[ { "path": ".devops/cloud-v-pipeline", "content": "node('x86_runner1'){ // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries\n stage('Cleanup'){\n cleanWs() // Cleaning previous CI build in workspace\n }\n stage('checkout repo'){\n retry(5){ // Retry if the cloning fails due to some reason\n checkout scm // Clone the repo on Runner\n }\n }\n stage('Compiling llama.cpp'){\n sh'''#!/bin/bash\n make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V\n '''\n }\n stage('Running llama.cpp'){\n sh'''#!/bin/bash\n module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc\n qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p \"Anything\" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64\n cat llama_log.txt # Printing results\n '''\n }\n}\n" }, { "path": ".devops/full-cuda.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n\n# This needs to generally match the container host's environment.\nARG CUDA_VERSION=11.7.1\n\n# Target the CUDA build image\nARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}\n\nFROM ${BASE_CUDA_DEV_CONTAINER} as build\n\n# Unless otherwise specified, we make a fat build.\nARG CUDA_DOCKER_ARCH=all\n\nRUN apt-get update && \\\n apt-get install -y build-essential python3 python3-pip git\n\nCOPY requirements.txt requirements.txt\n\nRUN pip install --upgrade pip setuptools wheel \\\n && pip install -r requirements.txt\n\nWORKDIR /app\n\nCOPY . .\n\n# Set nvcc architecture\nENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}\n# Enable cuBLAS\nENV LLAMA_CUBLAS=1\n\nRUN make\n\nENTRYPOINT [\"/app/.devops/tools.sh\"]\n" }, { "path": ".devops/full-rocm.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n\n# This needs to generally match the container host's environment.\nARG ROCM_VERSION=5.6\n\n# Target the CUDA build image\nARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete\n\nFROM ${BASE_ROCM_DEV_CONTAINER} as build\n\n# Unless otherwise specified, we make a fat build.\n# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878\n# This is mostly tied to rocBLAS supported archs.\nARG ROCM_DOCKER_ARCH=\\\n gfx803 \\\n gfx900 \\\n gfx906 \\\n gfx908 \\\n gfx90a \\\n gfx1010 \\\n gfx1030 \\\n gfx1100 \\\n gfx1101 \\\n gfx1102\n\nCOPY requirements.txt requirements.txt\n\nRUN pip install --upgrade pip setuptools wheel \\\n && pip install -r requirements.txt\n\nWORKDIR /app\n\nCOPY . .\n\n# Set nvcc architecture\nENV GPU_TARGETS=${ROCM_DOCKER_ARCH}\n# Enable ROCm\nENV LLAMA_HIPBLAS=1\nENV CC=/opt/rocm/llvm/bin/clang\nENV CXX=/opt/rocm/llvm/bin/clang++\n\nRUN make\n\nENTRYPOINT [\"/app/.devops/tools.sh\"]\n" }, { "path": ".devops/full.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n\nFROM ubuntu:$UBUNTU_VERSION as build\n\nRUN apt-get update && \\\n apt-get install -y build-essential python3 python3-pip git\n\nCOPY requirements.txt requirements.txt\n\nRUN pip install --upgrade pip setuptools wheel \\\n && pip install -r requirements.txt\n\nWORKDIR /app\n\nCOPY . .\n\nRUN make\n\nENV LC_ALL=C.utf8\n\nENTRYPOINT [\"/app/.devops/tools.sh\"]\n" }, { "path": ".devops/llama-cpp-clblast.srpm.spec", "content": "# SRPM for building from source and packaging an RPM for RPM-based distros.\n# https://fedoraproject.org/wiki/How_to_create_an_RPM_package\n# Built and maintained by John Boero - boeroboy@gmail.com\n# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal\n\n# Notes for llama.cpp:\n# 1. Tags are currently based on hash - which will not sort asciibetically.\n# We need to declare standard versioning if people want to sort latest releases.\n# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.\n# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.\n# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo\n# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.\n# It is up to the user to install the correct vendor-specific support.\n\nName: llama.cpp-clblast\nVersion: %( date \"+%%Y%%m%%d\" )\nRelease: 1%{?dist}\nSummary: OpenCL Inference of LLaMA model in C/C++\nLicense: MIT\nSource0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz\nBuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel\nRequires: clblast\nURL: https://github.com/ggerganov/llama.cpp\n\n%define debug_package %{nil}\n%define source_date_epoch_from_changelog 0\n\n%description\nCPU inference for Meta's Lllama2 models using default options.\n\n%prep\n%setup -n llama.cpp-master\n\n%build\nmake -j LLAMA_CLBLAST=1\n\n%install\nmkdir -p %{buildroot}%{_bindir}/\ncp -p main %{buildroot}%{_bindir}/llamaclblast\ncp -p server %{buildroot}%{_bindir}/llamaclblastserver\ncp -p simple %{buildroot}%{_bindir}/llamaclblastsimple\n\nmkdir -p %{buildroot}/usr/lib/systemd/system\n%{__cat} < %{buildroot}/usr/lib/systemd/system/llamaclblast.service\n[Unit]\nDescription=Llama.cpp server, CPU only (no GPU support in this build).\nAfter=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target\n\n[Service]\nType=simple\nEnvironmentFile=/etc/sysconfig/llama\nExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS\nExecReload=/bin/kill -s HUP $MAINPID\nRestart=never\n\n[Install]\nWantedBy=default.target\nEOF\n\nmkdir -p %{buildroot}/etc/sysconfig\n%{__cat} < %{buildroot}/etc/sysconfig/llama\nLLAMA_ARGS=\"-m /opt/llama2/ggml-model-f32.bin\"\nEOF\n\n%clean\nrm -rf %{buildroot}\nrm -rf %{_builddir}/*\n\n%files\n%{_bindir}/llamaclblast\n%{_bindir}/llamaclblastserver\n%{_bindir}/llamaclblastsimple\n/usr/lib/systemd/system/llamaclblast.service\n%config /etc/sysconfig/llama\n\n\n%pre\n\n%post\n\n%preun\n%postun\n\n%changelog\n" }, { "path": ".devops/llama-cpp-cublas.srpm.spec", "content": "# SRPM for building from source and packaging an RPM for RPM-based distros.\n# https://fedoraproject.org/wiki/How_to_create_an_RPM_package\n# Built and maintained by John Boero - boeroboy@gmail.com\n# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal\n\n# Notes for llama.cpp:\n# 1. Tags are currently based on hash - which will not sort asciibetically.\n# We need to declare standard versioning if people want to sort latest releases.\n# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.\n# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.\n# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo\n# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.\n# It is up to the user to install the correct vendor-specific support.\n\nName: llama.cpp-cublas\nVersion: %( date \"+%%Y%%m%%d\" )\nRelease: 1%{?dist}\nSummary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)\nLicense: MIT\nSource0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz\nBuildRequires: coreutils make gcc-c++ git cuda-toolkit\nRequires: cuda-toolkit\nURL: https://github.com/ggerganov/llama.cpp\n\n%define debug_package %{nil}\n%define source_date_epoch_from_changelog 0\n\n%description\nCPU inference for Meta's Lllama2 models using default options.\n\n%prep\n%setup -n llama.cpp-master\n\n%build\nmake -j LLAMA_CUBLAS=1\n\n%install\nmkdir -p %{buildroot}%{_bindir}/\ncp -p main %{buildroot}%{_bindir}/llamacppcublas\ncp -p server %{buildroot}%{_bindir}/llamacppcublasserver\ncp -p simple %{buildroot}%{_bindir}/llamacppcublassimple\n\nmkdir -p %{buildroot}/usr/lib/systemd/system\n%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacublas.service\n[Unit]\nDescription=Llama.cpp server, CPU only (no GPU support in this build).\nAfter=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target\n\n[Service]\nType=simple\nEnvironmentFile=/etc/sysconfig/llama\nExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS\nExecReload=/bin/kill -s HUP $MAINPID\nRestart=never\n\n[Install]\nWantedBy=default.target\nEOF\n\nmkdir -p %{buildroot}/etc/sysconfig\n%{__cat} < %{buildroot}/etc/sysconfig/llama\nLLAMA_ARGS=\"-m /opt/llama2/ggml-model-f32.bin\"\nEOF\n\n%clean\nrm -rf %{buildroot}\nrm -rf %{_builddir}/*\n\n%files\n%{_bindir}/llamacppcublas\n%{_bindir}/llamacppcublasserver\n%{_bindir}/llamacppcublassimple\n/usr/lib/systemd/system/llamacublas.service\n%config /etc/sysconfig/llama\n\n%pre\n\n%post\n\n%preun\n%postun\n\n%changelog\n" }, { "path": ".devops/llama-cpp.srpm.spec", "content": "# SRPM for building from source and packaging an RPM for RPM-based distros.\n# https://fedoraproject.org/wiki/How_to_create_an_RPM_package\n# Built and maintained by John Boero - boeroboy@gmail.com\n# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal\n\n# Notes for llama.cpp:\n# 1. Tags are currently based on hash - which will not sort asciibetically.\n# We need to declare standard versioning if people want to sort latest releases.\n# In the meantime, YYYYMMDD format will be used.\n# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.\n# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.\n# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo\n# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.\n# It is up to the user to install the correct vendor-specific support.\n\nName: llama.cpp\nVersion: %( date \"+%%Y%%m%%d\" )\nRelease: 1%{?dist}\nSummary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)\nLicense: MIT\nSource0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz\nBuildRequires: coreutils make gcc-c++ git libstdc++-devel\nRequires: libstdc++\nURL: https://github.com/ggerganov/llama.cpp\n\n%define debug_package %{nil}\n%define source_date_epoch_from_changelog 0\n\n%description\nCPU inference for Meta's Lllama2 models using default options.\nModels are not included in this package and must be downloaded separately.\n\n%prep\n%setup -n llama.cpp-master\n\n%build\nmake -j\n\n%install\nmkdir -p %{buildroot}%{_bindir}/\ncp -p main %{buildroot}%{_bindir}/llama\ncp -p server %{buildroot}%{_bindir}/llamaserver\ncp -p simple %{buildroot}%{_bindir}/llamasimple\n\nmkdir -p %{buildroot}/usr/lib/systemd/system\n%{__cat} < %{buildroot}/usr/lib/systemd/system/llama.service\n[Unit]\nDescription=Llama.cpp server, CPU only (no GPU support in this build).\nAfter=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target\n\n[Service]\nType=simple\nEnvironmentFile=/etc/sysconfig/llama\nExecStart=/usr/bin/llamaserver $LLAMA_ARGS\nExecReload=/bin/kill -s HUP $MAINPID\nRestart=never\n\n[Install]\nWantedBy=default.target\nEOF\n\nmkdir -p %{buildroot}/etc/sysconfig\n%{__cat} < %{buildroot}/etc/sysconfig/llama\nLLAMA_ARGS=\"-m /opt/llama2/ggml-model-f32.bin\"\nEOF\n\n%clean\nrm -rf %{buildroot}\nrm -rf %{_builddir}/*\n\n%files\n%{_bindir}/llama\n%{_bindir}/llamaserver\n%{_bindir}/llamasimple\n/usr/lib/systemd/system/llama.service\n%config /etc/sysconfig/llama\n\n%pre\n\n%post\n\n%preun\n%postun\n\n%changelog\n" }, { "path": ".devops/main-cuda.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n# This needs to generally match the container host's environment.\nARG CUDA_VERSION=11.7.1\n# Target the CUDA build image\nARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}\n# Target the CUDA runtime image\nARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}\n\nFROM ${BASE_CUDA_DEV_CONTAINER} as build\n\n# Unless otherwise specified, we make a fat build.\nARG CUDA_DOCKER_ARCH=all\n\nRUN apt-get update && \\\n apt-get install -y build-essential git\n\nWORKDIR /app\n\nCOPY . .\n\n# Set nvcc architecture\nENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}\n# Enable cuBLAS\nENV LLAMA_CUBLAS=1\n\nRUN make\n\nFROM ${BASE_CUDA_RUN_CONTAINER} as runtime\n\nCOPY --from=build /app/main /main\n\nENTRYPOINT [ \"/main\" ]\n" }, { "path": ".devops/main-rocm.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n\n# This needs to generally match the container host's environment.\nARG ROCM_VERSION=5.6\n\n# Target the CUDA build image\nARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete\n\nFROM ${BASE_ROCM_DEV_CONTAINER} as build\n\n# Unless otherwise specified, we make a fat build.\n# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878\n# This is mostly tied to rocBLAS supported archs.\nARG ROCM_DOCKER_ARCH=\\\n gfx803 \\\n gfx900 \\\n gfx906 \\\n gfx908 \\\n gfx90a \\\n gfx1010 \\\n gfx1030 \\\n gfx1100 \\\n gfx1101 \\\n gfx1102\n\nCOPY requirements.txt requirements.txt\n\nRUN pip install --upgrade pip setuptools wheel \\\n && pip install -r requirements.txt\n\nWORKDIR /app\n\nCOPY . .\n\n# Set nvcc architecture\nENV GPU_TARGETS=${ROCM_DOCKER_ARCH}\n# Enable ROCm\nENV LLAMA_HIPBLAS=1\nENV CC=/opt/rocm/llvm/bin/clang\nENV CXX=/opt/rocm/llvm/bin/clang++\n\nRUN make\n\nENTRYPOINT [ \"/app/main\" ]\n" }, { "path": ".devops/main.Dockerfile", "content": "ARG UBUNTU_VERSION=22.04\n\nFROM ubuntu:$UBUNTU_VERSION as build\n\nRUN apt-get update && \\\n apt-get install -y build-essential git\n\nWORKDIR /app\n\nCOPY . .\n\nRUN make\n\nFROM ubuntu:$UBUNTU_VERSION as runtime\n\nCOPY --from=build /app/main /main\n\nENV LC_ALL=C.utf8\n\nENTRYPOINT [ \"/main\" ]\n" }, { "path": ".devops/tools.sh", "content": "#!/bin/bash\nset -e\n\n# Read the first argument into a variable\narg1=\"$1\"\n\n# Shift the arguments to remove the first one\nshift\n\nif [[ \"$arg1\" == '--convert' || \"$arg1\" == '-c' ]]; then\n python3 ./convert.py \"$@\"\nelif [[ \"$arg1\" == '--quantize' || \"$arg1\" == '-q' ]]; then\n ./quantize \"$@\"\nelif [[ \"$arg1\" == '--run' || \"$arg1\" == '-r' ]]; then\n ./main \"$@\"\nelif [[ \"$arg1\" == '--all-in-one' || \"$arg1\" == '-a' ]]; then\n echo \"Converting PTH to GGML...\"\n for i in `ls $1/$2/ggml-model-f16.bin*`; do\n if [ -f \"${i/f16/q4_0}\" ]; then\n echo \"Skip model quantization, it already exists: ${i/f16/q4_0}\"\n else\n echo \"Converting PTH to GGML: $i into ${i/f16/q4_0}...\"\n ./quantize \"$i\" \"${i/f16/q4_0}\" q4_0\n fi\n done\nelif [[ \"$arg1\" == '--server' || \"$arg1\" == '-s' ]]; then\n ./server \"$@\"\nelse\n echo \"Unknown command: $arg1\"\n echo \"Available commands: \"\n echo \" --run (-r): Run a model previously converted into ggml\"\n echo \" ex: -m /models/7B/ggml-model-q4_0.bin -p \\\"Building a website can be done in 10 simple steps:\\\" -n 512\"\n echo \" --convert (-c): Convert a llama model into ggml\"\n echo \" ex: --outtype f16 \\\"/models/7B/\\\" \"\n echo \" --quantize (-q): Optimize with quantization process ggml\"\n echo \" ex: \\\"/models/7B/ggml-model-f16.bin\\\" \\\"/models/7B/ggml-model-q4_0.bin\\\" 2\"\n echo \" --all-in-one (-a): Execute --convert & --quantize\"\n echo \" ex: \\\"/models/\\\" 7B\"\n echo \" --server (-s): Run a model on the server\"\n echo \" ex: -m /models/7B/ggml-model-q4_0.bin -c 2048 -ngl 43 -mg 1 --port 8080\"\nfi\n" }, { "path": ".dockerignore", "content": "*.o\n*.a\n.cache/\n.git/\n.github/\n.gitignore\n.vs/\n.vscode/\n.DS_Store\n\nbuild*/\n\nmodels/*\n\n/main\n/quantize\n\narm_neon.h\ncompile_commands.json\nDockerfile\n" }, { "path": ".ecrc", "content": "{\n \"Disable\": {\n \"IndentSize\": true\n }\n}\n" }, { "path": ".editorconfig", "content": "# https://EditorConfig.org\n\n# Top-most EditorConfig file\nroot = true\n\n# Unix-style newlines with a newline ending every file, utf-8 charset\n[*]\nend_of_line = lf\ninsert_final_newline = true\ntrim_trailing_whitespace = true\ncharset = utf-8\nindent_style = space\nindent_size = 4\n\n[Makefile]\nindent_style = tab\n\n[prompts/*.txt]\ninsert_final_newline = unset\n\n[examples/server/public/*]\nindent_size = 2\n" }, { "path": ".flake8", "content": "[flake8]\nmax-line-length = 125\n" }, { "path": ".github/ISSUE_TEMPLATE/bug.md", "content": "---\nname: Bug template\nabout: Used to report bugs in PowerInfer\nlabels: [\"bug-unconfirmed\"]\nassignees: ''\n\n---\n\n# Prerequisites\n\nBefore submitting your issue, please ensure the following:\n\n- [ ] I am running the latest version of PowerInfer. Development is rapid, and as of now, there are no tagged versions.\n- [ ] I have carefully read and followed the instructions in the [README.md](https://github.com/SJTU-IPADS/PowerInfer/blob/main/README.md).\n- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).\n\n# Expected Behavior\n\nPlease provide a detailed written description of what you were trying to do, and what you expected PowerInfer to do.\n\n# Current Behavior\n\nPlease provide a detailed written description of what PowerInfer did, instead.\n\n# Environment and Context\n\nPlease provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.\n\n* Physical (or virtual) hardware you are using, e.g. for Linux:\n\n`$ lscpu`\n\n* Operating System, e.g. for Linux:\n\n`$ uname -a`\n\n* SDK version, e.g. for Linux:\n\n```\n$ python3 --version\n$ make --version\n$ g++ --version\n```\n\n# Failure Information (for bugs)\n\nPlease help provide information about the failure / bug.\n\n# Steps to Reproduce\n\nPlease provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.\n\n1. step 1\n2. step 2\n3. step 3\n4. etc.\n\n# Failure Logs\n\nPlease include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.\n\nAlso, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.\n\nExample environment info:\n```\nllama.cpp$ git log | head -1\ncommit 2af23d30434a677c6416812eea52ccc0af65119c\n\nllama.cpp$ lscpu | egrep \"AMD|Flags\"\nVendor ID: AuthenticAMD\nModel name: AMD Ryzen Threadripper 1950X 16-Core Processor\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev\nVirtualization: AMD-V\n\nllama.cpp$ python3 --version\nPython 3.10.9\n\nllama.cpp$ pip list | egrep \"torch|numpy|sentencepiece\"\nnumpy 1.24.2\nnumpydoc 1.5.0\nsentencepiece 0.1.97\ntorch 1.13.1\ntorchvision 0.14.1\n\nllama.cpp$ make --version | head -1\nGNU Make 4.3\n\n$ md5sum ./models/65B/ggml-model-q4_0.bin\ndbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin\n```\n\nExample run with the Linux command [perf](https://www.brendangregg.com/perf.html)\n```\nllama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p \"Please close your issue when it has been answered.\"\nmain: seed = 1679149377\nllama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...\nllama_model_load: n_vocab = 32000\nllama_model_load: n_ctx = 512\nllama_model_load: n_embd = 8192\nllama_model_load: n_mult = 256\nllama_model_load: n_head = 64\nllama_model_load: n_layer = 80\nllama_model_load: n_rot = 128\nllama_model_load: f16 = 2\nllama_model_load: n_ff = 22016\nllama_model_load: n_parts = 8\nllama_model_load: ggml ctx size = 41477.73 MB\nllama_model_load: memory_size = 2560.00 MB, n_mem = 40960\nllama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\nllama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'\nllama_model_load: .......................................................................................... done\nllama_model_load: model size = 4869.09 MB / num tensors = 723\n\nsystem_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |\n\nmain: prompt: 'Please close your issue when it has been answered.'\nmain: number of tokens in prompt = 11\n 1 -> ''\n 12148 -> 'Please'\n 3802 -> ' close'\n 596 -> ' your'\n 2228 -> ' issue'\n 746 -> ' when'\n 372 -> ' it'\n 756 -> ' has'\n 1063 -> ' been'\n 7699 -> ' answered'\n 29889 -> '.'\n\nsampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000\n\n\nPlease close your issue when it has been answered.\n@duncan-donut: I'm trying to figure out what kind of \"support\" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??\nI can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!\n@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]\n\n\nmain: mem per token = 71159620 bytes\nmain: load time = 19309.95 ms\nmain: sample time = 168.62 ms\nmain: predict time = 223895.61 ms / 888.47 ms per token\nmain: total time = 246406.42 ms\n\n Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':\n\n 3636882.89 msec task-clock # 14.677 CPUs utilized\n 13509 context-switches # 3.714 /sec\n 2436 cpu-migrations # 0.670 /sec\n 10476679 page-faults # 2.881 K/sec\n 13133115082869 cycles # 3.611 GHz (16.77%)\n 29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%)\n 10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%)\n 23479217109614 instructions # 1.79 insn per cycle\n # 0.44 stalled cycles per insn (16.76%)\n 2353072268027 branches # 647.002 M/sec (16.77%)\n 1998682780 branch-misses # 0.08% of all branches (16.76%)\n\n 247.802177522 seconds time elapsed\n\n 3618.573072000 seconds user\n 18.491698000 seconds sys\n```\n" }, { "path": ".github/ISSUE_TEMPLATE/enhancement.md", "content": "---\nname: Enhancement template\nabout: Used to request enhancements for PowerInfer\nlabels: [\"enhancement\"]\nassignees: ''\n\n---\n\n# Prerequisites\n\nBefore submitting your issue, please ensure the following:\n\n- [ ] I am running the latest version of PowerInfer. Development is rapid, and as of now, there are no tagged versions.\n- [ ] I have carefully read and followed the instructions in the [README.md](https://github.com/SJTU-IPADS/PowerInfer/blob/main/README.md).\n- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).\n\n# Feature Description\n\nPlease provide a detailed written description of what you were trying to do, and what you expected PowerInfer to do as an enhancement.\n\n# Motivation\n\nPlease provide a detailed written description of reasons why this feature is necessary and how it is useful to PowerInfer users.\n\n# Possible Implementation\n\nIf you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.\n" }, { "path": ".github/ISSUE_TEMPLATE/question.md", "content": "---\nname: Question template\nabout: Used for general questions and inquiries about PowerInfer\nlabels: [\"question\"]\nassignees: ''\n\n---\n\n# Prerequisites\n\nBefore submitting your question, please ensure the following:\n\n- [ ] I am running the latest version of PowerInfer. Development is rapid, and as of now, there are no tagged versions.\n- [ ] I have carefully read and followed the instructions in the [README.md](https://github.com/SJTU-IPADS/PowerInfer/blob/main/README.md).\n- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).\n\n# Question Details\n\nPlease provide a clear and concise description of your question. If applicable, include steps to reproduce the issue or behaviors you've observed.\n\n# Additional Context\n\nPlease provide any additional information that may be relevant to your question, such as specific system configurations, environment details, or any other context that could be helpful in addressing your inquiry.\n" }, { "path": ".github/workflows/build.yml", "content": "name: CI\n\non:\n workflow_dispatch: # allows manual triggering\n inputs:\n create_release:\n description: 'Create new release'\n required: true\n type: boolean\n push:\n branches:\n - master\n paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']\n pull_request:\n types: [opened, synchronize, reopened]\n paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']\n\nenv:\n BRANCH_NAME: ${{ github.head_ref || github.ref_name }}\n GGML_NLOOP: 3\n GGML_N_THREADS: 1\n\njobs:\n ubuntu-focal-make:\n runs-on: ubuntu-20.04\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n run: |\n sudo apt-get update\n sudo apt-get install build-essential gcc-8\n\n - name: Build\n id: make_build\n run: |\n CC=gcc-8 make -j $(nproc)\n\n - name: Test\n id: make_test\n run: |\n CC=gcc-8 make tests -j $(nproc)\n make test -j $(nproc)\n\n ubuntu-latest-cmake:\n runs-on: ubuntu-latest\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n run: |\n sudo apt-get update\n sudo apt-get install build-essential\n\n - name: Build\n id: cmake_build\n run: |\n mkdir build\n cd build\n cmake ..\n cmake --build . --config Release -j $(nproc)\n\n - name: Test\n id: cmake_test\n run: |\n cd build\n ctest --verbose --timeout 900\n\n ubuntu-latest-cmake-sanitizer:\n runs-on: ubuntu-latest\n\n continue-on-error: true\n\n strategy:\n matrix:\n sanitizer: [ADDRESS, THREAD, UNDEFINED]\n build_type: [Debug, Release]\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n run: |\n sudo apt-get update\n sudo apt-get install build-essential\n\n - name: Build\n id: cmake_build\n run: |\n mkdir build\n cd build\n cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}\n cmake --build . --config ${{ matrix.build_type }} -j $(nproc)\n\n - name: Test\n id: cmake_test\n run: |\n cd build\n ctest --verbose --timeout 900\n\n ubuntu-latest-cmake-mpi:\n runs-on: ubuntu-latest\n\n continue-on-error: true\n\n strategy:\n matrix:\n mpi_library: [mpich, libopenmpi-dev]\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n run: |\n sudo apt-get update\n sudo apt-get install build-essential ${{ matrix.mpi_library }}\n\n - name: Build\n id: cmake_build\n run: |\n mkdir build\n cd build\n cmake -DLLAMA_MPI=ON ..\n cmake --build . --config Release -j $(nproc)\n\n - name: Test\n id: cmake_test\n run: |\n cd build\n ctest --verbose\n\n macOS-latest-make:\n runs-on: macos-latest\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n continue-on-error: true\n run: |\n brew update\n\n - name: Build\n id: make_build\n run: |\n make -j $(sysctl -n hw.logicalcpu)\n\n - name: Test\n id: make_test\n run: |\n make tests -j $(sysctl -n hw.logicalcpu)\n make test -j $(sysctl -n hw.logicalcpu)\n\n macOS-latest-cmake:\n runs-on: macos-latest\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n id: depends\n continue-on-error: true\n run: |\n brew update\n\n - name: Build\n id: cmake_build\n run: |\n sysctl -a\n mkdir build\n cd build\n cmake ..\n cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)\n\n - name: Test\n id: cmake_test\n run: |\n cd build\n ctest --verbose --timeout 900\n\n macOS-latest-cmake-ios:\n runs-on: macos-latest\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v1\n\n - name: Dependencies\n id: depends\n continue-on-error: true\n run: |\n brew update\n\n - name: Build\n id: cmake_build\n run: |\n sysctl -a\n mkdir build\n cd build\n cmake -G Xcode .. \\\n -DLLAMA_BUILD_EXAMPLES=OFF \\\n -DLLAMA_BUILD_TESTS=OFF \\\n -DLLAMA_BUILD_SERVER=OFF \\\n -DCMAKE_SYSTEM_NAME=iOS \\\n -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0\n cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)\n\n macOS-latest-cmake-tvos:\n runs-on: macos-latest\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v1\n\n - name: Dependencies\n id: depends\n continue-on-error: true\n run: |\n brew update\n\n - name: Build\n id: cmake_build\n run: |\n sysctl -a\n mkdir build\n cd build\n cmake -G Xcode .. \\\n -DLLAMA_BUILD_EXAMPLES=OFF \\\n -DLLAMA_BUILD_TESTS=OFF \\\n -DLLAMA_BUILD_SERVER=OFF \\\n -DCMAKE_SYSTEM_NAME=tvOS \\\n -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0\n cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)\n\n macOS-latest-swift:\n runs-on: macos-latest\n\n strategy:\n matrix:\n destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v1\n\n - name: Dependencies\n id: depends\n continue-on-error: true\n run: |\n brew update\n\n - name: xcodebuild for swift package\n id: xcodebuild\n run: |\n xcodebuild -scheme llama -destination \"${{ matrix.destination }}\"\n\n - name: Build Swift Example\n id: make_build_swift_example\n run: |\n make swift\n\n windows-latest-cmake:\n runs-on: windows-latest\n\n env:\n OPENBLAS_VERSION: 0.3.23\n OPENCL_VERSION: 2023.04.17\n CLBLAST_VERSION: 1.6.0\n SDE_VERSION: 9.21.1-2023-04-24\n\n strategy:\n matrix:\n include:\n - build: 'noavx'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON'\n - build: 'avx2'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'\n - build: 'avx'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON'\n - build: 'avx512'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'\n - build: 'clblast'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH=\"$env:RUNNER_TEMP/clblast\"'\n - build: 'openblas'\n defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS=\"$env:RUNNER_TEMP/openblas/include\" -DBLAS_LIBRARIES=\"$env:RUNNER_TEMP/openblas/lib/openblas.lib\"'\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n with:\n fetch-depth: 0\n\n - name: Download OpenCL SDK\n id: get_opencl\n if: ${{ matrix.build == 'clblast' }}\n run: |\n curl.exe -o $env:RUNNER_TEMP/opencl.zip -L \"https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip\"\n mkdir $env:RUNNER_TEMP/opencl\n tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl\n\n - name: Download CLBlast\n id: get_clblast\n if: ${{ matrix.build == 'clblast' }}\n run: |\n curl.exe -o $env:RUNNER_TEMP/clblast.7z -L \"https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z\"\n curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L \"https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE\"\n 7z x \"-o${env:RUNNER_TEMP}\" $env:RUNNER_TEMP/clblast.7z\n rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast\n foreach ($f in (gci -Recurse -Path \"$env:RUNNER_TEMP/clblast\" -Filter '*.cmake')) {\n $txt = Get-Content -Path $f -Raw\n $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', \"$($env:RUNNER_TEMP.Replace('\\','/'))/opencl/\") | Set-Content -Path $f -Encoding UTF8\n }\n\n - name: Download OpenBLAS\n id: get_openblas\n if: ${{ matrix.build == 'openblas' }}\n run: |\n curl.exe -o $env:RUNNER_TEMP/openblas.zip -L \"https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip\"\n curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L \"https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE\"\n mkdir $env:RUNNER_TEMP/openblas\n tar.exe -xvf $env:RUNNER_TEMP/openblas.zip -C $env:RUNNER_TEMP/openblas\n $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath)\n $msvc = $(join-path $vcdir $('VC\\Tools\\MSVC\\'+$(gc -raw $(join-path $vcdir 'VC\\Auxiliary\\Build\\Microsoft.VCToolsVersion.default.txt')).Trim()))\n $lib = $(join-path $msvc 'bin\\Hostx64\\x64\\lib.exe')\n & $lib /machine:x64 \"/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def\" \"/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib\" /name:openblas.dll\n\n - name: Build\n id: cmake_build\n run: |\n mkdir build\n cd build\n cmake .. ${{ matrix.defines }}\n cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}\n\n - name: Add clblast.dll\n id: add_clblast_dll\n if: ${{ matrix.build == 'clblast' }}\n run: |\n cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release\n cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt\n\n - name: Add libopenblas.dll\n id: add_libopenblas_dll\n if: ${{ matrix.build == 'openblas' }}\n run: |\n cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll\n cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt\n\n - name: Check AVX512F support\n id: check_avx512f\n if: ${{ matrix.build == 'avx512' }}\n continue-on-error: true\n run: |\n cd build\n $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath)\n $msvc = $(join-path $vcdir $('VC\\Tools\\MSVC\\'+$(gc -raw $(join-path $vcdir 'VC\\Auxiliary\\Build\\Microsoft.VCToolsVersion.default.txt')).Trim()))\n $cl = $(join-path $msvc 'bin\\Hostx64\\x64\\cl.exe')\n echo 'int main(void){unsigned int a[4];__cpuid(a,7);return !(a[1]&65536);}' >> avx512f.c\n & $cl /O2 /GS- /kernel avx512f.c /link /nodefaultlib /entry:main\n .\\avx512f.exe && echo \"AVX512F: YES\" && ( echo HAS_AVX512F=1 >> $env:GITHUB_ENV ) || echo \"AVX512F: NO\"\n\n - name: Test\n id: cmake_test\n if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512\n run: |\n cd build\n ctest -C Release --verbose --timeout 900\n\n - name: Test (Intel SDE)\n id: cmake_test_sde\n if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation\n run: |\n curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L \"https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz\"\n # for some weird reason windows tar doesn't like sde tar.xz\n 7z x \"-o${env:RUNNER_TEMP}\" $env:RUNNER_TEMP/sde.tar.xz\n 7z x \"-o${env:RUNNER_TEMP}\" $env:RUNNER_TEMP/sde.tar\n $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)\n cd build\n & $sde -future -- ctest -C Release --verbose --timeout 900\n\n - name: Determine tag name\n id: tag\n shell: bash\n run: |\n BUILD_NUMBER=\"$(git rev-list --count HEAD)\"\n SHORT_HASH=\"$(git rev-parse --short=7 HEAD)\"\n if [[ \"${{ env.BRANCH_NAME }}\" == \"master\" ]]; then\n echo \"name=b${BUILD_NUMBER}\" >> $GITHUB_OUTPUT\n else\n SAFE_NAME=$(echo \"${{ env.BRANCH_NAME }}\" | tr '/' '-')\n echo \"name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}\" >> $GITHUB_OUTPUT\n fi\n\n - name: Pack artifacts\n id: pack_artifacts\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n run: |\n Copy-Item LICENSE .\\build\\bin\\Release\\llama.cpp.txt\n 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip .\\build\\bin\\Release\\*\n\n - name: Upload artifacts\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n uses: actions/upload-artifact@v3\n with:\n path: |\n llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip\n\n windows-latest-cmake-cublas:\n runs-on: windows-latest\n\n strategy:\n matrix:\n cuda: ['12.2.0', '11.7.1']\n build: ['cublas']\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n with:\n fetch-depth: 0\n\n - uses: Jimver/cuda-toolkit@v0.2.11\n id: cuda-toolkit\n with:\n cuda: ${{ matrix.cuda }}\n method: 'network'\n sub-packages: '[\"nvcc\", \"cudart\", \"cublas\", \"cublas_dev\", \"thrust\", \"visual_studio_integration\"]'\n\n - name: Build\n id: cmake_build\n run: |\n mkdir build\n cd build\n cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON -DBUILD_SHARED_LIBS=ON\n cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}\n\n - name: Determine tag name\n id: tag\n shell: bash\n run: |\n BUILD_NUMBER=\"$(git rev-list --count HEAD)\"\n SHORT_HASH=\"$(git rev-parse --short=7 HEAD)\"\n if [[ \"${{ env.BRANCH_NAME }}\" == \"master\" ]]; then\n echo \"name=b${BUILD_NUMBER}\" >> $GITHUB_OUTPUT\n else\n SAFE_NAME=$(echo \"${{ env.BRANCH_NAME }}\" | tr '/' '-')\n echo \"name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}\" >> $GITHUB_OUTPUT\n fi\n\n - name: Pack artifacts\n id: pack_artifacts\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n run: |\n 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\\build\\bin\\Release\\*\n\n - name: Upload artifacts\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n uses: actions/upload-artifact@v3\n with:\n path: |\n llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip\n\n - name: Copy and pack Cuda runtime\n run: |\n echo \"Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}\"\n $dst='.\\build\\bin\\cudart\\'\n robocopy \"${{steps.cuda-toolkit.outputs.CUDA_PATH}}\\bin\" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll\n 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip $dst\\*\n\n - name: Upload Cuda runtime\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n uses: actions/upload-artifact@v3\n with:\n path: |\n cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip\n\n# freeBSD-latest:\n# runs-on: macos-12\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Build\n# uses: cross-platform-actions/action@v0.19.0\n# with:\n# operating_system: freebsd\n# version: '13.2'\n# hypervisor: 'qemu'\n# run: |\n# sudo pkg update\n# sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas\n# gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`\n\n release:\n if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}\n\n runs-on: ubuntu-latest\n\n needs:\n - ubuntu-focal-make\n - ubuntu-latest-cmake\n - macOS-latest-make\n - macOS-latest-cmake\n - windows-latest-cmake\n - windows-latest-cmake-cublas\n\n steps:\n - name: Clone\n id: checkout\n uses: actions/checkout@v3\n with:\n fetch-depth: 0\n\n - name: Determine tag name\n id: tag\n shell: bash\n run: |\n BUILD_NUMBER=\"$(git rev-list --count HEAD)\"\n SHORT_HASH=\"$(git rev-parse --short=7 HEAD)\"\n if [[ \"${{ env.BRANCH_NAME }}\" == \"master\" ]]; then\n echo \"name=b${BUILD_NUMBER}\" >> $GITHUB_OUTPUT\n else\n SAFE_NAME=$(echo \"${{ env.BRANCH_NAME }}\" | tr '/' '-')\n echo \"name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}\" >> $GITHUB_OUTPUT\n fi\n\n - name: Download artifacts\n id: download-artifact\n uses: actions/download-artifact@v3\n\n - name: Create release\n id: create_release\n uses: anzz1/action-create-release@v1\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n with:\n tag_name: ${{ steps.tag.outputs.name }}\n\n - name: Upload release\n id: upload_release\n uses: actions/github-script@v3\n with:\n github-token: ${{secrets.GITHUB_TOKEN}}\n script: |\n const path = require('path');\n const fs = require('fs');\n const release_id = '${{ steps.create_release.outputs.id }}';\n for (let file of await fs.readdirSync('./artifact')) {\n if (path.extname(file) === '.zip') {\n console.log('uploadReleaseAsset', file);\n await github.repos.uploadReleaseAsset({\n owner: context.repo.owner,\n repo: context.repo.repo,\n release_id: release_id,\n name: file,\n data: await fs.readFileSync(`./artifact/${file}`)\n });\n }\n }\n\n# ubuntu-latest-gcc:\n# runs-on: ubuntu-latest\n#\n# strategy:\n# matrix:\n# build: [Debug, Release]\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Dependencies\n# run: |\n# sudo apt-get update\n# sudo apt-get install build-essential\n# sudo apt-get install cmake\n#\n# - name: Configure\n# run: cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}\n#\n# - name: Build\n# run: |\n# make\n#\n# ubuntu-latest-clang:\n# runs-on: ubuntu-latest\n#\n# strategy:\n# matrix:\n# build: [Debug, Release]\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Dependencies\n# run: |\n# sudo apt-get update\n# sudo apt-get install build-essential\n# sudo apt-get install cmake\n#\n# - name: Configure\n# run: cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }} -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_COMPILER=clang\n#\n# - name: Build\n# run: |\n# make\n#\n# ubuntu-latest-gcc-sanitized:\n# runs-on: ubuntu-latest\n#\n# strategy:\n# matrix:\n# sanitizer: [ADDRESS, THREAD, UNDEFINED]\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Dependencies\n# run: |\n# sudo apt-get update\n# sudo apt-get install build-essential\n# sudo apt-get install cmake\n#\n# - name: Configure\n# run: cmake . -DCMAKE_BUILD_TYPE=Debug -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON\n#\n# - name: Build\n# run: |\n# make\n#\n# windows:\n# runs-on: windows-latest\n#\n# strategy:\n# matrix:\n# build: [Release]\n# arch: [Win32, x64]\n# include:\n# - arch: Win32\n# s2arc: x86\n# - arch: x64\n# s2arc: x64\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Add msbuild to PATH\n# uses: microsoft/setup-msbuild@v1\n#\n# - name: Configure\n# run: >\n# cmake -S . -B ./build -A ${{ matrix.arch }}\n# -DCMAKE_BUILD_TYPE=${{ matrix.build }}\n#\n# - name: Build\n# run: |\n# cd ./build\n# msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}\n#\n# - name: Upload binaries\n# uses: actions/upload-artifact@v1\n# with:\n# name: llama-bin-${{ matrix.arch }}\n# path: build/bin/${{ matrix.build }}\n#\n# windows-blas:\n# runs-on: windows-latest\n#\n# strategy:\n# matrix:\n# build: [Release]\n# arch: [Win32, x64]\n# blas: [ON]\n# include:\n# - arch: Win32\n# obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x86.zip\n# s2arc: x86\n# - arch: x64\n# obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x64.zip\n# s2arc: x64\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Add msbuild to PATH\n# uses: microsoft/setup-msbuild@v1\n#\n# - name: Fetch OpenBLAS\n# if: matrix.blas == 'ON'\n# run: |\n# C:/msys64/usr/bin/wget.exe -qO blas.zip ${{ matrix.obzip }}\n# 7z x blas.zip -oblas -y\n# copy blas/include/cblas.h .\n# copy blas/include/openblas_config.h .\n# echo \"blasdir=$env:GITHUB_WORKSPACE/blas\" >> $env:GITHUB_ENV\n#\n# - name: Configure\n# run: >\n# cmake -S . -B ./build -A ${{ matrix.arch }}\n# -DCMAKE_BUILD_TYPE=${{ matrix.build }}\n# -DLLAMA_SUPPORT_OPENBLAS=${{ matrix.blas }}\n# -DCMAKE_LIBRARY_PATH=\"$env:blasdir/lib\"\n#\n# - name: Build\n# run: |\n# cd ./build\n# msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}\n#\n# - name: Copy libopenblas.dll\n# if: matrix.blas == 'ON'\n# run: copy \"$env:blasdir/bin/libopenblas.dll\" build/bin/${{ matrix.build }}\n#\n# - name: Upload binaries\n# if: matrix.blas == 'ON'\n# uses: actions/upload-artifact@v1\n# with:\n# name: llama-blas-bin-${{ matrix.arch }}\n# path: build/bin/${{ matrix.build }}\n#\n# emscripten:\n# runs-on: ubuntu-latest\n#\n# strategy:\n# matrix:\n# build: [Release]\n#\n# steps:\n# - name: Clone\n# uses: actions/checkout@v3\n#\n# - name: Dependencies\n# run: |\n# wget -q https://github.com/emscripten-core/emsdk/archive/master.tar.gz\n# tar -xvf master.tar.gz\n# emsdk-master/emsdk update\n# emsdk-master/emsdk install latest\n# emsdk-master/emsdk activate latest\n#\n# - name: Configure\n# run: echo \"tmp\"\n#\n# - name: Build\n# run: |\n# pushd emsdk-master\n# source ./emsdk_env.sh\n# popd\n# emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}\n# make\n" }, { "path": ".github/workflows/code-coverage.yml", "content": "name: Code Coverage\non: [push, pull_request]\n\nenv:\n GGML_NLOOP: 3\n GGML_N_THREADS: 1\n\njobs:\n run:\n runs-on: ubuntu-20.04\n steps:\n - name: Checkout\n uses: actions/checkout@v3\n\n - name: Dependencies\n run: |\n sudo apt-get update\n sudo apt-get install build-essential gcc-8 lcov\n\n - name: Build\n run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests\n\n - name: Run tests\n run: CC=gcc-8 make test\n\n - name: Generate coverage report\n run: |\n make coverage\n make lcov-report\n\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v3\n env:\n CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}\n with:\n files: lcov-report/coverage.info\n" }, { "path": ".github/workflows/docker.yml", "content": "# This workflow uses actions that are not certified by GitHub.\n# They are provided by a third-party and are governed by\n# separate terms of service, privacy policy, and support\n# documentation.\n\n# GitHub recommends pinning actions to a commit SHA.\n# To get a newer version, you will need to update the SHA.\n# You can also reference a tag or branch, but the action may change without warning.\n\nname: Publish Docker image\n\non:\n pull_request:\n push:\n branches:\n - master\n\njobs:\n push_to_registry:\n name: Push Docker image to Docker Hub\n if: github.event.pull_request.draft == false\n\n runs-on: ubuntu-latest\n env:\n COMMIT_SHA: ${{ github.sha }}\n strategy:\n matrix:\n config:\n - { tag: \"light\", dockerfile: \".devops/main.Dockerfile\", platforms: \"linux/amd64,linux/arm64\" }\n - { tag: \"full\", dockerfile: \".devops/full.Dockerfile\", platforms: \"linux/amd64,linux/arm64\" }\n # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I\n # have disabled them for now until the reason why\n # is understood.\n - { tag: \"light-cuda\", dockerfile: \".devops/main-cuda.Dockerfile\", platforms: \"linux/amd64\" }\n - { tag: \"full-cuda\", dockerfile: \".devops/full-cuda.Dockerfile\", platforms: \"linux/amd64\" }\n - { tag: \"light-rocm\", dockerfile: \".devops/main-rocm.Dockerfile\", platforms: \"linux/amd64,linux/arm64\" }\n - { tag: \"full-rocm\", dockerfile: \".devops/full-rocm.Dockerfile\", platforms: \"linux/amd64,linux/arm64\" }\n steps:\n - name: Check out the repo\n uses: actions/checkout@v3\n\n - name: Set up QEMU\n uses: docker/setup-qemu-action@v2\n\n - name: Set up Docker Buildx\n uses: docker/setup-buildx-action@v2\n\n - name: Log in to Docker Hub\n uses: docker/login-action@v2\n with:\n registry: ghcr.io\n username: ${{ github.repository_owner }}\n password: ${{ secrets.GITHUB_TOKEN }}\n\n - name: Build and push Docker image (versioned)\n if: github.event_name == 'push'\n uses: docker/build-push-action@v4\n with:\n context: .\n push: true\n platforms: ${{ matrix.config.platforms }}\n tags: \"ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}\"\n file: ${{ matrix.config.dockerfile }}\n\n - name: Build and push Docker image (tagged)\n uses: docker/build-push-action@v4\n with:\n context: .\n push: ${{ github.event_name == 'push' }}\n platforms: ${{ matrix.config.platforms }}\n tags: \"ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}\"\n file: ${{ matrix.config.dockerfile }}\n" }, { "path": ".github/workflows/editorconfig.yml", "content": "name: EditorConfig Checker\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\njobs:\n editorconfig:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - uses: editorconfig-checker/action-editorconfig-checker@main\n - run: editorconfig-checker\n" }, { "path": ".github/workflows/gguf-publish.yml", "content": "# This workflow will upload a Python Package using Twine when a GGUF release is created\n# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries\n\n# See `gguf-py/README.md` for how to make a release.\n\n# This workflow uses actions that are not certified by GitHub.\n# They are provided by a third-party and are governed by\n# separate terms of service, privacy policy, and support\n# documentation.\n\nname: Upload Python Package\n\non:\n workflow_dispatch:\n push:\n # Pattern matched against refs/tags\n tags:\n - 'gguf-v*' # Push events to every version tag\n\n\njobs:\n deploy:\n\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v2\n with:\n python-version: '3.9.x'\n - name: Install dependencies\n run: |\n cd gguf-py\n python -m pip install poetry\n poetry install\n\n - name: Build package\n run: cd gguf-py && poetry build\n - name: Publish package\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n password: ${{ secrets.PYPI_API_TOKEN }}\n packages-dir: gguf-py/dist\n" }, { "path": ".github/workflows/tidy-post.yml", "content": "name: clang-tidy review post comments\n\non:\n workflow_dispatch:\n workflows: [\"clang-tidy-review\"]\n types:\n - completed\n\njobs:\n build:\n runs-on: ubuntu-latest\n\n steps:\n - uses: ZedThree/clang-tidy-review/post@v0.13.0\n # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup\n with:\n # adjust options as necessary\n lgtm_comment_body: ''\n annotations: false\n max_comments: 25\n" }, { "path": ".github/workflows/tidy-review.yml", "content": "name: clang-tidy-review\n\non:\n pull_request:\n branches:\n - master\n\njobs:\n clang-tidy-review:\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v3\n\n - uses: ZedThree/clang-tidy-review@v0.13.0\n id: review\n with:\n lgtm_comment_body: ''\n build_dir: build\n cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on\n split_workflow: true\n\n - uses: ZedThree/clang-tidy-review/upload@v0.13.0\n" }, { "path": ".github/workflows/zig-build.yml", "content": "name: Zig CI\n\non:\n pull_request:\n push:\n branches:\n - master\n\njobs:\n build:\n strategy:\n fail-fast: false\n matrix:\n runs-on: [ubuntu-latest, macos-latest, windows-latest]\n runs-on: ${{ matrix.runs-on }}\n steps:\n - uses: actions/checkout@v3\n with:\n submodules: recursive\n fetch-depth: 0\n - uses: goto-bus-stop/setup-zig@v2\n with:\n version: 0.11.0\n - name: Build Summary\n run: zig build --summary all -freference-trace\n" }, { "path": ".gitignore", "content": "*.o\n*.a\n*.so\n*.gguf\n*.bin\n*.exe\n*.dll\n*.log\n*.gcov\n*.gcno\n*.gcda\n*.dot\n*.bat\n*.metallib\n.DS_Store\n.build/\n.cache/\n.ccls-cache/\n.direnv/\n.envrc\n.swiftpm\n.venv\n.clang-tidy\n.vs/\n.vscode/\n\nlcov-report/\ngcovr-report/\n\nbuild*/\nout/\ntmp/\n\nmodels/*\nmodels-mnt\n\n/Pipfile\n/baby-llama\n/beam-search\n/benchmark-matmult\n/convert-llama2c-to-ggml\n/embd-input-test\n/embedding\n/gguf\n/gguf-llama-simple\n/infill\n/libllama.so\n/llama-bench\n/llava-cli\n/main\n/metal\n/perplexity\n/q8dot\n/quantize\n/quantize-stats\n/result\n/save-load-state\n/server\n/simple\n/batched\n/batched-bench\n/export-lora\n/finetune\n/speculative\n/parallel\n/train-text-from-scratch\n/vdot\n/common/build-info.cpp\narm_neon.h\ncompile_commands.json\nCMakeSettings.json\n\n__pycache__\ndist\n\nzig-out/\nzig-cache/\n\nppl-*.txt\nqnt-*.txt\nperf-*.txt\n\nexamples/jeopardy/results.txt\n\npoetry.lock\npoetry.toml\n\n# Test binaries\ntests/test-grammar-parser\ntests/test-llama-grammar\ntests/test-double-float\ntests/test-grad0\ntests/test-opt\ntests/test-quantize-fns\ntests/test-quantize-perf\ntests/test-sampling\ntests/test-tokenizer-0-llama\ntests/test-tokenizer-0-falcon\ntests/test-tokenizer-1-llama\ntests/test-tokenizer-1-bpe\n\nbuild-info.h\n" }, { "path": ".gitmodules", "content": "[submodule \"smallthinker/ggml/src/ggml-kompute/kompute\"]\n\tpath = smallthinker/ggml/src/ggml-kompute/kompute\n\turl = https://github.com/nomic-ai/kompute.git\n[submodule \"smallthinker/powerinfer/third_part/perfetto\"]\n\tpath = smallthinker/powerinfer/third_part/perfetto\n\turl = https://github.com/google/perfetto.git\n[submodule \"smallthinker/powerinfer/third_part/benchmark\"]\n\tpath = smallthinker/powerinfer/third_part/benchmark\n\turl = https://github.com/google/benchmark.git\n[submodule \"smallthinker/powerinfer/third_part/googletest\"]\n\tpath = smallthinker/powerinfer/third_part/googletest\n\turl = https://github.com/google/googletest.git\n[submodule \"smallthinker/powerinfer/third_part/libaio\"]\n\tpath = smallthinker/powerinfer/third_part/libaio\n\turl = https://github.com/crossbuild/libaio.git\n[submodule \"smallthinker/powerinfer/third_part/liburing\"]\n\tpath = smallthinker/powerinfer/third_part/liburing\n\turl = https://github.com/axboe/liburing.git\n[submodule \"smallthinker/powerinfer/third_part/fmt\"]\n\tpath = smallthinker/powerinfer/third_part/fmt\n\turl = https://github.com/fmtlib/fmt.git\n" }, { "path": ".pre-commit-config.yaml", "content": "# See https://pre-commit.com for more information\n# See https://pre-commit.com/hooks.html for more hooks\nexclude: prompts/.*.txt\nrepos:\n- repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v3.2.0\n hooks:\n - id: trailing-whitespace\n - id: end-of-file-fixer\n - id: check-yaml\n - id: check-added-large-files\n- repo: https://github.com/PyCQA/flake8\n rev: 6.0.0\n hooks:\n - id: flake8\n" }, { "path": "CMakeLists.txt", "content": "cmake_minimum_required(VERSION 3.13) # for add_link_options\nproject(\"llama.cpp\" C CXX)\n\nset(CMAKE_EXPORT_COMPILE_COMMANDS ON)\n\nif (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)\n set(CMAKE_BUILD_TYPE Release CACHE STRING \"Build type\" FORCE)\n set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS \"Debug\" \"Release\" \"MinSizeRel\" \"RelWithDebInfo\")\nendif()\n\nset(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)\n\nif (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)\n set(LLAMA_STANDALONE ON)\n\n # configure project version\n # TODO\nelse()\n set(LLAMA_STANDALONE OFF)\nendif()\n\nif (EMSCRIPTEN)\n set(BUILD_SHARED_LIBS_DEFAULT OFF)\n\n option(LLAMA_WASM_SINGLE_FILE \"llama: embed WASM inside the generated llama.js\" ON)\nelse()\n if (MINGW)\n set(BUILD_SHARED_LIBS_DEFAULT OFF)\n else()\n set(BUILD_SHARED_LIBS_DEFAULT ON)\n endif()\nendif()\n\n\n#\n# Option list\n#\n\nif (APPLE)\n set(LLAMA_METAL_DEFAULT OFF) # metal has not been supported on Apple Silicon yet\nelse()\n set(LLAMA_METAL_DEFAULT OFF)\nendif()\n\n# general\noption(LLAMA_STATIC \"llama: static link libraries\" OFF)\noption(LLAMA_NATIVE \"llama: enable -march=native flag\" ON)\noption(LLAMA_LTO \"llama: enable link time optimization\" OFF)\n\n# debug\noption(LLAMA_ALL_WARNINGS \"llama: enable all compiler warnings\" ON)\noption(LLAMA_ALL_WARNINGS_3RD_PARTY \"llama: enable all compiler warnings in 3rd party libs\" OFF)\noption(LLAMA_GPROF \"llama: enable gprof\" OFF)\n\n# sanitizers\noption(LLAMA_SANITIZE_THREAD \"llama: enable thread sanitizer\" OFF)\noption(LLAMA_SANITIZE_ADDRESS \"llama: enable address sanitizer\" OFF)\noption(LLAMA_SANITIZE_UNDEFINED \"llama: enable undefined sanitizer\" OFF)\n\n# instruction set specific\nif (LLAMA_NATIVE)\n set(INS_ENB OFF)\nelse()\n set(INS_ENB ON)\nendif()\n\noption(LLAMA_AVX \"llama: enable AVX\" ${INS_ENB})\noption(LLAMA_AVX2 \"llama: enable AVX2\" ${INS_ENB})\noption(LLAMA_AVX512 \"llama: enable AVX512\" OFF)\noption(LLAMA_AVX512_VBMI \"llama: enable AVX512-VBMI\" OFF)\noption(LLAMA_AVX512_VNNI \"llama: enable AVX512-VNNI\" OFF)\noption(LLAMA_FMA \"llama: enable FMA\" ${INS_ENB})\n# in MSVC F16C is implied with AVX2/AVX512\nif (NOT MSVC)\n option(LLAMA_F16C \"llama: enable F16C\" ${INS_ENB})\nendif()\n\n# 3rd party libs\noption(LLAMA_ACCELERATE \"llama: enable Accelerate framework\" ON)\noption(LLAMA_BLAS \"llama: use BLAS\" OFF)\nset(LLAMA_BLAS_VENDOR \"Generic\" CACHE STRING \"llama: BLAS library vendor\")\noption(LLAMA_CUBLAS \"llama: use CUDA\" OFF)\n#option(LLAMA_CUDA_CUBLAS \"llama: use cuBLAS for prompt processing\" OFF)\noption(LLAMA_CUDA_FORCE_DMMV \"llama: use dmmv instead of mmvq CUDA kernels\" OFF)\noption(LLAMA_CUDA_FORCE_MMQ \"llama: use mmq kernels instead of cuBLAS\" OFF)\nset(LLAMA_CUDA_DMMV_X \"32\" CACHE STRING \"llama: x stride for dmmv CUDA kernels\")\nset(LLAMA_CUDA_MMV_Y \"1\" CACHE STRING \"llama: y block size for mmv CUDA kernels\")\noption(LLAMA_CUDA_F16 \"llama: use 16 bit floats for some calculations\" OFF)\nset(LLAMA_CUDA_KQUANTS_ITER \"2\" CACHE STRING \"llama: iters./thread per block for Q2_K/Q6_K\")\nset(LLAMA_CUDA_PEER_MAX_BATCH_SIZE \"128\" CACHE STRING\n \"llama: max. batch size for using peer access\")\noption(LLAMA_HIPBLAS \"llama: use hipBLAS\" OFF)\noption(LLAMA_CLBLAST \"llama: use CLBlast\" OFF)\noption(LLAMA_METAL \"llama: use Metal\" ${LLAMA_METAL_DEFAULT})\noption(LLAMA_METAL_NDEBUG \"llama: disable Metal debugging\" OFF)\noption(LLAMA_MPI \"llama: use MPI\" OFF)\noption(LLAMA_QKK_64 \"llama: use super-block size of 64 for k-quants\" OFF)\n\noption(LLAMA_BUILD_TESTS \"llama: build tests\" ${LLAMA_STANDALONE})\noption(LLAMA_BUILD_EXAMPLES \"llama: build examples\" ${LLAMA_STANDALONE})\noption(LLAMA_BUILD_SERVER \"llama: build server example\" ON)\n\n#\n# Compile flags\n#\n\nset(CMAKE_CXX_STANDARD 11)\nset(CMAKE_CXX_STANDARD_REQUIRED true)\nset(CMAKE_C_STANDARD 11)\nset(CMAKE_C_STANDARD_REQUIRED true)\nset(THREADS_PREFER_PTHREAD_FLAG ON)\nfind_package(Threads REQUIRED)\ninclude(CheckCXXCompilerFlag)\n\nif (NOT MSVC)\n if (LLAMA_SANITIZE_THREAD)\n add_compile_options(-fsanitize=thread)\n link_libraries(-fsanitize=thread)\n endif()\n\n if (LLAMA_SANITIZE_ADDRESS)\n add_compile_options(-fsanitize=address -fno-omit-frame-pointer)\n link_libraries(-fsanitize=address)\n endif()\n\n if (LLAMA_SANITIZE_UNDEFINED)\n add_compile_options(-fsanitize=undefined)\n link_libraries(-fsanitize=undefined)\n endif()\nendif()\n\nif (APPLE AND LLAMA_ACCELERATE)\n find_library(ACCELERATE_FRAMEWORK Accelerate)\n if (ACCELERATE_FRAMEWORK)\n message(STATUS \"Accelerate framework found\")\n\n add_compile_definitions(GGML_USE_ACCELERATE)\n add_compile_definitions(ACCELERATE_NEW_LAPACK)\n add_compile_definitions(ACCELERATE_LAPACK_ILP64)\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})\n else()\n message(WARNING \"Accelerate framework not found\")\n endif()\nendif()\n\nif (LLAMA_METAL)\n find_library(FOUNDATION_LIBRARY Foundation REQUIRED)\n find_library(METAL_FRAMEWORK Metal REQUIRED)\n find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)\n\n message(STATUS \"Metal framework found\")\n set(GGML_HEADERS_METAL ggml-metal.h)\n set(GGML_SOURCES_METAL ggml-metal.m)\n\n add_compile_definitions(GGML_USE_METAL)\n if (LLAMA_METAL_NDEBUG)\n add_compile_definitions(GGML_METAL_NDEBUG)\n endif()\n\n # get full path to the file\n #add_compile_definitions(GGML_METAL_DIR_KERNELS=\"${CMAKE_CURRENT_SOURCE_DIR}/\")\n\n # copy ggml-metal.metal to bin directory\n configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)\n\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}\n ${FOUNDATION_LIBRARY}\n ${METAL_FRAMEWORK}\n ${METALKIT_FRAMEWORK}\n )\nendif()\nif (LLAMA_BLAS)\n if (LLAMA_STATIC)\n set(BLA_STATIC ON)\n endif()\n if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)\n set(BLA_SIZEOF_INTEGER 8)\n endif()\n\n set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})\n find_package(BLAS)\n\n if (BLAS_FOUND)\n message(STATUS \"BLAS found, Libraries: ${BLAS_LIBRARIES}\")\n\n if (\"${BLAS_INCLUDE_DIRS}\" STREQUAL \"\")\n # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.\n # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268\n find_package(PkgConfig REQUIRED)\n if (${LLAMA_BLAS_VENDOR} MATCHES \"Generic\")\n pkg_check_modules(DepBLAS REQUIRED blas)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"OpenBLAS\")\n pkg_check_modules(DepBLAS REQUIRED openblas)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"FLAME\")\n pkg_check_modules(DepBLAS REQUIRED blis)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"ATLAS\")\n pkg_check_modules(DepBLAS REQUIRED blas-atlas)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"FlexiBLAS\")\n pkg_check_modules(DepBLAS REQUIRED flexiblas_api)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"Intel\")\n # all Intel* libraries share the same include path\n pkg_check_modules(DepBLAS REQUIRED mkl-sdl)\n elseif (${LLAMA_BLAS_VENDOR} MATCHES \"NVHPC\")\n # this doesn't provide pkg-config\n # suggest to assign BLAS_INCLUDE_DIRS on your own\n if (\"${NVHPC_VERSION}\" STREQUAL \"\")\n message(WARNING \"Better to set NVHPC_VERSION\")\n else()\n set(DepBLAS_FOUND ON)\n set(DepBLAS_INCLUDE_DIRS \"/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include\")\n endif()\n endif()\n if (DepBLAS_FOUND)\n set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})\n else()\n message(WARNING \"BLAS_INCLUDE_DIRS neither been provided nor been automatically\"\n \" detected by pkgconfig, trying to find cblas.h from possible paths...\")\n find_path(BLAS_INCLUDE_DIRS\n NAMES cblas.h\n HINTS\n /usr/include\n /usr/local/include\n /usr/include/openblas\n /opt/homebrew/opt/openblas/include\n /usr/local/opt/openblas/include\n /usr/include/x86_64-linux-gnu/openblas/include\n )\n endif()\n endif()\n\n message(STATUS \"BLAS found, Includes: ${BLAS_INCLUDE_DIRS}\")\n add_compile_options(${BLAS_LINKER_FLAGS})\n add_compile_definitions(GGML_USE_OPENBLAS)\n if (${BLAS_INCLUDE_DIRS} MATCHES \"mkl\" AND (${LLAMA_BLAS_VENDOR} MATCHES \"Generic\" OR ${LLAMA_BLAS_VENDOR} MATCHES \"Intel\"))\n add_compile_definitions(GGML_BLAS_USE_MKL)\n endif()\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})\n set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})\n\n else()\n message(WARNING \"BLAS not found, please refer to \"\n \"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors\"\n \" to set correct LLAMA_BLAS_VENDOR\")\n endif()\nendif()\n\nif (LLAMA_QKK_64)\n add_compile_definitions(GGML_QKK_64)\nendif()\n\nif (LLAMA_CUBLAS)\n cmake_minimum_required(VERSION 3.17)\n\n find_package(CUDAToolkit)\n if (CUDAToolkit_FOUND)\n message(STATUS \"cuBLAS found\")\n\n enable_language(CUDA)\n\n set(GGML_HEADERS_CUDA ggml-cuda.h)\n set(GGML_SOURCES_CUDA ggml-cuda.cu)\n\n add_compile_definitions(GGML_USE_CUBLAS)\n# if (LLAMA_CUDA_CUBLAS)\n# add_compile_definitions(GGML_CUDA_CUBLAS)\n# endif()\n if (LLAMA_CUDA_FORCE_DMMV)\n add_compile_definitions(GGML_CUDA_FORCE_DMMV)\n endif()\n if (LLAMA_CUDA_FORCE_MMQ)\n add_compile_definitions(GGML_CUDA_FORCE_MMQ)\n endif()\n add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})\n add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})\n if (DEFINED LLAMA_CUDA_DMMV_Y)\n add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility\n endif()\n if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)\n add_compile_definitions(GGML_CUDA_F16)\n endif()\n add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})\n add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})\n\n if (LLAMA_STATIC)\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)\n else()\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)\n endif()\n\n if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)\n # 52 == lowest CUDA 12 standard\n # 60 == f16 CUDA intrinsics\n # 61 == integer CUDA intrinsics\n # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster\n if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)\n set(CMAKE_CUDA_ARCHITECTURES \"60;61;70\") # needed for f16 CUDA intrinsics\n else()\n set(CMAKE_CUDA_ARCHITECTURES \"52;61;70\") # lowest CUDA 12 standard + lowest for integer intrinsics\n #set(CMAKE_CUDA_ARCHITECTURES \"\") # use this to compile much faster, but only F16 models work\n endif()\n endif()\n message(STATUS \"Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}\")\n\n else()\n message(WARNING \"cuBLAS not found\")\n endif()\nendif()\n\nif (LLAMA_MPI)\n cmake_minimum_required(VERSION 3.10)\n find_package(MPI)\n if (MPI_C_FOUND)\n message(STATUS \"MPI found\")\n set(GGML_HEADERS_MPI ggml-mpi.h)\n set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h)\n add_compile_definitions(GGML_USE_MPI)\n add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS})\n if (NOT MSVC)\n add_compile_options(-Wno-cast-qual)\n endif()\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES})\n set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS})\n # Even if you're only using the C header, C++ programs may bring in MPI\n # C++ functions, so more linkage is needed\n if (MPI_CXX_FOUND)\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_CXX_LIBRARIES})\n endif()\n else()\n message(WARNING \"MPI not found\")\n endif()\nendif()\n\nif (LLAMA_CLBLAST)\n find_package(CLBlast)\n if (CLBlast_FOUND)\n message(STATUS \"CLBlast found\")\n\n set(GGML_HEADERS_OPENCL ggml-opencl.h)\n set(GGML_SOURCES_OPENCL ggml-opencl.cpp)\n\n add_compile_definitions(GGML_USE_CLBLAST)\n\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)\n else()\n message(WARNING \"CLBlast not found\")\n endif()\nendif()\n\nif (LLAMA_HIPBLAS)\n list(APPEND CMAKE_PREFIX_PATH /opt/rocm)\n # enable fast atomic operation\n add_compile_options(-munsafe-fp-atomics)\n\n if (NOT ${CMAKE_C_COMPILER_ID} MATCHES \"Clang\")\n message(WARNING \"Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang\")\n endif()\n if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES \"Clang\")\n message(WARNING \"Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++\")\n endif()\n\n find_package(hip)\n find_package(hipblas)\n find_package(rocblas)\n\n if (${hipblas_FOUND} AND ${hip_FOUND})\n message(STATUS \"HIP and hipBLAS found\")\n add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)\n add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)\n if (BUILD_SHARED_LIBS)\n set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON)\n endif()\n if (LLAMA_CUDA_FORCE_DMMV)\n target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)\n endif()\n if (LLAMA_CUDA_FORCE_MMQ)\n target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ)\n endif()\n target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})\n target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})\n target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})\n set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)\n target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)\n\n if (LLAMA_STATIC)\n message(FATAL_ERROR \"Static linking not supported for HIP/ROCm\")\n endif()\n set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm)\n else()\n message(WARNING \"hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm\")\n endif()\nendif()\n\nif (LLAMA_ALL_WARNINGS)\n if (NOT MSVC)\n set(warning_flags -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)\n set(c_flags -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int -Werror=implicit-function-declaration)\n set(cxx_flags -Wmissing-declarations -Wmissing-noreturn)\n set(host_cxx_flags \"\")\n\n if (CMAKE_C_COMPILER_ID MATCHES \"Clang\")\n set(warning_flags ${warning_flags} -Wunreachable-code-break -Wunreachable-code-return)\n set(host_cxx_flags ${host_cxx_flags} -Wmissing-prototypes -Wextra-semi)\n\n if (\n (CMAKE_C_COMPILER_ID STREQUAL \"Clang\" AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 3.8.0) OR\n (CMAKE_C_COMPILER_ID STREQUAL \"AppleClang\" AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 7.3.0)\n )\n set(c_flags ${c_flags} -Wdouble-promotion)\n endif()\n elseif (CMAKE_C_COMPILER_ID STREQUAL \"GNU\")\n set(c_flags ${c_flags} -Wdouble-promotion)\n set(host_cxx_flags ${host_cxx_flags} -Wno-array-bounds)\n\n if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 7.1.0)\n set(host_cxx_flags ${host_cxx_flags} -Wno-format-truncation)\n endif()\n if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.1.0)\n set(host_cxx_flags ${host_cxx_flags} -Wextra-semi)\n endif()\n endif()\n else()\n # todo : msvc\n endif()\n\n set(c_flags ${c_flags} ${warning_flags})\n set(cxx_flags ${cxx_flags} ${warning_flags})\n add_compile_options(\"$<$:${c_flags}>\"\n \"$<$:${cxx_flags}>\"\n \"$<$:${host_cxx_flags}>\")\n\nendif()\n\nif (NOT MSVC)\n set(cuda_flags -Wno-pedantic)\nendif()\nset(cuda_flags ${cxx_flags} -use_fast_math ${cuda_flags})\n\nlist(JOIN host_cxx_flags \" \" cuda_host_flags) # pass host compiler flags as a single argument\nif (NOT cuda_host_flags STREQUAL \"\")\n set(cuda_flags -forward-unknown-to-host-compiler ${cuda_flags} -Xcompiler ${cuda_host_flags})\nendif()\n\nadd_compile_options(\"$<$:${cuda_flags}>\")\n\nif (WIN32)\n add_compile_definitions(_CRT_SECURE_NO_WARNINGS)\n\n if (BUILD_SHARED_LIBS)\n set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)\n endif()\nendif()\n\nif (LLAMA_LTO)\n include(CheckIPOSupported)\n check_ipo_supported(RESULT result OUTPUT output)\n if (result)\n set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)\n else()\n message(WARNING \"IPO is not supported: ${output}\")\n endif()\nendif()\n\n# this version of Apple ld64 is buggy\nexecute_process(\n COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v\n ERROR_VARIABLE output\n)\nif (output MATCHES \"dyld-1015\\.7\")\n add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)\nendif()\n\n# Architecture specific\n# TODO: probably these flags need to be tweaked on some architectures\n# feel free to update the Makefile for your architecture and send a pull request or issue\nmessage(STATUS \"CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}\")\nif (MSVC)\n string(TOLOWER \"${CMAKE_GENERATOR_PLATFORM}\" CMAKE_GENERATOR_PLATFORM_LWR)\n message(STATUS \"CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}\")\nelse ()\n set(CMAKE_GENERATOR_PLATFORM_LWR \"\")\nendif ()\n\nif (NOT MSVC)\n if (LLAMA_STATIC)\n add_link_options(-static)\n if (MINGW)\n add_link_options(-static-libgcc -static-libstdc++)\n endif()\n endif()\n if (LLAMA_GPROF)\n add_compile_options(-pg)\n endif()\nendif()\n\nif ((${CMAKE_SYSTEM_PROCESSOR} MATCHES \"arm\") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"aarch64\") OR (\"${CMAKE_GENERATOR_PLATFORM_LWR}\" MATCHES \"arm64\"))\n message(STATUS \"ARM detected\")\n if (MSVC)\n add_compile_definitions(__ARM_NEON)\n add_compile_definitions(__ARM_FEATURE_FMA)\n add_compile_definitions(__ARM_FEATURE_DOTPROD)\n # add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) # MSVC doesn't support vdupq_n_f16, vld1q_f16, vst1q_f16\n add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead\n else()\n check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)\n if (NOT \"${COMPILER_SUPPORTS_FP16_FORMAT_I3E}\" STREQUAL \"\")\n add_compile_options(-mfp16-format=ieee)\n endif()\n if (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"armv6\")\n # Raspberry Pi 1, Zero\n add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access)\n endif()\n if (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"armv7\")\n # Raspberry Pi 2\n add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)\n endif()\n if (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"armv8\")\n # Raspberry Pi 3, 4, Zero 2 (32-bit)\n add_compile_options(-mno-unaligned-access)\n endif()\n endif()\nelseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"^(x86_64|i686|AMD64)$\" OR \"${CMAKE_GENERATOR_PLATFORM_LWR}\" MATCHES \"^(x86_64|i686|amd64|x64)$\" )\n message(STATUS \"x86 detected\")\n if (MSVC)\n # instruction set detection for MSVC only\n if (LLAMA_NATIVE)\n include(cmake/FindSIMD.cmake)\n endif ()\n if (LLAMA_AVX512)\n add_compile_options($<$:/arch:AVX512>)\n add_compile_options($<$:/arch:AVX512>)\n # MSVC has no compile-time flags enabling specific\n # AVX512 extensions, neither it defines the\n # macros corresponding to the extensions.\n # Do it manually.\n if (LLAMA_AVX512_VBMI)\n add_compile_definitions($<$:__AVX512VBMI__>)\n add_compile_definitions($<$:__AVX512VBMI__>)\n endif()\n if (LLAMA_AVX512_VNNI)\n add_compile_definitions($<$:__AVX512VNNI__>)\n add_compile_definitions($<$:__AVX512VNNI__>)\n endif()\n elseif (LLAMA_AVX2)\n add_compile_options($<$:/arch:AVX2>)\n add_compile_options($<$:/arch:AVX2>)\n elseif (LLAMA_AVX)\n add_compile_options($<$:/arch:AVX>)\n add_compile_options($<$:/arch:AVX>)\n endif()\n else()\n if (LLAMA_NATIVE)\n add_compile_options(-march=native)\n endif()\n if (LLAMA_F16C)\n add_compile_options(-mf16c)\n endif()\n if (LLAMA_FMA)\n add_compile_options(-mfma)\n endif()\n if (LLAMA_AVX)\n add_compile_options(-mavx)\n endif()\n if (LLAMA_AVX2)\n add_compile_options(-mavx2)\n endif()\n if (LLAMA_AVX512)\n add_compile_options(-mavx512f)\n add_compile_options(-mavx512bw)\n endif()\n if (LLAMA_AVX512_VBMI)\n add_compile_options(-mavx512vbmi)\n endif()\n if (LLAMA_AVX512_VNNI)\n add_compile_options(-mavx512vnni)\n endif()\n endif()\nelseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES \"ppc64\")\n message(STATUS \"PowerPC detected\")\n add_compile_options(-mcpu=native -mtune=native)\n #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be)\nelse()\n message(STATUS \"Unknown architecture\")\nendif()\n\n#\n# POSIX conformance\n#\n\n# clock_gettime came in POSIX.1b (1993)\n# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional\n# posix_memalign came in POSIX.1-2001 / SUSv3\n# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)\nadd_compile_definitions(_XOPEN_SOURCE=600)\n\n# Somehow in OpenBSD whenever POSIX conformance is specified\n# some string functions rely on locale_t availability,\n# which was introduced in POSIX.1-2008, forcing us to go higher\nif (CMAKE_SYSTEM_NAME MATCHES \"OpenBSD\")\n remove_definitions(-D_XOPEN_SOURCE=600)\n add_compile_definitions(_XOPEN_SOURCE=700)\nendif()\n\n# Data types, macros and functions related to controlling CPU affinity and\n# some memory allocation are available on Linux through GNU extensions in libc\nif (CMAKE_SYSTEM_NAME MATCHES \"Linux\")\n add_compile_definitions(_GNU_SOURCE)\nendif()\n\n# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,\n# and on macOS its availability depends on enabling Darwin extensions\n# similarly on DragonFly, enabling BSD extensions is necessary\nif (\n CMAKE_SYSTEM_NAME MATCHES \"Darwin\" OR\n CMAKE_SYSTEM_NAME MATCHES \"iOS\" OR\n CMAKE_SYSTEM_NAME MATCHES \"tvOS\" OR\n CMAKE_SYSTEM_NAME MATCHES \"DragonFly\"\n)\n add_compile_definitions(_DARWIN_C_SOURCE)\nendif()\n\n# alloca is a non-standard interface that is not visible on BSDs when\n# POSIX conformance is specified, but not all of them provide a clean way\n# to enable it in such cases\nif (CMAKE_SYSTEM_NAME MATCHES \"FreeBSD\")\n add_compile_definitions(__BSD_VISIBLE)\nendif()\nif (CMAKE_SYSTEM_NAME MATCHES \"NetBSD\")\n add_compile_definitions(_NETBSD_SOURCE)\nendif()\nif (CMAKE_SYSTEM_NAME MATCHES \"OpenBSD\")\n add_compile_definitions(_BSD_SOURCE)\nendif()\n\n#\n# libraries\n#\n\n# ggml\n\nif (GGML_USE_CPU_HBM)\n add_definitions(-DGGML_USE_CPU_HBM)\n find_library(memkind memkind REQUIRED)\nendif()\n\nadd_library(ggml OBJECT\n ggml.c\n ggml.h\n ggml-alloc.c\n ggml-alloc.h\n ggml-backend.c\n ggml-backend.h\n ggml-quants.c\n ggml-quants.h\n ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}\n ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}\n ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}\n ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}\n ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}\n )\n\ntarget_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})\ntarget_compile_features(ggml PUBLIC c_std_11) # don't bump\ntarget_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})\nif (GGML_USE_CPU_HBM)\n target_link_libraries(ggml PUBLIC memkind)\nendif()\n\nadd_library(ggml_static STATIC $)\nif (BUILD_SHARED_LIBS)\n set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)\n add_library(ggml_shared SHARED $)\n target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})\n install(TARGETS ggml_shared LIBRARY)\nendif()\n\n# llama\n\nadd_library(llama\n llama.cpp\n llama.h\n )\n\ntarget_include_directories(llama PUBLIC .)\ntarget_compile_features(llama PUBLIC cxx_std_11) # don't bump\ntarget_link_libraries(llama PRIVATE\n ggml\n ${LLAMA_EXTRA_LIBS}\n )\n\nif (BUILD_SHARED_LIBS)\n set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)\n target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)\n if (LLAMA_METAL)\n set_target_properties(llama PROPERTIES RESOURCE \"${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal\")\n endif()\nendif()\n\n\n#\n# install\n#\n\ninclude(GNUInstallDirs)\ninclude(CMakePackageConfigHelpers)\n\nset(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR}\n CACHE PATH \"Location of header files\")\nset(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR}\n CACHE PATH \"Location of library files\")\nset(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR}\n CACHE PATH \"Location of binary files\")\nset(LLAMA_BUILD_NUMBER ${BUILD_NUMBER})\nset(LLAMA_BUILD_COMMIT ${BUILD_COMMIT})\nset(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})\nget_directory_property(LLAMA_TRANSIENT_DEFINES COMPILE_DEFINITIONS)\n\nconfigure_package_config_file(\n ${CMAKE_CURRENT_SOURCE_DIR}/scripts/LlamaConfig.cmake.in\n ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake\n INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama\n PATH_VARS LLAMA_INCLUDE_INSTALL_DIR\n LLAMA_LIB_INSTALL_DIR\n LLAMA_BIN_INSTALL_DIR )\n\nwrite_basic_package_version_file(\n ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake\n VERSION ${LLAMA_INSTALL_VERSION}\n COMPATIBILITY SameMajorVersion)\n\ninstall(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake\n ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake\n DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)\n\nset(GGML_PUBLIC_HEADERS \"ggml.h\"\n \"${GGML_HEADERS_CUDA}\" \"${GGML_HEADERS_OPENCL}\"\n \"${GGML_HEADERS_METAL}\" \"${GGML_HEADERS_MPI}\" \"${GGML_HEADERS_EXTRA}\")\n\nset_target_properties(ggml PROPERTIES PUBLIC_HEADER \"${GGML_PUBLIC_HEADERS}\")\ninstall(TARGETS ggml PUBLIC_HEADER)\n\nset_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/llama.h)\ninstall(TARGETS llama LIBRARY PUBLIC_HEADER)\n\ninstall(\n FILES convert.py\n PERMISSIONS\n OWNER_READ\n OWNER_WRITE\n OWNER_EXECUTE\n GROUP_READ\n GROUP_EXECUTE\n WORLD_READ\n WORLD_EXECUTE\n DESTINATION ${CMAKE_INSTALL_BINDIR})\ninstall(\n FILES convert-hf-to-powerinfer-gguf.py\n PERMISSIONS\n OWNER_READ\n OWNER_WRITE\n OWNER_EXECUTE\n GROUP_READ\n GROUP_EXECUTE\n WORLD_READ\n WORLD_EXECUTE\n DESTINATION ${CMAKE_INSTALL_BINDIR})\nif (LLAMA_METAL)\n install(\n FILES ggml-metal.metal\n PERMISSIONS\n OWNER_READ\n OWNER_WRITE\n GROUP_READ\n WORLD_READ\n DESTINATION ${CMAKE_INSTALL_BINDIR})\nendif()\n\n#\n# programs, examples and tests\n#\n\nadd_subdirectory(common)\n\nif (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)\n include(CTest)\n add_subdirectory(tests)\nendif ()\n\nif (LLAMA_BUILD_EXAMPLES)\n add_subdirectory(examples)\n add_subdirectory(pocs)\nendif()\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2023 Georgi Gerganov\nCopyright (c) 2023 SJTU-IPADS\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "Package.swift", "content": "// swift-tools-version:5.5\n\nimport PackageDescription\n\n#if arch(arm) || arch(arm64)\nlet platforms: [SupportedPlatform]? = [\n .macOS(.v12),\n .iOS(.v14),\n .watchOS(.v4),\n .tvOS(.v14)\n]\nlet exclude: [String] = []\nlet resources: [Resource] = [\n .process(\"ggml-metal.metal\")\n]\nlet additionalSources: [String] = [\"ggml-metal.m\"]\nlet additionalSettings: [CSetting] = [\n .unsafeFlags([\"-fno-objc-arc\"]),\n .define(\"GGML_USE_METAL\")\n]\n#else\nlet platforms: [SupportedPlatform]? = nil\nlet exclude: [String] = [\"ggml-metal.metal\"]\nlet resources: [Resource] = []\nlet additionalSources: [String] = []\nlet additionalSettings: [CSetting] = []\n#endif\n\nlet package = Package(\n name: \"llama\",\n platforms: platforms,\n products: [\n .library(name: \"llama\", targets: [\"llama\"]),\n ],\n targets: [\n .target(\n name: \"llama\",\n path: \".\",\n exclude: exclude,\n sources: [\n \"ggml.c\",\n \"llama.cpp\",\n \"ggml-alloc.c\",\n \"ggml-backend.c\",\n \"ggml-quants.c\",\n ] + additionalSources,\n resources: resources,\n publicHeadersPath: \"spm-headers\",\n cSettings: [\n .unsafeFlags([\"-Wno-shorten-64-to-32\", \"-O3\", \"-DNDEBUG\"]),\n .define(\"GGML_USE_ACCELERATE\")\n // NOTE: NEW_LAPACK will required iOS version 16.4+\n // We should consider add this in the future when we drop support for iOS 14\n // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)\n // .define(\"ACCELERATE_NEW_LAPACK\"),\n // .define(\"ACCELERATE_LAPACK_ILP64\")\n ] + additionalSettings,\n linkerSettings: [\n .linkedFramework(\"Accelerate\")\n ]\n )\n ],\n cxxLanguageStandard: .cxx11\n)\n" }, { "path": "README.md", "content": "# PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU\n\n## TL;DR\nPowerInfer is a CPU/GPU LLM inference engine leveraging **activation locality** for your device.\n\n\"SJTU-IPADS%2FPowerInfer\n\n[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n\n[Project Kanban](https://github.com/orgs/SJTU-IPADS/projects/2/views/2)\n\n## Latest News 🔥\n- [2026/1/5] We released **[Tiiny AI Pocket Lab](https://tiiny.ai/)**, the world's first pocket-size supercomputer. It runs GPT-OSS-120B (int4) locally at **20 tokens/s**. Featured at CES 2026.\n- [2025/7/27] We released [SmallThinker-21BA3B-Instruct](https://huggingface.co/PowerInfer/SmallThinker-21BA3B-Instruct) and [SmallThinker-4BA0.6B-Instruct](https://huggingface.co/PowerInfer/SmallThinker-4BA0.6B-Instruct). We also released a corresponding framework for efficient [on-device inference](./smallthinker/README.md). \n- [2024/6/11] We are thrilled to introduce [PowerInfer-2](https://arxiv.org/abs/2406.06282), our highly optimized inference framework designed specifically for smartphones. With TurboSparse-Mixtral-47B, it achieves an impressive speed of 11.68 tokens per second, which is up to 22 times faster than other state-of-the-art frameworks.\n- [2024/6/11] We are thrilled to present [Turbo Sparse](https://arxiv.org/abs/2406.05955), our TurboSparse models for fast inference. With just $0.1M, we sparsified the original Mistral and Mixtral model to nearly 90% sparsity while maintaining superior performance! For a Mixtral-level model, our TurboSparse-Mixtral activates only **4B** parameters!\n- [2024/5/20] **Competition Recruitment: CCF-TCArch Customized Computing Challenge 2024**. The CCF TCARCH CCC is a national competition organized by the Technical Committee on Computer Architecture (TCARCH) of the China Computer Federation (CCF). This year's competition aims to optimize the PowerInfer inference engine using the open-source ROCm/HIP. More information about the competition can be found [here](https://ccf-tcarch-ccc.github.io/2024/).\n- [2024/5/17] We now provide support for AMD devices with ROCm.\n- [2024/3/28] We are trilled to present [Bamboo LLM](https://github.com/SJTU-IPADS/Bamboo) that achieves both top-level performance and unparalleled speed with PowerInfer! Experience it with Bamboo-7B [Base](https://huggingface.co/PowerInfer/Bamboo-base-v0.1-gguf) / [DPO](https://huggingface.co/PowerInfer/Bamboo-DPO-v0.1-gguf).\n- [2024/3/14] We supported ProSparse Llama 2 ([7B](https://huggingface.co/SparseLLM/prosparse-llama-2-7b)/[13B](https://huggingface.co/SparseLLM/prosparse-llama-2-13b)), ReLU models with ~90% sparsity, matching original Llama 2's performance (Thanks THUNLP & ModelBest)!\n- [2024/1/11] We supported Windows with GPU inference!\n- [2023/12/24] We released an online [gradio demo](https://powerinfer-gradio.vercel.app/) for Falcon(ReLU)-40B-FP16!\n- [2023/12/19] We officially released PowerInfer!\n\n## Demo 🔥\n\nhttps://github.com/SJTU-IPADS/PowerInfer/assets/34213478/fe441a42-5fce-448b-a3e5-ea4abb43ba23\n\nPowerInfer v.s. llama.cpp on a single RTX 4090(24G) running Falcon(ReLU)-40B-FP16 with a 11x speedup!\n\nBoth PowerInfer and llama.cpp were running on the same hardware and fully utilized VRAM on RTX 4090.\n\n> [!NOTE]\n> **Live Demo Online⚡️**\n>\n> Try out our [Gradio server](https://powerinfer-gradio.vercel.app/) hosting Falcon(ReLU)-40B-FP16 on a RTX 4090!\n>\n> Experimental and without warranties 🚧\n\n## Abstract\n\nWe introduce PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC)\nequipped with a single consumer-grade GPU. The key underlying the design of PowerInfer is exploiting the high **locality**\ninherent in LLM inference, characterized by a power-law distribution in neuron activation.\n\nThis distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated\nacross inputs, while the majority, cold neurons, vary based on specific inputs.\nPowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine:\nhot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed\non the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers.\nPowerInfer further integrates adaptive predictors and neuron-aware sparse operators,\noptimizing the efficiency of neuron activation and computational sparsity.\n\nEvaluation shows that PowerInfer attains an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU,\nonly 18\\% lower than that achieved by a top-tier server-grade A100 GPU.\nThis significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy.\n\n## Features\nPowerInfer is a high-speed and easy-to-use inference engine for deploying LLMs locally.\n\nPowerInfer is fast with:\n\n- **Locality-centric design**: Utilizes sparse activation and 'hot'/'cold' neuron concept for efficient LLM inference, ensuring high speed with lower resource demands.\n- **Hybrid CPU/GPU Utilization**: Seamlessly integrates memory/computation capabilities of CPU and GPU for a balanced workload and faster processing.\n\nPowerInfer is flexible and easy to use with:\n\n- **Easy Integration**: Compatible with popular [ReLU-sparse models](https://huggingface.co/SparseLLM).\n- **Local Deployment Ease**: Designed and deeply optimized for local deployment on consumer-grade hardware, enabling low-latency LLM inference and serving on a single GPU.\n- **Backward Compatibility**: While distinct from llama.cpp, you can make use of most of `examples/` the same way as llama.cpp such as server and batched generation. PowerInfer also supports inference with llama.cpp's model weights for compatibility purposes, but there will be no performance gain.\n\nYou can use these models with PowerInfer today:\n\n- Falcon-40B\n- Llama2 family\n- ProSparse Llama2 family\n- Bamboo-7B\n\nWe have tested PowerInfer on the following platforms:\n\n- x86-64 CPUs with AVX2 instructions, with or without NVIDIA GPUs, under **Linux**.\n- x86-64 CPUs with AVX2 instructions, with or without NVIDIA GPUs, under **Windows**.\n- Apple M Chips (CPU only) on **macOS**. (As we do not optimize for Mac, the performance improvement is not significant now.)\n\nAnd new features coming soon:\n\n- Metal backend for sparse inference on macOS\n\nPlease kindly refer to our [Project Kanban](https://github.com/orgs/SJTU-IPADS/projects/2/views/2) for our current focus of development.\n\n## Getting Started\n\n- [Installation](#setup-and-installation)\n- [Model Weights](#model-weights)\n- [Inference](#inference)\n\n## Setup and Installation\n\n### Pre-requisites\n\nPowerInfer requires the following dependencies:\n\n- CMake (3.17+)\n- Python (3.8+) and pip (19.3+), for converting model weights and automatic FFN offloading\n\n### Get the Code\n\n```bash\ngit clone https://github.com/Tiiny-AI/PowerInfer\ncd PowerInfer\npip install -r requirements.txt # install Python helpers' dependencies\n```\n### Build\n\nIn order to build PowerInfer you have two different options. These commands are supposed to be run from the root directory of the project.\n\nUsing `CMake`(3.17+):\n* If you have an NVIDIA GPU:\n```bash\ncmake -S . -B build -DLLAMA_CUBLAS=ON\ncmake --build build --config Release\n```\n* If you have an AMD GPU:\n```bash\n# Replace '1100' to your card architecture name, you can get it by rocminfo\nCC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1100\ncmake --build build --config Release\n```\n\n* If you have just CPU:\n\n```bash\ncmake -S . -B build\ncmake --build build --config Release\n```\n\n## Model Weights\n\nPowerInfer models are stored in a special format called *PowerInfer GGUF* based on GGUF format, consisting of both LLM weights and predictor weights.\n\n### Download PowerInfer GGUF via Hugging Face\n\nYou can obtain PowerInfer GGUF weights at `*.powerinfer.gguf` as well as profiled model activation statistics for 'hot'-neuron offloading from each Hugging Face repo below.\n\n| Base Model | PowerInfer GGUF |\n| --------------------- | ------------------------------------------------------------------------------------------------------------- |\n| LLaMA(ReLU)-2-7B | [PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF) |\n| LLaMA(ReLU)-2-13B | [PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF) |\n| Falcon(ReLU)-40B | [PowerInfer/ReluFalcon-40B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluFalcon-40B-PowerInfer-GGUF) |\n| LLaMA(ReLU)-2-70B | [PowerInfer/ReluLLaMA-70B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-70B-PowerInfer-GGUF) |\n| ProSparse-LLaMA-2-7B | [PowerInfer/ProSparse-LLaMA-2-7B-GGUF](https://huggingface.co/PowerInfer/prosparse-llama-2-7b-gguf) |\n| ProSparse-LLaMA-2-13B | [PowerInfer/ProSparse-LLaMA-2-13B-GGUF](https://huggingface.co/PowerInfer/prosparse-llama-2-13b-gguf) |\n| Bamboo-base-7B 🌟 | [PowerInfer/Bamboo-base-v0.1-gguf](https://huggingface.co/PowerInfer/Bamboo-base-v0.1-gguf) |\n| Bamboo-DPO-7B 🌟 | [PowerInfer/Bamboo-DPO-v0.1-gguf](https://huggingface.co/PowerInfer/Bamboo-DPO-v0.1-gguf) |\n\nWe recommend using [`huggingface-cli`](https://huggingface.co/docs/huggingface_hub/guides/cli) to download the whole model repo. For example, the following command will download [PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF) into the `./ReluLLaMA-7B` directory.\n\n```shell\nhuggingface-cli download --resume-download --local-dir ReluLLaMA-7B --local-dir-use-symlinks False PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF\n```\n\nAs such, PowerInfer can automatically make use of the following directory structure for feature-complete model offloading:\n```\n.\n├── *.powerinfer.gguf (Unquantized PowerInfer model)\n├── *.q4.powerinfer.gguf (INT4 quantized PowerInfer model, if available)\n├── activation (Profiled activation statistics for fine-grained FFN offloading)\n│ ├── activation_x.pt (Profiled activation statistics for layer x)\n│ └── ...\n├── *.[q4].powerinfer.gguf.generated.gpuidx (Generated GPU index at runtime for corresponding model)\n```\n\n### Convert from Original Model Weights + Predictor Weights\n\nHugging Face limits single model weight to 50GiB. For unquantized models >= 40B, you can convert PowerInfer GGUF from the original model weights and predictor weights obtained from Hugging Face.\n\n| Base Model | Original Model | Predictor |\n| --------------------- | ----------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |\n| LLaMA(ReLU)-2-7B | [SparseLLM/ReluLLaMA-7B](https://huggingface.co/SparseLLM/ReluLLaMA-7B) | [PowerInfer/ReluLLaMA-7B-Predictor](https://huggingface.co/PowerInfer/ReluLLaMA-7B-Predictor) |\n| LLaMA(ReLU)-2-13B | [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B) | [PowerInfer/ReluLLaMA-13B-Predictor](https://huggingface.co/PowerInfer/ReluLLaMA-13B-Predictor) |\n| Falcon(ReLU)-40B | [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B) | [PowerInfer/ReluFalcon-40B-Predictor](https://huggingface.co/PowerInfer/ReluFalcon-40B-Predictor) |\n| LLaMA(ReLU)-2-70B | [SparseLLM/ReluLLaMA-70B](https://huggingface.co/SparseLLM/ReluLLaMA-70B) | [PowerInfer/ReluLLaMA-70B-Predictor](https://huggingface.co/PowerInfer/ReluLLaMA-70B-Predictor) |\n| ProSparse-LLaMA-2-7B | [SparseLLM/ProSparse-LLaMA-2-7B](https://huggingface.co/SparseLLM/prosparse-llama-2-7b) | [PowerInfer/ProSparse-LLaMA-2-7B-Predictor](https://huggingface.co/PowerInfer/prosparse-llama-2-7b-predictor) |\n| ProSparse-LLaMA-2-13B | [SparseLLM/ProSparse-LLaMA-2-13B](https://huggingface.co/SparseLLM/prosparse-llama-2-13b) | [PowerInfer/ProSparse-LLaMA-2-13B-Predictor](https://huggingface.co/PowerInfer/prosparse-llama-2-13b-predictor) |\n| Bamboo-base-7B 🌟 | [PowerInfer/Bamboo-base-v0.1](https://huggingface.co/PowerInfer/Bamboo-base-v0_1) | [PowerInfer/Bamboo-base-v0.1-predictor](https://huggingface.co/PowerInfer/Bamboo-base-v0.1-predictor) |\n| Bamboo-DPO-7B 🌟 | [PowerInfer/Bamboo-DPO-v0.1](https://huggingface.co/PowerInfer/Bamboo-DPO-v0_1) | [PowerInfer/Bamboo-DPO-v0.1-predictor](https://huggingface.co/PowerInfer/Bamboo-DPO-v0.1-predictor) |\n\nYou can use the following command to convert the original model weights and predictor weights to PowerInfer GGUF:\n```bash\n# make sure that you have done `pip install -r requirements.txt`\npython convert.py --outfile /PATH/TO/POWERINFER/GGUF/REPO/MODELNAME.powerinfer.gguf /PATH/TO/ORIGINAL/MODEL /PATH/TO/PREDICTOR\n# python convert.py --outfile ./ReluLLaMA-70B-PowerInfer-GGUF/llama-70b-relu.powerinfer.gguf ./SparseLLM/ReluLLaMA-70B ./PowerInfer/ReluLLaMA-70B-Predictor\n```\nFor the same reason, we suggest keeping the same directory structure as PowerInfer GGUF repos after conversion.\n\n
\n\nConvert Original models into dense GGUF models(compatible with llama.cpp)\n\n```bash\npython convert-dense.py --outfile /PATH/TO/DENSE/GGUF/REPO/MODELNAME.gguf /PATH/TO/ORIGINAL/MODEL\n# python convert-dense.py --outfile ./Bamboo-DPO-v0.1-gguf/bamboo-7b-dpo-v0.1.gguf --outtype f16 ./Bamboo-DPO-v0.1\n```\n\nPlease note that the generated dense GGUF models might not work properly with llama.cpp, as we have altered activation functions (for ReluLLaMA and Prosparse models), or the model architecture (for Bamboo models). The dense GGUF models generated by convert-dense.py can be used for PowerInfer in dense inference mode, but might not work properly with llama.cpp.\n\n
\n\n## Inference\n\nFor CPU-only and CPU-GPU hybrid inference with all available VRAM, you can use the following instructions to run PowerInfer:\n```bash\n./build/bin/main -m /PATH/TO/MODEL -n $output_token_count -t $thread_num -p $prompt\n# e.g.: ./build/bin/main -m ./ReluFalcon-40B-PowerInfer-GGUF/falcon-40b-relu.q4.powerinfer.gguf -n 128 -t 8 -p \"Once upon a time\"\n# For Windows: .\\build\\bin\\Release\\main.exe -m .\\ReluFalcon-40B-PowerInfer-GGUF\\falcon-40b-relu.q4.powerinfer.gguf -n 128 -t 8 -p \"Once upon a time\"\n```\n\nIf you want to limit the VRAM usage of GPU:\n```bash\n./build/bin/main -m /PATH/TO/MODEL -n $output_token_count -t $thread_num -p $prompt --vram-budget $vram_gb\n# e.g.: ./build/bin/main -m ./ReluLLaMA-7B-PowerInfer-GGUF/llama-7b-relu.powerinfer.gguf -n 128 -t 8 -p \"Once upon a time\" --vram-budget 8\n# For Windows: .\\build\\bin\\Release\\main.exe -m .\\ReluLLaMA-7B-PowerInfer-GGUF\\llama-7b-relu.powerinfer.gguf -n 128 -t 8 -p \"Once upon a time\" --vram-budget 8\n```\nUnder CPU-GPU hybrid inference, PowerInfer will automatically offload all dense activation blocks to GPU, then split FFN and offload to GPU if possible.\n\n
\nDense inference mode (limited support)\n\nIf you want to run PowerInfer to infer with the dense variants of the PowerInfer model family, you can use similarly as llama.cpp does:\n\n```bash\n./build/bin/main -m /PATH/TO/DENSE/MODEL -n $output_token_count -t $thread_num -p $prompt -ngl $num_gpu_layers\n# e.g.: ./build/bin/main -m ./Bamboo-base-v0.1-gguf/bamboo-7b-v0.1.gguf -n 128 -t 8 -p \"Once upon a time\" -ngl 12\n```\n\nSo is the case for other `examples/` like `server` and `batched_generation`. Please note that the dense inference mode is not a \"compatible mode\" for all models. We have altered activation functions (for ReluLLaMA and Prosparse models) in this mode to match with our model family. \n\n
\n\n## Serving, Perplexity Evaluation, and more applications\n\nPowerInfer supports serving and batched generation with the same instructions as llama.cpp. Generally, you can use the same command as llama.cpp, except for `-ngl` argument which has been replaced by `--vram-budget` for PowerInfer. Please refer to the detailed instructions in each `examples/` directory. For example:\n\n- [Serving](./examples/server/README.md)\n- [Perplexity Evaluation](./examples/perplexity/README.md)\n- [Batched Generation](./examples/batched/README.md)\n\n## Quantization\n\nPowerInfer has optimized quantization support for INT4(`Q4_0`) models. You can use the following instructions to quantize PowerInfer GGUF model:\n```bash\n./build/bin/quantize /PATH/TO/MODEL /PATH/TO/OUTPUT/QUANTIZED/MODEL Q4_0\n# e.g.: ./build/bin/quantize ./ReluFalcon-40B-PowerInfer-GGUF/falcon-40b-relu.powerinfer.gguf ./ReluFalcon-40B-PowerInfer-GGUF/falcon-40b-relu.q4.powerinfer.gguf Q4_0\n# For Windows: .\\build\\bin\\Release\\quantize.exe .\\ReluFalcon-40B-PowerInfer-GGUF\\falcon-40b-relu.powerinfer.gguf .\\ReluFalcon-40B-PowerInfer-GGUF\\falcon-40b-relu.q4.powerinfer.gguf Q4_0\n```\nThen you can use the quantized model for inference with PowerInfer with the same instructions as above.\n\n## More Documentation\n- [Performance troubleshooting](./docs/token_generation_performance_tips.md)\n\n## Evaluation\n\nWe evaluated PowerInfer vs. llama.cpp on a single RTX 4090(24G) with a series of FP16 ReLU models under inputs of length 64, and the results are shown below. PowerInfer achieves up to 11x speedup on Falcon 40B and up to 3x speedup on Llama 2 70B.\n\n![github-eval-4090](https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/d700fa6c-77ba-462f-a2fc-3fd21c898f33)\nThe X axis indicates the output length, and the Y axis represents the speedup compared with llama.cpp. The number above each bar indicates the end-to-end generation speed (total prompting + generation time / total tokens generated, in tokens/s).\n\nWe also evaluated PowerInfer on a single RTX 2080Ti(11G) with INT4 ReLU models under inputs of length 8, and the results are illustrated in the same way as above. PowerInfer achieves up to 8x speedup on Falcon 40B and up to 3x speedup on Llama 2 70B.\n\n![github-eval-2080ti-q4](https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/0fc1bfc4-aafc-4e82-a865-bec0143aff1a)\n\nPlease refer to our [paper](https://ipads.se.sjtu.edu.cn/_media/publications/powerinfer-20231219.pdf) for more evaluation details.\n\n## FAQs\n1. What if I encountered `CUDA_ERROR_OUT_OF_MEMORY`?\n - You can try to run with `--reset-gpu-index` argument to rebuild the GPU index for this model to avoid any stale cache.\n - Due to our current implementation, model offloading might not be as accurate as expected. You can try with `--vram-budget` with a slightly lower value or `--disable-gpu-index` to disable FFN offloading.\n\n2. Does PowerInfer support mistral, original llama, Qwen, ...?\n - Now we only support models with ReLU/ReGLU/Squared ReLU activation function. So we do not support these models now. It's worth mentioning that a [paper](https://arxiv.org/pdf/2310.04564.pdf) has demonstrated that using the ReLU/ReGLU activation function has a negligible impact on convergence and performance.\n\n3. Why is there a noticeable downgrade in the performance metrics of our current ReLU model, particularly the 70B model?\n - In contrast to the typical requirement of around 2T tokens for LLM training, our model's fine-tuning was conducted with only 5B tokens. This insufficient retraining has resulted in the model's inability to regain its original performance. We are actively working on updating to a more capable model, so please stay tuned.\n\n4. What if...\n - Issues are welcomed! Please feel free to open an issue and attach your running environment and running parameters. We will try our best to help you.\n\n## TODOs\nWe will release the code and data in the following order, please stay tuned!\n\n- [x] Release core code of PowerInfer, supporting Llama-2, Falcon-40B.\n- [x] Support ~~Mistral-7B~~ (Bamboo-7B)\n- [x] Support Windows\n- [ ] Support text-generation-webui\n- [x] Release perplexity evaluation code\n- [ ] Support Metal for Mac\n- [ ] Release code for OPT models\n- [ ] Release predictor training code\n- [x] Support online split for FFN network\n- [ ] Support Multi-GPU\n\n\n## Paper and Citation\nMore technical details can be found in our [paper](https://ipads.se.sjtu.edu.cn/_media/publications/powerinfer-20231219.pdf).\n\nIf you find PowerInfer useful or relevant to your project and research, please kindly cite our paper:\n\n```bibtex\n@misc{song2023powerinfer,\n title={PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU},\n author={Yixin Song and Zeyu Mi and Haotong Xie and Haibo Chen},\n year={2023},\n eprint={2312.12456},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n## Acknowledgement\nWe are thankful for the easily modifiable operator library [ggml](https://github.com/ggerganov/ggml) and execution runtime provided by [llama.cpp](https://github.com/ggerganov/llama.cpp). We also extend our gratitude to [THUNLP](https://nlp.csai.tsinghua.edu.cn/) for their support of ReLU-based sparse models. We also appreciate the research of [Deja Vu](https://proceedings.mlr.press/v202/liu23am.html), which inspires PowerInfer.\n" }, { "path": "SHA256SUMS", "content": "700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth\n666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin\nec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf models/7B/ggml-model-q4_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_1.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_1.bin\n7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json\n745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth\nd5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth\n2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808 models/13B/ggml-model-f16.bin\nfad169e6f0f575402cf75945961cb4a8ecd824ba4da6be2af831f320c4348fa5 models/13B/ggml-model-q4_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q4_1.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_1.bin\n4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json\ne23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth\n4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth\n24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth\n1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth\n7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37 models/30B/ggml-model-f16.bin\nd2a441403944819492ec8c2002cc36fa38468149bfb4b7b4c52afc7bd9a7166d models/30B/ggml-model-q4_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q4_1.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_1.bin\n2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json\n135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth\n9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth\ne7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/consolidated.02.pth\n73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e models/65B/consolidated.03.pth\n882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225 models/65B/consolidated.04.pth\na287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth\n72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth\nd27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth\n60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin\ncde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92 models/65B/ggml-model-q4_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_1.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_0.bin\nffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_1.bin\n999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json\n9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model\n" }, { "path": "atomic_windows.h", "content": "/*\n * C11 emulation header\n *\n * PLEASE LICENSE, (C) 2022, Michael Clark \n *\n * All rights to this work are granted for all purposes, with exception of\n * author's implied right of copyright to defend the free use of this work.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES\n * WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF\n * MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR\n * ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES\n * WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN\n * ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF\n * OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n */\n#ifdef _WIN32\n\n/*\n * C11 emulation\n *\n * This header can be included from C and uses C11 _Generic selection.\n * This header requires MSVC flags: \"/O2 /TC /std:c11 /volatile:iso\".\n *\n * Note: some primitives may be missing and some primitives may haver\n * stronger ordering than is required thus not produce optimal code,\n * and some primitives may be buggy.\n */\n#include \n#include \"winnt.h\"\n\n#define __concat2(x,y) x ## y\n#define __concat3(x,y,z) x ## y ## z\n\n#if UINTPTR_MAX == 0xFFFFFFFFFFFFFFFFull\n#define __intptr __int64\n#define __ptr i64\n#elif UINTPTR_MAX == 0xFFFFFFFFu\n#define __intptr __int32\n#define __ptr i32\n#else\n#error unable to determine pointer width\n#endif\n\n#define _Atomic volatile\n\n#define ATOMIC_BOOL_LOCK_FREE 1\n#define ATOMIC_CHAR_LOCK_FREE 1\n#define ATOMIC_SHORT_LOCK_FREE 1\n#define ATOMIC_INT_LOCK_FREE 1\n#define ATOMIC_LONG_LOCK_FREE 1\n#define ATOMIC_LLONG_LOCK_FREE 1\n#define ATOMIC_POINTER_LOCK_FREE 1\n\n#define ATOMIC_FLAG_INIT { 0 }\n\n#define __ATOMIC_RELAXED 0\n#define __ATOMIC_CONSUME 1\n#define __ATOMIC_ACQUIRE 2\n#define __ATOMIC_RELEASE 3\n#define __ATOMIC_ACQ_REL 4\n#define __ATOMIC_SEQ_CST 5\n\ntypedef enum memory_order {\n memory_order_relaxed = __ATOMIC_RELAXED,\n memory_order_consume = __ATOMIC_CONSUME,\n memory_order_acquire = __ATOMIC_ACQUIRE,\n memory_order_release = __ATOMIC_RELEASE,\n memory_order_acq_rel = __ATOMIC_ACQ_REL,\n memory_order_seq_cst = __ATOMIC_SEQ_CST\n} memory_order;\n\ntypedef long long llong;\ntypedef unsigned char uchar;\ntypedef unsigned short ushort;\ntypedef unsigned int uint;\ntypedef unsigned long ulong;\ntypedef unsigned long long ullong;\n\ntypedef _Atomic _Bool atomic_bool;\ntypedef _Atomic char atomic_char;\ntypedef _Atomic unsigned char atomic_uchar;\ntypedef _Atomic short atomic_short;\ntypedef _Atomic unsigned short atomic_ushort;\ntypedef _Atomic int atomic_int;\ntypedef _Atomic unsigned int atomic_uint;\ntypedef _Atomic long atomic_long;\ntypedef _Atomic unsigned long atomic_ulong;\ntypedef _Atomic long long atomic_llong;\ntypedef _Atomic unsigned long long atomic_ullong;\ntypedef _Atomic intptr_t atomic_intptr_t;\ntypedef _Atomic uintptr_t atomic_uintptr_t;\ntypedef _Atomic size_t atomic_size_t;\ntypedef _Atomic ptrdiff_t atomic_ptrdiff_t;\ntypedef _Atomic intmax_t atomic_intmax_t;\ntypedef _Atomic uintmax_t atomic_uintmax_t;\ntypedef void* _Atomic atomic_ptr;\n\ntypedef struct atomic_flag { atomic_bool _Value; } atomic_flag;\n\nstatic inline __int8 __msvc_xchg_i8(__int8 volatile* addr, __int8 val)\n{\n return _InterlockedExchange8(addr, val);\n}\nstatic inline __int16 __msvc_xchg_i16(__int16 volatile* addr, __int16 val)\n{\n return _InterlockedExchange16(addr, val);\n}\nstatic inline __int32 __msvc_xchg_i32(__int32 volatile* addr, __int32 val)\n{\n return _InterlockedExchange(addr, val);\n}\nstatic inline __int64 __msvc_xchg_i64(__int64 volatile* addr, __int64 val)\n{\n return _InterlockedExchange64(addr, val);\n}\n\n#define __msvc_xchg_ptr(ptr) __concat2(__msvc_xchg_,ptr)\n\nstatic inline char __c11_atomic_exchange__atomic_char(atomic_char* obj, char desired)\n{\n return (char)__msvc_xchg_i8((__int8 volatile*)obj, (__int8)desired);\n}\nstatic inline short __c11_atomic_exchange__atomic_short(atomic_short* obj, short desired)\n{\n return (short)__msvc_xchg_i16((__int16 volatile*)obj, (__int16)desired);\n}\nstatic inline int __c11_atomic_exchange__atomic_int(atomic_int* obj, int desired)\n{\n return (int)__msvc_xchg_i32((__int32 volatile*)obj, (__int32)desired);\n}\nstatic inline long __c11_atomic_exchange__atomic_long(atomic_long* obj, long desired)\n{\n return (int)__msvc_xchg_i32((__int32 volatile*)obj, (__int32)desired);\n}\nstatic inline llong __c11_atomic_exchange__atomic_llong(atomic_llong* obj, llong desired)\n{\n return (llong)__msvc_xchg_i64((__int64 volatile*)obj, (__int64)desired);\n}\nstatic inline uchar __c11_atomic_exchange__atomic_uchar(atomic_uchar* obj, uchar desired)\n{\n return (char)__msvc_xchg_i8((__int8 volatile*)obj, (__int8)desired);\n}\nstatic inline ushort __c11_atomic_exchange__atomic_ushort(atomic_ushort* obj, ushort desired)\n{\n return (short)__msvc_xchg_i16((__int16 volatile*)obj, (__int16)desired);\n}\nstatic inline uint __c11_atomic_exchange__atomic_uint(atomic_uint* obj, uint desired)\n{\n return (int)__msvc_xchg_i32((__int32 volatile*)obj, (__int32)desired);\n}\nstatic inline ulong __c11_atomic_exchange__atomic_ulong(atomic_ulong* obj, ulong desired)\n{\n return (int)__msvc_xchg_i32((__int32 volatile*)obj, (__int32)desired);\n}\nstatic inline ullong __c11_atomic_exchange__atomic_ullong(atomic_ullong* obj, ullong desired)\n{\n return (llong)__msvc_xchg_i64((__int64 volatile*)obj, (__int64)desired);\n}\nstatic inline void* __c11_atomic_exchange__atomic_ptr(atomic_ptr* obj, void* desired)\n{\n return (void*)__msvc_xchg_ptr(__ptr)((__intptr volatile*)obj, (ptrdiff_t)desired);\n}\n\n#define __c11_atomic_exchange(obj,desired) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_exchange__atomic_char, \\\natomic_uchar*: __c11_atomic_exchange__atomic_uchar, \\\natomic_short*: __c11_atomic_exchange__atomic_short, \\\natomic_ushort*: __c11_atomic_exchange__atomic_ushort, \\\natomic_int*: __c11_atomic_exchange__atomic_int, \\\natomic_uint*: __c11_atomic_exchange__atomic_uint, \\\natomic_long*: __c11_atomic_exchange__atomic_long, \\\natomic_ulong*: __c11_atomic_exchange__atomic_ulong, \\\natomic_llong*: __c11_atomic_exchange__atomic_llong, \\\natomic_ullong*: __c11_atomic_exchange__atomic_ullong \\\n)(obj,desired)\n\n#define atomic_exchange(obj,desired) __c11_atomic_exchange(obj,desired)\n#define atomic_store(obj,desired) __c11_atomic_exchange(obj,desired)\n#define atomic_exchange_explicit(obj,desired,mo) __c11_atomic_exchange(obj,desired)\n#define atomic_store_explicit(obj,desired,mo) __c11_atomic_exchange(obj,desired)\n\nstatic inline __int8 __msvc_cmpxchg_i8(__int8 volatile* addr, __int8 oldval, __int8 newval)\n{\n return _InterlockedCompareExchange8((__int8 volatile*)addr, newval, oldval);\n}\nstatic inline __int16 __msvc_cmpxchg_i16(__int16 volatile* addr, __int16 oldval, __int16 newval)\n{\n return _InterlockedCompareExchange16((__int16 volatile*)addr, newval, oldval);\n}\nstatic inline __int32 __msvc_cmpxchg_i32(__int32 volatile* addr, __int32 oldval, __int32 newval)\n{\n return _InterlockedCompareExchange((__int32 volatile*)addr, newval, oldval);\n}\nstatic inline __int64 __msvc_cmpxchg_i64(__int64 volatile* addr, __int64 oldval, __int64 newval)\n{\n return _InterlockedCompareExchange64((__int64 volatile*)addr, newval, oldval);\n}\n\n#define __msvc_cmpxchg_ptr(ptr) __concat2(__msvc_cmpxchg_,ptr)\n\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_char(atomic_char* obj, char* expected, char desired)\n{\n char cmp = *expected, val = __msvc_cmpxchg_i8((__int8 volatile*)obj, (__int8)cmp, (__int8)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_short(atomic_short* obj, short* expected, short desired)\n{\n short cmp = *expected, val = __msvc_cmpxchg_i16((__int16 volatile*)obj, (__int16)cmp, (__int16)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_int(atomic_int* obj, int* expected, int desired)\n{\n int cmp = *expected, val = __msvc_cmpxchg_i32((__int32 volatile*)obj, (__int32)cmp, (__int32)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_long(atomic_long* obj, long* expected, long desired)\n{\n long cmp = *expected, val = __msvc_cmpxchg_i32((__int32 volatile*)obj, (__int32)cmp, (__int32)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_llong(atomic_llong* obj, llong* expected, llong desired)\n{\n llong cmp = *expected, val = __msvc_cmpxchg_i64((__int64 volatile*)obj, (__int64)cmp, (__int64)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_uchar(atomic_uchar* obj, uchar* expected, uchar desired)\n{\n uchar cmp = *expected, val = __msvc_cmpxchg_i8((__int8 volatile*)obj, (__int8)cmp, (__int8)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_ushort(atomic_ushort* obj, ushort* expected, ushort desired)\n{\n ushort cmp = *expected, val = __msvc_cmpxchg_i16((__int16 volatile*)obj, (__int16)cmp, (__int16)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_uint(atomic_uint* obj, uint* expected, uint desired)\n{\n uint cmp = *expected, val = __msvc_cmpxchg_i32((__int32 volatile*)obj, (__int32)cmp, (__int32)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_ulong(atomic_ulong* obj, ulong* expected, ulong desired)\n{\n ulong cmp = *expected, val = __msvc_cmpxchg_i32((__int32 volatile*)obj, (__int32)cmp, (__int32)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_ullong(atomic_ullong* obj, ullong* expected, ullong desired)\n{\n ullong cmp = *expected, val = __msvc_cmpxchg_i64((__int64 volatile*)obj, (__int64)cmp, (__int64)desired); return val == cmp;\n}\nstatic inline _Bool __c11_atomic_compare_exchange_strong__atomic_ptr(atomic_ptr* obj, void** expected, void* desired)\n{\n ptrdiff_t cmp = *(ptrdiff_t*)expected, val = __msvc_cmpxchg_ptr(__ptr)((__intptr volatile*)obj, (ptrdiff_t)cmp, (ptrdiff_t)desired); return (ptrdiff_t)val == cmp;\n}\n\n\n#define __c11_atomic_compare_exchange_strong(obj,expected,desired) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_compare_exchange_strong__atomic_char, \\\natomic_uchar*: __c11_atomic_compare_exchange_strong__atomic_uchar, \\\natomic_short*: __c11_atomic_compare_exchange_strong__atomic_short, \\\natomic_ushort*: __c11_atomic_compare_exchange_strong__atomic_ushort, \\\natomic_int*: __c11_atomic_compare_exchange_strong__atomic_int, \\\natomic_uint*: __c11_atomic_compare_exchange_strong__atomic_uint, \\\natomic_long*: __c11_atomic_compare_exchange_strong__atomic_long, \\\natomic_ulong*: __c11_atomic_compare_exchange_strong__atomic_ulong, \\\natomic_llong*: __c11_atomic_compare_exchange_strong__atomic_llong, \\\natomic_ullong*: __c11_atomic_compare_exchange_strong__atomic_ullong, \\\natomic_ptr*: __c11_atomic_compare_exchange_strong__atomic_ptr \\\n)(obj,expected,desired)\n\n#define atomic_compare_exchange_weak(obj,expected,desired) __c11_atomic_compare_exchange_strong(obj,expected,desired)\n#define atomic_compare_exchange_strong(obj,expected,desired) __c11_atomic_compare_exchange_strong(obj,expected,desired)\n#define atomic_compare_exchange_weak_explicit(obj,expected,desired,smo,fmo) __c11_atomic_compare_exchange_strong(obj,expected,desired)\n#define atomic_compare_exchange_strong_explicit(obj,expected,desired,smo,fmo) __c11_atomic_compare_exchange_strong(obj,expected,desired)\n\n#if !(defined __STDC_VERSION__ && __STDC_VERSION__ > 201710L)\n#define ATOMIC_VAR_INIT(VALUE)\t(VALUE)\n#endif\n\n/*\n * atomic_fetch_add\n */\n\nstatic inline __int8 __msvc_xadd_i8(__int8 volatile* addr, __int8 val)\n{\n return _InterlockedExchangeAdd8(addr, val);\n}\nstatic inline __int16 __msvc_xadd_i16(__int16 volatile* addr, __int16 val)\n{\n return _InterlockedExchangeAdd16(addr, val);\n}\nstatic inline __int32 __msvc_xadd_i32(__int32 volatile* addr, __int32 val)\n{\n return _InterlockedExchangeAdd(addr, val);\n}\nstatic inline __int64 __msvc_xadd_i64(__int64 volatile* addr, __int64 val)\n{\n return _InterlockedExchangeAdd64(addr, val);\n}\n\n#define __msvc_xadd_ptr(ptr) __concat2(__msvc_xadd_,ptr)\n\nstatic inline char __c11_atomic_fetch_add__atomic_char(atomic_char* obj, char arg)\n{\n return (char)__msvc_xadd_i8((__int8 volatile*)obj, (__int8)arg);\n}\nstatic inline short __c11_atomic_fetch_add__atomic_short(atomic_short* obj, short arg)\n{\n return (short)__msvc_xadd_i16((__int16 volatile*)obj, (__int16)arg);\n}\nstatic inline int __c11_atomic_fetch_add__atomic_int(atomic_int* obj, int arg)\n{\n return (int)__msvc_xadd_i32((__int32 volatile*)obj, (__int32)arg);\n}\nstatic inline long __c11_atomic_fetch_add__atomic_long(atomic_long* obj, long arg)\n{\n return (long)__msvc_xadd_i32((__int32 volatile*)obj, (__int32)arg);\n}\nstatic inline llong __c11_atomic_fetch_add__atomic_llong(atomic_llong* obj, llong arg)\n{\n return (llong)__msvc_xadd_i64((__int64 volatile*)obj, (__int64)arg);\n}\nstatic inline uchar __c11_atomic_fetch_add__atomic_uchar(atomic_uchar* obj, uchar arg)\n{\n return (uchar)__msvc_xadd_i8((__int8 volatile*)obj, (__int8)arg);\n}\nstatic inline ushort __c11_atomic_fetch_add__atomic_ushort(atomic_ushort* obj, ushort arg)\n{\n return (ushort)__msvc_xadd_i16((__int16 volatile*)obj, (__int16)arg);\n}\nstatic inline uint __c11_atomic_fetch_add__atomic_uint(atomic_uint* obj, uint arg)\n{\n return (uint)__msvc_xadd_i32((__int32 volatile*)obj, (__int32)arg);\n}\nstatic inline ulong __c11_atomic_fetch_add__atomic_ulong(atomic_ulong* obj, ulong arg)\n{\n return (ulong)__msvc_xadd_i32((__int32 volatile*)obj, (__int32)arg);\n}\nstatic inline ullong __c11_atomic_fetch_add__atomic_ullong(atomic_ullong* obj, ullong arg)\n{\n return (ullong)__msvc_xadd_i64((__int64 volatile*)obj, (__int64)arg);\n}\nstatic inline void* __c11_atomic_fetch_add__atomic_ptr(atomic_ptr* obj, void* arg)\n{\n return (void*)__msvc_xadd_ptr(__ptr)((__intptr volatile*)obj, (__intptr)arg);\n}\n\n#define __c11_atomic_fetch_add(obj,arg) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_fetch_add__atomic_char, \\\natomic_uchar*: __c11_atomic_fetch_add__atomic_uchar, \\\natomic_short*: __c11_atomic_fetch_add__atomic_short, \\\natomic_ushort*: __c11_atomic_fetch_add__atomic_ushort, \\\natomic_int*: __c11_atomic_fetch_add__atomic_int, \\\natomic_uint*: __c11_atomic_fetch_add__atomic_uint, \\\natomic_long*: __c11_atomic_fetch_add__atomic_long, \\\natomic_ulong*: __c11_atomic_fetch_add__atomic_ulong, \\\natomic_llong*: __c11_atomic_fetch_add__atomic_llong, \\\natomic_ullong*: __c11_atomic_fetch_add__atomic_ullong, \\\natomic_ptr*: __c11_atomic_fetch_add__atomic_ptr \\\n)(obj,arg)\n\n#define atomic_fetch_add(obj,arg) __c11_atomic_fetch_add(obj,arg)\n#define atomic_fetch_sub(obj,arg) __c11_atomic_fetch_add(obj,-(arg))\n#define atomic_fetch_add_explicit(obj,arg,mo) __c11_atomic_fetch_add(obj,arg)\n#define atomic_fetch_sub_explicit(obj,arg,mo) __c11_atomic_fetch_add(obj,-(arg))\n\n/*\n * atomic_load\n */\n\nstatic inline char __c11_atomic_load__atomic_char(atomic_char* obj)\n{\n char val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline short __c11_atomic_load__atomic_short(atomic_short* obj)\n{\n short val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline int __c11_atomic_load__atomic_int(atomic_int* obj)\n{\n int val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline long __c11_atomic_load__atomic_long(atomic_long* obj)\n{\n long val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline llong __c11_atomic_load__atomic_llong(atomic_llong* obj)\n{\n llong val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline uchar __c11_atomic_load__atomic_uchar(atomic_uchar* obj)\n{\n uchar val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline ushort __c11_atomic_load__atomic_ushort(atomic_ushort* obj)\n{\n ushort val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline uint __c11_atomic_load__atomic_uint(atomic_uint* obj)\n{\n uint val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline ulong __c11_atomic_load__atomic_ulong(atomic_ulong* obj)\n{\n ulong val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline ullong __c11_atomic_load__atomic_ullong(atomic_ullong* obj)\n{\n ullong val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\nstatic inline void* __c11_atomic_load__atomic_ptr(atomic_ptr* obj)\n{\n void* val; _ReadBarrier(); val = *obj; _ReadWriteBarrier(); return val;\n}\n\n#define __c11_atomic_load(obj) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_load__atomic_char, \\\natomic_uchar*: __c11_atomic_load__atomic_uchar, \\\natomic_short*: __c11_atomic_load__atomic_short, \\\natomic_ushort*: __c11_atomic_load__atomic_ushort, \\\natomic_int*: __c11_atomic_load__atomic_int, \\\natomic_uint*: __c11_atomic_load__atomic_uint, \\\natomic_long*: __c11_atomic_load__atomic_long, \\\natomic_ulong*: __c11_atomic_load__atomic_ulong, \\\natomic_llong*: __c11_atomic_load__atomic_llong, \\\natomic_ullong*: __c11_atomic_load__atomic_ullong, \\\natomic_ptr*: __c11_atomic_load__atomic_ptr \\\n)(obj)\n\n#define atomic_load(obj) __c11_atomic_load(obj)\n#define atomic_load_explicit(obj,mo) __c11_atomic_load(obj)\n\n/*\n * atomic_fetch_{op} template for {and,or,xor} using atomic_compare_exchange\n */\n\n#define __C11_ATOMIC_FETCH_OP_TEMPLATE(prefix,type,op) static inline type \\\n __concat3(prefix,atomic_,type)(__concat2(atomic_,type) *obj, type arg) { \\\n type oldval, newval; \\\n do { oldval = atomic_load(obj); newval = oldval op arg; } \\\n while (!atomic_compare_exchange_strong(obj, &oldval, newval)); \\\n return oldval; \\\n}\n\n#define __C11_ATOMIC_FETCH_OP_POINTER_TEMPLATE(prefix,op) static inline void* \\\n __concat2(prefix,atomic_ptr)(atomic_ptr *obj, void* arg) { \\\n ptrdiff_t oldval, newval; \\\n do { oldval = (ptrdiff_t)atomic_load(obj); newval = oldval op (ptrdiff_t)arg; } \\\n while (!atomic_compare_exchange_strong(obj, (void**)&oldval, (void*)newval)); \\\n return (void*)oldval;\t\t\t\t\t\t \\\n}\n\n /*\n * atomic_fetch_and\n */\n\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, char, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, short, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, int, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, long, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, llong, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, uchar, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, ushort, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, uint, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, ulong, &)\n__C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_and__, ullong, &)\n__C11_ATOMIC_FETCH_OP_POINTER_TEMPLATE(__c11_atomic_fetch_and__, &)\n\n#define __c11_atomic_fetch_and(obj,arg) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_fetch_and__atomic_char, \\\natomic_uchar*: __c11_atomic_fetch_and__atomic_uchar, \\\natomic_short*: __c11_atomic_fetch_and__atomic_short, \\\natomic_ushort*: __c11_atomic_fetch_and__atomic_ushort, \\\natomic_int*: __c11_atomic_fetch_and__atomic_int, \\\natomic_uint*: __c11_atomic_fetch_and__atomic_uint, \\\natomic_long*: __c11_atomic_fetch_and__atomic_long, \\\natomic_ulong*: __c11_atomic_fetch_and__atomic_ulong, \\\natomic_llong*: __c11_atomic_fetch_and__atomic_llong, \\\natomic_ullong*: __c11_atomic_fetch_and__atomic_ullong,\t \\\natomic_ptr*: __c11_atomic_fetch_and__atomic_ptr \\\n)(obj,arg)\n\n#define atomic_fetch_and(obj,arg) __c11_atomic_fetch_and(obj,arg)\n#define atomic_fetch_and_explicit(obj,arg,mo) __c11_atomic_fetch_and(obj,arg)\n\n/*\n * atomic_fetch_or\n */\n\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, char, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, short, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, int, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, long, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, llong, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, uchar, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, ushort, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, uint, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, ulong, | )\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_or__, ullong, | )\n __C11_ATOMIC_FETCH_OP_POINTER_TEMPLATE(__c11_atomic_fetch_or__, | )\n\n#define __c11_atomic_fetch_or(obj,arg)\t\t\t \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_fetch_or__atomic_char, \\\natomic_uchar*: __c11_atomic_fetch_or__atomic_uchar, \\\natomic_short*: __c11_atomic_fetch_or__atomic_short, \\\natomic_ushort*: __c11_atomic_fetch_or__atomic_ushort, \\\natomic_int*: __c11_atomic_fetch_or__atomic_int, \\\natomic_uint*: __c11_atomic_fetch_or__atomic_uint, \\\natomic_long*: __c11_atomic_fetch_or__atomic_long, \\\natomic_ulong*: __c11_atomic_fetch_or__atomic_ulong,\t \\\natomic_llong*: __c11_atomic_fetch_or__atomic_llong, \\\natomic_ullong*: __c11_atomic_fetch_or__atomic_ullong,\t \\\natomic_ptr*: __c11_atomic_fetch_or__atomic_ptr \\\n)(obj,arg)\n\n#define atomic_fetch_or(obj,arg) __c11_atomic_fetch_or(obj,arg)\n#define atomic_fetch_or_explicit(obj,arg,mo) __c11_atomic_fetch_or(obj,arg)\n\n /*\n * atomic_fetch_xor\n */\n\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, char, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, short, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, int, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, long, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, llong, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, uchar, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, ushort, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, uint, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, ulong, ^)\n __C11_ATOMIC_FETCH_OP_TEMPLATE(__c11_atomic_fetch_xor__, ullong, ^)\n __C11_ATOMIC_FETCH_OP_POINTER_TEMPLATE(__c11_atomic_fetch_xor__, ^)\n\n#define __c11_atomic_fetch_xor(obj,arg) \\\n_Generic((obj), \\\natomic_char*: __c11_atomic_fetch_xor__atomic_char, \\\natomic_uchar*: __c11_atomic_fetch_xor__atomic_uchar, \\\natomic_short*: __c11_atomic_fetch_xor__atomic_short, \\\natomic_ushort*: __c11_atomic_fetch_xor__atomic_ushort, \\\natomic_int*: __c11_atomic_fetch_xor__atomic_int, \\\natomic_uint*: __c11_atomic_fetch_xor__atomic_uint, \\\natomic_long*: __c11_atomic_fetch_xor__atomic_long, \\\natomic_ulong*: __c11_atomic_fetch_xor__atomic_ulong,\t \\\natomic_llong*: __c11_atomic_fetch_xor__atomic_llong, \\\natomic_ullong*: __c11_atomic_fetch_xor__atomic_ullong,\t \\\natomic_ptr*: __c11_atomic_fetch_xor__atomic_ptr \\\n)(obj,arg)\n\n#define atomic_fetch_xor(obj,arg) __c11_atomic_fetch_xor(obj,arg)\n#define atomic_fetch_xor_explicit(obj,arg,mo) __c11_atomic_fetch_xor(obj,arg)\n\n /*\n * atomic_flag_test_and_set, atomic_flag_clear\n */\n\n static inline _Bool atomic_flag_test_and_set(volatile atomic_flag* obj)\n{\n char o = 0;\n return atomic_compare_exchange_strong((atomic_char*)&obj->_Value, &o, 1) ? 0 : 1;\n}\n\nstatic inline void atomic_flag_clear(volatile atomic_flag* obj)\n{\n atomic_store_explicit((atomic_char*)&obj->_Value, 0, memory_order_release);\n}\n\n#define atomic_flag_test_and_set_explicit(obj,mo) atomic_flag_test_and_set(obj)\n#define atomic_flag_clear_explicit(obj,mo) atomic_flag_clear(obj)\n\n#endif" }, { "path": "build.zig", "content": "// Compatible with Zig Version 0.11.0\nconst std = @import(\"std\");\nconst ArrayList = std.ArrayList;\nconst Compile = std.Build.Step.Compile;\nconst ConfigHeader = std.Build.Step.ConfigHeader;\nconst Mode = std.builtin.Mode;\nconst CrossTarget = std.zig.CrossTarget;\n\nconst Maker = struct {\n builder: *std.build.Builder,\n target: CrossTarget,\n optimize: Mode,\n enable_lto: bool,\n\n include_dirs: ArrayList([]const u8),\n cflags: ArrayList([]const u8),\n cxxflags: ArrayList([]const u8),\n objs: ArrayList(*Compile),\n\n fn addInclude(m: *Maker, dir: []const u8) !void {\n try m.include_dirs.append(dir);\n }\n fn addProjectInclude(m: *Maker, path: []const []const u8) !void {\n try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path));\n }\n fn addCFlag(m: *Maker, flag: []const u8) !void {\n try m.cflags.append(flag);\n }\n fn addCxxFlag(m: *Maker, flag: []const u8) !void {\n try m.cxxflags.append(flag);\n }\n fn addFlag(m: *Maker, flag: []const u8) !void {\n try m.addCFlag(flag);\n try m.addCxxFlag(flag);\n }\n\n fn init(builder: *std.build.Builder) !Maker {\n const target = builder.standardTargetOptions(.{});\n const zig_version = @import(\"builtin\").zig_version_string;\n const commit_hash = try std.ChildProcess.exec(\n .{ .allocator = builder.allocator, .argv = &.{ \"git\", \"rev-parse\", \"HEAD\" } },\n );\n try std.fs.cwd().writeFile(\"common/build-info.cpp\", builder.fmt(\n \\\\int LLAMA_BUILD_NUMBER = {};\n \\\\char const *LLAMA_COMMIT = \"{s}\";\n \\\\char const *LLAMA_COMPILER = \"Zig {s}\";\n \\\\char const *LLAMA_BUILD_TARGET = \"{s}\";\n \\\\\n , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) }));\n var m = Maker{\n .builder = builder,\n .target = target,\n .optimize = builder.standardOptimizeOption(.{}),\n .enable_lto = false,\n .include_dirs = ArrayList([]const u8).init(builder.allocator),\n .cflags = ArrayList([]const u8).init(builder.allocator),\n .cxxflags = ArrayList([]const u8).init(builder.allocator),\n .objs = ArrayList(*Compile).init(builder.allocator),\n };\n\n try m.addCFlag(\"-std=c11\");\n try m.addCxxFlag(\"-std=c++11\");\n try m.addProjectInclude(&.{});\n try m.addProjectInclude(&.{\"common\"});\n return m;\n }\n\n fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile {\n const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });\n if (o.target.getAbi() != .msvc)\n o.defineCMacro(\"_GNU_SOURCE\", null);\n\n if (std.mem.endsWith(u8, src, \".c\")) {\n o.addCSourceFiles(&.{src}, m.cflags.items);\n o.linkLibC();\n } else {\n o.addCSourceFiles(&.{src}, m.cxxflags.items);\n if (o.target.getAbi() == .msvc) {\n o.linkLibC(); // need winsdk + crt\n } else {\n // linkLibCpp already add (libc++ + libunwind + libc)\n o.linkLibCpp();\n }\n }\n for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i });\n o.want_lto = m.enable_lto;\n return o;\n }\n\n fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile {\n const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize });\n e.addCSourceFiles(&.{src}, m.cxxflags.items);\n for (deps) |d| e.addObject(d);\n for (m.objs.items) |o| e.addObject(o);\n for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i });\n\n // https://github.com/ziglang/zig/issues/15448\n if (e.target.getAbi() == .msvc) {\n e.linkLibC(); // need winsdk + crt\n } else {\n // linkLibCpp already add (libc++ + libunwind + libc)\n e.linkLibCpp();\n }\n m.builder.installArtifact(e);\n e.want_lto = m.enable_lto;\n return e;\n }\n};\n\npub fn build(b: *std.build.Builder) !void {\n var make = try Maker.init(b);\n make.enable_lto = b.option(bool, \"lto\", \"Enable LTO optimization, (default: false)\") orelse false;\n\n const ggml = make.obj(\"ggml\", \"ggml.c\");\n const ggml_alloc = make.obj(\"ggml-alloc\", \"ggml-alloc.c\");\n const ggml_backend = make.obj(\"ggml-backend\", \"ggml-backend.c\");\n const ggml_quants = make.obj(\"ggml-quants\", \"ggml-quants.c\");\n const llama = make.obj(\"llama\", \"llama.cpp\");\n const buildinfo = make.obj(\"common\", \"common/build-info.cpp\");\n const common = make.obj(\"common\", \"common/common.cpp\");\n const console = make.obj(\"console\", \"common/console.cpp\");\n const sampling = make.obj(\"sampling\", \"common/sampling.cpp\");\n const grammar_parser = make.obj(\"grammar-parser\", \"common/grammar-parser.cpp\");\n const train = make.obj(\"train\", \"common/train.cpp\");\n const clip = make.obj(\"clip\", \"examples/llava/clip.cpp\");\n\n _ = make.exe(\"main\", \"examples/main/main.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });\n _ = make.exe(\"quantize\", \"examples/quantize/quantize.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });\n _ = make.exe(\"perplexity\", \"examples/perplexity/perplexity.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });\n _ = make.exe(\"embedding\", \"examples/embedding/embedding.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });\n _ = make.exe(\"finetune\", \"examples/finetune/finetune.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });\n _ = make.exe(\"train-text-from-scratch\", \"examples/train-text-from-scratch/train-text-from-scratch.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });\n\n const server = make.exe(\"server\", \"examples/server/server.cpp\", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip });\n if (server.target.isWindows()) {\n server.linkSystemLibrary(\"ws2_32\");\n }\n}\n" }, { "path": "ci/README.md", "content": "# CI\n\nIn addition to [Github Actions](https://github.com/ggerganov/llama.cpp/actions) `llama.cpp` uses a custom CI framework:\n\nhttps://github.com/ggml-org/ci\n\nIt monitors the `master` branch for new commits and runs the\n[ci/run.sh](https://github.com/ggerganov/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us\nto execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled\nto cover various hardware architectures, including GPU and Apple Silicon instances.\n\nCollaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.\nOnly the branches of this repo are monitored for this keyword.\n\nIt is a good practice, before publishing changes to execute the full CI locally on your machine:\n\n```bash\nmkdir tmp\n\n# CPU-only build\nbash ./ci/run.sh ./tmp/results ./tmp/mnt\n\n# with CUDA support\nGG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt\n```\n" }, { "path": "ci/run.sh", "content": "#/bin/bash\n#\n# sample usage:\n#\n# mkdir tmp\n#\n# # CPU-only build\n# bash ./ci/run.sh ./tmp/results ./tmp/mnt\n#\n# # with CUDA support\n# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt\n#\n\nif [ -z \"$2\" ]; then\n echo \"usage: $0 \"\n exit 1\nfi\n\nmkdir -p \"$1\"\nmkdir -p \"$2\"\n\nOUT=$(realpath \"$1\")\nMNT=$(realpath \"$2\")\n\nrm -v $OUT/*.log\nrm -v $OUT/*.exit\nrm -v $OUT/*.md\n\nsd=`dirname $0`\ncd $sd/../\nSRC=`pwd`\n\n## helpers\n\n# download a file if it does not exist or if it is outdated\nfunction gg_wget {\n local out=$1\n local url=$2\n\n local cwd=`pwd`\n\n mkdir -p $out\n cd $out\n\n # should not re-download if file is the same\n wget -nv -N $url\n\n cd $cwd\n}\n\nfunction gg_printf {\n printf -- \"$@\" >> $OUT/README.md\n}\n\nfunction gg_run {\n ci=$1\n\n set -o pipefail\n set -x\n\n gg_run_$ci | tee $OUT/$ci.log\n cur=$?\n echo \"$cur\" > $OUT/$ci.exit\n\n set +x\n set +o pipefail\n\n gg_sum_$ci\n\n ret=$((ret | cur))\n}\n\n## ci\n\n# ctest_debug\n\nfunction gg_run_ctest_debug {\n cd ${SRC}\n\n rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug\n\n set -e\n\n (time cmake -DCMAKE_BUILD_TYPE=Debug .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log\n (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log\n\n (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log\n\n set +e\n}\n\nfunction gg_sum_ctest_debug {\n gg_printf '### %s\\n\\n' \"${ci}\"\n\n gg_printf 'Runs ctest in debug mode\\n'\n gg_printf '- status: %s\\n' \"$(cat $OUT/${ci}.exit)\"\n gg_printf '```\\n'\n gg_printf '%s\\n' \"$(cat $OUT/${ci}-ctest.log)\"\n gg_printf '```\\n'\n gg_printf '\\n'\n}\n\n# ctest_release\n\nfunction gg_run_ctest_release {\n cd ${SRC}\n\n rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release\n\n set -e\n\n (time cmake -DCMAKE_BUILD_TYPE=Release .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log\n (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log\n\n if [ -z ${GG_BUILD_LOW_PERF} ]; then\n (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log\n else\n (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log\n fi\n\n set +e\n}\n\nfunction gg_sum_ctest_release {\n gg_printf '### %s\\n\\n' \"${ci}\"\n\n gg_printf 'Runs ctest in release mode\\n'\n gg_printf '- status: %s\\n' \"$(cat $OUT/${ci}.exit)\"\n gg_printf '```\\n'\n gg_printf '%s\\n' \"$(cat $OUT/${ci}-ctest.log)\"\n gg_printf '```\\n'\n}\n\n# open_llama_3b_v2\n\nfunction gg_run_open_llama_3b_v2 {\n cd ${SRC}\n\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin\n gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json\n\n gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip\n unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/\n head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw\n\n path_models=\"../models-mnt/open-llama/3B-v2\"\n path_wiki=\"../models-mnt/wikitext/wikitext-2-raw\"\n\n rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release\n\n set -e\n\n (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log\n (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log\n\n python3 ../convert.py ${path_models}\n\n model_f16=\"${path_models}/ggml-model-f16.gguf\"\n model_q8_0=\"${path_models}/ggml-model-q8_0.gguf\"\n model_q4_0=\"${path_models}/ggml-model-q4_0.gguf\"\n model_q4_1=\"${path_models}/ggml-model-q4_1.gguf\"\n model_q5_0=\"${path_models}/ggml-model-q5_0.gguf\"\n model_q5_1=\"${path_models}/ggml-model-q5_1.gguf\"\n model_q2_k=\"${path_models}/ggml-model-q2_k.gguf\"\n model_q3_k=\"${path_models}/ggml-model-q3_k.gguf\"\n model_q4_k=\"${path_models}/ggml-model-q4_k.gguf\"\n model_q5_k=\"${path_models}/ggml-model-q5_k.gguf\"\n model_q6_k=\"${path_models}/ggml-model-q6_k.gguf\"\n\n wiki_test_60=\"${path_wiki}/wiki.test-60.raw\"\n\n ./bin/quantize ${model_f16} ${model_q8_0} q8_0\n ./bin/quantize ${model_f16} ${model_q4_0} q4_0\n ./bin/quantize ${model_f16} ${model_q4_1} q4_1\n ./bin/quantize ${model_f16} ${model_q5_0} q5_0\n ./bin/quantize ${model_f16} ${model_q5_1} q5_1\n ./bin/quantize ${model_f16} ${model_q2_k} q2_k\n ./bin/quantize ${model_f16} ${model_q3_k} q3_k\n ./bin/quantize ${model_f16} ${model_q4_k} q4_k\n ./bin/quantize ${model_f16} ${model_q5_k} q5_k\n ./bin/quantize ${model_f16} ${model_q6_k} q6_k\n\n (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log\n (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log\n (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log\n (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log\n (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log\n (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log\n (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log\n (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log\n (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log\n (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log\n (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log\n\n (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log\n (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log\n (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log\n (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log\n (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log\n (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log\n (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log\n (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log\n (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log\n (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log\n (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log\n\n (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log\n\n function check_ppl {\n qnt=\"$1\"\n ppl=$(echo \"$2\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n\n if [ $(echo \"$ppl > 20.0\" | bc) -eq 1 ]; then\n printf ' - %s @ %s (FAIL: ppl > 20.0)\\n' \"$qnt\" \"$ppl\"\n return 20\n fi\n\n printf ' - %s @ %s OK\\n' \"$qnt\" \"$ppl\"\n return 0\n }\n\n check_ppl \"f16\" \"$(cat $OUT/${ci}-tg-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q8_0\" \"$(cat $OUT/${ci}-tg-q8_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_0\" \"$(cat $OUT/${ci}-tg-q4_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_1\" \"$(cat $OUT/${ci}-tg-q4_1.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_0\" \"$(cat $OUT/${ci}-tg-q5_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_1\" \"$(cat $OUT/${ci}-tg-q5_1.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q2_k\" \"$(cat $OUT/${ci}-tg-q2_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q3_k\" \"$(cat $OUT/${ci}-tg-q3_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_k\" \"$(cat $OUT/${ci}-tg-q4_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_k\" \"$(cat $OUT/${ci}-tg-q5_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q6_k\" \"$(cat $OUT/${ci}-tg-q6_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n\n # lora\n function compare_ppl {\n qnt=\"$1\"\n ppl1=$(echo \"$2\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n ppl2=$(echo \"$3\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n\n if [ $(echo \"$ppl1 < $ppl2\" | bc) -eq 1 ]; then\n printf ' - %s @ %s (FAIL: %s > %s)\\n' \"$qnt\" \"$ppl\" \"$ppl1\" \"$ppl2\"\n return 20\n fi\n\n printf ' - %s @ %s %s OK\\n' \"$qnt\" \"$ppl1\" \"$ppl2\"\n return 0\n }\n\n path_lora=\"../models-mnt/open-llama/3B-v2/lora\"\n path_shakespeare=\"../models-mnt/shakespeare\"\n\n shakespeare=\"${path_shakespeare}/shakespeare.txt\"\n lora_shakespeare=\"${path_lora}/ggml-adapter-model.bin\"\n\n gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json\n gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin\n gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt\n\n python3 ../convert-lora-to-ggml.py ${path_lora}\n\n # f16\n (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log\n (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log\n compare_ppl \"f16 shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n # q8_0\n (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log\n (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log\n compare_ppl \"q8_0 shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n # q8_0 + f16 lora-base\n (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log\n compare_ppl \"q8_0 / f16 base shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n\n set +e\n}\n\nfunction gg_sum_open_llama_3b_v2 {\n gg_printf '### %s\\n\\n' \"${ci}\"\n\n gg_printf 'OpenLLaMA 3B-v2:\\n'\n gg_printf '- status: %s\\n' \"$(cat $OUT/${ci}.exit)\"\n gg_printf '- perplexity:\\n%s\\n' \"$(cat $OUT/${ci}-ppl.log)\"\n gg_printf '- lora:\\n%s\\n' \"$(cat $OUT/${ci}-lora-ppl.log)\"\n gg_printf '- f16: \\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-f16.log)\"\n gg_printf '- q8_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q8_0.log)\"\n gg_printf '- q4_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_0.log)\"\n gg_printf '- q4_1:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_1.log)\"\n gg_printf '- q5_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_0.log)\"\n gg_printf '- q5_1:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_1.log)\"\n gg_printf '- q2_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q2_k.log)\"\n gg_printf '- q3_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q3_k.log)\"\n gg_printf '- q4_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_k.log)\"\n gg_printf '- q5_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_k.log)\"\n gg_printf '- q6_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q6_k.log)\"\n gg_printf '- save-load-state: \\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-save-load-state.log)\"\n gg_printf '- shakespeare (f16):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-f16.log)\"\n gg_printf '- shakespeare (f16 lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)\"\n gg_printf '- shakespeare (q8_0):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)\"\n gg_printf '- shakespeare (q8_0 lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)\"\n gg_printf '- shakespeare (q8_0 / f16 base lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)\"\n}\n\n# open_llama_7b_v2\n# requires: GG_BUILD_CUDA\n\nfunction gg_run_open_llama_7b_v2 {\n cd ${SRC}\n\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin\n gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json\n\n gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip\n unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/\n\n path_models=\"../models-mnt/open-llama/7B-v2\"\n path_wiki=\"../models-mnt/wikitext/wikitext-2-raw\"\n\n rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release\n\n set -e\n\n (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log\n (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log\n\n python3 ../convert.py ${path_models}\n\n model_f16=\"${path_models}/ggml-model-f16.gguf\"\n model_q8_0=\"${path_models}/ggml-model-q8_0.gguf\"\n model_q4_0=\"${path_models}/ggml-model-q4_0.gguf\"\n model_q4_1=\"${path_models}/ggml-model-q4_1.gguf\"\n model_q5_0=\"${path_models}/ggml-model-q5_0.gguf\"\n model_q5_1=\"${path_models}/ggml-model-q5_1.gguf\"\n model_q2_k=\"${path_models}/ggml-model-q2_k.gguf\"\n model_q3_k=\"${path_models}/ggml-model-q3_k.gguf\"\n model_q4_k=\"${path_models}/ggml-model-q4_k.gguf\"\n model_q5_k=\"${path_models}/ggml-model-q5_k.gguf\"\n model_q6_k=\"${path_models}/ggml-model-q6_k.gguf\"\n\n wiki_test=\"${path_wiki}/wiki.test.raw\"\n\n ./bin/quantize ${model_f16} ${model_q8_0} q8_0\n ./bin/quantize ${model_f16} ${model_q4_0} q4_0\n ./bin/quantize ${model_f16} ${model_q4_1} q4_1\n ./bin/quantize ${model_f16} ${model_q5_0} q5_0\n ./bin/quantize ${model_f16} ${model_q5_1} q5_1\n ./bin/quantize ${model_f16} ${model_q2_k} q2_k\n ./bin/quantize ${model_f16} ${model_q3_k} q3_k\n ./bin/quantize ${model_f16} ${model_q4_k} q4_k\n ./bin/quantize ${model_f16} ${model_q5_k} q5_k\n ./bin/quantize ${model_f16} ${model_q6_k} q6_k\n\n (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log\n (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log\n (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log\n (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log\n (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log\n (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log\n (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log\n (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log\n (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log\n (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log\n (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p \"I believe the meaning of life is\" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log\n\n (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log\n (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log\n (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log\n (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log\n (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log\n (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log\n (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log\n (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log\n (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log\n (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log\n (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log\n\n (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log\n\n function check_ppl {\n qnt=\"$1\"\n ppl=$(echo \"$2\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n\n if [ $(echo \"$ppl > 20.0\" | bc) -eq 1 ]; then\n printf ' - %s @ %s (FAIL: ppl > 20.0)\\n' \"$qnt\" \"$ppl\"\n return 20\n fi\n\n printf ' - %s @ %s OK\\n' \"$qnt\" \"$ppl\"\n return 0\n }\n\n check_ppl \"f16\" \"$(cat $OUT/${ci}-tg-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q8_0\" \"$(cat $OUT/${ci}-tg-q8_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_0\" \"$(cat $OUT/${ci}-tg-q4_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_1\" \"$(cat $OUT/${ci}-tg-q4_1.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_0\" \"$(cat $OUT/${ci}-tg-q5_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_1\" \"$(cat $OUT/${ci}-tg-q5_1.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q2_k\" \"$(cat $OUT/${ci}-tg-q2_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q3_k\" \"$(cat $OUT/${ci}-tg-q3_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q4_k\" \"$(cat $OUT/${ci}-tg-q4_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q5_k\" \"$(cat $OUT/${ci}-tg-q5_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n check_ppl \"q6_k\" \"$(cat $OUT/${ci}-tg-q6_k.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-ppl.log\n\n # lora\n function compare_ppl {\n qnt=\"$1\"\n ppl1=$(echo \"$2\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n ppl2=$(echo \"$3\" | grep -oE \"[0-9]+\\.[0-9]+\" | tail -n 1)\n\n if [ $(echo \"$ppl1 < $ppl2\" | bc) -eq 1 ]; then\n printf ' - %s @ %s (FAIL: %s > %s)\\n' \"$qnt\" \"$ppl\" \"$ppl1\" \"$ppl2\"\n return 20\n fi\n\n printf ' - %s @ %s %s OK\\n' \"$qnt\" \"$ppl1\" \"$ppl2\"\n return 0\n }\n\n path_lora=\"../models-mnt/open-llama/7B-v2/lora\"\n path_shakespeare=\"../models-mnt/shakespeare\"\n\n shakespeare=\"${path_shakespeare}/shakespeare.txt\"\n lora_shakespeare=\"${path_lora}/ggml-adapter-model.bin\"\n\n gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json\n gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin\n gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt\n\n python3 ../convert-lora-to-ggml.py ${path_lora}\n\n # f16\n (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log\n (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log\n compare_ppl \"f16 shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n # currently not supported by the CUDA backend\n # q8_0\n #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log\n #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log\n #compare_ppl \"q8_0 shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n # q8_0 + f16 lora-base\n #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log\n #compare_ppl \"q8_0 / f16 shakespeare\" \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep \"^\\[1\\]\")\" \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep \"^\\[1\\]\")\" | tee -a $OUT/${ci}-lora-ppl.log\n\n set +e\n}\n\nfunction gg_sum_open_llama_7b_v2 {\n gg_printf '### %s\\n\\n' \"${ci}\"\n\n gg_printf 'OpenLLaMA 7B-v2:\\n'\n gg_printf '- status: %s\\n' \"$(cat $OUT/${ci}.exit)\"\n gg_printf '- perplexity:\\n%s\\n' \"$(cat $OUT/${ci}-ppl.log)\"\n gg_printf '- lora:\\n%s\\n' \"$(cat $OUT/${ci}-lora-ppl.log)\"\n gg_printf '- f16: \\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-f16.log)\"\n gg_printf '- q8_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q8_0.log)\"\n gg_printf '- q4_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_0.log)\"\n gg_printf '- q4_1:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_1.log)\"\n gg_printf '- q5_0:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_0.log)\"\n gg_printf '- q5_1:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_1.log)\"\n gg_printf '- q2_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q2_k.log)\"\n gg_printf '- q3_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q3_k.log)\"\n gg_printf '- q4_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q4_k.log)\"\n gg_printf '- q5_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q5_k.log)\"\n gg_printf '- q6_k:\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-tg-q6_k.log)\"\n gg_printf '- save-load-state: \\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-save-load-state.log)\"\n gg_printf '- shakespeare (f16):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-f16.log)\"\n gg_printf '- shakespeare (f16 lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)\"\n #gg_printf '- shakespeare (q8_0):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)\"\n #gg_printf '- shakespeare (q8_0 lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)\"\n #gg_printf '- shakespeare (q8_0 / f16 base lora):\\n```\\n%s\\n```\\n' \"$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)\"\n}\n\n## main\n\nif [ -z ${GG_BUILD_LOW_PERF} ]; then\n rm -rf ${SRC}/models-mnt\n\n mnt_models=${MNT}/models\n mkdir -p ${mnt_models}\n ln -sfn ${mnt_models} ${SRC}/models-mnt\n\n python3 -m pip install -r ${SRC}/requirements.txt\n python3 -m pip install --editable gguf-py\nfi\n\nret=0\n\ntest $ret -eq 0 && gg_run ctest_debug\ntest $ret -eq 0 && gg_run ctest_release\n\nif [ -z ${GG_BUILD_LOW_PERF} ]; then\n if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then\n if [ -z ${GG_BUILD_CUDA} ]; then\n test $ret -eq 0 && gg_run open_llama_3b_v2\n else\n test $ret -eq 0 && gg_run open_llama_7b_v2\n fi\n fi\nfi\n\nexit $ret\n" }, { "path": "cmake/FindSIMD.cmake", "content": "include(CheckCSourceRuns)\n\nset(AVX_CODE \"\n #include \n int main()\n {\n __m256 a;\n a = _mm256_set1_ps(0);\n return 0;\n }\n\")\n\nset(AVX512_CODE \"\n #include \n int main()\n {\n __m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0);\n __m512i b = a;\n __mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ);\n return 0;\n }\n\")\n\nset(AVX2_CODE \"\n #include \n int main()\n {\n __m256i a = {0};\n a = _mm256_abs_epi16(a);\n __m256i x;\n _mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code\n return 0;\n }\n\")\n\nset(FMA_CODE \"\n #include \n int main()\n {\n __m256 acc = _mm256_setzero_ps();\n const __m256 d = _mm256_setzero_ps();\n const __m256 p = _mm256_setzero_ps();\n acc = _mm256_fmadd_ps( d, p, acc );\n return 0;\n }\n\")\n\nmacro(check_sse type flags)\n set(__FLAG_I 1)\n set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})\n foreach (__FLAG ${flags})\n if (NOT ${type}_FOUND)\n set(CMAKE_REQUIRED_FLAGS ${__FLAG})\n check_c_source_runs(\"${${type}_CODE}\" HAS_${type}_${__FLAG_I})\n if (HAS_${type}_${__FLAG_I})\n set(${type}_FOUND TRUE CACHE BOOL \"${type} support\")\n set(${type}_FLAGS \"${__FLAG}\" CACHE STRING \"${type} flags\")\n endif()\n math(EXPR __FLAG_I \"${__FLAG_I}+1\")\n endif()\n endforeach()\n set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})\n\n if (NOT ${type}_FOUND)\n set(${type}_FOUND FALSE CACHE BOOL \"${type} support\")\n set(${type}_FLAGS \"\" CACHE STRING \"${type} flags\")\n endif()\n\n mark_as_advanced(${type}_FOUND ${type}_FLAGS)\nendmacro()\n\n# flags are for MSVC only!\ncheck_sse(\"AVX\" \" ;/arch:AVX\")\nif (NOT ${AVX_FOUND})\n set(LLAMA_AVX OFF)\nelse()\n set(LLAMA_AVX ON)\nendif()\n\ncheck_sse(\"AVX2\" \" ;/arch:AVX2\")\ncheck_sse(\"FMA\" \" ;/arch:AVX2\")\nif ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))\n set(LLAMA_AVX2 OFF)\nelse()\n set(LLAMA_AVX2 ON)\nendif()\n\ncheck_sse(\"AVX512\" \" ;/arch:AVX512\")\nif (NOT ${AVX512_FOUND})\n set(LLAMA_AVX512 OFF)\nelse()\n set(LLAMA_AVX512 ON)\nendif()\n" }, { "path": "codecov.yml", "content": "comment: off\n\ncoverage:\n status:\n project:\n default:\n target: auto\n threshold: 0\n base: auto\n patch:\n default:\n target: auto\n threshold: 0\n base: auto\n" }, { "path": "common/CMakeLists.txt", "content": "# common\n\n\n# Build info header\n#\n\nif(EXISTS \"${CMAKE_CURRENT_SOURCE_DIR}/../.git\")\n set(GIT_DIR \"${CMAKE_CURRENT_SOURCE_DIR}/../.git\")\n\n # Is git submodule/worktree\n if(NOT IS_DIRECTORY \"${GIT_DIR}\")\n file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)\n string(REGEX REPLACE \"gitdir: (.*)\\n$\" \"\\\\1\" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})\n set(GIT_DIR \"${REAL_GIT_DIR}\")\n endif()\n\n set(GIT_INDEX \"${GIT_DIR}/index\")\nelse()\n message(WARNING \"Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.\")\n set(GIT_INDEX \"\")\nendif()\n\n# Add a custom command to rebuild build-info.cpp when .git/index changes\nadd_custom_command(\n OUTPUT \"${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp\"\n COMMENT \"Generating build details from Git\"\n COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}\n -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}\n -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P \"${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake\"\n WORKING_DIRECTORY \"${CMAKE_CURRENT_SOURCE_DIR}/..\"\n DEPENDS \"${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in\" ${GIT_INDEX}\n VERBATIM\n)\nset(TARGET build_info)\nadd_library(${TARGET} OBJECT build-info.cpp)\nif (BUILD_SHARED_LIBS)\n set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)\nendif()\n\n\nset(TARGET common)\n\nadd_library(${TARGET} STATIC\n base64.hpp\n common.h\n common.cpp\n sampling.h\n sampling.cpp\n console.h\n console.cpp\n grammar-parser.h\n grammar-parser.cpp\n train.h\n train.cpp\n )\n\nif (BUILD_SHARED_LIBS)\n set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)\nendif()\n\ntarget_include_directories(${TARGET} PUBLIC .)\ntarget_compile_features(${TARGET} PUBLIC cxx_std_11)\ntarget_link_libraries(${TARGET} PRIVATE llama build_info)\n" }, { "path": "common/base64.hpp", "content": "/*\nThis is free and unencumbered software released into the public domain.\n\nAnyone is free to copy, modify, publish, use, compile, sell, or\ndistribute this software, either in source code form or as a compiled\nbinary, for any purpose, commercial or non-commercial, and by any\nmeans.\n\nIn jurisdictions that recognize copyright laws, the author or authors\nof this software dedicate any and all copyright interest in the\nsoftware to the public domain. We make this dedication for the benefit\nof the public at large and to the detriment of our heirs and\nsuccessors. We intend this dedication to be an overt act of\nrelinquishment in perpetuity of all present and future rights to this\nsoftware under copyright law.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR\nOTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\nARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\nOTHER DEALINGS IN THE SOFTWARE.\n\nFor more information, please refer to \n*/\n\n#ifndef PUBLIC_DOMAIN_BASE64_HPP_\n#define PUBLIC_DOMAIN_BASE64_HPP_\n\n#include \n#include \n#include \n#include \n\nclass base64_error : public std::runtime_error\n{\npublic:\n using std::runtime_error::runtime_error;\n};\n\nclass base64\n{\npublic:\n enum class alphabet\n {\n /** the alphabet is detected automatically */\n auto_,\n /** the standard base64 alphabet is used */\n standard,\n /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/\n url_filename_safe\n };\n\n enum class decoding_behavior\n {\n /** if the input is not padded, the remaining bits are ignored */\n moderate,\n /** if a padding character is encounter decoding is finished */\n loose\n };\n\n /**\n Encodes all the elements from `in_begin` to `in_end` to `out`.\n\n @warning The source and destination cannot overlap. The destination must be able to hold at least\n `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator.\n\n @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than\n 8 bits\n @tparam Output_iterator the destination; the elements written to it are from the type `char`\n @param in_begin the beginning of the source\n @param in_end the ending of the source\n @param out the destination iterator\n @param alphabet which alphabet should be used\n @returns the iterator to the next element past the last element copied\n @throws see `Input_iterator` and `Output_iterator`\n */\n template\n static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,\n alphabet alphabet = alphabet::standard)\n {\n constexpr auto pad = '=';\n const char* alpha = alphabet == alphabet::url_filename_safe\n ? \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_\"\n : \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\";\n\n while (in_begin != in_end) {\n std::uint8_t i0 = 0, i1 = 0, i2 = 0;\n\n // first character\n i0 = static_cast(*in_begin);\n ++in_begin;\n\n *out = alpha[i0 >> 2 & 0x3f];\n ++out;\n\n // part of first character and second\n if (in_begin != in_end) {\n i1 = static_cast(*in_begin);\n ++in_begin;\n\n *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)];\n ++out;\n } else {\n *out = alpha[(i0 & 0x3) << 4];\n ++out;\n\n // last padding\n *out = pad;\n ++out;\n\n // last padding\n *out = pad;\n ++out;\n\n break;\n }\n\n // part of second character and third\n if (in_begin != in_end) {\n i2 = static_cast(*in_begin);\n ++in_begin;\n\n *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)];\n ++out;\n } else {\n *out = alpha[(i1 & 0xf) << 2];\n ++out;\n\n // last padding\n *out = pad;\n ++out;\n\n break;\n }\n\n // rest of third\n *out = alpha[i2 & 0x3f];\n ++out;\n }\n\n return out;\n }\n /**\n Encodes a string.\n\n @param str the string that should be encoded\n @param alphabet which alphabet should be used\n @returns the encoded base64 string\n @throws see base64::encode()\n */\n static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard)\n {\n std::string result;\n\n result.reserve(required_encode_size(str.length()) + 1);\n\n encode(str.begin(), str.end(), std::back_inserter(result), alphabet);\n\n return result;\n }\n /**\n Encodes a char array.\n\n @param buffer the char array\n @param size the size of the array\n @param alphabet which alphabet should be used\n @returns the encoded string\n */\n static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard)\n {\n std::string result;\n\n result.reserve(required_encode_size(size) + 1);\n\n encode(buffer, buffer + size, std::back_inserter(result), alphabet);\n\n return result;\n }\n /**\n Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`,\n in other words: inplace decoding is possible.\n\n @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`,\n otherwise the behavior depends on the output iterator.\n\n @tparam Input_iterator the source; the returned elements are cast to `char`\n @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t`\n @param in_begin the beginning of the source\n @param in_end the ending of the source\n @param out the destination iterator\n @param alphabet which alphabet should be used\n @param behavior the behavior when an error was detected\n @returns the iterator to the next element past the last element copied\n @throws base64_error depending on the set behavior\n @throws see `Input_iterator` and `Output_iterator`\n */\n template\n static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,\n alphabet alphabet = alphabet::auto_,\n decoding_behavior behavior = decoding_behavior::moderate)\n {\n //constexpr auto pad = '=';\n std::uint8_t last = 0;\n auto bits = 0;\n\n while (in_begin != in_end) {\n auto c = *in_begin;\n ++in_begin;\n\n if (c == '=') {\n break;\n }\n\n auto part = _base64_value(alphabet, c);\n\n // enough bits for one byte\n if (bits + 6 >= 8) {\n *out = (last << (8 - bits)) | (part >> (bits - 2));\n ++out;\n\n bits -= 2;\n } else {\n bits += 6;\n }\n\n last = part;\n }\n\n // check padding\n if (behavior != decoding_behavior::loose) {\n while (in_begin != in_end) {\n auto c = *in_begin;\n ++in_begin;\n\n if (c != '=') {\n throw base64_error(\"invalid base64 character.\");\n }\n }\n }\n\n return out;\n }\n /**\n Decodes a string.\n\n @param str the base64 encoded string\n @param alphabet which alphabet should be used\n @param behavior the behavior when an error was detected\n @returns the decoded string\n @throws see base64::decode()\n */\n static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_,\n decoding_behavior behavior = decoding_behavior::moderate)\n {\n std::string result;\n\n result.reserve(max_decode_size(str.length()));\n\n decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior);\n\n return result;\n }\n /**\n Decodes a string.\n\n @param buffer the base64 encoded buffer\n @param size the size of the buffer\n @param alphabet which alphabet should be used\n @param behavior the behavior when an error was detected\n @returns the decoded string\n @throws see base64::decode()\n */\n static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_,\n decoding_behavior behavior = decoding_behavior::moderate)\n {\n std::string result;\n\n result.reserve(max_decode_size(size));\n\n decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior);\n\n return result;\n }\n /**\n Decodes a string inplace.\n\n @param[in,out] str the base64 encoded string\n @param alphabet which alphabet should be used\n @param behavior the behavior when an error was detected\n @throws base64::decode_inplace()\n */\n static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_,\n decoding_behavior behavior = decoding_behavior::moderate)\n {\n str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin());\n }\n /**\n Decodes a char array inplace.\n\n @param[in,out] str the string array\n @param size the length of the array\n @param alphabet which alphabet should be used\n @param behavior the behavior when an error was detected\n @returns the pointer to the next element past the last element decoded\n @throws base64::decode_inplace()\n */\n static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_,\n decoding_behavior behavior = decoding_behavior::moderate)\n {\n return decode(str, str + size, str, alphabet, behavior);\n }\n /**\n Returns the required decoding size for a given size. The value is calculated with the following formula:\n\n $$\n \\lceil \\frac{size}{4} \\rceil \\cdot 3\n $$\n\n @param size the size of the encoded input\n @returns the size of the resulting decoded buffer; this the absolute maximum\n */\n static std::size_t max_decode_size(std::size_t size) noexcept\n {\n return (size / 4 + (size % 4 ? 1 : 0)) * 3;\n }\n /**\n Returns the required encoding size for a given size. The value is calculated with the following formula:\n\n $$\n \\lceil \\frac{size}{3} \\rceil \\cdot 4\n $$\n\n @param size the size of the decoded input\n @returns the size of the resulting encoded buffer\n */\n static std::size_t required_encode_size(std::size_t size) noexcept\n {\n return (size / 3 + (size % 3 ? 1 : 0)) * 4;\n }\n\nprivate:\n static std::uint8_t _base64_value(alphabet& alphabet, char c)\n {\n if (c >= 'A' && c <= 'Z') {\n return c - 'A';\n } else if (c >= 'a' && c <= 'z') {\n return c - 'a' + 26;\n } else if (c >= '0' && c <= '9') {\n return c - '0' + 52;\n }\n\n // comes down to alphabet\n if (alphabet == alphabet::standard) {\n if (c == '+') {\n return 62;\n } else if (c == '/') {\n return 63;\n }\n } else if (alphabet == alphabet::url_filename_safe) {\n if (c == '-') {\n return 62;\n } else if (c == '_') {\n return 63;\n }\n } // auto detect\n else {\n if (c == '+') {\n alphabet = alphabet::standard;\n\n return 62;\n } else if (c == '/') {\n alphabet = alphabet::standard;\n\n return 63;\n } else if (c == '-') {\n alphabet = alphabet::url_filename_safe;\n\n return 62;\n } else if (c == '_') {\n alphabet = alphabet::url_filename_safe;\n\n return 63;\n }\n }\n\n throw base64_error(\"invalid base64 character.\");\n }\n};\n\n#endif // !PUBLIC_DOMAIN_BASE64_HPP_\n" }, { "path": "common/build-info.cpp.in", "content": "int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@;\nchar const *LLAMA_COMMIT = \"@BUILD_COMMIT@\";\nchar const *LLAMA_COMPILER = \"@BUILD_COMPILER@\";\nchar const *LLAMA_BUILD_TARGET = \"@BUILD_TARGET@\";\n" }, { "path": "common/common.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined(__APPLE__) && defined(__MACH__)\n#include \n#include \n#endif\n\n#if defined(_WIN32)\n#define WIN32_LEAN_AND_MEAN\n#ifndef NOMINMAX\n# define NOMINMAX\n#endif\n#include \n#include \n#include \n#include \n#include \n#else\n#include \n#include \n#include \n#endif\n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nint32_t get_num_physical_cores() {\n#ifdef __linux__\n // enumerate the set of thread siblings, num entries is num cores\n std::unordered_set siblings;\n for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {\n std::ifstream thread_siblings(\"/sys/devices/system/cpu\"\n + std::to_string(cpu) + \"/topology/thread_siblings\");\n if (!thread_siblings.is_open()) {\n break; // no more cpus\n }\n std::string line;\n if (std::getline(thread_siblings, line)) {\n siblings.insert(line);\n }\n }\n if (!siblings.empty()) {\n return static_cast(siblings.size());\n }\n#elif defined(__APPLE__) && defined(__MACH__)\n int32_t num_physical_cores;\n size_t len = sizeof(num_physical_cores);\n int result = sysctlbyname(\"hw.perflevel0.physicalcpu\", &num_physical_cores, &len, NULL, 0);\n if (result == 0) {\n return num_physical_cores;\n }\n result = sysctlbyname(\"hw.physicalcpu\", &num_physical_cores, &len, NULL, 0);\n if (result == 0) {\n return num_physical_cores;\n }\n#elif defined(_WIN32)\n //TODO: Implement\n#endif\n unsigned int n_threads = std::thread::hardware_concurrency();\n return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;\n}\n\nvoid process_escapes(std::string& input) {\n std::size_t input_len = input.length();\n std::size_t output_idx = 0;\n\n for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {\n if (input[input_idx] == '\\\\' && input_idx + 1 < input_len) {\n switch (input[++input_idx]) {\n case 'n': input[output_idx++] = '\\n'; break;\n case 'r': input[output_idx++] = '\\r'; break;\n case 't': input[output_idx++] = '\\t'; break;\n case '\\'': input[output_idx++] = '\\''; break;\n case '\\\"': input[output_idx++] = '\\\"'; break;\n case '\\\\': input[output_idx++] = '\\\\'; break;\n case 'x':\n // Handle \\x12, etc\n if (input_idx + 2 < input_len) {\n const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };\n char *err_p = nullptr;\n const long val = std::strtol(x, &err_p, 16);\n if (err_p == x + 2) {\n input_idx += 2;\n input[output_idx++] = char(val);\n break;\n }\n }\n // fall through\n default: input[output_idx++] = '\\\\';\n input[output_idx++] = input[input_idx]; break;\n }\n } else {\n input[output_idx++] = input[input_idx];\n }\n }\n\n input.resize(output_idx);\n}\n\nbool gpt_params_parse(int argc, char ** argv, gpt_params & params) {\n bool result = true;\n try {\n if (!gpt_params_parse_ex(argc, argv, params)) {\n gpt_print_usage(argc, argv, gpt_params());\n exit(0);\n }\n }\n catch (const std::invalid_argument & ex) {\n fprintf(stderr, \"%s\\n\", ex.what());\n gpt_print_usage(argc, argv, gpt_params());\n exit(1);\n }\n return result;\n}\n\nbool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {\n bool invalid_param = false;\n std::string arg;\n const std::string arg_prefix = \"--\";\n llama_sampling_params & sparams = params.sparams;\n\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n\n if (arg == \"-s\" || arg == \"--seed\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.seed = std::stoul(argv[i]);\n } else if (arg == \"-t\" || arg == \"--threads\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_threads = std::stoi(argv[i]);\n if (params.n_threads <= 0) {\n params.n_threads = std::thread::hardware_concurrency();\n }\n } else if (arg == \"-tb\" || arg == \"--threads-batch\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_threads_batch = std::stoi(argv[i]);\n if (params.n_threads_batch <= 0) {\n params.n_threads_batch = std::thread::hardware_concurrency();\n }\n } else if (arg == \"-p\" || arg == \"--prompt\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.prompt = argv[i];\n } else if (arg == \"-e\" || arg == \"--escape\") {\n params.escape = true;\n } else if (arg == \"--prompt-cache\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.path_prompt_cache = argv[i];\n } else if (arg == \"--prompt-cache-all\") {\n params.prompt_cache_all = true;\n } else if (arg == \"--prompt-cache-ro\") {\n params.prompt_cache_ro = true;\n } else if (arg == \"-f\" || arg == \"--file\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n std::ifstream file(argv[i]);\n if (!file) {\n fprintf(stderr, \"error: failed to open file '%s'\\n\", argv[i]);\n invalid_param = true;\n break;\n }\n // store the external file name in params\n params.prompt_file = argv[i];\n std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt));\n if (!params.prompt.empty() && params.prompt.back() == '\\n') {\n params.prompt.pop_back();\n }\n } else if (arg == \"-n\" || arg == \"--n-predict\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_predict = std::stoi(argv[i]);\n } else if (arg == \"--top-k\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.top_k = std::stoi(argv[i]);\n } else if (arg == \"-c\" || arg == \"--ctx-size\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_ctx = std::stoi(argv[i]);\n } else if (arg == \"--rope-freq-base\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.rope_freq_base = std::stof(argv[i]);\n } else if (arg == \"--rope-freq-scale\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.rope_freq_scale = std::stof(argv[i]);\n } else if (arg == \"--rope-scaling\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n std::string value(argv[i]);\n /**/ if (value == \"none\") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }\n else if (value == \"linear\") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }\n else if (value == \"yarn\") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }\n else { invalid_param = true; break; }\n } else if (arg == \"--rope-scale\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.rope_freq_scale = 1.0f/std::stof(argv[i]);\n } else if (arg == \"--yarn-orig-ctx\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.yarn_orig_ctx = std::stoi(argv[i]);\n } else if (arg == \"--yarn-ext-factor\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.yarn_ext_factor = std::stof(argv[i]);\n } else if (arg == \"--yarn-attn-factor\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.yarn_attn_factor = std::stof(argv[i]);\n } else if (arg == \"--yarn-beta-fast\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.yarn_beta_fast = std::stof(argv[i]);\n } else if (arg == \"--yarn-beta-slow\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.yarn_beta_slow = std::stof(argv[i]);\n } else if (arg == \"--memory-f32\") {\n params.memory_f16 = false;\n } else if (arg == \"--top-p\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.top_p = std::stof(argv[i]);\n } else if (arg == \"--min-p\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.min_p = std::stof(argv[i]);\n } else if (arg == \"--temp\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.temp = std::stof(argv[i]);\n sparams.temp = std::max(sparams.temp, 0.0f);\n } else if (arg == \"--tfs\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.tfs_z = std::stof(argv[i]);\n } else if (arg == \"--typical\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.typical_p = std::stof(argv[i]);\n } else if (arg == \"--repeat-last-n\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.penalty_last_n = std::stoi(argv[i]);\n sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);\n } else if (arg == \"--repeat-penalty\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.penalty_repeat = std::stof(argv[i]);\n } else if (arg == \"--frequency-penalty\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.penalty_freq = std::stof(argv[i]);\n } else if (arg == \"--presence-penalty\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.penalty_present = std::stof(argv[i]);\n } else if (arg == \"--mirostat\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.mirostat = std::stoi(argv[i]);\n } else if (arg == \"--mirostat-lr\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.mirostat_eta = std::stof(argv[i]);\n } else if (arg == \"--mirostat-ent\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.mirostat_tau = std::stof(argv[i]);\n } else if (arg == \"--cfg-negative-prompt\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.cfg_negative_prompt = argv[i];\n } else if (arg == \"--cfg-negative-prompt-file\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n std::ifstream file(argv[i]);\n if (!file) {\n fprintf(stderr, \"error: failed to open file '%s'\\n\", argv[i]);\n invalid_param = true;\n break;\n }\n std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(sparams.cfg_negative_prompt));\n if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\\n') {\n sparams.cfg_negative_prompt.pop_back();\n }\n } else if (arg == \"--cfg-scale\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.cfg_scale = std::stof(argv[i]);\n } else if (arg == \"-b\" || arg == \"--batch-size\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_batch = std::stoi(argv[i]);\n } else if (arg == \"--keep\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_keep = std::stoi(argv[i]);\n } else if (arg == \"--draft\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_draft = std::stoi(argv[i]);\n } else if (arg == \"--chunks\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_chunks = std::stoi(argv[i]);\n } else if (arg == \"-np\" || arg == \"--parallel\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_parallel = std::stoi(argv[i]);\n } else if (arg == \"-ns\" || arg == \"--sequences\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.n_sequences = std::stoi(argv[i]);\n } else if (arg == \"--p-accept\" || arg == \"-pa\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.p_accept = std::stof(argv[i]);\n } else if (arg == \"--p-split\" || arg == \"-ps\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.p_split = std::stof(argv[i]);\n } else if (arg == \"-m\" || arg == \"--model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.model = argv[i];\n } else if (arg == \"-md\" || arg == \"--model-draft\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.model_draft = argv[i];\n } else if (arg == \"-a\" || arg == \"--alias\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.model_alias = argv[i];\n } else if (arg == \"--lora\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));\n params.use_mmap = false;\n } else if (arg == \"--lora-scaled\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n const char * lora_adapter = argv[i];\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));\n params.use_mmap = false;\n } else if (arg == \"--lora-base\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.lora_base = argv[i];\n } else if (arg == \"--reset-gpu-index\") {\n params.reset_gpu_index = true;\n } else if (arg == \"--disable-gpu-index\") {\n params.disale_gpu_index = true;\n } else if (arg == \"--mmproj\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.mmproj = argv[i];\n } else if (arg == \"--image\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.image = argv[i];\n } else if (arg == \"-i\" || arg == \"--interactive\") {\n params.interactive = true;\n } else if (arg == \"--embedding\") {\n params.embedding = true;\n } else if (arg == \"--interactive-first\") {\n params.interactive_first = true;\n } else if (arg == \"-ins\" || arg == \"--instruct\") {\n params.instruct = true;\n } else if (arg == \"--infill\") {\n params.infill = true;\n } else if (arg == \"--multiline-input\") {\n params.multiline_input = true;\n } else if (arg == \"--simple-io\") {\n params.simple_io = true;\n } else if (arg == \"-cb\" || arg == \"--cont-batching\") {\n params.cont_batching = true;\n } else if (arg == \"--color\") {\n params.use_color = true;\n } else if (arg == \"--mlock\") {\n params.use_mlock = true;\n } else if (arg == \"--gpu-layers\" || arg == \"-ngl\" || arg == \"--n-gpu-layers\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD\n params.n_gpu_layers = std::stoi(argv[i]);\n#else\n fprintf(stderr, \"warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\\n\");\n fprintf(stderr, \"warning: see main README.md for information on enabling GPU BLAS support\\n\");\n#endif\n } else if (arg == \"--gpu-layers-draft\" || arg == \"-ngld\" || arg == \"--n-gpu-layers-draft\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD\n params.n_gpu_layers_draft = std::stoi(argv[i]);\n#else\n fprintf(stderr, \"warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\\n\");\n fprintf(stderr, \"warning: see main README.md for information on enabling GPU BLAS support\\n\");\n#endif\n } else if (arg == \"--main-gpu\" || arg == \"-mg\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef GGML_USE_CUBLAS\n params.main_gpu = std::stoi(argv[i]);\n#else\n fprintf(stderr, \"warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\\n\");\n#endif\n } else if (arg == \"--tensor-split\" || arg == \"-ts\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef GGML_USE_CUBLAS\n std::string arg_next = argv[i];\n\n // split string by , and /\n const std::regex regex{R\"([,/]+)\"};\n std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};\n std::vector split_arg{it, {}};\n GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);\n\n for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {\n if (i < split_arg.size()) {\n params.tensor_split[i] = std::stof(split_arg[i]);\n } else {\n params.tensor_split[i] = 0.0f;\n }\n }\n#else\n fprintf(stderr, \"warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\\n\");\n#endif // GGML_USE_CUBLAS\n } else if (arg == \"--vram-budget\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef GGML_USE_CUBLAS\n params.vram_budget_gb = std::stof(argv[i]);\n#else\n fprintf(stderr, \"warning: PowerInfer was compiled without cuBLAS. It is not possible to set a VRAM budget.\\n\");\n#endif\n } else if (arg == \"--no-mul-mat-q\" || arg == \"-nommq\") {\n#ifdef GGML_USE_CUBLAS\n params.mul_mat_q = false;\n#else\n fprintf(stderr, \"warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\\n\");\n#endif // GGML_USE_CUBLAS\n } else if (arg == \"--no-mmap\") {\n params.use_mmap = false;\n } else if (arg == \"--numa\") {\n params.numa = true;\n } else if (arg == \"--verbose-prompt\") {\n params.verbose_prompt = true;\n } else if (arg == \"-r\" || arg == \"--reverse-prompt\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.antiprompt.push_back(argv[i]);\n } else if (arg == \"-ld\" || arg == \"--logdir\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.logdir = argv[i];\n\n if (params.logdir.back() != DIRECTORY_SEPARATOR) {\n params.logdir += DIRECTORY_SEPARATOR;\n }\n } else if (arg == \"--perplexity\" || arg == \"--all-logits\") {\n params.logits_all = true;\n } else if (arg == \"--ppl-stride\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.ppl_stride = std::stoi(argv[i]);\n } else if (arg == \"--ppl-output-type\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.ppl_output_type = std::stoi(argv[i]);\n } else if (arg == \"--hellaswag\") {\n params.hellaswag = true;\n } else if (arg == \"--hellaswag-tasks\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.hellaswag_tasks = std::stoi(argv[i]);\n } else if (arg == \"--ignore-eos\") {\n params.ignore_eos = true;\n } else if (arg == \"--no-penalize-nl\") {\n sparams.penalize_nl = false;\n } else if (arg == \"-l\" || arg == \"--logit-bias\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n std::stringstream ss(argv[i]);\n llama_token key;\n char sign;\n std::string value_str;\n try {\n if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {\n sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);\n } else {\n throw std::exception();\n }\n } catch (const std::exception&) {\n invalid_param = true;\n break;\n }\n } else if (arg == \"-h\" || arg == \"--help\") {\n return false;\n\n } else if (arg == \"--random-prompt\") {\n params.random_prompt = true;\n } else if (arg == \"--in-prefix-bos\") {\n params.input_prefix_bos = true;\n } else if (arg == \"--in-prefix\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.input_prefix = argv[i];\n } else if (arg == \"--in-suffix\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.input_suffix = argv[i];\n } else if (arg == \"--grammar\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n sparams.grammar = argv[i];\n } else if (arg == \"--grammar-file\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n std::ifstream file(argv[i]);\n if (!file) {\n fprintf(stderr, \"error: failed to open file '%s'\\n\", argv[i]);\n invalid_param = true;\n break;\n }\n std::copy(\n std::istreambuf_iterator(file),\n std::istreambuf_iterator(),\n std::back_inserter(sparams.grammar)\n );\n#ifndef LOG_DISABLE_LOGS\n // Parse args for logging parameters\n } else if ( log_param_single_parse( argv[i] ) ) {\n // Do nothing, log_param_single_parse automatically does it's thing\n // and returns if a match was found and parsed.\n } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {\n // We have a matching known parameter requiring an argument,\n // now we need to check if there is anything after this argv\n // and flag invalid_param or parse it.\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {\n invalid_param = true;\n break;\n }\n // End of Parse args for logging parameters\n#endif // LOG_DISABLE_LOGS\n } else {\n throw std::invalid_argument(\"error: unknown argument: \" + arg);\n }\n }\n if (invalid_param) {\n throw std::invalid_argument(\"error: invalid parameter for argument: \" + arg);\n }\n if (params.prompt_cache_all &&\n (params.interactive || params.interactive_first ||\n params.instruct)) {\n\n throw std::invalid_argument(\"error: --prompt-cache-all not supported in interactive mode yet\\n\");\n }\n\n if (params.escape) {\n process_escapes(params.prompt);\n process_escapes(params.input_prefix);\n process_escapes(params.input_suffix);\n process_escapes(sparams.cfg_negative_prompt);\n for (auto & antiprompt : params.antiprompt) {\n process_escapes(antiprompt);\n }\n }\n\n return true;\n}\n\nvoid gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {\n const llama_sampling_params & sparams = params.sparams;\n\n printf(\"\\n\");\n printf(\"usage: %s [options]\\n\", argv[0]);\n printf(\"\\n\");\n printf(\"options:\\n\");\n printf(\" -h, --help show this help message and exit\\n\");\n printf(\" -i, --interactive run in interactive mode\\n\");\n printf(\" --interactive-first run in interactive mode and wait for input right away\\n\");\n printf(\" -ins, --instruct run in instruction mode (use with Alpaca models)\\n\");\n printf(\" --multiline-input allows you to write or paste multiple lines without ending each in '\\\\'\\n\");\n printf(\" -r PROMPT, --reverse-prompt PROMPT\\n\");\n printf(\" halt generation at PROMPT, return control in interactive mode\\n\");\n printf(\" (can be specified more than once for multiple prompts).\\n\");\n printf(\" --color colorise output to distinguish prompt and user input from generations\\n\");\n printf(\" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\\n\");\n printf(\" -t N, --threads N number of threads to use during generation (default: %d)\\n\", params.n_threads);\n printf(\" -tb N, --threads-batch N\\n\");\n printf(\" number of threads to use during batch and prompt processing (default: same as --threads)\\n\");\n printf(\" -p PROMPT, --prompt PROMPT\\n\");\n printf(\" prompt to start generation with (default: empty)\\n\");\n printf(\" -e, --escape process prompt escapes sequences (\\\\n, \\\\r, \\\\t, \\\\', \\\\\\\", \\\\\\\\)\\n\");\n printf(\" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\\n\");\n printf(\" --prompt-cache-all if specified, saves user input and generations to cache as well.\\n\");\n printf(\" not supported with --interactive or other interactive options\\n\");\n printf(\" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\\n\");\n printf(\" --random-prompt start with a randomized prompt.\\n\");\n printf(\" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\\n\");\n printf(\" --in-prefix STRING string to prefix user inputs with (default: empty)\\n\");\n printf(\" --in-suffix STRING string to suffix after user inputs with (default: empty)\\n\");\n printf(\" -f FNAME, --file FNAME\\n\");\n printf(\" prompt file to start generation.\\n\");\n printf(\" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\\n\", params.n_predict);\n printf(\" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\\n\", params.n_ctx);\n printf(\" -b N, --batch-size N batch size for prompt processing (default: %d)\\n\", params.n_batch);\n printf(\" --top-k N top-k sampling (default: %d, 0 = disabled)\\n\", sparams.top_k);\n printf(\" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\\n\", (double)sparams.top_p);\n printf(\" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\\n\", (double)sparams.min_p);\n printf(\" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\\n\", (double)sparams.tfs_z);\n printf(\" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\\n\", (double)sparams.typical_p);\n printf(\" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\\n\", sparams.penalty_last_n);\n printf(\" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\\n\", (double)sparams.penalty_repeat);\n printf(\" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\\n\", (double)sparams.penalty_present);\n printf(\" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\\n\", (double)sparams.penalty_freq);\n printf(\" --mirostat N use Mirostat sampling.\\n\");\n printf(\" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\\n\");\n printf(\" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\\n\", sparams.mirostat);\n printf(\" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\\n\", (double)sparams.mirostat_eta);\n printf(\" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\\n\", (double)sparams.mirostat_tau);\n printf(\" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\\n\");\n printf(\" modifies the likelihood of token appearing in the completion,\\n\");\n printf(\" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\\n\");\n printf(\" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\\n\");\n printf(\" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\\n\");\n printf(\" --grammar-file FNAME file to read grammar from\\n\");\n printf(\" --cfg-negative-prompt PROMPT\\n\");\n printf(\" negative prompt to use for guidance. (default: empty)\\n\");\n printf(\" --cfg-negative-prompt-file FNAME\\n\");\n printf(\" negative prompt file to use for guidance. (default: empty)\\n\");\n printf(\" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\\n\", sparams.cfg_scale);\n printf(\" --rope-scaling {none,linear,yarn}\\n\");\n printf(\" RoPE frequency scaling method, defaults to linear unless specified by the model\\n\");\n printf(\" --rope-scale N RoPE context scaling factor, expands context by a factor of N\\n\");\n printf(\" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\\n\");\n printf(\" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\\n\");\n printf(\" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\\n\");\n printf(\" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\\n\");\n printf(\" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\\n\");\n printf(\" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\\n\", params.yarn_beta_slow);\n printf(\" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\\n\", params.yarn_beta_fast);\n printf(\" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\\n\");\n printf(\" --no-penalize-nl do not penalize newline token\\n\");\n printf(\" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\\n\");\n printf(\" not recommended: doubles context memory required and no measurable increase in quality\\n\");\n printf(\" --temp N temperature (default: %.1f)\\n\", (double)sparams.temp);\n printf(\" --vram-budget N VRAM budget in GiB (default: -1, -1 = available VRAM)\\n\");\n printf(\" --logits-all return logits for all tokens in the batch (default: disabled)\\n\");\n printf(\" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\\n\");\n printf(\" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\\n\", params.hellaswag_tasks);\n printf(\" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\\n\", params.n_keep);\n printf(\" --draft N number of tokens to draft for speculative decoding (default: %d)\\n\", params.n_draft);\n printf(\" --chunks N max number of chunks to process (default: %d, -1 = all)\\n\", params.n_chunks);\n printf(\" -np N, --parallel N number of parallel sequences to decode (default: %d)\\n\", params.n_parallel);\n printf(\" -ns N, --sequences N number of sequences to decode (default: %d)\\n\", params.n_sequences);\n printf(\" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\\n\", (double)params.p_accept);\n printf(\" -ps N, --p-split N speculative decoding split probability (default: %.1f)\\n\", (double)params.p_split);\n printf(\" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\\n\");\n printf(\" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\\n\");\n printf(\" --image IMAGE_FILE path to an image file. use with multimodal models\\n\");\n if (llama_mlock_supported()) {\n printf(\" --mlock force system to keep model in RAM rather than swapping or compressing\\n\");\n }\n if (llama_mmap_supported()) {\n printf(\" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\\n\");\n }\n printf(\" --numa attempt optimizations that help on some NUMA systems\\n\");\n printf(\" if run without this previously, it is recommended to drop the system page cache before using this\\n\");\n printf(\" see https://github.com/ggerganov/llama.cpp/issues/1437\\n\");\n#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD\n printf(\" -ngl N, --n-gpu-layers N\\n\");\n printf(\" number of layers to store in VRAM\\n\");\n printf(\" -ngld N, --n-gpu-layers-draft N\\n\");\n printf(\" number of layers to store in VRAM for the draft model\\n\");\n printf(\" -ts SPLIT --tensor-split SPLIT\\n\");\n printf(\" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\\n\");\n printf(\" -mg i, --main-gpu i the GPU to use for scratch and small tensors\\n\");\n#ifdef GGML_USE_CUBLAS\n printf(\" -nommq, --no-mul-mat-q\\n\");\n printf(\" use \" GGML_CUBLAS_NAME \" instead of custom mul_mat_q \" GGML_CUDA_NAME \" kernels.\\n\");\n printf(\" Not recommended since this is both slower and uses more VRAM.\\n\");\n#endif // GGML_USE_CUBLAS\n#endif\n printf(\" --verbose-prompt print prompt before generation\\n\");\n printf(\" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\\n\");\n printf(\" --lora FNAME apply LoRA adapter (implies --no-mmap)\\n\");\n printf(\" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\\n\");\n printf(\" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\\n\");\n printf(\" -m FNAME, --model FNAME\\n\");\n printf(\" model path (default: %s)\\n\", params.model.c_str());\n printf(\" -md FNAME, --model-draft FNAME\\n\");\n printf(\" draft model for speculative decoding (default: %s)\\n\", params.model.c_str());\n printf(\" -ld LOGDIR, --logdir LOGDIR\\n\");\n printf(\" path under which to save YAML logs (no logging if unset)\\n\");\n printf(\"\\n\");\n#ifndef LOG_DISABLE_LOGS\n log_print_usage();\n#endif // LOG_DISABLE_LOGS\n}\n\nstd::string get_system_info(const gpt_params & params) {\n std::ostringstream os;\n\n os << \"system_info: n_threads = \" << params.n_threads;\n if (params.n_threads_batch != -1) {\n os << \" (n_threads_batch = \" << params.n_threads_batch << \")\";\n }\n os << \" / \" << std::thread::hardware_concurrency() << \" | \" << llama_print_system_info();\n\n return os.str();\n}\n\nstd::string gpt_random_prompt(std::mt19937 & rng) {\n const int r = rng() % 10;\n switch (r) {\n case 0: return \"So\";\n case 1: return \"Once upon a time\";\n case 2: return \"When\";\n case 3: return \"The\";\n case 4: return \"After\";\n case 5: return \"If\";\n case 6: return \"import\";\n case 7: return \"He\";\n case 8: return \"She\";\n case 9: return \"They\";\n }\n\n GGML_UNREACHABLE();\n}\n\n//\n// Model utils\n//\n\nstruct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {\n auto mparams = llama_model_default_params();\n\n if (params.n_gpu_layers != -1) {\n mparams.n_gpu_layers = params.n_gpu_layers;\n }\n mparams.main_gpu = params.main_gpu;\n mparams.vram_budget_gb = params.vram_budget_gb;\n mparams.tensor_split = params.tensor_split;\n mparams.use_mmap = params.use_mmap;\n mparams.use_mlock = params.use_mlock;\n mparams.reset_gpu_index = params.reset_gpu_index;\n mparams.disable_gpu_index = params.disale_gpu_index;\n\n return mparams;\n}\n\nstruct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {\n auto cparams = llama_context_default_params();\n\n cparams.n_ctx = params.n_ctx;\n cparams.n_batch = params.n_batch;\n cparams.n_threads = params.n_threads;\n cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;\n cparams.mul_mat_q = params.mul_mat_q;\n cparams.seed = params.seed;\n cparams.f16_kv = params.memory_f16;\n cparams.logits_all = params.logits_all;\n cparams.embedding = params.embedding;\n cparams.rope_scaling_type = params.rope_scaling_type;\n cparams.rope_freq_base = params.rope_freq_base;\n cparams.rope_freq_scale = params.rope_freq_scale;\n cparams.yarn_ext_factor = params.yarn_ext_factor;\n cparams.yarn_attn_factor = params.yarn_attn_factor;\n cparams.yarn_beta_fast = params.yarn_beta_fast;\n cparams.yarn_beta_slow = params.yarn_beta_slow;\n cparams.yarn_orig_ctx = params.yarn_orig_ctx;\n\n return cparams;\n}\n\nvoid llama_batch_clear(struct llama_batch & batch) {\n batch.n_tokens = 0;\n}\n\nvoid llama_batch_add(\n struct llama_batch & batch,\n llama_token id,\n llama_pos pos,\n const std::vector & seq_ids,\n bool logits) {\n batch.token [batch.n_tokens] = id;\n batch.pos [batch.n_tokens] = pos,\n batch.n_seq_id[batch.n_tokens] = seq_ids.size();\n for (size_t i = 0; i < seq_ids.size(); ++i) {\n batch.seq_id[batch.n_tokens][i] = seq_ids[i];\n }\n batch.logits [batch.n_tokens] = logits;\n\n batch.n_tokens++;\n}\n\nstd::tuple llama_init_from_gpt_params(gpt_params & params) {\n auto mparams = llama_model_params_from_gpt_params(params);\n auto cparams = llama_context_params_from_gpt_params(params);\n\n llama_model * model = llama_load_model_from_file_with_context(params.model.c_str(), mparams, &cparams);\n if (model == NULL) {\n fprintf(stderr, \"%s: error: failed to load model '%s'\\n\", __func__, params.model.c_str());\n return std::make_tuple(nullptr, nullptr);\n }\n\n llama_context * lctx = llama_new_context_with_model(model, cparams);\n if (lctx == NULL) {\n fprintf(stderr, \"%s: error: failed to create context with model '%s'\\n\", __func__, params.model.c_str());\n llama_free_model(model);\n return std::make_tuple(nullptr, nullptr);\n }\n\n for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {\n const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);\n float lora_scale = std::get<1>(params.lora_adapter[i]);\n int err = llama_model_apply_lora_from_file(model,\n lora_adapter.c_str(),\n lora_scale,\n ((i > 0) || params.lora_base.empty())\n ? NULL\n : params.lora_base.c_str(),\n params.n_threads);\n if (err != 0) {\n fprintf(stderr, \"%s: error: failed to apply lora adapter\\n\", __func__);\n llama_free(lctx);\n llama_free_model(model);\n return std::make_tuple(nullptr, nullptr);\n }\n }\n\n if (params.ignore_eos) {\n params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;\n }\n\n\n\n {\n LOG(\"warming up the model with an empty run\\n\");\n\n std::vector tmp = { llama_token_bos(model), llama_token_eos(model), };\n llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));\n llama_kv_cache_clear(lctx);\n llama_reset_timings(lctx);\n }\n\n return std::make_tuple(model, lctx);\n}\n\n//\n// Vocab utils\n//\n\nstd::vector llama_tokenize(\n const struct llama_context * ctx,\n const std::string & text,\n bool add_bos,\n bool special) {\n return llama_tokenize(llama_get_model(ctx), text, add_bos, special);\n}\n\nstd::vector llama_tokenize(\n const struct llama_model * model,\n const std::string & text,\n bool add_bos,\n bool special) {\n // upper limit for the number of tokens\n int n_tokens = text.length() + add_bos;\n std::vector result(n_tokens);\n n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);\n if (n_tokens < 0) {\n result.resize(-n_tokens);\n int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);\n GGML_ASSERT(check == -n_tokens);\n } else {\n result.resize(n_tokens);\n }\n return result;\n}\n\nstd::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {\n std::vector result(8, 0);\n const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());\n if (n_tokens < 0) {\n result.resize(-n_tokens);\n int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());\n GGML_ASSERT(check == -n_tokens);\n } else {\n result.resize(n_tokens);\n }\n\n return std::string(result.data(), result.size());\n}\n\nstd::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) {\n const llama_token bos_id = llama_token_bos(llama_get_model(ctx));\n\n std::string piece;\n std::string result;\n\n for (size_t i = 0; i < tokens.size(); ++i) {\n piece = llama_token_to_piece(ctx, tokens[i]);\n\n // remove the leading space of the first non-BOS token\n if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {\n piece = piece.substr(1);\n }\n\n result += piece;\n }\n\n return result;\n}\n\nstd::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens) {\n std::string piece;\n std::string result;\n\n for (size_t i = 0; i < tokens.size(); ++i) {\n piece = llama_token_to_piece(ctx, tokens[i]);\n\n result += piece;\n }\n\n // NOTE: the original tokenizer decodes bytes after collecting the pieces.\n return result;\n}\n\n//\n// YAML utils\n//\n\n// returns true if successful, false otherwise\nbool create_directory_with_parents(const std::string & path) {\n#ifdef _WIN32\n std::wstring_convert> converter;\n std::wstring wpath = converter.from_bytes(path);\n\n // if the path already exists, check whether it's a directory\n const DWORD attributes = GetFileAttributesW(wpath.c_str());\n if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {\n return true;\n }\n\n size_t pos_slash = 0;\n\n // process path from front to back, procedurally creating directories\n while ((pos_slash = path.find('\\\\', pos_slash)) != std::string::npos) {\n const std::wstring subpath = wpath.substr(0, pos_slash);\n const wchar_t * test = subpath.c_str();\n\n const bool success = CreateDirectoryW(test, NULL);\n if (!success) {\n const DWORD error = GetLastError();\n\n // if the path already exists, ensure that it's a directory\n if (error == ERROR_ALREADY_EXISTS) {\n const DWORD attributes = GetFileAttributesW(subpath.c_str());\n if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {\n return false;\n }\n } else {\n return false;\n }\n }\n\n pos_slash += 1;\n }\n\n return true;\n#else\n // if the path already exists, check whether it's a directory\n struct stat info;\n if (stat(path.c_str(), &info) == 0) {\n return S_ISDIR(info.st_mode);\n }\n\n size_t pos_slash = 1; // skip leading slashes for directory creation\n\n // process path from front to back, procedurally creating directories\n while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {\n const std::string subpath = path.substr(0, pos_slash);\n struct stat info;\n\n // if the path already exists, ensure that it's a directory\n if (stat(subpath.c_str(), &info) == 0) {\n if (!S_ISDIR(info.st_mode)) {\n return false;\n }\n } else {\n // create parent directories\n const int ret = mkdir(subpath.c_str(), 0755);\n if (ret != 0) {\n return false;\n }\n }\n\n pos_slash += 1;\n }\n\n return true;\n#endif // _WIN32\n}\n\nvoid dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) {\n if (data.empty()) {\n fprintf(stream, \"%s:\\n\", prop_name);\n return;\n }\n\n fprintf(stream, \"%s: [\", prop_name);\n for (size_t i = 0; i < data.size() - 1; ++i) {\n fprintf(stream, \"%e, \", data[i]);\n }\n fprintf(stream, \"%e]\\n\", data.back());\n}\n\nvoid dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) {\n if (data.empty()) {\n fprintf(stream, \"%s:\\n\", prop_name);\n return;\n }\n\n fprintf(stream, \"%s: [\", prop_name);\n for (size_t i = 0; i < data.size() - 1; ++i) {\n fprintf(stream, \"%d, \", data[i]);\n }\n fprintf(stream, \"%d]\\n\", data.back());\n}\n\nvoid dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {\n std::string data_str(data == NULL ? \"\" : data);\n\n if (data_str.empty()) {\n fprintf(stream, \"%s:\\n\", prop_name);\n return;\n }\n\n size_t pos_start = 0;\n size_t pos_found = 0;\n\n if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {\n data_str = std::regex_replace(data_str, std::regex(\"\\n\"), \"\\\\n\");\n data_str = std::regex_replace(data_str, std::regex(\"\\\"\"), \"\\\\\\\"\");\n data_str = \"\\\"\" + data_str + \"\\\"\";\n fprintf(stream, \"%s: %s\\n\", prop_name, data_str.c_str());\n return;\n }\n\n if (data_str.find('\\n') == std::string::npos) {\n fprintf(stream, \"%s: %s\\n\", prop_name, data_str.c_str());\n return;\n }\n\n fprintf(stream, \"%s: |\\n\", prop_name);\n while ((pos_found = data_str.find('\\n', pos_start)) != std::string::npos) {\n fprintf(stream, \" %s\\n\", data_str.substr(pos_start, pos_found-pos_start).c_str());\n pos_start = pos_found + 1;\n }\n}\n\nstd::string get_sortable_timestamp() {\n using clock = std::chrono::system_clock;\n\n const clock::time_point current_time = clock::now();\n const time_t as_time_t = clock::to_time_t(current_time);\n char timestamp_no_ns[100];\n std::strftime(timestamp_no_ns, 100, \"%Y_%m_%d-%H_%M_%S\", std::localtime(&as_time_t));\n\n const int64_t ns = std::chrono::duration_cast(\n current_time.time_since_epoch() % 1000000000).count();\n char timestamp_ns[11];\n snprintf(timestamp_ns, 11, \"%09\" PRId64, ns);\n\n return std::string(timestamp_no_ns) + \".\" + std::string(timestamp_ns);\n}\n\nvoid dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,\n const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) {\n const llama_sampling_params & sparams = params.sparams;\n\n fprintf(stream, \"build_commit: %s\\n\", LLAMA_COMMIT);\n fprintf(stream, \"build_number: %d\\n\", LLAMA_BUILD_NUMBER);\n fprintf(stream, \"cpu_has_arm_fma: %s\\n\", ggml_cpu_has_arm_fma() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_avx: %s\\n\", ggml_cpu_has_avx() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_avx2: %s\\n\", ggml_cpu_has_avx2() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_avx512: %s\\n\", ggml_cpu_has_avx512() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_avx512_vbmi: %s\\n\", ggml_cpu_has_avx512_vbmi() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_avx512_vnni: %s\\n\", ggml_cpu_has_avx512_vnni() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_blas: %s\\n\", ggml_cpu_has_blas() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_cublas: %s\\n\", ggml_cpu_has_cublas() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_clblast: %s\\n\", ggml_cpu_has_clblast() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_fma: %s\\n\", ggml_cpu_has_fma() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_gpublas: %s\\n\", ggml_cpu_has_gpublas() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_neon: %s\\n\", ggml_cpu_has_neon() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_f16c: %s\\n\", ggml_cpu_has_f16c() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_fp16_va: %s\\n\", ggml_cpu_has_fp16_va() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_wasm_simd: %s\\n\", ggml_cpu_has_wasm_simd() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_blas: %s\\n\", ggml_cpu_has_blas() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_sse3: %s\\n\", ggml_cpu_has_sse3() ? \"true\" : \"false\");\n fprintf(stream, \"cpu_has_vsx: %s\\n\", ggml_cpu_has_vsx() ? \"true\" : \"false\");\n\n#ifdef NDEBUG\n fprintf(stream, \"debug: false\\n\");\n#else\n fprintf(stream, \"debug: true\\n\");\n#endif // NDEBUG\n\n fprintf(stream, \"model_desc: %s\\n\", model_desc);\n fprintf(stream, \"n_vocab: %d # output size of the final layer, 32001 for some models\\n\", llama_n_vocab(llama_get_model(lctx)));\n\n#ifdef __OPTIMIZE__\n fprintf(stream, \"optimize: true\\n\");\n#else\n fprintf(stream, \"optimize: false\\n\");\n#endif // __OPTIMIZE__\n\n fprintf(stream, \"time: %s\\n\", timestamp.c_str());\n\n fprintf(stream, \"\\n\");\n fprintf(stream, \"###############\\n\");\n fprintf(stream, \"# User Inputs #\\n\");\n fprintf(stream, \"###############\\n\");\n fprintf(stream, \"\\n\");\n\n fprintf(stream, \"alias: %s # default: unknown\\n\", params.model_alias.c_str());\n fprintf(stream, \"batch_size: %d # default: 512\\n\", params.n_batch);\n dump_string_yaml_multiline(stream, \"cfg_negative_prompt\", sparams.cfg_negative_prompt.c_str());\n fprintf(stream, \"cfg_scale: %f # default: 1.0\\n\", sparams.cfg_scale);\n fprintf(stream, \"chunks: %d # default: -1 (unlimited)\\n\", params.n_chunks);\n fprintf(stream, \"color: %s # default: false\\n\", params.use_color ? \"true\" : \"false\");\n fprintf(stream, \"ctx_size: %d # default: 512\\n\", params.n_ctx);\n fprintf(stream, \"escape: %s # default: false\\n\", params.escape ? \"true\" : \"false\");\n fprintf(stream, \"file: # never logged, see prompt instead. Can still be specified for input.\\n\");\n fprintf(stream, \"frequency_penalty: %f # default: 0.0 \\n\", sparams.penalty_freq);\n dump_string_yaml_multiline(stream, \"grammar\", sparams.grammar.c_str());\n fprintf(stream, \"grammar-file: # never logged, see grammar instead. Can still be specified for input.\\n\");\n fprintf(stream, \"hellaswag: %s # default: false\\n\", params.hellaswag ? \"true\" : \"false\");\n fprintf(stream, \"hellaswag_tasks: %zu # default: 400\\n\", params.hellaswag_tasks);\n\n const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));\n const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;\n fprintf(stream, \"ignore_eos: %s # default: false\\n\", ignore_eos ? \"true\" : \"false\");\n\n dump_string_yaml_multiline(stream, \"in_prefix\", params.input_prefix.c_str());\n fprintf(stream, \"in_prefix_bos: %s # default: false\\n\", params.input_prefix_bos ? \"true\" : \"false\");\n dump_string_yaml_multiline(stream, \"in_suffix\", params.input_prefix.c_str());\n fprintf(stream, \"instruct: %s # default: false\\n\", params.instruct ? \"true\" : \"false\");\n fprintf(stream, \"interactive: %s # default: false\\n\", params.interactive ? \"true\" : \"false\");\n fprintf(stream, \"interactive_first: %s # default: false\\n\", params.interactive_first ? \"true\" : \"false\");\n fprintf(stream, \"keep: %d # default: 0\\n\", params.n_keep);\n fprintf(stream, \"logdir: %s # default: unset (no logging)\\n\", params.logdir.c_str());\n\n fprintf(stream, \"logit_bias:\\n\");\n for (std::pair lb : sparams.logit_bias) {\n if (ignore_eos && lb.first == logit_bias_eos->first) {\n continue;\n }\n fprintf(stream, \" %d: %f\", lb.first, lb.second);\n }\n\n fprintf(stream, \"lora:\\n\");\n for (std::tuple la : params.lora_adapter) {\n if (std::get<1>(la) != 1.0f) {\n continue;\n }\n fprintf(stream, \" - %s\\n\", std::get<0>(la).c_str());\n }\n fprintf(stream, \"lora_scaled:\\n\");\n for (std::tuple la : params.lora_adapter) {\n if (std::get<1>(la) == 1.0f) {\n continue;\n }\n fprintf(stream, \" - %s: %f\\n\", std::get<0>(la).c_str(), std::get<1>(la));\n }\n fprintf(stream, \"lora_base: %s\\n\", params.lora_base.c_str());\n fprintf(stream, \"reset_gpu_index: %s\\n\", params.reset_gpu_index ? \"true\" : \"false\");\n fprintf(stream, \"disable_gpu_index: %s\\n\", params.disale_gpu_index? \"true\": \"false\");\n fprintf(stream, \"main_gpu: %d # default: 0\\n\", params.main_gpu);\n fprintf(stream, \"memory_f32: %s # default: false\\n\", !params.memory_f16 ? \"true\" : \"false\");\n fprintf(stream, \"mirostat: %d # default: 0 (disabled)\\n\", sparams.mirostat);\n fprintf(stream, \"mirostat_ent: %f # default: 5.0\\n\", sparams.mirostat_tau);\n fprintf(stream, \"mirostat_lr: %f # default: 0.1\\n\", sparams.mirostat_eta);\n fprintf(stream, \"mlock: %s # default: false\\n\", params.use_mlock ? \"true\" : \"false\");\n fprintf(stream, \"model: %s # default: models/7B/ggml-model.bin\\n\", params.model.c_str());\n fprintf(stream, \"model_draft: %s # default:\\n\", params.model_draft.c_str());\n fprintf(stream, \"multiline_input: %s # default: false\\n\", params.multiline_input ? \"true\" : \"false\");\n fprintf(stream, \"n_gpu_layers: %d # default: -1\\n\", params.n_gpu_layers);\n fprintf(stream, \"n_predict: %d # default: -1 (unlimited)\\n\", params.n_predict);\n fprintf(stream, \"n_probs: %d # only used by server binary, default: 0\\n\", sparams.n_probs);\n fprintf(stream, \"no_mmap: %s # default: false\\n\", !params.use_mmap ? \"true\" : \"false\");\n fprintf(stream, \"no_mul_mat_q: %s # default: false\\n\", !params.mul_mat_q ? \"true\" : \"false\");\n fprintf(stream, \"no_penalize_nl: %s # default: false\\n\", !sparams.penalize_nl ? \"true\" : \"false\");\n fprintf(stream, \"numa: %s # default: false\\n\", params.numa ? \"true\" : \"false\");\n fprintf(stream, \"ppl_output_type: %d # default: 0\\n\", params.ppl_output_type);\n fprintf(stream, \"ppl_stride: %d # default: 0\\n\", params.ppl_stride);\n fprintf(stream, \"presence_penalty: %f # default: 0.0\\n\", sparams.penalty_present);\n dump_string_yaml_multiline(stream, \"prompt\", params.prompt.c_str());\n fprintf(stream, \"prompt_cache: %s\\n\", params.path_prompt_cache.c_str());\n fprintf(stream, \"prompt_cache_all: %s # default: false\\n\", params.prompt_cache_all ? \"true\" : \"false\");\n fprintf(stream, \"prompt_cache_ro: %s # default: false\\n\", params.prompt_cache_ro ? \"true\" : \"false\");\n dump_vector_int_yaml(stream, \"prompt_tokens\", prompt_tokens);\n fprintf(stream, \"random_prompt: %s # default: false\\n\", params.random_prompt ? \"true\" : \"false\");\n fprintf(stream, \"repeat_penalty: %f # default: 1.1\\n\", sparams.penalty_repeat);\n\n fprintf(stream, \"reverse_prompt:\\n\");\n for (std::string ap : params.antiprompt) {\n size_t pos = 0;\n while ((pos = ap.find('\\n', pos)) != std::string::npos) {\n ap.replace(pos, 1, \"\\\\n\");\n pos += 1;\n }\n\n fprintf(stream, \" - %s\\n\", ap.c_str());\n }\n\n fprintf(stream, \"rope_freq_base: %f # default: 10000.0\\n\", params.rope_freq_base);\n fprintf(stream, \"rope_freq_scale: %f # default: 1.0\\n\", params.rope_freq_scale);\n fprintf(stream, \"seed: %d # default: -1 (random seed)\\n\", params.seed);\n fprintf(stream, \"simple_io: %s # default: false\\n\", params.simple_io ? \"true\" : \"false\");\n fprintf(stream, \"cont_batching: %s # default: false\\n\", params.cont_batching ? \"true\" : \"false\");\n fprintf(stream, \"temp: %f # default: 0.8\\n\", sparams.temp);\n\n const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);\n dump_vector_float_yaml(stream, \"tensor_split\", tensor_split_vector);\n\n fprintf(stream, \"tfs: %f # default: 1.0\\n\", sparams.tfs_z);\n fprintf(stream, \"threads: %d # default: %d\\n\", params.n_threads, std::thread::hardware_concurrency());\n fprintf(stream, \"top_k: %d # default: 40\\n\", sparams.top_k);\n fprintf(stream, \"top_p: %f # default: 0.95\\n\", sparams.top_p);\n fprintf(stream, \"min_p: %f # default: 0.0\\n\", sparams.min_p);\n fprintf(stream, \"typical_p: %f # default: 1.0\\n\", sparams.typical_p);\n fprintf(stream, \"verbose_prompt: %s # default: false\\n\", params.verbose_prompt ? \"true\" : \"false\");\n fprintf(stream, \"vram_budget: %f # default: -1.0 (all available VRAM)\\n\", params.vram_budget_gb);\n}\n" }, { "path": "common/common.h", "content": "// Various helper functions and utilities\n\n#pragma once\n\n#include \"llama.h\"\n\n#include \"sampling.h\"\n\n#define LOG_NO_FILE_LINE_FUNCTION\n#include \"log.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef _WIN32\n#define DIRECTORY_SEPARATOR '\\\\'\n#else\n#define DIRECTORY_SEPARATOR '/'\n#endif // _WIN32\n\n#define die(msg) do { fputs(\"error: \" msg \"\\n\", stderr); exit(1); } while (0)\n#define die_fmt(fmt, ...) do { fprintf(stderr, \"error: \" fmt \"\\n\", __VA_ARGS__); exit(1); } while (0)\n\n#define print_build_info() do { \\\n fprintf(stderr, \"%s: build = %d (%s)\\n\", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \\\n fprintf(stderr, \"%s: built with %s for %s\\n\", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \\\n} while(0)\n\n// build info\nextern int LLAMA_BUILD_NUMBER;\nextern char const *LLAMA_COMMIT;\nextern char const *LLAMA_COMPILER;\nextern char const *LLAMA_BUILD_TARGET;\n\n//\n// CLI argument parsing\n//\nint32_t get_num_physical_cores();\n\nstruct gpt_params {\n uint32_t seed = -1; // RNG seed\n\n int32_t n_threads = get_num_physical_cores();\n int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)\n int32_t n_predict = -1; // new tokens to predict\n int32_t n_ctx = 512; // context size\n int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)\n int32_t n_keep = 0; // number of tokens to keep from initial prompt\n int32_t n_draft = 16; // number of tokens to draft during speculative decoding\n int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)\n int32_t n_parallel = 1; // number of parallel sequences to decode\n int32_t n_sequences = 1; // number of sequences to decode\n float p_accept = 0.5f; // speculative decoding accept probability\n float p_split = 0.1f; // speculative decoding split probability\n int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)\n int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)\n int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors\n float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs\n int32_t n_beams = 0; // if non-zero then use beam search of given width.\n float rope_freq_base = 0.0f; // RoPE base frequency\n float rope_freq_scale = 0.0f; // RoPE frequency scaling factor\n float vram_budget_gb = -1.0f; // VRAM budget in GB (-1 - use available VRAM)\n float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor\n float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor\n float yarn_beta_fast = 32.0f; // YaRN low correction dim\n float yarn_beta_slow = 1.0f; // YaRN high correction dim\n int32_t yarn_orig_ctx = 0; // YaRN original context length\n int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment\n // pinging @cebtenzzre\n\n // // sampling parameters\n struct llama_sampling_params sparams;\n\n std::string model = \"models/7B/ggml-model-f16.gguf\"; // model path\n std::string model_draft = \"\"; // draft model for speculative decoding\n std::string model_alias = \"unknown\"; // model alias\n std::string prompt = \"\";\n std::string prompt_file = \"\"; // store the external prompt file name\n std::string path_prompt_cache = \"\"; // path to file for saving/loading prompt eval state\n std::string input_prefix = \"\"; // string to prefix user inputs with\n std::string input_suffix = \"\"; // string to suffix user inputs with\n std::vector antiprompt; // string upon seeing which more user input is prompted\n std::string logdir = \"\"; // directory in which to save YAML log files\n\n // TODO: avoid tuple, use struct\n std::vector> lora_adapter; // lora adapter path with user defined scale\n std::string lora_base = \"\"; // base model path for the lora adapter\n\n bool reset_gpu_index = false; // refresh the gpu index file\n bool disale_gpu_index = false; // disable loading gpu index and splitting ffn\n\n int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.\n int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line\n // (which is more convenient to use for plotting)\n //\n bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt\n size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score\n\n bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS\n bool memory_f16 = true; // use f16 instead of f32 for memory kv\n bool random_prompt = false; // do not randomize prompt if none provided\n bool use_color = false; // use color to distinguish generations and inputs\n bool interactive = false; // interactive mode\n bool prompt_cache_all = false; // save user input and generations to prompt cache\n bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it\n\n bool embedding = false; // get only sentence embedding\n bool escape = false; // escape \"\\n\", \"\\r\", \"\\t\", \"\\'\", \"\\\"\", and \"\\\\\"\n bool interactive_first = false; // wait for user input immediately\n bool multiline_input = false; // reverse the usage of `\\`\n bool simple_io = false; // improves compatibility with subprocesses and limited consoles\n bool cont_batching = false; // insert new sequences for decoding on-the-fly\n\n bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix\n bool ignore_eos = false; // ignore generated EOS tokens\n bool instruct = false; // instruction mode (used for Alpaca models)\n bool logits_all = false; // return logits for all tokens in the batch\n bool use_mmap = true; // use mmap for faster loads\n bool use_mlock = false; // use mlock to keep model in memory\n bool numa = false; // attempt optimizations that help on some NUMA systems\n bool verbose_prompt = false; // print prompt tokens before generation\n bool infill = false; // use infill mode\n\n // multimodal models (see examples/llava)\n std::string mmproj = \"\"; // path to multimodal projector\n std::string image = \"\"; // path to an image file\n};\n\nbool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);\n\nbool gpt_params_parse(int argc, char ** argv, gpt_params & params);\n\nvoid gpt_print_usage(int argc, char ** argv, const gpt_params & params);\n\nstd::string get_system_info(const gpt_params & params);\n\nstd::string gpt_random_prompt(std::mt19937 & rng);\n\nvoid process_escapes(std::string& input);\n\n//\n// Model utils\n//\n\n// TODO: avoid tuplue, use struct\nstd::tuple llama_init_from_gpt_params(gpt_params & params);\n\nstruct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);\nstruct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);\n\n// Batch utils\n\nvoid llama_batch_clear(struct llama_batch & batch);\n\nvoid llama_batch_add(\n struct llama_batch & batch,\n llama_token id,\n llama_pos pos,\n const std::vector & seq_ids,\n bool logits);\n\n//\n// Vocab utils\n//\n\n// tokenizes a string into a vector of tokens\n// should work similar to Python's `tokenizer.encode`\nstd::vector llama_tokenize(\n const struct llama_context * ctx,\n const std::string & text,\n bool add_bos,\n bool special = false);\n\nstd::vector llama_tokenize(\n const struct llama_model * model,\n const std::string & text,\n bool add_bos,\n bool special = false);\n\n// tokenizes a token into a piece\n// should work similar to Python's `tokenizer.id_to_piece`\nstd::string llama_token_to_piece(\n const struct llama_context * ctx,\n llama_token token);\n\n// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function\n// that takes into account the tokenizer type and decides how to handle the leading space\n//\n// detokenizes a vector of tokens into a string\n// should work similar to Python's `tokenizer.decode`\n// removes the leading space from the first non-BOS token\nstd::string llama_detokenize_spm(\n llama_context * ctx,\n const std::vector & tokens);\n\n// detokenizes a vector of tokens into a string\n// should work similar to Python's `tokenizer.decode`\nstd::string llama_detokenize_bpe(\n llama_context * ctx,\n const std::vector & tokens);\n\n//\n// YAML utils\n//\n\nbool create_directory_with_parents(const std::string & path);\nvoid dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data);\nvoid dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data);\nvoid dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);\nstd::string get_sortable_timestamp();\n\nvoid dump_non_result_info_yaml(\n FILE * stream, const gpt_params & params, const llama_context * lctx,\n const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc);\n" }, { "path": "common/console.cpp", "content": "#include \"console.h\"\n#include \n#include \n\n#if defined(_WIN32)\n#define WIN32_LEAN_AND_MEAN\n#ifndef NOMINMAX\n#define NOMINMAX\n#endif\n#include \n#include \n#include \n#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING\n#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004\n#endif\n#else\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#endif\n\n#define ANSI_COLOR_RED \"\\x1b[31m\"\n#define ANSI_COLOR_GREEN \"\\x1b[32m\"\n#define ANSI_COLOR_YELLOW \"\\x1b[33m\"\n#define ANSI_COLOR_BLUE \"\\x1b[34m\"\n#define ANSI_COLOR_MAGENTA \"\\x1b[35m\"\n#define ANSI_COLOR_CYAN \"\\x1b[36m\"\n#define ANSI_COLOR_RESET \"\\x1b[0m\"\n#define ANSI_BOLD \"\\x1b[1m\"\n\nnamespace console {\n\n //\n // Console state\n //\n\n static bool advanced_display = false;\n static bool simple_io = true;\n static display_t current_display = reset;\n\n static FILE* out = stdout;\n\n#if defined (_WIN32)\n static void* hConsole;\n#else\n static FILE* tty = nullptr;\n static termios initial_state;\n#endif\n\n //\n // Init and cleanup\n //\n\n void init(bool use_simple_io, bool use_advanced_display) {\n advanced_display = use_advanced_display;\n simple_io = use_simple_io;\n#if defined(_WIN32)\n // Windows-specific console initialization\n DWORD dwMode = 0;\n hConsole = GetStdHandle(STD_OUTPUT_HANDLE);\n if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {\n hConsole = GetStdHandle(STD_ERROR_HANDLE);\n if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) {\n hConsole = nullptr;\n simple_io = true;\n }\n }\n if (hConsole) {\n // Check conditions combined to reduce nesting\n if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) &&\n !SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {\n advanced_display = false;\n }\n // Set console output codepage to UTF8\n SetConsoleOutputCP(CP_UTF8);\n }\n HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);\n if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {\n // Set console input codepage to UTF16\n _setmode(_fileno(stdin), _O_WTEXT);\n\n // Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)\n if (simple_io) {\n dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;\n } else {\n dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);\n }\n if (!SetConsoleMode(hConIn, dwMode)) {\n simple_io = true;\n }\n }\n#else\n // POSIX-specific console initialization\n if (!simple_io) {\n struct termios new_termios;\n tcgetattr(STDIN_FILENO, &initial_state);\n new_termios = initial_state;\n new_termios.c_lflag &= ~(ICANON | ECHO);\n new_termios.c_cc[VMIN] = 1;\n new_termios.c_cc[VTIME] = 0;\n tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);\n\n tty = fopen(\"/dev/tty\", \"w+\");\n if (tty != nullptr) {\n out = tty;\n }\n }\n\n setlocale(LC_ALL, \"\");\n#endif\n }\n\n void cleanup() {\n // Reset console display\n set_display(reset);\n\n#if !defined(_WIN32)\n // Restore settings on POSIX systems\n if (!simple_io) {\n if (tty != nullptr) {\n out = stdout;\n fclose(tty);\n tty = nullptr;\n }\n tcsetattr(STDIN_FILENO, TCSANOW, &initial_state);\n }\n#endif\n }\n\n //\n // Display and IO\n //\n\n // Keep track of current display and only emit ANSI code if it changes\n void set_display(display_t display) {\n if (advanced_display && current_display != display) {\n fflush(stdout);\n switch(display) {\n case reset:\n fprintf(out, ANSI_COLOR_RESET);\n break;\n case prompt:\n fprintf(out, ANSI_COLOR_YELLOW);\n break;\n case user_input:\n fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);\n break;\n case error:\n fprintf(out, ANSI_BOLD ANSI_COLOR_RED);\n }\n current_display = display;\n fflush(out);\n }\n }\n\n static char32_t getchar32() {\n#if defined(_WIN32)\n HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);\n wchar_t high_surrogate = 0;\n\n while (true) {\n INPUT_RECORD record;\n DWORD count;\n if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {\n return WEOF;\n }\n\n if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {\n wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;\n if (wc == 0) {\n continue;\n }\n\n if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate\n high_surrogate = wc;\n continue;\n }\n if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate\n if (high_surrogate != 0) { // Check if we have a high surrogate\n return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;\n }\n }\n\n high_surrogate = 0; // Reset the high surrogate\n return static_cast(wc);\n }\n }\n#else\n wchar_t wc = getwchar();\n if (static_cast(wc) == WEOF) {\n return WEOF;\n }\n\n#if WCHAR_MAX == 0xFFFF\n if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate\n wchar_t low_surrogate = getwchar();\n if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate\n return (static_cast(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;\n }\n }\n if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair\n return 0xFFFD; // Return the replacement character U+FFFD\n }\n#endif\n\n return static_cast(wc);\n#endif\n }\n\n static void pop_cursor() {\n#if defined(_WIN32)\n if (hConsole != NULL) {\n CONSOLE_SCREEN_BUFFER_INFO bufferInfo;\n GetConsoleScreenBufferInfo(hConsole, &bufferInfo);\n\n COORD newCursorPosition = bufferInfo.dwCursorPosition;\n if (newCursorPosition.X == 0) {\n newCursorPosition.X = bufferInfo.dwSize.X - 1;\n newCursorPosition.Y -= 1;\n } else {\n newCursorPosition.X -= 1;\n }\n\n SetConsoleCursorPosition(hConsole, newCursorPosition);\n return;\n }\n#endif\n putc('\\b', out);\n }\n\n static int estimateWidth(char32_t codepoint) {\n#if defined(_WIN32)\n (void)codepoint;\n return 1;\n#else\n return wcwidth(codepoint);\n#endif\n }\n\n static int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) {\n#if defined(_WIN32)\n CONSOLE_SCREEN_BUFFER_INFO bufferInfo;\n if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) {\n // go with the default\n return expectedWidth;\n }\n COORD initialPosition = bufferInfo.dwCursorPosition;\n DWORD nNumberOfChars = length;\n WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);\n\n CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;\n GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);\n\n // Figure out our real position if we're in the last column\n if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {\n DWORD nNumberOfChars;\n WriteConsole(hConsole, &\" \\b\", 2, &nNumberOfChars, NULL);\n GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);\n }\n\n int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;\n if (width < 0) {\n width += newBufferInfo.dwSize.X;\n }\n return width;\n#else\n // We can trust expectedWidth if we've got one\n if (expectedWidth >= 0 || tty == nullptr) {\n fwrite(utf8_codepoint, length, 1, out);\n return expectedWidth;\n }\n\n fputs(\"\\033[6n\", tty); // Query cursor position\n int x1;\n int y1;\n int x2;\n int y2;\n int results = 0;\n results = fscanf(tty, \"\\033[%d;%dR\", &y1, &x1);\n\n fwrite(utf8_codepoint, length, 1, tty);\n\n fputs(\"\\033[6n\", tty); // Query cursor position\n results += fscanf(tty, \"\\033[%d;%dR\", &y2, &x2);\n\n if (results != 4) {\n return expectedWidth;\n }\n\n int width = x2 - x1;\n if (width < 0) {\n // Calculate the width considering text wrapping\n struct winsize w;\n ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);\n width += w.ws_col;\n }\n return width;\n#endif\n }\n\n static void replace_last(char ch) {\n#if defined(_WIN32)\n pop_cursor();\n put_codepoint(&ch, 1, 1);\n#else\n fprintf(out, \"\\b%c\", ch);\n#endif\n }\n\n static void append_utf8(char32_t ch, std::string & out) {\n if (ch <= 0x7F) {\n out.push_back(static_cast(ch));\n } else if (ch <= 0x7FF) {\n out.push_back(static_cast(0xC0 | ((ch >> 6) & 0x1F)));\n out.push_back(static_cast(0x80 | (ch & 0x3F)));\n } else if (ch <= 0xFFFF) {\n out.push_back(static_cast(0xE0 | ((ch >> 12) & 0x0F)));\n out.push_back(static_cast(0x80 | ((ch >> 6) & 0x3F)));\n out.push_back(static_cast(0x80 | (ch & 0x3F)));\n } else if (ch <= 0x10FFFF) {\n out.push_back(static_cast(0xF0 | ((ch >> 18) & 0x07)));\n out.push_back(static_cast(0x80 | ((ch >> 12) & 0x3F)));\n out.push_back(static_cast(0x80 | ((ch >> 6) & 0x3F)));\n out.push_back(static_cast(0x80 | (ch & 0x3F)));\n } else {\n // Invalid Unicode code point\n }\n }\n\n // Helper function to remove the last UTF-8 character from a string\n static void pop_back_utf8_char(std::string & line) {\n if (line.empty()) {\n return;\n }\n\n size_t pos = line.length() - 1;\n\n // Find the start of the last UTF-8 character (checking up to 4 bytes back)\n for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {\n if ((line[pos] & 0xC0) != 0x80) {\n break; // Found the start of the character\n }\n }\n line.erase(pos);\n }\n\n static bool readline_advanced(std::string & line, bool multiline_input) {\n if (out != stdout) {\n fflush(stdout);\n }\n\n line.clear();\n std::vector widths;\n bool is_special_char = false;\n bool end_of_stream = false;\n\n char32_t input_char;\n while (true) {\n fflush(out); // Ensure all output is displayed before waiting for input\n input_char = getchar32();\n\n if (input_char == '\\r' || input_char == '\\n') {\n break;\n }\n\n if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {\n end_of_stream = true;\n break;\n }\n\n if (is_special_char) {\n set_display(user_input);\n replace_last(line.back());\n is_special_char = false;\n }\n\n if (input_char == '\\033') { // Escape sequence\n char32_t code = getchar32();\n if (code == '[' || code == 0x1B) {\n // Discard the rest of the escape sequence\n while ((code = getchar32()) != (char32_t) WEOF) {\n if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {\n break;\n }\n }\n }\n } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace\n if (!widths.empty()) {\n int count;\n do {\n count = widths.back();\n widths.pop_back();\n // Move cursor back, print space, and move cursor back again\n for (int i = 0; i < count; i++) {\n replace_last(' ');\n pop_cursor();\n }\n pop_back_utf8_char(line);\n } while (count == 0 && !widths.empty());\n }\n } else {\n int offset = line.length();\n append_utf8(input_char, line);\n int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));\n if (width < 0) {\n width = 0;\n }\n widths.push_back(width);\n }\n\n if (!line.empty() && (line.back() == '\\\\' || line.back() == '/')) {\n set_display(prompt);\n replace_last(line.back());\n is_special_char = true;\n }\n }\n\n bool has_more = multiline_input;\n if (is_special_char) {\n replace_last(' ');\n pop_cursor();\n\n char last = line.back();\n line.pop_back();\n if (last == '\\\\') {\n line += '\\n';\n fputc('\\n', out);\n has_more = !has_more;\n } else {\n // llama will just eat the single space, it won't act as a space\n if (line.length() == 1 && line.back() == ' ') {\n line.clear();\n pop_cursor();\n }\n has_more = false;\n }\n } else {\n if (end_of_stream) {\n has_more = false;\n } else {\n line += '\\n';\n fputc('\\n', out);\n }\n }\n\n fflush(out);\n return has_more;\n }\n\n static bool readline_simple(std::string & line, bool multiline_input) {\n#if defined(_WIN32)\n std::wstring wline;\n if (!std::getline(std::wcin, wline)) {\n // Input stream is bad or EOF received\n line.clear();\n GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0);\n return false;\n }\n\n int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);\n line.resize(size_needed);\n WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);\n#else\n if (!std::getline(std::cin, line)) {\n // Input stream is bad or EOF received\n line.clear();\n return false;\n }\n#endif\n if (!line.empty()) {\n char last = line.back();\n if (last == '/') { // Always return control on '/' symbol\n line.pop_back();\n return false;\n }\n if (last == '\\\\') { // '\\\\' changes the default action\n line.pop_back();\n multiline_input = !multiline_input;\n }\n }\n line += '\\n';\n\n // By default, continue input if multiline_input is set\n return multiline_input;\n }\n\n bool readline(std::string & line, bool multiline_input) {\n set_display(user_input);\n\n if (simple_io) {\n return readline_simple(line, multiline_input);\n }\n return readline_advanced(line, multiline_input);\n }\n\n}\n" }, { "path": "common/console.h", "content": "// Console functions\n\n#pragma once\n\n#include \n\nnamespace console {\n enum display_t {\n reset = 0,\n prompt,\n user_input,\n error\n };\n\n void init(bool use_simple_io, bool use_advanced_display);\n void cleanup();\n void set_display(display_t display);\n bool readline(std::string & line, bool multiline_input);\n}\n" }, { "path": "common/grammar-parser.cpp", "content": "#include \"grammar-parser.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace grammar_parser {\n // NOTE: assumes valid utf8 (but checks for overrun)\n // copied from llama.cpp\n static std::pair decode_utf8(const char * src) {\n static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };\n uint8_t first_byte = static_cast(*src);\n uint8_t highbits = first_byte >> 4;\n int len = lookup[highbits];\n uint8_t mask = (1 << (8 - len)) - 1;\n uint32_t value = first_byte & mask;\n const char * end = src + len; // may overrun!\n const char * pos = src + 1;\n for ( ; pos < end && *pos; pos++) {\n value = (value << 6) + (static_cast(*pos) & 0x3F);\n }\n return std::make_pair(value, pos);\n }\n\n static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {\n uint32_t next_id = static_cast(state.symbol_ids.size());\n auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id));\n return result.first->second;\n }\n\n static uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) {\n uint32_t next_id = static_cast(state.symbol_ids.size());\n state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id;\n return next_id;\n }\n\n static void add_rule(\n parse_state & state,\n uint32_t rule_id,\n const std::vector & rule) {\n if (state.rules.size() <= rule_id) {\n state.rules.resize(rule_id + 1);\n }\n state.rules[rule_id] = rule;\n }\n\n static bool is_word_char(char c) {\n return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');\n }\n\n static std::pair parse_hex(const char * src, int size) {\n const char * pos = src;\n const char * end = src + size;\n uint32_t value = 0;\n for ( ; pos < end && *pos; pos++) {\n value <<= 4;\n char c = *pos;\n if ('a' <= c && c <= 'f') {\n value += c - 'a' + 10;\n } else if ('A' <= c && c <= 'F') {\n value += c - 'A' + 10;\n } else if ('0' <= c && c <= '9') {\n value += c - '0';\n } else {\n break;\n }\n }\n if (pos != end) {\n throw std::runtime_error(\"expecting \" + std::to_string(size) + \" hex chars at \" + src);\n }\n return std::make_pair(value, pos);\n }\n\n static const char * parse_space(const char * src, bool newline_ok) {\n const char * pos = src;\n while (*pos == ' ' || *pos == '\\t' || *pos == '#' ||\n (newline_ok && (*pos == '\\r' || *pos == '\\n'))) {\n if (*pos == '#') {\n while (*pos && *pos != '\\r' && *pos != '\\n') {\n pos++;\n }\n } else {\n pos++;\n }\n }\n return pos;\n }\n\n static const char * parse_name(const char * src) {\n const char * pos = src;\n while (is_word_char(*pos)) {\n pos++;\n }\n if (pos == src) {\n throw std::runtime_error(std::string(\"expecting name at \") + src);\n }\n return pos;\n }\n\n static std::pair parse_char(const char * src) {\n if (*src == '\\\\') {\n switch (src[1]) {\n case 'x': return parse_hex(src + 2, 2);\n case 'u': return parse_hex(src + 2, 4);\n case 'U': return parse_hex(src + 2, 8);\n case 't': return std::make_pair('\\t', src + 2);\n case 'r': return std::make_pair('\\r', src + 2);\n case 'n': return std::make_pair('\\n', src + 2);\n case '\\\\':\n case '\"':\n case '[':\n case ']':\n return std::make_pair(src[1], src + 2);\n default:\n throw std::runtime_error(std::string(\"unknown escape at \") + src);\n }\n } else if (*src) {\n return decode_utf8(src);\n }\n throw std::runtime_error(\"unexpected end of input\");\n }\n\n const char * parse_alternates(\n parse_state & state,\n const char * src,\n const std::string & rule_name,\n uint32_t rule_id,\n bool is_nested);\n\n static const char * parse_sequence(\n parse_state & state,\n const char * src,\n const std::string & rule_name,\n std::vector & out_elements,\n bool is_nested) {\n size_t last_sym_start = out_elements.size();\n const char * pos = src;\n while (*pos) {\n if (*pos == '\"') { // literal string\n pos++;\n last_sym_start = out_elements.size();\n while (*pos != '\"') {\n auto char_pair = parse_char(pos);\n pos = char_pair.second;\n out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});\n }\n pos = parse_space(pos + 1, is_nested);\n } else if (*pos == '[') { // char range(s)\n pos++;\n enum llama_gretype start_type = LLAMA_GRETYPE_CHAR;\n if (*pos == '^') {\n pos++;\n start_type = LLAMA_GRETYPE_CHAR_NOT;\n }\n last_sym_start = out_elements.size();\n while (*pos != ']') {\n auto char_pair = parse_char(pos);\n pos = char_pair.second;\n enum llama_gretype type = last_sym_start < out_elements.size()\n ? LLAMA_GRETYPE_CHAR_ALT\n : start_type;\n\n out_elements.push_back({type, char_pair.first});\n if (pos[0] == '-' && pos[1] != ']') {\n auto endchar_pair = parse_char(pos + 1);\n pos = endchar_pair.second;\n out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});\n }\n }\n pos = parse_space(pos + 1, is_nested);\n } else if (is_word_char(*pos)) { // rule reference\n const char * name_end = parse_name(pos);\n uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos);\n pos = parse_space(name_end, is_nested);\n last_sym_start = out_elements.size();\n out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id});\n } else if (*pos == '(') { // grouping\n // parse nested alternates into synthesized rule\n pos = parse_space(pos + 1, true);\n uint32_t sub_rule_id = generate_symbol_id(state, rule_name);\n pos = parse_alternates(state, pos, rule_name, sub_rule_id, true);\n last_sym_start = out_elements.size();\n // output reference to synthesized rule\n out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});\n if (*pos != ')') {\n throw std::runtime_error(std::string(\"expecting ')' at \") + pos);\n }\n pos = parse_space(pos + 1, is_nested);\n } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator\n if (last_sym_start == out_elements.size()) {\n throw std::runtime_error(std::string(\"expecting preceeding item to */+/? at \") + pos);\n }\n\n // apply transformation to previous symbol (last_sym_start to end) according to\n // rewrite rules:\n // S* --> S' ::= S S' |\n // S+ --> S' ::= S S' | S\n // S? --> S' ::= S |\n uint32_t sub_rule_id = generate_symbol_id(state, rule_name);\n std::vector sub_rule;\n // add preceding symbol to generated rule\n sub_rule.insert(\n sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());\n if (*pos == '*' || *pos == '+') {\n // cause generated rule to recurse\n sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});\n }\n // mark start of alternate def\n sub_rule.push_back({LLAMA_GRETYPE_ALT, 0});\n if (*pos == '+') {\n // add preceding symbol as alternate only for '+' (otherwise empty)\n sub_rule.insert(\n sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());\n }\n sub_rule.push_back({LLAMA_GRETYPE_END, 0});\n add_rule(state, sub_rule_id, sub_rule);\n\n // in original rule, replace previous symbol with reference to generated rule\n out_elements.resize(last_sym_start);\n out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});\n\n pos = parse_space(pos + 1, is_nested);\n } else {\n break;\n }\n }\n return pos;\n }\n\n const char * parse_alternates(\n parse_state & state,\n const char * src,\n const std::string & rule_name,\n uint32_t rule_id,\n bool is_nested) {\n std::vector rule;\n const char * pos = parse_sequence(state, src, rule_name, rule, is_nested);\n while (*pos == '|') {\n rule.push_back({LLAMA_GRETYPE_ALT, 0});\n pos = parse_space(pos + 1, true);\n pos = parse_sequence(state, pos, rule_name, rule, is_nested);\n }\n rule.push_back({LLAMA_GRETYPE_END, 0});\n add_rule(state, rule_id, rule);\n return pos;\n }\n\n static const char * parse_rule(parse_state & state, const char * src) {\n const char * name_end = parse_name(src);\n const char * pos = parse_space(name_end, false);\n size_t name_len = name_end - src;\n uint32_t rule_id = get_symbol_id(state, src, name_len);\n const std::string name(src, name_len);\n\n if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) {\n throw std::runtime_error(std::string(\"expecting ::= at \") + pos);\n }\n pos = parse_space(pos + 3, true);\n\n pos = parse_alternates(state, pos, name, rule_id, false);\n\n if (*pos == '\\r') {\n pos += pos[1] == '\\n' ? 2 : 1;\n } else if (*pos == '\\n') {\n pos++;\n } else if (*pos) {\n throw std::runtime_error(std::string(\"expecting newline or end at \") + pos);\n }\n return parse_space(pos, true);\n }\n\n parse_state parse(const char * src) {\n try {\n parse_state state;\n const char * pos = parse_space(src, true);\n while (*pos) {\n pos = parse_rule(state, pos);\n }\n return state;\n } catch (const std::exception & err) {\n fprintf(stderr, \"%s: error parsing grammar: %s\\n\", __func__, err.what());\n return parse_state();\n }\n }\n\n static void print_grammar_char(FILE * file, uint32_t c) {\n if (0x20 <= c && c <= 0x7f) {\n fprintf(file, \"%c\", static_cast(c));\n } else {\n // cop out of encoding UTF-8\n fprintf(file, \"\", c);\n }\n }\n\n static bool is_char_element(llama_grammar_element elem) {\n switch (elem.type) {\n case LLAMA_GRETYPE_CHAR: return true;\n case LLAMA_GRETYPE_CHAR_NOT: return true;\n case LLAMA_GRETYPE_CHAR_ALT: return true;\n case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;\n default: return false;\n }\n }\n\n static void print_rule_binary(FILE * file, const std::vector & rule) {\n for (auto elem : rule) {\n switch (elem.type) {\n case LLAMA_GRETYPE_END: fprintf(file, \"END\"); break;\n case LLAMA_GRETYPE_ALT: fprintf(file, \"ALT\"); break;\n case LLAMA_GRETYPE_RULE_REF: fprintf(file, \"RULE_REF\"); break;\n case LLAMA_GRETYPE_CHAR: fprintf(file, \"CHAR\"); break;\n case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, \"CHAR_NOT\"); break;\n case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, \"CHAR_RNG_UPPER\"); break;\n case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, \"CHAR_ALT\"); break;\n }\n switch (elem.type) {\n case LLAMA_GRETYPE_END:\n case LLAMA_GRETYPE_ALT:\n case LLAMA_GRETYPE_RULE_REF:\n fprintf(file, \"(%u) \", elem.value);\n break;\n case LLAMA_GRETYPE_CHAR:\n case LLAMA_GRETYPE_CHAR_NOT:\n case LLAMA_GRETYPE_CHAR_RNG_UPPER:\n case LLAMA_GRETYPE_CHAR_ALT:\n fprintf(file, \"(\\\"\");\n print_grammar_char(file, elem.value);\n fprintf(file, \"\\\") \");\n break;\n }\n }\n fprintf(file, \"\\n\");\n }\n\n static void print_rule(\n FILE * file,\n uint32_t rule_id,\n const std::vector & rule,\n const std::map & symbol_id_names) {\n if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) {\n throw std::runtime_error(\n \"malformed rule, does not end with LLAMA_GRETYPE_END: \" + std::to_string(rule_id));\n }\n fprintf(file, \"%s ::= \", symbol_id_names.at(rule_id).c_str());\n for (size_t i = 0, end = rule.size() - 1; i < end; i++) {\n llama_grammar_element elem = rule[i];\n switch (elem.type) {\n case LLAMA_GRETYPE_END:\n throw std::runtime_error(\n \"unexpected end of rule: \" + std::to_string(rule_id) + \",\" +\n std::to_string(i));\n case LLAMA_GRETYPE_ALT:\n fprintf(file, \"| \");\n break;\n case LLAMA_GRETYPE_RULE_REF:\n fprintf(file, \"%s \", symbol_id_names.at(elem.value).c_str());\n break;\n case LLAMA_GRETYPE_CHAR:\n fprintf(file, \"[\");\n print_grammar_char(file, elem.value);\n break;\n case LLAMA_GRETYPE_CHAR_NOT:\n fprintf(file, \"[^\");\n print_grammar_char(file, elem.value);\n break;\n case LLAMA_GRETYPE_CHAR_RNG_UPPER:\n if (i == 0 || !is_char_element(rule[i - 1])) {\n throw std::runtime_error(\n \"LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: \" +\n std::to_string(rule_id) + \",\" + std::to_string(i));\n }\n fprintf(file, \"-\");\n print_grammar_char(file, elem.value);\n break;\n case LLAMA_GRETYPE_CHAR_ALT:\n if (i == 0 || !is_char_element(rule[i - 1])) {\n throw std::runtime_error(\n \"LLAMA_GRETYPE_CHAR_ALT without preceding char: \" +\n std::to_string(rule_id) + \",\" + std::to_string(i));\n }\n print_grammar_char(file, elem.value);\n break;\n }\n if (is_char_element(elem)) {\n switch (rule[i + 1].type) {\n case LLAMA_GRETYPE_CHAR_ALT:\n case LLAMA_GRETYPE_CHAR_RNG_UPPER:\n break;\n default:\n fprintf(file, \"] \");\n }\n }\n }\n fprintf(file, \"\\n\");\n }\n\n void print_grammar(FILE * file, const parse_state & state) {\n try {\n std::map symbol_id_names;\n for (const auto & kv : state.symbol_ids) {\n symbol_id_names[kv.second] = kv.first;\n }\n for (size_t i = 0, end = state.rules.size(); i < end; i++) {\n // fprintf(file, \"%zu: \", i);\n // print_rule_binary(file, state.rules[i]);\n print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);\n // fprintf(file, \"\\n\");\n }\n } catch (const std::exception & err) {\n fprintf(stderr, \"\\n%s: error printing grammar: %s\\n\", __func__, err.what());\n }\n }\n\n std::vector parse_state::c_rules() {\n std::vector ret;\n ret.reserve(rules.size());\n for (const auto & rule : rules) {\n ret.push_back(rule.data());\n }\n return ret;\n }\n}\n" }, { "path": "common/grammar-parser.h", "content": "// Implements a parser for an extended Backus-Naur form (BNF), producing the\n// binary context-free grammar format specified by llama.h. Supports character\n// ranges, grouping, and repetition operators. As an example, a grammar for\n// arithmetic might look like:\n//\n// root ::= expr\n// expr ::= term ([-+*/] term)*\n// term ::= num | \"(\" space expr \")\" space\n// num ::= [0-9]+ space\n// space ::= [ \\t\\n]*\n\n#pragma once\n#include \"llama.h\"\n#include \n#include \n#include \n#include \n\nnamespace grammar_parser {\n struct parse_state {\n std::map symbol_ids;\n std::vector> rules;\n\n std::vector c_rules();\n };\n\n parse_state parse(const char * src);\n void print_grammar(FILE * file, const parse_state & state);\n}\n" }, { "path": "common/log.h", "content": "#pragma once\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// --------------------------------\n//\n// Basic usage:\n//\n// --------\n//\n// The LOG() and LOG_TEE() macros are ready to go by default\n// they do not require any initialization.\n//\n// LOGLN() and LOG_TEELN() are variants which automatically\n// include \\n character at the end of the log string.\n//\n// LOG() behaves exactly like printf, by default writing to a logfile.\n// LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ).\n//\n// Default logfile is named\n// \"llama..log\"\n// Default LOG_TEE() secondary output target is\n// stderr\n//\n// Logs can be dynamically disabled or enabled using functions:\n// log_disable()\n// and\n// log_enable()\n//\n// A log target can be changed with:\n// log_set_target( string )\n// creating and opening, or re-opening a file by string filename\n// or\n// log_set_target( FILE* )\n// allowing to point at stderr, stdout, or any valid FILE* file handler.\n//\n// --------\n//\n// End of Basic usage.\n//\n// --------------------------------\n\n// Specifies a log target.\n// default uses log_handler() with \"llama.log\" log file\n// this can be changed, by defining LOG_TARGET\n// like so:\n//\n// #define LOG_TARGET (a valid FILE*)\n// #include \"log.h\"\n//\n// or it can be simply redirected to stdout or stderr\n// like so:\n//\n// #define LOG_TARGET stderr\n// #include \"log.h\"\n//\n// The log target can also be redirected to a diffrent function\n// like so:\n//\n// #define LOG_TARGET log_handler_diffrent()\n// #include \"log.h\"\n//\n// FILE* log_handler_diffrent()\n// {\n// return stderr;\n// }\n//\n// or:\n//\n// #define LOG_TARGET log_handler_another_one(\"somelog.log\")\n// #include \"log.h\"\n//\n// FILE* log_handler_another_one(char*filename)\n// {\n// static FILE* logfile = nullptr;\n// (...)\n// if( !logfile )\n// {\n// fopen(...)\n// }\n// (...)\n// return logfile\n// }\n//\n#ifndef LOG_TARGET\n #define LOG_TARGET log_handler()\n#endif\n\n#ifndef LOG_TEE_TARGET\n #define LOG_TEE_TARGET stderr\n#endif\n\n// Utility for synchronizing log configuration state\n// since std::optional was introduced only in c++17\nenum LogTriState\n{\n LogTriStateSame,\n LogTriStateFalse,\n LogTriStateTrue\n};\n\n// Utility to obtain \"pid\" like unique process id and use it when creating log files.\ninline std::string log_get_pid()\n{\n static std::string pid;\n if (pid.empty())\n {\n // std::this_thread::get_id() is the most portable way of obtaining a \"process id\"\n // it's not the same as \"pid\" but is unique enough to solve multiple instances\n // trying to write to the same log.\n std::stringstream ss;\n ss << std::this_thread::get_id();\n pid = ss.str();\n }\n\n return pid;\n}\n\n// Utility function for generating log file names with unique id based on thread id.\n// invocation with log_filename_generator( \"llama\", \"log\" ) creates a string \"llama..log\"\n// where the number is a runtime id of the current thread.\n\n#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(LogTriStateSame, log_file_basename, log_file_extension)\n\n// INTERNAL, DO NOT USE\ninline std::string log_filename_generator_impl(LogTriState multilog, const std::string & log_file_basename, const std::string & log_file_extension)\n{\n static bool _multilog = false;\n\n if (multilog != LogTriStateSame)\n {\n _multilog = multilog == LogTriStateTrue;\n }\n\n std::stringstream buf;\n\n buf << log_file_basename;\n if (_multilog)\n {\n buf << \".\";\n buf << log_get_pid();\n }\n buf << \".\";\n buf << log_file_extension;\n\n return buf.str();\n}\n\n#ifndef LOG_DEFAULT_FILE_NAME\n #define LOG_DEFAULT_FILE_NAME log_filename_generator(\"llama\", \"log\")\n#endif\n\n// Utility for turning #define values into string literals\n// so we can have a define for stderr and\n// we can print \"stderr\" instead of literal stderr, etc.\n#define LOG_STRINGIZE1(s) #s\n#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s)\n\n#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET)\n\n// Allows disabling timestamps.\n// in order to disable, define LOG_NO_TIMESTAMPS\n// like so:\n//\n// #define LOG_NO_TIMESTAMPS\n// #include \"log.h\"\n//\n#ifndef LOG_NO_TIMESTAMPS\n #ifndef _MSC_VER\n #define LOG_TIMESTAMP_FMT \"[%\" PRIu64 \"] \"\n #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count()\n #else\n #define LOG_TIMESTAMP_FMT \"[%\" PRIu64 \"] \"\n #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count()\n #endif\n#else\n #define LOG_TIMESTAMP_FMT \"%s\"\n #define LOG_TIMESTAMP_VAL ,\"\"\n#endif\n\n#ifdef LOG_TEE_TIMESTAMPS\n #ifndef _MSC_VER\n #define LOG_TEE_TIMESTAMP_FMT \"[%\" PRIu64 \"] \"\n #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count()\n #else\n #define LOG_TEE_TIMESTAMP_FMT \"[%\" PRIu64 \"] \"\n #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count()\n #endif\n#else\n #define LOG_TEE_TIMESTAMP_FMT \"%s\"\n #define LOG_TEE_TIMESTAMP_VAL ,\"\"\n#endif\n\n// Allows disabling file/line/function prefix\n// in order to disable, define LOG_NO_FILE_LINE_FUNCTION\n// like so:\n//\n// #define LOG_NO_FILE_LINE_FUNCTION\n// #include \"log.h\"\n//\n#ifndef LOG_NO_FILE_LINE_FUNCTION\n #ifndef _MSC_VER\n #define LOG_FLF_FMT \"[%24s:%5d][%24s] \"\n #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__\n #else\n #define LOG_FLF_FMT \"[%24s:%5ld][%24s] \"\n #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__\n #endif\n#else\n #define LOG_FLF_FMT \"%s\"\n #define LOG_FLF_VAL ,\"\"\n#endif\n\n#ifdef LOG_TEE_FILE_LINE_FUNCTION\n #ifndef _MSC_VER\n #define LOG_TEE_FLF_FMT \"[%24s:%5d][%24s] \"\n #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__\n #else\n #define LOG_TEE_FLF_FMT \"[%24s:%5ld][%24s] \"\n #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__\n #endif\n#else\n #define LOG_TEE_FLF_FMT \"%s\"\n #define LOG_TEE_FLF_VAL ,\"\"\n#endif\n\n// INTERNAL, DO NOT USE\n// USE LOG() INSTEAD\n//\n#ifndef _MSC_VER\n #define LOG_IMPL(str, ...) \\\n do { \\\n if (LOG_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str \"%s\" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \\\n fflush(LOG_TARGET); \\\n } \\\n } while (0)\n#else\n #define LOG_IMPL(str, ...) \\\n do { \\\n if (LOG_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str \"%s\" LOG_TIMESTAMP_VAL LOG_FLF_VAL \"\", ##__VA_ARGS__); \\\n fflush(LOG_TARGET); \\\n } \\\n } while (0)\n#endif\n\n// INTERNAL, DO NOT USE\n// USE LOG_TEE() INSTEAD\n//\n#ifndef _MSC_VER\n #define LOG_TEE_IMPL(str, ...) \\\n do { \\\n if (LOG_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str \"%s\" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \\\n fflush(LOG_TARGET); \\\n } \\\n if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str \"%s\" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \\\n fflush(LOG_TEE_TARGET); \\\n } \\\n } while (0)\n#else\n #define LOG_TEE_IMPL(str, ...) \\\n do { \\\n if (LOG_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str \"%s\" LOG_TIMESTAMP_VAL LOG_FLF_VAL \"\", ##__VA_ARGS__); \\\n fflush(LOG_TARGET); \\\n } \\\n if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \\\n { \\\n fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str \"%s\" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL \"\", ##__VA_ARGS__); \\\n fflush(LOG_TEE_TARGET); \\\n } \\\n } while (0)\n#endif\n\n// The '\\0' as a last argument, is a trick to bypass the silly\n// \"warning: ISO C++11 requires at least one argument for the \"...\" in a variadic macro\"\n// so we can have a single macro which can be called just like printf.\n\n// Main LOG macro.\n// behaves like printf, and supports arguments the exact same way.\n//\n#ifndef _MSC_VER\n #define LOG(...) LOG_IMPL(__VA_ARGS__, \"\")\n#else\n #define LOG(str, ...) LOG_IMPL(\"%s\" str, \"\", __VA_ARGS__, \"\")\n#endif\n\n// Main TEE macro.\n// does the same as LOG\n// and\n// simultaneously writes stderr.\n//\n// Secondary target can be changed just like LOG_TARGET\n// by defining LOG_TEE_TARGET\n//\n#ifndef _MSC_VER\n #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, \"\")\n#else\n #define LOG_TEE(str, ...) LOG_TEE_IMPL(\"%s\" str, \"\", __VA_ARGS__, \"\")\n#endif\n\n// LOG macro variants with auto endline.\n#ifndef _MSC_VER\n #define LOGLN(...) LOG_IMPL(__VA_ARGS__, \"\\n\")\n #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, \"\\n\")\n#else\n #define LOGLN(str, ...) LOG_IMPL(\"%s\" str, \"\", __VA_ARGS__, \"\\n\")\n #define LOG_TEELN(str, ...) LOG_TEE_IMPL(\"%s\" str, \"\", __VA_ARGS__, \"\\n\")\n#endif\n\n// INTERNAL, DO NOT USE\ninline FILE *log_handler1_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)\n{\n static bool _initialized = false;\n static bool _append = false;\n static bool _disabled = filename.empty() && target == nullptr;\n static std::string log_current_filename{filename};\n static FILE *log_current_target{target};\n static FILE *logfile = nullptr;\n\n if (change)\n {\n if (append != LogTriStateSame)\n {\n _append = append == LogTriStateTrue;\n return logfile;\n }\n\n if (disable == LogTriStateTrue)\n {\n // Disable primary target\n _disabled = true;\n }\n // If previously disabled, only enable, and keep previous target\n else if (disable == LogTriStateFalse)\n {\n _disabled = false;\n }\n // Otherwise, process the arguments\n else if (log_current_filename != filename || log_current_target != target)\n {\n _initialized = false;\n }\n }\n\n if (_disabled)\n {\n // Log is disabled\n return nullptr;\n }\n\n if (_initialized)\n {\n // with fallback in case something went wrong\n return logfile ? logfile : stderr;\n }\n\n // do the (re)initialization\n if (target != nullptr)\n {\n if (logfile != nullptr && logfile != stdout && logfile != stderr)\n {\n fclose(logfile);\n }\n\n log_current_filename = LOG_DEFAULT_FILE_NAME;\n log_current_target = target;\n\n logfile = target;\n }\n else\n {\n if (log_current_filename != filename)\n {\n if (logfile != nullptr && logfile != stdout && logfile != stderr)\n {\n fclose(logfile);\n }\n }\n\n logfile = fopen(filename.c_str(), _append ? \"a\" : \"w\");\n }\n\n if (!logfile)\n {\n // Verify whether the file was opened, otherwise fallback to stderr\n logfile = stderr;\n\n fprintf(stderr, \"Failed to open logfile '%s' with error '%s'\\n\", filename.c_str(), std::strerror(errno));\n fflush(stderr);\n\n // At this point we let the init flag be to true below, and let the target fallback to stderr\n // otherwise we would repeatedly fopen() which was already unsuccessful\n }\n\n _initialized = true;\n\n return logfile ? logfile : stderr;\n}\n\n// INTERNAL, DO NOT USE\ninline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)\n{\n return log_handler1_impl(change, append, disable, filename, target);\n}\n\n// Disables logs entirely at runtime.\n// Makes LOG() and LOG_TEE() produce no output,\n// untill enabled back.\n#define log_disable() log_disable_impl()\n\n// INTERNAL, DO NOT USE\ninline FILE *log_disable_impl()\n{\n return log_handler1_impl(true, LogTriStateSame, LogTriStateTrue);\n}\n\n// Enables logs at runtime.\n#define log_enable() log_enable_impl()\n\n// INTERNAL, DO NOT USE\ninline FILE *log_enable_impl()\n{\n return log_handler1_impl(true, LogTriStateSame, LogTriStateFalse);\n}\n\n// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*)\n#define log_set_target(target) log_set_target_impl(target)\n\n// INTERNAL, DO NOT USE\ninline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, LogTriStateSame, filename); }\ninline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, LogTriStateSame, target); }\n\n// INTERNAL, DO NOT USE\ninline FILE *log_handler() { return log_handler1_impl(); }\n\n// Enable or disable creating separate log files for each run.\n// can ONLY be invoked BEFORE first log use.\n#define log_multilog(enable) log_filename_generator_impl((enable) ? LogTriStateTrue : LogTriStateFalse, \"\", \"\")\n// Enable or disable append mode for log file.\n// can ONLY be invoked BEFORE first log use.\n#define log_append(enable) log_append_impl(enable)\n// INTERNAL, DO NOT USE\ninline FILE *log_append_impl(bool enable)\n{\n return log_handler1_impl(true, enable ? LogTriStateTrue : LogTriStateFalse, LogTriStateSame);\n}\n\ninline void log_test()\n{\n log_disable();\n LOG(\"01 Hello World to nobody, because logs are disabled!\\n\");\n log_enable();\n LOG(\"02 Hello World to default output, which is \\\"%s\\\" ( Yaaay, arguments! )!\\n\", LOG_STRINGIZE(LOG_TARGET));\n LOG_TEE(\"03 Hello World to **both** default output and \" LOG_TEE_TARGET_STRING \"!\\n\");\n log_set_target(stderr);\n LOG(\"04 Hello World to stderr!\\n\");\n LOG_TEE(\"05 Hello World TEE with double printing to stderr prevented!\\n\");\n log_set_target(LOG_DEFAULT_FILE_NAME);\n LOG(\"06 Hello World to default log file!\\n\");\n log_set_target(stdout);\n LOG(\"07 Hello World to stdout!\\n\");\n log_set_target(LOG_DEFAULT_FILE_NAME);\n LOG(\"08 Hello World to default log file again!\\n\");\n log_disable();\n LOG(\"09 Hello World _1_ into the void!\\n\");\n log_enable();\n LOG(\"10 Hello World back from the void ( you should not see _1_ in the log or the output )!\\n\");\n log_disable();\n log_set_target(\"llama.anotherlog.log\");\n LOG(\"11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\\n\");\n log_enable();\n LOG(\"12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\\n\");\n log_set_target(\"llama.yetanotherlog.log\");\n LOG(\"13 Hello World this time in yet new file?\\n\");\n log_set_target(log_filename_generator(\"llama_autonamed\", \"log\"));\n LOG(\"14 Hello World in log with generated filename!\\n\");\n#ifdef _MSC_VER\n LOG_TEE(\"15 Hello msvc TEE without arguments\\n\");\n LOG_TEE(\"16 Hello msvc TEE with (%d)(%s) arguments\\n\", 1, \"test\");\n LOG_TEELN(\"17 Hello msvc TEELN without arguments\\n\");\n LOG_TEELN(\"18 Hello msvc TEELN with (%d)(%s) arguments\\n\", 1, \"test\");\n LOG(\"19 Hello msvc LOG without arguments\\n\");\n LOG(\"20 Hello msvc LOG with (%d)(%s) arguments\\n\", 1, \"test\");\n LOGLN(\"21 Hello msvc LOGLN without arguments\\n\");\n LOGLN(\"22 Hello msvc LOGLN with (%d)(%s) arguments\\n\", 1, \"test\");\n#endif\n}\n\ninline bool log_param_single_parse(const std::string & param)\n{\n if ( param == \"--log-test\")\n {\n log_test();\n return true;\n }\n\n if ( param == \"--log-disable\")\n {\n log_disable();\n return true;\n }\n\n if ( param == \"--log-enable\")\n {\n log_enable();\n return true;\n }\n\n if (param == \"--log-new\")\n {\n log_multilog(true);\n return true;\n }\n\n if (param == \"--log-append\")\n {\n log_append(true);\n return true;\n }\n\n return false;\n}\n\ninline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string())\n{\n if ( param == \"--log-file\")\n {\n if (!check_but_dont_parse)\n {\n log_set_target(log_filename_generator(next.empty() ? \"unnamed\" : next, \"log\"));\n }\n\n return true;\n }\n\n return false;\n}\n\ninline void log_print_usage()\n{\n printf(\"log options:\\n\");\n /* format\n printf(\" -h, --help show this help message and exit\\n\");*/\n /* spacing\n printf(\"__-param----------------Description\\n\");*/\n printf(\" --log-test Run simple logging test\\n\");\n printf(\" --log-disable Disable trace logs\\n\");\n printf(\" --log-enable Enable trace logs\\n\");\n printf(\" --log-file Specify a log filename (without extension)\\n\");\n printf(\" --log-new Create a separate new log file on start. \"\n \"Each log file will have unique name: \\\"..log\\\"\\n\");\n printf(\" --log-append Don't truncate the old log file.\\n\");\n}\n\n#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)\n\n// INTERNAL, DO NOT USE\ninline void log_dump_cmdline_impl(int argc, char **argv)\n{\n std::stringstream buf;\n for (int i = 0; i < argc; ++i)\n {\n if (std::string(argv[i]).find(' ') != std::string::npos)\n {\n buf << \" \\\"\" << argv[i] <<\"\\\"\";\n }\n else\n {\n buf << \" \" << argv[i];\n }\n }\n LOGLN(\"Cmd:%s\", buf.str().c_str());\n}\n\n#define log_tostr(var) log_var_to_string_impl(var).c_str()\n\ninline std::string log_var_to_string_impl(bool var)\n{\n return var ? \"true\" : \"false\";\n}\n\ninline std::string log_var_to_string_impl(std::string var)\n{\n return var;\n}\n\ninline std::string log_var_to_string_impl(const std::vector & var)\n{\n std::stringstream buf;\n buf << \"[ \";\n bool first = true;\n for (auto e : var)\n {\n if (first)\n {\n first = false;\n }\n else\n {\n buf << \", \";\n }\n buf << std::to_string(e);\n }\n buf << \" ]\";\n\n return buf.str();\n}\n\ntemplate \ninline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens)\n{\n std::stringstream buf;\n buf << \"[ \";\n\n bool first = true;\n for (const auto &token : tokens)\n {\n if (!first) {\n buf << \", \";\n } else {\n first = false;\n }\n\n auto detokenized = llama_token_to_piece(ctx, token);\n\n detokenized.erase(\n std::remove_if(\n detokenized.begin(),\n detokenized.end(),\n [](const unsigned char c) { return !std::isprint(c); }),\n detokenized.end());\n\n buf\n << \"'\" << detokenized << \"'\"\n << \":\" << std::to_string(token);\n }\n buf << \" ]\";\n\n return buf.str();\n}\n\ntemplate \ninline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch)\n{\n std::stringstream buf;\n buf << \"[ \";\n\n bool first = true;\n for (int i = 0; i < batch.n_tokens; ++i)\n {\n if (!first) {\n buf << \", \";\n } else {\n first = false;\n }\n\n auto detokenized = llama_token_to_piece(ctx, batch.token[i]);\n\n detokenized.erase(\n std::remove_if(\n detokenized.begin(),\n detokenized.end(),\n [](const unsigned char c) { return !std::isprint(c); }),\n detokenized.end());\n\n buf\n << \"\\n\" << std::to_string(i)\n << \":token '\" << detokenized << \"'\"\n << \":pos \" << std::to_string(batch.pos[i])\n << \":n_seq_id \" << std::to_string(batch.n_seq_id[i])\n << \":seq_id \" << std::to_string(batch.seq_id[i][0])\n << \":logits \" << std::to_string(batch.logits[i]);\n }\n buf << \" ]\";\n\n return buf.str();\n}\n\n#ifdef LOG_DISABLE_LOGS\n\n#undef LOG\n#define LOG(...) // dummy stub\n#undef LOGLN\n#define LOGLN(...) // dummy stub\n\n#undef LOG_TEE\n#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf\n\n#undef LOG_TEELN\n#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf\n\n#undef LOG_DISABLE\n#define LOG_DISABLE() // dummy stub\n\n#undef LOG_ENABLE\n#define LOG_ENABLE() // dummy stub\n\n#undef LOG_ENABLE\n#define LOG_ENABLE() // dummy stub\n\n#undef LOG_SET_TARGET\n#define LOG_SET_TARGET(...) // dummy stub\n\n#undef LOG_DUMP_CMDLINE\n#define LOG_DUMP_CMDLINE(...) // dummy stub\n\n#endif // LOG_DISABLE_LOGS\n" }, { "path": "common/sampling.cpp", "content": "#include \"sampling.h\"\n\nstruct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {\n struct llama_sampling_context * result = new llama_sampling_context();\n\n result->params = params;\n result->grammar = nullptr;\n\n // if there is a grammar, parse it\n if (!params.grammar.empty()) {\n result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());\n\n // will be empty (default) if there are parse errors\n if (result->parsed_grammar.rules.empty()) {\n fprintf(stderr, \"%s: failed to parse grammar\\n\", __func__);\n return nullptr;\n }\n\n std::vector grammar_rules(result->parsed_grammar.c_rules());\n\n result->grammar = llama_grammar_init(\n grammar_rules.data(),\n grammar_rules.size(), result->parsed_grammar.symbol_ids.at(\"root\"));\n }\n\n result->prev.resize(params.n_prev);\n\n return result;\n}\n\nvoid llama_sampling_free(struct llama_sampling_context * ctx) {\n if (ctx->grammar != NULL) {\n llama_grammar_free(ctx->grammar);\n }\n\n delete ctx;\n}\n\nvoid llama_sampling_reset(llama_sampling_context * ctx) {\n if (ctx->grammar != NULL) {\n llama_grammar_free(ctx->grammar);\n ctx->grammar = NULL;\n }\n\n if (!ctx->parsed_grammar.rules.empty()) {\n std::vector grammar_rules(ctx->parsed_grammar.c_rules());\n\n ctx->grammar = llama_grammar_init(\n grammar_rules.data(),\n grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at(\"root\"));\n }\n\n std::fill(ctx->prev.begin(), ctx->prev.end(), 0);\n ctx->cur.clear();\n}\n\nvoid llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {\n if (dst->grammar) {\n llama_grammar_free(dst->grammar);\n dst->grammar = nullptr;\n }\n\n if (src->grammar) {\n dst->grammar = llama_grammar_copy(src->grammar);\n }\n\n dst->prev = src->prev;\n}\n\nllama_token llama_sampling_last(llama_sampling_context * ctx) {\n return ctx->prev.back();\n}\n\nstd::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {\n const int size = ctx_sampling->prev.size();\n\n n = std::min(n, size);\n\n std::string result;\n\n for (int i = size - n; i < size; i++) {\n result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);\n }\n\n return result;\n}\n\nstd::string llama_sampling_print(const llama_sampling_params & params) {\n char result[1024];\n\n snprintf(result, sizeof(result),\n \"\\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\\n\"\n \"\\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\\n\"\n \"\\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f\",\n params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,\n params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,\n params.mirostat, params.mirostat_eta, params.mirostat_tau);\n\n return std::string(result);\n}\n\nllama_token llama_sampling_sample(\n struct llama_sampling_context * ctx_sampling,\n struct llama_context * ctx_main,\n struct llama_context * ctx_cfg,\n const int idx) {\n const llama_sampling_params & params = ctx_sampling->params;\n\n const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));\n\n const float temp = params.temp;\n const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;\n const float top_p = params.top_p;\n const float min_p = params.min_p;\n const float tfs_z = params.tfs_z;\n const float typical_p = params.typical_p;\n const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;\n const float penalty_repeat = params.penalty_repeat;\n const float penalty_freq = params.penalty_freq;\n const float penalty_present = params.penalty_present;\n const int mirostat = params.mirostat;\n const float mirostat_tau = params.mirostat_tau;\n const float mirostat_eta = params.mirostat_eta;\n const bool penalize_nl = params.penalize_nl;\n\n auto & prev = ctx_sampling->prev;\n auto & cur = ctx_sampling->cur;\n\n llama_token id = 0;\n\n float * logits = llama_get_logits_ith(ctx_main, idx);\n\n // apply params.logit_bias map\n for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {\n logits[it->first] += it->second;\n }\n\n cur.clear();\n\n for (llama_token token_id = 0; token_id < n_vocab; token_id++) {\n cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});\n }\n\n llama_token_data_array cur_p = { cur.data(), cur.size(), false };\n\n if (ctx_cfg) {\n llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);\n }\n\n // apply penalties\n if (!prev.empty()) {\n const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];\n\n llama_sample_repetition_penalties(ctx_main, &cur_p,\n prev.data() + prev.size() - penalty_last_n,\n penalty_last_n, penalty_repeat, penalty_freq, penalty_present);\n\n if (!penalize_nl) {\n for (size_t idx = 0; idx < cur_p.size; idx++) {\n if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {\n cur_p.data[idx].logit = nl_logit;\n break;\n }\n }\n }\n }\n\n if (ctx_sampling->grammar != NULL) {\n llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);\n }\n\n if (temp < 0.0) {\n // greedy sampling, with probs\n llama_sample_softmax(ctx_main, &cur_p);\n id = cur_p.data[0].id;\n } else if (temp == 0.0) {\n // greedy sampling, no probs\n id = llama_sample_token_greedy(ctx_main, &cur_p);\n } else {\n if (mirostat == 1) {\n const int mirostat_m = 100;\n llama_sample_temp(ctx_main, &cur_p, temp);\n id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);\n } else if (mirostat == 2) {\n llama_sample_temp(ctx_main, &cur_p, temp);\n id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);\n } else {\n // temperature sampling\n size_t min_keep = std::max(1, params.n_probs);\n\n llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);\n llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);\n llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);\n llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);\n llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);\n llama_sample_temp (ctx_main, &cur_p, temp);\n\n id = llama_sample_token(ctx_main, &cur_p);\n\n //{\n // const int n_top = 10;\n // LOG(\"top %d candidates:\\n\", n_top);\n\n // for (int i = 0; i < n_top; i++) {\n // const llama_token id = cur_p.data[i].id;\n // (void)id; // To avoid a warning that id is unused when logging is disabled.\n // LOG(\" - %5d: '%12s' (%.3f)\\n\", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);\n // }\n //}\n\n LOG(\"sampled token: %5d: '%s'\\n\", id, llama_token_to_piece(ctx_main, id).c_str());\n }\n }\n\n return id;\n}\n\nvoid llama_sampling_accept(\n struct llama_sampling_context * ctx_sampling,\n struct llama_context * ctx_main,\n llama_token id,\n bool apply_grammar) {\n ctx_sampling->prev.erase(ctx_sampling->prev.begin());\n ctx_sampling->prev.push_back(id);\n\n if (ctx_sampling->grammar != NULL && apply_grammar) {\n llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);\n }\n}\n" }, { "path": "common/sampling.h", "content": "#pragma once\n\n#include \"llama.h\"\n\n#include \"grammar-parser.h\"\n\n#include \n#include \n#include \n\n// sampling parameters\ntypedef struct llama_sampling_params {\n int32_t n_prev = 64; // number of previous tokens to remember\n int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.\n int32_t top_k = 40; // <= 0 to use vocab size\n float top_p = 0.95f; // 1.0 = disabled\n float min_p = 0.05f; // 0.0 = disabled\n float tfs_z = 1.00f; // 1.0 = disabled\n float typical_p = 1.00f; // 1.0 = disabled\n float temp = 0.80f; // 1.0 = disabled\n int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)\n float penalty_repeat = 1.10f; // 1.0 = disabled\n float penalty_freq = 0.00f; // 0.0 = disabled\n float penalty_present = 0.00f; // 0.0 = disabled\n int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0\n float mirostat_tau = 5.00f; // target entropy\n float mirostat_eta = 0.10f; // learning rate\n bool penalize_nl = true; // consider newlines as a repeatable token\n\n std::string grammar; // optional BNF-like grammar to constrain sampling\n\n // Classifier-Free Guidance\n // https://arxiv.org/abs/2306.17806\n std::string cfg_negative_prompt; // string to help guidance\n float cfg_scale = 1.f; // how strong is guidance\n\n std::unordered_map logit_bias; // logit bias for specific tokens\n} llama_sampling_params;\n\n// general sampler context\n// TODO: move to llama.h\nstruct llama_sampling_context {\n // parameters that will be used for sampling\n llama_sampling_params params;\n\n // mirostat sampler state\n float mirostat_mu;\n\n llama_grammar * grammar;\n\n // internal\n grammar_parser::parse_state parsed_grammar;\n\n // TODO: replace with ring-buffer\n std::vector prev;\n std::vector cur;\n};\n\n#include \"common.h\"\n\n// Create a new sampling context instance.\nstruct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params);\n\nvoid llama_sampling_free(struct llama_sampling_context * ctx);\n\n// Reset the sampler context\n// - clear prev tokens\n// - reset grammar\nvoid llama_sampling_reset(llama_sampling_context * ctx);\n\n// Copy the sampler context\nvoid llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);\n\n// Get the last sampled token\nllama_token llama_sampling_last(llama_sampling_context * ctx);\n\n// Get a string representation of the last sampled tokens\nstd::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n);\n\n// Print sampling parameters into a string\nstd::string llama_sampling_print(const llama_sampling_params & params);\n\n// this is a common sampling function used across the examples for convenience\n// it can serve as a starting point for implementing your own sampling function\n// Note: When using multiple sequences, it is the caller's responsibility to call\n// llama_sampling_reset when a sequence ends\n//\n// required:\n// - ctx_main: context to use for sampling\n// - ctx_sampling: sampling-specific context\n//\n// optional:\n// - ctx_cfg: context to use for classifier-free guidance\n// - idx: sample from llama_get_logits_ith(ctx, idx)\n//\n// returns:\n// - token: sampled token\n// - candidates: vector of candidate tokens\n//\nllama_token llama_sampling_sample(\n struct llama_sampling_context * ctx_sampling,\n struct llama_context * ctx_main,\n struct llama_context * ctx_cfg,\n int idx = 0);\n\nvoid llama_sampling_accept(\n struct llama_sampling_context * ctx_sampling,\n struct llama_context * ctx_main,\n llama_token id,\n bool apply_grammar);\n" }, { "path": "common/stb_image.h", "content": "/* stb_image - v2.28 - public domain image loader - http://nothings.org/stb\n no warranty implied; use at your own risk\n\n Do this:\n #define STB_IMAGE_IMPLEMENTATION\n before you include this file in *one* C or C++ file to create the implementation.\n\n // i.e. it should look like this:\n #include ...\n #include ...\n #include ...\n #define STB_IMAGE_IMPLEMENTATION\n #include \"stb_image.h\"\n\n You can #define STBI_ASSERT(x) before the #include to avoid using assert.h.\n And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free\n\n\n QUICK NOTES:\n Primarily of interest to game developers and other people who can\n avoid problematic images and only need the trivial interface\n\n JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib)\n PNG 1/2/4/8/16-bit-per-channel\n\n TGA (not sure what subset, if a subset)\n BMP non-1bpp, non-RLE\n PSD (composited view only, no extra channels, 8/16 bit-per-channel)\n\n GIF (*comp always reports as 4-channel)\n HDR (radiance rgbE format)\n PIC (Softimage PIC)\n PNM (PPM and PGM binary only)\n\n Animated GIF still needs a proper API, but here's one way to do it:\n http://gist.github.com/urraka/685d9a6340b26b830d49\n\n - decode from memory or through FILE (define STBI_NO_STDIO to remove code)\n - decode from arbitrary I/O callbacks\n - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON)\n\n Full documentation under \"DOCUMENTATION\" below.\n\n\nLICENSE\n\n See end of file for license information.\n\nRECENT REVISION HISTORY:\n\n 2.28 (2023-01-29) many error fixes, security errors, just tons of stuff\n 2.27 (2021-07-11) document stbi_info better, 16-bit PNM support, bug fixes\n 2.26 (2020-07-13) many minor fixes\n 2.25 (2020-02-02) fix warnings\n 2.24 (2020-02-02) fix warnings; thread-local failure_reason and flip_vertically\n 2.23 (2019-08-11) fix clang static analysis warning\n 2.22 (2019-03-04) gif fixes, fix warnings\n 2.21 (2019-02-25) fix typo in comment\n 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs\n 2.19 (2018-02-11) fix warning\n 2.18 (2018-01-30) fix warnings\n 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings\n 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes\n 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC\n 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs\n 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes\n 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes\n 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64\n RGB-format JPEG; remove white matting in PSD;\n allocate large structures on the stack;\n correct channel count for PNG & BMP\n 2.10 (2016-01-22) avoid warning introduced in 2.09\n 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED\n\n See end of file for full revision history.\n\n\n ============================ Contributors =========================\n\n Image formats Extensions, features\n Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info)\n Nicolas Schulz (hdr, psd) Martin \"SpartanJ\" Golini (stbi_info)\n Jonathan Dummer (tga) James \"moose2000\" Brown (iPhone PNG)\n Jean-Marc Lienher (gif) Ben \"Disch\" Wenger (io callbacks)\n Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG)\n Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip)\n Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD)\n github:urraka (animated gif) Junggon Kim (PNM comments)\n Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA)\n socks-the-fox (16-bit PNG)\n Jeremy Sawicki (handle all ImageNet JPGs)\n Optimizations & bugfixes Mikhail Morozov (1-bit BMP)\n Fabian \"ryg\" Giesen Anael Seghezzi (is-16-bit query)\n Arseny Kapoulkine Simon Breuss (16-bit PNM)\n John-Mark Allen\n Carmelo J Fdez-Aguera\n\n Bug & warning fixes\n Marc LeBlanc David Woo Guillaume George Martins Mozeiko\n Christpher Lloyd Jerry Jansson Joseph Thomson Blazej Dariusz Roszkowski\n Phil Jordan Dave Moore Roy Eltham\n Hayaki Saito Nathan Reed Won Chun\n Luke Graham Johan Duparc Nick Verigakis the Horde3D community\n Thomas Ruf Ronny Chevalier github:rlyeh\n Janez Zemva John Bartholomew Michal Cichon github:romigrou\n Jonathan Blow Ken Hamada Tero Hanninen github:svdijk\n Eugene Golushkov Laurent Gomila Cort Stratton github:snagar\n Aruelien Pocheville Sergio Gonzalez Thibault Reuille github:Zelex\n Cass Everitt Ryamond Barbiero github:grim210\n Paul Du Bois Engin Manap Aldo Culquicondor github:sammyhw\n Philipp Wiesemann Dale Weiler Oriol Ferrer Mesia github:phprus\n Josh Tobin Neil Bickford Matthew Gregan github:poppolopoppo\n Julian Raschke Gregory Mullen Christian Floisand github:darealshinji\n Baldur Karlsson Kevin Schmidt JR Smith github:Michaelangel007\n Brad Weinberger Matvey Cherevko github:mosra\n Luca Sas Alexander Veselov Zack Middleton [reserved]\n Ryan C. Gordon [reserved] [reserved]\n DO NOT ADD YOUR NAME HERE\n\n Jacko Dirks\n\n To add your name to the credits, pick a random blank space in the middle and fill it.\n 80% of merge conflicts on stb PRs are due to people adding their name at the end\n of the credits.\n*/\n\n#ifndef STBI_INCLUDE_STB_IMAGE_H\n#define STBI_INCLUDE_STB_IMAGE_H\n\n// DOCUMENTATION\n//\n// Limitations:\n// - no 12-bit-per-channel JPEG\n// - no JPEGs with arithmetic coding\n// - GIF always returns *comp=4\n//\n// Basic usage (see HDR discussion below for HDR usage):\n// int x,y,n;\n// unsigned char *data = stbi_load(filename, &x, &y, &n, 0);\n// // ... process data if not NULL ...\n// // ... x = width, y = height, n = # 8-bit components per pixel ...\n// // ... replace '0' with '1'..'4' to force that many components per pixel\n// // ... but 'n' will always be the number that it would have been if you said 0\n// stbi_image_free(data);\n//\n// Standard parameters:\n// int *x -- outputs image width in pixels\n// int *y -- outputs image height in pixels\n// int *channels_in_file -- outputs # of image components in image file\n// int desired_channels -- if non-zero, # of image components requested in result\n//\n// The return value from an image loader is an 'unsigned char *' which points\n// to the pixel data, or NULL on an allocation failure or if the image is\n// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels,\n// with each pixel consisting of N interleaved 8-bit components; the first\n// pixel pointed to is top-left-most in the image. There is no padding between\n// image scanlines or between pixels, regardless of format. The number of\n// components N is 'desired_channels' if desired_channels is non-zero, or\n// *channels_in_file otherwise. If desired_channels is non-zero,\n// *channels_in_file has the number of components that _would_ have been\n// output otherwise. E.g. if you set desired_channels to 4, you will always\n// get RGBA output, but you can check *channels_in_file to see if it's trivially\n// opaque because e.g. there were only 3 channels in the source image.\n//\n// An output image with N components has the following components interleaved\n// in this order in each pixel:\n//\n// N=#comp components\n// 1 grey\n// 2 grey, alpha\n// 3 red, green, blue\n// 4 red, green, blue, alpha\n//\n// If image loading fails for any reason, the return value will be NULL,\n// and *x, *y, *channels_in_file will be unchanged. The function\n// stbi_failure_reason() can be queried for an extremely brief, end-user\n// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS\n// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly\n// more user-friendly ones.\n//\n// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized.\n//\n// To query the width, height and component count of an image without having to\n// decode the full file, you can use the stbi_info family of functions:\n//\n// int x,y,n,ok;\n// ok = stbi_info(filename, &x, &y, &n);\n// // returns ok=1 and sets x, y, n if image is a supported format,\n// // 0 otherwise.\n//\n// Note that stb_image pervasively uses ints in its public API for sizes,\n// including sizes of memory buffers. This is now part of the API and thus\n// hard to change without causing breakage. As a result, the various image\n// loaders all have certain limits on image size; these differ somewhat\n// by format but generally boil down to either just under 2GB or just under\n// 1GB. When the decoded image would be larger than this, stb_image decoding\n// will fail.\n//\n// Additionally, stb_image will reject image files that have any of their\n// dimensions set to a larger value than the configurable STBI_MAX_DIMENSIONS,\n// which defaults to 2**24 = 16777216 pixels. Due to the above memory limit,\n// the only way to have an image with such dimensions load correctly\n// is for it to have a rather extreme aspect ratio. Either way, the\n// assumption here is that such larger images are likely to be malformed\n// or malicious. If you do need to load an image with individual dimensions\n// larger than that, and it still fits in the overall size limit, you can\n// #define STBI_MAX_DIMENSIONS on your own to be something larger.\n//\n// ===========================================================================\n//\n// UNICODE:\n//\n// If compiling for Windows and you wish to use Unicode filenames, compile\n// with\n// #define STBI_WINDOWS_UTF8\n// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert\n// Windows wchar_t filenames to utf8.\n//\n// ===========================================================================\n//\n// Philosophy\n//\n// stb libraries are designed with the following priorities:\n//\n// 1. easy to use\n// 2. easy to maintain\n// 3. good performance\n//\n// Sometimes I let \"good performance\" creep up in priority over \"easy to maintain\",\n// and for best performance I may provide less-easy-to-use APIs that give higher\n// performance, in addition to the easy-to-use ones. Nevertheless, it's important\n// to keep in mind that from the standpoint of you, a client of this library,\n// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all.\n//\n// Some secondary priorities arise directly from the first two, some of which\n// provide more explicit reasons why performance can't be emphasized.\n//\n// - Portable (\"ease of use\")\n// - Small source code footprint (\"easy to maintain\")\n// - No dependencies (\"ease of use\")\n//\n// ===========================================================================\n//\n// I/O callbacks\n//\n// I/O callbacks allow you to read from arbitrary sources, like packaged\n// files or some other source. Data read from callbacks are processed\n// through a small internal buffer (currently 128 bytes) to try to reduce\n// overhead.\n//\n// The three functions you must define are \"read\" (reads some bytes of data),\n// \"skip\" (skips some bytes of data), \"eof\" (reports if the stream is at the end).\n//\n// ===========================================================================\n//\n// SIMD support\n//\n// The JPEG decoder will try to automatically use SIMD kernels on x86 when\n// supported by the compiler. For ARM Neon support, you must explicitly\n// request it.\n//\n// (The old do-it-yourself SIMD API is no longer supported in the current\n// code.)\n//\n// On x86, SSE2 will automatically be used when available based on a run-time\n// test; if not, the generic C versions are used as a fall-back. On ARM targets,\n// the typical path is to have separate builds for NEON and non-NEON devices\n// (at least this is true for iOS and Android). Therefore, the NEON support is\n// toggled by a build flag: define STBI_NEON to get NEON loops.\n//\n// If for some reason you do not want to use any of SIMD code, or if\n// you have issues compiling it, you can disable it entirely by\n// defining STBI_NO_SIMD.\n//\n// ===========================================================================\n//\n// HDR image support (disable by defining STBI_NO_HDR)\n//\n// stb_image supports loading HDR images in general, and currently the Radiance\n// .HDR file format specifically. You can still load any file through the existing\n// interface; if you attempt to load an HDR file, it will be automatically remapped\n// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1;\n// both of these constants can be reconfigured through this interface:\n//\n// stbi_hdr_to_ldr_gamma(2.2f);\n// stbi_hdr_to_ldr_scale(1.0f);\n//\n// (note, do not use _inverse_ constants; stbi_image will invert them\n// appropriately).\n//\n// Additionally, there is a new, parallel interface for loading files as\n// (linear) floats to preserve the full dynamic range:\n//\n// float *data = stbi_loadf(filename, &x, &y, &n, 0);\n//\n// If you load LDR images through this interface, those images will\n// be promoted to floating point values, run through the inverse of\n// constants corresponding to the above:\n//\n// stbi_ldr_to_hdr_scale(1.0f);\n// stbi_ldr_to_hdr_gamma(2.2f);\n//\n// Finally, given a filename (or an open file or memory block--see header\n// file for details) containing image data, you can query for the \"most\n// appropriate\" interface to use (that is, whether the image is HDR or\n// not), using:\n//\n// stbi_is_hdr(char *filename);\n//\n// ===========================================================================\n//\n// iPhone PNG support:\n//\n// We optionally support converting iPhone-formatted PNGs (which store\n// premultiplied BGRA) back to RGB, even though they're internally encoded\n// differently. To enable this conversion, call\n// stbi_convert_iphone_png_to_rgb(1).\n//\n// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per\n// pixel to remove any premultiplied alpha *only* if the image file explicitly\n// says there's premultiplied data (currently only happens in iPhone images,\n// and only if iPhone convert-to-rgb processing is on).\n//\n// ===========================================================================\n//\n// ADDITIONAL CONFIGURATION\n//\n// - You can suppress implementation of any of the decoders to reduce\n// your code footprint by #defining one or more of the following\n// symbols before creating the implementation.\n//\n// STBI_NO_JPEG\n// STBI_NO_PNG\n// STBI_NO_BMP\n// STBI_NO_PSD\n// STBI_NO_TGA\n// STBI_NO_GIF\n// STBI_NO_HDR\n// STBI_NO_PIC\n// STBI_NO_PNM (.ppm and .pgm)\n//\n// - You can request *only* certain decoders and suppress all other ones\n// (this will be more forward-compatible, as addition of new decoders\n// doesn't require you to disable them explicitly):\n//\n// STBI_ONLY_JPEG\n// STBI_ONLY_PNG\n// STBI_ONLY_BMP\n// STBI_ONLY_PSD\n// STBI_ONLY_TGA\n// STBI_ONLY_GIF\n// STBI_ONLY_HDR\n// STBI_ONLY_PIC\n// STBI_ONLY_PNM (.ppm and .pgm)\n//\n// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still\n// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB\n//\n// - If you define STBI_MAX_DIMENSIONS, stb_image will reject images greater\n// than that size (in either width or height) without further processing.\n// This is to let programs in the wild set an upper bound to prevent\n// denial-of-service attacks on untrusted data, as one could generate a\n// valid image of gigantic dimensions and force stb_image to allocate a\n// huge block of memory and spend disproportionate time decoding it. By\n// default this is set to (1 << 24), which is 16777216, but that's still\n// very big.\n\n#ifndef STBI_NO_STDIO\n#include \n#endif // STBI_NO_STDIO\n\n#define STBI_VERSION 1\n\nenum {\n STBI_default = 0, // only used for desired_channels\n\n STBI_grey = 1,\n STBI_grey_alpha = 2,\n STBI_rgb = 3,\n STBI_rgb_alpha = 4\n};\n\n#include \ntypedef unsigned char stbi_uc;\ntypedef unsigned short stbi_us;\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#ifndef STBIDEF\n#ifdef STB_IMAGE_STATIC\n#define STBIDEF static\n#else\n#define STBIDEF extern\n#endif\n#endif\n\n//////////////////////////////////////////////////////////////////////////////\n//\n// PRIMARY API - works on images of any type\n//\n\n//\n// load image by filename, open file, or memory buffer\n//\n\ntypedef struct {\n int (*read)(void * user, char * data,\n int size); // fill 'data' with 'size' bytes. return number of bytes actually read\n void (*skip)(void * user, int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative\n int (*eof)(void * user); // returns nonzero if we are at end of file/data\n} stbi_io_callbacks;\n\n////////////////////////////////////\n//\n// 8-bits-per-channel interface\n//\n\nSTBIDEF stbi_uc * stbi_load_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file,\n int desired_channels);\nSTBIDEF stbi_uc * stbi_load_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y,\n int * channels_in_file, int desired_channels);\n\n#ifndef STBI_NO_STDIO\nSTBIDEF stbi_uc * stbi_load(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels);\nSTBIDEF stbi_uc * stbi_load_from_file(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels);\n// for stbi_load_from_file, file pointer is left pointing immediately after image\n#endif\n\n#ifndef STBI_NO_GIF\nSTBIDEF stbi_uc * stbi_load_gif_from_memory(stbi_uc const * buffer, int len, int ** delays, int * x, int * y, int * z,\n int * comp, int req_comp);\n#endif\n\n#ifdef STBI_WINDOWS_UTF8\nSTBIDEF int stbi_convert_wchar_to_utf8(char * buffer, size_t bufferlen, const wchar_t * input);\n#endif\n\n////////////////////////////////////\n//\n// 16-bits-per-channel interface\n//\n\nSTBIDEF stbi_us * stbi_load_16_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file,\n int desired_channels);\nSTBIDEF stbi_us * stbi_load_16_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y,\n int * channels_in_file, int desired_channels);\n\n#ifndef STBI_NO_STDIO\nSTBIDEF stbi_us * stbi_load_16(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels);\nSTBIDEF stbi_us * stbi_load_from_file_16(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels);\n#endif\n\n////////////////////////////////////\n//\n// float-per-channel interface\n//\n#ifndef STBI_NO_LINEAR\nSTBIDEF float * stbi_loadf_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file,\n int desired_channels);\nSTBIDEF float * stbi_loadf_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * channels_in_file,\n int desired_channels);\n\n#ifndef STBI_NO_STDIO\nSTBIDEF float * stbi_loadf(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels);\nSTBIDEF float * stbi_loadf_from_file(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels);\n#endif\n#endif\n\n#ifndef STBI_NO_HDR\nSTBIDEF void stbi_hdr_to_ldr_gamma(float gamma);\nSTBIDEF void stbi_hdr_to_ldr_scale(float scale);\n#endif // STBI_NO_HDR\n\n#ifndef STBI_NO_LINEAR\nSTBIDEF void stbi_ldr_to_hdr_gamma(float gamma);\nSTBIDEF void stbi_ldr_to_hdr_scale(float scale);\n#endif // STBI_NO_LINEAR\n\n// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR\nSTBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const * clbk, void * user);\nSTBIDEF int stbi_is_hdr_from_memory(stbi_uc const * buffer, int len);\n#ifndef STBI_NO_STDIO\nSTBIDEF int stbi_is_hdr(char const * filename);\nSTBIDEF int stbi_is_hdr_from_file(FILE * f);\n#endif // STBI_NO_STDIO\n\n// get a VERY brief reason for failure\n// on most compilers (and ALL modern mainstream compilers) this is threadsafe\nSTBIDEF const char * stbi_failure_reason(void);\n\n// free the loaded image -- this is just free()\nSTBIDEF void stbi_image_free(void * retval_from_stbi_load);\n\n// get image dimensions & components without fully decoding\nSTBIDEF int stbi_info_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp);\nSTBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp);\nSTBIDEF int stbi_is_16_bit_from_memory(stbi_uc const * buffer, int len);\nSTBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * clbk, void * user);\n\n#ifndef STBI_NO_STDIO\nSTBIDEF int stbi_info(char const * filename, int * x, int * y, int * comp);\nSTBIDEF int stbi_info_from_file(FILE * f, int * x, int * y, int * comp);\nSTBIDEF int stbi_is_16_bit(char const * filename);\nSTBIDEF int stbi_is_16_bit_from_file(FILE * f);\n#endif\n\n// for image formats that explicitly notate that they have premultiplied alpha,\n// we just return the colors as stored in the file. set this flag to force\n// unpremultiplication. results are undefined if the unpremultiply overflow.\nSTBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply);\n\n// indicate whether we should process iphone images back to canonical format,\n// or just pass them through \"as-is\"\nSTBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert);\n\n// flip the image vertically, so the first pixel in the output array is the bottom left\nSTBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip);\n\n// as above, but only applies to images loaded on the thread that calls the function\n// this function is only available if your compiler supports thread-local variables;\n// calling it will fail to link if your compiler doesn't\nSTBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply);\nSTBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert);\nSTBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip);\n\n// ZLIB client - used by PNG, available for other purposes\n\nSTBIDEF char * stbi_zlib_decode_malloc_guesssize(const char * buffer, int len, int initial_size, int * outlen);\nSTBIDEF char * stbi_zlib_decode_malloc_guesssize_headerflag(const char * buffer, int len, int initial_size, int * outlen,\n int parse_header);\nSTBIDEF char * stbi_zlib_decode_malloc(const char * buffer, int len, int * outlen);\nSTBIDEF int stbi_zlib_decode_buffer(char * obuffer, int olen, const char * ibuffer, int ilen);\n\nSTBIDEF char * stbi_zlib_decode_noheader_malloc(const char * buffer, int len, int * outlen);\nSTBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const char * ibuffer, int ilen);\n\n#ifdef __cplusplus\n}\n#endif\n\n//\n//\n//// end header file /////////////////////////////////////////////////////\n#endif // STBI_INCLUDE_STB_IMAGE_H\n\n#ifdef STB_IMAGE_IMPLEMENTATION\n\n#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) || defined(STBI_ONLY_TGA) || \\\n defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || \\\n defined(STBI_ONLY_PNM) || defined(STBI_ONLY_ZLIB)\n#ifndef STBI_ONLY_JPEG\n#define STBI_NO_JPEG\n#endif\n#ifndef STBI_ONLY_PNG\n#define STBI_NO_PNG\n#endif\n#ifndef STBI_ONLY_BMP\n#define STBI_NO_BMP\n#endif\n#ifndef STBI_ONLY_PSD\n#define STBI_NO_PSD\n#endif\n#ifndef STBI_ONLY_TGA\n#define STBI_NO_TGA\n#endif\n#ifndef STBI_ONLY_GIF\n#define STBI_NO_GIF\n#endif\n#ifndef STBI_ONLY_HDR\n#define STBI_NO_HDR\n#endif\n#ifndef STBI_ONLY_PIC\n#define STBI_NO_PIC\n#endif\n#ifndef STBI_ONLY_PNM\n#define STBI_NO_PNM\n#endif\n#endif\n\n#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB)\n#define STBI_NO_ZLIB\n#endif\n\n#include \n#include \n#include // ptrdiff_t on osx\n#include \n#include \n\n#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR)\n#include // ldexp, pow\n#endif\n\n#ifndef STBI_NO_STDIO\n#include \n#endif\n\n#ifndef STBI_ASSERT\n#include \n#define STBI_ASSERT(x) assert(x)\n#endif\n\n#ifdef __cplusplus\n#define STBI_EXTERN extern \"C\"\n#else\n#define STBI_EXTERN extern\n#endif\n\n#ifndef _MSC_VER\n#ifdef __cplusplus\n#define stbi_inline inline\n#else\n#define stbi_inline\n#endif\n#else\n#define stbi_inline __forceinline\n#endif\n\n#ifndef STBI_NO_THREAD_LOCALS\n#if defined(__cplusplus) && __cplusplus >= 201103L\n#define STBI_THREAD_LOCAL thread_local\n#elif defined(__GNUC__) && __GNUC__ < 5\n#define STBI_THREAD_LOCAL __thread\n#elif defined(_MSC_VER)\n#define STBI_THREAD_LOCAL __declspec(thread)\n#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L && !defined(__STDC_NO_THREADS__)\n#define STBI_THREAD_LOCAL _Thread_local\n#endif\n\n#ifndef STBI_THREAD_LOCAL\n#if defined(__GNUC__)\n#define STBI_THREAD_LOCAL __thread\n#endif\n#endif\n#endif\n\n#if defined(_MSC_VER) || defined(__SYMBIAN32__)\ntypedef unsigned short stbi__uint16;\ntypedef signed short stbi__int16;\ntypedef unsigned int stbi__uint32;\ntypedef signed int stbi__int32;\n#else\n#include \ntypedef uint16_t stbi__uint16;\ntypedef int16_t stbi__int16;\ntypedef uint32_t stbi__uint32;\ntypedef int32_t stbi__int32;\n#endif\n\n// should produce compiler error if size is wrong\ntypedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1];\n\n#ifdef _MSC_VER\n#define STBI_NOTUSED(v) (void)(v)\n#else\n#define STBI_NOTUSED(v) (void)sizeof(v)\n#endif\n\n#ifdef _MSC_VER\n#define STBI_HAS_LROTL\n#endif\n\n#ifdef STBI_HAS_LROTL\n#define stbi_lrot(x, y) _lrotl(x, y)\n#else\n#define stbi_lrot(x, y) (((x) << (y)) | ((x) >> (-(y)&31)))\n#endif\n\n#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED))\n// ok\n#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED)\n// ok\n#else\n#error \"Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED).\"\n#endif\n\n#ifndef STBI_MALLOC\n#define STBI_MALLOC(sz) malloc(sz)\n#define STBI_REALLOC(p, newsz) realloc(p, newsz)\n#define STBI_FREE(p) free(p)\n#endif\n\n#ifndef STBI_REALLOC_SIZED\n#define STBI_REALLOC_SIZED(p, oldsz, newsz) STBI_REALLOC(p, newsz)\n#endif\n\n// x86/x64 detection\n#if defined(__x86_64__) || defined(_M_X64)\n#define STBI__X64_TARGET\n#elif defined(__i386) || defined(_M_IX86)\n#define STBI__X86_TARGET\n#endif\n\n#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD)\n// gcc doesn't support sse2 intrinsics unless you compile with -msse2,\n// which in turn means it gets to use SSE2 everywhere. This is unfortunate,\n// but previous attempts to provide the SSE2 functions with runtime\n// detection caused numerous issues. The way architecture extensions are\n// exposed in GCC/Clang is, sadly, not really suited for one-file libs.\n// New behavior: if compiled with -msse2, we use SSE2 without any\n// detection; if not, we don't use it at all.\n#define STBI_NO_SIMD\n#endif\n\n#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD)\n// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET\n//\n// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the\n// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant.\n// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not\n// simultaneously enabling \"-mstackrealign\".\n//\n// See https://github.com/nothings/stb/issues/81 for more information.\n//\n// So default to no SSE2 on 32-bit MinGW. If you've read this far and added\n// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2.\n#define STBI_NO_SIMD\n#endif\n\n#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET))\n#define STBI_SSE2\n#include \n\n#ifdef _MSC_VER\n\n#if _MSC_VER >= 1400 // not VC6\n#include // __cpuid\nstatic int stbi__cpuid3(void) {\n int info[4];\n __cpuid(info, 1);\n return info[3];\n}\n#else\nstatic int stbi__cpuid3(void) {\n int res;\n __asm {\n mov eax,1\n cpuid\n mov res,edx\n }\n return res;\n}\n#endif\n\n#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name\n\n#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2)\nstatic int stbi__sse2_available(void) {\n int info3 = stbi__cpuid3();\n return ((info3 >> 26) & 1) != 0;\n}\n#endif\n\n#else // assume GCC-style if not VC++\n#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16)))\n\n#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2)\nstatic int stbi__sse2_available(void) {\n // If we're even attempting to compile this on GCC/Clang, that means\n // -msse2 is on, which means the compiler is allowed to use SSE2\n // instructions at will, and so are we.\n return 1;\n}\n#endif\n\n#endif\n#endif\n\n// ARM NEON\n#if defined(STBI_NO_SIMD) && defined(STBI_NEON)\n#undef STBI_NEON\n#endif\n\n#ifdef STBI_NEON\n#include \n#ifdef _MSC_VER\n#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name\n#else\n#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16)))\n#endif\n#endif\n\n#ifndef STBI_SIMD_ALIGN\n#define STBI_SIMD_ALIGN(type, name) type name\n#endif\n\n#ifndef STBI_MAX_DIMENSIONS\n#define STBI_MAX_DIMENSIONS (1 << 24)\n#endif\n\n///////////////////////////////////////////////\n//\n// stbi__context struct and start_xxx functions\n\n// stbi__context structure is our basic context used by all images, so it\n// contains all the IO context, plus some basic image information\ntypedef struct {\n stbi__uint32 img_x, img_y;\n int img_n, img_out_n;\n\n stbi_io_callbacks io;\n void * io_user_data;\n\n int read_from_callbacks;\n int buflen;\n stbi_uc buffer_start[128];\n int callback_already_read;\n\n stbi_uc *img_buffer, *img_buffer_end;\n stbi_uc *img_buffer_original, *img_buffer_original_end;\n} stbi__context;\n\nstatic void stbi__refill_buffer(stbi__context * s);\n\n// initialize a memory-decode context\nstatic void stbi__start_mem(stbi__context * s, stbi_uc const * buffer, int len) {\n s->io.read = NULL;\n s->read_from_callbacks = 0;\n s->callback_already_read = 0;\n s->img_buffer = s->img_buffer_original = (stbi_uc *)buffer;\n s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *)buffer + len;\n}\n\n// initialize a callback-based context\nstatic void stbi__start_callbacks(stbi__context * s, stbi_io_callbacks * c, void * user) {\n s->io = *c;\n s->io_user_data = user;\n s->buflen = sizeof(s->buffer_start);\n s->read_from_callbacks = 1;\n s->callback_already_read = 0;\n s->img_buffer = s->img_buffer_original = s->buffer_start;\n stbi__refill_buffer(s);\n s->img_buffer_original_end = s->img_buffer_end;\n}\n\n#ifndef STBI_NO_STDIO\n\nstatic int stbi__stdio_read(void * user, char * data, int size) { return (int)fread(data, 1, size, (FILE *)user); }\n\nstatic void stbi__stdio_skip(void * user, int n) {\n int ch;\n fseek((FILE *)user, n, SEEK_CUR);\n ch = fgetc((FILE *)user); /* have to read a byte to reset feof()'s flag */\n if (ch != EOF) {\n ungetc(ch, (FILE *)user); /* push byte back onto stream if valid. */\n }\n}\n\nstatic int stbi__stdio_eof(void * user) { return feof((FILE *)user) || ferror((FILE *)user); }\n\nstatic stbi_io_callbacks stbi__stdio_callbacks = {\n stbi__stdio_read,\n stbi__stdio_skip,\n stbi__stdio_eof,\n};\n\nstatic void stbi__start_file(stbi__context * s, FILE * f) { stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *)f); }\n\n// static void stop_file(stbi__context *s) { }\n\n#endif // !STBI_NO_STDIO\n\nstatic void stbi__rewind(stbi__context * s) {\n // conceptually rewind SHOULD rewind to the beginning of the stream,\n // but we just rewind to the beginning of the initial buffer, because\n // we only use it after doing 'test', which only ever looks at at most 92 bytes\n s->img_buffer = s->img_buffer_original;\n s->img_buffer_end = s->img_buffer_original_end;\n}\n\nenum { STBI_ORDER_RGB, STBI_ORDER_BGR };\n\ntypedef struct {\n int bits_per_channel;\n int num_channels;\n int channel_order;\n} stbi__result_info;\n\n#ifndef STBI_NO_JPEG\nstatic int stbi__jpeg_test(stbi__context * s);\nstatic void * stbi__jpeg_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__jpeg_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_PNG\nstatic int stbi__png_test(stbi__context * s);\nstatic void * stbi__png_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__png_info(stbi__context * s, int * x, int * y, int * comp);\nstatic int stbi__png_is16(stbi__context * s);\n#endif\n\n#ifndef STBI_NO_BMP\nstatic int stbi__bmp_test(stbi__context * s);\nstatic void * stbi__bmp_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__bmp_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_TGA\nstatic int stbi__tga_test(stbi__context * s);\nstatic void * stbi__tga_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__tga_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_PSD\nstatic int stbi__psd_test(stbi__context * s);\nstatic void * stbi__psd_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc);\nstatic int stbi__psd_info(stbi__context * s, int * x, int * y, int * comp);\nstatic int stbi__psd_is16(stbi__context * s);\n#endif\n\n#ifndef STBI_NO_HDR\nstatic int stbi__hdr_test(stbi__context * s);\nstatic float * stbi__hdr_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__hdr_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_PIC\nstatic int stbi__pic_test(stbi__context * s);\nstatic void * stbi__pic_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__pic_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_GIF\nstatic int stbi__gif_test(stbi__context * s);\nstatic void * stbi__gif_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic void * stbi__load_gif_main(stbi__context * s, int ** delays, int * x, int * y, int * z, int * comp, int req_comp);\nstatic int stbi__gif_info(stbi__context * s, int * x, int * y, int * comp);\n#endif\n\n#ifndef STBI_NO_PNM\nstatic int stbi__pnm_test(stbi__context * s);\nstatic void * stbi__pnm_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri);\nstatic int stbi__pnm_info(stbi__context * s, int * x, int * y, int * comp);\nstatic int stbi__pnm_is16(stbi__context * s);\n#endif\n\nstatic\n#ifdef STBI_THREAD_LOCAL\n STBI_THREAD_LOCAL\n#endif\n const char * stbi__g_failure_reason;\n\nSTBIDEF const char * stbi_failure_reason(void) { return stbi__g_failure_reason; }\n\n#ifndef STBI_NO_FAILURE_STRINGS\nstatic int stbi__err(const char * str) {\n stbi__g_failure_reason = str;\n return 0;\n}\n#endif\n\nstatic void * stbi__malloc(size_t size) { return STBI_MALLOC(size); }\n\n// stb_image uses ints pervasively, including for offset calculations.\n// therefore the largest decoded image size we can support with the\n// current code, even on 64-bit targets, is INT_MAX. this is not a\n// significant limitation for the intended use case.\n//\n// we do, however, need to make sure our size calculations don't\n// overflow. hence a few helper functions for size calculations that\n// multiply integers together, making sure that they're non-negative\n// and no overflow occurs.\n\n// return 1 if the sum is valid, 0 on overflow.\n// negative terms are considered invalid.\nstatic int stbi__addsizes_valid(int a, int b) {\n if (b < 0)\n return 0;\n // now 0 <= b <= INT_MAX, hence also\n // 0 <= INT_MAX - b <= INTMAX.\n // And \"a + b <= INT_MAX\" (which might overflow) is the\n // same as a <= INT_MAX - b (no overflow)\n return a <= INT_MAX - b;\n}\n\n// returns 1 if the product is valid, 0 on overflow.\n// negative factors are considered invalid.\nstatic int stbi__mul2sizes_valid(int a, int b) {\n if (a < 0 || b < 0)\n return 0;\n if (b == 0)\n return 1; // mul-by-0 is always safe\n // portable way to check for no overflows in a*b\n return a <= INT_MAX / b;\n}\n\n#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR)\n// returns 1 if \"a*b + add\" has no negative terms/factors and doesn't overflow\nstatic int stbi__mad2sizes_valid(int a, int b, int add) {\n return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a * b, add);\n}\n#endif\n\n// returns 1 if \"a*b*c + add\" has no negative terms/factors and doesn't overflow\nstatic int stbi__mad3sizes_valid(int a, int b, int c, int add) {\n return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && stbi__addsizes_valid(a * b * c, add);\n}\n\n// returns 1 if \"a*b*c*d + add\" has no negative terms/factors and doesn't overflow\n#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM)\nstatic int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) {\n return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && stbi__mul2sizes_valid(a * b * c, d) &&\n stbi__addsizes_valid(a * b * c * d, add);\n}\n#endif\n\n#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR)\n// mallocs with size overflow checking\nstatic void * stbi__malloc_mad2(int a, int b, int add) {\n if (!stbi__mad2sizes_valid(a, b, add))\n return NULL;\n return stbi__malloc(a * b + add);\n}\n#endif\n\nstatic void * stbi__malloc_mad3(int a, int b, int c, int add) {\n if (!stbi__mad3sizes_valid(a, b, c, add))\n return NULL;\n return stbi__malloc(a * b * c + add);\n}\n\n#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM)\nstatic void * stbi__malloc_mad4(int a, int b, int c, int d, int add) {\n if (!stbi__mad4sizes_valid(a, b, c, d, add))\n return NULL;\n return stbi__malloc(a * b * c * d + add);\n}\n#endif\n\n// returns 1 if the sum of two signed ints is valid (between -2^31 and 2^31-1 inclusive), 0 on overflow.\nstatic int stbi__addints_valid(int a, int b) {\n if ((a >= 0) != (b >= 0))\n return 1; // a and b have different signs, so no overflow\n if (a < 0 && b < 0)\n return a >= INT_MIN - b; // same as a + b >= INT_MIN; INT_MIN - b cannot overflow since b < 0.\n return a <= INT_MAX - b;\n}\n\n// returns 1 if the product of two signed shorts is valid, 0 on overflow.\nstatic int stbi__mul2shorts_valid(short a, short b) {\n if (b == 0 || b == -1)\n return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow\n if ((a >= 0) == (b >= 0))\n return a <= SHRT_MAX / b; // product is positive, so similar to mul2sizes_valid\n if (b < 0)\n return a <= SHRT_MIN / b; // same as a * b >= SHRT_MIN\n return a >= SHRT_MIN / b;\n}\n\n// stbi__err - error\n// stbi__errpf - error returning pointer to float\n// stbi__errpuc - error returning pointer to unsigned char\n\n#ifdef STBI_NO_FAILURE_STRINGS\n#define stbi__err(x, y) 0\n#elif defined(STBI_FAILURE_USERMSG)\n#define stbi__err(x, y) stbi__err(y)\n#else\n#define stbi__err(x, y) stbi__err(x)\n#endif\n\n#define stbi__errpf(x, y) ((float *)(size_t)(stbi__err(x, y) ? NULL : NULL))\n#define stbi__errpuc(x, y) ((unsigned char *)(size_t)(stbi__err(x, y) ? NULL : NULL))\n\nSTBIDEF void stbi_image_free(void * retval_from_stbi_load) { STBI_FREE(retval_from_stbi_load); }\n\n#ifndef STBI_NO_LINEAR\nstatic float * stbi__ldr_to_hdr(stbi_uc * data, int x, int y, int comp);\n#endif\n\n#ifndef STBI_NO_HDR\nstatic stbi_uc * stbi__hdr_to_ldr(float * data, int x, int y, int comp);\n#endif\n\nstatic int stbi__vertically_flip_on_load_global = 0;\n\nSTBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) {\n stbi__vertically_flip_on_load_global = flag_true_if_should_flip;\n}\n\n#ifndef STBI_THREAD_LOCAL\n#define stbi__vertically_flip_on_load stbi__vertically_flip_on_load_global\n#else\nstatic STBI_THREAD_LOCAL int stbi__vertically_flip_on_load_local, stbi__vertically_flip_on_load_set;\n\nSTBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip) {\n stbi__vertically_flip_on_load_local = flag_true_if_should_flip;\n stbi__vertically_flip_on_load_set = 1;\n}\n\n#define stbi__vertically_flip_on_load \\\n (stbi__vertically_flip_on_load_set ? stbi__vertically_flip_on_load_local : stbi__vertically_flip_on_load_global)\n#endif // STBI_THREAD_LOCAL\n\nstatic void * stbi__load_main(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc) {\n memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields\n ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed\n ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order\n ri->num_channels = 0;\n\n// test the formats with a very explicit header first (at least a FOURCC\n// or distinctive magic number first)\n#ifndef STBI_NO_PNG\n if (stbi__png_test(s))\n return stbi__png_load(s, x, y, comp, req_comp, ri);\n#endif\n#ifndef STBI_NO_BMP\n if (stbi__bmp_test(s))\n return stbi__bmp_load(s, x, y, comp, req_comp, ri);\n#endif\n#ifndef STBI_NO_GIF\n if (stbi__gif_test(s))\n return stbi__gif_load(s, x, y, comp, req_comp, ri);\n#endif\n#ifndef STBI_NO_PSD\n if (stbi__psd_test(s))\n return stbi__psd_load(s, x, y, comp, req_comp, ri, bpc);\n#else\n STBI_NOTUSED(bpc);\n#endif\n#ifndef STBI_NO_PIC\n if (stbi__pic_test(s))\n return stbi__pic_load(s, x, y, comp, req_comp, ri);\n#endif\n\n// then the formats that can end up attempting to load with just 1 or 2\n// bytes matching expectations; these are prone to false positives, so\n// try them later\n#ifndef STBI_NO_JPEG\n if (stbi__jpeg_test(s))\n return stbi__jpeg_load(s, x, y, comp, req_comp, ri);\n#endif\n#ifndef STBI_NO_PNM\n if (stbi__pnm_test(s))\n return stbi__pnm_load(s, x, y, comp, req_comp, ri);\n#endif\n\n#ifndef STBI_NO_HDR\n if (stbi__hdr_test(s)) {\n float * hdr = stbi__hdr_load(s, x, y, comp, req_comp, ri);\n return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp);\n }\n#endif\n\n#ifndef STBI_NO_TGA\n // test tga last because it's a crappy test!\n if (stbi__tga_test(s))\n return stbi__tga_load(s, x, y, comp, req_comp, ri);\n#endif\n\n return stbi__errpuc(\"unknown image type\", \"Image not of any known type, or corrupt\");\n}\n\nstatic stbi_uc * stbi__convert_16_to_8(stbi__uint16 * orig, int w, int h, int channels) {\n int i;\n int img_len = w * h * channels;\n stbi_uc * reduced;\n\n reduced = (stbi_uc *)stbi__malloc(img_len);\n if (reduced == NULL)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n\n for (i = 0; i < img_len; ++i)\n reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling\n\n STBI_FREE(orig);\n return reduced;\n}\n\nstatic stbi__uint16 * stbi__convert_8_to_16(stbi_uc * orig, int w, int h, int channels) {\n int i;\n int img_len = w * h * channels;\n stbi__uint16 * enlarged;\n\n enlarged = (stbi__uint16 *)stbi__malloc(img_len * 2);\n if (enlarged == NULL)\n return (stbi__uint16 *)stbi__errpuc(\"outofmem\", \"Out of memory\");\n\n for (i = 0; i < img_len; ++i)\n enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff\n\n STBI_FREE(orig);\n return enlarged;\n}\n\nstatic void stbi__vertical_flip(void * image, int w, int h, int bytes_per_pixel) {\n int row;\n size_t bytes_per_row = (size_t)w * bytes_per_pixel;\n stbi_uc temp[2048];\n stbi_uc * bytes = (stbi_uc *)image;\n\n for (row = 0; row < (h >> 1); row++) {\n stbi_uc * row0 = bytes + row * bytes_per_row;\n stbi_uc * row1 = bytes + (h - row - 1) * bytes_per_row;\n // swap row0 with row1\n size_t bytes_left = bytes_per_row;\n while (bytes_left) {\n size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp);\n memcpy(temp, row0, bytes_copy);\n memcpy(row0, row1, bytes_copy);\n memcpy(row1, temp, bytes_copy);\n row0 += bytes_copy;\n row1 += bytes_copy;\n bytes_left -= bytes_copy;\n }\n }\n}\n\n#ifndef STBI_NO_GIF\nstatic void stbi__vertical_flip_slices(void * image, int w, int h, int z, int bytes_per_pixel) {\n int slice;\n int slice_size = w * h * bytes_per_pixel;\n\n stbi_uc * bytes = (stbi_uc *)image;\n for (slice = 0; slice < z; ++slice) {\n stbi__vertical_flip(bytes, w, h, bytes_per_pixel);\n bytes += slice_size;\n }\n}\n#endif\n\nstatic unsigned char * stbi__load_and_postprocess_8bit(stbi__context * s, int * x, int * y, int * comp, int req_comp) {\n stbi__result_info ri;\n void * result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8);\n\n if (result == NULL)\n return NULL;\n\n // it is the responsibility of the loaders to make sure we get either 8 or 16 bit.\n STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16);\n\n if (ri.bits_per_channel != 8) {\n result = stbi__convert_16_to_8((stbi__uint16 *)result, *x, *y, req_comp == 0 ? *comp : req_comp);\n ri.bits_per_channel = 8;\n }\n\n // @TODO: move stbi__convert_format to here\n\n if (stbi__vertically_flip_on_load) {\n int channels = req_comp ? req_comp : *comp;\n stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc));\n }\n\n return (unsigned char *)result;\n}\n\nstatic stbi__uint16 * stbi__load_and_postprocess_16bit(stbi__context * s, int * x, int * y, int * comp, int req_comp) {\n stbi__result_info ri;\n void * result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16);\n\n if (result == NULL)\n return NULL;\n\n // it is the responsibility of the loaders to make sure we get either 8 or 16 bit.\n STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16);\n\n if (ri.bits_per_channel != 16) {\n result = stbi__convert_8_to_16((stbi_uc *)result, *x, *y, req_comp == 0 ? *comp : req_comp);\n ri.bits_per_channel = 16;\n }\n\n // @TODO: move stbi__convert_format16 to here\n // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision\n\n if (stbi__vertically_flip_on_load) {\n int channels = req_comp ? req_comp : *comp;\n stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16));\n }\n\n return (stbi__uint16 *)result;\n}\n\n#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR)\nstatic void stbi__float_postprocess(float * result, int * x, int * y, int * comp, int req_comp) {\n if (stbi__vertically_flip_on_load && result != NULL) {\n int channels = req_comp ? req_comp : *comp;\n stbi__vertical_flip(result, *x, *y, channels * sizeof(float));\n }\n}\n#endif\n\n#ifndef STBI_NO_STDIO\n\n#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8)\nSTBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char * str,\n int cbmb, wchar_t * widestr, int cchwide);\nSTBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags,\n const wchar_t * widestr, int cchwide, char * str, int cbmb,\n const char * defchar, int * used_default);\n#endif\n\n#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8)\nSTBIDEF int stbi_convert_wchar_to_utf8(char * buffer, size_t bufferlen, const wchar_t * input) {\n return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int)bufferlen, NULL, NULL);\n}\n#endif\n\nstatic FILE * stbi__fopen(char const * filename, char const * mode) {\n FILE * f;\n#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8)\n wchar_t wMode[64];\n wchar_t wFilename[1024];\n if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename) / sizeof(*wFilename)))\n return 0;\n\n if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode) / sizeof(*wMode)))\n return 0;\n\n#if defined(_MSC_VER) && _MSC_VER >= 1400\n if (0 != _wfopen_s(&f, wFilename, wMode))\n f = 0;\n#else\n f = _wfopen(wFilename, wMode);\n#endif\n\n#elif defined(_MSC_VER) && _MSC_VER >= 1400\n if (0 != fopen_s(&f, filename, mode))\n f = 0;\n#else\n f = fopen(filename, mode);\n#endif\n return f;\n}\n\nSTBIDEF stbi_uc * stbi_load(char const * filename, int * x, int * y, int * comp, int req_comp) {\n FILE * f = stbi__fopen(filename, \"rb\");\n unsigned char * result;\n if (!f)\n return stbi__errpuc(\"can't fopen\", \"Unable to open file\");\n result = stbi_load_from_file(f, x, y, comp, req_comp);\n fclose(f);\n return result;\n}\n\nSTBIDEF stbi_uc * stbi_load_from_file(FILE * f, int * x, int * y, int * comp, int req_comp) {\n unsigned char * result;\n stbi__context s;\n stbi__start_file(&s, f);\n result = stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp);\n if (result) {\n // need to 'unget' all the characters in the IO buffer\n fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR);\n }\n return result;\n}\n\nSTBIDEF stbi__uint16 * stbi_load_from_file_16(FILE * f, int * x, int * y, int * comp, int req_comp) {\n stbi__uint16 * result;\n stbi__context s;\n stbi__start_file(&s, f);\n result = stbi__load_and_postprocess_16bit(&s, x, y, comp, req_comp);\n if (result) {\n // need to 'unget' all the characters in the IO buffer\n fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR);\n }\n return result;\n}\n\nSTBIDEF stbi_us * stbi_load_16(char const * filename, int * x, int * y, int * comp, int req_comp) {\n FILE * f = stbi__fopen(filename, \"rb\");\n stbi__uint16 * result;\n if (!f)\n return (stbi_us *)stbi__errpuc(\"can't fopen\", \"Unable to open file\");\n result = stbi_load_from_file_16(f, x, y, comp, req_comp);\n fclose(f);\n return result;\n}\n\n#endif //! STBI_NO_STDIO\n\nSTBIDEF stbi_us * stbi_load_16_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file,\n int desired_channels) {\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels);\n}\n\nSTBIDEF stbi_us * stbi_load_16_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y,\n int * channels_in_file, int desired_channels) {\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user);\n return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels);\n}\n\nSTBIDEF stbi_uc * stbi_load_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp, int req_comp) {\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp);\n}\n\nSTBIDEF stbi_uc * stbi_load_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp,\n int req_comp) {\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user);\n return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp);\n}\n\n#ifndef STBI_NO_GIF\nSTBIDEF stbi_uc * stbi_load_gif_from_memory(stbi_uc const * buffer, int len, int ** delays, int * x, int * y, int * z,\n int * comp, int req_comp) {\n unsigned char * result;\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n\n result = (unsigned char *)stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp);\n if (stbi__vertically_flip_on_load) {\n stbi__vertical_flip_slices(result, *x, *y, *z, *comp);\n }\n\n return result;\n}\n#endif\n\n#ifndef STBI_NO_LINEAR\nstatic float * stbi__loadf_main(stbi__context * s, int * x, int * y, int * comp, int req_comp) {\n unsigned char * data;\n#ifndef STBI_NO_HDR\n if (stbi__hdr_test(s)) {\n stbi__result_info ri;\n float * hdr_data = stbi__hdr_load(s, x, y, comp, req_comp, &ri);\n if (hdr_data)\n stbi__float_postprocess(hdr_data, x, y, comp, req_comp);\n return hdr_data;\n }\n#endif\n data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp);\n if (data)\n return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp);\n return stbi__errpf(\"unknown image type\", \"Image not of any known type, or corrupt\");\n}\n\nSTBIDEF float * stbi_loadf_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp, int req_comp) {\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__loadf_main(&s, x, y, comp, req_comp);\n}\n\nSTBIDEF float * stbi_loadf_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp,\n int req_comp) {\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user);\n return stbi__loadf_main(&s, x, y, comp, req_comp);\n}\n\n#ifndef STBI_NO_STDIO\nSTBIDEF float * stbi_loadf(char const * filename, int * x, int * y, int * comp, int req_comp) {\n float * result;\n FILE * f = stbi__fopen(filename, \"rb\");\n if (!f)\n return stbi__errpf(\"can't fopen\", \"Unable to open file\");\n result = stbi_loadf_from_file(f, x, y, comp, req_comp);\n fclose(f);\n return result;\n}\n\nSTBIDEF float * stbi_loadf_from_file(FILE * f, int * x, int * y, int * comp, int req_comp) {\n stbi__context s;\n stbi__start_file(&s, f);\n return stbi__loadf_main(&s, x, y, comp, req_comp);\n}\n#endif // !STBI_NO_STDIO\n\n#endif // !STBI_NO_LINEAR\n\n// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is\n// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always\n// reports false!\n\nSTBIDEF int stbi_is_hdr_from_memory(stbi_uc const * buffer, int len) {\n#ifndef STBI_NO_HDR\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__hdr_test(&s);\n#else\n STBI_NOTUSED(buffer);\n STBI_NOTUSED(len);\n return 0;\n#endif\n}\n\n#ifndef STBI_NO_STDIO\nSTBIDEF int stbi_is_hdr(char const * filename) {\n FILE * f = stbi__fopen(filename, \"rb\");\n int result = 0;\n if (f) {\n result = stbi_is_hdr_from_file(f);\n fclose(f);\n }\n return result;\n}\n\nSTBIDEF int stbi_is_hdr_from_file(FILE * f) {\n#ifndef STBI_NO_HDR\n long pos = ftell(f);\n int res;\n stbi__context s;\n stbi__start_file(&s, f);\n res = stbi__hdr_test(&s);\n fseek(f, pos, SEEK_SET);\n return res;\n#else\n STBI_NOTUSED(f);\n return 0;\n#endif\n}\n#endif // !STBI_NO_STDIO\n\nSTBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const * clbk, void * user) {\n#ifndef STBI_NO_HDR\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user);\n return stbi__hdr_test(&s);\n#else\n STBI_NOTUSED(clbk);\n STBI_NOTUSED(user);\n return 0;\n#endif\n}\n\n#ifndef STBI_NO_LINEAR\nstatic float stbi__l2h_gamma = 2.2f, stbi__l2h_scale = 1.0f;\n\nSTBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; }\nSTBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; }\n#endif\n\nstatic float stbi__h2l_gamma_i = 1.0f / 2.2f, stbi__h2l_scale_i = 1.0f;\n\nSTBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1 / gamma; }\nSTBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1 / scale; }\n\n//////////////////////////////////////////////////////////////////////////////\n//\n// Common code used by all image loaders\n//\n\nenum { STBI__SCAN_load = 0, STBI__SCAN_type, STBI__SCAN_header };\n\nstatic void stbi__refill_buffer(stbi__context * s) {\n int n = (s->io.read)(s->io_user_data, (char *)s->buffer_start, s->buflen);\n s->callback_already_read += (int)(s->img_buffer - s->img_buffer_original);\n if (n == 0) {\n // at end of file, treat same as if from memory, but need to handle case\n // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file\n s->read_from_callbacks = 0;\n s->img_buffer = s->buffer_start;\n s->img_buffer_end = s->buffer_start + 1;\n *s->img_buffer = 0;\n } else {\n s->img_buffer = s->buffer_start;\n s->img_buffer_end = s->buffer_start + n;\n }\n}\n\nstbi_inline static stbi_uc stbi__get8(stbi__context * s) {\n if (s->img_buffer < s->img_buffer_end)\n return *s->img_buffer++;\n if (s->read_from_callbacks) {\n stbi__refill_buffer(s);\n return *s->img_buffer++;\n }\n return 0;\n}\n\n#if defined(STBI_NO_JPEG) && defined(STBI_NO_HDR) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM)\n// nothing\n#else\nstbi_inline static int stbi__at_eof(stbi__context * s) {\n if (s->io.read) {\n if (!(s->io.eof)(s->io_user_data))\n return 0;\n // if feof() is true, check if buffer = end\n // special case: we've only got the special 0 character at the end\n if (s->read_from_callbacks == 0)\n return 1;\n }\n\n return s->img_buffer >= s->img_buffer_end;\n}\n#endif\n\n#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && \\\n defined(STBI_NO_GIF) && defined(STBI_NO_PIC)\n// nothing\n#else\nstatic void stbi__skip(stbi__context * s, int n) {\n if (n == 0)\n return; // already there!\n if (n < 0) {\n s->img_buffer = s->img_buffer_end;\n return;\n }\n if (s->io.read) {\n int blen = (int)(s->img_buffer_end - s->img_buffer);\n if (blen < n) {\n s->img_buffer = s->img_buffer_end;\n (s->io.skip)(s->io_user_data, n - blen);\n return;\n }\n }\n s->img_buffer += n;\n}\n#endif\n\n#if defined(STBI_NO_PNG) && defined(STBI_NO_TGA) && defined(STBI_NO_HDR) && defined(STBI_NO_PNM)\n// nothing\n#else\nstatic int stbi__getn(stbi__context * s, stbi_uc * buffer, int n) {\n if (s->io.read) {\n int blen = (int)(s->img_buffer_end - s->img_buffer);\n if (blen < n) {\n int res, count;\n\n memcpy(buffer, s->img_buffer, blen);\n\n count = (s->io.read)(s->io_user_data, (char *)buffer + blen, n - blen);\n res = (count == (n - blen));\n s->img_buffer = s->img_buffer_end;\n return res;\n }\n }\n\n if (s->img_buffer + n <= s->img_buffer_end) {\n memcpy(buffer, s->img_buffer, n);\n s->img_buffer += n;\n return 1;\n } else\n return 0;\n}\n#endif\n\n#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC)\n// nothing\n#else\nstatic int stbi__get16be(stbi__context * s) {\n int z = stbi__get8(s);\n return (z << 8) + stbi__get8(s);\n}\n#endif\n\n#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC)\n// nothing\n#else\nstatic stbi__uint32 stbi__get32be(stbi__context * s) {\n stbi__uint32 z = stbi__get16be(s);\n return (z << 16) + stbi__get16be(s);\n}\n#endif\n\n#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF)\n// nothing\n#else\nstatic int stbi__get16le(stbi__context * s) {\n int z = stbi__get8(s);\n return z + (stbi__get8(s) << 8);\n}\n#endif\n\n#ifndef STBI_NO_BMP\nstatic stbi__uint32 stbi__get32le(stbi__context * s) {\n stbi__uint32 z = stbi__get16le(s);\n z += (stbi__uint32)stbi__get16le(s) << 16;\n return z;\n}\n#endif\n\n#define STBI__BYTECAST(x) ((stbi_uc)((x)&255)) // truncate int to byte without warnings\n\n#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && \\\n defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM)\n// nothing\n#else\n//////////////////////////////////////////////////////////////////////////////\n//\n// generic converter from built-in img_n to req_comp\n// individual types do this automatically as much as possible (e.g. jpeg\n// does all cases internally since it needs to colorspace convert anyway,\n// and it never has alpha, so very few cases ). png can automatically\n// interleave an alpha=255 channel, but falls back to this for other cases\n//\n// assume data buffer is malloced, so malloc a new one and free that one\n// only failure mode is malloc failing\n\nstatic stbi_uc stbi__compute_y(int r, int g, int b) { return (stbi_uc)(((r * 77) + (g * 150) + (29 * b)) >> 8); }\n#endif\n\n#if defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && \\\n defined(STBI_NO_PIC) && defined(STBI_NO_PNM)\n// nothing\n#else\nstatic unsigned char * stbi__convert_format(unsigned char * data, int img_n, int req_comp, unsigned int x, unsigned int y) {\n int i, j;\n unsigned char * good;\n\n if (req_comp == img_n)\n return data;\n STBI_ASSERT(req_comp >= 1 && req_comp <= 4);\n\n good = (unsigned char *)stbi__malloc_mad3(req_comp, x, y, 0);\n if (good == NULL) {\n STBI_FREE(data);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n\n for (j = 0; j < (int)y; ++j) {\n unsigned char * src = data + j * x * img_n;\n unsigned char * dest = good + j * x * req_comp;\n\n#define STBI__COMBO(a, b) ((a)*8 + (b))\n#define STBI__CASE(a, b) \\\n case STBI__COMBO(a, b): \\\n for (i = x - 1; i >= 0; --i, src += a, dest += b)\n // convert source image with img_n components to one with req_comp components;\n // avoid switch per pixel, so use switch per scanline and massive macros\n switch (STBI__COMBO(img_n, req_comp)) {\n STBI__CASE(1, 2) {\n dest[0] = src[0];\n dest[1] = 255;\n }\n break;\n STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; }\n break;\n STBI__CASE(1, 4) {\n dest[0] = dest[1] = dest[2] = src[0];\n dest[3] = 255;\n }\n break;\n STBI__CASE(2, 1) { dest[0] = src[0]; }\n break;\n STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; }\n break;\n STBI__CASE(2, 4) {\n dest[0] = dest[1] = dest[2] = src[0];\n dest[3] = src[1];\n }\n break;\n STBI__CASE(3, 4) {\n dest[0] = src[0];\n dest[1] = src[1];\n dest[2] = src[2];\n dest[3] = 255;\n }\n break;\n STBI__CASE(3, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); }\n break;\n STBI__CASE(3, 2) {\n dest[0] = stbi__compute_y(src[0], src[1], src[2]);\n dest[1] = 255;\n }\n break;\n STBI__CASE(4, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); }\n break;\n STBI__CASE(4, 2) {\n dest[0] = stbi__compute_y(src[0], src[1], src[2]);\n dest[1] = src[3];\n }\n break;\n STBI__CASE(4, 3) {\n dest[0] = src[0];\n dest[1] = src[1];\n dest[2] = src[2];\n }\n break;\n default:\n STBI_ASSERT(0);\n STBI_FREE(data);\n STBI_FREE(good);\n return stbi__errpuc(\"unsupported\", \"Unsupported format conversion\");\n }\n#undef STBI__CASE\n }\n\n STBI_FREE(data);\n return good;\n}\n#endif\n\n#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD)\n// nothing\n#else\nstatic stbi__uint16 stbi__compute_y_16(int r, int g, int b) { return (stbi__uint16)(((r * 77) + (g * 150) + (29 * b)) >> 8); }\n#endif\n\n#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD)\n// nothing\n#else\nstatic stbi__uint16 * stbi__convert_format16(stbi__uint16 * data, int img_n, int req_comp, unsigned int x, unsigned int y) {\n int i, j;\n stbi__uint16 * good;\n\n if (req_comp == img_n)\n return data;\n STBI_ASSERT(req_comp >= 1 && req_comp <= 4);\n\n good = (stbi__uint16 *)stbi__malloc(req_comp * x * y * 2);\n if (good == NULL) {\n STBI_FREE(data);\n return (stbi__uint16 *)stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n\n for (j = 0; j < (int)y; ++j) {\n stbi__uint16 * src = data + j * x * img_n;\n stbi__uint16 * dest = good + j * x * req_comp;\n\n#define STBI__COMBO(a, b) ((a)*8 + (b))\n#define STBI__CASE(a, b) \\\n case STBI__COMBO(a, b): \\\n for (i = x - 1; i >= 0; --i, src += a, dest += b)\n // convert source image with img_n components to one with req_comp components;\n // avoid switch per pixel, so use switch per scanline and massive macros\n switch (STBI__COMBO(img_n, req_comp)) {\n STBI__CASE(1, 2) {\n dest[0] = src[0];\n dest[1] = 0xffff;\n }\n break;\n STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; }\n break;\n STBI__CASE(1, 4) {\n dest[0] = dest[1] = dest[2] = src[0];\n dest[3] = 0xffff;\n }\n break;\n STBI__CASE(2, 1) { dest[0] = src[0]; }\n break;\n STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; }\n break;\n STBI__CASE(2, 4) {\n dest[0] = dest[1] = dest[2] = src[0];\n dest[3] = src[1];\n }\n break;\n STBI__CASE(3, 4) {\n dest[0] = src[0];\n dest[1] = src[1];\n dest[2] = src[2];\n dest[3] = 0xffff;\n }\n break;\n STBI__CASE(3, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); }\n break;\n STBI__CASE(3, 2) {\n dest[0] = stbi__compute_y_16(src[0], src[1], src[2]);\n dest[1] = 0xffff;\n }\n break;\n STBI__CASE(4, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); }\n break;\n STBI__CASE(4, 2) {\n dest[0] = stbi__compute_y_16(src[0], src[1], src[2]);\n dest[1] = src[3];\n }\n break;\n STBI__CASE(4, 3) {\n dest[0] = src[0];\n dest[1] = src[1];\n dest[2] = src[2];\n }\n break;\n default:\n STBI_ASSERT(0);\n STBI_FREE(data);\n STBI_FREE(good);\n return (stbi__uint16 *)stbi__errpuc(\"unsupported\", \"Unsupported format conversion\");\n }\n#undef STBI__CASE\n }\n\n STBI_FREE(data);\n return good;\n}\n#endif\n\n#ifndef STBI_NO_LINEAR\nstatic float * stbi__ldr_to_hdr(stbi_uc * data, int x, int y, int comp) {\n int i, k, n;\n float * output;\n if (!data)\n return NULL;\n output = (float *)stbi__malloc_mad4(x, y, comp, sizeof(float), 0);\n if (output == NULL) {\n STBI_FREE(data);\n return stbi__errpf(\"outofmem\", \"Out of memory\");\n }\n // compute number of non-alpha components\n if (comp & 1)\n n = comp;\n else\n n = comp - 1;\n for (i = 0; i < x * y; ++i) {\n for (k = 0; k < n; ++k) {\n output[i * comp + k] = (float)(pow(data[i * comp + k] / 255.0f, stbi__l2h_gamma) * stbi__l2h_scale);\n }\n }\n if (n < comp) {\n for (i = 0; i < x * y; ++i) {\n output[i * comp + n] = data[i * comp + n] / 255.0f;\n }\n }\n STBI_FREE(data);\n return output;\n}\n#endif\n\n#ifndef STBI_NO_HDR\n#define stbi__float2int(x) ((int)(x))\nstatic stbi_uc * stbi__hdr_to_ldr(float * data, int x, int y, int comp) {\n int i, k, n;\n stbi_uc * output;\n if (!data)\n return NULL;\n output = (stbi_uc *)stbi__malloc_mad3(x, y, comp, 0);\n if (output == NULL) {\n STBI_FREE(data);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n // compute number of non-alpha components\n if (comp & 1)\n n = comp;\n else\n n = comp - 1;\n for (i = 0; i < x * y; ++i) {\n for (k = 0; k < n; ++k) {\n float z = (float)pow(data[i * comp + k] * stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f;\n if (z < 0)\n z = 0;\n if (z > 255)\n z = 255;\n output[i * comp + k] = (stbi_uc)stbi__float2int(z);\n }\n if (k < comp) {\n float z = data[i * comp + k] * 255 + 0.5f;\n if (z < 0)\n z = 0;\n if (z > 255)\n z = 255;\n output[i * comp + k] = (stbi_uc)stbi__float2int(z);\n }\n }\n STBI_FREE(data);\n return output;\n}\n#endif\n\n//////////////////////////////////////////////////////////////////////////////\n//\n// \"baseline\" JPEG/JFIF decoder\n//\n// simple implementation\n// - doesn't support delayed output of y-dimension\n// - simple interface (only one output format: 8-bit interleaved RGB)\n// - doesn't try to recover corrupt jpegs\n// - doesn't allow partial loading, loading multiple at once\n// - still fast on x86 (copying globals into locals doesn't help x86)\n// - allocates lots of intermediate memory (full size of all components)\n// - non-interleaved case requires this anyway\n// - allows good upsampling (see next)\n// high-quality\n// - upsampled channels are bilinearly interpolated, even across blocks\n// - quality integer IDCT derived from IJG's 'slow'\n// performance\n// - fast huffman; reasonable integer IDCT\n// - some SIMD kernels for common paths on targets with SSE2/NEON\n// - uses a lot of intermediate memory, could cache poorly\n\n#ifndef STBI_NO_JPEG\n\n// huffman decoding acceleration\n#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache\n\ntypedef struct {\n stbi_uc fast[1 << FAST_BITS];\n // weirdly, repacking this into AoS is a 10% speed loss, instead of a win\n stbi__uint16 code[256];\n stbi_uc values[256];\n stbi_uc size[257];\n unsigned int maxcode[18];\n int delta[17]; // old 'firstsymbol' - old 'firstcode'\n} stbi__huffman;\n\ntypedef struct {\n stbi__context * s;\n stbi__huffman huff_dc[4];\n stbi__huffman huff_ac[4];\n stbi__uint16 dequant[4][64];\n stbi__int16 fast_ac[4][1 << FAST_BITS];\n\n // sizes for components, interleaved MCUs\n int img_h_max, img_v_max;\n int img_mcu_x, img_mcu_y;\n int img_mcu_w, img_mcu_h;\n\n // definition of jpeg image component\n struct {\n int id;\n int h, v;\n int tq;\n int hd, ha;\n int dc_pred;\n\n int x, y, w2, h2;\n stbi_uc * data;\n void *raw_data, *raw_coeff;\n stbi_uc * linebuf;\n short * coeff; // progressive only\n int coeff_w, coeff_h; // number of 8x8 coefficient blocks\n } img_comp[4];\n\n stbi__uint32 code_buffer; // jpeg entropy-coded buffer\n int code_bits; // number of valid bits\n unsigned char marker; // marker seen while filling entropy buffer\n int nomore; // flag if we saw a marker so must stop\n\n int progressive;\n int spec_start;\n int spec_end;\n int succ_high;\n int succ_low;\n int eob_run;\n int jfif;\n int app14_color_transform; // Adobe APP14 tag\n int rgb;\n\n int scan_n, order[4];\n int restart_interval, todo;\n\n // kernels\n void (*idct_block_kernel)(stbi_uc * out, int out_stride, short data[64]);\n void (*YCbCr_to_RGB_kernel)(stbi_uc * out, const stbi_uc * y, const stbi_uc * pcb, const stbi_uc * pcr, int count,\n int step);\n stbi_uc * (*resample_row_hv_2_kernel)(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs);\n} stbi__jpeg;\n\nstatic int stbi__build_huffman(stbi__huffman * h, int * count) {\n int i, j, k = 0;\n unsigned int code;\n // build size list for each symbol (from JPEG spec)\n for (i = 0; i < 16; ++i) {\n for (j = 0; j < count[i]; ++j) {\n h->size[k++] = (stbi_uc)(i + 1);\n if (k >= 257)\n return stbi__err(\"bad size list\", \"Corrupt JPEG\");\n }\n }\n h->size[k] = 0;\n\n // compute actual symbols (from jpeg spec)\n code = 0;\n k = 0;\n for (j = 1; j <= 16; ++j) {\n // compute delta to add to code to compute symbol id\n h->delta[j] = k - code;\n if (h->size[k] == j) {\n while (h->size[k] == j)\n h->code[k++] = (stbi__uint16)(code++);\n if (code - 1 >= (1u << j))\n return stbi__err(\"bad code lengths\", \"Corrupt JPEG\");\n }\n // compute largest code + 1 for this size, preshifted as needed later\n h->maxcode[j] = code << (16 - j);\n code <<= 1;\n }\n h->maxcode[j] = 0xffffffff;\n\n // build non-spec acceleration table; 255 is flag for not-accelerated\n memset(h->fast, 255, 1 << FAST_BITS);\n for (i = 0; i < k; ++i) {\n int s = h->size[i];\n if (s <= FAST_BITS) {\n int c = h->code[i] << (FAST_BITS - s);\n int m = 1 << (FAST_BITS - s);\n for (j = 0; j < m; ++j) {\n h->fast[c + j] = (stbi_uc)i;\n }\n }\n }\n return 1;\n}\n\n// build a table that decodes both magnitude and value of small ACs in\n// one go.\nstatic void stbi__build_fast_ac(stbi__int16 * fast_ac, stbi__huffman * h) {\n int i;\n for (i = 0; i < (1 << FAST_BITS); ++i) {\n stbi_uc fast = h->fast[i];\n fast_ac[i] = 0;\n if (fast < 255) {\n int rs = h->values[fast];\n int run = (rs >> 4) & 15;\n int magbits = rs & 15;\n int len = h->size[fast];\n\n if (magbits && len + magbits <= FAST_BITS) {\n // magnitude code followed by receive_extend code\n int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits);\n int m = 1 << (magbits - 1);\n if (k < m)\n k += (~0U << magbits) + 1;\n // if the result is small enough, we can fit it in fast_ac table\n if (k >= -128 && k <= 127)\n fast_ac[i] = (stbi__int16)((k * 256) + (run * 16) + (len + magbits));\n }\n }\n }\n}\n\nstatic void stbi__grow_buffer_unsafe(stbi__jpeg * j) {\n do {\n unsigned int b = j->nomore ? 0 : stbi__get8(j->s);\n if (b == 0xff) {\n int c = stbi__get8(j->s);\n while (c == 0xff)\n c = stbi__get8(j->s); // consume fill bytes\n if (c != 0) {\n j->marker = (unsigned char)c;\n j->nomore = 1;\n return;\n }\n }\n j->code_buffer |= b << (24 - j->code_bits);\n j->code_bits += 8;\n } while (j->code_bits <= 24);\n}\n\n// (1 << n) - 1\nstatic const stbi__uint32 stbi__bmask[17] = {0, 1, 3, 7, 15, 31, 63, 127, 255,\n 511, 1023, 2047, 4095, 8191, 16383, 32767, 65535};\n\n// decode a jpeg huffman value from the bitstream\nstbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg * j, stbi__huffman * h) {\n unsigned int temp;\n int c, k;\n\n if (j->code_bits < 16)\n stbi__grow_buffer_unsafe(j);\n\n // look at the top FAST_BITS and determine what symbol ID it is,\n // if the code is <= FAST_BITS\n c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1);\n k = h->fast[c];\n if (k < 255) {\n int s = h->size[k];\n if (s > j->code_bits)\n return -1;\n j->code_buffer <<= s;\n j->code_bits -= s;\n return h->values[k];\n }\n\n // naive test is to shift the code_buffer down so k bits are\n // valid, then test against maxcode. To speed this up, we've\n // preshifted maxcode left so that it has (16-k) 0s at the\n // end; in other words, regardless of the number of bits, it\n // wants to be compared against something shifted to have 16;\n // that way we don't need to shift inside the loop.\n temp = j->code_buffer >> 16;\n for (k = FAST_BITS + 1;; ++k)\n if (temp < h->maxcode[k])\n break;\n if (k == 17) {\n // error! code not found\n j->code_bits -= 16;\n return -1;\n }\n\n if (k > j->code_bits)\n return -1;\n\n // convert the huffman code to the symbol id\n c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k];\n if (c < 0 || c >= 256) // symbol id out of bounds!\n return -1;\n STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]);\n\n // convert the id to a symbol\n j->code_bits -= k;\n j->code_buffer <<= k;\n return h->values[c];\n}\n\n// bias[n] = (-1<code_bits < n)\n stbi__grow_buffer_unsafe(j);\n if (j->code_bits < n)\n return 0; // ran out of bits from stream, return 0s intead of continuing\n\n sgn = j->code_buffer >> 31; // sign bit always in MSB; 0 if MSB clear (positive), 1 if MSB set (negative)\n k = stbi_lrot(j->code_buffer, n);\n j->code_buffer = k & ~stbi__bmask[n];\n k &= stbi__bmask[n];\n j->code_bits -= n;\n return k + (stbi__jbias[n] & (sgn - 1));\n}\n\n// get some unsigned bits\nstbi_inline static int stbi__jpeg_get_bits(stbi__jpeg * j, int n) {\n unsigned int k;\n if (j->code_bits < n)\n stbi__grow_buffer_unsafe(j);\n if (j->code_bits < n)\n return 0; // ran out of bits from stream, return 0s intead of continuing\n k = stbi_lrot(j->code_buffer, n);\n j->code_buffer = k & ~stbi__bmask[n];\n k &= stbi__bmask[n];\n j->code_bits -= n;\n return k;\n}\n\nstbi_inline static int stbi__jpeg_get_bit(stbi__jpeg * j) {\n unsigned int k;\n if (j->code_bits < 1)\n stbi__grow_buffer_unsafe(j);\n if (j->code_bits < 1)\n return 0; // ran out of bits from stream, return 0s intead of continuing\n k = j->code_buffer;\n j->code_buffer <<= 1;\n --j->code_bits;\n return k & 0x80000000;\n}\n\n// given a value that's at position X in the zigzag stream,\n// where does it appear in the 8x8 matrix coded as row-major?\nstatic const stbi_uc stbi__jpeg_dezigzag[64 + 15] = {\n 0, 1, 8, 16, 9, 2, 3, 10, 17, 24, 32, 25, 18, 11, 4, 5, 12, 19, 26, 33, 40, 48, 41, 34, 27, 20, 13, 6, 7, 14, 21, 28, 35,\n 42, 49, 56, 57, 50, 43, 36, 29, 22, 15, 23, 30, 37, 44, 51, 58, 59, 52, 45, 38, 31, 39, 46, 53, 60, 61, 54, 47, 55, 62, 63,\n // let corrupt input sample past end\n 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63};\n\n// decode one 64-entry block--\nstatic int stbi__jpeg_decode_block(stbi__jpeg * j, short data[64], stbi__huffman * hdc, stbi__huffman * hac, stbi__int16 * fac,\n int b, stbi__uint16 * dequant) {\n int diff, dc, k;\n int t;\n\n if (j->code_bits < 16)\n stbi__grow_buffer_unsafe(j);\n t = stbi__jpeg_huff_decode(j, hdc);\n if (t < 0 || t > 15)\n return stbi__err(\"bad huffman code\", \"Corrupt JPEG\");\n\n // 0 all the ac values now so we can do it 32-bits at a time\n memset(data, 0, 64 * sizeof(data[0]));\n\n diff = t ? stbi__extend_receive(j, t) : 0;\n if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff))\n return stbi__err(\"bad delta\", \"Corrupt JPEG\");\n dc = j->img_comp[b].dc_pred + diff;\n j->img_comp[b].dc_pred = dc;\n if (!stbi__mul2shorts_valid(dc, dequant[0]))\n return stbi__err(\"can't merge dc and ac\", \"Corrupt JPEG\");\n data[0] = (short)(dc * dequant[0]);\n\n // decode AC components, see JPEG spec\n k = 1;\n do {\n unsigned int zig;\n int c, r, s;\n if (j->code_bits < 16)\n stbi__grow_buffer_unsafe(j);\n c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1);\n r = fac[c];\n if (r) { // fast-AC path\n k += (r >> 4) & 15; // run\n s = r & 15; // combined length\n if (s > j->code_bits)\n return stbi__err(\"bad huffman code\", \"Combined length longer than code bits available\");\n j->code_buffer <<= s;\n j->code_bits -= s;\n // decode into unzigzag'd location\n zig = stbi__jpeg_dezigzag[k++];\n data[zig] = (short)((r >> 8) * dequant[zig]);\n } else {\n int rs = stbi__jpeg_huff_decode(j, hac);\n if (rs < 0)\n return stbi__err(\"bad huffman code\", \"Corrupt JPEG\");\n s = rs & 15;\n r = rs >> 4;\n if (s == 0) {\n if (rs != 0xf0)\n break; // end block\n k += 16;\n } else {\n k += r;\n // decode into unzigzag'd location\n zig = stbi__jpeg_dezigzag[k++];\n data[zig] = (short)(stbi__extend_receive(j, s) * dequant[zig]);\n }\n }\n } while (k < 64);\n return 1;\n}\n\nstatic int stbi__jpeg_decode_block_prog_dc(stbi__jpeg * j, short data[64], stbi__huffman * hdc, int b) {\n int diff, dc;\n int t;\n if (j->spec_end != 0)\n return stbi__err(\"can't merge dc and ac\", \"Corrupt JPEG\");\n\n if (j->code_bits < 16)\n stbi__grow_buffer_unsafe(j);\n\n if (j->succ_high == 0) {\n // first scan for DC coefficient, must be first\n memset(data, 0, 64 * sizeof(data[0])); // 0 all the ac values now\n t = stbi__jpeg_huff_decode(j, hdc);\n if (t < 0 || t > 15)\n return stbi__err(\"can't merge dc and ac\", \"Corrupt JPEG\");\n diff = t ? stbi__extend_receive(j, t) : 0;\n\n if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff))\n return stbi__err(\"bad delta\", \"Corrupt JPEG\");\n dc = j->img_comp[b].dc_pred + diff;\n j->img_comp[b].dc_pred = dc;\n if (!stbi__mul2shorts_valid(dc, 1 << j->succ_low))\n return stbi__err(\"can't merge dc and ac\", \"Corrupt JPEG\");\n data[0] = (short)(dc * (1 << j->succ_low));\n } else {\n // refinement scan for DC coefficient\n if (stbi__jpeg_get_bit(j))\n data[0] += (short)(1 << j->succ_low);\n }\n return 1;\n}\n\n// @OPTIMIZE: store non-zigzagged during the decode passes,\n// and only de-zigzag when dequantizing\nstatic int stbi__jpeg_decode_block_prog_ac(stbi__jpeg * j, short data[64], stbi__huffman * hac, stbi__int16 * fac) {\n int k;\n if (j->spec_start == 0)\n return stbi__err(\"can't merge dc and ac\", \"Corrupt JPEG\");\n\n if (j->succ_high == 0) {\n int shift = j->succ_low;\n\n if (j->eob_run) {\n --j->eob_run;\n return 1;\n }\n\n k = j->spec_start;\n do {\n unsigned int zig;\n int c, r, s;\n if (j->code_bits < 16)\n stbi__grow_buffer_unsafe(j);\n c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1);\n r = fac[c];\n if (r) { // fast-AC path\n k += (r >> 4) & 15; // run\n s = r & 15; // combined length\n if (s > j->code_bits)\n return stbi__err(\"bad huffman code\", \"Combined length longer than code bits available\");\n j->code_buffer <<= s;\n j->code_bits -= s;\n zig = stbi__jpeg_dezigzag[k++];\n data[zig] = (short)((r >> 8) * (1 << shift));\n } else {\n int rs = stbi__jpeg_huff_decode(j, hac);\n if (rs < 0)\n return stbi__err(\"bad huffman code\", \"Corrupt JPEG\");\n s = rs & 15;\n r = rs >> 4;\n if (s == 0) {\n if (r < 15) {\n j->eob_run = (1 << r);\n if (r)\n j->eob_run += stbi__jpeg_get_bits(j, r);\n --j->eob_run;\n break;\n }\n k += 16;\n } else {\n k += r;\n zig = stbi__jpeg_dezigzag[k++];\n data[zig] = (short)(stbi__extend_receive(j, s) * (1 << shift));\n }\n }\n } while (k <= j->spec_end);\n } else {\n // refinement scan for these AC coefficients\n\n short bit = (short)(1 << j->succ_low);\n\n if (j->eob_run) {\n --j->eob_run;\n for (k = j->spec_start; k <= j->spec_end; ++k) {\n short * p = &data[stbi__jpeg_dezigzag[k]];\n if (*p != 0)\n if (stbi__jpeg_get_bit(j))\n if ((*p & bit) == 0) {\n if (*p > 0)\n *p += bit;\n else\n *p -= bit;\n }\n }\n } else {\n k = j->spec_start;\n do {\n int r, s;\n int rs = stbi__jpeg_huff_decode(\n j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh\n if (rs < 0)\n return stbi__err(\"bad huffman code\", \"Corrupt JPEG\");\n s = rs & 15;\n r = rs >> 4;\n if (s == 0) {\n if (r < 15) {\n j->eob_run = (1 << r) - 1;\n if (r)\n j->eob_run += stbi__jpeg_get_bits(j, r);\n r = 64; // force end of block\n } else {\n // r=15 s=0 should write 16 0s, so we just do\n // a run of 15 0s and then write s (which is 0),\n // so we don't have to do anything special here\n }\n } else {\n if (s != 1)\n return stbi__err(\"bad huffman code\", \"Corrupt JPEG\");\n // sign bit\n if (stbi__jpeg_get_bit(j))\n s = bit;\n else\n s = -bit;\n }\n\n // advance by r\n while (k <= j->spec_end) {\n short * p = &data[stbi__jpeg_dezigzag[k++]];\n if (*p != 0) {\n if (stbi__jpeg_get_bit(j))\n if ((*p & bit) == 0) {\n if (*p > 0)\n *p += bit;\n else\n *p -= bit;\n }\n } else {\n if (r == 0) {\n *p = (short)s;\n break;\n }\n --r;\n }\n }\n } while (k <= j->spec_end);\n }\n }\n return 1;\n}\n\n// take a -128..127 value and stbi__clamp it and convert to 0..255\nstbi_inline static stbi_uc stbi__clamp(int x) {\n // trick to use a single test to catch both cases\n if ((unsigned int)x > 255) {\n if (x < 0)\n return 0;\n if (x > 255)\n return 255;\n }\n return (stbi_uc)x;\n}\n\n#define stbi__f2f(x) ((int)(((x)*4096 + 0.5)))\n#define stbi__fsh(x) ((x)*4096)\n\n// derived from jidctint -- DCT_ISLOW\n#define STBI__IDCT_1D(s0, s1, s2, s3, s4, s5, s6, s7) \\\n int t0, t1, t2, t3, p1, p2, p3, p4, p5, x0, x1, x2, x3; \\\n p2 = s2; \\\n p3 = s6; \\\n p1 = (p2 + p3) * stbi__f2f(0.5411961f); \\\n t2 = p1 + p3 * stbi__f2f(-1.847759065f); \\\n t3 = p1 + p2 * stbi__f2f(0.765366865f); \\\n p2 = s0; \\\n p3 = s4; \\\n t0 = stbi__fsh(p2 + p3); \\\n t1 = stbi__fsh(p2 - p3); \\\n x0 = t0 + t3; \\\n x3 = t0 - t3; \\\n x1 = t1 + t2; \\\n x2 = t1 - t2; \\\n t0 = s7; \\\n t1 = s5; \\\n t2 = s3; \\\n t3 = s1; \\\n p3 = t0 + t2; \\\n p4 = t1 + t3; \\\n p1 = t0 + t3; \\\n p2 = t1 + t2; \\\n p5 = (p3 + p4) * stbi__f2f(1.175875602f); \\\n t0 = t0 * stbi__f2f(0.298631336f); \\\n t1 = t1 * stbi__f2f(2.053119869f); \\\n t2 = t2 * stbi__f2f(3.072711026f); \\\n t3 = t3 * stbi__f2f(1.501321110f); \\\n p1 = p5 + p1 * stbi__f2f(-0.899976223f); \\\n p2 = p5 + p2 * stbi__f2f(-2.562915447f); \\\n p3 = p3 * stbi__f2f(-1.961570560f); \\\n p4 = p4 * stbi__f2f(-0.390180644f); \\\n t3 += p1 + p4; \\\n t2 += p2 + p3; \\\n t1 += p2 + p4; \\\n t0 += p1 + p3;\n\nstatic void stbi__idct_block(stbi_uc * out, int out_stride, short data[64]) {\n int i, val[64], *v = val;\n stbi_uc * o;\n short * d = data;\n\n // columns\n for (i = 0; i < 8; ++i, ++d, ++v) {\n // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing\n if (d[8] == 0 && d[16] == 0 && d[24] == 0 && d[32] == 0 && d[40] == 0 && d[48] == 0 && d[56] == 0) {\n // no shortcut 0 seconds\n // (1|2|3|4|5|6|7)==0 0 seconds\n // all separate -0.047 seconds\n // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds\n int dcterm = d[0] * 4;\n v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm;\n } else {\n STBI__IDCT_1D(d[0], d[8], d[16], d[24], d[32], d[40], d[48], d[56])\n // constants scaled things up by 1<<12; let's bring them back\n // down, but keep 2 extra bits of precision\n x0 += 512;\n x1 += 512;\n x2 += 512;\n x3 += 512;\n v[0] = (x0 + t3) >> 10;\n v[56] = (x0 - t3) >> 10;\n v[8] = (x1 + t2) >> 10;\n v[48] = (x1 - t2) >> 10;\n v[16] = (x2 + t1) >> 10;\n v[40] = (x2 - t1) >> 10;\n v[24] = (x3 + t0) >> 10;\n v[32] = (x3 - t0) >> 10;\n }\n }\n\n for (i = 0, v = val, o = out; i < 8; ++i, v += 8, o += out_stride) {\n // no fast case since the first 1D IDCT spread components out\n STBI__IDCT_1D(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7])\n // constants scaled things up by 1<<12, plus we had 1<<2 from first\n // loop, plus horizontal and vertical each scale by sqrt(8) so together\n // we've got an extra 1<<3, so 1<<17 total we need to remove.\n // so we want to round that, which means adding 0.5 * 1<<17,\n // aka 65536. Also, we'll end up with -128 to 127 that we want\n // to encode as 0..255 by adding 128, so we'll add that before the shift\n x0 += 65536 + (128 << 17);\n x1 += 65536 + (128 << 17);\n x2 += 65536 + (128 << 17);\n x3 += 65536 + (128 << 17);\n // tried computing the shifts into temps, or'ing the temps to see\n // if any were out of range, but that was slower\n o[0] = stbi__clamp((x0 + t3) >> 17);\n o[7] = stbi__clamp((x0 - t3) >> 17);\n o[1] = stbi__clamp((x1 + t2) >> 17);\n o[6] = stbi__clamp((x1 - t2) >> 17);\n o[2] = stbi__clamp((x2 + t1) >> 17);\n o[5] = stbi__clamp((x2 - t1) >> 17);\n o[3] = stbi__clamp((x3 + t0) >> 17);\n o[4] = stbi__clamp((x3 - t0) >> 17);\n }\n}\n\n#ifdef STBI_SSE2\n// sse2 integer IDCT. not the fastest possible implementation but it\n// produces bit-identical results to the generic C version so it's\n// fully \"transparent\".\nstatic void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) {\n // This is constructed to match our regular (generic) integer IDCT exactly.\n __m128i row0, row1, row2, row3, row4, row5, row6, row7;\n __m128i tmp;\n\n// dot product constant: even elems=x, odd elems=y\n#define dct_const(x, y) _mm_setr_epi16((x), (y), (x), (y), (x), (y), (x), (y))\n\n// out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit)\n// out(1) = c1[even]*x + c1[odd]*y\n#define dct_rot(out0, out1, x, y, c0, c1) \\\n __m128i c0##lo = _mm_unpacklo_epi16((x), (y)); \\\n __m128i c0##hi = _mm_unpackhi_epi16((x), (y)); \\\n __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \\\n __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \\\n __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \\\n __m128i out1##_h = _mm_madd_epi16(c0##hi, c1)\n\n// out = in << 12 (in 16-bit, out 32-bit)\n#define dct_widen(out, in) \\\n __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \\\n __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4)\n\n// wide add\n#define dct_wadd(out, a, b) \\\n __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \\\n __m128i out##_h = _mm_add_epi32(a##_h, b##_h)\n\n// wide sub\n#define dct_wsub(out, a, b) \\\n __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \\\n __m128i out##_h = _mm_sub_epi32(a##_h, b##_h)\n\n// butterfly a/b, add bias, then shift by \"s\" and pack\n#define dct_bfly32o(out0, out1, a, b, bias, s) \\\n { \\\n __m128i abiased_l = _mm_add_epi32(a##_l, bias); \\\n __m128i abiased_h = _mm_add_epi32(a##_h, bias); \\\n dct_wadd(sum, abiased, b); \\\n dct_wsub(dif, abiased, b); \\\n out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \\\n out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \\\n }\n\n// 8-bit interleave step (for transposes)\n#define dct_interleave8(a, b) \\\n tmp = a; \\\n a = _mm_unpacklo_epi8(a, b); \\\n b = _mm_unpackhi_epi8(tmp, b)\n\n// 16-bit interleave step (for transposes)\n#define dct_interleave16(a, b) \\\n tmp = a; \\\n a = _mm_unpacklo_epi16(a, b); \\\n b = _mm_unpackhi_epi16(tmp, b)\n\n#define dct_pass(bias, shift) \\\n { \\\n /* even part */ \\\n dct_rot(t2e, t3e, row2, row6, rot0_0, rot0_1); \\\n __m128i sum04 = _mm_add_epi16(row0, row4); \\\n __m128i dif04 = _mm_sub_epi16(row0, row4); \\\n dct_widen(t0e, sum04); \\\n dct_widen(t1e, dif04); \\\n dct_wadd(x0, t0e, t3e); \\\n dct_wsub(x3, t0e, t3e); \\\n dct_wadd(x1, t1e, t2e); \\\n dct_wsub(x2, t1e, t2e); \\\n /* odd part */ \\\n dct_rot(y0o, y2o, row7, row3, rot2_0, rot2_1); \\\n dct_rot(y1o, y3o, row5, row1, rot3_0, rot3_1); \\\n __m128i sum17 = _mm_add_epi16(row1, row7); \\\n __m128i sum35 = _mm_add_epi16(row3, row5); \\\n dct_rot(y4o, y5o, sum17, sum35, rot1_0, rot1_1); \\\n dct_wadd(x4, y0o, y4o); \\\n dct_wadd(x5, y1o, y5o); \\\n dct_wadd(x6, y2o, y5o); \\\n dct_wadd(x7, y3o, y4o); \\\n dct_bfly32o(row0, row7, x0, x7, bias, shift); \\\n dct_bfly32o(row1, row6, x1, x6, bias, shift); \\\n dct_bfly32o(row2, row5, x2, x5, bias, shift); \\\n dct_bfly32o(row3, row4, x3, x4, bias, shift); \\\n }\n\n __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f));\n __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f(0.765366865f), stbi__f2f(0.5411961f));\n __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f));\n __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f));\n __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f(0.298631336f), stbi__f2f(-1.961570560f));\n __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f(3.072711026f));\n __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f(2.053119869f), stbi__f2f(-0.390180644f));\n __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f(1.501321110f));\n\n // rounding biases in column/row passes, see stbi__idct_block for explanation.\n __m128i bias_0 = _mm_set1_epi32(512);\n __m128i bias_1 = _mm_set1_epi32(65536 + (128 << 17));\n\n // load\n row0 = _mm_load_si128((const __m128i *)(data + 0 * 8));\n row1 = _mm_load_si128((const __m128i *)(data + 1 * 8));\n row2 = _mm_load_si128((const __m128i *)(data + 2 * 8));\n row3 = _mm_load_si128((const __m128i *)(data + 3 * 8));\n row4 = _mm_load_si128((const __m128i *)(data + 4 * 8));\n row5 = _mm_load_si128((const __m128i *)(data + 5 * 8));\n row6 = _mm_load_si128((const __m128i *)(data + 6 * 8));\n row7 = _mm_load_si128((const __m128i *)(data + 7 * 8));\n\n // column pass\n dct_pass(bias_0, 10);\n\n {\n // 16bit 8x8 transpose pass 1\n dct_interleave16(row0, row4);\n dct_interleave16(row1, row5);\n dct_interleave16(row2, row6);\n dct_interleave16(row3, row7);\n\n // transpose pass 2\n dct_interleave16(row0, row2);\n dct_interleave16(row1, row3);\n dct_interleave16(row4, row6);\n dct_interleave16(row5, row7);\n\n // transpose pass 3\n dct_interleave16(row0, row1);\n dct_interleave16(row2, row3);\n dct_interleave16(row4, row5);\n dct_interleave16(row6, row7);\n }\n\n // row pass\n dct_pass(bias_1, 17);\n\n {\n // pack\n __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7\n __m128i p1 = _mm_packus_epi16(row2, row3);\n __m128i p2 = _mm_packus_epi16(row4, row5);\n __m128i p3 = _mm_packus_epi16(row6, row7);\n\n // 8bit 8x8 transpose pass 1\n dct_interleave8(p0, p2); // a0e0a1e1...\n dct_interleave8(p1, p3); // c0g0c1g1...\n\n // transpose pass 2\n dct_interleave8(p0, p1); // a0c0e0g0...\n dct_interleave8(p2, p3); // b0d0f0h0...\n\n // transpose pass 3\n dct_interleave8(p0, p2); // a0b0c0d0...\n dct_interleave8(p1, p3); // a4b4c4d4...\n\n // store\n _mm_storel_epi64((__m128i *)out, p0);\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p0, 0x4e));\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, p2);\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p2, 0x4e));\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, p1);\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p1, 0x4e));\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, p3);\n out += out_stride;\n _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p3, 0x4e));\n }\n\n#undef dct_const\n#undef dct_rot\n#undef dct_widen\n#undef dct_wadd\n#undef dct_wsub\n#undef dct_bfly32o\n#undef dct_interleave8\n#undef dct_interleave16\n#undef dct_pass\n}\n\n#endif // STBI_SSE2\n\n#ifdef STBI_NEON\n\n// NEON integer IDCT. should produce bit-identical\n// results to the generic C version.\nstatic void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) {\n int16x8_t row0, row1, row2, row3, row4, row5, row6, row7;\n\n int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f));\n int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f));\n int16x4_t rot0_2 = vdup_n_s16(stbi__f2f(0.765366865f));\n int16x4_t rot1_0 = vdup_n_s16(stbi__f2f(1.175875602f));\n int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f));\n int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f));\n int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f));\n int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f));\n int16x4_t rot3_0 = vdup_n_s16(stbi__f2f(0.298631336f));\n int16x4_t rot3_1 = vdup_n_s16(stbi__f2f(2.053119869f));\n int16x4_t rot3_2 = vdup_n_s16(stbi__f2f(3.072711026f));\n int16x4_t rot3_3 = vdup_n_s16(stbi__f2f(1.501321110f));\n\n#define dct_long_mul(out, inq, coeff) \\\n int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \\\n int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff)\n\n#define dct_long_mac(out, acc, inq, coeff) \\\n int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \\\n int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff)\n\n#define dct_widen(out, inq) \\\n int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \\\n int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12)\n\n// wide add\n#define dct_wadd(out, a, b) \\\n int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \\\n int32x4_t out##_h = vaddq_s32(a##_h, b##_h)\n\n// wide sub\n#define dct_wsub(out, a, b) \\\n int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \\\n int32x4_t out##_h = vsubq_s32(a##_h, b##_h)\n\n// butterfly a/b, then shift using \"shiftop\" by \"s\" and pack\n#define dct_bfly32o(out0, out1, a, b, shiftop, s) \\\n { \\\n dct_wadd(sum, a, b); \\\n dct_wsub(dif, a, b); \\\n out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \\\n out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \\\n }\n\n#define dct_pass(shiftop, shift) \\\n { \\\n /* even part */ \\\n int16x8_t sum26 = vaddq_s16(row2, row6); \\\n dct_long_mul(p1e, sum26, rot0_0); \\\n dct_long_mac(t2e, p1e, row6, rot0_1); \\\n dct_long_mac(t3e, p1e, row2, rot0_2); \\\n int16x8_t sum04 = vaddq_s16(row0, row4); \\\n int16x8_t dif04 = vsubq_s16(row0, row4); \\\n dct_widen(t0e, sum04); \\\n dct_widen(t1e, dif04); \\\n dct_wadd(x0, t0e, t3e); \\\n dct_wsub(x3, t0e, t3e); \\\n dct_wadd(x1, t1e, t2e); \\\n dct_wsub(x2, t1e, t2e); \\\n /* odd part */ \\\n int16x8_t sum15 = vaddq_s16(row1, row5); \\\n int16x8_t sum17 = vaddq_s16(row1, row7); \\\n int16x8_t sum35 = vaddq_s16(row3, row5); \\\n int16x8_t sum37 = vaddq_s16(row3, row7); \\\n int16x8_t sumodd = vaddq_s16(sum17, sum35); \\\n dct_long_mul(p5o, sumodd, rot1_0); \\\n dct_long_mac(p1o, p5o, sum17, rot1_1); \\\n dct_long_mac(p2o, p5o, sum35, rot1_2); \\\n dct_long_mul(p3o, sum37, rot2_0); \\\n dct_long_mul(p4o, sum15, rot2_1); \\\n dct_wadd(sump13o, p1o, p3o); \\\n dct_wadd(sump24o, p2o, p4o); \\\n dct_wadd(sump23o, p2o, p3o); \\\n dct_wadd(sump14o, p1o, p4o); \\\n dct_long_mac(x4, sump13o, row7, rot3_0); \\\n dct_long_mac(x5, sump24o, row5, rot3_1); \\\n dct_long_mac(x6, sump23o, row3, rot3_2); \\\n dct_long_mac(x7, sump14o, row1, rot3_3); \\\n dct_bfly32o(row0, row7, x0, x7, shiftop, shift); \\\n dct_bfly32o(row1, row6, x1, x6, shiftop, shift); \\\n dct_bfly32o(row2, row5, x2, x5, shiftop, shift); \\\n dct_bfly32o(row3, row4, x3, x4, shiftop, shift); \\\n }\n\n // load\n row0 = vld1q_s16(data + 0 * 8);\n row1 = vld1q_s16(data + 1 * 8);\n row2 = vld1q_s16(data + 2 * 8);\n row3 = vld1q_s16(data + 3 * 8);\n row4 = vld1q_s16(data + 4 * 8);\n row5 = vld1q_s16(data + 5 * 8);\n row6 = vld1q_s16(data + 6 * 8);\n row7 = vld1q_s16(data + 7 * 8);\n\n // add DC bias\n row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0));\n\n // column pass\n dct_pass(vrshrn_n_s32, 10);\n\n // 16bit 8x8 transpose\n {\n// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively.\n// whether compilers actually get this is another story, sadly.\n#define dct_trn16(x, y) \\\n { \\\n int16x8x2_t t = vtrnq_s16(x, y); \\\n x = t.val[0]; \\\n y = t.val[1]; \\\n }\n#define dct_trn32(x, y) \\\n { \\\n int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); \\\n x = vreinterpretq_s16_s32(t.val[0]); \\\n y = vreinterpretq_s16_s32(t.val[1]); \\\n }\n#define dct_trn64(x, y) \\\n { \\\n int16x8_t x0 = x; \\\n int16x8_t y0 = y; \\\n x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); \\\n y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); \\\n }\n\n // pass 1\n dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6\n dct_trn16(row2, row3);\n dct_trn16(row4, row5);\n dct_trn16(row6, row7);\n\n // pass 2\n dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4\n dct_trn32(row1, row3);\n dct_trn32(row4, row6);\n dct_trn32(row5, row7);\n\n // pass 3\n dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0\n dct_trn64(row1, row5);\n dct_trn64(row2, row6);\n dct_trn64(row3, row7);\n\n#undef dct_trn16\n#undef dct_trn32\n#undef dct_trn64\n }\n\n // row pass\n // vrshrn_n_s32 only supports shifts up to 16, we need\n // 17. so do a non-rounding shift of 16 first then follow\n // up with a rounding shift by 1.\n dct_pass(vshrn_n_s32, 16);\n\n {\n // pack and round\n uint8x8_t p0 = vqrshrun_n_s16(row0, 1);\n uint8x8_t p1 = vqrshrun_n_s16(row1, 1);\n uint8x8_t p2 = vqrshrun_n_s16(row2, 1);\n uint8x8_t p3 = vqrshrun_n_s16(row3, 1);\n uint8x8_t p4 = vqrshrun_n_s16(row4, 1);\n uint8x8_t p5 = vqrshrun_n_s16(row5, 1);\n uint8x8_t p6 = vqrshrun_n_s16(row6, 1);\n uint8x8_t p7 = vqrshrun_n_s16(row7, 1);\n\n // again, these can translate into one instruction, but often don't.\n#define dct_trn8_8(x, y) \\\n { \\\n uint8x8x2_t t = vtrn_u8(x, y); \\\n x = t.val[0]; \\\n y = t.val[1]; \\\n }\n#define dct_trn8_16(x, y) \\\n { \\\n uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); \\\n x = vreinterpret_u8_u16(t.val[0]); \\\n y = vreinterpret_u8_u16(t.val[1]); \\\n }\n#define dct_trn8_32(x, y) \\\n { \\\n uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); \\\n x = vreinterpret_u8_u32(t.val[0]); \\\n y = vreinterpret_u8_u32(t.val[1]); \\\n }\n\n // sadly can't use interleaved stores here since we only write\n // 8 bytes to each scan line!\n\n // 8x8 8-bit transpose pass 1\n dct_trn8_8(p0, p1);\n dct_trn8_8(p2, p3);\n dct_trn8_8(p4, p5);\n dct_trn8_8(p6, p7);\n\n // pass 2\n dct_trn8_16(p0, p2);\n dct_trn8_16(p1, p3);\n dct_trn8_16(p4, p6);\n dct_trn8_16(p5, p7);\n\n // pass 3\n dct_trn8_32(p0, p4);\n dct_trn8_32(p1, p5);\n dct_trn8_32(p2, p6);\n dct_trn8_32(p3, p7);\n\n // store\n vst1_u8(out, p0);\n out += out_stride;\n vst1_u8(out, p1);\n out += out_stride;\n vst1_u8(out, p2);\n out += out_stride;\n vst1_u8(out, p3);\n out += out_stride;\n vst1_u8(out, p4);\n out += out_stride;\n vst1_u8(out, p5);\n out += out_stride;\n vst1_u8(out, p6);\n out += out_stride;\n vst1_u8(out, p7);\n\n#undef dct_trn8_8\n#undef dct_trn8_16\n#undef dct_trn8_32\n }\n\n#undef dct_long_mul\n#undef dct_long_mac\n#undef dct_widen\n#undef dct_wadd\n#undef dct_wsub\n#undef dct_bfly32o\n#undef dct_pass\n}\n\n#endif // STBI_NEON\n\n#define STBI__MARKER_none 0xff\n// if there's a pending marker from the entropy stream, return that\n// otherwise, fetch from the stream and get a marker. if there's no\n// marker, return 0xff, which is never a valid marker value\nstatic stbi_uc stbi__get_marker(stbi__jpeg * j) {\n stbi_uc x;\n if (j->marker != STBI__MARKER_none) {\n x = j->marker;\n j->marker = STBI__MARKER_none;\n return x;\n }\n x = stbi__get8(j->s);\n if (x != 0xff)\n return STBI__MARKER_none;\n while (x == 0xff)\n x = stbi__get8(j->s); // consume repeated 0xff fill bytes\n return x;\n}\n\n// in each scan, we'll have scan_n components, and the order\n// of the components is specified by order[]\n#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7)\n\n// after a restart interval, stbi__jpeg_reset the entropy decoder and\n// the dc prediction\nstatic void stbi__jpeg_reset(stbi__jpeg * j) {\n j->code_bits = 0;\n j->code_buffer = 0;\n j->nomore = 0;\n j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0;\n j->marker = STBI__MARKER_none;\n j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff;\n j->eob_run = 0;\n // no more than 1<<31 MCUs if no restart_interal? that's plenty safe,\n // since we don't even allow 1<<30 pixels\n}\n\nstatic int stbi__parse_entropy_coded_data(stbi__jpeg * z) {\n stbi__jpeg_reset(z);\n if (!z->progressive) {\n if (z->scan_n == 1) {\n int i, j;\n STBI_SIMD_ALIGN(short, data[64]);\n int n = z->order[0];\n // non-interleaved data, we just need to process one block at a time,\n // in trivial scanline order\n // number of blocks to do just depends on how many actual \"pixels\" this\n // component has, independent of interleaved MCU blocking and such\n int w = (z->img_comp[n].x + 7) >> 3;\n int h = (z->img_comp[n].y + 7) >> 3;\n for (j = 0; j < h; ++j) {\n for (i = 0; i < w; ++i) {\n int ha = z->img_comp[n].ha;\n if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha, z->fast_ac[ha], n,\n z->dequant[z->img_comp[n].tq]))\n return 0;\n z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data);\n // every data block is an MCU, so countdown the restart interval\n if (--z->todo <= 0) {\n if (z->code_bits < 24)\n stbi__grow_buffer_unsafe(z);\n // if it's NOT a restart, then just bail, so we get corrupt data\n // rather than no data\n if (!STBI__RESTART(z->marker))\n return 1;\n stbi__jpeg_reset(z);\n }\n }\n }\n return 1;\n } else { // interleaved\n int i, j, k, x, y;\n STBI_SIMD_ALIGN(short, data[64]);\n for (j = 0; j < z->img_mcu_y; ++j) {\n for (i = 0; i < z->img_mcu_x; ++i) {\n // scan an interleaved mcu... process scan_n components in order\n for (k = 0; k < z->scan_n; ++k) {\n int n = z->order[k];\n // scan out an mcu's worth of this component; that's just determined\n // by the basic H and V specified for the component\n for (y = 0; y < z->img_comp[n].v; ++y) {\n for (x = 0; x < z->img_comp[n].h; ++x) {\n int x2 = (i * z->img_comp[n].h + x) * 8;\n int y2 = (j * z->img_comp[n].v + y) * 8;\n int ha = z->img_comp[n].ha;\n if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha,\n z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq]))\n return 0;\n z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * y2 + x2, z->img_comp[n].w2,\n data);\n }\n }\n }\n // after all interleaved components, that's an interleaved MCU,\n // so now count down the restart interval\n if (--z->todo <= 0) {\n if (z->code_bits < 24)\n stbi__grow_buffer_unsafe(z);\n if (!STBI__RESTART(z->marker))\n return 1;\n stbi__jpeg_reset(z);\n }\n }\n }\n return 1;\n }\n } else {\n if (z->scan_n == 1) {\n int i, j;\n int n = z->order[0];\n // non-interleaved data, we just need to process one block at a time,\n // in trivial scanline order\n // number of blocks to do just depends on how many actual \"pixels\" this\n // component has, independent of interleaved MCU blocking and such\n int w = (z->img_comp[n].x + 7) >> 3;\n int h = (z->img_comp[n].y + 7) >> 3;\n for (j = 0; j < h; ++j) {\n for (i = 0; i < w; ++i) {\n short * data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w);\n if (z->spec_start == 0) {\n if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n))\n return 0;\n } else {\n int ha = z->img_comp[n].ha;\n if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha]))\n return 0;\n }\n // every data block is an MCU, so countdown the restart interval\n if (--z->todo <= 0) {\n if (z->code_bits < 24)\n stbi__grow_buffer_unsafe(z);\n if (!STBI__RESTART(z->marker))\n return 1;\n stbi__jpeg_reset(z);\n }\n }\n }\n return 1;\n } else { // interleaved\n int i, j, k, x, y;\n for (j = 0; j < z->img_mcu_y; ++j) {\n for (i = 0; i < z->img_mcu_x; ++i) {\n // scan an interleaved mcu... process scan_n components in order\n for (k = 0; k < z->scan_n; ++k) {\n int n = z->order[k];\n // scan out an mcu's worth of this component; that's just determined\n // by the basic H and V specified for the component\n for (y = 0; y < z->img_comp[n].v; ++y) {\n for (x = 0; x < z->img_comp[n].h; ++x) {\n int x2 = (i * z->img_comp[n].h + x);\n int y2 = (j * z->img_comp[n].v + y);\n short * data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w);\n if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n))\n return 0;\n }\n }\n }\n // after all interleaved components, that's an interleaved MCU,\n // so now count down the restart interval\n if (--z->todo <= 0) {\n if (z->code_bits < 24)\n stbi__grow_buffer_unsafe(z);\n if (!STBI__RESTART(z->marker))\n return 1;\n stbi__jpeg_reset(z);\n }\n }\n }\n return 1;\n }\n }\n}\n\nstatic void stbi__jpeg_dequantize(short * data, stbi__uint16 * dequant) {\n int i;\n for (i = 0; i < 64; ++i)\n data[i] *= dequant[i];\n}\n\nstatic void stbi__jpeg_finish(stbi__jpeg * z) {\n if (z->progressive) {\n // dequantize and idct the data\n int i, j, n;\n for (n = 0; n < z->s->img_n; ++n) {\n int w = (z->img_comp[n].x + 7) >> 3;\n int h = (z->img_comp[n].y + 7) >> 3;\n for (j = 0; j < h; ++j) {\n for (i = 0; i < w; ++i) {\n short * data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w);\n stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]);\n z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data);\n }\n }\n }\n }\n}\n\nstatic int stbi__process_marker(stbi__jpeg * z, int m) {\n int L;\n switch (m) {\n case STBI__MARKER_none: // no marker found\n return stbi__err(\"expected marker\", \"Corrupt JPEG\");\n\n case 0xDD: // DRI - specify restart interval\n if (stbi__get16be(z->s) != 4)\n return stbi__err(\"bad DRI len\", \"Corrupt JPEG\");\n z->restart_interval = stbi__get16be(z->s);\n return 1;\n\n case 0xDB: // DQT - define quantization table\n L = stbi__get16be(z->s) - 2;\n while (L > 0) {\n int q = stbi__get8(z->s);\n int p = q >> 4, sixteen = (p != 0);\n int t = q & 15, i;\n if (p != 0 && p != 1)\n return stbi__err(\"bad DQT type\", \"Corrupt JPEG\");\n if (t > 3)\n return stbi__err(\"bad DQT table\", \"Corrupt JPEG\");\n\n for (i = 0; i < 64; ++i)\n z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s));\n L -= (sixteen ? 129 : 65);\n }\n return L == 0;\n\n case 0xC4: // DHT - define huffman table\n L = stbi__get16be(z->s) - 2;\n while (L > 0) {\n stbi_uc * v;\n int sizes[16], i, n = 0;\n int q = stbi__get8(z->s);\n int tc = q >> 4;\n int th = q & 15;\n if (tc > 1 || th > 3)\n return stbi__err(\"bad DHT header\", \"Corrupt JPEG\");\n for (i = 0; i < 16; ++i) {\n sizes[i] = stbi__get8(z->s);\n n += sizes[i];\n }\n if (n > 256)\n return stbi__err(\"bad DHT header\", \"Corrupt JPEG\"); // Loop over i < n would write past end of values!\n L -= 17;\n if (tc == 0) {\n if (!stbi__build_huffman(z->huff_dc + th, sizes))\n return 0;\n v = z->huff_dc[th].values;\n } else {\n if (!stbi__build_huffman(z->huff_ac + th, sizes))\n return 0;\n v = z->huff_ac[th].values;\n }\n for (i = 0; i < n; ++i)\n v[i] = stbi__get8(z->s);\n if (tc != 0)\n stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th);\n L -= n;\n }\n return L == 0;\n }\n\n // check for comment block or APP blocks\n if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) {\n L = stbi__get16be(z->s);\n if (L < 2) {\n if (m == 0xFE)\n return stbi__err(\"bad COM len\", \"Corrupt JPEG\");\n else\n return stbi__err(\"bad APP len\", \"Corrupt JPEG\");\n }\n L -= 2;\n\n if (m == 0xE0 && L >= 5) { // JFIF APP0 segment\n static const unsigned char tag[5] = {'J', 'F', 'I', 'F', '\\0'};\n int ok = 1;\n int i;\n for (i = 0; i < 5; ++i)\n if (stbi__get8(z->s) != tag[i])\n ok = 0;\n L -= 5;\n if (ok)\n z->jfif = 1;\n } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment\n static const unsigned char tag[6] = {'A', 'd', 'o', 'b', 'e', '\\0'};\n int ok = 1;\n int i;\n for (i = 0; i < 6; ++i)\n if (stbi__get8(z->s) != tag[i])\n ok = 0;\n L -= 6;\n if (ok) {\n stbi__get8(z->s); // version\n stbi__get16be(z->s); // flags0\n stbi__get16be(z->s); // flags1\n z->app14_color_transform = stbi__get8(z->s); // color transform\n L -= 6;\n }\n }\n\n stbi__skip(z->s, L);\n return 1;\n }\n\n return stbi__err(\"unknown marker\", \"Corrupt JPEG\");\n}\n\n// after we see SOS\nstatic int stbi__process_scan_header(stbi__jpeg * z) {\n int i;\n int Ls = stbi__get16be(z->s);\n z->scan_n = stbi__get8(z->s);\n if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int)z->s->img_n)\n return stbi__err(\"bad SOS component count\", \"Corrupt JPEG\");\n if (Ls != 6 + 2 * z->scan_n)\n return stbi__err(\"bad SOS len\", \"Corrupt JPEG\");\n for (i = 0; i < z->scan_n; ++i) {\n int id = stbi__get8(z->s), which;\n int q = stbi__get8(z->s);\n for (which = 0; which < z->s->img_n; ++which)\n if (z->img_comp[which].id == id)\n break;\n if (which == z->s->img_n)\n return 0; // no match\n z->img_comp[which].hd = q >> 4;\n if (z->img_comp[which].hd > 3)\n return stbi__err(\"bad DC huff\", \"Corrupt JPEG\");\n z->img_comp[which].ha = q & 15;\n if (z->img_comp[which].ha > 3)\n return stbi__err(\"bad AC huff\", \"Corrupt JPEG\");\n z->order[i] = which;\n }\n\n {\n int aa;\n z->spec_start = stbi__get8(z->s);\n z->spec_end = stbi__get8(z->s); // should be 63, but might be 0\n aa = stbi__get8(z->s);\n z->succ_high = (aa >> 4);\n z->succ_low = (aa & 15);\n if (z->progressive) {\n if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13)\n return stbi__err(\"bad SOS\", \"Corrupt JPEG\");\n } else {\n if (z->spec_start != 0)\n return stbi__err(\"bad SOS\", \"Corrupt JPEG\");\n if (z->succ_high != 0 || z->succ_low != 0)\n return stbi__err(\"bad SOS\", \"Corrupt JPEG\");\n z->spec_end = 63;\n }\n }\n\n return 1;\n}\n\nstatic int stbi__free_jpeg_components(stbi__jpeg * z, int ncomp, int why) {\n int i;\n for (i = 0; i < ncomp; ++i) {\n if (z->img_comp[i].raw_data) {\n STBI_FREE(z->img_comp[i].raw_data);\n z->img_comp[i].raw_data = NULL;\n z->img_comp[i].data = NULL;\n }\n if (z->img_comp[i].raw_coeff) {\n STBI_FREE(z->img_comp[i].raw_coeff);\n z->img_comp[i].raw_coeff = 0;\n z->img_comp[i].coeff = 0;\n }\n if (z->img_comp[i].linebuf) {\n STBI_FREE(z->img_comp[i].linebuf);\n z->img_comp[i].linebuf = NULL;\n }\n }\n return why;\n}\n\nstatic int stbi__process_frame_header(stbi__jpeg * z, int scan) {\n stbi__context * s = z->s;\n int Lf, p, i, q, h_max = 1, v_max = 1, c;\n Lf = stbi__get16be(s);\n if (Lf < 11)\n return stbi__err(\"bad SOF len\", \"Corrupt JPEG\"); // JPEG\n p = stbi__get8(s);\n if (p != 8)\n return stbi__err(\"only 8-bit\", \"JPEG format not supported: 8-bit only\"); // JPEG baseline\n s->img_y = stbi__get16be(s);\n if (s->img_y == 0)\n return stbi__err(\"no header height\",\n \"JPEG format not supported: delayed height\"); // Legal, but we don't handle it--but neither does IJG\n s->img_x = stbi__get16be(s);\n if (s->img_x == 0)\n return stbi__err(\"0 width\", \"Corrupt JPEG\"); // JPEG requires\n if (s->img_y > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n if (s->img_x > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n c = stbi__get8(s);\n if (c != 3 && c != 1 && c != 4)\n return stbi__err(\"bad component count\", \"Corrupt JPEG\");\n s->img_n = c;\n for (i = 0; i < c; ++i) {\n z->img_comp[i].data = NULL;\n z->img_comp[i].linebuf = NULL;\n }\n\n if (Lf != 8 + 3 * s->img_n)\n return stbi__err(\"bad SOF len\", \"Corrupt JPEG\");\n\n z->rgb = 0;\n for (i = 0; i < s->img_n; ++i) {\n static const unsigned char rgb[3] = {'R', 'G', 'B'};\n z->img_comp[i].id = stbi__get8(s);\n if (s->img_n == 3 && z->img_comp[i].id == rgb[i])\n ++z->rgb;\n q = stbi__get8(s);\n z->img_comp[i].h = (q >> 4);\n if (!z->img_comp[i].h || z->img_comp[i].h > 4)\n return stbi__err(\"bad H\", \"Corrupt JPEG\");\n z->img_comp[i].v = q & 15;\n if (!z->img_comp[i].v || z->img_comp[i].v > 4)\n return stbi__err(\"bad V\", \"Corrupt JPEG\");\n z->img_comp[i].tq = stbi__get8(s);\n if (z->img_comp[i].tq > 3)\n return stbi__err(\"bad TQ\", \"Corrupt JPEG\");\n }\n\n if (scan != STBI__SCAN_load)\n return 1;\n\n if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0))\n return stbi__err(\"too large\", \"Image too large to decode\");\n\n for (i = 0; i < s->img_n; ++i) {\n if (z->img_comp[i].h > h_max)\n h_max = z->img_comp[i].h;\n if (z->img_comp[i].v > v_max)\n v_max = z->img_comp[i].v;\n }\n\n // check that plane subsampling factors are integer ratios; our resamplers can't deal with fractional ratios\n // and I've never seen a non-corrupted JPEG file actually use them\n for (i = 0; i < s->img_n; ++i) {\n if (h_max % z->img_comp[i].h != 0)\n return stbi__err(\"bad H\", \"Corrupt JPEG\");\n if (v_max % z->img_comp[i].v != 0)\n return stbi__err(\"bad V\", \"Corrupt JPEG\");\n }\n\n // compute interleaved mcu info\n z->img_h_max = h_max;\n z->img_v_max = v_max;\n z->img_mcu_w = h_max * 8;\n z->img_mcu_h = v_max * 8;\n // these sizes can't be more than 17 bits\n z->img_mcu_x = (s->img_x + z->img_mcu_w - 1) / z->img_mcu_w;\n z->img_mcu_y = (s->img_y + z->img_mcu_h - 1) / z->img_mcu_h;\n\n for (i = 0; i < s->img_n; ++i) {\n // number of effective pixels (e.g. for non-interleaved MCU)\n z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max - 1) / h_max;\n z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max - 1) / v_max;\n // to simplify generation, we'll allocate enough memory to decode\n // the bogus oversized data from using interleaved MCUs and their\n // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't\n // discard the extra data until colorspace conversion\n //\n // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier)\n // so these muls can't overflow with 32-bit ints (which we require)\n z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8;\n z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8;\n z->img_comp[i].coeff = 0;\n z->img_comp[i].raw_coeff = 0;\n z->img_comp[i].linebuf = NULL;\n z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15);\n if (z->img_comp[i].raw_data == NULL)\n return stbi__free_jpeg_components(z, i + 1, stbi__err(\"outofmem\", \"Out of memory\"));\n // align blocks for idct using mmx/sse\n z->img_comp[i].data = (stbi_uc *)(((size_t)z->img_comp[i].raw_data + 15) & ~15);\n if (z->progressive) {\n // w2, h2 are multiples of 8 (see above)\n z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8;\n z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8;\n z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15);\n if (z->img_comp[i].raw_coeff == NULL)\n return stbi__free_jpeg_components(z, i + 1, stbi__err(\"outofmem\", \"Out of memory\"));\n z->img_comp[i].coeff = (short *)(((size_t)z->img_comp[i].raw_coeff + 15) & ~15);\n }\n }\n\n return 1;\n}\n\n// use comparisons since in some cases we handle more than one case (e.g. SOF)\n#define stbi__DNL(x) ((x) == 0xdc)\n#define stbi__SOI(x) ((x) == 0xd8)\n#define stbi__EOI(x) ((x) == 0xd9)\n#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2)\n#define stbi__SOS(x) ((x) == 0xda)\n\n#define stbi__SOF_progressive(x) ((x) == 0xc2)\n\nstatic int stbi__decode_jpeg_header(stbi__jpeg * z, int scan) {\n int m;\n z->jfif = 0;\n z->app14_color_transform = -1; // valid values are 0,1,2\n z->marker = STBI__MARKER_none; // initialize cached marker to empty\n m = stbi__get_marker(z);\n if (!stbi__SOI(m))\n return stbi__err(\"no SOI\", \"Corrupt JPEG\");\n if (scan == STBI__SCAN_type)\n return 1;\n m = stbi__get_marker(z);\n while (!stbi__SOF(m)) {\n if (!stbi__process_marker(z, m))\n return 0;\n m = stbi__get_marker(z);\n while (m == STBI__MARKER_none) {\n // some files have extra padding after their blocks, so ok, we'll scan\n if (stbi__at_eof(z->s))\n return stbi__err(\"no SOF\", \"Corrupt JPEG\");\n m = stbi__get_marker(z);\n }\n }\n z->progressive = stbi__SOF_progressive(m);\n if (!stbi__process_frame_header(z, scan))\n return 0;\n return 1;\n}\n\nstatic int stbi__skip_jpeg_junk_at_end(stbi__jpeg * j) {\n // some JPEGs have junk at end, skip over it but if we find what looks\n // like a valid marker, resume there\n while (!stbi__at_eof(j->s)) {\n int x = stbi__get8(j->s);\n while (x == 255) { // might be a marker\n if (stbi__at_eof(j->s))\n return STBI__MARKER_none;\n x = stbi__get8(j->s);\n if (x != 0x00 && x != 0xff) {\n // not a stuffed zero or lead-in to another marker, looks\n // like an actual marker, return it\n return x;\n }\n // stuffed zero has x=0 now which ends the loop, meaning we go\n // back to regular scan loop.\n // repeated 0xff keeps trying to read the next byte of the marker.\n }\n }\n return STBI__MARKER_none;\n}\n\n// decode image to YCbCr format\nstatic int stbi__decode_jpeg_image(stbi__jpeg * j) {\n int m;\n for (m = 0; m < 4; m++) {\n j->img_comp[m].raw_data = NULL;\n j->img_comp[m].raw_coeff = NULL;\n }\n j->restart_interval = 0;\n if (!stbi__decode_jpeg_header(j, STBI__SCAN_load))\n return 0;\n m = stbi__get_marker(j);\n while (!stbi__EOI(m)) {\n if (stbi__SOS(m)) {\n if (!stbi__process_scan_header(j))\n return 0;\n if (!stbi__parse_entropy_coded_data(j))\n return 0;\n if (j->marker == STBI__MARKER_none) {\n j->marker = stbi__skip_jpeg_junk_at_end(j);\n // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0\n }\n m = stbi__get_marker(j);\n if (STBI__RESTART(m))\n m = stbi__get_marker(j);\n } else if (stbi__DNL(m)) {\n int Ld = stbi__get16be(j->s);\n stbi__uint32 NL = stbi__get16be(j->s);\n if (Ld != 4)\n return stbi__err(\"bad DNL len\", \"Corrupt JPEG\");\n if (NL != j->s->img_y)\n return stbi__err(\"bad DNL height\", \"Corrupt JPEG\");\n m = stbi__get_marker(j);\n } else {\n if (!stbi__process_marker(j, m))\n return 1;\n m = stbi__get_marker(j);\n }\n }\n if (j->progressive)\n stbi__jpeg_finish(j);\n return 1;\n}\n\n// static jfif-centered resampling (across block boundaries)\n\ntypedef stbi_uc * (*resample_row_func)(stbi_uc * out, stbi_uc * in0, stbi_uc * in1, int w, int hs);\n\n#define stbi__div4(x) ((stbi_uc)((x) >> 2))\n\nstatic stbi_uc * resample_row_1(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n STBI_NOTUSED(out);\n STBI_NOTUSED(in_far);\n STBI_NOTUSED(w);\n STBI_NOTUSED(hs);\n return in_near;\n}\n\nstatic stbi_uc * stbi__resample_row_v_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n // need to generate two samples vertically for every one in input\n int i;\n STBI_NOTUSED(hs);\n for (i = 0; i < w; ++i)\n out[i] = stbi__div4(3 * in_near[i] + in_far[i] + 2);\n return out;\n}\n\nstatic stbi_uc * stbi__resample_row_h_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n // need to generate two samples horizontally for every one in input\n int i;\n stbi_uc * input = in_near;\n\n if (w == 1) {\n // if only one sample, can't do any interpolation\n out[0] = out[1] = input[0];\n return out;\n }\n\n out[0] = input[0];\n out[1] = stbi__div4(input[0] * 3 + input[1] + 2);\n for (i = 1; i < w - 1; ++i) {\n int n = 3 * input[i] + 2;\n out[i * 2 + 0] = stbi__div4(n + input[i - 1]);\n out[i * 2 + 1] = stbi__div4(n + input[i + 1]);\n }\n out[i * 2 + 0] = stbi__div4(input[w - 2] * 3 + input[w - 1] + 2);\n out[i * 2 + 1] = input[w - 1];\n\n STBI_NOTUSED(in_far);\n STBI_NOTUSED(hs);\n\n return out;\n}\n\n#define stbi__div16(x) ((stbi_uc)((x) >> 4))\n\nstatic stbi_uc * stbi__resample_row_hv_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n // need to generate 2x2 samples for every one in input\n int i, t0, t1;\n if (w == 1) {\n out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2);\n return out;\n }\n\n t1 = 3 * in_near[0] + in_far[0];\n out[0] = stbi__div4(t1 + 2);\n for (i = 1; i < w; ++i) {\n t0 = t1;\n t1 = 3 * in_near[i] + in_far[i];\n out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8);\n out[i * 2] = stbi__div16(3 * t1 + t0 + 8);\n }\n out[w * 2 - 1] = stbi__div4(t1 + 2);\n\n STBI_NOTUSED(hs);\n\n return out;\n}\n\n#if defined(STBI_SSE2) || defined(STBI_NEON)\nstatic stbi_uc * stbi__resample_row_hv_2_simd(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n // need to generate 2x2 samples for every one in input\n int i = 0, t0, t1;\n\n if (w == 1) {\n out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2);\n return out;\n }\n\n t1 = 3 * in_near[0] + in_far[0];\n // process groups of 8 pixels for as long as we can.\n // note we can't handle the last pixel in a row in this loop\n // because we need to handle the filter boundary conditions.\n for (; i < ((w - 1) & ~7); i += 8) {\n#if defined(STBI_SSE2)\n // load and perform the vertical filtering pass\n // this uses 3*x + y = 4*x + (y - x)\n __m128i zero = _mm_setzero_si128();\n __m128i farb = _mm_loadl_epi64((__m128i *)(in_far + i));\n __m128i nearb = _mm_loadl_epi64((__m128i *)(in_near + i));\n __m128i farw = _mm_unpacklo_epi8(farb, zero);\n __m128i nearw = _mm_unpacklo_epi8(nearb, zero);\n __m128i diff = _mm_sub_epi16(farw, nearw);\n __m128i nears = _mm_slli_epi16(nearw, 2);\n __m128i curr = _mm_add_epi16(nears, diff); // current row\n\n // horizontal filter works the same based on shifted vers of current\n // row. \"prev\" is current row shifted right by 1 pixel; we need to\n // insert the previous pixel value (from t1).\n // \"next\" is current row shifted left by 1 pixel, with first pixel\n // of next block of 8 pixels added in.\n __m128i prv0 = _mm_slli_si128(curr, 2);\n __m128i nxt0 = _mm_srli_si128(curr, 2);\n __m128i prev = _mm_insert_epi16(prv0, t1, 0);\n __m128i next = _mm_insert_epi16(nxt0, 3 * in_near[i + 8] + in_far[i + 8], 7);\n\n // horizontal filter, polyphase implementation since it's convenient:\n // even pixels = 3*cur + prev = cur*4 + (prev - cur)\n // odd pixels = 3*cur + next = cur*4 + (next - cur)\n // note the shared term.\n __m128i bias = _mm_set1_epi16(8);\n __m128i curs = _mm_slli_epi16(curr, 2);\n __m128i prvd = _mm_sub_epi16(prev, curr);\n __m128i nxtd = _mm_sub_epi16(next, curr);\n __m128i curb = _mm_add_epi16(curs, bias);\n __m128i even = _mm_add_epi16(prvd, curb);\n __m128i odd = _mm_add_epi16(nxtd, curb);\n\n // interleave even and odd pixels, then undo scaling.\n __m128i int0 = _mm_unpacklo_epi16(even, odd);\n __m128i int1 = _mm_unpackhi_epi16(even, odd);\n __m128i de0 = _mm_srli_epi16(int0, 4);\n __m128i de1 = _mm_srli_epi16(int1, 4);\n\n // pack and write output\n __m128i outv = _mm_packus_epi16(de0, de1);\n _mm_storeu_si128((__m128i *)(out + i * 2), outv);\n#elif defined(STBI_NEON)\n // load and perform the vertical filtering pass\n // this uses 3*x + y = 4*x + (y - x)\n uint8x8_t farb = vld1_u8(in_far + i);\n uint8x8_t nearb = vld1_u8(in_near + i);\n int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb));\n int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2));\n int16x8_t curr = vaddq_s16(nears, diff); // current row\n\n // horizontal filter works the same based on shifted vers of current\n // row. \"prev\" is current row shifted right by 1 pixel; we need to\n // insert the previous pixel value (from t1).\n // \"next\" is current row shifted left by 1 pixel, with first pixel\n // of next block of 8 pixels added in.\n int16x8_t prv0 = vextq_s16(curr, curr, 7);\n int16x8_t nxt0 = vextq_s16(curr, curr, 1);\n int16x8_t prev = vsetq_lane_s16(t1, prv0, 0);\n int16x8_t next = vsetq_lane_s16(3 * in_near[i + 8] + in_far[i + 8], nxt0, 7);\n\n // horizontal filter, polyphase implementation since it's convenient:\n // even pixels = 3*cur + prev = cur*4 + (prev - cur)\n // odd pixels = 3*cur + next = cur*4 + (next - cur)\n // note the shared term.\n int16x8_t curs = vshlq_n_s16(curr, 2);\n int16x8_t prvd = vsubq_s16(prev, curr);\n int16x8_t nxtd = vsubq_s16(next, curr);\n int16x8_t even = vaddq_s16(curs, prvd);\n int16x8_t odd = vaddq_s16(curs, nxtd);\n\n // undo scaling and round, then store with even/odd phases interleaved\n uint8x8x2_t o;\n o.val[0] = vqrshrun_n_s16(even, 4);\n o.val[1] = vqrshrun_n_s16(odd, 4);\n vst2_u8(out + i * 2, o);\n#endif\n\n // \"previous\" value for next iter\n t1 = 3 * in_near[i + 7] + in_far[i + 7];\n }\n\n t0 = t1;\n t1 = 3 * in_near[i] + in_far[i];\n out[i * 2] = stbi__div16(3 * t1 + t0 + 8);\n\n for (++i; i < w; ++i) {\n t0 = t1;\n t1 = 3 * in_near[i] + in_far[i];\n out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8);\n out[i * 2] = stbi__div16(3 * t1 + t0 + 8);\n }\n out[w * 2 - 1] = stbi__div4(t1 + 2);\n\n STBI_NOTUSED(hs);\n\n return out;\n}\n#endif\n\nstatic stbi_uc * stbi__resample_row_generic(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) {\n // resample with nearest-neighbor\n int i, j;\n STBI_NOTUSED(in_far);\n for (i = 0; i < w; ++i)\n for (j = 0; j < hs; ++j)\n out[i * hs + j] = in_near[i];\n return out;\n}\n\n// this is a reduced-precision calculation of YCbCr-to-RGB introduced\n// to make sure the code produces the same results in both SIMD and scalar\n#define stbi__float2fixed(x) (((int)((x)*4096.0f + 0.5f)) << 8)\nstatic void stbi__YCbCr_to_RGB_row(stbi_uc * out, const stbi_uc * y, const stbi_uc * pcb, const stbi_uc * pcr, int count,\n int step) {\n int i;\n for (i = 0; i < count; ++i) {\n int y_fixed = (y[i] << 20) + (1 << 19); // rounding\n int r, g, b;\n int cr = pcr[i] - 128;\n int cb = pcb[i] - 128;\n r = y_fixed + cr * stbi__float2fixed(1.40200f);\n g = y_fixed + (cr * -stbi__float2fixed(0.71414f)) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000);\n b = y_fixed + cb * stbi__float2fixed(1.77200f);\n r >>= 20;\n g >>= 20;\n b >>= 20;\n if ((unsigned)r > 255) {\n if (r < 0)\n r = 0;\n else\n r = 255;\n }\n if ((unsigned)g > 255) {\n if (g < 0)\n g = 0;\n else\n g = 255;\n }\n if ((unsigned)b > 255) {\n if (b < 0)\n b = 0;\n else\n b = 255;\n }\n out[0] = (stbi_uc)r;\n out[1] = (stbi_uc)g;\n out[2] = (stbi_uc)b;\n out[3] = 255;\n out += step;\n }\n}\n\n#if defined(STBI_SSE2) || defined(STBI_NEON)\nstatic void stbi__YCbCr_to_RGB_simd(stbi_uc * out, stbi_uc const * y, stbi_uc const * pcb, stbi_uc const * pcr, int count,\n int step) {\n int i = 0;\n\n#ifdef STBI_SSE2\n // step == 3 is pretty ugly on the final interleave, and i'm not convinced\n // it's useful in practice (you wouldn't use it for textures, for example).\n // so just accelerate step == 4 case.\n if (step == 4) {\n // this is a fairly straightforward implementation and not super-optimized.\n __m128i signflip = _mm_set1_epi8(-0x80);\n __m128i cr_const0 = _mm_set1_epi16((short)(1.40200f * 4096.0f + 0.5f));\n __m128i cr_const1 = _mm_set1_epi16(-(short)(0.71414f * 4096.0f + 0.5f));\n __m128i cb_const0 = _mm_set1_epi16(-(short)(0.34414f * 4096.0f + 0.5f));\n __m128i cb_const1 = _mm_set1_epi16((short)(1.77200f * 4096.0f + 0.5f));\n __m128i y_bias = _mm_set1_epi8((char)(unsigned char)128);\n __m128i xw = _mm_set1_epi16(255); // alpha channel\n\n for (; i + 7 < count; i += 8) {\n // load\n __m128i y_bytes = _mm_loadl_epi64((__m128i *)(y + i));\n __m128i cr_bytes = _mm_loadl_epi64((__m128i *)(pcr + i));\n __m128i cb_bytes = _mm_loadl_epi64((__m128i *)(pcb + i));\n __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128\n __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128\n\n // unpack to short (and left-shift cr, cb by 8)\n __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes);\n __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased);\n __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased);\n\n // color transform\n __m128i yws = _mm_srli_epi16(yw, 4);\n __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw);\n __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw);\n __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1);\n __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1);\n __m128i rws = _mm_add_epi16(cr0, yws);\n __m128i gwt = _mm_add_epi16(cb0, yws);\n __m128i bws = _mm_add_epi16(yws, cb1);\n __m128i gws = _mm_add_epi16(gwt, cr1);\n\n // descale\n __m128i rw = _mm_srai_epi16(rws, 4);\n __m128i bw = _mm_srai_epi16(bws, 4);\n __m128i gw = _mm_srai_epi16(gws, 4);\n\n // back to byte, set up for transpose\n __m128i brb = _mm_packus_epi16(rw, bw);\n __m128i gxb = _mm_packus_epi16(gw, xw);\n\n // transpose to interleave channels\n __m128i t0 = _mm_unpacklo_epi8(brb, gxb);\n __m128i t1 = _mm_unpackhi_epi8(brb, gxb);\n __m128i o0 = _mm_unpacklo_epi16(t0, t1);\n __m128i o1 = _mm_unpackhi_epi16(t0, t1);\n\n // store\n _mm_storeu_si128((__m128i *)(out + 0), o0);\n _mm_storeu_si128((__m128i *)(out + 16), o1);\n out += 32;\n }\n }\n#endif\n\n#ifdef STBI_NEON\n // in this version, step=3 support would be easy to add. but is there demand?\n if (step == 4) {\n // this is a fairly straightforward implementation and not super-optimized.\n uint8x8_t signflip = vdup_n_u8(0x80);\n int16x8_t cr_const0 = vdupq_n_s16((short)(1.40200f * 4096.0f + 0.5f));\n int16x8_t cr_const1 = vdupq_n_s16(-(short)(0.71414f * 4096.0f + 0.5f));\n int16x8_t cb_const0 = vdupq_n_s16(-(short)(0.34414f * 4096.0f + 0.5f));\n int16x8_t cb_const1 = vdupq_n_s16((short)(1.77200f * 4096.0f + 0.5f));\n\n for (; i + 7 < count; i += 8) {\n // load\n uint8x8_t y_bytes = vld1_u8(y + i);\n uint8x8_t cr_bytes = vld1_u8(pcr + i);\n uint8x8_t cb_bytes = vld1_u8(pcb + i);\n int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip));\n int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip));\n\n // expand to s16\n int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4));\n int16x8_t crw = vshll_n_s8(cr_biased, 7);\n int16x8_t cbw = vshll_n_s8(cb_biased, 7);\n\n // color transform\n int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0);\n int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0);\n int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1);\n int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1);\n int16x8_t rws = vaddq_s16(yws, cr0);\n int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1);\n int16x8_t bws = vaddq_s16(yws, cb1);\n\n // undo scaling, round, convert to byte\n uint8x8x4_t o;\n o.val[0] = vqrshrun_n_s16(rws, 4);\n o.val[1] = vqrshrun_n_s16(gws, 4);\n o.val[2] = vqrshrun_n_s16(bws, 4);\n o.val[3] = vdup_n_u8(255);\n\n // store, interleaving r/g/b/a\n vst4_u8(out, o);\n out += 8 * 4;\n }\n }\n#endif\n\n for (; i < count; ++i) {\n int y_fixed = (y[i] << 20) + (1 << 19); // rounding\n int r, g, b;\n int cr = pcr[i] - 128;\n int cb = pcb[i] - 128;\n r = y_fixed + cr * stbi__float2fixed(1.40200f);\n g = y_fixed + cr * -stbi__float2fixed(0.71414f) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000);\n b = y_fixed + cb * stbi__float2fixed(1.77200f);\n r >>= 20;\n g >>= 20;\n b >>= 20;\n if ((unsigned)r > 255) {\n if (r < 0)\n r = 0;\n else\n r = 255;\n }\n if ((unsigned)g > 255) {\n if (g < 0)\n g = 0;\n else\n g = 255;\n }\n if ((unsigned)b > 255) {\n if (b < 0)\n b = 0;\n else\n b = 255;\n }\n out[0] = (stbi_uc)r;\n out[1] = (stbi_uc)g;\n out[2] = (stbi_uc)b;\n out[3] = 255;\n out += step;\n }\n}\n#endif\n\n// set up the kernels\nstatic void stbi__setup_jpeg(stbi__jpeg * j) {\n j->idct_block_kernel = stbi__idct_block;\n j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row;\n j->resample_row_hv_2_kernel = stbi__resample_row_hv_2;\n\n#ifdef STBI_SSE2\n if (stbi__sse2_available()) {\n j->idct_block_kernel = stbi__idct_simd;\n j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd;\n j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd;\n }\n#endif\n\n#ifdef STBI_NEON\n j->idct_block_kernel = stbi__idct_simd;\n j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd;\n j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd;\n#endif\n}\n\n// clean up the temporary component buffers\nstatic void stbi__cleanup_jpeg(stbi__jpeg * j) { stbi__free_jpeg_components(j, j->s->img_n, 0); }\n\ntypedef struct {\n resample_row_func resample;\n stbi_uc *line0, *line1;\n int hs, vs; // expansion factor in each axis\n int w_lores; // horizontal pixels pre-expansion\n int ystep; // how far through vertical expansion we are\n int ypos; // which pre-expansion row we're on\n} stbi__resample;\n\n// fast 0..255 * 0..255 => 0..255 rounded multiplication\nstatic stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) {\n unsigned int t = x * y + 128;\n return (stbi_uc)((t + (t >> 8)) >> 8);\n}\n\nstatic stbi_uc * load_jpeg_image(stbi__jpeg * z, int * out_x, int * out_y, int * comp, int req_comp) {\n int n, decode_n, is_rgb;\n z->s->img_n = 0; // make stbi__cleanup_jpeg safe\n\n // validate req_comp\n if (req_comp < 0 || req_comp > 4)\n return stbi__errpuc(\"bad req_comp\", \"Internal error\");\n\n // load a jpeg image from whichever source, but leave in YCbCr format\n if (!stbi__decode_jpeg_image(z)) {\n stbi__cleanup_jpeg(z);\n return NULL;\n }\n\n // determine actual number of components to generate\n n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1;\n\n is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif));\n\n if (z->s->img_n == 3 && n < 3 && !is_rgb)\n decode_n = 1;\n else\n decode_n = z->s->img_n;\n\n // nothing to do if no components requested; check this now to avoid\n // accessing uninitialized coutput[0] later\n if (decode_n <= 0) {\n stbi__cleanup_jpeg(z);\n return NULL;\n }\n\n // resample and color-convert\n {\n int k;\n unsigned int i, j;\n stbi_uc * output;\n stbi_uc * coutput[4] = {NULL, NULL, NULL, NULL};\n\n stbi__resample res_comp[4];\n\n for (k = 0; k < decode_n; ++k) {\n stbi__resample * r = &res_comp[k];\n\n // allocate line buffer big enough for upsampling off the edges\n // with upsample factor of 4\n z->img_comp[k].linebuf = (stbi_uc *)stbi__malloc(z->s->img_x + 3);\n if (!z->img_comp[k].linebuf) {\n stbi__cleanup_jpeg(z);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n\n r->hs = z->img_h_max / z->img_comp[k].h;\n r->vs = z->img_v_max / z->img_comp[k].v;\n r->ystep = r->vs >> 1;\n r->w_lores = (z->s->img_x + r->hs - 1) / r->hs;\n r->ypos = 0;\n r->line0 = r->line1 = z->img_comp[k].data;\n\n if (r->hs == 1 && r->vs == 1)\n r->resample = resample_row_1;\n else if (r->hs == 1 && r->vs == 2)\n r->resample = stbi__resample_row_v_2;\n else if (r->hs == 2 && r->vs == 1)\n r->resample = stbi__resample_row_h_2;\n else if (r->hs == 2 && r->vs == 2)\n r->resample = z->resample_row_hv_2_kernel;\n else\n r->resample = stbi__resample_row_generic;\n }\n\n // can't error after this so, this is safe\n output = (stbi_uc *)stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1);\n if (!output) {\n stbi__cleanup_jpeg(z);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n\n // now go ahead and resample\n for (j = 0; j < z->s->img_y; ++j) {\n stbi_uc * out = output + n * z->s->img_x * j;\n for (k = 0; k < decode_n; ++k) {\n stbi__resample * r = &res_comp[k];\n int y_bot = r->ystep >= (r->vs >> 1);\n coutput[k] = r->resample(z->img_comp[k].linebuf, y_bot ? r->line1 : r->line0, y_bot ? r->line0 : r->line1,\n r->w_lores, r->hs);\n if (++r->ystep >= r->vs) {\n r->ystep = 0;\n r->line0 = r->line1;\n if (++r->ypos < z->img_comp[k].y)\n r->line1 += z->img_comp[k].w2;\n }\n }\n if (n >= 3) {\n stbi_uc * y = coutput[0];\n if (z->s->img_n == 3) {\n if (is_rgb) {\n for (i = 0; i < z->s->img_x; ++i) {\n out[0] = y[i];\n out[1] = coutput[1][i];\n out[2] = coutput[2][i];\n out[3] = 255;\n out += n;\n }\n } else {\n z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);\n }\n } else if (z->s->img_n == 4) {\n if (z->app14_color_transform == 0) { // CMYK\n for (i = 0; i < z->s->img_x; ++i) {\n stbi_uc m = coutput[3][i];\n out[0] = stbi__blinn_8x8(coutput[0][i], m);\n out[1] = stbi__blinn_8x8(coutput[1][i], m);\n out[2] = stbi__blinn_8x8(coutput[2][i], m);\n out[3] = 255;\n out += n;\n }\n } else if (z->app14_color_transform == 2) { // YCCK\n z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);\n for (i = 0; i < z->s->img_x; ++i) {\n stbi_uc m = coutput[3][i];\n out[0] = stbi__blinn_8x8(255 - out[0], m);\n out[1] = stbi__blinn_8x8(255 - out[1], m);\n out[2] = stbi__blinn_8x8(255 - out[2], m);\n out += n;\n }\n } else { // YCbCr + alpha? Ignore the fourth channel for now\n z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);\n }\n } else\n for (i = 0; i < z->s->img_x; ++i) {\n out[0] = out[1] = out[2] = y[i];\n out[3] = 255; // not used if n==3\n out += n;\n }\n } else {\n if (is_rgb) {\n if (n == 1)\n for (i = 0; i < z->s->img_x; ++i)\n *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]);\n else {\n for (i = 0; i < z->s->img_x; ++i, out += 2) {\n out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]);\n out[1] = 255;\n }\n }\n } else if (z->s->img_n == 4 && z->app14_color_transform == 0) {\n for (i = 0; i < z->s->img_x; ++i) {\n stbi_uc m = coutput[3][i];\n stbi_uc r = stbi__blinn_8x8(coutput[0][i], m);\n stbi_uc g = stbi__blinn_8x8(coutput[1][i], m);\n stbi_uc b = stbi__blinn_8x8(coutput[2][i], m);\n out[0] = stbi__compute_y(r, g, b);\n out[1] = 255;\n out += n;\n }\n } else if (z->s->img_n == 4 && z->app14_color_transform == 2) {\n for (i = 0; i < z->s->img_x; ++i) {\n out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]);\n out[1] = 255;\n out += n;\n }\n } else {\n stbi_uc * y = coutput[0];\n if (n == 1)\n for (i = 0; i < z->s->img_x; ++i)\n out[i] = y[i];\n else\n for (i = 0; i < z->s->img_x; ++i) {\n *out++ = y[i];\n *out++ = 255;\n }\n }\n }\n }\n stbi__cleanup_jpeg(z);\n *out_x = z->s->img_x;\n *out_y = z->s->img_y;\n if (comp)\n *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output\n return output;\n }\n}\n\nstatic void * stbi__jpeg_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n unsigned char * result;\n stbi__jpeg * j = (stbi__jpeg *)stbi__malloc(sizeof(stbi__jpeg));\n if (!j)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n memset(j, 0, sizeof(stbi__jpeg));\n STBI_NOTUSED(ri);\n j->s = s;\n stbi__setup_jpeg(j);\n result = load_jpeg_image(j, x, y, comp, req_comp);\n STBI_FREE(j);\n return result;\n}\n\nstatic int stbi__jpeg_test(stbi__context * s) {\n int r;\n stbi__jpeg * j = (stbi__jpeg *)stbi__malloc(sizeof(stbi__jpeg));\n if (!j)\n return stbi__err(\"outofmem\", \"Out of memory\");\n memset(j, 0, sizeof(stbi__jpeg));\n j->s = s;\n stbi__setup_jpeg(j);\n r = stbi__decode_jpeg_header(j, STBI__SCAN_type);\n stbi__rewind(s);\n STBI_FREE(j);\n return r;\n}\n\nstatic int stbi__jpeg_info_raw(stbi__jpeg * j, int * x, int * y, int * comp) {\n if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) {\n stbi__rewind(j->s);\n return 0;\n }\n if (x)\n *x = j->s->img_x;\n if (y)\n *y = j->s->img_y;\n if (comp)\n *comp = j->s->img_n >= 3 ? 3 : 1;\n return 1;\n}\n\nstatic int stbi__jpeg_info(stbi__context * s, int * x, int * y, int * comp) {\n int result;\n stbi__jpeg * j = (stbi__jpeg *)(stbi__malloc(sizeof(stbi__jpeg)));\n if (!j)\n return stbi__err(\"outofmem\", \"Out of memory\");\n memset(j, 0, sizeof(stbi__jpeg));\n j->s = s;\n result = stbi__jpeg_info_raw(j, x, y, comp);\n STBI_FREE(j);\n return result;\n}\n#endif\n\n// public domain zlib decode v0.2 Sean Barrett 2006-11-18\n// simple implementation\n// - all input must be provided in an upfront buffer\n// - all output is written to a single output buffer (can malloc/realloc)\n// performance\n// - fast huffman\n\n#ifndef STBI_NO_ZLIB\n\n// fast-way is faster to check than jpeg huffman, but slow way is slower\n#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables\n#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1)\n#define STBI__ZNSYMS 288 // number of symbols in literal/length alphabet\n\n// zlib-style huffman encoding\n// (jpegs packs from left, zlib from right, so can't share code)\ntypedef struct {\n stbi__uint16 fast[1 << STBI__ZFAST_BITS];\n stbi__uint16 firstcode[16];\n int maxcode[17];\n stbi__uint16 firstsymbol[16];\n stbi_uc size[STBI__ZNSYMS];\n stbi__uint16 value[STBI__ZNSYMS];\n} stbi__zhuffman;\n\nstbi_inline static int stbi__bitreverse16(int n) {\n n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1);\n n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2);\n n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4);\n n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8);\n return n;\n}\n\nstbi_inline static int stbi__bit_reverse(int v, int bits) {\n STBI_ASSERT(bits <= 16);\n // to bit reverse n bits, reverse 16 and shift\n // e.g. 11 bits, bit reverse and shift away 5\n return stbi__bitreverse16(v) >> (16 - bits);\n}\n\nstatic int stbi__zbuild_huffman(stbi__zhuffman * z, const stbi_uc * sizelist, int num) {\n int i, k = 0;\n int code, next_code[16], sizes[17];\n\n // DEFLATE spec for generating codes\n memset(sizes, 0, sizeof(sizes));\n memset(z->fast, 0, sizeof(z->fast));\n for (i = 0; i < num; ++i)\n ++sizes[sizelist[i]];\n sizes[0] = 0;\n for (i = 1; i < 16; ++i)\n if (sizes[i] > (1 << i))\n return stbi__err(\"bad sizes\", \"Corrupt PNG\");\n code = 0;\n for (i = 1; i < 16; ++i) {\n next_code[i] = code;\n z->firstcode[i] = (stbi__uint16)code;\n z->firstsymbol[i] = (stbi__uint16)k;\n code = (code + sizes[i]);\n if (sizes[i])\n if (code - 1 >= (1 << i))\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n z->maxcode[i] = code << (16 - i); // preshift for inner loop\n code <<= 1;\n k += sizes[i];\n }\n z->maxcode[16] = 0x10000; // sentinel\n for (i = 0; i < num; ++i) {\n int s = sizelist[i];\n if (s) {\n int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s];\n stbi__uint16 fastv = (stbi__uint16)((s << 9) | i);\n z->size[c] = (stbi_uc)s;\n z->value[c] = (stbi__uint16)i;\n if (s <= STBI__ZFAST_BITS) {\n int j = stbi__bit_reverse(next_code[s], s);\n while (j < (1 << STBI__ZFAST_BITS)) {\n z->fast[j] = fastv;\n j += (1 << s);\n }\n }\n ++next_code[s];\n }\n }\n return 1;\n}\n\n// zlib-from-memory implementation for PNG reading\n// because PNG allows splitting the zlib stream arbitrarily,\n// and it's annoying structurally to have PNG call ZLIB call PNG,\n// we require PNG read all the IDATs and combine them into a single\n// memory buffer\n\ntypedef struct {\n stbi_uc *zbuffer, *zbuffer_end;\n int num_bits;\n stbi__uint32 code_buffer;\n\n char * zout;\n char * zout_start;\n char * zout_end;\n int z_expandable;\n\n stbi__zhuffman z_length, z_distance;\n} stbi__zbuf;\n\nstbi_inline static int stbi__zeof(stbi__zbuf * z) { return (z->zbuffer >= z->zbuffer_end); }\n\nstbi_inline static stbi_uc stbi__zget8(stbi__zbuf * z) { return stbi__zeof(z) ? 0 : *z->zbuffer++; }\n\nstatic void stbi__fill_bits(stbi__zbuf * z) {\n do {\n if (z->code_buffer >= (1U << z->num_bits)) {\n z->zbuffer = z->zbuffer_end; /* treat this as EOF so we fail. */\n return;\n }\n z->code_buffer |= (unsigned int)stbi__zget8(z) << z->num_bits;\n z->num_bits += 8;\n } while (z->num_bits <= 24);\n}\n\nstbi_inline static unsigned int stbi__zreceive(stbi__zbuf * z, int n) {\n unsigned int k;\n if (z->num_bits < n)\n stbi__fill_bits(z);\n k = z->code_buffer & ((1 << n) - 1);\n z->code_buffer >>= n;\n z->num_bits -= n;\n return k;\n}\n\nstatic int stbi__zhuffman_decode_slowpath(stbi__zbuf * a, stbi__zhuffman * z) {\n int b, s, k;\n // not resolved by fast table, so compute it the slow way\n // use jpeg approach, which requires MSbits at top\n k = stbi__bit_reverse(a->code_buffer, 16);\n for (s = STBI__ZFAST_BITS + 1;; ++s)\n if (k < z->maxcode[s])\n break;\n if (s >= 16)\n return -1; // invalid code!\n // code size is s, so:\n b = (k >> (16 - s)) - z->firstcode[s] + z->firstsymbol[s];\n if (b >= STBI__ZNSYMS)\n return -1; // some data was corrupt somewhere!\n if (z->size[b] != s)\n return -1; // was originally an assert, but report failure instead.\n a->code_buffer >>= s;\n a->num_bits -= s;\n return z->value[b];\n}\n\nstbi_inline static int stbi__zhuffman_decode(stbi__zbuf * a, stbi__zhuffman * z) {\n int b, s;\n if (a->num_bits < 16) {\n if (stbi__zeof(a)) {\n return -1; /* report error for unexpected end of data. */\n }\n stbi__fill_bits(a);\n }\n b = z->fast[a->code_buffer & STBI__ZFAST_MASK];\n if (b) {\n s = b >> 9;\n a->code_buffer >>= s;\n a->num_bits -= s;\n return b & 511;\n }\n return stbi__zhuffman_decode_slowpath(a, z);\n}\n\nstatic int stbi__zexpand(stbi__zbuf * z, char * zout, int n) // need to make room for n bytes\n{\n char * q;\n unsigned int cur, limit, old_limit;\n z->zout = zout;\n if (!z->z_expandable)\n return stbi__err(\"output buffer limit\", \"Corrupt PNG\");\n cur = (unsigned int)(z->zout - z->zout_start);\n limit = old_limit = (unsigned)(z->zout_end - z->zout_start);\n if (UINT_MAX - cur < (unsigned)n)\n return stbi__err(\"outofmem\", \"Out of memory\");\n while (cur + n > limit) {\n if (limit > UINT_MAX / 2)\n return stbi__err(\"outofmem\", \"Out of memory\");\n limit *= 2;\n }\n q = (char *)STBI_REALLOC_SIZED(z->zout_start, old_limit, limit);\n STBI_NOTUSED(old_limit);\n if (q == NULL)\n return stbi__err(\"outofmem\", \"Out of memory\");\n z->zout_start = q;\n z->zout = q + cur;\n z->zout_end = q + limit;\n return 1;\n}\n\nstatic const int stbi__zlength_base[31] = {3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 19, 23, 27, 31,\n 35, 43, 51, 59, 67, 83, 99, 115, 131, 163, 195, 227, 258, 0, 0};\n\nstatic const int stbi__zlength_extra[31] = {0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2,\n 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0, 0};\n\nstatic const int stbi__zdist_base[32] = {1, 2, 3, 4, 5, 7, 9, 13, 17, 25, 33,\n 49, 65, 97, 129, 193, 257, 385, 513, 769, 1025, 1537,\n 2049, 3073, 4097, 6145, 8193, 12289, 16385, 24577, 0, 0};\n\nstatic const int stbi__zdist_extra[32] = {0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6,\n 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13};\n\nstatic int stbi__parse_huffman_block(stbi__zbuf * a) {\n char * zout = a->zout;\n for (;;) {\n int z = stbi__zhuffman_decode(a, &a->z_length);\n if (z < 256) {\n if (z < 0)\n return stbi__err(\"bad huffman code\", \"Corrupt PNG\"); // error in huffman codes\n if (zout >= a->zout_end) {\n if (!stbi__zexpand(a, zout, 1))\n return 0;\n zout = a->zout;\n }\n *zout++ = (char)z;\n } else {\n stbi_uc * p;\n int len, dist;\n if (z == 256) {\n a->zout = zout;\n return 1;\n }\n if (z >= 286)\n return stbi__err(\"bad huffman code\",\n \"Corrupt PNG\"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data\n z -= 257;\n len = stbi__zlength_base[z];\n if (stbi__zlength_extra[z])\n len += stbi__zreceive(a, stbi__zlength_extra[z]);\n z = stbi__zhuffman_decode(a, &a->z_distance);\n if (z < 0 || z >= 30)\n return stbi__err(\"bad huffman code\",\n \"Corrupt PNG\"); // per DEFLATE, distance codes 30 and 31 must not appear in compressed data\n dist = stbi__zdist_base[z];\n if (stbi__zdist_extra[z])\n dist += stbi__zreceive(a, stbi__zdist_extra[z]);\n if (zout - a->zout_start < dist)\n return stbi__err(\"bad dist\", \"Corrupt PNG\");\n if (zout + len > a->zout_end) {\n if (!stbi__zexpand(a, zout, len))\n return 0;\n zout = a->zout;\n }\n p = (stbi_uc *)(zout - dist);\n if (dist == 1) { // run of one byte; common in images.\n stbi_uc v = *p;\n if (len) {\n do\n *zout++ = v;\n while (--len);\n }\n } else {\n if (len) {\n do\n *zout++ = *p++;\n while (--len);\n }\n }\n }\n }\n}\n\nstatic int stbi__compute_huffman_codes(stbi__zbuf * a) {\n static const stbi_uc length_dezigzag[19] = {16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15};\n stbi__zhuffman z_codelength;\n stbi_uc lencodes[286 + 32 + 137]; // padding for maximum single op\n stbi_uc codelength_sizes[19];\n int i, n;\n\n int hlit = stbi__zreceive(a, 5) + 257;\n int hdist = stbi__zreceive(a, 5) + 1;\n int hclen = stbi__zreceive(a, 4) + 4;\n int ntot = hlit + hdist;\n\n memset(codelength_sizes, 0, sizeof(codelength_sizes));\n for (i = 0; i < hclen; ++i) {\n int s = stbi__zreceive(a, 3);\n codelength_sizes[length_dezigzag[i]] = (stbi_uc)s;\n }\n if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19))\n return 0;\n\n n = 0;\n while (n < ntot) {\n int c = stbi__zhuffman_decode(a, &z_codelength);\n if (c < 0 || c >= 19)\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n if (c < 16)\n lencodes[n++] = (stbi_uc)c;\n else {\n stbi_uc fill = 0;\n if (c == 16) {\n c = stbi__zreceive(a, 2) + 3;\n if (n == 0)\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n fill = lencodes[n - 1];\n } else if (c == 17) {\n c = stbi__zreceive(a, 3) + 3;\n } else if (c == 18) {\n c = stbi__zreceive(a, 7) + 11;\n } else {\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n }\n if (ntot - n < c)\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n memset(lencodes + n, fill, c);\n n += c;\n }\n }\n if (n != ntot)\n return stbi__err(\"bad codelengths\", \"Corrupt PNG\");\n if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit))\n return 0;\n if (!stbi__zbuild_huffman(&a->z_distance, lencodes + hlit, hdist))\n return 0;\n return 1;\n}\n\nstatic int stbi__parse_uncompressed_block(stbi__zbuf * a) {\n stbi_uc header[4];\n int len, nlen, k;\n if (a->num_bits & 7)\n stbi__zreceive(a, a->num_bits & 7); // discard\n // drain the bit-packed data into header\n k = 0;\n while (a->num_bits > 0) {\n header[k++] = (stbi_uc)(a->code_buffer & 255); // suppress MSVC run-time check\n a->code_buffer >>= 8;\n a->num_bits -= 8;\n }\n if (a->num_bits < 0)\n return stbi__err(\"zlib corrupt\", \"Corrupt PNG\");\n // now fill header the normal way\n while (k < 4)\n header[k++] = stbi__zget8(a);\n len = header[1] * 256 + header[0];\n nlen = header[3] * 256 + header[2];\n if (nlen != (len ^ 0xffff))\n return stbi__err(\"zlib corrupt\", \"Corrupt PNG\");\n if (a->zbuffer + len > a->zbuffer_end)\n return stbi__err(\"read past buffer\", \"Corrupt PNG\");\n if (a->zout + len > a->zout_end)\n if (!stbi__zexpand(a, a->zout, len))\n return 0;\n memcpy(a->zout, a->zbuffer, len);\n a->zbuffer += len;\n a->zout += len;\n return 1;\n}\n\nstatic int stbi__parse_zlib_header(stbi__zbuf * a) {\n int cmf = stbi__zget8(a);\n int cm = cmf & 15;\n /* int cinfo = cmf >> 4; */\n int flg = stbi__zget8(a);\n if (stbi__zeof(a))\n return stbi__err(\"bad zlib header\", \"Corrupt PNG\"); // zlib spec\n if ((cmf * 256 + flg) % 31 != 0)\n return stbi__err(\"bad zlib header\", \"Corrupt PNG\"); // zlib spec\n if (flg & 32)\n return stbi__err(\"no preset dict\", \"Corrupt PNG\"); // preset dictionary not allowed in png\n if (cm != 8)\n return stbi__err(\"bad compression\", \"Corrupt PNG\"); // DEFLATE required for png\n // window = 1 << (8 + cinfo)... but who cares, we fully buffer output\n return 1;\n}\n\nstatic const stbi_uc stbi__zdefault_length[STBI__ZNSYMS] = {\n 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8};\nstatic const stbi_uc stbi__zdefault_distance[32] = {5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5};\n/*\nInit algorithm:\n{\n int i; // use <= to match clearly with spec\n for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8;\n for ( ; i <= 255; ++i) stbi__zdefault_length[i] = 9;\n for ( ; i <= 279; ++i) stbi__zdefault_length[i] = 7;\n for ( ; i <= 287; ++i) stbi__zdefault_length[i] = 8;\n\n for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5;\n}\n*/\n\nstatic int stbi__parse_zlib(stbi__zbuf * a, int parse_header) {\n int final, type;\n if (parse_header)\n if (!stbi__parse_zlib_header(a))\n return 0;\n a->num_bits = 0;\n a->code_buffer = 0;\n do {\n final = stbi__zreceive(a, 1);\n type = stbi__zreceive(a, 2);\n if (type == 0) {\n if (!stbi__parse_uncompressed_block(a))\n return 0;\n } else if (type == 3) {\n return 0;\n } else {\n if (type == 1) {\n // use fixed code lengths\n if (!stbi__zbuild_huffman(&a->z_length, stbi__zdefault_length, STBI__ZNSYMS))\n return 0;\n if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32))\n return 0;\n } else {\n if (!stbi__compute_huffman_codes(a))\n return 0;\n }\n if (!stbi__parse_huffman_block(a))\n return 0;\n }\n } while (!final);\n return 1;\n}\n\nstatic int stbi__do_zlib(stbi__zbuf * a, char * obuf, int olen, int exp, int parse_header) {\n a->zout_start = obuf;\n a->zout = obuf;\n a->zout_end = obuf + olen;\n a->z_expandable = exp;\n\n return stbi__parse_zlib(a, parse_header);\n}\n\nSTBIDEF char * stbi_zlib_decode_malloc_guesssize(const char * buffer, int len, int initial_size, int * outlen) {\n stbi__zbuf a;\n char * p = (char *)stbi__malloc(initial_size);\n if (p == NULL)\n return NULL;\n a.zbuffer = (stbi_uc *)buffer;\n a.zbuffer_end = (stbi_uc *)buffer + len;\n if (stbi__do_zlib(&a, p, initial_size, 1, 1)) {\n if (outlen)\n *outlen = (int)(a.zout - a.zout_start);\n return a.zout_start;\n } else {\n STBI_FREE(a.zout_start);\n return NULL;\n }\n}\n\nSTBIDEF char * stbi_zlib_decode_malloc(char const * buffer, int len, int * outlen) {\n return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen);\n}\n\nSTBIDEF char * stbi_zlib_decode_malloc_guesssize_headerflag(const char * buffer, int len, int initial_size, int * outlen,\n int parse_header) {\n stbi__zbuf a;\n char * p = (char *)stbi__malloc(initial_size);\n if (p == NULL)\n return NULL;\n a.zbuffer = (stbi_uc *)buffer;\n a.zbuffer_end = (stbi_uc *)buffer + len;\n if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) {\n if (outlen)\n *outlen = (int)(a.zout - a.zout_start);\n return a.zout_start;\n } else {\n STBI_FREE(a.zout_start);\n return NULL;\n }\n}\n\nSTBIDEF int stbi_zlib_decode_buffer(char * obuffer, int olen, char const * ibuffer, int ilen) {\n stbi__zbuf a;\n a.zbuffer = (stbi_uc *)ibuffer;\n a.zbuffer_end = (stbi_uc *)ibuffer + ilen;\n if (stbi__do_zlib(&a, obuffer, olen, 0, 1))\n return (int)(a.zout - a.zout_start);\n else\n return -1;\n}\n\nSTBIDEF char * stbi_zlib_decode_noheader_malloc(char const * buffer, int len, int * outlen) {\n stbi__zbuf a;\n char * p = (char *)stbi__malloc(16384);\n if (p == NULL)\n return NULL;\n a.zbuffer = (stbi_uc *)buffer;\n a.zbuffer_end = (stbi_uc *)buffer + len;\n if (stbi__do_zlib(&a, p, 16384, 1, 0)) {\n if (outlen)\n *outlen = (int)(a.zout - a.zout_start);\n return a.zout_start;\n } else {\n STBI_FREE(a.zout_start);\n return NULL;\n }\n}\n\nSTBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const char * ibuffer, int ilen) {\n stbi__zbuf a;\n a.zbuffer = (stbi_uc *)ibuffer;\n a.zbuffer_end = (stbi_uc *)ibuffer + ilen;\n if (stbi__do_zlib(&a, obuffer, olen, 0, 0))\n return (int)(a.zout - a.zout_start);\n else\n return -1;\n}\n#endif\n\n// public domain \"baseline\" PNG decoder v0.10 Sean Barrett 2006-11-18\n// simple implementation\n// - only 8-bit samples\n// - no CRC checking\n// - allocates lots of intermediate memory\n// - avoids problem of streaming data between subsystems\n// - avoids explicit window management\n// performance\n// - uses stb_zlib, a PD zlib implementation with fast huffman decoding\n\n#ifndef STBI_NO_PNG\ntypedef struct {\n stbi__uint32 length;\n stbi__uint32 type;\n} stbi__pngchunk;\n\nstatic stbi__pngchunk stbi__get_chunk_header(stbi__context * s) {\n stbi__pngchunk c;\n c.length = stbi__get32be(s);\n c.type = stbi__get32be(s);\n return c;\n}\n\nstatic int stbi__check_png_header(stbi__context * s) {\n static const stbi_uc png_sig[8] = {137, 80, 78, 71, 13, 10, 26, 10};\n int i;\n for (i = 0; i < 8; ++i)\n if (stbi__get8(s) != png_sig[i])\n return stbi__err(\"bad png sig\", \"Not a PNG\");\n return 1;\n}\n\ntypedef struct {\n stbi__context * s;\n stbi_uc *idata, *expanded, *out;\n int depth;\n} stbi__png;\n\nenum {\n STBI__F_none = 0,\n STBI__F_sub = 1,\n STBI__F_up = 2,\n STBI__F_avg = 3,\n STBI__F_paeth = 4,\n // synthetic filters used for first scanline to avoid needing a dummy row of 0s\n STBI__F_avg_first,\n STBI__F_paeth_first\n};\n\nstatic stbi_uc first_row_filter[5] = {STBI__F_none, STBI__F_sub, STBI__F_none, STBI__F_avg_first, STBI__F_paeth_first};\n\nstatic int stbi__paeth(int a, int b, int c) {\n int p = a + b - c;\n int pa = abs(p - a);\n int pb = abs(p - b);\n int pc = abs(p - c);\n if (pa <= pb && pa <= pc)\n return a;\n if (pb <= pc)\n return b;\n return c;\n}\n\nstatic const stbi_uc stbi__depth_scale_table[9] = {0, 0xff, 0x55, 0, 0x11, 0, 0, 0, 0x01};\n\n// create the png data from post-deflated data\nstatic int stbi__create_png_image_raw(stbi__png * a, stbi_uc * raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x,\n stbi__uint32 y, int depth, int color) {\n int bytes = (depth == 16 ? 2 : 1);\n stbi__context * s = a->s;\n stbi__uint32 i, j, stride = x * out_n * bytes;\n stbi__uint32 img_len, img_width_bytes;\n int k;\n int img_n = s->img_n; // copy it into a local for later\n\n int output_bytes = out_n * bytes;\n int filter_bytes = img_n * bytes;\n int width = x;\n\n STBI_ASSERT(out_n == s->img_n || out_n == s->img_n + 1);\n a->out = (stbi_uc *)stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into\n if (!a->out)\n return stbi__err(\"outofmem\", \"Out of memory\");\n\n if (!stbi__mad3sizes_valid(img_n, x, depth, 7))\n return stbi__err(\"too large\", \"Corrupt PNG\");\n img_width_bytes = (((img_n * x * depth) + 7) >> 3);\n img_len = (img_width_bytes + 1) * y;\n\n // we used to check for exact match between raw_len and img_len on non-interlaced PNGs,\n // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros),\n // so just check for raw_len < img_len always.\n if (raw_len < img_len)\n return stbi__err(\"not enough pixels\", \"Corrupt PNG\");\n\n for (j = 0; j < y; ++j) {\n stbi_uc * cur = a->out + stride * j;\n stbi_uc * prior;\n int filter = *raw++;\n\n if (filter > 4)\n return stbi__err(\"invalid filter\", \"Corrupt PNG\");\n\n if (depth < 8) {\n if (img_width_bytes > x)\n return stbi__err(\"invalid width\", \"Corrupt PNG\");\n cur += x * out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place\n filter_bytes = 1;\n width = img_width_bytes;\n }\n prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above\n\n // if first row, use special filter that doesn't sample previous row\n if (j == 0)\n filter = first_row_filter[filter];\n\n // handle first byte explicitly\n for (k = 0; k < filter_bytes; ++k) {\n switch (filter) {\n case STBI__F_none:\n cur[k] = raw[k];\n break;\n case STBI__F_sub:\n cur[k] = raw[k];\n break;\n case STBI__F_up:\n cur[k] = STBI__BYTECAST(raw[k] + prior[k]);\n break;\n case STBI__F_avg:\n cur[k] = STBI__BYTECAST(raw[k] + (prior[k] >> 1));\n break;\n case STBI__F_paeth:\n cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0, prior[k], 0));\n break;\n case STBI__F_avg_first:\n cur[k] = raw[k];\n break;\n case STBI__F_paeth_first:\n cur[k] = raw[k];\n break;\n }\n }\n\n if (depth == 8) {\n if (img_n != out_n)\n cur[img_n] = 255; // first pixel\n raw += img_n;\n cur += out_n;\n prior += out_n;\n } else if (depth == 16) {\n if (img_n != out_n) {\n cur[filter_bytes] = 255; // first pixel top byte\n cur[filter_bytes + 1] = 255; // first pixel bottom byte\n }\n raw += filter_bytes;\n cur += output_bytes;\n prior += output_bytes;\n } else {\n raw += 1;\n cur += 1;\n prior += 1;\n }\n\n // this is a little gross, so that we don't switch per-pixel or per-component\n if (depth < 8 || img_n == out_n) {\n int nk = (width - 1) * filter_bytes;\n#define STBI__CASE(f) \\\n case f: \\\n for (k = 0; k < nk; ++k)\n switch (filter) {\n // \"none\" filter turns into a memcpy here; make that explicit.\n case STBI__F_none:\n memcpy(cur, raw, nk);\n break;\n STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - filter_bytes]); }\n break;\n STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); }\n break;\n STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - filter_bytes]) >> 1)); }\n break;\n STBI__CASE(STBI__F_paeth) {\n cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], prior[k], prior[k - filter_bytes]));\n }\n break;\n STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - filter_bytes] >> 1)); }\n break;\n STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], 0, 0)); }\n break;\n }\n#undef STBI__CASE\n raw += nk;\n } else {\n STBI_ASSERT(img_n + 1 == out_n);\n#define STBI__CASE(f) \\\n case f: \\\n for (i = x - 1; i >= 1; --i, cur[filter_bytes] = 255, raw += filter_bytes, cur += output_bytes, prior += output_bytes) \\\n for (k = 0; k < filter_bytes; ++k)\n switch (filter) {\n STBI__CASE(STBI__F_none) { cur[k] = raw[k]; }\n break;\n STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - output_bytes]); }\n break;\n STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); }\n break;\n STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - output_bytes]) >> 1)); }\n break;\n STBI__CASE(STBI__F_paeth) {\n cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], prior[k], prior[k - output_bytes]));\n }\n break;\n STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - output_bytes] >> 1)); }\n break;\n STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], 0, 0)); }\n break;\n }\n#undef STBI__CASE\n\n // the loop above sets the high byte of the pixels' alpha, but for\n // 16 bit png files we also need the low byte set. we'll do that here.\n if (depth == 16) {\n cur = a->out + stride * j; // start at the beginning of the row again\n for (i = 0; i < x; ++i, cur += output_bytes) {\n cur[filter_bytes + 1] = 255;\n }\n }\n }\n }\n\n // we make a separate pass to expand bits to pixels; for performance,\n // this could run two scanlines behind the above code, so it won't\n // intefere with filtering but will still be in the cache.\n if (depth < 8) {\n for (j = 0; j < y; ++j) {\n stbi_uc * cur = a->out + stride * j;\n stbi_uc * in = a->out + stride * j + x * out_n - img_width_bytes;\n // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for\n // 1/2/4-bit png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that\n // will be skipped in the later loop\n stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range\n\n // note that the final byte might overshoot and write more data than desired.\n // we can allocate enough data that this never writes out of memory, but it\n // could also overwrite the next scanline. can it overwrite non-empty data\n // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel.\n // so we need to explicitly clamp the final ones\n\n if (depth == 4) {\n for (k = x * img_n; k >= 2; k -= 2, ++in) {\n *cur++ = scale * ((*in >> 4));\n *cur++ = scale * ((*in) & 0x0f);\n }\n if (k > 0)\n *cur++ = scale * ((*in >> 4));\n } else if (depth == 2) {\n for (k = x * img_n; k >= 4; k -= 4, ++in) {\n *cur++ = scale * ((*in >> 6));\n *cur++ = scale * ((*in >> 4) & 0x03);\n *cur++ = scale * ((*in >> 2) & 0x03);\n *cur++ = scale * ((*in) & 0x03);\n }\n if (k > 0)\n *cur++ = scale * ((*in >> 6));\n if (k > 1)\n *cur++ = scale * ((*in >> 4) & 0x03);\n if (k > 2)\n *cur++ = scale * ((*in >> 2) & 0x03);\n } else if (depth == 1) {\n for (k = x * img_n; k >= 8; k -= 8, ++in) {\n *cur++ = scale * ((*in >> 7));\n *cur++ = scale * ((*in >> 6) & 0x01);\n *cur++ = scale * ((*in >> 5) & 0x01);\n *cur++ = scale * ((*in >> 4) & 0x01);\n *cur++ = scale * ((*in >> 3) & 0x01);\n *cur++ = scale * ((*in >> 2) & 0x01);\n *cur++ = scale * ((*in >> 1) & 0x01);\n *cur++ = scale * ((*in) & 0x01);\n }\n if (k > 0)\n *cur++ = scale * ((*in >> 7));\n if (k > 1)\n *cur++ = scale * ((*in >> 6) & 0x01);\n if (k > 2)\n *cur++ = scale * ((*in >> 5) & 0x01);\n if (k > 3)\n *cur++ = scale * ((*in >> 4) & 0x01);\n if (k > 4)\n *cur++ = scale * ((*in >> 3) & 0x01);\n if (k > 5)\n *cur++ = scale * ((*in >> 2) & 0x01);\n if (k > 6)\n *cur++ = scale * ((*in >> 1) & 0x01);\n }\n if (img_n != out_n) {\n int q;\n // insert alpha = 255\n cur = a->out + stride * j;\n if (img_n == 1) {\n for (q = x - 1; q >= 0; --q) {\n cur[q * 2 + 1] = 255;\n cur[q * 2 + 0] = cur[q];\n }\n } else {\n STBI_ASSERT(img_n == 3);\n for (q = x - 1; q >= 0; --q) {\n cur[q * 4 + 3] = 255;\n cur[q * 4 + 2] = cur[q * 3 + 2];\n cur[q * 4 + 1] = cur[q * 3 + 1];\n cur[q * 4 + 0] = cur[q * 3 + 0];\n }\n }\n }\n }\n } else if (depth == 16) {\n // force the image data from big-endian to platform-native.\n // this is done in a separate pass due to the decoding relying\n // on the data being untouched, but could probably be done\n // per-line during decode if care is taken.\n stbi_uc * cur = a->out;\n stbi__uint16 * cur16 = (stbi__uint16 *)cur;\n\n for (i = 0; i < x * y * out_n; ++i, cur16++, cur += 2) {\n *cur16 = (cur[0] << 8) | cur[1];\n }\n }\n\n return 1;\n}\n\nstatic int stbi__create_png_image(stbi__png * a, stbi_uc * image_data, stbi__uint32 image_data_len, int out_n, int depth,\n int color, int interlaced) {\n int bytes = (depth == 16 ? 2 : 1);\n int out_bytes = out_n * bytes;\n stbi_uc * final;\n int p;\n if (!interlaced)\n return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color);\n\n // de-interlacing\n final = (stbi_uc *)stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0);\n if (!final)\n return stbi__err(\"outofmem\", \"Out of memory\");\n for (p = 0; p < 7; ++p) {\n int xorig[] = {0, 4, 0, 2, 0, 1, 0};\n int yorig[] = {0, 0, 4, 0, 2, 0, 1};\n int xspc[] = {8, 8, 4, 4, 2, 2, 1};\n int yspc[] = {8, 8, 8, 4, 4, 2, 2};\n int i, j, x, y;\n // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1\n x = (a->s->img_x - xorig[p] + xspc[p] - 1) / xspc[p];\n y = (a->s->img_y - yorig[p] + yspc[p] - 1) / yspc[p];\n if (x && y) {\n stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y;\n if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) {\n STBI_FREE(final);\n return 0;\n }\n for (j = 0; j < y; ++j) {\n for (i = 0; i < x; ++i) {\n int out_y = j * yspc[p] + yorig[p];\n int out_x = i * xspc[p] + xorig[p];\n memcpy(final + out_y * a->s->img_x * out_bytes + out_x * out_bytes, a->out + (j * x + i) * out_bytes,\n out_bytes);\n }\n }\n STBI_FREE(a->out);\n image_data += img_len;\n image_data_len -= img_len;\n }\n }\n a->out = final;\n\n return 1;\n}\n\nstatic int stbi__compute_transparency(stbi__png * z, stbi_uc tc[3], int out_n) {\n stbi__context * s = z->s;\n stbi__uint32 i, pixel_count = s->img_x * s->img_y;\n stbi_uc * p = z->out;\n\n // compute color-based transparency, assuming we've\n // already got 255 as the alpha value in the output\n STBI_ASSERT(out_n == 2 || out_n == 4);\n\n if (out_n == 2) {\n for (i = 0; i < pixel_count; ++i) {\n p[1] = (p[0] == tc[0] ? 0 : 255);\n p += 2;\n }\n } else {\n for (i = 0; i < pixel_count; ++i) {\n if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2])\n p[3] = 0;\n p += 4;\n }\n }\n return 1;\n}\n\nstatic int stbi__compute_transparency16(stbi__png * z, stbi__uint16 tc[3], int out_n) {\n stbi__context * s = z->s;\n stbi__uint32 i, pixel_count = s->img_x * s->img_y;\n stbi__uint16 * p = (stbi__uint16 *)z->out;\n\n // compute color-based transparency, assuming we've\n // already got 65535 as the alpha value in the output\n STBI_ASSERT(out_n == 2 || out_n == 4);\n\n if (out_n == 2) {\n for (i = 0; i < pixel_count; ++i) {\n p[1] = (p[0] == tc[0] ? 0 : 65535);\n p += 2;\n }\n } else {\n for (i = 0; i < pixel_count; ++i) {\n if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2])\n p[3] = 0;\n p += 4;\n }\n }\n return 1;\n}\n\nstatic int stbi__expand_png_palette(stbi__png * a, stbi_uc * palette, int len, int pal_img_n) {\n stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y;\n stbi_uc *p, *temp_out, *orig = a->out;\n\n p = (stbi_uc *)stbi__malloc_mad2(pixel_count, pal_img_n, 0);\n if (p == NULL)\n return stbi__err(\"outofmem\", \"Out of memory\");\n\n // between here and free(out) below, exitting would leak\n temp_out = p;\n\n if (pal_img_n == 3) {\n for (i = 0; i < pixel_count; ++i) {\n int n = orig[i] * 4;\n p[0] = palette[n];\n p[1] = palette[n + 1];\n p[2] = palette[n + 2];\n p += 3;\n }\n } else {\n for (i = 0; i < pixel_count; ++i) {\n int n = orig[i] * 4;\n p[0] = palette[n];\n p[1] = palette[n + 1];\n p[2] = palette[n + 2];\n p[3] = palette[n + 3];\n p += 4;\n }\n }\n STBI_FREE(a->out);\n a->out = temp_out;\n\n STBI_NOTUSED(len);\n\n return 1;\n}\n\nstatic int stbi__unpremultiply_on_load_global = 0;\nstatic int stbi__de_iphone_flag_global = 0;\n\nSTBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) {\n stbi__unpremultiply_on_load_global = flag_true_if_should_unpremultiply;\n}\n\nSTBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) {\n stbi__de_iphone_flag_global = flag_true_if_should_convert;\n}\n\n#ifndef STBI_THREAD_LOCAL\n#define stbi__unpremultiply_on_load stbi__unpremultiply_on_load_global\n#define stbi__de_iphone_flag stbi__de_iphone_flag_global\n#else\nstatic STBI_THREAD_LOCAL int stbi__unpremultiply_on_load_local, stbi__unpremultiply_on_load_set;\nstatic STBI_THREAD_LOCAL int stbi__de_iphone_flag_local, stbi__de_iphone_flag_set;\n\nSTBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply) {\n stbi__unpremultiply_on_load_local = flag_true_if_should_unpremultiply;\n stbi__unpremultiply_on_load_set = 1;\n}\n\nSTBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert) {\n stbi__de_iphone_flag_local = flag_true_if_should_convert;\n stbi__de_iphone_flag_set = 1;\n}\n\n#define stbi__unpremultiply_on_load \\\n (stbi__unpremultiply_on_load_set ? stbi__unpremultiply_on_load_local : stbi__unpremultiply_on_load_global)\n#define stbi__de_iphone_flag (stbi__de_iphone_flag_set ? stbi__de_iphone_flag_local : stbi__de_iphone_flag_global)\n#endif // STBI_THREAD_LOCAL\n\nstatic void stbi__de_iphone(stbi__png * z) {\n stbi__context * s = z->s;\n stbi__uint32 i, pixel_count = s->img_x * s->img_y;\n stbi_uc * p = z->out;\n\n if (s->img_out_n == 3) { // convert bgr to rgb\n for (i = 0; i < pixel_count; ++i) {\n stbi_uc t = p[0];\n p[0] = p[2];\n p[2] = t;\n p += 3;\n }\n } else {\n STBI_ASSERT(s->img_out_n == 4);\n if (stbi__unpremultiply_on_load) {\n // convert bgr to rgb and unpremultiply\n for (i = 0; i < pixel_count; ++i) {\n stbi_uc a = p[3];\n stbi_uc t = p[0];\n if (a) {\n stbi_uc half = a / 2;\n p[0] = (p[2] * 255 + half) / a;\n p[1] = (p[1] * 255 + half) / a;\n p[2] = (t * 255 + half) / a;\n } else {\n p[0] = p[2];\n p[2] = t;\n }\n p += 4;\n }\n } else {\n // convert bgr to rgb\n for (i = 0; i < pixel_count; ++i) {\n stbi_uc t = p[0];\n p[0] = p[2];\n p[2] = t;\n p += 4;\n }\n }\n }\n}\n\n#define STBI__PNG_TYPE(a, b, c, d) (((unsigned)(a) << 24) + ((unsigned)(b) << 16) + ((unsigned)(c) << 8) + (unsigned)(d))\n\nstatic int stbi__parse_png_file(stbi__png * z, int scan, int req_comp) {\n stbi_uc palette[1024], pal_img_n = 0;\n stbi_uc has_trans = 0, tc[3] = {0};\n stbi__uint16 tc16[3];\n stbi__uint32 ioff = 0, idata_limit = 0, i, pal_len = 0;\n int first = 1, k, interlace = 0, color = 0, is_iphone = 0;\n stbi__context * s = z->s;\n\n z->expanded = NULL;\n z->idata = NULL;\n z->out = NULL;\n\n if (!stbi__check_png_header(s))\n return 0;\n\n if (scan == STBI__SCAN_type)\n return 1;\n\n for (;;) {\n stbi__pngchunk c = stbi__get_chunk_header(s);\n switch (c.type) {\n case STBI__PNG_TYPE('C', 'g', 'B', 'I'):\n is_iphone = 1;\n stbi__skip(s, c.length);\n break;\n case STBI__PNG_TYPE('I', 'H', 'D', 'R'): {\n int comp, filter;\n if (!first)\n return stbi__err(\"multiple IHDR\", \"Corrupt PNG\");\n first = 0;\n if (c.length != 13)\n return stbi__err(\"bad IHDR len\", \"Corrupt PNG\");\n s->img_x = stbi__get32be(s);\n s->img_y = stbi__get32be(s);\n if (s->img_y > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n if (s->img_x > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n z->depth = stbi__get8(s);\n if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16)\n return stbi__err(\"1/2/4/8/16-bit only\", \"PNG not supported: 1/2/4/8/16-bit only\");\n color = stbi__get8(s);\n if (color > 6)\n return stbi__err(\"bad ctype\", \"Corrupt PNG\");\n if (color == 3 && z->depth == 16)\n return stbi__err(\"bad ctype\", \"Corrupt PNG\");\n if (color == 3)\n pal_img_n = 3;\n else if (color & 1)\n return stbi__err(\"bad ctype\", \"Corrupt PNG\");\n comp = stbi__get8(s);\n if (comp)\n return stbi__err(\"bad comp method\", \"Corrupt PNG\");\n filter = stbi__get8(s);\n if (filter)\n return stbi__err(\"bad filter method\", \"Corrupt PNG\");\n interlace = stbi__get8(s);\n if (interlace > 1)\n return stbi__err(\"bad interlace method\", \"Corrupt PNG\");\n if (!s->img_x || !s->img_y)\n return stbi__err(\"0-pixel image\", \"Corrupt PNG\");\n if (!pal_img_n) {\n s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0);\n if ((1 << 30) / s->img_x / s->img_n < s->img_y)\n return stbi__err(\"too large\", \"Image too large to decode\");\n } else {\n // if paletted, then pal_n is our final components, and\n // img_n is # components to decompress/filter.\n s->img_n = 1;\n if ((1 << 30) / s->img_x / 4 < s->img_y)\n return stbi__err(\"too large\", \"Corrupt PNG\");\n }\n // even with SCAN_header, have to scan to see if we have a tRNS\n break;\n }\n\n case STBI__PNG_TYPE('P', 'L', 'T', 'E'): {\n if (first)\n return stbi__err(\"first not IHDR\", \"Corrupt PNG\");\n if (c.length > 256 * 3)\n return stbi__err(\"invalid PLTE\", \"Corrupt PNG\");\n pal_len = c.length / 3;\n if (pal_len * 3 != c.length)\n return stbi__err(\"invalid PLTE\", \"Corrupt PNG\");\n for (i = 0; i < pal_len; ++i) {\n palette[i * 4 + 0] = stbi__get8(s);\n palette[i * 4 + 1] = stbi__get8(s);\n palette[i * 4 + 2] = stbi__get8(s);\n palette[i * 4 + 3] = 255;\n }\n break;\n }\n\n case STBI__PNG_TYPE('t', 'R', 'N', 'S'): {\n if (first)\n return stbi__err(\"first not IHDR\", \"Corrupt PNG\");\n if (z->idata)\n return stbi__err(\"tRNS after IDAT\", \"Corrupt PNG\");\n if (pal_img_n) {\n if (scan == STBI__SCAN_header) {\n s->img_n = 4;\n return 1;\n }\n if (pal_len == 0)\n return stbi__err(\"tRNS before PLTE\", \"Corrupt PNG\");\n if (c.length > pal_len)\n return stbi__err(\"bad tRNS len\", \"Corrupt PNG\");\n pal_img_n = 4;\n for (i = 0; i < c.length; ++i)\n palette[i * 4 + 3] = stbi__get8(s);\n } else {\n if (!(s->img_n & 1))\n return stbi__err(\"tRNS with alpha\", \"Corrupt PNG\");\n if (c.length != (stbi__uint32)s->img_n * 2)\n return stbi__err(\"bad tRNS len\", \"Corrupt PNG\");\n has_trans = 1;\n // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now.\n if (scan == STBI__SCAN_header) {\n ++s->img_n;\n return 1;\n }\n if (z->depth == 16) {\n for (k = 0; k < s->img_n; ++k)\n tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is\n } else {\n for (k = 0; k < s->img_n; ++k)\n tc[k] = (stbi_uc)(stbi__get16be(s) & 255) *\n stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger\n }\n }\n break;\n }\n\n case STBI__PNG_TYPE('I', 'D', 'A', 'T'): {\n if (first)\n return stbi__err(\"first not IHDR\", \"Corrupt PNG\");\n if (pal_img_n && !pal_len)\n return stbi__err(\"no PLTE\", \"Corrupt PNG\");\n if (scan == STBI__SCAN_header) {\n // header scan definitely stops at first IDAT\n if (pal_img_n)\n s->img_n = pal_img_n;\n return 1;\n }\n if (c.length > (1u << 30))\n return stbi__err(\"IDAT size limit\", \"IDAT section larger than 2^30 bytes\");\n if ((int)(ioff + c.length) < (int)ioff)\n return 0;\n if (ioff + c.length > idata_limit) {\n stbi__uint32 idata_limit_old = idata_limit;\n stbi_uc * p;\n if (idata_limit == 0)\n idata_limit = c.length > 4096 ? c.length : 4096;\n while (ioff + c.length > idata_limit)\n idata_limit *= 2;\n STBI_NOTUSED(idata_limit_old);\n p = (stbi_uc *)STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit);\n if (p == NULL)\n return stbi__err(\"outofmem\", \"Out of memory\");\n z->idata = p;\n }\n if (!stbi__getn(s, z->idata + ioff, c.length))\n return stbi__err(\"outofdata\", \"Corrupt PNG\");\n ioff += c.length;\n break;\n }\n\n case STBI__PNG_TYPE('I', 'E', 'N', 'D'): {\n stbi__uint32 raw_len, bpl;\n if (first)\n return stbi__err(\"first not IHDR\", \"Corrupt PNG\");\n if (scan != STBI__SCAN_load)\n return 1;\n if (z->idata == NULL)\n return stbi__err(\"no IDAT\", \"Corrupt PNG\");\n // initial guess for decoded data size to avoid unnecessary reallocs\n bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component\n raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */;\n z->expanded = (stbi_uc *)stbi_zlib_decode_malloc_guesssize_headerflag((char *)z->idata, ioff, raw_len,\n (int *)&raw_len, !is_iphone);\n if (z->expanded == NULL)\n return 0; // zlib should set error\n STBI_FREE(z->idata);\n z->idata = NULL;\n if ((req_comp == s->img_n + 1 && req_comp != 3 && !pal_img_n) || has_trans)\n s->img_out_n = s->img_n + 1;\n else\n s->img_out_n = s->img_n;\n if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace))\n return 0;\n if (has_trans) {\n if (z->depth == 16) {\n if (!stbi__compute_transparency16(z, tc16, s->img_out_n))\n return 0;\n } else {\n if (!stbi__compute_transparency(z, tc, s->img_out_n))\n return 0;\n }\n }\n if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2)\n stbi__de_iphone(z);\n if (pal_img_n) {\n // pal_img_n == 3 or 4\n s->img_n = pal_img_n; // record the actual colors we had\n s->img_out_n = pal_img_n;\n if (req_comp >= 3)\n s->img_out_n = req_comp;\n if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n))\n return 0;\n } else if (has_trans) {\n // non-paletted image with tRNS -> source image has (constant) alpha\n ++s->img_n;\n }\n STBI_FREE(z->expanded);\n z->expanded = NULL;\n // end of PNG chunk, read and skip CRC\n stbi__get32be(s);\n return 1;\n }\n\n default:\n // if critical, fail\n if (first)\n return stbi__err(\"first not IHDR\", \"Corrupt PNG\");\n if ((c.type & (1 << 29)) == 0) {\n#ifndef STBI_NO_FAILURE_STRINGS\n // not threadsafe\n static char invalid_chunk[] = \"XXXX PNG chunk not known\";\n invalid_chunk[0] = STBI__BYTECAST(c.type >> 24);\n invalid_chunk[1] = STBI__BYTECAST(c.type >> 16);\n invalid_chunk[2] = STBI__BYTECAST(c.type >> 8);\n invalid_chunk[3] = STBI__BYTECAST(c.type >> 0);\n#endif\n return stbi__err(invalid_chunk, \"PNG not supported: unknown PNG chunk type\");\n }\n stbi__skip(s, c.length);\n break;\n }\n // end of PNG chunk, read and skip CRC\n stbi__get32be(s);\n }\n}\n\nstatic void * stbi__do_png(stbi__png * p, int * x, int * y, int * n, int req_comp, stbi__result_info * ri) {\n void * result = NULL;\n if (req_comp < 0 || req_comp > 4)\n return stbi__errpuc(\"bad req_comp\", \"Internal error\");\n if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) {\n if (p->depth <= 8)\n ri->bits_per_channel = 8;\n else if (p->depth == 16)\n ri->bits_per_channel = 16;\n else\n return stbi__errpuc(\"bad bits_per_channel\", \"PNG not supported: unsupported color depth\");\n result = p->out;\n p->out = NULL;\n if (req_comp && req_comp != p->s->img_out_n) {\n if (ri->bits_per_channel == 8)\n result = stbi__convert_format((unsigned char *)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y);\n else\n result = stbi__convert_format16((stbi__uint16 *)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y);\n p->s->img_out_n = req_comp;\n if (result == NULL)\n return result;\n }\n *x = p->s->img_x;\n *y = p->s->img_y;\n if (n)\n *n = p->s->img_n;\n }\n STBI_FREE(p->out);\n p->out = NULL;\n STBI_FREE(p->expanded);\n p->expanded = NULL;\n STBI_FREE(p->idata);\n p->idata = NULL;\n\n return result;\n}\n\nstatic void * stbi__png_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n stbi__png p;\n p.s = s;\n return stbi__do_png(&p, x, y, comp, req_comp, ri);\n}\n\nstatic int stbi__png_test(stbi__context * s) {\n int r;\n r = stbi__check_png_header(s);\n stbi__rewind(s);\n return r;\n}\n\nstatic int stbi__png_info_raw(stbi__png * p, int * x, int * y, int * comp) {\n if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) {\n stbi__rewind(p->s);\n return 0;\n }\n if (x)\n *x = p->s->img_x;\n if (y)\n *y = p->s->img_y;\n if (comp)\n *comp = p->s->img_n;\n return 1;\n}\n\nstatic int stbi__png_info(stbi__context * s, int * x, int * y, int * comp) {\n stbi__png p;\n p.s = s;\n return stbi__png_info_raw(&p, x, y, comp);\n}\n\nstatic int stbi__png_is16(stbi__context * s) {\n stbi__png p;\n p.s = s;\n if (!stbi__png_info_raw(&p, NULL, NULL, NULL))\n return 0;\n if (p.depth != 16) {\n stbi__rewind(p.s);\n return 0;\n }\n return 1;\n}\n#endif\n\n// Microsoft/Windows BMP image\n\n#ifndef STBI_NO_BMP\nstatic int stbi__bmp_test_raw(stbi__context * s) {\n int r;\n int sz;\n if (stbi__get8(s) != 'B')\n return 0;\n if (stbi__get8(s) != 'M')\n return 0;\n stbi__get32le(s); // discard filesize\n stbi__get16le(s); // discard reserved\n stbi__get16le(s); // discard reserved\n stbi__get32le(s); // discard data offset\n sz = stbi__get32le(s);\n r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124);\n return r;\n}\n\nstatic int stbi__bmp_test(stbi__context * s) {\n int r = stbi__bmp_test_raw(s);\n stbi__rewind(s);\n return r;\n}\n\n// returns 0..31 for the highest set bit\nstatic int stbi__high_bit(unsigned int z) {\n int n = 0;\n if (z == 0)\n return -1;\n if (z >= 0x10000) {\n n += 16;\n z >>= 16;\n }\n if (z >= 0x00100) {\n n += 8;\n z >>= 8;\n }\n if (z >= 0x00010) {\n n += 4;\n z >>= 4;\n }\n if (z >= 0x00004) {\n n += 2;\n z >>= 2;\n }\n if (z >= 0x00002) {\n n += 1; /* >>= 1;*/\n }\n return n;\n}\n\nstatic int stbi__bitcount(unsigned int a) {\n a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2\n a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4\n a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits\n a = (a + (a >> 8)); // max 16 per 8 bits\n a = (a + (a >> 16)); // max 32 per 8 bits\n return a & 0xff;\n}\n\n// extract an arbitrarily-aligned N-bit value (N=bits)\n// from v, and then make it 8-bits long and fractionally\n// extend it to full full range.\nstatic int stbi__shiftsigned(unsigned int v, int shift, int bits) {\n static unsigned int mul_table[9] = {\n 0,\n 0xff /*0b11111111*/,\n 0x55 /*0b01010101*/,\n 0x49 /*0b01001001*/,\n 0x11 /*0b00010001*/,\n 0x21 /*0b00100001*/,\n 0x41 /*0b01000001*/,\n 0x81 /*0b10000001*/,\n 0x01 /*0b00000001*/,\n };\n static unsigned int shift_table[9] = {\n 0, 0, 0, 1, 0, 2, 4, 6, 0,\n };\n if (shift < 0)\n v <<= -shift;\n else\n v >>= shift;\n STBI_ASSERT(v < 256);\n v >>= (8 - bits);\n STBI_ASSERT(bits >= 0 && bits <= 8);\n return (int)((unsigned)v * mul_table[bits]) >> shift_table[bits];\n}\n\ntypedef struct {\n int bpp, offset, hsz;\n unsigned int mr, mg, mb, ma, all_a;\n int extra_read;\n} stbi__bmp_data;\n\nstatic int stbi__bmp_set_mask_defaults(stbi__bmp_data * info, int compress) {\n // BI_BITFIELDS specifies masks explicitly, don't override\n if (compress == 3)\n return 1;\n\n if (compress == 0) {\n if (info->bpp == 16) {\n info->mr = 31u << 10;\n info->mg = 31u << 5;\n info->mb = 31u << 0;\n } else if (info->bpp == 32) {\n info->mr = 0xffu << 16;\n info->mg = 0xffu << 8;\n info->mb = 0xffu << 0;\n info->ma = 0xffu << 24;\n info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0\n } else {\n // otherwise, use defaults, which is all-0\n info->mr = info->mg = info->mb = info->ma = 0;\n }\n return 1;\n }\n return 0; // error\n}\n\nstatic void * stbi__bmp_parse_header(stbi__context * s, stbi__bmp_data * info) {\n int hsz;\n if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M')\n return stbi__errpuc(\"not BMP\", \"Corrupt BMP\");\n stbi__get32le(s); // discard filesize\n stbi__get16le(s); // discard reserved\n stbi__get16le(s); // discard reserved\n info->offset = stbi__get32le(s);\n info->hsz = hsz = stbi__get32le(s);\n info->mr = info->mg = info->mb = info->ma = 0;\n info->extra_read = 14;\n\n if (info->offset < 0)\n return stbi__errpuc(\"bad BMP\", \"bad BMP\");\n\n if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124)\n return stbi__errpuc(\"unknown BMP\", \"BMP type not supported: unknown\");\n if (hsz == 12) {\n s->img_x = stbi__get16le(s);\n s->img_y = stbi__get16le(s);\n } else {\n s->img_x = stbi__get32le(s);\n s->img_y = stbi__get32le(s);\n }\n if (stbi__get16le(s) != 1)\n return stbi__errpuc(\"bad BMP\", \"bad BMP\");\n info->bpp = stbi__get16le(s);\n if (hsz != 12) {\n int compress = stbi__get32le(s);\n if (compress == 1 || compress == 2)\n return stbi__errpuc(\"BMP RLE\", \"BMP type not supported: RLE\");\n if (compress >= 4)\n return stbi__errpuc(\"BMP JPEG/PNG\",\n \"BMP type not supported: unsupported compression\"); // this includes PNG/JPEG modes\n if (compress == 3 && info->bpp != 16 && info->bpp != 32)\n return stbi__errpuc(\"bad BMP\", \"bad BMP\"); // bitfields requires 16 or 32 bits/pixel\n stbi__get32le(s); // discard sizeof\n stbi__get32le(s); // discard hres\n stbi__get32le(s); // discard vres\n stbi__get32le(s); // discard colorsused\n stbi__get32le(s); // discard max important\n if (hsz == 40 || hsz == 56) {\n if (hsz == 56) {\n stbi__get32le(s);\n stbi__get32le(s);\n stbi__get32le(s);\n stbi__get32le(s);\n }\n if (info->bpp == 16 || info->bpp == 32) {\n if (compress == 0) {\n stbi__bmp_set_mask_defaults(info, compress);\n } else if (compress == 3) {\n info->mr = stbi__get32le(s);\n info->mg = stbi__get32le(s);\n info->mb = stbi__get32le(s);\n info->extra_read += 12;\n // not documented, but generated by photoshop and handled by mspaint\n if (info->mr == info->mg && info->mg == info->mb) {\n // ?!?!?\n return stbi__errpuc(\"bad BMP\", \"bad BMP\");\n }\n } else\n return stbi__errpuc(\"bad BMP\", \"bad BMP\");\n }\n } else {\n // V4/V5 header\n int i;\n if (hsz != 108 && hsz != 124)\n return stbi__errpuc(\"bad BMP\", \"bad BMP\");\n info->mr = stbi__get32le(s);\n info->mg = stbi__get32le(s);\n info->mb = stbi__get32le(s);\n info->ma = stbi__get32le(s);\n if (compress != 3) // override mr/mg/mb unless in BI_BITFIELDS mode, as per docs\n stbi__bmp_set_mask_defaults(info, compress);\n stbi__get32le(s); // discard color space\n for (i = 0; i < 12; ++i)\n stbi__get32le(s); // discard color space parameters\n if (hsz == 124) {\n stbi__get32le(s); // discard rendering intent\n stbi__get32le(s); // discard offset of profile data\n stbi__get32le(s); // discard size of profile data\n stbi__get32le(s); // discard reserved\n }\n }\n }\n return (void *)1;\n}\n\nstatic void * stbi__bmp_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n stbi_uc * out;\n unsigned int mr = 0, mg = 0, mb = 0, ma = 0, all_a;\n stbi_uc pal[256][4];\n int psize = 0, i, j, width;\n int flip_vertically, pad, target;\n stbi__bmp_data info;\n STBI_NOTUSED(ri);\n\n info.all_a = 255;\n if (stbi__bmp_parse_header(s, &info) == NULL)\n return NULL; // error code already set\n\n flip_vertically = ((int)s->img_y) > 0;\n s->img_y = abs((int)s->img_y);\n\n if (s->img_y > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n if (s->img_x > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n\n mr = info.mr;\n mg = info.mg;\n mb = info.mb;\n ma = info.ma;\n all_a = info.all_a;\n\n if (info.hsz == 12) {\n if (info.bpp < 24)\n psize = (info.offset - info.extra_read - 24) / 3;\n } else {\n if (info.bpp < 16)\n psize = (info.offset - info.extra_read - info.hsz) >> 2;\n }\n if (psize == 0) {\n // accept some number of extra bytes after the header, but if the offset points either to before\n // the header ends or implies a large amount of extra data, reject the file as malformed\n int bytes_read_so_far = s->callback_already_read + (int)(s->img_buffer - s->img_buffer_original);\n int header_limit = 1024; // max we actually read is below 256 bytes currently.\n int extra_data_limit = 256 * 4; // what ordinarily goes here is a palette; 256 entries*4 bytes is its max size.\n if (bytes_read_so_far <= 0 || bytes_read_so_far > header_limit) {\n return stbi__errpuc(\"bad header\", \"Corrupt BMP\");\n }\n // we established that bytes_read_so_far is positive and sensible.\n // the first half of this test rejects offsets that are either too small positives, or\n // negative, and guarantees that info.offset >= bytes_read_so_far > 0. this in turn\n // ensures the number computed in the second half of the test can't overflow.\n if (info.offset < bytes_read_so_far || info.offset - bytes_read_so_far > extra_data_limit) {\n return stbi__errpuc(\"bad offset\", \"Corrupt BMP\");\n } else {\n stbi__skip(s, info.offset - bytes_read_so_far);\n }\n }\n\n if (info.bpp == 24 && ma == 0xff000000)\n s->img_n = 3;\n else\n s->img_n = ma ? 4 : 3;\n if (req_comp && req_comp >= 3) // we can directly decode 3 or 4\n target = req_comp;\n else\n target = s->img_n; // if they want monochrome, we'll post-convert\n\n // sanity-check size\n if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0))\n return stbi__errpuc(\"too large\", \"Corrupt BMP\");\n\n out = (stbi_uc *)stbi__malloc_mad3(target, s->img_x, s->img_y, 0);\n if (!out)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n if (info.bpp < 16) {\n int z = 0;\n if (psize == 0 || psize > 256) {\n STBI_FREE(out);\n return stbi__errpuc(\"invalid\", \"Corrupt BMP\");\n }\n for (i = 0; i < psize; ++i) {\n pal[i][2] = stbi__get8(s);\n pal[i][1] = stbi__get8(s);\n pal[i][0] = stbi__get8(s);\n if (info.hsz != 12)\n stbi__get8(s);\n pal[i][3] = 255;\n }\n stbi__skip(s, info.offset - info.extra_read - info.hsz - psize * (info.hsz == 12 ? 3 : 4));\n if (info.bpp == 1)\n width = (s->img_x + 7) >> 3;\n else if (info.bpp == 4)\n width = (s->img_x + 1) >> 1;\n else if (info.bpp == 8)\n width = s->img_x;\n else {\n STBI_FREE(out);\n return stbi__errpuc(\"bad bpp\", \"Corrupt BMP\");\n }\n pad = (-width) & 3;\n if (info.bpp == 1) {\n for (j = 0; j < (int)s->img_y; ++j) {\n int bit_offset = 7, v = stbi__get8(s);\n for (i = 0; i < (int)s->img_x; ++i) {\n int color = (v >> bit_offset) & 0x1;\n out[z++] = pal[color][0];\n out[z++] = pal[color][1];\n out[z++] = pal[color][2];\n if (target == 4)\n out[z++] = 255;\n if (i + 1 == (int)s->img_x)\n break;\n if ((--bit_offset) < 0) {\n bit_offset = 7;\n v = stbi__get8(s);\n }\n }\n stbi__skip(s, pad);\n }\n } else {\n for (j = 0; j < (int)s->img_y; ++j) {\n for (i = 0; i < (int)s->img_x; i += 2) {\n int v = stbi__get8(s), v2 = 0;\n if (info.bpp == 4) {\n v2 = v & 15;\n v >>= 4;\n }\n out[z++] = pal[v][0];\n out[z++] = pal[v][1];\n out[z++] = pal[v][2];\n if (target == 4)\n out[z++] = 255;\n if (i + 1 == (int)s->img_x)\n break;\n v = (info.bpp == 8) ? stbi__get8(s) : v2;\n out[z++] = pal[v][0];\n out[z++] = pal[v][1];\n out[z++] = pal[v][2];\n if (target == 4)\n out[z++] = 255;\n }\n stbi__skip(s, pad);\n }\n }\n } else {\n int rshift = 0, gshift = 0, bshift = 0, ashift = 0, rcount = 0, gcount = 0, bcount = 0, acount = 0;\n int z = 0;\n int easy = 0;\n stbi__skip(s, info.offset - info.extra_read - info.hsz);\n if (info.bpp == 24)\n width = 3 * s->img_x;\n else if (info.bpp == 16)\n width = 2 * s->img_x;\n else /* bpp = 32 and pad = 0 */\n width = 0;\n pad = (-width) & 3;\n if (info.bpp == 24) {\n easy = 1;\n } else if (info.bpp == 32) {\n if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000)\n easy = 2;\n }\n if (!easy) {\n if (!mr || !mg || !mb) {\n STBI_FREE(out);\n return stbi__errpuc(\"bad masks\", \"Corrupt BMP\");\n }\n // right shift amt to put high bit in position #7\n rshift = stbi__high_bit(mr) - 7;\n rcount = stbi__bitcount(mr);\n gshift = stbi__high_bit(mg) - 7;\n gcount = stbi__bitcount(mg);\n bshift = stbi__high_bit(mb) - 7;\n bcount = stbi__bitcount(mb);\n ashift = stbi__high_bit(ma) - 7;\n acount = stbi__bitcount(ma);\n if (rcount > 8 || gcount > 8 || bcount > 8 || acount > 8) {\n STBI_FREE(out);\n return stbi__errpuc(\"bad masks\", \"Corrupt BMP\");\n }\n }\n for (j = 0; j < (int)s->img_y; ++j) {\n if (easy) {\n for (i = 0; i < (int)s->img_x; ++i) {\n unsigned char a;\n out[z + 2] = stbi__get8(s);\n out[z + 1] = stbi__get8(s);\n out[z + 0] = stbi__get8(s);\n z += 3;\n a = (easy == 2 ? stbi__get8(s) : 255);\n all_a |= a;\n if (target == 4)\n out[z++] = a;\n }\n } else {\n int bpp = info.bpp;\n for (i = 0; i < (int)s->img_x; ++i) {\n stbi__uint32 v = (bpp == 16 ? (stbi__uint32)stbi__get16le(s) : stbi__get32le(s));\n unsigned int a;\n out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount));\n out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount));\n out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount));\n a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255);\n all_a |= a;\n if (target == 4)\n out[z++] = STBI__BYTECAST(a);\n }\n }\n stbi__skip(s, pad);\n }\n }\n\n // if alpha channel is all 0s, replace with all 255s\n if (target == 4 && all_a == 0)\n for (i = 4 * s->img_x * s->img_y - 1; i >= 0; i -= 4)\n out[i] = 255;\n\n if (flip_vertically) {\n stbi_uc t;\n for (j = 0; j < (int)s->img_y >> 1; ++j) {\n stbi_uc * p1 = out + j * s->img_x * target;\n stbi_uc * p2 = out + (s->img_y - 1 - j) * s->img_x * target;\n for (i = 0; i < (int)s->img_x * target; ++i) {\n t = p1[i];\n p1[i] = p2[i];\n p2[i] = t;\n }\n }\n }\n\n if (req_comp && req_comp != target) {\n out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y);\n if (out == NULL)\n return out; // stbi__convert_format frees input on failure\n }\n\n *x = s->img_x;\n *y = s->img_y;\n if (comp)\n *comp = s->img_n;\n return out;\n}\n#endif\n\n// Targa Truevision - TGA\n// by Jonathan Dummer\n#ifndef STBI_NO_TGA\n// returns STBI_rgb or whatever, 0 on error\nstatic int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int * is_rgb16) {\n // only RGB or RGBA (incl. 16bit) or grey allowed\n if (is_rgb16)\n *is_rgb16 = 0;\n switch (bits_per_pixel) {\n case 8:\n return STBI_grey;\n case 16:\n if (is_grey)\n return STBI_grey_alpha;\n // fallthrough\n case 15:\n if (is_rgb16)\n *is_rgb16 = 1;\n return STBI_rgb;\n case 24: // fallthrough\n case 32:\n return bits_per_pixel / 8;\n default:\n return 0;\n }\n}\n\nstatic int stbi__tga_info(stbi__context * s, int * x, int * y, int * comp) {\n int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp;\n int sz, tga_colormap_type;\n stbi__get8(s); // discard Offset\n tga_colormap_type = stbi__get8(s); // colormap type\n if (tga_colormap_type > 1) {\n stbi__rewind(s);\n return 0; // only RGB or indexed allowed\n }\n tga_image_type = stbi__get8(s); // image type\n if (tga_colormap_type == 1) { // colormapped (paletted) image\n if (tga_image_type != 1 && tga_image_type != 9) {\n stbi__rewind(s);\n return 0;\n }\n stbi__skip(s, 4); // skip index of first colormap entry and number of entries\n sz = stbi__get8(s); // check bits per palette color entry\n if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) {\n stbi__rewind(s);\n return 0;\n }\n stbi__skip(s, 4); // skip image x and y origin\n tga_colormap_bpp = sz;\n } else { // \"normal\" image w/o colormap - only RGB or grey allowed, +/- RLE\n if ((tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11)) {\n stbi__rewind(s);\n return 0; // only RGB or grey allowed, +/- RLE\n }\n stbi__skip(s, 9); // skip colormap specification and image x/y origin\n tga_colormap_bpp = 0;\n }\n tga_w = stbi__get16le(s);\n if (tga_w < 1) {\n stbi__rewind(s);\n return 0; // test width\n }\n tga_h = stbi__get16le(s);\n if (tga_h < 1) {\n stbi__rewind(s);\n return 0; // test height\n }\n tga_bits_per_pixel = stbi__get8(s); // bits per pixel\n stbi__get8(s); // ignore alpha bits\n if (tga_colormap_bpp != 0) {\n if ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) {\n // when using a colormap, tga_bits_per_pixel is the size of the indexes\n // I don't think anything but 8 or 16bit indexes makes sense\n stbi__rewind(s);\n return 0;\n }\n tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL);\n } else {\n tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL);\n }\n if (!tga_comp) {\n stbi__rewind(s);\n return 0;\n }\n if (x)\n *x = tga_w;\n if (y)\n *y = tga_h;\n if (comp)\n *comp = tga_comp;\n return 1; // seems to have passed everything\n}\n\nstatic int stbi__tga_test(stbi__context * s) {\n int res = 0;\n int sz, tga_color_type;\n stbi__get8(s); // discard Offset\n tga_color_type = stbi__get8(s); // color type\n if (tga_color_type > 1)\n goto errorEnd; // only RGB or indexed allowed\n sz = stbi__get8(s); // image type\n if (tga_color_type == 1) { // colormapped (paletted) image\n if (sz != 1 && sz != 9)\n goto errorEnd; // colortype 1 demands image type 1 or 9\n stbi__skip(s, 4); // skip index of first colormap entry and number of entries\n sz = stbi__get8(s); // check bits per palette color entry\n if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32))\n goto errorEnd;\n stbi__skip(s, 4); // skip image x and y origin\n } else { // \"normal\" image w/o colormap\n if ((sz != 2) && (sz != 3) && (sz != 10) && (sz != 11))\n goto errorEnd; // only RGB or grey allowed, +/- RLE\n stbi__skip(s, 9); // skip colormap specification and image x/y origin\n }\n if (stbi__get16le(s) < 1)\n goto errorEnd; // test width\n if (stbi__get16le(s) < 1)\n goto errorEnd; // test height\n sz = stbi__get8(s); // bits per pixel\n if ((tga_color_type == 1) && (sz != 8) && (sz != 16))\n goto errorEnd; // for colormapped images, bpp is size of an index\n if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32))\n goto errorEnd;\n\n res = 1; // if we got this far, everything's good and we can return 1 instead of 0\n\nerrorEnd:\n stbi__rewind(s);\n return res;\n}\n\n// read 16bit value and convert to 24bit RGB\nstatic void stbi__tga_read_rgb16(stbi__context * s, stbi_uc * out) {\n stbi__uint16 px = (stbi__uint16)stbi__get16le(s);\n stbi__uint16 fiveBitMask = 31;\n // we have 3 channels with 5bits each\n int r = (px >> 10) & fiveBitMask;\n int g = (px >> 5) & fiveBitMask;\n int b = px & fiveBitMask;\n // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later\n out[0] = (stbi_uc)((r * 255) / 31);\n out[1] = (stbi_uc)((g * 255) / 31);\n out[2] = (stbi_uc)((b * 255) / 31);\n\n // some people claim that the most significant bit might be used for alpha\n // (possibly if an alpha-bit is set in the \"image descriptor byte\")\n // but that only made 16bit test images completely translucent..\n // so let's treat all 15 and 16bit TGAs as RGB with no alpha.\n}\n\nstatic void * stbi__tga_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n // read in the TGA header stuff\n int tga_offset = stbi__get8(s);\n int tga_indexed = stbi__get8(s);\n int tga_image_type = stbi__get8(s);\n int tga_is_RLE = 0;\n int tga_palette_start = stbi__get16le(s);\n int tga_palette_len = stbi__get16le(s);\n int tga_palette_bits = stbi__get8(s);\n int tga_x_origin = stbi__get16le(s);\n int tga_y_origin = stbi__get16le(s);\n int tga_width = stbi__get16le(s);\n int tga_height = stbi__get16le(s);\n int tga_bits_per_pixel = stbi__get8(s);\n int tga_comp, tga_rgb16 = 0;\n int tga_inverted = stbi__get8(s);\n // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?)\n // image data\n unsigned char * tga_data;\n unsigned char * tga_palette = NULL;\n int i, j;\n unsigned char raw_data[4] = {0};\n int RLE_count = 0;\n int RLE_repeating = 0;\n int read_next_pixel = 1;\n STBI_NOTUSED(ri);\n STBI_NOTUSED(tga_x_origin); // @TODO\n STBI_NOTUSED(tga_y_origin); // @TODO\n\n if (tga_height > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n if (tga_width > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n\n // do a tiny bit of precessing\n if (tga_image_type >= 8) {\n tga_image_type -= 8;\n tga_is_RLE = 1;\n }\n tga_inverted = 1 - ((tga_inverted >> 5) & 1);\n\n // If I'm paletted, then I'll use the number of bits from the palette\n if (tga_indexed)\n tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16);\n else\n tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16);\n\n if (!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency\n return stbi__errpuc(\"bad format\", \"Can't find out TGA pixelformat\");\n\n // tga info\n *x = tga_width;\n *y = tga_height;\n if (comp)\n *comp = tga_comp;\n\n if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0))\n return stbi__errpuc(\"too large\", \"Corrupt TGA\");\n\n tga_data = (unsigned char *)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0);\n if (!tga_data)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n\n // skip to the data's starting position (offset usually = 0)\n stbi__skip(s, tga_offset);\n\n if (!tga_indexed && !tga_is_RLE && !tga_rgb16) {\n for (i = 0; i < tga_height; ++i) {\n int row = tga_inverted ? tga_height - i - 1 : i;\n stbi_uc * tga_row = tga_data + row * tga_width * tga_comp;\n stbi__getn(s, tga_row, tga_width * tga_comp);\n }\n } else {\n // do I need to load a palette?\n if (tga_indexed) {\n if (tga_palette_len == 0) { /* you have to have at least one entry! */\n STBI_FREE(tga_data);\n return stbi__errpuc(\"bad palette\", \"Corrupt TGA\");\n }\n\n // any data to skip? (offset usually = 0)\n stbi__skip(s, tga_palette_start);\n // load the palette\n tga_palette = (unsigned char *)stbi__malloc_mad2(tga_palette_len, tga_comp, 0);\n if (!tga_palette) {\n STBI_FREE(tga_data);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n }\n if (tga_rgb16) {\n stbi_uc * pal_entry = tga_palette;\n STBI_ASSERT(tga_comp == STBI_rgb);\n for (i = 0; i < tga_palette_len; ++i) {\n stbi__tga_read_rgb16(s, pal_entry);\n pal_entry += tga_comp;\n }\n } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) {\n STBI_FREE(tga_data);\n STBI_FREE(tga_palette);\n return stbi__errpuc(\"bad palette\", \"Corrupt TGA\");\n }\n }\n // load the data\n for (i = 0; i < tga_width * tga_height; ++i) {\n // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk?\n if (tga_is_RLE) {\n if (RLE_count == 0) {\n // yep, get the next byte as a RLE command\n int RLE_cmd = stbi__get8(s);\n RLE_count = 1 + (RLE_cmd & 127);\n RLE_repeating = RLE_cmd >> 7;\n read_next_pixel = 1;\n } else if (!RLE_repeating) {\n read_next_pixel = 1;\n }\n } else {\n read_next_pixel = 1;\n }\n // OK, if I need to read a pixel, do it now\n if (read_next_pixel) {\n // load however much data we did have\n if (tga_indexed) {\n // read in index, then perform the lookup\n int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s);\n if (pal_idx >= tga_palette_len) {\n // invalid index\n pal_idx = 0;\n }\n pal_idx *= tga_comp;\n for (j = 0; j < tga_comp; ++j) {\n raw_data[j] = tga_palette[pal_idx + j];\n }\n } else if (tga_rgb16) {\n STBI_ASSERT(tga_comp == STBI_rgb);\n stbi__tga_read_rgb16(s, raw_data);\n } else {\n // read in the data raw\n for (j = 0; j < tga_comp; ++j) {\n raw_data[j] = stbi__get8(s);\n }\n }\n // clear the reading flag for the next pixel\n read_next_pixel = 0;\n } // end of reading a pixel\n\n // copy data\n for (j = 0; j < tga_comp; ++j)\n tga_data[i * tga_comp + j] = raw_data[j];\n\n // in case we're in RLE mode, keep counting down\n --RLE_count;\n }\n // do I need to invert the image?\n if (tga_inverted) {\n for (j = 0; j * 2 < tga_height; ++j) {\n int index1 = j * tga_width * tga_comp;\n int index2 = (tga_height - 1 - j) * tga_width * tga_comp;\n for (i = tga_width * tga_comp; i > 0; --i) {\n unsigned char temp = tga_data[index1];\n tga_data[index1] = tga_data[index2];\n tga_data[index2] = temp;\n ++index1;\n ++index2;\n }\n }\n }\n // clear my palette, if I had one\n if (tga_palette != NULL) {\n STBI_FREE(tga_palette);\n }\n }\n\n // swap RGB - if the source data was RGB16, it already is in the right order\n if (tga_comp >= 3 && !tga_rgb16) {\n unsigned char * tga_pixel = tga_data;\n for (i = 0; i < tga_width * tga_height; ++i) {\n unsigned char temp = tga_pixel[0];\n tga_pixel[0] = tga_pixel[2];\n tga_pixel[2] = temp;\n tga_pixel += tga_comp;\n }\n }\n\n // convert to target component count\n if (req_comp && req_comp != tga_comp)\n tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height);\n\n // the things I do to get rid of an error message, and yet keep\n // Microsoft's C compilers happy... [8^(\n tga_palette_start = tga_palette_len = tga_palette_bits = tga_x_origin = tga_y_origin = 0;\n STBI_NOTUSED(tga_palette_start);\n // OK, done\n return tga_data;\n}\n#endif\n\n// *************************************************************************************************\n// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB\n\n#ifndef STBI_NO_PSD\nstatic int stbi__psd_test(stbi__context * s) {\n int r = (stbi__get32be(s) == 0x38425053);\n stbi__rewind(s);\n return r;\n}\n\nstatic int stbi__psd_decode_rle(stbi__context * s, stbi_uc * p, int pixelCount) {\n int count, nleft, len;\n\n count = 0;\n while ((nleft = pixelCount - count) > 0) {\n len = stbi__get8(s);\n if (len == 128) {\n // No-op.\n } else if (len < 128) {\n // Copy next len+1 bytes literally.\n len++;\n if (len > nleft)\n return 0; // corrupt data\n count += len;\n while (len) {\n *p = stbi__get8(s);\n p += 4;\n len--;\n }\n } else if (len > 128) {\n stbi_uc val;\n // Next -len+1 bytes in the dest are replicated from next source byte.\n // (Interpret len as a negative 8-bit int.)\n len = 257 - len;\n if (len > nleft)\n return 0; // corrupt data\n val = stbi__get8(s);\n count += len;\n while (len) {\n *p = val;\n p += 4;\n len--;\n }\n }\n }\n\n return 1;\n}\n\nstatic void * stbi__psd_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc) {\n int pixelCount;\n int channelCount, compression;\n int channel, i;\n int bitdepth;\n int w, h;\n stbi_uc * out;\n STBI_NOTUSED(ri);\n\n // Check identifier\n if (stbi__get32be(s) != 0x38425053) // \"8BPS\"\n return stbi__errpuc(\"not PSD\", \"Corrupt PSD image\");\n\n // Check file type version.\n if (stbi__get16be(s) != 1)\n return stbi__errpuc(\"wrong version\", \"Unsupported version of PSD image\");\n\n // Skip 6 reserved bytes.\n stbi__skip(s, 6);\n\n // Read the number of channels (R, G, B, A, etc).\n channelCount = stbi__get16be(s);\n if (channelCount < 0 || channelCount > 16)\n return stbi__errpuc(\"wrong channel count\", \"Unsupported number of channels in PSD image\");\n\n // Read the rows and columns of the image.\n h = stbi__get32be(s);\n w = stbi__get32be(s);\n\n if (h > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n if (w > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n\n // Make sure the depth is 8 bits.\n bitdepth = stbi__get16be(s);\n if (bitdepth != 8 && bitdepth != 16)\n return stbi__errpuc(\"unsupported bit depth\", \"PSD bit depth is not 8 or 16 bit\");\n\n // Make sure the color mode is RGB.\n // Valid options are:\n // 0: Bitmap\n // 1: Grayscale\n // 2: Indexed color\n // 3: RGB color\n // 4: CMYK color\n // 7: Multichannel\n // 8: Duotone\n // 9: Lab color\n if (stbi__get16be(s) != 3)\n return stbi__errpuc(\"wrong color format\", \"PSD is not in RGB color format\");\n\n // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.)\n stbi__skip(s, stbi__get32be(s));\n\n // Skip the image resources. (resolution, pen tool paths, etc)\n stbi__skip(s, stbi__get32be(s));\n\n // Skip the reserved data.\n stbi__skip(s, stbi__get32be(s));\n\n // Find out if the data is compressed.\n // Known values:\n // 0: no compression\n // 1: RLE compressed\n compression = stbi__get16be(s);\n if (compression > 1)\n return stbi__errpuc(\"bad compression\", \"PSD has an unknown compression format\");\n\n // Check size\n if (!stbi__mad3sizes_valid(4, w, h, 0))\n return stbi__errpuc(\"too large\", \"Corrupt PSD\");\n\n // Create the destination image.\n\n if (!compression && bitdepth == 16 && bpc == 16) {\n out = (stbi_uc *)stbi__malloc_mad3(8, w, h, 0);\n ri->bits_per_channel = 16;\n } else\n out = (stbi_uc *)stbi__malloc(4 * w * h);\n\n if (!out)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n pixelCount = w * h;\n\n // Initialize the data to zero.\n // memset( out, 0, pixelCount * 4 );\n\n // Finally, the image data.\n if (compression) {\n // RLE as used by .PSD and .TIFF\n // Loop until you get the number of unpacked bytes you are expecting:\n // Read the next source byte into n.\n // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally.\n // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times.\n // Else if n is 128, noop.\n // Endloop\n\n // The RLE-compressed data is preceded by a 2-byte data count for each row in the data,\n // which we're going to just skip.\n stbi__skip(s, h * channelCount * 2);\n\n // Read the RLE data by channel.\n for (channel = 0; channel < 4; channel++) {\n stbi_uc * p;\n\n p = out + channel;\n if (channel >= channelCount) {\n // Fill this channel with default data.\n for (i = 0; i < pixelCount; i++, p += 4)\n *p = (channel == 3 ? 255 : 0);\n } else {\n // Read the RLE data.\n if (!stbi__psd_decode_rle(s, p, pixelCount)) {\n STBI_FREE(out);\n return stbi__errpuc(\"corrupt\", \"bad RLE data\");\n }\n }\n }\n } else {\n // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...)\n // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image.\n\n // Read the data by channel.\n for (channel = 0; channel < 4; channel++) {\n if (channel >= channelCount) {\n // Fill this channel with default data.\n if (bitdepth == 16 && bpc == 16) {\n stbi__uint16 * q = ((stbi__uint16 *)out) + channel;\n stbi__uint16 val = channel == 3 ? 65535 : 0;\n for (i = 0; i < pixelCount; i++, q += 4)\n *q = val;\n } else {\n stbi_uc * p = out + channel;\n stbi_uc val = channel == 3 ? 255 : 0;\n for (i = 0; i < pixelCount; i++, p += 4)\n *p = val;\n }\n } else {\n if (ri->bits_per_channel == 16) { // output bpc\n stbi__uint16 * q = ((stbi__uint16 *)out) + channel;\n for (i = 0; i < pixelCount; i++, q += 4)\n *q = (stbi__uint16)stbi__get16be(s);\n } else {\n stbi_uc * p = out + channel;\n if (bitdepth == 16) { // input bpc\n for (i = 0; i < pixelCount; i++, p += 4)\n *p = (stbi_uc)(stbi__get16be(s) >> 8);\n } else {\n for (i = 0; i < pixelCount; i++, p += 4)\n *p = stbi__get8(s);\n }\n }\n }\n }\n }\n\n // remove weird white matte from PSD\n if (channelCount >= 4) {\n if (ri->bits_per_channel == 16) {\n for (i = 0; i < w * h; ++i) {\n stbi__uint16 * pixel = (stbi__uint16 *)out + 4 * i;\n if (pixel[3] != 0 && pixel[3] != 65535) {\n float a = pixel[3] / 65535.0f;\n float ra = 1.0f / a;\n float inv_a = 65535.0f * (1 - ra);\n pixel[0] = (stbi__uint16)(pixel[0] * ra + inv_a);\n pixel[1] = (stbi__uint16)(pixel[1] * ra + inv_a);\n pixel[2] = (stbi__uint16)(pixel[2] * ra + inv_a);\n }\n }\n } else {\n for (i = 0; i < w * h; ++i) {\n unsigned char * pixel = out + 4 * i;\n if (pixel[3] != 0 && pixel[3] != 255) {\n float a = pixel[3] / 255.0f;\n float ra = 1.0f / a;\n float inv_a = 255.0f * (1 - ra);\n pixel[0] = (unsigned char)(pixel[0] * ra + inv_a);\n pixel[1] = (unsigned char)(pixel[1] * ra + inv_a);\n pixel[2] = (unsigned char)(pixel[2] * ra + inv_a);\n }\n }\n }\n }\n\n // convert to desired output format\n if (req_comp && req_comp != 4) {\n if (ri->bits_per_channel == 16)\n out = (stbi_uc *)stbi__convert_format16((stbi__uint16 *)out, 4, req_comp, w, h);\n else\n out = stbi__convert_format(out, 4, req_comp, w, h);\n if (out == NULL)\n return out; // stbi__convert_format frees input on failure\n }\n\n if (comp)\n *comp = 4;\n *y = h;\n *x = w;\n\n return out;\n}\n#endif\n\n// *************************************************************************************************\n// Softimage PIC loader\n// by Tom Seddon\n//\n// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format\n// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/\n\n#ifndef STBI_NO_PIC\nstatic int stbi__pic_is4(stbi__context * s, const char * str) {\n int i;\n for (i = 0; i < 4; ++i)\n if (stbi__get8(s) != (stbi_uc)str[i])\n return 0;\n\n return 1;\n}\n\nstatic int stbi__pic_test_core(stbi__context * s) {\n int i;\n\n if (!stbi__pic_is4(s, \"\\x53\\x80\\xF6\\x34\"))\n return 0;\n\n for (i = 0; i < 84; ++i)\n stbi__get8(s);\n\n if (!stbi__pic_is4(s, \"PICT\"))\n return 0;\n\n return 1;\n}\n\ntypedef struct {\n stbi_uc size, type, channel;\n} stbi__pic_packet;\n\nstatic stbi_uc * stbi__readval(stbi__context * s, int channel, stbi_uc * dest) {\n int mask = 0x80, i;\n\n for (i = 0; i < 4; ++i, mask >>= 1) {\n if (channel & mask) {\n if (stbi__at_eof(s))\n return stbi__errpuc(\"bad file\", \"PIC file too short\");\n dest[i] = stbi__get8(s);\n }\n }\n\n return dest;\n}\n\nstatic void stbi__copyval(int channel, stbi_uc * dest, const stbi_uc * src) {\n int mask = 0x80, i;\n\n for (i = 0; i < 4; ++i, mask >>= 1)\n if (channel & mask)\n dest[i] = src[i];\n}\n\nstatic stbi_uc * stbi__pic_load_core(stbi__context * s, int width, int height, int * comp, stbi_uc * result) {\n int act_comp = 0, num_packets = 0, y, chained;\n stbi__pic_packet packets[10];\n\n // this will (should...) cater for even some bizarre stuff like having data\n // for the same channel in multiple packets.\n do {\n stbi__pic_packet * packet;\n\n if (num_packets == sizeof(packets) / sizeof(packets[0]))\n return stbi__errpuc(\"bad format\", \"too many packets\");\n\n packet = &packets[num_packets++];\n\n chained = stbi__get8(s);\n packet->size = stbi__get8(s);\n packet->type = stbi__get8(s);\n packet->channel = stbi__get8(s);\n\n act_comp |= packet->channel;\n\n if (stbi__at_eof(s))\n return stbi__errpuc(\"bad file\", \"file too short (reading packets)\");\n if (packet->size != 8)\n return stbi__errpuc(\"bad format\", \"packet isn't 8bpp\");\n } while (chained);\n\n *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel?\n\n for (y = 0; y < height; ++y) {\n int packet_idx;\n\n for (packet_idx = 0; packet_idx < num_packets; ++packet_idx) {\n stbi__pic_packet * packet = &packets[packet_idx];\n stbi_uc * dest = result + y * width * 4;\n\n switch (packet->type) {\n default:\n return stbi__errpuc(\"bad format\", \"packet has bad compression type\");\n\n case 0: { // uncompressed\n int x;\n\n for (x = 0; x < width; ++x, dest += 4)\n if (!stbi__readval(s, packet->channel, dest))\n return 0;\n break;\n }\n\n case 1: // Pure RLE\n {\n int left = width, i;\n\n while (left > 0) {\n stbi_uc count, value[4];\n\n count = stbi__get8(s);\n if (stbi__at_eof(s))\n return stbi__errpuc(\"bad file\", \"file too short (pure read count)\");\n\n if (count > left)\n count = (stbi_uc)left;\n\n if (!stbi__readval(s, packet->channel, value))\n return 0;\n\n for (i = 0; i < count; ++i, dest += 4)\n stbi__copyval(packet->channel, dest, value);\n left -= count;\n }\n } break;\n\n case 2: { // Mixed RLE\n int left = width;\n while (left > 0) {\n int count = stbi__get8(s), i;\n if (stbi__at_eof(s))\n return stbi__errpuc(\"bad file\", \"file too short (mixed read count)\");\n\n if (count >= 128) { // Repeated\n stbi_uc value[4];\n\n if (count == 128)\n count = stbi__get16be(s);\n else\n count -= 127;\n if (count > left)\n return stbi__errpuc(\"bad file\", \"scanline overrun\");\n\n if (!stbi__readval(s, packet->channel, value))\n return 0;\n\n for (i = 0; i < count; ++i, dest += 4)\n stbi__copyval(packet->channel, dest, value);\n } else { // Raw\n ++count;\n if (count > left)\n return stbi__errpuc(\"bad file\", \"scanline overrun\");\n\n for (i = 0; i < count; ++i, dest += 4)\n if (!stbi__readval(s, packet->channel, dest))\n return 0;\n }\n left -= count;\n }\n break;\n }\n }\n }\n }\n\n return result;\n}\n\nstatic void * stbi__pic_load(stbi__context * s, int * px, int * py, int * comp, int req_comp, stbi__result_info * ri) {\n stbi_uc * result;\n int i, x, y, internal_comp;\n STBI_NOTUSED(ri);\n\n if (!comp)\n comp = &internal_comp;\n\n for (i = 0; i < 92; ++i)\n stbi__get8(s);\n\n x = stbi__get16be(s);\n y = stbi__get16be(s);\n\n if (y > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n if (x > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n\n if (stbi__at_eof(s))\n return stbi__errpuc(\"bad file\", \"file too short (pic header)\");\n if (!stbi__mad3sizes_valid(x, y, 4, 0))\n return stbi__errpuc(\"too large\", \"PIC image too large to decode\");\n\n stbi__get32be(s); // skip `ratio'\n stbi__get16be(s); // skip `fields'\n stbi__get16be(s); // skip `pad'\n\n // intermediate buffer is RGBA\n result = (stbi_uc *)stbi__malloc_mad3(x, y, 4, 0);\n if (!result)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n memset(result, 0xff, x * y * 4);\n\n if (!stbi__pic_load_core(s, x, y, comp, result)) {\n STBI_FREE(result);\n result = 0;\n }\n *px = x;\n *py = y;\n if (req_comp == 0)\n req_comp = *comp;\n result = stbi__convert_format(result, 4, req_comp, x, y);\n\n return result;\n}\n\nstatic int stbi__pic_test(stbi__context * s) {\n int r = stbi__pic_test_core(s);\n stbi__rewind(s);\n return r;\n}\n#endif\n\n// *************************************************************************************************\n// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb\n\n#ifndef STBI_NO_GIF\ntypedef struct {\n stbi__int16 prefix;\n stbi_uc first;\n stbi_uc suffix;\n} stbi__gif_lzw;\n\ntypedef struct {\n int w, h;\n stbi_uc * out; // output buffer (always 4 components)\n stbi_uc * background; // The current \"background\" as far as a gif is concerned\n stbi_uc * history;\n int flags, bgindex, ratio, transparent, eflags;\n stbi_uc pal[256][4];\n stbi_uc lpal[256][4];\n stbi__gif_lzw codes[8192];\n stbi_uc * color_table;\n int parse, step;\n int lflags;\n int start_x, start_y;\n int max_x, max_y;\n int cur_x, cur_y;\n int line_size;\n int delay;\n} stbi__gif;\n\nstatic int stbi__gif_test_raw(stbi__context * s) {\n int sz;\n if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8')\n return 0;\n sz = stbi__get8(s);\n if (sz != '9' && sz != '7')\n return 0;\n if (stbi__get8(s) != 'a')\n return 0;\n return 1;\n}\n\nstatic int stbi__gif_test(stbi__context * s) {\n int r = stbi__gif_test_raw(s);\n stbi__rewind(s);\n return r;\n}\n\nstatic void stbi__gif_parse_colortable(stbi__context * s, stbi_uc pal[256][4], int num_entries, int transp) {\n int i;\n for (i = 0; i < num_entries; ++i) {\n pal[i][2] = stbi__get8(s);\n pal[i][1] = stbi__get8(s);\n pal[i][0] = stbi__get8(s);\n pal[i][3] = transp == i ? 0 : 255;\n }\n}\n\nstatic int stbi__gif_header(stbi__context * s, stbi__gif * g, int * comp, int is_info) {\n stbi_uc version;\n if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8')\n return stbi__err(\"not GIF\", \"Corrupt GIF\");\n\n version = stbi__get8(s);\n if (version != '7' && version != '9')\n return stbi__err(\"not GIF\", \"Corrupt GIF\");\n if (stbi__get8(s) != 'a')\n return stbi__err(\"not GIF\", \"Corrupt GIF\");\n\n stbi__g_failure_reason = \"\";\n g->w = stbi__get16le(s);\n g->h = stbi__get16le(s);\n g->flags = stbi__get8(s);\n g->bgindex = stbi__get8(s);\n g->ratio = stbi__get8(s);\n g->transparent = -1;\n\n if (g->w > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n if (g->h > STBI_MAX_DIMENSIONS)\n return stbi__err(\"too large\", \"Very large image (corrupt?)\");\n\n if (comp != 0)\n *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments\n\n if (is_info)\n return 1;\n\n if (g->flags & 0x80)\n stbi__gif_parse_colortable(s, g->pal, 2 << (g->flags & 7), -1);\n\n return 1;\n}\n\nstatic int stbi__gif_info_raw(stbi__context * s, int * x, int * y, int * comp) {\n stbi__gif * g = (stbi__gif *)stbi__malloc(sizeof(stbi__gif));\n if (!g)\n return stbi__err(\"outofmem\", \"Out of memory\");\n if (!stbi__gif_header(s, g, comp, 1)) {\n STBI_FREE(g);\n stbi__rewind(s);\n return 0;\n }\n if (x)\n *x = g->w;\n if (y)\n *y = g->h;\n STBI_FREE(g);\n return 1;\n}\n\nstatic void stbi__out_gif_code(stbi__gif * g, stbi__uint16 code) {\n stbi_uc *p, *c;\n int idx;\n\n // recurse to decode the prefixes, since the linked-list is backwards,\n // and working backwards through an interleaved image would be nasty\n if (g->codes[code].prefix >= 0)\n stbi__out_gif_code(g, g->codes[code].prefix);\n\n if (g->cur_y >= g->max_y)\n return;\n\n idx = g->cur_x + g->cur_y;\n p = &g->out[idx];\n g->history[idx / 4] = 1;\n\n c = &g->color_table[g->codes[code].suffix * 4];\n if (c[3] > 128) { // don't render transparent pixels;\n p[0] = c[2];\n p[1] = c[1];\n p[2] = c[0];\n p[3] = c[3];\n }\n g->cur_x += 4;\n\n if (g->cur_x >= g->max_x) {\n g->cur_x = g->start_x;\n g->cur_y += g->step;\n\n while (g->cur_y >= g->max_y && g->parse > 0) {\n g->step = (1 << g->parse) * g->line_size;\n g->cur_y = g->start_y + (g->step >> 1);\n --g->parse;\n }\n }\n}\n\nstatic stbi_uc * stbi__process_gif_raster(stbi__context * s, stbi__gif * g) {\n stbi_uc lzw_cs;\n stbi__int32 len, init_code;\n stbi__uint32 first;\n stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear;\n stbi__gif_lzw * p;\n\n lzw_cs = stbi__get8(s);\n if (lzw_cs > 12)\n return NULL;\n clear = 1 << lzw_cs;\n first = 1;\n codesize = lzw_cs + 1;\n codemask = (1 << codesize) - 1;\n bits = 0;\n valid_bits = 0;\n for (init_code = 0; init_code < clear; init_code++) {\n g->codes[init_code].prefix = -1;\n g->codes[init_code].first = (stbi_uc)init_code;\n g->codes[init_code].suffix = (stbi_uc)init_code;\n }\n\n // support no starting clear code\n avail = clear + 2;\n oldcode = -1;\n\n len = 0;\n for (;;) {\n if (valid_bits < codesize) {\n if (len == 0) {\n len = stbi__get8(s); // start new block\n if (len == 0)\n return g->out;\n }\n --len;\n bits |= (stbi__int32)stbi__get8(s) << valid_bits;\n valid_bits += 8;\n } else {\n stbi__int32 code = bits & codemask;\n bits >>= codesize;\n valid_bits -= codesize;\n // @OPTIMIZE: is there some way we can accelerate the non-clear path?\n if (code == clear) { // clear code\n codesize = lzw_cs + 1;\n codemask = (1 << codesize) - 1;\n avail = clear + 2;\n oldcode = -1;\n first = 0;\n } else if (code == clear + 1) { // end of stream code\n stbi__skip(s, len);\n while ((len = stbi__get8(s)) > 0)\n stbi__skip(s, len);\n return g->out;\n } else if (code <= avail) {\n if (first) {\n return stbi__errpuc(\"no clear code\", \"Corrupt GIF\");\n }\n\n if (oldcode >= 0) {\n p = &g->codes[avail++];\n if (avail > 8192) {\n return stbi__errpuc(\"too many codes\", \"Corrupt GIF\");\n }\n\n p->prefix = (stbi__int16)oldcode;\n p->first = g->codes[oldcode].first;\n p->suffix = (code == avail) ? p->first : g->codes[code].first;\n } else if (code == avail)\n return stbi__errpuc(\"illegal code in raster\", \"Corrupt GIF\");\n\n stbi__out_gif_code(g, (stbi__uint16)code);\n\n if ((avail & codemask) == 0 && avail <= 0x0FFF) {\n codesize++;\n codemask = (1 << codesize) - 1;\n }\n\n oldcode = code;\n } else {\n return stbi__errpuc(\"illegal code in raster\", \"Corrupt GIF\");\n }\n }\n }\n}\n\n// this function is designed to support animated gifs, although stb_image doesn't support it\n// two back is the image from two frames ago, used for a very specific disposal format\nstatic stbi_uc * stbi__gif_load_next(stbi__context * s, stbi__gif * g, int * comp, int req_comp, stbi_uc * two_back) {\n int dispose;\n int first_frame;\n int pi;\n int pcount;\n STBI_NOTUSED(req_comp);\n\n // on first frame, any non-written pixels get the background colour (non-transparent)\n first_frame = 0;\n if (g->out == 0) {\n if (!stbi__gif_header(s, g, comp, 0))\n return 0; // stbi__g_failure_reason set by stbi__gif_header\n if (!stbi__mad3sizes_valid(4, g->w, g->h, 0))\n return stbi__errpuc(\"too large\", \"GIF image is too large\");\n pcount = g->w * g->h;\n g->out = (stbi_uc *)stbi__malloc(4 * pcount);\n g->background = (stbi_uc *)stbi__malloc(4 * pcount);\n g->history = (stbi_uc *)stbi__malloc(pcount);\n if (!g->out || !g->background || !g->history)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n\n // image is treated as \"transparent\" at the start - ie, nothing overwrites the current background;\n // background colour is only used for pixels that are not rendered first frame, after that \"background\"\n // color refers to the color that was there the previous frame.\n memset(g->out, 0x00, 4 * pcount);\n memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent)\n memset(g->history, 0x00, pcount); // pixels that were affected previous frame\n first_frame = 1;\n } else {\n // second frame - how do we dispose of the previous one?\n dispose = (g->eflags & 0x1C) >> 2;\n pcount = g->w * g->h;\n\n if ((dispose == 3) && (two_back == 0)) {\n dispose = 2; // if I don't have an image to revert back to, default to the old background\n }\n\n if (dispose == 3) { // use previous graphic\n for (pi = 0; pi < pcount; ++pi) {\n if (g->history[pi]) {\n memcpy(&g->out[pi * 4], &two_back[pi * 4], 4);\n }\n }\n } else if (dispose == 2) {\n // restore what was changed last frame to background before that frame;\n for (pi = 0; pi < pcount; ++pi) {\n if (g->history[pi]) {\n memcpy(&g->out[pi * 4], &g->background[pi * 4], 4);\n }\n }\n } else {\n // This is a non-disposal case eithe way, so just\n // leave the pixels as is, and they will become the new background\n // 1: do not dispose\n // 0: not specified.\n }\n\n // background is what out is after the undoing of the previou frame;\n memcpy(g->background, g->out, 4 * g->w * g->h);\n }\n\n // clear my history;\n memset(g->history, 0x00, g->w * g->h); // pixels that were affected previous frame\n\n for (;;) {\n int tag = stbi__get8(s);\n switch (tag) {\n case 0x2C: /* Image Descriptor */\n {\n stbi__int32 x, y, w, h;\n stbi_uc * o;\n\n x = stbi__get16le(s);\n y = stbi__get16le(s);\n w = stbi__get16le(s);\n h = stbi__get16le(s);\n if (((x + w) > (g->w)) || ((y + h) > (g->h)))\n return stbi__errpuc(\"bad Image Descriptor\", \"Corrupt GIF\");\n\n g->line_size = g->w * 4;\n g->start_x = x * 4;\n g->start_y = y * g->line_size;\n g->max_x = g->start_x + w * 4;\n g->max_y = g->start_y + h * g->line_size;\n g->cur_x = g->start_x;\n g->cur_y = g->start_y;\n\n // if the width of the specified rectangle is 0, that means\n // we may not see *any* pixels or the image is malformed;\n // to make sure this is caught, move the current y down to\n // max_y (which is what out_gif_code checks).\n if (w == 0)\n g->cur_y = g->max_y;\n\n g->lflags = stbi__get8(s);\n\n if (g->lflags & 0x40) {\n g->step = 8 * g->line_size; // first interlaced spacing\n g->parse = 3;\n } else {\n g->step = g->line_size;\n g->parse = 0;\n }\n\n if (g->lflags & 0x80) {\n stbi__gif_parse_colortable(s, g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1);\n g->color_table = (stbi_uc *)g->lpal;\n } else if (g->flags & 0x80) {\n g->color_table = (stbi_uc *)g->pal;\n } else\n return stbi__errpuc(\"missing color table\", \"Corrupt GIF\");\n\n o = stbi__process_gif_raster(s, g);\n if (!o)\n return NULL;\n\n // if this was the first frame,\n pcount = g->w * g->h;\n if (first_frame && (g->bgindex > 0)) {\n // if first frame, any pixel not drawn to gets the background color\n for (pi = 0; pi < pcount; ++pi) {\n if (g->history[pi] == 0) {\n g->pal[g->bgindex][3] =\n 255; // just in case it was made transparent, undo that; It will be reset next frame if need be;\n memcpy(&g->out[pi * 4], &g->pal[g->bgindex], 4);\n }\n }\n }\n\n return o;\n }\n\n case 0x21: // Comment Extension.\n {\n int len;\n int ext = stbi__get8(s);\n if (ext == 0xF9) { // Graphic Control Extension.\n len = stbi__get8(s);\n if (len == 4) {\n g->eflags = stbi__get8(s);\n g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths.\n\n // unset old transparent\n if (g->transparent >= 0) {\n g->pal[g->transparent][3] = 255;\n }\n if (g->eflags & 0x01) {\n g->transparent = stbi__get8(s);\n if (g->transparent >= 0) {\n g->pal[g->transparent][3] = 0;\n }\n } else {\n // don't need transparent\n stbi__skip(s, 1);\n g->transparent = -1;\n }\n } else {\n stbi__skip(s, len);\n break;\n }\n }\n while ((len = stbi__get8(s)) != 0) {\n stbi__skip(s, len);\n }\n break;\n }\n\n case 0x3B: // gif stream termination code\n return (stbi_uc *)s; // using '1' causes warning on some compilers\n\n default:\n return stbi__errpuc(\"unknown code\", \"Corrupt GIF\");\n }\n }\n}\n\nstatic void * stbi__load_gif_main_outofmem(stbi__gif * g, stbi_uc * out, int ** delays) {\n STBI_FREE(g->out);\n STBI_FREE(g->history);\n STBI_FREE(g->background);\n\n if (out)\n STBI_FREE(out);\n if (delays && *delays)\n STBI_FREE(*delays);\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n}\n\nstatic void * stbi__load_gif_main(stbi__context * s, int ** delays, int * x, int * y, int * z, int * comp, int req_comp) {\n if (stbi__gif_test(s)) {\n int layers = 0;\n stbi_uc * u = 0;\n stbi_uc * out = 0;\n stbi_uc * two_back = 0;\n stbi__gif g;\n int stride;\n int out_size = 0;\n int delays_size = 0;\n\n STBI_NOTUSED(out_size);\n STBI_NOTUSED(delays_size);\n\n memset(&g, 0, sizeof(g));\n if (delays) {\n *delays = 0;\n }\n\n do {\n u = stbi__gif_load_next(s, &g, comp, req_comp, two_back);\n if (u == (stbi_uc *)s)\n u = 0; // end of animated gif marker\n\n if (u) {\n *x = g.w;\n *y = g.h;\n ++layers;\n stride = g.w * g.h * 4;\n\n if (out) {\n void * tmp = (stbi_uc *)STBI_REALLOC_SIZED(out, out_size, layers * stride);\n if (!tmp)\n return stbi__load_gif_main_outofmem(&g, out, delays);\n else {\n out = (stbi_uc *)tmp;\n out_size = layers * stride;\n }\n\n if (delays) {\n int * new_delays = (int *)STBI_REALLOC_SIZED(*delays, delays_size, sizeof(int) * layers);\n if (!new_delays)\n return stbi__load_gif_main_outofmem(&g, out, delays);\n *delays = new_delays;\n delays_size = layers * sizeof(int);\n }\n } else {\n out = (stbi_uc *)stbi__malloc(layers * stride);\n if (!out)\n return stbi__load_gif_main_outofmem(&g, out, delays);\n out_size = layers * stride;\n if (delays) {\n *delays = (int *)stbi__malloc(layers * sizeof(int));\n if (!*delays)\n return stbi__load_gif_main_outofmem(&g, out, delays);\n delays_size = layers * sizeof(int);\n }\n }\n memcpy(out + ((layers - 1) * stride), u, stride);\n if (layers >= 2) {\n two_back = out - 2 * stride;\n }\n\n if (delays) {\n (*delays)[layers - 1U] = g.delay;\n }\n }\n } while (u != 0);\n\n // free temp buffer;\n STBI_FREE(g.out);\n STBI_FREE(g.history);\n STBI_FREE(g.background);\n\n // do the final conversion after loading everything;\n if (req_comp && req_comp != 4)\n out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h);\n\n *z = layers;\n return out;\n } else {\n return stbi__errpuc(\"not GIF\", \"Image was not as a gif type.\");\n }\n}\n\nstatic void * stbi__gif_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n stbi_uc * u = 0;\n stbi__gif g;\n memset(&g, 0, sizeof(g));\n STBI_NOTUSED(ri);\n\n u = stbi__gif_load_next(s, &g, comp, req_comp, 0);\n if (u == (stbi_uc *)s)\n u = 0; // end of animated gif marker\n if (u) {\n *x = g.w;\n *y = g.h;\n\n // moved conversion to after successful load so that the same\n // can be done for multiple frames.\n if (req_comp && req_comp != 4)\n u = stbi__convert_format(u, 4, req_comp, g.w, g.h);\n } else if (g.out) {\n // if there was an error and we allocated an image buffer, free it!\n STBI_FREE(g.out);\n }\n\n // free buffers needed for multiple frame loading;\n STBI_FREE(g.history);\n STBI_FREE(g.background);\n\n return u;\n}\n\nstatic int stbi__gif_info(stbi__context * s, int * x, int * y, int * comp) { return stbi__gif_info_raw(s, x, y, comp); }\n#endif\n\n// *************************************************************************************************\n// Radiance RGBE HDR loader\n// originally by Nicolas Schulz\n#ifndef STBI_NO_HDR\nstatic int stbi__hdr_test_core(stbi__context * s, const char * signature) {\n int i;\n for (i = 0; signature[i]; ++i)\n if (stbi__get8(s) != signature[i])\n return 0;\n stbi__rewind(s);\n return 1;\n}\n\nstatic int stbi__hdr_test(stbi__context * s) {\n int r = stbi__hdr_test_core(s, \"#?RADIANCE\\n\");\n stbi__rewind(s);\n if (!r) {\n r = stbi__hdr_test_core(s, \"#?RGBE\\n\");\n stbi__rewind(s);\n }\n return r;\n}\n\n#define STBI__HDR_BUFLEN 1024\nstatic char * stbi__hdr_gettoken(stbi__context * z, char * buffer) {\n int len = 0;\n char c = '\\0';\n\n c = (char)stbi__get8(z);\n\n while (!stbi__at_eof(z) && c != '\\n') {\n buffer[len++] = c;\n if (len == STBI__HDR_BUFLEN - 1) {\n // flush to end of line\n while (!stbi__at_eof(z) && stbi__get8(z) != '\\n')\n ;\n break;\n }\n c = (char)stbi__get8(z);\n }\n\n buffer[len] = 0;\n return buffer;\n}\n\nstatic void stbi__hdr_convert(float * output, stbi_uc * input, int req_comp) {\n if (input[3] != 0) {\n float f1;\n // Exponent\n f1 = (float)ldexp(1.0f, input[3] - (int)(128 + 8));\n if (req_comp <= 2)\n output[0] = (input[0] + input[1] + input[2]) * f1 / 3;\n else {\n output[0] = input[0] * f1;\n output[1] = input[1] * f1;\n output[2] = input[2] * f1;\n }\n if (req_comp == 2)\n output[1] = 1;\n if (req_comp == 4)\n output[3] = 1;\n } else {\n switch (req_comp) {\n case 4:\n output[3] = 1; /* fallthrough */\n case 3:\n output[0] = output[1] = output[2] = 0;\n break;\n case 2:\n output[1] = 1; /* fallthrough */\n case 1:\n output[0] = 0;\n break;\n }\n }\n}\n\nstatic float * stbi__hdr_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n char buffer[STBI__HDR_BUFLEN];\n char * token;\n int valid = 0;\n int width, height;\n stbi_uc * scanline;\n float * hdr_data;\n int len;\n unsigned char count, value;\n int i, j, k, c1, c2, z;\n const char * headerToken;\n STBI_NOTUSED(ri);\n\n // Check identifier\n headerToken = stbi__hdr_gettoken(s, buffer);\n if (strcmp(headerToken, \"#?RADIANCE\") != 0 && strcmp(headerToken, \"#?RGBE\") != 0)\n return stbi__errpf(\"not HDR\", \"Corrupt HDR image\");\n\n // Parse header\n for (;;) {\n token = stbi__hdr_gettoken(s, buffer);\n if (token[0] == 0)\n break;\n if (strcmp(token, \"FORMAT=32-bit_rle_rgbe\") == 0)\n valid = 1;\n }\n\n if (!valid)\n return stbi__errpf(\"unsupported format\", \"Unsupported HDR format\");\n\n // Parse width and height\n // can't use sscanf() if we're not using stdio!\n token = stbi__hdr_gettoken(s, buffer);\n if (strncmp(token, \"-Y \", 3))\n return stbi__errpf(\"unsupported data layout\", \"Unsupported HDR format\");\n token += 3;\n height = (int)strtol(token, &token, 10);\n while (*token == ' ')\n ++token;\n if (strncmp(token, \"+X \", 3))\n return stbi__errpf(\"unsupported data layout\", \"Unsupported HDR format\");\n token += 3;\n width = (int)strtol(token, NULL, 10);\n\n if (height > STBI_MAX_DIMENSIONS)\n return stbi__errpf(\"too large\", \"Very large image (corrupt?)\");\n if (width > STBI_MAX_DIMENSIONS)\n return stbi__errpf(\"too large\", \"Very large image (corrupt?)\");\n\n *x = width;\n *y = height;\n\n if (comp)\n *comp = 3;\n if (req_comp == 0)\n req_comp = 3;\n\n if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0))\n return stbi__errpf(\"too large\", \"HDR image is too large\");\n\n // Read data\n hdr_data = (float *)stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0);\n if (!hdr_data)\n return stbi__errpf(\"outofmem\", \"Out of memory\");\n\n // Load image data\n // image data is stored as some number of sca\n if (width < 8 || width >= 32768) {\n // Read flat data\n for (j = 0; j < height; ++j) {\n for (i = 0; i < width; ++i) {\n stbi_uc rgbe[4];\n main_decode_loop:\n stbi__getn(s, rgbe, 4);\n stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp);\n }\n }\n } else {\n // Read RLE-encoded data\n scanline = NULL;\n\n for (j = 0; j < height; ++j) {\n c1 = stbi__get8(s);\n c2 = stbi__get8(s);\n len = stbi__get8(s);\n if (c1 != 2 || c2 != 2 || (len & 0x80)) {\n // not run-length encoded, so we have to actually use THIS data as a decoded\n // pixel (note this can't be a valid pixel--one of RGB must be >= 128)\n stbi_uc rgbe[4];\n rgbe[0] = (stbi_uc)c1;\n rgbe[1] = (stbi_uc)c2;\n rgbe[2] = (stbi_uc)len;\n rgbe[3] = (stbi_uc)stbi__get8(s);\n stbi__hdr_convert(hdr_data, rgbe, req_comp);\n i = 1;\n j = 0;\n STBI_FREE(scanline);\n goto main_decode_loop; // yes, this makes no sense\n }\n len <<= 8;\n len |= stbi__get8(s);\n if (len != width) {\n STBI_FREE(hdr_data);\n STBI_FREE(scanline);\n return stbi__errpf(\"invalid decoded scanline length\", \"corrupt HDR\");\n }\n if (scanline == NULL) {\n scanline = (stbi_uc *)stbi__malloc_mad2(width, 4, 0);\n if (!scanline) {\n STBI_FREE(hdr_data);\n return stbi__errpf(\"outofmem\", \"Out of memory\");\n }\n }\n\n for (k = 0; k < 4; ++k) {\n int nleft;\n i = 0;\n while ((nleft = width - i) > 0) {\n count = stbi__get8(s);\n if (count > 128) {\n // Run\n value = stbi__get8(s);\n count -= 128;\n if ((count == 0) || (count > nleft)) {\n STBI_FREE(hdr_data);\n STBI_FREE(scanline);\n return stbi__errpf(\"corrupt\", \"bad RLE data in HDR\");\n }\n for (z = 0; z < count; ++z)\n scanline[i++ * 4 + k] = value;\n } else {\n // Dump\n if ((count == 0) || (count > nleft)) {\n STBI_FREE(hdr_data);\n STBI_FREE(scanline);\n return stbi__errpf(\"corrupt\", \"bad RLE data in HDR\");\n }\n for (z = 0; z < count; ++z)\n scanline[i++ * 4 + k] = stbi__get8(s);\n }\n }\n }\n for (i = 0; i < width; ++i)\n stbi__hdr_convert(hdr_data + (j * width + i) * req_comp, scanline + i * 4, req_comp);\n }\n if (scanline)\n STBI_FREE(scanline);\n }\n\n return hdr_data;\n}\n\nstatic int stbi__hdr_info(stbi__context * s, int * x, int * y, int * comp) {\n char buffer[STBI__HDR_BUFLEN];\n char * token;\n int valid = 0;\n int dummy;\n\n if (!x)\n x = &dummy;\n if (!y)\n y = &dummy;\n if (!comp)\n comp = &dummy;\n\n if (stbi__hdr_test(s) == 0) {\n stbi__rewind(s);\n return 0;\n }\n\n for (;;) {\n token = stbi__hdr_gettoken(s, buffer);\n if (token[0] == 0)\n break;\n if (strcmp(token, \"FORMAT=32-bit_rle_rgbe\") == 0)\n valid = 1;\n }\n\n if (!valid) {\n stbi__rewind(s);\n return 0;\n }\n token = stbi__hdr_gettoken(s, buffer);\n if (strncmp(token, \"-Y \", 3)) {\n stbi__rewind(s);\n return 0;\n }\n token += 3;\n *y = (int)strtol(token, &token, 10);\n while (*token == ' ')\n ++token;\n if (strncmp(token, \"+X \", 3)) {\n stbi__rewind(s);\n return 0;\n }\n token += 3;\n *x = (int)strtol(token, NULL, 10);\n *comp = 3;\n return 1;\n}\n#endif // STBI_NO_HDR\n\n#ifndef STBI_NO_BMP\nstatic int stbi__bmp_info(stbi__context * s, int * x, int * y, int * comp) {\n void * p;\n stbi__bmp_data info;\n\n info.all_a = 255;\n p = stbi__bmp_parse_header(s, &info);\n if (p == NULL) {\n stbi__rewind(s);\n return 0;\n }\n if (x)\n *x = s->img_x;\n if (y)\n *y = s->img_y;\n if (comp) {\n if (info.bpp == 24 && info.ma == 0xff000000)\n *comp = 3;\n else\n *comp = info.ma ? 4 : 3;\n }\n return 1;\n}\n#endif\n\n#ifndef STBI_NO_PSD\nstatic int stbi__psd_info(stbi__context * s, int * x, int * y, int * comp) {\n int channelCount, dummy, depth;\n if (!x)\n x = &dummy;\n if (!y)\n y = &dummy;\n if (!comp)\n comp = &dummy;\n if (stbi__get32be(s) != 0x38425053) {\n stbi__rewind(s);\n return 0;\n }\n if (stbi__get16be(s) != 1) {\n stbi__rewind(s);\n return 0;\n }\n stbi__skip(s, 6);\n channelCount = stbi__get16be(s);\n if (channelCount < 0 || channelCount > 16) {\n stbi__rewind(s);\n return 0;\n }\n *y = stbi__get32be(s);\n *x = stbi__get32be(s);\n depth = stbi__get16be(s);\n if (depth != 8 && depth != 16) {\n stbi__rewind(s);\n return 0;\n }\n if (stbi__get16be(s) != 3) {\n stbi__rewind(s);\n return 0;\n }\n *comp = 4;\n return 1;\n}\n\nstatic int stbi__psd_is16(stbi__context * s) {\n int channelCount, depth;\n if (stbi__get32be(s) != 0x38425053) {\n stbi__rewind(s);\n return 0;\n }\n if (stbi__get16be(s) != 1) {\n stbi__rewind(s);\n return 0;\n }\n stbi__skip(s, 6);\n channelCount = stbi__get16be(s);\n if (channelCount < 0 || channelCount > 16) {\n stbi__rewind(s);\n return 0;\n }\n STBI_NOTUSED(stbi__get32be(s));\n STBI_NOTUSED(stbi__get32be(s));\n depth = stbi__get16be(s);\n if (depth != 16) {\n stbi__rewind(s);\n return 0;\n }\n return 1;\n}\n#endif\n\n#ifndef STBI_NO_PIC\nstatic int stbi__pic_info(stbi__context * s, int * x, int * y, int * comp) {\n int act_comp = 0, num_packets = 0, chained, dummy;\n stbi__pic_packet packets[10];\n\n if (!x)\n x = &dummy;\n if (!y)\n y = &dummy;\n if (!comp)\n comp = &dummy;\n\n if (!stbi__pic_is4(s, \"\\x53\\x80\\xF6\\x34\")) {\n stbi__rewind(s);\n return 0;\n }\n\n stbi__skip(s, 88);\n\n *x = stbi__get16be(s);\n *y = stbi__get16be(s);\n if (stbi__at_eof(s)) {\n stbi__rewind(s);\n return 0;\n }\n if ((*x) != 0 && (1 << 28) / (*x) < (*y)) {\n stbi__rewind(s);\n return 0;\n }\n\n stbi__skip(s, 8);\n\n do {\n stbi__pic_packet * packet;\n\n if (num_packets == sizeof(packets) / sizeof(packets[0]))\n return 0;\n\n packet = &packets[num_packets++];\n chained = stbi__get8(s);\n packet->size = stbi__get8(s);\n packet->type = stbi__get8(s);\n packet->channel = stbi__get8(s);\n act_comp |= packet->channel;\n\n if (stbi__at_eof(s)) {\n stbi__rewind(s);\n return 0;\n }\n if (packet->size != 8) {\n stbi__rewind(s);\n return 0;\n }\n } while (chained);\n\n *comp = (act_comp & 0x10 ? 4 : 3);\n\n return 1;\n}\n#endif\n\n// *************************************************************************************************\n// Portable Gray Map and Portable Pixel Map loader\n// by Ken Miller\n//\n// PGM: http://netpbm.sourceforge.net/doc/pgm.html\n// PPM: http://netpbm.sourceforge.net/doc/ppm.html\n//\n// Known limitations:\n// Does not support comments in the header section\n// Does not support ASCII image data (formats P2 and P3)\n\n#ifndef STBI_NO_PNM\n\nstatic int stbi__pnm_test(stbi__context * s) {\n char p, t;\n p = (char)stbi__get8(s);\n t = (char)stbi__get8(s);\n if (p != 'P' || (t != '5' && t != '6')) {\n stbi__rewind(s);\n return 0;\n }\n return 1;\n}\n\nstatic void * stbi__pnm_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) {\n stbi_uc * out;\n STBI_NOTUSED(ri);\n\n ri->bits_per_channel = stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n);\n if (ri->bits_per_channel == 0)\n return 0;\n\n if (s->img_y > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n if (s->img_x > STBI_MAX_DIMENSIONS)\n return stbi__errpuc(\"too large\", \"Very large image (corrupt?)\");\n\n *x = s->img_x;\n *y = s->img_y;\n if (comp)\n *comp = s->img_n;\n\n if (!stbi__mad4sizes_valid(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0))\n return stbi__errpuc(\"too large\", \"PNM too large\");\n\n out = (stbi_uc *)stbi__malloc_mad4(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0);\n if (!out)\n return stbi__errpuc(\"outofmem\", \"Out of memory\");\n if (!stbi__getn(s, out, s->img_n * s->img_x * s->img_y * (ri->bits_per_channel / 8))) {\n STBI_FREE(out);\n return stbi__errpuc(\"bad PNM\", \"PNM file truncated\");\n }\n\n if (req_comp && req_comp != s->img_n) {\n if (ri->bits_per_channel == 16) {\n out = (stbi_uc *)stbi__convert_format16((stbi__uint16 *)out, s->img_n, req_comp, s->img_x, s->img_y);\n } else {\n out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y);\n }\n if (out == NULL)\n return out; // stbi__convert_format frees input on failure\n }\n return out;\n}\n\nstatic int stbi__pnm_isspace(char c) { return c == ' ' || c == '\\t' || c == '\\n' || c == '\\v' || c == '\\f' || c == '\\r'; }\n\nstatic void stbi__pnm_skip_whitespace(stbi__context * s, char * c) {\n for (;;) {\n while (!stbi__at_eof(s) && stbi__pnm_isspace(*c))\n *c = (char)stbi__get8(s);\n\n if (stbi__at_eof(s) || *c != '#')\n break;\n\n while (!stbi__at_eof(s) && *c != '\\n' && *c != '\\r')\n *c = (char)stbi__get8(s);\n }\n}\n\nstatic int stbi__pnm_isdigit(char c) { return c >= '0' && c <= '9'; }\n\nstatic int stbi__pnm_getinteger(stbi__context * s, char * c) {\n int value = 0;\n\n while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) {\n value = value * 10 + (*c - '0');\n *c = (char)stbi__get8(s);\n if ((value > 214748364) || (value == 214748364 && *c > '7'))\n return stbi__err(\"integer parse overflow\", \"Parsing an integer in the PPM header overflowed a 32-bit int\");\n }\n\n return value;\n}\n\nstatic int stbi__pnm_info(stbi__context * s, int * x, int * y, int * comp) {\n int maxv, dummy;\n char c, p, t;\n\n if (!x)\n x = &dummy;\n if (!y)\n y = &dummy;\n if (!comp)\n comp = &dummy;\n\n stbi__rewind(s);\n\n // Get identifier\n p = (char)stbi__get8(s);\n t = (char)stbi__get8(s);\n if (p != 'P' || (t != '5' && t != '6')) {\n stbi__rewind(s);\n return 0;\n }\n\n *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm\n\n c = (char)stbi__get8(s);\n stbi__pnm_skip_whitespace(s, &c);\n\n *x = stbi__pnm_getinteger(s, &c); // read width\n if (*x == 0)\n return stbi__err(\"invalid width\", \"PPM image header had zero or overflowing width\");\n stbi__pnm_skip_whitespace(s, &c);\n\n *y = stbi__pnm_getinteger(s, &c); // read height\n if (*y == 0)\n return stbi__err(\"invalid width\", \"PPM image header had zero or overflowing width\");\n stbi__pnm_skip_whitespace(s, &c);\n\n maxv = stbi__pnm_getinteger(s, &c); // read max value\n if (maxv > 65535)\n return stbi__err(\"max value > 65535\", \"PPM image supports only 8-bit and 16-bit images\");\n else if (maxv > 255)\n return 16;\n else\n return 8;\n}\n\nstatic int stbi__pnm_is16(stbi__context * s) {\n if (stbi__pnm_info(s, NULL, NULL, NULL) == 16)\n return 1;\n return 0;\n}\n#endif\n\nstatic int stbi__info_main(stbi__context * s, int * x, int * y, int * comp) {\n#ifndef STBI_NO_JPEG\n if (stbi__jpeg_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_PNG\n if (stbi__png_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_GIF\n if (stbi__gif_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_BMP\n if (stbi__bmp_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_PSD\n if (stbi__psd_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_PIC\n if (stbi__pic_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_PNM\n if (stbi__pnm_info(s, x, y, comp))\n return 1;\n#endif\n\n#ifndef STBI_NO_HDR\n if (stbi__hdr_info(s, x, y, comp))\n return 1;\n#endif\n\n// test tga last because it's a crappy test!\n#ifndef STBI_NO_TGA\n if (stbi__tga_info(s, x, y, comp))\n return 1;\n#endif\n return stbi__err(\"unknown image type\", \"Image not of any known type, or corrupt\");\n}\n\nstatic int stbi__is_16_main(stbi__context * s) {\n#ifndef STBI_NO_PNG\n if (stbi__png_is16(s))\n return 1;\n#endif\n\n#ifndef STBI_NO_PSD\n if (stbi__psd_is16(s))\n return 1;\n#endif\n\n#ifndef STBI_NO_PNM\n if (stbi__pnm_is16(s))\n return 1;\n#endif\n return 0;\n}\n\n#ifndef STBI_NO_STDIO\nSTBIDEF int stbi_info(char const * filename, int * x, int * y, int * comp) {\n FILE * f = stbi__fopen(filename, \"rb\");\n int result;\n if (!f)\n return stbi__err(\"can't fopen\", \"Unable to open file\");\n result = stbi_info_from_file(f, x, y, comp);\n fclose(f);\n return result;\n}\n\nSTBIDEF int stbi_info_from_file(FILE * f, int * x, int * y, int * comp) {\n int r;\n stbi__context s;\n long pos = ftell(f);\n stbi__start_file(&s, f);\n r = stbi__info_main(&s, x, y, comp);\n fseek(f, pos, SEEK_SET);\n return r;\n}\n\nSTBIDEF int stbi_is_16_bit(char const * filename) {\n FILE * f = stbi__fopen(filename, \"rb\");\n int result;\n if (!f)\n return stbi__err(\"can't fopen\", \"Unable to open file\");\n result = stbi_is_16_bit_from_file(f);\n fclose(f);\n return result;\n}\n\nSTBIDEF int stbi_is_16_bit_from_file(FILE * f) {\n int r;\n stbi__context s;\n long pos = ftell(f);\n stbi__start_file(&s, f);\n r = stbi__is_16_main(&s);\n fseek(f, pos, SEEK_SET);\n return r;\n}\n#endif // !STBI_NO_STDIO\n\nSTBIDEF int stbi_info_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp) {\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__info_main(&s, x, y, comp);\n}\n\nSTBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const * c, void * user, int * x, int * y, int * comp) {\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)c, user);\n return stbi__info_main(&s, x, y, comp);\n}\n\nSTBIDEF int stbi_is_16_bit_from_memory(stbi_uc const * buffer, int len) {\n stbi__context s;\n stbi__start_mem(&s, buffer, len);\n return stbi__is_16_main(&s);\n}\n\nSTBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * c, void * user) {\n stbi__context s;\n stbi__start_callbacks(&s, (stbi_io_callbacks *)c, user);\n return stbi__is_16_main(&s);\n}\n\n#endif // STB_IMAGE_IMPLEMENTATION\n\n/*\n revision history:\n 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs\n 2.19 (2018-02-11) fix warning\n 2.18 (2018-01-30) fix warnings\n 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug\n 1-bit BMP\n *_is_16_bit api\n avoid warnings\n 2.16 (2017-07-23) all functions have 16-bit variants;\n STBI_NO_STDIO works again;\n compilation fixes;\n fix rounding in unpremultiply;\n optimize vertical flip;\n disable raw_len validation;\n documentation fixes\n 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode;\n warning fixes; disable run-time SSE detection on gcc;\n uniform handling of optional \"return\" values;\n thread-safe initialization of zlib tables\n 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs\n 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now\n 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes\n 2.11 (2016-04-02) allocate large structures on the stack\n remove white matting for transparent PSD\n fix reported channel count for PNG & BMP\n re-enable SSE2 in non-gcc 64-bit\n support RGB-formatted JPEG\n read 16-bit PNGs (only as 8-bit)\n 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED\n 2.09 (2016-01-16) allow comments in PNM files\n 16-bit-per-pixel TGA (not bit-per-component)\n info() for TGA could break due to .hdr handling\n info() for BMP to shares code instead of sloppy parse\n can use STBI_REALLOC_SIZED if allocator doesn't support realloc\n code cleanup\n 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA\n 2.07 (2015-09-13) fix compiler warnings\n partial animated GIF support\n limited 16-bpc PSD support\n #ifdef unused functions\n bug with < 92 byte PIC,PNM,HDR,TGA\n 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value\n 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning\n 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit\n 2.03 (2015-04-12) extra corruption checking (mmozeiko)\n stbi_set_flip_vertically_on_load (nguillemot)\n fix NEON support; fix mingw support\n 2.02 (2015-01-19) fix incorrect assert, fix warning\n 2.01 (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2\n 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG\n 2.00 (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg)\n progressive JPEG (stb)\n PGM/PPM support (Ken Miller)\n STBI_MALLOC,STBI_REALLOC,STBI_FREE\n GIF bugfix -- seemingly never worked\n STBI_NO_*, STBI_ONLY_*\n 1.48 (2014-12-14) fix incorrectly-named assert()\n 1.47 (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb)\n optimize PNG (ryg)\n fix bug in interlaced PNG with user-specified channel count (stb)\n 1.46 (2014-08-26)\n fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG\n 1.45 (2014-08-16)\n fix MSVC-ARM internal compiler error by wrapping malloc\n 1.44 (2014-08-07)\n various warning fixes from Ronny Chevalier\n 1.43 (2014-07-15)\n fix MSVC-only compiler problem in code changed in 1.42\n 1.42 (2014-07-09)\n don't define _CRT_SECURE_NO_WARNINGS (affects user code)\n fixes to stbi__cleanup_jpeg path\n added STBI_ASSERT to avoid requiring assert.h\n 1.41 (2014-06-25)\n fix search&replace from 1.36 that messed up comments/error messages\n 1.40 (2014-06-22)\n fix gcc struct-initialization warning\n 1.39 (2014-06-15)\n fix to TGA optimization when req_comp != number of components in TGA;\n fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite)\n add support for BMP version 5 (more ignored fields)\n 1.38 (2014-06-06)\n suppress MSVC warnings on integer casts truncating values\n fix accidental rename of 'skip' field of I/O\n 1.37 (2014-06-04)\n remove duplicate typedef\n 1.36 (2014-06-03)\n convert to header file single-file library\n if de-iphone isn't set, load iphone images color-swapped instead of returning NULL\n 1.35 (2014-05-27)\n various warnings\n fix broken STBI_SIMD path\n fix bug where stbi_load_from_file no longer left file pointer in correct place\n fix broken non-easy path for 32-bit BMP (possibly never used)\n TGA optimization by Arseny Kapoulkine\n 1.34 (unknown)\n use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case\n 1.33 (2011-07-14)\n make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements\n 1.32 (2011-07-13)\n support for \"info\" function for all supported filetypes (SpartanJ)\n 1.31 (2011-06-20)\n a few more leak fixes, bug in PNG handling (SpartanJ)\n 1.30 (2011-06-11)\n added ability to load files via callbacks to accomidate custom input streams (Ben Wenger)\n removed deprecated format-specific test/load functions\n removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks\n anyway error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) fix inefficiency in\n decoding 32-bit BMP (David Woo) 1.29 (2010-08-16) various warning fixes from Aurelien Pocheville 1.28 (2010-08-01)\n fix bug in GIF palette transparency (SpartanJ)\n 1.27 (2010-08-01)\n cast-to-stbi_uc to fix warnings\n 1.26 (2010-07-24)\n fix bug in file buffering for PNG reported by SpartanJ\n 1.25 (2010-07-17)\n refix trans_data warning (Won Chun)\n 1.24 (2010-07-12)\n perf improvements reading from files on platforms with lock-heavy fgetc()\n minor perf improvements for jpeg\n deprecated type-specific functions so we'll get feedback if they're needed\n attempt to fix trans_data warning (Won Chun)\n 1.23 fixed bug in iPhone support\n 1.22 (2010-07-10)\n removed image *writing* support\n stbi_info support from Jetro Lauha\n GIF support from Jean-Marc Lienher\n iPhone PNG-extensions from James Brown\n warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva)\n 1.21 fix use of 'stbi_uc' in header (reported by jon blow)\n 1.20 added support for Softimage PIC, by Tom Seddon\n 1.19 bug in interlaced PNG corruption check (found by ryg)\n 1.18 (2008-08-02)\n fix a threading bug (local mutable static)\n 1.17 support interlaced PNG\n 1.16 major bugfix - stbi__convert_format converted one too many pixels\n 1.15 initialize some fields for thread safety\n 1.14 fix threadsafe conversion bug\n header-file-only version (#define STBI_HEADER_FILE_ONLY before including)\n 1.13 threadsafe\n 1.12 const qualifiers in the API\n 1.11 Support installable IDCT, colorspace conversion routines\n 1.10 Fixes for 64-bit (don't use \"unsigned long\")\n optimized upsampling by Fabian \"ryg\" Giesen\n 1.09 Fix format-conversion for PSD code (bad global variables!)\n 1.08 Thatcher Ulrich's PSD code integrated by Nicolas Schulz\n 1.07 attempt to fix C++ warning/errors again\n 1.06 attempt to fix C++ warning/errors again\n 1.05 fix TGA loading to return correct *comp and use good luminance calc\n 1.04 default float alpha is 1, not 255; use 'void *' for stbi_image_free\n 1.03 bugfixes to STBI_NO_STDIO, STBI_NO_HDR\n 1.02 support for (subset of) HDR files, float interface for preferred access to them\n 1.01 fix bug: possible bug in handling right-side up bmps... not sure\n fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all\n 1.00 interface to zlib that skips zlib header\n 0.99 correct handling of alpha in palette\n 0.98 TGA loader by lonesock; dynamically add loaders (untested)\n 0.97 jpeg errors on too large a file; also catch another malloc failure\n 0.96 fix detection of invalid v value - particleman@mollyrocket forum\n 0.95 during header scan, seek to markers in case of padding\n 0.94 STBI_NO_STDIO to disable stdio usage; rename all #defines the same\n 0.93 handle jpegtran output; verbose errors\n 0.92 read 4,8,16,24,32-bit BMP files of several formats\n 0.91 output 24-bit Windows 3.0 BMP files\n 0.90 fix a few more warnings; bump version number to approach 1.0\n 0.61 bugfixes due to Marc LeBlanc, Christopher Lloyd\n 0.60 fix compiling as c++\n 0.59 fix warnings: merge Dave Moore's -Wall fixes\n 0.58 fix bug: zlib uncompressed mode len/nlen was wrong endian\n 0.57 fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available\n 0.56 fix bug: zlib uncompressed mode len vs. nlen\n 0.55 fix bug: restart_interval not initialized to 0\n 0.54 allow NULL for 'int *comp'\n 0.53 fix bug in png 3->4; speedup png decoding\n 0.52 png handles req_comp=3,4 directly; minor cleanup; jpeg comments\n 0.51 obey req_comp requests, 1-component jpegs return as 1-component,\n on 'test' only check type, not whether we support this variant\n 0.50 (2006-11-19)\n first released version\n*/\n\n/*\n------------------------------------------------------------------------------\nThis software is available under 2 licenses -- choose whichever you prefer.\n------------------------------------------------------------------------------\nALTERNATIVE A - MIT License\nCopyright (c) 2017 Sean Barrett\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\nof the Software, and to permit persons to whom the Software is furnished to do\nso, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n------------------------------------------------------------------------------\nALTERNATIVE B - Public Domain (www.unlicense.org)\nThis is free and unencumbered software released into the public domain.\nAnyone is free to copy, modify, publish, use, compile, sell, or distribute this\nsoftware, either in source code form or as a compiled binary, for any purpose,\ncommercial or non-commercial, and by any means.\nIn jurisdictions that recognize copyright laws, the author or authors of this\nsoftware dedicate any and all copyright interest in the software to the public\ndomain. We make this dedication for the benefit of the public at large and to\nthe detriment of our heirs and successors. We intend this dedication to be an\novert act of relinquishment in perpetuity of all present and future rights to\nthis software under copyright law.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\nACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\nWITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n------------------------------------------------------------------------------\n*/\n" }, { "path": "common/train.cpp", "content": "#include \"train.h\"\n#include \"common.h\"\n\n#include \n#include \n#include \n\nstruct random_normal_distribution {\n std::mt19937 gen;\n std::normal_distribution rd;\n float min;\n float max;\n};\n\nstruct random_uniform_distribution {\n std::mt19937 gen;\n std::uniform_real_distribution rd;\n};\n\nstruct train_state * init_train_state() {\n struct train_state * state = new struct train_state;\n state->train_its = 0;\n state->train_samples = 0;\n state->train_tokens = 0;\n state->train_epochs = 0;\n state->shuffle_samples_hash = 0;\n state->shuffle_sample_count = 0;\n state->shuffle_next_sample = 0;\n state->shuffle_rng_state_current = \"\";\n state->shuffle_rng_state_next = \"\";\n\n state->opt = new struct ggml_opt_context;\n state->opt->ctx = NULL;\n state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);\n state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;\n state->opt->loss_after = 0.0f;\n\n return state;\n}\n\nvoid free_train_state(struct train_state * state) {\n delete state->opt;\n delete state;\n}\n\nstruct random_normal_distribution * init_random_normal_distribution(\n int seed, float mean, float std, float min, float max\n) {\n struct random_normal_distribution * rnd = (struct random_normal_distribution *) malloc(sizeof(struct random_normal_distribution));\n rnd->gen = std::mt19937(seed);\n rnd->rd = std::normal_distribution{mean, std};\n rnd->min = min;\n rnd->max = max;\n return rnd;\n}\n\nstruct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max) {\n struct random_uniform_distribution * rnd = (struct random_uniform_distribution *) malloc(sizeof(struct random_uniform_distribution));\n rnd->gen = std::mt19937(seed);\n rnd->rd = std::uniform_real_distribution{min, max};\n return rnd;\n}\n\nvoid free_random_normal_distribution (struct random_normal_distribution * rnd) {\n free(rnd);\n}\n\nvoid free_random_uniform_distribution(struct random_uniform_distribution * rnd) {\n free(rnd);\n}\n\nstruct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {\n float scale = 1.0f; // xavier\n switch (tensor->n_dims) {\n case 1:\n scale /= sqrtf((float) tensor->ne[0]);\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);\n *dst = scale * frand_normal(rnd);\n }\n break;\n case 2:\n scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);\n *dst = scale * frand_normal(rnd);\n }\n }\n break;\n case 3:\n scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);\n for (int i2 = 0; i2 < tensor->ne[2]; i2++) {\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);\n *dst = scale * frand_normal(rnd);\n }\n }\n }\n break;\n case 4:\n scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);\n for (int i3 = 0; i3 < tensor->ne[3]; i3++) {\n for (int i2 = 0; i2 < tensor->ne[2]; i2++) {\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);\n *dst = scale * frand_normal(rnd);\n }\n }\n }\n }\n break;\n default:\n die(\"Unsupported tensor->n_dims\");\n };\n return tensor;\n}\n\nstruct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {\n switch (tensor->n_dims) {\n case 1:\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);\n *dst = frand_uniform(rnd);\n }\n break;\n case 2:\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);\n *dst = frand_uniform(rnd);\n }\n }\n break;\n case 3:\n for (int i2 = 0; i2 < tensor->ne[2]; i2++) {\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);\n *dst = frand_uniform(rnd);\n }\n }\n }\n break;\n case 4:\n for (int i3 = 0; i3 < tensor->ne[3]; i3++) {\n for (int i2 = 0; i2 < tensor->ne[2]; i2++) {\n for (int i1 = 0; i1 < tensor->ne[1]; i1++) {\n for (int i0 = 0; i0 < tensor->ne[0]; i0++) {\n float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);\n *dst = frand_uniform(rnd);\n }\n }\n }\n }\n break;\n default:\n die(\"Unsupported tensor->n_dims\");\n };\n return tensor;\n}\n\nfloat frand() {\n return (float)rand()/((float)(RAND_MAX) + 1.0f);\n}\n\nfloat frand_normal(struct random_normal_distribution * rnd) {\n return fclamp(rnd->rd(rnd->gen), rnd->min, rnd->max);\n}\n\nfloat frand_uniform(struct random_uniform_distribution * rnd) {\n return rnd->rd(rnd->gen);\n}\n\nint clamp(const int v, const int min, const int max) {\n return ((v < min) ? (min) : (v > max) ? (max) : v);\n}\n\nfloat fclamp(const float v, const float min, const float max) {\n return ((v < min) ? (min) : (v > max) ? (max) : v);\n}\n\nvoid assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {\n GGML_ASSERT(tensor->n_dims == 1);\n GGML_ASSERT(tensor->ne[0] == ne0);\n}\n\nvoid assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {\n GGML_ASSERT(tensor->n_dims == 2);\n GGML_ASSERT(tensor->ne[0] == ne0);\n GGML_ASSERT(tensor->ne[1] == ne1);\n}\n\nvoid assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {\n GGML_ASSERT(tensor->n_dims == 3);\n GGML_ASSERT(tensor->ne[0] == ne0);\n GGML_ASSERT(tensor->ne[1] == ne1);\n GGML_ASSERT(tensor->ne[2] == ne2);\n}\n\nvoid assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {\n GGML_ASSERT(tensor->n_dims == 4);\n GGML_ASSERT(tensor->ne[0] == ne0);\n GGML_ASSERT(tensor->ne[1] == ne1);\n GGML_ASSERT(tensor->ne[2] == ne2);\n GGML_ASSERT(tensor->ne[3] == ne3);\n}\n\nint64_t get_example_targets_batch(\n struct llama_context * lctx,\n struct ggml_tensor * tokens_input,\n struct ggml_tensor * target_probs,\n int64_t example_id,\n const size_t * samples_offs,\n const size_t * samples_begin,\n const size_t * samples_size,\n size_t samples_count,\n const llama_token * train_data,\n size_t n_train_data,\n bool separate_with_eos,\n bool separate_with_bos,\n bool fill_with_next_samples,\n bool sample_random_offsets\n) {\n GGML_ASSERT(samples_count > 0);\n GGML_ASSERT(tokens_input->n_dims == 2);\n GGML_ASSERT(target_probs->n_dims == 3);\n int64_t n_vocab = target_probs->ne[0];\n int64_t n_tokens = tokens_input->ne[0];\n int64_t n_batch = tokens_input->ne[1];\n GGML_ASSERT(n_vocab == target_probs->ne[0]);\n GGML_ASSERT(n_tokens == target_probs->ne[1]);\n GGML_ASSERT(n_batch == target_probs->ne[2]);\n\n int64_t used_samples = 0;\n\n ggml_set_f32(target_probs, 0.0f);\n llama_token bos = llama_token_bos(llama_get_model(lctx));\n llama_token eos = llama_token_eos(llama_get_model(lctx));\n // printf(\"%s: example_id=%d n_batch=%d n_train_samples=%zu\\n\", __func__, example_id, n_batch, n_train_samples);\n for (int k=0; k= sample_size && fill_with_next_samples) {\n if (!sample_separation_eos) {\n // insert eos token to separate samples\n sample_separation_eos = true;\n } else if (!sample_separation_bos) {\n // insert bos token to separate samples\n sample_separation_bos = true;\n token = bos;\n } else {\n // sample separation is done, continue with next sample\n sample_separation_eos = !separate_with_eos;\n sample_separation_bos = !separate_with_bos;\n sample_offs = 0;\n sample_idx = (example_id + used_samples) % samples_count;\n sample_begin = samples_begin[sample_idx];\n sample_size = samples_size[sample_idx];\n ++used_samples;\n }\n }\n // note: no else-if here\n if (sample_offs < sample_size) {\n token = clamp(train_data[sample_begin+sample_offs], 0, (llama_token) (n_vocab - 1));\n ++sample_offs;\n }\n ggml_set_f32_nd(target_probs, token, (int) i, (int) k, 0, +1.0f);\n if (i+1> rng;\n}\n\nstd::string mt19937_get_state(const std::mt19937& rng) {\n std::stringstream s_rng_state;\n s_rng_state.imbue(std::locale::classic());\n s_rng_state << rng;\n return s_rng_state.str();\n}\n\nstd::string mt19937_seed_to_state(unsigned seed) {\n std::mt19937 rng(seed);\n return mt19937_get_state(rng);\n}\n\nstd::string shuffle_samples(\n const std::string & rng_state,\n size_t * shuffled_offs,\n size_t * shuffled_begins,\n size_t * shuffled_sizes,\n const size_t * begins,\n const size_t * sizes,\n size_t count) {\n if (count == 0) return rng_state;\n\n std::mt19937 rng;\n mt19937_set_state(rng, rng_state);\n\n // sort indices by random value for each index\n std::vector idcs;\n {\n std::vector rnd;\n idcs.resize(count);\n rnd.resize(count);\n for (unsigned i=0; i h_string;\n std::hash h_ull;\n size_t h = h_string(std::string(fn));\n h = hash_combine(h, h_ull((unsigned long long) sample_count));\n for (size_t i=0; i< sample_count; ++i) {\n h = hash_combine(h, h_ull((unsigned long long) samples_begin[i]));\n h = hash_combine(h, h_ull((unsigned long long) samples_size[i]));\n }\n return h;\n}\n\nstd::string replace_str(const char * s, const char * needle, const char * replacement) {\n std::string str = s;\n size_t pos = str.find(needle);\n if (pos != std::string::npos) {\n str.replace(pos, strlen(needle), replacement);\n }\n return str;\n}\n\nvoid print_duration(double fmillis) {\n if (fmillis < 1000.0f) {\n printf(\"%.1fms\", (float) fmillis);\n return;\n }\n const int64_t one_sec = 1000;\n const int64_t one_min = one_sec * 60;\n const int64_t one_hour = one_min * 60;\n const int64_t one_day = one_hour * 24;\n\n int64_t millis = (int64_t) fmillis;\n int64_t days = millis/one_day;\n int64_t hours = (millis - days*one_day)/one_hour;\n int64_t minutes = (millis - days*one_day - hours*one_hour)/one_min;\n int64_t seconds = (millis - days*one_day - hours*one_hour - minutes*one_min)/one_sec;\n\n // to print int64_t either cast to (long long int) or use macro PRId64 from \n if (days > 0) {\n printf(\"%lldd \", (long long int) days);\n }\n printf(\"%02lld:%02lld:%02lld\", (long long int) hours, (long long int) minutes, (long long int) seconds);\n}\n\nfloat cosine_decay(int64_t step, int64_t decay_steps, float minimum) {\n if (step > decay_steps) {\n step = decay_steps;\n }\n const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps));\n const float decay = (1 - minimum)*cosine_decay + minimum;\n return decay;\n}\n\nfloat cosine_decay_restart(int64_t step, int64_t decay_steps, float minimum, float restart_step_mult) {\n while (step > decay_steps) {\n step -= decay_steps;\n decay_steps = (int64_t) (restart_step_mult * decay_steps);\n }\n return cosine_decay(step, decay_steps, minimum);\n}\n\nfloat learning_schedule(\n int64_t step,\n int64_t warmup_steps,\n int64_t cos_decay_steps,\n float learning_rate,\n float overall_minimum,\n float cos_decay_minimum,\n float cos_decay_restart_step_mult,\n bool enable_restart) {\n\n float result =\n (step < warmup_steps)\n ? (float) step / (float) warmup_steps\n : enable_restart\n ? cosine_decay_restart(\n step - warmup_steps,\n cos_decay_steps,\n cos_decay_minimum,\n cos_decay_restart_step_mult)\n : cosine_decay(\n step,\n cos_decay_steps,\n cos_decay_minimum);\n\n float min = overall_minimum / learning_rate;\n result = min + result * (1.0f - min);\n return result;\n}\n\nstatic bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) {\n GGML_ASSERT(a != NULL);\n GGML_ASSERT(b != NULL);\n GGML_ASSERT(a->type == b->type);\n GGML_ASSERT(ggml_are_same_shape(a, b));\n GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b));\n\n return true;\n}\n\nvoid copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) {\n if (dst == NULL) {\n return;\n }\n struct ggml_tensor * t = ggml_get_tensor(ctx, name);\n GGML_ASSERT(are_same_layout(dst, t));\n memcpy(dst->data, t->data, ggml_nbytes(t));\n\n if (strlen(ggml_get_name(dst)) == 0) {\n ggml_set_name(dst, name);\n }\n}\n\n// gguf constants\nstatic const char * LLM_KV_OPTIMIZER_TYPE = \"optimizer.type\";\nstatic const char * LLM_KV_OPTIMIZER_TYPE_ADAM = \"adam\";\nstatic const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = \"lbfgs\";\nstatic const char * LLM_KV_OPTIMIZER_FILE_VERSION = \"optimizer.file_version\";\nstatic const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = \"optimizer.convergence_past_count\";\nstatic const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT = \"optimizer.parameter_count\";\nstatic const char * LLM_KV_OPTIMIZER_ITERATION_COUNT = \"optimizer.iteration_count\";\nstatic const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED = \"optimizer.just_initialized\";\nstatic const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = \"optimizer.adam.best_loss\";\nstatic const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = \"optimizer.adam.previous_loss\";\nstatic const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = \"optimizer.adam.no_improvement_count\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = \"optimizer.lbfgs.approx_hessian_count\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = \"optimizer.lbfgs.best_loss\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = \"optimizer.lbfgs.line_search_step\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = \"optimizer.lbfgs.line_search_j\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = \"optimizer.lbfgs.line_search_k\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = \"optimizer.lbfgs.line_search_end\";\nstatic const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = \"optimizer.lbfgs.no_improvement_count\";\n\nstatic const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = \"optimizer.adam.first_moments\";\nstatic const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = \"optimizer.adam.second_moments\";\nstatic const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = \"optimizer.adam.past_loss_values\";\n\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = \"optimizer.lbfgs.current_parameters\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = \"optimizer.lbfgs.previous_parameters\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = \"optimizer.lbfgs.current_gradients\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = \"optimizer.lbfgs.previous_gradients\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = \"optimizer.lbfgs.search_direction\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = \"optimizer.lbfgs.past_loss_values\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = \"optimizer.lbfgs.memory_alpha\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = \"optimizer.lbfgs.memory_ys\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = \"optimizer.lbfgs.memory_s\";\nstatic const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = \"optimizer.lbfgs.memory_y\";\n\nstatic const char * LLM_KV_TRAINING_FILE_VERSION = \"training.file_version\";\nstatic const char * LLM_KV_TRAINING_ITERATION_COUNT = \"training.iteration_count\";\nstatic const char * LLM_KV_TRAINING_SAMPLE_COUNT = \"training.sample_count\";\nstatic const char * LLM_KV_TRAINING_TOKEN_COUNT = \"training.token_count\";\nstatic const char * LLM_KV_TRAINING_EPOCH_COUNT = \"training.epoch_count\";\nstatic const char * LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH = \"training.shuffle.samples_hash\";\nstatic const char * LLM_KV_TRAINING_SHUFFLE_RNG_STATE = \"training.shuffle.rng_state\";\nstatic const char * LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT = \"training.shuffle.sample_count\";\nstatic const char * LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE = \"training.shuffle.next_sample\";\n\n#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \\\n{ \\\n const std::string skey(key); \\\n const int kid = gguf_find_key(ctx, skey.c_str()); \\\n if (kid >= 0) { \\\n enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \\\n if (ktype != (type)) { \\\n die_fmt(\"key %s has wrong type: %s\", skey.c_str(), gguf_type_name(ktype)); \\\n } \\\n (dst) = func(ctx, kid); \\\n } else if (req) { \\\n die_fmt(\"key not found in model: %s\", skey.c_str()); \\\n } \\\n}\n\nvoid load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) {\n // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read\n\n uint32_t file_version;\n GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION);\n GGML_ASSERT(file_version == 0);\n\n GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT);\n GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT);\n GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED);\n\n uint64_t nx;\n GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT);\n opt->nx = (size_t) nx;\n\n // don't call ggml_opt_init until optimizer type and optimizer specific parameters are know\n\n std::string opt_type;\n GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);\n if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {\n opt->params.type = GGML_OPT_ADAM;\n\n GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);\n GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);\n GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT);\n\n ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);\n\n copy_tensor_by_name(opt->adam.m, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);\n copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);\n copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);\n } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {\n opt->params.type = GGML_OPT_LBFGS;\n\n GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);\n GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);\n GGUF_GET_KEY(fctx, opt->lbfgs.step, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP);\n GGUF_GET_KEY(fctx, opt->lbfgs.j, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J);\n GGUF_GET_KEY(fctx, opt->lbfgs.k, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K);\n GGUF_GET_KEY(fctx, opt->lbfgs.end, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END);\n GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT);\n\n ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);\n\n copy_tensor_by_name(opt->lbfgs.x, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);\n copy_tensor_by_name(opt->lbfgs.xp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);\n copy_tensor_by_name(opt->lbfgs.g, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);\n copy_tensor_by_name(opt->lbfgs.gp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);\n copy_tensor_by_name(opt->lbfgs.d, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);\n copy_tensor_by_name(opt->lbfgs.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);\n copy_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);\n copy_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);\n copy_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);\n copy_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);\n } else {\n die(\"unknown optimizer type\\n\");\n }\n}\n\nvoid save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) {\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0);\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past);\n gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx);\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter);\n gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);\n\n switch (opt->params.type) {\n case GGML_OPT_ADAM:\n {\n gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);\n gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best);\n gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, opt->adam.fx_prev);\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement);\n\n ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);\n ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);\n if (opt->adam.pf) {\n ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);\n }\n\n gguf_add_tensor(fctx, opt->adam.m);\n gguf_add_tensor(fctx, opt->adam.v);\n if (opt->adam.pf) {\n gguf_add_tensor(fctx, opt->adam.pf);\n }\n } break;\n case GGML_OPT_LBFGS:\n {\n gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);\n gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, opt->lbfgs.fx_best);\n gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, opt->lbfgs.step);\n gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, opt->lbfgs.j);\n gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, opt->lbfgs.k);\n gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, opt->lbfgs.end);\n gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement);\n\n ggml_set_name(opt->lbfgs.x, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);\n ggml_set_name(opt->lbfgs.xp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);\n ggml_set_name(opt->lbfgs.g, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);\n ggml_set_name(opt->lbfgs.gp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);\n ggml_set_name(opt->lbfgs.d, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);\n if (opt->lbfgs.pf) {\n ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);\n }\n ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);\n ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);\n ggml_set_name(opt->lbfgs.lms, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);\n ggml_set_name(opt->lbfgs.lmy, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);\n\n gguf_add_tensor(fctx, opt->lbfgs.x);\n gguf_add_tensor(fctx, opt->lbfgs.xp);\n gguf_add_tensor(fctx, opt->lbfgs.g);\n gguf_add_tensor(fctx, opt->lbfgs.gp);\n gguf_add_tensor(fctx, opt->lbfgs.d);\n if (opt->lbfgs.pf) {\n gguf_add_tensor(fctx, opt->lbfgs.pf);\n }\n gguf_add_tensor(fctx, opt->lbfgs.lmal);\n gguf_add_tensor(fctx, opt->lbfgs.lmys);\n gguf_add_tensor(fctx, opt->lbfgs.lms);\n gguf_add_tensor(fctx, opt->lbfgs.lmy);\n } break;\n }\n}\n\nbool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train) {\n if (gguf_find_key(fctx, LLM_KV_TRAINING_FILE_VERSION) < 0) {\n return false;\n }\n\n uint32_t file_version;\n GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION);\n GGML_ASSERT(file_version <= 1);\n\n if (file_version == 0) {\n\n GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT);\n GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT);\n GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT);\n\n } else if (file_version == 1) {\n\n GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_ITERATION_COUNT);\n GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_SAMPLE_COUNT);\n GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_TOKEN_COUNT);\n GGUF_GET_KEY(fctx, train->train_epochs, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_EPOCH_COUNT);\n\n GGUF_GET_KEY(fctx, train->shuffle_samples_hash, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH);\n GGUF_GET_KEY(fctx, train->shuffle_rng_state_current, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_SHUFFLE_RNG_STATE);\n GGUF_GET_KEY(fctx, train->shuffle_sample_count, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT);\n GGUF_GET_KEY(fctx, train->shuffle_next_sample, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE);\n }\n\n load_opt_context_gguf(fctx, f_ggml_ctx, train->opt);\n return true;\n}\n\nvoid save_train_state_gguf(struct gguf_context * fctx, struct train_state * train) {\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION, 1);\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_ITERATION_COUNT, train->train_its);\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_SAMPLE_COUNT, train->train_samples);\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_TOKEN_COUNT, train->train_tokens);\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_EPOCH_COUNT, train->train_epochs);\n\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH, (uint64_t) train->shuffle_samples_hash);\n gguf_set_val_str(fctx, LLM_KV_TRAINING_SHUFFLE_RNG_STATE, train->shuffle_rng_state_current.c_str());\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT, (uint64_t) train->shuffle_sample_count);\n gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE, (uint64_t) train->shuffle_next_sample);\n\n save_opt_context_gguf(fctx, train->opt);\n}\n\n\nstruct llama_file {\n // use FILE * so we don't have to re-open the file to mmap\n FILE * fp;\n size_t size;\n\n llama_file(const char * fname, const char * mode) {\n fp = std::fopen(fname, mode);\n if (fp == NULL) {\n size = 0;\n } else {\n seek(0, SEEK_END);\n size = tell();\n seek(0, SEEK_SET);\n }\n }\n\n size_t tell() const {\n#ifdef _WIN32\n __int64 ret = _ftelli64(fp);\n#else\n long ret = std::ftell(fp);\n#endif\n GGML_ASSERT(ret != -1); // this really shouldn't fail\n return (size_t) ret;\n }\n\n void seek(size_t offset, int whence) {\n#ifdef _WIN32\n int ret = _fseeki64(fp, (__int64) offset, whence);\n#else\n int ret = std::fseek(fp, (long) offset, whence);\n#endif\n GGML_ASSERT(ret == 0); // same\n }\n\n void read_raw(void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n std::size_t ret = std::fread(ptr, size, 1, fp);\n if (ferror(fp)) {\n die_fmt(\"read error: %s\", strerror(errno));\n }\n if (ret != 1) {\n die(\"unexpectedly reached end of file\");\n }\n }\n\n std::uint32_t read_u32() {\n std::uint32_t ret;\n read_raw(&ret, sizeof(ret));\n return ret;\n }\n\n std::string read_string(std::uint32_t len) {\n std::vector chars(len);\n read_raw(chars.data(), len);\n return std::string(chars.data(), len);\n }\n\n void write_raw(const void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n size_t ret = std::fwrite(ptr, size, 1, fp);\n if (ret != 1) {\n die_fmt(\"write error: %s\", strerror(errno));\n }\n }\n\n void write_u32(std::uint32_t val) {\n write_raw(&val, sizeof(val));\n }\n\n ~llama_file() {\n if (fp) {\n std::fclose(fp);\n }\n }\n};\n\nstatic size_t utf8_len(char src) {\n const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };\n uint8_t highbits = static_cast(src) >> 4;\n return lookup[highbits];\n}\n\n// mark each byte with its utf8 unit number.\n// returns the number of utf8 characters.\n// e.g. when bytes == '\\x61\\xD0\\xB0\\x62',\n// then utf8_units will become [0,0,1,0]\n// utf8_nunits will become [1,2,2,1] and 3 is returned.\n// bytes where utf8_units is zero, are the begin of an utf8 character.\nstatic size_t mark_utf8_units(const char* bytes, int * utf8_units, int * utf8_nunits, size_t count) {\n size_t offs = 0;\n size_t count_utf8 = 0;\n while(offs < count) {\n int len = (int) utf8_len(bytes[offs]);\n for (int i=0; i & out_tokens,\n std::vector & out_samples_begin,\n std::vector & out_samples_size) {\n struct llama_file f(filename, \"rb\");\n\n if (f.size == 0) {\n out_tokens.clear();\n out_samples_begin.clear();\n out_samples_size.clear();\n printf(\"%s: warning: empty or not existing training data file '%s'\\n\",\n __func__, filename);\n return out_tokens.size();\n }\n\n // account for possible leading whitespace that will be added by tokenizer\n // e.g. '\\t' will be tokenized by llama spm tokenizer to [29871, 12]\n const int n_max_tokens_overhead = 1;\n\n std::vector buf;\n buf.resize(f.size);\n\n f.read_raw(buf.data(), f.size);\n\n std::vector utf8_units;\n std::vector utf8_nunits;\n utf8_units.resize(buf.size());\n utf8_nunits.resize(buf.size());\n mark_utf8_units(buf.data(), utf8_units.data(), utf8_nunits.data(), buf.size());\n\n if (sample_start.size() == 0) {\n // tokenize all data at once\n out_tokens.resize(buf.size() + n_max_tokens_overhead);\n\n int n_tokens = llama_tokenize(\n llama_get_model(lctx),\n buf.data(),\n (int) buf.size(),\n out_tokens.data(),\n (int) out_tokens.size(),\n false, false);\n if (n_tokens < 0) {\n out_tokens.resize(-n_tokens);\n n_tokens = llama_tokenize(\n llama_get_model(lctx),\n buf.data(),\n (int) buf.size(),\n out_tokens.data(),\n (int) out_tokens.size(),\n false, false);\n }\n if (n_tokens >= 0) {\n out_tokens.resize(n_tokens);\n }\n\n // generate sample starts at all token positions\n out_samples_begin.clear();\n out_samples_begin.push_back(0);\n out_samples_size.push_back(std::min((size_t) context_length, out_tokens.size()));\n size_t end = (out_tokens.size() >= context_length) ? (out_tokens.size() - context_length) : 0;\n for (size_t sample_begin = 1; sample_begin < end; ++sample_begin) {\n out_samples_begin.push_back(sample_begin);\n out_samples_size.push_back(context_length);\n }\n } else {\n // split data into samples and tokenize each sample\n std::string data_str(buf.data(), buf.size());\n out_samples_begin.clear();\n out_samples_size.clear();\n out_tokens.clear();\n\n // find all positions of pattern sample_start\n size_t sample_begin = data_str.find(sample_start, 0);\n while (sample_begin != std::string::npos) {\n out_samples_begin.push_back(sample_begin);\n const size_t search_start = sample_begin + sample_start.size();\n sample_begin = data_str.find(sample_start, search_start);\n }\n if (out_samples_begin.size() == 0) {\n printf(\"%s: warning: sample start pattern '%s' not found. inserting single sample at data begin\\n\",\n __func__, sample_start.c_str());\n out_samples_begin.push_back(0);\n }\n\n out_samples_size.resize(out_samples_begin.size(), 0);\n\n std::vector buf_sample;\n std::vector tok_sample;\n\n const size_t sample_begin_offset = (include_sample_start ? 0 : sample_start.size());\n size_t found_too_big_sample = 0;\n size_t found_too_small_sample = 0;\n size_t found_empty_sample = 0;\n size_t found_min_sample_size = SIZE_MAX;\n size_t found_max_sample_size = 0;\n\n size_t max_token_text_size = 0;\n int n_vocab = llama_n_vocab(llama_get_model(lctx));\n for (llama_token token=0; token < n_vocab; ++token) {\n max_token_text_size = std::max(\n max_token_text_size,\n strlen(llama_token_get_text(llama_get_model(lctx), token)));\n }\n\n // upper bound of context byte length.\n // strings with this byte length should always tokenize to at least context_length tokens.\n size_t context_byte_len = max_token_text_size*context_length;\n\n for (unsigned i=0; i 0) {\n // sample end is in the middle of an utf8 character.\n // advance sample_end to the begin of the next utf8 character.\n sample_end += utf8_nunits[sample_end] - utf8_units[sample_end];\n }\n size_t sample_size = sample_end - sample_begin;\n if (sample_size == 0) {\n ++found_empty_sample;\n }\n\n if (sample_size > 0) {\n // llama_tokenize expects zero terminated string,\n // copy sample into buffer and zero terminate it.\n buf_sample.resize(sample_size);\n memcpy(buf_sample.data(), data_str.data() + sample_begin, sample_size);\n\n // printf(\"sample: '%s'\\n\", buf_sample.data());\n\n // tokenize the sample\n tok_sample.resize(buf_sample.size() + n_max_tokens_overhead);\n int n_tokens = llama_tokenize(llama_get_model(lctx),\n buf_sample.data(),\n (int) buf_sample.size(),\n tok_sample.data(),\n (int) tok_sample.size(),\n false, false);\n if (n_tokens < 0) {\n tok_sample.resize(-n_tokens);\n n_tokens = llama_tokenize(llama_get_model(lctx),\n buf_sample.data(),\n (int) buf_sample.size(),\n tok_sample.data(),\n (int) tok_sample.size(),\n false, false);\n GGML_ASSERT(n_tokens >= 0);\n }\n GGML_ASSERT(n_tokens <= (int) tok_sample.size());\n\n if ((size_t) n_tokens > context_length) {\n ++found_too_big_sample;\n } else if ((size_t) n_tokens < context_length) {\n ++found_too_small_sample;\n }\n found_max_sample_size = std::max(found_max_sample_size, (size_t) n_tokens);\n found_min_sample_size = std::min(found_min_sample_size, (size_t) n_tokens);\n\n // write out tokens, start and size of sample\n // overwrite the string start position with the token start position\n out_samples_begin[i] = out_tokens.size();\n out_samples_size[i] = (size_t) n_tokens;\n out_tokens.insert(out_tokens.end(), tok_sample.begin(), tok_sample.begin() + n_tokens);\n } else {\n out_samples_begin[i] = out_tokens.size();\n out_samples_size[i] = 0;\n }\n\n }\n if (found_too_big_sample > 0) {\n printf(\"%s: warning: found %zu samples (max length %zu) that exceed context length of %u. samples will be cut off.\\n\",\n __func__, found_too_big_sample, found_max_sample_size, context_length);\n }\n\n if (found_too_small_sample > 0) {\n printf(\"%s: warning: found %zu samples (min length %zu) that are shorter than context length of %u.\\n\",\n __func__, found_too_small_sample, found_min_sample_size, context_length);\n }\n\n if (found_empty_sample) {\n printf(\"%s: warning: found %zu empty samples.\\n\",\n __func__, found_empty_sample);\n }\n }\n printf(\"%s: total number of samples: %zu\\n\",\n __func__, out_samples_begin.size());\n\n GGML_ASSERT(out_samples_begin.size() == out_samples_size.size());\n\n return out_tokens.size();\n}\n\nstd::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration) {\n std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest);\n return replace_str(filename, pattern_it, sit.c_str());\n}\n\nstruct train_params_common get_default_train_params_common() {\n struct train_params_common params;\n params.fn_train_data = \"shakespeare.txt\";\n params.fn_checkpoint_in = \"checkpoint.gguf\";\n params.fn_checkpoint_out = \"checkpoint-ITERATION.gguf\";\n params.pattern_fn_it = \"ITERATION\";\n params.fn_latest = \"LATEST\";\n\n params.print_usage = false;\n\n params.save_every = 10;\n\n params.seed = -1;\n\n params.n_ctx = 128;\n params.n_threads = 6;\n params.n_batch = 8;\n params.n_gradient_accumulation = 1;\n params.n_epochs = -1;\n params.n_gpu_layers = 0;\n\n params.custom_n_ctx = false;\n\n params.use_flash = true;\n params.use_checkpointing = true;\n\n params.sample_start = \"\";\n params.include_sample_start = false;\n params.escape = false;\n params.overlapping_samples = false;\n params.fill_with_next_samples = false;\n params.separate_with_eos = false;\n params.separate_with_bos = true;\n params.sample_random_offsets = false;\n params.force_reshuffle = false;\n\n params.opt_past = 0;\n params.opt_delta = 1e-5f;\n params.opt_max_no_improvement = 0;\n\n params.warmup = 100;\n params.cos_decay_steps = 1000;\n params.cos_decay_restart = 1.1f;\n params.cos_decay_min = 0.1f;\n params.enable_restart = false;\n\n params.adam_n_iter = 256;\n params.adam_alpha = 1e-3f;\n params.adam_min_alpha = 0;\n params.adam_decay = 1e-1f;\n params.adam_decay_min_ndim = 2;\n params.adam_beta1 = 0.9f;\n params.adam_beta2 = 0.999f;\n params.adam_gclip = 1.0f;\n params.adam_eps_f = 0.0f;\n\n return params;\n}\n\nvoid print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train_params_common * params) {\n // fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n // fprintf(stderr, \"\\n\");\n // fprintf(stderr, \"options:\\n\");\n // fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n fprintf(stderr, \" --train-data FNAME path from which to load training data (default '%s')\\n\", params->fn_train_data);\n fprintf(stderr, \" --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\\n\", params->fn_checkpoint_in);\n fprintf(stderr, \" --checkpoint-out FNAME path to save training checkpoint (default '%s')\\n\", params->fn_checkpoint_out);\n fprintf(stderr, \" --pattern-fn-it STR pattern in output filenames to be replaced by iteration number (default '%s')\\n\", params->pattern_fn_it);\n fprintf(stderr, \" --fn-latest STR string to use instead of iteration number for saving latest output (default '%s')\\n\", params->fn_latest);\n fprintf(stderr, \" --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%d')\\n\", params->save_every);\n fprintf(stderr, \" -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\\n\");\n fprintf(stderr, \" -c N, --ctx N Context size used during training (default %d)\\n\", params->n_ctx);\n fprintf(stderr, \" -t N, --threads N Number of threads (default %d)\\n\", params->n_threads);\n fprintf(stderr, \" -b N, --batch N Parallel batch size (default %d)\\n\", params->n_batch);\n fprintf(stderr, \" --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\\n\", params->n_gradient_accumulation);\n fprintf(stderr, \" --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\\n\", params->sample_start.c_str());\n fprintf(stderr, \" --include-sample-start Include the sample start in the samples. (default off)\\n\");\n fprintf(stderr, \" --escape process sample start escapes sequences (\\\\n, \\\\r, \\\\t, \\\\', \\\\\\\", \\\\\\\\)\\n\");\n fprintf(stderr, \" --overlapping-samples Samples my overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\\n\");\n fprintf(stderr, \" --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\\n\");\n fprintf(stderr, \" --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\\n\", params->separate_with_eos ? \" (default)\" : \"\");\n fprintf(stderr, \" --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\\n\", params->separate_with_bos ? \" (default)\" : \"\");\n fprintf(stderr, \" --no-separate-with-eos When fill-with-next-samples, don't insert end-of-sequence token between samples.%s\\n\", !params->separate_with_eos ? \" (default)\" : \"\");\n fprintf(stderr, \" --no-separate-with-bos When fill-with-next-samples, don't insert begin-of-sequence token between samples.%s\\n\", !params->separate_with_bos ? \" (default)\" : \"\");\n fprintf(stderr, \" --sample-random-offsets Use samples beginning at random offsets. Together with fill-with-next-samples this may help for training endless text generation.%s\\n\", params->sample_random_offsets ? \" (default)\" : \"\");\n fprintf(stderr, \" --force-reshuffle Force a reshuffling of data at program start, otherwise the shuffling of loaded checkpoint is resumed.\\n\");\n fprintf(stderr, \" --no-flash Don't use flash attention \\n\");\n fprintf(stderr, \" --use-flash Use flash attention (default)\\n\");\n fprintf(stderr, \" --no-checkpointing Don't use gradient checkpointing\\n\");\n fprintf(stderr, \" --use-checkpointing Use gradient checkpointing (default)\\n\");\n fprintf(stderr, \" --warmup N Only for Adam optimizer. Number of warmup steps (default %d)\\n\", params->warmup);\n fprintf(stderr, \" --cos-decay-steps N Only for Adam optimizer. Number of cosine decay steps (default %d)\\n\", params->cos_decay_steps);\n fprintf(stderr, \" --cos-decay-restart N Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\\n\", params->cos_decay_restart);\n fprintf(stderr, \" --cos-decay-min N Only for Adam optimizer. Cosine decay minimum (default %f)\\n\", params->cos_decay_min);\n fprintf(stderr, \" --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay %s\\n\", params->enable_restart ? \"(default)\" : \"\");\n fprintf(stderr, \" --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay %s\\n\", !params->enable_restart ? \"(default)\" : \"\");\n fprintf(stderr, \" --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\\n\", params->opt_past);\n fprintf(stderr, \" --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\\n\", params->opt_delta);\n fprintf(stderr, \" --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\\n\", params->opt_max_no_improvement);\n fprintf(stderr, \" --epochs N Maximum number epochs to process. (default %d)\\n\", params->n_epochs);\n fprintf(stderr, \" --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\\n\", params->adam_n_iter);\n fprintf(stderr, \" --adam-alpha N Adam learning rate alpha (default %f)\\n\", params->adam_alpha);\n fprintf(stderr, \" --adam-min-alpha N Adam minimum learning rate alpha - including warmup phase (default %f)\\n\", params->adam_min_alpha);\n fprintf(stderr, \" --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\\n\", params->adam_decay);\n fprintf(stderr, \" --adam-decay-min-ndim N Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\\n\", params->adam_decay_min_ndim);\n fprintf(stderr, \" --adam-beta1 N AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\\n\", params->adam_beta1);\n fprintf(stderr, \" --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\\n\", params->adam_beta2);\n fprintf(stderr, \" --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\\n\", params->adam_gclip);\n fprintf(stderr, \" --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\\n\", params->adam_eps_f);\n fprintf(stderr, \"\\n\");\n}\n\nbool consume_common_train_arg(\n int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param\n) {\n int& i = *idx;\n std::string arg = argv[i];\n const std::string arg_prefix = \"--\";\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n if (arg == \"--train-data\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->fn_train_data = argv[i];\n } else if (arg == \"--checkpoint-in\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->fn_checkpoint_in = argv[i];\n } else if (arg == \"--checkpoint-out\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->fn_checkpoint_out = argv[i];\n } else if (arg == \"--pattern-fn-it\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->pattern_fn_it = argv[i];\n } else if (arg == \"--fn-latest\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->fn_latest = argv[i];\n } else if (arg == \"--save-every\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->save_every = std::stoi(argv[i]);\n } else if (arg == \"-s\" || arg == \"--seed\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->seed = std::stoi(argv[i]);\n } else if (arg == \"-c\" || arg == \"--ctx\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->n_ctx = std::stoi(argv[i]);\n params->custom_n_ctx = true;\n } else if (arg == \"-t\" || arg == \"--threads\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->n_threads = std::stoi(argv[i]);\n } else if (arg == \"-b\" || arg == \"--batch\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->n_batch = std::stoi(argv[i]);\n } else if (arg == \"--grad-acc\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->n_gradient_accumulation = std::max(1, std::stoi(argv[i]));\n } else if (arg == \"--sample-start\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->sample_start = std::string(argv[i]);\n } else if (arg == \"--escape\") {\n params->escape = true;\n } else if (arg == \"--include-sample-start\") {\n params->include_sample_start = true;\n } else if (arg == \"--overlapping-samples\") {\n params->overlapping_samples = true;\n } else if (arg == \"--fill-with-next-samples\") {\n params->fill_with_next_samples = true;\n } else if (arg == \"--separate-with-eos\") {\n params->separate_with_eos = true;\n } else if (arg == \"--separate-with-bos\") {\n params->separate_with_bos = true;\n } else if (arg == \"--no-separate-with-eos\") {\n params->separate_with_eos = false;\n } else if (arg == \"--no-separate-with-bos\") {\n params->separate_with_bos = false;\n } else if (arg == \"--sample-random-offsets\") {\n params->sample_random_offsets = true;\n } else if (arg == \"--force-reshuffle\") {\n params->force_reshuffle = true;\n } else if (arg == \"--no-flash\") {\n params->use_flash = false;\n } else if (arg == \"--use-flash\") {\n params->use_flash = true;\n } else if (arg == \"--no-checkpointing\") {\n params->use_checkpointing = false;\n } else if (arg == \"--use-checkpointing\") {\n params->use_checkpointing = true;\n } else if (arg == \"--warmup\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->warmup = std::stoi(argv[i]);\n } else if (arg == \"--cos-decay-steps\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->cos_decay_steps = std::stoi(argv[i]);\n } else if (arg == \"--cos-decay-restart\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->cos_decay_restart = std::stof(argv[i]);\n } else if (arg == \"--cos-decay-min\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->cos_decay_min = std::stof(argv[i]);\n } else if (arg == \"--enable-restart\") {\n params->enable_restart = true;\n } else if (arg == \"--disable-restart\") {\n params->enable_restart = false;\n } else if (arg == \"--opt-past\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->opt_past = std::stoi(argv[i]);\n } else if (arg == \"--opt-delta\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->opt_delta = std::stof(argv[i]);\n } else if (arg == \"--opt-max-no-improvement\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->opt_max_no_improvement = std::stoi(argv[i]);\n } else if (arg == \"--adam-epsf\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_eps_f = std::stof(argv[i]);\n } else if (arg == \"--epochs\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->n_epochs = std::stoi(argv[i]);\n } else if (arg == \"--adam-iter\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_n_iter = std::stoi(argv[i]);\n } else if (arg == \"--adam-alpha\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_alpha = std::stof(argv[i]);\n } else if (arg == \"--adam-min-alpha\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_min_alpha = std::stof(argv[i]);\n } else if (arg == \"--adam-decay\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_decay = std::stof(argv[i]);\n } else if (arg == \"--adam-decay-min-ndim\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_decay_min_ndim = std::stoi(argv[i]);\n } else if (arg == \"--adam-beta1\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_beta1 = std::stof(argv[i]);\n } else if (arg == \"--adam-beta2\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_beta2 = std::stof(argv[i]);\n } else if (arg == \"--adam-gclip\") {\n if (++i >= argc) {\n *invalid_param = true;\n return true;\n }\n params->adam_gclip = std::stof(argv[i]);\n } else if (arg == \"-h\" || arg == \"--help\") {\n params->print_usage = true;\n return true;\n } else {\n return false;\n }\n return true;\n}\n\nvoid finish_processing_train_args(struct train_params_common * params) {\n if (params->escape) {\n process_escapes(params->sample_start);\n }\n}\n\nvoid train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel) {\n struct train_opt_callback_data * data = (struct train_opt_callback_data *) vdata;\n struct train_params_common * params = data->params;\n struct train_state * train = data->train;\n struct ggml_opt_context * opt = train->opt;\n int n_batch = params->n_batch;\n int n_ctx = params->n_ctx;\n\n if (accum_step == 0) {\n // time measurement\n int64_t now = ggml_time_ms();\n if (now > data->last_time && opt->iter > data->first_iter) {\n double dt = (double) (now - data->last_time);\n if (data->millis_per_iter == 0.0) {\n data->millis_per_iter = dt;\n } else {\n const double gain = 0.7;\n data->millis_per_iter = data->millis_per_iter*(1.0-gain) + dt*gain;\n }\n }\n\n double remaining_millis = 0.0;\n if (data->millis_per_iter > 0.0) {\n const int n_iter = params->adam_n_iter;\n const int done_iter = opt->iter - data->first_iter;\n const int remaining_iter = n_iter - done_iter;\n remaining_millis = remaining_iter * data->millis_per_iter;\n }\n\n // file saving\n const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);\n if (save_now) {\n int new_iters = opt->iter - data->last_save_iter;\n train->train_its += new_iters;\n train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_ctx;\n\n if (data->save_cb) {\n data->save_cb(data->save_data, train);\n }\n\n data->last_save_iter = opt->iter;\n }\n\n // exclude file saving from time measurement, by measuring last_time after saving\n data->last_time = ggml_time_ms();\n\n *sched = learning_schedule(\n opt->iter,\n params->warmup,\n params->cos_decay_steps,\n params->adam_alpha,\n params->adam_min_alpha,\n params->cos_decay_min,\n params->cos_decay_restart,\n params->enable_restart);\n\n int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);\n if (impr_plot > 0) impr_plot = 0;\n if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0;\n printf(\"%s: iter=%6d sample=%zu/%zu sched=%f loss=%f\",\n __func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count,\n *sched, opt->loss_after);\n\n\n if (data->millis_per_iter > 0) {\n printf(\" dt=\");\n print_duration(data->millis_per_iter);\n printf(\" eta=\");\n print_duration(remaining_millis);\n }\n\n float improvement = opt->loss_before - opt->loss_after;\n const float plot_scale = 10.0f;\n int bar_len = (int)(1 + improvement*plot_scale + 0.5);\n printf(\" |\");\n for (int i=0; i\");\n printf(\"\\n\");\n }\n\n int64_t used_samples = get_example_targets_batch(\n data->lctx,\n data->tokens_input,\n data->target_probs,\n train->shuffle_next_sample,\n data->shuffled_samples_offs,\n data->shuffled_samples_begin,\n data->shuffled_samples_size,\n data->samples_count,\n data->tokens_data,\n data->tokens_size,\n params->separate_with_eos,\n params->separate_with_bos,\n params->fill_with_next_samples,\n params->sample_random_offsets);\n\n train->train_samples += used_samples;\n train->shuffle_next_sample += used_samples;\n\n if (train->shuffle_next_sample >= train->shuffle_sample_count) {\n ++train->train_epochs;\n printf(\"%s: reshuffle samples. completed epochs: %llu\\n\", __func__, (long long unsigned) train->train_epochs);\n // note: we may have used some samples from the current shuffling more than once\n train->shuffle_rng_state_current = train->shuffle_rng_state_next;\n train->shuffle_rng_state_next = shuffle_samples(\n train->shuffle_rng_state_current,\n data->shuffled_samples_offs,\n data->shuffled_samples_begin,\n data->shuffled_samples_size,\n data->samples_begin,\n data->samples_size,\n data->samples_count);\n train->shuffle_next_sample = 0;\n }\n\n const bool last_epoch_reached = (params->n_epochs > 0 && (int64_t) train->train_epochs - data->first_epoch >= params->n_epochs);\n if (last_epoch_reached) {\n // allow optimization iteration at last epoch to be completed before canceling\n if (data->iter_at_last_epoch < 0) {\n data->iter_at_last_epoch = opt->iter;\n } else if (opt->iter > data->iter_at_last_epoch) {\n *cancel = true;\n }\n }\n}\n" }, { "path": "common/train.h", "content": "// Various helper functions and utilities for training\n\n#pragma once\n\n#include \n#include \n#include \n\n#include \"ggml.h\"\n#include \"llama.h\"\n\n#define LLAMA_TRAIN_MAX_NODES 16384\n\ntypedef std::string mt19937_state;\n\nstruct train_state {\n struct ggml_opt_context * opt;\n\n uint64_t train_its;\n uint64_t train_samples;\n uint64_t train_tokens;\n uint64_t train_epochs;\n\n size_t shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes)\n mt19937_state shuffle_rng_state_current;\n mt19937_state shuffle_rng_state_next;\n size_t shuffle_sample_count;\n size_t shuffle_next_sample;\n};\n\nstruct train_params_common {\n const char * fn_train_data;\n const char * fn_checkpoint_in;\n const char * fn_checkpoint_out;\n const char * pattern_fn_it;\n const char * fn_latest;\n\n bool print_usage;\n\n int save_every;\n\n uint32_t seed;\n\n int n_ctx;\n int n_threads;\n int n_batch;\n int n_gradient_accumulation;\n int n_epochs;\n int n_gpu_layers;\n\n bool custom_n_ctx;\n\n bool use_flash;\n bool use_checkpointing;\n\n std::string sample_start;\n bool include_sample_start;\n bool escape;\n bool overlapping_samples;\n bool fill_with_next_samples;\n bool separate_with_eos;\n bool separate_with_bos;\n bool sample_random_offsets;\n\n bool force_reshuffle;\n\n int warmup;\n int cos_decay_steps;\n float cos_decay_restart;\n float cos_decay_min;\n bool enable_restart;\n\n int opt_past;\n float opt_delta;\n int opt_max_no_improvement;\n\n int adam_n_iter;\n float adam_alpha;\n float adam_min_alpha;\n float adam_decay;\n int adam_decay_min_ndim;\n float adam_beta1;\n float adam_beta2;\n float adam_gclip;\n float adam_eps_f;\n};\n\ntypedef void (*save_train_files_callback)(void * data, struct train_state * train);\n\nstruct train_opt_callback_data {\n struct train_params_common * params;\n struct train_state * train;\n save_train_files_callback save_cb;\n void * save_data;\n struct llama_context * lctx;\n int last_save_iter;\n llama_token * tokens_data;\n size_t tokens_size;\n size_t * samples_begin;\n size_t * samples_size;\n size_t * shuffled_samples_offs;\n size_t * shuffled_samples_begin;\n size_t * shuffled_samples_size;\n size_t samples_count;\n struct ggml_tensor * tokens_input;\n struct ggml_tensor * target_probs;\n int first_iter;\n int first_epoch;\n int iter_at_last_epoch;\n int64_t last_time;\n double millis_per_iter;\n};\n\nstruct train_state * init_train_state();\nvoid free_train_state(struct train_state * state);\n\nstruct train_params_common get_default_train_params_common();\nvoid print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params);\n\nbool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param);\nvoid finish_processing_train_args(struct train_params_common * params);\n\nstruct random_normal_distribution;\nstruct random_uniform_distribution;\n\nstruct random_normal_distribution * init_random_normal_distribution (int seed, float mean, float std, float min, float max);\nstruct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max);\n\nvoid free_random_normal_distribution (struct random_normal_distribution * rnd);\nvoid free_random_uniform_distribution(struct random_uniform_distribution * rnd);\n\nstruct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd);\nstruct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd);\n\n// generate random float in interval [0,1)\nfloat frand();\nfloat frand_normal (struct random_normal_distribution * rnd);\nfloat frand_uniform(struct random_uniform_distribution * rnd);\n\nint clamp (const int v, const int min, const int max);\nfloat fclamp(const float v, const float min, const float max);\n\nvoid assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0);\nvoid assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1);\nvoid assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2);\nvoid assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3);\n\nsize_t tokenize_file(\n struct llama_context * lctx,\n const char * filename,\n const std::string & sample_start,\n bool include_sample_start,\n bool overlapping_samples,\n unsigned context_length,\n std::vector & out_tokens,\n std::vector & out_samples_begin,\n std::vector & out_samples_size);\n\nint64_t get_example_targets_batch(\n struct llama_context * lctx,\n struct ggml_tensor * tokens_input,\n struct ggml_tensor * target_probs,\n int64_t example_id,\n const size_t * samples_offs,\n const size_t * samples_begin,\n const size_t * samples_size,\n size_t samples_count,\n const llama_token * train_data,\n size_t n_train_data,\n bool separate_with_eos,\n bool separate_with_bos,\n bool fill_with_next_samples,\n bool sample_random_offsets);\n\n\nvoid mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state);\nmt19937_state mt19937_get_state(const std::mt19937& rng);\nmt19937_state mt19937_seed_to_state(unsigned seed);\n\nmt19937_state shuffle_samples(\n const mt19937_state & rng_state,\n size_t * shuffled_offs,\n size_t * shuffled_begins,\n size_t * shuffled_sizes,\n const size_t * begins,\n const size_t * sizes,\n size_t count);\n\nsize_t hash_combine(size_t h1, size_t h2);\n\nsize_t compute_samples_hash(\n const char* fn,\n const size_t* samples_begin,\n const size_t* samples_size,\n size_t sample_count);\n\n\nstd::string replace_str(const char * s, const char * needle, const char * replacement);\n\nvoid print_duration(double milliseconds);\n\nfloat cosine_decay(\n int64_t step,\n int64_t decay_steps,\n float minimum);\n\nfloat cosine_decay_restart(\n int64_t step,\n int64_t decay_steps,\n float minimum,\n float restart_step_mult);\n\nfloat learning_schedule(\n int64_t step,\n int64_t warmup_steps,\n int64_t decay_steps,\n float learning_rate,\n float overall_minimum,\n float cos_decay_minimum,\n float cos_decay_restart_step_mult,\n bool enable_restart);\n\nvoid copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name);\n\nvoid load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt);\nvoid save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt);\n\nbool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train);\nvoid save_train_state_gguf(struct gguf_context * fctx, struct train_state * train);\n\nstd::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration);\n\nvoid train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel);\n" }, { "path": "convert-dense.py", "content": "# SPDX-License-Identifier: MIT\n# Copyright (c) 2023 Georgi Gerganov\n# Based on code from https://github.com/ggerganov/llama.cpp/tree/6bb4908a17150b49373b5f977685b2e180a04f6f\n\n#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport concurrent.futures\nimport enum\nimport faulthandler\nimport functools\nimport itertools\nimport json\nimport math\nimport mmap\nimport pickle\nimport re\nimport signal\nimport struct\nimport sys\nimport time\nimport zipfile\nfrom abc import ABCMeta, abstractmethod\nfrom concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar\n\nimport numpy as np\nfrom sentencepiece import SentencePieceProcessor\n\nimport os\nif 'NO_LOCAL_GGUF' not in os.environ:\n sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))\nimport gguf\n\nif TYPE_CHECKING:\n from typing import TypeAlias\n\nif hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):\n faulthandler.register(signal.SIGUSR1)\n\nNDArray: TypeAlias = 'np.ndarray[Any, Any]'\n\nDEFAULT_CONCURRENCY = 8\n#\n# data types\n#\n\n@dataclass(frozen=True)\nclass DataType:\n name: str\n dtype: np.dtype[Any]\n valid_conversions: list[str]\n\n def elements_to_bytes(self, n_elements: int) -> int:\n return n_elements * self.dtype.itemsize\n\n@dataclass(frozen=True)\nclass UnquantizedDataType(DataType):\n pass\n\nDT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])\nDT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])\nDT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])\nDT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])\n\n@dataclass(frozen=True)\nclass QuantizedDataType(DataType):\n block_size: int\n quantized_dtype: np.dtype[Any]\n ggml_type: gguf.GGMLQuantizationType\n\n def quantize(self, arr: NDArray) -> NDArray:\n raise NotImplementedError(f'Quantization for {self.name} not implemented')\n\n def elements_to_bytes(self, n_elements: int) -> int:\n assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'\n return self.quantized_dtype.itemsize * (n_elements // self.block_size)\n\n@dataclass(frozen=True)\nclass Q8_0QuantizedDataType(QuantizedDataType):\n # Mini Q8_0 quantization in Python!\n def quantize(self, arr: NDArray) -> NDArray:\n assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'\n assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'\n n_blocks = arr.size // self.block_size\n blocks = arr.reshape((n_blocks, self.block_size))\n # Much faster implementation of block quantization contributed by @Cebtenzzre\n def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:\n d = abs(blocks).max(axis = 1) / np.float32(127)\n with np.errstate(divide = 'ignore'):\n qs = (blocks / d[:, None]).round()\n qs[d == 0] = 0\n yield from zip(d, qs)\n return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)\n\nDT_Q8_0 = Q8_0QuantizedDataType('Q8_0',\n dtype = np.dtype(np.float32), valid_conversions = [],\n ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,\n quantized_dtype = np.dtype([('d', ' DataType:\n dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)\n if dt is None:\n raise ValueError(self)\n # 1D tensors are always F32.\n return dt if len(tensor.shape) > 1 else DT_F32\n\nGGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {\n GGMLFileType.AllF32 : DT_F32,\n GGMLFileType.MostlyF16 : DT_F16,\n GGMLFileType.MostlyQ8_0: DT_Q8_0,\n}\n\n#\n# hparams loading\n#\n\n@dataclass\nclass Params:\n n_vocab: int\n n_embd: int\n n_layer: int\n n_ctx: int\n n_ff: int\n n_head: int\n n_head_kv: int\n f_norm_eps: float\n\n arch: gguf.MODEL_ARCH = gguf.MODEL_ARCH.LLAMA\n\n rope_scaling_type: gguf.RopeScalingType | None = None\n f_rope_freq_base: float | None = None\n f_rope_scale: float | None = None\n n_orig_ctx: int | None = None\n rope_finetuned: bool | None = None\n\n ftype: GGMLFileType | None = None\n\n # path to the directory containing the model files\n path_model: Path | None = None\n\n @staticmethod\n def guessed(model: LazyModel) -> Params:\n # try transformer naming first\n n_vocab, n_embd = model[\"model.embed_tokens.weight\"].shape if \"model.embed_tokens.weight\" in model else model[\"tok_embeddings.weight\"].shape\n\n # try transformer naming first\n if \"model.layers.0.self_attn.q_proj.weight\" in model:\n n_layer=next(i for i in itertools.count() if f\"model.layers.{i}.self_attn.q_proj.weight\" not in model)\n elif \"model.layers.0.self_attn.W_pack.weight\" in model: # next: try baichuan naming\n n_layer=next(i for i in itertools.count() if f\"model.layers.{i}.self_attn.W_pack.weight\" not in model)\n else:\n n_layer=next(i for i in itertools.count() if f\"layers.{i}.attention.wq.weight\" not in model)\n\n if n_layer < 1:\n raise Exception(\"failed to guess 'n_layer'. This model is unknown or unsupported.\\n\"\n \"Suggestion: provide 'config.json' of the model in the same directory containing model files.\")\n\n n_head = n_embd // 128 # guessed\n n_mult = 256 # guessed\n\n # TODO: verify this\n n_ff = int(2 * (4 * n_embd) / 3)\n n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)\n\n return Params(\n n_vocab = n_vocab,\n n_embd = n_embd,\n n_layer = n_layer,\n n_ctx = -1,\n n_ff = n_ff,\n n_head = n_head,\n n_head_kv = n_head,\n f_norm_eps = 1e-5,\n )\n\n @staticmethod\n def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:\n config = json.load(open(config_path))\n\n rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None\n rope_scaling = config.get(\"rope_scaling\")\n\n if rope_scaling is not None and (typ := rope_scaling.get(\"type\")):\n rope_factor = rope_scaling.get(\"factor\")\n f_rope_scale = rope_factor\n if typ == \"linear\":\n rope_scaling_type = gguf.RopeScalingType.LINEAR\n elif typ == \"yarn\":\n rope_scaling_type = gguf.RopeScalingType.YARN\n n_orig_ctx = rope_scaling['original_max_position_embeddings']\n rope_finetuned = rope_scaling['finetuned']\n else:\n raise NotImplementedError(f'Unknown rope scaling type: {typ}')\n\n if \"max_sequence_length\" in config:\n n_ctx = config[\"max_sequence_length\"]\n elif \"max_position_embeddings\" in config:\n n_ctx = config[\"max_position_embeddings\"]\n else:\n raise Exception(\"failed to guess 'n_ctx'. This model is unknown or unsupported.\\n\"\n \"Suggestion: provide 'config.json' of the model in the same directory containing model files.\")\n\n params = Params(\n n_vocab = config[\"vocab_size\"],\n n_embd = config[\"hidden_size\"],\n n_layer = config[\"num_hidden_layers\"],\n n_ctx = n_ctx,\n n_ff = config[\"intermediate_size\"],\n n_head = (n_head := config[\"num_attention_heads\"]),\n n_head_kv = config.get(\"num_key_value_heads\", n_head),\n f_norm_eps = config[\"rms_norm_eps\"],\n f_rope_freq_base = config.get(\"rope_theta\"),\n rope_scaling_type = rope_scaling_type,\n f_rope_scale = f_rope_scale,\n n_orig_ctx = n_orig_ctx,\n rope_finetuned = rope_finetuned,\n )\n\n if config.get(\"model_type\", None) == \"bamboo\":\n params.arch = gguf.MODEL_ARCH.BAMBOO\n\n return params\n\n\n # LLaMA v2 70B params.json\n # {\"dim\": 8192, \"multiple_of\": 4096, \"ffn_dim_multiplier\": 1.3, \"n_heads\": 64, \"n_kv_heads\": 8, \"n_layers\": 80, \"norm_eps\": 1e-05, \"vocab_size\": -1}\n @staticmethod\n def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:\n config = json.load(open(config_path))\n\n # hack to determine LLaMA v1 vs v2 vs CodeLlama\n if config.get(\"rope_theta\") == 1000000:\n # CodeLlama\n n_ctx = 16384\n elif config[\"norm_eps\"] == 1e-05:\n # LLaMA v2\n n_ctx = 4096\n else:\n # LLaMA v1\n n_ctx = 2048\n\n return Params(\n n_vocab = config.get(\"vocab_size\", model[\"tok_embeddings.weight\"].shape[0]),\n n_embd = config[\"dim\"],\n n_layer = config[\"n_layers\"],\n n_ctx = n_ctx,\n n_ff = model[\"layers.0.feed_forward.w1.weight\"].shape[0],\n n_head = (n_head := config[\"n_heads\"]),\n n_head_kv = config.get(\"n_kv_heads\", n_head),\n f_norm_eps = config[\"norm_eps\"],\n f_rope_freq_base = config.get(\"rope_theta\"),\n )\n\n @staticmethod\n def load(model_plus: ModelPlus) -> Params:\n hf_config_path = model_plus.paths[0].parent / \"config.json\"\n orig_config_path = model_plus.paths[0].parent / \"params.json\"\n\n if hf_config_path.exists():\n params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)\n elif orig_config_path.exists():\n params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)\n elif model_plus.format != 'none':\n params = Params.guessed(model_plus.model)\n else:\n raise ValueError('Cannot guess params when model format is none')\n\n params.path_model = model_plus.paths[0].parent\n\n return params\n\n\n#\n# vocab\n#\n\nclass BpeVocab:\n def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:\n self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding=\"utf-8\").read())\n added_tokens: dict[str, int]\n if fname_added_tokens is not None:\n # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.\n added_tokens = json.load(open(fname_added_tokens, encoding=\"utf-8\"))\n else:\n # Fall back to trying to find the added tokens in tokenizer.json\n tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'\n if not tokenizer_json_file.is_file():\n added_tokens = {}\n else:\n tokenizer_json = json.load(open(tokenizer_json_file, encoding=\"utf-8\"))\n added_tokens = dict(\n (item['content'], item['id'])\n for item in tokenizer_json.get('added_tokens', [])\n # Added tokens here can be duplicates of the main vocabulary.\n if item['content'] not in self.bpe_tokenizer )\n\n vocab_size: int = len(self.bpe_tokenizer)\n expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))\n actual_ids = sorted(added_tokens.values())\n if expected_ids != actual_ids:\n expected_end_id = vocab_size + len(actual_ids) - 1\n raise Exception(f\"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}\")\n\n items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])\n self.added_tokens_list = [text for (text, idx) in items]\n self.vocab_size_base: int = vocab_size\n self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)\n self.fname_tokenizer = fname_tokenizer\n self.fname_added_tokens = fname_added_tokens\n\n def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n tokenizer = self.bpe_tokenizer\n from transformers.models.gpt2 import tokenization_gpt2\n reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}\n\n for i, _ in enumerate(tokenizer):\n yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL\n\n def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n for text in self.added_tokens_list:\n score = -1000.0\n yield text.encode(\"utf-8\"), score, gguf.TokenType.CONTROL\n\n def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n yield from self.bpe_tokens()\n yield from self.added_tokens()\n\n def __repr__(self) -> str:\n return f\"\"\n\n\nclass SentencePieceVocab:\n def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:\n self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))\n added_tokens: dict[str, int]\n if fname_added_tokens is not None:\n added_tokens = json.load(open(fname_added_tokens, encoding=\"utf-8\"))\n else:\n added_tokens = {}\n\n vocab_size: int = self.sentencepiece_tokenizer.vocab_size()\n\n new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}\n expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))\n actual_new_ids = sorted(new_tokens.keys())\n\n if expected_new_ids != actual_new_ids:\n raise ValueError(f\"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}\")\n\n # Token pieces that were added to the base vocabulary.\n self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]\n self.vocab_size_base = vocab_size\n self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)\n self.fname_tokenizer = fname_tokenizer\n self.fname_added_tokens = fname_added_tokens\n\n def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n tokenizer = self.sentencepiece_tokenizer\n for i in range(tokenizer.vocab_size()):\n piece = tokenizer.id_to_piece(i)\n text: bytes = piece.encode(\"utf-8\")\n score: float = tokenizer.get_score(i)\n\n toktype = gguf.TokenType.NORMAL\n if tokenizer.is_unknown(i):\n toktype = gguf.TokenType.UNKNOWN\n if tokenizer.is_control(i):\n toktype = gguf.TokenType.CONTROL\n\n # NOTE: I think added_tokens are user defined.\n # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto\n # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED\n\n if tokenizer.is_unused(i):\n toktype = gguf.TokenType.UNUSED\n if tokenizer.is_byte(i):\n toktype = gguf.TokenType.BYTE\n\n yield text, score, toktype\n\n def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n for text in self.added_tokens_list:\n score = -1000.0\n yield text.encode(\"utf-8\"), score, gguf.TokenType.USER_DEFINED\n\n def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n yield from self.sentencepiece_tokens()\n yield from self.added_tokens()\n\n def __repr__(self) -> str:\n return f\"\"\n\nVocab: TypeAlias = 'BpeVocab | SentencePieceVocab'\n\n#\n# data loading\n# TODO: reuse (probably move to gguf.py?)\n#\n\ndef permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:\n #print( \"permute debug \" + str(weights.shape[0]) + \" x \" + str(weights.shape[1]) + \" nhead \" + str(n_head) + \" nheadkv \" + str(n_kv_head) )\n if n_head_kv is not None and n_head != n_head_kv:\n n_head = n_head_kv\n return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])\n .swapaxes(1, 2)\n .reshape(weights.shape))\n\n\nclass Tensor(metaclass=ABCMeta):\n data_type: DataType\n\n @abstractmethod\n def astype(self, data_type: DataType) -> Tensor: ...\n @abstractmethod\n def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...\n @abstractmethod\n def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...\n @abstractmethod\n def part(self, n_part: int) -> UnquantizedTensor: ...\n @abstractmethod\n def to_ggml(self) -> GGMLCompatibleTensor: ...\n\n\ndef bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:\n assert bf16_arr.dtype == np.uint16, f\"Input array should be of dtype uint16, but got {bf16_arr.dtype}\"\n fp32_arr = bf16_arr.astype(np.uint32) << 16\n return fp32_arr.view(np.float32)\n\n\nclass UnquantizedTensor(Tensor):\n def __init__(self, ndarray: NDArray) -> None:\n assert isinstance(ndarray, np.ndarray)\n self.ndarray = ndarray\n self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]\n\n def astype(self, data_type: DataType) -> Tensor:\n dtype = data_type.dtype\n if self.data_type == DT_BF16:\n self.ndarray = bf16_to_fp32(self.ndarray)\n return UnquantizedTensor(self.ndarray.astype(dtype))\n\n def to_ggml(self) -> UnquantizedTensor:\n return self\n\n def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:\n r = self.ndarray.shape[0] // 3\n return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))\n\n def part(self, n_part: int) -> UnquantizedTensor:\n r = self.ndarray.shape[0] // 3\n return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])\n\n def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor:\n return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))\n\n\ndef load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray:\n tensor = lazy_tensor.load()\n assert isinstance(tensor, UnquantizedTensor)\n\n # double-check:\n actual_shape = list(tensor.ndarray.shape)\n assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape)\n if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype:\n if convert:\n tensor.ndarray = tensor.ndarray.astype(expected_dtype)\n else:\n raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}')\n\n return tensor.ndarray\n\n\nGGMLCompatibleTensor = UnquantizedTensor\n\n\n@dataclass\nclass LazyTensor:\n _load: Callable[[], Tensor]\n shape: list[int]\n data_type: DataType\n description: str\n\n def load(self) -> Tensor:\n ret = self._load()\n # Should be okay if it maps to the same numpy type?\n assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \\\n (self.data_type, ret.data_type, self.description)\n return ret\n\n def astype(self, data_type: DataType) -> LazyTensor:\n self.validate_conversion_to(data_type)\n\n def load() -> Tensor:\n return self.load().astype(data_type)\n return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')\n\n def validate_conversion_to(self, data_type: DataType) -> None:\n if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:\n raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')\n\n\nLazyModel: TypeAlias = 'dict[str, LazyTensor]'\n\n\n@dataclass\nclass ModelPlus:\n model: LazyModel\n paths: list[Path] # Where this was read from.\n format: Literal['ggml', 'torch', 'safetensors', 'none']\n vocab: Vocab | None # For GGML models (which have vocab built in), the vocab.\n\n\ndef merge_sharded(models: list[LazyModel]) -> LazyModel:\n # Original LLaMA models have each file contain one part of each tensor.\n # Use a dict instead of a set to preserve order.\n names = {name: None for model in models for name in model}\n\n def convert(name: str) -> LazyTensor:\n lazy_tensors: list[LazyTensor] = [model[name] for model in models]\n if len(lazy_tensors) == 1:\n # only one file; don't go through this procedure since there might\n # be quantized tensors\n return lazy_tensors[0]\n if len(lazy_tensors[0].shape) == 1:\n # the tensor is just duplicated in every file\n return lazy_tensors[0]\n if name.startswith('tok_embeddings.') or \\\n name.endswith('.attention.wo.weight') or \\\n name.endswith('.feed_forward.w2.weight'):\n # split by columns\n axis = 1\n else:\n # split by rows\n axis = 0\n concatenated_shape = list(lazy_tensors[0].shape)\n concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors)\n\n def load() -> UnquantizedTensor:\n ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]\n concatenated: NDArray = np.concatenate(ndarrays, axis=axis)\n return UnquantizedTensor(concatenated)\n description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'\n return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)\n return {name: convert(name) for name in names}\n\n\ndef merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:\n formats = set(mp.format for mp in models_plus)\n assert len(formats) == 1, \"different formats?\"\n format = formats.pop()\n paths = [path for mp in models_plus for path in mp.paths]\n # Use the first non-None vocab, if any.\n try:\n vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None)\n except StopIteration:\n vocab = None\n\n if any(\"model.embed_tokens.weight\" in mp.model for mp in models_plus):\n # Transformers models put different tensors in different files, but\n # don't split indivdual tensors between files.\n model: LazyModel = {}\n for mp in models_plus:\n model.update(mp.model)\n else:\n model = merge_sharded([mp.model for mp in models_plus])\n\n return ModelPlus(model, paths, format, vocab)\n\n\ndef permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().permute(n_head, n_head_kv)\n return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)\n\ndef permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)\n s = lazy_tensor.shape.copy()\n s[0] = s[0] // 3\n return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)\n\ndef part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().part(n_part)\n s = lazy_tensor.shape.copy()\n s[0] = s[0] // 3\n return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)\n\n\n# Functionality that simulates `torch.load` but where individual tensors are\n# only loaded into memory on demand, not all at once.\n# PyTorch can't do this natively as of time of writing:\n# - https://github.com/pytorch/pytorch/issues/64327\n# This allows us to de-shard without multiplying RAM usage, and also\n# conveniently drops the PyTorch dependency (though we still need numpy).\n\n\n@dataclass\nclass LazyStorageKind:\n data_type: DataType\n\n\n@dataclass\nclass LazyStorage:\n load: Callable[[int, int], NDArray]\n kind: LazyStorageKind\n description: str\n\n\nclass LazyUnpickler(pickle.Unpickler):\n def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile):\n super().__init__(fp)\n self.data_base_path = data_base_path\n self.zip_file = zip_file\n\n def persistent_load(self, pid: Any) -> Any:\n assert pid[0] == 'storage'\n assert isinstance(pid[1], LazyStorageKind)\n data_type = pid[1].data_type\n filename_stem = pid[2]\n filename = f'{self.data_base_path}/{filename_stem}'\n info = self.zip_file.getinfo(filename)\n\n def load(offset: int, elm_count: int) -> NDArray:\n dtype = data_type.dtype\n fp = self.zip_file.open(info)\n fp.seek(offset * dtype.itemsize)\n size = elm_count * dtype.itemsize\n data = fp.read(size)\n assert len(data) == size\n return np.frombuffer(data, dtype)\n description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'\n return LazyStorage(load=load, kind=pid[1], description=description)\n\n @staticmethod\n def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,\n requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:\n assert isinstance(storage, LazyStorage)\n\n def load() -> UnquantizedTensor:\n elm_count = stride[0] * size[0]\n return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size))\n description = f'pickled storage_offset={storage_offset} in {storage.description}'\n return LazyTensor(load, list(size), storage.kind.data_type, description)\n\n @staticmethod\n def rebuild_from_type_v2(func, new_type, args, state):\n return func(*args)\n\n CLASSES: dict[tuple[str, str], Any] = {\n # getattr used here as a workaround for mypy not being smart enough to detrmine\n # the staticmethods have a __func__ attribute.\n ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),\n ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),\n ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),\n ('torch', 'HalfStorage'): LazyStorageKind(DT_F16),\n ('torch', 'FloatStorage'): LazyStorageKind(DT_F32),\n ('torch', 'IntStorage'): LazyStorageKind(DT_I32),\n ('torch', 'Tensor'): LazyTensor,\n }\n\n def find_class(self, module: str, name: str) -> Any:\n if not module.startswith('torch'):\n return super().find_class(module, name)\n return self.CLASSES[(module, name)]\n\n\ndef lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:\n zf = zipfile.ZipFile(outer_fp)\n pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')]\n assert len(pickle_paths) == 1, pickle_paths\n pickle_fp = zf.open(pickle_paths[0], 'r')\n unpickler = LazyUnpickler(pickle_fp,\n data_base_path=pickle_paths[0][:-4],\n zip_file=zf)\n model = unpickler.load()\n as_dict = dict(model.items())\n return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)\n\n\ndef lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:\n header_size, = struct.unpack(' LazyTensor:\n data_type = SAFETENSORS_DATA_TYPES[info['dtype']]\n numpy_dtype = data_type.dtype\n shape: list[int] = info['shape']\n begin, end = info['data_offsets']\n assert 0 <= begin <= end <= len(byte_buf)\n assert end - begin == math.prod(shape) * numpy_dtype.itemsize\n buf = byte_buf[begin:end]\n\n def load() -> UnquantizedTensor:\n return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape))\n description = f'safetensors begin={begin} end={end} type={data_type} path={path}'\n return LazyTensor(load, shape, data_type, description)\n model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'}\n return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None)\n\n\ndef must_read(fp: IO[bytes], length: int) -> bytes:\n ret = fp.read(length)\n if len(ret) < length:\n raise Exception(\"unexpectedly reached end of file\")\n return ret\n\n\n@functools.lru_cache(maxsize=None)\ndef lazy_load_file(path: Path) -> ModelPlus:\n fp = open(path, 'rb')\n first8 = fp.read(8)\n fp.seek(0)\n if first8[:2] == b'PK':\n # A zip file, i.e. PyTorch format\n return lazy_load_torch_file(fp, path)\n elif struct.unpack(' Iterable[Out]:\n '''Parallel map, but with backpressure. If the caller doesn't call `next`\n fast enough, this will stop calling `func` at some point rather than\n letting results pile up in memory. Specifically, there is a max of one\n output value buffered per thread.'''\n if concurrency < 2:\n yield from map(func, iterable)\n # Not reached.\n iterable = iter(iterable)\n executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor]\n if use_processpool_executor:\n executor_class = ProcessPoolExecutor\n else:\n executor_class = ThreadPoolExecutor\n with executor_class(max_workers = max_workers) as executor:\n futures: list[concurrent.futures.Future[Out]] = []\n done = False\n for _ in range(concurrency):\n try:\n futures.append(executor.submit(func, next(iterable)))\n except StopIteration:\n done = True\n break\n\n while futures:\n result = futures.pop(0).result()\n while not done and len(futures) < concurrency:\n try:\n futures.append(executor.submit(func, next(iterable)))\n except StopIteration:\n done = True\n break\n yield result\n\ndef check_vocab_size(params: Params, vocab: Vocab) -> None:\n if params.n_vocab != vocab.vocab_size:\n assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)\n if params.n_vocab == vocab.vocab_size_base:\n print(\"Ignoring added_tokens.json since model matches vocab size without it.\")\n vocab.added_tokens_list = []\n vocab.vocab_size = vocab.vocab_size_base\n return\n msg = f\"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}\"\n if vocab.fname_added_tokens is not None:\n msg += f\" combined with {vocab.fname_added_tokens}\"\n msg += f\" has {vocab.vocab_size}).\"\n if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:\n msg += f\" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent}).\"\n raise Exception(msg)\n\n\nclass OutputFile:\n def __init__(self, fname_out: Path, arch: gguf.MODEL_ARCH, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:\n self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[arch], endianess=endianess)\n\n def add_meta_arch(self, params: Params) -> None:\n name = \"LLaMA\"\n\n # TODO: better logic to determine model name\n if params.n_ctx == 4096:\n name = \"LLaMA v2\"\n elif params.path_model is not None:\n name = str(params.path_model).split('/')[-1]\n\n self.gguf.add_name (name)\n self.gguf.add_context_length (params.n_ctx)\n self.gguf.add_embedding_length (params.n_embd)\n self.gguf.add_block_count (params.n_layer)\n self.gguf.add_feed_forward_length (params.n_ff)\n self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)\n self.gguf.add_head_count (params.n_head)\n self.gguf.add_head_count_kv (params.n_head_kv)\n self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)\n\n if params.f_rope_freq_base is not None:\n self.gguf.add_rope_freq_base(params.f_rope_freq_base)\n\n if params.rope_scaling_type:\n assert params.f_rope_scale is not None\n self.gguf.add_rope_scaling_type(params.rope_scaling_type)\n self.gguf.add_rope_scaling_factor(params.f_rope_scale)\n\n if params.n_orig_ctx is not None:\n self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)\n\n if params.rope_finetuned is not None:\n self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)\n\n if params.ftype is not None:\n self.gguf.add_file_type(params.ftype)\n\n def add_meta_vocab(self, vocab: Vocab) -> None:\n tokens = []\n scores = []\n toktypes = []\n # NOTE: `all_tokens` returns the base vocabulary and added tokens\n for text, score, toktype in vocab.all_tokens():\n tokens.append(text)\n scores.append(score)\n toktypes.append(toktype)\n\n if isinstance(vocab, SentencePieceVocab):\n self.gguf.add_tokenizer_model(\"llama\")\n elif isinstance(vocab, BpeVocab):\n self.gguf.add_tokenizer_model(\"gpt2\")\n else:\n raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')\n self.gguf.add_token_list(tokens)\n self.gguf.add_token_scores(scores)\n self.gguf.add_token_types(toktypes)\n\n def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None:\n svocab.add_to_gguf(self.gguf)\n\n def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:\n n_elements = int(np.prod(tensor.shape))\n raw_dtype = getattr(tensor.data_type, 'ggml_type', None)\n data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype\n data_nbytes = tensor.data_type.elements_to_bytes(n_elements)\n self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)\n\n def write_meta(self) -> None:\n self.gguf.write_header_to_file()\n self.gguf.write_kv_data_to_file()\n\n def write_tensor_info(self) -> None:\n self.gguf.write_ti_data_to_file()\n\n def close(self) -> None:\n self.gguf.close()\n\n @staticmethod\n def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:\n check_vocab_size(params, vocab)\n\n of = OutputFile(fname_out, params.arch, endianess=endianess)\n\n # meta data\n of.add_meta_arch(params)\n of.add_meta_vocab(vocab)\n of.add_meta_special_vocab(svocab)\n\n of.write_meta()\n\n of.close()\n\n @staticmethod\n def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]:\n name, lazy_tensor = item\n tensor = lazy_tensor.load().to_ggml()\n return (lazy_tensor.data_type, tensor.ndarray)\n\n @staticmethod\n def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray:\n dt, arr = item\n if not isinstance(dt, QuantizedDataType):\n return arr\n return dt.quantize(arr)\n\n @staticmethod\n def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:\n check_vocab_size(params, vocab)\n\n of = OutputFile(fname_out, params.arch, endianess=endianess)\n\n # meta data\n of.add_meta_arch(params)\n of.add_meta_vocab(vocab)\n of.add_meta_special_vocab(svocab)\n\n # tensor info\n for name, lazy_tensor in model.items():\n of.add_tensor_info(name, lazy_tensor)\n\n of.write_meta()\n of.write_tensor_info()\n\n # tensor data\n ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)\n if ftype == GGMLFileType.MostlyQ8_0:\n ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True)\n else:\n ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)\n\n start = time.time()\n for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):\n elapsed = time.time() - start\n size = ' x '.join(f\"{dim:6d}\" for dim in lazy_tensor.shape)\n padi = len(str(len(model)))\n print(f\"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}\")\n of.gguf.write_tensor_data(ndarray)\n\n of.close()\n\ndef pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:\n wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+\".weight\"].data_type\n\n if output_type_str == \"f32\" or (output_type_str is None and wq_type == DT_F32):\n return GGMLFileType.AllF32\n if output_type_str == \"f16\" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):\n return GGMLFileType.MostlyF16\n if output_type_str == \"q8_0\":\n return GGMLFileType.MostlyQ8_0\n\n name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}\n\n raise Exception(f\"Unexpected combination of types: {name_to_type}\")\n\ndef convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:\n return {name: tensor.astype(output_type.type_for_tensor(name, tensor))\n for (name, tensor) in model.items()}\n\ndef convert_model_names(model: LazyModel, params: Params) -> LazyModel:\n tmap = gguf.TensorNameMap(params.arch, params.n_layer)\n should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(params.arch, []))\n\n tmp = model\n\n # HF models permut or pack some of the tensors, so we need to undo that\n for i in itertools.count():\n if f\"model.layers.{i}.self_attn.q_proj.weight\" in model:\n print(f\"Permuting layer {i}\")\n tmp[f\"model.layers.{i}.self_attn.q_proj.weight\"] = permute_lazy(model[f\"model.layers.{i}.self_attn.q_proj.weight\"], params.n_head, params.n_head)\n tmp[f\"model.layers.{i}.self_attn.k_proj.weight\"] = permute_lazy(model[f\"model.layers.{i}.self_attn.k_proj.weight\"], params.n_head, params.n_head_kv)\n #tmp[f\"model.layers.{i}.self_attn.v_proj.weight\"] = model[f\"model.layers.{i}.self_attn.v_proj.weight\"]\n elif f\"model.layers.{i}.self_attn.W_pack.weight\" in model:\n print(f\"Unpacking and permuting layer {i}\")\n tmp[f\"model.layers.{i}.self_attn.q_proj.weight\"] = permute_part_lazy(model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 0, params.n_head, params.n_head)\n tmp[f\"model.layers.{i}.self_attn.k_proj.weight\"] = permute_part_lazy(model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 1, params.n_head, params.n_head_kv)\n tmp[f\"model.layers.{i}.self_attn.v_proj.weight\"] = part_lazy (model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 2)\n del tmp[f\"model.layers.{i}.self_attn.W_pack.weight\"]\n else:\n break\n\n out: LazyModel = {}\n for name, lazy_tensor in model.items():\n tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (\".weight\", \".bias\")) or (None, None)\n if name_new is None:\n raise Exception(f\"Unexpected tensor name: {name}\")\n\n if tensor_type in should_skip:\n print(f\"skipping tensor {name_new}\")\n continue\n\n print(f\"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}\")\n out[name_new] = lazy_tensor\n\n return out\n\ndef nth_multifile_path(path: Path, n: int) -> Path | None:\n '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return\n the nth path in the model.\n '''\n # Support the following patterns:\n patterns: list[tuple[str, str]] = [\n # - x.00.pth, x.01.pth, etc.\n (r'\\.[0-9]{2}\\.pth$', f'.{n:02}.pth'),\n # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.\n (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\\1'),\n # x.bin, x.bin.1, etc.\n (r'(\\.[0-9]+)?$', r'\\1' if n == 0 else fr'\\1.{n}')\n ]\n for regex, replacement in patterns:\n if re.search(regex, path.name):\n new_path = path.with_name(re.sub(regex, replacement, path.name))\n if new_path.exists():\n return new_path\n return None\n\n\ndef find_multifile_paths(path: Path) -> list[Path]:\n '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return\n the whole list of paths in the model.\n '''\n ret: list[Path] = []\n for i in itertools.count():\n nth_path = nth_multifile_path(path, i)\n if nth_path is None:\n break\n ret.append(nth_path)\n if not ret:\n # No matches. This should only happen if the file was named, e.g.,\n # foo.0, and there was no file named foo. Oh well, try to process it\n # as a single file.\n return [path]\n return ret\n\n\ndef load_some_model(path: Path) -> ModelPlus:\n '''Load a model of any supported format.'''\n # Be extra-friendly and accept either a file or a directory:\n if path.is_dir():\n # Check if it's a set of safetensors files first\n globs = [\"model-00001-of-*.safetensors\", \"model.safetensors\"]\n files = [file for glob in globs for file in path.glob(glob)]\n if not files:\n # Try the PyTorch patterns too, with lower priority\n globs = [\"consolidated.00.pth\", \"pytorch_model-00001-of-*.bin\", \"*.pt\", \"pytorch_model.bin\"]\n files = [file for glob in globs for file in path.glob(glob)]\n if not files:\n raise Exception(f\"Can't find model in directory {path}\")\n if len(files) > 1:\n raise Exception(f\"Found multiple models in {path}, not sure which to pick: {files}\")\n path = files[0]\n\n paths = find_multifile_paths(path)\n models_plus: list[ModelPlus] = []\n for path in paths:\n print(f\"Loading model file {path}\")\n models_plus.append(lazy_load_file(path))\n\n model_plus = merge_multifile_models(models_plus)\n return model_plus\n\n\ndef load_vocab(path: Path, vocabtype: str | None) -> Vocab:\n # Be extra-friendly and accept either a file or a directory. Also, if it's\n # a directory, it might be the model directory, and tokenizer.model might\n # be in the parent of that.\n if path.is_dir():\n vocab_file = \"tokenizer.model\"\n if vocabtype == 'bpe':\n vocab_file = \"vocab.json\"\n path2 = path / vocab_file\n # Use `.parent` instead of /.. to handle the symlink case better.\n path3 = path.parent / vocab_file\n if path2.exists():\n path = path2\n elif path3.exists():\n path = path3\n else:\n raise FileNotFoundError(\n f\"Could not find {vocab_file} in {path} or its parent; \"\n \"if it's in another directory, pass the directory as --vocab-dir\")\n\n print(f\"Loading vocab file '{path}', type '{vocabtype}'\")\n\n added_tokens_path = path.parent / \"added_tokens.json\"\n if vocabtype == \"bpe\":\n return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)\n elif vocabtype == \"spm\":\n return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)\n else:\n raise ValueError(f\"Unsupported vocabulary type {vocabtype}\")\n\n\ndef default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:\n namestr = {\n GGMLFileType.AllF32: \"f32\",\n GGMLFileType.MostlyF16: \"f16\",\n GGMLFileType.MostlyQ8_0:\"q8_0\",\n }[file_type]\n ret = model_paths[0].parent / f\"ggml-model-{namestr}.gguf\"\n if ret in model_paths:\n sys.stderr.write(\n f\"Error: Default output path ({ret}) would overwrite the input. \"\n \"Please explicitly specify a path using --outfile.\\n\")\n sys.exit(1)\n return ret\n\n\ndef do_dump_model(model_plus: ModelPlus) -> None:\n print(f\"model_plus.paths = {model_plus.paths!r}\")\n print(f\"model_plus.format = {model_plus.format!r}\")\n print(f\"model_plus.vocab = {model_plus.vocab!r}\")\n for name, lazy_tensor in model_plus.model.items():\n print(f\"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}\")\n\n\ndef main(args_in: list[str] | None = None) -> None:\n output_choices = [\"f32\", \"f16\"]\n if np.uint32(1) == np.uint32(1).newbyteorder(\"<\"):\n # We currently only support Q8_0 output on little endian systems.\n output_choices.append(\"q8_0\")\n parser = argparse.ArgumentParser(description=\"Convert a LLaMa model to a GGML compatible file\")\n parser.add_argument(\"--dump\", action=\"store_true\", help=\"don't convert, just show what's in the model\")\n parser.add_argument(\"--dump-single\", action=\"store_true\", help=\"don't convert, just show what's in a single model file\")\n parser.add_argument(\"--vocab-only\", action=\"store_true\", help=\"extract only the vocab\")\n parser.add_argument(\"--outtype\", choices=output_choices, help=\"output format - note: q8_0 may be very slow (default: f16 or f32 based on input)\")\n parser.add_argument(\"--vocab-dir\", type=Path, help=\"directory containing tokenizer.model, if separate from model file\")\n parser.add_argument(\"--outfile\", type=Path, help=\"path to write to; default: based on input\")\n parser.add_argument(\"model\", type=Path, help=\"directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)\")\n parser.add_argument(\"--vocabtype\", choices=[\"spm\", \"bpe\"], help=\"vocab format (default: spm)\", default=\"spm\")\n parser.add_argument(\"--ctx\", type=int, help=\"model training context (default: based on input)\")\n parser.add_argument(\"--concurrency\", type=int, help=f\"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})\", default = DEFAULT_CONCURRENCY)\n parser.add_argument(\"--bigendian\", action=\"store_true\", help=\"model is executed on big endian machine\")\n\n args = parser.parse_args(args_in)\n \n if args.dump_single:\n model_plus = lazy_load_file(args.model)\n do_dump_model(model_plus)\n return\n\n if not args.vocab_only:\n model_plus = load_some_model(args.model)\n else:\n model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)\n\n if args.dump:\n do_dump_model(model_plus)\n return\n endianess = gguf.GGUFEndian.LITTLE\n if args.bigendian:\n endianess = gguf.GGUFEndian.BIG\n\n params = Params.load(model_plus)\n if params.n_ctx == -1:\n if args.ctx is None:\n raise Exception(\"The model doesn't have a context size, and you didn't specify one with --ctx\\n\"\n \"Please specify one with --ctx:\\n\"\n \" - LLaMA v1: --ctx 2048\\n\"\n \" - LLaMA v2: --ctx 4096\\n\")\n params.n_ctx = args.ctx\n\n if args.outtype:\n params.ftype = {\n \"f32\": GGMLFileType.AllF32,\n \"f16\": GGMLFileType.MostlyF16,\n \"q8_0\": GGMLFileType.MostlyQ8_0,\n }[args.outtype]\n\n print(f\"params = {params}\")\n\n vocab: Vocab\n if args.vocab_only:\n if not args.outfile:\n raise ValueError(\"need --outfile if using --vocab-only\")\n # FIXME: Try to respect vocab_dir somehow?\n vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)\n special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,\n load_merges = args.vocabtype == 'bpe',\n n_vocab = vocab.vocab_size)\n outfile = args.outfile\n OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)\n print(f\"Wrote {outfile}\")\n return\n\n if model_plus.vocab is not None and args.vocab_dir is None:\n vocab = model_plus.vocab\n else:\n vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent\n vocab = load_vocab(vocab_dir, args.vocabtype)\n # FIXME: Try to respect vocab_dir somehow?\n special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,\n load_merges = args.vocabtype == 'bpe',\n n_vocab = vocab.vocab_size)\n\n model = model_plus.model\n model = convert_model_names(model, params)\n ftype = pick_output_type(model, args.outtype)\n model = convert_to_output_type(model, ftype)\n outfile = args.outfile or default_outfile(model_plus.paths, ftype)\n\n params.ftype = ftype\n print(f\"Writing {outfile}, format {ftype}\")\n\n OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)\n print(f\"Wrote {outfile}\")\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "convert-hf-to-powerinfer-gguf.py", "content": "#!/usr/bin/env python3\n\nfrom __future__ import annotations\nfrom abc import ABC, abstractmethod\n\nimport argparse\nimport contextlib\nimport json\nimport os\nimport re\nimport struct\nimport sys\nfrom enum import IntEnum\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, ContextManager, Iterator, Optional, cast\n\nimport numpy as np\nimport torch\nimport torch.nn as tnn\n\nfrom dataclasses import dataclass\n\nif TYPE_CHECKING:\n from torch import Tensor\n\nif \"NO_LOCAL_GGUF\" not in os.environ:\n sys.path.insert(1, str(Path(__file__).parent / \"gguf-py\"))\nimport gguf\n\n\n###### MODEL DEFINITIONS ######\n\n\nclass SentencePieceTokenTypes(IntEnum):\n NORMAL = 1\n UNKNOWN = 2\n CONTROL = 3\n USER_DEFINED = 4\n UNUSED = 5\n BYTE = 6\n\n\nclass ReluMLP(tnn.Module):\n def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):\n super(ReluMLP, self).__init__()\n self.fc1 = tnn.Linear(input_dim, hidden_dim, bias=False)\n self.relu = tnn.ReLU()\n self.fc2 = tnn.Linear(hidden_dim, output_dim, bias=False)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.relu(x)\n x = self.fc2(x)\n return x\n\n @staticmethod\n def from_file(model_file: Path):\n model = torch.load(model_file, map_location=\"cpu\")\n hidden_size, input_size = model.get(\"fc1.weight\").shape\n output_size, _ = model.get(\"fc2.weight\").shape\n mlp = ReluMLP(input_size, hidden_size, output_size)\n mlp.load_state_dict(model)\n return mlp\n\n\nclass Model(ABC):\n \"\"\"Base class for model conversion\"\"\"\n\n def __init__(\n self,\n dir_model: Path,\n dir_mlp_pred: Path,\n ftype: int,\n fname_out: Path,\n is_big_endian: bool,\n ):\n self.dir_model = dir_model\n self.dir_mlp_pred = dir_mlp_pred\n self.ftype = ftype\n self.fname_out = fname_out\n self.is_big_endian = is_big_endian\n self.endianess = (\n gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE\n )\n self.is_safetensors = self._is_model_safetensors()\n self.num_parts = Model.count_model_parts(\n self.dir_model, \".safetensors\" if self.is_safetensors else \".bin\"\n )\n self.part_names = self._get_part_names()\n self.hparams = Model.load_hparams(self.dir_model)\n self.model_arch = self._get_model_architecture()\n self.gguf_writer = gguf.GGUFWriter(\n fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file = False\n )\n\n def set_vocab(self):\n self._set_vocab_gpt2()\n\n def get_tensors(self) -> Iterator[tuple[str, Tensor]]:\n for model_layer, part_name in self._get_mlp_part_layer_names():\n print(f\"gguf: loading mlp part '{part_name}'\")\n mlp_model = ReluMLP.from_file(self.dir_mlp_pred / part_name)\n for name, data in mlp_model.state_dict().items():\n yield f\"blk.{model_layer}.{name}\", data\n\n for part_name in self.part_names:\n print(f\"gguf: loading model part '{part_name}'\")\n ctx: ContextManager[Any]\n if self.is_safetensors:\n from safetensors import safe_open\n\n ctx = cast(\n ContextManager[Any],\n safe_open(self.dir_model / part_name, framework=\"pt\", device=\"cpu\"),\n )\n else:\n ctx = contextlib.nullcontext(\n torch.load(self.dir_model / part_name, map_location=\"cpu\")\n )\n\n with ctx as model_part:\n for name in model_part.keys():\n data = (\n model_part.get_tensor(name)\n if self.is_safetensors\n else model_part[name]\n )\n yield name, data\n\n @abstractmethod\n def set_gguf_parameters(self):\n pass\n # self.gguf_writer.add_name(self.dir_model.name)\n # self.gguf_writer.add_block_count(\n # self.hparams.get(\n # \"n_layers\",\n # self.hparams.get(\"num_hidden_layers\", self.hparams.get(\"n_layer\")),\n # )\n # )\n # if (n_ctx := self.hparams.get(\"max_position_embeddings\")) is not None:\n # self.gguf_writer.add_context_length(n_ctx)\n # if (n_embd := self.hparams.get(\"hidden_size\")) is not None:\n # self.gguf_writer.add_embedding_length(n_embd)\n # if (n_ff := self.hparams.get(\"intermediate_size\")) is not None:\n # self.gguf_writer.add_feed_forward_length(n_ff)\n # if (n_head := self.hparams.get(\"num_attention_head\")) is not None:\n # self.gguf_writer.add_head_count(n_head)\n # self.gguf_writer.add_parallel_residual(\n # self.hparams.get(\"use_parallel_residual\", True)\n # )\n\n @abstractmethod\n def write_tensors(self):\n pass\n\n def write(self):\n self.write_tensors()\n self.gguf_writer.write_header_to_file()\n self.gguf_writer.write_kv_data_to_file()\n self.gguf_writer.write_tensors_to_file()\n self.gguf_writer.close()\n\n def write_vocab(self):\n self.gguf_writer.write_header_to_file()\n self.gguf_writer.write_kv_data_to_file()\n self.gguf_writer.close()\n\n @staticmethod\n def count_model_parts(dir_model: Path, prefix: str) -> int:\n num_parts = 0\n for filename in os.listdir(dir_model):\n if filename.endswith(prefix):\n num_parts += 1\n\n return num_parts\n\n @staticmethod\n def load_hparams(dir_model):\n with open(dir_model / \"config.json\", \"r\", encoding=\"utf-8\") as f:\n return json.load(f)\n\n @staticmethod\n def from_model_architecture(model_architecture):\n if model_architecture in (\"FalconForCausalLM\", \"RWForCausalLM\"):\n return FalconModel\n if model_architecture == \"LlamaForCausalLM\":\n return LlamaModel\n if model_architecture == \"OPTForCausalLM\":\n return OptModel\n\n raise NotImplementedError(f'Architecture \"{model_architecture}\" not supported!')\n\n def _is_model_safetensors(self) -> bool:\n return Model.count_model_parts(self.dir_model, \".safetensors\") > 0\n\n def _get_mlp_part_layer_names(self):\n \"\"\"Returns a generator of (index, name) for MLP predictors of each model layer\"\"\"\n n_mlp_parts = Model.count_model_parts(self.dir_mlp_pred, \".pt\")\n return ((n, f\"model_{n}.pt\") for n in range(n_mlp_parts))\n\n def _get_part_names(self):\n if self.is_safetensors:\n if self.num_parts == 1: # there's only one .safetensors file\n return (\"model.safetensors\",)\n return (\n f\"model-{n:05}-of-{self.num_parts:05}.safetensors\"\n for n in range(1, self.num_parts + 1)\n )\n\n if self.num_parts == 1: # there's only one .bin file\n return (\"pytorch_model.bin\",)\n return (\n f\"pytorch_model-{n:05}-of-{self.num_parts:05}.bin\"\n for n in range(1, self.num_parts + 1)\n )\n\n def _get_model_architecture(self) -> gguf.MODEL_ARCH:\n arch = self.hparams[\"architectures\"][0]\n if arch == \"FalconForCausalLM\":\n return gguf.MODEL_ARCH.FALCON\n if arch == \"RWForCausalLM\" or arch == \"LlamaForCausalLM\":\n return gguf.MODEL_ARCH.LLAMA\n if arch == \"OPTForCausalLM\":\n return gguf.MODEL_ARCH.OPT\n\n raise NotImplementedError(f'Architecture \"{arch}\" not supported!')\n\n def _translate_tensor_key(\n self, key: str, try_suffixes=(\".weight\", \".bias\")\n ) -> Optional[str]:\n block_count = self.hparams.get(\n \"n_layers\",\n self.hparams.get(\"num_hidden_layers\", self.hparams.get(\"n_layer\")),\n )\n tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)\n arch_tensor_key = tensor_map.get_name(key, try_suffixes=try_suffixes)\n if arch_tensor_key is not None:\n return arch_tensor_key\n # check and handle ReluMLP layers\n mlp_match = re.match(r\"^blk\\.\\d+\\.fc\\d\\.weight$\", key)\n if mlp_match:\n return mlp_match.group(0)\n return None\n\n def _set_vocab_gpt2(self):\n dir_model = self.dir_model\n hparams = self.hparams\n tokens: list[bytearray] = []\n toktypes: list[int] = []\n\n from transformers import AutoTokenizer # type: ignore[attr-defined]\n\n tokenizer = AutoTokenizer.from_pretrained(dir_model)\n vocab_size = hparams.get(\"vocab_size\", len(tokenizer.vocab))\n assert max(tokenizer.vocab.values()) < vocab_size\n\n reverse_vocab = {\n id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()\n }\n added_vocab = tokenizer.get_added_vocab()\n\n for i in range(vocab_size):\n if i not in reverse_vocab:\n pad_token = f\"[PAD{i}]\".encode(\"utf-8\")\n tokens.append(bytearray(pad_token))\n toktypes.append(gguf.TokenType.USER_DEFINED)\n elif reverse_vocab[i] in added_vocab:\n tokens.append(reverse_vocab[i])\n if tokenizer.added_tokens_decoder[i].special:\n toktypes.append(gguf.TokenType.CONTROL)\n else:\n toktypes.append(gguf.TokenType.USER_DEFINED)\n else:\n tokens.append(reverse_vocab[i])\n toktypes.append(gguf.TokenType.NORMAL)\n\n self.gguf_writer.add_tokenizer_model(\"gpt2\")\n self.gguf_writer.add_token_list(tokens)\n self.gguf_writer.add_token_types(toktypes)\n\n special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)\n special_vocab.add_to_gguf(self.gguf_writer)\n\n def _set_vocab_sentencepiece(self):\n from sentencepiece import SentencePieceProcessor\n\n tokenizer_path = self.dir_model / \"tokenizer.model\"\n\n tokens: list[bytes] = []\n scores: list[float] = []\n toktypes: list[int] = []\n\n if not tokenizer_path.is_file():\n print(f\"Error: Missing {tokenizer_path}\", file=sys.stderr)\n sys.exit(1)\n\n tokenizer = SentencePieceProcessor(str(tokenizer_path))\n vocab_size = self.hparams.get(\"vocab_size\", tokenizer.vocab_size())\n\n for token_id in range(vocab_size):\n piece = tokenizer.id_to_piece(token_id)\n text = piece.encode(\"utf-8\")\n score = tokenizer.get_score(token_id)\n\n toktype = SentencePieceTokenTypes.NORMAL\n if tokenizer.is_unknown(token_id):\n toktype = SentencePieceTokenTypes.UNKNOWN\n elif tokenizer.is_control(token_id):\n toktype = SentencePieceTokenTypes.CONTROL\n elif tokenizer.is_unused(token_id):\n toktype = SentencePieceTokenTypes.UNUSED\n elif tokenizer.is_byte(token_id):\n toktype = SentencePieceTokenTypes.BYTE\n\n tokens.append(text)\n scores.append(score)\n toktypes.append(toktype)\n\n added_tokens_file = self.dir_model / \"added_tokens.json\"\n if added_tokens_file.is_file():\n with open(added_tokens_file, \"r\", encoding=\"utf-8\") as f:\n added_tokens_json = json.load(f)\n\n for key in added_tokens_json:\n tokens.append(key.encode(\"utf-8\"))\n scores.append(-1000.0)\n toktypes.append(SentencePieceTokenTypes.USER_DEFINED)\n\n self.gguf_writer.add_tokenizer_model(\"llama\")\n self.gguf_writer.add_token_list(tokens)\n self.gguf_writer.add_token_scores(scores)\n self.gguf_writer.add_token_types(toktypes)\n\n special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))\n special_vocab.add_to_gguf(self.gguf_writer)\n\n\nclass LlamaModel(Model):\n def set_vocab(self):\n self._set_vocab_sentencepiece()\n\n def set_gguf_parameters(self, params: PredictorParams):\n self.gguf_writer.add_name(\"Llama\")\n self.gguf_writer.add_context_length(2048) # not in config.json\n self.gguf_writer.add_embedding_length(self.hparams[\"hidden_size\"])\n self.gguf_writer.add_block_count(self.hparams[\"num_hidden_layers\"])\n self.gguf_writer.add_feed_forward_length(self.hparams[\"intermediate_size\"])\n self.gguf_writer.add_rope_dimension_count(\n self.hparams[\"hidden_size\"] // self.hparams[\"num_attention_heads\"]\n )\n self.gguf_writer.add_head_count(self.hparams[\"num_attention_heads\"])\n self.gguf_writer.add_head_count_kv(self.hparams[\"num_key_value_heads\"])\n self.gguf_writer.add_layer_norm_rms_eps(self.hparams[\"rms_norm_eps\"])\n self.gguf_writer.add_rope_freq_base(self.hparams[\"rope_theta\"])\n self.gguf_writer.add_file_type(self.ftype)\n\n if params.sparse_threshold is not None:\n self.gguf_writer.add_sparse_threshold(params.sparse_threshold)\n\n def write_tensors(self):\n for name, data_torch in self.get_tensors():\n # we don't need these\n if name.endswith(\n (\n \".attention.masked_bias\",\n \".attention.bias\",\n \".attention.rotary_emb.inv_freq\",\n )\n ):\n continue\n\n old_dtype = data_torch.dtype\n\n # convert any unsupported data types to float32\n if data_torch.dtype not in (torch.float16, torch.float32):\n data_torch = data_torch.to(torch.float32)\n\n data = data_torch.squeeze().numpy()\n\n # map tensor names\n new_name = self._translate_tensor_key(name)\n if new_name is None:\n print(f\"Can not map tensor {name!r}\")\n sys.exit()\n\n # We need to transpose the weight matrices for the FFN Down layers to support the\n # Axpy operation in PowerInfer. So we don't need to transpose them at runtime.\n if \"ffn_down\" in new_name:\n new_name = new_name.replace(\"ffn_down\", \"ffn_down_t\")\n data = data.T\n\n n_dims = len(data.shape)\n data_dtype = data.dtype\n\n # if f32 desired, convert any float16 to float32\n if self.ftype == 0 and data_dtype == np.float16:\n data = data.astype(np.float32)\n\n # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32\n if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:\n data = data.astype(np.float32)\n\n # if f16 desired, convert any float32 2-dim weight tensors to float16\n if (\n self.ftype == 1\n and data_dtype == np.float32\n and name.endswith(\".weight\")\n and n_dims == 2\n ):\n data = data.astype(np.float16)\n\n print(f\"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}\")\n\n self.gguf_writer.add_tensor(new_name, data)\n\n\nclass FalconModel(Model):\n def set_gguf_parameters(self, params: PredictorParams):\n block_count = self.hparams.get(\"num_hidden_layers\")\n if block_count is None:\n block_count = self.hparams[\"n_layer\"] # old name\n\n n_head = self.hparams.get(\"num_attention_heads\")\n if n_head is None:\n n_head = self.hparams[\"n_head\"] # old name\n\n n_head_kv = self.hparams.get(\"num_kv_heads\")\n if n_head_kv is None:\n n_head_kv = self.hparams.get(\"n_head_kv\", 1) # old name\n\n self.gguf_writer.add_name(\"Falcon\")\n self.gguf_writer.add_context_length(2048) # not in config.json\n self.gguf_writer.add_tensor_data_layout(\"jploski\") # qkv tensor transform\n self.gguf_writer.add_embedding_length(self.hparams[\"hidden_size\"])\n self.gguf_writer.add_feed_forward_length(4 * self.hparams[\"hidden_size\"])\n self.gguf_writer.add_block_count(block_count)\n self.gguf_writer.add_head_count(n_head)\n self.gguf_writer.add_head_count_kv(n_head_kv)\n self.gguf_writer.add_layer_norm_eps(self.hparams[\"layer_norm_epsilon\"])\n self.gguf_writer.add_file_type(self.ftype)\n\n if params.sparse_threshold is not None:\n self.gguf_writer.add_sparse_threshold(params.sparse_threshold)\n\n def write_tensors(self):\n n_head = self.hparams.get(\"num_attention_heads\")\n if n_head is None:\n n_head = self.hparams[\"n_head\"] # old name\n\n n_head_kv = self.hparams.get(\"num_kv_heads\")\n if n_head_kv is None:\n n_head_kv = self.hparams.get(\"n_head_kv\", 1) # old name\n\n head_dim = self.hparams[\"hidden_size\"] // n_head\n\n for name, data_torch in self.get_tensors():\n old_dtype = data_torch.dtype\n\n # convert any unsupported data types to float32\n if data_torch.dtype not in (torch.float16, torch.float32):\n data_torch = data_torch.to(torch.float32)\n\n # QKV tensor transform\n # The original query_key_value tensor contains n_head_kv \"kv groups\",\n # each consisting of n_head/n_head_kv query weights followed by one key\n # and one value weight (shared by all query heads in the kv group).\n # This layout makes it a big pain to work with in GGML.\n # So we rearrange them here,, so that we have n_head query weights\n # followed by n_head_kv key weights followed by n_head_kv value weights,\n # in contiguous fashion.\n # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py\n\n if \"query_key_value\" in name:\n qkv = data_torch.view(\n n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head\n )\n q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)\n k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)\n v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)\n data_torch = torch.cat((q, k, v)).reshape_as(data_torch)\n\n data = data_torch.squeeze().numpy()\n\n # map tensor names\n new_name = self._translate_tensor_key(name)\n if new_name is None:\n print(f\"Can not map tensor {name!r}\")\n sys.exit()\n\n # We need to transpose the weight matrices for the FFN Down layers to support the\n # Axpy operation in PowerInfer. So we don't need to transpose them at runtime.\n if \"ffn_down\" in new_name:\n new_name = new_name.replace(\"ffn_down\", \"ffn_down_t\")\n data = data.T\n\n n_dims = len(data.shape)\n data_dtype = data.dtype\n\n # if f32 desired, convert any float16 to float32\n if self.ftype == 0 and data_dtype == np.float16:\n data = data.astype(np.float32)\n\n # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32\n if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:\n data = data.astype(np.float32)\n\n # if f16 desired, convert any float32 2-dim weight tensors to float16\n if (\n self.ftype == 1\n and data_dtype == np.float32\n and name.endswith(\".weight\")\n and n_dims == 2\n ):\n data = data.astype(np.float16)\n\n print(f\"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}\")\n\n self.gguf_writer.add_tensor(new_name, data)\n\nclass OptModel(Model):\n def set_gguf_parameters(self, params: PredictorParams):\n self.gguf_writer.add_name(\"opt\")\n self.gguf_writer.add_context_length(2050) # not in config.json\n self.gguf_writer.add_embedding_length(self.hparams[\"hidden_size\"])\n self.gguf_writer.add_block_count(self.hparams[\"num_hidden_layers\"])\n self.gguf_writer.add_feed_forward_length(self.hparams[\"ffn_dim\"])\n self.gguf_writer.add_head_count(self.hparams[\"num_attention_heads\"])\n # self.gguf_writer.add_vocab_size(self.hparams[\"vocab_size\"])\n self.gguf_writer.add_file_type(self.ftype)\n \n if params.sparse_threshold is not None:\n self.gguf_writer.add_sparse_threshold(params.sparse_threshold)\n\n def write_tensors(self):\n for name, data_torch in self.get_tensors():\n old_dtype = data_torch.dtype\n \n # convert any unsupported data types to float32\n if data_torch.dtype not in (torch.float16, torch.float32):\n data_torch = data_torch.to(torch.float32)\n \n data = data_torch.squeeze().numpy()\n \n # map tensor names\n new_name = self._translate_tensor_key(name)\n if new_name is None:\n print(f\"Can not map tensor {name!r}\")\n sys.exit()\n\n # We need to transpose the weight matrices for the FFN Down layers to support the\n # Axpy operation in PowerInfer. So we don't need to transpose them at runtime.\n if \"ffn_down\" in new_name:\n new_name = new_name.replace(\"ffn_down\", \"ffn_down_t\")\n data = data.T\n \n n_dims = len(data.shape) \n data_dtype = data.dtype\n\n # if f32 desired, convert any float16 to float32\n if self.ftype == 0 and data_dtype == np.float16:\n data = data.astype(np.float32)\n # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32\n if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:\n data = data.astype(np.float32)\n # if f16 desired, convert any float32 2-dim weight tensors to float16\n if (\n self.ftype == 1\n and data_dtype == np.float32\n and name.endswith(\".weight\")\n and n_dims == 2\n ):\n data = data.astype(np.float16)\n \n print(f\"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}\")\n \n self.gguf_writer.add_tensor(new_name, data)\n\n@dataclass\nclass PredictorParams:\n sparse_threshold: float | None = None\n\n @staticmethod\n def loadPredictorJson(config_path: Path) -> PredictorParams:\n config = json.load(open(config_path))\n return PredictorParams(\n sparse_threshold = config.get(\"sparse_threshold\"),\n )\n\n @staticmethod\n def load(model_instance: Model) -> PredictorParams:\n config_path = model_instance.dir_mlp_pred / \"config.json\"\n\n if config_path.exists():\n params = PredictorParams.loadPredictorJson(config_path)\n else:\n params = PredictorParams()\n\n return params\n\n###### CONVERSION LOGIC ######\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser(\n description=\"Convert a huggingface model to a GGML compatible file\"\n )\n parser.add_argument(\n \"--vocab-only\",\n action=\"store_true\",\n help=\"extract only the vocab\",\n )\n parser.add_argument(\n \"--outfile\",\n type=Path,\n help=\"path to write to; default: based on input\",\n )\n parser.add_argument(\n \"--outtype\",\n type=str,\n choices=[\"f32\", \"f16\"],\n default=\"f16\",\n help=\"output format - use f32 for float32, f16 for float16\",\n )\n parser.add_argument(\n \"--bigendian\",\n action=\"store_true\",\n help=\"model is executed on big endian machine\",\n )\n parser.add_argument(\n \"model\",\n type=Path,\n help=\"directory containing model file\",\n )\n parser.add_argument(\n \"mlp_predictors\",\n type=Path,\n help=\"directory containing MLP predictors for model\",\n )\n\n return parser.parse_args()\n\n\nargs = parse_args()\n\ndir_model = args.model\ndir_mlp_pred = args.mlp_predictors\nif not dir_model.is_dir():\n print(f\"Error: {args.model} is not a directory\", file=sys.stderr)\n sys.exit(1)\nif not dir_mlp_pred.is_dir():\n print(f\"Error: {args.mlp_predictors} is not a directory\", file=sys.stderr)\n sys.exit(1)\n\nftype_map = {\n \"f32\": gguf.GGMLQuantizationType.F32,\n \"f16\": gguf.GGMLQuantizationType.F16,\n}\n\nif args.outfile is not None:\n fname_out = args.outfile\nelse:\n # output in the same directory as the model by default\n fname_out = dir_model / f\"ggml-model-{args.outtype}.gguf\"\n\nprint(f\"Loading model: {dir_model.name}\")\n\nhparams = Model.load_hparams(dir_model)\n\nmodel_class = Model.from_model_architecture(hparams[\"architectures\"][0])\nmodel_instance = model_class(\n dir_model, dir_mlp_pred, ftype_map[args.outtype], fname_out, args.bigendian\n)\n\nprint(\"Set model parameters\")\nparams = PredictorParams.load(model_instance)\nmodel_instance.set_gguf_parameters(params)\n\nprint(\"Set model tokenizer\")\nmodel_instance.set_vocab()\n\nif args.vocab_only:\n print(f\"Exporting model vocab to '{fname_out}'\")\n model_instance.write_vocab()\nelse:\n print(f\"Exporting model to '{fname_out}'\")\n model_instance.write()\n\n# post-process: write another unique file header to distinguish from the origianl GGUF file\nwith open(fname_out, \"r+b\") as fout:\n POWERINFER_MAGIC = int.from_bytes(b\"PWRI\", \"little\")\n fout.write(struct.pack(\" int:\n return n_elements * self.dtype.itemsize\n\n@dataclass(frozen=True)\nclass UnquantizedDataType(DataType):\n pass\n\nDT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])\nDT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])\nDT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])\nDT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])\n\n@dataclass(frozen=True)\nclass QuantizedDataType(DataType):\n block_size: int\n quantized_dtype: np.dtype[Any]\n ggml_type: gguf.GGMLQuantizationType\n\n def quantize(self, arr: NDArray) -> NDArray:\n raise NotImplementedError(f'Quantization for {self.name} not implemented')\n\n def elements_to_bytes(self, n_elements: int) -> int:\n assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'\n return self.quantized_dtype.itemsize * (n_elements // self.block_size)\n\n@dataclass(frozen=True)\nclass Q8_0QuantizedDataType(QuantizedDataType):\n # Mini Q8_0 quantization in Python!\n def quantize(self, arr: NDArray) -> NDArray:\n assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'\n assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'\n n_blocks = arr.size // self.block_size\n blocks = arr.reshape((n_blocks, self.block_size))\n # Much faster implementation of block quantization contributed by @Cebtenzzre\n def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:\n d = abs(blocks).max(axis = 1) / np.float32(127)\n with np.errstate(divide = 'ignore'):\n qs = (blocks / d[:, None]).round()\n qs[d == 0] = 0\n yield from zip(d, qs)\n return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)\n\nDT_Q8_0 = Q8_0QuantizedDataType('Q8_0',\n dtype = np.dtype(np.float32), valid_conversions = [],\n ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,\n quantized_dtype = np.dtype([('d', ' DataType:\n dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)\n if dt is None:\n raise ValueError(self)\n # 1D tensors are always F32.\n return dt if len(tensor.shape) > 1 else DT_F32\n\nGGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {\n GGMLFileType.AllF32 : DT_F32,\n GGMLFileType.MostlyF16 : DT_F16,\n GGMLFileType.MostlyQ8_0: DT_Q8_0,\n}\n\n#\n# hparams loading\n#\n\n@dataclass\nclass PredictorParams:\n sparse_threshold: float | None = None\n\n @staticmethod\n def loadPredictorJson(model: LazyModel, config_path: Path) -> PredictorParams:\n config = json.load(open(config_path))\n return PredictorParams(\n sparse_threshold = config.get(\"sparse_threshold\"),\n )\n\n @staticmethod\n def load(model_plus: ModelPlus) -> PredictorParams:\n config_path = model_plus.paths[0].parent / \"config.json\"\n\n if config_path.exists():\n params = PredictorParams.loadPredictorJson(model_plus.model, config_path)\n else:\n params = PredictorParams()\n\n return params\n\n@dataclass\nclass Params:\n n_vocab: int\n n_embd: int\n n_layer: int\n n_ctx: int\n n_ff: int\n n_head: int\n n_head_kv: int\n f_norm_eps: float\n\n arch: gguf.MODEL_ARCH = gguf.MODEL_ARCH.LLAMA\n rope_scaling_type: gguf.RopeScalingType | None = None\n f_rope_freq_base: float | None = None\n f_rope_scale: float | None = None\n n_orig_ctx: int | None = None\n rope_finetuned: bool | None = None\n\n ftype: GGMLFileType | None = None\n\n # path to the directory containing the model files\n path_model: Path | None = None\n\n # MLP predictor parameters\n predictor_params: PredictorParams = dataclasses.field(default_factory=PredictorParams)\n\n @staticmethod\n def guessed(model: LazyModel) -> Params:\n # try transformer naming first\n n_vocab, n_embd = model[\"model.embed_tokens.weight\"].shape if \"model.embed_tokens.weight\" in model else model[\"tok_embeddings.weight\"].shape\n\n # try transformer naming first\n if \"model.layers.0.self_attn.q_proj.weight\" in model:\n n_layer=next(i for i in itertools.count() if f\"model.layers.{i}.self_attn.q_proj.weight\" not in model)\n elif \"model.layers.0.self_attn.W_pack.weight\" in model: # next: try baichuan naming\n n_layer=next(i for i in itertools.count() if f\"model.layers.{i}.self_attn.W_pack.weight\" not in model)\n else:\n n_layer=next(i for i in itertools.count() if f\"layers.{i}.attention.wq.weight\" not in model)\n\n if n_layer < 1:\n raise Exception(\"failed to guess 'n_layer'. This model is unknown or unsupported.\\n\"\n \"Suggestion: provide 'config.json' of the model in the same directory containing model files.\")\n\n n_head = n_embd // 128 # guessed\n n_mult = 256 # guessed\n\n # TODO: verify this\n n_ff = int(2 * (4 * n_embd) / 3)\n n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)\n\n return Params(\n n_vocab = n_vocab,\n n_embd = n_embd,\n n_layer = n_layer,\n n_ctx = -1,\n n_ff = n_ff,\n n_head = n_head,\n n_head_kv = n_head,\n f_norm_eps = 1e-5,\n )\n\n @staticmethod\n def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:\n config = json.load(open(config_path))\n\n rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None\n rope_scaling = config.get(\"rope_scaling\")\n\n if rope_scaling is not None and (typ := rope_scaling.get(\"type\")):\n rope_factor = rope_scaling.get(\"factor\")\n f_rope_scale = rope_factor\n if typ == \"linear\":\n rope_scaling_type = gguf.RopeScalingType.LINEAR\n elif typ == \"yarn\":\n rope_scaling_type = gguf.RopeScalingType.YARN\n n_orig_ctx = rope_scaling['original_max_position_embeddings']\n rope_finetuned = rope_scaling['finetuned']\n else:\n raise NotImplementedError(f'Unknown rope scaling type: {typ}')\n\n if \"max_sequence_length\" in config:\n n_ctx = config[\"max_sequence_length\"]\n elif \"max_position_embeddings\" in config:\n n_ctx = config[\"max_position_embeddings\"]\n else:\n raise Exception(\"failed to guess 'n_ctx'. This model is unknown or unsupported.\\n\"\n \"Suggestion: provide 'config.json' of the model in the same directory containing model files.\")\n\n params = Params(\n n_vocab = config[\"vocab_size\"],\n n_embd = config[\"hidden_size\"],\n n_layer = config[\"num_hidden_layers\"],\n n_ctx = n_ctx,\n n_ff = config[\"intermediate_size\"],\n n_head = (n_head := config[\"num_attention_heads\"]),\n n_head_kv = config.get(\"num_key_value_heads\", n_head),\n f_norm_eps = config[\"rms_norm_eps\"],\n f_rope_freq_base = config.get(\"rope_theta\"),\n rope_scaling_type = rope_scaling_type,\n f_rope_scale = f_rope_scale,\n n_orig_ctx = n_orig_ctx,\n rope_finetuned = rope_finetuned,\n )\n\n if config.get(\"model_type\", None) == \"bamboo\":\n params.arch = gguf.MODEL_ARCH.BAMBOO\n\n return params\n\n # LLaMA v2 70B params.json\n # {\"dim\": 8192, \"multiple_of\": 4096, \"ffn_dim_multiplier\": 1.3, \"n_heads\": 64, \"n_kv_heads\": 8, \"n_layers\": 80, \"norm_eps\": 1e-05, \"vocab_size\": -1}\n @staticmethod\n def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:\n config = json.load(open(config_path))\n\n # hack to determine LLaMA v1 vs v2 vs CodeLlama\n if config.get(\"rope_theta\") == 1000000:\n # CodeLlama\n n_ctx = 16384\n elif config[\"norm_eps\"] == 1e-05:\n # LLaMA v2\n n_ctx = 4096\n else:\n # LLaMA v1\n n_ctx = 2048\n\n return Params(\n n_vocab = config.get(\"vocab_size\", model[\"tok_embeddings.weight\"].shape[0]),\n n_embd = config[\"dim\"],\n n_layer = config[\"n_layers\"],\n n_ctx = n_ctx,\n n_ff = model[\"layers.0.feed_forward.w1.weight\"].shape[0],\n n_head = (n_head := config[\"n_heads\"]),\n n_head_kv = config.get(\"n_kv_heads\", n_head),\n f_norm_eps = config[\"norm_eps\"],\n f_rope_freq_base = config.get(\"rope_theta\"),\n )\n\n @staticmethod\n def load(model_plus: ModelPlus) -> Params:\n hf_config_path = model_plus.paths[0].parent / \"config.json\"\n orig_config_path = model_plus.paths[0].parent / \"params.json\"\n\n if hf_config_path.exists():\n params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)\n elif orig_config_path.exists():\n params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)\n elif model_plus.format != 'none':\n params = Params.guessed(model_plus.model)\n else:\n raise ValueError('Cannot guess params when model format is none')\n\n params.path_model = model_plus.paths[0].parent\n\n return params\n\n\n#\n# vocab\n#\n\nclass BpeVocab:\n def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:\n self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding=\"utf-8\").read())\n added_tokens: dict[str, int]\n if fname_added_tokens is not None:\n # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.\n added_tokens = json.load(open(fname_added_tokens, encoding=\"utf-8\"))\n else:\n # Fall back to trying to find the added tokens in tokenizer.json\n tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'\n if not tokenizer_json_file.is_file():\n added_tokens = {}\n else:\n tokenizer_json = json.load(open(tokenizer_json_file, encoding=\"utf-8\"))\n added_tokens = dict(\n (item['content'], item['id'])\n for item in tokenizer_json.get('added_tokens', [])\n # Added tokens here can be duplicates of the main vocabulary.\n if item['content'] not in self.bpe_tokenizer )\n\n vocab_size: int = len(self.bpe_tokenizer)\n expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))\n actual_ids = sorted(added_tokens.values())\n if expected_ids != actual_ids:\n expected_end_id = vocab_size + len(actual_ids) - 1\n raise Exception(f\"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}\")\n\n items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])\n self.added_tokens_list = [text for (text, idx) in items]\n self.vocab_size_base: int = vocab_size\n self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)\n self.fname_tokenizer = fname_tokenizer\n self.fname_added_tokens = fname_added_tokens\n\n def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n tokenizer = self.bpe_tokenizer\n from transformers.models.gpt2 import tokenization_gpt2\n reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}\n\n for i, _ in enumerate(tokenizer):\n yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL\n\n def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n for text in self.added_tokens_list:\n score = -1000.0\n yield text.encode(\"utf-8\"), score, gguf.TokenType.CONTROL\n\n def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n yield from self.bpe_tokens()\n yield from self.added_tokens()\n\n def __repr__(self) -> str:\n return f\"\"\n\n\nclass SentencePieceVocab:\n def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:\n self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))\n added_tokens: dict[str, int]\n if fname_added_tokens is not None:\n added_tokens = json.load(open(fname_added_tokens, encoding=\"utf-8\"))\n else:\n added_tokens = {}\n\n vocab_size: int = self.sentencepiece_tokenizer.vocab_size()\n\n new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}\n expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))\n actual_new_ids = sorted(new_tokens.keys())\n\n if expected_new_ids != actual_new_ids:\n raise ValueError(f\"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}\")\n\n # Token pieces that were added to the base vocabulary.\n self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]\n self.vocab_size_base = vocab_size\n self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)\n self.fname_tokenizer = fname_tokenizer\n self.fname_added_tokens = fname_added_tokens\n\n def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n tokenizer = self.sentencepiece_tokenizer\n for i in range(tokenizer.vocab_size()):\n piece = tokenizer.id_to_piece(i)\n text: bytes = piece.encode(\"utf-8\")\n score: float = tokenizer.get_score(i)\n\n toktype = gguf.TokenType.NORMAL\n if tokenizer.is_unknown(i):\n toktype = gguf.TokenType.UNKNOWN\n if tokenizer.is_control(i):\n toktype = gguf.TokenType.CONTROL\n\n # NOTE: I think added_tokens are user defined.\n # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto\n # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED\n\n if tokenizer.is_unused(i):\n toktype = gguf.TokenType.UNUSED\n if tokenizer.is_byte(i):\n toktype = gguf.TokenType.BYTE\n\n yield text, score, toktype\n\n def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n for text in self.added_tokens_list:\n score = -1000.0\n yield text.encode(\"utf-8\"), score, gguf.TokenType.USER_DEFINED\n\n def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:\n yield from self.sentencepiece_tokens()\n yield from self.added_tokens()\n\n def __repr__(self) -> str:\n return f\"\"\n\nVocab: TypeAlias = 'BpeVocab | SentencePieceVocab'\n\n#\n# data loading\n# TODO: reuse (probably move to gguf.py?)\n#\n\ndef permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:\n #print( \"permute debug \" + str(weights.shape[0]) + \" x \" + str(weights.shape[1]) + \" nhead \" + str(n_head) + \" nheadkv \" + str(n_kv_head) )\n if n_head_kv is not None and n_head != n_head_kv:\n n_head = n_head_kv\n return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])\n .swapaxes(1, 2)\n .reshape(weights.shape))\n\n\nclass Tensor(metaclass=ABCMeta):\n data_type: DataType\n\n @abstractmethod\n def astype(self, data_type: DataType) -> Tensor: ...\n @abstractmethod\n def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...\n @abstractmethod\n def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...\n @abstractmethod\n def part(self, n_part: int) -> UnquantizedTensor: ...\n @abstractmethod\n def to_ggml(self) -> GGMLCompatibleTensor: ...\n\n\ndef bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:\n assert bf16_arr.dtype == np.uint16, f\"Input array should be of dtype uint16, but got {bf16_arr.dtype}\"\n fp32_arr = bf16_arr.astype(np.uint32) << 16\n return fp32_arr.view(np.float32)\n\n\nclass UnquantizedTensor(Tensor):\n def __init__(self, ndarray: NDArray) -> None:\n assert isinstance(ndarray, np.ndarray)\n self.ndarray = ndarray\n self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]\n\n def astype(self, data_type: DataType) -> Tensor:\n dtype = data_type.dtype\n if self.data_type == DT_BF16:\n self.ndarray = bf16_to_fp32(self.ndarray)\n return UnquantizedTensor(self.ndarray.astype(dtype))\n\n def to_ggml(self) -> UnquantizedTensor:\n return self\n\n def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:\n r = self.ndarray.shape[0] // 3\n return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))\n\n def part(self, n_part: int) -> UnquantizedTensor:\n r = self.ndarray.shape[0] // 3\n return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])\n\n def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor:\n return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))\n\n\ndef load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray:\n tensor = lazy_tensor.load()\n assert isinstance(tensor, UnquantizedTensor)\n\n # double-check:\n actual_shape = list(tensor.ndarray.shape)\n assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape)\n if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype:\n if convert:\n tensor.ndarray = tensor.ndarray.astype(expected_dtype)\n else:\n raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}')\n\n return tensor.ndarray\n\n\nGGMLCompatibleTensor = UnquantizedTensor\n\n\n@dataclass\nclass LazyTensor:\n _load: Callable[[], Tensor]\n shape: list[int]\n data_type: DataType\n description: str\n\n def load(self) -> Tensor:\n ret = self._load()\n # Should be okay if it maps to the same numpy type?\n assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \\\n (self.data_type, ret.data_type, self.description)\n return ret\n\n def astype(self, data_type: DataType) -> LazyTensor:\n self.validate_conversion_to(data_type)\n\n def load() -> Tensor:\n return self.load().astype(data_type)\n return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')\n \n def transposed(self) -> LazyTensor:\n def load() -> Tensor:\n loaded = self.load()\n assert isinstance(loaded, UnquantizedTensor), f'Cannot transpose {loaded}'\n loaded.ndarray = loaded.ndarray.T\n return loaded\n return LazyTensor(load, self.shape[::-1], self.data_type, f'transpose {self.description}')\n\n def validate_conversion_to(self, data_type: DataType) -> None:\n if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:\n raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')\n\n\nLazyModel: TypeAlias = 'dict[str, LazyTensor]'\n\n\n@dataclass\nclass ModelPlus:\n model: LazyModel\n paths: list[Path] # Where this was read from.\n format: Literal['ggml', 'torch', 'safetensors', 'none']\n vocab: Vocab | None # For GGML models (which have vocab built in), the vocab.\n\n\ndef merge_sharded(models: list[LazyModel]) -> LazyModel:\n # Original LLaMA models have each file contain one part of each tensor.\n # Use a dict instead of a set to preserve order.\n names = {name: None for model in models for name in model}\n\n def convert(name: str) -> LazyTensor:\n lazy_tensors: list[LazyTensor] = [model[name] for model in models]\n if len(lazy_tensors) == 1:\n # only one file; don't go through this procedure since there might\n # be quantized tensors\n return lazy_tensors[0]\n if len(lazy_tensors[0].shape) == 1:\n # the tensor is just duplicated in every file\n return lazy_tensors[0]\n if name.startswith('tok_embeddings.') or \\\n name.endswith('.attention.wo.weight') or \\\n name.endswith('.feed_forward.w2.weight'):\n # split by columns\n axis = 1\n else:\n # split by rows\n axis = 0\n concatenated_shape = list(lazy_tensors[0].shape)\n concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors)\n\n def load() -> UnquantizedTensor:\n ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]\n concatenated: NDArray = np.concatenate(ndarrays, axis=axis)\n return UnquantizedTensor(concatenated)\n description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'\n return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)\n return {name: convert(name) for name in names}\n\n\ndef merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:\n formats = set(mp.format for mp in models_plus)\n # assert len(formats) == 1, \"different formats?\"\n format = formats.pop()\n paths = [path for mp in models_plus for path in mp.paths]\n # Use the first non-None vocab, if any.\n try:\n vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None)\n except StopIteration:\n vocab = None\n\n if any(\"model.embed_tokens.weight\" in mp.model for mp in models_plus) or \\\n any(\"model.layers.0.fc1.weight\" in mp.model for mp in models_plus):\n # Transformers models put different tensors in different files, but\n # don't split indivdual tensors between files.\n model: LazyModel = {}\n for mp in models_plus:\n model.update(mp.model)\n else:\n model = merge_sharded([mp.model for mp in models_plus])\n\n return ModelPlus(model, paths, format, vocab)\n\n\ndef permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().permute(n_head, n_head_kv)\n return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)\n\ndef permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)\n s = lazy_tensor.shape.copy()\n s[0] = s[0] // 3\n return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)\n\ndef part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:\n def load() -> Tensor:\n return lazy_tensor.load().part(n_part)\n s = lazy_tensor.shape.copy()\n s[0] = s[0] // 3\n return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)\n\n\n# Functionality that simulates `torch.load` but where individual tensors are\n# only loaded into memory on demand, not all at once.\n# PyTorch can't do this natively as of time of writing:\n# - https://github.com/pytorch/pytorch/issues/64327\n# This allows us to de-shard without multiplying RAM usage, and also\n# conveniently drops the PyTorch dependency (though we still need numpy).\n\n\n@dataclass\nclass LazyStorageKind:\n data_type: DataType\n\n\n@dataclass\nclass LazyStorage:\n load: Callable[[int, int], NDArray]\n kind: LazyStorageKind\n description: str\n\n\nclass LazyUnpickler(pickle.Unpickler):\n def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile):\n super().__init__(fp)\n self.data_base_path = data_base_path\n self.zip_file = zip_file\n\n def persistent_load(self, pid: Any) -> Any:\n assert pid[0] == 'storage'\n assert isinstance(pid[1], LazyStorageKind)\n data_type = pid[1].data_type\n filename_stem = pid[2]\n filename = f'{self.data_base_path}/{filename_stem}'\n info = self.zip_file.getinfo(filename)\n\n def load(offset: int, elm_count: int) -> NDArray:\n dtype = data_type.dtype\n fp = self.zip_file.open(info)\n fp.seek(offset * dtype.itemsize)\n size = elm_count * dtype.itemsize\n data = fp.read(size)\n assert len(data) == size\n return np.frombuffer(data, dtype)\n description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'\n return LazyStorage(load=load, kind=pid[1], description=description)\n\n @staticmethod\n def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,\n requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:\n assert isinstance(storage, LazyStorage)\n\n def load() -> UnquantizedTensor:\n elm_count = stride[0] * size[0]\n return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size))\n description = f'pickled storage_offset={storage_offset} in {storage.description}'\n return LazyTensor(load, list(size), storage.kind.data_type, description)\n\n @staticmethod\n def rebuild_from_type_v2(func, new_type, args, state):\n return func(*args)\n\n CLASSES: dict[tuple[str, str], Any] = {\n # getattr used here as a workaround for mypy not being smart enough to detrmine\n # the staticmethods have a __func__ attribute.\n ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),\n ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),\n ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),\n ('torch', 'HalfStorage'): LazyStorageKind(DT_F16),\n ('torch', 'FloatStorage'): LazyStorageKind(DT_F32),\n ('torch', 'IntStorage'): LazyStorageKind(DT_I32),\n ('torch', 'Tensor'): LazyTensor,\n }\n\n def find_class(self, module: str, name: str) -> Any:\n if not module.startswith('torch'):\n return super().find_class(module, name)\n return self.CLASSES[(module, name)]\n\n\ndef lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:\n zf = zipfile.ZipFile(outer_fp)\n pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')]\n assert len(pickle_paths) == 1, pickle_paths\n pickle_fp = zf.open(pickle_paths[0], 'r')\n unpickler = LazyUnpickler(pickle_fp,\n data_base_path=pickle_paths[0][:-4],\n zip_file=zf)\n model = unpickler.load()\n as_dict = dict(model.items())\n return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)\n\n\ndef lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:\n header_size, = struct.unpack(' LazyTensor:\n data_type = SAFETENSORS_DATA_TYPES[info['dtype']]\n numpy_dtype = data_type.dtype\n shape: list[int] = info['shape']\n begin, end = info['data_offsets']\n assert 0 <= begin <= end <= len(byte_buf)\n assert end - begin == math.prod(shape) * numpy_dtype.itemsize\n buf = byte_buf[begin:end]\n\n def load() -> UnquantizedTensor:\n return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape))\n description = f'safetensors begin={begin} end={end} type={data_type} path={path}'\n return LazyTensor(load, shape, data_type, description)\n model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'}\n return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None)\n\n\ndef must_read(fp: IO[bytes], length: int) -> bytes:\n ret = fp.read(length)\n if len(ret) < length:\n raise Exception(\"unexpectedly reached end of file\")\n return ret\n\n\n@functools.lru_cache(maxsize=None)\ndef lazy_load_file(path: Path) -> ModelPlus:\n fp = open(path, 'rb')\n first8 = fp.read(8)\n fp.seek(0)\n if first8[:2] == b'PK':\n # A zip file, i.e. PyTorch format\n return lazy_load_torch_file(fp, path)\n elif struct.unpack(' Iterable[Out]:\n '''Parallel map, but with backpressure. If the caller doesn't call `next`\n fast enough, this will stop calling `func` at some point rather than\n letting results pile up in memory. Specifically, there is a max of one\n output value buffered per thread.'''\n if concurrency < 2:\n yield from map(func, iterable)\n # Not reached.\n iterable = iter(iterable)\n executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor]\n if use_processpool_executor:\n executor_class = ProcessPoolExecutor\n else:\n executor_class = ThreadPoolExecutor\n with executor_class(max_workers = max_workers) as executor:\n futures: list[concurrent.futures.Future[Out]] = []\n done = False\n for _ in range(concurrency):\n try:\n futures.append(executor.submit(func, next(iterable)))\n except StopIteration:\n done = True\n break\n\n while futures:\n result = futures.pop(0).result()\n while not done and len(futures) < concurrency:\n try:\n futures.append(executor.submit(func, next(iterable)))\n except StopIteration:\n done = True\n break\n yield result\n\ndef check_vocab_size(params: Params, vocab: Vocab) -> None:\n if params.n_vocab != vocab.vocab_size:\n assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)\n if params.n_vocab == vocab.vocab_size_base:\n print(\"Ignoring added_tokens.json since model matches vocab size without it.\")\n vocab.added_tokens_list = []\n vocab.vocab_size = vocab.vocab_size_base\n return\n msg = f\"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}\"\n if vocab.fname_added_tokens is not None:\n msg += f\" combined with {vocab.fname_added_tokens}\"\n msg += f\" has {vocab.vocab_size}).\"\n if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:\n msg += f\" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent}).\"\n raise Exception(msg)\n\n\nclass OutputFile:\n def __init__(self, fname_out: Path, arch: gguf.MODEL_ARCH, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:\n self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[arch], endianess=endianess)\n\n def add_meta_arch(self, params: Params) -> None:\n name = \"LLaMA\"\n\n # TODO: better logic to determine model name\n if params.n_ctx == 4096:\n name = \"LLaMA v2\"\n elif params.path_model is not None:\n name = str(params.path_model).split('/')[-1]\n\n self.gguf.add_name (name)\n self.gguf.add_context_length (params.n_ctx)\n self.gguf.add_embedding_length (params.n_embd)\n self.gguf.add_block_count (params.n_layer)\n self.gguf.add_feed_forward_length (params.n_ff)\n self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)\n self.gguf.add_head_count (params.n_head)\n self.gguf.add_head_count_kv (params.n_head_kv)\n self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)\n\n if params.f_rope_freq_base is not None:\n self.gguf.add_rope_freq_base(params.f_rope_freq_base)\n\n if params.rope_scaling_type:\n assert params.f_rope_scale is not None\n self.gguf.add_rope_scaling_type(params.rope_scaling_type)\n self.gguf.add_rope_scaling_factor(params.f_rope_scale)\n\n if params.n_orig_ctx is not None:\n self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)\n\n if params.rope_finetuned is not None:\n self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)\n\n if params.ftype is not None:\n self.gguf.add_file_type(params.ftype)\n\n if params.predictor_params.sparse_threshold is not None:\n self.gguf.add_sparse_threshold(params.predictor_params.sparse_threshold)\n\n def add_meta_vocab(self, vocab: Vocab) -> None:\n tokens = []\n scores = []\n toktypes = []\n # NOTE: `all_tokens` returns the base vocabulary and added tokens\n for text, score, toktype in vocab.all_tokens():\n tokens.append(text)\n scores.append(score)\n toktypes.append(toktype)\n\n if isinstance(vocab, SentencePieceVocab):\n self.gguf.add_tokenizer_model(\"llama\")\n elif isinstance(vocab, BpeVocab):\n self.gguf.add_tokenizer_model(\"gpt2\")\n else:\n raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')\n self.gguf.add_token_list(tokens)\n self.gguf.add_token_scores(scores)\n self.gguf.add_token_types(toktypes)\n\n def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None:\n svocab.add_to_gguf(self.gguf)\n\n def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:\n n_elements = int(np.prod(tensor.shape))\n raw_dtype = getattr(tensor.data_type, 'ggml_type', None)\n data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype\n data_nbytes = tensor.data_type.elements_to_bytes(n_elements)\n self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)\n\n def write_meta(self) -> None:\n self.gguf.write_header_to_file()\n self.gguf.write_kv_data_to_file()\n\n def write_tensor_info(self) -> None:\n self.gguf.write_ti_data_to_file()\n\n def close(self) -> None:\n self.gguf.close()\n\n @staticmethod\n def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:\n check_vocab_size(params, vocab)\n\n of = OutputFile(fname_out, params.arch, endianess=endianess)\n\n # meta data\n of.add_meta_arch(params)\n of.add_meta_vocab(vocab)\n of.add_meta_special_vocab(svocab)\n\n of.write_meta()\n\n of.close()\n\n @staticmethod\n def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]:\n name, lazy_tensor = item\n tensor = lazy_tensor.load().to_ggml()\n return (lazy_tensor.data_type, tensor.ndarray)\n\n @staticmethod\n def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray:\n dt, arr = item\n if not isinstance(dt, QuantizedDataType):\n return arr\n return dt.quantize(arr)\n\n @staticmethod\n def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:\n check_vocab_size(params, vocab)\n\n of = OutputFile(fname_out, params.arch, endianess=endianess)\n\n # meta data\n of.add_meta_arch(params)\n of.add_meta_vocab(vocab)\n of.add_meta_special_vocab(svocab)\n\n # tensor info\n for name, lazy_tensor in model.items():\n of.add_tensor_info(name, lazy_tensor)\n\n of.write_meta()\n of.write_tensor_info()\n\n # tensor data\n ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)\n if ftype == GGMLFileType.MostlyQ8_0:\n ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True)\n else:\n ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)\n\n start = time.time()\n for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):\n elapsed = time.time() - start\n size = ' x '.join(f\"{dim:6d}\" for dim in lazy_tensor.shape)\n padi = len(str(len(model)))\n print(f\"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}\")\n of.gguf.write_tensor_data(ndarray)\n\n of.close()\n\ndef pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:\n wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+\".weight\"].data_type\n\n if output_type_str == \"f32\" or (output_type_str is None and wq_type == DT_F32):\n return GGMLFileType.AllF32\n if output_type_str == \"f16\" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):\n return GGMLFileType.MostlyF16\n if output_type_str == \"q8_0\":\n return GGMLFileType.MostlyQ8_0\n\n name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}\n\n raise Exception(f\"Unexpected combination of types: {name_to_type}\")\n\ndef convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:\n return {name: tensor.astype(output_type.type_for_tensor(name, tensor))\n for (name, tensor) in model.items()}\n\ndef convert_model_names(model: LazyModel, params: Params) -> LazyModel:\n tmap = gguf.TensorNameMap(params.arch, params.n_layer)\n should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(params.arch, []))\n\n tmp = model\n\n # HF models permut or pack some of the tensors, so we need to undo that\n for i in itertools.count():\n if f\"model.layers.{i}.self_attn.q_proj.weight\" in model:\n print(f\"Permuting layer {i}\")\n tmp[f\"model.layers.{i}.self_attn.q_proj.weight\"] = permute_lazy(model[f\"model.layers.{i}.self_attn.q_proj.weight\"], params.n_head, params.n_head)\n tmp[f\"model.layers.{i}.self_attn.k_proj.weight\"] = permute_lazy(model[f\"model.layers.{i}.self_attn.k_proj.weight\"], params.n_head, params.n_head_kv)\n #tmp[f\"model.layers.{i}.self_attn.v_proj.weight\"] = model[f\"model.layers.{i}.self_attn.v_proj.weight\"]\n elif f\"model.layers.{i}.self_attn.W_pack.weight\" in model:\n print(f\"Unpacking and permuting layer {i}\")\n tmp[f\"model.layers.{i}.self_attn.q_proj.weight\"] = permute_part_lazy(model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 0, params.n_head, params.n_head)\n tmp[f\"model.layers.{i}.self_attn.k_proj.weight\"] = permute_part_lazy(model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 1, params.n_head, params.n_head_kv)\n tmp[f\"model.layers.{i}.self_attn.v_proj.weight\"] = part_lazy (model[f\"model.layers.{i}.self_attn.W_pack.weight\"], 2)\n del tmp[f\"model.layers.{i}.self_attn.W_pack.weight\"]\n else:\n break\n\n out: LazyModel = {}\n for name, lazy_tensor in model.items():\n tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (\".weight\", \".bias\")) or (None, None)\n if name_new is None:\n raise Exception(f\"Unexpected tensor name: {name}\")\n\n if tensor_type in should_skip:\n print(f\"skipping tensor {name_new}\")\n continue\n\n print(f\"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}\")\n out[name_new] = lazy_tensor\n\n return out\n\ndef postprocess_transpose(model: LazyModel) -> LazyModel:\n \"\"\"Transpose ffn_down matrices for Axpy ops.\"\"\"\n out: LazyModel = {}\n \n for name, lazy_tensor in model.items():\n if name.endswith(\".ffn_down.weight\"):\n out[name.replace(\"ffn_down\", \"ffn_down_t\")] = lazy_tensor.transposed()\n else:\n out[name] = lazy_tensor\n \n return out\n\ndef nth_multifile_path(path: Path, n: int) -> Path | None:\n '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return\n the nth path in the model.\n '''\n # Support the following patterns:\n patterns: list[tuple[str, str]] = [\n # - x.00.pth, x.01.pth, etc.\n (r'\\.[0-9]{2}\\.pth$', f'.{n:02}.pth'),\n # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.\n (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\\1'),\n # x.bin, x.bin.1, etc.\n (r'(\\.[0-9]+)?$', r'\\1' if n == 0 else fr'\\1.{n}'),\n # x_0.pt, x_1.pt, etc.\n (r'(_[0-9]+)?\\.pt$', fr'_{n}.pt'),\n ]\n for regex, replacement in patterns:\n if re.search(regex, path.name):\n new_path = path.with_name(re.sub(regex, replacement, path.name))\n if new_path.exists():\n return new_path\n return None\n\n\ndef find_multifile_paths(path: Path) -> list[Path]:\n '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return\n the whole list of paths in the model.\n '''\n ret: list[Path] = []\n for i in itertools.count():\n nth_path = nth_multifile_path(path, i)\n if nth_path is None:\n break\n ret.append(nth_path)\n if not ret:\n # No matches. This should only happen if the file was named, e.g.,\n # foo.0, and there was no file named foo. Oh well, try to process it\n # as a single file.\n return [path]\n return ret\n\n\ndef load_some_model(path: Path) -> ModelPlus:\n '''Load a model of any supported format.'''\n # Be extra-friendly and accept either a file or a directory:\n if path.is_dir():\n # Check if it's a set of safetensors files first\n globs = [\"model-00001-of-*.safetensors\", \"model.safetensors\"]\n files = [file for glob in globs for file in path.glob(glob)]\n if not files:\n # Try the PyTorch patterns too, with lower priority\n globs = [\"consolidated.00.pth\", \"pytorch_model-00001-of-*.bin\", \"*.pt\", \"pytorch_model.bin\"]\n files = [file for glob in globs for file in path.glob(glob)]\n if not files:\n raise Exception(f\"Can't find model in directory {path}\")\n if len(files) > 1:\n raise Exception(f\"Found multiple models in {path}, not sure which to pick: {files}\")\n path = files[0]\n\n paths = find_multifile_paths(path)\n models_plus: list[ModelPlus] = []\n for path in paths:\n print(f\"Loading model file {path}\")\n models_plus.append(lazy_load_file(path))\n\n model_plus = merge_multifile_models(models_plus)\n return model_plus\n\ndef load_predictor_model(path: Path) -> ModelPlus:\n '''Load MLP models for sparse FFN inference from directory.'''\n assert path.is_dir(), f\"MLP model path {path} is not a directory\"\n \n first_model_path = path / \"model_0.pt\"\n assert first_model_path.resolve(), f\"MLP model path {path} does not contain model_0.pt\"\n\n model_paths = find_multifile_paths(first_model_path)\n models_plus: list[ModelPlus] = []\n for model_path in model_paths:\n # find number in model_path\n model_layer = int(re.search(r'model_(\\d+).pt', str(model_path)).group(1))\n print(f\"Loading MLP model file {model_path}\")\n mlp_model = lazy_load_file(model_path)\n mlp_model.model = {f\"model.layers.{model_layer}.{name}\": tensor for name, tensor in mlp_model.model.items()}\n models_plus.append(mlp_model)\n\n return merge_multifile_models(models_plus)\n\n\ndef load_vocab(path: Path, vocabtype: str | None) -> Vocab:\n # Be extra-friendly and accept either a file or a directory. Also, if it's\n # a directory, it might be the model directory, and tokenizer.model might\n # be in the parent of that.\n if path.is_dir():\n vocab_file = \"tokenizer.model\"\n if vocabtype == 'bpe':\n vocab_file = \"vocab.json\"\n path2 = path / vocab_file\n # Use `.parent` instead of /.. to handle the symlink case better.\n path3 = path.parent / vocab_file\n if path2.exists():\n path = path2\n elif path3.exists():\n path = path3\n else:\n raise FileNotFoundError(\n f\"Could not find {vocab_file} in {path} or its parent; \"\n \"if it's in another directory, pass the directory as --vocab-dir\")\n\n print(f\"Loading vocab file '{path}', type '{vocabtype}'\")\n\n added_tokens_path = path.parent / \"added_tokens.json\"\n if vocabtype == \"bpe\":\n return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)\n elif vocabtype == \"spm\":\n return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)\n else:\n raise ValueError(f\"Unsupported vocabulary type {vocabtype}\")\n\n\ndef default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:\n namestr = {\n GGMLFileType.AllF32: \"f32\",\n GGMLFileType.MostlyF16: \"f16\",\n GGMLFileType.MostlyQ8_0:\"q8_0\",\n }[file_type]\n ret = model_paths[0].parent / f\"ggml-model-{namestr}.gguf\"\n if ret in model_paths:\n sys.stderr.write(\n f\"Error: Default output path ({ret}) would overwrite the input. \"\n \"Please explicitly specify a path using --outfile.\\n\")\n sys.exit(1)\n return ret\n\n\ndef do_dump_model(model_plus: ModelPlus) -> None:\n print(f\"model_plus.paths = {model_plus.paths!r}\")\n print(f\"model_plus.format = {model_plus.format!r}\")\n print(f\"model_plus.vocab = {model_plus.vocab!r}\")\n for name, lazy_tensor in model_plus.model.items():\n print(f\"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}\")\n\n\ndef main(args_in: list[str] | None = None) -> None:\n output_choices = [\"f32\", \"f16\"]\n if np.uint32(1) == np.uint32(1).newbyteorder(\"<\"):\n # We currently only support Q8_0 output on little endian systems.\n output_choices.append(\"q8_0\")\n parser = argparse.ArgumentParser(description=\"Convert a LLaMa model to a GGML compatible file\")\n parser.add_argument(\"--dump\", action=\"store_true\", help=\"don't convert, just show what's in the model\")\n parser.add_argument(\"--dump-single\", action=\"store_true\", help=\"don't convert, just show what's in a single model file\")\n parser.add_argument(\"--vocab-only\", action=\"store_true\", help=\"extract only the vocab\")\n parser.add_argument(\"--outtype\", choices=output_choices, help=\"output format - note: q8_0 may be very slow (default: f16 or f32 based on input)\", default=\"f16\")\n parser.add_argument(\"--vocab-dir\", type=Path, help=\"directory containing tokenizer.model, if separate from model file\")\n parser.add_argument(\"--outfile\", type=Path, help=\"path to write to; default: based on input\")\n parser.add_argument(\"--ctx\", type=int, help=\"model training context (default: based on input)\")\n parser.add_argument(\"--concurrency\", type=int, help=f\"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})\", default = DEFAULT_CONCURRENCY)\n parser.add_argument(\"--bigendian\", action=\"store_true\", help=\"model is executed on big endian machine\")\n parser.add_argument(\"--vocabtype\", choices=[\"spm\", \"bpe\"], help=\"vocab format (default: spm)\", default=\"spm\")\n parser.add_argument(\"model\", type=Path, help=\"directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)\")\n parser.add_argument(\"sparse_predictor\", type=Path, help=\"predictors for sparse FFN inference\")\n\n args = parser.parse_args(args_in)\n\n try:\n with open(args.model / \"config.json\", \"r\", encoding=\"utf-8\") as f:\n hf_config = json.load(f)\n if model_type := hf_config.get(\"model_type\") not in (\"llama\", \"bamboo\"):\n # invoke another script to convert other models\n print(f\"Model architecture {model_type} is not supported by this `convert.py`. Trying with `convert-hf-to-powerinfer-gguf.py`...\")\n script_path = Path(__file__).resolve().parent / \"convert-hf-to-powerinfer-gguf.py\"\n subprocess.run([\"python3\", str(script_path.absolute())] + sys.argv[1:])\n return\n except FileNotFoundError:\n print(\"Could not find config.json under the original model directory. \", file=sys.stderr)\n sys.exit(1)\n\n if args.dump_single:\n model_plus = lazy_load_file(args.model)\n do_dump_model(model_plus)\n return\n\n if not args.vocab_only:\n model_plus = load_some_model(args.model)\n params = Params.load(model_plus)\n mlp_predictor_plus = load_predictor_model(args.sparse_predictor)\n params.predictor_params = PredictorParams.load(mlp_predictor_plus)\n model_plus = merge_multifile_models([model_plus, mlp_predictor_plus])\n else:\n model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)\n params = Params.load(model_plus)\n\n if args.dump:\n do_dump_model(model_plus)\n return\n endianess = gguf.GGUFEndian.LITTLE\n if args.bigendian:\n endianess = gguf.GGUFEndian.BIG\n\n if params.n_ctx == -1:\n if args.ctx is None:\n raise Exception(\"The model doesn't have a context size, and you didn't specify one with --ctx\\n\"\n \"Please specify one with --ctx:\\n\"\n \" - LLaMA v1: --ctx 2048\\n\"\n \" - LLaMA v2: --ctx 4096\\n\")\n params.n_ctx = args.ctx\n\n if args.outtype:\n params.ftype = {\n \"f32\": GGMLFileType.AllF32,\n \"f16\": GGMLFileType.MostlyF16,\n \"q8_0\": GGMLFileType.MostlyQ8_0,\n }[args.outtype]\n\n print(f\"params = {params}\")\n\n vocab: Vocab\n if args.vocab_only:\n if not args.outfile:\n raise ValueError(\"need --outfile if using --vocab-only\")\n # FIXME: Try to respect vocab_dir somehow?\n vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)\n special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,\n load_merges = args.vocabtype == 'bpe',\n n_vocab = vocab.vocab_size)\n outfile = args.outfile\n OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)\n print(f\"Wrote {outfile}\")\n return\n\n if model_plus.vocab is not None and args.vocab_dir is None:\n vocab = model_plus.vocab\n else:\n vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent\n vocab = load_vocab(vocab_dir, args.vocabtype)\n # FIXME: Try to respect vocab_dir somehow?\n special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,\n load_merges = args.vocabtype == 'bpe',\n n_vocab = vocab.vocab_size)\n\n model = model_plus.model\n model = convert_model_names(model, params)\n model = postprocess_transpose(model)\n ftype = pick_output_type(model, args.outtype)\n model = convert_to_output_type(model, ftype)\n outfile = args.outfile or default_outfile(model_plus.paths, ftype)\n\n params.ftype = ftype\n print(f\"Writing {outfile}, format {ftype}\")\n\n OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)\n print(f\"Wrote {outfile}\")\n\n # post-process: write another unique file header to distinguish from the origianl GGUF file\n with open(outfile, \"r+b\") as fout:\n POWERINFER_MAGIC = int.from_bytes(b\"PWRI\", \"little\")\n fout.write(struct.pack(\"\n#include \n#include \n#include \n#include \n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\n#ifdef LLAMA_DEFAULT_RMS_EPS\nconstexpr float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;\n#else\nconstexpr float rms_norm_eps = 5e-6f;\n#endif\n\nstatic void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) {\n struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);\n\n if (plan.work_size > 0) {\n buf.resize(plan.work_size);\n plan.work_data = buf.data();\n }\n\n ggml_graph_compute(graph, &plan);\n}\n\nstatic struct ggml_tensor * randomize_tensor(\n struct ggml_tensor * tensor, int ndims, const int64_t ne[], float fmin, float fmax\n) {\n switch (ndims) {\n case 1:\n for (int i0 = 0; i0 < ne[0]; i0++) {\n ((float *)tensor->data)[i0] = frand()*(fmax - fmin) + fmin;\n }\n break;\n case 2:\n for (int i1 = 0; i1 < ne[1]; i1++) {\n for (int i0 = 0; i0 < ne[0]; i0++) {\n ((float *)tensor->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;\n }\n }\n break;\n case 3:\n for (int i2 = 0; i2 < ne[2]; i2++) {\n for (int i1 = 0; i1 < ne[1]; i1++) {\n for (int i0 = 0; i0 < ne[0]; i0++) {\n ((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;\n }\n }\n }\n break;\n case 4:\n for (int i3 = 0; i3 < ne[3]; i3++) {\n for (int i2 = 0; i2 < ne[2]; i2++) {\n for (int i1 = 0; i1 < ne[1]; i1++) {\n for (int i0 = 0; i0 < ne[0]; i0++) {\n ((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;\n }\n }\n }\n }\n break;\n default:\n assert(false);\n }\n\n return tensor;\n}\n\nstruct llama_hparams {\n uint32_t n_vocab = 32000;\n uint32_t n_ctx = 512; // this is provided as user input?\n uint32_t n_embd = 4096;\n uint32_t n_mult = 4;\n uint32_t n_head = 32;\n uint32_t n_layer = 32;\n uint32_t n_rot = 64;\n\n bool operator!=(const llama_hparams & other) const {\n return memcmp(this, &other, sizeof(llama_hparams));\n }\n};\n\nstatic uint32_t get_n_ff(const struct llama_hparams* hparams) {\n const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;\n return n_ff;\n}\n\nstruct llama_hparams_lora {\n uint32_t n_vocab = 32000;\n uint32_t n_ctx = 512; // this is provided as user input?\n uint32_t n_embd = 4096;\n uint32_t n_mult = 4;\n uint32_t n_head = 32;\n uint32_t n_layer = 32;\n uint32_t n_rot = 64;\n uint32_t n_lora = 64;\n\n bool operator!=(const llama_hparams_lora & other) const {\n return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0;\n }\n};\n\nstruct llama_layer {\n // normalization\n struct ggml_tensor * attention_norm;\n\n // attention\n struct ggml_tensor * wq;\n struct ggml_tensor * wk;\n struct ggml_tensor * wv;\n struct ggml_tensor * wo;\n\n // normalization\n struct ggml_tensor * ffn_norm;\n\n // ff\n struct ggml_tensor * w1;\n struct ggml_tensor * w2;\n struct ggml_tensor * w3;\n};\n\nstruct llama_layer_lora {\n // normalization\n struct ggml_tensor * attention_norm;\n\n // attention\n struct ggml_tensor * wqa;\n struct ggml_tensor * wqb;\n struct ggml_tensor * wka;\n struct ggml_tensor * wkb;\n struct ggml_tensor * wva;\n struct ggml_tensor * wvb;\n struct ggml_tensor * woa;\n struct ggml_tensor * wob;\n\n // normalization\n struct ggml_tensor * ffn_norm;\n\n // ff\n struct ggml_tensor * w1;\n struct ggml_tensor * w2;\n struct ggml_tensor * w3;\n};\n\n\nstruct llama_kv_cache {\n struct ggml_context * ctx = NULL;\n\n struct ggml_tensor * k;\n struct ggml_tensor * v;\n\n // llama_ctx_buffer buf;\n\n int n; // number of tokens currently in the cache\n};\n\nstruct llama_model {\n struct ggml_context * ctx = NULL;\n\n llama_hparams hparams;\n\n struct ggml_tensor * tok_embeddings;\n\n struct ggml_tensor * norm;\n struct ggml_tensor * output;\n\n std::vector layers;\n};\n\nstruct llama_model_lora {\n struct ggml_context * ctx = NULL;\n\n llama_hparams_lora hparams;\n\n struct ggml_tensor * tok_embeddings;\n\n struct ggml_tensor * norm;\n struct ggml_tensor * outputa;\n struct ggml_tensor * outputb;\n\n std::vector layers;\n};\n\nstatic void init_model(struct llama_model * model) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_embd = hparams.n_embd;\n const uint32_t n_layer = hparams.n_layer;\n const uint32_t n_vocab = hparams.n_vocab;\n\n const uint32_t n_ff = get_n_ff(&hparams);\n\n struct ggml_context * ctx = model->ctx;\n\n model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // (\"tok_embeddings.weight\", {n_embd, n_vocab});\n model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (\"norm.weight\", {n_embd});\n model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // (\"output.weight\", {n_embd, n_vocab});\n\n model->layers.resize(n_layer);\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n\n // std::string layers_i = \"layers.\" + std::to_string(i);\n\n layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + \".attention_norm.weight\", {n_embd});\n\n layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + \".attention.wq.weight\", {n_embd, n_embd});\n layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + \".attention.wk.weight\", {n_embd, n_embd});\n layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + \".attention.wv.weight\", {n_embd, n_embd});\n layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + \".attention.wo.weight\", {n_embd, n_embd});\n\n layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + \".ffn_norm.weight\", {n_embd});\n\n layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + \".feed_forward.w1.weight\", {n_embd, n_ff});\n layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + \".feed_forward.w2.weight\", { n_ff, n_embd});\n layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + \".feed_forward.w3.weight\", {n_embd, n_ff});\n }\n}\n\n\nstatic void init_model_lora(struct llama_model_lora * model) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_embd = hparams.n_embd;\n const uint32_t n_mult = hparams.n_mult;\n const uint32_t n_layer = hparams.n_layer;\n const uint32_t n_vocab = hparams.n_vocab;\n const uint32_t n_lora = hparams.n_lora;\n\n const uint32_t n_ff = ((2*(4*n_embd)/3 + n_mult - 1)/n_mult)*n_mult;\n\n struct ggml_context * ctx = model->ctx;\n\n model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // (\"tok_embeddings.weight\", {n_embd, n_vocab});\n model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (\"norm.weight\", {n_embd});\n model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // (\"output.weight\", {n_embd, n_vocab});\n model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (\"output.weight\", {n_embd, n_vocab});\n\n model->layers.resize(n_layer);\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n\n // std::string layers_i = \"layers.\" + std::to_string(i);\n\n layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + \".attention_norm.weight\", {n_embd});\n\n layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + \".attention.wq.weight\", {n_embd, n_embd});\n layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + \".attention.wq.weight\", {n_embd, n_embd});\n layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + \".attention.wk.weight\", {n_embd, n_embd});\n layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + \".attention.wk.weight\", {n_embd, n_embd});\n layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + \".attention.wv.weight\", {n_embd, n_embd});\n layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + \".attention.wv.weight\", {n_embd, n_embd});\n layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + \".attention.wo.weight\", {n_embd, n_embd});\n layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + \".attention.wo.weight\", {n_embd, n_embd});\n\n layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + \".ffn_norm.weight\", {n_embd});\n\n layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + \".feed_forward.w1.weight\", {n_embd, n_ff});\n layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + \".feed_forward.w2.weight\", { n_ff, n_embd});\n layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + \".feed_forward.w3.weight\", {n_embd, n_ff});\n }\n}\n\nstatic void set_param_model(struct llama_model * model) {\n const auto& hparams = model->hparams;\n\n const uint32_t n_layer = hparams.n_layer;\n\n struct ggml_context* ctx = model->ctx;\n\n ggml_set_param(ctx, model->tok_embeddings);\n ggml_set_param(ctx, model->norm);\n ggml_set_param(ctx, model->output);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n\n ggml_set_param(ctx, layer.attention_norm);\n ggml_set_param(ctx, layer.wq);\n ggml_set_param(ctx, layer.wk);\n ggml_set_param(ctx, layer.wv);\n ggml_set_param(ctx, layer.wo);\n ggml_set_param(ctx, layer.ffn_norm);\n ggml_set_param(ctx, layer.w1);\n ggml_set_param(ctx, layer.w2);\n ggml_set_param(ctx, layer.w3);\n }\n}\n\nstatic void set_param_model_lora(struct llama_model_lora * model) {\n const auto& hparams = model->hparams;\n\n const uint32_t n_layer = hparams.n_layer;\n\n struct ggml_context* ctx = model->ctx;\n\n ggml_set_param(ctx, model->tok_embeddings);\n ggml_set_param(ctx, model->norm);\n ggml_set_param(ctx, model->outputa);\n ggml_set_param(ctx, model->outputb);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n\n ggml_set_param(ctx, layer.attention_norm);\n ggml_set_param(ctx, layer.wqa);\n ggml_set_param(ctx, layer.wqb);\n ggml_set_param(ctx, layer.wka);\n ggml_set_param(ctx, layer.wkb);\n ggml_set_param(ctx, layer.wva);\n ggml_set_param(ctx, layer.wvb);\n ggml_set_param(ctx, layer.woa);\n ggml_set_param(ctx, layer.wob);\n ggml_set_param(ctx, layer.ffn_norm);\n ggml_set_param(ctx, layer.w1);\n ggml_set_param(ctx, layer.w2);\n ggml_set_param(ctx, layer.w3);\n }\n}\n\nstatic void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_layer = hparams.n_layer;\n\n struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);\n\n randomize_tensor_normal(model->tok_embeddings , rnd);\n randomize_tensor_normal(model->norm , rnd);\n randomize_tensor_normal(model->output , rnd);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n randomize_tensor_normal(layer.attention_norm, rnd);\n\n randomize_tensor_normal(layer.wq, rnd);\n randomize_tensor_normal(layer.wk, rnd);\n randomize_tensor_normal(layer.wv, rnd);\n randomize_tensor_normal(layer.wo, rnd);\n\n randomize_tensor_normal(layer.ffn_norm, rnd);\n\n randomize_tensor_normal(layer.w1, rnd);\n randomize_tensor_normal(layer.w2, rnd);\n randomize_tensor_normal(layer.w3, rnd);\n }\n\n free_random_normal_distribution(rnd);\n}\n\n\nstatic void randomize_model_lora(\n struct llama_model_lora * model, int seed, float mean, float std, float min, float max\n) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_layer = hparams.n_layer;\n\n struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);\n\n randomize_tensor_normal(model->tok_embeddings, rnd);\n randomize_tensor_normal(model->norm , rnd);\n randomize_tensor_normal(model->outputa , rnd);\n randomize_tensor_normal(model->outputb , rnd);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n randomize_tensor_normal(layer.attention_norm, rnd);\n\n randomize_tensor_normal(layer.wqa, rnd);\n randomize_tensor_normal(layer.wqb, rnd);\n randomize_tensor_normal(layer.wka, rnd);\n randomize_tensor_normal(layer.wkb, rnd);\n randomize_tensor_normal(layer.wva, rnd);\n randomize_tensor_normal(layer.wvb, rnd);\n randomize_tensor_normal(layer.woa, rnd);\n randomize_tensor_normal(layer.wob, rnd);\n\n randomize_tensor_normal(layer.ffn_norm, rnd);\n\n randomize_tensor_normal(layer.w1, rnd);\n randomize_tensor_normal(layer.w2, rnd);\n randomize_tensor_normal(layer.w3, rnd);\n }\n\n free_random_normal_distribution(rnd);\n}\n\nstatic void init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_ctx = hparams.n_ctx;\n const uint32_t n_embd = hparams.n_embd;\n const uint32_t n_layer = hparams.n_layer;\n\n const int64_t n_mem = n_layer*n_ctx*n_batch;\n const int64_t n_elements = n_embd*n_mem;\n\n // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);\n\n // struct ggml_init_params params;\n // params.mem_size = cache.buf.size;\n // params.mem_buffer = cache.buf.addr;\n // params.no_alloc = false;\n if (!cache->ctx) {\n struct ggml_init_params params;\n params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;\n params.mem_buffer = NULL;\n params.no_alloc = false;\n\n cache->ctx = ggml_init(params);\n\n if (!cache->ctx) {\n fprintf(stderr, \"%s: failed to allocate memory for kv cache\\n\", __func__);\n exit(1);\n }\n }\n\n cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);\n cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);\n}\n\nstatic bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_ctx = hparams.n_ctx;\n const uint32_t n_embd = hparams.n_embd;\n const uint32_t n_layer = hparams.n_layer;\n\n const int64_t n_mem = n_layer*n_ctx*n_batch;\n const int64_t n_elements = n_embd*n_mem;\n\n // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);\n\n // struct ggml_init_params params;\n // params.mem_size = cache.buf.size;\n // params.mem_buffer = cache.buf.addr;\n // params.no_alloc = false;\n if (!cache->ctx) {\n struct ggml_init_params params;\n params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;\n params.mem_buffer = NULL;\n params.no_alloc = false;\n\n cache->ctx = ggml_init(params);\n\n if (!cache->ctx) {\n fprintf(stderr, \"%s: failed to allocate memory for kv cache\\n\", __func__);\n return false;\n }\n }\n\n cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);\n cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);\n\n return true;\n}\n\nstatic struct ggml_tensor * forward(\n struct llama_model * model,\n struct llama_kv_cache * cache,\n struct ggml_context * ctx0,\n struct ggml_cgraph * gf,\n struct ggml_tensor * tokens_input,\n const int n_tokens,\n const int n_past\n) {\n const int N = n_tokens;\n\n struct llama_kv_cache& kv_self = *cache;\n const auto & hparams = model->hparams;\n const int n_ctx = hparams.n_ctx;\n const int n_embd = hparams.n_embd;\n const int n_layer = hparams.n_layer;\n const int n_head = hparams.n_head;\n const int n_rot = hparams.n_rot;\n\n struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);\n memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));\n\n struct ggml_tensor * kc = kv_self.k;\n struct ggml_tensor * vc = kv_self.v;\n\n struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);\n {\n int * data = (int *) KQ_pos->data;\n for (int i = 0; i < N; ++i) {\n data[i] = n_past + i;\n }\n }\n\n // inpL shape [n_embd,N,1,1]\n struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);\n for (int il = 0; il < n_layer; ++il) {\n struct ggml_tensor * inpSA = inpL;\n\n struct ggml_tensor * cur;\n\n // lctx.use_buf(ctx0, 0);\n\n // norm\n {\n // cur shape [n_embd,N,1,1]\n cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n\n // cur = attention_norm*cur\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].attention_norm, cur),\n cur);\n }\n\n // self-attention\n {\n // compute Q and K and RoPE them\n // wq shape [n_embd, n_embd, 1, 1]\n // wk shape [n_embd, n_embd, 1, 1]\n // Qcur shape [n_embd/n_head, n_head, N, 1]\n // Kcur shape [n_embd/n_head, n_head, N, 1]\n struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);\n struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);\n\n // store key and value to memory\n {\n // compute the transposed [N, n_embd] V matrix\n // wv shape [n_embd, n_embd, 1, 1]\n // Vcur shape [n_embd, N, 1, 1]\n struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N)));\n\n // kv_self.k shape [n_embd * n_ctx * n_layer, 1]\n // kv_self.v shape [n_embd * n_ctx * n_layer, 1]\n // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]\n // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]\n\n /* {\n struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));\n struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,\n ( n_ctx)*ggml_element_size(kv_self.v),\n (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));\n\n // important: storing RoPE-ed version of K in the KV cache!\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));\n } //*/\n\n kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));\n vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),\n (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));\n }\n\n // Qcur shape [n_embd/n_head, n_head, N, 1]\n // Q shape [n_embd/n_head, N, n_head, 1]\n struct ggml_tensor * Q =\n ggml_permute(ctx0,\n Qcur,\n 0, 2, 1, 3);\n\n // kv_self.k shape [n_embd * n_ctx * n_layer, 1]\n // K shape [n_embd/n_head, n_past + N, n_head, 1]\n struct ggml_tensor * K =\n ggml_permute(ctx0,\n ggml_reshape_3d(ctx0,\n ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),\n n_embd/n_head, n_head, n_past + N),\n 0, 2, 1, 3);\n\n // K * Q\n // KQ shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);\n\n // KQ_scaled = KQ / sqrt(n_embd/n_head)\n // KQ_scaled shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_scaled =\n ggml_scale(ctx0,\n KQ,\n ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));\n\n // KQ_masked = mask_past(KQ_scaled)\n // KQ_masked shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);\n\n // KQ = soft_max(KQ_masked)\n // KQ_soft_max shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);\n\n // split cached V into n_head heads\n //// V shape [n_past + N, n_embd/n_head, n_head, 1]\n // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]\n struct ggml_tensor * V =\n ggml_view_3d(ctx0, vc,\n n_past + N, n_embd/n_head, n_head,\n n_ctx*ggml_element_size(vc),\n n_ctx*ggml_element_size(vc)*n_embd/n_head,\n il*n_ctx*ggml_element_size(vc)*n_embd);\n\n // KQV shape [n_embd/n_head, N, n_head, 1]\n struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);\n\n // KQV_merged = KQV.permute(0, 2, 1, 3)\n // KQV_merged shape [n_embd/n_head, n_head, N, 1]\n struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);\n // KQV_merged shape\n\n // cur = KQV_merged.contiguous().view(n_embd, N)\n // cur shape [n_embd,N,1,1]\n cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);\n // cur = ggml_cpy(ctx0,\n // KQV_merged,\n // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));\n\n // projection (no bias)\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].wo,\n cur);\n }\n\n // lctx.use_buf(ctx0, 1);\n\n // inpFF shape [n_embd,N,1,1]\n struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);\n\n // feed-forward network\n {\n // norm\n {\n // cur shape [n_embd,N,1,1]\n cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);\n\n // cur = ffn_norm*cur\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),\n cur);\n }\n\n // tmp shape [n_ff,N,1,1]\n struct ggml_tensor * tmp = ggml_mul_mat(ctx0,\n model->layers[il].w3,\n cur);\n\n // cur shape [n_ff,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w1,\n cur);\n\n // SILU activation\n // cur shape [n_ff,N,1,1]\n cur = ggml_silu(ctx0, cur);\n\n // cur shape [n_ff,N,1,1]\n cur = ggml_mul(ctx0, cur, tmp);\n\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w2,\n cur);\n }\n\n // cur shape [n_embd,N,1,1]\n cur = ggml_add(ctx0, cur, inpFF);\n\n // input for next layer\n // inpL shape [n_embd,N,1,1]\n inpL = cur;\n }\n\n // norm\n {\n\n // inpL shape [n_embd,N,1,1]\n inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n\n // inpL = norm*inpL\n // inpL shape [n_embd,N,1,1]\n inpL = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->norm, inpL),\n inpL);\n\n //embeddings = inpL;\n }\n\n // lm_head\n // inpL shape [n_vocab,N,1,1]\n inpL = ggml_mul_mat(ctx0, model->output, inpL);\n\n // run the computation\n ggml_build_forward_expand(gf, inpL);\n\n return inpL;\n}\n\nstatic struct ggml_tensor * forward_batch(\n struct llama_model * model,\n struct llama_kv_cache * cache,\n struct ggml_context * ctx0,\n struct ggml_cgraph * gf,\n struct ggml_tensor * tokens_input,\n const int n_tokens,\n const int n_past,\n const int n_batch\n) {\n const int N = n_tokens;\n\n struct llama_kv_cache& kv_self = *cache;\n const auto & hparams = model->hparams;\n const int n_ctx = hparams.n_ctx;\n const int n_vocab = hparams.n_vocab;\n const int n_embd = hparams.n_embd;\n const int n_layer = hparams.n_layer;\n const int n_head = hparams.n_head;\n const int n_rot = hparams.n_rot;\n const int n_ff = get_n_ff(&hparams);\n\n struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);\n memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);\n\n struct ggml_tensor * kc = kv_self.k;\n struct ggml_tensor * vc = kv_self.v;\n\n struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);\n {\n int * data = (int *) KQ_pos->data;\n for (int i = 0; i < N; ++i) {\n data[i] = n_past + i;\n }\n }\n\n // inpL shape [n_embd,N*n_batch,1]\n struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);\n assert_shape_2d(inpL, n_embd, N*n_batch);\n\n for (int il = 0; il < n_layer; ++il) {\n struct ggml_tensor * inpSA = inpL;\n\n struct ggml_tensor * cur;\n\n // lctx.use_buf(ctx0, 0);\n\n // norm\n {\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n assert_shape_2d(cur, n_embd, N*n_batch);\n\n // cur = attention_norm*cur\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].attention_norm, cur),\n cur);\n assert_shape_2d(cur, n_embd, N*n_batch);\n }\n\n // self-attention\n {\n // compute Q and K and RoPE them\n // wq shape [n_embd, n_embd, 1, 1]\n // wk shape [n_embd, n_embd, 1, 1]\n // Qcur shape [n_embd/n_head, n_head, N, n_batch]\n // Kcur shape [n_embd/n_head, n_head, N, n_batch]\n struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);\n struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);\n assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);\n assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);\n\n // store key and value to memory\n {\n // compute the transposed [N, n_embd] V matrix\n // wv shape [n_embd, n_embd, 1, 1]\n // Vcur shape [N, n_embd, n_batch, 1]\n struct ggml_tensor * Vcur = ggml_cont(ctx0,\n ggml_permute(ctx0,\n ggml_reshape_3d(ctx0,\n ggml_mul_mat(ctx0,\n model->layers[il].wv,\n cur),\n n_embd, N, n_batch),\n 1, 0, 2, 3));\n\n assert_shape_3d(Vcur, N, n_embd, n_batch);\n\n // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]\n // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]\n // k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il]\n // v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il]\n\n /* {\n struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));\n struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,\n ( n_ctx)*ggml_element_size(kv_self.v),\n (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));\n\n // important: storing RoPE-ed version of K in the KV cache!\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));\n } //*/\n\n kc = ggml_set_2d(ctx0, kc,\n ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch),\n ggml_element_size(kc)*n_embd*n_ctx,\n (ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past));\n vc = ggml_set_2d(ctx0, vc,\n ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch),\n ggml_element_size(vc)*n_ctx*n_embd,\n ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx));\n\n assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer);\n assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer);\n }\n\n // Qcur shape [n_embd/n_head, n_head, N, n_batch]\n // Q shape [n_embd/n_head, N, n_head, n_batch]\n struct ggml_tensor * Q =\n ggml_permute(ctx0,\n Qcur,\n 0, 2, 1, 3);\n assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);\n\n // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]\n // K shape [n_embd/n_head, n_past + N, n_head, n_batch]\n struct ggml_tensor * K =\n ggml_permute(ctx0,\n ggml_reshape_4d(ctx0,\n ggml_view_3d(ctx0,\n kc,\n n_embd,\n (n_past + N),\n n_batch,\n n_embd*ggml_element_size(kc),\n n_ctx*n_embd*ggml_element_size(kc),\n il*n_batch*n_ctx*n_embd*ggml_element_size(kc)),\n n_embd/n_head, n_head, n_past + N, n_batch),\n 0, 2, 1, 3);\n assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch);\n\n // K * Q\n // KQ shape [n_past + N, N, n_head, n_batch]\n struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);\n assert_shape_4d(KQ, n_past + N, N, n_head, n_batch);\n\n // KQ_scaled = KQ / sqrt(n_embd/n_head)\n // KQ_scaled shape [n_past + N, N, n_head, n_batch]\n struct ggml_tensor * KQ_scaled =\n ggml_scale(ctx0,\n KQ,\n ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));\n assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch);\n\n // KQ_masked = mask_past(KQ_scaled)\n // KQ_masked shape [n_past + N, N, n_head, n_batch]\n struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);\n assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch);\n\n // KQ = soft_max(KQ_masked)\n // KQ_soft_max shape [n_past + N, N, n_head, n_batch]\n struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);\n assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch);\n\n // split cached V into n_head heads\n // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]\n // V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il]\n struct ggml_tensor * V =\n ggml_view_4d(ctx0, vc,\n n_past + N, n_embd/n_head, n_head, n_batch,\n ggml_element_size(vc)*n_ctx,\n ggml_element_size(vc)*n_ctx*n_embd/n_head,\n ggml_element_size(vc)*n_ctx*n_embd,\n il*n_batch*n_ctx*n_embd*ggml_element_size(vc));\n assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch);\n\n // KQV shape [n_embd/n_head, N, n_head, n_batch]\n struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);\n assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);\n\n // KQV_merged = KQV.permute(0, 2, 1, 3)\n // KQV_merged shape [n_embd/n_head, n_head, N, n_batch]\n struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);\n assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);\n // KQV_merged shape\n\n // cur = KQV_merged.contiguous().view(n_embd, N)\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);\n assert_shape_2d(cur, n_embd, N*n_batch);\n // cur = ggml_cpy(ctx0,\n // KQV_merged,\n // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));\n\n // projection (no bias)\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].wo,\n cur);\n assert_shape_2d(cur, n_embd, N*n_batch);\n }\n\n // lctx.use_buf(ctx0, 1);\n\n // inpFF shape [n_embd,N*n_batch,1,1]\n struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);\n assert_shape_2d(inpFF, n_embd, N*n_batch);\n\n // feed-forward network\n {\n // norm\n {\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);\n assert_shape_2d(cur, n_embd, N*n_batch);\n\n // cur = ffn_norm*cur\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),\n cur);\n assert_shape_2d(cur, n_embd, N*n_batch);\n }\n\n // tmp shape [n_ff,N*n_batch,1,1]\n struct ggml_tensor * tmp = ggml_mul_mat(ctx0,\n model->layers[il].w3,\n cur);\n assert_shape_2d(tmp, n_ff, N*n_batch);\n\n // cur shape [n_ff,N*n_batch,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w1,\n cur);\n assert_shape_2d(cur, n_ff, N*n_batch);\n\n // SILU activation\n // cur shape [n_ff,N*n_batch,1,1]\n cur = ggml_silu(ctx0, cur);\n assert_shape_2d(cur, n_ff, N*n_batch);\n\n // cur shape [n_ff,N*n_batch,1,1]\n cur = ggml_mul(ctx0, cur, tmp);\n assert_shape_2d(cur, n_ff, N*n_batch);\n\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w2,\n cur);\n assert_shape_2d(cur, n_embd, N*n_batch);\n }\n\n // cur shape [n_embd,N*n_batch,1,1]\n cur = ggml_add(ctx0, cur, inpFF);\n assert_shape_2d(cur, n_embd, N*n_batch);\n\n // input for next layer\n // inpL shape [n_embd,N*n_batch,1,1]\n inpL = cur;\n assert_shape_2d(inpL, n_embd, N*n_batch);\n }\n\n // norm\n {\n\n // inpL shape [n_embd,N*n_batch,1,1]\n inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n assert_shape_2d(inpL, n_embd, N*n_batch);\n\n // inpL = norm*inpL\n // inpL shape [n_embd,N*n_batch,1,1]\n inpL = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->norm, inpL),\n inpL);\n\n assert_shape_2d(inpL, n_embd, N*n_batch);\n\n //embeddings = inpL;\n }\n\n // lm_head\n // inpL shape [n_vocab,N*n_batch,1,1]\n inpL = ggml_mul_mat(ctx0, model->output, inpL);\n assert_shape_2d(inpL, n_vocab, N*n_batch);\n\n {\n // inpL shape [n_vocab,N,n_batch,1]\n inpL = ggml_reshape_3d(ctx0,\n inpL,\n n_vocab, N, n_batch);\n assert_shape_3d(inpL, n_vocab, N, n_batch);\n }\n\n // run the computation\n ggml_build_forward_expand(gf, inpL);\n\n return inpL;\n}\n\nstatic struct ggml_tensor * forward_lora(\n struct llama_model_lora * model,\n struct llama_kv_cache * cache,\n struct ggml_context * ctx0,\n struct ggml_cgraph * gf,\n struct ggml_tensor * tokens_input,\n const int n_tokens,\n const int n_past\n) {\n const int N = n_tokens;\n\n struct llama_kv_cache& kv_self = *cache;\n const auto & hparams = model->hparams;\n\n const int n_ctx = hparams.n_ctx;\n const int n_embd = hparams.n_embd;\n const int n_layer = hparams.n_layer;\n const int n_head = hparams.n_head;\n const int n_rot = hparams.n_rot;\n\n struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);\n memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));\n\n struct ggml_tensor * kc = kv_self.k;\n struct ggml_tensor * vc = kv_self.v;\n\n struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);\n {\n int * data = (int *) KQ_pos->data;\n for (int i = 0; i < N; ++i) {\n data[i] = n_past + i;\n }\n }\n\n // inpL shape [n_embd,N,1,1]\n struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);\n for (int il = 0; il < n_layer; ++il) {\n struct ggml_tensor * inpSA = inpL;\n\n struct ggml_tensor * cur;\n\n // norm\n {\n // cur shape [n_embd,N,1,1]\n cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n\n // cur = attention_norm*cur\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].attention_norm, cur),\n cur);\n }\n\n // self-attention\n {\n // compute Q and K and RoPE them\n // wq shape [n_embd, n_embd, 1, 1]\n // wk shape [n_embd, n_embd, 1, 1]\n // Qcur shape [n_embd/n_head, n_head, N, 1]\n // Kcur shape [n_embd/n_head, n_head, N, 1]\n struct ggml_tensor * Qcur = ggml_rope(ctx0,\n ggml_reshape_3d(ctx0,\n ggml_mul_mat(ctx0,\n model->layers[il].wqa,\n ggml_mul_mat(ctx0,\n model->layers[il].wqb,\n cur)),\n n_embd/n_head, n_head, N),\n KQ_pos, n_rot, 0, 0);\n struct ggml_tensor * Kcur = ggml_rope(ctx0,\n ggml_reshape_3d(ctx0,\n ggml_mul_mat(ctx0,\n model->layers[il].wka,\n ggml_mul_mat(ctx0,\n model->layers[il].wkb,\n cur)),\n n_embd/n_head, n_head, N),\n KQ_pos, n_rot, 0, 0);\n\n // store key and value to memory\n {\n // compute the transposed [N, n_embd] V matrix\n // wv shape [n_embd, n_embd, 1, 1]\n // Vcur shape [n_embd, N, 1, 1]\n struct ggml_tensor * Vcur = ggml_cont(ctx0,\n ggml_transpose(ctx0,\n ggml_reshape_2d(ctx0,\n ggml_mul_mat(ctx0,\n model->layers[il].wva,\n ggml_mul_mat(ctx0,\n model->layers[il].wvb,\n cur)),\n n_embd, N)));\n\n // kv_self.k shape [n_embd * n_ctx * n_layer, 1]\n // kv_self.v shape [n_embd * n_ctx * n_layer, 1]\n // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]\n // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]\n\n /* {\n struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));\n struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,\n ( n_ctx)*ggml_element_size(kv_self.v),\n (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));\n\n // important: storing RoPE-ed version of K in the KV cache!\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));\n ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));\n } //*/\n\n kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));\n vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),\n (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));\n }\n\n // Qcur shape [n_embd/n_head, n_head, N, 1]\n // Q shape [n_embd/n_head, N, n_head, 1]\n struct ggml_tensor * Q =\n ggml_permute(ctx0,\n Qcur,\n 0, 2, 1, 3);\n\n // kv_self.k shape [n_embd * n_ctx * n_layer, 1]\n // K shape [n_embd/n_head, n_past + N, n_head, 1]\n struct ggml_tensor * K =\n ggml_permute(ctx0,\n ggml_reshape_3d(ctx0,\n ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),\n n_embd/n_head, n_head, n_past + N),\n 0, 2, 1, 3);\n\n // K * Q\n // KQ shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);\n\n // KQ_scaled = KQ / sqrt(n_embd/n_head)\n // KQ_scaled shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_scaled =\n ggml_scale(ctx0,\n KQ,\n ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));\n\n // KQ_masked = mask_past(KQ_scaled)\n // KQ_masked shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);\n\n // KQ = soft_max(KQ_masked)\n // KQ_soft_max shape [n_past + N, N, n_head, 1]\n struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);\n\n // split cached V into n_head heads\n //// V shape [n_past + N, n_embd/n_head, n_head, 1]\n // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]\n struct ggml_tensor * V =\n ggml_view_3d(ctx0, vc,\n n_past + N, n_embd/n_head, n_head,\n n_ctx*ggml_element_size(vc),\n n_ctx*ggml_element_size(vc)*n_embd/n_head,\n il*n_ctx*ggml_element_size(vc)*n_embd);\n\n // KQV shape [n_embd/n_head, N, n_head, 1]\n struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);\n\n // KQV_merged = KQV.permute(0, 2, 1, 3)\n // KQV_merged shape [n_embd/n_head, n_head, N, 1]\n struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);\n // KQV_merged shape\n\n // cur = KQV_merged.contiguous().view(n_embd, N)\n // cur shape [n_embd,N,1,1]\n cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);\n // cur = ggml_cpy(ctx0,\n // KQV_merged,\n // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));\n\n // projection (no bias)\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].woa,\n ggml_mul_mat(ctx0,\n model->layers[il].wob,\n cur));\n }\n\n // inpFF shape [n_embd,N,1,1]\n struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);\n\n // feed-forward network\n {\n // norm\n {\n // cur shape [n_embd,N,1,1]\n cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);\n\n // cur = ffn_norm*cur\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),\n cur);\n }\n\n // tmp shape [n_ff,N,1,1]\n struct ggml_tensor * tmp = ggml_mul_mat(ctx0,\n model->layers[il].w3,\n cur);\n\n // cur shape [n_ff,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w1,\n cur);\n\n // SILU activation\n // cur shape [n_ff,N,1,1]\n cur = ggml_silu(ctx0, cur);\n\n // cur shape [n_ff,N,1,1]\n cur = ggml_mul(ctx0, cur, tmp);\n\n // cur shape [n_embd,N,1,1]\n cur = ggml_mul_mat(ctx0,\n model->layers[il].w2,\n cur);\n }\n\n // cur shape [n_embd,N,1,1]\n cur = ggml_add(ctx0, cur, inpFF);\n\n // input for next layer\n // inpL shape [n_embd,N,1,1]\n inpL = cur;\n }\n\n // norm\n {\n\n // inpL shape [n_embd,N,1,1]\n inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);\n\n // inpL = norm*inpL\n // inpL shape [n_embd,N,1,1]\n inpL = ggml_mul(ctx0,\n ggml_repeat(ctx0, model->norm, inpL),\n inpL);\n\n //embeddings = inpL;\n }\n\n\n // lm_head\n // inpL shape [n_vocab,N,1,1]\n inpL = ggml_mul_mat(ctx0,\n model->outputa,\n ggml_mul_mat(ctx0,\n model->outputb,\n inpL));\n\n // ggml_set_scratch(ctx0, { 0, 0, nullptr, });\n // run the computation\n ggml_build_forward_expand(gf, inpL);\n\n return inpL;\n}\n\nstatic void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {\n assert(logits->n_dims == 2);\n assert(probs->n_dims == 2);\n assert(best_samples->n_dims == 1);\n assert(logits->ne[1] == best_samples->ne[0]);\n assert(logits->ne[0] == probs->ne[0]);\n assert(logits->ne[1] == probs->ne[1]);\n for (int i = 0; i < logits->ne[1]; ++i) {\n float max_logit = ggml_get_f32_1d(logits, i * logits->ne[0]);\n ggml_set_i32_1d(best_samples, i, 0);\n for (int k = 0; k < logits->ne[0]; ++k) {\n float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);\n if (logit > max_logit) {\n max_logit = logit;\n ggml_set_i32_1d(best_samples, i, k);\n }\n }\n float psum = 0;\n for (int k = 0; k < logits->ne[0]; ++k) {\n float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);\n float p = (logit == -INFINITY) ? 0 : expf(logit - max_logit);\n psum += p;\n ggml_set_f32_1d(probs, i * probs->ne[0] + k, p);\n }\n for (int k = 0; k < logits->ne[0]; ++k) {\n float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);\n ggml_set_f32_1d(probs, i * probs->ne[0] + k, p / psum);\n }\n }\n}\n\nstatic void sample_softmax_batch(\n struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs,\n struct ggml_tensor * best_samples\n) {\n GGML_ASSERT(best_samples->n_dims == 2);\n GGML_ASSERT(logits->n_dims == 3);\n GGML_ASSERT(probs->n_dims == 3);\n int n_tokens = best_samples->ne[0];\n int n_batch = best_samples->ne[1];\n int n_vocab = logits->ne[0];\n GGML_ASSERT(n_tokens == logits->ne[1]);\n GGML_ASSERT(n_batch == logits->ne[2]);\n GGML_ASSERT(n_vocab == probs->ne[0]);\n GGML_ASSERT(n_tokens == probs->ne[1]);\n GGML_ASSERT(n_batch == probs->ne[2]);\n\n for (int k = 0; k < n_batch; ++k) {\n struct ggml_tensor * best_samples_k = ggml_view_1d(ctx,\n best_samples,\n best_samples->ne[0],\n k*best_samples->nb[1]);\n struct ggml_tensor * logits_k = ggml_view_2d(ctx,\n logits,\n logits->ne[0],\n logits->ne[1],\n logits->nb[1],\n k*logits->nb[2]);\n struct ggml_tensor * probs_k = ggml_view_2d(ctx,\n probs,\n probs->ne[0],\n probs->ne[1],\n probs->nb[1],\n k*probs->nb[2]);\n sample_softmax(logits_k, probs_k, best_samples_k);\n }\n}\n\nstatic void print_row(struct ggml_tensor * probs, int i) {\n for (int k = 0; k < probs->ne[0]; ++k) {\n float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);\n printf(\" %.2f\", p);\n }\n printf(\"\\n\");\n}\n\nstatic void print_matrix(struct ggml_tensor * probs) {\n assert(probs->n_dims == 2);\n for (int i = 0; i < probs->ne[1]; ++i) {\n for (int k = 0; k < probs->ne[0]; ++k) {\n float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);\n printf(\" %.2f\", p);\n }\n printf(\"\\n\");\n }\n}\n\nstatic void print_token(int token, int n_vocab) {\n for (int k = 0; k < token; ++k) {\n printf(\" \");\n }\n printf(\"X\");\n for (int k = token+1; k < n_vocab; ++k) {\n printf(\" \");\n }\n printf(\"\\n\");\n}\n\nstatic void print_tokens(struct ggml_tensor * tokens, int n_vocab) {\n for (int i=0; ine[0]; ++i) {\n int token = ggml_get_i32_1d(tokens, i);\n print_token(token, n_vocab);\n }\n}\n\nstatic void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) {\n int n_tokens = tokens_input->ne[0];\n int n_vocab = targets->ne[0];\n float randomness = 0.0f;\n // ggml_set_zero(targets);\n ggml_set_f32(targets, -1.0f);\n ggml_set_i32_1d(tokens_input, 0, 0);\n for (int i=1; i 1.0f) ? 1.0f : z; // clamp to [0..1]\n int token = std::max(1,std::min(1+(int)(z*(float)(n_vocab-1)), n_vocab-1));\n ggml_set_f32_1d(targets, (i-1)*n_vocab + token, +1.0f);\n if (in_dims == 2);\n GGML_ASSERT( targets->n_dims == 3);\n int n_tokens = tokens_input->ne[0];\n int n_batch = tokens_input->ne[1];\n GGML_ASSERT(n_tokens == targets->ne[1]);\n GGML_ASSERT(n_batch == targets->ne[2]);\n\n for (int k=0; kne[0],\n k*tokens_input->nb[1]);\n struct ggml_tensor * targets_k = ggml_view_2d(ctx,\n targets,\n targets->ne[0],\n targets->ne[1],\n targets->nb[1],\n k*targets->nb[2]);\n get_example_targets(example_id*n_batch + k, tokens_input_k, targets_k);\n }\n}\n\nstatic void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) {\n int n_tokens = tokens_input->ne[0];\n int n_vocab = targets->ne[0];\n for (int i=0; i work_buffer;\n\n for (int ex=0; ex\n#include \n#include \n#include \n#include \n\nint main(int argc, char ** argv) {\n gpt_params params;\n\n if (argc == 1 || argv[1][0] == '-') {\n printf(\"usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [N_THREAD] [VRAM_BUDGET] [NGL]\\n\" , argv[0]);\n return 1 ;\n }\n\n // number of parallel batches\n int n_parallel = 1;\n\n // total length of the sequences including the prompt\n int n_len = 32;\n\n // number of layers to offload to the GPU\n int n_gpu_layers = 0;\n\n // vram budget in GiB\n double vram_budget = -1;\n\n if (argc >= 2) {\n params.model = argv[1];\n }\n\n if (argc >= 3) {\n params.prompt = argv[2];\n }\n\n if (argc >= 4) {\n n_parallel = std::atoi(argv[3]);\n }\n\n if (argc >= 5) {\n n_len = std::atoi(argv[4]);\n }\n\n if (argc >= 6) {\n params.n_threads = std::atoi(argv[5]);\n }\n\n if (argc >= 7) {\n vram_budget = std::atof(argv[6]);\n }\n\n if (argc >= 8) {\n n_gpu_layers = std::atoi(argv[7]);\n }\n\n printf(\"params: model = %s, prompt = %s, n_parallel = %d, n_len = %d, n_gpu_layers = %d, n_threads = %d, vram_budget = %.2f GiB, reset_gpu_index = true\\n\",\n params.model.c_str(), params.prompt.c_str(), n_parallel, n_len, n_gpu_layers, params.n_threads, vram_budget);\n\n if (params.prompt.empty()) {\n params.prompt = \"Hello my name is\";\n }\n\n // init LLM\n\n llama_backend_init(params.numa);\n\n // initialize the model\n\n llama_model_params model_params = llama_model_default_params();\n\n model_params.n_gpu_layers = n_gpu_layers;\n // For testing purposes, we always reset the GPU index\n model_params.reset_gpu_index = true;\n model_params.vram_budget_gb = vram_budget;\n\n llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);\n\n if (model == NULL) {\n fprintf(stderr , \"%s: error: unable to load model\\n\" , __func__);\n return 1;\n }\n\n // tokenize the prompt\n\n std::vector tokens_list;\n tokens_list = ::llama_tokenize(model, params.prompt, true);\n const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;\n\n // initialize the context\n\n llama_context_params ctx_params = llama_context_default_params();\n\n ctx_params.seed = 1234;\n ctx_params.n_ctx = n_kv_req;\n ctx_params.n_batch = std::max(n_len, n_parallel);\n ctx_params.n_threads = params.n_threads;\n ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;\n\n llama_context * ctx = llama_new_context_with_model(model, ctx_params);\n\n if (ctx == NULL) {\n fprintf(stderr , \"%s: error: failed to create the llama_context\\n\" , __func__);\n return 1;\n }\n\n const int n_ctx = llama_n_ctx(ctx);\n\n LOG_TEE(\"\\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\\n\", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);\n\n // make sure the KV cache is big enough to hold all the prompt and generated tokens\n if (n_kv_req > n_ctx) {\n LOG_TEE(\"%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\\n\", __func__, n_kv_req);\n LOG_TEE(\"%s: either reduce n_parallel or increase n_ctx\\n\", __func__);\n return 1;\n }\n\n // print the prompt token-by-token\n\n fprintf(stderr, \"\\n\");\n\n for (auto id : tokens_list) {\n fprintf(stderr, \"%s\", llama_token_to_piece(ctx, id).c_str());\n }\n\n fflush(stderr);\n\n // create a llama_batch\n // we use this object to submit token data for decoding\n llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);\n\n // evaluate the initial prompt\n for (size_t i = 0; i < tokens_list.size(); ++i) {\n llama_batch_add(batch, tokens_list[i], i, { 0 }, false);\n }\n GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());\n\n // llama_decode will output logits only for the last token of the prompt\n batch.logits[batch.n_tokens - 1] = true;\n\n if (llama_decode(ctx, batch) != 0) {\n LOG_TEE(\"%s: llama_decode() failed\\n\", __func__);\n return 1;\n }\n\n // assign the system KV cache to all parallel sequences\n // this way, the parallel sequences will \"reuse\" the prompt tokens without having to copy them\n for (int32_t i = 1; i < n_parallel; ++i) {\n llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);\n }\n\n if (n_parallel > 1) {\n LOG_TEE(\"\\n\\n%s: generating %d sequences ...\\n\", __func__, n_parallel);\n }\n\n // main loop\n\n // we will store the parallel decoded sequences in this vector\n std::vector streams(n_parallel);\n\n // remember the batch index of the last token for each parallel sequence\n // we need this to determine which logits to sample from\n std::vector i_batch(n_parallel, batch.n_tokens - 1);\n\n int n_cur = batch.n_tokens;\n int n_decode = 0;\n\n const auto t_main_start = ggml_time_us();\n\n while (n_cur <= n_len) {\n // prepare the next batch\n llama_batch_clear(batch);\n\n // sample the next token for each parallel sequence / stream\n for (int32_t i = 0; i < n_parallel; ++i) {\n if (i_batch[i] < 0) {\n // the stream has already finished\n continue;\n }\n\n auto n_vocab = llama_n_vocab(model);\n auto * logits = llama_get_logits_ith(ctx, i_batch[i]);\n\n std::vector candidates;\n candidates.reserve(n_vocab);\n\n for (llama_token token_id = 0; token_id < n_vocab; token_id++) {\n candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });\n }\n\n llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };\n\n const int top_k = 40;\n const float top_p = 0.9f;\n const float temp = 0.4f;\n\n llama_sample_top_k(ctx, &candidates_p, top_k, 1);\n llama_sample_top_p(ctx, &candidates_p, top_p, 1);\n llama_sample_temp (ctx, &candidates_p, temp);\n\n const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);\n\n //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);\n\n // is it an end of stream? -> mark the stream as finished\n if (new_token_id == llama_token_eos(model) || n_cur == n_len) {\n i_batch[i] = -1;\n LOG_TEE(\"\\n\");\n if (n_parallel > 1) {\n LOG_TEE(\"%s: stream %d finished at n_cur = %d\", __func__, i, n_cur);\n }\n\n continue;\n }\n\n // if there is only one stream, we print immediately to stdout\n if (n_parallel == 1) {\n LOG_TEE(\"%s\", llama_token_to_piece(ctx, new_token_id).c_str());\n fflush(stdout);\n }\n\n streams[i] += llama_token_to_piece(ctx, new_token_id);\n\n i_batch[i] = batch.n_tokens;\n\n // push this new token for next evaluation\n llama_batch_add(batch, new_token_id, n_cur, { i }, true);\n\n n_decode += 1;\n }\n\n // all streams are finished\n if (batch.n_tokens == 0) {\n break;\n }\n\n n_cur += 1;\n\n // evaluate the current batch with the transformer model\n if (llama_decode(ctx, batch)) {\n fprintf(stderr, \"%s : failed to eval, return code %d\\n\", __func__, 1);\n return 1;\n }\n }\n\n LOG_TEE(\"\\n\");\n\n if (n_parallel > 1) {\n LOG_TEE(\"\\n\");\n\n for (int32_t i = 0; i < n_parallel; ++i) {\n LOG_TEE(\"sequence %d:\\n\\n%s%s\\n\\n\", i, params.prompt.c_str(), streams[i].c_str());\n }\n }\n\n const auto t_main_end = ggml_time_us();\n\n LOG_TEE(\"%s: decoded %d tokens in %.2f s, speed: %.2f t/s\\n\",\n __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));\n\n llama_print_timings(ctx);\n\n fprintf(stderr, \"\\n\");\n\n llama_batch_free(batch);\n\n llama_free(ctx);\n llama_free_model(model);\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/batched-bench/CMakeLists.txt", "content": "set(TARGET batched-bench)\nadd_executable(${TARGET} batched-bench.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/batched-bench/README.md", "content": "# llama.cpp/example/batched-bench\n\nBenchmark the batched decoding performance of `llama.cpp`\n\n## Usage\n\nThere are 2 modes of operation:\n\n- `prompt not shared` - each batch has a separate prompt of size `PP` (i.e. `N_KV = B*(PP + TG)`)\n- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)\n\n```bash\n./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n\n# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared\n./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99\n\n# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared\n./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99\n\n# custom set of batches\n./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n```\n\n## Sample results\n\n- `PP` - prompt tokens per batch\n- `TG` - generated tokens per batch\n- `B` - number of batches\n- `N_KV` - required KV cache size\n- `T_PP` - prompt processing time (i.e. time to first token)\n- `S_PP` - prompt processing speed (`(B*PP)/T_PP` or `PP/T_PP`)\n- `T_TG` - time to generate all batches\n- `S_TG` - text generation speed (`(B*TG)/T_TG`)\n- `T` - total time\n- `S` - total speed (i.e. all tokens / total time)\n\n| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |\n|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|\n| 128 | 128 | 1 | 256 | 0.108 | 1186.64 | 3.079 | 41.57 | 3.187 | 80.32 |\n| 128 | 128 | 2 | 512 | 0.198 | 1295.19 | 5.029 | 50.90 | 5.227 | 97.95 |\n| 128 | 128 | 4 | 1024 | 0.373 | 1373.96 | 6.878 | 74.44 | 7.251 | 141.23 |\n| 128 | 128 | 8 | 2048 | 0.751 | 1363.27 | 7.344 | 139.43 | 8.095 | 252.99 |\n| 128 | 128 | 16 | 4096 | 1.570 | 1304.68 | 8.455 | 242.23 | 10.024 | 408.60 |\n| 128 | 128 | 32 | 8192 | 3.408 | 1201.73 | 8.801 | 465.40 | 12.209 | 670.96 |\n| 128 | 256 | 1 | 384 | 0.107 | 1196.70 | 6.329 | 40.45 | 6.436 | 59.67 |\n| 128 | 256 | 2 | 768 | 0.194 | 1317.45 | 10.239 | 50.00 | 10.433 | 73.61 |\n| 128 | 256 | 4 | 1536 | 0.366 | 1399.03 | 13.960 | 73.35 | 14.326 | 107.22 |\n| 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 |\n| 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 |\n| 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 |\n" }, { "path": "examples/batched-bench/batched-bench.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n// mutates the input string\nstatic std::vector parse_list(char * p) {\n std::vector ret;\n\n char * q = p;\n\n while (*p) {\n if (*p == ',') {\n *p = '\\0';\n ret.push_back(std::atoi(q));\n q = p + 1;\n }\n\n ++p;\n }\n\n ret.push_back(std::atoi(q));\n\n return ret;\n}\n\nint main(int argc, char ** argv) {\n gpt_params params;\n\n if (argc == 1 || argv[1][0] == '-') {\n printf(\"usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \\n\" , argv[0]);\n printf(\" , and PL are comma-separated lists of numbers without spaces\\n\\n\");\n printf(\" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\\n\\n\", argv[0]);\n return 1 ;\n }\n\n int n_kv_max = 2048;\n int is_pp_shared = 0;\n int n_gpu_layers = 0;\n int mmq = 0;\n\n std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };\n std::vector n_tg = { 128, 256, };\n std::vector n_pl = { 1, 2, 4, 8, 16, 32, };\n //std::vector n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, };\n\n if (argc >= 2) {\n params.model = argv[1];\n }\n\n if (argc >= 3) {\n n_kv_max = std::atoi(argv[2]);\n }\n\n if (argc >= 4) {\n is_pp_shared = std::atoi(argv[3]);\n }\n\n if (argc >= 5) {\n n_gpu_layers = std::atoi(argv[4]);\n }\n\n if (argc >= 6) {\n mmq = std::atoi(argv[5]);\n }\n\n if (argc >= 7) {\n n_pp = parse_list(argv[6]);\n }\n\n if (argc >= 8) {\n n_tg = parse_list(argv[7]);\n }\n\n if (argc >= 9) {\n n_pl = parse_list(argv[8]);\n }\n\n // init LLM\n\n llama_backend_init(params.numa);\n\n // initialize the model\n\n llama_model_params model_params = llama_model_default_params();\n\n model_params.n_gpu_layers = n_gpu_layers;\n\n llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);\n\n if (model == NULL) {\n fprintf(stderr , \"%s: error: unable to load model\\n\" , __func__);\n return 1;\n }\n\n llama_context_params ctx_params = llama_context_default_params();\n\n ctx_params.seed = 1234;\n ctx_params.n_ctx = n_kv_max;\n ctx_params.n_batch = 512;\n ctx_params.mul_mat_q = mmq;\n\n ctx_params.n_threads = params.n_threads;\n ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;\n\n llama_context * ctx = llama_new_context_with_model(model, ctx_params);\n\n if (ctx == NULL) {\n fprintf(stderr , \"%s: error: failed to create the llama_context\\n\" , __func__);\n return 1;\n }\n\n llama_batch batch = llama_batch_init(n_kv_max, 0, 1);\n\n // decode in batches of ctx_params.n_batch tokens\n auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) {\n for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {\n const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));\n\n llama_batch batch_view = {\n n_tokens,\n batch.token + i,\n nullptr,\n batch.pos + i,\n batch.n_seq_id + i,\n batch.seq_id + i,\n batch.logits + i,\n 0, 0, 0, // unused\n };\n\n const int ret = llama_decode(ctx, batch_view);\n if (ret != 0) {\n LOG_TEE(\"failed to decode the batch, n_batch = %d, ret = %d\\n\", n_batch, ret);\n return false;\n }\n }\n\n return true;\n };\n\n // warm up\n {\n for (int i = 0; i < 16; ++i) {\n llama_batch_add(batch, 0, i, { 0 }, false);\n }\n\n if (!decode_helper(ctx, batch, ctx_params.n_batch)) {\n LOG_TEE(\"%s: llama_decode() failed\\n\", __func__);\n return 1;\n }\n }\n\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\\n\", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);\n LOG_TEE(\"\\n\");\n\n LOG_TEE(\"|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\\n\", \"PP\", \"TG\", \"B\", \"N_KV\", \"T_PP s\", \"S_PP t/s\", \"T_TG s\", \"S_TG t/s\", \"T s\", \"S t/s\");\n LOG_TEE(\"|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\\n\", \"------\", \"------\", \"----\", \"------\", \"--------\", \"--------\", \"--------\", \"--------\", \"--------\", \"--------\");\n\n for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) {\n for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) {\n for (int i_pl = 0; i_pl < (int) n_pl.size(); ++i_pl) {\n const int pp = n_pp[i_pp];\n const int tg = n_tg[i_tg];\n const int pl = n_pl[i_pl];\n\n const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg);\n\n if (n_ctx_req > n_kv_max) {\n continue;\n }\n\n llama_batch_clear(batch);\n\n const int n_tokens = is_pp_shared ? pp : pl*pp;\n\n for (int i = 0; i < n_tokens; ++i) {\n llama_batch_add(batch, 0, i, { 0 }, false);\n }\n batch.logits[batch.n_tokens - 1] = true;\n\n const auto t_pp_start = ggml_time_us();\n\n llama_kv_cache_clear(ctx);\n\n if (!decode_helper(ctx, batch, ctx_params.n_batch)) {\n LOG_TEE(\"%s: llama_decode() failed\\n\", __func__);\n return 1;\n }\n\n if (is_pp_shared) {\n for (int32_t i = 1; i < pl; ++i) {\n llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);\n }\n }\n\n const auto t_pp_end = ggml_time_us();\n\n const auto t_tg_start = ggml_time_us();\n\n for (int i = 0; i < tg; ++i) {\n llama_batch_clear(batch);\n\n for (int j = 0; j < pl; ++j) {\n llama_batch_add(batch, 0, pp + i, { j }, true);\n }\n\n if (!decode_helper(ctx, batch, ctx_params.n_batch)) {\n LOG_TEE(\"%s: llama_decode() failed\\n\", __func__);\n return 1;\n }\n }\n\n const auto t_tg_end = ggml_time_us();\n\n const int32_t n_kv = n_ctx_req;\n\n const float t_pp = (t_pp_end - t_pp_start) / 1000000.0f;\n const float t_tg = (t_tg_end - t_tg_start) / 1000000.0f;\n const float t = t_pp + t_tg;\n\n const float speed_pp = is_pp_shared ? pp / t_pp : pl*pp / t_pp;\n const float speed_tg = pl*tg / t_tg;\n const float speed = n_kv / t;\n\n LOG_TEE(\"|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\\n\", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);\n }\n }\n }\n\n llama_print_timings(ctx);\n\n llama_batch_free(batch);\n\n llama_free(ctx);\n llama_free_model(model);\n\n llama_backend_free();\n\n fprintf(stderr, \"\\n\\n\");\n\n return 0;\n}\n" }, { "path": "examples/batched.swift/.gitignore", "content": ".DS_Store\n/.build\n/Packages\nxcuserdata/\nDerivedData/\n.swiftpm/configuration/registries.json\n.swiftpm/xcode/package.xcworkspace/contents.xcworkspacedata\n.netrc\nbatched_swift\n" }, { "path": "examples/batched.swift/Makefile", "content": ".PHONY: build\n\nbuild:\n\txcodebuild -scheme batched_swift -destination \"generic/platform=macOS\" -derivedDataPath build\n\trm -f ./batched_swift\n\tln -s ./build/Build/Products/Debug/batched_swift ./batched_swift\n" }, { "path": "examples/batched.swift/Package.swift", "content": "// swift-tools-version: 5.5\n// The swift-tools-version declares the minimum version of Swift required to build this package.\n\nimport PackageDescription\n\nlet package = Package(\n name: \"batched_swift\",\n platforms: [.macOS(.v12)],\n dependencies: [\n .package(name: \"llama\", path: \"../../\"),\n ],\n targets: [\n // Targets are the basic building blocks of a package, defining a module or a test suite.\n // Targets can depend on other targets in this package and products from dependencies.\n .executableTarget(\n name: \"batched_swift\",\n dependencies: [\"llama\"],\n path: \"Sources\",\n linkerSettings: [.linkedFramework(\"Foundation\"), .linkedFramework(\"AppKit\")]\n ),\n ]\n)\n" }, { "path": "examples/batched.swift/README.md", "content": "This is a swift clone of `examples/batched`.\n\n$ `make`\n$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`\n" }, { "path": "examples/batched.swift/Sources/main.swift", "content": "import Foundation\nimport llama\n\nlet arguments = CommandLine.arguments\n\n// Check that we have at least one argument (the model path)\nguard arguments.count > 1 else {\n print(\"Usage: swift MODEL_PATH [PROMPT] [PARALLEL]\")\n exit(1)\n}\n\nlet modelPath: String = arguments[1]\nlet prompt: String = arguments.count > 2 ? arguments[2] : \"Hello my name is\"\nlet n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(arguments[3])! : 1\n\n// total length of the sequences including the prompt\nlet n_len: Int = 32\n\n// init LLM\nllama_backend_init(false)\ndefer {\n llama_backend_free()\n}\n\nlet model_params = llama_model_default_params()\nguard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {\n print(\"Failed to load model\")\n exit(1)\n}\n\ndefer {\n llama_free_model(model)\n}\n\nvar tokens = tokenize(text: prompt, add_bos: true)\n\nlet n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel)\n\nvar context_params = llama_context_default_params()\ncontext_params.seed = 1234\ncontext_params.n_ctx = n_kv_req\ncontext_params.n_batch = UInt32(max(n_len, n_parallel))\ncontext_params.n_threads = 8\ncontext_params.n_threads_batch = 8\n\nlet context = llama_new_context_with_model(model, context_params)\nguard context != nil else {\n print(\"Failed to initialize context\")\n exit(1)\n}\n\ndefer {\n llama_free(context)\n}\n\nlet n_ctx = llama_n_ctx(context)\n\nprint(\"\\nn_len = \\(n_len), n_ctx = \\(n_ctx), n_batch = \\(context_params.n_batch), n_parallel = \\(n_parallel), n_kv_req = \\(n_kv_req)\\n\")\n\nif n_kv_req > n_ctx {\n print(\"error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\\n\", n_kv_req)\n exit(1)\n}\n\nvar buffer: [CChar] = []\nfor id: llama_token in tokens {\n print(token_to_piece(token: id, buffer: &buffer) ?? \"\", terminator: \"\")\n}\n\nprint(\"\\n\")\n\nvar batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0, 1)\ndefer {\n llama_batch_free(batch)\n}\n\n// evaluate the initial prompt\nbatch.n_tokens = Int32(tokens.count)\n\nfor (i, token) in tokens.enumerated() {\n batch.token[i] = token\n batch.pos[i] = Int32(i)\n batch.n_seq_id[i] = 1\n // batch.seq_id[i][0] = 0\n // TODO: is this the proper way to do this?\n if let seq_id = batch.seq_id[i] {\n seq_id[0] = 0\n }\n batch.logits[i] = 0\n}\n\n// llama_decode will output logits only for the last token of the prompt\nbatch.logits[Int(batch.n_tokens) - 1] = 1\n\nif llama_decode(context, batch) != 0 {\n print(\"llama_decode() failed\")\n exit(1)\n}\n\nfor i in 1 ..< n_parallel {\n llama_kv_cache_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)\n}\n\nif n_parallel > 1 {\n print(\"generating \\(n_parallel) sequences ...\\n\")\n}\n\nvar streams: [String] = .init(repeating: \"\", count: n_parallel)\nvar streamBuffers: [[CChar]] = .init(repeating: [], count: n_parallel)\nvar i_batch = [Int32](repeating: batch.n_tokens - 1, count: n_parallel)\n\nvar n_cur = batch.n_tokens\nvar n_decode = 0\n\nlet t_main_start = ggml_time_us()\n\nwhile n_cur <= n_len {\n // prepare the next batch\n batch.n_tokens = 0\n\n // sample the next token for each parallel sequence / stream\n for i in 0 ..< n_parallel {\n if i_batch[i] < 0 {\n // the stream has already finished\n continue\n }\n\n var n_vocab = llama_n_vocab(model)\n var logits = llama_get_logits_ith(context, i_batch[i])\n\n var candidates: [llama_token_data] = .init(repeating: llama_token_data(), count: Int(n_vocab))\n\n for token_id in 0 ..< n_vocab {\n candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))\n }\n\n var candidates_p: llama_token_data_array = .init(\n data: &candidates,\n size: candidates.count,\n sorted: false\n )\n\n let top_k: Int32 = 40\n let top_p: Float = 0.9\n let temp: Float = 0.4\n\n llama_sample_top_k(context, &candidates_p, top_k, 1)\n llama_sample_top_p(context, &candidates_p, top_p, 1)\n llama_sample_temp(context, &candidates_p, temp)\n\n let new_token_id = llama_sample_token(context, &candidates_p)\n\n // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);\n\n // is it an end of stream? -> mark the stream as finished\n if new_token_id == llama_token_eos(context) || n_cur == n_len {\n i_batch[i] = -1\n // print(\"\")\n if n_parallel > 1 {\n print(\"stream \\(i) finished at n_cur = \\(n_cur)\")\n }\n\n continue\n }\n\n let nextStringPiece = token_to_piece(token: new_token_id, buffer: &streamBuffers[i]) ?? \"\"\n\n // if there is only one stream, we print immediately to stdout\n if n_parallel == 1 {\n print(nextStringPiece, terminator: \"\")\n }\n streams[i] += nextStringPiece\n\n // push this new token for next evaluation\n batch.token[Int(batch.n_tokens)] = new_token_id\n batch.pos[Int(batch.n_tokens)] = n_cur\n batch.n_seq_id[Int(batch.n_tokens)] = 1\n if let seq_id = batch.seq_id[Int(batch.n_tokens)] {\n seq_id[0] = Int32(i)\n }\n batch.logits[Int(batch.n_tokens)] = 1\n\n i_batch[i] = batch.n_tokens\n\n batch.n_tokens += 1\n\n n_decode += 1\n }\n\n // all streams are finished\n if batch.n_tokens == 0 {\n break\n }\n\n n_cur += 1\n\n // evaluate the current batch with the transformer model\n if llama_decode(context, batch) != 0 {\n print(\"llama_decode() failed\")\n exit(1)\n }\n}\n\nif n_parallel > 1 {\n print(\"\\n\")\n for (i, stream) in streams.enumerated() {\n print(\"sequence \\(i):\\n\\n\\(prompt)\\(stream)\\n\")\n }\n}\n\nlet t_main_end = ggml_time_us()\n\nprint(\"decoded \\(n_decode) tokens in \\(String(format: \"%.2f\", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \\(String(format: \"%.2f\", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\\n\")\n\nllama_print_timings(context)\n\nprivate func tokenize(text: String, add_bos: Bool) -> [llama_token] {\n let n_tokens = text.count + (add_bos ? 1 : 0)\n let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens)\n let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)\n var swiftTokens: [llama_token] = []\n for i in 0 ..< tokenCount {\n swiftTokens.append(tokens[Int(i)])\n }\n tokens.deallocate()\n return swiftTokens\n}\n\nprivate func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {\n var result = [CChar](repeating: 0, count: 8)\n let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))\n if nTokens < 0 {\n if result.count >= -Int(nTokens) {\n result.removeLast(-Int(nTokens))\n } else {\n result.removeAll()\n }\n let check = llama_token_to_piece(\n model,\n token,\n &result,\n Int32(result.count)\n )\n assert(check == nTokens)\n } else {\n result.removeLast(result.count - Int(nTokens))\n }\n if buffer.isEmpty, let utfString = String(cString: result + [0], encoding: .utf8) {\n return utfString\n } else {\n buffer.append(contentsOf: result)\n let data = Data(buffer.map { UInt8(bitPattern: $0) })\n if buffer.count >= 4 { // 4 bytes is the max length of a utf8 character so if we're here we need to reset the buffer\n buffer = []\n }\n guard let bufferString = String(data: data, encoding: .utf8) else {\n return nil\n }\n buffer = []\n return bufferString\n }\n return nil\n}\n" }, { "path": "examples/beam-search/CMakeLists.txt", "content": "set(TARGET beam-search)\nadd_executable(${TARGET} beam-search.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/beam-search/beam-search.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))\n#include \n#include \n#elif defined (_WIN32)\n#define WIN32_LEAN_AND_MEAN\n#ifndef NOMINMAX\n# define NOMINMAX\n#endif\n#include \n#include \n#endif\n\n// Used for debugging to print out beam tokens.\nstruct ostream_beam_view {\n llama_context * ctx;\n llama_beam_view beam_view;\n};\n\nstatic std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) {\n os << \"p(\" << obv.beam_view.p << \") eob(\" << std::boolalpha << obv.beam_view.eob << \") tokens(\";\n for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {\n os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);\n }\n return os << ')';\n}\n\n// Put here anything you want back in beam_search_callback().\nstruct beam_search_callback_data {\n llama_context * ctx;\n std::vector response;\n};\n\n// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.\n// For example, eob can be flagged due to maximum token length, stop words, etc.\nstatic bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {\n return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));\n}\n\n// Function matching type llama_beam_search_callback_fn_t.\n// Custom callback example is called each time the beams lengths increase:\n// * Show progress by printing ',' following by number of convergent beam tokens if any.\n// * When all beams converge to a common prefix, they are made available in beams_state.beams[0].\n// This is also called when the stop condition is met.\n// Collect tokens into std::vector response which is pointed to by callback_data.\nstatic void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {\n auto& callback_data = *static_cast(callback_data_ptr);\n // Mark beams as EOS as needed.\n for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {\n llama_beam_view& beam_view = beams_state.beam_views[i];\n if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {\n beam_view.eob = true;\n }\n }\n printf(\",\"); // Show progress\n if (const size_t n = beams_state.common_prefix_length) {\n callback_data.response.resize(callback_data.response.size() + n);\n assert(0u < beams_state.n_beams);\n const llama_token * tokens = beams_state.beam_views[0].tokens;\n std::copy(tokens, tokens + n, callback_data.response.end() - n);\n printf(\"%zu\", n);\n }\n fflush(stdout);\n#if 1 // DEBUG: print current beams for this iteration\n std::cout << \"\\n\\nCurrent beams (last_call=\" << beams_state.last_call << \"):\\n\";\n for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {\n std::cout << \"beams[\"< 3 )\n {\n params.prompt = argv[3];\n }\n\n if ( params.prompt.empty() )\n {\n params.prompt = \"### Request:\\nHow many countries are there?\\n\\n### Response:\\n\";\n }\n\n //---------------------------------\n // Init LLM :\n //---------------------------------\n\n llama_backend_init(params.numa);\n\n llama_model * model;\n llama_context * ctx;\n\n std::tie(model, ctx) = llama_init_from_gpt_params( params );\n\n if ( model == NULL )\n {\n fprintf( stderr , \"%s: error: unable to load model\\n\" , __func__ );\n return 1;\n }\n\n //---------------------------------\n // Tokenize the prompt :\n //---------------------------------\n\n std::vector tokens_list = llama_tokenize(ctx, params.prompt, true);\n\n const size_t max_context_size = llama_n_ctx( ctx );\n const size_t max_tokens_list_size = max_context_size - 4 ;\n\n if (tokens_list.size() > max_tokens_list_size)\n {\n fprintf( stderr , \"%s: error: prompt too long (%zu tokens, max %zu)\\n\" ,\n __func__ , tokens_list.size() , max_tokens_list_size );\n return 1;\n }\n\n fprintf( stderr, \"\\n\\n\" );\n\n // Print the tokens from the prompt :\n\n for( auto id : tokens_list )\n {\n std::cout << llama_token_to_piece(ctx, id);\n }\n std::cout << std::flush;\n\n int n_past = 0;\n\n if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0)))\n {\n fprintf(stderr, \"%s : failed to eval prompt.\\n\" , __func__ );\n return 1;\n }\n n_past += tokens_list.size();\n\n beam_search_callback_data callback_data{ctx, {}};\n size_t const beam_width = static_cast(params.n_beams);\n int const n_predict = 256;\n llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict);\n\n std::cout << \"\\n\\n\";\n for (llama_token const token_id : callback_data.response) {\n std::cout << llama_token_to_piece(ctx,token_id);\n }\n std::cout << std::endl;\n\n llama_free( ctx );\n llama_free_model( model );\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/benchmark/CMakeLists.txt", "content": "set(TARGET benchmark)\nadd_executable(${TARGET} benchmark-matmult.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})\ntarget_include_directories(${TARGET} PRIVATE ../../common)\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/benchmark/benchmark-matmult.cpp", "content": "#include \"common.h\"\n#include \"ggml.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstatic void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) {\n struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);\n\n if (plan.work_size > 0) {\n buf.resize(plan.work_size);\n plan.work_data = buf.data();\n }\n\n ggml_graph_compute(graph, &plan);\n}\n\nstatic float tensor_sum_elements(const ggml_tensor * tensor) {\n double sum = 0;\n if (tensor->type == GGML_TYPE_F32) {\n for (int j = 0; j < tensor->ne[1]; j++) {\n for (int k = 0; k < tensor->ne[0]; k++) {\n sum += ((float *) tensor->data)[j*tensor->ne[0] + k];\n }\n }\n }\n return sum;\n}\n\nstatic void tensor_dump(const ggml_tensor * tensor, const char * name) {\n printf(\"%15s: type = %i (%5s) ne = %5\" PRIi64 \" x %5\" PRIi64 \" x %5\" PRIi64 \", nb = (%5zi, %5zi, %5zi) - \", name,\n tensor->type, ggml_type_name(tensor->type),\n tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);\n float sum = tensor_sum_elements(tensor);\n printf(\"Sum of tensor %s is %6.2f\\n\", name, sum);\n}\n\n#define TENSOR_DUMP(tensor) tensor_dump(tensor, #tensor)\n\nstruct benchmark_params_struct {\n int32_t n_threads = 1;\n int32_t n_iterations = 10;\n};\n\nstatic void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) {\n fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"options:\\n\");\n fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n fprintf(stderr, \" -t N, --threads N number of threads to use during computation (default: %d)\\n\", params.n_threads);\n fprintf(stderr, \" -i N, --iter N number of iterations to use during computation (default: %d)\\n\", params.n_iterations);\n fprintf(stderr, \"\\n\");\n}\n\nint main(int argc, char ** argv) {\n struct benchmark_params_struct benchmark_params;\n\n bool invalid_param = false;\n std::string arg;\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n\n if (arg == \"-t\" || arg == \"--threads\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n benchmark_params.n_threads = std::stoi(argv[i]);\n } else if (arg == \"-i\" || arg == \"--iter\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n benchmark_params.n_iterations = std::stoi(argv[i]);\n } else if (arg == \"-h\" || arg == \"--help\") {\n print_usage(argc, argv, benchmark_params);\n exit(0);\n }\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: %s\\n\", arg.c_str());\n print_usage(argc, argv, benchmark_params);\n exit(1);\n }\n\n print_build_info();\n printf(\"Starting Test\\n\");\n\n // create the ggml context\n struct ggml_context * ctx;\n //const int sizex = 4096;\n //const int sizey = 11008;\n\n#undef VERBOSE_DEBUGGING\n#ifndef VERBOSE_DEBUGGING\n const int sizey = 4096;\n const int sizex = 11008;\n const int sizez = 128;\n#else\n /* Working - let's increase size */\n const int sizey = 1;\n const int sizex = (8*32);\n const int sizez = 1;\n\n /*const int sizey = 1;\n const int sizex = 3*(8*32);\n const int sizez = 1;*/\n#endif\n\n //printf(\"Memsize required = %i\\n\", sizex*sizex);\n\n // TODO: perform the bench for all types or for a user specified type\n const ggml_type qtype = GGML_TYPE_Q4_1;\n\n size_t ctx_size = 0;\n ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);\n ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);\n ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32);\n ctx_size += sizex*sizey*ggml_type_sizef(qtype);\n ctx_size += sizex*sizey*ggml_type_sizef(qtype);\n ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS\n ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS\n ctx_size += 1024*1024*16;\n\n printf(\"Allocating Memory of size %zi bytes, %zi MB\\n\",ctx_size, (ctx_size/1024/1024));\n\n struct ggml_init_params params = {\n /*.mem_size =*/ ctx_size,\n /*.mem_buffer =*/ NULL,\n /* no_alloc =*/ 0\n };\n\n ctx = ggml_init(params);\n if (!ctx) {\n fprintf(stderr, \"%s: ggml_init() failed\\n\", __func__);\n return 1;\n }\n\n\n printf(\"Creating new tensors\\n\");\n // printf(\"Creating new tensor m1\\n\");\n struct ggml_tensor * m11 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);\n ggml_set_f32(m11, 1.0f);\n\n // printf(\"Creating new tensor m1\\n\");\n struct ggml_tensor * m12 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);\n ggml_set_f32(m12, 1.5f);\n\n // printf(\"Creating new tensor m2\\n\");\n struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez);\n ggml_set_f32(m2, 2.0f);\n\n printf(\"\\n------ Test 1 - Matrix Mult via F32 code\\n\");\n // printf(\"Creating new tensor m11xm2\\n\");\n struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);\n\n // printf(\"Creating compute graph\\n\");\n struct ggml_cgraph * gf = ggml_new_graph(ctx);\n ggml_build_forward_expand(gf, m11xm2);\n\n printf(\"n_threads=%i\\n\", benchmark_params.n_threads);\n\n TENSOR_DUMP(m11);\n TENSOR_DUMP(m2);\n\n std::vector work_buffer;\n\n ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);\n\n TENSOR_DUMP(gf->nodes[0]);\n\n printf(\"\\n------ Test 2 - Matrix Mult via %s code\\n\", ggml_type_name(qtype));\n\n int32_t nelements = sizex*sizey;\n\n std::vector hist_cur(1 << 4, 0);\n\n // Set up a the benchmark matrices\n // printf(\"Creating new tensor q11 & Running quantize\\n\");\n struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);\n ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data());\n\n // Set up a the compute graph\n // printf(\"Creating new tensor q31\\n\");\n struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2);\n\n // printf(\"Creating compute graph\\n\");\n struct ggml_cgraph * gf31 = ggml_new_graph(ctx);\n ggml_build_forward_expand(gf31, q31);\n\n // Set up a second graph computation to make sure we override the CPU cache lines\n // printf(\"Creating new tensor q12 & Running quantize\\n\");\n struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);\n ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data());\n\n // printf(\"Creating new tensor q32\\n\");\n struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);\n\n //printf(\"Creating compute graph\\n\");\n struct ggml_cgraph * gf32 = ggml_new_graph(ctx);\n ggml_build_forward_expand(gf32, q32);\n printf(\"n_threads=%i\\n\", benchmark_params.n_threads);\n\n const int dimx = sizex;\n const int dimy = sizey;\n const int dimz = sizez;\n long long int flops_per_dot_product = dimy + dimy;\n long long int flops_per_matrix = flops_per_dot_product * dimx * dimz; ;\n printf(\"Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - about %6.2f gFLOPS\\n\\n\", sizex, sizey, 1, sizex, sizez, 1, 1.0f*flops_per_matrix / 1000 / 1000 / 1000);\n\n\n // Let's use the F32 result from above as a reference for the quantized multiplication\n float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]);\n\n printf(\"Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\\n\");\n printf(\"=====================================================================================\\n\");\n\n double gflops_sum = 0;\n for (int i=0;inodes[0]);\n float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference);\n float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6\n\n if (delta > allowed_delta) {\n printf(\"\\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f (delta %6.2f > allowed_delta %6.2f)\\n\",\n sum_of_F32_reference,\n sum_of_Q4_result,\n delta,\n allowed_delta\n );\n exit(0);\n }\n\n // Running a different graph computation to make sure we override the CPU cache lines\n ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads);\n }\n printf(\"\\n\");\n printf(\"Average%78.2f\\n\",gflops_sum/((double)benchmark_params.n_iterations));\n printf(\"=====================================================================================\\n\");\n}\n" }, { "path": "examples/chat-13B.sh", "content": "#!/bin/bash\n\nset -e\n\ncd \"$(dirname \"$0\")/..\" || exit\n\nMODEL=\"${MODEL:-./models/13B/ggml-model-q4_0.bin}\"\nPROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}\nUSER_NAME=\"${USER_NAME:-USER}\"\nAI_NAME=\"${AI_NAME:-ChatLLaMa}\"\n\n# Adjust to the number of CPU cores you want to use.\nN_THREAD=\"${N_THREAD:-8}\"\n# Number of tokens to predict (made it larger than default because we want a long interaction)\nN_PREDICTS=\"${N_PREDICTS:-2048}\"\n\n# Note: you can also override the generation options by specifying them on the command line:\n# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024\nGEN_OPTIONS=\"${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}\"\n\nDATE_TIME=$(date +%H:%M)\nDATE_YEAR=$(date +%Y)\n\nPROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)\n\nsed -e \"s/\\[\\[USER_NAME\\]\\]/$USER_NAME/g\" \\\n -e \"s/\\[\\[AI_NAME\\]\\]/$AI_NAME/g\" \\\n -e \"s/\\[\\[DATE_TIME\\]\\]/$DATE_TIME/g\" \\\n -e \"s/\\[\\[DATE_YEAR\\]\\]/$DATE_YEAR/g\" \\\n $PROMPT_TEMPLATE > $PROMPT_FILE\n\n# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS\n./main $GEN_OPTIONS \\\n --model \"$MODEL\" \\\n --threads \"$N_THREAD\" \\\n --n_predict \"$N_PREDICTS\" \\\n --color --interactive \\\n --file ${PROMPT_FILE} \\\n --reverse-prompt \"${USER_NAME}:\" \\\n --in-prefix ' ' \\\n \"$@\"\n" }, { "path": "examples/chat-persistent.sh", "content": "#!/bin/bash\n\nset -euo pipefail\n\ncd \"$(dirname \"$0\")/..\" || exit\n\nif [[ -z \"${PROMPT_CACHE_FILE+x}\" || -z \"${CHAT_SAVE_DIR+x}\" ]]; then\n echo >&2 \"error: PROMPT_CACHE_FILE and CHAT_SAVE_DIR must be provided\"\n exit 1\nfi\n\nMODEL=\"${MODEL:-./models/llama-13b/ggml-model-q4_0.gguf}\"\nPROMPT_TEMPLATE=\"${PROMPT_TEMPLATE:-./prompts/chat.txt}\"\nUSER_NAME=\"${USER_NAME:-User}\"\nAI_NAME=\"${AI_NAME:-ChatLLaMa}\"\nDATE_TIME=\"$(date +%H:%M)\"\nDATE_YEAR=\"$(date +%Y)\"\n\nLOG=\"${CHAT_SAVE_DIR}/main.log\"\nLOG_BG=\"${CHAT_SAVE_DIR}/main-bg.log\"\nCUR_PROMPT_FILE=\"${CHAT_SAVE_DIR}/current-prompt.txt\"\nCUR_PROMPT_CACHE=\"${CHAT_SAVE_DIR}/current-cache.bin\"\nNEXT_PROMPT_FILE=\"${CHAT_SAVE_DIR}/next-prompt.txt\"\nNEXT_PROMPT_CACHE=\"${CHAT_SAVE_DIR}/next-cache.bin\"\n\nSESSION_SIZE_MSG_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'\nSAMPLE_TIME_MSG_PATTERN='sample time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'\nSED_DELETE_MESSAGES=\"/^(${USER_NAME}:|${AI_NAME}:|\\\\.\\\\.\\\\.)/,\\$d\"\n\nCTX_SIZE=2048\nCTX_ROTATE_POINT=$((CTX_SIZE * 3 / 5)) # REVIEW\nOPTS=(--model \"$MODEL\" --ctx_size \"$CTX_SIZE\" --repeat_last_n 256 \"$@\")\n\n# An unbuffered `tail -c+N`\nskip_bytes() {\n LANG=C IFS= read -r -n \"$1\" -d '' c\n while LANG=C IFS= read -r -n 1 -d '' c; do\n printf '%s' \"$c\"\n done\n}\n\nmkdir -p \"$CHAT_SAVE_DIR\"\necho >\"$LOG\"\ntrap \"tail -n100 ${LOG}\" EXIT\n\nif [[ ! -e \"$CUR_PROMPT_FILE\" ]]; then\n sed -e \"s/\\[\\[USER_NAME\\]\\]/${USER_NAME}/g\" \\\n -e \"s/\\[\\[AI_NAME\\]\\]/${AI_NAME}/g\" \\\n -e \"s/\\[\\[DATE_TIME\\]\\]/${DATE_TIME}/g\" \\\n -e \"s/\\[\\[DATE_YEAR\\]\\]/${DATE_YEAR}/g\" \\\n \"$PROMPT_TEMPLATE\" >\"$CUR_PROMPT_FILE\"\nfi\n\nif [[ ! -e \"$NEXT_PROMPT_FILE\" ]]; then\n sed -r \"$SED_DELETE_MESSAGES\" \"$CUR_PROMPT_FILE\" >\"$NEXT_PROMPT_FILE\"\nfi\n\nif [[ \"$(tail -c4 \"$NEXT_PROMPT_FILE\")\" != \"...\" ]]; then\n echo '...' >>\"$NEXT_PROMPT_FILE\"\nfi\n\nif [[ ! -e \"$PROMPT_CACHE_FILE\" ]]; then\n echo 'Prompt cache does not exist, building...'\n # Default batch_size to 64 here for better user feedback during initial prompt processing\n ./main 2>>\"$LOG\" \\\n --batch_size 64 \\\n \"${OPTS[@]}\" \\\n --prompt-cache \"$PROMPT_CACHE_FILE\" \\\n --file \"$CUR_PROMPT_FILE\" \\\n --n_predict 1\n echo\n echo 'Done!'\nfi\n\nif [[ ! -e \"$CUR_PROMPT_CACHE\" ]]; then\n cp \"$PROMPT_CACHE_FILE\" \"$CUR_PROMPT_CACHE\"\nfi\nif [[ ! -e \"$NEXT_PROMPT_CACHE\" ]]; then\n cp \"$PROMPT_CACHE_FILE\" \"$NEXT_PROMPT_CACHE\"\nfi\n\nprintf '%s ' \"$(< \"$CUR_PROMPT_FILE\")\"\nn_tokens=0\n\nwhile read -e line; do\n # Limit generation to remaining context, with a buffer and estimating 2 chars/token for input\n n_predict=$((CTX_SIZE - n_tokens - ${#line} / 2 - 32))\n\n # Swap prompts when we're about to run out of context\n if ((n_predict <= 0)); then\n wait # for background main (below) to finish with next prompt\n mv \"$NEXT_PROMPT_FILE\" \"$CUR_PROMPT_FILE\"\n mv \"$NEXT_PROMPT_CACHE\" \"$CUR_PROMPT_CACHE\"\n\n sed -r \"$SED_DELETE_MESSAGES\" \"$CUR_PROMPT_FILE\" >\"$NEXT_PROMPT_FILE\"\n echo '...' >>\"$NEXT_PROMPT_FILE\"\n cp \"$PROMPT_CACHE_FILE\" \"$NEXT_PROMPT_CACHE\"\n\n n_tokens=0\n n_predict=$((CTX_SIZE / 2))\n fi\n\n echo \" ${line}\" >>\"$CUR_PROMPT_FILE\"\n if ((n_tokens > CTX_ROTATE_POINT)); then\n echo \" ${line}\" >>\"$NEXT_PROMPT_FILE\"\n fi\n\n n_prompt_len_pre=$(($(wc -c <\"$CUR_PROMPT_FILE\")))\n\n printf '%s: ' \"$AI_NAME\" >>\"$CUR_PROMPT_FILE\"\n\n ./main 2>>\"$LOG\" \"${OPTS[@]}\" \\\n --prompt-cache \"$CUR_PROMPT_CACHE\" \\\n --prompt-cache-all \\\n --file \"$CUR_PROMPT_FILE\" \\\n --reverse-prompt \"${USER_NAME}:\" \\\n --n_predict \"$n_predict\" |\n skip_bytes 1 | # skip BOS token added by ./main\n tee \"$CUR_PROMPT_FILE.tmp\" | # save prompt + generation to tmp file\n skip_bytes \"$n_prompt_len_pre\" # print generation\n\n mv \"$CUR_PROMPT_FILE.tmp\" \"$CUR_PROMPT_FILE\"\n\n # if we hit n_predict instead of reverse-prompt, we need to add the prompt\n if [[ \"$(tail -n1 \"$CUR_PROMPT_FILE\")\" != \"${USER_NAME}:\" ]]; then\n printf '\\n%s:' \"$USER_NAME\"\n printf '\\n%s:' \"$USER_NAME\" >> \"$CUR_PROMPT_FILE\"\n fi\n\n printf ' '\n\n # HACK get num tokens from debug message\n # TODO get both messages in one go\n if ! session_size_msg=\"$(tail -n30 \"$LOG\" | grep -oE \"$SESSION_SIZE_MSG_PATTERN\")\" ||\n ! sample_time_msg=\"$(tail -n10 \"$LOG\" | grep -oE \"$SAMPLE_TIME_MSG_PATTERN\")\"; then\n echo >&2 \"Couldn't get number of tokens from ./main output!\"\n exit 1\n fi\n\n n_tokens=$(($(cut -d/ -f2 <<<\"$session_size_msg\") + $(cut -d/ -f2 <<<\"$sample_time_msg\")))\n\n if ((n_tokens > CTX_ROTATE_POINT)); then\n tail -c+$((n_prompt_len_pre + 1)) \"$CUR_PROMPT_FILE\" >>\"$NEXT_PROMPT_FILE\"\n fi\n\n # Update cache for next prompt in background, ideally during user input\n ./main >>\"$LOG_BG\" 2>&1 \"${OPTS[@]}\" \\\n --prompt-cache \"$NEXT_PROMPT_CACHE\" \\\n --file \"$NEXT_PROMPT_FILE\" \\\n --n_predict 1 &\ndone\n" }, { "path": "examples/chat-vicuna.sh", "content": "#!/bin/bash\n\nset -e\n\ncd \"$(dirname \"$0\")/..\" || exit\n\nMODEL=\"${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}\"\nPROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}\nUSER_NAME=\"### Human\"\nAI_NAME=\"### Assistant\"\n\n# Adjust to the number of CPU cores you want to use.\nN_THREAD=\"${N_THREAD:-8}\"\n# Number of tokens to predict (made it larger than default because we want a long interaction)\nN_PREDICTS=\"${N_PREDICTS:-2048}\"\n\n# Note: you can also override the generation options by specifying them on the command line:\n# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024\nGEN_OPTIONS=\"${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}\"\n\nDATE_TIME=$(date +%H:%M)\nDATE_YEAR=$(date +%Y)\n\nPROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)\n\nsed -e \"s/\\[\\[USER_NAME\\]\\]/$USER_NAME/g\" \\\n -e \"s/\\[\\[AI_NAME\\]\\]/$AI_NAME/g\" \\\n -e \"s/\\[\\[DATE_TIME\\]\\]/$DATE_TIME/g\" \\\n -e \"s/\\[\\[DATE_YEAR\\]\\]/$DATE_YEAR/g\" \\\n $PROMPT_TEMPLATE > $PROMPT_FILE\n\n# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS\n./bin/main $GEN_OPTIONS \\\n --model \"$MODEL\" \\\n --threads \"$N_THREAD\" \\\n --n_predict \"$N_PREDICTS\" \\\n --color --interactive \\\n --file ${PROMPT_FILE} \\\n --reverse-prompt \"### Human:\" \\\n --in-prefix ' ' \\\n \"$@\"\n" }, { "path": "examples/chat.sh", "content": "#!/bin/bash\n\n#\n# Temporary script - will be removed in the future\n#\n\ncd `dirname $0`\ncd ..\n\n# Important:\n#\n# \"--keep 48\" is based on the contents of prompts/chat-with-bob.txt\n#\n./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \\\n --repeat_penalty 1.0 --color -i \\\n -r \"User:\" -f prompts/chat-with-bob.txt\n" }, { "path": "examples/convert-llama2c-to-ggml/CMakeLists.txt", "content": "set(TARGET convert-llama2c-to-ggml)\nadd_executable(${TARGET} convert-llama2c-to-ggml.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/convert-llama2c-to-ggml/README.md", "content": "## Convert llama2.c model to ggml\n\nThis example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.\n\nTo convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:\n\n`$ make -j`\n\nAfter successful compilation, following usage options are available:\n```\nusage: ./convert-llama2c-to-ggml [options]\n\noptions:\n -h, --help show this help message and exit\n --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf')\n --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n --llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')\n```\n\nAn example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:\n\n`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`\n\nNow you can use the model with a command like:\n\n`$ ./main -m stories42M.gguf.bin -p \"One day, Lily met a Shoggoth\" -n 500 -c 256`\n" }, { "path": "examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp", "content": "#include \"ggml.h\"\n#include \"llama.h\"\n#include \"common.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// GGUF keys & tensor names.\n\n#define KV_GENERAL_ARCHITECTURE \"general.architecture\"\n#define KV_GENERAL_NAME \"general.name\"\n\n#define KV_TOKENIZER_MODEL \"tokenizer.ggml.model\"\n#define KV_TOKENIZER_LIST \"tokenizer.ggml.tokens\"\n#define KV_TOKENIZER_TOKEN_TYPE \"tokenizer.ggml.token_type\"\n#define KV_TOKENIZER_SCORES \"tokenizer.ggml.scores\"\n#define KV_TOKENIZER_BOS_ID \"tokenizer.ggml.bos_token_id\"\n#define KV_TOKENIZER_EOS_ID \"tokenizer.ggml.eos_token_id\"\n#define KV_TOKENIZER_UNK_ID \"tokenizer.ggml.unknown_token_id\"\n#define KV_TOKENIZER_SEP_ID \"tokenizer.ggml.seperator_token_id\"\n#define KV_TOKENIZER_PAD_ID \"tokenizer.ggml.padding_token_id\"\n#define KV_TOKENIZER_HF_JSON \"tokenizer.huggingface.json\"\n\n#define KV_CONTEXT_LENGTH \"llama.context_length\"\n#define KV_EMBEDDING_LENGTH \"llama.embedding_length\"\n#define KV_BLOCK_COUNT \"llama.block_count\"\n#define KV_FEED_FORWARD_LENGTH \"llama.feed_forward_length\"\n#define KV_ATTENTION_HEAD_COUNT \"llama.attention.head_count\"\n#define KV_ATTENTION_HEAD_COUNT_KV \"llama.attention.head_count_kv\"\n#define KV_ATTENTION_LAYERNORM_RMS_EPS \"llama.attention.layer_norm_rms_epsilon\"\n#define KV_ROPE_DIMENSION_COUNT \"llama.rope.dimension_count\"\n\n#define TN_TOKEN_EMBD \"token_embd.weight\"\n#define TN_OUTPUT_NORM \"output_norm.weight\"\n#define TN_OUTPUT \"output.weight\"\n#define TN_ATTN_NORM \"blk.%d.attn_norm.weight\"\n#define TN_ATTN_Q \"blk.%d.attn_q.weight\"\n#define TN_ATTN_K \"blk.%d.attn_k.weight\"\n#define TN_ATTN_V \"blk.%d.attn_v.weight\"\n#define TN_ATTN_OUTPUT \"blk.%d.attn_output.weight\"\n#define TN_FFN_NORM \"blk.%d.ffn_norm.weight\"\n#define TN_FFN_GATE \"blk.%d.ffn_gate.weight\"\n#define TN_FFN_DOWN \"blk.%d.ffn_down.weight\"\n#define TN_FFN_UP \"blk.%d.ffn_up.weight\"\n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\n#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'\n#define LLAMA_FILE_VERSION_GGJT_V3 3\n\n#define TOKENIZER_NAME \"llama\"\n#define UNKNOWN_TOKEN_ID 0\n#define BOS_TOKEN_ID 1\n#define EOS_TOKEN_ID 2\n\n//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.\ntypedef struct {\n int dim; // transformer dimension\n int hidden_dim; // for ffn layers\n int n_layers; // number of layers\n int n_heads; // number of query heads\n int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)\n int vocab_size; // vocabulary size, usually 256 (byte-level)\n int seq_len; // max sequence length\n} Config;\n\nstruct TransformerWeights {\n // token embedding table\n float* token_embedding_table; // (vocab_size, dim)\n // weights for rmsnorms\n float* rms_att_weight; // (layer, dim) rmsnorm weights\n float* rms_ffn_weight; // (layer, dim)\n // weights for matmuls\n float* wq; // (layer, dim, dim)\n float* wk; // (layer, dim, dim)\n float* wv; // (layer, dim, dim)\n float* wo; // (layer, dim, dim)\n // weights for ffn\n float* w1; // (layer, hidden_dim, dim)\n float* w2; // (layer, dim, hidden_dim)\n float* w3; // (layer, hidden_dim, dim)\n // final rmsnorm\n float* rms_final_weight; // (dim,)\n // freq_cis for RoPE relatively positional embeddings\n // float* freq_cis_real; // (seq_len, dim/2)\n // float* freq_cis_imag; // (seq_len, dim/2)\n // (optional) classifier weights for the logits, on the last layer\n float* wcls;\n\n ~TransformerWeights() {\n delete[] token_embedding_table;\n delete[] rms_att_weight;\n delete[] rms_ffn_weight;\n delete[] wq;\n delete[] wk;\n delete[] wv;\n delete[] wo;\n delete[] w1;\n delete[] w2;\n delete[] w3;\n delete[] rms_final_weight;\n delete[] wcls;\n }\n};\n\nstatic void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {\n // we calloc instead of malloc to keep valgrind happy\n w->token_embedding_table = new float[p->vocab_size * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\\n\",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);\n\n w->rms_att_weight = new float[p->n_layers * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\\n\",__func__,p->n_layers, p->dim, p->n_layers * p->dim);\n\n w->rms_ffn_weight = new float[p->n_layers * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\\n\",__func__,p->n_layers , p->dim, p->n_layers * p->dim);\n\n w->wq = new float[p->n_layers * p->dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\\n\",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);\n\n w->wk = new float[p->n_layers * p->dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\\n\",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);\n\n w->wv = new float[p->n_layers * p->dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\\n\",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);\n\n w->wo = new float[p->n_layers * p->dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\\n\",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);\n\n w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\\n\",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);\n\n w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\\n\",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);\n\n w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\\n\",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);\n\n w->rms_final_weight = new float[p->dim]();\n printf(\"[%s:AK] Allocating [%d] float space for w->rms_final_weight\\n\",__func__,p->dim);\n\n if (shared_weights) {\n w->wcls = NULL;\n } else {\n w->wcls = new float[p->vocab_size * p->dim]();\n printf(\"[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\\n\",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);\n }\n}\n\nstatic int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {\n if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1;\n if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1;\n if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1;\n if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1;\n if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1;\n if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1;\n if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1;\n if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1;\n if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast(p->n_layers * p->hidden_dim * p->dim)) return 1;\n if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1;\n if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast(p->dim)) return 1;\n\n // Skip freq_cis_real & freq_cis_imag\n int head_size = p->dim / p->n_heads;\n fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);\n\n if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1;\n\n // Check we didn't forget to read anything\n auto curr = ftell(f);\n fseek(f, 0, SEEK_END);\n auto end = ftell(f);\n if (curr != end) {\n printf(\"Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\\n\", curr, end);\n return 1;\n }\n\n return 0;\n}\n\nstatic void print_sample_weights(TransformerWeights *w){\n printf(\"----- Quick print of first of the weight vales of all the variables\\n\");\n printf(\"%f\\n\", w->token_embedding_table[0]);\n printf(\"%f\\n\", w->rms_att_weight[0]);\n printf(\"%f\\n\", w->rms_ffn_weight[0]);\n\n printf(\"%f\\n\", w->wq[0]);\n printf(\"%f\\n\", w->wk[0]);\n printf(\"%f\\n\", w->wv[0]);\n printf(\"%f\\n\", w->wo[0]);\n printf(\"%f\\n\", w->w1[0]);\n printf(\"%f\\n\", w->w2[0]);\n printf(\"%f\\n\", w->w3[0]);\n printf(\"%f\\n\", w->rms_att_weight[0]);\n if (w->wcls) printf(\"%f\\n\", w->wcls[0]);\n}\n////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\n//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.\n\nstruct llama_vocab {\n using id = int32_t;\n using token = std::string;\n using ttype = llama_token_type;\n\n struct token_data {\n token text;\n float score;\n ttype type;\n };\n\n std::unordered_map token_to_id;\n std::vector id_to_token;\n};\n\nstruct my_llama_hparams {\n uint32_t n_vocab = 32000;\n uint32_t n_ctx = 512; // this is provided as user input?\n uint32_t n_embd = 4096;\n uint32_t n_ff = 11008;\n uint32_t n_mult = 4;\n uint32_t n_head = 32;\n uint32_t n_layer = 32;\n uint32_t n_rot = 64;\n bool operator!=(const my_llama_hparams& other) const {\n return memcmp(this, &other, sizeof(my_llama_hparams));\n }\n};\n\nstruct my_llama_layer {\n // normalization\n struct ggml_tensor * attention_norm;\n\n // attention\n struct ggml_tensor * wq;\n struct ggml_tensor * wk;\n struct ggml_tensor * wv;\n struct ggml_tensor * wo;\n\n // normalization\n struct ggml_tensor * ffn_norm;\n\n // ff\n struct ggml_tensor * w1;\n struct ggml_tensor * w2;\n struct ggml_tensor * w3;\n};\n\nstruct my_llama_model {\n struct ggml_context * ctx = NULL;\n\n std::string name;\n\n my_llama_hparams hparams;\n\n struct ggml_tensor * tok_embeddings;\n\n struct ggml_tensor * norm;\n struct ggml_tensor * output;\n\n std::vector layers;\n\n uint32_t train_its = 0;\n uint32_t train_samples = 0;\n uint32_t train_tokens = 0;\n};\n\nstruct train_params {\n const char * fn_vocab_model;\n const char * fn_llama2c_model;\n const char * fn_llama2c_output_model;\n const char * fn_train_data;\n const char * fn_checkpoint_in;\n const char * fn_checkpoint_out;\n const char * fn_model_out;\n\n uint32_t seed;\n\n int n_ctx;\n int n_embd;\n int n_mult;\n int n_head;\n int n_layer;\n int n_rotmax;\n\n int n_threads;\n int n_batch;\n int n_examples;\n int n_predict;\n\n int print_info_interval;\n int print_details_interval;\n\n bool samples_start_after_nl;\n bool use_adam;\n bool use_flash;\n bool use_scratch;\n\n // only adam\n int warmup;\n int cos_decay_steps;\n float cos_decay_restart;\n float cos_decay_alpha;\n\n int lbfgs_n_iter;\n int adam_n_iter;\n float adam_alpha;\n float adam_decay;\n\n int mem_model_gb;\n int mem_compute_gb;\n int mem_compute0_gb;\n int mem_compute1_gb;\n};\n\nstatic void print_params(struct my_llama_hparams * params) {\n printf(\"%s: n_vocab: %d\\n\", __func__, params->n_vocab);\n printf(\"%s: n_ctx: %d\\n\", __func__, params->n_ctx);\n printf(\"%s: n_embd: %d\\n\", __func__, params->n_embd);\n printf(\"%s: n_mult: %d\\n\", __func__, params->n_mult);\n printf(\"%s: n_head: %d\\n\", __func__, params->n_head);\n printf(\"%s: n_ff: %d\\n\", __func__, params->n_ff);\n printf(\"%s: n_layer: %d\\n\", __func__, params->n_layer);\n printf(\"%s: n_rot: %d\\n\", __func__, params->n_rot);\n}\n\nstatic void init_model(struct my_llama_model * model) {\n const auto & hparams = model->hparams;\n\n const uint32_t n_embd = hparams.n_embd;\n const uint32_t n_layer = hparams.n_layer;\n const uint32_t n_vocab = hparams.n_vocab;\n\n const uint32_t n_ff = hparams.n_ff;\n struct ggml_context * ctx = model->ctx;\n\n model->train_its = 0;\n model->train_samples = 0;\n model->train_tokens = 0;\n\n model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);\n printf(\"[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\\n\",__func__,n_embd , n_vocab, n_embd * n_vocab);\n\n model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);\n printf(\"[%s:GG] Allocating [%d] float space for model->norm\\n\",__func__,n_embd);\n\n model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\\n\",__func__,n_embd, n_vocab, n_embd * n_vocab);\n\n // printing the per-layer allocations here so we dont print in the for loop.\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\\n\",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\\n\",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\\n\",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\\n\",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);\n\n printf(\"[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\\n\",__func__,n_embd, n_layer);\n\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\\n\",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\\n\",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);\n printf(\"[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\\n\",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);\n\n ggml_set_name(model->tok_embeddings, \"tok_embeddings.weight\");\n ggml_set_name(model->norm, \"norm.weight\");\n ggml_set_name(model->output, \"output.weight\");\n\n model->layers.resize(n_layer);\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = model->layers[i];\n\n std::string layers_i = \"layers.\" + std::to_string(i);\n\n layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);\n\n layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);\n layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);\n layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);\n layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);\n\n layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);\n\n layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);\n layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);\n layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);\n\n ggml_set_name(layer.attention_norm, (layers_i + \".attention_norm.weight\").c_str());\n\n ggml_set_name(layer.wq, (layers_i + \".attention.wq.weight\").c_str());\n ggml_set_name(layer.wk, (layers_i + \".attention.wk.weight\").c_str());\n ggml_set_name(layer.wv, (layers_i + \".attention.wv.weight\").c_str());\n ggml_set_name(layer.wo, (layers_i + \".attention.wo.weight\").c_str());\n\n ggml_set_name(layer.ffn_norm, (layers_i + \".ffn_norm.weight\").c_str());\n\n ggml_format_name(layer.w1, \"%s.feed_forward.w1.weight\", layers_i.c_str());\n ggml_format_name(layer.w2, \"%s.feed_forward.w2.weight\", layers_i.c_str());\n ggml_format_name(layer.w3, \"%s.feed_forward.w3.weight\", layers_i.c_str());\n }\n}\n\nstatic float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {\n float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);\n return *ptr;\n}\n\nstatic int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {\n int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);\n return *ptr;\n}\n\nstatic void print_row(struct ggml_tensor * probs, int i) {\n for (int k = 0; k < probs->ne[0]; ++k) {\n float p = get_f32_2d(probs, k, i);\n printf(\" %f\", p);\n }\n printf(\"\\n\");\n}\n\nstatic void print_matrix(struct ggml_tensor * probs) {\n assert(probs->n_dims == 2);\n for (int i = 0; i < probs->ne[1]; ++i) {\n for (int k = 0; k < probs->ne[0]; ++k) {\n float p = get_f32_2d(probs, k, i);\n printf(\" %.2f\", p);\n }\n printf(\"\\n\");\n }\n}\n\n#ifdef __GNUC__\n#ifdef __MINGW32__\n__attribute__((format(gnu_printf, 1, 2)))\n#else\n__attribute__((format(printf, 1, 2)))\n#endif\n#endif\nstatic std::string format(const char * fmt, ...) {\n va_list ap, ap2;\n va_start(ap, fmt);\n va_copy(ap2, ap);\n int size = vsnprintf(NULL, 0, fmt, ap);\n GGML_ASSERT(size >= 0 && size < INT_MAX);\n std::vector buf(size + 1);\n int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);\n GGML_ASSERT(size2 == size);\n va_end(ap2);\n va_end(ap);\n return std::string(buf.data(), size);\n}\n\nstruct llama_file {\n // use FILE * so we don't have to re-open the file to mmap\n FILE * fp;\n size_t size;\n\n llama_file(const char * fname, const char * mode) {\n fp = std::fopen(fname, mode);\n if (fp == NULL) {\n size = 0;\n } else {\n seek(0, SEEK_END);\n size = tell();\n seek(0, SEEK_SET);\n }\n }\n\n size_t tell() const {\n#ifdef _WIN32\n __int64 ret = _ftelli64(fp);\n#else\n long ret = std::ftell(fp);\n#endif\n GGML_ASSERT(ret != -1); // this really shouldn't fail\n return (size_t) ret;\n }\n\n void seek(size_t offset, int whence) {\n#ifdef _WIN32\n int ret = _fseeki64(fp, (__int64) offset, whence);\n#else\n int ret = std::fseek(fp, (long) offset, whence);\n#endif\n GGML_ASSERT(ret == 0); // same\n }\n\n void read_raw(void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n std::size_t ret = std::fread(ptr, size, 1, fp);\n if (ferror(fp)) {\n die_fmt(\"fread failed: %s\", strerror(errno));\n }\n if (ret != 1) {\n die(\"unexpectedly reached end of file\");\n }\n }\n\n std::uint32_t read_u32() {\n std::uint32_t ret;\n read_raw(&ret, sizeof(ret));\n return ret;\n }\n std::float_t read_f32() {\n std::float_t ret;\n read_raw(&ret, sizeof(ret));\n return ret;\n }\n\n std::string read_string(std::uint32_t len) {\n std::vector chars(len);\n read_raw(chars.data(), len);\n return std::string(chars.data(), len);\n }\n\n ~llama_file() {\n if (fp) {\n std::fclose(fp);\n }\n }\n};\n\nstatic bool is_ggml_file(const char * filename) {\n llama_file file(filename, \"rb\");\n if (file.size < 4) {\n return false;\n }\n std::string magic = file.read_string(4);\n return magic == GGUF_MAGIC;\n}\n\nstatic std::string llama_escape_whitespaces(const std::string & text) {\n std::ostringstream out;\n for (char c : text) {\n if (c == ' ') out << \"\\xe2\\x96\\x81\";\n else out << c;\n }\n return out.str();\n}\n\nstatic void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {\n if (is_ggml_file(filename)) {\n struct ggml_context * ctx_data = NULL;\n\n struct gguf_init_params params = {\n /*.no_alloc = */ false,\n /*.ctx = */ &ctx_data,\n };\n\n struct gguf_context * ctx = gguf_init_from_file(filename, params);\n GGML_ASSERT(ctx != NULL);\n\n const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);\n GGML_ASSERT(model_idx >= 0);\n std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);\n GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);\n\n const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);\n GGML_ASSERT(token_idx >= 0);\n\n const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);\n GGML_ASSERT(score_idx >= 0);\n const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);\n\n const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);\n GGML_ASSERT(toktype_idx >= 0);\n const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);\n\n const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);\n\n vocab->id_to_token.resize(n_vocab);\n\n for (uint32_t i = 0; i < n_vocab; i++) {\n std::string word = gguf_get_arr_str(ctx, token_idx, i);\n\n vocab->token_to_id[word] = i;\n\n auto & token_data = vocab->id_to_token[i];\n token_data.text = std::move(word);\n token_data.score = scores[i];\n token_data.type = (llama_token_type) toktypes[i];\n }\n ggml_free(ctx_data);\n gguf_free(ctx);\n } else {\n // assume llama2.c vocabulary\n printf(\"Assuming llama2.c vocabulary since %s is not a gguf file\\n\", filename);\n llama_file file(filename, \"rb\");\n if (!file.fp) {\n die_fmt(\"%s: %s\", strerror(errno), filename);\n }\n const int n_vocab = config->vocab_size;\n /* uint32_t max_token_length = */ file.read_u32(); // unused\n vocab->id_to_token.resize(n_vocab);\n for (llama_vocab::id id=0; id\";\n type = LLAMA_TOKEN_TYPE_UNKNOWN;\n } else if (id == BOS_TOKEN_ID) {\n text = \"\";\n type = LLAMA_TOKEN_TYPE_CONTROL;\n } else if (id == EOS_TOKEN_ID) {\n text = \"\";\n type = LLAMA_TOKEN_TYPE_CONTROL;\n } else if (text.empty()) {\n type = LLAMA_TOKEN_TYPE_CONTROL;\n } else if (sscanf(text.c_str(), \"<0x%02hhX>\", &byte_val) == 1) {\n // Text of byte tokens is already in the expected format.\n type = LLAMA_TOKEN_TYPE_BYTE;\n } else {\n type = LLAMA_TOKEN_TYPE_NORMAL;\n }\n text = llama_escape_whitespaces(text);\n\n vocab->id_to_token[id].text = text;\n vocab->id_to_token[id].score = score;\n vocab->id_to_token[id].type = type;\n vocab->token_to_id.emplace(text, id);\n }\n }\n}\n\nstatic void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {\n int ct;\n switch (gg_weights->n_dims){\n case 1:\n ct = 0;\n for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){\n float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);\n *ptr = karpathy_weights[ct];\n ct++;\n }\n break;\n case 2:\n ct = 0;\n for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {\n for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {\n float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);\n *ptr = karpathy_weights[ct];\n ct++;\n }\n }\n break;\n case 3:\n ct = 0;\n for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {\n for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {\n for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {\n float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);\n *ptr = karpathy_weights[ct];\n ct++;\n }\n }\n }\n break;\n }\n}\n\nstatic void save_as_llama_model(\n struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename\n) {\n // convert AK weights into GG weights one by one.\n // w->token_embedding_table -> model->tok_embeddings\n // float* -> struct ggml_tensor\n convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table);\n convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);\n\n convert_weights_ak_to_gg(model->norm, w->rms_final_weight);\n //print_row(model->norm, 0);\n\n // for rms-att-weight\n int row_length = model->hparams.n_embd;\n int n_ff = model->hparams.n_ff;\n\n for (uint32_t i = 0; i < model->hparams.n_layer; ++i){\n auto & layer = model->layers[i];\n // 1d\n convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);\n convert_weights_ak_to_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);\n\n // from 3d matrix layer x dim x dim to 2d matrix dim x dim\n convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]);\n convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]);\n convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]);\n convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]);\n\n convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]);\n convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]);\n convert_weights_ak_to_gg(layer.w3 , &w->w3[i*row_length*n_ff]);\n }\n\n struct gguf_context * ctx = gguf_init_empty();\n\n std::vector tokens;\n std::vector scores;\n std::vector token_types;\n for (const llama_vocab::token_data & token_data : vocab->id_to_token) {\n tokens.push_back(token_data.text.c_str());\n scores.push_back(token_data.score);\n token_types.push_back(token_data.type);\n }\n gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());\n gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());\n gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());\n\n gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);\n\n gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, \"llama\");\n gguf_set_val_str(ctx, KV_GENERAL_NAME, \"llama\");\n\n // special tokens\n gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);\n gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);\n gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);\n gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);\n gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);\n\n gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);\n gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);\n gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);\n gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);\n // n_head_kv is optional, default to n_head\n // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);\n gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);\n gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);\n gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);\n\n // write tensors\n ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);\n gguf_add_tensor(ctx, model->tok_embeddings);\n\n ggml_set_name(model->norm, TN_OUTPUT_NORM);\n gguf_add_tensor(ctx, model->norm);\n\n ggml_set_name(model->output, TN_OUTPUT);\n gguf_add_tensor(ctx, model->output);\n\n for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {\n auto & layer = model->layers[i];\n\n ggml_format_name(layer.wq, TN_ATTN_Q, i);\n gguf_add_tensor(ctx, layer.wq);\n\n ggml_format_name(layer.wk, TN_ATTN_K, i);\n gguf_add_tensor(ctx, layer.wk);\n\n ggml_format_name(layer.wv, TN_ATTN_V, i);\n gguf_add_tensor(ctx, layer.wv);\n\n ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);\n gguf_add_tensor(ctx, layer.wo);\n\n ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);\n gguf_add_tensor(ctx, layer.attention_norm);\n\n ggml_format_name(layer.w1, TN_FFN_GATE, i);\n gguf_add_tensor(ctx, layer.w1);\n\n ggml_format_name(layer.w2, TN_FFN_DOWN, i);\n gguf_add_tensor(ctx, layer.w2);\n\n ggml_format_name(layer.w3, TN_FFN_UP, i);\n gguf_add_tensor(ctx, layer.w3);\n\n ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);\n gguf_add_tensor(ctx, layer.ffn_norm);\n }\n\n gguf_write_to_file(ctx, filename, false);\n gguf_free(ctx);\n}\n\nstatic struct train_params get_default_train_params() {\n struct train_params params;\n params.fn_vocab_model = \"models/7B/ggml-model-f16.gguf\";\n params.fn_llama2c_output_model = \"ak_llama_model.bin\";\n params.fn_train_data = \"shakespeare.txt\";\n params.fn_checkpoint_in = \"checkpoint.bin\";\n params.fn_checkpoint_out = \"checkpoint.bin\";\n params.fn_model_out = \"ggml-checkpoint-f32.bin\";\n\n params.seed = -1;\n\n params.n_ctx = 128;\n params.n_embd = 256;\n params.n_mult = 256;\n params.n_head = 8;\n params.n_layer = 16;\n params.n_rotmax = 64;\n\n params.n_threads = 6;\n params.n_batch = 8;\n params.n_examples = 8;\n params.n_predict = 1024;\n\n params.print_info_interval = 1;\n params.print_details_interval = 2;\n\n params.samples_start_after_nl = false;\n params.use_adam = true;\n params.use_flash = true;\n params.use_scratch = true;\n\n // only adam\n params.warmup = 100;\n params.cos_decay_steps = 1000;\n params.cos_decay_restart = 1.1f;\n params.cos_decay_alpha = 0.0f;\n\n params.lbfgs_n_iter = 16;\n params.adam_n_iter = 16;\n params.adam_alpha = 1e-3f;\n params.adam_decay = 1e-3f;\n\n params.mem_model_gb = 2;\n params.mem_compute_gb = 24;\n params.mem_compute0_gb = 8;\n params.mem_compute1_gb = 2;\n\n return params;\n}\n\nstatic void print_usage(int /*argc*/, char ** argv, const struct train_params * params) {\n fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"options:\\n\");\n fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n fprintf(stderr, \" --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\\n\", params->fn_vocab_model);\n fprintf(stderr, \" --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\\n\");\n fprintf(stderr, \" --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\\n\", params->fn_llama2c_output_model);\n fprintf(stderr, \"\\n\");\n}\n\nstatic bool params_parse(int argc, char ** argv, struct train_params * params) {\n bool invalid_param = false;\n bool reqd_param_found = false;\n std::string arg;\n struct train_params default_params = get_default_train_params();\n const std::string arg_prefix = \"--\";\n\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n\n if (arg == \"--copy-vocab-from-model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_vocab_model = argv[i];\n } else if (arg == \"--llama2c-model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n reqd_param_found = true;\n params->fn_llama2c_model = argv[i];\n } else if (arg == \"--llama2c-output-model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_llama2c_output_model = argv[i];\n } else if (arg == \"-h\" || arg == \"--help\") {\n print_usage(argc, argv, &default_params);\n exit(0);\n } else {\n fprintf(stderr, \"error: unknown argument: %s\\n\", arg.c_str());\n print_usage(argc, argv, &default_params);\n exit(1);\n }\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: %s\\n\", arg.c_str());\n print_usage(argc, argv, &default_params);\n exit(1);\n }\n if (!reqd_param_found){\n fprintf(stderr, \"error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\\n\");\n print_usage(argc, argv, &default_params);\n exit(1);\n }\n\n return true;\n}\n\nstatic std::string basename(const std::string &path) {\n size_t pos = path.find_last_of(\"/\\\\\");\n if (pos == std::string::npos) {\n return path;\n }\n return path.substr(pos + 1);\n}\n\nint main(int argc, char ** argv) {\n struct train_params params = get_default_train_params();\n if (!params_parse(argc, argv, ¶ms)) {\n return 1;\n }\n Config config;\n TransformerWeights weights = {};\n {\n FILE *file = fopen(params.fn_llama2c_model, \"rb\");\n if (!file) { printf(\"Unable to open the checkpoint file %s!\\n\", params.fn_llama2c_model); return 1; }\n // read in the config header\n if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }\n auto shared_weights = config.vocab_size > 0;\n config.vocab_size = abs(config.vocab_size);\n\n // read in the Transformer weights\n malloc_weights(&weights, &config, shared_weights);\n if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }\n fclose(file);\n }\n\n struct llama_vocab vocab;\n load_vocab(params.fn_vocab_model, &config, &vocab);\n\n struct my_llama_model model;\n model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);\n model.hparams.n_ctx = params.n_ctx;\n model.hparams.n_embd = config.dim; //params.n_embd;\n model.hparams.n_ff = config.hidden_dim;\n model.hparams.n_mult = 32;//params.n_mult;\n model.hparams.n_head = config.n_heads; //params.n_head;\n model.hparams.n_layer = config.n_layers; //params.n_layer;\n model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);\n print_params(&model.hparams);\n struct ggml_init_params lcparams;\n lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);\n lcparams.mem_buffer = NULL;\n lcparams.no_alloc = false;\n\n model.ctx = ggml_init(lcparams);\n\n init_model(&model);\n model.name = basename(params.fn_llama2c_model);\n save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);\n\n printf(\"Saving llama.c model file %s in ggml format at %s\\n\", params.fn_llama2c_model, params.fn_llama2c_output_model);\n\n ggml_free(model.ctx);\n return 0;\n}\n" }, { "path": "examples/embedding/CMakeLists.txt", "content": "set(TARGET embedding)\nadd_executable(${TARGET} embedding.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/embedding/README.md", "content": "# llama.cpp/example/embedding\n\nThis example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp.\n\n## Quick Start\n\nTo get started right away, run the following command, making sure to use the correct path for the model you have:\n\n### Unix-based systems (Linux, macOS, etc.):\n\n```bash\n./embedding -m ./path/to/model --log-disable -p \"Hello World!\" 2>/dev/null\n```\n\n### Windows:\n\n```powershell\nembedding.exe -m ./path/to/model --log-disable -p \"Hello World!\" 2>$null\n```\n\nThe above command will output space-separated float values.\n" }, { "path": "examples/embedding/embedding.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nint main(int argc, char ** argv) {\n gpt_params params;\n\n if (!gpt_params_parse(argc, argv, params)) {\n return 1;\n }\n\n params.embedding = true;\n\n print_build_info();\n\n if (params.seed == LLAMA_DEFAULT_SEED) {\n params.seed = time(NULL);\n }\n\n fprintf(stderr, \"%s: seed = %u\\n\", __func__, params.seed);\n\n std::mt19937 rng(params.seed);\n if (params.random_prompt) {\n params.prompt = gpt_random_prompt(rng);\n }\n\n llama_backend_init(params.numa);\n\n llama_model * model;\n llama_context * ctx;\n\n // load the model\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n if (model == NULL) {\n fprintf(stderr, \"%s: error: unable to load model\\n\", __func__);\n return 1;\n }\n\n const int n_ctx_train = llama_n_ctx_train(model);\n const int n_ctx = llama_n_ctx(ctx);\n\n if (n_ctx > n_ctx_train) {\n fprintf(stderr, \"%s: warning: model was trained on only %d context tokens (%d specified)\\n\",\n __func__, n_ctx_train, n_ctx);\n }\n\n // print system information\n {\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"%s\\n\", get_system_info(params).c_str());\n }\n\n int n_past = 0;\n\n // tokenize the prompt\n auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);\n\n if (params.verbose_prompt) {\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"%s: prompt: '%s'\\n\", __func__, params.prompt.c_str());\n fprintf(stderr, \"%s: number of tokens in prompt = %zu\\n\", __func__, embd_inp.size());\n for (int i = 0; i < (int) embd_inp.size(); i++) {\n fprintf(stderr, \"%6d -> '%s'\\n\", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());\n }\n fprintf(stderr, \"\\n\");\n }\n\n if (embd_inp.size() > (size_t)n_ctx) {\n fprintf(stderr, \"%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\\n\",\n __func__, embd_inp.size(), n_ctx);\n return 1;\n }\n\n while (!embd_inp.empty()) {\n int n_tokens = std::min(params.n_batch, (int) embd_inp.size());\n if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0))) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return 1;\n }\n n_past += n_tokens;\n embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);\n }\n\n const int n_embd = llama_n_embd(model);\n const auto * embeddings = llama_get_embeddings(ctx);\n\n for (int i = 0; i < n_embd; i++) {\n printf(\"%f \", embeddings[i]);\n }\n printf(\"\\n\");\n\n llama_print_timings(ctx);\n llama_free(ctx);\n llama_free_model(model);\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/export-lora/CMakeLists.txt", "content": "set(TARGET export-lora)\nadd_executable(${TARGET} export-lora.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/export-lora/README.md", "content": "# export-lora\n\nApply LORA adapters to base model and export the resulting model.\n\n```\nusage: export-lora [options]\n\noptions:\n -h, --help show this help message and exit\n -m FNAME, --model-base FNAME model path from which to load base model (default '')\n -o FNAME, --model-out FNAME path to save exported model (default '')\n -l FNAME, --lora FNAME apply LoRA adapter\n -s FNAME S, --lora-scaled FNAME S apply LoRA adapter with user defined scaling S\n -t N, --threads N number of threads to use during computation (default: 4)\n```\n\nFor example:\n\n```bash\n./bin/export-lora \\\n -m open-llama-3b-v2-q8_0.gguf \\\n -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \\\n -l lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin\n```\n\nMultiple LORA adapters can be applied by passing multiple `-l FN` or `-s FN S` command line parameters.\n" }, { "path": "examples/export-lora/export-lora.cpp", "content": "\n#include \"common.h\"\n#include \"ggml.h\"\n#include \"ggml-alloc.h\"\n\n#include \n#include \n#include \n\nstatic const size_t tensor_alignment = 32;\n\nstruct lora_info {\n std::string filename;\n float scale;\n};\n\nstruct export_lora_params {\n std::string fn_model_base;\n std::string fn_model_out;\n std::vector lora;\n int n_threads;\n};\n\nstruct lora_data {\n struct lora_info info;\n std::vector data;\n struct ggml_context * ctx;\n\n uint32_t lora_r;\n uint32_t lora_alpha;\n};\n\nstruct llama_file {\n // use FILE * so we don't have to re-open the file to mmap\n FILE * fp;\n size_t size;\n\n llama_file(const char * fname, const char * mode) {\n fp = std::fopen(fname, mode);\n if (fp == NULL) {\n size = 0;\n } else {\n seek(0, SEEK_END);\n size = tell();\n seek(0, SEEK_SET);\n }\n }\n\n size_t tell() const {\n#ifdef _WIN32\n __int64 ret = _ftelli64(fp);\n#else\n long ret = std::ftell(fp);\n#endif\n GGML_ASSERT(ret != -1); // this really shouldn't fail\n return (size_t) ret;\n }\n\n void seek(size_t offset, int whence) {\n#ifdef _WIN32\n int ret = _fseeki64(fp, (__int64) offset, whence);\n#else\n int ret = std::fseek(fp, (long) offset, whence);\n#endif\n GGML_ASSERT(ret == 0); // same\n }\n\n void read_raw(void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n std::size_t ret = std::fread(ptr, size, 1, fp);\n if (ferror(fp)) {\n die_fmt(\"read error: %s\", strerror(errno));\n }\n if (ret != 1) {\n die(\"unexpectedly reached end of file\");\n }\n }\n\n std::uint32_t read_u32() {\n std::uint32_t ret;\n read_raw(&ret, sizeof(ret));\n return ret;\n }\n\n std::string read_string(std::uint32_t len) {\n std::vector chars(len);\n read_raw(chars.data(), len);\n return std::string(chars.data(), len);\n }\n\n void write_raw(const void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n size_t ret = std::fwrite(ptr, size, 1, fp);\n if (ret != 1) {\n die_fmt(\"write error: %s\", strerror(errno));\n }\n }\n\n void write_u32(std::uint32_t val) {\n write_raw(&val, sizeof(val));\n }\n\n bool eof() {\n return tell() >= size;\n }\n\n ~llama_file() {\n if (fp) {\n std::fclose(fp);\n }\n }\n};\n\nstatic struct export_lora_params get_default_export_lora_params() {\n struct export_lora_params result;\n result.fn_model_base = \"\";\n result.fn_model_out = \"\";\n result.n_threads = GGML_DEFAULT_N_THREADS;\n return result;\n}\n\nstatic void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * params) {\n fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"options:\\n\");\n fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n fprintf(stderr, \" -m FNAME, --model-base FNAME model path from which to load base model (default '%s')\\n\", params->fn_model_base.c_str());\n fprintf(stderr, \" -o FNAME, --model-out FNAME path to save exported model (default '%s')\\n\", params->fn_model_out.c_str());\n fprintf(stderr, \" -l FNAME, --lora FNAME apply LoRA adapter\\n\");\n fprintf(stderr, \" -s FNAME S, --lora-scaled FNAME S apply LoRA adapter with user defined scaling S\\n\");\n fprintf(stderr, \" -t N, --threads N number of threads to use during computation (default: %d)\\n\", params->n_threads);\n}\n\nstatic bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) {\n bool invalid_param = false;\n std::string arg;\n struct export_lora_params default_params = get_default_export_lora_params();\n const std::string arg_prefix = \"--\";\n\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n\n if (arg == \"-m\" || arg == \"--model-base\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_model_base = argv[i];\n } else if (arg == \"-o\" || arg == \"--model-out\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_model_out = argv[i];\n } else if (arg == \"-l\" || arg == \"--lora\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n struct lora_info lora;\n lora.filename = argv[i];\n lora.scale = 1.0f;\n params->lora.push_back(lora);\n } else if (arg == \"-s\" || arg == \"--lora-scaled\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n struct lora_info lora;\n lora.filename = argv[i];\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n lora.scale = std::stof(argv[i]);\n params->lora.push_back(lora);\n } else if (arg == \"-t\" || arg == \"--threads\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_threads = std::stoi(argv[i]);\n if (params->n_threads <= 0) {\n params->n_threads = std::thread::hardware_concurrency();\n }\n } else {\n fprintf(stderr, \"error: unknown argument: '%s'\\n\", arg.c_str());\n export_lora_print_usage(argc, argv, &default_params);\n exit(1);\n }\n }\n\n if (params->fn_model_base == default_params.fn_model_base) {\n fprintf(stderr, \"error: please specify a filename for model-base.\\n\");\n export_lora_print_usage(argc, argv, &default_params);\n exit(1);\n }\n if (params->fn_model_out == default_params.fn_model_out) {\n fprintf(stderr, \"error: please specify a filename for model-out.\\n\");\n export_lora_print_usage(argc, argv, &default_params);\n exit(1);\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: '%s'\\n\", arg.c_str());\n export_lora_print_usage(argc, argv, &default_params);\n exit(1);\n }\n return true;\n}\n\nstatic void free_lora(struct lora_data * lora) {\n if (lora->ctx != NULL) {\n ggml_free(lora->ctx);\n }\n delete lora;\n}\n\nstatic struct lora_data * load_lora(struct lora_info * info) {\n struct lora_data * result = new struct lora_data;\n result->info = *info;\n result->ctx = NULL;\n result->lora_r = 1;\n result->lora_alpha = 1;\n\n struct llama_file file(info->filename.c_str(), \"rb\");\n if (file.fp == NULL) {\n fprintf(stderr, \"warning: Could not open lora adapter '%s'. Ignoring this adapter.\\n\",\n info->filename.c_str());\n free_lora(result);\n return NULL;\n }\n\n struct ggml_init_params params_ggml;\n params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;\n params_ggml.mem_buffer = NULL;\n params_ggml.no_alloc = true;\n result->ctx = ggml_init(params_ggml);\n\n uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'\n uint32_t magic = file.read_u32();\n if (magic != LLAMA_FILE_MAGIC_LORA) {\n die_fmt(\"unexpected lora header file magic in '%s'\", info->filename.c_str());\n }\n uint32_t version = file.read_u32();\n if (version != 1) {\n die_fmt(\"unexpected lora file version '%u' in '%s'\", (unsigned) version, info->filename.c_str());\n }\n result->lora_r = file.read_u32();\n result->lora_alpha = file.read_u32();\n // read tensor infos from file\n std::vector name_buf;\n std::vector tensors;\n std::vector tensors_offset;\n size_t total_nbytes_pad = 0;\n while(!file.eof()) {\n int64_t ne[4] = {1,1,1,1};\n uint32_t n_dims = file.read_u32();\n uint32_t namelen = file.read_u32();\n uint32_t type = file.read_u32();\n for (uint32_t k = 0; k < n_dims; ++k) {\n ne[k] = (int64_t)file.read_u32();\n }\n name_buf.clear();\n name_buf.resize(namelen + 1, '\\0');\n file.read_raw(name_buf.data(), namelen);\n file.seek((0-file.tell()) & 31, SEEK_CUR);\n size_t offset = file.tell();\n struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne);\n ggml_set_name(tensor, name_buf.data());\n size_t nbytes = ggml_nbytes(tensor);\n size_t nbytes_pad = ggml_nbytes_pad(tensor);\n total_nbytes_pad += nbytes_pad;\n tensors.push_back(tensor);\n tensors_offset.push_back(offset);\n file.seek(nbytes, SEEK_CUR);\n }\n // read tensor data\n result->data.resize(total_nbytes_pad);\n size_t data_offset = 0;\n for (size_t i = 0; i < tensors.size(); ++i) {\n struct ggml_tensor * tensor = tensors[i];\n size_t offset = tensors_offset[i];\n size_t nbytes = ggml_nbytes(tensor);\n size_t nbytes_pad = ggml_nbytes_pad(tensor);\n file.seek(offset, SEEK_SET);\n tensor->data = result->data.data() + data_offset;\n file.read_raw(tensor->data, nbytes);\n data_offset += nbytes_pad;\n }\n return result;\n}\n\n\nstatic struct ggml_cgraph * build_graph_lora(\n struct ggml_context * ctx,\n struct ggml_tensor * tensor,\n struct ggml_tensor * lora_a,\n struct ggml_tensor * lora_b,\n float scaling\n) {\n struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);\n if (scaling != 1.0f) {\n ab = ggml_scale(ctx, ab, ggml_new_f32(ctx, scaling));\n }\n struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);\n\n struct ggml_cgraph * gf = ggml_new_graph(ctx);\n ggml_build_forward_expand (gf, res);\n return gf;\n}\n\nstatic bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) {\n if (lora->ctx == NULL) {\n return false;\n }\n std::string name = ggml_get_name(tensor);\n std::string name_a = name + std::string(\".loraA\");\n std::string name_b = name + std::string(\".loraB\");\n struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str());\n struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str());\n if (lora_a == NULL || lora_b == NULL) {\n return false;\n }\n\n float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;\n\n struct ggml_init_params params;\n params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;\n params.mem_buffer = NULL;\n params.no_alloc = true;\n struct ggml_context * ctx = NULL;\n struct ggml_allocr * alloc = NULL;\n struct ggml_cgraph * gf = NULL;\n\n ctx = ggml_init(params);\n alloc = ggml_allocr_new_measure(tensor_alignment);\n gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);\n size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf);\n ggml_allocr_free(alloc);\n ggml_free(ctx);\n\n static std::vector data_compute;\n data_compute.resize(alloc_size + tensor_alignment);\n\n ctx = ggml_init(params);\n alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment);\n gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);\n ggml_allocr_alloc_graph(alloc, gf);\n ggml_allocr_free(alloc);\n\n struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);\n static std::vector data_work;\n data_work.resize(cplan.work_size);\n cplan.work_data = data_work.data();\n\n ggml_graph_compute(gf, &cplan);\n\n ggml_free(ctx);\n return true;\n}\n\nstatic void export_lora(struct export_lora_params * params) {\n // load all loras\n std::vector loras;\n for (size_t i = 0; i < params->lora.size(); ++i) {\n struct lora_data * lora = load_lora(¶ms->lora[i]);\n if (lora != NULL) {\n loras.push_back(lora);\n }\n }\n if (loras.size() == 0) {\n fprintf(stderr, \"warning: no lora adapters will be applied.\\n\");\n }\n\n // open input file\n struct llama_file fin(params->fn_model_base.c_str(), \"rb\");\n if (!fin.fp) {\n die_fmt(\"Could not open file '%s'\\n\", params->fn_model_base.c_str());\n }\n\n // open base model gguf, read tensors without their data\n struct ggml_context * ctx_in;\n struct gguf_init_params params_gguf;\n params_gguf.no_alloc = true;\n params_gguf.ctx = &ctx_in;\n struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf);\n\n // create new gguf\n struct gguf_context * gguf_out = gguf_init_empty();\n\n // copy meta data from base model: kv and tensors\n gguf_set_kv(gguf_out, gguf_in);\n int n_tensors = gguf_get_n_tensors(gguf_in);\n for (int i=0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name(gguf_in, i);\n struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);\n gguf_add_tensor(gguf_out, tensor);\n }\n\n // create output file\n struct llama_file fout(params->fn_model_out.c_str(), \"wb\");\n if (!fout.fp) {\n die_fmt(\"Could not create file '%s'\\n\", params->fn_model_out.c_str());\n }\n\n // write gguf meta data\n std::vector meta;\n meta.resize(gguf_get_meta_size(gguf_out));\n gguf_get_meta_data(gguf_out, meta.data());\n fout.write_raw(meta.data(), meta.size());\n\n std::vector data;\n std::vector padding;\n for (int i=0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name(gguf_in, i);\n struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);\n\n // read tensor data\n data.resize(ggml_nbytes(tensor));\n tensor->data = data.data();\n size_t offset = gguf_get_tensor_offset(gguf_in, i);\n fin.seek(offset + meta.size(), SEEK_SET);\n fin.read_raw(data.data(), data.size());\n\n // apply all loras\n for (size_t k = 0; k < loras.size(); ++k) {\n apply_lora(tensor, loras[k], params->n_threads);\n }\n\n // write tensor data + padding\n padding.clear();\n padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0);\n\n GGML_ASSERT(fout.tell() == offset + meta.size());\n // fout.seek(offset + meta.size(), SEEK_SET);\n fout.write_raw(data.data(), data.size());\n fout.write_raw(padding.data(), padding.size());\n\n if (i % 2 == 0) {\n printf(\".\");\n }\n }\n printf(\"\\n\");\n\n // close gguf\n gguf_free(gguf_out);\n gguf_free(gguf_in);\n\n // free loras\n for (size_t i = 0; i < loras.size(); ++i) {\n free_lora(loras[i]);\n }\n}\n\nint main(int argc, char ** argv) {\n struct export_lora_params params = get_default_export_lora_params();\n\n if (!export_lora_params_parse(argc, argv, ¶ms)) {\n return 1;\n }\n\n export_lora(¶ms);\n\n return 0;\n}\n" }, { "path": "examples/finetune/CMakeLists.txt", "content": "set(TARGET finetune)\nadd_executable(${TARGET} finetune.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/finetune/README.md", "content": "# finetune\n\nBasic usage instructions:\n\n```bash\n# get training data\nwget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt\n\n# finetune LORA adapter\n./bin/finetune \\\n --model-base open-llama-3b-v2-q8_0.gguf \\\n --checkpoint-in chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \\\n --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \\\n --lora-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \\\n --train-data \"shakespeare.txt\" \\\n --save-every 10 \\\n --threads 6 --adam-iter 30 --batch 4 --ctx 64 \\\n --use-checkpointing\n\n# predict\n./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin\n```\n\nFinetune output files will be saved every N iterations (config with `--save-every N`).\nThe pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output.\nSo in above example after 10 iterations these files will be written:\n- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf\n- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf\n- lora-open-llama-3b-v2-q8_0-shakespeare-10.bin\n- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin\n\nAfter 10 more iterations:\n- chk-lora-open-llama-3b-v2-q8_0-shakespeare-20.gguf\n- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf\n- lora-open-llama-3b-v2-q8_0-shakespeare-20.bin\n- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin\n\nCheckpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter.\n\nllama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`.\nThese LORA adapters can then be used by `main` together with the base model, like in the 'predict' example command above.\n\nIn `main` you can also load multiple LORA adapters, which will then be mixed together.\n\nFor example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this:\n\n```bash\n./bin/main -m open-llama-3b-v2-q8_0.gguf \\\n --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \\\n --lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin\n```\n\nYou can change how strong each LORA adapter is applied to the base model by using `--lora-scaled FN SCALE` instead of `--lora FN`.\n\nFor example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one:\n\n```bash\n./bin/main -m open-llama-3b-v2-q8_0.gguf \\\n --lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \\\n --lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \\\n --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin\n```\n\nThe scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.\n\nGradient checkpointing reduces the memory requirements by ~50% but increases the runtime.\nIf you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.\n\nThe default LORA rank can be specified with `--lora-r N`.\nThe LORA rank can be configured for each model tensor type separately with these command line options:\n\n```bash\n --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default 4)\n --rank-att-norm N LORA rank for attention norm tensor (default 1)\n --rank-ffn-norm N LORA rank for feed-forward norm tensor (default 1)\n --rank-out-norm N LORA rank for output norm tensor (default 1)\n --rank-tok-embd N LORA rank for token embeddings tensor (default 4)\n --rank-out N LORA rank for output tensor (default 4)\n --rank-wq N LORA rank for wq tensor (default 4)\n --rank-wk N LORA rank for wk tensor (default 4)\n --rank-wv N LORA rank for wv tensor (default 4)\n --rank-wo N LORA rank for wo tensor (default 4)\n --rank-w1 N LORA rank for w1 tensor (default 4)\n --rank-w2 N LORA rank for w2 tensor (default 4)\n --rank-w3 N LORA rank for w3 tensor (default 4)\n```\n\nThe LORA rank of 'norm' tensors should always be 1.\n\nTo see all available options use `finetune --help`.\n" }, { "path": "examples/finetune/convert-finetune-checkpoint-to-gguf.py", "content": "#!/usr/bin/env python3\n# finetune checkpoint --> gguf conversion\n\nimport argparse\nimport gguf\nimport os\nimport struct\nimport sys\nimport numpy as np\nfrom pathlib import Path\n\n# gguf constants\nLLM_KV_OPTIMIZER_TYPE = \"optimizer.type\"\nLLM_KV_OPTIMIZER_TYPE_ADAM = \"adam\"\nLLM_KV_OPTIMIZER_TYPE_LBFGS = \"lbfgs\"\nLLM_KV_OPTIMIZER_FILE_VERSION = \"optimizer.file_version\"\nLLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = \"optimizer.convergence_past_count\"\nLLM_KV_OPTIMIZER_PARAMETER_COUNT = \"optimizer.parameter_count\"\nLLM_KV_OPTIMIZER_ITERATION_COUNT = \"optimizer.iteration_count\"\nLLM_KV_OPTIMIZER_JUST_INITIALIZED = \"optimizer.just_initialized\"\nLLM_KV_OPTIMIZER_ADAM_BEST_LOSS = \"optimizer.adam.best_loss\"\nLLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = \"optimizer.adam.previous_loss\"\nLLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = \"optimizer.adam.no_improvement_count\"\nLLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = \"optimizer.lbfgs.approx_hessian_count\"\nLLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = \"optimizer.lbfgs.best_loss\"\nLLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = \"optimizer.lbfgs.line_search_step\"\nLLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = \"optimizer.lbfgs.line_search_j\"\nLLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = \"optimizer.lbfgs.line_search_k\"\nLLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = \"optimizer.lbfgs.line_search_end\"\nLLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = \"optimizer.lbfgs.no_improvement_count\"\n\nLLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = \"optimizer.adam.first_moments\"\nLLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = \"optimizer.adam.second_moments\"\nLLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = \"optimizer.adam.past_loss_values\"\n\nLLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = \"optimizer.lbfgs.current_parameters\"\nLLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = \"optimizer.lbfgs.previous_parameters\"\nLLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = \"optimizer.lbfgs.current_gradients\"\nLLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = \"optimizer.lbfgs.previous_gradients\"\nLLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = \"optimizer.lbfgs.search_direction\"\nLLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = \"optimizer.lbfgs.past_loss_values\"\nLLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = \"optimizer.lbfgs.memory_alpha\"\nLLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = \"optimizer.lbfgs.memory_ys\"\nLLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = \"optimizer.lbfgs.memory_s\"\nLLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = \"optimizer.lbfgs.memory_y\"\n\nLLM_KV_TRAINING_TYPE_TRAIN_MODEL = \"train_model\"\nLLM_KV_TRAINING_TYPE_FINETUNE_LORA = \"finetune_lora\"\nLLM_KV_TRAINING_TYPE = \"training.type\"\nLLM_KV_TRAINING_FILE_VERSION = \"training.file_version\"\nLLM_KV_TRAINING_ITERATION_COUNT = \"training.iteration_count\"\nLLM_KV_TRAINING_SAMPLE_COUNT = \"training.sample_count\"\nLLM_KV_TRAINING_TOKEN_COUNT = \"training.token_count\"\n\nLLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = \"training.lora.rank.token_embd\"\nLLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = \"training.lora.rank.output_norm\"\nLLM_KV_TRAINING_LORA_RANK_OUTPUT = \"training.lora.rank.output\"\nLLM_KV_TRAINING_LORA_RANK_ATTN_NORM = \"training.lora.rank.attn_norm\"\nLLM_KV_TRAINING_LORA_RANK_ATTN_Q = \"training.lora.rank.attn_q\"\nLLM_KV_TRAINING_LORA_RANK_ATTN_K = \"training.lora.rank.attn_k\"\nLLM_KV_TRAINING_LORA_RANK_ATTN_V = \"training.lora.rank.attn_v\"\nLLM_KV_TRAINING_LORA_RANK_ATTN_OUT = \"training.lora.rank.attn_output\"\nLLM_KV_TRAINING_LORA_RANK_FFN_NORM = \"training.lora.rank.ffn_norm\"\nLLM_KV_TRAINING_LORA_RANK_FFN_GATE = \"training.lora.rank.ffn_gate\"\nLLM_KV_TRAINING_LORA_RANK_FFN_DOWN = \"training.lora.rank.ffn_down\"\nLLM_KV_TRAINING_LORA_RANK_FFN_UP = \"training.lora.rank.ffn_up\"\n\nclass Tensor:\n def __init__(self, dtype='f', ne=None):\n if ne is None:\n ne = []\n self.dtype = dtype\n self.ne = ne\n self.nbytes = 0\n if self.dtype == 'f':\n if len(self.ne) == 0:\n self.nbytes = 0\n else:\n self.nbytes = int(np.product(self.ne)) * 4\n else:\n raise ValueError(f\"Unhandled data type '{self.dtype}'\")\n\n def load(self, data, offset):\n nd = struct.unpack(' 0 else [])\n\n self.lbfgs_x = Tensor('f', [self.nx])\n self.lbfgs_xp = Tensor('f', [self.nx])\n self.lbfgs_g = Tensor('f', [self.nx])\n self.lbfgs_gp = Tensor('f', [self.nx])\n self.lbfgs_d = Tensor('f', [self.nx])\n self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])\n self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])\n self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])\n self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])\n self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])\n\n # forgot to save type in version 1:\n # guess self.type from number of remaining bytes\n size_type_0 = 12 + sum([t.max_storage_size() for t in\n [self.adam_m, self.adam_v]\n +([self.adam_pf] if (self.past > 0) else [])])\n size_type_1 = 24 + sum([t.max_storage_size() for t in\n [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,\n self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,\n self.lbfgs_lmal, self.lbfgs_lmys,\n self.lbfgs_lms, self.lbfgs_lmy]\n +([self.lbfgs_pf] if (self.past > 0) else [])])\n # due to alignment padding the size might not by exact\n # but the difference in size for both types is significant,\n # so we can just use whichever is closest\n remaining = len(data) - offset\n if abs(remaining - size_type_0) < abs(remaining - size_type_1):\n self.type = 0\n else:\n self.type = 1\n\n if self.type == 0:\n offset = self.adam_m.load(data, offset)\n offset = self.adam_v.load(data, offset)\n offset = self.adam_pf.load(data,offset)\n\n self.adam_fx_best = struct.unpack(' 0:\n self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)\n\n elif self.type == 1:\n gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)\n gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)\n gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)\n gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)\n gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)\n gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)\n gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)\n gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)\n\n self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)\n self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)\n self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)\n self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)\n self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)\n if self.past > 0:\n self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)\n self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)\n self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)\n self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)\n self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)\n else:\n raise ValueError('Unknown optimizer type')\n\nclass LoraParams:\n def __init__(self):\n pass\n\n def load(self, data, offset):\n self.n_rank_attention_norm = struct.unpack('\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstatic const size_t tensor_alignment = 32;\n\nstruct my_llama_hparams {\n uint32_t n_vocab = 32000;\n uint32_t n_ctx = 512;\n uint32_t n_embd = 4096;\n uint32_t n_ff = 11008;\n uint32_t n_head = 32;\n uint32_t n_head_kv = 32;\n uint32_t n_layer = 32;\n\n // float f_norm_eps = 1e-5f; // falcon\n float f_norm_rms_eps = 1e-5f; // llama\n\n float rope_freq_base = 10000.0f;\n float rope_freq_scale = 1.0f;\n\n uint32_t n_gqa() const {\n return n_head/n_head_kv;\n }\n\n uint32_t n_embd_head() const {\n return n_embd/n_head;\n }\n\n uint32_t n_embd_gqa() const {\n return n_embd/n_gqa();\n }\n\n bool operator!=(const my_llama_hparams& other) const {\n return memcmp(this, &other, sizeof(other));\n }\n};\n\nstruct my_llama_layer {\n // normalization\n struct ggml_tensor * attention_norm;\n\n // attention\n struct ggml_tensor * wq;\n struct ggml_tensor * wk;\n struct ggml_tensor * wv;\n struct ggml_tensor * wo;\n\n // normalization\n struct ggml_tensor * ffn_norm;\n\n // ff\n struct ggml_tensor * w1;\n struct ggml_tensor * w2;\n struct ggml_tensor * w3;\n};\n\nstruct my_llama_model {\n struct my_llama_hparams hparams;\n\n struct ggml_tensor * tok_embeddings;\n\n struct ggml_tensor * norm;\n struct ggml_tensor * output;\n\n std::vector layers;\n};\n\nstruct my_llama_lora_hparams {\n uint32_t lora_r = 1;\n uint32_t lora_alpha = 1;\n uint32_t n_rank_attention_norm = 1;\n uint32_t n_rank_wq = 4;\n uint32_t n_rank_wk = 4;\n uint32_t n_rank_wv = 4;\n uint32_t n_rank_wo = 4;\n uint32_t n_rank_ffn_norm = 1;\n uint32_t n_rank_w1 = 4;\n uint32_t n_rank_w2 = 4;\n uint32_t n_rank_w3 = 4;\n uint32_t n_rank_tok_embeddings = 4;\n uint32_t n_rank_norm = 1;\n uint32_t n_rank_output = 4;\n\n bool operator!=(const my_llama_lora_hparams& other) const {\n return memcmp(this, &other, sizeof(other));\n }\n};\n\nstruct my_llama_lora_layer {\n // normalization\n struct ggml_tensor * attention_norm_a;\n struct ggml_tensor * attention_norm_b;\n\n // attention\n struct ggml_tensor * wq_a;\n struct ggml_tensor * wq_b;\n struct ggml_tensor * wk_a;\n struct ggml_tensor * wk_b;\n struct ggml_tensor * wv_a;\n struct ggml_tensor * wv_b;\n struct ggml_tensor * wo_a;\n struct ggml_tensor * wo_b;\n\n // normalization\n struct ggml_tensor * ffn_norm_a;\n struct ggml_tensor * ffn_norm_b;\n\n // ff\n struct ggml_tensor * w1_a;\n struct ggml_tensor * w1_b;\n struct ggml_tensor * w2_a;\n struct ggml_tensor * w2_b;\n struct ggml_tensor * w3_a;\n struct ggml_tensor * w3_b;\n};\n\nstruct my_llama_lora {\n struct ggml_context * ctx = NULL;\n std::vector data;\n\n my_llama_lora_hparams hparams;\n\n struct ggml_tensor * tok_embeddings_a;\n struct ggml_tensor * tok_embeddings_b;\n\n struct ggml_tensor * norm_a;\n struct ggml_tensor * norm_b;\n struct ggml_tensor * output_a;\n struct ggml_tensor * output_b;\n\n std::vector layers;\n};\n\n// gguf constants\nstatic const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA = \"finetune_lora\";\nstatic const char * LLM_KV_TRAINING_TYPE = \"training.type\";\n\nstatic const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = \"training.lora.rank.token_embd\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = \"training.lora.rank.output_norm\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT = \"training.lora.rank.output\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = \"training.lora.rank.attn_norm\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q = \"training.lora.rank.attn_q\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K = \"training.lora.rank.attn_k\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V = \"training.lora.rank.attn_v\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = \"training.lora.rank.attn_output\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM = \"training.lora.rank.ffn_norm\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE = \"training.lora.rank.ffn_gate\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = \"training.lora.rank.ffn_down\";\nstatic const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP = \"training.lora.rank.ffn_up\";\n\n// gguf constants (sync with gguf.py)\n\nstatic const char * LLM_KV_GENERAL_ARCHITECTURE = \"general.architecture\";\nstatic const char * LLM_KV_GENERAL_FILE_TYPE = \"general.file_type\";\n\nstatic const char * LLM_KV_CONTEXT_LENGTH = \"%s.context_length\";\nstatic const char * LLM_KV_EMBEDDING_LENGTH = \"%s.embedding_length\";\nstatic const char * LLM_KV_BLOCK_COUNT = \"%s.block_count\";\nstatic const char * LLM_KV_FEED_FORWARD_LENGTH = \"%s.feed_forward_length\";\nstatic const char * LLM_KV_ATTENTION_HEAD_COUNT = \"%s.attention.head_count\";\nstatic const char * LLM_KV_ATTENTION_HEAD_COUNT_KV = \"%s.attention.head_count_kv\";\nstatic const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = \"%s.attention.layer_norm_rms_epsilon\";\nstatic const char * LLM_KV_ROPE_DIMENSION_COUNT = \"%s.rope.dimension_count\";\nstatic const char * LLM_KV_ROPE_FREQ_BASE = \"%s.rope.freq_base\"; // TODO load in llama.cpp\nstatic const char * LLM_KV_ROPE_SCALE_LINEAR = \"%s.rope.scale_linear\";\n\nstatic const char * LLM_TENSOR_TOKEN_EMBD = \"token_embd\";\nstatic const char * LLM_TENSOR_OUTPUT_NORM = \"output_norm\";\nstatic const char * LLM_TENSOR_OUTPUT = \"output\";\nstatic const char * LLM_TENSOR_ATTN_NORM = \"blk.%d.attn_norm\";\nstatic const char * LLM_TENSOR_ATTN_Q = \"blk.%d.attn_q\";\nstatic const char * LLM_TENSOR_ATTN_K = \"blk.%d.attn_k\";\nstatic const char * LLM_TENSOR_ATTN_V = \"blk.%d.attn_v\";\nstatic const char * LLM_TENSOR_ATTN_OUT = \"blk.%d.attn_output\";\nstatic const char * LLM_TENSOR_FFN_NORM = \"blk.%d.ffn_norm\";\nstatic const char * LLM_TENSOR_FFN_GATE = \"blk.%d.ffn_gate\";\nstatic const char * LLM_TENSOR_FFN_DOWN = \"blk.%d.ffn_down\";\nstatic const char * LLM_TENSOR_FFN_UP = \"blk.%d.ffn_up\";\n\nstatic void print_params(struct my_llama_hparams * params) {\n printf(\"%s: n_vocab: %u\\n\", __func__, params->n_vocab);\n printf(\"%s: n_ctx: %u\\n\", __func__, params->n_ctx);\n printf(\"%s: n_embd: %u\\n\", __func__, params->n_embd);\n printf(\"%s: n_ff: %u\\n\", __func__, params->n_ff);\n printf(\"%s: n_head: %u\\n\", __func__, params->n_head);\n printf(\"%s: n_head_kv: %u\\n\", __func__, params->n_head_kv);\n printf(\"%s: n_layer: %u\\n\", __func__, params->n_layer);\n printf(\"%s: norm_rms_eps : %f\\n\", __func__, params->f_norm_rms_eps);\n printf(\"%s: rope_freq_base : %f\\n\", __func__, params->rope_freq_base);\n printf(\"%s: rope_freq_scale : %f\\n\", __func__, params->rope_freq_scale);\n}\n\nstatic void print_lora_params(struct my_llama_lora_hparams * params) {\n printf(\"%s: n_rank_attention_norm : %u\\n\", __func__, params->n_rank_attention_norm);\n printf(\"%s: n_rank_wq : %u\\n\", __func__, params->n_rank_wq);\n printf(\"%s: n_rank_wk : %u\\n\", __func__, params->n_rank_wk);\n printf(\"%s: n_rank_wv : %u\\n\", __func__, params->n_rank_wv);\n printf(\"%s: n_rank_wo : %u\\n\", __func__, params->n_rank_wo);\n printf(\"%s: n_rank_ffn_norm : %u\\n\", __func__, params->n_rank_ffn_norm);\n printf(\"%s: n_rank_w1 : %u\\n\", __func__, params->n_rank_w1);\n printf(\"%s: n_rank_w2 : %u\\n\", __func__, params->n_rank_w2);\n printf(\"%s: n_rank_w3 : %u\\n\", __func__, params->n_rank_w3);\n printf(\"%s: n_rank_tok_embeddings : %u\\n\", __func__, params->n_rank_tok_embeddings);\n printf(\"%s: n_rank_norm : %u\\n\", __func__, params->n_rank_norm);\n printf(\"%s: n_rank_output : %u\\n\", __func__, params->n_rank_output);\n}\n\n#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \\\n{ \\\n const std::string skey(key); \\\n const int kid = gguf_find_key(ctx, skey.c_str()); \\\n if (kid >= 0) { \\\n enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \\\n if (ktype != (type)) { \\\n die_fmt(\"key %s has wrong type: %s\", skey.c_str(), gguf_type_name(ktype)); \\\n } \\\n (dst) = func(ctx, kid); \\\n } else if (req) { \\\n die_fmt(\"key not found in model: %s\", skey.c_str()); \\\n } \\\n}\n\nstatic void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) {\n std::string arch;\n\n GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);\n if (expected_arch != NULL) {\n if (arch != expected_arch) {\n printf(\"%s: arch=%s expected_arch=%s\\n\", __func__, arch.c_str(), expected_arch);\n }\n GGML_ASSERT(arch == expected_arch);\n }\n\n std::vector keybuf;\n keybuf.resize(512);\n auto kv = [&arch, &keybuf](const char * key) -> const char * {\n snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());\n return keybuf.data();\n };\n\n GGUF_GET_KEY(ctx, hparams->n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));\n GGUF_GET_KEY(ctx, hparams->n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));\n GGUF_GET_KEY(ctx, hparams->n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));\n GGUF_GET_KEY(ctx, hparams->n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));\n GGUF_GET_KEY(ctx, hparams->n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));\n\n // n_head_kv is optional, default to n_head\n hparams->n_head_kv = hparams->n_head;\n GGUF_GET_KEY(ctx, hparams->n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));\n\n float rope_freq_scale = 1.0f;\n GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));\n GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));\n GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));\n if (rope_freq_scale != 1.0f) {\n hparams->rope_freq_scale = 1.0f / rope_freq_scale;\n }\n}\n\nstatic void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) {\n auto & hparams = model->hparams;\n\n std::vector tn_buf;\n tn_buf.resize(GGML_MAX_NAME);\n auto tn = [&tn_buf](const char * key) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), \"%s.weight\", key);\n return tn_buf.data();\n };\n auto tni = [&tn_buf](const char * key, int bid) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), key, bid);\n std::string s = tn_buf.data();\n snprintf(tn_buf.data(), tn_buf.size(), \"%s.weight\", s.c_str());\n return tn_buf.data();\n };\n\n\n // get parameters directly from gguf file\n {\n struct gguf_init_params params = {\n /*.no_alloc = */ false,\n /*.ctx = */ NULL,\n };\n struct gguf_context * mctx = gguf_init_from_file(fn_model, params);\n\n load_model_hparams_gguf(mctx, &hparams, \"llama\");\n\n gguf_free(mctx);\n }\n hparams.n_vocab = llama_n_vocab(input);\n hparams.n_ctx = n_ctx;\n\n // get tensors from llama_model (possibly mmapped)\n model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));\n model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));\n model->output = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));\n\n assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab);\n assert_shape_1d(model->norm, hparams.n_embd);\n assert_shape_2d(model->output, hparams.n_embd, hparams.n_vocab);\n\n model->layers.resize(hparams.n_layer);\n for (uint32_t i = 0; i < hparams.n_layer; ++i) {\n auto & layer = model->layers[i];\n\n layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));\n layer.wq = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i));\n layer.wk = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i));\n layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));\n layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));\n layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));\n layer.w1 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));\n layer.w2 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));\n layer.w3 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));\n\n assert_shape_1d(layer.attention_norm, hparams.n_embd);\n assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd);\n assert_shape_2d(layer.wk, hparams.n_embd, hparams.n_embd_gqa());\n assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd_gqa());\n assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd);\n assert_shape_1d(layer.ffn_norm, hparams.n_embd);\n assert_shape_2d(layer.w1, hparams.n_embd, hparams.n_ff);\n assert_shape_2d(layer.w2, hparams.n_ff, hparams.n_embd);\n assert_shape_2d(layer.w3, hparams.n_embd, hparams.n_ff);\n }\n}\n\nstatic void set_param_lora(struct my_llama_lora * lora) {\n const uint32_t n_layer = lora->layers.size();\n\n struct ggml_context* ctx = lora->ctx;\n\n ggml_set_param(ctx, lora->tok_embeddings_a);\n ggml_set_param(ctx, lora->tok_embeddings_b);\n ggml_set_param(ctx, lora->norm_a);\n ggml_set_param(ctx, lora->norm_b);\n ggml_set_param(ctx, lora->output_a);\n ggml_set_param(ctx, lora->output_b);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = lora->layers[i];\n\n ggml_set_param(ctx, layer.attention_norm_a);\n ggml_set_param(ctx, layer.attention_norm_b);\n ggml_set_param(ctx, layer.wq_a);\n ggml_set_param(ctx, layer.wq_b);\n ggml_set_param(ctx, layer.wk_a);\n ggml_set_param(ctx, layer.wk_b);\n ggml_set_param(ctx, layer.wv_a);\n ggml_set_param(ctx, layer.wv_b);\n ggml_set_param(ctx, layer.wo_a);\n ggml_set_param(ctx, layer.wo_b);\n ggml_set_param(ctx, layer.ffn_norm_a);\n ggml_set_param(ctx, layer.ffn_norm_b);\n ggml_set_param(ctx, layer.w1_a);\n ggml_set_param(ctx, layer.w1_b);\n ggml_set_param(ctx, layer.w2_a);\n ggml_set_param(ctx, layer.w2_b);\n ggml_set_param(ctx, layer.w3_a);\n ggml_set_param(ctx, layer.w3_b);\n }\n}\n\nstatic void alloc_lora(struct ggml_allocr * alloc, struct my_llama_lora * lora) {\n ggml_allocr_alloc(alloc, lora->tok_embeddings_a);\n ggml_allocr_alloc(alloc, lora->tok_embeddings_b);\n ggml_allocr_alloc(alloc, lora->norm_a);\n ggml_allocr_alloc(alloc, lora->norm_b);\n ggml_allocr_alloc(alloc, lora->output_a);\n ggml_allocr_alloc(alloc, lora->output_b);\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n ggml_allocr_alloc(alloc, layer.attention_norm_a);\n ggml_allocr_alloc(alloc, layer.attention_norm_b);\n ggml_allocr_alloc(alloc, layer.wq_a);\n ggml_allocr_alloc(alloc, layer.wq_b);\n ggml_allocr_alloc(alloc, layer.wk_a);\n ggml_allocr_alloc(alloc, layer.wk_b);\n ggml_allocr_alloc(alloc, layer.wv_a);\n ggml_allocr_alloc(alloc, layer.wv_b);\n ggml_allocr_alloc(alloc, layer.wo_a);\n ggml_allocr_alloc(alloc, layer.wo_b);\n ggml_allocr_alloc(alloc, layer.ffn_norm_a);\n ggml_allocr_alloc(alloc, layer.ffn_norm_b);\n ggml_allocr_alloc(alloc, layer.w1_a);\n ggml_allocr_alloc(alloc, layer.w1_b);\n ggml_allocr_alloc(alloc, layer.w2_a);\n ggml_allocr_alloc(alloc, layer.w2_b);\n ggml_allocr_alloc(alloc, layer.w3_a);\n ggml_allocr_alloc(alloc, layer.w3_b);\n }\n ggml_allocr_alloc(alloc, lora->tok_embeddings_a->grad);\n ggml_allocr_alloc(alloc, lora->tok_embeddings_b->grad);\n ggml_allocr_alloc(alloc, lora->norm_a->grad);\n ggml_allocr_alloc(alloc, lora->norm_b->grad);\n ggml_allocr_alloc(alloc, lora->output_a->grad);\n ggml_allocr_alloc(alloc, lora->output_b->grad);\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n ggml_allocr_alloc(alloc, layer.attention_norm_a->grad);\n ggml_allocr_alloc(alloc, layer.attention_norm_b->grad);\n ggml_allocr_alloc(alloc, layer.wq_a->grad);\n ggml_allocr_alloc(alloc, layer.wq_b->grad);\n ggml_allocr_alloc(alloc, layer.wk_a->grad);\n ggml_allocr_alloc(alloc, layer.wk_b->grad);\n ggml_allocr_alloc(alloc, layer.wv_a->grad);\n ggml_allocr_alloc(alloc, layer.wv_b->grad);\n ggml_allocr_alloc(alloc, layer.wo_a->grad);\n ggml_allocr_alloc(alloc, layer.wo_b->grad);\n ggml_allocr_alloc(alloc, layer.ffn_norm_a->grad);\n ggml_allocr_alloc(alloc, layer.ffn_norm_b->grad);\n ggml_allocr_alloc(alloc, layer.w1_a->grad);\n ggml_allocr_alloc(alloc, layer.w1_b->grad);\n ggml_allocr_alloc(alloc, layer.w2_a->grad);\n ggml_allocr_alloc(alloc, layer.w2_b->grad);\n ggml_allocr_alloc(alloc, layer.w3_a->grad);\n ggml_allocr_alloc(alloc, layer.w3_b->grad);\n }\n}\n\nstatic void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {\n const auto & lparams = lora->hparams;\n\n const uint32_t n_embd = model->hparams.n_embd;\n const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();\n const uint32_t n_layer = model->hparams.n_layer;\n const uint32_t n_vocab = model->hparams.n_vocab;\n const uint32_t n_ff = model->hparams.n_ff;\n\n std::vector tn_buf;\n tn_buf.resize(GGML_MAX_NAME);\n auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), \"%s%s\", key, suffix);\n return tn_buf.data();\n };\n auto tni = [&tn_buf](const char * key, const char * suffix, int bid) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), key, bid);\n std::string s = tn_buf.data();\n snprintf(tn_buf.data(), tn_buf.size(), \"%s%s\", s.c_str(), suffix);\n return tn_buf.data();\n };\n\n // context for lora tensors without their data\n struct ggml_init_params ctx_lora_params;\n ctx_lora_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18);\n ctx_lora_params.mem_buffer = NULL;\n ctx_lora_params.no_alloc = true;\n\n struct ggml_context * ctx = ggml_init(ctx_lora_params);\n lora->ctx = ctx;\n\n lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd);\n lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab);\n lora->norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd);\n lora->norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1);\n lora->output_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd);\n lora->output_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab);\n\n ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, \".weight.lora_a\"));\n ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, \".weight.lora_b\"));\n ggml_set_name(lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, \".weight.lora_a\"));\n ggml_set_name(lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, \".weight.lora_b\"));\n ggml_set_name(lora->output_a, tn(LLM_TENSOR_OUTPUT, \".weight.lora_a\"));\n ggml_set_name(lora->output_b, tn(LLM_TENSOR_OUTPUT, \".weight.lora_b\"));\n\n lora->layers.resize(n_layer);\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = lora->layers[i];\n\n layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd);\n layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1);\n\n layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);\n layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);\n layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);\n layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);\n layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);\n layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);\n layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);\n layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);\n\n layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);\n layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);\n\n layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd);\n layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff);\n layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff);\n layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd);\n layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd);\n layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff);\n\n ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, \".weight.lora_a\", i));\n ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, \".weight.lora_b\", i));\n ggml_set_name(layer.wq_a, tni(LLM_TENSOR_ATTN_Q, \".weight.lora_a\", i));\n ggml_set_name(layer.wq_b, tni(LLM_TENSOR_ATTN_Q, \".weight.lora_b\", i));\n ggml_set_name(layer.wk_a, tni(LLM_TENSOR_ATTN_K, \".weight.lora_a\", i));\n ggml_set_name(layer.wk_b, tni(LLM_TENSOR_ATTN_K, \".weight.lora_b\", i));\n ggml_set_name(layer.wv_a, tni(LLM_TENSOR_ATTN_V, \".weight.lora_a\", i));\n ggml_set_name(layer.wv_b, tni(LLM_TENSOR_ATTN_V, \".weight.lora_b\", i));\n ggml_set_name(layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, \".weight.lora_a\", i));\n ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, \".weight.lora_b\", i));\n ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, \".weight.lora_a\", i));\n ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, \".weight.lora_b\", i));\n ggml_set_name(layer.w1_a, tni(LLM_TENSOR_FFN_GATE, \".weight.lora_a\", i));\n ggml_set_name(layer.w1_b, tni(LLM_TENSOR_FFN_GATE, \".weight.lora_b\", i));\n ggml_set_name(layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, \".weight.lora_a\", i));\n ggml_set_name(layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, \".weight.lora_b\", i));\n ggml_set_name(layer.w3_a, tni(LLM_TENSOR_FFN_UP, \".weight.lora_a\", i));\n ggml_set_name(layer.w3_b, tni(LLM_TENSOR_FFN_UP, \".weight.lora_b\", i));\n }\n\n set_param_lora(lora);\n\n // measure data size\n size_t size = 0;\n for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {\n size += GGML_PAD(ggml_nbytes(t), tensor_alignment);\n }\n\n // allocate data\n struct ggml_allocr * alloc = NULL;\n lora->data.resize(size + tensor_alignment);\n alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment);\n alloc_lora(alloc, lora);\n ggml_allocr_free(alloc);\n}\n\nstatic void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {\n const uint32_t n_layer = lora->layers.size();\n\n struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);\n\n randomize_tensor_normal(lora->tok_embeddings_a, rnd);\n randomize_tensor_normal(lora->tok_embeddings_b, rnd);\n randomize_tensor_normal(lora->norm_a, rnd);\n randomize_tensor_normal(lora->norm_b, rnd);\n randomize_tensor_normal(lora->output_a, rnd);\n randomize_tensor_normal(lora->output_b, rnd);\n\n for (uint32_t i = 0; i < n_layer; ++i) {\n auto & layer = lora->layers[i];\n randomize_tensor_normal(layer.attention_norm_a, rnd);\n randomize_tensor_normal(layer.attention_norm_b, rnd);\n\n randomize_tensor_normal(layer.wq_a, rnd);\n randomize_tensor_normal(layer.wq_b, rnd);\n randomize_tensor_normal(layer.wk_a, rnd);\n randomize_tensor_normal(layer.wk_b, rnd);\n randomize_tensor_normal(layer.wv_a, rnd);\n randomize_tensor_normal(layer.wv_b, rnd);\n randomize_tensor_normal(layer.wo_a, rnd);\n randomize_tensor_normal(layer.wo_b, rnd);\n\n randomize_tensor_normal(layer.ffn_norm_a, rnd);\n randomize_tensor_normal(layer.ffn_norm_b, rnd);\n\n randomize_tensor_normal(layer.w1_a, rnd);\n randomize_tensor_normal(layer.w1_b, rnd);\n randomize_tensor_normal(layer.w2_a, rnd);\n randomize_tensor_normal(layer.w2_b, rnd);\n randomize_tensor_normal(layer.w3_a, rnd);\n randomize_tensor_normal(layer.w3_b, rnd);\n }\n\n free_random_normal_distribution(rnd);\n}\n\nstatic struct ggml_tensor * llama_build_lora_finetune_graphs(\n struct my_llama_model * model,\n struct my_llama_lora * lora,\n struct ggml_allocr * alloc,\n struct ggml_context * ctx,\n struct ggml_cgraph * gf,\n struct ggml_cgraph * gb,\n struct ggml_cgraph * gb_tmp,\n struct ggml_tensor * * logits,\n struct ggml_tensor * tokens_input,\n struct ggml_tensor * targets,\n const int n_tokens,\n const int n_batch,\n const bool enable_flash_attn,\n const bool enable_checkpointing) {\n\n ggml_set_scratch(ctx, { 0, 0, nullptr, });\n const int n_past = 0;\n const int N = n_tokens;\n const auto & hparams = model->hparams;\n const int n_ctx = hparams.n_ctx;\n const int n_vocab = hparams.n_vocab;\n const int n_embd = hparams.n_embd;\n const int n_layer = hparams.n_layer;\n const int n_head = hparams.n_head;\n const int n_head_kv = hparams.n_head_kv;\n const int n_ff = hparams.n_ff;\n const int n_rot = hparams.n_embd_head();\n const int n_embd_head = hparams.n_embd_head();\n const int n_embd_gqa = hparams.n_embd_gqa();\n const float rms_norm_eps = hparams.f_norm_rms_eps;\n const float rope_freq_base = hparams.rope_freq_base;\n const float rope_freq_scale = hparams.rope_freq_scale;\n\n GGML_ASSERT((size_t) n_layer == lora->layers.size());\n\n auto set_name = [](struct ggml_tensor * t, const char * n) {\n ggml_set_name(t, n);\n if (t->grad) {\n ggml_format_name(t->grad, \"%s->grad\", n);\n }\n };\n\n // KQ_pos - contains the positions\n struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);\n ggml_allocr_alloc(alloc, KQ_pos);\n if (!ggml_allocr_is_measure(alloc)) {\n int * data = (int *) KQ_pos->data;\n for (int i = 0; i < N; ++i) {\n data[i] = n_past + i;\n }\n }\n\n // rope has so much parameters that we make a custom function for it\n auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]\n (struct ggml_tensor * t) -> struct ggml_tensor * {\n // not capturing these, to silcence warnings\n const int rope_mode = 0;\n\n return ggml_rope_custom(ctx,\n t, KQ_pos, n_rot, rope_mode, n_ctx, 0,\n rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f\n );\n };\n\n set_name(tokens_input, \"tokens_input\");\n set_name(targets, \"targets\");\n\n GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);\n\n auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {\n if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) {\n return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);\n } else if (a->type == GGML_TYPE_F32) {\n return ggml_add(ctx, a, b);\n } else {\n die_fmt(\"%s: Finetuning on tensors with type '%s' is not yet supported.\\n\",\n __func__, ggml_type_name(a->type));\n }\n };\n\n struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b));\n struct ggml_tensor * norm = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b));\n struct ggml_tensor * output = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b));\n\n struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, \"t00\"); assert_shape_1d(t00, N*n_batch);\n struct ggml_tensor * t01 = ggml_get_rows(ctx, tok_embeddings, t00); set_name(t01, \"t01\"); assert_shape_2d(t01, n_embd, N*n_batch);\n\n struct ggml_tensor * cur = t01;\n\n std::vector checkpoints;\n if (enable_checkpointing) {\n checkpoints.push_back(tokens_input);\n checkpoints.push_back(targets);\n checkpoints.push_back(t00);\n checkpoints.push_back(t01);\n }\n\n struct ggml_tensor * kv_scale = NULL;\n if (!enable_flash_attn) {\n kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));\n }\n\n for (int il = 0; il < n_layer; ++il) {\n struct my_llama_layer & layer = model->layers[il];\n struct my_llama_lora_layer & llayer = lora->layers[il];\n\n struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));\n struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));\n struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));\n struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));\n struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));\n struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));\n struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b));\n struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));\n struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));\n\n struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, \"t02\"); assert_shape_2d(t02, n_embd, N*n_batch);\n struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, \"t03\"); assert_shape_2d(t03, n_embd, N*n_batch);\n struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, \"t04\"); assert_shape_2d(t04, n_embd, N*n_batch);\n struct ggml_tensor * t05 = ggml_mul_mat (ctx, wq, t04); set_name(t05, \"t05\"); assert_shape_2d(t05, n_embd, N*n_batch);\n struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd_head, n_head, N, n_batch); set_name(t06, \"t06\"); assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);\n struct ggml_tensor * t07 = rope (t06); set_name(t07, \"t07\"); assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);\n struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, \"t08\"); assert_shape_2d(t08, n_embd_gqa, N*n_batch);\n struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, \"t09\"); assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);\n struct ggml_tensor * t10 = rope (t09); set_name(t10, \"t10\"); assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);\n\n struct ggml_tensor * t11;\n if (ggml_is_quantized(wv->type)) {\n struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, \"t11_1\"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);\n struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, \"t11_2\"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);\n t11 = ggml_cont (ctx, t11_2); set_name(t11, \"t11\"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);\n } else {\n t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, \"t11\"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);\n }\n\n struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, \"t12\"); assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);\n struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, \"t13\"); assert_shape_4d(t13, n_embd_head, N, n_head, n_batch);\n struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, \"t14\"); assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch);\n struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, \"t15\"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);\n struct ggml_tensor * t16;\n if (enable_flash_attn) {\n t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, \"t16\"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);\n } else {\n struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, \"t16_0\"); assert_shape_4d(t16_0, N, N, n_head, n_batch);\n struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, \"t16_1\"); assert_shape_4d(t16_1, N, N, n_head, n_batch);\n struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, \"t16_2\"); assert_shape_4d(t16_2, N, N, n_head, n_batch);\n struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, \"t16_3\"); assert_shape_4d(t16_3, N, N, n_head, n_batch);\n t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, \"t16\"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);\n }\n struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, \"t17\"); assert_shape_4d(t17, n_embd_head, n_head, N, n_batch);\n struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, \"t18\"); assert_shape_4d(t18, n_embd_head, n_head, N, n_batch);\n struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, \"t19\"); assert_shape_2d(t19, n_embd, N*n_batch);\n struct ggml_tensor * t20 = ggml_mul_mat (ctx, wo, t19); set_name(t20, \"t20\"); assert_shape_2d(t20, n_embd, N*n_batch);\n struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, \"t21\"); assert_shape_2d(t21, n_embd, N*n_batch);\n struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, \"t22\"); assert_shape_2d(t22, n_embd, N*n_batch);\n struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, \"t23\"); assert_shape_2d(t23, n_embd, N*n_batch);\n struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, \"t24\"); assert_shape_2d(t24, n_embd, N*n_batch);\n struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, \"t25\"); assert_shape_2d(t25, n_ff, N*n_batch);\n struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, \"t26\"); assert_shape_2d(t26, n_ff, N*n_batch);\n struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, \"t27\"); assert_shape_2d(t27, n_ff, N*n_batch);\n struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, \"t28\"); assert_shape_2d(t28, n_ff, N*n_batch);\n struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, \"t29\"); assert_shape_2d(t29, n_embd, N*n_batch);\n struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, \"t30\"); assert_shape_2d(t30, n_embd, N*n_batch);\n cur = t30;\n if (enable_checkpointing) {\n checkpoints.push_back(cur);\n }\n }\n struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, \"t31\"); assert_shape_2d(t31, n_embd, N*n_batch);\n struct ggml_tensor * t32 = ggml_repeat (ctx, norm, t31); set_name(t32, \"t32\"); assert_shape_2d(t32, n_embd, N*n_batch);\n struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, \"t33\"); assert_shape_2d(t33, n_embd, N*n_batch);\n struct ggml_tensor * t34 = ggml_mul_mat (ctx, output, t33); set_name(t34, \"t34\"); assert_shape_2d(t34, n_vocab, N*n_batch);\n struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, \"t35\"); assert_shape_3d(t35, n_vocab, N, n_batch);\n struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, \"t36\"); assert_shape_1d(t36, 1);\n\n if (enable_checkpointing) {\n checkpoints.push_back(t31);\n checkpoints.push_back(t32);\n checkpoints.push_back(t33);\n checkpoints.push_back(t34);\n checkpoints.push_back(t35);\n checkpoints.push_back(t36);\n }\n\n ggml_build_forward_expand(gf, t36);\n\n if (enable_checkpointing) {\n ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());\n } else {\n ggml_graph_cpy(gf, gb);\n ggml_build_backward_expand(ctx, gf, gb, true);\n }\n\n GGML_ASSERT(alloc != NULL);\n\n // make sure some tensors are not reallocated by inserting new temporary nodes depending on them\n int n_leafs_before = gb->n_leafs;\n int n_nodes_before = gb->n_nodes;\n struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);\n // output tensors\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));\n // input gradient\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));\n GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);\n ggml_allocr_alloc(alloc, t36->grad);\n // KQ_pos\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));\n\n // make sure base model tensors data cannot be used in viewable operations\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, one));\n for (int il = 0; il < n_layer; ++il) {\n struct my_llama_layer & layer = model->layers[il];\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, one));\n ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, one));\n }\n\n // allocating checkpoints in one block to reduce memory fragmentation\n // note: they will be freed in reverse order\n for (unsigned int i = 0; i < checkpoints.size(); ++i) {\n if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {\n ggml_allocr_alloc(alloc, checkpoints[i]);\n }\n }\n\n ggml_allocr_alloc_graph(alloc, gb);\n\n // remove the additional nodes and leafs\n for (int i = n_leafs_before; i < gb->n_leafs; ++i) {\n gb->leafs[i] = NULL;\n }\n for (int i = n_nodes_before; i < gb->n_nodes; ++i) {\n gb->nodes[i] = NULL;\n }\n gb->n_leafs = n_leafs_before;\n gb->n_nodes = n_nodes_before;\n\n *logits = t35;\n return t36;\n}\n\nstatic void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {\n // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read\n\n std::string arch;\n\n std::vector keybuf;\n keybuf.resize(512);\n\n GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);\n GGML_ASSERT(arch == \"llama\");\n\n uint32_t ftype_u;\n GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);\n GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);\n\n struct my_llama_hparams hparams;\n load_model_hparams_gguf(fctx, &hparams, arch.c_str());\n\n // parameters that define tensor shapes must match\n GGML_ASSERT(hparams.n_embd == model->hparams.n_embd);\n GGML_ASSERT(hparams.n_ff == model->hparams.n_ff);\n GGML_ASSERT(hparams.n_head == model->hparams.n_head);\n GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv);\n GGML_ASSERT(hparams.n_layer == model->hparams.n_layer);\n\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_output, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);\n GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);\n\n init_lora(model, lora);\n\n copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a));\n copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b));\n copy_tensor_by_name(lora->norm_a, f_ggml_ctx, ggml_get_name(lora->norm_a));\n copy_tensor_by_name(lora->norm_b, f_ggml_ctx, ggml_get_name(lora->norm_b));\n copy_tensor_by_name(lora->output_a, f_ggml_ctx, ggml_get_name(lora->output_a));\n copy_tensor_by_name(lora->output_b, f_ggml_ctx, ggml_get_name(lora->output_b));\n\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));\n copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b));\n copy_tensor_by_name(layer.wq_a, f_ggml_ctx, ggml_get_name(layer.wq_a));\n copy_tensor_by_name(layer.wq_b, f_ggml_ctx, ggml_get_name(layer.wq_b));\n copy_tensor_by_name(layer.wk_a, f_ggml_ctx, ggml_get_name(layer.wk_a));\n copy_tensor_by_name(layer.wk_b, f_ggml_ctx, ggml_get_name(layer.wk_b));\n copy_tensor_by_name(layer.wv_a, f_ggml_ctx, ggml_get_name(layer.wv_a));\n copy_tensor_by_name(layer.wv_b, f_ggml_ctx, ggml_get_name(layer.wv_b));\n copy_tensor_by_name(layer.wo_a, f_ggml_ctx, ggml_get_name(layer.wo_a));\n copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b));\n copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));\n copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));\n copy_tensor_by_name(layer.w1_a, f_ggml_ctx, ggml_get_name(layer.w1_a));\n copy_tensor_by_name(layer.w1_b, f_ggml_ctx, ggml_get_name(layer.w1_b));\n copy_tensor_by_name(layer.w2_a, f_ggml_ctx, ggml_get_name(layer.w2_a));\n copy_tensor_by_name(layer.w2_b, f_ggml_ctx, ggml_get_name(layer.w2_b));\n copy_tensor_by_name(layer.w3_a, f_ggml_ctx, ggml_get_name(layer.w3_a));\n copy_tensor_by_name(layer.w3_b, f_ggml_ctx, ggml_get_name(layer.w3_b));\n }\n}\n\nstatic void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) {\n const char * arch = \"llama\";\n enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;\n\n std::vector keybuf;\n keybuf.resize(512);\n auto kv = [arch, &keybuf](const char * key) -> const char * {\n snprintf(keybuf.data(), keybuf.size(), key, arch);\n return keybuf.data();\n };\n\n gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);\n gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);\n\n gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx);\n gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd);\n gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff);\n gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head);\n gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV), model->hparams.n_head_kv);\n gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer);\n gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_embd_head());\n gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps);\n gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base);\n gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), model->hparams.rope_freq_scale);\n\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, lora->hparams.n_rank_tok_embeddings);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, lora->hparams.n_rank_norm);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT, lora->hparams.n_rank_output);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, lora->hparams.n_rank_attention_norm);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q, lora->hparams.n_rank_wq);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K, lora->hparams.n_rank_wk);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_w1);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_w2);\n gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_w3);\n\n gguf_add_tensor(fctx, lora->tok_embeddings_a);\n gguf_add_tensor(fctx, lora->tok_embeddings_b);\n gguf_add_tensor(fctx, lora->norm_a);\n gguf_add_tensor(fctx, lora->norm_b);\n gguf_add_tensor(fctx, lora->output_a);\n gguf_add_tensor(fctx, lora->output_b);\n\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n\n gguf_add_tensor(fctx, layer.attention_norm_a);\n gguf_add_tensor(fctx, layer.attention_norm_b);\n gguf_add_tensor(fctx, layer.wq_a);\n gguf_add_tensor(fctx, layer.wq_b);\n gguf_add_tensor(fctx, layer.wk_a);\n gguf_add_tensor(fctx, layer.wk_b);\n gguf_add_tensor(fctx, layer.wv_a);\n gguf_add_tensor(fctx, layer.wv_b);\n gguf_add_tensor(fctx, layer.wo_a);\n gguf_add_tensor(fctx, layer.wo_b);\n gguf_add_tensor(fctx, layer.ffn_norm_a);\n gguf_add_tensor(fctx, layer.ffn_norm_b);\n gguf_add_tensor(fctx, layer.w1_a);\n gguf_add_tensor(fctx, layer.w1_b);\n gguf_add_tensor(fctx, layer.w2_a);\n gguf_add_tensor(fctx, layer.w2_b);\n gguf_add_tensor(fctx, layer.w3_a);\n gguf_add_tensor(fctx, layer.w3_b);\n }\n}\n\nstatic void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {\n std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;\n GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);\n GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);\n\n load_train_state_gguf(fctx, f_ggml_ctx, train);\n load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);\n}\n\nstatic void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {\n gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);\n save_llama_lora_gguf(fctx, model, lora);\n save_train_state_gguf(fctx, train);\n}\n\nstatic bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {\n struct ggml_context * f_ggml_ctx;\n struct gguf_init_params params;\n params.no_alloc = false;\n params.ctx = &f_ggml_ctx;\n struct gguf_context * fctx = gguf_init_from_file(filename, params);\n if (fctx == NULL) {\n return false;\n }\n\n load_checkpoint_lora_gguf(fctx, f_ggml_ctx, model, lora, train);\n\n gguf_free(fctx);\n return true;\n}\n\nstatic void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {\n printf(\"%s: saving to %s\\n\", __func__, filename);\n struct gguf_context * fctx = gguf_init_empty();\n\n save_checkpoint_lora_gguf(fctx, model, lora, train);\n\n // write file\n const bool only_meta = false;\n gguf_write_to_file(fctx, filename, only_meta);\n gguf_free(fctx);\n}\n\nstruct llama_file {\n // use FILE * so we don't have to re-open the file to mmap\n FILE * fp;\n size_t size;\n\n llama_file(const char * fname, const char * mode) {\n fp = std::fopen(fname, mode);\n if (fp == NULL) {\n size = 0;\n } else {\n seek(0, SEEK_END);\n size = tell();\n seek(0, SEEK_SET);\n }\n }\n\n size_t tell() const {\n#ifdef _WIN32\n __int64 ret = _ftelli64(fp);\n#else\n long ret = std::ftell(fp);\n#endif\n GGML_ASSERT(ret != -1); // this really shouldn't fail\n return (size_t) ret;\n }\n\n void seek(size_t offset, int whence) {\n#ifdef _WIN32\n int ret = _fseeki64(fp, (__int64) offset, whence);\n#else\n int ret = std::fseek(fp, (long) offset, whence);\n#endif\n GGML_ASSERT(ret == 0); // same\n }\n\n void read_raw(void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n std::size_t ret = std::fread(ptr, size, 1, fp);\n if (ferror(fp)) {\n die_fmt(\"read error: %s\", strerror(errno));\n }\n if (ret != 1) {\n die(\"unexpectedly reached end of file\");\n }\n }\n\n std::uint32_t read_u32() {\n std::uint32_t ret;\n read_raw(&ret, sizeof(ret));\n return ret;\n }\n\n std::string read_string(std::uint32_t len) {\n std::vector chars(len);\n read_raw(chars.data(), len);\n return std::string(chars.data(), len);\n }\n\n void write_raw(const void * ptr, size_t size) {\n if (size == 0) {\n return;\n }\n errno = 0;\n size_t ret = std::fwrite(ptr, size, 1, fp);\n if (ret != 1) {\n die_fmt(\"write error: %s\", strerror(errno));\n }\n }\n\n void write_u32(std::uint32_t val) {\n write_raw(&val, sizeof(val));\n }\n\n ~llama_file() {\n if (fp) {\n std::fclose(fp);\n }\n }\n};\n\nstatic void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) {\n if (tensor == NULL) {\n file->write_u32(0);\n file->write_u32(0);\n file->write_u32(GGML_TYPE_F32);\n file->seek((0-file->tell()) & 31, SEEK_CUR);\n return;\n }\n if (name == NULL) {\n name = ggml_get_name(tensor);\n }\n uint32_t name_len = strlen(name);\n uint32_t nd = tensor->n_dims;\n uint32_t ne[4] = { (uint32_t)tensor->ne[0],\n (uint32_t)tensor->ne[1],\n (uint32_t)tensor->ne[2],\n (uint32_t)tensor->ne[3] };\n file->write_u32(nd);\n file->write_u32(name_len);\n file->write_u32(tensor->type);\n file->write_raw(ne, sizeof(ne[0]) * nd);\n file->write_raw(name, name_len);\n file->seek((0-file->tell()) & 31, SEEK_CUR);\n file->write_raw(tensor->data, ggml_nbytes(tensor));\n}\n\nstatic void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) {\n printf(\"%s: saving to %s\\n\", __func__, filename);\n struct llama_file file(filename, \"wb\");\n if (file.fp == NULL) {\n return;\n }\n\n std::vector tn_buf;\n tn_buf.resize(GGML_MAX_NAME);\n\n auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), \"%s%s\", key, suffix);\n return tn_buf.data();\n };\n\n auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> const char * {\n snprintf(tn_buf.data(), tn_buf.size(), key, bid);\n std::string s = tn_buf.data();\n snprintf(tn_buf.data(), tn_buf.size(), \"%s%s\", s.c_str(), suffix);\n return tn_buf.data();\n };\n\n uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'\n // write_magic\n file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic\n file.write_u32(1); // version\n // write_hparams\n file.write_u32(lora->hparams.lora_r);\n file.write_u32(lora->hparams.lora_alpha);\n // write tensors\n write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, \".weight.loraA\"));\n write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, \".weight.loraB\"));\n write_tensor(&file, lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, \".weight.loraA\"));\n write_tensor(&file, lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, \".weight.loraB\"));\n write_tensor(&file, lora->output_a, tn(LLM_TENSOR_OUTPUT, \".weight.loraA\"));\n write_tensor(&file, lora->output_b, tn(LLM_TENSOR_OUTPUT, \".weight.loraB\"));\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, \".weight.loraA\"));\n write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, \".weight.loraB\"));\n write_tensor(&file, layer.wq_a, tni(LLM_TENSOR_ATTN_Q, i, \".weight.loraA\"));\n write_tensor(&file, layer.wq_b, tni(LLM_TENSOR_ATTN_Q, i, \".weight.loraB\"));\n write_tensor(&file, layer.wk_a, tni(LLM_TENSOR_ATTN_K, i, \".weight.loraA\"));\n write_tensor(&file, layer.wk_b, tni(LLM_TENSOR_ATTN_K, i, \".weight.loraB\"));\n write_tensor(&file, layer.wv_a, tni(LLM_TENSOR_ATTN_V, i, \".weight.loraA\"));\n write_tensor(&file, layer.wv_b, tni(LLM_TENSOR_ATTN_V, i, \".weight.loraB\"));\n write_tensor(&file, layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, i, \".weight.loraA\"));\n write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, \".weight.loraB\"));\n write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, \".weight.loraA\"));\n write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, \".weight.loraB\"));\n write_tensor(&file, layer.w1_a, tni(LLM_TENSOR_FFN_GATE, i, \".weight.loraA\"));\n write_tensor(&file, layer.w1_b, tni(LLM_TENSOR_FFN_GATE, i, \".weight.loraB\"));\n write_tensor(&file, layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, i, \".weight.loraA\"));\n write_tensor(&file, layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, i, \".weight.loraB\"));\n write_tensor(&file, layer.w3_a, tni(LLM_TENSOR_FFN_UP, i, \".weight.loraA\"));\n write_tensor(&file, layer.w3_b, tni(LLM_TENSOR_FFN_UP, i, \".weight.loraB\"));\n }\n}\n\nstruct train_params {\n struct train_params_common common;\n\n const char * fn_model_base;\n const char * fn_lora_out;\n\n bool only_write_lora;\n\n float f_norm_rms_eps;\n float rope_freq_base;\n float rope_freq_scale;\n\n bool custom_f_norm_rms_eps;\n bool custom_rope_freq_base;\n bool custom_rope_freq_scale;\n\n int32_t lora_r;\n int32_t lora_alpha;\n bool custom_lora_alpha;\n\n uint32_t n_rank_attention_norm;\n uint32_t n_rank_wq;\n uint32_t n_rank_wk;\n uint32_t n_rank_wv;\n uint32_t n_rank_wo;\n uint32_t n_rank_ffn_norm;\n uint32_t n_rank_w1;\n uint32_t n_rank_w2;\n uint32_t n_rank_w3;\n uint32_t n_rank_tok_embeddings;\n uint32_t n_rank_norm;\n uint32_t n_rank_output;\n\n bool custom_n_rank_attention_norm;\n bool custom_n_rank_wq;\n bool custom_n_rank_wk;\n bool custom_n_rank_wv;\n bool custom_n_rank_wo;\n bool custom_n_rank_ffn_norm;\n bool custom_n_rank_w1;\n bool custom_n_rank_w2;\n bool custom_n_rank_w3;\n bool custom_n_rank_tok_embeddings;\n bool custom_n_rank_norm;\n bool custom_n_rank_output;\n};\n\nstatic struct train_params get_default_train_params() {\n struct train_params params;\n params.common = get_default_train_params_common();\n params.fn_model_base = \"\";\n params.fn_lora_out = \"ggml-lora-ITERATION-f32.gguf\";\n\n params.only_write_lora = false;\n\n params.f_norm_rms_eps = 1e-5f;\n params.rope_freq_base = 10000.0f;\n params.rope_freq_scale = 1.0f;\n\n params.custom_f_norm_rms_eps = false;\n params.custom_rope_freq_base = false;\n params.custom_rope_freq_scale = false;\n\n params.lora_r = 4;\n params.lora_alpha = 4;\n params.custom_lora_alpha = false;\n\n params.n_rank_attention_norm = 1;\n params.n_rank_wq = 4;\n params.n_rank_wk = 4;\n params.n_rank_wv = 4;\n params.n_rank_wo = 4;\n params.n_rank_ffn_norm = 1;\n params.n_rank_w1 = 4;\n params.n_rank_w2 = 4;\n params.n_rank_w3 = 4;\n params.n_rank_tok_embeddings = 4;\n params.n_rank_norm = 1;\n params.n_rank_output = 4;\n\n params.custom_n_rank_attention_norm = false;\n params.custom_n_rank_wq = false;\n params.custom_n_rank_wk = false;\n params.custom_n_rank_wv = false;\n params.custom_n_rank_wo = false;\n params.custom_n_rank_ffn_norm = false;\n params.custom_n_rank_w1 = false;\n params.custom_n_rank_w2 = false;\n params.custom_n_rank_w3 = false;\n params.custom_n_rank_tok_embeddings = false;\n params.custom_n_rank_norm = false;\n params.custom_n_rank_output = false;\n\n return params;\n}\n\nstatic void train_print_usage(int argc, char ** argv, const struct train_params * params) {\n fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"options:\\n\");\n fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n\n fprintf(stderr, \" --model-base FNAME model path from which to load base model (default '%s')\\n\", params->fn_model_base);\n fprintf(stderr, \" --lora-out FNAME path to save llama lora (default '%s')\\n\", params->fn_lora_out);\n fprintf(stderr, \" --only-write-lora only save llama lora, don't do any training. use this if you only want to convert a checkpoint to a lora adapter.\\n\");\n fprintf(stderr, \" --norm-rms-eps F RMS-Norm epsilon value (default %f)\\n\", params->f_norm_rms_eps);\n fprintf(stderr, \" --rope-freq-base F Frequency base for ROPE (default %f)\\n\", params->rope_freq_base);\n fprintf(stderr, \" --rope-freq-scale F Frequency scale for ROPE (default %f)\\n\", params->rope_freq_scale);\n fprintf(stderr, \" --lora-alpha N LORA alpha : resulting LORA scaling is alpha/r. (default %d)\\n\", params->lora_alpha);\n fprintf(stderr, \" --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\\n\", params->lora_r);\n fprintf(stderr, \" --rank-att-norm N LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\\n\");\n fprintf(stderr, \" --rank-ffn-norm N LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\\n\");\n fprintf(stderr, \" --rank-out-norm N LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\\n\");\n fprintf(stderr, \" --rank-tok-embd N LORA rank for token embeddings tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-out N LORA rank for output tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-wq N LORA rank for wq tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-wk N LORA rank for wk tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-wv N LORA rank for wv tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-wo N LORA rank for wo tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-w1 N LORA rank for w1 tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-w2 N LORA rank for w2 tensor, overrides default rank.\\n\");\n fprintf(stderr, \" --rank-w3 N LORA rank for w3 tensor, overrides default rank.\\n\");\n\n print_common_train_usage(argc, argv, ¶ms->common);\n}\n\nstatic bool train_params_parse(int argc, char ** argv, struct train_params * params) {\n bool invalid_param = false;\n std::string arg;\n struct train_params default_params = get_default_train_params();\n const std::string arg_prefix = \"--\";\n\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n\n if (consume_common_train_arg(argc, argv, &i, ¶ms->common, &invalid_param)) {\n if (invalid_param) {\n break;\n } else if (params->common.print_usage) {\n train_print_usage(argc, argv, &default_params);\n exit(0);\n }\n } else if (arg == \"--model-base\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_model_base = argv[i];\n } else if (arg == \"--lora-out\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->fn_lora_out = argv[i];\n } else if (arg == \"--only-write-lora\") {\n params->only_write_lora = true;\n } else if (arg == \"--norm-rms-eps\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->f_norm_rms_eps = std::stof(argv[i]);\n params->custom_f_norm_rms_eps = true;\n } else if (arg == \"--rope-freq-base\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->rope_freq_base = std::stof(argv[i]);\n params->custom_rope_freq_base = true;\n } else if (arg == \"--rope-freq-scale\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->rope_freq_scale = std::stof(argv[i]);\n params->custom_rope_freq_scale = true;\n } else if (arg == \"--lora-alpha\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->lora_alpha = std::stoi(argv[i]);\n params->custom_lora_alpha = true;\n } else if (arg == \"--lora-r\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->lora_r = std::stoi(argv[i]);\n } else if (arg == \"--rank-att-norm\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_attention_norm = std::stoi(argv[i]);\n params->custom_n_rank_attention_norm = true;\n } else if (arg == \"--rank-ffn-norm\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_ffn_norm = std::stoi(argv[i]);\n params->custom_n_rank_ffn_norm = true;\n } else if (arg == \"--rank-out-norm\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_norm = std::stoi(argv[i]);\n params->custom_n_rank_norm = true;\n } else if (arg == \"--rank-tok-embd\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_tok_embeddings = std::stoi(argv[i]);\n params->custom_n_rank_tok_embeddings = true;\n } else if (arg == \"--rank-out\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_output = std::stoi(argv[i]);\n params->custom_n_rank_output = true;\n } else if (arg == \"--rank-wq\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_wq = std::stoi(argv[i]);\n params->custom_n_rank_wq = true;\n } else if (arg == \"--rank-wk\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_wk = std::stoi(argv[i]);\n params->custom_n_rank_wk = true;\n } else if (arg == \"--rank-wv\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_wv = std::stoi(argv[i]);\n params->custom_n_rank_wv = true;\n } else if (arg == \"--rank-wo\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_wo = std::stoi(argv[i]);\n params->custom_n_rank_wo = true;\n } else if (arg == \"--rank-w1\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_w1 = std::stoi(argv[i]);\n params->custom_n_rank_w1 = true;\n } else if (arg == \"--rank-w2\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_w2 = std::stoi(argv[i]);\n params->custom_n_rank_w2 = true;\n } else if (arg == \"--rank-w3\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params->n_rank_w3 = std::stoi(argv[i]);\n params->custom_n_rank_w3 = true;\n } else if (arg == \"--gpu-layers\" || arg == \"-ngl\" || arg == \"--n-gpu-layers\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD\n params->common.n_gpu_layers = std::stoi(argv[i]);\n#else\n fprintf(stderr, \"warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\\n\");\n fprintf(stderr, \"warning: see main README.md for information on enabling GPU BLAS support\\n\");\n#endif\n } else {\n fprintf(stderr, \"error: unknown argument: %s\\n\", arg.c_str());\n train_print_usage(argc, argv, &default_params);\n exit(1);\n }\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: %s\\n\", arg.c_str());\n train_print_usage(argc, argv, &default_params);\n exit(1);\n }\n finish_processing_train_args(¶ms->common);\n return true;\n}\n\nstruct save_train_files_data {\n const char * fn_checkpoint_out;\n const char * fn_lora_out;\n const char * pattern_fn_it;\n const char * fn_latest;\n struct my_llama_model * model;\n struct my_llama_lora * lora;\n};\n\nstatic void save_train_files(void * vdata, struct train_state * train) {\n struct save_train_files_data * data = (struct save_train_files_data *) vdata;\n\n int64_t iter = train->opt->iter;\n\n if (strlen(data->fn_checkpoint_out) > 0) {\n save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train);\n save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->model, data->lora, train);\n }\n if (strlen(data->fn_lora_out) > 0) {\n save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora);\n save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->lora);\n }\n}\n\nstatic int64_t get_parameter_count(struct my_llama_lora* lora) {\n int64_t nx = 0;\n nx += ggml_nelements(lora->tok_embeddings_a);\n nx += ggml_nelements(lora->tok_embeddings_b);\n nx += ggml_nelements(lora->norm_a);\n nx += ggml_nelements(lora->norm_b);\n nx += ggml_nelements(lora->output_a);\n nx += ggml_nelements(lora->output_b);\n\n for (uint32_t i = 0; i < lora->layers.size(); ++i) {\n auto & layer = lora->layers[i];\n nx += ggml_nelements(layer.attention_norm_a);\n nx += ggml_nelements(layer.attention_norm_b);\n nx += ggml_nelements(layer.wq_a);\n nx += ggml_nelements(layer.wq_b);\n nx += ggml_nelements(layer.wk_a);\n nx += ggml_nelements(layer.wk_b);\n nx += ggml_nelements(layer.wv_a);\n nx += ggml_nelements(layer.wv_b);\n nx += ggml_nelements(layer.wo_a);\n nx += ggml_nelements(layer.wo_b);\n nx += ggml_nelements(layer.ffn_norm_a);\n nx += ggml_nelements(layer.ffn_norm_b);\n nx += ggml_nelements(layer.w1_a);\n nx += ggml_nelements(layer.w1_b);\n nx += ggml_nelements(layer.w2_a);\n nx += ggml_nelements(layer.w2_b);\n nx += ggml_nelements(layer.w3_a);\n nx += ggml_nelements(layer.w3_b);\n }\n return nx;\n}\n\nint main(int argc, char ** argv) {\n struct train_params params = get_default_train_params();\n\n if (!train_params_parse(argc, argv, ¶ms)) {\n return 1;\n }\n\n if (params.common.seed == LLAMA_DEFAULT_SEED) {\n params.common.seed = time(NULL);\n }\n printf(\"%s: seed: %u\\n\", __func__, params.common.seed);\n srand(params.common.seed);\n\n struct llama_model_params llama_mparams = llama_model_default_params();\n llama_mparams.n_gpu_layers = params.common.n_gpu_layers;\n llama_mparams.vocab_only = false;\n\n printf(\"%s: model base = '%s'\\n\", __func__, params.fn_model_base);\n struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams);\n\n struct llama_context_params llama_cparams = llama_context_default_params();\n struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams);\n\n struct my_llama_model model;\n init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx);\n\n struct my_llama_lora lora;\n\n struct train_state * train = init_train_state();\n struct ggml_opt_context * opt = train->opt;\n\n // set params from command line\n if (params.custom_f_norm_rms_eps) {\n model.hparams.f_norm_rms_eps = params.f_norm_rms_eps;\n }\n if (params.custom_rope_freq_base) {\n model.hparams.rope_freq_base = params.rope_freq_base;\n }\n if (params.custom_rope_freq_scale) {\n model.hparams.rope_freq_scale = params.rope_freq_scale;\n }\n lora.hparams.lora_r = params.lora_r;\n lora.hparams.lora_alpha = params.custom_lora_alpha ? params.lora_alpha : params.lora_r;\n uint32_t n_rank_attention_norm = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1;\n uint32_t n_rank_wq = params.custom_n_rank_wq ? params.n_rank_wq : params.lora_r;\n uint32_t n_rank_wk = params.custom_n_rank_wk ? params.n_rank_wk : params.lora_r;\n uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r;\n uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r;\n uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1;\n uint32_t n_rank_w1 = params.custom_n_rank_w1 ? params.n_rank_w1 : params.lora_r;\n uint32_t n_rank_w2 = params.custom_n_rank_w2 ? params.n_rank_w2 : params.lora_r;\n uint32_t n_rank_w3 = params.custom_n_rank_w3 ? params.n_rank_w3 : params.lora_r;\n uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;\n uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1;\n uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r;\n lora.hparams.n_rank_attention_norm = n_rank_attention_norm;\n lora.hparams.n_rank_wq = n_rank_wq;\n lora.hparams.n_rank_wk = n_rank_wk;\n lora.hparams.n_rank_wv = n_rank_wv;\n lora.hparams.n_rank_wo = n_rank_wo;\n lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm;\n lora.hparams.n_rank_w1 = n_rank_w1;\n lora.hparams.n_rank_w2 = n_rank_w2;\n lora.hparams.n_rank_w3 = n_rank_w3;\n lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;\n lora.hparams.n_rank_norm = n_rank_norm;\n lora.hparams.n_rank_output = n_rank_output;\n\n // set opt params from command line\n opt->params = ggml_opt_default_params(GGML_OPT_ADAM);\n opt->params.print_forward_graph = false;\n opt->params.print_backward_graph = false;\n opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;\n opt->params.n_threads = params.common.n_threads;\n opt->params.past = params.common.opt_past;\n opt->params.delta = params.common.opt_delta;\n opt->params.max_no_improvement = params.common.opt_max_no_improvement;\n opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;\n opt->params.adam.n_iter = params.common.adam_n_iter;\n opt->params.adam.sched = 1.0f;\n opt->params.adam.alpha = params.common.adam_alpha;\n opt->params.adam.decay = params.common.adam_decay;\n opt->params.adam.decay_min_ndim = params.common.adam_decay_min_ndim;\n opt->params.adam.beta1 = params.common.adam_beta1;\n opt->params.adam.beta2 = params.common.adam_beta2;\n opt->params.adam.gclip = params.common.adam_gclip;\n opt->params.adam.eps_f = params.common.adam_eps_f;\n\n ggml_allocr * alloc = NULL;\n\n printf(\"%s: init model\\n\", __func__);\n bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);\n\n if (existed) {\n // overwrite last n_ctx with user provided n_ctx\n if (params.common.custom_n_ctx) {\n model.hparams.n_ctx = params.common.n_ctx;\n }\n\n const bool opt_param_count_changed = (\n (lora.hparams.n_rank_attention_norm != n_rank_attention_norm)\n || (lora.hparams.n_rank_wq != n_rank_wq)\n || (lora.hparams.n_rank_wk != n_rank_wk)\n || (lora.hparams.n_rank_wv != n_rank_wv)\n || (lora.hparams.n_rank_wo != n_rank_wo)\n || (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm)\n || (lora.hparams.n_rank_w1 != n_rank_w1)\n || (lora.hparams.n_rank_w2 != n_rank_w2)\n || (lora.hparams.n_rank_w3 != n_rank_w3)\n || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)\n || (lora.hparams.n_rank_norm != n_rank_norm)\n || (lora.hparams.n_rank_output != n_rank_output)\n );\n\n const bool opt_past_changed = opt->params.past != params.common.opt_past;\n\n if (opt_param_count_changed) {\n print_lora_params(&lora.hparams);\n die(\"Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting.\");\n // need to discard previous optimizer gradient statistics and opt_init with new shapes\n // TODO\n }\n if (opt_past_changed) {\n die(\"Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting\");\n // need to discard previous optimizer past function value statistics and opt_init with new shapes\n // TODO\n }\n } else { // existed == false\n init_lora(&model, &lora);\n randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);\n if (!params.only_write_lora) {\n ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora));\n }\n }\n opt->iter = train->train_its;\n\n print_params(&model.hparams);\n print_lora_params(&lora.hparams);\n printf(\"%s: total train_iterations %llu\\n\", __func__, (long long unsigned) train->train_its);\n printf(\"%s: seen train_samples %llu\\n\", __func__, (long long unsigned) train->train_samples);\n printf(\"%s: seen train_tokens %llu\\n\", __func__, (long long unsigned) train->train_tokens);\n printf(\"%s: completed train_epochs %llu\\n\", __func__, (long long unsigned) train->train_epochs);\n printf(\"%s: lora_size = %zu bytes (%.1f MB)\\n\", __func__, (ggml_used_mem(lora.ctx) + lora.data.size()), (float) (ggml_used_mem(lora.ctx) + lora.data.size()) / (1024.0f*1024.0f));\n\n if (params.only_write_lora) {\n save_train_files_data save_data;\n save_data.fn_checkpoint_out = \"\";\n save_data.fn_lora_out = params.fn_lora_out;\n save_data.pattern_fn_it = params.common.pattern_fn_it;\n save_data.fn_latest = params.common.fn_latest;\n save_data.model = &model;\n save_data.lora = &lora;\n\n save_train_files(&save_data, train);\n\n free_train_state(train);\n ggml_free(lora.ctx);\n llama_free(lctx);\n llama_free_model(lmodel);\n return 0;\n }\n\n printf(\"%s: opt_size = %zu bytes (%.1f MB)\\n\", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));\n printf(\"%s: opt iter %d\\n\", __func__, opt->iter);\n\n int n_tokens = model.hparams.n_ctx;\n int n_vocab = model.hparams.n_vocab;\n int n_batch = params.common.n_batch;\n\n\n std::vector mem_input_data;\n std::vector mem_compute_data;\n\n // context for input tensors without their data\n struct ggml_init_params ctx_input_params = {\n ggml_tensor_overhead() * 2, // mem_size\n NULL, // mem_buffer\n true, // no_alloc\n };\n struct ggml_context * ctx_input = ggml_init(ctx_input_params);\n\n // the input tensors\n struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);\n struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);\n\n // measure required memory for input tensors\n size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +\n GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +\n tensor_alignment;\n printf(\"%s: input_size = %zu bytes (%.1f MB)\\n\", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));\n\n // allocate input tensors\n mem_input_data.resize(max_input_size);\n alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);\n ggml_allocr_alloc(alloc, tokens_input);\n ggml_allocr_alloc(alloc, target_probs);\n ggml_allocr_free(alloc);\n\n // context for compute tensors without their data\n const size_t estimated_compute_size_wo_data = (\n 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +\n (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))\n );\n struct ggml_init_params ctx_compute_params = {\n estimated_compute_size_wo_data, // mem_size\n NULL, // mem_buffer\n true, // no_alloc\n };\n struct ggml_context * ctx_compute = NULL;\n\n struct ggml_tensor * loss = NULL;\n struct ggml_tensor * logits = NULL;\n\n struct ggml_cgraph * gf = NULL;\n struct ggml_cgraph * gb = NULL;\n struct ggml_cgraph * gb_tmp = NULL;\n\n // measure required memory for compute tensors\n size_t best_compute_size = SIZE_MAX;\n enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;\n // find best evaluation order\n for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {\n ctx_compute = ggml_init(ctx_compute_params);\n alloc = ggml_allocr_new_measure(tensor_alignment);\n gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);\n gf->order = (enum ggml_cgraph_eval_order) order;\n gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);\n gb_tmp = params.common.use_checkpointing\n ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)\n : NULL;\n loss = llama_build_lora_finetune_graphs(\n &model, &lora, alloc, ctx_compute,\n gf, gb, gb_tmp,\n &logits, tokens_input, target_probs,\n n_tokens, n_batch,\n params.common.use_flash,\n params.common.use_checkpointing\n );\n size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;\n if (max_compute_size < best_compute_size) {\n best_compute_size = max_compute_size;\n best_order = gf->order;\n }\n ggml_allocr_free(alloc);\n ggml_free(ctx_compute);\n }\n size_t max_compute_size = best_compute_size;\n printf(\"%s: compute_size = %zu bytes (%.1f MB)\\n\", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));\n printf(\"%s: evaluation order = %s\\n\", __func__,\n (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? \"LEFT_TO_RIGHT\" :\n (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? \"RIGHT_TO_LEFT\" :\n \"invalid\");\n\n // allocate compute tensors\n mem_compute_data.resize(max_compute_size);\n ctx_compute = ggml_init(ctx_compute_params);\n alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);\n gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);\n gf->order = best_order;\n gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);\n gb_tmp = params.common.use_checkpointing\n ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)\n : NULL;\n loss = llama_build_lora_finetune_graphs(\n &model, &lora, alloc, ctx_compute,\n gf, gb, gb_tmp,\n &logits, tokens_input, target_probs,\n n_tokens, n_batch,\n params.common.use_flash,\n params.common.use_checkpointing\n );\n ggml_allocr_free(alloc);\n\n // tokenize data\n std::vector train_tokens;\n std::vector train_samples_begin;\n std::vector train_samples_size;\n printf(\"%s: tokenize training data\\n\", __func__);\n tokenize_file(lctx,\n params.common.fn_train_data,\n params.common.sample_start,\n params.common.include_sample_start,\n params.common.overlapping_samples,\n n_tokens,\n train_tokens,\n train_samples_begin,\n train_samples_size);\n GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());\n\n printf(\"%s: number of training tokens: %zu\\n\", __func__, train_tokens.size());\n\n std::vector token_noccurs;\n token_noccurs.resize(model.hparams.n_vocab, 0);\n for (unsigned int i = 0; i < train_tokens.size(); ++i) {\n ++token_noccurs[train_tokens[i]];\n }\n int n_unique_tokens = 0;\n for (unsigned int i = 0; i < token_noccurs.size(); ++i) {\n if (token_noccurs[i] == 0) continue;\n ++n_unique_tokens;\n }\n printf(\"%s: number of unique tokens: %d\\n\", __func__, n_unique_tokens);\n\n size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());\n const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());\n if (changed_train_data) {\n printf(\"%s: train data seems to have changed. restarting shuffled epoch.\\n\", __func__);\n }\n if (params.common.force_reshuffle) {\n printf(\"%s: forced reshuffling of data. restarting with newly shuffled epoch.\\n\", __func__);\n }\n if ((train->shuffle_rng_state_current == \"\") || changed_train_data || params.common.force_reshuffle) {\n train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);\n train->shuffle_sample_count = train_samples_size.size();\n train->shuffle_next_sample = 0;\n train->shuffle_samples_hash = shuffle_samples_hash;\n }\n std::vector train_shuffled_samples_offs;\n std::vector train_shuffled_samples_begin;\n std::vector train_shuffled_samples_size;\n train_shuffled_samples_offs.resize(train_samples_begin.size());\n train_shuffled_samples_begin.resize(train_samples_begin.size());\n train_shuffled_samples_size.resize(train_samples_size.size());\n train->shuffle_rng_state_next = shuffle_samples(\n train->shuffle_rng_state_current,\n train_shuffled_samples_offs.data(),\n train_shuffled_samples_begin.data(),\n train_shuffled_samples_size.data(),\n train_samples_begin.data(),\n train_samples_size.data(),\n train_samples_size.size());\n\n printf(\"%s: begin training\\n\", __func__);\n\n save_train_files_data save_data;\n save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;\n save_data.fn_lora_out = params.fn_lora_out;\n save_data.pattern_fn_it = params.common.pattern_fn_it;\n save_data.fn_latest = params.common.fn_latest;\n save_data.model = &model;\n save_data.lora = &lora;\n\n struct train_opt_callback_data opt_cb_data;\n opt_cb_data.params = ¶ms.common;\n opt_cb_data.train = train;\n opt_cb_data.save_cb = &save_train_files;\n opt_cb_data.save_data = &save_data;\n opt_cb_data.lctx = lctx;\n opt_cb_data.last_save_iter = opt->iter;\n opt_cb_data.tokens_data = train_tokens.data();\n opt_cb_data.tokens_size = train_tokens.size();\n opt_cb_data.samples_begin = train_samples_begin.data();\n opt_cb_data.samples_size = train_samples_size.data();\n opt_cb_data.shuffled_samples_offs = train_shuffled_samples_offs.data();\n opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();\n opt_cb_data.shuffled_samples_size = train_shuffled_samples_size.data();\n opt_cb_data.samples_count = train_samples_size.size();\n opt_cb_data.tokens_input = tokens_input;\n opt_cb_data.target_probs = target_probs;\n opt_cb_data.first_iter = opt->iter;\n opt_cb_data.first_epoch = train->train_epochs;\n opt_cb_data.iter_at_last_epoch = -1;\n opt_cb_data.last_time = ggml_time_ms();\n opt_cb_data.millis_per_iter = 0.0;\n\n // measure required memory for work buffer\n size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;\n printf(\"%s: work_size = %zu bytes (%.1f MB)\\n\", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));\n\n // context for work buffer\n struct ggml_init_params ctx_work_params = {\n max_work_size, // mem_size\n NULL, // mem_buffer\n false, // no_alloc\n };\n struct ggml_context * ctx_work = ggml_init(ctx_work_params);\n\n int64_t t0 = ggml_time_ms();\n\n ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);\n\n ggml_free(ctx_work);\n ggml_free(ctx_compute);\n ggml_free(ctx_input);\n\n int64_t t1 = ggml_time_ms();\n printf(\"%s: total training time: \", __func__);\n print_duration((double) (t1 - t0));\n printf(\"\\n\");\n\n int new_iters = opt->iter - opt_cb_data.last_save_iter;\n if (new_iters > 0) {\n train->train_its += new_iters;\n train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;\n\n save_train_files(&save_data, train);\n opt_cb_data.last_save_iter = opt->iter;\n }\n\n ggml_free(opt->ctx);\n free_train_state(train);\n ggml_free(lora.ctx);\n llama_free(lctx);\n llama_free_model(lmodel);\n return 0;\n}\n" }, { "path": "examples/finetune/finetune.sh", "content": "#!/bin/bash\ncd `dirname $0`\ncd ../..\n\nEXE=\"./finetune\"\n\nif [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR=\"./models\"; fi\nif [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR=\".\"; fi\n\n# MODEL=\"$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf\" # This is the model the readme uses.\nMODEL=\"$LLAMA_MODEL_DIR/openllama-3b-v2.gguf\" # An f16 model. Note in this case with \"-g\", you get an f32-format .BIN file that isn't yet supported if you use it with \"main --lora\" with GPU inferencing.\n\nwhile getopts \"dg\" opt; do\n case $opt in\n d)\n DEBUGGER=\"gdb --args\"\n ;;\n g)\n EXE=\"./build/bin/Release/finetune\"\n GPUARG=\"--gpu-layers 25\"\n ;;\n esac\ndone\n\n$DEBUGGER $EXE \\\n --model-base $MODEL \\\n $GPUARG \\\n --checkpoint-in chk-ol3b-shakespeare-LATEST.gguf \\\n --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \\\n --lora-out lora-ol3b-shakespeare-ITERATION.bin \\\n --train-data \"$LLAMA_TRAINING_DIR\\shakespeare.txt\" \\\n --save-every 10 \\\n --threads 10 --adam-iter 30 --batch 4 --ctx 64 \\\n --use-checkpointing\n" }, { "path": "examples/gguf/CMakeLists.txt", "content": "set(TARGET gguf)\nadd_executable(${TARGET} gguf.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/gguf/gguf.cpp", "content": "#include \"ggml.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#undef MIN\n#undef MAX\n#define MIN(a, b) ((a) < (b) ? (a) : (b))\n#define MAX(a, b) ((a) > (b) ? (a) : (b))\n\ntemplate \nstatic std::string to_string(const T & val) {\n std::stringstream ss;\n ss << val;\n return ss.str();\n}\n\nstatic bool gguf_ex_write(const std::string & fname) {\n struct gguf_context * ctx = gguf_init_empty();\n\n gguf_set_val_u8 (ctx, \"some.parameter.uint8\", 0x12);\n gguf_set_val_i8 (ctx, \"some.parameter.int8\", -0x13);\n gguf_set_val_u16 (ctx, \"some.parameter.uint16\", 0x1234);\n gguf_set_val_i16 (ctx, \"some.parameter.int16\", -0x1235);\n gguf_set_val_u32 (ctx, \"some.parameter.uint32\", 0x12345678);\n gguf_set_val_i32 (ctx, \"some.parameter.int32\", -0x12345679);\n gguf_set_val_f32 (ctx, \"some.parameter.float32\", 0.123456789f);\n gguf_set_val_u64 (ctx, \"some.parameter.uint64\", 0x123456789abcdef0ull);\n gguf_set_val_i64 (ctx, \"some.parameter.int64\", -0x123456789abcdef1ll);\n gguf_set_val_f64 (ctx, \"some.parameter.float64\", 0.1234567890123456789);\n gguf_set_val_bool(ctx, \"some.parameter.bool\", true);\n gguf_set_val_str (ctx, \"some.parameter.string\", \"hello world\");\n\n gguf_set_arr_data(ctx, \"some.parameter.arr.i16\", GGUF_TYPE_INT16, std::vector{ 1, 2, 3, 4, }.data(), 4);\n gguf_set_arr_data(ctx, \"some.parameter.arr.f32\", GGUF_TYPE_FLOAT32, std::vector{ 3.145f, 2.718f, 1.414f, }.data(), 3);\n gguf_set_arr_str (ctx, \"some.parameter.arr.str\", std::vector{ \"hello\", \"world\", \"!\" }.data(), 3);\n\n struct ggml_init_params params = {\n /*.mem_size =*/ 128ull*1024ull*1024ull,\n /*.mem_buffer =*/ NULL,\n /*.no_alloc =*/ false,\n };\n\n struct ggml_context * ctx_data = ggml_init(params);\n\n const int n_tensors = 10;\n\n // tensor infos\n for (int i = 0; i < n_tensors; ++i) {\n const std::string name = \"tensor_\" + to_string(i);\n\n int64_t ne[GGML_MAX_DIMS] = { 1 };\n int32_t n_dims = rand() % GGML_MAX_DIMS + 1;\n\n for (int j = 0; j < n_dims; ++j) {\n ne[j] = rand() % 10 + 1;\n }\n\n struct ggml_tensor * cur = ggml_new_tensor(ctx_data, GGML_TYPE_F32, n_dims, ne);\n ggml_set_name(cur, name.c_str());\n\n {\n float * data = (float *) cur->data;\n for (int j = 0; j < ggml_nelements(cur); ++j) {\n data[j] = 100 + i;\n }\n }\n\n gguf_add_tensor(ctx, cur);\n }\n\n gguf_write_to_file(ctx, fname.c_str(), false);\n\n printf(\"%s: wrote file '%s;\\n\", __func__, fname.c_str());\n\n ggml_free(ctx_data);\n gguf_free(ctx);\n\n return true;\n}\n\n// just read tensor info\nstatic bool gguf_ex_read_0(const std::string & fname) {\n struct gguf_init_params params = {\n /*.no_alloc = */ false,\n /*.ctx = */ NULL,\n };\n\n struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);\n\n printf(\"%s: version: %d\\n\", __func__, gguf_get_version(ctx));\n printf(\"%s: alignment: %zu\\n\", __func__, gguf_get_alignment(ctx));\n printf(\"%s: data offset: %zu\\n\", __func__, gguf_get_data_offset(ctx));\n\n // kv\n {\n const int n_kv = gguf_get_n_kv(ctx);\n\n printf(\"%s: n_kv: %d\\n\", __func__, n_kv);\n\n for (int i = 0; i < n_kv; ++i) {\n const char * key = gguf_get_key(ctx, i);\n\n printf(\"%s: kv[%d]: key = %s\\n\", __func__, i, key);\n }\n }\n\n // find kv string\n {\n const char * findkey = \"some.parameter.string\";\n\n const int keyidx = gguf_find_key(ctx, findkey);\n if (keyidx == -1) {\n printf(\"%s: find key: %s not found.\\n\", __func__, findkey);\n } else {\n const char * key_value = gguf_get_val_str(ctx, keyidx);\n printf(\"%s: find key: %s found, kv[%d] value = %s\\n\", __func__, findkey, keyidx, key_value);\n }\n }\n\n // tensor info\n {\n const int n_tensors = gguf_get_n_tensors(ctx);\n\n printf(\"%s: n_tensors: %d\\n\", __func__, n_tensors);\n\n for (int i = 0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name (ctx, i);\n const size_t offset = gguf_get_tensor_offset(ctx, i);\n\n printf(\"%s: tensor[%d]: name = %s, offset = %zu\\n\", __func__, i, name, offset);\n }\n }\n\n gguf_free(ctx);\n\n return true;\n}\n\n// read and create ggml_context containing the tensors and their data\nstatic bool gguf_ex_read_1(const std::string & fname) {\n struct ggml_context * ctx_data = NULL;\n\n struct gguf_init_params params = {\n /*.no_alloc = */ false,\n /*.ctx = */ &ctx_data,\n };\n\n struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);\n\n printf(\"%s: version: %d\\n\", __func__, gguf_get_version(ctx));\n printf(\"%s: alignment: %zu\\n\", __func__, gguf_get_alignment(ctx));\n printf(\"%s: data offset: %zu\\n\", __func__, gguf_get_data_offset(ctx));\n\n // kv\n {\n const int n_kv = gguf_get_n_kv(ctx);\n\n printf(\"%s: n_kv: %d\\n\", __func__, n_kv);\n\n for (int i = 0; i < n_kv; ++i) {\n const char * key = gguf_get_key(ctx, i);\n\n printf(\"%s: kv[%d]: key = %s\\n\", __func__, i, key);\n }\n }\n\n // tensor info\n {\n const int n_tensors = gguf_get_n_tensors(ctx);\n\n printf(\"%s: n_tensors: %d\\n\", __func__, n_tensors);\n\n for (int i = 0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name (ctx, i);\n const size_t offset = gguf_get_tensor_offset(ctx, i);\n\n printf(\"%s: tensor[%d]: name = %s, offset = %zu\\n\", __func__, i, name, offset);\n }\n }\n\n // data\n {\n const int n_tensors = gguf_get_n_tensors(ctx);\n\n for (int i = 0; i < n_tensors; ++i) {\n printf(\"%s: reading tensor %d data\\n\", __func__, i);\n\n const char * name = gguf_get_tensor_name(ctx, i);\n\n struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);\n\n printf(\"%s: tensor[%d]: n_dims = %d, name = %s, data = %p\\n\", __func__, i, cur->n_dims, cur->name, cur->data);\n\n // print first 10 elements\n const float * data = (const float *) cur->data;\n\n printf(\"%s data[:10] : \", name);\n for (int j = 0; j < MIN(10, ggml_nelements(cur)); ++j) {\n printf(\"%f \", data[j]);\n }\n printf(\"\\n\\n\");\n\n // check data\n {\n const float * data = (const float *) cur->data;\n for (int j = 0; j < ggml_nelements(cur); ++j) {\n if (data[j] != 100 + i) {\n fprintf(stderr, \"%s: tensor[%d]: data[%d] = %f\\n\", __func__, i, j, data[j]);\n return false;\n }\n }\n }\n }\n }\n\n printf(\"%s: ctx_data size: %zu\\n\", __func__, ggml_get_mem_size(ctx_data));\n\n ggml_free(ctx_data);\n gguf_free(ctx);\n\n return true;\n}\n\nint main(int argc, char ** argv) {\n if (argc < 3) {\n printf(\"usage: %s data.gguf r|w\\n\", argv[0]);\n return -1;\n }\n\n const std::string fname(argv[1]);\n const std::string mode (argv[2]);\n\n GGML_ASSERT((mode == \"r\" || mode == \"w\") && \"mode must be r or w\");\n\n if (mode == \"w\") {\n GGML_ASSERT(gguf_ex_write(fname) && \"failed to write gguf file\");\n } else if (mode == \"r\") {\n GGML_ASSERT(gguf_ex_read_0(fname) && \"failed to read gguf file\");\n GGML_ASSERT(gguf_ex_read_1(fname) && \"failed to read gguf file\");\n }\n\n return 0;\n}\n" }, { "path": "examples/gpt4all.sh", "content": "#!/bin/bash\n\n#\n# Temporary script - will be removed in the future\n#\n\ncd `dirname $0`\ncd ..\n\n./main --color --instruct --threads 4 \\\n --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \\\n --file ./prompts/alpaca.txt \\\n --batch_size 8 --ctx_size 2048 -n -1 \\\n --repeat_last_n 64 --repeat_penalty 1.3 \\\n --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95\n" }, { "path": "examples/infill/CMakeLists.txt", "content": "set(TARGET infill)\nadd_executable(${TARGET} infill.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/infill/README.md", "content": "# llama.cpp/example/infill\n\nThis example shows how to use the infill mode with Code Llama models supporting infill mode.\nCurrently the 7B and 13B models support infill mode.\n\nInfill supports most of the options available in the main example.\n\nFor further information have a look at the main README.md in llama.cpp/example/main/README.md\n\n## Common Options\n\nIn this section, we cover the most commonly used options for running the `infill` program with the LLaMA models:\n\n- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).\n- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.\n- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.\n- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.\n\n## Input Prompts\n\nThe `infill` program provides several ways to interact with the LLaMA models using input prompts:\n\n- `--in-prefix PROMPT_BEFORE_CURSOR`: Provide the prefix directly as a command-line option.\n- `--in-suffix PROMPT_AFTER_CURSOR`: Provide the suffix directly as a command-line option.\n- `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)\n\n## Interaction\n\nThe `infill` program offers a seamless way to interact with LLaMA models, allowing users to receive real-time infill suggestions. The interactive mode can be triggered using `--interactive`, and `--interactive-first`\n\n### Interaction Options\n\n- `-i, --interactive`: Run the program in interactive mode, allowing users to get real time code suggestions from model.\n- `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.\n- `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.\n\n### Example\n\n```bash\n./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix \"def helloworld():\\n print(\\\"hell\" --in-suffix \"\\n print(\\\"goodbye world\\\")\\n \"\n```\n" }, { "path": "examples/infill/infill.cpp", "content": "#include \"common.h\"\n\n#include \"console.h\"\n#include \"llama.h\"\n#include \"grammar-parser.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))\n#include \n#include \n#elif defined (_WIN32)\n#define WIN32_LEAN_AND_MEAN\n#ifndef NOMINMAX\n#define NOMINMAX\n#endif\n#include \n#include \n#endif\n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstatic llama_context ** g_ctx;\nstatic llama_model ** g_model;\nstatic gpt_params * g_params;\nstatic std::vector * g_input_tokens;\nstatic std::ostringstream * g_output_ss;\nstatic std::vector * g_output_tokens;\n\nstatic bool is_interacting = false;\n\nstatic void write_logfile(\n const llama_context * ctx, const gpt_params & params, const llama_model * model,\n const std::vector & input_tokens, const std::string & output,\n const std::vector & output_tokens\n) {\n if (params.logdir.empty()) {\n return;\n }\n\n const std::string timestamp = get_sortable_timestamp();\n\n const bool success = create_directory_with_parents(params.logdir);\n if (!success) {\n fprintf(stderr, \"%s: warning: failed to create logdir %s, cannot write logfile\\n\",\n __func__, params.logdir.c_str());\n return;\n }\n\n const std::string logfile_path = params.logdir + timestamp + \".yml\";\n FILE * logfile = fopen(logfile_path.c_str(), \"w\");\n\n if (logfile == NULL) {\n fprintf(stderr, \"%s: failed to open logfile %s\\n\", __func__, logfile_path.c_str());\n return;\n }\n\n fprintf(logfile, \"binary: infill\\n\");\n char model_desc[128];\n llama_model_desc(model, model_desc, sizeof(model_desc));\n dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);\n\n fprintf(logfile, \"\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"# Generation Results #\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"\\n\");\n\n dump_string_yaml_multiline(logfile, \"output\", output.c_str());\n dump_vector_int_yaml(logfile, \"output_tokens\", output_tokens);\n\n llama_dump_timing_info_yaml(logfile, ctx);\n fclose(logfile);\n}\n\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)\nstatic void sigint_handler(int signo) {\n if (signo == SIGINT) {\n if (!is_interacting) {\n is_interacting = true;\n } else {\n console::cleanup();\n printf(\"\\n\");\n llama_print_timings(*g_ctx);\n write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);\n _exit(130);\n }\n }\n}\n#endif\n\nint main(int argc, char ** argv) {\n gpt_params params;\n llama_sampling_params & sparams = params.sparams;\n g_params = ¶ms;\n\n if (!gpt_params_parse(argc, argv, params)) {\n return 1;\n }\n\n#ifndef LOG_DISABLE_LOGS\n log_set_target(log_filename_generator(\"infill\", \"log\"));\n LOG_TEE(\"Log start\\n\");\n log_dump_cmdline(argc, argv);\n#endif // LOG_DISABLE_LOGS\n\n console::init(params.simple_io, params.use_color);\n atexit([]() { console::cleanup(); });\n\n if (params.logits_all) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'perplexity' tool for perplexity calculations\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n\n if (params.embedding) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'embedding' tool for embedding calculations\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n\n if (params.n_ctx != 0 && params.n_ctx < 8) {\n LOG_TEE(\"%s: warning: minimum context size is 8, using minimum size.\\n\", __func__);\n params.n_ctx = 8;\n }\n if (params.instruct) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'main' tool for instruct mode\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n if (!params.antiprompt.empty()) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'main' tool for antiprompt mode\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n if (params.random_prompt) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'main' tool for random prompt mode\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n if (!params.path_prompt_cache.empty()) {\n printf(\"\\n************\\n\");\n printf(\"%s: infill does not support prompt caching\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n\n if (params.rope_freq_base != 0.0) {\n LOG_TEE(\"%s: warning: changing RoPE frequency base to %g.\\n\", __func__, params.rope_freq_base);\n }\n\n if (params.rope_freq_scale != 0.0) {\n LOG_TEE(\"%s: warning: scaling RoPE frequency by %g.\\n\", __func__, params.rope_freq_scale);\n }\n\n LOG_TEE(\"%s: build = %d (%s)\\n\", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);\n LOG_TEE(\"%s: built with %s for %s\\n\", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);\n\n if (params.seed == LLAMA_DEFAULT_SEED) {\n params.seed = time(NULL);\n }\n\n LOG_TEE(\"%s: seed = %u\\n\", __func__, params.seed);\n\n std::mt19937 rng(params.seed);\n\n LOG(\"%s: llama backend init\\n\", __func__);\n llama_backend_init(params.numa);\n\n llama_model * model;\n llama_context * ctx;\n llama_context * ctx_guidance = NULL;\n g_model = &model;\n g_ctx = &ctx;\n\n // load the model and apply lora adapter, if any\n LOG(\"%s: load the model and apply lora adapter, if any\\n\", __func__);\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n if (sparams.cfg_scale > 1.f) {\n struct llama_context_params lparams = llama_context_params_from_gpt_params(params);\n ctx_guidance = llama_new_context_with_model(model, lparams);\n }\n\n if (model == NULL) {\n LOG_TEE(\"%s: error: unable to load model\\n\", __func__);\n return 1;\n }\n\n const int n_ctx_train = llama_n_ctx_train(model);\n const int n_ctx = llama_n_ctx(ctx);\n LOG(\"n_ctx: %d\\n\", n_ctx);\n\n if (n_ctx > n_ctx_train) {\n LOG_TEE(\"%s: warning: model was trained on only %d context tokens (%d specified)\\n\",\n __func__, n_ctx_train, n_ctx);\n }\n\n // print system information\n {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s\\n\", get_system_info(params).c_str());\n }\n const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM;\n LOG(\"add_bos: %d\\n\", add_bos);\n\n bool suff_rm_leading_spc = params.escape;\n if (suff_rm_leading_spc && params.input_suffix.find_first_of(\" \") == 0 && params.input_suffix.size() > 1) {\n params.input_suffix.erase(0, 1);\n suff_rm_leading_spc = false;\n }\n std::vector embd_inp;\n std::vector inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);\n std::vector inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);\n const int space_token = 29871;\n if (suff_rm_leading_spc && inp_sfx[0] == space_token) {\n inp_sfx.erase(inp_sfx.begin());\n }\n inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));\n if (add_bos) {\n inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));\n }\n inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));\n embd_inp = inp_pfx;\n embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());\n embd_inp.push_back(llama_token_middle(model));\n\n LOG(\"prefix: \\\"%s\\\"\\n\", log_tostr(params.input_prefix));\n LOG(\"suffix: \\\"%s\\\"\\n\", log_tostr(params.input_suffix));\n LOG(\"tokens: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());\n\n // Should not run without any tokens\n if (embd_inp.empty()) {\n embd_inp.push_back(llama_token_bos(model));\n LOG(\"embd_inp was considered empty and bos was added: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());\n }\n\n // Tokenize negative prompt\n std::vector guidance_inp;\n int guidance_offset = 0;\n int original_prompt_len = 0;\n if (ctx_guidance) {\n LOG(\"cfg_negative_prompt: \\\"%s\\\"\\n\", log_tostr(sparams.cfg_negative_prompt));\n\n guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);\n LOG(\"guidance_inp tokenized: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());\n\n std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);\n LOG(\"original_inp tokenized: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());\n\n original_prompt_len = original_inp.size();\n guidance_offset = (int)guidance_inp.size() - original_prompt_len;\n LOG(\"original_prompt_len: %s\", log_tostr(original_prompt_len));\n LOG(\"guidance_offset: %s\", log_tostr(guidance_offset));\n }\n\n if ((int) embd_inp.size() > n_ctx - 4) {\n LOG_TEE(\"%s: error: prompt is too long (%d tokens, max %d)\\n\", __func__, (int) embd_inp.size(), n_ctx - 4);\n return 1;\n }\n\n // number of tokens to keep when resetting context\n if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {\n params.n_keep = (int)embd_inp.size();\n }\n\n LOG(\"inp_pfx: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());\n LOG(\"inp_sfx: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());\n\n\n // enable interactive mode if interactive start is specified\n if (params.interactive_first) {\n params.interactive = true;\n }\n\n if (params.verbose_prompt) {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s: prompt: '%s'\\n\", __func__, params.prompt.c_str());\n LOG_TEE(\"%s: number of tokens in prompt = %zu\\n\", __func__, embd_inp.size());\n for (int i = 0; i < (int) embd_inp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());\n }\n\n if (ctx_guidance) {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s: negative prompt: '%s'\\n\", __func__, sparams.cfg_negative_prompt.c_str());\n LOG_TEE(\"%s: number of tokens in negative prompt = %zu\\n\", __func__, guidance_inp.size());\n for (int i = 0; i < (int) guidance_inp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());\n }\n }\n\n if (params.n_keep > 0) {\n LOG_TEE(\"%s: static prompt based on n_keep: '\", __func__);\n for (int i = 0; i < params.n_keep; i++) {\n LOG_TEE(\"%s\", llama_token_to_piece(ctx, embd_inp[i]).c_str());\n }\n LOG_TEE(\"'\\n\");\n }\n LOG_TEE(\"\\n\");\n }\n\n if (params.interactive) {\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))\n struct sigaction sigint_action;\n sigint_action.sa_handler = sigint_handler;\n sigemptyset (&sigint_action.sa_mask);\n sigint_action.sa_flags = 0;\n sigaction(SIGINT, &sigint_action, NULL);\n#elif defined (_WIN32)\n auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {\n return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;\n };\n SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true);\n#endif\n\n LOG_TEE(\"%s: interactive mode on.\\n\", __func__);\n\n if (params.input_prefix_bos) {\n LOG_TEE(\"Input prefix with BOS\\n\");\n }\n\n if (!params.input_prefix.empty()) {\n LOG_TEE(\"Input prefix: '%s'\\n\", params.input_prefix.c_str());\n }\n\n if (!params.input_suffix.empty()) {\n LOG_TEE(\"Input suffix: '%s'\\n\", params.input_suffix.c_str());\n }\n }\n LOG_TEE(\"sampling: \\n%s\\n\", llama_sampling_print(sparams).c_str());\n LOG_TEE(\"generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\\n\", n_ctx, params.n_batch, params.n_predict, params.n_keep);\n LOG_TEE(\"\\n\\n\");\n\n LOG_TEE(\"\\n##### Infill mode #####\\n\\n\");\n if (params.infill) {\n printf(\"\\n************\\n\");\n printf(\"no need to specify '--infill', always running infill\\n\");\n printf(\"************\\n\\n\");\n }\n if (params.interactive) {\n const char *control_message;\n if (params.multiline_input) {\n control_message = \" - To return control to LLaMa, end your input with '\\\\'.\\n\"\n \" - To return control without starting a new line, end your input with '/'.\\n\";\n } else {\n control_message = \" - Press Return to return control to LLaMa.\\n\"\n \" - To return control without starting a new line, end your input with '/'.\\n\"\n \" - If you want to submit another line, end your input with '\\\\'.\\n\";\n }\n LOG_TEE(\"== Running in interactive mode. ==\\n\");\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)\n LOG_TEE( \" - Press Ctrl+C to interject at any time.\\n\");\n#endif\n LOG_TEE( \"%s\\n\", control_message);\n\n is_interacting = params.interactive_first;\n }\n\n bool input_echo = true;\n\n int n_past = 0;\n int n_remain = params.n_predict;\n int n_consumed = 0;\n int n_past_guidance = 0;\n\n std::vector input_tokens; g_input_tokens = &input_tokens;\n std::vector output_tokens; g_output_tokens = &output_tokens;\n std::ostringstream output_ss; g_output_ss = &output_ss;\n\n // the first thing we will do is to output the prompt, so set color accordingly\n console::set_display(console::prompt);\n\n std::vector embd;\n std::vector embd_guidance;\n\n struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);\n\n while (n_remain != 0 || params.interactive) {\n // predict\n if (!embd.empty()) {\n // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via\n // --prompt or --file which uses the same value.\n int max_embd_size = n_ctx - 4;\n\n // Ensure the input doesn't exceed the context size by truncating embd if necessary.\n if ((int) embd.size() > max_embd_size) {\n const int skipped_tokens = (int) embd.size() - max_embd_size;\n embd.resize(max_embd_size);\n\n console::set_display(console::error);\n printf(\"<>\", skipped_tokens, skipped_tokens != 1 ? \"s\" : \"\");\n console::set_display(console::reset);\n fflush(stdout);\n }\n\n // infinite text generation via context swapping\n // if we run out of context:\n // - take the n_keep first tokens from the original prompt (via n_past)\n // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches\n if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) {\n if (params.n_predict == -2) {\n LOG_TEE(\"\\n\\n%s: context full and n_predict == -%d => stopping\\n\", __func__, params.n_predict);\n break;\n }\n\n const int n_left = n_past - params.n_keep - 1;\n const int n_discard = n_left/2;\n\n LOG(\"context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\\n\",\n n_past, n_left, n_ctx, params.n_keep, n_discard);\n\n llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);\n llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);\n\n n_past -= n_discard;\n\n if (ctx_guidance) {\n n_past_guidance -= n_discard;\n }\n\n LOG(\"after swap: n_past = %d, n_past_guidance = %d\\n\", n_past, n_past_guidance);\n\n LOG(\"embd: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());\n\n }\n\n // evaluate tokens in batches\n // embd is typically prepared beforehand to fit within a batch, but not always\n\n if (ctx_guidance) {\n int input_size = 0;\n llama_token * input_buf = NULL;\n\n if (n_past_guidance < (int) guidance_inp.size()) {\n // Guidance context should have the same data with these modifications:\n //\n // * Replace the initial prompt\n // * Shift everything by guidance_offset\n embd_guidance = guidance_inp;\n if (embd.begin() + original_prompt_len < embd.end()) {\n embd_guidance.insert(\n embd_guidance.end(),\n embd.begin() + original_prompt_len,\n embd.end()\n );\n }\n\n input_buf = embd_guidance.data();\n input_size = embd_guidance.size();\n\n LOG(\"guidance context: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());\n } else {\n input_buf = embd.data();\n input_size = embd.size();\n }\n\n for (int i = 0; i < input_size; i += params.n_batch) {\n int n_eval = std::min(input_size - i, params.n_batch);\n if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {\n LOG_TEE(\"%s : failed to eval\\n\", __func__);\n return 1;\n }\n\n n_past_guidance += n_eval;\n }\n }\n\n for (int i = 0; i < (int) embd.size(); i += params.n_batch) {\n int n_eval = (int) embd.size() - i;\n if (n_eval > params.n_batch) {\n n_eval = params.n_batch;\n }\n\n LOG(\"eval: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());\n\n if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {\n LOG_TEE(\"%s : failed to eval\\n\", __func__);\n return 1;\n }\n\n n_past += n_eval;\n\n LOG(\"n_past = %d\\n\", n_past);\n }\n\n }\n\n embd.clear();\n embd_guidance.clear();\n\n if ((int) embd_inp.size() <= n_consumed && !is_interacting) {\n\n const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);\n\n llama_sampling_accept(ctx_sampling, ctx, id, true);\n\n LOG(\"last: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());\n\n embd.push_back(id);\n\n // echo this to console\n input_echo = true;\n\n // decrement remaining sampling budget\n --n_remain;\n\n LOG(\"n_remain: %d\\n\", n_remain);\n } else {\n // some user input remains from prompt or interaction, forward it to processing\n LOG(\"embd_inp.size(): %d, n_consumed: %d\\n\", (int) embd_inp.size(), n_consumed);\n while ((int) embd_inp.size() > n_consumed) {\n embd.push_back(embd_inp[n_consumed]);\n\n // push the prompt in the sampling context in order to apply repetition penalties later\n // for the prompt, we don't apply grammar rules\n llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);\n\n ++n_consumed;\n if ((int) embd.size() >= params.n_batch) {\n break;\n }\n }\n }\n\n // display text\n if (input_echo) {\n for (auto id : embd) {\n const std::string token_str = llama_token_to_piece(ctx, id);\n printf(\"%s\", token_str.c_str());\n\n if (embd.size() > 1) {\n input_tokens.push_back(id);\n } else {\n output_tokens.push_back(id);\n output_ss << token_str;\n }\n }\n fflush(stdout);\n }\n // reset color to default if we there is no pending user input\n if (input_echo && (int) embd_inp.size() == n_consumed) {\n console::set_display(console::reset);\n }\n\n // if not currently processing queued inputs;\n if ((int) embd_inp.size() <= n_consumed) {\n\n // deal with eot token in infill mode\n if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){\n if(is_interacting && !params.interactive_first) {\n // print an eot token\n printf(\"%s\", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());\n }\n fflush(stdout);\n printf(\"\\n\");\n console::set_display(console::user_input);\n std::string buffer;\n std::string line;\n bool another_line=true;\n // set a new prefix via stdin\n do {\n another_line = console::readline(line, params.multiline_input);\n buffer += line;\n } while (another_line);\n // check if we got an empty line, if so we use the old input\n if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\\n')) {\n params.input_prefix = buffer;\n }\n buffer.clear();\n // set a new suffix via stdin\n do {\n another_line = console::readline(line, params.multiline_input);\n buffer += line;\n } while (another_line);\n // check if we got an empty line\n if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\\n')) {\n params.input_suffix = buffer;\n }\n buffer.clear();\n // done taking input, reset color\n console::set_display(console::reset);\n\n if (params.escape) {\n //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here\n process_escapes(params.input_prefix);\n process_escapes(params.input_suffix);\n }\n suff_rm_leading_spc = params.escape;\n if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {\n params.input_suffix.erase(0, 1);\n suff_rm_leading_spc = false;\n }\n // tokenize new prefix and suffix\n std::vector inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);\n std::vector inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);\n if (suff_rm_leading_spc && inp_sfx[0] == space_token) {\n inp_sfx.erase(inp_sfx.begin());\n }\n inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));\n if (add_bos) {\n inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));\n }\n inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));\n embd_inp = inp_pfx;\n embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());\n embd_inp.push_back(llama_token_middle(model));\n embd.clear();\n embd_guidance.clear();\n n_remain = params.n_predict;\n n_past = 0;\n n_consumed = 0;\n // LOG_TEE(\"took new input\\n\");\n is_interacting = false;\n }\n // deal with end of text token in interactive mode\n else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {\n LOG(\"found EOS token\\n\");\n\n if (params.interactive) {\n\n is_interacting = true;\n printf(\"\\n\");\n console::set_display(console::user_input);\n fflush(stdout);\n }\n }\n\n if (n_past > 0 && is_interacting && !params.interactive) {\n LOG(\"waiting for user input\\n\");\n\n if (params.input_prefix_bos) {\n LOG(\"adding input prefix BOS token\\n\");\n embd_inp.push_back(llama_token_bos(model));\n }\n\n std::string buffer;\n if (!params.input_prefix.empty()) {\n LOG(\"appending input prefix: '%s'\\n\", params.input_prefix.c_str());\n buffer += params.input_prefix;\n printf(\"%s\", buffer.c_str());\n }\n\n std::string line;\n bool another_line = true;\n do {\n another_line = console::readline(line, params.multiline_input);\n buffer += line;\n } while (another_line);\n\n // done taking input, reset color\n console::set_display(console::reset);\n\n // Add tokens to embd only if the input buffer is non-empty\n // Entering a empty line lets the user pass control back\n if (buffer.length() > 1) {\n // append input suffix if any\n if (!params.input_suffix.empty()) {\n LOG(\"appending input suffix: '%s'\\n\", params.input_suffix.c_str());\n buffer += params.input_suffix;\n printf(\"%s\", params.input_suffix.c_str());\n }\n\n LOG(\"buffer: '%s'\\n\", buffer.c_str());\n\n const size_t original_size = embd_inp.size();\n\n const auto line_inp = ::llama_tokenize(ctx, buffer, false);\n LOG(\"input tokens: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());\n\n embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());\n\n for (size_t i = original_size; i < embd_inp.size(); ++i) {\n const llama_token token = embd_inp[i];\n output_tokens.push_back(token);\n output_ss << llama_token_to_piece(ctx, token);\n }\n\n n_remain -= line_inp.size();\n LOG(\"n_remain: %d\\n\", n_remain);\n } else {\n LOG(\"empty line, passing control back\\n\");\n }\n\n input_echo = false; // do not echo this again\n }\n\n if (n_past > 0) {\n if (is_interacting) {\n llama_sampling_reset(ctx_sampling);\n }\n is_interacting = false;\n }\n }\n\n // end of text token\n if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {\n break;\n }\n\n // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.\n // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).\n if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {\n n_remain = params.n_predict;\n is_interacting = true;\n }\n }\n if (!params.interactive && n_remain <= 0) {\n printf(\"%s\", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());\n fflush(stdout);\n }\n\n llama_print_timings(ctx);\n write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);\n\n if (ctx_guidance) { llama_free(ctx_guidance); }\n llama_free(ctx);\n llama_free_model(model);\n\n llama_sampling_free(ctx_sampling);\n llama_backend_free();\n\n#ifndef LOG_DISABLE_LOGS\n LOG_TEE(\"Log end\\n\");\n#endif // LOG_DISABLE_LOGS\n\n return 0;\n}\n\n" }, { "path": "examples/jeopardy/README.md", "content": "# llama.cpp/example/jeopardy\n\nThis is pretty much just a straight port of aigoopy/llm-jeopardy/ with an added graph viewer.\n\nThe jeopardy test can be used to compare the fact knowledge of different models and compare them to each other. This is in contrast to some other tests, which test logical deduction, creativity, writing skills, etc.\n\n\nStep 1: Open jeopardy.sh and modify the following:\n```\nMODEL=(path to your model)\nMODEL_NAME=(name of your model)\nprefix=(basically, if you use vicuna it's Human: , if you use something else it might be User: , etc)\nopts=(add -instruct here if needed for your model, or anything else you want to test out)\n```\nStep 2: Run `jeopardy.sh` from the llama.cpp folder\n\nStep 3: Repeat steps 1 and 2 until you have all the results you need.\n\nStep 4: Run `graph.py`, and follow the instructions. At the end, it will generate your final graph.\n\nNote: The Human bar is based off of the full, original 100 sample questions. If you modify the question count or questions, it will not be valid.\n" }, { "path": "examples/jeopardy/graph.py", "content": "#!/usr/bin/env python3\nimport matplotlib.pyplot as plt\nimport os\nimport csv\n\nlabels = []\nnumbers = []\nnumEntries = 1\n\nrows = []\n\n\ndef bar_chart(numbers, labels, pos):\n plt.bar(pos, numbers, color='blue')\n plt.xticks(ticks=pos, labels=labels)\n plt.title(\"Jeopardy Results by Model\")\n plt.xlabel(\"Model\")\n plt.ylabel(\"Questions Correct\")\n plt.show()\n\n\ndef calculatecorrect():\n directory = os.fsencode(\"./examples/jeopardy/results/\")\n csv_reader = csv.reader(open(\"./examples/jeopardy/qasheet.csv\", 'rt'), delimiter=',')\n for row in csv_reader:\n global rows\n rows.append(row)\n for listing in os.listdir(directory):\n filename = os.fsdecode(listing)\n if filename.endswith(\".txt\"):\n file = open(\"./examples/jeopardy/results/\" + filename, \"rt\")\n global labels\n global numEntries\n global numbers\n labels.append(filename[:-4])\n numEntries += 1\n i = 1\n totalcorrect = 0\n for line in file.readlines():\n if line.strip() != \"------\":\n print(line)\n else:\n print(\"Correct answer: \" + rows[i][2] + \"\\n\")\n i += 1\n print(\"Did the AI get the question right? (y/n)\")\n if input() == \"y\":\n totalcorrect += 1\n numbers.append(totalcorrect)\n\n\nif __name__ == '__main__':\n calculatecorrect()\n pos = list(range(numEntries))\n labels.append(\"Human\")\n numbers.append(48.11)\n bar_chart(numbers, labels, pos)\n print(labels)\n print(numbers)\n" }, { "path": "examples/jeopardy/jeopardy.sh", "content": "#!/bin/bash\nset -e\n\nMODEL=./models/ggml-vicuna-13b-1.1-q4_0.bin\nMODEL_NAME=Vicuna\n\n# exec options\nprefix=\"Human: \" # Ex. Vicuna uses \"Human: \"\nopts=\"--temp 0 -n 80\" # additional flags\nnl='\n'\nintroduction=\"You will be playing a game of Jeopardy. Simply answer the question in the correct format (Ex. What is Paris, or Who is George Washington).\"\n\n# file options\nquestion_file=./examples/jeopardy/questions.txt\ntouch ./examples/jeopardy/results/$MODEL_NAME.txt\noutput_file=./examples/jeopardy/results/$MODEL_NAME.txt\n\ncounter=1\n\necho 'Running'\nwhile IFS= read -r question\ndo\n exe_cmd=\"./main -p \"\\\"$prefix$introduction$nl$prefix$question\\\"\" \"$opts\" -m \"\"\\\"$MODEL\\\"\"\" >> \"\"\\\"$output_file\\\"\"\n echo $counter\n echo \"Current Question: $question\"\n eval \"$exe_cmd\"\n echo -e \"\\n------\" >> $output_file\n counter=$((counter+1))\ndone < \"$question_file\"\n" }, { "path": "examples/jeopardy/qasheet.csv", "content": "Index,Original Category,Original Correct Question,Model Prompt\n1,The Oscars,Who is John Williams?,Which actor Born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?\n2,English Literature,What is Paradise Lost?,\"What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?\"\n3,Writers’ Lesser-Known Works,Who is Niccolò Machiavelli?,\"Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?\"\n4,Exploration,What is Easter Island (Rapa Nui)?,\"James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?\"\n5,The Bill of Rights,What is the Eighth Amendment?,England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?\n6,Nobel Peace Prize Winners,Who are Nelson Mandela & Desmond Tutu?,\"Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?\"\n7,Famous Names,Who is Walt Disney?,\"In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?\"\n8,Geography,What is Colombia?,\"Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?\"\n9,Fashion History,What are rhinestones?,\"Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?\"\n10,Movies of the ’80s,What is Driving Miss Daisy?,What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?\n11,Novelists,Who is John Grisham?,\"A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?\"\n12,20th Century Eponyms,What is the Maginot Line?,\"A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?\"\n13,City History,What is Stockholm?,\"Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?\"\n14,Brand Names,What is Jacuzzi?,\"The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?\"\n15,American Authors,Who is Washington Irving?,\"In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?\"\n16,Symbols,What is “less than”?,What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?\n17,Movie Theme Songs,Who is James Bond?,\"Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?\"\n18,American Novelists,Who is Joseph Heller?,\"What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?\"\n19,Medieval Places,\"What is Canterbury, England? (Canterbury Cathedral)\",\"In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?\"\n20,Countries of Africa,What is Morocco?,\"At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?\"\n21,Statehood,What is Wyoming?,Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?\n22,1980s Movies,What is Raiders of the Lost Ark?,\"A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?\"\n23,Art Exhibitions,Who is Rembrandt?,In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?\n24,Countries of the World,What is Mongolia?,\"Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?\"\n25,Literature,What is “Howl”?,A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?\n26,Invasions,Who is William of Orange?,\"Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?\"\n27,Landmarks,What is the Eiffel Tower?,\"After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?\"\n28,Geographic Name’s the Same,What is Dover?,\"The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?\"\n29,Names in the Bookstore,Who is Peter Mark Roget?,\"This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?\"\n30,U.S. History,Who is Dr. Samuel Mudd?,\"An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?\"\n31,American Literature,What is The Things They Carried?,\"Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?\"\n32,Nonfiction,What is The Communist Manifesto,\"What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?\"\n33, a new version was passed 81 years later,Laws in U.S. History,What is the Civil Rights Act?,,,,,,,,,,,,,,,,,,0, 2/3\n34,Names of Myth,Who is Helen of Troy?,\"Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?\"\n35,African Countries,What is Sudan?,\"Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?\"\n36,The Ancient World,What is Alexandria?,\"The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?\"\n37,Famous Names,Who is Andy Warhol?,\"For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?\"\n38,People & Places,What is Guam?,\"Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?\"\n39,Current World Leaders,What is the Philippines?,\"In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?\"\n40,Writers & The South,Who is Tennessee Williams?,In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?\n41,National Parks,What is Yellowstone?,\"What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?\"\n42,Sports,Who are the Harlem Globetrotters?,\"In 2010 who introduced the 4-point shot, 35 feet from the basket?\"\n43,The U.S. Military,What is “Top Gun”?,Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?\n44,Art & Science,What is Halley’s Comet?,\"A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?\"\n45,Words From World War I,What is “tank”?,\"In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?\"\n46,European History,What is Holy Roman Emperor?,\"Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?\"\n47,Theater History,Who is Peter Pan?,\"In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?\"\n48,European Cities,What is Aachen?,\"Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?\"\n49,Word Origins,What is mantra?,This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?\n50,Inventions,What is barbed wire?,1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?\n51,World War II,What is Schindler’s list?,\"Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?\"\n52, their offspring was the source of this mythical object,Mythology,What is the Golden Fleece?\n53,Literature,What is Pride and Prejudice?,\"Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?\"\n54, only these 2 west of the Mississippi River border each other,U.S. State Names,What are Oregon & Nevada?\n55,Word Origins,What is passion?,\"Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?\"\n56,World Cinema,What is La Vie en Rose?,\"The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?\"\n57,History,What is Santa Maria?,\"Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?\"\n58,Landmarks,What is a kremlin?,Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?\n59,Foreign-Born Authors,Who is Vladimir Nabokov?,In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?\n60,Astronomy & Geography,What is Capricorn?,\"At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?\"\n61,Television,What is Law & Order?,\"Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?\"\n62,British Landmarks,What is the Tower of London?,\"Like Sir Thomas More, 3 16th century English queens are buried at what British location?\"\n63,Early American History,What are witches?,\"In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person … be condemned'?\"\n64,Geography Mnemonics,What are Arkansas and Louisiana?,\"The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?\"\n65,Business Milestones,What is the Ford Model T?,\"What was first sold in 1908, at a price equivalent to about $27,000 today?\"\n66,In The Bookstore,Who is Tom Clancy?,The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?\n67,Historic Art,What is the Bayeux Tapestry?,The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?\n68,Pop Stars,Who is Madonna?,In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?\n69,Classic Tale Characters,Who is Scheherazade?,\"In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?\"\n70,USA,What is Jack Daniel’s?,\"Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?\"\n71,Historic People,Who was William Bligh?,\"After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?\"\n72,The Movies,What is The Godfather?,Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?\n73,Continental Geography,What is Colombia?,\"Until a 1903 secession, what country's contiguous territory spanned 2 continents?\"\n74,Foreign-Born Authors,Who is Isabel Allende?,\"Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?\"\n75,Historic Crimes,What is the Mona Lisa?,\"Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?\"\n76,U.S. Bodies of Water,What is Lake Mead?,\"Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?\"\n77,Gods & Goddesses,Who is Aurora (or Eos)?,\"Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?\"\n78,America At War,What is the Battle of New Orleans?,\"Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?\"\n79,Children’s Books,What is The Velveteen Rabbit?,\"Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?\"\n80,TV Finales,What is Grace and Frankie?,\"In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?\"\n81,American Poems,Who is Evangeline?,\"In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?\"\n82,Famous Names,Who is Banksy?,\"In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?\"\n83,Children’s Lit,What is Charlotte’s Web?,The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?\n84,Classic Songs,What is “Here Comes Santa Claus”?,The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?\n85,Brand Names,What are Milk Duds?,\"Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?\"\n86,Countries of the World,What is Italy?,\"What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?\"\n87,Action Movies,What is Die Hard?,\"What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?\"\n88,Presidential Facts,Who is Woodrow Wilson?,Only 3 presidents have married while in office— John Tyler was the first & which one was the last?\n89,19th Century Americans,Who is Frederick Douglass?,\"Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?\"\n90,Latin Phrases,What is “quid pro quo”?,\"Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?\"\n91,1970s Movies,What is Monty Python and the Holy Grail?,The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?\n92,Name’s The Same,What is Manhattan?,\"A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?\"\n93,U.S. Presidents,Who is Calvin Coolidge?,\"Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?\"\n94,Plays,What is The Tempest?,A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?\n95,Landmarks,What is the Berlin Wall?,\"In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?\"\n96,World Capitals,\"What is Vienna, Austria?\",\"Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?\"\n97,Language & Its Meanings,What is a night owl?,\"Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?\"\n98,Flags of Our Hemisphere,What is Brazil?,\"The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?\"\n99,Names in U.S. History,Who is Oliver Brown?,What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?\n100,Children’s Authors,\"Who is Sarah? (from Sarah, Plain and Tall)\",\"Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?\"\n,,,\nTOTALS,,,\n" }, { "path": "examples/jeopardy/questions.txt", "content": "Which man born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?\nWhat work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?\nKnown for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?\nJames Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?\nEngland's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?\nWhich nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?\nIn 1966, the year of who's death did he share plans for an experimental prototype community in Florida?\nOf the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?\nWhich decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?\nWhat 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?\nA 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?\nA 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?\nOver 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?\nThe success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?\nIn a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?\nWhat symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?\nMonty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?\nWhat American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?\nIn what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?\nAt one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?\nCongress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?\nA writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?\nIn 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?\nPart of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?\nA 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?\nBacked by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?\nAfter its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?\nThe busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?\nThis man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?\nAn 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?\nLetters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?\nWhat nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?\nA radical Republican championed what 1875 act but the Supreme Court struck it down in 1883; a new version was passed 81 years later?\nWhose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?\nOnce Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?\nThe ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?\nFor a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?\nThought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?\nIn office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?\nIn 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?\nWhat National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?\nIn 2010 who introduced the 4-point shot, 35 feet from the basket?\nLosses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?\nA craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?\nIn World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?\nUntil 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?\nIn 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?\nAlphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?\nThis Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?\n1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?\nMimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?\nPoseidon carried off the maiden Theophane & turned her into a ewe; their offspring was the source of what mythical object?\nPublished in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?\n5 U.S. states have 6-letter names; only which 2 west of the Mississippi River border each other?\nOriginally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?\nThe 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?\nReturning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?\nPskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?\nIn the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?\nAt the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?\nMike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?\nLike Sir Thomas More, 3 16th century English queens are buried at what British location?\nIn 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person be condemned'?\nThe Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?\nWhat was first sold in 1908, at a price equivalent to about $27,000 today?\nThe name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?\nThe artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?\nIn 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?\nIn one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?\nIronically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?\nAfter a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?\nLaurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?\nUntil a 1903 secession, what country's contiguous territory spanned 2 continents?\nEarly in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?\nSaying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?\nContinuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?\nEach morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?\nUntil the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?\nWhich children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?\nIn a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?\nIn an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?\nIn 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?\nThe title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?\nThe shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?\nUnable to make what candies perfectly round, the confectioner embraced this flawed name for the product?\nWhat country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?\nWhat action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?\nOnly 3 presidents have married while in office— John Tyler was the first & which one was the last?\nDemonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?\nOriginally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?\nThe 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?\nA cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?\nWhich US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?\nA 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?\nIn 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?\nAmong what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?\nNow meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?\nThe stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?\nWhat father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?\nReversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?\n" }, { "path": "examples/json-schema-to-grammar.py", "content": "#!/usr/bin/env python3\nimport argparse\nimport json\nimport re\nimport sys\n\n# whitespace is constrained to a single space char to prevent model \"running away\" in\n# whitespace. Also maybe improves generation quality?\nSPACE_RULE = '\" \"?'\n\nPRIMITIVE_RULES = {\n 'boolean': '(\"true\" | \"false\") space',\n 'number': '(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space',\n 'integer': '(\"-\"? ([0-9] | [1-9] [0-9]*)) space',\n 'string': r''' \"\\\"\" (\n [^\"\\\\] |\n \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n )* \"\\\"\" space ''',\n 'null': '\"null\" space',\n}\n\nINVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')\nGRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\\r\\n\"]')\nGRAMMAR_LITERAL_ESCAPES = {'\\r': '\\\\r', '\\n': '\\\\n', '\"': '\\\\\"'}\n\n\nclass SchemaConverter:\n def __init__(self, prop_order):\n self._prop_order = prop_order\n self._rules = {'space': SPACE_RULE}\n\n def _format_literal(self, literal):\n escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(\n lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal)\n )\n return f'\"{escaped}\"'\n\n def _add_rule(self, name, rule):\n esc_name = INVALID_RULE_CHARS_RE.sub('-', name)\n if esc_name not in self._rules or self._rules[esc_name] == rule:\n key = esc_name\n else:\n i = 0\n while f'{esc_name}{i}' in self._rules:\n i += 1\n key = f'{esc_name}{i}'\n self._rules[key] = rule\n return key\n\n def visit(self, schema, name):\n schema_type = schema.get('type')\n rule_name = name or 'root'\n\n if 'oneOf' in schema or 'anyOf' in schema:\n rule = ' | '.join((\n self.visit(alt_schema, f'{name}{\"-\" if name else \"\"}{i}')\n for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf'])\n ))\n return self._add_rule(rule_name, rule)\n\n elif 'const' in schema:\n return self._add_rule(rule_name, self._format_literal(schema['const']))\n\n elif 'enum' in schema:\n rule = ' | '.join((self._format_literal(v) for v in schema['enum']))\n return self._add_rule(rule_name, rule)\n\n elif schema_type == 'object' and 'properties' in schema:\n # TODO: `required` keyword\n prop_order = self._prop_order\n prop_pairs = sorted(\n schema['properties'].items(),\n # sort by position in prop_order (if specified) then by key\n key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]),\n )\n\n rule = '\"{\" space'\n for i, (prop_name, prop_schema) in enumerate(prop_pairs):\n prop_rule_name = self.visit(prop_schema, f'{name}{\"-\" if name else \"\"}{prop_name}')\n if i > 0:\n rule += ' \",\" space'\n rule += fr' {self._format_literal(prop_name)} space \":\" space {prop_rule_name}'\n rule += ' \"}\" space'\n\n return self._add_rule(rule_name, rule)\n\n elif schema_type == 'array' and 'items' in schema:\n # TODO `prefixItems` keyword\n item_rule_name = self.visit(schema['items'], f'{name}{\"-\" if name else \"\"}item')\n rule = f'\"[\" space ({item_rule_name} (\",\" space {item_rule_name})*)? \"]\" space'\n return self._add_rule(rule_name, rule)\n\n else:\n assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'\n return self._add_rule(\n 'root' if rule_name == 'root' else schema_type,\n PRIMITIVE_RULES[schema_type]\n )\n\n def format_grammar(self):\n return '\\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items()))\n\n\ndef main(args_in = None):\n parser = argparse.ArgumentParser(\n description='''\n Generates a grammar (suitable for use in ./main) that produces JSON conforming to a\n given JSON schema. Only a subset of JSON schema features are supported; more may be\n added in the future.\n ''',\n )\n parser.add_argument(\n '--prop-order',\n default=[],\n type=lambda s: s.split(','),\n help='''\n comma-separated property names defining the order of precedence for object properties;\n properties not specified here are given lower precedence than those that are, and are\n sorted alphabetically\n '''\n )\n parser.add_argument('schema', help='file containing JSON schema (\"-\" for stdin)')\n args = parser.parse_args(args_in)\n\n schema = json.load(sys.stdin if args.schema == '-' else open(args.schema))\n prop_order = {name: idx for idx, name in enumerate(args.prop_order)}\n converter = SchemaConverter(prop_order)\n converter.visit(schema, '')\n print(converter.format_grammar())\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "examples/llama-bench/CMakeLists.txt", "content": "set(TARGET llama-bench)\nadd_executable(${TARGET} llama-bench.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/llama-bench/README.md", "content": "# llama.cpp/example/llama-bench\n\nPerformance testing tool for llama.cpp.\n\n## Table of contents\n\n1. [Syntax](#syntax)\n2. [Examples](#examples)\n 1. [Text generation with different models](#text-generation-with-different-models)\n 2. [Prompt processing with different batch sizes](#prompt-processing-with-different-batch-sizes)\n 3. [Different numbers of threads](#different-numbers-of-threads)\n 4. [Different numbers of layers offloaded to the GPU](#different-numbers-of-layers-offloaded-to-the-gpu)\n3. [Output formats](#output-formats)\n 1. [Markdown](#markdown)\n 2. [CSV](#csv)\n 3. [JSON](#json)\n 4. [SQL](#sql)\n\n## Syntax\n\n```\nusage: ./llama-bench [options]\n\noptions:\n -h, --help\n -m, --model (default: models/7B/ggml-model-q4_0.gguf)\n -p, --n-prompt (default: 512)\n -n, --n-gen (default: 128)\n -b, --batch-size (default: 512)\n --memory-f32 <0|1> (default: 0)\n -t, --threads (default: 16)\n -ngl N, --n-gpu-layers (default: 99)\n -mg i, --main-gpu (default: 0)\n -mmq, --mul-mat-q <0|1> (default: 1)\n -ts, --tensor_split \n -r, --repetitions (default: 5)\n -o, --output (default: md)\n -v, --verbose (default: 0)\n\nMultiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n```\n\nllama-bench can perform two types of tests:\n\n- Prompt processing (pp): processing a prompt in batches (`-p`)\n- Text generation (tg): generating a sequence of tokens (`-n`)\n\nWith the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`).\n\nEach test is repeated the number of times given by `-r`, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition.\n\nFor a description of the other options, see the [main example](../main/README.md).\n\n## Examples\n\n### Text generation with different models\n\n```sh\n$ ./llama-bench -m models/7B/ggml-model-q4_0.gguf -m models/13B/ggml-model-q4_0.gguf -p 0 -n 128,256,512\n```\n\n| model | size | params | backend | ngl | test | t/s |\n| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 128 | 132.19 ± 0.55 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 256 | 129.37 ± 0.54 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 512 | 123.83 ± 0.25 |\n| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 128 | 82.17 ± 0.31 |\n| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 256 | 80.74 ± 0.23 |\n| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 512 | 78.08 ± 0.07 |\n\n### Prompt processing with different batch sizes\n\n```sh\n$ ./llama-bench -n 0 -p 1024 -b 128,256,512,1024\n```\n\n| model | size | params | backend | ngl | n_batch | test | t/s |\n| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------: | ---------- | ---------------: |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 128 | pp 1024 | 1436.51 ± 3.66 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 256 | pp 1024 | 1932.43 ± 23.48 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 512 | pp 1024 | 2254.45 ± 15.59 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 1024 | pp 1024 | 2498.61 ± 13.58 |\n\n### Different numbers of threads\n\n```sh\n$ ./llama-bench -n 0 -n 16 -p 64 -t 1,2,4,8,16,32\n```\n\n| model | size | params | backend | threads | test | t/s |\n| ------------------------------ | ---------: | ---------: | ---------- | ---------: | ---------- | ---------------: |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 1 | pp 64 | 6.17 ± 0.07 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 1 | tg 16 | 4.05 ± 0.02 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 2 | pp 64 | 12.31 ± 0.13 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 2 | tg 16 | 7.80 ± 0.07 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 4 | pp 64 | 23.18 ± 0.06 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 4 | tg 16 | 12.22 ± 0.07 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 8 | pp 64 | 32.29 ± 1.21 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 8 | tg 16 | 16.71 ± 0.66 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 16 | pp 64 | 33.52 ± 0.03 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 16 | tg 16 | 15.32 ± 0.05 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 32 | pp 64 | 59.00 ± 1.11 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 32 | tg 16 | 16.41 ± 0.79 ||\n\n### Different numbers of layers offloaded to the GPU\n\n```sh\n$ ./llama-bench -ngl 10,20,30,31,32,33,34,35\n```\n\n| model | size | params | backend | ngl | test | t/s |\n| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 10 | pp 512 | 373.36 ± 2.25 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 10 | tg 128 | 13.45 ± 0.93 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 20 | pp 512 | 472.65 ± 1.25 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 20 | tg 128 | 21.36 ± 1.94 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 30 | pp 512 | 631.87 ± 11.25 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 30 | tg 128 | 40.04 ± 1.82 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 31 | pp 512 | 657.89 ± 5.08 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 31 | tg 128 | 48.19 ± 0.81 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 32 | pp 512 | 688.26 ± 3.29 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 32 | tg 128 | 54.78 ± 0.65 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 33 | pp 512 | 704.27 ± 2.24 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 33 | tg 128 | 60.62 ± 1.76 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 34 | pp 512 | 881.34 ± 5.40 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 34 | tg 128 | 71.76 ± 0.23 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | pp 512 | 2400.01 ± 7.72 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | tg 128 | 131.66 ± 0.49 |\n\n## Output formats\n\nBy default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the `-o` option.\n\n### Markdown\n\n```sh\n$ ./llama-bench -o md\n```\n\n| model | size | params | backend | ngl | test | t/s |\n| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | pp 512 | 2368.80 ± 93.24 |\n| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 128 | 131.42 ± 0.59 |\n\n### CSV\n\n```sh\n$ ./llama-bench -o csv\n```\n\n```csv\nbuild_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts\n\"3469684\",\"1275\",\"1\",\"0\",\"0\",\"1\",\"1\",\"13th Gen Intel(R) Core(TM) i9-13900K\",\"NVIDIA GeForce RTX 3090 Ti\",\"models/7B/ggml-model-q4_0.gguf\",\"llama 7B mostly Q4_0\",\"3825065984\",\"6738415616\",\"512\",\"16\",\"1\",\"99\",\"0\",\"1\",\"0.00\",\"512\",\"0\",\"2023-09-23T12:09:01Z\",\"212155977\",\"732372\",\"2413.341687\",\"8.305961\"\n\"3469684\",\"1275\",\"1\",\"0\",\"0\",\"1\",\"1\",\"13th Gen Intel(R) Core(TM) i9-13900K\",\"NVIDIA GeForce RTX 3090 Ti\",\"models/7B/ggml-model-q4_0.gguf\",\"llama 7B mostly Q4_0\",\"3825065984\",\"6738415616\",\"512\",\"16\",\"1\",\"99\",\"0\",\"1\",\"0.00\",\"0\",\"128\",\"2023-09-23T12:09:02Z\",\"969320879\",\"2728399\",\"132.052051\",\"0.371342\"\n```\n\n### JSON\n\n```sh\n$ ./llama-bench -o json\n```\n\n```json\n[\n {\n \"build_commit\": \"3469684\",\n \"build_number\": 1275,\n \"cuda\": true,\n \"opencl\": false,\n \"metal\": false,\n \"gpu_blas\": true,\n \"blas\": true,\n \"cpu_info\": \"13th Gen Intel(R) Core(TM) i9-13900K\",\n \"gpu_info\": \"NVIDIA GeForce RTX 3090 Ti\",\n \"model_filename\": \"models/7B/ggml-model-q4_0.gguf\",\n \"model_type\": \"llama 7B mostly Q4_0\",\n \"model_size\": 3825065984,\n \"model_n_params\": 6738415616,\n \"n_batch\": 512,\n \"n_threads\": 16,\n \"f16_kv\": true,\n \"n_gpu_layers\": 99,\n \"main_gpu\": 0,\n \"mul_mat_q\": true,\n \"tensor_split\": \"0.00\",\n \"n_prompt\": 512,\n \"n_gen\": 0,\n \"test_time\": \"2023-09-23T12:09:57Z\",\n \"avg_ns\": 212365953,\n \"stddev_ns\": 985423,\n \"avg_ts\": 2410.974041,\n \"stddev_ts\": 11.163766,\n \"samples_ns\": [ 213837238, 211635853, 212328053, 211329715, 212698907 ],\n \"samples_ts\": [ 2394.34, 2419.25, 2411.36, 2422.75, 2407.16 ]\n },\n {\n \"build_commit\": \"3469684\",\n \"build_number\": 1275,\n \"cuda\": true,\n \"opencl\": false,\n \"metal\": false,\n \"gpu_blas\": true,\n \"blas\": true,\n \"cpu_info\": \"13th Gen Intel(R) Core(TM) i9-13900K\",\n \"gpu_info\": \"NVIDIA GeForce RTX 3090 Ti\",\n \"model_filename\": \"models/7B/ggml-model-q4_0.gguf\",\n \"model_type\": \"llama 7B mostly Q4_0\",\n \"model_size\": 3825065984,\n \"model_n_params\": 6738415616,\n \"n_batch\": 512,\n \"n_threads\": 16,\n \"f16_kv\": true,\n \"n_gpu_layers\": 99,\n \"main_gpu\": 0,\n \"mul_mat_q\": true,\n \"tensor_split\": \"0.00\",\n \"n_prompt\": 0,\n \"n_gen\": 128,\n \"test_time\": \"2023-09-23T12:09:59Z\",\n \"avg_ns\": 977425219,\n \"stddev_ns\": 9268593,\n \"avg_ts\": 130.965708,\n \"stddev_ts\": 1.238924,\n \"samples_ns\": [ 984472709, 974901233, 989474741, 970729355, 967548060 ],\n \"samples_ts\": [ 130.019, 131.295, 129.362, 131.86, 132.293 ]\n }\n]\n```\n\n### SQL\n\nSQL output is suitable for importing into a SQLite database. The output can be piped into the `sqlite3` command line tool to add the results to a database.\n\n```sh\n$ ./llama-bench -o sql\n```\n\n```sql\nCREATE TABLE IF NOT EXISTS test (\n build_commit TEXT,\n build_number INTEGER,\n cuda INTEGER,\n opencl INTEGER,\n metal INTEGER,\n gpu_blas INTEGER,\n blas INTEGER,\n cpu_info TEXT,\n gpu_info TEXT,\n model_filename TEXT,\n model_type TEXT,\n model_size INTEGER,\n model_n_params INTEGER,\n n_batch INTEGER,\n n_threads INTEGER,\n f16_kv INTEGER,\n n_gpu_layers INTEGER,\n main_gpu INTEGER,\n mul_mat_q INTEGER,\n tensor_split TEXT,\n n_prompt INTEGER,\n n_gen INTEGER,\n test_time TEXT,\n avg_ns INTEGER,\n stddev_ns INTEGER,\n avg_ts REAL,\n stddev_ts REAL\n);\n\nINSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');\nINSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');\n```\n" }, { "path": "examples/llama-bench/llama-bench.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"ggml.h\"\n#include \"llama.h\"\n#include \"common.h\"\n#include \"ggml-cuda.h\"\n\n// utils\nstatic uint64_t get_time_ns() {\n using clock = std::chrono::high_resolution_clock;\n return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();\n}\n\ntemplate\nstatic std::string join(const std::vector & values, const std::string & delim) {\n std::ostringstream str;\n for (size_t i = 0; i < values.size(); i++) {\n str << values[i];\n if (i < values.size() - 1) {\n str << delim;\n }\n }\n return str.str();\n}\n\ntemplate\nstatic std::vector split(const std::string & str, char delim) {\n std::vector values;\n std::istringstream str_stream(str);\n std::string token;\n while (std::getline(str_stream, token, delim)) {\n T value;\n std::istringstream token_stream(token);\n token_stream >> value;\n values.push_back(value);\n }\n return values;\n}\n\ntemplate\nstatic T avg(const std::vector & v) {\n if (v.empty()) {\n return 0;\n }\n T sum = std::accumulate(v.begin(), v.end(), T(0));\n return sum / (T)v.size();\n}\n\ntemplate\nstatic T stdev(const std::vector & v) {\n if (v.size() <= 1) {\n return 0;\n }\n T mean = avg(v);\n T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0));\n T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1));\n return stdev;\n}\n\nstatic std::string get_cpu_info() {\n std::string id;\n#ifdef __linux__\n FILE * f = fopen(\"/proc/cpuinfo\", \"r\");\n if (f) {\n char buf[1024];\n while (fgets(buf, sizeof(buf), f)) {\n if (strncmp(buf, \"model name\", 10) == 0) {\n char * p = strchr(buf, ':');\n if (p) {\n p++;\n while (std::isspace(*p)) {\n p++;\n }\n while (std::isspace(p[strlen(p) - 1])) {\n p[strlen(p) - 1] = '\\0';\n }\n id = p;\n break;\n }\n }\n }\n }\n#endif\n // TODO: other platforms\n return id;\n}\n\nstatic std::string get_gpu_info() {\n std::string id;\n#ifdef GGML_USE_CUBLAS\n int count = ggml_cuda_get_device_count();\n for (int i = 0; i < count; i++) {\n char buf[128];\n ggml_cuda_get_device_description(i, buf, sizeof(buf));\n id += buf;\n if (i < count - 1) {\n id += \"/\";\n }\n }\n#endif\n // TODO: other backends\n return id;\n}\n\n// command line params\nenum output_formats {CSV, JSON, MARKDOWN, SQL};\n\nstruct cmd_params {\n std::vector model;\n std::vector n_prompt;\n std::vector n_gen;\n std::vector n_batch;\n std::vector f32_kv;\n std::vector n_threads;\n std::vector n_gpu_layers;\n std::vector main_gpu;\n std::vector mul_mat_q;\n std::vector> tensor_split;\n int reps;\n bool verbose;\n output_formats output_format;\n};\n\nstatic const cmd_params cmd_params_defaults = {\n /* model */ {\"models/7B/ggml-model-q4_0.gguf\"},\n /* n_prompt */ {512},\n /* n_gen */ {128},\n /* n_batch */ {512},\n /* f32_kv */ {false},\n /* n_threads */ {get_num_physical_cores()},\n /* n_gpu_layers */ {99},\n /* main_gpu */ {0},\n /* mul_mat_q */ {true},\n /* tensor_split */ {{}},\n /* reps */ 5,\n /* verbose */ false,\n /* output_format */ MARKDOWN\n};\n\nstatic void print_usage(int /* argc */, char ** argv) {\n printf(\"usage: %s [options]\\n\", argv[0]);\n printf(\"\\n\");\n printf(\"options:\\n\");\n printf(\" -h, --help\\n\");\n printf(\" -m, --model (default: %s)\\n\", join(cmd_params_defaults.model, \",\").c_str());\n printf(\" -p, --n-prompt (default: %s)\\n\", join(cmd_params_defaults.n_prompt, \",\").c_str());\n printf(\" -n, --n-gen (default: %s)\\n\", join(cmd_params_defaults.n_gen, \",\").c_str());\n printf(\" -b, --batch-size (default: %s)\\n\", join(cmd_params_defaults.n_batch, \",\").c_str());\n printf(\" --memory-f32 <0|1> (default: %s)\\n\", join(cmd_params_defaults.f32_kv, \",\").c_str());\n printf(\" -t, --threads (default: %s)\\n\", join(cmd_params_defaults.n_threads, \",\").c_str());\n printf(\" -ngl, --n-gpu-layers (default: %s)\\n\", join(cmd_params_defaults.n_gpu_layers, \",\").c_str());\n printf(\" -mg, --main-gpu (default: %s)\\n\", join(cmd_params_defaults.main_gpu, \",\").c_str());\n printf(\" -mmq, --mul-mat-q <0|1> (default: %s)\\n\", join(cmd_params_defaults.mul_mat_q, \",\").c_str());\n printf(\" -ts, --tensor_split \\n\");\n printf(\" -r, --repetitions (default: %d)\\n\", cmd_params_defaults.reps);\n printf(\" -o, --output (default: %s)\\n\", cmd_params_defaults.output_format == CSV ? \"csv\" : cmd_params_defaults.output_format == JSON ? \"json\" : cmd_params_defaults.output_format == MARKDOWN ? \"md\" : \"sql\");\n printf(\" -v, --verbose (default: %s)\\n\", cmd_params_defaults.verbose ? \"1\" : \"0\");\n printf(\"\\n\");\n printf(\"Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\\n\");\n\n}\n\nstatic cmd_params parse_cmd_params(int argc, char ** argv) {\n cmd_params params;\n std::string arg;\n bool invalid_param = false;\n const std::string arg_prefix = \"--\";\n const char split_delim = ',';\n\n params.verbose = cmd_params_defaults.verbose;\n params.output_format = cmd_params_defaults.output_format;\n params.reps = cmd_params_defaults.reps;\n\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {\n std::replace(arg.begin(), arg.end(), '_', '-');\n }\n\n if (arg == \"-h\" || arg == \"--help\") {\n print_usage(argc, argv);\n exit(0);\n } else if (arg == \"-m\" || arg == \"--model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.model.insert(params.model.end(), p.begin(), p.end());\n } else if (arg == \"-p\" || arg == \"--n-prompt\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());\n } else if (arg == \"-n\" || arg == \"--n-gen\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());\n } else if (arg == \"-b\" || arg == \"--batch-size\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());\n } else if (arg == \"--memory-f32\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());\n } else if (arg == \"-t\" || arg == \"--threads\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());\n } else if (arg == \"-ngl\" || arg == \"--n-gpu-layers\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());\n } else if (arg == \"-mg\" || arg == \"--main-gpu\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.main_gpu = split(argv[i], split_delim);\n } else if (arg == \"-mmq\" || arg == \"--mul-mat-q\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n auto p = split(argv[i], split_delim);\n params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());\n } else if (arg == \"-ts\" || arg == \"--tensor-split\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n for (auto ts : split(argv[i], split_delim)) {\n // split string by ; and /\n const std::regex regex{R\"([;/]+)\"};\n std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};\n std::vector split_arg{it, {}};\n GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);\n\n std::array tensor_split;\n for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {\n if (i < split_arg.size()) {\n tensor_split[i] = std::stof(split_arg[i]);\n } else {\n tensor_split[i] = 0.0f;\n }\n }\n params.tensor_split.push_back(tensor_split);\n }\n } else if (arg == \"-r\" || arg == \"--repetitions\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.reps = std::stoi(argv[i]);\n } else if (arg == \"-o\" || arg == \"--output\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n if (argv[i] == std::string(\"csv\")) {\n params.output_format = CSV;\n } else if (argv[i] == std::string(\"json\")) {\n params.output_format = JSON;\n } else if (argv[i] == std::string(\"md\")) {\n params.output_format = MARKDOWN;\n } else if (argv[i] == std::string(\"sql\")) {\n params.output_format = SQL;\n } else {\n invalid_param = true;\n break;\n }\n } else if (arg == \"-v\" || arg == \"--verbose\") {\n params.verbose = true;\n } else {\n invalid_param = true;\n break;\n }\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: %s\\n\", arg.c_str());\n print_usage(argc, argv);\n exit(1);\n }\n\n // set defaults\n if (params.model.empty()) { params.model = cmd_params_defaults.model; }\n if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }\n if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }\n if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }\n if (params.f32_kv.empty()) { params.f32_kv = cmd_params_defaults.f32_kv; }\n if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }\n if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }\n if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }\n if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }\n if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }\n\n return params;\n}\n\nstruct cmd_params_instance {\n std::string model;\n int n_prompt;\n int n_gen;\n int n_batch;\n bool f32_kv;\n int n_threads;\n int n_gpu_layers;\n int main_gpu;\n bool mul_mat_q;\n std::array tensor_split;\n\n llama_model_params to_llama_mparams() const {\n llama_model_params mparams = llama_model_default_params();\n\n mparams.n_gpu_layers = n_gpu_layers;\n mparams.main_gpu = main_gpu;\n mparams.tensor_split = tensor_split.data();\n\n return mparams;\n }\n\n bool equal_mparams(const cmd_params_instance & other) const {\n return model == other.model &&\n n_gpu_layers == other.n_gpu_layers &&\n main_gpu == other.main_gpu &&\n tensor_split == other.tensor_split;\n }\n\n llama_context_params to_llama_cparams() const {\n llama_context_params cparams = llama_context_default_params();\n\n cparams.n_ctx = n_prompt + n_gen;\n cparams.n_batch = n_batch;\n cparams.f16_kv = !f32_kv;\n cparams.mul_mat_q = mul_mat_q;\n\n return cparams;\n }\n};\n\nstatic std::vector get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) {\n std::vector instances;\n\n for (const auto & m : params.model)\n for (const auto & nl : params.n_gpu_layers)\n for (const auto & mg : params.main_gpu)\n for (const auto & ts : params.tensor_split)\n for (const auto & nb : params.n_batch)\n for (const auto & fk : params.f32_kv)\n for (const auto & mmq : params.mul_mat_q)\n for (const auto & nt : params.n_threads) {\n cmd_params_instance instance = {\n /* .model = */ m,\n /* .n_prompt = */ n_prompt,\n /* .n_gen = */ n_gen,\n /* .n_batch = */ nb,\n /* .f32_kv = */ fk,\n /* .n_threads = */ nt,\n /* .n_gpu_layers = */ nl,\n /* .main_gpu = */ mg,\n /* .mul_mat_q = */ mmq,\n /* .tensor_split = */ ts,\n };\n instances.push_back(instance);\n }\n return instances;\n}\n\nstatic std::vector get_cmd_params_instances(const cmd_params & params) {\n std::vector instances;\n\n#if 1\n // this ordering minimizes the number of times that each model needs to be reloaded\n for (const auto & m : params.model)\n for (const auto & nl : params.n_gpu_layers)\n for (const auto & mg : params.main_gpu)\n for (const auto & ts : params.tensor_split)\n for (const auto & nb : params.n_batch)\n for (const auto & fk : params.f32_kv)\n for (const auto & mmq : params.mul_mat_q)\n for (const auto & nt : params.n_threads) {\n for (const auto & n_prompt : params.n_prompt) {\n if (n_prompt == 0) {\n continue;\n }\n cmd_params_instance instance = {\n /* .model = */ m,\n /* .n_prompt = */ n_prompt,\n /* .n_gen = */ 0,\n /* .n_batch = */ nb,\n /* .f32_kv = */ fk,\n /* .n_threads = */ nt,\n /* .n_gpu_layers = */ nl,\n /* .main_gpu = */ mg,\n /* .mul_mat_q = */ mmq,\n /* .tensor_split = */ ts,\n };\n instances.push_back(instance);\n }\n\n for (const auto & n_gen : params.n_gen) {\n if (n_gen == 0) {\n continue;\n }\n cmd_params_instance instance = {\n /* .model = */ m,\n /* .n_prompt = */ 0,\n /* .n_gen = */ n_gen,\n /* .n_batch = */ nb,\n /* .f32_kv = */ fk,\n /* .n_threads = */ nt,\n /* .n_gpu_layers = */ nl,\n /* .main_gpu = */ mg,\n /* .mul_mat_q = */ mmq,\n /* .tensor_split = */ ts,\n };\n instances.push_back(instance);\n }\n }\n#else\n // this ordering separates the prompt and generation tests\n for (const auto & n_prompt : params.n_prompt) {\n if (n_prompt == 0) {\n continue;\n }\n auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt);\n instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end());\n }\n\n for (const auto & n_gen : params.n_gen) {\n if (n_gen == 0) {\n continue;\n }\n auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);\n instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());\n }\n#endif\n\n return instances;\n}\n\nstruct test {\n static const std::string build_commit;\n static const int build_number;\n static const bool cuda;\n static const bool opencl;\n static const bool metal;\n static const bool gpu_blas;\n static const bool blas;\n static const std::string cpu_info;\n static const std::string gpu_info;\n std::string model_filename;\n std::string model_type;\n uint64_t model_size;\n uint64_t model_n_params;\n int n_batch;\n int n_threads;\n bool f32_kv;\n int n_gpu_layers;\n int main_gpu;\n bool mul_mat_q;\n std::array tensor_split;\n int n_prompt;\n int n_gen;\n std::string test_time;\n std::vector samples_ns;\n\n test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {\n model_filename = inst.model;\n char buf[128];\n llama_model_desc(lmodel, buf, sizeof(buf));\n model_type = buf;\n model_size = llama_model_size(lmodel);\n model_n_params = llama_model_n_params(lmodel);\n n_batch = inst.n_batch;\n n_threads = inst.n_threads;\n f32_kv = inst.f32_kv;\n n_gpu_layers = inst.n_gpu_layers;\n main_gpu = inst.main_gpu;\n mul_mat_q = inst.mul_mat_q;\n tensor_split = inst.tensor_split;\n n_prompt = inst.n_prompt;\n n_gen = inst.n_gen;\n // RFC 3339 date-time format\n time_t t = time(NULL);\n std::strftime(buf, sizeof(buf), \"%FT%TZ\", gmtime(&t));\n test_time = buf;\n\n (void) ctx;\n }\n\n uint64_t avg_ns() const {\n return ::avg(samples_ns);\n }\n\n uint64_t stdev_ns() const {\n return ::stdev(samples_ns);\n }\n\n std::vector get_ts() const {\n int n_tokens = n_prompt + n_gen;\n std::vector ts;\n std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });\n return ts;\n }\n\n double avg_ts() const {\n return ::avg(get_ts());\n }\n\n double stdev_ts() const {\n return ::stdev(get_ts());\n }\n\n static std::string get_backend() {\n if (cuda) {\n return GGML_CUDA_NAME;\n }\n if (opencl) {\n return \"OpenCL\";\n }\n if (metal) {\n return \"Metal\";\n }\n if (gpu_blas) {\n return \"GPU BLAS\";\n }\n if (blas) {\n return \"BLAS\";\n }\n return \"CPU\";\n }\n\n static const std::vector & get_fields() {\n static const std::vector fields = {\n \"build_commit\", \"build_number\",\n \"cuda\", \"opencl\", \"metal\", \"gpu_blas\", \"blas\",\n \"cpu_info\", \"gpu_info\",\n \"model_filename\", \"model_type\", \"model_size\", \"model_n_params\",\n \"n_batch\", \"n_threads\", \"f16_kv\",\n \"n_gpu_layers\", \"main_gpu\", \"mul_mat_q\", \"tensor_split\",\n \"n_prompt\", \"n_gen\", \"test_time\",\n \"avg_ns\", \"stddev_ns\",\n \"avg_ts\", \"stddev_ts\"\n };\n return fields;\n }\n\n enum field_type {STRING, BOOL, INT, FLOAT};\n\n static field_type get_field_type(const std::string & field) {\n if (field == \"build_number\" || field == \"n_batch\" || field == \"n_threads\" ||\n field == \"model_size\" || field == \"model_n_params\" ||\n field == \"n_gpu_layers\" || field == \"main_gpu\" ||\n field == \"n_prompt\" || field == \"n_gen\" ||\n field == \"avg_ns\" || field == \"stddev_ns\") {\n return INT;\n }\n if (field == \"cuda\" || field == \"opencl\" || field == \"metal\" || field == \"gpu_blas\" || field == \"blas\" ||\n field == \"f16_kv\" || field == \"mul_mat_q\") {\n return BOOL;\n }\n if (field == \"avg_ts\" || field == \"stddev_ts\") {\n return FLOAT;\n }\n return STRING;\n }\n\n std::vector get_values() const {\n std::string tensor_split_str;\n int max_nonzero = 0;\n for (int i = 0; i < LLAMA_MAX_DEVICES; i++) {\n if (tensor_split[i] > 0) {\n max_nonzero = i;\n }\n }\n for (int i = 0; i <= max_nonzero; i++) {\n char buf[32];\n snprintf(buf, sizeof(buf), \"%.2f\", tensor_split[i]);\n tensor_split_str += buf;\n if (i < max_nonzero) {\n tensor_split_str += \"/\";\n }\n }\n std::vector values = {\n build_commit, std::to_string(build_number),\n std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),\n cpu_info, gpu_info,\n model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),\n std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),\n std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,\n std::to_string(n_prompt), std::to_string(n_gen), test_time,\n std::to_string(avg_ns()), std::to_string(stdev_ns()),\n std::to_string(avg_ts()), std::to_string(stdev_ts())\n };\n return values;\n }\n\n std::map get_map() const {\n std::map map;\n auto fields = get_fields();\n auto values = get_values();\n std::transform(fields.begin(), fields.end(), values.begin(),\n std::inserter(map, map.end()), std::make_pair);\n return map;\n }\n};\n\nconst std::string test::build_commit = LLAMA_COMMIT;\nconst int test::build_number = LLAMA_BUILD_NUMBER;\nconst bool test::cuda = !!ggml_cpu_has_cublas();\nconst bool test::opencl = !!ggml_cpu_has_clblast();\nconst bool test::metal = !!ggml_cpu_has_metal();\nconst bool test::gpu_blas = !!ggml_cpu_has_gpublas();\nconst bool test::blas = !!ggml_cpu_has_blas();\nconst std::string test::cpu_info = get_cpu_info();\nconst std::string test::gpu_info = get_gpu_info();\n\nstruct printer {\n virtual ~printer() {}\n\n FILE * fout;\n virtual void print_header(const cmd_params & params) { (void) params; }\n virtual void print_test(const test & t) = 0;\n virtual void print_footer() { }\n};\n\nstruct csv_printer : public printer {\n static std::string escape_csv(const std::string & field) {\n std::string escaped = \"\\\"\";\n for (auto c : field) {\n if (c == '\"') {\n escaped += \"\\\"\";\n }\n escaped += c;\n }\n escaped += \"\\\"\";\n return escaped;\n }\n\n void print_header(const cmd_params & params) override {\n std::vector fields = test::get_fields();\n fprintf(fout, \"%s\\n\", join(fields, \",\").c_str());\n (void) params;\n }\n\n void print_test(const test & t) override {\n std::vector values = t.get_values();\n std::transform(values.begin(), values.end(), values.begin(), escape_csv);\n fprintf(fout, \"%s\\n\", join(values, \",\").c_str());\n }\n};\n\nstruct json_printer : public printer {\n bool first = true;\n\n static std::string escape_json(const std::string & value) {\n std::string escaped;\n for (auto c : value) {\n if (c == '\"') {\n escaped += \"\\\\\\\"\";\n } else if (c == '\\\\') {\n escaped += \"\\\\\\\\\";\n } else if (c <= 0x1f) {\n char buf[8];\n snprintf(buf, sizeof(buf), \"\\\\u%04x\", c);\n escaped += buf;\n } else {\n escaped += c;\n }\n }\n return escaped;\n }\n\n static std::string format_value(const std::string & field, const std::string & value) {\n switch (test::get_field_type(field)) {\n case test::STRING:\n return \"\\\"\" + escape_json(value) + \"\\\"\";\n case test::BOOL:\n return value == \"0\" ? \"false\" : \"true\";\n default:\n return value;\n }\n }\n\n void print_header(const cmd_params & params) override {\n fprintf(fout, \"[\\n\");\n (void) params;\n }\n\n void print_fields(const std::vector & fields, const std::vector & values) {\n assert(fields.size() == values.size());\n for (size_t i = 0; i < fields.size(); i++) {\n fprintf(fout, \" \\\"%s\\\": %s,\\n\", fields.at(i).c_str(), format_value(fields.at(i), values.at(i)).c_str());\n }\n }\n\n void print_test(const test & t) override {\n if (first) {\n first = false;\n } else {\n fprintf(fout, \",\\n\");\n }\n fprintf(fout, \" {\\n\");\n print_fields(test::get_fields(), t.get_values());\n fprintf(fout, \" \\\"samples_ns\\\": [ %s ],\\n\", join(t.samples_ns, \", \").c_str());\n fprintf(fout, \" \\\"samples_ts\\\": [ %s ]\\n\", join(t.get_ts(), \", \").c_str());\n fprintf(fout, \" }\");\n fflush(fout);\n }\n\n void print_footer() override {\n fprintf(fout, \"\\n]\\n\");\n }\n};\n\nstruct markdown_printer : public printer {\n std::vector fields;\n\n static int get_field_width(const std::string & field) {\n if (field == \"model\") {\n return -30;\n }\n if (field == \"t/s\") {\n return 16;\n }\n if (field == \"size\" || field == \"params\") {\n return 10;\n }\n if (field == \"n_gpu_layers\") {\n return 3;\n }\n\n int width = std::max((int)field.length(), 10);\n\n if (test::get_field_type(field) == test::STRING) {\n return -width;\n }\n return width;\n }\n\n static std::string get_field_display_name(const std::string & field) {\n if (field == \"n_gpu_layers\") {\n return \"ngl\";\n }\n if (field == \"n_threads\") {\n return \"threads\";\n }\n if (field == \"mul_mat_q\") {\n return \"mmq\";\n }\n if (field == \"tensor_split\") {\n return \"ts\";\n }\n return field;\n }\n\n void print_header(const cmd_params & params) override {\n // select fields to print\n fields.push_back(\"model\");\n fields.push_back(\"size\");\n fields.push_back(\"params\");\n fields.push_back(\"backend\");\n bool is_cpu_backend = test::get_backend() == \"CPU\" || test::get_backend() == \"BLAS\";\n if (!is_cpu_backend) {\n fields.push_back(\"n_gpu_layers\");\n }\n if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {\n fields.push_back(\"n_threads\");\n }\n if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {\n fields.push_back(\"n_batch\");\n }\n if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {\n fields.push_back(\"f16_kv\");\n }\n if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {\n fields.push_back(\"main_gpu\");\n }\n if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {\n fields.push_back(\"mul_mat_q\");\n }\n if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {\n fields.push_back(\"tensor_split\");\n }\n fields.push_back(\"test\");\n fields.push_back(\"t/s\");\n\n fprintf(fout, \"|\");\n for (const auto & field : fields) {\n fprintf(fout, \" %*s |\", get_field_width(field), get_field_display_name(field).c_str());\n }\n fprintf(fout, \"\\n\");\n fprintf(fout, \"|\");\n for (const auto & field : fields) {\n int width = get_field_width(field);\n fprintf(fout, \" %s%s |\", std::string(std::abs(width) - 1, '-').c_str(), width > 0 ? \":\" : \"-\");\n }\n fprintf(fout, \"\\n\");\n }\n\n void print_test(const test & t) override {\n std::map vmap = t.get_map();\n\n fprintf(fout, \"|\");\n for (const auto & field : fields) {\n std::string value;\n char buf[128];\n if (field == \"model\") {\n value = t.model_type;\n } else if (field == \"size\") {\n if (t.model_size < 1024*1024*1024) {\n snprintf(buf, sizeof(buf), \"%.2f MiB\", t.model_size / 1024.0 / 1024.0);\n } else {\n snprintf(buf, sizeof(buf), \"%.2f GiB\", t.model_size / 1024.0 / 1024.0 / 1024.0);\n }\n value = buf;\n } else if (field == \"params\") {\n if (t.model_n_params < 1000*1000*1000) {\n snprintf(buf, sizeof(buf), \"%.2f M\", t.model_n_params / 1e6);\n } else {\n snprintf(buf, sizeof(buf), \"%.2f B\", t.model_n_params / 1e9);\n }\n value = buf;\n } else if (field == \"backend\") {\n value = test::get_backend();\n } else if (field == \"test\") {\n if (t.n_prompt > 0 && t.n_gen == 0) {\n snprintf(buf, sizeof(buf), \"pp %d\", t.n_prompt);\n } else if (t.n_gen > 0 && t.n_prompt == 0) {\n snprintf(buf, sizeof(buf), \"tg %d\", t.n_gen);\n } else {\n assert(false);\n exit(1);\n }\n value = buf;\n } else if (field == \"t/s\") {\n snprintf(buf, sizeof(buf), \"%.2f ± %.2f\", t.avg_ts(), t.stdev_ts());\n value = buf;\n } else if (vmap.find(field) != vmap.end()) {\n value = vmap.at(field);\n } else {\n assert(false);\n exit(1);\n }\n\n int width = get_field_width(field);\n if (field == \"t/s\") {\n // HACK: the utf-8 character is 2 bytes\n width += 1;\n }\n fprintf(fout, \" %*s |\", width, value.c_str());\n }\n fprintf(fout, \"\\n\");\n }\n\n void print_footer() override {\n fprintf(fout, \"\\nbuild: %s (%d)\\n\", test::build_commit.c_str(), test::build_number);\n }\n};\n\nstruct sql_printer : public printer {\n static std::string get_sql_field_type(const std::string & field) {\n switch (test::get_field_type(field)) {\n case test::STRING:\n return \"TEXT\";\n case test::BOOL:\n case test::INT:\n return \"INTEGER\";\n case test::FLOAT:\n return \"REAL\";\n default:\n assert(false);\n exit(1);\n }\n }\n\n void print_header(const cmd_params & params) override {\n std::vector fields = test::get_fields();\n fprintf(fout, \"CREATE TABLE IF NOT EXISTS test (\\n\");\n for (size_t i = 0; i < fields.size(); i++) {\n fprintf(fout, \" %s %s%s\\n\", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), i < fields.size() - 1 ? \",\" : \"\");\n }\n fprintf(fout, \");\\n\");\n fprintf(fout, \"\\n\");\n (void) params;\n }\n\n void print_test(const test & t) override {\n fprintf(fout, \"INSERT INTO test (%s) \", join(test::get_fields(), \", \").c_str());\n fprintf(fout, \"VALUES (\");\n std::vector values = t.get_values();\n for (size_t i = 0; i < values.size(); i++) {\n fprintf(fout, \"'%s'%s\", values.at(i).c_str(), i < values.size() - 1 ? \", \" : \"\");\n }\n fprintf(fout, \");\\n\");\n }\n};\n\nstatic void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {\n std::vector tokens(n_batch, llama_token_bos(llama_get_model(ctx)));\n int n_processed = 0;\n\n llama_set_n_threads(ctx, n_threads, n_threads);\n\n while (n_processed < n_prompt) {\n int n_tokens = std::min(n_prompt - n_processed, n_batch);\n llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));\n n_processed += n_tokens;\n }\n}\n\nstatic void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {\n llama_token token = llama_token_bos(llama_get_model(ctx));\n\n llama_set_n_threads(ctx, n_threads, n_threads);\n\n for (int i = 0; i < n_gen; i++) {\n llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));\n }\n}\n\nstatic void llama_null_log_callback(enum ggml_log_level level, const char * text, void * user_data) {\n (void) level;\n (void) text;\n (void) user_data;\n}\n\nint main(int argc, char ** argv) {\n // try to set locale for unicode characters in markdown\n setlocale(LC_CTYPE, \".UTF-8\");\n\n#if !defined(NDEBUG)\n fprintf(stderr, \"warning: asserts enabled, performance may be affected\\n\");\n#endif\n\n#if (defined(_MSC_VER) && defined(_DEBUG)) || (!defined(_MSC_VER) && !defined(__OPTIMIZE__))\n fprintf(stderr, \"warning: debug build, performance may be affected\\n\");\n#endif\n\n#if defined(__SANITIZE_ADDRESS__) || defined(__SANITIZE_THREAD__)\n fprintf(stderr, \"warning: sanitizer enabled, performance may be affected\\n\");\n#endif\n\n cmd_params params = parse_cmd_params(argc, argv);\n\n // initialize llama.cpp\n if (!params.verbose) {\n llama_log_set(llama_null_log_callback, NULL);\n }\n bool numa = false;\n llama_backend_init(numa);\n\n // initialize printer\n std::unique_ptr p;\n switch (params.output_format) {\n case CSV:\n p.reset(new csv_printer());\n break;\n case JSON:\n p.reset(new json_printer());\n break;\n case MARKDOWN:\n p.reset(new markdown_printer());\n break;\n case SQL:\n p.reset(new sql_printer());\n break;\n default:\n assert(false);\n exit(1);\n }\n p->fout = stdout;\n p->print_header(params);\n\n std::vector params_instances = get_cmd_params_instances(params);\n\n llama_model * lmodel = nullptr;\n const cmd_params_instance * prev_inst = nullptr;\n\n for (const auto & inst : params_instances) {\n // keep the same model between tests when possible\n if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {\n if (lmodel) {\n llama_free_model(lmodel);\n }\n\n lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams());\n if (lmodel == NULL) {\n fprintf(stderr, \"%s: error: failed to load model '%s'\\n\", __func__, inst.model.c_str());\n return 1;\n }\n prev_inst = &inst;\n }\n\n llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams());\n if (ctx == NULL) {\n fprintf(stderr, \"%s: error: failed to create context with model '%s'\\n\", __func__, inst.model.c_str());\n llama_free_model(lmodel);\n return 1;\n }\n\n test t(inst, lmodel, ctx);\n\n llama_kv_cache_clear(ctx);\n\n // warmup run\n if (t.n_prompt > 0) {\n test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);\n }\n if (t.n_gen > 0) {\n test_gen(ctx, 1, 0, t.n_threads);\n }\n\n for (int i = 0; i < params.reps; i++) {\n llama_kv_cache_clear(ctx);\n\n uint64_t t_start = get_time_ns();\n if (t.n_prompt > 0) {\n test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);\n }\n if (t.n_gen > 0) {\n test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);\n }\n uint64_t t_ns = get_time_ns() - t_start;\n t.samples_ns.push_back(t_ns);\n }\n\n p->print_test(t);\n\n llama_print_timings(ctx);\n\n llama_free(ctx);\n }\n\n llama_free_model(lmodel);\n\n p->print_footer();\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/llama.vim", "content": "\" Requires an already running llama.cpp server\n\" To install either copy or symlink to ~/.vim/autoload/llama.vim\n\" Then start with either :call llama#doLlamaGen(),\n\" or add a keybind to your vimrc such as\n\" nnoremap Z :call llama#doLlamaGen()\n\" Similarly, you could add an insert mode keybind with\n\" inoremap call llama#doLlamaGen()\n\"\n\" g:llama_api_url and g:llama_overrides can be configured in your .vimrc\n\" let g:llama_api_url = \"192.168.1.10:8080\"\n\" llama_overrides can also be set through buffer/window scopes. For instance\n\" autocmd filetype python let b:llama_overrides = {\"temp\": 0.2}\n\" Could be added to your .vimrc to automatically set a lower temperature when\n\" editing a python script\n\" Additionally, an override dict can be stored at the top of a file\n\" !*{\"stop\": [\"User:\"]}\n\" Could be added to the start of your chatlog.txt to set the stopping token\n\" These parameter dicts are merged together from lowest to highest priority:\n\" server default -> g:llama_overrides -> w:llama_overrides ->\n\" b:llama_overrides -> in file (!*) overrides\n\"\n\" Sublists (like logit_bias and stop) are overridden, not merged\n\" Example override:\n\" !*{\"logit_bias\": [[13, -5], [2, false]], \"temperature\": 1, \"top_k\": 5, \"top_p\": 0.5, \"n_predict\": 256, \"repeat_last_n\": 256, \"repeat_penalty\": 1.17647}\nif !exists(\"g:llama_api_url\")\n let g:llama_api_url= \"127.0.0.1:8080\"\nendif\nif !exists(\"g:llama_overrides\")\n let g:llama_overrides = {}\nendif\nconst s:querydata = {\"n_predict\": 256, \"stop\": [ \"\\n\" ], \"stream\": v:true }\nconst s:curlcommand = ['curl','--data-raw', \"{\\\"prompt\\\":\\\"### System:\\\"}\", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', \"Content-Type: application/json\"]\nlet s:linedict = {}\n\nfunc s:callbackHandler(bufn, channel, msg)\n if len(a:msg) < 3\n return\n elseif a:msg[0] == \"d\"\n let l:msg = a:msg[6:-1]\n else\n let l:msg = a:msg\n endif\n let l:decoded_msg = json_decode(l:msg)\n let l:newtext = split(l:decoded_msg['content'], \"\\n\", 1)\n if len(l:newtext) > 0\n call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0])\n else\n echo \"nothing genned\"\n endif\n if len(newtext) > 1\n let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1])\n let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1\n endif\n if has_key(l:decoded_msg, \"stop\") && l:decoded_msg.stop\n echo \"Finished generation\"\n endif\nendfunction\n\nfunc llama#doLlamaGen()\n if exists(\"b:job\")\n if job_status(b:job) == \"run\"\n call job_stop(b:job)\n return\n endif\n endif\n\n let l:cbuffer = bufnr(\"%\")\n let s:linedict[l:cbuffer] = line('$')\n let l:buflines = getbufline(l:cbuffer, 1, 1000)\n let l:querydata = copy(s:querydata)\n call extend(l:querydata, g:llama_overrides)\n if exists(\"w:llama_overrides\")\n call extend(l:querydata, w:llama_overrides)\n endif\n if exists(\"b:llama_overrides\")\n call extend(l:querydata, b:llama_overrides)\n endif\n if l:buflines[0][0:1] == '!*'\n let l:userdata = json_decode(l:buflines[0][2:-1])\n call extend(l:querydata, l:userdata)\n let l:buflines = l:buflines[1:-1]\n endif\n let l:querydata.prompt = join(l:buflines, \"\\n\")\n let l:curlcommand = copy(s:curlcommand)\n let l:curlcommand[2] = json_encode(l:querydata)\n let b:job = job_start(l:curlcommand, {\"callback\": function(\"s:callbackHandler\", [l:cbuffer])})\nendfunction\n\n\" Echos the tokkenization of the provided string , or cursor to end of word\n\" Onus is placed on the user to include the preceding space\nfunc llama#tokenizeWord(...)\n if (a:0 > 0)\n let l:input = a:1\n else\n exe \"normal \\\"*ye\"\n let l:input = @*\n endif\n let l:querydata = {\"content\": l:input}\n let l:curlcommand = copy(s:curlcommand)\n let l:curlcommand[2] = json_encode(l:querydata)\n let l:curlcommand[8] = g:llama_api_url .. \"/tokenize\"\n let s:token_job = job_start(l:curlcommand, {\"callback\": function(\"s:tokenizeWordCallback\", [l:input])})\nendfunction\n\nfunc s:tokenizeWordCallback(plaintext, channel, msg)\n echo '\"' .. a:plaintext ..'\" - ' .. string(json_decode(a:msg).tokens)\nendfunction\n\n\n\" Echos the token count of the entire buffer (or provided string)\n\" Example usage :echo llama#tokenCount()\nfunc llama#tokenCount(...)\n if (a:0 > 0)\n let l:buflines = a:1\n else\n let l:buflines = getline(1,1000)\n if l:buflines[0][0:1] == '!*'\n let l:buflines = l:buflines[1:-1]\n endif\n let l:buflines = join(l:buflines, \"\\n\")\n endif\n let l:querydata = {\"content\": l:buflines}\n let l:curlcommand = copy(s:curlcommand)\n let l:curlcommand[2] = json_encode(l:querydata)\n let l:curlcommand[8] = g:llama_api_url .. \"/tokenize\"\n let s:token_job = job_start(l:curlcommand, {\"callback\": \"s:tokenCountCallback\"})\nendfunction\n\nfunc s:tokenCountCallback(channel, msg)\n let resp = json_decode(a:msg)\n echo len(resp.tokens)\nendfunction\n" }, { "path": "examples/llama2-13b.sh", "content": "#!/bin/bash\n\n#\n# Temporary script - will be removed in the future\n#\n\ncd `dirname $0`\ncd ..\n\n./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \\\n --color \\\n --ctx_size 2048 \\\n -n -1 \\\n -ins -b 256 \\\n --top_k 10000 \\\n --temp 0.2 \\\n --repeat_penalty 1.1 \\\n -t 8\n" }, { "path": "examples/llama2.sh", "content": "#!/bin/bash\n\n#\n# Temporary script - will be removed in the future\n#\n\ncd `dirname $0`\ncd ..\n\n./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \\\n --color \\\n --ctx_size 2048 \\\n -n -1 \\\n -ins -b 256 \\\n --top_k 10000 \\\n --temp 0.2 \\\n --repeat_penalty 1.1 \\\n -t 8\n" }, { "path": "examples/llava/CMakeLists.txt", "content": "add_library(llava OBJECT\n llava.cpp\n llava.h\n clip.cpp\n clip.h\n )\n\ntarget_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})\n\ntarget_include_directories(llava PUBLIC .)\ntarget_include_directories(llava PUBLIC ../..)\ntarget_include_directories(llava PUBLIC ../../common)\n\ntarget_compile_features(llava PRIVATE cxx_std_11)\n\nadd_library(llava_static STATIC $)\nif (BUILD_SHARED_LIBS)\n set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON)\n target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD)\n add_library(llava_shared SHARED $)\n target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})\n install(TARGETS llava_shared LIBRARY)\nendif()\n\nif (NOT MSVC)\n target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h\n endif()\nif(TARGET BUILD_INFO)\n add_dependencies(llava BUILD_INFO)\nendif()\n\nset(TARGET llava-cli)\nadd_executable(llava-cli llava-cli.cpp)\ninstall(TARGETS llava-cli RUNTIME)\ntarget_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(llava PRIVATE cxx_std_11)\n" }, { "path": "examples/llava/README.md", "content": "# LLaVA\n\nCurrently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants.\n\nThe pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)\nand [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)\nmodels are available.\n\nAfter API is confirmed, more models will be supported / uploaded.\n\n## Usage\nBuild with cmake or run `make llava-cli` to build it.\n\nAfter building, run: `./llava-cli` to see the usage. For example:\n\n```sh\n./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg\n```\n\n**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.\n\n## Model conversion\n\n- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally:\n\n```sh\ngit clone https://huggingface.co/liuhaotian/llava-v1.5-7b\n\ngit clone https://huggingface.co/openai/clip-vit-large-patch14-336\n```\n\n2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:\n\n```sh\npython ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b\n```\n\n3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:\n\n```sh\npython ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b\n```\n\n4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:\n\n```sh\npython ./convert.py ../llava-v1.5-7b\n```\n\nNow both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.\n\n## TODO\n\n- [ ] Support non-CPU backend for the image encoding part.\n- [ ] Support different sampling methods.\n- [ ] Support more model variants.\n" }, { "path": "examples/llava/clip.cpp", "content": "// NOTE: This is modified from clip.cpp only for LLaVA,\n// so there might be still unnecessary artifacts hanging around\n// I'll gradually clean and extend it\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"clip.h\"\n#include \"ggml.h\"\n#include \"ggml-alloc.h\"\n\n#define STB_IMAGE_IMPLEMENTATION\n#include \"stb_image.h\"\n\n#define CLIP_DEBUG\n\nstatic std::string format(const char * fmt, ...) {\n va_list ap;\n va_list ap2;\n va_start(ap, fmt);\n va_copy(ap2, ap);\n int size = vsnprintf(NULL, 0, fmt, ap);\n GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT\n std::vector buf(size + 1);\n int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);\n GGML_ASSERT(size2 == size);\n va_end(ap2);\n va_end(ap);\n return std::string(buf.data(), buf.size());\n}\n\n//\n// key constants\n//\n\n#define KEY_FTYPE \"general.file_type\"\n#define KEY_NAME \"general.name\"\n#define KEY_DESCRIPTION \"general.description\"\n#define KEY_HAS_TEXT_ENC \"clip.has_text_encoder\"\n#define KEY_HAS_VIS_ENC \"clip.has_vision_encoder\"\n#define KEY_HAS_LLAVA_PROJ \"clip.has_llava_projector\"\n#define KEY_USE_GELU \"clip.use_gelu\"\n#define KEY_N_EMBD \"clip.%s.embedding_length\"\n#define KEY_N_FF \"clip.%s.feed_forward_length\"\n#define KEY_N_BLOCK \"clip.%s.block_count\"\n#define KEY_N_HEAD \"clip.%s.attention.head_count\"\n#define KEY_LAYER_NORM_EPS \"clip.%s.attention.layer_norm_epsilon\"\n#define KEY_PROJ_DIM \"clip.%s.projection_dim\"\n#define KEY_TOKENS \"tokenizer.ggml.tokens\"\n#define KEY_N_POSITIONS \"clip.text.context_length\"\n#define KEY_IMAGE_SIZE \"clip.vision.image_size\"\n#define KEY_PATCH_SIZE \"clip.vision.patch_size\"\n#define KEY_IMAGE_MEAN \"clip.vision.image_mean\"\n#define KEY_IMAGE_STD \"clip.vision.image_std\"\n\n//\n// tensor name constants\n//\n\n#define TN_TOKEN_EMBD \"%s.token_embd.weight\"\n#define TN_POS_EMBD \"%s.position_embd.weight\"\n#define TN_CLASS_EMBD \"v.class_embd\"\n#define TN_PATCH_EMBD \"v.patch_embd.weight\"\n#define TN_ATTN_K \"%s.blk.%d.attn_k.%s\"\n#define TN_ATTN_Q \"%s.blk.%d.attn_q.%s\"\n#define TN_ATTN_V \"%s.blk.%d.attn_v.%s\"\n#define TN_ATTN_OUTPUT \"%s.blk.%d.attn_out.%s\"\n#define TN_FFN_DOWN \"%s.blk.%d.ffn_down.%s\"\n#define TN_FFN_UP \"%s.blk.%d.ffn_up.%s\"\n#define TN_LN_1 \"%s.blk.%d.ln1.%s\"\n#define TN_LN_2 \"%s.blk.%d.ln2.%s\"\n#define TN_LN_PRE \"%s.pre_ln.%s\"\n#define TN_LN_POST \"%s.post_ln.%s\"\n#define TN_TEXT_PROJ \"text_projection.weight\"\n#define TN_VIS_PROJ \"visual_projection.weight\"\n#define TN_LLAVA_PROJ \"mm.%d.%s\"\n\n//\n// utilities to get data from a gguf file\n//\n\nstatic int get_key_idx(const gguf_context * ctx, const char * key) {\n int i = gguf_find_key(ctx, key);\n if (i == -1) {\n fprintf(stderr, \"key %s not found in file\\n\", key);\n throw std::runtime_error(format(\"Missing required key: %s\", key));\n }\n\n return i;\n}\n\nstatic uint32_t get_u32(const gguf_context * ctx, const std::string & key) {\n const int i = get_key_idx(ctx, key.c_str());\n\n return gguf_get_val_u32(ctx, i);\n}\n\nstatic float get_f32(const gguf_context * ctx, const std::string & key) {\n const int i = get_key_idx(ctx, key.c_str());\n\n return gguf_get_val_f32(ctx, i);\n}\n\nstatic struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::string & name) {\n struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());\n if (!cur) {\n throw std::runtime_error(format(\"%s: unable to find tensor %s\\n\", __func__, name.c_str()));\n }\n\n return cur;\n}\n\nstatic std::string get_ftype(int ftype) {\n switch (ftype) {\n case 0:\n return \"f32\";\n case 1:\n return \"f16\";\n case 2:\n return \"q4_0\";\n case 3:\n return \"q4_1\";\n case 6:\n return \"q5_0\";\n case 7:\n return \"q5_1\";\n case 8:\n return \"q8_0\";\n default:\n throw std::runtime_error(format(\"%s: Unrecognized file type: %d\\n\", __func__, ftype));\n }\n}\n\n//\n// clip layers\n//\n\nstruct clip_layer {\n // attention\n struct ggml_tensor * k_w;\n struct ggml_tensor * k_b;\n struct ggml_tensor * q_w;\n struct ggml_tensor * q_b;\n struct ggml_tensor * v_w;\n struct ggml_tensor * v_b;\n\n struct ggml_tensor * o_w;\n struct ggml_tensor * o_b;\n\n // layernorm 1\n struct ggml_tensor * ln_1_w;\n struct ggml_tensor * ln_1_b;\n\n // ff\n struct ggml_tensor * ff_i_w;\n struct ggml_tensor * ff_i_b;\n\n struct ggml_tensor * ff_o_w;\n struct ggml_tensor * ff_o_b;\n\n // layernorm 2\n struct ggml_tensor * ln_2_w;\n struct ggml_tensor * ln_2_b;\n};\n\nstruct clip_vision_model {\n struct clip_vision_hparams hparams;\n\n // embeddings\n struct ggml_tensor * class_embedding;\n struct ggml_tensor * patch_embeddings;\n struct ggml_tensor * position_embeddings;\n\n struct ggml_tensor * pre_ln_w;\n struct ggml_tensor * pre_ln_b;\n\n std::vector layers;\n\n struct ggml_tensor * post_ln_w;\n struct ggml_tensor * post_ln_b;\n\n struct ggml_tensor * projection;\n\n // LLaVA projection\n struct ggml_tensor * mm_0_w;\n struct ggml_tensor * mm_0_b;\n struct ggml_tensor * mm_2_w;\n struct ggml_tensor * mm_2_b;\n};\n\n// Replacement for std::vector that doesn't require zero-initialization.\nstruct clip_buffer {\n uint8_t * data = NULL;\n size_t size = 0;\n\n void resize(size_t size) {\n delete[] data;\n data = new uint8_t[size];\n this->size = size;\n }\n\n ~clip_buffer() { delete[] data; }\n};\n\nstruct clip_ctx {\n bool has_text_encoder = false;\n bool has_vision_encoder = false;\n bool has_llava_projector = false;\n struct clip_vision_model vision_model;\n float image_mean[3];\n float image_std[3];\n bool use_gelu = false;\n int32_t ftype = 1;\n struct ggml_context * ctx;\n struct gguf_context * ctx_gguf;\n\n // memory buffers to evaluate the model\n clip_buffer buf_compute;\n clip_buffer buf_alloc;\n ggml_allocr * alloc = NULL;\n};\n\nstatic ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) {\n if (!ctx->has_vision_encoder) {\n printf(\"This gguf file seems to have no vision encoder\\n\");\n return nullptr;\n }\n\n const auto & model = ctx->vision_model;\n const auto & hparams = model.hparams;\n\n const int image_size = hparams.image_size;\n const int patch_size = hparams.patch_size;\n const int num_patches = ((image_size / patch_size) * (image_size / patch_size));\n const int num_positions = num_patches + 1;\n const int hidden_size = hparams.hidden_size;\n const int n_head = hparams.n_head;\n const int d_head = hidden_size / n_head;\n const int n_layer = hparams.n_layer;\n //const int n_intermediate = hparams.n_intermediate;\n //const int projection_dim = hparams.projection_dim;\n const float eps = hparams.eps;\n int batch_size = imgs->size;\n if(ctx->has_llava_projector) {\n GGML_ASSERT(batch_size == 1);\n }\n\n const auto & buf_compute = ctx->buf_compute;\n\n struct ggml_init_params params = {\n /*.mem_size =*/ buf_compute.size,\n /*.mem_buffer =*/ buf_compute.data,\n /*.no_alloc =*/ false,\n };\n\n params.no_alloc = true;\n\n struct ggml_context * ctx0 = ggml_init(params);\n struct ggml_cgraph * gf = ggml_new_graph(ctx0);\n\n struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);\n ggml_allocr_alloc(ctx->alloc, inp_raw);\n\n if (!ggml_allocr_is_measure(ctx->alloc)) {\n float * data = (float *)ggml_get_data(inp_raw);\n\n for (size_t i = 0; i < imgs->size; i++) {\n const int nx = imgs->data[i].nx;\n const int ny = imgs->data[i].ny;\n GGML_ASSERT(nx == image_size && ny == image_size);\n\n const int n = nx * ny;\n\n for (int b = 0; b < batch_size; b++) {\n for (int k = 0; k < 3; k++) {\n for (int y = 0; y < ny; y++) {\n for (int x = 0; x < nx; x++) {\n data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k];\n }\n }\n }\n }\n }\n }\n\n struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);\n\n inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);\n inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));\n\n // concat class_embeddings and patch_embeddings\n struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);\n ggml_allocr_alloc(ctx->alloc, embeddings);\n if (!ggml_allocr_is_measure(ctx->alloc)) {\n ggml_set_zero(embeddings);\n }\n\n struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size);\n ggml_allocr_alloc(ctx->alloc, temp);\n\n embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, model.class_embedding, temp), embeddings->nb[1],\n embeddings->nb[2], embeddings->nb[3], 0);\n embeddings =\n ggml_acc(ctx0, embeddings, inp, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);\n\n struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);\n ggml_allocr_alloc(ctx->alloc, positions);\n if (!ggml_allocr_is_measure(ctx->alloc)) {\n for (int i = 0; i < num_positions; i++) {\n ggml_set_i32_1d(positions, i, i);\n }\n }\n\n embeddings =\n ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings));\n\n // pre-layernorm\n {\n embeddings = ggml_norm(ctx0, embeddings, eps);\n\n embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.pre_ln_w, embeddings), embeddings),\n ggml_repeat(ctx0, model.pre_ln_b, embeddings));\n }\n\n struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);\n ggml_allocr_alloc(ctx->alloc, KQ_scale);\n if (!ggml_allocr_is_measure(ctx->alloc)) {\n ggml_set_f32(KQ_scale, 1.0f / sqrt((float)d_head));\n }\n\n // loop over layers\n for (int il = 0; il < n_layer - 1; il++) {\n struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states\n\n //const size_t nb_q_w = model.layers[il].q_w->nb[0];\n\n // layernorm1\n {\n cur = ggml_norm(ctx0, cur, eps);\n\n cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur),\n ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));\n }\n\n // self-attention\n {\n\n struct ggml_tensor * Q =\n ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur));\n\n Q = ggml_scale_inplace(ctx0, Q, KQ_scale);\n Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);\n Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));\n Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);\n\n struct ggml_tensor * K =\n ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur));\n\n K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);\n K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));\n K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);\n\n struct ggml_tensor * V =\n ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur));\n\n V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);\n V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));\n V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);\n\n struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);\n KQ = ggml_soft_max_inplace(ctx0, KQ);\n struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);\n KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);\n KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));\n\n cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size));\n }\n\n // attention output\n cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur));\n\n // re-add the layer input, e.g., residual\n cur = ggml_add(ctx0, cur, embeddings);\n\n embeddings = cur; // embeddings = residual, cur = hidden_states\n\n // layernorm2\n {\n cur = ggml_norm(ctx0, cur, eps);\n\n cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur),\n ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));\n }\n\n cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);\n cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_i_b, cur), cur);\n\n if (ctx->use_gelu) {\n cur = ggml_gelu_inplace(ctx0, cur);\n } else {\n cur = ggml_gelu_quick_inplace(ctx0, cur);\n }\n\n cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);\n cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_o_b, cur), cur);\n\n // residual 2\n cur = ggml_add(ctx0, embeddings, cur);\n\n embeddings = cur;\n }\n\n // llava projector\n {\n embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);\n\n struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);\n ggml_allocr_alloc(ctx->alloc, patches);\n if (!ggml_allocr_is_measure(ctx->alloc)) {\n for (int i = 0; i < num_patches; ++i) {\n ggml_set_i32_1d(patches, i, i+1);\n }\n }\n\n embeddings = ggml_get_rows(ctx0, embeddings, patches);\n\n // mm projection 0\n embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);\n embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_0_b, embeddings), embeddings);\n\n embeddings = ggml_gelu(ctx0, embeddings);\n\n embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);\n embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_2_b, embeddings), embeddings);\n }\n\n // build the graph\n ggml_build_forward_expand(gf, embeddings);\n\n ggml_free(ctx0);\n\n return gf;\n}\n\n// read and create ggml_context containing the tensors and their data\nstruct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {\n\n struct ggml_context * meta = NULL;\n\n struct gguf_init_params params = {\n /*.no_alloc = */ true,\n /*.ctx = */ &meta,\n };\n\n struct gguf_context * ctx = gguf_init_from_file(fname, params);\n if (!ctx) {\n throw std::runtime_error(format(\"%s: failed to load CLIP model from %s. Does this file exist?\\n\", __func__, fname));\n }\n\n if (verbosity >= 1) {\n const int n_tensors = gguf_get_n_tensors(ctx);\n const int n_kv = gguf_get_n_kv(ctx);\n const int ftype = get_u32(ctx, KEY_FTYPE);\n const std::string ftype_str = get_ftype(ftype);\n const int idx_desc = get_key_idx(ctx, KEY_DESCRIPTION);\n const std::string description = gguf_get_val_str(ctx, idx_desc);\n const int idx_name = gguf_find_key(ctx, KEY_NAME);\n if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug\n const std::string name = gguf_get_val_str(ctx, idx_name);\n printf(\"%s: model name: %s\\n\", __func__, name.c_str());\n }\n printf(\"%s: description: %s\\n\", __func__, description.c_str());\n printf(\"%s: GGUF version: %d\\n\", __func__, gguf_get_version(ctx));\n printf(\"%s: alignment: %zu\\n\", __func__, gguf_get_alignment(ctx));\n printf(\"%s: n_tensors: %d\\n\", __func__, n_tensors);\n printf(\"%s: n_kv: %d\\n\", __func__, n_kv);\n printf(\"%s: ftype: %s\\n\", __func__, ftype_str.c_str());\n printf(\"\\n\");\n }\n\n // kv\n if (verbosity >= 3) {\n const int n_kv = gguf_get_n_kv(ctx);\n\n for (int i = 0; i < n_kv; ++i) {\n const char * key = gguf_get_key(ctx, i);\n\n printf(\"%s: kv[%d]: key = %s\\n\", __func__, i, key);\n }\n printf(\"\\n\");\n }\n\n // data\n size_t ctx_size = 0;\n {\n const int n_tensors = gguf_get_n_tensors(ctx);\n\n for (int i = 0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name(ctx, i);\n const size_t offset = gguf_get_tensor_offset(ctx, i);\n\n struct ggml_tensor * cur = ggml_get_tensor(meta, name);\n ctx_size += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;\n size_t tensor_size = ggml_nbytes(cur);\n size_t padded_size = ggml_nbytes_pad(cur);\n ctx_size += padded_size;\n if (verbosity >= 3) {\n printf(\"%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\\n\", __func__, i,\n cur->n_dims, cur->name, tensor_size, padded_size, offset);\n }\n }\n }\n\n clip_ctx * new_clip = new clip_ctx;\n\n // model size and capabilities\n {\n int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);\n new_clip->has_text_encoder = gguf_get_val_bool(ctx, idx);\n\n idx = get_key_idx(ctx, KEY_HAS_VIS_ENC);\n new_clip->has_vision_encoder = gguf_get_val_bool(ctx, idx);\n\n idx = gguf_find_key(ctx, KEY_HAS_LLAVA_PROJ);\n if (idx != -1) {\n new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx);\n }\n\n GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search\n GGML_ASSERT(new_clip->has_vision_encoder);\n GGML_ASSERT(!new_clip->has_text_encoder);\n\n idx = get_key_idx(ctx, KEY_USE_GELU);\n new_clip->use_gelu = gguf_get_val_bool(ctx, idx);\n\n if (verbosity >= 1) {\n printf(\"%s: text_encoder: %d\\n\", __func__, new_clip->has_text_encoder);\n printf(\"%s: vision_encoder: %d\\n\", __func__, new_clip->has_vision_encoder);\n printf(\"%s: llava_projector: %d\\n\", __func__, new_clip->has_llava_projector);\n printf(\"%s: model size: %.2f MB\\n\", __func__, (ctx_size / 1024.0 / 1024.0));\n printf(\"%s: metadata size: %.2f MB\\n\", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);\n }\n }\n\n // load tensors\n {\n struct ggml_init_params params = {\n /*.mem_size =*/ ctx_size,\n /*.mem_buffer =*/ NULL,\n /*.no_alloc =*/ false,\n };\n\n new_clip->ctx = ggml_init(params);\n if (!new_clip->ctx) {\n fprintf(stderr, \"%s: ggml_init() failed\\n\", __func__);\n clip_free(new_clip);\n return nullptr;\n }\n\n auto fin = std::ifstream(fname, std::ios::binary);\n if (!fin) {\n printf(\"cannot open model file for loading tensors\\n\");\n clip_free(new_clip);\n return nullptr;\n }\n\n const int n_tensors = gguf_get_n_tensors(ctx);\n for (int i = 0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name(ctx, i);\n struct ggml_tensor * t = ggml_get_tensor(meta, name);\n struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t);\n ggml_set_name(cur, name);\n\n const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);\n fin.seekg(offset, std::ios::beg);\n if (!fin) {\n printf(\"%s: failed to seek for tensor %s\\n\", __func__, name);\n clip_free(new_clip);\n return nullptr;\n }\n\n fin.read(reinterpret_cast(cur->data), ggml_nbytes(t));\n }\n\n fin.close();\n }\n\n // vision model\n if (new_clip->has_vision_encoder) {\n // load vision model\n auto & vision_model = new_clip->vision_model;\n auto & hparams = vision_model.hparams;\n hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, \"vision\"));\n hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, \"vision\"));\n hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, \"vision\"));\n hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, \"vision\"));\n hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE);\n hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE);\n hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, \"vision\"));\n hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, \"vision\"));\n\n int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);\n int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);\n for (int i = 0; i < 3; ++i) {\n new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));\n new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));\n }\n\n if (verbosity >= 2) {\n printf(\"\\n%s: vision model hparams\\n\", __func__);\n printf(\"image_size %d\\n\", hparams.image_size);\n printf(\"patch_size %d\\n\", hparams.patch_size);\n printf(\"v_hidden_size %d\\n\", hparams.hidden_size);\n printf(\"v_n_intermediate %d\\n\", hparams.n_intermediate);\n printf(\"v_projection_dim %d\\n\", hparams.projection_dim);\n printf(\"v_n_head %d\\n\", hparams.n_head);\n printf(\"v_n_layer %d\\n\", hparams.n_layer);\n }\n\n vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD);\n vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD);\n vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, \"v\"));\n vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, \"v\", \"weight\"));\n vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, \"v\", \"bias\"));\n vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, \"weight\"));\n vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, \"bias\"));\n vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, \"weight\"));\n vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, \"bias\"));\n\n vision_model.layers.resize(hparams.n_layer);\n for (int il = 0; il < hparams.n_layer; ++il) {\n auto & layer = vision_model.layers[il];\n layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, \"v\", il, \"weight\"));\n layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, \"v\", il, \"weight\"));\n layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, \"v\", il, \"weight\"));\n layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, \"v\", il, \"weight\"));\n layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, \"v\", il, \"weight\"));\n layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, \"v\", il, \"weight\"));\n layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, \"v\", il, \"weight\"));\n layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, \"v\", il, \"weight\"));\n layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, \"v\", il, \"bias\"));\n layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, \"v\", il, \"bias\"));\n layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, \"v\", il, \"bias\"));\n layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, \"v\", il, \"bias\"));\n layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, \"v\", il, \"bias\"));\n layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, \"v\", il, \"bias\"));\n layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, \"v\", il, \"bias\"));\n layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, \"v\", il, \"bias\"));\n }\n }\n\n ggml_free(meta);\n\n new_clip->ctx_gguf = ctx;\n\n// measure mem requirement and allocate\n {\n static const size_t tensor_alignment = 32;\n new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead());\n new_clip->alloc = ggml_allocr_new_measure(tensor_alignment);\n clip_image_f32_batch batch;\n batch.size = 1;\n ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);\n size_t alloc_size = ggml_allocr_alloc_graph(new_clip->alloc, gf) + tensor_alignment;\n ggml_allocr_free(new_clip->alloc);\n new_clip->buf_alloc.resize(alloc_size);\n new_clip->alloc = ggml_allocr_new(new_clip->buf_alloc.data, new_clip->buf_alloc.size, tensor_alignment);\n\n printf(\"%s: total allocated memory: %.2f MB\\n\", __func__, (new_clip->buf_compute.size + alloc_size)/1024.0/1024.0);\n }\n\n return new_clip;\n}\n\nclip_image_u8 * make_clip_image_u8() {\n auto img = new clip_image_u8();\n return img;\n}\nclip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }\n\nvoid clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; }\nvoid clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; }\n\nstatic void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {\n img->nx = nx;\n img->ny = ny;\n img->size = nx * ny * 3;\n img->data = new uint8_t[img->size]();\n memcpy(img->data, data, img->size);\n}\n\nbool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {\n int nx, ny, nc;\n auto data = stbi_load(fname, &nx, &ny, &nc, 3);\n if (!data) {\n fprintf(stderr, \"%s: failed to load image '%s'\\n\", __func__, fname);\n return false;\n }\n build_clip_img_from_data(data, nx, ny, img);\n stbi_image_free(data);\n return true;\n}\n\nbool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {\n int nx, ny, nc;\n auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);\n if (!data) {\n fprintf(stderr, \"%s: failed to decode image bytes\\n\", __func__);\n return false;\n }\n build_clip_img_from_data(data, nx, ny, img);\n stbi_image_free(data);\n return true;\n}\n\n// normalize: x = (x - mean) / std\n// TODO: implement bicubic interpolation instead of linear.\nbool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {\n if (!ctx->has_vision_encoder) {\n printf(\"This gguf file seems to have no vision encoder\\n\");\n return false;\n }\n\n // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)\n // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156\n\n clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily\n if (pad2square && img->nx != img->ny) {\n int longer_side = std::max(img->nx, img->ny);\n temp->nx = longer_side;\n temp->ny = longer_side;\n temp->size = 3 * longer_side * longer_side;\n temp->data = new uint8_t[temp->size]();\n uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA\n\n // fill with background color\n for (size_t i = 0; i < temp->size; i++) {\n temp->data[i] = bc[i % 3];\n }\n\n // copy from the input image\n for (int y = 0; y < img->ny; y++) {\n for (int x = 0; x < img->nx; x++) {\n const int i = 3 * (y * img->nx + x);\n const int j = 3 * (y * temp->nx + x);\n temp->data[j] = img->data[i];\n temp->data[j+1] = img->data[i+1];\n temp->data[j+2] = img->data[i+2];\n }\n }\n } else {\n temp->nx = img->nx;\n temp->ny = img->ny;\n temp->size = img->size;\n temp->data = new uint8_t[temp->size]();\n memcpy(&temp->data[0], &img->data[0], temp->size); // copy\n }\n\n const int nx = temp->nx;\n const int ny = temp->ny;\n\n const int nx2 = ctx->vision_model.hparams.image_size;\n const int ny2 = ctx->vision_model.hparams.image_size;\n\n res->nx = nx2;\n res->ny = ny2;\n res->size = 3 * nx2 * ny2;\n res->data = new float[res->size]();\n\n const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;\n\n const int nx3 = int(nx / scale + 0.5f);\n const int ny3 = int(ny / scale + 0.5f);\n\n const auto & m3 = ctx->image_mean; // {0.48145466f, 0.4578275f, 0.40821073f};\n const auto & s3 = ctx->image_std; // {0.26862954f, 0.26130258f, 0.27577711f};\n\n for (int y = 0; y < ny3; y++) {\n for (int x = 0; x < nx3; x++) {\n for (int c = 0; c < 3; c++) {\n // linear interpolation\n const float sx = (x + 0.5f) * scale - 0.5f;\n const float sy = (y + 0.5f) * scale - 0.5f;\n\n const int x0 = std::max(0, (int)std::floor(sx));\n const int y0 = std::max(0, (int)std::floor(sy));\n\n const int x1 = std::min(x0 + 1, nx - 1);\n const int y1 = std::min(y0 + 1, ny - 1);\n\n const float dx = sx - x0;\n const float dy = sy - y0;\n\n const int j00 = 3 * (y0 * nx + x0) + c;\n const int j01 = 3 * (y0 * nx + x1) + c;\n const int j10 = 3 * (y1 * nx + x0) + c;\n const int j11 = 3 * (y1 * nx + x1) + c;\n\n const float v00 = temp->data[j00];\n const float v01 = temp->data[j01];\n const float v10 = temp->data[j10];\n const float v11 = temp->data[j11];\n\n const float v0 = v00 * (1.0f - dx) + v01 * dx;\n const float v1 = v10 * (1.0f - dx) + v11 * dx;\n\n const float v = v0 * (1.0f - dy) + v1 * dy;\n\n const uint8_t v2 = std::min(std::max(std::round(v), 0.0f), 255.0f);\n\n const int i = 3 * (y * nx3 + x) + c;\n\n res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];\n }\n }\n }\n clip_image_u8_free(temp);\n\n return true;\n}\n\nvoid clip_free(clip_ctx * ctx) {\n ggml_free(ctx->ctx);\n gguf_free(ctx->ctx_gguf);\n delete ctx;\n}\n\nbool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {\n if (!ctx->has_vision_encoder) {\n printf(\"This gguf file seems to have no vision encoder\\n\");\n return false;\n }\n\n clip_image_f32_batch imgs{};\n imgs.size = 1;\n imgs.data = img;\n return clip_image_batch_encode(ctx, n_threads, &imgs, vec);\n}\n\nbool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {\n\n if (!ctx->has_vision_encoder) {\n printf(\"This gguf file seems to have no vision encoder\\n\");\n return false;\n }\n\n int batch_size = imgs->size;\n if(ctx->has_llava_projector) {\n GGML_ASSERT(batch_size == 1); // TODO: support multiple images\n }\n\n // reset alloc buffer to clean the memory from previous invocations\n ggml_allocr_reset(ctx->alloc);\n\n // build the inference graph\n ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);\n ggml_allocr_alloc_graph(ctx->alloc, gf);\n\n struct ggml_cplan plan = ggml_graph_plan(gf, n_threads);\n if (plan.work_size > 0) {\n plan.work_data = (uint8_t *)malloc(plan.work_size);\n }\n\n ggml_graph_compute(gf, &plan);\n\n // the last node is the embedding tensor\nstruct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];\n\n // copy the embeddings to the location passed by the user\n memcpy(vec, ggml_get_data_f32(embeddings), ggml_nbytes(embeddings));\n\n if (plan.work_size > 0) {\n free(plan.work_data);\n }\n\n return true;\n}\n\nbool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {\n\n ggml_type type = GGML_TYPE_Q4_1;\n\n switch (itype) {\n case 2:\n type = GGML_TYPE_Q4_0;\n break;\n case 3:\n type = GGML_TYPE_Q4_1;\n break;\n case 6:\n type = GGML_TYPE_Q5_0;\n break;\n case 7:\n type = GGML_TYPE_Q5_1;\n break;\n case 8:\n type = GGML_TYPE_Q8_0;\n break;\n default:\n fprintf(stderr, \"%s: invalid quantization type %d\\n\", __func__, itype);\n return false;\n };\n\n auto ctx_clip = clip_model_load(fname_inp, 2);\n const auto & ctx_src = ctx_clip->ctx_gguf;\n const auto & ctx_data = ctx_clip->ctx;\n\n auto ctx_out = gguf_init_empty();\n gguf_set_kv(ctx_out, ctx_src);\n gguf_set_val_u32(ctx_out, \"general.quantization_version\", GGML_QNT_VERSION);\n gguf_set_val_u32(ctx_out, \"general.file_type\", itype);\n\n auto fout = std::ofstream(fname_out, std::ios::binary);\n\n const int n_tensors = gguf_get_n_tensors(ctx_src);\n\n for (int i = 0; i < n_tensors; ++i) {\n const char * name = gguf_get_tensor_name(ctx_src, i);\n struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);\n gguf_add_tensor(ctx_out, cur);\n }\n\n const size_t meta_size = gguf_get_meta_size(ctx_out);\n for (size_t i = 0; i < meta_size; ++i) {\n fout.put(0);\n }\n\n // regexes of tensor names to be quantized\n const std::vector k_names = {\n \".*weight\",\n };\n\n std::vector read_data(512);\n std::vector work(512);\n std::vector conv_buf(512);\n std::vector hist_all(1 << 4, 0);\n size_t total_size_org = 0;\n size_t total_size_new = 0;\n\n for (int i = 0; i < n_tensors; ++i) {\n const std::string name = gguf_get_tensor_name(ctx_src, i);\n struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());\n\n enum ggml_type new_type;\n void * new_data;\n size_t new_size;\n\n bool quantize = false;\n for (const auto & s : k_names) {\n if (std::regex_match(name, std::regex(s))) {\n quantize = true;\n break;\n }\n }\n\n // quantize only 2D tensors\n quantize &= (cur->n_dims == 2);\n\n if (quantize) {\n new_type = type;\n const size_t n_elms = ggml_nelements(cur);\n float * f32_data;\n\n switch (cur->type) {\n case GGML_TYPE_F32:\n f32_data = (float *)cur->data;\n break;\n case GGML_TYPE_F16:\n if (conv_buf.size() < n_elms) {\n conv_buf.resize(n_elms);\n }\n for (size_t j = 0; j < n_elms; ++j) {\n conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]);\n }\n f32_data = (float *)conv_buf.data();\n break;\n default:\n printf(\"Please use an input file in f32 or f16\\n\");\n return false;\n }\n\n if (work.size() < n_elms * 4) {\n work.resize(n_elms * 4);\n }\n new_data = work.data();\n\n std::vector hist_cur(1 << 4, 0);\n\n switch (new_type) {\n case GGML_TYPE_Q4_0: {\n new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());\n } break;\n case GGML_TYPE_Q4_1: {\n new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());\n } break;\n case GGML_TYPE_Q5_0: {\n new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());\n } break;\n case GGML_TYPE_Q5_1: {\n new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());\n } break;\n case GGML_TYPE_Q8_0: {\n new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());\n } break;\n default: {\n fprintf(stderr, \"%s: unsupported quantization type %d\\n\", __func__, new_type);\n return false;\n }\n }\n\n for (size_t j = 0; j < hist_cur.size(); ++j) {\n hist_all[j] += hist_cur[j];\n }\n } else {\n new_type = cur->type;\n new_data = cur->data;\n new_size = ggml_nbytes(cur);\n }\n const size_t orig_size = ggml_nbytes(cur);\n total_size_org += orig_size;\n total_size_new += new_size;\n gguf_set_tensor_type(ctx_out, name.c_str(), new_type);\n gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);\n fout.write((const char *)new_data, new_size);\n size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;\n for (size_t j = 0; j < pad; ++j) {\n fout.put(0);\n }\n\n printf(\"%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\\n\", name.c_str(), cur->n_dims, quantize,\n orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);\n }\n\n // go back to beginning of file and write the updated metadata\n fout.seekp(0, std::ios::beg);\n std::vector meta(meta_size);\n gguf_get_meta_data(ctx_out, meta.data());\n fout.write((const char *)meta.data(), meta_size);\n\n fout.close();\n\n clip_free(ctx_clip);\n gguf_free(ctx_out);\n\n {\n printf(\"%s: original size = %8.2f MB\\n\", __func__, total_size_org / 1024.0 / 1024.0);\n printf(\"%s: quantized size = %8.2f MB\\n\", __func__, total_size_new / 1024.0 / 1024.0);\n\n int64_t sum_all = 0;\n for (size_t i = 0; i < hist_all.size(); ++i) {\n sum_all += hist_all[i];\n }\n\n printf(\"%s: hist: \", __func__);\n for (size_t i = 0; i < hist_all.size(); ++i) {\n printf(\"%5.3f \", hist_all[i] / (float)sum_all);\n }\n printf(\"\\n\");\n }\n\n return true;\n}\n\nint clip_n_mmproj_embd(const struct clip_ctx * ctx) {\n return ctx->vision_model.mm_2_b->ne[0];\n}\n\nint clip_n_patches(const struct clip_ctx * ctx) {\n auto & params = ctx->vision_model.hparams;\n\n return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);\n}\n\nsize_t clip_embd_nbytes(const struct clip_ctx * ctx) {\n return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);\n}\n" }, { "path": "examples/llava/clip.h", "content": "#ifndef CLIP_H\n#define CLIP_H\n\n#include \n#include \n\n#ifdef LLAMA_SHARED\n# if defined(_WIN32) && !defined(__MINGW32__)\n# ifdef LLAMA_BUILD\n# define CLIP_API __declspec(dllexport)\n# else\n# define CLIP_API __declspec(dllimport)\n# endif\n# else\n# define CLIP_API __attribute__ ((visibility (\"default\")))\n# endif\n#else\n# define CLIP_API\n#endif\n\nstruct clip_ctx;\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nstruct clip_vision_hparams {\n int32_t image_size;\n int32_t patch_size;\n int32_t hidden_size;\n int32_t n_intermediate;\n int32_t projection_dim;\n int32_t n_head;\n int32_t n_layer;\n float eps;\n};\n\n/** load mmproj model */\nCLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity);\n/** free mmproj model */\nCLIP_API void clip_free(struct clip_ctx * ctx);\n\nsize_t clip_embd_nbytes(const struct clip_ctx * ctx);\nint clip_n_patches(const struct clip_ctx * ctx);\nint clip_n_mmproj_embd(const struct clip_ctx * ctx);\n\n// RGB uint8 image\nstruct clip_image_u8 {\n int nx;\n int ny;\n uint8_t * data = NULL;\n size_t size;\n};\n\n// RGB float32 image (NHWC)\n// Memory layout: RGBRGBRGB...\nstruct clip_image_f32 {\n int nx;\n int ny;\n float * data = NULL;\n size_t size;\n};\n\nstruct clip_image_u8_batch {\n struct clip_image_u8 * data;\n size_t size;\n};\n\nstruct clip_image_f32_batch {\n struct clip_image_f32 * data;\n size_t size;\n};\n\nstruct clip_image_u8 * make_clip_image_u8();\nstruct clip_image_f32 * make_clip_image_f32();\nCLIP_API void clip_image_u8_free(clip_image_u8 * img);\nCLIP_API void clip_image_f32_free(clip_image_f32 * img);\nCLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);\n/** interpret bytes as an image file with length bytes_length, and use the result to populate img */\nCLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);\n\nbool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);\nbool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);\n\nbool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs,\n float * vec);\n\nbool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype);\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif // CLIP_H\n" }, { "path": "examples/llava/convert-image-encoder-to-gguf.py", "content": "import argparse\nimport os\nimport json\n\nimport torch\nimport numpy as np\nfrom gguf import *\nfrom transformers import CLIPModel, CLIPProcessor\n\nTEXT = \"clip.text\"\nVISION = \"clip.vision\"\n\n\ndef k(raw_key: str, arch: str) -> str:\n return raw_key.format(arch=arch)\n\n\ndef should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:\n if name in (\n \"logit_scale\",\n \"text_model.embeddings.position_ids\",\n \"vision_model.embeddings.position_ids\",\n ):\n return True\n\n if has_llava and name in [\"visual_projection.weight\", \"vision_model.post_layernorm.weight\", \"vision_model.post_layernorm.bias\"]:\n return True\n\n if name.startswith(\"v\") and not has_vision:\n return True\n\n if name.startswith(\"t\") and not has_text:\n return True\n\n return False\n\n\ndef get_tensor_name(name: str) -> str:\n if \"projection\" in name:\n return name\n\n if \"mm_projector\" in name:\n return name.replace(\"model.mm_projector\", \"mm\")\n\n return name.replace(\"text_model\", \"t\").replace(\"vision_model\", \"v\").replace(\"encoder.layers\", \"blk\").replace(\"embeddings.\", \"\").replace(\"_proj\", \"\").replace(\"self_attn.\", \"attn_\").replace(\"layer_norm\", \"ln\").replace(\"layernorm\", \"ln\").replace(\"mlp.fc1\", \"ffn_down\").replace(\"mlp.fc2\", \"ffn_up\").replace(\"embedding\", \"embd\").replace(\"final\", \"post\").replace(\"layrnorm\", \"ln\")\n\n\ndef bytes_to_unicode():\n \"\"\"\n Returns list of utf-8 byte and a corresponding list of unicode strings.\n The reversible bpe codes work on unicode strings.\n This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.\n This is a signficant percentage of your normal, say, 32K bpe vocab.\n To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = (\n list(range(ord(\"!\"), ord(\"~\") + 1))\n + list(range(ord(\"¡\"), ord(\"¬\") + 1))\n + list(range(ord(\"®\"), ord(\"ÿ\") + 1))\n )\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\nap = argparse.ArgumentParser(prog=\"convert_hf_to_gguf.py\")\nap.add_argument(\"-m\", \"--model-dir\", help=\"Path to model directory cloned from HF Hub\", required=True)\nap.add_argument(\"--use-f32\", action=\"store_true\", default=False, help=\"Use f32 instead of f16\")\nap.add_argument(\"--text-only\", action=\"store_true\", required=False,\n help=\"Save a text-only model. It can't be used to encode images\")\nap.add_argument(\"--vision-only\", action=\"store_true\", required=False,\n help=\"Save a vision-only model. It can't be used to encode texts\")\nap.add_argument(\"--llava-projector\", help=\"Path to llava.projector file. If specified, save an image encoder for LLaVA models.\")\nap.add_argument(\"--image-mean\", nargs=3, type=float, required=False, help=\"Override image mean values\")\nap.add_argument(\"--image-std\", nargs=3, type=float, required=False, help=\"Override image std values\")\nap.add_argument(\"-o\", \"--output-dir\", help=\"Directory to save GGUF files. Default is the original model directory\", default=None)\n\nargs = ap.parse_args()\n\n\nif args.text_only and args.vision_only:\n print(\"--text-only and --image-only arguments cannot be specified at the same time.\")\n exit(1)\n\nif args.use_f32:\n print(\"WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.\")\n\n# output in the same directory as the model if output_dir is None\ndir_model = args.model_dir\n\n\nwith open(dir_model + \"/vocab.json\", \"r\", encoding=\"utf-8\") as f:\n vocab = json.load(f)\n tokens = [key for key in vocab]\n\nwith open(dir_model + \"/config.json\", \"r\", encoding=\"utf-8\") as f:\n config = json.load(f)\n v_hparams = config[\"vision_config\"]\n t_hparams = config[\"text_config\"]\n\n# possible data types\n# ftype == 0 -> float32\n# ftype == 1 -> float16\n#\n# map from ftype to string\nftype_str = [\"f32\", \"f16\"]\n\nftype = 1\nif args.use_f32:\n ftype = 0\n\n\nmodel = CLIPModel.from_pretrained(dir_model)\nprocessor = CLIPProcessor.from_pretrained(dir_model)\n\nfname_middle = None\nhas_text_encoder = True\nhas_vision_encoder = True\nhas_llava_projector = False\nif args.text_only:\n fname_middle = \"text-\"\n has_vision_encoder = False\nelif args.vision_only:\n fname_middle = \"vision-\"\n has_text_encoder = False\nelif args.llava_projector is not None:\n fname_middle = \"mmproj-\"\n has_text_encoder = False\n has_llava_projector = True\nelse:\n fname_middle = \"\"\n\noutput_dir = args.output_dir if args.output_dir is not None else dir_model\nos.makedirs(output_dir, exist_ok=True)\noutput_prefix = os.path.basename(output_dir).replace(\"ggml_\", \"\")\nfname_out = os.path.join(output_dir, f\"{fname_middle}model-{ftype_str[ftype]}.gguf\")\nfout = GGUFWriter(path=fname_out, arch=\"clip\")\n\nfout.add_bool(\"clip.has_text_encoder\", has_text_encoder)\nfout.add_bool(\"clip.has_vision_encoder\", has_vision_encoder)\nfout.add_bool(\"clip.has_llava_projector\", has_llava_projector)\nfout.add_file_type(ftype)\nmodel_name = config[\"_name_or_path\"] if \"_name_or_path\" in config else os.path.basename(dir_model)\nfout.add_name(model_name)\nif args.text_only:\n fout.add_description(\"text-only CLIP model\")\nelif args.vision_only and not has_llava_projector:\n fout.add_description(\"vision-only CLIP model\")\nelif has_llava_projector:\n fout.add_description(\"image encoder for LLaVA\")\nelse:\n fout.add_description(\"two-tower CLIP model\")\n\nif has_text_encoder:\n # text_model hparams\n fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams[\"max_position_embeddings\"])\n fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams[\"hidden_size\"])\n fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams[\"intermediate_size\"])\n fout.add_uint32(\"clip.text.projection_dim\", t_hparams.get(\"projection_dim\", config[\"projection_dim\"]))\n fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams[\"num_attention_heads\"])\n fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams[\"layer_norm_eps\"])\n fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams[\"num_hidden_layers\"])\n fout.add_token_list(tokens)\n\nif has_vision_encoder:\n # vision_model hparams\n fout.add_uint32(\"clip.vision.image_size\", v_hparams[\"image_size\"])\n fout.add_uint32(\"clip.vision.patch_size\", v_hparams[\"patch_size\"])\n fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams[\"hidden_size\"])\n fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams[\"intermediate_size\"])\n fout.add_uint32(\"clip.vision.projection_dim\", v_hparams.get(\"projection_dim\", config[\"projection_dim\"]))\n fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams[\"num_attention_heads\"])\n fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams[\"layer_norm_eps\"])\n block_count = v_hparams[\"num_hidden_layers\"] - 1 if has_llava_projector else v_hparams[\"num_hidden_layers\"]\n fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)\n\n image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean\n image_std = processor.image_processor.image_std if args.image_std is None else args.image_std\n fout.add_array(\"clip.vision.image_mean\", image_mean)\n fout.add_array(\"clip.vision.image_std\", image_std)\n\nuse_gelu = v_hparams[\"hidden_act\"] == \"gelu\"\nfout.add_bool(\"clip.use_gelu\", use_gelu)\n\n\nif has_llava_projector:\n model.vision_model.encoder.layers.pop(-1)\n projector = torch.load(args.llava_projector)\n for name, data in projector.items():\n name = get_tensor_name(name)\n if data.ndim == 2:\n data = data.squeeze().numpy().astype(np.float16)\n else:\n data = data.squeeze().numpy().astype(np.float32)\n\n fout.add_tensor(name, data)\n\n print(\"Projector tensors added\\n\")\n\nstate_dict = model.state_dict()\nfor name, data in state_dict.items():\n if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):\n # we don't need this\n print(f\"skipping parameter: {name}\")\n continue\n\n name = get_tensor_name(name)\n data = data.squeeze().numpy()\n\n n_dims = len(data.shape)\n\n # ftype == 0 -> float32, ftype == 1 -> float16\n ftype_cur = 0\n if n_dims == 4:\n print(f\"tensor {name} is always saved in f16\")\n data = data.astype(np.float16)\n ftype_cur = 1\n elif ftype == 1:\n if name[-7:] == \".weight\" and n_dims == 2:\n print(\" Converting to float16\")\n data = data.astype(np.float16)\n ftype_cur = 1\n else:\n print(\" Converting to float32\")\n data = data.astype(np.float32)\n ftype_cur = 0\n else:\n if data.dtype != np.float32:\n print(\" Converting to float32\")\n data = data.astype(np.float32)\n ftype_cur = 0\n\n print(f\"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}\")\n fout.add_tensor(name, data)\n\n\nfout.write_header_to_file()\nfout.write_kv_data_to_file()\nfout.write_tensors_to_file()\nfout.close()\n\nprint(\"Done. Output file: \" + fname_out)\n" }, { "path": "examples/llava/llava-cli.cpp", "content": "#include \"ggml.h\"\n#include \"common.h\"\n#include \"clip.h\"\n#include \"llava.h\"\n#include \"llama.h\"\n\n#include \"base64.hpp\"\n\n#include \n#include \n#include \n\nstatic bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) {\n int N = (int) tokens.size();\n for (int i = 0; i < N; i += n_batch) {\n int n_eval = (int) tokens.size() - i;\n if (n_eval > n_batch) {\n n_eval = n_batch;\n }\n if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {\n fprintf(stderr, \"%s : failed to eval. token %d/%d (batch size %d, n_past %d)\\n\", __func__, i, N, n_batch, *n_past);\n return false;\n }\n *n_past += n_eval;\n }\n return true;\n}\n\nstatic bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {\n std::vector tokens;\n tokens.push_back(id);\n return eval_tokens(ctx_llama, tokens, 1, n_past);\n}\n\nstatic bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){\n std::string str2 = str;\n std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);\n eval_tokens(ctx_llama, embd_inp, n_batch, n_past);\n return true;\n}\n\n// TODO: use common/sampling.h\nstatic llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {\n auto & sparams = params.sparams;\n\n // out of user input, sample next token\n const float temp = sparams.temp;\n const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k;\n const float top_p = sparams.top_p;\n const float tfs_z = sparams.tfs_z;\n const float typical_p = sparams.typical_p;\n // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n;\n // const float repeat_penalty = sparams.repeat_penalty;\n // const float alpha_presence = sparams.presence_penalty;\n // const float alpha_frequency = sparams.frequency_penalty;\n const int mirostat = sparams.mirostat;\n const float mirostat_tau = sparams.mirostat_tau;\n const float mirostat_eta = sparams.mirostat_eta;\n // const bool penalize_nl = sparams.penalize_nl;\n\n llama_token id = 0;\n {\n auto logits = llama_get_logits(ctx_llama);\n auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));\n\n // Apply params.logit_bias map\n for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {\n logits[it->first] += it->second;\n }\n\n std::vector candidates;\n candidates.reserve(n_vocab);\n for (llama_token token_id = 0; token_id < n_vocab; token_id++) {\n candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});\n }\n\n llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };\n\n if (temp <= 0) {\n // Greedy sampling\n id = llama_sample_token_greedy(ctx_llama, &candidates_p);\n } else {\n if (mirostat == 1) {\n static float mirostat_mu = 2.0f * mirostat_tau;\n const int mirostat_m = 100;\n llama_sample_temp(ctx_llama, &candidates_p, temp);\n id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);\n } else if (mirostat == 2) {\n static float mirostat_mu = 2.0f * mirostat_tau;\n llama_sample_temp(ctx_llama, &candidates_p, temp);\n id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);\n } else {\n // Temperature sampling\n llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);\n llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);\n llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);\n llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);\n llama_sample_temp(ctx_llama, &candidates_p, temp);\n id = llama_sample_token(ctx_llama, &candidates_p);\n }\n }\n }\n\n return id;\n}\n\nstatic const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {\n int id = sample_id(ctx_llama, params);\n static std::string ret;\n if (id == llama_token_eos(llama_get_model(ctx_llama))) {\n ret = \"\";\n } else {\n ret = llama_token_to_piece(ctx_llama, id);\n }\n eval_id(ctx_llama, id, n_past);\n return ret.c_str();\n}\n\nstatic const char* IMG_BASE64_TAG_BEGIN = \"\";\n\nstatic void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {\n begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);\n end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);\n}\n\nstatic bool prompt_contains_image(const std::string& prompt) {\n size_t begin, end;\n find_image_tag_in_prompt(prompt, begin, end);\n return (begin != std::string::npos);\n}\n\n// replaces the base64 image tag in the prompt with `replacement`\nstatic llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {\n size_t img_base64_str_start, img_base64_str_end;\n find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);\n if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {\n fprintf(stderr, \"%s: invalid base64 image tag. must be %s%s\\n\", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);\n return NULL;\n }\n\n auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);\n auto base64_bytes_count = img_base64_str_end - base64_bytes_start;\n auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );\n\n auto required_bytes = base64::required_encode_size(base64_str.size());\n auto img_bytes = std::vector(required_bytes);\n base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());\n\n auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());\n if (!embed) {\n fprintf(stderr, \"%s: could not load image from base64 string.\\n\", __func__);\n return NULL;\n }\n\n return embed;\n}\n\nstatic std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = \"\") {\n size_t begin, end;\n find_image_tag_in_prompt(prompt, begin, end);\n if (begin == std::string::npos || end == std::string::npos) {\n return prompt;\n }\n auto pre = prompt.substr(0, begin);\n auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));\n return pre + replacement + post;\n}\n\nstruct llava_context {\n struct clip_ctx * ctx_clip = NULL;\n struct llama_context * ctx_llama = NULL;\n struct llama_model * model = NULL;\n};\n\nstatic void show_additional_info(int /*argc*/, char ** argv) {\n printf(\"\\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \\\"describe the image in detail.\\\"]\\n\", argv[0]);\n printf(\" note: a lower temperature value like 0.1 is recommended for better quality.\\n\");\n}\n\nstatic struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {\n\n // load and preprocess the image\n llava_image_embed * embed = NULL;\n auto prompt = params->prompt;\n if (prompt_contains_image(prompt)) {\n if (!params->image.empty()) {\n printf(\"using base64 encoded image instead of command line image path\\n\");\n }\n embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);\n if (!embed) {\n fprintf(stderr, \"%s: can't load image from prompt\\n\", __func__);\n return NULL;\n }\n params->prompt = remove_image_from_prompt(prompt);\n } else {\n embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());\n if (!embed) {\n fprintf(stderr, \"%s: is %s really an image file?\\n\", __func__, params->image.c_str());\n return NULL;\n }\n }\n\n return embed;\n}\n\nstatic void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) {\n int n_past = 0;\n\n const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;\n\n // llava chat format is \"\\nUSER:\\n\\nASSISTANT:\"\n eval_string(ctx_llava->ctx_llama, \"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\\nUSER:\", params->n_batch, &n_past, true);\n llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);\n eval_string(ctx_llava->ctx_llama, (prompt + \"\\nASSISTANT:\").c_str(), params->n_batch, &n_past, false);\n\n // generate the response\n\n printf(\"\\n\");\n\n for (int i = 0; i < max_tgt_len; i++) {\n const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past);\n if (strcmp(tmp, \"\") == 0) break;\n\n printf(\"%s\", tmp);\n fflush(stdout);\n }\n\n printf(\"\\n\");\n}\n\n\nstatic struct llava_context * llava_init(gpt_params * params) {\n const char * clip_path = params->mmproj.c_str();\n\n auto prompt = params->prompt;\n if (prompt.empty()) {\n prompt = \"describe the image in detail.\";\n }\n\n auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);\n\n llama_backend_init(params->numa);\n\n llama_model_params model_params = llama_model_params_from_gpt_params(*params);\n\n llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);\n if (model == NULL) {\n fprintf(stderr , \"%s: error: unable to load model\\n\" , __func__);\n return NULL;\n }\n\n llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);\n ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings\n\n llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);\n\n if (ctx_llama == NULL) {\n fprintf(stderr , \"%s: error: failed to create the llama_context\\n\" , __func__);\n return NULL;\n }\n\n auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));\n\n ctx_llava->ctx_llama = ctx_llama;\n ctx_llava->ctx_clip = ctx_clip;\n ctx_llava->model = model;\n return ctx_llava;\n}\n\nstatic void llava_free(struct llava_context * ctx_llava) {\n if (ctx_llava->ctx_clip) {\n clip_free(ctx_llava->ctx_clip);\n ctx_llava->ctx_clip = NULL;\n }\n\n llama_free(ctx_llava->ctx_llama);\n llama_free_model(ctx_llava->model);\n llama_backend_free();\n}\n\nint main(int argc, char ** argv) {\n ggml_time_init();\n\n gpt_params params;\n\n if (!gpt_params_parse(argc, argv, params)) {\n show_additional_info(argc, argv);\n return 1;\n }\n if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {\n gpt_print_usage(argc, argv, params);\n show_additional_info(argc, argv);\n return 1;\n }\n\n auto ctx_llava = llava_init(¶ms);\n if (ctx_llava == NULL) {\n fprintf(stderr, \"%s: error: failed to init llava\\n\", __func__);\n return 1;\n }\n\n auto image_embed = load_image(ctx_llava, ¶ms);\n\n // process the prompt\n process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);\n\n llama_print_timings(ctx_llava->ctx_llama);\n\n llava_image_embed_free(image_embed);\n llava_free(ctx_llava);\n return 0;\n}\n" }, { "path": "examples/llava/llava-surgery.py", "content": "import argparse\nimport glob\nimport os\nimport torch\n\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-m\", \"--model\", help=\"Path to LLaVA v1.5 model\")\nargs = ap.parse_args()\n\n# find the model part that includes the the multimodal projector weights\npath = sorted(glob.glob(f\"{args.model}/pytorch_model*.bin\"))[-1]\ncheckpoint = torch.load(path)\n\n# get a list of mm tensor names\nmm_tensors = [k for k, v in checkpoint.items() if k.startswith(\"model.mm_projector\")]\n\n# store these tensors in a new dictionary and torch.save them\nprojector = {name: checkpoint[name].float() for name in mm_tensors}\ntorch.save(projector, f\"{args.model}/llava.projector\")\n\n# remove these tensors from the checkpoint and save it again\nfor name in mm_tensors:\n del checkpoint[name]\n\n# BakLLaVA models contain CLIP tensors in it\nclip_tensors = [k for k, v in checkpoint.items() if k.startswith(\"model.vision_tower\")]\nif len(clip_tensors) > 0:\n clip = {name.replace(\"vision_tower.vision_tower.\", \"\"): checkpoint[name].float() for name in clip_tensors}\n torch.save(clip, f\"{args.model}/llava.clip\")\n\n # remove these tensors\n for name in clip_tensors:\n del checkpoint[name]\n\n # added tokens should be removed to be able to convert Mistral models\n if os.path.exists(f\"{args.model}/added_tokens.json\"):\n with open(f\"{args.model}/added_tokens.json\", \"w\") as f:\n f.write(\"{}\\n\")\n\n\ntorch.save(checkpoint, path)\n\nprint(\"Done!\")\nprint(f\"Now you can convert {args.model} to a a regular LLaMA GGUF file.\")\nprint(f\"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.\")\n" }, { "path": "examples/llava/llava.cpp", "content": "#include \"clip.h\"\n#include \"common.h\"\n#include \"llama.h\"\n#include \"llava.h\"\n\n#include \n#include \n#include \n\n#include \"base64.hpp\"\n\nstatic bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {\n clip_image_f32 * img_res = make_clip_image_f32();\n if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {\n fprintf(stderr, \"%s: unable to preprocess image\\n\", __func__);\n clip_image_f32_free(img_res);\n return false;\n }\n\n *n_img_pos = clip_n_patches(ctx_clip);\n\n const int64_t t_img_enc_start_us = ggml_time_us();\n bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);\n clip_image_f32_free(img_res);\n if (!encoded) {\n fprintf(stderr, \"Unable to encode image\\n\");\n\n return false;\n }\n\n const int64_t t_img_enc_end_us = ggml_time_us();\n float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;\n\n printf(\"\\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\\n\", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);\n\n return true;\n}\n\nbool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {\n // make sure that the correct mmproj was used, i.e., compare apples to apples\n int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));\n auto n_image_embd = clip_n_mmproj_embd(ctx_clip);\n if (n_image_embd != n_llama_embd) {\n printf(\"%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\\n\", __func__, n_image_embd, n_llama_embd);\n return false;\n }\n return true;\n}\n\nstatic bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {\n float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));\n if (!image_embd) {\n fprintf(stderr, \"Unable to allocate memory for image embeddings\\n\");\n free(image_embd);\n return false;\n }\n\n int n_img_pos;\n if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {\n fprintf(stderr, \"%s: cannot encode image, aborting\\n\", __func__);\n free(image_embd);\n return false;\n }\n *image_embd_out = image_embd;\n *n_img_pos_out = n_img_pos;\n\n return true;\n}\n\nbool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {\n int n_embd = llama_n_embd(llama_get_model(ctx_llama));\n\n for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {\n int n_eval = image_embed->n_image_pos - i;\n if (n_eval > n_batch) {\n n_eval = n_batch;\n }\n llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };\n if (llama_decode(ctx_llama, batch)) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return false;\n }\n *n_past += n_eval;\n }\n return true;\n}\n\nLLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {\n clip_image_u8 * img = make_clip_image_u8();\n if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {\n clip_image_u8_free(img);\n fprintf(stderr, \"%s: can't load image from bytes, is it a valid image?\", __func__);\n return NULL;\n }\n\n float* image_embed = NULL;\n int n_image_pos = 0;\n bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);\n if (!image_embed_result) {\n clip_image_u8_free(img);\n fprintf(stderr, \"%s: coulnd't embed the image\\n\", __func__);\n return NULL;\n }\n\n clip_image_u8_free(img);\n auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));\n result->embed = image_embed;\n result->n_image_pos = n_image_pos;\n return result;\n}\n\nstatic bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {\n auto file = fopen(path, \"rb\");\n if (file == NULL) {\n fprintf(stderr, \"%s: can't read file %s\\n\", __func__, path);\n return false;\n }\n\n fseek(file, 0, SEEK_END);\n auto fileSize = ftell(file);\n fseek(file, 0, SEEK_SET);\n\n auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data\n if (buffer == NULL) {\n fprintf(stderr, \"%s: failed to alloc %ld bytes for file %s\\n\", __func__, fileSize, path);\n perror(\"Memory allocation error\");\n fclose(file);\n return false;\n }\n fread(buffer, 1, fileSize, file); // Read the file into the buffer\n fclose(file); // Close the file\n\n *bytesOut = buffer;\n *sizeOut = fileSize;\n return true;\n}\n\nLLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {\n unsigned char* image_bytes;\n long image_bytes_length;\n auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);\n if (!loaded) {\n fprintf(stderr, \"%s: failed to load %s\\n\", __func__, image_path);\n return NULL;\n }\n\n auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);\n free(image_bytes);\n\n return embed;\n}\n\nLLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) {\n free(embed->embed);\n free(embed);\n}\n" }, { "path": "examples/llava/llava.h", "content": "#ifndef LLAVA_H\n#define LLAVA_H\n\n#include \"ggml.h\"\n\n\n#ifdef LLAMA_SHARED\n# if defined(_WIN32) && !defined(__MINGW32__)\n# ifdef LLAMA_BUILD\n# define LLAVA_API __declspec(dllexport)\n# else\n# define LLAVA_API __declspec(dllimport)\n# endif\n# else\n# define LLAVA_API __attribute__ ((visibility (\"default\")))\n# endif\n#else\n# define LLAVA_API\n#endif\n\nstruct clip_ctx;\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nstruct llava_image_embed {\n float * embed;\n int n_image_pos;\n};\n\n/** sanity check for clip <-> llava embed size match */\nLLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);\n\n/** build an image embed from image file bytes */\nLLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);\n/** build an image embed from a path to an image filename */\nLLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);\nLLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);\n/** free an embedding made with llava_image_embed_make_* */\n\n/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */\nLLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);\n\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif\n" }, { "path": "examples/llm.vim", "content": "\" Basic plugin example\n\nfunction! Llm()\n\n let url = \"http://127.0.0.1:8080/completion\"\n\n \" Get the content of the current buffer\n let buffer_content = join(getline(1, '$'), \"\\n\")\n\n \" Create the JSON payload\n let json_payload = {\"temp\":0.72,\"top_k\":100,\"top_p\":0.73,\"repeat_penalty\":1.100000023841858,\"n_predict\":256,\"stop\": [\"\\n\\n\\n\"],\"stream\": v:false}\n let json_payload.prompt = buffer_content\n\n \" Define the curl command\n let curl_command = 'curl -k -s -X POST -H \"Content-Type: application/json\" -d @- ' . url\n let response = system(curl_command, json_encode(json_payload))\n\n \" Extract the content field from the response\n let content = json_decode(response).content\n\n let split_newlines = split(content, '\\n', 1)\n\n \" Insert the content at the cursor position\n call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:])\nendfunction\n\ncommand! Llm call Llm()\nnoremap :Llm\n" }, { "path": "examples/main/CMakeLists.txt", "content": "set(TARGET main)\nadd_executable(${TARGET} main.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/main/README.md", "content": "# llama.cpp/example/main\n\nThis example program allows you to use various LLaMA language models in an easy and efficient way. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.\n\n## Table of Contents\n\n1. [Quick Start](#quick-start)\n2. [Common Options](#common-options)\n3. [Input Prompts](#input-prompts)\n4. [Interaction](#interaction)\n5. [Context Management](#context-management)\n6. [Generation Flags](#generation-flags)\n7. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options)\n8. [Additional Options](#additional-options)\n\n## Quick Start\n\nTo get started right away, run the following command, making sure to use the correct path for the model you have:\n\n#### Unix-based systems (Linux, macOS, etc.):\n\n```bash\n./main -m models/7B/ggml-model.bin --prompt \"Once upon a time\"\n```\n\n#### Windows:\n\n```powershell\nmain.exe -m models\\7B\\ggml-model.bin --prompt \"Once upon a time\"\n```\n\nFor an interactive experience, try this command:\n\n#### Unix-based systems (Linux, macOS, etc.):\n\n```bash\n./main -m models/7B/ggml-model.bin -n -1 --color -r \"User:\" --in-prefix \" \" -i -p \\\n'User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:'\n```\n\n#### Windows:\n\n```powershell\nmain.exe -m models\\7B\\ggml-model.bin -n -1 --color -r \"User:\" --in-prefix \" \" -i -e -p \"User: Hi\\nAI: Hello. I am an AI chatbot. Would you like to talk?\\nUser: Sure!\\nAI: What would you like to talk about?\\nUser:\"\n```\n\nThe following command generates \"infinite\" text from a starting prompt (you can use `Ctrl-C` to stop it):\n\n#### Unix-based systems (Linux, macOS, etc.):\n\n```bash\n./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt\n```\n\n#### Windows:\n\n```powershell\nmain.exe -m models\\7B\\ggml-model.bin --ignore-eos -n -1 --random-prompt\n```\n\n## Common Options\n\nIn this section, we cover the most commonly used options for running the `main` program with the LLaMA models:\n\n- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).\n- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.\n- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.\n- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.\n- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.\n\n## Input Prompts\n\nThe `main` program provides several ways to interact with the LLaMA models using input prompts:\n\n- `--prompt PROMPT`: Provide a prompt directly as a command-line option.\n- `--file FNAME`: Provide a file containing a prompt or multiple prompts.\n- `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)\n- `--random-prompt`: Start with a randomized prompt.\n\n## Interaction\n\nThe `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive`, `--interactive-first`, and `--instruct`.\n\nIn interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\\`) and continue typing.\n\n### Interaction Options\n\n- `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model.\n- `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.\n- `-ins, --instruct`: Run the program in instruction mode, which is specifically designed to work with Alpaca models that excel in completing tasks based on user instructions.\n- `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.\n\nBy understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.\n\n### Reverse Prompts\n\nReverse prompts are a powerful way to create a chat-like experience with a LLaMA model by pausing the text generation when specific text strings are encountered:\n\n- `-r PROMPT, --reverse-prompt PROMPT`: Specify one or multiple reverse prompts to pause text generation and switch to interactive mode. For example, `-r \"User:\"` can be used to jump back into the conversation whenever it's the user's turn to speak. This helps create a more interactive and conversational experience. However, the reverse prompt doesn't work when it ends with a space.\n\nTo overcome this limitation, you can use the `--in-prefix` flag to add a space or any other characters after the reverse prompt.\n\n### In-Prefix\n\nThe `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag:\n\n```sh\n./main -r \"User:\" --in-prefix \" \"\n```\n\n### In-Suffix\n\nThe `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an \"Assistant:\" prompt after the user's input. It's added after the new-line character (`\\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag:\n\n```sh\n./main -r \"User:\" --in-prefix \" \" --in-suffix \"Assistant:\"\n```\n\n### Instruction Mode\n\nInstruction mode is particularly useful when working with Alpaca models, which are designed to follow user instructions for specific tasks:\n\n- `-ins, --instruct`: Enable instruction mode to leverage the capabilities of Alpaca models in completing tasks based on user-provided instructions.\n\nTechnical detail: the user's input is internally prefixed with the reverse prompt (or `### Instruction:` as the default), and followed by `### Response:` (except if you just press Return without any input, to keep generating a longer response).\n\nBy understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.\n\n## Context Management\n\nDuring text generation, LLaMA models have a limited context size, which means they can only consider a certain number of tokens from the input and generated text. When the context fills up, the model resets internally, potentially losing some information from the beginning of the conversation or instructions. Context management options help maintain continuity and coherence in these situations.\n\n### Context Size\n\nThe `--ctx-size` option allows you to set the size of the prompt context used by the LLaMA models during text generation. A larger context size helps the model to better comprehend and generate responses for longer input or conversations.\n\n- `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.\n\n### Extended Context Size\n\nSome fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.\n\n- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.\n\n### Keep Prompt\n\nThe `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.\n\n- `--keep N`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.\n\nBy utilizing context management options like `--ctx-size` and `--keep`, you can maintain a more coherent and consistent interaction with the LLaMA models, ensuring that the generated text remains relevant to the original prompt or conversation.\n\n## Generation Flags\n\nThe following options allow you to control the text generation process and fine-tune the diversity, creativity, and quality of the generated text according to your needs. By adjusting these options and experimenting with different combinations of values, you can find the best settings for your specific use case.\n\n### Number of Tokens to Predict\n\n- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity, -2 = until context filled)\n\nThe `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.\n\nA value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--n-keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in significant pause in output.\n\nIf the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.\n\nIt is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.\n\n### Temperature\n\n- `--temp N`: Adjust the randomness of the generated text (default: 0.8).\n\nTemperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.\n\nExample usage: `--temp 0.5`\n\n### Repeat Penalty\n\n- `--repeat-penalty N`: Control the repetition of token sequences in the generated text (default: 1.1).\n- `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).\n- `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.\n\nThe `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.1.\n\nThe `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).\n\nUse the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.\n\nExample usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`\n\n### Top-K Sampling\n\n- `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40).\n\nTop-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top-k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text. The default value is 40.\n\nExample usage: `--top-k 30`\n\n### Top-P Sampling\n\n- `--top-p N`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).\n\nTop-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top-p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. The default value is 0.9.\n\nExample usage: `--top-p 0.95`\n\n### Min P Sampling\n\n- `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.05).\n\nThe Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.\n\nExample usage: `--min-p 0.05`\n\n### Tail Free Sampling (TFS)\n\n- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).\n\nTail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS.\n\nExample usage: `--tfs 0.95`\n\n### Locally Typical Sampling\n\n- `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).\n\nLocally typical sampling promotes the generation of contextually coherent and diverse text by sampling tokens that are typical or expected based on the surrounding context. By setting the parameter p between 0 and 1, you can control the balance between producing text that is locally coherent and diverse. A value closer to 1 will promote more contextually coherent tokens, while a value closer to 0 will promote more diverse tokens. A value equal to 1 disables locally typical sampling.\n\nExample usage: `--typical 0.9`\n\n### Mirostat Sampling\n\n- `--mirostat N`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).\n- `--mirostat-lr N`: Set the Mirostat learning rate, parameter eta (default: 0.1).\n- `--mirostat-ent N`: Set the Mirostat target entropy, parameter tau (default: 5.0).\n\nMirostat is an algorithm that actively maintains the quality of generated text within a desired range during text generation. It aims to strike a balance between coherence and diversity, avoiding low-quality output caused by excessive repetition (boredom traps) or incoherence (confusion traps).\n\nThe `--mirostat-lr` option sets the Mirostat learning rate (eta). The learning rate influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. The default value is `0.1`.\n\nThe `--mirostat-ent` option sets the Mirostat target entropy (tau), which represents the desired perplexity value for the generated text. Adjusting the target entropy allows you to control the balance between coherence and diversity in the generated text. A lower value will result in more focused and coherent text, while a higher value will lead to more diverse and potentially less coherent text. The default value is `5.0`.\n\nExample usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`\n\n### Logit Bias\n\n- `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion.\n\nThe logit bias option allows you to manually adjust the likelihood of specific tokens appearing in the generated text. By providing a token ID and a positive or negative bias value, you can increase or decrease the probability of that token being generated.\n\nFor example, use `--logit-bias 15043+1` to increase the likelihood of the token 'Hello', or `--logit-bias 15043-1` to decrease its likelihood. Using a value of negative infinity, `--logit-bias 15043-inf` ensures that the token `Hello` is never produced.\n\nA more practical use case might be to prevent the generation of `\\code{begin}` and `\\code{end}` by setting the `\\` token (29905) to negative infinity with `-l 29905-inf`. (This is due to the prevalence of LaTeX codes that show up in LLaMA model inference.)\n\nExample usage: `--logit-bias 29905-inf`\n\n### RNG Seed\n\n- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).\n\nThe RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.\n\n## Performance Tuning and Memory Options\n\nThese options help improve the performance and memory usage of the LLaMA models. By adjusting these settings, you can fine-tune the model's behavior to better suit your system's capabilities and achieve optimal performance for your specific use case.\n\n### Number of Threads\n\n- `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.\n- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. In some systems, it is beneficial to use a higher number of threads during batch processing than during generation. If not specified, the number of threads used for batch processing will be the same as the number of threads used for generation.\n\n### Mlock\n\n- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.\n\n### No Memory Mapping\n\n- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.\n\n### NUMA support\n\n- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.\n\n### Memory Float 32\n\n- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended.\n\n### Batch Size\n\n- `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled (\"BLAS=0\"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.\n\n### Prompt Caching\n\n- `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.\n\n### Grammars\n\n- `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax.\n\n### Quantization\n\nFor information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run).\n\n## Additional Options\n\nThese options provide extra functionality and customization when running the LLaMA models:\n\n- `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.\n- `--verbose-prompt`: Print the prompt before generating text.\n- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.\n- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.\n- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, \"3,2\" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.\n- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.\n- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.\n" }, { "path": "examples/main/main.cpp", "content": "#include \"common.h\"\n\n#include \"console.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))\n#include \n#include \n#elif defined (_WIN32)\n#define WIN32_LEAN_AND_MEAN\n#ifndef NOMINMAX\n#define NOMINMAX\n#endif\n#include \n#include \n#endif\n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstatic llama_context ** g_ctx;\nstatic llama_model ** g_model;\nstatic gpt_params * g_params;\nstatic std::vector * g_input_tokens;\nstatic std::ostringstream * g_output_ss;\nstatic std::vector * g_output_tokens;\nstatic bool is_interacting = false;\n\n\nstatic void write_logfile(\n const llama_context * ctx, const gpt_params & params, const llama_model * model,\n const std::vector & input_tokens, const std::string & output,\n const std::vector & output_tokens\n) {\n if (params.logdir.empty()) {\n return;\n }\n\n const std::string timestamp = get_sortable_timestamp();\n\n const bool success = create_directory_with_parents(params.logdir);\n if (!success) {\n fprintf(stderr, \"%s: warning: failed to create logdir %s, cannot write logfile\\n\",\n __func__, params.logdir.c_str());\n return;\n }\n\n const std::string logfile_path = params.logdir + timestamp + \".yml\";\n FILE * logfile = fopen(logfile_path.c_str(), \"w\");\n\n if (logfile == NULL) {\n fprintf(stderr, \"%s: failed to open logfile %s\\n\", __func__, logfile_path.c_str());\n return;\n }\n\n fprintf(logfile, \"binary: main\\n\");\n char model_desc[128];\n llama_model_desc(model, model_desc, sizeof(model_desc));\n dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);\n\n fprintf(logfile, \"\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"# Generation Results #\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"\\n\");\n\n dump_string_yaml_multiline(logfile, \"output\", output.c_str());\n dump_vector_int_yaml(logfile, \"output_tokens\", output_tokens);\n\n llama_dump_timing_info_yaml(logfile, ctx);\n fclose(logfile);\n}\n\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)\nstatic void sigint_handler(int signo) {\n if (signo == SIGINT) {\n if (!is_interacting) {\n is_interacting = true;\n } else {\n console::cleanup();\n printf(\"\\n\");\n llama_print_timings(*g_ctx);\n write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);\n _exit(130);\n }\n }\n}\n#endif\n\nint main(int argc, char ** argv) {\n gpt_params params;\n g_params = ¶ms;\n\n if (!gpt_params_parse(argc, argv, params)) {\n return 1;\n }\n llama_sampling_params & sparams = params.sparams;\n\n#ifndef LOG_DISABLE_LOGS\n log_set_target(log_filename_generator(\"main\", \"log\"));\n LOG_TEE(\"Log start\\n\");\n log_dump_cmdline(argc, argv);\n#endif // LOG_DISABLE_LOGS\n\n // TODO: Dump params ?\n //LOG(\"Params perplexity: %s\\n\", LOG_TOSTR(params.perplexity));\n\n // save choice to use color for later\n // (note for later: this is a slightly awkward choice)\n console::init(params.simple_io, params.use_color);\n atexit([]() { console::cleanup(); });\n\n if (params.logits_all) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'perplexity' tool for perplexity calculations\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n\n if (params.embedding) {\n printf(\"\\n************\\n\");\n printf(\"%s: please use the 'embedding' tool for embedding calculations\\n\", __func__);\n printf(\"************\\n\\n\");\n\n return 0;\n }\n\n if (params.n_ctx != 0 && params.n_ctx < 8) {\n LOG_TEE(\"%s: warning: minimum context size is 8, using minimum size.\\n\", __func__);\n params.n_ctx = 8;\n }\n\n if (params.rope_freq_base != 0.0) {\n LOG_TEE(\"%s: warning: changing RoPE frequency base to %g.\\n\", __func__, params.rope_freq_base);\n }\n\n if (params.rope_freq_scale != 0.0) {\n LOG_TEE(\"%s: warning: scaling RoPE frequency by %g.\\n\", __func__, params.rope_freq_scale);\n }\n\n LOG_TEE(\"%s: build = %d (%s)\\n\", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);\n LOG_TEE(\"%s: built with %s for %s\\n\", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);\n\n if (params.seed == LLAMA_DEFAULT_SEED) {\n params.seed = time(NULL);\n }\n\n LOG_TEE(\"%s: seed = %u\\n\", __func__, params.seed);\n\n std::mt19937 rng(params.seed);\n if (params.random_prompt) {\n params.prompt = gpt_random_prompt(rng);\n }\n\n LOG(\"%s: llama backend init\\n\", __func__);\n llama_backend_init(params.numa);\n\n llama_model * model;\n llama_context * ctx;\n llama_context * ctx_guidance = NULL;\n g_model = &model;\n g_ctx = &ctx;\n\n // load the model and apply lora adapter, if any\n LOG(\"%s: load the model and apply lora adapter, if any\\n\", __func__);\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n if (sparams.cfg_scale > 1.f) {\n struct llama_context_params lparams = llama_context_params_from_gpt_params(params);\n ctx_guidance = llama_new_context_with_model(model, lparams);\n }\n\n if (model == NULL) {\n LOG_TEE(\"%s: error: unable to load model\\n\", __func__);\n return 1;\n }\n\n const int n_ctx_train = llama_n_ctx_train(model);\n const int n_ctx = llama_n_ctx(ctx);\n LOG(\"n_ctx: %d\\n\", n_ctx);\n\n if (n_ctx > n_ctx_train) {\n LOG_TEE(\"%s: warning: model was trained on only %d context tokens (%d specified)\\n\",\n __func__, n_ctx_train, n_ctx);\n }\n\n // print system information\n {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s\\n\", get_system_info(params).c_str());\n }\n\n std::string path_session = params.path_prompt_cache;\n std::vector session_tokens;\n\n if (!path_session.empty()) {\n LOG_TEE(\"%s: attempting to load saved session from '%s'\\n\", __func__, path_session.c_str());\n\n // fopen to check for existing session\n FILE * fp = std::fopen(path_session.c_str(), \"rb\");\n if (fp != NULL) {\n std::fclose(fp);\n\n session_tokens.resize(n_ctx);\n size_t n_token_count_out = 0;\n if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {\n LOG_TEE(\"%s: error: failed to load session file '%s'\\n\", __func__, path_session.c_str());\n return 1;\n }\n session_tokens.resize(n_token_count_out);\n llama_set_rng_seed(ctx, params.seed);\n\n LOG_TEE(\"%s: loaded a session with prompt size of %d tokens\\n\", __func__, (int) session_tokens.size());\n } else {\n LOG_TEE(\"%s: session file does not exist, will create\\n\", __func__);\n }\n }\n\n const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM;\n LOG(\"add_bos: %d\\n\", add_bos);\n\n std::vector embd_inp;\n\n if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {\n LOG(\"tokenize the prompt\\n\");\n embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);\n } else {\n LOG(\"use session tokens\\n\");\n embd_inp = session_tokens;\n }\n\n LOG(\"prompt: \\\"%s\\\"\\n\", log_tostr(params.prompt));\n LOG(\"tokens: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());\n\n // Should not run without any tokens\n if (embd_inp.empty()) {\n embd_inp.push_back(llama_token_bos(model));\n LOG(\"embd_inp was considered empty and bos was added: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());\n }\n\n // Tokenize negative prompt\n std::vector guidance_inp;\n int guidance_offset = 0;\n int original_prompt_len = 0;\n if (ctx_guidance) {\n LOG(\"cfg_negative_prompt: \\\"%s\\\"\\n\", log_tostr(sparams.cfg_negative_prompt));\n\n guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);\n LOG(\"guidance_inp tokenized: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());\n\n std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);\n LOG(\"original_inp tokenized: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());\n\n original_prompt_len = original_inp.size();\n guidance_offset = (int)guidance_inp.size() - original_prompt_len;\n LOG(\"original_prompt_len: %s\", log_tostr(original_prompt_len));\n LOG(\"guidance_offset: %s\", log_tostr(guidance_offset));\n }\n\n if ((int) embd_inp.size() > n_ctx - 4) {\n LOG_TEE(\"%s: error: prompt is too long (%d tokens, max %d)\\n\", __func__, (int) embd_inp.size(), n_ctx - 4);\n return 1;\n }\n\n // debug message about similarity of saved session, if applicable\n size_t n_matching_session_tokens = 0;\n if (!session_tokens.empty()) {\n for (llama_token id : session_tokens) {\n if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {\n break;\n }\n n_matching_session_tokens++;\n }\n if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {\n LOG_TEE(\"%s: using full prompt from session file\\n\", __func__);\n } else if (n_matching_session_tokens >= embd_inp.size()) {\n LOG_TEE(\"%s: session file has exact match for prompt!\\n\", __func__);\n } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {\n LOG_TEE(\"%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\\n\",\n __func__, n_matching_session_tokens, embd_inp.size());\n } else {\n LOG_TEE(\"%s: session file matches %zu / %zu tokens of prompt\\n\",\n __func__, n_matching_session_tokens, embd_inp.size());\n }\n\n // remove any \"future\" tokens that we might have inherited from the previous session\n llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);\n }\n\n LOGLN(\n \"recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu\",\n log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());\n\n // if we will use the cache for the full prompt without reaching the end of the cache, force\n // reevaluation of the last token token to recalculate the cached logits\n if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {\n LOGLN(\"recalculate the cached logits (do): session_tokens.resize( %zu )\", embd_inp.size() - 1);\n\n session_tokens.resize(embd_inp.size() - 1);\n }\n\n // number of tokens to keep when resetting context\n if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {\n params.n_keep = (int)embd_inp.size();\n }\n\n // prefix & suffix for instruct mode\n const auto inp_pfx = ::llama_tokenize(ctx, \"\\n\\n### Instruction:\\n\\n\", add_bos, true);\n const auto inp_sfx = ::llama_tokenize(ctx, \"\\n\\n### Response:\\n\\n\", false, true);\n\n LOG(\"inp_pfx: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());\n LOG(\"inp_sfx: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());\n\n // in instruct mode, we inject a prefix and a suffix to each input by the user\n if (params.instruct) {\n params.interactive_first = true;\n params.antiprompt.push_back(\"### Instruction:\\n\\n\");\n }\n\n // enable interactive mode if interactive start is specified\n if (params.interactive_first) {\n params.interactive = true;\n }\n\n if (params.verbose_prompt) {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s: prompt: '%s'\\n\", __func__, params.prompt.c_str());\n LOG_TEE(\"%s: number of tokens in prompt = %zu\\n\", __func__, embd_inp.size());\n for (int i = 0; i < (int) embd_inp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());\n }\n\n if (ctx_guidance) {\n LOG_TEE(\"\\n\");\n LOG_TEE(\"%s: negative prompt: '%s'\\n\", __func__, sparams.cfg_negative_prompt.c_str());\n LOG_TEE(\"%s: number of tokens in negative prompt = %zu\\n\", __func__, guidance_inp.size());\n for (int i = 0; i < (int) guidance_inp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());\n }\n }\n\n if (params.n_keep > 0) {\n LOG_TEE(\"%s: static prompt based on n_keep: '\", __func__);\n for (int i = 0; i < params.n_keep; i++) {\n LOG_TEE(\"%s\", llama_token_to_piece(ctx, embd_inp[i]).c_str());\n }\n LOG_TEE(\"'\\n\");\n }\n LOG_TEE(\"\\n\");\n }\n\n if (params.interactive) {\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))\n struct sigaction sigint_action;\n sigint_action.sa_handler = sigint_handler;\n sigemptyset (&sigint_action.sa_mask);\n sigint_action.sa_flags = 0;\n sigaction(SIGINT, &sigint_action, NULL);\n#elif defined (_WIN32)\n auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {\n return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;\n };\n SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true);\n#endif\n\n LOG_TEE(\"%s: interactive mode on.\\n\", __func__);\n\n if (!params.antiprompt.empty()) {\n for (const auto & antiprompt : params.antiprompt) {\n LOG_TEE(\"Reverse prompt: '%s'\\n\", antiprompt.c_str());\n if (params.verbose_prompt) {\n auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);\n for (int i = 0; i < (int) tmp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());\n }\n }\n }\n }\n\n if (params.input_prefix_bos) {\n LOG_TEE(\"Input prefix with BOS\\n\");\n }\n\n if (!params.input_prefix.empty()) {\n LOG_TEE(\"Input prefix: '%s'\\n\", params.input_prefix.c_str());\n if (params.verbose_prompt) {\n auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);\n for (int i = 0; i < (int) tmp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());\n }\n }\n }\n\n if (!params.input_suffix.empty()) {\n LOG_TEE(\"Input suffix: '%s'\\n\", params.input_suffix.c_str());\n if (params.verbose_prompt) {\n auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);\n for (int i = 0; i < (int) tmp.size(); i++) {\n LOG_TEE(\"%6d -> '%s'\\n\", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());\n }\n }\n }\n }\n LOG_TEE(\"sampling: \\n%s\\n\", llama_sampling_print(sparams).c_str());\n LOG_TEE(\"generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\\n\", n_ctx, params.n_batch, params.n_predict, params.n_keep);\n LOG_TEE(\"\\n\\n\");\n\n if (params.interactive) {\n const char *control_message;\n if (params.multiline_input) {\n control_message = \" - To return control to LLaMa, end your input with '\\\\'.\\n\"\n \" - To return control without starting a new line, end your input with '/'.\\n\";\n } else {\n control_message = \" - Press Return to return control to LLaMa.\\n\"\n \" - To return control without starting a new line, end your input with '/'.\\n\"\n \" - If you want to submit another line, end your input with '\\\\'.\\n\";\n }\n LOG_TEE(\"== Running in interactive mode. ==\\n\");\n#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)\n LOG_TEE( \" - Press Ctrl+C to interject at any time.\\n\");\n#endif\n LOG_TEE( \"%s\\n\", control_message);\n\n is_interacting = params.interactive_first;\n }\n\n bool is_antiprompt = false;\n bool input_echo = true;\n bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();\n\n int n_past = 0;\n int n_remain = params.n_predict;\n int n_consumed = 0;\n int n_session_consumed = 0;\n int n_past_guidance = 0;\n\n std::vector input_tokens; g_input_tokens = &input_tokens;\n std::vector output_tokens; g_output_tokens = &output_tokens;\n std::ostringstream output_ss; g_output_ss = &output_ss;\n\n // the first thing we will do is to output the prompt, so set color accordingly\n console::set_display(console::prompt);\n\n std::vector embd;\n std::vector embd_guidance;\n\n struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);\n\n while ((n_remain != 0 && !is_antiprompt) || params.interactive) {\n // predict\n if (!embd.empty()) {\n // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via\n // --prompt or --file which uses the same value.\n int max_embd_size = n_ctx - 4;\n\n // Ensure the input doesn't exceed the context size by truncating embd if necessary.\n if ((int) embd.size() > max_embd_size) {\n const int skipped_tokens = (int) embd.size() - max_embd_size;\n embd.resize(max_embd_size);\n\n console::set_display(console::error);\n printf(\"<>\", skipped_tokens, skipped_tokens != 1 ? \"s\" : \"\");\n console::set_display(console::reset);\n fflush(stdout);\n }\n\n // infinite text generation via context swapping\n // if we run out of context:\n // - take the n_keep first tokens from the original prompt (via n_past)\n // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches\n if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) {\n if (params.n_predict == -2) {\n LOG_TEE(\"\\n\\n%s: context full and n_predict == -%d => stopping\\n\", __func__, params.n_predict);\n break;\n }\n\n const int n_left = n_past - params.n_keep - 1;\n const int n_discard = n_left/2;\n\n LOG(\"context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\\n\",\n n_past, n_left, n_ctx, params.n_keep, n_discard);\n\n llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);\n llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);\n\n n_past -= n_discard;\n\n if (ctx_guidance) {\n n_past_guidance -= n_discard;\n }\n\n LOG(\"after swap: n_past = %d, n_past_guidance = %d\\n\", n_past, n_past_guidance);\n\n LOG(\"embd: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());\n\n LOG(\"clear session path\\n\");\n path_session.clear();\n }\n\n // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)\n if (n_session_consumed < (int) session_tokens.size()) {\n size_t i = 0;\n for ( ; i < embd.size(); i++) {\n if (embd[i] != session_tokens[n_session_consumed]) {\n session_tokens.resize(n_session_consumed);\n break;\n }\n\n n_past++;\n n_session_consumed++;\n\n if (n_session_consumed >= (int) session_tokens.size()) {\n ++i;\n break;\n }\n }\n if (i > 0) {\n embd.erase(embd.begin(), embd.begin() + i);\n }\n }\n\n // evaluate tokens in batches\n // embd is typically prepared beforehand to fit within a batch, but not always\n if (ctx_guidance) {\n int input_size = 0;\n llama_token * input_buf = NULL;\n\n if (n_past_guidance < (int) guidance_inp.size()) {\n // Guidance context should have the same data with these modifications:\n //\n // * Replace the initial prompt\n // * Shift everything by guidance_offset\n embd_guidance = guidance_inp;\n if (embd.begin() + original_prompt_len < embd.end()) {\n embd_guidance.insert(\n embd_guidance.end(),\n embd.begin() + original_prompt_len,\n embd.end()\n );\n }\n\n input_buf = embd_guidance.data();\n input_size = embd_guidance.size();\n\n LOG(\"guidance context: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());\n } else {\n input_buf = embd.data();\n input_size = embd.size();\n }\n\n for (int i = 0; i < input_size; i += params.n_batch) {\n int n_eval = std::min(input_size - i, params.n_batch);\n if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {\n LOG_TEE(\"%s : failed to eval\\n\", __func__);\n return 1;\n }\n\n n_past_guidance += n_eval;\n }\n }\n\n for (int i = 0; i < (int) embd.size(); i += params.n_batch) {\n int n_eval = (int) embd.size() - i;\n if (n_eval > params.n_batch) {\n n_eval = params.n_batch;\n }\n\n LOG(\"eval: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());\n\n if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {\n LOG_TEE(\"%s : failed to eval\\n\", __func__);\n return 1;\n }\n\n n_past += n_eval;\n\n LOG(\"n_past = %d\\n\", n_past);\n }\n\n if (!embd.empty() && !path_session.empty()) {\n session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());\n n_session_consumed = session_tokens.size();\n }\n }\n\n embd.clear();\n embd_guidance.clear();\n\n if ((int) embd_inp.size() <= n_consumed && !is_interacting) {\n // optionally save the session on first sample (for faster prompt loading next time)\n if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {\n need_to_save_session = false;\n llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());\n\n LOG(\"saved session to %s\\n\", path_session.c_str());\n }\n\n const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);\n\n llama_sampling_accept(ctx_sampling, ctx, id, true);\n\n LOG(\"last: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());\n\n embd.push_back(id);\n\n // echo this to console\n input_echo = true;\n\n // decrement remaining sampling budget\n --n_remain;\n\n LOG(\"n_remain: %d\\n\", n_remain);\n } else {\n // some user input remains from prompt or interaction, forward it to processing\n LOG(\"embd_inp.size(): %d, n_consumed: %d\\n\", (int) embd_inp.size(), n_consumed);\n while ((int) embd_inp.size() > n_consumed) {\n embd.push_back(embd_inp[n_consumed]);\n\n // push the prompt in the sampling context in order to apply repetition penalties later\n // for the prompt, we don't apply grammar rules\n llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);\n\n ++n_consumed;\n if ((int) embd.size() >= params.n_batch) {\n break;\n }\n }\n }\n\n // display text\n if (input_echo) {\n for (auto id : embd) {\n const std::string token_str = llama_token_to_piece(ctx, id);\n printf(\"%s\", token_str.c_str());\n\n if (embd.size() > 1) {\n input_tokens.push_back(id);\n } else {\n output_tokens.push_back(id);\n output_ss << token_str;\n }\n }\n fflush(stdout);\n }\n // reset color to default if there is no pending user input\n if (input_echo && (int) embd_inp.size() == n_consumed) {\n console::set_display(console::reset);\n }\n\n // if not currently processing queued inputs;\n if ((int) embd_inp.size() <= n_consumed) {\n // check for reverse prompt in the last n_prev tokens\n if (!params.antiprompt.empty()) {\n const int n_prev = 32;\n const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev);\n\n is_antiprompt = false;\n // Check if each of the reverse prompts appears at the end of the output.\n // If we're not running interactively, the reverse prompt might be tokenized with some following characters\n // so we'll compensate for that by widening the search window a bit.\n for (std::string & antiprompt : params.antiprompt) {\n size_t extra_padding = params.interactive ? 0 : 2;\n size_t search_start_pos = last_output.length() > static_cast(antiprompt.length() + extra_padding)\n ? last_output.length() - static_cast(antiprompt.length() + extra_padding)\n : 0;\n\n if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {\n if (params.interactive) {\n is_interacting = true;\n }\n is_antiprompt = true;\n break;\n }\n }\n\n if (is_antiprompt) {\n LOG(\"found antiprompt: %s\\n\", last_output.c_str());\n }\n }\n\n // deal with end of text token in interactive mode\n if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {\n LOG(\"found EOS token\\n\");\n\n if (params.interactive) {\n if (!params.antiprompt.empty()) {\n // tokenize and inject first reverse prompt\n const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true);\n embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());\n is_antiprompt = true;\n }\n\n is_interacting = true;\n printf(\"\\n\");\n } else if (params.instruct) {\n is_interacting = true;\n }\n }\n\n if (n_past > 0 && is_interacting) {\n LOG(\"waiting for user input\\n\");\n\n if (params.instruct) {\n printf(\"\\n> \");\n }\n\n if (params.input_prefix_bos) {\n LOG(\"adding input prefix BOS token\\n\");\n embd_inp.push_back(llama_token_bos(model));\n }\n\n std::string buffer;\n if (!params.input_prefix.empty()) {\n LOG(\"appending input prefix: '%s'\\n\", params.input_prefix.c_str());\n printf(\"%s\", params.input_prefix.c_str());\n }\n\n // color user input only\n console::set_display(console::user_input);\n\n std::string line;\n bool another_line = true;\n do {\n another_line = console::readline(line, params.multiline_input);\n buffer += line;\n } while (another_line);\n\n // done taking input, reset color\n console::set_display(console::reset);\n\n // Add tokens to embd only if the input buffer is non-empty\n // Entering a empty line lets the user pass control back\n if (buffer.length() > 1) {\n // append input suffix if any\n if (!params.input_suffix.empty()) {\n LOG(\"appending input suffix: '%s'\\n\", params.input_suffix.c_str());\n printf(\"%s\", params.input_suffix.c_str());\n }\n\n LOG(\"buffer: '%s'\\n\", buffer.c_str());\n\n const size_t original_size = embd_inp.size();\n\n // instruct mode: insert instruction prefix\n if (params.instruct && !is_antiprompt) {\n LOG(\"inserting instruction prefix\\n\");\n n_consumed = embd_inp.size();\n embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());\n }\n if (params.escape) {\n process_escapes(buffer);\n }\n\n const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);\n const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);\n const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);\n LOG(\"input tokens: %s\\n\", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());\n\n embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());\n embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());\n embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());\n\n // instruct mode: insert response suffix\n if (params.instruct) {\n LOG(\"inserting instruction suffix\\n\");\n embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());\n }\n\n for (size_t i = original_size; i < embd_inp.size(); ++i) {\n const llama_token token = embd_inp[i];\n output_tokens.push_back(token);\n output_ss << llama_token_to_piece(ctx, token);\n }\n\n n_remain -= line_inp.size();\n LOG(\"n_remain: %d\\n\", n_remain);\n } else {\n LOG(\"empty line, passing control back\\n\");\n }\n\n input_echo = false; // do not echo this again\n }\n\n if (n_past > 0) {\n if (is_interacting) {\n llama_sampling_reset(ctx_sampling);\n }\n is_interacting = false;\n }\n }\n\n // end of text token\n if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive)) {\n LOG_TEE(\" [end of text]\\n\");\n break;\n }\n\n // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.\n // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).\n if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {\n n_remain = params.n_predict;\n is_interacting = true;\n }\n }\n\n if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {\n LOG_TEE(\"\\n%s: saving final output to session file '%s'\\n\", __func__, path_session.c_str());\n llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());\n }\n\n llama_print_timings(ctx);\n write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);\n\n if (ctx_guidance) { llama_free(ctx_guidance); }\n llama_free(ctx);\n llama_free_model(model);\n\n llama_sampling_free(ctx_sampling);\n llama_backend_free();\n\n#ifndef LOG_DISABLE_LOGS\n LOG_TEE(\"Log end\\n\");\n#endif // LOG_DISABLE_LOGS\n\n return 0;\n}\n" }, { "path": "examples/main-cmake-pkg/.gitignore", "content": "# Prerequisites\n*.d\n\n# Compiled Object files\n*.slo\n*.lo\n*.o\n*.obj\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Compiled Dynamic libraries\n*.so\n*.dylib\n*.dll\n\n# Fortran module files\n*.mod\n*.smod\n\n# Compiled Static libraries\n*.lai\n*.la\n*.a\n*.lib\n\n# Executables\n*.exe\n*.out\n*.app\n\n*.gguf\n\n*.log\n.DS_Store\n.build/\n.cache/\n.direnv/\n.envrc\n.swiftpm\n.venv\n.clang-tidy\n.vs/\n.vscode/\n\nbuild*/\nout/\ntmp/\n\n" }, { "path": "examples/main-cmake-pkg/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 3.12)\nproject(\"main-cmake-pkg\" C CXX)\nset(TARGET main-cmake-pkg)\n\nfind_package(Llama 0.0.1 REQUIRED)\n\n# Bake common functionality in with target. Because applications\n# using the relocatable Llama package should be outside of the\n# source tree, main-cmake-pkg pretends the dependencies are built-in.\n\nset(_common_path \"${CMAKE_CURRENT_LIST_DIR}/../../common\")\nadd_library(common OBJECT\n ${_common_path}/common.h\n ${_common_path}/common.cpp\n ${_common_path}/console.h\n ${_common_path}/console.cpp\n ${_common_path}/grammar-parser.h\n ${_common_path}/grammar-parser.cpp\n ${_common_path}/sampling.h\n ${_common_path}/sampling.cpp\n )\n\n# WARNING: because build-info.h is auto-generated, it will only\n# be available after the user has built the llama.cpp sources.\n#\nconfigure_file(${_common_path}/../build-info.h\n ${CMAKE_CURRENT_BINARY_DIR}/build-info.h\n COPYONLY)\n\ntarget_include_directories(common PUBLIC ${LLAMA_INCLUDE_DIR}\n ${CMAKE_CURRENT_BINARY_DIR})\n\n# If the common project was part of \"main-cmake-pkg\" the transient\n# defines would automatically be attached. Because the common func-\n# tionality is separate, but dependent upon the defines, it must be\n# explicitly extracted from the \"llama\" target.\n#\nget_target_property(_llama_transient_defines llama\n INTERFACE_COMPILE_DEFINITIONS)\n\ntarget_compile_definitions(common PRIVATE \"${_llama_transient_defines}\")\n\nadd_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp)\ntarget_include_directories(${TARGET} PRIVATE ${_common_path})\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n\n" }, { "path": "examples/main-cmake-pkg/README.md", "content": "# llama.cpp/example/main-cmake-pkg\n\nThis program builds the [main](../main) application using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.\n\n## Building\n\nBecause this example is \"outside of the source tree\", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions.\n\n### Considerations\n\nWhen hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.\n\n### Build llama.cpp and install to C:\\LlamaCPP directory\n\nIn this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`.\n\n```cmd\ngit clone https://github.com/ggerganov/llama.cpp\ncd llama.cpp\nmkdir build\ncd build\ncmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G \"Visual Studio 17 2022\" -A x64\ncmake --build . --config Release\ncmake --install . --prefix C:/LlamaCPP\n```\n\n### Build main-cmake-pkg\n\n\n```cmd\ncd ..\\examples\\main-cmake-pkg\nmkdir build\ncd build\ncmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH=\"C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama\" -G \"Visual Studio 17 2022\" -A x64\ncmake --build . --config Release\ncmake --install . --prefix C:/MyLlamaApp\n```\n" }, { "path": "examples/make-ggml.py", "content": "#!/usr/bin/env python3\n\"\"\"\nThis script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them.\n\nUsage:\npython make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]\n\nArguments:\n- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.\n- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.\n- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.\n- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.\n- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.\n- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.\n\nOld quant types (some base model types require these):\n- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M\n- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L\n- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M\n- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M\n\nNew quant types (recommended):\n- Q2_K: smallest, extreme quality loss - not recommended\n- Q3_K: alias for Q3_K_M\n- Q3_K_S: very small, very high quality loss\n- Q3_K_M: very small, very high quality loss\n- Q3_K_L: small, substantial quality loss\n- Q4_K: alias for Q4_K_M\n- Q4_K_S: small, significant quality loss\n- Q4_K_M: medium, balanced quality - recommended\n- Q5_K: alias for Q5_K_M\n- Q5_K_S: large, low quality loss - recommended\n- Q5_K_M: large, very low quality loss - recommended\n- Q6_K: very large, extremely low quality loss\n- Q8_0: very large, extremely low quality loss - not recommended\n- F16: extremely large, virtually no quality loss - not recommended\n- F32: absolutely huge, lossless - not recommended\n\"\"\"\nimport subprocess\nsubprocess.run(f\"pip install huggingface-hub==0.16.4\", shell=True, check=True)\n\nimport argparse\nimport os\nfrom huggingface_hub import snapshot_download\n\ndef main(model, model_type, outname, outdir, quants, keep_fp16):\n if not os.path.isdir(model):\n print(f\"Model not found at {model}. Downloading...\")\n try:\n if outname is None:\n outname = model.split('/')[-1]\n model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')\n except Exception as e:\n raise Exception(f\"Could not download the model: {e}\")\n\n if outdir is None:\n outdir = f'../models/{outname}'\n\n if not os.path.isfile(f\"{model}/config.json\"):\n raise Exception(f\"Could not find config.json in {model}\")\n\n os.makedirs(outdir, exist_ok=True)\n\n print(\"Building llama.cpp\")\n subprocess.run(f\"cd .. && make quantize\", shell=True, check=True)\n\n fp16 = f\"{outdir}/{outname}.gguf.fp16.bin\"\n\n print(f\"Making unquantised GGUF at {fp16}\")\n if not os.path.isfile(fp16):\n if model_type != \"llama\":\n subprocess.run(f\"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}\", shell=True, check=True)\n else:\n subprocess.run(f\"python3 ../convert.py {model} --outtype f16 --outfile {fp16}\", shell=True, check=True)\n else:\n print(f\"Unquantised GGML already exists at: {fp16}\")\n\n print(\"Making quants\")\n for type in quants:\n outfile = f\"{outdir}/{outname}.gguf.{type}.bin\"\n print(f\"Making {type} : {outfile}\")\n subprocess.run(f\"../quantize {fp16} {outfile} {type}\", shell=True, check=True)\n\n if not keep_fp16:\n os.remove(fp16)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')\n parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name')\n parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.')\n parser.add_argument('--outname', default=None, help='Output model(s) name')\n parser.add_argument('--outdir', default=None, help='Output directory')\n parser.add_argument('--quants', nargs='*', default=[\"Q4_K_M\", \"Q5_K_S\"], help='Quant types')\n parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)\n\n args = parser.parse_args()\n\n main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16)\n" }, { "path": "examples/metal/CMakeLists.txt", "content": "set(TEST_TARGET metal)\nadd_executable(${TEST_TARGET} metal.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TEST_TARGET} PRIVATE ggml)\n" }, { "path": "examples/metal/metal.cpp", "content": "// Evaluate a statically exported ggml computation graph with Metal\n//\n// - First, export a LLaMA graph:\n//\n// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export\n//\n// - Run this tool to evaluate the exported graph:\n//\n// $ ./bin/metal llama.ggml\n//\n// The purpose of this tool is mostly for debugging and demonstration purposes.\n// The main limitation of exporting computation graphs is that their sizes are static which often\n// can be a problem for real-world applications.\n//\n\n#include \"ggml.h\"\n#include \"ggml-metal.h\"\n\n#include \n#include \n#include \n\nint main(int argc, char ** argv) {\n ggml_time_init();\n\n if (argc != 2) {\n fprintf(stderr, \"Usage: %s llama.ggml\\n\", argv[0]);\n return -1;\n }\n\n const char * fname_cgraph = argv[1];\n\n // load the compute graph\n struct ggml_context * ctx_data = NULL;\n struct ggml_context * ctx_eval = NULL;\n\n struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);\n\n // this allocates all Metal resources and memory buffers\n auto * ctx_metal = ggml_metal_init(1);\n\n const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);\n const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);\n ggml_metal_add_buffer(ctx_metal, \"data\", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);\n ggml_metal_add_buffer(ctx_metal, \"eval\", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);\n\n // main\n {\n struct ggml_tensor * input = ggml_graph_get_tensor(gf, \"embd\");\n *(int32_t *) input->data = 1; // BOS\n\n ggml_metal_set_tensor(ctx_metal, input);\n\n // warmup\n ggml_metal_graph_compute(ctx_metal, gf);\n\n const int n_iter = 16;\n\n const int64_t t0 = ggml_time_us();\n\n // the actual inference happens here\n for (int i = 0; i < n_iter; ++i) {\n ggml_metal_graph_compute(ctx_metal, gf);\n }\n\n const int64_t t1 = ggml_time_us();\n\n printf(\"time: %.2f ms, %.2f ms/tok\\n\", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);\n }\n\n // debug output\n {\n struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];\n ggml_metal_get_tensor(ctx_metal, logits);\n\n float * ptr = (float *) ggml_get_data(logits);\n\n printf(\"logits: \");\n for (int i = 0; i < 10; i++) {\n printf(\"%8.4f \", ptr[i]);\n }\n printf(\"\\n\");\n int imax = 0;\n double sum = 0.0;\n double vmax = -1e9;\n for (int i = 0; i < 32000; i++) {\n sum += (double) ptr[i];\n if (ptr[i] > vmax) {\n vmax = ptr[i];\n imax = i;\n }\n }\n printf(\"sum: %f, imax = %d, vmax = %f\\n\", sum, imax, vmax);\n }\n\n ggml_metal_free(ctx_metal);\n\n ggml_free(ctx_data);\n ggml_free(ctx_eval);\n\n return 0;\n}\n\n" }, { "path": "examples/parallel/CMakeLists.txt", "content": "set(TARGET parallel)\nadd_executable(${TARGET} parallel.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/parallel/README.md", "content": "# llama.cpp/example/parallel\n\nSimplified simulation of serving incoming requests in parallel\n" }, { "path": "examples/parallel/parallel.cpp", "content": "// A basic application simulating a server with multiple clients.\n// The clients submite requests to the server and they are processed in parallel.\n\n#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n// trim whitespace from the beginning and end of a string\nstatic std::string trim(const std::string & str) {\n size_t start = 0;\n size_t end = str.size();\n\n while (start < end && isspace(str[start])) {\n start += 1;\n }\n\n while (end > start && isspace(str[end - 1])) {\n end -= 1;\n }\n\n return str.substr(start, end - start);\n}\n\nstatic std::string k_system =\nR\"(Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\n\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:)\";\n\nstatic std::vector k_prompts = {\n \"What is the meaning of life?\",\n \"Tell me an interesting fact about llamas.\",\n \"What is the best way to cook a steak?\",\n \"Are you familiar with the Special Theory of Relativity and can you explain it to me?\",\n \"Recommend some interesting books to read.\",\n \"What is the best way to learn a new language?\",\n \"How to get a job at Google?\",\n \"If you could have any superpower, what would it be?\",\n \"I want to learn how to play the piano.\",\n};\n\nstruct client {\n ~client() {\n if (ctx_sampling) {\n llama_sampling_free(ctx_sampling);\n }\n }\n\n int32_t id = 0;\n\n llama_seq_id seq_id = -1;\n\n llama_token sampled;\n\n int64_t t_start_prompt;\n int64_t t_start_gen;\n\n int32_t n_prompt = 0;\n int32_t n_decoded = 0;\n int32_t i_batch = -1;\n\n std::string input;\n std::string prompt;\n std::string response;\n\n struct llama_sampling_context * ctx_sampling = nullptr;\n};\n\nstatic void print_date_time() {\n std::time_t current_time = std::time(nullptr);\n std::tm* local_time = std::localtime(¤t_time);\n char buffer[80];\n strftime(buffer, sizeof(buffer), \"%Y-%m-%d %H:%M:%S\", local_time);\n\n printf(\"\\n\\033[35mrun parameters as at %s\\033[0m\\n\", buffer);\n}\n\n// Define a split string function to ...\nstatic std::vector split_string(const std::string& input, char delimiter) {\n std::vector tokens;\n std::istringstream stream(input);\n std::string token;\n while (std::getline(stream, token, delimiter)) {\n tokens.push_back(token);\n }\n return tokens;\n}\n\nint main(int argc, char ** argv) {\n srand(1234);\n\n gpt_params params;\n\n if (gpt_params_parse(argc, argv, params) == false) {\n return 1;\n }\n\n // number of simultaneous \"clients\" to simulate\n const int32_t n_clients = params.n_parallel;\n\n // requests to simulate\n const int32_t n_seq = params.n_sequences;\n\n // insert new requests as soon as the previous one is done\n const bool cont_batching = params.cont_batching;\n\n#ifndef LOG_DISABLE_LOGS\n log_set_target(log_filename_generator(\"parallel\", \"log\"));\n LOG_TEE(\"Log start\\n\");\n log_dump_cmdline(argc, argv);\n#endif // LOG_DISABLE_LOGS\n\n // init llama.cpp\n llama_backend_init(params.numa);\n\n llama_model * model = NULL;\n llama_context * ctx = NULL;\n\n // load the target model\n params.logits_all = true;\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n\n // load the prompts from an external file if there are any\n if (params.prompt.empty()) {\n printf(\"\\n\\033[32mNo new questions so proceed with build-in defaults.\\033[0m\\n\");\n } else {\n // Output each line of the input params.prompts vector and copy to k_prompts\n int index = 0;\n printf(\"\\n\\033[32mNow printing the external prompt file %s\\033[0m\\n\\n\", params.prompt_file.c_str());\n\n std::vector prompts = split_string(params.prompt, '\\n');\n for (const auto& prompt : prompts) {\n k_prompts.resize(index + 1);\n k_prompts[index] = prompt;\n index++;\n printf(\"%3d prompt: %s\\n\", index, prompt.c_str());\n }\n }\n\n fprintf(stderr, \"\\n\\n\");\n fflush(stderr);\n\n const int n_ctx = llama_n_ctx(ctx);\n\n std::vector clients(n_clients);\n for (size_t i = 0; i < clients.size(); ++i) {\n auto & client = clients[i];\n client.id = i;\n client.ctx_sampling = llama_sampling_init(params.sparams);\n }\n\n std::vector tokens_system;\n tokens_system = ::llama_tokenize(ctx, k_system, true);\n const int32_t n_tokens_system = tokens_system.size();\n\n llama_seq_id g_seq_id = 0;\n\n // the max batch size is as large as the context to handle cases where we get very long input prompt from multiple\n // users. regardless of the size, the main loop will chunk the batch into a maximum of params.n_batch tokens at a time\n llama_batch batch = llama_batch_init(n_ctx, 0, 1);\n\n int32_t n_total_prompt = 0;\n int32_t n_total_gen = 0;\n int32_t n_cache_miss = 0;\n\n const auto t_main_start = ggml_time_us();\n\n LOG_TEE(\"%s: Simulating parallel requests from clients:\\n\", __func__);\n LOG_TEE(\"%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\\n\", __func__, n_clients, n_seq, cont_batching, n_tokens_system);\n LOG_TEE(\"\\n\");\n\n {\n LOG_TEE(\"%s: Evaluating the system prompt ...\\n\", __func__);\n\n for (int32_t i = 0; i < n_tokens_system; ++i) {\n llama_batch_add(batch, tokens_system[i], i, { 0 }, false);\n }\n\n if (llama_decode(ctx, batch) != 0) {\n LOG_TEE(\"%s: llama_decode() failed\\n\", __func__);\n return 1;\n }\n\n // assign the system KV cache to all parallel sequences\n for (int32_t i = 1; i < n_clients; ++i) {\n llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);\n }\n\n LOG_TEE(\"\\n\");\n }\n\n LOG_TEE(\"Processing requests ...\\n\\n\");\n\n while (true) {\n llama_batch_clear(batch);\n\n // decode any currently ongoing sequences\n for (auto & client : clients) {\n if (client.seq_id == -1) {\n continue;\n }\n\n client.i_batch = batch.n_tokens;\n\n llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);\n\n client.n_decoded += 1;\n }\n\n if (batch.n_tokens == 0) {\n // all sequences have ended - clear the entire KV cache\n for (int i = 0; i < n_clients; ++i) {\n llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);\n }\n\n LOG_TEE(\"%s: clearing the KV cache\\n\", __func__);\n }\n\n // insert new sequences for decoding\n if (cont_batching || batch.n_tokens == 0) {\n for (auto & client : clients) {\n if (client.seq_id == -1 && g_seq_id < n_seq) {\n client.seq_id = g_seq_id;\n\n client.t_start_prompt = ggml_time_us();\n client.t_start_gen = 0;\n\n client.input = k_prompts[rand() % k_prompts.size()];\n client.prompt = client.input + \"\\nAssistant:\";\n client.response = \"\";\n\n llama_sampling_reset(client.ctx_sampling);\n\n // do not prepend BOS because we have a system prompt!\n std::vector tokens_prompt;\n tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);\n\n for (size_t i = 0; i < tokens_prompt.size(); ++i) {\n llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);\n }\n\n // extract the logits only for the last token\n if (batch.n_tokens > 0) {\n batch.logits[batch.n_tokens - 1] = true;\n }\n\n client.n_prompt = tokens_prompt.size();\n client.n_decoded = 0;\n client.i_batch = batch.n_tokens - 1;\n\n LOG_TEE(\"\\033[31mClient %3d, seq %4d, started decoding ...\\033[0m\\n\", client.id, client.seq_id);\n\n g_seq_id += 1;\n\n // insert new requests one-by-one\n //if (cont_batching) {\n // break;\n //}\n }\n }\n }\n\n if (batch.n_tokens == 0) {\n break;\n }\n\n // process in chunks of params.n_batch\n int32_t n_batch = params.n_batch;\n\n for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {\n // experiment: process in powers of 2\n //if (i + n_batch > (int32_t) batch.n_tokens && n_batch > 32) {\n // n_batch /= 2;\n // i -= n_batch;\n // continue;\n //}\n\n const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));\n\n llama_batch batch_view = {\n n_tokens,\n batch.token + i,\n nullptr,\n batch.pos + i,\n batch.n_seq_id + i,\n batch.seq_id + i,\n batch.logits + i,\n 0, 0, 0, // unused\n };\n\n const int ret = llama_decode(ctx, batch_view);\n if (ret != 0) {\n if (n_batch == 1 || ret < 0) {\n // if you get here, it means the KV cache is full - try increasing it via the context size\n LOG_TEE(\"%s : failed to decode the batch, n_batch = %d, ret = %d\\n\", __func__, n_batch, ret);\n return 1;\n }\n\n LOG(\"%s : failed to decode the batch, retrying with n_batch = %d\\n\", __func__, n_batch / 2);\n\n n_cache_miss += 1;\n\n // retry with half the batch size to try to find a free slot in the KV cache\n n_batch /= 2;\n i -= n_batch;\n\n continue;\n }\n\n LOG(\"%s : decoded batch of %d tokens\\n\", __func__, n_tokens);\n\n for (auto & client : clients) {\n if (client.i_batch < (int) i || client.i_batch >= (int) (i + n_tokens)) {\n continue;\n }\n\n //printf(\"client %d, seq %d, token %d, pos %d, batch %d\\n\",\n // client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);\n\n const llama_token id = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i);\n\n llama_sampling_accept(client.ctx_sampling, ctx, id, true);\n\n if (client.n_decoded == 1) {\n // start measuring generation time after the first token to make sure all concurrent clients\n // have their prompt already processed\n client.t_start_gen = ggml_time_us();\n }\n\n const std::string token_str = llama_token_to_piece(ctx, id);\n\n client.response += token_str;\n client.sampled = id;\n\n //printf(\"client %d, seq %d, token %d, pos %d, batch %d: %s\\n\",\n // client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());\n\n if (client.n_decoded > 2 &&\n (id == llama_token_eos(model) ||\n (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||\n client.response.find(\"User:\") != std::string::npos ||\n client.response.find('\\n') != std::string::npos)) {\n // basic reverse prompt\n const size_t pos = client.response.find(\"User:\");\n if (pos != std::string::npos) {\n client.response = client.response.substr(0, pos);\n }\n\n // delete only the generated part of the sequence, i.e. keep the system prompt in the cache\n llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1);\n\n const auto t_main_end = ggml_time_us();\n\n LOG_TEE(\"\\033[31mClient %3d, seq %3d/%3d, prompt %4d t, response %4d t, time %5.2f s, speed %5.2f t/s, cache miss %d \\033[0m \\nInput: %s\\n\\033[35mResponse: %s\\033[0m\\n\\n\",\n client.id, client.seq_id, n_seq, client.n_prompt, client.n_decoded,\n (t_main_end - client.t_start_prompt) / 1e6,\n (double) (client.n_prompt + client.n_decoded) / (t_main_end - client.t_start_prompt) * 1e6,\n n_cache_miss,\n ::trim(client.input).c_str(),\n ::trim(client.response).c_str());\n\n n_total_prompt += client.n_prompt;\n n_total_gen += client.n_decoded;\n\n client.seq_id = -1;\n }\n\n client.i_batch = -1;\n }\n }\n }\n\n const auto t_main_end = ggml_time_us();\n\n print_date_time();\n\n LOG_TEE(\"\\n%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\\n\", __func__, n_clients, n_seq, cont_batching, n_tokens_system);\n if (params.prompt_file.empty()) {\n params.prompt_file = \"used built-in defaults\";\n }\n LOG_TEE(\"External prompt file: \\033[32m%s\\033[0m\\n\", params.prompt_file.c_str());\n LOG_TEE(\"Model and path used: \\033[32m%s\\033[0m\\n\\n\", params.model.c_str());\n\n LOG_TEE(\"Total prompt tokens: %6d, speed: %5.2f t/s\\n\", n_total_prompt, (double) (n_total_prompt ) / (t_main_end - t_main_start) * 1e6);\n LOG_TEE(\"Total gen tokens: %6d, speed: %5.2f t/s\\n\", n_total_gen, (double) (n_total_gen ) / (t_main_end - t_main_start) * 1e6);\n LOG_TEE(\"Total speed (AVG): %6s speed: %5.2f t/s\\n\", \"\", (double) (n_total_prompt + n_total_gen) / (t_main_end - t_main_start) * 1e6);\n LOG_TEE(\"Cache misses: %6d\\n\", n_cache_miss);\n\n LOG_TEE(\"\\n\");\n\n llama_print_timings(ctx);\n\n llama_batch_free(batch);\n\n llama_free(ctx);\n llama_free_model(model);\n\n llama_backend_free();\n\n fprintf(stderr, \"\\n\\n\");\n\n return 0;\n}\n" }, { "path": "examples/perplexity/CMakeLists.txt", "content": "set(TARGET perplexity)\nadd_executable(${TARGET} perplexity.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/perplexity/README.md", "content": "# perplexity\n\nTODO\n\n## Llama 2 70B Scorechart\nQuantization | Model size (GiB) | Perplexity | Delta to fp16\n-- | -- | -- | --\nQ4_0 | 36.20 | 3.5550 | 3.61%\nQ4_1 | 40.20 | 3.5125 | 2.37%\nQ5_0 | 44.20 | 3.4744 | 1.26%\nQ2_K | 27.27 | 3.7339 | 8.82%\nQ3_K_S | 27.86 | 3.7019 | 7.89%\nQ3_K_M | 30.83 | 3.5932 | 4.72%\nQ3_K_L | 33.67 | 3.5617 | 3.80%\nQ4_K_S | 36.39 | 3.4852 | 1.57%\nQ4_K_M | 38.54 | 3.4725 | 1.20%\nQ5_K_S | 44.20 | 3.4483 | 0.50%\nQ5_K_M | 45.41 | 3.4451 | 0.40%\nQ6_K | 52.70 | 3.4367 | 0.16%\nfp16 | 128.5 | 3.4313 | -\n\n" }, { "path": "examples/perplexity/perplexity.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstruct results_perplexity {\n std::vector tokens;\n double ppl_value;\n std::vector logits;\n std::vector probs;\n};\n\nstruct results_log_softmax {\n double log_softmax;\n float logit;\n float prob;\n};\n\nstatic void write_logfile(\n const llama_context * ctx, const gpt_params & params, const llama_model * model,\n const struct results_perplexity & results\n) {\n if (params.logdir.empty()) {\n return;\n }\n\n if (params.hellaswag) {\n fprintf(stderr, \"%s: warning: logging results is not implemented for HellaSwag. No files will be written.\\n\", __func__);\n return;\n }\n\n const std::string timestamp = get_sortable_timestamp();\n\n const bool success = create_directory_with_parents(params.logdir);\n if (!success) {\n fprintf(stderr, \"%s: warning: failed to create logdir %s, cannot write logfile\\n\",\n __func__, params.logdir.c_str());\n return;\n }\n\n const std::string logfile_path = params.logdir + timestamp + \".yml\";\n FILE * logfile = fopen(logfile_path.c_str(), \"w\");\n\n if (logfile == NULL) {\n fprintf(stderr, \"%s: failed to open logfile %s\\n\", __func__, logfile_path.c_str());\n return;\n }\n\n fprintf(logfile, \"binary: main\\n\");\n char model_desc[128];\n llama_model_desc(model, model_desc, sizeof(model_desc));\n dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);\n\n fprintf(logfile, \"\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"# Perplexity Results #\\n\");\n fprintf(logfile, \"######################\\n\");\n fprintf(logfile, \"\\n\");\n\n dump_vector_float_yaml(logfile, \"logits\", results.logits);\n fprintf(logfile, \"ppl_value: %f\\n\", results.ppl_value);\n dump_vector_float_yaml(logfile, \"probs\", results.probs);\n\n llama_dump_timing_info_yaml(logfile, ctx);\n fclose(logfile);\n}\n\nstatic std::vector softmax(const std::vector& logits) {\n std::vector probs(logits.size());\n float max_logit = logits[0];\n for (float v : logits) {\n max_logit = std::max(max_logit, v);\n }\n double sum_exp = 0.0;\n for (size_t i = 0; i < logits.size(); i++) {\n // Subtract the maximum logit value from the current logit value for numerical stability\n const float logit = logits[i] - max_logit;\n const float exp_logit = expf(logit);\n sum_exp += exp_logit;\n probs[i] = exp_logit;\n }\n for (size_t i = 0; i < probs.size(); i++) {\n probs[i] /= sum_exp;\n }\n return probs;\n}\n\nstatic results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {\n float max_logit = logits[0];\n for (int i = 1; i < n_vocab; ++i) {\n max_logit = std::max(max_logit, logits[i]);\n }\n double sum_exp = 0.0;\n for (int i = 0; i < n_vocab; ++i) {\n sum_exp += expf(logits[i] - max_logit);\n }\n return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};\n}\n\nstatic void process_logits(\n int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers,\n double & nll, double & nll2, float * logit_history, float * prob_history\n) {\n std::mutex mutex;\n int counter = 0;\n auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {\n double local_nll = 0;\n double local_nll2 = 0;\n while (true) {\n std::unique_lock lock(mutex);\n int i = counter++;\n if (i >= n_token) {\n nll += local_nll; nll2 += local_nll2;\n break;\n }\n lock.unlock();\n const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);\n const double v = -results.log_softmax;\n local_nll += v;\n local_nll2 += v*v;\n\n logit_history[i] = results.logit;\n prob_history[i] = results.prob;\n }\n };\n for (auto & w : workers) {\n w = std::thread(compute);\n }\n compute();\n for (auto & w : workers) {\n w.join();\n }\n}\n\nstatic results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {\n // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research\n // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`\n // Output: `perplexity: 13.5106 [114/114]`\n // BOS tokens will be added for each chunk before eval\n\n const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;\n const bool add_bos = is_spm;\n\n fprintf(stderr, \"%s: tokenizing the input ..\\n\", __func__);\n\n std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos);\n\n const int n_ctx = llama_n_ctx(ctx);\n\n if (int(tokens.size()) < 2*n_ctx) {\n fprintf(stderr, \"%s: you need at least %d tokens to evaluate perplexity with a context of %d\\n\",__func__,2*n_ctx,\n n_ctx);\n fprintf(stderr, \"%s: the data file you provided tokenizes to only %zu tokens\\n\",__func__,tokens.size());\n return {std::move(tokens), 0., {}, {}};\n }\n\n std::vector logit_history;\n std::vector prob_history;\n\n logit_history.resize(tokens.size());\n prob_history.resize(tokens.size());\n\n if (params.ppl_stride <= 0) {\n fprintf(stderr, \"%s: stride is %d but must be greater than zero!\\n\",__func__,params.ppl_stride);\n return {tokens, -1, logit_history, prob_history};\n }\n\n const int calc_chunk = n_ctx;\n\n fprintf(stderr, \"%s: have %zu tokens. Calculation chunk = %d\\n\", __func__, tokens.size(), calc_chunk);\n\n if (int(tokens.size()) <= calc_chunk) {\n fprintf(stderr, \"%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\\n\",__func__,\n tokens.size(), n_ctx, params.ppl_stride);\n return {tokens, -1, logit_history, prob_history};\n }\n\n const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;\n\n const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);\n const int n_vocab = llama_n_vocab(llama_get_model(ctx));\n const int n_batch = params.n_batch;\n\n int count = 0;\n double nll = 0.0;\n\n fprintf(stderr, \"%s: calculating perplexity over %d chunks, batch_size=%d\\n\", __func__, n_chunk, n_batch);\n\n for (int i = 0; i < n_chunk; ++i) {\n const int start = i * params.ppl_stride;\n const int end = start + calc_chunk;\n\n const int num_batches = (calc_chunk + n_batch - 1) / n_batch;\n //fprintf(stderr, \"%s: evaluating %d...%d using %d batches\\n\", __func__, start, end, num_batches);\n\n std::vector logits;\n\n const auto t_start = std::chrono::high_resolution_clock::now();\n\n // clear the KV cache\n llama_kv_cache_clear(ctx);\n\n for (int j = 0; j < num_batches; ++j) {\n const int batch_start = start + j * n_batch;\n const int batch_size = std::min(end - batch_start, n_batch);\n\n //fprintf(stderr, \" Batch %d: starts at %d, size is %d, n_past is %d\\n\",j,batch_start,batch_size,j * n_batch);\n if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {\n //fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return {tokens, -1, logit_history, prob_history};\n }\n\n // save original token and restore it after eval\n const auto token_org = tokens[batch_start];\n\n // add BOS token for the first batch of each chunk\n if (add_bos && j == 0) {\n tokens[batch_start] = llama_token_bos(llama_get_model(ctx));\n }\n\n const auto batch_logits = llama_get_logits(ctx);\n logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);\n\n if (j == 0) {\n tokens[batch_start] = token_org;\n }\n }\n\n const auto t_end = std::chrono::high_resolution_clock::now();\n\n if (i == 0) {\n const float t_total = std::chrono::duration(t_end - t_start).count();\n fprintf(stderr, \"%s: %.2f seconds per pass - ETA \", __func__, t_total);\n int total_seconds = (int)(t_total * n_chunk);\n if (total_seconds >= 60*60) {\n fprintf(stderr, \"%d hours \", total_seconds / (60*60));\n total_seconds = total_seconds % (60*60);\n }\n fprintf(stderr, \"%.2f minutes\\n\", total_seconds / 60.0);\n }\n\n //fprintf(stderr, \"%s: using tokens %d...%d\\n\",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);\n for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) {\n\n // Calculate probability of next token, given the previous ones.\n const std::vector tok_logits(\n logits.begin() + (j + 0) * n_vocab,\n logits.begin() + (j + 1) * n_vocab);\n\n const float prob = softmax(tok_logits)[tokens[start + j + 1]];\n logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];\n prob_history[start + j + 1] = prob;\n\n nll += -std::log(prob);\n ++count;\n }\n // perplexity is e^(average negative log-likelihood)\n if (params.ppl_output_type == 0) {\n printf(\"[%d]%.4lf,\", i + 1, std::exp(nll / count));\n } else {\n printf(\"%8d %.4lf\\n\", i*params.ppl_stride, std::exp(nll / count));\n }\n fflush(stdout);\n }\n printf(\"\\n\");\n\n return {tokens, std::exp(nll / count), logit_history, prob_history};\n}\n\nstatic results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {\n if (params.ppl_stride > 0) {\n return perplexity_v2(ctx, params);\n }\n\n // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research\n // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`\n // Output: `perplexity: 13.5106 [114/114]`\n // BOS tokens will be added for each chunk before eval\n\n const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;\n const bool add_bos = is_spm;\n const int n_ctx = llama_n_ctx(ctx);\n\n auto tim1 = std::chrono::high_resolution_clock::now();\n fprintf(stderr, \"%s: tokenizing the input ..\\n\", __func__);\n\n std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos);\n\n auto tim2 = std::chrono::high_resolution_clock::now();\n fprintf(stderr, \"%s: tokenization took %g ms\\n\",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count());\n\n if (int(tokens.size()) < 2*n_ctx) {\n fprintf(stderr, \"%s: you need at least %d tokens to evaluate perplexity with a context of %d\\n\",__func__,2*n_ctx,\n n_ctx);\n fprintf(stderr, \"%s: the data file you provided tokenizes to only %zu tokens\\n\",__func__,tokens.size());\n return {std::move(tokens), 0., {}, {}};\n }\n\n std::vector logit_history;\n logit_history.resize(tokens.size());\n\n std::vector prob_history;\n prob_history.resize(tokens.size());\n\n const int n_chunk_max = tokens.size() / n_ctx;\n\n const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);\n const int n_vocab = llama_n_vocab(llama_get_model(ctx));\n const int n_batch = params.n_batch;\n\n int count = 0;\n double nll = 0.0;\n double nll2 = 0.0;\n\n fprintf(stderr, \"%s: calculating perplexity over %d chunks, batch_size=%d\\n\", __func__, n_chunk, n_batch);\n\n std::vector workers(std::thread::hardware_concurrency() - 1);\n\n for (int i = 0; i < n_chunk; ++i) {\n const int start = i * n_ctx;\n const int end = start + n_ctx;\n\n const int num_batches = (n_ctx + n_batch - 1) / n_batch;\n\n std::vector logits;\n\n const auto t_start = std::chrono::high_resolution_clock::now();\n\n // clear the KV cache\n llama_kv_cache_clear(ctx);\n\n for (int j = 0; j < num_batches; ++j) {\n const int batch_start = start + j * n_batch;\n const int batch_size = std::min(end - batch_start, n_batch);\n\n // save original token and restore it after eval\n const auto token_org = tokens[batch_start];\n\n // add BOS token for the first batch of each chunk\n if (add_bos && j == 0) {\n tokens[batch_start] = llama_token_bos(llama_get_model(ctx));\n }\n\n if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return {tokens, -1, logit_history, prob_history};\n }\n\n // restore the original token in case it was set to BOS\n tokens[batch_start] = token_org;\n\n const auto * batch_logits = llama_get_logits(ctx);\n logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);\n }\n\n const auto t_end = std::chrono::high_resolution_clock::now();\n\n if (i == 0) {\n const float t_total = std::chrono::duration(t_end - t_start).count();\n fprintf(stderr, \"%s: %.2f seconds per pass - ETA \", __func__, t_total);\n int total_seconds = (int)(t_total * n_chunk);\n if (total_seconds >= 60*60) {\n fprintf(stderr, \"%d hours \", total_seconds / (60*60));\n total_seconds = total_seconds % (60*60);\n }\n fprintf(stderr, \"%.2f minutes\\n\", total_seconds / 60.0);\n }\n\n // We get the logits for all the tokens in the context window (params.n_ctx)\n // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,\n // calculate the perplexity over the last half of the window (so the model always has\n // some context to predict the token).\n //\n // We rely on the fact that attention in the forward pass only looks at previous\n // tokens here, so the logits returned for each token are an accurate representation\n // of what the model would have predicted at that point.\n //\n // Example, we have a context window of 512, we will compute perplexity for each of the\n // last 256 tokens. Then, we split the input up into context window size chunks to\n // process the entire prompt.\n const int first = n_ctx/2;\n process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,\n workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);\n count += n_ctx - first - 1;\n\n // perplexity is e^(average negative log-likelihood)\n if (params.ppl_output_type == 0) {\n printf(\"[%d]%.4lf,\", i + 1, std::exp(nll / count));\n } else {\n double av = nll/count;\n double av2 = nll2/count - av*av;\n if (av2 > 0) av2 = sqrt(av2/(count-1));\n printf(\"%8d %.4lf %4lf %4lf\\n\", i*n_ctx, std::exp(nll / count), av, av2);\n }\n fflush(stdout);\n }\n printf(\"\\n\");\n\n nll2 /= count;\n nll /= count;\n const double ppl = exp(nll);\n nll2 -= nll * nll;\n if (nll2 > 0) {\n nll2 = sqrt(nll2/(count-1));\n printf(\"Final estimate: PPL = %.4lf +/- %.5lf\\n\", ppl, nll2*ppl);\n } else {\n printf(\"Unexpected negative standard deviation of log(prob)\\n\");\n }\n\n return {tokens, ppl, logit_history, prob_history};\n}\n\nstatic std::vector hellaswag_evaluate_tokens(\n llama_context * ctx, std::vector & tokens, int n_past, int n_batch, int n_vocab\n) {\n std::vector result;\n result.reserve(tokens.size() * n_vocab);\n size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch;\n for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {\n size_t n_tokens = tokens.size() - i_chunk * n_batch;\n n_tokens = std::min(n_tokens, size_t(n_batch));\n if (llama_decode(ctx, llama_batch_get_one(tokens.data() + i_chunk * n_batch, n_tokens, n_past, 0))) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return {};\n }\n\n const auto logits = llama_get_logits(ctx);\n result.insert(result.end(), logits, logits + n_tokens * n_vocab);\n\n n_past += n_tokens;\n }\n return result;\n}\n\nstatic void hellaswag_score(llama_context * ctx, const gpt_params & params) {\n // Calculates hellaswag score (acc_norm) from prompt\n //\n // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl\n // All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68\n //\n // All 10042 tasks should be extracted to keep the results standardized like other implementations.\n //\n // Datafile layout:\n // ['??'] denotes json fields\n // 6 lines per task:\n // ['activity_label'] + \": \" +['ctx'] - The first part of the query, the context\n // ['label'] - The index the best common sense ending aka gold ending\n // ['endings'][0] - Endings added to the first part of the query\n // ['endings'][1]\n // ['endings'][2]\n // ['endings'][3]\n\n std::vector prompt_lines;\n std::istringstream strstream(params.prompt);\n std::string line;\n\n while (std::getline(strstream,line,'\\n')) {\n prompt_lines.push_back(line);\n }\n\n if( prompt_lines.size() % 6 != 0) {\n fprintf(stderr, \"%s : number of lines in prompt not a multiple of 6.\\n\", __func__);\n return;\n }\n\n size_t hs_task_count = prompt_lines.size()/6;\n fprintf(stderr, \"%s : loaded %zu tasks from prompt.\\n\", __func__, hs_task_count);\n\n const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;\n fprintf(stderr, \"================================= is_spm = %d\\n\", is_spm);\n\n // This is needed as usual for LLaMA models\n const bool add_bos = is_spm;\n\n // Number of tasks to use when computing the score\n if ( params.hellaswag_tasks < hs_task_count ) {\n hs_task_count = params.hellaswag_tasks;\n }\n\n // The tasks should be randomized so the score stabilizes quickly.\n bool randomize_tasks = true;\n\n // The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now\n std::mt19937 rng(1);\n\n // Dataholder for hellaswag tasks\n struct hs_data_t {\n std::string context;\n size_t gold_ending_idx;\n std::string ending[4];\n size_t ending_logprob_count[4];\n double ending_logprob[4];\n };\n\n fprintf(stderr, \"%s : selecting %zu %s tasks.\\n\", __func__, hs_task_count, (randomize_tasks?\"randomized\":\"the first\") );\n\n // Select and read data from prompt lines\n hs_data_t *hs_data = new hs_data_t[hs_task_count];\n for (size_t i=0; i < hs_task_count; i++) {\n size_t idx = i;\n\n // Select a random example of those left in the prompt\n if (randomize_tasks) {\n std::uniform_int_distribution dist(0, prompt_lines.size()/6-1 ) ;\n idx = dist(rng);\n }\n\n hs_data[i].context = prompt_lines[idx*6];\n hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );\n for (size_t j=0; j < 4; j++) {\n hs_data[i].ending[j] = prompt_lines[idx*6+2+j];\n }\n\n // Delete the selected random example from the prompt\n if (randomize_tasks) {\n prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );\n }\n }\n\n fprintf(stderr, \"%s : calculating hellaswag score over selected tasks.\\n\", __func__);\n printf(\"\\ntask\\tacc_norm\\n\");\n\n double acc = 0.0f;\n const int n_vocab = llama_n_vocab(llama_get_model(ctx));\n const int n_ctx = llama_n_ctx(ctx);\n\n std::vector> ending_tokens(4);\n\n std::vector tok_logits(n_vocab);\n\n for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {\n // Tokenize the context to count tokens\n std::vector context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);\n size_t context_size = context_embd.size();\n\n for (int i = 0; i < 4; ++i) {\n ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + \" \" + hs_data[task_idx].ending[i], add_bos);\n for (int k = 0; k < int(context_size); ++k) {\n if (ending_tokens[i][k] != context_embd[k]) {\n fprintf(stderr, \"Oops: ending %d of task %d differs from context at position %d\\n\",i,int(task_idx),k);\n break;\n }\n }\n }\n\n // Do the 1st ending\n // In this case we include the context when evaluating\n //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);\n auto query_embd = ending_tokens[0];\n auto query_size = query_embd.size();\n\n // Stop if query wont fit the ctx window\n if (query_size > (size_t)n_ctx) {\n fprintf(stderr, \"%s : number of tokens in query %zu > n_ctxl\\n\", __func__, query_size);\n return;\n }\n\n // Speedup small evaluations by evaluating atleast 32 tokens\n if (query_size < 32) {\n query_embd.resize(32);\n }\n\n // clear the KV cache\n llama_kv_cache_clear(ctx);\n\n auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);\n if (logits.empty()) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return;\n }\n\n std::memcpy(tok_logits.data(), logits.data() + (context_size-1)*n_vocab, n_vocab*sizeof(float));\n const auto first_probs = softmax(tok_logits);\n\n hs_data[task_idx].ending_logprob_count[0] = 1;\n hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);\n\n // Calculate the logprobs over the ending\n for (size_t j = context_size; j < query_size - 1; j++) {\n\n std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));\n\n const float prob = softmax(tok_logits)[query_embd[j + 1]];\n\n hs_data[task_idx].ending_logprob[0] += std::log(prob);\n hs_data[task_idx].ending_logprob_count[0]++;\n }\n\n // Calculate the mean token logprob for acc_norm\n hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];\n\n // Do the remaining endings\n // For these, we use the bare ending with n_past = context_size\n //\n for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {\n\n // Tokenize the query\n query_embd.resize(ending_tokens[ending_idx].size() - context_size);\n std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));\n query_size = query_embd.size();\n\n // Stop if query wont fit the ctx window\n if (context_size + query_size > (size_t)n_ctx) {\n fprintf(stderr, \"%s : number of tokens in query %zu > n_ctxl\\n\", __func__, query_size);\n return;\n }\n\n // Speedup small evaluations by evaluating atleast 32 tokens\n // No, resizing to 32 is actually slightly slower (at least on CUDA)\n //if (query_size < 32) {\n // query_embd.resize(32);\n //}\n\n // Evaluate the query\n logits = hellaswag_evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab);\n if (logits.empty()) {\n fprintf(stderr, \"%s : failed to eval\\n\", __func__);\n return;\n }\n\n hs_data[task_idx].ending_logprob_count[ending_idx] = 1;\n hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);\n\n // Calculate the logprobs over the ending\n for (size_t j = 0; j < query_size - 1; j++) {\n std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));\n\n const float prob = softmax(tok_logits)[query_embd[j + 1]];\n\n hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);\n hs_data[task_idx].ending_logprob_count[ending_idx]++;\n }\n\n // Calculate the mean token logprob for acc_norm\n hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];\n\n\n// printf(\"task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\\n\",\n// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );\n }\n\n // Find the ending with maximum logprob\n size_t ending_logprob_max_idx = 0;\n double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];\n for (size_t j = 1; j < 4; j++) {\n if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {\n ending_logprob_max_idx = j;\n ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];\n }\n }\n\n// printf(\"max logprob ending idx %lu, gold ending idx %lu\\n\", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);\n\n // If the gold ending got the maximum logprobe add one accuracy point\n if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {\n acc += 1.0;\n }\n\n // Print the accumulated accuracy mean x 100\n printf(\"%zu\\t%.8lf\\n\",task_idx+1, acc/double(task_idx+1)*100.0);\n fflush(stdout);\n }\n\n delete [] hs_data;\n\n printf(\"\\n\");\n}\n\nint main(int argc, char ** argv) {\n gpt_params params;\n\n params.n_batch = 512;\n if (!gpt_params_parse(argc, argv, params)) {\n return 1;\n }\n\n params.logits_all = true;\n params.n_batch = std::min(params.n_batch, params.n_ctx);\n\n if (params.ppl_stride > 0) {\n fprintf(stderr, \"Will perform strided perplexity calculation -> adjusting context size from %d to %d\\n\",\n params.n_ctx, params.n_ctx + params.ppl_stride/2);\n params.n_ctx += params.ppl_stride/2;\n }\n\n print_build_info();\n\n if (params.seed == LLAMA_DEFAULT_SEED) {\n params.seed = time(NULL);\n }\n\n fprintf(stderr, \"%s: seed = %u\\n\", __func__, params.seed);\n\n std::mt19937 rng(params.seed);\n if (params.random_prompt) {\n params.prompt = gpt_random_prompt(rng);\n }\n\n llama_backend_init(params.numa);\n\n llama_model * model;\n llama_context * ctx;\n\n // load the model and apply lora adapter, if any\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n if (model == NULL) {\n fprintf(stderr, \"%s: error: unable to load model\\n\", __func__);\n return 1;\n }\n\n const int n_ctx_train = llama_n_ctx_train(model);\n if (params.n_ctx > n_ctx_train) {\n fprintf(stderr, \"%s: warning: model was trained on only %d context tokens (%d specified)\\n\",\n __func__, n_ctx_train, params.n_ctx);\n }\n\n // print system information\n {\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"%s\\n\", get_system_info(params).c_str());\n }\n\n struct results_perplexity results;\n if (params.hellaswag) {\n hellaswag_score(ctx, params);\n } else {\n results = perplexity(ctx, params);\n }\n\n llama_print_timings(ctx);\n write_logfile(ctx, params, model, results);\n\n llama_free(ctx);\n llama_free_model(model);\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/quantize/CMakeLists.txt", "content": "set(TARGET quantize)\nadd_executable(${TARGET} quantize.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})\ntarget_include_directories(${TARGET} PRIVATE ../../common)\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/quantize/README.md", "content": "# quantize\n\nTODO\n\n## Llama 2 7B\n\nQuantization | Bits per Weight (BPW)\n-- | --\nQ2_K | 3.35\nQ3_K_S | 3.50\nQ3_K_M | 3.91\nQ3_K_L | 4.27\nQ4_K_S | 4.58\nQ4_K_M | 4.84\nQ5_K_S | 5.52\nQ5_K_M | 5.68\nQ6_K | 6.56\n\n## Llama 2 13B\nQuantization | Bits per Weight (BPW)\n-- | --\nQ2_K | 3.34\nQ3_K_S | 3.48\nQ3_K_M | 3.89\nQ3_K_L | 4.26\nQ4_K_S | 4.56\nQ4_K_M | 4.83\nQ5_K_S | 5.51\nQ5_K_M | 5.67\nQ6_K | 6.56\n\n# Llama 2 70B\n\nQuantization | Bits per Weight (BPW)\n-- | --\nQ2_K | 3.40\nQ3_K_S | 3.47\nQ3_K_M | 3.85\nQ3_K_L | 4.19\nQ4_K_S | 4.53\nQ4_K_M | 4.80\nQ5_K_S | 5.50\nQ5_K_M | 5.65\nQ6_K | 6.56\n" }, { "path": "examples/quantize/quantize.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n\nstruct quant_option {\n std::string name;\n llama_ftype ftype;\n std::string desc;\n};\n\nstatic const std::vector QUANT_OPTIONS = {\n { \"Q4_0\", LLAMA_FTYPE_MOSTLY_Q4_0, \" 3.56G, +0.2166 ppl @ LLaMA-v1-7B\", },\n { \"Q4_1\", LLAMA_FTYPE_MOSTLY_Q4_1, \" 3.90G, +0.1585 ppl @ LLaMA-v1-7B\", },\n { \"Q5_0\", LLAMA_FTYPE_MOSTLY_Q5_0, \" 4.33G, +0.0683 ppl @ LLaMA-v1-7B\", },\n { \"Q5_1\", LLAMA_FTYPE_MOSTLY_Q5_1, \" 4.70G, +0.0349 ppl @ LLaMA-v1-7B\", },\n { \"Q2_K\", LLAMA_FTYPE_MOSTLY_Q2_K, \" 2.63G, +0.6717 ppl @ LLaMA-v1-7B\", },\n { \"Q3_K\", LLAMA_FTYPE_MOSTLY_Q3_K_M, \"alias for Q3_K_M\" },\n { \"Q3_K_S\", LLAMA_FTYPE_MOSTLY_Q3_K_S, \" 2.75G, +0.5551 ppl @ LLaMA-v1-7B\", },\n { \"Q3_K_M\", LLAMA_FTYPE_MOSTLY_Q3_K_M, \" 3.07G, +0.2496 ppl @ LLaMA-v1-7B\", },\n { \"Q3_K_L\", LLAMA_FTYPE_MOSTLY_Q3_K_L, \" 3.35G, +0.1764 ppl @ LLaMA-v1-7B\", },\n { \"Q4_K\", LLAMA_FTYPE_MOSTLY_Q4_K_M, \"alias for Q4_K_M\", },\n { \"Q4_K_S\", LLAMA_FTYPE_MOSTLY_Q4_K_S, \" 3.59G, +0.0992 ppl @ LLaMA-v1-7B\", },\n { \"Q4_K_M\", LLAMA_FTYPE_MOSTLY_Q4_K_M, \" 3.80G, +0.0532 ppl @ LLaMA-v1-7B\", },\n { \"Q5_K\", LLAMA_FTYPE_MOSTLY_Q5_K_M, \"alias for Q5_K_M\", },\n { \"Q5_K_S\", LLAMA_FTYPE_MOSTLY_Q5_K_S, \" 4.33G, +0.0400 ppl @ LLaMA-v1-7B\", },\n { \"Q5_K_M\", LLAMA_FTYPE_MOSTLY_Q5_K_M, \" 4.45G, +0.0122 ppl @ LLaMA-v1-7B\", },\n { \"Q6_K\", LLAMA_FTYPE_MOSTLY_Q6_K, \" 5.15G, -0.0008 ppl @ LLaMA-v1-7B\", },\n { \"Q8_0\", LLAMA_FTYPE_MOSTLY_Q8_0, \" 6.70G, +0.0004 ppl @ LLaMA-v1-7B\", },\n { \"F16\", LLAMA_FTYPE_MOSTLY_F16, \"13.00G @ 7B\", },\n { \"F32\", LLAMA_FTYPE_ALL_F32, \"26.00G @ 7B\", },\n // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.\n { \"COPY\", LLAMA_FTYPE_ALL_F32, \"only copy tensors, no quantizing\", },\n};\n\n\nstatic bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {\n std::string ftype_str;\n\n for (auto ch : ftype_str_in) {\n ftype_str.push_back(std::toupper(ch));\n }\n for (auto & it : QUANT_OPTIONS) {\n if (it.name == ftype_str) {\n ftype = it.ftype;\n ftype_str_out = it.name;\n return true;\n }\n }\n try {\n int ftype_int = std::stoi(ftype_str);\n for (auto & it : QUANT_OPTIONS) {\n if (it.ftype == ftype_int) {\n ftype = it.ftype;\n ftype_str_out = it.name;\n return true;\n }\n }\n }\n catch (...) {\n // stoi failed\n }\n return false;\n}\n\n// usage:\n// ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]\n//\n[[noreturn]]\nstatic void usage(const char * executable) {\n printf(\"usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\\n\\n\", executable);\n printf(\" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\\n\");\n printf(\" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\\n\");\n printf(\" --pure: Disable k-quant mixtures and quantize all tensors to the same type\\n\");\n printf(\"\\nAllowed quantization types:\\n\");\n for (auto & it : QUANT_OPTIONS) {\n if (it.name != \"COPY\") {\n printf(\" %2d or \", it.ftype);\n } else {\n printf(\" \");\n }\n printf(\"%-6s : %s\\n\", it.name.c_str(), it.desc.c_str());\n }\n exit(1);\n}\n\nint main(int argc, char ** argv) {\n if (argc < 3) {\n usage(argv[0]);\n }\n\n llama_model_quantize_params params = llama_model_quantize_default_params();\n\n int arg_idx = 1;\n\n for (; arg_idx < argc && strncmp(argv[arg_idx], \"--\", 2) == 0; arg_idx++) {\n if (strcmp(argv[arg_idx], \"--leave-output-tensor\") == 0) {\n params.quantize_output_tensor = false;\n } else if (strcmp(argv[arg_idx], \"--allow-requantize\") == 0) {\n params.allow_requantize = true;\n } else if (strcmp(argv[arg_idx], \"--pure\") == 0) {\n params.pure = true;\n } else {\n usage(argv[0]);\n }\n }\n\n if (argc - arg_idx < 2) {\n usage(argv[0]);\n }\n\n llama_backend_init(false);\n\n // parse command line arguments\n const std::string fname_inp = argv[arg_idx];\n arg_idx++;\n std::string fname_out;\n\n std::string ftype_str;\n if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {\n std::string fpath;\n const size_t pos = fname_inp.find_last_of(\"/\\\\\");\n if (pos != std::string::npos) {\n fpath = fname_inp.substr(0, pos + 1);\n }\n // export as [inp path]/ggml-model-[ftype].gguf\n fname_out = fpath + \"ggml-model-\" + ftype_str + \".gguf\";\n arg_idx++;\n if (ftype_str == \"COPY\") {\n params.only_copy = true;\n }\n }\n else {\n fname_out = argv[arg_idx];\n arg_idx++;\n\n if (argc <= arg_idx) {\n fprintf(stderr, \"%s: missing ftype\\n\", __func__);\n return 1;\n }\n if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {\n fprintf(stderr, \"%s: invalid ftype '%s'\\n\", __func__, argv[3]);\n return 1;\n }\n if (ftype_str == \"COPY\") {\n params.only_copy = true;\n }\n arg_idx++;\n }\n\n // parse nthreads\n if (argc > arg_idx) {\n try {\n params.nthread = std::stoi(argv[arg_idx]);\n }\n catch (const std::exception & e) {\n fprintf(stderr, \"%s: invalid nthread '%s' (%s)\\n\", __func__, argv[arg_idx], e.what());\n return 1;\n }\n }\n\n print_build_info();\n\n fprintf(stderr, \"%s: quantizing '%s' to '%s' as %s\", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());\n if (params.nthread > 0) {\n fprintf(stderr, \" using %d threads\", params.nthread);\n }\n fprintf(stderr, \"\\n\");\n\n const int64_t t_main_start_us = llama_time_us();\n\n int64_t t_quantize_us = 0;\n\n // load the model\n {\n const int64_t t_start_us = llama_time_us();\n\n if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ¶ms)) {\n fprintf(stderr, \"%s: failed to quantize model from '%s'\\n\", __func__, fname_inp.c_str());\n return 1;\n }\n\n t_quantize_us = llama_time_us() - t_start_us;\n }\n\n // report timing\n {\n const int64_t t_main_end_us = llama_time_us();\n\n printf(\"\\n\");\n printf(\"%s: quantize time = %8.2f ms\\n\", __func__, t_quantize_us/1000.0);\n printf(\"%s: total time = %8.2f ms\\n\", __func__, (t_main_end_us - t_main_start_us)/1000.0);\n }\n\n llama_backend_free();\n\n return 0;\n}\n" }, { "path": "examples/quantize-stats/CMakeLists.txt", "content": "set(TARGET quantize-stats)\nadd_executable(${TARGET} quantize-stats.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})\ntarget_include_directories(${TARGET} PRIVATE ../../common)\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/quantize-stats/quantize-stats.cpp", "content": "#define LLAMA_API_INTERNAL\n#include \"common.h\"\n#include \"ggml.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if defined(_MSC_VER)\n#pragma warning(disable: 4244 4267) // possible loss of data\n#endif\n\nstruct quantize_stats_params {\n std::string model = \"models/7B/ggml-model-f16.gguf\";\n bool verbose = false;\n bool per_layer_stats = false;\n bool print_histogram = false;\n bool reference = false;\n std::vector include_layers;\n std::vector exclude_layers;\n std::vector include_types;\n};\n\nconstexpr size_t HISTOGRAM_BUCKETS = 150;\nconstexpr double HISTOGRAM_RANGE = 0.03;\n\nstruct error_stats {\n size_t num_samples;\n double total_error;\n double max_error;\n uint64_t error_histogram[HISTOGRAM_BUCKETS];\n};\n\nstatic void quantize_stats_print_usage(int /*argc*/, char ** argv) {\n quantize_stats_params params;\n fprintf(stderr, \"usage: %s [options]\\n\", argv[0]);\n fprintf(stderr, \"\\n\");\n fprintf(stderr, \"options:\\n\");\n fprintf(stderr, \" -h, --help show this help message and exit\\n\");\n fprintf(stderr, \" -m FNAME, --model FNAME\\n\");\n fprintf(stderr, \" model path (default: %s)\\n\", params.model.c_str());\n fprintf(stderr, \" -r, --reference\\n\");\n fprintf(stderr, \" use reference implementation (default: false)\\n\");\n fprintf(stderr, \" -v, --verbose\\n\");\n fprintf(stderr, \" verbose output (default: false)\\n\");\n fprintf(stderr, \" -p, --per-layer-stats\\n\");\n fprintf(stderr, \" print stats per layer (default: false)\\n\");\n fprintf(stderr, \" --histogram\\n\");\n fprintf(stderr, \" print error histogram (default: false)\\n\");\n fprintf(stderr, \" -l LAYER, --include-layer LAYER\\n\");\n fprintf(stderr, \" only test layers matching pattern\\n\");\n fprintf(stderr, \" -L LAYER, --exclude-layer LAYER\\n\");\n fprintf(stderr, \" exclude layers matching pattern\\n\");\n fprintf(stderr, \" -t TYPE, --type TYPE\\n\");\n fprintf(stderr, \" only test given type (q4_0, q4_1)\\n\");\n fprintf(stderr, \"\\n\");\n}\n\n// Check if a layer is included/excluded by command line\nstatic bool layer_included(const quantize_stats_params & params, const std::string & layer) {\n for (const auto& excluded : params.exclude_layers) {\n if (std::regex_search(layer, std::regex(excluded))) {\n return false;\n }\n }\n for (const auto& included : params.include_layers) {\n if (std::regex_search(layer, std::regex(included))) {\n return true;\n }\n }\n return params.include_layers.empty();\n}\n\n// Update error statistics given vectors with the before/after result of quantization\nstatic void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) {\n for (int64_t i = 0; i < nelements; i++) {\n double diff = input[i] - output[i];\n stats.total_error += diff * diff;\n stats.max_error = fmax(fabs(diff), stats.max_error);\n stats.error_histogram[std::max(std::min((size_t) floor(fabs(diff) / HISTOGRAM_RANGE * HISTOGRAM_BUCKETS), HISTOGRAM_BUCKETS-1), (size_t) 0)]++;\n }\n stats.num_samples += nelements;\n}\n\nstatic void combine_error_stats(error_stats & into, const error_stats & from) {\n into.num_samples += from.num_samples;\n into.total_error += from.total_error;\n if (from.max_error > into.max_error) into.max_error = from.max_error;\n for (size_t i=0; i= sum*quantile) {\n return (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;\n }\n }\n return INFINITY;\n}\n\nstatic void print_error_stats(const std::string & name, const error_stats & stats, bool print_histogram) {\n double rmse = sqrt(stats.total_error / (double) stats.num_samples);\n double median = find_quantile(stats, .5);\n double pct95 = find_quantile(stats, .95);\n printf(\"%-50s: rmse %.8f, maxerr %.8f, 95pct<%.4f, median<%.4f\\n\", name.c_str(), rmse, stats.max_error, pct95, median);\n if (print_histogram) {\n printf(\"Error distribution:\\n\");\n for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {\n double lower = i * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;\n double upper = (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;\n if (i == HISTOGRAM_BUCKETS -1) upper = INFINITY;\n printf(\"[%3.4f, %3.4f): %11\" PRIu64 \"\\n\", lower, upper, stats.error_histogram[i]);\n }\n }\n}\n\n// copied from ggml.h - verify that we can access this as a flat array\nstatic bool tensor_is_contiguous(const struct ggml_tensor * tensor) {\n static_assert(GGML_MAX_DIMS == 4, \"GGML_MAX_DIMS is not 4 - update this function\");\n\n return\n tensor->nb[0] == ggml_type_size(tensor->type) &&\n tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&\n tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&\n tensor->nb[3] == tensor->nb[2]*tensor->ne[2];\n}\n\nstatic void test_roundtrip_on_chunk(\n const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits_t & qfns, bool use_reference,\n float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats\n) {\n if (layer->type == GGML_TYPE_F16) {\n for (int i = 0; i < chunk_size; i++) {\n input_scratch[i] = ggml_get_f32_1d(layer, i + offset);\n }\n } else {\n input_scratch = ggml_get_data_f32(layer) + offset;\n }\n\n if (use_reference) {\n qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);\n } else {\n qfns.from_float(input_scratch, quantized_scratch, chunk_size);\n }\n qfns.to_float(quantized_scratch, output_scratch, chunk_size);\n\n update_error_stats(chunk_size, input_scratch, output_scratch, stats);\n}\n\n\n// Run quantization function for a single layer and update error stats\nstatic void test_roundtrip_on_layer(\n std::string & name, bool print_layer_stats, const ggml_type_traits_t & qfns, bool use_reference,\n const ggml_tensor * layer, std::vector & input_scratch, std::vector & quantized_scratch,\n std::vector & output_scratch, error_stats & total_error, int max_thread = 0\n) {\n assert(tensor_is_contiguous(layer));\n error_stats layer_error {};\n uint64_t nelements = ggml_nelements(layer);\n\n float* input_scratch_ptr = nullptr;\n if (layer->type == GGML_TYPE_F16) {\n if (input_scratch.size() < nelements) input_scratch.resize(nelements);\n input_scratch_ptr = input_scratch.data();\n }\n if (quantized_scratch.size() < 4*nelements) quantized_scratch.resize(4*nelements);\n if (output_scratch.size() < nelements) output_scratch.resize(nelements);\n\n if (max_thread < 1) max_thread = std::thread::hardware_concurrency();\n int chunk_size = 32*512;\n int num_chunks = (nelements + chunk_size - 1)/chunk_size;\n\n if (num_chunks < 2 || max_thread < 2) {\n test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),\n output_scratch.data(), print_layer_stats ? layer_error : total_error);\n } else {\n auto & stats = print_layer_stats ? layer_error : total_error;\n std::mutex mutex;\n uint64_t counter = 0;\n auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,\n &quantized_scratch, &output_scratch, chunk_size] () {\n error_stats local_stats {};\n while (true) {\n std::unique_lock lock(mutex);\n uint64_t offset = counter; counter += chunk_size;\n if (offset >= nelements) {\n combine_error_stats(stats, local_stats);\n break;\n }\n lock.unlock();\n uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;\n test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,\n quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);\n }\n };\n int nthread = std::min(num_chunks, max_thread);\n std::vector workers(nthread-1);\n for (auto& w : workers) w = std::thread(compute);\n compute();\n for (auto& w : workers) w.join();\n }\n\n if (print_layer_stats) {\n print_error_stats(name, layer_error, false);\n combine_error_stats(total_error, layer_error);\n }\n}\n\nint main(int argc, char ** argv) {\n ggml_time_init();\n\n quantize_stats_params params;\n\n // read command line\n\n int max_thread = 0;\n bool invalid_param = false;\n std::string arg;\n for (int i = 1; i < argc; i++) {\n arg = argv[i];\n\n if (arg == \"-h\" || arg == \"--help\") {\n quantize_stats_print_usage(argc, argv);\n exit(0);\n } else if (arg == \"-r\" || arg == \"--reference\") {\n params.reference = true;\n } else if (arg == \"-v\") {\n params.verbose = true;\n } else if (arg == \"-p\" || arg == \"--per-layer-stats\") {\n params.per_layer_stats = true;\n } else if (arg == \"--histogram\") {\n params.print_histogram = true;\n } else if (arg == \"-m\" || arg == \"--model\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.model = argv[i];\n } else if (arg == \"-l\" || arg == \"--include-layer\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.include_layers.push_back(argv[i]);\n } else if (arg == \"-L\" || arg == \"--exclude-layer\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n params.exclude_layers.push_back(argv[i]);\n } else if (arg == \"-t\" || arg == \"--type\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n int j;\n for (j = 0; j < GGML_TYPE_COUNT; ++j) {\n const auto * name = ggml_type_name((ggml_type) j);\n if (name && strcmp(argv[i], name) == 0) break;\n }\n if (j < GGML_TYPE_COUNT) {\n params.include_types.push_back((ggml_type) j);\n } else {\n fprintf(stderr, \"error: %s not in list of types\\n\", argv[i]);\n invalid_param = true;\n }\n } else if (arg == \"-n\" || arg == \"--num-threads\") {\n if (++i >= argc) {\n invalid_param = true;\n break;\n }\n max_thread = atoi(argv[i]);\n } else {\n fprintf(stderr, \"error: unknown argument: %s\\n\", arg.c_str());\n quantize_stats_print_usage(argc, argv);\n return 1;\n }\n }\n if (invalid_param) {\n fprintf(stderr, \"error: invalid parameter for argument: %s\\n\", arg.c_str());\n quantize_stats_print_usage(argc, argv);\n return 1;\n }\n\n print_build_info();\n\n // load the model\n fprintf(stderr, \"Loading model\\n\");\n\n const int64_t t_main_start_us = ggml_time_us();\n llama_model * model;\n llama_context * ctx;\n\n {\n auto mparams = llama_model_default_params();\n mparams.use_mlock = false;\n\n model = llama_load_model_from_file(params.model.c_str(), mparams);\n\n if (model == NULL) {\n fprintf(stderr, \"%s: error: failed to load model '%s'\\n\", __func__, params.model.c_str());\n return 1;\n }\n\n auto cparams = llama_context_default_params();\n cparams.n_ctx = 256;\n cparams.seed = 1;\n cparams.f16_kv = false;\n\n ctx = llama_new_context_with_model(model, cparams);\n\n if (ctx == NULL) {\n fprintf(stderr, \"%s: error: failed to create context with model '%s'\\n\", __func__, params.model.c_str());\n llama_free_model(model);\n return 1;\n }\n }\n\n const auto &tensors = llama_internal_get_tensor_map(ctx);\n\n // check layer tensors\n int included_layers = 0;\n int64_t max_nelements = 0;\n bool is_f16 = false;\n for (const auto& kv_tensor : tensors) {\n if (!layer_included(params, kv_tensor.first)) {\n continue;\n }\n if (params.verbose) {\n printf(\"%s: type %s, size %\" PRId64 \"\\n\", kv_tensor.first.c_str(), ggml_type_name(kv_tensor.second->type), ggml_nelements(kv_tensor.second));\n }\n if (kv_tensor.second->type == GGML_TYPE_F16) {\n is_f16 = true;\n } else if (kv_tensor.second->type != GGML_TYPE_F32) {\n fprintf(stderr, \"%s: error: Quantization should be tested with a float model, \"\n \"this model contains already quantized layers (%s is type %d)\\n\", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);\n llama_free(ctx);\n llama_free_model(model);\n return 1;\n }\n included_layers++;\n max_nelements = std::max(max_nelements, ggml_nelements(kv_tensor.second));\n }\n\n if (is_f16) {\n printf(\"note: source model is f16\\n\");\n }\n printf(\"testing %d layers with max size %\" PRId64 \"\\n\", included_layers, max_nelements);\n // allocate scratch space\n std::vector input_scratch;\n std::vector quantized_scratch;\n std::vector output_scratch;\n\n // loop throught quantization types\n for (int i = 0; i < GGML_TYPE_COUNT; i++) {\n const ggml_type type = (ggml_type) i;\n if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {\n continue;\n }\n ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);\n if (qfns.from_float && qfns.to_float) {\n if (params.verbose) {\n printf(\"testing %s ...\\n\", ggml_type_name(type));\n }\n\n error_stats global_stats {};\n\n for (const auto& kv_tensor : tensors) {\n if (!layer_included(params, kv_tensor.first)) {\n continue;\n }\n if (params.verbose) {\n printf(\" %s ...\\n\", kv_tensor.first.c_str());\n }\n std::string layer_name { ggml_type_name(type) };\n layer_name += \"::\" + kv_tensor.first;\n test_roundtrip_on_layer(\n layer_name,\n params.per_layer_stats,\n qfns,\n params.reference,\n kv_tensor.second,\n input_scratch,\n quantized_scratch,\n output_scratch,\n global_stats,\n max_thread\n );\n }\n\n print_error_stats(ggml_type_name(type), global_stats, params.print_histogram);\n }\n }\n\n\n llama_free(ctx);\n llama_free_model(model);\n // report timing\n {\n const int64_t t_main_end_us = ggml_time_us();\n\n printf(\"\\n\");\n printf(\"%s: total time = %8.2f ms\\n\", __func__, (t_main_end_us - t_main_start_us)/1000.0);\n }\n\n return 0;\n}\n" }, { "path": "examples/reason-act.sh", "content": "#!/bin/bash\n\ncd `dirname $0`\ncd ..\n\n# get -m model parameter otherwise defer to default\nif [ \"$1\" == \"-m\" ]; then\n MODEL=\"-m $2 \"\nfi\n\n./main $MODEL --color \\\n -f ./prompts/reason-act.txt \\\n -i --interactive-first \\\n --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7 -c 2048 \\\n -r \"Question:\" -r \"Observation:\" --in-prefix \" \" \\\n -n -1\n" }, { "path": "examples/save-load-state/CMakeLists.txt", "content": "set(TARGET save-load-state)\nadd_executable(${TARGET} save-load-state.cpp)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/save-load-state/save-load-state.cpp", "content": "#include \"common.h\"\n#include \"llama.h\"\n\n#include \n#include \n#include \n\nint main(int argc, char ** argv) {\n gpt_params params;\n\n params.prompt = \"The quick brown fox\";\n\n if (!gpt_params_parse(argc, argv, params)) {\n return 1;\n }\n\n print_build_info();\n\n if (params.n_predict < 0) {\n params.n_predict = 16;\n }\n\n auto n_past = 0;\n\n std::string result0;\n std::string result1;\n\n // init\n llama_model * model;\n llama_context * ctx;\n\n std::tie(model, ctx) = llama_init_from_gpt_params(params);\n if (model == nullptr || ctx == nullptr) {\n fprintf(stderr, \"%s : failed to init\\n\", __func__);\n return 1;\n }\n\n // tokenize prompt\n auto tokens = llama_tokenize(ctx, params.prompt, true);\n\n // evaluate prompt\n llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));\n n_past += tokens.size();\n\n // save state (rng, logits, embedding and kv_cache) to file\n {\n std::vector state_mem(llama_get_state_size(ctx));\n\n {\n FILE *fp_write = fopen(\"dump_state.bin\", \"wb\");\n llama_copy_state_data(ctx, state_mem.data()); // could also copy directly to memory mapped file\n fwrite(state_mem.data(), 1, state_mem.size(), fp_write);\n fclose(fp_write);\n }\n }\n\n // save state (last tokens)\n const auto n_past_saved = n_past;\n\n // first run\n printf(\"\\nfirst run: %s\", params.prompt.c_str());\n\n for (auto i = 0; i < params.n_predict; i++) {\n auto * logits = llama_get_logits(ctx);\n auto n_vocab = llama_n_vocab(model);\n\n std::vector candidates;\n candidates.reserve(n_vocab);\n for (llama_token token_id = 0; token_id < n_vocab; token_id++) {\n candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});\n }\n llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };\n auto next_token = llama_sample_token(ctx, &candidates_p);\n auto next_token_str = llama_token_to_piece(ctx, next_token);\n\n printf(\"%s\", next_token_str.c_str());\n result0 += next_token_str;\n\n if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {\n fprintf(stderr, \"\\n%s : failed to evaluate\\n\", __func__);\n llama_free(ctx);\n llama_free_model(model);\n return 1;\n }\n n_past += 1;\n }\n\n printf(\"\\n\\n\");\n\n // free old context\n llama_free(ctx);\n\n // make new context\n auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));\n\n printf(\"\\nsecond run: %s\", params.prompt.c_str());\n\n // load state (rng, logits, embedding and kv_cache) from file\n {\n std::vector state_mem(llama_get_state_size(ctx2));\n\n FILE * fp_read = fopen(\"dump_state.bin\", \"rb\");\n\n const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read);\n if (ret != state_mem.size()) {\n fprintf(stderr, \"\\n%s : failed to read state\\n\", __func__);\n llama_free(ctx2);\n llama_free_model(model);\n return 1;\n }\n\n llama_set_state_data(ctx2, state_mem.data());\n\n fclose(fp_read);\n }\n\n // restore state (last tokens)\n n_past = n_past_saved;\n\n // second run\n for (auto i = 0; i < params.n_predict; i++) {\n auto * logits = llama_get_logits(ctx2);\n auto n_vocab = llama_n_vocab(model);\n std::vector candidates;\n candidates.reserve(n_vocab);\n for (llama_token token_id = 0; token_id < n_vocab; token_id++) {\n candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});\n }\n llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };\n auto next_token = llama_sample_token(ctx2, &candidates_p);\n auto next_token_str = llama_token_to_piece(ctx2, next_token);\n\n printf(\"%s\", next_token_str.c_str());\n result1 += next_token_str;\n\n if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {\n fprintf(stderr, \"\\n%s : failed to evaluate\\n\", __func__);\n llama_free(ctx2);\n llama_free_model(model);\n return 1;\n }\n n_past += 1;\n }\n\n printf(\"\\n\");\n\n llama_free(ctx2);\n llama_free_model(model);\n\n if (result0 != result1) {\n fprintf(stderr, \"\\n%s : error : the 2 generations are different\\n\", __func__);\n return 1;\n }\n\n fprintf(stderr, \"\\n%s : success\\n\", __func__);\n\n return 0;\n}\n" }, { "path": "examples/server/CMakeLists.txt", "content": "set(TARGET server)\noption(LLAMA_SERVER_VERBOSE \"Build verbose logging option for Server\" ON)\ninclude_directories(${CMAKE_CURRENT_SOURCE_DIR})\nadd_executable(${TARGET} server.cpp json.hpp httplib.h)\ninstall(TARGETS ${TARGET} RUNTIME)\ntarget_compile_definitions(${TARGET} PRIVATE\n SERVER_VERBOSE=$\n)\ntarget_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})\nif (WIN32)\n TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)\nendif()\ntarget_compile_features(${TARGET} PRIVATE cxx_std_11)\n" }, { "path": "examples/server/README.md", "content": "# llama.cpp/example/server\n\nThis example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.\n\nCommand line options:\n\n- `--threads N`, `-t N`: Set the number of threads to use during generation.\n- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.\n- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).\n- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.\n- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.\n- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.\n- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.\n- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, \"3,2\" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.\n- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.\n- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.\n- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.\n- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.\n- `--numa`: Attempt optimizations that help on some NUMA systems.\n- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.\n- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.\n- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.\n- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.\n- `--port`: Set the port to listen. Default: `8080`.\n- `--path`: path from which to serve static files (default examples/server/public)\n- `--embedding`: Enable embedding extraction, Default: disabled.\n- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)\n- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)\n- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load \"a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)\n- `--vram-budget N`: VRAM budget in GiB (default: -1, -1 = available VRAM)\n- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.\n\n## Build\n\nserver is build alongside everything else from the root of the project\n\n- Using `make`:\n\n ```bash\n make\n ```\n\n- Using `CMake`:\n\n ```bash\n cmake --build . --config Release\n ```\n\n## Quick Start\n\nTo get started right away, run the following command, making sure to use the correct path for the model you have:\n\n### Unix-based systems (Linux, macOS, etc.):\n\n```bash\n./server -m models/7B/ggml-model.gguf -c 2048\n```\n\n### Windows:\n\n```powershell\nserver.exe -m models\\7B\\ggml-model.gguf -c 2048\n```\nThe above command will start a server that by default listens on `127.0.0.1:8080`.\nYou can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.\n\n## Testing with CURL\n\nUsing [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.\n\n```sh\ncurl --request POST \\\n --url http://localhost:8080/completion \\\n --header \"Content-Type: application/json\" \\\n --data '{\"prompt\": \"Building a website can be done in 10 simple steps:\",\"n_predict\": 128}'\n```\n\n## Node JS Test\n\nYou need to have [Node.js](https://nodejs.org/en) installed.\n\n```bash\nmkdir llama-client\ncd llama-client\n```\n\nCreate a index.js file and put inside this:\n\n```javascript\nconst prompt = `Building a website can be done in 10 simple steps:`;\n\nasync function Test() {\n let response = await fetch(\"http://127.0.0.1:8080/completion\", {\n method: 'POST',\n body: JSON.stringify({\n prompt,\n n_predict: 512,\n })\n })\n console.log((await response.json()).content)\n}\n\nTest()\n```\n\nAnd run it:\n\n```bash\nnode index.js\n```\n\n## API Endpoints\n\n- **POST** `/completion`: Given a `prompt`, it returns the predicted completion.\n\n *Options:*\n\n `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the \"unseen\" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.\n\n `temperature`: Adjust the randomness of the generated text (default: 0.8).\n\n `top_k`: Limit the next token selection to the K most probable tokens (default: 40).\n\n `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).\n\n `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).\n\n `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).\n\n `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.\n By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.\n\n `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.\n\n `stop`: Specify a JSON array of stopping strings.\n These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).\n\n `tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).\n\n `typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).\n\n `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).\n\n `repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).\n\n `penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).\n\n `presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).\n\n `frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);\n\n `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).\n\n `mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).\n\n `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).\n\n `grammar`: Set grammar for grammar-based sampling (default: no grammar)\n\n `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).\n\n `ignore_eos`: Ignore end of stream token and continue generating (default: false).\n\n `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `\"logit_bias\": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `\"logit_bias\": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `\"logit_bias\": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).\n\n `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)\n\n `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., \"image_data\": [{\"data\": \"\", \"id\": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.\n\n *Result JSON:*\n\n Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.\n\n `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.\n\n `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)\n\n `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`\n\n `model`: The path to the model loaded with `-m`\n\n `prompt`: The provided `prompt`\n\n `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token\n\n `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered\n\n `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided\n\n `stopping_word`: The stopping word encountered which stopped the generation (or \"\" if not stopped due to a stopping word)\n\n `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`\n\n `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)\n\n `tokens_evaluated`: Number of tokens evaluated in total from the prompt\n\n `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)\n\n `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)\n\n `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)\n\n `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)\n\n- **POST** `/tokenize`: Tokenize a given text.\n\n *Options:*\n\n `content`: Set the text to tokenize.\n\n Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.\n\n- **POST** `/detokenize`: Convert tokens to text.\n\n *Options:*\n\n `tokens`: Set the tokens to detokenize.\n\n- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.\n\n *Options:*\n\n `content`: Set the text to process.\n\n **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.\n\n *Options:*\n\n `input_prefix`: Set the prefix of the code to infill.\n\n `input_suffix`: Set the suffix of the code to infill.\n\n It also accepts all the options of `/completion` except `stream` and `prompt`.\n\n- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.\n\n## More examples\n\n### Change system prompt on runtime\n\nTo use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.\n\n`prompt`: Specify a context that you want all connecting clients to respect.\n\n`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint.\n\n`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint.\n\n```json\n{\n \"system_prompt\": {\n \"prompt\": \"Transcript of a never ending dialog, where the User interacts with an Assistant.\\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\\nUser: Recommend a nice restaurant in the area.\\nAssistant: I recommend the restaurant \\\"The Golden Duck\\\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\\nUser: Who is Richard Feynman?\\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \\\"Surely You're Joking, Mr. Feynman!\\\" and \\\"What Do You Care What Other People Think?\\\".\\nUser:\",\n \"anti_prompt\": \"User:\",\n \"assistant_name\": \"Assistant:\"\n }\n}\n```\n\n**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`.\n\n### Interactive mode\n\nCheck the sample in [chat.mjs](chat.mjs).\nRun with NodeJS version 16 or later:\n\n```sh\nnode chat.mjs\n```\n\nAnother sample in [chat.sh](chat.sh).\nRequires [bash](https://www.gnu.org/software/bash/), [curl](https://curl.se) and [jq](https://jqlang.github.io/jq/).\nRun with bash:\n\n```sh\nbash chat.sh\n```\n\n### API like OAI\n\nAPI example using Python Flask: [api_like_OAI.py](api_like_OAI.py)\nThis example must be used with server.cpp\n\n```sh\npython api_like_OAI.py\n```\n\nAfter running the API server, you can use it in Python by setting the API base URL.\n```python\nopenai.api_base = \"http://:port\"\n```\n\nThen you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API\n\n### Extending or building alternative Web Front End\n\nThe default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.\n\nRead the documentation in `/completion.js` to see convenient ways to access llama.\n\nA simple example is below:\n\n```html\n\n \n 
\n      \n
\n \n\n```\n" }, { "path": "examples/server/api_like_OAI.py", "content": "#!/usr/bin/env python3\nimport argparse\nfrom flask import Flask, jsonify, request, Response\nimport urllib.parse\nimport requests\nimport time\nimport json\n\n\napp = Flask(__name__)\nslot_id = -1\n\nparser = argparse.ArgumentParser(description=\"An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.\")\nparser.add_argument(\"--chat-prompt\", type=str, help=\"the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\\\n')\", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\\\n')\nparser.add_argument(\"--user-name\", type=str, help=\"USER name in chat completions(default: '\\\\nUSER: ')\", default=\"\\\\nUSER: \")\nparser.add_argument(\"--ai-name\", type=str, help=\"ASSISTANT name in chat completions(default: '\\\\nASSISTANT: ')\", default=\"\\\\nASSISTANT: \")\nparser.add_argument(\"--system-name\", type=str, help=\"SYSTEM name in chat completions(default: '\\\\nASSISTANT's RULE: ')\", default=\"\\\\nASSISTANT's RULE: \")\nparser.add_argument(\"--stop\", type=str, help=\"the end of response in chat completions(default: '')\", default=\"\")\nparser.add_argument(\"--llama-api\", type=str, help=\"Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)\", default='http://127.0.0.1:8080')\nparser.add_argument(\"--api-key\", type=str, help=\"Set the api key to allow only few user(default: NULL)\", default=\"\")\nparser.add_argument(\"--host\", type=str, help=\"Set the ip address to listen.(default: 127.0.0.1)\", default='127.0.0.1')\nparser.add_argument(\"--port\", type=int, help=\"Set the port to listen.(default: 8081)\", default=8081)\n\nargs = parser.parse_args()\n\ndef is_present(json, key):\n try:\n buf = json[key]\n except KeyError:\n return False\n if json[key] == None:\n return False\n return True\n\n#convert chat to prompt\ndef convert_chat(messages):\n prompt = \"\" + args.chat_prompt.replace(\"\\\\n\", \"\\n\")\n\n system_n = args.system_name.replace(\"\\\\n\", \"\\n\")\n user_n = args.user_name.replace(\"\\\\n\", \"\\n\")\n ai_n = args.ai_name.replace(\"\\\\n\", \"\\n\")\n stop = args.stop.replace(\"\\\\n\", \"\\n\")\n\n\n for line in messages:\n if (line[\"role\"] == \"system\"):\n prompt += f\"{system_n}{line['content']}\"\n if (line[\"role\"] == \"user\"):\n prompt += f\"{user_n}{line['content']}\"\n if (line[\"role\"] == \"assistant\"):\n prompt += f\"{ai_n}{line['content']}{stop}\"\n prompt += ai_n.rstrip()\n\n return prompt\n\ndef make_postData(body, chat=False, stream=False):\n postData = {}\n if (chat):\n postData[\"prompt\"] = convert_chat(body[\"messages\"])\n else:\n postData[\"prompt\"] = body[\"prompt\"]\n if(is_present(body, \"temperature\")): postData[\"temperature\"] = body[\"temperature\"]\n if(is_present(body, \"top_k\")): postData[\"top_k\"] = body[\"top_k\"]\n if(is_present(body, \"top_p\")): postData[\"top_p\"] = body[\"top_p\"]\n if(is_present(body, \"max_tokens\")): postData[\"n_predict\"] = body[\"max_tokens\"]\n if(is_present(body, \"presence_penalty\")): postData[\"presence_penalty\"] = body[\"presence_penalty\"]\n if(is_present(body, \"frequency_penalty\")): postData[\"frequency_penalty\"] = body[\"frequency_penalty\"]\n if(is_present(body, \"repeat_penalty\")): postData[\"repeat_penalty\"] = body[\"repeat_penalty\"]\n if(is_present(body, \"mirostat\")): postData[\"mirostat\"] = body[\"mirostat\"]\n if(is_present(body, \"mirostat_tau\")): postData[\"mirostat_tau\"] = body[\"mirostat_tau\"]\n if(is_present(body, \"mirostat_eta\")): postData[\"mirostat_eta\"] = body[\"mirostat_eta\"]\n if(is_present(body, \"seed\")): postData[\"seed\"] = body[\"seed\"]\n if(is_present(body, \"logit_bias\")): postData[\"logit_bias\"] = [[int(token), body[\"logit_bias\"][token]] for token in body[\"logit_bias\"].keys()]\n if (args.stop != \"\"):\n postData[\"stop\"] = [args.stop]\n else:\n postData[\"stop\"] = []\n if(is_present(body, \"stop\")): postData[\"stop\"] += body[\"stop\"]\n postData[\"n_keep\"] = -1\n postData[\"stream\"] = stream\n postData[\"cache_prompt\"] = True\n postData[\"slot_id\"] = slot_id\n return postData\n\ndef make_resData(data, chat=False, promptToken=[]):\n resData = {\n \"id\": \"chatcmpl\" if (chat) else \"cmpl\",\n \"object\": \"chat.completion\" if (chat) else \"text_completion\",\n \"created\": int(time.time()),\n \"truncated\": data[\"truncated\"],\n \"model\": \"LLaMA_CPP\",\n \"usage\": {\n \"prompt_tokens\": data[\"tokens_evaluated\"],\n \"completion_tokens\": data[\"tokens_predicted\"],\n \"total_tokens\": data[\"tokens_evaluated\"] + data[\"tokens_predicted\"]\n }\n }\n if (len(promptToken) != 0):\n resData[\"promptToken\"] = promptToken\n if (chat):\n #only one choice is supported\n resData[\"choices\"] = [{\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": data[\"content\"],\n },\n \"finish_reason\": \"stop\" if (data[\"stopped_eos\"] or data[\"stopped_word\"]) else \"length\"\n }]\n else:\n #only one choice is supported\n resData[\"choices\"] = [{\n \"text\": data[\"content\"],\n \"index\": 0,\n \"logprobs\": None,\n \"finish_reason\": \"stop\" if (data[\"stopped_eos\"] or data[\"stopped_word\"]) else \"length\"\n }]\n return resData\n\ndef make_resData_stream(data, chat=False, time_now = 0, start=False):\n resData = {\n \"id\": \"chatcmpl\" if (chat) else \"cmpl\",\n \"object\": \"chat.completion.chunk\" if (chat) else \"text_completion.chunk\",\n \"created\": time_now,\n \"model\": \"LLaMA_CPP\",\n \"choices\": [\n {\n \"finish_reason\": None,\n \"index\": 0\n }\n ]\n }\n slot_id = data[\"slot_id\"]\n if (chat):\n if (start):\n resData[\"choices\"][0][\"delta\"] = {\n \"role\": \"assistant\"\n }\n else:\n resData[\"choices\"][0][\"delta\"] = {\n \"content\": data[\"content\"]\n }\n if (data[\"stop\"]):\n resData[\"choices\"][0][\"finish_reason\"] = \"stop\" if (data[\"stopped_eos\"] or data[\"stopped_word\"]) else \"length\"\n else:\n resData[\"choices\"][0][\"text\"] = data[\"content\"]\n if (data[\"stop\"]):\n resData[\"choices\"][0][\"finish_reason\"] = \"stop\" if (data[\"stopped_eos\"] or data[\"stopped_word\"]) else \"length\"\n\n return resData\n\n\n@app.route('/chat/completions', methods=['POST'])\n@app.route('/v1/chat/completions', methods=['POST'])\ndef chat_completions():\n if (args.api_key != \"\" and request.headers[\"Authorization\"].split()[1] != args.api_key):\n return Response(status=403)\n body = request.get_json()\n stream = False\n tokenize = False\n if(is_present(body, \"stream\")): stream = body[\"stream\"]\n if(is_present(body, \"tokenize\")): tokenize = body[\"tokenize\"]\n postData = make_postData(body, chat=True, stream=stream)\n\n promptToken = []\n if (tokenize):\n tokenData = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/tokenize\"), data=json.dumps({\"content\": postData[\"prompt\"]})).json()\n promptToken = tokenData[\"tokens\"]\n\n if (not stream):\n data = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/completion\"), data=json.dumps(postData))\n print(data.json())\n resData = make_resData(data.json(), chat=True, promptToken=promptToken)\n return jsonify(resData)\n else:\n def generate():\n data = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/completion\"), data=json.dumps(postData), stream=True)\n time_now = int(time.time())\n resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)\n yield 'data: {}\\n'.format(json.dumps(resData))\n for line in data.iter_lines():\n if line:\n decoded_line = line.decode('utf-8')\n resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)\n yield 'data: {}\\n'.format(json.dumps(resData))\n return Response(generate(), mimetype='text/event-stream')\n\n\n@app.route('/completions', methods=['POST'])\n@app.route('/v1/completions', methods=['POST'])\ndef completion():\n if (args.api_key != \"\" and request.headers[\"Authorization\"].split()[1] != args.api_key):\n return Response(status=403)\n body = request.get_json()\n stream = False\n tokenize = False\n if(is_present(body, \"stream\")): stream = body[\"stream\"]\n if(is_present(body, \"tokenize\")): tokenize = body[\"tokenize\"]\n postData = make_postData(body, chat=False, stream=stream)\n\n promptToken = []\n if (tokenize):\n tokenData = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/tokenize\"), data=json.dumps({\"content\": postData[\"prompt\"]})).json()\n promptToken = tokenData[\"tokens\"]\n\n if (not stream):\n data = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/completion\"), data=json.dumps(postData))\n print(data.json())\n resData = make_resData(data.json(), chat=False, promptToken=promptToken)\n return jsonify(resData)\n else:\n def generate():\n data = requests.request(\"POST\", urllib.parse.urljoin(args.llama_api, \"/completion\"), data=json.dumps(postData), stream=True)\n time_now = int(time.time())\n for line in data.iter_lines():\n if line:\n decoded_line = line.decode('utf-8')\n resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)\n yield 'data: {}\\n'.format(json.dumps(resData))\n return Response(generate(), mimetype='text/event-stream')\n\nif __name__ == '__main__':\n app.run(args.host, port=args.port)\n" }, { "path": "examples/server/chat-llama2.sh", "content": "#!/bin/bash\n\nAPI_URL=\"${API_URL:-http://127.0.0.1:8080}\"\n\nCHAT=(\n \"Hello, Assistant.\"\n \"Hello. How may I help you today?\"\n)\n\nINSTRUCTION=\"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\"\n\ntrim() {\n shopt -s extglob\n set -- \"${1##+([[:space:]])}\"\n printf \"%s\" \"${1%%+([[:space:]])}\"\n}\n\ntrim_trailing() {\n shopt -s extglob\n printf \"%s\" \"${1%%+([[:space:]])}\"\n}\n\nformat_prompt() {\n if [[ \"${#CHAT[@]}\" -eq 0 ]]; then\n echo -n \"[INST] <>\\n${INSTRUCTION}\\n<>\"\n else\n LAST_INDEX=$(( ${#CHAT[@]} - 1 ))\n echo -n \"${CHAT[$LAST_INDEX]}\\n[INST] $1 [/INST]\"\n fi\n}\n\ntokenize() {\n curl \\\n --silent \\\n --request POST \\\n --url \"${API_URL}/tokenize\" \\\n --header \"Content-Type: application/json\" \\\n --data-raw \"$(jq -ns --arg content \"$1\" '{content:$content}')\" \\\n | jq '.tokens[]'\n}\n\nN_KEEP=$(tokenize \"[INST] <>\\n${INSTRUCTION}\\n<>\" | wc -l)\n\nchat_completion() {\n PROMPT=\"$(trim_trailing \"$(format_prompt \"$1\")\")\"\n DATA=\"$(echo -n \"$PROMPT\" | jq -Rs --argjson n_keep $N_KEEP '{\n prompt: .,\n temperature: 0.2,\n top_k: 40,\n top_p: 0.9,\n n_keep: $n_keep,\n n_predict: 1024,\n stop: [\"[INST]\"],\n stream: true\n }')\"\n\n # Create a temporary file to hold the Python output\n TEMPFILE=$(mktemp)\n\n exec 3< <(curl \\\n --silent \\\n --no-buffer \\\n --request POST \\\n --url \"${API_URL}/completion\" \\\n --header \"Content-Type: application/json\" \\\n --data-raw \"${DATA}\")\n\n python -c \"\nimport json\nimport sys\n\nanswer = ''\nwhile True:\n line = sys.stdin.readline()\n if not line:\n break\n if line.startswith('data: '):\n json_content = line[6:].strip()\n content = json.loads(json_content)['content']\n sys.stdout.write(content)\n sys.stdout.flush()\n answer += content\n\nanswer = answer.rstrip('\\n')\n\n# Write the answer to the temporary file\nwith open('$TEMPFILE', 'w') as f:\n f.write(answer)\n \" <&3\n\n exec 3<&-\n\n # Read the answer from the temporary file\n ANSWER=$(cat $TEMPFILE)\n\n # Clean up the temporary file\n rm $TEMPFILE\n\n printf \"\\n\"\n\n CHAT+=(\"$1\" \"$(trim \"$ANSWER\")\")\n}\n\nwhile true; do\n echo -en \"\\033[0;32m\" # Green color\n read -r -e -p \"> \" QUESTION\n echo -en \"\\033[0m\" # Reset color\n chat_completion \"${QUESTION}\"\ndone\n" }, { "path": "examples/server/chat.mjs", "content": "import * as readline from 'node:readline'\nimport { stdin, stdout } from 'node:process'\nimport { readFileSync } from 'node:fs'\nimport { SchemaConverter } from './public/json-schema-to-grammar.mjs'\n\nconst args = process.argv.slice(2);\nconst grammarJsonSchemaFile = args.find(\n (_, index) => args[index - 1] === \"--grammar-json-schema\"\n);\n\nconst no_cached_prompt = args.find(\n (_, index) => args[index - 1] === \"--no-cache-prompt\"\n) ?? \"false\";\n\nconst grammarFile = args.find((_, index) => args[index - 1] === \"--grammar\");\n\n// Example usage: function,arguments\nconst grammarJsonSchemaPropOrder = args.find(\n (_, index) => args[index - 1] === \"--grammar-json-schema-prop-order\"\n);\nconst propOrder = grammarJsonSchemaPropOrder\n ? grammarJsonSchemaPropOrder\n .split(\",\")\n .reduce((acc, cur, index) => ({ ...acc, [cur]: index }), {})\n : {};\n\nlet grammar = null\nif (grammarJsonSchemaFile) {\n const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))\n const converter = new SchemaConverter(propOrder)\n converter.visit(schema, '')\n grammar = converter.formatGrammar()\n}\nif (grammarFile) {\n grammar = readFileSync(grammarFile, 'utf-8')\n}\n\n// for cached prompt\nlet slot_id = -1;\n\nconst API_URL = 'http://127.0.0.1:8080'\n\nconst chat = [\n {\n human: \"Hello, Assistant.\",\n assistant: \"Hello. How may I help you today?\"\n },\n {\n human: \"Please tell me the largest city in Europe.\",\n assistant: \"Sure. The largest city in Europe is Moscow, the capital of Russia.\"\n },\n]\n\nconst instruction = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.`\n\nfunction format_prompt(question) {\n return `${instruction}\\n${\n chat.map(m =>`### Human: ${m.human}\\n### Assistant: ${m.assistant}`).join(\"\\n\")\n }\\n### Human: ${question}\\n### Assistant:`\n}\n\nasync function tokenize(content) {\n const result = await fetch(`${API_URL}/tokenize`, {\n method: 'POST',\n body: JSON.stringify({ content })\n })\n\n if (!result.ok) {\n return []\n }\n\n return await result.json().tokens\n}\n\nconst n_keep = await tokenize(instruction).length\n\nasync function chat_completion(question) {\n const result = await fetch(`${API_URL}/completion`, {\n method: 'POST',\n body: JSON.stringify({\n prompt: format_prompt(question),\n temperature: 0.2,\n top_k: 40,\n top_p: 0.9,\n n_keep: n_keep,\n n_predict: 256,\n cache_prompt: no_cached_prompt === \"false\",\n slot_id: slot_id,\n stop: [\"\\n### Human:\"], // stop completion after generating this\n grammar,\n stream: true,\n })\n })\n\n if (!result.ok) {\n return\n }\n\n let answer = ''\n\n for await (var chunk of result.body) {\n const t = Buffer.from(chunk).toString('utf8')\n if (t.startsWith('data: ')) {\n const message = JSON.parse(t.substring(6))\n slot_id = message.slot_id\n answer += message.content\n process.stdout.write(message.content)\n if (message.stop) {\n if (message.truncated) {\n chat.shift()\n }\n break\n }\n }\n }\n\n process.stdout.write('\\n')\n chat.push({ human: question, assistant: answer.trimStart() })\n}\n\nconst rl = readline.createInterface({ input: stdin, output: stdout });\n\nconst readlineQuestion = (rl, query, options) => new Promise((resolve, reject) => {\n rl.question(query, options, resolve)\n});\n\nwhile(true) {\n const question = await readlineQuestion(rl, '> ')\n await chat_completion(question)\n}\n" }, { "path": "examples/server/chat.sh", "content": "#!/bin/bash\n\nAPI_URL=\"${API_URL:-http://127.0.0.1:8080}\"\n\nCHAT=(\n \"Hello, Assistant.\"\n \"Hello. How may I help you today?\"\n \"Please tell me the largest city in Europe.\"\n \"Sure. The largest city in Europe is Moscow, the capital of Russia.\"\n)\n\nINSTRUCTION=\"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\"\n\ntrim() {\n shopt -s extglob\n set -- \"${1##+([[:space:]])}\"\n printf \"%s\" \"${1%%+([[:space:]])}\"\n}\n\ntrim_trailing() {\n shopt -s extglob\n printf \"%s\" \"${1%%+([[:space:]])}\"\n}\n\nformat_prompt() {\n echo -n \"${INSTRUCTION}\"\n printf \"\\n### Human: %s\\n### Assistant: %s\" \"${CHAT[@]}\" \"$1\"\n}\n\ntokenize() {\n curl \\\n --silent \\\n --request POST \\\n --url \"${API_URL}/tokenize\" \\\n --header \"Content-Type: application/json\" \\\n --data-raw \"$(jq -ns --arg content \"$1\" '{content:$content}')\" \\\n | jq '.tokens[]'\n}\n\nN_KEEP=$(tokenize \"${INSTRUCTION}\" | wc -l)\n\nchat_completion() {\n PROMPT=\"$(trim_trailing \"$(format_prompt \"$1\")\")\"\n DATA=\"$(echo -n \"$PROMPT\" | jq -Rs --argjson n_keep $N_KEEP '{\n prompt: .,\n temperature: 0.2,\n top_k: 40,\n top_p: 0.9,\n n_keep: $n_keep,\n n_predict: 256,\n stop: [\"\\n### Human:\"],\n stream: true\n }')\"\n\n ANSWER=''\n\n while IFS= read -r LINE; do\n if [[ $LINE = data:* ]]; then\n CONTENT=\"$(echo \"${LINE:5}\" | jq -r '.content')\"\n printf \"%s\" \"${CONTENT}\"\n ANSWER+=\"${CONTENT}\"\n fi\n done < <(curl \\\n --silent \\\n --no-buffer \\\n --request POST \\\n --url \"${API_URL}/completion\" \\\n --header \"Content-Type: application/json\" \\\n --data-raw \"${DATA}\")\n\n printf \"\\n\"\n\n CHAT+=(\"$1\" \"$(trim \"$ANSWER\")\")\n}\n\nwhile true; do\n read -r -e -p \"> \" QUESTION\n chat_completion \"${QUESTION}\"\ndone\n" }, { "path": "examples/server/completion.js.hpp", "content": "unsigned char completion_js[] = {\n 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44,\n 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a,\n 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72,\n 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64,\n 0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20,\n 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0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d,\n 0x61, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d,\n 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e,\n 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67, 0x65,\n 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74,\n 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20,\n 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,\n 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61,\n 0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22,\n 0x2f, 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22,\n 0x29, 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20,\n 0x72, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20,\n 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20,\n 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73,\n 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a\n};\nunsigned int completion_js_len = 5099;\n" }, { "path": "examples/server/deps.sh", "content": "#!/bin/bash\n# Download and update deps for binary\n\n# get the directory of this script file\nDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nPUBLIC=$DIR/public\n\necho \"download js bundle files\"\ncurl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js\necho >> $PUBLIC/index.js # add newline\n\nFILES=$(ls $PUBLIC)\n\ncd $PUBLIC\nfor FILE in $FILES; do\n echo \"generate $FILE.hpp\"\n\n # use simple flag for old version of xxd\n xxd -i $FILE > $DIR/$FILE.hpp\ndone\n" }, { "path": "examples/server/httplib.h", "content": "//\n// httplib.h\n//\n// Copyright (c) 2023 Yuji Hirose. All rights reserved.\n// MIT License\n//\n\n#ifndef CPPHTTPLIB_HTTPLIB_H\n#define CPPHTTPLIB_HTTPLIB_H\n\n#define CPPHTTPLIB_VERSION \"0.12.2\"\n\n/*\n * Configuration\n */\n\n#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND\n#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND 5\n#endif\n\n#ifndef CPPHTTPLIB_KEEPALIVE_MAX_COUNT\n#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 5\n#endif\n\n#ifndef CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND\n#define CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND 300\n#endif\n\n#ifndef CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND\n#define CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND 0\n#endif\n\n#ifndef CPPHTTPLIB_READ_TIMEOUT_SECOND\n#define CPPHTTPLIB_READ_TIMEOUT_SECOND 5\n#endif\n\n#ifndef CPPHTTPLIB_READ_TIMEOUT_USECOND\n#define CPPHTTPLIB_READ_TIMEOUT_USECOND 0\n#endif\n\n#ifndef CPPHTTPLIB_WRITE_TIMEOUT_SECOND\n#define CPPHTTPLIB_WRITE_TIMEOUT_SECOND 5\n#endif\n\n#ifndef CPPHTTPLIB_WRITE_TIMEOUT_USECOND\n#define CPPHTTPLIB_WRITE_TIMEOUT_USECOND 0\n#endif\n\n#ifndef CPPHTTPLIB_IDLE_INTERVAL_SECOND\n#define CPPHTTPLIB_IDLE_INTERVAL_SECOND 0\n#endif\n\n#ifndef CPPHTTPLIB_IDLE_INTERVAL_USECOND\n#ifdef _WIN32\n#define CPPHTTPLIB_IDLE_INTERVAL_USECOND 10000\n#else\n#define CPPHTTPLIB_IDLE_INTERVAL_USECOND 0\n#endif\n#endif\n\n#ifndef CPPHTTPLIB_REQUEST_URI_MAX_LENGTH\n#define CPPHTTPLIB_REQUEST_URI_MAX_LENGTH 8192\n#endif\n\n#ifndef CPPHTTPLIB_HEADER_MAX_LENGTH\n#define CPPHTTPLIB_HEADER_MAX_LENGTH 8192\n#endif\n\n#ifndef CPPHTTPLIB_REDIRECT_MAX_COUNT\n#define CPPHTTPLIB_REDIRECT_MAX_COUNT 20\n#endif\n\n#ifndef CPPHTTPLIB_MULTIPART_FORM_DATA_FILE_MAX_COUNT\n#define CPPHTTPLIB_MULTIPART_FORM_DATA_FILE_MAX_COUNT 1024\n#endif\n\n#ifndef CPPHTTPLIB_PAYLOAD_MAX_LENGTH\n#define CPPHTTPLIB_PAYLOAD_MAX_LENGTH ((std::numeric_limits::max)())\n#endif\n\n#ifndef CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH\n#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192\n#endif\n\n#ifndef CPPHTTPLIB_TCP_NODELAY\n#define CPPHTTPLIB_TCP_NODELAY false\n#endif\n\n#ifndef CPPHTTPLIB_RECV_BUFSIZ\n#define CPPHTTPLIB_RECV_BUFSIZ size_t(4096u)\n#endif\n\n#ifndef CPPHTTPLIB_COMPRESSION_BUFSIZ\n#define CPPHTTPLIB_COMPRESSION_BUFSIZ size_t(16384u)\n#endif\n\n#ifndef CPPHTTPLIB_THREAD_POOL_COUNT\n#define CPPHTTPLIB_THREAD_POOL_COUNT \\\n ((std::max)(8u, std::thread::hardware_concurrency() > 0 \\\n ? std::thread::hardware_concurrency() - 1 \\\n : 0))\n#endif\n\n#ifndef CPPHTTPLIB_RECV_FLAGS\n#define CPPHTTPLIB_RECV_FLAGS 0\n#endif\n\n#ifndef CPPHTTPLIB_SEND_FLAGS\n#define CPPHTTPLIB_SEND_FLAGS 0\n#endif\n\n#ifndef CPPHTTPLIB_LISTEN_BACKLOG\n#define CPPHTTPLIB_LISTEN_BACKLOG 5\n#endif\n\n/*\n * Headers\n */\n\n#ifdef _WIN32\n#ifndef _CRT_SECURE_NO_WARNINGS\n#define _CRT_SECURE_NO_WARNINGS\n#endif //_CRT_SECURE_NO_WARNINGS\n\n#ifndef _CRT_NONSTDC_NO_DEPRECATE\n#define _CRT_NONSTDC_NO_DEPRECATE\n#endif //_CRT_NONSTDC_NO_DEPRECATE\n\n#if defined(_MSC_VER)\n#if _MSC_VER < 1900\n#error Sorry, Visual Studio versions prior to 2015 are not supported\n#endif\n\n#pragma comment(lib, \"ws2_32.lib\")\n\n#ifdef _WIN64\nusing ssize_t = __int64;\n#else\nusing ssize_t = long;\n#endif\n#endif // _MSC_VER\n\n#ifndef S_ISREG\n#define S_ISREG(m) (((m)&S_IFREG) == S_IFREG)\n#endif // S_ISREG\n\n#ifndef S_ISDIR\n#define S_ISDIR(m) (((m)&S_IFDIR) == S_IFDIR)\n#endif // S_ISDIR\n\n#ifndef NOMINMAX\n#define NOMINMAX\n#endif // NOMINMAX\n\n#include \n#include \n#include \n\n#ifndef WSA_FLAG_NO_HANDLE_INHERIT\n#define WSA_FLAG_NO_HANDLE_INHERIT 0x80\n#endif\n\n#ifndef strcasecmp\n#define strcasecmp _stricmp\n#endif // strcasecmp\n\nusing socket_t = SOCKET;\n#ifdef CPPHTTPLIB_USE_POLL\n#define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout)\n#endif\n\n#else // not _WIN32\n\n#include \n#ifndef _AIX\n#include \n#endif\n#include \n#include \n#include \n#ifdef __linux__\n#include \n#endif\n#include \n#ifdef CPPHTTPLIB_USE_POLL\n#include \n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n\nusing socket_t = int;\n#ifndef INVALID_SOCKET\n#define INVALID_SOCKET (-1)\n#endif\n#endif //_WIN32\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n#ifdef _WIN32\n#include \n\n// these are defined in wincrypt.h and it breaks compilation if BoringSSL is\n// used\n#undef X509_NAME\n#undef X509_CERT_PAIR\n#undef X509_EXTENSIONS\n#undef PKCS7_SIGNER_INFO\n\n#ifdef _MSC_VER\n#pragma comment(lib, \"crypt32.lib\")\n#pragma comment(lib, \"cryptui.lib\")\n#endif\n#elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__)\n#include \n#if TARGET_OS_OSX\n#include \n#include \n#endif // TARGET_OS_OSX\n#endif // _WIN32\n\n#include \n#include \n#include \n#include \n\n#if defined(_WIN32) && defined(OPENSSL_USE_APPLINK)\n#include \n#endif\n\n#include \n#include \n\n#if OPENSSL_VERSION_NUMBER < 0x1010100fL\n#error Sorry, OpenSSL versions prior to 1.1.1 are not supported\n#elif OPENSSL_VERSION_NUMBER < 0x30000000L\n#define SSL_get1_peer_certificate SSL_get_peer_certificate\n#endif\n\n#endif\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n#include \n#endif\n\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\n#include \n#include \n#endif\n\n/*\n * Declaration\n */\nnamespace httplib {\n\nnamespace detail {\n\n/*\n * Backport std::make_unique from C++14.\n *\n * NOTE: This code came up with the following stackoverflow post:\n * https://stackoverflow.com/questions/10149840/c-arrays-and-make-unique\n *\n */\n\ntemplate \ntypename std::enable_if::value, std::unique_ptr>::type\nmake_unique(Args &&...args) {\n return std::unique_ptr(new T(std::forward(args)...));\n}\n\ntemplate \ntypename std::enable_if::value, std::unique_ptr>::type\nmake_unique(std::size_t n) {\n typedef typename std::remove_extent::type RT;\n return std::unique_ptr(new RT[n]);\n}\n\nstruct ci {\n bool operator()(const std::string &s1, const std::string &s2) const {\n return std::lexicographical_compare(s1.begin(), s1.end(), s2.begin(),\n s2.end(),\n [](unsigned char c1, unsigned char c2) {\n return ::tolower(c1) < ::tolower(c2);\n });\n }\n};\n\n// This is based on\n// \"http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2014/n4189\".\n\nstruct scope_exit {\n explicit scope_exit(std::function &&f)\n : exit_function(std::move(f)), execute_on_destruction{true} {}\n\n scope_exit(scope_exit &&rhs)\n : exit_function(std::move(rhs.exit_function)),\n execute_on_destruction{rhs.execute_on_destruction} {\n rhs.release();\n }\n\n ~scope_exit() {\n if (execute_on_destruction) { this->exit_function(); }\n }\n\n void release() { this->execute_on_destruction = false; }\n\nprivate:\n scope_exit(const scope_exit &) = delete;\n void operator=(const scope_exit &) = delete;\n scope_exit &operator=(scope_exit &&) = delete;\n\n std::function exit_function;\n bool execute_on_destruction;\n};\n\n} // namespace detail\n\nusing Headers = std::multimap;\n\nusing Params = std::multimap;\nusing Match = std::smatch;\n\nusing Progress = std::function;\n\nstruct Response;\nusing ResponseHandler = std::function;\n\nstruct MultipartFormData {\n std::string name;\n std::string content;\n std::string filename;\n std::string content_type;\n};\nusing MultipartFormDataItems = std::vector;\nusing MultipartFormDataMap = std::multimap;\n\nclass DataSink {\npublic:\n DataSink() : os(&sb_), sb_(*this) {}\n\n DataSink(const DataSink &) = delete;\n DataSink &operator=(const DataSink &) = delete;\n DataSink(DataSink &&) = delete;\n DataSink &operator=(DataSink &&) = delete;\n\n std::function write;\n std::function done;\n std::function done_with_trailer;\n std::ostream os;\n\nprivate:\n class data_sink_streambuf : public std::streambuf {\n public:\n explicit data_sink_streambuf(DataSink &sink) : sink_(sink) {}\n\n protected:\n std::streamsize xsputn(const char *s, std::streamsize n) {\n sink_.write(s, static_cast(n));\n return n;\n }\n\n private:\n DataSink &sink_;\n };\n\n data_sink_streambuf sb_;\n};\n\nusing ContentProvider =\n std::function;\n\nusing ContentProviderWithoutLength =\n std::function;\n\nusing ContentProviderResourceReleaser = std::function;\n\nstruct MultipartFormDataProvider {\n std::string name;\n ContentProviderWithoutLength provider;\n std::string filename;\n std::string content_type;\n};\nusing MultipartFormDataProviderItems = std::vector;\n\nusing ContentReceiverWithProgress =\n std::function;\n\nusing ContentReceiver =\n std::function;\n\nusing MultipartContentHeader =\n std::function;\n\nclass ContentReader {\npublic:\n using Reader = std::function;\n using MultipartReader = std::function;\n\n ContentReader(Reader reader, MultipartReader multipart_reader)\n : reader_(std::move(reader)),\n multipart_reader_(std::move(multipart_reader)) {}\n\n bool operator()(MultipartContentHeader header,\n ContentReceiver receiver) const {\n return multipart_reader_(std::move(header), std::move(receiver));\n }\n\n bool operator()(ContentReceiver receiver) const {\n return reader_(std::move(receiver));\n }\n\n Reader reader_;\n MultipartReader multipart_reader_;\n};\n\nusing Range = std::pair;\nusing Ranges = std::vector;\n\nstruct Request {\n std::string method;\n std::string path;\n Headers headers;\n std::string body;\n\n std::string remote_addr;\n int remote_port = -1;\n std::string local_addr;\n int local_port = -1;\n\n // for server\n std::string version;\n std::string target;\n Params params;\n MultipartFormDataMap files;\n Ranges ranges;\n Match matches;\n\n // for client\n ResponseHandler response_handler;\n ContentReceiverWithProgress content_receiver;\n Progress progress;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n const SSL *ssl = nullptr;\n#endif\n\n bool has_header(const std::string &key) const;\n std::string get_header_value(const std::string &key, size_t id = 0) const;\n template \n T get_header_value(const std::string &key, size_t id = 0) const;\n size_t get_header_value_count(const std::string &key) const;\n void set_header(const std::string &key, const std::string &val);\n\n bool has_param(const std::string &key) const;\n std::string get_param_value(const std::string &key, size_t id = 0) const;\n size_t get_param_value_count(const std::string &key) const;\n\n bool is_multipart_form_data() const;\n\n bool has_file(const std::string &key) const;\n MultipartFormData get_file_value(const std::string &key) const;\n std::vector get_file_values(const std::string &key) const;\n\n // private members...\n size_t redirect_count_ = CPPHTTPLIB_REDIRECT_MAX_COUNT;\n size_t content_length_ = 0;\n ContentProvider content_provider_;\n bool is_chunked_content_provider_ = false;\n size_t authorization_count_ = 0;\n};\n\nstruct Response {\n std::string version;\n int status = -1;\n std::string reason;\n Headers headers;\n std::string body;\n std::string location; // Redirect location\n\n bool has_header(const std::string &key) const;\n std::string get_header_value(const std::string &key, size_t id = 0) const;\n template \n T get_header_value(const std::string &key, size_t id = 0) const;\n size_t get_header_value_count(const std::string &key) const;\n void set_header(const std::string &key, const std::string &val);\n\n void set_redirect(const std::string &url, int status = 302);\n void set_content(const char *s, size_t n, const std::string &content_type);\n void set_content(const std::string &s, const std::string &content_type);\n\n void set_content_provider(\n size_t length, const std::string &content_type, ContentProvider provider,\n ContentProviderResourceReleaser resource_releaser = nullptr);\n\n void set_content_provider(\n const std::string &content_type, ContentProviderWithoutLength provider,\n ContentProviderResourceReleaser resource_releaser = nullptr);\n\n void set_chunked_content_provider(\n const std::string &content_type, ContentProviderWithoutLength provider,\n ContentProviderResourceReleaser resource_releaser = nullptr);\n\n Response() = default;\n Response(const Response &) = default;\n Response &operator=(const Response &) = default;\n Response(Response &&) = default;\n Response &operator=(Response &&) = default;\n ~Response() {\n if (content_provider_resource_releaser_) {\n content_provider_resource_releaser_(content_provider_success_);\n }\n }\n\n // private members...\n size_t content_length_ = 0;\n ContentProvider content_provider_;\n ContentProviderResourceReleaser content_provider_resource_releaser_;\n bool is_chunked_content_provider_ = false;\n bool content_provider_success_ = false;\n};\n\nclass Stream {\npublic:\n virtual ~Stream() = default;\n\n virtual bool is_readable() const = 0;\n virtual bool is_writable() const = 0;\n\n virtual ssize_t read(char *ptr, size_t size) = 0;\n virtual ssize_t write(const char *ptr, size_t size) = 0;\n virtual void get_remote_ip_and_port(std::string &ip, int &port) const = 0;\n virtual void get_local_ip_and_port(std::string &ip, int &port) const = 0;\n virtual socket_t socket() const = 0;\n\n template \n ssize_t write_format(const char *fmt, const Args &...args);\n ssize_t write(const char *ptr);\n ssize_t write(const std::string &s);\n};\n\nclass TaskQueue {\npublic:\n TaskQueue() = default;\n virtual ~TaskQueue() = default;\n\n virtual void enqueue(std::function fn) = 0;\n virtual void shutdown() = 0;\n\n virtual void on_idle() {}\n};\n\nclass ThreadPool : public TaskQueue {\npublic:\n explicit ThreadPool(size_t n) : shutdown_(false) {\n while (n) {\n threads_.emplace_back(worker(*this));\n n--;\n }\n }\n\n ThreadPool(const ThreadPool &) = delete;\n ~ThreadPool() override = default;\n\n void enqueue(std::function fn) override {\n {\n std::unique_lock lock(mutex_);\n jobs_.push_back(std::move(fn));\n }\n\n cond_.notify_one();\n }\n\n void shutdown() override {\n // Stop all worker threads...\n {\n std::unique_lock lock(mutex_);\n shutdown_ = true;\n }\n\n cond_.notify_all();\n\n // Join...\n for (auto &t : threads_) {\n t.join();\n }\n }\n\nprivate:\n struct worker {\n explicit worker(ThreadPool &pool) : pool_(pool) {}\n\n void operator()() {\n for (;;) {\n std::function fn;\n {\n std::unique_lock lock(pool_.mutex_);\n\n pool_.cond_.wait(\n lock, [&] { return !pool_.jobs_.empty() || pool_.shutdown_; });\n\n if (pool_.shutdown_ && pool_.jobs_.empty()) { break; }\n\n fn = std::move(pool_.jobs_.front());\n pool_.jobs_.pop_front();\n }\n\n assert(true == static_cast(fn));\n fn();\n }\n }\n\n ThreadPool &pool_;\n };\n friend struct worker;\n\n std::vector threads_;\n std::list> jobs_;\n\n bool shutdown_;\n\n std::condition_variable cond_;\n std::mutex mutex_;\n};\n\nusing Logger = std::function;\n\nusing SocketOptions = std::function;\n\nvoid default_socket_options(socket_t sock);\n\nclass Server {\npublic:\n using Handler = std::function;\n\n using ExceptionHandler =\n std::function;\n\n enum class HandlerResponse {\n Handled,\n Unhandled,\n };\n using HandlerWithResponse =\n std::function;\n\n using HandlerWithContentReader = std::function;\n\n using Expect100ContinueHandler =\n std::function;\n\n Server();\n\n virtual ~Server();\n\n virtual bool is_valid() const;\n\n Server &Get(const std::string &pattern, Handler handler);\n Server &Post(const std::string &pattern, Handler handler);\n Server &Post(const std::string &pattern, HandlerWithContentReader handler);\n Server &Put(const std::string &pattern, Handler handler);\n Server &Put(const std::string &pattern, HandlerWithContentReader handler);\n Server &Patch(const std::string &pattern, Handler handler);\n Server &Patch(const std::string &pattern, HandlerWithContentReader handler);\n Server &Delete(const std::string &pattern, Handler handler);\n Server &Delete(const std::string &pattern, HandlerWithContentReader handler);\n Server &Options(const std::string &pattern, Handler handler);\n\n bool set_base_dir(const std::string &dir,\n const std::string &mount_point = std::string());\n bool set_mount_point(const std::string &mount_point, const std::string &dir,\n Headers headers = Headers());\n bool remove_mount_point(const std::string &mount_point);\n Server &set_file_extension_and_mimetype_mapping(const std::string &ext,\n const std::string &mime);\n Server &set_file_request_handler(Handler handler);\n\n Server &set_error_handler(HandlerWithResponse handler);\n Server &set_error_handler(Handler handler);\n Server &set_exception_handler(ExceptionHandler handler);\n Server &set_pre_routing_handler(HandlerWithResponse handler);\n Server &set_post_routing_handler(Handler handler);\n\n Server &set_expect_100_continue_handler(Expect100ContinueHandler handler);\n Server &set_logger(Logger logger);\n\n Server &set_address_family(int family);\n Server &set_tcp_nodelay(bool on);\n Server &set_socket_options(SocketOptions socket_options);\n\n Server &set_default_headers(Headers headers);\n\n Server &set_keep_alive_max_count(size_t count);\n Server &set_keep_alive_timeout(time_t sec);\n\n Server &set_read_timeout(time_t sec, time_t usec = 0);\n template \n Server &set_read_timeout(const std::chrono::duration &duration);\n\n Server &set_write_timeout(time_t sec, time_t usec = 0);\n template \n Server &set_write_timeout(const std::chrono::duration &duration);\n\n Server &set_idle_interval(time_t sec, time_t usec = 0);\n template \n Server &set_idle_interval(const std::chrono::duration &duration);\n\n Server &set_payload_max_length(size_t length);\n\n bool bind_to_port(const std::string &host, int port, int socket_flags = 0);\n int bind_to_any_port(const std::string &host, int socket_flags = 0);\n bool listen_after_bind();\n\n bool listen(const std::string &host, int port, int socket_flags = 0);\n\n bool is_running() const;\n void wait_until_ready() const;\n void stop();\n\n std::function new_task_queue;\n\nprotected:\n bool process_request(Stream &strm, bool close_connection,\n bool &connection_closed,\n const std::function &setup_request);\n\n std::atomic svr_sock_{INVALID_SOCKET};\n size_t keep_alive_max_count_ = CPPHTTPLIB_KEEPALIVE_MAX_COUNT;\n time_t keep_alive_timeout_sec_ = CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND;\n time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND;\n time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND;\n time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND;\n time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND;\n time_t idle_interval_sec_ = CPPHTTPLIB_IDLE_INTERVAL_SECOND;\n time_t idle_interval_usec_ = CPPHTTPLIB_IDLE_INTERVAL_USECOND;\n size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH;\n\nprivate:\n using Handlers = std::vector>;\n using HandlersForContentReader =\n std::vector>;\n\n socket_t create_server_socket(const std::string &host, int port,\n int socket_flags,\n SocketOptions socket_options) const;\n int bind_internal(const std::string &host, int port, int socket_flags);\n bool listen_internal();\n\n bool routing(Request &req, Response &res, Stream &strm);\n bool handle_file_request(const Request &req, Response &res,\n bool head = false);\n bool dispatch_request(Request &req, Response &res, const Handlers &handlers);\n bool\n dispatch_request_for_content_reader(Request &req, Response &res,\n ContentReader content_reader,\n const HandlersForContentReader &handlers);\n\n bool parse_request_line(const char *s, Request &req);\n void apply_ranges(const Request &req, Response &res,\n std::string &content_type, std::string &boundary);\n bool write_response(Stream &strm, bool close_connection, const Request &req,\n Response &res);\n bool write_response_with_content(Stream &strm, bool close_connection,\n const Request &req, Response &res);\n bool write_response_core(Stream &strm, bool close_connection,\n const Request &req, Response &res,\n bool need_apply_ranges);\n bool write_content_with_provider(Stream &strm, const Request &req,\n Response &res, const std::string &boundary,\n const std::string &content_type);\n bool read_content(Stream &strm, Request &req, Response &res);\n bool\n read_content_with_content_receiver(Stream &strm, Request &req, Response &res,\n ContentReceiver receiver,\n MultipartContentHeader multipart_header,\n ContentReceiver multipart_receiver);\n bool read_content_core(Stream &strm, Request &req, Response &res,\n ContentReceiver receiver,\n MultipartContentHeader multipart_header,\n ContentReceiver multipart_receiver);\n\n virtual bool process_and_close_socket(socket_t sock);\n\n struct MountPointEntry {\n std::string mount_point;\n std::string base_dir;\n Headers headers;\n };\n std::vector base_dirs_;\n\n std::atomic is_running_{false};\n std::atomic done_{false};\n std::map file_extension_and_mimetype_map_;\n Handler file_request_handler_;\n Handlers get_handlers_;\n Handlers post_handlers_;\n HandlersForContentReader post_handlers_for_content_reader_;\n Handlers put_handlers_;\n HandlersForContentReader put_handlers_for_content_reader_;\n Handlers patch_handlers_;\n HandlersForContentReader patch_handlers_for_content_reader_;\n Handlers delete_handlers_;\n HandlersForContentReader delete_handlers_for_content_reader_;\n Handlers options_handlers_;\n HandlerWithResponse error_handler_;\n ExceptionHandler exception_handler_;\n HandlerWithResponse pre_routing_handler_;\n Handler post_routing_handler_;\n Logger logger_;\n Expect100ContinueHandler expect_100_continue_handler_;\n\n int address_family_ = AF_UNSPEC;\n bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;\n SocketOptions socket_options_ = default_socket_options;\n\n Headers default_headers_;\n};\n\nenum class Error {\n Success = 0,\n Unknown,\n Connection,\n BindIPAddress,\n Read,\n Write,\n ExceedRedirectCount,\n Canceled,\n SSLConnection,\n SSLLoadingCerts,\n SSLServerVerification,\n UnsupportedMultipartBoundaryChars,\n Compression,\n ConnectionTimeout,\n\n // For internal use only\n SSLPeerCouldBeClosed_,\n};\n\nstd::string to_string(const Error error);\n\nstd::ostream &operator<<(std::ostream &os, const Error &obj);\n\nclass Result {\npublic:\n Result(std::unique_ptr &&res, Error err,\n Headers &&request_headers = Headers{})\n : res_(std::move(res)), err_(err),\n request_headers_(std::move(request_headers)) {}\n // Response\n operator bool() const { return res_ != nullptr; }\n bool operator==(std::nullptr_t) const { return res_ == nullptr; }\n bool operator!=(std::nullptr_t) const { return res_ != nullptr; }\n const Response &value() const { return *res_; }\n Response &value() { return *res_; }\n const Response &operator*() const { return *res_; }\n Response &operator*() { return *res_; }\n const Response *operator->() const { return res_.get(); }\n Response *operator->() { return res_.get(); }\n\n // Error\n Error error() const { return err_; }\n\n // Request Headers\n bool has_request_header(const std::string &key) const;\n std::string get_request_header_value(const std::string &key,\n size_t id = 0) const;\n template \n T get_request_header_value(const std::string &key, size_t id = 0) const;\n size_t get_request_header_value_count(const std::string &key) const;\n\nprivate:\n std::unique_ptr res_;\n Error err_;\n Headers request_headers_;\n};\n\nclass ClientImpl {\npublic:\n explicit ClientImpl(const std::string &host);\n\n explicit ClientImpl(const std::string &host, int port);\n\n explicit ClientImpl(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path);\n\n virtual ~ClientImpl();\n\n virtual bool is_valid() const;\n\n Result Get(const std::string &path);\n Result Get(const std::string &path, const Headers &headers);\n Result Get(const std::string &path, Progress progress);\n Result Get(const std::string &path, const Headers &headers,\n Progress progress);\n Result Get(const std::string &path, ContentReceiver content_receiver);\n Result Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, ContentReceiver content_receiver,\n Progress progress);\n Result Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver, Progress progress);\n Result Get(const std::string &path, ResponseHandler response_handler,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress);\n Result Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler, ContentReceiver content_receiver,\n Progress progress);\n\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, Progress progress = nullptr);\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, ContentReceiver content_receiver,\n Progress progress = nullptr);\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress = nullptr);\n\n Result Head(const std::string &path);\n Result Head(const std::string &path, const Headers &headers);\n\n Result Post(const std::string &path);\n Result Post(const std::string &path, const Headers &headers);\n Result Post(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers, const char *body,\n size_t content_length, const std::string &content_type);\n Result Post(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Post(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type);\n Result Post(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Params ¶ms);\n Result Post(const std::string &path, const Headers &headers,\n const Params ¶ms);\n Result Post(const std::string &path, const MultipartFormDataItems &items);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items, const std::string &boundary);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items);\n\n Result Put(const std::string &path);\n Result Put(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers, const char *body,\n size_t content_length, const std::string &content_type);\n Result Put(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Put(const std::string &path, size_t content_length,\n ContentProvider content_provider, const std::string &content_type);\n Result Put(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Params ¶ms);\n Result Put(const std::string &path, const Headers &headers,\n const Params ¶ms);\n Result Put(const std::string &path, const MultipartFormDataItems &items);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items, const std::string &boundary);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items);\n\n Result Patch(const std::string &path);\n Result Patch(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type);\n Result Patch(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Patch(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n\n Result Delete(const std::string &path);\n Result Delete(const std::string &path, const Headers &headers);\n Result Delete(const std::string &path, const char *body,\n size_t content_length, const std::string &content_type);\n Result Delete(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type);\n Result Delete(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Delete(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n\n Result Options(const std::string &path);\n Result Options(const std::string &path, const Headers &headers);\n\n bool send(Request &req, Response &res, Error &error);\n Result send(const Request &req);\n\n size_t is_socket_open() const;\n\n socket_t socket() const;\n\n void stop();\n\n void set_hostname_addr_map(std::map addr_map);\n\n void set_default_headers(Headers headers);\n\n void set_address_family(int family);\n void set_tcp_nodelay(bool on);\n void set_socket_options(SocketOptions socket_options);\n\n void set_connection_timeout(time_t sec, time_t usec = 0);\n template \n void\n set_connection_timeout(const std::chrono::duration &duration);\n\n void set_read_timeout(time_t sec, time_t usec = 0);\n template \n void set_read_timeout(const std::chrono::duration &duration);\n\n void set_write_timeout(time_t sec, time_t usec = 0);\n template \n void set_write_timeout(const std::chrono::duration &duration);\n\n void set_basic_auth(const std::string &username, const std::string &password);\n void set_bearer_token_auth(const std::string &token);\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_digest_auth(const std::string &username,\n const std::string &password);\n#endif\n\n void set_keep_alive(bool on);\n void set_follow_location(bool on);\n\n void set_url_encode(bool on);\n\n void set_compress(bool on);\n\n void set_decompress(bool on);\n\n void set_interface(const std::string &intf);\n\n void set_proxy(const std::string &host, int port);\n void set_proxy_basic_auth(const std::string &username,\n const std::string &password);\n void set_proxy_bearer_token_auth(const std::string &token);\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_proxy_digest_auth(const std::string &username,\n const std::string &password);\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_ca_cert_path(const std::string &ca_cert_file_path,\n const std::string &ca_cert_dir_path = std::string());\n void set_ca_cert_store(X509_STORE *ca_cert_store);\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void enable_server_certificate_verification(bool enabled);\n#endif\n\n void set_logger(Logger logger);\n\nprotected:\n struct Socket {\n socket_t sock = INVALID_SOCKET;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n SSL *ssl = nullptr;\n#endif\n\n bool is_open() const { return sock != INVALID_SOCKET; }\n };\n\n virtual bool create_and_connect_socket(Socket &socket, Error &error);\n\n // All of:\n // shutdown_ssl\n // shutdown_socket\n // close_socket\n // should ONLY be called when socket_mutex_ is locked.\n // Also, shutdown_ssl and close_socket should also NOT be called concurrently\n // with a DIFFERENT thread sending requests using that socket.\n virtual void shutdown_ssl(Socket &socket, bool shutdown_gracefully);\n void shutdown_socket(Socket &socket);\n void close_socket(Socket &socket);\n\n bool process_request(Stream &strm, Request &req, Response &res,\n bool close_connection, Error &error);\n\n bool write_content_with_provider(Stream &strm, const Request &req,\n Error &error);\n\n void copy_settings(const ClientImpl &rhs);\n\n // Socket endpoint information\n const std::string host_;\n const int port_;\n const std::string host_and_port_;\n\n // Current open socket\n Socket socket_;\n mutable std::mutex socket_mutex_;\n std::recursive_mutex request_mutex_;\n\n // These are all protected under socket_mutex\n size_t socket_requests_in_flight_ = 0;\n std::thread::id socket_requests_are_from_thread_ = std::thread::id();\n bool socket_should_be_closed_when_request_is_done_ = false;\n\n // Hostname-IP map\n std::map addr_map_;\n\n // Default headers\n Headers default_headers_;\n\n // Settings\n std::string client_cert_path_;\n std::string client_key_path_;\n\n time_t connection_timeout_sec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND;\n time_t connection_timeout_usec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND;\n time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND;\n time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND;\n time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND;\n time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND;\n\n std::string basic_auth_username_;\n std::string basic_auth_password_;\n std::string bearer_token_auth_token_;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n std::string digest_auth_username_;\n std::string digest_auth_password_;\n#endif\n\n bool keep_alive_ = false;\n bool follow_location_ = false;\n\n bool url_encode_ = true;\n\n int address_family_ = AF_UNSPEC;\n bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;\n SocketOptions socket_options_ = nullptr;\n\n bool compress_ = false;\n bool decompress_ = true;\n\n std::string interface_;\n\n std::string proxy_host_;\n int proxy_port_ = -1;\n\n std::string proxy_basic_auth_username_;\n std::string proxy_basic_auth_password_;\n std::string proxy_bearer_token_auth_token_;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n std::string proxy_digest_auth_username_;\n std::string proxy_digest_auth_password_;\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n std::string ca_cert_file_path_;\n std::string ca_cert_dir_path_;\n\n X509_STORE *ca_cert_store_ = nullptr;\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n bool server_certificate_verification_ = true;\n#endif\n\n Logger logger_;\n\nprivate:\n bool send_(Request &req, Response &res, Error &error);\n Result send_(Request &&req);\n\n socket_t create_client_socket(Error &error) const;\n bool read_response_line(Stream &strm, const Request &req, Response &res);\n bool write_request(Stream &strm, Request &req, bool close_connection,\n Error &error);\n bool redirect(Request &req, Response &res, Error &error);\n bool handle_request(Stream &strm, Request &req, Response &res,\n bool close_connection, Error &error);\n std::unique_ptr send_with_content_provider(\n Request &req, const char *body, size_t content_length,\n ContentProvider content_provider,\n ContentProviderWithoutLength content_provider_without_length,\n const std::string &content_type, Error &error);\n Result send_with_content_provider(\n const std::string &method, const std::string &path,\n const Headers &headers, const char *body, size_t content_length,\n ContentProvider content_provider,\n ContentProviderWithoutLength content_provider_without_length,\n const std::string &content_type);\n ContentProviderWithoutLength get_multipart_content_provider(\n const std::string &boundary, const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items);\n\n std::string adjust_host_string(const std::string &host) const;\n\n virtual bool process_socket(const Socket &socket,\n std::function callback);\n virtual bool is_ssl() const;\n};\n\nclass Client {\npublic:\n // Universal interface\n explicit Client(const std::string &scheme_host_port);\n\n explicit Client(const std::string &scheme_host_port,\n const std::string &client_cert_path,\n const std::string &client_key_path);\n\n // HTTP only interface\n explicit Client(const std::string &host, int port);\n\n explicit Client(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path);\n\n Client(Client &&) = default;\n\n ~Client();\n\n bool is_valid() const;\n\n Result Get(const std::string &path);\n Result Get(const std::string &path, const Headers &headers);\n Result Get(const std::string &path, Progress progress);\n Result Get(const std::string &path, const Headers &headers,\n Progress progress);\n Result Get(const std::string &path, ContentReceiver content_receiver);\n Result Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, ContentReceiver content_receiver,\n Progress progress);\n Result Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver, Progress progress);\n Result Get(const std::string &path, ResponseHandler response_handler,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver);\n Result Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler, ContentReceiver content_receiver,\n Progress progress);\n Result Get(const std::string &path, ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress);\n\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, Progress progress = nullptr);\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, ContentReceiver content_receiver,\n Progress progress = nullptr);\n Result Get(const std::string &path, const Params ¶ms,\n const Headers &headers, ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress = nullptr);\n\n Result Head(const std::string &path);\n Result Head(const std::string &path, const Headers &headers);\n\n Result Post(const std::string &path);\n Result Post(const std::string &path, const Headers &headers);\n Result Post(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers, const char *body,\n size_t content_length, const std::string &content_type);\n Result Post(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Post(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type);\n Result Post(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Post(const std::string &path, const Params ¶ms);\n Result Post(const std::string &path, const Headers &headers,\n const Params ¶ms);\n Result Post(const std::string &path, const MultipartFormDataItems &items);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items, const std::string &boundary);\n Result Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items);\n\n Result Put(const std::string &path);\n Result Put(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers, const char *body,\n size_t content_length, const std::string &content_type);\n Result Put(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Put(const std::string &path, size_t content_length,\n ContentProvider content_provider, const std::string &content_type);\n Result Put(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Put(const std::string &path, const Params ¶ms);\n Result Put(const std::string &path, const Headers &headers,\n const Params ¶ms);\n Result Put(const std::string &path, const MultipartFormDataItems &items);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items, const std::string &boundary);\n Result Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items);\n\n Result Patch(const std::string &path);\n Result Patch(const std::string &path, const char *body, size_t content_length,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type);\n Result Patch(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n Result Patch(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n size_t content_length, ContentProvider content_provider,\n const std::string &content_type);\n Result Patch(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type);\n\n Result Delete(const std::string &path);\n Result Delete(const std::string &path, const Headers &headers);\n Result Delete(const std::string &path, const char *body,\n size_t content_length, const std::string &content_type);\n Result Delete(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type);\n Result Delete(const std::string &path, const std::string &body,\n const std::string &content_type);\n Result Delete(const std::string &path, const Headers &headers,\n const std::string &body, const std::string &content_type);\n\n Result Options(const std::string &path);\n Result Options(const std::string &path, const Headers &headers);\n\n bool send(Request &req, Response &res, Error &error);\n Result send(const Request &req);\n\n size_t is_socket_open() const;\n\n socket_t socket() const;\n\n void stop();\n\n void set_hostname_addr_map(std::map addr_map);\n\n void set_default_headers(Headers headers);\n\n void set_address_family(int family);\n void set_tcp_nodelay(bool on);\n void set_socket_options(SocketOptions socket_options);\n\n void set_connection_timeout(time_t sec, time_t usec = 0);\n template \n void\n set_connection_timeout(const std::chrono::duration &duration);\n\n void set_read_timeout(time_t sec, time_t usec = 0);\n template \n void set_read_timeout(const std::chrono::duration &duration);\n\n void set_write_timeout(time_t sec, time_t usec = 0);\n template \n void set_write_timeout(const std::chrono::duration &duration);\n\n void set_basic_auth(const std::string &username, const std::string &password);\n void set_bearer_token_auth(const std::string &token);\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_digest_auth(const std::string &username,\n const std::string &password);\n#endif\n\n void set_keep_alive(bool on);\n void set_follow_location(bool on);\n\n void set_url_encode(bool on);\n\n void set_compress(bool on);\n\n void set_decompress(bool on);\n\n void set_interface(const std::string &intf);\n\n void set_proxy(const std::string &host, int port);\n void set_proxy_basic_auth(const std::string &username,\n const std::string &password);\n void set_proxy_bearer_token_auth(const std::string &token);\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_proxy_digest_auth(const std::string &username,\n const std::string &password);\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void enable_server_certificate_verification(bool enabled);\n#endif\n\n void set_logger(Logger logger);\n\n // SSL\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n void set_ca_cert_path(const std::string &ca_cert_file_path,\n const std::string &ca_cert_dir_path = std::string());\n\n void set_ca_cert_store(X509_STORE *ca_cert_store);\n\n long get_openssl_verify_result() const;\n\n SSL_CTX *ssl_context() const;\n#endif\n\nprivate:\n std::unique_ptr cli_;\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n bool is_ssl_ = false;\n#endif\n};\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\nclass SSLServer : public Server {\npublic:\n SSLServer(const char *cert_path, const char *private_key_path,\n const char *client_ca_cert_file_path = nullptr,\n const char *client_ca_cert_dir_path = nullptr,\n const char *private_key_password = nullptr);\n\n SSLServer(X509 *cert, EVP_PKEY *private_key,\n X509_STORE *client_ca_cert_store = nullptr);\n\n SSLServer(\n const std::function &setup_ssl_ctx_callback);\n\n ~SSLServer() override;\n\n bool is_valid() const override;\n\n SSL_CTX *ssl_context() const;\n\nprivate:\n bool process_and_close_socket(socket_t sock) override;\n\n SSL_CTX *ctx_;\n std::mutex ctx_mutex_;\n};\n\nclass SSLClient : public ClientImpl {\npublic:\n explicit SSLClient(const std::string &host);\n\n explicit SSLClient(const std::string &host, int port);\n\n explicit SSLClient(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path);\n\n explicit SSLClient(const std::string &host, int port, X509 *client_cert,\n EVP_PKEY *client_key);\n\n ~SSLClient() override;\n\n bool is_valid() const override;\n\n void set_ca_cert_store(X509_STORE *ca_cert_store);\n\n long get_openssl_verify_result() const;\n\n SSL_CTX *ssl_context() const;\n\nprivate:\n bool create_and_connect_socket(Socket &socket, Error &error) override;\n void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override;\n void shutdown_ssl_impl(Socket &socket, bool shutdown_socket);\n\n bool process_socket(const Socket &socket,\n std::function callback) override;\n bool is_ssl() const override;\n\n bool connect_with_proxy(Socket &sock, Response &res, bool &success,\n Error &error);\n bool initialize_ssl(Socket &socket, Error &error);\n\n bool load_certs();\n\n bool verify_host(X509 *server_cert) const;\n bool verify_host_with_subject_alt_name(X509 *server_cert) const;\n bool verify_host_with_common_name(X509 *server_cert) const;\n bool check_host_name(const char *pattern, size_t pattern_len) const;\n\n SSL_CTX *ctx_;\n std::mutex ctx_mutex_;\n std::once_flag initialize_cert_;\n\n std::vector host_components_;\n\n long verify_result_ = 0;\n\n friend class ClientImpl;\n};\n#endif\n\n/*\n * Implementation of template methods.\n */\n\nnamespace detail {\n\ntemplate \ninline void duration_to_sec_and_usec(const T &duration, U callback) {\n auto sec = std::chrono::duration_cast(duration).count();\n auto usec = std::chrono::duration_cast(\n duration - std::chrono::seconds(sec))\n .count();\n callback(static_cast(sec), static_cast(usec));\n}\n\ntemplate \ninline T get_header_value(const Headers & /*headers*/,\n const std::string & /*key*/, size_t /*id*/ = 0,\n uint64_t /*def*/ = 0) {}\n\ntemplate <>\ninline uint64_t get_header_value(const Headers &headers,\n const std::string &key, size_t id,\n uint64_t def) {\n auto rng = headers.equal_range(key);\n auto it = rng.first;\n std::advance(it, static_cast(id));\n if (it != rng.second) {\n return std::strtoull(it->second.data(), nullptr, 10);\n }\n return def;\n}\n\n} // namespace detail\n\ntemplate \ninline T Request::get_header_value(const std::string &key, size_t id) const {\n return detail::get_header_value(headers, key, id, 0);\n}\n\ntemplate \ninline T Response::get_header_value(const std::string &key, size_t id) const {\n return detail::get_header_value(headers, key, id, 0);\n}\n\ntemplate \ninline ssize_t Stream::write_format(const char *fmt, const Args &...args) {\n const auto bufsiz = 2048;\n std::array buf{};\n\n auto sn = snprintf(buf.data(), buf.size() - 1, fmt, args...);\n if (sn <= 0) { return sn; }\n\n auto n = static_cast(sn);\n\n if (n >= buf.size() - 1) {\n std::vector glowable_buf(buf.size());\n\n while (n >= glowable_buf.size() - 1) {\n glowable_buf.resize(glowable_buf.size() * 2);\n n = static_cast(\n snprintf(&glowable_buf[0], glowable_buf.size() - 1, fmt, args...));\n }\n return write(&glowable_buf[0], n);\n } else {\n return write(buf.data(), n);\n }\n}\n\ninline void default_socket_options(socket_t sock) {\n int yes = 1;\n#ifdef _WIN32\n setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes),\n sizeof(yes));\n setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE,\n reinterpret_cast(&yes), sizeof(yes));\n#else\n#ifdef SO_REUSEPORT\n setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast(&yes),\n sizeof(yes));\n#else\n setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes),\n sizeof(yes));\n#endif\n#endif\n}\n\ntemplate \ninline Server &\nServer::set_read_timeout(const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(\n duration, [&](time_t sec, time_t usec) { set_read_timeout(sec, usec); });\n return *this;\n}\n\ntemplate \ninline Server &\nServer::set_write_timeout(const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(\n duration, [&](time_t sec, time_t usec) { set_write_timeout(sec, usec); });\n return *this;\n}\n\ntemplate \ninline Server &\nServer::set_idle_interval(const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(\n duration, [&](time_t sec, time_t usec) { set_idle_interval(sec, usec); });\n return *this;\n}\n\ninline std::string to_string(const Error error) {\n switch (error) {\n case Error::Success: return \"Success (no error)\";\n case Error::Connection: return \"Could not establish connection\";\n case Error::BindIPAddress: return \"Failed to bind IP address\";\n case Error::Read: return \"Failed to read connection\";\n case Error::Write: return \"Failed to write connection\";\n case Error::ExceedRedirectCount: return \"Maximum redirect count exceeded\";\n case Error::Canceled: return \"Connection handling canceled\";\n case Error::SSLConnection: return \"SSL connection failed\";\n case Error::SSLLoadingCerts: return \"SSL certificate loading failed\";\n case Error::SSLServerVerification: return \"SSL server verification failed\";\n case Error::UnsupportedMultipartBoundaryChars:\n return \"Unsupported HTTP multipart boundary characters\";\n case Error::Compression: return \"Compression failed\";\n case Error::ConnectionTimeout: return \"Connection timed out\";\n case Error::Unknown: return \"Unknown\";\n default: break;\n }\n\n return \"Invalid\";\n}\n\ninline std::ostream &operator<<(std::ostream &os, const Error &obj) {\n os << to_string(obj);\n os << \" (\" << static_cast::type>(obj) << ')';\n return os;\n}\n\ntemplate \ninline T Result::get_request_header_value(const std::string &key,\n size_t id) const {\n return detail::get_header_value(request_headers_, key, id, 0);\n}\n\ntemplate \ninline void ClientImpl::set_connection_timeout(\n const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(duration, [&](time_t sec, time_t usec) {\n set_connection_timeout(sec, usec);\n });\n}\n\ntemplate \ninline void ClientImpl::set_read_timeout(\n const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(\n duration, [&](time_t sec, time_t usec) { set_read_timeout(sec, usec); });\n}\n\ntemplate \ninline void ClientImpl::set_write_timeout(\n const std::chrono::duration &duration) {\n detail::duration_to_sec_and_usec(\n duration, [&](time_t sec, time_t usec) { set_write_timeout(sec, usec); });\n}\n\ntemplate \ninline void Client::set_connection_timeout(\n const std::chrono::duration &duration) {\n cli_->set_connection_timeout(duration);\n}\n\ntemplate \ninline void\nClient::set_read_timeout(const std::chrono::duration &duration) {\n cli_->set_read_timeout(duration);\n}\n\ntemplate \ninline void\nClient::set_write_timeout(const std::chrono::duration &duration) {\n cli_->set_write_timeout(duration);\n}\n\n/*\n * Forward declarations and types that will be part of the .h file if split into\n * .h + .cc.\n */\n\nstd::string hosted_at(const std::string &hostname);\n\nvoid hosted_at(const std::string &hostname, std::vector &addrs);\n\nstd::string append_query_params(const std::string &path, const Params ¶ms);\n\nstd::pair make_range_header(Ranges ranges);\n\nstd::pair\nmake_basic_authentication_header(const std::string &username,\n const std::string &password,\n bool is_proxy = false);\n\nnamespace detail {\n\nstd::string encode_query_param(const std::string &value);\n\nstd::string decode_url(const std::string &s, bool convert_plus_to_space);\n\nvoid read_file(const std::string &path, std::string &out);\n\nstd::string trim_copy(const std::string &s);\n\nvoid split(const char *b, const char *e, char d,\n std::function fn);\n\nbool process_client_socket(socket_t sock, time_t read_timeout_sec,\n time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec,\n std::function callback);\n\nsocket_t create_client_socket(\n const std::string &host, const std::string &ip, int port,\n int address_family, bool tcp_nodelay, SocketOptions socket_options,\n time_t connection_timeout_sec, time_t connection_timeout_usec,\n time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec, const std::string &intf, Error &error);\n\nconst char *get_header_value(const Headers &headers, const std::string &key,\n size_t id = 0, const char *def = nullptr);\n\nstd::string params_to_query_str(const Params ¶ms);\n\nvoid parse_query_text(const std::string &s, Params ¶ms);\n\nbool parse_multipart_boundary(const std::string &content_type,\n std::string &boundary);\n\nbool parse_range_header(const std::string &s, Ranges &ranges);\n\nint close_socket(socket_t sock);\n\nssize_t send_socket(socket_t sock, const void *ptr, size_t size, int flags);\n\nssize_t read_socket(socket_t sock, void *ptr, size_t size, int flags);\n\nenum class EncodingType { None = 0, Gzip, Brotli };\n\nEncodingType encoding_type(const Request &req, const Response &res);\n\nclass BufferStream : public Stream {\npublic:\n BufferStream() = default;\n ~BufferStream() override = default;\n\n bool is_readable() const override;\n bool is_writable() const override;\n ssize_t read(char *ptr, size_t size) override;\n ssize_t write(const char *ptr, size_t size) override;\n void get_remote_ip_and_port(std::string &ip, int &port) const override;\n void get_local_ip_and_port(std::string &ip, int &port) const override;\n socket_t socket() const override;\n\n const std::string &get_buffer() const;\n\nprivate:\n std::string buffer;\n size_t position = 0;\n};\n\nclass compressor {\npublic:\n virtual ~compressor() = default;\n\n typedef std::function Callback;\n virtual bool compress(const char *data, size_t data_length, bool last,\n Callback callback) = 0;\n};\n\nclass decompressor {\npublic:\n virtual ~decompressor() = default;\n\n virtual bool is_valid() const = 0;\n\n typedef std::function Callback;\n virtual bool decompress(const char *data, size_t data_length,\n Callback callback) = 0;\n};\n\nclass nocompressor : public compressor {\npublic:\n virtual ~nocompressor() = default;\n\n bool compress(const char *data, size_t data_length, bool /*last*/,\n Callback callback) override;\n};\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\nclass gzip_compressor : public compressor {\npublic:\n gzip_compressor();\n ~gzip_compressor();\n\n bool compress(const char *data, size_t data_length, bool last,\n Callback callback) override;\n\nprivate:\n bool is_valid_ = false;\n z_stream strm_;\n};\n\nclass gzip_decompressor : public decompressor {\npublic:\n gzip_decompressor();\n ~gzip_decompressor();\n\n bool is_valid() const override;\n\n bool decompress(const char *data, size_t data_length,\n Callback callback) override;\n\nprivate:\n bool is_valid_ = false;\n z_stream strm_;\n};\n#endif\n\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\nclass brotli_compressor : public compressor {\npublic:\n brotli_compressor();\n ~brotli_compressor();\n\n bool compress(const char *data, size_t data_length, bool last,\n Callback callback) override;\n\nprivate:\n BrotliEncoderState *state_ = nullptr;\n};\n\nclass brotli_decompressor : public decompressor {\npublic:\n brotli_decompressor();\n ~brotli_decompressor();\n\n bool is_valid() const override;\n\n bool decompress(const char *data, size_t data_length,\n Callback callback) override;\n\nprivate:\n BrotliDecoderResult decoder_r;\n BrotliDecoderState *decoder_s = nullptr;\n};\n#endif\n\n// NOTE: until the read size reaches `fixed_buffer_size`, use `fixed_buffer`\n// to store data. The call can set memory on stack for performance.\nclass stream_line_reader {\npublic:\n stream_line_reader(Stream &strm, char *fixed_buffer,\n size_t fixed_buffer_size);\n const char *ptr() const;\n size_t size() const;\n bool end_with_crlf() const;\n bool getline();\n\nprivate:\n void append(char c);\n\n Stream &strm_;\n char *fixed_buffer_;\n const size_t fixed_buffer_size_;\n size_t fixed_buffer_used_size_ = 0;\n std::string glowable_buffer_;\n};\n\n} // namespace detail\n\n// ----------------------------------------------------------------------------\n\n/*\n * Implementation that will be part of the .cc file if split into .h + .cc.\n */\n\nnamespace detail {\n\ninline bool is_hex(char c, int &v) {\n if (0x20 <= c && isdigit(c)) {\n v = c - '0';\n return true;\n } else if ('A' <= c && c <= 'F') {\n v = c - 'A' + 10;\n return true;\n } else if ('a' <= c && c <= 'f') {\n v = c - 'a' + 10;\n return true;\n }\n return false;\n}\n\ninline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt,\n int &val) {\n if (i >= s.size()) { return false; }\n\n val = 0;\n for (; cnt; i++, cnt--) {\n if (!s[i]) { return false; }\n int v = 0;\n if (is_hex(s[i], v)) {\n val = val * 16 + v;\n } else {\n return false;\n }\n }\n return true;\n}\n\ninline std::string from_i_to_hex(size_t n) {\n const char *charset = \"0123456789abcdef\";\n std::string ret;\n do {\n ret = charset[n & 15] + ret;\n n >>= 4;\n } while (n > 0);\n return ret;\n}\n\ninline size_t to_utf8(int code, char *buff) {\n if (code < 0x0080) {\n buff[0] = (code & 0x7F);\n return 1;\n } else if (code < 0x0800) {\n buff[0] = static_cast(0xC0 | ((code >> 6) & 0x1F));\n buff[1] = static_cast(0x80 | (code & 0x3F));\n return 2;\n } else if (code < 0xD800) {\n buff[0] = static_cast(0xE0 | ((code >> 12) & 0xF));\n buff[1] = static_cast(0x80 | ((code >> 6) & 0x3F));\n buff[2] = static_cast(0x80 | (code & 0x3F));\n return 3;\n } else if (code < 0xE000) { // D800 - DFFF is invalid...\n return 0;\n } else if (code < 0x10000) {\n buff[0] = static_cast(0xE0 | ((code >> 12) & 0xF));\n buff[1] = static_cast(0x80 | ((code >> 6) & 0x3F));\n buff[2] = static_cast(0x80 | (code & 0x3F));\n return 3;\n } else if (code < 0x110000) {\n buff[0] = static_cast(0xF0 | ((code >> 18) & 0x7));\n buff[1] = static_cast(0x80 | ((code >> 12) & 0x3F));\n buff[2] = static_cast(0x80 | ((code >> 6) & 0x3F));\n buff[3] = static_cast(0x80 | (code & 0x3F));\n return 4;\n }\n\n // NOTREACHED\n return 0;\n}\n\n// NOTE: This code came up with the following stackoverflow post:\n// https://stackoverflow.com/questions/180947/base64-decode-snippet-in-c\ninline std::string base64_encode(const std::string &in) {\n static const auto lookup =\n \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\";\n\n std::string out;\n out.reserve(in.size());\n\n int val = 0;\n int valb = -6;\n\n for (auto c : in) {\n val = (val << 8) + static_cast(c);\n valb += 8;\n while (valb >= 0) {\n out.push_back(lookup[(val >> valb) & 0x3F]);\n valb -= 6;\n }\n }\n\n if (valb > -6) { out.push_back(lookup[((val << 8) >> (valb + 8)) & 0x3F]); }\n\n while (out.size() % 4) {\n out.push_back('=');\n }\n\n return out;\n}\n\ninline bool is_file(const std::string &path) {\n#ifdef _WIN32\n return _access_s(path.c_str(), 0) == 0;\n#else\n struct stat st;\n return stat(path.c_str(), &st) >= 0 && S_ISREG(st.st_mode);\n#endif\n}\n\ninline bool is_dir(const std::string &path) {\n struct stat st;\n return stat(path.c_str(), &st) >= 0 && S_ISDIR(st.st_mode);\n}\n\ninline bool is_valid_path(const std::string &path) {\n size_t level = 0;\n size_t i = 0;\n\n // Skip slash\n while (i < path.size() && path[i] == '/') {\n i++;\n }\n\n while (i < path.size()) {\n // Read component\n auto beg = i;\n while (i < path.size() && path[i] != '/') {\n i++;\n }\n\n auto len = i - beg;\n assert(len > 0);\n\n if (!path.compare(beg, len, \".\")) {\n ;\n } else if (!path.compare(beg, len, \"..\")) {\n if (level == 0) { return false; }\n level--;\n } else {\n level++;\n }\n\n // Skip slash\n while (i < path.size() && path[i] == '/') {\n i++;\n }\n }\n\n return true;\n}\n\ninline std::string encode_query_param(const std::string &value) {\n std::ostringstream escaped;\n escaped.fill('0');\n escaped << std::hex;\n\n for (auto c : value) {\n if (std::isalnum(static_cast(c)) || c == '-' || c == '_' ||\n c == '.' || c == '!' || c == '~' || c == '*' || c == '\\'' || c == '(' ||\n c == ')') {\n escaped << c;\n } else {\n escaped << std::uppercase;\n escaped << '%' << std::setw(2)\n << static_cast(static_cast(c));\n escaped << std::nouppercase;\n }\n }\n\n return escaped.str();\n}\n\ninline std::string encode_url(const std::string &s) {\n std::string result;\n result.reserve(s.size());\n\n for (size_t i = 0; s[i]; i++) {\n switch (s[i]) {\n case ' ': result += \"%20\"; break;\n case '+': result += \"%2B\"; break;\n case '\\r': result += \"%0D\"; break;\n case '\\n': result += \"%0A\"; break;\n case '\\'': result += \"%27\"; break;\n case ',': result += \"%2C\"; break;\n // case ':': result += \"%3A\"; break; // ok? probably...\n case ';': result += \"%3B\"; break;\n default:\n auto c = static_cast(s[i]);\n if (c >= 0x80) {\n result += '%';\n char hex[4];\n auto len = snprintf(hex, sizeof(hex) - 1, \"%02X\", c);\n assert(len == 2);\n result.append(hex, static_cast(len));\n } else {\n result += s[i];\n }\n break;\n }\n }\n\n return result;\n}\n\ninline std::string decode_url(const std::string &s,\n bool convert_plus_to_space) {\n std::string result;\n\n for (size_t i = 0; i < s.size(); i++) {\n if (s[i] == '%' && i + 1 < s.size()) {\n if (s[i + 1] == 'u') {\n int val = 0;\n if (from_hex_to_i(s, i + 2, 4, val)) {\n // 4 digits Unicode codes\n char buff[4];\n size_t len = to_utf8(val, buff);\n if (len > 0) { result.append(buff, len); }\n i += 5; // 'u0000'\n } else {\n result += s[i];\n }\n } else {\n int val = 0;\n if (from_hex_to_i(s, i + 1, 2, val)) {\n // 2 digits hex codes\n result += static_cast(val);\n i += 2; // '00'\n } else {\n result += s[i];\n }\n }\n } else if (convert_plus_to_space && s[i] == '+') {\n result += ' ';\n } else {\n result += s[i];\n }\n }\n\n return result;\n}\n\ninline void read_file(const std::string &path, std::string &out) {\n std::ifstream fs(path, std::ios_base::binary);\n fs.seekg(0, std::ios_base::end);\n auto size = fs.tellg();\n fs.seekg(0);\n out.resize(static_cast(size));\n fs.read(&out[0], static_cast(size));\n}\n\ninline std::string file_extension(const std::string &path) {\n std::smatch m;\n static auto re = std::regex(\"\\\\.([a-zA-Z0-9]+)$\");\n if (std::regex_search(path, m, re)) { return m[1].str(); }\n return std::string();\n}\n\ninline bool is_space_or_tab(char c) { return c == ' ' || c == '\\t'; }\n\ninline std::pair trim(const char *b, const char *e, size_t left,\n size_t right) {\n while (b + left < e && is_space_or_tab(b[left])) {\n left++;\n }\n while (right > 0 && is_space_or_tab(b[right - 1])) {\n right--;\n }\n return std::make_pair(left, right);\n}\n\ninline std::string trim_copy(const std::string &s) {\n auto r = trim(s.data(), s.data() + s.size(), 0, s.size());\n return s.substr(r.first, r.second - r.first);\n}\n\ninline void split(const char *b, const char *e, char d,\n std::function fn) {\n size_t i = 0;\n size_t beg = 0;\n\n while (e ? (b + i < e) : (b[i] != '\\0')) {\n if (b[i] == d) {\n auto r = trim(b, e, beg, i);\n if (r.first < r.second) { fn(&b[r.first], &b[r.second]); }\n beg = i + 1;\n }\n i++;\n }\n\n if (i) {\n auto r = trim(b, e, beg, i);\n if (r.first < r.second) { fn(&b[r.first], &b[r.second]); }\n }\n}\n\ninline stream_line_reader::stream_line_reader(Stream &strm, char *fixed_buffer,\n size_t fixed_buffer_size)\n : strm_(strm), fixed_buffer_(fixed_buffer),\n fixed_buffer_size_(fixed_buffer_size) {}\n\ninline const char *stream_line_reader::ptr() const {\n if (glowable_buffer_.empty()) {\n return fixed_buffer_;\n } else {\n return glowable_buffer_.data();\n }\n}\n\ninline size_t stream_line_reader::size() const {\n if (glowable_buffer_.empty()) {\n return fixed_buffer_used_size_;\n } else {\n return glowable_buffer_.size();\n }\n}\n\ninline bool stream_line_reader::end_with_crlf() const {\n auto end = ptr() + size();\n return size() >= 2 && end[-2] == '\\r' && end[-1] == '\\n';\n}\n\ninline bool stream_line_reader::getline() {\n fixed_buffer_used_size_ = 0;\n glowable_buffer_.clear();\n\n for (size_t i = 0;; i++) {\n char byte;\n auto n = strm_.read(&byte, 1);\n\n if (n < 0) {\n return false;\n } else if (n == 0) {\n if (i == 0) {\n return false;\n } else {\n break;\n }\n }\n\n append(byte);\n\n if (byte == '\\n') { break; }\n }\n\n return true;\n}\n\ninline void stream_line_reader::append(char c) {\n if (fixed_buffer_used_size_ < fixed_buffer_size_ - 1) {\n fixed_buffer_[fixed_buffer_used_size_++] = c;\n fixed_buffer_[fixed_buffer_used_size_] = '\\0';\n } else {\n if (glowable_buffer_.empty()) {\n assert(fixed_buffer_[fixed_buffer_used_size_] == '\\0');\n glowable_buffer_.assign(fixed_buffer_, fixed_buffer_used_size_);\n }\n glowable_buffer_ += c;\n }\n}\n\ninline int close_socket(socket_t sock) {\n#ifdef _WIN32\n return closesocket(sock);\n#else\n return close(sock);\n#endif\n}\n\ntemplate inline ssize_t handle_EINTR(T fn) {\n ssize_t res = false;\n while (true) {\n res = fn();\n if (res < 0 && errno == EINTR) { continue; }\n break;\n }\n return res;\n}\n\ninline ssize_t read_socket(socket_t sock, void *ptr, size_t size, int flags) {\n return handle_EINTR([&]() {\n return recv(sock,\n#ifdef _WIN32\n static_cast(ptr), static_cast(size),\n#else\n ptr, size,\n#endif\n flags);\n });\n}\n\ninline ssize_t send_socket(socket_t sock, const void *ptr, size_t size,\n int flags) {\n return handle_EINTR([&]() {\n return send(sock,\n#ifdef _WIN32\n static_cast(ptr), static_cast(size),\n#else\n ptr, size,\n#endif\n flags);\n });\n}\n\ninline ssize_t select_read(socket_t sock, time_t sec, time_t usec) {\n#ifdef CPPHTTPLIB_USE_POLL\n struct pollfd pfd_read;\n pfd_read.fd = sock;\n pfd_read.events = POLLIN;\n\n auto timeout = static_cast(sec * 1000 + usec / 1000);\n\n return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });\n#else\n#ifndef _WIN32\n if (sock >= FD_SETSIZE) { return 1; }\n#endif\n\n fd_set fds;\n FD_ZERO(&fds);\n FD_SET(sock, &fds);\n\n timeval tv;\n tv.tv_sec = static_cast(sec);\n tv.tv_usec = static_cast(usec);\n\n return handle_EINTR([&]() {\n return select(static_cast(sock + 1), &fds, nullptr, nullptr, &tv);\n });\n#endif\n}\n\ninline ssize_t select_write(socket_t sock, time_t sec, time_t usec) {\n#ifdef CPPHTTPLIB_USE_POLL\n struct pollfd pfd_read;\n pfd_read.fd = sock;\n pfd_read.events = POLLOUT;\n\n auto timeout = static_cast(sec * 1000 + usec / 1000);\n\n return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });\n#else\n#ifndef _WIN32\n if (sock >= FD_SETSIZE) { return 1; }\n#endif\n\n fd_set fds;\n FD_ZERO(&fds);\n FD_SET(sock, &fds);\n\n timeval tv;\n tv.tv_sec = static_cast(sec);\n tv.tv_usec = static_cast(usec);\n\n return handle_EINTR([&]() {\n return select(static_cast(sock + 1), nullptr, &fds, nullptr, &tv);\n });\n#endif\n}\n\ninline Error wait_until_socket_is_ready(socket_t sock, time_t sec,\n time_t usec) {\n#ifdef CPPHTTPLIB_USE_POLL\n struct pollfd pfd_read;\n pfd_read.fd = sock;\n pfd_read.events = POLLIN | POLLOUT;\n\n auto timeout = static_cast(sec * 1000 + usec / 1000);\n\n auto poll_res = handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });\n\n if (poll_res == 0) { return Error::ConnectionTimeout; }\n\n if (poll_res > 0 && pfd_read.revents & (POLLIN | POLLOUT)) {\n int error = 0;\n socklen_t len = sizeof(error);\n auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,\n reinterpret_cast(&error), &len);\n auto successful = res >= 0 && !error;\n return successful ? Error::Success : Error::Connection;\n }\n\n return Error::Connection;\n#else\n#ifndef _WIN32\n if (sock >= FD_SETSIZE) { return Error::Connection; }\n#endif\n\n fd_set fdsr;\n FD_ZERO(&fdsr);\n FD_SET(sock, &fdsr);\n\n auto fdsw = fdsr;\n auto fdse = fdsr;\n\n timeval tv;\n tv.tv_sec = static_cast(sec);\n tv.tv_usec = static_cast(usec);\n\n auto ret = handle_EINTR([&]() {\n return select(static_cast(sock + 1), &fdsr, &fdsw, &fdse, &tv);\n });\n\n if (ret == 0) { return Error::ConnectionTimeout; }\n\n if (ret > 0 && (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) {\n int error = 0;\n socklen_t len = sizeof(error);\n auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,\n reinterpret_cast(&error), &len);\n auto successful = res >= 0 && !error;\n return successful ? Error::Success : Error::Connection;\n }\n return Error::Connection;\n#endif\n}\n\ninline bool is_socket_alive(socket_t sock) {\n const auto val = detail::select_read(sock, 0, 0);\n if (val == 0) {\n return true;\n } else if (val < 0 && errno == EBADF) {\n return false;\n }\n char buf[1];\n return detail::read_socket(sock, &buf[0], sizeof(buf), MSG_PEEK) > 0;\n}\n\nclass SocketStream : public Stream {\npublic:\n SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec,\n time_t write_timeout_sec, time_t write_timeout_usec);\n ~SocketStream() override;\n\n bool is_readable() const override;\n bool is_writable() const override;\n ssize_t read(char *ptr, size_t size) override;\n ssize_t write(const char *ptr, size_t size) override;\n void get_remote_ip_and_port(std::string &ip, int &port) const override;\n void get_local_ip_and_port(std::string &ip, int &port) const override;\n socket_t socket() const override;\n\nprivate:\n socket_t sock_;\n time_t read_timeout_sec_;\n time_t read_timeout_usec_;\n time_t write_timeout_sec_;\n time_t write_timeout_usec_;\n\n std::vector read_buff_;\n size_t read_buff_off_ = 0;\n size_t read_buff_content_size_ = 0;\n\n static const size_t read_buff_size_ = 1024 * 4;\n};\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\nclass SSLSocketStream : public Stream {\npublic:\n SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec,\n time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec);\n ~SSLSocketStream() override;\n\n bool is_readable() const override;\n bool is_writable() const override;\n ssize_t read(char *ptr, size_t size) override;\n ssize_t write(const char *ptr, size_t size) override;\n void get_remote_ip_and_port(std::string &ip, int &port) const override;\n void get_local_ip_and_port(std::string &ip, int &port) const override;\n socket_t socket() const override;\n\nprivate:\n socket_t sock_;\n SSL *ssl_;\n time_t read_timeout_sec_;\n time_t read_timeout_usec_;\n time_t write_timeout_sec_;\n time_t write_timeout_usec_;\n};\n#endif\n\ninline bool keep_alive(socket_t sock, time_t keep_alive_timeout_sec) {\n using namespace std::chrono;\n auto start = steady_clock::now();\n while (true) {\n auto val = select_read(sock, 0, 10000);\n if (val < 0) {\n return false;\n } else if (val == 0) {\n auto current = steady_clock::now();\n auto duration = duration_cast(current - start);\n auto timeout = keep_alive_timeout_sec * 1000;\n if (duration.count() > timeout) { return false; }\n std::this_thread::sleep_for(std::chrono::milliseconds(1));\n } else {\n return true;\n }\n }\n}\n\ntemplate \ninline bool\nprocess_server_socket_core(const std::atomic &svr_sock, socket_t sock,\n size_t keep_alive_max_count,\n time_t keep_alive_timeout_sec, T callback) {\n assert(keep_alive_max_count > 0);\n auto ret = false;\n auto count = keep_alive_max_count;\n while (svr_sock != INVALID_SOCKET && count > 0 &&\n keep_alive(sock, keep_alive_timeout_sec)) {\n auto close_connection = count == 1;\n auto connection_closed = false;\n ret = callback(close_connection, connection_closed);\n if (!ret || connection_closed) { break; }\n count--;\n }\n return ret;\n}\n\ntemplate \ninline bool\nprocess_server_socket(const std::atomic &svr_sock, socket_t sock,\n size_t keep_alive_max_count,\n time_t keep_alive_timeout_sec, time_t read_timeout_sec,\n time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec, T callback) {\n return process_server_socket_core(\n svr_sock, sock, keep_alive_max_count, keep_alive_timeout_sec,\n [&](bool close_connection, bool &connection_closed) {\n SocketStream strm(sock, read_timeout_sec, read_timeout_usec,\n write_timeout_sec, write_timeout_usec);\n return callback(strm, close_connection, connection_closed);\n });\n}\n\ninline bool process_client_socket(socket_t sock, time_t read_timeout_sec,\n time_t read_timeout_usec,\n time_t write_timeout_sec,\n time_t write_timeout_usec,\n std::function callback) {\n SocketStream strm(sock, read_timeout_sec, read_timeout_usec,\n write_timeout_sec, write_timeout_usec);\n return callback(strm);\n}\n\ninline int shutdown_socket(socket_t sock) {\n#ifdef _WIN32\n return shutdown(sock, SD_BOTH);\n#else\n return shutdown(sock, SHUT_RDWR);\n#endif\n}\n\ntemplate \nsocket_t create_socket(const std::string &host, const std::string &ip, int port,\n int address_family, int socket_flags, bool tcp_nodelay,\n SocketOptions socket_options,\n BindOrConnect bind_or_connect) {\n // Get address info\n const char *node = nullptr;\n struct addrinfo hints;\n struct addrinfo *result;\n\n memset(&hints, 0, sizeof(struct addrinfo));\n hints.ai_socktype = SOCK_STREAM;\n hints.ai_protocol = 0;\n\n if (!ip.empty()) {\n node = ip.c_str();\n // Ask getaddrinfo to convert IP in c-string to address\n hints.ai_family = AF_UNSPEC;\n hints.ai_flags = AI_NUMERICHOST;\n } else {\n if (!host.empty()) { node = host.c_str(); }\n hints.ai_family = address_family;\n hints.ai_flags = socket_flags;\n }\n\n#ifndef _WIN32\n if (hints.ai_family == AF_UNIX) {\n const auto addrlen = host.length();\n if (addrlen > sizeof(sockaddr_un::sun_path)) return INVALID_SOCKET;\n\n auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol);\n if (sock != INVALID_SOCKET) {\n sockaddr_un addr{};\n addr.sun_family = AF_UNIX;\n std::copy(host.begin(), host.end(), addr.sun_path);\n\n hints.ai_addr = reinterpret_cast(&addr);\n hints.ai_addrlen = static_cast(\n sizeof(addr) - sizeof(addr.sun_path) + addrlen);\n\n fcntl(sock, F_SETFD, FD_CLOEXEC);\n if (socket_options) { socket_options(sock); }\n\n if (!bind_or_connect(sock, hints)) {\n close_socket(sock);\n sock = INVALID_SOCKET;\n }\n }\n return sock;\n }\n#endif\n\n auto service = std::to_string(port);\n\n if (getaddrinfo(node, service.c_str(), &hints, &result)) {\n#if defined __linux__ && !defined __ANDROID__\n res_init();\n#endif\n return INVALID_SOCKET;\n }\n\n for (auto rp = result; rp; rp = rp->ai_next) {\n // Create a socket\n#ifdef _WIN32\n auto sock =\n WSASocketW(rp->ai_family, rp->ai_socktype, rp->ai_protocol, nullptr, 0,\n WSA_FLAG_NO_HANDLE_INHERIT | WSA_FLAG_OVERLAPPED);\n /**\n * Since the WSA_FLAG_NO_HANDLE_INHERIT is only supported on Windows 7 SP1\n * and above the socket creation fails on older Windows Systems.\n *\n * Let's try to create a socket the old way in this case.\n *\n * Reference:\n * https://docs.microsoft.com/en-us/windows/win32/api/winsock2/nf-winsock2-wsasocketa\n *\n * WSA_FLAG_NO_HANDLE_INHERIT:\n * This flag is supported on Windows 7 with SP1, Windows Server 2008 R2 with\n * SP1, and later\n *\n */\n if (sock == INVALID_SOCKET) {\n sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol);\n }\n#else\n auto sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol);\n#endif\n if (sock == INVALID_SOCKET) { continue; }\n\n#ifndef _WIN32\n if (fcntl(sock, F_SETFD, FD_CLOEXEC) == -1) {\n close_socket(sock);\n continue;\n }\n#endif\n\n if (tcp_nodelay) {\n int yes = 1;\n setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&yes),\n sizeof(yes));\n }\n\n if (socket_options) { socket_options(sock); }\n\n if (rp->ai_family == AF_INET6) {\n int no = 0;\n setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, reinterpret_cast(&no),\n sizeof(no));\n }\n\n // bind or connect\n if (bind_or_connect(sock, *rp)) {\n freeaddrinfo(result);\n return sock;\n }\n\n close_socket(sock);\n }\n\n freeaddrinfo(result);\n return INVALID_SOCKET;\n}\n\ninline void set_nonblocking(socket_t sock, bool nonblocking) {\n#ifdef _WIN32\n auto flags = nonblocking ? 1UL : 0UL;\n ioctlsocket(sock, FIONBIO, &flags);\n#else\n auto flags = fcntl(sock, F_GETFL, 0);\n fcntl(sock, F_SETFL,\n nonblocking ? (flags | O_NONBLOCK) : (flags & (~O_NONBLOCK)));\n#endif\n}\n\ninline bool is_connection_error() {\n#ifdef _WIN32\n return WSAGetLastError() != WSAEWOULDBLOCK;\n#else\n return errno != EINPROGRESS;\n#endif\n}\n\ninline bool bind_ip_address(socket_t sock, const std::string &host) {\n struct addrinfo hints;\n struct addrinfo *result;\n\n memset(&hints, 0, sizeof(struct addrinfo));\n hints.ai_family = AF_UNSPEC;\n hints.ai_socktype = SOCK_STREAM;\n hints.ai_protocol = 0;\n\n if (getaddrinfo(host.c_str(), \"0\", &hints, &result)) { return false; }\n\n auto ret = false;\n for (auto rp = result; rp; rp = rp->ai_next) {\n const auto &ai = *rp;\n if (!::bind(sock, ai.ai_addr, static_cast(ai.ai_addrlen))) {\n ret = true;\n break;\n }\n }\n\n freeaddrinfo(result);\n return ret;\n}\n\n#if !defined _WIN32 && !defined ANDROID && !defined _AIX\n#define USE_IF2IP\n#endif\n\n#ifdef USE_IF2IP\ninline std::string if2ip(int address_family, const std::string &ifn) {\n struct ifaddrs *ifap;\n getifaddrs(&ifap);\n std::string addr_candidate;\n for (auto ifa = ifap; ifa; ifa = ifa->ifa_next) {\n if (ifa->ifa_addr && ifn == ifa->ifa_name &&\n (AF_UNSPEC == address_family ||\n ifa->ifa_addr->sa_family == address_family)) {\n if (ifa->ifa_addr->sa_family == AF_INET) {\n auto sa = reinterpret_cast(ifa->ifa_addr);\n char buf[INET_ADDRSTRLEN];\n if (inet_ntop(AF_INET, &sa->sin_addr, buf, INET_ADDRSTRLEN)) {\n freeifaddrs(ifap);\n return std::string(buf, INET_ADDRSTRLEN);\n }\n } else if (ifa->ifa_addr->sa_family == AF_INET6) {\n auto sa = reinterpret_cast(ifa->ifa_addr);\n if (!IN6_IS_ADDR_LINKLOCAL(&sa->sin6_addr)) {\n char buf[INET6_ADDRSTRLEN] = {};\n if (inet_ntop(AF_INET6, &sa->sin6_addr, buf, INET6_ADDRSTRLEN)) {\n // equivalent to mac's IN6_IS_ADDR_UNIQUE_LOCAL\n auto s6_addr_head = sa->sin6_addr.s6_addr[0];\n if (s6_addr_head == 0xfc || s6_addr_head == 0xfd) {\n addr_candidate = std::string(buf, INET6_ADDRSTRLEN);\n } else {\n freeifaddrs(ifap);\n return std::string(buf, INET6_ADDRSTRLEN);\n }\n }\n }\n }\n }\n }\n freeifaddrs(ifap);\n return addr_candidate;\n}\n#endif\n\ninline socket_t create_client_socket(\n const std::string &host, const std::string &ip, int port,\n int address_family, bool tcp_nodelay, SocketOptions socket_options,\n time_t connection_timeout_sec, time_t connection_timeout_usec,\n time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec, const std::string &intf, Error &error) {\n auto sock = create_socket(\n host, ip, port, address_family, 0, tcp_nodelay, std::move(socket_options),\n [&](socket_t sock2, struct addrinfo &ai) -> bool {\n if (!intf.empty()) {\n#ifdef USE_IF2IP\n auto ip_from_if = if2ip(address_family, intf);\n if (ip_from_if.empty()) { ip_from_if = intf; }\n if (!bind_ip_address(sock2, ip_from_if.c_str())) {\n error = Error::BindIPAddress;\n return false;\n }\n#endif\n }\n\n set_nonblocking(sock2, true);\n\n auto ret =\n ::connect(sock2, ai.ai_addr, static_cast(ai.ai_addrlen));\n\n if (ret < 0) {\n if (is_connection_error()) {\n error = Error::Connection;\n return false;\n }\n error = wait_until_socket_is_ready(sock2, connection_timeout_sec,\n connection_timeout_usec);\n if (error != Error::Success) { return false; }\n }\n\n set_nonblocking(sock2, false);\n\n {\n#ifdef _WIN32\n auto timeout = static_cast(read_timeout_sec * 1000 +\n read_timeout_usec / 1000);\n setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,\n sizeof(timeout));\n#else\n timeval tv;\n tv.tv_sec = static_cast(read_timeout_sec);\n tv.tv_usec = static_cast(read_timeout_usec);\n setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));\n#endif\n }\n {\n\n#ifdef _WIN32\n auto timeout = static_cast(write_timeout_sec * 1000 +\n write_timeout_usec / 1000);\n setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,\n sizeof(timeout));\n#else\n timeval tv;\n tv.tv_sec = static_cast(write_timeout_sec);\n tv.tv_usec = static_cast(write_timeout_usec);\n setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));\n#endif\n }\n\n error = Error::Success;\n return true;\n });\n\n if (sock != INVALID_SOCKET) {\n error = Error::Success;\n } else {\n if (error == Error::Success) { error = Error::Connection; }\n }\n\n return sock;\n}\n\ninline bool get_ip_and_port(const struct sockaddr_storage &addr,\n socklen_t addr_len, std::string &ip, int &port) {\n if (addr.ss_family == AF_INET) {\n port = ntohs(reinterpret_cast(&addr)->sin_port);\n } else if (addr.ss_family == AF_INET6) {\n port =\n ntohs(reinterpret_cast(&addr)->sin6_port);\n } else {\n return false;\n }\n\n std::array ipstr{};\n if (getnameinfo(reinterpret_cast(&addr), addr_len,\n ipstr.data(), static_cast(ipstr.size()), nullptr,\n 0, NI_NUMERICHOST)) {\n return false;\n }\n\n ip = ipstr.data();\n return true;\n}\n\ninline void get_local_ip_and_port(socket_t sock, std::string &ip, int &port) {\n struct sockaddr_storage addr;\n socklen_t addr_len = sizeof(addr);\n if (!getsockname(sock, reinterpret_cast(&addr),\n &addr_len)) {\n get_ip_and_port(addr, addr_len, ip, port);\n }\n}\n\ninline void get_remote_ip_and_port(socket_t sock, std::string &ip, int &port) {\n struct sockaddr_storage addr;\n socklen_t addr_len = sizeof(addr);\n\n if (!getpeername(sock, reinterpret_cast(&addr),\n &addr_len)) {\n#ifndef _WIN32\n if (addr.ss_family == AF_UNIX) {\n#if defined(__linux__)\n struct ucred ucred;\n socklen_t len = sizeof(ucred);\n if (getsockopt(sock, SOL_SOCKET, SO_PEERCRED, &ucred, &len) == 0) {\n port = ucred.pid;\n }\n#elif defined(SOL_LOCAL) && defined(SO_PEERPID) // __APPLE__\n pid_t pid;\n socklen_t len = sizeof(pid);\n if (getsockopt(sock, SOL_LOCAL, SO_PEERPID, &pid, &len) == 0) {\n port = pid;\n }\n#endif\n return;\n }\n#endif\n get_ip_and_port(addr, addr_len, ip, port);\n }\n}\n\ninline constexpr unsigned int str2tag_core(const char *s, size_t l,\n unsigned int h) {\n return (l == 0)\n ? h\n : str2tag_core(\n s + 1, l - 1,\n // Unsets the 6 high bits of h, therefore no overflow happens\n (((std::numeric_limits::max)() >> 6) &\n h * 33) ^\n static_cast(*s));\n}\n\ninline unsigned int str2tag(const std::string &s) {\n return str2tag_core(s.data(), s.size(), 0);\n}\n\nnamespace udl {\n\ninline constexpr unsigned int operator\"\" _t(const char *s, size_t l) {\n return str2tag_core(s, l, 0);\n}\n\n} // namespace udl\n\ninline const char *\nfind_content_type(const std::string &path,\n const std::map &user_data) {\n auto ext = file_extension(path);\n\n auto it = user_data.find(ext);\n if (it != user_data.end()) { return it->second.c_str(); }\n\n using udl::operator\"\"_t;\n\n switch (str2tag(ext)) {\n default: return nullptr;\n case \"css\"_t: return \"text/css\";\n case \"csv\"_t: return \"text/csv\";\n case \"htm\"_t:\n case \"html\"_t: return \"text/html\";\n case \"js\"_t:\n case \"mjs\"_t: return \"text/javascript\";\n case \"txt\"_t: return \"text/plain\";\n case \"vtt\"_t: return \"text/vtt\";\n\n case \"apng\"_t: return \"image/apng\";\n case \"avif\"_t: return \"image/avif\";\n case \"bmp\"_t: return \"image/bmp\";\n case \"gif\"_t: return \"image/gif\";\n case \"png\"_t: return \"image/png\";\n case \"svg\"_t: return \"image/svg+xml\";\n case \"webp\"_t: return \"image/webp\";\n case \"ico\"_t: return \"image/x-icon\";\n case \"tif\"_t: return \"image/tiff\";\n case \"tiff\"_t: return \"image/tiff\";\n case \"jpg\"_t:\n case \"jpeg\"_t: return \"image/jpeg\";\n\n case \"mp4\"_t: return \"video/mp4\";\n case \"mpeg\"_t: return \"video/mpeg\";\n case \"webm\"_t: return \"video/webm\";\n\n case \"mp3\"_t: return \"audio/mp3\";\n case \"mpga\"_t: return \"audio/mpeg\";\n case \"weba\"_t: return \"audio/webm\";\n case \"wav\"_t: return \"audio/wave\";\n\n case \"otf\"_t: return \"font/otf\";\n case \"ttf\"_t: return \"font/ttf\";\n case \"woff\"_t: return \"font/woff\";\n case \"woff2\"_t: return \"font/woff2\";\n\n case \"7z\"_t: return \"application/x-7z-compressed\";\n case \"atom\"_t: return \"application/atom+xml\";\n case \"pdf\"_t: return \"application/pdf\";\n case \"json\"_t: return \"application/json\";\n case \"rss\"_t: return \"application/rss+xml\";\n case \"tar\"_t: return \"application/x-tar\";\n case \"xht\"_t:\n case \"xhtml\"_t: return \"application/xhtml+xml\";\n case \"xslt\"_t: return \"application/xslt+xml\";\n case \"xml\"_t: return \"application/xml\";\n case \"gz\"_t: return \"application/gzip\";\n case \"zip\"_t: return \"application/zip\";\n case \"wasm\"_t: return \"application/wasm\";\n }\n}\n\ninline const char *status_message(int status) {\n switch (status) {\n case 100: return \"Continue\";\n case 101: return \"Switching Protocol\";\n case 102: return \"Processing\";\n case 103: return \"Early Hints\";\n case 200: return \"OK\";\n case 201: return \"Created\";\n case 202: return \"Accepted\";\n case 203: return \"Non-Authoritative Information\";\n case 204: return \"No Content\";\n case 205: return \"Reset Content\";\n case 206: return \"Partial Content\";\n case 207: return \"Multi-Status\";\n case 208: return \"Already Reported\";\n case 226: return \"IM Used\";\n case 300: return \"Multiple Choice\";\n case 301: return \"Moved Permanently\";\n case 302: return \"Found\";\n case 303: return \"See Other\";\n case 304: return \"Not Modified\";\n case 305: return \"Use Proxy\";\n case 306: return \"unused\";\n case 307: return \"Temporary Redirect\";\n case 308: return \"Permanent Redirect\";\n case 400: return \"Bad Request\";\n case 401: return \"Unauthorized\";\n case 402: return \"Payment Required\";\n case 403: return \"Forbidden\";\n case 404: return \"Not Found\";\n case 405: return \"Method Not Allowed\";\n case 406: return \"Not Acceptable\";\n case 407: return \"Proxy Authentication Required\";\n case 408: return \"Request Timeout\";\n case 409: return \"Conflict\";\n case 410: return \"Gone\";\n case 411: return \"Length Required\";\n case 412: return \"Precondition Failed\";\n case 413: return \"Payload Too Large\";\n case 414: return \"URI Too Long\";\n case 415: return \"Unsupported Media Type\";\n case 416: return \"Range Not Satisfiable\";\n case 417: return \"Expectation Failed\";\n case 418: return \"I'm a teapot\";\n case 421: return \"Misdirected Request\";\n case 422: return \"Unprocessable Entity\";\n case 423: return \"Locked\";\n case 424: return \"Failed Dependency\";\n case 425: return \"Too Early\";\n case 426: return \"Upgrade Required\";\n case 428: return \"Precondition Required\";\n case 429: return \"Too Many Requests\";\n case 431: return \"Request Header Fields Too Large\";\n case 451: return \"Unavailable For Legal Reasons\";\n case 501: return \"Not Implemented\";\n case 502: return \"Bad Gateway\";\n case 503: return \"Service Unavailable\";\n case 504: return \"Gateway Timeout\";\n case 505: return \"HTTP Version Not Supported\";\n case 506: return \"Variant Also Negotiates\";\n case 507: return \"Insufficient Storage\";\n case 508: return \"Loop Detected\";\n case 510: return \"Not Extended\";\n case 511: return \"Network Authentication Required\";\n\n default:\n case 500: return \"Internal Server Error\";\n }\n}\n\ninline bool can_compress_content_type(const std::string &content_type) {\n using udl::operator\"\"_t;\n\n auto tag = str2tag(content_type);\n\n switch (tag) {\n case \"image/svg+xml\"_t:\n case \"application/javascript\"_t:\n case \"application/json\"_t:\n case \"application/xml\"_t:\n case \"application/protobuf\"_t:\n case \"application/xhtml+xml\"_t: return true;\n\n default:\n return !content_type.rfind(\"text/\", 0) && tag != \"text/event-stream\"_t;\n }\n}\n\ninline EncodingType encoding_type(const Request &req, const Response &res) {\n auto ret =\n detail::can_compress_content_type(res.get_header_value(\"Content-Type\"));\n if (!ret) { return EncodingType::None; }\n\n const auto &s = req.get_header_value(\"Accept-Encoding\");\n (void)(s);\n\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\n // TODO: 'Accept-Encoding' has br, not br;q=0\n ret = s.find(\"br\") != std::string::npos;\n if (ret) { return EncodingType::Brotli; }\n#endif\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n // TODO: 'Accept-Encoding' has gzip, not gzip;q=0\n ret = s.find(\"gzip\") != std::string::npos;\n if (ret) { return EncodingType::Gzip; }\n#endif\n\n return EncodingType::None;\n}\n\ninline bool nocompressor::compress(const char *data, size_t data_length,\n bool /*last*/, Callback callback) {\n if (!data_length) { return true; }\n return callback(data, data_length);\n}\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\ninline gzip_compressor::gzip_compressor() {\n std::memset(&strm_, 0, sizeof(strm_));\n strm_.zalloc = Z_NULL;\n strm_.zfree = Z_NULL;\n strm_.opaque = Z_NULL;\n\n is_valid_ = deflateInit2(&strm_, Z_DEFAULT_COMPRESSION, Z_DEFLATED, 31, 8,\n Z_DEFAULT_STRATEGY) == Z_OK;\n}\n\ninline gzip_compressor::~gzip_compressor() { deflateEnd(&strm_); }\n\ninline bool gzip_compressor::compress(const char *data, size_t data_length,\n bool last, Callback callback) {\n assert(is_valid_);\n\n do {\n constexpr size_t max_avail_in =\n (std::numeric_limits::max)();\n\n strm_.avail_in = static_cast(\n (std::min)(data_length, max_avail_in));\n strm_.next_in = const_cast(reinterpret_cast(data));\n\n data_length -= strm_.avail_in;\n data += strm_.avail_in;\n\n auto flush = (last && data_length == 0) ? Z_FINISH : Z_NO_FLUSH;\n int ret = Z_OK;\n\n std::array buff{};\n do {\n strm_.avail_out = static_cast(buff.size());\n strm_.next_out = reinterpret_cast(buff.data());\n\n ret = deflate(&strm_, flush);\n if (ret == Z_STREAM_ERROR) { return false; }\n\n if (!callback(buff.data(), buff.size() - strm_.avail_out)) {\n return false;\n }\n } while (strm_.avail_out == 0);\n\n assert((flush == Z_FINISH && ret == Z_STREAM_END) ||\n (flush == Z_NO_FLUSH && ret == Z_OK));\n assert(strm_.avail_in == 0);\n } while (data_length > 0);\n\n return true;\n}\n\ninline gzip_decompressor::gzip_decompressor() {\n std::memset(&strm_, 0, sizeof(strm_));\n strm_.zalloc = Z_NULL;\n strm_.zfree = Z_NULL;\n strm_.opaque = Z_NULL;\n\n // 15 is the value of wbits, which should be at the maximum possible value\n // to ensure that any gzip stream can be decoded. The offset of 32 specifies\n // that the stream type should be automatically detected either gzip or\n // deflate.\n is_valid_ = inflateInit2(&strm_, 32 + 15) == Z_OK;\n}\n\ninline gzip_decompressor::~gzip_decompressor() { inflateEnd(&strm_); }\n\ninline bool gzip_decompressor::is_valid() const { return is_valid_; }\n\ninline bool gzip_decompressor::decompress(const char *data, size_t data_length,\n Callback callback) {\n assert(is_valid_);\n\n int ret = Z_OK;\n\n do {\n constexpr size_t max_avail_in =\n (std::numeric_limits::max)();\n\n strm_.avail_in = static_cast(\n (std::min)(data_length, max_avail_in));\n strm_.next_in = const_cast(reinterpret_cast(data));\n\n data_length -= strm_.avail_in;\n data += strm_.avail_in;\n\n std::array buff{};\n while (strm_.avail_in > 0) {\n strm_.avail_out = static_cast(buff.size());\n strm_.next_out = reinterpret_cast(buff.data());\n\n auto prev_avail_in = strm_.avail_in;\n\n ret = inflate(&strm_, Z_NO_FLUSH);\n\n if (prev_avail_in - strm_.avail_in == 0) { return false; }\n\n assert(ret != Z_STREAM_ERROR);\n switch (ret) {\n case Z_NEED_DICT:\n case Z_DATA_ERROR:\n case Z_MEM_ERROR: inflateEnd(&strm_); return false;\n }\n\n if (!callback(buff.data(), buff.size() - strm_.avail_out)) {\n return false;\n }\n }\n\n if (ret != Z_OK && ret != Z_STREAM_END) return false;\n\n } while (data_length > 0);\n\n return true;\n}\n#endif\n\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\ninline brotli_compressor::brotli_compressor() {\n state_ = BrotliEncoderCreateInstance(nullptr, nullptr, nullptr);\n}\n\ninline brotli_compressor::~brotli_compressor() {\n BrotliEncoderDestroyInstance(state_);\n}\n\ninline bool brotli_compressor::compress(const char *data, size_t data_length,\n bool last, Callback callback) {\n std::array buff{};\n\n auto operation = last ? BROTLI_OPERATION_FINISH : BROTLI_OPERATION_PROCESS;\n auto available_in = data_length;\n auto next_in = reinterpret_cast(data);\n\n for (;;) {\n if (last) {\n if (BrotliEncoderIsFinished(state_)) { break; }\n } else {\n if (!available_in) { break; }\n }\n\n auto available_out = buff.size();\n auto next_out = buff.data();\n\n if (!BrotliEncoderCompressStream(state_, operation, &available_in, &next_in,\n &available_out, &next_out, nullptr)) {\n return false;\n }\n\n auto output_bytes = buff.size() - available_out;\n if (output_bytes) {\n callback(reinterpret_cast(buff.data()), output_bytes);\n }\n }\n\n return true;\n}\n\ninline brotli_decompressor::brotli_decompressor() {\n decoder_s = BrotliDecoderCreateInstance(0, 0, 0);\n decoder_r = decoder_s ? BROTLI_DECODER_RESULT_NEEDS_MORE_INPUT\n : BROTLI_DECODER_RESULT_ERROR;\n}\n\ninline brotli_decompressor::~brotli_decompressor() {\n if (decoder_s) { BrotliDecoderDestroyInstance(decoder_s); }\n}\n\ninline bool brotli_decompressor::is_valid() const { return decoder_s; }\n\ninline bool brotli_decompressor::decompress(const char *data,\n size_t data_length,\n Callback callback) {\n if (decoder_r == BROTLI_DECODER_RESULT_SUCCESS ||\n decoder_r == BROTLI_DECODER_RESULT_ERROR) {\n return 0;\n }\n\n const uint8_t *next_in = (const uint8_t *)data;\n size_t avail_in = data_length;\n size_t total_out;\n\n decoder_r = BROTLI_DECODER_RESULT_NEEDS_MORE_OUTPUT;\n\n std::array buff{};\n while (decoder_r == BROTLI_DECODER_RESULT_NEEDS_MORE_OUTPUT) {\n char *next_out = buff.data();\n size_t avail_out = buff.size();\n\n decoder_r = BrotliDecoderDecompressStream(\n decoder_s, &avail_in, &next_in, &avail_out,\n reinterpret_cast(&next_out), &total_out);\n\n if (decoder_r == BROTLI_DECODER_RESULT_ERROR) { return false; }\n\n if (!callback(buff.data(), buff.size() - avail_out)) { return false; }\n }\n\n return decoder_r == BROTLI_DECODER_RESULT_SUCCESS ||\n decoder_r == BROTLI_DECODER_RESULT_NEEDS_MORE_INPUT;\n}\n#endif\n\ninline bool has_header(const Headers &headers, const std::string &key) {\n return headers.find(key) != headers.end();\n}\n\ninline const char *get_header_value(const Headers &headers,\n const std::string &key, size_t id,\n const char *def) {\n auto rng = headers.equal_range(key);\n auto it = rng.first;\n std::advance(it, static_cast(id));\n if (it != rng.second) { return it->second.c_str(); }\n return def;\n}\n\ninline bool compare_case_ignore(const std::string &a, const std::string &b) {\n if (a.size() != b.size()) { return false; }\n for (size_t i = 0; i < b.size(); i++) {\n if (::tolower(a[i]) != ::tolower(b[i])) { return false; }\n }\n return true;\n}\n\ntemplate \ninline bool parse_header(const char *beg, const char *end, T fn) {\n // Skip trailing spaces and tabs.\n while (beg < end && is_space_or_tab(end[-1])) {\n end--;\n }\n\n auto p = beg;\n while (p < end && *p != ':') {\n p++;\n }\n\n if (p == end) { return false; }\n\n auto key_end = p;\n\n if (*p++ != ':') { return false; }\n\n while (p < end && is_space_or_tab(*p)) {\n p++;\n }\n\n if (p < end) {\n auto key = std::string(beg, key_end);\n auto val = compare_case_ignore(key, \"Location\")\n ? std::string(p, end)\n : decode_url(std::string(p, end), false);\n fn(std::move(key), std::move(val));\n return true;\n }\n\n return false;\n}\n\ninline bool read_headers(Stream &strm, Headers &headers) {\n const auto bufsiz = 2048;\n char buf[bufsiz];\n stream_line_reader line_reader(strm, buf, bufsiz);\n\n for (;;) {\n if (!line_reader.getline()) { return false; }\n\n // Check if the line ends with CRLF.\n auto line_terminator_len = 2;\n if (line_reader.end_with_crlf()) {\n // Blank line indicates end of headers.\n if (line_reader.size() == 2) { break; }\n#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR\n } else {\n // Blank line indicates end of headers.\n if (line_reader.size() == 1) { break; }\n line_terminator_len = 1;\n }\n#else\n } else {\n continue; // Skip invalid line.\n }\n#endif\n\n if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }\n\n // Exclude line terminator\n auto end = line_reader.ptr() + line_reader.size() - line_terminator_len;\n\n parse_header(line_reader.ptr(), end,\n [&](std::string &&key, std::string &&val) {\n headers.emplace(std::move(key), std::move(val));\n });\n }\n\n return true;\n}\n\ninline bool read_content_with_length(Stream &strm, uint64_t len,\n Progress progress,\n ContentReceiverWithProgress out) {\n char buf[CPPHTTPLIB_RECV_BUFSIZ];\n\n uint64_t r = 0;\n while (r < len) {\n auto read_len = static_cast(len - r);\n auto n = strm.read(buf, (std::min)(read_len, CPPHTTPLIB_RECV_BUFSIZ));\n if (n <= 0) { return false; }\n\n if (!out(buf, static_cast(n), r, len)) { return false; }\n r += static_cast(n);\n\n if (progress) {\n if (!progress(r, len)) { return false; }\n }\n }\n\n return true;\n}\n\ninline void skip_content_with_length(Stream &strm, uint64_t len) {\n char buf[CPPHTTPLIB_RECV_BUFSIZ];\n uint64_t r = 0;\n while (r < len) {\n auto read_len = static_cast(len - r);\n auto n = strm.read(buf, (std::min)(read_len, CPPHTTPLIB_RECV_BUFSIZ));\n if (n <= 0) { return; }\n r += static_cast(n);\n }\n}\n\ninline bool read_content_without_length(Stream &strm,\n ContentReceiverWithProgress out) {\n char buf[CPPHTTPLIB_RECV_BUFSIZ];\n uint64_t r = 0;\n for (;;) {\n auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ);\n if (n < 0) {\n return false;\n } else if (n == 0) {\n return true;\n }\n\n if (!out(buf, static_cast(n), r, 0)) { return false; }\n r += static_cast(n);\n }\n\n return true;\n}\n\ntemplate \ninline bool read_content_chunked(Stream &strm, T &x,\n ContentReceiverWithProgress out) {\n const auto bufsiz = 16;\n char buf[bufsiz];\n\n stream_line_reader line_reader(strm, buf, bufsiz);\n\n if (!line_reader.getline()) { return false; }\n\n unsigned long chunk_len;\n while (true) {\n char *end_ptr;\n\n chunk_len = std::strtoul(line_reader.ptr(), &end_ptr, 16);\n\n if (end_ptr == line_reader.ptr()) { return false; }\n if (chunk_len == ULONG_MAX) { return false; }\n\n if (chunk_len == 0) { break; }\n\n if (!read_content_with_length(strm, chunk_len, nullptr, out)) {\n return false;\n }\n\n if (!line_reader.getline()) { return false; }\n\n if (strcmp(line_reader.ptr(), \"\\r\\n\")) { return false; }\n\n if (!line_reader.getline()) { return false; }\n }\n\n assert(chunk_len == 0);\n\n // Trailer\n if (!line_reader.getline()) { return false; }\n\n while (strcmp(line_reader.ptr(), \"\\r\\n\")) {\n if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }\n\n // Exclude line terminator\n constexpr auto line_terminator_len = 2;\n auto end = line_reader.ptr() + line_reader.size() - line_terminator_len;\n\n parse_header(line_reader.ptr(), end,\n [&](std::string &&key, std::string &&val) {\n x.headers.emplace(std::move(key), std::move(val));\n });\n\n if (!line_reader.getline()) { return false; }\n }\n\n return true;\n}\n\ninline bool is_chunked_transfer_encoding(const Headers &headers) {\n return !strcasecmp(get_header_value(headers, \"Transfer-Encoding\", 0, \"\"),\n \"chunked\");\n}\n\ntemplate \nbool prepare_content_receiver(T &x, int &status,\n ContentReceiverWithProgress receiver,\n bool decompress, U callback) {\n if (decompress) {\n std::string encoding = x.get_header_value(\"Content-Encoding\");\n std::unique_ptr decompressor;\n\n if (encoding == \"gzip\" || encoding == \"deflate\") {\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n decompressor = detail::make_unique();\n#else\n status = 415;\n return false;\n#endif\n } else if (encoding.find(\"br\") != std::string::npos) {\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\n decompressor = detail::make_unique();\n#else\n status = 415;\n return false;\n#endif\n }\n\n if (decompressor) {\n if (decompressor->is_valid()) {\n ContentReceiverWithProgress out = [&](const char *buf, size_t n,\n uint64_t off, uint64_t len) {\n return decompressor->decompress(buf, n,\n [&](const char *buf2, size_t n2) {\n return receiver(buf2, n2, off, len);\n });\n };\n return callback(std::move(out));\n } else {\n status = 500;\n return false;\n }\n }\n }\n\n ContentReceiverWithProgress out = [&](const char *buf, size_t n, uint64_t off,\n uint64_t len) {\n return receiver(buf, n, off, len);\n };\n return callback(std::move(out));\n}\n\ntemplate \nbool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,\n Progress progress, ContentReceiverWithProgress receiver,\n bool decompress) {\n return prepare_content_receiver(\n x, status, std::move(receiver), decompress,\n [&](const ContentReceiverWithProgress &out) {\n auto ret = true;\n auto exceed_payload_max_length = false;\n\n if (is_chunked_transfer_encoding(x.headers)) {\n ret = read_content_chunked(strm, x, out);\n } else if (!has_header(x.headers, \"Content-Length\")) {\n ret = read_content_without_length(strm, out);\n } else {\n auto len = get_header_value(x.headers, \"Content-Length\");\n if (len > payload_max_length) {\n exceed_payload_max_length = true;\n skip_content_with_length(strm, len);\n ret = false;\n } else if (len > 0) {\n ret = read_content_with_length(strm, len, std::move(progress), out);\n }\n }\n\n if (!ret) { status = exceed_payload_max_length ? 413 : 400; }\n return ret;\n });\n} // namespace detail\n\ninline ssize_t write_headers(Stream &strm, const Headers &headers) {\n ssize_t write_len = 0;\n for (const auto &x : headers) {\n auto len =\n strm.write_format(\"%s: %s\\r\\n\", x.first.c_str(), x.second.c_str());\n if (len < 0) { return len; }\n write_len += len;\n }\n auto len = strm.write(\"\\r\\n\");\n if (len < 0) { return len; }\n write_len += len;\n return write_len;\n}\n\ninline bool write_data(Stream &strm, const char *d, size_t l) {\n size_t offset = 0;\n while (offset < l) {\n auto length = strm.write(d + offset, l - offset);\n if (length < 0) { return false; }\n offset += static_cast(length);\n }\n return true;\n}\n\ntemplate \ninline bool write_content(Stream &strm, const ContentProvider &content_provider,\n size_t offset, size_t length, T is_shutting_down,\n Error &error) {\n size_t end_offset = offset + length;\n auto ok = true;\n DataSink data_sink;\n\n data_sink.write = [&](const char *d, size_t l) -> bool {\n if (ok) {\n if (strm.is_writable() && write_data(strm, d, l)) {\n offset += l;\n } else {\n ok = false;\n }\n }\n return ok;\n };\n\n while (offset < end_offset && !is_shutting_down()) {\n if (!strm.is_writable()) {\n error = Error::Write;\n return false;\n } else if (!content_provider(offset, end_offset - offset, data_sink)) {\n error = Error::Canceled;\n return false;\n } else if (!ok) {\n error = Error::Write;\n return false;\n }\n }\n\n error = Error::Success;\n return true;\n}\n\ntemplate \ninline bool write_content(Stream &strm, const ContentProvider &content_provider,\n size_t offset, size_t length,\n const T &is_shutting_down) {\n auto error = Error::Success;\n return write_content(strm, content_provider, offset, length, is_shutting_down,\n error);\n}\n\ntemplate \ninline bool\nwrite_content_without_length(Stream &strm,\n const ContentProvider &content_provider,\n const T &is_shutting_down) {\n size_t offset = 0;\n auto data_available = true;\n auto ok = true;\n DataSink data_sink;\n\n data_sink.write = [&](const char *d, size_t l) -> bool {\n if (ok) {\n offset += l;\n if (!strm.is_writable() || !write_data(strm, d, l)) { ok = false; }\n }\n return ok;\n };\n\n data_sink.done = [&](void) { data_available = false; };\n\n while (data_available && !is_shutting_down()) {\n if (!strm.is_writable()) {\n return false;\n } else if (!content_provider(offset, 0, data_sink)) {\n return false;\n } else if (!ok) {\n return false;\n }\n }\n return true;\n}\n\ntemplate \ninline bool\nwrite_content_chunked(Stream &strm, const ContentProvider &content_provider,\n const T &is_shutting_down, U &compressor, Error &error) {\n size_t offset = 0;\n auto data_available = true;\n auto ok = true;\n DataSink data_sink;\n\n data_sink.write = [&](const char *d, size_t l) -> bool {\n if (ok) {\n data_available = l > 0;\n offset += l;\n\n std::string payload;\n if (compressor.compress(d, l, false,\n [&](const char *data, size_t data_len) {\n payload.append(data, data_len);\n return true;\n })) {\n if (!payload.empty()) {\n // Emit chunked response header and footer for each chunk\n auto chunk =\n from_i_to_hex(payload.size()) + \"\\r\\n\" + payload + \"\\r\\n\";\n if (!strm.is_writable() ||\n !write_data(strm, chunk.data(), chunk.size())) {\n ok = false;\n }\n }\n } else {\n ok = false;\n }\n }\n return ok;\n };\n\n auto done_with_trailer = [&](const Headers *trailer) {\n if (!ok) { return; }\n\n data_available = false;\n\n std::string payload;\n if (!compressor.compress(nullptr, 0, true,\n [&](const char *data, size_t data_len) {\n payload.append(data, data_len);\n return true;\n })) {\n ok = false;\n return;\n }\n\n if (!payload.empty()) {\n // Emit chunked response header and footer for each chunk\n auto chunk = from_i_to_hex(payload.size()) + \"\\r\\n\" + payload + \"\\r\\n\";\n if (!strm.is_writable() ||\n !write_data(strm, chunk.data(), chunk.size())) {\n ok = false;\n return;\n }\n }\n\n static const std::string done_marker(\"0\\r\\n\");\n if (!write_data(strm, done_marker.data(), done_marker.size())) {\n ok = false;\n }\n\n // Trailer\n if (trailer) {\n for (const auto &kv : *trailer) {\n std::string field_line = kv.first + \": \" + kv.second + \"\\r\\n\";\n if (!write_data(strm, field_line.data(), field_line.size())) {\n ok = false;\n }\n }\n }\n\n static const std::string crlf(\"\\r\\n\");\n if (!write_data(strm, crlf.data(), crlf.size())) { ok = false; }\n };\n\n data_sink.done = [&](void) { done_with_trailer(nullptr); };\n\n data_sink.done_with_trailer = [&](const Headers &trailer) {\n done_with_trailer(&trailer);\n };\n\n while (data_available && !is_shutting_down()) {\n if (!strm.is_writable()) {\n error = Error::Write;\n return false;\n } else if (!content_provider(offset, 0, data_sink)) {\n error = Error::Canceled;\n return false;\n } else if (!ok) {\n error = Error::Write;\n return false;\n }\n }\n\n error = Error::Success;\n return true;\n}\n\ntemplate \ninline bool write_content_chunked(Stream &strm,\n const ContentProvider &content_provider,\n const T &is_shutting_down, U &compressor) {\n auto error = Error::Success;\n return write_content_chunked(strm, content_provider, is_shutting_down,\n compressor, error);\n}\n\ntemplate \ninline bool redirect(T &cli, Request &req, Response &res,\n const std::string &path, const std::string &location,\n Error &error) {\n Request new_req = req;\n new_req.path = path;\n new_req.redirect_count_ -= 1;\n\n if (res.status == 303 && (req.method != \"GET\" && req.method != \"HEAD\")) {\n new_req.method = \"GET\";\n new_req.body.clear();\n new_req.headers.clear();\n }\n\n Response new_res;\n\n auto ret = cli.send(new_req, new_res, error);\n if (ret) {\n req = new_req;\n res = new_res;\n res.location = location;\n }\n return ret;\n}\n\ninline std::string params_to_query_str(const Params ¶ms) {\n std::string query;\n\n for (auto it = params.begin(); it != params.end(); ++it) {\n if (it != params.begin()) { query += \"&\"; }\n query += it->first;\n query += \"=\";\n query += encode_query_param(it->second);\n }\n return query;\n}\n\ninline void parse_query_text(const std::string &s, Params ¶ms) {\n std::set cache;\n split(s.data(), s.data() + s.size(), '&', [&](const char *b, const char *e) {\n std::string kv(b, e);\n if (cache.find(kv) != cache.end()) { return; }\n cache.insert(kv);\n\n std::string key;\n std::string val;\n split(b, e, '=', [&](const char *b2, const char *e2) {\n if (key.empty()) {\n key.assign(b2, e2);\n } else {\n val.assign(b2, e2);\n }\n });\n\n if (!key.empty()) {\n params.emplace(decode_url(key, true), decode_url(val, true));\n }\n });\n}\n\ninline bool parse_multipart_boundary(const std::string &content_type,\n std::string &boundary) {\n auto boundary_keyword = \"boundary=\";\n auto pos = content_type.find(boundary_keyword);\n if (pos == std::string::npos) { return false; }\n auto end = content_type.find(';', pos);\n auto beg = pos + strlen(boundary_keyword);\n boundary = content_type.substr(beg, end - beg);\n if (boundary.length() >= 2 && boundary.front() == '\"' &&\n boundary.back() == '\"') {\n boundary = boundary.substr(1, boundary.size() - 2);\n }\n return !boundary.empty();\n}\n\n#ifdef CPPHTTPLIB_NO_EXCEPTIONS\ninline bool parse_range_header(const std::string &s, Ranges &ranges) {\n#else\ninline bool parse_range_header(const std::string &s, Ranges &ranges) try {\n#endif\n static auto re_first_range = std::regex(R\"(bytes=(\\d*-\\d*(?:,\\s*\\d*-\\d*)*))\");\n std::smatch m;\n if (std::regex_match(s, m, re_first_range)) {\n auto pos = static_cast(m.position(1));\n auto len = static_cast(m.length(1));\n bool all_valid_ranges = true;\n split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) {\n if (!all_valid_ranges) return;\n static auto re_another_range = std::regex(R\"(\\s*(\\d*)-(\\d*))\");\n std::cmatch cm;\n if (std::regex_match(b, e, cm, re_another_range)) {\n ssize_t first = -1;\n if (!cm.str(1).empty()) {\n first = static_cast(std::stoll(cm.str(1)));\n }\n\n ssize_t last = -1;\n if (!cm.str(2).empty()) {\n last = static_cast(std::stoll(cm.str(2)));\n }\n\n if (first != -1 && last != -1 && first > last) {\n all_valid_ranges = false;\n return;\n }\n ranges.emplace_back(std::make_pair(first, last));\n }\n });\n return all_valid_ranges;\n }\n return false;\n#ifdef CPPHTTPLIB_NO_EXCEPTIONS\n}\n#else\n} catch (...) { return false; }\n#endif\n\nclass MultipartFormDataParser {\npublic:\n MultipartFormDataParser() = default;\n\n void set_boundary(std::string &&boundary) {\n boundary_ = boundary;\n dash_boundary_crlf_ = dash_ + boundary_ + crlf_;\n crlf_dash_boundary_ = crlf_ + dash_ + boundary_;\n }\n\n bool is_valid() const { return is_valid_; }\n\n bool parse(const char *buf, size_t n, const ContentReceiver &content_callback,\n const MultipartContentHeader &header_callback) {\n\n // TODO: support 'filename*'\n static const std::regex re_content_disposition(\n R\"~(^Content-Disposition:\\s*form-data;\\s*name=\"(.*?)\"(?:;\\s*filename=\"(.*?)\")?(?:;\\s*filename\\*=\\S+)?\\s*$)~\",\n std::regex_constants::icase);\n\n buf_append(buf, n);\n\n while (buf_size() > 0) {\n switch (state_) {\n case 0: { // Initial boundary\n buf_erase(buf_find(dash_boundary_crlf_));\n if (dash_boundary_crlf_.size() > buf_size()) { return true; }\n if (!buf_start_with(dash_boundary_crlf_)) { return false; }\n buf_erase(dash_boundary_crlf_.size());\n state_ = 1;\n break;\n }\n case 1: { // New entry\n clear_file_info();\n state_ = 2;\n break;\n }\n case 2: { // Headers\n auto pos = buf_find(crlf_);\n if (pos > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }\n while (pos < buf_size()) {\n // Empty line\n if (pos == 0) {\n if (!header_callback(file_)) {\n is_valid_ = false;\n return false;\n }\n buf_erase(crlf_.size());\n state_ = 3;\n break;\n }\n\n static const std::string header_name = \"content-type:\";\n const auto header = buf_head(pos);\n if (start_with_case_ignore(header, header_name)) {\n file_.content_type = trim_copy(header.substr(header_name.size()));\n } else {\n std::smatch m;\n if (std::regex_match(header, m, re_content_disposition)) {\n file_.name = m[1];\n file_.filename = m[2];\n } else {\n is_valid_ = false;\n return false;\n }\n }\n buf_erase(pos + crlf_.size());\n pos = buf_find(crlf_);\n }\n if (state_ != 3) { return true; }\n break;\n }\n case 3: { // Body\n if (crlf_dash_boundary_.size() > buf_size()) { return true; }\n auto pos = buf_find(crlf_dash_boundary_);\n if (pos < buf_size()) {\n if (!content_callback(buf_data(), pos)) {\n is_valid_ = false;\n return false;\n }\n buf_erase(pos + crlf_dash_boundary_.size());\n state_ = 4;\n } else {\n auto len = buf_size() - crlf_dash_boundary_.size();\n if (len > 0) {\n if (!content_callback(buf_data(), len)) {\n is_valid_ = false;\n return false;\n }\n buf_erase(len);\n }\n return true;\n }\n break;\n }\n case 4: { // Boundary\n if (crlf_.size() > buf_size()) { return true; }\n if (buf_start_with(crlf_)) {\n buf_erase(crlf_.size());\n state_ = 1;\n } else {\n if (dash_crlf_.size() > buf_size()) { return true; }\n if (buf_start_with(dash_crlf_)) {\n buf_erase(dash_crlf_.size());\n is_valid_ = true;\n buf_erase(buf_size()); // Remove epilogue\n } else {\n return true;\n }\n }\n break;\n }\n }\n }\n\n return true;\n }\n\nprivate:\n void clear_file_info() {\n file_.name.clear();\n file_.filename.clear();\n file_.content_type.clear();\n }\n\n bool start_with_case_ignore(const std::string &a,\n const std::string &b) const {\n if (a.size() < b.size()) { return false; }\n for (size_t i = 0; i < b.size(); i++) {\n if (::tolower(a[i]) != ::tolower(b[i])) { return false; }\n }\n return true;\n }\n\n const std::string dash_ = \"--\";\n const std::string crlf_ = \"\\r\\n\";\n const std::string dash_crlf_ = \"--\\r\\n\";\n std::string boundary_;\n std::string dash_boundary_crlf_;\n std::string crlf_dash_boundary_;\n\n size_t state_ = 0;\n bool is_valid_ = false;\n MultipartFormData file_;\n\n // Buffer\n bool start_with(const std::string &a, size_t spos, size_t epos,\n const std::string &b) const {\n if (epos - spos < b.size()) { return false; }\n for (size_t i = 0; i < b.size(); i++) {\n if (a[i + spos] != b[i]) { return false; }\n }\n return true;\n }\n\n size_t buf_size() const { return buf_epos_ - buf_spos_; }\n\n const char *buf_data() const { return &buf_[buf_spos_]; }\n\n std::string buf_head(size_t l) const { return buf_.substr(buf_spos_, l); }\n\n bool buf_start_with(const std::string &s) const {\n return start_with(buf_, buf_spos_, buf_epos_, s);\n }\n\n size_t buf_find(const std::string &s) const {\n auto c = s.front();\n\n size_t off = buf_spos_;\n while (off < buf_epos_) {\n auto pos = off;\n while (true) {\n if (pos == buf_epos_) { return buf_size(); }\n if (buf_[pos] == c) { break; }\n pos++;\n }\n\n auto remaining_size = buf_epos_ - pos;\n if (s.size() > remaining_size) { return buf_size(); }\n\n if (start_with(buf_, pos, buf_epos_, s)) { return pos - buf_spos_; }\n\n off = pos + 1;\n }\n\n return buf_size();\n }\n\n void buf_append(const char *data, size_t n) {\n auto remaining_size = buf_size();\n if (remaining_size > 0 && buf_spos_ > 0) {\n for (size_t i = 0; i < remaining_size; i++) {\n buf_[i] = buf_[buf_spos_ + i];\n }\n }\n buf_spos_ = 0;\n buf_epos_ = remaining_size;\n\n if (remaining_size + n > buf_.size()) { buf_.resize(remaining_size + n); }\n\n for (size_t i = 0; i < n; i++) {\n buf_[buf_epos_ + i] = data[i];\n }\n buf_epos_ += n;\n }\n\n void buf_erase(size_t size) { buf_spos_ += size; }\n\n std::string buf_;\n size_t buf_spos_ = 0;\n size_t buf_epos_ = 0;\n};\n\ninline std::string to_lower(const char *beg, const char *end) {\n std::string out;\n auto it = beg;\n while (it != end) {\n out += static_cast(::tolower(*it));\n it++;\n }\n return out;\n}\n\ninline std::string make_multipart_data_boundary() {\n static const char data[] =\n \"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\";\n\n // std::random_device might actually be deterministic on some\n // platforms, but due to lack of support in the c++ standard library,\n // doing better requires either some ugly hacks or breaking portability.\n std::random_device seed_gen;\n\n // Request 128 bits of entropy for initialization\n std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), seed_gen()};\n std::mt19937 engine(seed_sequence);\n\n std::string result = \"--cpp-httplib-multipart-data-\";\n\n for (auto i = 0; i < 16; i++) {\n result += data[engine() % (sizeof(data) - 1)];\n }\n\n return result;\n}\n\ninline bool is_multipart_boundary_chars_valid(const std::string &boundary) {\n auto valid = true;\n for (size_t i = 0; i < boundary.size(); i++) {\n auto c = boundary[i];\n if (!std::isalnum(c) && c != '-' && c != '_') {\n valid = false;\n break;\n }\n }\n return valid;\n}\n\ntemplate \ninline std::string\nserialize_multipart_formdata_item_begin(const T &item,\n const std::string &boundary) {\n std::string body = \"--\" + boundary + \"\\r\\n\";\n body += \"Content-Disposition: form-data; name=\\\"\" + item.name + \"\\\"\";\n if (!item.filename.empty()) {\n body += \"; filename=\\\"\" + item.filename + \"\\\"\";\n }\n body += \"\\r\\n\";\n if (!item.content_type.empty()) {\n body += \"Content-Type: \" + item.content_type + \"\\r\\n\";\n }\n body += \"\\r\\n\";\n\n return body;\n}\n\ninline std::string serialize_multipart_formdata_item_end() { return \"\\r\\n\"; }\n\ninline std::string\nserialize_multipart_formdata_finish(const std::string &boundary) {\n return \"--\" + boundary + \"--\\r\\n\";\n}\n\ninline std::string\nserialize_multipart_formdata_get_content_type(const std::string &boundary) {\n return \"multipart/form-data; boundary=\" + boundary;\n}\n\ninline std::string\nserialize_multipart_formdata(const MultipartFormDataItems &items,\n const std::string &boundary, bool finish = true) {\n std::string body;\n\n for (const auto &item : items) {\n body += serialize_multipart_formdata_item_begin(item, boundary);\n body += item.content + serialize_multipart_formdata_item_end();\n }\n\n if (finish) body += serialize_multipart_formdata_finish(boundary);\n\n return body;\n}\n\ninline std::pair\nget_range_offset_and_length(const Request &req, size_t content_length,\n size_t index) {\n auto r = req.ranges[index];\n\n if (r.first == -1 && r.second == -1) {\n return std::make_pair(0, content_length);\n }\n\n auto slen = static_cast(content_length);\n\n if (r.first == -1) {\n r.first = (std::max)(static_cast(0), slen - r.second);\n r.second = slen - 1;\n }\n\n if (r.second == -1) { r.second = slen - 1; }\n return std::make_pair(r.first, static_cast(r.second - r.first) + 1);\n}\n\ninline std::string make_content_range_header_field(size_t offset, size_t length,\n size_t content_length) {\n std::string field = \"bytes \";\n field += std::to_string(offset);\n field += \"-\";\n field += std::to_string(offset + length - 1);\n field += \"/\";\n field += std::to_string(content_length);\n return field;\n}\n\ntemplate \nbool process_multipart_ranges_data(const Request &req, Response &res,\n const std::string &boundary,\n const std::string &content_type,\n SToken stoken, CToken ctoken,\n Content content) {\n for (size_t i = 0; i < req.ranges.size(); i++) {\n ctoken(\"--\");\n stoken(boundary);\n ctoken(\"\\r\\n\");\n if (!content_type.empty()) {\n ctoken(\"Content-Type: \");\n stoken(content_type);\n ctoken(\"\\r\\n\");\n }\n\n auto offsets = get_range_offset_and_length(req, res.body.size(), i);\n auto offset = offsets.first;\n auto length = offsets.second;\n\n ctoken(\"Content-Range: \");\n stoken(make_content_range_header_field(offset, length, res.body.size()));\n ctoken(\"\\r\\n\");\n ctoken(\"\\r\\n\");\n if (!content(offset, length)) { return false; }\n ctoken(\"\\r\\n\");\n }\n\n ctoken(\"--\");\n stoken(boundary);\n ctoken(\"--\\r\\n\");\n\n return true;\n}\n\ninline bool make_multipart_ranges_data(const Request &req, Response &res,\n const std::string &boundary,\n const std::string &content_type,\n std::string &data) {\n return process_multipart_ranges_data(\n req, res, boundary, content_type,\n [&](const std::string &token) { data += token; },\n [&](const std::string &token) { data += token; },\n [&](size_t offset, size_t length) {\n if (offset < res.body.size()) {\n data += res.body.substr(offset, length);\n return true;\n }\n return false;\n });\n}\n\ninline size_t\nget_multipart_ranges_data_length(const Request &req, Response &res,\n const std::string &boundary,\n const std::string &content_type) {\n size_t data_length = 0;\n\n process_multipart_ranges_data(\n req, res, boundary, content_type,\n [&](const std::string &token) { data_length += token.size(); },\n [&](const std::string &token) { data_length += token.size(); },\n [&](size_t /*offset*/, size_t length) {\n data_length += length;\n return true;\n });\n\n return data_length;\n}\n\ntemplate \ninline bool write_multipart_ranges_data(Stream &strm, const Request &req,\n Response &res,\n const std::string &boundary,\n const std::string &content_type,\n const T &is_shutting_down) {\n return process_multipart_ranges_data(\n req, res, boundary, content_type,\n [&](const std::string &token) { strm.write(token); },\n [&](const std::string &token) { strm.write(token); },\n [&](size_t offset, size_t length) {\n return write_content(strm, res.content_provider_, offset, length,\n is_shutting_down);\n });\n}\n\ninline std::pair\nget_range_offset_and_length(const Request &req, const Response &res,\n size_t index) {\n auto r = req.ranges[index];\n\n if (r.second == -1) {\n r.second = static_cast(res.content_length_) - 1;\n }\n\n return std::make_pair(r.first, r.second - r.first + 1);\n}\n\ninline bool expect_content(const Request &req) {\n if (req.method == \"POST\" || req.method == \"PUT\" || req.method == \"PATCH\" ||\n req.method == \"PRI\" || req.method == \"DELETE\") {\n return true;\n }\n // TODO: check if Content-Length is set\n return false;\n}\n\ninline bool has_crlf(const std::string &s) {\n auto p = s.c_str();\n while (*p) {\n if (*p == '\\r' || *p == '\\n') { return true; }\n p++;\n }\n return false;\n}\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline std::string message_digest(const std::string &s, const EVP_MD *algo) {\n auto context = std::unique_ptr(\n EVP_MD_CTX_new(), EVP_MD_CTX_free);\n\n unsigned int hash_length = 0;\n unsigned char hash[EVP_MAX_MD_SIZE];\n\n EVP_DigestInit_ex(context.get(), algo, nullptr);\n EVP_DigestUpdate(context.get(), s.c_str(), s.size());\n EVP_DigestFinal_ex(context.get(), hash, &hash_length);\n\n std::stringstream ss;\n for (auto i = 0u; i < hash_length; ++i) {\n ss << std::hex << std::setw(2) << std::setfill('0')\n << (unsigned int)hash[i];\n }\n\n return ss.str();\n}\n\ninline std::string MD5(const std::string &s) {\n return message_digest(s, EVP_md5());\n}\n\ninline std::string SHA_256(const std::string &s) {\n return message_digest(s, EVP_sha256());\n}\n\ninline std::string SHA_512(const std::string &s) {\n return message_digest(s, EVP_sha512());\n}\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n#ifdef _WIN32\n// NOTE: This code came up with the following stackoverflow post:\n// https://stackoverflow.com/questions/9507184/can-openssl-on-windows-use-the-system-certificate-store\ninline bool load_system_certs_on_windows(X509_STORE *store) {\n auto hStore = CertOpenSystemStoreW((HCRYPTPROV_LEGACY)NULL, L\"ROOT\");\n if (!hStore) { return false; }\n\n auto result = false;\n PCCERT_CONTEXT pContext = NULL;\n while ((pContext = CertEnumCertificatesInStore(hStore, pContext)) !=\n nullptr) {\n auto encoded_cert =\n static_cast(pContext->pbCertEncoded);\n\n auto x509 = d2i_X509(NULL, &encoded_cert, pContext->cbCertEncoded);\n if (x509) {\n X509_STORE_add_cert(store, x509);\n X509_free(x509);\n result = true;\n }\n }\n\n CertFreeCertificateContext(pContext);\n CertCloseStore(hStore, 0);\n\n return result;\n}\n#elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__)\n#if TARGET_OS_OSX\ntemplate \nusing CFObjectPtr =\n std::unique_ptr::type, void (*)(CFTypeRef)>;\n\ninline void cf_object_ptr_deleter(CFTypeRef obj) {\n if (obj) { CFRelease(obj); }\n}\n\ninline bool retrieve_certs_from_keychain(CFObjectPtr &certs) {\n CFStringRef keys[] = {kSecClass, kSecMatchLimit, kSecReturnRef};\n CFTypeRef values[] = {kSecClassCertificate, kSecMatchLimitAll,\n kCFBooleanTrue};\n\n CFObjectPtr query(\n CFDictionaryCreate(nullptr, reinterpret_cast(keys), values,\n sizeof(keys) / sizeof(keys[0]),\n &kCFTypeDictionaryKeyCallBacks,\n &kCFTypeDictionaryValueCallBacks),\n cf_object_ptr_deleter);\n\n if (!query) { return false; }\n\n CFTypeRef security_items = nullptr;\n if (SecItemCopyMatching(query.get(), &security_items) != errSecSuccess ||\n CFArrayGetTypeID() != CFGetTypeID(security_items)) {\n return false;\n }\n\n certs.reset(reinterpret_cast(security_items));\n return true;\n}\n\ninline bool retrieve_root_certs_from_keychain(CFObjectPtr &certs) {\n CFArrayRef root_security_items = nullptr;\n if (SecTrustCopyAnchorCertificates(&root_security_items) != errSecSuccess) {\n return false;\n }\n\n certs.reset(root_security_items);\n return true;\n}\n\ninline bool add_certs_to_x509_store(CFArrayRef certs, X509_STORE *store) {\n auto result = false;\n for (int i = 0; i < CFArrayGetCount(certs); ++i) {\n const auto cert = reinterpret_cast(\n CFArrayGetValueAtIndex(certs, i));\n\n if (SecCertificateGetTypeID() != CFGetTypeID(cert)) { continue; }\n\n CFDataRef cert_data = nullptr;\n if (SecItemExport(cert, kSecFormatX509Cert, 0, nullptr, &cert_data) !=\n errSecSuccess) {\n continue;\n }\n\n CFObjectPtr cert_data_ptr(cert_data, cf_object_ptr_deleter);\n\n auto encoded_cert = static_cast(\n CFDataGetBytePtr(cert_data_ptr.get()));\n\n auto x509 =\n d2i_X509(NULL, &encoded_cert, CFDataGetLength(cert_data_ptr.get()));\n\n if (x509) {\n X509_STORE_add_cert(store, x509);\n X509_free(x509);\n result = true;\n }\n }\n\n return result;\n}\n\ninline bool load_system_certs_on_macos(X509_STORE *store) {\n auto result = false;\n CFObjectPtr certs(nullptr, cf_object_ptr_deleter);\n if (retrieve_certs_from_keychain(certs) && certs) {\n result = add_certs_to_x509_store(certs.get(), store);\n }\n\n if (retrieve_root_certs_from_keychain(certs) && certs) {\n result = add_certs_to_x509_store(certs.get(), store) || result;\n }\n\n return result;\n}\n#endif // TARGET_OS_OSX\n#endif // _WIN32\n#endif // CPPHTTPLIB_OPENSSL_SUPPORT\n\n#ifdef _WIN32\nclass WSInit {\npublic:\n WSInit() {\n WSADATA wsaData;\n if (WSAStartup(0x0002, &wsaData) == 0) is_valid_ = true;\n }\n\n ~WSInit() {\n if (is_valid_) WSACleanup();\n }\n\n bool is_valid_ = false;\n};\n\nstatic WSInit wsinit_;\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline std::pair make_digest_authentication_header(\n const Request &req, const std::map &auth,\n size_t cnonce_count, const std::string &cnonce, const std::string &username,\n const std::string &password, bool is_proxy = false) {\n std::string nc;\n {\n std::stringstream ss;\n ss << std::setfill('0') << std::setw(8) << std::hex << cnonce_count;\n nc = ss.str();\n }\n\n std::string qop;\n if (auth.find(\"qop\") != auth.end()) {\n qop = auth.at(\"qop\");\n if (qop.find(\"auth-int\") != std::string::npos) {\n qop = \"auth-int\";\n } else if (qop.find(\"auth\") != std::string::npos) {\n qop = \"auth\";\n } else {\n qop.clear();\n }\n }\n\n std::string algo = \"MD5\";\n if (auth.find(\"algorithm\") != auth.end()) { algo = auth.at(\"algorithm\"); }\n\n std::string response;\n {\n auto H = algo == \"SHA-256\" ? detail::SHA_256\n : algo == \"SHA-512\" ? detail::SHA_512\n : detail::MD5;\n\n auto A1 = username + \":\" + auth.at(\"realm\") + \":\" + password;\n\n auto A2 = req.method + \":\" + req.path;\n if (qop == \"auth-int\") { A2 += \":\" + H(req.body); }\n\n if (qop.empty()) {\n response = H(H(A1) + \":\" + auth.at(\"nonce\") + \":\" + H(A2));\n } else {\n response = H(H(A1) + \":\" + auth.at(\"nonce\") + \":\" + nc + \":\" + cnonce +\n \":\" + qop + \":\" + H(A2));\n }\n }\n\n auto opaque = (auth.find(\"opaque\") != auth.end()) ? auth.at(\"opaque\") : \"\";\n\n auto field = \"Digest username=\\\"\" + username + \"\\\", realm=\\\"\" +\n auth.at(\"realm\") + \"\\\", nonce=\\\"\" + auth.at(\"nonce\") +\n \"\\\", uri=\\\"\" + req.path + \"\\\", algorithm=\" + algo +\n (qop.empty() ? \", response=\\\"\"\n : \", qop=\" + qop + \", nc=\" + nc + \", cnonce=\\\"\" +\n cnonce + \"\\\", response=\\\"\") +\n response + \"\\\"\" +\n (opaque.empty() ? \"\" : \", opaque=\\\"\" + opaque + \"\\\"\");\n\n auto key = is_proxy ? \"Proxy-Authorization\" : \"Authorization\";\n return std::make_pair(key, field);\n}\n#endif\n\ninline bool parse_www_authenticate(const Response &res,\n std::map &auth,\n bool is_proxy) {\n auto auth_key = is_proxy ? \"Proxy-Authenticate\" : \"WWW-Authenticate\";\n if (res.has_header(auth_key)) {\n static auto re = std::regex(R\"~((?:(?:,\\s*)?(.+?)=(?:\"(.*?)\"|([^,]*))))~\");\n auto s = res.get_header_value(auth_key);\n auto pos = s.find(' ');\n if (pos != std::string::npos) {\n auto type = s.substr(0, pos);\n if (type == \"Basic\") {\n return false;\n } else if (type == \"Digest\") {\n s = s.substr(pos + 1);\n auto beg = std::sregex_iterator(s.begin(), s.end(), re);\n for (auto i = beg; i != std::sregex_iterator(); ++i) {\n auto m = *i;\n auto key = s.substr(static_cast(m.position(1)),\n static_cast(m.length(1)));\n auto val = m.length(2) > 0\n ? s.substr(static_cast(m.position(2)),\n static_cast(m.length(2)))\n : s.substr(static_cast(m.position(3)),\n static_cast(m.length(3)));\n auth[key] = val;\n }\n return true;\n }\n }\n }\n return false;\n}\n\n// https://stackoverflow.com/questions/440133/how-do-i-create-a-random-alpha-numeric-string-in-c/440240#answer-440240\ninline std::string random_string(size_t length) {\n auto randchar = []() -> char {\n const char charset[] = \"0123456789\"\n \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n \"abcdefghijklmnopqrstuvwxyz\";\n const size_t max_index = (sizeof(charset) - 1);\n return charset[static_cast(std::rand()) % max_index];\n };\n std::string str(length, 0);\n std::generate_n(str.begin(), length, randchar);\n return str;\n}\n\nclass ContentProviderAdapter {\npublic:\n explicit ContentProviderAdapter(\n ContentProviderWithoutLength &&content_provider)\n : content_provider_(content_provider) {}\n\n bool operator()(size_t offset, size_t, DataSink &sink) {\n return content_provider_(offset, sink);\n }\n\nprivate:\n ContentProviderWithoutLength content_provider_;\n};\n\n} // namespace detail\n\ninline std::string hosted_at(const std::string &hostname) {\n std::vector addrs;\n hosted_at(hostname, addrs);\n if (addrs.empty()) { return std::string(); }\n return addrs[0];\n}\n\ninline void hosted_at(const std::string &hostname,\n std::vector &addrs) {\n struct addrinfo hints;\n struct addrinfo *result;\n\n memset(&hints, 0, sizeof(struct addrinfo));\n hints.ai_family = AF_UNSPEC;\n hints.ai_socktype = SOCK_STREAM;\n hints.ai_protocol = 0;\n\n if (getaddrinfo(hostname.c_str(), nullptr, &hints, &result)) {\n#if defined __linux__ && !defined __ANDROID__\n res_init();\n#endif\n return;\n }\n\n for (auto rp = result; rp; rp = rp->ai_next) {\n const auto &addr =\n *reinterpret_cast(rp->ai_addr);\n std::string ip;\n int dummy = -1;\n if (detail::get_ip_and_port(addr, sizeof(struct sockaddr_storage), ip,\n dummy)) {\n addrs.push_back(ip);\n }\n }\n\n freeaddrinfo(result);\n}\n\ninline std::string append_query_params(const std::string &path,\n const Params ¶ms) {\n std::string path_with_query = path;\n const static std::regex re(\"[^?]+\\\\?.*\");\n auto delm = std::regex_match(path, re) ? '&' : '?';\n path_with_query += delm + detail::params_to_query_str(params);\n return path_with_query;\n}\n\n// Header utilities\ninline std::pair make_range_header(Ranges ranges) {\n std::string field = \"bytes=\";\n auto i = 0;\n for (auto r : ranges) {\n if (i != 0) { field += \", \"; }\n if (r.first != -1) { field += std::to_string(r.first); }\n field += '-';\n if (r.second != -1) { field += std::to_string(r.second); }\n i++;\n }\n return std::make_pair(\"Range\", std::move(field));\n}\n\ninline std::pair\nmake_basic_authentication_header(const std::string &username,\n const std::string &password, bool is_proxy) {\n auto field = \"Basic \" + detail::base64_encode(username + \":\" + password);\n auto key = is_proxy ? \"Proxy-Authorization\" : \"Authorization\";\n return std::make_pair(key, std::move(field));\n}\n\ninline std::pair\nmake_bearer_token_authentication_header(const std::string &token,\n bool is_proxy = false) {\n auto field = \"Bearer \" + token;\n auto key = is_proxy ? \"Proxy-Authorization\" : \"Authorization\";\n return std::make_pair(key, std::move(field));\n}\n\n// Request implementation\ninline bool Request::has_header(const std::string &key) const {\n return detail::has_header(headers, key);\n}\n\ninline std::string Request::get_header_value(const std::string &key,\n size_t id) const {\n return detail::get_header_value(headers, key, id, \"\");\n}\n\ninline size_t Request::get_header_value_count(const std::string &key) const {\n auto r = headers.equal_range(key);\n return static_cast(std::distance(r.first, r.second));\n}\n\ninline void Request::set_header(const std::string &key,\n const std::string &val) {\n if (!detail::has_crlf(key) && !detail::has_crlf(val)) {\n headers.emplace(key, val);\n }\n}\n\ninline bool Request::has_param(const std::string &key) const {\n return params.find(key) != params.end();\n}\n\ninline std::string Request::get_param_value(const std::string &key,\n size_t id) const {\n auto rng = params.equal_range(key);\n auto it = rng.first;\n std::advance(it, static_cast(id));\n if (it != rng.second) { return it->second; }\n return std::string();\n}\n\ninline size_t Request::get_param_value_count(const std::string &key) const {\n auto r = params.equal_range(key);\n return static_cast(std::distance(r.first, r.second));\n}\n\ninline bool Request::is_multipart_form_data() const {\n const auto &content_type = get_header_value(\"Content-Type\");\n return !content_type.rfind(\"multipart/form-data\", 0);\n}\n\ninline bool Request::has_file(const std::string &key) const {\n return files.find(key) != files.end();\n}\n\ninline MultipartFormData Request::get_file_value(const std::string &key) const {\n auto it = files.find(key);\n if (it != files.end()) { return it->second; }\n return MultipartFormData();\n}\n\ninline std::vector\nRequest::get_file_values(const std::string &key) const {\n std::vector values;\n auto rng = files.equal_range(key);\n for (auto it = rng.first; it != rng.second; it++) {\n values.push_back(it->second);\n }\n return values;\n}\n\n// Response implementation\ninline bool Response::has_header(const std::string &key) const {\n return headers.find(key) != headers.end();\n}\n\ninline std::string Response::get_header_value(const std::string &key,\n size_t id) const {\n return detail::get_header_value(headers, key, id, \"\");\n}\n\ninline size_t Response::get_header_value_count(const std::string &key) const {\n auto r = headers.equal_range(key);\n return static_cast(std::distance(r.first, r.second));\n}\n\ninline void Response::set_header(const std::string &key,\n const std::string &val) {\n if (!detail::has_crlf(key) && !detail::has_crlf(val)) {\n headers.emplace(key, val);\n }\n}\n\ninline void Response::set_redirect(const std::string &url, int stat) {\n if (!detail::has_crlf(url)) {\n set_header(\"Location\", url);\n if (300 <= stat && stat < 400) {\n this->status = stat;\n } else {\n this->status = 302;\n }\n }\n}\n\ninline void Response::set_content(const char *s, size_t n,\n const std::string &content_type) {\n body.assign(s, n);\n\n auto rng = headers.equal_range(\"Content-Type\");\n headers.erase(rng.first, rng.second);\n set_header(\"Content-Type\", content_type);\n}\n\ninline void Response::set_content(const std::string &s,\n const std::string &content_type) {\n set_content(s.data(), s.size(), content_type);\n}\n\ninline void Response::set_content_provider(\n size_t in_length, const std::string &content_type, ContentProvider provider,\n ContentProviderResourceReleaser resource_releaser) {\n set_header(\"Content-Type\", content_type);\n content_length_ = in_length;\n if (in_length > 0) { content_provider_ = std::move(provider); }\n content_provider_resource_releaser_ = resource_releaser;\n is_chunked_content_provider_ = false;\n}\n\ninline void Response::set_content_provider(\n const std::string &content_type, ContentProviderWithoutLength provider,\n ContentProviderResourceReleaser resource_releaser) {\n set_header(\"Content-Type\", content_type);\n content_length_ = 0;\n content_provider_ = detail::ContentProviderAdapter(std::move(provider));\n content_provider_resource_releaser_ = resource_releaser;\n is_chunked_content_provider_ = false;\n}\n\ninline void Response::set_chunked_content_provider(\n const std::string &content_type, ContentProviderWithoutLength provider,\n ContentProviderResourceReleaser resource_releaser) {\n set_header(\"Content-Type\", content_type);\n content_length_ = 0;\n content_provider_ = detail::ContentProviderAdapter(std::move(provider));\n content_provider_resource_releaser_ = resource_releaser;\n is_chunked_content_provider_ = true;\n}\n\n// Result implementation\ninline bool Result::has_request_header(const std::string &key) const {\n return request_headers_.find(key) != request_headers_.end();\n}\n\ninline std::string Result::get_request_header_value(const std::string &key,\n size_t id) const {\n return detail::get_header_value(request_headers_, key, id, \"\");\n}\n\ninline size_t\nResult::get_request_header_value_count(const std::string &key) const {\n auto r = request_headers_.equal_range(key);\n return static_cast(std::distance(r.first, r.second));\n}\n\n// Stream implementation\ninline ssize_t Stream::write(const char *ptr) {\n return write(ptr, strlen(ptr));\n}\n\ninline ssize_t Stream::write(const std::string &s) {\n return write(s.data(), s.size());\n}\n\nnamespace detail {\n\n// Socket stream implementation\ninline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec,\n time_t read_timeout_usec,\n time_t write_timeout_sec,\n time_t write_timeout_usec)\n : sock_(sock), read_timeout_sec_(read_timeout_sec),\n read_timeout_usec_(read_timeout_usec),\n write_timeout_sec_(write_timeout_sec),\n write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {}\n\ninline SocketStream::~SocketStream() {}\n\ninline bool SocketStream::is_readable() const {\n return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;\n}\n\ninline bool SocketStream::is_writable() const {\n return select_write(sock_, write_timeout_sec_, write_timeout_usec_) > 0 &&\n is_socket_alive(sock_);\n}\n\ninline ssize_t SocketStream::read(char *ptr, size_t size) {\n#ifdef _WIN32\n size =\n (std::min)(size, static_cast((std::numeric_limits::max)()));\n#else\n size = (std::min)(size,\n static_cast((std::numeric_limits::max)()));\n#endif\n\n if (read_buff_off_ < read_buff_content_size_) {\n auto remaining_size = read_buff_content_size_ - read_buff_off_;\n if (size <= remaining_size) {\n memcpy(ptr, read_buff_.data() + read_buff_off_, size);\n read_buff_off_ += size;\n return static_cast(size);\n } else {\n memcpy(ptr, read_buff_.data() + read_buff_off_, remaining_size);\n read_buff_off_ += remaining_size;\n return static_cast(remaining_size);\n }\n }\n\n if (!is_readable()) { return -1; }\n\n read_buff_off_ = 0;\n read_buff_content_size_ = 0;\n\n if (size < read_buff_size_) {\n auto n = read_socket(sock_, read_buff_.data(), read_buff_size_,\n CPPHTTPLIB_RECV_FLAGS);\n if (n <= 0) {\n return n;\n } else if (n <= static_cast(size)) {\n memcpy(ptr, read_buff_.data(), static_cast(n));\n return n;\n } else {\n memcpy(ptr, read_buff_.data(), size);\n read_buff_off_ = size;\n read_buff_content_size_ = static_cast(n);\n return static_cast(size);\n }\n } else {\n return read_socket(sock_, ptr, size, CPPHTTPLIB_RECV_FLAGS);\n }\n}\n\ninline ssize_t SocketStream::write(const char *ptr, size_t size) {\n if (!is_writable()) { return -1; }\n\n#if defined(_WIN32) && !defined(_WIN64)\n size =\n (std::min)(size, static_cast((std::numeric_limits::max)()));\n#endif\n\n return send_socket(sock_, ptr, size, CPPHTTPLIB_SEND_FLAGS);\n}\n\ninline void SocketStream::get_remote_ip_and_port(std::string &ip,\n int &port) const {\n return detail::get_remote_ip_and_port(sock_, ip, port);\n}\n\ninline void SocketStream::get_local_ip_and_port(std::string &ip,\n int &port) const {\n return detail::get_local_ip_and_port(sock_, ip, port);\n}\n\ninline socket_t SocketStream::socket() const { return sock_; }\n\n// Buffer stream implementation\ninline bool BufferStream::is_readable() const { return true; }\n\ninline bool BufferStream::is_writable() const { return true; }\n\ninline ssize_t BufferStream::read(char *ptr, size_t size) {\n#if defined(_MSC_VER) && _MSC_VER < 1910\n auto len_read = buffer._Copy_s(ptr, size, size, position);\n#else\n auto len_read = buffer.copy(ptr, size, position);\n#endif\n position += static_cast(len_read);\n return static_cast(len_read);\n}\n\ninline ssize_t BufferStream::write(const char *ptr, size_t size) {\n buffer.append(ptr, size);\n return static_cast(size);\n}\n\ninline void BufferStream::get_remote_ip_and_port(std::string & /*ip*/,\n int & /*port*/) const {}\n\ninline void BufferStream::get_local_ip_and_port(std::string & /*ip*/,\n int & /*port*/) const {}\n\ninline socket_t BufferStream::socket() const { return 0; }\n\ninline const std::string &BufferStream::get_buffer() const { return buffer; }\n\n} // namespace detail\n\n// HTTP server implementation\ninline Server::Server()\n : new_task_queue(\n [] { return new ThreadPool(CPPHTTPLIB_THREAD_POOL_COUNT); }) {\n#ifndef _WIN32\n signal(SIGPIPE, SIG_IGN);\n#endif\n}\n\ninline Server::~Server() {}\n\ninline Server &Server::Get(const std::string &pattern, Handler handler) {\n get_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Post(const std::string &pattern, Handler handler) {\n post_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Post(const std::string &pattern,\n HandlerWithContentReader handler) {\n post_handlers_for_content_reader_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Put(const std::string &pattern, Handler handler) {\n put_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Put(const std::string &pattern,\n HandlerWithContentReader handler) {\n put_handlers_for_content_reader_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Patch(const std::string &pattern, Handler handler) {\n patch_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Patch(const std::string &pattern,\n HandlerWithContentReader handler) {\n patch_handlers_for_content_reader_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Delete(const std::string &pattern, Handler handler) {\n delete_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Delete(const std::string &pattern,\n HandlerWithContentReader handler) {\n delete_handlers_for_content_reader_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline Server &Server::Options(const std::string &pattern, Handler handler) {\n options_handlers_.push_back(\n std::make_pair(std::regex(pattern), std::move(handler)));\n return *this;\n}\n\ninline bool Server::set_base_dir(const std::string &dir,\n const std::string &mount_point) {\n return set_mount_point(mount_point, dir);\n}\n\ninline bool Server::set_mount_point(const std::string &mount_point,\n const std::string &dir, Headers headers) {\n if (detail::is_dir(dir)) {\n std::string mnt = !mount_point.empty() ? mount_point : \"/\";\n if (!mnt.empty() && mnt[0] == '/') {\n base_dirs_.push_back({mnt, dir, std::move(headers)});\n return true;\n }\n }\n return false;\n}\n\ninline bool Server::remove_mount_point(const std::string &mount_point) {\n for (auto it = base_dirs_.begin(); it != base_dirs_.end(); ++it) {\n if (it->mount_point == mount_point) {\n base_dirs_.erase(it);\n return true;\n }\n }\n return false;\n}\n\ninline Server &\nServer::set_file_extension_and_mimetype_mapping(const std::string &ext,\n const std::string &mime) {\n file_extension_and_mimetype_map_[ext] = mime;\n return *this;\n}\n\ninline Server &Server::set_file_request_handler(Handler handler) {\n file_request_handler_ = std::move(handler);\n return *this;\n}\n\ninline Server &Server::set_error_handler(HandlerWithResponse handler) {\n error_handler_ = std::move(handler);\n return *this;\n}\n\ninline Server &Server::set_error_handler(Handler handler) {\n error_handler_ = [handler](const Request &req, Response &res) {\n handler(req, res);\n return HandlerResponse::Handled;\n };\n return *this;\n}\n\ninline Server &Server::set_exception_handler(ExceptionHandler handler) {\n exception_handler_ = std::move(handler);\n return *this;\n}\n\ninline Server &Server::set_pre_routing_handler(HandlerWithResponse handler) {\n pre_routing_handler_ = std::move(handler);\n return *this;\n}\n\ninline Server &Server::set_post_routing_handler(Handler handler) {\n post_routing_handler_ = std::move(handler);\n return *this;\n}\n\ninline Server &Server::set_logger(Logger logger) {\n logger_ = std::move(logger);\n return *this;\n}\n\ninline Server &\nServer::set_expect_100_continue_handler(Expect100ContinueHandler handler) {\n expect_100_continue_handler_ = std::move(handler);\n\n return *this;\n}\n\ninline Server &Server::set_address_family(int family) {\n address_family_ = family;\n return *this;\n}\n\ninline Server &Server::set_tcp_nodelay(bool on) {\n tcp_nodelay_ = on;\n return *this;\n}\n\ninline Server &Server::set_socket_options(SocketOptions socket_options) {\n socket_options_ = std::move(socket_options);\n return *this;\n}\n\ninline Server &Server::set_default_headers(Headers headers) {\n default_headers_ = std::move(headers);\n return *this;\n}\n\ninline Server &Server::set_keep_alive_max_count(size_t count) {\n keep_alive_max_count_ = count;\n return *this;\n}\n\ninline Server &Server::set_keep_alive_timeout(time_t sec) {\n keep_alive_timeout_sec_ = sec;\n return *this;\n}\n\ninline Server &Server::set_read_timeout(time_t sec, time_t usec) {\n read_timeout_sec_ = sec;\n read_timeout_usec_ = usec;\n return *this;\n}\n\ninline Server &Server::set_write_timeout(time_t sec, time_t usec) {\n write_timeout_sec_ = sec;\n write_timeout_usec_ = usec;\n return *this;\n}\n\ninline Server &Server::set_idle_interval(time_t sec, time_t usec) {\n idle_interval_sec_ = sec;\n idle_interval_usec_ = usec;\n return *this;\n}\n\ninline Server &Server::set_payload_max_length(size_t length) {\n payload_max_length_ = length;\n return *this;\n}\n\ninline bool Server::bind_to_port(const std::string &host, int port,\n int socket_flags) {\n if (bind_internal(host, port, socket_flags) < 0) return false;\n return true;\n}\ninline int Server::bind_to_any_port(const std::string &host, int socket_flags) {\n return bind_internal(host, 0, socket_flags);\n}\n\ninline bool Server::listen_after_bind() {\n auto se = detail::scope_exit([&]() { done_ = true; });\n return listen_internal();\n}\n\ninline bool Server::listen(const std::string &host, int port,\n int socket_flags) {\n auto se = detail::scope_exit([&]() { done_ = true; });\n return bind_to_port(host, port, socket_flags) && listen_internal();\n}\n\ninline bool Server::is_running() const { return is_running_; }\n\ninline void Server::wait_until_ready() const {\n while (!is_running() && !done_) {\n std::this_thread::sleep_for(std::chrono::milliseconds{1});\n }\n}\n\ninline void Server::stop() {\n if (is_running_) {\n assert(svr_sock_ != INVALID_SOCKET);\n std::atomic sock(svr_sock_.exchange(INVALID_SOCKET));\n detail::shutdown_socket(sock);\n detail::close_socket(sock);\n }\n}\n\ninline bool Server::parse_request_line(const char *s, Request &req) {\n auto len = strlen(s);\n if (len < 2 || s[len - 2] != '\\r' || s[len - 1] != '\\n') { return false; }\n len -= 2;\n\n {\n size_t count = 0;\n\n detail::split(s, s + len, ' ', [&](const char *b, const char *e) {\n switch (count) {\n case 0: req.method = std::string(b, e); break;\n case 1: req.target = std::string(b, e); break;\n case 2: req.version = std::string(b, e); break;\n default: break;\n }\n count++;\n });\n\n if (count != 3) { return false; }\n }\n\n static const std::set methods{\n \"GET\", \"HEAD\", \"POST\", \"PUT\", \"DELETE\",\n \"CONNECT\", \"OPTIONS\", \"TRACE\", \"PATCH\", \"PRI\"};\n\n if (methods.find(req.method) == methods.end()) { return false; }\n\n if (req.version != \"HTTP/1.1\" && req.version != \"HTTP/1.0\") { return false; }\n\n {\n // Skip URL fragment\n for (size_t i = 0; i < req.target.size(); i++) {\n if (req.target[i] == '#') {\n req.target.erase(i);\n break;\n }\n }\n\n size_t count = 0;\n\n detail::split(req.target.data(), req.target.data() + req.target.size(), '?',\n [&](const char *b, const char *e) {\n switch (count) {\n case 0:\n req.path = detail::decode_url(std::string(b, e), false);\n break;\n case 1: {\n if (e - b > 0) {\n detail::parse_query_text(std::string(b, e), req.params);\n }\n break;\n }\n default: break;\n }\n count++;\n });\n\n if (count > 2) { return false; }\n }\n\n return true;\n}\n\ninline bool Server::write_response(Stream &strm, bool close_connection,\n const Request &req, Response &res) {\n return write_response_core(strm, close_connection, req, res, false);\n}\n\ninline bool Server::write_response_with_content(Stream &strm,\n bool close_connection,\n const Request &req,\n Response &res) {\n return write_response_core(strm, close_connection, req, res, true);\n}\n\ninline bool Server::write_response_core(Stream &strm, bool close_connection,\n const Request &req, Response &res,\n bool need_apply_ranges) {\n assert(res.status != -1);\n\n if (400 <= res.status && error_handler_ &&\n error_handler_(req, res) == HandlerResponse::Handled) {\n need_apply_ranges = true;\n }\n\n std::string content_type;\n std::string boundary;\n if (need_apply_ranges) { apply_ranges(req, res, content_type, boundary); }\n\n // Prepare additional headers\n if (close_connection || req.get_header_value(\"Connection\") == \"close\") {\n res.set_header(\"Connection\", \"close\");\n } else {\n std::stringstream ss;\n ss << \"timeout=\" << keep_alive_timeout_sec_\n << \", max=\" << keep_alive_max_count_;\n res.set_header(\"Keep-Alive\", ss.str());\n }\n\n if (!res.has_header(\"Content-Type\") &&\n (!res.body.empty() || res.content_length_ > 0 || res.content_provider_)) {\n res.set_header(\"Content-Type\", \"text/plain\");\n }\n\n if (!res.has_header(\"Content-Length\") && res.body.empty() &&\n !res.content_length_ && !res.content_provider_) {\n res.set_header(\"Content-Length\", \"0\");\n }\n\n if (!res.has_header(\"Accept-Ranges\") && req.method == \"HEAD\") {\n res.set_header(\"Accept-Ranges\", \"bytes\");\n }\n\n if (post_routing_handler_) { post_routing_handler_(req, res); }\n\n // Response line and headers\n {\n detail::BufferStream bstrm;\n\n if (!bstrm.write_format(\"HTTP/1.1 %d %s\\r\\n\", res.status,\n detail::status_message(res.status))) {\n return false;\n }\n\n if (!detail::write_headers(bstrm, res.headers)) { return false; }\n\n // Flush buffer\n auto &data = bstrm.get_buffer();\n detail::write_data(strm, data.data(), data.size());\n }\n\n // Body\n auto ret = true;\n if (req.method != \"HEAD\") {\n if (!res.body.empty()) {\n if (!detail::write_data(strm, res.body.data(), res.body.size())) {\n ret = false;\n }\n } else if (res.content_provider_) {\n if (write_content_with_provider(strm, req, res, boundary, content_type)) {\n res.content_provider_success_ = true;\n } else {\n res.content_provider_success_ = false;\n ret = false;\n }\n }\n }\n\n // Log\n if (logger_) { logger_(req, res); }\n\n return ret;\n}\n\ninline bool\nServer::write_content_with_provider(Stream &strm, const Request &req,\n Response &res, const std::string &boundary,\n const std::string &content_type) {\n auto is_shutting_down = [this]() {\n return this->svr_sock_ == INVALID_SOCKET;\n };\n\n if (res.content_length_ > 0) {\n if (req.ranges.empty()) {\n return detail::write_content(strm, res.content_provider_, 0,\n res.content_length_, is_shutting_down);\n } else if (req.ranges.size() == 1) {\n auto offsets =\n detail::get_range_offset_and_length(req, res.content_length_, 0);\n auto offset = offsets.first;\n auto length = offsets.second;\n return detail::write_content(strm, res.content_provider_, offset, length,\n is_shutting_down);\n } else {\n return detail::write_multipart_ranges_data(\n strm, req, res, boundary, content_type, is_shutting_down);\n }\n } else {\n if (res.is_chunked_content_provider_) {\n auto type = detail::encoding_type(req, res);\n\n std::unique_ptr compressor;\n if (type == detail::EncodingType::Gzip) {\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n compressor = detail::make_unique();\n#endif\n } else if (type == detail::EncodingType::Brotli) {\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\n compressor = detail::make_unique();\n#endif\n } else {\n compressor = detail::make_unique();\n }\n assert(compressor != nullptr);\n\n return detail::write_content_chunked(strm, res.content_provider_,\n is_shutting_down, *compressor);\n } else {\n return detail::write_content_without_length(strm, res.content_provider_,\n is_shutting_down);\n }\n }\n}\n\ninline bool Server::read_content(Stream &strm, Request &req, Response &res) {\n MultipartFormDataMap::iterator cur;\n auto file_count = 0;\n if (read_content_core(\n strm, req, res,\n // Regular\n [&](const char *buf, size_t n) {\n if (req.body.size() + n > req.body.max_size()) { return false; }\n req.body.append(buf, n);\n return true;\n },\n // Multipart\n [&](const MultipartFormData &file) {\n if (file_count++ == CPPHTTPLIB_MULTIPART_FORM_DATA_FILE_MAX_COUNT) {\n return false;\n }\n cur = req.files.emplace(file.name, file);\n return true;\n },\n [&](const char *buf, size_t n) {\n auto &content = cur->second.content;\n if (content.size() + n > content.max_size()) { return false; }\n content.append(buf, n);\n return true;\n })) {\n const auto &content_type = req.get_header_value(\"Content-Type\");\n if (!content_type.find(\"application/x-www-form-urlencoded\")) {\n if (req.body.size() > CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH) {\n res.status = 413; // NOTE: should be 414?\n return false;\n }\n detail::parse_query_text(req.body, req.params);\n }\n return true;\n }\n return false;\n}\n\ninline bool Server::read_content_with_content_receiver(\n Stream &strm, Request &req, Response &res, ContentReceiver receiver,\n MultipartContentHeader multipart_header,\n ContentReceiver multipart_receiver) {\n return read_content_core(strm, req, res, std::move(receiver),\n std::move(multipart_header),\n std::move(multipart_receiver));\n}\n\ninline bool Server::read_content_core(Stream &strm, Request &req, Response &res,\n ContentReceiver receiver,\n MultipartContentHeader multipart_header,\n ContentReceiver multipart_receiver) {\n detail::MultipartFormDataParser multipart_form_data_parser;\n ContentReceiverWithProgress out;\n\n if (req.is_multipart_form_data()) {\n const auto &content_type = req.get_header_value(\"Content-Type\");\n std::string boundary;\n if (!detail::parse_multipart_boundary(content_type, boundary)) {\n res.status = 400;\n return false;\n }\n\n multipart_form_data_parser.set_boundary(std::move(boundary));\n out = [&](const char *buf, size_t n, uint64_t /*off*/, uint64_t /*len*/) {\n /* For debug\n size_t pos = 0;\n while (pos < n) {\n auto read_size = (std::min)(1, n - pos);\n auto ret = multipart_form_data_parser.parse(\n buf + pos, read_size, multipart_receiver, multipart_header);\n if (!ret) { return false; }\n pos += read_size;\n }\n return true;\n */\n return multipart_form_data_parser.parse(buf, n, multipart_receiver,\n multipart_header);\n };\n } else {\n out = [receiver](const char *buf, size_t n, uint64_t /*off*/,\n uint64_t /*len*/) { return receiver(buf, n); };\n }\n\n if (req.method == \"DELETE\" && !req.has_header(\"Content-Length\")) {\n return true;\n }\n\n if (!detail::read_content(strm, req, payload_max_length_, res.status, nullptr,\n out, true)) {\n return false;\n }\n\n if (req.is_multipart_form_data()) {\n if (!multipart_form_data_parser.is_valid()) {\n res.status = 400;\n return false;\n }\n }\n\n return true;\n}\n\ninline bool Server::handle_file_request(const Request &req, Response &res,\n bool head) {\n for (const auto &entry : base_dirs_) {\n // Prefix match\n if (!req.path.compare(0, entry.mount_point.size(), entry.mount_point)) {\n std::string sub_path = \"/\" + req.path.substr(entry.mount_point.size());\n if (detail::is_valid_path(sub_path)) {\n auto path = entry.base_dir + sub_path;\n if (path.back() == '/') { path += \"index.html\"; }\n\n if (detail::is_file(path)) {\n detail::read_file(path, res.body);\n auto type =\n detail::find_content_type(path, file_extension_and_mimetype_map_);\n if (type) { res.set_header(\"Content-Type\", type); }\n for (const auto &kv : entry.headers) {\n res.set_header(kv.first.c_str(), kv.second);\n }\n res.status = req.has_header(\"Range\") ? 206 : 200;\n if (!head && file_request_handler_) {\n file_request_handler_(req, res);\n }\n return true;\n }\n }\n }\n }\n return false;\n}\n\ninline socket_t\nServer::create_server_socket(const std::string &host, int port,\n int socket_flags,\n SocketOptions socket_options) const {\n return detail::create_socket(\n host, std::string(), port, address_family_, socket_flags, tcp_nodelay_,\n std::move(socket_options),\n [](socket_t sock, struct addrinfo &ai) -> bool {\n if (::bind(sock, ai.ai_addr, static_cast(ai.ai_addrlen))) {\n return false;\n }\n if (::listen(sock, CPPHTTPLIB_LISTEN_BACKLOG)) { return false; }\n return true;\n });\n}\n\ninline int Server::bind_internal(const std::string &host, int port,\n int socket_flags) {\n if (!is_valid()) { return -1; }\n\n svr_sock_ = create_server_socket(host, port, socket_flags, socket_options_);\n if (svr_sock_ == INVALID_SOCKET) { return -1; }\n\n if (port == 0) {\n struct sockaddr_storage addr;\n socklen_t addr_len = sizeof(addr);\n if (getsockname(svr_sock_, reinterpret_cast(&addr),\n &addr_len) == -1) {\n return -1;\n }\n if (addr.ss_family == AF_INET) {\n return ntohs(reinterpret_cast(&addr)->sin_port);\n } else if (addr.ss_family == AF_INET6) {\n return ntohs(reinterpret_cast(&addr)->sin6_port);\n } else {\n return -1;\n }\n } else {\n return port;\n }\n}\n\ninline bool Server::listen_internal() {\n auto ret = true;\n is_running_ = true;\n auto se = detail::scope_exit([&]() { is_running_ = false; });\n\n {\n std::unique_ptr task_queue(new_task_queue());\n\n while (svr_sock_ != INVALID_SOCKET) {\n#ifndef _WIN32\n if (idle_interval_sec_ > 0 || idle_interval_usec_ > 0) {\n#endif\n auto val = detail::select_read(svr_sock_, idle_interval_sec_,\n idle_interval_usec_);\n if (val == 0) { // Timeout\n task_queue->on_idle();\n continue;\n }\n#ifndef _WIN32\n }\n#endif\n socket_t sock = accept(svr_sock_, nullptr, nullptr);\n\n if (sock == INVALID_SOCKET) {\n if (errno == EMFILE) {\n // The per-process limit of open file descriptors has been reached.\n // Try to accept new connections after a short sleep.\n std::this_thread::sleep_for(std::chrono::milliseconds(1));\n continue;\n } else if (errno == EINTR || errno == EAGAIN) {\n continue;\n }\n if (svr_sock_ != INVALID_SOCKET) {\n detail::close_socket(svr_sock_);\n ret = false;\n } else {\n ; // The server socket was closed by user.\n }\n break;\n }\n\n {\n#ifdef _WIN32\n auto timeout = static_cast(read_timeout_sec_ * 1000 +\n read_timeout_usec_ / 1000);\n setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,\n sizeof(timeout));\n#else\n timeval tv;\n tv.tv_sec = static_cast(read_timeout_sec_);\n tv.tv_usec = static_cast(read_timeout_usec_);\n setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));\n#endif\n }\n {\n\n#ifdef _WIN32\n auto timeout = static_cast(write_timeout_sec_ * 1000 +\n write_timeout_usec_ / 1000);\n setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,\n sizeof(timeout));\n#else\n timeval tv;\n tv.tv_sec = static_cast(write_timeout_sec_);\n tv.tv_usec = static_cast(write_timeout_usec_);\n setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));\n#endif\n }\n\n task_queue->enqueue([this, sock]() { process_and_close_socket(sock); });\n }\n\n task_queue->shutdown();\n }\n\n return ret;\n}\n\ninline bool Server::routing(Request &req, Response &res, Stream &strm) {\n if (pre_routing_handler_ &&\n pre_routing_handler_(req, res) == HandlerResponse::Handled) {\n return true;\n }\n\n // File handler\n bool is_head_request = req.method == \"HEAD\";\n if ((req.method == \"GET\" || is_head_request) &&\n handle_file_request(req, res, is_head_request)) {\n return true;\n }\n\n if (detail::expect_content(req)) {\n // Content reader handler\n {\n ContentReader reader(\n [&](ContentReceiver receiver) {\n return read_content_with_content_receiver(\n strm, req, res, std::move(receiver), nullptr, nullptr);\n },\n [&](MultipartContentHeader header, ContentReceiver receiver) {\n return read_content_with_content_receiver(strm, req, res, nullptr,\n std::move(header),\n std::move(receiver));\n });\n\n if (req.method == \"POST\") {\n if (dispatch_request_for_content_reader(\n req, res, std::move(reader),\n post_handlers_for_content_reader_)) {\n return true;\n }\n } else if (req.method == \"PUT\") {\n if (dispatch_request_for_content_reader(\n req, res, std::move(reader),\n put_handlers_for_content_reader_)) {\n return true;\n }\n } else if (req.method == \"PATCH\") {\n if (dispatch_request_for_content_reader(\n req, res, std::move(reader),\n patch_handlers_for_content_reader_)) {\n return true;\n }\n } else if (req.method == \"DELETE\") {\n if (dispatch_request_for_content_reader(\n req, res, std::move(reader),\n delete_handlers_for_content_reader_)) {\n return true;\n }\n }\n }\n\n // Read content into `req.body`\n if (!read_content(strm, req, res)) { return false; }\n }\n\n // Regular handler\n if (req.method == \"GET\" || req.method == \"HEAD\") {\n return dispatch_request(req, res, get_handlers_);\n } else if (req.method == \"POST\") {\n return dispatch_request(req, res, post_handlers_);\n } else if (req.method == \"PUT\") {\n return dispatch_request(req, res, put_handlers_);\n } else if (req.method == \"DELETE\") {\n return dispatch_request(req, res, delete_handlers_);\n } else if (req.method == \"OPTIONS\") {\n return dispatch_request(req, res, options_handlers_);\n } else if (req.method == \"PATCH\") {\n return dispatch_request(req, res, patch_handlers_);\n }\n\n res.status = 400;\n return false;\n}\n\ninline bool Server::dispatch_request(Request &req, Response &res,\n const Handlers &handlers) {\n for (const auto &x : handlers) {\n const auto &pattern = x.first;\n const auto &handler = x.second;\n\n if (std::regex_match(req.path, req.matches, pattern)) {\n handler(req, res);\n return true;\n }\n }\n return false;\n}\n\ninline void Server::apply_ranges(const Request &req, Response &res,\n std::string &content_type,\n std::string &boundary) {\n if (req.ranges.size() > 1) {\n boundary = detail::make_multipart_data_boundary();\n\n auto it = res.headers.find(\"Content-Type\");\n if (it != res.headers.end()) {\n content_type = it->second;\n res.headers.erase(it);\n }\n\n res.headers.emplace(\"Content-Type\",\n \"multipart/byteranges; boundary=\" + boundary);\n }\n\n auto type = detail::encoding_type(req, res);\n\n if (res.body.empty()) {\n if (res.content_length_ > 0) {\n size_t length = 0;\n if (req.ranges.empty()) {\n length = res.content_length_;\n } else if (req.ranges.size() == 1) {\n auto offsets =\n detail::get_range_offset_and_length(req, res.content_length_, 0);\n auto offset = offsets.first;\n length = offsets.second;\n auto content_range = detail::make_content_range_header_field(\n offset, length, res.content_length_);\n res.set_header(\"Content-Range\", content_range);\n } else {\n length = detail::get_multipart_ranges_data_length(req, res, boundary,\n content_type);\n }\n res.set_header(\"Content-Length\", std::to_string(length));\n } else {\n if (res.content_provider_) {\n if (res.is_chunked_content_provider_) {\n res.set_header(\"Transfer-Encoding\", \"chunked\");\n if (type == detail::EncodingType::Gzip) {\n res.set_header(\"Content-Encoding\", \"gzip\");\n } else if (type == detail::EncodingType::Brotli) {\n res.set_header(\"Content-Encoding\", \"br\");\n }\n }\n }\n }\n } else {\n if (req.ranges.empty()) {\n ;\n } else if (req.ranges.size() == 1) {\n auto offsets =\n detail::get_range_offset_and_length(req, res.body.size(), 0);\n auto offset = offsets.first;\n auto length = offsets.second;\n auto content_range = detail::make_content_range_header_field(\n offset, length, res.body.size());\n res.set_header(\"Content-Range\", content_range);\n if (offset < res.body.size()) {\n res.body = res.body.substr(offset, length);\n } else {\n res.body.clear();\n res.status = 416;\n }\n } else {\n std::string data;\n if (detail::make_multipart_ranges_data(req, res, boundary, content_type,\n data)) {\n res.body.swap(data);\n } else {\n res.body.clear();\n res.status = 416;\n }\n }\n\n if (type != detail::EncodingType::None) {\n std::unique_ptr compressor;\n std::string content_encoding;\n\n if (type == detail::EncodingType::Gzip) {\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n compressor = detail::make_unique();\n content_encoding = \"gzip\";\n#endif\n } else if (type == detail::EncodingType::Brotli) {\n#ifdef CPPHTTPLIB_BROTLI_SUPPORT\n compressor = detail::make_unique();\n content_encoding = \"br\";\n#endif\n }\n\n if (compressor) {\n std::string compressed;\n if (compressor->compress(res.body.data(), res.body.size(), true,\n [&](const char *data, size_t data_len) {\n compressed.append(data, data_len);\n return true;\n })) {\n res.body.swap(compressed);\n res.set_header(\"Content-Encoding\", content_encoding);\n }\n }\n }\n\n auto length = std::to_string(res.body.size());\n res.set_header(\"Content-Length\", length);\n }\n}\n\ninline bool Server::dispatch_request_for_content_reader(\n Request &req, Response &res, ContentReader content_reader,\n const HandlersForContentReader &handlers) {\n for (const auto &x : handlers) {\n const auto &pattern = x.first;\n const auto &handler = x.second;\n\n if (std::regex_match(req.path, req.matches, pattern)) {\n handler(req, res, content_reader);\n return true;\n }\n }\n return false;\n}\n\ninline bool\nServer::process_request(Stream &strm, bool close_connection,\n bool &connection_closed,\n const std::function &setup_request) {\n std::array buf{};\n\n detail::stream_line_reader line_reader(strm, buf.data(), buf.size());\n\n // Connection has been closed on client\n if (!line_reader.getline()) { return false; }\n\n Request req;\n Response res;\n\n res.version = \"HTTP/1.1\";\n\n for (const auto &header : default_headers_) {\n if (res.headers.find(header.first) == res.headers.end()) {\n res.headers.insert(header);\n }\n }\n\n#ifdef _WIN32\n // TODO: Increase FD_SETSIZE statically (libzmq), dynamically (MySQL).\n#else\n#ifndef CPPHTTPLIB_USE_POLL\n // Socket file descriptor exceeded FD_SETSIZE...\n if (strm.socket() >= FD_SETSIZE) {\n Headers dummy;\n detail::read_headers(strm, dummy);\n res.status = 500;\n return write_response(strm, close_connection, req, res);\n }\n#endif\n#endif\n\n // Check if the request URI doesn't exceed the limit\n if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) {\n Headers dummy;\n detail::read_headers(strm, dummy);\n res.status = 414;\n return write_response(strm, close_connection, req, res);\n }\n\n // Request line and headers\n if (!parse_request_line(line_reader.ptr(), req) ||\n !detail::read_headers(strm, req.headers)) {\n res.status = 400;\n return write_response(strm, close_connection, req, res);\n }\n\n if (req.get_header_value(\"Connection\") == \"close\") {\n connection_closed = true;\n }\n\n if (req.version == \"HTTP/1.0\" &&\n req.get_header_value(\"Connection\") != \"Keep-Alive\") {\n connection_closed = true;\n }\n\n strm.get_remote_ip_and_port(req.remote_addr, req.remote_port);\n req.set_header(\"REMOTE_ADDR\", req.remote_addr);\n req.set_header(\"REMOTE_PORT\", std::to_string(req.remote_port));\n\n strm.get_local_ip_and_port(req.local_addr, req.local_port);\n req.set_header(\"LOCAL_ADDR\", req.local_addr);\n req.set_header(\"LOCAL_PORT\", std::to_string(req.local_port));\n\n if (req.has_header(\"Range\")) {\n const auto &range_header_value = req.get_header_value(\"Range\");\n if (!detail::parse_range_header(range_header_value, req.ranges)) {\n res.status = 416;\n return write_response(strm, close_connection, req, res);\n }\n }\n\n if (setup_request) { setup_request(req); }\n\n if (req.get_header_value(\"Expect\") == \"100-continue\") {\n auto status = 100;\n if (expect_100_continue_handler_) {\n status = expect_100_continue_handler_(req, res);\n }\n switch (status) {\n case 100:\n case 417:\n strm.write_format(\"HTTP/1.1 %d %s\\r\\n\\r\\n\", status,\n detail::status_message(status));\n break;\n default: return write_response(strm, close_connection, req, res);\n }\n }\n\n // Rounting\n bool routed = false;\n#ifdef CPPHTTPLIB_NO_EXCEPTIONS\n routed = routing(req, res, strm);\n#else\n try {\n routed = routing(req, res, strm);\n } catch (std::exception &e) {\n if (exception_handler_) {\n auto ep = std::current_exception();\n exception_handler_(req, res, ep);\n routed = true;\n } else {\n res.status = 500;\n std::string val;\n auto s = e.what();\n for (size_t i = 0; s[i]; i++) {\n switch (s[i]) {\n case '\\r': val += \"\\\\r\"; break;\n case '\\n': val += \"\\\\n\"; break;\n default: val += s[i]; break;\n }\n }\n res.set_header(\"EXCEPTION_WHAT\", val);\n }\n } catch (...) {\n if (exception_handler_) {\n auto ep = std::current_exception();\n exception_handler_(req, res, ep);\n routed = true;\n } else {\n res.status = 500;\n res.set_header(\"EXCEPTION_WHAT\", \"UNKNOWN\");\n }\n }\n#endif\n\n if (routed) {\n if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; }\n return write_response_with_content(strm, close_connection, req, res);\n } else {\n if (res.status == -1) { res.status = 404; }\n return write_response(strm, close_connection, req, res);\n }\n}\n\ninline bool Server::is_valid() const { return true; }\n\ninline bool Server::process_and_close_socket(socket_t sock) {\n auto ret = detail::process_server_socket(\n svr_sock_, sock, keep_alive_max_count_, keep_alive_timeout_sec_,\n read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,\n write_timeout_usec_,\n [this](Stream &strm, bool close_connection, bool &connection_closed) {\n return process_request(strm, close_connection, connection_closed,\n nullptr);\n });\n\n detail::shutdown_socket(sock);\n detail::close_socket(sock);\n return ret;\n}\n\n// HTTP client implementation\ninline ClientImpl::ClientImpl(const std::string &host)\n : ClientImpl(host, 80, std::string(), std::string()) {}\n\ninline ClientImpl::ClientImpl(const std::string &host, int port)\n : ClientImpl(host, port, std::string(), std::string()) {}\n\ninline ClientImpl::ClientImpl(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path)\n : host_(host), port_(port),\n host_and_port_(adjust_host_string(host) + \":\" + std::to_string(port)),\n client_cert_path_(client_cert_path), client_key_path_(client_key_path) {}\n\ninline ClientImpl::~ClientImpl() {\n std::lock_guard guard(socket_mutex_);\n shutdown_socket(socket_);\n close_socket(socket_);\n}\n\ninline bool ClientImpl::is_valid() const { return true; }\n\ninline void ClientImpl::copy_settings(const ClientImpl &rhs) {\n client_cert_path_ = rhs.client_cert_path_;\n client_key_path_ = rhs.client_key_path_;\n connection_timeout_sec_ = rhs.connection_timeout_sec_;\n read_timeout_sec_ = rhs.read_timeout_sec_;\n read_timeout_usec_ = rhs.read_timeout_usec_;\n write_timeout_sec_ = rhs.write_timeout_sec_;\n write_timeout_usec_ = rhs.write_timeout_usec_;\n basic_auth_username_ = rhs.basic_auth_username_;\n basic_auth_password_ = rhs.basic_auth_password_;\n bearer_token_auth_token_ = rhs.bearer_token_auth_token_;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n digest_auth_username_ = rhs.digest_auth_username_;\n digest_auth_password_ = rhs.digest_auth_password_;\n#endif\n keep_alive_ = rhs.keep_alive_;\n follow_location_ = rhs.follow_location_;\n url_encode_ = rhs.url_encode_;\n address_family_ = rhs.address_family_;\n tcp_nodelay_ = rhs.tcp_nodelay_;\n socket_options_ = rhs.socket_options_;\n compress_ = rhs.compress_;\n decompress_ = rhs.decompress_;\n interface_ = rhs.interface_;\n proxy_host_ = rhs.proxy_host_;\n proxy_port_ = rhs.proxy_port_;\n proxy_basic_auth_username_ = rhs.proxy_basic_auth_username_;\n proxy_basic_auth_password_ = rhs.proxy_basic_auth_password_;\n proxy_bearer_token_auth_token_ = rhs.proxy_bearer_token_auth_token_;\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n proxy_digest_auth_username_ = rhs.proxy_digest_auth_username_;\n proxy_digest_auth_password_ = rhs.proxy_digest_auth_password_;\n#endif\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n ca_cert_file_path_ = rhs.ca_cert_file_path_;\n ca_cert_dir_path_ = rhs.ca_cert_dir_path_;\n ca_cert_store_ = rhs.ca_cert_store_;\n#endif\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n server_certificate_verification_ = rhs.server_certificate_verification_;\n#endif\n logger_ = rhs.logger_;\n}\n\ninline socket_t ClientImpl::create_client_socket(Error &error) const {\n if (!proxy_host_.empty() && proxy_port_ != -1) {\n return detail::create_client_socket(\n proxy_host_, std::string(), proxy_port_, address_family_, tcp_nodelay_,\n socket_options_, connection_timeout_sec_, connection_timeout_usec_,\n read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,\n write_timeout_usec_, interface_, error);\n }\n\n // Check is custom IP specified for host_\n std::string ip;\n auto it = addr_map_.find(host_);\n if (it != addr_map_.end()) ip = it->second;\n\n return detail::create_client_socket(\n host_, ip, port_, address_family_, tcp_nodelay_, socket_options_,\n connection_timeout_sec_, connection_timeout_usec_, read_timeout_sec_,\n read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, interface_,\n error);\n}\n\ninline bool ClientImpl::create_and_connect_socket(Socket &socket,\n Error &error) {\n auto sock = create_client_socket(error);\n if (sock == INVALID_SOCKET) { return false; }\n socket.sock = sock;\n return true;\n}\n\ninline void ClientImpl::shutdown_ssl(Socket & /*socket*/,\n bool /*shutdown_gracefully*/) {\n // If there are any requests in flight from threads other than us, then it's\n // a thread-unsafe race because individual ssl* objects are not thread-safe.\n assert(socket_requests_in_flight_ == 0 ||\n socket_requests_are_from_thread_ == std::this_thread::get_id());\n}\n\ninline void ClientImpl::shutdown_socket(Socket &socket) {\n if (socket.sock == INVALID_SOCKET) { return; }\n detail::shutdown_socket(socket.sock);\n}\n\ninline void ClientImpl::close_socket(Socket &socket) {\n // If there are requests in flight in another thread, usually closing\n // the socket will be fine and they will simply receive an error when\n // using the closed socket, but it is still a bug since rarely the OS\n // may reassign the socket id to be used for a new socket, and then\n // suddenly they will be operating on a live socket that is different\n // than the one they intended!\n assert(socket_requests_in_flight_ == 0 ||\n socket_requests_are_from_thread_ == std::this_thread::get_id());\n\n // It is also a bug if this happens while SSL is still active\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n assert(socket.ssl == nullptr);\n#endif\n if (socket.sock == INVALID_SOCKET) { return; }\n detail::close_socket(socket.sock);\n socket.sock = INVALID_SOCKET;\n}\n\ninline bool ClientImpl::read_response_line(Stream &strm, const Request &req,\n Response &res) {\n std::array buf{};\n\n detail::stream_line_reader line_reader(strm, buf.data(), buf.size());\n\n if (!line_reader.getline()) { return false; }\n\n#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR\n const static std::regex re(\"(HTTP/1\\\\.[01]) (\\\\d{3})(?: (.*?))?\\r\\n\");\n#else\n const static std::regex re(\"(HTTP/1\\\\.[01]) (\\\\d{3})(?: (.*?))?\\r?\\n\");\n#endif\n\n std::cmatch m;\n if (!std::regex_match(line_reader.ptr(), m, re)) {\n return req.method == \"CONNECT\";\n }\n res.version = std::string(m[1]);\n res.status = std::stoi(std::string(m[2]));\n res.reason = std::string(m[3]);\n\n // Ignore '100 Continue'\n while (res.status == 100) {\n if (!line_reader.getline()) { return false; } // CRLF\n if (!line_reader.getline()) { return false; } // next response line\n\n if (!std::regex_match(line_reader.ptr(), m, re)) { return false; }\n res.version = std::string(m[1]);\n res.status = std::stoi(std::string(m[2]));\n res.reason = std::string(m[3]);\n }\n\n return true;\n}\n\ninline bool ClientImpl::send(Request &req, Response &res, Error &error) {\n std::lock_guard request_mutex_guard(request_mutex_);\n auto ret = send_(req, res, error);\n if (error == Error::SSLPeerCouldBeClosed_) {\n assert(!ret);\n ret = send_(req, res, error);\n }\n return ret;\n}\n\ninline bool ClientImpl::send_(Request &req, Response &res, Error &error) {\n {\n std::lock_guard guard(socket_mutex_);\n\n // Set this to false immediately - if it ever gets set to true by the end of\n // the request, we know another thread instructed us to close the socket.\n socket_should_be_closed_when_request_is_done_ = false;\n\n auto is_alive = false;\n if (socket_.is_open()) {\n is_alive = detail::is_socket_alive(socket_.sock);\n if (!is_alive) {\n // Attempt to avoid sigpipe by shutting down nongracefully if it seems\n // like the other side has already closed the connection Also, there\n // cannot be any requests in flight from other threads since we locked\n // request_mutex_, so safe to close everything immediately\n const bool shutdown_gracefully = false;\n shutdown_ssl(socket_, shutdown_gracefully);\n shutdown_socket(socket_);\n close_socket(socket_);\n }\n }\n\n if (!is_alive) {\n if (!create_and_connect_socket(socket_, error)) { return false; }\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n // TODO: refactoring\n if (is_ssl()) {\n auto &scli = static_cast(*this);\n if (!proxy_host_.empty() && proxy_port_ != -1) {\n auto success = false;\n if (!scli.connect_with_proxy(socket_, res, success, error)) {\n return success;\n }\n }\n\n if (!scli.initialize_ssl(socket_, error)) { return false; }\n }\n#endif\n }\n\n // Mark the current socket as being in use so that it cannot be closed by\n // anyone else while this request is ongoing, even though we will be\n // releasing the mutex.\n if (socket_requests_in_flight_ > 1) {\n assert(socket_requests_are_from_thread_ == std::this_thread::get_id());\n }\n socket_requests_in_flight_ += 1;\n socket_requests_are_from_thread_ = std::this_thread::get_id();\n }\n\n for (const auto &header : default_headers_) {\n if (req.headers.find(header.first) == req.headers.end()) {\n req.headers.insert(header);\n }\n }\n\n auto ret = false;\n auto close_connection = !keep_alive_;\n\n auto se = detail::scope_exit([&]() {\n // Briefly lock mutex in order to mark that a request is no longer ongoing\n std::lock_guard guard(socket_mutex_);\n socket_requests_in_flight_ -= 1;\n if (socket_requests_in_flight_ <= 0) {\n assert(socket_requests_in_flight_ == 0);\n socket_requests_are_from_thread_ = std::thread::id();\n }\n\n if (socket_should_be_closed_when_request_is_done_ || close_connection ||\n !ret) {\n shutdown_ssl(socket_, true);\n shutdown_socket(socket_);\n close_socket(socket_);\n }\n });\n\n ret = process_socket(socket_, [&](Stream &strm) {\n return handle_request(strm, req, res, close_connection, error);\n });\n\n if (!ret) {\n if (error == Error::Success) { error = Error::Unknown; }\n }\n\n return ret;\n}\n\ninline Result ClientImpl::send(const Request &req) {\n auto req2 = req;\n return send_(std::move(req2));\n}\n\ninline Result ClientImpl::send_(Request &&req) {\n auto res = detail::make_unique();\n auto error = Error::Success;\n auto ret = send(req, *res, error);\n return Result{ret ? std::move(res) : nullptr, error, std::move(req.headers)};\n}\n\ninline bool ClientImpl::handle_request(Stream &strm, Request &req,\n Response &res, bool close_connection,\n Error &error) {\n if (req.path.empty()) {\n error = Error::Connection;\n return false;\n }\n\n auto req_save = req;\n\n bool ret;\n\n if (!is_ssl() && !proxy_host_.empty() && proxy_port_ != -1) {\n auto req2 = req;\n req2.path = \"http://\" + host_and_port_ + req.path;\n ret = process_request(strm, req2, res, close_connection, error);\n req = req2;\n req.path = req_save.path;\n } else {\n ret = process_request(strm, req, res, close_connection, error);\n }\n\n if (!ret) { return false; }\n\n if (300 < res.status && res.status < 400 && follow_location_) {\n req = req_save;\n ret = redirect(req, res, error);\n }\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n if ((res.status == 401 || res.status == 407) &&\n req.authorization_count_ < 5) {\n auto is_proxy = res.status == 407;\n const auto &username =\n is_proxy ? proxy_digest_auth_username_ : digest_auth_username_;\n const auto &password =\n is_proxy ? proxy_digest_auth_password_ : digest_auth_password_;\n\n if (!username.empty() && !password.empty()) {\n std::map auth;\n if (detail::parse_www_authenticate(res, auth, is_proxy)) {\n Request new_req = req;\n new_req.authorization_count_ += 1;\n new_req.headers.erase(is_proxy ? \"Proxy-Authorization\"\n : \"Authorization\");\n new_req.headers.insert(detail::make_digest_authentication_header(\n req, auth, new_req.authorization_count_, detail::random_string(10),\n username, password, is_proxy));\n\n Response new_res;\n\n ret = send(new_req, new_res, error);\n if (ret) { res = new_res; }\n }\n }\n }\n#endif\n\n return ret;\n}\n\ninline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {\n if (req.redirect_count_ == 0) {\n error = Error::ExceedRedirectCount;\n return false;\n }\n\n auto location = res.get_header_value(\"location\");\n if (location.empty()) { return false; }\n\n const static std::regex re(\n R\"((?:(https?):)?(?://(?:\\[([\\d:]+)\\]|([^:/?#]+))(?::(\\d+))?)?([^?#]*)(\\?[^#]*)?(?:#.*)?)\");\n\n std::smatch m;\n if (!std::regex_match(location, m, re)) { return false; }\n\n auto scheme = is_ssl() ? \"https\" : \"http\";\n\n auto next_scheme = m[1].str();\n auto next_host = m[2].str();\n if (next_host.empty()) { next_host = m[3].str(); }\n auto port_str = m[4].str();\n auto next_path = m[5].str();\n auto next_query = m[6].str();\n\n auto next_port = port_;\n if (!port_str.empty()) {\n next_port = std::stoi(port_str);\n } else if (!next_scheme.empty()) {\n next_port = next_scheme == \"https\" ? 443 : 80;\n }\n\n if (next_scheme.empty()) { next_scheme = scheme; }\n if (next_host.empty()) { next_host = host_; }\n if (next_path.empty()) { next_path = \"/\"; }\n\n auto path = detail::decode_url(next_path, true) + next_query;\n\n if (next_scheme == scheme && next_host == host_ && next_port == port_) {\n return detail::redirect(*this, req, res, path, location, error);\n } else {\n if (next_scheme == \"https\") {\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n SSLClient cli(next_host.c_str(), next_port);\n cli.copy_settings(*this);\n if (ca_cert_store_) { cli.set_ca_cert_store(ca_cert_store_); }\n return detail::redirect(cli, req, res, path, location, error);\n#else\n return false;\n#endif\n } else {\n ClientImpl cli(next_host.c_str(), next_port);\n cli.copy_settings(*this);\n return detail::redirect(cli, req, res, path, location, error);\n }\n }\n}\n\ninline bool ClientImpl::write_content_with_provider(Stream &strm,\n const Request &req,\n Error &error) {\n auto is_shutting_down = []() { return false; };\n\n if (req.is_chunked_content_provider_) {\n // TODO: Brotli support\n std::unique_ptr compressor;\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n if (compress_) {\n compressor = detail::make_unique();\n } else\n#endif\n {\n compressor = detail::make_unique();\n }\n\n return detail::write_content_chunked(strm, req.content_provider_,\n is_shutting_down, *compressor, error);\n } else {\n return detail::write_content(strm, req.content_provider_, 0,\n req.content_length_, is_shutting_down, error);\n }\n}\n\ninline bool ClientImpl::write_request(Stream &strm, Request &req,\n bool close_connection, Error &error) {\n // Prepare additional headers\n if (close_connection) {\n if (!req.has_header(\"Connection\")) {\n req.headers.emplace(\"Connection\", \"close\");\n }\n }\n\n if (!req.has_header(\"Host\")) {\n if (is_ssl()) {\n if (port_ == 443) {\n req.headers.emplace(\"Host\", host_);\n } else {\n req.headers.emplace(\"Host\", host_and_port_);\n }\n } else {\n if (port_ == 80) {\n req.headers.emplace(\"Host\", host_);\n } else {\n req.headers.emplace(\"Host\", host_and_port_);\n }\n }\n }\n\n if (!req.has_header(\"Accept\")) { req.headers.emplace(\"Accept\", \"*/*\"); }\n\n#ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT\n if (!req.has_header(\"User-Agent\")) {\n auto agent = std::string(\"cpp-httplib/\") + CPPHTTPLIB_VERSION;\n req.headers.emplace(\"User-Agent\", agent);\n }\n#endif\n\n if (req.body.empty()) {\n if (req.content_provider_) {\n if (!req.is_chunked_content_provider_) {\n if (!req.has_header(\"Content-Length\")) {\n auto length = std::to_string(req.content_length_);\n req.headers.emplace(\"Content-Length\", length);\n }\n }\n } else {\n if (req.method == \"POST\" || req.method == \"PUT\" ||\n req.method == \"PATCH\") {\n req.headers.emplace(\"Content-Length\", \"0\");\n }\n }\n } else {\n if (!req.has_header(\"Content-Type\")) {\n req.headers.emplace(\"Content-Type\", \"text/plain\");\n }\n\n if (!req.has_header(\"Content-Length\")) {\n auto length = std::to_string(req.body.size());\n req.headers.emplace(\"Content-Length\", length);\n }\n }\n\n if (!basic_auth_password_.empty() || !basic_auth_username_.empty()) {\n if (!req.has_header(\"Authorization\")) {\n req.headers.insert(make_basic_authentication_header(\n basic_auth_username_, basic_auth_password_, false));\n }\n }\n\n if (!proxy_basic_auth_username_.empty() &&\n !proxy_basic_auth_password_.empty()) {\n if (!req.has_header(\"Proxy-Authorization\")) {\n req.headers.insert(make_basic_authentication_header(\n proxy_basic_auth_username_, proxy_basic_auth_password_, true));\n }\n }\n\n if (!bearer_token_auth_token_.empty()) {\n if (!req.has_header(\"Authorization\")) {\n req.headers.insert(make_bearer_token_authentication_header(\n bearer_token_auth_token_, false));\n }\n }\n\n if (!proxy_bearer_token_auth_token_.empty()) {\n if (!req.has_header(\"Proxy-Authorization\")) {\n req.headers.insert(make_bearer_token_authentication_header(\n proxy_bearer_token_auth_token_, true));\n }\n }\n\n // Request line and headers\n {\n detail::BufferStream bstrm;\n\n const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path;\n bstrm.write_format(\"%s %s HTTP/1.1\\r\\n\", req.method.c_str(), path.c_str());\n\n detail::write_headers(bstrm, req.headers);\n\n // Flush buffer\n auto &data = bstrm.get_buffer();\n if (!detail::write_data(strm, data.data(), data.size())) {\n error = Error::Write;\n return false;\n }\n }\n\n // Body\n if (req.body.empty()) {\n return write_content_with_provider(strm, req, error);\n }\n\n if (!detail::write_data(strm, req.body.data(), req.body.size())) {\n error = Error::Write;\n return false;\n }\n\n return true;\n}\n\ninline std::unique_ptr ClientImpl::send_with_content_provider(\n Request &req, const char *body, size_t content_length,\n ContentProvider content_provider,\n ContentProviderWithoutLength content_provider_without_length,\n const std::string &content_type, Error &error) {\n if (!content_type.empty()) {\n req.headers.emplace(\"Content-Type\", content_type);\n }\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n if (compress_) { req.headers.emplace(\"Content-Encoding\", \"gzip\"); }\n#endif\n\n#ifdef CPPHTTPLIB_ZLIB_SUPPORT\n if (compress_ && !content_provider_without_length) {\n // TODO: Brotli support\n detail::gzip_compressor compressor;\n\n if (content_provider) {\n auto ok = true;\n size_t offset = 0;\n DataSink data_sink;\n\n data_sink.write = [&](const char *data, size_t data_len) -> bool {\n if (ok) {\n auto last = offset + data_len == content_length;\n\n auto ret = compressor.compress(\n data, data_len, last,\n [&](const char *compressed_data, size_t compressed_data_len) {\n req.body.append(compressed_data, compressed_data_len);\n return true;\n });\n\n if (ret) {\n offset += data_len;\n } else {\n ok = false;\n }\n }\n return ok;\n };\n\n while (ok && offset < content_length) {\n if (!content_provider(offset, content_length - offset, data_sink)) {\n error = Error::Canceled;\n return nullptr;\n }\n }\n } else {\n if (!compressor.compress(body, content_length, true,\n [&](const char *data, size_t data_len) {\n req.body.append(data, data_len);\n return true;\n })) {\n error = Error::Compression;\n return nullptr;\n }\n }\n } else\n#endif\n {\n if (content_provider) {\n req.content_length_ = content_length;\n req.content_provider_ = std::move(content_provider);\n req.is_chunked_content_provider_ = false;\n } else if (content_provider_without_length) {\n req.content_length_ = 0;\n req.content_provider_ = detail::ContentProviderAdapter(\n std::move(content_provider_without_length));\n req.is_chunked_content_provider_ = true;\n req.headers.emplace(\"Transfer-Encoding\", \"chunked\");\n } else {\n req.body.assign(body, content_length);\n ;\n }\n }\n\n auto res = detail::make_unique();\n return send(req, *res, error) ? std::move(res) : nullptr;\n}\n\ninline Result ClientImpl::send_with_content_provider(\n const std::string &method, const std::string &path, const Headers &headers,\n const char *body, size_t content_length, ContentProvider content_provider,\n ContentProviderWithoutLength content_provider_without_length,\n const std::string &content_type) {\n Request req;\n req.method = method;\n req.headers = headers;\n req.path = path;\n\n auto error = Error::Success;\n\n auto res = send_with_content_provider(\n req, body, content_length, std::move(content_provider),\n std::move(content_provider_without_length), content_type, error);\n\n return Result{std::move(res), error, std::move(req.headers)};\n}\n\ninline std::string\nClientImpl::adjust_host_string(const std::string &host) const {\n if (host.find(':') != std::string::npos) { return \"[\" + host + \"]\"; }\n return host;\n}\n\ninline bool ClientImpl::process_request(Stream &strm, Request &req,\n Response &res, bool close_connection,\n Error &error) {\n // Send request\n if (!write_request(strm, req, close_connection, error)) { return false; }\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n if (is_ssl()) {\n auto is_proxy_enabled = !proxy_host_.empty() && proxy_port_ != -1;\n if (!is_proxy_enabled) {\n char buf[1];\n if (SSL_peek(socket_.ssl, buf, 1) == 0 &&\n SSL_get_error(socket_.ssl, 0) == SSL_ERROR_ZERO_RETURN) {\n error = Error::SSLPeerCouldBeClosed_;\n return false;\n }\n }\n }\n#endif\n\n // Receive response and headers\n if (!read_response_line(strm, req, res) ||\n !detail::read_headers(strm, res.headers)) {\n error = Error::Read;\n return false;\n }\n\n // Body\n if ((res.status != 204) && req.method != \"HEAD\" && req.method != \"CONNECT\") {\n auto redirect = 300 < res.status && res.status < 400 && follow_location_;\n\n if (req.response_handler && !redirect) {\n if (!req.response_handler(res)) {\n error = Error::Canceled;\n return false;\n }\n }\n\n auto out =\n req.content_receiver\n ? static_cast(\n [&](const char *buf, size_t n, uint64_t off, uint64_t len) {\n if (redirect) { return true; }\n auto ret = req.content_receiver(buf, n, off, len);\n if (!ret) { error = Error::Canceled; }\n return ret;\n })\n : static_cast(\n [&](const char *buf, size_t n, uint64_t /*off*/,\n uint64_t /*len*/) {\n if (res.body.size() + n > res.body.max_size()) {\n return false;\n }\n res.body.append(buf, n);\n return true;\n });\n\n auto progress = [&](uint64_t current, uint64_t total) {\n if (!req.progress || redirect) { return true; }\n auto ret = req.progress(current, total);\n if (!ret) { error = Error::Canceled; }\n return ret;\n };\n\n int dummy_status;\n if (!detail::read_content(strm, res, (std::numeric_limits::max)(),\n dummy_status, std::move(progress), std::move(out),\n decompress_)) {\n if (error != Error::Canceled) { error = Error::Read; }\n return false;\n }\n }\n\n if (res.get_header_value(\"Connection\") == \"close\" ||\n (res.version == \"HTTP/1.0\" && res.reason != \"Connection established\")) {\n // TODO this requires a not-entirely-obvious chain of calls to be correct\n // for this to be safe. Maybe a code refactor (such as moving this out to\n // the send function and getting rid of the recursiveness of the mutex)\n // could make this more obvious.\n\n // This is safe to call because process_request is only called by\n // handle_request which is only called by send, which locks the request\n // mutex during the process. It would be a bug to call it from a different\n // thread since it's a thread-safety issue to do these things to the socket\n // if another thread is using the socket.\n std::lock_guard guard(socket_mutex_);\n shutdown_ssl(socket_, true);\n shutdown_socket(socket_);\n close_socket(socket_);\n }\n\n // Log\n if (logger_) { logger_(req, res); }\n\n return true;\n}\n\ninline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(\n const std::string &boundary, const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items) {\n size_t cur_item = 0, cur_start = 0;\n // cur_item and cur_start are copied to within the std::function and maintain\n // state between successive calls\n return [&, cur_item, cur_start](size_t offset,\n DataSink &sink) mutable -> bool {\n if (!offset && items.size()) {\n sink.os << detail::serialize_multipart_formdata(items, boundary, false);\n return true;\n } else if (cur_item < provider_items.size()) {\n if (!cur_start) {\n const auto &begin = detail::serialize_multipart_formdata_item_begin(\n provider_items[cur_item], boundary);\n offset += begin.size();\n cur_start = offset;\n sink.os << begin;\n }\n\n DataSink cur_sink;\n bool has_data = true;\n cur_sink.write = sink.write;\n cur_sink.done = [&]() { has_data = false; };\n\n if (!provider_items[cur_item].provider(offset - cur_start, cur_sink))\n return false;\n\n if (!has_data) {\n sink.os << detail::serialize_multipart_formdata_item_end();\n cur_item++;\n cur_start = 0;\n }\n return true;\n } else {\n sink.os << detail::serialize_multipart_formdata_finish(boundary);\n sink.done();\n return true;\n }\n };\n}\n\ninline bool\nClientImpl::process_socket(const Socket &socket,\n std::function callback) {\n return detail::process_client_socket(\n socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,\n write_timeout_usec_, std::move(callback));\n}\n\ninline bool ClientImpl::is_ssl() const { return false; }\n\ninline Result ClientImpl::Get(const std::string &path) {\n return Get(path, Headers(), Progress());\n}\n\ninline Result ClientImpl::Get(const std::string &path, Progress progress) {\n return Get(path, Headers(), std::move(progress));\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers) {\n return Get(path, headers, Progress());\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers,\n Progress progress) {\n Request req;\n req.method = \"GET\";\n req.path = path;\n req.headers = headers;\n req.progress = std::move(progress);\n\n return send_(std::move(req));\n}\n\ninline Result ClientImpl::Get(const std::string &path,\n ContentReceiver content_receiver) {\n return Get(path, Headers(), nullptr, std::move(content_receiver), nullptr);\n}\n\ninline Result ClientImpl::Get(const std::string &path,\n ContentReceiver content_receiver,\n Progress progress) {\n return Get(path, Headers(), nullptr, std::move(content_receiver),\n std::move(progress));\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver) {\n return Get(path, headers, nullptr, std::move(content_receiver), nullptr);\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver,\n Progress progress) {\n return Get(path, headers, nullptr, std::move(content_receiver),\n std::move(progress));\n}\n\ninline Result ClientImpl::Get(const std::string &path,\n ResponseHandler response_handler,\n ContentReceiver content_receiver) {\n return Get(path, Headers(), std::move(response_handler),\n std::move(content_receiver), nullptr);\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver) {\n return Get(path, headers, std::move(response_handler),\n std::move(content_receiver), nullptr);\n}\n\ninline Result ClientImpl::Get(const std::string &path,\n ResponseHandler response_handler,\n ContentReceiver content_receiver,\n Progress progress) {\n return Get(path, Headers(), std::move(response_handler),\n std::move(content_receiver), std::move(progress));\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver,\n Progress progress) {\n Request req;\n req.method = \"GET\";\n req.path = path;\n req.headers = headers;\n req.response_handler = std::move(response_handler);\n req.content_receiver =\n [content_receiver](const char *data, size_t data_length,\n uint64_t /*offset*/, uint64_t /*total_length*/) {\n return content_receiver(data, data_length);\n };\n req.progress = std::move(progress);\n\n return send_(std::move(req));\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Params ¶ms,\n const Headers &headers, Progress progress) {\n if (params.empty()) { return Get(path, headers); }\n\n std::string path_with_query = append_query_params(path, params);\n return Get(path_with_query.c_str(), headers, progress);\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Params ¶ms,\n const Headers &headers,\n ContentReceiver content_receiver,\n Progress progress) {\n return Get(path, params, headers, nullptr, content_receiver, progress);\n}\n\ninline Result ClientImpl::Get(const std::string &path, const Params ¶ms,\n const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver,\n Progress progress) {\n if (params.empty()) {\n return Get(path, headers, response_handler, content_receiver, progress);\n }\n\n std::string path_with_query = append_query_params(path, params);\n return Get(path_with_query.c_str(), headers, response_handler,\n content_receiver, progress);\n}\n\ninline Result ClientImpl::Head(const std::string &path) {\n return Head(path, Headers());\n}\n\ninline Result ClientImpl::Head(const std::string &path,\n const Headers &headers) {\n Request req;\n req.method = \"HEAD\";\n req.headers = headers;\n req.path = path;\n\n return send_(std::move(req));\n}\n\ninline Result ClientImpl::Post(const std::string &path) {\n return Post(path, std::string(), std::string());\n}\n\ninline Result ClientImpl::Post(const std::string &path,\n const Headers &headers) {\n return Post(path, headers, nullptr, 0, std::string());\n}\n\ninline Result ClientImpl::Post(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return Post(path, Headers(), body, content_length, content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return send_with_content_provider(\"POST\", path, headers, body, content_length,\n nullptr, nullptr, content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return Post(path, Headers(), body, content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return send_with_content_provider(\"POST\", path, headers, body.data(),\n body.size(), nullptr, nullptr,\n content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Params ¶ms) {\n return Post(path, Headers(), params);\n}\n\ninline Result ClientImpl::Post(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return Post(path, Headers(), content_length, std::move(content_provider),\n content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return Post(path, Headers(), std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"POST\", path, headers, nullptr,\n content_length, std::move(content_provider),\n nullptr, content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"POST\", path, headers, nullptr, 0, nullptr,\n std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n const Params ¶ms) {\n auto query = detail::params_to_query_str(params);\n return Post(path, headers, query, \"application/x-www-form-urlencoded\");\n}\n\ninline Result ClientImpl::Post(const std::string &path,\n const MultipartFormDataItems &items) {\n return Post(path, Headers(), items);\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items) {\n const auto &boundary = detail::make_multipart_data_boundary();\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n const auto &body = detail::serialize_multipart_formdata(items, boundary);\n return Post(path, headers, body, content_type.c_str());\n}\n\ninline Result ClientImpl::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const std::string &boundary) {\n if (!detail::is_multipart_boundary_chars_valid(boundary)) {\n return Result{nullptr, Error::UnsupportedMultipartBoundaryChars};\n }\n\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n const auto &body = detail::serialize_multipart_formdata(items, boundary);\n return Post(path, headers, body, content_type.c_str());\n}\n\ninline Result\nClientImpl::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items) {\n const auto &boundary = detail::make_multipart_data_boundary();\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n return send_with_content_provider(\n \"POST\", path, headers, nullptr, 0, nullptr,\n get_multipart_content_provider(boundary, items, provider_items),\n content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path) {\n return Put(path, std::string(), std::string());\n}\n\ninline Result ClientImpl::Put(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return Put(path, Headers(), body, content_length, content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return send_with_content_provider(\"PUT\", path, headers, body, content_length,\n nullptr, nullptr, content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return Put(path, Headers(), body, content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return send_with_content_provider(\"PUT\", path, headers, body.data(),\n body.size(), nullptr, nullptr,\n content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return Put(path, Headers(), content_length, std::move(content_provider),\n content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return Put(path, Headers(), std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"PUT\", path, headers, nullptr,\n content_length, std::move(content_provider),\n nullptr, content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"PUT\", path, headers, nullptr, 0, nullptr,\n std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Params ¶ms) {\n return Put(path, Headers(), params);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n const Params ¶ms) {\n auto query = detail::params_to_query_str(params);\n return Put(path, headers, query, \"application/x-www-form-urlencoded\");\n}\n\ninline Result ClientImpl::Put(const std::string &path,\n const MultipartFormDataItems &items) {\n return Put(path, Headers(), items);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items) {\n const auto &boundary = detail::make_multipart_data_boundary();\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n const auto &body = detail::serialize_multipart_formdata(items, boundary);\n return Put(path, headers, body, content_type);\n}\n\ninline Result ClientImpl::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const std::string &boundary) {\n if (!detail::is_multipart_boundary_chars_valid(boundary)) {\n return Result{nullptr, Error::UnsupportedMultipartBoundaryChars};\n }\n\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n const auto &body = detail::serialize_multipart_formdata(items, boundary);\n return Put(path, headers, body, content_type);\n}\n\ninline Result\nClientImpl::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items) {\n const auto &boundary = detail::make_multipart_data_boundary();\n const auto &content_type =\n detail::serialize_multipart_formdata_get_content_type(boundary);\n return send_with_content_provider(\n \"PUT\", path, headers, nullptr, 0, nullptr,\n get_multipart_content_provider(boundary, items, provider_items),\n content_type);\n}\ninline Result ClientImpl::Patch(const std::string &path) {\n return Patch(path, std::string(), std::string());\n}\n\ninline Result ClientImpl::Patch(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return Patch(path, Headers(), body, content_length, content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return send_with_content_provider(\"PATCH\", path, headers, body,\n content_length, nullptr, nullptr,\n content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path,\n const std::string &body,\n const std::string &content_type) {\n return Patch(path, Headers(), body, content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return send_with_content_provider(\"PATCH\", path, headers, body.data(),\n body.size(), nullptr, nullptr,\n content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return Patch(path, Headers(), content_length, std::move(content_provider),\n content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return Patch(path, Headers(), std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"PATCH\", path, headers, nullptr,\n content_length, std::move(content_provider),\n nullptr, content_type);\n}\n\ninline Result ClientImpl::Patch(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return send_with_content_provider(\"PATCH\", path, headers, nullptr, 0, nullptr,\n std::move(content_provider), content_type);\n}\n\ninline Result ClientImpl::Delete(const std::string &path) {\n return Delete(path, Headers(), std::string(), std::string());\n}\n\ninline Result ClientImpl::Delete(const std::string &path,\n const Headers &headers) {\n return Delete(path, headers, std::string(), std::string());\n}\n\ninline Result ClientImpl::Delete(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return Delete(path, Headers(), body, content_length, content_type);\n}\n\ninline Result ClientImpl::Delete(const std::string &path,\n const Headers &headers, const char *body,\n size_t content_length,\n const std::string &content_type) {\n Request req;\n req.method = \"DELETE\";\n req.headers = headers;\n req.path = path;\n\n if (!content_type.empty()) {\n req.headers.emplace(\"Content-Type\", content_type);\n }\n req.body.assign(body, content_length);\n\n return send_(std::move(req));\n}\n\ninline Result ClientImpl::Delete(const std::string &path,\n const std::string &body,\n const std::string &content_type) {\n return Delete(path, Headers(), body.data(), body.size(), content_type);\n}\n\ninline Result ClientImpl::Delete(const std::string &path,\n const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return Delete(path, headers, body.data(), body.size(), content_type);\n}\n\ninline Result ClientImpl::Options(const std::string &path) {\n return Options(path, Headers());\n}\n\ninline Result ClientImpl::Options(const std::string &path,\n const Headers &headers) {\n Request req;\n req.method = \"OPTIONS\";\n req.headers = headers;\n req.path = path;\n\n return send_(std::move(req));\n}\n\ninline size_t ClientImpl::is_socket_open() const {\n std::lock_guard guard(socket_mutex_);\n return socket_.is_open();\n}\n\ninline socket_t ClientImpl::socket() const { return socket_.sock; }\n\ninline void ClientImpl::stop() {\n std::lock_guard guard(socket_mutex_);\n\n // If there is anything ongoing right now, the ONLY thread-safe thing we can\n // do is to shutdown_socket, so that threads using this socket suddenly\n // discover they can't read/write any more and error out. Everything else\n // (closing the socket, shutting ssl down) is unsafe because these actions are\n // not thread-safe.\n if (socket_requests_in_flight_ > 0) {\n shutdown_socket(socket_);\n\n // Aside from that, we set a flag for the socket to be closed when we're\n // done.\n socket_should_be_closed_when_request_is_done_ = true;\n return;\n }\n\n // Otherwise, still holding the mutex, we can shut everything down ourselves\n shutdown_ssl(socket_, true);\n shutdown_socket(socket_);\n close_socket(socket_);\n}\n\ninline void ClientImpl::set_connection_timeout(time_t sec, time_t usec) {\n connection_timeout_sec_ = sec;\n connection_timeout_usec_ = usec;\n}\n\ninline void ClientImpl::set_read_timeout(time_t sec, time_t usec) {\n read_timeout_sec_ = sec;\n read_timeout_usec_ = usec;\n}\n\ninline void ClientImpl::set_write_timeout(time_t sec, time_t usec) {\n write_timeout_sec_ = sec;\n write_timeout_usec_ = usec;\n}\n\ninline void ClientImpl::set_basic_auth(const std::string &username,\n const std::string &password) {\n basic_auth_username_ = username;\n basic_auth_password_ = password;\n}\n\ninline void ClientImpl::set_bearer_token_auth(const std::string &token) {\n bearer_token_auth_token_ = token;\n}\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void ClientImpl::set_digest_auth(const std::string &username,\n const std::string &password) {\n digest_auth_username_ = username;\n digest_auth_password_ = password;\n}\n#endif\n\ninline void ClientImpl::set_keep_alive(bool on) { keep_alive_ = on; }\n\ninline void ClientImpl::set_follow_location(bool on) { follow_location_ = on; }\n\ninline void ClientImpl::set_url_encode(bool on) { url_encode_ = on; }\n\ninline void\nClientImpl::set_hostname_addr_map(std::map addr_map) {\n addr_map_ = std::move(addr_map);\n}\n\ninline void ClientImpl::set_default_headers(Headers headers) {\n default_headers_ = std::move(headers);\n}\n\ninline void ClientImpl::set_address_family(int family) {\n address_family_ = family;\n}\n\ninline void ClientImpl::set_tcp_nodelay(bool on) { tcp_nodelay_ = on; }\n\ninline void ClientImpl::set_socket_options(SocketOptions socket_options) {\n socket_options_ = std::move(socket_options);\n}\n\ninline void ClientImpl::set_compress(bool on) { compress_ = on; }\n\ninline void ClientImpl::set_decompress(bool on) { decompress_ = on; }\n\ninline void ClientImpl::set_interface(const std::string &intf) {\n interface_ = intf;\n}\n\ninline void ClientImpl::set_proxy(const std::string &host, int port) {\n proxy_host_ = host;\n proxy_port_ = port;\n}\n\ninline void ClientImpl::set_proxy_basic_auth(const std::string &username,\n const std::string &password) {\n proxy_basic_auth_username_ = username;\n proxy_basic_auth_password_ = password;\n}\n\ninline void ClientImpl::set_proxy_bearer_token_auth(const std::string &token) {\n proxy_bearer_token_auth_token_ = token;\n}\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void ClientImpl::set_proxy_digest_auth(const std::string &username,\n const std::string &password) {\n proxy_digest_auth_username_ = username;\n proxy_digest_auth_password_ = password;\n}\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void ClientImpl::set_ca_cert_path(const std::string &ca_cert_file_path,\n const std::string &ca_cert_dir_path) {\n ca_cert_file_path_ = ca_cert_file_path;\n ca_cert_dir_path_ = ca_cert_dir_path;\n}\n\ninline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) {\n if (ca_cert_store && ca_cert_store != ca_cert_store_) {\n ca_cert_store_ = ca_cert_store;\n }\n}\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void ClientImpl::enable_server_certificate_verification(bool enabled) {\n server_certificate_verification_ = enabled;\n}\n#endif\n\ninline void ClientImpl::set_logger(Logger logger) {\n logger_ = std::move(logger);\n}\n\n/*\n * SSL Implementation\n */\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\nnamespace detail {\n\ntemplate \ninline SSL *ssl_new(socket_t sock, SSL_CTX *ctx, std::mutex &ctx_mutex,\n U SSL_connect_or_accept, V setup) {\n SSL *ssl = nullptr;\n {\n std::lock_guard guard(ctx_mutex);\n ssl = SSL_new(ctx);\n }\n\n if (ssl) {\n set_nonblocking(sock, true);\n auto bio = BIO_new_socket(static_cast(sock), BIO_NOCLOSE);\n BIO_set_nbio(bio, 1);\n SSL_set_bio(ssl, bio, bio);\n\n if (!setup(ssl) || SSL_connect_or_accept(ssl) != 1) {\n SSL_shutdown(ssl);\n {\n std::lock_guard guard(ctx_mutex);\n SSL_free(ssl);\n }\n set_nonblocking(sock, false);\n return nullptr;\n }\n BIO_set_nbio(bio, 0);\n set_nonblocking(sock, false);\n }\n\n return ssl;\n}\n\ninline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl,\n bool shutdown_gracefully) {\n // sometimes we may want to skip this to try to avoid SIGPIPE if we know\n // the remote has closed the network connection\n // Note that it is not always possible to avoid SIGPIPE, this is merely a\n // best-efforts.\n if (shutdown_gracefully) { SSL_shutdown(ssl); }\n\n std::lock_guard guard(ctx_mutex);\n SSL_free(ssl);\n}\n\ntemplate \nbool ssl_connect_or_accept_nonblocking(socket_t sock, SSL *ssl,\n U ssl_connect_or_accept,\n time_t timeout_sec,\n time_t timeout_usec) {\n int res = 0;\n while ((res = ssl_connect_or_accept(ssl)) != 1) {\n auto err = SSL_get_error(ssl, res);\n switch (err) {\n case SSL_ERROR_WANT_READ:\n if (select_read(sock, timeout_sec, timeout_usec) > 0) { continue; }\n break;\n case SSL_ERROR_WANT_WRITE:\n if (select_write(sock, timeout_sec, timeout_usec) > 0) { continue; }\n break;\n default: break;\n }\n return false;\n }\n return true;\n}\n\ntemplate \ninline bool process_server_socket_ssl(\n const std::atomic &svr_sock, SSL *ssl, socket_t sock,\n size_t keep_alive_max_count, time_t keep_alive_timeout_sec,\n time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec, T callback) {\n return process_server_socket_core(\n svr_sock, sock, keep_alive_max_count, keep_alive_timeout_sec,\n [&](bool close_connection, bool &connection_closed) {\n SSLSocketStream strm(sock, ssl, read_timeout_sec, read_timeout_usec,\n write_timeout_sec, write_timeout_usec);\n return callback(strm, close_connection, connection_closed);\n });\n}\n\ntemplate \ninline bool\nprocess_client_socket_ssl(SSL *ssl, socket_t sock, time_t read_timeout_sec,\n time_t read_timeout_usec, time_t write_timeout_sec,\n time_t write_timeout_usec, T callback) {\n SSLSocketStream strm(sock, ssl, read_timeout_sec, read_timeout_usec,\n write_timeout_sec, write_timeout_usec);\n return callback(strm);\n}\n\nclass SSLInit {\npublic:\n SSLInit() {\n OPENSSL_init_ssl(\n OPENSSL_INIT_LOAD_SSL_STRINGS | OPENSSL_INIT_LOAD_CRYPTO_STRINGS, NULL);\n }\n};\n\n// SSL socket stream implementation\ninline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl,\n time_t read_timeout_sec,\n time_t read_timeout_usec,\n time_t write_timeout_sec,\n time_t write_timeout_usec)\n : sock_(sock), ssl_(ssl), read_timeout_sec_(read_timeout_sec),\n read_timeout_usec_(read_timeout_usec),\n write_timeout_sec_(write_timeout_sec),\n write_timeout_usec_(write_timeout_usec) {\n SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY);\n}\n\ninline SSLSocketStream::~SSLSocketStream() {}\n\ninline bool SSLSocketStream::is_readable() const {\n return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;\n}\n\ninline bool SSLSocketStream::is_writable() const {\n return select_write(sock_, write_timeout_sec_, write_timeout_usec_) > 0 &&\n is_socket_alive(sock_);\n}\n\ninline ssize_t SSLSocketStream::read(char *ptr, size_t size) {\n if (SSL_pending(ssl_) > 0) {\n return SSL_read(ssl_, ptr, static_cast(size));\n } else if (is_readable()) {\n auto ret = SSL_read(ssl_, ptr, static_cast(size));\n if (ret < 0) {\n auto err = SSL_get_error(ssl_, ret);\n int n = 1000;\n#ifdef _WIN32\n while (--n >= 0 && (err == SSL_ERROR_WANT_READ ||\n (err == SSL_ERROR_SYSCALL &&\n WSAGetLastError() == WSAETIMEDOUT))) {\n#else\n while (--n >= 0 && err == SSL_ERROR_WANT_READ) {\n#endif\n if (SSL_pending(ssl_) > 0) {\n return SSL_read(ssl_, ptr, static_cast(size));\n } else if (is_readable()) {\n std::this_thread::sleep_for(std::chrono::milliseconds(1));\n ret = SSL_read(ssl_, ptr, static_cast(size));\n if (ret >= 0) { return ret; }\n err = SSL_get_error(ssl_, ret);\n } else {\n return -1;\n }\n }\n }\n return ret;\n }\n return -1;\n}\n\ninline ssize_t SSLSocketStream::write(const char *ptr, size_t size) {\n if (is_writable()) {\n auto handle_size = static_cast(\n std::min(size, (std::numeric_limits::max)()));\n\n auto ret = SSL_write(ssl_, ptr, static_cast(handle_size));\n if (ret < 0) {\n auto err = SSL_get_error(ssl_, ret);\n int n = 1000;\n#ifdef _WIN32\n while (--n >= 0 && (err == SSL_ERROR_WANT_WRITE ||\n (err == SSL_ERROR_SYSCALL &&\n WSAGetLastError() == WSAETIMEDOUT))) {\n#else\n while (--n >= 0 && err == SSL_ERROR_WANT_WRITE) {\n#endif\n if (is_writable()) {\n std::this_thread::sleep_for(std::chrono::milliseconds(1));\n ret = SSL_write(ssl_, ptr, static_cast(handle_size));\n if (ret >= 0) { return ret; }\n err = SSL_get_error(ssl_, ret);\n } else {\n return -1;\n }\n }\n }\n return ret;\n }\n return -1;\n}\n\ninline void SSLSocketStream::get_remote_ip_and_port(std::string &ip,\n int &port) const {\n detail::get_remote_ip_and_port(sock_, ip, port);\n}\n\ninline void SSLSocketStream::get_local_ip_and_port(std::string &ip,\n int &port) const {\n detail::get_local_ip_and_port(sock_, ip, port);\n}\n\ninline socket_t SSLSocketStream::socket() const { return sock_; }\n\nstatic SSLInit sslinit_;\n\n} // namespace detail\n\n// SSL HTTP server implementation\ninline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,\n const char *client_ca_cert_file_path,\n const char *client_ca_cert_dir_path,\n const char *private_key_password) {\n ctx_ = SSL_CTX_new(TLS_server_method());\n\n if (ctx_) {\n SSL_CTX_set_options(ctx_,\n SSL_OP_NO_COMPRESSION |\n SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION);\n\n SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);\n\n // add default password callback before opening encrypted private key\n if (private_key_password != nullptr && (private_key_password[0] != '\\0')) {\n SSL_CTX_set_default_passwd_cb_userdata(ctx_,\n (char *)private_key_password);\n }\n\n if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 ||\n SSL_CTX_use_PrivateKey_file(ctx_, private_key_path, SSL_FILETYPE_PEM) !=\n 1) {\n SSL_CTX_free(ctx_);\n ctx_ = nullptr;\n } else if (client_ca_cert_file_path || client_ca_cert_dir_path) {\n SSL_CTX_load_verify_locations(ctx_, client_ca_cert_file_path,\n client_ca_cert_dir_path);\n\n SSL_CTX_set_verify(\n ctx_, SSL_VERIFY_PEER | SSL_VERIFY_FAIL_IF_NO_PEER_CERT, nullptr);\n }\n }\n}\n\ninline SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key,\n X509_STORE *client_ca_cert_store) {\n ctx_ = SSL_CTX_new(TLS_server_method());\n\n if (ctx_) {\n SSL_CTX_set_options(ctx_,\n SSL_OP_NO_COMPRESSION |\n SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION);\n\n SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);\n\n if (SSL_CTX_use_certificate(ctx_, cert) != 1 ||\n SSL_CTX_use_PrivateKey(ctx_, private_key) != 1) {\n SSL_CTX_free(ctx_);\n ctx_ = nullptr;\n } else if (client_ca_cert_store) {\n SSL_CTX_set_cert_store(ctx_, client_ca_cert_store);\n\n SSL_CTX_set_verify(\n ctx_, SSL_VERIFY_PEER | SSL_VERIFY_FAIL_IF_NO_PEER_CERT, nullptr);\n }\n }\n}\n\ninline SSLServer::SSLServer(\n const std::function &setup_ssl_ctx_callback) {\n ctx_ = SSL_CTX_new(TLS_method());\n if (ctx_) {\n if (!setup_ssl_ctx_callback(*ctx_)) {\n SSL_CTX_free(ctx_);\n ctx_ = nullptr;\n }\n }\n}\n\ninline SSLServer::~SSLServer() {\n if (ctx_) { SSL_CTX_free(ctx_); }\n}\n\ninline bool SSLServer::is_valid() const { return ctx_; }\n\ninline SSL_CTX *SSLServer::ssl_context() const { return ctx_; }\n\ninline bool SSLServer::process_and_close_socket(socket_t sock) {\n auto ssl = detail::ssl_new(\n sock, ctx_, ctx_mutex_,\n [&](SSL *ssl2) {\n return detail::ssl_connect_or_accept_nonblocking(\n sock, ssl2, SSL_accept, read_timeout_sec_, read_timeout_usec_);\n },\n [](SSL * /*ssl2*/) { return true; });\n\n auto ret = false;\n if (ssl) {\n ret = detail::process_server_socket_ssl(\n svr_sock_, ssl, sock, keep_alive_max_count_, keep_alive_timeout_sec_,\n read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,\n write_timeout_usec_,\n [this, ssl](Stream &strm, bool close_connection,\n bool &connection_closed) {\n return process_request(strm, close_connection, connection_closed,\n [&](Request &req) { req.ssl = ssl; });\n });\n\n // Shutdown gracefully if the result seemed successful, non-gracefully if\n // the connection appeared to be closed.\n const bool shutdown_gracefully = ret;\n detail::ssl_delete(ctx_mutex_, ssl, shutdown_gracefully);\n }\n\n detail::shutdown_socket(sock);\n detail::close_socket(sock);\n return ret;\n}\n\n// SSL HTTP client implementation\ninline SSLClient::SSLClient(const std::string &host)\n : SSLClient(host, 443, std::string(), std::string()) {}\n\ninline SSLClient::SSLClient(const std::string &host, int port)\n : SSLClient(host, port, std::string(), std::string()) {}\n\ninline SSLClient::SSLClient(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path)\n : ClientImpl(host, port, client_cert_path, client_key_path) {\n ctx_ = SSL_CTX_new(TLS_client_method());\n\n detail::split(&host_[0], &host_[host_.size()], '.',\n [&](const char *b, const char *e) {\n host_components_.emplace_back(std::string(b, e));\n });\n\n if (!client_cert_path.empty() && !client_key_path.empty()) {\n if (SSL_CTX_use_certificate_file(ctx_, client_cert_path.c_str(),\n SSL_FILETYPE_PEM) != 1 ||\n SSL_CTX_use_PrivateKey_file(ctx_, client_key_path.c_str(),\n SSL_FILETYPE_PEM) != 1) {\n SSL_CTX_free(ctx_);\n ctx_ = nullptr;\n }\n }\n}\n\ninline SSLClient::SSLClient(const std::string &host, int port,\n X509 *client_cert, EVP_PKEY *client_key)\n : ClientImpl(host, port) {\n ctx_ = SSL_CTX_new(TLS_client_method());\n\n detail::split(&host_[0], &host_[host_.size()], '.',\n [&](const char *b, const char *e) {\n host_components_.emplace_back(std::string(b, e));\n });\n\n if (client_cert != nullptr && client_key != nullptr) {\n if (SSL_CTX_use_certificate(ctx_, client_cert) != 1 ||\n SSL_CTX_use_PrivateKey(ctx_, client_key) != 1) {\n SSL_CTX_free(ctx_);\n ctx_ = nullptr;\n }\n }\n}\n\ninline SSLClient::~SSLClient() {\n if (ctx_) { SSL_CTX_free(ctx_); }\n // Make sure to shut down SSL since shutdown_ssl will resolve to the\n // base function rather than the derived function once we get to the\n // base class destructor, and won't free the SSL (causing a leak).\n shutdown_ssl_impl(socket_, true);\n}\n\ninline bool SSLClient::is_valid() const { return ctx_; }\n\ninline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) {\n if (ca_cert_store) {\n if (ctx_) {\n if (SSL_CTX_get_cert_store(ctx_) != ca_cert_store) {\n // Free memory allocated for old cert and use new store `ca_cert_store`\n SSL_CTX_set_cert_store(ctx_, ca_cert_store);\n }\n } else {\n X509_STORE_free(ca_cert_store);\n }\n }\n}\n\ninline long SSLClient::get_openssl_verify_result() const {\n return verify_result_;\n}\n\ninline SSL_CTX *SSLClient::ssl_context() const { return ctx_; }\n\ninline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) {\n return is_valid() && ClientImpl::create_and_connect_socket(socket, error);\n}\n\n// Assumes that socket_mutex_ is locked and that there are no requests in flight\ninline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,\n bool &success, Error &error) {\n success = true;\n Response res2;\n if (!detail::process_client_socket(\n socket.sock, read_timeout_sec_, read_timeout_usec_,\n write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {\n Request req2;\n req2.method = \"CONNECT\";\n req2.path = host_and_port_;\n return process_request(strm, req2, res2, false, error);\n })) {\n // Thread-safe to close everything because we are assuming there are no\n // requests in flight\n shutdown_ssl(socket, true);\n shutdown_socket(socket);\n close_socket(socket);\n success = false;\n return false;\n }\n\n if (res2.status == 407) {\n if (!proxy_digest_auth_username_.empty() &&\n !proxy_digest_auth_password_.empty()) {\n std::map auth;\n if (detail::parse_www_authenticate(res2, auth, true)) {\n Response res3;\n if (!detail::process_client_socket(\n socket.sock, read_timeout_sec_, read_timeout_usec_,\n write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {\n Request req3;\n req3.method = \"CONNECT\";\n req3.path = host_and_port_;\n req3.headers.insert(detail::make_digest_authentication_header(\n req3, auth, 1, detail::random_string(10),\n proxy_digest_auth_username_, proxy_digest_auth_password_,\n true));\n return process_request(strm, req3, res3, false, error);\n })) {\n // Thread-safe to close everything because we are assuming there are\n // no requests in flight\n shutdown_ssl(socket, true);\n shutdown_socket(socket);\n close_socket(socket);\n success = false;\n return false;\n }\n }\n } else {\n res = res2;\n return false;\n }\n }\n\n return true;\n}\n\ninline bool SSLClient::load_certs() {\n bool ret = true;\n\n std::call_once(initialize_cert_, [&]() {\n std::lock_guard guard(ctx_mutex_);\n if (!ca_cert_file_path_.empty()) {\n if (!SSL_CTX_load_verify_locations(ctx_, ca_cert_file_path_.c_str(),\n nullptr)) {\n ret = false;\n }\n } else if (!ca_cert_dir_path_.empty()) {\n if (!SSL_CTX_load_verify_locations(ctx_, nullptr,\n ca_cert_dir_path_.c_str())) {\n ret = false;\n }\n } else {\n auto loaded = false;\n#ifdef _WIN32\n loaded =\n detail::load_system_certs_on_windows(SSL_CTX_get_cert_store(ctx_));\n#elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__)\n#if TARGET_OS_OSX\n loaded = detail::load_system_certs_on_macos(SSL_CTX_get_cert_store(ctx_));\n#endif // TARGET_OS_OSX\n#endif // _WIN32\n if (!loaded) { SSL_CTX_set_default_verify_paths(ctx_); }\n }\n });\n\n return ret;\n}\n\ninline bool SSLClient::initialize_ssl(Socket &socket, Error &error) {\n auto ssl = detail::ssl_new(\n socket.sock, ctx_, ctx_mutex_,\n [&](SSL *ssl2) {\n if (server_certificate_verification_) {\n if (!load_certs()) {\n error = Error::SSLLoadingCerts;\n return false;\n }\n SSL_set_verify(ssl2, SSL_VERIFY_NONE, nullptr);\n }\n\n if (!detail::ssl_connect_or_accept_nonblocking(\n socket.sock, ssl2, SSL_connect, connection_timeout_sec_,\n connection_timeout_usec_)) {\n error = Error::SSLConnection;\n return false;\n }\n\n if (server_certificate_verification_) {\n verify_result_ = SSL_get_verify_result(ssl2);\n\n if (verify_result_ != X509_V_OK) {\n error = Error::SSLServerVerification;\n return false;\n }\n\n auto server_cert = SSL_get1_peer_certificate(ssl2);\n\n if (server_cert == nullptr) {\n error = Error::SSLServerVerification;\n return false;\n }\n\n if (!verify_host(server_cert)) {\n X509_free(server_cert);\n error = Error::SSLServerVerification;\n return false;\n }\n X509_free(server_cert);\n }\n\n return true;\n },\n [&](SSL *ssl2) {\n SSL_set_tlsext_host_name(ssl2, host_.c_str());\n return true;\n });\n\n if (ssl) {\n socket.ssl = ssl;\n return true;\n }\n\n shutdown_socket(socket);\n close_socket(socket);\n return false;\n}\n\ninline void SSLClient::shutdown_ssl(Socket &socket, bool shutdown_gracefully) {\n shutdown_ssl_impl(socket, shutdown_gracefully);\n}\n\ninline void SSLClient::shutdown_ssl_impl(Socket &socket,\n bool shutdown_gracefully) {\n if (socket.sock == INVALID_SOCKET) {\n assert(socket.ssl == nullptr);\n return;\n }\n if (socket.ssl) {\n detail::ssl_delete(ctx_mutex_, socket.ssl, shutdown_gracefully);\n socket.ssl = nullptr;\n }\n assert(socket.ssl == nullptr);\n}\n\ninline bool\nSSLClient::process_socket(const Socket &socket,\n std::function callback) {\n assert(socket.ssl);\n return detail::process_client_socket_ssl(\n socket.ssl, socket.sock, read_timeout_sec_, read_timeout_usec_,\n write_timeout_sec_, write_timeout_usec_, std::move(callback));\n}\n\ninline bool SSLClient::is_ssl() const { return true; }\n\ninline bool SSLClient::verify_host(X509 *server_cert) const {\n /* Quote from RFC2818 section 3.1 \"Server Identity\"\n\n If a subjectAltName extension of type dNSName is present, that MUST\n be used as the identity. Otherwise, the (most specific) Common Name\n field in the Subject field of the certificate MUST be used. Although\n the use of the Common Name is existing practice, it is deprecated and\n Certification Authorities are encouraged to use the dNSName instead.\n\n Matching is performed using the matching rules specified by\n [RFC2459]. If more than one identity of a given type is present in\n the certificate (e.g., more than one dNSName name, a match in any one\n of the set is considered acceptable.) Names may contain the wildcard\n character * which is considered to match any single domain name\n component or component fragment. E.g., *.a.com matches foo.a.com but\n not bar.foo.a.com. f*.com matches foo.com but not bar.com.\n\n In some cases, the URI is specified as an IP address rather than a\n hostname. In this case, the iPAddress subjectAltName must be present\n in the certificate and must exactly match the IP in the URI.\n\n */\n return verify_host_with_subject_alt_name(server_cert) ||\n verify_host_with_common_name(server_cert);\n}\n\ninline bool\nSSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {\n auto ret = false;\n\n auto type = GEN_DNS;\n\n struct in6_addr addr6;\n struct in_addr addr;\n size_t addr_len = 0;\n\n#ifndef __MINGW32__\n if (inet_pton(AF_INET6, host_.c_str(), &addr6)) {\n type = GEN_IPADD;\n addr_len = sizeof(struct in6_addr);\n } else if (inet_pton(AF_INET, host_.c_str(), &addr)) {\n type = GEN_IPADD;\n addr_len = sizeof(struct in_addr);\n }\n#endif\n\n auto alt_names = static_cast(\n X509_get_ext_d2i(server_cert, NID_subject_alt_name, nullptr, nullptr));\n\n if (alt_names) {\n auto dsn_matched = false;\n auto ip_matched = false;\n\n auto count = sk_GENERAL_NAME_num(alt_names);\n\n for (decltype(count) i = 0; i < count && !dsn_matched; i++) {\n auto val = sk_GENERAL_NAME_value(alt_names, i);\n if (val->type == type) {\n auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5);\n auto name_len = (size_t)ASN1_STRING_length(val->d.ia5);\n\n switch (type) {\n case GEN_DNS: dsn_matched = check_host_name(name, name_len); break;\n\n case GEN_IPADD:\n if (!memcmp(&addr6, name, addr_len) ||\n !memcmp(&addr, name, addr_len)) {\n ip_matched = true;\n }\n break;\n }\n }\n }\n\n if (dsn_matched || ip_matched) { ret = true; }\n }\n\n GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names);\n return ret;\n}\n\ninline bool SSLClient::verify_host_with_common_name(X509 *server_cert) const {\n const auto subject_name = X509_get_subject_name(server_cert);\n\n if (subject_name != nullptr) {\n char name[BUFSIZ];\n auto name_len = X509_NAME_get_text_by_NID(subject_name, NID_commonName,\n name, sizeof(name));\n\n if (name_len != -1) {\n return check_host_name(name, static_cast(name_len));\n }\n }\n\n return false;\n}\n\ninline bool SSLClient::check_host_name(const char *pattern,\n size_t pattern_len) const {\n if (host_.size() == pattern_len && host_ == pattern) { return true; }\n\n // Wildcard match\n // https://bugs.launchpad.net/ubuntu/+source/firefox-3.0/+bug/376484\n std::vector pattern_components;\n detail::split(&pattern[0], &pattern[pattern_len], '.',\n [&](const char *b, const char *e) {\n pattern_components.emplace_back(std::string(b, e));\n });\n\n if (host_components_.size() != pattern_components.size()) { return false; }\n\n auto itr = pattern_components.begin();\n for (const auto &h : host_components_) {\n auto &p = *itr;\n if (p != h && p != \"*\") {\n auto partial_match = (p.size() > 0 && p[p.size() - 1] == '*' &&\n !p.compare(0, p.size() - 1, h));\n if (!partial_match) { return false; }\n }\n ++itr;\n }\n\n return true;\n}\n#endif\n\n// Universal client implementation\ninline Client::Client(const std::string &scheme_host_port)\n : Client(scheme_host_port, std::string(), std::string()) {}\n\ninline Client::Client(const std::string &scheme_host_port,\n const std::string &client_cert_path,\n const std::string &client_key_path) {\n const static std::regex re(\n R\"((?:([a-z]+):\\/\\/)?(?:\\[([\\d:]+)\\]|([^:/?#]+))(?::(\\d+))?)\");\n\n std::smatch m;\n if (std::regex_match(scheme_host_port, m, re)) {\n auto scheme = m[1].str();\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n if (!scheme.empty() && (scheme != \"http\" && scheme != \"https\")) {\n#else\n if (!scheme.empty() && scheme != \"http\") {\n#endif\n#ifndef CPPHTTPLIB_NO_EXCEPTIONS\n std::string msg = \"'\" + scheme + \"' scheme is not supported.\";\n throw std::invalid_argument(msg);\n#endif\n return;\n }\n\n auto is_ssl = scheme == \"https\";\n\n auto host = m[2].str();\n if (host.empty()) { host = m[3].str(); }\n\n auto port_str = m[4].str();\n auto port = !port_str.empty() ? std::stoi(port_str) : (is_ssl ? 443 : 80);\n\n if (is_ssl) {\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n cli_ = detail::make_unique(host, port, client_cert_path,\n client_key_path);\n is_ssl_ = is_ssl;\n#endif\n } else {\n cli_ = detail::make_unique(host, port, client_cert_path,\n client_key_path);\n }\n } else {\n cli_ = detail::make_unique(scheme_host_port, 80,\n client_cert_path, client_key_path);\n }\n}\n\ninline Client::Client(const std::string &host, int port)\n : cli_(detail::make_unique(host, port)) {}\n\ninline Client::Client(const std::string &host, int port,\n const std::string &client_cert_path,\n const std::string &client_key_path)\n : cli_(detail::make_unique(host, port, client_cert_path,\n client_key_path)) {}\n\ninline Client::~Client() {}\n\ninline bool Client::is_valid() const {\n return cli_ != nullptr && cli_->is_valid();\n}\n\ninline Result Client::Get(const std::string &path) { return cli_->Get(path); }\ninline Result Client::Get(const std::string &path, const Headers &headers) {\n return cli_->Get(path, headers);\n}\ninline Result Client::Get(const std::string &path, Progress progress) {\n return cli_->Get(path, std::move(progress));\n}\ninline Result Client::Get(const std::string &path, const Headers &headers,\n Progress progress) {\n return cli_->Get(path, headers, std::move(progress));\n}\ninline Result Client::Get(const std::string &path,\n ContentReceiver content_receiver) {\n return cli_->Get(path, std::move(content_receiver));\n}\ninline Result Client::Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver) {\n return cli_->Get(path, headers, std::move(content_receiver));\n}\ninline Result Client::Get(const std::string &path,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, std::move(content_receiver), std::move(progress));\n}\ninline Result Client::Get(const std::string &path, const Headers &headers,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, headers, std::move(content_receiver),\n std::move(progress));\n}\ninline Result Client::Get(const std::string &path,\n ResponseHandler response_handler,\n ContentReceiver content_receiver) {\n return cli_->Get(path, std::move(response_handler),\n std::move(content_receiver));\n}\ninline Result Client::Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver) {\n return cli_->Get(path, headers, std::move(response_handler),\n std::move(content_receiver));\n}\ninline Result Client::Get(const std::string &path,\n ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, std::move(response_handler),\n std::move(content_receiver), std::move(progress));\n}\ninline Result Client::Get(const std::string &path, const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, headers, std::move(response_handler),\n std::move(content_receiver), std::move(progress));\n}\ninline Result Client::Get(const std::string &path, const Params ¶ms,\n const Headers &headers, Progress progress) {\n return cli_->Get(path, params, headers, progress);\n}\ninline Result Client::Get(const std::string &path, const Params ¶ms,\n const Headers &headers,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, params, headers, content_receiver, progress);\n}\ninline Result Client::Get(const std::string &path, const Params ¶ms,\n const Headers &headers,\n ResponseHandler response_handler,\n ContentReceiver content_receiver, Progress progress) {\n return cli_->Get(path, params, headers, response_handler, content_receiver,\n progress);\n}\n\ninline Result Client::Head(const std::string &path) { return cli_->Head(path); }\ninline Result Client::Head(const std::string &path, const Headers &headers) {\n return cli_->Head(path, headers);\n}\n\ninline Result Client::Post(const std::string &path) { return cli_->Post(path); }\ninline Result Client::Post(const std::string &path, const Headers &headers) {\n return cli_->Post(path, headers);\n}\ninline Result Client::Post(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return cli_->Post(path, body, content_length, content_type);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return cli_->Post(path, headers, body, content_length, content_type);\n}\ninline Result Client::Post(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return cli_->Post(path, body, content_type);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return cli_->Post(path, headers, body, content_type);\n}\ninline Result Client::Post(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Post(path, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Post(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Post(path, std::move(content_provider), content_type);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Post(path, headers, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Post(path, headers, std::move(content_provider), content_type);\n}\ninline Result Client::Post(const std::string &path, const Params ¶ms) {\n return cli_->Post(path, params);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n const Params ¶ms) {\n return cli_->Post(path, headers, params);\n}\ninline Result Client::Post(const std::string &path,\n const MultipartFormDataItems &items) {\n return cli_->Post(path, items);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items) {\n return cli_->Post(path, headers, items);\n}\ninline Result Client::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const std::string &boundary) {\n return cli_->Post(path, headers, items, boundary);\n}\ninline Result\nClient::Post(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items) {\n return cli_->Post(path, headers, items, provider_items);\n}\ninline Result Client::Put(const std::string &path) { return cli_->Put(path); }\ninline Result Client::Put(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return cli_->Put(path, body, content_length, content_type);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return cli_->Put(path, headers, body, content_length, content_type);\n}\ninline Result Client::Put(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return cli_->Put(path, body, content_type);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return cli_->Put(path, headers, body, content_type);\n}\ninline Result Client::Put(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Put(path, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Put(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Put(path, std::move(content_provider), content_type);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Put(path, headers, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Put(path, headers, std::move(content_provider), content_type);\n}\ninline Result Client::Put(const std::string &path, const Params ¶ms) {\n return cli_->Put(path, params);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n const Params ¶ms) {\n return cli_->Put(path, headers, params);\n}\ninline Result Client::Put(const std::string &path,\n const MultipartFormDataItems &items) {\n return cli_->Put(path, items);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items) {\n return cli_->Put(path, headers, items);\n}\ninline Result Client::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const std::string &boundary) {\n return cli_->Put(path, headers, items, boundary);\n}\ninline Result\nClient::Put(const std::string &path, const Headers &headers,\n const MultipartFormDataItems &items,\n const MultipartFormDataProviderItems &provider_items) {\n return cli_->Put(path, headers, items, provider_items);\n}\ninline Result Client::Patch(const std::string &path) {\n return cli_->Patch(path);\n}\ninline Result Client::Patch(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return cli_->Patch(path, body, content_length, content_type);\n}\ninline Result Client::Patch(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return cli_->Patch(path, headers, body, content_length, content_type);\n}\ninline Result Client::Patch(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return cli_->Patch(path, body, content_type);\n}\ninline Result Client::Patch(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return cli_->Patch(path, headers, body, content_type);\n}\ninline Result Client::Patch(const std::string &path, size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Patch(path, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Patch(const std::string &path,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Patch(path, std::move(content_provider), content_type);\n}\ninline Result Client::Patch(const std::string &path, const Headers &headers,\n size_t content_length,\n ContentProvider content_provider,\n const std::string &content_type) {\n return cli_->Patch(path, headers, content_length, std::move(content_provider),\n content_type);\n}\ninline Result Client::Patch(const std::string &path, const Headers &headers,\n ContentProviderWithoutLength content_provider,\n const std::string &content_type) {\n return cli_->Patch(path, headers, std::move(content_provider), content_type);\n}\ninline Result Client::Delete(const std::string &path) {\n return cli_->Delete(path);\n}\ninline Result Client::Delete(const std::string &path, const Headers &headers) {\n return cli_->Delete(path, headers);\n}\ninline Result Client::Delete(const std::string &path, const char *body,\n size_t content_length,\n const std::string &content_type) {\n return cli_->Delete(path, body, content_length, content_type);\n}\ninline Result Client::Delete(const std::string &path, const Headers &headers,\n const char *body, size_t content_length,\n const std::string &content_type) {\n return cli_->Delete(path, headers, body, content_length, content_type);\n}\ninline Result Client::Delete(const std::string &path, const std::string &body,\n const std::string &content_type) {\n return cli_->Delete(path, body, content_type);\n}\ninline Result Client::Delete(const std::string &path, const Headers &headers,\n const std::string &body,\n const std::string &content_type) {\n return cli_->Delete(path, headers, body, content_type);\n}\ninline Result Client::Options(const std::string &path) {\n return cli_->Options(path);\n}\ninline Result Client::Options(const std::string &path, const Headers &headers) {\n return cli_->Options(path, headers);\n}\n\ninline bool Client::send(Request &req, Response &res, Error &error) {\n return cli_->send(req, res, error);\n}\n\ninline Result Client::send(const Request &req) { return cli_->send(req); }\n\ninline size_t Client::is_socket_open() const { return cli_->is_socket_open(); }\n\ninline socket_t Client::socket() const { return cli_->socket(); }\n\ninline void Client::stop() { cli_->stop(); }\n\ninline void\nClient::set_hostname_addr_map(std::map addr_map) {\n cli_->set_hostname_addr_map(std::move(addr_map));\n}\n\ninline void Client::set_default_headers(Headers headers) {\n cli_->set_default_headers(std::move(headers));\n}\n\ninline void Client::set_address_family(int family) {\n cli_->set_address_family(family);\n}\n\ninline void Client::set_tcp_nodelay(bool on) { cli_->set_tcp_nodelay(on); }\n\ninline void Client::set_socket_options(SocketOptions socket_options) {\n cli_->set_socket_options(std::move(socket_options));\n}\n\ninline void Client::set_connection_timeout(time_t sec, time_t usec) {\n cli_->set_connection_timeout(sec, usec);\n}\n\ninline void Client::set_read_timeout(time_t sec, time_t usec) {\n cli_->set_read_timeout(sec, usec);\n}\n\ninline void Client::set_write_timeout(time_t sec, time_t usec) {\n cli_->set_write_timeout(sec, usec);\n}\n\ninline void Client::set_basic_auth(const std::string &username,\n const std::string &password) {\n cli_->set_basic_auth(username, password);\n}\ninline void Client::set_bearer_token_auth(const std::string &token) {\n cli_->set_bearer_token_auth(token);\n}\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void Client::set_digest_auth(const std::string &username,\n const std::string &password) {\n cli_->set_digest_auth(username, password);\n}\n#endif\n\ninline void Client::set_keep_alive(bool on) { cli_->set_keep_alive(on); }\ninline void Client::set_follow_location(bool on) {\n cli_->set_follow_location(on);\n}\n\ninline void Client::set_url_encode(bool on) { cli_->set_url_encode(on); }\n\ninline void Client::set_compress(bool on) { cli_->set_compress(on); }\n\ninline void Client::set_decompress(bool on) { cli_->set_decompress(on); }\n\ninline void Client::set_interface(const std::string &intf) {\n cli_->set_interface(intf);\n}\n\ninline void Client::set_proxy(const std::string &host, int port) {\n cli_->set_proxy(host, port);\n}\ninline void Client::set_proxy_basic_auth(const std::string &username,\n const std::string &password) {\n cli_->set_proxy_basic_auth(username, password);\n}\ninline void Client::set_proxy_bearer_token_auth(const std::string &token) {\n cli_->set_proxy_bearer_token_auth(token);\n}\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void Client::set_proxy_digest_auth(const std::string &username,\n const std::string &password) {\n cli_->set_proxy_digest_auth(username, password);\n}\n#endif\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void Client::enable_server_certificate_verification(bool enabled) {\n cli_->enable_server_certificate_verification(enabled);\n}\n#endif\n\ninline void Client::set_logger(Logger logger) { cli_->set_logger(logger); }\n\n#ifdef CPPHTTPLIB_OPENSSL_SUPPORT\ninline void Client::set_ca_cert_path(const std::string &ca_cert_file_path,\n const std::string &ca_cert_dir_path) {\n cli_->set_ca_cert_path(ca_cert_file_path, ca_cert_dir_path);\n}\n\ninline void Client::set_ca_cert_store(X509_STORE *ca_cert_store) {\n if (is_ssl_) {\n static_cast(*cli_).set_ca_cert_store(ca_cert_store);\n } else {\n cli_->set_ca_cert_store(ca_cert_store);\n }\n}\n\ninline long Client::get_openssl_verify_result() const {\n if (is_ssl_) {\n return static_cast(*cli_).get_openssl_verify_result();\n }\n return -1; // NOTE: -1 doesn't match any of X509_V_ERR_???\n}\n\ninline SSL_CTX *Client::ssl_context() const {\n if (is_ssl_) { return static_cast(*cli_).ssl_context(); }\n return nullptr;\n}\n#endif\n\n// ----------------------------------------------------------------------------\n\n} // namespace httplib\n\n#if defined(_WIN32) && defined(CPPHTTPLIB_USE_POLL)\n#undef poll\n#endif\n\n#endif // CPPHTTPLIB_HTTPLIB_H\n" }, { "path": "examples/server/index.html.hpp", "content": "unsigned char index_html[] = {\n 0x3c, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 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0x75, 0x72, 0x6e, 0x20, 0x67, 0x72, 0x61, 0x6d,\n 0x6d, 0x61, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a\n};\nunsigned int json_schema_to_grammar_mjs_len = 3695;\n" }, { "path": "examples/server/json.hpp", "content": "// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n/****************************************************************************\\\n * Note on documentation: The source files contain links to the online *\n * documentation of the public API at https://json.nlohmann.me. This URL *\n * contains the most recent documentation and should also be applicable to *\n * previous versions; documentation for deprecated functions is not *\n * removed, but marked deprecated. See \"Generate documentation\" section in *\n * file docs/README.md. *\n\\****************************************************************************/\n\n#ifndef INCLUDE_NLOHMANN_JSON_HPP_\n#define INCLUDE_NLOHMANN_JSON_HPP_\n\n#include // all_of, find, for_each\n#include // nullptr_t, ptrdiff_t, size_t\n#include // hash, less\n#include // initializer_list\n#ifndef JSON_NO_IO\n #include // istream, ostream\n#endif // JSON_NO_IO\n#include // random_access_iterator_tag\n#include // unique_ptr\n#include // accumulate\n#include // string, stoi, to_string\n#include // declval, forward, move, pair, swap\n#include // vector\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include \n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n// This file contains all macro definitions affecting or depending on the ABI\n\n#ifndef JSON_SKIP_LIBRARY_VERSION_CHECK\n #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)\n #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2\n #warning \"Already included a different version of the library!\"\n #endif\n #endif\n#endif\n\n#define NLOHMANN_JSON_VERSION_MAJOR 3 // NOLINT(modernize-macro-to-enum)\n#define NLOHMANN_JSON_VERSION_MINOR 11 // NOLINT(modernize-macro-to-enum)\n#define NLOHMANN_JSON_VERSION_PATCH 2 // NOLINT(modernize-macro-to-enum)\n\n#ifndef JSON_DIAGNOSTICS\n #define JSON_DIAGNOSTICS 0\n#endif\n\n#ifndef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON\n #define JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON 0\n#endif\n\n#if JSON_DIAGNOSTICS\n #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS _diag\n#else\n #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS\n#endif\n\n#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON\n #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON _ldvcmp\n#else\n #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON\n#endif\n\n#ifndef NLOHMANN_JSON_NAMESPACE_NO_VERSION\n #define NLOHMANN_JSON_NAMESPACE_NO_VERSION 0\n#endif\n\n// Construct the namespace ABI tags component\n#define NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b) json_abi ## a ## b\n#define NLOHMANN_JSON_ABI_TAGS_CONCAT(a, b) \\\n NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b)\n\n#define NLOHMANN_JSON_ABI_TAGS \\\n NLOHMANN_JSON_ABI_TAGS_CONCAT( \\\n NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS, \\\n NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON)\n\n// Construct the namespace version component\n#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch) \\\n _v ## major ## _ ## minor ## _ ## patch\n#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(major, minor, patch) \\\n NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch)\n\n#if NLOHMANN_JSON_NAMESPACE_NO_VERSION\n#define NLOHMANN_JSON_NAMESPACE_VERSION\n#else\n#define NLOHMANN_JSON_NAMESPACE_VERSION \\\n NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(NLOHMANN_JSON_VERSION_MAJOR, \\\n NLOHMANN_JSON_VERSION_MINOR, \\\n NLOHMANN_JSON_VERSION_PATCH)\n#endif\n\n// Combine namespace components\n#define NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b) a ## b\n#define NLOHMANN_JSON_NAMESPACE_CONCAT(a, b) \\\n NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b)\n\n#ifndef NLOHMANN_JSON_NAMESPACE\n#define NLOHMANN_JSON_NAMESPACE \\\n nlohmann::NLOHMANN_JSON_NAMESPACE_CONCAT( \\\n NLOHMANN_JSON_ABI_TAGS, \\\n NLOHMANN_JSON_NAMESPACE_VERSION)\n#endif\n\n#ifndef NLOHMANN_JSON_NAMESPACE_BEGIN\n#define NLOHMANN_JSON_NAMESPACE_BEGIN \\\n namespace nlohmann \\\n { \\\n inline namespace NLOHMANN_JSON_NAMESPACE_CONCAT( \\\n NLOHMANN_JSON_ABI_TAGS, \\\n NLOHMANN_JSON_NAMESPACE_VERSION) \\\n {\n#endif\n\n#ifndef NLOHMANN_JSON_NAMESPACE_END\n#define NLOHMANN_JSON_NAMESPACE_END \\\n } /* namespace (inline namespace) NOLINT(readability/namespace) */ \\\n } // namespace nlohmann\n#endif\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // transform\n#include // array\n#include // forward_list\n#include // inserter, front_inserter, end\n#include // map\n#include // string\n#include // tuple, make_tuple\n#include // is_arithmetic, is_same, is_enum, underlying_type, is_convertible\n#include // unordered_map\n#include // pair, declval\n#include // valarray\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // nullptr_t\n#include // exception\n#include // runtime_error\n#include // to_string\n#include // vector\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // array\n#include // size_t\n#include // uint8_t\n#include // string\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // declval, pair\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include \n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\ntemplate struct make_void\n{\n using type = void;\n};\ntemplate using void_t = typename make_void::type;\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\n// https://en.cppreference.com/w/cpp/experimental/is_detected\nstruct nonesuch\n{\n nonesuch() = delete;\n ~nonesuch() = delete;\n nonesuch(nonesuch const&) = delete;\n nonesuch(nonesuch const&&) = delete;\n void operator=(nonesuch const&) = delete;\n void operator=(nonesuch&&) = delete;\n};\n\ntemplate class Op,\n class... Args>\nstruct detector\n{\n using value_t = std::false_type;\n using type = Default;\n};\n\ntemplate class Op, class... Args>\nstruct detector>, Op, Args...>\n{\n using value_t = std::true_type;\n using type = Op;\n};\n\ntemplate class Op, class... Args>\nusing is_detected = typename detector::value_t;\n\ntemplate class Op, class... Args>\nstruct is_detected_lazy : is_detected { };\n\ntemplate class Op, class... Args>\nusing detected_t = typename detector::type;\n\ntemplate class Op, class... Args>\nusing detected_or = detector;\n\ntemplate class Op, class... Args>\nusing detected_or_t = typename detected_or::type;\n\ntemplate class Op, class... Args>\nusing is_detected_exact = std::is_same>;\n\ntemplate class Op, class... Args>\nusing is_detected_convertible =\n std::is_convertible, To>;\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n\n\n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-FileCopyrightText: 2016-2021 Evan Nemerson \n// SPDX-License-Identifier: MIT\n\n/* Hedley - https://nemequ.github.io/hedley\n * Created by Evan Nemerson \n */\n\n#if !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < 15)\n#if defined(JSON_HEDLEY_VERSION)\n #undef JSON_HEDLEY_VERSION\n#endif\n#define JSON_HEDLEY_VERSION 15\n\n#if defined(JSON_HEDLEY_STRINGIFY_EX)\n #undef JSON_HEDLEY_STRINGIFY_EX\n#endif\n#define JSON_HEDLEY_STRINGIFY_EX(x) #x\n\n#if defined(JSON_HEDLEY_STRINGIFY)\n #undef JSON_HEDLEY_STRINGIFY\n#endif\n#define JSON_HEDLEY_STRINGIFY(x) JSON_HEDLEY_STRINGIFY_EX(x)\n\n#if defined(JSON_HEDLEY_CONCAT_EX)\n #undef JSON_HEDLEY_CONCAT_EX\n#endif\n#define JSON_HEDLEY_CONCAT_EX(a,b) a##b\n\n#if defined(JSON_HEDLEY_CONCAT)\n #undef JSON_HEDLEY_CONCAT\n#endif\n#define JSON_HEDLEY_CONCAT(a,b) JSON_HEDLEY_CONCAT_EX(a,b)\n\n#if defined(JSON_HEDLEY_CONCAT3_EX)\n #undef JSON_HEDLEY_CONCAT3_EX\n#endif\n#define JSON_HEDLEY_CONCAT3_EX(a,b,c) a##b##c\n\n#if defined(JSON_HEDLEY_CONCAT3)\n #undef JSON_HEDLEY_CONCAT3\n#endif\n#define JSON_HEDLEY_CONCAT3(a,b,c) JSON_HEDLEY_CONCAT3_EX(a,b,c)\n\n#if defined(JSON_HEDLEY_VERSION_ENCODE)\n #undef JSON_HEDLEY_VERSION_ENCODE\n#endif\n#define JSON_HEDLEY_VERSION_ENCODE(major,minor,revision) (((major) * 1000000) + ((minor) * 1000) + (revision))\n\n#if defined(JSON_HEDLEY_VERSION_DECODE_MAJOR)\n #undef JSON_HEDLEY_VERSION_DECODE_MAJOR\n#endif\n#define JSON_HEDLEY_VERSION_DECODE_MAJOR(version) ((version) / 1000000)\n\n#if defined(JSON_HEDLEY_VERSION_DECODE_MINOR)\n #undef JSON_HEDLEY_VERSION_DECODE_MINOR\n#endif\n#define JSON_HEDLEY_VERSION_DECODE_MINOR(version) (((version) % 1000000) / 1000)\n\n#if defined(JSON_HEDLEY_VERSION_DECODE_REVISION)\n #undef JSON_HEDLEY_VERSION_DECODE_REVISION\n#endif\n#define JSON_HEDLEY_VERSION_DECODE_REVISION(version) ((version) % 1000)\n\n#if defined(JSON_HEDLEY_GNUC_VERSION)\n #undef JSON_HEDLEY_GNUC_VERSION\n#endif\n#if defined(__GNUC__) && defined(__GNUC_PATCHLEVEL__)\n #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__)\n#elif defined(__GNUC__)\n #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, 0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_VERSION_CHECK)\n #undef JSON_HEDLEY_GNUC_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_GNUC_VERSION)\n #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GNUC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_MSVC_VERSION)\n #undef JSON_HEDLEY_MSVC_VERSION\n#endif\n#if defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 140000000) && !defined(__ICL)\n #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 10000000, (_MSC_FULL_VER % 10000000) / 100000, (_MSC_FULL_VER % 100000) / 100)\n#elif defined(_MSC_FULL_VER) && !defined(__ICL)\n #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 1000000, (_MSC_FULL_VER % 1000000) / 10000, (_MSC_FULL_VER % 10000) / 10)\n#elif defined(_MSC_VER) && !defined(__ICL)\n #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_VER / 100, _MSC_VER % 100, 0)\n#endif\n\n#if defined(JSON_HEDLEY_MSVC_VERSION_CHECK)\n #undef JSON_HEDLEY_MSVC_VERSION_CHECK\n#endif\n#if !defined(JSON_HEDLEY_MSVC_VERSION)\n #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (0)\n#elif defined(_MSC_VER) && (_MSC_VER >= 1400)\n #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 10000000) + (minor * 100000) + (patch)))\n#elif defined(_MSC_VER) && (_MSC_VER >= 1200)\n #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 1000000) + (minor * 10000) + (patch)))\n#else\n #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_VER >= ((major * 100) + (minor)))\n#endif\n\n#if defined(JSON_HEDLEY_INTEL_VERSION)\n #undef JSON_HEDLEY_INTEL_VERSION\n#endif\n#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && !defined(__ICL)\n #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, __INTEL_COMPILER_UPDATE)\n#elif defined(__INTEL_COMPILER) && !defined(__ICL)\n #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, 0)\n#endif\n\n#if defined(JSON_HEDLEY_INTEL_VERSION_CHECK)\n #undef JSON_HEDLEY_INTEL_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_INTEL_VERSION)\n #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_INTEL_CL_VERSION)\n #undef JSON_HEDLEY_INTEL_CL_VERSION\n#endif\n#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && defined(__ICL)\n #define JSON_HEDLEY_INTEL_CL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER, __INTEL_COMPILER_UPDATE, 0)\n#endif\n\n#if defined(JSON_HEDLEY_INTEL_CL_VERSION_CHECK)\n #undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_INTEL_CL_VERSION)\n #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_CL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_PGI_VERSION)\n #undef JSON_HEDLEY_PGI_VERSION\n#endif\n#if defined(__PGI) && defined(__PGIC__) && defined(__PGIC_MINOR__) && defined(__PGIC_PATCHLEVEL__)\n #define JSON_HEDLEY_PGI_VERSION JSON_HEDLEY_VERSION_ENCODE(__PGIC__, __PGIC_MINOR__, __PGIC_PATCHLEVEL__)\n#endif\n\n#if defined(JSON_HEDLEY_PGI_VERSION_CHECK)\n #undef JSON_HEDLEY_PGI_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_PGI_VERSION)\n #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PGI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_SUNPRO_VERSION)\n #undef JSON_HEDLEY_SUNPRO_VERSION\n#endif\n#if defined(__SUNPRO_C) && (__SUNPRO_C > 0x1000)\n #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_C >> 16) & 0xf) * 10) + ((__SUNPRO_C >> 12) & 0xf), (((__SUNPRO_C >> 8) & 0xf) * 10) + ((__SUNPRO_C >> 4) & 0xf), (__SUNPRO_C & 0xf) * 10)\n#elif defined(__SUNPRO_C)\n #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_C >> 8) & 0xf, (__SUNPRO_C >> 4) & 0xf, (__SUNPRO_C) & 0xf)\n#elif defined(__SUNPRO_CC) && (__SUNPRO_CC > 0x1000)\n #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_CC >> 16) & 0xf) * 10) + ((__SUNPRO_CC >> 12) & 0xf), (((__SUNPRO_CC >> 8) & 0xf) * 10) + ((__SUNPRO_CC >> 4) & 0xf), (__SUNPRO_CC & 0xf) * 10)\n#elif defined(__SUNPRO_CC)\n #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_CC >> 8) & 0xf, (__SUNPRO_CC >> 4) & 0xf, (__SUNPRO_CC) & 0xf)\n#endif\n\n#if defined(JSON_HEDLEY_SUNPRO_VERSION_CHECK)\n #undef JSON_HEDLEY_SUNPRO_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_SUNPRO_VERSION)\n #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_SUNPRO_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)\n #undef JSON_HEDLEY_EMSCRIPTEN_VERSION\n#endif\n#if defined(__EMSCRIPTEN__)\n #define JSON_HEDLEY_EMSCRIPTEN_VERSION JSON_HEDLEY_VERSION_ENCODE(__EMSCRIPTEN_major__, __EMSCRIPTEN_minor__, __EMSCRIPTEN_tiny__)\n#endif\n\n#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK)\n #undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)\n #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_EMSCRIPTEN_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_ARM_VERSION)\n #undef JSON_HEDLEY_ARM_VERSION\n#endif\n#if defined(__CC_ARM) && defined(__ARMCOMPILER_VERSION)\n #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCOMPILER_VERSION / 1000000, (__ARMCOMPILER_VERSION % 1000000) / 10000, (__ARMCOMPILER_VERSION % 10000) / 100)\n#elif defined(__CC_ARM) && defined(__ARMCC_VERSION)\n #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCC_VERSION / 1000000, (__ARMCC_VERSION % 1000000) / 10000, (__ARMCC_VERSION % 10000) / 100)\n#endif\n\n#if defined(JSON_HEDLEY_ARM_VERSION_CHECK)\n #undef JSON_HEDLEY_ARM_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_ARM_VERSION)\n #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_ARM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_IBM_VERSION)\n #undef JSON_HEDLEY_IBM_VERSION\n#endif\n#if defined(__ibmxl__)\n #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ibmxl_version__, __ibmxl_release__, __ibmxl_modification__)\n#elif defined(__xlC__) && defined(__xlC_ver__)\n #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, (__xlC_ver__ >> 8) & 0xff)\n#elif defined(__xlC__)\n #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, 0)\n#endif\n\n#if defined(JSON_HEDLEY_IBM_VERSION_CHECK)\n #undef JSON_HEDLEY_IBM_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_IBM_VERSION)\n #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IBM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_VERSION)\n #undef JSON_HEDLEY_TI_VERSION\n#endif\n#if \\\n defined(__TI_COMPILER_VERSION__) && \\\n ( \\\n defined(__TMS470__) || defined(__TI_ARM__) || \\\n defined(__MSP430__) || \\\n defined(__TMS320C2000__) \\\n )\n#if (__TI_COMPILER_VERSION__ >= 16000000)\n #define JSON_HEDLEY_TI_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n#endif\n\n#if defined(JSON_HEDLEY_TI_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_VERSION)\n #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL2000_VERSION)\n #undef JSON_HEDLEY_TI_CL2000_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C2000__)\n #define JSON_HEDLEY_TI_CL2000_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL2000_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_CL2000_VERSION)\n #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL2000_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL430_VERSION)\n #undef JSON_HEDLEY_TI_CL430_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && defined(__MSP430__)\n #define JSON_HEDLEY_TI_CL430_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL430_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_CL430_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_CL430_VERSION)\n #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL430_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)\n #undef JSON_HEDLEY_TI_ARMCL_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && (defined(__TMS470__) || defined(__TI_ARM__))\n #define JSON_HEDLEY_TI_ARMCL_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_ARMCL_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)\n #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_ARMCL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL6X_VERSION)\n #undef JSON_HEDLEY_TI_CL6X_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C6X__)\n #define JSON_HEDLEY_TI_CL6X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL6X_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_CL6X_VERSION)\n #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL6X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL7X_VERSION)\n #undef JSON_HEDLEY_TI_CL7X_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && defined(__C7000__)\n #define JSON_HEDLEY_TI_CL7X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_CL7X_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_CL7X_VERSION)\n #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL7X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)\n #undef JSON_HEDLEY_TI_CLPRU_VERSION\n#endif\n#if defined(__TI_COMPILER_VERSION__) && defined(__PRU__)\n #define JSON_HEDLEY_TI_CLPRU_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))\n#endif\n\n#if defined(JSON_HEDLEY_TI_CLPRU_VERSION_CHECK)\n #undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)\n #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CLPRU_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_CRAY_VERSION)\n #undef JSON_HEDLEY_CRAY_VERSION\n#endif\n#if defined(_CRAYC)\n #if defined(_RELEASE_PATCHLEVEL)\n #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, _RELEASE_PATCHLEVEL)\n #else\n #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, 0)\n #endif\n#endif\n\n#if defined(JSON_HEDLEY_CRAY_VERSION_CHECK)\n #undef JSON_HEDLEY_CRAY_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_CRAY_VERSION)\n #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_CRAY_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_IAR_VERSION)\n #undef JSON_HEDLEY_IAR_VERSION\n#endif\n#if defined(__IAR_SYSTEMS_ICC__)\n #if __VER__ > 1000\n #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE((__VER__ / 1000000), ((__VER__ / 1000) % 1000), (__VER__ % 1000))\n #else\n #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE(__VER__ / 100, __VER__ % 100, 0)\n #endif\n#endif\n\n#if defined(JSON_HEDLEY_IAR_VERSION_CHECK)\n #undef JSON_HEDLEY_IAR_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_IAR_VERSION)\n #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IAR_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_TINYC_VERSION)\n #undef JSON_HEDLEY_TINYC_VERSION\n#endif\n#if defined(__TINYC__)\n #define JSON_HEDLEY_TINYC_VERSION JSON_HEDLEY_VERSION_ENCODE(__TINYC__ / 1000, (__TINYC__ / 100) % 10, __TINYC__ % 100)\n#endif\n\n#if defined(JSON_HEDLEY_TINYC_VERSION_CHECK)\n #undef JSON_HEDLEY_TINYC_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_TINYC_VERSION)\n #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TINYC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_DMC_VERSION)\n #undef JSON_HEDLEY_DMC_VERSION\n#endif\n#if defined(__DMC__)\n #define JSON_HEDLEY_DMC_VERSION JSON_HEDLEY_VERSION_ENCODE(__DMC__ >> 8, (__DMC__ >> 4) & 0xf, __DMC__ & 0xf)\n#endif\n\n#if defined(JSON_HEDLEY_DMC_VERSION_CHECK)\n #undef JSON_HEDLEY_DMC_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_DMC_VERSION)\n #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_DMC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_COMPCERT_VERSION)\n #undef JSON_HEDLEY_COMPCERT_VERSION\n#endif\n#if defined(__COMPCERT_VERSION__)\n #define JSON_HEDLEY_COMPCERT_VERSION JSON_HEDLEY_VERSION_ENCODE(__COMPCERT_VERSION__ / 10000, (__COMPCERT_VERSION__ / 100) % 100, __COMPCERT_VERSION__ % 100)\n#endif\n\n#if defined(JSON_HEDLEY_COMPCERT_VERSION_CHECK)\n #undef JSON_HEDLEY_COMPCERT_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_COMPCERT_VERSION)\n #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_COMPCERT_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_PELLES_VERSION)\n #undef JSON_HEDLEY_PELLES_VERSION\n#endif\n#if defined(__POCC__)\n #define JSON_HEDLEY_PELLES_VERSION JSON_HEDLEY_VERSION_ENCODE(__POCC__ / 100, __POCC__ % 100, 0)\n#endif\n\n#if defined(JSON_HEDLEY_PELLES_VERSION_CHECK)\n #undef JSON_HEDLEY_PELLES_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_PELLES_VERSION)\n #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PELLES_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_MCST_LCC_VERSION)\n #undef JSON_HEDLEY_MCST_LCC_VERSION\n#endif\n#if defined(__LCC__) && defined(__LCC_MINOR__)\n #define JSON_HEDLEY_MCST_LCC_VERSION JSON_HEDLEY_VERSION_ENCODE(__LCC__ / 100, __LCC__ % 100, __LCC_MINOR__)\n#endif\n\n#if defined(JSON_HEDLEY_MCST_LCC_VERSION_CHECK)\n #undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_MCST_LCC_VERSION)\n #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_MCST_LCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_VERSION)\n #undef JSON_HEDLEY_GCC_VERSION\n#endif\n#if \\\n defined(JSON_HEDLEY_GNUC_VERSION) && \\\n !defined(__clang__) && \\\n !defined(JSON_HEDLEY_INTEL_VERSION) && \\\n !defined(JSON_HEDLEY_PGI_VERSION) && \\\n !defined(JSON_HEDLEY_ARM_VERSION) && \\\n !defined(JSON_HEDLEY_CRAY_VERSION) && \\\n !defined(JSON_HEDLEY_TI_VERSION) && \\\n !defined(JSON_HEDLEY_TI_ARMCL_VERSION) && \\\n !defined(JSON_HEDLEY_TI_CL430_VERSION) && \\\n !defined(JSON_HEDLEY_TI_CL2000_VERSION) && \\\n !defined(JSON_HEDLEY_TI_CL6X_VERSION) && \\\n !defined(JSON_HEDLEY_TI_CL7X_VERSION) && \\\n !defined(JSON_HEDLEY_TI_CLPRU_VERSION) && \\\n !defined(__COMPCERT__) && \\\n !defined(JSON_HEDLEY_MCST_LCC_VERSION)\n #define JSON_HEDLEY_GCC_VERSION JSON_HEDLEY_GNUC_VERSION\n#endif\n\n#if defined(JSON_HEDLEY_GCC_VERSION_CHECK)\n #undef JSON_HEDLEY_GCC_VERSION_CHECK\n#endif\n#if defined(JSON_HEDLEY_GCC_VERSION)\n #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))\n#else\n #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (0)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_ATTRIBUTE)\n #undef JSON_HEDLEY_HAS_ATTRIBUTE\n#endif\n#if \\\n defined(__has_attribute) && \\\n ( \\\n (!defined(JSON_HEDLEY_IAR_VERSION) || JSON_HEDLEY_IAR_VERSION_CHECK(8,5,9)) \\\n )\n# define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) __has_attribute(attribute)\n#else\n# define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_ATTRIBUTE)\n #undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE\n#endif\n#if defined(__has_attribute)\n #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)\n#else\n #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_ATTRIBUTE)\n #undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE\n#endif\n#if defined(__has_attribute)\n #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)\n#else\n #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE)\n #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE\n#endif\n#if \\\n defined(__has_cpp_attribute) && \\\n defined(__cplusplus) && \\\n (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0))\n #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) __has_cpp_attribute(attribute)\n#else\n #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) (0)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS)\n #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS\n#endif\n#if !defined(__cplusplus) || !defined(__has_cpp_attribute)\n #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)\n#elif \\\n !defined(JSON_HEDLEY_PGI_VERSION) && \\\n !defined(JSON_HEDLEY_IAR_VERSION) && \\\n (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0)) && \\\n (!defined(JSON_HEDLEY_MSVC_VERSION) || JSON_HEDLEY_MSVC_VERSION_CHECK(19,20,0))\n #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(ns::attribute)\n#else\n #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE)\n #undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE\n#endif\n#if defined(__has_cpp_attribute) && defined(__cplusplus)\n #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)\n#else\n #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE)\n #undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE\n#endif\n#if defined(__has_cpp_attribute) && defined(__cplusplus)\n #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)\n#else\n #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_BUILTIN)\n #undef JSON_HEDLEY_HAS_BUILTIN\n#endif\n#if defined(__has_builtin)\n #define JSON_HEDLEY_HAS_BUILTIN(builtin) __has_builtin(builtin)\n#else\n #define JSON_HEDLEY_HAS_BUILTIN(builtin) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_BUILTIN)\n #undef JSON_HEDLEY_GNUC_HAS_BUILTIN\n#endif\n#if defined(__has_builtin)\n #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)\n#else\n #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_BUILTIN)\n #undef JSON_HEDLEY_GCC_HAS_BUILTIN\n#endif\n#if defined(__has_builtin)\n #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)\n#else\n #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_FEATURE)\n #undef JSON_HEDLEY_HAS_FEATURE\n#endif\n#if defined(__has_feature)\n #define JSON_HEDLEY_HAS_FEATURE(feature) __has_feature(feature)\n#else\n #define JSON_HEDLEY_HAS_FEATURE(feature) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_FEATURE)\n #undef JSON_HEDLEY_GNUC_HAS_FEATURE\n#endif\n#if defined(__has_feature)\n #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)\n#else\n #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_FEATURE)\n #undef JSON_HEDLEY_GCC_HAS_FEATURE\n#endif\n#if defined(__has_feature)\n #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)\n#else\n #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_EXTENSION)\n #undef JSON_HEDLEY_HAS_EXTENSION\n#endif\n#if defined(__has_extension)\n #define JSON_HEDLEY_HAS_EXTENSION(extension) __has_extension(extension)\n#else\n #define JSON_HEDLEY_HAS_EXTENSION(extension) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_EXTENSION)\n #undef JSON_HEDLEY_GNUC_HAS_EXTENSION\n#endif\n#if defined(__has_extension)\n #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)\n#else\n #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_EXTENSION)\n #undef JSON_HEDLEY_GCC_HAS_EXTENSION\n#endif\n#if defined(__has_extension)\n #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)\n#else\n #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE)\n #undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE\n#endif\n#if defined(__has_declspec_attribute)\n #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) __has_declspec_attribute(attribute)\n#else\n #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE)\n #undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE\n#endif\n#if defined(__has_declspec_attribute)\n #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)\n#else\n #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE)\n #undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE\n#endif\n#if defined(__has_declspec_attribute)\n #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)\n#else\n #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_HAS_WARNING)\n #undef JSON_HEDLEY_HAS_WARNING\n#endif\n#if defined(__has_warning)\n #define JSON_HEDLEY_HAS_WARNING(warning) __has_warning(warning)\n#else\n #define JSON_HEDLEY_HAS_WARNING(warning) (0)\n#endif\n\n#if defined(JSON_HEDLEY_GNUC_HAS_WARNING)\n #undef JSON_HEDLEY_GNUC_HAS_WARNING\n#endif\n#if defined(__has_warning)\n #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)\n#else\n #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_GCC_HAS_WARNING)\n #undef JSON_HEDLEY_GCC_HAS_WARNING\n#endif\n#if defined(__has_warning)\n #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)\n#else\n #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if \\\n (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \\\n defined(__clang__) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,0,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0) || \\\n JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,17) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(8,0,0) || \\\n (JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) && defined(__C99_PRAGMA_OPERATOR))\n #define JSON_HEDLEY_PRAGMA(value) _Pragma(#value)\n#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)\n #define JSON_HEDLEY_PRAGMA(value) __pragma(value)\n#else\n #define JSON_HEDLEY_PRAGMA(value)\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_PUSH)\n #undef JSON_HEDLEY_DIAGNOSTIC_PUSH\n#endif\n#if defined(JSON_HEDLEY_DIAGNOSTIC_POP)\n #undef JSON_HEDLEY_DIAGNOSTIC_POP\n#endif\n#if defined(__clang__)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"clang diagnostic push\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"clang diagnostic pop\")\n#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"warning(push)\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"warning(pop)\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"GCC diagnostic push\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"GCC diagnostic pop\")\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH __pragma(warning(push))\n #define JSON_HEDLEY_DIAGNOSTIC_POP __pragma(warning(pop))\n#elif JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"push\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"pop\")\n#elif \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,4,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"diag_push\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"diag_pop\")\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma(\"warning(push)\")\n #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma(\"warning(pop)\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_PUSH\n #define JSON_HEDLEY_DIAGNOSTIC_POP\n#endif\n\n/* JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ is for\n HEDLEY INTERNAL USE ONLY. API subject to change without notice. */\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_\n#endif\n#if defined(__cplusplus)\n# if JSON_HEDLEY_HAS_WARNING(\"-Wc++98-compat\")\n# if JSON_HEDLEY_HAS_WARNING(\"-Wc++17-extensions\")\n# if JSON_HEDLEY_HAS_WARNING(\"-Wc++1z-extensions\")\n# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++98-compat\\\"\") \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++17-extensions\\\"\") \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++1z-extensions\\\"\") \\\n xpr \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# else\n# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++98-compat\\\"\") \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++17-extensions\\\"\") \\\n xpr \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# endif\n# else\n# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wc++98-compat\\\"\") \\\n xpr \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# endif\n# endif\n#endif\n#if !defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(x) x\n#endif\n\n#if defined(JSON_HEDLEY_CONST_CAST)\n #undef JSON_HEDLEY_CONST_CAST\n#endif\n#if defined(__cplusplus)\n# define JSON_HEDLEY_CONST_CAST(T, expr) (const_cast(expr))\n#elif \\\n JSON_HEDLEY_HAS_WARNING(\"-Wcast-qual\") || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n# define JSON_HEDLEY_CONST_CAST(T, expr) (__extension__ ({ \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL \\\n ((T) (expr)); \\\n JSON_HEDLEY_DIAGNOSTIC_POP \\\n }))\n#else\n# define JSON_HEDLEY_CONST_CAST(T, expr) ((T) (expr))\n#endif\n\n#if defined(JSON_HEDLEY_REINTERPRET_CAST)\n #undef JSON_HEDLEY_REINTERPRET_CAST\n#endif\n#if defined(__cplusplus)\n #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) (reinterpret_cast(expr))\n#else\n #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) ((T) (expr))\n#endif\n\n#if defined(JSON_HEDLEY_STATIC_CAST)\n #undef JSON_HEDLEY_STATIC_CAST\n#endif\n#if defined(__cplusplus)\n #define JSON_HEDLEY_STATIC_CAST(T, expr) (static_cast(expr))\n#else\n #define JSON_HEDLEY_STATIC_CAST(T, expr) ((T) (expr))\n#endif\n\n#if defined(JSON_HEDLEY_CPP_CAST)\n #undef JSON_HEDLEY_CPP_CAST\n#endif\n#if defined(__cplusplus)\n# if JSON_HEDLEY_HAS_WARNING(\"-Wold-style-cast\")\n# define JSON_HEDLEY_CPP_CAST(T, expr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wold-style-cast\\\"\") \\\n ((T) (expr)) \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# elif JSON_HEDLEY_IAR_VERSION_CHECK(8,3,0)\n# define JSON_HEDLEY_CPP_CAST(T, expr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"diag_suppress=Pe137\") \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# else\n# define JSON_HEDLEY_CPP_CAST(T, expr) ((T) (expr))\n# endif\n#else\n# define JSON_HEDLEY_CPP_CAST(T, expr) (expr)\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wdeprecated-declarations\")\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"clang diagnostic ignored \\\"-Wdeprecated-declarations\\\"\")\n#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"warning(disable:1478 1786)\")\n#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:1478 1786))\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"diag_suppress 1215,1216,1444,1445\")\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"diag_suppress 1215,1444\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"GCC diagnostic ignored \\\"-Wdeprecated-declarations\\\"\")\n#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:4996))\n#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"diag_suppress 1215,1444\")\n#elif \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"diag_suppress 1291,1718\")\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && !defined(__cplusplus)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"error_messages(off,E_DEPRECATED_ATT,E_DEPRECATED_ATT_MESS)\")\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && defined(__cplusplus)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"error_messages(off,symdeprecated,symdeprecated2)\")\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"diag_suppress=Pe1444,Pe1215\")\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma(\"warn(disable:2241)\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wunknown-pragmas\")\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"clang diagnostic ignored \\\"-Wunknown-pragmas\\\"\")\n#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"warning(disable:161)\")\n#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:161))\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"diag_suppress 1675\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"GCC diagnostic ignored \\\"-Wunknown-pragmas\\\"\")\n#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:4068))\n#elif \\\n JSON_HEDLEY_TI_VERSION_CHECK(16,9,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"diag_suppress 163\")\n#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"diag_suppress 163\")\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"diag_suppress=Pe161\")\n#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma(\"diag_suppress 161\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wunknown-attributes\")\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"clang diagnostic ignored \\\"-Wunknown-attributes\\\"\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"GCC diagnostic ignored \\\"-Wdeprecated-declarations\\\"\")\n#elif JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"warning(disable:1292)\")\n#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:1292))\n#elif JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:5030))\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"diag_suppress 1097,1098\")\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"diag_suppress 1097\")\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"error_messages(off,attrskipunsup)\")\n#elif \\\n JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"diag_suppress 1173\")\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"diag_suppress=Pe1097\")\n#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma(\"diag_suppress 1097\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wcast-qual\")\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma(\"clang diagnostic ignored \\\"-Wcast-qual\\\"\")\n#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma(\"warning(disable:2203 2331)\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma(\"GCC diagnostic ignored \\\"-Wcast-qual\\\"\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL\n#endif\n\n#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION)\n #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wunused-function\")\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma(\"clang diagnostic ignored \\\"-Wunused-function\\\"\")\n#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma(\"GCC diagnostic ignored \\\"-Wunused-function\\\"\")\n#elif JSON_HEDLEY_MSVC_VERSION_CHECK(1,0,0)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION __pragma(warning(disable:4505))\n#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma(\"diag_suppress 3142\")\n#else\n #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION\n#endif\n\n#if defined(JSON_HEDLEY_DEPRECATED)\n #undef JSON_HEDLEY_DEPRECATED\n#endif\n#if defined(JSON_HEDLEY_DEPRECATED_FOR)\n #undef JSON_HEDLEY_DEPRECATED_FOR\n#endif\n#if \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated(\"Since \" # since))\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated(\"Since \" #since \"; use \" #replacement))\n#elif \\\n (JSON_HEDLEY_HAS_EXTENSION(attribute_deprecated_with_message) && !defined(JSON_HEDLEY_IAR_VERSION)) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(18,1,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__(\"Since \" #since)))\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__(\"Since \" #since \"; use \" #replacement)))\n#elif defined(__cplusplus) && (__cplusplus >= 201402L)\n #define JSON_HEDLEY_DEPRECATED(since) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated(\"Since \" #since)]])\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated(\"Since \" #since \"; use \" #replacement)]])\n#elif \\\n JSON_HEDLEY_HAS_ATTRIBUTE(deprecated) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)\n #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__))\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__))\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \\\n JSON_HEDLEY_PELLES_VERSION_CHECK(6,50,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated)\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated)\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_DEPRECATED(since) _Pragma(\"deprecated\")\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) _Pragma(\"deprecated\")\n#else\n #define JSON_HEDLEY_DEPRECATED(since)\n #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement)\n#endif\n\n#if defined(JSON_HEDLEY_UNAVAILABLE)\n #undef JSON_HEDLEY_UNAVAILABLE\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(warning) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_UNAVAILABLE(available_since) __attribute__((__warning__(\"Not available until \" #available_since)))\n#else\n #define JSON_HEDLEY_UNAVAILABLE(available_since)\n#endif\n\n#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT)\n #undef JSON_HEDLEY_WARN_UNUSED_RESULT\n#endif\n#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT_MSG)\n #undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(warn_unused_result) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_WARN_UNUSED_RESULT __attribute__((__warn_unused_result__))\n #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) __attribute__((__warn_unused_result__))\n#elif (JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard) >= 201907L)\n #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])\n #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard(msg)]])\n#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard)\n #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])\n #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])\n#elif defined(_Check_return_) /* SAL */\n #define JSON_HEDLEY_WARN_UNUSED_RESULT _Check_return_\n #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) _Check_return_\n#else\n #define JSON_HEDLEY_WARN_UNUSED_RESULT\n #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg)\n#endif\n\n#if defined(JSON_HEDLEY_SENTINEL)\n #undef JSON_HEDLEY_SENTINEL\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(sentinel) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_SENTINEL(position) __attribute__((__sentinel__(position)))\n#else\n #define JSON_HEDLEY_SENTINEL(position)\n#endif\n\n#if defined(JSON_HEDLEY_NO_RETURN)\n #undef JSON_HEDLEY_NO_RETURN\n#endif\n#if JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_NO_RETURN __noreturn\n#elif \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))\n#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L\n #define JSON_HEDLEY_NO_RETURN _Noreturn\n#elif defined(__cplusplus) && (__cplusplus >= 201103L)\n #define JSON_HEDLEY_NO_RETURN JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[noreturn]])\n#elif \\\n JSON_HEDLEY_HAS_ATTRIBUTE(noreturn) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,2,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)\n #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)\n #define JSON_HEDLEY_NO_RETURN _Pragma(\"does_not_return\")\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)\n#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)\n #define JSON_HEDLEY_NO_RETURN _Pragma(\"FUNC_NEVER_RETURNS;\")\n#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)\n #define JSON_HEDLEY_NO_RETURN __attribute((noreturn))\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)\n #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)\n#else\n #define JSON_HEDLEY_NO_RETURN\n#endif\n\n#if defined(JSON_HEDLEY_NO_ESCAPE)\n #undef JSON_HEDLEY_NO_ESCAPE\n#endif\n#if JSON_HEDLEY_HAS_ATTRIBUTE(noescape)\n #define JSON_HEDLEY_NO_ESCAPE __attribute__((__noescape__))\n#else\n #define JSON_HEDLEY_NO_ESCAPE\n#endif\n\n#if defined(JSON_HEDLEY_UNREACHABLE)\n #undef JSON_HEDLEY_UNREACHABLE\n#endif\n#if defined(JSON_HEDLEY_UNREACHABLE_RETURN)\n #undef JSON_HEDLEY_UNREACHABLE_RETURN\n#endif\n#if defined(JSON_HEDLEY_ASSUME)\n #undef JSON_HEDLEY_ASSUME\n#endif\n#if \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_ASSUME(expr) __assume(expr)\n#elif JSON_HEDLEY_HAS_BUILTIN(__builtin_assume)\n #define JSON_HEDLEY_ASSUME(expr) __builtin_assume(expr)\n#elif \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)\n #if defined(__cplusplus)\n #define JSON_HEDLEY_ASSUME(expr) std::_nassert(expr)\n #else\n #define JSON_HEDLEY_ASSUME(expr) _nassert(expr)\n #endif\n#endif\n#if \\\n (JSON_HEDLEY_HAS_BUILTIN(__builtin_unreachable) && (!defined(JSON_HEDLEY_ARM_VERSION))) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(18,10,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(13,1,5) || \\\n JSON_HEDLEY_CRAY_VERSION_CHECK(10,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_UNREACHABLE() __builtin_unreachable()\n#elif defined(JSON_HEDLEY_ASSUME)\n #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)\n#endif\n#if !defined(JSON_HEDLEY_ASSUME)\n #if defined(JSON_HEDLEY_UNREACHABLE)\n #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, ((expr) ? 1 : (JSON_HEDLEY_UNREACHABLE(), 1)))\n #else\n #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, expr)\n #endif\n#endif\n#if defined(JSON_HEDLEY_UNREACHABLE)\n #if \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)\n #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (JSON_HEDLEY_STATIC_CAST(void, JSON_HEDLEY_ASSUME(0)), (value))\n #else\n #define JSON_HEDLEY_UNREACHABLE_RETURN(value) JSON_HEDLEY_UNREACHABLE()\n #endif\n#else\n #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (value)\n#endif\n#if !defined(JSON_HEDLEY_UNREACHABLE)\n #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)\n#endif\n\nJSON_HEDLEY_DIAGNOSTIC_PUSH\n#if JSON_HEDLEY_HAS_WARNING(\"-Wpedantic\")\n #pragma clang diagnostic ignored \"-Wpedantic\"\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wc++98-compat-pedantic\") && defined(__cplusplus)\n #pragma clang diagnostic ignored \"-Wc++98-compat-pedantic\"\n#endif\n#if JSON_HEDLEY_GCC_HAS_WARNING(\"-Wvariadic-macros\",4,0,0)\n #if defined(__clang__)\n #pragma clang diagnostic ignored \"-Wvariadic-macros\"\n #elif defined(JSON_HEDLEY_GCC_VERSION)\n #pragma GCC diagnostic ignored \"-Wvariadic-macros\"\n #endif\n#endif\n#if defined(JSON_HEDLEY_NON_NULL)\n #undef JSON_HEDLEY_NON_NULL\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(nonnull) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)\n #define JSON_HEDLEY_NON_NULL(...) __attribute__((__nonnull__(__VA_ARGS__)))\n#else\n #define JSON_HEDLEY_NON_NULL(...)\n#endif\nJSON_HEDLEY_DIAGNOSTIC_POP\n\n#if defined(JSON_HEDLEY_PRINTF_FORMAT)\n #undef JSON_HEDLEY_PRINTF_FORMAT\n#endif\n#if defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && !defined(__USE_MINGW_ANSI_STDIO)\n #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(ms_printf, string_idx, first_to_check)))\n#elif defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && defined(__USE_MINGW_ANSI_STDIO)\n #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(gnu_printf, string_idx, first_to_check)))\n#elif \\\n JSON_HEDLEY_HAS_ATTRIBUTE(format) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(__printf__, string_idx, first_to_check)))\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(6,0,0)\n #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __declspec(vaformat(printf,string_idx,first_to_check))\n#else\n #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check)\n#endif\n\n#if defined(JSON_HEDLEY_CONSTEXPR)\n #undef JSON_HEDLEY_CONSTEXPR\n#endif\n#if defined(__cplusplus)\n #if __cplusplus >= 201103L\n #define JSON_HEDLEY_CONSTEXPR JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(constexpr)\n #endif\n#endif\n#if !defined(JSON_HEDLEY_CONSTEXPR)\n #define JSON_HEDLEY_CONSTEXPR\n#endif\n\n#if defined(JSON_HEDLEY_PREDICT)\n #undef JSON_HEDLEY_PREDICT\n#endif\n#if defined(JSON_HEDLEY_LIKELY)\n #undef JSON_HEDLEY_LIKELY\n#endif\n#if defined(JSON_HEDLEY_UNLIKELY)\n #undef JSON_HEDLEY_UNLIKELY\n#endif\n#if defined(JSON_HEDLEY_UNPREDICTABLE)\n #undef JSON_HEDLEY_UNPREDICTABLE\n#endif\n#if JSON_HEDLEY_HAS_BUILTIN(__builtin_unpredictable)\n #define JSON_HEDLEY_UNPREDICTABLE(expr) __builtin_unpredictable((expr))\n#endif\n#if \\\n (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect_with_probability) && !defined(JSON_HEDLEY_PGI_VERSION)) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(9,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n# define JSON_HEDLEY_PREDICT(expr, value, probability) __builtin_expect_with_probability( (expr), (value), (probability))\n# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) __builtin_expect_with_probability(!!(expr), 1 , (probability))\n# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) __builtin_expect_with_probability(!!(expr), 0 , (probability))\n# define JSON_HEDLEY_LIKELY(expr) __builtin_expect (!!(expr), 1 )\n# define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect (!!(expr), 0 )\n#elif \\\n (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,27) || \\\n JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n# define JSON_HEDLEY_PREDICT(expr, expected, probability) \\\n (((probability) >= 0.9) ? __builtin_expect((expr), (expected)) : (JSON_HEDLEY_STATIC_CAST(void, expected), (expr)))\n# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) \\\n (__extension__ ({ \\\n double hedley_probability_ = (probability); \\\n ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 1) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 0) : !!(expr))); \\\n }))\n# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) \\\n (__extension__ ({ \\\n double hedley_probability_ = (probability); \\\n ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 0) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 1) : !!(expr))); \\\n }))\n# define JSON_HEDLEY_LIKELY(expr) __builtin_expect(!!(expr), 1)\n# define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect(!!(expr), 0)\n#else\n# define JSON_HEDLEY_PREDICT(expr, expected, probability) (JSON_HEDLEY_STATIC_CAST(void, expected), (expr))\n# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) (!!(expr))\n# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) (!!(expr))\n# define JSON_HEDLEY_LIKELY(expr) (!!(expr))\n# define JSON_HEDLEY_UNLIKELY(expr) (!!(expr))\n#endif\n#if !defined(JSON_HEDLEY_UNPREDICTABLE)\n #define JSON_HEDLEY_UNPREDICTABLE(expr) JSON_HEDLEY_PREDICT(expr, 1, 0.5)\n#endif\n\n#if defined(JSON_HEDLEY_MALLOC)\n #undef JSON_HEDLEY_MALLOC\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(malloc) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_MALLOC __attribute__((__malloc__))\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)\n #define JSON_HEDLEY_MALLOC _Pragma(\"returns_new_memory\")\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_MALLOC __declspec(restrict)\n#else\n #define JSON_HEDLEY_MALLOC\n#endif\n\n#if defined(JSON_HEDLEY_PURE)\n #undef JSON_HEDLEY_PURE\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(pure) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(2,96,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n# define JSON_HEDLEY_PURE __attribute__((__pure__))\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)\n# define JSON_HEDLEY_PURE _Pragma(\"does_not_write_global_data\")\n#elif defined(__cplusplus) && \\\n ( \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) \\\n )\n# define JSON_HEDLEY_PURE _Pragma(\"FUNC_IS_PURE;\")\n#else\n# define JSON_HEDLEY_PURE\n#endif\n\n#if defined(JSON_HEDLEY_CONST)\n #undef JSON_HEDLEY_CONST\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(const) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(2,5,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_CONST __attribute__((__const__))\n#elif \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)\n #define JSON_HEDLEY_CONST _Pragma(\"no_side_effect\")\n#else\n #define JSON_HEDLEY_CONST JSON_HEDLEY_PURE\n#endif\n\n#if defined(JSON_HEDLEY_RESTRICT)\n #undef JSON_HEDLEY_RESTRICT\n#endif\n#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && !defined(__cplusplus)\n #define JSON_HEDLEY_RESTRICT restrict\n#elif \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,4) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \\\n defined(__clang__) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_RESTRICT __restrict\n#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,3,0) && !defined(__cplusplus)\n #define JSON_HEDLEY_RESTRICT _Restrict\n#else\n #define JSON_HEDLEY_RESTRICT\n#endif\n\n#if defined(JSON_HEDLEY_INLINE)\n #undef JSON_HEDLEY_INLINE\n#endif\n#if \\\n (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \\\n (defined(__cplusplus) && (__cplusplus >= 199711L))\n #define JSON_HEDLEY_INLINE inline\n#elif \\\n defined(JSON_HEDLEY_GCC_VERSION) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(6,2,0)\n #define JSON_HEDLEY_INLINE __inline__\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,1,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_INLINE __inline\n#else\n #define JSON_HEDLEY_INLINE\n#endif\n\n#if defined(JSON_HEDLEY_ALWAYS_INLINE)\n #undef JSON_HEDLEY_ALWAYS_INLINE\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(always_inline) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)\n# define JSON_HEDLEY_ALWAYS_INLINE __attribute__((__always_inline__)) JSON_HEDLEY_INLINE\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n# define JSON_HEDLEY_ALWAYS_INLINE __forceinline\n#elif defined(__cplusplus) && \\\n ( \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) \\\n )\n# define JSON_HEDLEY_ALWAYS_INLINE _Pragma(\"FUNC_ALWAYS_INLINE;\")\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n# define JSON_HEDLEY_ALWAYS_INLINE _Pragma(\"inline=forced\")\n#else\n# define JSON_HEDLEY_ALWAYS_INLINE JSON_HEDLEY_INLINE\n#endif\n\n#if defined(JSON_HEDLEY_NEVER_INLINE)\n #undef JSON_HEDLEY_NEVER_INLINE\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(noinline) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \\\n JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \\\n (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \\\n (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \\\n (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \\\n JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \\\n JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \\\n JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)\n #define JSON_HEDLEY_NEVER_INLINE __attribute__((__noinline__))\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)\n#elif JSON_HEDLEY_PGI_VERSION_CHECK(10,2,0)\n #define JSON_HEDLEY_NEVER_INLINE _Pragma(\"noinline\")\n#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)\n #define JSON_HEDLEY_NEVER_INLINE _Pragma(\"FUNC_CANNOT_INLINE;\")\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n #define JSON_HEDLEY_NEVER_INLINE _Pragma(\"inline=never\")\n#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)\n #define JSON_HEDLEY_NEVER_INLINE __attribute((noinline))\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)\n #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)\n#else\n #define JSON_HEDLEY_NEVER_INLINE\n#endif\n\n#if defined(JSON_HEDLEY_PRIVATE)\n #undef JSON_HEDLEY_PRIVATE\n#endif\n#if defined(JSON_HEDLEY_PUBLIC)\n #undef JSON_HEDLEY_PUBLIC\n#endif\n#if defined(JSON_HEDLEY_IMPORT)\n #undef JSON_HEDLEY_IMPORT\n#endif\n#if defined(_WIN32) || defined(__CYGWIN__)\n# define JSON_HEDLEY_PRIVATE\n# define JSON_HEDLEY_PUBLIC __declspec(dllexport)\n# define JSON_HEDLEY_IMPORT __declspec(dllimport)\n#else\n# if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(visibility) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \\\n JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \\\n ( \\\n defined(__TI_EABI__) && \\\n ( \\\n (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) \\\n ) \\\n ) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n# define JSON_HEDLEY_PRIVATE __attribute__((__visibility__(\"hidden\")))\n# define JSON_HEDLEY_PUBLIC __attribute__((__visibility__(\"default\")))\n# else\n# define JSON_HEDLEY_PRIVATE\n# define JSON_HEDLEY_PUBLIC\n# endif\n# define JSON_HEDLEY_IMPORT extern\n#endif\n\n#if defined(JSON_HEDLEY_NO_THROW)\n #undef JSON_HEDLEY_NO_THROW\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(nothrow) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_NO_THROW __attribute__((__nothrow__))\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(13,1,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)\n #define JSON_HEDLEY_NO_THROW __declspec(nothrow)\n#else\n #define JSON_HEDLEY_NO_THROW\n#endif\n\n#if defined(JSON_HEDLEY_FALL_THROUGH)\n #undef JSON_HEDLEY_FALL_THROUGH\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(fallthrough) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(7,0,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_FALL_THROUGH __attribute__((__fallthrough__))\n#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(clang,fallthrough)\n #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[clang::fallthrough]])\n#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(fallthrough)\n #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[fallthrough]])\n#elif defined(__fallthrough) /* SAL */\n #define JSON_HEDLEY_FALL_THROUGH __fallthrough\n#else\n #define JSON_HEDLEY_FALL_THROUGH\n#endif\n\n#if defined(JSON_HEDLEY_RETURNS_NON_NULL)\n #undef JSON_HEDLEY_RETURNS_NON_NULL\n#endif\n#if \\\n JSON_HEDLEY_HAS_ATTRIBUTE(returns_nonnull) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_RETURNS_NON_NULL __attribute__((__returns_nonnull__))\n#elif defined(_Ret_notnull_) /* SAL */\n #define JSON_HEDLEY_RETURNS_NON_NULL _Ret_notnull_\n#else\n #define JSON_HEDLEY_RETURNS_NON_NULL\n#endif\n\n#if defined(JSON_HEDLEY_ARRAY_PARAM)\n #undef JSON_HEDLEY_ARRAY_PARAM\n#endif\n#if \\\n defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && \\\n !defined(__STDC_NO_VLA__) && \\\n !defined(__cplusplus) && \\\n !defined(JSON_HEDLEY_PGI_VERSION) && \\\n !defined(JSON_HEDLEY_TINYC_VERSION)\n #define JSON_HEDLEY_ARRAY_PARAM(name) (name)\n#else\n #define JSON_HEDLEY_ARRAY_PARAM(name)\n#endif\n\n#if defined(JSON_HEDLEY_IS_CONSTANT)\n #undef JSON_HEDLEY_IS_CONSTANT\n#endif\n#if defined(JSON_HEDLEY_REQUIRE_CONSTEXPR)\n #undef JSON_HEDLEY_REQUIRE_CONSTEXPR\n#endif\n/* JSON_HEDLEY_IS_CONSTEXPR_ is for\n HEDLEY INTERNAL USE ONLY. API subject to change without notice. */\n#if defined(JSON_HEDLEY_IS_CONSTEXPR_)\n #undef JSON_HEDLEY_IS_CONSTEXPR_\n#endif\n#if \\\n JSON_HEDLEY_HAS_BUILTIN(__builtin_constant_p) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,19) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \\\n JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \\\n (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) && !defined(__cplusplus)) || \\\n JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \\\n JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)\n #define JSON_HEDLEY_IS_CONSTANT(expr) __builtin_constant_p(expr)\n#endif\n#if !defined(__cplusplus)\n# if \\\n JSON_HEDLEY_HAS_BUILTIN(__builtin_types_compatible_p) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \\\n JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \\\n JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,24)\n#if defined(__INTPTR_TYPE__)\n #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0)), int*)\n#else\n #include \n #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((intptr_t) ((expr) * 0)) : (int*) 0)), int*)\n#endif\n# elif \\\n ( \\\n defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) && \\\n !defined(JSON_HEDLEY_SUNPRO_VERSION) && \\\n !defined(JSON_HEDLEY_PGI_VERSION) && \\\n !defined(JSON_HEDLEY_IAR_VERSION)) || \\\n (JSON_HEDLEY_HAS_EXTENSION(c_generic_selections) && !defined(JSON_HEDLEY_IAR_VERSION)) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0) || \\\n JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \\\n JSON_HEDLEY_ARM_VERSION_CHECK(5,3,0)\n#if defined(__INTPTR_TYPE__)\n #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0), int*: 1, void*: 0)\n#else\n #include \n #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((intptr_t) * 0) : (int*) 0), int*: 1, void*: 0)\n#endif\n# elif \\\n defined(JSON_HEDLEY_GCC_VERSION) || \\\n defined(JSON_HEDLEY_INTEL_VERSION) || \\\n defined(JSON_HEDLEY_TINYC_VERSION) || \\\n defined(JSON_HEDLEY_TI_ARMCL_VERSION) || \\\n JSON_HEDLEY_TI_CL430_VERSION_CHECK(18,12,0) || \\\n defined(JSON_HEDLEY_TI_CL2000_VERSION) || \\\n defined(JSON_HEDLEY_TI_CL6X_VERSION) || \\\n defined(JSON_HEDLEY_TI_CL7X_VERSION) || \\\n defined(JSON_HEDLEY_TI_CLPRU_VERSION) || \\\n defined(__clang__)\n# define JSON_HEDLEY_IS_CONSTEXPR_(expr) ( \\\n sizeof(void) != \\\n sizeof(*( \\\n 1 ? \\\n ((void*) ((expr) * 0L) ) : \\\n((struct { char v[sizeof(void) * 2]; } *) 1) \\\n ) \\\n ) \\\n )\n# endif\n#endif\n#if defined(JSON_HEDLEY_IS_CONSTEXPR_)\n #if !defined(JSON_HEDLEY_IS_CONSTANT)\n #define JSON_HEDLEY_IS_CONSTANT(expr) JSON_HEDLEY_IS_CONSTEXPR_(expr)\n #endif\n #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (JSON_HEDLEY_IS_CONSTEXPR_(expr) ? (expr) : (-1))\n#else\n #if !defined(JSON_HEDLEY_IS_CONSTANT)\n #define JSON_HEDLEY_IS_CONSTANT(expr) (0)\n #endif\n #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (expr)\n#endif\n\n#if defined(JSON_HEDLEY_BEGIN_C_DECLS)\n #undef JSON_HEDLEY_BEGIN_C_DECLS\n#endif\n#if defined(JSON_HEDLEY_END_C_DECLS)\n #undef JSON_HEDLEY_END_C_DECLS\n#endif\n#if defined(JSON_HEDLEY_C_DECL)\n #undef JSON_HEDLEY_C_DECL\n#endif\n#if defined(__cplusplus)\n #define JSON_HEDLEY_BEGIN_C_DECLS extern \"C\" {\n #define JSON_HEDLEY_END_C_DECLS }\n #define JSON_HEDLEY_C_DECL extern \"C\"\n#else\n #define JSON_HEDLEY_BEGIN_C_DECLS\n #define JSON_HEDLEY_END_C_DECLS\n #define JSON_HEDLEY_C_DECL\n#endif\n\n#if defined(JSON_HEDLEY_STATIC_ASSERT)\n #undef JSON_HEDLEY_STATIC_ASSERT\n#endif\n#if \\\n !defined(__cplusplus) && ( \\\n (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) || \\\n (JSON_HEDLEY_HAS_FEATURE(c_static_assert) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \\\n JSON_HEDLEY_GCC_VERSION_CHECK(6,0,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \\\n defined(_Static_assert) \\\n )\n# define JSON_HEDLEY_STATIC_ASSERT(expr, message) _Static_assert(expr, message)\n#elif \\\n (defined(__cplusplus) && (__cplusplus >= 201103L)) || \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(16,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n# define JSON_HEDLEY_STATIC_ASSERT(expr, message) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(static_assert(expr, message))\n#else\n# define JSON_HEDLEY_STATIC_ASSERT(expr, message)\n#endif\n\n#if defined(JSON_HEDLEY_NULL)\n #undef JSON_HEDLEY_NULL\n#endif\n#if defined(__cplusplus)\n #if __cplusplus >= 201103L\n #define JSON_HEDLEY_NULL JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(nullptr)\n #elif defined(NULL)\n #define JSON_HEDLEY_NULL NULL\n #else\n #define JSON_HEDLEY_NULL JSON_HEDLEY_STATIC_CAST(void*, 0)\n #endif\n#elif defined(NULL)\n #define JSON_HEDLEY_NULL NULL\n#else\n #define JSON_HEDLEY_NULL ((void*) 0)\n#endif\n\n#if defined(JSON_HEDLEY_MESSAGE)\n #undef JSON_HEDLEY_MESSAGE\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wunknown-pragmas\")\n# define JSON_HEDLEY_MESSAGE(msg) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \\\n JSON_HEDLEY_PRAGMA(message msg) \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n#elif \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,4,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message msg)\n#elif JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0)\n# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(_CRI message msg)\n#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)\n# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))\n#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,0,0)\n# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))\n#else\n# define JSON_HEDLEY_MESSAGE(msg)\n#endif\n\n#if defined(JSON_HEDLEY_WARNING)\n #undef JSON_HEDLEY_WARNING\n#endif\n#if JSON_HEDLEY_HAS_WARNING(\"-Wunknown-pragmas\")\n# define JSON_HEDLEY_WARNING(msg) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \\\n JSON_HEDLEY_PRAGMA(clang warning msg) \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n#elif \\\n JSON_HEDLEY_GCC_VERSION_CHECK(4,8,0) || \\\n JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \\\n JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)\n# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(GCC warning msg)\n#elif \\\n JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(message(msg))\n#else\n# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_MESSAGE(msg)\n#endif\n\n#if defined(JSON_HEDLEY_REQUIRE)\n #undef JSON_HEDLEY_REQUIRE\n#endif\n#if defined(JSON_HEDLEY_REQUIRE_MSG)\n #undef JSON_HEDLEY_REQUIRE_MSG\n#endif\n#if JSON_HEDLEY_HAS_ATTRIBUTE(diagnose_if)\n# if JSON_HEDLEY_HAS_WARNING(\"-Wgcc-compat\")\n# define JSON_HEDLEY_REQUIRE(expr) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wgcc-compat\\\"\") \\\n __attribute__((diagnose_if(!(expr), #expr, \"error\"))) \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# define JSON_HEDLEY_REQUIRE_MSG(expr,msg) \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"clang diagnostic ignored \\\"-Wgcc-compat\\\"\") \\\n __attribute__((diagnose_if(!(expr), msg, \"error\"))) \\\n JSON_HEDLEY_DIAGNOSTIC_POP\n# else\n# define JSON_HEDLEY_REQUIRE(expr) __attribute__((diagnose_if(!(expr), #expr, \"error\")))\n# define JSON_HEDLEY_REQUIRE_MSG(expr,msg) __attribute__((diagnose_if(!(expr), msg, \"error\")))\n# endif\n#else\n# define JSON_HEDLEY_REQUIRE(expr)\n# define JSON_HEDLEY_REQUIRE_MSG(expr,msg)\n#endif\n\n#if defined(JSON_HEDLEY_FLAGS)\n #undef JSON_HEDLEY_FLAGS\n#endif\n#if JSON_HEDLEY_HAS_ATTRIBUTE(flag_enum) && (!defined(__cplusplus) || JSON_HEDLEY_HAS_WARNING(\"-Wbitfield-enum-conversion\"))\n #define JSON_HEDLEY_FLAGS __attribute__((__flag_enum__))\n#else\n #define JSON_HEDLEY_FLAGS\n#endif\n\n#if defined(JSON_HEDLEY_FLAGS_CAST)\n #undef JSON_HEDLEY_FLAGS_CAST\n#endif\n#if JSON_HEDLEY_INTEL_VERSION_CHECK(19,0,0)\n# define JSON_HEDLEY_FLAGS_CAST(T, expr) (__extension__ ({ \\\n JSON_HEDLEY_DIAGNOSTIC_PUSH \\\n _Pragma(\"warning(disable:188)\") \\\n ((T) (expr)); \\\n JSON_HEDLEY_DIAGNOSTIC_POP \\\n }))\n#else\n# define JSON_HEDLEY_FLAGS_CAST(T, expr) JSON_HEDLEY_STATIC_CAST(T, expr)\n#endif\n\n#if defined(JSON_HEDLEY_EMPTY_BASES)\n #undef JSON_HEDLEY_EMPTY_BASES\n#endif\n#if \\\n (JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,23918) && !JSON_HEDLEY_MSVC_VERSION_CHECK(20,0,0)) || \\\n JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)\n #define JSON_HEDLEY_EMPTY_BASES __declspec(empty_bases)\n#else\n #define JSON_HEDLEY_EMPTY_BASES\n#endif\n\n/* Remaining macros are deprecated. */\n\n#if defined(JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK)\n #undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK\n#endif\n#if defined(__clang__)\n #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) (0)\n#else\n #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)\n#endif\n\n#if defined(JSON_HEDLEY_CLANG_HAS_ATTRIBUTE)\n #undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE\n#endif\n#define JSON_HEDLEY_CLANG_HAS_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE)\n #undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE\n#endif\n#define JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_BUILTIN)\n #undef JSON_HEDLEY_CLANG_HAS_BUILTIN\n#endif\n#define JSON_HEDLEY_CLANG_HAS_BUILTIN(builtin) JSON_HEDLEY_HAS_BUILTIN(builtin)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_FEATURE)\n #undef JSON_HEDLEY_CLANG_HAS_FEATURE\n#endif\n#define JSON_HEDLEY_CLANG_HAS_FEATURE(feature) JSON_HEDLEY_HAS_FEATURE(feature)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_EXTENSION)\n #undef JSON_HEDLEY_CLANG_HAS_EXTENSION\n#endif\n#define JSON_HEDLEY_CLANG_HAS_EXTENSION(extension) JSON_HEDLEY_HAS_EXTENSION(extension)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE)\n #undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE\n#endif\n#define JSON_HEDLEY_CLANG_HAS_DECLSPEC_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute)\n\n#if defined(JSON_HEDLEY_CLANG_HAS_WARNING)\n #undef JSON_HEDLEY_CLANG_HAS_WARNING\n#endif\n#define JSON_HEDLEY_CLANG_HAS_WARNING(warning) JSON_HEDLEY_HAS_WARNING(warning)\n\n#endif /* !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < X) */\n\n\n// This file contains all internal macro definitions (except those affecting ABI)\n// You MUST include macro_unscope.hpp at the end of json.hpp to undef all of them\n\n// #include \n\n\n// exclude unsupported compilers\n#if !defined(JSON_SKIP_UNSUPPORTED_COMPILER_CHECK)\n #if defined(__clang__)\n #if (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) < 30400\n #error \"unsupported Clang version - see https://github.com/nlohmann/json#supported-compilers\"\n #endif\n #elif defined(__GNUC__) && !(defined(__ICC) || defined(__INTEL_COMPILER))\n #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40800\n #error \"unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers\"\n #endif\n #endif\n#endif\n\n// C++ language standard detection\n// if the user manually specified the used c++ version this is skipped\n#if !defined(JSON_HAS_CPP_20) && !defined(JSON_HAS_CPP_17) && !defined(JSON_HAS_CPP_14) && !defined(JSON_HAS_CPP_11)\n #if (defined(__cplusplus) && __cplusplus >= 202002L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L)\n #define JSON_HAS_CPP_20\n #define JSON_HAS_CPP_17\n #define JSON_HAS_CPP_14\n #elif (defined(__cplusplus) && __cplusplus >= 201703L) || (defined(_HAS_CXX17) && _HAS_CXX17 == 1) // fix for issue #464\n #define JSON_HAS_CPP_17\n #define JSON_HAS_CPP_14\n #elif (defined(__cplusplus) && __cplusplus >= 201402L) || (defined(_HAS_CXX14) && _HAS_CXX14 == 1)\n #define JSON_HAS_CPP_14\n #endif\n // the cpp 11 flag is always specified because it is the minimal required version\n #define JSON_HAS_CPP_11\n#endif\n\n#ifdef __has_include\n #if __has_include()\n #include \n #endif\n#endif\n\n#if !defined(JSON_HAS_FILESYSTEM) && !defined(JSON_HAS_EXPERIMENTAL_FILESYSTEM)\n #ifdef JSON_HAS_CPP_17\n #if defined(__cpp_lib_filesystem)\n #define JSON_HAS_FILESYSTEM 1\n #elif defined(__cpp_lib_experimental_filesystem)\n #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1\n #elif !defined(__has_include)\n #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1\n #elif __has_include()\n #define JSON_HAS_FILESYSTEM 1\n #elif __has_include()\n #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1\n #endif\n\n // std::filesystem does not work on MinGW GCC 8: https://sourceforge.net/p/mingw-w64/bugs/737/\n #if defined(__MINGW32__) && defined(__GNUC__) && __GNUC__ == 8\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n\n // no filesystem support before GCC 8: https://en.cppreference.com/w/cpp/compiler_support\n #if defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 8\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n\n // no filesystem support before Clang 7: https://en.cppreference.com/w/cpp/compiler_support\n #if defined(__clang_major__) && __clang_major__ < 7\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n\n // no filesystem support before MSVC 19.14: https://en.cppreference.com/w/cpp/compiler_support\n #if defined(_MSC_VER) && _MSC_VER < 1914\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n\n // no filesystem support before iOS 13\n #if defined(__IPHONE_OS_VERSION_MIN_REQUIRED) && __IPHONE_OS_VERSION_MIN_REQUIRED < 130000\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n\n // no filesystem support before macOS Catalina\n #if defined(__MAC_OS_X_VERSION_MIN_REQUIRED) && __MAC_OS_X_VERSION_MIN_REQUIRED < 101500\n #undef JSON_HAS_FILESYSTEM\n #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #endif\n #endif\n#endif\n\n#ifndef JSON_HAS_EXPERIMENTAL_FILESYSTEM\n #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 0\n#endif\n\n#ifndef JSON_HAS_FILESYSTEM\n #define JSON_HAS_FILESYSTEM 0\n#endif\n\n#ifndef JSON_HAS_THREE_WAY_COMPARISON\n #if defined(__cpp_impl_three_way_comparison) && __cpp_impl_three_way_comparison >= 201907L \\\n && defined(__cpp_lib_three_way_comparison) && __cpp_lib_three_way_comparison >= 201907L\n #define JSON_HAS_THREE_WAY_COMPARISON 1\n #else\n #define JSON_HAS_THREE_WAY_COMPARISON 0\n #endif\n#endif\n\n#ifndef JSON_HAS_RANGES\n // ranges header shipping in GCC 11.1.0 (released 2021-04-27) has syntax error\n #if defined(__GLIBCXX__) && __GLIBCXX__ == 20210427\n #define JSON_HAS_RANGES 0\n #elif defined(__cpp_lib_ranges)\n #define JSON_HAS_RANGES 1\n #else\n #define JSON_HAS_RANGES 0\n #endif\n#endif\n\n#ifdef JSON_HAS_CPP_17\n #define JSON_INLINE_VARIABLE inline\n#else\n #define JSON_INLINE_VARIABLE\n#endif\n\n#if JSON_HEDLEY_HAS_ATTRIBUTE(no_unique_address)\n #define JSON_NO_UNIQUE_ADDRESS [[no_unique_address]]\n#else\n #define JSON_NO_UNIQUE_ADDRESS\n#endif\n\n// disable documentation warnings on clang\n#if defined(__clang__)\n #pragma clang diagnostic push\n #pragma clang diagnostic ignored \"-Wdocumentation\"\n #pragma clang diagnostic ignored \"-Wdocumentation-unknown-command\"\n#endif\n\n// allow disabling exceptions\n#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) && !defined(JSON_NOEXCEPTION)\n #define JSON_THROW(exception) throw exception\n #define JSON_TRY try\n #define JSON_CATCH(exception) catch(exception)\n #define JSON_INTERNAL_CATCH(exception) catch(exception)\n#else\n #include \n #define JSON_THROW(exception) std::abort()\n #define JSON_TRY if(true)\n #define JSON_CATCH(exception) if(false)\n #define JSON_INTERNAL_CATCH(exception) if(false)\n#endif\n\n// override exception macros\n#if defined(JSON_THROW_USER)\n #undef JSON_THROW\n #define JSON_THROW JSON_THROW_USER\n#endif\n#if defined(JSON_TRY_USER)\n #undef JSON_TRY\n #define JSON_TRY JSON_TRY_USER\n#endif\n#if defined(JSON_CATCH_USER)\n #undef JSON_CATCH\n #define JSON_CATCH JSON_CATCH_USER\n #undef JSON_INTERNAL_CATCH\n #define JSON_INTERNAL_CATCH JSON_CATCH_USER\n#endif\n#if defined(JSON_INTERNAL_CATCH_USER)\n #undef JSON_INTERNAL_CATCH\n #define JSON_INTERNAL_CATCH JSON_INTERNAL_CATCH_USER\n#endif\n\n// allow overriding assert\n#if !defined(JSON_ASSERT)\n #include // assert\n #define JSON_ASSERT(x) assert(x)\n#endif\n\n// allow to access some private functions (needed by the test suite)\n#if defined(JSON_TESTS_PRIVATE)\n #define JSON_PRIVATE_UNLESS_TESTED public\n#else\n #define JSON_PRIVATE_UNLESS_TESTED private\n#endif\n\n/*!\n@brief macro to briefly define a mapping between an enum and JSON\n@def NLOHMANN_JSON_SERIALIZE_ENUM\n@since version 3.4.0\n*/\n#define NLOHMANN_JSON_SERIALIZE_ENUM(ENUM_TYPE, ...) \\\n template \\\n inline void to_json(BasicJsonType& j, const ENUM_TYPE& e) \\\n { \\\n static_assert(std::is_enum::value, #ENUM_TYPE \" must be an enum!\"); \\\n static const std::pair m[] = __VA_ARGS__; \\\n auto it = std::find_if(std::begin(m), std::end(m), \\\n [e](const std::pair& ej_pair) -> bool \\\n { \\\n return ej_pair.first == e; \\\n }); \\\n j = ((it != std::end(m)) ? it : std::begin(m))->second; \\\n } \\\n template \\\n inline void from_json(const BasicJsonType& j, ENUM_TYPE& e) \\\n { \\\n static_assert(std::is_enum::value, #ENUM_TYPE \" must be an enum!\"); \\\n static const std::pair m[] = __VA_ARGS__; \\\n auto it = std::find_if(std::begin(m), std::end(m), \\\n [&j](const std::pair& ej_pair) -> bool \\\n { \\\n return ej_pair.second == j; \\\n }); \\\n e = ((it != std::end(m)) ? it : std::begin(m))->first; \\\n }\n\n// Ugly macros to avoid uglier copy-paste when specializing basic_json. They\n// may be removed in the future once the class is split.\n\n#define NLOHMANN_BASIC_JSON_TPL_DECLARATION \\\n template class ObjectType, \\\n template class ArrayType, \\\n class StringType, class BooleanType, class NumberIntegerType, \\\n class NumberUnsignedType, class NumberFloatType, \\\n template class AllocatorType, \\\n template class JSONSerializer, \\\n class BinaryType>\n\n#define NLOHMANN_BASIC_JSON_TPL \\\n basic_json\n\n// Macros to simplify conversion from/to types\n\n#define NLOHMANN_JSON_EXPAND( x ) x\n#define NLOHMANN_JSON_GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, _61, _62, _63, _64, NAME,...) NAME\n#define NLOHMANN_JSON_PASTE(...) NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_GET_MACRO(__VA_ARGS__, \\\n NLOHMANN_JSON_PASTE64, \\\n NLOHMANN_JSON_PASTE63, \\\n NLOHMANN_JSON_PASTE62, \\\n NLOHMANN_JSON_PASTE61, \\\n NLOHMANN_JSON_PASTE60, \\\n NLOHMANN_JSON_PASTE59, \\\n NLOHMANN_JSON_PASTE58, \\\n NLOHMANN_JSON_PASTE57, \\\n NLOHMANN_JSON_PASTE56, \\\n NLOHMANN_JSON_PASTE55, \\\n NLOHMANN_JSON_PASTE54, \\\n NLOHMANN_JSON_PASTE53, \\\n NLOHMANN_JSON_PASTE52, \\\n NLOHMANN_JSON_PASTE51, \\\n NLOHMANN_JSON_PASTE50, \\\n NLOHMANN_JSON_PASTE49, \\\n NLOHMANN_JSON_PASTE48, \\\n NLOHMANN_JSON_PASTE47, \\\n NLOHMANN_JSON_PASTE46, \\\n NLOHMANN_JSON_PASTE45, \\\n NLOHMANN_JSON_PASTE44, \\\n NLOHMANN_JSON_PASTE43, \\\n NLOHMANN_JSON_PASTE42, \\\n NLOHMANN_JSON_PASTE41, \\\n NLOHMANN_JSON_PASTE40, \\\n NLOHMANN_JSON_PASTE39, \\\n NLOHMANN_JSON_PASTE38, \\\n NLOHMANN_JSON_PASTE37, \\\n NLOHMANN_JSON_PASTE36, \\\n NLOHMANN_JSON_PASTE35, \\\n NLOHMANN_JSON_PASTE34, \\\n NLOHMANN_JSON_PASTE33, \\\n NLOHMANN_JSON_PASTE32, \\\n NLOHMANN_JSON_PASTE31, \\\n NLOHMANN_JSON_PASTE30, \\\n NLOHMANN_JSON_PASTE29, \\\n NLOHMANN_JSON_PASTE28, \\\n NLOHMANN_JSON_PASTE27, \\\n NLOHMANN_JSON_PASTE26, \\\n NLOHMANN_JSON_PASTE25, \\\n NLOHMANN_JSON_PASTE24, \\\n NLOHMANN_JSON_PASTE23, \\\n NLOHMANN_JSON_PASTE22, \\\n NLOHMANN_JSON_PASTE21, \\\n NLOHMANN_JSON_PASTE20, \\\n NLOHMANN_JSON_PASTE19, \\\n NLOHMANN_JSON_PASTE18, \\\n NLOHMANN_JSON_PASTE17, \\\n NLOHMANN_JSON_PASTE16, \\\n NLOHMANN_JSON_PASTE15, \\\n NLOHMANN_JSON_PASTE14, \\\n NLOHMANN_JSON_PASTE13, \\\n NLOHMANN_JSON_PASTE12, \\\n NLOHMANN_JSON_PASTE11, \\\n NLOHMANN_JSON_PASTE10, \\\n NLOHMANN_JSON_PASTE9, \\\n NLOHMANN_JSON_PASTE8, \\\n NLOHMANN_JSON_PASTE7, \\\n NLOHMANN_JSON_PASTE6, \\\n NLOHMANN_JSON_PASTE5, \\\n NLOHMANN_JSON_PASTE4, \\\n NLOHMANN_JSON_PASTE3, \\\n NLOHMANN_JSON_PASTE2, \\\n NLOHMANN_JSON_PASTE1)(__VA_ARGS__))\n#define NLOHMANN_JSON_PASTE2(func, v1) func(v1)\n#define NLOHMANN_JSON_PASTE3(func, v1, v2) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE2(func, v2)\n#define NLOHMANN_JSON_PASTE4(func, v1, v2, v3) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE3(func, v2, v3)\n#define NLOHMANN_JSON_PASTE5(func, v1, v2, v3, v4) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE4(func, v2, v3, v4)\n#define NLOHMANN_JSON_PASTE6(func, v1, v2, v3, v4, v5) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE5(func, v2, v3, v4, v5)\n#define NLOHMANN_JSON_PASTE7(func, v1, v2, v3, v4, v5, v6) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE6(func, v2, v3, v4, v5, v6)\n#define NLOHMANN_JSON_PASTE8(func, v1, v2, v3, v4, v5, v6, v7) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE7(func, v2, v3, v4, v5, v6, v7)\n#define NLOHMANN_JSON_PASTE9(func, v1, v2, v3, v4, v5, v6, v7, v8) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE8(func, v2, v3, v4, v5, v6, v7, v8)\n#define NLOHMANN_JSON_PASTE10(func, v1, v2, v3, v4, v5, v6, v7, v8, v9) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE9(func, v2, v3, v4, v5, v6, v7, v8, v9)\n#define NLOHMANN_JSON_PASTE11(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE10(func, v2, v3, v4, v5, v6, v7, v8, v9, v10)\n#define NLOHMANN_JSON_PASTE12(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE11(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11)\n#define NLOHMANN_JSON_PASTE13(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE12(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12)\n#define NLOHMANN_JSON_PASTE14(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE13(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13)\n#define NLOHMANN_JSON_PASTE15(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE14(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14)\n#define NLOHMANN_JSON_PASTE16(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE15(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)\n#define NLOHMANN_JSON_PASTE17(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE16(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16)\n#define NLOHMANN_JSON_PASTE18(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE17(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17)\n#define NLOHMANN_JSON_PASTE19(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE18(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18)\n#define NLOHMANN_JSON_PASTE20(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE19(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19)\n#define NLOHMANN_JSON_PASTE21(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE20(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20)\n#define NLOHMANN_JSON_PASTE22(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE21(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21)\n#define NLOHMANN_JSON_PASTE23(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE22(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22)\n#define NLOHMANN_JSON_PASTE24(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE23(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23)\n#define NLOHMANN_JSON_PASTE25(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE24(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24)\n#define NLOHMANN_JSON_PASTE26(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE25(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25)\n#define NLOHMANN_JSON_PASTE27(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE26(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26)\n#define NLOHMANN_JSON_PASTE28(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE27(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27)\n#define NLOHMANN_JSON_PASTE29(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE28(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28)\n#define NLOHMANN_JSON_PASTE30(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE29(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29)\n#define NLOHMANN_JSON_PASTE31(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE30(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30)\n#define NLOHMANN_JSON_PASTE32(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE31(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31)\n#define NLOHMANN_JSON_PASTE33(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE32(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32)\n#define NLOHMANN_JSON_PASTE34(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE33(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33)\n#define NLOHMANN_JSON_PASTE35(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE34(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34)\n#define NLOHMANN_JSON_PASTE36(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE35(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35)\n#define NLOHMANN_JSON_PASTE37(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE36(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36)\n#define NLOHMANN_JSON_PASTE38(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE37(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37)\n#define NLOHMANN_JSON_PASTE39(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE38(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38)\n#define NLOHMANN_JSON_PASTE40(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE39(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39)\n#define NLOHMANN_JSON_PASTE41(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE40(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40)\n#define NLOHMANN_JSON_PASTE42(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE41(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41)\n#define NLOHMANN_JSON_PASTE43(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE42(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42)\n#define NLOHMANN_JSON_PASTE44(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE43(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43)\n#define NLOHMANN_JSON_PASTE45(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE44(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44)\n#define NLOHMANN_JSON_PASTE46(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE45(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45)\n#define NLOHMANN_JSON_PASTE47(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE46(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46)\n#define NLOHMANN_JSON_PASTE48(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE47(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47)\n#define NLOHMANN_JSON_PASTE49(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE48(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48)\n#define NLOHMANN_JSON_PASTE50(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE49(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49)\n#define NLOHMANN_JSON_PASTE51(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE50(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50)\n#define NLOHMANN_JSON_PASTE52(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE51(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51)\n#define NLOHMANN_JSON_PASTE53(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE52(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52)\n#define NLOHMANN_JSON_PASTE54(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE53(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53)\n#define NLOHMANN_JSON_PASTE55(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE54(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54)\n#define NLOHMANN_JSON_PASTE56(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE55(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55)\n#define NLOHMANN_JSON_PASTE57(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE56(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56)\n#define NLOHMANN_JSON_PASTE58(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE57(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57)\n#define NLOHMANN_JSON_PASTE59(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE58(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58)\n#define NLOHMANN_JSON_PASTE60(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE59(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59)\n#define NLOHMANN_JSON_PASTE61(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE60(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60)\n#define NLOHMANN_JSON_PASTE62(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE61(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61)\n#define NLOHMANN_JSON_PASTE63(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE62(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62)\n#define NLOHMANN_JSON_PASTE64(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE63(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63)\n\n#define NLOHMANN_JSON_TO(v1) nlohmann_json_j[#v1] = nlohmann_json_t.v1;\n#define NLOHMANN_JSON_FROM(v1) nlohmann_json_j.at(#v1).get_to(nlohmann_json_t.v1);\n#define NLOHMANN_JSON_FROM_WITH_DEFAULT(v1) nlohmann_json_t.v1 = nlohmann_json_j.value(#v1, nlohmann_json_default_obj.v1);\n\n/*!\n@brief macro\n@def NLOHMANN_DEFINE_TYPE_INTRUSIVE\n@since version 3.9.0\n*/\n#define NLOHMANN_DEFINE_TYPE_INTRUSIVE(Type, ...) \\\n friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \\\n friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }\n\n#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...) \\\n friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \\\n friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }\n\n/*!\n@brief macro\n@def NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE\n@since version 3.9.0\n*/\n#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Type, ...) \\\n inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \\\n inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }\n\n#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...) \\\n inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \\\n inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }\n\n\n// inspired from https://stackoverflow.com/a/26745591\n// allows to call any std function as if (e.g. with begin):\n// using std::begin; begin(x);\n//\n// it allows using the detected idiom to retrieve the return type\n// of such an expression\n#define NLOHMANN_CAN_CALL_STD_FUNC_IMPL(std_name) \\\n namespace detail { \\\n using std::std_name; \\\n \\\n template \\\n using result_of_##std_name = decltype(std_name(std::declval()...)); \\\n } \\\n \\\n namespace detail2 { \\\n struct std_name##_tag \\\n { \\\n }; \\\n \\\n template \\\n std_name##_tag std_name(T&&...); \\\n \\\n template \\\n using result_of_##std_name = decltype(std_name(std::declval()...)); \\\n \\\n template \\\n struct would_call_std_##std_name \\\n { \\\n static constexpr auto const value = ::nlohmann::detail:: \\\n is_detected_exact::value; \\\n }; \\\n } /* namespace detail2 */ \\\n \\\n template \\\n struct would_call_std_##std_name : detail2::would_call_std_##std_name \\\n { \\\n }\n\n#ifndef JSON_USE_IMPLICIT_CONVERSIONS\n #define JSON_USE_IMPLICIT_CONVERSIONS 1\n#endif\n\n#if JSON_USE_IMPLICIT_CONVERSIONS\n #define JSON_EXPLICIT\n#else\n #define JSON_EXPLICIT explicit\n#endif\n\n#ifndef JSON_DISABLE_ENUM_SERIALIZATION\n #define JSON_DISABLE_ENUM_SERIALIZATION 0\n#endif\n\n#ifndef JSON_USE_GLOBAL_UDLS\n #define JSON_USE_GLOBAL_UDLS 1\n#endif\n\n#if JSON_HAS_THREE_WAY_COMPARISON\n #include // partial_ordering\n#endif\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\n///////////////////////////\n// JSON type enumeration //\n///////////////////////////\n\n/*!\n@brief the JSON type enumeration\n\nThis enumeration collects the different JSON types. It is internally used to\ndistinguish the stored values, and the functions @ref basic_json::is_null(),\n@ref basic_json::is_object(), @ref basic_json::is_array(),\n@ref basic_json::is_string(), @ref basic_json::is_boolean(),\n@ref basic_json::is_number() (with @ref basic_json::is_number_integer(),\n@ref basic_json::is_number_unsigned(), and @ref basic_json::is_number_float()),\n@ref basic_json::is_discarded(), @ref basic_json::is_primitive(), and\n@ref basic_json::is_structured() rely on it.\n\n@note There are three enumeration entries (number_integer, number_unsigned, and\nnumber_float), because the library distinguishes these three types for numbers:\n@ref basic_json::number_unsigned_t is used for unsigned integers,\n@ref basic_json::number_integer_t is used for signed integers, and\n@ref basic_json::number_float_t is used for floating-point numbers or to\napproximate integers which do not fit in the limits of their respective type.\n\n@sa see @ref basic_json::basic_json(const value_t value_type) -- create a JSON\nvalue with the default value for a given type\n\n@since version 1.0.0\n*/\nenum class value_t : std::uint8_t\n{\n null, ///< null value\n object, ///< object (unordered set of name/value pairs)\n array, ///< array (ordered collection of values)\n string, ///< string value\n boolean, ///< boolean value\n number_integer, ///< number value (signed integer)\n number_unsigned, ///< number value (unsigned integer)\n number_float, ///< number value (floating-point)\n binary, ///< binary array (ordered collection of bytes)\n discarded ///< discarded by the parser callback function\n};\n\n/*!\n@brief comparison operator for JSON types\n\nReturns an ordering that is similar to Python:\n- order: null < boolean < number < object < array < string < binary\n- furthermore, each type is not smaller than itself\n- discarded values are not comparable\n- binary is represented as a b\"\" string in python and directly comparable to a\n string; however, making a binary array directly comparable with a string would\n be surprising behavior in a JSON file.\n\n@since version 1.0.0\n*/\n#if JSON_HAS_THREE_WAY_COMPARISON\n inline std::partial_ordering operator<=>(const value_t lhs, const value_t rhs) noexcept // *NOPAD*\n#else\n inline bool operator<(const value_t lhs, const value_t rhs) noexcept\n#endif\n{\n static constexpr std::array order = {{\n 0 /* null */, 3 /* object */, 4 /* array */, 5 /* string */,\n 1 /* boolean */, 2 /* integer */, 2 /* unsigned */, 2 /* float */,\n 6 /* binary */\n }\n };\n\n const auto l_index = static_cast(lhs);\n const auto r_index = static_cast(rhs);\n#if JSON_HAS_THREE_WAY_COMPARISON\n if (l_index < order.size() && r_index < order.size())\n {\n return order[l_index] <=> order[r_index]; // *NOPAD*\n }\n return std::partial_ordering::unordered;\n#else\n return l_index < order.size() && r_index < order.size() && order[l_index] < order[r_index];\n#endif\n}\n\n// GCC selects the built-in operator< over an operator rewritten from\n// a user-defined spaceship operator\n// Clang, MSVC, and ICC select the rewritten candidate\n// (see GCC bug https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105200)\n#if JSON_HAS_THREE_WAY_COMPARISON && defined(__GNUC__)\ninline bool operator<(const value_t lhs, const value_t rhs) noexcept\n{\n return std::is_lt(lhs <=> rhs); // *NOPAD*\n}\n#endif\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\n/*!\n@brief replace all occurrences of a substring by another string\n\n@param[in,out] s the string to manipulate; changed so that all\n occurrences of @a f are replaced with @a t\n@param[in] f the substring to replace with @a t\n@param[in] t the string to replace @a f\n\n@pre The search string @a f must not be empty. **This precondition is\nenforced with an assertion.**\n\n@since version 2.0.0\n*/\ntemplate\ninline void replace_substring(StringType& s, const StringType& f,\n const StringType& t)\n{\n JSON_ASSERT(!f.empty());\n for (auto pos = s.find(f); // find first occurrence of f\n pos != StringType::npos; // make sure f was found\n s.replace(pos, f.size(), t), // replace with t, and\n pos = s.find(f, pos + t.size())) // find next occurrence of f\n {}\n}\n\n/*!\n * @brief string escaping as described in RFC 6901 (Sect. 4)\n * @param[in] s string to escape\n * @return escaped string\n *\n * Note the order of escaping \"~\" to \"~0\" and \"/\" to \"~1\" is important.\n */\ntemplate\ninline StringType escape(StringType s)\n{\n replace_substring(s, StringType{\"~\"}, StringType{\"~0\"});\n replace_substring(s, StringType{\"/\"}, StringType{\"~1\"});\n return s;\n}\n\n/*!\n * @brief string unescaping as described in RFC 6901 (Sect. 4)\n * @param[in] s string to unescape\n * @return unescaped string\n *\n * Note the order of escaping \"~1\" to \"/\" and \"~0\" to \"~\" is important.\n */\ntemplate\nstatic void unescape(StringType& s)\n{\n replace_substring(s, StringType{\"~1\"}, StringType{\"/\"});\n replace_substring(s, StringType{\"~0\"}, StringType{\"~\"});\n}\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // size_t\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\n/// struct to capture the start position of the current token\nstruct position_t\n{\n /// the total number of characters read\n std::size_t chars_read_total = 0;\n /// the number of characters read in the current line\n std::size_t chars_read_current_line = 0;\n /// the number of lines read\n std::size_t lines_read = 0;\n\n /// conversion to size_t to preserve SAX interface\n constexpr operator size_t() const\n {\n return chars_read_total;\n }\n};\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-FileCopyrightText: 2018 The Abseil Authors\n// SPDX-License-Identifier: MIT\n\n\n\n#include // array\n#include // size_t\n#include // conditional, enable_if, false_type, integral_constant, is_constructible, is_integral, is_same, remove_cv, remove_reference, true_type\n#include // index_sequence, make_index_sequence, index_sequence_for\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\ntemplate\nusing uncvref_t = typename std::remove_cv::type>::type;\n\n#ifdef JSON_HAS_CPP_14\n\n// the following utilities are natively available in C++14\nusing std::enable_if_t;\nusing std::index_sequence;\nusing std::make_index_sequence;\nusing std::index_sequence_for;\n\n#else\n\n// alias templates to reduce boilerplate\ntemplate\nusing enable_if_t = typename std::enable_if::type;\n\n// The following code is taken from https://github.com/abseil/abseil-cpp/blob/10cb35e459f5ecca5b2ff107635da0bfa41011b4/absl/utility/utility.h\n// which is part of Google Abseil (https://github.com/abseil/abseil-cpp), licensed under the Apache License 2.0.\n\n//// START OF CODE FROM GOOGLE ABSEIL\n\n// integer_sequence\n//\n// Class template representing a compile-time integer sequence. An instantiation\n// of `integer_sequence` has a sequence of integers encoded in its\n// type through its template arguments (which is a common need when\n// working with C++11 variadic templates). `absl::integer_sequence` is designed\n// to be a drop-in replacement for C++14's `std::integer_sequence`.\n//\n// Example:\n//\n// template< class T, T... Ints >\n// void user_function(integer_sequence);\n//\n// int main()\n// {\n// // user_function's `T` will be deduced to `int` and `Ints...`\n// // will be deduced to `0, 1, 2, 3, 4`.\n// user_function(make_integer_sequence());\n// }\ntemplate \nstruct integer_sequence\n{\n using value_type = T;\n static constexpr std::size_t size() noexcept\n {\n return sizeof...(Ints);\n }\n};\n\n// index_sequence\n//\n// A helper template for an `integer_sequence` of `size_t`,\n// `absl::index_sequence` is designed to be a drop-in replacement for C++14's\n// `std::index_sequence`.\ntemplate \nusing index_sequence = integer_sequence;\n\nnamespace utility_internal\n{\n\ntemplate \nstruct Extend;\n\n// Note that SeqSize == sizeof...(Ints). It's passed explicitly for efficiency.\ntemplate \nstruct Extend, SeqSize, 0>\n{\n using type = integer_sequence < T, Ints..., (Ints + SeqSize)... >;\n};\n\ntemplate \nstruct Extend, SeqSize, 1>\n{\n using type = integer_sequence < T, Ints..., (Ints + SeqSize)..., 2 * SeqSize >;\n};\n\n// Recursion helper for 'make_integer_sequence'.\n// 'Gen::type' is an alias for 'integer_sequence'.\ntemplate \nstruct Gen\n{\n using type =\n typename Extend < typename Gen < T, N / 2 >::type, N / 2, N % 2 >::type;\n};\n\ntemplate \nstruct Gen\n{\n using type = integer_sequence;\n};\n\n} // namespace utility_internal\n\n// Compile-time sequences of integers\n\n// make_integer_sequence\n//\n// This template alias is equivalent to\n// `integer_sequence`, and is designed to be a drop-in\n// replacement for C++14's `std::make_integer_sequence`.\ntemplate \nusing make_integer_sequence = typename utility_internal::Gen::type;\n\n// make_index_sequence\n//\n// This template alias is equivalent to `index_sequence<0, 1, ..., N-1>`,\n// and is designed to be a drop-in replacement for C++14's\n// `std::make_index_sequence`.\ntemplate \nusing make_index_sequence = make_integer_sequence;\n\n// index_sequence_for\n//\n// Converts a typename pack into an index sequence of the same length, and\n// is designed to be a drop-in replacement for C++14's\n// `std::index_sequence_for()`\ntemplate \nusing index_sequence_for = make_index_sequence;\n\n//// END OF CODE FROM GOOGLE ABSEIL\n\n#endif\n\n// dispatch utility (taken from ranges-v3)\ntemplate struct priority_tag : priority_tag < N - 1 > {};\ntemplate<> struct priority_tag<0> {};\n\n// taken from ranges-v3\ntemplate\nstruct static_const\n{\n static JSON_INLINE_VARIABLE constexpr T value{};\n};\n\n#ifndef JSON_HAS_CPP_17\n template\n constexpr T static_const::value;\n#endif\n\ntemplate\ninline constexpr std::array make_array(Args&& ... args)\n{\n return std::array {{static_cast(std::forward(args))...}};\n}\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // numeric_limits\n#include // false_type, is_constructible, is_integral, is_same, true_type\n#include // declval\n#include // tuple\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n#include // random_access_iterator_tag\n\n// #include \n\n// #include \n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\nnamespace detail\n{\n\ntemplate\nstruct iterator_types {};\n\ntemplate\nstruct iterator_types <\n It,\n void_t>\n{\n using difference_type = typename It::difference_type;\n using value_type = typename It::value_type;\n using pointer = typename It::pointer;\n using reference = typename It::reference;\n using iterator_category = typename It::iterator_category;\n};\n\n// This is required as some compilers implement std::iterator_traits in a way that\n// doesn't work with SFINAE. See https://github.com/nlohmann/json/issues/1341.\ntemplate\nstruct iterator_traits\n{\n};\n\ntemplate\nstruct iterator_traits < T, enable_if_t < !std::is_pointer::value >>\n : iterator_types\n{\n};\n\ntemplate\nstruct iterator_traits::value>>\n{\n using iterator_category = std::random_access_iterator_tag;\n using value_type = T;\n using difference_type = ptrdiff_t;\n using pointer = T*;\n using reference = T&;\n};\n\n} // namespace detail\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\n\nNLOHMANN_CAN_CALL_STD_FUNC_IMPL(begin);\n\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n\n\n// #include \n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\n\nNLOHMANN_CAN_CALL_STD_FUNC_IMPL(end);\n\nNLOHMANN_JSON_NAMESPACE_END\n\n// #include \n\n// #include \n\n// #include \n// __ _____ _____ _____\n// __| | __| | | | JSON for Modern C++\n// | | |__ | | | | | | version 3.11.2\n// |_____|_____|_____|_|___| https://github.com/nlohmann/json\n//\n// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann \n// SPDX-License-Identifier: MIT\n\n#ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_\n #define INCLUDE_NLOHMANN_JSON_FWD_HPP_\n\n #include // int64_t, uint64_t\n #include // map\n #include // allocator\n #include // string\n #include // vector\n\n // #include \n\n\n /*!\n @brief namespace for Niels Lohmann\n @see https://github.com/nlohmann\n @since version 1.0.0\n */\n NLOHMANN_JSON_NAMESPACE_BEGIN\n\n /*!\n @brief default JSONSerializer template argument\n\n This serializer ignores the template arguments and uses ADL\n ([argument-dependent lookup](https://en.cppreference.com/w/cpp/language/adl))\n for serialization.\n */\n template\n struct adl_serializer;\n\n /// a class to store JSON values\n /// @sa https://json.nlohmann.me/api/basic_json/\n template class ObjectType =\n std::map,\n template class ArrayType = std::vector,\n class StringType = std::string, class BooleanType = bool,\n class NumberIntegerType = std::int64_t,\n class NumberUnsignedType = std::uint64_t,\n class NumberFloatType = double,\n template class AllocatorType = std::allocator,\n template class JSONSerializer =\n adl_serializer,\n class BinaryType = std::vector>\n class basic_json;\n\n /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document\n /// @sa https://json.nlohmann.me/api/json_pointer/\n template\n class json_pointer;\n\n /*!\n @brief default specialization\n @sa https://json.nlohmann.me/api/json/\n */\n using json = basic_json<>;\n\n /// @brief a minimal map-like container that preserves insertion order\n /// @sa https://json.nlohmann.me/api/ordered_map/\n template\n struct ordered_map;\n\n /// @brief specialization that maintains the insertion order of object keys\n /// @sa https://json.nlohmann.me/api/ordered_json/\n using ordered_json = basic_json;\n\n NLOHMANN_JSON_NAMESPACE_END\n\n#endif // INCLUDE_NLOHMANN_JSON_FWD_HPP_\n\n\nNLOHMANN_JSON_NAMESPACE_BEGIN\n/*!\n@brief detail namespace with internal helper functions\n\nThis namespace collects functions that should not be exposed,\nimplementations of some @ref basic_json methods, and meta-programming helpers.\n\n@since version 2.1.0\n*/\nnamespace detail\n{\n\n/////////////\n// helpers //\n/////////////\n\n// Note to maintainers:\n//\n// Every trait in this file expects a non CV-qualified type.\n// The only exceptions are in the 'aliases for detected' section\n// (i.e. those of the form: decltype(T::member_function(std::declval())))\n//\n// In this case, T has to be properly CV-qualified to constraint the function arguments\n// (e.g. to_json(BasicJsonType&, const T&))\n\ntemplate struct is_basic_json : std::false_type {};\n\nNLOHMANN_BASIC_JSON_TPL_DECLARATION\nstruct is_basic_json : std::true_type {};\n\n// used by exceptions create() member functions\n// true_type for pointer to possibly cv-qualified basic_json or std::nullptr_t\n// false_type otherwise\ntemplate\nstruct is_basic_json_context :\n std::integral_constant < bool,\n is_basic_json::type>::type>::value\n || std::is_same::value >\n{};\n\n//////////////////////\n// json_ref helpers //\n//////////////////////\n\ntemplate\nclass json_ref;\n\ntemplate\nstruct is_json_ref : std::false_type {};\n\ntemplate\nstruct is_json_ref> : std::true_type {};\n\n//////////////////////////\n// aliases for detected //\n//////////////////////////\n\ntemplate\nusing mapped_type_t = typename T::mapped_type;\n\ntemplate\nusing key_type_t = typename T::key_type;\n\ntemplate\nusing value_type_t = typename T::value_type;\n\ntemplate\nusing difference_type_t = typename T::difference_type;\n\ntemplate\nusing pointer_t = typename T::pointer;\n\ntemplate\nusing reference_t = typename T::reference;\n\ntemplate\nusing iterator_category_t = typename T::iterator_category;\n\ntemplate\nusing to_json_function = decltype(T::to_json(std::declval()...));\n\ntemplate\nusing from_json_function = decltype(T::from_json(std::declval()...));\n\ntemplate\nusing get_template_function = decltype(std::declval().template get());\n\n// trait checking if JSONSerializer::from_json(json const&, udt&) exists\ntemplate\nstruct has_from_json : std::false_type {};\n\n// trait checking if j.get is valid\n// use this trait instead of std::is_constructible or std::is_convertible,\n// both rely on, or make use of implicit conversions, and thus fail when T\n// has several constructors/operator= (see https://github.com/nlohmann/json/issues/958)\ntemplate \nstruct is_getable\n{\n static constexpr bool value = is_detected::value;\n};\n\ntemplate\nstruct has_from_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >>\n{\n using serializer = typename BasicJsonType::template json_serializer;\n\n static constexpr bool value =\n is_detected_exact::value;\n};\n\n// This trait checks if JSONSerializer::from_json(json const&) exists\n// this overload is used for non-default-constructible user-defined-types\ntemplate\nstruct has_non_default_from_json : std::false_type {};\n\ntemplate\nstruct has_non_default_from_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >>\n{\n using serializer = typename BasicJsonType::template json_serializer;\n\n static constexpr bool value =\n is_detected_exact::value;\n};\n\n// This trait checks if BasicJsonType::json_serializer::to_json exists\n// Do not evaluate the trait when T is a basic_json type, to avoid template instantiation infinite recursion.\ntemplate\nstruct has_to_json : std::false_type {};\n\ntemplate\nstruct has_to_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >>\n{\n using serializer = typename BasicJsonType::template json_serializer;\n\n static constexpr bool value =\n is_detected_exact::value;\n};\n\ntemplate\nusing detect_key_compare = typename T::key_compare;\n\ntemplate\nstruct has_key_compare : std::integral_constant::value> {};\n\n// obtains the actual object key comparator\ntemplate\nstruct actual_object_comparator\n{\n using object_t = typename BasicJsonType::object_t;\n using object_comparator_t = typename BasicJsonType::default_object_comparator_t;\n using type = typename std::conditional < has_key_compare::value,\n typename object_t::key_compare, object_comparator_t>::type;\n};\n\ntemplate\nusing actual_object_comparator_t = typename actual_object_comparator::type;\n\n///////////////////\n// is_ functions //\n///////////////////\n\n// https://en.cppreference.com/w/cpp/types/conjunction\ntemplate struct conjunction : std::true_type { };\ntemplate struct conjunction : B { };\ntemplate\nstruct conjunction\n: std::conditional(B::value), conjunction, B>::type {};\n\n// https://en.cppreference.com/w/cpp/types/negation\ntemplate struct negation : std::integral_constant < bool, !B::value > { };\n\n// Reimplementation of is_constructible and is_default_constructible, due to them being broken for\n// std::pair and std::tuple until LWG 2367 fix (see https://cplusplus.github.io/LWG/lwg-defects.html#2367).\n// This causes compile errors in e.g. clang 3.5 or gcc 4.9.\ntemplate \nstruct is_default_constructible : std::is_default_constructible {};\n\ntemplate \nstruct is_default_constructible>\n : conjunction, is_default_constructible> {};\n\ntemplate \nstruct is_default_constructible>\n : conjunction, is_default_constructible> {};\n\ntemplate \nstruct is_default_constructible>\n : conjunction...> {};\n\ntemplate \nstruct is_default_constructible>\n : conjunction...> {};\n\n\ntemplate \nstruct is_constructible : std::is_constructible {};\n\ntemplate \nstruct is_constructible> : is_default_constructible> {};\n\ntemplate \nstruct is_constructible> : is_default_constructible> {};\n\ntemplate \nstruct is_constructible> : is_default_constructible> {};\n\ntemplate \nstruct is_constructible> : is_default_constructible> {};\n\n\ntemplate\nstruct is_iterator_traits : std::false_type {};\n\ntemplate\nstruct is_iterator_traits>\n{\n private:\n using traits = iterator_traits;\n\n public:\n static constexpr auto value =\n is_detected::value &&\n is_detected::value &&\n is_detected::value &&\n is_detected::value &&\n is_detected::value;\n};\n\ntemplate\nstruct is_range\n{\n private:\n using t_ref = typename std::add_lvalue_reference::type;\n\n using iterator = detected_t;\n using sentinel = detected_t;\n\n // to be 100% correct, it should use https://en.cppreference.com/w/cpp/iterator/input_or_output_iterator\n // and https://en.cppreference.com/w/cpp/iterator/sentinel_for\n // but reimplementing these would be too much work, as a lot of other concepts are used underneath\n static constexpr auto is_iterator_begin =\n is_iterator_traits>::value;\n\n public:\n static constexpr bool value = !std::is_same::value && !std::is_same::value && is_iterator_begin;\n};\n\ntemplate\nusing iterator_t = enable_if_t::value, result_of_begin())>>;\n\ntemplate\nusing range_value_t = value_type_t>>;\n\n// The following implementation of is_complete_type is taken from\n// https://blogs.msdn.microsoft.com/vcblog/2015/12/02/partial-support-for-expression-sfinae-in-vs-2015-update-1/\n// and is written by Xiang Fan who agreed to using it in this library.\n\ntemplate\nstruct is_complete_type : std::false_type {};\n\ntemplate\nstruct is_complete_type : std::true_type {};\n\ntemplate\nstruct is_compatible_object_type_impl : std::false_type {};\n\ntemplate\nstruct is_compatible_object_type_impl <\n BasicJsonType, CompatibleObjectType,\n enable_if_t < is_detected::value&&\n is_detected::value >>\n{\n using object_t = typename BasicJsonType::object_t;\n\n // macOS's is_constructible does not play well with nonesuch...\n static constexpr bool value =\n is_constructible::value &&\n is_constructible::value;\n};\n\ntemplate\nstruct is_compatible_object_type\n : is_compatible_object_type_impl {};\n\ntemplate\nstruct is_constructible_object_type_impl : std::false_type {};\n\ntemplate\nstruct is_constructible_object_type_impl <\n BasicJsonType, ConstructibleObjectType,\n enable_if_t < is_detected::value&&\n is_detected::value >>\n{\n using object_t = typename BasicJsonType::object_t;\n\n static constexpr bool value =\n (is_default_constructible::value &&\n (std::is_move_assignable::value ||\n std::is_copy_assignable::value) &&\n (is_constructible::value &&\n std::is_same <\n typename object_t::mapped_type,\n typename ConstructibleObjectType::mapped_type >::value)) ||\n (has_from_json::value ||\n has_non_default_from_json <\n BasicJsonType,\n typename ConstructibleObjectType::mapped_type >::value);\n};\n\ntemplate\nstruct is_constructible_object_type\n : is_constructible_object_type_impl {};\n\ntemplate\nstruct is_compatible_string_type\n{\n static constexpr auto value =\n is_constructible::value;\n};\n\ntemplate\nstruct is_constructible_string_type\n{\n // launder type through decltype() to fix compilation failure on ICPC\n#ifdef __INTEL_COMPILER\n using laundered_type = decltype(std::declval());\n#else\n using laundered_type = ConstructibleStringType;\n#endif\n\n static constexpr auto value =\n conjunction <\n is_constructible,\n is_detected_exact>::value;\n};\n\ntemplate\nstruct is_compatible_array_type_impl : std::false_type {};\n\ntemplate\nstruct is_compatible_array_type_impl <\n BasicJsonType, CompatibleArrayType,\n enable_if_t <\n is_detected::value&&\n is_iterator_traits>>::value&&\n// special case for types like std::filesystem::path whose iterator's value_type are themselves\n// c.f. https://github.com/nlohmann/json/pull/3073\n !std::is_same>::value >>\n{\n static constexpr bool value =\n is_constructible>::value;\n};\n\ntemplate\nstruct is_compatible_array_type\n : is_compatible_array_type_impl {};\n\ntemplate\nstruct is_constructible_array_type_impl : std::false_type {};\n\ntemplate\nstruct is_constructible_array_type_impl <\n BasicJsonType, ConstructibleArrayType,\n enable_if_t::value >>\n : std::true_type {};\n\ntemplate\nstruct is_constructible_array_type_impl <\n BasicJsonType, ConstructibleArrayType,\n enable_if_t < !std::is_same::value&&\n !is_compatible_string_type::value&&\n is_default_constructible::value&&\n(std::is_move_assignable::value ||\n std::is_copy_assignable::value)&&\nis_detected::value&&\nis_iterator_traits>>::value&&\nis_detected::value&&\n// special case for types like std::filesystem::path whose iterator's value_type are themselves\n// c.f. https://github.com/nlohmann/json/pull/3073\n!std::is_same>::value&&\n is_complete_type <\n detected_t>::value >>\n{\n using value_type = range_value_t;\n\n static constexpr bool value =\n std::is_same::value ||\n has_from_json::value ||\n has_non_default_from_json <\n BasicJsonType,\n value_type >::value;\n};\n\ntemplate\nstruct is_constructible_array_type\n : is_constructible_array_type_impl {};\n\ntemplate\nstruct is_compatible_integer_type_impl : std::false_type {};\n\ntemplate\nstruct is_compatible_integer_type_impl <\n RealIntegerType, CompatibleNumberIntegerType,\n enable_if_t < std::is_integral::value&&\n std::is_integral::value&&\n !std::is_same::value >>\n{\n // is there an assert somewhere on overflows?\n using RealLimits = std::numeric_limits;\n using CompatibleLimits = std::numeric_limits;\n\n static constexpr auto value =\n is_constructible::value &&\n CompatibleLimits::is_integer &&\n RealLimits::is_signed == CompatibleLimits::is_signed;\n};\n\ntemplate\nstruct is_compatible_integer_type\n : is_compatible_integer_type_impl {};\n\ntemplate\nstruct is_compatible_type_impl: std::false_type {};\n\ntemplate\nstruct is_compatible_type_impl <\n BasicJsonType, CompatibleType,\n enable_if_t::value >>\n{\n static constexpr bool value =\n has_to_json::value;\n};\n\ntemplate\nstruct is_compatible_type\n : is_compatible_type_impl {};\n\ntemplate\nstruct is_constructible_tuple : std::false_type {};\n\ntemplate\nstruct is_constructible_tuple> : conjunction...> {};\n\ntemplate\nstruct is_json_iterator_of : std::false_type {};\n\ntemplate\nstruct is_json_iterator_of : std::true_type {};\n\ntemplate\nstruct is_json_iterator_of : std::true_type\n{};\n\n// checks if a given type T is a template specialization of Primary\ntemplate