Full Code of mukel/llama2.java for AI

Repository: mukel/llama2.java
Branch: main
Commit: 7a98616aafb9
Files: 6
Total size: 52.0 KB

Directory structure:
gitextract_pb0q9obi/

├── .gitignore
├── LICENSE
├── Llama2.java
├── Makefile
├── README.md
└── run.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Compiled class file
*.class

# Log file
*.log

# BlueJ files
*.ctxt

# Mobile Tools for Java (J2ME)
.mtj.tmp/

# Package Files #
*.jar
*.war
*.nar
*.ear
*.zip
*.tar.gz
*.rar

# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
hs_err_pid*
replay_pid*


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2023 Alfonso² Peterssen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: Llama2.java
================================================
///usr/bin/env jbang "$0" "$@" ; exit $?
//JAVA 21
//COMPILE_OPTIONS --enable-preview -source 21 --add-modules=jdk.incubator.vector
//RUNTIME_OPTIONS --enable-preview --add-modules=jdk.incubator.vector
//NATIVE_OPTIONS  --enable-preview --add-modules=jdk.incubator.vector --initialize-at-build-time=Llama2 -Dllama2.VectorAPI=false

/* Inference for Llama-2 Transformer model in pure Java */

// ----------------------------------------------------------------------------
// Transformer model

import jdk.incubator.vector.FloatVector;
import jdk.incubator.vector.VectorOperators;
import jdk.incubator.vector.VectorSpecies;

import java.io.BufferedInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.lang.foreign.MemorySegment;
import java.lang.foreign.Arena;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.FloatBuffer;
import java.nio.channels.FileChannel;
import java.nio.charset.StandardCharsets;
import java.nio.file.Paths;
import java.nio.file.StandardOpenOption;
import java.util.*;
import java.util.stream.IntStream;

final class Config {
    final int dim; // transformer dimension
    final int hidden_dim; // for ffn layers
    final int n_layers; // number of layers
    final int n_heads; // number of query heads
    final int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
    final int vocab_size; // vocabulary size, usually 256 (byte-level)
    final int seq_len; // max sequence length
    final boolean shared_weights;
    final int head_size;

    Config(ByteBuffer buffer) {
        this.dim = buffer.getInt();
        this.hidden_dim = buffer.getInt();
        this.n_layers = buffer.getInt();
        this.n_heads = buffer.getInt();
        this.n_kv_heads = buffer.getInt();
        int vocab_size = buffer.getInt();
        this.vocab_size = Math.abs(vocab_size);
        this.seq_len = buffer.getInt();
        this.shared_weights = vocab_size > 0;
        this.head_size = dim / n_heads;
    }

    @Override
    public String toString() {
        return "Config{" +
                "dim=" + dim +
                ", hidden_dim=" + hidden_dim +
                ", n_layers=" + n_layers +
                ", n_heads=" + n_heads +
                ", n_kv_heads=" + n_kv_heads +
                ", vocab_size=" + vocab_size +
                ", seq_len=" + seq_len +
                ", shared_weights=" + shared_weights +
                ", head_size=" + head_size +
                '}';
    }
}

final class Weights {
    // token embedding table
    final FloatBuffer token_embedding_table; // (vocab_size, dim)
    // weights for rmsnorms
    final FloatBuffer[] rms_att_weight; // (layer, dim) rmsnorm weights
    // weights for matmuls. note dim == n_heads * head_size
    final FloatBuffer[] wq; // (layer, dim, n_heads * head_size)
    final FloatBuffer[] wk; // (layer, dim, n_kv_heads * head_size)
    final FloatBuffer[] wv; // (layer, dim, n_kv_heads * head_size)
    final FloatBuffer[] wo; // (layer, n_heads * head_size, dim)
    final FloatBuffer[] rms_ffn_weight; // (layer, dim)
    // weights for ffn
    final FloatBuffer[] w1; // (layer, hidden_dim, dim)
    final FloatBuffer[] w2; // (layer, dim, hidden_dim)
    final FloatBuffer[] w3; // (layer, hidden_dim, dim)
    // final rmsnorm
    final FloatBuffer rms_final_weight; // (dim,)
    // (optional) classifier weights for the logits, on the last layer
    final FloatBuffer wcls; // (vocab_size, dim)

    static FloatBuffer takeFloats(MemorySegment memorySegment, long[] position, int... dims) {
        long totalBytes = 1;
        for (int d : dims) {
            totalBytes *= d;
        }
        totalBytes *= Float.BYTES;
        MemorySegment slice = memorySegment.asSlice(position[0], totalBytes);
        position[0] += totalBytes;
        return slice.asByteBuffer().order(ByteOrder.LITTLE_ENDIAN).asFloatBuffer();
    }

    static FloatBuffer[] takeArray(MemorySegment memorySegment, long[] position, int dim0, int... dims) {
        FloatBuffer[] segments = new FloatBuffer[dim0];
        for (int i = 0; i < dim0; ++i) {
            segments[i] = takeFloats(memorySegment, position, dims);
        }
        return segments;
    }

// ----------------------------------------------------------------------------
// initialization: read from checkpoint

    Weights(Config config, MemorySegment memorySegment) {
        long[] position = new long[]{0};
        this.token_embedding_table = takeFloats(memorySegment, position, config.vocab_size, config.dim);
        this.rms_att_weight = takeArray(memorySegment, position, config.n_layers, config.dim);
        this.wq = takeArray(memorySegment, position, config.n_layers, config.dim, config.n_heads * config.head_size);
        this.wk = takeArray(memorySegment, position, config.n_layers, config.dim, config.n_kv_heads * config.head_size);
        this.wv = takeArray(memorySegment, position, config.n_layers, config.dim, config.n_kv_heads * config.head_size);
        this.wo = takeArray(memorySegment, position, config.n_layers, config.n_heads * config.head_size, config.dim);
        this.rms_ffn_weight = takeArray(memorySegment, position, config.n_layers, config.dim);
        this.w1 = takeArray(memorySegment, position, config.n_layers, config.hidden_dim, config.dim);
        this.w2 = takeArray(memorySegment, position, config.n_layers, config.dim, config.hidden_dim);
        this.w3 = takeArray(memorySegment, position, config.n_layers, config.hidden_dim, config.dim);
        this.rms_final_weight = takeFloats(memorySegment, position, config.dim);
        position[0] += (config.seq_len * config.head_size / 2) * Float.BYTES; // skip what used to be freq_cis_real (for RoPE)
        position[0] += (config.seq_len * config.head_size / 2) * Float.BYTES; // skip what used to be freq_cis_imag (for RoPE)
        this.wcls = config.shared_weights
                ? this.token_embedding_table
                : takeFloats(memorySegment, position, config.vocab_size, config.dim);
    }
}

final class RunState {
    // current wave of activations
    final float[] x; // activation at current time stamp (dim,)
    final float[] xb; // same, but inside a residual branch (dim,)
    final float[] xb2; // an additional buffer just for convenience (dim,)
    final float[] hb; // buffer for hidden dimension in the ffn (hidden_dim,)
    final float[] hb2; // buffer for hidden dimension in the ffn (hidden_dim,)
    final float[] q; // query (dim,)
    final float[] k; // key (dim,)
    final float[] v; // value (dim,)
    final float[] att; // buffer for scores/attention values (n_heads, seq_len)
    final float[] logits; // output logits
    // kv cache
    final float[][] key_cache;   // (layer, seq_len, dim)
    final float[][] value_cache; // (layer, seq_len, dim)

    RunState(Config config) {
        int kv_dim = (config.dim * config.n_kv_heads) / config.n_heads;
        this.x = new float[config.dim];
        this.xb = new float[config.dim];
        this.xb2 = new float[config.dim];
        this.hb = new float[config.hidden_dim];
        this.hb2 = new float[config.hidden_dim];
        this.q = new float[config.dim];
        this.k = new float[kv_dim];
        this.v = new float[kv_dim];
        this.att = new float[config.n_heads * config.seq_len];
        this.logits = new float[config.vocab_size];
        this.key_cache = new float[config.n_layers][config.seq_len * kv_dim];
        this.value_cache = new float[config.n_layers][config.seq_len * kv_dim];
    }
}

final class Transformer {
    final Config config; // the hyperparameters of the architecture (the blueprint)
    final Weights weights; // the weights of the model
    final RunState state; // buffers for the "wave" of activations in the forward pass
    // some more state needed to properly clean up the memory mapping (sigh)
    final Arena memoryArena; // scope of the memory mapping
    final MemorySegment data; // memory mapped data pointer
    final long file_size; // size of the checkpoint file in bytes

    Transformer(String checkpoint_path) throws IOException {
        try (FileChannel fileChannel = FileChannel.open(Paths.get(checkpoint_path), StandardOpenOption.READ)) {
            this.file_size = fileChannel.size();
            this.memoryArena = Arena.ofAuto();
            MemorySegment mappedFile = fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, this.file_size, this.memoryArena);
            this.data = mappedFile;
            int configSize = 7 * Integer.BYTES;
            // read in the config header
            ByteBuffer configBuffer = mappedFile.asSlice(0, configSize).asByteBuffer().order(ByteOrder.LITTLE_ENDIAN);
            this.config = new Config(configBuffer);
            System.out.println(config);
            this.state = new RunState(config);
            this.weights = new Weights(config, mappedFile.asSlice(configSize));
        }
    }
}

final class Tokenizer {
    final String[] vocab;
    final float[] vocab_scores;
    final int vocab_size;
    final int max_token_length;
    Map<String, Integer> sorted_vocab;

    Tokenizer(String tokenizer_path, int vocab_size) throws IOException {
        // i should have written the vocab_size into the tokenizer file... sigh
        this.vocab_size = vocab_size;
        // malloc space to hold the scores and the strings
        this.vocab = new String[vocab_size];
        this.vocab_scores = new float[vocab_size];

        // read in the file
        try (FileChannel channel = FileChannel.open(Paths.get(tokenizer_path), StandardOpenOption.READ)) {
            ByteBuffer tokBuffer = channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size());
            tokBuffer.order(ByteOrder.LITTLE_ENDIAN);
            this.max_token_length = tokBuffer.getInt();
            for (int i = 0; i < vocab_size; i++) {
                this.vocab_scores[i] = tokBuffer.getFloat();
                int len = tokBuffer.getInt();
                byte[] bytes = new byte[len];
                tokBuffer.get(bytes);
                this.vocab[i] = new String(bytes, StandardCharsets.UTF_8);
            }
        }
    }
}

final class Sampler {
    final int vocab_size;
    final int[] probindex; // buffer used in top-p sampling
    final float temperature;
    final float topp;
    long rng_seed;

    Sampler(int vocab_size, float temperature, float topp, long rng_seed) {
        this.vocab_size = vocab_size;
        this.temperature = temperature;
        this.topp = topp;
        this.rng_seed = rng_seed;
        // buffer only used with nucleus sampling; may not need but it's ~small
        this.probindex = new int[vocab_size];
    }

    int random_u32() {
        // xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
        rng_seed ^= rng_seed >> 12;
        rng_seed ^= rng_seed << 25;
        rng_seed ^= rng_seed >> 27;
        return (int) ((rng_seed * 0x2545F4914F6CDD1DL) >> 32);
    }

    float random_f32() { // random float32 in [0,1)
        return (random_u32() >>> 8) / 16777216.0f;
    }
}

class Llama2 {

// ----------------------------------------------------------------------------
// neural net blocks; the dynamics of the Transformer

    static void rmsnorm(float[] o, float[] x, FloatBuffer weight, int size) {
        // calculate sum of squares
        float ss = 0.0f;
        for (int j = 0; j < size; j++) {
            ss += x[j] * x[j];
        }
        ss /= size;
        ss += 1e-5f;
        ss = 1.0f / (float) Math.sqrt(ss);
        // normalize and scale
        for (int j = 0; j < size; j++) {
            o[j] = weight.get(j) * (ss * x[j]);
        }
    }

    static void softmax(float[] x, int xOffset, int size) {
        // find max value (for numerical stability)
        float max_val = x[0 + xOffset];
        for (int i = 1; i < size; i++) {
            if (x[i + xOffset] > max_val) {
                max_val = x[i + xOffset];
            }
        }
        // exp and sum
        float sum = 0.0f;
        for (int i = 0; i < size; i++) {
            x[i + xOffset] = (float) Math.exp(x[i + xOffset] - max_val);
            sum += x[i + xOffset];
        }
        // normalize
        for (int i = 0; i < size; i++) {
            x[i + xOffset] /= sum;
        }
    }

    static final boolean USE_VECTOR_API = "true".equalsIgnoreCase(System.getProperty("llama2.VectorAPI", "true"));

    static void matmul(float[] xout, float[] x, FloatBuffer w, int n, int d) {
        // W (d,n) @ x (n,) -> xout (d,)
        // by far the most amount of time is spent inside this little function
        MemorySegment wSegment = MemorySegment.ofBuffer(w);
        IntStream.range(0, d).parallel().forEach(i -> {
            float val = 0f;
            int j = 0;
            if (USE_VECTOR_API) {
                VectorSpecies<Float> species = FloatVector.SPECIES_256;
                FloatVector sum0 = FloatVector.zero(species);
                FloatVector sum1 = FloatVector.zero(species);
                FloatVector sum2 = FloatVector.zero(species);
                FloatVector sum3 = FloatVector.zero(species);
                int width = species.length();
                int upperBound = n - n % (4 * width);
                for (; j < upperBound; j += 4 * width) {
                    var wj0 = FloatVector.fromMemorySegment(species, wSegment, (i * n + j + 0 * width) * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var wj1 = FloatVector.fromMemorySegment(species, wSegment, (i * n + j + 1 * width) * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var wj2 = FloatVector.fromMemorySegment(species, wSegment, (i * n + j + 2 * width) * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var wj3 = FloatVector.fromMemorySegment(species, wSegment, (i * n + j + 3 * width) * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var xj0 = FloatVector.fromArray(species, x, j + 0 * width);
                    var xj1 = FloatVector.fromArray(species, x, j + 1 * width);
                    var xj2 = FloatVector.fromArray(species, x, j + 2 * width);
                    var xj3 = FloatVector.fromArray(species, x, j + 3 * width);
                    sum0 = wj0.fma(xj0, sum0);
                    sum1 = wj1.fma(xj1, sum1);
                    sum2 = wj2.fma(xj2, sum2);
                    sum3 = wj3.fma(xj3, sum3);
                }
                val = sum0.add(sum1).add(sum2).add(sum3).reduceLanes(VectorOperators.ADD);
            }

            // Graal's auto-vectorization.
            int upperBound = n & ~3;
            float[] sum = new float[4];
            for (; j < upperBound; j += sum.length) {
                sum[0] += w.get(i * n + j + 0) * x[j + 0];
                sum[1] += w.get(i * n + j + 1) * x[j + 1];
                sum[2] += w.get(i * n + j + 2) * x[j + 2];
                sum[3] += w.get(i * n + j + 3) * x[j + 3];
            }
            val += sum[0] + sum[1] + sum[2] + sum[3];

            for (; j < n; j++) {
                val += w.get(i * n + j) * x[j];
            }
            xout[i] = val;
        });
    }

    static float[] forward(Transformer transformer, int token, int pos) {
        // a few convenience variables
        Config p = transformer.config;
        Weights w = transformer.weights;
        RunState s = transformer.state;
        int dim = p.dim;
        int hidden_dim = p.hidden_dim;
        int head_size = p.head_size;
        int kv_dim = (p.dim * p.n_kv_heads) / p.n_heads;
        int kv_mul = p.n_heads / p.n_kv_heads; // integer multiplier of the kv sharing in multiquery

        // copy the token embedding into x
        w.token_embedding_table.get(token * dim, s.x, 0, dim);

        // forward all the layers
        for (int l = 0; l < p.n_layers; l++) {

            // attention rmsnorm
            rmsnorm(s.xb, s.x, w.rms_att_weight[l], dim);

            // qkv matmuls for this position
            matmul(s.q, s.xb, w.wq[l], dim, dim);
            matmul(s.k, s.xb, w.wk[l], dim, kv_dim);
            matmul(s.v, s.xb, w.wv[l], dim, kv_dim);

            // RoPE relative positional encoding: complex-valued rotate q and k in each head
            for (int i = 0; i < dim; i+=2) {
                int head_dim = i % head_size;
                float freq = (float) (1.0 / Math.pow(10000.0f, head_dim / (float) head_size));
                float val = pos * freq;
                float fcr = (float) Math.cos(val);
                float fci = (float) Math.sin(val);
                int rotn = i < kv_dim ? 2 : 1; // how many vectors? 2 = q & k, 1 = q only
                for (int v = 0; v < rotn; v++) {
                    float[] vec = v == 0 ? s.q : s.k; // the vector to rotate (query or key)
                    float v0 = vec[i];
                    float v1 = vec[i + 1];
                    vec[i] = v0 * fcr - v1 * fci;
                    vec[i + 1] = v0 * fci + v1 * fcr;
                }
            }

            // save key,value at this time step (pos) to our kv cache
            //int loff = l * p.seq_len * kv_dim; // kv cache layer offset for convenience
            System.arraycopy(s.k, 0, s.key_cache[l], pos * kv_dim, kv_dim);
            System.arraycopy(s.v, 0, s.value_cache[l], pos * kv_dim, kv_dim);


            final int curLayer = l;

            // multihead attention. iterate over all heads
            IntStream.range(0, p.n_heads).parallel().forEach(h -> {
                // get the query vector for this head
                // float* q = s.q + h * head_size;
                int qOffset = h * head_size;

                // attention scores for this head
                // float* att = s.att + h * p.seq_len;
                int attOffset = h * p.seq_len;

                // iterate over all timesteps, including the current one
                for (int t = 0; t <= pos; t++) {
                    // get the key vector for this head and at this timestep
                    // float* k = s->key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
                    int keyCacheOffset = t * kv_dim + (h / kv_mul) * head_size;
                    // calculate the attention score as the dot product of q and k
                    float score = 0.0f;
                    for (int i = 0; i < head_size; i++) {
                        score += s.q[qOffset + i] * s.key_cache[curLayer][keyCacheOffset + i];
                    }
                    score /= (float) Math.sqrt(head_size);
                    // save the score to the attention buffer
                    s.att[attOffset + t] = score;
                }

                // softmax the scores to get attention weights, from 0..pos inclusively
                softmax(s.att, attOffset, pos + 1);

                // weighted sum of the values, store back into xb
                // float* xb = s.xb + h * head_size;
                int xbOffset = h * head_size;
                // memset(xb, 0, head_size * sizeof(float));
                Arrays.fill(s.xb, xbOffset, xbOffset + head_size, 0f);

                for (int t = 0; t <= pos; t++) {
                    // get the value vector for this head and at this timestep
                    // float* v = s->value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
                    int vOffset = t * kv_dim + (h / kv_mul) * head_size;
                    // get the attention weight for this timestep
                    float a = s.att[attOffset + t];
                    // accumulate the weighted value inconfigto xb
                    for (int i = 0; i < head_size; i++) {
                        s.xb[xbOffset + i] += a * s.value_cache[curLayer][vOffset + i];
                    }
                }
            });

            // final matmul to get the output of the attention
            matmul(s.xb2, s.xb, w.wo[l], dim, dim);

            // residual connection back into x
            for (int i = 0; i < dim; i++) {
                s.x[i] += s.xb2[i];
            }

            // ffn rmsnorm
            rmsnorm(s.xb, s.x, w.rms_ffn_weight[l], dim);

            // Now for FFN in PyTorch we have: self.w2(F.silu(self.w1(x)) * self.w3(x))
            // first calculate self.w1(x) and self.w3(x)
            matmul(s.hb, s.xb, w.w1[l], dim, p.hidden_dim);
            matmul(s.hb2, s.xb, w.w3[l], dim, p.hidden_dim);

            // SwiGLU non-linearity
            for (int i = 0; i < hidden_dim; i++) {
                float val = s.hb[i];
                // silu(x)=x*σ(x), where σ(x) is the logistic sigmoid
                val *= (1.0f / (1.0f + Math.exp(-val)));
                // elementwise multiply with w3(x)
                s.hb[i] = val;
            }

            // elementwise multiply with w3(x)
            for (int i = 0; i < hidden_dim; i++) {
                s.hb[i] = s.hb[i] * s.hb2[i];
            }

            // final matmul to get the output of the ffn
            matmul(s.xb, s.hb, w.w2[l], p.hidden_dim, dim);

            // residual connection
            for (int i = 0; i < dim; i++) {
                s.x[i] += s.xb[i];
            }
        }

        // final rmsnorm
        rmsnorm(s.x, s.x, w.rms_final_weight, dim);

        // classifier into logits
        matmul(s.logits, s.x, w.wcls, dim, p.vocab_size);
        return s.logits;
    }

// ----------------------------------------------------------------------------

// The Byte Pair Encoding (BPE) Tokenizer that translates strings <-> tokens

    static String decode(Tokenizer t, int prev_token, int token) {
        String piece = t.vocab[token];
        // following BOS (1) token, sentencepiece decoder strips any leading whitespace (see PR #89)
        if (prev_token == 1 && piece.charAt(0) == ' ') {
            piece = piece.substring(1);
        }
        // careful, some tokens designate raw bytes, and look like e.g. '<0x01>'
        String prefix = "<0x";
        String suffix = ">";
        if (piece.length() == 6 && piece.startsWith(prefix) && piece.endsWith(suffix)) {
            String hex2 = piece.substring(prefix.length(), prefix.length() + 2);
            char ch = (char) Integer.parseInt(hex2, 16);
            // ok this token is a raw byte token, carefuly to only print printable chars or whitespace
            // some of the other bytes can be various control codes, backspace, etc. => skip
            piece = Character.toString(ch);
        }
        return piece;
    }

    static void safe_printf(String piece) {
        // piece might be a raw byte token, and we only want to print printable chars or whitespace
        // because some of the other bytes can be various control codes, backspace, etc.
        if (piece == null) { return; }
        if (piece.isEmpty()) { return; }
        if (piece.length() == 1) {
            char ch = piece.charAt(0);
            boolean isPrintable = (32 <= ch && ch < 127);
            if (!(isPrintable || Character.isWhitespace(ch))) {
                return ;
            }
        }
        System.out.print(piece);
    }

    static int str_lookup(String str, Map<String, Integer> sorted_vocab) {
        // efficiently find the perfect match for str in vocab, return its index or -1 if not found
        return sorted_vocab.getOrDefault(str, -1);
    }

    static int encode(Tokenizer t, String text, boolean bos, boolean eos, int[] tokens) {
        // encode the string text (input) into an upper-bound preallocated tokens[] array
        // bos != 0 means prepend the BOS token (=1), eos != 0 means append the EOS token (=2)
        if (text == null) {
            System.err.println("cannot encode NULL text");
            System.exit(1);
        }

        if (t.sorted_vocab == null) {
            // sort vocabulary
            t.sorted_vocab = new HashMap<>();
            for (int i = 0; i < t.vocab_size; i++) {
                assert !t.sorted_vocab.containsKey(t.vocab[i]);
                t.sorted_vocab.put(t.vocab[i], i);
            }
        }

        // start at 0 tokens
        int n_tokens = 0; // the number of tokens

        // add optional BOS (=1) token, if desired
        if (bos) {
            tokens[n_tokens++] = 1;
        }

        // so prepend a dummy prefix token to the input string, but only if text != ""
        // TODO: pretty sure this isn't correct in the general case but I don't have the
        // energy to read more of the sentencepiece code to figure out what it's doing
        if (!"".equals(text)) {
            int dummy_prefix = str_lookup(" ", t.sorted_vocab);
            tokens[n_tokens++] = dummy_prefix;
        }

        // first encode every individual codepoint in the input string
        for (int i = 0, cpi; i < text.length(); i += Character.charCount(cpi)) {
            cpi = text.codePointAt(i);

            String singleCodepoint = Character.toString(cpi);
            int id = str_lookup(singleCodepoint, t.sorted_vocab);

            if (id != -1) {
                // we found this codepoint in vocab, add it as a token
                tokens[n_tokens++] = id;
            } else {
                // byte_fallback encoding: just encode each byte as a token
                // +3 is here because the first 3 vocab elements are <unk>, <s>, </s>
                // so the individual bytes only start at index 3
                for (byte b : singleCodepoint.getBytes(StandardCharsets.UTF_8)) {
                    tokens[n_tokens++] = Byte.toUnsignedInt(b) + 3;
                }
            }
        }

        // merge the best consecutive pair each iteration, according the scores in vocab_scores
        while (true) {
            float best_score = -1e10f;
            int best_id = -1;
            int best_idx = -1;

            for (int i = 0; i < n_tokens - 1; ++i) {
                // check if we can merge the pair (tokens[i], tokens[i+1])
                String str_buffer = t.vocab[tokens[i]] + t.vocab[tokens[i + 1]];
                int id = str_lookup(str_buffer, t.sorted_vocab);
                if (id != -1 && t.vocab_scores[id] > best_score) {
                    // this merge pair exists in vocab! record its score and position
                    best_score = t.vocab_scores[id];
                    best_id = id;
                    best_idx = i;
                }
            }

            if (best_idx == -1) {
                break; // we couldn't find any more pairs to merge, so we're done
            }

            // merge the consecutive pair (best_idx, best_idx+1) into new token best_id
            tokens[best_idx] = best_id;
            // delete token at position best_idx+1, shift the entire sequence back 1
            for (int i = best_idx + 1; i < n_tokens - 1; i++) {
                tokens[i] = tokens[i + 1];
            }
            n_tokens--; // token length decreased
        }

        // add optional EOS (=2) token, if desired
        if (eos) {
            tokens[n_tokens++] = 2;
        }

        return n_tokens;
    }

// ----------------------------------------------------------------------------
// utilities: time / rng

    static long time_in_ms() {
        // return time in milliseconds, for benchmarking the model speed
        return System.nanoTime() / 1_000_000;
    }


// ----------------------------------------------------------------------------
// generation loop

    static void generate(Transformer transformer, Tokenizer tokenizer, Sampler sampler, String prompt, int steps) {
        String empty_prompt = "";
        if (prompt == null) {
            prompt = empty_prompt;
        }

        // encode the (string) prompt into tokens sequence
        int num_prompt_tokens = 0; // the total number of prompt tokens
        int[] prompt_tokens = new int[prompt.length() * 2 + 3]; // +3 for '\0', ?BOS, ?EOS
        num_prompt_tokens = encode(tokenizer, prompt, true, false, prompt_tokens);
        if (num_prompt_tokens < 1) {
            System.err.println("something is wrong, expected at least 1 prompt token");
            System.exit(1);
        }

        // start the main loop
        long start = 0;  // used to time our code, only initialized after first iteration
        int next;        // will store the next token in the sequence
        int token = prompt_tokens[0]; // kick off with the first token in the prompt
        int pos = 0;     // position in the sequence
        while (pos < steps) {
            // forward the transformer to get logits for the next token
            float[] logits = forward(transformer, token, pos);

            // advance the state machine
            if (pos < num_prompt_tokens - 1) {
                // if we are still processing the input prompt, force the next prompt token
                next = prompt_tokens[pos + 1];
            } else {
                // otherwise sample the next token from the logits
                next = sample(sampler, logits);
            }
            pos++;

            // data-dependent terminating condition: the BOS (=1) token delimits sequences
            if (next == 1) {
                break;
            }

            // print the token as string, decode it with the Tokenizer object
            String piece = decode(tokenizer, token, next);
            safe_printf(piece);

            System.out.flush();
            token = next;

            // init the timer here because the first iteration can be slower
            if (start == 0) {
                start = time_in_ms();
            }
        }

        System.out.println();

        // report achieved tok/s (pos-1 because the timer starts after first iteration)
        if (pos > 1) {
            long end = time_in_ms();
            System.err.printf("\nachieved tok/s: %f\n", (pos - 1) / (double) (end - start) * 1000);
        }
    }


// ----------------------------------------------------------------------------
// sampling can be done in a few ways: greedy argmax, sampling, top-p sampling

    static int sample_argmax(float[] probabilities, int n) {
        // return the index that has the highest probability
        int max_i = 0;
        float max_p = probabilities[0];
        for (int i = 1; i < n; i++) {
            if (probabilities[i] > max_p) {
                max_i = i;
                max_p = probabilities[i];
            }
        }
        return max_i;
    }

    static int sample_mult(float[] probabilities, int n, float coin) {
        // sample index from probabilities (they must sum to 1!)
        float cdf = 0.0f;
        for (int i = 0; i < n; i++) {
            cdf += probabilities[i];
            if (coin < cdf) {
                return i;
            }
        }
        return n - 1; // in case of rounding errors
    }

    static void swap(int[] array, int from, int to) {
        int tmp = array[from];
        array[from] = array[to];
        array[to] = tmp;
    }

    static void siftDown(int[] array, int from, int n, Comparator<Integer> comparator) {
        int prev = from, next;
        while ((next = 2 * prev + 1) < n) {
            int r = 2 * prev + 2;
            if (r < n && comparator.compare(array[r], array[next]) < 0) {
                next = r;
            }
            if (comparator.compare(array[next], array[prev]) < 0) {
                swap(array, prev, next);
                prev = next;
            } else {
                break;
            }
        }
    }

    static int sample_topp(float[] probabilities, int n, float topp, int[] indices, float coin) {
        // top-p sampling (or "nucleus sampling") samples from the smallest set of
        // tokens that exceed probability topp. This way we never sample tokens that
        // have very low probabilities and are less likely to go "off the rails".
        // coin is a random number in [0, 1), usually from random_f32()
        Comparator<Integer> comparator = Comparator.<Integer>comparingDouble(i -> probabilities[i]).reversed();

        int head = 0;
        int tail = n - 1;
        // values smaller than (1 - topp) / (n - 1) cannot be part of the result
        // so for efficiency we crop these out as candidates before sorting
        float cutoff = (1.0f - topp) / (n - 1);
        for (int i = 0; i < indices.length; i++) {
            if (probabilities[i] >= cutoff) {
                indices[head++] = i;
            } else {
                indices[tail--] = i;
            }
        }

        int n0 = head;
        // build heap O(n0)
        for (int i = n0 / 2 - 1; i >= 0; --i) {
            siftDown(indices, i, n0, comparator);
        }

        // truncate the list where cumulative probability of the largest k elements exceeds topp
        // O(k lg n0)
        float cumulative_prob = 0.0f;
        int last_idx = 0;
        for (int i = n0 - 1; i >= 0; i--) {
            swap(indices, 0, i);
            cumulative_prob += probabilities[indices[i]];
            if (cumulative_prob > topp) {
                last_idx = i;
                break; // we've exceeded topp by including last_idx
            }
            siftDown(indices, 0, i - 1, comparator);
        }

        // sample from the truncated list
        float r = coin * cumulative_prob;
        float cdf = 0.0f;
        for (int i = n0 - 1; i >= last_idx; i--) {
            cdf += probabilities[indices[i]];
            if (r < cdf) {
                return indices[i];
            }
        }

        return indices[last_idx]; // in case of rounding errors
    }

    static int sample(Sampler sampler, float[] logits) {
        // sample the token given the logits and some hyperparameters
        int next;
        if (sampler.temperature == 0.0f) {
            // greedy argmax sampling: take the token with the highest probability
            next = sample_argmax(logits, sampler.vocab_size);
        } else {
            // apply the temperature to the logits
            for (int q = 0; q < sampler.vocab_size; q++) {
                logits[q] /= sampler.temperature;
            }
            // apply softmax to the logits to get the probabilities for next token
            softmax(logits, 0, sampler.vocab_size);
            // flip a (float) coin (this is our source of entropy for sampling)
            float coin = sampler.random_f32();
            // we sample from this distribution to get the next token
            if (sampler.topp <= 0 || sampler.topp >= 1) {
                // simply sample from the predicted probability distribution
                next = sample_mult(logits, sampler.vocab_size, coin);
            } else {
                // top-p (nucleus) sampling, clamping the least likely tokens to zero
                next = sample_topp(logits, sampler.vocab_size, sampler.topp, sampler.probindex, coin);
            }
        }
        return next;
    }

    static String read_stdin(String guide) {
        // read a line from stdin, up to but not including \n
        System.out.print(guide);
        Scanner scanner = new Scanner(System.in);
        if (scanner.hasNextLine()) {
            return scanner.nextLine();
        }
        return null;
    }

// ----------------------------------------------------------------------------
// chat loop
// I manually inspected the tokens for a few chat conversations compared to
// python reference and that seemed ok, but this was not thoroughly tested and
// is not safely implemented, it's more a proof of concept atm.

    static void chat(Transformer transformer, Tokenizer tokenizer, Sampler sampler,
              String cli_user_prompt, String cli_system_prompt, int steps) {

        // buffers for reading the system prompt and user prompt from stdin
        String system_prompt = null;
        String user_prompt = null;
        String rendered_prompt = null;
        int num_prompt_tokens = 0;
        int[] prompt_tokens = new int[512];
        int user_idx = 0;

        // start the main loop
        boolean user_turn = true; // user starts
        int next = 0;        // will store the next token in the sequence
        int token = 0;       // stores the current token to feed into the transformer
        int prev_token;
        int pos = 0;     // position in the sequence
        while (pos < steps) {

            // when it is the user's turn to contribute tokens to the dialog...
            if (user_turn) {
                // get the (optional) system prompt at position 0
                if (pos == 0) {
                    // at position 0, the user can also contribute a system prompt
                    if (cli_system_prompt == null) {
                        // system prompt was not passed in, attempt to get it from stdin
                        system_prompt = read_stdin("Enter system prompt (optional): ");
                    } else {
                        // system prompt was passed in, use it
                        system_prompt = cli_system_prompt;
                    }
                }
                // get the user prompt
                if (pos == 0 && cli_user_prompt != null) {
                    // user prompt for position 0 was passed in, use it
                    user_prompt = cli_user_prompt;
                } else {
                    // otherwise get user prompt from stdin
                    user_prompt = read_stdin("User: ");
                }
                // render user/system prompts into the Llama 2 Chat schema
                if (pos == 0 && system_prompt.isEmpty()) {
                    String system_template = "[INST] <<SYS>>\n%s\n<</SYS>>\n\n%s [/INST]";
                    rendered_prompt = system_template.formatted(system_prompt, user_prompt);
                } else {
                    String user_template = "[INST] %s [/INST]";
                    rendered_prompt = user_template.formatted(user_prompt);
                }
                // encode the rendered prompt into tokens
                num_prompt_tokens = encode(tokenizer, rendered_prompt, true, false, prompt_tokens);
                user_idx = 0; // reset the user index
                user_turn = false;
                System.out.print("Assistant: ");
            }

            // determine the token to pass into the transformer next
            if (user_idx < num_prompt_tokens) {
                // if we are still processing the input prompt, force the next prompt token
                token = prompt_tokens[user_idx++];
            } else {
                // otherwise use the next token sampled from previous turn
                token = next;
            }
            // EOS (=2) token ends the Assistant turn
            if (token == 2) {
                user_turn = true;
            }

            // forward the transformer to get logits for the next token
            float[] logits = forward(transformer, token, pos);
            next = sample(sampler, logits);
            pos++;

            if (user_idx >= num_prompt_tokens && next != 2) {
                // the Assistant is responding, so print its output
                String piece = decode(tokenizer, token, next);
                safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
                System.out.flush();
            }
            if (next == 2) {
                System.out.println();
            }
        }
        System.out.println();
    }


// ----------------------------------------------------------------------------
// int main

    static void error_usage() {
        System.err.println("Usage:   java Llama2 <checkpoint> [options]");
        System.err.println("Example: java Lamma2 model.bin -n 256 -i \"Once upon a time\"");
        System.err.println("Options:");
        System.err.println("  -t <float>  temperature in [0,inf], default 1.0");
        System.err.println("  -p <float>  p value in top-p (nucleus) sampling in [0,1] default 0.9");
        System.err.println("  -s <int>    random seed, default time(NULL)");
        System.err.println("  -n <int>    number of steps to run for, default 256. 0 = max_seq_len");
        System.err.println("  -i <string> input prompt");
        System.err.println("  -z <string> optional path to custom tokenizer");
        System.err.println("  -m <string> mode: generate|chat, default: generate");
        System.err.println("  -y <string> (optional) system prompt in chat mode");
        System.exit(1);
    }

    public static void main(String[] args) throws IOException {
        // default parameters
        String checkpoint_path = null; // e.g. out/model.bin
        String tokenizer_path = "tokenizer.bin";
        float temperature = 1.0f;    // 0.0 = greedy deterministic. 1.0 = original. don't set higher
        float topp = 0.9f;           // top-p in nucleus sampling. 1.0 = off. 0.9 works well, but slower
        long rng_seed = 0;           // seed rng with time by default
        int steps = 256;             // max number of steps to run for, 0: use seq_len
        String prompt = null;        // prompt string
        String mode = "generate";    // generate|chat
        String system_prompt = null; // the (optional) system prompt to use in chat mode

        // poor man's C argparse so we can override the defaults above from the command line
        if (args.length >= 1) {
            checkpoint_path = args[0];
        } else {
            error_usage();
        }
        for (int i = 1; i < args.length; i += 2) {
            // do some basic validation
            if (i + 1 >= args.length) { error_usage(); } // must have arg after flag
            if (args[i].charAt(0) != '-') { error_usage(); } // must start with dash
            if (args[i].length() != 2) { error_usage(); } // must be -x (one dash, one letter)
            // read in the args
            switch (args[i].charAt(1)) {
                case 't' -> temperature = Float.parseFloat(args[i + 1]);
                case 'p' -> topp = Float.parseFloat(args[i + 1]);
                case 's' -> rng_seed = Integer.parseInt(args[i + 1]);
                case 'n' -> steps = Integer.parseInt(args[i + 1]);
                case 'i' -> prompt = args[i + 1];
                case 'z' -> tokenizer_path = args[i + 1];
                case 'm' -> mode = args[i + 1];
                case 'y' -> system_prompt = args[i + 1];
                default -> error_usage();
            }
        }

        // parameter validation/overrides
        if (rng_seed <= 0) {
            rng_seed = System.currentTimeMillis();
        }
        if (temperature < 0.0) {
            temperature = 0.0f;
        }
        if (topp < 0.0 || 1.0 < topp) {
            topp = 0.9f;
        }
        if (steps <= 0) {
            steps = 0;
        }

        // build the Transformer via the model .bin file
        Transformer transformer = new Transformer(checkpoint_path);
        if (steps == 0 || steps > transformer.config.seq_len) {
            steps = transformer.config.seq_len; // ovrerride to ~max length
        }

        // build the Tokenizer via the tokenizer .bin file
        Tokenizer tokenizer = new Tokenizer(tokenizer_path, transformer.config.vocab_size);

        // build the Sampler
        Sampler sampler = new Sampler(transformer.config.vocab_size, temperature, topp, rng_seed);

        // run!
        switch (mode) {
            case "generate" -> generate(transformer, tokenizer, sampler, prompt, steps);
            case "chat" -> chat(transformer, tokenizer, sampler, prompt, system_prompt, steps);
            default -> {
                System.err.println("unknown mode: " + mode);
                error_usage();
            }
        }
    }
}


================================================
FILE: Makefile
================================================
ifdef JAVA_HOME
	JAVAC ?= ${JAVA_HOME}/bin/javac
	JAVA ?= ${JAVA_HOME}/bin/java
	JAR ?= ${JAVA_HOME}/bin/jar
	NATIVE_IMAGE ?= ${JAVA_HOME}/bin/native-image
endif

JAVAC ?= javac
JAVA ?= java
JAR ?= jar
NATIVE_IMAGE ?= native-image

JAVA_COMPILE_OPTIONS = --enable-preview -source 21 -g --add-modules jdk.incubator.vector
JAVA_RUNTIME_OPTIONS += --enable-preview --add-modules jdk.incubator.vector
NATIVE_IMAGE_OPTIONS += --enable-preview --add-modules jdk.incubator.vector

JAVA_MAIN_CLASS = Llama2
JAR_FILE = llama2.jar

JAVA_SOURCES = $(wildcard *.java)
JAVA_CLASSES = $(patsubst %.java, target/classes/%.class, $(JAVA_SOURCES))

# Bundle all classes in a jar
$(JAR_FILE): $(JAVA_CLASSES) target/META-INF/MANIFEST.MF
	$(JAR) -cvfm $(JAR_FILE) target/META-INF/MANIFEST.MF -C target/classes .

jar: $(JAR_FILE)

# Compile the Java source files
compile: $(JAVA_CLASSES)
	$(info Java source files: $(JAVA_SOURCES))
	$(info Java .class files: $(JAVA_CLASSES))

# Prints the command to run the Java main class
run-command:
	@echo $(JAVA) $(JAVA_RUNTIME_OPTIONS) -cp target/classes $(JAVA_MAIN_CLASS)

# Prints the command to run the $(JAR_FILE)
run-jar-command:
	@echo $(JAVA) $(JAVA_RUNTIME_OPTIONS) -jar $(JAR_FILE)

# Clean the target directory
clean:
	rm -rf target
	rm $(JAR_FILE)
	rm default.iprof
	rm llama2

# Creates the manifest for the .jar file
target/META-INF/MANIFEST.MF:
	mkdir -p target/META-INF
	@echo "Manifest-Version: 1.0" > target/META-INF/MANIFEST.MF
	@echo "Class-Path: ." >> target/META-INF/MANIFEST.MF
	@echo "Main-Class: $(JAVA_MAIN_CLASS)" >> target/META-INF/MANIFEST.MF
	@echo "" >> target/META-INF/MANIFEST.MF

# Create a standalone executable of the llama2.jar using GraalVM
native-image: $(JAR_FILE)
	$(NATIVE_IMAGE) $(NATIVE_IMAGE_OPTIONS) -jar $(JAR_FILE)

# Compile the Java source files
target/classes/%.class: %.java
	$(JAVAC) $(JAVA_COMPILE_OPTIONS) -d target/classes $<

# Create the target directory
target/classes:
	mkdir -p target/classes

# Make the target directory a dependency of the Java class files
$(JAVA_CLASSES): target/classes
compile: target/classes
default: target/classes

.PHONY: compile clean jar run-command run-jar-command
.SUFFIXES: .java .class .jar .MF


================================================
FILE: README.md
================================================
# A Java port of Andrej Karpathy's llama2.c

****Check the successor of this project: [Llama3.java](https://github.com/mukel/llama3.java): Practical Llama (3) inference in a single Java file, with additional features, including a `--chat` mode.**

This is a pure Java port of Andrej Karpathy's awesome [llama2.c](https://github.com/karpathy/llama2.c), a very simple implementation
to run inference of models with a [Llama2](https://arxiv.org/pdf/2302.13971.pdf)-like transformer-based LLM architecture.  

<p align="center">
  <img width="600" src="https://github.com/mukel/llama2.java/assets/1896283/66a8a650-f1a9-4540-9587-b112294e5e6b">
</p>

Currently, there isn't anything really original here, but I'll continue polishing it while keeping it in sync with the original.  
Besides the educational value, this project will be used to test and tune compiler optimizations on the JVM, particularly for the [Graal compiler](https://www.graalvm.org/latest/reference-manual/java/compiler).
This port used [llama2.scala](https://github.com/jrudolph/llama2.scala) initially as a reference.

## Build
Java 21+ is required, in particular the [`MemorySegment` mmap-ing feature](https://docs.oracle.com/en/java/javase/21/docs/api/java.base/java/nio/channels/FileChannel.html#map(java.nio.channels.FileChannel.MapMode,long,long,java.lang.foreign.Arena)).  

The code expects [`tokenizer.bin`](https://github.com/karpathy/llama2.c/raw/master/tokenizer.bin) in the current directory.
You can use [TinyStories](https://huggingface.co/karpathy/tinyllamas/tree/main) checkpoints or get LLama2 models by [following instructions](https://github.com/karpathy/llama2.c#metas-llama-2-models).

```bash
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.bin
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.bin
```

To build and run manually:
```bash
javac --enable-preview -source 21 --add-modules=jdk.incubator.vector Llama2.java
java --enable-preview --add-modules=jdk.incubator.vector Llama2 stories15M.bin
```

Or run it directly with [JBang](https://www.jbang.dev/):
```bash
jbang Llama2.java stories15M.bin
# With additional -D options and custom Java home.
JAVA_HOME=/path/to/java/home jbang -Djava.util.concurrent.ForkJoinPool.common.parallelism=0 -Dllama2.VectorAPI=false Llama2.java stories15M.bin
```

A `Makefile` and a `run.sh` script are also provided:

```bash
make # optional, run.sh already runs make

JAVA_HOME=$GRAALVM_HOME \
JAVA_RUNTIME_OPTIONS=-Djava.util.concurrent.ForkJoinPool.common.parallelism=8 \
./run.sh stories15M.bin
```

#### Native image

A standalone native image can be created with [GraalVM](https://www.graalvm.org/)
```bash
JAVA_HOME=$GRAALVM_HOME NATIVE_IMAGE_OPTIONS="-march=native" make native-image
./llama2 stories15M.bin
```

Or can also be built with [Profile-Guided Optimizations (PGO)](https://www.graalvm.org/dev/reference-manual/native-image/guides/optimize-native-executable-with-pgo), on Oracle GaaalVM:
```bash
JAVA_HOME=$GRAALVM_HOME \
NATIVE_IMAGE_OPTIONS="--pgo-instrument -march=native --initialize-at-build-time=Llama2 -Dllama2.VectorAPI=false" \
make native-image

# Profile run to generate default.iprof, with no parallelism to speedup profiling.
./llama2 -Djava.util.concurrent.ForkJoinPool.common.parallelism=0 stories15M.bin

# Build optimized image
JAVA_HOME=$GRAALVM_HOME \
NATIVE_IMAGE_OPTIONS="--pgo -march=native --initialize-at-build-time=Llama2 -Dllama2.VectorAPI=false" \
make native-image

# Should run ~2X faster than regular image.
./llama2 stories15M.bin
```

## Performance

Quick numbers on an AMD Ryzen 3950X 64GB, Arch Linux.  
`llama2.java` executed on OpenJDK 20.0.2+9.  
To make things fair w.r.t. to vectorization, the Java version has a matmul implementation using the [Vector API](https://openjdk.org/jeps/448).  
In these measurements the JVM is warmed up enough to reach peak tokens/s.  
On GraalVM, please note that the Graal compiler doesn't support the Vector API yet, to avoid unexpected performance degradation, run with `-Dllama2.VectorAPI=false`.

****Notes**  
*The numbers below were collected using aggressive (gcc) compiler flags e.g. regular `gcc -O2 ...` wouldn't be as fast.*

### Single-threaded

`llama2.c` compiled with `gcc -Ofast -march=native run.c -lm -o run -march=native`  
`llama2.java` executed with `-Djava.util.concurrent.ForkJoinPool.common.parallelism=0`

| Model | Tokens per second | Speedup vs. llama2.c | Implementation |  
| ------|------------------ | -------------------- | -------------- | 
| stories15M.bin  |   363 |  1.0 | llama2.c    |
| stories15M.bin  |   237 | 0.65 | llama2.java |
| stories110M.bin | 51.71 |  1.0 | llama2.c    |
| stories110M.bin | 42.20 | 0.81 | llama2.java |
| llama2_7B.bin   |  0.92 |  1.0 | llama2.c    |
| llama2_7B.bin   |  0.88 | 0.95 | llama2.java |

### Multi-threaded

`llama2.c` compiled with `gcc -Ofast -fopenmp -march=native run.c -lm -o run -march=native`  
`llama2.c` executed with `OMP_NUM_THREADS=8`  
`llama2.java` executed with `-Djava.util.concurrent.ForkJoinPool.common.parallelism=8`  

| Model | Tokens per second | Speedup vs. llama2.c | Implementation |  
| ------|------------------ | -------------------- | -------------- |
|  stories15M.bin |  1233 |  1.0 | llama2.c    |
|  stories15M.bin |   438 | 0.35 | llama2.java |
| stories110M.bin |    90 |  1.0 | llama2.c    |
| stories110M.bin |    80 | 0.88 | llama2.java |
|   llama2_7B.bin |  1.68 |  1.0 | llama2.c    |
|   llama2_7B.bin |  1.65 | 0.98 | llama2.java |

****Notes**  
*In `stories15M.bin`, the C version shows a huge speedup, very likely a cache effect, this is considered an outlier.
Running with 16/32 threads may actually cause a slowdown; the performance is, in most cases, U-shaped w.r.t to the # of threads.
With that many threads, vectorization does not give any advantage, since throughput is limited by memory bandwidth.*

Performance is already comparable to the original C code, bar vectorization, even if the Java code has not been optimized yet.

## License

MIT


================================================
FILE: run.sh
================================================
#!/bin/bash
make compile
`make run-command` "$@"