Repository: twitter/the-algorithm-ml Branch: main Commit: b85210863f7a Files: 111 Total size: 376.7 KB Directory structure: gitextract_800cdojr/ ├── .github/ │ └── workflows/ │ └── main.yml ├── .gitignore ├── .pre-commit-config.yaml ├── COPYING ├── LICENSE.torchrec ├── README.md ├── common/ │ ├── __init__.py │ ├── batch.py │ ├── checkpointing/ │ │ ├── __init__.py │ │ └── snapshot.py │ ├── device.py │ ├── filesystem/ │ │ ├── __init__.py │ │ ├── test_infer_fs.py │ │ └── util.py │ ├── log_weights.py │ ├── modules/ │ │ └── embedding/ │ │ ├── config.py │ │ └── embedding.py │ ├── run_training.py │ ├── test_device.py │ ├── testing_utils.py │ ├── utils.py │ └── wandb.py ├── core/ │ ├── __init__.py │ ├── config/ │ │ ├── __init__.py │ │ ├── base_config.py │ │ ├── base_config_test.py │ │ ├── config_load.py │ │ ├── test_config_load.py │ │ └── training.py │ ├── custom_training_loop.py │ ├── debug_training_loop.py │ ├── loss_type.py │ ├── losses.py │ ├── metric_mixin.py │ ├── metrics.py │ ├── test_metrics.py │ ├── test_train_pipeline.py │ └── train_pipeline.py ├── images/ │ ├── init_venv.sh │ └── requirements.txt ├── machines/ │ ├── environment.py │ ├── get_env.py │ ├── is_venv.py │ └── list_ops.py ├── metrics/ │ ├── __init__.py │ ├── aggregation.py │ ├── auroc.py │ └── rce.py ├── ml_logging/ │ ├── __init__.py │ ├── absl_logging.py │ ├── test_torch_logging.py │ └── torch_logging.py ├── model.py ├── optimizers/ │ ├── __init__.py │ ├── config.py │ └── optimizer.py ├── projects/ │ ├── __init__.py │ ├── home/ │ │ └── recap/ │ │ ├── FEATURES.md │ │ ├── README.md │ │ ├── __init__.py │ │ ├── config/ │ │ │ ├── home_recap_2022/ │ │ │ │ └── segdense.json │ │ │ └── local_prod.yaml │ │ ├── config.py │ │ ├── data/ │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ ├── dataset.py │ │ │ ├── generate_random_data.py │ │ │ ├── preprocessors.py │ │ │ ├── tfe_parsing.py │ │ │ └── util.py │ │ ├── embedding/ │ │ │ └── config.py │ │ ├── main.py │ │ ├── model/ │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ ├── entrypoint.py │ │ │ ├── feature_transform.py │ │ │ ├── mask_net.py │ │ │ ├── mlp.py │ │ │ ├── model_and_loss.py │ │ │ └── numeric_calibration.py │ │ ├── optimizer/ │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ └── optimizer.py │ │ └── script/ │ │ ├── create_random_data.sh │ │ └── run_local.sh │ └── twhin/ │ ├── README.md │ ├── config/ │ │ └── local.yaml │ ├── config.py │ ├── data/ │ │ ├── config.py │ │ ├── data.py │ │ ├── edges.py │ │ ├── test_data.py │ │ └── test_edges.py │ ├── machines.yaml │ ├── metrics.py │ ├── models/ │ │ ├── config.py │ │ ├── models.py │ │ └── test_models.py │ ├── optimizer.py │ ├── run.py │ ├── scripts/ │ │ ├── docker_run.sh │ │ └── run_in_docker.sh │ └── test_optimizer.py ├── pyproject.toml ├── reader/ │ ├── __init__.py │ ├── dataset.py │ ├── dds.py │ ├── test_dataset.py │ ├── test_utils.py │ └── utils.py └── tools/ └── pq.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/main.yml ================================================ name: Python package on: [push] jobs: build: runs-on: ubuntu-latest strategy: matrix: python-version: ["3.10"] steps: - uses: actions/checkout@v3 # - uses: pre-commit/action@v3.0.0 # name: Run pre-commit checks (pylint/yapf/isort) # env: # SKIP: insert-license # with: # extra_args: --hook-stage push --all-files - uses: actions/setup-python@v4 with: python-version: "3.10" cache: "pip" # caching pip dependencies - name: install packages run: | /usr/bin/python -m pip install --upgrade pip pip install --no-deps -r images/requirements.txt # - name: ssh access # uses: lhotari/action-upterm@v1 # with: # limit-access-to-actor: true # limit-access-to-users: arashd - name: run tests run: | # Environment variables are reset in between steps. mkdir /tmp/github_testing ln -s $GITHUB_WORKSPACE /tmp/github_testing/tml export PYTHONPATH="/tmp/github_testing:$PYTHONPATH" pytest -vv ================================================ FILE: .gitignore ================================================ # Mac .DS_Store # Vim *.py.swp # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] # C extensions *.so # Distribution / packaging build/ develop-eggs/ dist/ eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports .hypothesis venv ================================================ FILE: .pre-commit-config.yaml ================================================ repos: - repo: https://github.com/pausan/cblack rev: release-22.3.0 hooks: - id: cblack name: cblack description: "Black: The uncompromising Python code formatter - 2 space indent fork" entry: cblack . -l 100 - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.3.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-added-large-files - id: check-merge-conflict ================================================ FILE: COPYING ================================================ GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a "Source" link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements. You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see . ================================================ FILE: LICENSE.torchrec ================================================ A few files here (where it is specifically noted in comments) are based on code from torchrec but adapted for our use. Torchrec license is below: BSD 3-Clause License Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: README.md ================================================ This project open sources some of the ML models used at Twitter. Currently these are: 1. The "For You" Heavy Ranker (projects/home/recap). 2. TwHIN embeddings (projects/twhin) https://arxiv.org/abs/2202.05387 This project can be run inside a python virtualenv. We have only tried this on Linux machines and because we use torchrec it works best with an Nvidia GPU. To setup run `./images/init_venv.sh` (Linux only). The READMEs of each project contain instructions about how to run each project. ================================================ FILE: common/__init__.py ================================================ ================================================ FILE: common/batch.py ================================================ """Extension of torchrec.dataset.utils.Batch to cover any dataset. """ # flake8: noqa from __future__ import annotations from typing import Dict import abc from dataclasses import dataclass import dataclasses import torch from torchrec.streamable import Pipelineable class BatchBase(Pipelineable, abc.ABC): @abc.abstractmethod def as_dict(self) -> Dict: raise NotImplementedError def to(self, device: torch.device, non_blocking: bool = False): args = {} for feature_name, feature_value in self.as_dict().items(): args[feature_name] = feature_value.to(device=device, non_blocking=non_blocking) return self.__class__(**args) def record_stream(self, stream: torch.cuda.streams.Stream) -> None: for feature_value in self.as_dict().values(): feature_value.record_stream(stream) def pin_memory(self): args = {} for feature_name, feature_value in self.as_dict().items(): args[feature_name] = feature_value.pin_memory() return self.__class__(**args) def __repr__(self) -> str: def obj2str(v): return f"{v.size()}" if hasattr(v, "size") else f"{v.length_per_key()}" return "\n".join([f"{k}: {obj2str(v)}," for k, v in self.as_dict().items()]) @property def batch_size(self) -> int: for tensor in self.as_dict().values(): if tensor is None: continue if not isinstance(tensor, torch.Tensor): continue return tensor.shape[0] raise Exception("Could not determine batch size from tensors.") @dataclass class DataclassBatch(BatchBase): @classmethod def feature_names(cls): return list(cls.__dataclass_fields__.keys()) def as_dict(self): return { feature_name: getattr(self, feature_name) for feature_name in self.feature_names() if hasattr(self, feature_name) } @staticmethod def from_schema(name: str, schema): """Instantiates a custom batch subclass if all columns can be represented as a torch.Tensor.""" return dataclasses.make_dataclass( cls_name=name, fields=[(name, torch.Tensor, dataclasses.field(default=None)) for name in schema.names], bases=(DataclassBatch,), ) @staticmethod def from_fields(name: str, fields: dict): return dataclasses.make_dataclass( cls_name=name, fields=[(_name, _type, dataclasses.field(default=None)) for _name, _type in fields.items()], bases=(DataclassBatch,), ) class DictionaryBatch(BatchBase, dict): def as_dict(self) -> Dict: return self ================================================ FILE: common/checkpointing/__init__.py ================================================ from tml.common.checkpointing.snapshot import get_checkpoint, Snapshot ================================================ FILE: common/checkpointing/snapshot.py ================================================ import os import time from typing import Any, Dict, List, Optional from tml.ml_logging.torch_logging import logging from tml.common.filesystem import infer_fs, is_gcs_fs import torchsnapshot DONE_EVAL_SUBDIR = "evaled_by" GCS_PREFIX = "gs://" class Snapshot: """Checkpoints using torchsnapshot. Also saves step to be updated by the training loop. """ def __init__(self, save_dir: str, state: Dict[str, Any]) -> None: self.save_dir = save_dir self.state = state self.state["extra_state"] = torchsnapshot.StateDict(step=0, walltime=0.0) @property def step(self): return self.state["extra_state"]["step"] @step.setter def step(self, step: int) -> None: self.state["extra_state"]["step"] = step @property def walltime(self): return self.state["extra_state"]["walltime"] @walltime.setter def walltime(self, walltime: float) -> None: self.state["extra_state"]["walltime"] = walltime def save(self, global_step: int) -> "PendingSnapshot": """Saves checkpoint with given global_step.""" path = os.path.join(self.save_dir, str(global_step)) logging.info(f"Saving snapshot global_step {global_step} to {path}.") start_time = time.time() # Take a snapshot in async manner, the snapshot is consistent that state changes after this method returns have no effect on the snapshot. It performs storage I/O in the background. snapshot = torchsnapshot.Snapshot.async_take( app_state=self.state, path=path, # commented out because DistributedModelParallel model saving # errors with this on multi-GPU. With it removed, CPU, single # GPU, and multi-GPU training all successfully checkpoint. # replicated=["**"], ) logging.info(f"Snapshot saved to {snapshot.path} ({time.time() - start_time:.05}s") return snapshot def restore(self, checkpoint: str) -> None: """Restores a given checkpoint.""" snapshot = torchsnapshot.Snapshot(path=checkpoint) logging.info(f"Restoring snapshot from {snapshot.path}.") start_time = time.time() # We can remove the try-except when we are confident that we no longer need to restore from # checkpoints from before walltime was added try: # checkpoints that do not have extra_state[walltime] will fail here snapshot.restore(self.state) except RuntimeError: # extra_state[walltime] does not exist in the checkpoint, but step should be there so restore it self.state["extra_state"] = torchsnapshot.StateDict(step=0) snapshot.restore(self.state) # we still need to ensure that extra_state has walltime in it self.state["extra_state"] = torchsnapshot.StateDict(step=self.step, walltime=0.0) logging.info(f"Restored snapshot from {snapshot.path}. ({time.time() - start_time:.05}s") @classmethod def get_torch_snapshot( cls, snapshot_path: str, global_step: Optional[int] = None, missing_ok: bool = False, ) -> torchsnapshot.Snapshot: """Get torch stateless snapshot, without actually loading it. Args: snapshot_path: path to the model snapshot global_step: restores from this checkpoint if specified. missing_ok: if True and checkpoints do not exist, returns without restoration. """ path = get_checkpoint(snapshot_path, global_step, missing_ok) logging.info(f"Loading snapshot from {path}.") return torchsnapshot.Snapshot(path=path) @classmethod def load_snapshot_to_weight( cls, embedding_snapshot: torchsnapshot.Snapshot, snapshot_emb_name: str, weight_tensor, ) -> None: """Loads pretrained embedding from the snapshot to the model. Utilise partial lodaing meachanism from torchsnapshot. Args: embedding_snapshot: Path to the snapshot containing pretrained embeddings (EBC). snapshot_emb_name: Name of the layer in the *snapshot* model, containing the EBC. weight_tensor: embeddings tensor of *current* model, where the embeddings will be loaded. """ start_time = time.time() manifest = embedding_snapshot.get_manifest() for path in manifest.keys(): if path.startswith("0") and snapshot_emb_name in path: snapshot_path_to_load = path embedding_snapshot.read_object(snapshot_path_to_load, weight_tensor) logging.info( f"Loaded embedding snapshot from {snapshot_path_to_load}: {time.time() - start_time:.05}s", rank=-1, ) logging.info(f"Snapshot loaded to {weight_tensor.metadata()}", rank=-1) def _eval_subdir(checkpoint_path: str) -> str: return os.path.join(checkpoint_path, DONE_EVAL_SUBDIR) def _eval_done_path(checkpoint_path: str, eval_partition: str) -> str: return os.path.join(_eval_subdir(checkpoint_path), f"{eval_partition}_DONE") def is_done_eval(checkpoint_path: str, eval_partition: str): return get_checkpoint(checkpoint_path).exists(_eval_done_path(checkpoint_path, eval_partition)) def mark_done_eval(checkpoint_path: str, eval_partition: str): infer_fs(checkpoint_path).touch(_eval_done_path(checkpoint_path, eval_partition)) def step_from_checkpoint(checkpoint: str) -> int: return int(os.path.basename(checkpoint)) def checkpoints_iterator(save_dir: str, seconds_to_sleep: int = 30, timeout: int = 1800): """Simplified equivalent of tf.train.checkpoints_iterator. Args: seconds_to_sleep: time between polling calls. timeout: how long to wait for a new checkpoint. """ def _poll(last_checkpoint: Optional[str] = None): stop_time = time.time() + timeout while True: _checkpoint_path = get_checkpoint(save_dir, missing_ok=True) if not _checkpoint_path or _checkpoint_path == last_checkpoint: if time.time() + seconds_to_sleep > stop_time: logging.info( f"Timed out waiting for next available checkpoint from {save_dir} for {timeout}s." ) return None logging.info(f"Waiting for next available checkpoint from {save_dir}.") time.sleep(seconds_to_sleep) else: logging.info(f"Found latest checkpoint {_checkpoint_path}.") return _checkpoint_path checkpoint_path = None while True: new_checkpoint = _poll(checkpoint_path) if not new_checkpoint: return checkpoint_path = new_checkpoint yield checkpoint_path def get_checkpoint( save_dir: str, global_step: Optional[int] = None, missing_ok: bool = False, ) -> str: """Gets latest checkpoint or checkpoint at specified global_step. Args: global_step: Finds this checkpoint if specified. missing_ok: if True and checkpoints do not exist, returns without restoration. """ checkpoints = get_checkpoints(save_dir) if not checkpoints: if not missing_ok: raise Exception(f"No checkpoints found at {save_dir}") else: logging.info(f"No checkpoints found for restoration at {save_dir}.") return "" if global_step is None: return checkpoints[-1] logging.info(f"Found checkpoints: {checkpoints}") for checkpoint in checkpoints: step = step_from_checkpoint(checkpoint) if global_step == step: chosen_checkpoint = checkpoint break else: raise Exception(f"Desired checkpoint at {global_step} not found in {save_dir}") return chosen_checkpoint def get_checkpoints(save_dir: str) -> List[str]: """Gets all checkpoints that have been fully written.""" checkpoints = [] fs = infer_fs(save_dir) if fs.exists(save_dir): prefix = GCS_PREFIX if is_gcs_fs(fs) else "" checkpoints = list(f"{prefix}{elem}" for elem in fs.ls(save_dir, detail=False)) # Only take checkpoints that were fully written. checkpoints = list( filter( lambda path: fs.exists(f"{path}/{torchsnapshot.snapshot.SNAPSHOT_METADATA_FNAME}"), checkpoints, ) ) checkpoints = sorted(checkpoints, key=lambda path: int(os.path.basename(path))) return checkpoints def wait_for_evaluators( save_dir: str, partition_names: List[str], global_step: int, timeout: int, ) -> None: logging.info("Waiting for all evaluators to finish.") start_time = time.time() for checkpoint in checkpoints_iterator(save_dir): step = step_from_checkpoint(checkpoint) logging.info(f"Considering checkpoint {checkpoint} for global step {global_step}.") if step == global_step: while partition_names: if is_done_eval(checkpoint, partition_names[-1]): logging.info( f"Checkpoint {checkpoint} marked as finished eval for partition {partition_names[-1]} at step {step}, still waiting for {partition_names}." ) partition_names.pop() if time.time() - start_time >= timeout: logging.warning( f"Not all evaluators finished after waiting for {time.time() - start_time}" ) return time.sleep(10) logging.info("All evaluators finished.") return if time.time() - start_time >= timeout: logging.warning(f"Not all evaluators finished after waiting for {time.time() - start_time}") return ================================================ FILE: common/device.py ================================================ import os import torch import torch.distributed as dist def maybe_setup_tensorflow(): try: import tensorflow as tf except ImportError: pass else: tf.config.set_visible_devices([], "GPU") # disable tf gpu def setup_and_get_device(tf_ok: bool = True) -> torch.device: if tf_ok: maybe_setup_tensorflow() device = torch.device("cpu") backend = "gloo" if torch.cuda.is_available(): rank = os.environ["LOCAL_RANK"] device = torch.device(f"cuda:{rank}") backend = "nccl" torch.cuda.set_device(device) if not torch.distributed.is_initialized(): dist.init_process_group(backend) return device ================================================ FILE: common/filesystem/__init__.py ================================================ from tml.common.filesystem.util import infer_fs, is_gcs_fs, is_local_fs ================================================ FILE: common/filesystem/test_infer_fs.py ================================================ """Minimal test for infer_fs. Mostly a test that it returns an object """ from tml.common.filesystem import infer_fs def test_infer_fs(): local_path = "/tmp/local_path" gcs_path = "gs://somebucket/somepath" local_fs = infer_fs(local_path) gcs_fs = infer_fs(gcs_path) # This should return two different objects assert local_fs != gcs_fs ================================================ FILE: common/filesystem/util.py ================================================ """Utilities for interacting with the file systems.""" from fsspec.implementations.local import LocalFileSystem import gcsfs GCS_FS = gcsfs.GCSFileSystem(cache_timeout=-1) LOCAL_FS = LocalFileSystem() def infer_fs(path: str): if path.startswith("gs://"): return GCS_FS elif path.startswith("hdfs://"): # We can probably use pyarrow HDFS to support this. raise NotImplementedError("HDFS not yet supported") else: return LOCAL_FS def is_local_fs(fs): return fs == LOCAL_FS def is_gcs_fs(fs): return fs == GCS_FS ================================================ FILE: common/log_weights.py ================================================ """For logging model weights.""" import itertools from typing import Callable, Dict, List, Optional, Union from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined] import torch import torch.distributed as dist from torchrec.distributed.model_parallel import DistributedModelParallel def weights_to_log( model: torch.nn.Module, how_to_log: Optional[Union[Callable, Dict[str, Callable]]] = None, ): """Creates dict of reduced weights to log to give sense of training. Args: model: model to traverse. how_to_log: if a function, then applies this to every parameter, if a dict then only applies and logs specified parameters. """ if not how_to_log: return to_log = dict() named_parameters = model.named_parameters() logging.info(f"Using DMP: {isinstance(model, DistributedModelParallel)}") if isinstance(model, DistributedModelParallel): named_parameters = itertools.chain( named_parameters, model._dmp_wrapped_module.named_parameters() ) logging.info( f"Using dmp parameters: {list(name for name, _ in model._dmp_wrapped_module.named_parameters())}" ) for param_name, params in named_parameters: if callable(how_to_log): how = how_to_log else: how = how_to_log.get(param_name) # type: ignore[assignment] if not how: continue # type: ignore to_log[f"model/{how.__name__}/{param_name}"] = how(params.detach()).cpu().numpy() return to_log def log_ebc_norms( model_state_dict, ebc_keys: List[str], sample_size: int = 4_000_000, ) -> Dict[str, torch.Tensor]: """Logs the norms of the embedding tables as specified by ebc_keys. As of now, log average norm per rank. Args: model_state_dict: model.state_dict() ebc_keys: list of embedding keys from state_dict to log. Must contain full name, i.e. model.embeddings.ebc.embedding_bags.meta__user_id.weight sample_size: Limits number of rows per rank to compute average on to avoid OOM. """ norm_logs = dict() for emb_key in ebc_keys: norms = (torch.ones(1, dtype=torch.float32) * -1).to(torch.device(f"cuda:{dist.get_rank()}")) if emb_key in model_state_dict: emb_weight = model_state_dict[emb_key] try: emb_weight_tensor = emb_weight.local_tensor() except AttributeError as e: logging.info(e) emb_weight_tensor = emb_weight logging.info("Running Tensor.detach()") emb_weight_tensor = emb_weight_tensor.detach() sample_mask = torch.randperm(emb_weight_tensor.shape[0])[ : min(sample_size, emb_weight_tensor.shape[0]) ] # WARNING: .cpu() transfer executes malloc that may be the cause of memory leaks # Change sample_size if the you observe frequent OOM errors or remove weight logging. norms = emb_weight_tensor[sample_mask].cpu().norm(dim=1).to(torch.float32) logging.info(f"Norm shape before reduction: {norms.shape}", rank=-1) norms = norms.mean().to(torch.device(f"cuda:{dist.get_rank()}")) all_norms = [ torch.zeros(1, dtype=norms.dtype).to(norms.device) for _ in range(dist.get_world_size()) ] dist.all_gather(all_norms, norms) for idx, norm in enumerate(all_norms): if norm != -1.0: norm_logs[f"{emb_key}-norm-{idx}"] = norm logging.info(f"Norm Logs are {norm_logs}") return norm_logs ================================================ FILE: common/modules/embedding/config.py ================================================ from typing import List from enum import Enum import tml.core.config as base_config from tml.optimizers.config import OptimizerConfig import pydantic class DataType(str, Enum): FP32 = "fp32" FP16 = "fp16" class EmbeddingSnapshot(base_config.BaseConfig): """Configuration for Embedding snapshot""" emb_name: str = pydantic.Field( ..., description="Name of the embedding table from the loaded snapshot" ) embedding_snapshot_uri: str = pydantic.Field( ..., description="Path to torchsnapshot of the embedding" ) class EmbeddingBagConfig(base_config.BaseConfig): """Configuration for EmbeddingBag.""" name: str = pydantic.Field(..., description="name of embedding bag") num_embeddings: int = pydantic.Field(..., description="size of embedding dictionary") embedding_dim: int = pydantic.Field(..., description="size of each embedding vector") pretrained: EmbeddingSnapshot = pydantic.Field(None, description="Snapshot properties") vocab: str = pydantic.Field( None, description="Directory to parquet files of mapping from entity ID to table index." ) # make sure to use an optimizer that matches: # https://github.com/pytorch/FBGEMM/blob/4c58137529d221390575e47e88d3c05ce65b66fd/fbgemm_gpu/fbgemm_gpu/split_embedding_configs.py#L15 optimizer: OptimizerConfig data_type: DataType class LargeEmbeddingsConfig(base_config.BaseConfig): """Configuration for EmbeddingBagCollection. The tables listed in this config are gathered into a single torchrec EmbeddingBagCollection. """ tables: List[EmbeddingBagConfig] = pydantic.Field(..., description="list of embedding tables") tables_to_log: List[str] = pydantic.Field( None, description="list of embedding table names that we want to log during training" ) class Mode(str, Enum): """Job modes.""" TRAIN = "train" EVALUATE = "evaluate" INFERENCE = "inference" ================================================ FILE: common/modules/embedding/embedding.py ================================================ from tml.common.modules.embedding.config import LargeEmbeddingsConfig, DataType from tml.ml_logging.torch_logging import logging import torch from torch import nn import torchrec from torchrec.modules import embedding_configs from torchrec import EmbeddingBagConfig, EmbeddingBagCollection from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor import numpy as np class LargeEmbeddings(nn.Module): def __init__( self, large_embeddings_config: LargeEmbeddingsConfig, ): super().__init__() tables = [] for table in large_embeddings_config.tables: data_type = ( embedding_configs.DataType.FP32 if (table.data_type == DataType.FP32) else embedding_configs.DataType.FP16 ) tables.append( EmbeddingBagConfig( embedding_dim=table.embedding_dim, feature_names=[table.name], # restricted to 1 feature per table for now name=table.name, num_embeddings=table.num_embeddings, pooling=torchrec.PoolingType.SUM, data_type=data_type, ) ) self.ebc = EmbeddingBagCollection( device="meta", tables=tables, ) logging.info("********************** EBC named params are **********") logging.info(list(self.ebc.named_parameters())) # This hook is used to perform post-processing surgery # on large_embedding models to prep them for serving self.surgery_cut_point = torch.nn.Identity() def forward( self, sparse_features: KeyedJaggedTensor, ) -> KeyedTensor: pooled_embs = self.ebc(sparse_features) # a KeyedTensor return self.surgery_cut_point(pooled_embs) ================================================ FILE: common/run_training.py ================================================ import os import subprocess import sys from typing import Optional from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined] from twitter.ml.tensorflow.experimental.distributed import utils import torch import torch.distributed.run def is_distributed_worker(): world_size = os.environ.get("WORLD_SIZE", None) rank = os.environ.get("RANK", None) return world_size is not None and rank is not None def maybe_run_training( train_fn, module_name, nproc_per_node: Optional[int] = None, num_nodes: Optional[int] = None, set_python_path_in_subprocess: bool = False, is_chief: Optional[bool] = False, **training_kwargs, ): """Wrapper function for single node, multi-GPU Pytorch training. If the necessary distributed Pytorch environment variables (WORLD_SIZE, RANK) have been set, then this function executes `train_fn(**training_kwargs)`. Otherwise, this function calls torchrun and points at the calling module `module_name`. After this call, the necessary environment variables are set and training will commence. Args: train_fn: The function that is responsible for training module_name: The name of the module that this function was called from; used to indicate torchrun entrypoint. nproc_per_node: Number of workers per node; supported values. num_nodes: Number of nodes, otherwise inferred from environment. is_chief: If process is running on chief. set_python_path_in_subprocess: A bool denoting whether to set PYTHONPATH. """ machines = utils.machine_from_env() if num_nodes is None: num_nodes = 1 if machines.num_workers: num_nodes += machines.num_workers if is_distributed_worker(): # world_size, rank, etc are set; assuming any other env vars are set (checks to come) # start the actual training! train_fn(**training_kwargs) else: if nproc_per_node is None: if torch.cuda.is_available(): nproc_per_node = torch.cuda.device_count() else: nproc_per_node = machines.chief.num_accelerators # Rejoin all arguments to send back through torchrec # this is a temporary measure, will replace the os.system call # with torchrun API calls args = list(f"--{key}={val}" for key, val in training_kwargs.items()) cmd = [ "--nnodes", str(num_nodes), ] if nproc_per_node: cmd.extend(["--nproc_per_node", str(nproc_per_node)]) if num_nodes > 1: cluster_resolver = utils.cluster_resolver() backend_address = cluster_resolver.cluster_spec().task_address("chief", 0) cmd.extend( [ "--rdzv_backend", "c10d", "--rdzv_id", backend_address, ] ) # Set localhost on chief because of https://github.com/pytorch/pytorch/issues/79388 if is_chief: cmd.extend(["--rdzv_endpoint", "localhost:2222"]) else: cmd.extend(["--rdzv_endpoint", backend_address]) else: cmd.append("--standalone") cmd.extend( [ str(module_name), *args, ] ) logging.info(f"""Distributed running with cmd: '{" ".join(cmd)}'""") # Call torchrun on this module; will spawn new processes and re-run this # function, eventually calling "train_fn". The following line sets the PYTHONPATH to accommodate # bazel stubbing for the main binary. if set_python_path_in_subprocess: subprocess.run(["torchrun"] + cmd, env={**os.environ, "PYTHONPATH": ":".join(sys.path)}) else: torch.distributed.run.main(cmd) ================================================ FILE: common/test_device.py ================================================ """Minimal test for device. Mostly a test that this can be imported properly even tho moved. """ from unittest.mock import patch import tml.common.device as device_utils def test_device(): with patch("tml.common.device.dist.init_process_group"): device = device_utils.setup_and_get_device(tf_ok=False) assert device.type == "cpu" ================================================ FILE: common/testing_utils.py ================================================ from contextlib import contextmanager import datetime import os from unittest.mock import patch import torch.distributed as dist from tml.ml_logging.torch_logging import logging MOCK_ENV = { "LOCAL_RANK": "0", "WORLD_SIZE": "1", "LOCAL_WORLD_SIZE": "1", "MASTER_ADDR": "localhost", "MASTER_PORT": "29501", "RANK": "0", } @contextmanager def mock_pg(): with patch.dict(os.environ, MOCK_ENV): try: dist.init_process_group( backend="gloo", timeout=datetime.timedelta(1), ) yield except: dist.destroy_process_group() raise finally: dist.destroy_process_group() ================================================ FILE: common/utils.py ================================================ import yaml import getpass import os import string from typing import Tuple, Type, TypeVar from tml.core.config import base_config import fsspec C = TypeVar("C", bound=base_config.BaseConfig) def _read_file(f): with fsspec.open(f) as f: return f.read() def setup_configuration( config_type: Type[C], yaml_path: str, substitute_env_variable: bool = False, ) -> Tuple[C, str]: """Resolves a config at a yaml path. Args: config_type: Pydantic config class to load. yaml_path: yaml path of the config file. substitute_env_variable: If True substitute string in the format $VAR or ${VAR} by their environment variable value whenever possible. If an environment variable doesn't exist, the string is left unchanged. Returns: The pydantic config object. """ def _substitute(s): if substitute_env_variable: return string.Template(s).safe_substitute(os.environ, USER=getpass.getuser()) return s assert config_type is not None, "can't use all_config without config_type" content = _substitute(yaml.safe_load(_read_file(yaml_path))) return config_type.parse_obj(content) ================================================ FILE: common/wandb.py ================================================ from typing import Any, Dict, List import tml.core.config as base_config import pydantic class WandbConfig(base_config.BaseConfig): host: str = pydantic.Field( "https://https--wandb--prod--wandb.service.qus1.twitter.biz/", description="Host of Weights and Biases instance, passed to login.", ) key_path: str = pydantic.Field(description="Path to key file.") name: str = pydantic.Field(None, description="Name of the experiment, passed to init.") entity: str = pydantic.Field(None, description="Name of user/service account, passed to init.") project: str = pydantic.Field(None, description="Name of wandb project, passed to init.") tags: List[str] = pydantic.Field([], description="List of tags, passed to init.") notes: str = pydantic.Field(None, description="Notes, passed to init.") metadata: Dict[str, Any] = pydantic.Field(None, description="Additional metadata to log.") ================================================ FILE: core/__init__.py ================================================ ================================================ FILE: core/config/__init__.py ================================================ from tml.core.config.base_config import BaseConfig from tml.core.config.config_load import load_config_from_yaml # Make mypy happy by explicitly rexporting the symbols intended for end user use. __all__ = ["BaseConfig", "load_config_from_yaml"] ================================================ FILE: core/config/base_config.py ================================================ """Base class for all config (forbids extra fields).""" import collections import functools import yaml import pydantic class BaseConfig(pydantic.BaseModel): """Base class for all derived config classes. This class provides some convenient functionality: - Disallows extra fields when constructing an object. User error should be reduced by exact arguments. - "one_of" fields. A subclass can group optional fields and enforce that only one of the fields be set. For example: ``` class ExampleConfig(BaseConfig): x: int = Field(None, one_of="group_1") y: int = Field(None, one_of="group_1") ExampleConfig(x=1) # ok ExampleConfig(y=1) # ok ExampleConfig(x=1, y=1) # throws error ``` """ class Config: """Forbids extras.""" extra = pydantic.Extra.forbid # noqa @classmethod @functools.lru_cache() def _field_data_map(cls, field_data_name): """Create a map of fields with provided the field data.""" schema = cls.schema() one_of = collections.defaultdict(list) for field, fdata in schema["properties"].items(): if field_data_name in fdata: one_of[fdata[field_data_name]].append(field) return one_of @pydantic.root_validator def _one_of_check(cls, values): """Validate that all 'one of' fields are appear exactly once.""" one_of_map = cls._field_data_map("one_of") for one_of, field_names in one_of_map.items(): if sum([values.get(n, None) is not None for n in field_names]) != 1: raise ValueError(f"Exactly one of {','.join(field_names)} required.") return values @pydantic.root_validator def _at_most_one_of_check(cls, values): """Validate that all 'at_most_one_of' fields appear at most once.""" at_most_one_of_map = cls._field_data_map("at_most_one_of") for one_of, field_names in at_most_one_of_map.items(): if sum([values.get(n, None) is not None for n in field_names]) > 1: raise ValueError(f"At most one of {','.join(field_names)} can be set.") return values def pretty_print(self) -> str: """Return a human legible (yaml) representation of the config useful for logging.""" return yaml.dump(self.dict()) ================================================ FILE: core/config/base_config_test.py ================================================ from unittest import TestCase from tml.core.config import BaseConfig import pydantic class BaseConfigTest(TestCase): def test_extra_forbidden(self): class Config(BaseConfig): x: int Config(x=1) with self.assertRaises(pydantic.ValidationError): Config(x=1, y=2) def test_one_of(self): class Config(BaseConfig): x: int = pydantic.Field(None, one_of="f") y: int = pydantic.Field(None, one_of="f") with self.assertRaises(pydantic.ValidationError): Config() Config(x=1) Config(y=1) with self.assertRaises(pydantic.ValidationError): Config(x=1, y=3) def test_at_most_one_of(self): class Config(BaseConfig): x: int = pydantic.Field(None, at_most_one_of="f") y: str = pydantic.Field(None, at_most_one_of="f") Config() Config(x=1) Config(y="a") with self.assertRaises(pydantic.ValidationError): Config(x=1, y="a") ================================================ FILE: core/config/config_load.py ================================================ import yaml import string import getpass import os from typing import Type from tml.core.config.base_config import BaseConfig def load_config_from_yaml(config_type: Type[BaseConfig], yaml_path: str): """Recommend method to load a config file (a yaml file) and parse it. Because we have a shared filesystem the recommended route to running jobs it put modified config files with the desired parameters somewhere on the filesytem and run jobs pointing to them. """ def _substitute(s): return string.Template(s).safe_substitute(os.environ, USER=getpass.getuser()) with open(yaml_path, "r") as f: raw_contents = f.read() obj = yaml.safe_load(_substitute(raw_contents)) return config_type.parse_obj(obj) ================================================ FILE: core/config/test_config_load.py ================================================ from unittest import TestCase from tml.core.config import BaseConfig, load_config_from_yaml import pydantic import getpass import pydantic class _PointlessConfig(BaseConfig): a: int user: str def test_load_config_from_yaml(tmp_path): yaml_path = tmp_path.joinpath("test.yaml").as_posix() with open(yaml_path, "w") as yaml_file: yaml_file.write("""a: 3\nuser: ${USER}\n""") pointless_config = load_config_from_yaml(_PointlessConfig, yaml_path) assert pointless_config.a == 3 assert pointless_config.user == getpass.getuser() ================================================ FILE: core/config/training.py ================================================ from typing import Any, Dict, List, Optional from tml.common.wandb import WandbConfig from tml.core.config import base_config from tml.projects.twhin.data.config import TwhinDataConfig from tml.projects.twhin.models.config import TwhinModelConfig import pydantic class RuntimeConfig(base_config.BaseConfig): wandb: WandbConfig = pydantic.Field(None) enable_tensorfloat32: bool = pydantic.Field( False, description="Use tensorfloat32 if on Ampere devices." ) enable_amp: bool = pydantic.Field(False, description="Enable automatic mixed precision.") class TrainingConfig(base_config.BaseConfig): save_dir: str = pydantic.Field("/tmp/model", description="Directory to save checkpoints.") num_train_steps: pydantic.PositiveInt = 10000 initial_checkpoint_dir: str = pydantic.Field( None, description="Directory of initial checkpoints", at_most_one_of="initialization" ) checkpoint_every_n: pydantic.PositiveInt = 1000 checkpoint_max_to_keep: pydantic.PositiveInt = pydantic.Field( None, description="Maximum number of checkpoints to keep. Defaults to keeping all." ) train_log_every_n: pydantic.PositiveInt = 1000 num_eval_steps: int = pydantic.Field( 16384, description="Number of evaluation steps. If < 0 the entire dataset will be used." ) eval_log_every_n: pydantic.PositiveInt = 5000 eval_timeout_in_s: pydantic.PositiveFloat = 60 * 60 gradient_accumulation: int = pydantic.Field( None, description="Number of replica steps to accumulate gradients." ) num_epochs: pydantic.PositiveInt = 1 ================================================ FILE: core/custom_training_loop.py ================================================ """Torch and torchrec specific training and evaluation loops. Features (go/100_enablements): - CUDA data-fetch, compute, gradient-push overlap - Large learnable embeddings through torchrec - On/off-chief evaluation - Warmstart/checkpoint management - go/dataset-service 0-copy integration """ import datetime import os from typing import Callable, Dict, Iterable, List, Mapping, Optional from tml.common import log_weights import tml.common.checkpointing.snapshot as snapshot_lib from tml.core.losses import get_global_loss_detached from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined] from tml.core.train_pipeline import TrainPipelineSparseDist import tree import torch import torch.distributed as dist from torch.optim.lr_scheduler import _LRScheduler import torchmetrics as tm def get_new_iterator(iterable: Iterable): """ This obtain a new iterator from the iterable. If the iterable uses tf.data.Dataset internally, getting a new iterator each N steps will avoid memory leak. To avoid the memory leak calling iter(iterable) should return a "fresh" iterator using a fresh (new instance of) tf.data.Iterator. In particular, iterable can be a torch.utils.data.IterableDataset or a torch.utils.data.DataLoader. When using DDS, performing this reset does not change the order in which elements are received (excluding elements already prefetched) provided that iter(iterable) internally uses a new instance of tf.data.Dataset created by calling from_dataset_id. This requirement is satisfied by RecapDataset. :param iterable: :return: """ return iter(iterable) def _get_step_fn(pipeline, data_iterator, training: bool): def step_fn(): # It turns out that model.train() and model.eval() simply switch a single field inside the model # class,so it's somewhat safer to wrap in here. if training: pipeline._model.train() else: pipeline._model.eval() outputs = pipeline.progress(data_iterator) return tree.map_structure(lambda elem: elem.detach(), outputs) return step_fn @torch.no_grad() def _run_evaluation( pipeline, dataset, eval_steps: int, metrics: tm.MetricCollection, eval_batch_size: int, logger=None, ): """Runs the evaluation loop over all evaluation iterators.""" dataset = get_new_iterator(dataset) step_fn = _get_step_fn(pipeline, dataset, training=False) last_time = datetime.datetime.now() logging.info(f"Starting {eval_steps} steps of evaluation.") for _ in range(eval_steps): outputs = step_fn() metrics.update(outputs) eval_ex_per_s = ( eval_batch_size * eval_steps / (datetime.datetime.now() - last_time).total_seconds() ) logging.info(f"eval examples_per_s : {eval_ex_per_s}") metrics_result = metrics.compute() # Resetting at end to release metrics memory not in use. # Reset metrics to prevent accumulation between multiple evaluation splits and not report a # running average. metrics.reset() return metrics_result def train( model: torch.nn.Module, optimizer: torch.optim.Optimizer, device: str, save_dir: str, logging_interval: int, train_steps: int, checkpoint_frequency: int, dataset: Iterable, worker_batch_size: int, num_workers: Optional[int] = 0, enable_amp: bool = False, initial_checkpoint_dir: Optional[str] = None, gradient_accumulation: Optional[int] = None, logger_initializer: Optional[Callable] = None, scheduler: _LRScheduler = None, metrics: Optional[tm.MetricCollection] = None, parameters_to_log: Optional[Dict[str, Callable]] = None, tables_to_log: Optional[List[str]] = None, ) -> None: """Runs training and eval on the given TrainPipeline Args: dataset: data iterator for the training set evaluation_iterators: data iterators for the different evaluation sets scheduler: optional learning rate scheduler output_transform_for_metrics: optional transformation functions to transorm the model output and labels into a format the metrics can understand """ train_pipeline = TrainPipelineSparseDist( model=model, optimizer=optimizer, device=device, enable_amp=enable_amp, grad_accum=gradient_accumulation, ) # type: ignore[var-annotated] # We explicitly initialize optimizer state here so that checkpoint will work properly. if hasattr(train_pipeline._optimizer, "init_state"): train_pipeline._optimizer.init_state() save_state = { "model": train_pipeline._model, "optimizer": train_pipeline._optimizer, "scaler": train_pipeline._grad_scaler, } chosen_checkpoint = None checkpoint_handler = snapshot_lib.Snapshot( save_dir=save_dir, state=save_state, ) if save_dir: chosen_checkpoint = snapshot_lib.get_checkpoint(save_dir=save_dir, missing_ok=True) start_step = 0 start_walltime = 0.0 if chosen_checkpoint: # Skip restoration and exit if we should be finished. chosen_checkpoint_global_step = snapshot_lib.step_from_checkpoint(chosen_checkpoint) if not chosen_checkpoint_global_step < dist.get_world_size() * train_steps: logging.info( "Not restoring and finishing training as latest checkpoint " f"{chosen_checkpoint} found " f"at global_step ({chosen_checkpoint_global_step}) >= " f"train_steps ({dist.get_world_size() * train_steps})" ) return logging.info(f"Restoring latest checkpoint from global_step {chosen_checkpoint_global_step}") checkpoint_handler.restore(chosen_checkpoint) start_step = checkpoint_handler.step start_walltime = checkpoint_handler.walltime elif initial_checkpoint_dir: base, ckpt_step = os.path.split(initial_checkpoint_dir) warmstart_handler = snapshot_lib.Snapshot( save_dir=base, state=save_state, ) ckpt = snapshot_lib.get_checkpoint(save_dir=base, missing_ok=False, global_step=int(ckpt_step)) logging.info( f"Restoring from initial_checkpoint_dir: {initial_checkpoint_dir}, but keeping starting step as 0." ) warmstart_handler.restore(ckpt) train_logger = logger_initializer(mode="train") if logger_initializer else None train_step_fn = _get_step_fn(train_pipeline, get_new_iterator(dataset), training=True) # Counting number of parameters in the model directly when creating it. nb_param = 0 for p in model.parameters(): nb_param += p.numel() logging.info(f"Model has {nb_param} parameters") last_time = datetime.datetime.now() start_time = last_time last_pending_snapshot = None for step in range(start_step, train_steps + 1): checkpoint_handler.step = step outputs = train_step_fn() step_done_time = datetime.datetime.now() checkpoint_handler.walltime = (step_done_time - start_time).total_seconds() + start_walltime if scheduler: scheduler.step() if step % logging_interval == 0: interval_time = (step_done_time - last_time).total_seconds() steps_per_s = logging_interval / interval_time worker_example_per_s = steps_per_s * worker_batch_size global_example_per_s = worker_example_per_s * (1 + (num_workers or 0)) global_step = step log_values = { "global_step": global_step, "loss": get_global_loss_detached(outputs["loss"]), "steps_per_s": steps_per_s, "global_example_per_s": global_example_per_s, "worker_examples_per_s": worker_example_per_s, "active_training_walltime": checkpoint_handler.walltime, } if parameters_to_log: log_values.update( log_weights.weights_to_log( model=model, how_to_log=parameters_to_log, ) ) log_values = tree.map_structure(lambda elem: torch.as_tensor(elem).cpu(), log_values) if tables_to_log: log_values.update( log_weights.log_ebc_norms( model_state_dict=train_pipeline._model.state_dict(), ebc_keys=tables_to_log, ) ) if train_logger: train_logger.log(log_values, step=global_step) log_line = ", ".join(f"{name}: {value}" for name, value in log_values.items()) logging.info(f"Step: {step}, training. {log_line}") last_time = step_done_time # If we just restored, do not save again. if checkpoint_frequency and step > start_step and step % checkpoint_frequency == 0: if last_pending_snapshot and not last_pending_snapshot.done(): logging.warning( "Begin a new snapshot and the last one hasn't finished. That probably indicates " "either you're snapshotting really often or something is wrong. Will now block and " "wait for snapshot to finish before beginning the next one." ) last_pending_snapshot.wait() last_pending_snapshot = checkpoint_handler.save(global_step=step * dist.get_world_size()) # Save if we did not just save. if checkpoint_frequency and step % checkpoint_frequency != 0: # For the final save, wait for the checkpoint to write to make sure the process doesn't finish # before its completed. last_pending_snapshot = checkpoint_handler.save(global_step=step * dist.get_world_size()) logging.info(f"Finished training steps: {step}, global_steps: {step * dist.get_world_size()}") if last_pending_snapshot: logging.info(f"Waiting for any checkpoints to finish.") last_pending_snapshot.wait() def log_eval_results( results, eval_logger, partition_name: str, step: int, ): results = tree.map_structure(lambda elem: torch.as_tensor(elem).cpu(), results) logging.info(f"Step: {step}, evaluation ({partition_name}).") for metric_name, metric_value in results.items(): logging.info(f"\t{metric_name}: {metric_value:1.4e}") if eval_logger: eval_logger.log(results, step=step, commit=True) def only_evaluate( model: torch.nn.Module, optimizer: torch.optim.Optimizer, device: str, save_dir: str, num_train_steps: int, dataset: Iterable, eval_batch_size: int, num_eval_steps: int, eval_timeout_in_s: int, eval_logger: Callable, partition_name: str, metrics: Optional[tm.MetricCollection] = None, ): logging.info(f"Evaluating on partition {partition_name}.") logging.info("Computing metrics:") logging.info(metrics) eval_pipeline = TrainPipelineSparseDist(model, optimizer, device) # type: ignore[var-annotated] save_state = { "model": eval_pipeline._model, "optimizer": eval_pipeline._optimizer, } checkpoint_handler = snapshot_lib.Snapshot( save_dir=save_dir, state=save_state, ) for checkpoint_path in snapshot_lib.checkpoints_iterator(save_dir, timeout=eval_timeout_in_s): checkpoint_handler.restore(checkpoint_path) step = checkpoint_handler.step dataset = get_new_iterator(dataset) results = _run_evaluation( pipeline=eval_pipeline, dataset=dataset, eval_steps=num_eval_steps, eval_batch_size=eval_batch_size, metrics=metrics, ) log_eval_results(results, eval_logger, partition_name, step=step) rank = dist.get_rank() if dist.is_initialized() else 0 if rank == 0: snapshot_lib.mark_done_eval(checkpoint_path, partition_name) if step >= num_train_steps: return ================================================ FILE: core/debug_training_loop.py ================================================ """This is a very limited feature training loop useful for interactive debugging. It is not intended for actual model tranining (it is not fast, doesn't compile the model). It does not support checkpointing. suggested use: from tml.core import debug_training_loop debug_training_loop.train(...) """ from typing import Iterable, Optional, Dict, Callable, List import torch from torch.optim.lr_scheduler import _LRScheduler import torchmetrics as tm from tml.ml_logging.torch_logging import logging def train( model: torch.nn.Module, optimizer: torch.optim.Optimizer, train_steps: int, dataset: Iterable, scheduler: _LRScheduler = None, # Accept any arguments (to be compatible with the real training loop) # but just ignore them. *args, **kwargs, ) -> None: logging.warning("Running debug training loop, don't use for model training.") data_iter = iter(dataset) for step in range(0, train_steps + 1): x = next(data_iter) optimizer.zero_grad() loss, outputs = model.forward(x) loss.backward() optimizer.step() if scheduler: scheduler.step() logging.info(f"Step {step} completed. Loss = {loss}") ================================================ FILE: core/loss_type.py ================================================ """Loss type enums.""" from enum import Enum class LossType(str, Enum): CROSS_ENTROPY = "cross_entropy" BCE_WITH_LOGITS = "bce_with_logits" ================================================ FILE: core/losses.py ================================================ """Loss functions -- including multi task ones.""" import typing from tml.core.loss_type import LossType from tml.ml_logging.torch_logging import logging import torch def _maybe_warn(reduction: str): """ Warning for reduction different than mean. """ if reduction != "mean": logging.warn( f"For the same global_batch_size, the gradient in DDP is guaranteed to be equal," f"to the gradient without DDP only for mean reduction. If you need this property for" f"the provided reduction {reduction}, it needs to be implemented." ) def build_loss( loss_type: LossType, reduction="mean", ): _maybe_warn(reduction) f = _LOSS_TYPE_TO_FUNCTION[loss_type] def loss_fn(logits, labels): return f(logits, labels.type_as(logits), reduction=reduction) return loss_fn def get_global_loss_detached(local_loss, reduction="mean"): """ Perform all_reduce to obtain the global loss function using the provided reduction. :param local_loss: The local loss of the current rank. :param reduction: The reduction to use for all_reduce. Should match the reduction used by DDP. :return: The reduced & detached global loss. """ if reduction != "mean": logging.warn( f"The reduction used in this function should be the same as the one used by " f"the DDP model. By default DDP uses mean, So ensure that DDP is appropriately" f"modified for reduction {reduction}." ) if reduction not in ["mean", "sum"]: raise ValueError(f"Reduction {reduction} is currently unsupported.") global_loss = local_loss.detach() if reduction == "mean": global_loss.div_(torch.distributed.get_world_size()) torch.distributed.all_reduce(global_loss) return global_loss def build_multi_task_loss( loss_type: LossType, tasks: typing.List[str], task_loss_reduction="mean", global_reduction="mean", pos_weights=None, ): _maybe_warn(global_reduction) _maybe_warn(task_loss_reduction) f = _LOSS_TYPE_TO_FUNCTION[loss_type] loss_reduction_fns = { "mean": torch.mean, "sum": torch.sum, "min": torch.min, "max": torch.max, "median": torch.median, } def loss_fn(logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor): if pos_weights is None: torch_weights = torch.ones([len(tasks)]) else: torch_weights = torch.tensor(pos_weights) losses = {} for task_idx, task in enumerate(tasks): task_logits = logits[:, task_idx] label = labels[:, task_idx].type_as(task_logits) loss = f( task_logits, label, reduction=task_loss_reduction, pos_weight=torch_weights[task_idx], weight=weights[:, task_idx], ) losses[f"loss/{task}"] = loss losses["loss"] = loss_reduction_fns[global_reduction](torch.stack(list(losses.values()))) return losses return loss_fn _LOSS_TYPE_TO_FUNCTION = { LossType.BCE_WITH_LOGITS: torch.nn.functional.binary_cross_entropy_with_logits } ================================================ FILE: core/metric_mixin.py ================================================ """ Mixin that requires a transform to munge output dictionary of tensors a model produces to a form that the torchmetrics.Metric.update expects. By unifying on our signature for `update`, we can also now use torchmetrics.MetricCollection which requires all metrics have the same call signature. To use, override this with a transform that munges `outputs` into a kwargs dict that the inherited metric.update accepts. Here are two examples of how to extend torchmetrics.SumMetric so that it accepts an output dictionary of tensors and munges it to what SumMetric expects (single `value`) for its update method. 1. Using as a mixin to inherit from or define a new metric class. class Count(MetricMixin, SumMetric): def transform(self, outputs): return {'value': 1} 2. Redefine an existing metric class. SumMetric = prepend_transform(SumMetric, lambda outputs: {'value': 1}) """ from abc import abstractmethod from typing import Callable, Dict, List from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined] import torch import torchmetrics class MetricMixin: @abstractmethod def transform(self, outputs: Dict[str, torch.Tensor]) -> Dict: ... def update(self, outputs: Dict[str, torch.Tensor]): results = self.transform(outputs) # Do not try to update if any tensor is empty as a result of stratification. for value in results.values(): if torch.is_tensor(value) and not value.nelement(): return super().update(**results) class TaskMixin: def __init__(self, task_idx: int = -1, **kwargs): super().__init__(**kwargs) self._task_idx = task_idx class StratifyMixin: def __init__( self, stratifier=None, **kwargs, ): super().__init__(**kwargs) self._stratifier = stratifier def maybe_apply_stratification( self, outputs: Dict[str, torch.Tensor], value_names: List[str] ) -> Dict[str, torch.Tensor]: """Pick out examples with values for which the stratifier feature is equal to a specific stratifier indicator value.""" outputs = outputs.copy() if not self._stratifier: return outputs stratifiers = outputs.get("stratifiers") if not stratifiers: return outputs if stratifiers.get(self._stratifier.name) is None: return outputs mask = torch.flatten(outputs["stratifiers"][self._stratifier.name] == self._stratifier.value) target_slice = torch.squeeze(mask.nonzero(), -1) for value_name in value_names: target = outputs[value_name] outputs[value_name] = torch.index_select(target, 0, target_slice) return outputs def prepend_transform(base_metric: torchmetrics.Metric, transform: Callable): """Returns new class using MetricMixin and given base_metric. Functionally the same using inheritance, just saves some lines of code if no need for class attributes. """ def transform_method(_self, *args, **kwargs): return transform(*args, **kwargs) return type( base_metric.__name__, ( MetricMixin, base_metric, ), {"transform": transform_method}, ) ================================================ FILE: core/metrics.py ================================================ """Common metrics that also support multi task. We assume multi task models will output [task_idx, ...] predictions """ from typing import Any, Dict from tml.core.metric_mixin import MetricMixin, StratifyMixin, TaskMixin import torch import torchmetrics as tm def probs_and_labels( outputs: Dict[str, torch.Tensor], task_idx: int, ) -> Dict[str, torch.Tensor]: preds = outputs["probabilities"] target = outputs["labels"] if task_idx >= 0: preds = preds[:, task_idx] target = target[:, task_idx] return { "preds": preds, "target": target.int(), } class Count(StratifyMixin, TaskMixin, MetricMixin, tm.SumMetric): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["labels"]) value = outputs["labels"] if self._task_idx >= 0: value = value[:, self._task_idx] return {"value": value} class Ctr(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["labels"]) value = outputs["labels"] if self._task_idx >= 0: value = value[:, self._task_idx] return {"value": value} class Pctr(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["probabilities"]) value = outputs["probabilities"] if self._task_idx >= 0: value = value[:, self._task_idx] return {"value": value} class Precision(StratifyMixin, TaskMixin, MetricMixin, tm.Precision): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["probabilities", "labels"]) return probs_and_labels(outputs, self._task_idx) class Recall(StratifyMixin, TaskMixin, MetricMixin, tm.Recall): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["probabilities", "labels"]) return probs_and_labels(outputs, self._task_idx) class TorchMetricsRocauc(StratifyMixin, TaskMixin, MetricMixin, tm.AUROC): def transform(self, outputs): outputs = self.maybe_apply_stratification(outputs, ["probabilities", "labels"]) return probs_and_labels(outputs, self._task_idx) class Auc(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): """ Based on: https://github.com/facebookresearch/PyTorch-BigGraph/blob/a11ff0eb644b7e4cb569067c280112b47f40ef62/torchbiggraph/util.py#L420 """ def __init__(self, num_samples, **kwargs): super().__init__(**kwargs) self.num_samples = num_samples def transform(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, Any]: scores, labels = outputs["logits"], outputs["labels"] pos_scores = scores[labels == 1] neg_scores = scores[labels == 0] result = { "value": pos_scores[torch.randint(len(pos_scores), (self.num_samples,))] > neg_scores[torch.randint(len(neg_scores), (self.num_samples,))] } return result class PosRanks(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): """ The ranks of all positives Based on: https://github.com/facebookresearch/PyTorch-BigGraph/blob/a11ff0eb644b7e4cb569067c280112b47f40ef62/torchbiggraph/eval.py#L73 """ def __init__(self, **kwargs): super().__init__(**kwargs) def transform(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, Any]: scores, labels = outputs["logits"], outputs["labels"] _, sorted_indices = scores.sort(descending=True) pos_ranks = labels[sorted_indices].nonzero(as_tuple=True)[0] + 1 # all ranks start from 1 result = {"value": pos_ranks} return result class ReciprocalRank(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): """ The reciprocal of the ranks of all Based on: https://github.com/facebookresearch/PyTorch-BigGraph/blob/a11ff0eb644b7e4cb569067c280112b47f40ef62/torchbiggraph/eval.py#L74 """ def __init__(self, **kwargs): super().__init__(**kwargs) def transform(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, Any]: scores, labels = outputs["logits"], outputs["labels"] _, sorted_indices = scores.sort(descending=True) pos_ranks = labels[sorted_indices].nonzero(as_tuple=True)[0] + 1 # all ranks start from 1 result = {"value": torch.div(torch.ones_like(pos_ranks), pos_ranks)} return result class HitAtK(StratifyMixin, TaskMixin, MetricMixin, tm.MeanMetric): """ The fraction of positives that rank in the top K among their negatives Note that this is basically precision@k Based on: https://github.com/facebookresearch/PyTorch-BigGraph/blob/a11ff0eb644b7e4cb569067c280112b47f40ef62/torchbiggraph/eval.py#L75 """ def __init__(self, k: int, **kwargs): super().__init__(**kwargs) self.k = k def transform(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, Any]: scores, labels = outputs["logits"], outputs["labels"] _, sorted_indices = scores.sort(descending=True) pos_ranks = labels[sorted_indices].nonzero(as_tuple=True)[0] + 1 # all ranks start from 1 result = {"value": (pos_ranks <= self.k).float()} return result ================================================ FILE: core/test_metrics.py ================================================ from dataclasses import dataclass from tml.core import metrics as core_metrics from tml.core.metric_mixin import MetricMixin, prepend_transform import torch from torchmetrics import MaxMetric, MetricCollection, SumMetric @dataclass class MockStratifierConfig: name: str index: int value: int class Count(MetricMixin, SumMetric): def transform(self, outputs): return {"value": 1} Max = prepend_transform(MaxMetric, lambda outputs: {"value": outputs["value"]}) def test_count_metric(): num_examples = 123 examples = [ {"stuff": 0}, ] * num_examples metric = Count() for outputs in examples: metric.update(outputs) assert metric.compute().item() == num_examples def test_collections(): max_metric = Max() count_metric = Count() metric = MetricCollection([max_metric, count_metric]) examples = [{"value": idx} for idx in range(123)] for outputs in examples: metric.update(outputs) assert metric.compute() == { max_metric.__class__.__name__: len(examples) - 1, count_metric.__class__.__name__: len(examples), } def test_task_dependent_ctr(): num_examples = 144 batch_size = 1024 outputs = [ { "stuff": 0, "labels": torch.arange(0, 6).repeat(batch_size, 1), } for idx in range(num_examples) ] for task_idx in range(5): metric = core_metrics.Ctr(task_idx=task_idx) for output in outputs: metric.update(output) assert metric.compute().item() == task_idx def test_stratified_ctr(): outputs = [ { "stuff": 0, # [bsz, tasks] "labels": torch.tensor( [ [0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 0], ] ), "stratifiers": { # [bsz] "level": torch.tensor( [9, 0, 9], ), }, } ] stratifier = MockStratifierConfig(name="level", index=2, value=9) for task_idx in range(5): metric = core_metrics.Ctr(task_idx=1, stratifier=stratifier) for output in outputs: metric.update(output) # From the dataset of: # [ # [0, 1, 2, 3], # [1, 2, 3, 4], # [2, 3, 4, 0], # ] # we pick out # [ # [0, 1, 2, 3], # [2, 3, 4, 0], # ] # and with Ctr task_idx, we pick out # [ # [1,], # [3,], # ] assert metric.compute().item() == (1 + 3) / 2 def test_auc(): num_samples = 10000 metric = core_metrics.Auc(num_samples) target = torch.tensor([0, 0, 1, 1, 1]) preds_correct = torch.tensor([-1.0, -1.0, 1.0, 1.0, 1.0]) outputs_correct = {"logits": preds_correct, "labels": target} preds_bad = torch.tensor([1.0, 1.0, -1.0, -1.0, -1.0]) outputs_bad = {"logits": preds_bad, "labels": target} metric.update(outputs_correct) assert metric.compute().item() == 1.0 metric.reset() metric.update(outputs_bad) assert metric.compute().item() == 0.0 def test_pos_rank(): metric = core_metrics.PosRanks() target = torch.tensor([0, 0, 1, 1, 1]) preds_correct = torch.tensor([-1.0, -1.0, 0.5, 1.0, 1.5]) outputs_correct = {"logits": preds_correct, "labels": target} preds_bad = torch.tensor([1.0, 1.0, -1.5, -1.0, -0.5]) outputs_bad = {"logits": preds_bad, "labels": target} metric.update(outputs_correct) assert metric.compute().item() == 2.0 metric.reset() metric.update(outputs_bad) assert metric.compute().item() == 4.0 def test_reciprocal_rank(): metric = core_metrics.ReciprocalRank() target = torch.tensor([0, 0, 1, 1, 1]) preds_correct = torch.tensor([-1.0, -1.0, 0.5, 1.0, 1.5]) outputs_correct = {"logits": preds_correct, "labels": target} preds_bad = torch.tensor([1.0, 1.0, -1.5, -1.0, -0.5]) outputs_bad = {"logits": preds_bad, "labels": target} metric.update(outputs_correct) assert abs(metric.compute().item() - 0.6111) < 0.001 metric.reset() metric.update(outputs_bad) assert abs(metric.compute().item() == 0.2611) < 0.001 def test_hit_k(): hit1_metric = core_metrics.HitAtK(1) target = torch.tensor([0, 0, 1, 1, 1]) preds_correct = torch.tensor([-1.0, 1.0, 0.5, -0.1, 1.5]) outputs_correct = {"logits": preds_correct, "labels": target} preds_bad = torch.tensor([1.0, 1.0, -1.5, -1.0, -0.5]) outputs_bad = {"logits": preds_bad, "labels": target} hit1_metric.update(outputs_correct) assert abs(hit1_metric.compute().item() - 0.3333) < 0.0001 hit1_metric.reset() hit1_metric.update(outputs_bad) assert hit1_metric.compute().item() == 0 hit3_metric = core_metrics.HitAtK(3) hit3_metric.update(outputs_correct) assert (hit3_metric.compute().item() - 0.66666) < 0.0001 hit3_metric.reset() hit3_metric.update(outputs_bad) assert abs(hit3_metric.compute().item() - 0.3333) < 0.0001 ================================================ FILE: core/test_train_pipeline.py ================================================ from dataclasses import dataclass from typing import Tuple from tml.common.batch import DataclassBatch from tml.common.testing_utils import mock_pg from tml.core import train_pipeline import torch from torchrec.distributed import DistributedModelParallel @dataclass class MockDataclassBatch(DataclassBatch): continuous_features: torch.Tensor labels: torch.Tensor class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.model = torch.nn.Linear(10, 1) self.loss_fn = torch.nn.BCEWithLogitsLoss() def forward(self, batch: MockDataclassBatch) -> Tuple[torch.Tensor, torch.Tensor]: pred = self.model(batch.continuous_features) loss = self.loss_fn(pred, batch.labels) return (loss, pred) def create_batch(bsz: int): return MockDataclassBatch( continuous_features=torch.rand(bsz, 10).float(), labels=torch.bernoulli(torch.empty(bsz, 1).uniform_(0, 1)).float(), ) def test_sparse_pipeline(): device = torch.device("cpu") model = MockModule().to(device) steps = 8 example = create_batch(1) dataloader = iter(example for _ in range(steps + 2)) results = [] with mock_pg(): d_model = DistributedModelParallel(model) pipeline = train_pipeline.TrainPipelineSparseDist( model=d_model, optimizer=torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9), device=device, grad_accum=2, ) for _ in range(steps): results.append(pipeline.progress(dataloader)) results = [elem.detach().numpy() for elem in results] # Check gradients are accumulated, i.e. results do not change for every 0th and 1th. for first, second in zip(results[::2], results[1::2]): assert first == second, results # Check we do update gradients, i.e. results do change for every 1th and 2nd. for first, second in zip(results[1::2], results[2::2]): assert first != second, results def test_amp(): device = torch.device("cpu") model = MockModule().to(device) steps = 8 example = create_batch(1) dataloader = iter(example for _ in range(steps + 2)) results = [] with mock_pg(): d_model = DistributedModelParallel(model) pipeline = train_pipeline.TrainPipelineSparseDist( model=d_model, optimizer=torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9), device=device, enable_amp=True, # Not supported on CPU. enable_grad_scaling=False, ) for _ in range(steps): results.append(pipeline.progress(dataloader)) results = [elem.detach() for elem in results] for value in results: assert value.dtype == torch.bfloat16 ================================================ FILE: core/train_pipeline.py ================================================ """ Taken from https://raw.githubusercontent.com/pytorch/torchrec/v0.3.2/torchrec/distributed/train_pipeline.py with TrainPipelineSparseDist.progress modified to support gradient accumulation. """ import abc from dataclasses import dataclass, field import logging from typing import ( Any, cast, Dict, Generic, Iterator, List, Optional, Set, Tuple, TypeVar, ) import torch from torch.autograd.profiler import record_function from torch.fx.node import Node from torchrec.distributed.model_parallel import ( DistributedModelParallel, ShardedModule, ) from torchrec.distributed.types import Awaitable from torchrec.modules.feature_processor import BaseGroupedFeatureProcessor from torchrec.streamable import Multistreamable, Pipelineable logger: logging.Logger = logging.getLogger(__name__) In = TypeVar("In", bound=Pipelineable) Out = TypeVar("Out") class TrainPipeline(abc.ABC, Generic[In, Out]): @abc.abstractmethod def progress(self, dataloader_iter: Iterator[In]) -> Out: pass def _to_device(batch: In, device: torch.device, non_blocking: bool) -> In: assert isinstance( batch, (torch.Tensor, Pipelineable) ), f"{type(batch)} must implement Pipelineable interface" return cast(In, batch.to(device=device, non_blocking=non_blocking)) def _wait_for_batch(batch: In, stream: Optional[torch.cuda.streams.Stream]) -> None: if stream is None: return torch.cuda.current_stream().wait_stream(stream) # As mentioned in https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html, # PyTorch uses the "caching allocator" for memory allocation for tensors. When a tensor is # freed, its memory is likely to be reused by newly constructed tenosrs. By default, # this allocator traces whether a tensor is still in use by only the CUDA stream where it # was created. When a tensor is used by additional CUDA streams, we need to call record_stream # to tell the allocator about all these streams. Otherwise, the allocator might free the # underlying memory of the tensor once it is no longer used by the creator stream. This is # a notable programming trick when we write programs using multi CUDA streams. cur_stream = torch.cuda.current_stream() assert isinstance( batch, (torch.Tensor, Multistreamable) ), f"{type(batch)} must implement Multistreamable interface" batch.record_stream(cur_stream) class TrainPipelineBase(TrainPipeline[In, Out]): """ This class runs training iterations using a pipeline of two stages, each as a CUDA stream, namely, the current (default) stream and `self._memcpy_stream`. For each iteration, `self._memcpy_stream` moves the input from host (CPU) memory to GPU memory, and the default stream runs forward, backward, and optimization. """ def __init__( self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, device: torch.device, ) -> None: self._model = model self._optimizer = optimizer self._device = device self._memcpy_stream: Optional[torch.cuda.streams.Stream] = ( torch.cuda.Stream() if device.type == "cuda" else None ) self._cur_batch: Optional[In] = None self._connected = False def _connect(self, dataloader_iter: Iterator[In]) -> None: cur_batch = next(dataloader_iter) self._cur_batch = cur_batch with torch.cuda.stream(self._memcpy_stream): self._cur_batch = _to_device(cur_batch, self._device, non_blocking=True) self._connected = True def progress(self, dataloader_iter: Iterator[In]) -> Out: if not self._connected: self._connect(dataloader_iter) # Fetch next batch with record_function("## next_batch ##"): next_batch = next(dataloader_iter) cur_batch = self._cur_batch assert cur_batch is not None if self._model.training: with record_function("## zero_grad ##"): self._optimizer.zero_grad() with record_function("## wait_for_batch ##"): _wait_for_batch(cur_batch, self._memcpy_stream) with record_function("## forward ##"): (losses, output) = self._model(cur_batch) if self._model.training: with record_function("## backward ##"): torch.sum(losses, dim=0).backward() # Copy the next batch to GPU self._cur_batch = cur_batch = next_batch with record_function("## copy_batch_to_gpu ##"): with torch.cuda.stream(self._memcpy_stream): self._cur_batch = _to_device(cur_batch, self._device, non_blocking=True) # Update if self._model.training: with record_function("## optimizer ##"): self._optimizer.step() return output class Tracer(torch.fx.Tracer): # Disable proxying buffers during tracing. Ideally, proxying buffers would # be disabled, but some models are currently mutating buffer values, which # causes errors during tracing. If those models can be rewritten to not do # that, we can likely remove this line proxy_buffer_attributes = False def __init__(self, leaf_modules: Optional[List[str]] = None) -> None: super().__init__() self._leaf_modules: List[str] = leaf_modules if leaf_modules is not None else [] def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool: if isinstance(m, ShardedModule) or module_qualified_name in self._leaf_modules: return True return super().is_leaf_module(m, module_qualified_name) @dataclass class TrainPipelineContext: # pyre-ignore [4] input_dist_requests: Dict[str, Awaitable[Any]] = field(default_factory=dict) module_contexts: Dict[str, Multistreamable] = field(default_factory=dict) # pyre-ignore [4] feature_processor_forwards: List[Any] = field(default_factory=list) @dataclass class ArgInfo: # attributes of input batch, e.g. batch.attr1.attr2 call # will produce ["attr1", "attr2"] input_attrs: List[str] # batch[attr1].attr2 will produce [True, False] is_getitems: List[bool] # name for kwarg of pipelined forward() call or None # for a positional arg name: Optional[str] class PipelinedForward: def __init__( self, name: str, args: List[ArgInfo], module: ShardedModule, context: TrainPipelineContext, dist_stream: Optional[torch.cuda.streams.Stream], ) -> None: self._name = name self._args = args self._module = module self._context = context self._dist_stream = dist_stream # pyre-ignore [2, 24] def __call__(self, *input, **kwargs) -> Awaitable: assert self._name in self._context.input_dist_requests request = self._context.input_dist_requests[self._name] assert isinstance(request, Awaitable) with record_function("## wait_sparse_data_dist ##"): # Finish waiting on the dist_stream, # in case some delayed stream scheduling happens during the wait() call. with torch.cuda.stream(self._dist_stream): data = request.wait() # Make sure that both result of input_dist and context # are properly transferred to the current stream. if self._dist_stream is not None: torch.cuda.current_stream().wait_stream(self._dist_stream) cur_stream = torch.cuda.current_stream() assert isinstance( data, (torch.Tensor, Multistreamable) ), f"{type(data)} must implement Multistreamable interface" # pyre-fixme[6]: For 1st param expected `Stream` but got `Stream`. data.record_stream(cur_stream) ctx = self._context.module_contexts[self._name] ctx.record_stream(cur_stream) if len(self._context.feature_processor_forwards) > 0: with record_function("## feature_processor ##"): for sparse_feature in data: if sparse_feature.id_score_list_features is not None: for fp_forward in self._context.feature_processor_forwards: sparse_feature.id_score_list_features = fp_forward( sparse_feature.id_score_list_features ) return self._module.compute_and_output_dist(self._context.module_contexts[self._name], data) @property def name(self) -> str: return self._name @property def args(self) -> List[ArgInfo]: return self._args def _start_data_dist( pipelined_modules: List[ShardedModule], batch: In, context: TrainPipelineContext, ) -> None: context.input_dist_requests.clear() context.module_contexts.clear() for module in pipelined_modules: forward = module.forward assert isinstance(forward, PipelinedForward) # Retrieve argument for the input_dist of EBC # is_getitem True means this argument could be retrieved by a list # False means this argument is getting while getattr # and this info was done in the _rewrite_model by tracing the # entire model to get the arg_info_list args = [] kwargs = {} for arg_info in forward.args: if arg_info.input_attrs: arg = batch for attr, is_getitem in zip(arg_info.input_attrs, arg_info.is_getitems): if is_getitem: arg = arg[attr] else: arg = getattr(arg, attr) if arg_info.name: kwargs[arg_info.name] = arg else: args.append(arg) else: args.append(None) # Start input distribution. module_ctx = module.create_context() context.module_contexts[forward.name] = module_ctx context.input_dist_requests[forward.name] = module.input_dist(module_ctx, *args, **kwargs) # Call wait on the first awaitable in the input dist for the tensor splits for key, awaitable in context.input_dist_requests.items(): context.input_dist_requests[key] = awaitable.wait() def _get_node_args_helper( # pyre-ignore arguments, num_found: int, feature_processor_arguments: Optional[List[Node]] = None, ) -> Tuple[List[ArgInfo], int]: """ Goes through the args/kwargs of a node and arranges them into a list of `ArgInfo`s. It also counts the number of (args + kwargs) found. """ arg_info_list = [ArgInfo([], [], None) for _ in range(len(arguments))] for arg, arg_info in zip(arguments, arg_info_list): if arg is None: num_found += 1 continue while True: if not isinstance(arg, torch.fx.Node): break child_node = arg if child_node.op == "placeholder": num_found += 1 break # skip this fp node elif feature_processor_arguments is not None and child_node in feature_processor_arguments: arg = child_node.args[0] elif ( child_node.op == "call_function" and child_node.target.__module__ == "builtins" # pyre-ignore[16] and child_node.target.__name__ == "getattr" ): arg_info.input_attrs.insert(0, child_node.args[1]) arg_info.is_getitems.insert(0, False) arg = child_node.args[0] elif ( child_node.op == "call_function" and child_node.target.__module__ == "_operator" # pyre-ignore[16] and child_node.target.__name__ == "getitem" ): arg_info.input_attrs.insert(0, child_node.args[1]) arg_info.is_getitems.insert(0, True) arg = child_node.args[0] else: break return arg_info_list, num_found def _get_node_args( node: Node, feature_processor_nodes: Optional[List[Node]] = None ) -> Tuple[List[ArgInfo], int]: num_found = 0 pos_arg_info_list, num_found = _get_node_args_helper( node.args, num_found, feature_processor_nodes ) kwargs_arg_info_list, num_found = _get_node_args_helper(node.kwargs.values(), num_found) # Replace with proper names for kwargs for name, arg_info_list in zip(node.kwargs, kwargs_arg_info_list): arg_info_list.name = name arg_info_list = pos_arg_info_list + kwargs_arg_info_list return arg_info_list, num_found def _get_unsharded_module_names_helper( model: torch.nn.Module, path: str, unsharded_module_names: Set[str], ) -> bool: sharded_children = set() for name, child in model.named_children(): curr_path = path + name if isinstance(child, ShardedModule): sharded_children.add(name) else: child_sharded = _get_unsharded_module_names_helper( child, curr_path + ".", unsharded_module_names, ) if child_sharded: sharded_children.add(name) if len(sharded_children) > 0: for name, _ in model.named_children(): if name not in sharded_children: unsharded_module_names.add(path + name) return len(sharded_children) > 0 def _get_unsharded_module_names(model: torch.nn.Module) -> List[str]: """ Returns a list of top level modules do not contain any sharded sub modules. """ unsharded_module_names: Set[str] = set() _get_unsharded_module_names_helper( model, "", unsharded_module_names, ) return list(unsharded_module_names) def _rewrite_model( # noqa C901 model: torch.nn.Module, context: TrainPipelineContext, dist_stream: Optional[torch.cuda.streams.Stream], ) -> List[ShardedModule]: # Get underlying nn.Module if isinstance(model, DistributedModelParallel): model = model.module # Collect a list of sharded modules. sharded_modules = {} fp_modules = {} for name, m in model.named_modules(): if isinstance(m, ShardedModule): sharded_modules[name] = m if isinstance(m, BaseGroupedFeatureProcessor): fp_modules[name] = m # Trace a model. tracer = Tracer(leaf_modules=_get_unsharded_module_names(model)) graph = tracer.trace(model) feature_processor_nodes = [] # find the fp node for node in graph.nodes: if node.op == "call_module" and node.target in fp_modules: feature_processor_nodes.append(node) # Select sharded modules, which are top-level in the forward call graph, # i.e. which don't have input transformations, i.e. # rely only on 'builtins.getattr'. ret = [] for node in graph.nodes: if node.op == "call_module" and node.target in sharded_modules: total_num_args = len(node.args) + len(node.kwargs) if total_num_args == 0: continue arg_info_list, num_found = _get_node_args(node, feature_processor_nodes) if num_found == total_num_args: logger.info(f"Module '{node.target}'' will be pipelined") child = sharded_modules[node.target] child.forward = PipelinedForward( node.target, arg_info_list, child, context, dist_stream, ) ret.append(child) return ret class TrainPipelineSparseDist(TrainPipeline[In, Out]): """ This pipeline overlaps device transfer, and `ShardedModule.input_dist()` with forward and backward. This helps hide the all2all latency while preserving the training forward / backward ordering. stage 3: forward, backward - uses default CUDA stream stage 2: ShardedModule.input_dist() - uses data_dist CUDA stream stage 1: device transfer - uses memcpy CUDA stream `ShardedModule.input_dist()` is only done for top-level modules in the call graph. To be considered a top-level module, a module can only depend on 'getattr' calls on input. Input model must be symbolically traceable with the exception of `ShardedModule` and `DistributedDataParallel` modules. """ synced_pipeline_id: Dict[int, int] = {} def __init__( self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, device: torch.device, enable_amp: bool = False, enable_grad_scaling: bool = True, grad_accum: Optional[int] = None, ) -> None: self._model = model self._optimizer = optimizer self._device = device self._enable_amp = enable_amp # NOTE: Pending upstream feedback, but two flags because we can run AMP without CUDA but cannot scale gradients without CUDA. # Background on gradient/loss scaling # https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html#lossscaling # https://pytorch.org/docs/stable/amp.html#gradient-scaling self._enable_grad_scaling = enable_grad_scaling self._grad_scaler = torch.cuda.amp.GradScaler( enabled=self._enable_amp and self._enable_grad_scaling ) logging.info(f"Amp is enabled: {self._enable_amp}") # use two data streams to support two concurrent batches if device.type == "cuda": self._memcpy_stream: Optional[torch.cuda.streams.Stream] = torch.cuda.Stream() self._data_dist_stream: Optional[torch.cuda.streams.Stream] = torch.cuda.Stream() else: if self._enable_amp: logging.warning("Amp is enabled, but no CUDA available") self._memcpy_stream: Optional[torch.cuda.streams.Stream] = None self._data_dist_stream: Optional[torch.cuda.streams.Stream] = None self._batch_i: Optional[In] = None self._batch_ip1: Optional[In] = None self._batch_ip2: Optional[In] = None self._connected = False self._context = TrainPipelineContext() self._pipelined_modules: List[ShardedModule] = [] self._progress_calls = 0 if grad_accum is not None: assert isinstance(grad_accum, int) and grad_accum > 0 self._grad_accum = grad_accum def _connect(self, dataloader_iter: Iterator[In]) -> None: # batch 1 with torch.cuda.stream(self._memcpy_stream): batch_i = next(dataloader_iter) self._batch_i = batch_i = _to_device(batch_i, self._device, non_blocking=True) # Try to pipeline input data dist. self._pipelined_modules = _rewrite_model(self._model, self._context, self._data_dist_stream) with torch.cuda.stream(self._data_dist_stream): _wait_for_batch(batch_i, self._memcpy_stream) _start_data_dist(self._pipelined_modules, batch_i, self._context) # batch 2 with torch.cuda.stream(self._memcpy_stream): batch_ip1 = next(dataloader_iter) self._batch_ip1 = batch_ip1 = _to_device(batch_ip1, self._device, non_blocking=True) self._connected = True self.__class__.synced_pipeline_id[id(self._model)] = id(self) def progress(self, dataloader_iter: Iterator[In]) -> Out: """ NOTE: This method has been updated to perform gradient accumulation. If `_grad_accum` is set, then loss values are scaled by this amount and optimizer update/reset is skipped for `_grad_accum` calls of `progress` (congruent to training steps), and then update/reset on every `_grad_accum`th step. """ should_step_optimizer = ( self._grad_accum is not None and self._progress_calls > 0 and (self._progress_calls + 1) % self._grad_accum == 0 ) or self._grad_accum is None should_reset_optimizer = ( self._grad_accum is not None and self._progress_calls > 0 and (self._progress_calls + 2) % self._grad_accum == 0 ) or self._grad_accum is None if not self._connected: self._connect(dataloader_iter) elif self.__class__.synced_pipeline_id.get(id(self._model), None) != id(self): self._sync_pipeline() self.__class__.synced_pipeline_id[id(self._model)] = id(self) if self._model.training and should_reset_optimizer: with record_function("## zero_grad ##"): self._optimizer.zero_grad() with record_function("## copy_batch_to_gpu ##"): with torch.cuda.stream(self._memcpy_stream): batch_ip2 = next(dataloader_iter) self._batch_ip2 = batch_ip2 = _to_device(batch_ip2, self._device, non_blocking=True) batch_i = cast(In, self._batch_i) batch_ip1 = cast(In, self._batch_ip1) with record_function("## wait_for_batch ##"): _wait_for_batch(batch_i, self._data_dist_stream) # Forward with record_function("## forward ##"): # if using multiple streams (ie. CUDA), create an event in default stream # before starting forward pass if self._data_dist_stream: event = torch.cuda.current_stream().record_event() if self._enable_amp: # conditionally apply the model to the batch in the autocast context # it appears that `enabled=self._enable_amp` should handle this, # but it does not. with torch.autocast( device_type=self._device.type, dtype=torch.bfloat16, enabled=self._enable_amp, ): (losses, output) = cast(Tuple[torch.Tensor, Out], self._model(batch_i)) else: (losses, output) = cast(Tuple[torch.Tensor, Out], self._model(batch_i)) # Data Distribution with record_function("## sparse_data_dist ##"): with torch.cuda.stream(self._data_dist_stream): _wait_for_batch(batch_ip1, self._memcpy_stream) # Ensure event in default stream has been called before # starting data dist if self._data_dist_stream: # pyre-ignore [61]: Local variable `event` is undefined, or not always defined self._data_dist_stream.wait_event(event) _start_data_dist(self._pipelined_modules, batch_ip1, self._context) if self._model.training: # Backward with record_function("## backward ##"): # Loss is normalize by number of accumulation steps. # The reported loss in `output['loss']` remains the unnormalized value. if self._grad_accum is not None: losses = losses / self._grad_accum self._grad_scaler.scale(torch.sum(losses, dim=0)).backward() if should_step_optimizer: # Update with record_function("## optimizer ##"): self._grad_scaler.step(self._optimizer) self._grad_scaler.update() self._batch_i = batch_ip1 self._batch_ip1 = batch_ip2 if self._model.training: self._progress_calls += 1 return output def _sync_pipeline(self) -> None: """ Syncs `PipelinedForward` for sharded modules with context and dist stream of the current train pipeline. Used when switching between train pipelines for the same model. """ for module in self._pipelined_modules: module.forward._context = self._context module.forward._dist_stream = self._data_dist_stream ================================================ FILE: images/init_venv.sh ================================================ #! /bin/sh if [[ "$(uname)" == "Darwin" ]]; then echo "Only supported on Linux." exit 1 fi # You may need to point this to a version of python 3.10 PYTHONBIN="/opt/ee/python/3.10/bin/python3.10" echo Using "PYTHONBIN=$PYTHONBIN" # Put venv in tmp, these things are not made to last, just rebuild. VENV_PATH="$HOME/tml_venv" rm -rf "$VENV_PATH" "$PYTHONBIN" -m venv "$VENV_PATH" # shellcheck source=/dev/null . "$VENV_PATH/bin/activate" pip --require-virtual install -U pip pip --require-virtualenv install --no-deps -r images/requirements.txt ln -s "$(pwd)" "$VENV_PATH/lib/python3.10/site-packages/tml" echo "Now run source ${VENV_PATH}/bin/activate" to get going. ================================================ FILE: images/requirements.txt ================================================ absl-py==1.4.0 aiofiles==22.1.0 aiohttp==3.8.3 aiosignal==1.3.1 appdirs==1.4.4 arrow==1.2.3 asttokens==2.2.1 astunparse==1.6.3 async-timeout==4.0.2 attrs==22.1.0 backcall==0.2.0 black==22.6.0 cachetools==5.3.0 cblack==22.6.0 certifi==2022.12.7 cfgv==3.3.1 charset-normalizer==2.1.1 click==8.1.3 cmake==3.25.0 Cython==0.29.32 decorator==5.1.1 distlib==0.3.6 distro==1.8.0 dm-tree==0.1.6 docker==6.0.1 docker-pycreds==0.4.0 docstring-parser==0.8.1 exceptiongroup==1.1.0 executing==1.2.0 fbgemm-gpu-cpu==0.3.2 filelock==3.8.2 fire==0.5.0 flatbuffers==1.12 frozenlist==1.3.3 fsspec==2022.11.0 gast==0.4.0 gcsfs==2022.11.0 gitdb==4.0.10 GitPython==3.1.31 google-api-core==2.8.2 google-auth==2.16.0 google-auth-oauthlib==0.4.6 google-cloud-core==2.3.2 google-cloud-storage==2.7.0 google-crc32c==1.5.0 google-pasta==0.2.0 google-resumable-media==2.4.1 googleapis-common-protos==1.56.4 grpcio==1.51.1 h5py==3.8.0 hypothesis==6.61.0 identify==2.5.17 idna==3.4 importlib-metadata==6.0.0 iniconfig==2.0.0 iopath==0.1.10 ipdb==0.13.11 ipython==8.10.0 jedi==0.18.2 Jinja2==3.1.2 keras==2.9.0 Keras-Preprocessing==1.1.2 libclang==15.0.6.1 libcst==0.4.9 Markdown==3.4.1 MarkupSafe==2.1.1 matplotlib-inline==0.1.6 moreorless==0.4.0 multidict==6.0.4 mypy==1.0.1 mypy-extensions==0.4.3 nest-asyncio==1.5.6 ninja==1.11.1 nodeenv==1.7.0 numpy==1.22.0 nvidia-cublas-cu11==11.10.3.66 nvidia-cuda-nvrtc-cu11==11.7.99 nvidia-cuda-runtime-cu11==11.7.99 nvidia-cudnn-cu11==8.5.0.96 oauthlib==3.2.2 opt-einsum==3.3.0 packaging==22.0 pandas==1.5.3 parso==0.8.3 pathspec==0.11.0 pathtools==0.1.2 pexpect==4.8.0 pickleshare==0.7.5 platformdirs==3.0.0 pluggy==1.0.0 portalocker==2.6.0 portpicker==1.5.2 pre-commit==3.0.4 prompt-toolkit==3.0.36 protobuf==3.20.2 psutil==5.9.4 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==10.0.1 pyasn1==0.4.8 pyasn1-modules==0.2.8 pydantic==1.9.0 pyDeprecate==0.3.2 Pygments==2.14.0 pyparsing==3.0.9 pyre-extensions==0.0.27 pytest==7.2.1 pytest-mypy==0.10.3 python-dateutil==2.8.2 pytz==2022.6 PyYAML==6.0.0 requests==2.28.1 requests-oauthlib==1.3.1 rsa==4.9 scikit-build==0.16.3 sentry-sdk==1.16.0 setproctitle==1.3.2 six==1.16.0 smmap==5.0.0 sortedcontainers==2.4.0 stack-data==0.6.2 stdlibs==2022.10.9 tabulate==0.9.0 tensorboard==2.9.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.1 tensorflow==2.9.3 tensorflow-estimator==2.9.0 tensorflow-io-gcs-filesystem==0.30.0 termcolor==2.2.0 toml==0.10.2 tomli==2.0.1 torch==1.13.1 torchmetrics==0.11.0 torchrec==0.3.2 torchsnapshot==0.1.0 torchx==0.3.0 tqdm==4.64.1 trailrunner==1.2.1 traitlets==5.9.0 typing-inspect==0.8.0 typing_extensions==4.4.0 urllib3==1.26.13 usort==1.0.5 virtualenv==20.19.0 wandb==0.13.11 wcwidth==0.2.6 websocket-client==1.4.2 Werkzeug==2.2.3 wrapt==1.14.1 yarl==1.8.2 zipp==3.12.1 ================================================ FILE: machines/environment.py ================================================ import json import os from typing import List KF_DDS_PORT: int = 5050 SLURM_DDS_PORT: int = 5051 FLIGHT_SERVER_PORT: int = 2222 def on_kf(): return "SPEC_TYPE" in os.environ def has_readers(): if on_kf(): machines_config_env = json.loads(os.environ["MACHINES_CONFIG"]) return machines_config_env["dataset_worker"] is not None return os.environ.get("HAS_READERS", "False") == "True" def get_task_type(): if on_kf(): return os.environ["SPEC_TYPE"] return os.environ["TASK_TYPE"] def is_chief() -> bool: return get_task_type() == "chief" def is_reader() -> bool: return get_task_type() == "datasetworker" def is_dispatcher() -> bool: return get_task_type() == "datasetdispatcher" def get_task_index(): if on_kf(): pod_name = os.environ["MY_POD_NAME"] return int(pod_name.split("-")[-1]) else: raise NotImplementedError def get_reader_port(): if on_kf(): return KF_DDS_PORT return SLURM_DDS_PORT def get_dds(): if not has_readers(): return None dispatcher_address = get_dds_dispatcher_address() if dispatcher_address: return f"grpc://{dispatcher_address}" else: raise ValueError("Job does not have DDS.") def get_dds_dispatcher_address(): if not has_readers(): return None if on_kf(): job_name = os.environ["JOB_NAME"] dds_host = f"{job_name}-datasetdispatcher-0" else: dds_host = os.environ["SLURM_JOB_NODELIST_HET_GROUP_0"] return f"{dds_host}:{get_reader_port()}" def get_dds_worker_address(): if not has_readers(): return None if on_kf(): job_name = os.environ["JOB_NAME"] task_index = get_task_index() return f"{job_name}-datasetworker-{task_index}:{get_reader_port()}" else: node = os.environ["SLURMD_NODENAME"] return f"{node}:{get_reader_port()}" def get_num_readers(): if not has_readers(): return 0 if on_kf(): machines_config_env = json.loads(os.environ["MACHINES_CONFIG"]) return int(machines_config_env["num_dataset_workers"] or 0) return len(os.environ["SLURM_JOB_NODELIST_HET_GROUP_1"].split(",")) def get_flight_server_addresses(): if on_kf(): job_name = os.environ["JOB_NAME"] return [ f"grpc://{job_name}-datasetworker-{task_index}:{FLIGHT_SERVER_PORT}" for task_index in range(get_num_readers()) ] else: raise NotImplementedError def get_dds_journaling_dir(): return os.environ.get("DATASET_JOURNALING_DIR", None) ================================================ FILE: machines/get_env.py ================================================ import tml.machines.environment as env from absl import app, flags FLAGS = flags.FLAGS flags.DEFINE_string("property", None, "Which property of the current environment to fetch.") def main(argv): if FLAGS.property == "using_dds": print(f"{env.has_readers()}", flush=True) if FLAGS.property == "has_readers": print(f"{env.has_readers()}", flush=True) elif FLAGS.property == "get_task_type": print(f"{env.get_task_type()}", flush=True) elif FLAGS.property == "is_datasetworker": print(f"{env.is_reader()}", flush=True) elif FLAGS.property == "is_dds_dispatcher": print(f"{env.is_dispatcher()}", flush=True) elif FLAGS.property == "get_task_index": print(f"{env.get_task_index()}", flush=True) elif FLAGS.property == "get_dataset_service": print(f"{env.get_dds()}", flush=True) elif FLAGS.property == "get_dds_dispatcher_address": print(f"{env.get_dds_dispatcher_address()}", flush=True) elif FLAGS.property == "get_dds_worker_address": print(f"{env.get_dds_worker_address()}", flush=True) elif FLAGS.property == "get_dds_port": print(f"{env.get_reader_port()}", flush=True) elif FLAGS.property == "get_dds_journaling_dir": print(f"{env.get_dds_journaling_dir()}", flush=True) elif FLAGS.property == "should_start_dds": print(env.is_reader() or env.is_dispatcher(), flush=True) if __name__ == "__main__": app.run(main) ================================================ FILE: machines/is_venv.py ================================================ """This is intended to be run as a module. e.g. python -m tml.machines.is_venv Exits with 0 ii running in venv, otherwise 1. """ import sys import logging def is_venv(): # See https://stackoverflow.com/questions/1871549/determine-if-python-is-running-inside-virtualenv return sys.base_prefix != sys.prefix def _main(): if is_venv(): logging.info("In venv %s", sys.prefix) sys.exit(0) else: logging.error("Not in venv") sys.exit(1) if __name__ == "__main__": _main() ================================================ FILE: machines/list_ops.py ================================================ """ Simple str.split() parsing of input string usage example: python list_ops.py --input_list=$INPUT [--sep=","] [--op=] [--elem=$INDEX] Args: - input_list: input string - sep (default ","): separator string - elem (default 0): integer index - op (default "select"): either `len` or `select` - len: prints len(input_list.split(sep)) - select: prints input_list.split(sep)[elem] Typical usage would be in a bash script, e.g.: LIST_LEN=$(python list_ops.py --input_list=$INPUT --op=len) """ import tml.machines.environment as env from absl import app, flags FLAGS = flags.FLAGS flags.DEFINE_string("input_list", None, "string to parse as list") flags.DEFINE_integer("elem", 0, "which element to take") flags.DEFINE_string("sep", ",", "separator") flags.DEFINE_string("op", "select", "operation to do") def main(argv): split_list = FLAGS.input_list.split(FLAGS.sep) if FLAGS.op == "select": print(split_list[FLAGS.elem], flush=True) elif FLAGS.op == "len": print(len(split_list), flush=True) else: raise ValueError(f"operation {FLAGS.op} not recognized.") if __name__ == "__main__": app.run(main) ================================================ FILE: metrics/__init__.py ================================================ from .aggregation import StableMean # noqa from .auroc import AUROCWithMWU # noqa from .rce import NRCE, RCE # noqa ================================================ FILE: metrics/aggregation.py ================================================ """ Contains aggregation metrics. """ from typing import Tuple, Union import torch import torchmetrics def update_mean( current_mean: torch.Tensor, current_weight_sum: torch.Tensor, value: torch.Tensor, weight: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Update the mean according to Welford formula: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Weighted_batched_version. See also https://nullbuffer.com/articles/welford_algorithm.html for more information. Args: current_mean: The value of the current accumulated mean. current_weight_sum: The current weighted sum. value: The new value that needs to be added to get a new mean. weight: The weights for the new value. Returns: The updated mean and updated weighted sum. """ weight = torch.broadcast_to(weight, value.shape) # Avoiding (on purpose) in-place operation when using += in case # current_mean and current_weight_sum share the same storage current_weight_sum = current_weight_sum + torch.sum(weight) current_mean = current_mean + torch.sum((weight / current_weight_sum) * (value - current_mean)) return current_mean, current_weight_sum def stable_mean_dist_reduce_fn(state: torch.Tensor) -> torch.Tensor: """ Merge the state from multiple workers. Args: state: A tensor with the first dimension indicating workers. Returns: The accumulated mean from all workers. """ mean, weight_sum = update_mean( current_mean=torch.as_tensor(0.0, dtype=state.dtype, device=state.device), current_weight_sum=torch.as_tensor(0.0, dtype=state.dtype, device=state.device), value=state[:, 0], weight=state[:, 1], ) return torch.stack([mean, weight_sum]) class StableMean(torchmetrics.Metric): """ This implements a numerical stable mean metrics computation using Welford algorithm according to https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Weighted_batched_version. For example when using float32, the algorithm will give a valid output even if the "sum" is larger than the maximum float32 as far as the mean is within the limit of float32. See also https://nullbuffer.com/articles/welford_algorithm.html for more information. """ def __init__(self, **kwargs): """ Args: **kwargs: Additional parameters supported by all torchmetrics.Metric. """ super().__init__(**kwargs) self.add_state( "mean_and_weight_sum", default=torch.zeros(2), dist_reduce_fx=stable_mean_dist_reduce_fn, ) def update(self, value: torch.Tensor, weight: Union[float, torch.Tensor] = 1.0) -> None: """ Update the current mean. Args: value: Value to update the mean with. weight: weight to use. Shape should be broadcastable to that of value. """ mean, weight_sum = self.mean_and_weight_sum[0], self.mean_and_weight_sum[1] if not isinstance(weight, torch.Tensor): weight = torch.as_tensor(weight, dtype=value.dtype, device=value.device) self.mean_and_weight_sum[0], self.mean_and_weight_sum[1] = update_mean( mean, weight_sum, value, torch.as_tensor(weight) ) def compute(self) -> torch.Tensor: """ Compute and return the accumulated mean. """ return self.mean_and_weight_sum[0] ================================================ FILE: metrics/auroc.py ================================================ """ AUROC metrics. """ from typing import Union from tml.ml_logging.torch_logging import logging import torch import torchmetrics from torchmetrics.utilities.data import dim_zero_cat def _compute_helper( predictions: torch.Tensor, target: torch.Tensor, weights: torch.Tensor, max_positive_negative_weighted_sum: torch.Tensor, min_positive_negative_weighted_sum: torch.Tensor, equal_predictions_as_incorrect: bool, ) -> torch.Tensor: """ Compute AUROC. Args: predictions: The predictions probabilities. target: The target. weights: The sample weights to assign to each sample in the batch. max_positive_negative_weighted_sum: The sum of the weights for the positive labels. min_positive_negative_weighted_sum: equal_predictions_as_incorrect: For positive & negative labels having identical scores, we assume that they are correct prediction (i.e weight = 1) when ths is False. Otherwise, we assume that they are correct prediction (i.e weight = 0). """ dim = 0 # Sort predictions based on key (score, true_label). The order is ascending for score. # For true_label, order is ascending if equal_predictions_as_incorrect is True; # otherwise it is descending. target_order = torch.argsort(target, dim=dim, descending=equal_predictions_as_incorrect) score_order = torch.sort(torch.gather(predictions, dim, target_order), stable=True, dim=dim)[1] score_order = torch.gather(target_order, dim, score_order) sorted_target = torch.gather(target, dim, score_order) sorted_weights = torch.gather(weights, dim, score_order) negatives_from_left = torch.cumsum((1.0 - sorted_target) * sorted_weights, 0) numerator = torch.sum( sorted_weights * (sorted_target * negatives_from_left / max_positive_negative_weighted_sum) ) return numerator / min_positive_negative_weighted_sum class AUROCWithMWU(torchmetrics.Metric): """ AUROC using Mann-Whitney U-test. See https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve. This AUROC implementation is well suited to (non-zero) low-CTR. In particular it will return the correct AUROC even if the predicted probabilities are all close to 0. Currently only support binary classification. """ def __init__(self, label_threshold: float = 0.5, raise_missing_class: bool = False, **kwargs): """ Args: label_threshold: Labels strictly above this threshold are considered positive labels, otherwise, they are considered negative. raise_missing_class: If True, an error will be raise if negative or positive class is missing. Otherwise, we will simply log a warning. **kwargs: Additional parameters supported by all torchmetrics.Metric. """ super().__init__(**kwargs) self.add_state("predictions", default=[], dist_reduce_fx="cat") self.add_state("target", default=[], dist_reduce_fx="cat") self.add_state("weights", default=[], dist_reduce_fx="cat") self.label_threshold = label_threshold self.raise_missing_class = raise_missing_class def update( self, predictions: torch.Tensor, target: torch.Tensor, weight: Union[float, torch.Tensor] = 1.0, ) -> None: """ Update the current auroc. Args: predictions: Predicted values, 1D Tensor or 2D Tensor of shape batch_size x 1. target: Ground truth. Must have same shape as predictions. weight: The weight to use for the predicted values. Shape should be broadcastable to that of predictions. """ self.predictions.append(predictions) self.target.append(target) if not isinstance(weight, torch.Tensor): weight = torch.as_tensor(weight, dtype=predictions.dtype, device=target.device) self.weights.append(torch.broadcast_to(weight, predictions.size())) def compute(self) -> torch.Tensor: """ Compute and return the accumulated AUROC. """ weights = dim_zero_cat(self.weights) predictions = dim_zero_cat(self.predictions) target = dim_zero_cat(self.target).type_as(predictions) negative_mask = target <= self.label_threshold positive_mask = torch.logical_not(negative_mask) if not negative_mask.any(): msg = "Negative class missing. AUROC returned will be meaningless." if self.raise_missing_class: raise ValueError(msg) else: logging.warn(msg) if not positive_mask.any(): msg = "Positive class missing. AUROC returned will be meaningless." if self.raise_missing_class: raise ValueError(msg) else: logging.warn(msg) weighted_actual_negative_sum = torch.sum( torch.where(negative_mask, weights, torch.zeros_like(weights)) ) weighted_actual_positive_sum = torch.sum( torch.where(positive_mask, weights, torch.zeros_like(weights)) ) max_positive_negative_weighted_sum = torch.max( weighted_actual_negative_sum, weighted_actual_positive_sum ) min_positive_negative_weighted_sum = torch.min( weighted_actual_negative_sum, weighted_actual_positive_sum ) # Compute auroc with the weight set to 1 when positive & negative have identical scores. auroc_le = _compute_helper( target=target, weights=weights, predictions=predictions, min_positive_negative_weighted_sum=min_positive_negative_weighted_sum, max_positive_negative_weighted_sum=max_positive_negative_weighted_sum, equal_predictions_as_incorrect=False, ) # Compute auroc with the weight set to 0 when positive & negative have identical scores. auroc_lt = _compute_helper( target=target, weights=weights, predictions=predictions, min_positive_negative_weighted_sum=min_positive_negative_weighted_sum, max_positive_negative_weighted_sum=max_positive_negative_weighted_sum, equal_predictions_as_incorrect=True, ) # Compute auroc with the weight set to 1/2 when positive & negative have identical scores. return auroc_le - (auroc_le - auroc_lt) / 2.0 ================================================ FILE: metrics/rce.py ================================================ """ Contains RCE metrics. """ import copy from functools import partial from typing import Union from tml.metrics import aggregation import torch import torchmetrics def _smooth( value: torch.Tensor, label_smoothing: Union[float, torch.Tensor] ) -> Union[float, torch.Tensor]: """ Smooth given values. Args: value: Value to smooth. label_smoothing: smoothing constant. Returns: Smoothed values. """ return value * (1.0 - label_smoothing) + 0.5 * label_smoothing def _binary_cross_entropy_with_clipping( predictions: torch.Tensor, target: torch.Tensor, epsilon: Union[float, torch.Tensor], reduction: str = "none", ) -> torch.Tensor: """ Clip Predictions and apply binary cross entropy. This is done to match the implementation in keras at https://github.com/keras-team/keras/blob/r2.9/keras/backend.py#L5294-L5300 Args: predictions: Predicted probabilities. target: Ground truth. epsilon: Epsilon fuzz factor used to clip the predictions. reduction: The reduction method to use. Returns: Binary cross entropy on the clipped predictions. """ predictions = torch.clamp(predictions, epsilon, 1.0 - epsilon) bce = -target * torch.log(predictions + epsilon) bce -= (1.0 - target) * torch.log(1.0 - predictions + epsilon) if reduction == "mean": return torch.mean(bce) return bce class RCE(torchmetrics.Metric): """ Compute the relative cross entropy (`RCE `_). RCE is metric used for models predicting probability of success (p), i.e. pCTR. RCE represents the binary `cross entropy ` of the model compared to a reference straw man model. Binary cross entropy is defined as: y = label; p = prediction; binary cross entropy(example) = - y * log(p) - (1-y) * log(1-p) Where y in {0, 1} Cross entropy of a model is defined as: CE(model) = average(binary cross entropy(example)) Over all the examples we aggregate on. The straw man model is quite simple, it is a constant predictor, always predicting the average over the labels. RCE of a model is defined as: RCE(model) = 100 * (CE(reference model) - CE(model)) / CE(reference model) .. note:: Maximizing the likelihood is the same as minimizing the cross entropy or maximizing the RCE. Since cross entropy is the average minus likelihood for the binary case. .. note:: Binary cross entropy of an example is non negative, and equal to the `KL divergence <(https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence #Properties>` since p is constant, and its entropy is equal to zero. .. note:: 0% RCE means as good as the straw man model. 100% means always predicts exactly the label. Namely, cross entropy of the model is always zero. In practice 100% is impossible to achieve due to clipping. Negative RCE means that the model is doing worse than the straw man. This usually means an un-calibrated model, namely, the average prediction is "far" from the average label. Examining NRCE might help identifying if that is the case. .. note:: RCE is not a "ratio" in the statistical `level of measurement sense `. The higher the model's RCE is the harder it is to improve it by an extra point. For example: Let CE(model) = 0.5 CE(reference model), then the RCE(model) = 50. Now take a "twice as good" model: Let CE(better model) = 0.5 CE(model) = 0.25 CE(reference model), then the RCE(better model) = 75 and not 100. .. note:: In order to keep the log function stable, typically p is limited to lie in [CLAMP_EPSILON, 1-CLAMP_EPSILON], where CLAMP_EPSILON is some small constant like: 1e-7. Old implementation used 1e-5 clipping by default, current uses tf.keras.backend.epsilon() whose default is 1e-7. .. note:: Since the reference model prediction is constant (probability), CE(reference model) = H(average(label)) Where H is the standard `entropy ` function. .. note:: Must have at least 1 positive and 1 negative sample accumulated, or RCE will come out as NaN. """ def __init__( self, from_logits: bool = False, label_smoothing: float = 0, epsilon: float = 1e-7, **kwargs ): """ Args: from_logits: whether or not predictions are logits or probabilities. label_smoothing: label smoothing constant. epsilon: Epsilon fuzz factor used on the predictions probabilities when from_logits is False. **kwargs: Additional parameters supported by all torchmetrics.Metric. """ super().__init__(**kwargs) self.from_logits = from_logits self.label_smoothing = label_smoothing self.epsilon = epsilon self.kwargs = kwargs self.mean_label = aggregation.StableMean(**kwargs) self.binary_cross_entropy = aggregation.StableMean(**kwargs) if self.from_logits: self.bce_loss_fn = torch.nn.functional.binary_cross_entropy_with_logits else: self.bce_loss_fn = partial(_binary_cross_entropy_with_clipping, epsilon=self.epsilon) # Used to compute non-accumulated batch metric if `forward` or `__call__` functions are used. self.batch_metric = copy.deepcopy(self) def update( self, predictions: torch.Tensor, target: torch.Tensor, weight: float = 1.0 ) -> torch.Tensor: """ Update the current rce. Args: predictions: Predicted values. target: Ground truth. Should have same shape as predictions. weight: The weight to use for the predicted values. Shape should be broadcastable to that of predictions. """ target = _smooth(target, self.label_smoothing) self.mean_label.update(target, weight) self.binary_cross_entropy.update( self.bce_loss_fn(predictions, target, reduction="none"), weight ) def compute(self) -> torch.Tensor: """ Compute and return the accumulated rce. """ baseline_mean = self.mean_label.compute() baseline_ce = _binary_cross_entropy_with_clipping( baseline_mean, baseline_mean, reduction="mean", epsilon=self.epsilon ) pred_ce = self.binary_cross_entropy.compute() return (1.0 - (pred_ce / baseline_ce)) * 100 def reset(self): """ Reset the metric to its initial state. """ super().reset() self.mean_label.reset() self.binary_cross_entropy.reset() def forward(self, *args, **kwargs): """ Serves the dual purpose of both computing the metric on the current batch of inputs but also add the batch statistics to the overall accumulating metric state. Input arguments are the exact same as corresponding ``update`` method. The returned output is the exact same as the output of ``compute``. """ self.update(*args, **kwargs) self.batch_metric.update(*args, **kwargs) batch_result = self.batch_metric.compute() self.batch_metric.reset() return batch_result class NRCE(RCE): """ Calculate the RCE of the normalizes model. Where the normalized model prediction average is normalized to the average label seen so far. Namely, the the normalized model prediction: normalized model prediction(example) = (model prediction(example) * average(label)) / average(model prediction) Where the average is over all previously seen examples. .. note:: average(normalized model prediction) = average(label) .. note:: NRCE can be misleading since it is oblivious to mis-calibrations. The common interpretation of NRCE is to measure how good your model could potentially perform if it was well calibrated. .. note:: A big gap between NRCE and RCE might indicate a badly calibrated model, """ def __init__( self, from_logits: bool = False, label_smoothing: float = 0, epsilon: float = 1e-7, **kwargs ): """ Args: from_logits: whether or not predictions are logits or probabilities. label_smoothing: label smoothing constant. epsilon: Epsilon fuzz factor used on the predictions probabilities when from_logits is False. It only used when computing the cross entropy but not when normalizing. **kwargs: Additional parameters supported by all torchmetrics.Metric. """ super().__init__(from_logits=False, label_smoothing=0, epsilon=epsilon, **kwargs) self.nrce_from_logits = from_logits self.nrce_label_smoothing = label_smoothing self.mean_prediction = aggregation.StableMean() # Used to compute non-accumulated batch metric if `forward` or `__call__` functions are used. self.batch_metric = copy.deepcopy(self) def update( self, predictions: torch.Tensor, target: torch.Tensor, weight: Union[float, torch.Tensor] = 1.0, ): """ Update the current nrce. Args: predictions: Predicted values. target: Ground truth. Should have same shape as predictions. weight: The weight to use for the predicted values. Shape should be broadcastable to that of predictions. """ predictions = torch.sigmoid(predictions) if self.nrce_from_logits else predictions target = _smooth(target, self.nrce_label_smoothing) self.mean_label.update(target, weight) self.mean_prediction.update(predictions, weight) normalizer = self.mean_label.compute() / self.mean_prediction.compute() predictions = predictions * normalizer self.binary_cross_entropy.update( self.bce_loss_fn(predictions, target, reduction="none"), weight ) def reset(self): """ Reset the metric to its initial state. """ super().reset() self.mean_prediction.reset() ================================================ FILE: ml_logging/__init__.py ================================================ ================================================ FILE: ml_logging/absl_logging.py ================================================ """Sets up logging through absl for training usage. - Redirects logging to sys.stdout so that severity levels in GCP Stackdriver are accurate. Usage: >>> from twitter.ml.logging.absl_logging import logging >>> logging.info(f"Properly logged as INFO level in GCP Stackdriver.") """ import logging as py_logging import sys from absl import logging as logging def setup_absl_logging(): """Make sure that absl logging pushes to stdout rather than stderr.""" logging.get_absl_handler().python_handler.stream = sys.stdout formatter = py_logging.Formatter( fmt="[%(module)s.%(funcName)s:%(lineno)s - %(levelname)s] %(message)s" ) logging.get_absl_handler().setFormatter(formatter) logging.set_verbosity(logging.INFO) setup_absl_logging() ================================================ FILE: ml_logging/test_torch_logging.py ================================================ import unittest from tml.ml_logging.torch_logging import logging class Testtlogging(unittest.TestCase): def test_warn_once(self): with self.assertLogs(level="INFO") as captured_logs: logging.info("first info") logging.warning("first warning") logging.warning("first warning") logging.info("second info") self.assertEqual( captured_logs.output, [ "INFO:absl:first info", "WARNING:absl:first warning", "INFO:absl:second info", ], ) ================================================ FILE: ml_logging/torch_logging.py ================================================ """Overrides absl logger to be rank-aware for distributed pytorch usage. >>> # in-bazel import >>> from twitter.ml.logging.torch_logging import logging >>> # out-bazel import >>> from ml.logging.torch_logging import logging >>> logging.info(f"This only prints on rank 0 if distributed, otherwise prints normally.") >>> logging.info(f"This prints on all ranks if distributed, otherwise prints normally.", rank=-1) """ import functools from typing import Optional from tml.ml_logging.absl_logging import logging as logging from absl import logging as absl_logging import torch.distributed as dist def rank_specific(logger): """Ensures that we only override a given logger once.""" if hasattr(logger, "_ALREADY_OVERWRITTEN_TO_BE_RANK_SPECIFIC"): return logger def _if_rank(logger_method, limit: Optional[int] = None): if limit: # If we are limiting redundant logs, wrap logging call with a cache # to not execute if already cached. def _wrap(_call): @functools.lru_cache(limit) def _logger_method(*args, **kwargs): _call(*args, **kwargs) return _logger_method logger_method = _wrap(logger_method) def _inner(msg, *args, rank: int = 0, **kwargs): if not dist.is_initialized(): logger_method(msg, *args, **kwargs) elif dist.get_rank() == rank: logger_method(msg, *args, **kwargs) elif rank < 0: logger_method(f"Rank{dist.get_rank()}: {msg}", *args, **kwargs) # Register this stack frame with absl logging so that it doesn't trample logging lines. absl_logging.ABSLLogger.register_frame_to_skip(__file__, _inner.__name__) return _inner logger.fatal = _if_rank(logger.fatal) logger.error = _if_rank(logger.error) logger.warning = _if_rank(logger.warning, limit=1) logger.info = _if_rank(logger.info) logger.debug = _if_rank(logger.debug) logger.exception = _if_rank(logger.exception) logger._ALREADY_OVERWRITTEN_TO_BE_RANK_SPECIFIC = True rank_specific(logging) ================================================ FILE: model.py ================================================ """Wraps servable model in loss and RecapBatch passing to be trainable.""" # flake8: noqa from typing import Callable from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined] import torch import torch.distributed as dist from torchrec.distributed.model_parallel import DistributedModelParallel class ModelAndLoss(torch.nn.Module): # Reconsider our approach at a later date: https://ppwwyyxx.com/blog/2022/Loss-Function-Separation/ def __init__( self, model, loss_fn: Callable, ) -> None: """ Args: model: torch module to wrap. loss_fn: Function for calculating loss, should accept logits and labels. """ super().__init__() self.model = model self.loss_fn = loss_fn def forward(self, batch: "RecapBatch"): # type: ignore[name-defined] """Runs model forward and calculates loss according to given loss_fn. NOTE: The input signature here needs to be a Pipelineable object for prefetching purposes during training using torchrec's pipeline. However the underlying model signature needs to be exportable to onnx, requiring generic python types. see https://pytorch.org/docs/stable/onnx.html#types. """ outputs = self.model(batch) losses = self.loss_fn(outputs["logits"], batch.labels.float(), batch.weights.float()) outputs.update( { "loss": losses, "labels": batch.labels, "weights": batch.weights, } ) # Allow multiple losses. return losses, outputs def maybe_shard_model( model, device: torch.device, ): """Set up and apply DistributedModelParallel to a model if running in a distributed environment. If in a distributed environment, constructs Topology, sharders, and ShardingPlan, then applies DistributedModelParallel. If not in a distributed environment, returns model directly. """ if dist.is_initialized(): logging.info("***** Wrapping in DistributedModelParallel *****") logging.info(f"Model before wrapping: {model}") model = DistributedModelParallel( module=model, device=device, ) logging.info(f"Model after wrapping: {model}") return model def log_sharded_tensor_content(weight_name: str, table_name: str, weight_tensor) -> None: """Handy function to log the content of EBC embedding layer. Only works for single GPU machines. Args: weight_name: name of tensor, as defined in model table_name: name of the EBC table the weight is taken from weight_tensor: embedding weight tensor """ logging.info(f"{weight_name}, {table_name}", rank=-1) logging.info(f"{weight_tensor.metadata()}", rank=-1) output_tensor = torch.zeros(*weight_tensor.size(), device=torch.device("cuda:0")) weight_tensor.gather(out=output_tensor) logging.info(f"{output_tensor}", rank=-1) ================================================ FILE: optimizers/__init__.py ================================================ from tml.optimizers.optimizer import compute_lr ================================================ FILE: optimizers/config.py ================================================ """Optimization configurations for models.""" import typing import tml.core.config as base_config import pydantic class PiecewiseConstant(base_config.BaseConfig): learning_rate_boundaries: typing.List[int] = pydantic.Field(None) learning_rate_values: typing.List[float] = pydantic.Field(None) class LinearRampToConstant(base_config.BaseConfig): learning_rate: float num_ramp_steps: pydantic.PositiveInt = pydantic.Field( description="Number of steps to ramp this up from zero." ) class LinearRampToCosine(base_config.BaseConfig): learning_rate: float final_learning_rate: float num_ramp_steps: pydantic.PositiveInt = pydantic.Field( description="Number of steps to ramp this up from zero." ) final_num_steps: pydantic.PositiveInt = pydantic.Field( description="Final number of steps where decay stops." ) class LearningRate(base_config.BaseConfig): constant: float = pydantic.Field(None, one_of="lr") linear_ramp_to_cosine: LinearRampToCosine = pydantic.Field(None, one_of="lr") linear_ramp_to_constant: LinearRampToConstant = pydantic.Field(None, one_of="lr") piecewise_constant: PiecewiseConstant = pydantic.Field(None, one_of="lr") class OptimizerAlgorithmConfig(base_config.BaseConfig): """Base class for optimizer configurations.""" lr: float ... class AdamConfig(OptimizerAlgorithmConfig): # see https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam lr: float betas: typing.Tuple[float, float] = [0.9, 0.999] eps: float = 1e-7 # Numerical stability in denominator. class SgdConfig(OptimizerAlgorithmConfig): lr: float momentum: float = 0.0 class AdagradConfig(OptimizerAlgorithmConfig): lr: float eps: float = 0 class OptimizerConfig(base_config.BaseConfig): learning_rate: LearningRate = pydantic.Field( None, description="Constant learning rates", ) adam: AdamConfig = pydantic.Field(None, one_of="optimizer") sgd: SgdConfig = pydantic.Field(None, one_of="optimizer") adagrad: AdagradConfig = pydantic.Field(None, one_of="optimizer") def get_optimizer_algorithm_config(optimizer_config: OptimizerConfig): if optimizer_config.adam is not None: return optimizer_config.adam elif optimizer_config.sgd is not None: return optimizer_config.sgd elif optimizer_config.adagrad is not None: return optimizer_config.adagrad else: raise ValueError(f"No optimizer selected in optimizer_config, passed {optimizer_config}") ================================================ FILE: optimizers/optimizer.py ================================================ from typing import Dict, Tuple import math import bisect from tml.optimizers.config import ( LearningRate, OptimizerConfig, ) import torch from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from tml.ml_logging.torch_logging import logging def compute_lr(lr_config, step): """Compute a learning rate.""" if lr_config.constant is not None: return lr_config.constant elif lr_config.piecewise_constant is not None: return lr_config.piecewise_constant.learning_rate_values[ bisect.bisect_right(lr_config.piecewise_constant.learning_rate_boundaries, step) ] elif lr_config.linear_ramp_to_constant is not None: slope = ( lr_config.linear_ramp_to_constant.learning_rate / lr_config.linear_ramp_to_constant.num_ramp_steps ) return min(lr_config.linear_ramp_to_constant.learning_rate, slope * step) elif lr_config.linear_ramp_to_cosine is not None: cfg = lr_config.linear_ramp_to_cosine if step < cfg.num_ramp_steps: slope = cfg.learning_rate / cfg.num_ramp_steps return slope * step elif step <= cfg.final_num_steps: return cfg.final_learning_rate + (cfg.learning_rate - cfg.final_learning_rate) * 0.5 * ( 1.0 + math.cos( math.pi * (step - cfg.num_ramp_steps) / (cfg.final_num_steps - cfg.num_ramp_steps) ) ) else: return cfg.final_learning_rate else: raise ValueError(f"No option selected in lr_config, passed {lr_config}") class LRShim(_LRScheduler): """Shim to get learning rates into a LRScheduler. This adheres to the torch.optim scheduler API and can be plugged anywhere that e.g. exponential decay can be used. """ def __init__( self, optimizer, lr_dict: Dict[str, LearningRate], last_epoch=-1, verbose=False, ): self.optimizer = optimizer self.lr_dict = lr_dict self.group_names = list(self.lr_dict.keys()) num_param_groups = sum(1 for _, _optim in optimizer._optims for _ in _optim.param_groups) if num_param_groups != len(lr_dict): raise ValueError( f"Optimizer had {len(optimizer.param_groups)}, but config had {len(lr_dict)}." ) super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: logging.warn( "To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning, ) return self._get_closed_form_lr() def _get_closed_form_lr(self): return [compute_lr(lr_config, self.last_epoch) for lr_config in self.lr_dict.values()] def get_optimizer_class(optimizer_config: OptimizerConfig): if optimizer_config.adam is not None: return torch.optim.Adam elif optimizer_config.sgd is not None: return torch.optim.SGD elif optimizer_config.adagrad is not None: return torch.optim.Adagrad def build_optimizer( model: torch.nn.Module, optimizer_config: OptimizerConfig ) -> Tuple[Optimizer, _LRScheduler]: """Builds an optimizer and LR scheduler from an OptimizerConfig. Note: use this when you want the same optimizer and learning rate schedule for all your parameters. """ optimizer_class = get_optimizer_class(optimizer_config) optimizer = optimizer_class(model.parameters(), **optimizer_config.sgd.dict()) # We're passing everything in as one group here scheduler = LRShim(optimizer, lr_dict={"ALL_PARAMS": optimizer_config.learning_rate}) return optimizer, scheduler ================================================ FILE: projects/__init__.py ================================================ ================================================ FILE: projects/home/recap/FEATURES.md ================================================ # Overview Below is a description of the major feature groups which are input to the Twitter Heavy Ranking model. Note that not every request will have every feature available due to user settings or other constraints and there may be some differences in ranking "For You" based on different variables. ## Aggregate Features Twitter's aggregate features comprise the bulk of Twitter's feature count and are generated by maintaining rolling aggregations of feature values within a specific scope within a specific time window. We compute aggregates over the long-term (50 days count) and short-term ("real-time" - under 3 days count and typically 30 mins count).
Show Details Aggregate features are groups of multiple features generated as Cartesian crosses from a template and have the format
Feature Group Name Engagement Scope Feature To Aggregate Aggregation Spec
  • The Feature Group Name is both the name of the aggregate feature and contains internally the aggregation scope, that is, what entities are aggregated over.
    • For example, "user_aggregate" aggregates over unique user_ids, and "user_author_aggregate" aggregates over all user-author pairs. It also determines what fields the feature is joined to when being used. In the case of "user_author_aggregate", the feature is joined to data corresponding to the specific user and the specific author.
    • The raw feature group names are often verbose and are simplified in the below presentation.
  • Engagement Scope is the subset of tweets within the aggregation scope that will be aggregated over. Typically this is the name of an output engagement, like recap.engagement.is_favorited. In that case, we only aggregate over Tweets which are also Liked.
  • The Feature To Aggregate is the feature we are accumulating over. If this value is any_feature, that means we aggregate the Tweet count. For example user_aggregate_v2.pair.recap.engagement.is_favorited.any_feature.50.days.count will be the number of Liked records for every user over the last 50 days.
  • The Aggregation Spec is what aggregate to compute - what function and over what time window.
For every Feature Group, we generate one feature for every possible combination of Engagement Scope, Feature To Aggregate, and Aggregation Spec. In particular, every row in the below tables generate one feature for every possible cross between columns. Example: For example, one such feature may be user_aggregate_v2.pair.recap.engagement.is_favorited.engagement_features.in_network.replies.count.50.days.count, which can be parsed into
Feature Group Name Engagement Scope Feature To Aggregate Aggregation Spec
user_aggregate_v2.pair recap.engagement.is_favorited engagement_features.in_network.replies.count 50.days.count
This means that this feature aggregates
  1. (Over every user),
  2. (Over only tweets favorited by the user),
  3. In network replies sent out by this user,
  4. (Counted over the last 50 days)
This feature is then made available as a feature for the particular user.
The list of our aggregate features are below:
author_aggregate These features aggregate over the author (or original author) of a tweet. Some of the features are short-duration (30 minutes) and some longer (50 days). The features track how many of an author's tweets were engaged with.
author (real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed
any_feature
30.minutes.count
original_author (real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed
any_feature
30.minutes.count
original_author (real_time) timelines.engagement.is_share_menu_clicked
timelines.engagement.is_shared
any_feature
30.minutes.count
1.days.count
original_author recap.engagement.is_replied_reply_favorited_by_author
recap.engagement.is_replied_reply_impressed_by_author
recap.engagement.is_replied_reply_replied_by_author
any_feature
50.days.count
author-topic_aggregate These features aggregate over a specific tweet author and a specific topic. We only accumulate long (50 day) counts.
author-topic any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count
list_aggregate These features aggregate short term and long term engagement between a user and a list.
user_list any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count
list (real_time) timelines.engagement.is_block_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_mute_clicked
timelines.engagement.is_replied
timelines.engagement.is_report_tweet_clicked
timelines.engagement.is_retweeted
any_feature
30.minutes.count
user_aggregate These features aggregate short term and long term engagement from a specific user.
user_v2 any_label
recap.engagement.is_favorited
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
any_feature
engagement_features.in_network.favorites.count
engagement_features.in_network.replies.count
engagement_features.in_network.retweets.count
realgraph.num_favorites.days_since_last
realgraph.num_favorites.elapsed_days
realgraph.num_favorites.ewma
realgraph.num_favorites.non_zero_days
realgraph.num_inspected_tweets.days_since_last
realgraph.num_inspected_tweets.elapsed_days
realgraph.num_inspected_tweets.ewma
realgraph.num_inspected_tweets.non_zero_days
realgraph.num_mentions.days_since_last
realgraph.num_mentions.elapsed_days
realgraph.num_mentions.ewma
realgraph.num_mentions.non_zero_days
realgraph.num_profile_views.days_since_last
realgraph.num_profile_views.elapsed_days
realgraph.num_profile_views.ewma
realgraph.num_profile_views.non_zero_days
realgraph.num_retweets.days_since_last
realgraph.num_retweets.elapsed_days
realgraph.num_retweets.ewma
realgraph.num_retweets.non_zero_days
realgraph.num_tweet_clicks.days_since_last
realgraph.num_tweet_clicks.elapsed_days
realgraph.num_tweet_clicks.ewma
realgraph.num_tweet_clicks.non_zero_days
realgraph.total_dwell_time.days_since_last
realgraph.total_dwell_time.elapsed_days
realgraph.total_dwell_time.ewma
realgraph.total_dwell_time.non_zero_days
recap.earlybird.fav_count_v2
recap.earlybird.reply_count_v2
recap.earlybird.retweet_count_v2
recap.searchfeature.blender_score
recap.searchfeature.fav_count
recap.searchfeature.reply_count
recap.searchfeature.retweet_count
recap.searchfeature.text_score
recap.tweetfeature.bidirectional_fav_count
recap.tweetfeature.bidirectional_reply_count
recap.tweetfeature.bidirectional_retweet_count
recap.tweetfeature.contains_media
recap.tweetfeature.conversational_count
recap.tweetfeature.embeds_impression_count
recap.tweetfeature.embeds_url_count
recap.tweetfeature.from_mutual_follow
recap.tweetfeature.has_card
recap.tweetfeature.has_image
recap.tweetfeature.has_link
recap.tweetfeature.has_multiple_media
recap.tweetfeature.has_news
recap.tweetfeature.has_periscope
recap.tweetfeature.has_pro_video
recap.tweetfeature.has_trend
recap.tweetfeature.has_video
recap.tweetfeature.has_vine
recap.tweetfeature.has_visible_link
recap.tweetfeature.is_business_score
recap.tweetfeature.is_extended_reply
recap.tweetfeature.is_reply
recap.tweetfeature.is_retweet
recap.tweetfeature.is_sensitive
recap.tweetfeature.link_count
recap.tweetfeature.link_language
recap.tweetfeature.match_searcher_langs
recap.tweetfeature.match_searcher_main_lang
recap.tweetfeature.match_ui_lang
recap.tweetfeature.mention_searcher
recap.tweetfeature.num_hashtags
recap.tweetfeature.num_mentions
recap.tweetfeature.reply_other
recap.tweetfeature.reply_searcher
recap.tweetfeature.retweet_other
recap.tweetfeature.retweet_searcher
recap.tweetfeature.tweet_count_from_user_in_snapshot
recap.tweetfeature.unidirectiona_fav_count
recap.tweetfeature.unidirectional_reply_count
recap.tweetfeature.unidirectional_retweet_count
recap.tweetfeature.user_rep
recap.tweetfeature.video_view_count
50.days.count
50.days.sum
user_v5 any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
 		any_feature
time_features.earlybird.last_favorite_since_creation_hrs
time_features.earlybird.last_quote_since_creation_hrs
time_features.earlybird.last_reply_since_creation_hrs
time_features.earlybird.last_retweet_since_creation_hrs
time_features.earlybird.time_since_last_favorite
time_features.earlybird.time_since_last_quote
time_features.earlybird.time_since_last_reply
time_features.earlybird.time_since_last_retweet
timelines.earlybird.decayed_favorite_count
timelines.earlybird.decayed_quote_count
timelines.earlybird.decayed_reply_count
timelines.earlybird.decayed_retweet_count
timelines.earlybird.embeds_impression_count_v2
timelines.earlybird.embeds_url_count_v2
timelines.earlybird.fake_favorite_count
timelines.earlybird.fake_quote_count
timelines.earlybird.fake_reply_count
timelines.earlybird.fake_retweet_count
timelines.earlybird.quote_count
timelines.earlybird.visible_token_ratio
timelines.earlybird.weighted_fav_count
timelines.earlybird.weighted_quote_count
timelines.earlybird.weighted_reply_count
timelines.earlybird.weighted_retweet_count
 		50.days.count
50.days.sum
50.days.sumsq
user_v6 recap.engagement.is_replied_reply_favorited_by_author
recap.engagement.is_replied_reply_impressed_by_author
recap.engagement.is_replied_reply_replied_by_author
 		any_feature
50.days.count
user (twitter_wide) any_label
recap.engagement.is_favorited
recap.engagement.is_replied
recap.engagement.is_retweeted
 		any_feature
recap.tweetfeature.contains_media
recap.tweetfeature.has_card
recap.tweetfeature.has_hashtag
recap.tweetfeature.has_link
recap.tweetfeature.has_mention
recap.tweetfeature.is_reply
timelines.earlybird.has_quote
 		50.days.count
user (real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed
 		any_feature
client_log_event.tweet.has_consumer_video
client_log_event.tweet.photo_count
 		30.minutes.count
user (48h_real_time_v5) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed
 		any_feature
client_log_event.tweet.has_consumer_video
client_log_event.tweet.photo_count
 		2.days.count
user (72h_real_time_v6) timelines.engagement.is_block_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_mute_clicked
timelines.engagement.is_report_tweet_clicked
 		timelines.author.user_state.is_user_heavy_non_tweeter
timelines.author.user_state.is_user_heavy_tweeter
timelines.author.user_state.is_user_light
timelines.author.user_state.is_user_medium_non_tweeter
timelines.author.user_state.is_user_medium_tweeter
timelines.author.user_state.is_user_new
 		3.days.count
user (profile_real_time_v6) profile.engagement.is_clicked
profile.engagement.is_dwelled
profile.engagement.is_favorited
profile.engagement.is_replied
profile.engagement.is_retweeted
 		any_feature
client_log_event.tweet.has_consumer_video
client_log_event.tweet.photo_count
 		30.minutes.count
user (real_time) timelines.engagement.is_share_menu_clicked
timelines.engagement.is_shared
any_feature
client_log_event.tweet.has_consumer_video
client_log_event.tweet.photo_count
 		1.days.count
30.minutes.count
user (real_time) timelines.engagement.is_fullscreen_video_dwelled
timelines.engagement.is_fullscreen_video_dwelled_10_sec
timelines.engagement.is_fullscreen_video_dwelled_20_sec
timelines.engagement.is_fullscreen_video_dwelled_30_sec
timelines.engagement.is_fullscreen_video_dwelled_5_sec
timelines.engagement.is_profile_dwelled
timelines.engagement.is_profile_dwelled_10_sec
timelines.engagement.is_profile_dwelled_20_sec
timelines.engagement.is_profile_dwelled_30_sec
timelines.engagement.is_tweet_detail_dwelled
timelines.engagement.is_tweet_detail_dwelled_15_sec
timelines.engagement.is_tweet_detail_dwelled_25_sec
timelines.engagement.is_tweet_detail_dwelled_30_sec
timelines.engagement.is_tweet_detail_dwelled_8_sec
 		any_feature
1.days.count
30.minutes.count
user_author_aggregate These features aggregate over user-author pairs.
user_author_v2 any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
 		engagement_features.in_network.favorites.count
engagement_features.in_network.replies.count
engagement_features.in_network.retweets.count
recap.earlybird.fav_count_v2
recap.earlybird.reply_count_v2
recap.earlybird.retweet_count_v2
recap.searchfeature.blender_score
recap.searchfeature.fav_count
recap.searchfeature.reply_count
recap.searchfeature.retweet_count
recap.searchfeature.text_score
recap.tweetfeature.embeds_impression_count
recap.tweetfeature.embeds_url_count
recap.tweetfeature.has_card
recap.tweetfeature.has_image
recap.tweetfeature.has_link
recap.tweetfeature.has_multiple_media
recap.tweetfeature.has_news
recap.tweetfeature.has_periscope
recap.tweetfeature.has_pro_video
recap.tweetfeature.has_trend
recap.tweetfeature.has_video
recap.tweetfeature.has_vine
recap.tweetfeature.has_visible_link
recap.tweetfeature.is_reply
recap.tweetfeature.is_retweet
recap.tweetfeature.num_mentions
 		50.days.count
50.days.sum
user_author_v5 any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
 		any_feature
timelines.earlybird.has_quote
timelines.earlybird.label_abusive_flag
timelines.earlybird.label_abusive_hi_rcl_flag
timelines.earlybird.label_dup_content_flag
timelines.earlybird.label_nsfw_hi_prc_flag
timelines.earlybird.label_nsfw_hi_rcl_flag
timelines.earlybird.label_spam_flag
timelines.earlybird.label_spam_hi_rcl_flag
 		50.days.count
user_author (tweetsource_v1 -
These features are sourced from a different underlying dataset) 		any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
 		any_feature
tweetsource.tweet.media.num_tags
tweetsource.tweet.media.video_duration
tweetsource.tweet.text.has_question
tweetsource.tweet.text.length
 		50.days.count
50.days.sum
user_author (twitter_wide -
These features are sourced from a different underlying dataset) 		recap.engagement.is_favorited
recap.engagement.is_replied
recap.engagement.is_retweeted
 		any_feature
recap.tweetfeature.contains_media
recap.tweetfeature.has_card
recap.tweetfeature.has_hashtag
recap.tweetfeature.has_link
recap.tweetfeature.has_mention
recap.tweetfeature.is_reply
timelines.earlybird.has_quote
 		50.days.count
user_original_author (real_time) timelines.engagement.is_shared
 		any_feature
 		1.days.count
30.minutes.count
user_original_author recap.engagement.is_replied_reply_favorited_by_author
recap.engagement.is_replied_reply_impressed_by_author
recap.engagement.is_replied_reply_replied_by_author
 		any_feature
50.days.count
user_author (real_time, shared) timelines.engagement.is_clicked
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_negative_feedback_union
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_share_menu_clicked
timelines.engagement.is_video_playback_50 		any_feature 1.days.count
30.minutes.count
user_engager_aggregate These features aggregate counts of user interaction with other engagers of tweets that the user interacts with. For example, the user_engager.recap.engagement.is_favorited.any_feature.50.days.count.sparse_top1 feature can be parsed as follows: For all tweets that a user Likes, accumulate a running count over 50 days where the number of engagement events for every other user who has engaged with the Tweet is accumulated. Engagement is defined as Like or reply. We now have a list of engagement counts for other users that have engaged with the Tweets that the user has Liked, and we take the top count as the feature value.
user_engager
any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count.sparse_mean
50.days.count.sparse_nonzero
50.days.count.sparse_sum
50.days.count.sparse_top1
50.days.count.sparse_top2
user_inferred_topic_aggregate These features aggregate short term and long term engagement between a user and tweets from our internally predicted inferred topic (whether or not the tweet is actually tagged to that topic).
user_inferred_topic_v1 any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50 		any_feature
50.days.count.sparse_mean
50.days.count.sparse_nonzero
50.days.count.sparse_sum
50.days.count.sparse_top1
50.days.count.sparse_top2
user_inferred_topic_v2 recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
engagement_features.in_network.favorites.count
engagement_features.in_network.retweets.count
recap.searchfeature.fav_count
recap.tweetfeature.contains_media
recap.tweetfeature.has_card
recap.tweetfeature.has_image
recap.tweetfeature.has_link
recap.tweetfeature.has_news
recap.tweetfeature.has_trend
recap.tweetfeature.has_video
recap.tweetfeature.is_reply
recap.tweetfeature.is_retweet
recap.tweetfeature.is_sensitive
recap.tweetfeature.match_searcher_langs
recap.tweetfeature.match_searcher_main_lang
recap.tweetfeature.match_ui_lang
recap.tweetfeature.mention_searcher
recap.tweetfeature.reply_other
recap.tweetfeature.reply_searcher
recap.tweetfeature.retweet_other
recap.tweetfeature.retweet_searcher
tweetsource.tweet.media.aspect_ratio_den
tweetsource.tweet.text.num_caps
tweetsource.tweet.text.num_newlines
tweetsource.v2.tweet.media.has_description
tweetsource.v2.tweet.media.has_selected_preview_image
tweetsource.v2.tweet.media.has_title
tweetsource.v2.tweet.media.has_visit_site_call_to_action
tweetsource.v2.tweet.media.has_watch_now_call_to_action
tweetsource.v2.tweet.media.is_360
tweetsource.v2.tweet.media.is_managed
tweetsource.v2.tweet.media.is_monetizable
50.days.count.sparse_mean
50.days.count.sparse_nonzero
50.days.count.sparse_sum
50.days.count.sparse_top1
50.days.count.sparse_top2
user_media_annotation_aggregate These features aggregate how often a user interacts with different types of media (photo, video, etc)
user_media_annotation (keyed by user and media type) any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature 50.days.count.sparse_mean
50.days.count.sparse_nonzero
50.days.count.sparse_sum
50.days.count.sparse_top1
50.days.count.sparse_top2
user_mention_aggregate These features aggregate counts of user interactions with Tweets that mention other users. Let the original user who viewed a Tweet be user1, and let user2, user3, ..., user_n be users mentioned in a tweet. This feature group aggregates the interactions between user1 and other Tweets that mention user2, user3,..., user_n. Here sparse_sum means we sum the aggregate values over all mentioned users, sparse_top1 means we take the max of the aggregate values for the mentioned authors, sparse_top1 means we take the second-highest of the aggregate values for the mentioned authors, and so on.
user_mention
any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50 any_feature.50.days.count
any_feature
50.days.count.sparse_mean
50.days.count.sparse_nonzero
50.days.count.sparse_sum
50.days.count.sparse_top1
50.days.count.sparse_top2
user_request_context_aggregate These features aggregate engagements over the request context, which is either the same day of week (dow) or hour of day (hour), to account for temporal effects.
dow
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count
hour
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count
user_topic_aggregate These features aggregate long term feature values between a user and tweets from a particular topic.
user_topic_v1 any_label
recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
any_feature
50.days.count
user_topic_v2 recap.engagement.is_clicked
recap.engagement.is_favorited
recap.engagement.is_open_linked
recap.engagement.is_photo_expanded
recap.engagement.is_profile_clicked
recap.engagement.is_replied
recap.engagement.is_retweeted
recap.engagement.is_video_playback_50
engagement_features.in_network.favorites.count
engagement_features.in_network.retweets.count
recap.searchfeature.fav_count
recap.tweetfeature.contains_media
recap.tweetfeature.has_card
recap.tweetfeature.has_image
recap.tweetfeature.has_link
recap.tweetfeature.has_news
recap.tweetfeature.has_trend
recap.tweetfeature.has_video
recap.tweetfeature.is_reply
recap.tweetfeature.is_retweet
recap.tweetfeature.is_sensitive
recap.tweetfeature.match_searcher_langs
recap.tweetfeature.match_searcher_main_lang
recap.tweetfeature.match_ui_lang
recap.tweetfeature.mention_searcher
recap.tweetfeature.reply_other
recap.tweetfeature.reply_searcher
recap.tweetfeature.retweet_other
recap.tweetfeature.retweet_searcher
tweetsource.tweet.media.aspect_ratio_den
tweetsource.tweet.text.num_caps
tweetsource.tweet.text.num_newlines
tweetsource.v2.tweet.media.has_description
tweetsource.v2.tweet.media.has_selected_preview_image
tweetsource.v2.tweet.media.has_title
tweetsource.v2.tweet.media.has_visit_site_call_to_action
tweetsource.v2.tweet.media.has_watch_now_call_to_action
tweetsource.v2.tweet.media.is_360
tweetsource.v2.tweet.media.is_managed
tweetsource.v2.tweet.media.is_monetizable
50.days.count
topic_aggregate These features aggregate values for tweets that come from a particular topic.
topic (real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_not_interested_in_topic
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed
any_feature
30.minutes.count
topic (24_hour_real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_block_clicked
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_mute_clicked
timelines.engagement.is_not_about_topic
timelines.engagement.is_not_interested_in_topic
timelines.engagement.is_not_recent
timelines.engagement.is_not_relevant
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_report_tweet_clicked
timelines.engagement.is_retweeted
timelines.engagement.is_see_fewer
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_unfollow_topic
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed 		any_feature 1.days.count
topic-country_code (real_time) timelines.engagement.is_block_clicked
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_impressed
timelines.engagement.is_mute_clicked
timelines.engagement.is_not_about_topic
timelines.engagement.is_not_interested_in_topic
timelines.engagement.is_not_recent
timelines.engagement.is_not_relevant
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_replied
timelines.engagement.is_report_tweet_clicked
timelines.engagement.is_retweeted
timelines.engagement.is_see_fewer
timelines.engagement.is_share_menu_clicked
timelines.engagement.is_shared
timelines.engagement.is_unfollow_topic
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed 		any_feature 3.days.count
30.minutes.count
topic-share (real_time) timelines.engagement.is_share_menu_clicked
timelines.engagement.is_shared 		any_feature 1.days.count
30.minutes.count
tweet_aggregate These features aggregate values corresponding to a tweet.
tweet (real_time) timelines.enagagement.is_retweeted_without_quote
timelines.engagement.is_clicked
timelines.engagement.is_dont_like
timelines.engagement.is_dwelled
timelines.engagement.is_favorited
timelines.engagement.is_followed
timelines.engagement.is_open_linked
timelines.engagement.is_photo_expanded
timelines.engagement.is_profile_clicked
timelines.engagement.is_quoted
timelines.engagement.is_replied
timelines.engagement.is_retweeted
timelines.engagement.is_tweet_share_dm_clicked
timelines.engagement.is_tweet_share_dm_sent
timelines.engagement.is_video_playback_50
timelines.engagement.is_video_quality_viewed
timelines.engagement.is_video_viewed 		any_feature 30.minutes.count
Duration.Top.count
tweet_v2 (real_time) timelines.engagement.is_block_clicked
timelines.engagement.is_mute_clicked
timelines.engagement.is_report_tweet_clicked
any_feature
30.minutes.count
Duration.Top.count
tweet (real_time dwell) timelines.engagement.is_fullscreen_video_dwelled
timelines.engagement.is_fullscreen_video_dwelled_10_sec
timelines.engagement.is_fullscreen_video_dwelled_20_sec
timelines.engagement.is_fullscreen_video_dwelled_30_sec
timelines.engagement.is_fullscreen_video_dwelled_5_sec
timelines.engagement.is_profile_dwelled
timelines.engagement.is_profile_dwelled_10_sec
timelines.engagement.is_profile_dwelled_20_sec
timelines.engagement.is_profile_dwelled_30_sec
timelines.engagement.is_tweet_detail_dwelled
timelines.engagement.is_tweet_detail_dwelled_15_sec
timelines.engagement.is_tweet_detail_dwelled_25_sec
timelines.engagement.is_tweet_detail_dwelled_30_sec
timelines.engagement.is_tweet_detail_dwelled_8_sec		 any_feature 1.days.count
30.minutes.count
tweet (real_time shared) timelines.engagement.is_share_menu_clicked
timelines.engagement.is_shared 		any_feature 1.days.count
30.minutes.count
## Non Aggregate Features We have a number of standalone features capturing information about the user, the tweet, the author, and the tweet context.
two_hop
This feature group contains features about interactions which are "two-hop" between a user and the tweet author. Examples of two-top interactions are: If user 1 favorites a tweet by user 2, and user 2 favorites a tweet by user 3, there will be a positive value for the "favorite.favorited_by" two-hop feature between user 1 and user 3. The feature group consists of all possible crosses of the below features.
two_hop favorite
following
mutual_follow
favorited_by
followed_by
mentioned_by
retweeted_by
normalized
two_hop favorited_by
favorited_by
mentioned_by
retweeted_by 		right_degree
realgraph
This feature group contains features about interactions between the user and the Tweet author. The feature group consists of all possible crosses of the below features.
realgraph dst_id
src_id
realgraph num_address_book_email
num_address_book_in_both
num_address_book_mutual_edge_email
num_address_book_mutual_edge_in_both
num_address_book_mutual_edge_phone
num_address_book_phone
num_blocks
num_direct_messages
num_favorites
num_follow
num_inspected_tweets
num_link_clicks
num_mentions
num_mutes
num_mutual_follow
num_photo_tags
num_profile_views
num_report_as_abuses
num_report_as_spams
num_retweets
num_sms_follow
num_tweet_clicks
total_dwell_time
weight 		days_since_last
days_since_last.sparse_avg
days_since_last.sparse_max
days_since_last.sparse_sum
elapsed_days
elapsed_days.sparse_avg
elapsed_days.sparse_max
elapsed_days.sparse_sum
ewma
ewma.sparse_avg
ewma.sparse_max
ewma.sparse_sum
is_missing
m2ForVariance.sparse_avg
m2ForVariance.sparse_max
m2ForVariance.sparse_sum
mean
mean.sparse_avg
mean.sparse_max
mean.sparse_sum
non_zero_days
non_zero_days.sparse_avg
non_zero_days.sparse_max
non_zero_days.sparse_sum
sparse_avg
sparse_max
sparse_sum
variance
authors.realgraph This feature group contains features about interactions between the user and various other users including
  1. the Tweet author
  2. any users mentioned in the Tweet
  3. in-network engagers with the Tweet
  4. upstream authors if the Tweet was part of a reply chain
Note that all the above users are included in the interaction set, not just the Tweet author. The feature group consists of all possible crosses of the below features.
authors.realgraph weight sparse_avg
sparse_max
sparse_sum
authors.realgraph num_address_book_email
num_address_book_in_both
num_address_book_mutual_edge_email
num_address_book_mutual_edge_in_both
num_address_book_phone
num_blocks
num_direct_messages
num_favorites
num_follow
num_inspected_tweets
num_link_clicks
num_mentions
num_mutes
num_mutual_follow
num_photo_tags
num_profile_views
num_report_as_abuses
num_report_as_spams
num_retweets
num_sms_follow
num_tweet_clicks
total_dwell_time
days_since_last
elapsed_days
ewma
m2ForVariance
mean
non_zero_days
sparse_avg
sparse_max
sparse_sum
recap.tweetfeature, recap.searchfeature, etc
This feature group contains features about the tweet, whether from the tweets service or the search service ("Earlybird"). It also contains features related to the user's device type.
recap.earlybird.fav_count_v2
recap.earlybird.reply_count_v2
recap.earlybird.retweet_count_v2
recap.searchfeature.blender_score
recap.searchfeature.fav_count
recap.searchfeature.reply_count
recap.searchfeature.retweet_count
recap.searchfeature.text_score
recap.source.type
recap.tweetfeature.bidirectional_fav_count
recap.tweetfeature.bidirectional_reply_count
recap.tweetfeature.bidirectional_retweet_count
recap.tweetfeature.contains_media
recap.tweetfeature.conversational_count
recap.tweetfeature.embeds_impression_count
recap.tweetfeature.embeds_url_count
recap.tweetfeature.from_inactive_user
recap.tweetfeature.from_mutual_follow
recap.tweetfeature.from_verified_account
recap.tweetfeature.has_card
recap.tweetfeature.has_consumer_video
recap.tweetfeature.has_hashtag
recap.tweetfeature.has_image
recap.tweetfeature.has_link
recap.tweetfeature.has_mention
recap.tweetfeature.has_multiple_hashtag_or_trend
recap.tweetfeature.has_multiple_media
recap.tweetfeature.has_native_image
recap.tweetfeature.has_native_video
recap.tweetfeature.has_news
recap.tweetfeature.has_periscope
recap.tweetfeature.has_pro_video
recap.tweetfeature.has_trend
recap.tweetfeature.has_video
recap.tweetfeature.has_vine
recap.tweetfeature.has_visible_link
recap.tweetfeature.is_author_bot
recap.tweetfeature.is_author_new
recap.tweetfeature.is_author_profile_egg
recap.tweetfeature.is_author_spam
recap.tweetfeature.is_business_score
recap.tweetfeature.is_extended_reply
recap.tweetfeature.is_offensive
recap.tweetfeature.is_reply
recap.tweetfeature.is_retweet
recap.tweetfeature.is_sensitive
recap.tweetfeature.language
recap.tweetfeature.link_count
recap.tweetfeature.link_language
recap.tweetfeature.match_searcher_langs
recap.tweetfeature.match_searcher_main_lang
recap.tweetfeature.match_ui_lang
recap.tweetfeature.mention_searcher
recap.tweetfeature.num_hashtags
recap.tweetfeature.num_mentions
recap.tweetfeature.prev_user_tweet_enagagement
recap.tweetfeature.reply_other
recap.tweetfeature.reply_searcher
recap.tweetfeature.retweet_other
recap.tweetfeature.retweet_searcher
recap.tweetfeature.signature
recap.tweetfeature.tweet_count_from_user_in_snapshot
recap.tweetfeature.unidirectiona_fav_count
recap.tweetfeature.unidirectional_reply_count
recap.tweetfeature.unidirectional_retweet_count
recap.tweetfeature.user_rep
recap.tweetfeature.video_view_count
recap.user_agent.client_name
recap.user_agent.client_source
recap.user_agent.client_version
recap.user_agent.client_version_code
recap.user_agent.device
recap.user_agent.manufacturer
recap.user_agent.network_connection
recap.user_agent.sdk_version
recap.v2.tweetfeature.is_retweet_directed_at_user_in_first_degree
recap.v2.tweetfeature.is_retweet_of_reply
recap.v2.tweetfeature.is_retweeter_bot
recap.v2.tweetfeature.is_retweeter_new
recap.v2.tweetfeature.is_retweeter_nsfw
recap.v2.tweetfeature.is_retweeter_profile_egg
recap.v2.tweetfeature.is_retweeter_spam
recap.v2.tweetfeature.retweet_of_mutual_follow
recap.v2.tweetfeature.source_author_rep
recap.v3.tweetfeature.probably_from_follow
tweetsource
This feature group contains features about the tweet media as well as conversation-related features about the tweet.

tweetsource.tweet.media.aspect_ratio_den
tweetsource.tweet.media.aspect_ratio_num
tweetsource.tweet.media.bit_rate
tweetsource.tweet.media.height_1
tweetsource.tweet.media.height_2
tweetsource.tweet.media.height_3
tweetsource.tweet.media.height_4
tweetsource.tweet.media.num_tags
tweetsource.tweet.media.resize_method_1
tweetsource.tweet.media.resize_method_2
tweetsource.tweet.media.resize_method_3
tweetsource.tweet.media.resize_method_4
tweetsource.tweet.media.video_duration
tweetsource.tweet.media.width_1
tweetsource.tweet.media.width_2
tweetsource.tweet.media.width_3
tweetsource.tweet.media.width_4
tweetsource.tweet.text.has_question
tweetsource.tweet.text.length
tweetsource.tweet.text.length_type
tweetsource.tweet.text.num_caps
tweetsource.tweet.text.num_newlines
tweetsource.tweet.text.num_whitespaces
tweetsource.v2.tweet.media.color_1_blue
tweetsource.v2.tweet.media.color_1_green
tweetsource.v2.tweet.media.color_1_percentage
tweetsource.v2.tweet.media.color_1_red
tweetsource.v2.tweet.media.face_areas
tweetsource.v2.tweet.media.has_app_install_call_to_action
tweetsource.v2.tweet.media.has_description
tweetsource.v2.tweet.media.has_selected_preview_image
tweetsource.v2.tweet.media.has_title
tweetsource.v2.tweet.media.has_visit_site_call_to_action
tweetsource.v2.tweet.media.has_watch_now_call_to_action
tweetsource.v2.tweet.media.is_360
tweetsource.v2.tweet.media.is_embeddable
tweetsource.v2.tweet.media.is_managed
tweetsource.v2.tweet.media.is_monetizable
tweetsource.v2.tweet.media.num_color_pallette_items
tweetsource.v2.tweet.media.num_faces
tweetsource.v2.tweet.media.num_stickers
tweetsource.v2.tweet.media.view_count
in_reply_to_tweet
If the tweet was a reply, this feature group contains the features of the replied to tweet.
in_reply_to_tweet.recap.earlybird.fav_count_v2
in_reply_to_tweet.recap.earlybird.reply_count_v2
in_reply_to_tweet.recap.earlybird.retweet_count_v2
in_reply_to_tweet.recap.searchfeature.fav_count
in_reply_to_tweet.recap.searchfeature.reply_count
in_reply_to_tweet.recap.searchfeature.retweet_count
in_reply_to_tweet.recap.searchfeature.text_score
in_reply_to_tweet.recap.tweetfeature.bidirectional_fav_count
in_reply_to_tweet.recap.tweetfeature.bidirectional_reply_count
in_reply_to_tweet.recap.tweetfeature.bidirectional_retweet_count
in_reply_to_tweet.recap.tweetfeature.conversational_count
in_reply_to_tweet.recap.tweetfeature.from_mutual_follow
in_reply_to_tweet.recap.tweetfeature.from_verified_account
in_reply_to_tweet.recap.tweetfeature.has_hashtag
in_reply_to_tweet.recap.tweetfeature.has_image
in_reply_to_tweet.recap.tweetfeature.has_mention
in_reply_to_tweet.recap.tweetfeature.has_news
in_reply_to_tweet.recap.tweetfeature.has_video
in_reply_to_tweet.recap.tweetfeature.has_visible_link
in_reply_to_tweet.recap.tweetfeature.is_author_bot
in_reply_to_tweet.recap.tweetfeature.is_author_new
in_reply_to_tweet.recap.tweetfeature.is_author_nsfw
in_reply_to_tweet.recap.tweetfeature.is_author_spam
in_reply_to_tweet.recap.tweetfeature.is_offensive
in_reply_to_tweet.recap.tweetfeature.is_reply
in_reply_to_tweet.recap.tweetfeature.is_sensitive
in_reply_to_tweet.recap.tweetfeature.num_mentions
in_reply_to_tweet.recap.tweetfeature.prev_user_tweet_enagagement
in_reply_to_tweet.recap.tweetfeature.unidirectiona_fav_count
in_reply_to_tweet.recap.tweetfeature.unidirectional_reply_count
in_reply_to_tweet.recap.tweetfeature.unidirectional_retweet_count
in_reply_to_tweet.recap.tweetfeature.user_rep
in_reply_to_tweet.timelines.earlybird.decayed_favorite_count
in_reply_to_tweet.timelines.earlybird.decayed_quote_count
in_reply_to_tweet.timelines.earlybird.decayed_reply_count
in_reply_to_tweet.timelines.earlybird.decayed_retweet_count
in_reply_to_tweet.timelines.earlybird.has_quote
in_reply_to_tweet.timelines.earlybird.quote_count
in_reply_to_tweet.timelines.earlybird.weighted_fav_count
in_reply_to_tweet.timelines.earlybird.weighted_quote_count
in_reply_to_tweet.timelines.earlybird.weighted_reply_count
in_reply_to_tweet.timelines.earlybird.weighted_retweet_count
in_reply_to_tweet.timelines.earlybird_score
in_reply_to_tweet.tweetsource.tweet.media.aspect_ratio_den
in_reply_to_tweet.tweetsource.tweet.media.aspect_ratio_num
in_reply_to_tweet.tweetsource.tweet.media.height_1
in_reply_to_tweet.tweetsource.tweet.media.height_2
in_reply_to_tweet.tweetsource.tweet.media.video_duration
in_reply_to_tweet.tweetsource.tweet.text.has_question
in_reply_to_tweet.tweetsource.tweet.text.length
in_reply_to_tweet.tweetsource.tweet.text.num_caps
timelines.earlybird
This feature group passes on features used by the search and light ranking service ("Earlybird") to the Heavy Ranker.
timelines.earlybird.decayed_favorite_count
timelines.earlybird.decayed_quote_count
timelines.earlybird.decayed_reply_count
timelines.earlybird.decayed_retweet_count
timelines.earlybird.embeds_impression_count_v2
timelines.earlybird.embeds_url_count_v2
timelines.earlybird.fake_favorite_count
timelines.earlybird.fake_quote_count
timelines.earlybird.fake_reply_count
timelines.earlybird.fake_retweet_count
timelines.earlybird.has_quote
timelines.earlybird.is_composer_source_camera
timelines.earlybird.label_abusive_flag
timelines.earlybird.label_abusive_hi_rcl_flag
timelines.earlybird.label_dup_content_flag
timelines.earlybird.label_nsfw_hi_prc_flag
timelines.earlybird.label_nsfw_hi_rcl_flag
timelines.earlybird.label_spam_flag
timelines.earlybird.label_spam_hi_rcl_flag
timelines.earlybird.periscope_exists
timelines.earlybird.periscope_has_been_featured
timelines.earlybird.periscope_is_currently_featured
timelines.earlybird.periscope_is_from_quality_source
timelines.earlybird.periscope_is_live
timelines.earlybird.preported_tweet_score
timelines.earlybird.quote_count
timelines.earlybird.visible_token_ratio
timelines.earlybird.weighted_fav_count
timelines.earlybird.weighted_quote_count
timelines.earlybird.weighted_reply_count
timelines.earlybird.weighted_retweet_count
realtime_interaction_graph
User-author interaction features. Similar to RealGraph but updated more rapidly.
realtime_interaction_graph.click.count
realtime_interaction_graph.click.days_since_last
realtime_interaction_graph.fav.count
realtime_interaction_graph.fav.days_since_last
realtime_interaction_graph.mention.count
realtime_interaction_graph.mention.days_since_last
realtime_interaction_graph.profile_view.count
realtime_interaction_graph.profile_view.days_since_last
realtime_interaction_graph.retweet.count
realtime_interaction_graph.retweet.days_since_last
realtime_interaction_graph.soft_follow.count
realtime_interaction_graph.soft_follow.days_since_last
user_tweet.recommendations
Similarity of a tweet to a user's recent engaged tweets.
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.fav_1d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.fav_1d_last_10_max
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.fav_7d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.fav_7d_last_10_max
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.follow_30d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.follow_30d_last_10_max
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.follow_7d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.follow_7d_last_10_max
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.retweet_1d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.retweet_1d_last_10_max
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.retweet_7d_last_10_avg
user_tweet.recommendations.sim_clusters_recent_engagement_similarity.retweet_7d_last_10_max
user-tweet.recommendations.sim_clusters_scores.user_interested_in_tweet_embedding_dot_product_20m_145k_2020
other
Here we list individual features not covered in any feature group
author_health.num_connect
author_health.num_connect_days
author_health.num_followers
engagement_features.in_network.favorites.count
engagement_features.in_network.replies.count
engagement_features.in_network.retweets.count
request_context.display_dpi
request_context.display_height
request_context.display_width
request_context.is_get_initial
request_context.is_get_middle
request_context.is_get_newer
request_context.is_get_older
request_context.is_session_start
time_features.earlybird.last_favorite_since_creation_hrs
time_features.earlybird.last_quote_since_creation_hrs
time_features.earlybird.last_reply_since_creation_hrs
time_features.earlybird.last_retweet_since_creation_hrs
time_features.earlybird.time_since_last_favorite
time_features.earlybird.time_since_last_quote
time_features.earlybird.time_since_last_reply
time_features.earlybird.time_since_last_retweet
time_features.is_tweet_recycled
time_features.non_polling_requests_since_tweet_creation
time_features.time_between_non_polling_requests_avg
time_features.time_since_last_non_polling_request
time_features.time_since_source_tweet_creation
time_features.time_since_tweet_creation
time_features.time_since_viewer_account_creation_secs
time_features.tweet_age_ratio
## Embeddings Features [Twhin](https://arxiv.org/pdf/2202.05387.pdf) is a large graph embedding trained on Twitter data. We use three 200-dimensional embeddings sourced from the Twhin algorithm.
Twhin Follow Embeddings
We have two embeddings trained on the user-user follow graph, one representing who is likely to follow a user and the other representing who a user is likely to follow. Each embedding is 200-dimensional.
Twhin Engagement Embeddings
We have one embedding trained on the user-tweet engagement graph, representing users based on the Tweets they are likely to engage with. This embedding is 200 dimensional. ================================================ FILE: projects/home/recap/README.md ================================================ # Heavy Ranker ## Overview The heavy ranker is a machine learning model used to rank tweets for the "For You" timeline which have passed through the candidate retrieval stage. It is one of the final stages of the funnel, succeeded primarily by a set of filtering heuristics. The model receives features describing a Tweet and the user that the Tweet is being recommended to (see [FEATURES.md](./FEATURES.md)). The model architecture is a parallel [MaskNet](https://arxiv.org/abs/2102.07619) which outputs a set of numbers between 0 and 1, with each output representing the probability that the user will engage with the tweet in a particular way. The predicted engagement types are explained below: ``` scored_tweets_model_weight_fav: The probability the user will favorite the Tweet. scored_tweets_model_weight_retweet: The probability the user will Retweet the Tweet. scored_tweets_model_weight_reply: The probability the user replies to the Tweet. scored_tweets_model_weight_good_profile_click: The probability the user opens the Tweet author profile and Likes or replies to a Tweet. scored_tweets_model_weight_video_playback50: The probability (for a video Tweet) that the user will watch at least half of the video. scored_tweets_model_weight_reply_engaged_by_author: The probability the user replies to the Tweet and this reply is engaged by the Tweet author. scored_tweets_model_weight_good_click: The probability the user will click into the conversation of this Tweet and reply or Like a Tweet. scored_tweets_model_weight_good_click_v2: The probability the user will click into the conversation of this Tweet and stay there for at least 2 minutes. scored_tweets_model_weight_negative_feedback_v2: The probability the user will react negatively (requesting "show less often" on the Tweet or author, block or mute the Tweet author). scored_tweets_model_weight_report: The probability the user will click Report Tweet. ``` The outputs of the model are combined into a final model score by doing a weighted sum across the predicted engagement probabilities. The weight of each engagement probability comes from a configuration file, read by the serving stack [here](https://github.com/twitter/the-algorithm/blob/main/home-mixer/server/src/main/scala/com/twitter/home_mixer/product/scored_tweets/param/ScoredTweetsParam.scala#L84). The exact weights in the file can be adjusted at any time, but the current weighting of probabilities (April 5, 2023) is as follows: ``` scored_tweets_model_weight_fav: 0.5 scored_tweets_model_weight_retweet: 1.0 scored_tweets_model_weight_reply: 13.5 scored_tweets_model_weight_good_profile_click: 12.0 scored_tweets_model_weight_video_playback50: 0.005 scored_tweets_model_weight_reply_engaged_by_author: 75.0 scored_tweets_model_weight_good_click: 11.0 scored_tweets_model_weight_good_click_v2: 10.0 scored_tweets_model_weight_negative_feedback_v2: -74.0 scored_tweets_model_weight_report: -369.0 ``` Essentially, the formula is: ``` score = sum_i { (weight of engagement i) * (probability of engagement i) } ``` Since each engagement has a different average probability, the weights were originally set so that, on average, each weighted engagement probability contributes a near-equal amount to the score. Since then, we have periodically adjusted the weights to optimize for platform metrics. Some disclaimers: - Due to the need to make sure this runs independently from other parts of Twitter codebase, there may be small differences from the production model. - We cannot release the real training data due to privacy restrictions. However, we have included a script to generate random data to ensure you can run the model training code. ## Development After following the repo setup instructions, you can run the following script from a virtual environment to create a random training dataset in `$HOME/tmp/recap_local_random_data`: ```sh projects/home/recap/scripts/create_random_data.sh ``` You can then train the model using the following script. Checkpoints and logs will be written to `$HOME/tmp/runs/recap_local_debug`: ```sh projects/home/recap/scripts/run_local.sh ``` The model training can be configured in `projects/home/recap/config/local_prod.yaml` ================================================ FILE: projects/home/recap/__init__.py ================================================ ================================================ FILE: projects/home/recap/config/home_recap_2022/segdense.json ================================================ { "schema": [ { "dtype": "int64_list", "feature_name": "home_recap_2022_discrete__segdense_vals", "length": 320 }, { "dtype": "float_list", "feature_name": "home_recap_2022_cont__segdense_vals", "length": 6000 }, { "dtype": "int64_list", "feature_name": "home_recap_2022_binary__segdense_vals", "length": 512 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_tweet_detail_dwelled_15_sec", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_profile_clicked_and_profile_engaged", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_replied_reply_engaged_by_author", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_video_playback_50", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_report_tweet_clicked", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_replied", "length": 1 }, { "dtype": "int64_list", "feature_name": "meta.author_id", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_negative_feedback_v2", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_retweeted", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_favorited", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied", "length": 1 }, { "dtype": "int64_list", "feature_name": "meta.tweet_id", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_good_clicked_convo_desc_v2", "length": 1 }, { "dtype": "int64_list", "feature_name": "meta.user_id", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_bookmarked", "length": 1 }, { "dtype": "int64_list", "feature_name": "recap.engagement.is_shared", "length": 1 }, { "dtype": "float_list", "feature_name": "user.timelines.twhin_user_engagement_embeddings.twhin_user_engagement_embeddings", "length": 200 }, { "dtype": "float_list", "feature_name": "original_author.timelines.twhin_author_follow_embeddings.twhin_author_follow_embeddings", "length": 200 }, { "dtype": "float_list", "feature_name": "user.timelines.twhin_user_follow_embeddings.twhin_user_follow_embeddings", "length": 200 } ] } ================================================ FILE: projects/home/recap/config/local_prod.yaml ================================================ training: num_train_steps: 10 num_eval_steps: 5 checkpoint_every_n: 5 train_log_every_n: 1 eval_log_every_n: 1 save_dir: ${HOME}/tmp/runs/recap_local_debug eval_timeout_in_s: 7200 model: backbone: affine_map: null dcn_config: null dlrm_config: null mask_net_config: mask_blocks: - aggregation_size: 1024 input_layer_norm: false output_size: 1024 reduction_factor: null - aggregation_size: 1024 input_layer_norm: false output_size: 1024 reduction_factor: null - aggregation_size: 1024 input_layer_norm: false output_size: 1024 reduction_factor: null - aggregation_size: 1024 input_layer_norm: false output_size: 1024 reduction_factor: null mlp: batch_norm: null dropout: null final_layer_activation: true layer_sizes: - 2048 use_parallel: true mlp_config: null pos_weight: 1.0 featurization_config: clip_log1p_abs_config: null double_norm_log_config: batch_norm_config: affine: true momentum: 0.01 clip_magnitude: 5.0 layer_norm_config: axis: -1 center: true epsilon: 0.0 scale: true feature_names_to_concat: - binary log1p_abs_config: null z_score_log_config: null large_embeddings: null multi_task_type: share_all position_debias_config: null small_embeddings: null stratifiers: null tasks: recap.engagement.is_favorited: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_good_clicked_convo_desc_favorited_or_replied: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_good_clicked_convo_desc_v2: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_negative_feedback_v2: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: rate: 0.1 final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_profile_clicked_and_profile_engaged: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_replied: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_replied_reply_engaged_by_author: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_report_tweet_clicked: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: rate: 0.2 final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_retweeted: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 recap.engagement.is_video_playback_50: affine_map: null dcn_config: null dlrm_config: null mask_net_config: null mlp_config: batch_norm: affine: false momentum: 0.1 dropout: null final_layer_activation: false layer_sizes: - 256 - 128 - 1 pos_weight: 1.0 train_data: global_batch_size: 128 dataset_service_compression: AUTO inputs: &data_root "${HOME}/tmp/recap_local_random_data/*.gz" seg_dense_schema: &seg_dense_schema schema_path: "${TML_BASE}/projects/home/recap/config/home_recap_2022/segdense.json" renamed_features: "continuous": "home_recap_2022_cont__segdense_vals" "binary": "home_recap_2022_binary__segdense_vals" "discrete": "home_recap_2022_discrete__segdense_vals" "author_embedding": "original_author.timelines.twhin_author_follow_embeddings.twhin_author_follow_embeddings" "user_embedding": "user.timelines.twhin_user_follow_embeddings.twhin_user_follow_embeddings" "user_eng_embedding": "user.timelines.twhin_user_engagement_embeddings.twhin_user_engagement_embeddings" "meta__author_id": "meta.author_id" "meta__user_id": "meta.user_id" "meta__tweet_id": "meta.tweet_id" tasks: &data_tasks "recap.engagement.is_bookmarked": {} "recap.engagement.is_favorited": {} "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied": {} "recap.engagement.is_good_clicked_convo_desc_v2": {} "recap.engagement.is_negative_feedback_v2": {} "recap.engagement.is_profile_clicked_and_profile_engaged": {} "recap.engagement.is_replied": {} "recap.engagement.is_replied_reply_engaged_by_author": {} "recap.engagement.is_report_tweet_clicked": {} "recap.engagement.is_retweeted": {} "recap.engagement.is_shared": {} "recap.engagement.is_tweet_detail_dwelled_15_sec": {} "recap.engagement.is_video_playback_50": {} preprocess: &preprocess truncate_and_slice: continuous_feature_truncation: 2117 binary_feature_truncation: 59 validation_data: validation: &validation global_batch_size: &eval_batch_size 128 inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: *data_tasks preprocess: *preprocess train: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: *data_tasks preprocess: *preprocess recap.engagement.is_favorited: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_favorited": pos_downsampling_rate: 0.8387 neg_downsampling_rate: 0.01 evaluation_tasks: - "recap.engagement.is_favorited" preprocess: *preprocess recap.engagement.is_good_clicked_convo_desc_favorited_or_replied: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied": pos_downsampling_rate: 0.9164 neg_downsampling_rate: 0.00195 evaluation_tasks: - "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied" preprocess: *preprocess recap.engagement.is_good_clicked_convo_desc_v2: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_good_clicked_convo_desc_v2": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.00174 evaluation_tasks: - "recap.engagement.is_good_clicked_convo_desc_v2" preprocess: *preprocess recap.engagement.is_negative_feedback_v2: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_negative_feedback_v2": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.00280 evaluation_tasks: - "recap.engagement.is_negative_feedback_v2" preprocess: *preprocess recap.engagement.is_profile_clicked_and_profile_engaged: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_profile_clicked_and_profile_engaged": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.0015 evaluation_tasks: - "recap.engagement.is_profile_clicked_and_profile_engaged" preprocess: *preprocess recap.engagement.is_replied: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_replied": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.005 evaluation_tasks: - "recap.engagement.is_replied" preprocess: *preprocess recap.engagement.is_replied_reply_engaged_by_author: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_replied_reply_engaged_by_author": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.001 evaluation_tasks: - "recap.engagement.is_replied_reply_engaged_by_author" preprocess: *preprocess recap.engagement.is_report_tweet_clicked: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_report_tweet_clicked": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.000014 evaluation_tasks: - "recap.engagement.is_report_tweet_clicked" preprocess: *preprocess recap.engagement.is_retweeted: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_retweeted": pos_downsampling_rate: 0.9561 neg_downsampling_rate: 0.004 evaluation_tasks: - "recap.engagement.is_retweeted" preprocess: *preprocess recap.engagement.is_video_playback_50: global_batch_size: *eval_batch_size inputs: *data_root seg_dense_schema: *seg_dense_schema tasks: <<: *data_tasks "recap.engagement.is_video_playback_50": pos_downsampling_rate: 1.0 neg_downsampling_rate: 0.00427 evaluation_tasks: - "recap.engagement.is_video_playback_50" preprocess: *preprocess optimizer: adam: beta_1: 0.95 beta_2: 0.999 epsilon: 1.0e-07 multi_task_learning_rates: backbone_learning_rate: constant: null linear_ramp_to_constant: learning_rate: 0.0001 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null tower_learning_rates: recap.engagement.is_favorited: constant: null linear_ramp_to_constant: learning_rate: 0.0008 num_ramp_steps: 5000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_good_clicked_convo_desc_favorited_or_replied: constant: null linear_ramp_to_constant: learning_rate: 0.0001 num_ramp_steps: 2000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_good_clicked_convo_desc_v2: constant: null linear_ramp_to_constant: learning_rate: 0.0002 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_negative_feedback_v2: constant: null linear_ramp_to_constant: learning_rate: 0.0005 num_ramp_steps: 5000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_profile_clicked_and_profile_engaged: constant: null linear_ramp_to_constant: learning_rate: 0.0003 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_replied: constant: null linear_ramp_to_constant: learning_rate: 0.001 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_replied_reply_engaged_by_author: constant: null linear_ramp_to_constant: learning_rate: 0.0001 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_report_tweet_clicked: constant: null linear_ramp_to_constant: learning_rate: 0.0001 num_ramp_steps: 3000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_retweeted: constant: null linear_ramp_to_constant: learning_rate: 0.0001 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null recap.engagement.is_video_playback_50: constant: null linear_ramp_to_constant: learning_rate: 0.003 num_ramp_steps: 1000 linear_ramp_to_cosine: null piecewise_constant: null single_task_learning_rate: null ================================================ FILE: projects/home/recap/config.py ================================================ from tml.core import config as config_mod import tml.projects.home.recap.data.config as data_config import tml.projects.home.recap.model.config as model_config import tml.projects.home.recap.optimizer.config as optimizer_config from enum import Enum from typing import Dict, Optional import pydantic class TrainingConfig(config_mod.BaseConfig): save_dir: str = "/tmp/model" num_train_steps: pydantic.PositiveInt = 1000000 initial_checkpoint_dir: str = pydantic.Field( None, description="Directory of initial checkpoints", at_most_one_of="initialization" ) checkpoint_every_n: pydantic.PositiveInt = 1000 checkpoint_max_to_keep: pydantic.PositiveInt = pydantic.Field( None, description="Maximum number of checkpoints to keep. Defaults to keeping all." ) train_log_every_n: pydantic.PositiveInt = 1000 num_eval_steps: int = pydantic.Field( 16384, description="Number of evaluation steps. If < 0 the entire dataset " "will be used." ) eval_log_every_n: pydantic.PositiveInt = 5000 eval_timeout_in_s: pydantic.PositiveFloat = 60 * 60 gradient_accumulation: int = pydantic.Field( None, description="Number of replica steps to accumulate gradients." ) class RecapConfig(config_mod.BaseConfig): training: TrainingConfig = pydantic.Field(TrainingConfig()) model: model_config.ModelConfig train_data: data_config.RecapDataConfig validation_data: Dict[str, data_config.RecapDataConfig] optimizer: optimizer_config.RecapOptimizerConfig which_metrics: Optional[str] = pydantic.Field(None, description="which metrics to pick.") # DANGER DANGER! You might expect validators here to ensure that multi task learning setups are # the same as the data. Unfortunately, this throws opaque errors when the model configuration is # invalid. In our judgement, that is a more frequency and worse occurrence than tasks not matching # the data. class JobMode(str, Enum): """Job modes.""" TRAIN = "train" EVALUATE = "evaluate" INFERENCE = "inference" ================================================ FILE: projects/home/recap/data/__init__.py ================================================ ================================================ FILE: projects/home/recap/data/config.py ================================================ import typing from enum import Enum from tml.core import config as base_config import pydantic class ExplicitDateInputs(base_config.BaseConfig): """Arguments to select train/validation data using end_date and days of data.""" data_root: str = pydantic.Field(..., description="Data path prefix.") end_date: str = pydantic.Field(..., description="Data end date, inclusive.") days: int = pydantic.Field(..., description="Number of days of data for dataset.") num_missing_days_tol: int = pydantic.Field( 0, description="We tolerate <= num_missing_days_tol days of missing data." ) class ExplicitDatetimeInputs(base_config.BaseConfig): """Arguments to select train/validation data using end_datetime and hours of data.""" data_root: str = pydantic.Field(..., description="Data path prefix.") end_datetime: str = pydantic.Field(..., description="Data end datetime, inclusive.") hours: int = pydantic.Field(..., description="Number of hours of data for dataset.") num_missing_hours_tol: int = pydantic.Field( 0, description="We tolerate <= num_missing_hours_tol hours of missing data." ) class DdsCompressionOption(str, Enum): """The only valid compression option is 'AUTO'""" AUTO = "AUTO" class DatasetConfig(base_config.BaseConfig): inputs: str = pydantic.Field( None, description="A glob for selecting data.", one_of="date_inputs_format" ) explicit_datetime_inputs: ExplicitDatetimeInputs = pydantic.Field( None, one_of="date_inputs_format" ) explicit_date_inputs: ExplicitDateInputs = pydantic.Field(None, one_of="date_inputs_format") global_batch_size: pydantic.PositiveInt num_files_to_keep: pydantic.PositiveInt = pydantic.Field( None, description="Number of shards to keep." ) repeat_files: bool = pydantic.Field( True, description="DEPRICATED. Files are repeated no matter what this is set to." ) file_batch_size: pydantic.PositiveInt = pydantic.Field(16, description="File batch size") cache: bool = pydantic.Field( False, description="Cache dataset in memory. Careful to only use this when you" " have enough memory to fit entire dataset.", ) data_service_dispatcher: str = pydantic.Field(None) ignore_data_errors: bool = pydantic.Field( False, description="Whether to ignore tf.data errors. DANGER DANGER, may wedge jobs." ) dataset_service_compression: DdsCompressionOption = pydantic.Field( None, description="Compress the dataset for DDS worker -> training host. Disabled by default and the only valid option is 'AUTO'", ) # tf.data.Dataset options examples_shuffle_buffer_size: int = pydantic.Field(1024, description="Size of shuffle buffers.") map_num_parallel_calls: pydantic.PositiveInt = pydantic.Field( None, description="Number of parallel calls." ) interleave_num_parallel_calls: pydantic.PositiveInt = pydantic.Field( None, description="Number of shards to interleave." ) class TruncateAndSlice(base_config.BaseConfig): # Apply truncation and then slice. continuous_feature_truncation: pydantic.PositiveInt = pydantic.Field( None, description="Experimental. Truncates continuous features to this amount for efficiency." ) binary_feature_truncation: pydantic.PositiveInt = pydantic.Field( None, description="Experimental. Truncates binary features to this amount for efficiency." ) continuous_feature_mask_path: str = pydantic.Field( None, description="Path of mask used to slice input continuous features." ) binary_feature_mask_path: str = pydantic.Field( None, description="Path of mask used to slice input binary features." ) class DataType(str, Enum): BFLOAT16 = "bfloat16" BOOL = "bool" FLOAT32 = "float32" FLOAT16 = "float16" UINT8 = "uint8" class DownCast(base_config.BaseConfig): # Apply down casting to selected features. features: typing.Dict[str, DataType] = pydantic.Field( None, description="Map features to down cast data types." ) class TaskData(base_config.BaseConfig): pos_downsampling_rate: float = pydantic.Field( 1.0, description="Downsampling rate of positives used to generate dataset.", ) neg_downsampling_rate: float = pydantic.Field( 1.0, description="Downsampling rate of negatives used to generate dataset.", ) class SegDenseSchema(base_config.BaseConfig): schema_path: str = pydantic.Field(..., description="Path to feature config json.") features: typing.List[str] = pydantic.Field( [], description="List of features (in addition to the renamed features) to read from schema path above.", ) renamed_features: typing.Dict[str, str] = pydantic.Field( {}, description="Dictionary of renamed features." ) mask_mantissa_features: typing.Dict[str, int] = pydantic.Field( {}, description="(experimental) Number of mantissa bits to mask to simulate lower precision data.", ) class RectifyLabels(base_config.BaseConfig): label_rectification_window_in_hours: float = pydantic.Field( 3.0, description="overlap time in hours for which to flip labels" ) served_timestamp_field: str = pydantic.Field( ..., description="input field corresponding to served time" ) impressed_timestamp_field: str = pydantic.Field( ..., description="input field corresponding to impressed time" ) label_to_engaged_timestamp_field: typing.Dict[str, str] = pydantic.Field( ..., description="label to the input field corresponding to engagement time" ) class ExtractFeaturesRow(base_config.BaseConfig): name: str = pydantic.Field( ..., description="name of the new field name to be created", ) source_tensor: str = pydantic.Field( ..., description="name of the dense tensor to look for the feature", ) index: int = pydantic.Field( ..., description="index of the feature in the dense tensor", ) class ExtractFeatures(base_config.BaseConfig): extract_feature_table: typing.List[ExtractFeaturesRow] = pydantic.Field( [], description="list of features to be extracted with their name, source tensor and index", ) class DownsampleNegatives(base_config.BaseConfig): batch_multiplier: int = pydantic.Field( None, description="batch multiplier", ) engagements_list: typing.List[str] = pydantic.Field( [], description="engagements with kept positives", ) num_engagements: int = pydantic.Field( ..., description="number engagements used in the model, including ones excluded in engagements_list", ) class Preprocess(base_config.BaseConfig): truncate_and_slice: TruncateAndSlice = pydantic.Field(None, description="Truncation and slicing.") downcast: DownCast = pydantic.Field(None, description="Down cast to features.") rectify_labels: RectifyLabels = pydantic.Field( None, description="Rectify labels for a given overlap window" ) extract_features: ExtractFeatures = pydantic.Field( None, description="Extract features from dense tensors." ) downsample_negatives: DownsampleNegatives = pydantic.Field( None, description="Downsample negatives." ) class Sampler(base_config.BaseConfig): """Assumes function is defined in data/samplers.py. Only use this for quick experimentation. If samplers are useful, we should sample from upstream data generation. DEPRICATED, DO NOT USE. """ name: str kwargs: typing.Dict class RecapDataConfig(DatasetConfig): seg_dense_schema: SegDenseSchema tasks: typing.Dict[str, TaskData] = pydantic.Field( description="Description of individual tasks in this dataset." ) evaluation_tasks: typing.List[str] = pydantic.Field( [], description="If specified, lists the tasks we're generating metrics for." ) preprocess: Preprocess = pydantic.Field( None, description="Function run in tf.data.Dataset at train/eval, in-graph at inference." ) sampler: Sampler = pydantic.Field( None, description="""DEPRICATED, DO NOT USE. Sampling function for offline experiments.""", ) @pydantic.root_validator() def _validate_evaluation_tasks(cls, values): if values.get("evaluation_tasks") is not None: for task in values["evaluation_tasks"]: if task not in values["tasks"]: raise KeyError(f"Evaluation task {task} must be in tasks. Received {values['tasks']}") return values ================================================ FILE: projects/home/recap/data/dataset.py ================================================ from dataclasses import dataclass from typing import Callable, List, Optional, Tuple, Dict import functools import torch import tensorflow as tf from tml.common.batch import DataclassBatch from tml.projects.home.recap.data.config import RecapDataConfig, TaskData from tml.projects.home.recap.data import preprocessors from tml.projects.home.recap.config import JobMode from tml.projects.home.recap.data.tfe_parsing import get_seg_dense_parse_fn from tml.projects.home.recap.data.util import ( keyed_jagged_tensor_from_tensors_dict, sparse_or_dense_tf_to_torch, ) from absl import logging import torch.distributed as dist @dataclass class RecapBatch(DataclassBatch): """Holds features and labels from the Recap dataset.""" continuous_features: torch.Tensor binary_features: torch.Tensor discrete_features: torch.Tensor sparse_features: "KeyedJaggedTensor" # type: ignore[name-defined] # noqa: F821 labels: torch.Tensor user_embedding: torch.Tensor = None user_eng_embedding: torch.Tensor = None author_embedding: torch.Tensor = None weights: torch.Tensor = None def __post_init__(self): if self.weights is None: self.weights = torch.ones_like(self.labels) for feature_name, feature_value in self.as_dict().items(): if ("embedding" in feature_name) and (feature_value is None): setattr(self, feature_name, torch.empty([0, 0])) def to_batch(x, sparse_feature_names: Optional[List[str]] = None) -> RecapBatch: """Converts a torch data loader output into `RecapBatch`.""" x = tf.nest.map_structure(functools.partial(sparse_or_dense_tf_to_torch, pin_memory=False), x) try: features_in, labels = x except ValueError: # For Mode.INFERENCE, we do not expect to recieve labels as part of the input tuple features_in, labels = x, None sparse_features = keyed_jagged_tensor_from_tensors_dict({}) if sparse_feature_names: sparse_features = keyed_jagged_tensor_from_tensors_dict( {embedding_name: features_in[embedding_name] for embedding_name in sparse_feature_names} ) user_embedding, user_eng_embedding, author_embedding = None, None, None if "user_embedding" in features_in: if sparse_feature_names and "meta__user_id" in sparse_feature_names: raise ValueError("Only one source of embedding for user is supported") else: user_embedding = features_in["user_embedding"] if "user_eng_embedding" in features_in: if sparse_feature_names and "meta__user_eng_id" in sparse_feature_names: raise ValueError("Only one source of embedding for user is supported") else: user_eng_embedding = features_in["user_eng_embedding"] if "author_embedding" in features_in: if sparse_feature_names and "meta__author_id" in sparse_feature_names: raise ValueError("Only one source of embedding for user is supported") else: author_embedding = features_in["author_embedding"] return RecapBatch( continuous_features=features_in["continuous"], binary_features=features_in["binary"], discrete_features=features_in["discrete"], sparse_features=sparse_features, user_embedding=user_embedding, user_eng_embedding=user_eng_embedding, author_embedding=author_embedding, labels=labels, weights=features_in.get("weights", None), # Defaults to torch.ones_like(labels) ) def _chain(param, f1, f2): """ Reduce multiple functions into one chained function _chain(x, f1, f2) -> f2(f1(x)) """ output = param fns = [f1, f2] for f in fns: output = f(output) return output def _add_weights(inputs, tasks: Dict[str, TaskData]): """Adds weights based on label sampling for positive and negatives. This is useful for numeric calibration etc. This mutates inputs. Args: inputs: A dictionary of strings to tensor-like structures. tasks: A dict of string (label) to `TaskData` specifying inputs. Returns: A tuple of features and labels; weights are added to features. """ weights = [] for key, task in tasks.items(): label = inputs[key] float_label = tf.cast(label, tf.float32) weights.append( float_label / task.pos_downsampling_rate + (1.0 - float_label) / task.neg_downsampling_rate ) # Ensure we are batch-major (assumes we batch before this call). inputs["weights"] = tf.squeeze(tf.transpose(tf.convert_to_tensor(weights)), axis=0) return inputs def get_datetimes(explicit_datetime_inputs): """Compute list datetime strings for train/validation data.""" datetime_format = "%Y/%m/%d/%H" end = datetime.strptime(explicit_datetime_inputs.end_datetime, datetime_format) dates = sorted( [ (end - timedelta(hours=i + 1)).strftime(datetime_format) for i in range(int(explicit_datetime_inputs.hours)) ] ) return dates def get_explicit_datetime_inputs_files(explicit_datetime_inputs): """ Compile list of files for training/validation. Used with DataConfigs that use the `explicit_datetime_inputs` format to specify data. For each hour of data, if the directory is missing or empty, we increment a counter to keep track of the number of missing data hours. Returns only files with a `.gz` extension. Args: explicit_datetime_inputs: An `ExplicitDatetimeInputs` object within a `datasets.DataConfig` object Returns: data_files: Sorted list of files to read corresponding to data at the desired datetimes num_hours_missing: Number of hours that we are missing data """ datetimes = get_datetimes(explicit_datetime_inputs) folders = [os.path.join(explicit_datetime_inputs.data_root, datetime) for datetime in datetimes] data_files = [] num_hours_missing = 0 for folder in folders: try: files = tf.io.gfile.listdir(folder) if not files: logging.warning(f"{folder} contained no data files") num_hours_missing += 1 data_files.extend( [ os.path.join(folder, filename) for filename in files if filename.rsplit(".", 1)[-1].lower() == "gz" ] ) except tf.errors.NotFoundError as e: num_hours_missing += 1 logging.warning(f"Cannot find directory {folder}. Missing one hour of data. Error: \n {e}") return sorted(data_files), num_hours_missing def _map_output_for_inference( inputs, tasks: Dict[str, TaskData], preprocessor: tf.keras.Model = None, add_weights: bool = False ): if preprocessor: raise ValueError("No preprocessor should be used at inference time.") if add_weights: raise NotImplementedError() # Add zero weights. inputs["weights"] = tf.zeros_like(tf.expand_dims(inputs["continuous"][:, 0], -1)) for label in tasks: del inputs[label] return inputs def _map_output_for_train_eval( inputs, tasks: Dict[str, TaskData], preprocessor: tf.keras.Model = None, add_weights: bool = False ): if add_weights: inputs = _add_weights_based_on_sampling_rates(inputs, tasks) # Warning this has to happen first as it changes the input if preprocessor: inputs = preprocessor(inputs) label_values = tf.squeeze(tf.stack([inputs[label] for label in tasks], axis=1), axis=[-1]) for label in tasks: del inputs[label] return inputs, label_values def _add_weights_based_on_sampling_rates(inputs, tasks: Dict[str, TaskData]): """Adds weights based on label sampling for positive and negatives. This is useful for numeric calibration etc. This mutates inputs. Args: inputs: A dictionary of strings to tensor-like structures. tasks: A dict of string (label) to `TaskData` specifying inputs. Returns: A tuple of features and labels; weights are added to features. """ weights = [] for key, task in tasks.items(): label = inputs[key] float_label = tf.cast(label, tf.float32) weights.append( float_label / task.pos_downsampling_rate + (1.0 - float_label) / task.neg_downsampling_rate ) # Ensure we are batch-major (assumes we batch before this call). inputs["weights"] = tf.squeeze(tf.transpose(tf.convert_to_tensor(weights)), axis=0) return inputs class RecapDataset(torch.utils.data.IterableDataset): def __init__( self, data_config: RecapDataConfig, dataset_service: Optional[str] = None, mode: JobMode = JobMode.TRAIN, compression: Optional[str] = "AUTO", repeat: bool = False, vocab_mapper: tf.keras.Model = None, ): logging.info("***** Labels *****") logging.info(list(data_config.tasks.keys())) self._data_config = data_config self._parse_fn = get_seg_dense_parse_fn(data_config) self._mode = mode self._repeat = repeat self._num_concurrent_iterators = 1 self._vocab_mapper = vocab_mapper self.dataset_service = dataset_service preprocessor = None self._batch_size_multiplier = 1 if data_config.preprocess: preprocessor = preprocessors.build_preprocess(data_config.preprocess, mode=mode) if data_config.preprocess.downsample_negatives: self._batch_size_multiplier = data_config.preprocess.downsample_negatives.batch_multiplier self._preprocessor = preprocessor if mode == JobMode.INFERENCE: if preprocessor is not None: raise ValueError("Expect no preprocessor at inference time.") should_add_weights = False output_map_fn = _map_output_for_inference # (features,) else: # Only add weights if there is a reason to! If all weights will # be equal to 1.0, save bandwidth between DDS and Chief by simply # relying on the fact that weights default to 1.0 in `RecapBatch` # WARNING: Weights may still be added as a side effect of a preprocessor # such as `DownsampleNegatives`. should_add_weights = any( [ task_cfg.pos_downsampling_rate != 1.0 or task_cfg.neg_downsampling_rate != 1.0 for task_cfg in data_config.tasks.values() ] ) output_map_fn = _map_output_for_train_eval # (features, labels) self._output_map_fn = functools.partial( output_map_fn, tasks=data_config.tasks, preprocessor=preprocessor, add_weights=should_add_weights, ) sparse_feature_names = list(vocab_mapper.vocabs.keys()) if vocab_mapper else None self._tf_dataset = self._create_tf_dataset() self._init_tensor_spec() def _init_tensor_spec(self): def _tensor_spec_to_torch_shape(spec): if spec.shape is None: return None shape = [x if x is not None else -1 for x in spec.shape] return torch.Size(shape) self.torch_element_spec = tf.nest.map_structure( _tensor_spec_to_torch_shape, self._tf_dataset.element_spec ) def _create_tf_dataset(self): if hasattr(self, "_tf_dataset"): raise ValueError("Do not call `_create_tf_dataset` more than once.") world_size = dist.get_world_size() if dist.is_initialized() else 1 per_replica_bsz = ( self._batch_size_multiplier * self._data_config.global_batch_size // world_size ) dataset: tf.data.Dataset = self._create_base_tf_dataset( batch_size=per_replica_bsz, ) if self._repeat: logging.info("Repeating dataset") dataset = dataset.repeat() if self.dataset_service: if self._num_concurrent_iterators > 1: if not self.machines_config: raise ValueError( "Must supply a machine_config for autotuning in order to use >1 concurrent iterators" ) dataset = dataset_lib.with_auto_tune_budget( dataset, machine_config=self.machines_config.chief, num_concurrent_iterators=self.num_concurrent_iterators, on_chief=False, ) self.dataset_id, self.job_name = register_dataset( dataset=dataset, dataset_service=self.dataset_service, compression=self.compression ) dataset = distribute_from_dataset_id( dataset_id=self.dataset_id, # type: ignore[arg-type] job_name=self.job_name, dataset_service=self.dataset_service, compression=self.compression, ) elif self._num_concurrent_iterators > 1: if not self.machines_config: raise ValueError( "Must supply a machine_config for autotuning in order to use >1 concurrent iterators" ) dataset = dataset_lib.with_auto_tune_budget( dataset, machine_config=self.machines_config.chief, num_concurrent_iterators=self._num_concurrent_iterators, on_chief=True, ) # Vocabulary mapping happens on the training node, not in dds because of size. if self._vocab_mapper: dataset = dataset.map(self._vocab_mapper) return dataset.prefetch(world_size * 2) def _create_base_tf_dataset(self, batch_size: int): if self._data_config.inputs: glob = self._data_config.inputs filenames = sorted(tf.io.gfile.glob(glob)) elif self._data_config.explicit_datetime_inputs: num_missing_hours_tol = self._data_config.explicit_datetime_inputs.num_missing_hours_tol filenames, num_hours_missing = get_explicit_datetime_inputs_files( self._data_config.explicit_datetime_inputs, increment="hourly", ) if num_hours_missing > num_missing_hours_tol: raise ValueError( f"We are missing {num_hours_missing} hours of data" f"more than tolerance {num_missing_hours_tol}." ) elif self._data_config.explicit_date_inputs: num_missing_days_tol = self._data_config.explicit_date_inputs.num_missing_days_tol filenames, num_days_missing = get_explicit_datetime_inputs_files( self._data_config.explicit_date_inputs, increment="daily", ) if num_days_missing > num_missing_days_tol: raise ValueError( f"We are missing {num_days_missing} days of data" f"more than tolerance {num_missing_days_tol}." ) else: raise ValueError( "Must specifiy either `inputs`, `explicit_datetime_inputs`, or `explicit_date_inputs` in data_config" ) num_files = len(filenames) logging.info(f"Found {num_files} data files") if num_files < 1: raise ValueError("No data files found") if self._data_config.num_files_to_keep is not None: filenames = filenames[: self._data_config.num_files_to_keep] logging.info(f"Retaining only {len(filenames)} files.") filenames_ds = ( tf.data.Dataset.from_tensor_slices(filenames).shuffle(len(filenames)) # Because of drop_remainder, if our dataset does not fill # up a batch, it will emit nothing without this repeat. .repeat(-1) ) if self._data_config.file_batch_size: filenames_ds = filenames_ds.batch(self._data_config.file_batch_size) def per_shard_dataset(filename): ds = tf.data.TFRecordDataset([filename], compression_type="GZIP") return ds.prefetch(4) ds = filenames_ds.interleave( per_shard_dataset, block_length=4, deterministic=False, num_parallel_calls=self._data_config.interleave_num_parallel_calls or tf.data.experimental.AUTOTUNE, ) # Combine functions into one map call to reduce overhead. map_fn = functools.partial( _chain, f1=self._parse_fn, f2=self._output_map_fn, ) # Shuffle -> Batch -> Parse is the correct ordering # Shuffling needs to be performed before batching otherwise there is not much point # Batching happens before parsing because tf.Example parsing is actually vectorized # and works much faster overall on batches of data. ds = ( # DANGER DANGER: there is a default shuffle size here. ds.shuffle(self._data_config.examples_shuffle_buffer_size) .batch(batch_size=batch_size, drop_remainder=True) .map( map_fn, num_parallel_calls=self._data_config.map_num_parallel_calls or tf.data.experimental.AUTOTUNE, ) ) if self._data_config.cache: ds = ds.cache() if self._data_config.ignore_data_errors: ds = ds.apply(tf.data.experimental.ignore_errors()) options = tf.data.Options() options.experimental_deterministic = False ds = ds.with_options(options) return ds def _gen(self): for x in self._tf_dataset: yield to_batch(x) def to_dataloader(self) -> Dict[str, torch.Tensor]: return torch.utils.data.DataLoader(self, batch_size=None) def __iter__(self): return iter(self._gen()) ================================================ FILE: projects/home/recap/data/generate_random_data.py ================================================ import os import json from absl import app, flags, logging import tensorflow as tf from typing import Dict from tml.projects.home.recap.data import tfe_parsing from tml.core import config as tml_config_mod import tml.projects.home.recap.config as recap_config_mod flags.DEFINE_string("config_path", None, "Path to hyperparameters for model.") flags.DEFINE_integer("n_examples", 100, "Numer of examples to generate.") FLAGS = flags.FLAGS def _generate_random_example( tf_example_schema: Dict[str, tf.io.FixedLenFeature] ) -> Dict[str, tf.Tensor]: example = {} for feature_name, feature_spec in tf_example_schema.items(): dtype = feature_spec.dtype if (dtype == tf.int64) or (dtype == tf.int32): x = tf.experimental.numpy.random.randint(0, high=10, size=feature_spec.shape, dtype=dtype) elif (dtype == tf.float32) or (dtype == tf.float64): x = tf.random.uniform(shape=[feature_spec.shape], dtype=dtype) else: raise NotImplementedError(f"Unknown type {dtype}") example[feature_name] = x return example def _float_feature(value): return tf.train.Feature(float_list=tf.train.FloatList(value=value)) def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) def _serialize_example(x: Dict[str, tf.Tensor]) -> bytes: feature = {} serializers = {tf.float32: _float_feature, tf.int64: _int64_feature} for feature_name, tensor in x.items(): feature[feature_name] = serializers[tensor.dtype](tensor) example_proto = tf.train.Example(features=tf.train.Features(feature=feature)) return example_proto.SerializeToString() def generate_data(data_path: str, config: recap_config_mod.RecapConfig): with tf.io.gfile.GFile(config.train_data.seg_dense_schema.schema_path, "r") as f: seg_dense_schema = json.load(f)["schema"] tf_example_schema = tfe_parsing.create_tf_example_schema( config.train_data, seg_dense_schema, ) record_filename = os.path.join(data_path, "random.tfrecord.gz") with tf.io.TFRecordWriter(record_filename, "GZIP") as writer: random_example = _generate_random_example(tf_example_schema) serialized_example = _serialize_example(random_example) writer.write(serialized_example) def _generate_data_main(unused_argv): config = tml_config_mod.load_config_from_yaml(recap_config_mod.RecapConfig, FLAGS.config_path) # Find the path where to put the data data_path = os.path.dirname(config.train_data.inputs) logging.info("Putting random data in %s", data_path) generate_data(data_path, config) if __name__ == "__main__": app.run(_generate_data_main) ================================================ FILE: projects/home/recap/data/preprocessors.py ================================================ """ Preprocessors applied on DDS workers in order to modify the dataset on the fly. Some of these preprocessors are also applied to the model at serving time. """ from tml.projects.home.recap import config as config_mod from absl import logging import tensorflow as tf import numpy as np class TruncateAndSlice(tf.keras.Model): """Class for truncating and slicing.""" def __init__(self, truncate_and_slice_config): super().__init__() self._truncate_and_slice_config = truncate_and_slice_config if self._truncate_and_slice_config.continuous_feature_mask_path: with tf.io.gfile.GFile( self._truncate_and_slice_config.continuous_feature_mask_path, "rb" ) as f: self._continuous_mask = np.load(f).nonzero()[0] logging.info(f"Slicing {np.sum(self._continuous_mask)} continuous features.") else: self._continuous_mask = None if self._truncate_and_slice_config.binary_feature_mask_path: with tf.io.gfile.GFile(self._truncate_and_slice_config.binary_feature_mask_path, "rb") as f: self._binary_mask = np.load(f).nonzero()[0] logging.info(f"Slicing {np.sum(self._binary_mask)} binary features.") else: self._binary_mask = None def call(self, inputs, training=None, mask=None): outputs = tf.nest.pack_sequence_as(inputs, tf.nest.flatten(inputs)) if self._truncate_and_slice_config.continuous_feature_truncation: logging.info("Truncating continuous") outputs["continuous"] = outputs["continuous"][ :, : self._truncate_and_slice_config.continuous_feature_truncation ] if self._truncate_and_slice_config.binary_feature_truncation: logging.info("Truncating binary") outputs["binary"] = outputs["binary"][ :, : self._truncate_and_slice_config.binary_feature_truncation ] if self._continuous_mask is not None: outputs["continuous"] = tf.gather(outputs["continuous"], self._continuous_mask, axis=1) if self._binary_mask is not None: outputs["binary"] = tf.gather(outputs["binary"], self._binary_mask, axis=1) return outputs class DownCast(tf.keras.Model): """Class for Down casting dataset before serialization and transferring to training host. Depends on the data type and the actual data range, the down casting can be lossless or not. It is strongly recommended to compare the metrics before and after down casting. """ def __init__(self, downcast_config): super().__init__() self.config = downcast_config self._type_map = { "bfloat16": tf.bfloat16, "bool": tf.bool, } def call(self, inputs, training=None, mask=None): outputs = tf.nest.pack_sequence_as(inputs, tf.nest.flatten(inputs)) for feature, type_str in self.config.features.items(): assert type_str in self._type_map if type_str == "bfloat16": logging.warning( "Although bfloat16 and float32 have the same number of exponent bits, this down casting is not 100% lossless. Please double check metrics." ) down_cast_data_type = self._type_map[type_str] outputs[feature] = tf.cast(outputs[feature], dtype=down_cast_data_type) return outputs class RectifyLabels(tf.keras.Model): """Class for rectifying labels""" def __init__(self, rectify_label_config): super().__init__() self._config = rectify_label_config self._window = int(self._config.label_rectification_window_in_hours * 60 * 60 * 1000) def call(self, inputs, training=None, mask=None): served_ts_field = self._config.served_timestamp_field impressed_ts_field = self._config.impressed_timestamp_field for label, engaged_ts_field in self._config.label_to_engaged_timestamp_field.items(): impressed = inputs[impressed_ts_field] served = inputs[served_ts_field] engaged = inputs[engaged_ts_field] keep = tf.math.logical_and(inputs[label] > 0, impressed - served < self._window) keep = tf.math.logical_and(keep, engaged - served < self._window) inputs[label] = tf.where(keep, inputs[label], tf.zeros_like(inputs[label])) return inputs class ExtractFeatures(tf.keras.Model): """Class for extracting individual features from dense tensors by their index.""" def __init__(self, extract_features_config): super().__init__() self._config = extract_features_config def call(self, inputs, training=None, mask=None): for row in self._config.extract_feature_table: inputs[row.name] = inputs[row.source_tensor][:, row.index] return inputs class DownsampleNegatives(tf.keras.Model): """Class for down-sampling/dropping negatives and updating the weights. If inputs['fav'] = [1, 0, 0, 0] and inputs['weights'] = [1.0, 1.0, 1.0, 1.0] inputs are transformed to inputs['fav'] = [1, 0] and inputs['weights'] = [1.0, 3.0] when batch_multiplier=2 and engagements_list=['fav'] It supports multiple engagements (union/logical_or is used to aggregate engagements), so we don't drop positives for any engagement. """ def __init__(self, downsample_negatives_config): super().__init__() self.config = downsample_negatives_config def call(self, inputs, training=None, mask=None): labels = self.config.engagements_list # union of engagements mask = tf.squeeze(tf.reduce_any(tf.stack([inputs[label] == 1 for label in labels], 1), 1)) n_positives = tf.reduce_sum(tf.cast(mask, tf.int32)) batch_size = tf.cast(tf.shape(inputs[labels[0]])[0] / self.config.batch_multiplier, tf.int32) negative_weights = tf.math.divide_no_nan( tf.cast(self.config.batch_multiplier * batch_size - n_positives, tf.float32), tf.cast(batch_size - n_positives, tf.float32), ) new_weights = tf.cast(mask, tf.float32) + (1 - tf.cast(mask, tf.float32)) * negative_weights def _split_by_label_concatenate_and_truncate(input_tensor): # takes positive examples and concatenate with negative examples and truncate # DANGER: if n_positives > batch_size down-sampling is incorrect (do not use pb_50) return tf.concat( [ input_tensor[mask], input_tensor[tf.math.logical_not(mask)], ], 0, )[:batch_size] if "weights" not in inputs: # add placeholder so logic below applies even if weights aren't present in inputs inputs["weights"] = tf.ones([tf.shape(inputs[labels[0]])[0], self.config.num_engagements]) for tensor in inputs: if tensor == "weights": inputs[tensor] = inputs[tensor] * tf.reshape(new_weights, [-1, 1]) inputs[tensor] = _split_by_label_concatenate_and_truncate(inputs[tensor]) return inputs def build_preprocess(preprocess_config, mode=config_mod.JobMode.TRAIN): """Builds a preprocess model to apply all preprocessing stages.""" if mode == config_mod.JobMode.INFERENCE: logging.info("Not building preprocessors for dataloading since we are in Inference mode.") return None preprocess_models = [] if preprocess_config.downsample_negatives: preprocess_models.append(DownsampleNegatives(preprocess_config.downsample_negatives)) if preprocess_config.truncate_and_slice: preprocess_models.append(TruncateAndSlice(preprocess_config.truncate_and_slice)) if preprocess_config.downcast: preprocess_models.append(DownCast(preprocess_config.downcast)) if preprocess_config.rectify_labels: preprocess_models.append(RectifyLabels(preprocess_config.rectify_labels)) if preprocess_config.extract_features: preprocess_models.append(ExtractFeatures(preprocess_config.extract_features)) if len(preprocess_models) == 0: raise ValueError("No known preprocessor.") class PreprocessModel(tf.keras.Model): def __init__(self, preprocess_models): super().__init__() self.preprocess_models = preprocess_models def call(self, inputs, training=None, mask=None): outputs = inputs for model in self.preprocess_models: outputs = model(outputs, training, mask) return outputs if len(preprocess_models) > 1: logging.warning( "With multiple preprocessing models, we apply these models in a predefined order. Future works may introduce customized models and orders." ) return PreprocessModel(preprocess_models) ================================================ FILE: projects/home/recap/data/tfe_parsing.py ================================================ import functools import json from tml.projects.home.recap.data import config as recap_data_config from absl import logging import tensorflow as tf DEFAULTS_MAP = {"int64_list": 0, "float_list": 0.0, "bytes_list": ""} DTYPE_MAP = {"int64_list": tf.int64, "float_list": tf.float32, "bytes_list": tf.string} def create_tf_example_schema( data_config: recap_data_config.SegDenseSchema, segdense_schema, ): """Generate schema for deseralizing tf.Example. Args: segdense_schema: List of dicts of segdense features (includes feature_name, dtype, length). labels: List of strings denoting labels. Returns: A dictionary schema suitable for deserializing tf.Example. """ segdense_config = data_config.seg_dense_schema labels = list(data_config.tasks.keys()) used_features = ( segdense_config.features + list(segdense_config.renamed_features.values()) + labels ) logging.info(used_features) tfe_schema = {} for entry in segdense_schema: feature_name = entry["feature_name"] if feature_name in used_features: length = entry["length"] dtype = entry["dtype"] if feature_name in labels: logging.info(f"Label: feature name is {feature_name} type is {dtype}") tfe_schema[feature_name] = tf.io.FixedLenFeature( length, DTYPE_MAP[dtype], DEFAULTS_MAP[dtype] ) elif length == -1: tfe_schema[feature_name] = tf.io.VarLenFeature(DTYPE_MAP[dtype]) else: tfe_schema[feature_name] = tf.io.FixedLenFeature( length, DTYPE_MAP[dtype], [DEFAULTS_MAP[dtype]] * length ) for feature_name in used_features: if feature_name not in tfe_schema: raise ValueError(f"{feature_name} missing from schema: {segdense_config.schema_path}.") return tfe_schema @functools.lru_cache(1) def make_mantissa_mask(mask_length: int) -> tf.Tensor: """For experimentating with emulating bfloat16 or less precise types.""" return tf.constant((1 << 32) - (1 << mask_length), dtype=tf.int32) def mask_mantissa(tensor: tf.Tensor, mask_length: int) -> tf.Tensor: """For experimentating with emulating bfloat16 or less precise types.""" mask: tf.Tensor = make_mantissa_mask(mask_length) return tf.bitcast(tf.bitwise.bitwise_and(tf.bitcast(tensor, tf.int32), mask), tensor.dtype) def parse_tf_example( serialized_example, tfe_schema, seg_dense_schema_config, ): """Parse serialized tf.Example into dict of tensors. Args: serialized_example: Serialized tf.Example to be parsed. tfe_schema: Dictionary schema suitable for deserializing tf.Example. Returns: Dictionary of tensors to be used as model input. """ inputs = tf.io.parse_example(serialized=serialized_example, features=tfe_schema) for new_feature_name, old_feature_name in seg_dense_schema_config.renamed_features.items(): inputs[new_feature_name] = inputs.pop(old_feature_name) # This should not actually be used except for experimentation with low precision floats. if "mask_mantissa_features" in seg_dense_schema_config: for feature_name, mask_length in seg_dense_schema_config.mask_mantissa_features.items(): inputs[feature_name] = mask_mantissa(inputs[feature_name], mask_length) # DANGER DANGER: This default seems really scary, and it's only here because it has to be visible # at TF level. # We should not return empty tensors if we dont use embeddings. # Otherwise, it breaks numpy->pt conversion renamed_keys = list(seg_dense_schema_config.renamed_features.keys()) for renamed_key in renamed_keys: if "embedding" in renamed_key and (renamed_key not in inputs): inputs[renamed_key] = tf.zeros([], tf.float32) logging.info(f"parsed example and inputs are {inputs}") return inputs def get_seg_dense_parse_fn(data_config: recap_data_config.RecapDataConfig): """Placeholder for seg dense. In the future, when we use more seg dense variations, we can change this. """ with tf.io.gfile.GFile(data_config.seg_dense_schema.schema_path, "r") as f: seg_dense_schema = json.load(f)["schema"] tf_example_schema = create_tf_example_schema( data_config, seg_dense_schema, ) logging.info("***** TF Example Schema *****") logging.info(tf_example_schema) parse = functools.partial( parse_tf_example, tfe_schema=tf_example_schema, seg_dense_schema_config=data_config.seg_dense_schema, ) return parse ================================================ FILE: projects/home/recap/data/util.py ================================================ from typing import Mapping, Tuple, Union import torch import torchrec import numpy as np import tensorflow as tf def keyed_tensor_from_tensors_dict( tensor_map: Mapping[str, torch.Tensor] ) -> "torchrec.KeyedTensor": """ Convert a dictionary of torch tensor to torchrec keyed tensor Args: tensor_map: Returns: """ keys = list(tensor_map.keys()) # We expect batch size to be first dim. However, if we get a shape [Batch_size], # KeyedTensor will not find the correct batch_size. So, in those cases we make sure the shape is # [Batch_size x 1]. values = [ tensor_map[key] if len(tensor_map[key].shape) > 1 else torch.unsqueeze(tensor_map[key], -1) for key in keys ] return torchrec.KeyedTensor.from_tensor_list(keys, values) def _compute_jagged_tensor_from_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: if tensor.is_sparse: x = tensor.coalesce() # Ensure that the indices are ordered. lengths = torch.bincount(x.indices()[0]) values = x.values() else: values = tensor lengths = torch.ones(tensor.shape[0], dtype=torch.int32, device=tensor.device) return values, lengths def jagged_tensor_from_tensor(tensor: torch.Tensor) -> "torchrec.JaggedTensor": """ Convert a torch tensor to torchrec jagged tensor. Note: Currently only support shape of [Batch_size] or [Batch_size x N] for dense tensors. For sparse tensor the shape of .values() should be [Batch_size] or [Batch_size x N]; the dense_shape of the sparse tensor can be arbitrary. Args: tensor: a torch (sparse) tensor. Returns: """ values, lengths = _compute_jagged_tensor_from_tensor(tensor) return torchrec.JaggedTensor(values=values, lengths=lengths) def keyed_jagged_tensor_from_tensors_dict( tensor_map: Mapping[str, torch.Tensor] ) -> "torchrec.KeyedJaggedTensor": """ Convert a dictionary of (sparse) torch tensors to torchrec keyed jagged tensor. Note: Currently only support shape of [Batch_size] or [Batch_size x 1] for dense tensors. For sparse tensor the shape of .values() should be [Batch_size] or [Batch_size x 1]; the dense_shape of the sparse tensor can be arbitrary. Args: tensor_map: Returns: """ if not tensor_map: return torchrec.KeyedJaggedTensor( keys=[], values=torch.zeros(0, dtype=torch.int), lengths=torch.zeros(0, dtype=torch.int), ) values = [] lengths = [] for tensor in tensor_map.values(): tensor_val, tensor_len = _compute_jagged_tensor_from_tensor(tensor) values.append(torch.squeeze(tensor_val)) lengths.append(tensor_len) values = torch.cat(values, axis=0) lengths = torch.cat(lengths, axis=0) return torchrec.KeyedJaggedTensor( keys=list(tensor_map.keys()), values=values, lengths=lengths, ) def _tf_to_numpy(tf_tensor: tf.Tensor) -> np.ndarray: return tf_tensor._numpy() # noqa def _dense_tf_to_torch(tensor: tf.Tensor, pin_memory: bool) -> torch.Tensor: tensor = _tf_to_numpy(tensor) # Pytorch does not support bfloat16, up cast to float32 to keep the same number of bits on exponent if tensor.dtype.name == "bfloat16": tensor = tensor.astype(np.float32) tensor = torch.from_numpy(tensor) if pin_memory: tensor = tensor.pin_memory() return tensor def sparse_or_dense_tf_to_torch( tensor: Union[tf.Tensor, tf.SparseTensor], pin_memory: bool ) -> torch.Tensor: if isinstance(tensor, tf.SparseTensor): tensor = torch.sparse_coo_tensor( _dense_tf_to_torch(tensor.indices, pin_memory).t(), _dense_tf_to_torch(tensor.values, pin_memory), torch.Size(_tf_to_numpy(tensor.dense_shape)), ) else: tensor = _dense_tf_to_torch(tensor, pin_memory) return tensor ================================================ FILE: projects/home/recap/embedding/config.py ================================================ from typing import List, Optional import tml.core.config as base_config from tml.optimizers import config as optimizer_config import pydantic class EmbeddingSnapshot(base_config.BaseConfig): """Configuration for Embedding snapshot""" emb_name: str = pydantic.Field( ..., description="Name of the embedding table from the loaded snapshot" ) embedding_snapshot_uri: str = pydantic.Field( ..., description="Path to torchsnapshot of the embedding" ) # https://pytorch.org/torchrec/torchrec.modules.html#torchrec.modules.embedding_configs.EmbeddingBagConfig class EmbeddingBagConfig(base_config.BaseConfig): """Configuration for EmbeddingBag.""" name: str = pydantic.Field(..., description="name of embedding bag") num_embeddings: int = pydantic.Field(..., description="size of embedding dictionary") embedding_dim: int = pydantic.Field(..., description="size of each embedding vector") pretrained: EmbeddingSnapshot = pydantic.Field(None, description="Snapshot properties") vocab: str = pydantic.Field( None, description="Directory to parquet files of mapping from entity ID to table index." ) class EmbeddingOptimizerConfig(base_config.BaseConfig): learning_rate: optimizer_config.LearningRate = pydantic.Field( None, description="learning rate scheduler for the EBC" ) init_learning_rate: float = pydantic.Field(description="initial learning rate for the EBC") # NB: Only sgd is supported right now and implicitly. # FBGemm only supports simple exact_sgd which only takes LR as an argument. class LargeEmbeddingsConfig(base_config.BaseConfig): """Configuration for EmbeddingBagCollection. The tables listed in this config are gathered into a single torchrec EmbeddingBagCollection. """ tables: List[EmbeddingBagConfig] = pydantic.Field(..., description="list of embedding tables") optimizer: EmbeddingOptimizerConfig tables_to_log: List[str] = pydantic.Field( None, description="list of embedding table names that we want to log during training" ) class StratifierConfig(base_config.BaseConfig): name: str index: int value: int class SmallEmbeddingBagConfig(base_config.BaseConfig): """Configuration for SmallEmbeddingBag.""" name: str = pydantic.Field(..., description="name of embedding bag") num_embeddings: int = pydantic.Field(..., description="size of embedding dictionary") embedding_dim: int = pydantic.Field(..., description="size of each embedding vector") index: int = pydantic.Field(..., description="index in the discrete tensor to look for") class SmallEmbeddingBagConfig(base_config.BaseConfig): """Configuration for SmallEmbeddingBag.""" name: str = pydantic.Field(..., description="name of embedding bag") num_embeddings: int = pydantic.Field(..., description="size of embedding dictionary") embedding_dim: int = pydantic.Field(..., description="size of each embedding vector") index: int = pydantic.Field(..., description="index in the discrete tensor to look for") class SmallEmbeddingsConfig(base_config.BaseConfig): """Configuration for SmallEmbeddingConfig. Here we can use discrete features that already are present in our TFRecords generated using segdense conversion as "home_recap_2022_discrete__segdense_vals" which are available in the model as "discrete_features", and embed a user-defined set of them with configurable dimensions and vocabulary sizes. Compared with LargeEmbedding, this config is for small embedding tables that can fit inside the model, whereas LargeEmbedding usually is meant to be hydrated outside the model at serving time due to size (>>1 GB). This small embeddings table uses the same optimizer as the rest of the model.""" tables: List[SmallEmbeddingBagConfig] = pydantic.Field( ..., description="list of embedding tables" ) ================================================ FILE: projects/home/recap/main.py ================================================ import datetime import os from typing import Callable, List, Optional, Tuple import tensorflow as tf import tml.common.checkpointing.snapshot as snapshot_lib from tml.common.device import setup_and_get_device from tml.core import config as tml_config_mod import tml.core.custom_training_loop as ctl from tml.core import debug_training_loop from tml.core import losses from tml.core.loss_type import LossType from tml.model import maybe_shard_model import tml.projects.home.recap.data.dataset as ds import tml.projects.home.recap.config as recap_config_mod import tml.projects.home.recap.optimizer as optimizer_mod # from tml.projects.home.recap import feature import tml.projects.home.recap.model as model_mod import torchmetrics as tm import torch import torch.distributed as dist from torchrec.distributed.model_parallel import DistributedModelParallel from absl import app, flags, logging flags.DEFINE_string("config_path", None, "Path to hyperparameters for model.") flags.DEFINE_bool("debug_loop", False, "Run with debug loop (slow)") FLAGS = flags.FLAGS def run(unused_argv: str, data_service_dispatcher: Optional[str] = None): print("#" * 100) config = tml_config_mod.load_config_from_yaml(recap_config_mod.RecapConfig, FLAGS.config_path) logging.info("Config: %s", config.pretty_print()) device = setup_and_get_device() # Always enable tensorfloat on supported devices. torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True loss_fn = losses.build_multi_task_loss( loss_type=LossType.BCE_WITH_LOGITS, tasks=list(config.model.tasks.keys()), pos_weights=[task.pos_weight for task in config.model.tasks.values()], ) # Since the prod model doesn't use large embeddings, for now we won't support them. assert config.model.large_embeddings is None train_dataset = ds.RecapDataset( data_config=config.train_data, dataset_service=data_service_dispatcher, mode=recap_config_mod.JobMode.TRAIN, compression=config.train_data.dataset_service_compression, vocab_mapper=None, repeat=True, ) train_iterator = iter(train_dataset.to_dataloader()) torch_element_spec = train_dataset.torch_element_spec model = model_mod.create_ranking_model( data_spec=torch_element_spec[0], config=config, loss_fn=loss_fn, device=device, ) optimizer, scheduler = optimizer_mod.build_optimizer(model, config.optimizer, None) model = maybe_shard_model(model, device) datetime_str = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M") print(f"{datetime_str}\n", end="") if FLAGS.debug_loop: logging.warning("Running debug mode, slow!") train_mod = debug_training_loop else: train_mod = ctl train_mod.train( model=model, optimizer=optimizer, device=device, save_dir=config.training.save_dir, logging_interval=config.training.train_log_every_n, train_steps=config.training.num_train_steps, checkpoint_frequency=config.training.checkpoint_every_n, dataset=train_iterator, worker_batch_size=config.train_data.global_batch_size, enable_amp=False, initial_checkpoint_dir=config.training.initial_checkpoint_dir, gradient_accumulation=config.training.gradient_accumulation, scheduler=scheduler, ) if __name__ == "__main__": app.run(run) ================================================ FILE: projects/home/recap/model/__init__.py ================================================ from tml.projects.home.recap.model.entrypoint import ( create_ranking_model, sanitize, unsanitize, MultiTaskRankingModel, ) from tml.projects.home.recap.model.model_and_loss import ModelAndLoss ================================================ FILE: projects/home/recap/model/config.py ================================================ """Configuration for the main Recap model.""" import enum from typing import List, Optional, Dict import tml.core.config as base_config from tml.projects.home.recap.embedding import config as embedding_config import pydantic class DropoutConfig(base_config.BaseConfig): """Configuration for the dropout layer.""" rate: pydantic.PositiveFloat = pydantic.Field( 0.1, description="Fraction of inputs to be dropped." ) class LayerNormConfig(base_config.BaseConfig): """Configruation for the layer normalization.""" epsilon: float = pydantic.Field( 1e-3, description="Small float added to variance to avoid dividing by zero." ) axis: int = pydantic.Field(-1, description="Axis or axes to normalize across.") center: bool = pydantic.Field(True, description="Whether to add learnable center.") scale: bool = pydantic.Field(True, description="Whether to add learnable scale.") class BatchNormConfig(base_config.BaseConfig): """Configuration of the batch normalization layer.""" epsilon: pydantic.PositiveFloat = 1e-5 momentum: pydantic.PositiveFloat = 0.9 training_mode_at_inference_time: bool = False use_renorm: bool = False center: bool = pydantic.Field(True, description="Whether to add learnable center.") scale: bool = pydantic.Field(True, description="Whether to add learnable scale.") class DenseLayerConfig(base_config.BaseConfig): layer_size: pydantic.PositiveInt dropout: DropoutConfig = pydantic.Field(None, description="Optional dropout config for layer.") class MlpConfig(base_config.BaseConfig): """Configuration for MLP model.""" layer_sizes: List[pydantic.PositiveInt] = pydantic.Field(None, one_of="mlp_layer_definition") layers: List[DenseLayerConfig] = pydantic.Field(None, one_of="mlp_layer_definition") class BatchNormConfig(base_config.BaseConfig): """Configuration for the batch norm layer.""" affine: bool = pydantic.Field(True, description="Use affine transformation.") momentum: pydantic.PositiveFloat = pydantic.Field( 0.1, description="Forgetting parameter in moving average." ) class DoubleNormLogConfig(base_config.BaseConfig): batch_norm_config: Optional[BatchNormConfig] = pydantic.Field(None) clip_magnitude: float = pydantic.Field( 5.0, description="Threshold to clip the normalized input values." ) layer_norm_config: Optional[LayerNormConfig] = pydantic.Field(None) class Log1pAbsConfig(base_config.BaseConfig): """Simple configuration where only the log transform is performed.""" class ClipLog1pAbsConfig(base_config.BaseConfig): clip_magnitude: pydantic.NonNegativeFloat = pydantic.Field( 3e38, description="Threshold to clip the input values." ) class ZScoreLogConfig(base_config.BaseConfig): analysis_path: str schema_path: str = pydantic.Field( None, description="Schema path which feaure statistics are generated with. Can be different from scehma in data config.", ) clip_magnitude: float = pydantic.Field( 5.0, description="Threshold to clip the normalized input values." ) use_batch_norm: bool = pydantic.Field( False, description="Option to use batch normalization on the inputs." ) use_renorm: bool = pydantic.Field( False, description="Option to use batch renormalization for trainig and serving consistency." ) use_bq_stats: bool = pydantic.Field( False, description="Option to load the partitioned json files from BQ as statistics." ) class FeaturizationConfig(base_config.BaseConfig): """Configuration for featurization.""" log1p_abs_config: Log1pAbsConfig = pydantic.Field(None, one_of="featurization") clip_log1p_abs_config: ClipLog1pAbsConfig = pydantic.Field(None, one_of="featurization") z_score_log_config: ZScoreLogConfig = pydantic.Field(None, one_of="featurization") double_norm_log_config: DoubleNormLogConfig = pydantic.Field(None, one_of="featurization") feature_names_to_concat: List[str] = pydantic.Field( ["binary"], description="Feature names to concatenate as raw values with continuous features." ) class DropoutConfig(base_config.BaseConfig): """Configuration for the dropout layer.""" rate: pydantic.PositiveFloat = pydantic.Field( 0.1, description="Fraction of inputs to be dropped." ) class MlpConfig(base_config.BaseConfig): """Configuration for MLP model.""" layer_sizes: List[pydantic.PositiveInt] batch_norm: BatchNormConfig = pydantic.Field( None, description="Optional batch norm configuration." ) dropout: DropoutConfig = pydantic.Field(None, description="Optional dropout configuration.") final_layer_activation: bool = pydantic.Field( False, description="Whether to include activation on final layer." ) class DcnConfig(base_config.BaseConfig): """Config for DCN model.""" poly_degree: pydantic.PositiveInt projection_dim: pydantic.PositiveInt = pydantic.Field( None, description="Factorizes main DCN matmul with projection." ) parallel_mlp: Optional[MlpConfig] = pydantic.Field( None, description="Config for the mlp if used. If None, only the cross layers are used." ) use_parallel: bool = pydantic.Field(True, description="Whether to use parallel DCN.") output_mlp: Optional[MlpConfig] = pydantic.Field(None, description="Config for the output mlp.") class MaskBlockConfig(base_config.BaseConfig): output_size: int reduction_factor: Optional[pydantic.PositiveFloat] = pydantic.Field( None, one_of="aggregation_size" ) aggregation_size: Optional[pydantic.PositiveInt] = pydantic.Field( None, description="Specify the aggregation size directly.", one_of="aggregation_size" ) input_layer_norm: bool class MaskNetConfig(base_config.BaseConfig): mask_blocks: List[MaskBlockConfig] mlp: Optional[MlpConfig] = pydantic.Field(None, description="MLP Configuration for parallel") use_parallel: bool = pydantic.Field(False, description="Whether to use parallel MaskNet.") class PositionDebiasConfig(base_config.BaseConfig): """ Configuration for Position Debias. """ max_position: int = pydantic.Field(256, description="Bucket all later positions.") num_dims: pydantic.PositiveInt = pydantic.Field( 64, description="Number of dimensions in embedding." ) drop_probability: float = pydantic.Field(0.5, description="Probability of dropping position.") # Currently it should be 51 based on dataset being tested at the time of writing this model # However, no default provided here to make sure user of the model is aware of its importance. position_feature_index: int = pydantic.Field( description="The index of the position feature in the discrete features" ) class AffineMap(base_config.BaseConfig): """An affine map that scales the logits into the appropriate range.""" scale: float = pydantic.Field(1.0) bias: float = pydantic.Field(0.0) class DLRMConfig(base_config.BaseConfig): bottom_mlp: MlpConfig = pydantic.Field( ..., description="Bottom mlp, the output to be combined with sparse features and feed to interaction", ) top_mlp: MlpConfig = pydantic.Field(..., description="Top mlp, generate the final output") class TaskModel(base_config.BaseConfig): mlp_config: MlpConfig = pydantic.Field(None, one_of="architecture") dcn_config: DcnConfig = pydantic.Field(None, one_of="architecture") dlrm_config: DLRMConfig = pydantic.Field(None, one_of="architecture") mask_net_config: MaskNetConfig = pydantic.Field(None, one_of="architecture") affine_map: AffineMap = pydantic.Field( None, description="Affine map applied to logits so we can represent a broader range of probabilities.", ) # DANGER DANGER: not implemented yet. # loss_weight: float = pydantic.Field(1.0, description="Weight for task in loss.") pos_weight: float = pydantic.Field(1.0, description="Weight of positive in loss.") class MultiTaskType(str, enum.Enum): SHARE_NONE = "share_none" # Tasks are separate. SHARE_ALL = "share_all" # Tasks share same backbone. SHARE_PARTIAL = "share_partial" # Tasks share some backbone, but have their own portions. class ModelConfig(base_config.BaseConfig): """Specify model architecture.""" tasks: Dict[str, TaskModel] = pydantic.Field( description="Specification of architecture per task." ) large_embeddings: embedding_config.LargeEmbeddingsConfig = pydantic.Field(None) small_embeddings: embedding_config.SmallEmbeddingsConfig = pydantic.Field(None) # Not implemented yet. # multi_task_loss_reduction_fn: str = "mean" position_debias_config: PositionDebiasConfig = pydantic.Field( default=None, description="position debias model configuration" ) featurization_config: FeaturizationConfig = pydantic.Field(None) multi_task_type: MultiTaskType = pydantic.Field( MultiTaskType.SHARE_NONE, description="Multi task architecture" ) backbone: TaskModel = pydantic.Field(None, description="Type of architecture for the backbone.") stratifiers: List[embedding_config.StratifierConfig] = pydantic.Field( default=None, description="Discrete features and values to stratify metrics by." ) @pydantic.root_validator() def _validate_mtl(cls, values): if values.get("multi_task_type", None) is None: return values elif values["multi_task_type"] in [MultiTaskType.SHARE_ALL, MultiTaskType.SHARE_PARTIAL]: if values.get("backbone", None) is None: raise ValueError("Require `backbone` for SHARE_ALL and SHARE_PARTIAL.") elif values["multi_task_type"] in [ MultiTaskType.SHARE_NONE, ]: if values.get("backbone", None) is not None: raise ValueError("Can not have backbone if the share type is SHARE_NONE") return values ================================================ FILE: projects/home/recap/model/entrypoint.py ================================================ from __future__ import annotations from absl import logging import torch from typing import Optional, Callable, Mapping, Dict, Sequence, TYPE_CHECKING from tml.projects.home.recap.model import feature_transform from tml.projects.home.recap.model import config as model_config_mod from tml.projects.home.recap.model import mlp from tml.projects.home.recap.model import mask_net from tml.projects.home.recap.model import numeric_calibration from tml.projects.home.recap.model.model_and_loss import ModelAndLoss import tml.projects.home.recap.model.config as model_config_mod if TYPE_CHECKING: from tml.projects.home.recap import config as config_mod from tml.projects.home.recap.data.config import RecapDataConfig from tml.projects.home.recap.model.config import ModelConfig def sanitize(task_name): return task_name.replace(".", "__") def unsanitize(sanitized_task_name): return sanitized_task_name.replace("__", ".") def _build_single_task_model(task: model_config_mod.TaskModel, input_shape: int): """ "Builds a model for a single task""" if task.mlp_config: return mlp.Mlp(in_features=input_shape, mlp_config=task.mlp_config) elif task.dcn_config: return dcn.Dcn(dcn_config=task.dcn_config, in_features=input_shape) elif task.mask_net_config: return mask_net.MaskNet(mask_net_config=task.mask_net_config, in_features=input_shape) else: raise ValueError("This should never be reached.") class MultiTaskRankingModel(torch.nn.Module): """Multi-task ranking model.""" def __init__( self, input_shapes: Mapping[str, torch.Size], config: ModelConfig, data_config: RecapDataConfig, return_backbone: bool = False, ): """Constructor for Multi task learning. Assumptions made: 1. Tasks specified in data config match model architecture. These are all validated in config. """ super().__init__() self._config = config self._data_config = data_config self._preprocessor = feature_transform.build_features_preprocessor( config.featurization_config, input_shapes ) self.return_backbone = return_backbone self.embeddings = None self.small_embeddings = None embedding_dims = 0 if config.large_embeddings: from large_embeddings.models.learnable_embeddings import LargeEmbeddings self.embeddings = LargeEmbeddings(large_embeddings_config=config.large_embeddings) embedding_dims += sum([table.embedding_dim for table in config.large_embeddings.tables]) logging.info(f"Emb dim: {embedding_dims}") if config.small_embeddings: self.small_embeddings = SmallEmbedding(config.small_embeddings) embedding_dims += sum([table.embedding_dim for table in config.small_embeddings.tables]) logging.info(f"Emb dim (with small embeddings): {embedding_dims}") if "user_embedding" in data_config.seg_dense_schema.renamed_features: embedding_dims += input_shapes["user_embedding"][-1] self._user_embedding_layer_norm = torch.nn.LayerNorm(input_shapes["user_embedding"][-1]) else: self._user_embedding_layer_norm = None if "user_eng_embedding" in data_config.seg_dense_schema.renamed_features: embedding_dims += input_shapes["user_eng_embedding"][-1] self._user_eng_embedding_layer_norm = torch.nn.LayerNorm( input_shapes["user_eng_embedding"][-1] ) else: self._user_eng_embedding_layer_norm = None if "author_embedding" in data_config.seg_dense_schema.renamed_features: embedding_dims += input_shapes["author_embedding"][-1] self._author_embedding_layer_norm = torch.nn.LayerNorm(input_shapes["author_embedding"][-1]) else: self._author_embedding_layer_norm = None input_dims = input_shapes["continuous"][-1] + input_shapes["binary"][-1] + embedding_dims if config.position_debias_config: self.position_debias_model = PositionDebias(config.position_debias_config) input_dims += self.position_debias_model.out_features else: self.position_debias_model = None logging.info(f"input dim: {input_dims}") if config.multi_task_type in [ model_config_mod.MultiTaskType.SHARE_ALL, model_config_mod.MultiTaskType.SHARE_PARTIAL, ]: self._backbone = _build_single_task_model(config.backbone, input_dims) else: self._backbone = None _towers: Dict[str, torch.nn.Module] = {} _calibrators: Dict[str, torch.nn.Module] = {} _affine_maps: Dict[str, torch.nn.Module] = {} for task_name, task_architecture in config.tasks.items(): safe_name = sanitize(task_name) # Complex input dimension calculation. if config.multi_task_type == model_config_mod.MultiTaskType.SHARE_NONE: num_inputs = input_dims elif config.multi_task_type == model_config_mod.MultiTaskType.SHARE_ALL: num_inputs = self._backbone.out_features elif config.multi_task_type == model_config_mod.MultiTaskType.SHARE_PARTIAL: num_inputs = input_dims + self._backbone.out_features else: raise ValueError("Unreachable branch of enum.") # Annoyingly, ModuleDict doesn't allow . inside key names. _towers[safe_name] = _build_single_task_model(task_architecture, num_inputs) if task_architecture.affine_map: affine_map = torch.nn.Linear(1, 1) affine_map.weight.data = torch.tensor([[task_architecture.affine_map.scale]]) affine_map.bias.data = torch.tensor([task_architecture.affine_map.bias]) _affine_maps[safe_name] = affine_map else: _affine_maps[safe_name] = torch.nn.Identity() _calibrators[safe_name] = numeric_calibration.NumericCalibration( pos_downsampling_rate=data_config.tasks[task_name].pos_downsampling_rate, neg_downsampling_rate=data_config.tasks[task_name].neg_downsampling_rate, ) self._task_names = list(config.tasks.keys()) self._towers = torch.nn.ModuleDict(_towers) self._affine_maps = torch.nn.ModuleDict(_affine_maps) self._calibrators = torch.nn.ModuleDict(_calibrators) self._counter = torch.autograd.Variable(torch.tensor(0), requires_grad=False) def forward( self, continuous_features: torch.Tensor, binary_features: torch.Tensor, discrete_features: Optional[torch.Tensor] = None, sparse_features=None, # Optional[KeyedJaggedTensor] user_embedding: Optional[torch.Tensor] = None, user_eng_embedding: Optional[torch.Tensor] = None, author_embedding: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, ): concat_dense_features = [ self._preprocessor(continuous_features=continuous_features, binary_features=binary_features) ] if self.embeddings: concat_dense_features.append(self.embeddings(sparse_features)) # Twhin embedding layer norms if self.small_embeddings: if discrete_features is None: raise ValueError( "Forward arg discrete_features is None, but since small_embeddings are used, a Tensor is expected." ) concat_dense_features.append(self.small_embeddings(discrete_features)) if self._user_embedding_layer_norm: if user_embedding is None: raise ValueError( "Forward arg user_embedding is None, but since Twhin user_embeddings are used by the model, a Tensor is expected." ) concat_dense_features.append(self._user_embedding_layer_norm(user_embedding)) if self._user_eng_embedding_layer_norm: if user_eng_embedding is None: raise ValueError( "Forward arg user_eng_embedding is None, but since Twhin user_eng_embeddings are used by the model, a Tensor is expected." ) concat_dense_features.append(self._user_eng_embedding_layer_norm(user_eng_embedding)) if self._author_embedding_layer_norm: if author_embedding is None: raise ValueError( "Forward arg author_embedding is None, but since Twhin author_embeddings are used by the model, a Tensor is expected." ) concat_dense_features.append(self._author_embedding_layer_norm(author_embedding)) if self.position_debias_model: if discrete_features is None: raise ValueError( "Forward arg discrete_features is None, but since position_debias_model is used, a Tensor is expected." ) concat_dense_features.append(self.position_debias_model(discrete_features)) if discrete_features is not None and not (self.position_debias_model or self.small_embeddings): logging.warning("Forward arg discrete_features is passed, but never used.") concat_dense_features = torch.cat(concat_dense_features, dim=1) if self._backbone: if self._config.multi_task_type == model_config_mod.MultiTaskType.SHARE_ALL: net = self._backbone(concat_dense_features)["output"] elif self._config.multi_task_type == model_config_mod.MultiTaskType.SHARE_PARTIAL: net = torch.cat( [concat_dense_features, self._backbone(concat_dense_features)["output"]], dim=1 ) else: net = concat_dense_features backbone_result = net all_logits = [] all_probabilities = [] all_calibrated_probabilities = [] for task_name in self._task_names: safe_name = sanitize(task_name) tower_outputs = self._towers[safe_name](net) logits = tower_outputs["output"] scaled_logits = self._affine_maps[safe_name](logits) probabilities = torch.sigmoid(scaled_logits) calibrated_probabilities = self._calibrators[safe_name](probabilities) all_logits.append(scaled_logits) all_probabilities.append(probabilities) all_calibrated_probabilities.append(calibrated_probabilities) results = { "logits": torch.squeeze(torch.stack(all_logits, dim=1), dim=-1), "probabilities": torch.squeeze(torch.stack(all_probabilities, dim=1), dim=-1), "calibrated_probabilities": torch.squeeze( torch.stack(all_calibrated_probabilities, dim=1), dim=-1 ), } # Returning the backbone is intended for stitching post-tf conversion # Leaving this on will ~200x the size of the output # and could slow things down if self.return_backbone: results["backbone"] = backbone_result return results def create_ranking_model( data_spec, # Used for planner to be batch size aware. config: config_mod.RecapConfig, device: torch.device, loss_fn: Optional[Callable] = None, data_config=None, return_backbone=False, ): if list(config.model.tasks.values())[0].dlrm_config: raise NotImplementedError() model = EmbeddingRankingModel( input_shapes=data_spec, config=all_config.model, data_config=all_config.train_data, ) else: model = MultiTaskRankingModel( input_shapes=data_spec, config=config.model, data_config=data_config if data_config is not None else config.train_data, return_backbone=return_backbone, ) logging.info("***** Model Architecture *****") logging.info(model) logging.info("***** Named Parameters *****") for elem in model.named_parameters(): logging.info(elem[0]) if loss_fn: logging.info("***** Wrapping in loss *****") model = ModelAndLoss( model=model, loss_fn=loss_fn, stratifiers=config.model.stratifiers, ) return model ================================================ FILE: projects/home/recap/model/feature_transform.py ================================================ from typing import Mapping, Sequence, Union from tml.projects.home.recap.model.config import ( BatchNormConfig, DoubleNormLogConfig, FeaturizationConfig, LayerNormConfig, ) import torch def log_transform(x: torch.Tensor) -> torch.Tensor: """Safe log transform that works across both negative, zero, and positive floats.""" return torch.sign(x) * torch.log1p(torch.abs(x)) class BatchNorm(torch.nn.Module): def __init__(self, num_features: int, config: BatchNormConfig): super().__init__() self.layer = torch.nn.BatchNorm1d(num_features, affine=config.affine, momentum=config.momentum) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.layer(x) class LayerNorm(torch.nn.Module): def __init__(self, normalized_shape: Union[int, Sequence[int]], config: LayerNormConfig): super().__init__() if config.axis != -1: raise NotImplementedError if config.center != config.scale: raise ValueError( f"Center and scale must match in torch, received {config.center}, {config.scale}" ) self.layer = torch.nn.LayerNorm( normalized_shape, eps=config.epsilon, elementwise_affine=config.center ) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.layer(x) class Log1pAbs(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x: torch.Tensor) -> torch.Tensor: return log_transform(x) class InputNonFinite(torch.nn.Module): def __init__(self, fill_value: float = 0): super().__init__() self.register_buffer( "fill_value", torch.as_tensor(fill_value, dtype=torch.float32), persistent=False ) def forward(self, x: torch.Tensor) -> torch.Tensor: return torch.where(torch.isfinite(x), x, self.fill_value) class Clamp(torch.nn.Module): def __init__(self, min_value: float, max_value: float): super().__init__() # Using buffer to make sure they are on correct device (and not moved every time). # Will also be part of state_dict. self.register_buffer( "min_value", torch.as_tensor(min_value, dtype=torch.float32), persistent=True ) self.register_buffer( "max_value", torch.as_tensor(max_value, dtype=torch.float32), persistent=True ) def forward(self, x: torch.Tensor) -> torch.Tensor: return torch.clamp(x, min=self.min_value, max=self.max_value) class DoubleNormLog(torch.nn.Module): """Performs a batch norm and clamp on continuous features followed by a layer norm on binary and continuous features.""" def __init__( self, input_shapes: Mapping[str, Sequence[int]], config: DoubleNormLogConfig, ): super().__init__() _before_concat_layers = [ InputNonFinite(), Log1pAbs(), ] if config.batch_norm_config: _before_concat_layers.append( BatchNorm(input_shapes["continuous"][-1], config.batch_norm_config) ) _before_concat_layers.append( Clamp(min_value=-config.clip_magnitude, max_value=config.clip_magnitude) ) self._before_concat_layers = torch.nn.Sequential(*_before_concat_layers) self.layer_norm = None if config.layer_norm_config: last_dim = input_shapes["continuous"][-1] + input_shapes["binary"][-1] self.layer_norm = LayerNorm(last_dim, config.layer_norm_config) def forward( self, continuous_features: torch.Tensor, binary_features: torch.Tensor ) -> torch.Tensor: x = self._before_concat_layers(continuous_features) x = torch.cat([x, binary_features], dim=1) if self.layer_norm: return self.layer_norm(x) return x def build_features_preprocessor( config: FeaturizationConfig, input_shapes: Mapping[str, Sequence[int]] ): """Trivial right now, but we will change in the future.""" return DoubleNormLog(input_shapes, config.double_norm_log_config) ================================================ FILE: projects/home/recap/model/mask_net.py ================================================ """MaskNet: Wang et al. (https://arxiv.org/abs/2102.07619).""" from tml.projects.home.recap.model import config, mlp import torch def _init_weights(module): if isinstance(module, torch.nn.Linear): torch.nn.init.xavier_uniform_(module.weight) torch.nn.init.constant_(module.bias, 0) class MaskBlock(torch.nn.Module): def __init__( self, mask_block_config: config.MaskBlockConfig, input_dim: int, mask_input_dim: int ) -> None: super(MaskBlock, self).__init__() self.mask_block_config = mask_block_config output_size = mask_block_config.output_size if mask_block_config.input_layer_norm: self._input_layer_norm = torch.nn.LayerNorm(input_dim) else: self._input_layer_norm = None if mask_block_config.reduction_factor: aggregation_size = int(mask_input_dim * mask_block_config.reduction_factor) elif mask_block_config.aggregation_size is not None: aggregation_size = mask_block_config.aggregation_size else: raise ValueError("Need one of reduction factor or aggregation size.") self._mask_layer = torch.nn.Sequential( torch.nn.Linear(mask_input_dim, aggregation_size), torch.nn.ReLU(), torch.nn.Linear(aggregation_size, input_dim), ) self._mask_layer.apply(_init_weights) self._hidden_layer = torch.nn.Linear(input_dim, output_size) self._hidden_layer.apply(_init_weights) self._layer_norm = torch.nn.LayerNorm(output_size) def forward(self, net: torch.Tensor, mask_input: torch.Tensor): if self._input_layer_norm: net = self._input_layer_norm(net) hidden_layer_output = self._hidden_layer(net * self._mask_layer(mask_input)) return self._layer_norm(hidden_layer_output) class MaskNet(torch.nn.Module): def __init__(self, mask_net_config: config.MaskNetConfig, in_features: int): super().__init__() self.mask_net_config = mask_net_config mask_blocks = [] if mask_net_config.use_parallel: total_output_mask_blocks = 0 for mask_block_config in mask_net_config.mask_blocks: mask_blocks.append(MaskBlock(mask_block_config, in_features, in_features)) total_output_mask_blocks += mask_block_config.output_size self._mask_blocks = torch.nn.ModuleList(mask_blocks) else: input_size = in_features for mask_block_config in mask_net_config.mask_blocks: mask_blocks.append(MaskBlock(mask_block_config, input_size, in_features)) input_size = mask_block_config.output_size self._mask_blocks = torch.nn.ModuleList(mask_blocks) total_output_mask_blocks = mask_block_config.output_size if mask_net_config.mlp: self._dense_layers = mlp.Mlp(total_output_mask_blocks, mask_net_config.mlp) self.out_features = mask_net_config.mlp.layer_sizes[-1] else: self.out_features = total_output_mask_blocks self.shared_size = total_output_mask_blocks def forward(self, inputs: torch.Tensor): if self.mask_net_config.use_parallel: mask_outputs = [] for mask_layer in self._mask_blocks: mask_outputs.append(mask_layer(mask_input=inputs, net=inputs)) # Share the outputs of the MaskBlocks. all_mask_outputs = torch.cat(mask_outputs, dim=1) output = ( all_mask_outputs if self.mask_net_config.mlp is None else self._dense_layers(all_mask_outputs)["output"] ) return {"output": output, "shared_layer": all_mask_outputs} else: net = inputs for mask_layer in self._mask_blocks: net = mask_layer(net=net, mask_input=inputs) # Share the output of the stacked MaskBlocks. output = net if self.mask_net_config.mlp is None else self._dense_layers[net]["output"] return {"output": output, "shared_layer": net} ================================================ FILE: projects/home/recap/model/mlp.py ================================================ """MLP feed forward stack in torch.""" from tml.projects.home.recap.model.config import MlpConfig import torch from absl import logging def _init_weights(module): if isinstance(module, torch.nn.Linear): torch.nn.init.xavier_uniform_(module.weight) torch.nn.init.constant_(module.bias, 0) class Mlp(torch.nn.Module): def __init__(self, in_features: int, mlp_config: MlpConfig): super().__init__() self._mlp_config = mlp_config input_size = in_features layer_sizes = mlp_config.layer_sizes modules = [] for layer_size in layer_sizes[:-1]: modules.append(torch.nn.Linear(input_size, layer_size, bias=True)) if mlp_config.batch_norm: modules.append( torch.nn.BatchNorm1d( layer_size, affine=mlp_config.batch_norm.affine, momentum=mlp_config.batch_norm.momentum ) ) modules.append(torch.nn.ReLU()) if mlp_config.dropout: modules.append(torch.nn.Dropout(mlp_config.dropout.rate)) input_size = layer_size modules.append(torch.nn.Linear(input_size, layer_sizes[-1], bias=True)) if mlp_config.final_layer_activation: modules.append(torch.nn.ReLU()) self.layers = torch.nn.ModuleList(modules) self.layers.apply(_init_weights) def forward(self, x: torch.Tensor) -> torch.Tensor: net = x for i, layer in enumerate(self.layers): net = layer(net) if i == 1: # Share the first (widest?) set of activations for other applications. shared_layer = net return {"output": net, "shared_layer": shared_layer} @property def shared_size(self): return self._mlp_config.layer_sizes[-1] @property def out_features(self): return self._mlp_config.layer_sizes[-1] ================================================ FILE: projects/home/recap/model/model_and_loss.py ================================================ from typing import Callable, Optional, List from tml.projects.home.recap.embedding import config as embedding_config_mod import torch from absl import logging class ModelAndLoss(torch.nn.Module): def __init__( self, model, loss_fn: Callable, stratifiers: Optional[List[embedding_config_mod.StratifierConfig]] = None, ) -> None: """ Args: model: torch module to wrap. loss_fn: Function for calculating loss, should accept logits and labels. straitifiers: mapping of stratifier name and index of discrete features to emit for metrics stratification. """ super().__init__() self.model = model self.loss_fn = loss_fn self.stratifiers = stratifiers def forward(self, batch: "RecapBatch"): # type: ignore[name-defined] """Runs model forward and calculates loss according to given loss_fn. NOTE: The input signature here needs to be a Pipelineable object for prefetching purposes during training using torchrec's pipeline. However the underlying model signature needs to be exportable to onnx, requiring generic python types. see https://pytorch.org/docs/stable/onnx.html#types. """ outputs = self.model( continuous_features=batch.continuous_features, binary_features=batch.binary_features, discrete_features=batch.discrete_features, sparse_features=batch.sparse_features, user_embedding=batch.user_embedding, user_eng_embedding=batch.user_eng_embedding, author_embedding=batch.author_embedding, labels=batch.labels, weights=batch.weights, ) losses = self.loss_fn(outputs["logits"], batch.labels.float(), batch.weights.float()) if self.stratifiers: logging.info(f"***** Adding stratifiers *****\n {self.stratifiers}") outputs["stratifiers"] = {} for stratifier in self.stratifiers: outputs["stratifiers"][stratifier.name] = batch.discrete_features[:, stratifier.index] # In general, we can have a large number of losses returned by our loss function. if isinstance(losses, dict): return losses["loss"], { **outputs, **losses, "labels": batch.labels, "weights": batch.weights, } else: # Assume that this is a float. return losses, { **outputs, "loss": losses, "labels": batch.labels, "weights": batch.weights, } ================================================ FILE: projects/home/recap/model/numeric_calibration.py ================================================ import torch class NumericCalibration(torch.nn.Module): def __init__( self, pos_downsampling_rate: float, neg_downsampling_rate: float, ): super().__init__() # Using buffer to make sure they are on correct device (and not moved every time). # Will also be part of state_dict. self.register_buffer( "ratio", torch.as_tensor(neg_downsampling_rate / pos_downsampling_rate), persistent=True ) def forward(self, probs: torch.Tensor): return probs * self.ratio / (1.0 - probs + (self.ratio * probs)) ================================================ FILE: projects/home/recap/optimizer/__init__.py ================================================ from tml.projects.home.recap.optimizer.optimizer import build_optimizer ================================================ FILE: projects/home/recap/optimizer/config.py ================================================ """Optimization configurations for models.""" import typing import tml.core.config as base_config import tml.optimizers.config as optimizers_config_mod import pydantic class RecapAdamConfig(base_config.BaseConfig): beta_1: float = 0.9 # Momentum term. beta_2: float = 0.999 # Exponential weighted decay factor. epsilon: float = 1e-7 # Numerical stability in denominator. class MultiTaskLearningRates(base_config.BaseConfig): tower_learning_rates: typing.Dict[str, optimizers_config_mod.LearningRate] = pydantic.Field( description="Learning rates for different towers of the model." ) backbone_learning_rate: optimizers_config_mod.LearningRate = pydantic.Field( None, description="Learning rate for backbone of the model." ) class RecapOptimizerConfig(base_config.BaseConfig): multi_task_learning_rates: MultiTaskLearningRates = pydantic.Field( None, description="Multiple learning rates for different tasks.", one_of="lr" ) single_task_learning_rate: optimizers_config_mod.LearningRate = pydantic.Field( None, description="Single task learning rates", one_of="lr" ) adam: RecapAdamConfig = pydantic.Field(one_of="optimizer") ================================================ FILE: projects/home/recap/optimizer/optimizer.py ================================================ """Build optimizers and learning rate schedules.""" import bisect from collections import defaultdict import functools import math import typing from typing import Optional import warnings # from large_embeddings.config import EmbeddingOptimizerConfig from tml.projects.home.recap import model as model_mod from tml.optimizers import config from tml.optimizers import compute_lr from absl import logging # type: ignore[attr-defined] import torch from torchrec.optim import keyed _DEFAULT_LR = 24601.0 # NaN the model if we're not using the learning rate. _BACKBONE = "backbone" _DENSE_EMBEDDINGS = "dense_ebc" class RecapLRShim(torch.optim.lr_scheduler._LRScheduler): """Shim to get learning rates into a LRScheduler. This adheres to the torch.optim scheduler API and can be plugged anywhere that e.g. exponential decay can be used. """ def __init__( self, optimizer, lr_dict: typing.Dict[str, config.LearningRate], emb_learning_rate, last_epoch=-1, verbose=False, ): self.optimizer = optimizer self.lr_dict = lr_dict self.group_names = list(self.lr_dict.keys()) self.emb_learning_rate = emb_learning_rate # We handle sparse LR scheduling separately, so only validate LR groups against dense param groups num_dense_param_groups = sum( 1 for _, _optim in optimizer._optims for _ in _optim.param_groups if isinstance(_optim, keyed.KeyedOptimizerWrapper) ) if num_dense_param_groups != len(lr_dict): raise ValueError( f"Optimizer had {len(optimizer.param_groups)}, but config had {len(lr_dict)}." ) super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn( "To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning, ) return self._get_closed_form_lr() def _get_closed_form_lr(self): learning_rates = [] for lr_config in self.lr_dict.values(): learning_rates.append(compute_lr(lr_config, self.last_epoch)) # WARNING: The order of appending is important. if self.emb_learning_rate: learning_rates.append(compute_lr(self.emb_learning_rate, self.last_epoch)) return learning_rates def build_optimizer( model: torch.nn.Module, optimizer_config: config.OptimizerConfig, emb_optimizer_config: None = None, # Optional[EmbeddingOptimizerConfig] = None, ): """Builds an optimizer and scheduler. Args: model: A torch model, probably with DDP/DMP. optimizer_config: An OptimizerConfig object that specifies learning rates per tower. Returns: A torch.optim instance, and a scheduler instance. """ optimizer_fn = functools.partial( torch.optim.Adam, lr=_DEFAULT_LR, betas=(optimizer_config.adam.beta_1, optimizer_config.adam.beta_2), eps=optimizer_config.adam.epsilon, maximize=False, ) if optimizer_config.multi_task_learning_rates: logging.info("***** Parameter groups for optimization *****") # Importantly, we preserve insertion order in dictionaries here. parameter_groups: typing.Dict[str, typing.Dict] = defaultdict(dict) added_parameters: typing.Set[str] = set() for task in optimizer_config.multi_task_learning_rates.tower_learning_rates: for name, parameter in model.named_parameters(): if f".{model_mod.sanitize(task)}." in name: parameter_groups[task][name] = parameter logging.info(f"{task}: {name}") if name in added_parameters: raise ValueError(f"Parameter {name} matched multiple tasks.") added_parameters.add(name) for name, parameter in model.named_parameters(): if name not in added_parameters and "embedding_bags" not in name: parameter_groups[_BACKBONE][name] = parameter added_parameters.add(name) logging.info(f"{_BACKBONE}: {name}") for name, parameter in model.named_parameters(): if name not in added_parameters and "embedding_bags" in name: parameter_groups[_DENSE_EMBEDDINGS][name] = parameter logging.info(f"{_DENSE_EMBEDDINGS}: {name}") all_learning_rates = optimizer_config.multi_task_learning_rates.tower_learning_rates.copy() if optimizer_config.multi_task_learning_rates.backbone_learning_rate is not None: all_learning_rates[ _BACKBONE ] = optimizer_config.multi_task_learning_rates.backbone_learning_rate if _DENSE_EMBEDDINGS in parameter_groups and emb_optimizer_config: all_learning_rates[_DENSE_EMBEDDINGS] = emb_optimizer_config.learning_rate.copy() else: parameter_groups = dict(model.named_parameters()) all_learning_rates = {"single_task": optimizer_config.single_task_learning_rate} optimizers = [ keyed.KeyedOptimizerWrapper(param_group, optimizer_fn) for param_name, param_group in parameter_groups.items() if param_name != _DENSE_EMBEDDINGS ] # Making EBC optimizer to be SGD to match fused optimiser if _DENSE_EMBEDDINGS in parameter_groups: optimizers.append( keyed.KeyedOptimizerWrapper( parameter_groups[_DENSE_EMBEDDINGS], functools.partial(torch.optim.SGD, lr=_DEFAULT_LR, maximize=False, momentum=False), ) ) if not parameter_groups.keys() == all_learning_rates.keys(): raise ValueError("Learning rates do not match optimizers") # If the optimiser is dense, model.fused_optimizer will be empty (but not None) emb_learning_rate = None if hasattr(model, "fused_optimizer") and model.fused_optimizer.optimizers: logging.info(f"Model fused optimiser: {model.fused_optimizer}") optimizers.append(model.fused_optimizer) if emb_optimizer_config: emb_learning_rate = emb_optimizer_config.learning_rate.copy() else: raise ValueError("Fused kernel exists, but LR is not set") logging.info(f"***** Combining optimizers: {optimizers} *****") optimizer = keyed.CombinedOptimizer(optimizers) scheduler = RecapLRShim(optimizer, all_learning_rates, emb_learning_rate) logging.info(f"***** Combined optimizer after init: {optimizer} *****") return optimizer, scheduler ================================================ FILE: projects/home/recap/script/create_random_data.sh ================================================ #!/usr/bin/env bash # Runs from inside venv rm -rf $HOME/tmp/runs/recap_local_random_data python -m tml.machines.is_venv || exit 1 export TML_BASE="$(git rev-parse --show-toplevel)" mkdir -p $HOME/tmp/recap_local_random_data python projects/home/recap/data/generate_random_data.py --config_path $(pwd)/projects/home/recap/config/local_prod.yaml ================================================ FILE: projects/home/recap/script/run_local.sh ================================================ #!/usr/bin/env bash # Runs from inside venv rm -rf $HOME/tmp/runs/recap_local_debug mkdir -p $HOME/tmp/runs/recap_local_debug python -m tml.machines.is_venv || exit 1 export TML_BASE="$(git rev-parse --show-toplevel)" torchrun \ --standalone \ --nnodes 1 \ --nproc_per_node 1 \ projects/home/recap/main.py \ --config_path $(pwd)/projects/home/recap/config/local_prod.yaml \ $@ ================================================ FILE: projects/twhin/README.md ================================================ Twhin in torchrec This project contains code for pretraining dense vector embedding features for Twitter entities. Within Twitter, these embeddings are used for candidate retrieval and as model features in a variety of recommender system models. We obtain entity embeddings based on a variety of graph data within Twitter such as: "User follows User" "User favorites Tweet" "User clicks Advertisement" While we cannot release the graph data used to train TwHIN embeddings due to privacy restrictions, heavily subsampled, anonymized open-sourced graph data can used: https://huggingface.co/datasets/Twitter/TwitterFollowGraph https://huggingface.co/datasets/Twitter/TwitterFaveGraph The code expects parquet files with three columns: lhs, rel, rhs that refer to the vocab index of the left-hand-side node, relation type, and right-hand-side node of each edge in a graph respectively. The location of the data must be specified in the configuration yaml files in projects/twhin/configs. Workflow ======== - Build local development images `./scripts/build_images.sh` - Run with `./scripts/docker_run.sh` - Iterate in image with `./scripts/idocker.sh` - Run tests with `./scripts/docker_test.sh` ================================================ FILE: projects/twhin/config/local.yaml ================================================ runtime: enable_amp: false training: save_dir: "/tmp/model" num_train_steps: 100000 checkpoint_every_n: 100000 train_log_every_n: 10 num_eval_steps: 1000 eval_log_every_n: 500 eval_timeout_in_s: 10000 num_epochs: 5 model: translation_optimizer: sgd: lr: 0.05 learning_rate: constant: 0.05 embeddings: tables: - name: user num_embeddings: 424_241 embedding_dim: 4 data_type: fp32 optimizer: sgd: lr: 0.01 learning_rate: constant: 0.01 - name: tweet num_embeddings: 72_543 embedding_dim: 4 data_type: fp32 optimizer: sgd: lr: 0.005 learning_rate: constant: 0.005 relations: - name: fav lhs: user rhs: tweet operator: translation - name: reply lhs: user rhs: tweet operator: translation - name: retweet lhs: user rhs: tweet operator: translation - name: magic_recs lhs: user rhs: tweet operator: translation train_data: data_root: "gs://follows_tml_01/tweet_eng/2023-01-23/large/edges/*" per_replica_batch_size: 500 global_negatives: 0 in_batch_negatives: 10 limit: 9990 validation_data: data_root: "gs://follows_tml_01/tweet_eng/2023-01-23/large/edges/*" per_replica_batch_size: 500 global_negatives: 0 in_batch_negatives: 10 limit: 10 offset: 9990 ================================================ FILE: projects/twhin/config.py ================================================ from tml.core.config import base_config from tml.projects.twhin.data.config import TwhinDataConfig from tml.projects.twhin.models.config import TwhinModelConfig from tml.core.config.training import RuntimeConfig, TrainingConfig import pydantic class TwhinConfig(base_config.BaseConfig): runtime: RuntimeConfig = pydantic.Field(RuntimeConfig()) training: TrainingConfig = pydantic.Field(TrainingConfig()) model: TwhinModelConfig train_data: TwhinDataConfig validation_data: TwhinDataConfig ================================================ FILE: projects/twhin/data/config.py ================================================ from tml.core.config import base_config import pydantic class TwhinDataConfig(base_config.BaseConfig): data_root: str per_replica_batch_size: pydantic.PositiveInt global_negatives: int in_batch_negatives: int limit: pydantic.PositiveInt offset: pydantic.PositiveInt = pydantic.Field( None, description="The offset to start reading from." ) ================================================ FILE: projects/twhin/data/data.py ================================================ from tml.projects.twhin.data.config import TwhinDataConfig from tml.projects.twhin.models.config import TwhinModelConfig from tml.projects.twhin.data.edges import EdgesDataset def create_dataset(data_config: TwhinDataConfig, model_config: TwhinModelConfig): tables = model_config.embeddings.tables table_sizes = {table.name: table.num_embeddings for table in tables} relations = model_config.relations pos_batch_size = data_config.per_replica_batch_size return EdgesDataset( file_pattern=data_config.data_root, relations=relations, table_sizes=table_sizes, batch_size=pos_batch_size, ) ================================================ FILE: projects/twhin/data/edges.py ================================================ from collections import defaultdict from dataclasses import dataclass from typing import Dict, List, Tuple from tml.common.batch import DataclassBatch from tml.reader.dataset import Dataset from tml.projects.twhin.models.config import Relation import numpy as np import pyarrow as pa import pyarrow.compute as pc import torch from torchrec.sparse.jagged_tensor import KeyedJaggedTensor @dataclass class EdgeBatch(DataclassBatch): nodes: KeyedJaggedTensor labels: torch.Tensor rels: torch.Tensor weights: torch.Tensor class EdgesDataset(Dataset): rng = np.random.default_rng() def __init__( self, file_pattern: str, table_sizes: Dict[str, int], relations: List[Relation], lhs_column_name: str = "lhs", rhs_column_name: str = "rhs", rel_column_name: str = "rel", **dataset_kwargs ): self.batch_size = dataset_kwargs["batch_size"] self.table_sizes = table_sizes self.num_tables = len(table_sizes) self.table_names = list(table_sizes.keys()) self.relations = relations self.relations_t = torch.tensor( [ [self.table_names.index(relation.lhs), self.table_names.index(relation.rhs)] for relation in relations ] ) self.lhs_column_name = lhs_column_name self.rhs_column_name = rhs_column_name self.rel_column_name = rel_column_name self.label_column_name = "label" super().__init__(file_pattern=file_pattern, **dataset_kwargs) def pa_to_batch(self, batch: pa.RecordBatch): lhs = torch.from_numpy(batch.column(self.lhs_column_name).to_numpy()) rhs = torch.from_numpy(batch.column(self.rhs_column_name).to_numpy()) rel = torch.from_numpy(batch.column(self.rel_column_name).to_numpy()) label = torch.from_numpy(batch.column(self.label_column_name).to_numpy()) nodes = self._to_kjt(lhs, rhs, rel) return EdgeBatch( nodes=nodes, rels=rel, labels=label, weights=torch.ones(batch.num_rows), ) def _to_kjt( self, lhs: torch.Tensor, rhs: torch.Tensor, rel: torch.Tensor ) -> Tuple[KeyedJaggedTensor, List[Tuple[int, int]]]: """Process edges that contain lhs index, rhs index, relation index. Example: ``` tables = ["f0", "f1", "f2", "f3"] relations = [["f0", "f1"], ["f1", "f2"], ["f1", "f0"], ["f2", "f1"], ["f0", "f2"]] self.relations_t = torch.Tensor([[0, 1], [1, 2], [1, 0], [2, 1], [0, 2]]) lhs = [1, 6, 3, 1, 8] rhs = [6, 3, 4, 4, 9] rel = [0, 2, 1, 3, 4] This corresponds to the following "edges": edges = [ {"lhs": 1, "rhs": 6, "relation": ["f0", "f1"]}, {"lhs": 6, "rhs": 3, "relation": ["f1", "f0"]}, {"lhs": 3, "rhs": 4, "relation": ["f1", "f2"]}, {"lhs": 1, "rhs": 4, "relation": ["f2", "f1"]}, {"lhs": 8, "rhs": 9, "relation": ["f0", "f2"]}, ] ``` Returns a KeyedJaggedTensor used to look up all embeddings. Note: We treat the lhs and rhs as though they're separate lookups: `len(lenghts) == 2 * bsz * len(tables)`. This differs from the DLRM pattern where we have `len(lengths) = bsz * len(tables)`. For the example above: ``` lookups = tensor([ [0., 1.], [1., 6.], [1., 6.], [0., 3.], [1., 3.], [2., 4.], [2., 1.], [1., 4.], [0., 8.], [2., 9.] ]) kjt = KeyedJaggedTensor( features=["f0", "f1", "f2"] values=[ 1, 3, 8, # f0 6, 6, 3, 4, # f1 4, 1, 9 # f2 ] lengths=[ 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, # f0 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, # f1 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, # f2 ) ``` Note: - values = [values for f0] + [values for f1] + [values for f2] - lengths are always 0 or 1, and sum(lengths) = len(values) = 2 * bsz """ lookups = torch.concat((lhs[:, None], self.relations_t[rel], rhs[:, None]), dim=1) index = torch.LongTensor([1, 0, 2, 3]) lookups = lookups[:, index].reshape(2 * self.batch_size, 2) # values is just the row indices into each table, ordered by the table indices _, indices = torch.sort(lookups[:, 0], dim=0, stable=True) values = lookups[indices][:, 1].int() # lengths[table_idx * batch_size + i] == whether the ith lookup is for the table with index table_idx lengths = torch.arange(self.num_tables)[:, None].eq(lookups[:, 0]) lengths = lengths.reshape(-1).int() return KeyedJaggedTensor(keys=self.table_names, values=values, lengths=lengths) def to_batches(self): ds = super().to_batches() batch_size = self._dataset_kwargs["batch_size"] names = [ self.lhs_column_name, self.rhs_column_name, self.rel_column_name, self.label_column_name, ] for _, batch in enumerate(ds): # Pass along positive edges lhs = batch.column(self.lhs_column_name) rhs = batch.column(self.rhs_column_name) rel = batch.column(self.rel_column_name) label = pa.array(np.ones(batch_size, dtype=np.int64)) yield pa.RecordBatch.from_arrays( arrays=[lhs, rhs, rel, label], names=names, ) ================================================ FILE: projects/twhin/data/test_data.py ================================================ import pytest from unittest.mock import Mock def test_create_dataset(): pass ================================================ FILE: projects/twhin/data/test_edges.py ================================================ """Tests edges dataset functionality.""" from unittest.mock import patch import os import tempfile from tml.projects.twhin.data.edges import EdgesDataset from tml.projects.twhin.models.config import Relation from fsspec.implementations.local import LocalFileSystem import numpy as np import pyarrow as pa import pyarrow.compute as pc import pyarrow.parquet as pq import torch TABLE_SIZES = {"user": 16, "author": 32} RELATIONS = [ Relation(name="fav", lhs="user", rhs="author"), Relation(name="engaged_with_reply", lhs="author", rhs="user"), ] def test_gen(): import os import tempfile from fsspec.implementations.local import LocalFileSystem import pyarrow as pa import pyarrow.parquet as pq lhs = pa.array(np.arange(4)) rhs = pa.array(np.flip(np.arange(4))) rel = pa.array([0, 1, 0, 0]) names = ["lhs", "rhs", "rel"] with tempfile.TemporaryDirectory() as tmpdir: table = pa.Table.from_arrays([lhs, rhs, rel], names=names) writer = pq.ParquetWriter( os.path.join(tmpdir, "example.parquet"), table.schema, ) writer.write_table(table) writer.close() ds = EdgesDataset( file_pattern=os.path.join(tmpdir, "*"), table_sizes=TABLE_SIZES, relations=RELATIONS, batch_size=4, ) ds.FS = LocalFileSystem() dl = ds.dataloader() batch = next(iter(dl)) # labels should be positive labels = batch.labels assert (labels[:4] == 1).sum() == 4 # make sure positive examples are what we expect kjt_values = batch.nodes.values() users, authors = torch.split(kjt_values, 4, dim=0) assert torch.equal(users[:4], torch.tensor([0, 2, 2, 3])) assert torch.equal(authors[:4], torch.tensor([3, 1, 1, 0])) ================================================ FILE: projects/twhin/machines.yaml ================================================ chief: &gpu mem: 1.4Ti cpu: 24 num_accelerators: 16 accelerator_type: a100 dataset_dispatcher: mem: 2Gi cpu: 2 num_dataset_workers: 4 dataset_worker: mem: 14Gi cpu: 2 ================================================ FILE: projects/twhin/metrics.py ================================================ import torch import torchmetrics as tm import tml.core.metrics as core_metrics def create_metrics( device: torch.device, ): metrics = dict() metrics.update( { "AUC": core_metrics.Auc(128), } ) metrics = tm.MetricCollection(metrics).to(device) return metrics ================================================ FILE: projects/twhin/models/config.py ================================================ import typing import enum from tml.common.modules.embedding.config import LargeEmbeddingsConfig from tml.core.config import base_config from tml.optimizers.config import OptimizerConfig import pydantic from pydantic import validator class TwhinEmbeddingsConfig(LargeEmbeddingsConfig): @validator("tables") def embedding_dims_match(cls, tables): embedding_dim = tables[0].embedding_dim data_type = tables[0].data_type for table in tables: assert table.embedding_dim == embedding_dim, "Embedding dimensions for all nodes must match." assert table.data_type == data_type, "Data types for all nodes must match." return tables class Operator(str, enum.Enum): TRANSLATION = "translation" class Relation(pydantic.BaseModel): """graph relationship properties and operator""" name: str = pydantic.Field(..., description="Relationship name.") lhs: str = pydantic.Field( ..., description="Name of the entity on the left-hand-side of this relation. Must match a table name.", ) rhs: str = pydantic.Field( ..., description="Name of the entity on the right-hand-side of this relation. Must match a table name.", ) operator: Operator = pydantic.Field( Operator.TRANSLATION, description="Transformation to apply to lhs embedding before dot product." ) class TwhinModelConfig(base_config.BaseConfig): embeddings: TwhinEmbeddingsConfig relations: typing.List[Relation] translation_optimizer: OptimizerConfig @validator("relations", each_item=True) def valid_node_types(cls, relation, values, **kwargs): table_names = [table.name for table in values["embeddings"].tables] assert relation.lhs in table_names, f"Invalid lhs node type: {relation.lhs}" assert relation.rhs in table_names, f"Invalid rhs node type: {relation.rhs}" return relation ================================================ FILE: projects/twhin/models/models.py ================================================ from typing import Callable import math from tml.projects.twhin.data.edges import EdgeBatch from tml.projects.twhin.models.config import TwhinModelConfig from tml.projects.twhin.data.config import TwhinDataConfig from tml.common.modules.embedding.embedding import LargeEmbeddings from tml.optimizers.optimizer import get_optimizer_class from tml.optimizers.config import get_optimizer_algorithm_config import torch from torch import nn from torchrec.optim.apply_optimizer_in_backward import apply_optimizer_in_backward class TwhinModel(nn.Module): def __init__(self, model_config: TwhinModelConfig, data_config: TwhinDataConfig): super().__init__() self.batch_size = data_config.per_replica_batch_size self.table_names = [table.name for table in model_config.embeddings.tables] self.large_embeddings = LargeEmbeddings(model_config.embeddings) self.embedding_dim = model_config.embeddings.tables[0].embedding_dim self.num_tables = len(model_config.embeddings.tables) self.in_batch_negatives = data_config.in_batch_negatives self.global_negatives = data_config.global_negatives self.num_relations = len(model_config.relations) # one bias per relation self.all_trans_embs = torch.nn.parameter.Parameter( torch.nn.init.uniform_(torch.empty(self.num_relations, self.embedding_dim)) ) def forward(self, batch: EdgeBatch): # B x D trans_embs = self.all_trans_embs.data[batch.rels] # KeyedTensor outs = self.large_embeddings(batch.nodes) # 2B x TD x = outs.values() # 2B x T x D x = x.reshape(2 * self.batch_size, -1, self.embedding_dim) # 2B x D x = torch.sum(x, 1) # B x 2 x D x = x.reshape(self.batch_size, 2, self.embedding_dim) # translated translated = x[:, 1, :] + trans_embs negs = [] if self.in_batch_negatives: # construct dot products for negatives via matmul for relation in range(self.num_relations): rel_mask = batch.rels == relation rel_count = rel_mask.sum() if not rel_count: continue # R x D lhs_matrix = x[rel_mask, 0, :] rhs_matrix = x[rel_mask, 1, :] lhs_perm = torch.randperm(lhs_matrix.shape[0]) # repeat until we have enough negatives lhs_perm = lhs_perm.repeat(math.ceil(float(self.in_batch_negatives) / rel_count)) lhs_indices = lhs_perm[: self.in_batch_negatives] sampled_lhs = lhs_matrix[lhs_indices] rhs_perm = torch.randperm(rhs_matrix.shape[0]) # repeat until we have enough negatives rhs_perm = rhs_perm.repeat(math.ceil(float(self.in_batch_negatives) / rel_count)) rhs_indices = rhs_perm[: self.in_batch_negatives] sampled_rhs = rhs_matrix[rhs_indices] # RS negs_rhs = torch.flatten(torch.matmul(lhs_matrix, sampled_rhs.t())) negs_lhs = torch.flatten(torch.matmul(rhs_matrix, sampled_lhs.t())) negs.append(negs_lhs) negs.append(negs_rhs) # dot product for positives x = (x[:, 0, :] * translated).sum(-1) # concat positives and negatives x = torch.cat([x, *negs]) return { "logits": x, "probabilities": torch.sigmoid(x), } def apply_optimizers(model: TwhinModel, model_config: TwhinModelConfig): for table in model_config.embeddings.tables: optimizer_class = get_optimizer_class(table.optimizer) optimizer_kwargs = get_optimizer_algorithm_config(table.optimizer).dict() params = [ param for name, param in model.large_embeddings.ebc.named_parameters() if (name.startswith(f"embedding_bags.{table.name}")) ] apply_optimizer_in_backward( optimizer_class=optimizer_class, params=params, optimizer_kwargs=optimizer_kwargs, ) return model class TwhinModelAndLoss(torch.nn.Module): def __init__( self, model, loss_fn: Callable, data_config: TwhinDataConfig, device: torch.device, ) -> None: """ Args: model: torch module to wrap. loss_fn: Function for calculating loss, should accept logits and labels. """ super().__init__() self.model = model self.loss_fn = loss_fn self.batch_size = data_config.per_replica_batch_size self.in_batch_negatives = data_config.in_batch_negatives self.device = device def forward(self, batch: "RecapBatch"): # type: ignore[name-defined] """Runs model forward and calculates loss according to given loss_fn. NOTE: The input signature here needs to be a Pipelineable object for prefetching purposes during training using torchrec's pipeline. However the underlying model signature needs to be exportable to onnx, requiring generic python types. see https://pytorch.org/docs/stable/onnx.html#types. """ outputs = self.model(batch) logits = outputs["logits"] num_negatives = 2 * self.batch_size * self.in_batch_negatives num_positives = self.batch_size neg_weight = float(num_positives) / num_negatives labels = torch.cat([batch.labels.float(), torch.ones(num_negatives).to(self.device)]) weights = torch.cat( [batch.weights.float(), (torch.ones(num_negatives) * neg_weight).to(self.device)] ) losses = self.loss_fn(logits, labels, weights) outputs.update( { "loss": losses, "labels": labels, "weights": weights, } ) # Allow multiple losses. return losses, outputs ================================================ FILE: projects/twhin/models/test_models.py ================================================ from tml.projects.twhin.models.config import TwhinEmbeddingsConfig, TwhinModelConfig from tml.projects.twhin.data.config import TwhinDataConfig from tml.common.modules.embedding.config import DataType, EmbeddingBagConfig from tml.optimizers.config import OptimizerConfig, SgdConfig from tml.model import maybe_shard_model from tml.projects.twhin.models.models import apply_optimizers, TwhinModel from tml.projects.twhin.models.config import Operator, Relation from tml.common.testing_utils import mock_pg import torch import torch.nn.functional as F from pydantic import ValidationError import pytest NUM_EMBS = 10_000 EMB_DIM = 128 def twhin_model_config() -> TwhinModelConfig: sgd_config_0 = OptimizerConfig(sgd=SgdConfig(lr=0.01)) sgd_config_1 = OptimizerConfig(sgd=SgdConfig(lr=0.02)) table0 = EmbeddingBagConfig( name="table0", num_embeddings=NUM_EMBS, embedding_dim=EMB_DIM, optimizer=sgd_config_0, data_type=DataType.FP32, ) table1 = EmbeddingBagConfig( name="table1", num_embeddings=NUM_EMBS, embedding_dim=EMB_DIM, optimizer=sgd_config_1, data_type=DataType.FP32, ) embeddings_config = TwhinEmbeddingsConfig( tables=[table0, table1], ) model_config = TwhinModelConfig( embeddings=embeddings_config, translation_optimizer=sgd_config_0, relations=[ Relation(name="rel0", lhs="table0", rhs="table1", operator=Operator.TRANSLATION), Relation(name="rel1", lhs="table1", rhs="table0", operator=Operator.TRANSLATION), ], ) return model_config def twhin_data_config() -> TwhinDataConfig: data_config = TwhinDataConfig( data_root="/", per_replica_batch_size=10, global_negatives=10, in_batch_negatives=10, limit=1, offset=1, ) return data_config def test_twhin_model(): model_config = twhin_model_config() loss_fn = F.binary_cross_entropy_with_logits with mock_pg(): data_config = twhin_data_config() model = TwhinModel(model_config=model_config, data_config=data_config) apply_optimizers(model, model_config) for tensor in model.state_dict().values(): if tensor.size() == (NUM_EMBS, EMB_DIM): assert str(tensor.device) == "meta" else: assert str(tensor.device) == "cpu" model = maybe_shard_model(model, device=torch.device("cpu")) def test_unequal_dims(): sgd_config_1 = OptimizerConfig(sgd=SgdConfig(lr=0.02)) sgd_config_2 = OptimizerConfig(sgd=SgdConfig(lr=0.05)) table0 = EmbeddingBagConfig( name="table0", num_embeddings=10_000, embedding_dim=128, optimizer=sgd_config_1, data_type=DataType.FP32, ) table1 = EmbeddingBagConfig( name="table1", num_embeddings=10_000, embedding_dim=64, optimizer=sgd_config_2, data_type=DataType.FP32, ) with pytest.raises(ValidationError): _ = TwhinEmbeddingsConfig( tables=[table0, table1], ) ================================================ FILE: projects/twhin/optimizer.py ================================================ import functools from tml.projects.twhin.models.config import TwhinModelConfig from tml.projects.twhin.models.models import TwhinModel from tml.optimizers.optimizer import get_optimizer_class, LRShim from tml.optimizers.config import get_optimizer_algorithm_config, LearningRate from tml.ml_logging.torch_logging import logging from torchrec.optim.optimizers import in_backward_optimizer_filter from torchrec.optim import keyed FUSED_OPT_KEY = "fused_opt" TRANSLATION_OPT_KEY = "operator_opt" def _lr_from_config(optimizer_config): if optimizer_config.learning_rate is not None: return optimizer_config.learning_rate else: # treat None as constant lr lr_value = get_optimizer_algorithm_config(optimizer_config).lr return LearningRate(constant=lr_value) def build_optimizer(model: TwhinModel, config: TwhinModelConfig): """Builds an optimizer for a Twhin model combining the embeddings optimizer with an optimizer for per-relation translations. Args: model: TwhinModel to build optimizer for. config: TwhinConfig for model. Returns: Optimizer for model. """ translation_optimizer_fn = functools.partial( get_optimizer_class(config.translation_optimizer), **get_optimizer_algorithm_config(config.translation_optimizer).dict(), ) translation_optimizer = keyed.KeyedOptimizerWrapper( dict(in_backward_optimizer_filter(model.named_parameters())), optim_factory=translation_optimizer_fn, ) lr_dict = {} for table in config.embeddings.tables: lr_dict[table.name] = _lr_from_config(table.optimizer) lr_dict[TRANSLATION_OPT_KEY] = _lr_from_config(config.translation_optimizer) logging.info(f"***** LR dict: {lr_dict} *****") logging.info( f"***** Combining fused optimizer {model.fused_optimizer} with operator optimizer: {translation_optimizer} *****" ) optimizer = keyed.CombinedOptimizer( [ (FUSED_OPT_KEY, model.fused_optimizer), (TRANSLATION_OPT_KEY, translation_optimizer), ] ) # scheduler = LRShim(optimizer, lr_dict) scheduler = None logging.info(f"***** Combined optimizer after init: {optimizer} *****") return optimizer, scheduler ================================================ FILE: projects/twhin/run.py ================================================ from absl import app, flags import json from typing import Optional import os import sys import torch # isort: on from tml.common.device import setup_and_get_device from tml.common.utils import setup_configuration import tml.core.custom_training_loop as ctl import tml.machines.environment as env from tml.projects.twhin.models.models import apply_optimizers, TwhinModel, TwhinModelAndLoss from tml.model import maybe_shard_model from tml.projects.twhin.metrics import create_metrics from tml.projects.twhin.config import TwhinConfig from tml.projects.twhin.data.data import create_dataset from tml.projects.twhin.optimizer import build_optimizer from tml.ml_logging.torch_logging import logging import torch.distributed as dist from torch.nn import functional as F from torchrec.optim.apply_optimizer_in_backward import apply_optimizer_in_backward from torchrec.distributed.model_parallel import get_module FLAGS = flags.FLAGS flags.DEFINE_bool("overwrite_save_dir", False, "Whether to clear preexisting save directories.") flags.DEFINE_string("save_dir", None, "If provided, overwrites the save directory.") flags.DEFINE_string("config_yaml_path", None, "Path to hyperparameters for model.") flags.DEFINE_string("task", None, "Task to run if this is local. Overrides TF_CONFIG etc.") def run( all_config: TwhinConfig, save_dir: Optional[str] = None, ): train_dataset = create_dataset(all_config.train_data, all_config.model) if env.is_reader(): train_dataset.serve() if env.is_chief(): device = setup_and_get_device(tf_ok=False) logging.info(f"device: {device}") logging.info(f"WORLD_SIZE: {dist.get_world_size()}") # validation_dataset = create_dataset(all_config.validation_data, all_config.model) global_batch_size = all_config.train_data.per_replica_batch_size * dist.get_world_size() metrics = create_metrics(device) model = TwhinModel(all_config.model, all_config.train_data) apply_optimizers(model, all_config.model) model = maybe_shard_model(model, device=device) optimizer, scheduler = build_optimizer(model=model, config=all_config.model) loss_fn = F.binary_cross_entropy_with_logits model_and_loss = TwhinModelAndLoss( model, loss_fn, data_config=all_config.train_data, device=device ) ctl.train( model=model_and_loss, optimizer=optimizer, device=device, save_dir=save_dir, logging_interval=all_config.training.train_log_every_n, train_steps=all_config.training.num_train_steps, checkpoint_frequency=all_config.training.checkpoint_every_n, dataset=train_dataset.dataloader(remote=False), worker_batch_size=global_batch_size, num_workers=0, scheduler=scheduler, initial_checkpoint_dir=all_config.training.initial_checkpoint_dir, gradient_accumulation=all_config.training.gradient_accumulation, ) def main(argv): logging.info("Starting") logging.info(f"parsing config from {FLAGS.config_yaml_path}...") all_config = setup_configuration( # type: ignore[var-annotated] TwhinConfig, yaml_path=FLAGS.config_yaml_path, ) run( all_config, save_dir=FLAGS.save_dir, ) if __name__ == "__main__": app.run(main) ================================================ FILE: projects/twhin/scripts/docker_run.sh ================================================ #! /bin/sh docker run -it --rm \ -v $HOME/workspace/tml:/usr/src/app/tml \ -v $HOME/.config:/root/.config \ -w /usr/src/app \ -e PYTHONPATH="/usr/src/app/" \ --network host \ -e SPEC_TYPE=chief \ local/torch \ bash tml/projects/twhin/scripts/run_in_docker.sh ================================================ FILE: projects/twhin/scripts/run_in_docker.sh ================================================ #! /bin/sh torchrun \ --standalone \ --nnodes 1 \ --nproc_per_node 2 \ /usr/src/app/tml/projects/twhin/run.py \ --config_yaml_path="/usr/src/app/tml/projects/twhin/config/local.yaml" \ --save_dir="/some/save/dir" ================================================ FILE: projects/twhin/test_optimizer.py ================================================ import pytest import unittest from tml.projects.twhin.models.models import TwhinModel, apply_optimizers from tml.projects.twhin.models.test_models import twhin_model_config, twhin_data_config from tml.projects.twhin.optimizer import build_optimizer from tml.model import maybe_shard_model from tml.common.testing_utils import mock_pg import torch from torch.nn import functional as F def test_twhin_optimizer(): model_config = twhin_model_config() data_config = twhin_data_config() loss_fn = F.binary_cross_entropy_with_logits with mock_pg(): model = TwhinModel(model_config, data_config) apply_optimizers(model, model_config) model = maybe_shard_model(model, device=torch.device("cpu")) optimizer, _ = build_optimizer(model, model_config) # make sure there is one combined fused optimizer and one translation optimizer assert len(optimizer.optimizers) == 2 fused_opt_tup, _ = optimizer.optimizers _, fused_opt = fused_opt_tup # make sure there are two tables for which the fused opt has parameters assert len(fused_opt.param_groups) == 2 ================================================ FILE: pyproject.toml ================================================ [tool.black] line-length = 100 include = '\.pyi?$' exclude = ''' /( \.git | \.hg | \.pem | \.mypy_cache | \.tox | \.venv | _build | buck-out | build | dist )/ ''' ================================================ FILE: reader/__init__.py ================================================ ================================================ FILE: reader/dataset.py ================================================ """Dataset to be overwritten that can work with or without distributed reading. - Override `pa_to_batch` for dataset specific imputation, negative sampling, or coercion to Batch. - Readers can be colocated or off trainer machines. """ import abc import functools import random from typing import Optional from fsspec.implementations.local import LocalFileSystem import pyarrow.dataset as pads import pyarrow as pa import pyarrow.parquet import pyarrow.flight from pyarrow.ipc import IpcWriteOptions import torch from tml.common.batch import DataclassBatch from tml.machines import environment as env import tml.reader.utils as reader_utils from tml.common.filesystem import infer_fs from tml.ml_logging.torch_logging import logging class _Reader(pa.flight.FlightServerBase): """Distributed reader flight server wrapping a dataset.""" def __init__(self, location: str, ds: "Dataset"): super().__init__(location=location) self._location = location self._ds = ds def do_get(self, _, __): # NB: An updated schema (to account for column selection) has to be given the stream. schema = next(iter(self._ds.to_batches())).schema batches = self._ds.to_batches() return pa.flight.RecordBatchStream( data_source=pa.RecordBatchReader.from_batches( schema=schema, batches=batches, ), options=IpcWriteOptions(use_threads=True), ) class Dataset(torch.utils.data.IterableDataset): LOCATION = "grpc://0.0.0.0:2222" def __init__(self, file_pattern: str, **dataset_kwargs) -> None: """Specify batch size and column to select for. Refer to https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Scanner.html#pyarrow.dataset.Scanner.from_dataset. """ self._file_pattern = file_pattern self._fs = infer_fs(self._file_pattern) self._dataset_kwargs = dataset_kwargs logging.info(f"Using dataset_kwargs: {self._dataset_kwargs}") self._files = self._fs.glob(self._file_pattern) assert len(self._files) > 0, f"No files found at {self._file_pattern}" logging.info(f"Found {len(self._files)} files: {', '.join(self._files[:4])}, ...") self._schema = pa.parquet.read_schema(self._files[0], filesystem=self._fs) self._validate_columns() def _validate_columns(self): columns = set(self._dataset_kwargs.get("columns", [])) wrong_columns = set(columns) - set(self._schema.names) if wrong_columns: raise Exception(f"Specified columns {list(wrong_columns)} not in schema.") def serve(self): self.reader = _Reader(location=self.LOCATION, ds=self) self.reader.serve() def _create_dataset(self): return pads.dataset( source=random.sample(self._files, len(self._files))[0], format="parquet", filesystem=self._fs, exclude_invalid_files=False, ) def to_batches(self): """This allows the init to control reading settings. Refer to https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Scanner.html#pyarrow.dataset.Scanner.from_dataset. Perform `drop_remainder` behavior to afix batch size. This does not shift our data distribution bc of volume and file-level shuffling on every repeat. """ batch_size = self._dataset_kwargs["batch_size"] while True: ds = self._create_dataset() for batch in ds.to_batches(**self._dataset_kwargs): if batch.num_rows < batch_size: logging.info(f"Dropping remainder ({batch.num_rows}/{batch_size})") break yield batch @abc.abstractmethod def pa_to_batch(self, batch: pa.RecordBatch) -> DataclassBatch: raise NotImplementedError def dataloader(self, remote: bool = False): if not remote: return map(self.pa_to_batch, self.to_batches()) readers = get_readers(2) return map(self.pa_to_batch, reader_utils.roundrobin(*readers)) GRPC_OPTIONS = [ ("GRPC_ARG_KEEPALIVE_TIME_MS", 60000), ("GRPC_ARG_MIN_RECONNECT_BACKOFF_MS", 2000), ("GRPC_ARG_MAX_METADATA_SIZE", 1024 * 1024 * 1024), ] def get_readers(num_readers_per_worker: int): addresses = env.get_flight_server_addresses() readers = [] for worker in addresses: logging.info(f"Attempting connection to reader {worker}.") client = pa.flight.connect(worker, generic_options=GRPC_OPTIONS) client.wait_for_available(60) reader = client.do_get(None).to_reader() logging.info(f"Connected reader to {worker}.") readers.append(reader) return readers ================================================ FILE: reader/dds.py ================================================ """Dataset service orchestrated by a TFJob """ from typing import Optional import uuid from tml.ml_logging.torch_logging import logging import tml.machines.environment as env import packaging.version import tensorflow as tf try: import tensorflow_io as tfio except: pass from tensorflow.python.data.experimental.ops.data_service_ops import ( _from_dataset_id, _register_dataset, ) import torch.distributed as dist def maybe_start_dataset_service(): if not env.has_readers(): return if packaging.version.parse(tf.__version__) < packaging.version.parse("2.5"): raise Exception(f"maybe_distribute_dataset requires TF >= 2.5; got {tf.__version__}") if env.is_dispatcher(): logging.info(f"env.get_reader_port() = {env.get_reader_port()}") logging.info(f"env.get_dds_journaling_dir() = {env.get_dds_journaling_dir()}") work_dir = env.get_dds_journaling_dir() server = tf.data.experimental.service.DispatchServer( tf.data.experimental.service.DispatcherConfig( port=env.get_reader_port(), protocol="grpc", work_dir=work_dir, fault_tolerant_mode=bool(work_dir), ) ) server.join() elif env.is_reader(): logging.info(f"env.get_reader_port() = {env.get_reader_port()}") logging.info(f"env.get_dds_dispatcher_address() = {env.get_dds_dispatcher_address()}") logging.info(f"env.get_dds_worker_address() = {env.get_dds_worker_address()}") server = tf.data.experimental.service.WorkerServer( tf.data.experimental.service.WorkerConfig( port=env.get_reader_port(), dispatcher_address=env.get_dds_dispatcher_address(), worker_address=env.get_dds_worker_address(), protocol="grpc", ) ) server.join() def register_dataset( dataset: tf.data.Dataset, dataset_service: str, compression: Optional[str] = "AUTO" ): if dist.get_rank() == 0: dataset_id = _register_dataset( service=dataset_service, dataset=dataset, compression=compression, ) job_name = uuid.uuid4().hex[:8] id_and_job = [dataset_id.numpy(), job_name] logging.info(f"rank{dist.get_rank()}: Created dds job with {dataset_id.numpy()}, {job_name}") else: id_and_job = [None, None] dist.broadcast_object_list(id_and_job, src=0) return tuple(id_and_job) def distribute_from_dataset_id( dataset_service: str, dataset_id: int, job_name: Optional[str], compression: Optional[str] = "AUTO", prefetch: Optional[int] = tf.data.experimental.AUTOTUNE, ) -> tf.data.Dataset: logging.info(f"rank{dist.get_rank()}: Consuming dds job with {dataset_id}, {job_name}") dataset = _from_dataset_id( processing_mode="parallel_epochs", service=dataset_service, dataset_id=dataset_id, job_name=job_name, element_spec=None, compression=compression, ) if prefetch is not None: dataset = dataset.prefetch(prefetch) return dataset def maybe_distribute_dataset(dataset: tf.data.Dataset) -> tf.data.Dataset: """Torch-compatible and distributed-training-aware dataset service distributor. - rank 0 process will register the given dataset. - rank 0 process will broadcast job name and dataset id. - all rank processes will consume from the same job/dataset. Without this, dataset workers will try to serve 1 job per rank process and OOM. """ if not env.has_readers(): return dataset dataset_service = env.get_dds() logging.info(f"using DDS = {dataset_service}") dataset_id, job_name = register_dataset(dataset=dataset, dataset_service=dataset_service) dataset = distribute_from_dataset_id( dataset_service=dataset_service, dataset_id=dataset_id, job_name=job_name ) return dataset if __name__ == "__main__": maybe_start_dataset_service() ================================================ FILE: reader/test_dataset.py ================================================ import multiprocessing as mp import os from unittest.mock import patch import tml.reader.utils as reader_utils from tml.reader.dataset import Dataset import pyarrow as pa import pyarrow.parquet as pq import pytest import torch def create_dataset(tmpdir): table = pa.table( { "year": [2020, 2022, 2021, 2022, 2019, 2021], "n_legs": [2, 2, 4, 4, 5, 100], } ) file_path = tmpdir pq.write_to_dataset(table, root_path=str(file_path)) class MockDataset(Dataset): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._pa_to_batch = reader_utils.create_default_pa_to_batch(self._schema) def pa_to_batch(self, batch): return self._pa_to_batch(batch) return MockDataset(file_pattern=str(file_path / "*"), batch_size=2) def test_dataset(tmpdir): ds = create_dataset(tmpdir) batch = next(iter(ds.dataloader(remote=False))) assert batch.batch_size == 2 assert torch.equal(batch.year, torch.Tensor([2020, 2022])) assert torch.equal(batch.n_legs, torch.Tensor([2, 2])) @pytest.mark.skipif( os.environ.get("GITHUB_WORKSPACE") is not None, reason="Multiprocessing doesn't work on github yet.", ) def test_distributed_dataset(tmpdir): MOCK_ENV = {"TEMP_SLURM_NUM_READERS": "1"} def _client(): with patch.dict(os.environ, MOCK_ENV): with patch( "tml.reader.dataset.env.get_flight_server_addresses", return_value=["grpc://localhost:2222"] ): ds = create_dataset(tmpdir) batch = next(iter(ds.dataloader(remote=True))) assert batch.batch_size == 2 assert torch.equal(batch.year, torch.Tensor([2020, 2022])) assert torch.equal(batch.n_legs, torch.Tensor([2, 2])) def _worker(): ds = create_dataset(tmpdir) ds.serve() worker = mp.Process(target=_worker) client = mp.Process(target=_client) worker.start() client.start() client.join() assert not client.exitcode worker.kill() client.kill() ================================================ FILE: reader/test_utils.py ================================================ import tml.reader.utils as reader_utils def test_rr(): options = ["a", "b", "c"] rr = reader_utils.roundrobin(options) for i, v in enumerate(rr): assert v == options[i % 3] if i > 4: break ================================================ FILE: reader/utils.py ================================================ """Reader utilities.""" import itertools import time from typing import Optional from tml.common.batch import DataclassBatch from tml.ml_logging.torch_logging import logging import pyarrow as pa import torch def roundrobin(*iterables): """Round robin through provided iterables, useful for simple load balancing. Adapted from https://docs.python.org/3/library/itertools.html. """ num_active = len(iterables) nexts = itertools.cycle(iter(it).__next__ for it in iterables) while num_active: try: for _next in nexts: result = _next() yield result except StopIteration: # Remove the iterator we just exhausted from the cycle. num_active -= 1 nexts = itertools.cycle(itertools.islice(nexts, num_active)) logging.warning(f"Iterable exhausted, {num_active} iterables left.") except Exception as exc: logging.warning(f"Iterable raised exception {exc}, ignoring.") # continue raise def speed_check(data_loader, max_steps: int, frequency: int, peek: Optional[int]): num_examples = 0 prev = time.perf_counter() for idx, batch in enumerate(data_loader): if idx > max_steps: break if peek and idx % peek == 0: logging.info(f"Batch: {batch}") num_examples += batch.batch_size if idx % frequency == 0: now = time.perf_counter() elapsed = now - prev logging.info( f"step: {idx}, " f"elapsed(s): {elapsed}, " f"examples: {num_examples}, " f"ex/s: {num_examples / elapsed}, " ) prev = now num_examples = 0 def pa_to_torch(array: pa.array) -> torch.Tensor: return torch.from_numpy(array.to_numpy()) def create_default_pa_to_batch(schema) -> DataclassBatch: """ """ _CustomBatch = DataclassBatch.from_schema("DefaultBatch", schema=schema) def get_imputation_value(pa_type): type_map = { pa.float64(): pa.scalar(0, type=pa.float64()), pa.int64(): pa.scalar(0, type=pa.int64()), pa.string(): pa.scalar("", type=pa.string()), } if pa_type not in type_map: raise Exception(f"Imputation for type {pa_type} not supported.") return type_map[pa_type] def _impute(array: pa.array) -> pa.array: return array.fill_null(get_imputation_value(array.type)) def _column_to_tensor(record_batch: pa.RecordBatch): tensors = { col_name: pa_to_torch(_impute(record_batch.column(col_name))) for col_name in record_batch.schema.names } return _CustomBatch(**tensors) return _column_to_tensor ================================================ FILE: tools/pq.py ================================================ """Local reader of parquet files. 1. Make sure you are initialized locally: ``` ./images/init_venv_macos.sh ``` 2. Activate ``` source ~/tml_venv/bin/activate ``` 3. Use tool, e.g. `head` prints the first `--num` rows of the dataset. ``` python3 tools/pq.py \ --num 5 --path "tweet_eng/small/edges/all/*" \ head ``` `distinct` prints the observed values in the first `--num` rows for the specified columns. ``` python3 tools/pq.py \ --num 1000000000 --columns '["rel"]' \ --path "tweet_eng/small/edges/all/*" \ distinct ``` """ from typing import List, Optional from tml.common.filesystem import infer_fs import fire import pandas as pd import pyarrow as pa import pyarrow.dataset as pads import pyarrow.parquet as pq def _create_dataset(path: str): fs = infer_fs(path) files = fs.glob(path) return pads.dataset(files, format="parquet", filesystem=fs) class PqReader: def __init__( self, path: str, num: int = 10, batch_size: int = 1024, columns: Optional[List[str]] = None ): self._ds = _create_dataset(path) self._batch_size = batch_size self._num = num self._columns = columns def __iter__(self): batches = self._ds.to_batches(batch_size=self._batch_size, columns=self._columns) rows_seen = 0 for count, record in enumerate(batches): if self._num and rows_seen >= self._num: break yield record rows_seen += record.data.num_rows def _head(self): total_read = self._num * self.bytes_per_row if total_read >= int(500e6): raise Exception( "Sorry you're trying to read more than 500 MB " f"into memory ({total_read} bytes)." ) return self._ds.head(self._num, columns=self._columns) @property def bytes_per_row(self) -> int: nbits = 0 for t in self._ds.schema.types: try: nbits += t.bit_width except: # Just estimate size if it is variable nbits += 8 return nbits // 8 def schema(self): print(f"\n# Schema\n{self._ds.schema}") def head(self): """Displays first --num rows.""" print(self._head().to_pandas()) def distinct(self): """Displays unique values seen in specified columns in the first `--num` rows. Useful for getting an approximate vocabulary for certain columns. """ for col_name, column in zip(self._head().column_names, self._head().columns): print(col_name) print("unique:", column.unique().to_pylist()) if __name__ == "__main__": pd.set_option("display.max_columns", None) pd.set_option("display.max_rows", None) fire.Fire(PqReader)