The Algorithm ML

.github/workflows/main.yml

@@ -0,0 +1,37 @@
+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.
+          export PYTHONPATH="$GITHUB_WORKSPACE/../:$PYTHONPATH"
+          pytest -vv

.gitignore

@@ -0,0 +1,35 @@
+# 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

.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+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

LICENSE.torchrec

@@ -0,0 +1,33 @@
+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.

README.md

@@ -0,0 +1,16 @@
+This project open sources some of the ML models used at Twitter.
+
+Note these are released to be open about our recommendation system. However, these are not supported Twitter products and we are unable to provide support for the use of this code.
+
+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.

common/batch.py

@@ -0,0 +1,85 @@
+"""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

common/checkpointing/__init__.py

@@ -0,0 +1 @@
+from tml.common.checkpointing.snapshot import get_checkpoint, Snapshot

common/checkpointing/snapshot.py

@@ -0,0 +1,260 @@
+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

common/device.py

@@ -0,0 +1,30 @@
+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

common/filesystem/__init__.py

@@ -0,0 +1 @@
+from tml.common.filesystem.util import infer_fs, is_gcs_fs, is_local_fs

common/filesystem/test_infer_fs.py

@@ -0,0 +1,16 @@
+"""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

common/filesystem/util.py

@@ -0,0 +1,25 @@
+"""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

common/log_weights.py

@@ -0,0 +1,90 @@
+"""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

common/modules/embedding/config.py

@@ -0,0 +1,59 @@
+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"

common/modules/embedding/embedding.py

@@ -0,0 +1,58 @@
+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)

common/run_training.py

@@ -0,0 +1,109 @@
+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)

common/test_device.py

@@ -0,0 +1,13 @@
+"""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"

common/testing_utils.py

@@ -0,0 +1,33 @@
+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()

common/utils.py

@@ -0,0 +1,44 @@
+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)

common/wandb.py

@@ -0,0 +1,20 @@
+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.")

core/config/__init__.py

@@ -0,0 +1,5 @@
+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"]

core/config/base_config.py

@@ -0,0 +1,66 @@
+"""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())

core/config/base_config_test.py

@@ -0,0 +1,38 @@
+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")

core/config/config_load.py

@@ -0,0 +1,24 @@
+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)

core/config/test_config_load.py

@@ -0,0 +1,23 @@
+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()

core/config/training.py

@@ -0,0 +1,40 @@
+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

core/custom_training_loop.py

@@ -0,0 +1,316 @@
+"""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

core/debug_training_loop.py

@@ -0,0 +1,45 @@
+"""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}")

core/loss_type.py

@@ -0,0 +1,7 @@
+"""Loss type enums."""
+from enum import Enum
+
+
+class LossType(str, Enum):
+  CROSS_ENTROPY = "cross_entropy"
+  BCE_WITH_LOGITS = "bce_with_logits"

core/losses.py

@@ -0,0 +1,109 @@
+"""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
+}

core/metric_mixin.py

@@ -0,0 +1,104 @@
+"""
+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},
+  )

core/metrics.py

@@ -0,0 +1,148 @@
+"""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

core/test_metrics.py

@@ -0,0 +1,187 @@
+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

core/test_train_pipeline.py

@@ -0,0 +1,91 @@
+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

core/train_pipeline.py

@@ -0,0 +1,626 @@
+"""
+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

images/init_venv.sh

@@ -0,0 +1,25 @@
+#! /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.

images/requirements.txt

@@ -0,0 +1,152 @@
+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

machines/environment.py

@@ -0,0 +1,108 @@
+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)

machines/get_env.py

@@ -0,0 +1,38 @@
+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)

machines/is_venv.py

@@ -0,0 +1,26 @@
+"""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()

machines/list_ops.py

@@ -0,0 +1,43 @@
+"""
+Simple str.split() parsing of input string
+
+usage example:
+  python list_ops.py --input_list=$INPUT [--sep=","] [--op=<len|select>] [--elem=$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)

metrics/__init__.py

@@ -0,0 +1,3 @@
+from .aggregation import StableMean  # noqa
+from .auroc import AUROCWithMWU  # noqa
+from .rce import NRCE, RCE  # noqa

metrics/aggregation.py

@@ -0,0 +1,97 @@
+"""
+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]

metrics/auroc.py

@@ -0,0 +1,162 @@
+"""
+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

metrics/rce.py

@@ -0,0 +1,280 @@
+"""
+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 <http://go/rce>`_).
+
+  RCE is metric used for models predicting probability of success (p), i.e. pCTR.
+  RCE represents the binary `cross entropy <https://en.wikipedia.org/wiki/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 <https://en.wikipedia.org/wiki/Level_of_measurement>`.
+            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 <https://en.wikipedia.org/wiki/Entropy_(information_theory)>` 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()

ml_logging/absl_logging.py

@@ -0,0 +1,26 @@
+"""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()

ml_logging/test_torch_logging.py

@@ -0,0 +1,21 @@
+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",
+      ],
+    )

ml_logging/torch_logging.py

@@ -0,0 +1,61 @@
+"""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)

model.py

@@ -0,0 +1,89 @@
+"""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)

optimizers/__init__.py

@@ -0,0 +1 @@
+from tml.optimizers.optimizer import compute_lr

optimizers/config.py

@@ -0,0 +1,82 @@
+"""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}")

optimizers/optimizer.py

@@ -0,0 +1,105 @@
+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

projects/home/recap/README.md

@@ -0,0 +1,48 @@
+This project is the "heavy ranker" used on the "For You" timeline. This is used to generate the ranking of Tweet after candidate retrieval and light ranker (note the final ordering of the Tweet is not directly the highest -> lowest scoring, because after scoring other heuristics are used).
+
+This model captures the ranking model used for the majority of users of Twitter "For You" timeline in early March 2023. 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.
+
+The model receives various features, describing the Tweet and the user whose timeline is being constructed as input (see FEATURES.md for more details). The model outputs multiple binary predictions about how the user will respond if shown the Tweet.
+
+
+Those are:
+  "recap.engagement.is_favorited": The probability the user will favorite the Tweet.
+  "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied": The probability the user will click into the conversation of this Tweet and reply or Like a Tweet.
+  "recap.engagement.is_good_clicked_convo_desc_v2": The probability the user will click into the conversation of this Tweet and stay there for at least 2 minutes.
+  "recap.engagement.is_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)
+  "recap.engagement.is_profile_clicked_and_profile_engaged": The probability the user opens the Tweet author profile and Likes or replies to a Tweet.
+  "recap.engagement.is_replied": The probability the user replies to the Tweet.
+  "recap.engagement.is_replied_reply_engaged_by_author": The probability the user replies to the Tweet and this reply is engaged by the Tweet author.
+  "recap.engagement.is_report_tweet_clicked": The probability the user will click Report Tweet.
+  "recap.engagement.is_retweeted": The probability the user will ReTweet the Tweet.
+  "recap.engagement.is_video_playback_50": The probability (for a video Tweet) that the user will watch at least half of the video
+
+For ranking the candidates these predictions are combined into a score by weighting them:
+  "recap.engagement.is_favorited": 0.5
+  "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied": 11* (the maximum prediction from these two "good click" features is used and weighted by 11, the other prediction is ignored).
+  "recap.engagement.is_good_clicked_convo_desc_v2": 11*
+  "recap.engagement.is_negative_feedback_v2": -74
+  "recap.engagement.is_profile_clicked_and_profile_engaged": 12
+  "recap.engagement.is_replied": 27
+  "recap.engagement.is_replied_reply_engaged_by_author": 75
+  "recap.engagement.is_report_tweet_clicked": -369
+  "recap.engagement.is_retweeted": 1
+  "recap.engagement.is_video_playback_50": 0.005
+
+
+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.
+
+To try training the model (assuming you have already followed the repo setup instructions and are inside a virtualenv).
+
+Run
+$ ./projects/home/recap/scripts/create_random_data.sh
+
+This will create some random data (in $HOME/tmp/recap_local_random_data).
+
+$ ./projects/home/recap/scripts/run_local.sh
+
+This will train the model (for a small number of iterations). Checkpoints and logs will be written to $HOME/tmp/runs/recap_local_debug.
+
+The model training is configured through a yaml file (./projects/home/recap/config/local_prod.yaml).
+
+The model architecture is a parallel masknet (https://arxiv.org/abs/2102.07619).

projects/home/recap/config.py

@@ -0,0 +1,54 @@
+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"

projects/home/recap/config/home_recap_2022/segdense.json

@@ -0,0 +1,114 @@
+{
+  "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
+    }
+  ]
+}

projects/home/recap/config/local_prod.yaml

@@ -0,0 +1,477 @@
+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

projects/home/recap/data/config.py

@@ -0,0 +1,246 @@
+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

projects/home/recap/data/dataset.py

@@ -0,0 +1,477 @@
+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())

projects/home/recap/data/generate_random_data.py

@@ -0,0 +1,81 @@
+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)

projects/home/recap/data/preprocessors.py

@@ -0,0 +1,206 @@
+"""
+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)

projects/home/recap/data/tfe_parsing.py

@@ -0,0 +1,129 @@
+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

projects/home/recap/data/util.py

@@ -0,0 +1,120 @@
+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

projects/home/recap/embedding/config.py

@@ -0,0 +1,94 @@
+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"
+  )

projects/home/recap/main.py

@@ -0,0 +1,108 @@
+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)

projects/home/recap/model/__init__.py

@@ -0,0 +1,7 @@
+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

projects/home/recap/model/config.py

@@ -0,0 +1,260 @@
+"""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

projects/home/recap/model/entrypoint.py

@@ -0,0 +1,304 @@
+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

projects/home/recap/model/feature_transform.py

@@ -0,0 +1,122 @@
+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)

projects/home/recap/model/mask_net.py

@@ -0,0 +1,98 @@
+"""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}

projects/home/recap/model/mlp.py

@@ -0,0 +1,58 @@
+"""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]

projects/home/recap/model/model_and_loss.py

@@ -0,0 +1,67 @@
+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,
+      }

projects/home/recap/model/numeric_calibration.py

@@ -0,0 +1,19 @@
+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))

projects/home/recap/optimizer/__init__.py

@@ -0,0 +1 @@
+from tml.projects.home.recap.optimizer.optimizer import build_optimizer

projects/home/recap/optimizer/config.py

@@ -0,0 +1,36 @@
+"""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")

projects/home/recap/optimizer/optimizer.py

@@ -0,0 +1,166 @@
+"""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

projects/home/recap/schema/segdense_tfexample_schema_home_recap_2022.json

@@ -0,0 +1,499 @@
+{
+  "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" : "bytes_list",
+      "feature_name" : "home_recap_2022_string__segdense_vals",
+      "length" : 64
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "home_recap_2022_blob__segdense_vals",
+      "length" : 64
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_photo_expanded",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_tweet_detail_dwelled_15_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "meta.timestamp",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_replied",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_profile_clicked_and_profile_engaged",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_retweeted",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_profile_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_shared",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_share_menu_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_replied_reply_engaged_by_author",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_profile_clicked_and_profile_engaged",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_profile_dwelled_30_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_video_playback_50",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_negative_feedback_v2",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_strong_negative_feedback",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_photo_expanded",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_share_menu_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_report_tweet_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_dwell",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_replied",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "meta.author_id",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_weak_negative_feedback",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_favorited",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_weak_negative_feedback",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_negative_feedback_v2",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_profile_dwelled_20_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_quoted",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_profile_dwelled_20_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_retweeted",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_profile_dwelled_10_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_report_tweet_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_strong_negative_feedback",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_dwelled",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_profile_dwelled_10_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "timelines.meta.candidate_tweet_source_id",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_favorited",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_good_clicked_convo_desc_v2",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "timelines.engagement.dwell_time_ms",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_good_clicked_convo_desc_favorited_or_replied",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_tweet_detail_dwelled_15_sec",
+      "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" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_good_clicked_convo_desc_favorited_or_replied",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_replied_reply_engaged_by_author",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_bookmarked",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "meta.record_weight/type=recap",
+      "length" : 1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "recap.engagement_predicted.is_video_playback_50",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_shared",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "timelines.meta.prediction_request_id",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_quoted",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_profile_clicked",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "recap.engagement.is_profile_dwelled_30_sec",
+      "length" : 1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.good_profile_click",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v3.tweet.text.emoji_tokens",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "entities.inferred_topic_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "semantic_core.media_understanding.high_recall.sensitive.entity_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "user.timelines.twhin_user_engagement_embeddings.twhin_user_engagement_embeddings",
+      "length" : 200
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "user.interests_discovery.user_topic_relationships.not_interested_topics",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v2.tweet.media.providers",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v3.tweet.text.pos_unigrams",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.share_menu_click",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "original_author.twhin.tw_hi_n.author_follow_as_float_tensor",
+      "length" : 200
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.tweet.media.sticker_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "original_author.timelines.twhin_author_follow_embeddings.twhin_author_follow_embeddings",
+      "length" : 200
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.good_click",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v2.tweet.media.classification_labels__key0",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.share",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "semantic_core.media_understanding.high_recall.non_sensitive.entity_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "semantic_core.media_understanding.high_precision.non_sensitive.entity_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.reported",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "tweet.cuad.hashspace.tweet_embedding_tensor",
+      "length" : 256
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "semantic_core.common.entity_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.negative_feedback_v_2",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.retweet",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.tweet.media.tag_screen_names",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "user.twhin.tw_hi_n.user_follow_as_float_tensor",
+      "length" : 200
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "tweetsource.v2.tweet.media.classification_labels__vals",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "in_reply_to_tweet.tweetsource.v4.tweet.text.tokens",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.playback_50",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.reply",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "recap.tweetfeature.url_domains",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.reply_engaged_by_author",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "user.twhin.tw_hi_n.user_engagement_as_float_tensor",
+      "length" : 200
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v3.tweet.text.pos_bigrams",
+      "length" : -1
+    },
+    {
+      "dtype" : "float_list",
+      "feature_name" : "user.timelines.twhin_user_follow_embeddings.twhin_user_follow_embeddings",
+      "length" : 200
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "in_reply_to_tweet.tweetsource.v3.tweet.text.emoji_tokens",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.good_click_v_2",
+      "length" : -1
+    },
+    {
+      "dtype" : "int64_list",
+      "feature_name" : "user.recommender_systems_research.recap_hashspace_pq_encoded.fav",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "user.interests_discovery.user_topic_relationships.followed_topics",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "semantic_core.media_understanding.high_precision.sensitive.entity_ids",
+      "length" : -1
+    },
+    {
+      "dtype" : "bytes_list",
+      "feature_name" : "tweetsource.v3.tweet.text.emoticon_tokens",
+      "length" : -1
+    }
+  ]
+}

projects/home/recap/script/create_random_data.sh

@@ -0,0 +1,10 @@
+#!/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

projects/home/recap/script/run_local.sh

@@ -0,0 +1,15 @@
+#!/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 \
+  $@

projects/twhin/README.md

@@ -0,0 +1,28 @@
+Twhin in torchrec
+
+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`
+
+
+Dump twhin user follows author prod data to gcs parquet in index-coordinates
+===========
+## Dump twhin user follows author prod data to gcs parquet in index-coordinates
+Assumes the graph has a single partition
+
+```
+EXPORT DATA
+  OPTIONS ( uri = 'gs://follows_tml_01/tweet_eng/2023-01-23/small/edges/0/*',
+    format = 'PARQUET', compression="ZSTD",
+    overwrite = TRUE) AS ( (
+    SELECT
+      lhs, rhs, rel
+    FROM
+      `twttr-twhin-stg.tweet_eng_tml_01.tweet_eng_small_graph`
+    WHERE
+      snapshot_date = "2023-01-23" AND rel = 0)
+  )
+```

projects/twhin/config.py

@@ -0,0 +1,14 @@
+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

projects/twhin/config/base_twhin.yaml

@@ -0,0 +1,72 @@
+runtime:
+  wandb:
+    key_path: "/var/lib/tss/keys/${USER}/wandb.key"
+    name: "twhin-test"
+    entity: "-"
+    project: "twhin-test"
+    host: "https://https--wandb--prod--wandb.service.qus1.twitter.biz/"
+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_060
+        embedding_dim: 128
+        data_type: fp32
+        optimizer:
+          sgd:
+            lr: 0.01
+          learning_rate:
+            constant: 0.01
+      - name: tweet
+        num_embeddings: 72_543_984
+        embedding_dim: 128
+        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: 60652
+  global_negatives: 0
+  in_batch_negatives: 0
+  limit: 9990
+validation_data:
+  data_root: "gs://follows_tml_01/tweet_eng/2023-01-23/large/edges/*"
+  per_replica_batch_size: 60652
+  global_negatives: 0
+  in_batch_negatives: 0
+  limit: 10
+  offset: 9990

projects/twhin/config/local.yaml

@@ -0,0 +1,72 @@
+runtime:
+  wandb:
+    key_path: "/var/lib/tss/keys/${USER}/wandb.key"
+    name: "twhin-test"
+    entity: "-"
+    project: "twhin-test"
+    host: "https://https--wandb--prod--wandb.service.qus1.twitter.biz/"
+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

projects/twhin/config/local_docker_compose.yaml

@@ -0,0 +1,38 @@
+version: "3.9"
+services:
+  chief-0:
+    depends_on:
+      - datasetworker-0
+      - datasetworker-1
+    image: local/torch
+    command: ['python', 'tml/projects/twhin/toy.py']
+    volumes: &volumes
+      - $HOME/workspace/tml:/usr/src/app/tml
+      - $HOME/.config:/root/.config
+    environment:
+      - SLURM_RANK_INDICATOR=chief
+      - TEMP_SLURM_NUM_READERS=2
+      - RANK=0
+      - WORLD_SIZE=1
+      - MASTER_ADDR=localhost
+      - MASTER_PORT=2020
+  datasetworker-0: &worker
+    image: local/torch
+    command: ['python', 'tml/projects/twhin/toy.py']
+    volumes: *volumes
+    ports:
+      - "2222:2222"
+    environment:
+      - SLURM_RANK_INDICATOR=reader
+      - TEMP_SLURM_NUM_READERS=2
+  datasetworker-1:
+    image: local/torch
+    command: ['python', 'tml/projects/twhin/toy.py']
+    volumes: *volumes
+    ports:
+      - "2223:2222"
+    environment:
+      - SLURM_RANK_INDICATOR=reader
+      - TEMP_SLURM_NUM_READERS=2
+networks:
+  host:

projects/twhin/config/slurm.yaml

@@ -0,0 +1,72 @@
+runtime:
+  wandb:
+    key_path: "/var/lib/tss/keys/${USER}/wandb.key"
+    name: "twhin-test"
+    entity: "-"
+    project: "twhin-test"
+    host: "https://https--wandb--prod--wandb.service.qus1.twitter.biz/"
+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: 700_410_729
+        embedding_dim: 64
+        data_type: fp16
+        optimizer:
+          sgd:
+            lr: 0.01
+          learning_rate:
+            constant: 0.01
+      - name: tweet
+        num_embeddings: 250_543_984
+        embedding_dim: 64
+        data_type: fp16
+        optimizer:
+          adagrad:
+            lr: 0.01
+          learning_rate:
+            constant: 0.01
+  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: "/home/-/twhin-sample/*"
+  per_replica_batch_size: 65536
+  global_negatives: 100
+  in_batch_negatives: 100
+  limit: 9990
+validation_data:
+  data_root: "/home/-/twhin-sample/*"
+  per_replica_batch_size: 65536
+  global_negatives: 100
+  in_batch_negatives: 100
+  limit: 10
+  offset: 9990

projects/twhin/data/config.py

@@ -0,0 +1,14 @@
+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."
+  )

projects/twhin/data/data.py

@@ -0,0 +1,22 @@
+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
+  global_negatives = data_config.global_negatives
+  in_batch_negatives = data_config.in_batch_negatives
+
+  return EdgesDataset(
+    file_pattern=data_config.data_root,
+    relations=relations,
+    table_sizes=table_sizes,
+    global_negatives=global_negatives,
+    in_batch_negatives=in_batch_negatives,
+    batch_size=pos_batch_size,
+  )

projects/twhin/data/edges.py

@@ -0,0 +1,169 @@
+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,
+      )

projects/twhin/data/test_data.py

@@ -0,0 +1,6 @@
+import pytest
+from unittest.mock import Mock
+
+
+def test_create_dataset():
+  pass

projects/twhin/data/test_edges.py

@@ -0,0 +1,66 @@
+"""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]))

projects/twhin/machines.yaml

@@ -0,0 +1,12 @@
+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

projects/twhin/metrics.py

@@ -0,0 +1,17 @@
+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

projects/twhin/models/config.py

@@ -0,0 +1,54 @@
+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

projects/twhin/models/models.py

@@ -0,0 +1,172 @@
+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