the-algorithm-ml/projects/home/recap/model/model_and_loss.py

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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,
}