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):
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"""
PyTorch module that combines a neural network model and loss function.
This module wraps a neural network model and facilitates the forward pass through the model
while also calculating the loss based on the model's predictions and provided labels.
Args:
model: The torch module to wrap.
loss_fn (Callable): Function for calculating the loss, which should accept logits and labels.
stratifiers (Optional[List[embedding_config_mod.StratifierConfig]]): A list of stratifier configurations
for metrics stratification. Each stratifier config includes the name and index of discrete features
to emit for stratification.
Example:
To use `ModelAndLoss` in a PyTorch training loop, you can create an instance of it and pass your model
and loss function as arguments:
```python
# Create a neural network model
model = YourNeuralNetworkModel()
# Define a loss function
loss_fn = torch.nn.CrossEntropyLoss()
# Create an instance of ModelAndLoss
model_and_loss = ModelAndLoss(model, loss_fn)
# Generate a batch of training data (e.g., RecapBatch)
batch = generate_training_batch()
# Perform a forward pass through the model and calculate the loss
loss, outputs = model_and_loss(batch)
# You can now backpropagate and optimize using the computed loss
loss.backward()
optimizer.step()
```
Note:
The `ModelAndLoss` class simplifies the process of running forward passes through a model and
calculating loss, making it easier to integrate the model into your training loop. Additionally,
it supports the addition of stratifiers for metrics stratification, if needed.
Warning:
This class is intended for internal use within neural network architectures and should not be
directly accessed or modified by external code.
"""
def __init__(
self,
model,
loss_fn: Callable,
stratifiers: Optional[List[embedding_config_mod.StratifierConfig]] = None,
) -> None:
"""
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Initializes the ModelAndLoss module.
Args:
model: The torch module to wrap.
loss_fn (Callable): Function for calculating the loss, which should accept logits and labels.
stratifiers (Optional[List[embedding_config_mod.StratifierConfig]]): A list of stratifier configurations
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,
}