118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
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):
|
|
"""
|
|
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:
|
|
"""
|
|
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,
|
|
}
|