mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-11-16 21:29:24 +01:00
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""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)
|