mirror of
https://github.com/twitter/the-algorithm-ml.git
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90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
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"""Wraps servable model in loss and RecapBatch passing to be trainable."""
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# flake8: noqa
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from typing import Callable
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from tml.ml_logging.torch_logging import logging # type: ignore[attr-defined]
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import torch
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import torch.distributed as dist
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from torchrec.distributed.model_parallel import DistributedModelParallel
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class ModelAndLoss(torch.nn.Module):
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# Reconsider our approach at a later date: https://ppwwyyxx.com/blog/2022/Loss-Function-Separation/
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def __init__(
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self,
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model,
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loss_fn: Callable,
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) -> None:
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"""
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Args:
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model: torch module to wrap.
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loss_fn: Function for calculating loss, should accept logits and labels.
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"""
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super().__init__()
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self.model = model
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self.loss_fn = loss_fn
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def forward(self, batch: "RecapBatch"): # type: ignore[name-defined]
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"""Runs model forward and calculates loss according to given loss_fn.
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NOTE: The input signature here needs to be a Pipelineable object for
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prefetching purposes during training using torchrec's pipeline. However
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the underlying model signature needs to be exportable to onnx, requiring
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generic python types. see https://pytorch.org/docs/stable/onnx.html#types.
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"""
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outputs = self.model(batch)
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losses = self.loss_fn(outputs["logits"], batch.labels.float(), batch.weights.float())
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outputs.update(
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{
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"loss": losses,
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"labels": batch.labels,
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"weights": batch.weights,
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}
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)
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# Allow multiple losses.
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return losses, outputs
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def maybe_shard_model(
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model,
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device: torch.device,
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):
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"""Set up and apply DistributedModelParallel to a model if running in a distributed environment.
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If in a distributed environment, constructs Topology, sharders, and ShardingPlan, then applies
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DistributedModelParallel.
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If not in a distributed environment, returns model directly.
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"""
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if dist.is_initialized():
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logging.info("***** Wrapping in DistributedModelParallel *****")
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logging.info(f"Model before wrapping: {model}")
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model = DistributedModelParallel(
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module=model,
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device=device,
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)
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logging.info(f"Model after wrapping: {model}")
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return model
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def log_sharded_tensor_content(weight_name: str, table_name: str, weight_tensor) -> None:
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"""Handy function to log the content of EBC embedding layer.
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Only works for single GPU machines.
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Args:
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weight_name: name of tensor, as defined in model
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table_name: name of the EBC table the weight is taken from
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weight_tensor: embedding weight tensor
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"""
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logging.info(f"{weight_name}, {table_name}", rank=-1)
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logging.info(f"{weight_tensor.metadata()}", rank=-1)
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output_tensor = torch.zeros(*weight_tensor.size(), device=torch.device("cuda:0"))
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weight_tensor.gather(out=output_tensor)
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logging.info(f"{output_tensor}", rank=-1)
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