85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
# Copyright (c) 2023, Tri Dao.
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import torch
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import torch.nn as nn
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from flash_attn.ops.triton.cross_entropy import cross_entropy_loss
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class CrossEntropyLoss(nn.Module):
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def __init__(
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self,
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ignore_index=-100,
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reduction="mean",
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label_smoothing=0.0,
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logit_scale=1.0,
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lse_square_scale=0.0,
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inplace_backward=False,
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process_group=None,
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return_z_loss=False,
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):
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"""
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Arguments:
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ignored_index: int. If labels == ignored_index, the loss is set to 0.0.
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label_smoothing: float
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lse_square_scale: float. If > 0, we add lse_square_scale * lse(logits) ^ 2 to the loss.
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This is also referred to as "z-loss".
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inplace_backward: bool. If True, we do the backward pass in-place by modifying the logits.
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This saves memory.
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process_group: if not None, we're doing Tensor Parallel: each process is responsible for
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one part of the vocab. The loss will be aggregated across processes.
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return_z_loss: bool. If True, we return the component of the loss contributed by
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the lse_square_scale value. This value is only for logging and does not support
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backprop.
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"""
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super().__init__()
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if reduction not in ["mean", "none", "sum"]:
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raise NotImplementedError("Only support reduction = 'mean' or 'none' or 'sum'")
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self.ignore_index = ignore_index
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self.reduction = reduction
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self.label_smoothing = label_smoothing
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self.logit_scale = logit_scale
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self.lse_square_scale = lse_square_scale
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self.inplace_backward = inplace_backward
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self.process_group = process_group
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self.return_z_loss = return_z_loss
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def forward(self, input, target):
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"""
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Arguments:
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input: (batch, vocab_size)
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target: (batch,)
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Returns:
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losses: (batch,) if reduction is 'none', else (1,), dtype float
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z_loss: (batch,) if reduction is 'none', else (1,), dtype float (if self.return_z_loss)
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"""
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assert input.is_cuda and target.is_cuda, "Only support CUDA tensors"
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loss, z_loss = cross_entropy_loss(
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input,
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target,
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label_smoothing=self.label_smoothing,
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logit_scale=self.logit_scale,
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lse_square_scale=self.lse_square_scale,
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ignored_index=self.ignore_index,
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inplace_backward=self.inplace_backward,
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process_group=self.process_group,
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)
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if self.reduction == "mean":
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loss = loss.sum() / (target != self.ignore_index).sum()
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elif self.reduction == "sum":
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loss = loss.sum()
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else:
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loss = loss
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if not self.return_z_loss:
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return loss
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if self.reduction == "mean":
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z_loss = z_loss.sum() / (target != self.ignore_index).sum()
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elif self.reduction == "sum":
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z_loss = z_loss.sum()
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else:
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z_loss = z_loss
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return loss, z_loss
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