Spaces:
Sleeping
Sleeping
File size: 3,214 Bytes
e45d058 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# Copyright (c) 2023, Tri Dao.
import torch
import torch.nn as nn
from flash_attn.ops.triton.cross_entropy import cross_entropy_loss
class CrossEntropyLoss(nn.Module):
def __init__(
self,
ignore_index=-100,
reduction="mean",
label_smoothing=0.0,
logit_scale=1.0,
lse_square_scale=0.0,
inplace_backward=False,
process_group=None,
return_z_loss=False,
):
"""
Arguments:
ignore_index: int. If labels == ignore_index, the loss is set to 0.0.
label_smoothing: float
lse_square_scale: float. If > 0, we add lse_square_scale * lse(logits) ^ 2 to the loss.
This is also referred to as "z-loss".
inplace_backward: bool. If True, we do the backward pass in-place by modifying the logits.
This saves memory.
process_group: if not None, we're doing Tensor Parallel: each process is responsible for
one part of the vocab. The loss will be aggregated across processes.
return_z_loss: bool. If True, we return the component of the loss contributed by
the lse_square_scale value. This value is only for logging and does not support
backprop.
"""
super().__init__()
if reduction not in ["mean", "none", "sum"]:
raise NotImplementedError("Only support reduction = 'mean' or 'none' or 'sum'")
self.ignore_index = ignore_index
self.reduction = reduction
self.label_smoothing = label_smoothing
self.logit_scale = logit_scale
self.lse_square_scale = lse_square_scale
self.inplace_backward = inplace_backward
self.process_group = process_group
self.return_z_loss = return_z_loss
def forward(self, input, target):
"""
Arguments:
input: (batch, vocab_size)
target: (batch,)
Returns:
losses: (batch,) if reduction is 'none', else (1,), dtype float
z_loss: (batch,) if reduction is 'none', else (1,), dtype float (if self.return_z_loss)
"""
assert input.is_cuda and target.is_cuda, "Only support CUDA tensors"
loss, z_loss = cross_entropy_loss(
input,
target,
label_smoothing=self.label_smoothing,
logit_scale=self.logit_scale,
lse_square_scale=self.lse_square_scale,
ignore_index=self.ignore_index,
inplace_backward=self.inplace_backward,
process_group=self.process_group,
)
if self.reduction == "mean":
loss = loss.sum() / (target != self.ignore_index).sum()
elif self.reduction == "sum":
loss = loss.sum()
else:
loss = loss
if not self.return_z_loss:
return loss
if self.reduction == "mean":
z_loss = z_loss.sum() / (target != self.ignore_index).sum()
elif self.reduction == "sum":
z_loss = z_loss.sum()
else:
z_loss = z_loss
return loss, z_loss
|