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106 lines
4.1 KiB
106 lines
4.1 KiB
import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import Function
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class DistCrossEntropy(Function):
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r"""
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Overwrite the forward and backward function to calculate the cross entropy loss before gather
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Args:
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Function (:class:`torch.autograd.Function`): default
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"""
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@staticmethod
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def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: int):
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r"""
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Calculate the cross entropy loss before gather, the origin loss function is as follows:
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loss = -log(exp(x[class])/sum(exp(x[i]))
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and can be rewrite as:
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loss = log(sum(exp(x[i])) - x[class]
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To avoid the `nan` of log(sum(exp(x[i]))), we minus the max of x[i]
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Args:
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vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is
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[batch_size, seq_len, vocab_size]
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labels (:class:`torch.Tensor`): The labels of the vocabulary, shape is
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[batch_size, seq_len]
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Returns:
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:class:`torch.Tensor`: The cross entropy loss
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"""
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# get the max
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logits_max = torch.max(vocab_logits, dim=-1)[0]
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dist.all_reduce(logits_max, op=dist.ReduceOp.MAX)
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# minus the max to avoid the result of sum of exp is too large and the log is nan
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vocab_logits = vocab_logits - logits_max.unsqueeze(dim=-1)
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# mask the target in the local device
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partition_vocab_size = vocab_logits.size()[-1]
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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global_vocab_size = partition_vocab_size * world_size
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# [down, up) => false, other device and -100 => true
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delta = (global_vocab_size + world_size - 1) // world_size
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down_shreshold = rank * delta
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up_shreshold = down_shreshold + delta
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mask = (target < down_shreshold) | (target >= up_shreshold)
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masked_target = target.clone() - down_shreshold
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masked_target[mask] = 0
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# reshape the logist and target
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# reshape the vocab_logits to [bath_size * seq_len, vocab_size]
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# reshape the labels to [bath_size * seq_len]
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logits_2d = vocab_logits.view(-1, partition_vocab_size)
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masked_target_1d = masked_target.view(-1)
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# extract the x[class] and set the x[other device] to zero
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pred_logits_1d = logits_2d[torch.arange(start=0, end=logits_2d.shape[0], device=logits_2d.device),
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masked_target_1d]
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pred_logits_1d = pred_logits_1d.clone().contiguous()
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pred_logits = pred_logits_1d.view_as(target)
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pred_logits[mask] = 0.0
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# allreduce the get all x(i,y)
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dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM)
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exp_logits = vocab_logits
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torch.exp(vocab_logits, out=exp_logits)
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sum_exp_logits = torch.sum(exp_logits, dim=-1)
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dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM)
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# calculate the loss
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# loss = log(sum(exp(x[i]))) - x[class]
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loss = torch.where(target == ignore_index, 0.0, torch.log(sum_exp_logits) - pred_logits)
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loss = torch.sum(loss).div_(torch.sum(loss != 0.0))
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# caculate the softmax
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exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
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ctx.save_for_backward(exp_logits, mask, masked_target_1d)
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return loss
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@staticmethod
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def backward(ctx, grad_output):
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# retrieve the saved tensors
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exp_logits, mask, masked_target_1d = ctx.saved_tensors
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# use exp logits as the input grad
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grad_logits = exp_logits
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partion_vocab_size = grad_logits.shape[-1]
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grad_logits_2d = grad_logits.view(-1, partion_vocab_size)
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update = 1.0 - mask.view(-1).float()
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grad_logits_2d[torch.arange(0, grad_logits_2d.shape[0]), masked_target_1d] -= update
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grad_logits.mul_(grad_output.unsqueeze(dim=-1))
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return grad_logits, None, None
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def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor:
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return DistCrossEntropy.apply(vocab_logits, labels, ignore_index)
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