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106 lines
4.6 KiB
106 lines
4.6 KiB
import torch
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import torch.distributed as dist
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.registry import LOSSES
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from torch.cuda.amp import custom_bwd, custom_fwd
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from torch.nn.modules.loss import _Loss
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class _VocabParallelCrossEntropy1D(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, vocab_parallel_logits, targets, process_group):
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if process_group is None:
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process_group = gpc.get_group(ParallelMode.PARALLEL_1D)
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# Maximum value along vocab dimension across all GPUs.
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logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
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torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=process_group)
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# Subtract the maximum value.
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vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
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# Get the partition's vocab indecies
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partition_vocab_size = vocab_parallel_logits.size()[-1]
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rank = dist.get_rank(process_group)
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vocab_start_index = partition_vocab_size * rank
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vocab_end_index = vocab_start_index + partition_vocab_size
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# Create a mask of valid vocab ids (1 means it needs to be masked).
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target_mask = (targets < vocab_start_index) | (targets >= vocab_end_index)
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masked_target = targets.clone() - vocab_start_index
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masked_target[target_mask] = 0
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# Get predicted-logits = logits[target].
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# For Simplicity, we convert logits to a 2-D tensor with size
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# [*, partition-vocab-size] and target to a 1-D tensor of size [*].
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logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
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masked_target_1d = masked_target.view(-1)
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arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
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predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
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predicted_logits_1d = predicted_logits_1d.clone().contiguous()
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predicted_logits = predicted_logits_1d.view_as(targets)
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predicted_logits[target_mask] = 0.0
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# All reduce is needed to get the chunks from other GPUs.
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torch.distributed.all_reduce(predicted_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
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# Sum of exponential of logits along vocab dimension across all GPUs.
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exp_logits = torch.exp(vocab_parallel_logits)
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sum_exp_logits = exp_logits.sum(dim=-1)
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torch.distributed.all_reduce(sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
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# Loss = log(sum(exp(logits))) - predicted-logit.
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loss = torch.log(sum_exp_logits) - predicted_logits
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# Store softmax, target-mask and masked-target for backward pass.
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exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
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ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
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return loss
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_output):
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# Retreive tensors from the forward path.
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softmax, target_mask, masked_target_1d = ctx.saved_tensors
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# All the inputs have softmax as thier gradient.
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grad_input = softmax
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# For simplicity, work with the 2D gradient.
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partition_vocab_size = softmax.size()[-1]
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grad_2d = grad_input.view(-1, partition_vocab_size)
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# Add the gradient from matching classes.
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arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
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grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
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# Finally elementwise multiplication with the output gradients.
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grad_input.mul_(grad_output.unsqueeze(dim=-1))
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return grad_input, None, None
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@LOSSES.register_module
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class VocabParallelCrossEntropyLoss1D(_Loss):
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"""Vocab parallel cross entropy loss for 1D parallelism.
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Args:
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reduction (bool, optional): whether to average the loss, defaults to True.
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"""
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def __init__(self, reduction=True):
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super().__init__()
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self.reduction_mean = reduction
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def forward(self, logits, targets, process_group=None):
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"""Calculate loss between logits and targets.
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Args:
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logits (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
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targets (:class:`torch.tensor`): Ground truth class indices or class probabilities.
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"""
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loss = _VocabParallelCrossEntropy1D.apply(logits, targets, process_group)
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if self.reduction_mean:
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loss = loss.mean()
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return loss
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