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import torch |
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import deepspeed.comm as dist |
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class _VocabSequenceParallelCrossEntropy(torch.autograd.Function): |
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@staticmethod |
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def forward(ctx, vocab_seq_parallel_logits, target, sp_group): |
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softmax = torch.nn.functional.softmax(vocab_seq_parallel_logits, dim=-1) |
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ctx.vocab_size = vocab_seq_parallel_logits.size(2) |
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loss = torch.nn.functional.nll_loss(softmax.log().view(-1, ctx.vocab_size), target.view(-1), reduction='none') |
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sp_world_size = dist.get_world_size(sp_group) |
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sp_rank = dist.get_rank(sp_group) |
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ctx.sp_world_size = sp_world_size |
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ctx.sp_rank = sp_rank |
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ctx.seqlen = vocab_seq_parallel_logits.size(0) * sp_world_size |
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batch_size = vocab_seq_parallel_logits.size(1) |
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loss_all = torch.empty(ctx.seqlen, |
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batch_size, |
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dtype=vocab_seq_parallel_logits.dtype, |
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device=vocab_seq_parallel_logits.device) |
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dist.all_gather_into_tensor(loss_all, loss, group=sp_group) |
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ctx.save_for_backward(softmax, target) |
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return loss_all |
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@staticmethod |
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def backward(ctx, grad_output): |
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softmax, target = ctx.saved_tensors |
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step_seqlen = ctx.seqlen // ctx.sp_world_size |
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sp_rank = ctx.sp_rank |
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grad_output_part = grad_output[step_seqlen * sp_rank:step_seqlen * (sp_rank + 1), :] |
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grad_input = softmax |
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grad_2d = grad_input.view(-1, ctx.vocab_size) |
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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, target.view(-1)] -= 1 |
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grad_input.mul_(grad_output_part.unsqueeze(dim=-1)) |
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return grad_input, None, None, None |
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def vocab_sequence_parallel_cross_entropy(vocab_parallel_logits, target, sp_group): |
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return _VocabSequenceParallelCrossEntropy.apply(vocab_parallel_logits, target, sp_group) |
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