mirror of https://github.com/hpcaitech/ColossalAI
146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
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.nn.layer.parallel_2d import reduce_by_batch_2d, split_tensor_2d
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from colossalai.nn.layer.parallel_2d._utils import assert_summa_initialization
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from colossalai.registry import LOSSES
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from colossalai.utils import get_current_device
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from torch.cuda.amp import custom_bwd, custom_fwd
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from torch.nn.functional import cross_entropy
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from torch.nn.modules.loss import _Loss
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@LOSSES.register_module
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class CrossEntropyLoss2D(_Loss):
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"""
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Cross entropy loss for 2D parallelism
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:param reduction: whether to average the loss, defaults to True
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:param args: Args for loss function
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:param kwargs: Kwargs for loss function
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:type reduction: bool, optional
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"""
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def __init__(self, reduction=True, *args, **kwargs):
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super().__init__()
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assert_summa_initialization()
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self.reduction_mean = reduction
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self.loss_args = args
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self.loss_kwargs = kwargs
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def forward(self, logits, targets):
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"""Calculate loss between logits and targets
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:param logits: Output logits of model
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:param targets: True targets from data
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"""
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targets = split_tensor_2d(targets)
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loss = cross_entropy(logits, targets, reduction='none', *self.loss_args, **self.loss_kwargs)
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if self.reduction_mean:
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loss = loss.mean()
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loss = reduce_by_batch_2d(loss, True)
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return loss
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class _VocabParallelCrossEntropy2D(torch.autograd.Function):
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### Modified based on megatron.mpu.cross_entropy ###
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, logits, targets):
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# logits: [b/q, h/q]
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# labels: [b/q]
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# loss: [b/q]
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# vocab_parallel_logits: [b/q, s, v/q]
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# target: [b/q, s]
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logits_max = torch.max(logits, dim=-1)[0]
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torch.distributed.all_reduce(logits_max,
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op=torch.distributed.ReduceOp.MAX,
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group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
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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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logits = logits - logits_max.unsqueeze(dim=-1)
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vocab_size = logits.size(-1)
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
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vocab_start = rank * (vocab_size)
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vocab_end = (rank + 1) * (vocab_size) - 1
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target_mask = (targets < vocab_start) | (targets > vocab_end)
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masked_target = targets.clone() - vocab_start
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masked_target[target_mask] = 0
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arange_1d = torch.arange(
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start=0,
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end=logits.size()[0],
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)
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predicted_logits = logits[arange_1d, masked_target]
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predicted_logits[target_mask] = 0.
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dist.all_reduce(predicted_logits, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
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exp_logits = torch.exp(logits)
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sum_exp_logits = exp_logits.sum(dim=1)
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dist.all_reduce(sum_exp_logits, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
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loss = torch.log(sum_exp_logits) - predicted_logits
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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)
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return loss
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@staticmethod
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@custom_bwd
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def backward(ctx, output_grad):
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# Retreive tensors from the forward path.
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softmax, target_mask, masked_target = ctx.saved_tensors
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# All the inputs have softmax as their 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=get_current_device())
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grad_2d[arange_1d, masked_target] -= (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_(output_grad.unsqueeze(dim=-1))
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return grad_input, None
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@LOSSES.register_module
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class VocabParallelCrossEntropyLoss2D(_Loss):
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"""
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Vocab parallel cross entropy loss for 2D parallelism
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:param reduction: whether to average the loss, defaults to True
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:type reduction: bool, optional
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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):
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"""Calculate loss between logits and targets
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:param logits: Output logits of model
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:param targets: True targets from data
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"""
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targets = split_tensor_2d(targets)
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loss = _VocabParallelCrossEntropy2D.apply(
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logits,
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targets,
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)
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if self.reduction_mean:
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loss = loss.mean()
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loss = reduce_by_batch_2d(loss, True)
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return loss
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