mirror of https://github.com/hpcaitech/ColossalAI
59 lines
2.4 KiB
Python
59 lines
2.4 KiB
Python
import torch
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from colossalai.tensor.op_wrapper import colo_op_impl
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from colossalai.tensor.colo_tensor import ColoTensor
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from colossalai.context import ParallelMode
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from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input
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from colossalai.nn.layer.utils import divide
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from colossalai.core import global_context as gpc
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from packaging import version
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from colossalai.utils.cuda import get_current_device
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@colo_op_impl(torch.nn.functional.linear)
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def colo_linear(types, args, kwargs, pg):
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"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
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This method computes a linear.
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"""
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input_tensor = args[0]
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weight = args[1]
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if version.parse(torch.__version__) > version.parse("1.11.0"):
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if len(args) == 3:
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bias = args[2]
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else:
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bias = None
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else:
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bias = kwargs.get('bias', None)
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if isinstance(bias, ColoTensor):
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bias = bias.torch_tensor()
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# Add communication logic before and after linear call.
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if isinstance(weight, ColoTensor):
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if weight.shard_spec == None:
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return torch.nn.functional.linear(input_tensor, weight.torch_tensor(), bias)
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elif weight.shard_spec == '1Drow':
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# Input:S[1] x Weight:S[0] = Output:P
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# All-Reduce(Output) + bias = res
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assert divide(input_tensor.shape[-1], gpc.tensor_parallel_size) == weight.size(-1), \
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'Invalid shapes in 1Drow forward: input={}, weight={}. Expected last dim of input {}.'.format(
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input_tensor.shape, weight.size, weight.size[-1] * gpc.tensor_parallel_size)
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# Input:S[1]
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input_per_partition = split_forward_gather_backward(input_tensor, ParallelMode.PARALLEL_1D, dim=-1)
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# Output:P
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device = get_current_device() # TODO where to put to(deivce)?
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weight_ = weight.torch_tensor().to(device)
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partial_output = torch.nn.functional.linear(input_per_partition, weight_)
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# Reduce(Output)
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output = reduce_input(partial_output, ParallelMode.PARALLEL_1D)
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# Bias
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if bias is not None:
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bias_ = bias.to(device)
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output = output + bias_
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return output
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else:
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raise NotImplementedError
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else:
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return torch.nn.functional.linear(input_tensor, weight, bias)
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