mirror of https://github.com/hpcaitech/ColossalAI
119 lines
5.3 KiB
Python
119 lines
5.3 KiB
Python
import torch
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from colossalai.tensor.op_wrapper import colo_op_impl
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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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gather_forward_split_backward, reduce_grad
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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.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
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def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
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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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# Input:S[1]
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if input_tensor.is_gathered():
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# Not splited yet.
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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_per_partition = split_forward_gather_backward(input_tensor.torch_tensor(),
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parallel_action.parallel_mode,
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dim=-1)
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elif input_tensor.shard_pattern == ShardPattern.Col:
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# Splited by 1Dcol
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assert input_tensor.shape[-1] == 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))
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input_per_partition = input_tensor.torch_tensor()
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else:
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raise NotImplementedError
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# Output:P
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partial_output = torch.nn.functional.linear(input_per_partition, weight.torch_tensor())
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# Reduce(Output)
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output = reduce_input(partial_output, parallel_action.parallel_mode)
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# Bias
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if bias is not None:
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assert not bias.has_spec(), 'Invalid bias spec for 1Drow Linear op'
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output = output + bias.torch_tensor()
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output = ColoTensor.init_from_torch_tensor(output)
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return output
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def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
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# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
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# All-Gather(Output)
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# Input:B
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
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if input_tensor.is_gathered():
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# Not splited yet.
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assert input_tensor.shape[-1] == weight.size(-1), \
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'Invalid shapes in 1Dcol forward: input={}, weight={}. Expected last dim of input {}.'.format(
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input_tensor.shape, weight.size, weight.size(-1))
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input_parallel = reduce_grad(input_tensor.torch_tensor(), parallel_action.parallel_mode)
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# Bias:S[1]
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if bias is not None:
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assert bias.has_spec() and bias.shard_spec.num_action == 1 and \
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bias.shard_pattern in [ShardPattern.Col, ShardPattern.Row], \
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'Invalid bias spec for 1Dcol Linear op'
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output_parallel = torch.nn.functional.linear(input_parallel, weight.torch_tensor(), bias.torch_tensor())
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output = ColoTensor.init_from_torch_tensor(output_parallel)
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out_parallel_action_list = [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]
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output_spec = TensorSpec(out_parallel_action_list)
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output.set_spec(output_spec, shard=False)
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output.set_shard_pattern(ShardPattern.Col)
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if parallel_action.gather_out:
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# All-Gather(Output)
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output.gather()
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return output
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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 not isinstance(input_tensor, ColoTensor):
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input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
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if not isinstance(weight, ColoTensor):
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weight = ColoTensor.init_from_torch_tensor(weight)
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if bias is not None and not isinstance(bias, ColoTensor):
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bias = ColoTensor.init_from_torch_tensor(bias)
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# Add communication logic before and after linear call.
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if not weight.has_spec(): # No Model Parallel Applied
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assert not bias.has_spec(), 'Invalid bias spec for native Linear op'
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input_tensor = input_tensor.torch_tensor()
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weight = weight.torch_tensor()
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bias = bias.torch_tensor()
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return ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
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elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
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compute_patterns = weight.shard_spec.compute_patterns
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if ComputePattern.TP1DRow in compute_patterns:
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return colo_linear_1Drow(input_tensor, weight, bias)
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elif ComputePattern.TP1DCol in compute_patterns:
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return colo_linear_1Dcol(input_tensor, weight, bias)
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else:
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raise NotImplementedError
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else:
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raise NotImplementedError
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