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198 lines
5.2 KiB
198 lines
5.2 KiB
import torch
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from typing import Union, Optional
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from colossalai.tensor import ColoTensor
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import torch
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import torch.distributed as dist
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from colossalai.global_variables import tensor_parallel_env as env
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from colossalai.nn.layer.utils import divide
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from colossalai.tensor import ProcessGroup, ColoTensorSpec
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GeneralTensor = Union[ColoTensor, torch.Tensor]
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Number = Union[int, float]
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def convert_to_colo_tensor(tensor: Optional[GeneralTensor], pg: ProcessGroup) -> Optional[ColoTensor]:
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if tensor is not None and not isinstance(tensor, ColoTensor):
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tensor = ColoTensor.from_torch_tensor(tensor, ColoTensorSpec(pg))
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return tensor
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def set_parallel_input(input_parallel: bool):
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env.parallel_input_1d = input_parallel
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def get_parallel_input():
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return env.parallel_input_1d
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def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank):
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index_f = rank * per_partition_vocab_size
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index_l = index_f + per_partition_vocab_size
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return index_f, index_l
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def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
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per_partition_vocab_size = divide(global_vocab_size, world_size)
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return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank)
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def _reduce(input_, pg: ProcessGroup):
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# skip if only one rank involved
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if pg.tp_world_size() == 1:
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return input_
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assert input_.device.type == 'cuda'
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group = pg.tp_process_group()
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dist.all_reduce(input_, group=group)
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return input_
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def _split(input_, pg: ProcessGroup, dim=-1):
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# skip if only one rank involved
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world_size = pg.tp_world_size()
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if world_size == 1:
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return input_
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# Split along last dimension.
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dim_size = input_.size(dim)
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assert dim_size % world_size == 0, \
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f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \
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f'cannot split tensor evenly'
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tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
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rank = pg.tp_local_rank()
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output = tensor_list[rank].contiguous()
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return output
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def _gather(input_, pg: ProcessGroup, dim=-1):
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# skip if only one rank involved
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world_size = pg.tp_world_size()
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if world_size == 1:
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return input_
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# all gather
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rank = pg.tp_local_rank()
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tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
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tensor_list[rank] = input_
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assert input_.device.type == 'cuda'
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group = pg.tp_process_group()
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torch.distributed.all_gather(tensor_list, input_, group=group)
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# concat
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output = torch.cat(tensor_list, dim=dim).contiguous()
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return output
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class _ReduceGrad(torch.autograd.Function):
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"""
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Pass the input to the model parallel region.
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Args:
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input_: input matrix.
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process_group: parallel mode.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return input_
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@staticmethod
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def forward(ctx, input_, process_group):
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ctx.mode = process_group
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return input_
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@staticmethod
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def backward(ctx, grad_output):
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return _reduce(grad_output, ctx.mode), None
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class _ReduceInput(torch.autograd.Function):
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"""
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All-reduce the input from the model parallel region.
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Args:
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input_: input matrix.
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process_group: parallel mode.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _reduce(input_)
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@staticmethod
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def forward(ctx, input_, process_group):
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return _reduce(input_, process_group)
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@staticmethod
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def backward(ctx, grad_output):
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return grad_output, None
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class _SplitForwardGatherBackward(torch.autograd.Function):
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"""
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Split the input and keep only the corresponding chuck to the rank.
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Args:
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input_: input matrix.
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process_group: parallel mode.
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dim: dimension
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _split(input_)
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@staticmethod
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def forward(ctx, input_, process_group, dim):
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ctx.mode = process_group
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ctx.dim = dim
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return _split(input_, process_group, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _gather(grad_output, ctx.mode, ctx.dim), None, None
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class _GatherForwardSplitBackward(torch.autograd.Function):
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"""Gather the input from model parallel region and concatenate.
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Args:
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input_: input matrix.
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process_group: parallel mode.
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dim: dimension
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _gather(input_)
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@staticmethod
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def forward(ctx, input_, process_group, dim):
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ctx.mode = process_group
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ctx.dim = dim
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return _gather(input_, process_group, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _split(grad_output, ctx.mode, ctx.dim), None, None
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def reduce_grad(input_, process_group):
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return _ReduceGrad.apply(input_, process_group)
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def reduce_input(input_, process_group):
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return _ReduceInput.apply(input_, process_group)
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def split_forward_gather_backward(input_, process_group, dim):
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return _SplitForwardGatherBackward.apply(input_, process_group, dim)
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def gather_forward_split_backward(input_, process_group, dim):
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return _GatherForwardSplitBackward.apply(input_, process_group, dim)
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