mirror of https://github.com/hpcaitech/ColossalAI
284 lines
8.5 KiB
Python
284 lines
8.5 KiB
Python
from typing import List, Optional, Union
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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.legacy.nn.layer.utils import divide
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from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
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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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def _all_to_all(x: torch.Tensor, pg: ProcessGroup, scatter_dim: int, gather_dim: int) -> torch.Tensor:
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world_size = pg.tp_world_size()
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if world_size == 1:
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return x
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# TODO: enabling mpi backend to support CPU all_to_all
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assert x.device.type == 'cuda', f"Currently, the collective function dual_all_to_all only supports nccl backend"
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shapes = list(x.size())
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shapes[scatter_dim] = shapes[scatter_dim] // world_size
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scatter_list = [each.contiguous() for each in torch.tensor_split(x, world_size, scatter_dim)]
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gather_list = [torch.empty(*shapes, dtype=x.dtype, device=x.device) for _ in range(world_size)]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, dim=gather_dim).contiguous()
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class _DualAllToAll(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, pg, scatter_dim, gather_dim):
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ctx.scatter_dim = scatter_dim
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ctx.gather_dim = gather_dim
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ctx.pg = pg
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return _all_to_all(x, pg, scatter_dim, gather_dim)
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@staticmethod
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def backward(ctx, grad):
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return _all_to_all(grad, ctx.pg, ctx.gather_dim, ctx.scatter_dim), None, None, None
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def dual_all_to_all(x, pg, scatter_dim: int, gather_dim: int):
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return _DualAllToAll.apply(x, pg, scatter_dim, gather_dim)
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# table wise embedding shard
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def _all_to_all_for_tablewise(x: torch.Tensor,
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pg: ProcessGroup,
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scatter_strides: List[int],
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gather_strides: List[int],
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forward=True) -> torch.Tensor:
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world_size = pg.tp_world_size()
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rank = pg.tp_local_rank()
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if world_size == 1:
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return x
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assert x.device.type == 'cuda', f"Currently, the collective function dual_all_to_all only supports nccl backend"
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if forward:
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scatter_list = list(x.split(scatter_strides, 0))
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gather_list = [
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torch.empty(scatter_strides[rank], gather_strides[i], dtype=x.dtype, device=x.device)
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for i in range(world_size)
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]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 1).contiguous()
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else:
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# split on dim 1, lose contiguity
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scatter_list = [each.contiguous() for each in x.split(scatter_strides, 1)]
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gather_list = [
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torch.empty(gather_strides[i], scatter_strides[rank], dtype=x.dtype, device=x.device)
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for i in range(world_size)
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]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 0).contiguous()
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class _DualAllToAllForTablewise(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, pg, scatter_strides, gather_strides):
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ctx.pg = pg
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ctx.scatter_strides = scatter_strides
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ctx.gather_strides = gather_strides
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return _all_to_all_for_tablewise(x, pg, scatter_strides, gather_strides, forward=True)
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@staticmethod
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def backward(ctx, grad):
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return _all_to_all_for_tablewise(grad, ctx.pg, ctx.gather_strides, ctx.scatter_strides,
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forward=False), None, None, None
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def dual_all_to_all_tablewise(x, pg, scatter_strides, gather_strides):
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return _DualAllToAllForTablewise.apply(x, pg, scatter_strides, gather_strides)
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