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249 lines
11 KiB
249 lines
11 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch
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import torch.distributed as dist
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from torch import Tensor
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from torch.distributed import ReduceOp
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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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_all_gather_func = dist._all_gather_base \
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if "all_gather_into_tensor" not in dir(dist) else dist.all_gather_into_tensor
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_reduce_scatter_func = dist._reduce_scatter_base \
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if "reduce_scatter_tensor" not in dir(dist) else dist.reduce_scatter_tensor
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def all_gather(tensor: Tensor, dim: int, parallel_mode: ParallelMode, async_op: bool = False) -> Tensor:
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r"""Gathers all tensors from the parallel group and concatenates them in a
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specific dimension.
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Note:
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The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
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in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be gathered.
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dim (int): The dimension concatenating in.
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parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
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async_op (bool, optional): Whether operations are asynchronous.
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Returns:
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Union[tuple(:class:`torch.Tensor`, work handle), :class:`torch.Tensor`]: The result of all-together only,
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if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
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"""
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depth = gpc.get_world_size(parallel_mode)
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if depth == 1:
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out = tensor
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work = None
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else:
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tensor_in = tensor.contiguous() if dim == 0 else tensor.transpose(0, dim).contiguous()
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out_shape = (tensor_in.shape[0] * depth,) + tensor_in.shape[1:]
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tensor_out = torch.empty(out_shape, dtype=tensor.dtype, device=tensor.device)
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group = gpc.get_cpu_group(parallel_mode) if tensor.device.type == "cpu" else gpc.get_group(parallel_mode)
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work = _all_gather_func(tensor_out, tensor_in, group=group, async_op=async_op)
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out = tensor_out if dim == 0 else tensor_out.transpose(0, dim)
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if async_op:
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return out, work
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else:
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return out
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def reduce_scatter(tensor: Tensor,
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dim: int,
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parallel_mode: ParallelMode,
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op: ReduceOp = ReduceOp.SUM,
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async_op: bool = False) -> Tensor:
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r"""Reduces all tensors then scatters it in a specific dimension to all
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members in the parallel group.
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Note:
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The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
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in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be reduce_scattered.
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dim (int): The dimension concatenating in.
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parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
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op (torch.distributed.ReduceOp, optional): The type of reduce operation,
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should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR].
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More details about ReduceOp please refer to
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`ReduceOp <https://pytorch.org/docs/stable/distributed.html#torch.distributed.ReduceOp>`_.
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async_op (bool, optional): Whether operations are asynchronous.
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Returns:
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Union[tuple(:class:`torch.Tensor`, work handle), :class:`torch.Tensor`]: The result of reduce_scatter only,
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if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
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"""
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depth = gpc.get_world_size(parallel_mode)
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if depth == 1:
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out = tensor
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work = None
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else:
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tensor_in = tensor.contiguous() if dim == 0 else tensor.transpose(0, dim).contiguous()
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out_shape = (tensor_in.shape[0] // depth,) + tensor_in.shape[1:]
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tensor_out = torch.empty(out_shape, dtype=tensor.dtype, device=tensor.device)
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group = gpc.get_cpu_group(parallel_mode) if tensor.device.type == "cpu" else gpc.get_group(parallel_mode)
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work = _reduce_scatter_func(tensor_out, tensor_in, op=op, group=group, async_op=async_op)
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out = tensor_out if dim == 0 else tensor_out.transpose(0, dim)
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if async_op:
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return out, work
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else:
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return out
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def all_reduce(tensor: Tensor,
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parallel_mode: ParallelMode,
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op: ReduceOp = ReduceOp.SUM,
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async_op: bool = False) -> Tensor:
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r"""Reduces the tensor data across whole parallel group in such a way that all get the final result.
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Note:
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The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
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in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be all-reduced.
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parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
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op (torch.distributed.ReduceOp, optional): The type of reduce operation,
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should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR].
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More details about ReduceOp please refer to
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`ReduceOp <https://pytorch.org/docs/stable/distributed.html#torch.distributed.ReduceOp>`_.
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async_op (bool, optional): Whether operations are asynchronous.
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Returns:
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Union[tuple(:class:`torch.Tensor`, work handle), :class:`torch.Tensor`]: The result of all-gather only,
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if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
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"""
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depth = gpc.get_world_size(parallel_mode)
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if depth == 1:
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out = tensor
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work = None
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else:
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out = tensor.contiguous()
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group = gpc.get_cpu_group(parallel_mode) if tensor.device.type == "cpu" else gpc.get_group(parallel_mode)
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work = dist.all_reduce(out, op=op, group=group, async_op=async_op)
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if async_op:
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return out, work
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else:
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return out
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def broadcast(tensor: Tensor, src: int, parallel_mode: ParallelMode, async_op: bool = False):
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r"""Broadcast tensors to whole parallel group. Tensor must have the same
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number of elements in all processes participating in the collective.
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Note:
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The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
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in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be broadcast.
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src (int): Source rank.
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parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
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async_op (bool, optional): Whether operations are asynchronous.
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Returns:
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Union[tuple(:class:`torch.Tensor`, work handle), :class:`torch.Tensor`]: The tensor need to be broadcast only,
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if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
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"""
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depth = gpc.get_world_size(parallel_mode)
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if depth == 1:
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out = tensor
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work = None
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else:
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out = tensor.contiguous()
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group = gpc.get_cpu_group(parallel_mode) if tensor.device.type == "cpu" else gpc.get_group(parallel_mode)
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work = dist.broadcast(out, src=src, group=group, async_op=async_op)
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if async_op:
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return out, work
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else:
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return out
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def reduce(tensor: Tensor, dst: int, parallel_mode: ParallelMode, op: ReduceOp = ReduceOp.SUM, async_op: bool = False):
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r"""Reduce tensors across whole parallel group. Only the process with
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rank ``dst`` is going to receive the final result.
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Note:
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The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
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in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be reduced.
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dst (int): Destination rank.
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parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
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async_op (bool, optional): Whether operations are asynchronous.
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Returns:
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Union[tuple(:class:`torch.Tensor`, work handle), :class:`torch.Tensor`]: The result of reduce only,
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if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
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"""
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depth = gpc.get_world_size(parallel_mode)
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if depth == 1:
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out = tensor
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work = None
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else:
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out = tensor.contiguous()
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group = gpc.get_cpu_group(parallel_mode) if tensor.device.type == "cpu" else gpc.get_group(parallel_mode)
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work = dist.reduce(out, dst=dst, op=op, group=group, async_op=async_op)
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if async_op:
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return out, work
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else:
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return out
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def scatter_object_list(scatter_object_output_list, scatter_object_input_list, src=0, group=None) -> None:
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r"""Modified from `torch.distributed.scatter_object_list
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<https://pytorch.org/docs/stable/_modules/torch/distributed/distributed_c10d.html#scatter_object_list>` to fix issues
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"""
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if dist.distributed_c10d._rank_not_in_group(group):
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return
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if (not isinstance(scatter_object_output_list, list) or len(scatter_object_output_list) < 1):
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raise RuntimeError("Expected argument scatter_object_output_list to be a list of size at least 1.")
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# set tensor device to cuda if backend is nccl
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device = torch.cuda.current_device() if dist.get_backend(group) == 'nccl' else torch.device("cpu")
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my_rank = dist.get_rank() # use global rank
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if my_rank == src:
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tensor_list, tensor_sizes = zip(
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*[dist.distributed_c10d._object_to_tensor(obj) for obj in scatter_object_input_list])
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tensor_list = list(map(lambda x: x.to(device), tensor_list))
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tensor_sizes = list(map(lambda x: x.to(device), tensor_sizes))
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# Src rank broadcasts the maximum tensor size. This is because all ranks are
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# expected to call into scatter() with equal-sized tensors.
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if my_rank == src:
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max_tensor_size = max(tensor_sizes)
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for tensor in tensor_list:
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tensor.resize_(max_tensor_size)
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else:
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max_tensor_size = torch.tensor([0], dtype=torch.long).to(device)
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dist.broadcast(max_tensor_size, src=src, group=group)
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# Scatter actual serialized objects
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output_tensor = torch.empty(max_tensor_size.item(), dtype=torch.uint8).to(device)
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dist.scatter(
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output_tensor,
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scatter_list=None if my_rank != src else tensor_list,
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src=src,
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group=group,
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)
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# Scatter per-object sizes to trim tensors when deserializing back to object
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obj_tensor_size = torch.tensor([0], dtype=torch.long).to(device)
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dist.scatter(
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obj_tensor_size,
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scatter_list=None if my_rank != src else tensor_sizes,
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src=src,
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group=group,
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)
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output_tensor, obj_tensor_size = output_tensor.cpu(), obj_tensor_size.cpu()
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# Deserialize back to object
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scatter_object_output_list[0] = dist.distributed_c10d._tensor_to_object(output_tensor, obj_tensor_size)
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