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333
colossalai/communication/p2p.py
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333
colossalai/communication/p2p.py
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#!/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 colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.utils import get_current_device
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def _communicate(tensor_send_next=None,
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tensor_send_prev=None,
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recv_prev=False,
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recv_next=False,
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recv_prev_shape=None,
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recv_next_shape=None,
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prev_rank=None,
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next_rank=None,
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up_group=None,
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down_group=None,
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dtype=None):
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"""
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Adapted from megatron.p2p_communication.
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Communicate tensors between stages. Used as helper method in other
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communication methods that are used in pipeline schedule.
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Takes the following arguments:
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tensor_send_next: tensor to send to next rank (no tensor sent if
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set to None).
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tensor_send_prev: tensor to send to prev rank (no tensor sent if
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set to None).
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recv_prev: boolean for whether tensor should be received from
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previous rank.
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recv_next: boolean for whether tensor should be received from
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next rank.
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Returns:
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(tensor_recv_prev, tensor_recv_next)
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"""
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# Create placeholder tensors for receive in forward and backward directions
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# if needed.
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tensor_recv_prev = None
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tensor_recv_next = None
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if recv_prev:
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assert recv_prev_shape is not None
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tensor_recv_prev = torch.empty(recv_prev_shape,
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requires_grad=True,
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device=get_current_device(),
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dtype=dtype)
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if recv_next:
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assert recv_next_shape is not None
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tensor_recv_next = torch.empty(recv_next_shape,
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requires_grad=True,
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device=get_current_device(),
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dtype=dtype)
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if tensor_send_prev is not None or recv_prev:
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if prev_rank is None:
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prev_rank = gpc.get_prev_global_rank(
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ParallelMode.PIPELINE)
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if up_group is None:
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up_group = gpc.get_group(ParallelMode.PIPELINE_PREV)
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if tensor_send_next is not None or recv_next:
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if next_rank is None:
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next_rank = gpc.get_next_global_rank(
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ParallelMode.PIPELINE)
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if down_group is None:
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down_group = gpc.get_group(ParallelMode.PIPELINE_NEXT)
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# rank = dist.get_rank()
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rank = gpc.get_global_rank()
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ops = []
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if tensor_send_prev is not None:
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send_prev_op = dist.broadcast(tensor_send_prev,
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src=rank,
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group=up_group,
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async_op=True)
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ops.append(send_prev_op)
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if tensor_recv_prev is not None:
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recv_prev_op = dist.broadcast(tensor_recv_prev,
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src=prev_rank,
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group=up_group,
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async_op=True)
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ops.append(recv_prev_op)
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if tensor_recv_next is not None:
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recv_next_op = dist.broadcast(tensor_recv_next,
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src=next_rank,
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group=down_group,
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async_op=True)
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ops.append(recv_next_op)
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if tensor_send_next is not None:
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send_next_op = dist.broadcast(tensor_send_next,
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src=rank,
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group=down_group,
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async_op=True)
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ops.append(send_next_op)
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for req in ops:
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req.wait()
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# To protect against race condition when using batch_isend_irecv().
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torch.cuda.synchronize()
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return tensor_recv_prev, tensor_recv_next
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def recv_forward(input_tensor_shape, prev_rank=None, up_group=None):
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"""Receives the input tensor from the previous member in pipeline.
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:param input_tensor_shape: The shape of the tensor to be recieved
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:param prev_rank: The rank of the source of the tensor
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:param up_group: Communication group including the previous member in pipeline parallel group
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:type input_tensor_shape: torch.Size
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:type prev_rank: int, optional
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:type up_group: ProcessGroup, optional
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:return: The input tensor in forward step
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:rtype: Tensor
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"""
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if gpc.is_first_rank(ParallelMode.PIPELINE):
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input_tensor = None
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else:
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input_tensor, _ = _communicate(recv_prev=True,
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recv_prev_shape=input_tensor_shape,
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prev_rank=prev_rank,
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up_group=up_group)
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return input_tensor
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def recv_backward(output_grad_shape, next_rank=None, down_group=None):
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"""Receives the grad tensor from the next member in pipeline.
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:param output_grad_shape: The shape of the tensor to be recieved
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:param next_rank: The rank of the source of the tensor
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:param down_group: Communication group including the next member in pipeline parallel group
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:type output_grad_shape: torch.Size
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:type next_rank: int, optional
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:type down_group: ProcessGroup, optional
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:return: The grad of output tensor in forward step
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:rtype: Tensor
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"""
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if gpc.is_last_rank(ParallelMode.PIPELINE):
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output_tensor_grad = None
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else:
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_, output_tensor_grad = _communicate(recv_next=True,
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recv_next_shape=output_grad_shape,
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next_rank=next_rank,
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down_group=down_group)
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return output_tensor_grad
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def send_forward(output_tensor,
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next_rank=None,
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down_group=None):
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"""Sends the input tensor to the next member in pipeline.
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:param output_tensor: Tensor to be sent
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:param next_rank: The rank of the recipient of the tensor
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:param down_group: Communication group including the next member in pipeline parallel group
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:type output_tensor: Tensor
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:type next_rank: int, optional
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:type down_group: ProcessGroup, optional
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"""
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if not gpc.is_last_rank(ParallelMode.PIPELINE):
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_communicate(tensor_send_next=output_tensor,
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next_rank=next_rank,
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down_group=down_group)
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def send_backward(input_tensor_grad,
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prev_rank=None,
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up_group=None):
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"""Sends the grad tensor to the previous member in pipeline.
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:param input_tensor_grad: Tensor to be sent
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:param prev_rank: The rank of the recipient of the tensor
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:param up_group: Communication group including the previous member in pipeline parallel group
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:type input_tensor_grad: Tensor
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:type prev_rank: int, optional
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:type up_group: ProcessGroup, optional
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"""
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if not gpc.is_first_rank(ParallelMode.PIPELINE):
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_communicate(tensor_send_prev=input_tensor_grad,
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prev_rank=prev_rank,
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up_group=up_group)
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def send_forward_recv_backward(output_tensor,
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output_grad_shape,
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recv_next=True,
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next_rank=None,
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down_group=None):
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"""Batched communication operation. Sends the input tensor to the
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next member in pipeline, while recieves the grad tensor from the
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next member in pipeline.
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:param output_tensor: Tensor to be sent
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:param output_grad_shape: The shape of the tensor to be recieved
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:type output_tensor: Tensor
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:type output_grad_shape: torch.Size
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:return: The grad of output tensor in forward step
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:rtype: Tensor
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"""
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if gpc.is_last_rank(ParallelMode.PIPELINE):
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output_tensor_grad = None
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else:
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_, output_tensor_grad = _communicate(tensor_send_next=output_tensor,
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recv_next=recv_next,
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recv_next_shape=output_grad_shape,
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next_rank=next_rank,
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down_group=down_group)
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return output_tensor_grad
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def send_backward_recv_forward(input_tensor_grad,
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input_tensor_shape,
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recv_prev=True,
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prev_rank=None,
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up_group=None):
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"""Batched communication operation. Sends the grad tensor to the
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previous member in pipeline, while recieves the input tensor from the
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previous member in pipeline.
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:param input_tensor_grad: Tensor to be sent
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:param input_tensor_shape: The shape of the tensor to be recieved
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:type input_tensor_grad: Tensor
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:type input_tensor_shape: torch.Size
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:return: The input tensor in forward step
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:rtype: Tensor
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"""
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if gpc.is_first_rank(ParallelMode.PIPELINE):
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input_tensor = None
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else:
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input_tensor, _ = _communicate(tensor_send_prev=input_tensor_grad,
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recv_prev=recv_prev,
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recv_prev_shape=input_tensor_shape,
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prev_rank=prev_rank,
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up_group=up_group)
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return input_tensor
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def send_forward_recv_forward(output_tensor,
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input_tensor_shape,
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recv_prev=True,
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prev_rank=None,
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next_rank=None,
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up_group=None,
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down_group=None):
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"""Batched communication operation. Sends the input tensor to the
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next member in pipeline, while recieves the input tensor from the
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previous member in pipeline.
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:param output_tensor: Tensor to be sent
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:param input_tensor_shape: The shape of the tensor to be recieved
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:type output_tensor: Tensor
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:type input_tensor_shape: torch.Size
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:return: The input tensor in forward step
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:rtype: Tensor
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"""
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input_tensor, _ = _communicate(tensor_send_next=output_tensor,
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recv_prev=recv_prev,
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recv_prev_shape=input_tensor_shape,
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prev_rank=prev_rank,
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next_rank=next_rank,
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up_group=up_group,
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down_group=down_group)
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return input_tensor
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def send_backward_recv_backward(input_tensor_grad,
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output_grad_shape,
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recv_next=True,
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prev_rank=None,
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next_rank=None,
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up_group=None,
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down_group=None):
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"""Batched communication operation. Sends the grad tensor to the
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previous member in pipeline, while recieves the grad tensor from the
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next member in pipeline.
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:param input_tensor_grad: Tensor to be sent
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:param output_grad_shape: The shape of the tensor to be recieved
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:type input_tensor_grad: Tensor
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:type output_grad_shape: torch.Size
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:return: The grad of output tensor in forward step
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:rtype: Tensor
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"""
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_, output_tensor_grad = _communicate(tensor_send_prev=input_tensor_grad,
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recv_next=recv_next,
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recv_next_shape=output_grad_shape,
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prev_rank=prev_rank,
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next_rank=next_rank,
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up_group=up_group,
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down_group=down_group)
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return output_tensor_grad
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def send_forward_backward_recv_forward_backward(output_tensor,
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input_tensor_grad,
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input_tensor_shape,
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output_grad_shape,
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recv_prev=True,
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recv_next=True,
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prev_rank=None,
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next_rank=None,
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up_group=None,
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down_group=None):
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"""Batched communication operation. Sends the input tensor to the next and
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the grad tensor to the previous, while recieves the grad tensor from the
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next and the input tensor from the previous.
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:param output_tensor: Tensor sent to the next
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:param input_tensor_grad: Tensor sent to the previous
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:param input_tensor_shape: The shape of the tensor recieved from the previous
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:param output_grad_shape: The shape of the tensor recieved from the next
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:type output_tensor: Tensor
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:type input_tensor_grad: Tensor
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:type input_tensor_shape: torch.Size
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:type output_grad_shape: torch.Size
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:return: (the input tensor in forward step, the grad of output tensor in forward step)
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:rtype: (Tensor, Tensor)
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"""
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input_tensor, output_tensor_grad = _communicate(
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tensor_send_next=output_tensor,
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tensor_send_prev=input_tensor_grad,
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recv_prev=recv_prev,
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recv_next=recv_next,
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recv_prev_shape=input_tensor_shape,
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recv_next_shape=output_grad_shape,
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prev_rank=prev_rank,
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next_rank=next_rank,
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up_group=up_group,
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down_group=down_group)
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return input_tensor, output_tensor_grad
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