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357 lines
15 KiB
357 lines
15 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import List, Tuple, Union
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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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from functools import reduce
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import operator
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from .utils import split_tensor_into_1d_equal_chunks, gather_split_1d_tensor
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TensorShape = Union[torch.Size, List[int], Tuple[int]]
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def _get_tensor_shape(tensor_shape: TensorShape, chunk_tensor: bool = False) -> Tuple[TensorShape, bool]:
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"""get the exact tensor shape when communicating and return whether the tensor is a chunk
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:param tensor_shape: shape of tensor
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:type tensor_shape: TensorShape
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:param chunk_tensor: whether to chunk tensor, defaults to False
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:type chunk_tensor: bool, optional
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:return: exact tensor shape, whether to chunk tensor
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:rtype: Tuple[Union[torch.Size, List[int], Tuple[int]], bool]
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"""
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if chunk_tensor:
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tensor_chunk_shape = reduce(operator.mul, tensor_shape, 1)
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tensor_parallel_world_size = gpc.get_world_size(ParallelMode.TENSOR)
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if tensor_chunk_shape % tensor_parallel_world_size == 0:
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tensor_chunk_shape = tensor_chunk_shape // tensor_parallel_world_size
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else:
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tensor_chunk_shape = tensor_shape
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chunk_tensor = False
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else:
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tensor_chunk_shape = tensor_shape
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return tensor_chunk_shape, chunk_tensor
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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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dtype=None,
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scatter_gather_tensors=False):
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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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recv_prev_chunk_shape, recv_prev_split = _get_tensor_shape(recv_prev_shape, scatter_gather_tensors)
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tensor_recv_prev = torch.empty(recv_prev_chunk_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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recv_next_chunk_shape, recv_next_split = _get_tensor_shape(recv_next_shape, scatter_gather_tensors)
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tensor_recv_next = torch.empty(recv_next_chunk_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 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 tensor_send_prev is not None:
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send_prev_split = _get_tensor_shape(tensor_send_prev.shape, scatter_gather_tensors)[1]
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if send_prev_split:
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tensor_send_prev = split_tensor_into_1d_equal_chunks(tensor_send_prev)
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if tensor_send_next is not None:
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send_next_split = _get_tensor_shape(tensor_send_next.shape, scatter_gather_tensors)[1]
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if send_next_split:
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tensor_send_next = split_tensor_into_1d_equal_chunks(tensor_send_next)
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ops = []
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if tensor_send_prev is not None:
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send_prev_op = dist.P2POp(dist.isend, tensor_send_prev, prev_rank)
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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.P2POp(dist.irecv, tensor_recv_prev, prev_rank)
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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.P2POp(dist.irecv, tensor_recv_next, next_rank)
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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.P2POp(dist.isend, tensor_send_next, next_rank)
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ops.append(send_next_op)
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if len(ops) > 0:
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reqs = dist.batch_isend_irecv(ops)
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for req in reqs:
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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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if recv_prev and recv_prev_split:
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tensor_recv_prev = gather_split_1d_tensor(tensor_recv_prev).view(recv_prev_shape).requires_grad_()
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if recv_next and recv_next_split:
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tensor_recv_next = gather_split_1d_tensor(tensor_recv_next).view(recv_next_shape).requires_grad_()
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return tensor_recv_prev, tensor_recv_next
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def recv_forward(input_tensor_shape, prev_rank=None, dtype=torch.float, scatter_gather_tensors=False):
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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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:type input_tensor_shape: torch.Size
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:type prev_rank: int, optional
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:return: The input tensor in forward step
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:rtype: :class:`torch.Tensor`
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"""
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if gpc.is_pipeline_first_stage():
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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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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return input_tensor
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def recv_backward(output_grad_shape, next_rank=None, dtype=torch.float, scatter_gather_tensors=False):
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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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:type output_grad_shape: torch.Size
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:type next_rank: int, optional
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:return: The grad of output tensor in forward step
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:rtype: :class:`torch.Tensor`
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"""
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if gpc.is_pipeline_last_stage():
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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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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return output_tensor_grad
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def send_forward(output_tensor, next_rank=None, scatter_gather_tensors=False):
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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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:type output_tensor: :class:`torch.Tensor`
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:type next_rank: int, optional
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"""
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if not gpc.is_pipeline_last_stage():
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_communicate(tensor_send_next=output_tensor,
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next_rank=next_rank,
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scatter_gather_tensors=scatter_gather_tensors)
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def send_backward(input_tensor_grad, prev_rank=None, scatter_gather_tensors=False):
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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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:type input_tensor_grad: :class:`torch.Tensor`
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:type prev_rank: int, optional
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"""
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if not gpc.is_pipeline_first_stage():
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_communicate(tensor_send_prev=input_tensor_grad,
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prev_rank=prev_rank,
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scatter_gather_tensors=scatter_gather_tensors)
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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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dtype=torch.float,
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scatter_gather_tensors=False):
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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: :class:`torch.Tensor`
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:type output_grad_shape: :class:`torch.Size`
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:return: The grad of output tensor in forward step
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:rtype: :class:`torch.Tensor`
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"""
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if gpc.is_pipeline_last_stage():
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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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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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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dtype=torch.float,
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scatter_gather_tensors=False):
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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: :class:`torch.Tensor`
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:type input_tensor_shape: :class:`torch.Size`
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:return: The input tensor in forward step
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:rtype: :class:`torch.Tensor`
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"""
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if gpc.is_pipeline_first_stage():
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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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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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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dtype=torch.float,
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scatter_gather_tensors=False):
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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: :class:`torch.Tensor`
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:type input_tensor_shape: :class:`torch.Size`
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:return: The input tensor in forward step
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:rtype: :class:`torch.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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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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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dtype=torch.float,
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scatter_gather_tensors=False):
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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: :class:`torch.Tensor`
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:type output_grad_shape: :class:`torch.Size`
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:return: The grad of output tensor in forward step
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:rtype: :class:`torch.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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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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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dtype=torch.float,
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scatter_gather_tensors=False):
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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: :class:`torch.Tensor`
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:type input_tensor_grad: :class:`torch.Tensor`
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:type input_tensor_shape: :class:`torch.Size`
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:type output_grad_shape: :class:`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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dtype=dtype,
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scatter_gather_tensors=scatter_gather_tensors)
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return input_tensor, output_tensor_grad
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