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[legacy] move communication and nn to legacy and refactor logger (#4671)
* [legacy] move communication to legacy (#4640) * [legacy] refactor logger and clean up legacy codes (#4654) * [legacy] make logger independent to gpc * [legacy] make optim independent to registry * [legacy] move test engine to legacy * [legacy] move nn to legacy (#4656) * [legacy] move nn to legacy * [checkpointio] fix save hf config * [test] remove useledd rpc pp test * [legacy] fix nn init * [example] skip tutorial hybriad parallel example * [devops] test doc check * [devops] test doc check
This commit is contained in:
Hongxin Liu
GitHub
407
colossalai/legacy/communication/p2p.py
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407
colossalai/legacy/communication/p2p.py
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@@ -0,0 +1,407 @@
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import operator
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from functools import reduce
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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 .utils import gather_split_1d_tensor, split_tensor_into_1d_equal_chunks
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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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Args:
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tensor_shape (:class:`torch.Size`): shape of tensor
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chunk_tensor (bool, optional): whether to chunk tensor, defaults to False
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Returns:
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Tuple[Union[:class:`torch.Size`, List[int], Tuple[int]], bool]: exact tensor shape, whether to chunk tensor
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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 create_recv_buffer_with_shapes(recv_shapes, dtype, scatter_gather_tensors):
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if isinstance(recv_shapes, torch.Size):
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recv_chunk_shape, recv_split = _get_tensor_shape(recv_shapes, scatter_gather_tensors)
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buffer_recv = torch.empty(recv_chunk_shape, requires_grad=True, device=get_current_device(), dtype=dtype)
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return buffer_recv, recv_split
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buffer_recv = []
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for recv_shape in recv_shapes:
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recv_chunk_shape, recv_split = _get_tensor_shape(recv_shape, scatter_gather_tensors)
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tensor_recv = torch.empty(recv_chunk_shape, requires_grad=True, device=get_current_device(), dtype=dtype)
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buffer_recv.append(tensor_recv)
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return buffer_recv, recv_split
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def process_object_to_send(object_send, scatter_gather_tensors):
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if isinstance(object_send, torch.Tensor):
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send_split = _get_tensor_shape(object_send.shape, scatter_gather_tensors)[1]
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if send_split:
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object_send = split_tensor_into_1d_equal_chunks(object_send)
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return object_send
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object_send_list = []
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for tensor_send in object_send:
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send_split = _get_tensor_shape(tensor_send.shape, scatter_gather_tensors)[1]
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if send_split:
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object_send_list.append(split_tensor_into_1d_equal_chunks(tensor_send))
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else:
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object_send_list.append(tensor_send)
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object_send = tuple(object_send_list)
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return object_send
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def filling_ops_queue(obj, comm_op, comm_rank, ops_queue):
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if isinstance(obj, torch.Tensor):
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op_to_add = dist.P2POp(comm_op, obj, comm_rank)
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ops_queue.append(op_to_add)
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else:
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for tensor_to_comm in obj:
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op_to_add = dist.P2POp(comm_op, tensor_to_comm, comm_rank)
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ops_queue.append(op_to_add)
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def _communicate(object_send_next: Union[torch.Tensor, List[torch.Tensor]] = None,
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object_send_prev: Union[torch.Tensor, List[torch.Tensor]] = None,
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recv_prev: bool = False,
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recv_next: bool = False,
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recv_prev_shape: Union[torch.Size, List[torch.Size]] = None,
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recv_next_shape: Union[torch.Size, List[torch.Size]] = None,
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prev_rank: int = None,
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next_rank: int = None,
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dtype: torch.dtype = None,
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scatter_gather_tensors: bool = False) -> Tuple[Union[torch.Tensor, List[torch.Tensor]]]:
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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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object_send_next (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): tensor to send to next rank (no tensor sent if
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set to None).
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object_send_prev (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): tensor to send to prev rank (no tensor sent if
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set to None).
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recv_prev (bool): boolean for whether tensor should be received from
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previous rank.
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recv_next (bool): boolean for whether tensor should be received from
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next rank.
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recv_prev_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the previous stage, defaults to None.
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recv_next_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the next stage, defaults to None.
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prev_rank (int): the rank of the previous pipeline stage, defaults to None,
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next_rank (int): the rank of the next pipeline stage, defaults to None,
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dtype (torch.dtype): data type of intermediate buffers, defaults to None
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scatter_gather_tensors (bool): whether to scatter and gather tensor between pipeline stages, defaults to False
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Returns:
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Tuple[Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]]: returns 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, recv_prev_split = create_recv_buffer_with_shapes(recv_prev_shape, dtype,
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scatter_gather_tensors)
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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, recv_next_split = create_recv_buffer_with_shapes(recv_next_shape, dtype,
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scatter_gather_tensors)
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if object_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(ParallelMode.PIPELINE)
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if object_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(ParallelMode.PIPELINE)
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if object_send_prev is not None:
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object_send_prev = process_object_to_send(object_send_prev, scatter_gather_tensors)
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if object_send_next is not None:
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object_send_next = process_object_to_send(object_send_next, scatter_gather_tensors)
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ops = []
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if object_send_prev is not None:
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filling_ops_queue(object_send_prev, dist.isend, prev_rank, ops)
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if tensor_recv_prev is not None:
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filling_ops_queue(tensor_recv_prev, dist.irecv, prev_rank, ops)
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if tensor_recv_next is not None:
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filling_ops_queue(tensor_recv_next, dist.irecv, next_rank, ops)
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if object_send_next is not None:
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filling_ops_queue(object_send_next, dist.isend, next_rank, ops)
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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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if isinstance(tensor_recv_prev, torch.Tensor):
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tensor_recv_prev = gather_split_1d_tensor(tensor_recv_prev).view(recv_prev_shape).requires_grad_()
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else:
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for index in range(len(tensor_recv_prev)):
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tensor_recv_prev[index] = gather_split_1d_tensor(tensor_recv_prev[index]).view(
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recv_prev_shape[index]).requires_grad_()
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if recv_next and recv_next_split:
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if isinstance(tensor_recv_next, torch.Tensor):
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tensor_recv_next = gather_split_1d_tensor(tensor_recv_next).view(recv_next_shape).requires_grad_()
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else:
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for index in range(len(tensor_recv_next)):
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tensor_recv_next[index] = gather_split_1d_tensor(tensor_recv_next[index]).view(
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recv_next_shape[index]).requires_grad_()
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return tensor_recv_prev, tensor_recv_next
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def recv_forward(input_tensor_shape,
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prev_rank=None,
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dtype=torch.float,
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scatter_gather_tensors=False) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Copy the forward output from the previous stage in pipeline as the input tensor of this stage.
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Args:
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input_tensor_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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prev_rank (int, optional): The rank of the source of the tensor.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input tensor or input tensor list.
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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,
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next_rank=None,
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dtype=torch.float,
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scatter_gather_tensors=False) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
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Args:
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output_grad_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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next_rank (int, optional): The rank of the source of the tensor.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input gradient tensor or gradient tensor list.
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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) -> None:
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"""Sends the input tensor to the next stage in pipeline.
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Args:
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output_tensor (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent.
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next_rank (int, optional): The rank of the recipient of the tensor.
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"""
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if not gpc.is_pipeline_last_stage():
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_communicate(object_send_next=output_tensor, next_rank=next_rank, scatter_gather_tensors=scatter_gather_tensors)
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def send_backward(input_tensor_grad, prev_rank=None, scatter_gather_tensors=False) -> None:
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"""Sends the gradient tensor to the previous stage in pipeline.
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Args:
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input_tensor_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent
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prev_rank (int, optional): The rank of the recipient of the tensor
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"""
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if not gpc.is_pipeline_first_stage():
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_communicate(object_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) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Batched communication operation. Sends the input tensor to the
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next stage in pipeline, while receives the gradient tensor from the
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next stage in pipeline as the input gradient tensor of this stage.
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Args:
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output_tensor (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent.
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output_grad_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input gradient 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(object_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) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Batched communication operation. Sends the gradient tensor to the
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previous stage in pipeline, while receives the output tensor from the
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previous stage in pipeline as the input of this stage.
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Args:
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input_tensor_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent.
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input_tensor_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input 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(object_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) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Batched communication operation. Sends the input tensor to the
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next stage in pipeline, while receives the output tensor from the
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previous stage in pipeline as the input of this stage.
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Args:
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output_tensor (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent.
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input_tensor_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input tensor.
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"""
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input_tensor, _ = _communicate(object_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) -> Union[torch.Tensor, List[torch.Tensor]]:
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"""Batched communication operation. Sends the gradient tensor to the
|
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previous stage in pipeline, while receives the gradient tensor from the
|
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next member in pipeline as the input of this stage.
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Args:
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input_tensor_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent.
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output_grad_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
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Returns:
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Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input gradient tensor.
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"""
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_, output_tensor_grad = _communicate(object_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(
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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) -> Tuple[Union[torch.Tensor, List[torch.Tensor]]]:
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"""Batched communication operation. Sends the input tensor to the next stage in pipeline and
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the gradient tensor to the previous stage, while receives the input gradient tensor from the
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next stage and the input tensor from the previous stage.
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Args:
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output_tensor (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor sent to the next.
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input_tensor_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor sent to the previous.
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input_tensor_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor received from the previous.
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output_grad_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor received from the next.
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Returns:
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Tuple(Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]], Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): (the input tensor, the input gradient tensor)
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"""
|
||||
input_tensor, output_tensor_grad = _communicate(object_send_next=output_tensor,
|
||||
object_send_prev=input_tensor_grad,
|
||||
recv_prev=recv_prev,
|
||||
recv_next=recv_next,
|
||||
recv_prev_shape=input_tensor_shape,
|
||||
recv_next_shape=output_grad_shape,
|
||||
prev_rank=prev_rank,
|
||||
next_rank=next_rank,
|
||||
dtype=dtype,
|
||||
scatter_gather_tensors=scatter_gather_tensors)
|
||||
return input_tensor, output_tensor_grad
|
||||
Reference in New Issue
Block a user