mirror of https://github.com/InternLM/InternLM
126 lines
4.9 KiB
Python
126 lines
4.9 KiB
Python
# adopted from https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/communication
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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 internlm.core.context import ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.utils.common import get_current_device
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TensorShape = Union[torch.Size, List[int], Tuple[int]]
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def send_meta_helper(obj, next_rank, tensor_kwargs):
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send_shape = torch.tensor(obj.size(), **tensor_kwargs)
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send_ndims = torch.tensor(len(obj.size()), **tensor_kwargs)
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dist.send(send_ndims, next_rank)
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dist.send(send_shape, next_rank)
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def send_obj_meta(obj, next_rank=None):
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"""Sends obj meta information before sending a specific obj.
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Since the recipient must know the shape of the obj in p2p communications,
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meta information of the obj should be sent before communications. This function
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synchronizes with :func:`recv_obj_meta`.
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Args:
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obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): obj to be sent.
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need_meta (bool, optional): If False, meta information won't be sent.
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next_rank (int): The rank of the next member in pipeline parallel group.
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Returns:
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bool: False
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"""
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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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tensor_kwargs = {"dtype": torch.long, "device": get_current_device()}
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if isinstance(obj, torch.Tensor):
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send_obj_nums = torch.tensor(1, **tensor_kwargs)
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dist.send(send_obj_nums, next_rank)
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send_meta_helper(obj, next_rank, tensor_kwargs)
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else:
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send_obj_nums = torch.tensor(len(obj), **tensor_kwargs)
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dist.send(send_obj_nums, next_rank)
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for tensor_to_send in obj:
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send_meta_helper(tensor_to_send, next_rank, tensor_kwargs)
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def recv_meta_helper(prev_rank, tensor_kwargs):
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recv_ndims = torch.empty((), **tensor_kwargs)
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dist.recv(recv_ndims, prev_rank)
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recv_shape = torch.empty(recv_ndims, **tensor_kwargs)
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dist.recv(recv_shape, prev_rank)
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return recv_shape
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def recv_obj_meta(prev_rank=None) -> torch.Size:
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"""Receives obj meta information before receiving a specific obj.
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Since the recipient must know the shape of the obj in p2p communications,
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meta information of the obj should be received before communications. This function
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synchronizes with :func:`send_obj_meta`.
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Args:
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obj_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the obj to be received.
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prev_rank (int): The rank of the source of the obj.
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Returns:
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Union[:class:`torch.Size`, List[:class:`torch.Size`]]: The shape of the obj to be received.
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"""
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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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tensor_kwargs = {"dtype": torch.long, "device": get_current_device()}
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recv_obj_nums = torch.empty((), **tensor_kwargs)
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dist.recv(recv_obj_nums, prev_rank)
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if recv_obj_nums.item() == 1:
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recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
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obj_shape = torch.Size(recv_shape)
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else:
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obj_shape = []
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for _ in range(recv_obj_nums.item()):
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recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
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obj_shape.append(torch.Size(recv_shape))
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return obj_shape
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def split_tensor_into_1d_equal_chunks(tensor: torch.Tensor, new_buffer=False) -> torch.Tensor:
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"""Break a tensor into equal 1D chunks.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be split before communication.
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new_buffer (bool, optional): Whether to use a new buffer to store sliced tensor.
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Returns:
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:class:`torch.Tensor`: The split tensor
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"""
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partition_size = torch.numel(tensor) // gpc.get_world_size(ParallelMode.TENSOR)
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start_index = partition_size * gpc.get_local_rank(ParallelMode.TENSOR)
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end_index = start_index + partition_size
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if new_buffer:
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data = torch.empty(partition_size, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False)
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data.copy_(tensor.view(-1)[start_index:end_index])
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else:
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data = tensor.view(-1)[start_index:end_index]
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return data
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def gather_split_1d_tensor(tensor: torch.Tensor) -> torch.Tensor:
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"""Opposite of above function, gather values from model parallel ranks.
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Args:
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tensor (:class:`torch.Tensor`): Tensor to be gathered after communication.
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Returns:
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:class:`torch.Tensor`: The gathered tensor.
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"""
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world_size = gpc.get_world_size(ParallelMode.TENSOR)
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numel = torch.numel(tensor)
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numel_gathered = world_size * numel
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gathered = torch.empty(numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False)
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chunks = [gathered[i * numel : (i + 1) * numel] for i in range(world_size)]
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dist.all_gather(chunks, tensor, group=gpc.get_group(ParallelMode.TENSOR))
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return gathered
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