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102 lines
3.8 KiB
102 lines
3.8 KiB
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 send_tensor_meta(tensor, need_meta=True, next_rank=None):
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"""Sends tensor meta information before sending a specific tensor.
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Since the recipient must know the shape of the tensor in p2p communications,
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meta information of the tensor should be sent before communications. This function
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synchronizes with :func:`recv_tensor_meta`.
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Args:
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tensor (torch.Tensor): Tensor 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 need_meta:
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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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send_shape = torch.tensor(tensor.size(), **tensor_kwargs)
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send_ndims = torch.tensor(len(tensor.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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return False
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def recv_tensor_meta(tensor_shape, prev_rank=None):
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"""Receives tensor meta information before receiving a specific tensor.
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Since the recipient must know the shape of the tensor in p2p communications,
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meta information of the tensor should be received before communications. This function
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synchronizes with :func:`send_tensor_meta`.
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Args:
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tensor_shape (torch.Size): The shape of the tensor to be received.
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prev_rank (int): The rank of the source of the tensor.
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Returns:
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:class:`torch.Size`: The shape of the tensor to be received.
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"""
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if tensor_shape is None:
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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_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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tensor_shape = torch.Size(recv_shape)
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return tensor_shape
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def split_tensor_into_1d_equal_chunks(tensor, new_buffer=False):
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"""Break a tensor into equal 1D chunks.
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Args:
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tensor (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.Size`: The split tensor
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"""
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partition_size = torch.numel(tensor) // gpc.get_world_size(ParallelMode.PARALLEL_1D)
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start_index = partition_size * gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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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):
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"""Opposite of above function, gather values from model parallel ranks.
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Args:
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tensor (torch.Tensor): Tensor to be gathered after communication.
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Returns:
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:class:`torch.Size`: The gathered tensor.
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"""
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world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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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.PARALLEL_1D))
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return gathered
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