mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
5.0 KiB
128 lines
5.0 KiB
from typing import List, Tuple, Union
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
from colossalai.accelerator import get_accelerator
|
|
from colossalai.legacy.context.parallel_mode import ParallelMode
|
|
from colossalai.legacy.core import global_context as gpc
|
|
|
|
TensorShape = Union[torch.Size, List[int], Tuple[int]]
|
|
|
|
|
|
def send_meta_helper(obj, next_rank, tensor_kwargs):
|
|
send_shape = torch.tensor(obj.size(), **tensor_kwargs)
|
|
send_ndims = torch.tensor(len(obj.size()), **tensor_kwargs)
|
|
dist.send(send_ndims, next_rank)
|
|
dist.send(send_shape, next_rank)
|
|
|
|
|
|
def send_obj_meta(obj, need_meta=True, next_rank=None) -> bool:
|
|
"""Sends obj meta information before sending a specific obj.
|
|
Since the recipient must know the shape of the obj in p2p communications,
|
|
meta information of the obj should be sent before communications. This function
|
|
synchronizes with :func:`recv_obj_meta`.
|
|
|
|
Args:
|
|
obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): obj to be sent.
|
|
need_meta (bool, optional): If False, meta information won't be sent.
|
|
next_rank (int): The rank of the next member in pipeline parallel group.
|
|
|
|
Returns:
|
|
bool: False
|
|
"""
|
|
if need_meta:
|
|
if next_rank is None:
|
|
next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE)
|
|
|
|
tensor_kwargs = {"dtype": torch.long, "device": get_accelerator().get_current_device()}
|
|
if isinstance(obj, torch.Tensor):
|
|
send_obj_nums = torch.tensor(1, **tensor_kwargs)
|
|
dist.send(send_obj_nums, next_rank)
|
|
send_meta_helper(obj, next_rank, tensor_kwargs)
|
|
else:
|
|
send_obj_nums = torch.tensor(len(obj), **tensor_kwargs)
|
|
dist.send(send_obj_nums, next_rank)
|
|
for tensor_to_send in obj:
|
|
send_meta_helper(tensor_to_send, next_rank, tensor_kwargs)
|
|
|
|
return False
|
|
|
|
|
|
def recv_meta_helper(prev_rank, tensor_kwargs):
|
|
recv_ndims = torch.empty((), **tensor_kwargs)
|
|
dist.recv(recv_ndims, prev_rank)
|
|
recv_shape = torch.empty(recv_ndims, **tensor_kwargs)
|
|
dist.recv(recv_shape, prev_rank)
|
|
return recv_shape
|
|
|
|
|
|
def recv_obj_meta(obj_shape, prev_rank=None) -> torch.Size:
|
|
"""Receives obj meta information before receiving a specific obj.
|
|
Since the recipient must know the shape of the obj in p2p communications,
|
|
meta information of the obj should be received before communications. This function
|
|
synchronizes with :func:`send_obj_meta`.
|
|
|
|
Args:
|
|
obj_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the obj to be received.
|
|
prev_rank (int): The rank of the source of the obj.
|
|
|
|
Returns:
|
|
Union[:class:`torch.Size`, List[:class:`torch.Size`]]: The shape of the obj to be received.
|
|
"""
|
|
if obj_shape is None:
|
|
if prev_rank is None:
|
|
prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE)
|
|
|
|
tensor_kwargs = {"dtype": torch.long, "device": get_accelerator().get_current_device()}
|
|
recv_obj_nums = torch.empty((), **tensor_kwargs)
|
|
dist.recv(recv_obj_nums, prev_rank)
|
|
if recv_obj_nums.item() == 1:
|
|
recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
|
|
obj_shape = torch.Size(recv_shape)
|
|
else:
|
|
obj_shape = []
|
|
for i in range(recv_obj_nums.item()):
|
|
recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
|
|
obj_shape.append(torch.Size(recv_shape))
|
|
|
|
return obj_shape
|
|
|
|
|
|
def split_tensor_into_1d_equal_chunks(tensor: torch.Tensor, new_buffer=False) -> torch.Tensor:
|
|
"""Break a tensor into equal 1D chunks.
|
|
|
|
Args:
|
|
tensor (:class:`torch.Tensor`): Tensor to be split before communication.
|
|
new_buffer (bool, optional): Whether to use a new buffer to store sliced tensor.
|
|
|
|
Returns:
|
|
:class:`torch.Tensor`: The split tensor
|
|
"""
|
|
partition_size = torch.numel(tensor) // gpc.get_world_size(ParallelMode.PARALLEL_1D)
|
|
start_index = partition_size * gpc.get_local_rank(ParallelMode.PARALLEL_1D)
|
|
end_index = start_index + partition_size
|
|
if new_buffer:
|
|
data = torch.empty(partition_size, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False)
|
|
data.copy_(tensor.view(-1)[start_index:end_index])
|
|
else:
|
|
data = tensor.view(-1)[start_index:end_index]
|
|
return data
|
|
|
|
|
|
def gather_split_1d_tensor(tensor: torch.Tensor) -> torch.Tensor:
|
|
"""Opposite of above function, gather values from model parallel ranks.
|
|
|
|
Args:
|
|
tensor (:class:`torch.Tensor`): Tensor to be gathered after communication.
|
|
Returns:
|
|
:class:`torch.Tensor`: The gathered tensor.
|
|
"""
|
|
world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
|
|
numel = torch.numel(tensor)
|
|
numel_gathered = world_size * numel
|
|
gathered = torch.empty(numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False)
|
|
chunks = [gathered[i * numel : (i + 1) * numel] for i in range(world_size)]
|
|
dist.all_gather(chunks, tensor, group=gpc.get_group(ParallelMode.PARALLEL_1D))
|
|
return gathered
|