chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import warnings
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from typing import TYPE_CHECKING
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import paddle
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import paddle.distributed as dist
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from paddle import framework
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from paddle.base import data_feeder
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from paddle.distributed.communication.group import (
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_get_global_group,
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_get_or_throw_group_rank,
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_warn_cur_rank_not_in_group,
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)
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from paddle import Tensor
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from paddle.base.core import task
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from paddle.distributed.communication.group import Group
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def _scatter_tensor_in_dygraph(
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out_tensor, in_tensor, src_rank_in_group, group, sync_op, use_calc_stream
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):
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nranks = group.nranks
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if use_calc_stream:
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return group.process_group.scatter_tensor_on_calc_stream(
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out_tensor, in_tensor, src_rank_in_group
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)
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task = group.process_group.scatter_tensor(
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out_tensor, in_tensor, src_rank_in_group, sync_op
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)
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if sync_op:
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task.wait()
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return task
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def _scatter_in_dygraph(
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tensor, tensor_list, src_rank_in_group, group, sync_op, use_calc_stream
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):
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nranks = group.nranks
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if group.rank == src_rank_in_group:
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if len(tensor_list) == 0:
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raise RuntimeError(
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"The tensor_list should not be empty on src rank."
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)
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else:
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tensor_list = [tensor for _ in range(nranks)]
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if use_calc_stream:
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return group.process_group.scatter_on_calc_stream(
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tensor, tensor_list, src_rank_in_group
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)
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task = group.process_group.scatter(
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tensor, tensor_list, src_rank_in_group, sync_op
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)
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if sync_op:
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task.wait()
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return task
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def _scatter_in_static_mode(
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tensor,
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tensor_or_tensor_list,
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src_rank_in_group,
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group,
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sync_op,
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use_calc_stream,
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):
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nranks = dist.get_world_size() if group is None else group.nranks
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rank = dist.get_rank()
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input_tensor = tensor_or_tensor_list
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if isinstance(tensor_or_tensor_list, list):
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tensor_list = tensor_or_tensor_list
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if rank == src_rank_in_group:
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if len(tensor_list) == 0:
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raise RuntimeError(
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"The tensor_list should not be empty on src rank."
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)
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else:
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tensor_list = [tensor for _ in range(nranks)]
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# 0-D use stack/unstack while others use concat/split
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if len(tensor_list[0].shape) == 0:
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input_tensor = paddle.stack(tensor_list, axis=0)
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else:
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input_tensor = paddle.concat(tensor_list, axis=0)
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ring_id = 0 if group is None else group.id
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data_feeder.check_variable_and_dtype(
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tensor,
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'tensor',
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[
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'float16',
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'float32',
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'float64',
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'int32',
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'int64',
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'int8',
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'uint8',
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'bool',
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],
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'scatter',
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)
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op_type = 'c_scatter'
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helper = framework.LayerHelper(op_type, **locals())
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helper.append_op(
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type=op_type,
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inputs={'X': [input_tensor]},
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outputs={'Out': [tensor]},
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attrs={
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'ring_id': ring_id,
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'root': src_rank_in_group,
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'use_calc_stream': sync_op,
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'nranks': nranks,
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},
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)
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def scatter(
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tensor: Tensor,
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tensor_or_tensor_list: Tensor | Sequence[Tensor] | None = None,
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src: int = 0,
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group: Group | None = None,
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sync_op: bool = True,
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use_calc_stream: bool = False,
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) -> task | None:
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"""
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Scatter a tensor (or a tensor list) across devices.
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Args:
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tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
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float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
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tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The input to scatter (default is `None`, must be specified on the source rank).
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If it is a tensor, it should be correctly-sized. If it is a list, it should contain correctly-sized tensors.
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src (int, optional): Rank of the source device. If none is given, use `0` as default.
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group (Group|None, optional): Communicate in which group. If none is given, use the global group as default.
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sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
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use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
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option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
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Returns:
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Return a task object.
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Warning:
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This API only supports the dygraph mode now.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +REQUIRES(env: DISTRIBUTED)
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>>> import paddle
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>>> import paddle.distributed as dist
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>>> dist.init_parallel_env()
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>>> if dist.get_rank() == 0:
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... data1 = paddle.to_tensor([7, 8, 9])
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... data2 = paddle.to_tensor([10, 11, 12])
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... dist.stream.scatter(data1, src=1)
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>>> else:
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... data1 = paddle.to_tensor([1, 2, 3])
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... data2 = paddle.to_tensor([4, 5, 6])
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... dist.stream.scatter(data1, [data1, data2], src=1)
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>>> out = data1.numpy()
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>>> print(out)
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>>> # [1, 2, 3] (2 GPUs, out for rank 0)
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>>> # [4, 5, 6] (2 GPUs, out for rank 1)
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"""
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if _warn_cur_rank_not_in_group(group):
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return
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if not sync_op and use_calc_stream:
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raise RuntimeError(
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"use_calc_stream can only be true in sync op behavior."
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)
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# NOTE(liyurui): Only the source rank needs to specific the tensor_or_tensor_list argument.
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# Other ranks which pass this argument in will be ignored with a warning.
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# If a tensor_list passed in, we need to concat it to a tensor before invoke C++ API.
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# If a tensor passed in, concat is not needed.
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# The passed in type for non-src rank is meaningless, for it will be ignored.
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if src != dist.get_rank():
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if tensor_or_tensor_list is not None:
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warnings.warn(
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"Specific `tensor_or_tensor_list` is meaningless for rank which is not src."
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)
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tensor_or_tensor_list = []
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if framework.in_dynamic_mode():
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group = _get_global_group() if group is None else group
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src_rank_in_group = _get_or_throw_group_rank(src, group)
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if paddle.is_tensor(tensor_or_tensor_list):
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return _scatter_tensor_in_dygraph(
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tensor,
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tensor_or_tensor_list,
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src_rank_in_group,
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group,
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sync_op,
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use_calc_stream,
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)
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else:
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return _scatter_in_dygraph(
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tensor,
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tensor_or_tensor_list,
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src_rank_in_group,
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group,
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sync_op,
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use_calc_stream,
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)
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else:
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assert group is None, (
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"Group can not be used in static graph mode for now."
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)
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return _scatter_in_static_mode(
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tensor,
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tensor_or_tensor_list,
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src,
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group,
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sync_op,
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use_calc_stream,
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)
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