scatter.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
import paddle
import paddle.distributed as dist
import paddle.fluid.framework as framework
import paddle.fluid.data_feeder as data_feeder
import paddle.fluid.layer_helper as layer_helper
from paddle.distributed.communication.group import (
    _get_global_group,
    _warn_cur_rank_not_in_group,
    _get_or_throw_group_rank,
)


def _check_tensor_shape(tensor, shape, nranks=1):
    expect_shape = list(shape)
    expect_shape[0] //= nranks
    if list(tensor.shape) != expect_shape:
        raise RuntimeError("The in_tensor for scatter is not correctly-sized.")


def _check_tensor_list_shape(tensor_list, shape, nranks=1):
    if len(tensor_list) != nranks:
        raise RuntimeError(
            "The tensor_list for scatter is not correctly-sized."
        )
    for tensor in tensor_list:
        if tensor.shape != shape:
            raise RuntimeError(
                "The tensor_list for scatter is not correctly-sized."
            )


def _scatter_tensor_in_dygraph(
    out_tensor, in_tensor, src_rank_in_group, group, sync_op, use_calc_stream
):
    nranks = group.nranks
    if group.rank == src_rank_in_group:
        _check_tensor_shape(out_tensor, in_tensor.shape, nranks)

    if use_calc_stream:
        return group.process_group.scatter_tensor_on_calc_stream(
            in_tensor, out_tensor, src_rank_in_group
        )

    task = group.process_group.scatter_tensor(
        in_tensor, out_tensor, src_rank_in_group, sync_op
    )
    if sync_op:
        task.wait()

    return task


def _scatter_in_dygraph(
    tensor, tensor_list, src_rank_in_group, group, sync_op, use_calc_stream
):
    nranks = group.nranks
    if group.rank == src_rank_in_group:
        if len(tensor_list) == 0:
            raise RuntimeError(
                "The tensor_list should not be empty on src rank."
            )
        _check_tensor_list_shape(tensor_list, tensor.shape, nranks)
    else:
        tensor_list = [tensor for _ in range(nranks)]

    if use_calc_stream:
        return group.process_group.scatter_on_calc_stream(
            tensor_list, tensor, src_rank_in_group
        )

    task = group.process_group.scatter(
        tensor_list, tensor, src_rank_in_group, sync_op
    )
    if sync_op:
        task.wait()

    return task


def _scatter_in_static_mode(
    tensor,
    tensor_or_tensor_list,
    src_rank_in_group,
    group,
    sync_op,
    use_calc_stream,
):
    nranks = dist.get_world_size() if group is None else group.nranks
    rank = dist.get_rank()

    input_tensor = tensor_or_tensor_list
    if isinstance(tensor_or_tensor_list, list):
        tensor_list = tensor_or_tensor_list
        if rank == src_rank_in_group:
            if len(tensor_list) == 0:
                raise RuntimeError(
                    "The tensor_list should not be empty on src rank."
                )
        else:
            tensor_list = [tensor for _ in range(nranks)]
        input_tensor = paddle.concat(tensor_list, axis=0)

    ring_id = 0 if group is None else group.id

    data_feeder.check_variable_and_dtype(
        tensor,
        'tensor',
        [
            'float16',
            'float32',
            'float64',
            'int32',
            'int64',
            'int8',
            'uint8',
            'bool',
        ],
        'scatter',
    )

    op_type = 'c_scatter'
    helper = layer_helper.LayerHelper(op_type, **locals())
    helper.append_op(
        type=op_type,
        inputs={'X': [input_tensor]},
        outputs={'Out': [tensor]},
        attrs={
            'ring_id': ring_id,
            'root': src_rank_in_group,
            'use_calc_stream': sync_op,
            'nranks': nranks,
        },
    )

    return None


def scatter(
    tensor,
    tensor_or_tensor_list=None,
    src=0,
    group=None,
    sync_op=True,
    use_calc_stream=False,
):
    """

    Scatter a tensor (or a tensor list) across devices.

    Args:
        tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
            float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
        tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The input to scatter (default is `None`, must be specified on the source rank).
            If it is a tensor, it should be correctly-sized. If it is a list, it should contain correctly-sized tensors.
        src (int, optional): Rank of the source device. If none is given, use `0` as default.
        group (Group, optional): Communicate in which group. If none is given, use the global group as default.
        sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
        use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
            option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.

    Returns:
        Return a task object.

    Warning:
        This API only supports the dygraph mode now.

    Examples:
        .. code-block:: python

            # required: distributed
            import paddle
            import paddle.distributed as dist

            dist.init_parallel_env()
            if dist.get_rank() == 0:
                data1 = paddle.to_tensor([7, 8, 9])
                data2 = paddle.to_tensor([10, 11, 12])
                dist.stream.scatter(data1, src=1)
            else:
                data1 = paddle.to_tensor([1, 2, 3])
                data2 = paddle.to_tensor([4, 5, 6])
                dist.stream.scatter(data1, [data1, data2], src=1)
            out = data1.numpy()
            # [1, 2, 3] (2 GPUs, out for rank 0)
            # [4, 5, 6] (2 GPUs, out for rank 1)
    """
    if _warn_cur_rank_not_in_group(group):
        return

    if not sync_op and use_calc_stream:
        raise RuntimeError(
            "use_calc_stream can only be true in sync op behavior."
        )

    # NOTE(liyurui): Only the source rank needs to specific the tensor_or_tensor_list argument.
    # Other ranks which pass this argument in will be ignored with a warning.
    # If a tensor_list passed in, we need to concat it to a tensor before invoke C++ API.
    # If a tensor passed in, concat is not needed.
    # The passed in type for non-src rank is meaningless, for it will be ignored.
    if src != dist.get_rank():
        if tensor_or_tensor_list is not None:
            warnings.warn(
                "Specific `tensor_or_tensor_list` is meaningless for rank which is not src."
            )
        tensor_or_tensor_list = []

    if framework.in_dygraph_mode():
        group = _get_global_group() if group is None else group
        src_rank_in_group = _get_or_throw_group_rank(src, group)
        if paddle.is_tensor(tensor_or_tensor_list):
            return _scatter_tensor_in_dygraph(
                tensor,
                tensor_or_tensor_list,
                src_rank_in_group,
                group,
                sync_op,
                use_calc_stream,
            )
        else:
            return _scatter_in_dygraph(
                tensor,
                tensor_or_tensor_list,
                src_rank_in_group,
                group,
                sync_op,
                use_calc_stream,
            )
    else:
        assert group is None, "Group can not be used in static mode for now."

        return _scatter_in_static_mode(
            tensor,
            tensor_or_tensor_list,
            src,
            group,
            sync_op,
            use_calc_stream,
        )