# Copyright (c) 2020 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.

from legacy_test.test_collective_api_base import (
    TestCollectiveAPIRunnerBase,
    runtime_main,
)

import paddle
import paddle.distributed as dist
from paddle import base, framework
from paddle.base import data_feeder

paddle.enable_static()


def alltoall_new(
    in_tensor_or_tensor_list,
    out_tensor_or_tensor_list,
    group=None,
    sync_op=True,
):
    op_type = 'all_to_all'

    ring_id = 0 if group is None else group.id
    nranks = dist.get_world_size()
    helper = framework.LayerHelper(op_type, **locals())

    in_tensor = in_tensor_or_tensor_list
    if isinstance(in_tensor_or_tensor_list, list):
        if len(in_tensor_or_tensor_list) == 0:
            raise RuntimeError("The input tensor_list should not be empty.")
        # 0-D use stack/unstack while others use concat/split
        if len(in_tensor_or_tensor_list[0].shape) == 0:
            in_tensor = paddle.stack(in_tensor_or_tensor_list, axis=0)
        else:
            in_tensor = paddle.concat(in_tensor_or_tensor_list, axis=0)

    out_tensor = out_tensor_or_tensor_list
    if isinstance(out_tensor_or_tensor_list, list):
        if len(out_tensor_or_tensor_list) != 0:
            raise ValueError(
                "The 'out_tensor_list' for all_to_all " "must be an empty list."
            )
        out_tensor = helper.create_variable_for_type_inference(
            dtype=in_tensor.dtype
        )

    data_feeder.check_variable_and_dtype(
        in_tensor,
        'in_tensor',
        [
            'float16',
            'float32',
            'float64',
            'int32',
            'int64',
            'int8',
            'uint8',
            'bool',
            'uint16',
        ],
        'all_to_all',
    )
    helper.append_op(
        type=op_type,
        inputs={'x': [in_tensor]},
        outputs={'out': [out_tensor]},
        attrs={
            'ring_id': ring_id,
        },
    )
    # NOTE(liyurui): If the argument `out_tensor_or_tensor_list` is a tensor_list,
    # we need to split the result. So we should wait the result of all_to_all
    # before split if the communication is not on calc stream.
    if isinstance(out_tensor_or_tensor_list, list):
        if not sync_op:
            dist.wait(out_tensor, use_calc_stream=False)
        # 0-D use stack/unstack while others use concat/split
        if len(in_tensor_or_tensor_list[0].shape) == 0:
            out_tensor_or_tensor_list.extend(paddle.unstack(out_tensor, 0))
        else:
            out_tensor_or_tensor_list.extend(
                paddle.split(out_tensor, nranks, 0)
            )


class TestCollectiveAllToAllAPI(TestCollectiveAPIRunnerBase):
    def __init__(self):
        self.global_ring_id = 0

    def get_model(self, main_prog, startup_program, rank):
        with base.program_guard(main_prog, startup_program):
            tindata = paddle.static.data(
                name="tindata", shape=[-1, 10, 1000], dtype='float32'
            )
            tindata.desc.set_need_check_feed(False)
            tindata = paddle.split(tindata, 2, axis=0)
            tout_data = []
            paddle.distributed.alltoall(tindata, tout_data)
            return tout_data

    def get_model_new(
        self, main_prog, startup_program, rank, dtype=None, reduce_type=None
    ):
        with base.program_guard(main_prog, startup_program):
            tindata = paddle.static.data(
                name="tindata", shape=[-1, 10, 1000], dtype=dtype
            )
            tindata.desc.set_need_check_feed(False)
            tindata = paddle.split(tindata, 2, axis=0)
            tout_data = []
            alltoall_new(tindata, tout_data)
            return tout_data

    def get_model_new_comm(
        self, main_prog, startup_program, rank, dtype='float32'
    ):
        with base.program_guard(main_prog, startup_program):
            tindata = paddle.static.data(
                name="tindata", shape=[-1, 10, 1000], dtype=dtype
            )
            tindata.desc.set_need_check_feed(False)
            tindata = paddle.split(tindata, 2, axis=0)
            tout_data = []
            paddle.distributed.alltoall(tindata, tout_data)
            return tout_data


if __name__ == "__main__":
    runtime_main(TestCollectiveAllToAllAPI, "alltoall")
