# 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 os

import numpy as np
import test_collective_api_base as test_collective_base

import paddle
import paddle.distributed as dist


class StreamAllgatherTestCase:
    def __init__(self):
        self._sync_op = eval(os.getenv("sync_op"))
        self._use_calc_stream = eval(os.getenv("use_calc_stream"))
        self._backend = os.getenv("backend")
        self._shape = eval(os.getenv("shape"))
        self._dtype = os.getenv("dtype")
        self._seeds = eval(os.getenv("seeds"))
        if self._backend not in ["nccl", "gloo"]:
            raise NotImplementedError(
                "Only support nccl and gloo as the backend for now."
            )
        os.environ["PADDLE_DISTRI_BACKEND"] = self._backend

    def run_test_case(self):
        dist.init_parallel_env()

        test_data_list = []
        for seed in self._seeds:
            test_data_list.append(
                test_collective_base.create_test_data(
                    shape=self._shape, dtype=self._dtype, seed=seed
                )
            )

        rank = dist.get_rank()
        tensor = paddle.to_tensor(test_data_list[rank])

        # case 1: pass an empty tensor list
        empty_tensor_list = []
        task = dist.stream.all_gather(
            empty_tensor_list,
            tensor,
            sync_op=self._sync_op,
            use_calc_stream=self._use_calc_stream,
        )
        if not self._sync_op:
            task.wait()
        np.testing.assert_allclose(
            empty_tensor_list, test_data_list, rtol=1e-05, atol=1e-05
        )

        # case 2: pass a pre-sized tensor list
        full_tensor_list = [paddle.empty_like(tensor) for _ in test_data_list]
        task = dist.stream.all_gather(
            full_tensor_list,
            tensor,
            sync_op=self._sync_op,
            use_calc_stream=self._use_calc_stream,
        )
        if not self._sync_op:
            task.wait()
        np.testing.assert_allclose(
            full_tensor_list, test_data_list, rtol=1e-05, atol=1e-05
        )

        # case 3: pass a pre-sized tensor
        result_tensor = paddle.concat(
            [paddle.to_tensor(data) for data in test_data_list]
        )
        out_tensor = paddle.empty_like(result_tensor)
        task = dist.stream.all_gather(
            out_tensor,
            tensor,
            sync_op=self._sync_op,
            use_calc_stream=self._use_calc_stream,
        )
        if not self._sync_op:
            task.wait()
        np.testing.assert_allclose(
            out_tensor, result_tensor, rtol=1e-05, atol=1e-05
        )


if __name__ == "__main__":
    StreamAllgatherTestCase().run_test_case()
