# 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 random
import unittest

import numpy as np

import paddle
import paddle.distributed as dist


def init_process_group(strategy=None):
    nranks = paddle.distributed.ParallelEnv().nranks
    rank = dist.ParallelEnv().local_rank
    is_master = True if rank == 0 else False
    pg_group = dist.init_parallel_env()

    return pg_group.process_group


class TestProcessGroupFp32(unittest.TestCase):
    def setUp(self):
        paddle.seed(2022)
        random.seed(2022)
        np.random.seed(2022)
        self.config()

    def config(self):
        self.dtype = "float32"
        self.shape = (2, 10, 5)

    def test_create_process_group_nccl(self):
        device_id = paddle.distributed.ParallelEnv().dev_id
        paddle.set_device('gpu:%d' % device_id)

        assert paddle.distributed.is_available()

        pg = init_process_group()
        print("rank:", pg.rank(), "size:", pg.size(), "name:", pg.name())
        print("test new group api ok")

        assert paddle.distributed.get_backend() == "NCCL"

        # test allreduce sum
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        sum_result = tensor_x + tensor_y
        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x)
            assert np.array_equal(tensor_x, sum_result)
        else:
            task = dist.all_reduce(tensor_y)
            assert np.array_equal(tensor_y, sum_result)

        print("test allreduce sum api ok")

        # test allreduce sum with shape = []
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random([]).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        sum_result = tensor_x + tensor_y
        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x)
            assert np.array_equal(tensor_x, sum_result)
        else:
            task = dist.all_reduce(tensor_y)
            assert np.array_equal(tensor_y, sum_result)

        print("test allreduce sum api with = [] ok")

        # test allreduce max
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        max_result = paddle.maximum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.MAX, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, max_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.MAX, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, max_result)

        print("test allreduce max api ok")

        # test allreduce max with shape = []
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random([]).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        max_result = paddle.maximum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.MAX, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, max_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.MAX, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, max_result)

        print("test allreduce max api with shape = [] ok")

        # test allreduce min
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        min_result = paddle.minimum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.MIN, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, min_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.MIN, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, min_result)

        print("test allreduce min api ok")

        # test allreduce min with shape = []
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random([]).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        min_result = paddle.minimum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.MIN, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, min_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.MIN, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, min_result)

        print("test allreduce min api with shape [] ok")

        # test allreduce prod
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        prod_result = np.multiply(x, y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.PROD, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, prod_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.PROD, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, prod_result)

        print("test allreduce prod api ok")

        # test allreduce prod with shape = []
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random([]).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        prod_result = np.multiply(x, y)

        if pg.rank() == 0:
            task = dist.all_reduce(tensor_x, dist.ReduceOp.PROD, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, prod_result)
        else:
            task = dist.all_reduce(tensor_y, dist.ReduceOp.PROD, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, prod_result)

        print("test allreduce prod api with shape = [] ok")

        # test broadcast
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        broadcast_result = paddle.assign(tensor_x)
        if pg.rank() == 0:
            task = dist.broadcast(tensor_x, 0, sync_op=False)
            task.synchronize()
            paddle.device.cuda.synchronize()
            assert task.is_completed()
            assert np.array_equal(broadcast_result, tensor_x)
        else:
            task = dist.broadcast(tensor_y, 0)
            paddle.device.cuda.synchronize()
            assert np.array_equal(broadcast_result, tensor_y)

        print("test broadcast api ok")

        # test broadcast with shape=[]
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random([]).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        broadcast_result = paddle.assign(tensor_x)
        if pg.rank() == 0:
            task = dist.broadcast(tensor_x, 0, sync_op=False)
            task.synchronize()
            paddle.device.cuda.synchronize()
            assert task.is_completed()
            assert np.array_equal(broadcast_result, tensor_x)
        else:
            task = dist.broadcast(tensor_y, 0)
            paddle.device.cuda.synchronize()
            assert np.array_equal(broadcast_result, tensor_y)
        assert tensor_y.shape == []

        print("test broadcast api with shape=[] ok")

        # test barrier
        # rank 0
        if pg.rank() == 0:
            pg.barrier(device_id)
        # rank 1
        else:
            task = pg.barrier(device_id)
            task.wait()

        print("test barrier api ok\n")

        # test allgather
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        out_shape = list(self.shape)
        out_shape[0] *= 2
        out = np.random.random(out_shape).astype(self.dtype)
        tensor_out = paddle.to_tensor(out)
        if pg.rank() == 0:
            task = pg.all_gather(tensor_x, tensor_out)
            task.wait()
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            tensor_out_list = [
                paddle.empty_like(tensor_x),
                paddle.empty_like(tensor_x),
            ]
            task = dist.all_gather(tensor_out_list, tensor_y, sync_op=False)
            paddle.device.cuda.synchronize()
            tensor_out = paddle.concat(tensor_out_list)
        out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
        out_2 = paddle.slice(
            tensor_out, [0], [out_shape[0] // 2], [out_shape[0]]
        )
        assert np.array_equal(tensor_x, out_1)
        assert np.array_equal(tensor_y, out_2)
        print("test allgather api ok\n")

        if pg.rank() == 0:
            task = pg.all_gather(tensor_x, tensor_out)
            task.wait()
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            tensor_out_list = []
            task = dist.all_gather(tensor_out_list, tensor_y, sync_op=False)
            paddle.device.cuda.synchronize()
            tensor_out = paddle.concat(tensor_out_list)
        out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
        out_2 = paddle.slice(
            tensor_out, [0], [out_shape[0] // 2], [out_shape[0]]
        )
        assert np.array_equal(tensor_x, out_1)
        assert np.array_equal(tensor_y, out_2)
        print("test allgather api2 ok\n")

        # test allgather with shape = []
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        y = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        tensor_out_list = []
        if pg.rank() == 0:
            task = dist.all_gather(tensor_out_list, tensor_x)
            task.wait()
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            task = dist.all_gather(tensor_out_list, tensor_y, sync_op=False)
            paddle.device.cuda.synchronize()
        out_1 = tensor_out_list[0]
        out_2 = tensor_out_list[1]
        assert np.array_equal(tensor_x, out_1)
        assert np.array_equal(tensor_y, out_2)
        print("test allgather api with shape [] ok\n")

        # test alltoall
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        y = np.random.random(self.shape).astype(self.dtype)
        out1 = np.random.random(self.shape).astype(self.dtype)
        out2 = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        tensor_out1 = paddle.to_tensor(out1)
        tensor_out2 = paddle.to_tensor(out2)
        raw_tensor_x_2 = paddle.slice(
            tensor_x, [0], [self.shape[0] // 2], [self.shape[0]]
        )
        raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [self.shape[0] // 2])
        if pg.rank() == 0:
            task = pg.alltoall(tensor_x, tensor_out1)
            task.wait()
        # rank 1
        else:
            in_1, in_2 = paddle.split(tensor_y, 2)
            out_1, out_2 = paddle.split(tensor_out2, 2)
            out_tensor_list = [out_1, out_2]
            task = dist.alltoall([in_1, in_2], out_tensor_list)
            paddle.device.cuda.synchronize()
            tensor_out2 = paddle.concat(out_tensor_list)
        out1_2 = paddle.slice(
            tensor_out1, [0], [self.shape[0] // 2], [self.shape[0]]
        )
        out2_1 = paddle.slice(tensor_out2, [0], [0], [self.shape[0] // 2])
        if pg.rank() == 0:
            assert np.array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
        else:
            assert np.array_equal(out2_1, raw_tensor_x_2)
        print("test alltoall api ok\n")

        x = np.random.random(self.shape).astype(self.dtype)
        y = np.random.random(self.shape).astype(self.dtype)
        out1 = np.random.random(self.shape).astype(self.dtype)
        out2 = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        tensor_out1 = paddle.to_tensor(out1)
        tensor_out2 = paddle.to_tensor(out2)
        raw_tensor_x_2 = paddle.slice(
            tensor_x, [0], [self.shape[0] // 2], [self.shape[0]]
        )
        raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [self.shape[0] // 2])
        if pg.rank() == 0:
            task = pg.alltoall(tensor_x, tensor_out1)
            task.wait()
        # rank 1
        else:
            in_1, in_2 = paddle.split(tensor_y, 2)
            out_1, out_2 = paddle.split(tensor_out2, 2)
            out_tensor_list = []
            task = dist.alltoall([in_1, in_2], out_tensor_list)
            paddle.device.cuda.synchronize()
            tensor_out2 = paddle.concat(out_tensor_list)
        out1_2 = paddle.slice(
            tensor_out1, [0], [self.shape[0] // 2], [self.shape[0]]
        )
        out2_1 = paddle.slice(tensor_out2, [0], [0], [self.shape[0] // 2])
        if pg.rank() == 0:
            assert np.array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
        else:
            assert np.array_equal(out2_1, raw_tensor_x_2)
        print("test alltoall api2 ok\n")

        # test Reduce
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        sum_result = tensor_x + tensor_y
        if pg.rank() == 0:
            task = dist.reduce(tensor_x, 0, sync_op=True)
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            task = dist.reduce(tensor_y, 0, sync_op=False)
            task.wait()
            paddle.device.cuda.synchronize()
        if pg.rank() == 0:
            assert np.array_equal(tensor_x, sum_result)
        print("test reduce sum api ok\n")

        # test reduce max
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        max_result = paddle.maximum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.reduce(tensor_x, 0, dist.ReduceOp.MAX, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, max_result)
        else:
            task = dist.reduce(tensor_y, 0, dist.ReduceOp.MAX, sync_op=False)
            task.wait()

        print("test reduce max api ok")

        # test reduce min
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        min_result = paddle.minimum(tensor_x, tensor_y)

        if pg.rank() == 0:
            task = dist.reduce(tensor_x, 0, dist.ReduceOp.MIN, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, min_result)
        else:
            task = dist.reduce(tensor_y, 0, dist.ReduceOp.MIN, sync_op=False)
            task.wait()

        print("test reduce min api ok")

        # test reduce product
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        prod_result = np.multiply(x, y)

        if pg.rank() == 0:
            task = dist.reduce(tensor_x, 0, dist.ReduceOp.PROD, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_x, prod_result)
        else:
            task = dist.reduce(tensor_y, 0, dist.ReduceOp.PROD, sync_op=False)
            task.wait()

        print("test reduce prod api ok")

        test_reduce_with_zero_dim([], self.dtype, pg)

        # test Scatter
        # rank 0
        in_shape = list(self.shape)
        in_shape[0] *= 2
        x = np.random.random(in_shape).astype(self.dtype)
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        if pg.rank() == 0:
            in_1, in_2 = paddle.split(tensor_x, 2)
            task = dist.scatter(tensor_y, [in_1, in_2], 0, sync_op=True)
            # task.wait()
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            task = dist.scatter(tensor_y, [], 0, sync_op=False)
            task.wait()
            paddle.device.cuda.synchronize()
        out1 = paddle.slice(tensor_x, [0], [0], [self.shape[0]])
        out2 = paddle.slice(tensor_x, [0], [self.shape[0]], [self.shape[0] * 2])
        if pg.rank() == 0:
            assert np.array_equal(tensor_y, out1)
        else:
            assert np.array_equal(tensor_y, out2)
        print("test scatter api ok\n")

        # test Scatter with shape=[]
        # rank 0
        x = np.random.random([]).astype(self.dtype)
        y = np.random.random([]).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        tensor_y = paddle.to_tensor(y)
        if pg.rank() == 0:
            in_1, in_2 = tensor_x, tensor_x + 1
            task = dist.scatter(tensor_y, [in_1, in_2], 0, sync_op=True)
            paddle.device.cuda.synchronize()
        # rank 1
        else:
            task = dist.scatter(tensor_y, [], 0, sync_op=True)
            task.wait()
            paddle.device.cuda.synchronize()
        out1 = paddle.assign(tensor_x)
        out2 = paddle.assign(tensor_x + 1)
        if pg.rank() == 0:
            assert np.array_equal(tensor_y, out1)
        else:
            assert np.array_equal(tensor_y, out2), f"{tensor_y}, {out2}"
        assert tensor_y.shape == []
        print("test scatter api with shape=[] ok\n")

        # test send min
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        if pg.rank() == 0:
            task = dist.send(tensor_x, 1, sync_op=False)
            task.wait()
        else:
            task = dist.recv(tensor_y, 0, sync_op=False)
            task.wait()
            assert np.array_equal(tensor_y, tensor_x)

        print("test send api ok")

        # test send min
        # rank 0
        x = np.random.random(self.shape).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.random.random(self.shape).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        if pg.rank() == 0:
            task = dist.send(tensor_x, 1, sync_op=True)
        else:
            task = dist.recv(tensor_y, 0, sync_op=True)
            assert np.array_equal(tensor_y, tensor_x)

        print("test send api ok")

        # test send 0-d tensor
        # rank 0
        x = np.random.uniform(-1, 1, []).astype(self.dtype)
        tensor_x = paddle.to_tensor(x)
        # rank 1
        y = np.array(0.2022).astype(self.dtype)
        tensor_y = paddle.to_tensor(y)

        if pg.rank() == 0:
            task = dist.send(tensor_x, 1, sync_op=True)
        else:
            task = dist.recv(tensor_y, 0, sync_op=True)
            assert np.array_equal(tensor_y, tensor_x) and tensor_y.shape == []

        print("test send & recv 0-d tensor ok")


class TestProcessGroupFp16(TestProcessGroupFp32):
    def setUp(self):
        paddle.seed(2022)
        random.seed(2022)
        np.random.seed(2022)
        self.config()

    def config(self):
        self.dtype = "float16"
        self.shape = (4, 20, 20)


def test_reduce_with_zero_dim(shape, dtype, pg):
    # test Reduce With Zero Dim
    # rank 0
    x = np.random.random(shape).astype(dtype)
    y = np.random.random(shape).astype(dtype)
    tensor_x = paddle.to_tensor(x)
    tensor_y = paddle.to_tensor(y)
    sum_result = tensor_x + tensor_y
    if pg.rank() == 0:
        task = dist.reduce(tensor_x, 0, sync_op=True)
        paddle.device.cuda.synchronize()
    # rank 1
    else:
        task = dist.reduce(tensor_y, 0, sync_op=False)
        task.wait()
        paddle.device.cuda.synchronize()
    if pg.rank() == 0:
        assert np.array_equal(tensor_x, sum_result) and len(tensor_x.shape) == 0
    print("test reduce with zero dim sum api ok\n")

    # test reduce with zero dim max
    # rank 0
    x = np.random.random(shape).astype(dtype)
    tensor_x = paddle.to_tensor(x)
    # rank 1
    y = np.random.random(shape).astype(dtype)
    tensor_y = paddle.to_tensor(y)

    max_result = paddle.maximum(tensor_x, tensor_y)

    if pg.rank() == 0:
        task = dist.reduce(tensor_x, 0, dist.ReduceOp.MAX, sync_op=False)
        task.wait()
        assert np.array_equal(tensor_x, max_result) and len(tensor_x.shape) == 0
    else:
        task = dist.reduce(tensor_y, 0, dist.ReduceOp.MAX, sync_op=False)
        task.wait()

    print("test reduce with zero dim max api ok")

    # test reduce with zero dim min
    # rank 0
    x = np.random.random(shape).astype(dtype)
    tensor_x = paddle.to_tensor(x)
    # rank 1
    y = np.random.random(shape).astype(dtype)
    tensor_y = paddle.to_tensor(y)

    min_result = paddle.minimum(tensor_x, tensor_y)

    if pg.rank() == 0:
        task = dist.reduce(tensor_x, 0, dist.ReduceOp.MIN, sync_op=False)
        task.wait()
        assert np.array_equal(tensor_x, min_result) and len(tensor_x.shape) == 0
    else:
        task = dist.reduce(tensor_y, 0, dist.ReduceOp.MIN, sync_op=False)
        task.wait()

    print("test reduce with zero dim min api ok")

    # test reduce with zero dim product
    # rank 0
    x = np.random.random(shape).astype(dtype)
    tensor_x = paddle.to_tensor(x)
    # rank 1
    y = np.random.random(shape).astype(dtype)
    tensor_y = paddle.to_tensor(y)

    prod_result = np.multiply(x, y)

    if pg.rank() == 0:
        task = dist.reduce(tensor_x, 0, dist.ReduceOp.PROD, sync_op=False)
        task.wait()
        assert (
            np.array_equal(tensor_x, prod_result) and len(tensor_x.shape) == 0
        )
    else:
        task = dist.reduce(tensor_y, 0, dist.ReduceOp.PROD, sync_op=False)
        task.wait()

    print("test reduce with zero dim prod api ok")


if __name__ == "__main__":
    unittest.main()