process_group_bkcl.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 random
import sys
import unittest

import numpy as np

import paddle
import paddle.distributed as dist
from paddle.fluid.dygraph.parallel import ParallelEnv


def init_process_group(strategy=None):
    nranks = ParallelEnv().nranks
    rank = 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_bkcl(self):
        device_id = paddle.distributed.ParallelEnv().dev_id
        paddle.set_device('xpu:%d' % device_id)

        pg = init_process_group()
        sys.stdout.write(
            "rank {}: size {} name {}\n".format(pg.rank(), pg.size(), pg.name())
        )
        sys.stdout.write("rank {}: test new group api ok\n".format(pg.rank()))

        # TODO(zhangxiaoci) allreduce unittest raise error
        # 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)

        # sys.stdout.write(
        #    "rank {}: test allreduce sum api ok\n".format(pg.rank())
        # )

        # 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:
            # XPU don't support event query by now, so just use sync op here
            task = dist.broadcast(tensor_x, 0)
            paddle.device.xpu.synchronize()
            assert np.array_equal(broadcast_result, tensor_x)
        else:
            task = dist.broadcast(tensor_y, 0)
            paddle.device.xpu.synchronize()
            assert np.array_equal(broadcast_result, tensor_y)

        sys.stdout.write("rank {}: test broadcast api ok\n".format(pg.rank()))

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

        sys.stdout.write("rank {}: test barrier api ok\n".format(pg.rank()))

        # 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.xpu.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)
            paddle.device.xpu.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)
        sys.stdout.write("rank {}: test allgather api ok\n".format(pg.rank()))

        if pg.rank() == 0:
            task = pg.all_gather(tensor_x, tensor_out)
            task.wait()
            paddle.device.xpu.synchronize()
        # rank 1
        else:
            tensor_out_list = []
            task = dist.all_gather(tensor_out_list, tensor_y)
            paddle.device.xpu.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)
        sys.stdout.write("rank {}: test allgather api2 ok\n".format(pg.rank()))

        # 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)
        old_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.xpu.synchronize()
        # rank 1
        else:
            task = dist.reduce(tensor_y, 0, sync_op=False)
            task.wait()
            paddle.device.xpu.synchronize()
        if pg.rank() == 0:
            assert np.array_equal(tensor_x, sum_result)
        assert np.array_equal(tensor_y, old_tensor_y)
        sys.stdout.write("rank {}: test reduce sum api ok\n".format(pg.rank()))

        # test send async api
        # 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)

        # test send sync api
        # 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)

        # 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 == []

        sys.stdout.write("rank {}: test send api ok\n".format(pg.rank()))


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)


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