process_group_xccl.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 unittest
import random
import numpy as np

import paddle
from paddle.fluid import core
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
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
    store = paddle.fluid.core.TCPStore("127.0.0.1", 6173, is_master, nranks)
    pg_group = core.ProcessGroupCustom(
        store,
        ParallelEnv().device_type,
        rank,
        nranks,
    )

    return pg_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_xccl(self):
        with _test_eager_guard():
            device_id = paddle.distributed.ParallelEnv().dev_id
            paddle.set_device('custom_cpu:%d' % device_id)

            pg = init_process_group()

            x = np.random.random(self.shape).astype(self.dtype)
            tensor_x = paddle.to_tensor(x)
            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 = pg.all_reduce(tensor_x, core.ReduceOp.SUM, sync_op=True)
                task.wait()
                # assert np.array_equal(tensor_x, sum_result)
            else:
                task = pg.all_reduce(tensor_y, core.ReduceOp.SUM, sync_op=True)
                task.wait()
                # assert np.array_equal(tensor_y, sum_result)

            print("test allreduce sum api ok")

            x = np.random.random(self.shape).astype(self.dtype)
            tensor_x = paddle.to_tensor(x)
            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 = pg.all_reduce(tensor_x, core.ReduceOp.MAX, sync_op=True)
                task.wait()
                # assert np.array_equal(tensor_x, max_result)
            else:
                task = pg.all_reduce(tensor_y, core.ReduceOp.MAX, sync_op=True)
                task.wait()
                # assert np.array_equal(tensor_y, max_result)

            print("test allreduce max api 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 = pg.broadcast(tensor_x, 0, sync_op=True)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
                assert task.is_completed()
                # assert np.array_equal(broadcast_result, tensor_x)
            else:
                task = pg.broadcast(tensor_y, 0, sync_op=True)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
                assert task.is_completed()
                # assert np.array_equal(broadcast_result, tensor_y)

            print("test broadcast api ok")

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

            print("test barrier api ok\n")
            return

            # 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_out, tensor_x, sync_op=True)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            # rank 1
            else:
                task = pg.all_gather(tensor_out, tensor_y, sync_op=True)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            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")

            # 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()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            # rank 1
            else:
                task = pg.alltoall(tensor_y, tensor_out2)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            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")

            # 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 = pg.reduce(tensor_x, 0)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            # rank 1
            else:
                task = pg.reduce(tensor_y, 0)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            # if pg.rank() == 0:
            #     assert np.array_equal(tensor_x, sum_result)
            print("test reduce sum api ok\n")

            # 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:
                task = pg.scatter(tensor_x, tensor_y, 0)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            # rank 1
            else:
                task = pg.scatter(tensor_x, tensor_y, 0)
                task.wait()
                # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1)
            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")


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