wehub-resource-sync
494 lines
15 KiB
Python
494 lines
15 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import ctypes
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import random
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import unittest
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.base import core
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from paddle.base.framework import _set_expected_place
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from paddle.distributed.collective import (
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Group,
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_default_group_name,
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_set_group_map,
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_set_group_map_backend,
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_set_group_map_by_name,
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)
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ctypes.CDLL("libmpi.so", mode=ctypes.RTLD_GLOBAL)
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def init_process_group(strategy=None):
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gid = 0
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pg = core.ProcessGroupMPI.create([], gid)
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rank = pg.get_rank()
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world_size = pg.get_world_size()
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# support CPU
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place = core.CPUPlace()
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_set_expected_place(place)
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group = Group(
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rank,
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world_size,
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id=0,
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ranks=list(range(world_size)),
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pg=pg,
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name=_default_group_name,
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)
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_set_group_map_by_name(_default_group_name, group)
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_set_group_map(gid, group)
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_set_group_map_backend(group, "mpi")
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return group
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def test_allreduce_sum(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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sum_result = tensor_x + tensor_y
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if pg.rank() == 0:
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task = dist.all_reduce(tensor_x)
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np.testing.assert_array_equal(tensor_x, sum_result)
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else:
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task = dist.all_reduce(tensor_y)
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np.testing.assert_array_equal(tensor_y, sum_result)
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print("test allreduce sum api ok")
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def test_allreduce_max(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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max_result = paddle.maximum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = dist.all_reduce(
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tensor_x, dist.ReduceOp.MAX, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, max_result)
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else:
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task = dist.all_reduce(
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tensor_y, dist.ReduceOp.MAX, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_y, max_result)
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print("test allreduce max api ok")
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def test_allreduce_min(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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min_result = paddle.minimum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = dist.all_reduce(
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tensor_x, dist.ReduceOp.MIN, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, min_result)
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else:
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task = dist.all_reduce(
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tensor_y, dist.ReduceOp.MIN, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_y, min_result)
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print("test allreduce min api ok")
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def test_allreduce_prod(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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prod_result = np.multiply(x, y)
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if pg.rank() == 0:
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task = dist.all_reduce(
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tensor_x, dist.ReduceOp.PROD, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, prod_result)
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else:
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task = dist.all_reduce(
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tensor_y, dist.ReduceOp.PROD, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_y, prod_result)
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print("test allreduce prod api ok")
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def test_broadcast(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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broadcast_result = paddle.assign(tensor_x)
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if pg.rank() == 0:
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task = dist.broadcast(tensor_x, 0, use_calc_stream=False)
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task.synchronize()
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assert task.is_completed()
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np.testing.assert_array_equal(broadcast_result, tensor_x)
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else:
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task = dist.broadcast(tensor_y, 0)
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np.testing.assert_array_equal(broadcast_result, tensor_y)
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print("test broadcast api ok")
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def test_barrier(pg):
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# rank 0
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if pg.rank() == 0:
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dist.barrier()
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# rank 1
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else:
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task = pg.barrier()
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task.wait()
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print("test barrier api ok\n")
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def test_allgather(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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y = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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out_shape = list(shape)
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out_shape[0] *= 2
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out = np.random.random(out_shape).astype(dtype)
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tensor_out = paddle.to_tensor(out)
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if pg.rank() == 0:
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task = pg.all_gather(tensor_x, tensor_out)
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task.wait()
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# rank 1
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else:
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tensor_out_list = [
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paddle.empty_like(tensor_x),
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paddle.empty_like(tensor_x),
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]
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task = dist.all_gather(tensor_out_list, tensor_y, use_calc_stream=False)
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tensor_out = paddle.concat(tensor_out_list)
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out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
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out_2 = paddle.slice(tensor_out, [0], [out_shape[0] // 2], [out_shape[0]])
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np.testing.assert_array_equal(tensor_x, out_1)
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np.testing.assert_array_equal(tensor_y, out_2)
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print("test allgather api ok\n")
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if pg.rank() == 0:
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task = pg.all_gather(tensor_x, tensor_out)
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task.wait()
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# rank 1
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else:
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tensor_out_list = []
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task = dist.all_gather(tensor_out_list, tensor_y, use_calc_stream=False)
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tensor_out = paddle.concat(tensor_out_list)
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out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
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out_2 = paddle.slice(tensor_out, [0], [out_shape[0] // 2], [out_shape[0]])
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np.testing.assert_array_equal(tensor_x, out_1)
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np.testing.assert_array_equal(tensor_y, out_2)
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print("test allgather api2 ok\n")
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def test_all2all(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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y = np.random.random(shape).astype(dtype)
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out1 = np.random.random(shape).astype(dtype)
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out2 = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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tensor_out1 = paddle.to_tensor(out1)
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tensor_out2 = paddle.to_tensor(out2)
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raw_tensor_x_2 = paddle.slice(tensor_x, [0], [shape[0] // 2], [shape[0]])
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raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [shape[0] // 2])
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if pg.rank() == 0:
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task = pg.alltoall(tensor_out1, tensor_x)
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task.wait()
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# rank 1
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else:
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in_1, in_2 = paddle.split(tensor_y, 2)
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out_1, out_2 = paddle.split(tensor_out2, 2)
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out_tensor_list = [out_1, out_2]
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task = dist.alltoall(out_tensor_list, [in_1, in_2])
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tensor_out2 = paddle.concat(out_tensor_list)
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out1_2 = paddle.slice(tensor_out1, [0], [shape[0] // 2], [shape[0]])
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out2_1 = paddle.slice(tensor_out2, [0], [0], [shape[0] // 2])
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if pg.rank() == 0:
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np.testing.assert_array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
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else:
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np.testing.assert_array_equal(out2_1, raw_tensor_x_2)
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print("test alltoall api ok\n")
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x = np.random.random(shape).astype(dtype)
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y = np.random.random(shape).astype(dtype)
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out1 = np.random.random(shape).astype(dtype)
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out2 = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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tensor_out1 = paddle.to_tensor(out1)
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tensor_out2 = paddle.to_tensor(out2)
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raw_tensor_x_2 = paddle.slice(tensor_x, [0], [shape[0] // 2], [shape[0]])
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raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [shape[0] // 2])
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if pg.rank() == 0:
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task = pg.alltoall(tensor_out1, tensor_x)
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task.wait()
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# rank 1
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else:
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in_1, in_2 = paddle.split(tensor_y, 2)
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out_1, out_2 = paddle.split(tensor_out2, 2)
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out_tensor_list = []
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task = dist.alltoall(out_tensor_list, [in_1, in_2])
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tensor_out2 = paddle.concat(out_tensor_list)
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out1_2 = paddle.slice(tensor_out1, [0], [shape[0] // 2], [shape[0]])
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out2_1 = paddle.slice(tensor_out2, [0], [0], [shape[0] // 2])
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if pg.rank() == 0:
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np.testing.assert_array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
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else:
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np.testing.assert_array_equal(out2_1, raw_tensor_x_2)
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print("test alltoall api2 ok\n")
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def test_reduce_sum(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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y = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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sum_result = tensor_x + tensor_y
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if pg.rank() == 0:
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task = dist.reduce(tensor_x, 0, use_calc_stream=True)
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# rank 1
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else:
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task = dist.reduce(tensor_y, 0, use_calc_stream=False)
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task.wait()
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if pg.rank() == 0:
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np.testing.assert_array_equal(tensor_x, sum_result)
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print("test reduce sum api ok\n")
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def test_reduce_max(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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max_result = paddle.maximum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = dist.reduce(
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tensor_x, 0, dist.ReduceOp.MAX, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, max_result)
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else:
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task = dist.reduce(
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tensor_y, 0, dist.ReduceOp.MAX, use_calc_stream=False
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)
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task.wait()
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print("test reduce max api ok")
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def test_reduce_min(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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min_result = paddle.minimum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = dist.reduce(
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tensor_x, 0, dist.ReduceOp.MIN, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, min_result)
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else:
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task = dist.reduce(
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tensor_y, 0, dist.ReduceOp.MIN, use_calc_stream=False
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)
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task.wait()
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print("test reduce min api ok")
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def test_reduce_prod(pg, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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prod_result = np.multiply(x, y)
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if pg.rank() == 0:
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task = dist.reduce(
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tensor_x, 0, dist.ReduceOp.PROD, use_calc_stream=False
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)
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task.wait()
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np.testing.assert_array_equal(tensor_x, prod_result)
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else:
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task = dist.reduce(
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tensor_y, 0, dist.ReduceOp.PROD, use_calc_stream=False
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)
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task.wait()
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print("test reduce prod api ok")
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def test_scatter(pg, shape, dtype):
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# rank 0
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in_shape = list(shape)
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in_shape[0] *= 2
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x = np.random.random(in_shape).astype(dtype)
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y = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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if pg.rank() == 0:
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in_1, in_2 = paddle.split(tensor_x, 2)
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task = dist.scatter(tensor_y, [in_1, in_2], 0, use_calc_stream=True)
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# rank 1
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else:
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task = dist.scatter(tensor_y, [], 0, use_calc_stream=False)
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task.wait()
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out1 = paddle.slice(tensor_x, [0], [0], [shape[0]])
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out2 = paddle.slice(tensor_x, [0], [shape[0]], [shape[0] * 2])
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if pg.rank() == 0:
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np.testing.assert_array_equal(tensor_y, out1)
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else:
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np.testing.assert_array_equal(tensor_y, out2)
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print("test scatter api ok\n")
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def test_send_recv(pg, sub_group, shape, dtype):
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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if pg.rank() == 0:
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task = dist.send(tensor_x, 1, group=sub_group, use_calc_stream=False)
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task.wait()
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elif pg.rank() == 1:
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task = dist.recv(tensor_y, 0, group=sub_group, use_calc_stream=False)
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task.wait()
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np.testing.assert_array_equal(tensor_y, tensor_x)
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print("test send api ok")
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# test send min
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# rank 0
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x = np.random.random(shape).astype(dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(shape).astype(dtype)
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tensor_y = paddle.to_tensor(y)
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if pg.rank() == 0:
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task = dist.send(tensor_x, 1, group=sub_group, use_calc_stream=True)
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elif pg.rank() == 1:
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task = dist.recv(tensor_y, 0, group=sub_group, use_calc_stream=True)
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np.testing.assert_array_equal(tensor_y, tensor_x)
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print("test send api ok")
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class TestProcessGroup(unittest.TestCase):
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def setUp(self):
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paddle.seed(2022)
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random.seed(2022)
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np.random.seed(2022)
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self.config()
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def config(self):
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self.dtype = "float32"
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self.shape = (2, 10, 5)
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def test_create_process_group_mpi(self):
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group = init_process_group()
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pg = group.process_group
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# test allreduce sum
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test_allreduce_sum(pg, self.shape, self.dtype)
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# test allreduce max
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test_allreduce_max(pg, self.shape, self.dtype)
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# test allreduce min
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test_allreduce_min(pg, self.shape, self.dtype)
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# test allreduce prod
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test_allreduce_prod(pg, self.shape, self.dtype)
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# test broadcast
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test_broadcast(pg, self.shape, self.dtype)
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# test barrier
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test_barrier(pg)
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# test allgather
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test_allgather(pg, self.shape, self.dtype)
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# test alltoall
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test_all2all(pg, self.shape, self.dtype)
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# test Reduce
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test_reduce_sum(pg, self.shape, self.dtype)
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# test reduce max
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test_reduce_max(pg, self.shape, self.dtype)
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# test reduce min
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test_reduce_min(pg, self.shape, self.dtype)
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# test reduce product
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test_reduce_prod(pg, self.shape, self.dtype)
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# test Scatter
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test_scatter(pg, self.shape, self.dtype)
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# test send recv.
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test_send_recv(pg, group, self.shape, self.dtype)
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if __name__ == "__main__":
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unittest.main()
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