# 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 os import shutil import paddle from paddle.fluid import core from datetime import timedelta 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, rank, nranks, paddle.CustomPlace(ParallelEnv().device_type, ParallelEnv().device_id)) 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(): paddle.set_device('custom_cpu:%d' % paddle.distributed.ParallelEnv().dev_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.allreduce(tensor_x) task.wait() # assert np.array_equal(tensor_x, sum_result) else: task = pg.allreduce(tensor_y) 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.allreduce(tensor_x, core.ReduceOp.MAX) task.wait() # assert np.array_equal(tensor_x, max_result) else: task = pg.allreduce(tensor_y, core.ReduceOp.MAX) 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) task.synchronize() # 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) task.synchronize() # 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() task.wait() # rank 1 else: task = pg.barrier() 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_x, tensor_out) task.wait() # paddle.fluid.core._custom_device_synchronize("custom_cpu", -1) # rank 1 else: task = pg.all_gather(tensor_y, tensor_out) 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()