wehub-resource-sync
234 lines
7.7 KiB
Python
234 lines
7.7 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 random
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import unittest
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from copy import deepcopy
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import numpy as np
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import paddle
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from paddle.base import core
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class TestProcessGroupFp32(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_gloo(self):
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nranks = paddle.distributed.ParallelEnv().nranks
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rank = paddle.distributed.ParallelEnv().local_rank
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is_master = True if rank == 0 else False
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store = paddle.base.core.TCPStore(
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"127.0.0.1", 6272, is_master, nranks, 30
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)
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pg = paddle.base.core.ProcessGroupGloo.create(store, rank, nranks)
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# test allreduce sum
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# rank 0
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paddle.device.set_device('cpu')
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x = np.random.random(self.shape).astype(self.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(self.shape).astype(self.dtype)
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tensor_y = paddle.to_tensor(y)
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sum_result = x + y
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if rank == 0:
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task = pg.allreduce(tensor_x)
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task.wait()
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np.testing.assert_equal(tensor_x, sum_result)
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else:
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task = pg.allreduce(tensor_y)
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task.wait()
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np.testing.assert_equal(tensor_y, sum_result)
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print("test allreduce sum api ok")
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# test allreduce max
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# rank 0
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x = np.random.random(self.shape).astype(self.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(self.shape).astype(self.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 rank == 0:
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task = pg.allreduce(tensor_x, core.ReduceOp.MAX)
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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 = pg.allreduce(tensor_y, core.ReduceOp.MAX)
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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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# test broadcast
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# rank 0
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x = np.random.random(self.shape).astype(self.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(self.shape).astype(self.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 rank == 0:
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task = pg.broadcast(tensor_x, 0)
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np.testing.assert_array_equal(broadcast_result, tensor_x)
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else:
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task = pg.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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# test send_recv
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# rank 0
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x = np.random.random(self.shape).astype(self.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(self.shape).astype(self.dtype)
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tensor_y_1 = paddle.to_tensor(y)
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tensor_y_2 = deepcopy(tensor_y_1)
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send_recv_result_1 = paddle.assign(tensor_x)
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send_recv_result_2 = paddle.assign(tensor_y_2)
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if pg.rank() == 0:
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task = pg.send(tensor_x, pg.size() - 1, True)
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elif pg.rank() == pg.size() - 1:
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task = pg.recv(tensor_y_1, 0, True)
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np.testing.assert_array_equal(send_recv_result_1, tensor_y_1)
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if pg.rank() == 0:
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task = pg.recv(tensor_x, pg.size() - 1, True)
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np.testing.assert_array_equal(send_recv_result_2, tensor_x)
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elif pg.rank() == pg.size() - 1:
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task = pg.send(tensor_y_2, 0, True)
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print("test send_recv api ok")
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# test barrier
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# rank 0
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if pg.rank() == 0:
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task = pg.barrier()
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task.wait()
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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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# test allgather
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.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(self.shape)
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out_shape[0] *= 2
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out = np.random.random(out_shape).astype(self.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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paddle.device.cuda.synchronize()
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# rank 1
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else:
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task = pg.all_gather(tensor_y, tensor_out)
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task.wait()
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out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
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out_2 = paddle.slice(
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tensor_out, [0], [out_shape[0] // 2], [out_shape[0]]
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)
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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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# test Reduce
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.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 = pg.reduce(tensor_x, 0)
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task.wait()
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# rank 1
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else:
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task = pg.reduce(tensor_y, 0)
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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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# test Scatter
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# rank 0
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in_shape = list(self.shape)
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in_shape[0] *= 2
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x = np.random.random(in_shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.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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task = pg.scatter(tensor_x, tensor_y, 0)
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task.wait()
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# rank 1
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else:
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task = pg.scatter(tensor_x, tensor_y, 0)
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task.wait()
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out1 = paddle.slice(tensor_x, [0], [0], [self.shape[0]])
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out2 = paddle.slice(tensor_x, [0], [self.shape[0]], [self.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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# test Gather
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def test_gather(root):
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tensor_x = [
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paddle.zeros(self.shape).astype(self.dtype)
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for _ in range(pg.size())
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]
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tensor_y = [
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paddle.to_tensor(
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np.random.random(self.shape).astype(self.dtype)
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)
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for _ in range(pg.size())
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]
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if pg.rank() == root:
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task = pg.gather(tensor_y[root], tensor_x, root, True)
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task.wait()
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np.testing.assert_array_equal(tensor_x, tensor_y)
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else:
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task = pg.gather(tensor_y[pg.rank()], tensor_x, root, True)
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task.wait()
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test_gather(0)
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test_gather(pg.size() - 1)
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print("test gather api ok\n")
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if __name__ == "__main__":
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unittest.main()
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