wehub-resource-sync
695 lines
24 KiB
Python
695 lines
24 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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import numpy as np
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import paddle
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import paddle.distributed as dist
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def init_process_group(strategy=None):
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nranks = paddle.distributed.ParallelEnv().nranks
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rank = dist.ParallelEnv().local_rank
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is_master = True if rank == 0 else False
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pg_group = dist.init_parallel_env()
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return pg_group.process_group
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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_nccl(self):
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device_id = paddle.distributed.ParallelEnv().dev_id
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paddle.set_device(f'gpu:{device_id}')
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assert paddle.distributed.is_available()
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pg = init_process_group()
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print("rank:", pg.rank(), "size:", pg.size(), "name:", pg.name())
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print("test new group api ok")
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assert paddle.distributed.get_backend() == "NCCL"
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# test allreduce sum
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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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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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# test allreduce sum with shape = []
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# rank 0
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x = np.random.random([]).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([]).astype(self.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 with = [] 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 pg.rank() == 0:
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task = dist.all_reduce(tensor_x, dist.ReduceOp.MAX, sync_op=False)
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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(tensor_y, dist.ReduceOp.MAX, sync_op=False)
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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 allreduce max with shape = []
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# rank 0
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x = np.random.random([]).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([]).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 pg.rank() == 0:
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task = dist.all_reduce(tensor_x, dist.ReduceOp.MAX, sync_op=False)
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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(tensor_y, dist.ReduceOp.MAX, sync_op=False)
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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 with shape = [] ok")
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# test allreduce min
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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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min_result = paddle.minimum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = dist.all_reduce(tensor_x, dist.ReduceOp.MIN, sync_op=False)
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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(tensor_y, dist.ReduceOp.MIN, sync_op=False)
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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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# test allreduce min with shape = []
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# rank 0
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x = np.random.random([]).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([]).astype(self.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(tensor_x, dist.ReduceOp.MIN, sync_op=False)
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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(tensor_y, dist.ReduceOp.MIN, sync_op=False)
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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 with shape [] ok")
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# test allreduce prod
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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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prod_result = np.multiply(x, y)
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if pg.rank() == 0:
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task = dist.all_reduce(tensor_x, dist.ReduceOp.PROD, sync_op=False)
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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(tensor_y, dist.ReduceOp.PROD, sync_op=False)
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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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# test allreduce prod with shape = []
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# rank 0
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x = np.random.random([]).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([]).astype(self.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(tensor_x, dist.ReduceOp.PROD, sync_op=False)
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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(tensor_y, dist.ReduceOp.PROD, sync_op=False)
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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 with shape = [] 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 pg.rank() == 0:
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task = dist.broadcast(tensor_x, 0, sync_op=False)
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task.synchronize()
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paddle.device.cuda.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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paddle.device.cuda.synchronize()
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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 broadcast with shape=[]
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# rank 0
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x = np.random.random([]).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([]).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 pg.rank() == 0:
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task = dist.broadcast(tensor_x, 0, sync_op=False)
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task.synchronize()
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paddle.device.cuda.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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paddle.device.cuda.synchronize()
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np.testing.assert_array_equal(broadcast_result, tensor_y)
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assert tensor_y.shape == []
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print("test broadcast api with shape=[] 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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pg.barrier(device_id)
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# rank 1
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else:
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task = pg.barrier(device_id)
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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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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, sync_op=False)
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paddle.device.cuda.synchronize()
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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(
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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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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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tensor_out_list = []
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task = dist.all_gather(tensor_out_list, tensor_y, sync_op=False)
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paddle.device.cuda.synchronize()
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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(
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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 api2 ok\n")
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# test allgather with shape = []
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# rank 0
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x = np.random.random([]).astype(self.dtype)
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y = np.random.random([]).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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tensor_out_list = []
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if pg.rank() == 0:
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task = dist.all_gather(tensor_out_list, tensor_x)
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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 = dist.all_gather(tensor_out_list, tensor_y, sync_op=False)
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paddle.device.cuda.synchronize()
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out_1 = tensor_out_list[0]
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out_2 = tensor_out_list[1]
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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 with shape [] ok\n")
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# test alltoall
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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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out1 = np.random.random(self.shape).astype(self.dtype)
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out2 = 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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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(
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tensor_x, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [self.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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paddle.device.cuda.synchronize()
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tensor_out2 = paddle.concat(out_tensor_list)
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out1_2 = paddle.slice(
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tensor_out1, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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out2_1 = paddle.slice(tensor_out2, [0], [0], [self.shape[0] // 2])
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if pg.rank() == 0:
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np.testing.assert_array_equal(
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out1_2.numpy(), raw_tensor_y_1.numpy()
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)
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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(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.dtype)
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out1 = np.random.random(self.shape).astype(self.dtype)
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out2 = 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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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(
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tensor_x, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [self.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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paddle.device.cuda.synchronize()
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tensor_out2 = paddle.concat(out_tensor_list)
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out1_2 = paddle.slice(
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tensor_out1, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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out2_1 = paddle.slice(tensor_out2, [0], [0], [self.shape[0] // 2])
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if pg.rank() == 0:
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np.testing.assert_array_equal(
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out1_2.numpy(), raw_tensor_y_1.numpy()
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)
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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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# 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 = dist.reduce(tensor_x, 0, sync_op=True)
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paddle.device.cuda.synchronize()
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# rank 1
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else:
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task = dist.reduce(tensor_y, 0, sync_op=False)
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task.wait()
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paddle.device.cuda.synchronize()
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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 reduce 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 pg.rank() == 0:
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task = dist.reduce(tensor_x, 0, dist.ReduceOp.MAX, sync_op=False)
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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(tensor_y, 0, dist.ReduceOp.MAX, sync_op=False)
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task.wait()
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print("test reduce max api ok")
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# test reduce min
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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)
|
|
# rank 1
|
|
y = np.random.random(self.shape).astype(self.dtype)
|
|
tensor_y = paddle.to_tensor(y)
|
|
|
|
min_result = paddle.minimum(tensor_x, tensor_y)
|
|
|
|
if pg.rank() == 0:
|
|
task = dist.reduce(tensor_x, 0, dist.ReduceOp.MIN, sync_op=False)
|
|
task.wait()
|
|
np.testing.assert_array_equal(tensor_x, min_result)
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, dist.ReduceOp.MIN, sync_op=False)
|
|
task.wait()
|
|
|
|
print("test reduce min api ok")
|
|
|
|
# test reduce product
|
|
# 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)
|
|
|
|
prod_result = np.multiply(x, y)
|
|
|
|
if pg.rank() == 0:
|
|
task = dist.reduce(tensor_x, 0, dist.ReduceOp.PROD, sync_op=False)
|
|
task.wait()
|
|
np.testing.assert_array_equal(tensor_x, prod_result)
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, dist.ReduceOp.PROD, sync_op=False)
|
|
task.wait()
|
|
|
|
print("test reduce prod api ok")
|
|
|
|
test_reduce_with_zero_dim([], self.dtype, pg)
|
|
|
|
# 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:
|
|
in_1, in_2 = paddle.split(tensor_x, 2)
|
|
task = dist.scatter(tensor_y, [in_1, in_2], 0, sync_op=True)
|
|
# task.wait()
|
|
paddle.device.cuda.synchronize()
|
|
# rank 1
|
|
else:
|
|
task = dist.scatter(tensor_y, [], 0, sync_op=False)
|
|
task.wait()
|
|
paddle.device.cuda.synchronize()
|
|
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:
|
|
np.testing.assert_array_equal(tensor_y, out1)
|
|
else:
|
|
np.testing.assert_array_equal(tensor_y, out2)
|
|
print("test scatter api ok\n")
|
|
|
|
# test Scatter with shape=[]
|
|
# rank 0
|
|
x = np.random.random([]).astype(self.dtype)
|
|
y = np.random.random([]).astype(self.dtype)
|
|
tensor_x = paddle.to_tensor(x)
|
|
tensor_y = paddle.to_tensor(y)
|
|
if pg.rank() == 0:
|
|
in_1, in_2 = tensor_x, tensor_x + 1
|
|
task = dist.scatter(tensor_y, [in_1, in_2], 0, sync_op=True)
|
|
paddle.device.cuda.synchronize()
|
|
# rank 1
|
|
else:
|
|
task = dist.scatter(tensor_y, [], 0, sync_op=True)
|
|
task.wait()
|
|
paddle.device.cuda.synchronize()
|
|
out1 = paddle.assign(tensor_x)
|
|
out2 = paddle.assign(tensor_x + 1)
|
|
if pg.rank() == 0:
|
|
np.testing.assert_array_equal(tensor_y, out1)
|
|
else:
|
|
np.testing.assert_array_equal(tensor_y, out2)
|
|
assert tensor_y.shape == []
|
|
print("test scatter api with shape=[] ok\n")
|
|
|
|
# test send min
|
|
# 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()
|
|
np.testing.assert_array_equal(tensor_y, tensor_x)
|
|
|
|
print("test send api ok")
|
|
|
|
# test send min
|
|
# 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)
|
|
np.testing.assert_array_equal(tensor_y, tensor_x)
|
|
|
|
print("test send api ok")
|
|
|
|
# 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 == []
|
|
|
|
print("test send & recv 0-d tensor ok")
|
|
|
|
|
|
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)
|
|
|
|
|
|
def test_reduce_with_zero_dim(shape, dtype, pg):
|
|
# test Reduce With Zero Dim
|
|
# rank 0
|
|
x = np.random.random(shape).astype(dtype)
|
|
y = np.random.random(shape).astype(dtype)
|
|
tensor_x = paddle.to_tensor(x)
|
|
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.cuda.synchronize()
|
|
# rank 1
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, sync_op=False)
|
|
task.wait()
|
|
paddle.device.cuda.synchronize()
|
|
if pg.rank() == 0:
|
|
assert np.array_equal(tensor_x, sum_result) and len(tensor_x.shape) == 0
|
|
print("test reduce with zero dim sum api ok\n")
|
|
|
|
# test reduce with zero dim max
|
|
# rank 0
|
|
x = np.random.random(shape).astype(dtype)
|
|
tensor_x = paddle.to_tensor(x)
|
|
# rank 1
|
|
y = np.random.random(shape).astype(dtype)
|
|
tensor_y = paddle.to_tensor(y)
|
|
|
|
max_result = paddle.maximum(tensor_x, tensor_y)
|
|
|
|
if pg.rank() == 0:
|
|
task = dist.reduce(tensor_x, 0, dist.ReduceOp.MAX, sync_op=False)
|
|
task.wait()
|
|
assert np.array_equal(tensor_x, max_result) and len(tensor_x.shape) == 0
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, dist.ReduceOp.MAX, sync_op=False)
|
|
task.wait()
|
|
|
|
print("test reduce with zero dim max api ok")
|
|
|
|
# test reduce with zero dim min
|
|
# rank 0
|
|
x = np.random.random(shape).astype(dtype)
|
|
tensor_x = paddle.to_tensor(x)
|
|
# rank 1
|
|
y = np.random.random(shape).astype(dtype)
|
|
tensor_y = paddle.to_tensor(y)
|
|
|
|
min_result = paddle.minimum(tensor_x, tensor_y)
|
|
|
|
if pg.rank() == 0:
|
|
task = dist.reduce(tensor_x, 0, dist.ReduceOp.MIN, sync_op=False)
|
|
task.wait()
|
|
assert np.array_equal(tensor_x, min_result) and len(tensor_x.shape) == 0
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, dist.ReduceOp.MIN, sync_op=False)
|
|
task.wait()
|
|
|
|
print("test reduce with zero dim min api ok")
|
|
|
|
# test reduce with zero dim product
|
|
# rank 0
|
|
x = np.random.random(shape).astype(dtype)
|
|
tensor_x = paddle.to_tensor(x)
|
|
# rank 1
|
|
y = np.random.random(shape).astype(dtype)
|
|
tensor_y = paddle.to_tensor(y)
|
|
|
|
prod_result = np.multiply(x, y)
|
|
|
|
if pg.rank() == 0:
|
|
task = dist.reduce(tensor_x, 0, dist.ReduceOp.PROD, sync_op=False)
|
|
task.wait()
|
|
assert (
|
|
np.array_equal(tensor_x, prod_result) and len(tensor_x.shape) == 0
|
|
)
|
|
else:
|
|
task = dist.reduce(tensor_y, 0, dist.ReduceOp.PROD, sync_op=False)
|
|
task.wait()
|
|
|
|
print("test reduce with zero dim prod api ok")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|