wehub-resource-sync
128 lines
4.1 KiB
Python
128 lines
4.1 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 os
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import legacy_test.test_collective_api_base as test_collective_base
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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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class StreamAllToAllTestCase:
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def __init__(self):
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self._sync_op = eval(os.getenv("sync_op"))
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self._use_calc_stream = eval(os.getenv("use_calc_stream"))
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self._backend = os.getenv("backend")
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self._shape = eval(os.getenv("shape"))
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self._dtype = os.getenv("dtype")
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self._seeds = eval(os.getenv("seeds"))
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if self._backend not in ["nccl", "gloo", "flagcx"]:
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raise NotImplementedError(
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"Only support nccl and gloo as the backend for now."
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)
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os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
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def run_test_case(self):
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dist.init_parallel_env()
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test_data_list = []
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for seed in self._seeds:
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test_data_list.append(
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test_collective_base.create_test_data(
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shape=self._shape, dtype=self._dtype, seed=seed
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)
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)
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nranks = len(test_data_list)
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data1 = test_data_list[0]
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data2 = test_data_list[1]
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result1 = np.vstack(
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[
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data1[0 : data1.shape[0] // 2, :],
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data2[0 : data2.shape[0] // 2, :],
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]
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)
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result2 = np.vstack(
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[data1[data1.shape[0] // 2 :, :], data2[data2.shape[0] // 2 :, :]]
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)
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rank = dist.get_rank()
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tensor = paddle.to_tensor(test_data_list[rank])
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t1, t2 = paddle.split(tensor, nranks, axis=0)
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# case 1: pass an empty tensor list
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empty_tensor_list = []
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task = dist.stream.alltoall(
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empty_tensor_list,
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[t1, t2],
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sync_op=self._sync_op,
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use_calc_stream=self._use_calc_stream,
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)
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if not self._sync_op:
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task.wait()
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result_tensor_list = np.vstack(empty_tensor_list)
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if rank == 0:
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np.testing.assert_allclose(
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result_tensor_list, result1, rtol=1e-05, atol=1e-05
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)
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else:
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np.testing.assert_allclose(
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result_tensor_list, result2, rtol=1e-05, atol=1e-05
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)
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# case 2: pass a pre-sized tensor list
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full_tensor_list = [paddle.empty_like(t1) for _ in test_data_list]
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task = dist.stream.alltoall(
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full_tensor_list,
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[t1, t2],
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sync_op=self._sync_op,
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use_calc_stream=self._use_calc_stream,
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)
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if not self._sync_op:
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task.wait()
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result_tensor_list = np.vstack(full_tensor_list)
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if rank == 0:
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np.testing.assert_allclose(
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result_tensor_list, result1, rtol=1e-05, atol=1e-05
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)
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else:
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np.testing.assert_allclose(
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result_tensor_list, result2, rtol=1e-05, atol=1e-05
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)
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# case 3: pass a pre-sized tensor
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out_tensor = paddle.empty_like(tensor)
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task = dist.stream.alltoall(
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out_tensor,
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tensor,
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sync_op=self._sync_op,
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use_calc_stream=self._use_calc_stream,
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)
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if not self._sync_op:
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task.wait()
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if rank == 0:
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np.testing.assert_allclose(
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out_tensor, result1, rtol=1e-05, atol=1e-05
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)
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else:
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np.testing.assert_allclose(
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out_tensor, result2, rtol=1e-05, atol=1e-05
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)
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if __name__ == "__main__":
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StreamAllToAllTestCase().run_test_case()
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