wehub-resource-sync
93 lines
3.0 KiB
Python
93 lines
3.0 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 StreamScatterTestCase:
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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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src_rank = 1
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src_data = test_data_list[src_rank]
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result1 = src_data[0 : src_data.shape[0] // 2]
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result2 = src_data[src_data.shape[0] // 2 :]
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rank = dist.get_rank()
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# case 1: pass a pre-sized tensor list
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tensor = paddle.to_tensor(test_data_list[rank])
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t1, t2 = paddle.split(tensor, 2, axis=0)
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task = dist.stream.scatter(
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t1,
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[t1, t2],
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src=src_rank,
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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 == src_rank:
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np.testing.assert_allclose(t1, result2, rtol=1e-05, atol=1e-05)
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else:
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np.testing.assert_allclose(t1, result1, rtol=1e-05, atol=1e-05)
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# case 2: pass a pre-sized tensor
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tensor = paddle.to_tensor(src_data)
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t1 = paddle.empty_like(t1)
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task = dist.stream.scatter(
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t1,
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tensor,
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src=src_rank,
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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 == src_rank:
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np.testing.assert_allclose(t1, result2, rtol=1e-05, atol=1e-05)
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else:
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np.testing.assert_allclose(t1, result1, rtol=1e-05, atol=1e-05)
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if __name__ == "__main__":
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StreamScatterTestCase().run_test_case()
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