wehub-resource-sync
73 lines
2.0 KiB
Python
73 lines
2.0 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 numpy as np
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import paddle
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import paddle.distributed as dist
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paddle.device.set_device("cpu")
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def add(a, b):
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a = paddle.to_tensor(a, dtype="float32")
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b = paddle.to_tensor(b, dtype="float32")
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res = paddle.add(a, b).numpy()
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return res
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def rpc_add(to, args):
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res = dist.rpc.rpc_sync(to, add, args=args)
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return res
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def worker_name(rank):
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return f"worker{rank}"
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def main():
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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dist.rpc.init_rpc(worker_name(rank))
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if rank == 0:
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mmap_data1 = np.memmap(
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"rpc_launch_data1.npy",
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dtype=np.float32,
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mode="r",
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shape=(10 * world_size, 100),
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)
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mmap_data2 = np.memmap(
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"rpc_launch_data2.npy",
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dtype=np.float32,
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mode="r",
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shape=(10 * world_size, 100),
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)
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mmap_out = np.memmap(
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"rpc_launch_result.npy",
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dtype=np.float32,
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mode="w+",
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shape=(10 * world_size, 100),
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)
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for i in range(world_size):
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a = mmap_data1[i * 10 : (i + 1) * 10, :]
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b = mmap_data2[i * 10 : (i + 1) * 10, :]
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args = (a, b)
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out = rpc_add(worker_name(i), args)
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mmap_out[i * 10 : (i + 1) * 10, :] = out[:]
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dist.rpc.shutdown()
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if __name__ == "__main__":
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main()
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