wehub-resource-sync
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
# Copyright (c) 2021 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 sys
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import numpy as np
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sys.path.append("..")
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import paddle
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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runtime_main,
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)
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np.random.seed(2021)
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paddle.seed(1024)
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batch_size = 4
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batch_num = 1000
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.net_a = paddle.nn.Sequential(
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paddle.nn.Linear(10, 20),
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paddle.nn.Linear(20, 20),
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paddle.nn.Linear(20, 5),
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)
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self.net_b = paddle.nn.Sequential(
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paddle.nn.Linear(10, 20),
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paddle.nn.Linear(20, 20),
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paddle.nn.Linear(20, 5),
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)
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self.step = 0
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def forward(self, x):
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return paddle.to_tensor([0.0], dtype='float32')
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def fake_sample_reader():
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def __reader__():
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for i in range(batch_num):
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x_data = np.random.random_sample((10,)).astype('float32')
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yield x_data
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return __reader__
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class TestSimpleNet(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = SimpleNet()
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train_reader = paddle.batch(
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fake_sample_reader(), batch_size=batch_size, drop_last=True
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=model.parameters()
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)
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return model, train_reader, optimizer
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def run_one_loop(self, model, optimizer, batch):
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x_data = np.array(list(batch))
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x_data = x_data.reshape((-1, 10))
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x = paddle.to_tensor(x_data)
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out = model(x)
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loss = out.sum() / len(batch)
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return loss
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if __name__ == "__main__":
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runtime_main(TestSimpleNet)
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