wehub-resource-sync
134 lines
4.3 KiB
Python
134 lines
4.3 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 tempfile
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import unittest
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import numpy as np
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from op_test_ipu import IPUOpTest
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import paddle
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import paddle.static
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@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
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class TestBase(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_data_feed()
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self.set_feed_attr()
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self.set_attrs()
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self.temp_dir = tempfile.TemporaryDirectory()
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self.model_path = os.path.join(self.temp_dir.name, "weight_decay")
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def tearDown(self):
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super().tearDown()
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self.temp_dir.cleanup()
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def set_atol(self):
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self.atol = 1e-6
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def set_data_feed(self):
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self.feed = {
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"image": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
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}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed.values()]
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self.feed_list = list(self.feed.keys())
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self.feed_dtype = [x.dtype for x in self.feed.values()]
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def set_attrs(self):
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self.attrs = {
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"weight_decay": 4.0,
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"loss_scaling": 1.0,
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}
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def _test_optimizer(self, run_ipu=True):
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def exclude_fn(param):
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return param.name.endswith('.w_0')
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scope = paddle.static.Scope()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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paddle.seed(self.SEED)
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np.random.seed(self.SEED)
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with paddle.static.scope_guard(scope):
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with paddle.static.program_guard(main_prog, startup_prog):
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image = paddle.static.data(
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name='image', shape=[1, 3, 10, 10], dtype='float32'
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)
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bias = paddle.create_parameter(
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shape=[1, 3, 10, 10], is_bias=True, dtype='float32'
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)
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add1 = image + bias
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conv1 = paddle.nn.Conv2D(
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in_channels=add1.shape[1],
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out_channels=3,
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kernel_size=3,
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bias_attr=False,
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)(add1)
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loss = paddle.mean(conv1)
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opt = paddle.optimizer.Lamb(
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learning_rate=1e-3,
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lamb_weight_decay=self.attrs['weight_decay'],
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exclude_from_weight_decay_fn=exclude_fn,
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)
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opt.minimize(loss)
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if run_ipu:
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place = paddle.IPUPlace()
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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paddle.static.save(main_prog, self.model_path)
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if run_ipu:
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feed_list = [image.name]
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fetch_list = [loss.name]
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ipu_strategy = paddle.static.IpuStrategy()
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ipu_strategy.set_graph_config(is_training=True)
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ipu_strategy.set_options(
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{'loss_scaling': self.attrs["loss_scaling"]}
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)
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program = paddle.static.IpuCompiledProgram(
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main_prog, ipu_strategy=ipu_strategy
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).compile(feed_list, fetch_list)
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else:
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program = main_prog
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result = []
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for epoch in range(100):
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loss_res = exe.run(program, feed=self.feed, fetch_list=[loss])
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result.append(loss_res)
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return np.array(result)
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def test(self):
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# cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
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ipu_loss = self._test_optimizer(True).flatten()
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cpu_loss = self._test_optimizer(False).flatten()
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np.testing.assert_allclose(
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ipu_loss, cpu_loss, rtol=1e-05, atol=self.atol
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)
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if __name__ == "__main__":
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unittest.main()
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