wehub-resource-sync
147 lines
4.3 KiB
Python
147 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 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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class TestBase(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_training()
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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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@property
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def fp16_enabled(self):
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return False
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def set_training(self):
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self.is_training = True
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self.epoch = 100
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def set_data_feed(self):
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data = np.random.uniform(size=[1, 3, 10, 10]).astype('float32')
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self.feed_fp32 = {"image": data.astype(np.float32)}
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self.feed_fp16 = {"image": data.astype(np.float16)}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed_fp32.values()]
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self.feed_list = list(self.feed_fp32.keys())
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self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
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def set_attrs(self):
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self.attrs = {
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"optimizer": 'lamb',
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"weight_decay": 0.0,
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"scaled_optimizer_state": True,
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}
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@IPUOpTest.static_graph
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def build_model(self):
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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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conv1 = paddle.nn.Conv2D(
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in_channels=3, out_channels=3, kernel_size=3, bias_attr=False
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)(image)
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loss = paddle.mean(conv1)
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weight_decay = self.attrs['weight_decay']
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opt = paddle.optimizer.Adam(
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learning_rate=1e-1, weight_decay=weight_decay
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)
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if self.attrs['optimizer'] == 'lamb':
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opt = paddle.optimizer.Lamb(
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learning_rate=1e-1, lamb_weight_decay=weight_decay
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)
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opt.minimize(loss)
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self.feed_list = [image.name]
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self.fetch_list = [loss]
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def run_model(self, exec_mode):
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ipu_strategy = paddle.static.IpuStrategy()
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ipu_strategy.set_graph_config(is_training=self.is_training)
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if self.is_ipu_mode(exec_mode):
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if "use_no_bias_optimizer" in self.attrs.keys():
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ipu_strategy.set_options(
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{
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"use_no_bias_optimizer": self.attrs[
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"use_no_bias_optimizer"
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]
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}
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)
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if "scaled_optimizer_state" in self.attrs.keys():
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ipu_strategy.set_options(
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{
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"scaled_optimizer_state": self.attrs[
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"scaled_optimizer_state"
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]
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}
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)
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self.run_op_test(exec_mode, ipu_strategy=ipu_strategy)
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def test(self):
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for m in IPUOpTest.ExecutionMode:
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if not self.skip_mode(m):
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self.build_model()
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self.run_model(m)
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self.check()
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class TestScaledAdam(TestBase):
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def set_attrs(self):
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self.attrs = {
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"optimizer": 'adam',
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"weight_decay": 0.0,
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"scaled_optimizer_state": True,
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}
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def set_atol(self):
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super().set_atol()
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self.atol = 1e-5
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self.rtol = 1e-5
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@unittest.skip('cpu do not support AdamNoBias')
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class TestScaledAdamNoBias(TestBase):
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def set_attrs(self):
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self.attrs = {
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"optimizer": 'adam',
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"weight_decay": 0.0,
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"use_no_bias_optimizer": True,
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"scaled_optimizer_state": True,
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}
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@unittest.skip('cpu do not support LambNoBias')
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class TestScaledLambNoBias(TestBase):
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def set_attrs(self):
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self.attrs = {
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"optimizer": 'lamb',
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"weight_decay": 0.0,
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"use_no_bias_optimizer": True,
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"scaled_optimizer_state": True,
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}
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if __name__ == "__main__":
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unittest.main()
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