wehub-resource-sync
162 lines
4.9 KiB
Python
162 lines
4.9 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 unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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)
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import paddle
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# NOTE(SigureMo): In PIR, we convert dygraph EagerParamBase to Variable by
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# _jst.Ld instead of param_guard. So this unittest name maybe confusing.
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# But the test case is still useful.
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class NetWithParameterList(paddle.nn.Layer):
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def __init__(self, in_size, out_size):
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super().__init__()
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weight = self.create_parameter([in_size, out_size])
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bias = self.create_parameter([out_size], is_bias=True)
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self.params = paddle.nn.ParameterList([weight, bias])
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def forward(self, x):
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out = paddle.matmul(x, self.params[0])
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out = paddle.add(out, self.params[1])
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out = paddle.tanh(out)
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return out
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class NetWithParameterListIter(NetWithParameterList):
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def __init__(self, in_size, out_size):
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super().__init__(in_size, out_size)
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def forward(self, x):
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# NOTE: manually trigger `__iter__` logic.
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params = list(self.params.__iter__())
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out = paddle.matmul(x, params[0])
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out = paddle.add(out, params[1])
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out = paddle.tanh(out)
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return out
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class TestParameterList(Dy2StTestBase):
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def setUp(self):
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self.seed = 2021
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self.iter_num = 5
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def train(self, is_iter, to_static: bool):
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paddle.seed(self.seed)
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np.random.seed(self.seed)
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with enable_to_static_guard(to_static):
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if is_iter:
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net = paddle.jit.to_static(NetWithParameterList(10, 3))
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else:
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net = paddle.jit.to_static(NetWithParameterListIter(10, 3))
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sgd = paddle.optimizer.SGD(0.1, parameters=net.parameters())
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for batch_id in range(self.iter_num):
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x = paddle.rand([4, 10], dtype='float32')
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out = net(x)
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loss = paddle.mean(out)
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loss.backward()
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sgd.step()
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sgd.clear_grad()
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return loss
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def test_parameter_list(self):
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static_loss = self.train(False, to_static=True)
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dygraph_loss = self.train(False, to_static=False)
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np.testing.assert_allclose(dygraph_loss, static_loss, rtol=1e-05)
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class NetWithRawParamList(paddle.nn.Layer):
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def __init__(self, in_size, out_size):
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super().__init__()
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weight = self.add_parameter(
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'w', self.create_parameter([in_size, out_size])
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)
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bias = self.add_parameter(
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'b', self.create_parameter([out_size], is_bias=True)
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)
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self.params = [weight]
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self.bias_dict = {'b': bias}
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def forward(self, x):
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out = paddle.matmul(x, self.params[0])
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out = paddle.add(out, self.bias_dict['b'])
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out = paddle.tanh(out)
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return out
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class TestRawParameterList(Dy2StTestBase):
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def setUp(self):
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self.seed = 2021
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self.iter_num = 5
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def init_net(self):
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self.net = paddle.jit.to_static(NetWithRawParamList(10, 3))
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def train(self, to_static: bool):
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paddle.seed(self.seed)
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np.random.seed(self.seed)
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with enable_to_static_guard(to_static):
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self.init_net()
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sgd = paddle.optimizer.SGD(0.1, parameters=self.net.parameters())
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for batch_id in range(self.iter_num):
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x = paddle.rand([4, 10], dtype='float32')
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out = self.net(x)
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loss = paddle.mean(out)
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loss.backward()
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sgd.step()
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sgd.clear_grad()
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return loss
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def test_parameter_list(self):
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static_loss = self.train(to_static=True)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(dygraph_loss, static_loss, rtol=1e-05)
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class NetWithSubLayerParamList(paddle.nn.Layer):
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def __init__(self, sub_layer):
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super().__init__()
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self.sub_layer = sub_layer
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self.params = [sub_layer.weight]
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self.bias_dict = {'b': sub_layer.bias}
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def forward(self, x):
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out = paddle.matmul(x, self.params[0])
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out = paddle.add(out, self.bias_dict['b'])
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out = paddle.tanh(out)
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return out
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class TestSubLayerParameterList(TestRawParameterList):
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def init_net(self):
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fc = paddle.nn.Linear(10, 3)
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self.net = paddle.jit.to_static(NetWithSubLayerParamList(fc))
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if __name__ == '__main__':
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unittest.main()
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