wehub-resource-sync
161 lines
5.4 KiB
Python
161 lines
5.4 KiB
Python
# Copyright (c) 2018 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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import paddle
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from paddle import base
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class dy_to_st(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._param_attr = base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.1)
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)
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self.w1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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self.b1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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@paddle.jit.to_static(full_graph=True)
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def forward(self, x):
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self.x = x
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self.y = paddle.matmul(self.x, self.w1)
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self.z = paddle.add(self.y, self.b1)
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self.k = paddle.tanh(self.z)
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return self.k
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@paddle.jit.to_static(full_graph=True)
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def backward(self, x, k_grad):
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x = x
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y = paddle.matmul(x, self.w1)
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z = paddle.add(y, self.b1)
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k = paddle.tanh(z)
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z_grad = paddle._C_ops.tanh_grad(k, k_grad)
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y_grad, b1_grad = paddle._C_ops.add_grad(y, self.b1, z_grad, -1)
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x_grad, w1_grad = paddle._C_ops.matmul_grad(
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x, self.w1, y_grad, False, False
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)
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return x_grad, z_grad, y_grad, w1_grad, b1_grad
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class dygraph(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._param_attr = base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.1)
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)
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self.w1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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self.b1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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def forward(self, x):
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self.x = x
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self.y = paddle.matmul(self.x, self.w1)
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self.z = paddle.add(self.y, self.b1)
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self.k = paddle.tanh(self.z)
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return self.k
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def backward(self, k_grad):
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z_grad = paddle._C_ops.tanh_grad(self.k, k_grad)
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y_grad, b1_grad = paddle._C_ops.add_grad(self.y, self.b1, z_grad, -1)
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x_grad, w1_grad = paddle._C_ops.matmul_grad(
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self.x, self.w1, y_grad, False, False
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)
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return x_grad, z_grad, y_grad, w1_grad, b1_grad
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class dygraph_inplace(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._param_attr = base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.1)
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)
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self.w1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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self.b1 = self.create_parameter(
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attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False
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)
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def forward(self, x):
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self.x = x
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self.k = paddle.tanh(self.x)
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return self.k
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def backward(self, k_grad):
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z_grad = paddle._C_ops.tanh_grad_(self.k, k_grad)
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return z_grad
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class TestBaseLayer(unittest.TestCase):
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def test_dy_to_st(self):
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layer = dy_to_st()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
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out_grad = paddle.to_tensor([[1.0, 1.0], [1.0, 1.0]], dtype='float32')
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x.stop_gradient = False
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out = layer(x)
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with paddle.no_grad():
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x_grad, z_grad, y_grad, w1_grad, b1_grad = layer.backward(
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x, out_grad
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)
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out.backward(out_grad)
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x_grad_check = x.grad
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w1_grad_check = layer.w1.grad
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b1_grad_check = layer.b1.grad
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np.testing.assert_allclose(x_grad.numpy(), x_grad_check.numpy())
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np.testing.assert_allclose(w1_grad.numpy(), w1_grad_check.numpy())
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np.testing.assert_allclose(b1_grad.numpy(), b1_grad_check.numpy())
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def test_dygraph(self):
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layer = dygraph()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
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out_grad = paddle.to_tensor([[1.0, 1.0], [1.0, 1.0]], dtype='float32')
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x.stop_gradient = False
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out = layer(x)
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x_grad, z_grad, y_grad, w1_grad, b1_grad = layer.backward(out_grad)
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out.backward(out_grad)
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x_grad_check = x.grad
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w1_grad_check = layer.w1.grad
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b1_grad_check = layer.b1.grad
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np.testing.assert_allclose(x_grad.numpy(), x_grad_check.numpy())
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np.testing.assert_allclose(w1_grad.numpy(), w1_grad_check.numpy())
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np.testing.assert_allclose(b1_grad.numpy(), b1_grad_check.numpy())
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def test_dygraph_inplace(self):
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layer = dygraph_inplace()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
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out_grad = paddle.to_tensor([[1.0, 1.0], [1.0, 1.0]], dtype='float32')
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x.stop_gradient = False
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out = layer(x)
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x_grad = layer.backward(out_grad)
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np.testing.assert_allclose(out_grad.numpy(), x_grad.numpy())
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if __name__ == '__main__':
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unittest.main()
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