wehub-resource-sync
124 lines
4.4 KiB
Python
124 lines
4.4 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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import paddle.nn.functional as F
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from paddle import _legacy_C_ops, base
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class TestVariable(unittest.TestCase):
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def setUp(self):
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self.shape = [512, 768]
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self.dtype = np.float32
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self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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def test_elementwise_add(self):
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with base.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = paddle.to_tensor(a)
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y = paddle.to_tensor(b)
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x.stop_gradient = False
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res1 = paddle.add(x, y)
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res2 = _legacy_C_ops.elementwise_add(x, y)
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np.testing.assert_array_equal(res1.numpy(), res2.numpy())
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def test_elementwise_mul(self):
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with base.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = paddle.to_tensor(a)
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y = paddle.to_tensor(b)
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res1 = paddle.multiply(x, y)
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res2 = _legacy_C_ops.elementwise_mul(x, y)
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np.testing.assert_array_equal(res1.numpy(), res2.numpy())
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def test_relu(self):
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with base.dygraph.guard():
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a = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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x = paddle.to_tensor(a)
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res1 = F.relu(x)
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res2 = _legacy_C_ops.relu(x)
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np.testing.assert_array_equal(res1.numpy(), res2.numpy())
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def test_trace_backward(self):
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with base.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = paddle.to_tensor(a)
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y = paddle.to_tensor(b)
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x.stop_gradient = False
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y.stop_gradient = False
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x.retain_grads()
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y.retain_grads()
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loss = _legacy_C_ops.elementwise_mul(x, y)
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loss.retain_grads()
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loss.backward()
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x_grad = x.gradient()
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y_grad = y.gradient()
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np.testing.assert_array_equal(x_grad, loss.gradient() * b)
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np.testing.assert_array_equal(y_grad, loss.gradient() * a)
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def test_retain_grad(self):
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"""Test retain_grad() for both leaf nodes and intermediate nodes (new API)"""
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with base.dygraph.guard():
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# Prepare input data
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = paddle.to_tensor(a)
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y = paddle.to_tensor(b)
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x.stop_gradient = False
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y.stop_gradient = False
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# ===== Test leaf nodes (x, y) =====
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# Create scalar loss for leaf nodes (must be scalar)
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loss_leaf = paddle.sum(_legacy_C_ops.elementwise_mul(x, y))
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x.retain_grad()
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y.retain_grad()
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loss_leaf.backward()
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# Verify leaf node gradients (x.grad = y, y.grad = x)
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np.testing.assert_array_equal(x.gradient(), b)
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np.testing.assert_array_equal(y.gradient(), a)
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# ===== Test intermediate node (z = x * y) =====
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# Create intermediate node z
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z = _legacy_C_ops.elementwise_mul(x, y)
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z.retain_grad() # Retain gradient for intermediate node
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# Create scalar loss for intermediate node
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loss_mid = paddle.sum(z)
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loss_mid.backward()
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# Verify intermediate node gradient (d(loss_mid)/dz = 1)
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expected_z_grad = np.ones_like(a)
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np.testing.assert_array_equal(z.gradient(), expected_z_grad)
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if __name__ == '__main__':
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unittest.main()
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