wehub-resource-sync
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
# Copyright (c) 2026 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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"""
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CPU unit test for L1NormGradKernel
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(paddle/phi/kernels/cpu/l1_norm_grad_kernel.cc).
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Kernel formula: dX = dout * sign(X)
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sign(x) = +1 if x > 0, -1 if x < 0, 0 if x == 0
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"""
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import unittest
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import numpy as np
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import paddle
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class TestL1NormGradKernel(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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paddle.set_device('cpu')
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def test_basic(self):
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"""dX = dout * sign(X); loss = l1_norm(x), dout = 1."""
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x_np = np.array([-3.0, 1.0, -2.0, 4.0], dtype='float32')
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x = paddle.to_tensor(x_np, stop_gradient=False)
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loss = paddle.norm(x, p=1) # sum(abs(x))
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loss.backward()
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# expected: sign(x) * 1.0
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expected = np.sign(x_np).astype('float32')
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-6)
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def test_2d_input(self):
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"""2-D input flattened inside the kernel; same formula applies."""
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x_np = np.array([[1.0, -2.0], [-3.0, 4.0]], dtype='float32')
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x = paddle.to_tensor(x_np, stop_gradient=False)
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loss = paddle.norm(x, p=1)
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loss.backward()
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expected = np.sign(x_np).astype('float32')
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-6)
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def test_scaled_dout(self):
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"""Wrap l1_norm in a chain to get dout != 1: loss = 3 * l1_norm(x)."""
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x_np = np.array([-1.0, 2.0, -3.0], dtype='float32')
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x = paddle.to_tensor(x_np, stop_gradient=False)
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loss = 3.0 * paddle.norm(x, p=1)
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loss.backward()
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expected = 3.0 * np.sign(x_np).astype('float32')
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-6)
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if __name__ == '__main__':
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unittest.main()
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