wehub-resource-sync
377 lines
13 KiB
Python
377 lines
13 KiB
Python
# Copyright (c) 2025 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 scipy.special import log_softmax as scipy_log_softmax
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import paddle
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import paddle.nn.functional as F
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class TestLogSoftmaxAPI(unittest.TestCase):
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"""Test paddle.nn.functional.log_softmax (API 1/5)."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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# 1. Paddle positional arguments
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out1 = F.log_softmax(x, -1)
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# 2. Paddle keyword arguments
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out2 = F.log_softmax(x=x, axis=-1, dtype=None, name=None)
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# 3. PyTorch positional arguments
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out3 = F.log_softmax(x, -1, None)
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# 4. PyTorch keyword arguments (alias)
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out4 = F.log_softmax(input=x, dim=-1)
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# 5. Mixed arguments (positional + keyword)
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out5 = F.log_softmax(x, dim=-1)
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# 6. out parameter
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out6 = paddle.empty_like(x)
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F.log_softmax(x, -1, out=out6)
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# 7. Tensor method - positional args
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out7 = x.log_softmax(-1)
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# 8. Tensor method - keyword args
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out8 = x.log_softmax(dim=-1)
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# Verify all outputs match reference
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for out in [out1, out2, out3, out4, out5, out6, out7, out8]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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def test_static_Compatibility(self):
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# 9. Dynamic and static graph modes
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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# Paddle positional args
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out1 = F.log_softmax(x, -1)
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# Paddle keyword args
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out2 = F.log_softmax(x=x, axis=-1)
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# PyTorch keyword args (alias)
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out3 = F.log_softmax(input=x, dim=-1)
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exe = paddle.base.Executor()
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fetches = exe.run(
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main,
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feed={"x": self.np_x},
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fetch_list=[out1, out2, out3],
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)
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for out in fetches:
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np.testing.assert_allclose(
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out, self.ref_out, rtol=1e-5, atol=1e-6
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)
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paddle.disable_static()
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class TestPaddleLogSoftmaxAPI(unittest.TestCase):
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"""Test paddle.log_softmax (API 2/5)."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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# 1. Positional arguments
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out1 = paddle.log_softmax(x, -1)
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# 2. Keyword arguments
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out2 = paddle.log_softmax(input=x, dim=-1, dtype=None)
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# 3. Mixed arguments
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out3 = paddle.log_softmax(x, dim=-1)
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# 4. out parameter
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out4 = paddle.empty_like(x)
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paddle.log_softmax(x, -1, out=out4)
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for out in [out1, out2, out3, out4]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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class TestTensorLogSoftmaxAPI(unittest.TestCase):
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"""Test paddle.Tensor.log_softmax (API 3/5)."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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# 1. Positional arguments
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out1 = x.log_softmax(-1)
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# 2. Keyword arguments
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out2 = x.log_softmax(dim=-1)
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# 3. with dtype
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out3 = x.log_softmax(-1, dtype='float64')
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self.assertEqual(out3.dtype, paddle.float64)
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for out in [out1, out2]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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class TestSpecialLogSoftmaxAPI(unittest.TestCase):
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"""Test paddle.special.log_softmax (API 4/5)."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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# 1. Positional arguments
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out1 = paddle.special.log_softmax(x, -1)
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# 2. Keyword arguments
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out2 = paddle.special.log_softmax(input=x, dim=-1)
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# 3. out parameter
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out3 = paddle.empty_like(x)
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paddle.special.log_softmax(x, -1, out=out3)
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for out in [out1, out2, out3]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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class TestCompatLogSoftmaxAPI(unittest.TestCase):
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"""Test paddle.compat.nn.functional.log_softmax (API 5/5)."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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compat_fn = paddle.compat.nn.functional.log_softmax
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# 1. Positional arguments
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out1 = compat_fn(x, -1)
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# 2. Keyword arguments
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out2 = compat_fn(input=x, dim=-1, dtype=None)
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# 3. Mixed arguments (positional + keyword)
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out3 = compat_fn(x, dim=-1)
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# 4. out parameter
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out4 = paddle.empty_like(x)
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compat_fn(x, -1, out=out4)
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for out in [out1, out2, out3, out4]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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class TestLogSoftmaxAllAliasesConsistent(unittest.TestCase):
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"""Test that all five log_softmax entry points produce the same results."""
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def setUp(self):
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np.random.seed(2025)
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self.shape = [2, 3, 4]
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self.dtype = 'float32'
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self.np_x = np.random.randn(*self.shape).astype(self.dtype)
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self.ref_out = scipy_log_softmax(self.np_x, axis=-1).astype(self.dtype)
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def test_all_aliases_consistent(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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out1 = F.log_softmax(x, -1)
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out2 = paddle.log_softmax(x, -1)
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out3 = x.log_softmax(-1)
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out4 = paddle.special.log_softmax(x, -1)
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out5 = paddle.compat.nn.functional.log_softmax(x, dim=-1)
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for out in [out1, out2, out3, out4, out5]:
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np.testing.assert_allclose(
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self.ref_out, out.numpy(), rtol=1e-5, atol=1e-6
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)
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class TestCompatLogSoftmaxDimNoneDefault(unittest.TestCase):
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"""Test PyTorch-compatible dim=None default behavior."""
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def setUp(self):
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paddle.disable_static()
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def test_0d_defaults_to_dim0(self):
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x = paddle.to_tensor(1.0)
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out = paddle.compat.nn.functional.log_softmax(x)
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expected = paddle.compat.nn.functional.log_softmax(x, dim=0)
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np.testing.assert_allclose(out.numpy(), expected.numpy())
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def test_1d_defaults_to_dim0(self):
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x = paddle.randn([4], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(x)
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expected = paddle.compat.nn.functional.log_softmax(x, dim=0)
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np.testing.assert_allclose(out.numpy(), expected.numpy())
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def test_2d_defaults_to_dim1(self):
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x = paddle.randn([3, 4], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(x)
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expected = paddle.compat.nn.functional.log_softmax(x, dim=1)
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np.testing.assert_allclose(out.numpy(), expected.numpy())
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def test_3d_defaults_to_dim0(self):
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x = paddle.randn([2, 3, 4], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(x)
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expected = paddle.compat.nn.functional.log_softmax(x, dim=0)
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np.testing.assert_allclose(out.numpy(), expected.numpy())
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def test_4d_defaults_to_dim1(self):
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x = paddle.randn([2, 3, 4, 5], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(x)
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expected = paddle.compat.nn.functional.log_softmax(x, dim=1)
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np.testing.assert_allclose(out.numpy(), expected.numpy())
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class TestCompatLogSoftmaxDtype(unittest.TestCase):
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"""Test dtype casting behavior."""
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def setUp(self):
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paddle.disable_static()
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def test_float32_to_float64(self):
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x = paddle.randn([2, 3, 4], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(
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x, dim=-1, dtype='float64'
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)
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self.assertEqual(out.dtype, paddle.float64)
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x64 = x.cast('float64')
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expected = F.log_softmax(x64, axis=-1)
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np.testing.assert_allclose(
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out.numpy(), expected.numpy(), rtol=1e-10, atol=1e-10
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)
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def test_float64_to_float32(self):
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x = paddle.randn([2, 3], dtype=paddle.float64)
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out = paddle.compat.nn.functional.log_softmax(x, dim=1, dtype='float32')
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self.assertEqual(out.dtype, paddle.float32)
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def test_dtype_none_preserves_input_dtype(self):
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for dtype in [paddle.float32, paddle.float64]:
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x = paddle.randn([3, 4], dtype=dtype)
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out = paddle.compat.nn.functional.log_softmax(x, dim=-1)
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self.assertEqual(out.dtype, dtype)
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def test_dtype_as_paddle_dtype(self):
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x = paddle.randn([2, 3], dtype=paddle.float32)
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out = paddle.compat.nn.functional.log_softmax(
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x, dim=1, dtype=paddle.float64
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)
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self.assertEqual(out.dtype, paddle.float64)
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class TestCompatLogSoftmaxStacklevel(unittest.TestCase):
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"""Test that _stacklevel is silently ignored (torch compat)."""
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def setUp(self):
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paddle.disable_static()
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def test_stacklevel_ignored(self):
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x = paddle.randn([3, 4], dtype=paddle.float32)
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out1 = paddle.compat.nn.functional.log_softmax(x, dim=-1)
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out2 = paddle.compat.nn.functional.log_softmax(x, dim=-1, _stacklevel=5)
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np.testing.assert_allclose(out1.numpy(), out2.numpy())
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class TestCompatLogSoftmaxErrorHandling(unittest.TestCase):
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"""Test that paddle-style keyword arguments are rejected by compat API."""
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def setUp(self):
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paddle.disable_static()
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def test_rejects_x_keyword(self):
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x = paddle.randn([3, 4])
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msg = (
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"paddle.compat.nn.functional.log_softmax() received unexpected keyword argument 'x'. "
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"\nDid you mean to use paddle.nn.functional.log_softmax() instead?"
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)
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with self.assertRaises(TypeError) as cm:
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paddle.compat.nn.functional.log_softmax(x=x, dim=-1)
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self.assertEqual(str(cm.exception), msg)
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def test_rejects_axis_keyword(self):
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x = paddle.randn([3, 4])
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msg = (
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"paddle.compat.nn.functional.log_softmax() received unexpected keyword argument 'axis'. "
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"\nDid you mean to use paddle.nn.functional.log_softmax() instead?"
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)
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with self.assertRaises(TypeError) as cm:
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paddle.compat.nn.functional.log_softmax(x, axis=-1)
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self.assertEqual(str(cm.exception), msg)
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def test_rejects_name_keyword(self):
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x = paddle.randn([3, 4])
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msg = (
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"paddle.compat.nn.functional.log_softmax() received unexpected keyword argument 'name'. "
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"\nDid you mean to use paddle.nn.functional.log_softmax() instead?"
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)
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with self.assertRaises(TypeError) as cm:
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paddle.compat.nn.functional.log_softmax(x, dim=-1, name='test')
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self.assertEqual(str(cm.exception), msg)
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if __name__ == "__main__":
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unittest.main()
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