wehub-resource-sync
477 lines
17 KiB
Python
477 lines
17 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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import paddle
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class TestTensorRequiresGrad(unittest.TestCase):
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def setUp(self):
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"""Set up test fixtures before each test method."""
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paddle.disable_static()
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np.random.seed(1919)
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def tearDown(self):
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"""Clean up after each test method."""
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paddle.disable_static()
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def test_basic_requires_grad_property(self):
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"""Test basic requires_grad property functionality"""
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# Test default behavior - new tensors have stop_gradient=True by default
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x = paddle.randn([2, 3])
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test setting requires_grad to True
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x.requires_grad = True
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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# Test setting requires_grad to False
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x.requires_grad = False
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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def test_requires_grad_consistency_with_stop_gradient(self):
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"""Test that requires_grad is always the opposite of stop_gradient"""
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x = paddle.randn([3, 4])
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# Test multiple state changes
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states = [True, False, True, False]
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for requires_grad_state in states:
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x.requires_grad = requires_grad_state
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self.assertEqual(x.requires_grad, requires_grad_state)
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self.assertEqual(x.stop_gradient, not requires_grad_state)
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# Also test setting stop_gradient directly
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x.stop_gradient = requires_grad_state
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self.assertEqual(x.requires_grad, not requires_grad_state)
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self.assertEqual(x.stop_gradient, requires_grad_state)
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def test_requires_grad_type_checking(self):
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"""Test type checking for requires_grad setter"""
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x = paddle.randn([2, 2])
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# Valid boolean values should work
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x.requires_grad = True
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x.requires_grad = False
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# Invalid types should raise TypeError
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invalid_values = ["true", 1, 0, None, [], {}]
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for invalid_value in invalid_values:
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with self.assertRaises(TypeError) as cm:
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x.requires_grad = invalid_value
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self.assertIn("requires_grad must be bool", str(cm.exception))
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def test_requires_grad_with_parameter(self):
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"""Test requires_grad behavior with Parameter tensors"""
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# Create a parameter - Parameters have stop_gradient=False by default (trainable)
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param = paddle.create_parameter([3, 4], dtype='float32')
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self.assertTrue(
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param.requires_grad
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) # Parameters require grad by default
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self.assertFalse(
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param.stop_gradient
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) # Parameters are trainable by default
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# Test changing requires_grad on parameter
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param.requires_grad = False
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self.assertFalse(param.requires_grad)
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self.assertTrue(param.stop_gradient)
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def test_requires_grad_in_gradient_computation(self):
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"""Test requires_grad behavior in actual gradient computation"""
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x = paddle.randn([2, 3])
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y = paddle.randn([2, 3])
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# Set both tensors to require grad
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x.requires_grad = True
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y.requires_grad = True
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z = x * y + x.sum()
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z.backward()
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self.assertIsNotNone(x.grad)
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self.assertIsNotNone(y.grad)
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# Clear gradients and test with requires_grad=False
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x.grad._clear_data()
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y.grad._clear_data()
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x.requires_grad = False
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y.requires_grad = True
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z = x * y + x.sum()
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z.backward()
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self.assertIsNone(x.grad) # x doesn't require grad
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self.assertIsNotNone(y.grad) # y requires grad
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def test_requires_grad_with_different_tensor_types(self):
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"""Test requires_grad with different tensor creation methods"""
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# Test with different tensor creation functions
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tensor_creators = [
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lambda: paddle.randn([2, 3]),
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lambda: paddle.zeros([2, 3]),
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lambda: paddle.ones([2, 3]),
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lambda: paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32'),
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lambda: paddle.arange(6, dtype='float32').reshape([2, 3]),
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]
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for creator in tensor_creators:
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x = creator()
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# All newly created tensors should have requires_grad=False by default
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test modification
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x.requires_grad = True
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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def test_requires_grad_with_tensor_operations(self):
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"""Test requires_grad preservation through tensor operations"""
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x = paddle.randn([3, 3])
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y = paddle.randn([3, 3])
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x.requires_grad = True
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y.requires_grad = False
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# Operations should preserve requires_grad appropriately
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z1 = x + y # Should require grad (x requires grad)
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z2 = x * 2.0 # Should require grad (x requires grad)
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z3 = y.sin() # Should not require grad (y doesn't require grad)
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self.assertTrue(z1.requires_grad)
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self.assertTrue(z2.requires_grad)
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self.assertFalse(z3.requires_grad)
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def test_requires_grad_with_detach(self):
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"""Test requires_grad behavior with detach operation"""
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x = paddle.randn([2, 3])
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x.requires_grad = True
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y = x.detach()
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# Detached tensor should not require grad
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self.assertTrue(x.requires_grad)
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self.assertFalse(y.requires_grad)
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self.assertTrue(y.stop_gradient)
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def test_requires_grad_static_mode(self):
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"""Test requires_grad behavior in static mode"""
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paddle.enable_static()
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try:
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name='x', shape=[2, 3], dtype='float32')
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# In static mode, variables also have stop_gradient=True by default
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test setting requires_grad in static mode
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x.requires_grad = True
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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finally:
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paddle.disable_static()
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def test_requires_grad_edge_cases(self):
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"""Test edge cases for requires_grad"""
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# Test with scalar tensor
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scalar = paddle.to_tensor(3.14)
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self.assertFalse(scalar.requires_grad) # False
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scalar.requires_grad = True
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self.assertTrue(scalar.requires_grad)
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# Test with empty tensor
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empty = paddle.empty([0, 3])
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self.assertFalse(empty.requires_grad) # False
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empty.requires_grad = True
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self.assertTrue(empty.requires_grad)
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# Test with different dtypes
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dtypes = [paddle.float32, paddle.float64, paddle.int32, paddle.int64]
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for dtype in dtypes:
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x = paddle.ones([2, 2], dtype=dtype)
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# All tensors should have requires_grad=False by default
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self.assertFalse(x.requires_grad)
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# Float tensors should support requires_grad
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if dtype in [paddle.float32, paddle.float64]:
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x.requires_grad = True
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self.assertTrue(x.requires_grad)
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class TestTensorRequiresGrad_(unittest.TestCase):
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def setUp(self):
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"""Set up test fixtures before each test method."""
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paddle.disable_static()
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np.random.seed(1919)
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def tearDown(self):
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"""Clean up after each test method."""
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paddle.disable_static()
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def test_basic_requires_grad_property(self):
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"""Test basic requires_grad property functionality"""
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# Test default behavior - new tensors have stop_gradient=True by default
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x = paddle.randn([2, 3])
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test setting requires_grad to True
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x.requires_grad_(True)
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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# Test setting requires_grad to False
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x.requires_grad_(False)
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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def test_requires_grad_consistency_with_stop_gradient(self):
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"""Test that requires_grad is always the opposite of stop_gradient"""
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x = paddle.randn([3, 4])
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# Test multiple state changes
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states = [True, False, True, False]
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for requires_grad_state in states:
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x.requires_grad_(requires_grad_state)
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self.assertEqual(x.requires_grad, requires_grad_state)
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self.assertEqual(x.stop_gradient, not requires_grad_state)
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# Also test setting stop_gradient directly
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x.stop_gradient = requires_grad_state
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self.assertEqual(x.requires_grad, not requires_grad_state)
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self.assertEqual(x.stop_gradient, requires_grad_state)
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def test_requires_grad_type_checking(self):
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"""Test type checking for requires_grad setter"""
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x = paddle.randn([2, 2])
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# Valid boolean values should work
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x.requires_grad_(True)
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x.requires_grad_(False)
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# Invalid types should raise TypeError
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invalid_values = ["true", 1, 0, None, [], {}]
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for invalid_value in invalid_values:
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with self.assertRaises(TypeError) as cm:
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x.requires_grad_(invalid_value)
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self.assertIn("requires_grad must be bool", str(cm.exception))
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def test_requires_grad_with_parameter(self):
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"""Test requires_grad behavior with Parameter tensors"""
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# Create a parameter - Parameters have stop_gradient=False by default (trainable)
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param = paddle.create_parameter([3, 4], dtype='float32')
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self.assertTrue(
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param.requires_grad
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) # Parameters require grad by default
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self.assertFalse(
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param.stop_gradient
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) # Parameters are trainable by default
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# Test changing requires_grad on parameter
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param.requires_grad_(False)
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self.assertFalse(param.requires_grad)
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self.assertTrue(param.stop_gradient)
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def test_requires_grad_in_gradient_computation(self):
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"""Test requires_grad behavior in actual gradient computation"""
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x = paddle.randn([2, 3])
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y = paddle.randn([2, 3])
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# Set both tensors to require grad
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x.requires_grad_(True)
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y.requires_grad_(True)
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z = x * y + x.sum()
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z.backward()
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self.assertIsNotNone(x.grad)
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self.assertIsNotNone(y.grad)
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# Clear gradients and test with requires_grad=False
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x.grad._clear_data()
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y.grad._clear_data()
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x.requires_grad_(False)
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y.requires_grad_(True)
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z = x * y + x.sum()
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z.backward()
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self.assertIsNone(x.grad) # x doesn't require grad
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self.assertIsNotNone(y.grad) # y requires grad
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def test_requires_grad_with_different_tensor_types(self):
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"""Test requires_grad with different tensor creation methods"""
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# Test with different tensor creation functions
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tensor_creators = [
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lambda: paddle.randn([2, 3]),
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lambda: paddle.zeros([2, 3]),
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lambda: paddle.ones([2, 3]),
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lambda: paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32'),
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lambda: paddle.arange(6, dtype='float32').reshape([2, 3]),
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]
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for creator in tensor_creators:
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x = creator()
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# All newly created tensors should have requires_grad=False by default
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test modification
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x.requires_grad_(True)
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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def test_requires_grad_with_tensor_operations(self):
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"""Test requires_grad preservation through tensor operations"""
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x = paddle.randn([3, 3])
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y = paddle.randn([3, 3])
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x.requires_grad_(True)
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y.requires_grad_(False)
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# Operations should preserve requires_grad appropriately
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z1 = x + y # Should require grad (x requires grad)
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z2 = x * 2.0 # Should require grad (x requires grad)
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z3 = y.sin() # Should not require grad (y doesn't require grad)
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self.assertTrue(z1.requires_grad)
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self.assertTrue(z2.requires_grad)
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self.assertFalse(z3.requires_grad)
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def test_requires_grad_with_detach(self):
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"""Test requires_grad behavior with detach operation"""
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x = paddle.randn([2, 3])
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x.requires_grad_(True)
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y = x.detach()
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# Detached tensor should not require grad
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self.assertTrue(x.requires_grad)
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self.assertFalse(y.requires_grad)
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self.assertTrue(y.stop_gradient)
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def test_requires_grad_old_static_mode(self):
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"""Test requires_grad behavior in static mode"""
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paddle.enable_static()
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with paddle.pir_utils.OldIrGuard():
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x = paddle.static.data(name='x', shape=[2, 3], dtype='float32')
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# In static mode, variables also have stop_gradient=True by default
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test setting requires_grad in static mode
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x.requires_grad_(True)
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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def test_requires_grad_static_mode(self):
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"""Test requires_grad behavior in static mode"""
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name='x', shape=[2, 3], dtype='float32')
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# In static mode, variables also have stop_gradient=True by default
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self.assertFalse(x.requires_grad)
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self.assertTrue(x.stop_gradient)
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# Test setting requires_grad in static mode
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x.requires_grad_(True)
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self.assertTrue(x.requires_grad)
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self.assertFalse(x.stop_gradient)
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def test_requires_grad_edge_cases(self):
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"""Test edge cases for requires_grad"""
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# Test with scalar tensor
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scalar = paddle.to_tensor(3.14)
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self.assertFalse(scalar.requires_grad) # False
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scalar.requires_grad_(True)
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self.assertTrue(scalar.requires_grad)
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# Test with empty tensor
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empty = paddle.empty([0, 3])
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self.assertFalse(empty.requires_grad) # False
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empty.requires_grad_(True)
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self.assertTrue(empty.requires_grad)
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# Test with different dtypes
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dtypes = [paddle.float32, paddle.float64, paddle.int32, paddle.int64]
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for dtype in dtypes:
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x = paddle.ones([2, 2], dtype=dtype)
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# All tensors should have requires_grad=False by default
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self.assertFalse(x.requires_grad)
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# Float tensors should support requires_grad
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if dtype in [paddle.float32, paddle.float64]:
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x.requires_grad_(True)
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self.assertTrue(x.requires_grad)
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class TestAPI(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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def assert_api(self, api_func, require_grad):
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = api_func()
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self.assertEqual(x.stop_gradient, require_grad)
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# test for setter
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x.requires_grad_(require_grad)
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self.assertEqual(x.stop_gradient, not require_grad)
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def test_full(self):
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api = lambda: paddle.full(shape=[2, 3], fill_value=1.0)
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self.assert_api(api, True)
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def test_data(self):
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api = lambda: paddle.static.data('x', [4, 4], dtype='float32')
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self.assert_api(api, True)
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# TODO(Aurelius84): Add more test cases after API is migrated.
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class TestParameters(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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def test_create_param(self):
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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w = paddle.create_parameter(shape=[784, 200], dtype='float32')
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self.assertEqual(w.stop_gradient, False)
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self.assertEqual(w.persistable, True)
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# test for setter
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w.requires_grad_(False)
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w.persistable = False
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self.assertEqual(w.stop_gradient, True)
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self.assertEqual(w.persistable, False)
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if __name__ == '__main__':
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unittest.main()
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