wehub-resource-sync
276 lines
10 KiB
Python
276 lines
10 KiB
Python
# Copyright (c) 2019 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.base import backward
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class BackwardNet:
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"""
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Abstract Base Class.
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All Net inherited this Class should implement two functions:
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build_model: build net to test the logic of backward
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init_data: fake input data to test all programs.
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"""
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def __init__(self):
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self.stop_gradient_grad_vars = set()
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self.no_grad_vars = set()
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self.params_names = set()
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self.op_path = []
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def build_model(self):
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"""
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Build net to test the logic of backward.
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:return: loss
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"""
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raise NotImplementedError
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def init_data(self):
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"""
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Fake input data to test all programs.
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:return: dict, {'var_name': var_data}
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"""
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raise NotImplementedError
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# TODO(Aurelius84): add conditional network test
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class ConditionalNet(BackwardNet):
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def __init__(self):
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super().__init__()
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class TestBackwardUninitializedVariable(unittest.TestCase):
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"""this case is found in yolov5 while to_static.
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gradient aggregation may cause sum a invalid variable.
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"""
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def test(self):
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paddle.enable_static()
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main_prg, startup_prg = paddle.static.Program(), paddle.static.Program()
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with paddle.static.program_guard(main_prg, startup_prg):
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gt = paddle.static.data(name='gt', shape=[4], dtype='float32')
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x = paddle.static.data(name='x', shape=[2], dtype='float32')
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gt.stop_gradient = True
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x.stop_gradient = False
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gt = gt.reshape([4, 1]).reshape([4])
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loss = (
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paddle.nn.functional.binary_cross_entropy(x, gt[:2])
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+ (gt[2:4] * x).sum()
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)
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exe = paddle.static.Executor()
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paddle.base.backward.gradients(loss, [])
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exe.run(startup_prg)
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# Optimizer
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out = exe.run(
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main_prg,
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feed={
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'gt': np.array([1.0, 1.0, 0.0, 0.0], dtype='float32'),
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'x': np.array([0.5, 0.5], dtype='float32'),
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},
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fetch_list=[loss],
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)
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print(out)
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class TestStripGradSuffix(unittest.TestCase):
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def test_strip_grad_suffix(self):
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cases = (
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('x@GRAD', 'x'),
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('x@GRAD@GRAD', 'x'),
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('x@GRAD@RENAME@1', 'x'),
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('x@GRAD_slice_0@GRAD', 'x@GRAD_slice_0'),
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('grad/grad/x@GRAD@RENAME@block0@1@GRAD', 'x'),
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)
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for input_, desired in cases:
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self.assertEqual(backward._strip_grad_suffix_(input_), desired)
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class TestBackwardParamAlias(unittest.TestCase):
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"""Test backward() with parameter alias: grad_tensor -> gradient"""
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def setUp(self):
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paddle.disable_static()
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def test_backward_with_grad_tensor_param(self):
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"""Test backward using grad_tensor parameter name."""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32', stop_gradient=False)
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y = x**2
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z = y.sum()
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grad_tensor = paddle.to_tensor(1.0, dtype='float32')
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z.backward(grad_tensor=grad_tensor)
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expected = [2.0, 4.0]
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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def test_backward_with_gradient_alias(self):
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"""Test backward using gradient alias parameter name."""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32', stop_gradient=False)
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y = x**2
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z = y.sum()
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grad_tensor = paddle.to_tensor(1.0, dtype='float32')
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z.backward(gradient=grad_tensor)
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expected = [2.0, 4.0]
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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def test_backward_alias_with_custom_grad(self):
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"""Test gradient alias with custom gradient values."""
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x = paddle.to_tensor([[1.0, 2.0]], dtype='float32', stop_gradient=False)
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y = x * 3
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loss = y.sum()
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custom_grad = paddle.to_tensor(2.0, dtype='float32')
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loss.backward(gradient=custom_grad)
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expected = [[6.0, 6.0]]
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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def test_backward_alias_with_retain_graph(self):
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"""Test gradient alias combined with retain_graph parameter."""
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x = paddle.to_tensor([2.0], dtype='float32', stop_gradient=False)
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y = x**2
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loss = y.sum()
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grad_tensor = paddle.to_tensor(1.0, dtype='float32')
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loss.backward(gradient=grad_tensor, retain_graph=True)
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expected = [4.0]
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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x.clear_grad()
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loss.backward(gradient=grad_tensor)
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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def test_backward_alias_with_create_graph(self):
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"""Test gradient alias combined with create_graph parameter."""
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x = paddle.to_tensor([1.0], dtype='float32', stop_gradient=False)
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y = x**2
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loss = y.sum()
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grad_tensor = paddle.to_tensor(1.0, dtype='float32')
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loss.backward(gradient=grad_tensor, create_graph=True)
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self.assertIsNotNone(x.grad)
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np.testing.assert_allclose(x.grad.numpy(), [2.0], rtol=1e-5)
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class TestBackwardCreateGraph(unittest.TestCase):
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"""Test backward with create_graph parameter for higher-order gradients."""
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def setUp(self):
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paddle.disable_static()
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def test_tensor_backward_with_create_graph(self):
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"""Test backward with create_graph=True for second-order gradients"""
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x = paddle.to_tensor(
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np.array([[1.0, 2.0], [3.0, 4.0]]), dtype='float32'
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)
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x.stop_gradient = False
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y = x * x
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loss = paddle.sum(y)
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# First backward with create_graph=True
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loss.backward(create_graph=True)
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# Verify first-order gradients
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self.assertIsNotNone(x.grad)
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first_grad = x.grad.numpy()
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np.testing.assert_allclose(
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first_grad, np.array([[2.0, 4.0], [6.0, 8.0]]), rtol=1e-7
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)
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# Compute second-order gradients
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grad_sum = paddle.sum(x.grad)
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grad_sum.backward()
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# Verify second-order gradients
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self.assertIsNotNone(x.grad)
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second_grad = x.grad.numpy()
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np.testing.assert_allclose(
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second_grad, np.array([[4.0, 6.0], [8.0, 10.0]]), rtol=1e-7
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)
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def test_backward_create_graph_second_order(self):
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"""Test computing second-order gradients using create_graph=True."""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32', stop_gradient=False)
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y = x**2
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loss = y.sum()
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# First backward with create_graph=True
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# x.grad should be [2.0, 4.0]
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paddle.autograd.backward(loss, create_graph=True)
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grad_sum = x.grad.sum()
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grad_sum.backward()
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# Check second-order gradients
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# sum up to [4.0, 6.0]
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self.assertIsNotNone(x.grad)
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np.testing.assert_allclose(x.grad.numpy(), [4.0, 6.0], rtol=1e-5)
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def test_backward_create_graph_with_multiple_tensors(self):
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"""Test backward with create_graph on multiple output tensors."""
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x = paddle.to_tensor(
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[[1.0, 2.0], [3.0, 4.0]], dtype='float32', stop_gradient=False
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)
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z1 = x**2
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z2 = x * 3
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# Backward on z1
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paddle.autograd.backward(z1, create_graph=True)
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self.assertIsNotNone(x.grad)
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self.assertFalse(x.grad.stop_gradient)
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x.clear_grad()
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# Backward on z2
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paddle.autograd.backward(z2, create_graph=True)
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self.assertIsNotNone(x.grad)
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self.assertFalse(x.grad.stop_gradient)
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def test_backward_create_graph_with_grad_tensors(self):
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"""Test backward with create_graph and custom grad_tensors."""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32', stop_gradient=False)
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y = x**2
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z = y.sum()
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grad_tensor = paddle.to_tensor([1.0, 2.0], dtype='float32')
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paddle.autograd.backward(y, grad_tensors=grad_tensor, create_graph=True)
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# Check gradients with custom weights
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self.assertIsNotNone(x.grad)
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expected = [2.0 * 1.0, 4.0 * 2.0] # dy/dx * weights
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np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
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self.assertFalse(x.grad.stop_gradient)
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def test_backward_create_graph_retain_graph(self):
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"""Test backward with create_graph=True and retain_graph=True."""
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x = paddle.to_tensor([2.0], dtype='float32', stop_gradient=False)
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y = x**3
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loss = y.sum()
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# First backward
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paddle.autograd.backward(loss, create_graph=True, retain_graph=True)
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grad1 = x.grad.clone()
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x.clear_grad()
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# Second backward with same graph
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paddle.autograd.backward(loss, create_graph=True, retain_graph=False)
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grad2 = x.grad
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# Gradients should be the same
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np.testing.assert_allclose(grad1.numpy(), grad2.numpy(), rtol=1e-5)
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def test_backward_create_graph_chain_rule(self):
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"""Test chain rule with higher-order gradients."""
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x = paddle.to_tensor([1.0], dtype='float32', stop_gradient=False)
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y = x**3
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loss = y**2
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# First backward
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paddle.autograd.backward(loss, create_graph=True, retain_graph=True)
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# At x=1: x.grad should be 6.0
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np.testing.assert_allclose(x.grad.numpy(), [6.0], rtol=1e-5)
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# Second backward
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grad_sum = paddle.sum(x.grad)
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paddle.autograd.backward(grad_sum)
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# At x=1: x.grad should be 30
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# Sum up to 36
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np.testing.assert_allclose(x.grad.numpy(), [36.0], rtol=1e-5)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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