# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle.fluid as fluid import paddle from paddle.fluid.wrapped_decorator import wrap_decorator from paddle.vision.models import resnet50, resnet101 import unittest from unittest import TestCase import numpy as np from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph, _in_eager_without_dygraph_check def _dygraph_guard_(func): def __impl__(*args, **kwargs): if fluid._non_static_mode(): return func(*args, **kwargs) else: with fluid.dygraph.guard(): return func(*args, **kwargs) return __impl__ dygraph_guard = wrap_decorator(_dygraph_guard_) def random_var(size, low=-1, high=1, dtype='float32'): np.random.seed(2021) x_np = np.random.uniform(low=low, high=high, size=size).astype(dtype) return fluid.dygraph.to_variable(x_np) class TestDygraphTripleGrad(TestCase): def setUp(self): self.sort_sum_gradient = False self.shape = [5, 5] def grad(self, outputs, inputs, grad_outputs=None, no_grad_vars=None, retain_graph=None, create_graph=False, allow_unused=False): fluid.set_flags({'FLAGS_sort_sum_gradient': self.sort_sum_gradient}) return fluid.dygraph.grad( outputs=outputs, inputs=inputs, grad_outputs=grad_outputs, no_grad_vars=no_grad_vars, retain_graph=retain_graph, create_graph=create_graph, allow_unused=allow_unused) @dygraph_guard def func_exception(self): with self.assertRaises(AssertionError): self.grad(None, None) shape = self.shape with self.assertRaises(AssertionError): self.grad(1, random_var(shape)) with self.assertRaises(AssertionError): self.grad(random_var(shape), 1) with self.assertRaises(AssertionError): self.grad([1], [random_var(shape)]) with self.assertRaises(AssertionError): self.grad([random_var(shape)], [1]) with self.assertRaises(AssertionError): self.grad([random_var(shape), random_var(shape)], [random_var(shape)], [random_var(shape)]) with self.assertRaises(AssertionError): self.grad( [random_var(shape)], [random_var(shape)], no_grad_vars=[1]) with self.assertRaises(AssertionError): self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1) @dygraph_guard def func_example_with_gradient_and_create_graph(self): x = random_var(self.shape) x_np = x.numpy() x.stop_gradient = False y = random_var(self.shape) y_np = y.numpy() y.stop_gradient = False z = random_var(self.shape) z_np = z.numpy() numel = z_np.size z.stop_gradient = False out = fluid.layers.sigmoid(paddle.matmul(x, y) + z) out_np = out.numpy() dx_actual, = self.grad([out], [x], create_graph=True) # Theoritical result based on math calculation dout = np.ones(self.shape).astype('float32') dx_expected = np.matmul(dout * out_np * (1 - out_np), np.transpose(y_np)) self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected)) ddx_actual, = self.grad([dx_actual], [x], create_graph=True) # Theoritical result based on math calculation DDY = np.zeros(self.shape).astype('float32') DDX = np.ones(self.shape).astype('float32') double_grad_tmp1 = np.matmul(dout * out_np * (1 - out_np), np.transpose(DDY)) double_grad_tmp2 = np.matmul(DDX, y_np) + np.matmul(x_np, DDY) double_grad_tmp3 = ( 1 - 2 * out_np) * dout * double_grad_tmp2 * out_np * (1 - out_np) ddx_expected = double_grad_tmp1 + np.matmul(double_grad_tmp3, np.transpose(y_np)) self.assertTrue(np.allclose(ddx_actual.numpy(), ddx_expected)) # Theoritical result based on math calculation d_ddout = np.zeros(self.shape).astype('float32') tmp0 = np.matmul(DDX, y_np) + np.matmul(x_np, DDY) tmp1 = (1 - 2 * out_np) * ((1 - 2 * out_np) * dout * tmp0 * tmp0) tmp2 = tmp0 * (1 - 2 * out_np) * d_ddout - 2 * dout * ( 1 - out_np) * out_np * tmp0 * tmp0 dddx_expected = np.matmul(((tmp1 + tmp2) * out_np * (1 - out_np)), np.transpose(y_np)) ddx_actual.backward() dddx_grad_actual = x.gradient() self.assertTrue(np.allclose(dddx_grad_actual, dddx_expected)) def test_all_cases(self): if _in_legacy_dygraph(): self.func_exception() self.func_example_with_gradient_and_create_graph() class TestDygraphTripleGradBradcastCase(TestCase): def setUp(self): self.sort_sum_gradient = False self.x_shape = [3, 2, 2] self.y_shape = [1, 2, 2] self.z_shape = [2, 2] def grad(self, outputs, inputs, grad_outputs=None, no_grad_vars=None, retain_graph=None, create_graph=False, allow_unused=False): fluid.set_flags({'FLAGS_sort_sum_gradient': self.sort_sum_gradient}) return fluid.dygraph.grad( outputs=outputs, inputs=inputs, grad_outputs=grad_outputs, no_grad_vars=no_grad_vars, retain_graph=retain_graph, create_graph=create_graph, allow_unused=allow_unused) @dygraph_guard def func_example_with_gradient_and_create_graph(self): x = random_var(self.x_shape) x_np = x.numpy() x.stop_gradient = False y = random_var(self.y_shape) y_np = y.numpy() y.stop_gradient = False z = random_var(self.z_shape) z_np = z.numpy() numel = z_np.size z.stop_gradient = False out = fluid.layers.sigmoid(paddle.matmul(x, y) + z) out_np = out.numpy() dx_actual, = self.grad([out], [x], create_graph=True) # Theoritical result based on math calculation dout = np.ones(self.x_shape).astype('float32') dx_expected = np.matmul( dout * out_np * (1 - out_np), np.transpose( y_np, axes=(0, 2, 1))) self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected)) ddx_actual, = self.grad([dx_actual], [x], create_graph=True) # Theoritical result based on math calculation DDY = np.zeros(self.y_shape).astype('float32') DDX = np.ones(self.x_shape).astype('float32') double_grad_tmp1 = np.matmul( dout * out_np * (1 - out_np), np.transpose( DDY, axes=(0, 2, 1))) double_grad_tmp2 = np.matmul(DDX, y_np) + np.matmul(x_np, DDY) double_grad_tmp3 = ( 1 - 2 * out_np) * dout * double_grad_tmp2 * out_np * (1 - out_np) ddx_expected = double_grad_tmp1 + np.matmul( double_grad_tmp3, np.transpose( y_np, axes=(0, 2, 1))) self.assertTrue(np.allclose(ddx_actual.numpy(), ddx_expected)) # Theoritical result based on math calculation d_ddout = np.zeros(self.x_shape).astype('float32') tmp0 = np.matmul(DDX, y_np) + np.matmul(x_np, DDY) tmp1 = (1 - 2 * out_np) * ((1 - 2 * out_np) * dout * tmp0 * tmp0) tmp2 = tmp0 * (1 - 2 * out_np) * d_ddout - 2 * dout * ( 1 - out_np) * out_np * tmp0 * tmp0 dddx_expected = np.matmul( ((tmp1 + tmp2) * out_np * (1 - out_np)), np.transpose( y_np, axes=(0, 2, 1))) ddx_actual.backward() dddx_grad_actual = x.gradient() self.assertTrue(np.allclose(dddx_grad_actual, dddx_expected)) def test_all_cases(self): if _in_legacy_dygraph(): self.func_example_with_gradient_and_create_graph() if __name__ == '__main__': unittest.main()