test_imperative_triple_grad.py

# 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 TestDygraphTripleGradMatmul(TestCase):

    def test_matmul_triple_grad(self):
        input_numpy = np.ones([3, 3]) * 2
        with _test_eager_guard():
            x = paddle.to_tensor(input_numpy,
                                 stop_gradient=False,
                                 dtype='float32')
            y = paddle.to_tensor(input_numpy,
                                 stop_gradient=False,
                                 dtype='float32')
            out = paddle.matmul(x, y, False, False)

            new_out_g = paddle.to_tensor(np.ones([3, 3]),
                                         stop_gradient=False,
                                         dtype='float32')
            new_x_g, new_y_g = paddle.grad([out], [x, y], [new_out_g],
                                           retain_graph=True,
                                           create_graph=True)

            new_x_g_g = paddle.to_tensor(np.ones([3, 3]),
                                         stop_gradient=False,
                                         dtype='float32')
            new_y_g_g = paddle.to_tensor(np.ones([3, 3]),
                                         stop_gradient=False,
                                         dtype='float32')
            new_a, new_b, new_c = paddle.grad([new_x_g, new_y_g],
                                              [x, y, new_out_g],
                                              [new_x_g_g, new_y_g_g],
                                              retain_graph=True,
                                              create_graph=True)

            new_a.backward()

            out_ref = np.ones([3, 3]) * 12.0
            self.assertTrue(np.array_equal(out.numpy(), out_ref))

            new_x_g_ref = np.ones([3, 3]) * 6.0
            new_y_g_ref = np.ones([3, 3]) * 6.0
            self.assertTrue(np.array_equal(new_x_g.numpy(), new_x_g_ref))
            self.assertTrue(np.array_equal(new_y_g.numpy(), new_y_g_ref))

            new_a_ref = np.ones([3, 3]) * 3.0
            new_b_ref = np.ones([3, 3]) * 3.0
            new_c_ref = np.ones([3, 3]) * 12.0

            self.assertTrue(np.array_equal(new_a.numpy(), new_a_ref))
            self.assertTrue(np.array_equal(new_b.numpy(), new_b_ref))
            self.assertTrue(np.array_equal(new_c.numpy(), new_c_ref))

            x_grad_ref = np.ones([3, 3]) * 0.0
            self.assertTrue(np.array_equal(x.grad.numpy(), x_grad_ref))

            y_grad_ref = np.ones([3, 3]) * 0.0
            self.assertTrue(np.array_equal(y.grad.numpy(), y_grad_ref))

            new_out_g_ref = np.ones([3, 3]) * 3.0
            self.assertTrue(
                np.array_equal(new_out_g.grad.numpy(), new_out_g_ref))

            new_x_g_g_ref = np.ones([3, 3]) * 0.0
            new_y_g_g_ref = np.ones([3, 3]) * 3.0
            self.assertTrue(
                np.array_equal(new_x_g_g.grad.numpy(), new_x_g_g_ref))
            self.assertTrue(
                np.array_equal(new_y_g_g.grad.numpy(), new_y_g_g_ref))


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):
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        self.func_exception()
        self.func_example_with_gradient_and_create_graph()
        with _test_eager_guard():
            self.func_exception()
            self.func_example_with_gradient_and_create_graph()
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})


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):
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        self.func_example_with_gradient_and_create_graph()
        with _test_eager_guard():
            self.func_example_with_gradient_and_create_graph()
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})


if __name__ == '__main__':
    unittest.main()