/* Copyright 2016 The TensorFlow 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.
==============================================================================*/

#include "tensorflow/cc/framework/gradient_checker.h"
#include "tensorflow/cc/framework/grad_op_registry.h"
#include "tensorflow/cc/framework/testutil.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/equal_graph_def.h"

namespace tensorflow {
using namespace ops;  // NOLINT(build/namespaces)

namespace {

TEST(GradientCheckerTest, BasicFloat) {
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Square(scope, x);
  float max_error;
  TF_ASSERT_OK((ComputeGradientError<float, float, float>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-4);
}

TEST(GradientCheckerTest, BasicDouble) {
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_DOUBLE, Placeholder::Shape(shape));
  auto y = Square(scope, x);
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, BasicComplex64) {
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_COMPLEX64, Placeholder::Shape(shape));
  auto y = Square(scope, x);
  float max_error;
  TF_ASSERT_OK((ComputeGradientError<complex64, complex64, float>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-4);
}

TEST(GradientCheckerTest, BasicComplex128) {
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_COMPLEX128, Placeholder::Shape(shape));
  auto y = Square(scope, x);
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<complex128, complex128, double>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, FloatToComplex64) {
  // Test an op whose inputs are real and outputs are complex
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Complex(scope, x, x);
  float max_error;
  TF_ASSERT_OK((ComputeGradientError<float, complex64, float>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-4);
}

TEST(GradientCheckerTest, Complex64ToFloat) {
  // Test an op whose inputs are complex and outputs are real
  Scope scope = Scope::NewRootScope();
  TensorShape shape({2, 4, 3});
  auto x = Placeholder(scope, DT_COMPLEX64, Placeholder::Shape(shape));
  auto y = Real(scope, x);
  float max_error;
  TF_ASSERT_OK((ComputeGradientError<complex64, float, float>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_LT(max_error, 1e-4);
}

TEST(GradientCheckerTest, MatMulGrad) {
  Scope scope = Scope::NewRootScope();

  TensorShape x_shape({4, 3});
  TensorShape y_shape({3, 2});
  TensorShape z_shape({4, 2});

  auto x = Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape));
  auto y = Const(scope, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, y_shape);
  auto z = MatMul(scope, x, y);
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, {x}, {x_shape}, {z}, {z_shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, SplitGrad) {
  // Split is an op with single inputs and multiple outputs.
  Scope scope = Scope::NewRootScope();
  TensorShape x_shape({5, 2});
  auto x = Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape));
  // Split along the second dimension.
  auto split_dim = Const(scope, 1, {});
  auto y = Split(scope, split_dim, x, /* num_split */ 2);
  TensorShape y_shape = TensorShape({5, 1});
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, {x}, {x_shape}, y.output, {y_shape, y_shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, StackGrad) {
  // Stack is an op with multiple inputs and a single output.
  Scope scope = Scope::NewRootScope();
  TensorShape x_shape({1, 2, 3});
  std::vector<Output> xs;
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  auto y = Stack(scope, xs, Stack::Axis(0));
  TensorShape y_shape({2, 1, 2, 3});
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, xs, {x_shape, x_shape}, {y}, {y_shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, StackUnstackGrad) {
  // Chaining a Stack op to an Unstack op allows us to test the gradient checker
  // in a multiple input/output scenario.
  Scope scope = Scope::NewRootScope();
  TensorShape shape({1, 2, 3});
  std::vector<Output> xs;
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(shape)));
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(shape)));
  auto tmp = Stack(scope, xs, Stack::Axis(0));
  auto y = Unstack(scope, tmp, 2, Unstack::Axis(0));
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, xs, {shape, shape}, y.output, {shape, shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

}  // namespace
}  // namespace tensorflow
