/* Copyright 2017 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/core/grappler/optimizers/model_pruner.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/grappler/devices.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/grappler/utils/grappler_test.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace grappler {
namespace {

class ModelPrunerTest : public GrapplerTest {};

TEST_F(ModelPrunerTest, NoPruning) {
  // This trivial graph is so basic there's nothing to prune.
  TrivialTestGraphInputYielder fake_input(4, 1, 10, false, {"CPU:0"});
  GrapplerItem item;
  CHECK(fake_input.NextItem(&item));

  ModelPruner pruner;
  GraphDef output;
  Status s = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(s);

  EXPECT_EQ(item.graph.node_size(), output.node_size());
  for (int i = 0; i < item.graph.node_size(); ++i) {
    const NodeDef& original = item.graph.node(i);
    const NodeDef& optimized = output.node(i);
    EXPECT_EQ(original.name(), optimized.name());
    EXPECT_EQ(original.op(), optimized.op());
    EXPECT_EQ(original.input_size(), optimized.input_size());
    for (int j = 0; j < original.input_size(); ++j) {
      EXPECT_EQ(original.input(j), optimized.input(j));
    }
  }
}

TEST_F(ModelPrunerTest, StopGradientPruning) {
  // Build a simple graph with a few trivially prunable ops.
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
  Output b = ops::Sqrt(s.WithOpName("b"), {a});
  Output c = ops::StopGradient(s.WithOpName("c"), b);
  Output d = ops::StopGradient(s.WithOpName("d"), c);
  Output e = ops::Sqrt(s.WithOpName("e"), {d});

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  EXPECT_EQ(5, output.node_size());
  const NodeDef& new_a = output.node(0);
  EXPECT_EQ(NodeName(a.name()), new_a.name());
  const NodeDef& new_b = output.node(1);
  EXPECT_EQ(NodeName(b.name()), new_b.name());
  const NodeDef& new_c = output.node(2);
  EXPECT_EQ(NodeName(c.name()), new_c.name());
  const NodeDef& new_d = output.node(3);
  EXPECT_EQ(NodeName(d.name()), new_d.name());
  const NodeDef& new_e = output.node(4);
  EXPECT_EQ(NodeName(e.name()), new_e.name());

  EXPECT_EQ(1, new_e.input_size());
  EXPECT_EQ(NodeName(b.name()), new_e.input(0));
  EXPECT_EQ(1, new_d.input_size());
  EXPECT_EQ(NodeName(b.name()), new_d.input(0));

  std::vector<string> fetch = {"e"};
  auto expected_tensors = EvaluateNodes(item.graph, fetch);
  auto actual_tensors = EvaluateNodes(output, fetch);
  EXPECT_EQ(1, expected_tensors.size());
  EXPECT_EQ(1, actual_tensors.size());
  test::ExpectTensorEqual<float>(expected_tensors[0], actual_tensors[0]);
}

TEST_F(ModelPrunerTest, IdentityPruning) {
  // Build a simple graph with a few trivially prunable ops.
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
  Output b = ops::Sqrt(s.WithOpName("b"), {a});
  Output c = ops::Identity(s.WithOpName("c"), b);
  Output d = ops::Identity(s.WithOpName("d"), c);
  Output e = ops::Sqrt(s.WithOpName("e"), {d});

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  EXPECT_EQ(5, output.node_size());
  const NodeDef& new_a = output.node(0);
  EXPECT_EQ(NodeName(a.name()), new_a.name());
  const NodeDef& new_b = output.node(1);
  EXPECT_EQ(NodeName(b.name()), new_b.name());
  const NodeDef& new_c = output.node(2);
  EXPECT_EQ(NodeName(c.name()), new_c.name());
  const NodeDef& new_d = output.node(3);
  EXPECT_EQ(NodeName(d.name()), new_d.name());
  const NodeDef& new_e = output.node(4);
  EXPECT_EQ(NodeName(e.name()), new_e.name());

  EXPECT_EQ(1, new_e.input_size());
  EXPECT_EQ(NodeName(b.name()), new_e.input(0));
  EXPECT_EQ(1, new_d.input_size());
  EXPECT_EQ(NodeName(b.name()), new_d.input(0));
  EXPECT_EQ(1, new_c.input_size());
  EXPECT_EQ(NodeName(b.name()), new_c.input(0));

  std::vector<string> fetch = {"e"};
  auto expected_tensors = EvaluateNodes(item.graph, fetch);
  auto actual_tensors = EvaluateNodes(output, fetch);
  EXPECT_EQ(1, expected_tensors.size());
  EXPECT_EQ(1, actual_tensors.size());
  test::ExpectTensorEqual<float>(expected_tensors[0], actual_tensors[0]);
}

TEST_F(ModelPrunerTest, NoOpPruning) {
  // Build a simple graph with a few trivially prunable ops.
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
  Output b = ops::AddN(s.WithOpName("b"), {a});
  Output c = ops::AddN(s.WithOpName("c"), {b});
  Output d = ops::AddN(s.WithOpName("d").WithControlDependencies(b), {c});
  Output e = ops::AddN(s.WithOpName("e"), {d});

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  EXPECT_EQ(5, output.node_size());
  const NodeDef& new_a = output.node(0);
  EXPECT_EQ(NodeName(a.name()), new_a.name());
  const NodeDef& new_b = output.node(1);
  EXPECT_EQ(NodeName(b.name()), new_b.name());
  const NodeDef& new_c = output.node(2);
  EXPECT_EQ(NodeName(c.name()), new_c.name());
  const NodeDef& new_d = output.node(3);
  EXPECT_EQ(NodeName(d.name()), new_d.name());
  const NodeDef& new_e = output.node(4);
  EXPECT_EQ(NodeName(e.name()), new_e.name());

  for (const auto& new_node : output.node()) {
    if (new_node.name() != "a") {
      EXPECT_EQ(1, new_node.input_size());
      EXPECT_EQ("a", new_node.input(0));
    }
  }

  std::vector<string> fetch = {"e"};
  auto expected_tensors = EvaluateNodes(item.graph, fetch);
  auto actual_tensors = EvaluateNodes(output, fetch);
  EXPECT_EQ(1, expected_tensors.size());
  EXPECT_EQ(1, actual_tensors.size());
  test::ExpectTensorEqual<float>(expected_tensors[0], actual_tensors[0]);
}

TEST_F(ModelPrunerTest, PreserveIdentities) {
  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
  ops::Variable v_in(scope.WithOpName("v_in"), {3}, DT_FLOAT);
  ops::Variable v_ctrl(scope.WithOpName("v_ctrl"), {}, DT_BOOL);
  ops::Switch s(scope.WithOpName("switch"), v_in, v_ctrl);
  // id0 is preserved because it is fed by a Switch and drives a
  // control dependency.
  Output id0 = ops::Identity(scope.WithOpName("id0"), s.output_true);
  // id1 is preserved because it feeds a Merge.
  Output id1 = ops::Identity(
      scope.WithOpName("id1").WithControlDependencies(v_ctrl), s.output_false);
  Output id2 = ops::Identity(scope.WithOpName("id2"), id0);
  Output id3 =
      ops::Identity(scope.WithOpName("id3").WithControlDependencies(id0), id1);
  auto merge = ops::Merge(scope.WithOpName("merge"), {id0, id1});

  GrapplerItem item;
  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
  item.fetch.push_back("id2");
  item.fetch.push_back("id3");
  item.fetch.push_back("merge");

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);

  TF_EXPECT_OK(status);
  EXPECT_EQ(item.graph.node_size(), output.node_size());

  auto v_in_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({3}));
  Tensor v_ctrl_t(DT_BOOL, TensorShape({}));
  v_ctrl_t.flat<bool>()(0) = true;
  auto expected_tensors = EvaluateNodes(
      item.graph, {"merge", "id2"}, {{"v_in", v_in_t}, {"v_ctrl", v_ctrl_t}});
  auto actual_tensors = EvaluateNodes(output, {"merge", "id2"},
                                      {{"v_in", v_in_t}, {"v_ctrl", v_ctrl_t}});
  EXPECT_EQ(2, expected_tensors.size());
  EXPECT_EQ(2, actual_tensors.size());
  for (int i = 0; i < expected_tensors.size(); i++) {
    test::ExpectTensorEqual<float>(expected_tensors[i], actual_tensors[i]);
  }
}

TEST_F(ModelPrunerTest, PruningSkipsRefOutputs) {
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  // Make graph of Identity(Identity(Identity(Identity(Variable)))).
  Output a = ops::Variable(s.WithOpName("a"), {}, DT_INT64);
  Output b = ops::Identity(s.WithOpName("b"), a);
  Output c = ops::Identity(s.WithOpName("c"), b);
  Output d = ops::Identity(s.WithOpName("d"), c);
  Output e = ops::Identity(s.WithOpName("e"), d);

  // Run pruner.
  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));
  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  // Get the updated nodes.
  ASSERT_EQ(5, output.node_size());
  const NodeDef& new_a = output.node(0);
  const NodeDef& new_b = output.node(1);
  const NodeDef& new_c = output.node(2);
  const NodeDef& new_d = output.node(3);
  const NodeDef& new_e = output.node(4);
  EXPECT_EQ("a", new_a.name());
  EXPECT_EQ("b", new_b.name());
  EXPECT_EQ("c", new_c.name());
  EXPECT_EQ("d", new_d.name());
  EXPECT_EQ("e", new_e.name());

  // Verify the connections. Identity "b" can't be removed from the chain
  // because it is converting a reference input to a non-reference, so c,d,e all
  // refer to it as an input.
  EXPECT_EQ("a", new_b.input(0));
  EXPECT_EQ("b", new_c.input(0));
  EXPECT_EQ("b", new_d.input(0));
  EXPECT_EQ("b", new_e.input(0));

  std::vector<string> fetch = {"e"};
  auto a_t = GenerateRandomTensor<DT_INT64>(TensorShape({}));
  auto expected_tensors = EvaluateNodes(item.graph, fetch, {{"a", a_t}});
  auto actual_tensors = EvaluateNodes(output, fetch, {{"a", a_t}});
  EXPECT_EQ(1, expected_tensors.size());
  EXPECT_EQ(1, actual_tensors.size());
  test::ExpectTensorEqual<int64>(expected_tensors[0], actual_tensors[0]);
}

// TODO(rmlarsen): Reenable this test when the issues with
// //robotics/learning/sensor_predict:utils_multi_sensor_rnn_test
// have been resolved.
/*
TEST_F(ModelPrunerTest, PruningForwardsCtrlDependencies) {
  // Build a simple graph with a few trivially prunable ops.
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
  Output b = ops::Sqrt(s.WithOpName("b"), {a});
  Output c = ops::Sqrt(s.WithOpName("c"), {a});
  Output d = ops::Identity(s.WithOpName("d").WithControlDependencies(b), c);
  Output e = ops::Identity(s.WithOpName("e").WithControlDependencies(c), d);
  Output f = ops::Sqrt(s.WithOpName("f"), {d});
  Output g = ops::Sqrt(s.WithOpName("g"), {e});

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));
  item.fetch.push_back("f");
  item.fetch.push_back("g");

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);
  LOG(INFO) << "After: " << output.DebugString();

  EXPECT_EQ(5, output.node_size());
  for (const auto& new_node : output.node()) {
    // "d" and "e" should be removed.
    EXPECT_NE("d", new_node.name());
    EXPECT_NE("e", new_node.name());
    if (new_node.name() == "g") {
      EXPECT_EQ(2, new_node.input_size());
      // The input from switch should be forwarded to id3.
      EXPECT_EQ("c", new_node.input(0));
      EXPECT_EQ("^b", new_node.input(1));
    }
    if (new_node.name() == "f") {
      EXPECT_EQ(2, new_node.input_size());
      // The input from switch should be forwarded to id3.
      EXPECT_EQ("c", new_node.input(0));
      EXPECT_EQ("^b", new_node.input(1));
    }
  }
}
*/

TEST_F(ModelPrunerTest, PruningPerservesFetch) {
  // Build a simple graph with a few trivially prunable ops.
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

  Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10});
  Output b = ops::Sqrt(s.WithOpName("b"), {a});
  Output c = ops::Identity(s.WithOpName("c"), b);
  Output d = ops::Identity(s.WithOpName("d"), c);

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));
  item.fetch.push_back("c");

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  EXPECT_EQ(3, output.node_size());
  const NodeDef& new_a = output.node(0);
  EXPECT_EQ(NodeName(a.name()), new_a.name());
  const NodeDef& new_b = output.node(1);
  EXPECT_EQ(NodeName(b.name()), new_b.name());
  const NodeDef& new_c = output.node(2);
  EXPECT_EQ(NodeName(c.name()), new_c.name());

  auto expected_tensors = EvaluateNodes(item.graph, item.fetch);
  auto actual_tensors = EvaluateNodes(output, item.fetch);
  EXPECT_EQ(1, expected_tensors.size());
  EXPECT_EQ(1, actual_tensors.size());
  test::ExpectTensorEqual<float>(expected_tensors[0], actual_tensors[0]);
}

TEST_F(ModelPrunerTest, PruningPerservesCrossDeviceIdentity) {
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
  Output c = ops::Const(s.WithOpName("c").WithDevice("/cpu:0"), 0.0f, {10, 10});

  // Node i1 should be preserved.
  Output i1 = ops::Identity(s.WithOpName("i1").WithDevice("/device:GPU:0"), c);
  Output a1 = ops::Sqrt(s.WithOpName("a1").WithDevice("/device:GPU:0"), {i1});
  Output a2 = ops::Sqrt(s.WithOpName("a2").WithDevice("/device:GPU:0"), {i1});

  // Node i2 should be pruned since it resides on the sender's device.
  Output i2 = ops::Identity(s.WithOpName("i2").WithDevice("/cpu:0"), c);
  Output a3 = ops::Sqrt(s.WithOpName("a3").WithDevice("/device:GPU:0"), {i2});
  Output a4 = ops::Sqrt(s.WithOpName("a4").WithDevice("/device:GPU:0"), {i2});

  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));
  item.fetch = {"a1", "a2", "a3", "a4"};

  ModelPruner pruner;
  GraphDef output;
  Status status = pruner.Optimize(nullptr, item, &output);
  TF_EXPECT_OK(status);

  for (const auto& node : output.node()) {
    if (node.name() == "a1" || node.name() == "a2") {
      EXPECT_EQ("i1", node.input(0));
    } else if (node.name() == "a3" || node.name() == "a4") {
      EXPECT_EQ("c", node.input(0));
    }
  }
  if (GetNumAvailableGPUs() > 0) {
    auto expected_tensors = EvaluateNodes(item.graph, item.fetch);
    auto actual_tensors = EvaluateNodes(output, item.fetch);
    EXPECT_EQ(4, expected_tensors.size());
    EXPECT_EQ(4, actual_tensors.size());
    for (int i = 0; i < expected_tensors.size(); i++) {
      test::ExpectTensorNear<float>(expected_tensors[i], actual_tensors[i],
                                    1e-6);
    }
  }
}

}  // namespace
}  // namespace grappler
}  // namespace tensorflow