/* Copyright 2018 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/scoped_allocator_optimizer.h"

#include <unordered_set>

#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/tensor.pb.h"  // NOLINT
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/graph/testlib.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/public/session_options.h"

namespace tensorflow {
namespace grappler {
namespace {

class ScopedAllocatorOptimizerTest : public ::testing::Test {
 public:
  std::unique_ptr<Session> CreateSession(const GraphDef& graph,
                                         const ConfigProto& config) {
    SessionOptions options;
    options.config = config;
    (*options.config.mutable_device_count())["CPU"] = 2;
    Session* session = NewSession(options);
    TF_CHECK_OK(session->Create(graph));
    return std::unique_ptr<Session>(session);
  }

  std::vector<Tensor> EvaluateNodes(const GraphDef& graph,
                                    const std::vector<string>& fetch) {
    SessionOptions options;
    std::unique_ptr<tensorflow::Session> session(NewSession(options));
    TF_CHECK_OK(session->Create(graph));
    RunOptions run_options;
    std::vector<Tensor> output_tensors;
    TF_CHECK_OK(
        session->Run(run_options, {}, fetch, fetch, &output_tensors, nullptr));
    TF_CHECK_OK(session->Close());
    return output_tensors;
  }

  // Constructs the following graph.
  // (Flow is top to bottom, like nature intends.)
  //
  // The intended optimization is to have s1 and s2 allocate from
  // an new ScopedAllocator, then replace a1 and a2 with a3 that
  // reads from the backing buffer.
  /*
        a    b    c
         \  / \  /
          s1   s2
          |    |
          a1   a2
          |    |
          r1   r2
  */
  void BuildAbsGraph(GraphDef* graph_def) {
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    s = s.WithDevice("/job:localhost/replica:0/task:0/device:CPU:0");

    Output a =
        ops::Const<float>(s.WithOpName("a"), {1.0, 0.0, 0.0, -1.0}, {2, 2});
    Output b =
        ops::Const<float>(s.WithOpName("b"), {1.0, -2.0, 3.0, 4.0}, {2, 2});
    Output c =
        ops::Const<float>(s.WithOpName("c"), {-5.0, -2.0, 0.0, -2.0}, {2, 2});
    Output s1 = ops::Add(s.WithOpName("s1"), a, b);
    Output s2 = ops::Add(s.WithOpName("s2"), b, c);
    Output a1 = ops::Abs(s.WithOpName("a1"), s1);
    Output a2 = ops::Abs(s.WithOpName("a2"), s2);
    Output r1 = ops::Reshape(s.WithOpName("r1"), a1, {1, 4});
    Output r2 = ops::Reshape(s.WithOpName("r2"), a2, {4, 1});
    TF_CHECK_OK(s.ToGraphDef(graph_def));
  }

  void SetShapes(GraphDef* graph_def) {
    TensorShapeProto shape_proto;
    shape_proto.add_dim()->set_size(2);
    shape_proto.add_dim()->set_size(2);

    for (NodeDef& n : *graph_def->mutable_node()) {
      if (n.op() == "Add" || n.op() == "Abs") {
        AddNodeAttr("_output_shapes", {shape_proto}, &n);
      }
    }
  }
};

TEST_F(ScopedAllocatorOptimizerTest, UnaryRewriteOnly) {
  // Tests that Rewrite of program with parallel unary Ops is done as
  // anticipated.
  GrapplerItem item;
  BuildAbsGraph(&item.graph);
  SetShapes(&item.graph);

  ScopedAllocatorOptions opts;
  opts.add_enable_op("Abs");
  ScopedAllocatorOptimizer sao(RewriterConfig::ON, opts);
  ScopedAllocatorOptimizer::OpNameSet ons;
  ons.insert("Abs");

  GraphDef optimized_graph;
  TF_ASSERT_OK(sao.Optimize(nullptr /*cluster*/, item, &optimized_graph));

  // Examine the resulting graph def.
  NodeMap node_map(&optimized_graph);
  NodeDef* nd = node_map.GetNode("scoped_allocator_1");
  ASSERT_TRUE(nd);
  {
    auto& nd_set = node_map.GetOutputs(nd->name());
    ASSERT_EQ(3, nd_set.size());
    std::unordered_set<string> expected = {"scoped_allocator_concat_1", "s1",
                                           "s2"};
    for (auto it : nd_set) {
      ASSERT_NE(expected.find(it->name()), expected.end())
          << "Failed to find " << it->name();
    }
  }
  {
    auto& nd_set = node_map.GetOutputs("scoped_allocator_concat_1");
    ASSERT_EQ(1, nd_set.size());
    for (auto it : nd_set) {
      ASSERT_EQ("scoped_allocator_1_Abs", it->name());
    }
  }
  {
    auto& nd_set = node_map.GetOutputs("scoped_allocator_1_Abs");
    ASSERT_EQ(1, nd_set.size());
    for (auto it : nd_set) {
      ASSERT_EQ("scoped_allocator_split_1", it->name());
    }
  }
  {
    auto& nd_set = node_map.GetOutputs("scoped_allocator_split_1");
    ASSERT_EQ(2, nd_set.size());
    std::unordered_set<string> name_set;
    for (auto it : nd_set) {
      name_set.insert(it->name());
    }
    ASSERT_TRUE(name_set.find("r1") != name_set.end());
    ASSERT_TRUE(name_set.find("r2") != name_set.end());
  }
}

TEST_F(ScopedAllocatorOptimizerTest, UnaryExecute) {
  // Constructs the same graph as UnaryRewriteOnly, but actually executes it.
  GrapplerItem item;
  BuildAbsGraph(&item.graph);

  // Turn off all optimization except the ScopedAllocatorOptimizer
  // to avoid anything that would alter the expected graph input/output,
  // e.g. by constant folding away all calculations.
  ConfigProto config;
  GraphOptions* gopt = config.mutable_graph_options();
  OptimizerOptions* opts = gopt->mutable_optimizer_options();
  opts->set_do_common_subexpression_elimination(false);
  opts->set_do_constant_folding(false);
  opts->set_do_function_inlining(false);
  opts->set_opt_level(OptimizerOptions::L0);
  RewriterConfig* rwcfg = gopt->mutable_rewrite_options();
  rwcfg->clear_optimizers();
  (*rwcfg->add_optimizers()) = "scoped_allocator";
  rwcfg->mutable_scoped_allocator_opts()->add_enable_op("Abs");
  std::unique_ptr<Session> session(CreateSession(item.graph, config));

  std::vector<std::pair<string, Tensor>> inputs;

  // Request two targets: one fetch output and one non-fetched output.
  std::vector<string> output_names = {"r1:0", "r2:0",
                                      "scoped_allocator_1_Abs:0"};
  std::vector<string> target_nodes = {};
  std::vector<Tensor> outputs;
  Status s = session->Run(inputs, output_names, target_nodes, &outputs);
  TF_ASSERT_OK(s);
  ASSERT_EQ(outputs.size(), 3);
  std::vector<float> expected_r1({2, 2, 3, 3});
  std::vector<float> expected_r2({4, 4, 3, 2});
  // a + b == 2, -2, 3, 3
  // b + c == -4, -4, 3, 2
  for (int oi = 0; oi < outputs.size(); ++oi) {
    for (int i = 0; i < outputs[oi].NumElements(); ++i) {
      VLOG(1) << "output vec " << oi << " index " << i << " = "
              << outputs[oi].flat<float>()(i);
    }
    if (oi == 0) {
      ASSERT_EQ(expected_r1.size(), outputs[oi].NumElements());
      for (int i = 0; i < expected_r1.size(); ++i) {
        EXPECT_EQ(expected_r1[i], outputs[oi].flat<float>()(i));
      }
    } else if (oi == 1) {
      ASSERT_EQ(expected_r2.size(), outputs[oi].NumElements());
      for (int i = 0; i < expected_r2.size(); ++i) {
        EXPECT_EQ(expected_r2[i], outputs[oi].flat<float>()(i));
      }
    }
  }
}

// Tests static ScopedAllocatorOptimizer::ExtendNodeAttr.
// Maybe this should be moved elsewhere?
TEST_F(ScopedAllocatorOptimizerTest, Extend) {
  NodeDef nd;
  ScopedAllocatorOptimizer::ExtendNodeAttr("_scoped_allocator", {0, 2}, &nd);
  ScopedAllocatorOptimizer::ExtendNodeAttr("_scoped_allocator", {6, 7}, &nd);
  ScopedAllocatorOptimizer::ExtendNodeAttr("_scoped_allocator", {2, 3}, &nd);
  VLOG(0) << "nd: " << nd.DebugString();
  std::vector<int> scoped_allocator_attrs;
  AttrSlice slice(nd);
  Status sa_status =
      GetNodeAttr(slice, "_scoped_allocator", &scoped_allocator_attrs);
  for (int i : scoped_allocator_attrs) {
    VLOG(0) << "extracted: " << i;
  }
  NodeDef nd2;
  AddNodeAttr("_scoped_allocator", {0, 2}, &nd2);
  AddNodeAttr("_scoped_allocator", {6, 7}, &nd2);
  AddNodeAttr("_scoped_allocator", {2, 3}, &nd2);
  VLOG(0) << "nd2: " << nd2.DebugString();
}

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