/* 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/compiler/jit/build_xla_ops_pass.h"

#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/resource_variable_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/compiler/jit/defs.h"
#include "tensorflow/compiler/jit/encapsulate_subgraphs_pass.h"
#include "tensorflow/compiler/jit/node_matchers.h"
#include "tensorflow/compiler/jit/test_util.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/public/session_options.h"

namespace tensorflow {
namespace {

class BuildXlaOpsTest : public ::testing::Test {
 protected:
  void SetUp() override {
    // This is needed to register the XLA_* devices.
    CHECK(DeviceFactory::AddDevices(
              SessionOptions(), "/job:localhost/replica:0/task:0", &devices_)
              .ok());
  }

 private:
  std::vector<std::unique_ptr<Device>> devices_;
};

using ::tensorflow::testing::FindNodeByName;
using ::tensorflow::testing::matchers::Attr;
using ::tensorflow::testing::matchers::CtrlDeps;
using ::tensorflow::testing::matchers::Inputs;
using ::tensorflow::testing::matchers::NodeWith;
using ::tensorflow::testing::matchers::Op;
using ::tensorflow::testing::matchers::Out;
using ::testing::_;

absl::Status BuildXlaOps(const Scope& s, const FunctionDefLibrary& fdef_lib,
                         std::unique_ptr<Graph>* result) {
  auto graph = std::make_unique<Graph>(OpRegistry::Global());
  TF_RETURN_IF_ERROR(s.ToGraph(graph.get()));
  FunctionLibraryDefinition flib_def(graph->op_registry(), fdef_lib);

  // Assign all nodes to the CPU device.
  static const char* kCpuDevice = "/job:localhost/replica:0/task:0/cpu:0";
  for (Node* n : graph->nodes()) {
    if (n->requested_device().empty()) {
      n->set_assigned_device_name(kCpuDevice);
    } else {
      n->set_assigned_device_name(n->requested_device());
    }
  }

  FixupSourceAndSinkEdges(graph.get());

  GraphOptimizationPassWrapper wrapper;
  GraphOptimizationPassOptions opt_options =
      wrapper.CreateGraphOptimizationPassOptions(&graph);
  opt_options.flib_def = &flib_def;

  BuildXlaOpsPass pass(/*enable_lazy_compilation=*/true);
  TF_RETURN_IF_ERROR(pass.Run(opt_options));
  VLOG(3) << graph->ToGraphDefDebug().DebugString();
  *result = std::move(graph);
  return absl::OkStatus();
}

absl::Status MakeXlaCompiledKernel(Graph* graph, const string& callee_name,
                                   const string& node_name,
                                   int num_constant_args, int num_resource_args,
                                   Node** result) {
  NodeDef call_node;
  call_node.set_name(node_name);
  call_node.set_op(callee_name);
  AddNodeAttr(kXlaCompiledKernelAttr, true, &call_node);
  AddNodeAttr(kXlaNumConstantArgsAttr, num_constant_args, &call_node);
  AddNodeAttr(kXlaNumResourceArgsAttr, num_resource_args, &call_node);
  TF_ASSIGN_OR_RETURN(*result, graph->AddNode(call_node));
  return absl::OkStatus();
}

absl::Status MakeXlaCompiledKernel(Graph* graph, const string& callee_name,
                                   const string& node_name, Node** result) {
  return MakeXlaCompiledKernel(graph, callee_name, node_name,
                               /*num_constant_args=*/0, /*num_resource_args=*/0,
                               result);
}

Node* MakeWrite(const Scope& scope, Output value_to_write, const string& id) {
  Output var_handle = ops::VarHandleOp(scope.WithOpName("Var_" + id), DT_FLOAT,
                                       TensorShape({}));
  ops::AssignVariableOp assign_op(scope.WithOpName("Assignee_" + id),
                                  var_handle, value_to_write);
  return assign_op.operation.node();
}

Node* MakeWrite(const Scope& scope, const string& id) {
  return MakeWrite(
      scope, ops::Const(scope.WithOpName("ValueToAssign" + id), 1.0f), id);
}

FunctionDefLibrary CreateFunctionDefLibWithConstFunction(const string& name) {
  FunctionDefLibrary fdef_lib;
  FunctionDef func = FunctionDefHelper::Create(
      /*function_name=*/name, /*in_def=*/{}, /*out_def=*/{"out: float"},
      /*attr_def*/
      {}, /*node_def=*/{FunctionDefHelper::Const("one", 1.0f)},
      /*ret_def=*/{{"out", "out:output:0"}});
  *fdef_lib.add_function() = std::move(func);
  return fdef_lib;
}

TEST_F(BuildXlaOpsTest, ControlDepsPreserved) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:XLA_CPU:0";
  Scope root = Scope::NewRootScope().WithDevice(kXlaDeviceName).ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithConstFunction("cluster_0");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));
  Node* call;
  TF_ASSERT_OK(MakeXlaCompiledKernel(root.graph(), "cluster_0", "C", &call));
  call->AddAttr(kXlaHasReferenceVarsAttr, false);
  call->set_requested_device(kXlaDeviceName);
  Node* write_op = MakeWrite(root, "write");
  write_op->AddAttr(kXlaHasReferenceVarsAttr, false);
  root.graph()->AddControlEdge(call, write_op);

  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(BuildXlaOps(root, fdef_lib, &graph));

  Node* write_op_new = FindNodeByName(graph.get(), write_op->name());
  ASSERT_NE(write_op_new, nullptr);
  EXPECT_THAT(write_op_new, NodeWith(CtrlDeps(NodeWith(Op("_XlaRun")))));
}

TEST_F(BuildXlaOpsTest, CleanFailureOnBogusAttr) {
  Scope root = Scope::NewRootScope().ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithConstFunction("cluster_0");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));

  Node* call;
  TF_ASSERT_OK(
      MakeXlaCompiledKernel(root.graph(), "cluster_0", "C", 100, 100, &call));

  Node* write_op = MakeWrite(root, "write");
  root.graph()->AddControlEdge(call, write_op);

  std::unique_ptr<Graph> graph;
  absl::Status failure_status = BuildXlaOps(root, fdef_lib, &graph);
  ASSERT_FALSE(failure_status.ok());
  EXPECT_EQ(failure_status.code(), error::INVALID_ARGUMENT);
}

TEST_F(BuildXlaOpsTest, OnNonXlaDevice) {
  Scope root = Scope::NewRootScope().ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithConstFunction("cluster_0");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));

  Node* call;
  TF_ASSERT_OK(MakeXlaCompiledKernel(root.graph(), "cluster_0", "C", &call));
  TF_ASSERT_OK(root.DoShapeInference(call));
  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  Node* write_op = MakeWrite(root, Output(call), "write_result");
  write_op->AddAttr(kXlaHasReferenceVarsAttr, false);

  auto xla_compile = NodeWith(Op("_XlaCompile"), Attr("must_compile", false));
  auto predicated_compilation_key =
      NodeWith(Op("Switch"), Inputs(Out(0, xla_compile), Out(1, xla_compile)));
  auto xla_run =
      NodeWith(Op("_XlaRun"), Inputs(Out(1, predicated_compilation_key)));
  auto tf_call =
      NodeWith(Op("StatefulPartitionedCall"),
               CtrlDeps(NodeWith(Op("Identity"),
                                 Inputs(Out(0, predicated_compilation_key)))));
  auto merge = NodeWith(Op("_XlaMerge"), Inputs(Out(tf_call), Out(xla_run)));
  auto assign_var = NodeWith(Op("AssignVariableOp"), Inputs(_, Out(merge)));

  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(BuildXlaOps(root, fdef_lib, &graph));

  Node* write_op_new = FindNodeByName(graph.get(), write_op->name());
  ASSERT_NE(write_op_new, nullptr);
  EXPECT_THAT(write_op_new, assign_var);
}

TEST_F(BuildXlaOpsTest, OnXlaDevice) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:XLA_CPU:0";
  Scope root = Scope::NewRootScope().WithDevice(kXlaDeviceName).ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithConstFunction("cluster_0");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));

  Node* call;
  TF_ASSERT_OK(MakeXlaCompiledKernel(root.graph(), "cluster_0", "C", &call));
  call->set_requested_device(kXlaDeviceName);
  TF_ASSERT_OK(root.DoShapeInference(call));
  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  Node* write_op = MakeWrite(root, Output(call), "write_result");
  write_op->AddAttr(kXlaHasReferenceVarsAttr, false);

  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(BuildXlaOps(root, fdef_lib, &graph));

  auto xla_op =
      NodeWith(Op("_XlaRun"), Inputs(Out(NodeWith(Op("_XlaCompile")))));
  auto assign_var =
      NodeWith(Op("AssignVariableOp"), Inputs(Out(NodeWith()), Out(xla_op)));

  Node* write_op_new = FindNodeByName(graph.get(), write_op->name());
  ASSERT_NE(write_op_new, nullptr);
  EXPECT_THAT(write_op_new, assign_var);
}

TEST_F(BuildXlaOpsTest, NoExtraMergeForEdgeToSink) {
  Scope root = Scope::NewRootScope().ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithConstFunction("cluster_0");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));
  Node* call;
  TF_ASSERT_OK(MakeXlaCompiledKernel(root.graph(), "cluster_0", "C", &call));
  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(BuildXlaOps(root, fdef_lib, &graph));

  Node* sink_node = graph->sink_node();
  EXPECT_THAT(sink_node,
              NodeWith(CtrlDeps(NodeWith(Op("_XlaRun")),
                                NodeWith(Op("StatefulPartitionedCall")),
                                NodeWith(Op("NoOp")))));
}

#ifdef GOOGLE_CUDA
FunctionDefLibrary CreateFunctionDefLibWithInt32Input(const string& name) {
  FunctionDefLibrary fdef_lib;
  FunctionDef func = FunctionDefHelper::Create(
      /*function_name=*/name, /*in_def=*/{"in: int32"},
      /*out_def=*/{"out: int32"},
      /*attr_def=*/{}, /*node_def=*/{{{"out"}, "Identity", {"in"}}},
      /*ret_def=*/{{"out", "out:output:0"}});
  *fdef_lib.add_function() = std::move(func);
  return fdef_lib;
}

// This tests a rewrite that only makes sense and is active in a CUDA-enabled
// build.  Specifically we check that we insert an IdentityN op to avoid extra
// device-to-host copies.
TEST_F(BuildXlaOpsTest, NoDeviceToHostCopiesForClustersWithInt32Inputs) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:GPU:0";
  Scope root = Scope::NewRootScope()
                   .WithDevice(kXlaDeviceName)
                   .WithAssignedDevice(kXlaDeviceName)
                   .ExitOnError();

  FunctionDefLibrary fdef_lib =
      CreateFunctionDefLibWithInt32Input("cluster_int32");
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(fdef_lib));
  Node* call;
  TF_ASSERT_OK(
      MakeXlaCompiledKernel(root.graph(), "cluster_int32", "C", &call));
  call->set_requested_device(kXlaDeviceName);
  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  auto var =
      ops::VarHandleOp(root.WithOpName("var"), DT_INT32, TensorShape({}));
  auto int32_on_device =
      ops::ReadVariableOp(root.WithOpName("int32_on_device"), var, DT_INT32);

  root.graph()->AddEdge(int32_on_device.node(), 0, call, 0);

  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(BuildXlaOps(root, fdef_lib, &graph));

  Node* stateful_partitioned_call_op = nullptr;
  for (Node* n : graph->op_nodes()) {
    if (n->type_string() == "StatefulPartitionedCall") {
      ASSERT_EQ(stateful_partitioned_call_op, nullptr);
      stateful_partitioned_call_op = n;
    }
  }

  ASSERT_NE(stateful_partitioned_call_op, nullptr);
  auto xla_compile = NodeWith(Op("_XlaCompile"));
  auto switch_on_compilation_pred =
      NodeWith(Op("Switch"), Inputs(Out(0, xla_compile), Out(1, xla_compile)));
  auto ctrl_dep =
      NodeWith(Op("Identity"), Inputs(Out(0, switch_on_compilation_pred)));
  // Check that we pipe int32 inputs through an IdentityN to avoid extra D2H
  // copies.
  EXPECT_THAT(
      stateful_partitioned_call_op,
      NodeWith(Inputs(Out(NodeWith(Op("IdentityN"), CtrlDeps(ctrl_dep))))));
}
#endif

}  // namespace
}  // namespace tensorflow
