/* 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/compiler/tf2xla/graph_compiler.h"

#include <deque>
#include <numeric>
#include <vector>
#include "tensorflow/compiler/tf2xla/const_analysis.h"
#include "tensorflow/compiler/tf2xla/literal_util.h"
#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/side_effect_util.h"
#include "tensorflow/compiler/tf2xla/type_util.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_context.h"
#include "tensorflow/compiler/tf2xla/xla_expression.h"
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/xla/client/client_library.h"
#include "tensorflow/compiler/xla/client/xla_builder.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/executor.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/common_runtime/graph_optimizer.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/graph/graph_constructor.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/validate.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/version.h"
#include "tensorflow/core/util/dump_graph.h"

namespace tensorflow {

namespace {
Status PrepareArguments(XlaOpKernelContext* ctx, Graph* graph,
                        const std::vector<const XlaExpression*>& expressions,
                        std::vector<XlaCompiler::Argument>* args) {
  auto client = ctx->compiler()->client();
  std::vector<bool> arg_must_be_compile_time_constant(expressions.size());

  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(
      *graph, &arg_must_be_compile_time_constant,
      /*compile_time_const_nodes=*/nullptr, ctx->function_library()));

  args->resize(expressions.size());
  for (int i = 0; i < args->size(); ++i) {
    XlaCompiler::Argument& arg = (*args)[i];
    arg.type = ctx->input_type(i);
    arg.shape = ctx->InputShape(i);

    switch (expressions[i]->kind()) {
      case XlaExpression::Kind::kConstant:
        arg.kind = XlaCompiler::Argument::kConstant;
        arg.constant_value = expressions[i]->constant_value();
        break;
      case XlaExpression::Kind::kXlaOp:
        if (arg_must_be_compile_time_constant[i]) {
          TF_ASSIGN_OR_RETURN(absl::optional<Tensor> value,
                              expressions[i]->ResolveConstant(client));
          if (!value.has_value()) {
            return errors::InvalidArgument(
                "Argument to function must be a compile-time constant, but "
                "unable to resolve argument value to a constant.");
          }
          arg.kind = XlaCompiler::Argument::kConstant;
          arg.constant_value = *value;
        } else {
          arg.kind = XlaCompiler::Argument::kParameter;
        }
        break;
      case XlaExpression::Kind::kResource: {
        XlaResource* resource = expressions[i]->resource();

        arg.initialized = resource->initialized();
        arg.kind = XlaCompiler::Argument::kResource;
        arg.resource_kind = resource->kind();
        arg.type = resource->type();
        arg.shape = resource->shape();
        arg.max_array_size = resource->max_array_size();
        arg.name = resource->name();
        break;
      }
      case XlaExpression::Kind::kTensorList: {
        arg.kind = XlaCompiler::Argument::kTensorList;
        const xla::XlaOp& tensor_list = expressions[i]->handle();
        arg.shape = tensor_list.builder()->GetShape(tensor_list).ValueOrDie();
        break;
      }
      case XlaExpression::Kind::kInvalid:
        return errors::InvalidArgument("Invalid function argument");
    }
  }
  return Status::OK();
}
}  // namespace
Status GraphCompiler::Compile() {
  // Check that the graph has no illegal cycles.
  TF_RETURN_IF_ERROR(graph::ValidateGraphHasNoCycle(*graph_));
  // Maintain a mapping from node id to node outputs.
  using NodeOutputs = std::vector<TensorValue>;
  std::vector<NodeOutputs> output_registry(graph_->num_node_ids());
  auto output_registry_cleanup = gtl::MakeCleanup([&output_registry] {
    for (const NodeOutputs& outputs : output_registry) {
      for (const TensorValue& value : outputs) {
        CHECK(!value.is_ref());
        delete value.tensor;
      }
    }
  });

  // XLA requires determinism, generate a stable ordering from DFS.
  std::vector<Node*> topo_sorted_nodes;
  GetReversePostOrder(*graph_, &topo_sorted_nodes,
                      /*stable_comparator=*/NodeComparatorName());

  OpKernelContext::Params params;
  PartiallySetupParams(&params);

  for (Node* n : topo_sorted_nodes) {
    OpKernel* op_kernel_raw = nullptr;
    // The kernel is not actually run for functional ops, we just need it
    // for metadata.
    Status s = flib_->CreateKernel(n->def(), &op_kernel_raw);
    // Transfer ownership of the kernel to a local smart pointer.
    std::unique_ptr<OpKernel> op_kernel(op_kernel_raw);

    if (!s.ok()) {
      s = AttachDef(s, *n);
      LOG(ERROR) << "Executor failed to create kernel. " << s;
      return s;
    }

    TF_RET_CHECK(!n->IsRecv() && !n->IsSend() && !n->IsSwitch())
        << "Not supported node: " << n->DebugString();
    params.op_kernel = op_kernel.get();
    absl::InlinedVector<AllocatorAttributes, 4> output_attr(n->num_outputs());
    params.output_attr_array = output_attr.data();

    // tensor_inputs_ is a buffer reused across graph traversal. We clean up and
    // reinitialize the buffer before we visit a new node.
    tensor_inputs_.clear();
    tensor_inputs_.resize(n->num_inputs());

    // Set up inputs from outputs of previous nodes.
    for (auto* e : n->in_edges()) {
      if (e->IsControlEdge()) continue;
      const Node* src = e->src();
      TF_RET_CHECK(src->id() < output_registry.size());
      const NodeOutputs& src_outputs = output_registry[src->id()];

      tensor_inputs_.at(e->dst_input()) = src_outputs.at(e->src_output());
    }

    OpKernelContext op_context(&params, n->num_outputs());
    VLOG(3) << "Translating " << params.op_kernel->name();
    if (IsFunctionCall(*flib_->GetFunctionLibraryDefinition(), *n)) {
      TF_RETURN_IF_ERROR(CompileFunctionalNode(n, &op_context));
    } else {
      device_->Compute(CHECK_NOTNULL(params.op_kernel), &op_context);
      Status s = op_context.status();
      if (!s.ok()) {
        return AttachDef(s, n->def());
      }
    }

    // Set up outputs. Also check if outputs from the previous computation is
    // valid.
    NodeOutputs& outputs = output_registry[n->id()];
    outputs.resize(n->num_outputs());
    for (int o = 0; o < n->num_outputs(); ++o) {
      outputs[o] = op_context.release_output(o);
      if (outputs[o].tensor == nullptr) {
        return errors::Internal("Missing xla_context ", o, "-th output from ",
                                FormatNodeForError(*n));
      }
    }
  }
  return Status::OK();
}

namespace {

Status GetFunctionNameAndAttr(const FunctionLibraryRuntime& flib,
                              const Node& node, NameAttrList* func) {
  if (node.IsPartitionedCall()) {
    const AttrValue* attr_value;
    TF_RETURN_IF_ERROR(
        node.attrs().Find(FunctionLibraryDefinition::kFuncAttr, &attr_value));
    if (!attr_value->has_func()) {
      return errors::InvalidArgument(
          "The attribute value for attribute 'f' in node ", node.DebugString(),
          " does not have 'func' field set");
    }
    *func = attr_value->func();
    return Status::OK();
  }

  if (flib.GetFunctionLibraryDefinition()->Find(node.def().op())) {
    func->set_name(node.type_string());
  } else {
    func->set_name(FunctionLibraryDefinition::kGradientOp);
  }
  *func->mutable_attr() = node.def().attr();
  return Status::OK();
}

}  // namespace

Status GraphCompiler::CompileFunctionalNode(Node* n,
                                            OpKernelContext* op_context) {
  TF_RET_CHECK(IsFunctionCall(*flib_->GetFunctionLibraryDefinition(), *n));
  // For functional nodes, compile them using compiler from the context and call
  // into the functions.
  XlaOpKernelContext xla_op_context(op_context);

  XlaContext& context = XlaContext::Get(op_context);
  auto* b = context.builder();

  XlaCompiler* compiler = xla_op_context.compiler();

  NameAttrList func;
  TF_RETURN_IF_ERROR(GetFunctionNameAndAttr(*flib_, *n, &func));

  std::vector<const XlaExpression*> expressions;

  for (auto tensor : tensor_inputs_) {
    auto expression =
        reinterpret_cast<const XlaExpression*>(tensor->tensor_data().data());
    expressions.push_back(expression);
  }

  // Prepare the arguments and compile the function.
  std::vector<XlaCompiler::Argument> arguments;
  const FunctionBody* fbody;
  TF_RETURN_IF_ERROR(compiler->FindFunctionBody(func, &fbody));

  auto graph = compiler->GetGraph(fbody);

  TF_RETURN_IF_ERROR(
      PrepareArguments(&xla_op_context, graph.get(), expressions, &arguments));

  bool add_token_input_output =
      func.attr().find(kXlaTokenInputNodesAttrName) != func.attr().end();

  XlaCompiler::CompileOptions compile_options;
  compile_options.is_entry_computation = false;
  compile_options.add_token_input_output = add_token_input_output;
  XlaCompiler::CompilationResult result;
  TF_RETURN_IF_ERROR(
      compiler->CompileFunction(compile_options, func, arguments, &result));

  TF_RET_CHECK(arguments.size() == expressions.size());

  std::vector<xla::XlaOp> handles;
  for (int64 i = 0; i < expressions.size(); ++i) {
    if (arguments[i].kind == XlaCompiler::Argument::kConstant) {
      continue;
    }
    if (arguments[i].kind == XlaCompiler::Argument::kResource) {
      handles.push_back(expressions[i]->resource()->value());
    } else {
      handles.push_back(expressions[i]->handle());
    }
  }
  if (add_token_input_output) {
    std::vector<string> token_input_nodes;
    TF_RETURN_IF_ERROR(GetNodeAttr(AttrSlice(&func.attr()),
                                   kXlaTokenInputNodesAttrName,
                                   &token_input_nodes));
    std::vector<xla::XlaOp> token_inputs;
    for (const string& node_name : token_input_nodes) {
      auto token_or = compiler->GetNodeToken(node_name);
      TF_RETURN_IF_ERROR(token_or.status());
      token_inputs.push_back(token_or.ConsumeValueOrDie());
    }
    xla::XlaOp token_input = xla::AfterAll(b, token_inputs);
    handles.push_back(token_input);
  }

  auto output_handle = xla::Call(b, *result.computation, handles);
  // The output handle of `Call` computation is a tuple type. Unzip it so
  // that it can fit into future computations.
  int computation_output = 0;
  for (int64 i = 0; i < n->num_outputs(); ++i) {
    if (result.outputs[i].is_constant) {
      xla_op_context.SetConstantOutput(i, result.outputs[i].constant_value);
    } else {
      if (result.outputs[i].is_tensor_list) {
        xla_op_context.SetTensorListOutput(
            i, xla::GetTupleElement(output_handle, computation_output));
      } else {
        xla_op_context.SetOutput(
            i, xla::GetTupleElement(output_handle, computation_output));
      }
      ++computation_output;
    }
  }

  for (int64 i = 0; i < result.resource_updates.size(); i++) {
    if (result.resource_updates[i].modified) {
      XlaResource* resource =
          expressions[result.resource_updates[i].input_index]->resource();
      xla::XlaOp updated_value =
          xla::GetTupleElement(output_handle, i + n->num_outputs());
      TF_RETURN_IF_ERROR(resource->SetValue(updated_value));
    }
  }

  if (add_token_input_output) {
    TF_RETURN_IF_ERROR(compiler->SetNodeToken(
        n->name(), xla::GetTupleElement(output_handle, computation_output)));
  }
  return b->first_error();
}

void GraphCompiler::PartiallySetupParams(OpKernelContext::Params* params) {
  params->device = device_;
  params->inputs = &tensor_inputs_;
  params->step_container = step_container_;
  params->resource_manager = device_->resource_manager();
  params->function_library = flib_;
}

}  // namespace tensorflow
