/* 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/xla_context.h"

#include <memory>
#include <utility>
#include <vector>

#include "absl/types/span.h"
#include "tensorflow/compiler/tf2xla/literal_util.h"
#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/type_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.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/compiler/xla/client/xla_computation.h"
#include "tensorflow/compiler/xla/layout_util.h"
#include "tensorflow/compiler/xla/literal.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/core/common_runtime/dma_helper.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

const char XlaContext::kXlaContextResourceName[] = "_xla_context";

// Looks up the context associated with the current step. It is stored
// in a resource container managed by the device.
/* static */ XlaContext& XlaContext::Get(const OpKernelContext* ctx) {
  // When an Op kernel wants to use an XLA JIT context, the
  // per-step context is looked up in the resource manager. The
  // JIT will prepopulate the JITContext.
  XlaContext* context;
  TF_CHECK_OK(ctx->resource_manager()->Lookup(
      ctx->step_container()->name(), kXlaContextResourceName, &context));
  // The resource manager handed us a fresh reference to 'context', but retains
  // a reference itself so the context won't be freed. The resource manager will
  // outlive the JIT compilation.
  context->Unref();
  return *context;
}

/* static */ XlaContext& XlaContext::Get(const XlaOpKernelContext* ctx) {
  return Get(ctx->op_kernel_context());
}

void XlaContext::set_args(std::vector<XlaExpression> args) {
  args_ = std::move(args);
}

XlaContext::XlaContext(
    XlaCompiler* compiler, xla::XlaBuilder* builder,
    bool allow_cpu_custom_calls, bool resolve_compile_time_constants,
    bool is_entry_computation,
    const std::function<xla::StatusOr<TensorShape>(
        const TensorShape&, DataType)>* shape_representation_fn)
    : compiler_(compiler),
      builder_(builder),
      allow_cpu_custom_calls_(allow_cpu_custom_calls),
      resolve_compile_time_constants_(resolve_compile_time_constants),
      is_entry_computation_(is_entry_computation),
      shape_representation_fn_(shape_representation_fn) {}

string XlaContext::DebugString() { return "TLA JIT context"; }

// This is called by the Retval Op to associate a computed value
// with a specific return value of the subgraph.
void XlaContext::AddRetval(int retval_index, DataType type,
                           const TensorShape& shape, const xla::XlaOp& handle) {
  VLOG(1) << "Added retval index " << retval_index << " to XLA computation";
  // Add the return value to the list being built up.
  if (retvals_.size() <= retval_index) {
    retvals_.resize(retval_index + 1);
  }
  XlaExpression e;
  e.set_handle(handle);
  retvals_[retval_index] = Retval{type, shape, e};
}

Status XlaContext::AddConstRetval(int retval_index, DataType dtype,
                                  const xla::LiteralSlice& literal) {
  VLOG(1) << "Adding retval index " << retval_index
          << " with non-data-dependent tensor to XLA computation";
  if (retvals_.size() <= retval_index) {
    retvals_.resize(retval_index + 1);
  }
  Tensor value;
  TF_RETURN_IF_ERROR(LiteralToHostTensor(literal, dtype, &value));
  XlaExpression e;
  e.set_constant_value(value);
  retvals_[retval_index] = Retval{dtype, value.shape(), e};
  return Status::OK();
}

Status XlaContext::AddResourceRetval(int retval_index, XlaResource* resource) {
  VLOG(1) << "Adding retval index " << retval_index << " with resource "
          << resource->name() << ":" << resource->shape().DebugString()
          << " to XLA computation";
  if (retvals_.size() <= retval_index) {
    retvals_.resize(retval_index + 1);
  }
  XlaExpression e;
  e.set_resource(resource);
  retvals_[retval_index] = Retval{DT_RESOURCE, resource->shape(), e};
  return Status::OK();
}

Status XlaContext::AppendTokenRetval(const xla::XlaOp& token) {
  VLOG(1) << "Adding retval index " << retvals_.size()
          << " with token to XLA computation";
  XlaExpression e;
  e.set_handle(token);
  // We use DT_INVALID because there is no TF DataType which corresponds to XLA
  // token. XlaCompiler handles this case separately, so putting it here is OK.
  retvals_.push_back(Retval{DT_INVALID, TensorShape(), e});
  return Status::OK();
}

xla::XlaBuilder* XlaContext::builder() { return builder_; }

Status XlaContext::CreateResource(
    XlaResource::Kind kind, int arg_num, string name, DataType type,
    TensorShape shape, const xla::XlaOp& handle, int64 tensor_array_size,
    const std::set<string>& tensor_array_gradients, XlaResource** resource) {
  resources_.emplace_back(
      new XlaResource(kind, arg_num, std::move(name), type, std::move(shape),
                      handle, tensor_array_size, tensor_array_gradients));
  *resource = resources_.back().get();
  return Status::OK();
}

xla::StatusOr<TensorShape> XlaContext::RepresentationShape(
    const TensorShape& shape, DataType type) const {
  return (*shape_representation_fn_)(shape, type);
}

const xla::XlaComputation* XlaContext::GetOrCreateMax(const DataType type) {
  return LookupOrCreate(type, &max_func_, [this, type] {
    const string type_string = DataTypeString(type);
    VLOG(1) << "Building Max() for " << type_string;
    xla::XlaBuilder b("max<" + type_string + ">");
    xla::PrimitiveType xla_type;
    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
    auto x =
        xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
    auto y =
        xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y");
    xla::Max(x, y);
    return b.Build().ConsumeValueOrDie();
  });
}

const xla::XlaComputation* XlaContext::GetOrCreateMin(const DataType type) {
  return LookupOrCreate(type, &min_func_, [this, type] {
    const string type_string = DataTypeString(type);
    VLOG(1) << "Building Min() for " << type_string;
    xla::XlaBuilder b("min<" + type_string + ">");
    xla::PrimitiveType xla_type;
    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
    auto x =
        xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
    auto y =
        xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y");
    xla::Min(x, y);
    return b.Build().ConsumeValueOrDie();
  });
}

const xla::XlaComputation* XlaContext::GetOrCreateAdd(const DataType type) {
  return LookupOrCreate(type, &add_func_, [this, type] {
    const string type_string = DataTypeString(type);
    VLOG(1) << "Building Add() for " << type_string;
    xla::XlaBuilder b("add<" + type_string + ">");
    xla::PrimitiveType xla_type;
    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
    auto x =
        xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
    auto y =
        xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y");
    xla::Add(x, y);
    return b.Build().ConsumeValueOrDie();
  });
}

const xla::XlaComputation* XlaContext::GetOrCreateMul(const DataType type) {
  return LookupOrCreate(type, &mul_func_, [this, type] {
    const string type_string = DataTypeString(type);
    VLOG(1) << "Building Mul() for " << type_string;
    xla::XlaBuilder b("mul<" + type_string + ">");
    xla::PrimitiveType xla_type;
    TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type));
    auto x =
        xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x");
    auto y =
        xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y");
    xla::Mul(x, y);
    return b.Build().ConsumeValueOrDie();
  });
}

const xla::XlaComputation* XlaContext::LookupOrCreate(
    DataType type, ComputationMap* out,
    const std::function<xla::XlaComputation()>& create) {
  {
    const auto& entry = (*out)[type];
    if (!entry.IsNull()) {
      return &entry;
    }
  }
  auto new_entry = create();
  {
    // Somebody else might have made one concurrently.
    auto& entry = (*out)[type];
    if (entry.IsNull()) {
      entry = std::move(new_entry);
    }
    return &entry;
  }
}

}  // namespace tensorflow