tensorflow/compiler/mlir/tensorflow/transforms/mark_initialized_variables.cc

/* Copyright 2021 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/mlir/tensorflow/transforms/mark_initialized_variables.h"

#include <string>
#include <vector>

#include "llvm/ADT/SmallVector.h"
#include "mlir/IR/Block.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/Threading.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/session_utils.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/rendezvous.h"
#include "tensorflow/core/framework/resource_var.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/public/session.h"

namespace mlir {
namespace tf_saved_model {

// Returns true if the variable 'var_handle_op' is initialized in 'session'.
bool IsVariableInitialized(mlir::TF::VarHandleOp var_handle_op,
                           llvm::StringRef device_name,
                           const tensorflow::DeviceMgr* mgr,
                           tensorflow::Session* session) {
  auto* var_ptr = GetVariableFromSession(var_handle_op, device_name, mgr);
  if (!var_ptr) return false;
  auto* tensor = var_ptr->tensor();
  bool is_initialized = tensor && tensor->IsInitialized();
  var_ptr->Unref();
  return is_initialized;
}

LogicalResult MarkInitializedVariablesInFunction(func::FuncOp function,
                                                 tensorflow::Session* session) {
  if (!session || !llvm::hasSingleElement(function)) return success();
  Block& block = function.front();

  const tensorflow::DeviceMgr* mgr = nullptr;
  auto status = session->LocalDeviceManager(&mgr);
  if (!status.ok())
    return function->emitError("failed to fetch device manager: " +
                               status.error_message());

  // Fetch all varHandleOp in the function.
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;
  for (auto var_handle_op : block.getOps<TF::VarHandleOp>())
    var_ops.emplace_back(var_handle_op);

  // Get resources from Session.
  auto resource_tensors_or = GetResourcesFromSession(var_ops, session);
  if (!resource_tensors_or.ok())
    return function->emitError(resource_tensors_or.status().message().data());

  MLIRContext* context = function.getContext();
  for (auto var_and_tensor : llvm::zip(var_ops, resource_tensors_or.value())) {
    auto& var_op = std::get<0>(var_and_tensor);
    auto& resource_tensor = std::get<1>(var_and_tensor);
    bool is_variable_initialized = false;
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
      is_variable_initialized = true;
    } else {
      auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();
      is_variable_initialized =
          IsVariableInitialized(var_op, handle.device(), mgr, session);
    }
    var_op->setAttr("_is_initialized",
                    BoolAttr::get(context, is_variable_initialized));
  }
  return success();
}

LogicalResult MarkInitializedVariablesInFunction(ModuleOp module,
                                                 tensorflow::Session* session) {
  auto functions_range = module.getOps<func::FuncOp>();
  return mlir::failableParallelForEach(
      module.getContext(), functions_range.begin(), functions_range.end(),
      [&](func::FuncOp function) {
        return MarkInitializedVariablesInFunction(function, session);
      });
}

}  // namespace tf_saved_model
}  // namespace mlir