/* Copyright 2015 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/common_runtime/threadpool_device.h"

#include "tensorflow/core/common_runtime/local_device.h"
#include "tensorflow/core/common_runtime/scoped_allocator.h"
#include "tensorflow/core/common_runtime/scoped_allocator_mgr.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/allocator_registry.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.pb_text.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/graph/types.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/platform/tracing.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session_options.h"

#ifdef INTEL_MKL
#ifdef _OPENMP
#include <omp.h>
#endif
#include "tensorflow/core/common_runtime/mkl_cpu_allocator.h"
#include "tensorflow/core/platform/cpu_info.h"
#endif

namespace tensorflow {

ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options,
                                   const string& name, Bytes memory_limit,
                                   const DeviceLocality& locality,
                                   Allocator* allocator)
    : LocalDevice(options, Device::BuildDeviceAttributes(
                               name, DEVICE_CPU, memory_limit, locality)),
      allocator_(allocator),
      scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) {
#ifdef INTEL_MKL
#ifdef _OPENMP
  const char* user_omp_threads = getenv("OMP_NUM_THREADS");
  if (user_omp_threads == nullptr) {
    // OMP_NUM_THREADS controls MKL's intra-op parallelization
    // Default to available physical cores
    const int mkl_intra_op = port::NumSchedulableCPUs();
    const int ht = port::NumHyperthreadsPerCore();
    omp_set_num_threads((mkl_intra_op + ht - 1) / ht);
  } else {
    uint64 user_val = 0;
    if (strings::safe_strtou64(user_omp_threads, &user_val)) {
      // Superflous but triggers OpenMP loading
      omp_set_num_threads(user_val);
    }
  }
#endif  // _OPENMP
#endif  // INTEL_MKL
}

ThreadPoolDevice::~ThreadPoolDevice() {}

void ThreadPoolDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) {
  // When Xprof/ThreadScape profiling is off (which is the default), the
  // following code is simple enough that its overhead is negligible.
  tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(),
                                   op_kernel->IsExpensive());
  tracing::ScopedRegion region(tracing::EventCategory::kCompute,
                               op_kernel->name());

  op_kernel->Compute(context);
}

Allocator* ThreadPoolDevice::GetAllocator(AllocatorAttributes attr) {
  return allocator_;
}

Allocator* ThreadPoolDevice::GetScopedAllocator(AllocatorAttributes attr,
                                                int64 step_id) {
  if (attr.scope_id > 0) {
    return scoped_allocator_mgr_->GetContainer(step_id)->GetInstance(
        attr.scope_id);
  }
  LOG(FATAL) << "Unexpected call to ThreadPoolDevice::GetScopedAllocator "
             << "attr.scope_id = " << attr.scope_id;
  return allocator_;
}

Status ThreadPoolDevice::MakeTensorFromProto(
    const TensorProto& tensor_proto, const AllocatorAttributes alloc_attrs,
    Tensor* tensor) {
  if (tensor_proto.dtype() > 0 && tensor_proto.dtype() <= DataType_MAX) {
    Tensor parsed(tensor_proto.dtype());
    if (parsed.FromProto(cpu_allocator(), tensor_proto)) {
      *tensor = std::move(parsed);
      return Status::OK();
    }
  }
  return errors::InvalidArgument("Cannot parse tensor from proto: ",
                                 ProtoDebugString(tensor_proto));
}

#ifdef INTEL_MKL
REGISTER_MEM_ALLOCATOR("MklCPUAllocator", 200, MklCPUAllocator);
#endif

}  // namespace tensorflow
