text: /* 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/core/common_runtime/eager/attr_builder.h"
  
  #include "tensorflow/core/common_runtime/device_factory.h"
  #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
  #include "tensorflow/core/framework/allocator.h"
  #include "tensorflow/core/framework/node_def.pb.h"
  #include "tensorflow/core/lib/core/errors.h"
  #include "tensorflow/core/lib/gtl/map_util.h"
  #include "tensorflow/core/lib/gtl/stl_util.h"
  #include "tensorflow/core/platform/fingerprint.h"
  #include "tensorflow/core/platform/mutex.h"
  #include "tensorflow/core/public/version.h"
  #include "tensorflow/core/util/tensor_slice_reader_cache.h"
  
  namespace tensorflow {
  namespace {
  
  mutex g_op_name_to_attr_type_map_lock(LINKER_INITIALIZED);
  
  std::unordered_map<string, const AttrTypeMap*>* OpNameToAttrTypeMap() {
    static auto* const m = new std::unordered_map<string, const AttrTypeMap*>;
    return m;
  }
  
  const uint32 kIsList = 1U << 31;
  
  AttrTypeMap* DefaultFunctionAttrTypeMap() {
    AttrTypeMap* map = new AttrTypeMap();
    (*map)["executor_type"] = TF_ATTR_STRING;
    (*map)["config_proto"] = TF_ATTR_STRING;
    return map;
  }
  
  const AttrTypeMap* GetDefaultFunctionAttrTypeMap() {
    static const AttrTypeMap* map = DefaultFunctionAttrTypeMap();
    return map;
  }
  
  }  // namespace
  
  Status OpDefForOp(const char* op_name, const OpDef** op_def) {
    const OpRegistrationData* op_reg_data = nullptr;
    Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data);
    if (s.ok()) {
      *op_def = &op_reg_data->op_def;
    }
    return s;
  }
  
  Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out,
                          bool* is_function) {
    mutex_lock l(g_op_name_to_attr_type_map_lock);
    *is_function = false;
    *out = gtl::FindPtrOrNull(*OpNameToAttrTypeMap(), op_name);
    if (*out != nullptr) return Status::OK();
    const OpDef* op_def = nullptr;
    Status s = OpDefForOp(op_name, &op_def);
    if (errors::IsNotFound(s)) {
      // If we did not find the op def, we assume `op_name` is a function.
      // If it is actually a misspelled op, user will get another error when
      // trying to run it.
      // TODO(iga): If we ever have a use case for different attribute specs
      // in different functions, we will need to look at the OpDef in the
      // function def to retrieve their types.
      *out = GetDefaultFunctionAttrTypeMap();
      *is_function = true;
      return Status::OK();
    } else if (!s.ok()) {
      return s;
    }
    std::unique_ptr<AttrTypeMap> m(new AttrTypeMap);
    // TODO(agarwal): Avoid having to create this "registry" at runtime,
    // perhaps can be done at op registration time?
    for (const auto& attr : op_def->attr()) {
      string type = attr.type();
      const bool is_list = (type.length() > 6 && type.compare(0, 4, "list") == 0);
      if (is_list) {
        type = type.substr(5, type.length() - 6);
      }
      uint32 t = is_list ? kIsList : 0;
      if (type == "string") {
        t |= TF_ATTR_STRING;
      } else if (type == "int") {
        t |= TF_ATTR_INT;
      } else if (type == "float") {
        t |= TF_ATTR_FLOAT;
      } else if (type == "bool") {
        t |= TF_ATTR_BOOL;
      } else if (type == "type") {
        t |= TF_ATTR_TYPE;
      } else if (type == "shape") {
        t |= TF_ATTR_SHAPE;
      } else if (type == "tensor") {
        t |= TF_ATTR_TENSOR;
      } else if (type == "func") {
        t |= TF_ATTR_FUNC;
      } else {
        return errors::Unimplemented(
            "TODO(agarwal): Enable support for ops with attributes of type '",
            type, "'");
      }
      gtl::InsertIfNotPresent(m.get(), attr.name(), t);
    }
    *out = m.get();
    (*OpNameToAttrTypeMap())[op_name] = m.release();
    return Status::OK();
  }
  
  #define DEFINE_GET_ATTR(TYPE, FIELD, ATTR_TYPE)                         \
    template <>                                                           \
    Status AttrBuilder::Get(StringPiece attr_name, TYPE* value) const {   \
      auto it = encoded_attrs_.find(string(attr_name));                   \
      if (it == encoded_attrs_.end()) {                                   \
        return errors::NotFound("No attr named'", attr_name,              \
                                "' found in AttrBuilder for ", op_name_); \
      }                                                                   \
      attr_tmp_.ParseFromString(it->second);                              \
      TF_RETURN_IF_ERROR(AttrValueHasType(attr_tmp_, ATTR_TYPE));         \
      *value = attr_tmp_.FIELD();                                         \
      return Status::OK();                                                \
    }
  
  DEFINE_GET_ATTR(float, f, "float");
  DEFINE_GET_ATTR(int, i, "int");
  DEFINE_GET_ATTR(bool, b, "bool");
  DEFINE_GET_ATTR(tensorflow::DataType, type, "type");
  
  #undef DEFINE_GET_ATTR
  
  AttrBuilder& AttrBuilder::NumInputs(int n) {
    DCHECK(!node_def_finalized_) << "Calling NumInputs after BuildNodeDef.";
    num_inputs_ = n;
    return *this;
  }
  
  void AttrBuilder::FillAttrValueMap(AttrValueMap* m) const {
    for (auto& entry : encoded_attrs_) {
      attr_tmp_.ParseFromString(entry.second);
      m->insert(AttrValueMap::value_type(entry.first, attr_tmp_));
    }
    // For any attr-value pairs that exist in the op def (from op registry) but
    // not `m`, fill them into `m`, so that we can run a TFE_Op without having to
    // specify all the default attr values (e.g. for matmul, the `transpose_a`
    // attr defaults to false).
    const OpDef* op_def = nullptr;
    Status s = OpDefForOp(op_name_.c_str(), &op_def);
    // This is expected, if this op is a custom function, and is therefore not
    // present in the op registry.
    if (!s.ok()) return;
  
    DCHECK(op_def);
    for (const auto& attr_def : op_def->attr()) {
      if (attr_def.has_default_value() && !m->count(attr_def.name())) {
        SetInAttrValueMap(m, attr_def.name(), attr_def.default_value());
      }
    }
  }
  
  void AttrBuilder::AddAttrIfNotPresent(StringPiece attr_name,
                                        const AttrValue& value) {
    encoded_attrs_.emplace(string(attr_name), value.SerializeAsString());
  }
  
  const NodeDef& AttrBuilder::BuildNodeDef() {
    if (node_def_finalized_) return *node_def_;
    MayBeInitializeNodeDef();
    for (int i = 0; i < num_inputs_; ++i) {
      node_def_->add_input("dummy_input");
    }
    FillAttrValueMap(node_def_->mutable_attr());
    node_def_finalized_ = true;
    return *node_def_;
  }
  
  Status AttrTypeByName(const AttrTypeMap& m, const string& attr_name,
                        TF_AttrType* out, unsigned char* is_list) {
    auto* t = gtl::FindOrNull(m, attr_name);
    if (t == nullptr) {
      return errors::InvalidArgument("Attribute '", attr_name,
                                     "' does not exist for this operation");
    }
    *out = static_cast<TF_AttrType>(*t & ~kIsList);
    if (*t & kIsList) {
      *is_list = 1;
    } else {
      *is_list = 0;
    }
    return Status::OK();
  }
  
  namespace {
  inline tensorflow::Fprint128 FingerprintCat128(const tensorflow::Fprint128& a,
                                                 const tensorflow::Fprint128& b) {
    return {tensorflow::FingerprintCat64(a.low64, b.low64),
            tensorflow::FingerprintCat64(a.high64, b.high64)};
  }
  
  void CombineUnordered(const tensorflow::Fprint128& a,
                        tensorflow::Fprint128* b) {
    b->low64 += a.low64;
    b->high64 += a.high64;
  }
  
  inline tensorflow::Fprint128 CacheKeyHelper(StringPiece s,
                                              const tensorflow::Fprint128& b) {
    tensorflow::Fprint128 a = tensorflow::Fingerprint128(s);
    return FingerprintCat128(a, b);
  }
  
  inline tensorflow::Fprint128 CacheKeyHelper(StringPiece s, uint64 b) {
    return CacheKeyHelper(s, {b, b});
  }
  
  }  // namespace
  
  tensorflow::Fprint128 AttrBuilder::CacheKey(const StringPiece device) {
    if (!cached_cache_key_ || device != device_for_cached_cache_key_) {
      cached_cache_key_ = BuildCacheKeyForDevice(device);
      device_for_cached_cache_key_ = string(device);
    }
  
    return *cached_cache_key_;
  }
  
  tensorflow::Fprint128 AttrBuilder::BuildCacheKeyForDevice(
      const StringPiece device) const {
    tensorflow::Fprint128 f = tensorflow::Fingerprint128(op_name_);
    f = tensorflow::FingerprintCat128(f, tensorflow::Fingerprint128(device));
    for (const auto& p : encoded_attrs_) {
      CombineUnordered(
          CacheKeyHelper(p.first, tensorflow::Fingerprint128(p.second)), &f);
    }
    return f;
  }
  
  void AttrBuilder::MayBeInitializeNodeDef() {
    if (node_def_ == nullptr) {
      node_def_.reset(new NodeDef());
      node_def_->set_name(op_name_);
      node_def_->set_op(op_name_);
    }
  }
  
  }  // namespace tensorflow
  