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Fixing build of when TFLite and TF are linked in one binary.

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PiperOrigin-RevId: 344907413
Change-Id: Ib5f58788aebcef37f47bb0d8e2637223b844ce27
This commit is contained in:
A. Unique TensorFlower 2020-11-30 16:02:54 -08:00 committed by TensorFlower Gardener
parent db3b92e813
commit d73bbf5c61
3 changed files with 51 additions and 78 deletions
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4
tensorflow/core/util/BUILD
View File

@ -108,7 +108,6 @@ filegroup(
"ptr_util.h", "ptr_util.h",
"ragged_to_dense_util.cc", "ragged_to_dense_util.cc",
"ragged_to_dense_util.h", "ragged_to_dense_util.h",
"ragged_to_dense_util_common.cc",
"ragged_to_dense_util_common.h", "ragged_to_dense_util_common.h",
"reffed_status_callback.h", "reffed_status_callback.h",
"saved_tensor_slice_util.cc", "saved_tensor_slice_util.cc",
@ -388,9 +387,6 @@ cc_library(
cc_library( cc_library(
name = "ragged_to_dense_util_common", name = "ragged_to_dense_util_common",
srcs = [
"ragged_to_dense_util_common.cc",
],
hdrs = [ hdrs = [
"ragged_to_dense_util_common.h", "ragged_to_dense_util_common.h",
], ],

70
tensorflow/core/util/ragged_to_dense_util_common.cc
View File

@ -1,70 +0,0 @@
/* Copyright 2019 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/util/ragged_to_dense_util_common.h"
#include <unordered_map>
namespace tensorflow {
std::string RowPartitionTypeToString(RowPartitionType row_partition_type) {
switch (row_partition_type) {
case RowPartitionType::FIRST_DIM_SIZE:
return "FIRST_DIM_SIZE";
case RowPartitionType::VALUE_ROWIDS:
return "VALUE_ROWIDS";
case RowPartitionType::ROW_LENGTHS:
return "ROW_LENGTHS";
case RowPartitionType::ROW_SPLITS:
return "ROW_SPLITS";
case RowPartitionType::ROW_LIMITS:
return "ROW_LIMITS";
case RowPartitionType::ROW_STARTS:
return "ROW_STARTS";
default:
return "UNKNOWN ROW PARTITION TYPE";
}
}
std::vector<RowPartitionType> GetRowPartitionTypesHelper(
const std::vector<std::string>& row_partition_type_strings) {
static const auto kStringToType =
new std::unordered_map<std::string, RowPartitionType>(
{{"FIRST_DIM_SIZE", RowPartitionType::FIRST_DIM_SIZE},
{"VALUE_ROWIDS", RowPartitionType::VALUE_ROWIDS},
{"ROW_LENGTHS", RowPartitionType::ROW_LENGTHS},
{"ROW_SPLITS", RowPartitionType::ROW_SPLITS},
{"ROW_LIMITS", RowPartitionType::ROW_LIMITS},
{"ROW_STARTS", RowPartitionType::ROW_STARTS}});
std::vector<RowPartitionType> result;
for (const auto& type_str : row_partition_type_strings) {
const auto iter = kStringToType->find(type_str);
if (iter == kStringToType->end()) {
break;
}
result.push_back(iter->second);
}
return result;
}
int GetRaggedRank(const std::vector<RowPartitionType>& row_partition_types) {
if (row_partition_types.empty()) {
return 0;
}
if (row_partition_types[0] == RowPartitionType::FIRST_DIM_SIZE) {
return row_partition_types.size() - 1;
}
return row_partition_types.size();
}
} // namespace tensorflow

55
tensorflow/core/util/ragged_to_dense_util_common.h
View File

@ -17,6 +17,7 @@ limitations under the License.
#define TENSORFLOW_CORE_UTIL_RAGGED_TO_DENSE_UTIL_COMMON_H_ #define TENSORFLOW_CORE_UTIL_RAGGED_TO_DENSE_UTIL_COMMON_H_
#include <string> #include <string>
#include <unordered_map>
#include <vector> #include <vector>
namespace tensorflow { namespace tensorflow {
@ -29,12 +30,58 @@ enum class RowPartitionType {
ROW_STARTS ROW_STARTS
}; };
std::string RowPartitionTypeToString(RowPartitionType row_partition_type); inline std::string RowPartitionTypeToString(
RowPartitionType row_partition_type) {
switch (row_partition_type) {
case RowPartitionType::FIRST_DIM_SIZE:
return "FIRST_DIM_SIZE";
case RowPartitionType::VALUE_ROWIDS:
return "VALUE_ROWIDS";
case RowPartitionType::ROW_LENGTHS:
return "ROW_LENGTHS";
case RowPartitionType::ROW_SPLITS:
return "ROW_SPLITS";
case RowPartitionType::ROW_LIMITS:
return "ROW_LIMITS";
case RowPartitionType::ROW_STARTS:
return "ROW_STARTS";
default:
return "UNKNOWN ROW PARTITION TYPE";
}
}
std::vector<RowPartitionType> GetRowPartitionTypesHelper( inline std::vector<RowPartitionType> GetRowPartitionTypesHelper(
const std::vector<std::string>& row_partition_type_strings); const std::vector<std::string>& row_partition_type_strings) {
static const auto kStringToType =
new std::unordered_map<std::string, RowPartitionType>(
{{"FIRST_DIM_SIZE", RowPartitionType::FIRST_DIM_SIZE},
{"VALUE_ROWIDS", RowPartitionType::VALUE_ROWIDS},
{"ROW_LENGTHS", RowPartitionType::ROW_LENGTHS},
{"ROW_SPLITS", RowPartitionType::ROW_SPLITS},
{"ROW_LIMITS", RowPartitionType::ROW_LIMITS},
{"ROW_STARTS", RowPartitionType::ROW_STARTS}});
std::vector<RowPartitionType> result;
for (const auto& type_str : row_partition_type_strings) {
const auto iter = kStringToType->find(type_str);
if (iter == kStringToType->end()) {
break;
}
result.push_back(iter->second);
}
return result;
}
inline int GetRaggedRank(
const std::vector<RowPartitionType>& row_partition_types) {
if (row_partition_types.empty()) {
return 0;
}
if (row_partition_types[0] == RowPartitionType::FIRST_DIM_SIZE) {
return row_partition_types.size() - 1;
}
return row_partition_types.size();
}
int GetRaggedRank(const std::vector<RowPartitionType>& row_partition_types);
} // namespace tensorflow } // namespace tensorflow
#endif // TENSORFLOW_CORE_UTIL_RAGGED_TO_DENSE_UTIL_COMMON_H_ #endif // TENSORFLOW_CORE_UTIL_RAGGED_TO_DENSE_UTIL_COMMON_H_