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Experimental C API and framework build target for the benchmark tool

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PiperOrigin-RevId: 279883195
Change-Id: I3212b33b2be4e97cf6f50e9fb25756988d5fad7e
This commit is contained in:
YoungSeok Yoon 2019-11-11 21:36:17 -08:00 committed by TensorFlower Gardener
parent 7d8e271251
commit 7089aaa395
6 changed files with 989 additions and 1 deletions

3
configure.py
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@ -60,7 +60,8 @@ NCCL_LIB_PATHS = [
APPLE_BAZEL_FILES = [
'tensorflow/lite/experimental/ios/BUILD',
'tensorflow/lite/experimental/objc/BUILD',
'tensorflow/lite/experimental/swift/BUILD'
'tensorflow/lite/experimental/swift/BUILD',
'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]
# List of files to move when building for iOS.

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tensorflow/lite/tools/benchmark/experimental/c/BUILD Normal file
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load("//tensorflow/lite:build_def.bzl", "tflite_copts")
package(
default_visibility = ["benchmark"],
licenses = ["notice"], # Apache 2.0
)
package_group(
name = "benchmark",
packages = [
"//tensorflow/lite/tools/benchmark/...",
],
)
cc_library(
name = "benchmark_c_api",
srcs = ["benchmark_c_api.cc"],
hdrs = [
"benchmark_c_api.h",
"c_api_types.h",
],
copts = tflite_copts(),
visibility = [
"benchmark",
],
deps = [
"//tensorflow/core:stats_calculator_portable",
"//tensorflow/lite/tools/benchmark:benchmark_tflite_model_lib",
],
)

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tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.cc Normal file
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/* 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/lite/tools/benchmark/experimental/c/benchmark_c_api.h"
#include "tensorflow/core/util/stats_calculator.h"
#include "tensorflow/lite/tools/benchmark/benchmark_tflite_model.h"
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkResults type.
// -----------------------------------------------------------------------------
struct TfLiteBenchmarkResults {
const tflite::benchmark::BenchmarkResults* results;
};
// Converts the given int64_t stat into a TfLiteBenchmarkInt64Stat struct.
TfLiteBenchmarkInt64Stat ConvertStat(const tensorflow::Stat<int64_t>& stat) {
return {
stat.empty(), stat.first(), stat.newest(), stat.max(),
stat.min(), stat.count(), stat.sum(), stat.squared_sum(),
stat.all_same(), stat.avg(), stat.std_deviation(),
};
}
TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetInferenceTimeMicroseconds(
const TfLiteBenchmarkResults* results) {
return ConvertStat(results->results->inference_time_us());
}
TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetWarmupTimeMicroseconds(
const TfLiteBenchmarkResults* results) {
return ConvertStat(results->results->warmup_time_us());
}
int64_t TfLiteBenchmarkResultsGetStartupLatencyMicroseconds(
const TfLiteBenchmarkResults* results) {
return results->results->startup_latency_us();
}
uint64_t TfLiteBenchmarkResultsGetInputBytes(
const TfLiteBenchmarkResults* results) {
return results->results->input_bytes();
}
double TfLiteBenchmarkResultsGetThroughputMbPerSecond(
const TfLiteBenchmarkResults* results) {
return results->results->throughput_MB_per_second();
}
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkListener type.
// -----------------------------------------------------------------------------
class BenchmarkListenerAdapter : public tflite::benchmark::BenchmarkListener {
public:
void OnBenchmarkStart(
const tflite::benchmark::BenchmarkParams& params) override {
if (on_benchmark_start_fn_ != nullptr) {
on_benchmark_start_fn_();
}
}
void OnSingleRunStart(tflite::benchmark::RunType runType) override {
if (on_single_run_start_fn_ != nullptr) {
on_single_run_start_fn_(runType == tflite::benchmark::WARMUP
? TfLiteBenchmarkWarmup
: TfLiteBenchmarkRegular);
}
}
void OnSingleRunEnd() override {
if (on_single_run_end_fn_ != nullptr) {
on_single_run_end_fn_();
}
}
void OnBenchmarkEnd(
const tflite::benchmark::BenchmarkResults& results) override {
if (on_benchmark_end_fn_ != nullptr) {
TfLiteBenchmarkResults* wrapper = new TfLiteBenchmarkResults{&results};
on_benchmark_end_fn_(wrapper);
delete wrapper;
}
}
// Function pointers set by the TfLiteBenchmarkListenerSetCallbacks call.
// Only non-null callbacks will be actually called.
void (*on_benchmark_start_fn_)();
void (*on_single_run_start_fn_)(TfLiteBenchmarkRunType runType);
void (*on_single_run_end_fn_)();
void (*on_benchmark_end_fn_)(TfLiteBenchmarkResults* results);
};
struct TfLiteBenchmarkListener {
std::unique_ptr<BenchmarkListenerAdapter> adapter;
};
TfLiteBenchmarkListener* TfLiteBenchmarkListenerCreate() {
std::unique_ptr<BenchmarkListenerAdapter> adapter(
new BenchmarkListenerAdapter());
return new TfLiteBenchmarkListener{std::move(adapter)};
}
void TfLiteBenchmarkListenerDelete(TfLiteBenchmarkListener* listener) {
delete listener;
}
void TfLiteBenchmarkListenerSetCallbacks(
TfLiteBenchmarkListener* listener, void (*on_benchmark_start_fn)(),
void (*on_single_run_start_fn)(TfLiteBenchmarkRunType runType),
void (*on_single_run_end_fn)(),
void (*on_benchmark_end_fn)(TfLiteBenchmarkResults* results)) {
listener->adapter->on_benchmark_start_fn_ = on_benchmark_start_fn;
listener->adapter->on_single_run_start_fn_ = on_single_run_start_fn;
listener->adapter->on_single_run_end_fn_ = on_single_run_end_fn;
listener->adapter->on_benchmark_end_fn_ = on_benchmark_end_fn;
}
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkTfLiteModel type.
// -----------------------------------------------------------------------------
struct TfLiteBenchmarkTfLiteModel {
std::unique_ptr<tflite::benchmark::BenchmarkTfLiteModel> benchmark_model;
};
TfLiteBenchmarkTfLiteModel* TfLiteBenchmarkTfLiteModelCreate() {
std::unique_ptr<tflite::benchmark::BenchmarkTfLiteModel> benchmark_model(
new tflite::benchmark::BenchmarkTfLiteModel());
return new TfLiteBenchmarkTfLiteModel{std::move(benchmark_model)};
}
void TfLiteBenchmarkTfLiteModelDelete(
TfLiteBenchmarkTfLiteModel* benchmark_model) {
delete benchmark_model;
}
TfLiteStatus TfLiteBenchmarkTfLiteModelInit(
TfLiteBenchmarkTfLiteModel* benchmark_model) {
return benchmark_model->benchmark_model->Init();
}
TfLiteStatus TfLiteBenchmarkTfLiteModelRun(
TfLiteBenchmarkTfLiteModel* benchmark_model) {
return benchmark_model->benchmark_model->Run();
}
TfLiteStatus TfLiteBenchmarkTfLiteModelRunWithArgs(
TfLiteBenchmarkTfLiteModel* benchmark_model, int argc, char** argv) {
return benchmark_model->benchmark_model->Run(argc, argv);
}
void TfLiteBenchmarkTfLiteModelAddListener(
TfLiteBenchmarkTfLiteModel* benchmark_model,
const TfLiteBenchmarkListener* listener) {
return benchmark_model->benchmark_model->AddListener(listener->adapter.get());
}
#ifdef __cplusplus
}
#endif // __cplusplus

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tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.h Normal file
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_
#define TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_
#include "c_api_types.h"
// -----------------------------------------------------------------------------
// Experimental C APIs for the benchmark tool, mainly intended to be used for
// building a standalone TensorFlow Lite benchmark framework for iOS. This
// header only has a minimal dependency to the C API types, which can be
// included in the framework itself.
// -----------------------------------------------------------------------------
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
typedef enum {
TfLiteBenchmarkWarmup,
TfLiteBenchmarkRegular,
} TfLiteBenchmarkRunType;
// -----------------------------------------------------------------------------
// C APIs corresponding to tensorflow::Stat<int64_t> type.
// -----------------------------------------------------------------------------
typedef struct TfLiteBenchmarkInt64Stat {
bool empty;
int64_t first;
int64_t newest;
int64_t max;
int64_t min;
int64_t count;
int64_t sum;
double squared_sum;
bool all_same;
double avg;
int64_t std_deviation;
} TfLiteBenchmarkInt64Stat;
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkResults type.
// -----------------------------------------------------------------------------
typedef struct TfLiteBenchmarkResults TfLiteBenchmarkResults;
extern TfLiteBenchmarkInt64Stat
TfLiteBenchmarkResultsGetInferenceTimeMicroseconds(
const TfLiteBenchmarkResults* results);
extern TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetWarmupTimeMicroseconds(
const TfLiteBenchmarkResults* results);
extern int64_t TfLiteBenchmarkResultsGetStartupLatencyMicroseconds(
const TfLiteBenchmarkResults* results);
extern uint64_t TfLiteBenchmarkResultsGetInputBytes(
const TfLiteBenchmarkResults* results);
extern double TfLiteBenchmarkResultsGetThroughputMbPerSecond(
const TfLiteBenchmarkResults* results);
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkListener type.
// -----------------------------------------------------------------------------
typedef struct TfLiteBenchmarkListener TfLiteBenchmarkListener;
extern TfLiteBenchmarkListener* TfLiteBenchmarkListenerCreate();
extern void TfLiteBenchmarkListenerDelete(TfLiteBenchmarkListener* listener);
// Sets the listener callbacks. Only non-null callback functions will be called
// when the following events occur.
//
// - on_benchmark_start: Called before the (outer) inference loop begins. Note
// that this is called *after* the interpreter has been initialized, but
// *before* any warmup runs have been executed.
// - on_single_run_start: Called before a single (inner) inference call starts.
// - on_single_run_end: Called before a single (inner) inference call ends.
// - on_benchmark_end: Called after the (outer) inference loop ends.
//
// In case of `on_benchmark_end` callback, the passed in `results` pointer is
// only valid during the callback function execution, and will be destroyed
// afterwards.
extern void TfLiteBenchmarkListenerSetCallbacks(
TfLiteBenchmarkListener* listener, void (*on_benchmark_start_fn)(),
void (*on_single_run_start_fn)(TfLiteBenchmarkRunType runType),
void (*on_single_run_end_fn)(),
void (*on_benchmark_end_fn)(TfLiteBenchmarkResults* results));
// -----------------------------------------------------------------------------
// C APIs corresponding to tflite::benchmark::BenchmarkTfLiteModel type.
// -----------------------------------------------------------------------------
typedef struct TfLiteBenchmarkTfLiteModel TfLiteBenchmarkTfLiteModel;
// TODO(b/144321502): Support BenchmarkParams.
extern TfLiteBenchmarkTfLiteModel* TfLiteBenchmarkTfLiteModelCreate();
extern void TfLiteBenchmarkTfLiteModelDelete(
TfLiteBenchmarkTfLiteModel* benchmark_model);
extern TfLiteStatus TfLiteBenchmarkTfLiteModelInit(
TfLiteBenchmarkTfLiteModel* benchmark_model);
extern TfLiteStatus TfLiteBenchmarkTfLiteModelRun(
TfLiteBenchmarkTfLiteModel* benchmark_model);
extern TfLiteStatus TfLiteBenchmarkTfLiteModelRunWithArgs(
TfLiteBenchmarkTfLiteModel* benchmark_model, int argc, char** argv);
extern void TfLiteBenchmarkTfLiteModelAddListener(
TfLiteBenchmarkTfLiteModel* benchmark_model,
const TfLiteBenchmarkListener* listener);
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
#endif // TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_

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/* 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.
==============================================================================*/
// This file defines a C API for implementing operations in tflite.
// These operations can be defined using c++ but the interface between
// the interpreter and the operations are C.
//
// Summary of abstractions
// TF_LITE_ENSURE - Self-sufficient error checking
// TfLiteStatus - Status reporting
// TfLiteIntArray - stores tensor shapes (dims),
// TfLiteContext - allows an op to access the tensors
// TfLiteTensor - tensor (a multidimensional array)
// TfLiteNode - a single node or operation
// TfLiteRegistration - the implementation of a conceptual operation.
//
// Some abstractions in this file are created and managed by Interpreter.
#ifndef TENSORFLOW_LITE_C_C_API_INTERNAL_H_
#define TENSORFLOW_LITE_C_C_API_INTERNAL_H_
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
typedef enum { kTfLiteOk = 0, kTfLiteError = 1 } TfLiteStatus;
// The list of external context types known to TF Lite. This list exists solely
// to avoid conflicts and to ensure ops can share the external contexts they
// need. Access to the external contexts is controled by one of the
// corresponding support files.
typedef enum {
kTfLiteEigenContext = 0, // include eigen_support.h to use.
kTfLiteGemmLowpContext = 1, // include gemm_support.h to use.
kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support.
kTfLiteCpuBackendContext = 3, // include cpu_backend_support.h to use.
kTfLiteMaxExternalContexts = 4
} TfLiteExternalContextType;
// Forward declare so dependent structs and methods can reference these types
// prior to the struct definitions.
struct TfLiteContext;
struct TfLiteDelegate;
struct TfLiteRegistration;
// An external context is a collection of information unrelated to the TF Lite
// framework, but useful to a subset of the ops. TF Lite knows very little
// about about the actual contexts, but it keeps a list of them, and is able to
// refresh them if configurations like the number of recommended threads
// change.
typedef struct {
TfLiteExternalContextType type;
TfLiteStatus (*Refresh)(struct TfLiteContext* context);
} TfLiteExternalContext;
#define kOptionalTensor (-1)
// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
// indices
typedef struct {
int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1
int data[0];
#else
int data[];
#endif
} TfLiteIntArray;
// Given the size (number of elements) in a TfLiteIntArray, calculate its size
// in bytes.
int TfLiteIntArrayGetSizeInBytes(int size);
// Create a array of a given `size` (uninitialized entries).
// This returns a pointer, that you must free using TfLiteIntArrayFree().
TfLiteIntArray* TfLiteIntArrayCreate(int size);
// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b);
// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
const int b_data[]);
// Create a copy of an array passed as `src`.
// You are expected to free memory with TfLiteIntArrayFree
TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src);
// Free memory of array `a`.
void TfLiteIntArrayFree(TfLiteIntArray* a);
// Fixed size list of floats. Used for per-channel quantization.
typedef struct {
int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1
float data[0];
#else
float data[];
#endif
} TfLiteFloatArray;
// Given the size (number of elements) in a TfLiteFloatArray, calculate its size
// in bytes.
int TfLiteFloatArrayGetSizeInBytes(int size);
// Create a array of a given `size` (uninitialized entries).
// This returns a pointer, that you must free using TfLiteFloatArrayFree().
TfLiteFloatArray* TfLiteFloatArrayCreate(int size);
// Free memory of array `a`.
void TfLiteFloatArrayFree(TfLiteFloatArray* a);
// Since we must not depend on any libraries, define a minimal subset of
// error macros while avoiding names that have pre-conceived meanings like
// assert and check.
// Check whether value is true, and if not return kTfLiteError from
// the current function (and report the error string msg).
#define TF_LITE_ENSURE_MSG(context, value, msg) \
do { \
if (!(value)) { \
(context)->ReportError((context), __FILE__ " " msg); \
return kTfLiteError; \
} \
} while (0)
// Check whether the value `a` is true, and if not return kTfLiteError from
// the current function, while also reporting the location of the error.
#define TF_LITE_ENSURE(context, a) \
do { \
if (!(a)) { \
(context)->ReportError((context), "%s:%d %s was not true.", __FILE__, \
__LINE__, #a); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_STATUS(a) \
do { \
if ((a) != kTfLiteOk) { \
return kTfLiteError; \
} \
} while (0)
// Check whether the value `a == b` is true, and if not return kTfLiteError from
// the current function, while also reporting the location of the error.
// `a` and `b` may be evaluated more than once, so no side effects or
// extremely expensive computations should be done.
#define TF_LITE_ENSURE_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
(context)->ReportError((context), "%s:%d %s != %s (%d != %d)", __FILE__, \
__LINE__, #a, #b, (a), (b)); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_TYPES_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
(context)->ReportError((context), "%s:%d %s != %s (%s != %s)", __FILE__, \
__LINE__, #a, #b, TfLiteTypeGetName(a), \
TfLiteTypeGetName(b)); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_OK(context, status) \
do { \
if ((status) != kTfLiteOk) { \
return kTfLiteError; \
} \
} while (0)
// Single-precision complex data type compatible with the C99 definition.
typedef struct {
float re, im; // real and imaginary parts, respectively.
} TfLiteComplex64;
// Half precision data type compatible with the C99 definition.
typedef struct {
uint16_t data;
} TfLiteFloat16;
// Types supported by tensor
typedef enum {
kTfLiteNoType = 0,
kTfLiteFloat32 = 1,
kTfLiteInt32 = 2,
kTfLiteUInt8 = 3,
kTfLiteInt64 = 4,
kTfLiteString = 5,
kTfLiteBool = 6,
kTfLiteInt16 = 7,
kTfLiteComplex64 = 8,
kTfLiteInt8 = 9,
kTfLiteFloat16 = 10,
} TfLiteType;
// Return the name of a given type, for error reporting purposes.
const char* TfLiteTypeGetName(TfLiteType type);
// SupportedQuantizationTypes.
typedef enum {
// No quantization.
kTfLiteNoQuantization = 0,
// Affine quantization (with support for per-channel quantization).
// Corresponds to TfLiteAffineQuantization.
kTfLiteAffineQuantization = 1,
} TfLiteQuantizationType;
// Structure specifying the quantization used by the tensor, if-any.
typedef struct {
// The type of quantization held by params.
TfLiteQuantizationType type;
// Holds a reference to one of the quantization param structures specified
// below.
void* params;
} TfLiteQuantization;
// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
// If per-layer quantization is specified this field will still be populated in
// addition to TfLiteAffineQuantization.
// Parameters for asymmetric quantization. Quantized values can be converted
// back to float using:
// real_value = scale * (quantized_value - zero_point)
typedef struct {
float scale;
int32_t zero_point;
} TfLiteQuantizationParams;
// Parameters for asymmetric quantization across a dimension (i.e per output
// channel quantization).
// quantized_dimension specifies which dimension the scales and zero_points
// correspond to.
// For a particular value in quantized_dimension, quantized values can be
// converted back to float using:
// real_value = scale * (quantized_value - zero_point)
typedef struct {
TfLiteFloatArray* scale;
TfLiteIntArray* zero_point;
int32_t quantized_dimension;
} TfLiteAffineQuantization;
/* A union of pointers that points to memory for a given tensor. */
typedef union {
/* Do not access these members directly, if possible, use
* GetTensorData<TYPE>(tensor) instead, otherwise only access .data, as other
* members are deprecated. */
int32_t* i32;
int64_t* i64;
float* f;
TfLiteFloat16* f16;
char* raw;
const char* raw_const;
uint8_t* uint8;
bool* b;
int16_t* i16;
TfLiteComplex64* c64;
int8_t* int8;
/* Only use this member. */
void* data;
} TfLitePtrUnion;
// Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped
// data (or data externally allocated). kTfLiteArenaRw is arena allocated
// data. kTfLiteDynamic is for tensors that are allocated during evaluation.
typedef enum {
kTfLiteMemNone = 0,
kTfLiteMmapRo,
kTfLiteArenaRw,
kTfLiteArenaRwPersistent,
kTfLiteDynamic,
} TfLiteAllocationType;
// The delegates should use zero or positive integers to represent handles.
// -1 is reserved from unallocated status.
typedef int TfLiteBufferHandle;
enum {
kTfLiteNullBufferHandle = -1,
};
// An tensor in the interpreter system which is a wrapper around a buffer of
// data including a dimensionality (or NULL if not currently defined).
typedef struct {
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
// A union of data pointers. The appropriate type should be used for a typed
// tensor based on `type`.
TfLitePtrUnion data;
// A pointer to a structure representing the dimensionality interpretation
// that the buffer should have. NOTE: the product of elements of `dims`
// and the element datatype size should be equal to `bytes` below.
TfLiteIntArray* dims;
// Quantization information.
TfLiteQuantizationParams params;
// How memory is mapped
// kTfLiteMmapRo: Memory mapped read only.
// i.e. weights
// kTfLiteArenaRw: Arena allocated read write memory
// (i.e. temporaries, outputs).
TfLiteAllocationType allocation_type;
// The number of bytes required to store the data of this Tensor. I.e.
// (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if
// type is kTfLiteFloat32 and dims = {3, 2} then
// bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
size_t bytes;
// An opaque pointer to a tflite::MMapAllocation
const void* allocation;
// Null-terminated name of this tensor.
const char* name;
// The delegate which knows how to handle `buffer_handle`.
// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
// An integer buffer handle that can be handled by `delegate`.
// The value is valid only when delegate is not null.
// WARNING: This is an experimental interface that is subject to change.
TfLiteBufferHandle buffer_handle;
// If the delegate uses its own buffer (e.g. GPU memory), the delegate is
// responsible to set data_is_stale to true.
// `delegate->CopyFromBufferHandle` can be called to copy the data from
// delegate buffer.
// WARNING: This is an // experimental interface that is subject to change.
bool data_is_stale;
// True if the tensor is a variable.
bool is_variable;
// Quantization information. Replaces params field above.
TfLiteQuantization quantization;
} TfLiteTensor;
// Free data memory of tensor `t`.
void TfLiteTensorDataFree(TfLiteTensor* t);
// Free quantization data.
void TfLiteQuantizationFree(TfLiteQuantization* quantization);
// Free memory of tensor `t`.
void TfLiteTensorFree(TfLiteTensor* t);
// Set all of a tensor's fields (and free any previously allocated data).
void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
TfLiteQuantizationParams quantization, char* buffer,
size_t size, TfLiteAllocationType allocation_type,
const void* allocation, bool is_variable,
TfLiteTensor* tensor);
// Resize the allocated data of a (dynamic) tensor. Tensors with allocation
// types other than kTfLiteDynamic will be ignored.
void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor);
// A structure representing an instance of a node.
// This structure only exhibits the inputs, outputs and user defined data, not
// other features like the type.
typedef struct {
// Inputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* inputs;
// Outputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* outputs;
// intermediate tensors to this node expressed as indices into the simulator's
// tensors.
TfLiteIntArray* intermediates;
// Temporary tensors uses during the computations. This usually contains no
// tensors, but ops are allowed to change that if they need scratch space of
// any sort.
TfLiteIntArray* temporaries;
// Opaque data provided by the node implementer through `Registration.init`.
void* user_data;
// Opaque data provided to the node if the node is a builtin. This is usually
// a structure defined in builtin_op_data.h
void* builtin_data;
// Custom initial data. This is the opaque data provided in the flatbuffer.
// WARNING: This is an experimental interface that is subject to change.
const void* custom_initial_data;
int custom_initial_data_size;
// The pointer to the delegate. This is non-null only when the node is
// created by calling `interpreter.ModifyGraphWithDelegate`.
// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
} TfLiteNode;
typedef struct TfLiteContext {
// Number of tensors in the context.
size_t tensors_size;
// The execution plan contains a list of the node indices in execution
// order. execution_plan->size is the current number of nodes. And,
// execution_plan->data[0] is the first node that needs to be run.
// TfLiteDelegates can traverse the current execution plan by iterating
// through each member of this array and using GetNodeAndRegistration() to
// access details about a node. i.e.
// TfLiteIntArray* execution_plan;
// TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan));
// for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) {
// int node_index = execution_plan->data[exec_index];
// TfLiteNode* node;
// TfLiteRegistration* reg;
// context->GetNodeAndRegistration(context, node_index, &node, &reg);
// }
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
TfLiteIntArray** execution_plan);
// An array of tensors in the interpreter context (of length `tensors_size`)
TfLiteTensor* tensors;
// opaque full context ptr (an opaque c++ data structure)
void* impl_;
// Request memory pointer be resized. Updates dimensions on the tensor.
// NOTE: ResizeTensor takes ownership of newSize.
TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor,
TfLiteIntArray* new_size);
// Request that an error be reported with format string msg.
void (*ReportError)(struct TfLiteContext*, const char* msg, ...);
// Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If
// non-null, the value pointed to by `first_new_tensor_index` will be set to
// the index of the first new tensor.
TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add,
int* first_new_tensor_index);
// Get a Tensor node by node_index.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetNodeAndRegistration)(
struct TfLiteContext*, int node_index, TfLiteNode** node,
struct TfLiteRegistration** registration);
// Replace ops with one or more stub delegate operations. This function
// does not take ownership of `nodes_to_replace`.
TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)(
struct TfLiteContext*, struct TfLiteRegistration registration,
const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate);
// Number of threads that are recommended to subsystems like gemmlowp and
// eigen.
int recommended_num_threads;
// Access external contexts by type.
// WARNING: This is an experimental interface that is subject to change.
TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*,
TfLiteExternalContextType);
// Set the value of a external context. Does not take ownership of the
// pointer.
// WARNING: This is an experimental interface that is subject to change.
void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType,
TfLiteExternalContext*);
// Flag for allowing float16 precision for FP32 calculation.
// default: false.
// WARNING: This is an experimental API and subject to change.
bool allow_fp32_relax_to_fp16;
// Pointer to the op-level profiler, if set; nullptr otherwise.
void* profiler;
} TfLiteContext;
typedef struct TfLiteRegistration {
// Initializes the op from serialized data.
// If a built-in op:
// `buffer` is the op's params data (TfLiteLSTMParams*).
// `length` is zero.
// If custom op:
// `buffer` is the op's `custom_options`.
// `length` is the size of the buffer.
//
// Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
// or an instance of a struct).
//
// The returned pointer will be stored with the node in the `user_data` field,
// accessible within prepare and invoke functions below.
// NOTE: if the data is already in the desired format, simply implement this
// function to return `nullptr` and implement the free function to be a no-op.
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
// The pointer `buffer` is the data previously returned by an init invocation.
void (*free)(TfLiteContext* context, void* buffer);
// prepare is called when the inputs this node depends on have been resized.
// context->ResizeTensor() can be called to request output tensors to be
// resized.
//
// Returns kTfLiteOk on success.
TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
// Execute the node (should read node->inputs and output to node->outputs).
// Returns kTfLiteOk on success.
TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
// profiling_string is called during summarization of profiling information
// in order to group executions together. Providing a value here will cause a
// given op to appear multiple times is the profiling report. This is
// particularly useful for custom ops that can perform significantly
// different calculations depending on their `user-data`.
const char* (*profiling_string)(const TfLiteContext* context,
const TfLiteNode* node);
// Builtin codes. If this kernel refers to a builtin this is the code
// of the builtin. This is so we can do marshaling to other frameworks like
// NN API.
// Note: It is the responsibility of the registration binder to set this
// properly.
int32_t builtin_code;
// Custom op name. If the op is a builtin, this will be null.
// Note: It is the responsibility of the registration binder to set this
// properly.
// WARNING: This is an experimental interface that is subject to change.
const char* custom_name;
// The version of the op.
// Note: It is the responsibility of the registration binder to set this
// properly.
int version;
} TfLiteRegistration;
// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
// values should be 1, 2, 4, 8, ...etc.
typedef enum {
kTfLiteDelegateFlagsNone = 0,
// The flag is set if the delegate can handle dynamic sized tensors.
// For example, the output shape of a `Resize` op with non-constant shape
// can only be inferred when the op is invoked.
// In this case, the Delegate is responsible for calling
// `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
// `ResizeTensor` when invoking the op.
//
// If the delegate isn't capable to handle dynamic tensors, this flag need
// to be set to false.
kTfLiteDelegateFlagsAllowDynamicTensors = 1
} TfLiteDelegateFlags;
// WARNING: This is an experimental interface that is subject to change.
typedef struct TfLiteDelegate {
// Data that delegate needs to identify itself. This data is owned by the
// delegate. The delegate is owned in the user code, so the delegate is
// responsible for doing this when it is destroyed.
void* data_;
// Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
// delegate a view of the current graph through TfLiteContext*. It typically
// will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
// to ask the TensorFlow lite runtime to create macro-nodes to represent
// delegated subgraphs of the original graph.
TfLiteStatus (*Prepare)(TfLiteContext* context,
struct TfLiteDelegate* delegate);
// Copy the data from delegate buffer handle into raw memory of the given
// 'tensor'. This cannot be null. The delegate is allowed to allocate the raw
// bytes as long as it follows the rules for kTfLiteDynamic tensors.
TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
// Copy the data from raw memory of the given 'tensor' to delegate buffer
// handle. This can be null if the delegate doesn't use its own buffer.
TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
// Free the Delegate Buffer Handle. Note: This only frees the handle, but
// this doesn't release the underlying resource (e.g. textures). The
// resources are either owned by application layer or the delegate.
// This can be null if the delegate doesn't use its own buffer.
void (*FreeBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle* handle);
// Bitmask flags. See the comments in `TfLiteDelegateFlags`.
int64_t flags;
} TfLiteDelegate;
// Build a 'null' delegate, with all the fields properly set to their default
// values.
TfLiteDelegate TfLiteDelegateCreate();
// WARNING: This is an experimental interface that is subject to change.
//
// Currently, TfLiteDelegateParams has to be allocated in a way that it's
// trivially destructable. It will be stored as `builtin_data` field in
// `TfLiteNode` of the delegate node.
//
// See also the `CreateDelegateParams` function in `interpreter.cc` details.
typedef struct {
TfLiteDelegate* delegate;
TfLiteIntArray* nodes_to_replace;
TfLiteIntArray* input_tensors;
TfLiteIntArray* output_tensors;
} TfLiteDelegateParams;
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
#endif // TENSORFLOW_LITE_C_C_API_INTERNAL_H_

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tensorflow/lite/tools/benchmark/experimental/ios/BUILD.apple Normal file
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load("//tensorflow/lite/experimental/ios:ios.bzl", "TFL_MINIMUM_OS_VERSION")
load("@build_bazel_rules_apple//apple:ios.bzl", "ios_static_framework")
package(
licenses = ["notice"], # Apache 2.0
)
# Main target for the benchmark tool iOS framework.
# bazel build --config=ios_fat -c opt //tensorflow/lite/tools/benchmark/experimental/ios:TensorFlowLiteBenchmarkC_framework
ios_static_framework(
name = "TensorFlowLiteBenchmarkC_framework",
hdrs = [
"//tensorflow/lite/tools/benchmark:logging.h",
"//tensorflow/lite/tools/benchmark/experimental/c:benchmark_c_api.h",
"//tensorflow/lite/tools/benchmark/experimental/c:c_api_types.h",
],
bundle_name = "TensorFlowLiteBenchmarkC",
minimum_os_version = TFL_MINIMUM_OS_VERSION,
deps = [
"//tensorflow/lite/tools/benchmark/experimental/c:benchmark_c_api",
],
)