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Update tflite

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jomjol
2020-11-08 03:27:52 +01:00
parent 05a0f6fa62
commit 84cea8e3d6
169 changed files with 16367 additions and 11456 deletions
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282
code/lib/tfmicro/tensorflow/lite/c/common.h
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@@ -29,6 +29,9 @@ limitations under the License.
// TfLiteDelegate - allows delegation of nodes to alternative backends.
//
// Some abstractions in this file are created and managed by Interpreter.
//
// NOTE: The order of values in these structs are "semi-ABI stable". New values
// should be added only to the end of structs and never reordered.
#ifndef TENSORFLOW_LITE_C_COMMON_H_
#define TENSORFLOW_LITE_C_COMMON_H_
@@ -43,8 +46,18 @@ extern "C" {
typedef enum TfLiteStatus {
kTfLiteOk = 0,
// Generally referring to an error in the runtime (i.e. interpreter)
kTfLiteError = 1,
kTfLiteDelegateError = 2
// Generally referring to an error from a TfLiteDelegate itself.
kTfLiteDelegateError = 2,
// Generally referring to an error in applying a delegate due to
// incompatibility between runtime and delegate, e.g., this error is returned
// when trying to apply a TfLite delegate onto a model graph that's already
// immutable.
kTfLiteApplicationError = 3
} TfLiteStatus;
// The list of external context types known to TF Lite. This list exists solely
@@ -55,7 +68,7 @@ typedef enum TfLiteExternalContextType {
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.
kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use.
kTfLiteMaxExternalContexts = 4
} TfLiteExternalContextType;
@@ -83,8 +96,9 @@ typedef struct TfLiteIntArray {
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
#if (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1) || \
defined(HEXAGON) || (__clang_major__ == 7 && __clang_minor__ == 1)
int data[0];
#else
int data[];
@@ -122,6 +136,7 @@ typedef struct TfLiteFloatArray {
int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
// This also applies to the toolchain used for Qualcomm Hexagon DSPs.
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1
float data[0];
@@ -200,6 +215,7 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a);
// 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.
// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes.
#define TF_LITE_ENSURE_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
@@ -219,6 +235,17 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a);
} \
} while (0)
#define TF_LITE_ENSURE_NEAR(context, a, b, epsilon) \
do { \
auto delta = ((a) > (b)) ? ((a) - (b)) : ((b) - (a)); \
if (delta > epsilon) { \
TF_LITE_KERNEL_LOG((context), "%s:%d %s not near %s (%f != %f)", \
__FILE__, __LINE__, #a, #b, static_cast<double>(a), \
static_cast<double>(b)); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_OK(context, status) \
do { \
const TfLiteStatus s = (status); \
@@ -227,11 +254,32 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a);
} \
} while (0)
// Define TFL_CAPI_EXPORT macro to export a function properly with a shared
// library.
#ifdef SWIG
#define TFL_CAPI_EXPORT
#else
#if defined(_WIN32)
#ifdef TFL_COMPILE_LIBRARY
#define TFL_CAPI_EXPORT __declspec(dllexport)
#else
#define TFL_CAPI_EXPORT __declspec(dllimport)
#endif // TFL_COMPILE_LIBRARY
#else
#define TFL_CAPI_EXPORT __attribute__((visibility("default")))
#endif // _WIN32
#endif // SWIG
// Single-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex64 {
float re, im; // real and imaginary parts, respectively.
} TfLiteComplex64;
// Double-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex128 {
double re, im; // real and imaginary parts, respectively.
} TfLiteComplex128;
// Half precision data type compatible with the C99 definition.
typedef struct TfLiteFloat16 {
uint16_t data;
@@ -251,6 +299,7 @@ typedef enum {
kTfLiteInt8 = 9,
kTfLiteFloat16 = 10,
kTfLiteFloat64 = 11,
kTfLiteComplex128 = 12,
} TfLiteType;
// Return the name of a given type, for error reporting purposes.
@@ -307,26 +356,39 @@ typedef union TfLitePtrUnion {
int64_t* i64;
float* f;
TfLiteFloat16* f16;
double* f64;
char* raw;
const char* raw_const;
uint8_t* uint8;
bool* b;
int16_t* i16;
TfLiteComplex64* c64;
TfLiteComplex128* c128;
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.
// Memory allocation strategies.
// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated.
// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence,
// and available during eval.
// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and
// only available during eval.
// * kTfLiteDynamic: Allocated during eval, or for string tensors.
// * kTfLitePersistentRo: Allocated and populated during prepare. This is
// useful for tensors that can be computed during prepare and treated
// as constant inputs for downstream ops (also in prepare).
// * kTfLiteCustom: Custom memory allocation provided by the user. See
// TfLiteCustomAllocation below.
typedef enum TfLiteAllocationType {
kTfLiteMemNone = 0,
kTfLiteMmapRo,
kTfLiteArenaRw,
kTfLiteArenaRwPersistent,
kTfLiteDynamic,
kTfLitePersistentRo,
kTfLiteCustom,
} TfLiteAllocationType;
// The delegates should use zero or positive integers to represent handles.
@@ -359,8 +421,18 @@ typedef struct TfLiteSparsity {
int dim_metadata_size;
} TfLiteSparsity;
// An tensor in the interpreter system which is a wrapper around a buffer of
// Defines a custom memory allocation not owned by the runtime.
// `data` should be aligned to kDefaultTensorAlignment defined in
// lite/util.h. (Currently 64 bytes)
// NOTE: See Interpreter.SetCustomAllocationForTensor for details on usage.
typedef struct TfLiteCustomAllocation {
void* data;
size_t bytes;
} TfLiteCustomAllocation;
// A tensor in the interpreter system which is a wrapper around a buffer of
// data including a dimensionality (or NULL if not currently defined).
#ifndef TF_LITE_STATIC_MEMORY
typedef struct TfLiteTensor {
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
@@ -426,31 +498,6 @@ typedef struct TfLiteTensor {
const TfLiteIntArray* dims_signature;
} TfLiteTensor;
#ifndef TF_LITE_STATIC_MEMORY
// Free data memory of tensor `t`.
void TfLiteTensorDataFree(TfLiteTensor* t);
// Free quantization data.
void TfLiteQuantizationFree(TfLiteQuantization* quantization);
// Free sparsity parameters.
void TfLiteSparsityFree(TfLiteSparsity* sparsity);
// 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);
#endif // TF_LITE_STATIC_MEMORY
// 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.
@@ -487,6 +534,130 @@ typedef struct TfLiteNode {
// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
} TfLiteNode;
#else // defined(TF_LITE_STATIC_MEMORY)?
// NOTE: This flag is opt-in only at compile time.
//
// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct
// contains only the minimum fields required to initialize and prepare a micro
// inference graph. The fields in this struct have been ordered from
// largest-to-smallest for optimal struct sizeof.
//
// This struct does not use:
// - allocation
// - buffer_handle
// - data_is_stale
// - delegate
// - dims_signature
// - name
// - sparsity
typedef struct TfLiteTensor {
// TODO(b/155784997): Consider consolidating these quantization fields:
// Quantization information. Replaces params field above.
TfLiteQuantization quantization;
// Quantization information.
TfLiteQuantizationParams params;
// 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;
// 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;
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
// 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;
// True if the tensor is a variable.
bool is_variable;
} TfLiteTensor;
// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains
// only the minimum fields required to represent a node.
//
// This struct does not use:
// - delegate
// - intermediates
// - temporaries
typedef struct TfLiteNode {
// 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;
// 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;
} TfLiteNode;
#endif // TF_LITE_STATIC_MEMORY
// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount
// of information required for a kernel to run during TfLiteRegistration::Eval.
// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM
// builds with this flag by default internally.
typedef struct TfLiteEvalTensor {
// 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.
TfLiteIntArray* dims;
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
} TfLiteEvalTensor;
#ifndef TF_LITE_STATIC_MEMORY
// Free data memory of tensor `t`.
void TfLiteTensorDataFree(TfLiteTensor* t);
// Free quantization data.
void TfLiteQuantizationFree(TfLiteQuantization* quantization);
// Free sparsity parameters.
void TfLiteSparsityFree(TfLiteSparsity* sparsity);
// 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);
#endif // TF_LITE_STATIC_MEMORY
// WARNING: This is an experimental interface that is subject to change.
//
@@ -578,12 +749,11 @@ typedef struct TfLiteContext {
void* profiler;
// Allocate persistent buffer which has the same life time as the interpreter.
// Returns nullptr on failure.
// The memory is allocated from heap for TFL, and from tail in TFLM.
// If *ptr is not nullptr, the pointer will be reallocated.
// This method is only available in Prepare stage.
// This method is only available in Init or Prepare stage.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*AllocatePersistentBuffer)(struct TfLiteContext* ctx,
size_t bytes, void** ptr);
void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes);
// Allocate a buffer which will be deallocated right after invoke phase.
// The memory is allocated from heap in TFL, and from volatile arena in TFLM.
@@ -638,6 +808,18 @@ typedef struct TfLiteContext {
TfLiteStatus (*PreviewDelegatePartitioning)(
struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions);
// Returns a TfLiteTensor struct for a given index.
// WARNING: This is an experimental interface that is subject to change.
// WARNING: This method may not be available on all platforms.
TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context,
int tensor_idx);
// Returns a TfLiteEvalTensor struct for a given index.
// WARNING: This is an experimental interface that is subject to change.
// WARNING: This method may not be available on all platforms.
TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context,
int tensor_idx);
} TfLiteContext;
typedef struct TfLiteRegistration {
@@ -712,7 +894,26 @@ typedef enum TfLiteDelegateFlags {
//
// If the delegate isn't capable to handle dynamic tensors, this flag need
// to be set to false.
kTfLiteDelegateFlagsAllowDynamicTensors = 1
kTfLiteDelegateFlagsAllowDynamicTensors = 1,
// This flag can be used by delegates (that allow dynamic tensors) to ensure
// applicable tensor shapes are automatically propagated in the case of tensor
// resizing.
// This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors
// of a delegate kernel will have correct shapes before its Prepare() method
// is called. The runtime leverages TFLite builtin ops in the original
// execution plan to propagate shapes.
//
// A few points to note:
// 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is
// false, this one is redundant since the delegate kernels are re-initialized
// every time tensors are resized.
// 2. Enabling this flag adds some overhead to AllocateTensors(), since extra
// work is required to prepare the original execution plan.
// 3. This flag requires that the original execution plan only have ops with
// valid registrations (and not 'dummy' custom ops like with Flex).
// WARNING: This feature is experimental and subject to change.
kTfLiteDelegateFlagsRequirePropagatedShapes = 2
} TfLiteDelegateFlags;
// WARNING: This is an experimental interface that is subject to change.
@@ -731,8 +932,9 @@ typedef struct TfLiteDelegate {
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.
// 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
// long as it follows the rules for kTfLiteDynamic tensors, in which case this
// cannot be null.
TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,