Mirror/AI-on-the-edge-device
1
0
Fork 0
mirror of https://github.com/jomjol/AI-on-the-edge-device.git synced 2025-12-10 13:36:54 +03:00
Code Issues Packages Projects Releases Wiki Activity

v11.2.0

Browse Source
This commit is contained in:
jomjol
2022-08-28 19:52:51 +02:00
parent 338184712d
commit c9b7a5f84c
223 changed files with 13226 additions and 2342 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/common.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
#include <algorithm>
#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK
#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK

10
code/components/tflite-lib/tensorflow/lite/kernels/internal/compatibility.h
View File

@@ -86,6 +86,16 @@ using int32 = std::int32_t;
using uint32 = std::uint32_t;
#endif // !defined(TF_LITE_STATIC_MEMORY)
// Allow for cross-compiler usage of function signatures - currently used for
// specifying named RUY profiler regions in templated methods.
#if defined(_MSC_VER)
#define TFLITE_PRETTY_FUNCTION __FUNCSIG__
#elif defined(__GNUC__)
#define TFLITE_PRETTY_FUNCTION __PRETTY_FUNCTION__
#else
#define TFLITE_PRETTY_FUNCTION __func__
#endif
// TFLITE_DEPRECATED()
//
// Duplicated from absl/base/macros.h to avoid pulling in that library.

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/portable_tensor_utils.h
View File

@@ -324,7 +324,7 @@ void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input,
// - n_input: the size for input and output.
// - output: the 16 bit output
// The input is in Qm.15-m format and the output is in Q0.15 format.
void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch,
void ApplyTanh(int32_t intger_bits, const int16_t* input, int32_t n_batch,
int32_t n_input, int16_t* output);
// Apply Tanh to a quantized vector. Tbe internal calculation is in float.

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/add.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
#include <algorithm>
#include <type_traits>
#include "fixedpoint/fixedpoint.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/concatenation.h
View File

@@ -16,6 +16,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/conv.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"

247
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/div.h Normal file
View File

@@ -0,0 +1,247 @@
/* Copyright 2020 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_KERNELS_INTERNAL_REFERENCE_DIV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_ops {
template <typename T>
inline void DivCheckArithmeticParams(const ArithmeticParams& params) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
// Input offset is negative input zero point. Activation tensors are
// asymmetric quantized so they span the full int8 range.
constexpr int32_t max_value =
static_cast<int32_t>(std::numeric_limits<T>::max());
TFLITE_DCHECK_GE(params.input1_offset, -max_value);
TFLITE_DCHECK_LE(params.input1_offset, max_value);
TFLITE_DCHECK_GE(params.input2_offset, -max_value);
TFLITE_DCHECK_LE(params.input2_offset, max_value);
TFLITE_DCHECK_GE(params.output_offset, -max_value);
TFLITE_DCHECK_LE(params.output_offset, max_value);
}
// Element-wise div that can often be used for inner loop of broadcast Div as
// well as the non-broadcast Div.
template <typename T>
inline void DivElementwise(int size, const ArithmeticParams& params,
const T* input1_data, const T* input2_data,
T* output_data) {
DivCheckArithmeticParams<T>(params);
for (int i = 0; i < size; ++i) {
int32_t input1_val = params.input1_offset + input1_data[i];
int32_t input2_val = params.input2_offset + input2_data[i];
TFLITE_DCHECK_NE(input2_val, 0);
if (input2_val < 0) {
// Invert signs to avoid a negative input2_val as input2_inv needs to be
// positive to be used as multiplier of MultiplyByQuantizedMultiplier.
input1_val = -input1_val;
input2_val = -input2_val;
}
int recip_shift;
const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
const int headroom = CountLeadingSignBits(input1_val);
const int32_t unscaled_quotient =
MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
headroom);
const int total_shift = params.output_shift - recip_shift - headroom;
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplierSmallerThanOneExp(
unscaled_quotient, params.output_multiplier, total_shift);
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[i] = static_cast<T>(clamped_output);
}
}
inline void Div(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const uint8_t* input1_data,
const RuntimeShape& input2_shape, const uint8_t* input2_data,
const RuntimeShape& output_shape, uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
DivElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void Div(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int8_t* input1_data,
const RuntimeShape& input2_shape, const int8_t* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
DivElementwise(flat_size, params, input1_data, input2_data, output_data);
}
template <typename T, int N = 5>
inline void BroadcastDivSlowQuantized(
const ArithmeticParams& params, const RuntimeShape& unextended_input1_shape,
const T* input1_data, const RuntimeShape& unextended_input2_shape,
const T* input2_data, const RuntimeShape& unextended_output_shape,
T* output_data) {
TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
unextended_input2_shape, &desc1, &desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
&output_desc);
DivCheckArithmeticParams<T>(params);
auto div_func = [&](int indexes[N]) {
int32_t input1_val =
params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
int32_t input2_val =
params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
TFLITE_DCHECK_NE(input2_val, 0);
if (input2_val < 0) {
// Invert signs to avoid a negative input2_val as input2_inv needs to be
// positive to be used as multiplier of MultiplyByQuantizedMultiplier.
input1_val = -input1_val;
input2_val = -input2_val;
}
int recip_shift;
const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
const int headroom = CountLeadingSignBits(input1_val);
const int32_t unscaled_quotient =
MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
headroom);
const int total_shift = params.output_shift - recip_shift - headroom;
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplierSmallerThanOneExp(
unscaled_quotient, params.output_multiplier, total_shift);
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[SubscriptToIndex(output_desc, indexes)] =
static_cast<T>(clamped_output);
};
NDOpsHelper<N>(output_desc, div_func);
}
template <int N = 5>
inline void BroadcastDivSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const uint8_t* input1_data,
const RuntimeShape& unextended_input2_shape,
const uint8_t* input2_data,
const RuntimeShape& unextended_output_shape,
uint8_t* output_data) {
BroadcastDivSlowQuantized<uint8_t, N>(
params, unextended_input1_shape, input1_data, unextended_input2_shape,
input2_data, unextended_output_shape, output_data);
}
template <int N = 5>
inline void BroadcastDivSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const int8_t* input1_data,
const RuntimeShape& unextended_input2_shape,
const int8_t* input2_data,
const RuntimeShape& unextended_output_shape,
int8_t* output_data) {
BroadcastDivSlowQuantized<int8_t, N>(
params, unextended_input1_shape, input1_data, unextended_input2_shape,
input2_data, unextended_output_shape, output_data);
}
// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary
// dimensionality if the runtime code does a single loop over one dimension
// that handles broadcasting as the base case. The code generator would then
// generate max(D1, D2) nested for loops.
template <typename T, int N = 5>
void BroadcastDivSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const T* input1_data,
const RuntimeShape& unextended_input2_shape,
const T* input2_data,
const RuntimeShape& unextended_output_shape,
T* output_data) {
T output_activation_min;
T output_activation_max;
GetActivationParams(params, &output_activation_min, &output_activation_max);
TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
unextended_input2_shape, &desc1, &desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
&output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest
// stride, typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
auto div_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, indexes)] /
input2_data[SubscriptToIndex(desc2, indexes)],
output_activation_min, output_activation_max);
};
NDOpsHelper<N>(output_desc, div_func);
}
template <typename T>
inline void Div(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
T output_activation_min;
T output_activation_max;
GetActivationParams(params, &output_activation_min, &output_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] / input2_data[i], output_activation_min,
output_activation_max);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/fully_connected.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
#include <algorithm>
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"

8
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
#include <algorithm>
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
@@ -23,9 +25,9 @@ namespace tflite {
namespace reference_ops {
inline int16_t SaturatingLeftShift(int16_t value, int amount) {
int32_t result = static_cast<int32_t>(value) * (1 << amount);
result = std::min<int32_t>(result, std::numeric_limits<int16_t>::max());
result = std::max<int32_t>(result, std::numeric_limits<int16_t>::min());
int64_t result = static_cast<int64_t>(value) * (1 << amount);
result = std::min<int64_t>(result, std::numeric_limits<int16_t>::max());
result = std::max<int64_t>(result, std::numeric_limits<int16_t>::min());
return result;
}

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/add.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
#include <algorithm>
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

90
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h
View File

@@ -15,11 +15,101 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
// For per-channel functions, since it is defined in quantization spec that
// weights are symmetric
// (https://www.tensorflow.org/lite/performance/quantization_spec#symmetric_vs_asymmetric),
// zero_point (params.weights_offset) is always 0.
// However, for per-tensor functions, params.weights_offset is still applied for
// backward compatibility.
inline void FullyConnectedPerChannel(
const FullyConnectedParams& params, const int32_t* output_multiplier,
const int* output_shift, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int8_t* output_data) {
const int32_t input_offset = params.input_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int filter_dim_count = filter_shape.DimensionsCount();
const int batches = output_shape.Dims(0);
const int output_depth = output_shape.Dims(1);
TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
int32_t acc = 0;
for (int d = 0; d < accum_depth; ++d) {
int32_t input_val = input_data[b * accum_depth + d];
int32_t filter_val = filter_data[out_c * accum_depth + d];
acc += filter_val * (input_val + input_offset);
}
if (bias_data) {
acc += bias_data[out_c];
}
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier[out_c],
output_shift[out_c]);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[out_c + output_depth * b] = static_cast<int8_t>(acc);
}
}
}
template <typename AccumScalar>
inline void FullyConnectedPerChannel(
const FullyConnectedParams& params, const int32_t* output_multiplier,
const int* output_shift, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const AccumScalar* bias_data, const RuntimeShape& output_shape,
int16_t* output_data) {
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int filter_dim_count = filter_shape.DimensionsCount();
const int output_dim_count = output_shape.DimensionsCount();
const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1);
const int output_depth = output_shape.Dims(output_dim_count - 1);
TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
AccumScalar acc = 0;
for (int d = 0; d < accum_depth; ++d) {
int32_t input_val = input_data[b * accum_depth + d];
int32_t filter_val = filter_data[out_c * accum_depth + d];
acc += filter_val * input_val;
}
if (bias_data) {
acc += bias_data[out_c];
}
int32_t acc_scaled = MultiplyByQuantizedMultiplier(
acc, output_multiplier[out_c], output_shift[out_c]);
acc_scaled = std::max(acc_scaled, output_activation_min);
acc_scaled = std::min(acc_scaled, output_activation_max);
output_data[out_c + output_depth * b] = static_cast<int16_t>(acc_scaled);
}
}
}
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h
View File

@@ -15,7 +15,9 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
#include <algorithm>
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
#include <algorithm>
#include "fixedpoint/fixedpoint.h"
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h
View File

@@ -15,7 +15,9 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
#include <algorithm>
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
#include <algorithm>
#include <limits>
#include "fixedpoint/fixedpoint.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/mul.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/pooling.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/prelu.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/reduce.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
#include <algorithm>
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/requantize.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
#include <algorithm>
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
#include <algorithm>
#include <cmath>
#include "tensorflow/lite/kernels/internal/cppmath.h"

1
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/softmax.h
View File

@@ -15,6 +15,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#include <algorithm>
#include <limits>
#include "fixedpoint/fixedpoint.h"

2
code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/transpose_conv.h
View File

@@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_
#include <algorithm>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"

9
code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h
View File

@@ -27,6 +27,11 @@ class RuntimeShape {
public:
RuntimeShape& operator=(RuntimeShape const&) = delete;
// RuntimeShape in TFLM supports up to 5 dimensions.
// The name kMaxSmallSize comes from the same file of the upstream
// tensorflow lite repo and need to be kept the same for max reuse.
static constexpr int kMaxSmallSize = 5;
RuntimeShape() : size_(0) {}
explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) {}
@@ -104,11 +109,9 @@ class RuntimeShape {
sizeof(int32_t) * shape.DimensionsCount());
}
// A maximum of 4 dimensions are supported on TFLM.
static constexpr int kMaxSize = 5;
int32_t size_;
union {
int32_t dims_[kMaxSize];
int32_t dims_[kMaxSmallSize];
};
};

10
code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h
View File

@@ -974,11 +974,11 @@ struct StridedSliceParams {
int8_t strides_count;
int32_t strides[5];
int16_t begin_mask;
int16_t ellipsis_mask;
int16_t end_mask;
int16_t new_axis_mask;
int16_t shrink_axis_mask;
uint16_t begin_mask;
uint16_t ellipsis_mask;
uint16_t end_mask;
uint16_t new_axis_mask;
uint16_t shrink_axis_mask;
};
struct TanhParams {

10
code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h
View File

@@ -177,6 +177,14 @@ inline int64_t NumElements(const TfLiteTensor* t) {
return NumElements(t->dims);
}
inline int64_t NumElements(const int* dims, int num_dims) {
int64_t count = 1;
for (int i = 0; i < num_dims; ++i) {
count *= dims[i];
}
return count;
}
// Determines whether tensor is constant.
// TODO(b/138199592): Introduce new query which checks for constant OR
// persistent-read-only, which would be useful for most tensor kernels that
@@ -308,7 +316,7 @@ TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
const TfLiteTensor* input3,
TfLiteIntArray** output_shape);
// Return the size of given type in bytes. Return 0 in in case of string.
// Return the size of given type in bytes. Return 0 in case of string.
int TfLiteTypeGetSize(TfLiteType type);
// Whether the current platform is mobile (Android or iOS).