OSSForks/tensorflow
1
0
Fork 0
mirror of https://github.com/zebrajr/tensorflow.git synced 2025-12-06 12:20:11 +01:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity
ca0bb02924
tensorflow/tensorflow/lite/kernels/test_util.h
Jae H. Yoo ca0bb02924 Bump up Cast op version op to 7 with bfloat16 runtime kernel support 
This CL can resolve the latest bfloat16 TFLite flatbuffers' interpreter executation & quantization. It's required because quantization checks the TFLite float FB validation with interpreter.

PiperOrigin-RevId: 695529144
2024-11-11 17:04:39 -08:00

1408 lines
52 KiB
C++
Raw Blame History

/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_TEST_UTIL_H_
#define TENSORFLOW_LITE_KERNELS_TEST_UTIL_H_
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <string.h>
#include <algorithm>
#include <cmath>
#include <complex>
#include <cstdlib>
#include <functional>
#include <initializer_list>
#include <limits>
#include <map>
#include <memory>
#include <optional>
#include <ostream>
#include <string>
#include <tuple>
#include <type_traits>
#include <utility>
#include <vector>
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "absl/algorithm/container.h"
#include "absl/types/span.h"
#include "Eigen/Core" // from @eigen_archive
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/core/api/op_resolver.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/core/interpreter.h"
#include "tensorflow/lite/kernels/internal/tensor_utils.h"
#include "tensorflow/lite/kernels/internal/utils/sparsity_format_converter.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/portable_type_to_tflitetype.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/string_type.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/testing/util.h" // IWYU pragma: keep
#include "tensorflow/lite/tools/optimize/quantization_utils.h"
#include "tensorflow/lite/type_to_tflitetype.h"
#include "tensorflow/lite/util.h"
#include "tsl/platform/logging.h"
namespace tflite {
// This constant indicates the error bound is derived automatically in functions
// like ArrayFloatNear.
constexpr float kFpErrorAuto = -1;
// Returns whether we allow FP16 precision for FP32 operations, i.e. in FP16
// mode.
bool AllowFp16PrecisionForFp32();
// It checks if the actual number almost equals the expected number with the
// tolerance of 4 FP16 ULPs in FP16 mode; 4 FP32 ULPs in FP32 mode.
// Given float x, 2^e <= |x| <= 2^(e+1), then ULP(x) = 2^(max(e, e_min)-p+1)
// where e_min is -24 for FP16, -126 for FP32; p is 10 for FP16, 23 for FP32.
::testing::Matcher<std::tuple<float, float>> FloatingPointAlmostEq();
// In FP32 mode, it equals to Eq(), which means the error bound is zero (no
// error allowed); in FP16 mode, it checks if the actual number almost equals
// the expected number with the tolerance of 4 FP16 ULPs.
::testing::Matcher<std::tuple<float, float>> FloatingPointEq();
// A gmock matcher that check that elements of a float vector match to a given
// tolerance. In FP32 mode, the tolerance is max(max_abs_err, value *
// max_rel_err). In FP16 mode, the tolerance is max(fp16_max_abs_err, value *
// fp16_max_rel_err). If fp16_max_abs_err is kFpErrorAuto, it is set to
// std::max(max_abs_err, sqrt(max_abs_err)) automatically.
std::vector<::testing::Matcher<float>> ArrayFloatNear(
const std::vector<float>& values, float max_abs_err = 1e-5,
float fp16_max_abs_err = kFpErrorAuto, float max_rel_err = 0,
float fp16_max_rel_err = 0.01);
// TODO(b/280061335): Add FP16 logic as ArrayFloatNear does.
// A gmock matcher that check that elements of a complex vector match to a given
// tolerance.
std::vector<::testing::Matcher<std::complex<float>>> ArrayComplex64Near(
const std::vector<std::complex<float>>& values, float max_abs_error = 1e-5);
template <typename T>
inline std::vector<T> Quantize(const std::vector<float>& data, float scale,
int32_t zero_point,
TfLiteType type = kTfLiteNoType) {
std::vector<T> q;
T min = std::numeric_limits<T>::min();
T max = std::numeric_limits<T>::max();
if (type == kTfLiteInt4) {
min = -7;
max = 7;
}
q.reserve(data.size());
for (const auto& f : data) {
q.push_back(static_cast<T>(std::max<float>(
min, std::min<float>(max, std::round(zero_point + (f / scale))))));
}
return q;
}
template <typename T>
inline std::vector<float> Dequantize(const std::vector<T>& data, float scale,
int32_t zero_point) {
std::vector<float> f;
f.reserve(data.size());
for (const T& q : data) {
f.push_back(scale * (q - zero_point));
}
return f;
}
template <>
constexpr TfLiteType typeToTfLiteType<Eigen::half>() {
return kTfLiteFloat16;
}
template <>
constexpr TfLiteType typeToTfLiteType<Eigen::bfloat16>() {
return kTfLiteBFloat16;
}
// A test model that contains a single operator. All operator inputs and
// output are external to the model, so the tests can directly access them.
// Typical usage:
// SingleOpModel m;
// int a = m.AddInput({TensorType_FLOAT32, a_shape});
// int b = m.AddInput({TensorType_FLOAT32, b_shape});
// int c = m.AddOutput({TensorType_FLOAT32, {}});
// m.SetBuiltinOp(...);
// m.BuildInterpreter({GetShape(a), GetShape(b)});
// m.PopulateTensor(a, {...});
// m.PopulateTensor(b, {...});
// m.Invoke();
// EXPECT_THAT(m.ExtractVector<float>(c), ArrayFloatNear({...}));
//
// A helper struct to construct test tensors. This is particularly useful for
// quantized tensor which must have their scale and zero_point defined before
// the actual data is known. This mimics what happens in practice: quantization
// parameters are calculated during training or post training..
struct TensorData {
// NOLINTNEXTLINE
TensorData(TensorType type = TensorType_FLOAT32, std::vector<int> shape = {},
float min = 0.0f, float max = 0.0f, float scale = 0.0f,
int32_t zero_point = 0, bool per_channel_quantization = false,
std::vector<float> per_channel_quantization_scales = {},
std::vector<int64_t> per_channel_quantization_offsets = {},
int32_t channel_index = 0, std::vector<int> traversal_order = {},
std::vector<TfLiteDimensionType> format = {},
std::vector<int> block_size = {}, std::vector<int> block_map = {},
std::vector<int> shape_signature = {})
: type(type),
shape(shape),
min(min),
max(max),
scale(scale),
zero_point(zero_point),
per_channel_quantization(per_channel_quantization),
per_channel_quantization_scales(
std::move(per_channel_quantization_scales)),
per_channel_quantization_offsets(
std::move(per_channel_quantization_offsets)),
channel_index(channel_index),
traversal_order(traversal_order),
format(format),
block_size(block_size),
block_map(block_map),
shape_signature(shape_signature) {}
TensorType type;
std::vector<int> shape;
float min;
float max;
float scale;
int32_t zero_point;
bool per_channel_quantization;
std::vector<float> per_channel_quantization_scales;
std::vector<int64_t> per_channel_quantization_offsets;
int32_t channel_index;
std::vector<int> traversal_order;
std::vector<TfLiteDimensionType> format;
std::vector<int> block_size;
std::vector<int> block_map;
std::vector<int> shape_signature;
};
class SingleOpResolver : public OpResolver {
public:
SingleOpResolver(const BuiltinOperator op, TfLiteRegistration* registration,
int version = 1)
: op_(op), registration_(*registration) {
registration_.builtin_code = static_cast<int32_t>(op);
registration_.version = version;
}
const TfLiteRegistration* FindOp(BuiltinOperator op,
int version) const override {
if (op == op_) {
return &registration_;
}
return nullptr;
}
const TfLiteRegistration* FindOp(const char* op, int version) const override {
return nullptr;
}
private:
const BuiltinOperator op_;
TfLiteRegistration registration_;
};
class SingleOpModel;
class AccelerationValidator {
public:
using Callback = std::function<void(const SingleOpModel& model)>;
// Returns a global AccelerationValidator instance.
static AccelerationValidator* Get();
// Adds a callback function that will be invoked at the end of a kernel test
// to validate acceleration.
void AddCallback(Callback callback);
// Performs acceleration validation with all registered callbacks.
void Validate(const SingleOpModel& model) const;
private:
std::vector<Callback> callbacks_;
};
class SingleOpModel {
public:
SingleOpModel() = default;
~SingleOpModel();
// Set a delegate that is applied right after graph is prepared. This is
// useful for testing other runtimes like NN API or GPU.
// Note: the caller still owns the memory of the passed-in `delegate`.
void SetDelegate(TfLiteDelegate* delegate) {
delegate_ = delegate;
// As this is a manually-set TF Lite delegate, we assume the intention of
// the test is to test against the particular delegate, hence bypassing
// applying TfLite default delegates (i.e. the XNNPACK delegate).
if (delegate_ != nullptr) {
SetBypassDefaultDelegates();
}
}
TfLiteStatus ApplyDelegate();
// Copying or assignment is disallowed to simplify ownership semantics.
SingleOpModel(const SingleOpModel&) = delete;
SingleOpModel& operator=(const SingleOpModel&) = delete;
// Add a TensorType input tensor and return its index.
int AddInput(const TensorData& t);
int AddVariableInput(const TensorData& t);
int AddIntermediate(TensorType type, const std::vector<float>& scale,
const std::vector<int64_t>& zero_point);
// Returns the input tensor at position `index`.
TfLiteTensor* GetInputTensor(int index) {
return interpreter_->input_tensor(index);
}
// Returns the output tensor at position `index`.
TfLiteTensor* GetOutputTensor(int index) {
return interpreter_->output_tensor(index);
}
// Templated version of AddConstInput() taking pointer and size.
template <typename T>
int AddConstInput(const TensorData& t, const T* data, size_t size) {
int id = 0;
if (t.per_channel_quantization) {
id = AddTensorPerChannelQuant(t, data, size);
} else {
id = AddTensor(t, data, size);
}
inputs_.push_back(id);
return id;
}
// Templated version of AddConstInput() taking vector and shape.
template <typename T>
int AddConstInput(TensorType type, const std::vector<T>& data,
std::initializer_list<int> shape) {
return AddConstInput(TensorData{type, shape}, data.data(), data.size());
}
// Templated version of AddConstInput() taking TensorType, initializer_list
// and shape.
template <typename T>
int AddConstInput(TensorType type, std::initializer_list<T> data,
std::initializer_list<int> shape) {
return AddConstInput<T>(TensorData{type, shape}, data.begin(), data.size());
}
// Templated version of AddConstInput() taking TensorData, initializer_list
// and shape.
template <typename T>
int AddConstInput(const TensorData& t, std::initializer_list<T> data) {
return AddConstInput(t, data.begin(), data.size());
}
// Templated version of AddConstInput() taking TensorData and vector.
template <typename T>
int AddConstInput(const TensorData& t, const std::vector<T>& data) {
return AddConstInput(t, data.data(), data.size());
}
// TODO(b/166202747): Use a better way to do type specialization. Reduce
// duplicate code in the two functions below.
int AddConstSparseInput(const TensorData& t,
const std::vector<int8_t>& data) {
int id = tensors_.size();
const int dims_count = t.traversal_order.size();
std::vector<int8_t> dense_data(data);
tflite::internal::sparsity::FormatConverter<int8_t> converter(
t.shape, t.traversal_order, t.format, t.block_size, t.block_map);
converter.DenseToSparse(dense_data.data());
const auto& dim_metadata = converter.GetDimMetadata();
const auto& sparse_data = converter.GetData();
// Build sparsity parameter.
std::vector<flatbuffers::Offset<DimensionMetadata>> fb_dim_metadata(
dims_count);
for (int i = 0; i < dims_count; i++) {
const int metadata_idx = 2 * i;
if (i < t.shape.size() &&
t.format[t.traversal_order[i]] == kTfLiteDimSparseCSR) {
auto array_segments =
CreateInt32Vector(builder_, builder_.CreateVector<int>(
dim_metadata[metadata_idx]))
.Union();
auto array_indices =
CreateInt32Vector(builder_, builder_.CreateVector<int>(
dim_metadata[metadata_idx + 1]))
.Union();
fb_dim_metadata[i] = CreateDimensionMetadata(
builder_, DimensionType_SPARSE_CSR, 0,
SparseIndexVector_Int32Vector, array_segments,
SparseIndexVector_Int32Vector, array_indices);
} else {
fb_dim_metadata[i] = CreateDimensionMetadata(
builder_, DimensionType_DENSE, dim_metadata[metadata_idx][0]);
}
}
flatbuffers::Offset<SparsityParameters> s_param = CreateSparsityParameters(
builder_, builder_.CreateVector<int>(t.traversal_order),
builder_.CreateVector<int>(t.block_map),
builder_.CreateVector(fb_dim_metadata));
int buffer_id = 0;
if (!data.empty()) {
// Initialize buffers list with empty buffer to allow for non-const
// tensors.
if (buffers_.empty()) {
buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector({})));
}
// Add compressed data as a Buffer to buffers list.
buffer_id = buffers_.size();
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(sparse_data.data()),
sparse_data.size());
buffers_.push_back(CreateBuffer(builder_, data_buffer));
}
tensors_.push_back(CreateTensor(
builder_, builder_.CreateVector<int>(t.shape), t.type,
/*buffer=*/buffer_id,
/*name=*/0, /*quantization=*/0, /*is_variable=*/false, s_param));
inputs_.push_back(id);
tensor_data_[id] = t;
return id;
}
// Add a constant sparse tensor as input.
template <typename T>
int AddConstSparseInput(const TensorData& t, const std::vector<T>& data,
bool symmetric_quantize = false) {
int id = tensors_.size();
const int dims_count = t.traversal_order.size();
std::vector<T> dense_data(data);
tflite::internal::sparsity::FormatConverter<T> converter(
t.shape, t.traversal_order, t.format, t.block_size, t.block_map);
converter.DenseToSparse(dense_data.data());
const auto dim_metadata = converter.GetDimMetadata();
const auto sparse_data = converter.GetData();
// Build sparsity parameter.
std::vector<flatbuffers::Offset<DimensionMetadata>> fb_dim_metadata(
dims_count);
for (int i = 0; i < dims_count; i++) {
const int metadata_idx = 2 * i;
if (i < t.shape.size() &&
t.format[t.traversal_order[i]] == kTfLiteDimSparseCSR) {
auto array_segments =
CreateInt32Vector(builder_,
builder_.CreateVector(dim_metadata[metadata_idx]))
.Union();
auto array_indices =
CreateInt32Vector(
builder_, builder_.CreateVector(dim_metadata[metadata_idx + 1]))
.Union();
fb_dim_metadata[i] = CreateDimensionMetadata(
builder_, DimensionType_SPARSE_CSR, 0,
SparseIndexVector_Int32Vector, array_segments,
SparseIndexVector_Int32Vector, array_indices);
} else {
fb_dim_metadata[i] = CreateDimensionMetadata(
builder_, DimensionType_DENSE, dim_metadata[metadata_idx][0]);
}
}
flatbuffers::Offset<SparsityParameters> s_param = CreateSparsityParameters(
builder_, builder_.CreateVector(t.traversal_order),
builder_.CreateVector(t.block_map),
builder_.CreateVector(fb_dim_metadata));
flatbuffers::Offset<QuantizationParameters> q_params = 0;
int buffer_id = 0;
if (!data.empty()) {
// Initialize buffers list with empty buffer to allow for non-const
// tensors.
if (buffers_.empty()) {
buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector({})));
}
// Add compressed data as a Buffer to buffers list.
buffer_id = buffers_.size();
// When the quantization parameter is set for the added tensor, we
// quantize the given data.
bool is_quantized = (t.min != 0 || t.max != 0 || t.scale != 0 ||
(t.per_channel_quantization &&
!t.per_channel_quantization_scales.empty() &&
!t.per_channel_quantization_offsets.empty()));
if (symmetric_quantize) {
const int length = sparse_data.size();
std::vector<int8_t> q(length);
float min, max, scaling_factor;
tensor_utils::SymmetricQuantizeFloats(
sparse_data.data(), length, q.data(), &min, &max, &scaling_factor);
std::vector<float> scales{scaling_factor};
std::vector<int64_t> zero_points{0};
q_params = CreateQuantizationParameters(
builder_, 0, 0, builder_.CreateVector<float>(scales),
builder_.CreateVector<int64_t>(zero_points));
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(q.data()), q.size());
buffers_.push_back(CreateBuffer(builder_, data_buffer));
} else if (is_quantized) {
CHECK_EQ(t.type, TensorType_INT8)
<< "The INT8 quantization is only supported for sparsified tensor";
std::vector<int8_t> quantized_output(sparse_data.size());
std::vector<float> scales;
std::vector<int64_t> zero_points;
if (t.per_channel_quantization) {
CHECK_EQ(t.per_channel_quantization_scales.size(), // NOLINT
t.per_channel_quantization_offsets.size())
<< "Per channel quantization scales and offsets should have the "
"same size";
std::vector<int8_t> temp_data(dense_data.size());
const int32_t num_channel = t.shape[t.channel_index];
std::vector<float> scales_inv(num_channel);
for (int i = 0; i < num_channel; ++i) {
scales_inv[i] = 1.0f / t.per_channel_quantization_scales[i];
}
optimize::utils::SymmetricPerChannelQuantizeValues(
dense_data.data(), scales_inv, t.shape, t.channel_index,
&temp_data, kTfLiteInt8);
tflite::internal::sparsity::FormatConverter<int8_t> quant_converter(
t.shape, t.traversal_order, t.format, t.block_size, t.block_map);
quant_converter.DenseToSparse(temp_data.data());
quantized_output = std::move(quant_converter.GetData());
scales = t.per_channel_quantization_scales;
zero_points = t.per_channel_quantization_offsets;
} else {
quantized_output =
std::move(Quantize<int8_t>(sparse_data, t.scale, t.zero_point));
scales = {t.scale};
zero_points = {0};
}
q_params = CreateQuantizationParameters(
builder_, t.min, t.max, builder_.CreateVector<float>(scales),
builder_.CreateVector<int64_t>(zero_points));
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(quantized_output.data()),
quantized_output.size());
buffers_.push_back(CreateBuffer(builder_, data_buffer));
} else {
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(sparse_data.data()),
sizeof(T) * sparse_data.size());
buffers_.push_back(CreateBuffer(builder_, data_buffer));
}
}
tensors_.push_back(
CreateTensor(builder_, builder_.CreateVector<int>(t.shape),
symmetric_quantize ? TensorType_INT8 : t.type,
/*buffer=*/buffer_id,
/*name=*/0, q_params, /*is_variable=*/false, s_param));
inputs_.push_back(id);
tensor_data_[id] = t;
return id;
}
// Add a null input tensor (optional input) and return kTfLiteOptionalTensor.
int AddNullInput();
// Add a TensorType output tensor and return its index.
int AddOutput(const TensorData& t);
template <typename T>
void QuantizeAndPopulate(int index, const std::vector<float>& data) {
TfLiteTensor* t = interpreter_->tensor(index);
auto q = Quantize<T>(data, t->params.scale, t->params.zero_point, t->type);
PopulateTensor(index, 0, q.data(), q.data() + q.size());
}
void QuantizeAndPopulate4bit(int index, const std::vector<float>& data) {
TfLiteTensor* t = interpreter_->tensor(index);
t->type = kTfLiteInt4;
std::vector<int8_t> quantized_output =
Quantize<int8_t>(data, t->params.scale, t->params.zero_point, t->type);
PopulateTensor4bit(index, /*offset=*/0, quantized_output.data(),
quantized_output.data() + quantized_output.size());
}
void SymmetricQuantizeAndPopulate(int index, const std::vector<float>& data) {
std::vector<int8_t> q = QuantizeTensor(index, data);
PopulateTensor(index, /*offset=*/0, reinterpret_cast<uint8_t*>(q.data()),
reinterpret_cast<uint8_t*>(q.data() + q.size()));
}
void SignedSymmetricQuantizeAndPopulate(int index,
const std::vector<float>& data) {
TfLiteTensor* t = interpreter_->tensor(index);
if (t->type == kTfLiteInt4) {
std::vector<int8_t> q = Quantize<int8_t>(data, t->params.scale,
t->params.zero_point, t->type);
PopulateTensor4bit(index, /*offset=*/0, q.data(), q.data() + q.size());
} else {
std::vector<int8_t> q = QuantizeTensor(index, data);
PopulateTensor(index, /*offset=*/0, q.data(), q.data() + q.size());
}
}
// Quantize and populate data for filter with per channel quantization.
void PerChannelSymmetricQuantizeAndPopulate(
int index, const std::vector<float>& input_data) {
TfLiteTensor* t = interpreter_->tensor(index);
auto* params =
reinterpret_cast<TfLiteAffineQuantization*>(t->quantization.params);
const int channel_index = params->quantized_dimension;
std::vector<int32_t> shape(t->dims->size);
for (size_t i = 0; i < shape.size(); ++i) {
shape[i] = t->dims->data[i];
}
const int32_t num_inputs = input_data.size();
const int32_t num_channel = shape[channel_index];
std::vector<int8_t> quantized_output(num_inputs);
std::vector<float> scales_inv(num_channel);
for (int i = 0; i < num_channel; ++i) {
const float scale = params->scale->size == 1 ? params->scale->data[0]
: params->scale->data[i];
scales_inv[i] = 1.0f / scale;
}
optimize::utils::SymmetricPerChannelQuantizeValues(
input_data.data(), scales_inv, shape, channel_index, &quantized_output,
t->type);
if (t->type == kTfLiteInt4) {
PopulateTensor4bit(index, /*offset=*/0, quantized_output.data(),
quantized_output.data() + quantized_output.size());
} else {
PopulateTensor(index, /*offset=*/0, quantized_output.data(),
quantized_output.data() + quantized_output.size());
}
}
template <typename T>
void PerChannelQuantizeBiasPopulateTensor(
const std::vector<float>& input_data, int index,
TfLiteAffineQuantization* params) {
const int32_t num_inputs = input_data.size();
std::vector<T> quantized_output(num_inputs);
for (int i = 0; i < num_inputs; ++i) {
const float scale = params->scale->size == 1 ? params->scale->data[0]
: params->scale->data[i];
quantized_output[i] = input_data[i] / scale;
}
}
template <typename T>
void PerChannelQuantizeBiasPopulateTensor(
int index, const std::vector<float>& input_data,
const TfLiteAffineQuantization* params) {
const int32_t num_inputs = input_data.size();
std::vector<T> quantized_output(num_inputs);
for (int i = 0; i < num_inputs; ++i) {
const float scale = params->scale->size == 1 ? params->scale->data[0]
: params->scale->data[i];
quantized_output[i] = input_data[i] / scale;
}
PopulateTensor(index, /*offset=*/0, quantized_output.data(),
quantized_output.data() + quantized_output.size());
}
// Quantize and populate data for bias with per channel quantization.
void PerChannelQuantizeBias(int index, const std::vector<float>& input_data) {
TfLiteTensor* t = interpreter_->tensor(index);
auto* params =
reinterpret_cast<TfLiteAffineQuantization*>(t->quantization.params);
CHECK(t->type == kTfLiteInt32 || t->type == kTfLiteInt64);
if (t->type == kTfLiteInt32) {
PerChannelQuantizeBiasPopulateTensor<int32_t>(index, input_data, params);
} else {
PerChannelQuantizeBiasPopulateTensor<int64_t>(index, input_data, params);
}
}
const std::vector<int>& GetShape(int id) { return tensor_data_.at(id).shape; }
float GetScale(int id) { return tensor_data_.at(id).scale; }
int32_t GetZeroPoint(int id) { return tensor_data_.at(id).zero_point; }
// Define the operator in this model.
void SetBuiltinOp(BuiltinOperator type, BuiltinOptions builtin_options_type,
flatbuffers::Offset<void> builtin_options);
void SetBuiltinOp(BuiltinOperator type,
BuiltinOptions2 builtin_options_2_type,
flatbuffers::Offset<void> builtin_options_2);
void SetCustomOp(const string& name,
const std::vector<uint8_t>& custom_option,
const std::function<TfLiteRegistration*()>& registration);
// Allocate tensors and apply delegate.
// Note that this is called by default in BuiltInterpreter().
void AllocateAndDelegate(bool apply_delegate);
// Build the interpreter for this model. Also, resize and allocate all
// tensors given the shapes of the inputs.
// Note, if `allocate_and_delegate` is `false`, then the value of
// `apply_delegate` is ignored.
void BuildInterpreter(std::vector<std::vector<int>> input_shapes,
int num_threads, bool allow_fp32_relax_to_fp16,
bool apply_delegate, bool allocate_and_delegate = true,
bool use_simple_allocator = false);
void BuildInterpreter(std::vector<std::vector<int>> input_shapes,
bool use_simple_allocator = false);
// Executes inference and return status code.
TfLiteStatus Invoke();
void PopulateStringTensor(int index, const std::vector<string>& content) {
auto tensor = interpreter_->tensor(index);
DynamicBuffer buf;
for (const string& s : content) {
buf.AddString(s.data(), s.length());
}
buf.WriteToTensor(tensor, /*new_shape=*/nullptr);
}
// Populates the tensor given its index.
template <typename T>
void PopulateTensor(int index, const std::initializer_list<T>& data) {
PopulateTensorImpl<T>(index, /*offset=*/0, data);
}
// Populates the tensor given its index.
template <typename T>
void PopulateTensor(int index, const std::vector<T>& data) {
PopulateTensorImpl<T>(index, /*offset=*/0, data);
}
// Populates the tensor given its index.
template <typename T>
void PopulateTensor(int index, absl::Span<const T> data) {
PopulateTensorImpl<T>(index, /*offset=*/0, data);
}
// Partially populates the tensor, starting at the given offset.
template <typename T>
void PopulateTensor(int index, int offset, T* begin, T* end) {
PopulateTensorImpl<T>(index, offset, absl::Span<T>(begin, end - begin));
}
// Return a vector with the flattened contents of a tensor.
template <typename T>
std::vector<T> ExtractVector(int index) const {
const T* v = interpreter_->typed_tensor<T>(index);
const auto* tensor = interpreter_->tensor(index);
CHECK(v) << "Could not extract vector at index: " << index;
int tensor_size;
if (tensor->sparsity) {
// Getting the size of the sparse buffer this way is based on the
// assumption that the last dimension of the tensor is a compressed
// dimension.
tensor_size = tensor->sparsity
->dim_metadata[tensor->sparsity->dim_metadata_size - 1]
.array_indices->size;
} else {
tensor_size = GetTensorSize(index);
}
return std::vector<T>(v, v + tensor_size);
}
// Return the TFLite model buffer, only available after BuildInterpreter.
const uint8_t* GetModelBuffer() { return builder_.GetBufferPointer(); }
std::vector<int> GetTensorShape(int index) {
std::vector<int> result;
TfLiteTensor* t = interpreter_->tensor(index);
result.reserve(t->dims->size);
for (int i = 0; i < t->dims->size; ++i) {
result.push_back(t->dims->data[i]);
}
return result;
}
// Sets the number of threads available to the interpreter.
// Reconstruct the interpreter if reset_interpreter is true.
void SetNumThreads(int num_threads, bool reset_interpreter = false) {
CHECK(interpreter_ != nullptr);
if (reset_interpreter) {
// Reconstruct interpreter as number of threads may affect internal state,
// e.g. stratch buffer allocation.
BuildInterpreter(input_shapes_, num_threads, allow_fp32_relax_to_fp16_,
apply_delegate_, allocate_and_delegate_);
}
interpreter_->SetNumThreads(num_threads);
}
void SetResolver(std::unique_ptr<OpResolver> resolver) {
resolver_ = std::move(resolver);
}
// Indicate whether the test has the NNAPI delegate applied.
static bool GetForceUseNnapi();
int CountOpsExecutedByCpuKernel();
int CountNumberOfDelegatedPartitions() const;
int GetNumberOfAppliedDelegates() const { return num_applied_delegates_; }
// Return the most recent return status of ApplyDelegate.
std::optional<TfLiteStatus> GetDelegateApplicationStatus() const {
return delegate_application_status_;
}
void SetDelegateApplicationStatus(std::optional<TfLiteStatus> status) {
delegate_application_status_ = status;
}
// Tell TF Lite runtime to apply default delegates (i.e. XNNPACK delegate)
// when handling this op-level model.
void SetApplyDefaultDelegates() { bypass_default_delegates_ = false; }
protected:
int32_t GetTensorSize(int index) const;
// Tell TF Lite runtime to skip applying default delegates (i.e. XNNPACK
// delegate) when handling this op-level model.
void SetBypassDefaultDelegates() { bypass_default_delegates_ = true; }
flatbuffers::FlatBufferBuilder builder_;
std::unique_ptr<tflite::Interpreter> interpreter_;
std::unique_ptr<OpResolver> resolver_;
std::vector<flatbuffers::Offset<OperatorCode>> opcodes_;
std::vector<flatbuffers::Offset<Operator>> operators_;
std::map<string, std::function<TfLiteRegistration*()>> custom_registrations_;
template <typename T>
int AddTensor(TensorData t, const T* data, size_t size,
bool is_variable = false) {
int id = tensors_.size();
// This is slightly different depending on whether we are adding a
// quantized or a regular tensor.
bool is_quantized = (t.min != 0 || t.max != 0 || t.scale != 0);
flatbuffers::Offset<QuantizationParameters> q_params = 0;
if (is_quantized) {
if (t.min != 0 || t.max != 0) {
if (t.type == TensorType_UINT8) {
std::tie(t.scale, t.zero_point) =
QuantizationParams<uint8_t>(t.min, t.max);
} else if (t.type == TensorType_INT8) {
std::tie(t.scale, t.zero_point) =
QuantizationParams<int8_t>(t.min, t.max);
} else if (t.type == TensorType_INT32) {
std::tie(t.scale, t.zero_point) =
QuantizationParams<int32_t>(t.min, t.max);
} else if (t.type == TensorType_INT16) {
std::tie(t.scale, t.zero_point) =
QuantizationParams<int16_t>(t.min, t.max);
} else if (t.type == TensorType_INT4) {
std::tie(t.scale, t.zero_point) =
QuantizationParams<int8_t>(t.min, t.max, kTfLiteInt4);
} else {
LOG(FATAL) << "No support for the requested quantized type";
}
t.min = 0;
t.max = 0;
}
std::vector<float> scales{t.scale};
std::vector<int64_t> zero_points{t.zero_point};
q_params = CreateQuantizationParameters(
builder_, /*min=*/0, /*max=*/0, builder_.CreateVector<float>(scales),
builder_.CreateVector<int64_t>(zero_points));
}
int buffer_id = 0;
if (size) {
// Initialize buffers list with empty buffer to allow for non-const
// tensors.
if (buffers_.empty()) {
buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector({})));
}
builder_.ForceVectorAlignment(size, sizeof(T), 16);
// Add data as a Buffer to buffers list.
buffer_id = buffers_.size();
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(data), sizeof(T) * size);
buffers_.push_back(CreateBuffer(builder_, data_buffer));
}
tensors_.push_back(CreateTensor(
builder_, builder_.CreateVector<int>(t.shape), t.type,
/*buffer=*/buffer_id,
/*name=*/0, q_params, is_variable,
/*sparsity=*/0, builder_.CreateVector<int>(t.shape_signature)));
tensor_data_[id] = t;
return id;
}
template <typename T>
std::pair<float, int32_t> QuantizationParams(
float f_min, float f_max, TfLiteType type = kTfLiteNoType) {
int32_t zero_point = 0;
float scale = 0;
T qmin;
T qmax;
if (type == kTfLiteInt4) {
qmin = -7;
qmax = 7;
} else {
qmin = std::numeric_limits<T>::min();
qmax = std::numeric_limits<T>::max();
}
const float qmin_double = qmin;
const float qmax_double = qmax;
// 0 should always be a representable value. Let's assume that the initial
// min,max range contains 0.
CHECK_LE(f_min, 0);
CHECK_GE(f_max, 0);
if (f_min == f_max) {
// Special case where the min,max range is a point. Should be {0}.
CHECK_EQ(f_min, 0);
CHECK_EQ(f_max, 0);
return {scale, zero_point};
}
// General case.
//
// First determine the scale.
scale = (f_max - f_min) / (qmax_double - qmin_double);
// Zero-point computation.
// First the initial floating-point computation. The zero-point can be
// determined from solving an affine equation for any known pair
// (real value, corresponding quantized value).
// We know two such pairs: (rmin, qmin) and (rmax, qmax).
// The arithmetic error on the zero point computed from either pair
// will be roughly machine_epsilon * (sum of absolute values of terms)
// so we want to use the variant that adds the smaller terms.
const float zero_point_from_min = qmin_double - f_min / scale;
const float zero_point_from_max = qmax_double - f_max / scale;
const float zero_point_from_min_error =
std::abs(qmin_double) + std::abs(f_min / scale);
const float zero_point_from_max_error =
std::abs(qmax_double) + std::abs(f_max / scale);
const float zero_point_double =
zero_point_from_min_error < zero_point_from_max_error
? zero_point_from_min
: zero_point_from_max;
// Now we need to nudge the zero point to be an integer
// (our zero points are integer, and this is motivated by the requirement
// to be able to represent the real value "0" exactly as a quantized value,
// which is required in multiple places, for example in Im2col with SAME
// padding).
T nudged_zero_point = 0;
if (zero_point_double < qmin_double) {
nudged_zero_point = qmin;
} else if (zero_point_double > qmax_double) {
nudged_zero_point = qmax;
} else {
nudged_zero_point = static_cast<T>(std::round(zero_point_double));
}
// The zero point should always be in the range of quantized value,
// // [qmin, qmax].
CHECK_GE(nudged_zero_point, qmin);
CHECK_LE(nudged_zero_point, qmax);
zero_point = nudged_zero_point;
// finally, return the values
return {scale, zero_point};
}
void AddSubgraphs(int subgraphs_to_add,
int* first_new_subgraph_index = nullptr) {
interpreter_->AddSubgraphs(subgraphs_to_add, first_new_subgraph_index);
}
// Partially populates the tensor, starting at the given offset.
void PopulateTensor4bit(int index, int offset, const int8_t* begin,
const int8_t* end) {
auto data = absl::Span<const int8_t>(begin, end - begin);
TfLiteTensor* tensor_ptr = interpreter_->tensor(index);
uint8_t* v = nullptr;
if (tensor_ptr) {
v = reinterpret_cast<uint8_t*>(tensor_ptr->data.data);
}
if (!v) {
auto* t = interpreter_->tensor(index);
CHECK(t) << "No tensor with index " << index << ".";
CHECK(t->data.raw) << "Empty data for tensor with index " << index << ".";
LOG(FATAL) << "Unknown tensor error.";
}
absl::c_copy(data, v + offset);
PackInt4ValuesDenselyInPlace(v, ElementCount(*tensor_ptr->dims));
tensor_ptr->bytes = ((ElementCount(*tensor_ptr->dims) + 1) / 2);
}
private:
// Populates the tensor starting at offset using given data.
template <typename T, typename Container>
void PopulateTensorImpl(int index, int offset, const Container& data) {
T* v = interpreter_->typed_tensor<T>(index);
if (!v) {
auto* t = interpreter_->tensor(index);
CHECK(t) << "No tensor with index " << index << ".";
CHECK(t->data.raw) << "Empty data for tensor with index " << index << ".";
CHECK_EQ(t->type, typeToTfLiteType<T>())
<< "Type mismatch for tensor with index " << index << ". Requested "
<< TfLiteTypeGetName(typeToTfLiteType<T>()) << ", got "
<< TfLiteTypeGetName(t->type) << ".";
LOG(FATAL) << "Unknown tensor error.";
}
absl::c_copy(data, v + offset);
}
void PackInt4ValuesDenselyInPlace(uint8_t* src_buffer, int buffer_size) {
for (int i = 0; i < buffer_size; ++i) {
if (i % 2 == 0) {
src_buffer[i / 2] = src_buffer[i] & 0x0F;
} else {
src_buffer[i / 2] |= src_buffer[i] << 4;
}
}
// the rest of the buffer should be empty since half of it is packed with
// the values
memset(src_buffer + (buffer_size + 1) / 2, 0, buffer_size / 2);
}
int ElementCount(TfLiteIntArray& dims) {
int result = 1;
for (int i = 0; i < dims.size; ++i) {
result *= dims.data[i];
}
return result;
}
int AddTensorPerChannelQuant(const TensorData& t) {
// type does not matter when adding empty data.
return AddTensorPerChannelQuant<uint8_t>(t, nullptr, 0);
}
template <typename T>
int AddTensorPerChannelQuant(const TensorData& t, const T* data,
size_t size) {
const int id = tensors_.size();
flatbuffers::Offset<QuantizationParameters> q_params = 0;
q_params = CreateQuantizationParameters(
builder_, /*min=*/0, /*max=*/0,
/*scale=*/
builder_.CreateVector<float>(t.per_channel_quantization_scales),
/*zero point=*/
builder_.CreateVector<int64_t>(t.per_channel_quantization_offsets),
QuantizationDetails_NONE, 0, t.channel_index);
int buffer_id = 0;
if (size) {
// Initialize buffers list with empty buffer to allow for non-const
// tensors.
if (buffers_.empty()) {
buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector({})));
}
// Add data as a Buffer to buffers list.
buffer_id = buffers_.size();
auto data_buffer = builder_.CreateVector(
reinterpret_cast<const uint8_t*>(data), sizeof(T) * size);
buffers_.push_back(CreateBuffer(builder_, data_buffer));
}
tensors_.push_back(
CreateTensor(builder_, builder_.CreateVector<int>(t.shape), t.type,
/*buffer=*/buffer_id,
/*name=*/0, q_params, /*is_variable=*/false));
tensor_data_[id] = t;
return id;
}
std::vector<int8_t> QuantizeTensor(int index,
const std::vector<float>& data) {
TfLiteTensor* t = interpreter_->tensor(index);
const int length = data.size();
std::vector<int8_t> q(length);
float min, max, scaling_factor;
tensor_utils::SymmetricQuantizeFloats(data.data(), length, q.data(), &min,
&max, &scaling_factor);
// Update quantization params.
t->params.scale = scaling_factor;
t->params.zero_point = 0;
// Populate the new quantization params.
TfLiteQuantizationFree(&t->quantization);
t->quantization.type = kTfLiteAffineQuantization;
auto* affine_quantization = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
affine_quantization->quantized_dimension = 0;
affine_quantization->scale = TfLiteFloatArrayCreate(1);
affine_quantization->zero_point = TfLiteIntArrayCreate(1);
affine_quantization->scale->data[0] = scaling_factor;
affine_quantization->zero_point->data[0] = 0;
t->quantization.params = affine_quantization;
return q;
}
// Checks if acceleration has been done as expected.
// Currently supports only NNAPI.
// It verifies if the test was configured to run with NNAPI acceleration
// or not (SetForceUseNnapi(true)).
// In affirmative case it checks if:
// - the test case has been listed in the list of nnapi-accelerated cases
// - the test is running on a device (NNAPI has been loaded)
//
// The list of nnapi-accelerated test cases is a file containing regex to
// include or exclude specific test cases plus the minimum android SDK version
// the acceleration should be enabled for. For example:
// To enable the test BorderFloat in TopKV2OpTest only from
// android_sdk_version 29:
//
// TopKV2OpTest/BorderFloat,29
//
// And to have it always excluded while enabling all other Float tests
// (the order of the rules is important, the first one matching is used):
//
// -TopKV2OpTest/BorderFloat
// TopKV2OpTest/.+Float
void ValidateAcceleration();
// If the test was configured to use NNAPI and NNAPI was actually loaded,
// checks if the single operation in the model has been accelerated.
void ExpectOpAcceleratedWithNnapi(const std::string& test_id);
std::map<int, TensorData> tensor_data_;
std::vector<int32_t> inputs_;
std::vector<int32_t> intermediates_;
std::vector<int32_t> outputs_;
std::vector<flatbuffers::Offset<Tensor>> tensors_;
std::vector<flatbuffers::Offset<Buffer>> buffers_;
TfLiteDelegate* delegate_ = nullptr; // not own the memory.
std::optional<TfLiteStatus> delegate_application_status_ = std::nullopt;
std::vector<std::vector<int>> input_shapes_;
int num_applied_delegates_ = 0;
bool allow_fp32_relax_to_fp16_ = false;
bool apply_delegate_ = true;
bool allocate_and_delegate_ = true;
// Whether to bypass the application of TF Lite default delegates (i.e.
// XNNPACK delegate) at rutnime.
// True by default as delegated graphs are tested elsewhere.
bool bypass_default_delegates_ = true;
};
// Populate string tensors.
template <>
inline void SingleOpModel::PopulateTensor<string>(
int index, const std::initializer_list<string>& data) {
PopulateStringTensor(index, data);
}
// Base class for single op unit tests.
// The tests are parameterized to test multiple kernels for a single op.
// The parameters are strings like "optimized" and "reference" to have better
// readability in test reports.
//
// To use this class:
// * Define a constant map from strings to TfLiteRegistration.
// * Implement a test class that inherits SingleOpTest.
// * Instantiate the test cases with SingleOpTest::GetKernelTags helper
// function.
// * Call GetRegistration to get the TfLiteRegistration to be used before
// building the interpreter.
class SingleOpTest : public ::testing::TestWithParam<string> {
public:
static std::vector<string> GetKernelTags(
const std::map<string, TfLiteRegistration*>& kernel_map) {
std::vector<string> tags;
tags.reserve(kernel_map.size());
for (const auto& it : kernel_map) {
tags.push_back(it.first);
}
return tags;
}
protected:
virtual const std::map<string, TfLiteRegistration*>& GetKernelMap() = 0;
TfLiteRegistration* GetRegistration() {
return GetKernelMap().at(GetParam());
}
};
// Maps the native C++ types to the corresponding TFLite tensor type enum
// values.
template <class T>
struct TensorTypeFor;
#define TFLITE_TENSOR_TYPE_ASSOC(CPP_TYPE, TENSORTYPE_VALUE) \
template <> \
struct TensorTypeFor<CPP_TYPE> { \
static constexpr TensorType value = TENSORTYPE_VALUE; \
};
TFLITE_TENSOR_TYPE_ASSOC(bool, TensorType_BOOL);
TFLITE_TENSOR_TYPE_ASSOC(int8_t, TensorType_INT8);
TFLITE_TENSOR_TYPE_ASSOC(int16_t, TensorType_INT16);
TFLITE_TENSOR_TYPE_ASSOC(int32_t, TensorType_INT32);
TFLITE_TENSOR_TYPE_ASSOC(int64_t, TensorType_INT64);
TFLITE_TENSOR_TYPE_ASSOC(uint8_t, TensorType_UINT8);
TFLITE_TENSOR_TYPE_ASSOC(uint16_t, TensorType_UINT16);
TFLITE_TENSOR_TYPE_ASSOC(uint32_t, TensorType_UINT32);
TFLITE_TENSOR_TYPE_ASSOC(uint64_t, TensorType_UINT64);
TFLITE_TENSOR_TYPE_ASSOC(TfLiteFloat16, TensorType_FLOAT16);
TFLITE_TENSOR_TYPE_ASSOC(Eigen::half, TensorType_FLOAT16);
TFLITE_TENSOR_TYPE_ASSOC(TfLiteBFloat16, TensorType_BFLOAT16);
TFLITE_TENSOR_TYPE_ASSOC(Eigen::bfloat16, TensorType_BFLOAT16);
TFLITE_TENSOR_TYPE_ASSOC(float, TensorType_FLOAT32);
TFLITE_TENSOR_TYPE_ASSOC(double, TensorType_FLOAT64);
TFLITE_TENSOR_TYPE_ASSOC(std::string, TensorType_STRING);
#undef TFLITE_TENSOR_TYPE_ASSOC
// Returns the corresponding TensorType given the type T.
template <typename T>
constexpr TensorType GetTensorType() {
return TensorTypeFor<T>::value;
}
// Strings have a special implementation that is in test_util.cc
template <>
std::vector<string> SingleOpModel::ExtractVector(int index) const;
// The TypeUnion struct specializations hold a collection of related types.
// Each struct holds: 1. a primitive type (e.g. float), 2. a TensorType (e.g.
// TensorType_FLOAT32, and 3. a TfLiteType (e.g. kTfLiteFloat32). The latter
// two are actually enum values and not raw types, but these specializations
// make it easy to use gUnit Typed Test Suite:
// https://github.com/google/googletest/blob/master/googletest/docs/advanced.md#typed-tests
template <typename T>
struct TypeUnion;
template <>
struct TypeUnion<float> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_FLOAT32;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteFloat32;
typedef float ScalarType;
};
template <>
struct TypeUnion<int32_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_INT32;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt32;
typedef int32_t ScalarType;
};
template <>
struct TypeUnion<uint32_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_UINT32;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteUInt32;
typedef uint32_t ScalarType;
};
template <>
struct TypeUnion<int16_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_INT16;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt16;
typedef int16_t ScalarType;
};
template <>
struct TypeUnion<uint16_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_UINT16;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteUInt16;
typedef uint16_t ScalarType;
};
template <>
struct TypeUnion<int8_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_INT8;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt8;
typedef int8_t ScalarType;
};
template <>
struct TypeUnion<uint8_t> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_UINT8;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteUInt8;
typedef uint8_t ScalarType;
};
template <>
struct TypeUnion<Eigen::half> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_FLOAT16;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteFloat16;
typedef Eigen::half ScalarType;
};
template <>
struct TypeUnion<Eigen::bfloat16> {
public:
// NOLINTNEXTLINE
static constexpr TensorType tensor_type = TensorType::TensorType_BFLOAT16;
// NOLINTNEXTLINE
static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteBFloat16;
typedef Eigen::bfloat16 ScalarType;
};
class MultiOpModel : public SingleOpModel {
public:
MultiOpModel() : SingleOpModel() {}
~MultiOpModel() = default;
void AddBuiltinOp(BuiltinOperator type, BuiltinOptions builtin_options_type,
const flatbuffers::Offset<void>& builtin_options,
const std::vector<int32_t>& inputs,
const std::vector<int32_t>& outputs);
void AddCustomOp(const string& name,
const std::vector<uint8_t>& custom_option,
const std::function<TfLiteRegistration*()>& registration,
const std::vector<int32_t>& inputs,
const std::vector<int32_t>& outputs);
template <typename T>
int AddInnerTensor(TensorData t) {
return AddTensor<T>(t, {}, false);
}
};
// This Matcher can be used to check the dimensions of a `TfLiteTensor`
// in a readable way. It will print a helpful error message in the
// case of a failure.
//
// EXAMPLE:
// TfLiteTensor* t = (TfLiteTensor*)malloc(sizeof(TfLiteTensor));
// t->dims = ConvertVectorToTfLiteIntArray({2, 2});
// EXPECT_EQ(t, DimsAre({2, 2}));
//
// To check for scalar, simply pass empty initializer list: `DimsAre({})`.
class DimsAreMatcher {
public:
using is_gtest_matcher = void;
explicit DimsAreMatcher(const std::vector<int>& dims) {
dims_ = std::shared_ptr<TfLiteIntArray>(ConvertVectorToTfLiteIntArray(dims),
TfLiteIntArrayFree);
}
// Required method to implement for matcher objects. We overload on
// both `TfLiteTensor*` and `TfLiteIntArray` for flexibility.
bool MatchAndExplain(const TfLiteIntArray* arg,
testing::MatchResultListener* result_listener) const {
if (arg == nullptr) {
*result_listener << "dims are null";
return false;
}
if (TfLiteIntArrayEqual(arg, dims_.get())) {
return true;
}
*result_listener << "has dims " << tflite::GetShapeDebugString(arg);
return false;
}
bool MatchAndExplain(const TfLiteTensor* arg,
testing::MatchResultListener* result_listener) const {
if (arg == nullptr) {
*result_listener << "tensor is null";
return false;
}
return MatchAndExplain(arg->dims, result_listener);
}
void DescribeTo(std::ostream* os) const {
*os << "dims equal to ";
*os << tflite::GetShapeDebugString(dims_.get());
}
void DescribeNegationTo(std::ostream* os) const {
*os << "dims not equal to ";
*os << tflite::GetShapeDebugString(dims_.get());
}
private:
// gUnit uses the implicit copy constructor of `DimsAreMatcher`. Use
// `std::shared_ptr` here so copies of `DimsAreMatcher` can share the
// same `TfLiteIntArray`.
std::shared_ptr<TfLiteIntArray> dims_;
};
inline DimsAreMatcher DimsAre(std::initializer_list<int> dims_list) {
return DimsAreMatcher(dims_list);
}
inline DimsAreMatcher DimsAre(const std::vector<int>& dims) {
return DimsAreMatcher(dims);
}
inline DimsAreMatcher DimsAre(absl::Span<int> dims) {
return DimsAreMatcher(std::vector<int>(dims.begin(), dims.end()));
}
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_TEST_UTIL_H_
Reference in New Issue View Git Blame Copy Permalink