Utils.h

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/*
 * Copyright (c) 2017-2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef ARM_COMPUTE_TEST_UTILS_H
#define ARM_COMPUTE_TEST_UTILS_H
 
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "support/StringSupport.h"
#include "support/ToolchainSupport.h"
 
#ifdef ARM_COMPUTE_CL
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#endif /* ARM_COMPUTE_CL */
 
#include <cmath>
#include <cstddef>
#include <limits>
#include <memory>
#include <random>
#include <sstream>
#include <string>
#include <type_traits>
#include <vector>
 
#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
#include "arm_compute/runtime/CPP/CPPScheduler.h"
#include "arm_compute/runtime/RuntimeContext.h"
 
namespace arm_compute
{
#ifdef ARM_COMPUTE_CL
class CLTensor;
#endif /* ARM_COMPUTE_CL */
namespace test
{
/** Round floating-point value with half value rounding to positive infinity.
 *
 * @param[in] value floating-point value to be rounded.
 *
 * @return Floating-point value of rounded @p value.
 */
template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
inline T round_half_up(T value)
{
    return std::floor(value + 0.5f);
}
 
/** Round floating-point value with half value rounding to nearest even.
 *
 * @param[in] value   floating-point value to be rounded.
 * @param[in] epsilon precision.
 *
 * @return Floating-point value of rounded @p value.
 */
template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
inline T round_half_even(T value, T epsilon = std::numeric_limits<T>::epsilon())
{
    T positive_value = std::abs(value);
    T ipart          = 0;
    std::modf(positive_value, &ipart);
    // If 'value' is exactly halfway between two integers
    if(std::abs(positive_value - (ipart + 0.5f)) < epsilon)
    {
        // If 'ipart' is even then return 'ipart'
        if(std::fmod(ipart, 2.f) < epsilon)
        {
            return support::cpp11::copysign(ipart, value);
        }
        // Else return the nearest even integer
        return support::cpp11::copysign(std::ceil(ipart + 0.5f), value);
    }
    // Otherwise use the usual round to closest
    return support::cpp11::copysign(support::cpp11::round(positive_value), value);
}
 
namespace traits
{
// *INDENT-OFF*
// clang-format off
/** Promote a type */
template <typename T> struct promote { };
/** Promote uint8_t to uint16_t */
template <> struct promote<uint8_t> { using type = uint16_t; /**< Promoted type */ };
/** Promote int8_t to int16_t */
template <> struct promote<int8_t> { using type = int16_t; /**< Promoted type */ };
/** Promote uint16_t to uint32_t */
template <> struct promote<uint16_t> { using type = uint32_t; /**< Promoted type */ };
/** Promote int16_t to int32_t */
template <> struct promote<int16_t> { using type = int32_t; /**< Promoted type */ };
/** Promote uint32_t to uint64_t */
template <> struct promote<uint32_t> { using type = uint64_t; /**< Promoted type */ };
/** Promote int32_t to int64_t */
template <> struct promote<int32_t> { using type = int64_t; /**< Promoted type */ };
/** Promote float to float */
template <> struct promote<float> { using type = float; /**< Promoted type */ };
/** Promote half to half */
template <> struct promote<half> { using type = half; /**< Promoted type */ };
 
/** Get promoted type */
template <typename T>
using promote_t = typename promote<T>::type;
 
template <typename T>
using make_signed_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_signed<T>, std::common_type<T>>::type;
 
template <typename T>
using make_unsigned_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_unsigned<T>, std::common_type<T>>::type;
 
// clang-format on
// *INDENT-ON*
} // namespace traits
 
/** Look up the format corresponding to a channel.
 *
 * @param[in] channel Channel type.
 *
 * @return Format that contains the given channel.
 */
inline Format get_format_for_channel(Channel channel)
{
    switch(channel)
    {
        case Channel::R:
        case Channel::G:
        case Channel::B:
            return Format::RGB888;
        default:
            throw std::runtime_error("Unsupported channel");
    }
}
 
/** Return the format of a channel.
 *
 * @param[in] channel Channel type.
 *
 * @return Format of the given channel.
 */
inline Format get_channel_format(Channel channel)
{
    switch(channel)
    {
        case Channel::R:
        case Channel::G:
        case Channel::B:
            return Format::U8;
        default:
            throw std::runtime_error("Unsupported channel");
    }
}
 
/** Base case of foldl.
 *
 * @return value.
 */
template <typename F, typename T>
inline T foldl(F &&, const T &value)
{
    return value;
}
 
/** Base case of foldl.
 *
 * @return func(value1, value2).
 */
template <typename F, typename T, typename U>
inline auto foldl(F &&func, T &&value1, U &&value2) -> decltype(func(value1, value2))
{
    return func(value1, value2);
}
 
/** Fold left.
 *
 * @param[in] func    Binary function to be called.
 * @param[in] initial Initial value.
 * @param[in] value   Argument passed to the function.
 * @param[in] values  Remaining arguments.
 */
template <typename F, typename I, typename T, typename... Vs>
inline I foldl(F &&func, I &&initial, T &&value, Vs &&... values)
{
    return foldl(std::forward<F>(func), func(std::forward<I>(initial), std::forward<T>(value)), std::forward<Vs>(values)...);
}
 
/** Create a valid region based on tensor shape, border mode and border size
 *
 * @param[in] a_shape          Shape used as size of the valid region.
 * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined.
 * @param[in] border_size      (Optional) Border size used to specify the region to exclude.
 *
 * @return A valid region starting at (0, 0, ...) with size of @p shape if @p border_undefined is false; otherwise
 *  return A valid region starting at (@p border_size.left, @p border_size.top, ...) with reduced size of @p shape.
 */
inline ValidRegion shape_to_valid_region(const TensorShape &a_shape, bool border_undefined = false, BorderSize border_size = BorderSize(0))
{
    ValidRegion valid_region{ Coordinates(), a_shape };
 
    Coordinates &anchor = valid_region.anchor;
    TensorShape &shape  = valid_region.shape;
 
    if(border_undefined)
    {
        ARM_COMPUTE_ERROR_ON(shape.num_dimensions() < 2);
 
        anchor.set(0, border_size.left);
        anchor.set(1, border_size.top);
 
        const int valid_shape_x = std::max(0, static_cast<int>(shape.x()) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right));
        const int valid_shape_y = std::max(0, static_cast<int>(shape.y()) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom));
 
        shape.set(0, valid_shape_x);
        shape.set(1, valid_shape_y);
    }
 
    return valid_region;
}
 
/** Write the value after casting the pointer according to @p data_type.
 *
 * @warning The type of the value must match the specified data type.
 *
 * @param[out] ptr       Pointer to memory where the @p value will be written.
 * @param[in]  value     Value that will be written.
 * @param[in]  data_type Data type that will be written.
 */
template <typename T>
void store_value_with_data_type(void *ptr, T value, DataType data_type)
{
    switch(data_type)
    {
        case DataType::U8:
        case DataType::QASYMM8:
            *reinterpret_cast<uint8_t *>(ptr) = value;
            break;
        case DataType::S8:
        case DataType::QASYMM8_SIGNED:
        case DataType::QSYMM8:
        case DataType::QSYMM8_PER_CHANNEL:
            *reinterpret_cast<int8_t *>(ptr) = value;
            break;
        case DataType::U16:
        case DataType::QASYMM16:
            *reinterpret_cast<uint16_t *>(ptr) = value;
            break;
        case DataType::S16:
        case DataType::QSYMM16:
            *reinterpret_cast<int16_t *>(ptr) = value;
            break;
        case DataType::U32:
            *reinterpret_cast<uint32_t *>(ptr) = value;
            break;
        case DataType::S32:
            *reinterpret_cast<int32_t *>(ptr) = value;
            break;
        case DataType::U64:
            *reinterpret_cast<uint64_t *>(ptr) = value;
            break;
        case DataType::S64:
            *reinterpret_cast<int64_t *>(ptr) = value;
            break;
        case DataType::BFLOAT16:
            *reinterpret_cast<bfloat16 *>(ptr) = bfloat16(value);
            break;
        case DataType::F16:
            *reinterpret_cast<half *>(ptr) = value;
            break;
        case DataType::F32:
            *reinterpret_cast<float *>(ptr) = value;
            break;
        case DataType::F64:
            *reinterpret_cast<double *>(ptr) = value;
            break;
        case DataType::SIZET:
            *reinterpret_cast<size_t *>(ptr) = value;
            break;
        default:
            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
    }
}
 
/** Saturate a value of type T against the numeric limits of type U.
 *
 * @param[in] val Value to be saturated.
 *
 * @return saturated value.
 */
template <typename U, typename T>
T saturate_cast(T val)
{
    if(val > static_cast<T>(std::numeric_limits<U>::max()))
    {
        val = static_cast<T>(std::numeric_limits<U>::max());
    }
    if(val < static_cast<T>(std::numeric_limits<U>::lowest()))
    {
        val = static_cast<T>(std::numeric_limits<U>::lowest());
    }
    return val;
}
 
/** Find the signed promoted common type.
 */
template <typename... T>
struct common_promoted_signed_type
{
    /** Common type */
    using common_type = typename std::common_type<T...>::type;
    /** Promoted type */
    using promoted_type = traits::promote_t<common_type>;
    /** Intermediate type */
    using intermediate_type = typename traits::make_signed_conditional_t<promoted_type>::type;
};
 
/** Find the unsigned promoted common type.
 */
template <typename... T>
struct common_promoted_unsigned_type
{
    /** Common type */
    using common_type = typename std::common_type<T...>::type;
    /** Promoted type */
    using promoted_type = traits::promote_t<common_type>;
    /** Intermediate type */
    using intermediate_type = typename traits::make_unsigned_conditional_t<promoted_type>::type;
};
 
/** Convert a linear index into n-dimensional coordinates.
 *
 * @param[in] shape Shape of the n-dimensional tensor.
 * @param[in] index Linear index specifying the i-th element.
 *
 * @return n-dimensional coordinates.
 */
inline Coordinates index2coord(const TensorShape &shape, int index)
{
    int num_elements = shape.total_size();
 
    ARM_COMPUTE_ERROR_ON_MSG(index < 0 || index >= num_elements, "Index has to be in [0, num_elements]");
    ARM_COMPUTE_ERROR_ON_MSG(num_elements == 0, "Cannot create coordinate from empty shape");
 
    Coordinates coord{ 0 };
 
    for(int d = shape.num_dimensions() - 1; d >= 0; --d)
    {
        num_elements /= shape[d];
        coord.set(d, index / num_elements);
        index %= num_elements;
    }
 
    return coord;
}
 
/** Linearise the given coordinate.
 *
 * Transforms the given coordinate into a linear offset in terms of
 * elements.
 *
 * @param[in] shape Shape of the n-dimensional tensor.
 * @param[in] coord The to be converted coordinate.
 *
 * @return Linear offset to the element.
 */
inline int coord2index(const TensorShape &shape, const Coordinates &coord)
{
    ARM_COMPUTE_ERROR_ON_MSG(shape.total_size() == 0, "Cannot get index from empty shape");
    ARM_COMPUTE_ERROR_ON_MSG(coord.num_dimensions() == 0, "Cannot get index of empty coordinate");
 
    int index    = 0;
    int dim_size = 1;
 
    for(unsigned int i = 0; i < coord.num_dimensions(); ++i)
    {
        index += coord[i] * dim_size;
        dim_size *= shape[i];
    }
 
    return index;
}
 
/** Check if a coordinate is within a valid region */
inline bool is_in_valid_region(const ValidRegion &valid_region, Coordinates coord)
{
    for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
    {
        if(coord[d] < valid_region.start(d) || coord[d] >= valid_region.end(d))
        {
            return false;
        }
    }
 
    return true;
}
 
/** Create and initialize a tensor of the given type.
 *
 * @param[in] info Tensor information to be used to create the tensor
 * @param[in] ctx  (Optional) Pointer to the runtime context.
 *
 * @return Initialized tensor of given type.
 */
template <typename T>
inline T create_tensor(const TensorInfo &info, IRuntimeContext *ctx = nullptr)
{
    T tensor(ctx);
    tensor.allocator()->init(info);
    return tensor;
}
 
/** Create and initialize a tensor of the given type.
 *
 * @param[in] shape             Tensor shape.
 * @param[in] data_type         Data type.
 * @param[in] num_channels      (Optional) Number of channels.
 * @param[in] quantization_info (Optional) Quantization info for asymmetric quantized types.
 * @param[in] data_layout       (Optional) Data layout. Default is NCHW.
 * @param[in] ctx               (Optional) Pointer to the runtime context.
 *
 * @return Initialized tensor of given type.
 */
template <typename T>
inline T create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1,
                       QuantizationInfo quantization_info = QuantizationInfo(), DataLayout data_layout = DataLayout::NCHW, IRuntimeContext *ctx = nullptr)
{
    T          tensor(ctx);
    TensorInfo info(shape, num_channels, data_type);
    info.set_quantization_info(quantization_info);
    info.set_data_layout(data_layout);
 
    return create_tensor<T>(info, ctx);
}
 
/** Create and initialize a tensor of the given type.
 *
 * @param[in] shape  Tensor shape.
 * @param[in] format Format type.
 * @param[in] ctx    (Optional) Pointer to the runtime context.
 *
 * @return Initialized tensor of given type.
 */
template <typename T>
inline T create_tensor(const TensorShape &shape, Format format, IRuntimeContext *ctx = nullptr)
{
    TensorInfo info(shape, format);
 
    return create_tensor<T>(info, ctx);
}
 
/** Create a vector with a uniform distribution of floating point values across the specified range.
 *
 * @param[in] num_values The number of values to be created.
 * @param[in] min        The minimum value in distribution (inclusive).
 * @param[in] max        The maximum value in distribution (inclusive).
 * @param[in] seed       The random seed to be used.
 *
 * @return A vector that contains the requested number of random floating point values
 */
template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
inline std::vector<T> generate_random_real(unsigned int num_values, T min, T max, std::random_device::result_type seed)
{
    std::vector<T>                    v(num_values);
    std::mt19937                      gen(seed);
    std::uniform_real_distribution<T> dist(min, max);
 
    for(unsigned int i = 0; i < num_values; ++i)
    {
        v.at(i) = dist(gen);
    }
 
    return v;
}
 
template <typename T, typename ArrayAccessor_T>
inline void fill_array(ArrayAccessor_T &&array, const std::vector<T> &v)
{
    array.resize(v.size());
    std::memcpy(array.buffer(), v.data(), v.size() * sizeof(T));
}
 
/** Obtain numpy type string from DataType.
 *
 * @param[in] data_type Data type.
 *
 * @return numpy type string.
 */
inline std::string get_typestring(DataType data_type)
{
    // Check endianness
    const unsigned int i = 1;
    const char        *c = reinterpret_cast<const char *>(&i);
    std::string        endianness;
    if(*c == 1)
    {
        endianness = std::string("<");
    }
    else
    {
        endianness = std::string(">");
    }
    const std::string no_endianness("|");
 
    switch(data_type)
    {
        case DataType::U8:
            return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
        case DataType::S8:
            return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
        case DataType::U16:
            return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
        case DataType::S16:
            return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
        case DataType::U32:
            return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
        case DataType::S32:
            return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
        case DataType::U64:
            return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
        case DataType::S64:
            return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
        case DataType::F32:
            return endianness + "f" + support::cpp11::to_string(sizeof(float));
        case DataType::F64:
            return endianness + "f" + support::cpp11::to_string(sizeof(double));
        case DataType::SIZET:
            return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
        default:
            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
    }
}
 
/** Sync if necessary.
 */
template <typename TensorType>
inline void sync_if_necessary()
{
#ifdef ARM_COMPUTE_CL
    if(opencl_is_available() && std::is_same<typename std::decay<TensorType>::type, arm_compute::CLTensor>::value)
    {
        CLScheduler::get().sync();
    }
#endif /* ARM_COMPUTE_CL */
}
 
/** Sync tensor if necessary.
 *
 * @note: If the destination tensor not being used on OpenGL ES, GPU will optimize out the operation.
 *
 * @param[in] tensor Tensor to be sync.
 */
template <typename TensorType>
inline void sync_tensor_if_necessary(TensorType &tensor)
{
    ARM_COMPUTE_UNUSED(tensor);
}
 
/** Construct and return object for dimensions' state filled with the given value
 *
 * @param[in] value The value to fill
 *
 * @return Constructed class
 */
inline ITensorInfo::TensorDimsState construct_dims_state(int32_t value)
{
    auto states = ITensorInfo::TensorDimsState{};
    std::fill(states.begin(), states.end(), value);
    return states;
}
 
/** Construct and return object for dimensions' state filled with the value for dynamic state
 *
 * @return Constructed class filled with the value for dynamic state
 */
inline ITensorInfo::TensorDimsState construct_dynamic_dims_state()
{
    return construct_dims_state(ITensorInfo::get_dynamic_state_value());
}
 
/** Construct and return object for dimensions' state filled with the value for non-dynamic state
 *
 * @return Constructed class filled with the value for non-dynamic state
 */
inline ITensorInfo::TensorDimsState construct_static_dims_state()
{
    return construct_dims_state(ITensorInfo::get_static_state_value());
}
 
/** Set the dimension states of the given tensor to dynamic
 *
 * @param[in] t The tensor to set to dynamic state
 *
 */
template <typename TensorType>
void set_tensor_dynamic(TensorType &t)
{
    t.info()->set_tensor_dims_state(construct_dynamic_dims_state());
}
 
/** Set the dimension states of the given tensor to state
 *
 * @param[in] t The tensor to set to static state
 *
 */
template <typename TensorType>
void set_tensor_static(TensorType &t)
{
    t.info()->set_tensor_dims_state(construct_static_dims_state());
}
 
inline experimental::dynamic_fusion::Conv2dAttributes convert_pad_stride_info_to_conv_attr(const PadStrideInfo &info, const Size2D &dialation)
{
    const Padding2D info_pad(info.pad_left(), info.pad_right(), info.pad_top(), info.pad_bottom());
    const Size2D    info_stride(info.stride().first, info.stride().second);
    return arm_compute::experimental::dynamic_fusion::Conv2dAttributes().pad(info_pad).stride(info_stride).dilation(dialation);
}
 
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_UTILS_H */