src/cpu/kernels/CpuQuantizeKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2022 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
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "src/cpu/kernels/CpuQuantizeKernel.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "src/core/NEON/NEAsymm.h"
 #include "src/core/NEON/NEMath.h"
 #include "src/core/NEON/wrapper/wrapper.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include "src/core/CPP/Validate.h"

 #include <arm_neon.h>
 #include <map>

 namespace arm_compute
 {
 namespace cpu
 {
 namespace kernels
 {
 namespace
 {
 constexpr auto window_step = 16;

 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QSYMM8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);

     return Status{};
 }

 template <typename T>
 inline float32x4x4_t load_value(const T *input_ptr)
 {
     using Tx16_t = typename wrapper::traits::neon_vector<T, 16>::type;
     return arm_compute::convert_to_float32x4x4<Tx16_t>(wrapper::vloadq(input_ptr));
 }

 template <>
 inline float32x4x4_t load_value(const float *input_ptr)
 {
     return { wrapper::vloadq(input_ptr),
              wrapper::vloadq(input_ptr + 4),
              wrapper::vloadq(input_ptr + 8),
              wrapper::vloadq(input_ptr + 12) };
 }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 template <>
 inline float32x4x4_t load_value(const float16_t *input_ptr)
 {
     return { vcvt_f32_f16(wrapper::vload(input_ptr)),
              vcvt_f32_f16(wrapper::vload(input_ptr + 4)),
              vcvt_f32_f16(wrapper::vload(input_ptr + 8)),
              vcvt_f32_f16(wrapper::vload(input_ptr + 12)) };
 }

 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

 template <typename element_type>
 using vector_type = wrapper::traits::neon_vector_t<element_type, window_step>;

 template <typename quantized_type>
 vector_type<quantized_type> vquantize_qasymm8(const float32x4x4_t &qv, const UniformQuantizationInfo &qi);

 template <>
 vector_type<uint8_t> vquantize_qasymm8<uint8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
 {
     return vquantize(qv, qi);
 }

 template <>
 vector_type<int8_t> vquantize_qasymm8<int8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
 {
     return vquantize_signed(qv, qi);
 }

 } // namespace

 void CpuQuantizeKernel::configure(const ITensorInfo *src, ITensorInfo *dst)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst));

     static const std::map<std::string, QuantizeFunctionExecutorPtr> quant_map =
     {
         { "op_QASYMM8_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<uint8_t, uint8_t> },
         { "op_QASYMM8_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<uint8_t, int8_t> },
         { "op_QASYMM8_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<uint8_t> },

         { "op_QASYMM8_SIGNED_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<int8_t, uint8_t> },
         { "op_QASYMM8_SIGNED_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<int8_t, int8_t> },
         { "op_QASYMM8_SIGNED_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<int8_t> },

         { "op_F32_QSYMM8", &CpuQuantizeKernel::run_quantize_qsymm8<float, int8_t> },

         { "op_F32_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<float, uint8_t> },
         { "op_F32_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<float, int8_t> },
         { "op_F32_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<float> },

 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         { "op_F16_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<float16_t, uint8_t> },
         { "op_F16_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<float16_t, int8_t> },
         { "op_F16_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<float16_t> },
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/
     };

     std::string function_to_call("op_");
     function_to_call += string_from_data_type(src->data_type()) + "_";
     function_to_call += string_from_data_type(dst->data_type());

     auto it = quant_map.find(function_to_call);

     if(it == quant_map.end())
     {
         ARM_COMPUTE_ERROR("Unsupported combination of input and output data types");
     }
     _func = it->second;

     // Configure kernel window
     Window win_config = calculate_max_window(*src, Steps());
     ICpuKernel::configure(win_config);
 }

 Status CpuQuantizeKernel::validate(const ITensorInfo *src, const ITensorInfo *dst)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst));
     return Status{};
 }

 template <typename TIn, typename TOut>
 void CpuQuantizeKernel::run_quantize_qsymm8(const ITensor *src, ITensor *dst, const Window &window)
 {
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
     UniformQuantizationInfo       uqinfo    = dst->info()->quantization_info().uniform();
     if(is_data_type_quantized_asymmetric(src->info()->data_type()))
     {
         uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
     }
     // Collapse window and reset first dimension to handle tail calculations manually
     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator input(src, win_collapsed);
     Iterator output(dst, win_collapsed);
     execute_window_loop(win_collapsed, [&](const Coordinates &)
     {
         auto input_ptr  = reinterpret_cast<const TIn *>(input.ptr());
         auto output_ptr = reinterpret_cast<TOut *>(output.ptr());
         int x = window_start_x;
         for(; x <= (window_end_x - window_step); x += window_step)
         {
             wrapper::vstore(&output_ptr[x], vquantize_qasymm8<TOut>(load_value(&input_ptr[x]), uqinfo));
         }
         // Compute left-over elements
         for(; x < window_end_x; ++x)
         {
             output_ptr[x] = quantize_qsymm8(input_ptr[x], dst->info()->quantization_info());
         }
     },
     input, output);
 }

 template <typename TIn, typename TOut>
 void CpuQuantizeKernel::run_quantize_qasymm8(const ITensor *src, ITensor *dst, const Window &window)
 {
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
     UniformQuantizationInfo       uqinfo    = dst->info()->quantization_info().uniform();
     if(is_data_type_quantized_asymmetric(src->info()->data_type()))
     {
         uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
     }
 #ifdef __aarch64__
     constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
 #else  //__aarch64__
     constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
 #endif //__aarch64__

     // Collapse window and reset first dimension to handle tail calculations manually
     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator input(src, win_collapsed);
     Iterator output(dst, win_collapsed);
     execute_window_loop(win_collapsed, [&](const Coordinates &)
     {
         auto input_ptr  = reinterpret_cast<const TIn *>(input.ptr());
         auto output_ptr = reinterpret_cast<TOut *>(output.ptr());

         int x = window_start_x;
         for(; x <= (window_end_x - window_step); x += window_step)
         {
             wrapper::vstore(&output_ptr[x], vquantize_qasymm8<TOut>(load_value(&input_ptr[x]), uqinfo));
         }
         // Compute left-over elements
         for(; x < window_end_x; ++x)
         {
             output_ptr[x] = Qasymm8QuantizationHelper<TOut>::quantize(input_ptr[x], uqinfo, rounding_policy);
         }
     },
     input, output);
 }

 template <typename T>
 void CpuQuantizeKernel::run_quantize_qasymm16(const ITensor *src, ITensor *dst, const Window &window)
 {
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
     UniformQuantizationInfo       uqinfo    = dst->info()->quantization_info().uniform();
     if(is_data_type_quantized_asymmetric(src->info()->data_type()))
     {
         uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
     }
 #ifdef __aarch64__
     constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
 #else  //__aarch64__
     constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
 #endif //__aarch64__

     // Collapse window and reset first dimension to handle tail calculations manually
     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator input(src, win_collapsed);
     Iterator output(dst, win_collapsed);
     execute_window_loop(win_collapsed, [&](const Coordinates &)
     {
         auto input_ptr  = reinterpret_cast<const T *>(input.ptr());
         auto output_ptr = reinterpret_cast<uint16_t *>(output.ptr());

         int x = window_start_x;
         for(; x <= (window_end_x - window_step); x += window_step)
         {
             uint16x8x2_t tmp = vquantize_qasymm16(load_value(&input_ptr[x]), uqinfo);
             vst1q_u16(&output_ptr[x], tmp.val[0]);
             vst1q_u16(&output_ptr[x + 8], tmp.val[1]);
         }
         // Compute left-over elements
         for(; x < window_end_x; ++x)
         {
             output_ptr[x] = quantize_qasymm16(input_ptr[x], uqinfo, rounding_policy);
         }
     },
     input, output);
 }

 void CpuQuantizeKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
     ARM_COMPUTE_ERROR_ON(_func == nullptr);

     const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
     auto       dst = tensors.get_tensor(TensorType::ACL_DST);
     (this->*_func)(src, dst, window);
 }

 const char *CpuQuantizeKernel::name() const
 {
     return "CpuQuantizeKernel";
 }
 } // namespace kernels
 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2022 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
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "src/cpu/kernels/CpuQuantizeKernel.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "src/core/NEON/NEAsymm.h"
	#include "src/core/NEON/NEMath.h"
	#include "src/core/NEON/wrapper/wrapper.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include "src/core/CPP/Validate.h"

	#include <arm_neon.h>
	#include <map>

	namespace arm_compute
	{
	namespace cpu
	{
	namespace kernels
	{
	namespace
	{
	constexpr auto window_step = 16;

	Status validate_arguments(const ITensorInfo src, const ITensorInfo dst)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QSYMM8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);

	return Status{};
	}

	template <typename T>
	inline float32x4x4_t load_value(const T *input_ptr)
	{
	using Tx16_t = typename wrapper::traits::neon_vector<T, 16>::type;
	return arm_compute::convert_to_float32x4x4<Tx16_t>(wrapper::vloadq(input_ptr));
	}

	template <>
	inline float32x4x4_t load_value(const float *input_ptr)
	{
	return { wrapper::vloadq(input_ptr),
	wrapper::vloadq(input_ptr + 4),
	wrapper::vloadq(input_ptr + 8),
	wrapper::vloadq(input_ptr + 12) };
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	template <>
	inline float32x4x4_t load_value(const float16_t *input_ptr)
	{
	return { vcvt_f32_f16(wrapper::vload(input_ptr)),
	vcvt_f32_f16(wrapper::vload(input_ptr + 4)),
	vcvt_f32_f16(wrapper::vload(input_ptr + 8)),
	vcvt_f32_f16(wrapper::vload(input_ptr + 12)) };
	}

	#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

	template <typename element_type>
	using vector_type = wrapper::traits::neon_vector_t<element_type, window_step>;

	template <typename quantized_type>
	vector_type<quantized_type> vquantize_qasymm8(const float32x4x4_t &qv, const UniformQuantizationInfo &qi);

	template <>
	vector_type<uint8_t> vquantize_qasymm8<uint8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
	{
	return vquantize(qv, qi);
	}

	template <>
	vector_type<int8_t> vquantize_qasymm8<int8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
	{
	return vquantize_signed(qv, qi);
	}

	} // namespace

	void CpuQuantizeKernel::configure(const ITensorInfo src, ITensorInfo dst)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst));

	static const std::map<std::string, QuantizeFunctionExecutorPtr> quant_map =
	{
	{ "op_QASYMM8_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<uint8_t, uint8_t> },
	{ "op_QASYMM8_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<uint8_t, int8_t> },
	{ "op_QASYMM8_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<uint8_t> },

	{ "op_QASYMM8_SIGNED_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<int8_t, uint8_t> },
	{ "op_QASYMM8_SIGNED_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<int8_t, int8_t> },
	{ "op_QASYMM8_SIGNED_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<int8_t> },

	{ "op_F32_QSYMM8", &CpuQuantizeKernel::run_quantize_qsymm8<float, int8_t> },

	{ "op_F32_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<float, uint8_t> },
	{ "op_F32_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<float, int8_t> },
	{ "op_F32_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<float> },

	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	{ "op_F16_QASYMM8", &CpuQuantizeKernel::run_quantize_qasymm8<float16_t, uint8_t> },
	{ "op_F16_QASYMM8_SIGNED", &CpuQuantizeKernel::run_quantize_qasymm8<float16_t, int8_t> },
	{ "op_F16_QASYMM16", &CpuQuantizeKernel::run_quantize_qasymm16<float16_t> },
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/
	};

	std::string function_to_call("op_");
	function_to_call += string_from_data_type(src->data_type()) + "_";
	function_to_call += string_from_data_type(dst->data_type());

	auto it = quant_map.find(function_to_call);

	if(it == quant_map.end())
	{
	ARM_COMPUTE_ERROR("Unsupported combination of input and output data types");
	}
	_func = it->second;

	// Configure kernel window
	Window win_config = calculate_max_window(*src, Steps());
	ICpuKernel::configure(win_config);
	}

	Status CpuQuantizeKernel::validate(const ITensorInfo src, const ITensorInfo dst)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst));
	return Status{};
	}

	template <typename TIn, typename TOut>
	void CpuQuantizeKernel::run_quantize_qsymm8(const ITensor src, ITensor dst, const Window &window)
	{
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
	UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform();
	if(is_data_type_quantized_asymmetric(src->info()->data_type()))
	{
	uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
	}
	// Collapse window and reset first dimension to handle tail calculations manually
	Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
	win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator input(src, win_collapsed);
	Iterator output(dst, win_collapsed);
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	auto input_ptr = reinterpret_cast<const TIn *>(input.ptr());
	auto output_ptr = reinterpret_cast<TOut *>(output.ptr());
	int x = window_start_x;
	for(; x <= (window_end_x - window_step); x += window_step)
	{
	wrapper::vstore(&output_ptr[x], vquantize_qasymm8<TOut>(load_value(&input_ptr[x]), uqinfo));
	}
	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	output_ptr[x] = quantize_qsymm8(input_ptr[x], dst->info()->quantization_info());
	}
	},
	input, output);
	}

	template <typename TIn, typename TOut>
	void CpuQuantizeKernel::run_quantize_qasymm8(const ITensor src, ITensor dst, const Window &window)
	{
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
	UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform();
	if(is_data_type_quantized_asymmetric(src->info()->data_type()))
	{
	uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
	}
	#ifdef __aarch64__
	constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
	#else //__aarch64__
	constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
	#endif //__aarch64__

	// Collapse window and reset first dimension to handle tail calculations manually
	Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
	win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator input(src, win_collapsed);
	Iterator output(dst, win_collapsed);
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	auto input_ptr = reinterpret_cast<const TIn *>(input.ptr());
	auto output_ptr = reinterpret_cast<TOut *>(output.ptr());

	int x = window_start_x;
	for(; x <= (window_end_x - window_step); x += window_step)
	{
	wrapper::vstore(&output_ptr[x], vquantize_qasymm8<TOut>(load_value(&input_ptr[x]), uqinfo));
	}
	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	output_ptr[x] = Qasymm8QuantizationHelper<TOut>::quantize(input_ptr[x], uqinfo, rounding_policy);
	}
	},
	input, output);
	}

	template <typename T>
	void CpuQuantizeKernel::run_quantize_qasymm16(const ITensor src, ITensor dst, const Window &window)
	{
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
	UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform();
	if(is_data_type_quantized_asymmetric(src->info()->data_type()))
	{
	uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
	}
	#ifdef __aarch64__
	constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
	#else //__aarch64__
	constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
	#endif //__aarch64__

	// Collapse window and reset first dimension to handle tail calculations manually
	Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
	win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator input(src, win_collapsed);
	Iterator output(dst, win_collapsed);
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	auto input_ptr = reinterpret_cast<const T *>(input.ptr());
	auto output_ptr = reinterpret_cast<uint16_t *>(output.ptr());

	int x = window_start_x;
	for(; x <= (window_end_x - window_step); x += window_step)
	{
	uint16x8x2_t tmp = vquantize_qasymm16(load_value(&input_ptr[x]), uqinfo);
	vst1q_u16(&output_ptr[x], tmp.val[0]);
	vst1q_u16(&output_ptr[x + 8], tmp.val[1]);
	}
	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	output_ptr[x] = quantize_qasymm16(input_ptr[x], uqinfo, rounding_policy);
	}
	},
	input, output);
	}

	void CpuQuantizeKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
	ARM_COMPUTE_ERROR_ON(_func == nullptr);

	const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
	auto dst = tensors.get_tensor(TensorType::ACL_DST);
	(this->*_func)(src, dst, window);
	}

	const char *CpuQuantizeKernel::name() const
	{
	return "CpuQuantizeKernel";
	}
	} // namespace kernels
	} // namespace cpu
	} // namespace arm_compute