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

 /*
  * Copyright (c) 2017-2021 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/CpuDequantizeKernel.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/CPP/Validate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/NEON/NEAsymm.h"
 #include "src/core/NEON/NESymm.h"
 #include "src/core/NEON/wrapper/wrapper.h"

 #include <arm_neon.h>

 namespace arm_compute
 {
 namespace cpu
 {
 namespace kernels
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                          DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8,
                                                          DataType::QSYMM16);

     if (dst->tensor_shape().total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(dst);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);
     }

     return Status{};
 }

 template <typename T>
 inline void store_result(T *ptr, const float32x4x4_t &v)
 {
     ARM_COMPUTE_UNUSED(ptr, v);
 }

 template <>
 inline void store_result<float>(float *ptr, const float32x4x4_t &v)
 {
     wrapper::vstore(ptr, v.val[0]);
     wrapper::vstore(ptr + 4, v.val[1]);
     wrapper::vstore(ptr + 8, v.val[2]);
     wrapper::vstore(ptr + 12, v.val[3]);
 }

 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 template <>
 inline void store_result<float16_t>(float16_t *ptr, const float32x4x4_t &v)
 {
     wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
     wrapper::vstore(ptr + 8, vcombine_f16(vcvt_f16_f32(v.val[2]), vcvt_f16_f32(v.val[3])));
 }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

 template <typename T>
 inline void store_result(T *ptr, const float32x4x2_t &v)
 {
     ARM_COMPUTE_UNUSED(ptr, v);
 }

 template <>
 inline void store_result<float>(float *ptr, const float32x4x2_t &v)
 {
     wrapper::vstore(ptr, v.val[0]);
     wrapper::vstore(ptr + 4, v.val[1]);
 }

 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 template <>
 inline void store_result<float16_t>(float16_t *ptr, const float32x4x2_t &v)
 {
     wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
 }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

 template <typename TOut, typename TIn>
 void run_dequantization_qasymm8(const ITensor *input, ITensor *output, const Window &window)
 {
     const UniformQuantizationInfo &qinfo  = input->info()->quantization_info().uniform();
     const float                    scale  = qinfo.scale;
     const int32_t                  offset = qinfo.offset;

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     // 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));

     // Create iterators
     Iterator in(input, win_collapsed);
     Iterator out(output, win_collapsed);

     execute_window_loop(
         win_collapsed,
         [&](const Coordinates &)
         {
             const auto in_ptr  = reinterpret_cast<const TIn *>(in.ptr());
             const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());

             int x = window_start_x;
             for (; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 const auto vin  = wrapper::vloadq(in_ptr + x);
                 const auto vdeq = vdequantize(vin, scale, offset);

                 store_result(reinterpret_cast<TOut *>(out_ptr + x), vdeq);
             }

             // Compute left-over elements
             for (; x < window_end_x; ++x)
             {
                 auto val       = *(in_ptr + x);
                 *(out_ptr + x) = static_cast<TOut>(Qasymm8QuantizationHelper<TIn>::dequantize(val, qinfo));
             }
         },
         in, out);
 }

 template <typename T>
 void run_dequantization_qsymm8_per_channel_nchw(const ITensor *input, ITensor *output, const Window &window)
 {
     const auto scale = input->info()->quantization_info().scale();

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     // Reset first dimension to handle tail calculations manually
     Window win(window);
     win.set(Window::DimX, Window::Dimension(0, 1, 1));

     // Create iterators
     Iterator in(input, win);
     Iterator out(output, win);

     execute_window_loop(
         win,
         [&](const Coordinates &id)
         {
             const auto in_ptr  = reinterpret_cast<const int8_t *>(in.ptr());
             const auto out_ptr = reinterpret_cast<T *>(out.ptr());

             int x = window_start_x;
             for (; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 const auto vin  = wrapper::vloadq(in_ptr + x);
                 const auto vdeq = vdequantize(vin, scale[id.z()]);

                 store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
             }

             // Compute left-over elements
             for (; x < window_end_x; ++x)
             {
                 int8_t val     = *(in_ptr + x);
                 *(out_ptr + x) = static_cast<T>(dequantize(val, scale[id.z()]));
             }
         },
         in, out);
 }

 template <typename T>
 void run_dequantization_qsymm8_per_channel_nhwc(const ITensor *input, ITensor *output, const Window &window)
 {
     const auto scale = input->info()->quantization_info().scale();

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     // Reset first dimension to handle tail calculations manually
     Window win(window);
     win.set(Window::DimX, Window::Dimension(0, 1, 1));

     // Create iterators
     Iterator in(input, win);
     Iterator out(output, win);

     execute_window_loop(
         win,
         [&](const Coordinates &)
         {
             const auto in_ptr  = reinterpret_cast<const int8_t *>(in.ptr());
             const auto out_ptr = reinterpret_cast<T *>(out.ptr());

             int x = window_start_x;
             for (; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 const float32x4x4_t vscale = {{scale[x + 0], scale[x + 1], scale[x + 2], scale[x + 3], scale[x + 4],
                                                scale[x + 5], scale[x + 6], scale[x + 7], scale[x + 8], scale[x + 9],
                                                scale[x + 10], scale[x + 11], scale[x + 12], scale[x + 13],
                                                scale[x + 14], scale[x + 15]}};
                 const auto          vin    = wrapper::vloadq(in_ptr + x);
                 const auto          vdeq   = vdequantize(vin, vscale);

                 store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
             }

             // Compute left-over elements
             for (; x < window_end_x; ++x)
             {
                 int8_t val     = *(in_ptr + x);
                 *(out_ptr + x) = static_cast<T>(dequantize(val, scale[x]));
             }
         },
         in, out);
 }

 template <typename T>
 void run_dequantization_qsymm8(const ITensor *input, ITensor *output, const Window &window)
 {
     const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
     const float                    scale = qinfo.scale;

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     // 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));

     // Create iterators
     Iterator in(input, win_collapsed);
     Iterator out(output, win_collapsed);

     execute_window_loop(
         win_collapsed,
         [&](const Coordinates &)
         {
             const auto in_ptr  = reinterpret_cast<const int8_t *>(in.ptr());
             const auto out_ptr = reinterpret_cast<T *>(out.ptr());

             int x = window_start_x;
             for (; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 const auto vin  = wrapper::vloadq(in_ptr + x);
                 const auto vdeq = vdequantize(vin, scale);

                 store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
             }

             // Compute left-over elements
             for (; x < window_end_x; ++x)
             {
                 int8_t val     = *(in_ptr + x);
                 *(out_ptr + x) = static_cast<T>(dequantize(val, scale));
             }
         },
         in, out);
 }

 template <typename T>
 void run_dequantization_qsymm16(const ITensor *input, ITensor *output, const Window &window)
 {
     const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
     const float                    scale = qinfo.scale;

     const int  window_step_x  = 8;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     // 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));

     // Create iterators
     Iterator in(input, win_collapsed);
     Iterator out(output, win_collapsed);

     execute_window_loop(
         win_collapsed,
         [&](const Coordinates &)
         {
             const auto in_ptr  = reinterpret_cast<const int16_t *>(in.ptr());
             const auto out_ptr = reinterpret_cast<T *>(out.ptr());

             int x = window_start_x;
             for (; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 const auto vin  = wrapper::vloadq(in_ptr + x);
                 const auto vdeq = vdequantize_int16(vin, scale);

                 store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
             }

             // Compute left-over elements
             for (; x < window_end_x; ++x)
             {
                 int16_t val    = *(in_ptr + x);
                 *(out_ptr + x) = static_cast<T>(dequantize_qsymm16(val, scale));
             }
         },
         in, out);
 }

 template <typename T>
 void run_dequantization_core(const ITensor *input, ITensor *output, const Window &window)
 {
     switch (input->info()->data_type())
     {
         case DataType::QASYMM8:
             run_dequantization_qasymm8<T, uint8_t>(input, output, window);
             break;
         case DataType::QASYMM8_SIGNED:
             run_dequantization_qasymm8<T, int8_t>(input, output, window);
             break;
         case DataType::QSYMM8_PER_CHANNEL:
             input->info()->data_layout() == DataLayout::NHWC
                 ? run_dequantization_qsymm8_per_channel_nhwc<T>(input, output, window)
                 : run_dequantization_qsymm8_per_channel_nchw<T>(input, output, window);
             break;
         case DataType::QSYMM8:
             run_dequantization_qsymm8<T>(input, output, window);
             break;
         case DataType::QSYMM16:
             run_dequantization_qsymm16<T>(input, output, window);
             break;
         default:
             ARM_COMPUTE_ERROR("Unsupported data type.");
     }
 }
 } // namespace

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

     // Configure kernel window
     Window win = calculate_max_window(*src, Steps());

     // Output tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, src->tensor_shape(), 1, DataType::F32);

     ICpuKernel::configure(win);
 }

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

 void CpuDequantizeKernel::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);

     const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
     auto       dst = tensors.get_tensor(TensorType::ACL_DST);

     switch (dst->info()->data_type())
     {
         case DataType::F32:
             run_dequantization_core<float>(src, dst, window);
             break;
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
             run_dequantization_core<float16_t>(src, dst, window);
             break;
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
         default:
             ARM_COMPUTE_ERROR("Unsupported data type.");
     }
 }
 const char *CpuDequantizeKernel::name() const
 {
     return "CpuDequantizeKernel";
 }
 } // namespace kernels
 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2021 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/CpuDequantizeKernel.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/CPP/Validate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "src/core/NEON/NEAsymm.h"
	#include "src/core/NEON/NESymm.h"
	#include "src/core/NEON/wrapper/wrapper.h"

	#include <arm_neon.h>

	namespace arm_compute
	{
	namespace cpu
	{
	namespace kernels
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo src, const ITensorInfo dst)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
	DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8,
	DataType::QSYMM16);

	if (dst->tensor_shape().total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(dst);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);
	}

	return Status{};
	}

	template <typename T>
	inline void store_result(T *ptr, const float32x4x4_t &v)
	{
	ARM_COMPUTE_UNUSED(ptr, v);
	}

	template <>
	inline void store_result<float>(float *ptr, const float32x4x4_t &v)
	{
	wrapper::vstore(ptr, v.val[0]);
	wrapper::vstore(ptr + 4, v.val[1]);
	wrapper::vstore(ptr + 8, v.val[2]);
	wrapper::vstore(ptr + 12, v.val[3]);
	}

	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	template <>
	inline void store_result<float16_t>(float16_t *ptr, const float32x4x4_t &v)
	{
	wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
	wrapper::vstore(ptr + 8, vcombine_f16(vcvt_f16_f32(v.val[2]), vcvt_f16_f32(v.val[3])));
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

	template <typename T>
	inline void store_result(T *ptr, const float32x4x2_t &v)
	{
	ARM_COMPUTE_UNUSED(ptr, v);
	}

	template <>
	inline void store_result<float>(float *ptr, const float32x4x2_t &v)
	{
	wrapper::vstore(ptr, v.val[0]);
	wrapper::vstore(ptr + 4, v.val[1]);
	}

	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	template <>
	inline void store_result<float16_t>(float16_t *ptr, const float32x4x2_t &v)
	{
	wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

	template <typename TOut, typename TIn>
	void run_dequantization_qasymm8(const ITensor input, ITensor output, const Window &window)
	{
	const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
	const float scale = qinfo.scale;
	const int32_t offset = qinfo.offset;

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	// 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));

	// Create iterators
	Iterator in(input, win_collapsed);
	Iterator out(output, win_collapsed);

	execute_window_loop(
	win_collapsed,
	[&](const Coordinates &)
	{
	const auto in_ptr = reinterpret_cast<const TIn *>(in.ptr());
	const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());

	int x = window_start_x;
	for (; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	const auto vin = wrapper::vloadq(in_ptr + x);
	const auto vdeq = vdequantize(vin, scale, offset);

	store_result(reinterpret_cast<TOut *>(out_ptr + x), vdeq);
	}

	// Compute left-over elements
	for (; x < window_end_x; ++x)
	{
	auto val = *(in_ptr + x);
	*(out_ptr + x) = static_cast<TOut>(Qasymm8QuantizationHelper<TIn>::dequantize(val, qinfo));
	}
	},
	in, out);
	}

	template <typename T>
	void run_dequantization_qsymm8_per_channel_nchw(const ITensor input, ITensor output, const Window &window)
	{
	const auto scale = input->info()->quantization_info().scale();

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	// Reset first dimension to handle tail calculations manually
	Window win(window);
	win.set(Window::DimX, Window::Dimension(0, 1, 1));

	// Create iterators
	Iterator in(input, win);
	Iterator out(output, win);

	execute_window_loop(
	win,
	[&](const Coordinates &id)
	{
	const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T *>(out.ptr());

	int x = window_start_x;
	for (; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	const auto vin = wrapper::vloadq(in_ptr + x);
	const auto vdeq = vdequantize(vin, scale[id.z()]);

	store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
	}

	// Compute left-over elements
	for (; x < window_end_x; ++x)
	{
	int8_t val = *(in_ptr + x);
	*(out_ptr + x) = static_cast<T>(dequantize(val, scale[id.z()]));
	}
	},
	in, out);
	}

	template <typename T>
	void run_dequantization_qsymm8_per_channel_nhwc(const ITensor input, ITensor output, const Window &window)
	{
	const auto scale = input->info()->quantization_info().scale();

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	// Reset first dimension to handle tail calculations manually
	Window win(window);
	win.set(Window::DimX, Window::Dimension(0, 1, 1));

	// Create iterators
	Iterator in(input, win);
	Iterator out(output, win);

	execute_window_loop(
	win,
	[&](const Coordinates &)
	{
	const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T *>(out.ptr());

	int x = window_start_x;
	for (; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	const float32x4x4_t vscale = {{scale[x + 0], scale[x + 1], scale[x + 2], scale[x + 3], scale[x + 4],
	scale[x + 5], scale[x + 6], scale[x + 7], scale[x + 8], scale[x + 9],
	scale[x + 10], scale[x + 11], scale[x + 12], scale[x + 13],
	scale[x + 14], scale[x + 15]}};
	const auto vin = wrapper::vloadq(in_ptr + x);
	const auto vdeq = vdequantize(vin, vscale);

	store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
	}

	// Compute left-over elements
	for (; x < window_end_x; ++x)
	{
	int8_t val = *(in_ptr + x);
	*(out_ptr + x) = static_cast<T>(dequantize(val, scale[x]));
	}
	},
	in, out);
	}

	template <typename T>
	void run_dequantization_qsymm8(const ITensor input, ITensor output, const Window &window)
	{
	const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
	const float scale = qinfo.scale;

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	// 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));

	// Create iterators
	Iterator in(input, win_collapsed);
	Iterator out(output, win_collapsed);

	execute_window_loop(
	win_collapsed,
	[&](const Coordinates &)
	{
	const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T *>(out.ptr());

	int x = window_start_x;
	for (; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	const auto vin = wrapper::vloadq(in_ptr + x);
	const auto vdeq = vdequantize(vin, scale);

	store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
	}

	// Compute left-over elements
	for (; x < window_end_x; ++x)
	{
	int8_t val = *(in_ptr + x);
	*(out_ptr + x) = static_cast<T>(dequantize(val, scale));
	}
	},
	in, out);
	}

	template <typename T>
	void run_dequantization_qsymm16(const ITensor input, ITensor output, const Window &window)
	{
	const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
	const float scale = qinfo.scale;

	const int window_step_x = 8;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	// 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));

	// Create iterators
	Iterator in(input, win_collapsed);
	Iterator out(output, win_collapsed);

	execute_window_loop(
	win_collapsed,
	[&](const Coordinates &)
	{
	const auto in_ptr = reinterpret_cast<const int16_t *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T *>(out.ptr());

	int x = window_start_x;
	for (; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	const auto vin = wrapper::vloadq(in_ptr + x);
	const auto vdeq = vdequantize_int16(vin, scale);

	store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
	}

	// Compute left-over elements
	for (; x < window_end_x; ++x)
	{
	int16_t val = *(in_ptr + x);
	*(out_ptr + x) = static_cast<T>(dequantize_qsymm16(val, scale));
	}
	},
	in, out);
	}

	template <typename T>
	void run_dequantization_core(const ITensor input, ITensor output, const Window &window)
	{
	switch (input->info()->data_type())
	{
	case DataType::QASYMM8:
	run_dequantization_qasymm8<T, uint8_t>(input, output, window);
	break;
	case DataType::QASYMM8_SIGNED:
	run_dequantization_qasymm8<T, int8_t>(input, output, window);
	break;
	case DataType::QSYMM8_PER_CHANNEL:
	input->info()->data_layout() == DataLayout::NHWC
	? run_dequantization_qsymm8_per_channel_nhwc<T>(input, output, window)
	: run_dequantization_qsymm8_per_channel_nchw<T>(input, output, window);
	break;
	case DataType::QSYMM8:
	run_dequantization_qsymm8<T>(input, output, window);
	break;
	case DataType::QSYMM16:
	run_dequantization_qsymm16<T>(input, output, window);
	break;
	default:
	ARM_COMPUTE_ERROR("Unsupported data type.");
	}
	}
	} // namespace

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

	// Configure kernel window
	Window win = calculate_max_window(*src, Steps());

	// Output tensor auto initialization if not yet initialized
	auto_init_if_empty(*dst, src->tensor_shape(), 1, DataType::F32);

	ICpuKernel::configure(win);
	}

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

	void CpuDequantizeKernel::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);

	const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
	auto dst = tensors.get_tensor(TensorType::ACL_DST);

	switch (dst->info()->data_type())
	{
	case DataType::F32:
	run_dequantization_core<float>(src, dst, window);
	break;
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	run_dequantization_core<float16_t>(src, dst, window);
	break;
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
	default:
	ARM_COMPUTE_ERROR("Unsupported data type.");
	}
	}
	const char *CpuDequantizeKernel::name() const
	{
	return "CpuDequantizeKernel";
	}
	} // namespace kernels
	} // namespace cpu
	} // namespace arm_compute