| /* |
| * Copyright (c) 2017-2022, 2024 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/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/NEMath.h" |
| #include "src/core/NEON/wrapper/wrapper.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); |
| } |
| |
| template <typename TOut, typename = typename std::enable_if<std::is_signed<TOut>::value, bool>::type> |
| inline int8x16_t recombine_8_16(int16x8_t lower, int16x8_t upper) |
| { |
| return wrapper::vcombine(wrapper::vqmovn(lower), wrapper::vqmovn(upper)); |
| } |
| |
| template <typename TOut, typename = typename std::enable_if<std::is_unsigned<TOut>::value, bool>::type> |
| inline uint8x16_t recombine_8_16(int16x8_t lower, int16x8_t upper) |
| { |
| return wrapper::vcombine(wrapper::vqmovun(lower), wrapper::vqmovun(upper)); |
| } |
| |
| } // 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>}, |
| |
| // Functions for offset only requantization |
| {"op_OFFSET_ONLY_QASYMM8_QASYMM8", &CpuQuantizeKernel::run_requantize_offset_only<uint8_t, uint8_t>}, |
| {"op_OFFSET_ONLY_QASYMM8_QASYMM8_SIGNED", &CpuQuantizeKernel::run_requantize_offset_only<uint8_t, int8_t>}, |
| {"op_OFFSET_ONLY_QASYMM8_SIGNED_QASYMM8", &CpuQuantizeKernel::run_requantize_offset_only<int8_t, uint8_t>}, |
| {"op_OFFSET_ONLY_QASYMM8_SIGNED_QASYMM8_SIGNED", |
| &CpuQuantizeKernel::run_requantize_offset_only<int8_t, int8_t>}, |
| |
| // Functions for offset uint8 to int8 and vice versa quantization (no scale changes) |
| {"op_OFFSET_ONLY_CONVERT_QASYMM8_SIGNED_QASYMM8", |
| &CpuQuantizeKernel::run_requantize_offset_only_convert<int8_t, uint8_t>}, |
| {"op_OFFSET_ONLY_CONVERT_QASYMM8_QASYMM8_SIGNED", |
| &CpuQuantizeKernel::run_requantize_offset_only_convert<uint8_t, 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_"); |
| |
| // For offset only functions - must be 8-bit and have identical scale values. |
| if (src->quantization_info().scale() == dst->quantization_info().scale() && |
| (is_data_type_quantized_asymmetric_char(src->data_type()) && |
| is_data_type_quantized_asymmetric_char(dst->data_type()))) |
| { |
| function_to_call += "OFFSET_ONLY_"; |
| // For optimized datatype conversion 8-bit re-quantization offset only functions. |
| // These must have an offset of exactly 128 to match requirements - has specific circumstances to match use case. |
| auto uqinfo = |
| compute_requantization_scale_offset(src->quantization_info().uniform(), dst->quantization_info().uniform()); |
| const auto src_dt = src->data_type(); |
| if (src->data_type() != dst->data_type() && ((src_dt == DataType::QASYMM8_SIGNED && uqinfo.offset == 128) || |
| (src_dt == DataType::QASYMM8 && uqinfo.offset == -128))) |
| { |
| function_to_call += "CONVERT_"; |
| } |
| } |
| |
| // Specify datatype for function |
| 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; |
| |
| // Calculate window. Squash if possible. |
| Window win; |
| std::tie(win, _split_dimension) = calculate_squashed_or_max_window(*src); |
| |
| ICpuKernel::configure(win); |
| } |
| |
| 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(); |
| 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_requantize_offset_only_convert(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()); |
| |
| // Calculate output offset difference. |
| const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform(); |
| UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform(); |
| 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)); |
| |
| // Duplicate offset in signed vector format |
| const int8x16_t offset = wrapper::vdup_n(static_cast<int8_t>(uqinfo.offset), wrapper::traits::vector_128_tag{}); |
| |
| 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) |
| { |
| const wrapper::traits::neon_vector_t<TIn, window_step> qv = |
| wrapper::vloadq(input_ptr + x); // load 128 bit vector of 8 bit datatype |
| |
| // Signed addition. |
| auto res = vaddq_s8(reinterpret_cast<int8x16_t>(qv), offset); |
| |
| // Output is dependent on datatype. |
| wrapper::vstore(&output_ptr[x], |
| reinterpret_cast<wrapper::traits::neon_vector_t<TOut, window_step>>(res)); |
| } |
| // Compute left-over elements |
| for (; x < window_end_x; ++x) |
| { |
| auto result = uqinfo.offset + static_cast<int32_t>(input_ptr[x]); |
| output_ptr[x] = static_cast<TOut>(result); |
| } |
| }, |
| input, output); |
| } |
| |
| template <typename TIn, typename TOut> |
| void CpuQuantizeKernel::run_requantize_offset_only(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(); |
| 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)); |
| |
| // Duplicate offset in signed vector format |
| const int16x8_t offset = wrapper::vdup_n(static_cast<int16_t>(uqinfo.offset), wrapper::traits::vector_128_tag{}); |
| |
| const int32_t low_bound = (dst->info()->data_type() == DataType::QASYMM8) ? 0 : -128; |
| const int32_t upper_bound = (dst->info()->data_type() == DataType::QASYMM8) ? 255 : 127; |
| |
| 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()); |
| TOut *output_ptr = reinterpret_cast<TOut *>(output.ptr()); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step); x += window_step) |
| { |
| const auto qv = wrapper::vloadq(input_ptr + x); // load 128 bit vector of 8 bit datatype |
| int16x8_t lower = reinterpret_cast<int16x8_t>(wrapper::vmovl(wrapper::vgetlow(qv))); |
| int16x8_t upper = reinterpret_cast<int16x8_t>(wrapper::vmovl(wrapper::vgethigh(qv))); |
| |
| // Signed addition. |
| lower = wrapper::vqadd(lower, offset); |
| upper = wrapper::vqadd(upper, offset); |
| |
| // Output is dependent on datatype. |
| auto res = recombine_8_16<TOut>(lower, upper); |
| wrapper::vstore(&output_ptr[x], res); |
| } |
| // Compute left-over elements |
| for (; x < window_end_x; ++x) |
| { |
| // Add offset and clamp result to within the range of the output datatype. |
| int32_t result = uqinfo.offset + static_cast<int32_t>(input_ptr[x]); |
| result = utility::clamp<int32_t>(result, low_bound, upper_bound); |
| |
| // Cast result to output datatype. |
| output_ptr[x] = static_cast<TOut>(result); |
| } |
| }, |
| 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 |