| /* |
| * 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/core/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::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_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_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 |