| /* |
| * 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/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/NEON/NEAsymm.h" |
| #include "src/core/NEON/NESymm.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.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 |