| /* |
| * Copyright (c) 2019-2021 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/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/NEON/NESymm.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 *bias, const ITensorInfo *dst, int min, int max) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON(min > max); |
| |
| // Check biases if exist |
| if(bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bias); |
| ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != bias->dimension(0)); |
| } |
| |
| if(dst->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QSYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, src); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| template <bool is_bounded_relu> |
| void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal(const ITensor *src, const ITensor *bias, ITensor *dst, const Window &window) |
| { |
| const int16x8_t min_s16 = vdupq_n_s16(static_cast<int16_t>(_min)); |
| const int16x8_t max_s16 = vdupq_n_s16(static_cast<int16_t>(_max)); |
| |
| ARM_COMPUTE_UNUSED(min_s16); |
| ARM_COMPUTE_UNUSED(max_s16); |
| |
| 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()); |
| |
| Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); |
| win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator in(src, win_collapsed); |
| Iterator out(dst, win_collapsed); |
| if(bias != nullptr) |
| { |
| Window win_biases; |
| win_biases.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win_biases.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| Iterator bias_i(bias, win_biases); |
| execute_window_loop(win_collapsed, [&](const Coordinates &) |
| { |
| // Compute 16 elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| int32x4x2_t in_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4) |
| } |
| }; |
| |
| const int32x4x2_t bias_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 4) |
| } |
| }; |
| |
| // Add the bias to GEMM's result |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]); |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x); |
| int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); |
| |
| // Add bias |
| in_value += bias_value; |
| // Finalize and store the result |
| *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min), |
| static_cast<int16_t>(_max)); |
| } |
| }, |
| in, out, bias_i); |
| } |
| else |
| { |
| execute_window_loop(win_collapsed, [&](const Coordinates &) |
| { |
| // Compute 16 elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| int32x4x2_t in_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4) |
| } |
| }; |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); |
| ARM_COMPUTE_UNUSED(in_value); |
| // Finalize and store the result |
| *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min), |
| static_cast<int16_t>(_max)); |
| } |
| }, |
| in, out); |
| } |
| } |
| |
| void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(ITensorInfo *src, ITensorInfo *bias, ITensorInfo *dst, int result_fixedpoint_multiplier, int result_shift, |
| int min, int max) |
| { |
| // Perform validate step |
| ARM_COMPUTE_UNUSED(bias, dst); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, bias, dst, min, max)); |
| |
| _result_fixedpoint_multiplier = result_fixedpoint_multiplier; |
| _result_shift = result_shift; |
| _min = min; |
| _max = max; |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*src, src->clone()->set_data_type(DataType::QSYMM16)); |
| // Configure kernel window |
| Window win_config = calculate_max_window(*src, Steps()); |
| ICpuKernel::configure(win_config); |
| |
| // Check if we need to clamp the result using min and max |
| const bool is_bounded_relu = !(min <= -32768 && max >= 32767); |
| _func = is_bounded_relu ? &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<true> : |
| &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<false>; |
| } |
| |
| Status CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); |
| return Status{}; |
| } |
| |
| void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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_MSG(tensors.empty(), "No inputs provided"); |
| |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC); |
| auto bias = tensors.get_const_tensor(TensorType::ACL_BIAS); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| (this->*_func)(src, bias, dst, window); |
| } |
| |
| const char *CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::name() const |
| { |
| return "CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |