| /* | |
| * Copyright (c) 2020-2021 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/CpuGemmLowpQuantizeDownInt32ScaleKernel.h" | |
| #include "arm_compute/core/Error.h" | |
| #include "arm_compute/core/Helpers.h" | |
| #include "arm_compute/core/ITensor.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/quantization/AsymmHelpers.h" | |
| #include "src/core/AccessWindowStatic.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 *bias, const ITensorInfo *dst, const GEMMLowpOutputStageInfo *output_stage) | |
| { | |
| 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(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)) | |
| || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound); | |
| // 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) | |
| { | |
| if(dst->data_type() != output_stage->output_data_type && (output_stage->output_data_type == DataType::QASYMM8 || output_stage->output_data_type == DataType::QASYMM8_SIGNED)) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_MSG("Mismatching data types"); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst); | |
| } | |
| return Status{}; | |
| } | |
| inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int) | |
| { | |
| // Add the offset terms to GEMM's result | |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32); | |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32); | |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32); | |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32); | |
| // Multiply by result_mult_int | |
| in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int); | |
| in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int); | |
| in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int); | |
| in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int); | |
| } | |
| template <typename T> | |
| inline typename std::enable_if<std::is_same<T, uint8_t>::value, | |
| typename wrapper::traits::neon_vector<T, 16>::type>::type | |
| convert_to_8bit(const int16x8x2_t in_s16) | |
| { | |
| return wrapper::vcombine(wrapper::vqmovun(in_s16.val[0]), wrapper::vqmovun(in_s16.val[1])); | |
| } | |
| template <typename T> | |
| inline typename std::enable_if<std::is_same<T, int8_t>::value, | |
| typename wrapper::traits::neon_vector<T, 16>::type>::type | |
| convert_to_8bit(const int16x8x2_t in_s16) | |
| { | |
| return wrapper::vcombine(wrapper::vqmovn(in_s16.val[0]), wrapper::vqmovn(in_s16.val[1])); | |
| } | |
| template <typename T> | |
| inline typename wrapper::traits::neon_vector<T, 16>::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector<T, 16>::type min, | |
| typename wrapper::traits::neon_vector<T, 16>::type max) | |
| { | |
| // Shift final result (negative value shift right) | |
| in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32); | |
| in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32); | |
| in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32); | |
| in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32); | |
| // Convert S32 to S16 | |
| const int16x8x2_t in_s16 = | |
| { | |
| { | |
| vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])), | |
| vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3])) | |
| } | |
| }; | |
| // Convert S16 to S8 or U8 | |
| typename wrapper::traits::neon_vector<T, 16>::type out = convert_to_8bit<T>(in_s16); | |
| out = wrapper::vmax(out, min); | |
| out = wrapper::vmin(out, max); | |
| return out; | |
| } | |
| } // namespace | |
| template <typename T> | |
| void CpuGemmLowpQuantizeDownInt32ScaleKernel::run_internal(const ITensor *src, const ITensor *bias, ITensor *dst, const Window &window) | |
| { | |
| using VectorType = typename wrapper::traits::neon_vector<T, 16>::type; | |
| const int32x4_t result_offset_s32 = vdupq_n_s32(_output_stage->gemmlowp_offset); | |
| const int32x4_t result_shift_s32 = vdupq_n_s32(-_output_stage->gemmlowp_shift); | |
| 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()); | |
| const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits<T>::lowest(); | |
| const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits<T>::max(); | |
| VectorType min = wrapper::vdup_n(static_cast<T>(clamp_min), wrapper::traits::vector_128_tag{}); | |
| VectorType max = wrapper::vdup_n(static_cast<T>(clamp_max), wrapper::traits::vector_128_tag{}); | |
| Window win(window); | |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); | |
| Iterator in(src, win); | |
| Iterator out(dst, win); | |
| 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, [&](const Coordinates &) | |
| { | |
| // Compute 16 elements per iteration | |
| int x = window_start_x; | |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) | |
| { | |
| int32x4x4_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), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) | |
| } | |
| }; | |
| const int32x4x4_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), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 8), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 12) | |
| } | |
| }; | |
| // 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]); | |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]); | |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]); | |
| // Add the offset terms to GEMM's result and multiply by result_mult_int | |
| scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier); | |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max)); | |
| } | |
| // Compute left-over elements | |
| for(; x < window_end_x; ++x) | |
| { | |
| const int bias_value = *(reinterpret_cast<const int *>(bias_i.ptr()) + x); | |
| int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x); | |
| // Quantize | |
| in_value = ((in_value + bias_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift; | |
| // Store the result | |
| *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max)); | |
| } | |
| }, | |
| in, bias_i, out); | |
| } | |
| else | |
| { | |
| execute_window_loop(win, [&](const Coordinates &) | |
| { | |
| // Compute 16 elements per iteration | |
| int x = window_start_x; | |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) | |
| { | |
| int32x4x4_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), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), | |
| vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) | |
| } | |
| }; | |
| // Add the offset terms to GEMM's result and multiply by result_mult_int | |
| scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier); | |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max)); | |
| } | |
| // Compute left-over elements | |
| for(; x < window_end_x; ++x) | |
| { | |
| int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x); | |
| // Quantize | |
| in_value = ((in_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift; | |
| // Store the result | |
| *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max)); | |
| } | |
| }, | |
| in, out); | |
| } | |
| } | |
| void CpuGemmLowpQuantizeDownInt32ScaleKernel::configure(ITensorInfo *src, ITensorInfo *bias, ITensorInfo *dst, const GEMMLowpOutputStageInfo *output_stage) | |
| { | |
| ARM_COMPUTE_UNUSED(bias); | |
| // Perform validate step | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, output_stage); | |
| // Output auto inizialitation if not yet initialized | |
| auto_init_if_empty(*dst, src->clone()->set_data_type(output_stage->output_data_type)); | |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, | |
| bias, | |
| dst, | |
| output_stage)); | |
| _output_stage = output_stage; | |
| // Configure kernel window | |
| Window win = calculate_max_window(*src, Steps()); | |
| ICpuKernel::configure(win); | |
| // Check if we need to clamp the result using min and max | |
| _is_bounded_relu = ((_output_stage->gemmlowp_min_bound != _output_stage->gemmlowp_max_bound) | |
| && !(_output_stage->gemmlowp_min_bound == std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)) | |
| && _output_stage->gemmlowp_max_bound == std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)))); | |
| if(_output_stage->output_data_type == DataType::QASYMM8) | |
| { | |
| _func = &CpuGemmLowpQuantizeDownInt32ScaleKernel::run_internal<uint8_t>; | |
| } | |
| else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED) | |
| { | |
| _func = &CpuGemmLowpQuantizeDownInt32ScaleKernel::run_internal<int8_t>; | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR("Data type not supported"); | |
| } | |
| } | |
| Status CpuGemmLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst, const GEMMLowpOutputStageInfo *output_stage) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, bias, dst, output_stage)); | |
| return Status{}; | |
| } | |
| void CpuGemmLowpQuantizeDownInt32ScaleKernel::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 *CpuGemmLowpQuantizeDownInt32ScaleKernel::name() const | |
| { | |
| return "CpuGemmLowpQuantizeDownInt32ScaleKernel"; | |
| } | |
| } // namespace kernels | |
| } // namespace cpu | |
| } // namespace arm_compute |