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Diffstat (limited to 'src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.cpp | 337 |
1 files changed, 337 insertions, 0 deletions
diff --git a/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.cpp b/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.cpp new file mode 100644 index 0000000000..eefc294700 --- /dev/null +++ b/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.cpp @@ -0,0 +1,337 @@ +/* + * 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/utils/quantization/AsymmHelpers.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include "src/core/AccessWindowStatic.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/wrapper/wrapper.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 |