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Diffstat (limited to 'src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp | 234 |
1 files changed, 234 insertions, 0 deletions
diff --git a/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp b/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp new file mode 100644 index 0000000000..a5c09c9977 --- /dev/null +++ b/src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp @@ -0,0 +1,234 @@ +/* + * 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/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/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/NESymm.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 |