diff options
Diffstat (limited to 'src/core/cpu/kernels/CpuDirectConv2dOutputStageKernel.cpp')
| -rw-r--r-- | src/core/cpu/kernels/CpuDirectConv2dOutputStageKernel.cpp | 513 |
1 files changed, 0 insertions, 513 deletions
diff --git a/src/core/cpu/kernels/CpuDirectConv2dOutputStageKernel.cpp b/src/core/cpu/kernels/CpuDirectConv2dOutputStageKernel.cpp deleted file mode 100644 index 662d052941..0000000000 --- a/src/core/cpu/kernels/CpuDirectConv2dOutputStageKernel.cpp +++ /dev/null @@ -1,513 +0,0 @@ -/* - * 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/CpuDirectConv2dOutputStageKernel.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/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/Traits.h" -#include "src/core/CPP/Validate.h" -#include "src/core/NEON/NEAsymm.h" -#include "src/core/NEON/NEFixedPoint.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> - -namespace arm_compute -{ -namespace cpu -{ -namespace kernels -{ -namespace -{ -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst, - const DirectConvolutionLayerOutputStageKernelInfo &info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); - ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::S32, DataType::F32); - - if(bias != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bias); - ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != src->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL))); - ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); - } - - if(src->data_type() == DataType::S32) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst == nullptr, "In-place computation not allowed for quantized output"); - } - - // Checks performed when output is configured - if((dst != nullptr) && (dst->total_size() != 0)) - { - if(is_data_type_float(src->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); - } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst); - } - else if(src->data_type() == DataType::S32) - { - // In case of quantized computation and unconfigured output, the output data type must be provided through DirectConvolutionLayerOutputStageKernelInfo - ARM_COMPUTE_RETURN_ERROR_ON((info.output_data_type != DataType::QASYMM8) && (info.output_data_type != DataType::QASYMM8_SIGNED)); - } - - return Status{}; -} - -template <typename T> -typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type -output_stage_nchw(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst, - int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift) -{ - const bool has_bias = bias != nullptr; - /** SIMD vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; - - ARM_COMPUTE_ERROR_ON(src->info()->data_layout() == DataLayout::UNKNOWN); - ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier); - ARM_COMPUTE_UNUSED(result_shift); - ARM_COMPUTE_UNUSED(result_offset_after_shift); - - const int window_start_x = window.x().start(); - const int window_end_x = window.x().end(); - const int window_step_x = 16 / src->info()->element_size(); - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(src, win); - Iterator out(dst, win); - execute_window_loop(win, [&](const Coordinates & id) - { - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get bias and pointer to input - const auto in_ptr = reinterpret_cast<const T *>(in.ptr()) + x; - auto v_in = wrapper::vloadq(in_ptr); - - // Accumulate bias - if(has_bias) - { - const auto vb = wrapper::vdup_n(*reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{}); - v_in = wrapper::vadd(v_in, vb); - } - - const auto out_ptr = reinterpret_cast<T *>(out.ptr()) + x; - wrapper::vstore(out_ptr, v_in); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Get bias and pointer to input - auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x); - - // Accumulate bias - if(has_bias) - { - const auto b = *reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))); - s_in += b; - } - - *(reinterpret_cast<T *>(out.ptr()) + x) = s_in; - } - - }, - in, out); -} - -template <typename T> -typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type -output_stage_nhwc(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst, - int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift) -{ - const bool has_bias = bias != nullptr; - ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier); - ARM_COMPUTE_UNUSED(result_shift); - ARM_COMPUTE_UNUSED(result_offset_after_shift); - - Window window_bias = window; - window_bias.set(Window::DimX, Window::Dimension(0, 1, 1)); - window_bias.set(Window::DimY, Window::Dimension(0, 0, 0)); - window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0)); - window_bias.set(3, Window::Dimension(0, 0, 0)); - - const int window_start_x = window.x().start(); - const int window_end_x = window.x().end(); - const int window_step_x = 16 / src->info()->element_size(); - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(src, win); - Iterator bi(bias, window_bias); - Iterator out(dst, win); - - execute_window_loop(win, [&](const Coordinates &) - { - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get bias and pointer to input - const auto in_ptr = reinterpret_cast<const T *>(in.ptr()); - auto v_in = wrapper::vloadq(in_ptr + x); - - // Accumulate bias - if(has_bias) - { - const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x; - v_in = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr)); - } - - const auto out_ptr = reinterpret_cast<T *>(out.ptr()); - wrapper::vstore(out_ptr + x, v_in); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Get bias and pointer to input - auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x); - - // Accumulate bias - if(has_bias) - { - const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x; - s_in += *bias_ptr; - } - - const auto out_ptr = reinterpret_cast<T *>(out.ptr()); - *(out_ptr + x) = s_in; - } - }, - in, bi, out); -} - -// Quantized case -template < typename TOut, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 > -void output_stage_nchw(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst, - int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift) -{ - const bool has_bias = bias != nullptr; - using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>; - using TagType = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>; - - const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift); - - const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{}); - const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{}); - - const int window_start_x = window.x().start(); - const int window_end_x = window.x().end(); - const int window_step_x = 16 / src->info()->element_size(); - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(src, win); - Iterator out(dst, win); - - execute_window_loop(win, [&](const Coordinates & id) - { - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get bias and pointer to input - const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x; - int32x4x4_t v_in = - { - { - wrapper::vloadq(in_ptr), - wrapper::vloadq(in_ptr + 4), - wrapper::vloadq(in_ptr + 8), - wrapper::vloadq(in_ptr + 12) - } - }; - - // Accumulate bias - if(has_bias) - { - const auto vb = wrapper::vdup_n(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))), TagType{}); - v_in = - { - { - wrapper::vadd(v_in.val[0], vb), - wrapper::vadd(v_in.val[1], vb), - wrapper::vadd(v_in.val[2], vb), - wrapper::vadd(v_in.val[3], vb) - } - }; - } - - const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x; - wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, - min, max, false)); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Get bias and pointer to input - int32_t s_in = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Accumulate bias - if(has_bias) - { - const auto b = *reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))); - s_in += b; - } - - const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x; - *out_ptr = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, - std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false); - } - }, - in, out); -} -template < typename TOut, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 > -void output_stage_nhwc(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst, - int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift) -{ - const bool has_bias = bias != nullptr; - using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>; - using TagType = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>; - - const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift); - - const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{}); - const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{}); - - Window window_bias = window; - window_bias.set(Window::DimX, Window::Dimension(0, 1, 1)); - window_bias.set(Window::DimY, Window::Dimension(0, 0, 0)); - window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0)); - window_bias.set(3, Window::Dimension(0, 0, 0)); - - const int window_start_x = window.x().start(); - const int window_end_x = window.x().end(); - const int window_step_x = 16 / src->info()->element_size(); - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(src, win); - Iterator bi(bias, window_bias); - Iterator out(dst, win); - - execute_window_loop(win, [&](const Coordinates &) - { - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get bias and pointer to input - const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x; - int32x4x4_t v_in = - { - { - wrapper::vloadq(in_ptr), - wrapper::vloadq(in_ptr + 4), - wrapper::vloadq(in_ptr + 8), - wrapper::vloadq(in_ptr + 12), - } - }; - - // Accumulate bias - if(has_bias) - { - const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x; - - wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr)); - wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4)); - wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8)); - wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12)); - } - - const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x; - wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false)); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Get bias and pointer to input - const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x; - int32_t s_in = *in_ptr; - - // Accumulate bias - if(has_bias) - { - const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x; - s_in += *bias_ptr; - } - - const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x; - *out_ptr = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, - std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false); - } - }, - in, bi, out); -} -} // namespace - -void CpuDirectConv2dOutputStageKernel::configure(ITensorInfo *src, const ITensorInfo *bias, ITensorInfo *dst, - const DirectConvolutionLayerOutputStageKernelInfo &info) -{ - ARM_COMPUTE_UNUSED(bias); - // Perform validation step - ARM_COMPUTE_ERROR_ON_NULLPTR(src); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, bias, dst, info)); - - _func = nullptr; - _result_fixedpoint_multiplier = info.result_fixedpoint_multiplier; - _result_shift = info.result_shift; - _result_offset_after_shift = info.result_offset_after_shift; - - // Auto-initialize output output if required - if(dst != nullptr) - { - // Work out expected output data type - const DataType output_dt = (src->data_type() == DataType::S32) ? info.output_data_type : DataType::S32; - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*dst, src->clone()->set_data_type(output_dt)); - } - - Window win = calculate_max_window(*src, Steps()); - - ICpuKernel::configure(win); - - const bool is_qasymm8_signed = (dst != nullptr) ? is_data_type_quantized_asymmetric_signed(dst->data_type()) : false; - - // Set appropriate function - if(src->data_layout() == DataLayout::NCHW) - { - switch(src->data_type()) - { - case DataType::S32: - { - if(is_qasymm8_signed) - { - _func = &output_stage_nchw<int8_t>; - } - else - { - _func = &output_stage_nchw<uint8_t>; - } - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - _func = &output_stage_nchw<float16_t>; - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::F32: - { - _func = &output_stage_nchw<float>; - break; - } - default: - { - ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs."); - } - } - } - else - { - switch(src->data_type()) - { - case DataType::S32: - { - if(is_qasymm8_signed) - { - _func = &output_stage_nhwc<int8_t>; - } - else - { - _func = &output_stage_nhwc<uint8_t>; - } - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - _func = &output_stage_nhwc<float16_t>; - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::F32: - { - _func = &output_stage_nhwc<float>; - break; - } - default: - { - ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs."); - } - } - } -} - -Status CpuDirectConv2dOutputStageKernel::validate(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst, - const DirectConvolutionLayerOutputStageKernelInfo &info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, bias, dst, info)); - return Status{}; -} - -void CpuDirectConv2dOutputStageKernel::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(_func == nullptr); - - auto src = tensors.get_tensor(TensorType::ACL_SRC_0); - auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_1); - auto dst = tensors.get_tensor(TensorType::ACL_DST); - - (*_func)(src, bias, window, dst, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift); -} - -const char *CpuDirectConv2dOutputStageKernel::name() const -{ - return "CpuDirectConv2dOutputStageKernel"; -} -} // namespace kernels -} // namespace cpu -} // namespace arm_compute |