| /* |
| * 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 |