diff options
Diffstat (limited to 'src')
9 files changed, 637 insertions, 41 deletions
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 4bc4a4827e..a4b88b8eb2 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -238,6 +238,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "gemmlowp_mm_interleaved_transposed", "gemmlowp.cl" }, { "gemmlowp_offset_contribution", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down", "gemmlowp.cl" }, + { "gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl" }, { "harris_score_3x3", "harris_corners.cl" }, { "harris_score_5x5", "harris_corners.cl" }, { "harris_score_7x7", "harris_corners.cl" }, diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index 16f8fe9f7f..a8e8e600fe 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -508,6 +508,9 @@ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src), int16 input_values = vload16(0, (__global int *)src.ptr); + // Add the offset terms to GEMM's result + input_values += (int16)RESULT_OFFSET; + #if defined(ADD_BIAS) // Add bias const int16 biases_values = vload16(0, (__global int *)biases.ptr); @@ -515,10 +518,90 @@ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src), #endif // defined(ADD_BIAS) // Multiply by result_mult_int and shift - input_values = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(input_values, RESULT_MULT_INT, RESULT_SHIFT, 16); + input_values *= RESULT_MULT_INT; + + input_values >>= RESULT_SHIFT; + + uchar16 res = convert_uchar16_sat(input_values); + +#if defined(MIN_BOUND) + res = max(res, (uchar16)MIN_BOUND); +#endif // defined(MIN_BOUND) +#if defined(MAX_BOUND) + res = min(res, (uchar16)MAX_BOUND); +#endif // defined(MAX_BOUND) + + // Store the result + vstore16(res, 0, dst.ptr); +} +#endif // defined(RESULT_OFFSET) && defined(RESULT_MULT_INT) && defined(RESULT_SHIFT) + +#if defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) +/** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 + * + * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8 value. + * The following computations will be performed by the kernel: + * + * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier + * -# Add bias to final result if bias tensor is not a nullptr + * -# Round to nearest division by a power-of-two using result_shift + * -# Add offset to each result + * -# Clamp the value between the specified min and max bounds + * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. + * + * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT + * + * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time + * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. + * These values can be used to implement "rectified linear unit" activation functions + * + * @param[in] src_ptr Pointer to the source tensor. Supported data type: S32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] biases_ptr Pointer to the biases tensor. Supported data type: same as @p src_ptr + * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) + * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor + * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATION(src), +#if defined(ADD_BIAS) + VECTOR_DECLARATION(biases), +#endif // defined(ADD_BIAS) + TENSOR3D_DECLARATION(dst)) +{ + // Compute source and destination addresses + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); +#if defined(ADD_BIAS) + Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); +#endif // defined(ADD_BIAS) + + int16 input_values = vload16(0, (__global int *)src.ptr); + +#if defined(ADD_BIAS) + // Add bias + const int16 biases_values = vload16(0, (__global int *)biases.ptr); + input_values += (int16)biases_values; +#endif // defined(ADD_BIAS) + + // Multiply by result_mult_int and shift + input_values = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(input_values, RESULT_FIXEDPOINT_MULTIPLIER, RESULT_SHIFT, 16); // Add the offset terms to GEMM's result - input_values += (int16)RESULT_OFFSET; + input_values += (int16)RESULT_OFFSET_AFTER_SHIFT; uchar16 res = convert_uchar16_sat(input_values); @@ -532,4 +615,4 @@ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src), // Store the result vstore16(res, 0, dst.ptr); } -#endif // defined(RESULT_OFFSET) && defined(RESULT_MULT_INT) && defined(RESULT_SHIFT)
\ No newline at end of file +#endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT)
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp new file mode 100644 index 0000000000..37a430e8b0 --- /dev/null +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -0,0 +1,167 @@ +/* + * Copyright (c) 2017 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 "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "support/ToolchainSupport.h" + +using namespace arm_compute; + +namespace arm_compute +{ +namespace +{ +Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON(max > 255); + ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); + + // Check biases if exist + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); + } + return Error{}; +} + +std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) +{ + constexpr unsigned int num_elems_processed_per_iteration = 16; + + // Configure kernel window + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, + input_access, + output_result_access); + + if(bias != nullptr) + { + AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]); + window_changed = window_changed || update_window_and_padding(win, bias_access); + } + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + + Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; + return std::make_pair(err, win); +} +} // namespace + +class Coordinates; +} // namespace arm_compute + +CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() + : _input(nullptr), _bias(nullptr), _output(nullptr) +{ +} + +Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), + (bias != nullptr) ? bias->clone().get() : nullptr, + output->clone().get()) + .first); + + return Error{}; +} + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, + int result_offset_after_shift, int min, int max) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), + (bias != nullptr) ? bias->info() : nullptr, + output->info(), + min, + max)); + + _input = input; + _bias = bias; + _output = output; + + // Set the arguments to pass at compile time + CLBuildOptions build_opts; + build_opts.add_option("-DRESULT_OFFSET_AFTER_SHIFT=" + support::cpp11::to_string(result_offset_after_shift)); + build_opts.add_option("-DRESULT_FIXEDPOINT_MULTIPLIER=" + support::cpp11::to_string(result_fixedpoint_multiplier)); + build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(result_shift)); + build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); + build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); + build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_fixedpoint", build_opts.options())); + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure(win_config.second); +} + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + unsigned int idx1 = num_arguments_per_3D_tensor(); + if(_bias != nullptr) + { + Window biases_slice(slice); + biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1)); + biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1)); + add_1D_tensor_argument(idx1, _bias, biases_slice); + } + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx1, _output, slice); + enqueue(queue, *this, slice); + } + while(collapsed.slide_window_slice_3D(slice)); +}
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index b5a007e832..343c31c73d 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -36,6 +36,53 @@ using namespace arm_compute; namespace arm_compute { +namespace +{ +Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON(max > 255); + ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); + + // Check biases if exist + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); + } + return Error{}; +} + +std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) +{ + constexpr unsigned int num_elems_processed_per_iteration = 16; + + // Configure kernel window + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, + input_access, + output_result_access); + + if(bias != nullptr) + { + AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]); + window_changed = window_changed || update_window_and_padding(win, bias_access); + } + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + + Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; + return std::make_pair(err, win); +} +} // namespace + class Coordinates; } // namespace arm_compute @@ -43,25 +90,31 @@ CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::CLGEMMLowpQuantizeDownInt32ToUint : _input(nullptr), _bias(nullptr), _output(nullptr) { } +Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), + (bias != nullptr) ? bias->clone().get() : nullptr, + output->clone().get()) + .first); + + return Error{}; +} void CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_ERROR_ON(max > 255); - ARM_COMPUTE_ERROR_ON(min < 0 || min > max); - - if(bias != nullptr) - { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); - ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1); - ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != bias->info()->dimension(0)); - } + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), + (bias != nullptr) ? bias->info() : nullptr, + output->info(), + min, + max)); _input = input; _bias = bias; @@ -79,29 +132,10 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ICLTensor *i // Create kernel _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down", build_opts.options())); - constexpr unsigned int num_elems_processed_per_iteration = 16; - // Configure kernel window - Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); - - AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output->info(), 0, num_elems_processed_per_iteration); - - update_window_and_padding(win, - input_access, - output_result_access); - - if(bias != nullptr) - { - AccessWindowStatic bias_access(bias->info(), 0, 0, ceil_to_multiple(bias->info()->dimension(0), num_elems_processed_per_iteration), bias->info()->tensor_shape()[1]); - - update_window_and_padding(win, - bias_access); - } - - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); - - ICLKernel::configure(win); + auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure(win_config.second); } void CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window, cl::CommandQueue &queue) diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp new file mode 100644 index 0000000000..102d08c7ba --- /dev/null +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -0,0 +1,274 @@ +/* + * Copyright (c) 2017 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 "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/NEAsymm.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_neon.h> +#include <cstddef> +#include <cstdint> + +using namespace arm_compute; + +namespace +{ +Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON(max > 255); + ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); + + // Check biases if exist + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); + } + return Error{}; +} + +std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) +{ + constexpr unsigned int num_elems_processed_per_iteration = 16; + + // Configure kernel window + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, + input_access, + output_result_access); + + if(bias != nullptr) + { + AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]); + window_changed = window_changed || update_window_and_padding(win, bias_access); + } + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + + Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; + return std::make_pair(err, win); +} + +template <bool is_bounded_relu> +inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int result_fixedpoint_multiplier, int32_t result_shift, int32x4_t result_offset_after_shift_s32, uint8x16_t min_u8, + uint8x16_t max_u8) +{ + const static int32x4_t zero_s32 = vdupq_n_s32(0); + + // Fixed point multiplication with vector saturating rounding doubling multiply high with scalar + in_s32.val[0] = vqrdmulhq_n_s32(in_s32.val[0], result_fixedpoint_multiplier); + in_s32.val[1] = vqrdmulhq_n_s32(in_s32.val[1], result_fixedpoint_multiplier); + in_s32.val[2] = vqrdmulhq_n_s32(in_s32.val[2], result_fixedpoint_multiplier); + in_s32.val[3] = vqrdmulhq_n_s32(in_s32.val[3], result_fixedpoint_multiplier); + + // Round to the nearest division by a power-of-two using result_shift_s32 + in_s32.val[0] = rounding_divide_by_pow2(in_s32.val[0], result_shift); + in_s32.val[1] = rounding_divide_by_pow2(in_s32.val[1], result_shift); + in_s32.val[2] = rounding_divide_by_pow2(in_s32.val[2], result_shift); + in_s32.val[3] = rounding_divide_by_pow2(in_s32.val[3], result_shift); + + // Add the offset terms + in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_after_shift_s32); + in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_after_shift_s32); + in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_after_shift_s32); + in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_after_shift_s32); + + // Saturate negative values + in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32); + in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32); + in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32); + in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_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 U8 + uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1])); + + if(is_bounded_relu) + { + out_u8 = vmaxq_u8(out_u8, min_u8); + out_u8 = vminq_u8(out_u8, max_u8); + } + + return out_u8; +} +} // namespace + +namespace arm_compute +{ +class Coordinates; +} // namespace arm_compute + +template <bool is_bounded_relu> +void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window) +{ + const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift); + const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(_min)); + const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(_max)); + + ARM_COMPUTE_UNUSED(min_u8); + ARM_COMPUTE_UNUSED(max_u8); + + Iterator in(_input, window); + Iterator out(_output, window); + + if(_bias != nullptr) + { + Window win_biases; + win_biases.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().step())); + win_biases.set(Window::DimY, Window::Dimension(0, 1, 1)); + + Iterator bias(_bias, win_biases); + execute_window_loop(window, [&](const Coordinates & id) + { + int32x4x4_t in_s32 = + { + { + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12) + } + }; + + const int32x4x4_t bias_s32 = + { + { + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 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]); + + vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8)); + }, + in, bias, out); + } + else + { + execute_window_loop(window, [&](const Coordinates & id) + { + int32x4x4_t in_s32 = + { + { + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12) + } + }; + + vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8)); + }, + in, out); + } +} + +NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() + : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0) +{ +} + +void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, + int result_offset_after_shift, int min, int max) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), + (bias != nullptr) ? bias->info() : nullptr, + output->info(), + min, + max)); + + _input = input; + _bias = bias; + _output = output; + _result_fixedpoint_multiplier = result_fixedpoint_multiplier; + _result_shift = result_shift; + _result_offset_after_shift = result_offset_after_shift; + _min = min; + _max = max; + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); + + // Check if we need to clamp the result using min and max + const bool is_bounded_relu = ((min != max) && !(min == 0 && max == 255)); + _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>; +} + +Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), + (bias != nullptr) ? bias->clone().get() : nullptr, + output->clone().get()) + .first); + + return Error{}; +} + +void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + + (this->*_func)(window); +}
\ No newline at end of file diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index 670b11fe67..edd6a9fd80 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -78,7 +78,7 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); @@ -186,15 +186,15 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window) } }; - // Add the offset terms to GEMM's result and multiply by result_mult_int - scale_input(in_s32, result_offset_s32, _result_mult_int); - // 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, _result_mult_int); + vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); }, in, bias, out); @@ -231,6 +231,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *inp { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp index 88aaf1cae8..6cc2f4bdb7 100644 --- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp +++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp @@ -173,7 +173,7 @@ void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *w float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; int output_multiplier, output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output->info()->quantization_info().offset, output_multiplier, output_shift); + _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset); _gemmlowp_output.allocator()->allocate(); } diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index b1d620d8a2..f26ff5f938 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -24,6 +24,7 @@ #include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h" #include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h" #include "support/ToolchainSupport.h" @@ -34,4 +35,22 @@ void CLGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ICLTensor *input, auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>(); k->configure(input, bias, output, result_offset, result_mult_int, result_shift, min, max); _kernel = std::move(k); +} + +Error CLGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max); +} + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, + int result_offset_after_shift, int min, int max) +{ + auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>(); + k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); + _kernel = std::move(k); +} + +Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max); }
\ No newline at end of file diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp index ed51291e95..53e5ae2f5a 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp @@ -24,6 +24,7 @@ #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" #include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h" #include "support/ToolchainSupport.h" @@ -39,4 +40,17 @@ void NEGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ITensor *input, co Error NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max); +} + +void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, + int result_offset_after_shift, int min, int max) +{ + auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>(); + k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); + _kernel = std::move(k); +} + +Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +{ + return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max); }
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