From 51e53a324dd314367de09ea24c8d25b8b42a2f87 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 22 Oct 2018 13:49:08 +0100 Subject: COMPMID-1451: Perform CLOutputStage using floats. Change-Id: Ic8312a5b6790aa7cd4468d42f08d557ad40e9441 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154570 Tested-by: bsgcomp Reviewed-by: Gian Marco Iodice --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/gemmlowp.cl | 91 +++++++++ ...pQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp | 207 +++++++++++++++++++++ .../CL/functions/CLGEMMConvolutionLayer.cpp | 12 +- src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp | 16 ++ 5 files changed, 317 insertions(+), 10 deletions(-) create mode 100644 src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp (limited to 'src') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 12a7c38dfd..880963de7b 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -269,6 +269,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "gemmlowp_offset_contribution", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl" }, + { "gemmlowp_output_stage_quantize_down_float", "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 0fc3868341..80b5d00cf2 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -2276,3 +2276,94 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATIO vstore16(res, 0, dst.ptr); } #endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) + +#if defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET) +/** 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 + * -# Requantize + * -# 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 and scalar scale factor must be passed at compile time using -DRESULT_OFFSET, -DREAL_MULTIPLIER + * + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w src_stride_w * number of elements along W 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_float(TENSOR3D_DECLARATION(src), +#if defined(ADD_BIAS) + VECTOR_DECLARATION(biases), +#endif // defined(ADD_BIAS) +#if defined(DST_HEIGHT) + TENSOR4D_DECLARATION(dst)) +#else // defined(DST_HEIGHT) + TENSOR3D_DECLARATION(dst)) +#endif // defined(DST_HEIGHT) +{ + // Compute source and destination addresses + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); +#if defined(DST_HEIGHT) + Tensor4D dst = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(dst, 1); + dst.ptr += get_global_id(0) * dst_step_x + (get_global_id(1) % DST_HEIGHT) * dst_step_y + (get_global_id(1) / DST_HEIGHT) * dst_step_z + get_global_id(2) * dst_step_w; +#else // defined(DST_HEIGHT) + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); +#endif // defined(DST_HEIGHT) + +#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) + + // Convert to float + float16 input_values_f = convert_float16(input_values); + input_values_f = round(input_values_f * (float)REAL_MULTIPLIER + (float)OUTPUT_OFFSET); + + uchar16 res = convert_uchar16_sat(input_values_f); + +#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(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET) diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp new file mode 100644 index 0000000000..f0096bd3ad --- /dev/null +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp @@ -0,0 +1,207 @@ +/* + * Copyright (c) 2018 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/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.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/TensorInfo.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/ShapeCalculator.h" + +#include "support/ToolchainSupport.h" + +using namespace arm_compute; + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + 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)); + } + + if(output->total_size() != 0) + { + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth, true); + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + } + + return Status{}; +} + +std::pair 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(*input, Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, + input_access); + + if(output->total_size() != 0) + { + Window win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win_out, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } + + 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); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +class Coordinates; +} // namespace arm_compute + +CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel() + : _input(nullptr), _bias(nullptr), _output(nullptr), _reinterpret_as_3d(false) +{ +} + +Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), + (bias != nullptr) ? bias->clone().get() : nullptr, + output->clone().get()) + .first); + + return Status{}; +} + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + float multiplier, int offset, + int min, int max, unsigned int output_3d_depth) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output auto inizialitation if not yet initialized + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth, true); + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape)); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), + min, max, output_3d_depth)); + + _input = input; + _bias = bias; + _output = output; + _reinterpret_as_3d = output_3d_depth > 1; + + // Set the arguments to pass at compile time + CLBuildOptions build_opts; + build_opts.add_option("-DREAL_MULTIPLIER=" + float_to_string_with_full_precision(multiplier)); + build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(offset)); + 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"); + build_opts.add_option_if(_reinterpret_as_3d, "-DDST_HEIGHT=" + support::cpp11::to_string(input->info()->tensor_shape().y() / output_3d_depth)); + + // Create kernel + _kernel = static_cast(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_float", 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_internal(win_config.second); +} + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + // Create input window + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + // Setup bias slice + 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); + } + + if(_reinterpret_as_3d) + { + // Create output window + Window window_out; + window_out.use_tensor_dimensions(_output->info()->tensor_shape()); + Window collapsed_out = window_out.collapse_if_possible(window_out, 3); + Window slice_out = collapsed.first_slice_window_4D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_4D_tensor_argument(idx1, _output, slice_out); + enqueue(queue, *this, slice); + } + while(collapsed.slide_window_slice_3D(slice) && collapsed_out.slide_window_slice_4D(slice_out)); + } + else + { + 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)); + } +} diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index f41a12ae48..61180fd5d3 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -284,17 +284,14 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * { const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info(); - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; - int output_multiplier, output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - if(!_skip_col2im) { _memory_group.manage(&_tmp_output); gemm_output_staged_to_use = &_tmp_output; } - _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset); + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; + _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, multiplier, output_quant_info.offset); } if(!_skip_col2im) @@ -448,17 +445,12 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI if(is_quantized) { - float multiplier = input->quantization_info().scale * weights_to_use->quantization_info().scale / output->quantization_info().scale; - int output_multiplier, output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - if(!skip_col2im) { tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); tmp_info.set_quantization_info(output->quantization_info()); gemm_output_staged_to_use = &tmp_info; } - // Validate output stage for quantized case CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use); } diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index b18d23fac9..f5dc655776 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -25,6 +25,7 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h" #include "support/ToolchainSupport.h" @@ -56,4 +57,19 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITens { return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth); } + +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + float multiplier, int offset, + int min, int max, unsigned int output_3d_depth) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(input, bias, output, multiplier, offset, min, max, output_3d_depth); + _kernel = std::move(k); +} + +Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) +{ + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(input, bias, output, min, max, output_3d_depth); +} } // namespace arm_compute \ No newline at end of file -- cgit v1.2.1