From 9c9b70b9d30482d34f4f9c9dbc6479df163f96a1 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Mon, 1 Jul 2019 17:35:56 +0100 Subject: COMPMID-2410: Create a new GEMMLowpQuantizeDownInt32ToInt16ScaleKernel for CL Change-Id: Iab74b72f7adf712a1baf16aab916ea7c8d2bf92f Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/1497 Tested-by: Arm Jenkins Reviewed-by: Pablo Marquez Comments-Addressed: Arm Jenkins --- arm_compute/core/CL/CLKernels.h | 1 + ...antizeDownInt32ToInt16ScaleByFixedPointKernel.h | 94 +++++++++++ .../runtime/CL/functions/CLGEMMLowpOutputStage.h | 60 ++++++- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/gemmlowp.cl | 83 ++++++++++ ...tizeDownInt32ToInt16ScaleByFixedPointKernel.cpp | 180 +++++++++++++++++++++ src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp | 19 ++- tests/validation/CL/GEMMLowp.cpp | 63 ++++++++ 8 files changed, 499 insertions(+), 2 deletions(-) create mode 100644 arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h create mode 100644 src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h index 8fbc4770b0..cd9eadc1cd 100644 --- a/arm_compute/core/CL/CLKernels.h +++ b/arm_compute/core/CL/CLKernels.h @@ -81,6 +81,7 @@ #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.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" diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h new file mode 100644 index 0000000000..2bd2bb6afb --- /dev/null +++ b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2019 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. + */ +#ifndef __ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H__ +#define __ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** CL kernel used to quantize down the int32 accumulator values of GEMMLowp to QSYMM16 + * + * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 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 + * -# Clamp the value between the specified min and max bounds + * -# Clamp the resulting int32 values to the [-32768, 32767] range and cast to QSYMM16. + * + */ +class CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel : public ICLKernel +{ +public: + /** Constructor */ + CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers)*/ + CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(const CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers)*/ + CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(const CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete; + /** Allow instances of this class to be moved */ + CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default; + /** Allow instances of this class to be moved */ + CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default; + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[out] output Output tensor. Data type supported: Data type supported: QSYMM16 + * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add + * @param[in] result_shift Integer value used to round to nearest division by a power-of-two the result after the fixed point multiplication + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16. + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. + */ + void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0); + /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel + * + * @param[in] input Input tensor info. Data type supported: S32 + * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor info with dimensions [OFM]. Data type supported: Same as @p input. + * @param[in] output Output tensor info. Data type supported: Data type supported: QSYMM16 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + const ICLTensor *_bias; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h index cfd1f08519..0e70223998 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -188,5 +188,63 @@ public: */ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; +/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint on OpenCL. + * + * CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint depends on 2 parameters: + * + * result_fixedpoint_multiplier, result_shift + * + * The final result is: + * + * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift) + * + * where FixedPointMul(x, y) is the nearest integer to the following + * mathematical expression, evaluated without overflow or intermediate rounding: + * + * (x * y) / 2^31 + * + * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68 + * + * In case the bias tensor is provided, the final result is: + * + * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift + * + * This function calls the following NEON kernels: + * + * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel + * + * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions + * after the result is shifted right by result_shift +*/ +class CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's inputs, output + * + * @param[in] input Input tensor. Data type supported: S32 + * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[out] output Output tensor. Data type supported: Data type supported: QSYMM16 + * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add + * @param[in] result_shift Number of bits to shift right the result after the fixed point multiplication + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16. + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. + */ + void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0); + /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint + * + * @param[in] input Input tensor info. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32 + * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required. + * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. + * @param[in] output Output tensor info. Data type supported: Data type supported: QSYMM16 + * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. + * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16, + * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); +}; } // namespace arm_compute #endif /*__ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H__ */ \ No newline at end of file diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 36d8bed5b9..8b64b1f20e 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -342,6 +342,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "gemmlowp_offset_contribution_quantize_down_fixedpoint", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl" }, + { "gemmlowp_output_stage_quantize_down_fixedpoint_qsymm16", "gemmlowp.cl" }, { "gemmlowp_output_stage_quantize_down_float", "gemmlowp.cl" }, { "generate_proposals_compute_all_anchors", "generate_proposals.cl" }, { "harris_score_3x3", "harris_corners.cl" }, diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index 65c31efe2b..4b869554c5 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -2861,6 +2861,89 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATIO } #endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) +#if defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) + +/** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM16 + * + * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 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 [-32768..32767] range and cast to QSYMM16. + * + * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_FIXEDPOINT_MULTIPLIER 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 (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr + * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) 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_qsymm16(TENSOR3D_DECLARATION(src), +#if defined(ADD_BIAS) + VECTOR_DECLARATION(biases), +#endif // defined(ADD_BIAS) + TENSOR3D_DECLARATION(dst)) +{ + // Compute source and destination addresses + int x = get_global_id(0) * 4; + int y = get_global_id(1); + int z = get_global_id(2); + + __global short *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; + + __global short *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * 2 + y * dst_stride_y + z * dst_stride_z; + + int4 input_values = vload4(0, (__global int *)src_addr); + +#if defined(ADD_BIAS) + // Add bias + __global short *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); + + int4 biases_values = vload4(0, (__global int *)bias_addr); + input_values += (int4)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, 4); + + short4 res = convert_short4_sat(input_values); + +#if defined(MIN_BOUND) + res = max(res, (short4)MIN_BOUND); +#endif // defined(MIN_BOUND) +#if defined(MAX_BOUND) + res = min(res, (short4)MAX_BOUND); +#endif // defined(MAX_BOUND) + + // Store the result + vstore4(res, 0, dst_addr); +} +#endif // 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 * diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp new file mode 100644 index 0000000000..557e82dc50 --- /dev/null +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp @@ -0,0 +1,180 @@ +/* + * Copyright (c) 2017-2019 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/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.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) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(max > 32767); + ARM_COMPUTE_RETURN_ERROR_ON(min < -32768 || 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) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QSYMM16); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input); + } + + return Status{}; +} + +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) +{ + constexpr unsigned int num_elems_processed_per_iteration = 4; + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QSYMM16)); + + // 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 + +CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel() + : _input(nullptr), _bias(nullptr), _output(nullptr) +{ +} + +Status CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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)); + 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 CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + int result_fixedpoint_multiplier, int result_shift, + int min, int max) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + 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_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 != -32768) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); + build_opts.add_option_if((max != 32767) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); + build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); + + // Create kernel + _kernel = static_cast(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_fixedpoint_qsymm16", 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 CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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); + } + + 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/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index f1282cbde9..020fbbe52c 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -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/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.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" @@ -72,4 +73,20 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::validate(const ITensorInf { return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(input, bias, output, min, max); } + +void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + int result_fixedpoint_multiplier, int result_shift, + int min, int max) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, min, max); + _kernel = std::move(k); +} + +Status CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max) +{ + return CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, min, max); +} + } // namespace arm_compute \ No newline at end of file diff --git a/tests/validation/CL/GEMMLowp.cpp b/tests/validation/CL/GEMMLowp.cpp index efefbd645b..b8dfc030a2 100644 --- a/tests/validation/CL/GEMMLowp.cpp +++ b/tests/validation/CL/GEMMLowp.cpp @@ -295,7 +295,70 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedP } TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToUint8ScaleByFixedPoint +TEST_SUITE(QuantizeDownInt32ToInt16ScaleByFixedPoint) +const auto quantize_down_int32_to_int16_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, + 2) + * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); + +const auto quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, + 2) + * framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true }); + +using CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture = + GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture; + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( + framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), + TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max + TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Wrong output data type + }), + framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32), + TensorInfo(TensorShape(21U), 1, DataType::S32), + TensorInfo(TensorShape(21U), 1, DataType::S32), + })), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), + TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), + TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), + })), + framework::dataset::make("Min",{ -205, + -60000, + -180, + })), + framework::dataset::make("Max",{ 205, + 60000, + 180, + })), + framework::dataset::make("Expected", { true, false, false })), + a_info, b_info, output_info, min, max, expected) +{ + // Lock tensors + Status status = CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(true), + &b_info.clone()->set_is_resizable(true), + &output_info.clone()->set_is_resizable(true), + min, + max); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), + quantize_down_int32_to_int16_scale_by_fixedpoint_cases)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +TEST_SUITE(BoundedReLu) +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), + quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +TEST_SUITE_END() // BoundedReLu +TEST_SUITE_END() // QuantizeDownInt32ToInt16ScaleByFixedPoint TEST_SUITE_END() // OutputStage TEST_SUITE_END() // GEMMLowp TEST_SUITE_END() // CL -- cgit v1.2.1