diff options
-rw-r--r-- | Android.bp | 2 | ||||
-rw-r--r-- | arm_compute/core/CL/CLKernels.h | 2 | ||||
-rw-r--r-- | arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h (renamed from arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h) | 54 | ||||
-rw-r--r-- | arm_compute/core/Types.h | 1 | ||||
-rw-r--r-- | arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h | 5 | ||||
-rw-r--r-- | src/core/CL/cl_kernels/gemmlowp.cl | 24 | ||||
-rw-r--r-- | src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.cpp (renamed from src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp) | 60 | ||||
-rw-r--r-- | src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp | 28 | ||||
-rw-r--r-- | tests/validation/CL/GEMMLowp.cpp | 40 | ||||
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 103 | ||||
-rw-r--r-- | tests/validation/reference/GEMMLowp.cpp | 63 | ||||
-rw-r--r-- | tests/validation/reference/GEMMLowp.h | 8 |
12 files changed, 315 insertions, 75 deletions
diff --git a/Android.bp b/Android.bp index f9a41000dd..0d5c9e949d 100644 --- a/Android.bp +++ b/Android.bp @@ -126,11 +126,11 @@ cc_library_static { "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp", "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp", "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp", + "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.cpp", "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.cpp", "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp", "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp", "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp", - "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp", "src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp", "src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp", "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp", diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h index f2e16ca139..b265aa2fe7 100644 --- a/arm_compute/core/CL/CLKernels.h +++ b/arm_compute/core/CL/CLKernels.h @@ -79,11 +79,11 @@ #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/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" -#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h index 900a8c3b5d..439f569d07 100644 --- a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h +++ b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFLOATKERNEL_H -#define ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFLOATKERNEL_H +#ifndef ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32SCALEBYFLOATKERNEL_H +#define ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32SCALEBYFLOATKERNEL_H #include "arm_compute/core/CL/ICLKernel.h" @@ -31,9 +31,9 @@ namespace arm_compute // Forward declarations class ICLTensor; -/** OpenCL kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 +/** OpenCL kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8/QASYMM8_SIGNED * - * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8 value. + * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value. * The following computations will be performed by the kernel: * * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier @@ -41,47 +41,43 @@ class ICLTensor; * -# 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. + * -# Clamp the resulting int32 values to + * - to the [0..255] range and cast to QASYMM8. + * - to the [-128..127] range and cast to QASYMM8_SIGNED. */ -class CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel : public ICLKernel +class CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel : public ICLKernel { public: /** Constructor */ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel(); + CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel(); /** Prevent instances of this class from being copied (As this class contains pointers)*/ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel(const CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &) = delete; + CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel(const CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers)*/ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &operator=(const CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &) = delete; + CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &operator=(const CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &) = delete; /** Allow instances of this class to be moved */ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &&) = default; + CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel(CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &&) = default; /** Allow instances of this class to be moved */ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &operator=(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel &&) = default; + CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &operator=(CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel &&) = 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: QASYMM8 - * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix - * @param[in] offset Offset to be applied to result before converting it back to QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions + * @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: QASYMM8/QASYMM8_SIGNED + * @param[in] info Output stage info. Used to pass the quantized output data type */ - void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = 0, int max = 0); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel + void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *info); + /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel * * @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[in] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions + * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED + * @param[in] info Output stage info. Used to pass the quantized output data type * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *info); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -92,4 +88,4 @@ private: ICLTensor *_output; }; } // namespace arm_compute -#endif /* ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFLOATKERNEL_H */ +#endif /* ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32SCALEBYFLOATKERNEL_H */ diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index 711b68f236..37a9679a21 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -1956,6 +1956,7 @@ struct GEMMLowpOutputStageInfo int32_t gemmlowp_max_bound{ std::numeric_limits<int32_t>::max() }; /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */ std::vector<int32_t> gemmlowp_multipliers{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */ std::vector<int32_t> gemmlowp_shifts{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */ + float gemmlowp_real_multiplier{ 0 }; /**< GEMMLowp output stage real multiplier used for quantizing to QASYMM8 */ bool is_quantized_per_channel{ false }; /**< GEMMLowp quantized per-channel flag */ DataType output_data_type{ DataType::UNKNOWN }; /**< Output tensor data type to use if the output is not initialized */ }; diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h index 184d827d4b..05cffa6680 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h @@ -217,7 +217,7 @@ public: * * This function calls the following OpenCL kernels: * - * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel + * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel * * @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 @@ -237,6 +237,7 @@ public: * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer. */ + ARM_COMPUTE_DEPRECATED_REL(20.05) void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max()); /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint @@ -251,6 +252,7 @@ public: * * @return a status */ + ARM_COMPUTE_DEPRECATED_REL(20.05) static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max()); }; /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint on OpenCL. @@ -317,6 +319,7 @@ public: * This function calls the following CL kernels: * * -# @ref CLGEMMLowpQuantizeDownInt32ScaleKernel + * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel * -# @ref CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel */ diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index 3fba781ede..7f2828689a 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -2317,9 +2317,9 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint_qsymm16(TENSOR3D_DE #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 +/** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8/QASYMM8_SIGNED * - * This kernel takes a final int32 accumulator value (the output of matrix multiplication), and processes it to obtain the final QASYMM8 value. + * This kernel takes a final int32 accumulator value (the output of matrix multiplication), and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value. * The following computations will be performed by the kernel: * * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier @@ -2327,11 +2327,14 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint_qsymm16(TENSOR3D_DE * -# 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. + * -# Clamp the resulting int32 values: + * - to the [0..255] range and cast to QASYMM8. + * - to the [-128..127] range and cast to QASYMM8_SIGNED. * * @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 The output datatype should be passed at compile time using -DOUTPUT_DATA_TYPE * @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 * @@ -2388,19 +2391,20 @@ __kernel void gemmlowp_output_stage_quantize_down_float(TENSOR3D_DECLARATION(src #endif // defined(ADD_BIAS) // Convert to float - float16 input_values_f = convert_float4(input_values); - input_values_f = round(input_values_f * (float)REAL_MULTIPLIER + (float)OUTPUT_OFFSET); + float4 input_values_f = convert_float4(input_values); + input_values_f = round(input_values_f * (float)REAL_MULTIPLIER + (float)OUTPUT_OFFSET); - uchar4 res = convert_uchar4_sat(input_values_f); + VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4) + res = CONVERT_SAT(input_values_f, VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4)); #if defined(MIN_BOUND) - res = max(res, (uchar4)MIN_BOUND); + res = max(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) - res = min(res, (uchar4)MAX_BOUND); + res = min(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result - vstore4(res, 0, dst_addr); + vstore4(res, 0, (__global OUTPUT_DATA_TYPE *)dst_addr); } -#endif // defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET) +#endif // defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET)
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.cpp index 7097dc9248..5a554f3111 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.cpp @@ -21,9 +21,10 @@ * 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/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h" #include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" @@ -32,7 +33,7 @@ #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" - +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "support/StringSupport.h" namespace arm_compute @@ -40,10 +41,13 @@ namespace arm_compute namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min, int max) + const GEMMLowpOutputStageInfo *info) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(min > max); + ARM_COMPUTE_RETURN_ERROR_ON((info->output_data_type != DataType::QASYMM8) && (info->output_data_type != DataType::QASYMM8_SIGNED)); + ARM_COMPUTE_RETURN_ERROR_ON(info->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(info->output_data_type))); + ARM_COMPUTE_RETURN_ERROR_ON(info->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(info->output_data_type)) + || info->gemmlowp_min_bound > info->gemmlowp_max_bound); // Check biases if exist if(bias != nullptr) @@ -55,15 +59,18 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con if(output->total_size() != 0) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() != info->output_data_type, "Mismatching output data type"); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, DataType output_data_type) { + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_data_type(output_data_type)); + constexpr unsigned int num_elems_processed_per_iteration = 4; // Output auto inizialitation if not yet initialized @@ -77,14 +84,9 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen 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())); - } + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); if(bias != nullptr) { @@ -98,39 +100,39 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen } // namespace class Coordinates; -CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel() +CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel() : _input(nullptr), _bias(nullptr), _output(nullptr) { } -Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +Status CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + const GEMMLowpOutputStageInfo *info) { 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); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, info)); return Status{}; } -void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, - float multiplier, int offset, - int min, int max) +void CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + const GEMMLowpOutputStageInfo *info) { // 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)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), info)); _input = input; _bias = bias; _output = output; + auto min = info->gemmlowp_min_bound; + auto max = info->gemmlowp_max_bound; + // 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("-DREAL_MULTIPLIER=" + float_to_string_with_full_precision(info->gemmlowp_real_multiplier)); + build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(info->gemmlowp_offset)); + build_opts.add_option("-DOUTPUT_DATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type())); build_opts.add_option_if((min > 0), "-DMIN_BOUND=" + support::cpp11::to_string(min)); build_opts.add_option_if((max < 255), "-DMAX_BOUND=" + support::cpp11::to_string(max)); build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); @@ -139,12 +141,12 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTe _kernel = static_cast<cl::Kernel>(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()); + auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), info->output_data_type); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); } -void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::run(const Window &window, cl::CommandQueue &queue) +void CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index e86f303ff4..fbd1820098 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -24,11 +24,11 @@ #include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h" #include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" -#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h" #include "support/MemorySupport.h" namespace arm_compute @@ -90,15 +90,24 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::configure(const ICLTensor * float multiplier, int offset, int min, int max) { - auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel>(); - k->configure(input, bias, output, multiplier, offset, min, max); + GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo(); + info.gemmlowp_offset = offset; + info.gemmlowp_real_multiplier = multiplier; + info.gemmlowp_min_bound = min; + info.gemmlowp_max_bound = max; + + auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel>(); + k->configure(input, bias, output, &info); _kernel = std::move(k); } Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { - return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(input, bias, output, min, max); + GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo(); + info.gemmlowp_min_bound = min; + info.gemmlowp_max_bound = max; + return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info); } void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, @@ -165,6 +174,13 @@ void CLGEMMLowpOutputStage::configure(const ICLTensor *input, const ICLTensor *b } break; } + case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT: + { + auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel>(); + k->configure(input, bias, output, &info); + _kernel = std::move(k); + break; + } default: ARM_COMPUTE_ERROR("Unsupported GEMMLowpOutputStage type."); } @@ -202,6 +218,10 @@ Status CLGEMMLowpOutputStage::validate(const ITensorInfo *input, const ITensorIn return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type."); } } + case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT: + { + return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info); + } default: return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type."); } diff --git a/tests/validation/CL/GEMMLowp.cpp b/tests/validation/CL/GEMMLowp.cpp index 3d7c76aa2b..8aa81d0962 100644 --- a/tests/validation/CL/GEMMLowp.cpp +++ b/tests/validation/CL/GEMMLowp.cpp @@ -389,6 +389,46 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedP TEST_SUITE_END() // MultGreater1 TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToInt16ScaleByFixedPoint + +TEST_SUITE(QuantizeDownInt32ScaleByFloat) + +TEST_SUITE(QASYMM8) +using CLGEMMLowpQuantizeDownInt32ScaleByFloatFixture = + GEMMLowpQuantizeDownInt32ScaleByFloatValidationFixture<CLTensor, CLAccessor, CLGEMMLowpOutputStage, uint8_t>; + +FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMLowpQuantizeDownInt32ScaleByFloatFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), + datasets::TinyShapes()), + framework::dataset::make("result_real_multiplier", 0.33f)), + framework::dataset::make("result_offset", 2, 3)), + framework::dataset::make("min", 0)), + framework::dataset::make("max", 255)), + framework::dataset::make("addBias", { false, true }))) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +TEST_SUITE_END() // QASYMM8 + +TEST_SUITE(QASYMM8_SIGNED) +using CLGEMMLowpQuantizeDownInt32ScaleByFloatFixture_Signed = + GEMMLowpQuantizeDownInt32ScaleByFloatValidationFixture<CLTensor, CLAccessor, CLGEMMLowpOutputStage, int8_t>; +FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMLowpQuantizeDownInt32ScaleByFloatFixture_Signed, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(framework::dataset::make("DataType", DataType::QASYMM8_SIGNED), + datasets::TinyShapes()), + framework::dataset::make("result_real_multiplier", 0.33f)), + framework::dataset::make("result_offset", 2, 3)), + framework::dataset::make("min", -128)), + framework::dataset::make("max", 127)), + framework::dataset::make("addBias", { false, true }))) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +TEST_SUITE_END() // QASYMM8_SIGNED + +TEST_SUITE_END() // QuantizeDownInt32ScaleByFloat + TEST_SUITE_END() // OutputStage TEST_SUITE_END() // GEMMLowp TEST_SUITE_END() // CL diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 0207f4c5ae..be9ce96dcb 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -556,6 +556,109 @@ protected: SimpleTensor<uint8_t> _reference{}; }; +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class GEMMLowpQuantizeDownInt32ScaleByFloatValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(DataType data_type, TensorShape shape, float result_real_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias) + { + _target = compute_target(data_type, shape, result_real_multiplier, result_offset, min, max, add_bias); + _reference = compute_reference(shape, result_real_multiplier, result_offset, min, max, add_bias); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + // To avoid data all being clampped + std::uniform_int_distribution<> distribution(-500, 500); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(DataType data_type, const TensorShape &shape, float result_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias) + { + TensorShape shape_bias(shape[0]); + + // Create tensors + TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1); + TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1); + TensorType c = create_tensor<TensorType>(shape, data_type, 1); + + // create output stage info + GEMMLowpOutputStageInfo info; + info.gemmlowp_max_bound = max; + info.gemmlowp_min_bound = min; + info.gemmlowp_real_multiplier = result_multiplier; + info.gemmlowp_offset = result_offset; + info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT; + info.output_data_type = data_type; + + // Create and configure function + FunctionType output_stage; + output_stage.configure(&a, add_bias ? &b : nullptr, &c, info); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + a.allocator()->allocate(); + c.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensor + fill(AccessorType(a), 0); + + if(add_bias) + { + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate bias tensor + b.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensor + fill(AccessorType(b), 1); + } + + // Compute GEMM function + output_stage.run(); + return c; + } + + SimpleTensor<T> compute_reference(const TensorShape &shape, float_t result_real_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias) + { + // Create reference + TensorShape shape_bias(shape[0]); + + SimpleTensor<int32_t> a{ shape, DataType::S32, 1 }; + SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 }; + + // Fill reference + fill(a, 0); + + const std::vector<float_t> result_float_multiplier_vec = { result_real_multiplier }; + + if(add_bias) + { + // Fill bias + fill(b, 1); + + return reference::gemmlowp_quantize_down_scale_by_float<int32_t, T>(a, b, result_float_multiplier_vec, result_offset, min, max); + } + else + { + return reference::gemmlowp_quantize_down_scale_by_float<int32_t, T>(a, result_float_multiplier_vec, result_offset, min, max); + } + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; +}; + template <typename TensorType, typename AccessorType, typename FunctionType> class GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture : public framework::Fixture { diff --git a/tests/validation/reference/GEMMLowp.cpp b/tests/validation/reference/GEMMLowp.cpp index 99d08e34f1..61617c8aae 100644 --- a/tests/validation/reference/GEMMLowp.cpp +++ b/tests/validation/reference/GEMMLowp.cpp @@ -131,6 +131,39 @@ void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const Simple std::min<TIn>(std::numeric_limits<TOut>::max(), result))); } } + +template <typename TIn, typename TOut> +void quantize_down_scale_by_float(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<float_t> result_real_multiplier, + int32_t result_offset, int32_t min, int32_t max) +{ + const int cols_in = in->shape().x(); + const bool is_per_channel = result_real_multiplier.size() > 1; + + for(int i = 0; i < in->num_elements(); ++i) + { + TIn result = (*in)[i]; + + if(bias != nullptr) + { + result += (*bias)[i % cols_in]; + } + + // Float multiplication + const float_t multiplier = (is_per_channel) ? result_real_multiplier[i % cols_in] : result_real_multiplier[0]; + + float_t result_f = static_cast<float_t>(result) * multiplier + static_cast<float_t>(result_offset); + result = static_cast<TIn>(std::round(result_f)); + + // Bounded ReLu + if(min != max) + { + result = std::max(min, std::min(max, result)); + } + + (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(), + std::min<TIn>(std::numeric_limits<TOut>::max(), result))); + } +} } // namespace template <typename T_out, typename T_in, typename T_in_1> @@ -237,6 +270,36 @@ SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor return dst; } +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max) +{ + SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); + + quantize_down_scale_by_float<TIn, TOut>(&in, &bias, &dst, result_real_multiplier, result_offset, min, max); + + return dst; +} + +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max) +{ + SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); + + quantize_down_scale_by_float<TIn, TOut>(&in, nullptr, &dst, result_real_multiplier, result_offset, min, max); + + return dst; +} + +template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max); +template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max); +template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max); +template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max); template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, diff --git a/tests/validation/reference/GEMMLowp.h b/tests/validation/reference/GEMMLowp.h index 7d711263e8..5de48dab52 100644 --- a/tests/validation/reference/GEMMLowp.h +++ b/tests/validation/reference/GEMMLowp.h @@ -59,6 +59,14 @@ SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor template <typename TIn, typename TOut> SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); + +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min = 0, int32_t max = 0); + +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, + std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min = 0, int32_t max = 0); } // namespace reference } // namespace validation } // namespace test |