"src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp",
+ "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp",
- "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp",
"src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp",
"src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp",
"src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp",
* @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] output_stage Output stage info. Used to pass the quantized output data type
+ * @param[in] output_stage GEMMLowp output stage metadata.
*/
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ScaleKernel
* @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/QASYMM8_SIGNED
- * @param[in] output_stage Output stage info. Used to pass the quantized output data type
+ * @param[in] output_stage GEMMLowp output stage metadata.
*
* @return a status
*/
/*
- * Copyright (c) 2016-2019 ARM Limited.
+ * Copyright (c) 2016-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
#include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h"
--- /dev/null
+/*
+ * Copyright (c) 2020 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_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H
+#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H
+
+#include "arm_compute/core/NEON/INEKernel.h"
+
+namespace arm_compute
+{
+class ITensor;
+
+/** NEON 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 NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value.
+ * The following computations will be performed by the kernel:
+ *
+ * -# Add offset terms to final result
+ * -# Multiply each entry of result by result_mult_int
+ * -# Add bias to final result if bias tensor is not a nullptr
+ * -# Shift the int32 accumulator by result_shift
+ * -# Clamp the value between the specified min and max bounds
+ * -# Clamp the resulting int32 values:
+ * -# -to the [0..255] range and cast to QASYMM8.
+ * -# -to the [-128..127] range and cast to QASYMM8_SIGNED.
+ *
+ */
+class NEGEMMLowpQuantizeDownInt32ScaleKernel : public INEKernel
+{
+public:
+ const char *name() const override
+ {
+ return "NEGEMMLowpQuantizeDownInt32ScaleKernel";
+ }
+ /** Constructor */
+ NEGEMMLowpQuantizeDownInt32ScaleKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers)*/
+ NEGEMMLowpQuantizeDownInt32ScaleKernel(const NEGEMMLowpQuantizeDownInt32ScaleKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers)*/
+ NEGEMMLowpQuantizeDownInt32ScaleKernel &operator=(const NEGEMMLowpQuantizeDownInt32ScaleKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ NEGEMMLowpQuantizeDownInt32ScaleKernel(NEGEMMLowpQuantizeDownInt32ScaleKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ NEGEMMLowpQuantizeDownInt32ScaleKernel &operator=(NEGEMMLowpQuantizeDownInt32ScaleKernel &&) = 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/QASYMM8_SIGNED
+ * @param[out] output_stage GEMMLowp output stage metadata.
+ */
+ void configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage);
+ /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ScaleKernel
+ *
+ * @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/QASYMM8_SIGNED
+ * @param[out] output_stage GEMMLowp output stage metadata.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage);
+
+ // Inherited methods overridden:
+ void run(const Window &window, const ThreadInfo &info) override;
+
+private:
+ /** Template function to run the NEGEMMLowpQuantizeDownInt32ScaleKernel
+ *
+ * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
+ */
+ template <typename T>
+ void run(const Window &window);
+
+ /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ScaleKernel functions
+ *
+ * @param[in] window Region on which to execute the kernel.
+ */
+ using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ScaleKernel::*)(const Window &window);
+
+ QuantizeDownFunctionPtr _func;
+ const ITensor *_input;
+ const ITensor *_bias;
+ ITensor *_output;
+ const GEMMLowpOutputStageInfo *_output_stage;
+ bool _is_bounded_relu;
+};
+} // namespace arm_compute
+
+#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H */
+++ /dev/null
-/*
- * 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.
- */
-#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEKERNEL_H
-#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEKERNEL_H
-
-#include "arm_compute/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** NEON kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8
- *
- * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8 value.
- * The following computations will be performed by the kernel:
- *
- * -# Add offset terms to final result
- * -# Multiply each entry of result by result_mult_int
- * -# Add bias to final result if bias tensor is not a nullptr
- * -# Shift the int32 accumulator by result_shift
- * -# Clamp the value between the specified min and max bounds
- * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8.
- *
- */
-class NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel";
- }
- /** Constructor */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &operator=(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel(NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &operator=(NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel &&) = 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] result_offset Offset to be added to each element of the input matrix
- * @param[in] result_mult_int 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 before converting 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
- */
- void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min = 0, int max = 0);
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
- *
- * @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
- *
- * @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, const ThreadInfo &info) override;
-
-private:
- /** Template function to run the NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
- *
- * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
- */
- template <bool is_bounded_relu>
- void run(const Window &window);
-
- /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel functions
- *
- * @param[in] window Region on which to execute the kernel.
- */
- using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::*)(const Window &window);
-
- QuantizeDownFunctionPtr _func;
- const ITensor *_input;
- const ITensor *_bias;
- ITensor *_output;
- int _result_offset;
- int _result_mult_int;
- int _result_shift;
- int _min;
- int _max;
-};
-} // namespace arm_compute
-
-#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEKERNEL_H */
*
* This function calls the following NEON kernels:
*
- * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
+ * -# @ref NEGEMMLowpQuantizeDownInt32ScaleKernel
*
* @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
* @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 ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, 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 NEGEMMLowpQuantizeDownInt32ToUint8Scale
*
* @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());
};
*
* This function calls the following NEON kernels:
*
- * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
+ * -# @ref NEGEMMLowpQuantizeDownInt32ScaleKernel
* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
* -# @ref NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
* -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
- @ref NEDepthwiseConvolutionLayer3x3Kernel / NEDepthwiseIm2ColKernel / @ref NEGEMMMatrixVectorMultiplyKernel / NEDepthwiseVectorToTensorKernel / @ref NEDepthwiseConvolutionLayer
- @ref NEGEMMLowpOffsetContributionKernel / @ref NEGEMMLowpMatrixAReductionKernel / @ref NEGEMMLowpMatrixBReductionKernel / @ref NEGEMMLowpMatrixMultiplyCore
- @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- - @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale
- NEWinogradLayer / NEWinogradLayerKernel
- New OpenCL kernels / functions
--- /dev/null
+/*
+ * Copyright (c) 2020 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/NEGEMMLowpQuantizeDownInt32ScaleKernel.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/wrapper/wrapper.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_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+namespace arm_compute
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)));
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+ || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound);
+
+ // 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)
+ {
+ if(output->data_type() != output_stage->output_data_type && (output_stage->output_data_type == DataType::QASYMM8 || output_stage->output_data_type == DataType::QASYMM8_SIGNED))
+ {
+ ARM_COMPUTE_RETURN_ERROR_MSG("Mismatching data types");
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ }
+
+ return Status{};
+}
+
+inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
+{
+ // Add the offset terms to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
+ in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
+ in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
+
+ // Multiply by result_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value,
+ typename wrapper::traits::neon_vector<T, 16>::type>::type
+ convert_to_8bit(const int16x8x2_t in_s16)
+{
+ return wrapper::vcombine(wrapper::vqmovun(in_s16.val[0]), wrapper::vqmovun(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value,
+ typename wrapper::traits::neon_vector<T, 16>::type>::type
+ convert_to_8bit(const int16x8x2_t in_s16)
+{
+ return wrapper::vcombine(wrapper::vqmovn(in_s16.val[0]), wrapper::vqmovn(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename wrapper::traits::neon_vector<T, 16>::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector<T, 16>::type min,
+ typename wrapper::traits::neon_vector<T, 16>::type max)
+{
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_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 S8 or U8
+ typename wrapper::traits::neon_vector<T, 16>::type out = convert_to_8bit<T>(in_s16);
+
+ out = wrapper::vmax(out, min);
+ out = wrapper::vmin(out, max);
+
+ return out;
+}
+
+class Coordinates;
+
+template <typename T>
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window)
+{
+ using VectorType = typename wrapper::traits::neon_vector<T, 16>::type;
+
+ const int32x4_t result_offset_s32 = vdupq_n_s32(_output_stage->gemmlowp_offset);
+ const int32x4_t result_shift_s32 = vdupq_n_s32(-_output_stage->gemmlowp_shift);
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits<T>::lowest();
+ const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits<T>::max();
+
+ VectorType min = wrapper::vdup_n(static_cast<T>(clamp_min), wrapper::traits::vector_128_tag{});
+ VectorType max = wrapper::vdup_n(static_cast<T>(clamp_max), wrapper::traits::vector_128_tag{});
+
+ Window win(window);
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(_input, win);
+ Iterator out(_output, win);
+
+ if(_bias != nullptr)
+ {
+ Window win_biases;
+ win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
+ win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ Iterator bias(_bias, win_biases);
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+ }
+ };
+
+ const int32x4x4_t bias_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 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]);
+
+ // Add the offset terms to GEMM's result and multiply by result_mult_int
+ scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
+ int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+ // Quantize
+ in_value = ((in_value + bias_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+ // Store the result
+ *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+ }
+ },
+ in, bias, out);
+ }
+ else
+ {
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+ }
+ };
+
+ // Add the offset terms to GEMM's result and multiply by result_mult_int
+ scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+ // Quantize
+ in_value = ((in_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+ // Store the result
+ *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+ }
+ },
+ in, out);
+ }
+}
+
+NEGEMMLowpQuantizeDownInt32ScaleKernel::NEGEMMLowpQuantizeDownInt32ScaleKernel()
+ : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _output_stage(nullptr), _is_bounded_relu(false)
+{
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ // Perform validate step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, output_stage);
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_stage->output_data_type));
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
+ (bias != nullptr) ? bias->info() : nullptr,
+ output->info(),
+ output_stage));
+
+ _input = input;
+ _bias = bias;
+ _output = output;
+ _output_stage = output_stage;
+
+ // Configure kernel window
+ Window win = calculate_max_window(*input->info(), Steps());
+ Coordinates coord;
+ coord.set_num_dimensions(output->info()->num_dimensions());
+ output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
+
+ INEKernel::configure(win);
+
+ // Check if we need to clamp the result using min and max
+ _is_bounded_relu = ((_output_stage->gemmlowp_min_bound != _output_stage->gemmlowp_max_bound)
+ && !(_output_stage->gemmlowp_min_bound == std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+ && _output_stage->gemmlowp_max_bound == std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))));
+ if(_output_stage->output_data_type == DataType::QASYMM8)
+ {
+ _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<uint8_t>;
+ }
+ else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED)
+ {
+ _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<int8_t>;
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Data type not supported");
+ }
+}
+
+Status NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, output_stage));
+
+ return Status{};
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::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);
+}
+} // namespace arm_compute
\ No newline at end of file
+++ /dev/null
-/*
- * Copyright (c) 2017-2020 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/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.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/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
-{
-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(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::QASYMM8);
- 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)
-{
- // Note: This kernel performs 16 elements per iteration.
- // However, since we use a left-over for loop, we cannot have any read or write out of memory
- // For this reason num_elems_processed_per_iteration is set to 1
- constexpr unsigned int num_elems_processed_per_iteration = 1;
-
- // 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);
-
- bool window_changed = update_window_and_padding(win,
- input_access);
-
- if(output->total_size() != 0)
- {
- 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)
- {
- AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(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);
-}
-
-inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
-{
- // Add the offset terms to GEMM's result
- in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
- in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
- in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
- in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
-
- // Multiply by result_mult_int
- in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
- in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
- in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
- in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
-}
-
-template <bool is_bounded_relu>
-inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
-{
- const static int32x4_t zero_s32 = vdupq_n_s32(0);
-
- // Shift final result (negative value shift right)
- in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
- in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
- in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
- in_s32.val[3] = vshlq_s32(in_s32.val[3], result_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 NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
-{
- const int32x4_t result_offset_s32 = vdupq_n_s32(_result_offset);
- const int32x4_t result_shift_s32 = vdupq_n_s32(-_result_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);
-
- const int window_step_x = 16;
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
-
- Window win(window);
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator in(_input, win);
- Iterator out(_output, win);
-
- if(_bias != nullptr)
- {
- Window win_biases;
- win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
- win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Iterator bias(_bias, win_biases);
- execute_window_loop(win, [&](const Coordinates &)
- {
- // Compute 16 elements per iteration
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- int32x4x4_t in_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
- }
- };
-
- const int32x4x4_t bias_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 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]);
-
- // 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() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
- int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
- // Quantize
- in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;
-
- // Finalize and store the result
- if(is_bounded_relu)
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
- }
- else
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
- }
- }
- },
- in, bias, out);
- }
- else
- {
- execute_window_loop(win, [&](const Coordinates &)
- {
- // Compute 16 elements per iteration
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- int32x4x4_t in_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
- }
- };
-
- // 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() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
- // Quantize
- in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;
-
- // Finalize and store the result
- if(is_bounded_relu)
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
- }
- else
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
- }
- }
- },
- in, out);
- }
-}
-
-NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel()
- : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0)
-{
-}
-
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_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_offset = result_offset;
- _result_mult_int = result_mult_int;
- _result_shift = result_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 <= 0 && max >= 255);
- _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<false>;
-}
-
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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 NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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);
-}
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- switch(info.output_data_type)
- {
- case DataType::QASYMM8:
- case DataType::QASYMM8_SIGNED:
- {
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
- k->configure(input, bias, output, &info);
- _kernel = std::move(k);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Unsupported output data type.");
- break;
- }
- }
+ auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
+ _kernel = std::move(k);
break;
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
}
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN:
- {
- switch(output->data_type())
- {
- case DataType::QASYMM8:
- case DataType::QASYMM8_SIGNED:
- {
- return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
- }
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
- }
- }
+ return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
- {
return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info);
- }
default:
return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
}
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
#include "arm_compute/core/Validate.h"
#include "support/MemorySupport.h"
{
void NEGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
- k->configure(input, bias, output, result_offset, result_mult_int, result_shift, min, max);
+ GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+ info.gemmlowp_offset = result_offset;
+ info.gemmlowp_multiplier = result_mult_int;
+ info.gemmlowp_shift = result_shift;
+ info.gemmlowp_min_bound = min;
+ info.gemmlowp_max_bound = max;
+
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
_kernel = std::move(k);
}
Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
{
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max);
+ GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+ info.gemmlowp_min_bound = min;
+ info.gemmlowp_max_bound = max;
+
+ return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
}
void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpOutputStage::validate(input->info(), bias != nullptr ? bias->info() : nullptr, output->info(), info));
- if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+ switch(info.type)
{
- switch(output->info()->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
{
- case DataType::QASYMM8:
+ switch(info.output_data_type)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
+ case DataType::QASYMM8:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ case DataType::QASYMM8_SIGNED:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ case DataType::QSYMM16:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
+ }
}
- default:
- ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
}
- }
- else if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
- {
- switch(output->info()->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- case DataType::QASYMM8:
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
- }
- case DataType::QASYMM8_SIGNED:
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
- }
- case DataType::QSYMM16:
+ switch(info.output_data_type)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
+ _kernel = std::move(k);
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
+ }
}
- default:
- ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
}
- }
- else
- {
- ARM_COMPUTE_ERROR("Unsupported output stage quantization type.");
+ default:
+ ARM_COMPUTE_ERROR("Unsupported GEMMLowpOutputStage type.");
}
}
ARM_COMPUTE_RETURN_ERROR_ON((info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN) && (info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT));
- if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+ switch(info.type)
{
- switch(output->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
{
- case DataType::QASYMM8:
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ switch(output->data_type())
+ {
+ case DataType::QASYMM8:
+ return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ case DataType::QASYMM8_SIGNED:
+ return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ case DataType::QSYMM16:
+ return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ }
}
- }
- else
- {
- switch(output->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- case DataType::QASYMM8:
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- case DataType::QASYMM8_SIGNED:
- return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- case DataType::QSYMM16:
- return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ switch(output->data_type())
+ {
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ }
}
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
}
}
} // namespace arm_compute
TEST_SUITE(OutputStage)
+TEST_SUITE(QuantizeDownInt32Scale)
+
+TEST_SUITE(QASYMM8)
+
+const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
+ 3)
+ * framework::dataset::make("min", 0) * framework::dataset::make("max", 255) * framework::dataset::make("addBias", { false, true });
+
+const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1,
+ 2)
+ * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 173) * framework::dataset::make("addBias", { false, true });
+
+using CLGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<CLTensor, CLAccessor, CLGEMMLowpOutputStage>;
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+
+TEST_SUITE(BoundedReLu)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+
+TEST_SUITE_END() // BoundedReLu
+TEST_SUITE_END() // QASYMM8
+
+TEST_SUITE(QASYMM8_SIGNED)
+
+const auto quantize_down_int32_to_int8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
+ 3)
+ * framework::dataset::make("min", -128) * framework::dataset::make("max", 127) * framework::dataset::make("addBias", { false, true });
+
+const auto quantize_down_int32_to_int8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1,
+ 2)
+ * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", -100, -98) * framework::dataset::make("max", 71, 73) * framework::dataset::make("addBias", { false, true });
+
+using CLGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture<CLTensor, CLAccessor, CLGEMMLowpOutputStage>;
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_cases))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+
+TEST_SUITE(BoundedReLu)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_relu_cases))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+
+TEST_SUITE_END() // BoundedReLu
+TEST_SUITE_END() // QASYMM8_SIGNED
+TEST_SUITE_END() // QuantizeDownInt32Scale
+
TEST_SUITE(QuantizeDownInt32ToUint8ScaleByFixedPoint)
const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
2)
TEST_SUITE(OutputStage)
-TEST_SUITE(QuantizeDownInt32ToUint8Scale)
+TEST_SUITE(QuantizeDownInt32Scale)
+
+TEST_SUITE(QASYMM8)
const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
3)
2)
* framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true });
-using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>;
+using NEGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpOutputStage>;
// *INDENT-OFF*
// clang-format off
framework::dataset::make("Expected", { true, false })),
a_info, b_info, output_info, min, max, expected)
{
+
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
+ output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage.gemmlowp_min_bound = min;
+ output_stage.gemmlowp_max_bound = max;
+ output_stage.output_data_type = DataType::QASYMM8;
+
// Lock tensors
- Status status = NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(&a_info.clone()->set_is_resizable(false),
+ Status status = NEGEMMLowpOutputStage::validate(&a_info.clone()->set_is_resizable(false),
&b_info.clone()->set_is_resizable(false),
&output_info.clone()->set_is_resizable(false),
- min,
- max);
+ output_stage);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases),
- shape, result_offset, result_mult_int, result_shift, min, max, add_bias)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
{
- TensorShape shape_bias(shape[0]);
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
- // Create tensors
- Tensor in = create_tensor<Tensor>(shape, DataType::S32);
- Tensor bias = create_tensor<Tensor>(shape_bias, DataType::S32);
- Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8);
+TEST_SUITE(BoundedReLu)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
- ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS);
+TEST_SUITE_END() // BoundedReLu
- // Create and configure function
- NEGEMMLowpQuantizeDownInt32ToUint8Scale output_stage;
- output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_offset, result_mult_int, result_shift, min, max);
+TEST_SUITE_END() // QASYMM8
- // Validate valid region input and output
- const ValidRegion valid_region = shape_to_valid_region(shape);
- validate(in.info()->valid_region(), valid_region);
- validate(out.info()->valid_region(), valid_region);
+TEST_SUITE(QASYMM8_SIGNED)
- // Validate valid region bias
- if(add_bias)
- {
- const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias);
- validate(bias.info()->valid_region(), valid_region_bias);
- }
+const auto quantize_down_int32_to_int8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
+ 3)
+ * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true });
- // Validate padding
- const PaddingSize padding(0);
- validate(in.info()->padding(), padding);
- validate(out.info()->padding(), padding);
+const auto quantize_down_int32_to_int8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1,
+ 2)
+ * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", -100, -98) * framework::dataset::make("max", 71, 74) * framework::dataset::make("addBias", { false, true });
- if(add_bias)
- {
- validate(bias.info()->padding(), padding);
- }
-}
+using NEGEMMLowpQuantizeDownInt32ScaleFixture = GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpOutputStage>;
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
+// *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), // Input not a multiple of 16
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max
+ TensorInfo(TensorShape(20U, 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(20U), 1, DataType::S32),
+ })),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
+ TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
+ })),
+ framework::dataset::make("Min",{ -10,
+ -200,
+ -113,
+ })),
+ framework::dataset::make("Max",{ 105,
+ 300,
+ -18,
+ })),
+ framework::dataset::make("Expected", { true, false, false })),
+ a_info, b_info, output_info, min, max, expected)
{
- // Validate output
- validate(Accessor(_target), _reference);
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
+ output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage.gemmlowp_min_bound = min;
+ output_stage.gemmlowp_max_bound = max;
+ output_stage.output_data_type = DataType::QASYMM8_SIGNED;
+
+ // Lock tensors
+ Status status = NEGEMMLowpOutputStage::validate(&a_info.clone()->set_is_resizable(false),
+ &b_info.clone()->set_is_resizable(false),
+ &output_info.clone()->set_is_resizable(false),
+ output_stage);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
+// clang-format on
+// *INDENT-ON*
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_cases))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_cases))
{
// Validate output
validate(Accessor(_target), _reference);
}
TEST_SUITE(BoundedReLu)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int8_scale_relu_cases))
{
// Validate output
validate(Accessor(_target), _reference);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
-{
- // Validate output
- validate(Accessor(_target), _reference);
-}
TEST_SUITE_END() // BoundedReLu
-TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale
+TEST_SUITE_END() // QASYMM8_SIGNED
+
+TEST_SUITE_END() // QuantizeDownInt32Scale
TEST_SUITE(QuantizeDownInt32ToUint8ScaleByFixedPoint)
TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);
// Create and configure function
- FunctionType output_stage;
- output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max);
+ FunctionType output_stage;
+ GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
+ output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage_info.gemmlowp_offset = result_offset;
+ output_stage_info.gemmlowp_multiplier = result_mult_int;
+ output_stage_info.gemmlowp_shift = result_shift;
+ output_stage_info.gemmlowp_min_bound = min;
+ output_stage_info.gemmlowp_max_bound = max;
+ output_stage_info.output_data_type = DataType::QASYMM8;
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info);
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
SimpleTensor<uint8_t> _reference{};
};
+template <typename TensorType, typename AccessorType, typename FunctionType>
+class GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ _target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
+ _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_int_distribution<> distribution(-6000, 6000);
+ library->fill(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, 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, DataType::QASYMM8_SIGNED, 1);
+
+ // Create and configure function
+ FunctionType output_stage;
+ GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
+ output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
+ output_stage_info.gemmlowp_offset = result_offset;
+ output_stage_info.gemmlowp_multiplier = result_mult_int;
+ output_stage_info.gemmlowp_shift = result_shift;
+ output_stage_info.gemmlowp_min_bound = min;
+ output_stage_info.gemmlowp_max_bound = max;
+ output_stage_info.output_data_type = DataType::QASYMM8_SIGNED;
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_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<int8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, 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<int32_t> result_mult_int_vec = { result_mult_int };
+ const std::vector<int32_t> result_shift_vec = { result_shift };
+
+ if(add_bias)
+ {
+ // Fill bias
+ fill(b, 1);
+
+ return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ }
+ else
+ {
+ return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ }
+ }
+
+ TensorType _target{};
+ SimpleTensor<int8_t> _reference{};
+};
+
template <typename TensorType, typename AccessorType, typename FunctionType>
class GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture : public framework::Fixture
{
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