Port NEGEMMLowp Part 1
Details:
Port NEGEMMLowpQuantizeDownInt32ScaleKernel to CpuGemmLowpQuantizeDownInt32ScaleKernel
Port NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
Port NEGEMMLowpOutputStage functions to CpuGemmLowpOutputStage operators
Partially Resolves: COMPMID-4403
Change-Id: I6d5f45e43f35d731d564ed3b5c0e804d2a318fb1
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5833
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
index fa5f5e3..232344e 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
@@ -25,7 +25,7 @@
#define ARM_COMPUTE_NEGEMMLOWPOUTPUTSTAGE_H
#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/NEON/INESimpleFunctionNoBorder.h"
+#include "arm_compute/runtime/IFunction.h"
/** This file contains all available output stages for GEMMLowp.
*
@@ -39,237 +39,17 @@
{
class ITensor;
class ITensorInfo;
-
-/** Basic function to execute NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint.
- *
- * NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint depends on 3 parameters:
- *
- * result_fixedpoint_multiplier, result_shift, result_offset_after_shift
- *
- * The final result is:
- *
- * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift
- *
- * where FixedPointMul(x, y) is the nearest integer to the following
- * mathematical expression, evaluated without overflow or intermediate rounding:
- *
- * (x * y) / 2^31
- *
- * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
- *
- * In case the bias tensor is provided, the final result is:
- *
- * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
- *
- * This function calls the following kernels:
- *
- * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint : public INESimpleFunctionNoBorder
-{
-public:
- /** Constructor */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint() = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &operator=(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint(NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &&) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &operator=(NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint &&) = delete;
- /** Default destructor */
- ~NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint();
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8
- * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result after the fixed point multiplication
- * @param[in] result_offset_after_shift 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. Defaults to the minimum possible 32-bit signed integer.
- * @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.
- */
- void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_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 NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- *
- * @param[in] input Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. 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. Defaults to the minimum possible 32-bit signed integer.
- * @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.
- *
- * @return a status
- */
- 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 NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint.
- *
- * NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint depends on 3 parameters:
- *
- * result_fixedpoint_multiplier, result_shift, result_offset_after_shift
- *
- * The final result is:
- *
- * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift
- *
- * where FixedPointMul(x, y) is the nearest integer to the following
- * mathematical expression, evaluated without overflow or intermediate rounding:
- *
- * (x * y) / 2^31
- *
- * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
- *
- * In case the bias tensor is provided, the final result is:
- *
- * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
- *
- * This function calls the following kernels:
- *
- * -# @ref NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint : public INESimpleFunctionNoBorder
-{
-public:
- /** Constructor */
- NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint() = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint(const NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &operator=(const NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint(NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &&) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &operator=(NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint &&) = delete;
- /** Default destructor */
- ~NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint();
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8_SIGNED
- * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result after the fixed point multiplication
- * @param[in] result_offset_after_shift Offset to be applied to result before converting it back to QASYMM8_SIGNED
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED,
- * 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.
- */
- void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_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 NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint
- *
- * @param[in] input Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8_SIGNED
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED,
- * 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.
- *
- * @return a status
- */
- 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 NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint.
- *
- * NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint depends on 2 parameters:
- *
- * result_fixedpoint_multiplier, result_shift
- *
- * The final result is:
- *
- * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift)
- *
- * where FixedPointMul(x, y) is the nearest integer to the following
- * mathematical expression, evaluated without overflow or intermediate rounding:
- *
- * (x * y) / 2^31
- *
- * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
- *
- * In case the bias tensor is provided, the final result is:
- *
- * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
- *
- * This function calls the following kernels:
- *
- * -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint : public INESimpleFunctionNoBorder
-{
-public:
- /** Constructor */
- NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint() = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &operator=(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &&) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &operator=(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint &&) = delete;
- /** Default destructor */
- ~NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint();
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: Data type supported: QSYMM16
- * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result after the fixed point multiplication
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16.
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, 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 NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- *
- * @param[in] input Input tensor info. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor info. Data type supported: Data type supported: QSYMM16
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- *
- * @return a status
- */
- 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 GEMMLowpQuantizeDown kernels.
*
- * This function calls the following kernels:
+ * This function calls the following operators:
*
- * -# @ref NEGEMMLowpQuantizeDownInt32ScaleKernel
- * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
- * -# @ref NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
- * -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
+ * -# @ref cpu::CpuGemmLowpOutputStage
*/
-class NEGEMMLowpOutputStage : public INESimpleFunctionNoBorder
+class NEGEMMLowpOutputStage : public IFunction
{
public:
/** Constructor */
- NEGEMMLowpOutputStage() = default;
+ NEGEMMLowpOutputStage();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEGEMMLowpOutputStage(const NEGEMMLowpOutputStage &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
@@ -310,6 +90,13 @@
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info);
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ struct Impl;
+ std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /*ARM_COMPUTE_NEGEMMLOWPOUTPUTSTAGE_H */