Port NEGEMMLowp Part 2
Details:
Extend NEConvertQuantizedSignednessKernel
Port NEGEMMInterleave4x4Kernel to CpuGemmInterleave4x4Kernel
Port NEGEMMTranspose1xWKernel to CpuGemmTranspose1xWKernel
Port NEGEMMLowpMatrixAReductionKernel to CpuGemmLowpMatrixAReductionKernel
Port NEGEMMLowpMatrixBReductionKernel to CpuGemmLowpMatrixBReductionKernel
Port NEGEMMLowpOffsetContributionOutputStageKernel to CpuGemmLowpOffsetContributionOutputStageKernel
Port NEGEMMLowpOffsetContributionKernel to CpuGemmLowpOffsetContributionKernel
Resolves: COMPMID-4403
Change-Id: I3227f052f25e7b41d073bbea1da8a881fcd78b8e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5875
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h
index 69c8d7b..cd09544 100644
--- a/src/core/NEON/NEKernels.h
+++ b/src/core/NEON/NEKernels.h
@@ -33,7 +33,6 @@
#include "src/core/NEON/kernels/NEBoundingBoxTransformKernel.h"
#include "src/core/NEON/kernels/NEChannelShuffleLayerKernel.h"
#include "src/core/NEON/kernels/NECol2ImKernel.h"
-#include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h"
#include "src/core/NEON/kernels/NECropKernel.h"
#include "src/core/NEON/kernels/NEDepthToSpaceLayerKernel.h"
#include "src/core/NEON/kernels/NEFFTDigitReverseKernel.h"
@@ -41,10 +40,6 @@
#include "src/core/NEON/kernels/NEFFTScaleKernel.h"
#include "src/core/NEON/kernels/NEFillBorderKernel.h"
#include "src/core/NEON/kernels/NEFuseBatchNormalizationKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
#include "src/core/NEON/kernels/NEGatherKernel.h"
#include "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.h"
#include "src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.h"
diff --git a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h b/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h
deleted file mode 100644
index 67d5ca2..0000000
--- a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Copyright (c) 2019-2021 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_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H
-#define ARM_COMPUTE_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H
-
-#include "arm_compute/core/Types.h"
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-// Forward declarations
-class ITensor;
-
-/** Kernel to convert asymmetric signed to asymmetric signed and vice-versa */
-class NEConvertQuantizedSignednessKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEConvertQuantizedSignednessKernel";
- }
- /** Default constructor */
- NEConvertQuantizedSignednessKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers). */
- NEConvertQuantizedSignednessKernel(const NEConvertQuantizedSignednessKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers). */
- NEConvertQuantizedSignednessKernel &operator=(const NEConvertQuantizedSignednessKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEConvertQuantizedSignednessKernel(NEConvertQuantizedSignednessKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEConvertQuantizedSignednessKernel &operator=(NEConvertQuantizedSignednessKernel &&) = default;
- /** Default destructor */
- ~NEConvertQuantizedSignednessKernel() = default;
- /** Initialize the kernel's input, output.
- *
- * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED.
- * @param[out] output Destination tensor. Data types supported: opposite of @p input.
- */
- void configure(const ITensor *input, ITensor *output);
- /** Static function to check if given info will lead to a valid configuration of @ref NEConvertQuantizedSignednessKernel
- *
- * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED.
- * @param[in] output Destination tensor. Data types supported: opposite of @p input.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- const ITensor *_input;
- ITensor *_output;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H */
diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h
deleted file mode 100644
index b9a1b5e..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h
+++ /dev/null
@@ -1,92 +0,0 @@
-/*
- * Copyright (c) 2017-2021 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_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H
-#define ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** Kernel to multiply matrices
- *
- * @note @ref NEGEMMLowpMatrixMultiplyKernel low precision matrix product kernel
- * This kernel performs the following computation:
- *
- * -# Convert a values from int8 to int32
- * -# Convert b values from int8 to int32
- * -# Compute the int32 matrix product of the resulting a * b and store the result as int32
- *
- */
-class NEGEMMLowpMatrixMultiplyKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpMatrixMultiplyKernel";
- }
- /** Constructor */
- NEGEMMLowpMatrixMultiplyKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpMatrixMultiplyKernel(const NEGEMMLowpMatrixMultiplyKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpMatrixMultiplyKernel &operator=(const NEGEMMLowpMatrixMultiplyKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixMultiplyKernel(NEGEMMLowpMatrixMultiplyKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixMultiplyKernel &operator=(NEGEMMLowpMatrixMultiplyKernel &&) = default;
- /** Default destructor */
- ~NEGEMMLowpMatrixMultiplyKernel() = default;
- /** Initialise the kernel's input and output.
- *
- * The input matrices @p input0 and @p input1 must be the output of the kernels: cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel. These two
- * kernels change the layout of the original matrices to be more cache-friendly.
- *
- * @param[in] input0 Input tensor containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED
- * @param[in] input1 Input tensor containing the transposed1xW Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: S32
- */
- void configure(const ITensor *input0, const ITensor *input1, ITensor *output);
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixMultiplyKernel
- *
- * @param[in] input0 Input tensor info containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED
- * @param[in] input1 Input tensor info containing the transposed Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[in] output Output tensor info to store the result of matrix multiplication. Data type supported: S32
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- const ITensor *_input0;
- const ITensor *_input1;
- ITensor *_output;
- bool _slide_matrix_b;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H*/
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h
deleted file mode 100644
index f71929f..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (c) 2017-2021 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_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H
-#define ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** Kernel used to add the offset contribution after @ref NEGEMMLowpMatrixMultiplyKernel. The computation is performed in-place
- *
- * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel),
- * and adds to it the offset contribution of matrix A and matrix B in-place.
- *
- * The final result is:
- *
- * mm_result[i][k] = mm_result[i][k] +
- * (vector_sum_col[k] * a_offset) +
- * (vector_sum_row[i] * b_offset) +
- * (a_offset * b_offset * k)
- *
- */
-class NEGEMMLowpOffsetContributionKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpOffsetContributionKernel";
- }
- /** Constructor */
- NEGEMMLowpOffsetContributionKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpOffsetContributionKernel(const NEGEMMLowpOffsetContributionKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpOffsetContributionKernel &operator=(const NEGEMMLowpOffsetContributionKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpOffsetContributionKernel(NEGEMMLowpOffsetContributionKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpOffsetContributionKernel &operator=(NEGEMMLowpOffsetContributionKernel &&) = default;
- /** Default destructor */
- ~NEGEMMLowpOffsetContributionKernel() = default;
- /** Initialise the kernel's input and output.
- *
- * @param[in, out] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
- * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
- * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
- * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
- * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
- * @param[in] k Number of matrix A columns or Matrix B rows
- * @param[in] a_offset Offset to be added to each element of the matrix A.
- * @param[in] b_offset Offset to be added to each element of the matrix B.
- */
- void configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset);
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpOffsetContributionKernel
- *
- * @param[in] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
- * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
- * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
- * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
- * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
- * @param[in] a_offset Offset to be added to each element of the matrix A.
- * @param[in] b_offset Offset to be added to each element of the matrix B.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, int32_t a_offset, int32_t b_offset);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- const ITensor *_vector_sum_col;
- const ITensor *_vector_sum_row;
- ITensor *_mm_result;
- int32_t _a_offset;
- int32_t _b_offset;
- int32_t _k_offset;
- bool _slide_vector_sum_col;
-};
-} // namespace arm_compute
-
-#endif /* ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H */
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h
deleted file mode 100644
index 6908f37..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2019-2021 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_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H
-#define ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** Kernel used to add the offset contribution and perform the output stage after @ref NEGEMMLowpMatrixMultiplyKernel.
- *
- * The computation is performed in-place
- *
- * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel),
- * and adds to it the offset contribution of matrix A and matrix B in-place.
- *
- * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8.
- * The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8.
- *
- * For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is:
- *
- * ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift
- *
- * For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is:
- *
- * (FixedPointMul(mm_result'[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
- *
- * and mm_result'[i][k] = mm_result[i][k] +
- * (vector_sum_col[k] * a_offset) +
- * (vector_sum_row[i] * b_offset) +
- * (a_offset * b_offset * k)
- */
-
-class NEGEMMLowpOffsetContributionOutputStageKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpOffsetContributionOutputStageKernel";
- }
- /** Constructor */
- NEGEMMLowpOffsetContributionOutputStageKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpOffsetContributionOutputStageKernel(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- NEGEMMLowpOffsetContributionOutputStageKernel &operator=(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpOffsetContributionOutputStageKernel(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpOffsetContributionOutputStageKernel &operator=(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default;
- /** Default destructor */
- ~NEGEMMLowpOffsetContributionOutputStageKernel() = default;
- /** Initialise the kernel's input and output.
- *
- * @param[in] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
- * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
- * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
- * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
- * @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 mm_result.
- * @param[out] output Output tensor containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED
- * @param[in] k Number of matrix A columns or Matrix B rows
- * @param[in] a_offset Offset to be added to each element of the matrix A.
- * @param[in] b_offset Offset to be added to each element of the matrix B.
- * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters.
- */
- void configure(const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, int32_t k, int32_t a_offset, int32_t b_offset,
- GEMMLowpOutputStageInfo output_stage);
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpOffsetContributionOutputStageKernel
- *
- * @param[in] mm_result Input tensor info containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
- * @param[in] vector_sum_col Tensor info for the input row-vector of sums of all the entries in each column of matrix B.
- * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
- * @param[in] vector_sum_row Tensor info for the input row-vector of sums of all the entries in each row of matrix A.
- * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
- * @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 mm_result.
- * @param[in] output Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED
- * @param[in] a_offset Offset to be added to each element of the matrix A.
- * @param[in] b_offset Offset to be added to each element of the matrix B.
- * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, int32_t a_offset,
- int32_t b_offset,
- GEMMLowpOutputStageInfo output_stage);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- /** Function to use for the particular tensors passed to configure() */
- const ITensor *_vector_sum_col;
- const ITensor *_vector_sum_row;
- const ITensor *_bias;
- const ITensor *_mm_result;
- ITensor *_output;
- int32_t _a_offset;
- int32_t _b_offset;
- int32_t _k_offset;
- bool _slide_vector_sum_col;
- GEMMLowpOutputStageInfo _output_stage;
-};
-} // namespace arm_compute
-
-#endif /* ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H */
diff --git a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h b/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h
deleted file mode 100644
index 9be618d..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*
- * Copyright (c) 2017-2021 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_NEGEMMLOWREDUCTIONKERNEL_H
-#define ARM_COMPUTE_NEGEMMLOWREDUCTIONKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-// Forward declarations
-class ITensor;
-struct GEMMLowpReductionKernelInfo;
-
-/** Common interface for all reduction kernels */
-class INEGEMMLowpReductionKernel : public INEKernel
-{
-public:
- /** Constructor */
- INEGEMMLowpReductionKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- INEGEMMLowpReductionKernel(const INEGEMMLowpReductionKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers)*/
- INEGEMMLowpReductionKernel &operator=(const INEGEMMLowpReductionKernel &) = delete;
- /** Allow instances of this class to be moved */
- INEGEMMLowpReductionKernel(INEGEMMLowpReductionKernel &&) = default;
- /** Allow instances of this class to be moved */
- INEGEMMLowpReductionKernel &operator=(INEGEMMLowpReductionKernel &&) = default;
- /** Default destructor */
- virtual ~INEGEMMLowpReductionKernel() = default;
-
- /** Initialise the kernel's input and output.
- *
- * @param[in] input Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[out] output Output row-vector of sums of all the entries in each row/col of input tensor. Data type supported: S32
- * @param[in] info Kernel metadata:
- * - k Number of matrix columns/rows depending on the type of reduction.
- * - is_reshaped True if the matrix has been reshaped.
- * - scalar Scalar value to multiply each reduced column/row by.
- * - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
- */
- virtual void configure(const ITensor *input, ITensor *output, const GEMMLowpReductionKernelInfo &info) = 0;
-
-protected:
- const ITensor *_input;
- ITensor *_output;
- int32_t _k;
- int32_t _scalar;
- bool _mul_by_scalar;
-};
-
-/** Kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A.
- *
- * @note This stage is needed to handle the offset of matrix product
- * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
- */
-class NEGEMMLowpMatrixAReductionKernel : public INEGEMMLowpReductionKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpMatrixAReductionKernel";
- }
- /** Default constructor */
- NEGEMMLowpMatrixAReductionKernel() = default;
- /** Prevent instances of this class from being copied */
- NEGEMMLowpMatrixAReductionKernel(const NEGEMMLowpMatrixAReductionKernel &) = delete;
- /** Prevent instances of this class from being copied */
- NEGEMMLowpMatrixAReductionKernel &operator=(const NEGEMMLowpMatrixAReductionKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixAReductionKernel(NEGEMMLowpMatrixAReductionKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixAReductionKernel &operator=(NEGEMMLowpMatrixAReductionKernel &&) = default;
- /** Default destructor */
- ~NEGEMMLowpMatrixAReductionKernel() = default;
- /** Initialise the kernel's input and output.
- *
- * @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[out] vector_sum_row Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32
- * @param[in] info Kernel metadata:
- * - k (num_mtx_a_cols) Number of matrix A columns
- * - is_reshaped (is_interleaved4x4) True if the matrix A has been interleaved4x4
- * - scalar Scalar value to multiply each reduced row by.
- * - mul_byscalar True if each reduced column must be multiplied by a scalar value.
- */
- void configure(const ITensor *mtx_a, ITensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) override;
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixAReductionKernel
- *
- * @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[in] vector_sum_row Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32
- * @param[in] info Kernel metadata:
- * - k (num_mtx_a_cols) Number of matrix A columns
- * - is_reshaped (is_interleaved4x4) True if the matrix A has been interleaved4x4
- * - scalar Scalar value to multiply each reduced row by.
- * - mul_byscalar True if each reduced column must be multiplied by a scalar value.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- /** Execution of the reduction kernel specialized on the input type
- *
- * @param[in] window Execution window
- */
- template <typename T>
- void run_internal(const Window &window);
-};
-
-/** Kernel used to compute the row-vectors of sums of all the entries in each column of Matrix B.
- *
- * @note This stage is needed to handle the offset of matrix product
- * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
- */
-class NEGEMMLowpMatrixBReductionKernel : public INEGEMMLowpReductionKernel
-{
-public:
- const char *name() const override
- {
- return "NEGEMMLowpMatrixBReductionKernel";
- }
- /** Default constructor */
- NEGEMMLowpMatrixBReductionKernel() = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpMatrixBReductionKernel(const NEGEMMLowpMatrixBReductionKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEGEMMLowpMatrixBReductionKernel &operator=(const NEGEMMLowpMatrixBReductionKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixBReductionKernel(NEGEMMLowpMatrixBReductionKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEGEMMLowpMatrixBReductionKernel &operator=(NEGEMMLowpMatrixBReductionKernel &&) = default;
- /** Default destructor */
- ~NEGEMMLowpMatrixBReductionKernel() = default;
- /** Initialise the kernel's input and output.
- *
- * @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[out] vector_sum_col Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32
- * @param[in] info Kernel metadata:
- * - k (num_mtx_b_rows) Number of matrix B rows.
- * - is_reshaped (is_transposed1xW) True if the input tensor is transposed 1xW.
- * - scalar Scalar value to multiply each reduced row by.
- * - mul_byscalar True if each reduced row must be multiplied by a scalar value.
- */
- void configure(const ITensor *mtx_b, ITensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) override;
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixBReductionKernel
- *
- * @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
- * @param[in] vector_sum_col Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32
- * @param[in] info Kernel metadata:
- * - k (num_mtx_b_rows) Number of matrix B rows.
- * - is_reshaped (is_transposed1xW) True if the input tensor is transposed 1xW.
- * - scalar Scalar value to multiply each reduced row by.
- * - mul_byscalar True if each reduced row must be multiplied by a scalar value.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info);
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
-private:
- /** Execution of the reduction kernel specialized on the input type
- *
- * @param[in] window Execution window
- * @param[in] info Thread-related information
- */
- template <typename T>
- void run_internal(const Window &window, const ThreadInfo &info);
-};
-} // namespace arm_compute
-
-#endif /* ARM_COMPUTE_NEGEMMLOWREDUCTIONKERNEL_H */
diff --git a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp b/src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.cpp
similarity index 62%
rename from src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp
rename to src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.cpp
index 1f2170f..26cbb48 100644
--- a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp
+++ b/src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h"
+#include "src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -35,76 +35,73 @@
namespace arm_compute
{
+namespace cpu
+{
+namespace kernels
+{
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
// Validate output if initialized
- if(output->total_size() != 0)
+ if(dst->total_size() != 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape());
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(src->tensor_shape(), dst->tensor_shape());
}
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src, ITensorInfo *dst)
{
// Output auto inizialitation if not yet initialized
{
- const bool is_input_signed = input->data_type() == DataType::QASYMM8_SIGNED;
+ const bool is_input_signed = src->data_type() == DataType::QASYMM8_SIGNED;
const DataType dt = is_input_signed ? DataType::QASYMM8 : DataType::QASYMM8_SIGNED;
- const UniformQuantizationInfo qinfo = input->quantization_info().uniform();
+ const UniformQuantizationInfo qinfo = src->quantization_info().uniform();
const int offset_correction = is_input_signed ? -128 : 128;
const QuantizationInfo corrected_qinfo = QuantizationInfo(qinfo.scale, qinfo.offset + offset_correction);
- auto_init_if_empty(*output, input->clone()->set_data_type(dt).set_quantization_info(corrected_qinfo));
+ auto_init_if_empty(*dst, src->clone()->set_data_type(dt).set_quantization_info(corrected_qinfo));
}
- return std::make_pair(Status{}, calculate_max_window(*output));
+ return std::make_pair(Status{}, calculate_max_window(*dst));
}
} // namespace
-NEConvertQuantizedSignednessKernel::NEConvertQuantizedSignednessKernel()
- : _input(nullptr), _output(nullptr)
+void CpuConvertQuantizedSignednessKernel::configure(const ITensorInfo *src, ITensorInfo *dst)
{
-}
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst));
-void NEConvertQuantizedSignednessKernel::configure(const ITensor *input, ITensor *output)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
-
- _input = input;
- _output = output;
-
- std::pair<Status, Window> win_config = validate_and_configure_window(input->info(), output->info());
+ std::pair<Status, Window> win_config = validate_and_configure_window(src, dst);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- INEKernel::configure(win_config.second);
+ ICpuKernel::configure(win_config.second);
}
-Status NEConvertQuantizedSignednessKernel::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output)
+Status CpuConvertQuantizedSignednessKernel::validate(const ITensorInfo *src, const ITensorInfo *dst)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst));
return Status{};
}
-void NEConvertQuantizedSignednessKernel::run(const Window &window, const ThreadInfo &info)
+void CpuConvertQuantizedSignednessKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
+ auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input(_input, win_collapsed);
- Iterator output(_output, win_collapsed);
+ Iterator input(src, win_collapsed);
+ Iterator output(dst, win_collapsed);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
@@ -135,4 +132,11 @@
},
input, output);
}
+
+const char *CpuConvertQuantizedSignednessKernel::name() const
+{
+ return "CpuConvertQuantizedSignednessKernel";
+}
+} // namespace kernels
+} // namespace cpu
} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h b/src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h
new file mode 100644
index 0000000..2a8f6c3
--- /dev/null
+++ b/src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2019-2021 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_CPU_CONVERTQUANTIZEDSIGNEDNESS_KERNEL_H
+#define ARM_COMPUTE_CPU_CONVERTQUANTIZEDSIGNEDNESS_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel to convert asymmetric signed to asymmetric signed and vice-versa */
+class CpuConvertQuantizedSignednessKernel : public ICpuKernel
+{
+public:
+ CpuConvertQuantizedSignednessKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuConvertQuantizedSignednessKernel);
+ /** Initialize the kernel input and output info.
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED.
+ * @param[out] dst Destination tensor info. Data types supported: opposite of @p src.
+ */
+ void configure(const ITensorInfo *src, ITensorInfo *dst);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuConvertQuantizedSignednessKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_CONVERTQUANTIZEDSIGNEDNESS_KERNEL_H */
diff --git a/src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h b/src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h
index 8f1a543..0c55886 100644
--- a/src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h
+++ b/src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h
@@ -56,7 +56,6 @@
{
public:
CpuGemmInterleave4x4Kernel() = default;
- ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmInterleave4x4Kernel);
/** Initialise the kernel's src and dst.
*
* @param[in] src Input tensor info. Data types supported: All
diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp b/src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.cpp
similarity index 92%
rename from src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
rename to src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.cpp
index 6bcf59e..35e542f 100644
--- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.cpp
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -36,10 +36,12 @@
#include <arm_neon.h>
-using namespace arm_compute;
-
namespace arm_compute
{
+namespace cpu
+{
+namespace kernels
+{
namespace
{
void inline vector_matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, int width_out, size_t stride_b, const Window &window)
@@ -860,19 +862,16 @@
},
ina, inb, out);
}
-} // namespace
-namespace
+Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst)
{
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S8, DataType::U8);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::S8, DataType::U8);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S8, DataType::U8);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::S8, DataType::U8);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
- TensorShape in0_shape = input0->tensor_shape();
- TensorShape in1_shape = input1->tensor_shape();
- TensorShape out_shape = output->tensor_shape();
+ TensorShape in0_shape = src0->tensor_shape();
+ TensorShape in1_shape = src1->tensor_shape();
+ TensorShape out_shape = dst->tensor_shape();
// Check vector-by-matrix case
if(out_shape[1] == 1)
@@ -894,63 +893,58 @@
}
} // namespace
-NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel()
- : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true)
+void CpuGemmLowpMatrixMultiplyKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst)
{
-}
+ ARM_COMPUTE_UNUSED(src0);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst));
-void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
-
- TensorShape in1_shape = input1->info()->tensor_shape();
+ TensorShape in1_shape = src1->tensor_shape();
in1_shape.collapse(2);
- _input0 = input0;
- _input1 = input1;
- _output = output;
_slide_matrix_b = in1_shape[2] != 1;
constexpr unsigned int num_elems_processed_per_iteration_x = 16;
constexpr unsigned int num_elems_processed_per_iteration_y = 4;
Window win;
-
// Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
- if((output->info()->dimension(1) == 1))
+ if((dst->dimension(1) == 1))
{
// Configure kernel window
- win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+ win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x));
}
else
{
- win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
}
- INEKernel::configure(win);
+ ICpuKernel::configure(win);
}
-Status NEGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+Status CpuGemmLowpMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
-
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst));
return Status{};
}
-void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpMatrixMultiplyKernel::run_op(ITensorPack &tensors, 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);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+ auto src0 = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ auto src1 = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
// Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication path
- if((_output->info()->dimension(1) == 1))
+ if((dst->info()->dimension(1) == 1))
{
- const auto width_matrix_a = static_cast<int>(_input0->info()->dimension(0));
- const auto width_matrix_b = static_cast<int>(_input1->info()->dimension(0));
- const auto width_out = static_cast<int>(_output->info()->dimension(0));
- const auto in_b_stride = static_cast<int>(_input1->info()->strides_in_bytes()[1] / data_size_from_type(_input1->info()->data_type()));
+ const auto width_matrix_a = static_cast<int>(src0->info()->dimension(0));
+ const auto width_matrix_b = static_cast<int>(src1->info()->dimension(0));
+ const auto width_out = static_cast<int>(dst->info()->dimension(0));
+ const auto in_b_stride = static_cast<int>(src1->info()->strides_in_bytes()[1] / data_size_from_type(src1->info()->data_type()));
// The implementation computes 16 elements per iteration
const int window_start_x = 16 * info.thread_id;
@@ -969,18 +963,18 @@
Window win_b;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(_input1->info()->num_dimensions() >= 3)
+ if(src1->info()->num_dimensions() >= 3)
{
win_b = window;
}
win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
- Iterator ina(_input0, win_a);
- Iterator inb(_input1, win_b);
- Iterator out(_output, win_out);
+ Iterator ina(src0, win_a);
+ Iterator inb(src1, win_b);
+ Iterator out(dst, win_out);
- switch(_input0->info()->data_type())
+ switch(src0->info()->data_type())
{
case DataType::S8:
case DataType::QASYMM8_SIGNED:
@@ -1003,8 +997,8 @@
}
else
{
- const size_t in_b_stride = _input1->info()->strides_in_bytes()[1];
- const int width_b = _input1->info()->dimension(0);
+ const size_t in_b_stride = src1->info()->strides_in_bytes()[1];
+ const int width_b = src1->info()->dimension(0);
// Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
Window win_a(window);
@@ -1023,22 +1017,22 @@
win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
// The step x and step y for the output matrix has been already set using in configure()
- Iterator ina(_input0, win_a);
- Iterator inb(_input1, win_b);
- Iterator out(_output, window);
+ Iterator ina(src0, win_a);
+ Iterator inb(src1, win_b);
+ Iterator out(dst, window);
- switch(_input0->info()->data_type())
+ switch(src0->info()->data_type())
{
case DataType::S8:
case DataType::QASYMM8_SIGNED:
{
- matrix_multiply_s8(ina, inb, out, width_b, *_output->info(), window);
+ matrix_multiply_s8(ina, inb, out, width_b, *dst->info(), window);
break;
}
case DataType::U8:
case DataType::QASYMM8:
{
- matrix_multiply_u8(ina, inb, out, width_b, *_output->info(), window);
+ matrix_multiply_u8(ina, inb, out, width_b, *dst->info(), window);
break;
}
default:
@@ -1049,4 +1043,11 @@
}
}
}
-} // namespace arm_compute
+
+const char *CpuGemmLowpMatrixMultiplyKernel::name() const
+{
+ return "CpuGemmLowpMatrixMultiplyKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h b/src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h
new file mode 100644
index 0000000..77d8741
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2017-2021 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_CPU_GEMMLOWP_MATRIXMULTIPLY_KERNEL_H
+#define ARM_COMPUTE_CPU_GEMMLOWP_MATRIXMULTIPLY_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel to multiply matrices
+ *
+ * @note @ref CpuGemmLowpMatrixMultiplyKernel low precision matrix product kernel
+ * This kernel performs the following computation:
+ *
+ * -# Convert a values from int8 to int32
+ * -# Convert b values from int8 to int32
+ * -# Compute the int32 matrix product of the resulting a * b and store the result as int32
+ *
+ */
+class CpuGemmLowpMatrixMultiplyKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuGemmLowpMatrixMultiplyKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpMatrixMultiplyKernel);
+ /** Initialise the kernel's input and output.
+ *
+ * The input matrices @p src0 and @p src1 must be the output of the kernels: @ref CpuGemmInterleave4x4Kernel and @ref CpuGemmTranspose1xWKernel. These two
+ * kernels change the layout of the original matrices to be more cache-friendly.
+ *
+ * @param[in] src0 Input tensor info containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED
+ * @param[in] src1 Input tensor info containing the transposed1xW Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
+ * @param[out] dst Output tensor info to store the result of matrix multiplication. Data type supported: S32
+ */
+ void configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpMatrixMultiplyKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ bool _slide_matrix_b{ true };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_GEMMLOWP_MATRIXMULTIPLY_KERNEL_H*/
diff --git a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp b/src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp
similarity index 72%
rename from src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp
rename to src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp
index dfbfbd6..270abc8 100644
--- a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp
+++ b/src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/KernelDescriptors.h"
@@ -32,68 +32,80 @@
namespace arm_compute
{
+namespace cpu
+{
+namespace kernels
+{
namespace
{
-Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output)
+Status validate_arguments_matrix_a_reduction(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
- if(output->total_size() > 0)
+ if(dst->total_size() > 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(1), "Output vector must have length equal to the number of rows of the input matrix");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->dimension(0) != src->dimension(1), "Output vector must have length equal to the number of rows of the input matrix");
}
return Status{};
}
-Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output)
+Status validate_arguments_matrix_b_reduction(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
- if(output->total_size() > 0)
+ if(dst->total_size() > 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(0), "Output vector must have length equal to the number of columns of the input matrix");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->dimension(0) != src->dimension(0), "Output vector must have length equal to the number of columns of the input matrix");
}
return Status{};
}
} // namespace
-INEGEMMLowpReductionKernel::INEGEMMLowpReductionKernel()
- : _input(), _output(), _k(0), _scalar(0), _mul_by_scalar(false)
-{
-}
-
-void NEGEMMLowpMatrixAReductionKernel::configure(const ITensor *mtx_a, ITensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info)
+void CpuGemmLowpMatrixAReductionKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
{
// Perform validate step
- ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
- ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported");
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a->info(), vector_sum_row->info()));
- _input = mtx_a;
- _output = vector_sum_row;
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(src, dst, info));
_k = info.k;
_scalar = info.scalar;
_mul_by_scalar = info.mul_by_scalar;
+ switch(src->data_type())
+ {
+ case DataType::QASYMM8:
+ _func = &CpuGemmLowpMatrixAReductionKernel::run_internal<uint8_t>;
+ break;
+ case DataType::QASYMM8_SIGNED:
+ case DataType::QSYMM8:
+ case DataType::QSYMM8_PER_CHANNEL:
+ _func = &CpuGemmLowpMatrixAReductionKernel::run_internal<int8_t>;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
+
// Output auto initialization if not yet initialized
- auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(1)), 1, DataType::S32);
+ auto_init_if_empty(*dst, TensorShape(src->dimension(1)), 1, DataType::S32);
- Window win = calculate_max_window(*_output->info(), Steps(1));
-
- INEKernel::configure(win);
+ Window win = calculate_max_window(*dst, Steps(1));
+ ICpuKernel::configure(win);
}
-Status NEGEMMLowpMatrixAReductionKernel::validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info)
+Status CpuGemmLowpMatrixAReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
{
- ARM_COMPUTE_UNUSED(info);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(src, dst, info));
return Status{};
}
template <typename T>
-void NEGEMMLowpMatrixAReductionKernel::run_internal(const arm_compute::Window &window)
+void CpuGemmLowpMatrixAReductionKernel::run_internal(const ITensor *src, ITensor *dst, const arm_compute::Window &window)
{
// Intermediate and final accumulator types
using TIAcc = wrapper::traits::promote_t<T>;
@@ -106,15 +118,15 @@
win_input.set(Window::DimY, Window::Dimension(0, 0, 0));
win_input.set(Window::DimZ, Window::Dimension(0, 0, 0));
- Iterator in(_input, win_input);
- Iterator out(_output, collapsed_window);
+ Iterator in(src, win_input);
+ Iterator out(dst, collapsed_window);
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
auto vsum_row = wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{});
TAcc sum_row = 0;
- const T *matrix_a = reinterpret_cast<const T *>((in.ptr() + id.x() * _input->info()->strides_in_bytes()[1] + id.y() * _input->info()->strides_in_bytes()[2]));
+ const T *matrix_a = reinterpret_cast<const T *>((in.ptr() + id.x() * src->info()->strides_in_bytes()[1] + id.y() * src->info()->strides_in_bytes()[2]));
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(matrix_a));
@@ -160,36 +172,28 @@
in, out);
}
-void NEGEMMLowpMatrixAReductionKernel::run(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpMatrixAReductionKernel::run_op(ITensorPack &tensors, 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);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
- switch(_input->info()->data_type())
- {
- case DataType::QASYMM8:
- run_internal<uint8_t>(window);
- break;
- case DataType::QASYMM8_SIGNED:
- case DataType::QSYMM8:
- case DataType::QSYMM8_PER_CHANNEL:
- run_internal<int8_t>(window);
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported data type");
- }
+ auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ (this->*_func)(src, dst, window);
}
-void NEGEMMLowpMatrixBReductionKernel::configure(const ITensor *mtx_b, ITensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info)
+const char *CpuGemmLowpMatrixAReductionKernel::name() const
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col);
- ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported");
+ return "CpuGemmLowpMatrixAReductionKernel";
+}
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b->info(), vector_sum_col->info()));
+void CpuGemmLowpMatrixBReductionKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(src, dst, info));
- _input = mtx_b;
- _output = vector_sum_col;
_k = info.k;
_scalar = info.scalar;
_mul_by_scalar = info.mul_by_scalar;
@@ -197,24 +201,36 @@
// Configure kernel window
constexpr unsigned int num_elems_processed_per_iteration = 16;
+ switch(src->data_type())
+ {
+ case DataType::QASYMM8:
+ _func = &CpuGemmLowpMatrixBReductionKernel::run_internal<uint8_t>;
+ break;
+ case DataType::QASYMM8_SIGNED:
+ case DataType::QSYMM8:
+ case DataType::QSYMM8_PER_CHANNEL:
+ _func = &CpuGemmLowpMatrixBReductionKernel::run_internal<int8_t>;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
+
// Output auto initialization if not yet initialized
- auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(0)), 1, DataType::S32);
+ auto_init_if_empty(*dst, TensorShape(src->dimension(0)), 1, DataType::S32);
// Configure kernel window
- Window win = calculate_max_window_horizontal(*_output->info(), Steps(num_elems_processed_per_iteration));
- INEKernel::configure(win);
+ Window win = calculate_max_window_horizontal(*dst, Steps(num_elems_processed_per_iteration));
+ ICpuKernel::configure(win);
}
-Status NEGEMMLowpMatrixBReductionKernel::validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info)
+Status CpuGemmLowpMatrixBReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info)
{
- ARM_COMPUTE_UNUSED(info);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));
-
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(src, dst, info));
return Status{};
}
template <typename T>
-void NEGEMMLowpMatrixBReductionKernel::run_internal(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpMatrixBReductionKernel::run_internal(const ITensor *src, ITensor *dst, const Window &window, const ThreadInfo &info)
{
// Intermediate and final accumulator types
using TIAcc = wrapper::traits::promote_t<T>;
@@ -223,8 +239,8 @@
Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimY);
const auto vec_scalar = wrapper::vdup_n(static_cast<TAcc>(_scalar), wrapper::traits::vector_128_tag{});
- const auto width_matrix_b = static_cast<int>(_input->info()->dimension(0));
- const auto in_b_stride = static_cast<int>(_input->info()->strides_in_bytes()[1]);
+ const auto width_matrix_b = static_cast<int>(src->info()->dimension(0));
+ const auto in_b_stride = static_cast<int>(src->info()->strides_in_bytes()[1]);
// The implementation computes 16 elements per iteration
const int window_start_x = 16 * info.thread_id;
@@ -239,8 +255,8 @@
win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
win_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
- Iterator inb(_input, win_in);
- Iterator out(_output, win_out);
+ Iterator inb(src, win_in);
+ Iterator out(dst, win_out);
execute_window_loop(win_out, [&](const Coordinates & id)
{
@@ -258,7 +274,7 @@
wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{})
};
- const auto *matrix_b = reinterpret_cast<const T *>(inb.ptr() + id.y() * _input->info()->strides_in_bytes()[2]);
+ const auto *matrix_b = reinterpret_cast<const T *>(inb.ptr() + id.y() * src->info()->strides_in_bytes()[2]);
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(matrix_b));
@@ -359,24 +375,22 @@
inb, out);
}
-void NEGEMMLowpMatrixBReductionKernel::run(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpMatrixBReductionKernel::run_op(ITensorPack &tensors, 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);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
- switch(_input->info()->data_type())
- {
- case DataType::QASYMM8:
- run_internal<uint8_t>(window, info);
- break;
- case DataType::QASYMM8_SIGNED:
- case DataType::QSYMM8:
- case DataType::QSYMM8_PER_CHANNEL:
- run_internal<int8_t>(window, info);
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported data type");
- }
+ auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ (this->*_func)(src, dst, window, info);
}
-} // namespace arm_compute
+
+const char *CpuGemmLowpMatrixBReductionKernel::name() const
+{
+ return "CpuGemmLowpMatrixBReductionKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h b/src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h
new file mode 100644
index 0000000..106980f
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h
@@ -0,0 +1,157 @@
+/*
+ * Copyright (c) 2017-2021 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_CPU_GEMMLOWP_REDUCTION_KERNEL_H
+#define ARM_COMPUTE_CPU_GEMMLOWP_REDUCTION_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+// Forward declarations
+struct GEMMLowpReductionKernelInfo;
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A.
+ *
+ * @note This stage is needed to handle the offset of matrix product
+ * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
+ */
+class CpuGemmLowpMatrixAReductionKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuGemmLowpMatrixAReductionKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpMatrixAReductionKernel);
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] src Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
+ * @param[out] dst Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32
+ * @param[in] info Kernel metadata:
+ * - k (num_mtx_a_cols) Number of matrix A columns
+ * - is_reshaped (is_interleaved4x4) True if the matrix A has been interleaved4x4
+ * - scalar Scalar value to multiply each reduced row by.
+ * - mul_byscalar True if each reduced column must be multiplied by a scalar value.
+ */
+ void configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpMatrixAReductionKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ /** Execution of the reduction kernel specialized on the input type
+ *
+ * @param[in] src Input tensor
+ * @param[in] dst Output tensor
+ * @param[in] window Execution window
+ */
+ template <typename T>
+ void run_internal(const ITensor *src, ITensor *dst, const Window &window);
+
+ /** Common signature for all reduction functions
+ *
+ * @param[in] src Input tensor
+ * @param[out] dst Output tensor
+ * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
+ */
+ using CpuGemmLowpMatrixAReductionKernelPtr = void (CpuGemmLowpMatrixAReductionKernel::*)(const ITensor *src, ITensor *dst, const Window &window);
+
+ CpuGemmLowpMatrixAReductionKernelPtr _func{ nullptr };
+ int32_t _k{ 0 };
+ int32_t _scalar{ 0 };
+ bool _mul_by_scalar{ false };
+};
+
+/** Kernel used to compute the row-vectors of sums of all the entries in each column of Matrix B.
+ *
+ * @note This stage is needed to handle the offset of matrix product
+ * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
+ */
+class CpuGemmLowpMatrixBReductionKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuGemmLowpMatrixBReductionKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpMatrixBReductionKernel);
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] src Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
+ * @param[out] dst Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32
+ * @param[in] info Kernel metadata:
+ * - k (num_mtx_b_rows) Number of matrix B rows.
+ * - is_reshaped (is_transposed1xW) True if the input tensor is transposed 1xW.
+ * - scalar Scalar value to multiply each reduced row by.
+ * - mul_byscalar True if each reduced row must be multiplied by a scalar value.
+ */
+ void configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpMatrixBReductionKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ /** Execution of the reduction kernel specialized on the input type
+ *
+ * @param[in] src Input tensor
+ * @param[in] dst Output tensor
+ * @param[in] window Execution window
+ * @param[in] info Thread-related information
+ */
+ template <typename T>
+ void run_internal(const ITensor *src, ITensor *dst, const Window &window, const ThreadInfo &info);
+
+ /** Common signature for all reduction functions
+ *
+ * @param[in] src Input tensor
+ * @param[out] dst Output tensor
+ * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
+ */
+ using CpuGemmLowpMatrixBReductionKernelPtr = void (CpuGemmLowpMatrixBReductionKernel::*)(const ITensor *src, ITensor *dst, const Window &window, const ThreadInfo &info);
+
+ CpuGemmLowpMatrixBReductionKernelPtr _func{ nullptr };
+ int32_t _k{ 0 };
+ int32_t _scalar{ 0 };
+ bool _mul_by_scalar{ false };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_GEMMLOWP_REDUCTION_KERNEL_H */
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.cpp
similarity index 88%
rename from src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
rename to src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.cpp
index 867beca..9b1bf08 100644
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.cpp
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -38,6 +38,10 @@
namespace arm_compute
{
+namespace cpu
+{
+namespace kernels
+{
namespace
{
Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
@@ -354,26 +358,16 @@
}
} // namespace
-NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel()
- : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true)
-{
-}
-
-void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
+void CpuGemmLowpOffsetContributionKernel::configure(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
{
// Perform validate step
+ ARM_COMPUTE_UNUSED(vector_sum_row);
ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
- vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
- vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
- a_offset, b_offset)); // NOLINT
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
- _vector_sum_col = vector_sum_col;
- _vector_sum_row = vector_sum_row;
- _mm_result = mm_result;
- _a_offset = a_offset;
- _b_offset = b_offset;
- _k_offset = a_offset * b_offset * k;
+ _a_offset = a_offset;
+ _b_offset = b_offset;
+ _k_offset = a_offset * b_offset * k;
// If a_offset == 0, vector_sum_col can be a nullptr
if(a_offset != 0)
@@ -381,33 +375,43 @@
// Check if vector_sum_col_shape should be slidden or not
// Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
+ _slide_vector_sum_col = vector_sum_col->tensor_shape().num_dimensions() > 1;
}
// Configure kernel window
- Window win = calculate_max_window(*mm_result->info(), Steps());
- INEKernel::configure(win);
+ Window win = calculate_max_window(*mm_result, Steps());
+ ICpuKernel::configure(win);
}
-Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
- int32_t a_offset, int32_t b_offset)
+Status CpuGemmLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
+ int32_t a_offset, int32_t b_offset)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
-
return Status{};
}
-void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpOffsetContributionKernel::run_op(ITensorPack &tensors, 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);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+ auto vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ auto mm_result = tensors.get_tensor(TensorType::ACL_DST);
// Check if input is a 3D reinterpretation
- const bool reinterpret_as_3d = _vector_sum_row != nullptr
- && _mm_result->info()->num_dimensions() > 1
- && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x();
+ const bool reinterpret_as_3d = vector_sum_row != nullptr
+ && mm_result->info()->num_dimensions() > 1
+ && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();
- run_offset_contribution(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d);
+ run_offset_contribution(window, mm_result, vector_sum_col, vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d);
}
-} // namespace arm_compute
+
+const char *CpuGemmLowpOffsetContributionKernel::name() const
+{
+ return "CpuGemmLowpOffsetContributionKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h
new file mode 100644
index 0000000..f23a46c
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2017-2021 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_CPU_GEMMLOWP_OFFSETCONTRIBUTION_KERNEL_H
+#define ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel used to add the offset contribution after @ref CpuGemmLowpMatrixMultiplyKernel. The computation is performed in-place
+ *
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel),
+ * and adds to it the offset contribution of matrix A and matrix B in-place.
+ *
+ * The final result is:
+ *
+ * mm_result[i][k] = mm_result[i][k] +
+ * (vector_sum_col[k] * a_offset) +
+ * (vector_sum_row[i] * b_offset) +
+ * (a_offset * b_offset * k)
+ *
+ */
+class CpuGemmLowpOffsetContributionKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuGemmLowpOffsetContributionKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpOffsetContributionKernel);
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in, out] mm_result Input tensor containing the result of @ref CpuGemmLowpMatrixMultiplyKernel. Data type supported: S32
+ * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
+ * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
+ * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] k Number of matrix A columns or Matrix B rows
+ * @param[in] a_offset Offset to be added to each element of the matrix A.
+ * @param[in] b_offset Offset to be added to each element of the matrix B.
+ */
+ void configure(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpOffsetContributionKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, int32_t a_offset, int32_t b_offset);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ int32_t _a_offset{ 0 };
+ int32_t _b_offset{ 0 };
+ int32_t _k_offset{ 0 };
+ bool _slide_vector_sum_col{ true };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_KERNEL_H */
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
similarity index 91%
rename from src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
rename to src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
index dfed7f0..332ce6f 100644
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
+++ b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -37,12 +37,13 @@
#include "src/core/helpers/WindowHelpers.h"
#include <arm_neon.h>
-#include <cstddef>
-#include <cstdint>
-#include <map>
namespace arm_compute
{
+namespace cpu
+{
+namespace kernels
+{
namespace
{
inline int32x4x4_t load_results_input(const Iterator &mm_result_it, int32_t x)
@@ -836,53 +837,22 @@
return Status{};
}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *output)
-{
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output, mm_result->clone()->set_data_type(DataType::QASYMM8));
-
- // Configure kernel window
- Window win = calculate_max_window(*mm_result, Steps());
-
- // 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 1 and so update_window_and_padding() can be skipped
-
- return std::make_pair(Status{}, win);
-}
} // namespace
-NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageKernel()
- : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _bias(nullptr), _mm_result(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true),
- _output_stage(GEMMLowpOutputStageInfo())
-
+void CpuGemmLowpOffsetContributionOutputStageKernel::configure(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row, const ITensorInfo *bias, ITensorInfo *dst,
+ int32_t k, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage)
{
-}
-
-void NEGEMMLowpOffsetContributionOutputStageKernel::configure(const ITensor *mm_result, const ITensor *vector_sum_col,
- const ITensor *vector_sum_row, const ITensor *bias, ITensor *output,
- int32_t k, int32_t a_offset, int32_t b_offset,
- GEMMLowpOutputStageInfo output_stage)
-{
+ ARM_COMPUTE_UNUSED(vector_sum_row, bias);
// Perform validate step
- ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, dst, a_offset, b_offset, output_stage));
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
- vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
- vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
- bias != nullptr ? bias->info() : nullptr, // NOLINT
- output->info(), a_offset, b_offset, output_stage)); // NOLINT
-
- _vector_sum_col = vector_sum_col;
- _vector_sum_row = vector_sum_row;
- _bias = bias;
- _mm_result = mm_result;
- _output = output;
- _a_offset = a_offset;
- _b_offset = b_offset;
- _k_offset = a_offset * b_offset * k;
- _output_stage = output_stage;
+ _a_offset = a_offset;
+ _b_offset = b_offset;
+ _k_offset = a_offset * b_offset * k;
+ _output_stage = output_stage;
// If a_offset == 0, vector_sum_col can be a nullptr
if(a_offset != 0)
@@ -890,40 +860,51 @@
// Check if vector_sum_col_shape should be slidden or not
// Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
+ _slide_vector_sum_col = vector_sum_col->tensor_shape().num_dimensions() > 1;
}
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*dst, mm_result->clone()->set_data_type(DataType::QASYMM8));
+
// Configure kernel window
- auto win_config = validate_and_configure_window(mm_result->info(), output->info());
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- INEKernel::configure(win_config.second);
+ Window win = calculate_max_window(*mm_result, Steps());
+
+ // 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 1 and so update_window_and_padding() can be skipped
+ ICpuKernel::configure(win);
}
-Status NEGEMMLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col,
- const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output,
- int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+Status CpuGemmLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output,
+ int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), output->clone().get()).first);
return Status{};
}
-void NEGEMMLowpOffsetContributionOutputStageKernel::run(const Window &window, const ThreadInfo &info)
+void CpuGemmLowpOffsetContributionOutputStageKernel::run_op(ITensorPack &tensors, 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);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+ auto mm_result = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ auto vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_2);
+ auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_3);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
PixelValue type_min{};
PixelValue type_max{};
- std::tie(type_min, type_max) = get_min_max(_output->info()->data_type());
+ std::tie(type_min, type_max) = get_min_max(dst->info()->data_type());
int32_t type_min_int = type_min.get<int32_t>();
int32_t type_max_int = type_max.get<int32_t>();
- const bool reinterpret_as_3d = _vector_sum_row != nullptr
- && _mm_result->info()->num_dimensions() > 1
- && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x();
+ const bool reinterpret_as_3d = vector_sum_row != nullptr
+ && mm_result->info()->num_dimensions() > 1
+ && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();
const bool is_bounded_relu = !(_output_stage.gemmlowp_min_bound <= type_min_int && _output_stage.gemmlowp_max_bound >= type_max_int);
@@ -931,29 +912,35 @@
const bool is_fixed_point = _output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN;
// Check if symmetric per-channel execution
- const bool is_signed = _output->info()->data_type() == DataType::QASYMM8_SIGNED;
+ const bool is_signed = dst->info()->data_type() == DataType::QASYMM8_SIGNED;
// Check if symmetric per-channel execution
const bool is_symm = _output_stage.is_quantized_per_channel;
if(is_symm)
{
- run_offset_contribution_output_stage_symm(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage_symm(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
else
{
if(is_signed)
{
- run_offset_contribution_output_stage<int8_t>(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage<int8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
else
{
- run_offset_contribution_output_stage<uint8_t>(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage<uint8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
}
}
+const char *CpuGemmLowpOffsetContributionOutputStageKernel::name() const
+{
+ return "CpuGemmLowpOffsetContributionOutputStageKernel";
+}
+} // namespace kernels
+} // namespace cpu
} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h
new file mode 100644
index 0000000..404f2c9
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2019-2021 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_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H
+#define ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H
+
+#include "arm_compute/core/KernelDescriptors.h"
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel used to add the offset contribution and perform the output stage after @ref CpuGemmLowpMatrixMultiplyKernel.
+ *
+ * The computation is performed in-place
+ *
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel),
+ * and adds to it the offset contribution of matrix A and matrix B in-place.
+ *
+ * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8.
+ * The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8.
+ *
+ * For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is:
+ *
+ * ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift
+ *
+ * For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is:
+ *
+ * (FixedPointMul(mm_result'[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
+ *
+ * and mm_result'[i][k] = mm_result[i][k] +
+ * (vector_sum_col[k] * a_offset) +
+ * (vector_sum_row[i] * b_offset) +
+ * (a_offset * b_offset * k)
+ */
+
+class CpuGemmLowpOffsetContributionOutputStageKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuGemmLowpOffsetContributionOutputStageKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpOffsetContributionOutputStageKernel);
+ /** Initialise the kernel inputs and output.
+ *
+ * @param[in] mm_result Input tensor info containing the result of @ref CpuGemmLowpMatrixMultiplyKernel. Data type supported: S32
+ * @param[in] vector_sum_col Input row-vector tensor info of sums of all the entries in each column of matrix B.
+ * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] vector_sum_row Input row-vector tensor info of sums of all the entries in each row of matrix A.
+ * @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 mm_result.
+ * @param[out] dst Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED
+ * @param[in] k Number of matrix A columns or Matrix B rows
+ * @param[in] a_offset Offset to be added to each element of the matrix A.
+ * @param[in] b_offset Offset to be added to each element of the matrix B.
+ * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters.
+ */
+ void configure(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, ITensorInfo *dst, int32_t k, int32_t a_offset,
+ int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpOffsetContributionOutputStageKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *dst, int32_t a_offset,
+ int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ /** Function to use for the particular tensors passed to configure() */
+ int32_t _a_offset{ 0 };
+ int32_t _b_offset{ 0 };
+ int32_t _k_offset{ 0 };
+ bool _slide_vector_sum_col{ true };
+ GEMMLowpOutputStageInfo _output_stage{ GEMMLowpOutputStageInfo() };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H */
diff --git a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.h b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.h
index f3cdbdc..ca5e1b4 100644
--- a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.h
+++ b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ScaleKernel.h
@@ -38,7 +38,7 @@
{
/** 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.
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel), 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
diff --git a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h
index 7a1197d..e360e65 100644
--- a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h
+++ b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h
@@ -38,7 +38,7 @@
{
/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QSYMM16
*
- * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 value.
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 value.
* The following computations will be performed by the kernel:
*
* -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier
diff --git a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h
index 9ebb529..9c213ab 100644
--- a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h
+++ b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h
@@ -38,7 +38,7 @@
{
/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8_SIGNED
*
- * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8_SIGNED value.
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel), and processes it to obtain the final 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
diff --git a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
index 312cad9..13b30f3 100644
--- a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
+++ b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
@@ -38,7 +38,7 @@
{
/** 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.
+ * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8 value.
* The following computations will be performed by the kernel:
*
* -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier