COMPMID-1413 - Improve the performance of GEMMLowp with 8 bit dot product on OpenCL
COMPMID-1424 - Add dot product support for CLDepthwise QASYMM8 3x3 NHWC non-unit stride
With this patch we are able to improve the performance of MobileNet v1-qasymm8 by 37 %
Tried to use the dot product instruction in CLDepthwise QASYMM8 3x3 NHWC non-unit stride
but I have not seen any benefit (maybe because we have few arithemtic operation and we
do not have more load instructions). However Depthwise convolution has been improved by
30%
Change-Id: Id768a99c2e53a04276707e427af5d0ec93419ada
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155082
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
index 48b8801..fbf0c08 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
@@ -157,43 +157,48 @@
private:
/** Configures the appropriate matrix multiply routine
*
- * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
- * @param[in] weights Weights tensor. Data type supported: Same as @p input.
- * @param[in, out] output Output tensor. Data types supported: Same as @p input,
- * except for input of QASYMM8 type where output should be of S32 type.
- * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
+ * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights Weights tensor. Data type supported: Same as @p input.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[in, out] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ * @param[in] gemmlowp_output_stage GEMMLowp output stage info
+ * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
*/
- void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, int gemm_3d_depth = 1);
+ void configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth = 1);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer matrix multiply routines
*
- * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
- * @param[in] weights Weights tensor. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data types supported: Same as @p input,
- * except for input of QASYMM8 type where output should be of S32 type.
- * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
- * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
+ * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights Weights tensor. Data type supported: Same as @p input.
+ * @param[in] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[in] gemmlowp_output_stage GEMMLowp output stage info
+ * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
+ * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
*
* @return a status
*/
- static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth = 1, bool skip_im2col = false);
+ static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
+ int gemm_3d_depth = 1, bool skip_im2col = false);
private:
- CLMemoryGroup _memory_group;
- CLConvolutionLayerReshapeWeights _reshape_weights;
- CLIm2ColKernel _im2col_kernel;
- CLGEMM _mm_gemm;
- CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
- CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat _gemmlowp_output_stage;
- CLCol2ImKernel _col2im_kernel;
- CLActivationLayer _activationlayer_function;
- CLArithmeticAdditionKernel _add_bias_kernel;
+ CLMemoryGroup _memory_group;
+ CLConvolutionLayerReshapeWeights _reshape_weights;
+ CLIm2ColKernel _im2col_kernel;
+ CLGEMM _mm_gemm;
+ CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
+ CLCol2ImKernel _col2im_kernel;
+ CLActivationLayer _activationlayer_function;
+ CLArithmeticAdditionKernel _add_bias_kernel;
const ICLTensor *_original_weights;
CLTensor _im2col_output;
CLTensor _weights_reshaped;
CLTensor _gemm_output;
- CLTensor _tmp_output;
DataLayout _data_layout;
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index f404ccd..82f307a 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -27,6 +27,7 @@
#include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMTranspose1xWKernel.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
@@ -45,7 +46,8 @@
* -# @ref CLGEMMLowpMatrixMultiplyKernel
* -# @ref CLGEMMLowpMatrixAReductionKernel (if the offset of matrix B is not 0)
* -# @ref CLGEMMLowpMatrixBReductionKernel (if the offset of matrix A is not 0)
- * -# @ref CLGEMMLowpOffsetContributionKernel
+ * -# @ref CLGEMMLowpOffsetContributionKernel (if gemm_info.gemmlowp_output_stage == NONE)
+ * -# @ref CLGEMMLowpOffsetContributionOutputStageKernel (if gemm_info.gemmlowp_output_stage != NONE)
*
*/
class CLGEMMLowpMatrixMultiplyCore : public IFunction
@@ -63,54 +65,60 @@
CLGEMMLowpMatrixMultiplyCore &operator=(CLGEMMLowpMatrixMultiplyCore &&) = default;
/** Initialise the kernel's inputs, output
*
- * @note GEMM_LOWP: low precision GEMM kernel
+ * @note GEMMLowp: low precision GEMM kernel. [A * B + C]
* This kernel performs the following computations:
*
* -# Convert a values from QASYMM8 to int32 and add a_offset to each of them.
* -# Convert b values from QASYMM8 to int32 add b_offset to each of them.
* -# Compute the matrix product of the resulting a * b in int32.
+ * -# Quantize to uint8 if gemm_info.gemmlowp_output_stage != NONE
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a
- * @param[out] output Output tensor. Data type supported: Data type supported: S32
+ * @param[in] c Third input tensor (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[out] output Output tensor. Data type supported: S32 or QASYMM8 if gemm_info.gemmlowp_output_stage != NONE
* @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
* if the reshape of matrix B should be executed only for the first run
*/
- void configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info = GEMMInfo());
+ void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info = GEMMInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyCore
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a
- * @param[in] output Output tensor. Data type supported: Data type supported: S32
+ * @param[in] c Third input tensor (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[in] output Output tensor. Data type supported: S32 or QASYMM8 if gemm_info.gemmlowp_output_stage != NONE
* @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
* if the reshape of matrix B should be executed only for the first run
*
* @return a status
*/
- static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
+ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
- CLMemoryGroup _memory_group;
- CLGEMMLowpMatrixMultiplyKernel _mm_kernel;
- CLGEMMInterleave4x4Kernel _mtx_a_reshape_kernel;
- CLGEMMTranspose1xWKernel _mtx_b_reshape_kernel;
- CLGEMMLowpMatrixAReductionKernel _mtx_a_reduction_kernel;
- CLGEMMLowpMatrixBReductionKernel _mtx_b_reduction_kernel;
- CLGEMMLowpOffsetContributionKernel _offset_contribution_kernel;
- CLTensor _vector_sum_col;
- CLTensor _vector_sum_row;
- CLTensor _tmp_a;
- CLTensor _tmp_b;
- const ICLTensor *_original_b;
- int32_t _a_offset;
- int32_t _b_offset;
- bool _is_interleaved_transposed;
- bool _reshape_b_only_on_first_run;
- bool _is_prepared;
+ CLMemoryGroup _memory_group;
+ CLGEMMLowpMatrixMultiplyKernel _mm_kernel;
+ CLGEMMInterleave4x4Kernel _mtx_a_reshape_kernel;
+ CLGEMMTranspose1xWKernel _mtx_b_reshape_kernel;
+ CLGEMMLowpMatrixAReductionKernel _mtx_a_reduction_kernel;
+ CLGEMMLowpMatrixBReductionKernel _mtx_b_reduction_kernel;
+ CLGEMMLowpOffsetContributionKernel _offset_contribution_kernel;
+ CLGEMMLowpOffsetContributionOutputStageKernel _offset_contribution_output_stage_kernel;
+ CLTensor _vector_sum_col;
+ CLTensor _vector_sum_row;
+ CLTensor _tmp_a;
+ CLTensor _tmp_b;
+ CLTensor _mm_result_s32;
+ const ICLTensor *_original_b;
+ int32_t _a_offset;
+ int32_t _b_offset;
+ bool _is_interleaved_transposed;
+ bool _reshape_b_only_on_first_run;
+ bool _is_prepared;
+ bool _fuse_output_stage;
};
}
#endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
index 51fcbe9..3330b40 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
@@ -131,24 +131,22 @@
* @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
* @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
- * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1)
*/
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
- int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+ int min = 0, int max = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
*
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+ * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+ * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
+ * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+ * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
- * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1)
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
};
/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat on OpenCL.