diff options
Diffstat (limited to 'arm_compute/runtime')
3 files changed, 52 insertions, 31 deletions
diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h index 04ce1cf635..8dfb6c86c0 100644 --- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h @@ -78,7 +78,8 @@ public: * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @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[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. @@ -98,7 +99,8 @@ public: * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input. * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. @@ -120,7 +122,8 @@ public: * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h index 017bf78938..3392f11b06 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h @@ -60,7 +60,7 @@ public: /** Set the input and output tensors. * * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. - * Data type supported: QASYMM8/F16/F32. + * Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. * @param[out] output Destination tensor. Data types supported: Same as @p weights. * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout @@ -69,7 +69,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayerReshapeWeights * * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. - * Data type supported: QASYMM8/F16/F32. + * Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. * @param[in] output Destination tensor. Data types supported: Same as @p weights. * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout @@ -168,7 +168,8 @@ public: * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @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[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. @@ -187,7 +188,8 @@ public: * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @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[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. @@ -212,7 +214,7 @@ 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] weights Weights tensor. Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. * @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, @@ -225,12 +227,12 @@ private: const ActivationLayerInfo &act_info); /** 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] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * @param[in] input Input tensor info. Data types supported: QASYMM8/F16/F32. + * @param[in] weights Weights tensor info. Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8. + * @param[in] biases Biases tensor info. 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] output Output tensor info. 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 Depth of GEMM 3D * @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h index 6aacbf6abd..b364653a36 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h @@ -24,6 +24,7 @@ #ifndef __ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ #define __ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ +#include "arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" @@ -49,6 +50,7 @@ class ICLTensor; * -# @ref CLGEMMLowpMatrixBReductionKernel (if the offset of matrix A is not 0) * -# @ref CLGEMMLowpOffsetContributionKernel (if gemm_info.gemmlowp_output_stage == NONE) * -# @ref CLGEMMLowpOffsetContributionOutputStageKernel (if gemm_info.gemmlowp_output_stage != NONE) + * -# @ref CLDepthConvertLayerKernel * */ class CLGEMMLowpMatrixMultiplyCore : public IFunction @@ -84,10 +86,10 @@ public: 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] 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] a First input tensor info (Matrix A). Data type supported: QASYMM8. + * @param[in] b Second input tensor info (Matrix B). Data type supported: same as @p a + * @param[in] c Third input tensor info (Matrix C). It can be a nullptr. Data type supported: S32 + * @param[in] output Output tensor info. 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 * @@ -100,7 +102,10 @@ public: void prepare() override; private: - MemoryGroup _memory_group; + MemoryGroup _memory_group; + + // Kernels used + CLDepthConvertLayerKernel _weights_to_qasymm8; CLGEMMLowpMatrixMultiplyKernel _mm_midgard_kernel; CLGEMMLowpMatrixMultiplyNativeKernel _mm_native_kernel; CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel _mm_reshaped_only_rhs_kernel; @@ -109,18 +114,29 @@ private: 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_b; - CLTensor _mm_result_s32; - const ICLTensor *_original_b; - int32_t _a_offset; - int32_t _b_offset; - bool _is_gemm_reshaped; - bool _is_midgard; - bool _reshape_b_only_on_first_run; - bool _is_prepared; - bool _fuse_output_stage; + + // Temporary tensors + CLTensor _qasymm8_weights; + CLTensor _vector_sum_col; + CLTensor _vector_sum_row; + CLTensor _tmp_b; + CLTensor _mm_result_s32; + CLTensor _gemm_output_stage_multipliers; + CLTensor _gemm_output_stage_shifts; + + // Tensor pointers + const ICLTensor *_matrix_a; + const ICLTensor *_original_b; + const ICLTensor *_output; + + int32_t _a_offset; + int32_t _b_offset; + bool _is_gemm_reshaped; + bool _is_midgard; + bool _reshape_b_only_on_first_run; + bool _is_prepared; + bool _fuse_output_stage; + bool _convert_to_qasymm8; }; -} +} // namespace arm_compute #endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ */
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