From d1f54767fc9d6398a5eea38e639dd0ce3df8e5d8 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Fri, 19 Jul 2019 09:54:47 +0100 Subject: COMPMID-1979: Fuse Activation Function in CLGEMM - part 3 Fused beta*bias in in the old cl gemm kernels Fused activation function in the old cl gemm kernels Change-Id: I695fb9189e6d4792010abd256784624982d17d79 Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/1587 Reviewed-by: Giuseppe Rossini Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- .../core/CL/kernels/CLGEMMMatrixMultiplyKernel.h | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) (limited to 'arm_compute/core/CL') diff --git a/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h index 724a7d67e6..8e6e07973c 100644 --- a/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h +++ b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h @@ -30,13 +30,12 @@ namespace arm_compute { class ICLTensor; -/** OpenCL kernel to multiply two input matrices "A" and "B" and add a vector "C" if provided. All elements of the output matrix will be multiplied by alpha. In case vector C is passed, it will be added to the previous result (a broadcast addition will be performed). +/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result. + * For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object * * @note If the input tensors @p input0 and @p input1 have been reshaped respectively with @ref CLGEMMReshapeLHSMatrixKernel" and @ref CLGEMMReshapeRHSMatrixKernel, * the flag @p is_interleaved_transposed must be set to true * - * @attention Vector C (@p input2) must be 1D. A broadcast addition is performed. - * * @attention @p input1 tensor must have at least 2 dimensions (matrix) * */ @@ -57,22 +56,23 @@ public: * * @param[in] input0 Input tensor containing the Matrix A. Data types supported: F16/F32 * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0 - * @param[in] input2 Input tensor containing the Vector C. Can be nullptr. Data type supported: same as @p input0 + * @param[in] input2 Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p input0 * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 * @param[in] alpha Weight of the matrix product * @param[in] beta (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported. * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy + * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication * */ void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f, - bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false); + bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyKernel * * @param[in] input0 Input tensor containing the Matrix A info. Data types supported: F16/F32 * @param[in] input1 Input tensor containing the Matrix B info. Data type supported: same as @p input0 - * @param[in] input2 Input tensor containing the Vector C info. Can be nullptr. Data type supported: same as @p input0 + * @param[in] input2 Input tensor containing the Matrix C (bias) info. Can be nullptr. Data type supported: same as @p input0 * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 * @param[in] alpha Weight of the matrix product * @param[in] beta Weight of vector C. Default value is 0. Only beta = 1 is currently supported. @@ -80,11 +80,12 @@ public: * @param[in] reshape_info GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped * @param[in] gpu_target GPU Target * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy + * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication * * @return a status */ static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, - bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false); + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -97,7 +98,8 @@ public: bool _slide_matrix_b; bool _reinterpret_input_as_3d; bool _reinterpret_output_as_3d; - bool _has_vec_c; + bool _add_bias; + bool _broadcast_bias; }; } // namespace arm_compute #endif /* __ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H__ */ -- cgit v1.2.1