diff options
| -rw-r--r-- | arm_compute/core/CL/CLKernels.h | 1 | ||||
| -rw-r--r-- | arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h | 92 | ||||
| -rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 1 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/gemm_helpers.h | 24 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/gemmlowp.cl | 172 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp | 321 | ||||
| -rw-r--r-- | tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp | 152 | ||||
| -rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 189 |
8 files changed, 940 insertions, 12 deletions
diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h index bdf14c356b..7f625aca67 100644 --- a/arm_compute/core/CL/CLKernels.h +++ b/arm_compute/core/CL/CLKernels.h @@ -75,6 +75,7 @@ #include "arm_compute/core/CL/kernels/CLFloorKernel.h" #include "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h new file mode 100644 index 0000000000..bac2c99413 --- /dev/null +++ b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2019 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_CLGEMMLOWPMATRIXMULTIPLYNATIVEKERNEL_H__ +#define __ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYNATIVEKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to multiply matrices with QASYMM8 data type */ +class CLGEMMLowpMatrixMultiplyNativeKernel : public ICLKernel +{ +public: + /** Default Constructor */ + CLGEMMLowpMatrixMultiplyNativeKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLGEMMLowpMatrixMultiplyNativeKernel(const CLGEMMLowpMatrixMultiplyNativeKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLGEMMLowpMatrixMultiplyNativeKernel &operator=(const CLGEMMLowpMatrixMultiplyNativeKernel &) = delete; + /** Allow instances of this class to be moved */ + CLGEMMLowpMatrixMultiplyNativeKernel(CLGEMMLowpMatrixMultiplyNativeKernel &&) = default; + /** Allow instances of this class to be moved */ + CLGEMMLowpMatrixMultiplyNativeKernel &operator=(CLGEMMLowpMatrixMultiplyNativeKernel &&) = default; + /** Initialise the kernel's input and output. + * + * @param[in] input0 Input tensor containing the LHS matrix. Data type supported: QASYMM8 + * @param[in] input1 Input tensor containing the RHS matrix. Data type supported: same as @p input0 + * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: S32 + * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread + * lhs_info.m0: 2,3,4,5,6,7,8 + * lhs_info.k0: 2,3,4,8,16 + * @param[in] rhs_info RHS matrix information used to retrieve the number of columns to be processed by each thread + * rhs_info.n0: 2,3,4,8,16 + * rhs_info.k0: same as lhs_info.k0 + * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices + */ + void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info); + /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyNativeKernel + * + * @param[in] input0 Input tensor info for the LHS matrix. Data type supported: QASYMM8 + * @param[in] input1 Input tensor info for the RHS matrix. Data type supported: same as @p input0 + * @param[in] output Output tensor info. Data type supported: S32 + * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread + * lhs_info.m0: 2,3,4,5,6,7,8 + * lhs_info.k0: 2,3,4,8,16 + * @param[in] rhs_info RHS matrix information used to retrieve the number of columns to be processed by each thread + * rhs_info.n0: 2,3,4,8,16 + * rhs_info.k0: same as lhs_info.k0 + * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices + * + * @return a status + */ + static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + const GEMMReshapeInfo &gemm_info); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input0; + const ICLTensor *_input1; + ICLTensor *_output; + bool _slide_matrix_b; + bool _reinterpret_input_as_3d; + bool _reinterpret_output_as_3d; + bool _use_dummy_work_items; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYNATIVEKERNEL_H__*/ diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 4c9808d7a4..904575a53c 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -333,6 +333,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "gemmlowp_mm_bifrost_dot8", "gemmlowp.cl" }, { "gemmlowp_mm_midgard", "gemmlowp.cl" }, { "gemmlowp_mm_interleaved_transposed_midgard", "gemmlowp.cl" }, + { "gemmlowp_mm_native", "gemmlowp.cl" }, { "gemmlowp_mm_reshaped_lhs_nt_rhs_t", "gemmlowp.cl" }, { "gemmlowp_mm_reshaped_only_rhs_t", "gemmlowp.cl" }, { "gemmlowp_offset_contribution", "gemmlowp.cl" }, diff --git a/src/core/CL/cl_kernels/gemm_helpers.h b/src/core/CL/cl_kernels/gemm_helpers.h index cd2d39b433..3fd5950b01 100644 --- a/src/core/CL/cl_kernels/gemm_helpers.h +++ b/src/core/CL/cl_kernels/gemm_helpers.h @@ -430,18 +430,18 @@ #define SCALE_BLOCK(N, DATA_TYPE, BASENAME, SCALE) SCALE_BLOCK_STR(N, DATA_TYPE, BASENAME, SCALE) /** Given a set of vectors of size K0, these macros create a new vector to contain the values at index IDX_COL (with IDX_COL < N0) for all input vectors */ -#define COLUMN_VECTOR1(IDX_COL, BASENAME, B) \ - uchar BASENAME##IDX_COL = (uchar)((B##0).s##IDX_COL); -#define COLUMN_VECTOR2(IDX_COL, BASENAME, B) \ - uchar2 BASENAME##IDX_COL = (uchar2)((B##0).s##IDX_COL, (B##1).s##IDX_COL); -#define COLUMN_VECTOR3(IDX_COL, BASENAME, B) \ - uchar3 BASENAME##IDX_COL = (uchar3)((B##0).s##IDX_COL, (B##1).s##IDX_COL, (B##2).s##IDX_COL); -#define COLUMN_VECTOR4(IDX_COL, BASENAME, B) \ - uchar4 BASENAME##IDX_COL = (uchar4)((B##0).s##IDX_COL, (B##1).s##IDX_COL, (B##2).s##IDX_COL, (B##3).s##IDX_COL); -#define COLUMN_VECTOR8(IDX_COL, BASENAME, B) \ - uchar8 BASENAME##IDX_COL = (uchar8)((B##0).s##IDX_COL, (B##1).s##IDX_COL, (B##2).s##IDX_COL, (B##3).s##IDX_COL, (B##4).s##IDX_COL, (B##5).s##IDX_COL, (B##6).s##IDX_COL, (B##7).s##IDX_COL); -#define COLUMN_VECTOR16(IDX_COL, BASENAME, B) \ - uchar16 BASENAME##N0 = (uchar16)((B##0).s##IDX_COL, (B##1).s##IDX_COL, (B##2).s##IDX_COL, (B##3).s##IDX_COL, (B##4).s##IDX_COL, (B##5).s##IDX_COL, (B##6).s##IDX_COL, (B##7).s##IDX_COL, (B##8).s##IDX_COL, (B##9).s##IDX_COL, (B##A).s##IDX_COL, (B##B).s##IDX_COL, (B##C).s##IDX_COL, (B##D).s##IDX_COL, (B##E).s##IDX_COL, (B##F).s##IDX_COL); +#define COLUMN_VECTOR1(IDX_COL, BASENAME, X) \ + uchar BASENAME##IDX_COL = (uchar)((X##0).s##IDX_COL); +#define COLUMN_VECTOR2(IDX_COL, BASENAME, X) \ + uchar2 BASENAME##IDX_COL = (uchar2)((X##0).s##IDX_COL, (X##1).s##IDX_COL); +#define COLUMN_VECTOR3(IDX_COL, BASENAME, X) \ + uchar3 BASENAME##IDX_COL = (uchar3)((X##0).s##IDX_COL, (X##1).s##IDX_COL, (X##2).s##IDX_COL); +#define COLUMN_VECTOR4(IDX_COL, BASENAME, X) \ + uchar4 BASENAME##IDX_COL = (uchar4)((X##0).s##IDX_COL, (X##1).s##IDX_COL, (X##2).s##IDX_COL, (X##3).s##IDX_COL); +#define COLUMN_VECTOR8(IDX_COL, BASENAME, X) \ + uchar8 BASENAME##IDX_COL = (uchar8)((X##0).s##IDX_COL, (X##1).s##IDX_COL, (X##2).s##IDX_COL, (X##3).s##IDX_COL, (X##4).s##IDX_COL, (X##5).s##IDX_COL, (X##6).s##IDX_COL, (X##7).s##IDX_COL); +#define COLUMN_VECTOR16(IDX_COL, BASENAME, X) \ + uchar16 BASENAME##IDX_COL = (uchar16)((X##0).s##IDX_COL, (X##1).s##IDX_COL, (X##2).s##IDX_COL, (X##3).s##IDX_COL, (X##4).s##IDX_COL, (X##5).s##IDX_COL, (X##6).s##IDX_COL, (X##7).s##IDX_COL, (X##8).s##IDX_COL, (X##9).s##IDX_COL, (X##A).s##IDX_COL, (X##B).s##IDX_COL, (X##C).s##IDX_COL, (X##D).s##IDX_COL, (X##E).s##IDX_COL, (X##F).s##IDX_COL); /** Given N0 vectors of size K0, these macros create K0 vectors of size N0 which are the result of a transposition */ #define TRANSPOSE_K0X1(K0, BASENAME, B) \ diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index 0080369705..54ffea184c 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -1843,6 +1843,178 @@ __kernel void gemmlowp_mm_reshaped_only_rhs_t(IMAGE_DECLARATION(lhs), } #endif // defined(M0) && defined(N0) && defined(K0) && defined(H0) && defined(DATA_TYPE) && defined(K) +#if defined(M0) && defined(N0) && defined(K0) && defined(K) + +/** This OpenCL kernel computes the matrix multiplication between 2 matrices. + * The LHS matrix is NOT reshaped + * The RHS matrix is NOT reshaped + * + * @note The number of columns of LHS matrix must be passed at compile time using -DK (i.e. -DK=64) + * @note The number of M0 rows to process must be passed at compile time using -DM0 (i.e. -DM0=2) + * @note The number of N0 columns to process must be passed at compile time using -DN0 (i.e. -DN0=2) + * @note The number of K0 partial accumulations must be passed at compile time using -DK0 (i.e., -DK0=2) + * @note Only the following configurations of M0, N0 and K0 are currently supported: + * - M0 = 1, 2, 3, 4, 5, 6, 7, 8 + * - N0 = 2, 3, 4, 8, 16 + * - K0 = 2, 3, 4, 8, 16 + * + * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time: + * -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D + * -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D + * -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor. + * -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor + * (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix + * + * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: F16/F32 + * @param[in] lhs_stride_x Stride of the LHS reshaped matrix in X dimension (in bytes) + * @param[in] lhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] lhs_stride_y Stride of the LHS reshaped matrix in Y dimension (in bytes) + * @param[in] lhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS reshaped matrix + * @param[in] rhs_ptr Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr + * @param[in] rhs_stride_x Stride of the RHS reshaped matrix in X dimension (in bytes) + * @param[in] rhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] rhs_stride_y Stride of the RHS reshaped matrix in Y dimension (in bytes) + * @param[in] rhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix + * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr + * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix + * @param[in] lhs_stride_z Stride of the LHS reshaped matrix in Z dimension (in bytes) + * @param[in] rhs_stride_z Stride of the RHS reshaped matrix in Z dimension (in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] lhs_cross_plane_pad (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D) + * @param[in] dst_cross_plane_pad (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) + */ +__kernel void gemmlowp_mm_native(IMAGE_DECLARATION(lhs), + IMAGE_DECLARATION(rhs), + IMAGE_DECLARATION(dst), + uint lhs_stride_z, + uint rhs_stride_z, + uint dst_stride_z +#if defined(REINTERPRET_INPUT_AS_3D) + , + uint lhs_cross_plane_pad +#endif // REINTERPRET_INPUT_AS_3D +#if defined(REINTERPRET_OUTPUT_AS_3D) + , + uint dst_cross_plane_pad +#endif // REINTERPRET_OUTPUT_AS_3D + ) +{ + uint x = get_global_id(0); + uint y = get_global_id(1); + uint z = get_global_id(2); + +#if defined(DUMMY_WORK_ITEMS) + if((x * N0 >= N) || (y * M0 >= M)) + { + return; + } +#endif // defined(DUMMY_WORK_ITEMS) + + // Compute LHS matrix address + uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y; + + // Compute RHS matrix address + uint rhs_offset = rhs_offset_first_element_in_bytes + x * N0; + +#if defined(MATRIX_B_DEPTH) + // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 + rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z; +#else // defined(MATRIX_B_DEPTH) + rhs_offset += z * rhs_stride_z; +#endif // defined(MATRIX_B_DEPTH) + + REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); + REPEAT_VAR_INIT_TO_CONST(16, uint, zrhs, 0); + +#if defined(REINTERPRET_INPUT_AS_3D) + // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D + CALCULATE_Z_OFFSET(M0, uint, zlhs, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y); + + // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we + // multiply lhs_stride_z by DEPTH_GEMM3D + lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D; + +#else // defined(REINTERPRET_INPUT_AS_3D) + + // Add offset for batched GEMM + lhs_offset += z * lhs_stride_z; + +#endif // defined(REINTERPRET_INPUT_AS_3D) + + // Initialize the accumulators + REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(uint, N0), c, 0); //VEC_DATA_TYPE(uint, N0) c0=0,c1=0,c2=0,... c(M0-1)=0; + + int i = 0; + + for(; i <= (K - K0); i += K0) + { + // Load values from LHS matrix + LOAD_BLOCK(M0, K0, uchar, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs); + + // Load values from RHS matrix + LOAD_BLOCK(K0, N0, uchar, b, rhs_ptr, rhs_offset, rhs_stride_y, zrhs); + + // Transpose the values from RHS matrix + TRANSPOSE_K0XN0(K0, N0, b_t, b); + + // Partial matrix multiplication M0,N0,K0 + ARM_MM_K0XN0XM0(M0, N0, K0, a, b_t, c); + + // Update the offset + lhs_offset += K0; + rhs_offset += K0 * rhs_stride_y; + } + + // Left-over for loop + for(; i < K; ++i) + { + // Load values from LHS matrix + LOAD_BLOCK(M0, 1, uchar, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs); + + // Load values from RHS matrix + LOAD_BLOCK(1, N0, uchar, b, rhs_ptr, rhs_offset, rhs_stride_y, zrhs); + + // Transpose the values from RHS matrix + TRANSPOSE_K0XN0(1, N0, b_t, b); + + // Partial matrix multiplication M0,N0,1 + ARM_MM_K0XN0XM0(M0, N0, 1, a, b_t, c); + + // Update the offset + lhs_offset += 1; + rhs_offset += rhs_stride_y; + } + + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(int) + (y * (uint)M0 * dst_stride_y); + + REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0; + +#if defined(REINTERPRET_OUTPUT_AS_3D) + // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D + CALCULATE_Z_OFFSET(M0, uint, zout, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y); + + // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we + // multiply dst_stride_z by DEPTH_GEMM3D + dst_addr += z * dst_stride_z * DEPTH_GEMM3D; + +#else // defined(REINTERPRET_OUTPUT_AS_3D) + + // Add offset for batched GEMM + dst_addr += z * dst_stride_z; + +#endif // defined(REINTERPRET_OUTPUT_AS_3D) + + // Convert and store output block + CONVERT_STORE_BLOCK(M0, N0, int, c, dst_addr, dst_stride_y, zout); +} +#endif // defined(M0) && defined(N0) && defined(K0) && defined(K) + #if defined(COLS_A) /** OpenCL kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A. * diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp new file mode 100644 index 0000000000..fa2c544899 --- /dev/null +++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp @@ -0,0 +1,321 @@ +/* + * Copyright (c) 2019 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. + */ +#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/OpenCL.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "support/ToolchainSupport.h" + +#include <cstddef> +#include <cstdint> +#include <tuple> + +namespace arm_compute +{ +using namespace misc::shape_calculator; + +class Coordinates; + +namespace +{ +using ElementsProcessed = Steps; + +Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + const GEMMReshapeInfo &gemm_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 != rhs_info.k0); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(((lhs_info.k0 & (lhs_info.k0 - 1)) && lhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0"); + ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 > 16); + ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0"); + + const int m = gemm_info.m(); + const int n = gemm_info.n(); + const int k = gemm_info.k(); + + ARM_COMPUTE_UNUSED(m); + ARM_COMPUTE_UNUSED(n); + ARM_COMPUTE_UNUSED(k); + + ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k)); + ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != static_cast<unsigned int>(n)); + ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != static_cast<unsigned int>(k)); + if(gemm_info.reinterpret_input_as_3d()) + { + ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m)); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m)); + } + + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed) +{ + unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; + unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); + + Window win{}; + Window win_out{}; + bool window_changed = false; + + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_output_as_3d = false; + } + + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)).set_data_type(DataType::S32)); + + TensorInfo tmp_info(*output); + + if(reinterpret_output_as_3d) + { + // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // the window needs to be constructed on the 2D collapsed version of the tensor + TensorShape tmp_shape(output->tensor_shape()); + tmp_shape.collapse(2U, 1U); + tmp_info.set_tensor_shape(tmp_shape); + } + + // Configure kernel window + num_elems_processed_per_iteration_x = rhs_info.n0; + num_elems_processed_per_iteration_y = lhs_info.m0; + + // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor + // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic + const int m = reinterpret_output_as_3d ? gemm_info.m() : input0->dimension(1); + const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; + + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + + AccessWindowStatic input0_access(input0, 0, 0, + input0->dimension(0), + input0->dimension(1) + bottom_pad); + AccessWindowStatic input1_access(input1, 0, 0, + ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), + input1->dimension(1)); + AccessWindowStatic output_access(output, 0, 0, + ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), + output->dimension(1) + bottom_pad); + + window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + + output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape())); + + // Collapse along the Z direction + // This collapse needs to be here in order to tune the Z dimension of LWS + Window collapsed = win; + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); + collapsed = win.collapse(win, dimension_to_collapse); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, collapsed); +} +} // namespace + +CLGEMMLowpMatrixMultiplyNativeKernel::CLGEMMLowpMatrixMultiplyNativeKernel() + : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false) +{ +} + +void CLGEMMLowpMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + const GEMMReshapeInfo &gemm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info)); + + _input0 = input0; + _input1 = input1; + _output = output; + _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); + _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); + + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) + { + _reinterpret_input_as_3d = false; + _reinterpret_output_as_3d = false; + } + + // Check if we need to slide the matrix B + const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions(); + _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); + + ElementsProcessed num_elements_processed{}; + + // Configure kernel window + auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // Create build options + CLBuildOptions build_opts; + build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1))); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); + build_opts.add_option("-DM=" + support::cpp11::to_string(input0->info()->dimension(1))); + build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n())); + build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k())); + build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0)); + build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0)); + build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0)); + + std::string kernel_name("gemmlowp_mm_native"); + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name; + _config_id += "_"; + _config_id += dot8_supported(CLKernelLibrary::get().get_device()) ? "_dot8" : ""; + _config_id += "_"; + _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); + _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); + _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(gemm_info.k()); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += "_"; + _config_id += support::cpp11::to_string(lhs_info.m0); + _config_id += "_"; + _config_id += support::cpp11::to_string(rhs_info.n0); + _config_id += "_"; + _config_id += support::cpp11::to_string(lhs_info.k0); +} + +Status CLGEMMLowpMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info) +{ + ElementsProcessed num_elements_processed{}; + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), + input1->clone().get(), + output->clone().get(), + lhs_info, + rhs_info, + gemm_info, + num_elements_processed) + .first); + + return Status{}; +} + +void CLGEMMLowpMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + if(_input1->info()->num_dimensions() < 3) + { + // The stride_z for matrix B must be zero if we do not slice + ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); + } + + Window slice = window.first_slice_window_3D(); + Window slice_matrix_b = slice; + + slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1)); + slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); + + if(_reinterpret_input_as_3d) + { + // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3; + const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; + _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); + } + + if(_reinterpret_output_as_3d) + { + // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); + const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; + _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); + } + + do + { + Window slice_b = slice; + // 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 matrix multiplication is used to perform a convolution operation + if(!_slide_matrix_b) + { + slice_b = slice_matrix_b; + } + + unsigned int idx = 0; + add_2D_tensor_argument(idx, _input0, slice); + add_2D_tensor_argument(idx, _input1, slice_b); + add_2D_tensor_argument(idx, _output, slice); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); + enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); + } + while(window.slide_window_slice_3D(slice)); +} +} // namespace arm_compute
\ No newline at end of file diff --git a/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp new file mode 100644 index 0000000000..1fc8cc47c4 --- /dev/null +++ b/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2019 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. + */ +#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/GEMMLowpFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::misc::shape_calculator; + +// Create function for CLGEMMMatrixMultiplyNativeKernel +using CLGEMMLowpMatrixMultiplyNative = CLSynthetizeFunction<CLGEMMLowpMatrixMultiplyNativeKernel>; + +// Fixture for CLGEMMLowpMatrixMultiplyNative +using CLGEMMLowpMatrixMultiplyNativeFixture = GEMMLowpMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyNative>; + +// Fixture for CLGEMMMatrixMultiplyNative3D +using CLGEMMLowpMatrixMultiplyNative3DFixture = GEMMLowpMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyNative>; + +namespace +{ +// *INDENT-OFF* +// clang-format off +/** M values to test */ +const auto m_values = framework::dataset::make("M", 37); + +/** M_W values to test */ +const auto m_w_values = framework::dataset::make("M_W", 5); + +/** M_H values to test */ +const auto m_h_values = framework::dataset::make("M_H", 7); + +/** N values to test */ +const auto n_values = framework::dataset::make("N", 51); + +/** K values to test */ +const auto k_values = framework::dataset::make("K", 23); + +/** Batch size values to test */ +const auto b_values = framework::dataset::make("batch_size", 1, 3); + +/** M0 values to test - Precommit */ +const auto m0_values_precommit = framework::dataset::make("M0", {4, 6}); + +/** N0 values to test - Precommit */ +const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 }); + +/** K0 values to test - Precommit */ +const auto k0_values_precommit = framework::dataset::make("K0", { 4 }); + +/** M0 values to test - Nightly */ +const auto m0_values_nightly = framework::dataset::make("M0", 2, 7); + +/** N0 values to test - Nightly */ +const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 }); + +/** K0 values to test - Nightly */ +const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 }); +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(GEMMLowpMatrixMultiplyNative) +FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(m_values, + n_values), + k_values), + b_values), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMLowpMatrixMultiplyNativeFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(m_values, + n_values), + k_values), + b_values), + m0_values_nightly), + n0_values_nightly), + k0_values_nightly)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} + +FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMLowpMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(m_w_values, + m_h_values), + n_values), + k_values), + b_values), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} + +FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMLowpMatrixMultiplyNative3DFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(m_w_values, + m_h_values), + n_values), + k_values), + b_values), + m0_values_nightly), + n0_values_nightly), + k0_values_nightly)) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +TEST_SUITE_END() // GEMMLowpMatrixMultiplyNative +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 5793ebdd2d..ad5acfc418 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -819,6 +819,195 @@ protected: TensorType _target{}; SimpleTensor<int32_t> _reference{}; }; + +template <typename TensorType, typename AccessorType, typename GEMMFunctionType> +class GEMMLowpMatrixMultiplyNativeValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + + _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info); + _reference = compute_reference(lhs_shape, rhs_shape); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info) + { + // Create tensors + TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // The output tensor will be auto-initialized within the function + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; + SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor<int32_t> _reference{}; +}; + +template <typename TensorType, typename AccessorType, typename GEMMFunctionType> +class GEMMLowpMatrixMultiplyNative3DValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + + // In case of GEMM3D, m is the product between m_w and m_h + const unsigned int m = m_w * m_h; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + + _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h); + _reference = compute_reference(lhs_shape, rhs_shape, m_h); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h) + { + // Create tensors + TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // The output tensor will be auto-initialized within the function + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(&lhs, &rhs, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h) + { + TensorShape dst_shape = lhs_shape; + dst_shape.set(0, rhs_shape[0]); + dst_shape.set(1, lhs_shape[1] / m_h); + dst_shape.set(2, m_h); + dst_shape.set(3, lhs_shape[2]); + + // Create reference + SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; + SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor<int32_t> _reference{}; +}; } // namespace validation } // namespace test } // namespace arm_compute |