/* * Copyright (c) 2017-2019 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 "gemm_helpers.h" #include "helpers_asymm.h" #include "repeat.h" #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) #if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) #define ARM_DOT(x, y, val) val = arm_dot_acc((x), (y), (val)); #else // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) #define ARM_DOT(x, y, val) val += arm_dot((x), (y)); #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8) #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) /** Specialized macros to perform the dot product instruction between two vectors of size N [1,16]. These macros use the dot8 instruction */ #define ARM_DOT1(a, b, c) \ ({ \ ARM_DOT((uchar4)(a, (uchar3)0), (uchar4)(b, (uchar3)0), c); \ }) #define ARM_DOT2(a, b, c) \ ({ \ ARM_DOT((uchar4)(a, (uchar2)0), (uchar4)(b, (uchar2)0), c); \ }) #define ARM_DOT3(a, b, c) \ ({ \ ARM_DOT((uchar4)(a, (uchar)0), (uchar4)(b, (uchar)0), c); \ }) #define ARM_DOT4(a, b, c) \ ({ \ ARM_DOT(a, b, c); \ }) #define ARM_DOT8(a, b, c) \ ({ \ ARM_DOT4((a.lo), (b.lo), c); \ ARM_DOT4((a.hi), (b.hi), c); \ }) #define ARM_DOT16(a, b, c) \ ({ \ ARM_DOT8((a.lo), (b.lo), c); \ ARM_DOT8((a.hi), (b.hi), c); \ }) #else // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) /** Specialized macros to perform the dot product instruction between two vectors of size K0 [1,16] without using the dot8 instruction. */ #define ARM_DOT1(a, b, c) \ ({ \ c += (uint)a * b; \ }) #define ARM_DOT2(a, b, c) \ ({ \ c += (uint)a.s0 * b.s0; \ c += (uint)a.s1 * b.s1; \ }) #define ARM_DOT3(a, b, c) \ ({ \ ARM_DOT2(a, b, c); \ c += (uint)a.s2 * b.s2; \ }) #define ARM_DOT4(a, b, c) \ ({ \ ARM_DOT3(a, b, c); \ c += (uint)a.s3 * b.s3; \ }) #define ARM_DOT8(a, b, c) \ ({ \ ARM_DOT4((a.lo), (b.lo), c); \ ARM_DOT4((a.hi), (b.hi), c); \ }) #define ARM_DOT16(a, b, c) \ ({ \ ARM_DOT8((a.lo), (b.lo), c); \ ARM_DOT8((a.hi), (b.hi), c); \ }) #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) /** Specialized macros to perform a broadcast dot product operation between one vector "a" and N0 vectors "b" of size K0 [1,16] */ #define ARM_DOT_K0X2(k0, a, b, c) \ ({ \ ARM_DOT_K0(k0, (a), (b##0), (c.s0)); \ ARM_DOT_K0(k0, (a), (b##1), (c.s1)); \ }) #define ARM_DOT_K0X3(k0, a, b, c) \ ({ \ ARM_DOT_K0X2(k0, a, b, c); \ ARM_DOT_K0(k0, (a), (b##2), (c.s2)); \ }) #define ARM_DOT_K0X4(k0, a, b, c) \ ({ \ ARM_DOT_K0X3(k0, a, b, c); \ ARM_DOT_K0(k0, (a), (b##3), (c.s3)); \ }) #define ARM_DOT_K0X8(k0, a, b, c) \ ({ \ ARM_DOT_K0X4(k0, a, b, c); \ ARM_DOT_K0(k0, (a), (b##4), (c.s4)); \ ARM_DOT_K0(k0, (a), (b##5), (c.s5)); \ ARM_DOT_K0(k0, (a), (b##6), (c.s6)); \ ARM_DOT_K0(k0, (a), (b##7), (c.s7)); \ }) #define ARM_DOT_K0X16(k0, a, b, c) \ ({ \ ARM_DOT_K0X8(k0, a, b, c); \ ARM_DOT_K0(k0, (a), (b##8), (c.s8)); \ ARM_DOT_K0(k0, (a), (b##9), (c.s9)); \ ARM_DOT_K0(k0, (a), (b##A), (c.sA)); \ ARM_DOT_K0(k0, (a), (b##B), (c.sB)); \ ARM_DOT_K0(k0, (a), (b##C), (c.sC)); \ ARM_DOT_K0(k0, (a), (b##D), (c.sD)); \ ARM_DOT_K0(k0, (a), (b##E), (c.sE)); \ ARM_DOT_K0(k0, (a), (b##F), (c.sF)); \ }) /** Specialized macros to perform a a partial matrix multiplication with dimensions M0,N0,K0 */ #define ARM_MM_K0XN0X1(n0, k0, a, b, c) \ ({ \ ARM_DOT_K0XN0(n0, k0, (a##0), b, (c##0)); \ }) #define ARM_MM_K0XN0X2(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X1(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##1), b, (c##1)); \ }) #define ARM_MM_K0XN0X3(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X2(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##2), b, (c##2)); \ }) #define ARM_MM_K0XN0X4(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X3(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##3), b, (c##3)); \ }) #define ARM_MM_K0XN0X5(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X4(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##4), b, (c##4)); \ }) #define ARM_MM_K0XN0X6(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X5(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##5), b, (c##5)); \ }) #define ARM_MM_K0XN0X7(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X6(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##6), b, (c##6)); \ }) #define ARM_MM_K0XN0X8(n0, k0, a, b, c) \ ({ \ ARM_MM_K0XN0X7(n0, k0, a, b, c); \ ARM_DOT_K0XN0(n0, k0, (a##7), b, (c##7)); \ }) #define ARM_DOT_K0(k0, a, b, c) \ ({ \ CONCAT(ARM_DOT, k0) \ ((a), (b), (c)); \ }) #define ARM_DOT_K0XN0(n0, k0, a, b, c) \ ({ \ CONCAT(ARM_DOT_K0X, n0) \ (k0, (a), b, (c)); \ }) #define ARM_MM_K0XN0XM0(m0, n0, k0, a, b, c) \ ({ \ CONCAT(ARM_MM_K0XN0X, m0) \ (n0, k0, a, b, c); \ }) #if defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_Y) && defined(COLS_A) #define VECTOR_UCHAR VEC_DATA_TYPE(uchar, NUM_ELEMS_PROCESSED_PER_THREAD_X) #define VECTOR_UINT VEC_DATA_TYPE(uint, NUM_ELEMS_PROCESSED_PER_THREAD_X) #define VECTOR_INT VEC_DATA_TYPE(int, NUM_ELEMS_PROCESSED_PER_THREAD_X) /** This OpenCL kernel computes the matrix multiplication between matrix A (src0) and matrix B (src1) in case both matrices have not beed reshaped * * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data type: QASYMM8 * @param[in] src0_stride_x Stride of the source matrix in X dimension (in bytes) * @param[in] src0_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src0_stride_y Stride of the source matrix in Y dimension (in bytes) * @param[in] src0_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src0_offset_first_element_in_bytes The offset of the first element in the source matrix * @param[in] src1_ptr Pointer to the source matrix. Supported data type: same as @p src0_ptr * @param[in] src1_stride_x Stride of the source matrix in X dimension (in bytes) * @param[in] src1_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src1_stride_y Stride of the source matrix in Y dimension (in bytes) * @param[in] src1_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src1_offset_first_element_in_bytes The offset of the first element in the source matrix * @param[out] dst_ptr Pointer to the destination matrix Supported data type: S32 * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes) * @param[in] dst_step_x dst_gx_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_gx_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] src0_stride_z Stride of the source matrix in Z dimension (in bytes) * @param[in] src1_stride_z Stride of the source matrix in Z dimension (in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] src_cross_plane_pad (Optional) Bottom paddings in unit of elements for the input tensor (only if defined REINTERPRET_INPUT_AS_3D) * @param[in] dst_cross_plane_pad (Optional) Bottom paddings in unit of elements for the output tensor (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemmlowp_mm_midgard(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_INPUT_AS_3D) , uint src_cross_plane_pad #endif // REINTERPRET_INPUT_AS_3D #if defined(REINTERPRET_OUTPUT_AS_3D) , uint dst_cross_plane_pad #endif // REINTERPRET_OUTPUT_AS_3D ) { int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X; // Compute starting address for matrix A and Matrix B int2 src_addr = ((int2)(src0_offset_first_element_in_bytes, src1_offset_first_element_in_bytes)); // Update address for the matrix A src_addr.s0 += get_global_id(1) * src0_stride_y * NUM_ELEMS_PROCESSED_PER_THREAD_Y; // Update address for the matrix B src_addr.s1 += idx; #if defined(REINTERPRET_INPUT_AS_3D) // Since we load a 2D input tile from a 3D tensor, we need to check when the plane changes across the z dimension // in order to take into account the presence of possible cross plane paddings // // | | // | plane0 | // | | // |__________________| // |******************| // | cross_plane_pad | // |******************| // | | // | plane1 | // | | // |__________________| // The plane (zin) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint4 zin = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D; zin = min(DEPTH_GEMM3D - 1, zin); // Add offset due to the cross plane paddings zin *= (src_cross_plane_pad * src0_stride_y); // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we // multiply src0_stride_z by DEPTH_GEMM3D src_addr.s0 += get_global_id(2) * src0_stride_z * DEPTH_GEMM3D; #else // defined(REINTERPRET_INPUT_AS_3D) // Add offset for batched GEMM src_addr.s0 += get_global_id(2) * src0_stride_z; #endif // defined(REINTERPRET_INPUT_AS_3D) #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z; #else // defined(MATRIX_B_DEPTH) src_addr.s1 += get_global_id(2) * src1_stride_z; #endif // defined(MATRIX_B_DEPTH) int end_row_vec_a = src_addr.s0 + COLS_A; VECTOR_UINT acc0 = 0; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VECTOR_UINT acc1 = 0; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 VECTOR_UINT acc2 = 0; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 VECTOR_UINT acc3 = 0; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 VECTOR_UINT acc4 = 0; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 for(; src_addr.s0 <= (end_row_vec_a - 2); src_addr += (int2)(2, 2 * src1_stride_y)) { // Load values from matrix A uchar2 a0 = vload2(0, src0_ptr + src_addr.s0 + 0 * src0_stride_y); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 uchar2 a1 = vload2(0, src0_ptr + src_addr.s0 + 1 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 uchar2 a2 = vload2(0, src0_ptr + src_addr.s0 + 2 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 uchar2 a3 = vload2(0, src0_ptr + src_addr.s0 + 3 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 uchar2 a4 = vload2(0, src0_ptr + src_addr.s0 + 4 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 // Load values from matrix B VECTOR_UCHAR b0 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, src1_ptr + src_addr.s1); VECTOR_UCHAR b1 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, src1_ptr + src_addr.s1 + src1_stride_y); // Accumulate acc0 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a0.s0; acc0 += CONVERT(b1, VECTOR_UINT) * (VECTOR_UINT)a0.s1; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a1.s0; acc1 += CONVERT(b1, VECTOR_UINT) * (VECTOR_UINT)a1.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a2.s0; acc2 += CONVERT(b1, VECTOR_UINT) * (VECTOR_UINT)a2.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a3.s0; acc3 += CONVERT(b1, VECTOR_UINT) * (VECTOR_UINT)a3.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 acc4 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a4.s0; acc4 += CONVERT(b1, VECTOR_UINT) * (VECTOR_UINT)a4.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 } for(; src_addr.s0 < end_row_vec_a; src_addr += (int2)(1, src1_stride_y)) { // Load values from matrix A uchar a0 = *(src0_ptr + src_addr.s0 + 0 * src0_stride_y); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 uchar a1 = *(src0_ptr + src_addr.s0 + 1 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 uchar a2 = *(src0_ptr + src_addr.s0 + 2 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 uchar a3 = *(src0_ptr + src_addr.s0 + 3 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 uchar a4 = *(src0_ptr + src_addr.s0 + 4 * src0_stride_y); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 // Load values from matrix B VECTOR_UCHAR b0 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, src1_ptr + src_addr.s1); // Accumulate acc0 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a0; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a2; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a3; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 acc4 += CONVERT(b0, VECTOR_UINT) * (VECTOR_UINT)a4; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 } const int z = get_global_id(2); // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); #if defined(REINTERPRET_OUTPUT_AS_3D) // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension // in order to take into account the presence of possible cross plane paddings // // | | // | plane0 | // | | // |__________________| // |******************| // | cross_plane_pad | // |******************| // | | // | plane1 | // | | // |__________________| // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint8 zout = ((uint8)(0, 1, 2, 3, 4, 5, 6, 7) + (uint8)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint8)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the cross plane paddings zout *= (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.ptr += z * dst_stride_z * DEPTH_GEMM3D; // Store the result VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc0, VECTOR_INT), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc1, VECTOR_INT), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc2, VECTOR_INT), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc3, VECTOR_INT), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc4, VECTOR_INT), 0, (__global int *)(dst.ptr + 4 * dst_stride_y + zout.s4)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst.ptr += z * dst_stride_z; // Store the result VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc0, VECTOR_INT), 0, (__global int *)(dst.ptr + 0 * dst_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc1, VECTOR_INT), 0, (__global int *)(dst.ptr + 1 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc2, VECTOR_INT), 0, (__global int *)(dst.ptr + 2 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc3, VECTOR_INT), 0, (__global int *)(dst.ptr + 3 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (CONVERT(acc4, VECTOR_INT), 0, (__global int *)(dst.ptr + 4 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4 #endif // defined(REINTERPRET_OUTPUT_AS_3D) } #endif // defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_Y) && defined(COLS_A) #if defined(M0) && defined(N0) && defined(K0) && defined(V0) && defined(H0) && defined(M) && defined(N) /** This OpenCL kernel computes the matrix multiplication between 2 matrices with QASYMM data type. * The LHS matrix must be reshaped with @ref CLGEMMReshapeLHSMatrixKernel and the M0xK0 must be NOT transposed * The RHS matrix must be reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the K0xN0 must be transposed * * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time. * @note The GEMM's dimensions M and N must be passed at compile time using -DM and -DN (i.e. -DM=52 and -DN=90). * @note The block's dimensions used for reshaping the LHS matrix and the RHS matrix (M0, N0 and K0) must be passed at compile time using -DM0, -DN0 and -DK0 (i.e. -DM0=4, -DN0=8, -DK0=4). * @note The number of M0xK0 vertical blocks stored on the same output row of the reshaped LHS matrix must be passed at compile time using -DV0 (i.e. -DV0=2) * @note The number of K0xN0 horizontal blocks stored on the same output row of the reshaped RHS matrix must be passed at compile time using -DH0 (i.e. -DH0=2) * @note If the M0xK0 blocks in the reshaped LHS matrix have been interleaved, the option -DLHS_INTERLEAVE must passed at compile time. * @note If the K0xN0 blocks in the reshaped RHS matrix have been interleaved, the option -DRHS_INTERLEAVE must passed at compile time. * @note Only the following configurations of M0, N0 and K0 are currently supported: * - M0 = 2, 3, 4, 5, 6, 7, 8 * - N0 = 2, 3, 4, 8, 16 * - K0 = 2, 3, 4, 8, 16 * - V0 >= 1 * - H0 >= 1 * * @note In case the output has to be reinterpreted as a 3D tensor (i.e. output of convolution layer), the following information must be passed at compile time: * -# 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 NOT reshaped * * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: QASYMM8 * @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] k Number of columns in LHS matrix and rows in RHS matrix not reshaped. * @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] dst_cross_plane_pad (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemmlowp_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs), IMAGE_DECLARATION(rhs), IMAGE_DECLARATION(dst), uint k, uint lhs_stride_z, uint rhs_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint dst_cross_plane_pad #endif // REINTERPRET_OUTPUT_AS_3D ) { // Block size #define LHS_BLOCK_SIZE ((K0) * (M0)) #if defined(LHS_INTERLEAVE) #define LHS_OFFSET_X (K0) #define LHS_STEP_X ((K0) * (V0)) #define LHS_STEP_LOOP (1) #else // defined(INTERLEAVE) #define LHS_OFFSET_X (LHS_BLOCK_SIZE) #define LHS_STEP_X (K0) #define LHS_STEP_LOOP (V0) #endif // defined(INTERLEAVE) // Block size #define RHS_BLOCK_SIZE ((K0) * (N0)) // RHS offset and step X #if defined(RHS_INTERLEAVE) #define RHS_OFFSET_X (K0) #define RHS_STEP_X ((K0) * (H0)) #define RHS_STEP_LOOP (1) #else // defined(RHS_INTERLEAVE) #define RHS_OFFSET_X (RHS_BLOCK_SIZE) #define RHS_STEP_X (K0) #define RHS_STEP_LOOP (H0) #endif // defined(RHS_INTERLEAVE) 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 __global uchar *lhs_addr = lhs_ptr + lhs_offset_first_element_in_bytes + (y % V0) * (uint)LHS_OFFSET_X + (y / V0) * (uint)lhs_stride_y + (z * lhs_stride_z); // Compute RHS matrix address __global uchar *rhs_addr = rhs_ptr + rhs_offset_first_element_in_bytes + (x % H0) * (uint)RHS_OFFSET_X + (x / (uint)H0) * rhs_stride_y; #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 rhs_addr += (z % MATRIX_B_DEPTH) * rhs_stride_z; #else // defined(MATRIX_B_DEPTH) rhs_addr += z * rhs_stride_z; #endif // defined(MATRIX_B_DEPTH) REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0; REPEAT_VAR_INIT_TO_CONST(16, uint, zrhs, 0); // 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; for(int i = 0; i < k; i += K0) { // Load values from LHS matrix LOAD_BLOCK(M0, K0, uchar, a, lhs_addr, 0, LHS_STEP_X, zlhs); // Load values from RHS matrix LOAD_BLOCK(N0, K0, uchar, b, rhs_addr, 0, RHS_STEP_X, zrhs); // Partial matrix multiplication M0,N0,K0 ARM_MM_K0XN0XM0(M0, N0, K0, a, b, c); // Update address lhs_addr += (M0 * LHS_STEP_X * LHS_STEP_LOOP); rhs_addr += (N0 * RHS_STEP_X * RHS_STEP_LOOP); } __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(8, 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); #undef LHS_BLOCK_SIZE #undef LHS_OFFSET_X #undef LHS_STEP_X #undef RHS_BLOCK_SIZE #undef RHS_OFFSET_X #undef RHS_STEP_X } #endif // defined(M0) && defined(N0) && defined(K0) && defined(V0) && defined(H0) && defined(K) #if defined(M0) && defined(N0) && defined(K0) && defined(H0) && defined(K) /** This OpenCL kernel computes the matrix multiplication between 2 matrices. * The LHS matrix is NOT reshaped * The RHS matrix is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is transposed * * @note The number of columns of LHS matrix must be passed at compile time using -DK (i.e. -DK=64) * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (i.e. -DN0=8, -DK0=4). * @note The number of M0 rows to process must be passed at compile time using -DM0 (i.e. -DM0=2) * @note The number of K0xN0 horizontal blocks stored on the same output row of the reshaped RHS matrix must be passed at compile time using -DH0 (i.e. -DH0=2) * @note If the K0xN0 blocks in the reshaped RHS matrix have been interleaved, the option -DRHS_INTERLEAVE must passed at compile time. * @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 * - H0 >= 1 * * @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_reshaped_only_rhs_t(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 ) { // Block size #define RHS_BLOCK_SIZE ((K0) * (N0)) // RHS offset and step X #if defined(RHS_INTERLEAVE) #define RHS_OFFSET_X (K0) #define RHS_STEP_X ((K0) * (H0)) #define RHS_STEP_LOOP (1) #else // defined(RHS_INTERLEAVE) #define RHS_OFFSET_X (RHS_BLOCK_SIZE) #define RHS_STEP_X (K0) #define RHS_STEP_LOOP (H0) #endif // defined(RHS_INTERLEAVE) 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 % H0) * (uint)RHS_OFFSET_X + (x / (uint)H0) * rhs_stride_y; #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); //uint zout0=0,zout1=0,zout2=0,... zout7=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(N0-1)=0; for(int i = 0; i < K; 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(N0, K0, uchar, b, rhs_ptr, rhs_offset, RHS_STEP_X, zrhs); // Partial matrix multiplication M0,N0,K0 ARM_MM_K0XN0XM0(M0, N0, K0, a, b, c); lhs_offset += K0; rhs_offset += N0 * RHS_STEP_X * RHS_STEP_LOOP; } __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(8, 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); #undef RHS_BLOCK_SIZE #undef RHS_OFFSET_X #undef RHS_STEP_X } #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. * * @note This stage is needed to handle the offset of matrix product * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md * * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A * * @param[in] src_ptr Pointer to the source tensor. Supported data type: QASYMM8 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: S32 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_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 tensor */ __kernel void gemmlowp_matrix_a_reduction(TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst)) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Image dst = CONVERT_TO_IMAGE_STRUCT(dst); uint4 sum_row_u32 = (uint4)0; uint sum_row = 0; __global const uchar *matrix_a = (__global const uchar *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z); int i = 0; // This for loop performs 16 accumulations for(; i <= ((int)COLS_A - 16); i += 16) { const uchar16 a0_u8 = vload16(0, matrix_a + i); sum_row_u32 += convert_uint4(a0_u8.s0123) + convert_uint4(a0_u8.s4567) + convert_uint4(a0_u8.s89AB) + convert_uint4(a0_u8.sCDEF); } // This for loop performs the leftover accumulations for(; i < COLS_A; ++i) { sum_row += matrix_a[i]; } sum_row += sum_row_u32.s0 + sum_row_u32.s1 + sum_row_u32.s2 + sum_row_u32.s3; *((__global int *)dst.ptr) = (int)sum_row; } #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) /** OpenCL kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A using the arm dot product instruction * * @note This stage is needed to handle the offset of matrix product * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md * * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A * * @param[in] src_ptr Pointer to the source tensor. Supported data type: QASYMM8 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: S32 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_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 tensor */ __kernel void gemmlowp_matrix_a_reduction_dot8(TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst)) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Image dst = CONVERT_TO_IMAGE_STRUCT(dst); uint sum_row = 0; __global const uchar *matrix_a = (__global const uchar *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z); int i = 0; // This for loop performs 16 accumulations for(; i <= ((int)COLS_A - 32); i += 32) { uchar16 a0_u8 = vload16(0, matrix_a + i); sum_row += arm_dot(a0_u8.s0123, (uchar4)(1)); sum_row += arm_dot(a0_u8.s4567, (uchar4)(1)); sum_row += arm_dot(a0_u8.s89AB, (uchar4)(1)); sum_row += arm_dot(a0_u8.sCDEF, (uchar4)(1)); a0_u8 = vload16(1, matrix_a + i); sum_row += arm_dot(a0_u8.s0123, (uchar4)(1)); sum_row += arm_dot(a0_u8.s4567, (uchar4)(1)); sum_row += arm_dot(a0_u8.s89AB, (uchar4)(1)); sum_row += arm_dot(a0_u8.sCDEF, (uchar4)(1)); } // This for loop performs the leftover accumulations for(; i < COLS_A; ++i) { sum_row += matrix_a[i]; } *((__global int *)dst.ptr) = (int)sum_row; } #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) #endif // defined(COLS_A) #if defined(COLS_B) && defined(ROWS_B) /** OpenCL 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 * * @attention The number of matrix B columns and rows needs to be passed at compile time using -DCOLS_B and -DROWS_B * * @param[in] src_ptr Pointer to the source tensor. Supported data type: QASYMM8 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: S32 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_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 tensor */ __kernel void gemmlowp_matrix_b_reduction(TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst)) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Image dst = CONVERT_TO_IMAGE_STRUCT(dst); uint16 sum_col_u32 = (uint16)0; __global const uchar *matrix_b = (__global const uchar *)(src.ptr + get_global_id(1) * src_stride_z); int i = 0; // This for loop performs 4 accumulations for(; i <= ((int)ROWS_B - 4); i += 4) { const uchar16 b0_u8 = vload16(0, matrix_b + 0 * src_stride_y); const uchar16 b1_u8 = vload16(0, matrix_b + 1 * src_stride_y); const uchar16 b2_u8 = vload16(0, matrix_b + 2 * src_stride_y); const uchar16 b3_u8 = vload16(0, matrix_b + 3 * src_stride_y); sum_col_u32 += convert_uint16(b0_u8) + convert_uint16(b1_u8) + convert_uint16(b2_u8) + convert_uint16(b3_u8); matrix_b += 4 * src_stride_y; } // This for loop perfoms the leftover accumulations for(; i < (int)ROWS_B; ++i) { const uchar16 b0_u8 = vload16(0, matrix_b); sum_col_u32 += convert_uint16(b0_u8); matrix_b += src_stride_y; } vstore16(convert_int16(sum_col_u32), 0, (__global int *)dst.ptr); } #endif // defined(COLS_B) && defined(ROWS_B) #if defined(K_OFFSET) /* Helper function used to calculate the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel. * * This kernel takes a final int32 accumulator value (the output of @CLGEMMLowpMatrixMultiplyKernel), * and calculates the offset contribution of matrix A and matrix B. * * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * @param[in] x get_global_id(0) * 4 * @param[in] y get_global_id(1) * @param[in] z get_global_id(2) * @param[in] sum_col_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_col_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_col_step_x (Optional) sum_col_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_col_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_col_step_y (Optional) sum_col_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_col_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] sum_row_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_row_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_row_step_x (Optional) sum_row_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_row_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_row_step_y (Optional) sum_row_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_row_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor */ inline int4 offset_contribution( int x, int y, int z #if defined(A_OFFSET) , IMAGE_DECLARATION(sum_col) #endif // defined(A_OFFSET) #if defined(B_OFFSET) , IMAGE_DECLARATION(sum_row) #endif // defined(B_OFFSET) #if defined(ADD_BIAS) , VECTOR_DECLARATION(biases) #endif // defined(ADD_BIAS) ) { int4 a_offset_s32 = (int4)0; int4 b_offset_s32 = (int4)0; int batch_id = z; #if defined(DEPTH_INPUT3D) batch_id /= (int)DEPTH_INPUT3D; #endif // defined(DEPTH_INPUT3D) #if defined(A_OFFSET) // Compute the offset contribution due to A_OFFSET __global uchar *sum_col_addr = sum_col_ptr + sum_col_offset_first_element_in_bytes + x * sizeof(int); // Compute the offset contribution due to A_OFFSET #if defined(SUM_COL_HAS_BATCHES) a_offset_s32 = vload4(0, (__global int *)(sum_col_addr + batch_id * sum_col_stride_y)); #else // defined(SUM_COL_HAS_BATCHES) a_offset_s32 = vload4(0, (__global int *)sum_col_addr); #endif // defined(SUM_COL_HAS_BATCHES) a_offset_s32 *= (int4)A_OFFSET; #endif // defined(A_OFFSET) #if defined(B_OFFSET) // Compute the offset contribution due to A_OFFSET __global uchar *sum_row_addr = sum_row_ptr + sum_row_offset_first_element_in_bytes + y * sizeof(int); // Compute the offset contribution due to B_OFFSET #if defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D) b_offset_s32 = (int4) * (((__global int *)(sum_row_addr + batch_id * sum_row_stride_y)) + (z % (int)DEPTH_INPUT3D) * (int)HEIGHT_INPUT3D); #else // defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D) b_offset_s32 = (int4) * (((__global int *)(sum_row_addr + batch_id * sum_row_stride_y))); #endif // defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D) b_offset_s32 *= (int4)B_OFFSET; #endif // defined(B_OFFSET) #if defined(ADD_BIAS) // Add bias __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); int4 biases_values = vload4(0, (__global int *)bias_addr); b_offset_s32 += (int4)biases_values; #endif // defined(ADD_BIAS) return (int4)K_OFFSET + a_offset_s32 + b_offset_s32; } /* OpenCL kernel used to add the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel. The computation is performed in-place * * This kernel takes a final int32 accumulator value (the output of @CLGEMMLowpMatrixMultiplyKernel), * and adds to it the offset contribution of matrix A and matrix B in-place. * * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * The final result is: * * mm_result[i][k] = mm_result[i][k] + * (sum_col[k] * A_OFFSET) + * (sum_row[i] * B_OFFSET) + * (K_OFFSET) * * @param[in] mm_result_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] mm_result_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] mm_result_step_x mm_result_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mm_result_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] mm_result_step_y mm_result_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] mm_result_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] mm_result_step_z mm_result_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] mm_result_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] sum_col_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_col_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_col_step_x (Optional) sum_col_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_col_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_col_step_y (Optional) sum_col_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_col_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] sum_row_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_row_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_row_step_x (Optional) sum_row_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_row_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_row_step_y (Optional) sum_row_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_row_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor */ __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result) #if defined(A_OFFSET) , IMAGE_DECLARATION(sum_col) #endif // defined(A_OFFSET) #if defined(B_OFFSET) , IMAGE_DECLARATION(sum_row) #endif // defined(B_OFFSET) #if defined(ADD_BIAS) , VECTOR_DECLARATION(biases) #endif // defined(ADD_BIAS)) ) { const int x = get_global_id(0) * 4; const int y = get_global_id(1); const int z = get_global_id(2); // Compute offset contribution int4 offset_term_s32 = offset_contribution( x, y, z #if defined(A_OFFSET) , sum_col_ptr, sum_col_stride_x, sum_col_step_x, sum_col_stride_y, sum_col_step_y, sum_col_offset_first_element_in_bytes #endif // defined(A_OFFSET) #if defined(B_OFFSET) , sum_row_ptr, sum_row_stride_x, sum_row_step_x, sum_row_stride_y, sum_row_step_y, sum_row_offset_first_element_in_bytes #endif // defined(B_OFFSET) #if defined(ADD_BIAS) , biases_ptr, biases_stride_x, biases_step_x, biases_offset_first_element_in_bytes #endif // defined(ADD_BIAS) ); __global uchar *mm_result_addr = mm_result_ptr + mm_result_offset_first_element_in_bytes + x * sizeof(int) + y * mm_result_stride_y + z * mm_result_stride_z; int4 in_s32 = vload4(0, (__global int *)mm_result_addr); // Add the offset terms to GEMM's result in_s32 += offset_term_s32; // Store the result with the offset contribution vstore4(in_s32, 0, (__global int *)mm_result_addr); } #if defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT) /* OpenCL kernel used to add the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel and it quantizes down to uint8. * * This kernel takes a final int32 accumulator value (the output of @CLGEMMLowpMatrixMultiplyKernel), adds to it the offset contribution of matrix A and matrix B and quantizes to uint8 through the output stage. * * * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * The result before the output stage is: * * mm_result[i][k] = mm_result[i][k] + * (sum_col[k] * A_OFFSET) + * (sum_row[i] * B_OFFSET) + * (K_OFFSET) * * This result is quantized down to uint8 using the output stage. The output stage computes the following operations: * * -# Add offset terms to final result * -# Multiply each entry of result by result_mult_int * -# Add bias to final result (if -DADD_BIAS is passed at compile time) * -# Shift the int32 accumulator by result_shift * -# Clamp the value between the specified min and max bounds (if -DMIN_BOUND and/or -DMAX_BOUND are passed at compile time) * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. * * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] mm_result_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] mm_result_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] mm_result_step_x mm_result_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mm_result_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] mm_result_step_y mm_result_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] mm_result_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] mm_result_step_z mm_result_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] mm_result_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] sum_col_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_col_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_col_step_x (Optional) sum_col_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_col_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_col_step_y (Optional) sum_col_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_col_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] sum_row_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_row_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_row_step_x (Optional) sum_row_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_row_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_row_step_y (Optional) sum_row_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_row_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_offset_contribution_quantize_down(TENSOR3D_DECLARATION(mm_result) #if defined(A_OFFSET) , IMAGE_DECLARATION(sum_col) #endif // defined(A_OFFSET) #if defined(B_OFFSET) , IMAGE_DECLARATION(sum_row) #endif // defined(B_OFFSET) , #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) TENSOR3D_DECLARATION(dst)) { const int x = get_global_id(0) * 4; const int y = get_global_id(1); const int z = get_global_id(2); __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; // Compute offset contribution int4 offset_term_s32 = offset_contribution( x, y, z #if defined(A_OFFSET) , sum_col_ptr, sum_col_stride_x, sum_col_step_x, sum_col_stride_y, sum_col_step_y, sum_col_offset_first_element_in_bytes #endif // defined(A_OFFSET) #if defined(B_OFFSET) , sum_row_ptr, sum_row_stride_x, sum_row_step_x, sum_row_stride_y, sum_row_step_y, sum_row_offset_first_element_in_bytes #endif // defined(B_OFFSET) #if defined(ADD_BIAS) , biases_ptr, biases_stride_x, biases_step_x, biases_offset_first_element_in_bytes #endif // defined(ADD_BIAS) ); __global uchar *mm_result_addr = mm_result_ptr + mm_result_offset_first_element_in_bytes + x * sizeof(int) + y * mm_result_stride_y + z * mm_result_stride_z; int4 in_s32 = vload4(0, (__global int *)mm_result_addr); // Add the offset terms to GEMM's result in_s32 += offset_term_s32; // -------------- OUTPUT STAGE // Add the offset terms to GEMM's result in_s32 += (int4)RESULT_OFFSET; // Multiply by result_mult_int and shift in_s32 *= RESULT_MULTIPLIER; in_s32 >>= RESULT_SHIFT; uchar4 res = convert_uchar4_sat(in_s32); #if defined(MIN_BOUND) res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, dst_addr); } /* OpenCL kernel used to add the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel and it quantizes down to uint8. * * This kernel takes a final int32 accumulator value (the output of @CLGEMMLowpMatrixMultiplyKernel), adds to it the offset contribution of matrix A and matrix B and quantizes to uint8 through the output stage. * * * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * The result before the output stage is: * * mm_result[i][k] = mm_result[i][k] + * (sum_col[k] * A_OFFSET) + * (sum_row[i] * B_OFFSET) + * (K_OFFSET) * * This result is quantized down to uint8 using the output stage. The output stage computes the following operations: * * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier * -# Add bias to final result if bias tensor is not a nullptr * -# Round to nearest division by a power-of-two using result_shift * -# Add offset to each result * -# Clamp the value between the specified min and max bounds * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. * * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] mm_result_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] mm_result_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] mm_result_step_x mm_result_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mm_result_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] mm_result_step_y mm_result_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] mm_result_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] mm_result_step_z mm_result_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] mm_result_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] sum_col_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_col_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_col_step_x (Optional) sum_col_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_col_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_col_step_y (Optional) sum_col_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_col_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] sum_row_ptr (Optional) Pointer to the source tensor. Supported data type: same as @p mm_result_ptr * @param[in] sum_row_stride_x (Optional) Stride of the source tensor in X dimension (in bytes) * @param[in] sum_row_step_x (Optional) sum_row_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_row_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes) * @param[in] sum_row_step_y (Optional) sum_row_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_row_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_offset_contribution_quantize_down_fixedpoint(TENSOR3D_DECLARATION(mm_result) #if defined(A_OFFSET) , IMAGE_DECLARATION(sum_col) #endif // defined(A_OFFSET) #if defined(B_OFFSET) , IMAGE_DECLARATION(sum_row) #endif // defined(B_OFFSET) , #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) TENSOR3D_DECLARATION(dst)) { const int x = get_global_id(0) * 4; const int y = get_global_id(1); const int z = get_global_id(2); // Compute offset contribution int4 offset_term_s32 = offset_contribution( x, y, z #if defined(A_OFFSET) , sum_col_ptr, sum_col_stride_x, sum_col_step_x, sum_col_stride_y, sum_col_step_y, sum_col_offset_first_element_in_bytes #endif // defined(A_OFFSET) #if defined(B_OFFSET) , sum_row_ptr, sum_row_stride_x, sum_row_step_x, sum_row_stride_y, sum_row_step_y, sum_row_offset_first_element_in_bytes #endif // defined(B_OFFSET) #if defined(ADD_BIAS) , biases_ptr, biases_stride_x, biases_step_x, biases_offset_first_element_in_bytes #endif // defined(ADD_BIAS) ); __global uchar *mm_result_addr = mm_result_ptr + mm_result_offset_first_element_in_bytes + x * sizeof(int) + y * mm_result_stride_y + z * mm_result_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; int4 in_s32 = vload4(0, (__global int *)mm_result_addr); // Add the offset terms to GEMM's result in_s32 += offset_term_s32; // -------------- OUTPUT STAGE // Multiply by result_mult_int and shift in_s32 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(in_s32, RESULT_MULTIPLIER, RESULT_SHIFT, 4); // Add the offset terms to GEMM's result in_s32 += (int4)RESULT_OFFSET; uchar4 res = convert_uchar4_sat(in_s32); #if defined(MIN_BOUND) res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, dst_addr); } #endif // defined(K_OFFSET) && defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT) #endif // defined(K_OFFSET) #if defined(RESULT_OFFSET) && defined(RESULT_MULT_INT) && defined(RESULT_SHIFT) /** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 * * This kernel takes a final int32 accumulator value and processes it to obtain the final QASYMM8 value. * The following computations will be performed by the kernel: * * -# Add offset terms to final result * -# Multiply each entry of result by result_mult_int * -# Add bias to final result (if -DADD_BIAS is passed at compile time) * -# Shift the int32 accumulator by result_shift * -# Clamp the value between the specified min and max bounds (if -DMIN_BOUND and/or -DMAX_BOUND are passed at compile time) * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. * * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] src_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src), #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses int x = get_global_id(0) * 4; int y = get_global_id(1); int z = get_global_id(2); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; int4 input_values = vload4(0, (__global int *)src_addr); #if defined(ADD_BIAS) // Add bias __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); int4 biases_values = vload4(0, (__global int *)bias_addr); input_values += (int4)biases_values; #endif // defined(ADD_BIAS) // Add the offset terms to GEMM's result input_values += (int4)RESULT_OFFSET; // Multiply by result_mult_int and shift input_values *= RESULT_MULT_INT; input_values >>= RESULT_SHIFT; uchar4 res = convert_uchar4_sat(input_values); #if defined(MIN_BOUND) res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, dst_addr); } #endif // defined(RESULT_OFFSET) && defined(RESULT_MULT_INT) && defined(RESULT_SHIFT) #if defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) /** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 * * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), 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 * -# Add bias to final result if bias tensor is not a nullptr * -# Round to nearest division by a power-of-two using result_shift * -# Add offset to each result * -# Clamp the value between the specified min and max bounds * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. * * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET_AFTER_SHIFT, -DRESULT_FIXEDPOINT_MULTIPLIER and -DRESULT_SHIFT * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] src_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATION(src), #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses int x = get_global_id(0) * 4; int y = get_global_id(1); int z = get_global_id(2); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; int4 input_values = vload4(0, (__global int *)src_addr); #if defined(ADD_BIAS) // Add bias __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); int4 biases_values = vload4(0, (__global int *)bias_addr); input_values += (int4)biases_values; #endif // defined(ADD_BIAS) // Multiply by result_mult_int and shift input_values = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(input_values, RESULT_FIXEDPOINT_MULTIPLIER, RESULT_SHIFT, 4); // Add the offset terms to GEMM's result input_values += (int4)RESULT_OFFSET_AFTER_SHIFT; uchar4 res = convert_uchar4_sat(input_values); #if defined(MIN_BOUND) res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, dst_addr); } #endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) #if defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) /** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QSYMM16 * * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), 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 * -# Add bias to final result if bias tensor is not a nullptr * -# Round to nearest division by a power-of-two using result_shift * -# Add offset to each result * -# Clamp the value between the specified min and max bounds * -# Clamp the resulting int32 values to the [-32768..32767] range and cast to QSYMM16. * * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_FIXEDPOINT_MULTIPLIER and -DRESULT_SHIFT * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] src_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint_qsymm16(TENSOR3D_DECLARATION(src), #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses int x = get_global_id(0) * 4; int y = get_global_id(1); int z = get_global_id(2); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * 2 + y * dst_stride_y + z * dst_stride_z; int4 input_values = vload4(0, (__global int *)src_addr); #if defined(ADD_BIAS) // Add bias __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); int4 biases_values = vload4(0, (__global int *)bias_addr); input_values += (int4)biases_values; #endif // defined(ADD_BIAS) // Multiply by result_mult_int and shift input_values = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(input_values, RESULT_FIXEDPOINT_MULTIPLIER, RESULT_SHIFT, 4); short4 res = convert_short4_sat(input_values); #if defined(MIN_BOUND) res = max(res, (short4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (short4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, (__global short *)dst_addr); } #endif // defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) #if defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET) /** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 * * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), 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 * -# Add bias to final result if bias tensor is not a nullptr * -# Requantize * -# Add offset to each result * -# Clamp the value between the specified min and max bounds * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. * * @attention The offset and scalar scale factor must be passed at compile time using -DRESULT_OFFSET, -DREAL_MULTIPLIER * * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND. * These values can be used to implement "rectified linear unit" activation functions * * @param[in] src_ptr Pointer to the source tensor. Supported data type: S32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] biases_ptr Pointer to the biases tensor. Supported data type: same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[out] dst_ptr Pointer to the destination tensor Supported data type: QASYMM8 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) * @param[in] dst_step_w src_stride_w * number of elements along W processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_output_stage_quantize_down_float(TENSOR3D_DECLARATION(src), #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) #if defined(DST_HEIGHT) TENSOR4D_DECLARATION(dst)) #else // defined(DST_HEIGHT) TENSOR3D_DECLARATION(dst)) #endif // defined(DST_HEIGHT) { // Compute source and destination addresses int x = get_global_id(0) * 4; int y = get_global_id(1); int z = get_global_id(2); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(int) + y * src_stride_y + z * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x + y * dst_stride_y + z * dst_stride_z; int4 input_values = vload4(0, (__global int *)src_addr); #if defined(ADD_BIAS) // Add bias __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(int); int4 biases_values = vload4(0, (__global int *)bias_addr); input_values += (int4)biases_values; #endif // defined(ADD_BIAS) // Convert to float float16 input_values_f = convert_float4(input_values); input_values_f = round(input_values_f * (float)REAL_MULTIPLIER + (float)OUTPUT_OFFSET); uchar4 res = convert_uchar4_sat(input_values_f); #if defined(MIN_BOUND) res = max(res, (uchar4)MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (uchar4)MAX_BOUND); #endif // defined(MAX_BOUND) // Store the result vstore4(res, 0, dst_addr); } #endif // defined(REAL_MULTIPLIER) && defined(OUTPUT_OFFSET)