/* * Copyright (c) 2017-2018 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 "helpers.h" #if defined(TRANSPOSE_W) && defined(MULT_TRANSPOSE1XW_WIDTH) #if ELEMENT_SIZE == 1 #define DATA_TYPE uchar #elif ELEMENT_SIZE == 2 #define DATA_TYPE ushort #elif ELEMENT_SIZE == 4 #define DATA_TYPE uint #else // ELEMENT_SIZE == 1 #error "Element size not supported" #endif // ELEMENT_SIZE /** This OpenCL kernel computes the "vector" 1xW transposition of input matrix * * @note The transposition width must be passed at compile time using -DTRANSPOSE_W (i.e. -DTRANSPOSE_W) * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2) * * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 * @param[in] src_stride_x Stride of the source matrix 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 matrix 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 matrix * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_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_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_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 matrix */ __kernel void gemm_transpose1xW(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst)) { uint x = get_global_id(0); uint y = get_global_id(1); uint z = get_global_id(2); // Compute address for Matrix B - source Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); // Compute address for Matrix B transposed - destination. X and Y are swapped uint dst_addr_in_bytes = dst_offset_first_element_in_bytes + y * TRANSPOSE_W * sizeof(DATA_TYPE) * MULT_TRANSPOSE1XW_WIDTH + (x / MULT_TRANSPOSE1XW_WIDTH) * dst_stride_y + (x % MULT_TRANSPOSE1XW_WIDTH) * TRANSPOSE_W * sizeof(DATA_TYPE); // Add offset for batched GEMM dst_addr_in_bytes += z * dst_stride_z; VEC_DATA_TYPE(DATA_TYPE, TRANSPOSE_W) b0 = VLOAD(TRANSPOSE_W)(0, (__global DATA_TYPE *)src.ptr); VSTORE(TRANSPOSE_W) (b0, 0, (__global DATA_TYPE *)(dst_ptr + dst_addr_in_bytes)); } #endif // defined(TRANSPOSE_W) && defined(MULT_TRANSPOSE1XW_WIDTH) #if defined(MULT_INTERLEAVE4X4_HEIGHT) && defined(DATA_TYPE) /** This OpenCL kernel reshapes the input matrix transposing each 4x4 block and interleaving the values * * @note The data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=float) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 * @param[in] src_stride_x Stride of the source matrix 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 matrix 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 matrix * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_ptr * @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_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_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 matrix */ __kernel void gemm_interleave4x4(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses uint x = get_global_id(0); uint y = get_global_id(1); uint z = get_global_id(2); // Compute address for source tensor Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); // Compute address for Matrix B transposed - destination. X and Y are swapped uint dst_addr_in_bytes = dst_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * 16 * MULT_INTERLEAVE4X4_HEIGHT + (y / MULT_INTERLEAVE4X4_HEIGHT) * dst_stride_y + (y % MULT_INTERLEAVE4X4_HEIGHT) * 4 * sizeof(DATA_TYPE); // Add offset for batched GEMM dst_addr_in_bytes += z * dst_stride_z; __global uchar *input_ptr = src.ptr; // Load values from Matrix A VEC_DATA_TYPE(DATA_TYPE, 4) a0 = vload4(0, (__global DATA_TYPE *)(input_ptr + 0 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 4) a1 = vload4(0, (__global DATA_TYPE *)(input_ptr + 1 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 4) a2 = vload4(0, (__global DATA_TYPE *)(input_ptr + 2 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 4) a3 = vload4(0, (__global DATA_TYPE *)(input_ptr + 3 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 4) val0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(a0.s0, a1.s0, a2.s0, a3.s0); vstore4(val0, 0, ((__global DATA_TYPE *)(dst_ptr + dst_addr_in_bytes) + 0 * MULT_INTERLEAVE4X4_HEIGHT)); val0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(a0.s1, a1.s1, a2.s1, a3.s1); vstore4(val0, 0, ((__global DATA_TYPE *)(dst_ptr + dst_addr_in_bytes) + 4 * MULT_INTERLEAVE4X4_HEIGHT)); val0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(a0.s2, a1.s2, a2.s2, a3.s2); vstore4(val0, 0, ((__global DATA_TYPE *)(dst_ptr + dst_addr_in_bytes) + 8 * MULT_INTERLEAVE4X4_HEIGHT)); val0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(a0.s3, a1.s3, a2.s3, a3.s3); vstore4(val0, 0, ((__global DATA_TYPE *)(dst_ptr + dst_addr_in_bytes) + 12 * MULT_INTERLEAVE4X4_HEIGHT)); } #endif // defined(MULT_INTERLEAVE4X4_HEIGHT) && defined(DATA_TYPE) #if defined(COLS_B) && defined(MULT_TRANSPOSE1XW_WIDTH) && defined(MULT_INTERLEAVE4X4_HEIGHT) /** This OpenCL kernel is optimised for Midgard. It computes the matrix multiplication between matrix A (src0) and matrix B (src1) * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_32bit and @ref gemm_transpose1x4 before running the matrix multiplication * * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F32 * @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 types: 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 types: same as @p src0_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] 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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_interleaved_transposed_f32(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #endif // REINTERPRET_OUTPUT_AS_3D ) { int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH; int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT; int z = get_global_id(2); // Offset const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4; const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 4; // src_addr_a = address of matrix A // src_addr_b = address of matrix B int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes; int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes; #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z; #else // defined(MATRIX_B_DEPTH) src1_addr_in_bytes += z * src1_stride_z; #endif // defined(MATRIX_B_DEPTH) __global float *src_addr_a = (__global float *)(src0_ptr + src0_addr_in_bytes); __global float *src_addr_b = (__global float *)(src1_ptr + src1_addr_in_bytes); // Compute end row address for matrix B __global float *src_end_addr_b = src_addr_b + COLS_B; src_addr_a += offset_row_a; src_addr_b += offset_row_b; // Reset accumulators float4 c00 = 0.0f; float4 c10 = 0.0f; float4 c20 = 0.0f; float4 c30 = 0.0f; for(; src_addr_b <= (src_end_addr_b - (int)(8 * MULT_TRANSPOSE1XW_WIDTH)); src_addr_a += 8 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH) { // Load values from matrix A (interleaved) and matrix B (transposed) float4 a0 = vload4(0, src_addr_a); float4 b0 = vload4(0, src_addr_b); c00 += (float4)a0.s0 * b0; c10 += (float4)a0.s1 * b0; c20 += (float4)a0.s2 * b0; c30 += (float4)a0.s3 * b0; // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a + 4 * MULT_INTERLEAVE4X4_HEIGHT); b0 = vload4(0, src_addr_b + 4 * MULT_TRANSPOSE1XW_WIDTH); c00 += (float4)a0.s0 * b0; c10 += (float4)a0.s1 * b0; c20 += (float4)a0.s2 * b0; c30 += (float4)a0.s3 * b0; } for(; src_addr_b < src_end_addr_b; src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH) { // Load values from matrix A (interleaved) and matrix B (transposed) float4 a0 = vload4(0, src_addr_a); float4 b0 = vload4(0, src_addr_b); c00 += (float4)a0.s0 * b0; c10 += (float4)a0.s1 * b0; c20 += (float4)a0.s2 * b0; c30 += (float4)a0.s3 * b0; } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); #if defined(ALPHA) // Multiply by the weight of matrix product c00 = c00 * (float4)ALPHA; c10 = c10 * (float4)ALPHA; c20 = c20 * (float4)ALPHA; c30 = c30 * (float4)ALPHA; #endif // defined(ALPHA) // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store 4x4 block vstore4(c00, 0, (__global float *)(dst_addr + 0 * dst_stride_y + zout.s0)); vstore4(c10, 0, (__global float *)(dst_addr + 1 * dst_stride_y + zout.s1)); vstore4(c20, 0, (__global float *)(dst_addr + 2 * dst_stride_y + zout.s2)); vstore4(c30, 0, (__global float *)(dst_addr + 3 * dst_stride_y + zout.s3)); #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store 4x4 block vstore4(c00, 0, (__global float *)(dst_addr + 0 * dst_stride_y)); vstore4(c10, 0, (__global float *)(dst_addr + 1 * dst_stride_y)); vstore4(c20, 0, (__global float *)(dst_addr + 2 * dst_stride_y)); vstore4(c30, 0, (__global float *)(dst_addr + 3 * dst_stride_y)); #endif // defined(REINTERPRET_OUTPUT_AS_3D) } /** This OpenCL kernel is optimized for Bifrost. It computes the matrix multiplication between matrix A (src0) and matrix B (src1) * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_32bit and @ref gemm_transpose1x4 before running the matrix multiplication * * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F32 * @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 types: 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 types: same as @p src0_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] 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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_interleaved_transposed_f32_bifrost(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #endif // REINTERPRET_OUTPUT_AS_3D ) { int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH; int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT; int z = get_global_id(2); // Offset const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4; const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 4; // src_addr_a = address of matrix A // src_addr_b = address of matrix B int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes; int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes; #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z; #else // defined(MATRIX_B_DEPTH) src1_addr_in_bytes += z * src1_stride_z; #endif // defined(MATRIX_B_DEPTH) __global float *src_addr_a = (__global float *)(src0_ptr + src0_addr_in_bytes); __global float *src_addr_b = (__global float *)(src1_ptr + src1_addr_in_bytes); src_addr_a += offset_row_a; src_addr_b += offset_row_b; // Reset accumulators float c00 = 0.0f; float c01 = 0.0f; float c02 = 0.0f; float c03 = 0.0f; float c10 = 0.0f; float c11 = 0.0f; float c12 = 0.0f; float c13 = 0.0f; float c20 = 0.0f; float c21 = 0.0f; float c22 = 0.0f; float c23 = 0.0f; float c30 = 0.0f; float c31 = 0.0f; float c32 = 0.0f; float c33 = 0.0f; #define COLS_MTX_B (COLS_B / (4 * MULT_TRANSPOSE1XW_WIDTH)) int i = 0; for(; i <= (int)(COLS_MTX_B - 4); i += 4) { // Load values from matrix A (interleaved) and matrix B (transposed) float4 a0 = vload4(0, src_addr_a); float4 b0 = vload4(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma(a0.s0, b0.s0, c00); c01 = fma(a0.s0, b0.s1, c01); c02 = fma(a0.s0, b0.s2, c02); c03 = fma(a0.s0, b0.s3, c03); c10 = fma(a0.s1, b0.s0, c10); c11 = fma(a0.s1, b0.s1, c11); c12 = fma(a0.s1, b0.s2, c12); c13 = fma(a0.s1, b0.s3, c13); c20 = fma(a0.s2, b0.s0, c20); c21 = fma(a0.s2, b0.s1, c21); c22 = fma(a0.s2, b0.s2, c22); c23 = fma(a0.s2, b0.s3, c23); c30 = fma(a0.s3, b0.s0, c30); c31 = fma(a0.s3, b0.s1, c31); c32 = fma(a0.s3, b0.s2, c32); c33 = fma(a0.s3, b0.s3, c33); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload4(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma(a0.s0, b0.s0, c00); c01 = fma(a0.s0, b0.s1, c01); c02 = fma(a0.s0, b0.s2, c02); c03 = fma(a0.s0, b0.s3, c03); c10 = fma(a0.s1, b0.s0, c10); c11 = fma(a0.s1, b0.s1, c11); c12 = fma(a0.s1, b0.s2, c12); c13 = fma(a0.s1, b0.s3, c13); c20 = fma(a0.s2, b0.s0, c20); c21 = fma(a0.s2, b0.s1, c21); c22 = fma(a0.s2, b0.s2, c22); c23 = fma(a0.s2, b0.s3, c23); c30 = fma(a0.s3, b0.s0, c30); c31 = fma(a0.s3, b0.s1, c31); c32 = fma(a0.s3, b0.s2, c32); c33 = fma(a0.s3, b0.s3, c33); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload4(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma(a0.s0, b0.s0, c00); c01 = fma(a0.s0, b0.s1, c01); c02 = fma(a0.s0, b0.s2, c02); c03 = fma(a0.s0, b0.s3, c03); c10 = fma(a0.s1, b0.s0, c10); c11 = fma(a0.s1, b0.s1, c11); c12 = fma(a0.s1, b0.s2, c12); c13 = fma(a0.s1, b0.s3, c13); c20 = fma(a0.s2, b0.s0, c20); c21 = fma(a0.s2, b0.s1, c21); c22 = fma(a0.s2, b0.s2, c22); c23 = fma(a0.s2, b0.s3, c23); c30 = fma(a0.s3, b0.s0, c30); c31 = fma(a0.s3, b0.s1, c31); c32 = fma(a0.s3, b0.s2, c32); c33 = fma(a0.s3, b0.s3, c33); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload4(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma(a0.s0, b0.s0, c00); c01 = fma(a0.s0, b0.s1, c01); c02 = fma(a0.s0, b0.s2, c02); c03 = fma(a0.s0, b0.s3, c03); c10 = fma(a0.s1, b0.s0, c10); c11 = fma(a0.s1, b0.s1, c11); c12 = fma(a0.s1, b0.s2, c12); c13 = fma(a0.s1, b0.s3, c13); c20 = fma(a0.s2, b0.s0, c20); c21 = fma(a0.s2, b0.s1, c21); c22 = fma(a0.s2, b0.s2, c22); c23 = fma(a0.s2, b0.s3, c23); c30 = fma(a0.s3, b0.s0, c30); c31 = fma(a0.s3, b0.s1, c31); c32 = fma(a0.s3, b0.s2, c32); c33 = fma(a0.s3, b0.s3, c33); } for(; i < (int)(COLS_MTX_B); ++i) { // Load values from matrix A (interleaved) and matrix B (transposed) float4 a0 = vload4(0, src_addr_a); float4 b0 = vload4(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 4 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma(a0.s0, b0.s0, c00); c01 = fma(a0.s0, b0.s1, c01); c02 = fma(a0.s0, b0.s2, c02); c03 = fma(a0.s0, b0.s3, c03); c10 = fma(a0.s1, b0.s0, c10); c11 = fma(a0.s1, b0.s1, c11); c12 = fma(a0.s1, b0.s2, c12); c13 = fma(a0.s1, b0.s3, c13); c20 = fma(a0.s2, b0.s0, c20); c21 = fma(a0.s2, b0.s1, c21); c22 = fma(a0.s2, b0.s2, c22); c23 = fma(a0.s2, b0.s3, c23); c30 = fma(a0.s3, b0.s0, c30); c31 = fma(a0.s3, b0.s1, c31); c32 = fma(a0.s3, b0.s2, c32); c33 = fma(a0.s3, b0.s3, c33); } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); #if defined(ALPHA) // Multiply by the weight of matrix product c00 = c00 * ALPHA; c01 = c01 * ALPHA; c02 = c02 * ALPHA; c03 = c03 * ALPHA; c10 = c10 * ALPHA; c11 = c11 * ALPHA; c12 = c12 * ALPHA; c13 = c13 * ALPHA; c20 = c20 * ALPHA; c21 = c21 * ALPHA; c22 = c22 * ALPHA; c23 = c23 * ALPHA; c30 = c30 * ALPHA; c31 = c31 * ALPHA; c32 = c32 * ALPHA; c33 = c33 * ALPHA; #endif // defined(ALPHA) // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store 4x4 block vstore4((float4)(c00, c01, c02, c03), 0, (__global float *)(dst_addr + 0 * dst_stride_y + zout.s0)); vstore4((float4)(c10, c11, c12, c13), 0, (__global float *)(dst_addr + 1 * dst_stride_y + zout.s1)); vstore4((float4)(c20, c21, c22, c23), 0, (__global float *)(dst_addr + 2 * dst_stride_y + zout.s2)); vstore4((float4)(c30, c31, c32, c33), 0, (__global float *)(dst_addr + 3 * dst_stride_y + zout.s3)); #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store 4x4 block vstore4((float4)(c00, c01, c02, c03), 0, (__global float *)(dst_addr + 0 * dst_stride_y)); vstore4((float4)(c10, c11, c12, c13), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); vstore4((float4)(c20, c21, c22, c23), 0, (__global float *)(dst_addr + 2 * dst_stride_y)); vstore4((float4)(c30, c31, c32, c33), 0, (__global float *)(dst_addr + 3 * dst_stride_y)); #endif // defined(REINTERPRET_OUTPUT_AS_3D) } // Undefine local defines #undef COLS_MTX_B #if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) /** This OpenCL kernel computes the matrix multiplication between matrix A (src0) and matrix B (src1) * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_16bit and @ref gemm_transpose1x8 before running the matrix multiplication * * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16 * @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 types: 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 types: same as @p src0_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] 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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_interleaved_transposed_f16(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #endif // REINTERPRET_OUTPUT_AS_3D ) { int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH; int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT; int z = get_global_id(2); // Offset const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4; const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 8; // src_addr_a = address of matrix A // src_addr_b = address of matrix B int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes; int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes; #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z; #else // defined(MATRIX_B_DEPTH) src1_addr_in_bytes += z * src1_stride_z; #endif // defined(MATRIX_B_DEPTH) __global half *src_addr_a = (__global half *)(src0_ptr + src0_addr_in_bytes); __global half *src_addr_b = (__global half *)(src1_ptr + src1_addr_in_bytes); // Compute end row address for matrix B __global half *src_end_addr_b = src_addr_b + COLS_B; src_addr_a += offset_row_a; src_addr_b += offset_row_b; // Reset accumulators half8 c00 = 0.0f; half8 c10 = 0.0f; half8 c20 = 0.0f; half8 c30 = 0.0f; for(; src_addr_b <= (src_end_addr_b - (int)(16 * MULT_TRANSPOSE1XW_WIDTH)); src_addr_a += 8 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 16 * MULT_TRANSPOSE1XW_WIDTH) { // Load values from matrix A (interleaved) and matrix B (transposed) half4 a0 = vload4(0, src_addr_a); half8 b0 = vload8(0, src_addr_b); c00 += (half8)a0.s0 * b0; c10 += (half8)a0.s1 * b0; c20 += (half8)a0.s2 * b0; c30 += (half8)a0.s3 * b0; // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a + 4 * MULT_INTERLEAVE4X4_HEIGHT); b0 = vload8(0, src_addr_b + 8 * MULT_TRANSPOSE1XW_WIDTH); c00 += (half8)a0.s0 * b0; c10 += (half8)a0.s1 * b0; c20 += (half8)a0.s2 * b0; c30 += (half8)a0.s3 * b0; } for(; src_addr_b < src_end_addr_b; src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH) { // Load values from matrix A (interleaved) and matrix B (transposed) half4 a0 = vload4(0, src_addr_a); half8 b0 = vload8(0, src_addr_b); c00 += (half8)a0.s0 * b0; c10 += (half8)a0.s1 * b0; c20 += (half8)a0.s2 * b0; c30 += (half8)a0.s3 * b0; } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); #if defined(ALPHA) // Multiply by the weight of matrix product c00 = c00 * (half8)ALPHA; c10 = c10 * (half8)ALPHA; c20 = c20 * (half8)ALPHA; c30 = c30 * (half8)ALPHA; #endif // defined(ALPHA) // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store 4x8 block vstore8(c00, 0, (__global half *)(dst_addr + 0 * dst_stride_y + zout.s0)); vstore8(c10, 0, (__global half *)(dst_addr + 1 * dst_stride_y + zout.s1)); vstore8(c20, 0, (__global half *)(dst_addr + 2 * dst_stride_y + zout.s2)); vstore8(c30, 0, (__global half *)(dst_addr + 3 * dst_stride_y + zout.s3)); #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store 4x8 block vstore8(c00, 0, (__global half *)(dst_addr + 0 * dst_stride_y)); vstore8(c10, 0, (__global half *)(dst_addr + 1 * dst_stride_y)); vstore8(c20, 0, (__global half *)(dst_addr + 2 * dst_stride_y)); vstore8(c30, 0, (__global half *)(dst_addr + 3 * dst_stride_y)); #endif // defined(REINTERPRET_OUTPUT_AS_3D) } /** This OpenCL kernel optimized for Bifrost architectures computes the matrix multiplication between matrix A (src0) and matrix B (src1) * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_16bit and @ref gemm_transpose1x8 before running the matrix multiplication * * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2) * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2) * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16 * @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 types: 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 types: same as @p src0_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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_interleaved_transposed_f16_bifrost(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #endif // REINTERPRET_OUTPUT_AS_3D ) { int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH; int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT; int z = get_global_id(2); // Offset const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4; const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 8; // src_addr_a = address of matrix A // src_addr_b = address of matrix B int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes; int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes; #if defined(MATRIX_B_DEPTH) // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3 src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z; #else // defined(MATRIX_B_DEPTH) src1_addr_in_bytes += z * src1_stride_z; #endif // defined(MATRIX_B_DEPTH) __global half *src_addr_a = (__global half *)(src0_ptr + src0_addr_in_bytes); __global half *src_addr_b = (__global half *)(src1_ptr + src1_addr_in_bytes); // Compute end row address for matrix B __global half *src_end_addr_b = src_addr_b + COLS_B; src_addr_a += offset_row_a; src_addr_b += offset_row_b; // Reset accumulators half8 c00 = 0.0f; half8 c10 = 0.0f; half8 c20 = 0.0f; half8 c30 = 0.0f; #define COLS_MTX_B (COLS_B / (8 * MULT_TRANSPOSE1XW_WIDTH)) int i = 0; for(; i <= (int)(COLS_MTX_B - 4); i += 4) { #if MULT_INTERLEAVE4X4_HEIGHT == 1 // Load values from matrix A (interleaved) and matrix B (transposed) half8 a0 = vload8(0, src_addr_a); half8 b0 = vload8(0, src_addr_b); src_addr_a += 8 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); // Load values from matrix B (transposed) b0 = vload8(0, src_addr_b); src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s4, b0, c00); c10 = fma((half8)a0.s5, b0, c10); c20 = fma((half8)a0.s6, b0, c20); c30 = fma((half8)a0.s7, b0, c30); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload8(0, src_addr_a); b0 = vload8(0, src_addr_b); src_addr_a += 8 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); // Load values from matrix B (transposed) b0 = vload8(0, src_addr_b); src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s4, b0, c00); c10 = fma((half8)a0.s5, b0, c10); c20 = fma((half8)a0.s6, b0, c20); c30 = fma((half8)a0.s7, b0, c30); #else // MULT_INTERLEAVE4X4_HEIGHT == 1 // Load values from matrix A (interleaved) and matrix B (transposed) half4 a0 = vload4(0, src_addr_a); half8 b0 = vload8(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload8(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload8(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); // Load values from matrix A (interleaved) and matrix B (transposed) a0 = vload4(0, src_addr_a); b0 = vload8(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); #endif // MULT_INTERLEAVE4X4_HEIGHT == 1 } for(; i < (int)(COLS_MTX_B); ++i) { // Load values from matrix A (interleaved) and matrix B (transposed) half4 a0 = vload4(0, src_addr_a); half8 b0 = vload8(0, src_addr_b); src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT; src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH; c00 = fma((half8)a0.s0, b0, c00); c10 = fma((half8)a0.s1, b0, c10); c20 = fma((half8)a0.s2, b0, c20); c30 = fma((half8)a0.s3, b0, c30); } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); #if defined(ALPHA) // Multiply by the weight of matrix product c00 = c00 * (half8)ALPHA; c10 = c10 * (half8)ALPHA; c20 = c20 * (half8)ALPHA; c30 = c30 * (half8)ALPHA; #endif // defined(ALPHA) // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store 4x8 block vstore8(c00, 0, (__global half *)(dst_addr + 0 * dst_stride_y + zout.s0)); vstore8(c10, 0, (__global half *)(dst_addr + 1 * dst_stride_y + zout.s1)); vstore8(c20, 0, (__global half *)(dst_addr + 2 * dst_stride_y + zout.s2)); vstore8(c30, 0, (__global half *)(dst_addr + 3 * dst_stride_y + zout.s3)); #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store 4x8 block vstore8(c00, 0, (__global half *)(dst_addr + 0 * dst_stride_y)); vstore8(c10, 0, (__global half *)(dst_addr + 1 * dst_stride_y)); vstore8(c20, 0, (__global half *)(dst_addr + 2 * dst_stride_y)); vstore8(c30, 0, (__global half *)(dst_addr + 3 * dst_stride_y)); #endif // defined(REINTERPRET_OUTPUT_AS_3D) } // Undefine local defines #undef COLS_MTX_B #endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) #endif // defined(COLS_B) && defined(MULT_TRANSPOSE1XW_WIDTH) && defined(MULT_INTERLEAVE4X4_HEIGHT) #if defined(COLS_A) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && (NUM_ELEMS_PROCESSED_PER_THREAD_Y) #if defined(DATA_TYPE) #define VECTOR_TYPE VEC_DATA_TYPE(DATA_TYPE, NUM_ELEMS_PROCESSED_PER_THREAD_X) /** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not been reshaped * * @note This OpenCL kernel works with floating point data types (F16/F32) * @note The floating point data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float) * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y * @note The number of matrix A columns and the optional alpha's value need to be passed at compile time using -DCOLS_A and -DALPHA * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16/F32 * @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 types: 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 types: same as @p src0_ptr * @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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_floating_point(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #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 * sizeof(DATA_TYPE); // Add offset for batched GEMM src_addr.s0 += get_global_id(2) * src0_stride_z; #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 * sizeof(DATA_TYPE)); VECTOR_TYPE acc0 = 0.0f; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VECTOR_TYPE acc1 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 VECTOR_TYPE acc2 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 VECTOR_TYPE acc3 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 for(; src_addr.s0 <= (end_row_vec_a - 2 * (int)sizeof(DATA_TYPE)); src_addr += (int2)(2 * sizeof(DATA_TYPE), 2 * src1_stride_y)) { // Load values from matrix A VEC_DATA_TYPE(DATA_TYPE, 2) a0 = vload2(0, (__global DATA_TYPE *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VEC_DATA_TYPE(DATA_TYPE, 2) a1 = vload2(0, (__global DATA_TYPE *)(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 VEC_DATA_TYPE(DATA_TYPE, 2) a2 = vload2(0, (__global DATA_TYPE *)(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 VEC_DATA_TYPE(DATA_TYPE, 2) a3 = vload2(0, (__global DATA_TYPE *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B VECTOR_TYPE b0 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, (__global DATA_TYPE *)(src1_ptr + src_addr.s1)); VECTOR_TYPE b1 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, (__global DATA_TYPE *)(src1_ptr + src_addr.s1 + src1_stride_y)); // Accumulate acc0 += b0 * (VECTOR_TYPE)a0.s0; acc0 += b1 * (VECTOR_TYPE)a0.s1; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 += b0 * (VECTOR_TYPE)a1.s0; acc1 += b1 * (VECTOR_TYPE)a1.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 += b0 * (VECTOR_TYPE)a2.s0; acc2 += b1 * (VECTOR_TYPE)a2.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 += b0 * (VECTOR_TYPE)a3.s0; acc3 += b1 * (VECTOR_TYPE)a3.s1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 } for(; src_addr.s0 < end_row_vec_a; src_addr += (int2)(sizeof(DATA_TYPE), src1_stride_y)) { // Load values from matrix A DATA_TYPE a0 = *((__global DATA_TYPE *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 DATA_TYPE a1 = *((__global DATA_TYPE *)(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 DATA_TYPE a2 = *((__global DATA_TYPE *)(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 DATA_TYPE a3 = *((__global DATA_TYPE *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B VECTOR_TYPE b0 = VLOAD(NUM_ELEMS_PROCESSED_PER_THREAD_X)(0, (__global DATA_TYPE *)(src1_ptr + src_addr.s1)); // Accumulate acc0 += b0 * (VECTOR_TYPE)a0; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 += b0 * (VECTOR_TYPE)a1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 += b0 * (VECTOR_TYPE)a2; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 += b0 * (VECTOR_TYPE)a3; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) acc0 = acc0 * (VECTOR_TYPE)ALPHA; #endif // defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) acc1 = acc1 * (VECTOR_TYPE)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) acc2 = acc2 * (VECTOR_TYPE)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) acc3 = acc3 * (VECTOR_TYPE)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) int z = get_global_id(2); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store output block VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (acc0, 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y + zout.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (acc1, 0, (__global DATA_TYPE *)(dst_addr + 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) (acc2, 0, (__global DATA_TYPE *)(dst_addr + 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) (acc3, 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y + zout.s3)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store output block VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (acc0, 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X) (acc1, 0, (__global DATA_TYPE *)(dst_addr + 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) (acc2, 0, (__global DATA_TYPE *)(dst_addr + 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) (acc3, 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #endif // defined(REINTERPRET_OUTPUT_AS_3D) } #endif // defined(DATA_TYPE) /** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not been reshaped * * @note This OpenCL kernel works with the 32-bit floating point data type (float) and uses the fma units. * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y. * This kernel optimally uses -DNUM_ELEMS_PROCESSED_PER_THREAD_X=4. * @note The number of matrix A columns must be passed at compile time using -DCOLS_A. * @note The optional value of scalar alpha is passed at compile time using -DALPHA=alpha * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16/F32 * @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 types: 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 types: same as @p src0_ptr * @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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_floating_point_f32_bifrost(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #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 matrix A src_addr.s0 += get_global_id(1) * src0_stride_y * NUM_ELEMS_PROCESSED_PER_THREAD_Y; // Update address for matrix B src_addr.s1 += idx * sizeof(float); // Add offset for batched GEMM src_addr.s0 += get_global_id(2) * src0_stride_z; #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) // Initialize accumulators float acc00 = 0.0f; float acc01 = 0.0f; float acc02 = 0.0f; float acc03 = 0.0f; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 float acc10 = 0.0f; float acc11 = 0.0f; float acc12 = 0.0f; float acc13 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 float acc20 = 0.0f; float acc21 = 0.0f; float acc22 = 0.0f; float acc23 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 float acc30 = 0.0f; float acc31 = 0.0f; float acc32 = 0.0f; float acc33 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // A and B src indices get incremented at the same time. int i = 0; for(; i <= ((int)COLS_A - 4); i += 4) { // Load values from matrix A and matrix B float4 a0 = vload4(0, (__global float *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 float4 a1 = vload4(0, (__global float *)(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 float4 a2 = vload4(0, (__global float *)(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 float4 a3 = vload4(0, (__global float *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 float4 b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0.s0, b0.s0, acc00); acc01 = fma(a0.s0, b0.s1, acc01); acc02 = fma(a0.s0, b0.s2, acc02); acc03 = fma(a0.s0, b0.s3, acc03); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1.s0, b0.s0, acc10); acc11 = fma(a1.s0, b0.s1, acc11); acc12 = fma(a1.s0, b0.s2, acc12); acc13 = fma(a1.s0, b0.s3, acc13); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2.s0, b0.s0, acc20); acc21 = fma(a2.s0, b0.s1, acc21); acc22 = fma(a2.s0, b0.s2, acc22); acc23 = fma(a2.s0, b0.s3, acc23); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3.s0, b0.s0, acc30); acc31 = fma(a3.s0, b0.s1, acc31); acc32 = fma(a3.s0, b0.s2, acc32); acc33 = fma(a3.s0, b0.s3, acc33); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix A and matrix B b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0.s1, b0.s0, acc00); acc01 = fma(a0.s1, b0.s1, acc01); acc02 = fma(a0.s1, b0.s2, acc02); acc03 = fma(a0.s1, b0.s3, acc03); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1.s1, b0.s0, acc10); acc11 = fma(a1.s1, b0.s1, acc11); acc12 = fma(a1.s1, b0.s2, acc12); acc13 = fma(a1.s1, b0.s3, acc13); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2.s1, b0.s0, acc20); acc21 = fma(a2.s1, b0.s1, acc21); acc22 = fma(a2.s1, b0.s2, acc22); acc23 = fma(a2.s1, b0.s3, acc23); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3.s1, b0.s0, acc30); acc31 = fma(a3.s1, b0.s1, acc31); acc32 = fma(a3.s1, b0.s2, acc32); acc33 = fma(a3.s1, b0.s3, acc33); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix A and matrix B b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0.s2, b0.s0, acc00); acc01 = fma(a0.s2, b0.s1, acc01); acc02 = fma(a0.s2, b0.s2, acc02); acc03 = fma(a0.s2, b0.s3, acc03); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1.s2, b0.s0, acc10); acc11 = fma(a1.s2, b0.s1, acc11); acc12 = fma(a1.s2, b0.s2, acc12); acc13 = fma(a1.s2, b0.s3, acc13); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2.s2, b0.s0, acc20); acc21 = fma(a2.s2, b0.s1, acc21); acc22 = fma(a2.s2, b0.s2, acc22); acc23 = fma(a2.s2, b0.s3, acc23); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3.s2, b0.s0, acc30); acc31 = fma(a3.s2, b0.s1, acc31); acc32 = fma(a3.s2, b0.s2, acc32); acc33 = fma(a3.s2, b0.s3, acc33); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix A and matrix B b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0.s3, b0.s0, acc00); acc01 = fma(a0.s3, b0.s1, acc01); acc02 = fma(a0.s3, b0.s2, acc02); acc03 = fma(a0.s3, b0.s3, acc03); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1.s3, b0.s0, acc10); acc11 = fma(a1.s3, b0.s1, acc11); acc12 = fma(a1.s3, b0.s2, acc12); acc13 = fma(a1.s3, b0.s3, acc13); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2.s3, b0.s0, acc20); acc21 = fma(a2.s3, b0.s1, acc21); acc22 = fma(a2.s3, b0.s2, acc22); acc23 = fma(a2.s3, b0.s3, acc23); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3.s3, b0.s0, acc30); acc31 = fma(a3.s3, b0.s1, acc31); acc32 = fma(a3.s3, b0.s2, acc32); acc33 = fma(a3.s3, b0.s3, acc33); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 src_addr.s0 += 4 * sizeof(float); } for(; i < (int)COLS_A; ++i) { // Load values from matrix A float a0 = *((__global float *)(src0_ptr + src_addr.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 float a1 = *((__global float *)(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 float a2 = *((__global float *)(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 float a3 = *((__global float *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B float4 b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0, b0.s0, acc00); acc01 = fma(a0, b0.s1, acc01); acc02 = fma(a0, b0.s2, acc02); acc03 = fma(a0, b0.s3, acc03); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1, b0.s0, acc10); acc11 = fma(a1, b0.s1, acc11); acc12 = fma(a1, b0.s2, acc12); acc13 = fma(a1, b0.s3, acc13); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2, b0.s0, acc20); acc21 = fma(a2, b0.s1, acc21); acc22 = fma(a2, b0.s2, acc22); acc23 = fma(a2, b0.s3, acc23); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3, b0.s0, acc30); acc31 = fma(a3, b0.s1, acc31); acc32 = fma(a3, b0.s2, acc32); acc33 = fma(a3, b0.s3, acc33); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 src_addr.s0 += sizeof(float); } int z = get_global_id(2); // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) acc00 = acc00 * ALPHA; acc01 = acc01 * ALPHA; acc02 = acc02 * ALPHA; acc03 = acc03 * ALPHA; #endif // defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) acc10 = acc10 * ALPHA; acc11 = acc11 * ALPHA; acc12 = acc12 * ALPHA; acc13 = acc13 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) acc20 = acc20 * ALPHA; acc21 = acc21 * ALPHA; acc22 = acc22 * ALPHA; acc23 = acc23 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) acc30 = acc30 * ALPHA; acc31 = acc31 * ALPHA; acc32 = acc32 * ALPHA; acc33 = acc33 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store the output block vstore4((float4)(acc00, acc01, acc02, acc03), 0, (__global float *)(dst_addr + 0 * dst_stride_y + zout.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore4((float4)(acc10, acc11, acc12, acc13), 0, (__global float *)(dst_addr + 1 * dst_stride_y + zout.s1)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore4((float4)(acc20, acc21, acc22, acc23), 0, (__global float *)(dst_addr + 2 * dst_stride_y + zout.s2)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore4((float4)(acc30, acc31, acc32, acc33), 0, (__global float *)(dst_addr + 3 * dst_stride_y + zout.s3)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store the output block vstore4((float4)(acc00, acc01, acc02, acc03), 0, (__global float *)(dst_addr + 0 * dst_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore4((float4)(acc10, acc11, acc12, acc13), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore4((float4)(acc20, acc21, acc22, acc23), 0, (__global float *)(dst_addr + 2 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore4((float4)(acc30, acc31, acc32, acc33), 0, (__global float *)(dst_addr + 3 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #endif // defined(REINTERPRET_OUTPUT_AS_3D) } /** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not been reshaped * * @note This OpenCL kernel works with the 32-bit floating point data type (float) and uses the fma units. * This OpenCL kernel is optimized for Bifrost when the number of matrix B columns is less or equal to 1000. * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y. * This kernel optimally uses -DNUM_ELEMS_PROCESSED_PER_THREAD_X=2. * @note The number of matrix A columns must be passed at compile time using -DCOLS_A. * @note The optional value of scalar alpha is passed at compile time using -DALPHA=alpha if alpha!=1.0f. * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16/F32 * @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 types: 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 types: same as @p src0_ptr * @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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_floating_point_f32_bifrost_1000(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #endif // REINTERPRET_OUTPUT_AS_3D ) { // Requires 2 NUM_ELEMS_PROCESSED_PER_THREAD_X, C vect2, A vect4, B (2 vload2) // to fix for NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 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 * sizeof(float); // Add offset for batched GEMM src_addr.s0 += get_global_id(2) * src0_stride_z; #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) // Initialize accumulators float acc00 = 0.0f; float acc01 = 0.0f; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 float acc10 = 0.0f; float acc11 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 float acc20 = 0.0f; float acc21 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 float acc30 = 0.0f; float acc31 = 0.0f; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // A and B src indices get incremented at the same time. int i = 0; for(; i <= ((int)COLS_A - 8); i += 8) { // Load values from matrix A float8 a0 = vload8(0, (__global float *)(src0_ptr + src_addr.s0)); // Load values from matrix B float2 b0 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b1 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b2 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b3 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b4 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b5 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b6 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; float2 b7 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0.s0, b0.s0, acc00); acc00 = fma(a0.s1, b1.s0, acc00); acc00 = fma(a0.s2, b2.s0, acc00); acc00 = fma(a0.s3, b3.s0, acc00); acc00 = fma(a0.s4, b4.s0, acc00); acc00 = fma(a0.s5, b5.s0, acc00); acc00 = fma(a0.s6, b6.s0, acc00); acc00 = fma(a0.s7, b7.s0, acc00); acc01 = fma(a0.s0, b0.s1, acc01); acc01 = fma(a0.s1, b1.s1, acc01); acc01 = fma(a0.s2, b2.s1, acc01); acc01 = fma(a0.s3, b3.s1, acc01); acc01 = fma(a0.s4, b4.s1, acc01); acc01 = fma(a0.s5, b5.s1, acc01); acc01 = fma(a0.s6, b6.s1, acc01); acc01 = fma(a0.s7, b7.s1, acc01); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 a0 = vload8(0, (__global float *)(src0_ptr + src_addr.s0 + 1 * src0_stride_y)); acc10 = fma(a0.s0, b0.s0, acc10); acc10 = fma(a0.s1, b1.s0, acc10); acc10 = fma(a0.s2, b2.s0, acc10); acc10 = fma(a0.s3, b3.s0, acc10); acc10 = fma(a0.s4, b4.s0, acc10); acc10 = fma(a0.s5, b5.s0, acc10); acc10 = fma(a0.s6, b6.s0, acc10); acc10 = fma(a0.s7, b7.s0, acc10); acc11 = fma(a0.s0, b0.s1, acc11); acc11 = fma(a0.s1, b1.s1, acc11); acc11 = fma(a0.s2, b2.s1, acc11); acc11 = fma(a0.s3, b3.s1, acc11); acc11 = fma(a0.s4, b4.s1, acc11); acc11 = fma(a0.s5, b5.s1, acc11); acc11 = fma(a0.s6, b6.s1, acc11); acc11 = fma(a0.s7, b7.s1, acc11); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 a0 = vload8(0, (__global float *)(src0_ptr + src_addr.s0 + 2 * src0_stride_y)); acc20 = fma(a0.s0, b0.s0, acc20); acc20 = fma(a0.s1, b1.s0, acc20); acc20 = fma(a0.s2, b2.s0, acc20); acc20 = fma(a0.s3, b3.s0, acc20); acc20 = fma(a0.s4, b4.s0, acc20); acc20 = fma(a0.s5, b5.s0, acc20); acc20 = fma(a0.s6, b6.s0, acc20); acc20 = fma(a0.s7, b7.s0, acc20); acc21 = fma(a0.s0, b0.s1, acc21); acc21 = fma(a0.s1, b1.s1, acc21); acc21 = fma(a0.s2, b2.s1, acc21); acc21 = fma(a0.s3, b3.s1, acc21); acc21 = fma(a0.s4, b4.s1, acc21); acc21 = fma(a0.s5, b5.s1, acc21); acc21 = fma(a0.s6, b6.s1, acc21); acc21 = fma(a0.s7, b7.s1, acc21); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 a0 = vload8(0, (__global float *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); acc30 = fma(a0.s0, b0.s0, acc30); acc30 = fma(a0.s1, b1.s0, acc30); acc30 = fma(a0.s2, b2.s0, acc30); acc30 = fma(a0.s3, b3.s0, acc30); acc30 = fma(a0.s4, b4.s0, acc30); acc30 = fma(a0.s5, b5.s0, acc30); acc30 = fma(a0.s6, b6.s0, acc30); acc30 = fma(a0.s7, b7.s0, acc30); acc31 = fma(a0.s0, b0.s1, acc31); acc31 = fma(a0.s1, b1.s1, acc31); acc31 = fma(a0.s2, b2.s1, acc31); acc31 = fma(a0.s3, b3.s1, acc31); acc31 = fma(a0.s4, b4.s1, acc31); acc31 = fma(a0.s5, b5.s1, acc31); acc31 = fma(a0.s6, b6.s1, acc31); acc31 = fma(a0.s7, b7.s1, acc31); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 src_addr.s0 += sizeof(float) * 8; } // float size increment for(; i < (int)COLS_A; ++i) { // Load values from matrix A float a0 = *((__global float *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 float a1 = *((__global float *)(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 float a2 = *((__global float *)(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 float a3 = *((__global float *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B float2 b0 = vload2(0, (__global float *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Multiply and accumulate acc00 = fma(a0, b0.s0, acc00); acc01 = fma(a0, b0.s1, acc01); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc10 = fma(a1, b0.s0, acc10); acc11 = fma(a1, b0.s1, acc11); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc20 = fma(a2, b0.s0, acc20); acc21 = fma(a2, b0.s1, acc21); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc30 = fma(a3, b0.s0, acc30); acc31 = fma(a3, b0.s1, acc31); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 src_addr.s0 += sizeof(float); } // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) acc00 = acc00 * ALPHA; acc01 = acc01 * ALPHA; #endif // defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) acc10 = acc10 * ALPHA; acc11 = acc11 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) acc20 = acc20 * ALPHA; acc21 = acc21 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) acc30 = acc30 * ALPHA; acc31 = acc31 * ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) int z = get_global_id(2); // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store the output block vstore2((float2)(acc00, acc01), 0, (__global float *)(dst_addr + 0 * dst_stride_y + zout.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore2((float2)(acc10, acc11), 0, (__global float *)(dst_addr + 1 * dst_stride_y + zout.s1)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore2((float2)(acc20, acc21), 0, (__global float *)(dst_addr + 2 * dst_stride_y + zout.s2)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore2((float2)(acc30, acc31), 0, (__global float *)(dst_addr + 3 * dst_stride_y + zout.s3)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store the output block vstore2((float2)(acc00, acc01), 0, (__global float *)(dst_addr + 0 * dst_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore2((float2)(acc10, acc11), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore2((float2)(acc20, acc21), 0, (__global float *)(dst_addr + 2 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore2((float2)(acc30, acc31), 0, (__global float *)(dst_addr + 3 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #endif // defined(REINTERPRET_OUTPUT_AS_3D) } #if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) /** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not beed reshaped * * @note This OpenCL kernel works with the 16-bit floating point data type (half) and uses the fma units. * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y. * This kernel optimally uses -DNUM_ELEMS_PROCESSED_PER_THREAD_X=4. * @note The number of matrix A columns must be passed at compile time using -DCOLS_A. * @note The optional value of scalar alpha is passed at compile time using -DALPHA=alpha * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16) * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16]) * * @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 matrix A NOT reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F16 * @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 types: 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 types: same as @p src0_ptr * @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] pad_bottom Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_floating_point_f16_bifrost(IMAGE_DECLARATION(src0), IMAGE_DECLARATION(src1), IMAGE_DECLARATION(dst), uint src0_stride_z, uint src1_stride_z, uint dst_stride_z #if defined(REINTERPRET_OUTPUT_AS_3D) , uint pad_bottom #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 * sizeof(half); // Add offset for batched GEMM src_addr.s0 += get_global_id(2) * src0_stride_z; #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) half8 acc0 = 0.0h; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 half8 acc1 = 0.0h; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 half8 acc2 = 0.0h; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 half8 acc3 = 0.0h; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 int i = 0; for(; i <= ((int)COLS_A - 4); i += 4) { // Load values from matrix A half4 a0 = vload4(0, (__global half *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 half4 a1 = vload4(0, (__global half *)(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 half4 a2 = vload4(0, (__global half *)(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 half4 a3 = vload4(0, (__global half *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B half8 b0 = vload8(0, (__global half *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; // Accumulate acc0 = fma(b0, (half8)a0.s0, acc0); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 = fma(b0, (half8)a1.s0, acc1); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 = fma(b0, (half8)a2.s0, acc2); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 = fma(b0, (half8)a3.s0, acc3); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 b0 = vload8(0, (__global half *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; acc0 = fma(b0, (half8)a0.s1, acc0); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 = fma(b0, (half8)a1.s1, acc1); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 = fma(b0, (half8)a2.s1, acc2); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 = fma(b0, (half8)a3.s1, acc3); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 b0 = vload8(0, (__global half *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; acc0 = fma(b0, (half8)a0.s2, acc0); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 = fma(b0, (half8)a1.s2, acc1); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 = fma(b0, (half8)a2.s2, acc2); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 = fma(b0, (half8)a3.s2, acc3); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 b0 = vload8(0, (__global half *)(src1_ptr + src_addr.s1)); src_addr.s1 += src1_stride_y; acc0 = fma(b0, (half8)a0.s3, acc0); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 = fma(b0, (half8)a1.s3, acc1); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 = fma(b0, (half8)a2.s3, acc2); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 = fma(b0, (half8)a3.s3, acc3); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 src_addr.s0 += 4 * sizeof(half); } for(; i < (int)COLS_A; ++i) { // Load values from matrix A half a0 = *((__global half *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 half a1 = *((__global half *)(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 half a2 = *((__global half *)(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 half a3 = *((__global half *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 // Load values from matrix B half8 b0 = vload8(0, (__global half *)(src1_ptr + src_addr.s1)); src_addr += (int2)(sizeof(half), src1_stride_y); // Accumulate acc0 = fma(b0, (half8)a0, acc0); // b0 * (half8)a0; #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 acc1 = fma(b0, (half8)a1, acc1); // b0 * (half8)a1; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 acc2 = fma(b0, (half8)a2, acc2); // b0 * (half8)a2; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 acc3 = fma(b0, (half8)a3, acc3); // b0 * (half8)a3; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 } // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) acc0 = acc0 * (half8)ALPHA; #endif // defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) acc1 = acc1 * (half8)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) acc2 = acc2 * (half8)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 && defined(ALPHA) #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) acc3 = acc3 * (half8)ALPHA; #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 && defined(ALPHA) int z = get_global_id(2); // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); // Compute dst address __global uchar *dst_addr = offset(&dst, 0, 0); #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 bottom paddings // // | | // | plane0 | // | | // |_____________| // |*************| // | pad_bottom | // |*************| // | | // | plane1 | // | | // |_____________| // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D; zout = min(DEPTH_GEMM3D - 1, zout); // Add offset due to the bottom paddings zout *= (pad_bottom * 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; // Store the output block vstore8(acc0, 0, (__global half *)(dst_addr + 0 * dst_stride_y + zout.s0)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore8(acc1, 0, (__global half *)(dst_addr + 1 * dst_stride_y + zout.s1)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore8(acc2, 0, (__global half *)(dst_addr + 2 * dst_stride_y + zout.s2)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore8(acc3, 0, (__global half *)(dst_addr + 3 * dst_stride_y + zout.s3)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #else // defined(REINTERPRET_OUTPUT_AS_3D) // Add offset for batched GEMM dst_addr += z * dst_stride_z; // Store the output block vstore8(acc0, 0, (__global half *)(dst_addr + 0 * dst_stride_y)); #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 vstore8(acc1, 0, (__global half *)(dst_addr + 1 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 vstore8(acc2, 0, (__global half *)(dst_addr + 2 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 vstore8(acc3, 0, (__global half *)(dst_addr + 3 * dst_stride_y)); #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3 #endif // REINTERPRET_OUTPUT_AS_3D } #endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) #endif // defined(COLS_A) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && (NUM_ELEMS_PROCESSED_PER_THREAD_Y) #if defined(BETA) /** This OpenCL kernel performs the in-place matrix addition between 2 matrices taking into account that the second matrix might be weighted by a scalar value beta: * * @note The beta's value need to be passed at compile time using -DBETA * * @param[in] src_ptr Pointer to the source matrix. Supported data types: F32 * @param[in] src_stride_x Stride of the source matrix 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 matrix 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 destination tensor in Z dimension (in bytes) * @param[in] src_step_z dst_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 matrix * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_ptr * @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_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_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 matrix */ __kernel void gemm_ma_f32(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); // Load values from A x B float4 alpha_ab = vload4(0, (__global float *)dst.ptr); // Load values from Matrix C float4 c = vload4(0, (__global float *)src.ptr); // Computes alpha * axb + beta * c float4 out = alpha_ab + (float4)BETA * c; // Store final result in axb matrix vstore4(out, 0, (__global float *)dst.ptr); } #if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) /** This OpenCL kernel performs the in-place matrix addition between 2 matrices taking into account that the second matrix might be weighted by a scalar value beta: * * @note The beta's value need to be passed at compile time using -DBETA * * @param[in] src_ptr Pointer to the source matrix. Supported data types: F16 * @param[in] src_stride_x Stride of the source matrix 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 matrix 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 destination tensor in Z dimension (in bytes) * @param[in] src_step_z dst_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 matrix * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_ptr * @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_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_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 matrix */ __kernel void gemm_ma_f16(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst)) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); // Load values from A x B half8 alpha_ab = vload8(0, (__global half *)dst.ptr); // Load values from Matrix C half8 c = vload8(0, (__global half *)src.ptr); // Computes alpha * axb + beta * c half8 out = alpha_ab + (half8)BETA * c; // Store final result in axb matrix vstore8(out, 0, (__global half *)dst.ptr); } #endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) #endif // defined(BETA) #if defined(WIDTH_VECTOR_A) /** This OpenCL kernel computes the vector by matrix multiplication between each row of A (src0) and matrix B (src1) used for locally connected layer * * @note The width of A need to be passed at compile time using -DWIDTH_VECTOR_A * * @note The input A and matrix B must not be reshaped * * @param[in] src0_ptr Pointer to the source matrix. Supported data types: F32 * @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 types: 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_stride_z Stride of the source matrix in Z dimension (in bytes) * @param[in] src1_step_z src_stride_z * number of elements along Z 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 types: same as @p src0_ptr * @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 */ __kernel void gemm_lc_vm_f32(IMAGE_DECLARATION(src0), TENSOR3D_DECLARATION(src1), IMAGE_DECLARATION(dst)) { int idx = get_global_id(0) * 4; int idy = get_global_id(1); // Compute the address for the vector A and matrix B int2 src_addr = ((int2)(src0_offset_first_element_in_bytes + src0_stride_y * idy, src1_offset_first_element_in_bytes + src1_stride_z * idy)); src_addr.s1 += idx * sizeof(float); int end_row_vec_a = src_addr.s0 + (WIDTH_VECTOR_A * sizeof(float)); float4 acc = 0.0f; for(; src_addr.s0 <= (end_row_vec_a - 2 * (int)sizeof(float)); src_addr += (int2)(2 * sizeof(float), 2 * src1_stride_y)) { float2 a0 = vload2(0, (__global float *)(src0_ptr + src_addr.s0)); float4 b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); float4 b1 = vload4(0, (__global float *)(src1_ptr + src_addr.s1 + src1_stride_y)); acc += b0 * (float4)a0.s0; acc += b1 * (float4)a0.s1; } for(; src_addr.s0 < end_row_vec_a; src_addr += (int2)(sizeof(float), src1_stride_y)) { float a0 = *((__global float *)(src0_ptr + src_addr.s0)); float4 b0 = vload4(0, (__global float *)(src1_ptr + src_addr.s1)); acc += b0 * (float4)a0; } // Compute destination address Image dst = CONVERT_TO_IMAGE_STRUCT(dst); vstore4(acc, 0, (__global float *)(offset(&dst, 0, 0))); } #endif // defined(WIDTH_VECTOR_A) /** This kernel accumulates each row with the biases vector. * * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=short. * @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=16. * * @param[in, out] accum_ptr Pointer to the accumulate tensor. Supported data type: U8/S8/U16/S16/F16/U32/S32/F32 * @param[in] accum_stride_x Stride of the accmulate tensor in X dimension (in bytes) * @param[in] accum_step_x accum_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] accum_stride_y Stride of the accumlulate tensor in Y dimension (in bytes) * @param[in] accum_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] accum_offset_first_element_in_bytes The offset of the first element in the accumulate tensor * @param[in] biases_ptr Pointer to the biases vector. Same as @p accum_ptr * @param[in] biases_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] biases_step_x dst_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 destination tensor */ #if defined(DATA_TYPE) && defined(VECTOR_SIZE) __kernel void gemm_accumulate_biases( IMAGE_DECLARATION(accum), VECTOR_DECLARATION(biases)) { Image accum = CONVERT_TO_IMAGE_STRUCT(accum); Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); // Vector size, i.e. number of vector elements. VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) accum_value = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)accum.ptr); VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) biases_value = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)biases.ptr); accum_value = biases_value + accum_value; // Store result in the accumulate buffer VSTORE(VECTOR_SIZE) (accum_value, 0, (__global DATA_TYPE *)accum.ptr); } #endif // defined(DATA_TYPE) && defined(VECTOR_SIZE)