/* * Copyright (c) 2022-2023 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 "activation_float_helpers.h" #include "helpers.h" #include "tile_helpers.h" #if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL) /** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension: * * The LHS matrix is NOT reshaped * The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed * * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4). * @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2) * @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2) * @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2) * @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2) * @note Only the following configurations of M0, N0 and K0 are currently supported: * - M0 > 0 * - N0 = 1, 2, 3, 4, 8, 16 * - K0 = 1 * * @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively. * The activation function is performed after the bias addition * * @param[in] lhs_ptr Pointer to the LHS tensor. Supported data types: F16/F32 * @param[in] lhs_stride_y Stride of the LHS tensor in Y dimension (in bytes) * @param[in] lhs_stride_z Stride of the LHS tensor in Z dimension (in bytes) * @param[in] lhs_w The size of the width dimension of the LHS tensor * @param[in] lhs_h The size of the height dimension of the LHS tensor * @param[in] lhs_n The size of the depth dimension of the LHS tensor * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor * @param[in] rhs_ptr Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr * @param[in] rhs_stride_y Stride of the RHS tensor in Y dimension (in bytes) * @param[in] rhs_stride_z Stride of the RHS tensor in Z dimension (in bytes) * @param[in] rhs_w The size of the width dimension of the RHS tensor * @param[in] rhs_h The size of the height dimension of the RHS tensor * @param[in] rhs_n The size of the depth dimension of the RHS tensor * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor * @param[in] bia_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr * @param[in] bia_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) * @param[in] bia_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) * @param[in] bia_w (Optional) The size of the width dimension of the bias tensor * @param[in] bia_h (Optional) The size of the height dimension of the bias tensor * @param[in] bia_n (Optional) The size of the depth dimension of the bias tensor * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p lhs_ptr * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_w The size of the width dimension of the destination tensor * @param[in] dst_h The size of the height dimension of the destination tensor * @param[in] dst_n The size of the depth dimension of the destination tensor * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] M Number of rows in LHS matrix not reshaped * @param[in] N Number of columns in RHS matrix not reshaped * @param[in] K Number of columns in LHS matrix and rows in RHS matrix not reshaped */ __kernel void gemm_mm_reshaped_only_rhs_nt_mmul( TENSOR3D_T(lhs, BUFFER), TENSOR3D_T(rhs, BUFFER), #if defined(BETA) TENSOR3D_T(bia, BUFFER), #endif // defined(BETA) TENSOR3D_T(dst, BUFFER), const int M, const int N, const int K) { #define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0) uint x0 = get_global_id(0); // (N / N0) * MMUL_K0 uint y0 = get_global_id(1); // (M / M0) / MMUL_M0 uint z = get_global_id(2); // Batch // Get block ID and thread ID within the block uint block_id = (x0 / MMUL_BLOCK_SIZE); uint thread_id = (x0 % MMUL_BLOCK_SIZE); // Coordinate within a block uint block_x = thread_id % MMUL_N0; uint block_y = (thread_id / MMUL_M0); // Starting destination coordinates uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1)); uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0)); // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results // Starting LHS coordinates uint lhs_x = block_x; uint lhs_y = dst_y; // Starting RHS coordinates uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0; uint rhs_y = block_id; // Compute LHS/RHS/DST matrix address #ifdef REINTERPRET_INPUT_AS_3D lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + (lhs_y + z * M) * lhs_stride_y; #else // REINTERPRET_INPUT_AS_3D lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; #endif // REINTERPRET_INPUT_AS_3D #ifdef BATCHED_RHS rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; #else // BATCHED_RHS rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y; #endif // BATCHED_RHS #ifdef REINTERPRET_OUTPUT_AS_3D dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + (dst_y + z * M) * dst_stride_y; #else // REINTERPRET_OUTPUT_AS_3D dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; #endif // REINTERPRET_OUTPUT_AS_3D // Note: If RHS derives from the weights of convolution 2d layer, RHS will always be 2D and rhs_stride_z will always be equal to 0 for // not sliding the tensor // Initialize the accumulators // MMUL extension accumulate the result in F32 for both F32 and F16 TILE(float, M0, N0, c_f32); #if !defined(HALF_PRECISION) #define c c_f32 #endif // !defined(HALF_PRECISION) LOOP_UNROLLING(int, i, 0, 1, M0, { c_f32[i].v = 0; }) for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0) { TILE(DATA_TYPE, M0, 1, a); TILE(DATA_TYPE, 1, N0, b); // Load tile from the lhs/rhs tensors T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, 0, b); LOOP_UNROLLING(int, m0, 0, 1, M0, { LOOP_UNROLLING(int, n0, 0, 1, N0, { c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]); }) }) lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE); rhs_offset_first_element_in_bytes += MMUL_K0 * MMUL_N0 * N0 * sizeof(DATA_TYPE); } if(block_x * N0 + block_id * MMUL_N0 * N0 >= N) { return; } if(block_y * M0 + y0 * M0 * MMUL_M0 >= M) { return; } #if defined(HALF_PRECISION) TILE(DATA_TYPE, M0, N0, c); // Conversion required for the half precision LOOP_UNROLLING(int, m0, 0, 1, M0, { LOOP_UNROLLING(int, n0, 0, 1, N0, { c[m0].s[n0] = c_f32[m0].s[n0]; }) }) #endif // defined(HALF_PRECISION) // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c); #endif // defined(ALPHA) // Add beta*bias #if defined(BETA) #if defined(BROADCAST_BIAS) bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE); TILE(DATA_TYPE, 1, N0, bias0); if(dst_x + N0 <= N || N0_LEFTOVER == 0) { bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes)); } else { VLOAD_PARTIAL(N0, N0_LEFTOVER) (bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes)); } #ifndef UNIT_BETA T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0); #endif // UNIT_BIAS // c = c + bias[broadcasted] T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c); #else // defined(BROADCAST_BIAS) TILE(DATA_TYPE, M0, N0, bias0); bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z; if(dst_x + N0 <= N || N0_LEFTOVER == 0) { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y)); } }) } else { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VLOAD_PARTIAL(N0, N0_LEFTOVER) (bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y)); } }) } #ifndef UNIT_BETA T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0); #endif // UNIT_BIAS // c = c + bias T_ADD(DATA_TYPE, M0, N0, c, bias0, c); // c = c + bias #endif // defined(BROADCAST_BIAS) #endif // defined(BETA) T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c); // Store if(dst_x + N0 <= N || N0_LEFTOVER == 0) { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VSTORE(N0) (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); } }) } else { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VSTORE_PARTIAL(N0, N0_LEFTOVER) (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); } }) } #undef RHS_BLOCK_SIZE #undef RHS_OFFSET_X #undef RHS_STEP_X } #endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL) #if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL_TEXTURE) /** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension and the OpenCL image for RHS: * * The LHS matrix is NOT reshaped * The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed * * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4). * @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2) * @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2) * @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2) * @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2) * @note Only the following configurations of M0, N0 and K0 are currently supported: * - M0 > 0 * - N0 = 1, 2, 3, 4, 8, 16 * - K0 = 1 * * @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively. * The activation function is performed after the bias addition * * @param[in] lhs_ptr Pointer to the LHS tensor. Supported data types: F16/F32 * @param[in] lhs_stride_y Stride of the LHS tensor in Y dimension (in bytes) * @param[in] lhs_stride_z Stride of the LHS tensor in Z dimension (in bytes) * @param[in] lhs_w The size of the width dimension of the LHS tensor * @param[in] lhs_h The size of the height dimension of the LHS tensor * @param[in] lhs_n The size of the depth dimension of the LHS tensor * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor * @param[in] rhs_ptr Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr * @param[in] rhs_stride_y Stride of the RHS tensor in Y dimension (in bytes) * @param[in] rhs_stride_z Stride of the RHS tensor in Z dimension (in bytes) * @param[in] rhs_w The size of the width dimension of the RHS tensor * @param[in] rhs_h The size of the height dimension of the RHS tensor * @param[in] rhs_n The size of the depth dimension of the RHS tensor * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor * @param[in] bia_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr * @param[in] bia_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) * @param[in] bia_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) * @param[in] bia_w (Optional) The size of the width dimension of the bias tensor * @param[in] bia_h (Optional) The size of the height dimension of the bias tensor * @param[in] bia_n (Optional) The size of the depth dimension of the bias tensor * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p lhs_ptr * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_w The size of the width dimension of the destination tensor * @param[in] dst_h The size of the height dimension of the destination tensor * @param[in] dst_n The size of the depth dimension of the destination tensor * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] M Number of rows in LHS matrix not reshaped * @param[in] N Number of columns in RHS matrix not reshaped * @param[in] K Number of columns in LHS matrix and rows in RHS matrix not reshaped */ __kernel void gemm_mm_reshaped_only_rhs_nt_mmul_texture( TENSOR3D_T(lhs, BUFFER), TENSOR3D_T(rhs, IMAGE), #if defined(BETA) TENSOR3D_T(bia, BUFFER), #endif // defined(BETA) TENSOR3D_T(dst, BUFFER), const int M, const int N, const int K) { #define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0) uint x0 = get_global_id(0); // (N / N0) * MMUL_K0 uint y0 = get_global_id(1); // (M / M0) / MMUL_M0 uint z = get_global_id(2); // Batch // Get block ID and thread ID within the block uint block_id = (x0 / MMUL_BLOCK_SIZE); uint thread_id = (x0 % MMUL_BLOCK_SIZE); // Coordinate within a block uint block_x = thread_id % MMUL_N0; uint block_y = (thread_id / MMUL_M0); // Starting destination coordinates uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1)); uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0)); // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results // Starting LHS coordinates uint lhs_x = block_x; uint lhs_y = dst_y; // Starting RHS coordinates uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0; #ifdef BATCHED_RHS uint rhs_y = block_id + z * rhs_h; #else // BATCHED_RHS uint rhs_y = block_id; #endif // BATCHED_RHS // Compute LHS/RHS/DST matrix address #ifdef REINTERPRET_INPUT_AS_3D lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + (lhs_y + z * M) * lhs_stride_y; #else // REINTERPRET_INPUT_AS_3D lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; #endif // REINTERPRET_INPUT_AS_3D #ifdef REINTERPRET_OUTPUT_AS_3D dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + (dst_y + z * M) * dst_stride_y; #else // REINTERPRET_OUTPUT_AS_3D dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; #endif // REINTERPRET_OUTPUT_AS_3D // Initialize the accumulators // MMUL extension accumulate the result in F32 for both F32 and F16 TILE(float, M0, N0, c_f32); #if !defined(HALF_PRECISION) #define c c_f32 #endif // !defined(HALF_PRECISION) LOOP_UNROLLING(int, i, 0, 1, M0, { c_f32[i].v = 0; }) for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0) { TILE(DATA_TYPE, M0, 1, a); TILE(DATA_TYPE, 1, N0, b); // Load tile from the lhs/rhs tensors T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); T_LOAD(DATA_TYPE, 1, N0, IMAGE, rhs, rhs_x, rhs_y, 1, rhs_stride_y, b); LOOP_UNROLLING(int, m0, 0, 1, M0, { LOOP_UNROLLING(int, n0, 0, 1, N0, { c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]); }) }) lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE); rhs_x += MMUL_K0 * MMUL_N0 * N0; } if(block_x * N0 + block_id * MMUL_N0 * N0 >= N) { return; } if(block_y * M0 + y0 * M0 * MMUL_M0 >= M) { return; } #if defined(HALF_PRECISION) TILE(DATA_TYPE, M0, N0, c); // Conversion required for the half precision LOOP_UNROLLING(int, m0, 0, 1, M0, { LOOP_UNROLLING(int, n0, 0, 1, N0, { c[m0].s[n0] = c_f32[m0].s[n0]; }) }) #endif // defined(HALF_PRECISION) // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c); #endif // defined(ALPHA) // Add beta*bias #if defined(BETA) #if defined(BROADCAST_BIAS) bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE); TILE(DATA_TYPE, 1, N0, bias0); if(dst_x + N0 <= N || N0_LEFTOVER == 0) { bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes)); } else { VLOAD_PARTIAL(N0, N0_LEFTOVER) (bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes)); } #ifndef UNIT_BETA T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0); #endif // UNIT_BIAS // c = c + bias[broadcasted] T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c); #else // defined(BROADCAST_BIAS) TILE(DATA_TYPE, M0, N0, bias0); bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z; if(dst_x + N0 <= N || N0_LEFTOVER == 0) { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y)); } }) } else { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VLOAD_PARTIAL(N0, N0_LEFTOVER) (bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y)); } }) } #ifndef UNIT_BETA T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0); #endif // UNIT_BIAS // c = c + bias T_ADD(DATA_TYPE, M0, N0, c, bias0, c); // c = c + bias #endif // defined(BROADCAST_BIAS) #endif // defined(BETA) T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c); // Store if(dst_x + N0 <= N || N0_LEFTOVER == 0) { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VSTORE(N0) (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); } }) } else { LOOP_UNROLLING(int, m0, 0, 1, M0, { if(dst_y + m0 < M || M0_LEFTOVER == 0) { VSTORE_PARTIAL(N0, N0_LEFTOVER) (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); } }) } #undef RHS_BLOCK_SIZE #undef RHS_OFFSET_X #undef RHS_STEP_X } #endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL_TEXTURE)