/* * Copyright (c) 2016-2018 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" #undef CONVERT_SAT #define ADD_OP(a, b) ((a) + (b)) #define MUL_OP(a, b) ((a) * (b)) #define CONVERT_SAT(a, b) ((a)) #if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if STRIDE_X == 1 #define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) #elif STRIDE_X == 2 /* STRIDE_X == 1 */ #define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) #else /* STRIDE_X not equals 1 or 2 */ #error "STRIDE_X larger than 2 is not supported" #endif /* STRIDE_X == 2 */ #define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \ ({ \ VEC_DATA_TYPE(DATA_TYPE, 3) \ weights_values0 = vload3(0, weights_row_ptr); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ src0 = vload8(0, src_row_ptr); \ VEC_DATA_TYPE(DATA_TYPE, 2) \ src1 = vload2(0, src_row_ptr + 8); \ \ acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \ }) #define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \ ({ \ VEC_DATA_TYPE(DATA_TYPE, 3) \ weights_values0 = vload3(0, weights_row_ptr); \ VEC_DATA_TYPE(DATA_TYPE, 16) \ src0 = vload16(0, src_row_ptr); \ DATA_TYPE src1 = *(src_row_ptr + 16); \ \ acc = ADD_OP(acc, MUL_OP(src0.even, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \ }) #if defined(DATA_LAYOUT_NHWC) #define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR)) #if STRIDE_X == 1 #define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr) #elif STRIDE_X == 2 /* STRIDE_X == 1 */ #define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr) #else /* STRIDE_X not equals 1 or 2 */ #error "STRIDE_X larger than 2 is not supported" #endif /* STRIDE_X == 2 */ #define CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr) \ { \ VEC_DATA_TYPE(DATA_TYPE, 8) \ src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( \ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE)); \ VEC_DATA_TYPE(DATA_TYPE, 2) \ src1 = (VEC_DATA_TYPE(DATA_TYPE, 2))( \ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE)); \ VEC_DATA_TYPE(DATA_TYPE, 3) \ weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \ PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \ PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \ PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \ acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \ } #define CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr) \ { \ VEC_DATA_TYPE(DATA_TYPE, 16) \ src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))( \ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), \ PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE)); \ DATA_TYPE src1 = PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE); \ VEC_DATA_TYPE(DATA_TYPE, 3) \ weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \ PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \ PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \ PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \ \ acc = ADD_OP(acc, MUL_OP(src0.s02468ACE, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \ } /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note This OpenCL kernel works with stride_x = 1 and 2 * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note If biases are used then -DHAS_BIAS has to be passed at compile time * * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor 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 tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Z 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 tensor * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension */ __kernel void direct_convolution3x3_nhwc( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8) values0 = 0; const int id0 = get_global_id(0); const int id1 = get_global_id(1); const int id2 = get_global_id(2); __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0); __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z; weights_addr += id0 * weights_stride_w; const int coordy = ((id2 * STRIDE_Y) - PAD_TOP); for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { #if PAD_TOP > 0 if(coordy < 0) // special case Z = -1 doesn't exists { //skip first row and load the two next ones CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z)); } else if(coordy == (SRC_HEIGHT - PAD_TOP - 1)) { // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the // Z axis has no padding at all. CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z)); } else { CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z)); } #else // PAD_TOP > 0 CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z)); CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z)); #endif // PAD_TOP > 0 src_addr += src_stride_x; weights_addr += weights_stride_x; } #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0)))); #endif /* defined(HAS_BIAS) */ *((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values0.s0; *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1; *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2; *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3; *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4; *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5; *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6; *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7; } #endif // defined(DATA_LAYOUT_NHWC) /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note This OpenCL kernel works with stride_x = 1 and 2 * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note If biases are used then -DHAS_BIAS has to be passed at compile time * * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor 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 tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Z 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 tensor * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension */ __kernel void direct_convolution3x3( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8) values0 = 0; __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0); __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); const int kernel_index = get_global_id(2); weights_addr += kernel_index * weights_stride_w; for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y)); CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y)); src_addr += src_stride_z; weights_addr += weights_stride_z; } #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)))); #endif /* defined(HAS_BIAS) */ vstore8(CONVERT_SAT(values0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr); } #endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if defined(WEIGHTS_DEPTH) #define CONVOLUTION1x3_BIFROST(acc, src0, src1, weights_row0) \ ({ \ acc.s0 = mad(src0.s0, weights_row0.s0, acc.s0); \ acc.s1 = mad(src0.s1, weights_row0.s0, acc.s1); \ acc.s2 = mad(src0.s2, weights_row0.s0, acc.s2); \ acc.s3 = mad(src0.s3, weights_row0.s0, acc.s3); \ acc.s0 = mad(src0.s1, weights_row0.s1, acc.s0); \ acc.s1 = mad(src0.s2, weights_row0.s1, acc.s1); \ acc.s2 = mad(src0.s3, weights_row0.s1, acc.s2); \ acc.s3 = mad(src1.s0, weights_row0.s1, acc.s3); \ acc.s0 = mad(src0.s2, weights_row0.s2, acc.s0); \ acc.s1 = mad(src0.s3, weights_row0.s2, acc.s1); \ acc.s2 = mad(src1.s0, weights_row0.s2, acc.s2); \ acc.s3 = mad(src1.s1, weights_row0.s2, acc.s3); \ }) /** An optimized direct convolution 3x3 OpenCL kernel for Bifrost architectures when the data type is F32 * * @note This OpenCL kernel works only with stride_x and stride_y equal to 1 * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note In case biases, -DHAS_BIAS must to be passed at compile * * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor 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 tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Z 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 tensor * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension */ __kernel void direct_convolution3x3_f32_bifrost( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w) { // Get the kernel index const int kernel_index = get_global_id(2); Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); float4 values0 = 0; float4 values1 = 0; float4 values2 = 0; __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w); __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); // Note: Since each work-item computes 4x3 elements, we need to load 5 rows from the input tensor for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) { // Load the weights float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); float4 src0; float2 src1; // Load values from row0 of input tensor src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); src1 = vload2(0, (__global float *)(src_addr + 0 * src_stride_y) + 4); CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row0); // Load values from row1 of input tensor src0 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); src1 = vload2(0, (__global float *)(src_addr + 1 * src_stride_y) + 4); // Accumulate CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row1); CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row0); // Load values from row2 of input tensor src0 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); src1 = vload2(0, (__global float *)(src_addr + 2 * src_stride_y) + 4); // Accumulate CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row2); CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row1); CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row0); // Load values from row3 of input tensor src0 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); src1 = vload2(0, (__global float *)(src_addr + 3 * src_stride_y) + 4); // Accumulate CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row2); CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row1); // Row4 src0 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); src1 = vload2(0, (__global float *)(src_addr + 4 * src_stride_y) + 4); // Accumulate CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row2); src_addr += src_stride_z; weights_addr += weights_stride_z; } #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index))); values0 += (float4)bias; values1 += (float4)bias; values2 += (float4)bias; #endif /* defined(HAS_BIAS) */ vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); vstore4(values2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); } #endif // defined(WEIGHTS_DEPTH)