From 3d319469e5f28066c507e4228dfeb6b9fdfb38a5 Mon Sep 17 00:00:00 2001 From: Pablo Tello Date: Thu, 21 Jun 2018 15:13:17 +0100 Subject: COMPMID-807: NHWC support in CLDirectConvolution. Change-Id: I8738aca2cc0104e4c4d7c9605762ab59fce10a33 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137333 Reviewed-by: Giorgio Arena Reviewed-by: Anthony Barbier Tested-by: Jenkins --- src/core/CL/cl_kernels/direct_convolution5x5.cl | 234 +++++++++++++++++++++--- 1 file changed, 209 insertions(+), 25 deletions(-) (limited to 'src/core/CL/cl_kernels/direct_convolution5x5.cl') diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl index e678f6f51b..70be058854 100644 --- a/src/core/CL/cl_kernels/direct_convolution5x5.cl +++ b/src/core/CL/cl_kernels/direct_convolution5x5.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016, 2017 ARM Limited. + * Copyright (c) 2016-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -69,6 +69,190 @@ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ }) +#if defined(DATA_LAYOUT_NHWC) + +#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR)) + +#if STRIDE_X == 1 +#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr) +#elif STRIDE_X == 2 /* STRIDE_X == 1 */ +#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE2_NHWC(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 CONVOLUTION1x5_STRIDE1_NHWC(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, 4) \ + src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ + 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)); \ + VEC_DATA_TYPE(DATA_TYPE, 4) \ + weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ + 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), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \ + DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \ + acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ + }) + +#define CONVOLUTION1x5_STRIDE2_NHWC(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)); \ + VEC_DATA_TYPE(DATA_TYPE, 4) \ + src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ + PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 17 * src_stride_y, DATA_TYPE), \ + PTR_TO_VALUE(row_ptr + 18 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 19 * src_stride_y, DATA_TYPE)); \ + VEC_DATA_TYPE(DATA_TYPE, 4) \ + weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ + 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), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \ + DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \ + acc += src0.s02468ACE * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ + \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ + acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ + }) + +/** This kernel performs a direct convolution to convolve the low three dimensions in a tensor with the NHWC data layout + * + * @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_convolution5x5_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, 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 - PAD_TOP; + + for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) + { +#if(PAD_TOP) + if(coordy < 0) // special case Z = -1 doesn't exists + { + //skip first row and load the two next ones + CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); + } + else if(coordy == (DST_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. + CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); + CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); + } + else + { + CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); + CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); + } +#else //PAD_TOP > 0 + CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); + CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); + CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (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 += (VEC_DATA_TYPE(DATA_TYPE, 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 The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float @@ -119,7 +303,7 @@ __kernel void direct_convolution5x5( Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); VEC_DATA_TYPE(DATA_TYPE, 8) - pixels0 = 0; + values0 = 0; __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0); __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); @@ -129,11 +313,11 @@ __kernel void direct_convolution5x5( for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { - CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr); - CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); - CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y)); - CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y)); - CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y)); + CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr); + CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); + CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y)); + CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y)); + CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y)); src_addr += src_stride_z; weights_addr += weights_stride_z; @@ -142,10 +326,10 @@ __kernel void direct_convolution5x5( #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - pixels0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))); + values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))); #endif /* defined(HAS_BIAS) */ - vstore8(pixels0, 0, (__global DATA_TYPE *)dst.ptr); + vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr); } #endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) @@ -226,8 +410,8 @@ __kernel void direct_convolution5x5_f32_bifrost( Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); - float4 pixels0 = 0.0f; - float4 pixels1 = 0.0f; + float4 values0 = 0.0f; + float4 values1 = 0.0f; __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); @@ -247,14 +431,14 @@ __kernel void direct_convolution5x5_f32_bifrost( src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y)); // Accumulate - CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); // Load values from row1 of input tensor src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y)); // Accumulate - CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11); - CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11); + CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); // Load values from row2 of input tensor src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y)); @@ -264,8 +448,8 @@ __kernel void direct_convolution5x5_f32_bifrost( weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4); // Accumulate - CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01); - CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11); + CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11); // Load values from row3 of input tensor src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y)); @@ -275,8 +459,8 @@ __kernel void direct_convolution5x5_f32_bifrost( weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4); // Accumulate - CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11); - CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11); + CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); // Load values from row4 of input tensor src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y)); @@ -285,14 +469,14 @@ __kernel void direct_convolution5x5_f32_bifrost( weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y)); weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4); - CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01); - CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11); + CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11); // Load values from row5 of input tensor src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y)); // Accumulate - CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01); + CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); src_addr += src_stride_z; weights_addr += weights_stride_z; @@ -303,11 +487,11 @@ __kernel void direct_convolution5x5_f32_bifrost( float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index))); - pixels0 += bias; - pixels1 += bias; + values0 += bias; + values1 += bias; #endif /* defined(HAS_BIAS) */ - vstore4(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore4(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); + vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); + vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); } #endif // defined(WEIGHTS_DEPTH) -- cgit v1.2.1