/* * 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(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if defined(DATA_LAYOUT_NHWC) #define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR)) /** This kernel performs a direct convolution to convolve the low three dimensions of a tensor with data layout NHWC * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32 * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1 * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * * @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_convolution1x1_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); #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* defined(HAS_BIAS) */ VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8) values = 0; const int id0 = get_global_id(0); const int id1 = get_global_id(1); const int id2 = get_global_id(2); weights.ptr += id0 * weights_stride_w; __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + id2 * STRIDE_Y * (int)src_stride_z; for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr; #if STRIDE_X == 1 VEC_DATA_TYPE(DATA_TYPE, 8) col0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( PTR_TO_VALUE(src_addr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 1 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 3 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 5 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 7 * src_stride_y, DATA_TYPE)); #elif STRIDE_X == 2 /* STRIDE_X == 1 */ VEC_DATA_TYPE(DATA_TYPE, 8) col0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( PTR_TO_VALUE(src_addr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 12 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(src_addr + 14 * src_stride_y, DATA_TYPE)); #else /* STRIDE_X not equals 1 or 2 */ #error "STRIDE_X larger than 2 is not supported" #endif /* STRIDE_X == 2 */ values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, col0)); src_addr += src_stride_x; weights.ptr += weights_stride_x; } #ifdef HAS_BIAS values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0)))); #endif /* defined(HAS_BIAS) */ *((__global DATA_TYPE *)dst.ptr) = values.s0; *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values.s1; *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values.s2; *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values.s3; *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values.s4; *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values.s5; *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values.s6; *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values.s7; } #endif // defined(DATA_LAYOUT_NHWC) #if STRIDE_X == 3 #define INPUT_PIXEL_STR(data_size) extract_input_stride3_##data_size #define INPUT_PIXEL(data_size) INPUT_PIXEL_STR(data_size) #elif STRIDE_X == 2 #define INPUT_PIXEL(data_size) extract_input_stride2 #elif STRIDE_X == 1 #define INPUT_PIXEL(data_size) extract_input_stride1 #else /* STRIDE_X not equals 1, 2 or 3 */ #error "Only support strides 1, 2 and 3" #endif /* STRIDE_X == 3 */ /** Extracts a 1D horizontal vector from the input tensor with stride as 1. * * @param[in] input_pixel Pointer to the first pixel. * * @return extracted input values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel) { return vload8(0, input_pixel); } /** Extracts a 1D horizontal vector from the input tensor with stride as 2. * * @param[in] input_pixel Pointer to the first pixel. * * @return extracted input values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel) { VEC_DATA_TYPE(DATA_TYPE, 16) temp = vload16(0, input_pixel); return temp.s02468ace; } /** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 32-bit data size. * * @param[in] input_pixel Pointer to the first pixel. * * @return extracted input values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_32(__global const DATA_TYPE *input_pixel) { VEC_DATA_TYPE(DATA_TYPE, 4) temp1 = vload4(0, input_pixel); VEC_DATA_TYPE(DATA_TYPE, 4) temp2 = vload4(0, input_pixel + 6); VEC_DATA_TYPE(DATA_TYPE, 4) temp3 = vload4(0, input_pixel + 12); VEC_DATA_TYPE(DATA_TYPE, 4) temp4 = vload4(0, input_pixel + 18); return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s03, temp2.s03, temp3.s03, temp4.s03); } /** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 16-bit data size. * * @param[in] input_pixel Pointer to the first pixel. * * @return extracted input values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_16(__global const DATA_TYPE *input_pixel) { VEC_DATA_TYPE(DATA_TYPE, 8) temp1 = vload8(0, input_pixel); VEC_DATA_TYPE(DATA_TYPE, 8) temp2 = vload8(0, input_pixel + 8); VEC_DATA_TYPE(DATA_TYPE, 8) temp3 = vload8(0, input_pixel + 16); return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s036, temp2.s147, temp3.s25); } /** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size. * * @param[in] input_pixel Pointer to the first pixel. * * @return extracted input values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_8(__global const DATA_TYPE *input_pixel) { VEC_DATA_TYPE(DATA_TYPE, 16) temp1 = vload16(0, input_pixel); VEC_DATA_TYPE(DATA_TYPE, 16) temp2 = vload16(0, input_pixel + 12); return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369); } /** 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 * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32 * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1 * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * * @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_convolution1x1( 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); #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* defined(HAS_BIAS) */ VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8) values = 0; const uint z_index = get_global_id(2); weights.ptr += z_index * weights_stride_w; for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr; VEC_DATA_TYPE(DATA_TYPE, 8) input_pixel = INPUT_PIXEL(DATA_SIZE)((__global DATA_TYPE *)src.ptr); values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, input_pixel)); src.ptr += src_stride_z; weights.ptr += weights_stride_z; } #ifdef HAS_BIAS values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index)))); #endif /* defined(HAS_BIAS) */ vstore8(CONVERT_SAT(values, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr); } #endif // defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if defined(WEIGHTS_DEPTH) #define CONVOLUTION1x1_BIFROST(acc, src, weight_value) \ ({ \ acc.s0 = mad(src.s0, weight_value, acc.s0); \ acc.s1 = mad(src.s1, weight_value, acc.s1); \ acc.s2 = mad(src.s2, weight_value, acc.s2); \ acc.s3 = mad(src.s3, weight_value, acc.s3); \ }) /** An optimized direct convolution 1x1 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_convolution1x1_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 acc0 = 0.0f; float4 acc1 = 0.0f; float4 acc2 = 0.0f; float4 acc3 = 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); for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) { // Load the weights float weight = *((__global float *)weights_addr); // Load values from row0 of input tensor float4 src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); float4 src1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); float4 src2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); float4 src3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); CONVOLUTION1x1_BIFROST(acc0, src0, weight); CONVOLUTION1x1_BIFROST(acc1, src1, weight); CONVOLUTION1x1_BIFROST(acc2, src2, weight); CONVOLUTION1x1_BIFROST(acc3, src3, weight); 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))); acc0.s0 += bias; acc0.s1 += bias; acc0.s2 += bias; acc0.s3 += bias; acc1.s0 += bias; acc1.s1 += bias; acc1.s2 += bias; acc1.s3 += bias; acc2.s0 += bias; acc2.s1 += bias; acc2.s2 += bias; acc2.s3 += bias; acc3.s0 += bias; acc3.s1 += bias; acc3.s2 += bias; acc3.s3 += bias; #endif /* defined(HAS_BIAS) */ vstore4(acc0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); vstore4(acc1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); vstore4(acc2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); vstore4(acc3, 0, (__global float *)(dst.ptr + 3 * dst_stride_y)); } #endif // defined(WEIGHTS_DEPTH)