/* * Copyright (c) 2017 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" /** This kernel reshapes the tensor's low three dimensions to single column * * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short * * @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 Y 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. Same as input * @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 Y 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] bias_ptr Pointer to the bias tensor. Same as input * @param[in] bias_stride_x Stride of the bias tensor in X dimension (in bytes) * @param[in] bias_step_x bias_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] bias_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] width The width of the input tensor * @param[in] height The height of the input tensor * @param[in] depth The depth of the input tensor * @param[in] total_filters Total number of filters. 4th dimension of the weights matrix */ __kernel void reshape_to_columns( TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst), #if defined HAS_BIAS VECTOR_DECLARATION(bias), #endif uint width, uint height, uint depth, uint total_filters) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); bool is_last_thread = (get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1)); __global uchar *tmp_src_ptr = src.ptr; __global uchar *tmp_dst_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(0) * dst_stride_y + get_global_id(1) * width * dst_stride_y + get_global_id( 2) * width * height * dst_stride_y; #if defined HAS_BIAS __global uchar *tmp_bias_ptr = bias_ptr + bias_offset_first_element_in_bytes; #endif if(is_last_thread) { for(uint i = 0; i < total_filters; ++i) { *((__global DATA_TYPE *)tmp_dst_ptr) = *((__global DATA_TYPE *)tmp_src_ptr); #if defined HAS_BIAS *((__global DATA_TYPE *)(tmp_dst_ptr + dst_stride_y)) = *((__global DATA_TYPE *)(tmp_bias_ptr)); tmp_bias_ptr += bias_stride_x; #endif tmp_src_ptr += depth * src_stride_z; tmp_dst_ptr += dst_stride_x; } } else { for(uint i = 0; i < total_filters; ++i) { *((__global DATA_TYPE *)tmp_dst_ptr) = *((__global DATA_TYPE *)tmp_src_ptr); tmp_src_ptr += depth * src_stride_z; tmp_dst_ptr += dst_stride_x; } } } /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @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: F16, F32 * @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 Y 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] kernel_size The convolution kernel size * @param[in] kernel_depth The kernel depth * @param[in] width The output tensor width * @param[in] input_dims The input tensor dimensions * @param[in] strides The strides of the im2col operation * @param[in] paddings The input tensor paddings */ __kernel void im2col_generic( TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst), int kernel_size, int kernel_depth, int width, int2 input_dims, int2 strides, int2 paddings) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Image dst = CONVERT_TO_IMAGE_STRUCT_NO_STEP(dst); // Determine output index uint idx = (get_global_id(1) * width + get_global_id(0)) * dst.stride_y; __global uchar *output_ptr = dst.ptr + idx; // Determine current input index const int top_left_x = get_global_id(0) * strides.x - paddings.x; const int top_left_y = get_global_id(1) * strides.y - paddings.y; // Linearize convolution elements for(int d = 0; d < kernel_depth; ++d) { for(int y = top_left_y, y_e = top_left_y + kernel_size; y < y_e; ++y) { for(int x = top_left_x, x_e = top_left_x + kernel_size; x < x_e; ++x, output_ptr += dst.stride_x) { if(x < 0 || x >= input_dims.x || y < 0 || y >= input_dims.y) { *((__global DATA_TYPE *)output_ptr) = 0; } else { *((__global DATA_TYPE *)output_ptr) = *((__global DATA_TYPE *)(tensor3D_offset(&src, x, y, d))); } } } } #if defined HAS_BIAS *((__global DATA_TYPE *)output_ptr) = 1; #endif } /** This kernel performs a reshaping of the output of the convolution layer. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * * @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_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: F16, F32 * @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 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 tensor * @param[in] width The output tensor width */ __kernel void col2im( IMAGE_DECLARATION(src), TENSOR3D_DECLARATION(dst), uint width) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(dst); int idx = get_global_id(0) * dst.stride_z + (get_global_id(1) / width) * dst.stride_y + (get_global_id(1) % width) * dst.stride_x; __global uchar *tmp_out_ptr = dst.ptr + idx; *((__global DATA_TYPE *)tmp_out_ptr) = *((__global DATA_TYPE *)(src.ptr)); } /** This kernel reshapes the tensor's low three dimensions to single row for GEMM operation * * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float * @note In case biases will be added in late stage, -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 Y 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. Same as input. * @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_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] width The width of the input tensor * @param[in] height The height of the input tensor */ __kernel void im2col_reduced( TENSOR3D_DECLARATION(src), VECTOR_DECLARATION(dst), uint width, uint height) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); const uint image_size = width * height; __global uchar *tmp_out_ptr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) + get_global_id(1) * width + get_global_id(2) * image_size) * dst_stride_x; *((__global DATA_TYPE *)tmp_out_ptr) = *((__global DATA_TYPE *)src.ptr); #if defined HAS_BIAS // If it is the last thread in the 3 dimensional workgroup if(get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1)) { tmp_out_ptr += dst_stride_x; *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1; } #endif }