/* * 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" #if defined(FIXED_POINT_POSITION) #include "fixed_point.h" #endif // FIXED_POINT_POSITION /** 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 @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 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 @p src_ptr * @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), #ifdef HAS_BIAS VECTOR_DECLARATION(bias), #endif /* HAS_BIAS */ 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; #ifdef HAS_BIAS __global uchar *tmp_bias_ptr = bias_ptr + bias_offset_first_element_in_bytes; #endif /* HAS_BIAS */ if(is_last_thread) { for(uint i = 0; i < total_filters; ++i) { *((__global DATA_TYPE *)tmp_dst_ptr) = *((__global DATA_TYPE *)tmp_src_ptr); #ifdef HAS_BIAS *((__global DATA_TYPE *)(tmp_dst_ptr + dst_stride_y)) = *((__global DATA_TYPE *)(tmp_bias_ptr)); tmp_bias_ptr += bias_stride_x; #endif /* HAS_BIAS */ 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; } } } #if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) /** 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: 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 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] filter_depth The depth of the used filter * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes). * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes). */ __kernel void im2col_generic( TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst), uint filter_depth, uint src_stride_w, uint dst_stride_w) { const int xc = get_global_id(0); // x coordinate in the convolved tensor const int yc = get_global_id(1); // y coordinate in the convolved tensor const int ch = get_global_id(2) % filter_depth; // input feature map const int batch = get_global_id(2) / filter_depth; // the batch // Calculate input indeces const int xi = xc * STRIDE_X - PAD_LEFT; const int yi = yc * STRIDE_Y - PAD_TOP; // Calculate output indeces const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w; __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo; // Linearize convolution elements for(int y = yi, y_e = yi + KERNEL_HEIGHT; y < y_e; ++y) { for(int x = xi, x_e = xi + KERNEL_WIDTH; x < x_e; ++x, ++output_ptr) { #if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); #else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) { *output_ptr = 0; } else { *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); } #endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 } } #ifdef HAS_BIAS if(ch == (KERNEL_DEPTH - 1)) { #ifdef FIXED_POINT_POSITION *output_ptr = (DATA_TYPE)(1 << FIXED_POINT_POSITION); #else // FIXED_POINT_POSITION *output_ptr = 1.0f; #endif // FIXED_POINT_POSITION } #endif // HAS_BIAS } /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3 and pad_x = pad_y = 0 * * @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: 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 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] filter_depth The depth of the used filter * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes). * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes). */ __kernel void im2col_kernel3x3_padx0_pady0( TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst), uint filter_depth, uint src_stride_w, uint dst_stride_w) { const int xc = get_global_id(0); // x coordinate in the convolved tensor const int yc = get_global_id(1); // y coordinate in the convolved tensor const int ch = get_global_id(2) % filter_depth; // input feature map const int batch = get_global_id(2) / filter_depth; // the batch // Calculate input indeces const int xi = xc * STRIDE_X; const int yi = yc * STRIDE_Y; // Calculate output indeces const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution // Get input and output address __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w; __global DATA_TYPE *output_ptr = (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w) + xo; VEC_DATA_TYPE(DATA_TYPE, 3) row0 = vload3(0, (__global DATA_TYPE *)(input_ptr + 0 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 3) row1 = vload3(0, (__global DATA_TYPE *)(input_ptr + 1 * src_stride_y)); VEC_DATA_TYPE(DATA_TYPE, 3) row2 = vload3(0, (__global DATA_TYPE *)(input_ptr + 2 * src_stride_y)); vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, output_ptr); *(output_ptr + 8) = row2.s2; #ifdef HAS_BIAS if(ch == (KERNEL_DEPTH - 1)) { #ifdef FIXED_POINT_POSITION *(output_ptr + 9) = (DATA_TYPE)(1 << FIXED_POINT_POSITION); #else // FIXED_POINT_POSITION *(output_ptr + 9) = 1.0f; #endif // FIXED_POINT_POSITION } #endif // HAS_BIAS } #endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) #if defined(WIDTH_OUTPUT) /** 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: 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 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] dst_stride_w Stride of the destination tensor in W dimension (in bytes) */ __kernel void col2im( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), uint dst_stride_w) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(dst); // Compute output offset int idx = get_global_id(0) * dst.stride_z + (get_global_id(1) / WIDTH_OUTPUT) * dst_stride_y + (get_global_id(1) % WIDTH_OUTPUT) * dst_stride_x + get_global_id(2) * dst_stride_w; // Store value *((__global DATA_TYPE *)(dst.ptr + idx)) = *((__global DATA_TYPE *)(src.ptr)); } #endif // defined(WIDTH_OUTPUT) /** 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: QS8/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 @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_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); #ifdef 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; #ifdef FIXED_POINT_POSITION *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)(1 << FIXED_POINT_POSITION); #else // FIXED_POINT_POSITION *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1; #endif // FIXED_POINT_POSITION } #endif // HAS_BIAS }