From 76faef88284e6fd51f53b23063374d3d3a884e4f Mon Sep 17 00:00:00 2001 From: Gian Marco Date: Mon, 29 Jan 2018 12:15:32 +0000 Subject: COMPMID-855 - Optimizing im2col on OpenCL (DCHW) Introduced optimizations for 1x1, 3x3, 5x5 and 11x11 Change-Id: Ibb7f7a9fbec01a7684746ed8513634078126e452 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118107 Tested-by: Jenkins Reviewed-by: Michalis Spyrou --- arm_compute/core/CL/kernels/CLIm2ColKernel.h | 5 +- src/core/CL/CLKernelLibrary.cpp | 21 +- src/core/CL/cl_kernels/col2im.cl | 68 +++ src/core/CL/cl_kernels/convolution_layer.cl | 320 +---------- src/core/CL/cl_kernels/fixed_point.h | 18 +- src/core/CL/cl_kernels/im2col.cl | 804 +++++++++++++++++++++++++++ src/core/CL/kernels/CLIm2ColKernel.cpp | 127 +++-- 7 files changed, 991 insertions(+), 372 deletions(-) create mode 100644 src/core/CL/cl_kernels/col2im.cl create mode 100644 src/core/CL/cl_kernels/im2col.cl diff --git a/arm_compute/core/CL/kernels/CLIm2ColKernel.h b/arm_compute/core/CL/kernels/CLIm2ColKernel.h index 88de1ba002..e38e7e8a49 100644 --- a/arm_compute/core/CL/kernels/CLIm2ColKernel.h +++ b/arm_compute/core/CL/kernels/CLIm2ColKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -25,11 +25,11 @@ #define __ARM_COMPUTE_CLIM2COLKERNEL_H__ #include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/Size2D.h" namespace arm_compute { class ICLTensor; -class Size2D; /** Interface for the im2col reshape kernel. * @@ -117,6 +117,7 @@ private: std::pair _convolved_dims; unsigned int _num_elems_processed_per_iteration; Im2ColFunction _run_func; + Size2D _kernel_dims; }; } // namespace arm_compute #endif /*__ARM_COMPUTE_CLIM2COLKERNEL_H__ */ diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index ae3553860a..d7ae6e3127 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -170,7 +170,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "combine_gradients_L2", "canny.cl" }, { "concatenate_depth", "concatenate.cl" }, { "convolution_rectangle", "convolution_rectangle.cl" }, - { "col2im", "convolution_layer.cl" }, + { "col2im", "col2im.cl" }, { "convolution3x3_static", "convolution3x3.cl" }, { "convolution5x5_static", "convolution5x5.cl" }, { "convolution7x7_static", "convolution7x7.cl" }, @@ -248,10 +248,13 @@ const std::map CLKernelLibrary::_kernel_program_map = { "hog_detector", "hog.cl" }, { "hog_orientation_binning", "hog.cl" }, { "hysteresis", "canny.cl" }, - { "im2col_generic", "convolution_layer.cl" }, - { "im2col_generic_padx0_pady0", "convolution_layer.cl" }, - { "im2col_kernel3x3_padx0_pady0", "convolution_layer.cl" }, - { "im2col_reduced", "convolution_layer.cl" }, + { "im2col1x1_stridex1_dchw", "im2col.cl" }, + { "im2col3x3_dchw", "im2col.cl" }, + { "im2col5x5_dchw", "im2col.cl" }, + { "im2col11x11_padx0_pady0_dchw", "im2col.cl" }, + { "im2col_generic_dchw", "im2col.cl" }, + { "im2col_generic_padx0_pady0_dchw", "im2col.cl" }, + { "im2col_reduced_dchw", "im2col.cl" }, { "init_level", "optical_flow_pyramid_lk.cl" }, { "init_level_max", "optical_flow_pyramid_lk.cl" }, { "init_level_max_initial_estimate", "optical_flow_pyramid_lk.cl" }, @@ -388,6 +391,10 @@ const std::map CLKernelLibrary::_program_source_map = { "channel_extract.cl", #include "./cl_kernels/channel_extract.clembed" + }, + { + "col2im.cl", +#include "./cl_kernels/col2im.clembed" }, { "concatenate.cl", @@ -520,6 +527,10 @@ const std::map CLKernelLibrary::_program_source_map = { "hog.cl", #include "./cl_kernels/hog.clembed" + }, + { + "im2col.cl", +#include "./cl_kernels/im2col.clembed" }, { "integral_image.cl", diff --git a/src/core/CL/cl_kernels/col2im.cl b/src/core/CL/cl_kernels/col2im.cl new file mode 100644 index 0000000000..58fb80a416 --- /dev/null +++ b/src/core/CL/cl_kernels/col2im.cl @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2017-2018 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" + +#if defined(FIXED_POINT_POSITION) +#include "fixed_point.h" +#endif // FIXED_POINT_POSITION + +#if defined(DATA_TYPE) && 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 + * @note The width of the output tensor must be passed at compile time using -DWIDTH_OUTPUT: e.g. -DWIDTH_OUTPUT=320 + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/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(DATA_TYPE) && defined(WIDTH_OUTPUT) \ No newline at end of file diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl index 77b9b64945..f8e0c27724 100644 --- a/src/core/CL/cl_kernels/convolution_layer.cl +++ b/src/core/CL/cl_kernels/convolution_layer.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -27,6 +27,7 @@ #include "fixed_point.h" #endif // FIXED_POINT_POSITION +#if defined(DATA_TYPE) /** 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 @@ -96,319 +97,4 @@ __kernel void reshape_to_columns( } } } - -#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) && defined(PAD_VALUE) -/** 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 The value to use for the paddings must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0 - * @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/QASYMM8/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] 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 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) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X - PAD_LEFT; - const int yi = yc * STRIDE_Y - PAD_TOP; - - // Calculate output indices - 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 = PAD_VALUE; - } - 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/QASYMM8/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] 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 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) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X; - const int yi = yc * STRIDE_Y; - - // Calculate output indices - 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/QASYMM8/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/QASYMM8/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 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 -} - -#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) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) -/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when - * the kernel width is greater than 1 (except 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 The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4. - * @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3. - * @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] 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_padx0_pady0( - TENSOR3D_DECLARATION(src), - IMAGE_DECLARATION(dst), - 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) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X; - const int yi = yc * STRIDE_Y; - // Calculate output indices - 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) - { - int last_x = 0; - for(int x = xi, x_e = xi + KERNEL_WIDTH; x + VECTOR_SIZE <= x_e; x += VECTOR_SIZE, output_ptr += VECTOR_SIZE) - { - VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) - row = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); - VSTORE(VECTOR_SIZE) - (row, 0, output_ptr); - last_x = x; - } - // Copy the remainder of the row by doing VLOAD(WIDTH_MOD_VECTOR_SIZE) and VSTORE(WIDTH_MOD_VECTOR_SIZE). - // Note that x and output_ptr have already been incremented by VECTOR_SIZE by the loop just before exit. -#if WIDTH_MOD_VECTOR_SIZE == 1 - *output_ptr = *((__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); -#elif WIDTH_MOD_VECTOR_SIZE > 1 - VEC_DATA_TYPE(DATA_TYPE, WIDTH_MOD_VECTOR_SIZE) - row = VLOAD(WIDTH_MOD_VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); - VSTORE(WIDTH_MOD_VECTOR_SIZE) - (row, 0, output_ptr); -#endif /* WIDTH_MOD_VECTOR_SIZE */ - output_ptr += WIDTH_MOD_VECTOR_SIZE; - } /* End of loop over KERNEL_HEIGHT */ - -#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 -} -#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) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) +#endif // defined(DATA_TYPE) \ No newline at end of file diff --git a/src/core/CL/cl_kernels/fixed_point.h b/src/core/CL/cl_kernels/fixed_point.h index d55346b532..46fa645c2b 100644 --- a/src/core/CL/cl_kernels/fixed_point.h +++ b/src/core/CL/cl_kernels/fixed_point.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -476,19 +476,19 @@ TANHQ_IMPL(qs16, qs16x8, 8) #define floatx16 float16 #define float16_TYPE float16 -#define CONVERTQ_DOWN_IMPL(in_type, out_type) \ - inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \ - { \ - return CONVERT(a * (1 << fixed_point_position) + select((in_type)-0.5, (in_type)0.5, isgreater(a, (in_type)0)), out_type); \ +#define CONVERTQ_DOWN_IMPL(in_type, out_type) \ + inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \ + { \ + return CONVERT(a * (1 << fixed_point_position) + select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), out_type); \ } CONVERTQ_DOWN_IMPL(float16, qs8x16) CONVERTQ_DOWN_IMPL(float16, qs16x16) -#define CONVERTQ_DOWN_SAT_IMPL(in_type, out_type) \ - inline out_type convert_##out_type##_##in_type##_sat(in_type a, int fixed_point_position) \ - { \ - return CONVERT_SAT(a * (1 << fixed_point_position) + select((in_type)-0.5, (in_type)0.5, isgreater(a, (in_type)0)), out_type); \ +#define CONVERTQ_DOWN_SAT_IMPL(in_type, out_type) \ + inline out_type convert_##out_type##_##in_type##_sat(in_type a, int fixed_point_position) \ + { \ + return CONVERT_SAT(a * (1 << fixed_point_position) + select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), out_type); \ } CONVERTQ_DOWN_SAT_IMPL(float16, qs8x16) diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl new file mode 100644 index 0000000000..75d99bda85 --- /dev/null +++ b/src/core/CL/cl_kernels/im2col.cl @@ -0,0 +1,804 @@ +/* + * Copyright (c) 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" + +#if defined(FIXED_POINT_POSITION) +#include "fixed_point.h" +#endif // FIXED_POINT_POSITION + +#if defined(DATA_TYPE) && defined(ELEMENT_SIZE) +#if !defined(FIXED_POINT_POSITION) + +#if ELEMENT_SIZE == 1 +#define COND_DATA_TYPE char +#elif ELEMENT_SIZE == 2 +#define COND_DATA_TYPE short +#elif ELEMENT_SIZE == 4 +#define COND_DATA_TYPE int +#else // ELEMENT_SIZE +#error "Element size not support" +#endif // ELEMENT_SIZE + +#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) +/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 1x1 and the stride_x = 1 + * + * @note This kernel computes 4 elements + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34 + * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3 + * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1 + * @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/QASYMM8/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] 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 im2col1x1_stridex1_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + uint src_stride_w, + uint dst_stride_w) +{ + const uint xc = get_global_id(0) * 4; // x coordinate in the convolved tensor + const uint yc = get_global_id(1); // y coordinate in the convolved tensor + const uint ch = get_global_id(2) % KERNEL_DEPTH; // input feature map + const uint batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Clamp xc + // The strategy clamps at "xc" as it will be a valid value for sure + uint4 xc_clamped = xc + (uint4)(0, 1, 2, 3); + + // Check which values are valid + const VEC_DATA_TYPE(COND_DATA_TYPE, 4) cond0 = CONVERT((xc_clamped < SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 4)); + + xc_clamped = select((uint4)xc, xc_clamped, convert_int4(cond0)); + + // Calculate input indices + const uint xi = xc; + const uint yi = yc * STRIDE_Y; + + // Calculate output indices + const uint xo = ch; + const uint4 yo = xc_clamped + 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 uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + batch * dst_stride_w; + + VEC_DATA_TYPE(DATA_TYPE, 4) + data = vload4(0, (__global DATA_TYPE *)input_ptr); + + // If out-of-bound, overwrite with the first element + data = select((VEC_DATA_TYPE(DATA_TYPE, 4))data.s0, data, cond0); + + *(__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) = data.s0; + *(__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) = data.s1; + *(__global DATA_TYPE *)(output_ptr + yo.s2 * dst_stride_y) = data.s2; + *(__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) = data.s3; + +#ifdef HAS_BIAS + if(ch == (KERNEL_DEPTH - 1)) + { + *((__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) + 1) = 1.0f; + *((__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) + 1) = 1.0f; + *((__global DATA_TYPE *)(output_ptr + yo.s2 * dst_stride_y) + 1) = 1.0f; + *((__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) + 1) = 1.0f; + } +#endif // HAS_BIAS +} +#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) + +#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) +/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3 + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128 + * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34 + * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3 + * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2 + * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0 + * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1 + * @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/QASYMM8/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] 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 im2col3x3_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + 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) % KERNEL_DEPTH; // input feature map + const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Calculate input indices + const int xi = xc * STRIDE_X - PAD_LEFT; + const int yi = yc * STRIDE_Y - PAD_TOP; + + // Calculate output indices + const int xo = ch * 9; // 3x3 + 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 * (int)src_stride_x + yi * (int)src_stride_y + ch * src_stride_z + batch * src_stride_w; + + __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w; + + 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)); + +#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + // Put 0 if the value is out-of-bound + int3 x = (int3)xi + (int3)(0, 1, 2); + int3 y = (int3)yi + (int3)(0, 1, 2); + + VEC_DATA_TYPE(COND_DATA_TYPE, 3) + cond0 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s0 >= 0 && y.s0 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3)); + VEC_DATA_TYPE(COND_DATA_TYPE, 3) + cond1 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s1 >= 0 && y.s1 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3)); + VEC_DATA_TYPE(COND_DATA_TYPE, 3) + cond2 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s2 >= 0 && y.s2 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3)); + + row0 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row0, cond0); + row1 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row1, cond1); + row2 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row2, cond2); +#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, (__global DATA_TYPE *)output_ptr); + *((__global DATA_TYPE *)output_ptr + 8) = row2.s2; + +#ifdef HAS_BIAS + if(ch == (KERNEL_DEPTH - 1)) + { + *((__global DATA_TYPE *)output_ptr + 9) = 1.0f; + } +#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 5x5 + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128 + * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34 + * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3 + * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2 + * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0 + * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1 + * @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/QASYMM8/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] 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 im2col5x5_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + 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) % KERNEL_DEPTH; // input feature map + const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Calculate input indices + const int xi = xc * STRIDE_X - PAD_LEFT; + const int yi = yc * STRIDE_Y - PAD_TOP; + + // Calculate output indices + const int xo = ch * 25; // 5x5 + const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution + +#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + // Put 0 if the value is out-of-bound + int4 x0 = (int4)xi + (int4)(0, 1, 2, 3); + int4 y0 = (int4)yi + (int4)(0, 1, 2, 3); + int x1 = xi + 4; + int y1 = yi + 4; + + // Check if we could have out-of-bounds elements in the x direction + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + x0_condition = CONVERT((x0 >= (int4)0 && x0 < (int4)SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 4)); + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + y0_condition = CONVERT((y0 >= (int4)0 && y0 < (int4)SRC_HEIGHT), VEC_DATA_TYPE(COND_DATA_TYPE, 4)); + COND_DATA_TYPE x1_condition = (COND_DATA_TYPE)(x1 >= 0 && x1 < SRC_WIDTH); + COND_DATA_TYPE y1_condition = (COND_DATA_TYPE)(y1 >= 0 && y1 < SRC_HEIGHT); +#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + + // Get input and output address + __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * (int)src_stride_x + yi * (int)src_stride_y + ch * src_stride_z + batch * src_stride_w; + + __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w; + + { + VEC_DATA_TYPE(DATA_TYPE, 4) + row00 = vload4(0, (__global DATA_TYPE *)input_ptr); + DATA_TYPE + row01 = *((__global DATA_TYPE *)input_ptr + 4); + + input_ptr += src_stride_y; + + VEC_DATA_TYPE(DATA_TYPE, 4) + row10 = vload4(0, (__global DATA_TYPE *)input_ptr); + DATA_TYPE + row11 = *((__global DATA_TYPE *)input_ptr + 4); + +#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s0; + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + cond10 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s1; + COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s0); + COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s1); + + // Replace with 0 if the value is not valid + row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00); + row10 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row10, cond10); + row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01); + row11 = select((DATA_TYPE)PAD_VALUE, row11, cond11); +#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s0123, row01, + row10.s012), + 0, (__global DATA_TYPE *)output_ptr); + vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(row10.s3, row11), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 10 * dst_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 4) + row00 = vload4(0, (__global DATA_TYPE *)input_ptr); + DATA_TYPE + row01 = *((__global DATA_TYPE *)input_ptr + 4); + + input_ptr += src_stride_y; + + VEC_DATA_TYPE(DATA_TYPE, 4) + row10 = vload4(0, (__global DATA_TYPE *)input_ptr); + DATA_TYPE + row11 = *((__global DATA_TYPE *)input_ptr + 4); + +#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s2; + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + cond10 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s3; + COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s2); + COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s3); + + // Replace with 0 if the value is not valid + row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00); + row10 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row10, cond10); + row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01); + row11 = select((DATA_TYPE)PAD_VALUE, row11, cond11); +#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s0123, row01, + row10.s012), + 0, (__global DATA_TYPE *)output_ptr); + vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(row10.s3, row11), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 10 * dst_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 4) + row00 = vload4(0, (__global DATA_TYPE *)input_ptr); + DATA_TYPE + row01 = *((__global DATA_TYPE *)input_ptr + 4); + + input_ptr += src_stride_y; + +#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + VEC_DATA_TYPE(COND_DATA_TYPE, 4) + cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y1_condition; + COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y1_condition); + + // Replace with 0 if the value is not valid + row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00); + row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01); +#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 + + vstore4(row00, 0, (__global DATA_TYPE *)output_ptr); + *((__global DATA_TYPE *)output_ptr + 4) = row01; + + output_ptr += 5 * dst_stride_x; + } + +#ifdef HAS_BIAS + if(ch == (KERNEL_DEPTH - 1)) + { + *((__global DATA_TYPE *)output_ptr) = 1.0f; + } +#endif // HAS_BIAS +} +#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) + +#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) +/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 11x11 + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34 + * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3 + * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1 + * @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/QASYMM8/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] 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 im2col11x11_padx0_pady0_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + 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) % KERNEL_DEPTH; // input feature map + const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Calculate input indices + const int xi = xc * STRIDE_X; + const int yi = yc * STRIDE_Y; + + // Calculate output indices + const int xo = ch * 121; // 11x11 + 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 uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w; + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + input_ptr += src_stride_y; + output_ptr += 11 * src_stride_x; + } + + { + VEC_DATA_TYPE(DATA_TYPE, 8) + row00 = vload8(0, (__global DATA_TYPE *)(input_ptr)); + VEC_DATA_TYPE(DATA_TYPE, 3) + row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8); + + vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr); + vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8); + + output_ptr += 11 * src_stride_x; + } + +#ifdef HAS_BIAS + if(ch == (KERNEL_DEPTH - 1)) + { + *((__global DATA_TYPE *)output_ptr) = 1.0f; + } +#endif // HAS_BIAS +} +#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) +#endif // !defined(FIXED_POINT_POSITION) + +#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) +/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when + * the kernel width is greater than 1 (except 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 The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4. + * @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3. + * @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] 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_padx0_pady0_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + 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) % KERNEL_DEPTH; // input feature map + const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Calculate input indices + const int xi = xc * STRIDE_X; + const int yi = yc * STRIDE_Y; + // Calculate output indices + 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) + { + int last_x = 0; + for(int x = xi, x_e = xi + KERNEL_WIDTH; x + VECTOR_SIZE <= x_e; x += VECTOR_SIZE, output_ptr += VECTOR_SIZE) + { + VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) + row = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); + VSTORE(VECTOR_SIZE) + (row, 0, output_ptr); + last_x = x; + } + // Copy the remainder of the row by doing VLOAD(WIDTH_MOD_VECTOR_SIZE) and VSTORE(WIDTH_MOD_VECTOR_SIZE). + // Note that x and output_ptr have already been incremented by VECTOR_SIZE by the loop just before exit. +#if WIDTH_MOD_VECTOR_SIZE == 1 + *output_ptr = *((__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); +#elif WIDTH_MOD_VECTOR_SIZE > 1 + VEC_DATA_TYPE(DATA_TYPE, WIDTH_MOD_VECTOR_SIZE) + row = VLOAD(WIDTH_MOD_VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); + VSTORE(WIDTH_MOD_VECTOR_SIZE) + (row, 0, output_ptr); +#endif /* WIDTH_MOD_VECTOR_SIZE */ + output_ptr += WIDTH_MOD_VECTOR_SIZE; + } /* End of loop over KERNEL_HEIGHT */ + +#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 +} +#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) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) + +#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) +/** 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 The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128 + * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34 + * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DKERNEL_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DKERNEL_DEPTH=64 + * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2 + * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0 + * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1 + * @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/QASYMM8/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] 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_dchw( + TENSOR3D_DECLARATION(src), + IMAGE_DECLARATION(dst), + 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) % KERNEL_DEPTH; // input feature map + const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size + + // Calculate input indices + const int xi = xc * STRIDE_X - PAD_LEFT; + const int yi = yc * STRIDE_Y - PAD_TOP; + + // Calculate output indices + 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 = PAD_VALUE; + } + 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 +} +#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) + +/**This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when + * the kernel width and height are the same of width and height of the input tensor + * + * @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/QASYMM8/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 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_dchw( + 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)1.0f; +#endif // FIXED_POINT_POSITION + } +#endif // HAS_BIAS +} +#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE) \ No newline at end of file diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp index 4f693187bd..d1fc50365e 100644 --- a/src/core/CL/kernels/CLIm2ColKernel.cpp +++ b/src/core/CL/kernels/CLIm2ColKernel.cpp @@ -59,7 +59,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b } // namespace CLIm2ColKernel::CLIm2ColKernel() - : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr) + : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims() { } @@ -70,8 +70,9 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const // Perform validation step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias)); - _input = input; - _output = output; + _input = input; + _output = output; + _kernel_dims = kernel_dims; const DataType data_type = input->info()->data_type(); const GPUTarget gpu_target = get_arch_from_target(get_target()); @@ -79,6 +80,7 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const // Create kernel CLBuildOptions build_opts; build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); + build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size())); build_opts.add_option_if(has_bias, "-DHAS_BIAS"); build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); @@ -93,13 +95,19 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const output->info()->tensor_shape().cbegin() + 1)) && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding()); - std::string kernel_name = "im2col_generic"; + bool is_optimized_path = false; + + _num_elems_processed_per_iteration = 1; + + std::string kernel_name; if(!run_img2col_reduced) { + // Default kernel name + kernel_name = "im2col_generic_dchw"; + _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_dims.width, kernel_dims.height, conv_info); - _num_elems_processed_per_iteration = output->info()->dimension(0); build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width)); build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height)); @@ -116,19 +124,50 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0"); - if(kernel_dims.width == 3 && kernel_dims.height == 3 && !conv_info.has_padding()) - { - kernel_name = "im2col_kernel3x3_padx0_pady0"; + const bool squared_im2col = kernel_dims.width == kernel_dims.height; - // Local work size optimized for the 3x3 MobileNets convolution on Bifrost. - if(gpu_target == GPUTarget::BIFROST && input->info()->dimension(0) == 224) + if(squared_im2col && !is_data_type_fixed_point(data_type)) + { + // Check if we can run an optimized im2col + switch(kernel_dims.width) { - _lws_hint = cl::NDRange(2, 3, 3); + case 1: + // Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false + if(conv_info.stride().first == 1 && !conv_info.has_padding()) + { + _num_elems_processed_per_iteration = 4; + is_optimized_path = true; + kernel_name = "im2col1x1_stridex1_dchw"; + } + break; + case 3: + _num_elems_processed_per_iteration = 1; + is_optimized_path = true; + kernel_name = "im2col3x3_dchw"; + break; + case 5: + _num_elems_processed_per_iteration = 1; + is_optimized_path = true; + kernel_name = "im2col5x5_dchw"; + break; + case 11: + // Optimized im2col11x11 if pad_x = pad_y = 0 + if(!conv_info.has_padding()) + { + _num_elems_processed_per_iteration = 1; + is_optimized_path = true; + kernel_name = "im2col11x11_padx0_pady0_dchw"; + } + break; + default: + is_optimized_path = false; + break; } } else if(kernel_dims.width > 1 && !conv_info.has_padding()) { - kernel_name = "im2col_generic_padx0_pady0"; + _num_elems_processed_per_iteration = 1; + kernel_name = "im2col_generic_padx0_pady0_dchw"; // Optimized im2col is performed using one or more vector operations with the specified vector size // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4 @@ -152,30 +191,12 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size)); build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size)); } - else - { - if(gpu_target == GPUTarget::BIFROST) - { - const size_t input_channels = input->info()->dimension(2); - if((input_channels & (input_channels - 1)) == 0) - { - // input_channels is a power of two - _lws_hint = cl::NDRange(1, 1, 4); - } - else if(input_channels < 192 && (input_channels % 4) == 0) - { - // input_channels is less than 192 and is a multiple of 4 - _lws_hint = cl::NDRange(1, 1, 2); - } - // otherwise the default is optimal - } - } _run_func = &CLIm2ColKernel::run_generic; } else { - kernel_name = "im2col_reduced"; _num_elems_processed_per_iteration = 1; + kernel_name = "im2col_reduced_dchw"; _run_func = &CLIm2ColKernel::run_reduced; } @@ -183,8 +204,30 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Configure kernel window - Window win = calculate_max_window(*input->info(), Steps()); - // The CLIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped + Window win; + if(is_optimized_path) + { + win = calculate_max_window(*input->info(), + Steps(_num_elems_processed_per_iteration), + false, + BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left())); + + const int x = -conv_info.pad_left(); + const int y = -conv_info.pad_top(); + const int w = kernel_dims.width * _num_elems_processed_per_iteration; + const int h = kernel_dims.height; + + AccessWindowRectangle input_access(input->info(), x, y, w, h); + + update_window_and_padding(win, input_access); + } + else + { + // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so + // update_window_and_padding() can be skipped + win = calculate_max_window(*input->info(), Steps()); + } + output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); if(!run_img2col_reduced) { @@ -195,8 +238,8 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLKernel::configure(win); // Set config_id for enabling LWS tuning - _config_id = "im2col_"; - _config_id += (run_img2col_reduced ? "reduced_" : ""); + _config_id = kernel_name; + _config_id += "_"; _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); @@ -233,9 +276,15 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue) Window slice_in = window_collapsed.first_slice_window_3D(); Window slice_out = window_collapsed.first_slice_window_3D(); - // Setup slice - slice.set(Window::DimX, Window::Dimension(0, static_cast(_convolved_dims.first), 1)); - slice.set(Window::DimY, Window::Dimension(0, static_cast(_convolved_dims.second), 1)); + // Setup slice if stride_x != 0 or stride_y != 0 + if(_convolved_dims.first != _input->info()->dimension(0) || _convolved_dims.second != _input->info()->dimension(1)) + { + // If the stride_x or stride_y are not 1, the output tensor of matrix multiply (Convolved tensor) will not + // have the same shape of the im2col input tensor + // In this case we need to re-compute the window using the shape of the tensor after matrix multiply (convolved_dims) + slice.set(Window::DimX, Window::Dimension(0, static_cast(_convolved_dims.first), 1)); + slice.set(Window::DimY, Window::Dimension(0, static_cast(_convolved_dims.second), 1)); + } // Setup input slice // The first three dimensions of the input are increased by the inner loops @@ -244,7 +293,7 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue) slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); // Setup output slice - slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _num_elems_processed_per_iteration)); + slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _kernel_dims.area())); slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1)); slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1)); -- cgit v1.2.1