From 7e4b23953e885e58d655a7d9f35a1afcc38365e4 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Thu, 22 Feb 2018 16:17:20 +0000 Subject: COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 2) Implemented Winograd Filter Transform 3x3 on OpenCL Change-Id: I8f2b2dd938c5c000ef7ce392a37fb7b8b4202a4e Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/122708 Reviewed-by: Georgios Pinitas Tested-by: Jenkins --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/winograd.cl | 97 +++++++++++++- .../CL/kernels/CLWinogradFilterTransformKernel.cpp | 139 +++++++++++++++++++++ 3 files changed, 236 insertions(+), 1 deletion(-) create mode 100644 src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp (limited to 'src') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 40aceb702a..4b7fa8a3b3 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -351,6 +351,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "warp_affine_bilinear", "warp_affine.cl" }, { "warp_perspective_nearest_neighbour", "warp_perspective.cl" }, { "warp_perspective_bilinear", "warp_perspective.cl" }, + { "winograd_filter_transform_2x2_3x3_nchw", "winograd.cl" }, { "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" }, { "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" }, { "YUYV422_to_IYUV_bt709", "color_convert.cl" }, diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl index fa06601c50..238e21a18a 100644 --- a/src/core/CL/cl_kernels/winograd.cl +++ b/src/core/CL/cl_kernels/winograd.cl @@ -205,4 +205,99 @@ __kernel void winograd_input_transform_2x2_3x3_stepz2_nchw( vstore2(out32, 0, (__global float *)(dst_addr + 14 * dst_stride_z)); vstore2(out33, 0, (__global float *)(dst_addr + 15 * dst_stride_z)); } -#endif //defined(NUM_TILES_X) \ No newline at end of file +#endif //defined(NUM_TILES_X) + +#if defined(NUM_CHANNELS) + +/** This OpenCL kernel performs Winograd filter transform 3x3 when the data format is NCHW and the output tile is 2x2 + * + * @note The number of channels must be passed at compile time using -DNUM_CHANNELS: e.g. -DNUM_CHANNELS=64 + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] 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] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void winograd_filter_transform_2x2_3x3_nchw( + TENSOR4D_DECLARATION(src), + TENSOR3D_DECLARATION(dst)) +{ + Tensor4D src = CONVERT_TO_TENSOR4D_STRUCT(src, NUM_CHANNELS); + + const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0); + + // Load the values from the input tensor + float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y)); + float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y)); + float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y)); + + // Transform the 3x3 tile in a 4x4 tile + float4 out0 = 0.0f; + float4 out1 = 0.0f; + float4 out2 = 0.0f; + float4 out3 = 0.0f; + + // Row 0 + out0.s0 = (w0.s0); + out0.s1 = (w0.s0 + w0.s1 + w0.s2) * 0.5f; + out0.s2 = (w0.s0 + w0.s2 - w0.s1) * 0.5f; + out0.s3 = (w0.s2); + + // Row 1 + out1.s0 = (w0.s0 + w1.s0 + w2.s0) * 0.5f; + out1.s1 = (w0.s0 + w1.s0 + w2.s0 + w0.s1 + w1.s1 + w2.s1 + w0.s2 + w1.s2 + w2.s2) * 0.25f; + out1.s2 = (w0.s0 + w1.s0 + w2.s0 + w0.s2 + w1.s2 + w2.s2 - w0.s1 - w1.s1 - w2.s1) * 0.25f; + out1.s3 = (w0.s2 + w1.s2 + w2.s2) * 0.5f; + + // Row 2 + out2.s0 = (w0.s0 + w2.s0 - w1.s0) * 0.5f; + out2.s1 = (w0.s0 + w2.s0 + w0.s1 + w2.s1 + w0.s2 + w2.s2 - w1.s0 - w1.s1 - w1.s2) * 0.25f; + out2.s2 = (w0.s0 + w2.s0 + w1.s1 + w0.s2 + w2.s2 - w1.s0 - w0.s1 - w2.s1 - w1.s2) * 0.25f; + out2.s3 = (w0.s2 + w2.s2 - w1.s2) * 0.5f; + + // Row 3 + out3.s0 = (w2.s0); + out3.s1 = (w2.s0 + w2.s1 + w2.s2) * 0.5f; + out3.s2 = (w2.s0 + w2.s2 - w2.s1) * 0.5f; + out3.s3 = (w2.s2); + + int z = get_global_id(2); + int x0 = z / NUM_CHANNELS; // idx filter + int y0 = z % NUM_CHANNELS; // idx channel + + // Get output address + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x0 * dst_stride_x + y0 * dst_stride_y; + + // Store the 16 values across the 16 channels + *(__global float *)(dst_addr + 0 * dst_stride_z) = out0.s0; + *(__global float *)(dst_addr + 1 * dst_stride_z) = out0.s1; + *(__global float *)(dst_addr + 2 * dst_stride_z) = out0.s2; + *(__global float *)(dst_addr + 3 * dst_stride_z) = out0.s3; + *(__global float *)(dst_addr + 4 * dst_stride_z) = out1.s0; + *(__global float *)(dst_addr + 5 * dst_stride_z) = out1.s1; + *(__global float *)(dst_addr + 6 * dst_stride_z) = out1.s2; + *(__global float *)(dst_addr + 7 * dst_stride_z) = out1.s3; + *(__global float *)(dst_addr + 8 * dst_stride_z) = out2.s0; + *(__global float *)(dst_addr + 9 * dst_stride_z) = out2.s1; + *(__global float *)(dst_addr + 10 * dst_stride_z) = out2.s2; + *(__global float *)(dst_addr + 11 * dst_stride_z) = out2.s3; + *(__global float *)(dst_addr + 12 * dst_stride_z) = out3.s0; + *(__global float *)(dst_addr + 13 * dst_stride_z) = out3.s1; + *(__global float *)(dst_addr + 14 * dst_stride_z) = out3.s2; + *(__global float *)(dst_addr + 15 * dst_stride_z) = out3.s3; +} +#endif // defined(NUM_CHANNELS) diff --git a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp new file mode 100644 index 0000000000..3dbbe157b2 --- /dev/null +++ b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp @@ -0,0 +1,139 @@ +/* + * 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 "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +#include "support/ToolchainSupport.h" + +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != 3); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != input->dimension(1)); + ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); + + // Checks performed when output is configured + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(compute_winograd_filter_transform_shape(*input)); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + constexpr unsigned int num_elems_processed_per_iteration_x = 3; + constexpr unsigned int num_elems_processed_per_iteration_y = 3; + + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + bool window_changed = false; + + AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); + AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); + window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, input->valid_region()); + + Window win_collapsed = win.collapse(win, Window::DimZ); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win_collapsed); +} +} // namespace + +CLWinogradFilterTransformKernel::CLWinogradFilterTransformKernel() + : _input(nullptr), _output(nullptr) +{ +} + +void CLWinogradFilterTransformKernel::configure(const ICLTensor *input, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output tensor auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_filter_transform_shape(*input->info()))); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); + + // Set build options + CLBuildOptions build_opts; + build_opts.add_option("-DNUM_CHANNELS=" + support::cpp11::to_string(input->info()->dimension(2))); + + // Create kernel + _kernel = static_cast(CLKernelLibrary::get().create_kernel("winograd_filter_transform_2x2_3x3_nchw", build_opts.options())); + + _input = input; + _output = output; + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure(win_config.second); +} + +Status CLWinogradFilterTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); + + return Status{}; +} + +void CLWinogradFilterTransformKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + // Setup output window + Window window_out; + window_out.use_tensor_dimensions(_output->info()->tensor_shape(), 0); + + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, window); + add_3D_tensor_argument(idx, _output, window_out); + enqueue(queue, *this, window); +} -- cgit v1.2.1