diff options
Diffstat (limited to 'test/routines/levelx/xconvgemm.hpp')
| -rw-r--r-- | test/routines/levelx/xconvgemm.hpp | 243 |
1 files changed, 243 insertions, 0 deletions
diff --git a/test/routines/levelx/xconvgemm.hpp b/test/routines/levelx/xconvgemm.hpp new file mode 100644 index 00000000..7fa4e701 --- /dev/null +++ b/test/routines/levelx/xconvgemm.hpp @@ -0,0 +1,243 @@ + +// ================================================================================================= +// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This +// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max- +// width of 100 characters per line. +// +// Author(s): +// Cedric Nugteren <www.cedricnugteren.nl> +// +// This file implements a class with static methods to describe the Xconvgemm routine. Examples of +// such 'descriptions' are how to calculate the size a of buffer or how to run the routine. These +// static methods are used by the correctness tester and the performance tester. +// +// ================================================================================================= + +#ifndef CLBLAST_TEST_ROUTINES_XCONVGEMM_H_ +#define CLBLAST_TEST_ROUTINES_XCONVGEMM_H_ + +#include "test/routines/common.hpp" + +namespace clblast { +// ================================================================================================= + +// See comment at top of file for a description of the class +template <typename T> +class TestXconvgemm { +public: + + // The BLAS level: 4 for the extra routines + static size_t BLASLevel() { return 4; } + + // The list of arguments relevant for this routine + static std::vector<std::string> GetOptions() { + return {kArgChannels, kArgHeight, kArgWidth, kArgKernelH, kArgKernelW, kArgPadH, kArgPadW, + kArgStrideH, kArgStrideW, kArgDilationH, kArgDilationW, kArgNumKernels, kArgBatchCount, + kArgAOffset, kArgBOffset, kArgCOffset}; + } + static std::vector<std::string> BuffersIn() { return {kBufMatA, kBufMatB, kBufMatC}; } + static std::vector<std::string> BuffersOut() { return {kBufMatC}; } + + // Describes how to obtain the sizes of the buffers + static size_t OutputHeight(const Arguments<T> &args) { + const auto size = args.height + 2 * args.pad_h; + const auto padding = args.dilation_h * (args.kernel_h - 1) + 1; + if (size >= padding) { return (size - padding) / args.stride_h + 1; } + return 1; + } + static size_t OutputWidth(const Arguments<T> &args) { + const auto size = args.width + 2 * args.pad_w; + const auto padding = args.dilation_w * (args.kernel_w - 1) + 1; + if (size >= padding) { return (size - padding) / args.stride_w + 1; } + return 1; + } + static size_t NumPatches(const Arguments<T> &args) { + return OutputHeight(args) * OutputWidth(args) * args.channels; + } + static size_t GetSizeA(const Arguments<T> &args) { // 4D: NCHW == batch-channel-height-width + return args.batch_count * args.channels * args.height * args.width + args.a_offset; + } + static size_t GetSizeB(const Arguments<T> &args) { // 4D: KCHW == kernel-channel-height-width + return args.num_kernels * args.channels * args.kernel_h * args.kernel_w + args.b_offset; + } + static size_t GetSizeC(const Arguments<T> &args) { // 4D: NCHW == batch-channel-height-width + return args.batch_count * args.num_kernels * OutputHeight(args) * OutputWidth(args) + args.c_offset; + } + + // Describes how to set the sizes of all the buffers + static void SetSizes(Arguments<T> &args, Queue&) { + args.a_size = GetSizeA(args); + args.b_size = GetSizeB(args); + args.c_size = GetSizeC(args); + } + + // Describes what the default values of the leading dimensions of the matrices are + static size_t DefaultLDA(const Arguments<T> &) { return 1; } // N/A for this routine + static size_t DefaultLDB(const Arguments<T> &) { return 1; } // N/A for this routine + static size_t DefaultLDC(const Arguments<T> &) { return 1; } // N/A for this routine + + // Describes which transpose options are relevant for this routine + using Transposes = std::vector<Transpose>; + static Transposes GetATransposes(const Transposes &) { return {}; } // N/A for this routine + static Transposes GetBTransposes(const Transposes &) { return {}; } // N/A for this routine + + // Describes how to prepare the input data + static void PrepareData(const Arguments<T>&, Queue&, const int, std::vector<T>&, + std::vector<T>&, std::vector<T>&, std::vector<T>&, std::vector<T>&, + std::vector<T>&, std::vector<T>&) {} // N/A for this routine + + // Describes how to run the CLBlast routine + static StatusCode RunRoutine(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) { +#ifdef OPENCL_API + auto queue_plain = queue(); + auto event = cl_event{}; + auto status = Convgemm<T>(args.channels, args.height, args.width, + args.kernel_h, args.kernel_w, + args.pad_h, args.pad_w, + args.stride_h, args.stride_w, + args.dilation_h, args.dilation_w, + args.num_kernels, args.batch_count, + buffers.a_mat(), args.a_offset, + buffers.b_mat(), args.b_offset, + buffers.c_mat(), args.c_offset, + &queue_plain, &event); + if (status == StatusCode::kSuccess) { clWaitForEvents(1, &event); clReleaseEvent(event); } +#elif CUDA_API + auto status = Convgemm<T>(args.channels, args.height, args.width, + args.kernel_h, args.kernel_w, + args.pad_h, args.pad_w, + args.stride_h, args.stride_w, + args.dilation_h, args.dilation_w, + args.num_kernels, args.batch_count, + buffers.a_mat(), args.a_offset, + buffers.b_mat(), args.b_offset, + buffers.c_mat(), args.c_offset, + queue.GetContext()(), queue.GetDevice()()); + cuStreamSynchronize(queue()); +#endif + return status; + } + + // Describes how to run a naive version of the routine (for correctness/performance comparison). + // Note that a proper clBLAS or CPU BLAS comparison is not available for non-BLAS routines. + static StatusCode RunReference1(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) { + auto buffers_host = BuffersHost<T>(); + DeviceToHost(args, buffers, buffers_host, queue, BuffersIn()); + const auto status = RunReference(args, buffers_host); + HostToDevice(args, buffers, buffers_host, queue, BuffersOut()); + return status; + } + + static StatusCode RunReference2(const Arguments<T> &args, BuffersHost<T> &buffers_host, Queue&) { + return RunReference(args, buffers_host); + } + static StatusCode RunReference3(const Arguments<T> &, BuffersCUDA<T> &, Queue &) { + return StatusCode::kUnknownError; + } + + // Describes how to download the results of the computation (more importantly: which buffer) + static std::vector<T> DownloadResult(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) { + std::vector<T> result(args.c_size, static_cast<T>(0)); + buffers.c_mat.Read(queue, args.c_size, result); + return result; + } + + // Describes how to compute the indices of the result buffer + static size_t ResultID1(const Arguments<T> &args) { return OutputHeight(args) * OutputWidth(args); } + static size_t ResultID2(const Arguments<T> &args) { return args.num_kernels * args.batch_count; } + static size_t GetResultIndex(const Arguments<T> &args, const size_t id1, const size_t id2) { + return id1 + OutputHeight(args) * OutputWidth(args) * id2 + args.c_offset; + } + + // Describes how to compute performance metrics + static size_t GetFlops(const Arguments<T> &args) { + const auto patch_size = args.kernel_h * args.kernel_w * args.channels; + const auto num_patches = OutputHeight(args) * OutputWidth(args); + return args.batch_count * 2 * num_patches * args.num_kernels * patch_size; + } + static size_t GetBytes(const Arguments<T> &args) { + return (GetSizeA(args) + GetSizeB(args) + GetSizeC(args)) * sizeof(T); + } +}; + +// ================================================================================================= + +template <typename T> +StatusCode RunReference(const Arguments<T> &args, BuffersHost<T> &buffers_host) { + const auto output_h = TestXconvgemm<T>::OutputHeight(args); + const auto output_w = TestXconvgemm<T>::OutputWidth(args); + for (auto batch_id = size_t{0}; batch_id < args.batch_count; ++batch_id) { + for (auto co_id = size_t{0}; co_id < args.num_kernels; ++co_id) { // output channels == num-kernels + for (auto ho_id = size_t{0}; ho_id < output_h; ++ho_id) { // image height + for (auto wo_id = size_t{0}; wo_id < output_w; ++wo_id) { // image width + auto result = ConstantZero<T>(); + + // 3D convolution + for (auto ci_id = size_t{0}; ci_id < args.channels; ++ci_id) { // input channels + for (auto kh_id = size_t{0}; kh_id < args.kernel_h; ++kh_id) { // kernel height + for (auto kw_id = size_t{0}; kw_id < args.kernel_w; ++kw_id) { // kernel width + + // Retrieves the value from the input image + const auto hi_id = kh_id * args.dilation_h + args.stride_h * ho_id - args.pad_h; + const auto wi_id = kw_id * args.dilation_w + args.stride_w * wo_id - args.pad_w; + if (hi_id >= 0 && hi_id < args.height && + wi_id >= 0 && wi_id < args.width) { + const auto input_index = wi_id + args.width * ( + hi_id + args.height * ( + ci_id + args.channels * ( + batch_id))); + const auto input_value = buffers_host.a_mat[input_index + args.a_offset]; + + // Multiplies with the kernel tensor + const auto kernel_index = kw_id + args.kernel_w * ( + kh_id + args.kernel_h * ( + ci_id + args.channels * ( + co_id))); + const auto kernel_value = buffers_host.b_mat[kernel_index + args.b_offset]; + result += input_value * kernel_value; + + } + } + } + } + + // Sets the output value (NCHW == batch-channel-height-width) + const auto output_index = wo_id + output_w * ( + ho_id + output_h * ( + co_id + args.num_kernels * ( + batch_id))); + buffers_host.c_mat[output_index + args.c_offset] = result; + } + } + } + } + return StatusCode::kSuccess; +} + +// Half-precision version calling the above reference implementation after conversions +template <> +StatusCode RunReference<half>(const Arguments<half> &args, BuffersHost<half> &buffers_host) { + auto a_buffer2 = HalfToFloatBuffer(buffers_host.a_mat); + auto b_buffer2 = HalfToFloatBuffer(buffers_host.b_mat); + auto c_buffer2 = HalfToFloatBuffer(buffers_host.c_mat); + auto dummy = std::vector<float>(0); + auto buffers2 = BuffersHost<float>{dummy, dummy, a_buffer2, b_buffer2, c_buffer2, dummy, dummy}; + auto args2 = Arguments<float>(); + args2.a_size = args.a_size; args2.b_size = args.b_size; args2.c_size = args.c_size; + args2.channels = args.channels; args2.height = args.height; args2.width = args.width; + args2.kernel_h = args.kernel_h; args2.kernel_w = args.kernel_w; + args2.pad_h = args.pad_h; args2.pad_w = args.pad_w; + args2.stride_h = args.stride_h; args2.stride_w = args.stride_w; + args2.dilation_h = args.dilation_h; args2.dilation_w = args.dilation_w; + args2.num_kernels = args.num_kernels; args2.batch_count = args.batch_count; + args2.a_offset = args.a_offset; args2.b_offset = args.b_offset; args2.c_offset = args.c_offset; + auto status = RunReference(args2, buffers2); + FloatToHalfBuffer(buffers_host.c_mat, buffers2.c_mat); + return status; +} + +// ================================================================================================= +} // namespace clblast + +// CLBLAST_TEST_ROUTINES_XCONVGEMM_H_ +#endif |