2018-05-06 11:35:34 +02:00
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// =================================================================================================
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// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
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// width of 100 characters per line.
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//
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// Author(s):
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// Cedric Nugteren <www.cedricnugteren.nl>
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//
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// This file implements a class with static methods to describe the Xconvgemm routine. Examples of
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// such 'descriptions' are how to calculate the size a of buffer or how to run the routine. These
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// static methods are used by the correctness tester and the performance tester.
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//
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// =================================================================================================
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#ifndef CLBLAST_TEST_ROUTINES_XCONVGEMM_H_
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#define CLBLAST_TEST_ROUTINES_XCONVGEMM_H_
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#include "test/routines/common.hpp"
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namespace clblast {
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// =================================================================================================
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// See comment at top of file for a description of the class
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template <typename T>
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class TestXconvgemm {
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public:
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// The BLAS level: 4 for the extra routines
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static size_t BLASLevel() { return 4; }
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// The list of arguments relevant for this routine
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static std::vector<std::string> GetOptions() {
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2018-12-17 13:57:35 +01:00
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return {kArgKernelMode,
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kArgChannels, kArgHeight, kArgWidth, kArgKernelH, kArgKernelW, kArgPadH, kArgPadW,
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kArgStrideH, kArgStrideW, kArgDilationH, kArgDilationW, kArgNumKernels, kArgBatchCount,
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kArgAOffset, kArgBOffset, kArgCOffset};
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}
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static std::vector<std::string> BuffersIn() { return {kBufMatA, kBufMatB, kBufMatC}; }
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static std::vector<std::string> BuffersOut() { return {kBufMatC}; }
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// Describes how to obtain the sizes of the buffers
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static size_t OutputHeight(const Arguments<T> &args) {
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const auto size = args.height + 2 * args.pad_h;
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const auto padding = args.dilation_h * (args.kernel_h - 1) + 1;
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if (size >= padding) { return (size - padding) / args.stride_h + 1; }
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return 1;
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}
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static size_t OutputWidth(const Arguments<T> &args) {
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const auto size = args.width + 2 * args.pad_w;
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const auto padding = args.dilation_w * (args.kernel_w - 1) + 1;
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if (size >= padding) { return (size - padding) / args.stride_w + 1; }
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return 1;
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}
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static size_t NumPatches(const Arguments<T> &args) {
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return OutputHeight(args) * OutputWidth(args) * args.channels;
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}
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static size_t GetSizeA(const Arguments<T> &args) { // 4D: NCHW == batch-channel-height-width
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return args.batch_count * args.channels * args.height * args.width + args.a_offset;
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}
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static size_t GetSizeB(const Arguments<T> &args) { // 4D: KCHW == kernel-channel-height-width
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return args.num_kernels * args.channels * args.kernel_h * args.kernel_w + args.b_offset;
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}
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static size_t GetSizeC(const Arguments<T> &args) { // 4D: NCHW == batch-channel-height-width
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return args.batch_count * args.num_kernels * OutputHeight(args) * OutputWidth(args) + args.c_offset;
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}
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// Describes how to set the sizes of all the buffers
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static void SetSizes(Arguments<T> &args, Queue&) {
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args.a_size = GetSizeA(args);
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args.b_size = GetSizeB(args);
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args.c_size = GetSizeC(args);
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}
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// Describes what the default values of the leading dimensions of the matrices are
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static size_t DefaultLDA(const Arguments<T> &) { return 1; } // N/A for this routine
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static size_t DefaultLDB(const Arguments<T> &) { return 1; } // N/A for this routine
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static size_t DefaultLDC(const Arguments<T> &) { return 1; } // N/A for this routine
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// Describes which transpose options are relevant for this routine
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using Transposes = std::vector<Transpose>;
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static Transposes GetATransposes(const Transposes &) { return {}; } // N/A for this routine
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static Transposes GetBTransposes(const Transposes &) { return {}; } // N/A for this routine
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// Describes how to prepare the input data
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static void PrepareData(const Arguments<T>&, Queue&, const int, std::vector<T>&,
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std::vector<T>&, std::vector<T>&, std::vector<T>&, std::vector<T>&,
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std::vector<T>&, std::vector<T>&) {} // N/A for this routine
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// Describes how to run the CLBlast routine
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static StatusCode RunRoutine(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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#ifdef OPENCL_API
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auto queue_plain = queue();
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auto event = cl_event{};
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auto status = Convgemm<T>(args.kernel_mode,
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args.channels, args.height, args.width,
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args.kernel_h, args.kernel_w,
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args.pad_h, args.pad_w,
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args.stride_h, args.stride_w,
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args.dilation_h, args.dilation_w,
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args.num_kernels, args.batch_count,
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buffers.a_mat(), args.a_offset,
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buffers.b_mat(), args.b_offset,
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buffers.c_mat(), args.c_offset,
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&queue_plain, &event);
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if (status == StatusCode::kSuccess) { clWaitForEvents(1, &event); clReleaseEvent(event); }
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#elif CUDA_API
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auto status = Convgemm<T>(args.kernel_mode,
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args.channels, args.height, args.width,
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2018-05-06 11:35:34 +02:00
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args.kernel_h, args.kernel_w,
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args.pad_h, args.pad_w,
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args.stride_h, args.stride_w,
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args.dilation_h, args.dilation_w,
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args.num_kernels, args.batch_count,
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buffers.a_mat(), args.a_offset,
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buffers.b_mat(), args.b_offset,
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buffers.c_mat(), args.c_offset,
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queue.GetContext()(), queue.GetDevice()());
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cuStreamSynchronize(queue());
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#endif
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return status;
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}
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// Describes how to run a naive version of the routine (for correctness/performance comparison).
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// Note that a proper clBLAS or CPU BLAS comparison is not available for non-BLAS routines.
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static StatusCode RunReference1(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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auto buffers_host = BuffersHost<T>();
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DeviceToHost(args, buffers, buffers_host, queue, BuffersIn());
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const auto status = RunReference(args, buffers_host);
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HostToDevice(args, buffers, buffers_host, queue, BuffersOut());
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return status;
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}
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static StatusCode RunReference2(const Arguments<T> &args, BuffersHost<T> &buffers_host, Queue&) {
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return RunReference(args, buffers_host);
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}
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static StatusCode RunReference3(const Arguments<T> &, BuffersCUDA<T> &, Queue &) {
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return StatusCode::kUnknownError;
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}
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// Describes how to download the results of the computation (more importantly: which buffer)
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static std::vector<T> DownloadResult(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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std::vector<T> result(args.c_size, static_cast<T>(0));
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buffers.c_mat.Read(queue, args.c_size, result);
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return result;
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}
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// Describes how to compute the indices of the result buffer
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static size_t ResultID1(const Arguments<T> &args) { return OutputHeight(args) * OutputWidth(args); }
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static size_t ResultID2(const Arguments<T> &args) { return args.num_kernels * args.batch_count; }
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static size_t GetResultIndex(const Arguments<T> &args, const size_t id1, const size_t id2) {
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return id1 + OutputHeight(args) * OutputWidth(args) * id2 + args.c_offset;
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}
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// Describes how to compute performance metrics
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static size_t GetFlops(const Arguments<T> &args) {
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const auto patch_size = args.kernel_h * args.kernel_w * args.channels;
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const auto num_patches = OutputHeight(args) * OutputWidth(args);
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return args.batch_count * 2 * num_patches * args.num_kernels * patch_size;
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}
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static size_t GetBytes(const Arguments<T> &args) {
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return (GetSizeA(args) + GetSizeB(args) + GetSizeC(args)) * sizeof(T);
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}
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};
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// =================================================================================================
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template <typename T>
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StatusCode RunReference(const Arguments<T> &args, BuffersHost<T> &buffers_host) {
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const auto output_h = TestXconvgemm<T>::OutputHeight(args);
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const auto output_w = TestXconvgemm<T>::OutputWidth(args);
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for (auto batch_id = size_t{0}; batch_id < args.batch_count; ++batch_id) {
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for (auto co_id = size_t{0}; co_id < args.num_kernels; ++co_id) { // output channels == num-kernels
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for (auto ho_id = size_t{0}; ho_id < output_h; ++ho_id) { // image height
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for (auto wo_id = size_t{0}; wo_id < output_w; ++wo_id) { // image width
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auto result = ConstantZero<T>();
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// 3D convolution
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for (auto ci_id = size_t{0}; ci_id < args.channels; ++ci_id) { // input channels
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for (auto kh_id = size_t{0}; kh_id < args.kernel_h; ++kh_id) { // kernel height
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for (auto kw_id = size_t{0}; kw_id < args.kernel_w; ++kw_id) { // kernel width
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// Retrieves the value from the input image
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const auto hi_id = kh_id * args.dilation_h + args.stride_h * ho_id - args.pad_h;
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const auto wi_id = kw_id * args.dilation_w + args.stride_w * wo_id - args.pad_w;
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if (hi_id >= 0 && hi_id < args.height &&
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wi_id >= 0 && wi_id < args.width) {
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const auto input_index = wi_id + args.width * (
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hi_id + args.height * (
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ci_id + args.channels * (
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batch_id)));
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const auto input_value = buffers_host.a_mat[input_index + args.a_offset];
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// Multiplies with the kernel tensor
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const auto kernel_index
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= (args.kernel_mode == KernelMode::kConvolution)
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? (args.kernel_w - kw_id - 1) + args.kernel_w * (
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(args.kernel_h - kh_id - 1) + args.kernel_h * (
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ci_id + args.channels * (
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co_id)))
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: kw_id + args.kernel_w * (
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kh_id + args.kernel_h * (
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ci_id + args.channels * (
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co_id)));
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const auto kernel_value = buffers_host.b_mat[kernel_index + args.b_offset];
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result += input_value * kernel_value;
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}
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}
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}
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}
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// Sets the output value (NCHW == batch-channel-height-width)
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const auto output_index = wo_id + output_w * (
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ho_id + output_h * (
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co_id + args.num_kernels * (
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batch_id)));
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buffers_host.c_mat[output_index + args.c_offset] = result;
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}
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}
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}
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}
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return StatusCode::kSuccess;
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}
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2018-09-07 22:04:08 +02:00
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// Half-precision version calling the above reference implementation after conversions
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template <>
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StatusCode RunReference<half>(const Arguments<half> &args, BuffersHost<half> &buffers_host) {
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auto a_buffer2 = HalfToFloatBuffer(buffers_host.a_mat);
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auto b_buffer2 = HalfToFloatBuffer(buffers_host.b_mat);
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auto c_buffer2 = HalfToFloatBuffer(buffers_host.c_mat);
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auto dummy = std::vector<float>(0);
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auto dummy_uint = std::vector<unsigned int>(0);
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auto buffers2 = BuffersHost<float>{dummy, dummy, a_buffer2, b_buffer2, c_buffer2, dummy, dummy, dummy_uint};
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auto args2 = Arguments<float>();
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args2.a_size = args.a_size; args2.b_size = args.b_size; args2.c_size = args.c_size;
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args2.kernel_mode = args.kernel_mode;
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args2.channels = args.channels; args2.height = args.height; args2.width = args.width;
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args2.kernel_h = args.kernel_h; args2.kernel_w = args.kernel_w;
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args2.pad_h = args.pad_h; args2.pad_w = args.pad_w;
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args2.stride_h = args.stride_h; args2.stride_w = args.stride_w;
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args2.dilation_h = args.dilation_h; args2.dilation_w = args.dilation_w;
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args2.num_kernels = args.num_kernels; args2.batch_count = args.batch_count;
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args2.a_offset = args.a_offset; args2.b_offset = args.b_offset; args2.c_offset = args.c_offset;
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auto status = RunReference(args2, buffers2);
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FloatToHalfBuffer(buffers_host.c_mat, buffers2.c_mat);
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return status;
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}
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2018-05-06 11:35:34 +02:00
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// =================================================================================================
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} // namespace clblast
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// CLBLAST_TEST_ROUTINES_XCONVGEMM_H_
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#endif
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