// ================================================================================================= // 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 // // This file implements the Routine base class (see the header for information about the class). // // ================================================================================================= #include #include #include #include "internal/routine.h" namespace clblast { // ================================================================================================= // The cache of compiled OpenCL programs and its mutex for thread safety template std::vector::ProgramCache> Routine::program_cache_; template std::mutex Routine::program_cache_mutex_; // Constructor: not much here, because no status codes can be returned template Routine::Routine(Queue &queue, Event &event, const std::string &name, const std::vector &routines, const Precision precision): precision_(precision), routine_name_(name), queue_(queue), event_(event), context_(queue_.GetContext()), device_(queue_.GetDevice()), device_name_(device_.Name()), max_work_item_dimensions_(device_.MaxWorkItemDimensions()), max_work_item_sizes_(device_.MaxWorkItemSizes()), max_work_group_size_(device_.MaxWorkGroupSize()), db_(queue_, routines, precision_) { } // ================================================================================================= // Separate set-up function to allow for status codes to be returned template StatusCode Routine::SetUp() { // Queries the cache to see whether or not the compiled kernel is already there. If not, it will // be built and added to the cache. if (!ProgramIsInCache()) { // Inspects whether or not cl_khr_fp64 is supported in case of double precision auto extensions = device_.Capabilities(); if (precision_ == Precision::kDouble || precision_ == Precision::kComplexDouble) { if (extensions.find(kKhronosDoublePrecision) == std::string::npos) { return StatusCode::kNoDoublePrecision; } } // As above, but for cl_khr_fp16 (half precision) if (precision_ == Precision::kHalf) { if (extensions.find(kKhronosHalfPrecision) == std::string::npos) { return StatusCode::kNoHalfPrecision; } } // Loads the common header (typedefs and defines and such) std::string common_header = #include "kernels/common.opencl" ; // Collects the parameters for this device in the form of defines, and adds the precision auto defines = db_.GetDefines(); defines += "#define PRECISION "+ToString(static_cast(precision_))+"\n"; // Adds the name of the routine as a define defines += "#define ROUTINE_"+routine_name_+"\n"; // For specific devices, use the non-IEE754 compilant OpenCL mad() instruction. This can improve // performance, but might result in a reduced accuracy. if (device_.Vendor() == "AMD") { defines += "#define USE_CL_MAD 1\n"; } // Combines everything together into a single source string auto source_string = defines + common_header + source_string_; // Compiles the kernel try { auto program = Program(context_, source_string); auto options = std::vector(); auto build_status = program.Build(device_, options); // Checks for compiler crashes/errors/warnings if (build_status == BuildStatus::kError) { auto message = program.GetBuildInfo(device_); fprintf(stdout, "OpenCL compiler error/warning: %s\n", message.c_str()); return StatusCode::kBuildProgramFailure; } if (build_status == BuildStatus::kInvalid) { return StatusCode::kInvalidBinary; } // Store the compiled program in the cache (atomic for thread-safety) program_cache_mutex_.lock(); program_cache_.push_back({program, device_name_, precision_, routine_name_}); program_cache_mutex_.unlock(); } catch (...) { return StatusCode::kBuildProgramFailure; } } // No errors, normal termination of this function return StatusCode::kSuccess; } // ================================================================================================= // Enqueues a kernel, waits for completion, and checks for errors template StatusCode Routine::RunKernel(Kernel &kernel, std::vector &global, const std::vector &local) { // Tests for validity of the local thread sizes if (local.size() > max_work_item_dimensions_) { return StatusCode::kInvalidLocalNumDimensions; } for (auto i=size_t{0}; i max_work_item_sizes_[i]) { return StatusCode::kInvalidLocalThreadsDim; } } auto local_size = size_t{1}; for (auto &item: local) { local_size *= item; } if (local_size > max_work_group_size_) { return StatusCode::kInvalidLocalThreadsTotal; } // Make sure the global thread sizes are at least equal to the local sizes for (auto i=size_t{0}; i StatusCode Routine::TestMatrixA(const size_t one, const size_t two, const Buffer &buffer, const size_t offset, const size_t ld, const size_t data_size) { if (ld < one) { return StatusCode::kInvalidLeadDimA; } try { auto required_size = (ld*two + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryA; } } catch (...) { return StatusCode::kInvalidMatrixA; } return StatusCode::kSuccess; } // Tests matrix B for validity: checks for a valid OpenCL buffer, a valid lead-dimension, and for a // sufficient buffer size. template StatusCode Routine::TestMatrixB(const size_t one, const size_t two, const Buffer &buffer, const size_t offset, const size_t ld, const size_t data_size) { if (ld < one) { return StatusCode::kInvalidLeadDimB; } try { auto required_size = (ld*two + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryB; } } catch (...) { return StatusCode::kInvalidMatrixB; } return StatusCode::kSuccess; } // Tests matrix C for validity: checks for a valid OpenCL buffer, a valid lead-dimension, and for a // sufficient buffer size. template StatusCode Routine::TestMatrixC(const size_t one, const size_t two, const Buffer &buffer, const size_t offset, const size_t ld, const size_t data_size) { if (ld < one) { return StatusCode::kInvalidLeadDimC; } try { auto required_size = (ld*two + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryC; } } catch (...) { return StatusCode::kInvalidMatrixC; } return StatusCode::kSuccess; } // Tests matrix AP for validity: checks for a valid OpenCL buffer and for a sufficient buffer size template StatusCode Routine::TestMatrixAP(const size_t n, const Buffer &buffer, const size_t offset, const size_t data_size) { try { auto required_size = (((n*(n+1))/2) + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryA; } } catch (...) { return StatusCode::kInvalidMatrixA; } return StatusCode::kSuccess; } // ================================================================================================= // Tests vector X for validity: checks for a valid increment, a valid OpenCL buffer, and for a // sufficient buffer size. template StatusCode Routine::TestVectorX(const size_t n, const Buffer &buffer, const size_t offset, const size_t inc, const size_t data_size) { if (inc == 0) { return StatusCode::kInvalidIncrementX; } try { auto required_size = (n*inc + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryX; } } catch (...) { return StatusCode::kInvalidVectorX; } return StatusCode::kSuccess; } // Tests vector Y for validity: checks for a valid increment, a valid OpenCL buffer, and for a // sufficient buffer size. template StatusCode Routine::TestVectorY(const size_t n, const Buffer &buffer, const size_t offset, const size_t inc, const size_t data_size) { if (inc == 0) { return StatusCode::kInvalidIncrementY; } try { auto required_size = (n*inc + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryY; } } catch (...) { return StatusCode::kInvalidVectorY; } return StatusCode::kSuccess; } // ================================================================================================= // Tests vector dot for validity: checks for a valid increment, a valid OpenCL buffer, and for a // sufficient buffer size. template StatusCode Routine::TestVectorDot(const size_t n, const Buffer &buffer, const size_t offset, const size_t data_size) { try { auto required_size = (n + offset)*data_size; auto buffer_size = buffer.GetSize(); if (buffer_size < required_size) { return StatusCode::kInsufficientMemoryDot; } } catch (...) { return StatusCode::kInvalidVectorDot; } return StatusCode::kSuccess; } // ================================================================================================= // Copies or transposes a matrix and pads/unpads it with zeros template StatusCode Routine::PadCopyTransposeMatrix(const size_t src_one, const size_t src_two, const size_t src_ld, const size_t src_offset, const Buffer &src, const size_t dest_one, const size_t dest_two, const size_t dest_ld, const size_t dest_offset, const Buffer &dest, const Program &program, const bool do_pad, const bool do_transpose, const bool do_conjugate, const bool upper, const bool lower, const bool diagonal_imag_zero) { // Determines whether or not the fast-version could potentially be used auto use_fast_kernel = (src_offset == 0) && (dest_offset == 0) && (do_conjugate == false) && (src_one == dest_one) && (src_two == dest_two) && (src_ld == dest_ld) && (upper == false) && (lower == false) && (diagonal_imag_zero == false); // Determines the right kernel auto kernel_name = std::string{}; if (do_transpose) { if (use_fast_kernel && IsMultiple(src_ld, db_["TRA_WPT"]) && IsMultiple(src_one, db_["TRA_WPT"]*db_["TRA_WPT"]) && IsMultiple(src_two, db_["TRA_WPT"]*db_["TRA_WPT"])) { kernel_name = "TransposeMatrix"; } else { use_fast_kernel = false; kernel_name = (do_pad) ? "PadTransposeMatrix" : "UnPadTransposeMatrix"; } } else { if (use_fast_kernel && IsMultiple(src_ld, db_["COPY_VW"]) && IsMultiple(src_one, db_["COPY_VW"]*db_["COPY_DIMX"]) && IsMultiple(src_two, db_["COPY_WPT"]*db_["COPY_DIMY"])) { kernel_name = "CopyMatrix"; } else { use_fast_kernel = false; kernel_name = (do_pad) ? "PadMatrix" : "UnPadMatrix"; } } // Retrieves the kernel from the compiled binary try { auto kernel = Kernel(program, kernel_name); // Sets the kernel arguments if (use_fast_kernel) { kernel.SetArgument(0, static_cast(src_ld)); kernel.SetArgument(1, src()); kernel.SetArgument(2, dest()); } else { kernel.SetArgument(0, static_cast(src_one)); kernel.SetArgument(1, static_cast(src_two)); kernel.SetArgument(2, static_cast(src_ld)); kernel.SetArgument(3, static_cast(src_offset)); kernel.SetArgument(4, src()); kernel.SetArgument(5, static_cast(dest_one)); kernel.SetArgument(6, static_cast(dest_two)); kernel.SetArgument(7, static_cast(dest_ld)); kernel.SetArgument(8, static_cast(dest_offset)); kernel.SetArgument(9, dest()); if (do_pad) { kernel.SetArgument(10, static_cast(do_conjugate)); } else { kernel.SetArgument(10, static_cast(upper)); kernel.SetArgument(11, static_cast(lower)); kernel.SetArgument(12, static_cast(diagonal_imag_zero)); } } // Launches the kernel and returns the error code. Uses global and local thread sizes based on // parameters in the database. auto status = StatusCode::kSuccess; if (do_transpose) { if (use_fast_kernel) { auto global = std::vector{dest_one / db_["TRA_WPT"], dest_two / db_["TRA_WPT"]}; auto local = std::vector{db_["TRA_DIM"], db_["TRA_DIM"]}; status = RunKernel(kernel, global, local); } else { auto global = std::vector{Ceil(CeilDiv(dest_one, db_["PADTRA_WPT"]), db_["PADTRA_TILE"]), Ceil(CeilDiv(dest_two, db_["PADTRA_WPT"]), db_["PADTRA_TILE"])}; auto local = std::vector{db_["PADTRA_TILE"], db_["PADTRA_TILE"]}; status = RunKernel(kernel, global, local); } } else { if (use_fast_kernel) { auto global = std::vector{dest_one / db_["COPY_VW"], dest_two / db_["COPY_WPT"]}; auto local = std::vector{db_["COPY_DIMX"], db_["COPY_DIMY"]}; status = RunKernel(kernel, global, local); } else { auto global = std::vector{Ceil(CeilDiv(dest_one, db_["PAD_WPTX"]), db_["PAD_DIMX"]), Ceil(CeilDiv(dest_two, db_["PAD_WPTY"]), db_["PAD_DIMY"])}; auto local = std::vector{db_["PAD_DIMX"], db_["PAD_DIMY"]}; status = RunKernel(kernel, global, local); } } return status; } catch (...) { return StatusCode::kInvalidKernel; } } // ================================================================================================= // Queries the cache and retrieves a matching program. Assumes that the match is available, throws // otherwise. template const Program& Routine::GetProgramFromCache() const { program_cache_mutex_.lock(); for (auto &cached_program: program_cache_) { if (cached_program.MatchInCache(device_name_, precision_, routine_name_)) { program_cache_mutex_.unlock(); return cached_program.program; } } program_cache_mutex_.unlock(); throw std::runtime_error("Internal CLBlast error: Expected program in cache, but found none."); } // Queries the cache to see whether or not the compiled kernel is already there template bool Routine::ProgramIsInCache() const { program_cache_mutex_.lock(); for (auto &cached_program: program_cache_) { if (cached_program.MatchInCache(device_name_, precision_, routine_name_)) { program_cache_mutex_.unlock(); return true; } } program_cache_mutex_.unlock(); return false; } // ================================================================================================= // Compiles the templated class template class Routine; template class Routine; template class Routine; template class Routine; // ================================================================================================= } // namespace clblast