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Diffstat (limited to 'src/routines/level3/xher2k.cpp')
| -rw-r--r-- | src/routines/level3/xher2k.cpp | 241 |
1 files changed, 241 insertions, 0 deletions
diff --git a/src/routines/level3/xher2k.cpp b/src/routines/level3/xher2k.cpp new file mode 100644 index 00000000..bd7a053e --- /dev/null +++ b/src/routines/level3/xher2k.cpp @@ -0,0 +1,241 @@ + +// ================================================================================================= +// 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 the Xher2k class (see the header for information about the class). +// +// ================================================================================================= + +#include "routines/level3/xher2k.hpp" + +#include <string> +#include <vector> + +namespace clblast { +// ================================================================================================= + +// Constructor: forwards to base class constructor +template <typename T, typename U> +Xher2k<T,U>::Xher2k(Queue &queue, EventPointer event, const std::string &name): + Routine(queue, event, name, {"Copy","Pad","Transpose","Padtranspose","Xgemm"}, PrecisionValue<T>()) { + source_string_ = + #include "../../kernels/level3/level3.opencl" + #include "../../kernels/level3/copy_fast.opencl" + #include "../../kernels/level3/copy_pad.opencl" + #include "../../kernels/level3/transpose_fast.opencl" + #include "../../kernels/level3/transpose_pad.opencl" + #include "../../kernels/level3/xgemm_part1.opencl" + #include "../../kernels/level3/xgemm_part2.opencl" + ; +} + +// ================================================================================================= + +// The main routine +template <typename T, typename U> +StatusCode Xher2k<T,U>::DoHer2k(const Layout layout, const Triangle triangle, const Transpose ab_transpose, + const size_t n, const size_t k, + const T alpha, + const Buffer<T> &a_buffer, const size_t a_offset, const size_t a_ld, + const Buffer<T> &b_buffer, const size_t b_offset, const size_t b_ld, + const U beta, + const Buffer<T> &c_buffer, const size_t c_offset, const size_t c_ld) { + + // Makes sure all dimensions are larger than zero + if ((n == 0) || (k == 0) ) { return StatusCode::kInvalidDimension; } + + // Determines whether to apply the conjugate transpose to matrix B (argument: no transpose) or + // to matrix A (argument: conjugate transpose) + auto ab_conjugate = (ab_transpose != Transpose::kNo); + + // Computes whether or not the matrices are transposed in memory. This is based on their layout + // (row or column-major) and whether or not they are requested to be pre-transposed. + auto ab_rotated = (layout == Layout::kColMajor && ab_conjugate) || + (layout == Layout::kRowMajor && !ab_conjugate); + auto c_rotated = (layout == Layout::kRowMajor); + + // Computes the first and second dimensions of the A and B matrices taking the layout into account + auto ab_one = (ab_rotated) ? k : n; + auto ab_two = (ab_rotated) ? n : k; + + // Tests the matrices (A, B, C) for validity, first from a perspective of the OpenCL buffers and + // their sizes, and then from a perspective of parameter values (e.g. n, k). Tests whether the + // OpenCL buffers are valid and non-zero and whether the OpenCL buffers have sufficient storage + // space. Also tests that the leading dimensions of: + // matrix A cannot be less than N when rotated, or less than K when not-rotated + // matrix B cannot be less than N when rotated, or less than K when not-rotated + // matrix C cannot be less than N + auto status = TestMatrixA(ab_one, ab_two, a_buffer, a_offset, a_ld); + if (ErrorIn(status)) { return status; } + status = TestMatrixB(ab_one, ab_two, b_buffer, b_offset, b_ld); + if (ErrorIn(status)) { return status; } + status = TestMatrixC(n, n, c_buffer, c_offset, c_ld); + if (ErrorIn(status)) { return status; } + + // Calculates the ceiled versions of n and k + auto n_ceiled = Ceil(n, db_["NWG"]); + auto k_ceiled = Ceil(k, db_["KWG"]); + + // Decides which kernel to run: the upper-triangular or lower-triangular version + auto kernel_name = (triangle == Triangle::kUpper) ? "XgemmUpper" : "XgemmLower"; + + // The padded/transposed input/output matrices: if memory allocation fails, throw an exception + try { + + // Loads the program from the database + const auto program = GetProgramFromCache(context_, PrecisionValue<T>(), routine_name_); + + // Determines whether or not temporary matrices are needed + auto a1_no_temp = ab_one == n_ceiled && ab_two == k_ceiled && a_ld == n_ceiled && a_offset == 0 && + ab_rotated == false && ab_conjugate == false; + auto a2_no_temp = ab_one == n_ceiled && ab_two == k_ceiled && a_ld == n_ceiled && a_offset == 0 && + ab_rotated == false && ab_conjugate == true; + auto b1_no_temp = ab_one == n_ceiled && ab_two == k_ceiled && b_ld == n_ceiled && b_offset == 0 && + ab_rotated == false && ab_conjugate == false; + auto b2_no_temp = ab_one == n_ceiled && ab_two == k_ceiled && b_ld == n_ceiled && b_offset == 0 && + ab_rotated == false && ab_conjugate == true; + + // Creates the temporary matrices + auto a1_temp = (a1_no_temp) ? a_buffer : Buffer<T>(context_, k_ceiled*n_ceiled); + auto a2_temp = (a2_no_temp) ? a_buffer : Buffer<T>(context_, k_ceiled*n_ceiled); + auto b1_temp = (b1_no_temp) ? b_buffer : Buffer<T>(context_, k_ceiled*n_ceiled); + auto b2_temp = (b2_no_temp) ? b_buffer : Buffer<T>(context_, k_ceiled*n_ceiled); + auto c_temp = Buffer<T>(context_, n_ceiled*n_ceiled); + + // Upload the scalar arguments as constant buffers to the device (needed for half-precision) + auto complex_beta = T{beta, static_cast<U>(0.0)}; + auto alpha_buffer = Buffer<T>(context_, 1); + auto beta_buffer = Buffer<T>(context_, 1); + alpha_buffer.Write(queue_, 1, &alpha); + beta_buffer.Write(queue_, 1, &complex_beta); + + // Events of all kernels (including pre/post processing kernels) + auto eventWaitList = std::vector<Event>(); + auto emptyEventList = std::vector<Event>(); + + // Runs the pre-processing kernels. This transposes the matrices A and B, but also pads zeros to + // to fill it up until it reaches a certain multiple of size (kernel parameter dependent). In + // case nothing has to be done, these kernels can be skipped. + if (!a1_no_temp) { + auto eventProcessA1 = Event(); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, eventProcessA1.pointer(), emptyEventList, + ab_one, ab_two, a_ld, a_offset, a_buffer, + n_ceiled, k_ceiled, n_ceiled, 0, a1_temp, + ConstantOne<T>(), program, + true, ab_rotated, ab_conjugate); + eventWaitList.push_back(eventProcessA1); + if (ErrorIn(status)) { return status; } + } + if (!a2_no_temp) { + auto eventProcessA2 = Event(); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, eventProcessA2.pointer(), emptyEventList, + ab_one, ab_two, a_ld, a_offset, a_buffer, + n_ceiled, k_ceiled, n_ceiled, 0, a2_temp, + ConstantOne<T>(), program, + true, ab_rotated, !ab_conjugate); + eventWaitList.push_back(eventProcessA2); + if (ErrorIn(status)) { return status; } + } + if (!b1_no_temp) { + auto eventProcessB1 = Event(); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, eventProcessB1.pointer(), emptyEventList, + ab_one, ab_two, b_ld, b_offset, b_buffer, + n_ceiled, k_ceiled, n_ceiled, 0, b1_temp, + ConstantOne<T>(), program, + true, ab_rotated, ab_conjugate); + eventWaitList.push_back(eventProcessB1); + if (ErrorIn(status)) { return status; } + } + if (!b2_no_temp) { + auto eventProcessB2 = Event(); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, eventProcessB2.pointer(), emptyEventList, + ab_one, ab_two, b_ld, b_offset, b_buffer, + n_ceiled, k_ceiled, n_ceiled, 0, b2_temp, + ConstantOne<T>(), program, + true, ab_rotated, !ab_conjugate); + eventWaitList.push_back(eventProcessB2); + if (ErrorIn(status)) { return status; } + } + + // Furthermore, also creates a (possibly padded) copy of matrix C, since it is not allowed to + // modify the other triangle. + auto eventProcessC = Event(); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, eventProcessC.pointer(), emptyEventList, + n, n, c_ld, c_offset, c_buffer, + n_ceiled, n_ceiled, n_ceiled, 0, c_temp, + ConstantOne<T>(), program, + true, c_rotated, false); + eventWaitList.push_back(eventProcessC); + if (ErrorIn(status)) { return status; } + + // Retrieves the XgemmUpper or XgemmLower kernel from the compiled binary + try { + auto kernel = Kernel(program, kernel_name); + + // Sets the kernel arguments + kernel.SetArgument(0, static_cast<int>(n_ceiled)); + kernel.SetArgument(1, static_cast<int>(k_ceiled)); + kernel.SetArgument(2, alpha_buffer()); + kernel.SetArgument(3, beta_buffer()); + kernel.SetArgument(4, a1_temp()); + kernel.SetArgument(5, b2_temp()); + kernel.SetArgument(6, c_temp()); + + // Computes the global and local thread sizes + auto global = std::vector<size_t>{ + (n_ceiled * db_["MDIMC"]) / db_["MWG"], + (n_ceiled * db_["NDIMC"]) / db_["NWG"] + }; + auto local = std::vector<size_t>{db_["MDIMC"], db_["NDIMC"]}; + + // Launches the kernel + auto eventKernel1 = Event(); + status = RunKernel(kernel, queue_, device_, global, local, eventKernel1.pointer(), eventWaitList); + if (ErrorIn(status)) { return status; } + eventWaitList.push_back(eventKernel1); + + // Swaps the arguments for matrices A and B, sets 'beta' to 1, and conjugate alpha + auto conjugate_alpha = T{alpha.real(), -alpha.imag()}; + auto complex_one = T{static_cast<U>(1.0), static_cast<U>(0.0)}; + alpha_buffer.Write(queue_, 1, &conjugate_alpha); + beta_buffer.Write(queue_, 1, &complex_one); + kernel.SetArgument(2, alpha_buffer()); + kernel.SetArgument(3, beta_buffer()); + kernel.SetArgument(4, b1_temp()); + kernel.SetArgument(5, a2_temp()); + + // Runs the kernel again + auto eventKernel2 = Event(); + status = RunKernel(kernel, queue_, device_, global, local, eventKernel2.pointer(), eventWaitList); + if (ErrorIn(status)) { return status; } + eventWaitList.push_back(eventKernel2); + + // Runs the post-processing kernel + auto upper = (triangle == Triangle::kUpper); + auto lower = (triangle == Triangle::kLower); + status = PadCopyTransposeMatrix(queue_, device_, context_, db_, event_, eventWaitList, + n_ceiled, n_ceiled, n_ceiled, 0, c_temp, + n, n, c_ld, c_offset, c_buffer, + ConstantOne<T>(), program, + false, c_rotated, false, upper, lower, true); + if (ErrorIn(status)) { return status; } + + // Successfully finished the computation + return StatusCode::kSuccess; + } catch (...) { return StatusCode::kInvalidKernel; } + } catch (...) { return StatusCode::kTempBufferAllocFailure; } +} + +// ================================================================================================= + +// Compiles the templated class +template class Xher2k<float2,float>; +template class Xher2k<double2,double>; + +// ================================================================================================= +} // namespace clblast |