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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * **************************************************************************************************/ /*! \file \brief Template for a pipelined GEMM kernel. Does not compute batching or support split-K. */ #pragma once #include "cutlass/cutlass.h" #include "cutlass/numeric_types.h" #include "cutlass/arch/arch.h" #include "cutlass/device_kernel.h" #include "cutlass/gemm/threadblock/threadblock_swizzle.h" #include "cutlass/gemm/kernel/gemm.h" #include "cutlass/gemm/kernel/default_gemm_complex.h" #include "cutlass/gemm/device/default_gemm_configuration.h" //////////////////////////////////////////////////////////////////////////////// namespace cutlass { namespace gemm { namespace device { ///////////////////////////////////////////////////////////////////////////////////////////////// /*! Gemm device-level operator. This is an interface to efficient CUTLASS GEMM kernels that may be invoked from host code. The contributions of this class are: 1. At compile time, it maps data types and high-level structural parameters onto specific CUTLASS components. 2. At runtime, it maps logical arguments to GEMM problems to kernel parameters. 3. At runtime, it launches kernels on the device. The intent is to provide a convenient mechanism for interacting with most plausible GEMM configurations for each supported architecture. Consequently, not all parameters are exposed to the top-level interface. Rather, sensible defaults at each level of the CUTLASS hierarchy are selected to tradeoff simplicity of the interface with flexibility. We expect most configurations to be specified at this level. Applications with more exotic requirements may construct their kernels of interest using CUTLASS components at the threadblock, warp, and thread levels of abstraction. CUTLASS exposes computations using the functor design pattern in which objects compose some internal state with an overloaded function call operator. This enables decoupling of initialization from execution, possibly reducing overhead during steady state phases of application execution. CUTLASS device-level operators expose an Arguments structure encompassing each logical input to the computation. This is distinct from the kernel-level Params structure pattern which contains application-specific precomputed state needed by the device code. Example of a CUTLASS GEMM operator implementing the functionality of cuBLAS's SGEMM NN is as follows: // // Instantiate the CUTLASS GEMM operator. // cutlass::gemm::device::Gemm< float, cutlass::layout::ColumnMajor, float, cutlass::layout::ColumnMajor, float, cutlass::layout::ColumnMajor > gemm_op; // // Launch the GEMM operation on the device // cutlass::Status status = gemm_op({ {m, n, k}, // GemmCoord problem_size, {A, lda}, // TensorRef ref_A, {B, ldb}, // TensorRef ref_B, {C, ldc}, // TensorRef ref_C, {D, ldd}, // TensorRef ref_D, {alpha, beta} // EpilogueOutputOp::Params epilogue_op_params }); A simplified view of the template is listed below. template < /// Element type for A matrix operand typename ElementA, /// Layout type for A matrix operand typename LayoutA, /// Element type for B matrix operand typename ElementB, /// Layout type for B matrix operand typename LayoutB, /// Element type for C and D matrix operands typename ElementC, /// Layout type for C and D matrix operands typename LayoutC, /// Element type for internal accumulation typename ElementAccumulator, /// Operator class tag typename OperatorClass, /// Tag indicating architecture to tune for. This is the minimum SM that /// supports the intended feature. The device kernel can be built /// targeting any SM larger than this number. typename ArchTag, /// Threadblock-level tile size (concept: GemmShape) typename ThreadblockShape, /// Warp-level tile size (concept: GemmShape) typename WarpShape, /// Warp-level tile size (concept: GemmShape) typename InstructionShape, /// Epilogue output operator typename EpilogueOutputOp, /// Threadblock-level swizzling operator typename ThreadblockSwizzle, /// Number of stages used in the pipelined mainloop int Stages > class Gemm; */ template < /// Element type for A matrix operand typename ElementA_, /// Layout type for A matrix operand typename LayoutA_, /// Element type for B matrix operand typename ElementB_, /// Layout type for B matrix operand typename LayoutB_, /// Element type for C and D matrix operands typename ElementC_, /// Layout type for C and D matrix operands typename LayoutC_, /// Element type for internal accumulation typename ElementAccumulator_ = ElementC_, /// Operator class tag typename OperatorClass_ = arch::OpClassSimt, /// Tag indicating architecture to tune for. typename ArchTag_ = arch::Sm70, /// Threadblock-level tile size (concept: GemmShape) typename ThreadblockShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::ThreadblockShape, /// Warp-level tile size (concept: GemmShape) typename WarpShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::WarpShape, /// Instruction-level tile size (concept: GemmShape) typename InstructionShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::InstructionShape, /// Epilogue output operator typename EpilogueOutputOp_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::EpilogueOutputOp, /// Threadblock-level swizzling operator typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle<>, /// Number of stages used in the pipelined mainloop int Stages = DefaultGemmConfiguration::kStages, /// Complex elementwise transformation on A operand ComplexTransform TransformA = ComplexTransform::kNone, /// Complex elementwise transformation on B operand ComplexTransform TransformB = ComplexTransform::kNone, /// Multiply-add operator // (selects complex or gaussian complex) typename Operator_ = arch::OpMultiplyAddComplex, /// If true, kernel supports split-K with serial reduction bool SplitKSerial = false> class GemmComplex { public: using ElementA = ElementA_; using LayoutA = LayoutA_; using TensorRefA = TensorRef; using ElementB = ElementB_; using LayoutB = LayoutB_; using TensorRefB = TensorRef; using ElementC = ElementC_; using LayoutC = LayoutC_; using TensorRefC = TensorRef; using TensorRefD = TensorRef; using ElementAccumulator = ElementAccumulator_; using OperatorClass = OperatorClass_; using ArchTag = ArchTag_; using ThreadblockShape = ThreadblockShape_; using WarpShape = WarpShape_; using InstructionShape = InstructionShape_; using EpilogueOutputOp = EpilogueOutputOp_; using ThreadblockSwizzle = ThreadblockSwizzle_; static int const kStages = Stages; static ComplexTransform const kTransformA = TransformA; static ComplexTransform const kTransformB = TransformB; using Operator = Operator_; static bool const kSplitKSerial = SplitKSerial; static int const kAlignmentA = 1; static int const kAlignmentB = 1; static int const kAlignmentC = EpilogueOutputOp::kCount; /// Define the kernel using GemmKernel = typename kernel::DefaultGemmComplex< ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ElementAccumulator, OperatorClass, ArchTag, ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp, ThreadblockSwizzle, kStages, kTransformA, kTransformB, Operator, kSplitKSerial >::GemmKernel; /// Argument structure struct Arguments { // // Data members // GemmCoord problem_size; TensorRef ref_A; TensorRef ref_B; TensorRef ref_C; TensorRef ref_D; typename EpilogueOutputOp::Params epilogue; int split_k_slices; // // Methods // /// Default ctor CUTLASS_HOST_DEVICE Arguments(): problem_size(0, 0, 0), split_k_slices(1) { } /// Constructs an Arguments structure CUTLASS_HOST_DEVICE Arguments( GemmCoord problem_size_, TensorRef ref_A_, TensorRef ref_B_, TensorRef ref_C_, TensorRef ref_D_, typename EpilogueOutputOp::Params epilogue_ = typename EpilogueOutputOp::Params(), int split_k_slices = 1 ): problem_size(problem_size_), ref_A(ref_A_), ref_B(ref_B_), ref_C(ref_C_), ref_D(ref_D_), epilogue(epilogue_), split_k_slices(split_k_slices) { } }; private: /// Kernel parameters object typename GemmKernel::Params params_; public: /// Constructs the GEMM. GemmComplex() { } /// Determines whether the GEMM can execute the given problem. static Status can_implement(Arguments const &args) { if (!kSplitKSerial && args.split_k_slices > 1) { return Status::kErrorInvalidProblem; } return Status::kSuccess; } /// Gets the workspace size static size_t get_workspace_size(Arguments const &args) { if (kSplitKSerial && args.split_k_slices > 1) { // Determine grid shape ThreadblockSwizzle threadblock_swizzle; cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape( args.problem_size, {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK}, args.split_k_slices); return sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n()); } return 0; } /// Initializes GEMM state from arguments. Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { // Determine grid shape ThreadblockSwizzle threadblock_swizzle; cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape( args.problem_size, {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK}, args.split_k_slices); if (kSplitKSerial) { if (args.split_k_slices > 1) { if (!workspace) { return Status::kErrorWorkspaceNull; } size_t bytes = get_workspace_size(args); cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream); if (result != cudaSuccess) { return Status::kErrorInternal; } } } else { if (args.split_k_slices > 1) { return Status::kErrorInvalidProblem; } } // Initialize the Params structure params_ = typename GemmKernel::Params{ args.problem_size, grid_shape, args.ref_A.non_const_ref(), args.ref_B.non_const_ref(), args.ref_C.non_const_ref(), args.ref_D, args.epilogue, static_cast(workspace) }; return Status::kSuccess; } /// Lightweight update given a subset of arguments Status update(Arguments const &args, void *workspace = nullptr) { if (kSplitKSerial && args.split_k_slices > 1) { if (!workspace) { return Status::kErrorWorkspaceNull; } } params_.ref_A.reset(args.ref_A.non_const_ref().data()); params_.ref_B.reset(args.ref_B.non_const_ref().data()); params_.ref_C.reset(args.ref_C.non_const_ref().data()); params_.ref_D.reset(args.ref_D.data()); params_.semaphore = static_cast(workspace); return Status::kSuccess; } /// Runs the kernel using initialized state. Status run(cudaStream_t stream = nullptr) { ThreadblockSwizzle threadblock_swizzle; dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape); dim3 block(GemmKernel::kThreadCount, 1, 1); cudaError_t result; int smem_size = int(sizeof(typename GemmKernel::SharedStorage)); if (smem_size >= (48 << 10)) { result = cudaFuncSetAttribute(Kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size); if (result != cudaSuccess) { return Status::kErrorInternal; } } cutlass::arch::synclog_setup(); cutlass::Kernel<<>>(params_); result = cudaGetLastError(); return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal; } /// Runs the kernel using initialized state. Status operator()(cudaStream_t stream = nullptr) { return run(stream); } /// Runs the kernel using initialized state. Status operator()( Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { Status status = initialize(args, workspace); if (status == Status::kSuccess) { status = run(stream); } return status; } }; //////////////////////////////////////////////////////////////////////////////// /// Partial specialization for column-major output exchanges problem size and operand. template < /// Element type for A matrix operand typename ElementA_, /// Layout type for A matrix operand typename LayoutA_, /// Element type for B matrix operand typename ElementB_, /// Layout type for B matrix operand typename LayoutB_, /// Element type for C and D matrix operands typename ElementC_, /// Element type for internal accumulation typename ElementAccumulator_, /// Operator class tag typename OperatorClass_, /// Tag indicating architecture to tune for typename ArchTag_, /// Threadblock-level tile size (concept: GemmShape) typename ThreadblockShape_, /// Warp-level tile size (concept: GemmShape) typename WarpShape_, /// Warp-level tile size (concept: GemmShape) typename InstructionShape_, /// Epilogue output operator typename EpilogueOutputOp_, /// Threadblock-level swizzling operator typename ThreadblockSwizzle_, /// Number of stages used in the pipelined mainloop int Stages, /// Complex elementwise transformation on A operand ComplexTransform TransformA, /// Complex elementwise transformation on B operand ComplexTransform TransformB, /// Multiply-add operator // (selects complex or gaussian complex) typename Operator_, /// If true, kernel supports split-K as a serial reduction bool SplitKSerial > class GemmComplex< ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, // partially specialized on LayoutC ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, TransformA, TransformB, Operator_, SplitKSerial > { public: using ElementA = ElementA_; using LayoutA = LayoutA_; using TensorRefA = TensorRef; using ElementB = ElementB_; using LayoutB = LayoutB_; using TensorRefB = TensorRef; using ElementC = ElementC_; using LayoutC = layout::ColumnMajor; using TensorRefC = TensorRef; using TensorRefD = TensorRef; using ElementAccumulator = ElementAccumulator_; using OperatorClass = OperatorClass_; using ArchTag = ArchTag_; using ThreadblockShape = ThreadblockShape_; using WarpShape = WarpShape_; using InstructionShape = InstructionShape_; using EpilogueOutputOp = EpilogueOutputOp_; using ThreadblockSwizzle = ThreadblockSwizzle_; static int const kStages = Stages; using Operator = Operator_; static bool const kSplitKSerial = SplitKSerial; using UnderlyingOperator = GemmComplex< ElementB, typename layout::LayoutTranspose::type, ElementA, typename layout::LayoutTranspose::type, ElementC, layout::RowMajor, ElementAccumulator, OperatorClass, ArchTag, ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp, ThreadblockSwizzle, Stages, TransformB, TransformA, Operator, SplitKSerial >; static int const kAlignmentA = UnderlyingOperator::kAlignmentB; static int const kAlignmentB = UnderlyingOperator::kAlignmentA; static int const kAlignmentC = UnderlyingOperator::kAlignmentC; static ComplexTransform const kTransformA = UnderlyingOperator::kTransformB; static ComplexTransform const kTransformB = UnderlyingOperator::kTransformA; using UnderlyingArguments = typename UnderlyingOperator::Arguments; using GemmKernel = typename UnderlyingOperator::GemmKernel; /// Argument structure struct Arguments { // // Data members // GemmCoord problem_size; TensorRef ref_A; TensorRef ref_B; TensorRef ref_C; TensorRef ref_D; typename EpilogueOutputOp::Params epilogue; int split_k_slices; // // Methods // /// Default ctor CUTLASS_HOST_DEVICE Arguments() { } /// Constructs an Arguments structure CUTLASS_HOST_DEVICE Arguments( GemmCoord problem_size_, TensorRef ref_A_, TensorRef ref_B_, TensorRef ref_C_, TensorRef ref_D_, typename EpilogueOutputOp::Params epilogue_ = typename EpilogueOutputOp::Params(), int split_k_slices = 1 ): problem_size(problem_size_), ref_A(ref_A_), ref_B(ref_B_), ref_C(ref_C_), ref_D(ref_D_), epilogue(epilogue_), split_k_slices(split_k_slices) { } }; private: UnderlyingOperator underlying_operator_; public: /// Constructs the GEMM. GemmComplex() { } /// Helper to construct a transposed equivalent for the underlying GEMM operator static UnderlyingArguments to_underlying_arguments(Arguments const &args) { return UnderlyingArguments( {args.problem_size.n(), args.problem_size.m(), args.problem_size.k()}, {args.ref_B.data(), args.ref_B.stride(0)}, {args.ref_A.data(), args.ref_A.stride(0)}, {args.ref_C.data(), args.ref_C.stride(0)}, {args.ref_D.data(), args.ref_D.stride(0)}, args.epilogue, args.split_k_slices ); } /// Determines whether the GEMM can execute the given problem. static Status can_implement(Arguments const &args) { return UnderlyingOperator::can_implement(to_underlying_arguments(args)); } /// Gets the workspace size static size_t get_workspace_size(Arguments const &args) { return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args)); } /// Initializes GEMM state from arguments. Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { return underlying_operator_.initialize(to_underlying_arguments(args), workspace); } /// Lightweight update given a subset of arguments Status update(Arguments const &args, void *workspace = nullptr) { return underlying_operator_.update(to_underlying_arguments(args), workspace); } /// Runs the kernel using initialized state. Status run(cudaStream_t stream = nullptr) { return underlying_operator_.run(stream); } /// Runs the kernel using initialized state. Status operator()(cudaStream_t stream = nullptr) { return run(stream); } /// Runs the kernel using initialized state. Status operator()( Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { Status status = initialize(args, workspace, stream); if (status == Status::kSuccess) { status = run(stream); } return status; } }; //////////////////////////////////////////////////////////////////////////////// } // namespace device } // namespace gemm } // namespace cutlass ////////////////////////////////////////////////////////////////////////////////