/*************************************************************************************************** * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * 3. Neither the name of the copyright holdvr nor the names of its * contributors may be used to endorse or promote products derived from * this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. 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. * **************************************************************************************************/ // Copyright (c) Microsoft Corporation. // SPDX-License-Identifier: Apache-2.0 // DeepSpeed Team /*! \file \brief Cutlass provides helper template functions to figure out the right datastructures to instantiate to run a GEMM with various parameters (see `cutlass/gemm/threadblock/default_mma.h`). However, due to template instantiation priority rules, it will only create an MmaMultiStage with kStages=3 (otherwise creates an MmePipelined - which is not compatible with FastF32). kStages=3 uses too much shared memory and we want to use kStages=2, so we just copy-pasted some code from `default_mma.h` and `default_mma_core.h` files and wrapped this template to allow our usecase. This is really only for the FastF32 case - aka using TensorCores with fp32. */ #pragma once #include "cutlass/gemm/threadblock/default_mma.h" #include "cutlass/gemm/threadblock/default_mma_core_simt.h" #include "cutlass/gemm/threadblock/default_mma_core_sm70.h" #include "cutlass/gemm/threadblock/default_mma_core_sm75.h" #include "cutlass/gemm/threadblock/default_mma_core_sm80.h" namespace cutlass { namespace gemm { namespace threadblock { template < /// Element type for A matrix operand typename ElementA, /// Layout type for A matrix operand typename LayoutA, /// Access granularity of A matrix in units of elements int kAlignmentA, /// Element type for B matrix operand typename ElementB, /// Layout type for B matrix operand typename LayoutB, /// Access granularity of B matrix in units of elements int kAlignmentB, /// Element type for internal accumulation typename ElementAccumulator, /// Layout type for C and D matrix operand typename LayoutC, /// 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, /// Instruction-level tile size (concept: GemmShape) typename InstructionShape, /// Number of stages used in the pipelined mainloop int Stages, /// Operation performed by GEMM typename Operator, typename Enable_ = void> struct FindDefaultMma { static constexpr bool AccumulatorsInRowMajor = false; static constexpr SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone; using DefaultMma = cutlass::gemm::threadblock::DefaultMma; }; /// Specialization for sm80 / FastF32 / multistage with kStages=2 template struct FindDefaultMma 1)>::type> { using LayoutC = layout::RowMajor; using OperatorClass = arch::OpClassTensorOp; using ArchTag = arch::Sm80; using DefaultMma_ = cutlass::gemm::threadblock::DefaultMma; struct DefaultMma : DefaultMma_ { using MmaCore_ = typename DefaultMma_::MmaCore; // Define the threadblock-scoped multistage matrix multiply using ThreadblockMma = cutlass::gemm::threadblock::MmaMultistage; }; }; } // namespace threadblock } // namespace gemm } // namespace cutlass