461 lines
12 KiB
Plaintext
461 lines
12 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief CUTLASS Dual-GEMM Example.
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Fused kernel that outputs `D0` and `D1`.
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We assume that B0/B1 have the same shape/layout
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```
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D0 = epilogue0(X @ B0, C0)
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D1 = epilogue1(X @ B1, C1)
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D2 = element_wise(D0, D1)
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```
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D0 and D1 will be optionally stored in gmem (`kStoreD0` / `kStoreD1`)
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*/
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#include <iostream>
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/device/gemm.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "device/dual_gemm.h"
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#include "thread/left_silu_and_mul.h"
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#include "dual_gemm_run.h"
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#include "test_run.h"
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////////////////////////////////////////////////////////////////////////////////
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cutlass::gemm::GemmCoord problem_size(4096, 4096, 8192);
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cutlass::gemm::GemmCoord batch_problem_size(321, 256, 512);
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constexpr int kStages = 3;
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constexpr bool kSplitKSerial = false;
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constexpr bool kUseBias = true;
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constexpr int kBatchCount = 37;
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#if 0
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using ElementOperandA = cutlass::bfloat16_t;
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using ElementOperandB = cutlass::bfloat16_t;
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using ElementOutput = cutlass::bfloat16_t;
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using ElementAccumulator = float;
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using ElementCompute = float;
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#else
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using ElementOperandA = cutlass::half_t;
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using ElementOperandB = cutlass::half_t;
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using ElementOutput = cutlass::half_t;
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using ElementAccumulator = cutlass::half_t;
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using ElementCompute = cutlass::half_t;
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#endif
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constexpr auto kScaleType = kUseBias ? cutlass::epilogue::thread::ScaleType::NoBetaScaling : (
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// No bias
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kSplitKSerial ? cutlass::epilogue::thread::ScaleType::Default : cutlass::epilogue::thread::ScaleType::Nothing
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);
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using EpilogueOutputOp0 = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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128 / cutlass::sizeof_bits<ElementOutput>::value,
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ElementAccumulator,
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ElementCompute,
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kScaleType
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>;
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using EpilogueOutputOp1 = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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128 / cutlass::sizeof_bits<ElementOutput>::value,
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ElementAccumulator,
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ElementCompute,
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kScaleType
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>;
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using EpilogueOutputOp2 = cutlass::epilogue::thread::LeftSiLUAndMul<
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ElementOutput,
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128 / cutlass::sizeof_bits<ElementOutput>::value,
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ElementOutput,
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ElementCompute
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>;
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const ElementCompute alpha0 = ElementCompute(1);
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const ElementCompute beta0 = ElementCompute(kUseBias ? 1 : 0);
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const ElementCompute alpha1 = ElementCompute(1);
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const ElementCompute beta1 = ElementCompute(kUseBias ? 1 : 0);
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bool run_nonfused_gemm_f16_sm80() {
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using ThreadblockShape = cutlass::gemm::GemmShape<128, 128, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
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using Gemm0 = cutlass::gemm::device::Gemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp0,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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8,
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8,
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kSplitKSerial
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>;
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using Gemm1 = cutlass::gemm::device::Gemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp1,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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8,
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8,
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kSplitKSerial
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>;
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NonFusedDualGemmRun<Gemm0, Gemm1> nonFusedGemm;
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std::cout << "Running Non-fused GEMMs FP16 TN GEMMs...\n";
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bool pass = nonFusedGemm.run(
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problem_size,
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alpha0,
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beta0,
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alpha1,
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beta1,
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true /* is_profiling */
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);
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if(pass)
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std::cout << "Pass\n";
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else
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std::cout << "Fail\n";
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return pass;
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}
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template <typename T>
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struct LeftSiLUAndMul {
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struct Params{};
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CUTLASS_HOST_DEVICE LeftSiLUAndMul(Params p) {}
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CUTLASS_HOST_DEVICE void set_k_partition(int, int) {}
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CUTLASS_HOST_DEVICE T operator() (
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T const &lhs,
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T const &rhs) const {
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cutlass::epilogue::thread::SiLu<T> silu;
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cutlass::multiplies<T> mul;
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auto silu_lhs = silu(lhs);
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return mul(silu_lhs, rhs);
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}
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template <int kCount>
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CUTLASS_HOST_DEVICE cutlass::Array<T, kCount> operator() (
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cutlass::Array<T, kCount> const &lhs,
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cutlass::Array<T, kCount> const &rhs) const {
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cutlass::epilogue::thread::SiLu<T> silu;
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cutlass::multiplies<T> mul;
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auto silu_lhs = silu(lhs);
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return mul(silu_lhs, rhs);
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}
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};
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bool run_fused_gemm_f16_sm80_shmem() {
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using ThreadblockShape = cutlass::gemm::GemmShape<128, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
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// Optionally, we might not need intermediate GEMM outputs
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constexpr bool kStoreD0 = true;
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constexpr bool kStoreD1 = true;
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using DualGemm = cutlass::gemm::device::DualGemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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cutlass::layout::ColumnMajor,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp0,
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EpilogueOutputOp1,
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EpilogueOutputOp2,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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kStoreD0,
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kStoreD1,
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kSplitKSerial
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>;
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DualFusedGemmRun<DualGemm> fusedGemm;
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std::cout << "Running Fused FP16 TN GEMMs + Epilogue2...\n";
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bool passed = fusedGemm.run(
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problem_size,
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alpha0,
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beta0,
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alpha1,
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beta1
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);
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if(passed)
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std::cout << "Pass\n";
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else
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std::cout << "Fail\n";
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return passed;
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}
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bool run_batched_fused_gemm_f16_sm80_shmem() {
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using ThreadblockShape = cutlass::gemm::GemmShape<128, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
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// Optionally, we might not need intermediate GEMM outputs
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constexpr bool kStoreD0 = true;
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constexpr bool kStoreD1 = true;
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using DualGemm = cutlass::gemm::device::DualGemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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cutlass::layout::ColumnMajor,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp0,
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EpilogueOutputOp1,
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EpilogueOutputOp2,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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kStoreD0,
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kStoreD1,
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kSplitKSerial
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>;
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DualFusedGemmRun<DualGemm> fusedGemm;
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std::cout << "Running Batched Fused FP16 TN GEMMs + Epilogue2...\n";
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bool passed = fusedGemm.run(
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batch_problem_size,
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alpha0,
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beta0,
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alpha1,
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beta1,
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kBatchCount,
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false, /* broadcast_b1 */
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false /* is_profiling */
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);
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if(passed)
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std::cout << "Pass\n";
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else
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std::cout << "Fail\n";
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return passed;
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}
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bool run_broadcast_fused_gemm_f16_sm80_shmem() {
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using ThreadblockShape = cutlass::gemm::GemmShape<128, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
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// Optionally, we might not need intermediate GEMM outputs
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constexpr bool kStoreD0 = true;
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constexpr bool kStoreD1 = true;
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using DualGemm = cutlass::gemm::device::DualGemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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// different LayoutB0 and B1
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cutlass::layout::RowMajor,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp0,
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EpilogueOutputOp1,
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EpilogueOutputOp2,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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kStoreD0,
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kStoreD1,
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kSplitKSerial
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>;
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DualFusedGemmRun<DualGemm> fusedGemm;
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std::cout << "Running Broadcast Fused FP16 TN GEMMs + Epilogue2...\n";
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bool passed = fusedGemm.run(
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problem_size,
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alpha0,
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beta0,
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alpha1,
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beta1,
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1, /* batch_count */
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true, /* broadcast_b1 */
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true /* is_profiling */
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);
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if(passed)
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std::cout << "Pass\n";
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else
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std::cout << "Fail\n";
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return passed;
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}
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bool run_batched_broadcast_fused_gemm_f16_sm80_shmem() {
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using ThreadblockShape = cutlass::gemm::GemmShape<128, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
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// Optionally, we might not need intermediate GEMM outputs
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constexpr bool kStoreD0 = true;
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constexpr bool kStoreD1 = true;
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using DualGemm = cutlass::gemm::device::DualGemm<
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ElementOperandA,
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cutlass::layout::RowMajor,
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ElementOperandB,
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// different LayoutB0 and B1
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cutlass::layout::RowMajor,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp0,
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EpilogueOutputOp1,
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EpilogueOutputOp2,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
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kStages,
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kStoreD0,
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kStoreD1,
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kSplitKSerial
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>;
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DualFusedGemmRun<DualGemm> fusedGemm;
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std::cout << "Running Batch Broadcast Fused FP16 TN GEMMs + Epilogue2...\n";
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bool passed = fusedGemm.run(
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batch_problem_size,
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alpha0,
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beta0,
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alpha1,
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beta1,
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kBatchCount,
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true, /* broadcast_b1 */
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false /* is_profiling */
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);
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if(passed)
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std::cout << "Pass\n";
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else
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std::cout << "Fail\n";
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return passed;
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}
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int main() {
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std::vector<bool (*)()>funcs = {
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&run_nonfused_gemm_f16_sm80,
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&run_fused_gemm_f16_sm80_shmem,
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&run_batched_fused_gemm_f16_sm80_shmem,
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&run_broadcast_fused_gemm_f16_sm80_shmem,
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&run_batched_broadcast_fused_gemm_f16_sm80_shmem
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};
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std::string test_name = (
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"dual-gemm f16 bias=" +
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std::to_string(kUseBias) +
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" split_k_serial=" +
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std::to_string(kSplitKSerial) +
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" batch_count=" +
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std::to_string(kBatchCount)
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);
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return testRun(80, funcs, test_name);
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}
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////////////////////////////////////////////////////////////////////////////////
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