mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-06-30 03:37:38 +00:00
210 lines
7.6 KiB
C++
210 lines
7.6 KiB
C++
#include <cstring>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_description/cluster_descriptor.hpp"
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#include "ck/tensor/tensor_view.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "reference_batched_gemm.hpp"
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#include "reference_batched_softmax.hpp"
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#include "flash_attention_fwd.hpp"
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/*
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* Toy code of flash attention forward pass
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* Assume simplest case.
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* Q [Batch, HeadNum, SeqenceLengthQ, HeadDim]
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* K [Batch, HeadNum, SeqenceLengthK, HeadDim]
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* V [Batch, HeadNum, HeadDim, SeqenceLengthK]
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* O [Batch, HeadNum, SeqenceLengthQ, HeadDim]
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*/
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int main(int argc, char* argv[])
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{
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using QDataType = ck::half_t;
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using KDataType = ck::half_t;
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using VDataType = ck::half_t;
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using SaccDataType = float;
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using SMPLComputeDataType = float;
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using PDataType = ck::half_t;
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using OaccDataType = float;
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using ODataType = ck::half_t;
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ck::index_t Batch = 64; // Batch Number * Head Number
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ck::index_t M0 = 4096; // SequenceLengthQ
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ck::index_t N0 = 4096; // SequencelengthK
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ck::index_t K0 = 128; // HeadDim
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ck::index_t N1 = 128; // HeadDim
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ck::index_t verification = 0;
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ck::index_t init_method = 1;
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ck::index_t time_kernel = 0;
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if(argc == 4)
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{
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init_method = std::stoi(argv[1]);
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time_kernel = std::stoi(argv[2]);
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verification = std::stoi(argv[3]);
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}
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if(argc == 9)
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{
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init_method = std::stoi(argv[1]);
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time_kernel = std::stoi(argv[2]);
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verification = std::stoi(argv[3]);
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Batch = std::stoi(argv[4]);
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M0 = std::stoi(argv[5]);
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N0 = std::stoi(argv[6]);
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K0 = std::stoi(argv[7]);
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N1 = std::stoi(argv[8]);
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}
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std::array<ck::index_t, 3> q_lengths{Batch, M0, K0};
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std::array<ck::index_t, 3> q_strides{M0 * K0, K0, 1};
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std::array<ck::index_t, 3> k_lengths{Batch, N0, K0};
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std::array<ck::index_t, 3> k_strides{N0 * K0, K0, 1};
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std::array<ck::index_t, 3> v_lengths{Batch, N1, N0};
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std::array<ck::index_t, 3> v_strides{N1 * N0, N0, 1};
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std::array<ck::index_t, 3> s_lengths{Batch, M0, N0};
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std::array<ck::index_t, 3> s_strides{M0 * N0, N0, 1};
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std::array<ck::index_t, 3> p_lengths{Batch, M0, N0};
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std::array<ck::index_t, 3> p_strides{M0 * N0, N0, 1};
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std::array<ck::index_t, 3> o_lengths{Batch, M0, N1};
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std::array<ck::index_t, 3> o_strides{M0 * N1, N1, 1};
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// host verify
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Tensor<QDataType> q_host(q_lengths, q_strides);
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Tensor<KDataType> k_host(k_lengths, k_strides);
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Tensor<VDataType> v_host(v_lengths, v_strides);
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Tensor<ODataType> o_host_dev(o_lengths, o_strides);
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switch(init_method)
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{
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case 0: break;
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case 1:
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ck::utils::FillUniformDistributionIntegerValue<QDataType>{-3.f, 3.f}(q_host);
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ck::utils::FillUniformDistributionIntegerValue<KDataType>{-3.f, 3.f}(k_host);
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ck::utils::FillUniformDistributionIntegerValue<VDataType>{-3.f, 3.f}(v_host);
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break;
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case 2:
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ck::utils::FillUniformDistribution<QDataType>{-3.f, 3.f}(q_host);
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ck::utils::FillUniformDistribution<KDataType>{-3.f, 3.f}(k_host);
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ck::utils::FillUniformDistribution<VDataType>{-3.f, 3.f}(v_host);
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break;
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default:
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ck::utils::FillUniformDistributionIntegerValue<QDataType>{-2.f, 2.f}(q_host);
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ck::utils::FillUniformDistributionIntegerValue<KDataType>{-2.f, 2.f}(k_host);
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ck::utils::FillUniformDistributionIntegerValue<VDataType>{-2.f, 2.f}(v_host);
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}
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DeviceMem q_buf(sizeof(QDataType) * q_host.GetElementSpaceSize());
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DeviceMem k_buf(sizeof(KDataType) * k_host.GetElementSpaceSize());
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DeviceMem v_buf(sizeof(VDataType) * v_host.GetElementSpaceSize());
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DeviceMem o_buf(sizeof(ODataType) * o_host_dev.GetElementSpaceSize());
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q_buf.ToDevice(q_host.mData.data());
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k_buf.ToDevice(k_host.mData.data());
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v_buf.ToDevice(v_host.mData.data());
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constexpr ck::index_t kM0PerBlock = 128;
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constexpr ck::index_t kN0PerBlock = 128;
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constexpr ck::index_t kK0PerBlock = 32;
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constexpr ck::index_t kN1PerBlock = 128;
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constexpr ck::index_t kK1PerBlock = 32;
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constexpr ck::index_t kBlockSize = 256;
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constexpr ck::index_t kHeadDim = 128;
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ck::index_t kGridSize = Batch * (M0 / kM0PerBlock) * (N1 / kN1PerBlock);
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std::cout << "grid size " << kGridSize << std::endl;
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constexpr ck::index_t kWarpPerCu = 8; // 2 warps per SIMD
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constexpr ck::index_t kWarpPerBlock = kBlockSize / warpSize;
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constexpr ck::index_t kBlockPerCu = kWarpPerCu / kWarpPerBlock;
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float ave_time = launch_kernel<kBlockSize, kBlockPerCu>(
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StreamConfig{nullptr, static_cast<bool>(time_kernel)},
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FlashAttentionFwd<QDataType,
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KDataType,
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VDataType,
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SaccDataType,
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SMPLComputeDataType,
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PDataType,
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OaccDataType,
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ODataType,
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kBlockSize,
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kHeadDim,
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kM0PerBlock,
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kN0PerBlock,
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kK0PerBlock,
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kN1PerBlock,
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kK1PerBlock>{},
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kGridSize,
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kBlockSize,
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0,
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static_cast<QDataType*>(q_buf.GetDeviceBuffer()),
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static_cast<KDataType*>(k_buf.GetDeviceBuffer()),
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static_cast<VDataType*>(v_buf.GetDeviceBuffer()),
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static_cast<ODataType*>(o_buf.GetDeviceBuffer()),
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M0,
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N0,
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K0,
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N1,
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Batch,
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K0, // StrideQ
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K0, // StrideK
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N0, // StrideV
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N1, // StrideO
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M0 * K0, // BatchStrideQ
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N0 * K0, // BatchStrideK
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N1 * N0, // BatchStrideV
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M0 * N1); // BatchStrideO
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// reference
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auto pass = true;
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if(verification)
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{
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o_buf.FromDevice(o_host_dev.mData.data());
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Tensor<SMPLComputeDataType> s_host_ref(s_lengths, s_strides);
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Tensor<PDataType> p_host_ref(p_lengths, p_strides);
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Tensor<ODataType> o_host_ref(o_lengths, o_strides);
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reference_batched_gemm<QDataType, KDataType, SaccDataType, SMPLComputeDataType>(
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q_host, k_host, s_host_ref);
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reference_batched_softmax<SMPLComputeDataType, SMPLComputeDataType, PDataType>(s_host_ref,
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p_host_ref);
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reference_batched_gemm<PDataType, VDataType, OaccDataType, ODataType>(
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p_host_ref, v_host, o_host_ref);
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pass &= ck::utils::check_err(o_host_dev, o_host_ref);
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}
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std::size_t flop =
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std::size_t(2) * Batch * M0 * N0 * K0 + std::size_t(2) * Batch * M0 * N1 * N0;
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std::size_t num_btype =
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sizeof(QDataType) * Batch * M0 * K0 + sizeof(KDataType) * Batch * N0 * K0 +
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sizeof(VDataType) * Batch * N1 * N0 + sizeof(ODataType) * Batch * M0 * N1;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
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<< std::endl;
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return !pass;
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}
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