Implement device_gemm_universal_preshuffle_instance for RDNA4

example/01_gemm/CMakeLists.txt

@@ -149,3 +149,7 @@ add_example_executable(example_gemm_wmma_fp16_fp8_v3 gemm_wmma_fp16_fp8_v3.cpp)
 add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_fp8_v3)
 add_example_executable(example_gemm_wmma_fp16_pk_i4_v3_b_scale gemm_wmma_fp16_pk_i4_v3_b_scale.cpp)
 add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_pk_i4_v3_b_scale)
+add_example_executable(example_gemm_wmma_fp8_bpreshuffle gemm_wmma_fp8_bpreshuffle.cpp)
+add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp8_bpreshuffle)
+add_example_executable(example_gemm_wmma_fp16_bpreshuffle gemm_wmma_fp16_bpreshuffle.cpp)
+add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_bpreshuffle)

example/01_gemm/gemm_wmma_fp16_bpreshuffle.cpp

@@ -0,0 +1,70 @@
+// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+// SPDX-License-Identifier: MIT
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/stream_config.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_wmma_cshuffle_v3_b_preshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/get_id.hpp"
+#include "ck/utility/scheduler_enum.hpp"
+
+#include <cstddef>
+#include <iostream>
+#include <type_traits>
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType        = F16;
+using BDataType        = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using CDataType        = F16;
+using ComputeTypeA     = F16;
+using ComputeTypeB     = F16;
+
+using ALayout = Row;
+using BLayout = Col;
+using CLayout = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+using CElementOp = PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+static constexpr bool PermuteA = false;
+static constexpr bool PermuteB = false;
+static constexpr int KPack     = 8; // int4 -> 32, fp8 -> 16, fp16 -> 8
+// clang-format off
+using DeviceOpInstance = 
+    ck::tensor_operation::device::DeviceGemm_Wmma_CShuffleV3_BPreshuffle<
+        ALayout,   BLayout,  CLayout,   
+        ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, 
+        AElementOp, BElementOp, CElementOp, GemmDefault, 
+        128,
+        32, 128, 128,
+        8, 8,
+        16, 16,
+        2, 2,
+        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        1, 1, S<1, 16, 1, 8>, S<4, 4, 1>,
+        ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, ComputeTypeA, ComputeTypeB, PermuteA, PermuteB>;
+// clang-format on
+
+#include "run_gemm_wmma_bpreshuffle_example.inc"
+
+int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }

example/01_gemm/gemm_wmma_fp8_bpreshuffle.cpp

@@ -0,0 +1,72 @@
+// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+// SPDX-License-Identifier: MIT
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/stream_config.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_wmma_cshuffle_v3_b_preshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/utility/amd_ck_fp8.hpp"
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/get_id.hpp"
+#include "ck/utility/scheduler_enum.hpp"
+
+#include <cstddef>
+#include <iostream>
+#include <type_traits>
+
+using F8  = ck::f8_t;
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType        = F8;
+using BDataType        = F8;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using CDataType        = F16;
+using ComputeTypeA     = F8;
+using ComputeTypeB     = F8;
+
+using ALayout = Row;
+using BLayout = Col;
+using CLayout = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+using CElementOp = PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+static constexpr bool PermuteA = false;
+static constexpr bool PermuteB = false;
+static constexpr int KPack     = 16; // int4 -> 32, fp8 -> 16, fp16 -> 8
+// clang-format off
+using DeviceOpInstance = 
+    ck::tensor_operation::device::DeviceGemm_Wmma_CShuffleV3_BPreshuffle<
+        ALayout,   BLayout,  CLayout,   
+        ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, 
+        AElementOp, BElementOp, CElementOp, GemmDefault, 
+        256,
+        32, 128, 256,
+        16, 16,
+        16, 16,
+        2, 1,
+        S<16, 16, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 16, 16, 0,
+        S<16, 16, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 16, 16, 0,
+        1, 1, S<1, 16, 1, 16>, S<8, 8, 1>,
+        ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, ComputeTypeA, ComputeTypeB, PermuteA, PermuteB>;
+// clang-format on
+
+#include "run_gemm_wmma_bpreshuffle_example.inc"
+
+int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }

example/01_gemm/run_gemm_wmma_bpreshuffle_example.inc

@@ -0,0 +1,206 @@
+// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+// SPDX-License-Identifier: MIT
+
+#pragma once
+
+template <typename ProblemType>
+bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
+{
+    using namespace ck::literals;
+
+    auto M       = problem_size.M;
+    auto N       = problem_size.N;
+    auto K       = problem_size.K;
+    auto StrideA = problem_size.StrideA;
+    auto StrideB = problem_size.StrideB;
+    auto StrideC = problem_size.StrideC;
+    auto KBatch  = problem_size.KBatch;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    auto f_get_default_stride =
+        [](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
+            if(stride == -1)
+            {
+                // give a chance if stride is -1, return a default packed stride
+                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+                {
+                    return static_cast<std::size_t>(col);
+                }
+                else
+                {
+                    return static_cast<std::size_t>(row);
+                }
+            }
+            else
+                return static_cast<std::size_t>(stride);
+        };
+
+    StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
+    StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
+    StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BDataType> b_k_n_preshuffled(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+
+    switch(config.init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{0, 2});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+    }
+
+    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "b_k_n_preshuffled: " << b_k_n_preshuffled.mDesc << std::endl;
+    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+
+    // weight pre-shuffle
+    int NPerWmma = device_op.GetPreShuffleParameters();
+    int KLane    = ck::get_warp_size() / NPerWmma;
+
+    int K0 = K / (KLane * KPack);
+    // K -> K0 KLane KPack
+    // N -> N0 NPerWmma
+    // N, K -> N0 K0 KLane NPerWmma KPack
+    int tempk;
+    for(int n = 0; n < N; ++n)
+    {
+        for(int k = 0; k < K; ++k)
+        {
+            int n0 = n / NPerWmma;
+            int n1 = n % NPerWmma;
+
+            int k0 = k / (KLane * KPack);
+            tempk  = k % (KLane * KPack);
+            int k1 = tempk / KPack;
+            int k2 = tempk % KPack;
+
+            int outputIndex = n0 * KPack * NPerWmma * KLane * K0 + k0 * KPack * NPerWmma * KLane +
+                              k1 * KPack * NPerWmma + n1 * KPack + k2;
+
+            b_k_n_preshuffled(outputIndex) = b_k_n(n * K + k);
+        }
+    }
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n_preshuffled.mData.data());
+    c_m_n_device_buf.ToDevice(c_m_n_device_result.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    auto invoker = device_op.MakeInvoker();
+
+    auto argument =
+        device_op.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                               static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                               static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                               M,
+                               N,
+                               K,
+                               StrideA,
+                               StrideB,
+                               StrideC,
+                               KBatch,
+                               a_element_op,
+                               b_element_op,
+                               c_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        std::cerr << device_op.GetTypeString() << " does not support this problem" << std::endl;
+
+        return true;
+    }
+
+    float ave_time =
+        invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 50, 50, false, 1});
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                CDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                PassThrough>;
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        invoker.Run(argument, StreamConfig{nullptr, false, 0});
+        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+        pass &= ck::utils::check_err(c_m_n_device_result,
+                                     c_m_n_host_result,
+                                     "Error: Incorrect results!",
+                                     get_rtol<CDataType>(),
+                                     get_atol<CDataType>());
+    }
+
+    if(config.time_kernel)
+    {
+        ave_time =
+            invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
+
+        std::size_t flop = 2_uz * M * N * K;
+        std::size_t num_btype =
+            sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << device_op.GetTypeString() << std::endl;
+    }
+
+    return pass;
+}
+
+bool run_gemm_splitk_example(int argc, char* argv[])
+{
+    ProblemSizeSplitK problem_size{3840, 4096, 4096, 4096, 4096, 4096, 1};
+    ExecutionConfig config;
+
+    return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
+}