Gemm + bias + c_permute (#312)

* init commit

* add desc

* finished c permute

* fixed vector lens

example/25_gemm_bias_c_permute/CMakeLists.txt

+add_example_executable(example_gemm_bias_c_permute_xdl_fp16 gemm_bias_c_permute_xdl_fp16.cpp)

example/25_gemm_bias_c_permute/gemm_bias_c_permute_xdl_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_bias_c_permute_xdl.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+
+#include "ck/library/host_tensor/device_memory.hpp"
+#include "ck/library/host_tensor/host_tensor.hpp"
+#include "ck/library/host_tensor/host_tensor_generator.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Add         = ck::tensor_operation::element_wise::Add;
+
+using ADataType        = F16;
+using BDataType        = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using DDataType        = F16;
+using EDataType        = F16;
+
+using ALayout = Row;
+using BLayout = Col;
+using DLayout = Row;
+using ELayout = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = Add;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmBiasCPermute_Xdl
+//######| ALayout| BLayout| ELayout|     AData|     BData|     AccData|         CShuffle|     DsData|     EData|           A|           B|          CDE|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+//######|        |        |        |      Type|      Type|        Type|         DataType|       Type|      Type| Elementwise| Elementwise|  Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+//######|        |        |        |          |          |            |                 |           |          |   Operation|   Operation|    Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+//######|        |        |        |          |          |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        < ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType,  DDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               1>;
+// clang-format on
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    ck::index_t M0 = 4;
+    ck::index_t M1 = 32;
+    ck::index_t M2 = 128;
+    ck::index_t N0 = 16;
+    ck::index_t N1 = 256;
+
+    // GEMM shape
+    ck::index_t M = M0 * M1 * M2;
+    ck::index_t N = N0 * N1;
+    ck::index_t K = 128;
+
+    ck::index_t stride_A = K;
+    ck::index_t stride_B = K;
+
+#if 1
+    // E = [M0, N0, M1, N1, M2]
+    ck::index_t stride_E_M0 = N0 * M1 * N1 * M2;
+    ck::index_t stride_E_M1 = N1 * M2;
+    ck::index_t stride_E_M2 = 1;
+    ck::index_t stride_E_N0 = M1 * N1 * M2;
+    ck::index_t stride_E_N1 = M2;
+
+    // D = [0, N0, 0, N1, 0]
+    ck::index_t stride_D_M0 = 0;
+    ck::index_t stride_D_M1 = 0;
+    ck::index_t stride_D_M2 = 0;
+    ck::index_t stride_D_N0 = N1;
+    ck::index_t stride_D_N1 = 1;
+#else
+    // D = [0, 0, 0, N0, N1]
+    ck::index_t stride_D_M0 = 0;
+    ck::index_t stride_D_M1 = 0;
+    ck::index_t stride_D_M2 = 0;
+    ck::index_t stride_D_N0 = N1;
+    ck::index_t stride_D_N1 = 1;
+
+    // E = [M0, M1, M2, N0, N1]
+    ck::index_t stride_E_M0 = M1 * M2 * N0 * N1;
+    ck::index_t stride_E_M1 = M2 * N0 * N1;
+    ck::index_t stride_E_M2 = N0 * N1;
+    ck::index_t stride_E_N0 = N1;
+    ck::index_t stride_E_N1 = 1;
+#endif
+
+    const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc{
+        M0, M1, M2, N0, N1, stride_D_M0, stride_D_M1, stride_D_M2, stride_D_N0, stride_D_N1};
+    const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc{
+        M0, M1, M2, N0, N1, stride_E_M0, stride_E_M1, stride_E_M2, stride_E_N0, stride_E_N1};
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    auto f_host_de_tensor_descriptor =
+        [](ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 de_grid_desc) {
+            std::size_t m0        = de_grid_desc.M0_;
+            std::size_t m1        = de_grid_desc.M1_;
+            std::size_t m2        = de_grid_desc.M2_;
+            std::size_t n0        = de_grid_desc.N0_;
+            std::size_t n1        = de_grid_desc.N1_;
+            std::size_t stride_m0 = de_grid_desc.stride_M0_;
+            std::size_t stride_m1 = de_grid_desc.stride_M1_;
+            std::size_t stride_m2 = de_grid_desc.stride_M2_;
+            std::size_t stride_n0 = de_grid_desc.stride_N0_;
+            std::size_t stride_n1 = de_grid_desc.stride_N1_;
+            return HostTensorDescriptor(
+                std::vector<std::size_t>({m0, m1, m2, n0, n1}),
+                std::vector<std::size_t>({stride_m0, stride_m1, stride_m2, stride_n0, stride_n1}));
+        };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
+    Tensor<DDataType> d_m0_m1_m2_n0_n1(f_host_de_tensor_descriptor(d_grid_desc));
+    Tensor<EDataType> e_m0_m1_m2_n0_n1_host_result(f_host_de_tensor_descriptor(e_grid_desc));
+    Tensor<EDataType> e_m0_m1_m2_n0_n1_device_result(f_host_de_tensor_descriptor(e_grid_desc));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "d_m0_m1_m2_n0_n1: " << d_m0_m1_m2_n0_n1.mDesc << std::endl;
+    std::cout << "e_m0_m1_m2_n0_n1: " << e_m0_m1_m2_n0_n1_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem d_m0_m1_m2_n0_n1_device_buf(sizeof(DDataType) *
+                                          d_m0_m1_m2_n0_n1.mDesc.GetElementSpace());
+    DeviceMem e_m0_m1_m2_n0_n1_device_buf(sizeof(EDataType) *
+                                          e_m0_m1_m2_n0_n1_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+    d_m0_m1_m2_n0_n1_device_buf.ToDevice(d_m0_m1_m2_n0_n1.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument  = device_op.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
+                                           b_k_n_device_buf.GetDeviceBuffer(),
+                                           d_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
+                                           e_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
+                                           M,
+                                           N,
+                                           K,
+                                           stride_A,
+                                           stride_B,
+                                           d_grid_desc,
+                                           e_grid_desc,
+                                           a_element_op,
+                                           b_element_op,
+                                           cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error("wrong! this device_op instance does not support this problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop      = std::size_t(2) * M * N * K;
+    std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
+                            sizeof(DDataType) * N + sizeof(EDataType) * 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;
+
+    if(do_verification)
+    {
+        Tensor<AccDataType> c_m_n(HostTensorDescriptor(
+            std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                AccDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                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, a_element_op, b_element_op, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m0 = 0; m0 < M0; ++m0)
+            for(int m1 = 0; m1 < M1; ++m1)
+                for(int m2 = 0; m2 < M2; ++m2)
+                    for(int n0 = 0; n0 < N0; ++n0)
+                        for(int n1 = 0; n1 < N1; ++n1)
+                        {
+                            int m = m0 * M1 * M2 + m1 * M2 + m2;
+                            int n = n0 * N1 + n1;
+
+                            cde_element_op(e_m0_m1_m2_n0_n1_host_result(m0, m1, m2, n0, n1),
+                                           ck::type_convert<EDataType>(c_m_n(m, n)),
+                                           d_m0_m1_m2_n0_n1(m0, m1, m2, n0, n1));
+                        }
+
+        e_m0_m1_m2_n0_n1_device_buf.FromDevice(e_m0_m1_m2_n0_n1_device_result.mData.data());
+
+        return ck::utils::check_err(e_m0_m1_m2_n0_n1_device_result.mData,
+                                    e_m0_m1_m2_n0_n1_host_result.mData)
+                   ? 0
+                   : 1;
+    }
+
+    return 0;
+}

example/CMakeLists.txt

@@ -42,3 +42,4 @@ add_subdirectory(20_convnd_bwd_weight_xdl)
 add_subdirectory(21_gemm_layernorm)
 add_subdirectory(22_cgemm)
 add_subdirectory(23_softmax)
+add_subdirectory(25_gemm_bias_c_permute)

include/ck/tensor_operation/gpu/device/device_gemm_bias_c_permute.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+
+#include "device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+struct DEGridDesc_M0_M1_M2_N0_N1
+{
+    ck::index_t M0_, M1_, M2_, N0_, N1_;
+    ck::index_t stride_M0_, stride_M1_, stride_M2_, stride_N0_, stride_N1_;
+};
+
+// input : A[M, K], B[K, N],
+// input : D[M, N], ...
+// output : E[M, N]
+// C = a_op(A) * b_op(B)
+// E = cde_op(C, D)
+template <typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceGemmBiasCPermute : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        const void* p_d,
+                        void* p_e,
+                        ck::index_t M,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t StrideA,
+                        ck::index_t StrideB,
+                        DEGridDesc_M0_M1_M2_N0_N1 d_gride_desc,
+                        DEGridDesc_M0_M1_M2_N0_N1 e_gride_desc,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+using DeviceGemmBiasCPermutePtr = std::unique_ptr<
+    DeviceGemmBiasCPermute<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp

@@ -11,8 +11,8 @@ namespace element_wise {
 
 struct Add
 {
-    template <typename T>
-    __host__ __device__ constexpr void operator()(T& y, const T& x0, const T& x1) const;
+    template <typename Y, typename X0, typename X1>
+    __host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const;
 
     template <>
     __host__ __device__ constexpr void
@@ -28,6 +28,13 @@ struct Add
         y = x0 + x1;
     };
 
+    template <>
+    __host__ __device__ constexpr void
+    operator()<half_t>(half_t& y, const float& x0, const half_t& x1) const
+    {
+        y = type_convert<half_t>(x0) + x1;
+    };
+
     // Question: should half_t be supported ?
     template <>
     __host__ __device__ constexpr void