Merge branch 'develop' into uif2-initial

example/01_gemm/gemm_xdl_fp16.cpp

@@ -9,13 +9,13 @@
 using ADataType        = ck::half_t;
 using BDataType        = ck::half_t;
 using AccDataType      = float;
-using CShuffleDataType = float;
+using CShuffleDataType = ck::half_t;
 using CDataType        = ck::half_t;
 
 using F16 = ck::half_t;
 
 using ALayout = Row;
-using BLayout = Col;
+using BLayout = Row;
 using CLayout = Row;
 
 using AElementOp = PassThrough;
@@ -39,7 +39,7 @@ using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffl
 // ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| 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, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    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>,               8>;
+         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           2,              S<1, 16, 1, 16>,               8, ck::LoopScheduler::Interwave, ck::PipelineVersion::v1>;
 // clang-format on
 
 using DeviceGemmInstance = DeviceGemmInstance1;

include/ck/host_utility/kernel_launch.hpp

@@ -33,10 +33,13 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
         printf("Warm up 1 time\n");
 #endif
         // warm up
-        kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
-        hip_check_error(hipGetLastError());
+        for(int i = 0; i < stream_config.cold_niters_; ++i)
+        {
+            kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+            hip_check_error(hipGetLastError());
+        }
 
-        const int nrepeat = 10;
+        const int nrepeat = stream_config.nrepeat_;
 #if DEBUG_LOG
         printf("Start running %d times...\n", nrepeat);
 #endif

include/ck/stream_config.hpp

@@ -11,4 +11,6 @@ struct StreamConfig
     hipStream_t stream_id_ = nullptr;
     bool time_kernel_      = false;
     int log_level_         = 0;
+    int cold_niters_       = 50;
+    int nrepeat_           = 200;
 };

library/include/ck/library/tensor_operation_instance/gpu/gemm.hpp

@@ -377,7 +377,7 @@ struct DeviceOperationInstanceFactory<
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
 #endif
@@ -386,7 +386,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
 #endif
@@ -395,7 +395,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
 #endif
@@ -404,7 +404,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
 #endif
@@ -418,7 +418,7 @@ struct DeviceOperationInstanceFactory<
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
                 add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
@@ -430,7 +430,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
                 add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
@@ -443,7 +443,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
                 add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
@@ -455,7 +455,7 @@ struct DeviceOperationInstanceFactory<
             else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
                               is_same_v<CLayout, Row>)
             {
-                add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
+                /// add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
 #ifdef DL_KERNELS
                 add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
                 add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);

profiler/include/profiler/profile_gemm_impl.hpp

@@ -6,6 +6,7 @@
 #include <iomanip>
 #include <iostream>
 #include <typeinfo>
+#include <unistd.h>
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
@@ -20,6 +21,7 @@
 #include "ck/library/utility/host_tensor_generator.hpp"
 #include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/fill.hpp"
 
 namespace ck {
 namespace profiler {
@@ -69,14 +71,17 @@ int profile_gemm_impl(int do_verification,
 
     switch(init_method)
     {
-    case 0: break;
+    case 0:
+        ck::utils::FillConstant<ADataType>{static_cast<ADataType>(1.f)}(a_m_k);
+        ck::utils::FillConstant<BDataType>{static_cast<BDataType>(1.f)}(b_k_n);
+        break;
     case 1:
-        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
-        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
         break;
     default:
-        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 0.1});
-        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.01, 0.01});
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
     }
 
     using AElementOp = ck::tensor_operation::element_wise::PassThrough;
@@ -130,11 +135,10 @@ int profile_gemm_impl(int do_verification,
         ref_invoker.Run(ref_argument);
     }
 
-    std::string best_op_name;
-    float best_avg_time   = 0;
-    float best_tflops     = 0;
-    float best_gb_per_sec = 0;
+    float best_tflops    = 0;
+    int best_instance_id = 0;
 
+    int instance_id = 0;
     // profile device op instances
     for(auto& op_ptr : op_ptrs)
     {
@@ -178,10 +182,8 @@ int profile_gemm_impl(int do_verification,
 
             if(tflops > best_tflops)
             {
-                best_op_name    = op_name;
-                best_tflops     = tflops;
-                best_avg_time   = avg_time;
-                best_gb_per_sec = gb_per_sec;
+                best_instance_id = instance_id;
+                best_tflops      = tflops;
             }
 
             if(do_verification)
@@ -205,53 +207,94 @@ int profile_gemm_impl(int do_verification,
         {
             std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
         }
-    }
 
-    if constexpr(is_same<CDataType, float>::value)
-    {
-        std::cout << "Best Perf for datatype = f32";
-    }
-    else if constexpr(is_same<CDataType, half_t>::value)
-    {
-        std::cout << "Best Perf for datatype = f16";
+        instance_id++;
     }
-    else if constexpr(is_same<CDataType, bhalf_t>::value)
-    {
-        std::cout << "Best Perf for datatype = bf16";
-    }
-    else if constexpr(is_same<CDataType, int8_t>::value)
+
+    sleep(2);
+
+    // Run the best instance again
     {
-        std::cout << "Best Perf for datatype = int8";
-    }
+        auto& op_ptr = op_ptrs[best_instance_id];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                        static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
+                                        static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                        M,
+                                        N,
+                                        K,
+                                        StrideA,
+                                        StrideB,
+                                        StrideC,
+                                        a_element_op,
+                                        b_element_op,
+                                        c_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            std::string op_name = op_ptr->GetTypeString();
+
+            float avg_time = invoker_ptr->Run(argument_ptr.get(),
+                                              StreamConfig{nullptr, time_kernel, 0, 50, 200});
+
+            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(CDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
+
+            float gb_per_sec = num_btype / 1.E6 / avg_time;
+
+            if constexpr(is_same<CDataType, float>::value)
+            {
+                std::cout << "Best Perf for datatype = f32";
+            }
+            else if constexpr(is_same<CDataType, half_t>::value)
+            {
+                std::cout << "Best Perf for datatype = f16";
+            }
+            else if constexpr(is_same<CDataType, bhalf_t>::value)
+            {
+                std::cout << "Best Perf for datatype = bf16";
+            }
+            else if constexpr(is_same<CDataType, int8_t>::value)
+            {
+                std::cout << "Best Perf for datatype = int8";
+            }
 #if defined CK_ENABLE_FP8
-    else if constexpr(is_same<CDataType, f8_t>::value)
-    {
-        std::cout << "Best Perf for datatype = fp8";
-    }
+            else if constexpr(is_same<CDataType, f8_t>::value)
+            {
+                std::cout << "Best Perf for datatype = fp8";
+            }
 #endif
 
-    if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
-    {
-        std::cout << " ALayout =  RowMajor";
-    }
-    else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
-    {
-        std::cout << " ALayout =  ColumnMajor";
-    }
+            if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
+            {
+                std::cout << " ALayout =  RowMajor";
+            }
+            else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
+            {
+                std::cout << " ALayout =  ColumnMajor";
+            }
 
-    if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
-    {
-        std::cout << " BLayout =  RowMajor";
-    }
-    else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
-    {
-        std::cout << " BLayout =  ColumnMajor";
-    }
+            if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
+            {
+                std::cout << " BLayout =  RowMajor";
+            }
+            else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
+            {
+                std::cout << " BLayout =  ColumnMajor";
+            }
 
-    std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
-              << " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << best_avg_time
-              << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
-              << best_op_name << std::endl;
+            std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
+                      << " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << avg_time
+                      << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << op_name
+                      << std::endl;
+        }
+    }
 
     return pass ? 0 : 1;
 }

profiler/src/profile_transpose.cpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
-
-#include <iostream>
-#include <numeric>
-#include <initializer_list>
-#include <cstdlib>
-
-#include "profiler/profile_transpose_impl.hpp"
-#include "profiler_operation_registry.hpp"
-
-enum struct MatrixLayout
-{
-    NCDHW, // 0
-    NCHWD, // 1
-};
-
-enum struct DataType
-{
-    F32_F32_F32_F32_F32, // 0
-    F16_F16_F16_F16_F16, // 1
-};
-
-#define OP_NAME "transpose"
-#define OP_DESC "Transpose"
-
-int profile_transpose(int argc, char* argv[])
-{
-    if(argc != 15)
-    {
-        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
-        printf("arg2: data type (0: fp32; 1: fp16)\n");
-        // printf("arg3: matrix layout (NCDHW -> NDCHW);\n");
-        printf("arg4: verification (0: no; 1: yes)\n");
-        printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
-        printf("arg6: print tensor value (0: no; 1: yes)\n");
-        printf("arg7: time kernel (0=no, 1=yes)\n");
-        printf("arg8 to 13: N, C, D, H, W\n");
-        exit(1);
-    }
-
-    const auto data_type = static_cast<DataType>(std::stoi(argv[2]));
-    // const auto layout          = static_cast<MatrixLayout>(std::stoi(argv[3]));
-    const bool do_verification   = std::stoi(argv[3]);
-    const int init_method        = std::stoi(argv[4]);
-    const bool do_log            = std::stoi(argv[5]);
-    const bool time_kernel       = std::stoi(argv[6]);
-    std::vector<index_t> lengths = std::stoi(argv[7]);
-
-    /**const int N = std::stoi(argv[7]);
-    const int C = std::stoi(argv[8]);
-    const int D = std::stoi(argv[9]);
-    const int H = std::stoi(argv[10]);
-    const int W = std::stoi(argv[11]);**/
-
-    using F32 = float;
-    using F16 = ck::half_t;
-
-    auto profile = [&](auto a_type, auto b_type) {
-        using ADataType = decltype(a_type);
-        using BDataType = decltype(b_type);
-
-        bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType>(
-            do_verification, init_method, do_log, time_kernel, lengths);
-
-        return pass ? 0 : 1;
-    };
-
-    if(data_type == GemmDataType::F32_F32_F32_F32_F32)
-    {
-        return profile(F32{}, F32{});
-    }
-    else if(data_type == GemmDataType::F16_F16_F16_F16_F16)
-    {
-        return profile(F16{}, F16{});
-    }
-    else
-    {
-        std::cout << "this data_type & layout is not implemented" << std::endl;
-
-        return 1;
-    }
-}
-
-REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_transpose);