Merge branch 'develop' into feature/use-larger-tile-size-for-chunk-prefill

include/ck_tile/remod.py

@@ -7,6 +7,7 @@ import copy
 
 NS = 'ck_tile'
 OPS = 'ops'
+REF = 'ref'
 OPS_COMMON = 'common' # common header will be duplicated into ops/* other module
 
 HEADER_COMMON = f"""// SPDX-License-Identifier: MIT
@@ -29,6 +30,9 @@ class submodule_t:
     def push(self, f):
         if len(f.parents) != 1: # ignore ./xxx.hpp
             mod = get_module(f)
+            # ref is supposed to include one header on demand
+            if mod == REF:
+                return
             if mod == OPS:
                 if mod not in self.m.keys():
                     self.m[mod] = dict()

library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn.hpp

@@ -52,6 +52,9 @@ using device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn_comp_instances =
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   256,   256,    32,   8,   8,  32,   32,    4,    4,     S<4, 64, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<4, 64, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   224,   256,    64,   8,   8,  16,   16,    7,    8,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           2,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   256,   224,    64,   8,   8,  16,   16,    8,    7,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          2,           1,                   S<1, 32, 1, 8>,          S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
+        DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   256,   160,    64,   8,   8,  16,   16,    8,    5,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          2,           1,                   S<1, 32, 1, 8>,          S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
+        DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   128,   160,    64,   8,   8,  32,   32,    1,    5,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 64, 1, 4>,          S<8>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
+        DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   160,   128,    64,   8,   8,  32,   32,    5,    1,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   128,   128,    64,   8,   8,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   128,   128,    64,   8,   8,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,     Row,     BF16,   BF16, DsDataType,  BF16,     F32,     BF16, PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   128,   128,    64,   8,   8,  32,   32,    2,    2,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,    S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,              8,              8,          0,          1,           1,                   S<1, 16, 1, 16>,         S<4>,  BlockGemmPipelineScheduler::Interwave, BlockGemmPipelineVersion::v1>

library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_f8_f8_bf16/device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp

@@ -42,6 +42,7 @@ using device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances = std
         //##################################|        |        |         |        | Type|  Type|         Type|  Type|    Type|     Type| Elementwise| Elementwise| Elementwise|Specialization|  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_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|                               Pipeline|                     Pipeline|
         //##################################|        |        |         |        |     |      |             |      |        |         |   Operation|   Operation|   Operation|              |      |      |      |      |    |    |    |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl|   _NWaveNPerXdl|                              Scheduler|                     Verision|
         //##################################|        |        |         |        |     |      |             |      |        |         |            |            |            |              |      |      |      |      |    |    |    |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                                 |                |                                       |                             |
+        
 #ifdef __gfx94__
         // Compute friendly
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,    Row,     F8,     F8, DsDataType, BF16,   F32,     BF16,  PassThrough, PassThrough, PassThrough,       GemmSpec,   256,   256,   256,    64,  16,  16,  32,   32,    4,    4,     S<4, 64, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,    S<4, 64, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,          1,           1,                   S<1, 32, 1, 8>,               S<8>,  BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v4, F8>,
@@ -72,6 +73,7 @@ using device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances = std:
         //##################################|        |        |         |        | Type|  Type|         Type|  Type|    Type|     Type| Elementwise| Elementwise| Elementwise|Specialization|  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_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|          Pipeline|                     Pipeline|
         //##################################|        |        |         |        |     |      |             |      |        |         |   Operation|   Operation|   Operation|              |      |      |      |      |    |    |    |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl|   _NWaveNPerXdl|         Scheduler|                     Verision|
         //##################################|        |        |         |        |     |      |             |      |        |         |            |            |            |              |      |      |      |      |    |    |    |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                                 |                |                  |                             |
+
 #if defined(__gfx94__) || defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH)
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,    Row,     F8,     F8, DsDataType,   BF16,   F32,     BF16,  PassThrough, PassThrough, PassThrough,       GemmSpec,   128,    32,   16,    128, 16,  16,  16,   16,    1,    1,     S<8, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,    S<8, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,          1,           1,                   S<1, 16, 1, 8>,               S<2>,  BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
         DeviceBatchedGemmMultiD_Xdl_CShuffle_V3<  Row,     Col, DsLayout,    Row,     F8,     F8, DsDataType,   BF16,   F32,     BF16,  PassThrough, PassThrough, PassThrough,       GemmSpec,    64,    16,   16,    128, 16,  16,  16,   16,    1,    1,     S<8,  8, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,    S<8,  8, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,          1,           1,                   S<1, 16, 1, 4>,               S<4>,  BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,

library/src/tensor_operation_instance/gpu/mha/CMakeLists.txt

@@ -6,7 +6,7 @@ set(CK_TILE_SRC_FOLDER ${CMAKE_SOURCE_DIR}/include/ck_tile/)
 # CK Codegen requires dataclass which is added in Python 3.7
 # Python version 3.8 is required for general good practice as it is default for Ubuntu 20.04
 if(NOT CK_USE_ALTERNATIVE_PYTHON)
-   find_package(PythonInterp 3 REQUIRED)
+   find_package(Python3 COMPONENTS Interpreter Development)
 else()
    message("Using alternative python version")
    set(EXTRA_PYTHON_PATH)
@@ -33,7 +33,7 @@ set(FMHA_KNOWN_APIS "fwd,fwd_splitkv,fwd_appendkv,bwd")
 # Note: The receipt 3 arg filters the generated backwards instances to reduce compilation time.
 # With receipt 3 set, we are generating instances for datatype == {fp16 || bfp16}, bias == {no || alibi}, deterministic == off, and dpad == dvpad.
 execute_process(
-  COMMAND ${PYTHON_EXECUTABLE} ${FMHA_SRC_FOLDER}/generate.py
+  COMMAND ${Python3_EXECUTABLE} ${FMHA_SRC_FOLDER}/generate.py
   --list_blobs ${FMHA_CPP_FOLDER}/blob_list.txt
   --api ${FMHA_KNOWN_APIS}
   --receipt 3
@@ -50,7 +50,7 @@ endif()
 # With receipt 3 set, we are generating instances for datatype == {fp16 || bfp16}, bias == {no || alibi}, deterministic == off, and dpad == dvpad.
 add_custom_command(
   OUTPUT ${FMHA_GEN_BLOBS}
-  COMMAND ${PYTHON_EXECUTABLE} ${FMHA_SRC_FOLDER}/generate.py
+  COMMAND ${Python3_EXECUTABLE} ${FMHA_SRC_FOLDER}/generate.py
   --output_dir ${FMHA_CPP_FOLDER}
   --api ${FMHA_KNOWN_APIS}
   --receipt 3

modified_files.txt

-example/01_gemm/gemm_xdl_fp8_streamk_v3.cpp
-example/01_gemm/run_gemm_example_streamk_v2.inc
-include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_streamk_v3.hpp
-include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_streamk_v3.hpp
-library/src/tensor_operation_instance/gpu/gemm_universal_streamk/device_gemm_xdl_universal_streamk_f16_f8_f16/device_gemm_xdl_universal_streamk_f16_f8_f16_mk_kn_mn_mem_v2_mnkpadding_instance.cpp
-library/src/tensor_operation_instance/gpu/gemm_universal_streamk/device_gemm_xdl_universal_streamk_f16_f8_f16/device_gemm_xdl_universal_streamk_f16_f8_f16_mk_nk_mn_comp_mnpadding_instance.cpp
-library/src/tensor_operation_instance/gpu/gemm_universal_streamk/device_gemm_xdl_universal_streamk_f8_f16_f16/device_gemm_xdl_universal_streamk_f8_f16_f16_mk_kn_mn_mem_v2_mnkpadding_instance.cpp
-library/src/tensor_operation_instance/gpu/gemm_universal_streamk/device_gemm_xdl_universal_streamk_f8_f16_f16/device_gemm_xdl_universal_streamk_f8_f16_f16_mk_nk_mn_comp_mnpadding_instance.cpp
-profiler/src/profile_gemm_universal_streamk.cpp
-modified_files.txt

profiler/include/profiler/profile_gemm_universal_batched_impl.hpp

@@ -48,6 +48,7 @@ bool profile_gemm_universal_batched_impl(int do_verification,
                                          int StrideB,
                                          int StrideC,
                                          int BatchCount,
+                                         int KBatch,
                                          int n_warmup,
                                          int n_iter,
                                          uint64_t rotating = 0)
@@ -147,14 +148,23 @@ bool profile_gemm_universal_batched_impl(int do_verification,
     float best_ave_time   = 0;
     float best_tflops     = 0;
     float best_gb_per_sec = 0;
+    float best_kbatch     = 0;
 
     // profile device op instances
     for(auto& op_ptr : op_ptrs)
     {
-        std::unique_ptr<tensor_operation::device::BaseArgument> argument_ptr;
-        // false branch for multi d dl kernel
+        std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38};
 
-        argument_ptr =
+        if(KBatch > 0)
+        {
+            kbatch_list = {KBatch};
+        }
+
+        for(std::size_t i = 0; i < kbatch_list.size(); i++)
+        {
+            auto kbatch_curr = kbatch_list[i];
+
+            auto argument_ptr =
                 op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
                                             static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
                                             {},
@@ -173,15 +183,13 @@ bool profile_gemm_universal_batched_impl(int do_verification,
                                             BatchStrideC,
                                             ck::tensor_operation::element_wise::PassThrough{},
                                             ck::tensor_operation::element_wise::PassThrough{},
-                                        ck::tensor_operation::element_wise::PassThrough{});
+                                            ck::tensor_operation::element_wise::PassThrough{},
+                                            kbatch_curr);
 
             auto invoker_ptr = op_ptr->MakeInvokerPointer();
 
             if(op_ptr->IsSupportedArgument(argument_ptr.get()))
             {
-            // re-init C to zero before profiling next kernel
-            c_device_buf.SetZero();
-
                 std::string op_name = op_ptr->GetTypeString();
 
                 float ave_time = invoker_ptr->Run(
@@ -199,7 +207,7 @@ bool profile_gemm_universal_batched_impl(int do_verification,
                 float gb_per_sec = num_btype / 1.E6 / ave_time;
 
                 std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
-                      << " GB/s, " << op_name << std::endl;
+                          << " GB/s, " << op_name << ", KBatch " << kbatch_curr << std::endl;
 
                 if(tflops > best_tflops)
                 {
@@ -207,6 +215,7 @@ bool profile_gemm_universal_batched_impl(int do_verification,
                     best_tflops     = tflops;
                     best_ave_time   = ave_time;
                     best_gb_per_sec = gb_per_sec;
+                    best_kbatch     = kbatch_curr;
                 }
 
                 if(do_verification)
@@ -219,7 +228,8 @@ bool profile_gemm_universal_batched_impl(int do_verification,
                     {
                         LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
                         LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
-                    LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
+                        LogRangeAsType<float>(
+                            std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
                             << std::endl;
                         LogRangeAsType<float>(
                             std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
@@ -229,7 +239,9 @@ bool profile_gemm_universal_batched_impl(int do_verification,
             }
             else
             {
-            std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
+                std::cout << op_ptr->GetTypeString() << " does not support this problem"
+                          << std::endl;
+            }
         }
     }
 
@@ -270,8 +282,8 @@ bool profile_gemm_universal_batched_impl(int do_verification,
 
     std::cout << " B = " << BatchCount << " M = " << M << " N = " << N << " K = " << K
               << " StrideA = " << StrideA << " StrideB = " << StrideB << " StrideC = " << StrideC
-              << ": " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
-              << " GB/s, " << best_op_name << std::endl;
+              << " KBatch = " << best_kbatch << ": " << best_ave_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
 
     return pass;
 }

profiler/src/profile_gemm_universal_batched.cpp

@@ -31,7 +31,7 @@ enum struct GemmDataType
 
 int profile_batched_gemm_universal(int argc, char* argv[])
 {
-    if(argc != 18 && argc != 21)
+    if(argc != 19 && argc != 22)
     {
         // clang-format off
         printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
@@ -44,11 +44,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
         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=n0, 1=yes)\n");
-        printf("arg8 to 17: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount\n");
+        printf("arg8 to 18: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount, KBatch\n");
         printf("optional:\n");
-        printf("arg18: number of warm-up cycles (default 1)\n");
-        printf("arg19: number of iterations (default 10)\n");
-        printf("arg20: memory for rotating buffer (default 0, size in MB)\n");
+        printf("arg19: number of warm-up cycles (default 1)\n");
+        printf("arg20: number of iterations (default 10)\n");
+        printf("arg21: memory for rotating buffer (default 0, size in MB)\n");
         // clang-format on
         exit(1);
     }
@@ -56,11 +56,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
     int n_warmup      = 1;
     int n_iter        = 10;
     uint64_t rotating = 0;
-    if(argc == 21)
+    if(argc == 22)
     {
-        n_warmup = std::stoi(argv[18]);
-        n_iter   = std::stoi(argv[19]);
-        rotating = std::stoull(argv[20]) * 1024 * 1024;
+        n_warmup = std::stoi(argv[19]);
+        n_iter   = std::stoi(argv[20]);
+        rotating = std::stoull(argv[21]) * 1024 * 1024;
     }
 
     const auto data_type       = static_cast<GemmDataType>(std::stoi(argv[2]));
@@ -83,6 +83,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
     const int BatchStrideC = std::stoi(argv[16]);
 
     const int BatchCount = std::stoi(argv[17]);
+    const int KBatch     = std::stoi(argv[18]);
 
 #if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
     using F8 = ck::f8_t;
@@ -159,6 +160,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
                                                                                     StrideB_,
                                                                                     StrideC_,
                                                                                     BatchCount,
+                                                                                    KBatch,
                                                                                     n_warmup,
                                                                                     n_iter,
                                                                                     rotating);

script/process_perf_data.py

@@ -82,7 +82,7 @@ def parse_logfile(logfile):
     StrideA=[]
     StrideB=[]
     StrideC=[]
-    if 'perf_gemm.log' in logfile:
+    if 'perf_gemm' in logfile and 'gemm_bilinear' not in logfile:
         for line in open(logfile):
             if 'Best Perf' in line:
                 lst=line.split()
@@ -260,7 +260,7 @@ def main():
         conn = sqlEngine.connect()
 
         #save gemm performance tests:
-        if 'perf_gemm.log' in filename:
+        if 'perf_gemm' in filename and 'gemm_bilinear' not in filename:
             #write the ck_gemm_test_params table only needed once the test set changes
             #post_test_params(test_list,conn)
             for i in range(1,len(results)+1):
@@ -332,7 +332,7 @@ def main():
             table_name="ck_fmha_bwd_tflops"
 
         tflops_base = get_baseline(table_name,conn)
-        store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)
+        store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, sqlEngine)
         conn.close()
 
     #compare the results to the baseline if baseline exists

script/process_perf_data.sh

@@ -11,9 +11,22 @@
 
 #process results
 python3 process_perf_data.py perf_gemm.log
+python3 process_perf_data.py perf_onnx_gemm.log
 python3 process_perf_data.py perf_resnet50_N256.log
 python3 process_perf_data.py perf_resnet50_N4.log
 
+file=./perf_onnx_gemm_gfx10.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx10.log
+fi
+file=./perf_onnx_gemm_gfx11.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx11.log
+fi
+file=./perf_onnx_gemm_gfx12.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx12.log
+fi
 file=./perf_fmha_fwd_gfx942.log
 if [ -e "$file" ]; then
     python3 process_perf_data.py perf_fmha_fwd_gfx942.log

script/process_qa_data.sh

@@ -24,6 +24,18 @@ python3 process_perf_data.py perf_splitK_gemm.log
 python3 process_perf_data.py perf_onnx_gemm.log
 python3 process_perf_data.py perf_mixed_gemm.log
 
+file=./perf_onnx_gemm_gfx10.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx10.log
+fi
+file=./perf_onnx_gemm_gfx11.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx11.log
+fi
+file=./perf_onnx_gemm_gfx12.log
+if [ -e "$file" ]; then
+    python3 process_perf_data.py perf_onnx_gemm_gfx12.log
+fi
 file=./perf_fmha_fwd_gfx942.log
 if [ -e "$file" ]; then
     python3 process_perf_data.py perf_fmha_fwd_gfx942.log

script/run_full_performance_tests.sh

@@ -5,7 +5,7 @@
 # post your new test results to the database and compare them to the baseline
 # please contact Illia.Silin@amd.com for more details
 #
-# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
+# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
 # input arguments: 
 # verification = 0 : do not verify result correctness on CPU
 #              = 1 : verifuy correctness on CPU (may take a long time)

script/run_gemm_performance_tests.sh

+#!/bin/bash 
+#
+# in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
+# run the script as "./run_gemm_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name> <arch>
+# input arguments: 
+# verification = 0 : do not verify result correctness on CPU
+#              = 1 : verify correctness on CPU (may take a long time)
+# environment tag  : a string describing the specifics of your test environment
+# branch name      : name of the branch in git repo (git status | grep -e 'On branch')
+# node name        : $hostname
+# arch             : GPU architecture, e.g. "gfx9" or "gfx1100"
+
+#get the command line arguments:
+export verify=$1
+echo 'Verification: ' $verify
+export env_type=$2
+echo 'Environment type: ' $env_type
+export branch=$3
+echo 'Branch name: ' $branch
+export host_name=$4
+echo 'Host name: ' $host_name
+export arch=$5
+echo 'GPU architecture: ' $arch
+
+function print_log_header(){
+	rm -f $1;
+	echo 'On branch ' $3 &> $1;
+	echo 'Node name: ' $4 >> $1;
+	#get GPU_arch and number of compute units from rocminfo
+	echo -n "GPU_arch: " >> $1; rocminfo | grep "Name:" | grep "gfx" >> $1;
+	rocminfo | grep "Compute Unit:" >> $1;
+	hipcc --version | grep -e 'HIP version'  >> $1;
+	echo 'Environment type: ' $2 >> $1;
+	/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> $1;
+}
+
+#run ONNX gemm tests
+export onnx_log="perf_onnx_gemm_$arch.log"
+print_log_header $onnx_log $env_type $branch $host_name
+./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
+./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log

script/run_performance_tests.sh

 #!/bin/bash 
 #
 # in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
-# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
+# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
 # input arguments: 
 # verification = 0 : do not verify result correctness on CPU
 #              = 1 : verify correctness on CPU (may take a long time)
@@ -51,20 +51,11 @@ print_log_header $gemm_log $env_type $branch $host_name
 ./profile_gemm.sh gemm 2 3 $verify 1 0 1 | tee -a $gemm_log
 ./profile_gemm.sh gemm 3 3 $verify 1 0 1 | tee -a $gemm_log
 
-#run grouped_fwd fp16 tests
-export grouped_conv_fwd_log="perf_grouped_conv_fwd_fp16.log"
-print_log_header $conv_fwd_log $env_type $branch $host_name
-./profile_grouped_conv_fwd.sh grouped_conv_fwd 1 1 0 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_fwd_log
-
-#run grouped_bwd_data fp16 tests
-export grouped_conv_bwd_data_log="perf_grouped_conv_bwd_data_fp16.log"
-print_log_header $grouped_conv_bwd_data_log $env_type $branch $host_name
-./profile_grouped_conv_bwd_data.sh grouped_conv_bwd_data 1 1 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_bwd_data_log
-
-#run grouped_bwd_weight fp16 tests
-export grouped_conv_bwd_weight_log="perf_grouped_conv_bwd_weight_fp16.log"
-print_log_header $grouped_conv_bwd_weight_log $env_type $branch $host_name
-./profile_grouped_conv_bwd_weight.sh grouped_conv_bwd_weight 1 1 $verify 1 0 1 256 1 2>&1 | tee -a $grouped_conv_bwd_weight_log
+#run ONNX gemm tests
+export onnx_log="perf_onnx_gemm.log"
+print_log_header $onnx_log $env_type $branch $host_name
+./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
+./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
 
 #run resnet50 tests
 export resnet256_log="perf_resnet50_N256.log"

test/ck_tile/gemm/test_gemm_mem_pipeline.cpp

@@ -10,33 +10,27 @@
 
 using F16       = ck_tile::half_t;
 using F32       = float;
-
 using Row       = ck_tile::tensor_layout::gemm::RowMajor;
 using Col       = ck_tile::tensor_layout::gemm::ColumnMajor;
-static constexpr auto Intrawave = ck_tile::GemmPipelineScheduler::Intrawave;
-static constexpr auto Interwave = ck_tile::GemmPipelineScheduler::Interwave;
-
-template <typename Tuple>
-class TestCkTileGemmMemPipelineIntrawave : public TestCkTileGemmMemPipeline<Tuple, Intrawave>
-{
-};
-
-template <typename Tuple>
-class TestCkTileGemmMemPipelineInterwave : public TestCkTileGemmMemPipeline<Tuple, Interwave>
-{
-};
+using Intrawave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
+                                             ck_tile::GemmPipelineScheduler::Intrawave>;
+using Interwave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
+                                             ck_tile::GemmPipelineScheduler::Interwave>;
 
 // clang-format off
 using KernelTypes = ::testing::Types<
-    //         ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType
-    std::tuple<    Row,     Col,     Row,       F16,       F16,         F32,      F16>,
-    std::tuple<    Col,     Row,     Row,       F16,       F16,         F32,      F16>,
-    std::tuple<    Row,     Row,     Row,       F16,       F16,         F32,      F16>,
-    std::tuple<    Col,     Col,     Row,       F16,       F16,         F32,      F16>
+    //         ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler
+    std::tuple<    Row,     Row,     Row,       F16,       F16,         F32,      F16,             Intrawave>,
+    std::tuple<    Row,     Row,     Row,       F16,       F16,         F32,      F16,             Interwave>,
+    std::tuple<    Row,     Col,     Row,       F16,       F16,         F32,      F16,             Intrawave>,
+    std::tuple<    Row,     Col,     Row,       F16,       F16,         F32,      F16,             Interwave>,
+    std::tuple<    Col,     Row,     Row,       F16,       F16,         F32,      F16,             Intrawave>,
+    std::tuple<    Col,     Row,     Row,       F16,       F16,         F32,      F16,             Interwave>,
+    std::tuple<    Col,     Col,     Row,       F16,       F16,         F32,      F16,             Intrawave>,
+    std::tuple<    Col,     Col,     Row,       F16,       F16,         F32,      F16,             Interwave>
     >;
 // clang-format on
 
-TYPED_TEST_SUITE(TestCkTileGemmMemPipelineIntrawave, KernelTypes);
-TYPED_TEST_SUITE(TestCkTileGemmMemPipelineInterwave, KernelTypes);
+TYPED_TEST_SUITE(TestCkTileGemmMemPipeline, KernelTypes);
 
 #include "test_gemm_mem_pipeline_ut_cases.inc"

test/grouped_convnd_bwd_data/CMakeLists.txt

-add_gtest_executable(test_grouped_convnd_bwd_data test_grouped_convnd_bwd_data_xdl_wmma.cpp)
+add_gtest_executable(test_grouped_convnd_bwd_data_xdl test_grouped_convnd_bwd_data_xdl.cpp)
 if(result EQUAL 0)
-    target_link_libraries(test_grouped_convnd_bwd_data PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
+    target_link_libraries(test_grouped_convnd_bwd_data_xdl PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
+endif()
+add_gtest_executable(test_grouped_convnd_bwd_data_wmma test_grouped_convnd_bwd_data_wmma.cpp)
+if(result EQUAL 0)
+    target_link_libraries(test_grouped_convnd_bwd_data_wmma PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
 endif()
 add_gtest_executable(test_grouped_convnd_bwd_data_interface_xdl test_grouped_convnd_bwd_data_interface_xdl.cpp)
 if(result EQUAL 0)