Merge branch 'develop' into avgpool_bwd

profiler/README.md

@@ -144,7 +144,7 @@ GB/s: 127.947
 
 ## Profile grouped convolution backward weight kernels
 ```bash
-# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
+# arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight)
 # arg2: data type (0: Input fp32, Weight fp32, Output fp32
 #                  1: Input fp16, Weight fp16, Output fp16
 #                  2: Input bf16, Weight fp32, Output bf16)
@@ -167,7 +167,7 @@ GB/s: 127.947
 # SplitK
 
  ################                   op   datatype  layout  verify  init  log  time  Ndims  G   N   K   C  Y  X  Hi  Wi  Sy  Sx  Dy  Dx  LeftPy  LeftPx  RightPy  RightPx  SplitK
-./bin/ckProfiler grouped_conv_bwd_data          1       0       1     1    0     1      2 32 256 256 512  3  3  28  28   1   1   1   1       1       0        0        0       1
+./bin/ckProfiler grouped_conv_bwd_weight          1       0       1     1    0     1      2 32 256 256 512  3  3  28  28   1   1   1   1       1       0        0        0       1
 
  ```
 

profiler/src/CMakeLists.txt

@@ -81,4 +81,5 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_in
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool_fwd_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
+target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
 rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)

profiler/src/profile_batched_gemm_multi_d.cpp

@@ -70,8 +70,10 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
 
     const int BatchCount = std::stoi(argv[17]);
 
-    using F16  = ck::half_t;
+    using F16 = ck::half_t;
+#ifdef __int8__
     using INT8 = int8_t;
+#endif
 
     using Row = ck::tensor_layout::gemm::RowMajor;
     using Col = ck::tensor_layout::gemm::ColumnMajor;
@@ -163,6 +165,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
     {
         return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{});
     }
+#ifdef __int8__
     else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{});
@@ -179,6 +182,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
     {
         return profile(INT8{}, INT8{}, INT8{}, Col{}, Col{}, Row{});
     }
+#endif
     else
     {
         std::cout << "this data_type & layout is not implemented" << std::endl;

profiler/src/profile_conv_bwd_data.cpp

@@ -77,7 +77,9 @@ int profile_conv_bwd_data(int argc, char* argv[])
     using F32  = float;
     using F16  = ck::half_t;
     using BF16 = ck::bhalf_t;
+#ifdef __int8__
     using INT8 = int8_t;
+#endif
 
     using NWC   = ck::tensor_layout::convolution::NWC;
     using NHWC  = ck::tensor_layout::convolution::NHWC;
@@ -138,10 +140,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
         {
             return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{});
         }
+#ifdef __int8__
         else if(data_type == ConvDataType::INT8_INT8_INT8)
         {
             return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{});
         }
+#endif
     }
     else if(num_dim_spatial == 2 && layout == ConvLayout::NHWC_KYXC_NHWK)
     {
@@ -157,10 +161,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
         {
             return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{});
         }
+#ifdef __int8__
         else if(data_type == ConvDataType::INT8_INT8_INT8)
         {
             return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{});
         }
+#endif
     }
     else if(num_dim_spatial == 3 && layout == ConvLayout::NHWC_KYXC_NHWK)
     {
@@ -176,10 +182,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
         {
             return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{});
         }
+#ifdef __int8__
         else if(data_type == ConvDataType::INT8_INT8_INT8)
         {
             return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{});
         }
+#endif
     }
 
     std::cout << "this data_type & layout is not implemented" << std::endl;

profiler/src/profile_gemm.cpp

@@ -67,11 +67,15 @@ int profile_gemm(int argc, char* argv[])
     const int StrideB = std::stoi(argv[12]);
     const int StrideC = std::stoi(argv[13]);
 
-    using F32   = float;
-    using F16   = ck::half_t;
-    using BF16  = ck::bhalf_t;
+    using F32 = float;
+    using F16 = ck::half_t;
+#ifdef __bf16__
+    using BF16 = ck::bhalf_t;
+#endif
+#ifdef __int8__
     using INT8  = int8_t;
     using INT32 = int32_t;
+#endif
 
     using Row = ck::tensor_layout::gemm::RowMajor;
     using Col = ck::tensor_layout::gemm::ColumnMajor;
@@ -149,6 +153,7 @@ int profile_gemm(int argc, char* argv[])
     {
         return profile(Col{}, Col{}, Row{}, F16{}, F16{}, F32{}, F16{});
     }
+#ifdef __bf16__
     else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         return profile(Row{}, Row{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
@@ -165,6 +170,8 @@ int profile_gemm(int argc, char* argv[])
     {
         return profile(Col{}, Col{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
     }
+#endif
+#ifdef __int8__
     else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         return profile(Row{}, Row{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
@@ -181,6 +188,7 @@ int profile_gemm(int argc, char* argv[])
     {
         return profile(Col{}, Col{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
     }
+#endif
     else
     {
         std::cout << "this data_type & layout is not implemented" << std::endl;

profiler/src/profile_grouped_conv_bwd_data.cpp

@@ -77,15 +77,10 @@ int profile_grouped_conv_bwd_data(int argc, char* argv[])
     using F16  = ck::half_t;
     using BF16 = ck::bhalf_t;
 
-    using GNHWC = ck::tensor_layout::convolution::GNHWC;
-    using NHWGC = ck::tensor_layout::convolution::NHWGC;
-
-    using GKYXC = ck::tensor_layout::convolution::GKYXC;
-
-    using GNHWK = ck::tensor_layout::convolution::GNHWK;
-    using NHWGK = ck::tensor_layout::convolution::NHWGK;
+    using namespace ck::tensor_layout::convolution;
 
     constexpr auto I2 = ck::Number<2>{};
+    constexpr auto I3 = ck::Number<3>{};
 
     auto profile = [&](auto num_dim_spatial_tmp,
                        auto out_layout,
@@ -116,36 +111,70 @@ int profile_grouped_conv_bwd_data(int argc, char* argv[])
         return pass ? 0 : 1;
     };
 
-    // GNHWC_GKYXC_GNHWK
-    if(num_dim_spatial == 2 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
+    if(num_dim_spatial == 2)
     {
-        if(data_type == ConvDataType::F32_F32_F32)
-        {
-            return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F32{}, F32{}, F32{});
-        }
-        else if(data_type == ConvDataType::F16_F16_F16)
+        if(layout == ConvLayout::GNHWC_GKYXC_GNHWK)
         {
-            return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F16{}, F16{}, F16{});
+            if(data_type == ConvDataType::F32_F32_F32)
+            {
+                return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F32{}, F32{}, F32{});
+            }
+            else if(data_type == ConvDataType::F16_F16_F16)
+            {
+                return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F16{}, F16{}, F16{});
+            }
+            else if(data_type == ConvDataType::BF16_BF16_BF16)
+            {
+                return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, BF16{}, BF16{}, BF16{});
+            }
         }
-        else if(data_type == ConvDataType::BF16_BF16_BF16)
+        else if(layout == ConvLayout::NHWGC_GKYXC_NHWGK)
         {
-            return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, BF16{}, BF16{}, BF16{});
+            if(data_type == ConvDataType::F32_F32_F32)
+            {
+                return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F32{}, F32{}, F32{});
+            }
+            else if(data_type == ConvDataType::F16_F16_F16)
+            {
+                return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F16{}, F16{}, F16{});
+            }
+            else if(data_type == ConvDataType::BF16_BF16_BF16)
+            {
+                return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, BF16{}, BF16{}, BF16{});
+            }
         }
     }
-    // NHWGC_GKYXC_NHWGK
-    else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
+    else if(num_dim_spatial == 3)
     {
-        if(data_type == ConvDataType::F32_F32_F32)
-        {
-            return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F32{}, F32{}, F32{});
-        }
-        else if(data_type == ConvDataType::F16_F16_F16)
+        if(layout == ConvLayout::GNHWC_GKYXC_GNHWK)
         {
-            return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F16{}, F16{}, F16{});
+            if(data_type == ConvDataType::F32_F32_F32)
+            {
+                return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, F32{}, F32{}, F32{});
+            }
+            else if(data_type == ConvDataType::F16_F16_F16)
+            {
+                return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, F16{}, F16{}, F16{});
+            }
+            else if(data_type == ConvDataType::BF16_BF16_BF16)
+            {
+                return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, BF16{}, BF16{}, BF16{});
+            }
         }
-        else if(data_type == ConvDataType::BF16_BF16_BF16)
+        else if(layout == ConvLayout::NHWGC_GKYXC_NHWGK)
         {
-            return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, BF16{}, BF16{}, BF16{});
+            if(data_type == ConvDataType::F32_F32_F32)
+            {
+                return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, F32{}, F32{}, F32{});
+            }
+            else if(data_type == ConvDataType::F16_F16_F16)
+            {
+                return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, F16{}, F16{}, F16{});
+            }
+            else if(data_type == ConvDataType::BF16_BF16_BF16)
+            {
+                return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, BF16{}, BF16{}, BF16{});
+            }
         }
     }
 

profiler/src/profile_grouped_conv_bwd_weight.cpp

@@ -83,19 +83,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
     using F16  = ck::half_t;
     using BF16 = ck::bhalf_t;
 
-    using GNWC   = ck::tensor_layout::convolution::GNWC;
-    using GNHWC  = ck::tensor_layout::convolution::GNHWC;
-    using NHWGC  = ck::tensor_layout::convolution::NHWGC;
-    using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
-
-    using GKXC   = ck::tensor_layout::convolution::GKXC;
-    using GKYXC  = ck::tensor_layout::convolution::GKYXC;
-    using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
-
-    using GNWK   = ck::tensor_layout::convolution::GNWK;
-    using GNHWK  = ck::tensor_layout::convolution::GNHWK;
-    using NHWGK  = ck::tensor_layout::convolution::NHWGK;
-    using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
+    using namespace ck::tensor_layout::convolution;
 
     constexpr auto I1 = ck::Number<1>{};
     constexpr auto I2 = ck::Number<2>{};
@@ -194,6 +182,22 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
             return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, BF16{}, F32{}, BF16{});
         }
     }
+    else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
+    {
+        if(data_type == ConvDataType::F32_F32_F32)
+        {
+            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{});
+        }
+        else if(data_type == ConvDataType::F16_F16_F16)
+        {
+            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{});
+        }
+        else if(data_type == ConvDataType::BF16_F32_BF16)
+        {
+            // fp32 atomic add is used for weight tensor in bf16 kernel
+            return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, F32{}, BF16{});
+        }
+    }
 
     std::cout << "this data_type & layout is not implemented" << std::endl;
 

test/batched_gemm_multi_d/test_batched_gemm_multi_d.cpp

@@ -68,7 +68,9 @@ using KernelTypes = ::testing::Types<std::tuple<Row, Row, Row>,
 } // namespace
 
 TYPED_TEST_SUITE(TestBatchedGemmMultiD, KernelTypes);
-
+#ifdef __fp16
 TYPED_TEST(TestBatchedGemmMultiD, f16) { this->template Run<F16>(); }
-
+#endif
+#ifdef __int8__
 TYPED_TEST(TestBatchedGemmMultiD, int8) { this->template Run<int8_t>(); }
+#endif

test/gemm/CMakeLists.txt

+if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
 add_test_executable(test_gemm_fp32 gemm_fp32.cpp)
 target_link_libraries(test_gemm_fp32 PRIVATE utility)
 target_link_libraries(test_gemm_fp32 PRIVATE device_gemm_instance)
-
+endif()
+if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
 add_test_executable(test_gemm_fp16 gemm_fp16.cpp)
 target_link_libraries(test_gemm_fp16 PRIVATE utility)
 target_link_libraries(test_gemm_fp16 PRIVATE device_gemm_instance)
-
-add_test_executable(test_gemm_bf16 gemm_bf16.cpp)
-target_link_libraries(test_gemm_bf16 PRIVATE utility)
-target_link_libraries(test_gemm_bf16 PRIVATE device_gemm_instance)
-
-add_test_executable(test_gemm_int8 gemm_int8.cpp)
-target_link_libraries(test_gemm_int8 PRIVATE utility)
-target_link_libraries(test_gemm_int8 PRIVATE device_gemm_instance)
-
 add_library(gemm_standalone_xdl_fp16_instances STATIC
     instance/gemm_f16_nn_instance.cpp
     instance/gemm_f16_nt_instance.cpp
@@ -24,3 +17,14 @@ add_library(gemm_standalone_xdl_fp16_instances STATIC
 add_test_executable(test_gemm_standalone_xdl_fp16 gemm_standalone_xdl_fp16.cpp)
 target_link_libraries(test_gemm_standalone_xdl_fp16 PRIVATE gemm_standalone_xdl_fp16_instances utility)
 target_include_directories(test_gemm_standalone_xdl_fp16 PRIVATE instance/)
+endif()
+if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
+add_test_executable(test_gemm_bf16 gemm_bf16.cpp)
+target_link_libraries(test_gemm_bf16 PRIVATE utility)
+target_link_libraries(test_gemm_bf16 PRIVATE device_gemm_instance)
+endif()
+if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
+add_test_executable(test_gemm_int8 gemm_int8.cpp)
+target_link_libraries(test_gemm_int8 PRIVATE utility)
+target_link_libraries(test_gemm_int8 PRIVATE device_gemm_instance)
+endif()
\ No newline at end of file

test/grouped_convnd_bwd_data/CMakeLists.txt

 if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR GPU_TARGETS MATCHES "gfx940")
     add_gtest_executable(test_grouped_convnd_bwd_data test_grouped_convnd_bwd_data.cpp)
-    target_link_libraries(test_grouped_convnd_bwd_data PRIVATE utility device_grouped_conv2d_bwd_data_instance)
+    target_link_libraries(test_grouped_convnd_bwd_data PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
     add_gtest_executable(test_grouped_convnd_bwd_data_interface test_grouped_convnd_bwd_data_interface.cpp)
     target_link_libraries(test_grouped_convnd_bwd_data_interface PRIVATE utility device_grouped_conv2d_bwd_data_instance)
 endif()
\ No newline at end of file

test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp

@@ -85,7 +85,10 @@ using KernelTypes2d = ::testing::Types<
 using KernelTypes3d = ::testing::Types<
     std::tuple<float, float, float, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
     std::tuple<ck::half_t, ck::half_t, ck::half_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
-    std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>>;
+    std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
+    std::tuple<float, float, float, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
+    std::tuple<ck::half_t, ck::half_t, ck::half_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
+    std::tuple<ck::bhalf_t, float, ck::bhalf_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>>;
 
 TYPED_TEST_SUITE(TestGroupedConvndBwdWeight1d, KernelTypes1d);
 TYPED_TEST_SUITE(TestGroupedConvndBwdWeight2d, KernelTypes2d);