Merge pull request #219 from ROCm/andriy/lwpck-2430

Add support of OCP FP8 data types in CK for gfx950 arch

CMakeLists.txt

@@ -184,14 +184,23 @@ if (SUPPORTED_GPU_TARGETS MATCHES "gfx9")
     message("Enabling XDL instances")
     add_definitions(-DCK_USE_XDL)
 endif()
-if (SUPPORTED_GPU_TARGETS MATCHES "gfx94")
-    message("Enabling FP8 gemms in ckProfiler")
+if (SUPPORTED_GPU_TARGETS MATCHES "gfx94" OR SUPPORTED_GPU_TARGETS MATCHES "gfx95")
+    message("Enabling FP8 gemms on native architectures")
     add_definitions(-DCK_USE_GFX94)
 endif()
 if (SUPPORTED_GPU_TARGETS MATCHES "gfx11" OR SUPPORTED_GPU_TARGETS MATCHES "gfx12")
     message("Enabling WMMA instances")
     add_definitions(-DCK_USE_WMMA)
 endif()
+if (SUPPORTED_GPU_TARGETS MATCHES "gfx12" OR SUPPORTED_GPU_TARGETS MATCHES "gfx950")
+    add_definitions(-DCK_USE_OCP_FP8)
+    set(CK_USE_OCP_FP8 "ON")
+endif()
+if (SUPPORTED_GPU_TARGETS MATCHES "gfx90a" OR SUPPORTED_GPU_TARGETS MATCHES "gfx94")
+    add_definitions(-DCK_USE_FNUZ_FP8)
+    set(CK_USE_FNUZ_FP8 "ON")
+endif()
+
 option(CK_USE_FP8_ON_UNSUPPORTED_ARCH "Enable FP8 GEMM instances on older architectures" OFF)
 if(CK_USE_FP8_ON_UNSUPPORTED_ARCH AND (SUPPORTED_GPU_TARGETS MATCHES "gfx90a" OR SUPPORTED_GPU_TARGETS MATCHES "gfx908"))
     add_definitions(-DCK_USE_FP8_ON_UNSUPPORTED_ARCH)

client_example/24_grouped_conv_activation/CMakeLists.txt

@@ -54,7 +54,7 @@ target_link_libraries(client_conv3d_fwd_convscale_relu_amax_fp8
                       PRIVATE composable_kernel::device_conv_operations
                               composable_kernel::device_other_operations
                               composable_kernel::device_reduction_operations
-                              utility)
+                              composable_kernel::utility)
 # Fwd convscale + AMAX
 add_executable(client_conv3d_fwd_convscale_amax_fp8
                grouped_convnd_fwd_convscale_reduce/conv3d_fwd_convscale_amax_fp8.cpp)
@@ -62,7 +62,7 @@ target_link_libraries(client_conv3d_fwd_convscale_amax_fp8
                       PRIVATE composable_kernel::device_conv_operations
                               composable_kernel::device_other_operations
                               composable_kernel::device_reduction_operations
-                              utility)
+                              composable_kernel::utility)
 # Fwd convscale
 add_executable(client_conv3d_fwd_convscale_fp8
                grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8.cpp)

client_example/CMakeLists.txt

@@ -56,13 +56,21 @@ if (GPU_TARGETS)
         add_definitions(-DCK_USE_WMMA)
         set(CK_USE_WMMA "ON")
     endif()
+    if (GPU_TARGETS MATCHES "gfx12" OR GPU_TARGETS MATCHES "gfx950")
+        add_definitions(-DCK_USE_OCP_FP8)
+        set(CK_USE_OCP_FP8 "ON")
+    endif()
+    if (GPU_TARGETS MATCHES "gfx90a" OR GPU_TARGETS MATCHES "gfx94")
+        add_definitions(-DCK_USE_FNUZ_FP8)
+        set(CK_USE_FNUZ_FP8 "ON")
+    endif()
 else()
     add_definitions(-DCK_USE_WMMA -DCK_USE_XDL)
     set(CK_USE_XDL "ON")
     set(CK_USE_WMMA "ON")
 endif()
 
-find_package(composable_kernel COMPONENTS device_other_operations device_gemm_operations device_conv_operations  device_reduction_operations)
+find_package(composable_kernel COMPONENTS device_other_operations device_gemm_operations device_conv_operations  device_reduction_operations utility)
 if(GPU_TARGETS MATCHES "gfx9")
     find_package(composable_kernel COMPONENTS device_contraction_operations)
 endif()

example/01_gemm/common.hpp

@@ -76,7 +76,7 @@ struct ProblemSizeSplitK final
 struct ExecutionConfig final
 {
     // 0 - no verification, 1 - CPU, 2 - GPU, 3 - CPU + GPU
-    int do_verification = 3;
+    int do_verification = 1;
     int init_method     = 2;
     bool time_kernel    = false;
 };

example/01_gemm/run_gemm_example.inc

@@ -143,8 +143,8 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
     switch(config.init_method)
     {
     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);
+        ck::utils::FillConstant<ADataType>{ck::type_convert<ADataType>(1.f)}(a_m_k);
+        ck::utils::FillConstant<BDataType>{ck::type_convert<BDataType>(1.f)}(b_k_n);
         break;
     case 1:
         ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);

example/04_gemm_add_add_fastgelu/CMakeLists.txt

@@ -16,7 +16,7 @@ if(USE_BITINT_EXTENSION_INT4)
     add_example_dependencies(example_gemm_add_add_fastgelu_xdl example_gemm_add_add_fastgelu_xdl_int4)
 endif(USE_BITINT_EXTENSION_INT4)
 
-list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942)
+list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942 gfx950)
 set(target 0)
 foreach(gpu IN LISTS GPU_TARGETS)
     if(gpu IN_LIST gpu_list AND target EQUAL 0)

example/15_grouped_gemm/grouped_gemm_multiple_d_splitk_xdl_fp16.cpp

@@ -186,15 +186,15 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             for(int j = 0; j < NumDMatrices; ++j)
             {
-                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
             }
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
             for(int j = 0; j < NumDMatrices; ++j)
             {
-                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<DDataType, 0>{});
             }
         }
     }

example/15_grouped_gemm/grouped_gemm_multiple_d_xdl_fp16.cpp

@@ -190,15 +190,15 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             for(int j = 0; j < NumDs; ++j)
             {
-                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
             }
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
             for(int j = 0; j < NumDs; ++j)
             {
-                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+                d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<DDataType, 0>{});
             }
         }
     }

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_bias_fp16.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
 #include <numeric>
@@ -167,11 +167,11 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
         }
 
-        d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<D0DataType, 1>{});
     }
 
     using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<1>;

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
 #include <numeric>
@@ -157,8 +157,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
         }
     }
 

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16_fp8.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
 #include <numeric>
@@ -158,8 +158,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
         }
     }
 

example/15_grouped_gemm/run_grouped_gemm_example.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
 
 struct ProblemSize final
@@ -124,8 +127,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
             break;
         default:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
         }
     }
 

example/21_gemm_layernorm/gemm_xdl_layernorm_naive_single_kernel_fp16.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
 #include <numeric>
@@ -175,8 +175,8 @@ int main(int argc, char* argv[])
         b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
         break;
     default:
-        a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-        b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
+        b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
     }
 
     c0_n_bias.GenerateTensorValue(GeneratorTensor_2<C0DataType>{-5, 5});

example/31_batched_gemm_gemm/run_batched_gemm_gemm_example.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -150,7 +150,7 @@ bool run_batched_gemm_gemm_example(int argc, char* argv[])
         break;
     default:
         a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
-        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
         b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }
 

example/32_batched_gemm_scale_softmax_gemm/run_batched_gemm_scale_softmax_gemm.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -157,7 +157,7 @@ int run(int argc, char* argv[])
         break;
     default:
         a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
-        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
         b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }
 

example/32_batched_gemm_scale_softmax_gemm/run_batched_gemm_scale_softmax_gemm_permute.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -118,7 +118,7 @@ int run(int argc, char* argv[])
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
         break;
     default:
-        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 2>{});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }

example/32_batched_gemm_scale_softmax_gemm/run_batched_gemm_scale_softmax_gemm_permute_wmma.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -153,7 +153,7 @@ int run(int argc, char* argv[])
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
         break;
     default:
-        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 2>{});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }

example/32_batched_gemm_scale_softmax_gemm/run_cross_attention_wmma.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -178,7 +178,7 @@ int run(int argc, char* argv[])
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
         break;
     default:
-        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 2>{});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }

example/32_batched_gemm_scale_softmax_gemm/run_grouped_gemm_scale_softmax_gemm_permute.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -152,7 +152,7 @@ int run(int argc, char* argv[])
             break;
         default:
             a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
-            b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
             b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
         }
 

example/32_batched_gemm_scale_softmax_gemm/run_grouped_query_attention_forward_wmma.inc

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 int run(int argc, char* argv[])
 {
@@ -156,7 +156,7 @@ int run(int argc, char* argv[])
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
         break;
     default:
-        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 2>{});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
     }