Extend GeneratorTensor_Sequential to produce values of prescribed data types.

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

@@ -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

@@ -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_1<ADataType>{1.0});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0});
         }
     }
 

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16_fp8.cpp

@@ -154,12 +154,12 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
             break;
         case 2:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{-1.0, 1.0});
             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_1<ADataType>{1.0});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0});
         }
     }
 
@@ -266,6 +266,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
                                                                                 BElementOp,
                                                                                 CDEElementOp>;
 
+        std::cout << "Running verification on CPU." << std::endl;
         for(std::size_t i = 0; i < gemm_descs.size(); i++)
         {
             c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data(),
@@ -285,6 +286,9 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
 
             pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
         }
+
+        if(pass)
+            std::cout << "Verification on CPU: PASS" << std::endl;
     }
 
     return pass;

example/15_grouped_gemm/run_grouped_gemm_example.inc

@@ -120,12 +120,12 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
             break;
         case 2:
-            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{-1.0, 1.0});
             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_1<ADataType>{1});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
         }
     }
 
@@ -184,6 +184,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
     bool pass = true;
     if(config.do_verification)
     {
+        std::cout << "Running verification on CPU." << std::endl;
         using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                                 BDataType,
                                                                                 EDataType,
@@ -215,6 +216,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
 #endif
         }
+        if(pass)
+            std::cout << "Verification on CPU: PASS" << std::endl;
     }
 
     if(config.time_kernel)

example/21_gemm_layernorm/gemm_xdl_layernorm_naive_single_kernel_fp16.cpp

@@ -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_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
     }
 
     c0_n_bias.GenerateTensorValue(GeneratorTensor_2<C0DataType>{-5, 5});

example/31_batched_gemm_gemm/run_batched_gemm_gemm_example.inc

@@ -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

@@ -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

@@ -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_grouped_gemm_scale_softmax_gemm_permute.inc

@@ -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/35_splitK_gemm/run_splitK_gemm_example.inc

@@ -66,8 +66,8 @@ bool run_splitK_gemm(const ProblemSize& problem_size, const ExecutionConfig& con
         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_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
     }
 
     DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());

example/37_batched_gemm_add_add_relu_gemm_add/batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp

@@ -377,7 +377,7 @@ int main(int argc, char* argv[])
         break;
     default:
         a0_g_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{1});
-        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
         d00_g_m_n.GenerateTensorValue(GeneratorTensor_1<D00DataType>{1});
         d01_g_m_n.GenerateTensorValue(GeneratorTensor_1<D01DataType>{1});
         b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});

example/47_gemm_bias_softmax_gemm_permute/gemm_bias_softmax_gemm_permute_xdl.cpp

@@ -248,7 +248,7 @@ int main(int argc, char* argv[])
         d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<D0DataType>{1});
         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>{});
         d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<D0DataType>{1});

example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8.cpp

@@ -194,9 +194,9 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b1_tensors[i].GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
             break;
         default:
-            a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
-            b1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a0_tensors[i].GenerateTensorValue(GeneratorTensor_1<A0DataType>{1});
+            b0_tensors[i].GenerateTensorValue(GeneratorTensor_1<B0DataType>{1});
+            b1_tensors[i].GenerateTensorValue(GeneratorTensor_1<B1DataType>{1});
         }
 
         d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});

example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp

@@ -184,9 +184,9 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
             b_tensors[i].GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
             break;
         default:
-            a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            a1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
-            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+            a0_tensors[i].GenerateTensorValue(GeneratorTensor_1<A0DataType>{1});
+            a1_tensors[i].GenerateTensorValue(GeneratorTensor_1<A1DataType>{1});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_1<B0DataType>{-1});
         }
 
         d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});

library/include/ck/library/utility/host_tensor_generator.hpp

@@ -256,14 +256,33 @@ struct GeneratorTensor_Checkboard
     }
 };
 
-template <ck::index_t Dim>
+/**
+ * @brief Is used to generate sequential values based on the specified dimension.
+ *
+ * @tparam T The type of the tensor values.
+ * @tparam Dim The specific dimension used for generation.
+ *
+ * GeneratorTensor_Sequential<1>{} will generate the following values for a 3x3 tensor:
+ *
+ * 0 1 2
+ * 0 1 2
+ * 0 1 2
+ *
+ * Essentially, the values generated are logical coordinates of the generated element that
+ * correspond to dimension Dim. E.g. for 2-dimensional tensor and Dim=1, the values are the column
+ * indices.
+ *
+ */
+template <typename T, ck::index_t Dim>
 struct GeneratorTensor_Sequential
 {
     template <typename... Ts>
-    float operator()(Ts... Xs) const
+    T operator()(Ts... Xs) const
     {
         std::array<ck::index_t, sizeof...(Ts)> dims = {{static_cast<ck::index_t>(Xs)...}};
-        return dims[Dim];
+
+        float tmp = dims[Dim];
+        return ck::type_convert<T>(tmp);
     }
 };