bug fixes and enharcement to gemm reductionK fusion (#682)

* add two missing files

* fix bunch of bugs of gemm-reducek fusion and add a device interface

* small changes

Co-authored-by: Haicheng Wu <haichengw@nvidia.com>

examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu

@@ -45,7 +45,7 @@ epilogue/threadblock/epilogue_gemm_k_reduction.h
 #include <sstream>
 
 #include "cutlass/cutlass.h"
-#include "cutlass/gemm/device/gemm_universal_adapter.h"
+#include "cutlass/gemm/device/gemm_with_k_reduction.h"
 #include "cutlass/gemm/kernel/default_gemm_with_k_reduction.h"
 #include "cutlass/reduction/device/reduce_split_k.h"
 #include "cutlass/reduction/kernel/reduce_split_k.h"
@@ -101,6 +101,12 @@ constexpr int NumStages = 4;
 // Reduce A or B operand along the K dimension
 constexpr bool ReduceKForA = true;
 
+// Alignment of A operand
+constexpr int AlignmentA = 8;
+
+// Alignment of B operand
+constexpr int AlignmentB = 8;
+
 // This code section describes the epilogue part of the kernel, we use default value
 using EpilogueOp = cutlass::epilogue::thread::LinearCombination<
     ElementOutput,                                        // Data type of output matrix.
@@ -110,9 +116,9 @@ using EpilogueOp = cutlass::epilogue::thread::LinearCombination<
     ElementAccumulator,                                   // Data type of accumulator
     ElementComputeEpilogue>;
 
-using GemmKernel = typename cutlass::gemm::kernel::DefaultGemmWithKReduction<
-  ElementInputA, LayoutInputA, cutlass::ComplexTransform::kNone, 8,
-  ElementInputB, LayoutInputB, cutlass::ComplexTransform::kNone, 8,
+using Gemm = typename cutlass::gemm::device::GemmWithKReduction<
+  ElementInputA, LayoutInputA,
+  ElementInputB, LayoutInputB,
   ElementOutput, LayoutOutput,
   ElementAccumulator,
   MMAOp,
@@ -124,10 +130,12 @@ using GemmKernel = typename cutlass::gemm::kernel::DefaultGemmWithKReduction<
   EpilogueOp,
   SwizzleThreadBlock,
   NumStages,
-  cutlass::arch::OpMultiplyAdd
->::GemmKernel;
-
-using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+  AlignmentA,
+  AlignmentB,
+  cutlass::arch::OpMultiplyAdd,
+  cutlass::ComplexTransform::kNone,
+  cutlass::ComplexTransform::kNone
+>;
 
 // Below is the reduction kernel used in the case of parallel split-k
 using ReduceGemmSplitKShape = cutlass::MatrixShape<4, 64>;;
@@ -368,21 +376,21 @@ Result profile(Options const &options) {
   // Fill input and output matrices on host using CUTLASS helper functions
   cutlass::reference::host::TensorFillRandomUniform(
       tensor_a.host_view(),
-      1,
+      1997,
       ElementInputA(2),
       ElementInputA(-2),
       0);  // <- Fill tensor A on host with uniform-distribution random data
 
   cutlass::reference::host::TensorFillRandomUniform(
       tensor_b.host_view(),
-      1,
+      2003,
       ElementInputB(2),
       ElementInputB(-2),
       0);  // <- Fill tensor B on host with uniform-distribution random data
 
   cutlass::reference::host::TensorFillRandomUniform(
       tensor_c.host_view(),
-      1,
+      2017,
       ElementOutput(2),
       ElementOutput(-2),
       0);  // <- Fill matrix C on host with uniform-distribution random data
@@ -561,7 +569,7 @@ Result profile(Options const &options) {
   
     tensor_reduction.sync_host();
   
-    // ReduceK in host code
+    // Reduce K in host code
     if (ReduceKForA) {
       for (int m = 0; m < options.problem_size.m(); ++m) {
         for (int k = 0; k < options.problem_size.k(); ++k) {
@@ -581,7 +589,7 @@ Result profile(Options const &options) {
     // Check if output from CUTLASS kernel and reference kernel are equal or not
     bool pass = cutlass::reference::host::TensorEquals(tensor_d.host_view(),
                                                        tensor_ref_d.host_view());
-  
+
     pass &= cutlass::reference::host::TensorEquals(tensor_ref_reduction.host_view(),
                                                    tensor_reduction.host_view());