Apply formatting suggestions from code review

Co-authored-by: Finlay <[email protected]>
codeplaysoftware · Dec 5, 2024 · dc12326 · dc12326
1 parent 81b70b6
commit dc12326
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Showing 2 changed files with 12 additions and 12 deletions.
diff --git a/examples/35_gemm_softmax/gemm_online_softmax.cpp b/examples/35_gemm_softmax/gemm_online_softmax.cpp
@@ -128,7 +128,7 @@ struct Options {
   /// Prints the usage statement.
   std::ostream & print_usage(std::ostream &out) const {
 
-    out << "14_ampere_tf32_tensorop_gemm_cute example\n\n"
+    out << "35_gemm_softmax example\n\n"
       << "  This example uses the CUTLASS Library to execute TF32 tensorop GEMM computations.\n\n"
       << "Options:\n\n"
       << "  --help                      If specified, displays this usage statement.\n\n"
@@ -238,7 +238,7 @@ struct ExampleRunner {
   // Methods
   //
   template<typename Element>
-  bool verify_tensor(std::vector<Element> vector_Input, \
+  bool verify_tensor(std::vector<Element> vector_Input,
                        std::vector<Element> vector_Input_Ref, const Options& options) {
 
     auto size = int64_t((vector_Input.size() < vector_Input_Ref.size()) ? vector_Input.size() : vector_Input_Ref.size());

diff --git a/examples/35_gemm_softmax/softmax_finalize.hpp b/examples/35_gemm_softmax/softmax_finalize.hpp
@@ -129,7 +129,7 @@ class SoftmaxFinalize {
     const int y_size = BlockDimY();
     const int batch_id = BlockIdxY();
 
-    if(m>=params.args.M){
+    if (m >= params.args.M) {
       return;
     }
 
@@ -146,43 +146,43 @@ class SoftmaxFinalize {
                                   make_layout(make_shape(NumThreadsPerWarp, MaxNumThreadsPerBlock / NumThreadsPerWarp)));
 
     ElementPartial max_val = std::numeric_limits<ElementPartial>::lowest();
-    for(int partial_n = idx_y; partial_n < params.args.partialN; partial_n += y_size){
+    for (int partial_n = idx_y; partial_n < params.args.partialN; partial_n += y_size){
         ElementPartial partial_max = mPartialMax(m, partial_n, batch_id);
         max_val = cutlass::fast_max(max_val, partial_max);
     }
-    sPartial(idx_x,idx_y) = max_val;
+    sPartial(idx_x, idx_y) = max_val;
     syncthreads();
     // tree-reduction could be better, although it does not seem to be a bottleneck
-    for(int idx_y2 = 0; idx_y2 < y_size; idx_y2++){
-        ElementPartial partial_max = sPartial(idx_x,idx_y2);
+    for (int idx_y2 = 0; idx_y2 < y_size; idx_y2++){
+        ElementPartial partial_max = sPartial(idx_x, idx_y2);
         max_val = cutlass::fast_max(max_val, partial_max);
     }
 
     ElementPartial sum_val = 0;
-    for(int partial_n = idx_y; partial_n < params.args.partialN; partial_n += y_size){
+    for (int partial_n = idx_y; partial_n < params.args.partialN; partial_n += y_size){
         ElementPartial partial_max = mPartialMax(m, partial_n, batch_id);
         ElementPartial partial_sum = mPartialSum(m, partial_n, batch_id);
         sum_val += partial_sum * cutlass::fast_exp(partial_max - max_val);
     }
     syncthreads();
-    sPartial(idx_x,idx_y) = sum_val;
+    sPartial(idx_x, idx_y) = sum_val;
     syncthreads();
     sum_val = 0;
     // tree-reduction could be better, although it does not seem to be a bottleneck
     for(int idx_y2 = 0; idx_y2 < y_size; idx_y2++){
-        ElementPartial partial_sum = sPartial(idx_x,idx_y2);
+        ElementPartial partial_sum = sPartial(idx_x, idx_y2);
         sum_val += partial_sum;
     }
 
     ElementPartial norm = 1 / sum_val;
 
-    for(int n = idx_y * 2; n < params.args.dataN; n += y_size * 2){
+    for (int n = idx_y * 2; n < params.args.dataN; n += y_size * 2){
       auto inVal = mIn(m, n, batch_id);
       auto inVal2 = mIn(m, n+1, batch_id);
       mOut(m, n, batch_id) = cutlass::fast_exp(inVal - max_val) * norm;
       mOut(m, n+1, batch_id) = cutlass::fast_exp(inVal2 - max_val) * norm;
     }
-    if(params.args.dataN % 2 == 1){
+    if (params.args.dataN % 2 == 1){
       int n = params.args.dataN - 1;
       auto inVal = mIn(m, n, batch_id);
       mOut(m, n, batch_id) = cutlass::fast_exp(inVal - max_val) * norm;