Apply clang-tidy readability rules

Signed-off-by: Yuanyuan Chen <cyyever@outlook.com>

.clang-tidy

@@ -60,9 +60,11 @@ performance-*,
 readability-container-size-empty,
 readability-delete-null-pointer,
 readability-duplicate-include,
+readability-named-parameter,
 readability-misplaced-array-index,
 readability-redundant*,
 readability-simplify-subscript-expr,
+readability-static-definition-in-anonymous-namespace
 readability-string-compare,
 -readability-redundant-access-specifiers,
 -readability-redundant-control-flow,

aten/src/ATen/native/cuda/Blas.cpp

@@ -4,7 +4,6 @@
 #include <c10/util/SmallVector.h>
 #include <c10/core/Scalar.h>
 #include <c10/core/ScalarType.h>
-#include <c10/util/Exception.h>
 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
 #include <ATen/core/Tensor.h>
 #include <ATen/core/NamedTensor.h>
@@ -1423,12 +1422,12 @@ _scaled_gemm(
 //                  to help cleanup v1 call structure.
 Tensor&
 _scaled_rowwise_rowwise(
-          const Tensor&, const Tensor&,
-          const Tensor&, const Tensor&,
-          const std::optional<Tensor>&,
-          const c10::ScalarType,
-          bool,
-          Tensor&);
+          const Tensor& /*mat_a*/, const Tensor& /*mat_b*/,
+          const Tensor& /*scale_a*/, const Tensor& /*scale_b*/,
+          const std::optional<Tensor>& /*bias*/,
+          const c10::ScalarType /*out_dtype*/,
+          bool /*use_fast_accum*/,
+          Tensor& /*out*/);
 
 
 // Computes matrix multiply + bias while applying scaling to input and output matrices
@@ -2058,7 +2057,7 @@ _scaled_rowwise_rowwise(
   auto dprops = at::cuda::getCurrentDeviceProperties();
   if (((dprops->major < 9 || CUBLAS_VERSION < 120900 || cublasLtGetVersion() < 120900)
       // cuBLAS only supports tiled 1D factor layout for 1D block scaling, no 2D block scales
-      ||  (dprops->major == 10 && (scale_a.sizes().size() || scale_b.sizes().size())))) {
+      ||  (dprops->major == 10 && (!scale_a.sizes().empty() || !scale_b.sizes().empty())))) {
     TORCH_CHECK_VALUE(out.dtype() == kBFloat16, "Only bf16 high precision output types are supported for row-wise scaling.");
     at::cuda::detail::f8f8bf16_rowwise(
         mat_a,
@@ -2423,7 +2422,7 @@ _scaled_mm_cuda_v2_out(
   // Check if the input matrix sizes can be multiplied
   // - if optional contraction dims are provided, use those
   //   -- mostly for < 1B formats (i.e. nvfp4x2) where cheap .t() is not available.
-  if (contraction_dim.size() > 0) {
+  if (!contraction_dim.empty()) {
     TORCH_CHECK_VALUE(contraction_dim.size() == 2, "contraction_dim must have exactly 2 elements");
     auto mat_a_dim = contraction_dim[0];
     auto mat_b_dim = contraction_dim[1];
@@ -2880,7 +2879,7 @@ _scaled_grouped_mm_cuda_v2(
 
   // NOTE(slayton): For sub-1B formats want contraction_dim argument?
   if (!a_is_2d || !b_is_2d) {
-    if (contraction_dim.size() > 0) {
+    if (!contraction_dim.empty()) {
       const int dim_a = contraction_dim[0], dim_b = mat_b.size(contraction_dim[1]);
       TORCH_CHECK_VALUE(mat_a.size(dim_a) == mat_b.size(dim_b),
           "Contraction dimensions (", dim_a, ",", dim_b, ") of mat_a and mat_b must match, got: ", mat_a.size(dim_a), " and ",

torch/csrc/inductor/aoti_package/model_package_loader.cpp

@@ -824,7 +824,7 @@ AOTIModelPackageLoader::AOTIModelPackageLoader(
   runner_ = registered_aoti_runner[device_key](
       so_path, num_runners, device.str(), cubin_dir, run_single_threaded);
 
-  if (weight_blob_filename != "") {
+  if (!weight_blob_filename.empty()) {
     runner_->update_constant_buffer_from_blob(weight_blob_filename);
   }
 }