Fix lints from HUD (pytorch#7110)

run lintrunner

Co-authored-by: lind <[email protected]>

backends/cadence/fusion_g3/operators/op_add.cpp

@@ -95,26 +95,26 @@ Tensor& add_out(
   }
 
   for (int i = 0; i < max_dim; i++) {
-    out_shape[i]  = 1;
+    out_shape[i] = 1;
     inp1_shape[i] = 1;
     inp2_shape[i] = 1;
   }
-  
-  int offset_out  = max_dim - out.dim();
+
+  int offset_out = max_dim - out.dim();
   int offset_inp1 = max_dim - a.dim();
   int offset_inp2 = max_dim - b.dim();
-  
+
   for (int i = 0; i < out.dim(); i++) {
     out_shape[i + offset_out] = out.size(i);
   }
   for (int i = 0; i < a.dim(); i++) {
     inp1_shape[i + offset_inp1] = a.size(i);
   }
   for (int i = 0; i < b.dim(); i++) {
-  inp2_shape[i + offset_inp2] = b.size(i); 
+    inp2_shape[i + offset_inp2] = b.size(i);
   }
 
-  if ((compute_type == ScalarType::Int) && (optimized)){
+  if ((compute_type == ScalarType::Int) && (optimized)) {
     const int* const inp1_data = a.const_data_ptr<int>();
     const int* const inp2_data = b.const_data_ptr<int>();
     int* const out_data = out.mutable_data_ptr<int>();

backends/cadence/fusion_g3/operators/op_mul.cpp

@@ -87,23 +87,23 @@ Tensor& mul_out(
   }
 
   for (int i = 0; i < max_dim; i++) {
-    out_shape[i]  = 1;
+    out_shape[i] = 1;
     inp1_shape[i] = 1;
     inp2_shape[i] = 1;
   }
-  
-  int offset_out  = max_dim - out.dim();
+
+  int offset_out = max_dim - out.dim();
   int offset_inp1 = max_dim - a.dim();
   int offset_inp2 = max_dim - b.dim();
-  
+
   for (int i = 0; i < out.dim(); i++) {
     out_shape[i + offset_out] = out.size(i);
   }
   for (int i = 0; i < a.dim(); i++) {
     inp1_shape[i + offset_inp1] = a.size(i);
   }
   for (int i = 0; i < b.dim(); i++) {
-  inp2_shape[i + offset_inp2] = b.size(i); 
+    inp2_shape[i + offset_inp2] = b.size(i);
   }
 
   if ((compute_type == ScalarType::Int) && (optimized)) {

backends/cadence/hifi/operators/op_maximum.cpp

@@ -23,7 +23,6 @@ using torch::executor::apply_binary_elementwise_fn;
 using torch::executor::Error;
 using torch::executor::resize_to_broadcast_target_size;
 
-
 namespace cadence {
 namespace impl {
 namespace HiFi {

backends/cadence/hifi/operators/op_pow.cpp

@@ -351,4 +351,3 @@ Tensor& pow_Scalar_out(
 } // namespace HiFi
 } // namespace impl
 } // namespace cadence
-

backends/cadence/hifi/operators/quantized_linear_out.cpp

@@ -26,8 +26,7 @@ using ::executorch::aten::Tensor;
 using ::executorch::runtime::getLeadingDims;
 using ::executorch::runtime::KernelRuntimeContext;
 
-
-    // The nnlib kernel to compute quantized linear via matmul.
+// The nnlib kernel to compute quantized linear via matmul.
 
 void _quantized_linear_asym8u(
     const Tensor& in,
@@ -48,22 +47,22 @@ void _quantized_linear_asym8u(
   const int32_t* __restrict__ bias_data = bias.const_data_ptr<int32_t>();
   uint8_t* __restrict__ out_data = out.mutable_data_ptr<uint8_t>();
   int32_t ret = xa_nn_matmul_asym8uxasym8u_asym8u(
-        out_data,
-        weight_data,
-        in_data,
-        bias_data,
-        out_dim,
-        in_dim,
-        in_dim,
-        leading_dims,
-        in_dim,
-        out_dim,
-        1,
+      out_data,
+      weight_data,
+      in_data,
+      bias_data,
+      out_dim,
+      in_dim,
+      in_dim,
+      leading_dims,
+      in_dim,
+      out_dim,
+      1,
       -weight_zero_point.const_data_ptr<int32_t>()[0], // mat1_zero_bias
       -in_zero_point, // mat2_zero_bias
-        out_multiplier.const_data_ptr<int32_t>()[0],
-        out_shift.const_data_ptr<int32_t>()[0],
-        out_zero_point);
+      out_multiplier.const_data_ptr<int32_t>()[0],
+      out_shift.const_data_ptr<int32_t>()[0],
+      out_zero_point);
   ET_DCHECK_MSG(ret == 0, "HiFi quantized::linear failed");
 }