[experimental][kleidi] linter

torchao/experimental/kernels/cpu/aarch64/kleidi/kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod.h

@@ -1,4 +1,3 @@
-// namespace example
 // Copyright (c) Meta Platforms, Inc. and affiliates.
 // All rights reserved.
 //
@@ -42,8 +41,9 @@ const Ukernel get_ukernel() {
 }
 
 int activation_data_size(int m, int k, int group_size) {
-  (void) group_size; // unused
-  return kai_matmul_clamp_f32_qai8dxp_qsi4c32p::activation_data_size(get_ukernel(), m, k);
+  (void)group_size; // unused
+  return kai_matmul_clamp_f32_qai8dxp_qsi4c32p::activation_data_size(
+      get_ukernel(), m, k);
 }
 
 void prepare_activation_data(
@@ -52,17 +52,14 @@ void prepare_activation_data(
     int k,
     int group_size,
     const float* activations) {
-  (void) group_size; // unused
+  (void)group_size; // unused
   kai_matmul_clamp_f32_qai8dxp_qsi4c32p::prepare_activation_data(
-      get_ukernel(),
-      activation_data,
-      m,
-      k,
-      activations);
+      get_ukernel(), activation_data, m, k, activations);
 }
 
 int weight_data_size(int n, int k, int group_size) {
-  return kai_matmul_clamp_f32_qai8dxp_qsi4c32p::weight_data_size(get_ukernel(), n, k, group_size);
+  return kai_matmul_clamp_f32_qai8dxp_qsi4c32p::weight_data_size(
+      get_ukernel(), n, k, group_size);
 }
 
 void prepare_weight_data(
@@ -96,24 +93,24 @@ void kernel(
     const float* bias,
     float clamp_min,
     float clamp_max) {
-    (void) bias; // unused - needs API fixing
-    assert(output_m_stride == n);
-    if (clamp_min == 0 && clamp_max == 0) {
-      clamp_min = std::numeric_limits<float>::lowest();
-      clamp_max = std::numeric_limits<float>::max();
-    }
+  (void)bias; // unused - needs API fixing
+  assert(output_m_stride == n);
+  if (clamp_min == 0 && clamp_max == 0) {
+    clamp_min = std::numeric_limits<float>::lowest();
+    clamp_max = std::numeric_limits<float>::max();
+  }
 
-    auto ukernel = get_ukernel(); 
-    ukernel.run_matmul(
+  auto ukernel = get_ukernel();
+  ukernel.run_matmul(
       m,
       n,
       k,
       group_size,
       activation_data,
       weight_data,
       output,
-      /*dst_stride_row=*/ n * sizeof(float),
-      /*dst_stride_col=*/ sizeof(float),
+      /*dst_stride_row=*/n * sizeof(float),
+      /*dst_stride_col=*/sizeof(float),
       clamp_min,
       clamp_max);
 }

torchao/experimental/kernels/cpu/aarch64/kleidi/kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p8x8_1x8x32_neon_dotprod.h

@@ -1,4 +1,3 @@
-// namespace example
 // Copyright (c) Meta Platforms, Inc. and affiliates.
 // All rights reserved.
 //

torchao/experimental/kernels/cpu/aarch64/kleidi/kai_matmul_clamp_f32_qai8dxp_qsi4c32p.h

@@ -1,4 +1,3 @@
-// namespace example
 // Copyright (c) Meta Platforms, Inc. and affiliates.
 // All rights reserved.
 //
@@ -7,10 +6,10 @@
 
 #pragma once
 
-#include <cstdint>
+#include <cassert>
 #include <cstddef>
+#include <cstdint>
 #include <cstring>
-#include <cassert>
 #include <limits>
 #include <vector>
 
@@ -25,7 +24,7 @@ namespace torchao::kernels::cpu::aarch64::kleidi {
 // TODO: find a better place for these?
 
 size_t roundup(size_t a, size_t b) {
-    return ((a + b - 1) / b) * b;
+  return ((a + b - 1) / b) * b;
 }
 
 uint16_t get_bf16_from_float(float f) {
@@ -111,13 +110,15 @@ void prepare_weight_data(
   uint8_t wzp = 8;
   for (size_t i = 0; i < n * k; i += 2) {
     const uint8_t low = static_cast<uint8_t>(weight_qvals[i] + wzp);
-    const uint8_t high = static_cast<uint8_t>(weight_qvals[i+1] + wzp);
+    const uint8_t high = static_cast<uint8_t>(weight_qvals[i + 1] + wzp);
     packed_weight_qvals[i / 2] = ((high << 4) | (low & 0xF));
   }
 
   // Parameters for packing
   rhs_packing::qparams_t qparams{
-      .lhs_zero_point=1, .rhs_zero_point=wzp, .scale_dt = kai_datatype::kai_dt_bf16};
+      .lhs_zero_point = 1,
+      .rhs_zero_point = wzp,
+      .scale_dt = kai_datatype::kai_dt_bf16};
 
   auto rhs_pack = get_rhs_packing();
 
@@ -133,7 +134,7 @@ void prepare_weight_data(
       /*rhs_stride=*/roundup(k, 2) / 2,
       /*bias=*/nullptr, // TODO fix APIs to move bias here
       /*scale=*/reinterpret_cast<const uint16_t*>(weight_scales_bf16.data()),
-      /*scale_stride=*/ sizeof(uint16_t) * (roundup(k, group_size) / group_size),
+      /*scale_stride=*/sizeof(uint16_t) * (roundup(k, group_size) / group_size),
       /*rhs_packed=*/weight_data,
       /*extra_bytes=*/0,
       /*qparams=*/&qparams);

torchao/experimental/kernels/cpu/aarch64/tests/test_linear.cpp

@@ -6,8 +6,8 @@
 
 #if defined(__aarch64__) || defined(__ARM_NEON)
 
-#include <vector>
 #include <arm_neon.h>
+#include <vector>
 
 #include <gtest/gtest.h>
 #include <torchao/experimental/kernels/cpu/aarch64/bitpacking/bitpack.h>
@@ -375,10 +375,10 @@ void test_kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod(
           has_clamp,
           /*weight_scale_bf16_round_trip=*/true);
 
-  using namespace torchao::kernels::cpu::aarch64::kleidi::kai_matmul_clamp_f32_qai8dxp_qsi4c32p::neon_dotprod_1x4x32;
+  using namespace torchao::kernels::cpu::aarch64::kleidi::
+      kai_matmul_clamp_f32_qai8dxp_qsi4c32p::neon_dotprod_1x4x32;
 
-  std::vector<char> activation_data(
-      activation_data_size(m, k, group_size));
+  std::vector<char> activation_data(activation_data_size(m, k, group_size));
 
   prepare_activation_data(
       (void*)activation_data.data(),
@@ -387,8 +387,7 @@ void test_kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod(
       group_size,
       test_case.activations.data());
 
-  std::vector<char> weight_data(
-      weight_data_size(n, k, group_size));
+  std::vector<char> weight_data(weight_data_size(n, k, group_size));
 
   prepare_weight_data(
       (void*)weight_data.data(),
@@ -462,8 +461,6 @@ TEST(
       /*m=*/1, /*k=*/128, /*n=*/182, /*group_size=*/128);
 }
 
-
-
 template <int weight_nbit, bool has_weight_zeros, bool has_bias, bool has_clamp>
 void test_kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p8x8_1x8x32_neon_dotprod(
     int m,
@@ -482,10 +479,10 @@ void test_kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p8x8_1x8x32_neon_dotprod(
           has_clamp,
           /*weight_scale_bf16_round_trip=*/true);
 
-  using namespace torchao::kernels::cpu::aarch64::kleidi::kai_matmul_clamp_f32_qai8dxp_qsi4c32p::neon_dotprod_1x8x32;
+  using namespace torchao::kernels::cpu::aarch64::kleidi::
+      kai_matmul_clamp_f32_qai8dxp_qsi4c32p::neon_dotprod_1x8x32;
 
-  std::vector<char> activation_data(
-      activation_data_size(m, k, group_size));
+  std::vector<char> activation_data(activation_data_size(m, k, group_size));
 
   prepare_activation_data(
       (void*)activation_data.data(),
@@ -494,8 +491,7 @@ void test_kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p8x8_1x8x32_neon_dotprod(
       group_size,
       test_case.activations.data());
 
-  std::vector<char> weight_data(
-      weight_data_size(n, k, group_size));
+  std::vector<char> weight_data(weight_data_size(n, k, group_size));
 
   prepare_weight_data(
       (void*)weight_data.data(),