diff --git a/colossalai/kernel/cuda_native/csrc/cpu_adam.cpp b/colossalai/kernel/cuda_native/csrc/cpu_adam.cpp
index feb612f9f..22bec7e27 100644
--- a/colossalai/kernel/cuda_native/csrc/cpu_adam.cpp
+++ b/colossalai/kernel/cuda_native/csrc/cpu_adam.cpp
@@ -20,12 +20,14 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE
 */
 #include "cpu_adam.h"
-#include <iostream>
+
 #include <math.h>
-#include <memory>
 #include <omp.h>
 #include <string.h>
 #include <torch/extension.h>
+
+#include <iostream>
+#include <memory>
 #include <type_traits>
 #include <unordered_map>
 
@@ -82,8 +84,7 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
 
   for (size_t t = 0; t < rounded_size; t += TILE) {
     size_t copy_size = TILE;
-    if ((t + TILE) > rounded_size)
-      copy_size = rounded_size - t;
+    if ((t + TILE) > rounded_size) copy_size = rounded_size - t;
     size_t offset = copy_size + t;
 
 #pragma omp parallel for
@@ -145,8 +146,7 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
   if (_param_size > rounded_size) {
     for (size_t t = rounded_size; t < _param_size; t += TILE) {
       size_t copy_size = TILE;
-      if ((t + TILE) > _param_size)
-        copy_size = _param_size - t;
+      if ((t + TILE) > _param_size) copy_size = _param_size - t;
       size_t offset = copy_size + t;
 
 #pragma omp parallel for
@@ -235,8 +235,7 @@ void Adam_Optimizer::Step_4(float *_params, float *grads, float *_exp_avg,
 
   for (size_t t = 0; t < rounded_size; t += TILE) {
     size_t copy_size = TILE;
-    if ((t + TILE) > rounded_size)
-      copy_size = rounded_size - t;
+    if ((t + TILE) > rounded_size) copy_size = rounded_size - t;
     size_t offset = copy_size + t;
 
 #pragma omp parallel for
@@ -321,7 +320,6 @@ int create_adam_optimizer(int optimizer_id, float alpha = 1e-3,
   s_optimizers[optimizer_id] = opt;
 
   if (should_log) {
-
     std::string avx_type = "";
 #if defined(__AVX512__)
     avx_type = "AVX512";
@@ -386,8 +384,7 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
 
   for (size_t t = 0; t < rounded_size; t += TILE) {
     size_t copy_size = TILE;
-    if ((t + TILE) > rounded_size)
-      copy_size = rounded_size - t;
+    if ((t + TILE) > rounded_size) copy_size = rounded_size - t;
     size_t offset = copy_size + t;
 
 #pragma omp parallel for
@@ -463,43 +460,29 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
            grad_half_precision, loss_scale);
 }
 
-int adam_step(int optimizer_id,
-                 size_t step,
-                 float lr,
-                 float beta1,
-                 float beta2,
-                 float epsilon,
-                 float weight_decay,
-                 bool bias_correction,
-                 torch::Tensor& params,
-                 torch::Tensor& grads,
-                 torch::Tensor& exp_avg,
-                 torch::Tensor& exp_avg_sq,
-                 float loss_scale)
-{
-    auto params_c = params.contiguous();
-    auto grads_c = grads.contiguous();
-    auto exp_avg_c = exp_avg.contiguous();
-    auto exp_avg_sq_c = exp_avg_sq.contiguous();
+int adam_step(int optimizer_id, size_t step, float lr, float beta1, float beta2,
+              float epsilon, float weight_decay, bool bias_correction,
+              torch::Tensor &params, torch::Tensor &grads,
+              torch::Tensor &exp_avg, torch::Tensor &exp_avg_sq,
+              float loss_scale) {
+  auto params_c = params.contiguous();
+  auto grads_c = grads.contiguous();
+  auto exp_avg_c = exp_avg.contiguous();
+  auto exp_avg_sq_c = exp_avg_sq.contiguous();
 
-    float* params_ptr = (float*)params_c.data_ptr();
-    float* grads_ptr = (float*)grads_c.data_ptr();
-    float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
-    float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
-    std::shared_ptr<Adam_Optimizer> opt =
-        std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
-    opt->IncrementStep(step, beta1, beta2);
-    opt->update_state(lr, epsilon, weight_decay, bias_correction);
-    opt->Step_8(params_ptr,
-                grads_ptr,
-                exp_avg_ptr,
-                exp_avg_sq_ptr,
-                params_c.numel(),
-                (params.options().dtype() == at::kHalf),
-                (grads.options().dtype() == at::kHalf),
-                loss_scale);
+  float *params_ptr = (float *)params_c.data_ptr();
+  float *grads_ptr = (float *)grads_c.data_ptr();
+  float *exp_avg_ptr = (float *)exp_avg_c.data_ptr();
+  float *exp_avg_sq_ptr = (float *)exp_avg_sq_c.data_ptr();
+  std::shared_ptr<Adam_Optimizer> opt =
+      std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
+  opt->IncrementStep(step, beta1, beta2);
+  opt->update_state(lr, epsilon, weight_decay, bias_correction);
+  opt->Step_8(params_ptr, grads_ptr, exp_avg_ptr, exp_avg_sq_ptr,
+              params_c.numel(), (params.options().dtype() == at::kHalf),
+              (grads.options().dtype() == at::kHalf), loss_scale);
 
-    return 0;
+  return 0;
 }
 
 int destroy_adam_optimizer(int optimizer_id) {