diff --git a/.github/workflows/clang-format.yml b/.github/workflows/clang-format.yml
index 68149d2dc019f..67454c40d4b53 100644
--- a/.github/workflows/clang-format.yml
+++ b/.github/workflows/clang-format.yml
@@ -6,6 +6,7 @@ on:
   push:
     branches:
       - main
+      - develop
     paths:
       - '**/*.h'
       - '**/*.cpp'
@@ -15,6 +16,7 @@ on:
   pull_request:
     branches:
       - main
+      - develop
     paths:
       - '**/*.h'
       - '**/*.cpp'
diff --git a/csrc/activation_kernels.cu b/csrc/activation_kernels.cu
index 94a1c4a678cf7..8c3e74e502a55 100644
--- a/csrc/activation_kernels.cu
+++ b/csrc/activation_kernels.cu
@@ -93,20 +93,21 @@ __device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
 
 // Launch activation and gating kernel.
 #ifdef USE_ROCM
-#define LAUNCH_SCALED_ACTIVATION_GATE_KERNEL(KERNEL)                           \
-  int d = input.size(-1) / 2;                                                  \
-  int64_t num_tokens = input.numel() / input.size(-1);                         \
-  dim3 grid(num_tokens);                                                       \
-  dim3 block(std::min(d, 1024));                                               \
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
-  VLLM_DISPATCH_FLOATING_TYPES(                                                \
-      input.scalar_type(), "scaled_act_and_mul_kernel", [&] {                  \
-        vllm::scaled_act_and_mul_kernel<scalar_t, KERNEL<scalar_t>>            \
-            <<<grid, block, 0, stream>>>(out.data_ptr<c10::Float8_e4m3fnuz>(), \
-                                         input.data_ptr<scalar_t>(), d,        \
-                                         1.0 / (*scale.data_ptr<float>()));    \
-      });
+  #define LAUNCH_SCALED_ACTIVATION_GATE_KERNEL(KERNEL)                \
+    int d = input.size(-1) / 2;                                       \
+    int64_t num_tokens = input.numel() / input.size(-1);              \
+    dim3 grid(num_tokens);                                            \
+    dim3 block(std::min(d, 1024));                                    \
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(input)); \
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();     \
+    VLLM_DISPATCH_FLOATING_TYPES(                                     \
+        input.scalar_type(), "scaled_act_and_mul_kernel", [&] {       \
+          vllm::scaled_act_and_mul_kernel<scalar_t, KERNEL<scalar_t>> \
+              <<<grid, block, 0, stream>>>(                           \
+                  out.data_ptr<c10::Float8_e4m3fnuz>(),               \
+                  input.data_ptr<scalar_t>(), d,                      \
+                  1.0 / (*scale.data_ptr<float>()));                  \
+        });
 #endif
 
 void silu_and_mul(torch::Tensor& out,    // [..., d]
diff --git a/csrc/layernorm_kernels.cu b/csrc/layernorm_kernels.cu
index de098a9ee0c19..405ba213628f6 100644
--- a/csrc/layernorm_kernels.cu
+++ b/csrc/layernorm_kernels.cu
@@ -247,7 +247,7 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
     LAUNCH_RMS_NORM(0);
   }
 #else
-    LAUNCH_RMS_NORM(0);
+  LAUNCH_RMS_NORM(0);
 #endif
 }