[Kernel] Use fused rmsnorm for some models like qwen3 series (#17735)

Signed-off-by: evian <eviantai@u.nus.edu>
Co-authored-by: evian <eviantai@u.nus.edu>

csrc/layernorm_kernels.cu

@@ -140,6 +140,10 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
               torch::Tensor& input,   // [..., hidden_size]
               torch::Tensor& weight,  // [hidden_size]
               double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(weight.is_contiguous());
+
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 

vllm/_custom_ops.py

@@ -186,7 +186,9 @@ def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
 # layer norm ops
 def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
              epsilon: float) -> None:
-    torch.ops._C.rms_norm(out, input, weight, epsilon)
+    # TODO: Remove this contiguous call when the kernel is updated to support non-contiguous input
+    input_contiguous = input.contiguous()
+    torch.ops._C.rms_norm(out, input_contiguous, weight, epsilon)
 
 
 def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,

vllm/model_executor/models/intern_vit.py

@@ -190,8 +190,8 @@ class InternParallelAttention(nn.Module):
         if self.tp_size > 1:
             q = tensor_model_parallel_all_gather(q.contiguous())
             k = tensor_model_parallel_all_gather(k.contiguous())
-        q = self.q_norm.forward_native(q)
-        k = self.k_norm.forward_native(k)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
         if self.tp_size > 1:
             splitter = partial(split_tensor_along_last_dim,
                                num_partitions=self.tp_size)
@@ -264,10 +264,8 @@ class InternSdpaAttention(nn.Module):
 
         if self.qk_normalization:
             B_, N_, H_, D_ = q.shape
-            q = self.q_norm.forward_native(q.flatten(-2,
-                                                     -1)).view(B_, N_, H_, D_)
-            k = self.k_norm.forward_native(k.flatten(-2,
-                                                     -1)).view(B_, N_, H_, D_)
+            q = self.q_norm(q.flatten(-2, -1)).view(B_, N_, H_, D_)
+            k = self.k_norm(k.flatten(-2, -1)).view(B_, N_, H_, D_)
         q = q.transpose(1, 2)
         k = k.transpose(1, 2)
         v = v.transpose(1, 2)

vllm/model_executor/models/molmo.py

@@ -438,8 +438,8 @@ class MolmoAttention(nn.Module):
         if self.tp_size > 1:
             q = tensor_model_parallel_all_gather(q.contiguous())
             k = tensor_model_parallel_all_gather(k.contiguous())
-        q = self.q_norm.forward_native(q)
-        k = self.k_norm.forward_native(k)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
         if self.tp_size > 1:
             splitter = partial(split_tensor_along_last_dim,
                                num_partitions=self.tp_size)

vllm/model_executor/models/olmo2.py

@@ -139,8 +139,8 @@ class Olmo2Attention(nn.Module):
         if self.tp_size > 1:
             q = tensor_model_parallel_all_gather(q.contiguous())
             k = tensor_model_parallel_all_gather(k.contiguous())
-        q = self.q_norm.forward_native(q)
-        k = self.k_norm.forward_native(k)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
         if self.tp_size > 1:
             splitter = partial(split_tensor_along_last_dim,
                                num_partitions=self.tp_size)

vllm/model_executor/models/qwen3.py

@@ -133,11 +133,11 @@ class Qwen3Attention(nn.Module):
         # Add qk-norm
         q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
                            self.head_dim)
-        q_by_head = self.q_norm.forward_native(q_by_head)
+        q_by_head = self.q_norm(q_by_head)
         q = q_by_head.view(q.shape)
         k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
                            self.head_dim)
-        k_by_head = self.k_norm.forward_native(k_by_head)
+        k_by_head = self.k_norm(k_by_head)
         k = k_by_head.view(k.shape)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v)

vllm/model_executor/models/qwen3_moe.py

@@ -225,12 +225,12 @@ class Qwen3MoeAttention(nn.Module):
         # Add qk-norm
         q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
                            self.head_dim)
-        q_by_head = self.q_norm.forward_native(q_by_head)
+        q_by_head = self.q_norm(q_by_head)
         q = q_by_head.view(q.shape)
 
         k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
                            self.head_dim)
-        k_by_head = self.k_norm.forward_native(k_by_head)
+        k_by_head = self.k_norm(k_by_head)
         k = k_by_head.view(k.shape)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v)