[Rebase] Rebase to vllm v0.21.0 (#3530)

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vllm_omni/model_executor/models/common/qwen3_code_predictor.py

@@ -351,8 +351,10 @@ class CodePredictorBaseModel(nn.Module):
         # native bf16 support (Turing, Volta).  The RMSNorm and RoPE
         # layers already upcast internally; this extends the same
         # treatment to attention and MLP.
+        # autocast to float32 is unsupported on CPU; skip fp32 upcast there
+        # (CPU uses full-precision intermediates internally).
         input_dtype = inputs_embeds.dtype
-        use_fp32 = input_dtype == torch.float16
+        use_fp32 = input_dtype == torch.float16 and inputs_embeds.device.type != "cpu"
         if use_fp32:
             inputs_embeds = inputs_embeds.float()
         hidden_states = inputs_embeds

vllm_omni/model_executor/models/common/snake_activation.py

@@ -61,8 +61,9 @@ class SnakeBeta(nn.Module):
             x = tl.load(x_ptr + bid * stride_b + cid * stride_c + t_off, mask=mask, other=0.0)
             ea = tl.load(exp_alpha_ptr + cid)
             ib = tl.load(inv_beta_ptr + cid)
-            sin_val = tl.sin(x * ea)
-            result = x + ib * sin_val * sin_val
+            x_float = x.to(tl.float32)
+            sin_val = tl.sin(x_float * ea)
+            result = x + (ib * sin_val * sin_val).to(x.dtype)
 
             tl.store(out_ptr + bid * stride_b + cid * stride_c + t_off, result, mask=mask)