Remove NaN check — incompatible with Dynamo fullgraph compilation

Dynamo fullgraph mode rejects BOTH data-dependent branching AND
torch.compiler.disable as graph breaks. The NaN check cannot coexist
with vLLM's AOT compilation. Use layertest/cudagraph_test for debugging.

vllm/patches/deepseek_v4.py

@@ -1210,24 +1210,6 @@ class DeepseekV4DecoderLayer(nn.Module):
         return x
 
 
-@torch.compiler.disable
-def _clawmine_nan_check(hidden_states: torch.Tensor, layer_idx: int):
-    """NaN/Inf detection — only active when CLAWMINE_NAN_CHECK=1.
-    Decorated with @torch.compiler.disable so Dynamo never traces
-    the data-dependent branching (if tensor.any())."""
-    if os.environ.get('CLAWMINE_NAN_CHECK', '0') != '1':
-        return
-    with torch.no_grad():
-        if torch.isnan(hidden_states).any():
-            nan_pct = torch.isnan(hidden_states).float().mean().item() * 100
-            print(f"[CLAWMINE] NaN after layer {layer_idx}! {nan_pct:.2f}% NaN, amax={hidden_states.amax().item():.4f}")
-        elif torch.isinf(hidden_states).any():
-            inf_pct = torch.isinf(hidden_states).float().mean().item() * 100
-            print(f"[CLAWMINE] Inf after layer {layer_idx}! {inf_pct:.2f}% Inf, amax={hidden_states.amax().item():.4f}")
-        elif layer_idx % 10 == 0:
-            print(f"[CLAWMINE] Layer {layer_idx}: amax={hidden_states.amax().item():.4f} mean={hidden_states.mean().item():.6f}")
-
-
 @support_torch_compile
 class DeepseekV4Model(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -1334,9 +1316,7 @@ class DeepseekV4Model(nn.Module):
                 positions,
                 input_ids,
             )
-            # NaN detection — guarded by env var, wrapped in torch.compiler.disable
-            # so Dynamo never traces the data-dependent branching.
-            _clawmine_nan_check(hidden_states, layer_idx)
+
 
         # Stash pre-hc_head residual for the MTP draft (captured copy_).
         num_tokens = hidden_states.shape[0]