feat: warm up CuTeDSL kernel during model loading

JIT compiles the MLIR→PTX during finalize_weights instead of on the
first inference request. Prevents vLLM's 5-min RPC timeout from
killing the engine while workers are busy compiling.

Warmup runs a single-token, single-expert forward pass — just enough
to trigger compilation. Takes ~1-2 min, same as layertest.

vllm/patches/deepseek_v4.py

@@ -429,7 +429,7 @@ class DeepseekV4MegaMoEExperts(nn.Module):
         l2_fp4, l2_sf, l2_gs = [], [], []
 
         from tqdm import tqdm
-        for e in tqdm(range(self.num_local_experts), desc="  uint8→NVFP4 experts", unit="exp"):
+        for e in tqdm(range(self.num_local_experts), desc="  (byte view-cast)uint8→NVFP4 experts", unit="exp"):
             # ── L1: gate + up (fused) ──
             gate_w = self.w13_weight.data[e, :self.intermediate_size]  # (intermediate, hidden//2) uint8
             up_w = self.w13_weight.data[e, self.intermediate_size:]    # (intermediate, hidden//2) uint8
@@ -505,6 +505,19 @@ class DeepseekV4MegaMoEExperts(nn.Module):
         self.w2_weight_scale_2 = None
         self.w2_input_scale = None
 
+        # Warm up the CuTeDSL kernel (JIT compiles MLIR→PTX on first call).
+        # This takes ~1-2 min but prevents the vLLM RPC timeout (5 min) from
+        # killing the engine when the first inference request triggers compilation.
+        print("  Warming up CuTeDSL NVFP4 kernel (first call compiles)...", flush=True)
+        dummy_hidden = torch.randn(1, self.hidden_size, dtype=torch.bfloat16, device=self._cutedsl_runner.l1_fp4[0].device)
+        dummy_ids = torch.zeros(1, 1, dtype=torch.int32, device=dummy_hidden.device)
+        dummy_weights = torch.ones(1, 1, dtype=torch.float32, device=dummy_hidden.device)
+        try:
+            self._cutedsl_runner.run(dummy_hidden, dummy_weights, dummy_ids, expert_indices=[0])
+            print("  CuTeDSL warmup complete ✓", flush=True)
+        except Exception:
+            print("  CuTeDSL warmup failed (will compile on first inference)", flush=True)
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -1623,7 +1636,7 @@ class DeepseekV4Model(nn.Module):
         _shard_index = self._build_shard_index("/model") if os.path.isdir("/model") else None
 
         from tqdm import tqdm
-        for layer_idx, layer in tqdm(enumerate(self.layers), total=len(self.layers), desc="  NVFP4→FP8/BF16 convert", unit="layer"):
+        for layer_idx, layer in tqdm(enumerate(self.layers), total=len(self.layers), desc="  (upcast)NVFP4→FP8/BF16 convert", unit="layer"):
             attn = layer.attn
             
             # FP8 conversion: only wo_a