fix: prepack cache key includes data_ptr, shape, dtype, device, N, K

Old cache used only tag ('l1'/'l2'), so layer 1 would reuse layer 0's
packed scales if the function object persisted. Now keyed by
(tag, data_ptr, shape, dtype, device, N, K) — safe across layers.

src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py

@@ -95,10 +95,19 @@ def _prepack_weight_sf(weight_sf, N, K, tag):
     Returns a tensor of shape (E, sfb_size) with SFB already in CUTLASS
     interleaved layout, skipping the per-call remap+memset+alloc.
     """
-    cache_attr = f"_prepacked_{tag}"
-    cached = getattr(_prepack_weight_sf, cache_attr, None)
-    if cached is not None:
-        return cached
+    cache_key = (
+        tag,
+        weight_sf.data_ptr(),
+        tuple(weight_sf.shape),
+        str(weight_sf.dtype),
+        weight_sf.device.index,
+        N,
+        K,
+    )
+    if not hasattr(_prepack_weight_sf, '_cache'):
+        _prepack_weight_sf._cache = {}
+    if cache_key in _prepack_weight_sf._cache:
+        return _prepack_weight_sf._cache[cache_key]
 
     from nvfp4_megamoe_kernel.cutlass_nvfp4_gemm.kernel import prepack_sfb
 
@@ -112,7 +121,7 @@ def _prepack_weight_sf(weight_sf, N, K, tag):
         packed.append(prepack_sfb(weight_sf[e], M_for_layout, N, K))
 
     packed = torch.stack(packed, dim=0).contiguous()
-    setattr(_prepack_weight_sf, cache_attr, packed)
+    _prepack_weight_sf._cache[cache_key] = packed
 
     if MEGA_MOE_DEBUG:
         print(f"[PREPACK] {tag}: E={E} N={N} K={K} packed_shape={packed.shape} "