[TRTLLM-13246][feat] Wave 1: migrate aliases to setup_aliases and stage GMS RO load

tensorrt_llm/_torch/memory/gpu_memory_backend.py

@@ -36,8 +36,16 @@
   CUDA memory pool. After loading, weights are committed for read-only
   access by other workers and the client transitions to RO mode in place.
 - **RO (Read-Only)**: Subsequent workers zero-copy import already-committed
-  weights from the GMS pool. `post_load_weights()` must run BEFORE
-  materialization so that module aliases are set up correctly.
+  weights from the GMS pool. `setup_aliases()` must run BEFORE
+  materialization so that module aliases are set up correctly, while derived
+  state is refreshed after real tensors are bound. RO is validated for models
+  whose `post_load_weights()` is pure alias wiring; models that additionally
+  rely on plain Python attributes set inside `post_load_weights()` (rather
+  than registered `nn.Buffer` / `nn.Parameter` assignments) need to migrate
+  those side effects to `cache_derived_state()` or another path that runs on
+  RO readers. The GMS RO reader runs `setup_aliases()` before
+  `materialize_module()` and `cache_derived_state()` afterward; it does not
+  run `transform_weights()`.
 """
 
 from contextlib import contextmanager
@@ -477,7 +485,7 @@ def materialize_module(self, model: nn.Module) -> None:
         by GPU pointers from the shared memory region — no data copies,
         no disk I/O, just CUDA VMM remapping. The model's submodule
         layout must already match the writer's at commit time, including
-        any aliases / derived buffers introduced by `post_load_weights`.
+        any aliases introduced by `setup_aliases`.
 
         Args:
             model: The `nn.Module` to materialize. Walks the full
@@ -489,11 +497,10 @@ def materialize_module(self, model: nn.Module) -> None:
             RuntimeError: If `connect()` has not been called yet.
 
         Note:
-            `post_load_weights()` must be called on the model BEFORE
-            this method. The order ensures that any aliases / derived
-            parameters created by post-load hooks are present on the
-            module tree at materialization time, so they are bound to
-            the same GMS storage as their primary tensor.
+            `setup_aliases()` must be called on the model BEFORE this method.
+            The order ensures that any structural aliases created by post-load
+            hooks are present on the module tree at materialization time, so
+            they are bound to the same GMS storage as their primary tensor.
         """
         if self._client is None:
             raise RuntimeError("GMS client not connected. Call connect() first.")

tensorrt_llm/_torch/models/modeling_deepseekv3.py

@@ -1921,7 +1921,7 @@ def load_weights(self, weights: ConsumableWeightsDict):
         weight_loader = DeepseekV3WeightLoader(self)
         weight_loader.load_weights(weights)
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:

tensorrt_llm/_torch/models/modeling_exaone_moe.py

@@ -725,7 +725,7 @@ def load_weights(
             allow_partial_loading=allow_partial_loading,
         )
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         # For the cross-layer residual+LN fusion.
         for idx, layer in enumerate(self.model.layers[: self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:

tensorrt_llm/_torch/models/modeling_glm.py

@@ -1074,7 +1074,7 @@ def load_weights(self, weights: ConsumableWeightsDict, allow_partial_loading: bo
         weight_loader = Glm4WeightLoader(self)
         weight_loader.load_weights(weights, allow_partial_loading=allow_partial_loading)
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(self.model.layers[: self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:
                 layer.next_layer_layernorm = self.model.norm

tensorrt_llm/_torch/models/modeling_gpt_oss.py

@@ -631,7 +631,7 @@ def load_weights(self, weights: Dict):
         else:
             self.load_hf_weights(weights)
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.block[:self.config.num_hidden_layers]):
             if idx == 0:

tensorrt_llm/_torch/models/modeling_llama.py

@@ -484,7 +484,7 @@ def __init__(
         self.input_layernorm = RMSNorm(hidden_size=config.hidden_size,
                                        eps=config.rms_norm_eps,
                                        dtype=config.torch_dtype)
-        # When post_load_weights() chains layernorms across layers,
+        # When setup_aliases() chains layernorms across layers,
         # this flag is set to True to skip the input layernorm in
         # forward() since it is handled by the previous layer.
         self.skip_input_layernorm = False
@@ -709,7 +709,7 @@ def __init__(
             quantize_type="nvfp4"
             if not self.disable_nvfp4_layernorm_fusion and self.is_nvfp4
             and not (differ_pp_stage_with_previous_layer) else None)
-        # When post_load_weights() chains layernorms across layers,
+        # When setup_aliases() chains layernorms across layers,
         # this flag is set to True to skip the input layernorm in
         # forward() since it is handled by the previous layer.
         self.skip_input_layernorm = False
@@ -983,7 +983,7 @@ def __init__(self, model_config: ModelConfig[LlamaConfig]):
         self.norm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)
-        # When post_load_weights() chains the final norm into the
+        # When setup_aliases() chains the final norm into the
         # last decoder layer, this flag is set to True to skip
         # applying it again in forward().
         self.skip_norm = False
@@ -1088,7 +1088,7 @@ def __init__(self, model_config: ModelConfig[LlamaConfig]):
         self.norm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)
-        # When post_load_weights() chains the final norm into the
+        # When setup_aliases() chains the final norm into the
         # last decoder layer, this flag is set to True to skip
         # applying it again in forward().
         self.skip_norm = False
@@ -1140,7 +1140,7 @@ def __init__(
     ):
         super().__init__(LlamaModel(model_config), model_config)
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:
@@ -1564,7 +1564,7 @@ def load_weights(self, weights: Dict, weight_mapper: BaseWeightMapper):
             if had_mm_encoder:
                 self.mm_encoder = saved_mm_encoder
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:

tensorrt_llm/_torch/models/modeling_qwen3_moe.py

@@ -417,7 +417,7 @@ def __init__(
         )
         self.preload_weight_modules = self.model.preload_weight_modules
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:

tensorrt_llm/_torch/models/modeling_qwen3_next.py

@@ -980,7 +980,7 @@ def load_weights(self, weights: dict, weight_mapper: BaseWeightMapper):
         new_weights = weight_mapper.preprocess_weights(weights)
         super().load_weights(new_weights, weight_mapper)
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
         for idx, layer in enumerate(
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:

tensorrt_llm/_torch/pyexecutor/model_loader.py

@@ -485,8 +485,9 @@ def init_meta_tensor(t: torch.Tensor):
             # post_load_* hooks itself, so the shared post-load block below
             # must skip them. RW handles them inside `mem_pool_scope` so the
             # committed pool reflects the post-post_load layout; RO runs
-            # `module.post_load_weights()` before `materialize_module` to
-            # wire aliases prior to zero-copy mapping.
+            # `setup_aliases()` before `materialize_module` to wire aliases
+            # prior to zero-copy mapping, then refreshes derived state after
+            # real GMS tensors are bound.
             gms_post_load_handled = False
             if load_format == LoadFormat.AUTO:
                 # Pass model= so format-specific loaders (e.g. MX) can
@@ -717,29 +718,31 @@ def init_meta_tensor_in_pool(t: torch.Tensor):
                         # Hook order:
                         #   1. `post_load_apply`: format-specific apply
                         #      work (e.g., MX preshard markers).
-                        #   2. Per-module `post_load_weights`: creates
-                        #      aliases/derived parameter attributes BEFORE
-                        #      `materialize_module` walks the final module
-                        #      tree (including `draft_model` for spec dec).
-                        #   3. `materialize_module`: zero-copy bind GMS
+                        #   2. Per-module `setup_aliases`: creates structural
+                        #      aliases BEFORE `materialize_module` walks the
+                        #      final module tree (including `draft_model` for
+                        #      spec dec).
+                        #   3. SourceIdentity gate: STRICT pre-materialize
+                        #      compatibility check (GMS has no disk fallback).
+                        #   4. `materialize_module`: zero-copy bind GMS
                         #      pool storage onto the model parameters.
-                        #   4. `post_load_publish`: any receiver-side
+                        #   5. Per-module `cache_derived_state`: recompute
+                        #      Python-side state from real, materialized
+                        #      tensors without re-running one-shot transforms.
+                        #   6. `post_load_publish`: any receiver-side
                         #      publish (no-op via the receiver guard).
                         checkpoint_loader.post_load_apply(
                             model, weights_preloaded=True)
 
-                        for module in model.modules():
-                            if hasattr(module,
-                                       'post_load_weights') and not getattr(
-                                           module, '_weights_removed', False):
-                                module.post_load_weights()
+                        self._setup_aliases(model)
 
                         # Pre-materialize compatibility gate. GMS has no
                         # disk-fallback path, so a mismatch raises under STRICT
                         # rather than falling back.
                         self._check_gms_source_identity(gms_backend)
 
                         gms_backend.materialize_module(model)
+                        self._walk_cache_state(model)
 
                         checkpoint_loader.post_load_publish(
                             model,
@@ -829,22 +832,24 @@ def _check_gms_source_identity(self, gms_backend) -> None:
 
     @staticmethod
     def _setup_aliases(model: DecoderModelForCausalLM) -> None:
-        """Run top-level structural alias setup if the model defines it.
+        """Run structural alias setup on eligible modules.
 
-        Alias wiring is a model-level concern. It is intentionally not a
-        recursive module walk, because migrated aliases are expected to be set
-        by the root model that owns the layer graph.
+        The walk is duck-typed so modules can opt in without inheriting a
+        shared base class. Modules whose weights were removed are skipped,
+        matching the legacy full post-load walk.
 
         Args:
-            model: Root decoder model whose top-level alias hook should run.
+            model: Root decoder model whose module tree should be visited.
 
         Returns:
             None.
         """
-        setup_aliases: Optional[Callable[[], None]] = getattr(
-            model, 'setup_aliases', None)
-        if setup_aliases is not None:
-            setup_aliases()
+        for module in model.modules():
+            setup_aliases: Optional[Callable[[], None]] = getattr(
+                module, 'setup_aliases', None)
+            if setup_aliases is not None and not getattr(
+                    module, '_weights_removed', False):
+                setup_aliases()
 
     @staticmethod
     def _walk_transform(model: DecoderModelForCausalLM) -> None:
@@ -935,8 +940,11 @@ def reload(self,
         """Reload model weights without running post-load hooks.
 
         Reload is used by incremental update paths that may provide only a
-        partial set of replacement weights. The owner of the update lifecycle is
-        responsible for running post-load processing once all bytes are present.
+        partial set of replacement weights. Full reloads reset transform guards
+        before rebinding fresh weights. Partial reloads keep existing transform
+        guards intact because untouched modules may already contain transformed
+        live weights. The owner of the update lifecycle is responsible for
+        running post-load processing once all bytes are present.
 
         Args:
             model: Model instance receiving the replacement weights.
@@ -952,6 +960,8 @@ def reload(self,
                 "Cannot reload weights: weight_mapper was not initialized. "
                 "This can happen when the initial load used GMS, MX P2P, or "
                 "VISION_ONLY, which bypass the standard weight mapping path.")
+        if not allow_partial_loading:
+            self._reset_weights_transformed(model)
         self._call_load_weights(model.load_weights,
                                 weights,
                                 self.weight_mapper,

tests/unittest/_torch/pyexecutor/test_model_loader_gms.py

@@ -44,7 +44,13 @@ def to(self, *args, **kwargs):
     def load_weights(self, weights, mapper):
         self._events.append("load_weights")
 
-    def post_load_weights(self):
+    def setup_aliases(self) -> None:
+        self._events.append("setup_aliases")
+
+    def cache_derived_state(self) -> None:
+        self._events.append("cache_derived_state")
+
+    def post_load_weights(self) -> None:
         self._events.append("post_load_weights")
 
 
@@ -76,7 +82,9 @@ def _make_loader(monkeypatch, *, events, spec_config=None):
     loader._call_load_weights = MagicMock(
         side_effect=lambda fn, weights, mapper, **kwargs: fn(weights, mapper)
     )
-    loader._load_and_validate_config = MagicMock(return_value=SimpleNamespace(name="config"))
+    loader._load_and_validate_config = MagicMock(
+        return_value=SimpleNamespace(name="config", mapping=SimpleNamespace())
+    )
 
     monkeypatch.setattr(model_loader_mod, "timing", lambda *_args, **_kwargs: nullcontext())
     monkeypatch.setattr(model_loader_mod, "maybe_create_moe_load_balancer", _moe_context)
@@ -147,7 +155,7 @@ def _spec_config_needing_draft_weights():
         ),
         pytest.param(
             False,
-            ["post_load_weights", "materialize"],
+            ["setup_aliases", "materialize", "cache_derived_state"],
             id="ro",
         ),
     ],
@@ -161,8 +169,9 @@ def test_gms_load_branch(monkeypatch, is_rw, expected_events):
             (``_apply`` for meta materialization, ``to('cuda')``, weight
             load, ``post_load_weights``) inside the pool, then commits via
             ``finalize_write`` once the scope exits.
-        ro: the reader runs ``post_load_weights`` to wire module aliases
-            first, then GMS materializes weights via zero-copy mapping.
+        ro: the reader runs ``setup_aliases`` to wire module aliases, checks
+            identity compatibility, materializes weights via zero-copy mapping,
+            then refreshes derived state from real tensors.
     """
     events = []
     loader = _make_loader(monkeypatch, events=events)
@@ -196,13 +205,55 @@ def test_gms_load_branch(monkeypatch, is_rw, expected_events):
         backend.move_untracked_params.assert_called_once_with(model)
         backend.finalize_write.assert_called_once_with(model)
     else:
-        # RO: post_load_weights() must run before the GMS materialize
-        # step so module aliases are wired up before zero-copy mapping.
+        # RO: setup_aliases() must run before the GMS materialize step so
+        # module aliases are wired up before zero-copy mapping.
         checkpoint_loader.load_weights.assert_not_called()
         loader._call_load_weights.assert_not_called()
         backend.materialize_module.assert_called_once_with(model)
 
 
+def test_gms_ro_materializes_between_alias_setup_and_cache_state(monkeypatch):
+    events = []
+    loader = _make_loader(monkeypatch, events=events)
+    backend = _build_gms_backend(is_rw=False, events=events)
+    _install_gms_backend(monkeypatch, backend)
+
+    checkpoint_loader = MagicMock(name="checkpoint_loader")
+    checkpoint_loader.checkpoint_format = "HF"
+
+    def record(event):
+        def _append(*_args, **_kwargs):
+            events.append(event)
+
+        return _append
+
+    checkpoint_loader.post_load_apply.side_effect = record("post_load_apply")
+    checkpoint_loader.post_load_publish.side_effect = record("post_load_publish")
+
+    # The STRICT pre-materialize identity gate runs between alias setup and
+    # materialization; record it to pin the ordering without exercising the
+    # comparison logic, which is covered in test_source_identity.py.
+    monkeypatch.setattr(
+        model_loader_mod,
+        "check_weight_sharing_compatibility",
+        lambda *_args, **_kwargs: events.append("check_source_identity"),
+    )
+
+    loader.load("/ckpt", checkpoint_loader)
+
+    assert events == [
+        "post_load_apply",
+        "setup_aliases",
+        "check_source_identity",
+        "materialize",
+        "cache_derived_state",
+        "post_load_publish",
+    ]
+    assert "post_load_weights" not in events
+    checkpoint_loader.load_weights.assert_not_called()
+    backend.materialize_module.assert_called_once()
+
+
 def test_gms_rw_post_load_runs_inside_pool_before_finalize(monkeypatch):
     """Every step that may allocate or rebind tensors must run inside the GMS pool.