fix: avoid non-parameter/buffer property scan

.github/dependabot.yml

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 version: 2
 updates:
   - package-ecosystem: github-actions
@@ -16,4 +20,4 @@ updates:
     groups:
       python-dependencies:
         patterns:
-          - "*"
+          - "*"

.github/scripts/deps.yaml

@@ -1,2 +1,6 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 common:
   - transformers

.github/scripts/install_deps.py

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 import os
 import subprocess
 import sys

.github/workflows/release.yml

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 name: Release
 
 concurrency:
@@ -118,4 +122,3 @@ jobs:
         with:
           name: ${{ env.WHL_NAME }}
           path: dist/${{ env.WHL_NAME }}
-

.github/workflows/unit_tests.yml

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 name: Unit Tests
 
 defaults:
@@ -155,4 +159,3 @@ jobs:
         run: |
           mkdir -p artifacts
           pytest --durations=0 tests/${{ matrix.test_script }}.py --junitxml=artifacts/${{ runner.os }}-${{ matrix.test_script }}.xml
-

.gitignore

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 ### Python template
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -156,4 +160,3 @@ dmypy.json
 cython_debug/
 
 .idea/
-

README.md

@@ -23,7 +23,7 @@ Depending on the model family, Defuser can:
 
 - patch a supported model class before load so HF instantiates a defused block directly
 - split fused tensors such as `gate_up_proj` into `gate_proj` + `up_proj`
-- convert 3D expert tensors into numbered expert `nn.Linear` modules
+- convert 3D expert tensors, including registered expert buffers, into numbered expert `nn.Linear` modules
 - preserve the original fused math while presenting a naive module structure again
 
 Public API:
@@ -33,8 +33,9 @@ from defuser import convert_model, replace_fused_blocks
 ```
 
 - `replace_fused_blocks(model_type)` patches supported HF model classes before `from_pretrained()` or direct model construction.
-- `convert_model(model, cleanup_original=True, max_layers=None, filter=None)` converts an already loaded model in place. This is the runtime defusion path for supported post-load expert and MLP conversions, including `qwen3_5_moe` style checkpoints.
+- `convert_model(model, cleanup_original=False, max_layers=None, filter=None)` converts an already loaded model in place. This is the runtime defusion path for supported post-load expert and MLP conversions, including `qwen3_5_moe` style checkpoints.
 - Defuser is designed and CI-tested for `transformers>=5.3.0`, and support is only offered for that version range. Older versions log a warning on these public APIs and are skipped as unsupported.
+- Some model families appear in both support tables. Full models can be prepatched with `replace_fused_blocks(...)`, while standalone fused expert modules from those same families can still be runtime-defused with `convert_model(...)`.
 
 `filter` is an optional list of PCRE regex rules evaluated against full module paths such as `model.layers.0.mlp.experts`:
 
@@ -46,7 +47,7 @@ from defuser import convert_model, replace_fused_blocks
 
 ## Supported Models
 
-Defuser currently supports the following `transformers==5.3.0` `model_type` values.
+Defuser currently supports the following `transformers>=5.3.0` `model_type` values.
 
 ### `replace_fused_blocks(model_type)` before load
 
@@ -65,7 +66,7 @@ Defuser currently supports the following `transformers==5.3.0` `model_type` valu
 
 | Pattern | Supported model types | Defused op performed |
 | --- | --- | --- |
-| Standard routed expert tensors | `deepseek_v2`, `dots1`, `ernie4_5_moe`, `ernie4_5_vl_moe`, `exaone_moe`, `flex_olmo`, `glm4_moe_lite`, `glm4v_moe`, `hunyuan_v1_moe`, `jamba`, `lfm2_moe`, `minimax`, `minimax_m2`, `olmoe`, `qwen3_vl_moe`, `solar_open` | Splits fused expert tensors into numbered expert `nn.Linear` modules with per-expert `gate_proj`, `up_proj`, and `down_proj`. |
+| Standard routed expert tensors | `deepseek_v2`, `dots1`, `ernie4_5_moe`, `ernie4_5_vl_moe`, `exaone_moe`, `flex_olmo`, `glm4_moe_lite`, `glm4v_moe`, `hunyuan_v1_moe`, `jamba`, `lfm2_moe`, `minimax`, `minimax_m2`, `olmoe`, `qwen3_vl_moe`, `solar_open` | Splits fused expert tensors or registered expert buffers into numbered expert `nn.Linear` modules with per-expert `gate_proj`, `up_proj`, and `down_proj`. |
 | Mixed sparse and shared experts | `deepseek_v3`, `glm_moe_dsa`, `qwen3_5_moe`, `qwen3_5_moe_text` | Runtime expert tensor defusion for routed experts while preserving the model's shared-expert path. |
 | Transposed or packed expert tensors | `gpt_oss`, `phimoe` | Splits transposed fused expert `gate_up_proj` tensors into per-expert `gate_proj` + `up_proj`, preserves expert bias when present, and converts expert tensors into numbered expert `nn.Linear` modules. |
 | Flattened expert layout | `dbrx` | Rebuilds the flattened DBRX expert FFN weights into numbered expert `gate_proj`, `up_proj`, and `down_proj` `nn.Linear` modules. |
@@ -100,6 +101,8 @@ converted = convert_model(model)
 print(converted)  # True when runtime defusion happened
 ```
 
+`convert_model(model)` also preserves meta-device construction for supported meta-initialized models, so structural validation can run without materializing weights.
+
 Use `filter` when only specific blocks should be defused:
 
 ```python

defuser/checkpoint_ops.py

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 import torch
 from transformers.core_model_loading import Chunk, Concatenate, ConversionOps, MergeModulelist
 

defuser/defuser.py

@@ -36,9 +36,26 @@ def get_checkpoint_conversion_mapping(model_type):
 
 
 class PatchError(Exception):
+    """Raised when Defuser cannot patch a registered Transformers class."""
+
     pass
 
 
+def _has_prebuilt_replacements(model: nn.Module, model_type: str) -> bool:
+    """Detect models that were already instantiated with registry-backed replacements."""
+    replacement_paths = MODEL_CONFIG[model_type].get(PATCH.REPLACE_MODULE, [])
+    replacement_class_paths = {custom_path for _, custom_path in replacement_paths}
+    if not replacement_class_paths:
+        return False
+
+    for module in model.modules():
+        class_path = f"{module.__class__.__module__}.{module.__class__.__name__}"
+        if class_path in replacement_class_paths:
+            return True
+
+    return False
+
+
 def replace_fused_blocks(model_type: str) -> bool:
     """Patch supported HF model classes so future loads instantiate defused blocks."""
     if warn_if_public_api_transformers_unsupported("replace_fused_blocks()", logger):
@@ -202,9 +219,10 @@ def convert_model(
 
     apply_model_patches(model, max_layers=max_layers, filter_rules=filter)
 
-    # If fused blocks have already been structurally replaced at load model before,
-    # there is no need to perform runtime defusing again
-    if MODEL_CONFIG[model.config.model_type].get(PATCH.REPLACE_MODULE):
+    # Full models patched at construction time already contain the defused
+    # replacement modules, but standalone experts from those model types can
+    # still use runtime tensor defusion.
+    if _has_prebuilt_replacements(model, model.config.model_type):
         return False
 
     # Perform runtime defusing of fused projections

defuser/modeling/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium

defuser/modeling/glm4v.py

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
 from torch import nn
 from transformers.activations import ACT2FN
 

defuser/modeling/moe_experts_interface.py

@@ -292,35 +292,66 @@ def _install_instance_forward(module: nn.Module, implementation: str) -> None:
 def _detect_expert_projections(module: nn.Module) -> dict[str, dict]:
     """Detect which expert projections exist in the module.
 
-    This function scans the module for any 3D nn.Parameter attributes.
-    It first checks known projection names, then discovers any unknown 3D parameters.
+    This function scans the module for any registered 3D Parameter / Tensor
+    attributes. It first checks known projection names, then discovers any
+    unknown 3D registered tensors.
 
     Returns:
         Dict mapping projection names to their config, only for projections that exist
-        as 3D nn.Parameter in the module.
+        as 3D registered Parameter / Tensor in the module.
     """
     detected = {}
+    # Only inspect registered tensors here. Scanning arbitrary attributes can
+    # trigger unrelated properties such as Transformers' `loss_function`.
+    local_tensors = {
+        name: tensor
+        for registry in (module._parameters, module._buffers)
+        for name, tensor in registry.items()
+        if isinstance(tensor, torch.Tensor)
+    }
 
     # First, check known projection patterns
     for proj_name, config in KNOWN_PROJECTION_PATTERNS.items():
-        param = getattr(module, proj_name, None)
-        if param is not None and isinstance(param, nn.Parameter) and param.dim() == 3:
+        param = local_tensors.get(proj_name)
+        if param is not None and param.dim() == 3:
             detected[proj_name] = config
 
-    # If no known patterns found, scan for any 3D Parameter (future-proofing)
+    # If no known patterns found, scan for any 3D registered tensor (future-proofing)
     if not detected:
-        for attr_name in dir(module):
+        for attr_name, param in local_tensors.items():
             if attr_name.startswith("_"):
                 continue
-            param = getattr(module, attr_name, None)
-            if param is not None and isinstance(param, nn.Parameter) and param.dim() == 3:
+            if param is not None and param.dim() == 3:
                 # Use default config for unknown projections
                 if DEBUG_ON: logger.debug(f"Discovered unknown 3D projection: {attr_name}")
                 detected[attr_name] = {"is_input_proj": True, "output_multiplier": 1}
 
     return detected
 
 
+def _get_registered_tensor(module: nn.Module, name: str) -> torch.Tensor | None:
+    """Return a direct registered parameter or buffer without touching properties."""
+
+    tensor = module._parameters.get(name)
+    if isinstance(tensor, torch.Tensor):
+        return tensor
+
+    tensor = module._buffers.get(name)
+    if isinstance(tensor, torch.Tensor):
+        return tensor
+
+    return None
+
+
+def _set_registered_tensor_like(module: nn.Module, name: str, tensor: torch.Tensor, source: torch.Tensor) -> None:
+    """Register ``tensor`` using the same parameter-vs-buffer kind as ``source``."""
+
+    if isinstance(source, nn.Parameter):
+        module.register_parameter(name, nn.Parameter(tensor, requires_grad=source.requires_grad))
+    else:
+        module.register_buffer(name, tensor)
+
+
 def _experts_supports_decorator(module: nn.Module) -> bool:
     """Check if experts module supports @use_experts_implementation decorator.
 
@@ -334,11 +365,11 @@ def _experts_supports_decorator(module: nn.Module) -> bool:
     return hasattr(forward_method, "__wrapped__")
 
 
-def _infer_dimensions(param: nn.Parameter, config: dict, is_transposed: bool) -> tuple[int, int]:
+def _infer_dimensions(param: torch.Tensor, config: dict, is_transposed: bool) -> tuple[int, int]:
     """Infer input and output dimensions for a projection.
 
     Args:
-        param: The 3D parameter (num_experts, dim1, dim2)
+        param: The 3D projection tensor (num_experts, dim1, dim2)
         config: Projection config with is_input_proj and output_multiplier
         is_transposed: Whether weights are stored transposed
 
@@ -370,7 +401,7 @@ def _unfuse_single_projection(
         dtype: torch.dtype,
         target_device: torch.device,
 ) -> list | None:
-    """Unfuse a single projection from 3D Parameter to a list of Linear layers.
+    """Unfuse a single projection from a 3D registered tensor to Linear layers.
 
     Optimized to keep peak device memory low while preserving the module's
     original device placement:
@@ -390,8 +421,8 @@ def _unfuse_single_projection(
     Returns:
         List of Linear layers, or None if projection doesn't exist
     """
-    param = getattr(module, proj_name, None)
-    if param is None or not isinstance(param, nn.Parameter) or param.dim() != 3:
+    param = _get_registered_tensor(module, proj_name)
+    if param is None or param.dim() != 3:
         return None
 
     # Get projection config
@@ -402,17 +433,17 @@ def _unfuse_single_projection(
 
     # Check for bias
     bias_name = f"{proj_name}_bias"
-    bias_param = getattr(module, bias_name, None)
-    has_bias = bias_param is not None
+    bias_param = _get_registered_tensor(module, bias_name)
+    has_bias = isinstance(bias_param, torch.Tensor)
 
     source_device = param.device
     is_meta = source_device.type == "meta"
-    weight_requires_grad = param.requires_grad
+    weight_requires_grad = param.requires_grad if isinstance(param, nn.Parameter) else False
 
     # Prepare weight slices on CPU in batch (single D2H transfer + batch transpose)
     if not is_meta:
         # Single transfer: GPU -> CPU (or no-op if already on CPU)
-        weights_cpu = param.data.cpu()  # (num_experts, dim1, dim2)
+        weights_cpu = param.detach().cpu()  # (num_experts, dim1, dim2)
         if is_transposed:
             # Batch transpose: (num_experts, in, out) -> (num_experts, out, in)
             weights_cpu = weights_cpu.transpose(1, 2)
@@ -425,12 +456,12 @@ def _unfuse_single_projection(
         weight_slices = weights_cpu.unbind(0)
 
         if has_bias:
-            bias_cpu = bias_param.data.cpu()
+            bias_cpu = bias_param.detach().cpu()
             # Ensure contiguous — bias may come from a chunk split (Phase 1)
             if not bias_cpu.is_contiguous():
                 bias_cpu = bias_cpu.contiguous()
             bias_slices = bias_cpu.unbind(0)
-            bias_requires_grad = bias_param.requires_grad
+            bias_requires_grad = bias_param.requires_grad if isinstance(bias_param, nn.Parameter) else False
 
         # Drop the original fused parameter before allocating the defused
         # per-expert linears back on the original device.
@@ -588,7 +619,7 @@ def _unfuse_experts_weights_inplace(
 
     # Get first projection to determine num_experts and layout
     first_proj_name = next(iter(detected_projections))
-    first_param = getattr(module, first_proj_name)
+    first_param = _get_registered_tensor(module, first_proj_name)
     num_experts = first_param.shape[0]
 
     # Detect if transposed
@@ -608,33 +639,33 @@ def _unfuse_experts_weights_inplace(
     dtype = first_param.dtype
     target_device = first_param.device if first_param.device.type != "meta" else "cpu"
 
-    # Phase 1: Split fused projections (e.g., gate_up_proj -> gate_proj + up_proj) into separate 3D params
+    # Phase 1: Split fused projections (e.g., gate_up_proj -> gate_proj + up_proj) into separate 3D tensors
     extra_projections = {}
     fused_to_remove = []
     for proj_name, config in detected_projections.items():
         split_into = config.get("split_into")
         if not split_into:
             continue
-        param = getattr(module, proj_name, None)
-        if param is None or not isinstance(param, nn.Parameter) or param.dim() != 3:
+        param = _get_registered_tensor(module, proj_name)
+        if param is None or param.dim() != 3:
             continue
         # Split along output dimension
         split_dim = 2 if is_transposed else 1
         split_params = param.chunk(len(split_into), dim=split_dim)
 
         # Also split bias if present (e.g., gate_up_proj_bias -> gate_proj_bias + up_proj_bias)
         bias_name = f"{proj_name}_bias"
-        bias_param = getattr(module, bias_name, None)
+        bias_param = _get_registered_tensor(module, bias_name)
         bias_splits = None
-        if bias_param is not None and isinstance(bias_param, nn.Parameter) and bias_param.dim() == 2:
+        if isinstance(bias_param, torch.Tensor) and bias_param.dim() == 2:
             bias_splits = bias_param.chunk(len(split_into), dim=1)
 
         for i, (split_name, split_param) in enumerate(zip(split_into, split_params)):
             # Avoid .contiguous() here — _unfuse_single_projection will handle it
             # during batch transpose/unbind, saving a full-tensor copy
-            setattr(module, split_name, nn.Parameter(split_param))
+            _set_registered_tensor_like(module, split_name, split_param, param)
             if bias_splits is not None:
-                setattr(module, f"{split_name}_bias", nn.Parameter(bias_splits[i]))
+                _set_registered_tensor_like(module, f"{split_name}_bias", bias_splits[i], bias_param)
             extra_projections[split_name] = KNOWN_PROJECTION_PATTERNS.get(
                 split_name, {"is_input_proj": True, "output_multiplier": 1}
             )
@@ -649,7 +680,7 @@ def _unfuse_experts_weights_inplace(
         del detected_projections[name]
     detected_projections.update(extra_projections)
 
-    # Phase 2: Unfuse all 3D params into per-expert Linear layers
+    # Phase 2: Unfuse all 3D tensors into per-expert Linear layers
     proj_linears = {}  # proj_name -> [Linear_expert0, Linear_expert1, ...]
     for proj_name in detected_projections:
         linears = _unfuse_single_projection(module, proj_name, num_experts, is_transposed, dtype, target_device)

defuser/modeling/unfused_moe/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium

defuser/utils/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium