[NVBug 6108145] Fix PTQ calibration and export for fused-experts MoE (Qwen3.5-MoE VLM)

modelopt/torch/export/quant_utils.py

@@ -42,6 +42,7 @@
     QuantizerAttrNames,
     quantizer_attr_names,
     reduce_block_amax,
+    representative_weight_quantizer,
     weight_attr_names,
 )
 from modelopt.torch.utils import clear_cuda_cache
@@ -546,7 +547,7 @@ def _compute_kv_cache_dtype(
 
 def get_weight_block_size(module: nn.Module, weight_name: str = "weight") -> int:
     """Returns the weight block size."""
-    weight_quantizer = getattr(module, quantizer_attr_names(weight_name).weight_quantizer, None)
+    weight_quantizer = representative_weight_quantizer(module, weight_name)
 
     if weight_quantizer is None:
         return 0
@@ -572,7 +573,11 @@ def get_quantization_format(module) -> str | None:
     """
 
     def _get_quantization_from_layer(layer, quantizer_attr_names: QuantizerAttrNames):
-        weight_quantizer = getattr(layer, quantizer_attr_names.weight_quantizer, None)
+        # Singular form first, plural ModuleList fallback (fused-experts).
+        # Strip the "_weight_quantizer" suffix to recover the weight attr name.
+        weight_attr = quantizer_attr_names.weight_quantizer
+        weight_name = weight_attr[: -len("_weight_quantizer")].rstrip("_") or "weight"
+        weight_quantizer = representative_weight_quantizer(layer, weight_name)
         input_quantizer = getattr(layer, quantizer_attr_names.input_quantizer, None)
 
         if weight_quantizer is None or not weight_quantizer.is_enabled:

modelopt/torch/export/unified_export_hf.py

@@ -88,6 +88,7 @@
     QUANTIZATION_W4A8_NVFP4_FP8,
 )
 from .model_utils import get_language_model_from_vl, is_multimodal_model
+from .moe_utils import _export_fused_experts
 from .plugins import SpeculativeDecodingExporter, has_spec_opt
 from .quant_utils import (
     fuse_prequant_layernorm,
@@ -642,11 +643,20 @@ def _process_quantized_modules(
         if is_modelopt_qlora and (hasattr(sub_module, "base_layer")):
             continue
 
+        # Preprocessing: restore unpacked weight so the export path can read
+        # the live quantizer state. Falls through to the export branches below.
         if hasattr(sub_module, "weight_packed") or (
             "QuantFP8Linear" in type(sub_module).__name__ and sub_module.weight.element_size() <= 1
         ):
             sub_module.unpack_weight()
-        if get_quantization_format(sub_module) != QUANTIZATION_NONE:
+
+        if hasattr(sub_module, "gate_up_proj_weight_quantizers"):
+            # _QuantFusedExperts uses plural `gate_up_proj_weight_quantizers` (ModuleList),
+            # which get_quantization_format's singular-weight_quantizer check misses. Handle
+            # it explicitly before the format gate so fused-experts get split + quantized.
+            with fsdp2_aware_weight_update(model, sub_module, reshard=False):
+                _export_fused_experts(sub_module, dtype)
+        elif get_quantization_format(sub_module) != QUANTIZATION_NONE:
             # Skip QuantMoELinear - it's handled separately in _reconstruct_fused_moe_linear
             if type(sub_module).__name__ == "QuantMoELinear":
                 continue
@@ -677,13 +687,6 @@ def _process_quantized_modules(
                 with fsdp2_aware_weight_update(model, sub_module, reshard=False):
                     for weight_name in ["gate_up_proj", "down_proj"]:
                         _export_quantized_weight(sub_module, dtype, weight_name)
-            elif hasattr(sub_module, "gate_up_proj_weight_quantizers"):
-                # Generic fused MoE experts (_QuantFusedExperts) with per-expert
-                # quantizer ModuleLists. Split into per-expert modules and export.
-                from modelopt.torch.export.moe_utils import _export_fused_experts
-
-                with fsdp2_aware_weight_update(model, sub_module, reshard=False):
-                    _export_fused_experts(sub_module, dtype)
 
 
 def _export_transformers_checkpoint(

modelopt/torch/quantization/conversion.py

@@ -16,6 +16,7 @@
 """Quantization conversion/restore utilities."""
 
 import fnmatch
+import re
 import warnings
 from collections.abc import Callable
 from contextlib import contextmanager
@@ -286,6 +287,33 @@ def set_quantizer_by_cfg(quant_model: nn.Module, quant_cfg: QuantizeQuantCfgType
             set_quantizer_attributes_full(quant_model, quantizer_name, attributes, parent_class)
 
 
+_FUSED_EXPERTS_QUANTIZER_LIST_RE = re.compile(
+    r"(weight_quantizers?|input_quantizers?)\.\d+(?=$|\.)"
+)
+
+
+def _normalize_fused_experts_quantizer_name(name: str) -> str:
+    """Strip the per-expert index from per-expert quantizer ModuleList names.
+
+    Fused-experts modules register per-expert weight/input quantizers in a
+    ``nn.ModuleList``; its children surface as dotted names like
+    ``...gate_up_proj_weight_quantizers.0`` (plural) or — if a variant uses
+    singular naming — ``...gate_up_proj_weight_quantizer.0``. Neither matches
+    the singular-suffix wildcards (``*weight_quantizer``) used in the stock
+    configs, so the experts stay at their defaults.
+
+    Return a normalized name where either ``weight_quantizer[s]?.N`` or
+    ``input_quantizer[s]?.N`` collapses to the singular form without the index
+    so the standard wildcards match.
+    """
+
+    def _repl(m: re.Match) -> str:
+        base = m.group(1)
+        return base.removesuffix("s")
+
+    return _FUSED_EXPERTS_QUANTIZER_LIST_RE.sub(_repl, name)
+
+
 def _match_quantizer(
     wildcard_or_filter_func: str | Callable,
     name: str,
@@ -296,7 +324,11 @@ def _match_quantizer(
     if not isinstance(module, (TensorQuantizer, SequentialQuantizer)):
         return False
     if isinstance(wildcard_or_filter_func, str):
-        if not fnmatch.fnmatch(name, wildcard_or_filter_func):
+        normalized = _normalize_fused_experts_quantizer_name(name)
+        if not (
+            fnmatch.fnmatch(name, wildcard_or_filter_func)
+            or (normalized != name and fnmatch.fnmatch(normalized, wildcard_or_filter_func))
+        ):
             return False
     elif callable(wildcard_or_filter_func):
         if not wildcard_or_filter_func(name):

modelopt/torch/quantization/plugins/huggingface.py

@@ -1419,6 +1419,38 @@ def register_fused_experts_on_the_fly(model):
             QuantModuleRegistry.register({mod_type: f"hf.{mod_type.__name__}"})(_QuantFusedExperts)
 
 
+def force_eager_experts_impl_on_the_fly(model):
+    """Force HF fused-experts modules onto the eager ``F.linear``-based forward.
+
+    HF transformers 5.0+ decorates fused-experts forwards with
+    ``@use_experts_implementation``, which may dispatch to ``torch._grouped_mm``
+    or ``torch.bmm`` backends. Those backends bypass ``F.linear`` and so bypass
+    ``_QuantFusedExperts``'s input/weight quantizer hooks — calibration silently
+    does nothing, no ``input_scale`` / ``amax`` is collected, and the exported
+    checkpoint produces garbage at inference.
+
+    Sets ``config._experts_implementation = "eager"`` on the model config (and
+    recursively on ``text_config`` / ``vision_config`` / ``audio_config`` /
+    ``speech_config``) whenever a fused-experts module is present.
+    """
+    if not any(_is_fused_experts_module(m) for m in model.modules()):
+        return
+
+    nested_cfg_attrs = ("text_config", "vision_config", "audio_config", "speech_config")
+
+    def _force(cfg):
+        if cfg is None:
+            return
+        if hasattr(cfg, "_experts_implementation"):
+            cfg._experts_implementation = "eager"
+        for sub in nested_cfg_attrs:
+            if hasattr(cfg, sub):
+                _force(getattr(cfg, sub))
+
+    if hasattr(model, "config"):
+        _force(model.config)
+
+
 def _is_supported_hf_model(model):
     """Check if the model a valid model for transformers quantization specific support."""
     supported_models = [transformers.PreTrainedModel]
@@ -1646,6 +1678,7 @@ def _reconstruct_fused_moe_linear(model: nn.Module) -> None:
         register_dbrx_moe_on_the_fly,
         register_step3p5_moe_on_the_fly,
         register_fused_experts_on_the_fly,
+        force_eager_experts_impl_on_the_fly,
         register_sparse_moe_on_the_fly,
         register_hf_attentions_on_the_fly,
         convert_hf_parallel_linears_on_the_fly,

modelopt/torch/quantization/utils/__init__.py

@@ -30,6 +30,7 @@
     "reduce_amax",
     "reduce_sum",
     "replace_function",
+    "representative_weight_quantizer",
     "update_quant_cfg_with_kv_cache_quant",
     "weight_attr_names",
 ]

modelopt/torch/quantization/utils/core_utils.py

@@ -202,27 +202,55 @@ def reduce_sum(input, axis=None, keepdims=True):
     return output
 
 
-def weight_attr_names(module: nn.Module) -> "Generator[str, None, None]":
-    """Get the weight param attribute names in a converted module, non-recursive.
+def representative_weight_quantizer(module: nn.Module, weight_name: str = "weight"):
+    """Return the representative weight quantizer for ``weight_name`` on ``module``.
+
+    Handles two layouts:
+    - singular ``<name>_weight_quantizer`` — standard ``nn.Linear`` / ``_QuantLinear``.
+    - plural ``<name>_weight_quantizers`` (``nn.ModuleList``) — fused-experts modules
+      (``_QuantFusedExperts``) hold one ``TensorQuantizer`` per expert. Per-expert
+      formats are identical, so the first element is representative.
 
-    We consider the following two cases for each weight param attribute:
-    - The standard weight attribute (e.g. nn.Linear).
-    - The custom `weight_attr_name`. (e.g. Llama4TextExperts has weight attributes `gate_up_proj` and `down_proj`)
+    Returns ``None`` if no matching quantizer is found.
     """
     from ..nn import SequentialQuantizer, TensorQuantizer
 
-    # the standard weight and quantizer case
-    weight = getattr(module, "weight", None)
-    weight_quantizer = getattr(module, "weight_quantizer", None)
-    if weight is not None and isinstance(weight_quantizer, (TensorQuantizer, SequentialQuantizer)):
-        yield "weight"
+    singular = quantizer_attr_names(weight_name).weight_quantizer
+    q = getattr(module, singular, None)
+    if isinstance(q, (TensorQuantizer, SequentialQuantizer)):
+        return q
 
-    # other weight and quantizer case
+    plural = getattr(module, singular + "s", None)
+    if isinstance(plural, nn.ModuleList) and len(plural) > 0:
+        first = plural[0]
+        if isinstance(first, (TensorQuantizer, SequentialQuantizer)):
+            return first
+    return None
+
+
+def weight_attr_names(module: nn.Module) -> "Generator[str, None, None]":
+    """Get the weight param attribute names in a converted module, non-recursive.
+
+    Covers three layouts:
+    - standard ``nn.Linear``: ``weight`` + ``weight_quantizer``.
+    - custom per-weight quantizer (e.g. ``Llama4TextExperts`` with ``gate_up_proj`` +
+      ``gate_up_proj_weight_quantizer``).
+    - fused-experts ``nn.ModuleList`` quantizers (``_QuantFusedExperts`` with
+      ``gate_up_proj`` + ``gate_up_proj_weight_quantizers`` plural list).
+    """
+    # standard: "weight" + "weight_quantizer" (singular) or "weight_quantizers" (plural)
+    if getattr(module, "weight", None) is not None:
+        if representative_weight_quantizer(module, "weight") is not None:
+            yield "weight"
+
+    # per-parameter custom attr names
     for name, _ in module.named_parameters(recurse=False):
+        if name == "weight":
+            continue
         weight = getattr(module, name, None)
-        weight_quantizer = getattr(module, f"{name}_weight_quantizer", None)
-        if isinstance(weight, nn.Parameter) and isinstance(
-            weight_quantizer, (TensorQuantizer, SequentialQuantizer)
+        if (
+            isinstance(weight, nn.Parameter)
+            and representative_weight_quantizer(module, name) is not None
         ):
             yield name
 

modelopt_recipes/general/ptq/nvfp4_experts_only-fp8_kv.yaml

@@ -20,7 +20,9 @@ quantize:
   algorithm:
     method: max
     # Max calibration is fast and does not typically need checkpointing.
-    layerwise: true
+    # layerwise=false required for VLMs where the decoder layers are nested under
+    # `model.language_model.layers` (layerwise_calibrate can't find them otherwise).
+    layerwise: false
   quant_cfg:
     - quantizer_name: '*'
       enable: false