Leave MTP layers unquantized at any depth in the default PTQ exclusions

CHANGELOG.rst

@@ -37,6 +37,7 @@ Changelog
 
 **Bug Fixes**
 
+- The default PTQ recipes now leave multi-token-prediction (MTP) layers unquantized in Megatron vision-language models too (``language_model.mtp.*``), as they already did at the model root.
 - Fix ONNX quantization placing a per-tensor Q/DQ pair on a rank-0 (scalar) constant input, which TensorRT's ONNX parser rejects (``convertAxis`` asserts the quantization axis lies in ``[0, nbDims]`` but ``nbDims == 0``). Rank-0 constant operands are now kept unquantized in the int8, fp8, and autotune export paths; the existing single-element Mul/Div/Pad stripping stays gated under ``target_dla``.
 - Fix PTQ of native Transformers FP8 experts to apply source weight scales during calibration and export. Re-quantize affected checkpoints from the original FP8 source.
 - Fix ONNX preprocessing with custom TensorRT plugin libraries on TensorRT 11.

modelopt_recipes/configs/ptq/units/default_disabled_quantizers.yaml

@@ -46,7 +46,7 @@
     enable: false
   - quantizer_name: '*router*'
     enable: false
-  - quantizer_name: 'mtp.*'
+  - quantizer_name: '*mtp.*'
     enable: false
   - quantizer_name: 'output.*'
     enable: false

tests/unit/recipe/test_presets.py

@@ -24,6 +24,7 @@
 import argparse
 
 import pytest
+import torch
 
 import modelopt.torch.quantization as mtq
 from modelopt.recipe import load_recipe, presets
@@ -96,6 +97,25 @@ def test_w4a16_nvfp4_preset_disables_vllm_marlin_incompatible_projections():
     } <= disabled_quantizers
 
 
+def test_default_exclusions_disable_mtp_at_any_depth():
+    # Megatron VLMs nest the MTP block under ``language_model.``; HF and Megatron LLMs keep it at the root.
+    model = torch.nn.Module()
+    for path in ("mtp", "language_model.mtp", "language_model.decoder"):
+        parent = model
+        for part in path.split("."):
+            if not hasattr(parent, part):
+                parent.add_module(part, torch.nn.Module())
+            parent = getattr(parent, part)
+        parent.add_module("proj", torch.nn.Linear(16, 16))
+
+    quant_cfg = load_recipe("general/ptq/fp8_default-kv_fp8").quantize.model_dump()
+    mtq.quantize(model, {**quant_cfg, "algorithm": None})
+
+    assert model.language_model.decoder.proj.input_quantizer.is_enabled
+    assert not model.mtp.proj.input_quantizer.is_enabled
+    assert not model.language_model.mtp.proj.input_quantizer.is_enabled
+
+
 def test_local_hessian_layerwise_is_scoped_to_the_model_recipe():
     # Layerwise calibration needs identifiable decoder layers, so it belongs to the model
     # recipe the published numbers come from -- not to the shared preset, which also backs