GLM-4.7 MTP support (#792)

## What does this PR do?

**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** Enable GLM-4.7 PTQ workflow, including loading the
standalone MTP modules and export as-is.

## Usage
<!-- You can potentially add a usage example below. -->

```python
python3 hf_ptq.py --pyt_ckpt_path /home/omniml_data_3/models/GLM-4.7 --qformat nvfp4_mlp_only --export_path /home/omniml_data_3/zhiyuc/checkpoints/GLM-4.7-NVFP4-0203 --trust_remote_code
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **New Features**
* Added quantization support for GLM-4.7 model with automatic handling
of specialized layer architecture.
* Added image-text data calibration capabilities for Nemotron VL model
quantization.

* **Documentation**
* Updated support matrix to reflect newly supported models and
quantization features.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>

Author: Edwardf0t1

CHANGELOG.rst

@@ -17,6 +17,8 @@ NVIDIA Model Optimizer Changelog (Linux)
 - Add support for calibration data with multiple samples in ``npz`` format in the ONNX Autocast workflow.
 - Add ``--opset`` option to ONNX quantization CLI to specify the target opset version for the quantized model.
 - Add support for context parallelism in Eagle speculative decoding for huggingface and megatron core models.
+- Add PTQ support for GLM-4.7, including loading MTP layer weights from a separate ``mtp.safetensors`` file and export as-is.
+- Add support for image-text data calibration in PTQ for Nemotron VL models.
 
 0.41 (2026-01-19)
 ^^^^^^^^^^^^^^^^^

examples/llm_ptq/README.md

@@ -109,6 +109,7 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 | QWen3 MOE, Next <sup>6</sup> | ✅ | - | - | - | ✅ |
 | QwQ | ✅ | - | - | - | ✅ |
 | DeepSeek V3, R1, V3.1, V3.2<sup>7</sup> | - | - | - | - | ✅ |
+| GLM-4.7<sup>8</sup> | ✅ | - | - | - | ✅ |
 | Kimi K2 | - | - | - | - | ✅ |
 | T5 | ✅ | ✅ | ✅ | ✅ | - |
 | Whisper | ✅ | ❌ | ❌ | ❌ | - |
@@ -121,7 +122,8 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 > *<sup>4.</sup>For some models, KV cache quantization may result in a higher accuracy penalty.* \
 > *<sup>5.</sup>A selective set of the popular models are internally tested. The actual model support list may be longer. NVFP4 inference requires Blackwell GPUs and TensorRT-LLM v0.17 or later* \
 > *<sup>6.</sup>Some models currently support export to HF format only.* \
-> *<sup>7.</sup>[PTQ for DeepSeek](../deepseek/README.md)*
+> *<sup>7.</sup>[PTQ for DeepSeek](../deepseek/README.md)* \
+> *<sup>8.</sup>GLM-4.7 has MTP (Multi-Token Prediction) layers that are automatically loaded and excluded from quantization.*
 
 > *The accuracy loss after PTQ may vary depending on the actual model and the quantization method. Different models may have different accuracy loss and usually the accuracy loss is more significant when the base model is small. If the accuracy after PTQ is not meeting the requirement, please try either modifying [hf_ptq.py](./hf_ptq.py) and disabling the KV cache quantization or using the [QAT](./../llm_qat/README.md) instead.*
 

examples/llm_ptq/example_utils.py

@@ -16,7 +16,9 @@
 import copy
 import glob
 import inspect
+import json
 import os
+import re
 import shutil
 import sys
 import warnings
@@ -27,6 +29,7 @@
 import transformers
 from accelerate import infer_auto_device_map, init_empty_weights
 from accelerate.utils import get_max_memory
+from safetensors.torch import load_file
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
@@ -314,6 +317,106 @@ def get_processor(
     return None
 
 
+def load_mtp_weights_if_needed(model: torch.nn.Module, model_path: str) -> list[str]:
+    """Load MTP weights from separate safetensors if needed (e.g., GLM-4.7).
+
+    Some models store additional layers in separate safetensors files with non-standard
+    names (e.g., mtp.safetensors). HuggingFace's from_pretrained() may not load these
+    files even though they're referenced in model.safetensors.index.json.
+
+    This function detects such cases and explicitly loads the missing weights.
+
+    Args:
+        model: The loaded model that may be missing weights
+        model_path: Path to the model directory
+
+    Returns:
+        List of layer prefixes that were loaded from non-standard safetensors files.
+        These layers should typically be excluded from quantization.
+        Empty list if no additional weights were loaded.
+    """
+    model_path = Path(model_path)
+    index_file = model_path / "model.safetensors.index.json"
+    mtp_layer_prefixes: list[str] = []
+
+    if not index_file.exists():
+        return mtp_layer_prefixes
+
+    # Load the index to find all referenced safetensors files
+    with open(index_file) as f:
+        index = json.load(f)
+
+    # Find all unique safetensors files referenced
+    all_files = set(index["weight_map"].values())
+
+    # Find non-standard shard files (not matching model-XXXXX-of-XXXXX.safetensors pattern)
+    standard_pattern = re.compile(r"model-\d{5}-of-\d{5}\.safetensors")
+    non_standard_files = [f for f in all_files if not standard_pattern.match(f)]
+
+    if not non_standard_files:
+        return mtp_layer_prefixes
+
+    # Check which non-standard files exist and have missing weights
+    model_state = model.state_dict()
+    total_loaded = 0
+
+    for filename in non_standard_files:
+        filepath = model_path / filename
+        if not filepath.exists():
+            continue
+
+        # Find keys that should be in this file
+        expected_keys = [k for k, v in index["weight_map"].items() if v == filename]
+
+        # Check which are missing from the model
+        missing_keys = [k for k in expected_keys if k not in model_state]
+
+        if not missing_keys:
+            # Even if weights are loaded, record the layer prefixes for exclusion
+            # Extract unique layer prefixes (e.g., "model.layers.92" from "model.layers.92.mlp.weight")
+            for key in expected_keys:
+                # Extract layer prefix like "model.layers.92" or "layers.92"
+                parts = key.split(".")
+                for i, part in enumerate(parts):
+                    if part == "layers" and i + 1 < len(parts) and parts[i + 1].isdigit():
+                        prefix = ".".join(parts[: i + 2])  # e.g., "model.layers.92"
+                        if prefix not in mtp_layer_prefixes:
+                            mtp_layer_prefixes.append(prefix)
+                        break
+            continue
+
+        print(f"Loading {len(missing_keys)} missing weights from {filename}...")
+
+        # Extract unique layer prefixes for exclusion from quantization
+        for key in missing_keys:
+            parts = key.split(".")
+            for i, part in enumerate(parts):
+                if part == "layers" and i + 1 < len(parts) and parts[i + 1].isdigit():
+                    prefix = ".".join(parts[: i + 2])  # e.g., "model.layers.92"
+                    if prefix not in mtp_layer_prefixes:
+                        mtp_layer_prefixes.append(prefix)
+                    break
+
+        # Load the weights to CPU first, load_state_dict will handle device placement
+        weights = load_file(str(filepath), device="cpu")
+        weights_to_load = {k: v for k, v in weights.items() if k in missing_keys}
+
+        # Load into model
+        missing, unexpected = model.load_state_dict(weights_to_load, strict=False)
+        total_loaded += len(weights_to_load)
+
+        if missing:
+            print(f"  Warning: {len(missing)} keys still missing after loading {filename}")
+
+    if total_loaded > 0:
+        print(f"✓ Successfully loaded {total_loaded} weights from non-standard safetensors files")
+
+    if mtp_layer_prefixes:
+        print(f"✓ Detected MTP layers to exclude from quantization: {mtp_layer_prefixes}")
+
+    return mtp_layer_prefixes
+
+
 def get_dtype(dtype):
     if dtype == "bf16":
         dtype = torch.bfloat16
@@ -473,6 +576,12 @@ def get_model(
     if device == "cuda" and not is_model_on_gpu(model):
         print("Warning: Some parameters are not on a GPU. Calibration can be slow or hit OOM")
 
+    # Load any missing weights from non-standard safetensors files (e.g., GLM-4.7's mtp.safetensors)
+    # Store the MTP layer prefixes on the model for later exclusion from quantization
+    mtp_layer_prefixes = load_mtp_weights_if_needed(model, ckpt_path)
+    if mtp_layer_prefixes:
+        model._mtp_layer_prefixes = mtp_layer_prefixes
+
     return model
 
 

examples/llm_ptq/hf_ptq.py

@@ -31,6 +31,7 @@
     get_tokenizer,
     is_enc_dec,
     is_nemotron_vl,
+    load_mtp_weights_if_needed,
     run_nemotron_vl_preview,
 )
 from torch.utils.data import DataLoader
@@ -348,6 +349,12 @@ def load_model(args: argparse.Namespace):
             )
         calibration_only = True
 
+        # Load any missing weights from non-standard safetensors (handled in get_model for non-low-memory mode)
+        # Store the MTP layer prefixes on the model for later exclusion from quantization
+        mtp_layer_prefixes = load_mtp_weights_if_needed(full_model, args.pyt_ckpt_path)
+        if mtp_layer_prefixes:
+            full_model._mtp_layer_prefixes = mtp_layer_prefixes
+
     model_type = get_model_type(full_model)
 
     device = full_model.device
@@ -878,6 +885,19 @@ def quantize_main(
             KV_QUANT_CFG_CHOICES,
         )
 
+        # Exclude MTP layers from quantization if detected (e.g., GLM-4.7's layer 92)
+        # These layers are typically speculative decoding layers that should be exported as-is
+        mtp_layer_prefixes = getattr(full_model, "_mtp_layer_prefixes", None)
+        if mtp_layer_prefixes:
+            import copy
+
+            quant_cfg = copy.deepcopy(quant_cfg)
+            for prefix in mtp_layer_prefixes:
+                # Add exclusion pattern for this MTP layer (e.g., "*layers.92*")
+                pattern = f"*{prefix.split('.')[-2]}.{prefix.split('.')[-1]}*"
+                quant_cfg["quant_cfg"][pattern] = {"enable": False}
+                print(f"Excluding MTP layer from quantization: {pattern}")
+
         if args.qformat in QUANT_CFG_CHOICES:
             mono_quantize(
                 args,

modelopt/torch/export/unified_export_hf.py

@@ -710,6 +710,18 @@ def _export_transformers_checkpoint(
 
     quant_config = get_quant_config(model, is_modelopt_qlora=is_modelopt_qlora)
 
+    # Add MTP layer prefixes to exclude_modules if they were excluded from quantization
+    # This ensures they appear in quantization_config["ignore"] in config.json
+    mtp_layer_prefixes = getattr(model, "_mtp_layer_prefixes", None)
+    if mtp_layer_prefixes:
+        exclude_modules = quant_config["quantization"].setdefault("exclude_modules", [])
+        for prefix in mtp_layer_prefixes:
+            # Add wildcard pattern to exclude all submodules under this MTP layer
+            pattern = f"{prefix}*"
+            if pattern not in exclude_modules:
+                exclude_modules.append(pattern)
+                print(f"Adding MTP layer to quantization_config ignore: {pattern}")
+
     # Process all quantized modules and export weights
     _process_quantized_modules(model, dtype, is_modelopt_qlora)