Fix: supporting gpt-oss HF eagle

examples/speculative_decoding/README.md

@@ -115,7 +115,7 @@ After training draft model, we can evaluate the saved modelopt checkpoint on MT-
 python ar_validate.py --model_path $OUTPUT_DIR
 ```
 
-Alternatively, we can export the checkpoint and run evaluation on serving frameworks. See sections below.
+**Note**: In-framework evaluation is supported only for online training. For offline training checkpoints, please export the model and evaluate it using serving frameworks.
 
 ## Export
 

modelopt/torch/export/plugins/hf_spec_export.py

@@ -48,17 +48,18 @@ def _check_state_dict_keys_match(draft_model: nn.Module, required_items: dict):
             raise ValueError(f"State dict keys mismatch!\nMissing in draft model: {required_key}")
 
 
-def rename_and_prune_if_spec_decoding(model: nn.Module, post_state_dict: dict):
+def spec_opt_only(model: nn.Module):
+    """Check if the model have only speculative decoding optimization."""
+    opt_modes = getattr(model, "_modelopt_state", None)
+    return (
+        isinstance(opt_modes, (list, tuple)) and len(opt_modes) == 1 and opt_modes[0][0] == "eagle"
+    )
+
+
+def export_spec_ckpt_state_dict(model: nn.Module):
     """Only return the state dict of the draft model in official format and ignore the base model."""
     # check the model has only speculative decoding
-    opt_modes = getattr(model, "_modelopt_state", None)
-    if (
-        not isinstance(opt_modes, (list, tuple))
-        or len(opt_modes) != 1
-        or opt_modes[0][0] != "eagle"
-    ):
-        # if there's other opts, return as is
-        return post_state_dict
+    assert spec_opt_only(model), "Not purely eagle model."
 
     # Check if the state dict keys match
     _check_state_dict_keys_match(model.eagle_module, EAGLE_MODELOPT_TO_OFFICIAL["required"])
@@ -80,16 +81,9 @@ def rename_and_prune_if_spec_decoding(model: nn.Module, post_state_dict: dict):
     return export_state_dict
 
 
-def set_config_if_spec_decoding(model: nn.Module, config_data: dict):
+def export_spec_ckpt_config(model: nn.Module):
     """Return the config of draft model in official format."""
-    opt_modes = getattr(model, "_modelopt_state", None)
-    if (
-        not isinstance(opt_modes, (list, tuple))
-        or len(opt_modes) != 1
-        or opt_modes[0][0] != "eagle"
-    ):
-        # return as is
-        return config_data
+    assert spec_opt_only(model), "Not purely eagle model."
 
     # This is the config keys in official checkpoint.
     template_config = {

modelopt/torch/export/unified_export_hf.py

@@ -26,6 +26,7 @@
 
 import torch
 import torch.nn as nn
+from safetensors.torch import save_file
 
 from modelopt.torch.quantization import set_quantizer_by_cfg_context
 from modelopt.torch.quantization.nn import SequentialQuantizer, TensorQuantizer
@@ -53,7 +54,7 @@
     QUANTIZATION_W4A8_AWQ,
     QUANTIZATION_W4A8_NVFP4_FP8,
 )
-from .plugins import rename_and_prune_if_spec_decoding, set_config_if_spec_decoding
+from .plugins import export_spec_ckpt_config, export_spec_ckpt_state_dict, spec_opt_only
 from .quant_utils import (
     fuse_prequant_layernorm,
     get_activation_scaling_factor,
@@ -507,18 +508,24 @@ def export_hf_checkpoint(
     """
     export_dir = Path(export_dir)
     export_dir.mkdir(parents=True, exist_ok=True)
+
+    # Early exit for speculative decoding models
+    # We do this since some spec models get error in convert_hf_quant_config_format
+    if spec_opt_only(model):
+        save_file(export_spec_ckpt_state_dict(model), f"{export_dir}/model.safetensors")
+        with open(f"{export_dir}/config.json", "w") as file:
+            json.dump(export_spec_ckpt_config(model), file, indent=4)
+        return
+
     try:
         post_state_dict, hf_quant_config = _export_hf_checkpoint(model, dtype)
 
-        # NOTE: (hg) Should we save hf_quant_config when there's no quantization applied?
         # Save hf_quant_config.json for backward compatibility
         with open(f"{export_dir}/hf_quant_config.json", "w") as file:
             json.dump(hf_quant_config, file, indent=4)
 
         hf_quant_config = convert_hf_quant_config_format(hf_quant_config)
 
-        post_state_dict = rename_and_prune_if_spec_decoding(model, post_state_dict)
-
         # Save model
         model.save_pretrained(
             export_dir, state_dict=post_state_dict, save_modelopt_state=save_modelopt_state
@@ -532,8 +539,6 @@ def export_hf_checkpoint(
 
         config_data["quantization_config"] = hf_quant_config
 
-        config_data = set_config_if_spec_decoding(model, config_data)
-
         with open(original_config, "w") as file:
             json.dump(config_data, file, indent=4)
 

modelopt/torch/speculative/eagle/default_config.py

@@ -47,4 +47,5 @@
     "use_mtp_layernorm": False,
     "parallel_draft_step": 1,
     "has_lm_head": False,
+    "head_dim": 128,
 }