Fix: supporting gpt-oss HF eagle

examples/speculative_decoding/README.md

@@ -43,14 +43,16 @@ pip install -U nvidia-modelopt[hf]
 pip install -r requirements.txt
 ```
 
-We use [Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater) dataset in this example. Download by:
+### Data Preparation
+
+We use [Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater) dataset in this example. Prepare data by:
 
 ```bash
-apt-get update && apt-get install -y git-lfs
-git lfs install --system
-git clone https://huggingface.co/datasets/nvidia/Daring-Anteater
+python prepare_input_conversations/add_daring_anteater.py
 ```
 
+See [other-datasets](#other-datasets) section for other dataset options and instruction for user-provided data.
+
 ## Getting Started: Simplified Workflow
 
 ```bash
@@ -71,7 +73,7 @@ For small base models that fit in GPU memory, we can collocate them with draft m
 ```bash
 ./launch_train.sh --model $BASE_MODEL \
             --output_dir $OUTPUT_DIR \
-            --data Daring-Anteater/train.jsonl  \
+            --data input_conversations/daring-anteater.jsonl  \
             --num_gpu $NUM_GPU \
             --num_epochs $NUM_EPOCH \
             --eagle_config eagle_config.json
@@ -91,7 +93,7 @@ We support two backends for generating base model hidden states. For better effc
 ```bash
 python collect_hidden_states/compute_hidden_states_trtllm.py \
             --model $BASE_MODEL \ 
-            --input-file Daring-Anteater/train.jsonl \
+            --input-file input_conversations/daring-anteater.jsonl \
             --output-dir $HIDDEN_STATES_DIR
 ```
 
@@ -102,7 +104,7 @@ Alternatively, you can generate the same hidden states with HF:
 ```bash
 python collect_hidden_states/compute_hidden_states_hf.py \
             --model $BASE_MODEL \
-            --input-file Daring-Anteater/train.jsonl  \
+            --input-file input_conversations/daring-anteater.jsonl  \
             --output-dir $HIDDEN_STATES_DIR
 ```
 
@@ -130,7 +132,7 @@ For online training checkpoints, we can run in-framework evaluation on MT-bench:
 python ar_validate.py --model_path $ONLINE_CKPT
 ```
 
-Offline checkpoints does not support this evaluation due to missing of base model modules. To evaluate offline checkpoint, please export first and evaluate with serving frameworks.
+**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
 
@@ -183,6 +185,28 @@ See more details on deployment of quantized model to TRTLLM [here](../llm_ptq/RE
 
 ## Advanced Usage
 
+### Other Datasets
+
+In addition to `daring-anteater`, we provide scripts for adding several other commonly used datasets in `prepare_input_conversations`:
+
+```text
+prepare_input_conversations/
+    ├── add_daring_anteater.py
+    ├── add_mtbench.py
+    ├── add_sharegpt.py
+    ├── add_ultrachat.py
+    └── example_make_prompt_dataset.sh
+```
+
+To use your own datasets, please preprocess your data into a `.jsonl` file with each line in the format:
+
+```json
+{
+    "conversation_id": <unique id>, 
+    "conversations": [{"role":<user or assistant>, "content":<content>}]
+}
+```
+
 ### Data Synthesis
 
 To achieve higher acceptance rates during speculative decoding, it is beneficial to use conversations generated by the base model as training data. This ensures that the draft model's output distribution closely aligns with that of the base model.
@@ -199,7 +223,7 @@ Note: Add `--quantization=modelopt` flag for quantized models.
 Then, we generate conversations with the base model using prompts from Daring-Anteater:
 
 ```bash
-python server_generate.py --data_path Daring-Anteater/train.jsonl --output_path synthetic/train.jsonl
+python server_generate.py --data_path input_conversations/daring-anteater.jsonl --output_path synthetic/train.jsonl
 ```
 
 To add a system prompt, use the `--system_prompt <system_prompt_text>` argument.
@@ -211,7 +235,7 @@ For large scale data generation, please see [SLURM prepare data](SLURM_prepare_d
 We can optionally use smaller vocab size for the draft model for faster training and inference. E.g. Llama3.2-1B has a vocab size of 128256. In this example, we construct a draft vocab mapping of size 32k by finding the most commonly appeared vocabs in our training set:
 
 ```bash
-python calibrate_draft_vocab.py --model meta-llama/Llama-3.2-1B-Instruct --data Daring-Anteater/train.jsonl --draft_vocab_size 32000 --save_dir draft_vocab_cache
+python calibrate_draft_vocab.py --model meta-llama/Llama-3.2-1B-Instruct --data input_conversations/daring-anteater.jsonl --draft_vocab_size 32000 --save_dir draft_vocab_cache
 ```
 
 This will produce a `d2t.pt` file in `save_dir`, which is the mapping from draft token to target token. During inference, draft tokens can be mapped back to target tokens by `target_token = draft_token + d2t[draft_token]`.

examples/speculative_decoding/eagle_utils.py

@@ -29,8 +29,6 @@
 
 try:
     import wandb
-
-    wandb.init()
 except ImportError:
     wandb = None
 
@@ -397,6 +395,8 @@ def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
 class ARValidationCallback(TrainerCallback):
     def __init__(self, ar_validate_steps: int = 1000):
         self.ar_validate_steps = ar_validate_steps
+        if wandb:
+            wandb.init()
 
     def on_step_end(self, args, state, control, **kwargs):
         if self.ar_validate_steps <= 0:

examples/speculative_decoding/train_eagle3_and_export.sh

@@ -20,7 +20,7 @@ set -eo pipefail
 # Set default values for BASE_MODEL, NUM_GPU, and DATA
 BASE_MODEL=meta-llama/Llama-3.2-1B-Instruct
 NUM_GPU=1
-DATA=Daring-Anteater/train.jsonl
+DATA=input_conversations/daring-anteater.jsonl
 
 # Parse input arguments --base_model, --num_gpu, and --data
 while [[ $# -gt 0 ]]; do

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,
@@ -511,18 +512,24 @@ def export_hf_checkpoint(
     """
     export_dir = Path(export_dir)
     export_dir.mkdir(parents=True, exist_ok=True)
+
+    # NOTE: (hg) Early exit for speculative decoding models
+    # This is a temp workaround to avoid error with offline spec ckpt during _export_hf_checkpoint
+    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
@@ -536,8 +543,6 @@ 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,
 }

tests/examples/speculative_decoding/test_eagle.py

@@ -27,6 +27,7 @@ def test_llama_eagle3(tiny_llama_path, num_gpus, tiny_daring_anteater_path, tmp_
         "intermediate_size": 64,
         "num_attention_heads": 2,
         "num_key_value_heads": 2,
+        "head_dim": 64,
     }
 
     # Write the tiny config to a temporary file