Adding modelopt_run_config.yaml and a main function for megatron data preprocessing

modelopt/torch/opt/plugins/mcore_dist_checkpointing.py

@@ -22,6 +22,7 @@
 from typing import Any
 
 import torch
+import yaml
 from megatron.core import dist_checkpointing, mpu
 from megatron.core.dist_checkpointing.serialization import get_default_load_sharded_strategy
 from megatron.core.dist_checkpointing.strategies.common import COMMON_STATE_FNAME
@@ -35,6 +36,21 @@
 
 SUPPORTED_WRAPPERS[Float16Module] = "module"
 
+DROP_SUBSTRINGS = [
+    "fp4",
+    "fp8",
+    "tp_",
+    "parallel",
+    "cuda_graph",
+    "init_",
+    "cpu",
+    "recompute",
+    "inference",
+    "pipeline",
+    "comm",
+    "batch",
+]
+
 
 def remove_per_module_state(
     modelopt_state: dict[str, Any],
@@ -122,6 +138,27 @@ def save_sharded_modelopt_state(
         sharded_strategy: configures sharded tensors saving behavior and backend
         prefix: the prefix to add to the modelopt_state keys ("model." for NeMo)
     """
+
+    def _parse_transformer_config(transformer_config: dict) -> dict:
+        config = {}
+
+        for k, v in transformer_config.items():
+            if any(substring in k for substring in DROP_SUBSTRINGS):
+                continue
+            if isinstance(v, (bool, int, str)):
+                config[k] = v
+            else:
+                config[k] = str(v)
+
+        return config
+
+    if dist.is_master():
+        run_config_name = f"{checkpoint_name}/modelopt_run_config.yaml"
+        config_dict = _parse_transformer_config(copy.deepcopy(model[0].config.__dict__))
+        config_dict["nvidia_modelopt_version"] = modelopt.__version__
+        with open(run_config_name, "w") as f:
+            yaml.dump(config_dict, f, default_flow_style=False)
+
     if not mto.ModeloptStateManager.is_converted(model[0]):
         return
     if len(model) > 1:

modelopt/torch/utils/plugins/megatron_preprocess_data.py

@@ -31,11 +31,14 @@
 ```
 """
 
+import argparse
 import json
 import multiprocessing
 import sys
 from pathlib import Path
 
+import requests
+from datasets import load_dataset
 from megatron.core.datasets import indexed_dataset
 from transformers import AutoTokenizer
 
@@ -198,3 +201,87 @@ def megatron_preprocess_data(
         final_enc_len += num_tokens
 
     print(f">>> Total number of tokens: {final_enc_len}")
+
+
+def main():
+    """Sample main function to process large data for pretraining.
+
+    Example usage:
+
+    >>> python megatron_preprocess_data.py \
+            --dataset "nvidia/Nemotron-Pretraining-Dataset-sample" \
+            --tokenizer "meta-llama/Llama-3.2-1B-Instruct" \
+            --output_dir "./processed_data"
+    """
+    parser = argparse.ArgumentParser(prog="megatron_preprocess_data")
+    parser.add_argument("--input_path", type=str, default=None, help="Input path.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="nvidia/Nemotron-Pretraining-Dataset-sample",
+        help="Hugging Face Hub dataset name or path",
+    )
+    parser.add_argument("--subset", type=str, default=None, help="Hugging Face Hub dataset subset")
+    parser.add_argument("--split", type=str, default="train", help="Hugging Face Hub dataset split")
+    parser.add_argument(
+        "--output_dir", type=str, default="./processed_data", help="Output directory"
+    )
+    parser.add_argument("--tokenizer", type=str, required=True, help="Tokenizer name or path")
+    parser.add_argument("--json_keys", nargs="+", default=["text"], help="JSON keys to tokenize")
+    parser.add_argument("--append_eod", action="store_true", help="Append <eod> token")
+    parser.add_argument(
+        "--max_sequence_length", type=int, default=None, help="Maximum sequence length"
+    )
+    parser.add_argument("--workers", type=int, default=8, help="Number of worker processes")
+    parser.add_argument("--log_interval", type=int, default=1000, help="Log interval")
+    args = parser.parse_args()
+
+    if args.input_path is None:
+        args.input_path = []
+
+        response = requests.get(
+            "https://datasets-server.huggingface.co/splits?dataset={}".format(args.dataset),
+            timeout=10,
+        )
+
+        for entry in response.json()["splits"]:
+            skip_processing = False
+            name = entry["dataset"]
+            subset = entry.get("config", None)
+            split = entry["split"]
+
+            if args.subset is not None and args.subset != subset:
+                skip_processing = True
+            if args.split is not None and args.split != split:
+                skip_processing = True
+
+            print(f"Loading dataset {name} with subset {subset} and split {split}")
+            dataset = load_dataset(name, subset, split=split)
+
+            for key in args.json_keys:
+                if key not in dataset.features:
+                    print(f"Key {key} not found in dataset features. Skipping...")
+                    skip_processing = True
+                    break
+
+            if skip_processing:
+                continue
+
+            json_file_path = args.output_dir + "/" + name + "_" + subset + "_" + split + ".jsonl"
+            dataset.to_json(json_file_path)
+            args.input_path += [json_file_path]
+
+    megatron_preprocess_data(
+        input_path=args.input_path,
+        output_dir=args.output_dir,
+        tokenizer_name_or_path=args.tokenizer,
+        json_keys=args.json_keys,
+        append_eod=args.append_eod,
+        max_sequence_length=args.max_sequence_length,
+        workers=args.workers,
+        log_interval=args.log_interval,
+    )
+
+
+if __name__ == "__main__":
+    main()