Add the model license file for mlflow (#915)

llmfoundry/callbacks/hf_checkpointer.py

@@ -6,6 +6,7 @@
 import logging
 import math
 import os
+import re
 import tempfile
 from pathlib import Path
 from typing import Optional, Sequence, Union
@@ -27,6 +28,23 @@
 
 log = logging.getLogger(__name__)
 
+_LICENSE_FILE_PATTERN = re.compile(r'license(\.[a-z]+|$)', re.IGNORECASE)
+
+
+def _maybe_get_license_filename(local_dir: str) -> Optional[str]:
+    """Returns the name of the license file if it exists in the local_dir.
+
+    Note: This is intended to be consistent with the code in MLflow.
+    https://github.com/mlflow/mlflow/blob/5d13d6ec620a02de9a5e31201bf1becdb9722ea5/mlflow/transformers/__init__.py#L1152
+
+    If the license file does not exist, returns None.
+    """
+    try:
+        return next(file for file in os.listdir(local_dir)
+                    if _LICENSE_FILE_PATTERN.search(file))
+    except StopIteration:
+        return None
+
 
 class HuggingFaceCheckpointer(Callback):
     """Save a huggingface formatted checkpoint during training.
@@ -279,6 +297,15 @@ def _save_checkpoint(self, state: State, logger: Logger):
                             path=local_save_path,
                             **self.mlflow_logging_config,
                         )
+
+                        license_filename = _maybe_get_license_filename(
+                            local_save_path)
+                        if license_filename is not None:
+                            mlflow_logger._mlflow_client.log_artifact(
+                                mlflow_logger._run_id,
+                                os.path.join(local_save_path, license_filename),
+                            )
+
                         mlflow_logger.register_model(
                             model_uri=local_save_path,
                             name=self.mlflow_registered_model_name,

setup.py

@@ -50,7 +50,8 @@
 ]
 
 install_requires = [
-    'mosaicml[libcloud,wandb,mlflow,oci,gcs]>=0.17.2,<0.18',
+    'mosaicml[libcloud,wandb,oci,gcs]>=0.17.2,<0.18',
+    'mlflow>=2.10,<3',
     'accelerate>=0.25,<0.26',  # for HF inference `device_map`
     'transformers>=4.37,<4.38',
     'mosaicml-streaming>=0.7.2,<0.8',

tests/a_scripts/inference/test_convert_composer_to_hf.py

@@ -6,7 +6,7 @@
 import pathlib
 import shutil
 from argparse import Namespace
-from typing import Callable, Optional, cast
+from typing import Any, Callable, Optional, cast
 from unittest.mock import ANY, MagicMock, patch
 
 import pytest
@@ -22,13 +22,18 @@
 
 from llmfoundry import COMPOSER_MODEL_REGISTRY
 from llmfoundry.callbacks import HuggingFaceCheckpointer
+from llmfoundry.callbacks.hf_checkpointer import _maybe_get_license_filename
 from llmfoundry.data.finetuning import build_finetuning_dataloader
 from llmfoundry.models.mpt.modeling_mpt import ComposerMPTCausalLM
 from llmfoundry.utils.builders import build_optimizer, build_tokenizer
 from scripts.inference.convert_composer_to_hf import convert_composer_to_hf
 from tests.data_utils import make_tiny_ft_dataset
 
 
+def _save_model_mock(*args: Any, path: str, **kwargs: Any):
+    os.makedirs(path, exist_ok=True)
+
+
 def check_hf_tokenizer_equivalence(tokenizer1: PreTrainedTokenizerBase,
                                    tokenizer2: PreTrainedTokenizerBase):
     """WARNING: Parameters are updated within the check so don't call check_hf_tokenizer_equivalence on the same
@@ -297,7 +302,7 @@ def test_huggingface_conversion_callback_interval(
 
     mlflow_logger_mock = MagicMock(spec=MLFlowLogger)
     mlflow_logger_mock.state_dict = lambda *args, **kwargs: {}
-    mlflow_logger_mock.save_model = MagicMock()
+    mlflow_logger_mock.save_model = MagicMock(wraps=_save_model_mock)
     mlflow_logger_mock.register_model = MagicMock()
     mlflow_logger_mock.model_registry_prefix = ''
     mlflow_logger_mock._experiment_id = 'mlflow-experiment-id'
@@ -533,7 +538,7 @@ def test_huggingface_conversion_callback(
 
     mlflow_logger_mock = MagicMock(spec=MLFlowLogger)
     mlflow_logger_mock.state_dict = lambda *args, **kwargs: {}
-    mlflow_logger_mock.save_model = MagicMock()
+    mlflow_logger_mock.save_model = MagicMock(wraps=_save_model_mock)
     mlflow_logger_mock.register_model = MagicMock()
     mlflow_logger_mock.model_registry_prefix = ''
     mlflow_logger_mock._experiment_id = 'mlflow-experiment-id'
@@ -817,3 +822,17 @@ def test_convert_and_generate_meta(tie_word_embeddings: str,
         assert torch.allclose(p1, p2)
 
     delete_transformers_cache()
+
+
+@pytest.mark.parametrize(
+    'license_file_name',
+    ['LICENSE', 'LICENSE.txt', 'license', 'license.md', None])
+def test_license_file_finder(tmp_path: pathlib.Path,
+                             license_file_name: Optional[str]):
+    if license_file_name is not None:
+        with open(os.path.join(tmp_path, license_file_name), 'w') as f:
+            f.write('test')
+
+    found_path = _maybe_get_license_filename(str(tmp_path))
+    assert (found_path == license_file_name
+           ) if license_file_name is not None else (found_path is None)