Fix passed metadata to mlflow logging

llmfoundry/callbacks/hf_checkpointer.py

@@ -74,12 +74,13 @@ def __init__(
         if self.mlflow_registered_model_name is not None:
             # Both the metadata and the task are needed in order for mlflow
             # and databricks optimized model serving to work
-            if 'metadata' not in mlflow_logging_config:
-                mlflow_logging_config['metadata'] = {
-                    'task': 'llm/v1/completions'
-                }
-            if 'task' not in mlflow_logging_config:
-                mlflow_logging_config['task'] = 'text-generation'
+            default_metadata = {'task': 'llm/v1/completions'}
+            passed_metadata = mlflow_logging_config.get('metadata', {})
+            mlflow_logging_config['metadata'] = {
+                **default_metadata,
+                **passed_metadata
+            }
+            mlflow_logging_config.setdefault('task', 'text-generation')
         self.mlflow_logging_config = mlflow_logging_config
 
         self.huggingface_folder_name_fstr = os.path.join(
@@ -93,7 +94,6 @@ def __init__(
         self.save_interval = save_interval
         self.check_interval = create_interval_scheduler(
             save_interval, include_end_of_training=True)
-
         self.remote_ud = maybe_create_remote_uploader_downloader_from_uri(
             save_folder, loggers=[])
         if self.remote_ud is not None:

llmfoundry/utils/builders.py

@@ -73,7 +73,8 @@ def build_icl_data_and_gauntlet(
     return icl_evaluators, logger_keys, eval_gauntlet_cb
 
 
-def build_callback(name: str, kwargs: Dict[str, Any]) -> Callback:
+def build_callback(name: str, kwargs: Union[DictConfig, Dict[str,
+                                                             Any]]) -> Callback:
     if name == 'lr_monitor':
         return LRMonitor()
     elif name == 'memory_monitor':
@@ -117,6 +118,8 @@ def build_callback(name: str, kwargs: Dict[str, Any]) -> Callback:
     elif name == 'early_stopper':
         return EarlyStopper(**kwargs)
     elif name == 'hf_checkpointer':
+        if isinstance(kwargs, DictConfig):
+            kwargs = om.to_object(kwargs)  # pyright: ignore
         return HuggingFaceCheckpointer(**kwargs)
     else:
         raise ValueError(f'Not sure how to build callback: {name}')

tests/test_builders.py

@@ -6,8 +6,10 @@
 
 import pytest
 from composer.callbacks import Generate
+from omegaconf import OmegaConf as om
 from transformers import PreTrainedTokenizerBase
 
+from llmfoundry.callbacks import HuggingFaceCheckpointer
 from llmfoundry.tokenizers.tiktoken import TiktokenTokenizerWrapper
 from llmfoundry.utils.builders import build_callback, build_tokenizer
 
@@ -78,3 +80,33 @@ def test_build_generate_callback_unspecified_interval():
                 'foo': 'bar',
                 'something': 'else',
             })
+
+
+def test_build_hf_checkpointer_callback():
+    with mock.patch.object(HuggingFaceCheckpointer,
+                           '__init__') as mock_hf_checkpointer:
+        mock_hf_checkpointer.return_value = None
+        save_folder = 'path_to_save_folder'
+        save_interval = 1
+        mlflow_logging_config_dict = {
+            'metadata': {
+                'databricks_model_family': 'MptForCausalLM',
+                'databricks_model_size_parameters': '7b',
+                'databricks_model_source': 'mosaic-fine-tuning',
+                'task': 'llm/v1/completions'
+            }
+        }
+        build_callback(name='hf_checkpointer',
+                       kwargs=om.create({
+                           'save_folder': save_folder,
+                           'save_interval': save_interval,
+                           'mlflow_logging_config': mlflow_logging_config_dict
+                       }))
+
+        assert mock_hf_checkpointer.call_count == 1
+        _, _, kwargs = mock_hf_checkpointer.mock_calls[0]
+        assert kwargs['save_folder'] == save_folder
+        assert kwargs['save_interval'] == save_interval
+        assert isinstance(kwargs['mlflow_logging_config'], dict)
+        assert isinstance(kwargs['mlflow_logging_config']['metadata'], dict)
+        assert kwargs['mlflow_logging_config'] == mlflow_logging_config_dict

tests/test_hf_conversion_script.py

@@ -5,7 +5,7 @@
 import os
 import pathlib
 import sys
-from unittest.mock import MagicMock, patch
+from unittest.mock import ANY, MagicMock, patch
 
 from composer import Trainer
 from composer.loggers import MLFlowLogger
@@ -242,9 +242,22 @@ def get_config(
     return cast(DictConfig, test_cfg)
 
 
-def test_callback_inits_with_defaults():
+def test_callback_inits():
+    # test with defaults
     _ = HuggingFaceCheckpointer(save_folder='test', save_interval='1ba')
 
+    # test default metatdata when mlflow registered name is given
+    hf_checkpointer = HuggingFaceCheckpointer(
+        save_folder='test',
+        save_interval='1ba',
+        mlflow_registered_model_name='test_model_name')
+    assert hf_checkpointer.mlflow_logging_config == {
+        'task': 'text-generation',
+        'metadata': {
+            'task': 'llm/v1/completions'
+        }
+    }
+
 
 @pytest.mark.world_size(2)
 @pytest.mark.gpu
@@ -425,10 +438,18 @@ def test_huggingface_conversion_callback(
     trainer.fit()
 
     if dist.get_global_rank() == 0:
-        assert mlflow_logger_mock.save_model.call_count == (1 if log_to_mlflow
-                                                            else 0)
-        assert mlflow_logger_mock.register_model.call_count == (
-            1 if log_to_mlflow else 0)
+        if log_to_mlflow:
+            assert mlflow_logger_mock.save_model.call_count == 1
+            mlflow_logger_mock.save_model.assert_called_with(
+                flavor='transformers',
+                transformers_model=ANY,
+                path=ANY,
+                task='text-generation',
+                metadata={'task': 'llm/v1/completions'})
+            assert mlflow_logger_mock.register_model.call_count == 1
+        else:
+            assert mlflow_logger_mock.save_model.call_count == 0
+            assert mlflow_logger_mock.register_model.call_count == 0
     else:
         assert mlflow_logger_mock.log_model.call_count == 0
         assert mlflow_logger_mock.register_model.call_count == 0