Add MLFLOW_FLATTEN_PARAMS support in MLflowCallback

src/transformers/integrations.py

@@ -23,7 +23,7 @@
 import tempfile
 from pathlib import Path
 
-from .utils import is_datasets_available, logging
+from .utils import flatten_dict, is_datasets_available, logging
 
 
 logger = logging.get_logger(__name__)
@@ -802,10 +802,13 @@ def setup(self, args, state, model):
                 Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint.
                 When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified
                 run ID and other parameters are ignored.
+            MLFLOW_FLATTEN_PARAMS (`str`, *optional*):
+                Whether to flatten the parameters dictionary before logging. Default to `False`.
         """
         self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
         self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES
         self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
+        self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
         self._run_id = os.getenv("MLFLOW_RUN_ID", None)
         logger.debug(
             f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run}, tags={self._nested_run}"
@@ -822,15 +825,15 @@ def setup(self, args, state, model):
             if hasattr(model, "config") and model.config is not None:
                 model_config = model.config.to_dict()
                 combined_dict = {**model_config, **combined_dict}
+            combined_dict = flatten_dict(combined_dict) if self._flatten_params else combined_dict
             # remove params that are too long for MLflow
             for name, value in list(combined_dict.items()):
                 # internally, all values are converted to str in MLflow
                 if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:
                     logger.warning(
-                        f"Trainer is attempting to log a value of "
-                        f'"{value}" for key "{name}" as a parameter. '
-                        f"MLflow's log_param() only accepts values no longer than "
-                        f"250 characters so we dropped this attribute."
+                        f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. '
+                        f"MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute. "
+                        f"You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and avoid this message."
                     )
                     del combined_dict[name]
             # MLflow cannot log more than 100 values in one go, so we have to split it
@@ -857,10 +860,8 @@ def on_log(self, args, state, control, logs, model=None, **kwargs):
                     metrics[k] = v
                 else:
                     logger.warning(
-                        f"Trainer is attempting to log a value of "
-                        f'"{v}" of type {type(v)} for key "{k}" as a metric. '
-                        f"MLflow's log_metric() only accepts float and "
-                        f"int types so we dropped this attribute."
+                        f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. '
+                        f"MLflow's log_metric() only accepts float and int types so we dropped this attribute."
                     )
             self._ml_flow.log_metrics(metrics=metrics, step=state.global_step)
 
@@ -875,7 +876,7 @@ def on_train_end(self, args, state, control, **kwargs):
     def __del__(self):
         # if the previous run is not terminated correctly, the fluent API will
         # not let you start a new run before the previous one is killed
-        if self._auto_end_run and self._ml_flow.active_run() is not None:
+        if self._auto_end_run and self._ml_flow and self._ml_flow.active_run() is not None:
             self._ml_flow.end_run()
 
 

src/transformers/utils/__init__.py

@@ -38,6 +38,7 @@
     TensorType,
     cached_property,
     find_labels,
+    flatten_dict,
     is_tensor,
     to_numpy,
     to_py_obj,

src/transformers/utils/generic.py

@@ -17,6 +17,7 @@
 
 import inspect
 from collections import OrderedDict, UserDict
+from collections.abc import MutableMapping
 from contextlib import ExitStack
 from dataclasses import fields
 from enum import Enum
@@ -310,3 +311,17 @@ def find_labels(model_class):
         return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
     else:
         return [p for p in signature.parameters if "label" in p]
+
+
+def flatten_dict(d: MutableMapping, parent_key: str = "", delimiter: str = "."):
+    """Flatten a nested dict into a single level dict."""
+
+    def _flatten_dict(d, parent_key="", delimiter="."):
+        for k, v in d.items():
+            key = str(parent_key) + delimiter + str(k) if parent_key else k
+            if v and isinstance(v, MutableMapping):
+                yield from flatten_dict(v, key, delimiter=delimiter).items()
+            else:
+                yield key, v
+
+    return dict(_flatten_dict(d, parent_key, delimiter))

tests/utils/test_generic.py

@@ -0,0 +1,45 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers.utils import flatten_dict
+
+
+class GenericTester(unittest.TestCase):
+    def test_flatten_dict(self):
+        input_dict = {
+            "task_specific_params": {
+                "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
+                "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
+                "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
+            }
+        }
+        expected_dict = {
+            "task_specific_params.summarization.length_penalty": 1.0,
+            "task_specific_params.summarization.max_length": 128,
+            "task_specific_params.summarization.min_length": 12,
+            "task_specific_params.summarization.num_beams": 4,
+            "task_specific_params.summarization_cnn.length_penalty": 2.0,
+            "task_specific_params.summarization_cnn.max_length": 142,
+            "task_specific_params.summarization_cnn.min_length": 56,
+            "task_specific_params.summarization_cnn.num_beams": 4,
+            "task_specific_params.summarization_xsum.length_penalty": 1.0,
+            "task_specific_params.summarization_xsum.max_length": 62,
+            "task_specific_params.summarization_xsum.min_length": 11,
+            "task_specific_params.summarization_xsum.num_beams": 6,
+        }
+
+        self.assertEqual(flatten_dict(input_dict), expected_dict)

utils/check_inits.py

@@ -249,7 +249,7 @@ def get_transformers_submodules():
             if fname == "__init__.py":
                 continue
             short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS))
-            submodule = short_path.replace(os.path.sep, ".").replace(".py", "")
+            submodule = short_path.replace(".py", "").replace(os.path.sep, ".")
             if len(submodule.split(".")) == 1:
                 submodules.append(submodule)
     return submodules

utils/tests_fetcher.py

@@ -268,7 +268,7 @@ def create_reverse_dependency_map():
     "feature_extraction_sequence_utils.py": "test_sequence_feature_extraction_common.py",
     "feature_extraction_utils.py": "test_feature_extraction_common.py",
     "file_utils.py": ["utils/test_file_utils.py", "utils/test_model_output.py"],
-    "utils/generic.py": ["utils/test_file_utils.py", "utils/test_model_output.py"],
+    "utils/generic.py": ["utils/test_file_utils.py", "utils/test_model_output.py", "utils/test_generic.py"],
     "utils/hub.py": "utils/test_file_utils.py",
     "modelcard.py": "utils/test_model_card.py",
     "modeling_flax_utils.py": "test_modeling_flax_common.py",