WIP: Support table logging for mlflow, too (#1506)

* WIP: Support table logging for mlflow, too

Create a `LogPredictionCallback` for both "wandb" and "mlflow" if
specified.

In `log_prediction_callback_factory`, create a generic table and make it
specific only if the newly added `logger` argument is set to "wandb"
resp. "mlflow".

See #1505

* chore: lint

* add additional clause for mlflow as it's optional

* Fix circular imports

---------

Co-authored-by: Dave Farago <[email protected]>
Co-authored-by: Wing Lian <[email protected]>

src/axolotl/core/trainer_builder.py

@@ -36,6 +36,7 @@
 from axolotl.loraplus import create_loraplus_optimizer
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
+from axolotl.utils import is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
@@ -71,10 +72,6 @@
 LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
-def is_mlflow_available():
-    return importlib.util.find_spec("mlflow") is not None
-
-
 def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
     if isinstance(tag_names, str):
         tag_names = [tag_names]
@@ -943,7 +940,16 @@ def get_post_trainer_create_callbacks(self, trainer):
         callbacks = []
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
-                trainer, self.tokenizer
+                trainer, self.tokenizer, "wandb"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
+        if (
+            self.cfg.use_mlflow
+            and is_mlflow_available()
+            and self.cfg.eval_table_size > 0
+        ):
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "mlflow"
             )
             callbacks.append(LogPredictionCallback(self.cfg))
 

src/axolotl/utils/__init__.py

@@ -0,0 +1,8 @@
+"""
+Basic utils for Axolotl
+"""
+import importlib
+
+
+def is_mlflow_available():
+    return importlib.util.find_spec("mlflow") is not None

src/axolotl/utils/callbacks/__init__.py

@@ -6,7 +6,7 @@
 import os
 from shutil import copyfile
 from tempfile import NamedTemporaryFile
-from typing import TYPE_CHECKING, Dict, List
+from typing import TYPE_CHECKING, Any, Dict, List
 
 import evaluate
 import numpy as np
@@ -27,7 +27,9 @@
 )
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
+from axolotl.utils import is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 from axolotl.utils.distributed import (
     barrier,
     broadcast_dict,
@@ -540,7 +542,7 @@ def predict_with_generate():
     return CausalLMBenchEvalCallback
 
 
-def log_prediction_callback_factory(trainer: Trainer, tokenizer):
+def log_prediction_callback_factory(trainer: Trainer, tokenizer, logger: str):
     class LogPredictionCallback(TrainerCallback):
         """Callback to log prediction values during each evaluation"""
 
@@ -597,15 +599,13 @@ def find_ranges(lst):
                 return ranges
 
             def log_table_from_dataloader(name: str, table_dataloader):
-                table = wandb.Table(  # type: ignore[attr-defined]
-                    columns=[
-                        "id",
-                        "Prompt",
-                        "Correct Completion",
-                        "Predicted Completion (model.generate)",
-                        "Predicted Completion (trainer.prediction_step)",
-                    ]
-                )
+                table_data: Dict[str, List[Any]] = {
+                    "id": [],
+                    "Prompt": [],
+                    "Correct Completion": [],
+                    "Predicted Completion (model.generate)": [],
+                    "Predicted Completion (trainer.prediction_step)": [],
+                }
                 row_index = 0
 
                 for batch in tqdm(table_dataloader):
@@ -709,16 +709,29 @@ def log_table_from_dataloader(name: str, table_dataloader):
                     ) in zip(
                         prompt_texts, completion_texts, predicted_texts, pred_step_texts
                     ):
-                        table.add_data(
-                            row_index,
-                            prompt_text,
-                            completion_text,
-                            prediction_text,
-                            pred_step_text,
+                        table_data["id"].append(row_index)
+                        table_data["Prompt"].append(prompt_text)
+                        table_data["Correct Completion"].append(completion_text)
+                        table_data["Predicted Completion (model.generate)"].append(
+                            prediction_text
                         )
+                        table_data[
+                            "Predicted Completion (trainer.prediction_step)"
+                        ].append(pred_step_text)
                         row_index += 1
-
-                wandb.run.log({f"{name} - Predictions vs Ground Truth": table})  # type: ignore[attr-defined]
+                if logger == "wandb":
+                    wandb.run.log({f"{name} - Predictions vs Ground Truth": pd.DataFrame(table_data)})  # type: ignore[attr-defined]
+                elif logger == "mlflow" and is_mlflow_available():
+                    import mlflow
+
+                    tracking_uri = AxolotlInputConfig(
+                        **self.cfg.to_dict()
+                    ).mlflow_tracking_uri
+                    mlflow.log_table(
+                        data=table_data,
+                        artifact_file="PredictionsVsGroundTruth.json",
+                        tracking_uri=tracking_uri,
+                    )
 
             if is_main_process():
                 log_table_from_dataloader("Eval", eval_dataloader)