Trainer: add logging through Weights & Biases (#3916)

* feat: add logging through Weights & Biases

* feat(wandb): make logging compatible with all scripts

* style(trainer.py): fix formatting

* [Trainer] Tweak wandb integration
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

.gitignore

@@ -131,6 +131,7 @@ proc_data
 # examples
 runs
 /runs_old
+/wandb
 examples/runs
 
 # data

src/transformers/trainer.py

@@ -52,6 +52,18 @@ def is_tensorboard_available():
     return _has_tensorboard
 
 
+try:
+    import wandb
+
+    _has_wandb = True
+except ImportError:
+    _has_wandb = False
+
+
+def is_wandb_available():
+    return _has_wandb
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -151,6 +163,10 @@ class Trainer:
             logger.warning(
                 "You are instantiating a Trainer but Tensorboard is not installed. You should consider installing it."
             )
+        if not is_wandb_available():
+            logger.info(
+                "You are instantiating a Trainer but wandb is not installed. Install it to use Weights & Biases logging."
+            )
         set_seed(self.args.seed)
         # Create output directory if needed
         if self.args.local_rank in [-1, 0]:
@@ -209,6 +225,12 @@ class Trainer:
         )
         return optimizer, scheduler
 
+    def _setup_wandb(self):
+        # Start a wandb run and log config parameters
+        wandb.init(name=self.args.logging_dir, config=vars(self.args))
+        # keep track of model topology and gradients
+        # wandb.watch(self.model)
+
     def train(self, model_path: Optional[str] = None):
         """
         Main training entry point.
@@ -263,6 +285,9 @@ class Trainer:
 
         if self.tb_writer is not None:
             self.tb_writer.add_text("args", self.args.to_json_string())
+            self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={})
+        if is_wandb_available():
+            self._setup_wandb()
 
         # Train!
         logger.info("***** Running training *****")
@@ -351,6 +376,9 @@ class Trainer:
                             if self.tb_writer:
                                 for k, v in logs.items():
                                     self.tb_writer.add_scalar(k, v, global_step)
+                            if is_wandb_available():
+                                wandb.log(logs, step=global_step)
+
                             epoch_iterator.write(json.dumps({**logs, **{"step": global_step}}))
 
                         if self.args.save_steps > 0 and global_step % self.args.save_steps == 0:
@@ -467,7 +495,7 @@ class Trainer:
             shutil.rmtree(checkpoint)
 
     def evaluate(
-        self, eval_dataset: Optional[Dataset] = None, prediction_loss_only: Optional[bool] = None
+        self, eval_dataset: Optional[Dataset] = None, prediction_loss_only: Optional[bool] = None,
     ) -> Dict[str, float]:
         """
         Run evaluation and return metrics.

src/transformers/training_args.py

@@ -2,7 +2,7 @@ import dataclasses
 import json
 import logging
 from dataclasses import dataclass, field
-from typing import Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 from .file_utils import cached_property, is_torch_available, torch_required
 
@@ -138,3 +138,13 @@ class TrainingArguments:
         Serializes this instance to a JSON string.
         """
         return json.dumps(dataclasses.asdict(self), indent=2)
+
+    def to_sanitized_dict(self) -> Dict[str, Any]:
+        """
+        Sanitized serialization to use with TensorBoard’s hparams
+        """
+        d = dataclasses.asdict(self)
+        valid_types = [bool, int, float, str]
+        if is_torch_available():
+            valid_types.append(torch.Tensor)
+        return {k: v if type(v) in valid_types else str(v) for k, v in d.items()}