feat: Upgrade Weights & Biases callback (#30135)

* feat: upgrade wandb callback with new features

* fix: ci issues with imports and run fixup

src/transformers/integrations/integration_utils.py

@@ -31,8 +31,17 @@
 import numpy as np
 import packaging.version
 
+from .. import PreTrainedModel, TFPreTrainedModel
 from .. import __version__ as version
-from ..utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
+from ..utils import (
+    PushToHubMixin,
+    flatten_dict,
+    is_datasets_available,
+    is_pandas_available,
+    is_tf_available,
+    is_torch_available,
+    logging,
+)
 
 
 logger = logging.get_logger(__name__)
@@ -69,6 +78,7 @@
         except importlib.metadata.PackageNotFoundError:
             _has_neptune = False
 
+from .. import modelcard  # noqa: E402
 from ..trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
 from ..trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
 from ..training_args import ParallelMode  # noqa: E402
@@ -663,6 +673,22 @@ def on_train_end(self, args, state, control, **kwargs):
             self.tb_writer = None
 
 
+def save_model_architecture_to_file(model: Any, output_dir: str):
+    with open(f"{output_dir}/model_architecture.txt", "w+") as f:
+        if isinstance(model, PreTrainedModel):
+            print(model, file=f)
+        elif is_tf_available() and isinstance(model, TFPreTrainedModel):
+
+            def print_to_file(s):
+                print(s, file=f)
+
+            model.summary(print_fn=print_to_file)
+        elif is_torch_available() and (
+            isinstance(model, (torch.nn.Module, PushToHubMixin)) and hasattr(model, "base_model")
+        ):
+            print(model, file=f)
+
+
 class WandbCallback(TrainerCallback):
     """
     A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
@@ -728,6 +754,9 @@ def setup(self, args, state, model, **kwargs):
             if hasattr(model, "config") and model.config is not None:
                 model_config = model.config.to_dict()
                 combined_dict = {**model_config, **combined_dict}
+            if hasattr(model, "peft_config") and model.peft_config is not None:
+                peft_config = model.peft_config
+                combined_dict = {**{"peft_config": peft_config}, **combined_dict}
             trial_name = state.trial_name
             init_args = {}
             if trial_name is not None:
@@ -756,6 +785,51 @@ def setup(self, args, state, model, **kwargs):
                 self._wandb.watch(model, log=_watch_model, log_freq=max(100, state.logging_steps))
             self._wandb.run._label(code="transformers_trainer")
 
+            # add number of model parameters to wandb config
+            if any(
+                (
+                    isinstance(model, PreTrainedModel),
+                    isinstance(model, PushToHubMixin),
+                    (is_tf_available() and isinstance(model, TFPreTrainedModel)),
+                    (is_torch_available() and isinstance(model, torch.nn.Module)),
+                )
+            ):
+                self._wandb.config["model/num_parameters"] = model.num_parameters()
+
+            # log the initial model and architecture to an artifact
+            with tempfile.TemporaryDirectory() as temp_dir:
+                model_name = (
+                    f"model-{self._wandb.run.id}"
+                    if (args.run_name is None or args.run_name == args.output_dir)
+                    else f"model-{self._wandb.run.name}"
+                )
+                model_artifact = self._wandb.Artifact(
+                    name=model_name,
+                    type="model",
+                    metadata={
+                        "model_config": model.config.to_dict() if hasattr(model, "config") else None,
+                        "num_parameters": self._wandb.config.get("model/num_parameters"),
+                        "initial_model": True,
+                    },
+                )
+                model.save_pretrained(temp_dir)
+                # add the architecture to a separate text file
+                save_model_architecture_to_file(model, temp_dir)
+
+                for f in Path(temp_dir).glob("*"):
+                    if f.is_file():
+                        with model_artifact.new_file(f.name, mode="wb") as fa:
+                            fa.write(f.read_bytes())
+                self._wandb.run.log_artifact(model_artifact, aliases=["base_model"])
+
+                badge_markdown = (
+                    f'[<img src="https://github.com/raw/wandb/assets/main/wandb-github-badge'
+                    f'-28.svg" alt="Visualize in Weights & Biases" width="20'
+                    f'0" height="32"/>]({self._wandb.run.get_url()})'
+                )
+
+                modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
+
     def on_train_begin(self, args, state, control, model=None, **kwargs):
         if self._wandb is None:
             return
@@ -786,20 +860,25 @@ def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwarg
                     else {
                         f"eval/{args.metric_for_best_model}": state.best_metric,
                         "train/total_floss": state.total_flos,
+                        "model/num_parameters": self._wandb.config.get("model/num_parameters"),
                     }
                 )
+                metadata["final_model"] = True
                 logger.info("Logging model artifacts. ...")
                 model_name = (
                     f"model-{self._wandb.run.id}"
                     if (args.run_name is None or args.run_name == args.output_dir)
                     else f"model-{self._wandb.run.name}"
                 )
+                # add the model architecture to a separate text file
+                save_model_architecture_to_file(model, temp_dir)
+
                 artifact = self._wandb.Artifact(name=model_name, type="model", metadata=metadata)
                 for f in Path(temp_dir).glob("*"):
                     if f.is_file():
                         with artifact.new_file(f.name, mode="wb") as fa:
                             fa.write(f.read_bytes())
-                self._wandb.run.log_artifact(artifact)
+                self._wandb.run.log_artifact(artifact, aliases=["final_model"])
 
     def on_log(self, args, state, control, model=None, logs=None, **kwargs):
         single_value_scalars = [
@@ -829,18 +908,30 @@ def on_save(self, args, state, control, **kwargs):
                 for k, v in dict(self._wandb.summary).items()
                 if isinstance(v, numbers.Number) and not k.startswith("_")
             }
+            checkpoint_metadata["model/num_parameters"] = self._wandb.config.get("model/num_parameters")
 
             ckpt_dir = f"checkpoint-{state.global_step}"
             artifact_path = os.path.join(args.output_dir, ckpt_dir)
             logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. ...")
             checkpoint_name = (
-                f"checkpoint-{self._wandb.run.id}"
+                f"model-{self._wandb.run.id}"
                 if (args.run_name is None or args.run_name == args.output_dir)
-                else f"checkpoint-{self._wandb.run.name}"
+                else f"model-{self._wandb.run.name}"
             )
             artifact = self._wandb.Artifact(name=checkpoint_name, type="model", metadata=checkpoint_metadata)
             artifact.add_dir(artifact_path)
-            self._wandb.log_artifact(artifact, aliases=[f"checkpoint-{state.global_step}"])
+            self._wandb.log_artifact(
+                artifact, aliases=[f"epoch_{round(state.epoch, 2)}", f"checkpoint_global_step_{state.global_step}"]
+            )
+
+    def on_predict(self, args, state, control, metrics, **kwargs):
+        if self._wandb is None:
+            return
+        if not self._initialized:
+            self.setup(args, state, **kwargs)
+        if state.is_world_process_zero:
+            metrics = rewrite_logs(metrics)
+            self._wandb.log(metrics)
 
 
 class CometCallback(TrainerCallback):