Add support for Trackio completions logging in GRPOTrainer

trl/trainer/grpo_config.py

@@ -234,7 +234,8 @@ class GRPOConfig(TrainingArguments):
 
         log_completions (`bool`, *optional*, defaults to `False`):
             Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed,
-            it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`.
+            it prints the sample. If `wandb` and/or `trackio` logging is enabled, it logs it to `wandb` and/or
+            `trackio`.
         num_completions_to_print (`int`, *optional*):
             Number of completions to print with `rich`. If `None`, all completions are logged.
         wandb_log_unique_prompts (`bool`, *optional*, defaults to `False`):

trl/trainer/grpo_trainer.py

@@ -42,6 +42,7 @@
     PreTrainedTokenizerBase,
     ProcessorMixin,
     TrainerCallback,
+    is_trackio_available,
     is_wandb_available,
 )
 from transformers.trainer_utils import seed_worker
@@ -94,6 +95,9 @@
 if is_wandb_available():
     import wandb
 
+if is_trackio_available():
+    import trackio
+
 
 logger = logging.get_logger(__name__)
 
@@ -1869,7 +1873,13 @@ def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> Non
                     self.num_completions_to_print,
                 )
 
+            logging_backends = []
             if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None:
+                logging_backends.append(wandb)
+            if self.args.report_to and "trackio" in self.args.report_to:
+                logging_backends.append(trackio)
+
+            if logging_backends:
                 import pandas as pd
 
                 table = {
@@ -1880,16 +1890,28 @@ def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> Non
                     "advantage": self._logs["advantages"],
                 }
 
-                if self._logs["images"]:
-                    table["images"] = []
-                    for image_list in self._logs["images"]:
-                        # Convert images to wandb Image objects for proper visualization
-                        table["images"].append([wandb.Image(image) for image in image_list])
+                df_base = pd.DataFrame(table)
+                images_raw = self._logs["images"] or []
+
+                for logging_backend in logging_backends:
+                    if images_raw:
+                        # Convert images per backend and derive a dataframe that shares base columns
+                        if logging_backend is wandb:
+                            images = []
+                            for image_list in self._logs["images"]:
+                                images.append([wandb.Image(image) for image in image_list])
+                            df = pd.concat([df_base, pd.Series(images, name="image")], axis=1, copy=False)
+                        elif logging_backend is trackio:
+                            # TODO: Implement once supported upstream https://github.com/gradio-app/trackio/issues/327
+                            logger.info("Skipping image logging for Trackio")
+                            df = df_base
+                    else:
+                        df = df_base
+
+                    if self.wandb_log_unique_prompts:
+                        df = df.drop_duplicates(subset=["prompt"])
 
-                df = pd.DataFrame(table)
-                if self.wandb_log_unique_prompts:
-                    df = df.drop_duplicates(subset=["prompt"])
-                wandb.log({"completions": wandb.Table(dataframe=df)})
+                    logging_backend.log({"completions": logging_backend.Table(dataframe=df)})
 
     # Ensure the model card is saved along with the checkpoint
     def _save_checkpoint(self, model, trial):

trl/trainer/rloo_config.py

@@ -186,7 +186,8 @@ class RLOOConfig(TrainingArguments):
 
         log_completions (`bool`, *optional*, defaults to `False`):
             Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed,
-            it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`.
+            it prints the sample. If `wandb` and/or `trackio` logging is enabled, it logs it to `wandb` and/or
+            `trackio`.
         num_completions_to_print (`int`, *optional*):
             Number of completions to print with `rich`. If `None`, all completions are logged.
         wandb_log_unique_prompts (`bool`, *optional*, defaults to `False`):

trl/trainer/rloo_trainer.py

@@ -42,6 +42,7 @@
     PreTrainedTokenizerBase,
     ProcessorMixin,
     TrainerCallback,
+    is_trackio_available,
     is_wandb_available,
 )
 from transformers.trainer_utils import seed_worker
@@ -90,6 +91,9 @@
 if is_wandb_available():
     import wandb
 
+if is_trackio_available():
+    import trackio
+
 
 logger = logging.get_logger(__name__)
 
@@ -1511,7 +1515,13 @@ def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> Non
                     self.num_completions_to_print,
                 )
 
+            logging_backends = []
             if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None:
+                logging_backends.append(wandb)
+            if self.args.report_to and "trackio" in self.args.report_to:
+                logging_backends.append(trackio)
+
+            if logging_backends:
                 import pandas as pd
 
                 table = {
@@ -1522,16 +1532,28 @@ def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> Non
                     "advantage": self._logs["advantages"],
                 }
 
-                if self._logs["images"]:
-                    table["images"] = []
-                    for image_list in self._logs["images"]:
-                        # Convert images to wandb Image objects for proper visualization
-                        table["images"].append([wandb.Image(image) for image in image_list])
+                df_base = pd.DataFrame(table)
+                images_raw = self._logs["images"] or []
+
+                for logging_backend in logging_backends:
+                    if images_raw:
+                        # Convert images per backend and derive a dataframe that shares base columns
+                        if logging_backend is wandb:
+                            images = []
+                            for image_list in self._logs["images"]:
+                                images.append([wandb.Image(image) for image in image_list])
+                            df = pd.concat([df_base, pd.Series(images, name="image")], axis=1, copy=False)
+                        elif logging_backend is trackio:
+                            # TODO: Implement once supported upstream https://github.com/gradio-app/trackio/issues/327
+                            logger.info("Skipping image logging for Trackio")
+                            df = df_base
+                    else:
+                        df = df_base
+
+                    if self.wandb_log_unique_prompts:
+                        df = df.drop_duplicates(subset=["prompt"])
 
-                df = pd.DataFrame(table)
-                if self.wandb_log_unique_prompts:
-                    df = df.drop_duplicates(subset=["prompt"])
-                wandb.log({"completions": wandb.Table(dataframe=df)})
+                    logging_backend.log({"completions": logging_backend.Table(dataframe=df)})
 
     # Ensure the model card is saved along with the checkpoint
     def _save_checkpoint(self, model, trial):