[Wandb Logger] add models, and args to wandb tables. (EvolvingLMMs-La…

…b#55)

* Refactor logging in lmms_eval package

* Refactor variable names in lmms_eval package

lmms_eval/__main__.py

@@ -201,12 +201,14 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
             results_list.append(results)
 
             accelerator.wait_for_everyone()
-            if is_main_process:
+            if is_main_process and args.wandb_args:
                 wandb_logger.post_init(results)
                 wandb_logger.log_eval_result()
                 if args.wandb_log_samples and samples is not None:
                     wandb_logger.log_eval_samples(samples)
 
+                wandb_logger.finish()
+
         except Exception as e:
             traceback.print_exc()
             eval_logger.error(f"Error during evaluation: {e}")
@@ -312,7 +314,7 @@ def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None:
                 for task_name, config in results["configs"].items():
                     filename = args.output_path.joinpath(f"{task_name}.json")
                     # Structure the data with 'args' and 'logs' keys
-                    data_to_dump = {"args": vars(args), "config": config, "logs": sorted(samples[task_name], key=lambda x: x["doc_id"])}  # Convert Namespace to dict
+                    data_to_dump = {"args": vars(args), "model_configs": config, "logs": sorted(samples[task_name], key=lambda x: x["doc_id"])}  # Convert Namespace to dict
                     samples_dumped = json.dumps(data_to_dump, indent=4, default=_handle_non_serializable)
                     filename.open("w").write(samples_dumped)
                     eval_logger.info(f"Saved samples to {filename}")

lmms_eval/evaluator.py

@@ -134,7 +134,7 @@ def simple_evaluate(
 
     if lm.rank == 0:
         # add info about the model and few shot config
-        results["config"] = {
+        results["model_configs"] = {
             "model": model if isinstance(model, str) else model.model.config._name_or_path,
             "model_args": model_args,
             "batch_size": batch_size,

lmms_eval/logging_utils.py

@@ -82,6 +82,9 @@ def __init__(self, args):
             os.environ["WANDB_MODE"] = "offline"
             self.init_run()
 
+    def finish(self):
+        self.run.finish()
+
     @tenacity.retry(wait=tenacity.wait_fixed(5), stop=tenacity.stop_after_attempt(5))
     def init_run(self):
         if "name" not in self.wandb_args:
@@ -152,6 +155,9 @@ def _sanitize_results_dict(self) -> Tuple[Dict[str, str], Dict[str, Any]]:
     def _log_results_as_table(self) -> None:
         """Generate and log evaluation results as a table to W&B."""
         columns = [
+            "Model",
+            "Args",
+            "Tasks",
             "Version",
             "Filter",
             "num_fewshot",
@@ -164,6 +170,9 @@ def make_table(columns: List[str], key: str = "results"):
             table = wandb.Table(columns=columns)
             results = copy.deepcopy(self.results)
 
+            model_name = results.get("model_configs").get("model")
+            model_args = results.get("model_configs").get("model_args")
+
             for k, dic in results.get(key).items():
                 if k in self.group_names and not key == "groups":
                     continue
@@ -183,14 +192,14 @@ def make_table(columns: List[str], key: str = "results"):
                         se = dic[m + "_stderr" + "," + f]
                         if se != "N/A":
                             se = "%.4f" % se
-                        table.add_data(*[k, version, f, n, m, str(v), str(se)])
+                        table.add_data(*[model_name, model_args, k, version, f, n, m, str(v), str(se)])
                     else:
-                        table.add_data(*[k, version, f, n, m, str(v), ""])
+                        table.add_data(*[model_name, model_args, k, version, f, n, m, str(v), ""])
 
             return table
 
         # log the complete eval result to W&B Table
-        table = make_table(["Tasks"] + columns, "results")
+        table = make_table(columns, "results")
         self.run.log({"evaluation/eval_results": table})
 
         if "groups" in self.results.keys():
@@ -209,7 +218,7 @@ def log_eval_result(self) -> None:
         """Log evaluation results to W&B."""
         # Log configs to wandb
         configs = self._get_config()
-        self.run.config.update(configs)
+        self.run.config.update(configs, allow_val_change=True)
 
         wandb_summary, self.wandb_results = self._sanitize_results_dict()
         # update wandb.run.summary with items that were removed

lmms_eval/tasks/internal_eval/d170_cn.yaml

@@ -4,8 +4,8 @@ dataset_kwargs:
 task: "d170_cn"
 test_split: test
 output_type: generate_until
-doc_to_visual: !function utils.doc_to_visual
 doc_to_text: !function utils.doc_to_text # Such that {{prompt}} will be replaced by doc["question"]
+doc_to_visual: !function d170_cn_utils.doc_to_visual
 doc_to_target: "{{annotation}}"
 generation_kwargs:
   until:

lmms_eval/tasks/mme/mme_test.yaml

@@ -14,7 +14,7 @@ generation_kwargs:
   num_beams: 1
   do_sample: false
 # The return value of process_results will be used by metrics
-process_results: !function utils.mme_process_result
+process_results: !function utils.mme_process_results
 # Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
 metric_list:
   - metric: mme_percetion_score

lmms_eval/tasks/mme/utils.py

@@ -67,7 +67,7 @@ def parse_pred_ans(pred_ans):
     return pred_label
 
 
-def mme_process_result(doc, results):
+def mme_process_results(doc, results):
     """
     Args:
         doc: a instance of the eval dataset