Wandb step handling bugfix and feature (#2580)

docs/interface.md

@@ -58,7 +58,7 @@ This mode supports a number of command-line arguments, the details of which can
 
 * `--seed`: Set seed for python's random, numpy and torch.  Accepts a comma-separated list of 3 values for python's random, numpy, and torch seeds, respectively, or a single integer to set the same seed for all three.  The values are either an integer or 'None' to not set the seed. Default is `0,1234,1234` (for backward compatibility).  E.g. `--seed 0,None,8` sets `random.seed(0)` and `torch.manual_seed(8)`. Here numpy's seed is not set since the second value is `None`.  E.g, `--seed 42` sets all three seeds to 42.
 
-* `--wandb_args`:  Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list [here](https://docs.wandb.ai/ref/python/init). e.g., ```--wandb_args project=test-project,name=test-run```
+* `--wandb_args`:  Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list [here](https://docs.wandb.ai/ref/python/init). e.g., ```--wandb_args project=test-project,name=test-run```. Also allows for the passing of the step to log things at (passed to `wandb.run.log`), e.g., `--wandb_args step=123`.
 
 * `--hf_hub_log_args` : Logs evaluation results to Hugging Face Hub. Accepts a string with the arguments separated by commas. Available arguments:
     * `hub_results_org` - organization name on Hugging Face Hub, e.g., `EleutherAI`. If not provided, the results will be pushed to the owner of the Hugging Face token,

lm_eval/loggers/wandb_logger.py

@@ -48,6 +48,9 @@ class WandbLogger:
 
         self.wandb_args: Dict[str, Any] = kwargs
 
+        # pop the step key from the args to save for all logging calls
+        self.step = self.wandb_args.pop("step", None)
+
         # initialize a W&B run
         if wandb.run is None:
             self.run = wandb.init(**self.wandb_args)
@@ -152,11 +155,11 @@ class WandbLogger:
 
         # log the complete eval result to W&B Table
         table = make_table(["Tasks"] + columns, "results")
-        self.run.log({"evaluation/eval_results": table})
+        self.run.log({"evaluation/eval_results": table}, step=self.step)
 
         if "groups" in self.results.keys():
             table = make_table(["Groups"] + columns, "groups")
-            self.run.log({"evaluation/group_eval_results": table})
+            self.run.log({"evaluation/group_eval_results": table}, step=self.step)
 
     def _log_results_as_artifact(self) -> None:
         """Log results as JSON artifact to W&B."""
@@ -174,13 +177,13 @@ class WandbLogger:
         """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=self.step is not None)
 
         wandb_summary, self.wandb_results = self._sanitize_results_dict()
         # update wandb.run.summary with items that were removed
         self.run.summary.update(wandb_summary)
         # Log the evaluation metrics to wandb
-        self.run.log(self.wandb_results)
+        self.run.log(self.wandb_results, step=self.step)
         # Log the evaluation metrics as W&B Table
         self._log_results_as_table()
         # Log the results dict as json to W&B Artifacts
@@ -329,7 +332,7 @@ class WandbLogger:
 
             # log the samples as a W&B Table
             df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))
-            self.run.log({f"{task_name}_eval_results": df})
+            self.run.log({f"{task_name}_eval_results": df}, step=self.step)
 
             # log the samples as a json file as W&B Artifact
             self._log_samples_as_artifact(eval_preds, task_name)
@@ -348,4 +351,4 @@ class WandbLogger:
                 # log the samples as a json file as W&B Artifact
                 self._log_samples_as_artifact(eval_preds, task_name)
 
-            self.run.log({f"{group}_eval_results": grouped_df})
+            self.run.log({f"{group}_eval_results": grouped_df}, step=self.step)

lm_eval/utils.py

@@ -104,7 +104,8 @@ def simple_parse_args_string(args_string):
         return {}
     arg_list = [arg for arg in args_string.split(",") if arg]
     args_dict = {
-        k: handle_arg_string(v) for k, v in [arg.split("=") for arg in arg_list]
+        kv[0]: handle_arg_string("=".join(kv[1:]))
+        for kv in [arg.split("=") for arg in arg_list]
     }
     return args_dict