bug fixing checkpoint and gradient step

pythia-training-metrics.py

@@ -85,27 +85,31 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
             """
             return list(range(max(0, step-5), min(step+6, 143_000))) 
 
-        for model_size in self.MODEL_SIZES:
+        for _idx, model_size in enumerate(self.MODEL_SIZES):
             for checkpoint_step in checkpoint_steps:
 
                 directory_path = f"./models/{model_size}/checkpoint_{checkpoint_step}"
 
                 if self.config.name == "activations": 
                     model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_activations.pickle")
-                    kwargs_checkpoint_steps.append(checkpoint_step)
+                    if _idx == 0:
+                        kwargs_checkpoint_steps.append(checkpoint_step)
                 elif self.config.name == "weights":
                     model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_weights.pickle")
-                    kwargs_checkpoint_steps.append(checkpoint_step)
+                    if _idx == 0:
+                        kwargs_checkpoint_steps.append(checkpoint_step)
                 elif self.config.name == "gradients":
                     for gradient_step in get_gradient_step(checkpoint_step):
                         model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_gradients_{gradient_step}.pickle")
-                        kwargs_checkpoint_steps.append(checkpoint_step)
-                        kwargs_gradient_steps.append(gradient_step)
+                        if _idx == 0:
+                            kwargs_checkpoint_steps.append(checkpoint_step)
+                            kwargs_gradient_steps.append(gradient_step)
                 elif self.config.name == "gradients_mini":
                     for gradient_step in get_gradient_step(checkpoint_step)[:2]:
                         model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_gradients_mini_{gradient_step}.pickle")
-                        kwargs_checkpoint_steps.append(checkpoint_step)
-                        kwargs_gradient_steps.append(gradient_step)
+                        if _idx == 0: 
+                            kwargs_checkpoint_steps.append(checkpoint_step)
+                            kwargs_gradient_steps.append(gradient_step)
                 else: 
                     raise Exception("Invalid config name")
 
@@ -117,17 +121,20 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepaths": downloaded_fps,
                     "checkpoint_steps": kwargs_checkpoint_steps,
-                    "gradient_steps": kwargs_gradient_steps,
+                    **({"gradient_steps": kwargs_gradient_steps} if self.config.name in ["gradients", "gradients_mini"] else {}),
                 }
             )  for model_size_name, downloaded_fps in downloaded_files.items()
         ]
 
-    def _generate_examples(self, filepaths, checkpoint_steps, gradient_steps):
+    def _generate_examples(self, filepaths, checkpoint_steps, **kwargs):
 
         # the filepaths should be a list of filepaths 
         if isinstance(filepaths, str):
             filepaths = [filepaths]
 
+        if self.config.name in ["gradients", "gradients_mini"]:
+            gradient_steps = kwargs["gradient_steps"]
+
         global_idx = 0 # the unique identifier for the example 
 
         for idx, filepath in enumerate(filepaths):