updating _generate_examples to take in checkpoint and grad step kwargs

pythia-training-metrics.py

@@ -73,6 +73,9 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
 
         model_size_to_fp = { model_size: [] for model_size in self.MODEL_SIZES }
 
+        kwargs_checkpoint_steps = []
+        kwargs_gradient_steps = [] 
+
         checkpoint_steps = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1000, ]
         checkpoint_steps.extend([3000 + (i * 10000) for i in range(0, 15)])
 
@@ -89,14 +92,20 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
 
                 if self.config.name == "activations": 
                     model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_activations.pickle")
+                    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)
                 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)
                 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)
                 else: 
                     raise Exception("Invalid config name")
 
@@ -106,12 +115,14 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=model_size_name,
                 gen_kwargs={
-                    "filepaths": downloaded_fps
+                    "filepaths": downloaded_fps,
+                    "checkpoint_steps": kwargs_checkpoint_steps,
+                    "gradient_steps": kwargs_gradient_steps,
                 }
             )  for model_size_name, downloaded_fps in downloaded_files.items()
         ]
 
-    def _generate_examples(self, filepaths):
+    def _generate_examples(self, filepaths, checkpoint_steps, gradient_steps):
 
         # the filepaths should be a list of filepaths 
         if isinstance(filepaths, str):
@@ -119,19 +130,15 @@ class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
 
         global_idx = 0 # the unique identifier for the example 
 
-        for filepath in filepaths:
+        for idx, filepath in enumerate(filepaths):
             with open(filepath, 'rb') as f:
                 data = pickle.load(f)
-
-                # extract checkpoint step from the filepath
-                checkpoint_step = int(filepath.split("/")[-2].split("_")[-1])
-                
+               
                 if self.config.name in ["activations", "weights"]:
                     for layer_name, layer_data in data.items():
-                        yield global_idx, {"checkpoint_step": checkpoint_step, "layer_name": layer_name, "data": layer_data}
+                        yield global_idx, {"checkpoint_step": checkpoint_steps[idx], "layer_name": layer_name, "data": layer_data}
                         global_idx += 1
                 elif self.config.name in ["gradients", "gradients_mini"]:
-                    gradient_step = int(filepath.split('/')[-1].split("_")[-1].split(".")[0])
                     for layer_name, layer_data in data.items():
-                        yield global_idx, {"checkpoint_step": checkpoint_step, "layer_name": layer_name, "gradient_step": gradient_step, "data": layer_data}
+                        yield global_idx, {"checkpoint_step": checkpoint_steps[idx], "layer_name": layer_name, "gradient_step": gradient_steps[idx], "data": layer_data}
                         global_idx += 1