Create pythia-training-metrics.py

pythia-training-metrics.py

@@ -0,0 +1,158 @@
+import datasets
+import pickle
+
+_DESCRIPTION = """\
+    Dataset for storing training metrics of pythia models 
+"""
+
+class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
+    
+    MODEL_SIZES = [ 
+        "14m"
+    ]
+
+    _GRADIENTS_DESCRIPTION = """\
+        Dataset for storing gradients of pythia models of the requested model size
+    """
+
+    _WEIGHTS_DESCRIPTION = """\
+        Dataset for storing weights of pythia models  of the requested model size
+    """
+
+    _WEIGHTS_MINI_DESCRIPTION = """\
+        Dataset for storing weights of pythia models (minimizes the amount of gradients per 
+        checkpoint to only 2) of the requested model size
+    """
+
+    _ACTIVATIONS_DESCRIPTION = """\
+        Dataset for storing activations of pythia models of the requested model size
+    """
+   
+    BUILDER_CONFIGS = []
+    for model_size in MODEL_SIZES:
+        BUILDER_CONFIGS.extend([
+            datasets.BuilderConfig(
+                name=f"{model_size}__gradients",
+                description=_WEIGHTS_DESCRIPTION,
+                version="1.0.0",
+            ),
+            datasets.BuilderConfig(
+                name=f"{model_size}__gradients_mini",
+                description=_WEIGHTS_MINI_DESCRIPTION,
+                version="1.0.0",
+            ),
+            datasets.BuilderConfig(
+                name=f"{model_size}__activations",
+                description=_ACTIVATIONS_DESCRIPTION,
+                version="1.0.0",
+            ),
+            datasets.BuilderConfig(
+                name=f"{model_size}__weights",
+                description=_WEIGHTS_DESCRIPTION,
+                version="1.0.0",
+            ),
+        ])
+
+    def _info(self):
+        """
+        NOTE: we might want to specify features, but since the features are different for each
+        model size it's annoying and kind of pointless since hf does it automatically 
+        """
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+        )
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """ 
+        Returns data for different splits - we define a split as a model size. 
+        """
+
+        to_download_files = []
+
+        kwargs_checkpoint_steps = []
+        kwargs_gradient_steps = [] 
+
+        checkpoint_steps = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1000, 2000, 3000, 4000, 4091]
+
+        def get_gradient_step(step: int): 
+            """
+            Return a list of the gradient steps that are used at a given checkpoint step. 
+            """
+            return list(range(max(0, step-5), min(step+6, 4091))) 
+
+        def get_gradient_mini_step(step: int):
+            """
+            Return a list of the gradient steps that are used at a given checkpoint step, we 
+            limit the amount of gradients to only 2. 
+            """
+            if step != checkpoint_steps[-1]:
+                return [step, step+1]
+            else:
+                return [step-2, step-1]
+
+        model_size = self.config.name.split("__")[0]
+
+        for checkpoint_step in checkpoint_steps:
+
+            directory_path = f"./models/{model_size}/checkpoint_{checkpoint_step}"
+
+            if "activations" in self.config.name:
+                to_download_files.append(f"{directory_path}/checkpoint_activations.pickle")
+                kwargs_checkpoint_steps.append(checkpoint_step)
+            elif "weights" in self.config.name:
+                to_download_files.append(f"{directory_path}/checkpoint_weights.pickle")
+                kwargs_checkpoint_steps.append(checkpoint_step)
+            elif "gradients" in self.config.name:
+                if "mini" in self.config.name:
+                    gradient_steps = get_gradient_mini_step(checkpoint_step)
+                else:
+                    gradient_steps = get_gradient_step(checkpoint_step)
+
+                for gradient_step in gradient_steps:
+                    to_download_files.append(f"{directory_path}/checkpoint_gradients_{gradient_step}.pickle")
+                    kwargs_checkpoint_steps.append(checkpoint_step)
+                    kwargs_gradient_steps.append(gradient_step)
+            else: 
+                raise Exception("Invalid config name")
+
+            downloaded_files = dl_manager.download_and_extract(to_download_files)
+
+        return [
+            datasets.SplitGenerator(
+                name='default',
+                gen_kwargs={
+                    "filepaths": downloaded_files,
+                    "checkpoint_steps": kwargs_checkpoint_steps,
+                    **({"gradient_steps": kwargs_gradient_steps} if "gradients" in self.config.name else {}),
+                }
+            )  
+        ]
+
+    def _generate_examples(self, filepaths, checkpoint_steps, **kwargs):
+
+        # the filepaths should be a list of filepaths 
+        if isinstance(filepaths, str):
+            filepaths = [filepaths]
+
+        if "gradients" in self.config.name:
+            gradient_steps = kwargs["gradient_steps"]
+
+        global_idx = 0 # the unique identifier for the example 
+
+        for idx, filepath in enumerate(filepaths):
+            with open(filepath, 'rb') as f:
+                data = pickle.load(f)
+
+                for layer_name, layer_data in data.items():
+                    record = {
+                        "checkpoint_step": checkpoint_steps[idx],
+                        "layer_name": layer_name,
+                        "data": layer_data, 
+                    }
+                    if "gradients" in self.config.name:
+                        record['gradient_step'] = gradient_steps[idx]
+               
+                    yield global_idx,  record
+                    global_idx += 1