Delete pythia-training-metrics.py

pythia-training-metrics.py

@@ -1,159 +0,0 @@
-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, ]
-        checkpoint_steps.extend([(i * 10000) for i in range(0, 15)])
-
-        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, 143_000))) 
-
-        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