File size: 5,771 Bytes
89bc030 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
import datasets
import pickle
_DESCRIPTION = """\
Dataset for storing training metrics of pythia models
"""
class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
MODEL_SIZES = [
"70m",
"160m",
"410m",
"1b",
"1.4b",
"2.8b",
"6.9b"
]
_GRADIENTS_DESCRIPTION = """\
Dataset for storing gradients of pythia models
"""
_WEIGHTS_DESCRIPTION = """\
Dataset for storing weights of pythia models
"""
_WEIGHTS_MINI_DESCRIPTION = """\
Dataset for storing weights of pythia models (minimizes the amount of gradients per
checkpoint to only 2)
"""
_ACTIVATIONS_DESCRIPTION = """\
Dataset for storing activations of pythia models
"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="gradients",
description=_WEIGHTS_DESCRIPTION,
version="1.0.0",
),
datasets.BuilderConfig(
name="gradients_mini",
description=_WEIGHTS_MINI_DESCRIPTION,
version="1.0.0",
),
datasets.BuilderConfig(
name="activations ",
description=_ACTIVATIONS_DESCRIPTION,
version="1.0.0",
),
datasets.BuilderConfig(
name="weights",
description=_WEIGHTS_DESCRIPTION,
version="1.0.0",
),
datasets.BuilderConfig(
name="all",
description="All the metrics",
version="1.0.0",
)
]
def _info(self):
"""
TODO: Got to figure out how to represent the features etc.
how do we do this if each feature is dependent on the model size?
"""
features_dict = {
"checkpoint_step": datasets.Value('int32'),
"layer_name": datasets.Value('string'),
}
if self.config.name in ["activations", "weights"]:
features_dict['data'] = datasets.Sequence(datasets.Value('float32'))
elif self.config_name in ["gradients", "gradients_mini"]:
features_dict['gradient_step'] = datasets.Value('int32')
features_dict['gradient'] = datasets.Sequence(datasets.Value('float32'))
features = datasets.Features(features_dict)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""
Returns data for different splits - we define a split as a model size.
"""
model_size_to_fp = { model_size: [] for model_size in self.MODEL_SIZES }
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)])
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)))
for model_size in 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")
elif self.config_name == "weights":
model_size_to_fp[model_size].append(f"{directory_path}/checkpoint_weights.pickle")
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")
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")
downloaded_files = dl_manager.download_and_extract(model_size_to_fp)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": downloaded_fps
}
) for downloaded_fps in downloaded_files.values()
]
def _generate_examples(self, filepaths):
# the filepaths should be a list of filepaths
if isinstance(filepaths, str):
filepaths = [filepaths]
global_idx = 0 # the unique identifier for the example
for filepath in filepaths:
with open(filepath, encoding="utf-8") as f:
data = pickle.load(f)
# extract checkpoint step from the filepath
checkpoint_step = int(filepath.split("/")[1].split("_")[-1])
if self.config.name in ["activations", "weights"]:
for layer_name, layer_data in data.items():
for data in layer_data:
yield global_idx, {"checkpoint_step": checkpoint_step, "layer_name": layer_name, "data": data}
global_idx += 1
elif self.config.name in ["gradients", "gradients_mini"]:
for layer_name, layer_data in data.items():
for gradient_step, gradient in layer_data.items():
yield global_idx, {"checkpoint_step": checkpoint_step, "layer_name": layer_name, "gradient_step": gradient_step, "gradient": gradient}
global_idx += 1
|