File size: 5,782 Bytes
89bc030 ebfb4b3 89bc030 2ebe38b 89bc030 ebfb4b3 89bc030 ebfb4b3 89bc030 ebfb4b3 89bc030 ebfb4b3 89bc030 ebfb4b3 89bc030 ebfb4b3 2178182 89bc030 ebfb4b3 89bc030 bc74a1c 89bc030 c03487d ebfb4b3 c03487d ebfb4b3 89bc030 ebfb4b3 89bc030 ebfb4b3 bc74a1c ebfb4b3 89bc030 ebfb4b3 89bc030 2b544b3 89bc030 2178182 ebfb4b3 2b544b3 89bc030 bc74a1c 2178182 89bc030 ebfb4b3 bc74a1c ebfb4b3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
import datasets
import pickle
_DESCRIPTION = """\
Dataset for storing training metrics of pythia models
"""
class PythiaTrainingMetrics(datasets.GeneratorBasedBuilder):
MODEL_SIZES = [
"70m",
"160m",
"410m",
"1.4b",
"2.8b",
]
_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([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)))
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
|