Page MenuHomec4science
Files F83704492

bertarize.py
No OneTemporary
Actions

File Metadata

Created
Wed, Sep 18, 14:24

bertarize.py
View Options

# Copyright 2020-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Once a model has been fine-pruned, the weights that are masked during the forward pass can be pruned once for all.
For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceClassification` is trained, it can be saved (and then loaded)
as a standard :class:`~transformers.BertForSequenceClassification`.
"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def main(args):
pruning_method = args.pruning_method
threshold = args.threshold
model_name_or_path = args.model_name_or_path.rstrip("/")
target_model_path = args.target_model_path
print(f"Load fine-pruned model from {model_name_or_path}")
model = torch.load(os.path.join(model_name_or_path, "pytorch_model.bin"))
pruned_model = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
elif "classifier" in name or "qa_output" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
elif "bias" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
else:
if pruning_method == "magnitude":
mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "topK":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
mask = TopKBinarizer.apply(scores, threshold)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
mask = ThresholdBinarizer.apply(scores, threshold, True)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "l0":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
l, r = -0.1, 1.1
s = torch.sigmoid(scores)
s_bar = s * (r - l) + l
mask = s_bar.clamp(min=0.0, max=1.0)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
else:
raise ValueError("Unknown pruning method")
if target_model_path is None:
target_model_path = os.path.join(
os.path.dirname(model_name_or_path), f"bertarized_{os.path.basename(model_name_or_path)}"
)
if not os.path.isdir(target_model_path):
shutil.copytree(model_name_or_path, target_model_path)
print(f"\nCreated folder {target_model_path}")
torch.save(pruned_model, os.path.join(target_model_path, "pytorch_model.bin"))
print("\nPruned model saved! See you later!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help="Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning, sigmoied_threshold = Soft movement pruning)",
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help="For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`",
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
args = parser.parse_args()
main(args)

Event Timeline

c4science · Help