Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Merge for comprehension when filtering parameters without grad #574

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
+2 −4
Open

Merge for comprehension when filtering parameters without grad #574

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a59abcf
merge for comprehension when filtering parameters without grad
tsdeng Nov 22, 2024
eabe78b
update comments
tsdeng Nov 22, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 2 additions & 4 deletions model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -261,10 +261,8 @@ def from_pretrained(cls, model_type, override_args=None):
return model

def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
# start with all of the candidate parameters
param_dict = {pn: p for pn, p in self.named_parameters()}
# filter out those that do not require grad
param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
# Filter out parameters that does not require grad.
param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
Expand Down