LARC asynchronous execution
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
Research direction
The issue only shows the LARC Python step method and describes sequential gradient updates for conv1 and conv2; no file or test is named. Start by locating that step implementation, then determine how asynchronous updates would affect optimizer semantics and how completion should be verified across layers.
Written by the indexing model from the issue text.
Description
Now the LARC code is:
def step(self):
with torch.no_grad():
for group in self.optim.param_groups:
# some code
for p in group['params']:
param_norm = torch.norm(p.data)
grad_norm = torch.norm(p.grad.data)
adaptive_lr = somecode
p.grad.data += weight_decay * p.data
p.grad.data *= adaptive_lr
If the model has 2 layers, conv1 and conv2
The update of conv2 grad is after the conv1. But actually it can asynchronous execution both the update of grad in conv1 and conv2.
- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
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