Skip to content

Update README.md #649

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

Merged
merged 1 commit into from
Aug 16, 2024
Merged

Update README.md #649

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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: 4 additions & 2 deletions torchao/quantization/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,12 +49,14 @@ import pickle
import torchao.quantization

# After the first forward pass (when quantization was done)
from torchao.quantization.autoquant import AUTOQUANT_CACHE
with open("quantization-cache.pkl", "wb") as f:
pickle.dump(torchao.quantization.AUTOQUANT_CACHE)
pickle.dump(AUTOQUANT_CACHE)

# On load
from torchao.quantization.autoquant import AUTOQUANT_CACHE
with open("quantization-cache.pkl", "rb") as f:
torchao.quantization.AUTOQUANT_CACHE.update(pickle.load(f))
AUTOQUANT_CACHE.update(pickle.load(f))
```
## Affine Quantization
Affine quantization refers to the type of quantization that maps from floating point numbers to quantized numbers (typically integer) with an affine transformation, i.e.: `quantized_val = float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data.
Expand Down
Loading