Usage
This module contains the public API.
Assume you have a function for batched computation of nearest neighbors using brute-force distance calculation.
import torch
def knn(x, y, batch_size, k: int = 3):
return torch.cat(
[
torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=1, largest=False).indices
for start in range(0, x.shape[0], batch_size)
],
dim=0,
)
Using maximize_memory_utilization()
you can decorate this function to reduce the batch size until no more
out-of-memory error occurs.
import torch
from torch_max_mem import maximize_memory_utilization
@maximize_memory_utilization()
def knn(x, y, batch_size, k: int = 3):
return torch.cat(
[
torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=1, largest=False).indices
for start in range(0, x.shape[0], batch_size)
],
dim=0,
)
In the code, you can now always pass the largest sensible batch size, e.g.,
x = torch.rand(100, 100, device="cuda")
y = torch.rand(200, 100, device="cuda")
knn(x, y, batch_size=x.shape[0])
- maximize_memory_utilization
alias of
MemoryUtilizationMaximizer