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Improve memory management in clustering_qr.kmeans_plusplus #775

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Improve memory management in clustering_qr.kmeans_plusplus #775

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5169a39
Improve memory management in clustering_qr.kmeans_plusplus
RobertoDF Sep 4, 2024
73afacf
Merge branch 'MouseLand:main' into Improved_memory_management_cluster…
RobertoDF Sep 6, 2024
73591f3
clear_cache arg in kmeans_plusplus
RobertoDF Sep 6, 2024
c7711ca
clear_cache arg in clustering_qr.run
RobertoDF Sep 6, 2024
0747ee8
Move tensor to CPU temporarily in kmeans_plusplus
RobertoDF Sep 6, 2024
d4bff08
Xd transfer to GPU moved within kmeans_plusplus, remove clear_cachhe arg
RobertoDF Sep 7, 2024
7680278
Refer to Xd not Xg ,Xg has been moved inside kmeans_plusplus
RobertoDF Sep 11, 2024
f73c7a5
Merge branch 'main' into Improved_memory_management_clustering_qr.kme…
RobertoDF Jan 30, 2025
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25 changes: 12 additions & 13 deletions kilosort/clustering_qr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,20 +131,18 @@ def cluster(Xd, iclust = None, kn = None, nskip = 20, n_neigh = 10, nclust = 200

m, ki, kj = Mstats(M, device=device)

#Xg = torch.from_numpy(Xd).to(dev)
Xg = Xd.to(device)
kn = torch.from_numpy(kn).to(device)

n_neigh = kn.shape[1]
NN, nfeat = Xg.shape
NN, nfeat = Xd.shape
nsub = (NN-1)//nskip + 1

rows_neigh = torch.arange(NN, device = device).unsqueeze(-1).tile((1,n_neigh))

tones2 = torch.ones((NN, n_neigh), device = device)

if iclust is None:
iclust_init = kmeans_plusplus(Xg, niter = nclust, seed = seed, device=device)
iclust_init = kmeans_plusplus(Xd, niter = nclust, seed = seed, device=device)
iclust = iclust_init.clone()
else:
iclust_init = iclust.clone()
Expand All @@ -165,13 +163,13 @@ def cluster(Xd, iclust = None, kn = None, nskip = 20, n_neigh = 10, nclust = 200

return iclust, isub, M, iclust_init


def kmeans_plusplus(Xg, niter = 200, seed = 1, device=torch.device('cuda')):
# Xg is number of spikes by number of features
# we are finding cluster centroids and assigning each spike to a centroid
def kmeans_plusplus(Xd, niter = 200, seed = 1, device=torch.device('cuda')):

#Xg = torch.from_numpy(Xd).to(dev)
vtot = (Xg**2).sum(1)
Xg = Xd.to(device)

Xd_squared = (Xd ** 2)
vtot = Xd_squared.sum(1).to(device)


n1 = vtot.shape[0]
if n1 > 2**24:
Expand Down Expand Up @@ -219,10 +217,11 @@ def kmeans_plusplus(Xg, niter = 200, seed = 1, device=torch.device('cuda')):
# Xc are the new centroids to be tested. The spikes themselves are used as centroids.
Xc = Xg[isamp]

# this is how much variance the new centroids would explain for each spike
vexp = 2 * Xg @ Xc.T - (Xc**2).sum(1)
Xc_squared_sum = (Xc ** 2).sum(1)
vexp = Xg @ Xc.T
vexp.mul_(2)
vexp = vexp - Xc_squared_sum

# this is the comparison between how much variance the new centroids explain, and the best explained variance so far
dexp = vexp - vexp0.unsqueeze(1)

# this gets relu-ed, since only the positive increases will actually re-assign a spike to this new cluster
Expand Down