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Fixes by format action #9

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e6abf47
feat: add keops full matrix multiplication
texlucas Jul 8, 2026
7b370f9
Auto code format
texlucas Jul 8, 2026
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118 changes: 90 additions & 28 deletions src/pyGroupedTransforms/GroupedTransform.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,12 @@
import numpy as np
import pykeops
import torch
from pykeops.torch import LazyTensor

from pyGroupedTransforms import *

from .NFFTtools import index_set_without_zeros

# All code that is linked to NFMTtools or to system = "mixed" is not tested yet....

systems = {
Expand Down Expand Up @@ -266,24 +271,90 @@ def __init__(
bandwidths=s.bandwidths, X=np.copy(X[:, u], order="C")
)
elif algorithm == "keops":
self.matrix = np.empty((0,0), dtype=object)
self.matrix = np.empty((0, 0), dtype=object)

self.transforms = [
DeferredLinearOperator()
for _ in range(len(self.settings))
]
self.transforms = [
DeferredLinearOperator() for _ in range(len(self.settings))
]

for idx, s in enumerate(self.settings):
D = X.shape[1]

if len(s.bandwidths) == 0:
u = (0,)
else:
u = s.u
freq_list = []

for s in self.settings:

if len(s.bandwidths) == 0:
full = np.zeros((1, D), dtype=np.int32)

else:
local = index_set_without_zeros(
np.array(s.bandwidths, dtype=np.int32)
).T

full = np.zeros((local.shape[0], D), dtype=np.int32)

for i, dim in enumerate(s.u):
full[:, dim] = local[:, i]

freq_list.append(full)

freq = np.vstack(freq_list)

X_torch = torch.tensor(X, dtype=torch.float64, device="cuda")
I_torch = torch.tensor(freq, dtype=torch.float64, device="cuda")

def trafo(fhat):
X_i = LazyTensor(X_torch[:, None, :].contiguous())
K_j = LazyTensor(I_torch[None, :, :].contiguous())
phase_fwd = (X_i * K_j).sum(-1)
two_pi_phase = -2 * torch.pi * phase_fwd
kernel_fwd = two_pi_phase.cos() + 1j * two_pi_phase.sin()
fhat_torch = torch.tensor(fhat, dtype=torch.complex128, device="cuda")
fhat_j = LazyTensor(fhat_torch[None, :, None].contiguous())

try:
result = (kernel_fwd * fhat_j).sum(dim=1)
torch.cuda.synchronize()
result = result.squeeze().cpu().numpy()
return result
except Exception as e:
print("Error in KeOps trafo:", e)
return None

def adjoint(f):
f_torch = torch.tensor(
f, dtype=torch.complex128, device="cuda"
).contiguous()

X_i = LazyTensor(X_torch[None, :, :].contiguous())
K_j = LazyTensor(I_torch[:, None, :].contiguous())

phase = (K_j * X_i).sum(-1)
kernel = (-2 * torch.pi * phase).cos() - 1j * (
-2 * torch.pi * phase
).sin()

f_i = LazyTensor(f_torch[None, :, None].contiguous())

try:
result = (kernel * f_i).sum(dim=1)
torch.cuda.synchronize()
result = result.squeeze().cpu().numpy()

return result
except Exception as e:
print("Error in KeOps adjoint:", e)
return None

self.transforms = [
DeferredLinearOperator(
dtype=np.complex128,
shape=(X.shape[0], len(freq)),
mfunc=trafo,
rmfunc=adjoint,
)
]

self.transforms[idx] = s.mode.get_transform_keops(
bandwidths=s.bandwidths,
X=np.copy(X[:,u], order="C")
)
else:
self.transforms = []
s1 = self.settings[0]
Expand Down Expand Up @@ -363,19 +434,15 @@ def adjoint_worker(i):
adjoint_worker(i)

return fhat

elif self.algorithm == "keops":
fhat = GroupedCoefficients(self.settings)

for i in range(len(self.transforms)):
adjoint_result = self.transforms[i].H @ other
fhat[self.settings[i].u] = adjoint_result
return GroupedCoefficients(self.settings, self.transforms[0].H @ other)

return fhat
elif self.algorithm == "direct":
return GroupedCoefficients(
self.settings, (self.matrix.conj()).T @ other
)

elif isinstance(other, GC): # `f = F*fhat` (fhat = other)
if self.settings != other.settings:
raise ValueError(
Expand Down Expand Up @@ -403,15 +470,10 @@ def worker(i):
worker(i)

return sum(results)

elif self.algorithm == "keops":
results = []

for i in range(len(self.transforms)):
u = self.settings[i].u
result = self.transforms[i] @ other[u]
results.append(result)
return self.transforms[0] @ other.data

return sum(results)
elif self.algorithm == "direct":
return self.matrix @ other.data
else:
Expand Down
81 changes: 3 additions & 78 deletions src/pyGroupedTransforms/NFFTtools.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,10 @@
import numpy as np

from pyGroupedTransforms import *

import torch
import pykeops
import torch
from pykeops.torch import LazyTensor

from pyGroupedTransforms import *


def datalength(
bandwidths: np.ndarray,
Expand Down Expand Up @@ -172,80 +171,6 @@ def adjoint(f): # function adjoint(f::Vector{ComplexF64})::Vector{ComplexF64}
return DeferredLinearOperator(
dtype=np.complex128, shape=(M, N), mfunc=trafo, rmfunc=adjoint
)

def get_transform_keops(
bandwidths: np.array, X: np.array
):
if bandwidths.ndim > 1 or bandwidths.dtype != "int32":
return "Please use an zero or one-dimensional numpy.array with dtype 'int32' as input"

M, d = np.shape(X)

if len(bandwidths) == 0:
return DeferredLinearOperator(
dtype=np.complex128,
shape=(M, 1),
mfunc=lambda fhat: np.full(M, fhat[0]),
rmfunc=lambda f: np.array([np.sum(f)]),
)

X_torch = torch.tensor(X, dtype=torch.float64, device="cuda")
freq = index_set_without_zeros(
np.array(bandwidths, dtype=np.int32)
).T

I_torch = torch.tensor(
freq,
dtype=torch.float64,
device="cuda"
)


def trafo(fhat):
X_i = LazyTensor(X_torch[:, None, :].contiguous())
K_j = LazyTensor(I_torch[None, :, :].contiguous())
phase_fwd = (X_i * K_j).sum(-1)
two_pi_phase = -2 * torch.pi * phase_fwd
kernel_fwd = two_pi_phase.cos() + 1j * two_pi_phase.sin()
fhat_torch = torch.tensor(fhat, dtype=torch.complex128, device="cuda")
fhat_j = LazyTensor(fhat_torch[None, :, None].contiguous())

try:
result = (kernel_fwd * fhat_j).sum(dim=1)
torch.cuda.synchronize()
result = result.squeeze().cpu().numpy()
return result
except Exception as e:
print("Error in KeOps trafo:", e)
return None

def adjoint(f):
f_torch = torch.tensor(f, dtype=torch.complex128, device="cuda").contiguous()

X_i = LazyTensor(X_torch[None, :, :].contiguous())
K_j = LazyTensor(I_torch[:, None, :].contiguous())

phase = (K_j * X_i).sum(-1)
kernel = (-2*torch.pi*phase).cos() - 1j*(-2*torch.pi*phase).sin()

f_i = LazyTensor(f_torch[None, :, None].contiguous())

try:
result = (kernel * f_i).sum(dim=1)
torch.cuda.synchronize()
result = result.squeeze().cpu().numpy()

return result
except Exception as e:
print("Error in KeOps adjoint:", e)
return None


Ncoeffs = np.prod(bandwidths - 1)

return DeferredLinearOperator(
dtype=np.complex128, shape=(M, Ncoeffs), mfunc=trafo, rmfunc=adjoint
)


def get_matrix(
Expand Down