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Fast KroneckerProduct.matmul, t_matmul and rmatmul #103

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Fast KroneckerProduct.matmul, t_matmul and rmatmul #103

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05d8eb6
use pyfastkron for kroneckerproduct (t/r)matmul
abhijangda Dec 8, 2024
8e62107
fix for linter
abhijangda Feb 3, 2025
9c145cb
ufmt suggested changes
abhijangda Feb 4, 2025
44215cb
Merge branch 'main' into main
abhijangda Mar 16, 2025
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25 changes: 10 additions & 15 deletions linear_operator/operators/kronecker_product_linear_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@

import torch
from jaxtyping import Float

from pyfastkron import fastkrontorch as fktorch
from torch import Tensor

from linear_operator import settings
Expand Down Expand Up @@ -267,14 +269,14 @@ def _matmul(
self: Float[LinearOperator, "*batch M N"],
rhs: Union[Float[torch.Tensor, "*batch2 N C"], Float[torch.Tensor, "*batch2 N"]],
) -> Union[Float[torch.Tensor, "... M C"], Float[torch.Tensor, "... M"]]:
is_vec = rhs.ndimension() == 1
if is_vec:
rhs = rhs.unsqueeze(-1)

res = _matmul(self.linear_ops, self.shape, rhs.contiguous())
res = fktorch.gekmm([op.to_dense() for op in self.linear_ops], rhs.contiguous())
return res

if is_vec:
res = res.squeeze(-1)
def rmatmul(
self: Float[LinearOperator, "... M N"],
other: Union[Float[Tensor, "... P M"], Float[Tensor, "... M"], Float[LinearOperator, "... P M"]],
) -> Union[Float[Tensor, "... P N"], Float[Tensor, "N"], Float[LinearOperator, "... P N"]]:
res = fktorch.gemkm(other.contiguous(), [op.to_dense() for op in self.linear_ops])
return res

@cached(name="root_decomposition")
Expand Down Expand Up @@ -357,14 +359,7 @@ def _t_matmul(
self: Float[LinearOperator, "*batch M N"],
rhs: Union[Float[Tensor, "*batch2 M P"], Float[LinearOperator, "*batch2 M P"]],
) -> Union[Float[LinearOperator, "... N P"], Float[Tensor, "... N P"]]:
is_vec = rhs.ndimension() == 1
if is_vec:
rhs = rhs.unsqueeze(-1)

res = _t_matmul(self.linear_ops, self.shape, rhs.contiguous())

if is_vec:
res = res.squeeze(-1)
res = fktorch.gekmm([op.to_dense().mT for op in self.linear_ops], rhs.contiguous())
return res

def _transpose_nonbatch(self: Float[LinearOperator, "*batch M N"]) -> Float[LinearOperator, "*batch N M"]:
Expand Down
1 change: 1 addition & 0 deletions setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@
"scipy",
"jaxtyping",
"mpmath>=0.19,<=1.3", # avoid incompatibiltiy with torch+sympy with mpmath 1.4
"pyfastkron"
]


Expand Down