51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
|
|
from typing import Union
|
||
|
|
|
||
|
|
import ixformer._C as ops
|
||
|
|
import torch
|
||
|
|
|
||
|
|
__all__ = ["solve", "ref_slove"]
|
||
|
|
|
||
|
|
|
||
|
|
def ref_slove(
|
||
|
|
A: torch.Tensor, B: torch.Tensor, *, left: bool = True, out: torch.Tensor = None
|
||
|
|
):
|
||
|
|
out = torch.linalg.solve(A, B, left=left)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def solve(
|
||
|
|
A: torch.Tensor, B: torch.Tensor, *, left: bool = True, out: torch.Tensor = None
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
A: (..., n, n) torch.float
|
||
|
|
B: (..., n) or (..., n, k) or (n,...) or (n, k) or (n) torch.float
|
||
|
|
left: bool
|
||
|
|
whether to solve the system AX=B or XA=B. Default: True, 目前只支持left =True
|
||
|
|
out: (..., n, k) torch.float
|
||
|
|
Returns:
|
||
|
|
out: (..., n, k) torch.float
|
||
|
|
"""
|
||
|
|
|
||
|
|
n = A.shape[-1]
|
||
|
|
batch_count = A.numel() // (n * n)
|
||
|
|
if B.dim() == 1:
|
||
|
|
k = 1
|
||
|
|
elif B.dim() == 2:
|
||
|
|
if A.dim() > 2 and B.shape == (batch_count, n):
|
||
|
|
k = 1
|
||
|
|
else:
|
||
|
|
nid = 0 if left else 1
|
||
|
|
k = B.shape[nid ^ 1]
|
||
|
|
else:
|
||
|
|
k = B.size(B.dim() - 1 if left else B.dim() - 2)
|
||
|
|
|
||
|
|
if n <= 64 and k <= 64 and left:
|
||
|
|
return ops.infer.solve(A, B, left)
|
||
|
|
else:
|
||
|
|
device = A.device
|
||
|
|
cpu_A = A.cpu()
|
||
|
|
cpu_B = B.cpu()
|
||
|
|
cpu_res = ref_slove(A=cpu_A, B=cpu_B, left=left)
|
||
|
|
return cpu_res.to(device)
|