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project_6/ixformer_sdk/inference/functions/linalg.py

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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)