DirectSolver yields incorrect result for random and close to random matrices
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Description
I was considering swapping cupy sparse solvers for nvmath Python/cuDSS DirectSolver, but I am unable to obtain correct results even for small problem instances.
Minimal example
In example01_cupy.py replace
n = 8
...
a += sp.diags([2.0] * n, format="csr", dtype="float64")
with
n = 100
...
# a += sp.diags([2.0] * n, format="csr", dtype="float64")
The same remains true, if I used small, but not tiny values for the diagonal like 0.01.
Example observation 1
||A||: 40.65133430135082
||b||: 14.142135623730951
det(A): 7.262125991606542e+28
02-10 17:35:07 userlogger INFO = SPECIFICATION PHASE =
02-10 17:35:07 userlogger INFO The LHS package is cupyx.
02-10 17:35:07 userlogger INFO The RHS package is cupy.
02-10 17:35:07 userlogger INFO The device_id=0, dtype = float64, index type = int32.
02-10 17:35:07 userlogger INFO The number of equations = 100.
02-10 17:35:07 userlogger INFO The operands' memory space is cuda, and the execution space is on device 0.
02-10 17:35:07 userlogger INFO The specified stream for the DirectSolver ctor is <cuda.core.experimental._stream.Stream object at 0x14b6709b87c0>.
02-10 17:35:07 userlogger INFO The library handle has been created: 94793135484384.
02-10 17:35:07 userlogger INFO The sparse direct solver operation has been created.
02-10 17:35:07 userlogger INFO Starting solver phase ANALYSIS...
02-10 17:35:07 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:35:07 userlogger INFO Starting solver phase FACTORIZATION...
02-10 17:35:07 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:35:07 userlogger INFO Starting solver phase SOLVE...
02-10 17:35:07 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:35:07 userlogger INFO The DirectSolver object's resources have been released.
CuDSS:
||x||: 29391823863.993862
||Ax - b||: 92575181911.9934
CuPy:
||x||: 10.647411227999223
||Ax - b||: 4.289313285599112e-14
NumPy:
||x||: 10.647411227999223
||Ax - b||: 4.29904389535207e-14
Note that I computed the determinant to check whether A is invertible.
Example observation 2
Vice versa I made the observation that DirectSolver sometimes returns not nan even though the determinant is 0. Note that here b = cp.ones((n, 1), order="F") was only a vector
||A||: 5.6922584694712395
||b||: 10.0
det(A): 0.0
02-10 17:30:48 userlogger INFO = SPECIFICATION PHASE =
02-10 17:30:48 userlogger INFO The LHS package is cupyx.
02-10 17:30:48 userlogger INFO The RHS package is cupy.
02-10 17:30:48 userlogger INFO The device_id=0, dtype = float64, index type = int32.
02-10 17:30:48 userlogger INFO The number of equations = 100.
02-10 17:30:48 userlogger INFO The operands' memory space is cuda, and the execution space is on device 0.
02-10 17:30:48 userlogger INFO The specified stream for the DirectSolver ctor is <cuda.core.experimental._stream.Stream object at 0x14c6b6504580>.
02-10 17:30:48 userlogger INFO The library handle has been created: 94064601263200.
02-10 17:30:48 userlogger INFO The sparse direct solver operation has been created.
02-10 17:30:48 userlogger INFO Starting solver phase ANALYSIS...
02-10 17:30:48 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:30:48 userlogger INFO Starting solver phase FACTORIZATION...
02-10 17:30:48 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:30:48 userlogger INFO Starting solver phase SOLVE...
02-10 17:30:48 userlogger INFO This call is non-blocking and will return immediately after the operation is launched on the device.
02-10 17:30:48 userlogger INFO The DirectSolver object's resources have been released.
CuDSS:
||x||: 1.5379474527090538e+102
||Ax - b||: 1.5379474527090537e+89
CuPy:
||x||: nan
||Ax - b||: nan
NumPy:
||x||: nan
||Ax - b||: nan
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with example01_cupy.py and reproduce the reported cases by varying n and the diagonal value. Compare DirectSolver results with the CuPy and NumPy outputs, including the norms and residuals shown in the issue. Done means identifying and correcting the cause of the incorrect results for nonsingular and singular matrices.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100