Add gradient and autodiff checks for linear operators

[ckolbPTB]

This supersedes #407 - it was too difficult to bring this up-to-date with main.

forward_mode_autodiff_of_linear_operator_test and gradient_of_linear_operator_test still need to be added to

• ZeroOp
• RearrangeOp
• PCACompOp
• GridSamplingOp

Do we want to run forward_mode_autodiff_of_linear_operator_test and gradient_of_linear_operator_test on all parametrize-options used for the adjointness tests?

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
walsh.py	16	1	94%	34
src/mrpro/algorithms/dcf
dcf_voronoi.py	53	4	92%	15, 48–49, 76
src/mrpro/algorithms/optimizers
adam.py	20	1	95%	69
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	16	43%	51–71, 85
IterativeSENSEReconstruction.py	13	1	92%	76
Reconstruction.py	50	22	56%	42, 54–56, 80–87, 104–113
RegularizedIterativeSENSEReconstruction.py	41	17	59%	96–100, 114–139
src/mrpro/data
AcqInfo.py	128	3	98%	26, 169, 207
CsmData.py	29	3	90%	15, 82–84
DcfData.py	45	8	82%	18, 66, 78–83
IData.py	67	9	87%	119, 125, 129, 159–167
IHeader.py	75	7	91%	75, 109, 127–131
KData.py	214	25	88%	111–112, 127, 134, 144, 152, 206–207, 245, 250–251, 270–281, 435, 437, 500, 515, 552, 583, 592
KHeader.py	153	17	89%	25, 119–123, 150, 199, 210, 217–218, 221, 228, 260–271
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	81	12	85%	108–113, 116–118, 203–207
MoveDataMixin.py	140	18	87%	15, 113, 129, 143–145, 207, 323–325, 338, 417, 437–438, 440, 455–456, 458
QData.py	39	7	82%	42, 65–73
Rotation.py	674	35	95%	100, 198, 335, 433, 477, 495, 581, 583, 592, 626, 628, 691, 768, 773, 776, 791, 808, 813, 889, 1077, 1082, 1085, 1109, 1113, 1240, 1242, 1250–1251, 1315, 1397, 1690, 1846, 1881, 1885, 1996
SpatialDimension.py	232	21	91%	34, 104, 141, 148, 154, 274–276, 289–291, 325, 343, 356, 369, 382, 395, 404–405, 420, 429
acq_filters.py	12	1	92%	47
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	25	2	92%	23, 45
KTrajectoryIsmrmrd.py	13	2	85%	41, 50
KTrajectoryPulseq.py	23	1	96%	55
src/mrpro/operators
CartesianSamplingOp.py	89	3	97%	118, 157, 280
ConstraintsOp.py	60	2	97%	46, 48
EndomorphOperator.py	65	2	97%	228, 234
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	158	3	98%	263, 381, 386
Functional.py	71	5	93%	20–22, 117, 119
GridSamplingOp.py	136	9	93%	72–73, 82–83, 90–91, 94, 96, 98
LinearOperator.py	168	9	95%	91, 190, 220, 261, 270, 278, 287, 295, 320
LinearOperatorMatrix.py	158	16	90%	82, 119, 152, 161, 166, 175–178, 191–194, 203, 215, 304, 331, 359
MultiIdentityOp.py	13	2	85%	43, 48
Operator.py	78	2	97%	25, 74
ProximableFunctionalSeparableSum.py	39	3	92%	50, 103, 110
SliceProjectionOp.py	173	8	95%	44, 61, 63, 69, 206, 227, 260, 300
WaveletOp.py	119	5	96%	151, 169, 204, 209, 232
ZeroPadOp.py	16	1	94%	30
src/mrpro/utils
filters.py	62	2	97%	44, 49
reshape.py	60	1	98%	191
slice_profiles.py	46	6	87%	20, 36, 113–116, 149
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	11	9	18%	20–29
typing.py	18	11	39%	8–23
zero_pad_or_crop.py	31	6	81%	26, 30, 54, 57, 60, 63
TOTAL	4890	359	93%

Tests	Skipped	Failures	Errors	Time
2479	0 💤	0 ❌	0 🔥	1m 22s ⏱️

[github-actions]

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[ckolbPTB]

Tests which currently do not pass:
FAILED tests/operators/test_slice_projection_op.py::test_slice_projection_op_grad - RuntimeError: Sparse CSR tensors do not have strides
FAILED tests/operators/test_slice_projection_op.py::test_slice_projection_op_forward_mode_autodiff - RuntimeError: Sparse CSR tensors do not have strides

[fzimmermann89]

That is a known issue #409 🙈
pytorch/pytorch#97286 pytorch/pytorch#136357

There is imho no feasibe way tfor us to support the slice projection operator in torch.func.

Fixing it in pytorch would also be infeasible for us, as the the functorch c backend seems to cause the issue, as sparse tensors or completely missing in its implementation,

We could replace the sparse-matrix/dense vector multiplication by manual indexing and dotproducts. This will make it quite a bit slower and way more complicated.

For now, I would ether remove the tests or mark them as failing.

This mainly means that we cant solve a superresolutionproblem with fista or pdgh if we keep using torch.func for the gradients.

[ckolbPTB]

@fzimmermann89 do you think you could add the missing tests for GridSamplingOp? Everthing else should now be covered...

[fzimmermann89]

I moved #604 out of this PR, to unblock #293