Skip to content

Simplify the TimeIndependentMDCObjectiveFunction class #515

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 18 commits into from
Jan 20, 2025
Merged

Simplify the TimeIndependentMDCObjectiveFunction class #515

Show file tree
Hide file tree
Changes from 13 commits
Commits
Show all changes
18 commits
Select commit Hold shift + click to select a range
f0edb04
initial commit simplifying TimeIndependentMDCObjectiveFunction
rileyjmurray Dec 16, 2024
768f248
bugfixes
rileyjmurray Dec 16, 2024
609b7a9
improve readability
rileyjmurray Dec 16, 2024
a62de79
inline comment
rileyjmurray Dec 16, 2024
dd764cf
add pytest dispatcher for MPI test in test_packages
rileyjmurray Dec 17, 2024
4590263
move simple MPI test from test/test_packages into test/unit, and rena…
rileyjmurray Dec 17, 2024
00e4d16
implement changes discussed in 12/17 dev meeting
rileyjmurray Dec 17, 2024
8b304c3
expand scope of exception handling
rileyjmurray Dec 18, 2024
8f641da
add separate TimeIndpendentMDCObjectiveFunction.dlsvec back
rileyjmurray Dec 18, 2024
1d32520
resolve an infuriating sign error. Will probably want to clean this up.
rileyjmurray Dec 24, 2024
3f8029a
check in slightly simpler (but still complicated) implementation. Nex…
rileyjmurray Dec 26, 2024
6efa45e
simplifications complete
rileyjmurray Dec 26, 2024
c33542e
adjust size of perturbation from zero in test_stdgst_prunedpath
rileyjmurray Jan 16, 2025
157d999
split out duplicated complicated line into its own function; remove T…
rileyjmurray Jan 17, 2025
588cd78
have perturbation from zero be closer to what it was before (only inc…
rileyjmurray Jan 17, 2025
ec105d6
fix LM bugs
rileyjmurray Jan 17, 2025
5779011
simplify bugfix from last commit
rileyjmurray Jan 17, 2025
59ab4d3
remove variable added for debugging
rileyjmurray Jan 17, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
712 changes: 177 additions & 535 deletions pygsti/objectivefns/objectivefns.py
View file
Open in desktop

Large diffs are not rendered by default.

2 changes: 1 addition & 1 deletion test/performance/mpi_2D_scaling/run.sh
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,4 +29,4 @@ export MKL_NUM_THREADS=1
# Note: This flags are useful on Kahuna to avoid error messages
# But the --mca flags are not necessary for performance
mpirun -np ${NUM_PROCS} --mca pml ucx --mca btl '^openib' \
python ./mpi_test.py &> ${PREFIX}.out
python ./run_me_with_mpirun.py &> ${PREFIX}.out
0 test/performance/mpi_2D_scaling/mpi_test.py → ...ance/mpi_2D_scaling/run_me_with_mpirun.py
View file
Open in desktop
File renamed without changes.
2 changes: 1 addition & 1 deletion test/test_packages/drivers/test_calcmethods1Q.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -237,7 +237,7 @@ def test_stdgst_prunedpath(self):
target_model.sim = pygsti.forwardsims.TermForwardSimulator(mode='pruned', max_order=3, desired_perr=0.01,
allowed_perr=0.1, max_paths_per_outcome=1000,
perr_heuristic='meanscaled', max_term_stages=5)
target_model.from_vector(1e-10 * np.ones(target_model.num_params)) # to seed term calc (starting with perfect zeros causes trouble)
target_model.from_vector(1e-9 * np.ones(target_model.num_params)) # to seed term calc (starting with perfect zeros causes trouble)
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Why this change?

Copy link
Contributor Author

@rileyjmurray rileyjmurray Jan 17, 2025 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Uhhh .... if I didn't make it, then the test failed nastily. The optimizer just didn't converge.

I can compare side by side and see what the threshold is for the old code vs new code.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@sserita, I'm very glad you asked this question. It turns out that the non-convergence I observed stemmed from a bug in the LM optimizer. Specifically, it stemmed from how the LM optimizer decided to update the "best_x."

The implementations on develop (i.e., both the default SimplerLMOptimizer and the backup CustomLMOptimizer) would only update best_x if norm_f < min_norm_f and a boolean flag called new_x_is_known_in_bounds was True. But new_x_is_known_in_bounds is really more of a sufficient condition for being in bounds than a necessary condition. It makes more sense to do this:

If norm_f < min_norm_f, then check if new_x_is_known_in_bounds == True. If it is, then we take this flag's word for it and update best_x[:] = x. Otherwise, we explicitly check if x is in bounds and we update best_x[:] = x if and only if that's the case.

If I make that change then the test passes without making the change that prompted your comment.

See below for my investigation, written to a future pyGSTi developer who might come across this.

Investigation

Stefan's comment concerned a change I made in test/test_packages/drivers/test_calcmethods1Q.py::test_stdgst_prunedpath. The change was to replace a scalar that was hard-coded to 1e-10 with a scalar hard-coded to 1e-9. Let's call that scalar perturb. So the old value is perturb=1e-10.

I ran this test on develop with different values of perturb; those runs passed when perturb >= 5e-13 and failed when perturb <= 2.5e-13. Then I ran this test on the current branch using the commit at the time that Stefan left his comment. Those runs passed when perturb >= 2e-10 and failed when perturb <= 1.125.

I started to write a report of these observations when I noticed something funny. In the test log it was clear that the LM optimizer was terminating outer iterations with a much larger value for norm_f than the minimum value seen across the outer iterations (specific logs are shown below). This made me suspect that best_x wasn't being set properly. Sure enough, I found this issue where a sufficient condition for an iterate being in-bounds (new_x_is_known_in_bounds) was effectively used as a necessary condition. After updating the termination criteria I was able to get this problematic unit test to pass when perturb==1e-10. In fact, it passed with perturb==1e-12. I didn't bother checking smaller values.

develop, passing with 5e-13

  Last iteration:
  --- dlogl GST ---
  Initial Term-stage model has 0 failures and uses 10.0% of allowed paths (ok=True).
    --- Outer Iter 0: norm_f = 166109, mu=1, |x|=2.44949e-12, |J|=2835.48
    --- Outer Iter 1: norm_f = 166104, mu=979.996, |x|=0.00324549, |J|=2835.51
    --- Outer Iter 2: norm_f = 608.149, mu=326.665, |x|=0.255707, |J|=3040.87
    --- Outer Iter 3: norm_f = 95.8116, mu=168.158, |x|=0.276211, |J|=3017.42
    --- Outer Iter 4: norm_f = 31.8847, mu=56.0528, |x|=0.268233, |J|=3017.41
    --- Outer Iter 5: norm_f = 31.864, mu=18.6843, |x|=0.268133, |J|=3017.33
    --- Outer Iter 6: norm_f = 166104, mu=979.996, |x|=0.00324549, |J|=3017.33
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    Least squares message = Objective function out-of-bounds! STOP
  2*Delta(log(L)) = 332207 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0)
Pruned path-integral: kept 13212 paths (10.0%) w/magnitude 135.6 (target=134.4, #circuits=66, #failed=0)
  (avg per circuit paths=200, magnitude=2.055, target=2.036)
  After adapting paths, num failures = 0, 10.0% of allowed paths used.
    --- Outer Iter 0: norm_f = 166104, mu=979.996, |x|=0.00324549, |J|=2835.51
    --- Outer Iter 1: norm_f = 608.149, mu=326.665, |x|=0.255707, |J|=3040.87
    --- Outer Iter 2: norm_f = 95.8116, mu=168.158, |x|=0.276211, |J|=3017.42
    --- Outer Iter 3: norm_f = 31.8847, mu=56.0528, |x|=0.268233, |J|=3017.41
    --- Outer Iter 4: norm_f = 31.864, mu=18.6843, |x|=0.268133, |J|=3017.33
    --- Outer Iter 5: norm_f = 608.149, mu=326.665, |x|=0.255707, |J|=3017.33
    --- Outer Iter 6: norm_f = 607.403, mu=1.52598e+07, |x|=0.255282, |J|=3040.98
    --- Outer Iter 7: norm_f = 591.59, mu=5.08659e+06, |x|=0.255303, |J|=3040.85
    --- Outer Iter 8: norm_f = 548.357, mu=1.69553e+06, |x|=0.255395, |J|=3040.46
    --- Outer Iter 9: norm_f = 448.118, mu=565177, |x|=0.255827, |J|=3039.43
    --- Outer Iter 10: norm_f = 289.143, mu=188392, |x|=0.257372, |J|=3037.08
    --- Outer Iter 11: norm_f = 150.619, mu=62797.4, |x|=0.260537, |J|=3032.8
    --- Outer Iter 12: norm_f = 68.2405, mu=20932.5, |x|=0.26454, |J|=3026.4
    --- Outer Iter 13: norm_f = 36.1194, mu=6977.49, |x|=0.267382, |J|=3020.41
    --- Outer Iter 14: norm_f = 31.9729, mu=2325.83, |x|=0.268035, |J|=3017.84
    --- Outer Iter 15: norm_f = 31.8644, mu=775.277, |x|=0.268122, |J|=3017.37
    --- Outer Iter 16: norm_f = 31.864, mu=258.426, |x|=0.268135, |J|=3017.33
    Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06
  2*Delta(log(L)) = 63.7279 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0.0168613)
  Term-states Converged!
  Completed in 0.7s
  Final optimization took 0.7s

develop, passing with 2e-10

  Last iteration:
  --- dlogl GST ---
  Initial Term-stage model has 0 failures and uses 10.0% of allowed paths (ok=True).
    --- Outer Iter 0: norm_f = 166109, mu=1, |x|=9.79796e-10, |J|=2835.48
    --- Outer Iter 1: norm_f = 166090, mu=979.996, |x|=0.00324842, |J|=2851.83
    --- Outer Iter 2: norm_f = 26560.4, mu=334505, |x|=0.094016, |J|=3265.36
    --- Outer Iter 3: norm_f = 3024.04, mu=117205, |x|=0.198562, |J|=3065.56
    --- Outer Iter 4: norm_f = 223.51, mu=39068.3, |x|=0.250841, |J|=3037.55
    --- Outer Iter 5: norm_f = 61.1352, mu=13022.8, |x|=0.264528, |J|=3026.48
    --- Outer Iter 6: norm_f = 34.14, mu=4340.92, |x|=0.267594, |J|=3019.84
    --- Outer Iter 7: norm_f = 31.9082, mu=1446.97, |x|=0.268025, |J|=3017.7
    --- Outer Iter 8: norm_f = 31.8645, mu=482.324, |x|=0.26812, |J|=3017.36
    --- Outer Iter 9: norm_f = 31.864, mu=160.775, |x|=0.268135, |J|=3017.33
    --- Outer Iter 10: norm_f = 166090, mu=979.996, |x|=0.00324842, |J|=3017.33
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    Least squares message = Objective function out-of-bounds! STOP
  2*Delta(log(L)) = 332181 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0)
Pruned path-integral: kept 13212 paths (10.0%) w/magnitude 135.6 (target=134.4, #circuits=66, #failed=0)
  (avg per circuit paths=200, magnitude=2.055, target=2.036)
  After adapting paths, num failures = 0, 10.0% of allowed paths used.
    --- Outer Iter 0: norm_f = 166090, mu=979.996, |x|=0.00324842, |J|=2851.83
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    --- Outer Iter 1: norm_f = 26560.4, mu=334505, |x|=0.094016, |J|=3265.36
    --- Outer Iter 2: norm_f = 3024.04, mu=117205, |x|=0.198562, |J|=3065.56
    --- Outer Iter 3: norm_f = 223.51, mu=39068.3, |x|=0.250841, |J|=3037.55
    --- Outer Iter 4: norm_f = 61.1352, mu=13022.8, |x|=0.264528, |J|=3026.48
    --- Outer Iter 5: norm_f = 34.14, mu=4340.92, |x|=0.267594, |J|=3019.84
    --- Outer Iter 6: norm_f = 31.9082, mu=1446.97, |x|=0.268025, |J|=3017.7
    --- Outer Iter 7: norm_f = 31.8645, mu=482.324, |x|=0.26812, |J|=3017.36
    --- Outer Iter 8: norm_f = 31.864, mu=160.775, |x|=0.268135, |J|=3017.33
    --- Outer Iter 9: norm_f = 26560.4, mu=334505, |x|=0.094016, |J|=3017.33
    --- Outer Iter 10: norm_f = 24207.2, mu=111502, |x|=0.0993976, |J|=3245.77
    --- Outer Iter 11: norm_f = 1743.06, mu=37167.3, |x|=0.216206, |J|=3057.74
    --- Outer Iter 12: norm_f = 177.863, mu=12389.1, |x|=0.26331, |J|=3032.07
    --- Outer Iter 13: norm_f = 47.4228, mu=4129.7, |x|=0.269905, |J|=3020.44
    --- Outer Iter 14: norm_f = 31.995, mu=1376.57, |x|=0.268271, |J|=3017.63
    --- Outer Iter 15: norm_f = 31.8641, mu=458.855, |x|=0.268137, |J|=3017.34
    --- Outer Iter 16: norm_f = 31.864, mu=137.657, |x|=0.268136, |J|=3017.33
    Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06
  2*Delta(log(L)) = 63.7279 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0.0168613)
  Term-states Converged!
  Completed in 0.6s
  Final optimization took 0.6s

This PR, passing with 2e-10

  Last iteration:
  --- dlogl GST ---
  Initial Term-stage model has 0 failures and uses 10.0% of allowed paths (ok=True).
    --- Outer Iter 0: norm_f = 166109, mu=1, |x|=9.79796e-10, |J|=2835.48
    --- Outer Iter 1: norm_f = 166090, mu=979.996, |x|=0.00324842, |J|=2851.83
    --- Outer Iter 2: norm_f = 26560.4, mu=334505, |x|=0.094016, |J|=3265.36
    --- Outer Iter 3: norm_f = 3024.04, mu=117205, |x|=0.198562, |J|=3065.56
    --- Outer Iter 4: norm_f = 223.51, mu=39068.3, |x|=0.250841, |J|=3037.55
    --- Outer Iter 5: norm_f = 61.1352, mu=13022.8, |x|=0.264528, |J|=3026.48
    --- Outer Iter 6: norm_f = 34.14, mu=4340.92, |x|=0.267594, |J|=3019.84
    --- Outer Iter 7: norm_f = 31.9082, mu=1446.97, |x|=0.268025, |J|=3017.7
    --- Outer Iter 8: norm_f = 31.8645, mu=482.324, |x|=0.26812, |J|=3017.36
    --- Outer Iter 9: norm_f = 31.864, mu=160.775, |x|=0.268135, |J|=3017.33
    --- Outer Iter 10: norm_f = 166090, mu=979.996, |x|=0.00324842, |J|=3017.33
    --- Outer Iter 11: norm_f = 166090, mu=293.999, |x|=0.0032486, |J|=2851.52
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    Least squares message = Objective function out-of-bounds! STOP
  2*Delta(log(L)) = 332180 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0)
Pruned path-integral: kept 13212 paths (10.0%) w/magnitude 135.6 (target=134.4, #circuits=66, #failed=0)
  (avg per circuit paths=200, magnitude=2.055, target=2.036)
  After adapting paths, num failures = 0, 10.0% of allowed paths used.
    --- Outer Iter 0: norm_f = 166090, mu=293.999, |x|=0.0032486, |J|=2851.52
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    --- Outer Iter 1: norm_f = 696.596, mu=100352, |x|=0.273829, |J|=3033.45
    --- Outer Iter 2: norm_f = 145.292, mu=33450.5, |x|=0.26568, |J|=3028.08
    --- Outer Iter 3: norm_f = 50.8609, mu=11150.2, |x|=0.267615, |J|=3021.55
    --- Outer Iter 4: norm_f = 33.0403, mu=3716.73, |x|=0.268405, |J|=3017.96
    --- Outer Iter 5: norm_f = 31.8829, mu=1238.91, |x|=0.268202, |J|=3017.31
    --- Outer Iter 6: norm_f = 31.8642, mu=412.97, |x|=0.268145, |J|=3017.31
    --- Outer Iter 7: norm_f = 31.864, mu=123.891, |x|=0.268137, |J|=3017.33
    --- Outer Iter 8: norm_f = 696.596, mu=100352, |x|=0.273829, |J|=3017.33
    --- Outer Iter 9: norm_f = 696.594, mu=30105.5, |x|=0.273829, |J|=3033.45
    --- Outer Iter 10: norm_f = 80.7071, mu=10035.2, |x|=0.269167, |J|=3023.24
    --- Outer Iter 11: norm_f = 34.0435, mu=3345.05, |x|=0.268725, |J|=3018.22
    --- Outer Iter 12: norm_f = 31.886, mu=1115.02, |x|=0.268213, |J|=3017.33
    --- Outer Iter 13: norm_f = 31.8642, mu=371.673, |x|=0.268145, |J|=3017.32
    --- Outer Iter 14: norm_f = 31.864, mu=111.502, |x|=0.268137, |J|=3017.33
    Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06
  2*Delta(log(L)) = 63.7279 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0.0168613)
  Term-states Converged!
  Completed in 0.6s
  Final optimization took 0.6s

This PR, failing with 1e-10

  Last iteration:
  --- dlogl GST ---
  Initial Term-stage model has 0 failures and uses 10.0% of allowed paths (ok=True).
    --- Outer Iter 0: norm_f = 166109, mu=1, |x|=4.89898e-10, |J|=2835.48
    --- Outer Iter 1: norm_f = 166100, mu=979.996, |x|=0.00324622, |J|=2839.6
    --- Outer Iter 2: norm_f = 52952, mu=334505, |x|=0.0493353, |J|=3716.22
    --- Outer Iter 3: norm_f = 8281.27, mu=203584, |x|=0.159582, |J|=3101.88
    --- Outer Iter 4: norm_f = 631.08, mu=67861.3, |x|=0.234876, |J|=3044.93
    --- Outer Iter 5: norm_f = 97.1942, mu=22620.4, |x|=0.260353, |J|=3031.15
    --- Outer Iter 6: norm_f = 40.7621, mu=7540.14, |x|=0.266602, |J|=3022.47
    --- Outer Iter 7: norm_f = 32.2466, mu=2513.38, |x|=0.26786, |J|=3018.4
    --- Outer Iter 8: norm_f = 31.8699, mu=837.794, |x|=0.268084, |J|=3017.45
    --- Outer Iter 9: norm_f = 31.864, mu=279.265, |x|=0.26813, |J|=3017.34
    --- Outer Iter 10: norm_f = 31.864, mu=83.7794, |x|=0.268136, |J|=3017.33
    --- Outer Iter 11: norm_f = 166100, mu=979.996, |x|=0.00324622, |J|=3017.33
    --- Outer Iter 12: norm_f = 166100, mu=293.999, |x|=0.00324625, |J|=2839.55
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    Least squares message = Objective function out-of-bounds! STOP
  2*Delta(log(L)) = 332201 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0)
Pruned path-integral: kept 13212 paths (10.0%) w/magnitude 135.6 (target=134.4, #circuits=66, #failed=0)
  (avg per circuit paths=200, magnitude=2.055, target=2.036)
  After adapting paths, num failures = 0, 10.0% of allowed paths used.
    --- Outer Iter 0: norm_f = 166100, mu=293.999, |x|=0.00324625, |J|=2839.55
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
Paths are insufficient! (Using meanscaled heuristic with error bound of 0.1)
    --- Outer Iter 1: norm_f = 8859.56, mu=100352, |x|=0.157859, |J|=3102.33
    --- Outer Iter 2: norm_f = 392.573, mu=33450.5, |x|=0.243907, |J|=3037.75
    --- Outer Iter 3: norm_f = 56.3503, mu=11150.2, |x|=0.265337, |J|=3024.63
    --- Outer Iter 4: norm_f = 33.251, mu=3716.73, |x|=0.268011, |J|=3018.92
    --- Outer Iter 5: norm_f = 31.8752, mu=1238.91, |x|=0.268108, |J|=3017.49
    --- Outer Iter 6: norm_f = 31.864, mu=412.97, |x|=0.268133, |J|=3017.34
    --- Outer Iter 7: norm_f = 8859.56, mu=100352, |x|=0.157859, |J|=3017.34
    Least squares message = Relative change, |dx|/|x|, is at most 1e-08
  2*Delta(log(L)) = 17719.1 (66 data params - 24 (approx) model params = expected mean of 42; p-value = 0)
  Term-states Converged!
  Completed in 0.6s
  Final optimization took 0.6s
    -- Performing 'go0' gauge optimization on GateSetTomography estimate --
MISFIT nSigma =  1928.7314162397222

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Fantastic work tracking this down, @rileyjmurray!

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Thanks, @coreyostrove! I feel pretty good about this one. Who knows how many past calls to LM suffered from this bug. It's wild to think about, since LM was already performing very well, and now it'll perform even better :)

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Agreed, nice work and as always, the detailed writeups are incredibly appreciated!

results = pygsti.run_long_sequence_gst(self.ds, target_model, std.prep_fiducials(), std.meas_fiducials(),
std.germs(lite=False), self.maxLengths, verbosity=3,
disable_checkpointing=True)
Expand Down
8 changes: 4 additions & 4 deletions test/test_packages/mpi/test_mpi.py → test/unit/mpi/run_me_with_mpiexec.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# This file is designed to be run via: mpiexec -np 4 python -W ignore testMPI.py
# This file is designed to be run via: mpiexec -np 4 python -W ignore run_me_with_mpiexec.py
# This does not use nosetests because I want to set verbosity differently based on rank (quiet if not rank 0)
# By wrapping asserts in comm.rank == 0, only rank 0 should fail (should help with output)
# Can run with different number of procs, but 4 is minimum to test all modes (pure MPI, pure shared mem, and mixed)
Expand Down Expand Up @@ -226,7 +226,7 @@ def run_fills(self, sim, natoms, nparams):
else:
raise RuntimeError("Improper sim type passed by test_fills_generator")

serial_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimension=nP)
serial_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimensions=(nP,))

nE = serial_layout.num_elements
nC = len(circuits)
Expand All @@ -246,7 +246,7 @@ def run_fills(self, sim, natoms, nparams):
global_serial_layout = serial_layout.global_layout

#Use a parallel layout to compute the same probabilities & their derivatives
local_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimension=nP,
local_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimensions=(nP,),
resource_alloc=self.ralloc)

vp_local = local_layout.allocate_local_array('e', 'd')
Expand Down Expand Up @@ -334,7 +334,7 @@ def setup_class(cls):
tester = PureMPIParallel_Test()
tester.setup_class()
tester.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm)
#tester.run_objfn_values('matrix','logl',4)
tester.run_objfn_values('matrix','logl',4)
tester.run_fills('map', 1, None)
tester.run_fills('map', 4, None)
tester.run_fills('matrix', 4, 15)
26 changes: 26 additions & 0 deletions test/unit/mpi/test_mpi.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,26 @@
import subprocess
import pytest
import os
from pathlib import Path

try:
from mpi4py import MPI
except (ImportError, RuntimeError):
MPI = None


class MPITester:

@pytest.mark.skipif(MPI is None, reason="mpi4py could not be imported")
def test_all(self, capfd: pytest.LogCaptureFixture):
current_filepath = Path(os.path.abspath(__file__))
to_run = current_filepath.parents[0] / Path('run_me_with_mpiexec.py')
subprocess_args = (f"mpiexec -np 4 python -W ignore {str(to_run)}").split(' ')

result = subprocess.run(subprocess_args, capture_output=False, text=True)
out, err = capfd.readouterr()
if len(out) + len(err) > 0:
msg = out + '\n'+ 80*'-' + err
raise RuntimeError(msg)
return

10 changes: 6 additions & 4 deletions test/unit/objects/test_objectivefns.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -300,10 +300,12 @@ def test_derivative(self):
places=3) # each *element* should match to 3 places

if self.computes_lsvec:
lsvec = objfn.lsvec().copy()
dlsvec = objfn.dlsvec().copy()
self.assertArraysAlmostEqual(dterms / nEls, 2 * lsvec[:, None] * dlsvec / nEls,
places=4) # each *element* should match to 4 places
arg1 = dterms / nEls
lsvec = objfn.lsvec(v0).copy()
dlsvec = objfn.dlsvec(v0).copy()
arg2 = 2 * lsvec[:, None] * dlsvec / nEls
self.assertArraysAlmostEqual(arg1, arg2, places=4) # each *element* should match to 4 places
return

def test_approximate_hessian(self):
if not self.enable_hessian_tests:
Expand Down
Loading