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Merged
merged 17 commits into from
May 5, 2023
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[MRG] Add github action test cuda #473

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13749c6
add workflow cuda
rflamary May 4, 2023
886eb51
try with conda
rflamary May 4, 2023
c8e763a
try again conda
rflamary May 4, 2023
1c5635b
and now?
rflamary May 4, 2023
dba6260
proper python version
rflamary May 4, 2023
07e985e
cleanup
rflamary May 4, 2023
d4f1d4c
try to use proper python
rflamary May 4, 2023
0ecc94f
again?
rflamary May 4, 2023
b98f4d5
cleanup stuff and use python3.10
rflamary May 4, 2023
4fb6f41
remove dr test
rflamary May 4, 2023
6e94eef
remove docstrng tetss for stoc methods
rflamary May 5, 2023
80204a6
add condition for cuda tests
rflamary May 5, 2023
113d88a
Merge branch 'master' into test-cuda
rflamary May 5, 2023
c466d97
remove unused files
rflamary May 5, 2023
0dd42de
Merge branch 'test-cuda' of github.com:PythonOT/POT into test-cuda
rflamary May 5, 2023
6f74dc3
upate release file
rflamary May 5, 2023
ec6661f
debug workflow
rflamary May 5, 2023
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25 changes: 25 additions & 0 deletions .github/workflows/build_tests_cuda.yml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,25 @@
name: Tests CUDA

on:
workflow_dispatch:
pull_request_review:
types: [submitted]
push:
branches:
- 'master' # Set a branch to run CI tests on

jobs:
linux-cuda:

runs-on: pc-cuda
if: github.event.review.state == 'approved' || github.event_name == 'workflow_dispatch' || (github.event_name == 'push' && github.event.branch == 'master')

steps:
- uses: actions/checkout@v1
- name: Install POT
run: |
python3.10 -m pip install --ignore-installed -e .
- name: Run tests
run: |
python3.10 -m pytest --durations=20 -v test/ ot/ --doctest-modules --color=yes --ignore=test/test_dr.py --ignore=ot.dr --ignore=ot.plot

2 changes: 2 additions & 0 deletions RELEASES.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,8 @@
#### New features
- Make alpha parameter in Fused Gromov Wasserstein differentiable (PR #463)
- Added the sparsity-constrained OT solver to `ot.smooth` and added ` projection_sparse_simplex` to `ot.utils` (PR #459)
- Add tests on GPU for master branch and approved PR (PR #473)

#### Closed issues

- Fix circleci-redirector action and codecov (PR #460)
Expand Down
194 changes: 0 additions & 194 deletions ot/stochastic.py
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Expand Up @@ -58,27 +58,6 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i):
-------
coordinate gradient : ndarray, shape (nt,)

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
[1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
[3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
[2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
[9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
[2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
[4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])


.. _references-coordinate-grad-semi-dual:
References
----------
Expand Down Expand Up @@ -137,27 +116,6 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None):
v : ndarray, shape (`nt`,)
Dual variable.

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
[1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
[3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
[2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
[9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
[2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
[4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])


.. _references-sag-entropic-transport:
References
----------
Expand Down Expand Up @@ -225,27 +183,6 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None):
ave_v : ndarray, shape (`nt`,)
dual variable

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
[1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
[3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
[2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
[9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
[2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
[4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])


.. _references-averaged-sgd-entropic-transport:
References
----------
Expand Down Expand Up @@ -304,27 +241,6 @@ def c_transform_entropic(b, M, reg, beta):
u : ndarray, shape (`ns`,)
Dual variable.

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
[1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
[3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
[2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
[9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
[2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
[4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])


.. _references-c-transform-entropic:
References
----------
Expand Down Expand Up @@ -399,27 +315,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
log : dict
log dictionary return only if log==True in parameters

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
[1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
[3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
[2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
[9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
[2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
[4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])


.. _references-solve-semi-dual-entropic:
References
----------
Expand Down Expand Up @@ -509,33 +404,6 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
grad : ndarray, shape (`ns`,)
partial grad F

Examples
--------
>>> import ot
>>> np.random.seed(0)
>>> n_source = 7
>>> n_target = 4
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> X_source = np.random.randn(n_source, 2)
>>> Y_target = np.random.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg=1, batch_size=3, numItermax=30000, lr=0.1, log=True)
>>> log['alpha']
array([0.71759102, 1.57057384, 0.85576566, 0.1208211 , 0.59190466,
1.197148 , 0.17805133])
>>> log['beta']
array([0.49741367, 0.57478564, 1.40075528, 2.75890102])
>>> sgd_dual_pi
array([[2.09730063e-02, 8.38169324e-02, 7.50365455e-03, 8.72731415e-09],
[5.58432437e-03, 5.89881299e-04, 3.09558411e-05, 8.35469849e-07],
[3.26489515e-03, 7.15536035e-02, 2.99778211e-02, 3.02601593e-10],
[4.05390622e-02, 5.31085068e-02, 6.65191787e-02, 1.55812785e-06],
[7.82299812e-02, 6.12099102e-03, 4.44989098e-02, 2.37719187e-03],
[5.06266486e-02, 2.16230494e-03, 2.26215141e-03, 6.81514609e-04],
[6.06713990e-02, 3.98139808e-02, 5.46829338e-02, 8.62371424e-06]])


.. _references-batch-grad-dual:
References
----------
Expand Down Expand Up @@ -600,37 +468,6 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr):
beta : ndarray, shape (nt,)
dual variable

Examples
--------
>>> import ot
>>> n_source = 7
>>> n_target = 4
>>> reg = 1
>>> numItermax = 20000
>>> lr = 0.1
>>> batch_size = 3
>>> log = True
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> rng = np.random.RandomState(0)
>>> X_source = rng.randn(n_source, 2)
>>> Y_target = rng.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log)
>>> log['alpha']
array([0.64171798, 1.27932201, 0.78132257, 0.15638935, 0.54888354,
1.03663469, 0.20595781])
>>> log['beta']
array([0.51207194, 0.58033189, 1.28922676, 2.26859736])
>>> sgd_dual_pi
array([[1.97276541e-02, 7.81248547e-02, 6.22136048e-03, 4.95442423e-09],
[4.23494310e-03, 4.43286263e-04, 2.06927079e-05, 3.82389139e-07],
[3.07542414e-03, 6.67897769e-02, 2.48904999e-02, 1.72030247e-10],
[4.26271990e-02, 5.53375455e-02, 6.16535024e-02, 9.88812650e-07],
[7.60423265e-02, 5.89585256e-03, 3.81267087e-02, 1.39458256e-03],
[4.37557504e-02, 1.85189176e-03, 1.72335760e-03, 3.55491279e-04],
[6.33096109e-02, 4.11683954e-02, 5.02962051e-02, 5.43097516e-06]])

References
----------
.. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. International Conference on Learning Representation (2018)
Expand Down Expand Up @@ -702,37 +539,6 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
log : dict
log dictionary return only if log==True in parameters

Examples
--------
>>> import ot
>>> n_source = 7
>>> n_target = 4
>>> reg = 1
>>> numItermax = 20000
>>> lr = 0.1
>>> batch_size = 3
>>> log = True
>>> a = ot.utils.unif(n_source)
>>> b = ot.utils.unif(n_target)
>>> rng = np.random.RandomState(0)
>>> X_source = rng.randn(n_source, 2)
>>> Y_target = rng.randn(n_target, 2)
>>> M = ot.dist(X_source, Y_target)
>>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log)
>>> log['alpha']
array([0.64057733, 1.2683513 , 0.75610161, 0.16024284, 0.54926534,
1.0514201 , 0.19958936])
>>> log['beta']
array([0.51372571, 0.58843489, 1.27993921, 2.24344807])
>>> sgd_dual_pi
array([[1.97377795e-02, 7.86706853e-02, 6.15682001e-03, 4.82586997e-09],
[4.19566963e-03, 4.42016865e-04, 2.02777272e-05, 3.68823708e-07],
[3.00379244e-03, 6.56562018e-02, 2.40462171e-02, 1.63579656e-10],
[4.28626062e-02, 5.60031599e-02, 6.13193826e-02, 9.67977735e-07],
[7.61972739e-02, 5.94609051e-03, 3.77886693e-02, 1.36046648e-03],
[4.44810042e-02, 1.89476742e-03, 1.73285847e-03, 3.51826036e-04],
[6.30118293e-02, 4.12398660e-02, 4.95148998e-02, 5.26247246e-06]])

References
----------
.. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. International Conference on Learning Representation (2018)
Expand Down