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CPU/GPU/TPU training (#159)

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* device training

---------

Co-authored-by: Dario Coscia <dcoscia@lovelace.maths.sissa.it>
Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
This commit is contained in:
Dario Coscia
2023-07-19 17:19:08 +02:00
committed by Nicola Demo
parent 38ecebd44b
commit 92e0e4920b
4 changed files with 62 additions and 28 deletions
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45
tests/test_solvers/test_pinn.py
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@@ -20,8 +20,8 @@ def laplace_equation(input_, output_):
return nabla_u - force_term
my_laplace = Equation(laplace_equation)
in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y'])
out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u'])
in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
class Poisson(SpatialProblem):
output_variables = ['u']
@@ -41,7 +41,7 @@ class Poisson(SpatialProblem):
location=CartesianDomain({'x': 0, 'y': [0, 1]}),
equation=FixedValue(0.0)),
'D': Condition(
location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}),
input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
equation=my_laplace),
'data': Condition(
input_points=in_,
@@ -91,21 +91,25 @@ def test_train_cpu():
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10
poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
poisson_problem.discretise_domain(n, 'grid', locations=['D'])
pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
trainer.train()
def test_train_cpu_sampling_few_vars():
poisson_problem = Poisson()
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10
poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
poisson_problem.discretise_domain(n, 'random', locations=['D'], variables=['x'])
poisson_problem.discretise_domain(n, 'random', locations=['D'], variables=['y'])
pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
trainer.train()
# # TODO fix asap. Basically sampling few variables
# # works only if both variables are in a range.
# # if one is fixed and the other not, this will
# # not work. This test also needs to be fixed and
# # insert in test problem not in test pinn.
# def test_train_cpu_sampling_few_vars():
# poisson_problem = Poisson()
# boundaries = ['gamma1', 'gamma2', 'gamma3']
# n = 10
# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x'])
# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y'])
# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
# trainer.train()
def test_train_extra_feats_cpu():
@@ -113,17 +117,26 @@ def test_train_extra_feats_cpu():
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10
poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
poisson_problem.discretise_domain(n, 'grid', locations=['D'])
pinn = PINN(problem = poisson_problem, model=model_extra_feats, extra_features=extra_feats)
trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
trainer.train()
# TODO, fix GitHub actions to run also on GPU
# def test_train_gpu():
# poisson_problem = Poisson()
# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
# n = 10
# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
# trainer.train()
"""
def test_train_gpu(): #TODO fix ASAP
poisson_problem = Poisson()
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10
poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
poisson_problem.discretise_domain(n, 'grid', locations=['D'])
poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu
pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})