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Check number of tensors at call time #21

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Nov 13, 2019
Merged

Check number of tensors at call time #21

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5 changes: 3 additions & 2 deletions cvxpylayers/tensorflow/cvxpylayer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,9 +100,10 @@ def __call__(self, *parameters, solver_args={}):
a list of optimal variable values, one for each CVXPY Variable
supplied to the constructor.
"""
# TODO(akshakya): Validate shapes of params.
if len(parameters) != len(self.params):
raise ValueError('A value must be provided for each CVXPY '
'parameter; received %d values, expected %d' % (
raise ValueError('A tensor must be provided for each CVXPY '
'parameter; received %d tensors, expected %d' % (
len(parameters), len(self.params)))
compute = tf.custom_gradient(
lambda *parameters: self._compute(parameters, solver_args))
Expand Down
18 changes: 15 additions & 3 deletions cvxpylayers/tensorflow/test_cvxpylayer.py
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Expand Up @@ -254,7 +254,19 @@ def test_not_enough_parameters(self):
objective = lam * cp.norm(x, 1) + lam2 * cp.sum_squares(x)
prob = cp.Problem(cp.Minimize(objective))
with self.assertRaisesRegex(ValueError, "The layer's parameters.*"):
CvxpyLayer(prob, [lam], [x])
CvxpyLayer(prob, [lam], [x]) # noqa: F841

def test_not_enough_parameters_at_call_time(self):
x = cp.Variable(1)
lam = cp.Parameter(1, nonneg=True)
lam2 = cp.Parameter(1, nonneg=True)
objective = lam * cp.norm(x, 1) + lam2 * cp.sum_squares(x)
prob = cp.Problem(cp.Minimize(objective))
layer = CvxpyLayer(prob, [lam, lam2], [x])
with self.assertRaisesRegex(
ValueError,
'A tensor must be provided for each CVXPY parameter.*'):
layer(lam)

def test_non_dpp(self):
x = cp.Variable(1)
Expand All @@ -263,7 +275,7 @@ def test_non_dpp(self):
objective = lam * cp.norm(x, 1)
prob = cp.Problem(cp.Minimize(objective))
with self.assertRaisesRegex(ValueError, 'Problem must be DPP.'):
CvxpyLayer(prob, [lam], [x, y])
CvxpyLayer(prob, [lam], [x, y]) # noqa: F841

def test_too_many_variables(self):
x = cp.Variable(1)
Expand All @@ -272,7 +284,7 @@ def test_too_many_variables(self):
objective = lam * cp.norm(x, 1)
prob = cp.Problem(cp.Minimize(objective))
with self.assertRaisesRegex(ValueError, 'Argument `variables`.*'):
CvxpyLayer(prob, [lam], [x, y])
CvxpyLayer(prob, [lam], [x, y]) # noqa: F841

def test_infeasible(self):
x = cp.Variable(1)
Expand Down
22 changes: 13 additions & 9 deletions cvxpylayers/torch/cvxpylayer.py
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Expand Up @@ -67,17 +67,16 @@ def __init__(self, problem, parameters, variables):
Variables determines the order of the optimal variable
values returned from the forward pass.
"""
if not problem.is_dpp():
raise ValueError('Problem must be DPP.')

super(CvxpyLayer, self).__init__()

assert set(problem.parameters()) == set(parameters), \
"The layer's parameters must exactly match problem.parameters"
assert set(variables).issubset(set(problem.variables())), \
"Argument variables must be a subset of problem.variables"
assert hasattr(problem, "get_problem_data"), \
"cvxpy problem does not support ASA form; please upgrade cvxpy"
if not problem.is_dpp():
raise ValueError('Problem must be DPP.')
if not set(problem.parameters()) == set(parameters):
raise ValueError("The layer's parameters must exactly match "
"problem.parameters")
if not set(variables).issubset(set(problem.variables())):
raise ValueError("Argument variables must be a subset of "
"problem.variables")

self.param_order = parameters
self.param_ids = [p.id for p in self.param_order]
Expand Down Expand Up @@ -105,6 +104,11 @@ def forward(self, *params, solver_args={}):
a list of optimal variable values, one for each CVXPY Variable
supplied to the constructor.
"""
# TODO(akshakya): Validate shapes of params.
if len(params) != len(self.param_ids):
raise ValueError('A tensor must be provided for each CVXPY '
'parameter; received %d tensors, expected %d' % (
len(params), len(self.param_ids)))
info = {}
f = _CvxpyLayerFn(
param_order=self.param_order,
Expand Down
18 changes: 15 additions & 3 deletions cvxpylayers/torch/test_cvxpylayer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -215,7 +215,7 @@ def test_sdp(self):
constraints)
layer = CvxpyLayer(prob, [C] + A + b, [X])
torch.autograd.gradcheck(lambda *x: layer(*x,
solver_args={"eps": 1e-12}),
solver_args={'eps': 1e-12}),
[C_tch] + A_tch + b_tch,
eps=1e-6,
atol=1e-3,
Expand All @@ -227,16 +227,28 @@ def test_not_enough_parameters(self):
lam2 = cp.Parameter(1, nonneg=True)
objective = lam * cp.norm(x, 1) + lam2 * cp.sum_squares(x)
prob = cp.Problem(cp.Minimize(objective))
with self.assertRaises(AssertionError):
with self.assertRaises(ValueError):
layer = CvxpyLayer(prob, [lam], [x]) # noqa: F841

def test_not_enough_parameters_at_call_time(self):
x = cp.Variable(1)
lam = cp.Parameter(1, nonneg=True)
lam2 = cp.Parameter(1, nonneg=True)
objective = lam * cp.norm(x, 1) + lam2 * cp.sum_squares(x)
prob = cp.Problem(cp.Minimize(objective))
layer = CvxpyLayer(prob, [lam, lam2], [x]) # noqa: F841
with self.assertRaisesRegex(
ValueError,
'A tensor must be provided for each CVXPY parameter.*'):
layer(lam)

def test_too_many_variables(self):
x = cp.Variable(1)
y = cp.Variable(1)
lam = cp.Parameter(1, nonneg=True)
objective = lam * cp.norm(x, 1)
prob = cp.Problem(cp.Minimize(objective))
with self.assertRaises(AssertionError):
with self.assertRaises(ValueError):
layer = CvxpyLayer(prob, [lam], [x, y]) # noqa: F841

def test_infeasible(self):
Expand Down