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Request sufficient accuracy in three gradient checks #243

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4 changes: 2 additions & 2 deletions tests/test_dual_variables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,7 +250,7 @@ def test_dual_gradcheck_inequality():

# Function that returns dual variable for gradcheck
def f(c_t):
x_opt, ineq_dual = layer(c_t)
x_opt, ineq_dual = layer(c_t, solver_args={"eps": 1e-10})
return ineq_dual

c_t = torch.tensor([1.0, -1.0], requires_grad=True)
Expand Down Expand Up @@ -303,7 +303,7 @@ def test_dual_gradcheck_vector_equality():
)

def f(A_t, b_t):
x_opt, eq_dual = layer(A_t, b_t)
x_opt, eq_dual = layer(A_t, b_t, solver_args={"eps": 1e-10})
return eq_dual.sum()

torch.manual_seed(42)
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5 changes: 4 additions & 1 deletion tests/test_torch.py
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Expand Up @@ -245,7 +245,10 @@ def test_sdp():
# Use a well-conditioned symmetric matrix
C_t = torch.tensor([[2.0, 0.5, 0.1], [0.5, 3.0, 0.2], [0.1, 0.2, 1.5]], requires_grad=True)

torch.autograd.gradcheck(layer, (C_t,), atol=1e-4, rtol=1e-3)
torch.autograd.gradcheck(
lambda C: layer(C, solver_args={"eps": 1e-10}),
(C_t,), atol=1e-4, rtol=1e-3,
)


def test_not_enough_parameters():
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