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Issues with pytorch bindings in the case where structural_feasibility=False. #414

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5b3671e
Add a test for the pytorch interface
oomcth Aug 8, 2025
10de54e
Fix batched unfeasible QP
oomcth Aug 8, 2025
ccfc91a
Rename cvxpy.py to cvxpy_test.py
oomcth Aug 8, 2025
e736b9c
Add some checks for the gradients
oomcth Aug 8, 2025
0166ef0
Fix some bugs for the backward infeasible case
oomcth Aug 8, 2025
1b75a6e
Merge branch 'Simple-Robotics:devel' into devel
oomcth Sep 29, 2025
be7f5af
Delete test/src/cvxpy_test.py
oomcth Sep 29, 2025
154e858
Add files via upload
oomcth Sep 29, 2025
fb9c80a
Add files via upload
oomcth Sep 29, 2025
acb0276
now test handle with and without eq/neq
oomcth Sep 29, 2025
538459e
Add back cvxpy.py
oomcth Sep 29, 2025
2630af4
fixed a ruff error
oomcth Sep 29, 2025
af8c876
Fixed a ruff error
oomcth Sep 29, 2025
03b3ee1
Added checks for gradients consistency
oomcth Oct 5, 2025
b52112a
Added fix for gradients
oomcth Oct 5, 2025
ea6f117
Gradients checks are not enough.
oomcth Oct 7, 2025
a25953b
Fixed some minor gradient bugs in the case where the inputs were batc…
oomcth Oct 7, 2025
a19542d
Fix minor error in docstrings
oomcth Oct 7, 2025
e507b34
changed max_iter_in value
oomcth Oct 7, 2025
c1be573
Delete test/src/QPLayer_test.py
oomcth Oct 7, 2025
4bde1fb
Add files via upload
oomcth Oct 7, 2025
f505e89
Delete bindings/python/proxsuite/torch/torch_bindings.py
oomcth Oct 7, 2025
1e4f6b6
Changed back max_iter_in to 100
oomcth Oct 7, 2025
81ff8fb
Merge branch 'Simple-Robotics:devel' into devel
oomcth Oct 7, 2025
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210 changes: 140 additions & 70 deletions bindings/python/proxsuite/torch/qplayer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,10 @@ def QPFunction(
class QPFunctionFn(Function):
@staticmethod
def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
nBatch = extract_nBatch(Q_, p_, A_, b_, G_, l_, u_)
if len(Q_.size()) == 3:
nBatch = Q_.size(0)
else:
nBatch = 1
Q, _ = expandParam(Q_, nBatch, 3)
p, _ = expandParam(p_, nBatch, 2)
G, _ = expandParam(G_, nBatch, 3)
Expand All @@ -103,8 +106,13 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
ctx.vector_of_qps = proxsuite.proxqp.dense.BatchQP()

ctx.nBatch = nBatch

_, nineq, nz = G.size()
do_neq = True
if len(G.size()) == 3 or len(G.size()) == 2:
nineq, nz = G.size()[1:]
else:
nineq = 0
nz = Q.size()[-1]
do_neq = False
neq = A.size(1) if A.nelement() > 0 else 0
assert neq > 0 or nineq > 0
ctx.neq, ctx.nineq, ctx.nz = neq, nineq, nz
Expand Down Expand Up @@ -134,21 +142,20 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
if p[i] is not None:
p__ = p[i].cpu().numpy()
G__ = None
if G[i] is not None:
if do_neq and G[i] is not None:
G__ = G[i].cpu().numpy()
u__ = None
if u[i] is not None:
if do_neq and u[i] is not None:
u__ = u[i].cpu().numpy()
l__ = None
if l[i] is not None:
if do_neq and l[i] is not None:
l__ = l[i].cpu().numpy()
A__ = None
if Ai is not None:
A__ = Ai.cpu().numpy()
b__ = None
if bi is not None:
b__ = bi.cpu().numpy()

qp.init(
H=H__, g=p__, A=A__, b=b__, C=G__, l=l__, u=u__, rho=default_rho
)
Expand Down Expand Up @@ -255,8 +262,22 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus):
class QPFunctionFn_infeas(Function):
@staticmethod
def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
n_in, nz = G_.size() # true double-sided inequality size
nBatch = extract_nBatch(Q_, p_, A_, b_, G_, l_, u_)

do_neq = True
if len(G_.size()) == 3:
_, n_in, nz = G_.size()
elif len(G_.size()) == 2:
n_in = G_.size()[-2]
nz = G_.size()[-1]
else:
n_in = Q_.size()[-1]
nz = Q_.size()[-1]
do_neq = False
ctx.G_size = G_.size()
if len(Q_.size()) == 3:
nBatch = Q_.size(0)
else:
nBatch = 1

Q, _ = expandParam(Q_, nBatch, 3)
p, _ = expandParam(p_, nBatch, 2)
Expand All @@ -268,32 +289,43 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):

h = torch.cat((-l, u), axis=1) # single-sided inequality
G = torch.cat((-G, G), axis=1) # single-sided inequality

_, nineq, nz = G.size()
neq = A.size(1) if A.nelement() > 0 else 0
if len(G.size()) == 3:
_, nineq, nz = G.size()
else:
nineq = 0
nz = Q.size()[-1]
if len(A.size()) == 3 or len(A.size()) == 2:
neq = A.size(-2) if A.nelement() > 0 else 0
else:
neq = 0
assert neq > 0 or nineq > 0
ctx.neq, ctx.nineq, ctx.nz = neq, nineq, nz

zhats = torch.empty((nBatch, ctx.nz), dtype=Q.dtype)
nus = torch.empty((nBatch, ctx.nineq), dtype=Q.dtype)
nus_sol = torch.empty(
(nBatch, n_in), dtype=Q.dtype
) # double-sided inequality multiplier
if do_neq:
nus_sol = torch.empty(
(nBatch, n_in), dtype=Q.dtype
) # double-sided inequality multiplier
else:
nus_sol = None
lams = (
torch.empty(nBatch, ctx.neq, dtype=Q.dtype)
if ctx.neq > 0
else torch.empty()
else torch.tensor([])
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Is this to ensure at least a 1st-order tensor? Does torch.empty() create a zero-dim tensor? Aren't there args for it to set its dimensions?

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torch.empty() does not work.

TypeError: empty() received an invalid combination of arguments - got (), but expected one of:
 * (tuple of ints size, *, tuple of names names, torch.memory_format memory_format = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (tuple of ints size, *, torch.memory_format memory_format = None, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

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https://docs.pytorch.org/docs/stable/generated/torch.empty.html
That means we just need to pass in arguments, no?

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We could do torch.empty(0)

>>> torch.empty(0)
tensor([])

)
s_e = (
torch.empty(nBatch, ctx.neq, dtype=Q.dtype)
if ctx.neq > 0
else torch.empty()
else torch.tensor([])
)
slacks = torch.empty((nBatch, ctx.nineq), dtype=Q.dtype)
s_i = torch.empty(
(nBatch, n_in), dtype=Q.dtype
) # this one is of size the one of the original n_in

if do_neq:
s_i = torch.empty(
(nBatch, n_in), dtype=Q.dtype
) # this one is of size the one of the original n_in
else:
s_i = None
vector_of_qps = proxsuite.proxqp.dense.BatchQP()

ctx.cpu = os.cpu_count()
Expand All @@ -311,18 +343,21 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
qp.settings.refactor_rho_threshold = default_rho # no refactorization
qp.settings.eps_abs = eps
Ai, bi = (A[i], b[i]) if neq > 0 else (None, None)

H__ = None
if Q[i] is not None:
H__ = Q[i].cpu().numpy()
p__ = None
if p[i] is not None:
p__ = p[i].cpu().numpy()
G__ = None
if G[i] is not None:
if do_neq and G[i] is not None:
G__ = G[i].cpu().numpy()
u__ = None
if h[i] is not None:
if do_neq and h[i] is not None:
u__ = h[i].cpu().numpy()
if not do_neq:
l = None
# l__ = None
# if (l[i] is not None):
# l__ = l[i].cpu().numpy()
Expand All @@ -332,7 +367,6 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
b__ = None
if bi is not None:
b__ = bi.cpu().numpy()

qp.init(H=H__, g=p__, A=A__, b=b__, C=G__, l=l, u=u__, rho=default_rho)

if proxqp_parallel:
Expand All @@ -348,16 +382,18 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
if nineq > 0:
# we re-convert the solution to a double sided inequality QP
slack = -h[i] + G[i] @ vector_of_qps.get(i).results.x
nus_sol[i] = torch.Tensor(
-vector_of_qps.get(i).results.z[:n_in]
+ vector_of_qps.get(i).results.z[n_in:]
) # de-projecting this one may provoke loss of information when using inexact solution
if do_neq:
nus_sol[i] = torch.Tensor(
-vector_of_qps.get(i).results.z[:n_in]
+ vector_of_qps.get(i).results.z[n_in:]
) # de-projecting this one may provoke loss of information when using inexact solution
nus[i] = torch.tensor(vector_of_qps.get(i).results.z)
slacks[i] = slack.clone().detach()
s_i[i] = torch.tensor(
-vector_of_qps.get(i).results.si[:n_in]
+ vector_of_qps.get(i).results.si[n_in:]
)
if do_neq:
s_i[i] = torch.tensor(
-vector_of_qps.get(i).results.si[:n_in]
+ vector_of_qps.get(i).results.si[n_in:]
)
if neq > 0:
lams[i] = torch.tensor(vector_of_qps.get(i).results.y)
s_e[i] = torch.tensor(vector_of_qps.get(i).results.se)
Expand All @@ -371,7 +407,10 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
@staticmethod
def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
zhats, s_e, Q, p, G, l, u, A, b = ctx.saved_tensors
nBatch = extract_nBatch(Q, p, A, b, G, l, u)
if len(Q.size()) == 3:
nBatch = Q.size(0)
else:
nBatch = 1

Q, Q_e = expandParam(Q, nBatch, 3)
p, p_e = expandParam(p, nBatch, 2)
Expand Down Expand Up @@ -414,7 +453,9 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):

for i in range(nBatch):
Q_i = Q[i].numpy()
C_i = G[i].numpy()
C_i = None
if G is not None and G.numel() != 0:
C_i = G[i].numpy()
A_i = None
if A is not None:
if A.shape[0] != 0:
Expand All @@ -436,16 +477,17 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
if neq > 0:
kkt[:dim, dim : dim + n_eq] = A_i.transpose()
kkt[dim : dim + n_eq, :dim] = A_i
kkt[
dim + n_eq + n_in : dim + 2 * n_eq + n_in, dim : dim + n_eq
] = -np.eye(n_eq)
kkt[dim + n_eq + n_in : dim + 2 * n_eq + n_in, dim : dim + n_eq] = (
-np.eye(n_eq)
)
kkt[
dim + n_eq + n_in : dim + 2 * n_eq + n_in,
dim + n_eq + 2 * n_in : 2 * dim + n_eq + 2 * n_in,
] = A_i

kkt[:dim, dim + n_eq : dim + n_eq + n_in] = C_i.transpose()
kkt[dim + n_eq : dim + n_eq + n_in, :dim] = C_i
if n_in > 0:
kkt[:dim, dim + n_eq : dim + n_eq + n_in] = C_i.transpose()
kkt[dim + n_eq : dim + n_eq + n_in, :dim] = C_i

D_1_c = np.eye(n_in) # represents [s_i]_- + z_i < 0
D_1_c[P_1, P_1] = 0.0
Expand Down Expand Up @@ -485,9 +527,9 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
rhs[dim + n_eq : dim + n_eq + n_in_sol][~active_set] = dl_dnus[
i
][~active_set]
rhs[dim + n_eq + n_in_sol : dim + n_eq + n_in][
active_set
] = -dl_dnus[i][active_set]
rhs[dim + n_eq + n_in_sol : dim + n_eq + n_in][active_set] = (
-dl_dnus[i][active_set]
)
Comment on lines +530 to +532
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Is this a file formatting change? Did it come from pre-commit and ruff?

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@oomcth oomcth Oct 7, 2025 •
edited
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Its a formatting change. It comes from ruff on my computer.

if dl_ds_e is not None:
if dl_ds_e.shape[0] != 0:
rhs[dim + n_eq + n_in : dim + 2 * n_eq + n_in] = -dl_ds_e[i]
Expand Down Expand Up @@ -515,9 +557,9 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):

qp.settings.primal_infeasibility_solving = True
qp.settings.eps_abs = eps_backward
qp.settings.max_iter = 10
qp.settings.default_rho = 1.0e-3
qp.settings.refactor_rho_threshold = 1.0e-3
qp.settings.max_iter = 1000
qp.settings.default_rho = 5.0e-5
qp.settings.refactor_rho_threshold = 5.0e-5
qp.init(
H,
g,
Expand All @@ -542,13 +584,19 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
)
if n_eq > 0:
dlam[i] = torch.from_numpy(
np.float64(vector_of_qps.get(i).results.x[dim : dim + n_eq])
vector_of_qps.get(i)
.results.x[dim : dim + n_eq]
.astype(np.float64)
)
dnu[i] = torch.from_numpy(
np.float64(
vector_of_qps.get(i).results.x[dim + n_eq : dim + n_eq + n_in]

if dnu is not None:
dnu[i] = torch.from_numpy(
np.float64(
vector_of_qps.get(i).results.x[
dim + n_eq : dim + n_eq + n_in
]
)
)
)
dim_ = 0
if n_eq > 0:
b_5[i] = torch.from_numpy(
Expand All @@ -566,16 +614,18 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
)

dps = dx
dGs = (
bger(dnu.double(), zhats.double())
+ bger(ctx.nus.double(), dx.double())
+ bger(P_2_c_s_i.double(), b_6.double())
)
if G_e:
dGs = dGs.mean(0)
dhs = -dnu
if h_e:
dhs = dhs.mean(0)
dGs = None
if dnu is not None:
dGs = (
bger(dnu.double(), zhats.double())
+ bger(ctx.nus.double(), dx.double())
+ bger(P_2_c_s_i.double(), b_6.double())
)
if G_e:
dGs = dGs.mean(0)
dhs = -dnu
if h_e:
dhs = dhs.mean(0)
if neq > 0:
dAs = (
bger(dlam.double(), zhats.double())
Expand All @@ -597,16 +647,36 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
if p_e:
dps = dps.mean(0)

grads = (
dQs,
dps,
dAs,
dbs,
dGs[n_in_sol:, :],
-dhs[:n_in_sol],
dhs[n_in_sol:],
)

if len(ctx.G_size) == 2:
grads = (
dQs,
dps,
dAs,
dbs,
dGs[n_in_sol:, :],
-dhs[:n_in_sol],
dhs[n_in_sol:],
)
elif len(ctx.G_size) == 3:
grads = (
dQs,
dps,
dAs,
dbs,
dGs[:, n_in_sol:, :],
-dhs[:, :n_in_sol],
dhs[:, n_in_sol:],
)
else:
grads = (
dQs,
dps,
dAs,
dbs,
None,
None,
None,
)
return grads

if structural_feasibility:
Expand Down
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