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import copy
from concurrent.futures import ThreadPoolExecutor
from contextlib import contextmanager
from threadpoolctl import threadpool_limits
@contextmanager
def _limit_native_threads(n: int = 1):
if threadpool_limits is None:
yield
else:
with threadpool_limits(limits=n):
yield
import cvxpy as cp
import numpy as np
import torch
try:
from cvxtorch import TorchExpression
except Exception:
TorchExpression = None
def _require_cvxtorch():
if TorchExpression is None:
raise ImportError(
"cvxtorch is required for this feature. Install it with:\n"
" pip install git+https://github.com/cvxpy/cvxtorch.git"
)
from cvxpy.constraints.exponential import ExpCone
from cvxpy.constraints.psd import PSD
from cvxpy.constraints.second_order import SOC
from .utils import to_numpy, to_torch, slice_params_for_batch
@torch.no_grad()
def _compare_grads(params_req, grads, ground_truth_grads):
est_chunks, gt_chunks = [], []
for p, ge, gg in zip(params_req, grads, ground_truth_grads):
ge = torch.zeros_like(p) if ge is None else ge.detach()
gg = torch.zeros_like(p) if gg is None else gg.detach()
est_chunks.append(ge.reshape(-1))
gt_chunks.append(gg.reshape(-1))
est = torch.cat(est_chunks)
gt = torch.cat(gt_chunks)
eps = 1e-12
denom = (est.norm() * gt.norm()).clamp_min(eps)
cos_sim = torch.dot(est, gt) / denom
l2_diff = (est - gt).norm()
return cos_sim, l2_diff
def _cvx_sum_or_zero(terms):
return cp.sum(terms) if len(terms) > 0 else cp.Constant(0.0)
def _has_pnorm_atom(expr) -> bool:
try:
nm_fn = getattr(expr, "name", None)
if callable(nm_fn):
nm = nm_fn()
if nm in {"pnorm", "norm1", "norm_inf"}:
return True
except Exception:
pass
try:
cls = expr.__class__.__name__.lower()
if cls in {"pnorm", "norm1", "norminf", "norm_inf"}:
return True
except Exception:
pass
for a in getattr(expr, "args", []) or []:
if _has_pnorm_atom(a):
return True
return False
def _infer_objective_expr(problem: cp.Problem):
obj = problem.objective
if isinstance(obj, cp.Minimize):
return obj.expr
if isinstance(obj, cp.Maximize):
return -obj.expr
expr = getattr(obj, "expr", None)
if expr is None:
raise ValueError("Unsupported objective type; expected Minimize/Maximize.")
return expr
def _expcone_dual_dot(u_triplet, c: ExpCone):
ux, uy, uz = u_triplet
x, y, z = c.args
return cp.sum(cp.multiply(ux, x)) + cp.sum(cp.multiply(uy, y)) + cp.sum(cp.multiply(uz, z))
def _split_expcone_dual_value(dv, shapes3):
if isinstance(dv, (list, tuple)) and len(dv) == 3:
out = [np.asarray(d, dtype=float) for d in dv]
for k in range(3):
if tuple(out[k].shape) != tuple(shapes3[k]):
if out[k].size == int(np.prod(shapes3[k])):
out[k] = out[k].reshape(shapes3[k])
else:
raise ValueError(f"ExpCone dual block {k} shape mismatch: got {out[k].shape}, expected {shapes3[k]}")
return out
dv_arr = np.asarray(dv, dtype=float)
if dv_arr.ndim >= 1 and dv_arr.shape[-1] == 3:
base = dv_arr.shape[:-1]
if tuple(base) == tuple(shapes3[0]) and tuple(base) == tuple(shapes3[1]) and tuple(base) == tuple(shapes3[2]):
return [dv_arr[..., k].reshape(shapes3[k]) for k in range(3)]
block = int(np.prod(shapes3[0]))
if int(dv_arr.size) == 3 * block and int(np.prod(shapes3[1])) == block and int(np.prod(shapes3[2])) == block:
tmp = dv_arr.reshape((block, 3))
return [tmp[:, k].reshape(shapes3[k]) for k in range(3)]
raise ValueError(
f"Cannot parse ExpCone dual_value with shape {dv_arr.shape} into 3 blocks of shapes {shapes3}."
)
def _active_counts_one(b, ctx, i: int, tol: float):
out = {}
out["eq"] = sum(int(np.prod(f.shape)) for f in b["eq_functions"])
out["ineq"] = sum(
int(np.sum(np.asarray(ctx.scalar_ineq_slack[j][i]) <= tol))
for j in range(len(b["scalar_ineq_functions"]))
)
soc_cnt = 0
for c in b["soc_constraints"]:
t_val = c.args[0].expr.value
x_val = c.args[1].expr.value
if t_val is None or x_val is None:
continue
t = np.asarray(t_val, dtype=float).reshape(-1) # (k,) or (1,)
x = np.asarray(x_val, dtype=float)
if t.size == 1:
soc_cnt += int((t.item() - np.linalg.norm(x.ravel())) <= tol)
else:
if x.ndim == 1:
norms = np.full(t.size, np.linalg.norm(x.ravel()))
elif x.shape[-1] == t.size:
norms = np.linalg.norm(x.reshape(-1, t.size), axis=0)
elif x.shape[0] == t.size:
norms = np.linalg.norm(x.reshape(t.size, -1), axis=1)
else:
flat = x.ravel()
if flat.size % t.size == 0:
norms = np.linalg.norm(flat.reshape(t.size, -1), axis=1)
else:
norms = np.full(t.size, np.linalg.norm(flat))
soc_cnt += int(np.sum((t - norms) <= tol))
out["soc"] = soc_cnt
exp_cnt = 0
for c in b["exp_cones"]:
x_val, y_val, z_val = (c.args[0].value, c.args[1].value, c.args[2].value)
if x_val is None or y_val is None or z_val is None:
continue
xv = np.asarray(x_val, dtype=float).reshape(-1) # (k,) or (1,)
yv = np.asarray(y_val, dtype=float).reshape(-1)
zv = np.asarray(z_val, dtype=float).reshape(-1)
k = max(xv.size, yv.size, zv.size)
# broadcast scalars to vector length k if needed
if xv.size == 1 and k > 1: xv = np.full(k, xv.item())
if yv.size == 1 and k > 1: yv = np.full(k, yv.item())
if zv.size == 1 and k > 1: zv = np.full(k, zv.item())
with np.errstate(over="ignore", divide="ignore", invalid="ignore"):
active = np.zeros(k, dtype=bool)
active[yv <= tol] = True
mask = yv > tol
resid = zv[mask] - yv[mask] * np.exp(xv[mask] / yv[mask])
active[mask] = resid <= tol
exp_cnt += int(np.sum(active))
out["exp"] = exp_cnt
psd_cnt = 0
for c in b["psd_cones"]:
X = np.asarray(c.expr.value, dtype=float)
X = 0.5 * (X + X.T)
psd_cnt += int(np.linalg.eigvalsh(X).min() <= tol)
out["psd"] = psd_cnt
out["cone_total"] = out["soc"] + out["exp"] + out["psd"]
out["total"] = out["eq"] + out["ineq"] + out["cone_total"]
return out
def active_counts_dict(ctx, tol = None, reduce: str = "sum"):
tol = float(ctx.mt.slack_tol if tol is None else tol)
per_batch = [_active_counts_one(ctx.bundles[i], ctx, i, tol) for i in range(ctx.batch_size)]
if reduce is None:
return per_batch
keys = per_batch[0].keys()
return {k: sum(d[k] for d in per_batch) for k in keys}
def _build_problem_bundle(
problem: cp.Problem,
parameters,
variables,
alpha: float,
dual_cutoff: float,
slack_tol: float,
eps: float,
):
"""
Build and return a dict containing EVERYTHING needed for one problem:
- forward problem, perturbed problem
- cvxpy Parameters for dvars/duals/masks
- torch callables for phi and each dual term
- pnorm tangent caches + TorchExpression for g (for gradient wrt variables)
"""
objective_expr = _infer_objective_expr(problem)
# ---- split constraints ----
eq_funcs = []
scalar_ineq_funcs = []
soc_constraints = []
exp_cones = []
psd_cones = []
for c in problem.constraints:
if isinstance(c, cp.constraints.zero.Equality):
eq_funcs.append(c.expr)
elif isinstance(c, cp.constraints.nonpos.Inequality):
scalar_ineq_funcs.append(c.expr)
elif isinstance(c, SOC):
soc_constraints.append(c)
elif isinstance(c, ExpCone):
exp_cones.append(c)
elif isinstance(c, PSD):
psd_cones.append(c)
else:
raise ValueError(f"Unsupported constraint type: {type(c)}")
param_order = list(parameters)
variables = list(variables)
# ---- original problem (forward) ----
eq_constraints = [f == 0 for f in eq_funcs]
scalar_ineq_constraints = [g <= 0 for g in scalar_ineq_funcs]
forward_problem = cp.Problem(
cp.Minimize(objective_expr),
eq_constraints + scalar_ineq_constraints + soc_constraints + exp_cones + psd_cones,
)
# ---- dvar params ----
dvar_params = [cp.Parameter(shape=v.shape) for v in variables]
# ---- dual params (old) for eq/scalar ineq ----
eq_dual_params = [cp.Parameter(shape=f.shape) for f in eq_funcs]
scalar_ineq_dual_params = [cp.Parameter(shape=g.shape, nonneg=True) for g in scalar_ineq_funcs]
# ---- scalar active masks ----
scalar_active_mask_params = [cp.Parameter(shape=g.shape, nonneg=True) for g in scalar_ineq_funcs]
# ---- SOC dual placeholders (old) and linear constraints ----
soc_dual_params_0 = [cp.Parameter(shape=c.dual_variables[0].shape, nonneg=True) for c in soc_constraints]
soc_dual_params_1 = [cp.Parameter(shape=c.dual_variables[1].shape) for c in soc_constraints]
soc_dual_product = _cvx_sum_or_zero([
cp.multiply(cp.pnorm(c.args[1].expr, p=2) - c.args[0].expr, u)
for u, c in zip(soc_dual_params_0, soc_constraints)
])
soc_lin_constraints = [
(soc_dual_params_1[j].T @ soc_constraints[j].args[1].expr
+ cp.multiply(soc_constraints[j].args[0].expr, soc_dual_params_0[j])) == 0
for j in range(len(soc_constraints))
]
# ---- ExpCone dual placeholders (old) ----
exp_dual_params = [[cp.Parameter(shape=dv.shape) for dv in c.dual_variables] for c in exp_cones]
exp_dual_product = _cvx_sum_or_zero([
_expcone_dual_dot(u3, c) for u3, c in zip(exp_dual_params, exp_cones)
])
# ---- PSD dual placeholders (old) ----
psd_dual_params = [cp.Parameter(shape=c.dual_variables[0].shape) for c in psd_cones]
psd_dual_product = _cvx_sum_or_zero([
cp.sum(cp.multiply(u, c.expr)) for u, c in zip(psd_dual_params, psd_cones)
])
# ---- pnorm tangent support for scalar inequalities (scalar-only) ----
pnorm_ineq_ids = []
non_pnorm_scalar_ids = []
pnorm_xstar_params = []
pnorm_grad_params = []
pnorm_tangent_constraints = []
pnorm_g_torch = []
for j, g in enumerate(scalar_ineq_funcs):
is_scalar = int(np.prod(g.shape)) == 1
is_pnorm = is_scalar and _has_pnorm_atom(g)
if not is_pnorm:
non_pnorm_scalar_ids.append(j)
continue
local_id = len(pnorm_ineq_ids)
pnorm_ineq_ids.append(j)
xs = []
gs = []
for v in variables:
xs.append(cp.Parameter(shape=v.shape))
gs.append(cp.Parameter(shape=v.shape))
pnorm_xstar_params.append(xs)
pnorm_grad_params.append(gs)
lin = cp.Constant(0.0)
for v_id, v in enumerate(variables):
dv = v - pnorm_xstar_params[local_id][v_id]
lin += cp.sum(cp.multiply(pnorm_grad_params[local_id][v_id], dv))
pnorm_tangent_constraints.append(cp.multiply(scalar_active_mask_params[j], lin) == 0)
pnorm_g_torch.append(
TorchExpression(
g,
provided_vars_list=[*variables, *param_order],
).torch_expression
)
# ---- perturbed problem ----
vars_dvars_product = _cvx_sum_or_zero([cp.sum(cp.multiply(dv, v)) for dv, v in zip(dvar_params, variables)])
scalar_ineq_dual_product = _cvx_sum_or_zero([
cp.sum(cp.multiply(lm, g)) for lm, g in zip(scalar_ineq_dual_params, scalar_ineq_funcs)
])
new_objective = (1.0 / float(alpha)) * vars_dvars_product + objective_expr
new_objective += scalar_ineq_dual_product + soc_dual_product + exp_dual_product
# Note: psd_dual_product is intentionally NOT added to the perturbed objective.
# For PSD cones, keeping them as explicit constraints in the perturbed problem
# lets the solver handle the conic geometry directly.
active_eq_constraints = [
cp.multiply(scalar_active_mask_params[j], scalar_ineq_funcs[j]) == 0
for j in non_pnorm_scalar_ids
]
perturbed_problem = cp.Problem(
cp.Minimize(new_objective),
eq_constraints + active_eq_constraints + soc_lin_constraints + pnorm_tangent_constraints + psd_cones,
)
# ---- TorchExpressions for loss pieces (phi and dual terms) ----
phi_torch = TorchExpression(
objective_expr,
provided_vars_list=[*variables, *param_order],
).torch_expression
eq_terms = [cp.sum(cp.multiply(du, f)) for du, f in zip(eq_dual_params, eq_funcs)]
eq_dual_term_torch = TorchExpression(
_cvx_sum_or_zero(eq_terms),
provided_vars_list=[*variables, *param_order, *eq_dual_params],
).torch_expression
ineq_terms = [cp.sum(cp.multiply(du, g)) for du, g in zip(scalar_ineq_dual_params, scalar_ineq_funcs)]
ineq_dual_term_torch = TorchExpression(
_cvx_sum_or_zero(ineq_terms),
provided_vars_list=[*variables, *param_order, *scalar_ineq_dual_params],
).torch_expression
if len(exp_cones) > 0:
exp_terms = [_expcone_dual_dot(du3, c) for du3, c in zip(exp_dual_params, exp_cones)]
exp_dual_term_torch = TorchExpression(
_cvx_sum_or_zero(exp_terms),
provided_vars_list=[*variables, *param_order, *[u for tri in exp_dual_params for u in tri]],
).torch_expression
else:
exp_dual_term_torch = None
if len(psd_cones) > 0:
psd_terms = [cp.sum(cp.multiply(du, c.expr)) for du, c in zip(psd_dual_params, psd_cones)]
psd_dual_term_torch = TorchExpression(
_cvx_sum_or_zero(psd_terms),
provided_vars_list=[*variables, *param_order, *psd_dual_params],
).torch_expression
else:
psd_dual_term_torch = None
non_pnorm_set = set(non_pnorm_scalar_ids)
pnorm_set = set(pnorm_ineq_ids)
pnorm_map = {j: lid for lid, j in enumerate(pnorm_ineq_ids)}
scalar_is_scalar = [int(np.prod(g.shape)) == 1 for g in scalar_ineq_funcs]
scalar_scalar_indices = [j for j, f in enumerate(scalar_is_scalar) if f]
scalar_nonscalar_indices = [j for j, f in enumerate(scalar_is_scalar) if not f]
return dict(
alpha=float(alpha),
dual_cutoff=float(dual_cutoff),
slack_tol=float(slack_tol),
eps=float(eps),
param_order=param_order,
variables=variables,
objective=objective_expr,
eq_functions=eq_funcs,
scalar_ineq_functions=scalar_ineq_funcs,
scalar_is_scalar=scalar_is_scalar,
scalar_scalar_indices=scalar_scalar_indices,
scalar_nonscalar_indices=scalar_nonscalar_indices,
soc_constraints=soc_constraints,
exp_cones=exp_cones,
psd_cones=psd_cones,
eq_constraints=eq_constraints,
scalar_ineq_constraints=scalar_ineq_constraints,
soc_lin_constraints=soc_lin_constraints,
active_eq_constraints=active_eq_constraints,
# problems
problem=forward_problem,
perturbed_problem=perturbed_problem,
# cvx params
dvar_params=dvar_params,
eq_dual_params=eq_dual_params,
scalar_ineq_dual_params=scalar_ineq_dual_params,
scalar_active_mask_params=scalar_active_mask_params,
soc_dual_params_0=soc_dual_params_0,
soc_dual_params_1=soc_dual_params_1,
exp_dual_params=exp_dual_params,
psd_dual_params=psd_dual_params,
# pnorm tangent
pnorm_ineq_ids=pnorm_ineq_ids,
non_pnorm_scalar_ids=non_pnorm_scalar_ids,
pnorm_xstar_params=pnorm_xstar_params,
pnorm_grad_params=pnorm_grad_params,
pnorm_tangent_constraints=pnorm_tangent_constraints,
pnorm_g_torch=pnorm_g_torch,
# precomputed sets/maps
non_pnorm_set=non_pnorm_set,
pnorm_set=pnorm_set,
pnorm_map=pnorm_map,
# torch callables
phi_torch=phi_torch,
eq_dual_term_torch=eq_dual_term_torch,
ineq_dual_term_torch=ineq_dual_term_torch,
exp_dual_term_torch=exp_dual_term_torch,
psd_dual_term_torch=psd_dual_term_torch,
)
def FFOLayer(
problem,
parameters,
variables,
alpha: float = 100.0,
dual_cutoff: float = 1e-3,
slack_tol: float = 1e-8,
eps: float = 1e-13,
compute_cos_sim: bool = False,
max_workers: int = 8,
backward_eps: float = 1e-3,
verbose: bool = False,
):
_require_cvxtorch()
print(f"FFOLayer forward eps = {eps}, backward eps = {backward_eps}")
return _FFOLayer(
problem=problem,
parameters=parameters,
variables=variables,
alpha=alpha,
dual_cutoff=dual_cutoff,
slack_tol=slack_tol,
eps=eps,
backward_eps=backward_eps,
compute_cos_sim=compute_cos_sim,
max_workers=max_workers,
verbose=verbose,
)
class _FFOLayer(torch.nn.Module):
def __init__(
self,
problem,
parameters,
variables,
alpha,
dual_cutoff,
slack_tol,
eps,
backward_eps,
compute_cos_sim,
max_workers: int = 8,
verbose: bool = False,
):
super().__init__()
self.alpha = float(alpha)
self.dual_cutoff = float(dual_cutoff)
self.slack_tol = float(slack_tol)
self.eps = float(eps)
self.backward_eps = float(backward_eps)
self._compute_cos_sim = bool(compute_cos_sim)
self.verbose = bool(verbose)
self._problem_proto = problem
# If problem is a list, user may pass parameters/variables as list-of-list (one list per problem).
self._params_list_proto = None
self._vars_list_proto = None
if isinstance(problem, (list, tuple)):
problem_list = list(problem)
if len(problem_list) == 0:
raise ValueError("Empty problem_list.")
# Case A: parameters/variables are list-of-list aligned with problem_list
if (
isinstance(parameters, (list, tuple)) and len(parameters) == len(problem_list)
and len(parameters) > 0 and isinstance(parameters[0], (list, tuple))
):
if not (isinstance(variables, (list, tuple)) and len(variables) == len(problem_list)
and len(variables) > 0 and isinstance(variables[0], (list, tuple))):
raise ValueError("When problem is a list and parameters is list-of-list, variables must be list-of-list too.")
self._params_list_proto = [list(pi) for pi in parameters]
self._vars_list_proto = [list(vi) for vi in variables]
self._param_templates = list(self._params_list_proto[0])
self._var_templates = list(self._vars_list_proto[0])
# Case B: parameters/variables are flat templates; we'll map by name in _lazy_init_from_B
else:
self._param_templates = list(parameters)
self._var_templates = list(variables)
else:
self._param_templates = list(parameters)
self._var_templates = list(variables)
# self._problem_proto = problem
# self._param_templates = list(parameters)
# self._var_templates = list(variables)
self._max_workers_user = max_workers
self._initialized = False
self.num_problems = 0
self.bundles = None
self.problem_list = None
self.perturbed_problem_list = None
self._ref_param_order = None
self._ref_vars = None
self._ws_primal_fwd = None
self._executor = None
self.forward_solve_time = 0.0
self.backward_solve_time = 0.0
self.forward_setup_time = 0.0
self.backward_setup_time = 0.0
self._solver_args_fwd = None
self._solver_args_bwd = None
def _infer_B_from_params(self, params):
ref_param_order = self._param_templates
batch_sizes = []
for i, (p, qtmpl) in enumerate(zip(params, ref_param_order)):
if p.ndimension() == qtmpl.ndim:
bs = 0
elif p.ndimension() == qtmpl.ndim + 1:
bs = int(p.size(0))
if bs <= 0:
raise ValueError(f"Parameter {i} has empty batch dimension.")
else:
raise ValueError(
f"Invalid dim for parameter {i}: got {p.ndimension()}, expected {qtmpl.ndim} or {qtmpl.ndim+1}."
)
p_shape = p.shape if bs == 0 else p.shape[1:]
if tuple(p_shape) != tuple(qtmpl.shape):
raise ValueError(f"Parameter {i} shape mismatch: expected {qtmpl.shape}, got {p.shape}.")
batch_sizes.append(bs)
batch_sizes = np.array(batch_sizes, dtype=int)
if np.any(batch_sizes > 0):
nonzero = batch_sizes[batch_sizes > 0]
B = int(nonzero[0])
if np.any(nonzero != B):
raise ValueError(f"Inconsistent batch sizes: {batch_sizes}.")
else:
B = 1
return B
def _lazy_init_from_B(self, B: int, solver_args: dict):
if self._initialized:
return
if isinstance(self._problem_proto, (list, tuple)):
problem_list = list(self._problem_proto)
if len(problem_list) != B:
raise ValueError(f"Got batch size B={B}, but problem_list has len={len(problem_list)}.")
# parameters_list = list(self._param_templates)
# variables_list = list(self._var_templates)
# if not (len(parameters_list) == len(variables_list) == len(problem_list)):
# raise ValueError("When passing problem as list, parameters and variables must be list-of-list aligned.")
if self._params_list_proto is not None:
parameters_list = self._params_list_proto
variables_list = self._vars_list_proto
if not (len(parameters_list) == len(variables_list) == len(problem_list)):
raise ValueError("When passing problem as list, parameters and variables must be list-of-list aligned.")
# sanity: each inner list length matches template length
P = len(self._param_templates)
V = len(self._var_templates)
for i in range(B):
if len(parameters_list[i]) != P:
raise ValueError(f"parameters_list[{i}] length mismatch: expected {P}, got {len(parameters_list[i])}")
if len(variables_list[i]) != V:
raise ValueError(f"variables_list[{i}] length mismatch: expected {V}, got {len(variables_list[i])}")
else:
# Otherwise, map by name from each problem
pnames = [p.name() for p in self._param_templates]
vnames = [v.name() for v in self._var_templates]
parameters_list, variables_list = [], []
for prob_i in problem_list:
pmap = prob_i.param_dict
vmap = prob_i.var_dict
parameters_list.append([pmap[n] for n in pnames])
variables_list.append([vmap[n] for n in vnames])
else:
pnames = [p.name() for p in self._param_templates]
vnames = [v.name() for v in self._var_templates]
problem_list, parameters_list, variables_list = [], [], []
# Clear solver cache before deepcopy – solver objects are not picklable
saved_cache = getattr(self._problem_proto, '_solver_cache', None)
if saved_cache is not None:
self._problem_proto._solver_cache = {}
for _ in range(int(B)):
prob_i = copy.deepcopy(self._problem_proto)
pmap = prob_i.param_dict
vmap = prob_i.var_dict
params_i = [pmap[n] for n in pnames]
vars_i = [vmap[n] for n in vnames]
problem_list.append(prob_i)
parameters_list.append(params_i)
variables_list.append(vars_i)
if saved_cache is not None:
self._problem_proto._solver_cache = saved_cache
self.num_problems = len(problem_list)
if self.num_problems == 0:
raise ValueError("Empty problem_list.")
self.max_workers = int(self._max_workers_user or min(os.cpu_count() or 1, self.num_problems))
print(f"max_workers: {self.max_workers}")
bundles = []
for prob_i, params_i, vars_i in zip(problem_list, parameters_list, variables_list):
bundles.append(_build_problem_bundle(
prob_i,
parameters=params_i,
variables=vars_i,
alpha=self.alpha,
dual_cutoff=self.dual_cutoff,
slack_tol=self.slack_tol,
eps=self.eps,
))
self.bundles = bundles
self.problem_list = [b["problem"] for b in bundles]
self.perturbed_problem_list = [b["perturbed_problem"] for b in bundles]
self._ref_param_order = bundles[0]["param_order"]
self._ref_vars = bundles[0]["variables"]
self._ws_cache_fwd = {} # key -> {scs_x, scs_y, scs_s}
self._ws_cache_bwd = {} # key -> {scs_x, scs_y, scs_s}
self._scs_solvers = {} # i -> SCS solver instance (for direct SCS path)
self._scs_data_hash = None # hash of (A, b) params to detect changes
self._scs_mapping = None # {primal_slice, eq_dual_slice, ineq_dual_slice, c_p_slice}
self._executor = ThreadPoolExecutor(max_workers=self.max_workers)
self._FFOLayerFn = _make_ffo_fn(self, solver_args=solver_args)
self._initialized = True
def close(self):
ex = getattr(self, "_executor", None)
if ex is not None:
ex.shutdown(wait=True)
self._executor = None
def __del__(self):
try:
self.close()
except Exception:
pass
def forward(self, *params, solver_args=None):
if solver_args is None:
solver_args = {}
solver = solver_args.get("solver", cp.SCS)
if solver == cp.SCS:
default_solver_args = dict(
solver=cp.SCS,
warm_start=False,
ignore_dpp=True,
max_iters=2500,
eps=self.eps,
verbose=False,
)
else:
default_solver_args = dict(ignore_dpp=False)
solver_args = {**default_solver_args, **solver_args}
self._warm_start = bool(solver_args.get("warm_start", False))
self._ws_keys = solver_args.pop("ws_keys", None)
if not self._initialized:
B = self._infer_B_from_params(params)
self._lazy_init_from_B(B, solver_args)
self._solver_args_fwd = dict(solver_args)
self._solver_args_bwd = dict(solver_args)
self._solver_args_bwd["max_iters"] = 2500
self._solver_args_bwd["warm_start"] = True
if "eps" in self._solver_args_bwd:
self._solver_args_bwd["eps"] = float(self.backward_eps)
# Fn = _make_ffo_fn(self, solver_args)
# return Fn.apply(*params)
return self._FFOLayerFn.apply(*params)
def _make_ffo_fn(mt: "_FFOLayer", solver_args: dict):
solver_args = dict(solver_args)
class _FFOLayerFn(torch.autograd.Function):
@staticmethod
def forward(ctx, *params):
ctx.mt = mt
ctx.bundles = mt.bundles
ctx.solver_args = solver_args
ctx.dtype = params[0].dtype
ctx.device = params[0].device if isinstance(params[0], torch.Tensor) else 'cpu'
ref_param_order = mt._ref_param_order
batch_sizes = []
for i, (p, qtmpl) in enumerate(zip(params, ref_param_order)):
if p.dtype != ctx.dtype or p.device != ctx.device:
raise ValueError(f"Parameter {i} dtype/device mismatch.")
if p.ndimension() == qtmpl.ndim:
bs = 0
elif p.ndimension() == qtmpl.ndim + 1:
bs = int(p.size(0))
if bs <= 0:
raise ValueError(f"Parameter {i} has empty batch dimension.")
else:
raise ValueError(f"Invalid dim for parameter {i}: got {p.ndimension()}, expected {qtmpl.ndim} or {qtmpl.ndim+1}.")
batch_sizes.append(bs)
p_shape = p.shape if bs == 0 else p.shape[1:]
if tuple(p_shape) != tuple(qtmpl.shape):
raise ValueError(f"Parameter {i} shape mismatch: expected {qtmpl.shape}, got {p.shape}.")
ctx.batch_sizes = np.array(batch_sizes, dtype=int)
ctx.batch = bool(np.any(ctx.batch_sizes > 0))
if ctx.batch:
nonzero = ctx.batch_sizes[ctx.batch_sizes > 0]
B = int(nonzero[0])
if np.any(nonzero != B):
raise ValueError(f"Inconsistent batch sizes: {ctx.batch_sizes}.")
else:
B = 1
if ctx.batch and B != mt.num_problems:
raise ValueError(f"Batch size ({B}) must equal number of problems ({mt.num_problems}).")
ctx.batch_size = B
params_np_all = [to_numpy(p) for p in params]
def _slice_params_np(i: int):
if ctx.batch:
return [arr[i] if bs > 0 else arr for arr, bs in zip(params_np_all, ctx.batch_sizes)]
return params_np_all
ref_bundle = ctx.bundles[0]
variables = ref_bundle["variables"]
eq_functions = ref_bundle["eq_functions"]
scalar_ineq_functions = ref_bundle["scalar_ineq_functions"]
soc_constraints = ref_bundle["soc_constraints"]
exp_cones = ref_bundle["exp_cones"]
psd_cones = ref_bundle["psd_cones"]
sol_numpy = [np.empty((B,) + v.shape, dtype=float) for v in variables]
eq_dual = [np.empty((B,) + f.shape, dtype=float) for f in eq_functions]
scalar_ineq_dual = [np.empty((B,) + g.shape, dtype=float) for g in scalar_ineq_functions]
scalar_ineq_slack = [np.empty((B,) + g.shape, dtype=float) for g in scalar_ineq_functions]
soc_dual_0 = [np.empty((B,) + c.dual_variables[0].shape, dtype=float) for c in soc_constraints]
soc_dual_1 = [np.empty((B,) + c.dual_variables[1].shape, dtype=float) for c in soc_constraints]
exp_dual = [
[np.empty((B,) + dv.shape, dtype=float) for dv in c.dual_variables]
for c in exp_cones
]
psd_dual = [np.empty((B,) + c.dual_variables[0].shape, dtype=float) for c in psd_cones]
pnorm_xstar = []
pnorm_grad = []
for _local_id in range(len(ref_bundle["pnorm_ineq_ids"])):
pnorm_xstar.append([np.empty((B,) + v.shape, dtype=float) for v in variables])
pnorm_grad.append([np.empty((B,) + v.shape, dtype=float) for v in variables])
def _slice_params_torch(i: int):
if ctx.batch:
return [p[i] if bs > 0 else p for p, bs in zip(params, ctx.batch_sizes)]
return list(params)
fwd_solver_iters = [0] * B
_fwd_solve_times = [0.0] * B
# ---- Direct SCS path: setup solvers for this forward pass ----
if mt._warm_start:
import scs as _scs
import time as _time_mod
# Detect if A/b changed by checking param tensor versions
_param_versions = tuple(
p.data_ptr() if hasattr(p, 'data_ptr') else id(p)
for p in params
)
if mt._scs_data_hash != _param_versions:
# Use first problem to get the SCS data template via CVXPY
b0 = ctx.bundles[0]
prob0 = mt.problem_list[0]
params_0_np = _slice_params_np(0)
for pval, pparam in zip(params_0_np, b0["param_order"]):
pparam.value = pval
data0, _, _ = prob0.get_problem_data(
solver=cp.SCS, ignore_dpp=True
)
cone = {
'z': data0['dims'].zero,
'l': data0['dims'].nonneg,
}
scs_args = dict(
max_iters=int(mt._solver_args_fwd.get('max_iters', 2500)),
eps_abs=float(mt._solver_args_fwd.get('eps', 1e-6)),
eps_rel=float(mt._solver_args_fwd.get('eps', 1e-6)),
verbose=False,
)
# Discover the mapping once
if mt._scs_mapping is None:
n_vars = sum(int(np.prod(v.shape)) for v in b0["variables"])
n_eq = sum(int(np.prod(f.shape)) for f in b0["eq_functions"])
n_ineq = sum(int(np.prod(f.shape)) for f in b0["scalar_ineq_functions"])
# SCS x layout: [aux(n_vars), y_var(n_vars)]
# SCS y layout: [aux_dual(n_vars), eq_dual(n_eq), ineq_dual(n_ineq)]
mt._scs_mapping = {
'primal_slice': slice(n_vars, 2 * n_vars),
'eq_dual_slice': slice(n_vars, n_vars + n_eq),
'ineq_dual_slice': slice(n_vars + n_eq, n_vars + n_eq + n_ineq),
'c_p_slice': slice(n_vars, 2 * n_vars),
'b_eq_slice': slice(n_vars, n_vars + n_eq),
}
# Store dimensions for later use
mt._scs_c_dim = len(data0['c'])
# Build SCS solvers in parallel (SCS releases GIL)
scs_template = {
'P': data0['P'],
'A': data0['A'],
'b': data0['b'].copy(),
'c': data0['c'].copy(),
}
def _build_scs(j):
sd = {k: (v.copy() if isinstance(v, np.ndarray) else v)
for k, v in scs_template.items()}
return _scs.SCS(sd, cone, **scs_args)
with _limit_native_threads(1):
futs = [mt._executor.submit(_build_scs, j) for j in range(B)]
mt._scs_solvers = {j: f.result() for j, f in enumerate(futs)}
mt._scs_data_hash = _param_versions
# Mark that backward template needs to be built
mt._bwd_scs_template = None
_m = mt._scs_mapping
def _solve_one(i: int):
import time as _time
b = ctx.bundles[i]
prob = mt.problem_list[i]
params_i_np = _slice_params_np(i)
# Always set CVXPY params (backward pass needs them)
for pval, pparam in zip(params_i_np, b["param_order"]):
pparam.value = pval
_t0 = _time.perf_counter()
if mt._warm_start and i in mt._scs_solvers:
# ---- Direct SCS path ----
scs_solver = mt._scs_solvers[i]
# Update c vector with current p (puzzle encoding)
# p is at index 1 in params: [Q, p, G, h, A, b]
p_np = params_i_np[1] # the puzzle-specific parameter
new_c = np.zeros(mt._scs_c_dim, dtype=float)
new_c[_m['c_p_slice']] = p_np
scs_solver.update(c=new_c)
# Warm start from cached SCS solution (same puzzle, prev epoch)
ws_key = mt._ws_keys[i] if mt._ws_keys is not None else None
ws_cached = mt._ws_cache_fwd.get(ws_key) if ws_key is not None else None
if ws_cached is not None and 'scs_x' in ws_cached:
sol = scs_solver.solve(
warm_start=True,
x=ws_cached['scs_x'],
y=ws_cached['scs_y'],
s=ws_cached['scs_s'],
)
else:
sol = scs_solver.solve(warm_start=False)
if sol['info']['status'] == 'solved' or sol['info']['status'] == 'solved_inaccurate':
x_scs = sol['x']
y_scs = sol['y']
# Cache full SCS state for warm starting next epoch
if ws_key is not None:
mt._ws_cache_fwd[ws_key] = {
'scs_x': sol['x'].copy(),
'scs_y': sol['y'].copy(),
'scs_s': sol['s'].copy(),
}
# Extract primal solution
y_var = x_scs[_m['primal_slice']]
for v_id, v in enumerate(b["variables"]):
vshape = v.shape
n_el = int(np.prod(vshape))
sol_numpy[v_id][i, ...] = y_var[:n_el].reshape(vshape)
# Extract dual values
eq_d = y_scs[_m['eq_dual_slice']]
offset = 0
for c_id, f in enumerate(b["eq_functions"]):
n_el = int(np.prod(f.shape))
eq_dual[c_id][i, ...] = eq_d[offset:offset+n_el].reshape(f.shape)
offset += n_el
ineq_d = y_scs[_m['ineq_dual_slice']]
offset = 0
for j, g_expr in enumerate(b["scalar_ineq_functions"]):
n_el = int(np.prod(g_expr.shape))
scalar_ineq_dual[j][i, ...] = ineq_d[offset:offset+n_el].reshape(g_expr.shape)
# slack = max(-(G@y - h), 0) = max(y_var, 0) for G=-I, h=0
scalar_ineq_slack[j][i, ...] = np.maximum(y_var[offset:offset+n_el].reshape(g_expr.shape), 0.0)
offset += n_el
fwd_solver_iters[i] = sol['info']['iter']
_fwd_solve_times[i] = _time.perf_counter() - _t0
return # skip CVXPY path
else:
print(f"[forward] SCS direct failed for problem {i}: {sol['info']['status']}, falling back to CVXPY")
# ---- CVXPY fallback path ----
try:
prob.solve(**mt._solver_args_fwd)
except Exception as e:
print(f"[forward] problem {i} solve failed: {e!r}")
try:
prob.solve(solver=cp.OSQP, warm_start=False, verbose=False)
except Exception as e2:
raise RuntimeError(f"[forward] problem {i} solve failed: {e!r} {e2!r}")
_fwd_solve_times[i] = _time.perf_counter() - _t0
if prob.status not in (cp.OPTIMAL, cp.OPTIMAL_INACCURATE):
raise RuntimeError(f"[forward] problem {i} status: {prob.status}")
if prob.solver_stats is not None:
fwd_solver_iters[i] = getattr(prob.solver_stats, 'num_iters', 0)
for v_id, v in enumerate(b["variables"]):
sol_numpy[v_id][i, ...] = v.value
for c_id, c in enumerate(b["eq_constraints"]):
eq_dual[c_id][i, ...] = c.dual_value
for j, g_expr in enumerate(b["scalar_ineq_functions"]):
g_val = np.asarray(g_expr.value, dtype=float)
scalar_ineq_dual[j][i, ...] = b["scalar_ineq_constraints"][j].dual_value
scalar_ineq_slack[j][i, ...] = np.maximum(-g_val, 0.0)
for c_id, c in enumerate(b["soc_constraints"]):
dv0, dv1 = c.dual_value
soc_dual_0[c_id][i, ...] = dv0
if hasattr(dv1, "shape") and len(dv1.shape) == 2 and dv1.shape[1] == 1:
soc_dual_1[c_id][i, ...] = dv1.reshape(-1)
else:
soc_dual_1[c_id][i, ...] = dv1
for c_id, c in enumerate(b["exp_cones"]):
shapes3 = [dv.shape for dv in c.dual_variables]
dv3 = _split_expcone_dual_value(c.dual_value, shapes3)
for k in range(3):
exp_dual[c_id][k][i, ...] = dv3[k]
for c_id, c in enumerate(b["psd_cones"]):
psd_dual[c_id][i, ...] = c.dual_value
if len(b["pnorm_ineq_ids"]) > 0:
with torch.enable_grad():
vars_star_t = [
torch.tensor(sol_numpy[v_id][i, ...], dtype=ctx.dtype, device=ctx.device, requires_grad=True)
for v_id in range(len(variables))
]
params_i_det = [t.detach() for t in _slice_params_torch(i)]
for local_id in range(len(b["pnorm_ineq_ids"])):
g_t = b["pnorm_g_torch"][local_id](*vars_star_t, *params_i_det).reshape(())
grads = torch.autograd.grad(
g_t,
vars_star_t,
retain_graph=False,
create_graph=False,
allow_unused=True,
)
for v_id, gv in enumerate(grads):
pnorm_xstar[local_id][v_id][i, ...] = to_numpy(vars_star_t[v_id].detach())
pnorm_grad[local_id][v_id][i, ...] = 0.0 if gv is None else to_numpy(gv.detach())
with _limit_native_threads(1):
futs = [mt._executor.submit(_solve_one, i) for i in range(B)]
for f in futs:
f.result()
# single thread for debugging
# for i in range(B):
# _solve_one(i)
if mt._warm_start:
total_fwd = sum(fwd_solver_iters)
max_solve = max(_fwd_solve_times)
sum_solve = sum(_fwd_solve_times)
fwd_cache_size = len(mt._ws_cache_fwd)
print(f"[forward] iters: avg={total_fwd/max(B,1):.0f}, max_solve={max_solve:.3f}s, sum_solve={sum_solve:.3f}s, cache={fwd_cache_size}")
elif mt.verbose:
total_fwd = sum(fwd_solver_iters)
print(f"[forward] solver iters: total={total_fwd}, avg={total_fwd/max(B,1):.1f}")
ctx.sol_numpy = sol_numpy
ctx.eq_dual = eq_dual
ctx.scalar_ineq_dual = scalar_ineq_dual
ctx.scalar_ineq_slack = scalar_ineq_slack
ctx.soc_dual_0 = soc_dual_0
ctx.soc_dual_1 = soc_dual_1
ctx.exp_dual = exp_dual
ctx.psd_dual = psd_dual
ctx.pnorm_xstar = pnorm_xstar
ctx.pnorm_grad = pnorm_grad
ctx.params = params
# if want to check active counts
if mt.verbose:
ctx.active_counts = active_counts_dict(ctx)
print(f"active_counts: {ctx.active_counts}")
sol_torch = [to_torch(arr, ctx.dtype, ctx.device) for arr in sol_numpy]
return tuple(sol_torch) # return the solution
@staticmethod
def backward(ctx, *dvars):
mt = ctx.mt
bundles = ctx.bundles
B = ctx.batch_size
ref = bundles[0]
num_vars = len(ref["variables"])
num_scalar_ineq = len(ref["scalar_ineq_functions"])
params_np_all = [to_numpy(p) for p in ctx.params]
dvars_np_all = [to_numpy(dv) for dv in dvars]
def _slice_params_np(i: int):
if ctx.batch:
return [arr[i] if bs > 0 else arr for arr, bs in zip(params_np_all, ctx.batch_sizes)]
return params_np_all
# def _slice_dvars_np(i: int):
# out = []
# for arr, v in zip(dvars_np_all, ref["variables"]):
# vshape = tuple(v.shape)
# if arr.shape == (B,) + vshape:
# out.append(arr[i])
# elif B == 1 and arr.ndim >= 1 and arr.shape[0] == 1 and tuple(arr.shape[1:]) == vshape:
# out.append(arr[0])
# else:
# out.append(arr)
# return out
def _slice_dvars_np(i: int):
if ctx.batch:
return [arr[i] for arr in dvars_np_all]
# Even when not batched, sol_numpy has leading B=1 dim,
# so dvars also has shape (1, *v.shape). Slice it out.
return [arr[i] if arr.ndim > len(v.shape) else arr
for arr, v in zip(dvars_np_all, ref["variables"])]
y_dim = int(np.prod((_slice_dvars_np(0)[0]).shape))
num_eq = int(np.prod(ctx.eq_dual[0][0].shape)) if (len(ctx.eq_dual) > 0 and ctx.batch) else (
int(np.prod(ctx.eq_dual[0].shape)) if len(ctx.eq_dual) > 0 else 0
)
cap_scalar = int(max(1, y_dim - num_eq))
new_sol_lagrangian = [np.empty_like(ctx.sol_numpy[k]) for k in range(num_vars)]
new_eq_dual = [np.empty_like(ctx.eq_dual[k]) for k in range(len(ref["eq_constraints"]))]
new_active_dual = [np.empty((B,) + c.shape, dtype=float) for c in ref["active_eq_constraints"]]
new_soc_lam = [np.zeros((B,), dtype=float) for _ in ref["soc_lin_constraints"]]
new_pnorm_lam = [np.zeros((B,), dtype=float) for _ in ref["pnorm_tangent_constraints"]]
new_exp_dual = [
[np.empty_like(ctx.exp_dual[j][k]) for k in range(3)]
for j in range(len(ref["exp_cones"]))
]
new_psd_dual = [np.empty_like(ctx.psd_dual[k]) for k in range(len(ref["psd_cones"]))]
def _slice_params_torch(i: int, params_src):
if ctx.batch:
return [p[i] if bs > 0 else p for p, bs in zip(params_src, ctx.batch_sizes)]
return list(params_src)
bwd_solver_iters = [0] * B
_bwd_solve_times = [0.0] * B
# ---- Rebuild backward SCS template when params change ----
if mt._warm_start and (getattr(mt, '_bwd_scs_template', None) is None
or getattr(mt, '_bwd_param_hash', None) != id(ctx.params)):
import scipy.sparse as _sp_init
b0 = bundles[0]
prob0_bwd = mt.perturbed_problem_list[0]
n_vars_total = y_dim
n_eq_total = num_eq
# Set params and mask=1 so all mask entries exist in A
params_0_np = _slice_params_np(0)
for pval, pparam in zip(params_0_np, b0["param_order"]):
pparam.value = pval
for dv in b0["dvar_params"]:
dv.value = np.zeros(dv.shape)
for dp in b0.get("eq_dual_params", []):
dp.value = np.zeros(dp.shape)
for dp in b0.get("scalar_ineq_dual_params", []):
dp.value = np.zeros(dp.shape)
for mp in b0.get("scalar_active_mask_params", []):
mp.value = np.ones(mp.shape)
bwd_data_ones, _, _ = prob0_bwd.get_problem_data(
solver=cp.SCS, ignore_dpp=True
)
bwd_A_csc = _sp_init.csc_matrix(bwd_data_ones['A'])
mask_row_start = n_vars_total + n_eq_total
mask_data_indices = np.empty(n_vars_total, dtype=int)
for k in range(n_vars_total):
col_s, col_e = bwd_A_csc.indptr[k], bwd_A_csc.indptr[k+1]
rows = bwd_A_csc.indices[col_s:col_e]
idx = np.searchsorted(rows, mask_row_start + k)
mask_data_indices[k] = col_s + idx
# Get baseline c with mask=0
for mp in b0.get("scalar_active_mask_params", []):
mp.value = np.zeros(mp.shape)
bwd_data_base, _, _ = prob0_bwd.get_problem_data(
solver=cp.SCS, ignore_dpp=True
)
bwd_eps = float(mt._solver_args_bwd.get('eps', 1e-5))
mt._bwd_scs_template = {
'c_base': bwd_data_base['c'].copy(),
'b': bwd_data_base['b'].copy(),
'P': bwd_data_ones['P'],
'A_base_data': bwd_A_csc.data.copy(),
'A_base_indices': bwd_A_csc.indices.copy(),
'A_base_indptr': bwd_A_csc.indptr.copy(),
'A_shape': bwd_A_csc.shape,
'mask_data_indices': mask_data_indices,
'alpha': float(b0['alpha']),
'cone': {
'z': bwd_data_ones['dims'].zero,
'l': bwd_data_ones['dims'].nonneg,
},
'scs_args': dict(
max_iters=int(mt._solver_args_bwd.get('max_iters', 2500)),
eps_abs=bwd_eps, eps_rel=bwd_eps, verbose=False,
),
}
mt._bwd_param_hash = id(ctx.params)
def _solve_perturbed_one(i: int):
import time as _time
b = bundles[i]
prob = mt.perturbed_problem_list[i]
params_i_np = _slice_params_np(i)
for pval, pparam in zip(params_i_np, b["param_order"]):
pparam.value = pval
dvals_i = _slice_dvars_np(i)
for j, v in enumerate(b["variables"]):
b["dvar_params"][j].value = dvals_i[j]
v.value = ctx.sol_numpy[j][i, ...]
for j in range(len(b["eq_functions"])):
b["eq_dual_params"][j].value = ctx.eq_dual[j][i]
cap = cap_scalar
scalar_candidates = []
for j in b["scalar_scalar_indices"]:
sl_s = float(np.asarray(ctx.scalar_ineq_slack[j][i]).reshape(()))
lam_s = float(np.asarray(ctx.scalar_ineq_dual[j][i]).reshape(()))
lam_s = 0.0 if lam_s < -1e-8 else max(lam_s, 0.0)
if sl_s <= mt.slack_tol and lam_s >= mt.dual_cutoff:
scalar_candidates.append((lam_s, j))
if len(scalar_candidates) > 0:
scalar_candidates.sort(key=lambda t: t[0])
active_scalar = set([j for _, j in scalar_candidates[-cap:]]) if len(scalar_candidates) > cap else set([j for _, j in scalar_candidates])
else:
active_scalar = set()
for j in range(num_scalar_ineq):
lam = np.asarray(ctx.scalar_ineq_dual[j][i], dtype=float)
lam = np.where(lam < -1e-8, lam, np.maximum(lam, 0.0))
b["scalar_ineq_dual_params"][j].value = lam
gshape = b["scalar_ineq_functions"][j].shape
if int(np.prod(gshape)) == 1:
b["scalar_active_mask_params"][j].value = 1.0 if (j in active_scalar) else 0.0
else:
sl = np.asarray(ctx.scalar_ineq_slack[j][i], dtype=float)
mask = (sl <= mt.slack_tol).astype(np.float64)
cap_vec = cap_scalar
if mask.sum() > cap_vec:
lam_flat = lam.reshape(-1)
idx = np.argpartition(lam_flat, -cap_vec)[-cap_vec:]
mask_flat = np.zeros_like(lam_flat, dtype=np.float64)
mask_flat[idx] = 1.0
mask = mask_flat.reshape(lam.shape)
b["scalar_active_mask_params"][j].value = mask
for j in range(len(b["soc_constraints"])):
b["soc_dual_params_0"][j].value = np.maximum(ctx.soc_dual_0[j][i], 0.0)
b["soc_dual_params_1"][j].value = ctx.soc_dual_1[j][i]
for j in range(len(b["exp_cones"])):
for k in range(3):
b["exp_dual_params"][j][k].value = ctx.exp_dual[j][k][i]
for j in range(len(b["psd_cones"])):
b["psd_dual_params"][j].value = ctx.psd_dual[j][i]
for local_id, j_scalar in enumerate(b["pnorm_ineq_ids"]):
for v_id in range(num_vars):
b["pnorm_xstar_params"][local_id][v_id].value = ctx.pnorm_xstar[local_id][v_id][i]
b["pnorm_grad_params"][local_id][v_id].value = ctx.pnorm_grad[local_id][v_id][i]
_bwd_t0 = _time.perf_counter()
if mt._warm_start and hasattr(mt, '_bwd_scs_template'):
# ---- Direct SCS path for backward (no CVXPY) ----
import scs as _scs_bwd
import scipy.sparse as _sp
try:
tmpl = mt._bwd_scs_template
# Backward SCS variable order: x = [y, t] (opposite of forward)
# c[0:y_dim] = p + dvar/alpha - lambda (all y-linear terms)
# c[y_dim:2*y_dim] = 0 (t has no linear cost)
new_c = tmpl['c_base'].copy()
dvar_i = dvals_i[0].ravel()
lam_i = np.asarray(b["scalar_ineq_dual_params"][0].value, dtype=float).ravel()
p_i = params_i_np[1].ravel()
new_c[:y_dim] = p_i + dvar_i / tmpl['alpha'] - lam_i
# Construct A: copy base, update mask diagonal
A_data = tmpl['A_base_data'].copy()
mask_i = np.asarray(b["scalar_active_mask_params"][0].value, dtype=float).ravel()
mask_data_idx = tmpl['mask_data_indices']
for k in range(y_dim):
A_data[mask_data_idx[k]] = -mask_i[k]
A_sparse = _sp.csc_matrix(
(A_data, tmpl['A_base_indices'], tmpl['A_base_indptr']),
shape=tmpl['A_shape'],
)
# Update b: b[y_dim:y_dim+num_eq] = b_eq (index 5 in params)
# b vector matches the A rows: [aux(y_dim), eq(num_eq), mask(y_dim)]
new_b = tmpl['b'].copy()
b_eq_i = params_i_np[5].ravel()
new_b[y_dim:y_dim + num_eq] = b_eq_i # same position confirmed earlier
bwd_sd = {
'P': tmpl['P'], 'A': A_sparse,
'b': new_b, 'c': new_c,
}
bwd_solver = _scs_bwd.SCS(bwd_sd, tmpl['cone'], **tmpl['scs_args'])
# Warm start backward from cached SCS state
bwd_ws_key = mt._ws_keys[i] if mt._ws_keys is not None else None
bwd_ws = mt._ws_cache_bwd.get(bwd_ws_key) if bwd_ws_key is not None else None
if bwd_ws is not None:
bwd_sol = bwd_solver.solve(
warm_start=True,
x=bwd_ws['scs_x'], y=bwd_ws['scs_y'], s=bwd_ws['scs_s'],
)
else:
bwd_sol = bwd_solver.solve(warm_start=False)
if bwd_sol['info']['status'] in ('solved', 'solved_inaccurate'):
x_bwd = bwd_sol['x']
y_bwd = bwd_sol['y']
bwd_solver_iters[i] = bwd_sol['info']['iter']
y_var = x_bwd[:y_dim]
for j, v in enumerate(b["variables"]):
vshape = v.shape
n_el = int(np.prod(vshape))
new_sol_lagrangian[j][i, ...] = y_var[:n_el].reshape(vshape)
eq_d = y_bwd[y_dim:y_dim + num_eq]
offset = 0
for c_id, f in enumerate(b["eq_functions"]):
n_el = int(np.prod(f.shape))
new_eq_dual[c_id][i, ...] = eq_d[offset:offset+n_el].reshape(f.shape)
offset += n_el
active_d = y_bwd[y_dim + num_eq:]
offset = 0
for c_id, c_expr in enumerate(b["active_eq_constraints"]):
n_el = int(np.prod(c_expr.shape))
new_active_dual[c_id][i, ...] = active_d[offset:offset+n_el].reshape(c_expr.shape)
offset += n_el
# Cache backward SCS state
if bwd_ws_key is not None:
mt._ws_cache_bwd[bwd_ws_key] = {
'scs_x': bwd_sol['x'].copy(),
'scs_y': bwd_sol['y'].copy(),
'scs_s': bwd_sol['s'].copy(),
}
_bwd_solve_times[i] = _time.perf_counter() - _bwd_t0
return
else:
print(f"[backward] SCS direct failed for {i}: {bwd_sol['info']['status']}, fallback")
except Exception as e:
print(f"[backward] SCS direct error for {i}: {e!r}, fallback")
# ---- CVXPY fallback path ----
try:
prob.solve(**mt._solver_args_bwd)
except Exception as e:
print(f"[backward] problem {i} perturbed solve failed: {e!r}")
try:
b["perturbed_problem"].solve(solver=cp.OSQP, eps_abs=1e-4, eps_rel=1e-4, warm_start=True, verbose=False)
except Exception as e2:
raise RuntimeError(f"[backward] problem {i} perturbed solve failed: {e!r} {e2!r}")
if prob.status not in (cp.OPTIMAL, cp.OPTIMAL_INACCURATE):
raise RuntimeError(f"[backward] perturbed problem {i} status: {prob.status}")
if prob.solver_stats is not None:
bwd_solver_iters[i] = getattr(prob.solver_stats, 'num_iters', 0)
for j, v in enumerate(b["variables"]):
new_sol_lagrangian[j][i, ...] = v.value
for c_id, c in enumerate(b["eq_constraints"]):
new_eq_dual[c_id][i, ...] = c.dual_value
for c_id, c in enumerate(b["active_eq_constraints"]):
new_active_dual[c_id][i, ...] = c.dual_value
for c_id, c in enumerate(b["soc_lin_constraints"]):
dv = c.dual_value
new_soc_lam[c_id][i] = 0.0 if dv is None else float(np.asarray(dv).reshape(()))
for c_id, c in enumerate(b["pnorm_tangent_constraints"]):
dv = c.dual_value
lam_val = 0.0 if dv is None else float(np.asarray(dv).reshape(()))
j_scalar = b["pnorm_ineq_ids"][c_id]
mval = float(np.asarray(b["scalar_active_mask_params"][j_scalar].value).reshape(()))
if mval < 0.5:
lam_val = 0.0
new_pnorm_lam[c_id][i] = lam_val
for c_id, c in enumerate(b["exp_cones"]):
shapes3 = [dv.shape for dv in c.dual_variables]
dv3 = _split_expcone_dual_value(c.dual_value, shapes3)
for k in range(3):
new_exp_dual[c_id][k][i, ...] = dv3[k]
for c_id, c in enumerate(b["psd_cones"]):
new_psd_dual[c_id][i, ...] = c.dual_value
with _limit_native_threads(1):
futs = [mt._executor.submit(_solve_perturbed_one, i) for i in range(B)]
for f in futs:
f.result()
if mt._warm_start or mt.verbose:
total_bwd = sum(bwd_solver_iters)
bwd_max = max(_bwd_solve_times)
bwd_sum = sum(_bwd_solve_times)
print(f"[backward] iters: avg={total_bwd/max(B,1):.0f}, max_solve={bwd_max:.3f}s, sum_solve={bwd_sum:.3f}s")
new_sol = [to_torch(v, ctx.dtype, ctx.device) for v in new_sol_lagrangian]
vars_old = [to_torch(ctx.sol_numpy[j], ctx.dtype, ctx.device) for j in range(num_vars)]
new_eq_dual_t = [to_torch(v, ctx.dtype, ctx.device) for v in new_eq_dual]
old_eq_dual_t = [to_torch(v, ctx.dtype, ctx.device) for v in ctx.eq_dual]
old_scalar_dual_t = [to_torch(v, ctx.dtype, ctx.device) for v in ctx.scalar_ineq_dual]
new_active_dual_t = [to_torch(v, ctx.dtype, ctx.device) for v in new_active_dual]
new_exp_dual_t = [
[to_torch(new_exp_dual[j][k], ctx.dtype, ctx.device) for k in range(3)]
for j in range(len(ref["exp_cones"]))
]
new_psd_dual_t = [to_torch(v, ctx.dtype, ctx.device) for v in new_psd_dual]
new_pnorm_lam_t = [to_torch(v, ctx.dtype, ctx.device) for v in new_pnorm_lam]
params_req = []
req_grad_mask = []
for p in ctx.params:
need = bool(getattr(p, "requires_grad", False))
q = p.detach()
if need:
q.requires_grad_(True)
params_req.append(q)
req_grad_mask.append(need)
loss = 0.0
with torch.enable_grad():
for i in range(B):
b = bundles[i]
vars_new_i = [v[i] for v in new_sol]
vars_old_i = [v[i] for v in vars_old]
params_i = slice_params_for_batch(params_req, ctx.batch_sizes, i) if ctx.batch else params_req
new_eq_dual_i = [d[i] for d in new_eq_dual_t]
old_eq_dual_i = [d[i] for d in old_eq_dual_t]
new_scalar_dual_full_i = []
ptr = 0
for j in range(num_scalar_ineq):
if j in b["non_pnorm_set"]:
new_scalar_dual_full_i.append(new_active_dual_t[ptr][i])
ptr += 1
elif j in b["pnorm_set"]:
lid = b["pnorm_map"][j]
new_scalar_dual_full_i.append(new_pnorm_lam_t[lid][i])
else:
new_scalar_dual_full_i.append(old_scalar_dual_t[j][i])
old_scalar_dual_full_i = [d[i] for d in old_scalar_dual_t]
new_exp_dual_i = []
for j in range(len(b["exp_cones"])):
for k in range(3):
new_exp_dual_i.append(new_exp_dual_t[j][k][i])
new_psd_dual_i = [d[i] for d in new_psd_dual_t]
phi_new = b["phi_torch"](*vars_new_i, *params_i)
phi_old = b["phi_torch"](*vars_old_i, *params_i)
eq_new = b["eq_dual_term_torch"](*vars_old_i, *params_i, *new_eq_dual_i)
eq_old = b["eq_dual_term_torch"](*vars_old_i, *params_i, *old_eq_dual_i)
ineq_new = b["ineq_dual_term_torch"](*vars_old_i, *params_i, *new_scalar_dual_full_i)
ineq_old = b["ineq_dual_term_torch"](*vars_old_i, *params_i, *old_scalar_dual_full_i)
if b["exp_dual_term_torch"] is not None:
exp_new = b["exp_dual_term_torch"](*vars_old_i, *params_i, *new_exp_dual_i)
else:
exp_new = 0.0
if b["psd_dual_term_torch"] is not None:
psd_new = b["psd_dual_term_torch"](*vars_old_i, *params_i, *new_psd_dual_i)
else:
psd_new = 0.0
loss = loss + (phi_new + ineq_new + eq_new + exp_new + psd_new - phi_old - eq_old - ineq_old)
loss = mt.alpha * loss
grads_req = torch.autograd.grad(
outputs=loss,
inputs=[q for q, need in zip(params_req, req_grad_mask) if need],
allow_unused=True,
retain_graph=False,
)
grads = []
it = iter(grads_req)
for need in req_grad_mask:
grads.append(next(it) if need else None)
return tuple(grads)
return _FFOLayerFn |