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# import diffcp
import time
from dataclasses import dataclass
from typing import Any
import cvxpy as cp
import tracemalloc
import os
import linecache
import torch
from scipy.linalg import block_diag
import pathlib
n_threads = os.cpu_count()
def _np(x):
if isinstance(x, torch.Tensor):
return x.detach().cpu().numpy()
return np.array(x)
def to_numpy(x):
# convert torch tensor to numpy array
if isinstance(x, torch.Tensor):
if x.device.type == 'cuda':
return x.cpu().detach().double().numpy()
else:
return x.detach().double().numpy()
else:
return np.array(x)
def to_torch(x, dtype, device):
# convert numpy array to torch tensor
return torch.from_numpy(x).type(dtype).to(device)
def slice_params_for_batch(params_req, batch_sizes, i):
"""Pick p[i] if that parameter was batched; else p."""
out = []
for p, bs in zip(params_req, batch_sizes):
out.append(p[i] if bs > 0 else p)
return out
def make_mask_torch_for_i(i, inequality_dual, inequality_functions, dual_cutoff, ctx):
mask_list = []
for j in range(len(inequality_functions)):
lam_ji = inequality_dual[j][i]
mask_np = (lam_ji > dual_cutoff).astype(np.float64)
mask_list.append(to_torch(mask_np, ctx.dtype, ctx.device))
return mask_list
def extract_nBatch(Q, p, G, h, A, b):
dims = [3, 2, 3, 2, 3, 2]
params = [Q, p, G, h, A, b]
for param, dim in zip(params, dims):
if param.ndim == dim:
return param.size(0)
return 1
def expandParam(X, nBatch, nDim):
if X.ndim in (0, nDim) or X.nelement() == 0:
return X, False
elif X.ndim == nDim - 1:
return X.unsqueeze(0).expand(*([nBatch] + list(X.size()))), True
else:
raise RuntimeError("Unexpected number of dimensions.")
# def forward_numpy(params_numpy, context):
# """Forward pass in numpy."""
#
# info = {}
#
# if context.gp:
# param_map = {}
# # construct a list of params for the DCP problem
# for param, value in zip(context.param_order, params_numpy):
# if param in context.old_params_to_new_params:
# new_id = context.old_params_to_new_params[param].id
# param_map[new_id] = np.log(value)
# else:
# new_id = param.id
# param_map[new_id] = value
# params_numpy = [param_map[pid] for pid in context.param_ids]
#
# # canonicalize problem
# start = time.time()
# As, bs, cs, cone_dicts, shapes = [], [], [], [], []
# for i in range(context.batch_size):
# params_numpy_i = [
# p if sz == 0 else p[i]
# for p, sz in zip(params_numpy, context.batch_sizes)]
# c, _, neg_A, b = context.compiler.apply_parameters(
# dict(zip(context.param_ids, params_numpy_i)),
# keep_zeros=True)
# A = -neg_A # cvxpy canonicalizes -A
# As.append(A)
# bs.append(b)
# cs.append(c)
# cone_dicts.append(context.cone_dims)
# shapes.append(A.shape)
# info['canon_time'] = time.time() - start
# info['shapes'] = shapes
#
# # compute solution and derivative function
# start = time.time()
# try:
# if context.solve_and_derivative:
# xs, _, _, _, DT_batch = diffcp.solve_and_derivative_batch(
# As, bs, cs, cone_dicts, **context.solver_args)
# info['DT_batch'] = DT_batch
# else:
# xs, _, _ = diffcp.solve_only_batch(
# As, bs, cs, cone_dicts, **context.solver_args)
# except diffcp.SolverError as e:
# print(
# "Please consider re-formulating your problem so that "
# "it is always solvable or increasing the number of "
# "solver iterations.")
# raise e
# info['solve_time'] = time.time() - start
#
# # extract solutions and append along batch dimension
# start = time.time()
# sol = [[] for i in range(len(context.variables))]
# for i in range(context.batch_size):
# sltn_dict = context.compiler.split_solution(
# xs[i], active_vars=context.var_dict)
# for j, v in enumerate(context.variables):
# sol[j].append(np.expand_dims(sltn_dict[v.id], axis=0))
# sol = [np.concatenate(s, axis=0) for s in sol]
#
# if not context.batch:
# sol = [np.squeeze(s, axis=0) for s in sol]
#
# if context.gp:
# sol = [np.exp(s) for s in sol]
# info['sol'] = sol
#
# return sol, info
def forward_single_np(Q, p, G, h, A, b,
solver='GUROBI',
solver_opts={"verbose": False}):
nz, neq, nineq = p.shape[0], A.shape[0] if A is not None else 0, G.shape[0]
z_ = cp.Variable(nz)
obj = cp.Minimize(0.5 * cp.quad_form(z_, Q) + p.T @ z_)
eqCon = A @ z_ == b if neq > 0 else None
if nineq > 0:
slacks = cp.Variable(nineq)
ineqCon = G @ z_ + slacks == h
slacksCon = slacks >= 0
else:
ineqCon = slacks = slacksCon = None
cons = [x for x in [eqCon, ineqCon, slacksCon] if x is not None]
prob = cp.Problem(obj, cons)
prob.solve(solver=solver, **solver_opts) # max_iters=5000)
# prob.solve()
# prob.solve(adaptive_rho = False) # solver=cp.SCS, max_iters=5000, verbose=False)
# prob.solve(solver=cp.SCS, max_iters=10000, verbose=True)
assert('optimal' in prob.status)
zhat = np.array(z_.value).ravel()
nu = np.array(eqCon.dual_value).ravel() if eqCon is not None else None
if ineqCon is not None:
lam = np.array(ineqCon.dual_value).ravel()
slacks = np.array(slacks.value).ravel()
else:
lam = slacks = None
return prob.value, zhat, nu, lam, slacks
def forward_single_np_eq_cst(Q, p, G, h, A, b):
""" -> kamo
min_z 1/2 * z.T Q z + p.T z
s.t. A z = b ; G z <= h
"""
nz, neq, nineq = p.shape[0], A.shape[0] if A is not None else 0, G.shape[0] if G is not None else 0
z_ = cp.Variable(nz)
obj = cp.Minimize(0.5 * cp.quad_form(z_, Q) + p.T @ z_)
eqCon = A @ z_ == b if neq > 0 else None
if nineq > 0:
slacks = cp.Variable(nineq)
ineqCon = G @ z_ + slacks == h
slacksCon = slacks >= 0
else:
ineqCon = slacks = slacksCon = None
cons = [x for x in [eqCon, ineqCon, slacksCon] if x is not None]
prob = cp.Problem(obj, cons)
prob.solve(solver=cp.GUROBI, verbose=False, **{"Threads": n_threads, "OutputFlag": 0} ) # max_iters=5000)
# prob.solve()
# prob.solve(adaptive_rho = False) # solver=cp.SCS, max_iters=5000, verbose=False)
# prob.solve(solver=cp.SCS, max_iters=10000, verbose=True)
assert('optimal' in prob.status)
zhat = np.array(z_.value).ravel()
nu = np.array(eqCon.dual_value).ravel() if eqCon is not None else None
if ineqCon is not None:
lam = np.array(ineqCon.dual_value).ravel()
slacks = np.array(slacks.value).ravel()
else:
lam = slacks = None
return prob.value, zhat, nu, lam, slacks
def forward_batch_np(Q, p, G, h, A, b,
solver='GUROBI',
solver_opts={"verbose": False}):
""" -> kamo
Q : (nb, nz, nz)
p : (nb, nz)
G : (nb, nineq, nz)
h : (nb, nineq)
A : (nb, neq, nz)
b : (nb, neq)
"""
nb = p.shape[0]
nz, neq, nineq = p.shape[1], A.shape[1] if A is not None else 0, G.shape[1] if G is not None else 0
z_ = cp.Variable(nz * nb)
Q_ = block_diag(*Q)
p_ = p.reshape(-1)
obj = cp.Minimize(0.5 * cp.quad_form(z_, Q_) + p_.T @ z_)
eqCon = None
if neq > 0:
A_ = block_diag(*A)
b_ = b.reshape(-1)
eqCon = A_ @ z_ == b_
if nineq > 0:
slacks = cp.Variable(nineq * nb)
G_ = block_diag(*G)
h_ = h.reshape(-1)
ineqCon = G_ @ z_ + slacks == h_
slacksCon = slacks >= 0
else:
ineqCon = slacks = slacksCon = None
cons = [x for x in [eqCon, ineqCon, slacksCon] if x is not None]
prob = cp.Problem(obj, cons)
# prob.solve(solver=cp.GUROBI, verbose=False, **{"Threads": n_threads, "OutputFlag": 0} )
prob.solve(solver=solver, **solver_opts)
# prob.solve(solver='SCS', max_iters=100, eps=1e-7, **solver_opts) # max_iters=5000)
# prob.solve()
# prob.solve(adaptive_rho = False) # solver=cp.SCS, max_iters=5000, verbose=False)
# prob.solve(solver=cp.SCS, max_iters=10000, verbose=True)
assert('optimal' in prob.status)
zhat = np.array(z_.value).reshape(nb, nz)
nu = np.array(eqCon.dual_value).reshape(nb, neq) if eqCon is not None else None
if ineqCon is not None:
lam = np.array(ineqCon.dual_value).reshape(nb, nineq)
slacks = np.array(slacks.value).reshape(nb, nineq)
else:
lam = slacks = None
return prob.value, zhat, nu, lam, slacks
def display_top(snapshot, key_type='lineno', limit=3):
snapshot = snapshot.filter_traces((
tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
tracemalloc.Filter(False, "<unknown>"),
))
top_stats = snapshot.statistics(key_type)
print("Top %s lines" % limit)
for index, stat in enumerate(top_stats[:limit], 1):
frame = stat.traceback[0]
# replace "/path/to/module/file.py" with "module/file.py"
filename = os.sep.join(frame.filename.split(os.sep)[-2:])
print("#%s: %s:%s: %.1f KiB"
% (index, filename, frame.lineno, stat.size / 1024))
line = linecache.getline(frame.filename, frame.lineno).strip()
if line:
print(' %s' % line)
other = top_stats[limit:]
if other:
size = sum(stat.size for stat in other)
print("%s other: %.1f KiB" % (len(other), size / 1024))
total = sum(stat.size for stat in top_stats)
print("Total allocated size: %.1f KiB" % (total / 1024))
def _dump_cvxpy(
save_dir, file_name, batch_i, *,
ctx,
param_order, variables,
alpha, dual_cutoff,
solver_used, trigger,
params_numpy,
sol_numpy,
equality_dual,
inequality_dual,
slack,
new_sol_lagrangian,
new_equality_dual,
new_active_dual,
active_mask_params,
dvars_numpy,
):
p = pathlib.Path(save_dir)
p.mkdir(parents=True, exist_ok=True)
meta = {
"tag": f"{file_name}",
"batch_i": int(batch_i),
"dtype": str(ctx.dtype),
"device": str(ctx.device),
"alpha": float(alpha),
"dual_cutoff": float(dual_cutoff),
"solver_used": solver_used,
"trigger": trigger,
"param_count": len(param_order),
"var_count": len(variables),
"eq_count": len(equality_dual),
"ineq_count": len(inequality_dual),
}
# (p / "meta.json").write_text(json.dumps(meta, indent=2))
arrs = {}
for k in range(len(param_order)):
arrs[f"param_{k}"] = _np(params_numpy[k][batch_i if ctx.batch else 0])
for j in range(len(variables)):
arrs[f"y_old_{j}"] = _np(sol_numpy[j][batch_i])
for l in range(len(equality_dual)):
arrs[f"dual_eq_old_{l}"] = _np(equality_dual[l][batch_i])
for m in range(len(inequality_dual)):
lam = inequality_dual[m][batch_i]
arrs[f"dual_ineq_old_{m}"] = _np(lam)
arrs[f"slack_old_{m}"] = _np(slack[m][batch_i])
try:
mask_val = active_mask_params[m].value
except Exception:
mask_val = (lam > dual_cutoff).astype(np.float64)
arrs[f"active_mask_used_{m}"] = _np(mask_val)
if dvars_numpy is not None:
for j in range(len(variables)):
dv = dvars_numpy[j]
if dv is None:
arrs[f"dvars_is_none_{j}"] = np.array([True])
else:
arrs[f"dvars_{j}"] = _np(dv[batch_i])
np.savez_compressed(p / f"{file_name}.npz", **arrs) |