def test_minimize_methods(dtype, device, method):
torch.manual_seed(400)
random.seed(100)
nr = 3
nbatch = 2
default_fwd_options = {
"max_niter": 50,
"f_tol": 1e-9,
"alpha": -1.0,
}
linearmixing_fwd_options = {
"max_niter": 50,
"f_tol": 3e-6,
"alpha": -0.3,
}
gd_fwd_options = {
"maxiter": 5000,
"f_rtol": 1e-10,
"x_rtol": 1e-10,
"step": 1e-2,
}
# list the methods and the options here
options = {
"broyden1": default_fwd_options,
"broyden2": default_fwd_options,
"linearmixing": linearmixing_fwd_options,
"gd": gd_fwd_options,
"adam": gd_fwd_options,
}[method]
# specify higher atol for non-ideal method
atol = defaultdict(lambda: 1e-8)
atol["linearmixing"] = 3e-6
A = torch.nn.Parameter((torch.randn(
(nr, nr)) * 0.5).to(dtype).requires_grad_())
diag = torch.nn.Parameter(
torch.randn((nbatch, nr)).to(dtype).requires_grad_())
# bias will be detached from the optimization line, so set it undifferentiable
bias = torch.zeros((nbatch, nr)).to(dtype)
y0 = torch.randn((nbatch, nr)).to(dtype)
activation = "square" # square activation makes it easy to optimize
fwd_options = {**options, "method": method}
model = DummyModule(A, addx=False, activation=activation, sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward, y0, **fwd_options)
# check the grad (must be close to 1)
with torch.enable_grad():
y1 = y.clone().requires_grad_()
f = model.forward(y1)
grady, = torch.autograd.grad(f, (y1, ))
assert torch.allclose(grady, grady * 0, atol=atol[method])
# check the hessian (must be posdef)
h = hess(model.forward, (y1, ), idxs=0).fullmatrix()
eigval, _ = torch.symeig(h)
assert torch.all(eigval >= 0)
def test_minimize(dtype, device, clss):
torch.manual_seed(400)
random.seed(100)
nr = 3
nbatch = 2
A = torch.nn.Parameter((torch.randn(
(nr, nr)) * 0.5).to(dtype).requires_grad_())
diag = torch.nn.Parameter(
torch.randn((nbatch, nr)).to(dtype).requires_grad_())
# bias will be detached from the optimization line, so set it undifferentiable
bias = torch.zeros((nbatch, nr)).to(dtype)
y0 = torch.randn((nbatch, nr)).to(dtype)
fwd_options = {
"method": "broyden1",
"max_niter": 50,
"f_tol": 1e-9,
"alpha": -0.5,
}
activation = "square" # square activation makes it easy to optimize
model = clss(A, addx=False, activation=activation, sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward, y0, **fwd_options)
# check the grad (must be close to 1)
with torch.enable_grad():
y1 = y.clone().requires_grad_()
f = model.forward(y1)
grady, = torch.autograd.grad(f, (y1, ))
assert torch.allclose(grady, grady * 0)
# check the hessian (must be posdef)
h = hess(model.forward, (y1, ), idxs=0).fullmatrix()
eigval, _ = torch.symeig(h)
assert torch.all(eigval >= 0)
def getloss(A, y0, diag, bias):
model = clss(A, addx=False, activation=activation, sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward, y0, **fwd_options)
return y
gradcheck(getloss, (A, y0, diag, bias))
gradgradcheck(getloss, (A, y0, diag, bias))
def test_minimize_methods(dtype, device):
torch.manual_seed(400)
random.seed(100)
dtype = torch.float64
nr = 3
nbatch = 2
default_fwd_options = {
"max_niter": 50,
"f_tol": 1e-9,
"alpha": -0.5,
}
# list the methods and the options here
methods_and_options = {
"broyden1": default_fwd_options,
}
A = torch.nn.Parameter((torch.randn(
(nr, nr)) * 0.5).to(dtype).requires_grad_())
diag = torch.nn.Parameter(
torch.randn((nbatch, nr)).to(dtype).requires_grad_())
# bias will be detached from the optimization line, so set it undifferentiable
bias = torch.zeros((nbatch, nr)).to(dtype)
y0 = torch.randn((nbatch, nr)).to(dtype)
activation = "square" # square activation makes it easy to optimize
for method in methods_and_options:
fwd_options = {**methods_and_options[method], "method": method}
model = DummyModule(A,
addx=False,
activation=activation,
sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward, y0, **fwd_options)
# check the grad (must be close to 1)
with torch.enable_grad():
y1 = y.clone().requires_grad_()
f = model.forward(y1)
grady, = torch.autograd.grad(f, (y1, ))
assert torch.allclose(grady, grady * 0)
# check the hessian (must be posdef)
h = hess(model.forward, (y1, ), idxs=0).fullmatrix()
eigval, _ = torch.symeig(h)
assert torch.all(eigval >= 0)
def test_hess_func():
na = 3
params = getfnparams(na)
nnparams = getnnparams(na)
nparams = len(params)
all_idxs = [None, (0, ), (1, ), (0, 1), (0, 1, 2)]
funcs = [hfunc1, hfunc2(*nnparams)]
for func in funcs:
for idxs in all_idxs:
if idxs is None:
gradparams = params
else:
gradparams = [params[i] for i in idxs]
hs = hess(func, params, idxs=idxs)
assert len(hs) == len(gradparams)
y = func(*params)
nins = [torch.numel(p) for p in gradparams]
w = [torch.rand_like(p) for p in gradparams]
for i in range(len(hs)):
assert list(hs[i].shape) == [nins[i], nins[i]]
# assert the values
dfdy = torch.autograd.grad(y, gradparams, create_graph=True)
hs_mv_man = [
torch.autograd.grad(dfdy[i], (gradparams[i], ),
grad_outputs=w[i],
retain_graph=True)[0]
for i in range(len(dfdy))
]
hs_mv = [
hs[i].mv(w[i].reshape(-1, nins[i])) for i in range(len(dfdy))
]
for i in range(len(dfdy)):
assert torch.allclose(hs_mv[i].view(-1), hs_mv_man[i].view(-1))
def test_hess_grad():
na = 3
params = getfnparams(na)
params2 = [torch.rand(1, dtype=dtype).requires_grad_() for p in params]
hs = hess(hfunc1, params)
def fcnl(i, v, *params):
hs = hess(hfunc1, params)
return hs[i].mv(v.view(-1))
def fcnl2(i, v, *params2):
fmv = get_pure_function(hs[i].mv)
params0 = v.view(-1)
params1 = fmv.objparams()
params12 = [p1 * p2 for (p1, p2) in zip(params1, params2)]
with fmv.useobjparams(params12):
return fmv(params0)
w = [torch.rand_like(p).requires_grad_() for p in params]
for i in range(len(hs)):
gradcheck(fcnl, (i, w[i], *params))
gradgradcheck(fcnl, (i, w[i], *params))
gradcheck(fcnl2, (i, w[i], *params2))
gradgradcheck(fcnl2, (i, w[i], *params2))
def fcnl(i, v, *params):
hs = hess(hfunc1, params)
return hs[i].mv(v.view(-1))
def test_minimize(dtype, device, clss, method):
torch.manual_seed(400)
random.seed(100)
method_fwd_options = {
"broyden1": {
"max_niter": 50,
"f_tol": 1e-9,
"alpha": -0.5,
},
"gd": {
"maxiter": 10000,
"f_rtol": 1e-14,
"x_rtol": 1e-14,
"step": 2e-2,
},
}
nr = 2
nbatch = 2
A = torch.nn.Parameter((torch.randn(
(nr, nr)) * 0.5).to(dtype).requires_grad_())
diag = torch.nn.Parameter(
torch.randn((nbatch, nr)).to(dtype).requires_grad_())
bias = torch.nn.Parameter(
torch.zeros((nbatch, nr)).to(dtype).requires_grad_())
y0 = torch.randn((nbatch, nr)).to(dtype)
fwd_options = method_fwd_options[method]
bck_options = {
"rtol": 1e-9,
"atol": 1e-9,
}
activation = "square" # square activation makes it easy to optimize
model = clss(A, addx=False, activation=activation, sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward, y0, method=method, **fwd_options)
# check the grad (must be close to 0)
with torch.enable_grad():
y1 = y.clone().requires_grad_()
f = model.forward(y1)
grady, = torch.autograd.grad(f, (y1, ))
assert torch.allclose(grady, grady * 0)
# check the hessian (must be posdef)
h = hess(model.forward, (y1, ), idxs=0).fullmatrix()
eigval, _ = torch.symeig(h)
assert torch.all(eigval >= 0)
def getloss(A, y0, diag, bias):
model = clss(A, addx=False, activation=activation, sumoutput=True)
model.set_diag_bias(diag, bias)
y = minimize(model.forward,
y0,
method=method,
bck_options=bck_options,
**fwd_options)
return y
gradcheck(getloss, (A, y0, diag, bias))
# pytorch 1.8's gradgradcheck fails if there are unrelated variables
# I have made a PR to solve this and will be in 1.9
gradgradcheck(getloss, (A, y0, diag, bias.detach()))
