def lanczos_iteration(Fvp_fn, dim, k=20):
v = torch.FloatTensor(dim).uniform_()
v /= torch.norm(v, 2)
diag = []
diag_adj = []
w = Fvp_fn(v)
alpha = w.dot(v)
w -= alpha * v
diag.append(alpha)
for i in range(k - 1):
beta = torch.norm(w, 2)
if beta == 0:
break
v_prev = v.clone()
v = w / beta
w = Fvp_fn(v)
alpha = w.dot(v)
diag.append(alpha)
diag_adj.append(beta)
w = w - alpha * v - beta * v_prev
diag, diag_adj = np.array(diag), np.array(diag_adj)
# print ("Lanc diag: ", diag)
# print ("Lanc diag_adj: ", diag_adj)
w = eigvalsh_tridiagonal(np.array(diag), np.array(diag_adj))
return w
def jacobi_sampler_tridiag(M_1, M_2, N, beta=2):
"""
.. seealso::
:cite:`KiNe04` Theorem 2
"""
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# c_odd = c_1, c_2, ..., c_2N-1
c_odd = np.random.beta(a=0.5 * beta * np.arange(M_1, M_1 - N, step=-1),
b=0.5 * beta * np.arange(M_2, M_2 - N, step=-1))
# c_even = c_0, c_2, c_2N-2
c_even = np.zeros(N)
c_even[1:] = np.random.beta(
a=0.5 * beta * np.arange(N - 1, 0, step=-1),
b=0.5 * beta *
np.arange(M_1 + M_2 - N, M_1 + M_2 - 2 * N + 1, step=-1))
# xi_odd = xi_2i-1 = (1-c_2i-2) c_2i-1
xi_odd = (1 - c_even) * c_odd
# xi_even = xi_0=0, xi_2, xi_2N-2
# xi_2i = (1-c_2i-1)*c_2i
xi_even = np.zeros(N)
xi_even[1:] = (1 - c_odd[:-1]) * c_even[1:]
# alpha_i = xi_2i-2 + xi_2i-1, xi_0 = 0
alpha_coef = xi_even + xi_odd
# beta_i+1 = xi_2i-1 * xi_2i
beta_coef = xi_odd[:-1] * xi_even[1:]
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def laguerre_sampler_tridiag(M, N, beta=2):
"""
.. seealso::
:cite:`DuEd02` III-B
"""
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# M=>N
# xi_odd = xi_1, ... , xi_2N-1
xi_odd = np.random.chisquare(beta * np.arange(M, M - N, step=-1))
# xi_even = xi_0=0, xi_2, ... ,xi_2N-2
xi_even = np.zeros(N)
xi_even[1:] = np.random.chisquare(beta * np.arange(N - 1, 0, step=-1))
# alpha_i = xi_2i-2 + xi_2i-1
# alpha_1 = xi_0 + xi_1 = xi_1
alpha_coef = xi_even + xi_odd
# beta_i+1 = xi_2i-1 * xi_2i
beta_coef = xi_odd[:-1] * xi_even[1:]
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def _levels(Ec, EJ, ng=0.0, gridSize=51, select_range=(0, 10)):
n = np.arange(gridSize) - gridSize // 2
w = eigvalsh_tridiagonal(4 * Ec * (n - ng)**2,
-EJ / 2 * np.ones(gridSize - 1),
select='i',
select_range=select_range)
return w
def _generate_GOE_tridiagonal_direct(size: int = 100,
seed: int = None,
dowarn: bool = True) -> ndarray:
"""See: Edelman, A., Sutton, B. D., & Wang, Y. (2014).
Random matrix theory, numerical computation and applications.
Modern Aspects of Random Matrix Theory, 72, 53.
"""
if dowarn:
warn(
"While this method is fast, and uses the least memory, it appears that"
"`eigvalsh_tridiagonal` is considerably less precise, and will result"
"in significant deviations from the expected values for the long range"
"spectral observables (e.g. spectral rigidity, level number variance)."
)
if seed is not None:
np.random.seed(seed)
size = size + 2
chi_range = size - 1 - np.arange(size - 1)
chi = np.sqrt(np.random.chisquare(chi_range))
diagonal = np.random.normal(0, np.sqrt(2), size) / np.sqrt(2)
eigs = eigvalsh_tridiagonal(
diagonal,
chi,
# select="a",
check_finite=False,
select="i",
select_range=(1, size - 2),
lapack_driver="stebz",
tol=4 * np.finfo(np.float64).eps,
)
return eigs
def mu_ref_normal_sampler_tridiag(loc=0.0, scale=1.0, beta=2, size=10,
random_state=None):
"""Implementation of the tridiagonal model to sample from
.. math::
\\Delta(x_{1}, \\dots, x_{N})^{\\beta}
\\prod_{n=1}^{N} \\exp(-\\frac{(x_i-\\mu)^2}{2\\sigma^2} ) dx_i
.. seealso::
:cite:`DuEd02` II-C
"""
rng = check_random_state(random_state)
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# beta/2*[N-1, N-2, ..., 1]
b_2_Ni = 0.5 * beta * np.arange(size - 1, 0, step=-1)
alpha_coef = rng.normal(loc=loc, scale=scale, size=size)
beta_coef = rng.gamma(shape=b_2_Ni, scale=scale**2)
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def mu_ref_gamma_sampler_tridiag(shape=1.0, scale=1.0, beta=2, size=10):
"""
.. seealso::
:cite:`DuEd02` III-B
"""
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# beta/2*[N-1, N-2, ..., 1, 0]
b_2_Ni = 0.5 * beta * np.arange(size - 1, -1, step=-1)
# xi_odd = xi_1, ... , xi_2N-1
xi_odd = np.random.gamma(shape=b_2_Ni + shape, scale=scale) # odd
# xi_even = xi_0=0, xi_2, ... ,xi_2N-2
xi_even = np.zeros(size)
xi_even[1:] = np.random.gamma(shape=b_2_Ni[:-1], scale=scale) # even
# alpha_i = xi_2i-2 + xi_2i-1
# alpha_1 = xi_0 + xi_1 = xi_1
alpha_coef = xi_even + xi_odd
# beta_i+1 = xi_2i-1 * xi_2i
beta_coef = xi_odd[:-1] * xi_even[1:]
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def jacobi_sampler_tridiag(M_1, M_2, N, beta=2): """ .. seealso:: :cite:`KiNe04` Theorem 2 """ # c_odd = c_1, c_2, ..., c_2N-1 c_odd = np.random.beta( 0.5*beta*np.arange(M_1, M_1-N, step=-1), 0.5*beta*np.arange(M_2, M_2-N, step=-1)) # c_even = c_0, c_2, c_2N-2 c_even = np.zeros(N) c_even[1:] = np.random.beta( 0.5*beta*np.arange(N-1, 0, step=-1), 0.5*beta*np.arange(M_1+M_2-N, M_1+M_2-2*N+1,step=-1)) # xi_odd = xi_2i-1 = (1-c_2i-2) c_2i-1 xi_odd = (1-c_even)*c_odd # xi_even = xi_0=0, xi_2, xi_2N-2 # xi_2i = (1-c_2i-1)*c_2i xi_even = np.zeros(N) xi_even[1:] = (1-c_odd[:-1])*c_even[1:] # alpha_i = xi_2i-2 + xi_2i-1 # alpha_1 = xi_0 + xi_1 = xi_1 alpha_coef = xi_even + xi_odd # beta_i+1 = xi_2i-1 * xi_2i beta_coef = xi_odd[:-1] * xi_even[1:] return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def mu_ref_beta_sampler_tridiag(a, b, beta=2, size=10): """ .. seealso:: :cite:`KiNe04` Theorem 2 """ # beta/2*[N-1, N-2, ..., 1, 0] b_2_Ni = 0.5*beta*np.arange(size-1,-1,step=-1) # c_odd = c_1, c_2, ..., c_2N-1 c_odd = np.random.beta( b_2_Ni + a, b_2_Ni + b) # c_even = c_0, c_2, c_2N-2 c_even = np.zeros(size) c_even[1:] = np.random.beta(b_2_Ni[:-1], b_2_Ni[1:] + a + b) # xi_odd = xi_2i-1 = (1-c_2i-2) c_2i-1 xi_odd = (1-c_even)*c_odd # xi_even = xi_0=0, xi_2, xi_2N-2 # xi_2i = (1-c_2i-1)*c_2i xi_even = np.zeros(size) xi_even[1:] = (1-c_odd[:-1])*c_even[1:] # alpha_i = xi_2i-2 + xi_2i-1 # alpha_1 = xi_0 + xi_1 = xi_1 alpha_coef = xi_even + xi_odd # beta_i+1 = xi_2i-1 * xi_2i beta_coef = xi_odd[:-1] * xi_even[1:] return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def hermite_sampler_tridiag(N, beta=2): """ .. seealso:: :cite:`DuEd02` II-C """ alpha_coef = np.sqrt(2)*np.random.randn(N) beta_coef = np.random.chisquare(beta*np.arange(N-1, 0, step=-1)) return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def hermite_sampler_tridiag(N, beta=2):
"""
.. seealso::
:cite:`DuEd02` II-C
"""
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
alpha_coef = np.sqrt(2) * np.random.randn(N)
beta_coef = np.random.chisquare(beta * np.arange(N - 1, 0, step=-1))
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def muref_normal_sampler_tridiag(loc=0.0, scale=1.0, beta=2, size=10): """ .. seealso:: :cite:`DuEd02` II-C """ # beta/2*[N-1, N-2, ..., 1] b_2_Ni = 0.5*beta*np.arange(size-1, 0, step=-1) alpha_coef = np.random.normal(loc=loc, scale=scale, size=size) beta_coef = np.random.gamma(shape=b_2_Ni, scale=scale**2) return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def mu_ref_normal_sampler_tridiag(loc=0.0, scale=1.0, beta=2, size=10):
"""
.. seealso::
:cite:`DuEd02` II-C
"""
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# beta/2*[N-1, N-2, ..., 1]
b_2_Ni = 0.5 * beta * np.arange(size - 1, 0, step=-1)
alpha_coef = np.random.normal(loc=loc, scale=scale, size=size)
beta_coef = np.random.gamma(shape=b_2_Ni, scale=scale**2)
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def mu_ref_beta_sampler_tridiag(a, b, beta=2, size=10,
random_state=None):
""" Implementation of the tridiagonal model given by Theorem 2 of :cite:`KiNe04` to sample from
.. math::
\\Delta(x_{1}, \\dots, x_{N})^{\\beta}
\\prod_{n=1}^{N} x^{a-1} (1-x)^{b-1} dx
.. seealso::
:cite:`KiNe04` Theorem 2
"""
rng = check_random_state(random_state)
if not (beta > 0):
raise ValueError('`beta` must be positive. Given: {}'.format(beta))
# beta/2*[N-1, N-2, ..., 1, 0]
b_2_Ni = 0.5 * beta * np.arange(size - 1, -1, step=-1)
# c_odd = c_1, c_3, ..., c_2N-1
c_odd = rng.beta(b_2_Ni + a, b_2_Ni + b)
# c_even = c_0, c_2, c_2N-2
c_even = np.zeros(size)
c_even[1:] = rng.beta(b_2_Ni[:-1], b_2_Ni[1:] + a + b)
# xi_odd = xi_2i-1 = (1-c_2i-2) c_2i-1
xi_odd = (1 - c_even) * c_odd
# xi_even = xi_0=0, xi_2, xi_2N-2
# xi_2i = (1-c_2i-1)*c_2i
xi_even = np.zeros(size)
xi_even[1:] = (1 - c_odd[:-1]) * c_even[1:]
# alpha_i = xi_2i-2 + xi_2i-1
# alpha_1 = xi_0 + xi_1 = xi_1
alpha_coef = xi_even + xi_odd
# beta_i+1 = xi_2i-1 * xi_2i
beta_coef = xi_odd[:-1] * xi_even[1:]
return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def laguerre_sampler_tridiag(M, N, beta=2): """ .. seealso:: :cite:`DuEd02` III-B """ # M=>N # xi_odd = xi_1, ... , xi_2N-1 xi_odd = np.random.chisquare(beta*np.arange(M, M-N, step=-1)) # odd # xi_even = xi_0=0, xi_2, ... ,xi_2N-2 xi_even = np.zeros(N) xi_even[1:] = np.random.chisquare(beta*np.arange(N-1, 0, step=-1)) # even # alpha_i = xi_2i-2 + xi_2i-1 # alpha_1 = xi_0 + xi_1 = xi_1 alpha_coef = xi_even + xi_odd # beta_i+1 = xi_2i-1 * xi_2i beta_coef = xi_odd[:-1] * xi_even[1:] return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
def step(
self,
closure,
execute_update=True): #Fvp_fn, execute_update=True, closure=None):
"""Performs a single optimization step.
Arguments:
Fvp_fn (callable): A closure that accepts a vector of parameters and a vector of length
equal to the number of model paramsters and returns the Fisher-vector product.
"""
state = self.state
# State initialization
if len(state) == 0:
state['step'] = 0
# Set shrinkage to defaults, i.e. no shrinkage
state['rho'] = 0.0
state['diag_shrunk'] = 1.0
state['step'] += 1
# Get flat grad
g = gradients_to_vector(self._params)
if 'ng_prior' not in state:
state['ng_prior'] = torch.zeros_like(g)
curv_type = self._param_group['curv_type']
if curv_type not in self.valid_curv_types:
raise ValueError("Invalid curv_type.")
# Create closure to pass to Lanczos and CG
if curv_type == 'fisher':
Fvp_theta_fn = make_fvp_fun(closure, self._params)
elif curv_type == 'gauss_newton':
Fvp_theta_fn = make_gnvp_fun(closure, self._params)
shrinkage_method = self._param_group['shrinkage_method']
lanczos_amortization = self._param_group['lanczos_amortization']
if shrinkage_method == 'lanczos' and (state['step'] -
1) % lanczos_amortization == 0:
# print ("Computing Lanczos shrinkage at step ", state['step'])
w = lanczos_iteration(Fvp_theta_fn,
self._numel(),
k=self._param_group['lanczos_iters'])
rho, diag_shrunk = estimate_shrinkage(
w, self._numel(), self._param_group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
M = None
if self._param_group['cg_precondition_empirical']:
# Empirical Fisher is g * g
M = (g * g + self._param_group['cg_precondition_regu_coef'] *
torch.ones_like(g))**self._param_group['cg_precondition_exp']
# Do CG solve with hvp fn closure
extract_tridiag = self._param_group['shrinkage_method'] == 'cg'
cg_result = cg_solve(
Fvp_theta_fn,
g.data.clone(),
x_0=self._param_group['cg_prev_init_coef'] * state['ng_prior'],
M=M,
cg_iters=self._param_group['cg_iters'],
cg_residual_tol=self._param_group['cg_residual_tol'],
shrunk=self._param_group['shrinkage_method'] is not None,
rho=state['rho'],
Dshrunk=state['diag_shrunk'],
extract_tridiag=extract_tridiag)
if extract_tridiag:
# print ("Computing CG shrinkage at step ", state['step'])
ng, (diag_elems, off_diag_elems) = cg_result
w = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
rho, diag_shrunk = estimate_shrinkage(
w, self._numel(), self._param_group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
else:
ng = cg_result
state['ng_prior'] = ng.data.clone()
# Normalize NG
lr = self._param_group['lr']
alpha = torch.sqrt(torch.abs(lr / (torch.dot(g, ng) + 1e-20)))
# Unflatten grad
vector_to_gradients(ng, self._params)
if execute_update:
# Apply step
for p in self._params:
if p.grad is None:
continue
d_p = p.grad.data
p.data.add_(-alpha, d_p)
return dict(alpha=alpha, delta=lr, natural_grad=ng)
def step(self, closure, execute_update=True):
"""Performs a single optimization step.
Arguments:
Fvp_fn (callable): A closure that accepts a vector of parameters and a vector of length
equal to the number of model paramsters and returns the Fisher-vector product.
"""
# Update theta old for all blocks first, only approx update is supported
params_i = 0
params_j = 0
for gi, group in enumerate(self.param_groups):
params = group['params']
params_j += len(params)
num_params = self._numel(gi, params)
# print ("num_params: ", num_params, params_i, params_j)
state = self.state[gi]
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['m'] = torch.zeros(num_params)
# Maintain adaptive preconditioner if needed
if group['cg_precondition_empirical']:
state['M'] = torch.zeros(num_params)
# Set shrinkage to defaults, i.e. no shrinkage
state['rho'] = 0.0
state['diag_shrunk'] = 1.0
state['lagged'] = []
for i in range(len(params)):
state['lagged'].append(params[i] +
torch.randn(params[i].shape) *
0.0001)
beta1, beta2 = group['betas']
theta = parameters_to_vector(params)
theta_old = parameters_to_vector(state['lagged'])
# Update theta_old beta2 portion towards theta
theta_old = beta2 * theta_old + (1 - beta2) * theta
vector_to_parameters(theta_old, state['lagged'])
# print (theta_old)
# input("")
info = {}
# If doing block diag, perform the update for each param group
params_i = 0
params_j = 0
for gi, group in enumerate(self.param_groups):
params = group['params']
params_j += len(params)
num_params = self._numel(gi, params)
# NOTE: state is initialized above
state = self.state[gi]
m = state['m']
beta1, beta2 = group['betas']
state['step'] += 1
params_old = state['lagged'] #
bias_correction1 = 1 - beta1**state['step']
bias_correction2 = 1 - beta2**state['step']
# Get flat grad
g = gradients_to_vector(params)
# Update moving average mean
m.mul_(beta1).add_(1 - beta1, g)
g_hat = m / bias_correction1
if 'ng_prior' not in state:
state['ng_prior'] = torch.zeros_like(
g) #g_hat) #g_hat.data.clone()
curv_type = group['curv_type']
if curv_type not in self.valid_curv_types:
raise ValueError("Invalid curv_type.")
# Now that theta_old has been updated, do CG with only theta old
if curv_type == 'fisher':
fvp_fn_div_beta2 = make_fvp_fun_idx(
closure,
params_old,
params_i,
params_j,
bias_correction2=bias_correction2)
elif curv_type == 'gauss_newton':
fvp_fn_div_beta2 = make_gnvp_fun(
closure, params_old, bias_correction2=bias_correction2)
shrinkage_method = group['shrinkage_method']
lanczos_amortization = group['lanczos_amortization']
if shrinkage_method == 'lanczos' and (
state['step'] - 1) % lanczos_amortization == 0:
# print ("Computing Lanczos shrinkage at step ", state['step'])
w = lanczos_iteration(fvp_fn_div_beta2,
num_params,
k=group['lanczos_iters'])
rho, diag_shrunk = estimate_shrinkage(w, num_params,
group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
M = None
if group['cg_precondition_empirical']:
# Empirical Fisher is g * g
V = state['M']
Mt = (g * g + group['cg_precondition_regu_coef'] *
torch.ones_like(g))**group['cg_precondition_exp']
Vhat = V.mul(beta2).add(1 - beta2, Mt) / bias_correction2
V = torch.max(V, Vhat)
M = V
extract_tridiag = group['shrinkage_method'] == 'cg'
cg_result = cg_solve(fvp_fn_div_beta2,
g_hat.data.clone(),
x_0=group['cg_prev_init_coef'] *
state['ng_prior'],
M=M,
cg_iters=group['cg_iters'],
cg_residual_tol=group['cg_residual_tol'],
shrunk=group['shrinkage_method'] is not None,
rho=state['rho'],
Dshrunk=state['diag_shrunk'],
extract_tridiag=extract_tridiag)
if extract_tridiag:
# print ("Computing CG shrinkage at step ", state['step'])
ng, (diag_elems, off_diag_elems) = cg_result
w = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
rho, diag_shrunk = estimate_shrinkage(w, num_params,
group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
else:
ng = cg_result
# print ("NG: ", ng)
state['ng_prior'] = ng.data.clone()
# Normalize NG
lr = group['lr']
alpha = torch.sqrt(torch.abs(lr / (torch.dot(g_hat, ng) + 1e-20)))
# Unflatten grad
vector_to_gradients(ng, params)
if execute_update:
# Apply step
for p in params:
if p.grad is None:
continue
d_p = p.grad.data
p.data.add_(-alpha, d_p)
params_i = params_j
info[gi] = dict(alpha=alpha, delta=lr, natural_grad=ng)
return info
return f
if __name__ == "__main__":
n = 100
P = np.random.random((100, 100))
A = P @ P.T
M = np.diag(A)
Minv_mat = np.diag(1.0/M)
w1 = np.linalg.eigvals(A)
w1b = np.linalg.eigvals(Minv_mat @ A)
b = np.ones((n,))
fvp_fn = make_fvp_fn(A)
cg_result = cg_solve(fvp_fn, b, cg_iters=n, extract_tridiag=True)
ng, (diag_elems, off_diag_elems) = cg_result
w2 = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
cg_result = cg_solve(fvp_fn, b, cg_iters=n, M=M, extract_tridiag=True)
ng, (diag_elems, off_diag_elems) = cg_result
w3 = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
w4 = Minv_mat @ np.diag(w3)
print ("Originals: ", np.max(w1), np.linalg.norm(w1))
print ("CG no prec: ", np.max(w2), np.linalg.norm(w2), np.max(w1)-np.max(w2), np.linalg.norm(w1-w2))
print ("CG w/ prec: ", np.max(w3), np.linalg.norm(w3), np.max(w1)-np.max(w3), np.linalg.norm(w1-w3))
print ("CG w/ prec vs orig: ", np.max(w4), np.linalg.norm(w4), np.max(w1)-np.max(w4), np.linalg.norm(w1-w4))
print ("CG w/ prec vs true MinvA: ", np.max(w4), np.linalg.norm(w4), np.max(w1b)-np.max(w4), np.linalg.norm(w1b-w4))
def step(self, closure, execute_update=True):
"""Performs a single optimization step.
Arguments:
Fvp_fn (callable): A closure that accepts a vector of length equal to the number of
model paramsters and returns the Fisher-vector product.
"""
state = self.state
param_vec = parameters_to_vector(self._params)
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['m'] = torch.zeros_like(param_vec.data)
# Maintain adaptive preconditioner if needed
if self._param_group['cg_precondition_empirical']:
state['M'] = torch.zeros_like(param_vec.data)
# Set shrinkage to defaults, i.e. no shrinkage
state['rho'] = 0.0
state['diag_shrunk'] = 1.0
m = state['m']
beta1, beta2 = self._param_group['betas']
state['step'] += 1
bias_correction1 = 1 - beta1**state['step']
bias_correction2 = 1 - beta2**state['step']
# Get flat grad
g = gradients_to_vector(self._params)
# Update moving average mean
m.mul_(beta1).add_(1 - beta1, g)
g_hat = m / bias_correction1
theta = parameters_to_vector(self._params)
theta_old = parameters_to_vector(self._params_old)
if 'ng_prior' not in state:
state['ng_prior'] = torch.zeros_like(g_hat) #g_hat.data.clone()
if 'max_fisher_spectral_norm' not in state:
state['max_fisher_spectral_norm'] = 0.0
curv_type = self._param_group['curv_type']
if curv_type not in self.valid_curv_types:
raise ValueError("Invalid curv_type.")
if curv_type == 'fisher':
weighted_fvp_fn_div_beta2 = self._make_combined_fvp_fun(
closure,
self._params,
self._params_old,
bias_correction2=bias_correction2)
elif curv_type == 'gauss_newton':
weighted_fvp_fn_div_beta2 = self._make_combined_gnvp_fun(
closure,
self._params,
self._params_old,
bias_correction2=bias_correction2)
fisher_norm = lanczos_iteration(weighted_fvp_fn_div_beta2,
self._numel(),
k=1)[0]
is_max_norm = fisher_norm > state['max_fisher_spectral_norm'] or state[
'step'] == 1
if is_max_norm:
state['max_fisher_spectral_norm'] = fisher_norm
if is_max_norm:
if self._param_group['assume_locally_linear']:
# Update theta_old beta2 portion towards theta
theta_old = beta2 * theta_old + (1 - beta2) * theta
else:
# Do linesearch first to update theta_old. Then can do CG with only one HVP at each itr.
ng = self.state['ng_prior'].clone(
) if state['step'] > 1 else g_hat.data.clone()
if curv_type == 'fisher':
weighted_fvp_fn = self._make_combined_fvp_fun(
closure, self._params, self._params_old)
f = make_fvp_obj_fun(closure, weighted_fvp_fn, ng)
elif curv_type == 'gauss_newton':
weighted_fvp_fn = self._make_combined_gnvp_fun(
closure, self._params, self._params_old)
f = make_gnvp_obj_fun(closure, weighted_fvp_fn, ng)
xmin, fmin, alpha = randomized_linesearch(
f, theta_old.data, theta.data)
theta_old = Variable(xmin.float())
vector_to_parameters(theta_old, self._params_old)
# Now that theta_old has been updated, do CG with only theta old
# If not max norm, then this will remain the old params.
if curv_type == 'fisher':
fvp_fn_div_beta2 = make_fvp_fun(closure,
self._params_old,
bias_correction2=bias_correction2)
elif curv_type == 'gauss_newton':
fvp_fn_div_beta2 = make_gnvp_fun(closure,
self._params_old,
bias_correction2=bias_correction2)
shrinkage_method = self._param_group['shrinkage_method']
lanczos_amortization = self._param_group['lanczos_amortization']
if shrinkage_method == 'lanczos' and (state['step'] -
1) % lanczos_amortization == 0:
# print ("Computing Lanczos shrinkage at step ", state['step'])
w = lanczos_iteration(fvp_fn_div_beta2,
self._numel(),
k=self._param_group['lanczos_iters'])
rho, diag_shrunk = estimate_shrinkage(
w, self._numel(), self._param_group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
M = None
if self._param_group['cg_precondition_empirical']:
# Empirical Fisher is g * g
V = state['M']
Mt = (g * g + self._param_group['cg_precondition_regu_coef'] *
torch.ones_like(g))**self._param_group['cg_precondition_exp']
Vhat = V.mul(beta2).add(1 - beta2, Mt) / bias_correction2
V = torch.max(V, Vhat)
M = V
extract_tridiag = self._param_group['shrinkage_method'] == 'cg'
cg_result = cg_solve(
fvp_fn_div_beta2,
g_hat.data.clone(),
x_0=self._param_group['cg_prev_init_coef'] * state['ng_prior'],
M=M,
cg_iters=self._param_group['cg_iters'],
cg_residual_tol=self._param_group['cg_residual_tol'],
shrunk=self._param_group['shrinkage_method'] is not None,
rho=state['rho'],
Dshrunk=state['diag_shrunk'],
extract_tridiag=extract_tridiag)
if extract_tridiag:
# print ("Computing CG shrinkage at step ", state['step'])
ng, (diag_elems, off_diag_elems) = cg_result
w = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
rho, diag_shrunk = estimate_shrinkage(
w, self._numel(), self._param_group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
else:
ng = cg_result
self.state['ng_prior'] = ng.data.clone()
# Normalize NG
lr = self._param_group['lr']
alpha = torch.sqrt(torch.abs(lr / (torch.dot(g_hat, ng) + 1e-20)))
# Unflatten grad
vector_to_gradients(ng, self._params)
if execute_update:
# Apply step
for p in self._params:
if p.grad is None:
continue
d_p = p.grad.data
p.data.add_(-alpha, d_p)
return dict(alpha=alpha, delta=lr, natural_grad=ng)
import numpy as np
from scipy.linalg import eigvalsh_tridiagonal, eigvalsh
diagonal = np.array([1.5833333333333332593, -0.01259572752922188954, 2.3690214303404664165, 0.06024096385542132559, 1.9941915593928158934, 1.0058084406071843286])
subdiagonal = np.array([-2.3964673074247340168, 0.93475927884341891705,-2.0788632064407330802, 6.3258425909268308882e-016, -0.075991464158134569562])
eigenvectors = eigvalsh_tridiagonal(diagonal, subdiagonal)
print("Eigenvectors of matrixA to: ")
print(eigenvectors)
import numpy as np
from scipy.linalg import eigvalsh_tridiagonal
diagonal = np.array([1.58333, -0.0125957, 2.36902, 0.060241, 1.90646, 1.09354])
subdiagonal = np.array([-2.396467, 0.9347593, -2.078863, 1.177896e-15, -0.2911902])
result = eigvalsh_tridiagonal(diagonal, subdiagonal)
print("Eigenvalues: ", result)
def sample_mcmc(self,
N=10,
nb_gibbs_passes=10,
sample_exact_cond=False,
nb_mala_steps=100,
return_chain_of_eig_vals=False,
return_chain_of_lambda_max=False,
random_state=None):
""" Gibbs sampler on Jacobi matrices to sample approximately from the corresponding :math:`\\beta`-ensemble.
:param N:
Number of points/size of the :math:`\\beta`-ensemble
:type N:
int
:param nb_gibbs_passes:
Number of passes/sweeps over the variables using the Gibbs sampler
:type nb_gibbs_passes:
int
:param sample_exact_cond:
Flag to force (``True``) exact sampling from the conditionals when it is possible.
Otherwise run MALA for ``nb_mala_steps` to sample from the conditionals.
:type sample_exact_cond:
bool (default 100)
:param nb_mala_steps:
Number of steps of Metropolis Ajusted Langevin Algorithm (MALA) to perform when the conditionals are sampled approximately
:type nb_mala_steps:
int, default 100
:param return_chain_of_eig_vals:
Flag to return the chain of eigenvalues associated to the chain of Jacobi matrices.
If ``True`` the whole chain of eigenvalues is returned
If ``False`` only the last sequence of eigenvalues is returned
:type return_chain_of_eig_vals:
bool (default False)
:param return_chain_of_lambda:
Flag to return the chain of the **largest** eigenvalues associated to the chain of Jacobi matrices.
If ``True`` the whole chain of the **largest** eigenvalues is returned
If ``False`` only the **largest** eigenvalue of the last Jacobi matrix is returned
:type return_chain_of_eig_vals:
bool (default False)
"""
rng = check_random_state(random_state)
if sample_exact_cond:
if self.V[3]:
raise ValueError(
'Sampling exactly the conditionals a_i |... from V = ... + x^3 + ... is not supported, given g_3={}. Conditionals are not log-concave, cannot use Dev12 sampler'
.format(self.V[3]))
if self.V.order >= 5:
raise ValueError(
'Sampling exactly the conditionals a_i |... from V = ... + x^5 + ... is not supported, deg(V)={}>=5. Conditionals are not log-concave, cannot use Dev12 sampler'
.format(self.V.order))
even_coefs_V = self.V.coef[::-1][2::2]
if not all(even_coefs_V >= 0):
raise ValueError('\n'.join([
'even coefs of V are not all >=0', ', '.join([
'g_{}={}'.format(2 * (n + 1), g_2n)
for n, g_2n in enumerate(even_coefs_V)
]),
'Conditionals are not log-concave, cannot use Dev12 sampler',
'You may retry swithching `sample_exact_cond` to False'
]))
self.N = N
self.nb_gibbs_passes = nb_gibbs_passes
a, b = np.zeros((2, N + 3))
if return_chain_of_eig_vals:
eig_vals = np.zeros((N, nb_gibbs_passes))
elif return_chain_of_lambda_max:
lambda_max = np.zeros(nb_gibbs_passes)
for p in range(nb_gibbs_passes):
if (p + 1) % 50 == 0:
print(p + 1)
for i in range(1, N + 1):
# a_i | ... propto exp - P_a_i
P_a_i = 0.5 * self.beta * N * P_a_cond(i, a, b, self.V)
if sample_exact_cond:
a[i], _ = sampler_exact_convex_quartic(P=P_a_i,
random_state=rng)
else:
a[i] = sampler_mala(a[i],
V=P_a_i,
sigma=0.01,
nb_steps=nb_mala_steps,
random_state=rng)
# b_i | ... propto x^(shape-1) * exp - P_b_i
if i < N:
P_b_i = 0.5 * self.beta * N * P_b_cond(i, a, b, self.V)
b[i], _ = sampler_exact_convex_quartic(P=P_b_i,
shape=0.5 *
self.beta * (N - i),
random_state=rng)
if return_chain_of_eig_vals:
eig_vals[:,
p] = la.eigvalsh_tridiagonal(a[1:N + 1],
np.sqrt(b[1:N]))
elif return_chain_of_lambda_max:
lambda_max[p] = la.eigvalsh_tridiagonal(
a[1:N + 1],
np.sqrt(b[1:N]),
select='i',
select_range=(N - 1, N - 1))[0]
if return_chain_of_eig_vals:
return eig_vals
if return_chain_of_lambda_max:
return lambda_max
return la.eigvalsh_tridiagonal(a[1:N + 1], np.sqrt(b[1:N]))
def comp_modes(dh, N2, f0=1.0, eivec=False, wmode=False, diag=False):
'''
Compute eigenvalues (and eigenvectors) of the sturm-liouville
equation
d ( f^2 d ) 1
-- ( --- -- psi) + ---- psi = 0
dz ( N^2 dz ) Rd^2
for a given stratification
The eigenvectors correspond to the matrices for the mode/layer
conversion
mod2lay[:,0] is the barotropic mode: should be 1..1
mod2lay[:,i] is the ith baroclinic mode
-To convert from physical to modal:
u_mod = np.dot(lay2mod[:,:],u_lev) # if u_lev is 1D
u_mod = np.einsum('ij,jkl->ikl',lay2mod,u_lev) # if u_lev is 3D
u_mod = np.einsum('ijkl,jkl->ikl',lay2mod,u_lev) #if u_lev is 3D and N2 variable
-To go back to the physical space:
u_lev = np.dot(mod2lay[:,:],u_mod)
u_lev = np.einsum('ij,jkl->ikl',mod2lay,u_mod) # if u_mod is 3D
u_lev = np.einsum('ijkl,jkl->ikl',mod2lay,u_mod) #if u_mod is 3D and N2 variable
the w_modes are related to the p_modes by
w_modes = -1/N2 d p_modes/dz
Parameters
----------
dh : array [nz]
N2 : array [nz (,ny,nx)]
f0 : scalar or array [(ny,nx)]
eivec : Bool
wmode : Bool
diag : Bool
Use transformation matrix to solve a symetric matrix
Returns
-------
if eivec == T
Rd: array [nz (,ny,nx)]
lay2mod: array [nz,nz (,ny,nx)]
mod2lay: array [nz,nz (,ny,nx)]
if eivec == F
Rd: array [nz (,ny,nx)]
'''
N2,f0 = reshape3d(dh,N2,f0)
nl,si_y,si_x = N2.shape
mat_format = "dense"
if diag:
mat_format = "sym_diag"
S = gamma_stretch(dh,N2,f0,wmode=wmode,squeeze=False,mat_format=mat_format)
nlt = (N2 == 0).argmax(axis=0)
nlt = np.where(nlt == 0,nl,nlt)
# put variables in right format
Ht = np.cumsum(dh)
# Ht = np.sum(dh)
dhi = 0.5*(dh[1:] + dh[:-1])
dhcol = dh[:,None]
dhicol = dhi[:,None]
if wmode:
Rd = np.zeros((nl,si_y,si_x))
if eivec:
mod2lay = np.zeros((nl,nl,si_y,si_x))
lay2mod = np.zeros((nl,nl,si_y,si_x))
else:
nlt = nlt + 1
Rd = np.zeros((nl+1,si_y,si_x))
if eivec:
mod2lay = np.zeros((nl+1,nl+1,si_y,si_x))
lay2mod = np.zeros((nl+1,nl+1,si_y,si_x))
for j,i in np.ndindex((si_y,si_x)):
if eivec:
if diag:
iRd2, eigs = la.eigh_tridiagonal(S[1,:nlt[j,i],j,i], S[0,1:nlt[j,i],j,i])
eigr = S[2,:nlt[j,i],j,i,None]*eigs # D*w
eigl = eigs/S[2,:nlt[j,i],j,i,None] # w*D^-1 if eigenvectors are stored in lines but eigl is eigl.T so we do D^-1*w
else:
iRd2, eigl,eigr= la.eig(S[:nlt[j,i],:nlt[j,i],j,i],left=True)
else:
if diag:
iRd2 = la.eigvalsh_tridiagonal(S[1,:nlt[j,i],j,i], S[0,1:nlt[j,i],j,i])
else:
iRd2 = la.eig(S[:nlt[j,i],:nlt[j,i],j,i],right=False)
iRd2 = -iRd2.real
idx = np.argsort(iRd2)
iRd2 = iRd2[idx]
with np.errstate(divide='ignore', invalid='ignore'):
Rd_loc = 1./np.sqrt(iRd2)
Rd[:nlt[j,i],j,i] = Rd_loc
if eivec:
eigl = eigl[:,idx]
eigr = eigr[:,idx]
# Normalize eigenvectors
N2col = N2[:nlt[j,i],j,i][:,None]
cm = Rd_loc[:nlt[j,i],None]*f0[j,i]
if wmode:
scap = np.sum(dhi[:nlt[j,i],None]*eigr*eigr*N2col*cm.T**2,0)
Htt = Ht[nlt[j,i]]
else:
scap = np.sum(dh[:nlt[j,i],None]*eigr*eigr,0)
Htt = Ht[nlt[j,i]-1]
flip = np.sign(eigr[0,:])
eigr = eigr*np.sqrt(Htt/scap)*flip
# # scalar product
# if wmode:
# check = np.sum(N2col.T*eigr[:,1]*eigr[:,1]*dhicol.T*(Rd_loc[1]*f0[j,i])**2)
# else:
# check = np.sum(dhcol.T*eigr[:,2]*eigr[:,2])/Ht
if diag:
eigl = eigl/np.sqrt(Htt/scap)*flip
else:
scap2 = np.sum(eigl*eigr,0)
eigl = eigl/scap2
lay2mod[:nlt[j,i],:nlt[j,i],j,i] = eigl.T
mod2lay[:nlt[j,i],:nlt[j,i],j,i] = eigr
if eivec:
return Rd.squeeze(), lay2mod.squeeze(), mod2lay.squeeze()
else:
return Rd.squeeze()
def step(self, closure, execute_update=True):
"""Performs a single optimization step.
Arguments:
Fvp_fn (callable): A closure that accepts a vector of parameters and a vector of length
equal to the number of model paramsters and returns the Fisher-vector product.
"""
info = {}
# If doing block diag, perform the update for each param group
params_i = 0
params_j = 0
for gi, group in enumerate(self.param_groups):
params = group['params']
params_j += len(params)
state = self.state[gi]
if len(state) == 0:
state['step'] = 0
# Set shrinkage to defaults, i.e. no shrinkage
state['rho'] = 0.0
state['diag_shrunk'] = 1.0
state['step'] += 1
g = gradients_to_vector(params)
if 'ng_prior' not in state:
state['ng_prior'] = torch.zeros_like(g)
curv_type = group['curv_type']
if curv_type not in self.valid_curv_types:
raise ValueError("Invalid curv_type.")
# Create closure to pass to Lanczos and CG
if curv_type == 'fisher':
Fvp_theta_fn = make_fvp_fun_idx(closure, params, params_i,
params_j)
elif curv_type == 'gauss_newton':
# Pass indices instead of actual params, since these params should be the same at
# the model params anyway. Then the closure should set only the subset of params
# and only return the tmp_params from that subset.
# This would require that the param groups are order in a specific manner?
Fvp_theta_fn = make_gnvp_fun_idx(closure, params, params_i,
params_j)
num_params = self._numel(gi, params)
shrinkage_method = group['shrinkage_method']
lanczos_amortization = group['lanczos_amortization']
if shrinkage_method == 'lanczos' and (
state['step'] - 1) % lanczos_amortization == 0:
# print ("Computing Lanczos shrinkage at step ", state['step'])
w = lanczos_iteration(Fvp_theta_fn,
num_params,
k=group['lanczos_iters'])
rho, diag_shrunk = estimate_shrinkage(w, num_params,
group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
M = None
if group['cg_precondition_empirical']:
# Empirical Fisher is g * g
M = (g * g + group['cg_precondition_regu_coef'] *
torch.ones_like(g))**group['cg_precondition_exp']
# Do CG solve with hvp fn closure
extract_tridiag = group['shrinkage_method'] == 'cg'
cg_result = cg_solve(Fvp_theta_fn,
g.data.clone(),
x_0=group['cg_prev_init_coef'] *
state['ng_prior'],
M=M,
cg_iters=group['cg_iters'],
cg_residual_tol=group['cg_residual_tol'],
shrunk=group['shrinkage_method'] is not None,
rho=state['rho'],
Dshrunk=state['diag_shrunk'],
extract_tridiag=extract_tridiag)
if extract_tridiag:
# print ("Computing CG shrinkage at step ", state['step'])
ng, (diag_elems, off_diag_elems) = cg_result
w = eigvalsh_tridiagonal(diag_elems, off_diag_elems)
rho, diag_shrunk = estimate_shrinkage(w, num_params,
group['batch_size'])
state['rho'] = rho
state['diag_shrunk'] = diag_shrunk
else:
ng = cg_result
state['ng_prior'] = ng.data.clone()
# Normalize NG
lr = group['lr']
alpha = torch.sqrt(torch.abs(lr / (torch.dot(g, ng) + 1e-20)))
# Unflatten grad
vector_to_gradients(ng, params)
if execute_update:
# Apply step
for p in params:
if p.grad is None:
continue
d_p = p.grad.data
p.data.add_(-alpha, d_p)
params_i = params_j
info[gi] = dict(alpha=alpha, delta=lr, natural_grad=ng)
return info