def main():
utils.init_out_dir()
last_epoch = utils.get_last_checkpoint_step()
if last_epoch >= args.epoch:
exit()
if last_epoch >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_epoch))
else:
utils.clear_log()
model = RNN(args.device, Number_qubits = args.N,charset_length = args.charset_length,\
hidden_size = args.hidden_size, num_layers = args.num_layers)
model.train(False)
print('number of qubits: ', model.Number_qubits)
my_log('Total nparams: {}'.format(utils.get_nparams(model)))
model.to(args.device)
params = [x for x in model.parameters() if x.requires_grad]
optimizer = torch.optim.AdamW(params,
lr=args.lr,
weight_decay=args.weight_decay)
if last_epoch >= 0:
utils.load_checkpoint(last_epoch, model, optimizer)
# Quantum state
ghz = GHZ(Number_qubits=args.N)
c_fidelity = classical_fidelity(model, ghz)
# c_fidelity = cfid(model, ghz, './data.txt')
print(c_fidelity)
def main():
start_time = time.time()
init_out_dir()
if args.clear_checkpoint:
clear_checkpoint()
last_step = get_last_checkpoint_step()
if last_step >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_step))
else:
clear_log()
print_args()
if args.net == 'made':
net = MADE(**vars(args))
elif args.net == 'pixelcnn':
net = PixelCNN(**vars(args))
elif args.net == 'bernoulli':
net = BernoulliMixture(**vars(args))
else:
raise ValueError('Unknown net: {}'.format(args.net))
net.to(args.device)
my_log('{}\n'.format(net))
params = list(net.parameters())
params = list(filter(lambda p: p.requires_grad, params))
nparams = int(sum([np.prod(p.shape) for p in params]))
my_log('Total number of trainable parameters: {}'.format(nparams))
named_params = list(net.named_parameters())
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(params, lr=args.lr)
elif args.optimizer == 'sgdm':
optimizer = torch.optim.SGD(params, lr=args.lr, momentum=0.9)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(params, lr=args.lr, alpha=0.99)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.9, 0.999))
elif args.optimizer == 'adam0.5':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.5, 0.999))
else:
raise ValueError('Unknown optimizer: {}'.format(args.optimizer))
if args.lr_schedule:
# 0.92**80 ~ 1e-3
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, factor=0.92, patience=100, threshold=1e-4, min_lr=1e-6)
if last_step >= 0:
state = torch.load('{}_save/{}.state'.format(args.out_filename,
last_step))
ignore_param(state['net'], net)
net.load_state_dict(state['net'])
if state.get('optimizer'):
optimizer.load_state_dict(state['optimizer'])
if args.lr_schedule and state.get('scheduler'):
scheduler.load_state_dict(state['scheduler'])
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
sample_time = 0
train_time = 0
start_time = time.time()
for step in range(last_step + 1, args.max_step + 1):
optimizer.zero_grad()
sample_start_time = time.time()
with torch.no_grad():
sample, x_hat = net.sample(args.batch_size)
assert not sample.requires_grad
assert not x_hat.requires_grad
sample_time += time.time() - sample_start_time
train_start_time = time.time()
log_prob = net.log_prob(sample)
# 0.998**9000 ~ 1e-8
beta = args.beta * (1 - args.beta_anneal**step)
with torch.no_grad():
energy = ising.energy(sample, args.ham, args.lattice,
args.boundary)
loss = log_prob + beta * energy
assert not energy.requires_grad
assert not loss.requires_grad
loss_reinforce = torch.mean((loss - loss.mean()) * log_prob)
loss_reinforce.backward()
if args.clip_grad:
nn.utils.clip_grad_norm_(params, args.clip_grad)
optimizer.step()
if args.lr_schedule:
scheduler.step(loss.mean())
train_time += time.time() - train_start_time
if args.print_step and step % args.print_step == 0:
free_energy_mean = loss.mean() / args.beta / args.L**2
free_energy_std = loss.std() / args.beta / args.L**2
entropy_mean = -log_prob.mean() / args.L**2
energy_mean = energy.mean() / args.L**2
mag = sample.mean(dim=0)
mag_mean = mag.mean()
mag_sqr_mean = (mag**2).mean()
if step > 0:
sample_time /= args.print_step
train_time /= args.print_step
used_time = time.time() - start_time
my_log(
'step = {}, F = {:.8g}, F_std = {:.8g}, S = {:.8g}, E = {:.8g}, M = {:.8g}, Q = {:.8g}, lr = {:.3g}, beta = {:.8g}, sample_time = {:.3f}, train_time = {:.3f}, used_time = {:.3f}'
.format(
step,
free_energy_mean.item(),
free_energy_std.item(),
entropy_mean.item(),
energy_mean.item(),
mag_mean.item(),
mag_sqr_mean.item(),
optimizer.param_groups[0]['lr'],
beta,
sample_time,
train_time,
used_time,
))
sample_time = 0
train_time = 0
if args.save_sample:
state = {
'sample': sample,
'x_hat': x_hat,
'log_prob': log_prob,
'energy': energy,
'loss': loss,
}
torch.save(state, '{}_save/{}.sample'.format(
args.out_filename, step))
if (args.out_filename and args.save_step
and step % args.save_step == 0):
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
}
if args.lr_schedule:
state['scheduler'] = scheduler.state_dict()
torch.save(state, '{}_save/{}.state'.format(
args.out_filename, step))
if (args.out_filename and args.visual_step
and step % args.visual_step == 0):
torchvision.utils.save_image(
sample,
'{}_img/{}.png'.format(args.out_filename, step),
nrow=int(sqrt(sample.shape[0])),
padding=0,
normalize=True)
if args.print_sample:
x_hat_np = x_hat.view(x_hat.shape[0], -1).cpu().numpy()
x_hat_std = np.std(x_hat_np, axis=0).reshape([args.L] * 2)
x_hat_cov = np.cov(x_hat_np.T)
x_hat_cov_diag = np.diag(x_hat_cov)
x_hat_corr = x_hat_cov / (
sqrt(x_hat_cov_diag[:, None] * x_hat_cov_diag[None, :]) +
args.epsilon)
x_hat_corr = np.tril(x_hat_corr, -1)
x_hat_corr = np.max(np.abs(x_hat_corr), axis=1)
x_hat_corr = x_hat_corr.reshape([args.L] * 2)
energy_np = energy.cpu().numpy()
energy_count = np.stack(
np.unique(energy_np, return_counts=True)).T
my_log(
'\nsample\n{}\nx_hat\n{}\nlog_prob\n{}\nenergy\n{}\nloss\n{}\nx_hat_std\n{}\nx_hat_corr\n{}\nenergy_count\n{}\n'
.format(
sample[:args.print_sample, 0],
x_hat[:args.print_sample, 0],
log_prob[:args.print_sample],
energy[:args.print_sample],
loss[:args.print_sample],
x_hat_std,
x_hat_corr,
energy_count,
))
if args.print_grad:
my_log('grad max_abs min_abs mean std')
for name, param in named_params:
if param.grad is not None:
grad = param.grad
grad_abs = torch.abs(grad)
my_log('{} {:.3g} {:.3g} {:.3g} {:.3g}'.format(
name,
torch.max(grad_abs).item(),
torch.min(grad_abs).item(),
torch.mean(grad).item(),
torch.std(grad).item(),
))
else:
my_log('{} None'.format(name))
my_log('')
def main():
start_time = time.time()
init_out_dir()
print_args()
args.n = 60 ##n=60
if args.ham == 'hop':
ham = HopfieldModel(args.n, args.beta, args.device, seed=args.seed)
elif args.ham == 'sk':
ham = SKModel(args.n, args.beta, args.device, seed=args.seed)
else:
raise ValueError('Unknown ham: {}'.format(args.ham))
ham.J.requires_grad = False
net = MADE(**vars(args))
net.to(args.device)
my_log('{}\n'.format(net))
params = list(net.parameters())
params = list(filter(lambda p: p.requires_grad, params))
nparams = int(sum([np.prod(p.shape) for p in params]))
my_log('Total number of trainable parameters: {}'.format(nparams))
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(params, lr=args.lr)
elif args.optimizer == 'sgdm':
optimizer = torch.optim.SGD(params, lr=args.lr, momentum=0.9)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(params, lr=args.lr, alpha=0.99)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.9, 0.999))
elif args.optimizer == 'adam0.5':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.5, 0.999))
else:
raise ValueError('Unknown optimizer: {}'.format(args.optimizer))
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
sample_time = 0
train_time = 0
start_time = time.time()
if args.beta_anneal_to < args.beta:
args.beta_anneal_to = args.beta
beta = args.beta
while beta <= args.beta_anneal_to:
for step in range(args.max_step):
optimizer.zero_grad()
sample_start_time = time.time()
with torch.no_grad():
sample, x_hat = net.sample(args.batch_size)
assert not sample.requires_grad
assert not x_hat.requires_grad
sample_time += time.time() - sample_start_time
train_start_time = time.time()
log_prob = net.log_prob(sample)
with torch.no_grad():
energy = ham.energy(sample)
#print('fffffffffffffffffff',energy.type())
loss = log_prob + beta * energy
assert not energy.requires_grad
assert not loss.requires_grad
loss_reinforce = torch.mean((loss - loss.mean()) * log_prob)
loss_reinforce.backward()
if args.clip_grad > 0:
# nn.utils.clip_grad_norm_(params, args.clip_grad)
parameters = list(filter(lambda p: p.grad is not None, params))
max_norm = float(args.clip_grad)
norm_type = 2
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item()**norm_type
total_norm = total_norm**(1 / norm_type)
clip_coef = max_norm / (total_norm + args.epsilon)
for p in parameters:
p.grad.data.mul_(clip_coef)
optimizer.step()
train_time += time.time() - train_start_time
if args.print_step and step % args.print_step == 0:
free_energy_mean = loss.mean() / beta / args.n
free_energy_std = loss.std() / beta / args.n
entropy_mean = -log_prob.mean() / args.n
energy_mean = energy.mean() / args.n
mag = sample.mean(dim=0)
mag_mean = mag.mean()
if step > 0:
sample_time /= args.print_step
train_time /= args.print_step
used_time = time.time() - start_time
my_log(
'beta = {:.3g}, # {}, F = {:.8g}, F_std = {:.8g}, S = {:.5g}, E = {:.5g}, M = {:.5g}, sample_time = {:.3f}, train_time = {:.3f}, used_time = {:.3f}'
.format(
beta,
step,
free_energy_mean.item(),
free_energy_std.item(),
entropy_mean.item(),
energy_mean.item(),
mag_mean.item(),
sample_time,
train_time,
used_time,
))
sample_time = 0
train_time = 0
with open(args.fname, 'a', newline='\n') as f:
f.write('{} {} {:.3g} {:.8g} {:.8g} {:.8g} {:.8g}\n'.format(
args.n,
args.seed,
beta,
free_energy_mean.item(),
free_energy_std.item(),
energy_mean.item(),
entropy_mean.item(),
))
if args.ham == 'hop':
ensure_dir(args.out_filename + '_sample/')
np.savetxt('{}_sample/sample{:.2f}.txt'.format(
args.out_filename, beta),
sample.cpu().numpy(),
delimiter=' ',
fmt='%d')
np.savetxt('{}_sample/log_prob{:.2f}.txt'.format(
args.out_filename, beta),
log_prob.cpu().detach().numpy(),
delimiter=' ',
fmt='%.5f')
beta += args.beta_inc
def main():
start_time = time()
init_out_dir()
last_step = get_last_ckpt_step()
if last_step >= 0:
my_log(f'\nCheckpoint found: {last_step}\n')
else:
clear_log()
print_args()
net_init, net_apply, net_init_cache, net_apply_fast = get_net()
rng, rng_net = jrand.split(jrand.PRNGKey(args.seed))
in_shape = (args.batch_size, args.L, args.L, 1)
out_shape, params_init = net_init(rng_net, in_shape)
_, cache_init = net_init_cache(params_init, jnp.zeros(in_shape), (-1, -1))
# sample_fun = get_sample_fun(net_apply, None)
sample_fun = get_sample_fun(net_apply_fast, cache_init)
log_q_fun = get_log_q_fun(net_apply)
need_beta_anneal = args.beta_anneal_step > 0
opt_init, opt_update, get_params = optimizers.adam(args.lr)
@jit
def update(step, opt_state, rng):
params = get_params(opt_state)
rng, rng_sample = jrand.split(rng)
spins = sample_fun(args.batch_size, params, rng_sample)
log_q = log_q_fun(params, spins) / args.L**2
energy = energy_fun(spins) / args.L**2
def neg_log_Z_fun(params, spins):
log_q = log_q_fun(params, spins) / args.L**2
energy = energy_fun(spins) / args.L**2
beta = args.beta
if need_beta_anneal:
beta *= jnp.minimum(step / args.beta_anneal_step, 1)
neg_log_Z = log_q + beta * energy
return neg_log_Z
loss_fun = partial(expect,
log_q_fun,
neg_log_Z_fun,
mean_grad_expected_is_zero=True)
grads = grad(loss_fun)(params, spins, spins)
opt_state = opt_update(step, grads, opt_state)
return spins, log_q, energy, opt_state, rng
if last_step >= 0:
params_init = load_ckpt(last_step)
opt_state = opt_init(params_init)
my_log('Training...')
for step in range(last_step + 1, args.max_step + 1):
spins, log_q, energy, opt_state, rng = update(step, opt_state, rng)
if args.print_step and step % args.print_step == 0:
# Use the final beta, not the annealed beta
free_energy = log_q / args.beta + energy
my_log(', '.join([
f'step = {step}',
f'F = {free_energy.mean():.8g}',
f'F_std = {free_energy.std():.8g}',
f'S = {-log_q.mean():.8g}',
f'E = {energy.mean():.8g}',
f'time = {time() - start_time:.3f}',
]))
if args.save_step and step % args.save_step == 0:
params = get_params(opt_state)
save_ckpt(params, step)
def main():
start_time = time.time()
# initialize output dir
init_out_dir()
# check point
if args.clear_checkpoint:
clear_checkpoint()
last_step = get_last_checkpoint_step()
if last_step >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_step))
else:
clear_log()
print_args()
if args.net == 'pixelcnn_xy':
net = PixelCNN(**vars(args))
else:
raise ValueError('Unknown net: {}'.format(args.net))
net.to(args.device)
my_log('{}\n'.format(net))
# parameters of networks
params = list(net.parameters())
params = list(filter(lambda p: p.requires_grad,
params)) # parameters with gradients
nparams = int(sum([np.prod(p.shape) for p in params]))
my_log('Total number of trainable parameters: {}'.format(nparams))
named_params = list(net.named_parameters())
# optimizers
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(params, lr=args.lr)
elif args.optimizer == 'sgdm':
optimizer = torch.optim.SGD(params, lr=args.lr, momentum=0.9)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(params, lr=args.lr, alpha=0.99)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.9, 0.999))
elif args.optimizer == 'adam0.5':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.5, 0.999))
else:
raise ValueError('Unknown optimizer: {}'.format(args.optimizer))
# learning rates
if args.lr_schedule:
# 0.92**80 ~ 1e-3
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.92,
patience=100,
threshold=1e-4,
min_lr=1e-6)
# read last step
if last_step >= 0:
state = torch.load('{}_save/{}.state'.format(args.out_filename,
last_step))
ignore_param(state['net'], net)
net.load_state_dict(state['net'])
if state.get('optimizer'):
optimizer.load_state_dict(state['optimizer'])
if args.lr_schedule and state.get('scheduler'):
scheduler.load_state_dict(state['scheduler'])
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
# start training
my_log('Training...')
sample_time = 0
train_time = 0
start_time = time.time()
for step in range(last_step + 1, args.max_step + 1):
optimizer.zero_grad() # clear last step
sample_start_time = time.time()
with torch.no_grad():
sample, x_hat = net.sample(
args.batch_size
) # sample from networks with batch_size = 10**3 (default)
assert not sample.requires_grad
assert not x_hat.requires_grad
sample_time += time.time() - sample_start_time
train_start_time = time.time()
# log probabilities
log_prob = net.log_prob(sample, args.batch_size)
# 0.998**9000 ~ 1e-8
beta = args.beta * (1 - args.beta_anneal**step
) # anneal process to avoid mode collapse
with torch.no_grad():
energy, vortices = xy.energy(sample, args.ham, args.lattice,
args.boundary)
loss = log_prob + beta * energy # construct loss function(free energy）from configurations
assert not energy.requires_grad
assert not loss.requires_grad
loss_reinforce = torch.mean((loss - loss.mean()) * log_prob)
loss_reinforce.backward() # back propagation
if args.clip_grad:
nn.utils.clip_grad_norm_(params, args.clip_grad)
optimizer.step()
if args.lr_schedule:
scheduler.step(loss.mean())
train_time += time.time() - train_start_time
# export physical observables
if args.print_step and step % args.print_step == 0:
free_energy_mean = loss.mean() / beta / (args.L**2
) # free energy density
free_energy_std = loss.std() / beta / (args.L**2)
entropy_mean = -log_prob.mean() / (args.L**2) # entropy density
energy_mean = (energy / (args.L**2)).mean() # energy density
energy_std = (energy / (args.L**2)).std()
vortices = vortices.mean() / args.L**2 # vortices density
# heat_capacity=(((energy/ (args.L**2))**2).mean()- ((energy/ (args.L**2)).mean())**2) *(beta**2)
# magnetization
# mag = torch.cos(sample).sum(dim=(2,3)).mean(dim=0) # M_x (M_x,M_y)=(cos(theta), sin(theta))
# mag_mean = mag.mean()
# mag_sqr_mean = (mag**2).mean()
# sus_mean = mag_sqr_mean/args.L**2
# log
if step > 0:
sample_time /= args.print_step
train_time /= args.print_step
used_time = time.time() - start_time
# hyperparameters in training
my_log(
'step = {}, lr = {:.3g}, loss={:.8g}, beta = {:.8g}, sample_time = {:.3f}, train_time = {:.3f}, used_time = {:.3f}'
.format(
step,
optimizer.param_groups[0]['lr'],
loss.mean(),
beta,
sample_time,
train_time,
used_time,
))
# observables
my_log(
'F = {:.8g}, F_std = {:.8g}, E = {:.8g}, E_std={:.8g}, v={:.8g}'
.format(
free_energy_mean.item(),
free_energy_std.item(),
energy_mean.item(),
energy_std.item(),
vortices.item(),
))
sample_time = 0
train_time = 0
# save sample
if args.save_sample and step % args.save_step == 0:
# save traning state
# state = {
# 'sample': sample,
# 'x_hat': x_hat,
# 'log_prob': log_prob,
# 'energy': energy,
# 'loss': loss,
# }
# torch.save(state, '{}_save/{}.sample'.format(
# args.out_filename, step))
# Recognize the Phase Transition
# helicity
with torch.no_grad():
correlations = helicity(sample)
helicity_modulus = -((energy / args.L**2).mean()) - (
args.beta * correlations**2 / args.L**2).mean()
my_log('Rho={:.8g}'.format(helicity_modulus.item()))
# save configurations
sample_array = sample.cpu().numpy()
np.savetxt(
'{}_save/sample{}.txt'.format(args.out_filename, step),
sample_array.reshape(args.batch_size, -1))
# save observables
np.savetxt(
'{}_save/results{}.csv'.format(args.out_filename, step), [
beta,
step,
free_energy_mean,
free_energy_std,
energy_mean,
energy_std,
vortices,
helicity_modulus,
])
# save net
if (args.out_filename and args.save_step
and step % args.save_step == 0):
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
}
if args.lr_schedule:
state['scheduler'] = scheduler.state_dict()
torch.save(state,
'{}_save/{}.state'.format(args.out_filename, step))
# visualization in each visual_step
if (args.out_filename and args.visual_step
and step % args.visual_step == 0):
# torchvision.utils.save_image(
# sample,
# '{}_img/{}.png'.format(args.out_filename, step),
# nrow=int(sqrt(sample.shape[0])),
# padding=0,
# normalize=True)
# print sample
if args.print_sample:
x_hat_alpha = x_hat[:, 0, :, :].view(x_hat.shape[0],
-1).cpu().numpy() # alpha
x_hat_std1 = np.std(x_hat_alpha, axis=0).reshape([args.L] * 2)
x_hat_beta = x_hat[:, 1, :, :].view(x_hat.shape[0],
-1).cpu().numpy() # beta
x_hat_std2 = np.std(x_hat_beta, axis=0).reshape([args.L] * 2)
energy_np = energy.cpu().numpy()
energy_count = np.stack(
np.unique(energy_np, return_counts=True)).T
my_log(
'\nsample\n{}\nalpha\n{}\nbeta\n{}\nlog_prob\n{}\nenergy\n{}\nloss\n{}\nalpha_std\n{}\nbeta_std\n{}\nenergy_count\n{}\n'
.format(
sample[:args.print_sample, 0],
x_hat[:args.print_sample, 0],
x_hat[:args.print_sample, 1],
log_prob[:args.print_sample],
energy[:args.print_sample],
loss[:args.print_sample],
x_hat_std1,
x_hat_std2,
energy_count,
))
# print gradient
if args.print_grad:
my_log('grad max_abs min_abs mean std')
for name, param in named_params:
if param.grad is not None:
grad = param.grad
grad_abs = torch.abs(grad)
my_log('{} {:.3g} {:.3g} {:.3g} {:.3g}'.format(
name,
torch.max(grad_abs).item(),
torch.min(grad_abs).item(),
torch.mean(grad).item(),
torch.std(grad).item(),
))
else:
my_log('{} None'.format(name))
my_log('')
def main():
start_time = time.time()
utils.init_out_dir()
last_epoch = utils.get_last_checkpoint_step()
if last_epoch >= args.epoch:
exit()
if last_epoch >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_epoch))
else:
utils.clear_log()
utils.print_args()
model = RNN(args.device, Number_qubits = args.N,charset_length = args.charset_length,\
hidden_size = args.hidden_size, num_layers = args.num_layers)
data = prepare_data(args.N, './data.txt')
ghz = GHZ(Number_qubits=args.N)
model.train(True)
my_log('Total nparams: {}'.format(utils.get_nparams(model)))
model.to(args.device)
params = [x for x in model.parameters() if x.requires_grad]
optimizer = torch.optim.AdamW(params,
lr=args.lr,
weight_decay=args.weight_decay)
if last_epoch >= 0:
utils.load_checkpoint(last_epoch, model, optimizer)
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
start_time = time.time()
best_fid = 0
trigger = 0 # once current fid is less than best fid, trigger+=1
for epoch_idx in range(last_epoch + 1, args.epoch + 1):
for batch_idx in range(int(args.Ns / args.batch_size)):
optimizer.zero_grad()
# idx = np.random.randint(low=0,high=int(args.Ns-1),size=(args.batch_size,))
idx = np.arange(args.batch_size) + batch_idx * args.batch_size
train_data = data[idx]
loss = -model.log_prob(
torch.from_numpy(train_data).to(args.device)).mean()
loss.backward()
if args.clip_grad:
clip_grad_norm_(params, args.clip_grad)
optimizer.step()
print('epoch_idx {} current loss {:.8g}'.format(
epoch_idx, loss.item()))
print('Evaluating...')
# Evaluation
current_fid = classical_fidelity(model, ghz, print_prob=False)
if current_fid > best_fid:
trigger = 0 # reset
my_log('epoch_idx {} loss {:.8g} fid {} time {:.3f}'.format(
epoch_idx, loss.item(), current_fid,
time.time() - start_time))
best_fid = current_fid
if (args.out_filename and args.save_epoch
and epoch_idx % args.save_epoch == 0):
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(
state, '{}_save/{}.state'.format(args.out_filename,
epoch_idx))
else:
trigger = trigger + 1
if trigger > 4:
break
def main():
start_time = time.time()
utils.init_out_dir()
last_epoch = utils.get_last_checkpoint_step()
if last_epoch >= args.epoch:
exit()
if last_epoch >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_epoch))
else:
utils.clear_log()
utils.print_args()
flow = build_mera()
flow.train(True)
my_log('nparams in each RG layer: {}'.format(
[utils.get_nparams(layer) for layer in flow.layers]))
my_log('Total nparams: {}'.format(utils.get_nparams(flow)))
# Use multiple GPUs
if args.cuda and torch.cuda.device_count() > 1:
flow = utils.data_parallel_wrap(flow)
params = [x for x in flow.parameters() if x.requires_grad]
optimizer = torch.optim.AdamW(params,
lr=args.lr,
weight_decay=args.weight_decay)
if last_epoch >= 0:
utils.load_checkpoint(last_epoch, flow, optimizer)
train_split, val_split, data_info = utils.load_dataset()
train_loader = torch.utils.data.DataLoader(train_split,
args.batch_size,
shuffle=True,
num_workers=1,
pin_memory=True)
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
start_time = time.time()
for epoch_idx in range(last_epoch + 1, args.epoch + 1):
for batch_idx, (x, _) in enumerate(train_loader):
optimizer.zero_grad()
x = x.to(args.device)
x, ldj_logit = utils.logit_transform(x)
log_prob = flow.log_prob(x)
loss = -(log_prob + ldj_logit) / (args.nchannels * args.L**2)
loss_mean = loss.mean()
loss_std = loss.std()
utils.check_nan(loss_mean)
loss_mean.backward()
if args.clip_grad:
clip_grad_norm_(params, args.clip_grad)
optimizer.step()
if args.print_step and batch_idx % args.print_step == 0:
bit_per_dim = (loss_mean.item() + log(256)) / log(2)
my_log(
'epoch {} batch {} bpp {:.8g} loss {:.8g} +- {:.8g} time {:.3f}'
.format(
epoch_idx,
batch_idx,
bit_per_dim,
loss_mean.item(),
loss_std.item(),
time.time() - start_time,
))
if (args.out_filename and args.save_epoch
and epoch_idx % args.save_epoch == 0):
state = {
'flow': flow.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state,
'{}_save/{}.state'.format(args.out_filename, epoch_idx))
if epoch_idx > 0 and (epoch_idx - 1) % args.keep_epoch != 0:
os.remove('{}_save/{}.state'.format(args.out_filename,
epoch_idx - 1))
if (args.plot_filename and args.plot_epoch
and epoch_idx % args.plot_epoch == 0):
with torch.no_grad():
do_plot(flow, epoch_idx)
def main():
start_time = time.time()
init_out_dir()
print_args()
fsave_name = 'n{}b{:.2f}D{}.pickle'.format(args.n, args.beta, args.seed)
with open(fsave_name, 'rb') as f:
sk_true = pickle.load(f)
sk_true.J = sk_true.J.to(args.device)
assert args.n == sk_true.n
assert args.beta == sk_true.beta
sk = SKModel(args.n, args.beta, args.device, seed=args.seed)
my_log('diff = {:.8g}'.format(sk_true.J_diff(sk.J)))
C_data = sk_true.C_model.to(args.device, default_dtype_torch)
C = C_data
C_inv = torch.inverse(C)
J_nmf = (torch.eye(args.n).to(C.device) - C_inv) / args.beta
idx = range(args.n)
J_nmf[idx, idx] = 0
diff_nmf = sk_true.J_diff(J_nmf.to(args.device))
my_log('diff_NMF = {:.8g}'.format(diff_nmf))
J_IP = 1 / 2 * torch.log((1 + C) / (1 - C + args.epsilon)) / args.beta
idx = range(args.n)
J_IP[idx, idx] = 0
diff_IP = sk_true.J_diff(J_IP.to(args.device))
my_log('diff_IP = {:.8g}'.format(diff_IP))
J_SM = -C_inv + J_IP * args.beta - C / (1 - C**2 + args.epsilon)
J_SM /= args.beta
idx = range(args.n)
J_SM[idx, idx] = 0
diff_SM = sk_true.J_diff(J_SM.to(args.device))
my_log('diff_SM = {:.8g}'.format(diff_SM))
sk.J.data[:] = 0
net = MADE(**vars(args))
net.to(args.device)
my_log('{}\n'.format(net))
params = list(net.parameters())
params = [sk.J]
params = list(filter(lambda p: p.requires_grad, params))
nparams = int(sum([np.prod(p.shape) for p in params]))
my_log('Total number of trainable parameters: {}'.format(nparams))
params2 = [sk.J]
optimizer = torch.optim.Adam(params, lr=args.lr)
optimizer2 = torch.optim.Adam(params2, lr=0.01)
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
sample_time = 0
train_time = 0
start_time = time.time()
diff = 10000
for step2 in range(5000):
optimizer2.zero_grad()
for step in range(args.max_step + 1):
optimizer.zero_grad()
sample_start_time = time.time()
with torch.no_grad():
sample, x_hat = net.sample(args.batch_size)
assert not sample.requires_grad
assert not x_hat.requires_grad
sample_time += time.time() - sample_start_time
train_start_time = time.time()
log_prob = net.log_prob(sample)
with torch.no_grad():
energy = sk.energy(sample)
loss = log_prob + args.beta * energy
assert not energy.requires_grad
assert not loss.requires_grad
loss_reinforce = torch.mean((loss - loss.mean()) * log_prob)
loss_reinforce.backward()
optimizer.step()
train_time += time.time() - train_start_time
with torch.no_grad():
sample, x_hat = net.sample(10**6)
assert not sample.requires_grad
assert not x_hat.requires_grad
m_model = torch.mean(sample, 0).view(sample.shape[1], 1)
C_model = sample.t() @ sample / sample.shape[0] - m_model @ m_model.t()
C_diff = C_data - C_model
C_norm = C_diff.norm(2)
sk.J.grad = -C_diff / (C_norm + args.epsilon)
optimizer2.step()
diff_old = diff
diff = sk_true.J_diff(sk.J)
my_log('# {}, diff_J = {}, diff_C = {}'.format(
step2, diff, torch.sqrt(torch.mean(C_diff**2))))
idx = range(args.n)
sk.J.data[idx, idx] = 0
with open(args.fname, 'a', newline='\n') as f:
f.write('{} {} {:.3g} {:.8g}\n'.format(args.n, args.seed, args.beta,
diff))