import time
import json
import logging
log = logging.getLogger(__name__)
import torch
#from memory_profiler import profile
import config as cfg
[docs]def store_checkpoint(checkpoint_file, state, optimizer, current_epoch, current_loss,\
verbosity=0):
r"""
:param checkpoint_file: target file
:param state: ipeps wavefunction
:param optimizer: Optimizer
:param current_epoch: current epoch
:param current_loss: current value of a loss function
:type checkpoint_file: str or Path
:type state: IPEPS
:type optimizer: torch.optim.Optimizer
:type current_epoch: int
:type current_loss: float
Store the current state of the optimization in ``checkpoint_file``.
"""
torch.save({
'epoch': current_epoch,
'loss': current_loss,
'parameters': state.get_checkpoint(),
'optimizer_state_dict': optimizer.state_dict()}, checkpoint_file)
if verbosity>0:
print(checkpoint_file)
[docs]def optimize_state(state, ctm_env_init, loss_fn, obs_fn=None, post_proc=None,
main_args=cfg.main_args, opt_args=cfg.opt_args, ctm_args=cfg.ctm_args,
global_args=cfg.global_args):
r"""
:param state: initial wavefunction
:param ctm_env_init: initial environment of ``state``
:param loss_fn: loss function
:param obs_fn: optional function to evaluate observables
:param post_proc: optional function for post-processing the state and environment
:param main_args: main configuration
:param opt_args: optimization configuration
:param ctm_args: CTM algorithm configuration
:param global_args: global configuration
:type state: IPEPS
:type ctm_env_init: ENV
:type loss_fn: function(IPEPS,ENV,dict)->torch.tensor
:type obs_fn: function(IPEPS,ENV,dict)->None
:type post_proc: function(IPEPS,ENV,dict)->None
:type main_args: MAINARGS
:type opt_args: OPTARGS
:type ctm_args: CTMARGS
:type global_args: GLOBALARGS
Optimizes initial wavefunction ``state`` with respect to ``loss_fn`` using
`LBFGS optimizer <https://pytorch.org/docs/stable/optim.html#torch.optim.LBFGS>`_.
The main parameters influencing the optimization process are given in :class:`config.OPTARGS`.
Calls to functions ``loss_fn``, ``obs_fn``, and ``post_proc`` pass the current configuration
as dictionary ``{"ctm_args":ctm_args, "opt_args":opt_args}``.
"""
verbosity = opt_args.verbosity_opt_epoch
checkpoint_file = main_args.out_prefix+"_checkpoint.p"
outputstatefile= main_args.out_prefix+"_state.json"
t_data = dict({"loss": [], "min_loss": float('inf')})
current_env=[ctm_env_init]
context= dict({"ctm_args":ctm_args, "opt_args":opt_args, "loss_history": t_data})
epoch= 0
parameters= state.get_parameters()
for A in parameters: A.requires_grad_(True)
optimizer = torch.optim.LBFGS(parameters, max_iter=opt_args.max_iter_per_epoch, lr=opt_args.lr, \
tolerance_grad=opt_args.tolerance_grad, tolerance_change=opt_args.tolerance_change, \
history_size=opt_args.history_size)
# load and/or modify optimizer state from checkpoint
if main_args.opt_resume is not None:
print(f"INFO: resuming from check point. resume = {main_args.opt_resume}")
checkpoint = torch.load(main_args.opt_resume)
epoch0 = checkpoint["epoch"]
loss0 = checkpoint["loss"]
cp_state_dict= checkpoint["optimizer_state_dict"]
cp_opt_params= cp_state_dict["param_groups"][0]
cp_opt_history= cp_state_dict["state"][cp_opt_params["params"][0]]
if main_args.opt_resume_override_params:
cp_opt_params["lr"] = opt_args.lr
cp_opt_params["max_iter"] = opt_args.max_iter_per_epoch
cp_opt_params["tolerance_grad"] = opt_args.tolerance_grad
cp_opt_params["tolerance_change"] = opt_args.tolerance_change
# resize stored old_dirs, old_stps, ro, al to new history size
cp_history_size= cp_opt_params["history_size"]
cp_opt_params["history_size"] = opt_args.history_size
if opt_args.history_size < cp_history_size:
if len(cp_opt_history["old_dirs"]) > opt_args.history_size:
cp_opt_history["old_dirs"]= cp_opt_history["old_dirs"][-opt_args.history_size:]
cp_opt_history["old_stps"]= cp_opt_history["old_stps"][-opt_args.history_size:]
cp_ro_filtered= list(filter(None,cp_opt_history["ro"]))
cp_al_filtered= list(filter(None,cp_opt_history["al"]))
if len(cp_ro_filtered) > opt_args.history_size:
cp_opt_history["ro"]= cp_ro_filtered[-opt_args.history_size:]
cp_opt_history["al"]= cp_al_filtered[-opt_args.history_size:]
else:
cp_opt_history["ro"]= cp_ro_filtered + [None for i in range(opt_args.history_size-len(cp_ro_filtered))]
cp_opt_history["al"]= cp_al_filtered + [None for i in range(opt_args.history_size-len(cp_ro_filtered))]
cp_state_dict["param_groups"][0]= cp_opt_params
cp_state_dict["state"][cp_opt_params["params"][0]]= cp_opt_history
optimizer.load_state_dict(cp_state_dict)
print(f"checkpoint.loss = {loss0}")
#@profile
def closure():
# 0) evaluate loss
optimizer.zero_grad()
loss, ctm_env, history, t_ctm, t_check = loss_fn(state, current_env[0], context)
# 1) store current state if the loss improves
t_data["loss"].append(loss.item())
if t_data["min_loss"] > t_data["loss"][-1]:
t_data["min_loss"]= t_data["loss"][-1]
state.write_to_file(outputstatefile, normalize=True)
# 2) log CTM metrics for debugging
if opt_args.opt_logging:
log_entry=dict({"id": epoch, "loss": t_data["loss"][-1], "t_ctm": t_ctm, \
"t_check": t_check})
log.info(json.dumps(log_entry))
# 3) compute desired observables
if obs_fn is not None:
obs_fn(state, ctm_env, context)
# 4) evaluate gradient
t_grad0= time.perf_counter()
loss.backward()
t_grad1= time.perf_counter()
# 5) log grad metrics for debugging
if opt_args.opt_logging:
log_entry=dict({"id": epoch, "t_grad": t_grad1-t_grad0})
log_entry["grad_mag"]= [p.grad.norm().item() for p in parameters]
if opt_args.opt_log_grad: log_entry["grad"]= [p.grad.tolist() for p in parameters]
log.info(json.dumps(log_entry))
# 6) detach current environment from autograd graph
current_env[0] = ctm_env.detach().clone()
return loss
for epoch in range(main_args.opt_max_iter):
# checkpoint the optimizer
# checkpointing before step, guarantees the correspondence between the wavefunction
# and the last computed value of loss t_data["loss"][-1]
if epoch>0:
store_checkpoint(checkpoint_file, state, optimizer, epoch, t_data["loss"][-1])
# After execution closure ``current_env`` **IS NOT** corresponding to ``state``, since
# the ``state`` on-site tensors have been modified by gradient.
optimizer.step(closure)
if post_proc is not None:
post_proc(state, current_env[0], context)
# terminate condition
if len(t_data["loss"])>1 and \
abs(t_data["loss"][-1]-t_data["loss"][-2])<opt_args.tolerance_change:
break
# optimization is over, store the last checkpoint
store_checkpoint(checkpoint_file, state, optimizer, \
main_args.opt_max_iter, t_data["loss"][-1])