|
|
import torch |
|
|
import os |
|
|
|
|
|
def get_rank(): |
|
|
"""Get rank of current process.""" |
|
|
|
|
|
print(os.environ.keys()) |
|
|
|
|
|
if "SLURM_PROCID" in os.environ: |
|
|
return int(os.environ["SLURM_PROCID"]) |
|
|
|
|
|
if not torch.distributed.is_available() or not torch.distributed.is_initialized(): |
|
|
return 0 |
|
|
|
|
|
return torch.distributed.get_rank() |
|
|
|
|
|
class InverseLR(torch.optim.lr_scheduler._LRScheduler): |
|
|
"""Implements an inverse decay learning rate schedule with an optional exponential |
|
|
warmup. When last_epoch=-1, sets initial lr as lr. |
|
|
inv_gamma is the number of steps/epochs required for the learning rate to decay to |
|
|
(1 / 2)**power of its original value. |
|
|
Args: |
|
|
optimizer (Optimizer): Wrapped optimizer. |
|
|
inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. |
|
|
power (float): Exponential factor of learning rate decay. Default: 1. |
|
|
warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) |
|
|
Default: 0. |
|
|
final_lr (float): The final learning rate. Default: 0. |
|
|
last_epoch (int): The index of last epoch. Default: -1. |
|
|
verbose (bool): If ``True``, prints a message to stdout for |
|
|
each update. Default: ``False``. |
|
|
""" |
|
|
|
|
|
def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., final_lr=0., |
|
|
last_epoch=-1, verbose=False): |
|
|
self.inv_gamma = inv_gamma |
|
|
self.power = power |
|
|
if not 0. <= warmup < 1: |
|
|
raise ValueError('Invalid value for warmup') |
|
|
self.warmup = warmup |
|
|
self.final_lr = final_lr |
|
|
super().__init__(optimizer, last_epoch, verbose) |
|
|
|
|
|
def get_lr(self): |
|
|
if not self._get_lr_called_within_step: |
|
|
import warnings |
|
|
warnings.warn("To get the last learning rate computed by the scheduler, " |
|
|
"please use `get_last_lr()`.") |
|
|
|
|
|
return self._get_closed_form_lr() |
|
|
|
|
|
def _get_closed_form_lr(self): |
|
|
warmup = 1 - self.warmup ** (self.last_epoch + 1) |
|
|
lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power |
|
|
return [warmup * max(self.final_lr, base_lr * lr_mult) |
|
|
for base_lr in self.base_lrs] |
|
|
|
|
|
def copy_state_dict(model, state_dict): |
|
|
"""Load state_dict to model, but only for keys that match exactly. |
|
|
|
|
|
Args: |
|
|
model (nn.Module): model to load state_dict. |
|
|
state_dict (OrderedDict): state_dict to load. |
|
|
""" |
|
|
model_state_dict = model.state_dict() |
|
|
for key in state_dict: |
|
|
if key in model_state_dict and state_dict[key].shape == model_state_dict[key].shape: |
|
|
if isinstance(state_dict[key], torch.nn.Parameter): |
|
|
|
|
|
state_dict[key] = state_dict[key].data |
|
|
model_state_dict[key] = state_dict[key] |
|
|
|
|
|
model.load_state_dict(model_state_dict, strict=False) |
|
|
|
|
|
def create_optimizer_from_config(optimizer_config, parameters): |
|
|
"""Create optimizer from config. |
|
|
|
|
|
Args: |
|
|
parameters (iterable): parameters to optimize. |
|
|
optimizer_config (dict): optimizer config. |
|
|
|
|
|
Returns: |
|
|
torch.optim.Optimizer: optimizer. |
|
|
""" |
|
|
|
|
|
optimizer_type = optimizer_config["type"] |
|
|
|
|
|
if optimizer_type == "FusedAdam": |
|
|
from deepspeed.ops.adam import FusedAdam |
|
|
optimizer = FusedAdam(parameters, **optimizer_config["config"]) |
|
|
else: |
|
|
optimizer_fn = getattr(torch.optim, optimizer_type) |
|
|
optimizer = optimizer_fn(parameters, **optimizer_config["config"]) |
|
|
return optimizer |
|
|
|
|
|
def create_scheduler_from_config(scheduler_config, optimizer): |
|
|
"""Create scheduler from config. |
|
|
|
|
|
Args: |
|
|
scheduler_config (dict): scheduler config. |
|
|
optimizer (torch.optim.Optimizer): optimizer. |
|
|
|
|
|
Returns: |
|
|
torch.optim.lr_scheduler._LRScheduler: scheduler. |
|
|
""" |
|
|
if scheduler_config["type"] == "InverseLR": |
|
|
scheduler_fn = InverseLR |
|
|
else: |
|
|
scheduler_fn = getattr(torch.optim.lr_scheduler, scheduler_config["type"]) |
|
|
scheduler = scheduler_fn(optimizer, **scheduler_config["config"]) |
|
|
return scheduler |
