mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 09:19:23 +01:00
96 lines
4.4 KiB
Python
96 lines
4.4 KiB
Python
from functools import partial
|
|
import torch
|
|
import transformers
|
|
import math
|
|
from torch.optim.lr_scheduler import LambdaLR
|
|
|
|
|
|
#FPHAM custom training scheduller block - should be extracted to separate file
|
|
last_print_label = ''
|
|
|
|
def _get_fp_cosine_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_firstepoch_steps: int):
|
|
|
|
global last_print_label
|
|
print_label = ''
|
|
|
|
num_warmup_steps = min(num_warmup_steps,num_firstepoch_steps)
|
|
|
|
if current_step < num_warmup_steps:
|
|
print_label = 'Scheduler: Warmup'
|
|
elif current_step < num_firstepoch_steps:
|
|
print_label = 'Scheduler: Hold'
|
|
else:
|
|
print_label = 'Scheduler: Annealing'
|
|
|
|
if print_label != last_print_label:
|
|
print(print_label)
|
|
|
|
last_print_label = print_label
|
|
|
|
if current_step < num_warmup_steps:
|
|
return float(current_step) / float(max(1, num_warmup_steps))
|
|
|
|
if current_step < num_firstepoch_steps:
|
|
return 1.0
|
|
|
|
progress = float(current_step - num_firstepoch_steps) / float(max(1, num_training_steps - num_firstepoch_steps))
|
|
num_cycles = 0.5
|
|
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
|
|
|
|
|
|
def custom_scheduler_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_firstepoch_steps, last_epoch=-1):
|
|
"""
|
|
Args:
|
|
optimizer ([`~torch.optim.Optimizer`]):
|
|
The optimizer for which to schedule the learning rate.
|
|
num_warmup_steps (`int`):
|
|
The number of steps for the warmup phase.
|
|
num_training_steps (`int`):
|
|
The total number of training steps.
|
|
last_epoch (`int`, *optional*, defaults to -1):
|
|
The index of the last epoch when resuming training.
|
|
|
|
Return:
|
|
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
|
|
"""
|
|
|
|
lr_lambda = partial(
|
|
_get_fp_cosine_schedule_with_warmup_lr_lambda,
|
|
num_warmup_steps=num_warmup_steps,
|
|
num_training_steps=num_training_steps,
|
|
num_firstepoch_steps = num_firstepoch_steps,
|
|
)
|
|
return LambdaLR(optimizer, lr_lambda, last_epoch)
|
|
|
|
class FPSchedulerTrainer(transformers.Trainer):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
|
|
#Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.
|
|
|
|
if self.args.lr_scheduler_type == 'cosine':
|
|
num_train_epochs = self.args.num_train_epochs
|
|
num_warmup_steps=self.args.get_warmup_steps(num_training_steps)
|
|
num_firstepoch_steps = math.ceil(num_training_steps/num_train_epochs)
|
|
num_warmup_acc = num_warmup_steps*self.args.gradient_accumulation_steps
|
|
num_firstepoch_steps_acc = num_firstepoch_steps*self.args.gradient_accumulation_steps
|
|
num_training_steps_acc = num_training_steps*self.args.gradient_accumulation_steps
|
|
num_warmup_acc_min = min(num_warmup_acc, num_firstepoch_steps_acc)
|
|
|
|
if num_warmup_acc>num_firstepoch_steps_acc:
|
|
print(f"\033[1;31;1mWARNING: The number of warmup steps is set too high! It will be clamped to 1 epoch, essentially going from warmup to annealing.\033[0;37;0m")
|
|
print (f"FP Scheduler Warmup: 0-[{num_warmup_acc_min}], Hold [{num_warmup_acc_min}]-{num_firstepoch_steps_acc}, Annealing {num_firstepoch_steps_acc}-{num_training_steps_acc}")
|
|
else:
|
|
print (f"FP Scheduler Warmup: 0-{num_warmup_acc_min}, Hold {num_warmup_acc_min}-{num_firstepoch_steps_acc}, Annealing {num_firstepoch_steps_acc}-{num_training_steps_acc}")
|
|
|
|
self.lr_scheduler = custom_scheduler_with_warmup(
|
|
optimizer=self.optimizer if optimizer is None else optimizer,
|
|
num_warmup_steps=num_warmup_steps,
|
|
num_training_steps=num_training_steps,
|
|
num_firstepoch_steps = num_firstepoch_steps,
|
|
)
|
|
self._created_lr_scheduler = True
|
|
return self.lr_scheduler
|
|
else:
|
|
return super().create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer) |