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