mirror of
https://github.com/oobabooga/text-generation-webui.git
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433 lines
18 KiB
Python
433 lines
18 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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from peft import (
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PeftModel,
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)
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RED = "\033[91m"
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YELLOW = "\033[93m"
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GREEN = "\033[92m"
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RESET = "\033[0m"
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last_print_label = ''
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custom_scheduler_params = {'trigger_loss': 0.0, 'ramp_down_ratio':1.0, 'current_loss': 0.0,'dynamic_scheduler_stop': False, 'calc_ramp_down_at_step': 0, 'calc_num_training_steps': 0}
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def custom_scheduler_global_update(current_loss: float):
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custom_scheduler_params.update({'current_loss': current_loss})
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def custom_scheduler_global_setup(trigger_loss: float, ramp_down_ratio: float):
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custom_scheduler_params.update({'trigger_loss': trigger_loss})
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custom_scheduler_params.update({'ramp_down_ratio': ramp_down_ratio})
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# calculates the total num steps after trigger
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custom_scheduler_params.update({'calc_num_training_steps': 0})
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#calculates steps when the ramp_down trigger occured
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custom_scheduler_params.update({'calc_ramp_down_at_step': 0})
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# triggers scheduler stopping after it reached calc_num_training_steps
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custom_scheduler_params.update({'dynamic_scheduler_stop': False})
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# hold constant to the half of epochs then cosine down to 0
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def _get_fp_half_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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half_steps = num_training_steps//2
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num_warmup_steps = min(num_warmup_steps,half_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 < half_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 < half_steps:
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return 1.0
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progress = float(current_step - half_steps) / float(max(1, num_training_steps - half_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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# raise up in cosine, then fall back in cosine
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def _get_fp_cosine_raise_and_fall_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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half_steps = num_training_steps//2
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#num_warmup_steps = min(num_warmup_steps,half_steps)
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if current_step < half_steps:
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print_label = 'Scheduler: Raise'
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else:
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print_label = 'Scheduler: Fall'
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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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# linear
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# return float(current_step) / float(max(1, num_warmup_steps))
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progress = float(current_step - half_steps) / float(max(1, num_training_steps - half_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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# constant to the first epochs then cosine down to 0 over the rest epochs
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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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# halve lr each epoch
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def _get_fp_cdrop_rate_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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current_epoch = (current_step // num_firstepoch_steps) + 1
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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: Drop Rate'
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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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# Compute the learning rate for the annealing phase
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learning_rate = 1.0 / float(2 ** (current_epoch - 1))
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return learning_rate
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# epoch decay: 1/(1 + decay * epoch)
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def custom_cosine_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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def custom_half_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_half_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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def custom_raise_fall_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_raise_and_fall_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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def neftune_forward(self, input: torch.Tensor):
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"""
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Implements the NEFTune forward pass for the model. Note this works only for
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torch.nn.Embedding layers. This method is slightly adapted from the original source code
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that can be found here: https://github.com/neelsjain/NEFTune
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Args:
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input (`torch.Tensor`):
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The input tensor to the model.
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noise_alpha (`float`):
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The noise alpha value to use for the NEFTune forward pass.
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"""
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embeddings = torch.nn.functional.embedding(
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input, self.weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse
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)
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if self.training:
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# Add noise to the embeddings
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dims = torch.tensor(embeddings.size(1) * embeddings.size(2))
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mag_norm = self.neftune_noise_alpha / torch.sqrt(dims)
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embeddings = embeddings + torch.zeros_like(embeddings).uniform_(-mag_norm, mag_norm)
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return embeddings
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class FPNEFtuneTrainer(transformers.Trainer):
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def __init__(self,neftune_noise_alpha:float = 0.0, model = None, *args, **kwargs):
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self.neftune_noise_alpha = neftune_noise_alpha
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if self.neftune_noise_alpha > 0.0:
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model = self._activate_neftune(model)
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super().__init__(model = model, *args, **kwargs)
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def _activate_neftune(self, model):
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r"""
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Activates the neftune as presented in this code: https://github.com/neelsjain/NEFTune and paper: https://arxiv.org/abs/2310.05914
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"""
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print(f"Activating {RED}NEFtune{RESET} with scale: {self.neftune_noise_alpha}")
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if isinstance(model, transformers.PreTrainedModel):
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embeddings = model.get_input_embeddings()
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elif isinstance(model, PeftModel):
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embeddings = model.base_model.get_input_embeddings()
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embeddings.neftune_noise_alpha = self.neftune_noise_alpha
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old_forward = embeddings.forward
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# This hack seems to be needed to properly use a custom forward pass
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# all credits to: https://discuss.pytorch.org/t/how-can-i-replace-the-forward-method-of-a-predefined-torchvision-model-with-my-customized-forward-function/54224/11
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bound_method = neftune_forward.__get__(embeddings, embeddings.__class__)
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setattr(embeddings, "forward", bound_method)
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# embeddings.forward = neftune_forward
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embeddings._trl_old_forward = old_forward
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return model
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def train(self, *args, **kwargs):
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output = super().train(*args, **kwargs)
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# After training we make sure to retrieve back the original forward pass method
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# for the embedding layer
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if self.neftune_noise_alpha is not None:
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if isinstance(self.model, transformers.PreTrainedModel):
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embeddings = self.model.get_input_embeddings()
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elif isinstance(self.model, PeftModel):
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embeddings = self.model.base_model.get_input_embeddings()
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if hasattr(embeddings, "_trl_old_forward"):
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embeddings.forward = embeddings._trl_old_forward
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del embeddings._trl_old_forward
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del embeddings.neftune_noise_alpha
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return output
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class FPSchedulerTrainer(transformers.Trainer):
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def __init__(self,neftune_noise_alpha:float = 0.0, model = None, *args, **kwargs):
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self.neftune_noise_alpha = neftune_noise_alpha
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if self.neftune_noise_alpha > 0.0:
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model = self._activate_neftune(model)
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super().__init__(model = model, *args, **kwargs)
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def _activate_neftune(self, model):
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r"""
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Activates the neftune as presented in this code: https://github.com/neelsjain/NEFTune and paper: https://arxiv.org/abs/2310.05914
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"""
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print(f"Activating {RED}NEFtune{RESET} with scale: {self.neftune_noise_alpha}")
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if isinstance(model, transformers.PreTrainedModel):
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embeddings = model.get_input_embeddings()
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elif isinstance(model, PeftModel):
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embeddings = model.base_model.get_input_embeddings()
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embeddings.neftune_noise_alpha = self.neftune_noise_alpha
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old_forward = embeddings.forward
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# This hack seems to be needed to properly use a custom forward pass
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# all credits to: https://discuss.pytorch.org/t/how-can-i-replace-the-forward-method-of-a-predefined-torchvision-model-with-my-customized-forward-function/54224/11
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bound_method = neftune_forward.__get__(embeddings, embeddings.__class__)
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setattr(embeddings, "forward", bound_method)
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# embeddings.forward = neftune_forward
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embeddings._trl_old_forward = old_forward
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return model
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def train(self, *args, **kwargs):
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output = super().train(*args, **kwargs)
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# After training we make sure to retrieve back the original forward pass method
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# for the embedding layer
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if self.neftune_noise_alpha is not None:
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if isinstance(self.model, transformers.PreTrainedModel):
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embeddings = self.model.get_input_embeddings()
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elif isinstance(self.model, PeftModel):
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embeddings = self.model.base_model.get_input_embeddings()
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if hasattr(embeddings, "_trl_old_forward"):
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embeddings.forward = embeddings._trl_old_forward
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del embeddings._trl_old_forward
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del embeddings.neftune_noise_alpha
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return output
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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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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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custom_scheduler_params.update({'dynamic_scheduler_stop': False})
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print (f"Warm-up steps aligned to Gradient accumulation ({self.args.gradient_accumulation_steps}) = {num_warmup_acc} actual warmup steps")
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if self.args.lr_scheduler_type == 'cosine':
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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_cosine_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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elif self.args.lr_scheduler_type == 'constant':
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half_step_acc = num_training_steps_acc//2
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num_warmup_acc_min = min(num_warmup_acc, half_step_acc)
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if num_warmup_acc>half_step_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 half of all epochs, essentially going from warmup to annealing in the middle.\033[0;37;0m")
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print (f"FP Scheduler Warmup: 0-[{num_warmup_acc_min}], Hold [{num_warmup_acc_min}]-{half_step_acc}, Annealing {half_step_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}-{half_step_acc}, Annealing {half_step_acc}-{num_training_steps_acc}")
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self.lr_scheduler = custom_half_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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elif self.args.lr_scheduler_type == 'constant_with_warmup':
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half_step_acc = num_training_steps_acc//2
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if num_warmup_steps>0:
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print(f"Warmup doesn't apply to this scheduler [Raise-Fall]")
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print (f"Scheduler Raise: 0-{half_step_acc}, Fall {half_step_acc}-{num_training_steps_acc}")
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self.lr_scheduler = custom_raise_fall_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) |