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script.py
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import os
os.environ["WANDB_MODE"] = "offline"
# os.environ["WANDB_DISABLED"] = "true"
import warnings
warnings.filterwarnings(action = "ignore", message="torch.utils.checkpoint:")
warnings.filterwarnings(action = "ignore", message="`do_sample` is set to `False`")
warnings.simplefilter(action='ignore', category=FutureWarning)
import json
import math
import random
import shutil
import sys
import threading
import time
import traceback
from datetime import datetime
from pathlib import Path
import gradio as gr
import pandas as pd
import torch
import transformers
from functools import partial
from .custom_scheduler import FPSchedulerTrainer, FPNEFtuneTrainer
from .matplotgraph import create_graph
from .train_utils import get_available_loras_local, precise_cut, sliding_block_cut, download_file_from_url
from peft.tuners.lora import QuantLinear
import bitsandbytes as bnb
from datasets import Dataset, load_dataset, DatasetDict
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_kbit_training,
set_peft_model_state_dict
)
from peft.utils.other import \
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as model_to_lora_modules
from transformers.models.auto.modeling_auto import (
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
)
from modules import shared, utils
from modules.ui import create_refresh_button
from modules.evaluate import (
calculate_perplexity,
generate_markdown_table,
save_past_evaluations
)
from modules.logging_colors import logger
from modules.models import reload_model, unload_model, load_model
from modules.utils import natural_keys
params = {
"display_name": "Training PRO",
"is_tab": True
}
non_serialized_params = {
"debug_slicer": False,
"Lora_sortedByTime": False,
"stop_at_loss": 0,
"stop_at_epoch": 0,
"save_steps_under_loss": 0.0,
"save_checkpoint_now": False,
"training_loop": False,
"current_stability": 0,
"save_epochs": 0,
"checkpoint_offset": 0,
"epoch_offset":0,
"safe_serialization": False,
"dump_dataset": False,
"dump_dataset_remove_s": True,
}
mapped_prompts = 0
MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
PARAMETERS = ["lora_name", "always_override", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after", "stop_at_loss", "add_eos_token", "min_chars", "report_to", "precize_slicing_overlap", "add_eos_token_type", "save_steps_under_loss", "add_bos_token", "training_projection","sliding_window","warmup_ratio","grad_accumulation","neft_noise_alpha","group_by_length","eliminate_long_blocks","lora_target_linear", "stop_at_epoch","datasetJSONL", "eval_datasetJSONL", "eval_stepsJSONL","hybrid_training", "hybrid_data_ratio","hybrid_text_ratio","lora_RS","lora_RS_alpha","lora_modulessave","use_grad_checkpoint"]
WANT_INTERRUPT = False
train_log = {}
train_template = {}
train_log_graph = []
train_choices = ["all","q-k-v-o","q-k-v","k-v-down","q-v"]
statistics = {
'loss': [],
'lr': [],
}
RED = "\033[91m"
YELLOW = "\033[93m"
GREEN = "\033[92m"
RESET = "\033[0m"
def ui():
with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
tmp = gr.State('')
with gr.Row():
with gr.Column():
# YY.MM.DD
gr.Markdown("`Ver: 25.01.10` This is enhanced version of QLora Training. [Maintained by FP](https://github.com/FartyPants/Training_PRO/tree/main)")
with gr.Row():
with gr.Column(scale=5):
with gr.Row():
copy_from = gr.Dropdown(label='Copy parameters from', value='None', choices=get_available_loras_local(non_serialized_params['Lora_sortedByTime']), elem_classes=['slim-dropdown'],allow_custom_value = True)
create_refresh_button(copy_from, lambda: None, lambda: {'choices': get_available_loras_local(non_serialized_params['Lora_sortedByTime'])}, 'refresh-button')
with gr.Column():
sort_byTime = gr.Checkbox(label='Sort list by Date', value=False, info='Sorts Loras by date created.', elem_classes=['no-background'])
with gr.Row():
with gr.Column(scale=5):
lora_name = gr.Textbox(label='Name', info='The name of your new LoRA file')
with gr.Column():
always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name is the same, checking will replace the existing file, and unchecking will load and continue from it (the rank must be the same).', elem_classes=['no-background'])
with gr.Row():
with gr.Column():
lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='Also called dimension count. Higher values = larger file, more content control. Smaller values = smaller file, less control. Use 4 or 8 for style, 128 or 256 to teach, 1024+ for fine-detail on big data. More VRAM is needed for higher ranks.')
lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='Alpha determines Scaling of the LoRA. A good standard value is 1x-2x of Rank. scale = LORA_alpha/rank')
with gr.Accordion(label='Rank Stabilised LoRA', open=False):
with gr.Row():
lora_RS = gr.Checkbox(label='Use rsLoRA', value=False, info='scale = rsLoRA_Alpha/sqrt(rank)')
lora_RS_alpha = gr.Number(label='rsLoRA Alpha', value=16)
batch_size = gr.Slider(visible= False, label='Batch Size', value=0, minimum=0, maximum=1024, step=4, info='Now Replaced with Gradient accumulation. Keeping it for sake of old saved data')
micro_batch_size = gr.Slider(label='True Batch Size', value=4, minimum=1, maximum=128, step=1, info='Specifies how many text blocks per step will be trained. The higher value, the better the concept of training will be, but it requires more GPU memory and it reduces speed.')
grad_accumulation = gr.Slider(label='Gradient Accumulation Steps', value=1, minimum=1, maximum=256, step=1, info="Virtually multiplies the Batch Size by averaging the learning over more than one step. VRAM friendly. Evens out loss fluctuations but can also degrade training fidelity.")
with gr.Column():
epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.')
learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='In scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.')
lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt', 'FP_low_epoch_annealing', 'FP_half_time_annealing','FP_raise_fall_creative','FP_3epoch_raise_hold_fall','FP_step_decay_with_warmup'], info='Learning rate scheduler - defines how the learning rate changes over time. (FP_ = my Own Custom schedulers)', elem_classes=['slim-dropdown'])
with gr.Accordion(label='Checkpoints', open=True):
with gr.Row():
with gr.Column():
save_steps = gr.Number(label='Save every n steps', value=0, info='A checkpoint will be saved every n steps and at each Epoch boundary. (0 = OFF)')
with gr.Column():
save_steps_under_loss = gr.Slider(label='Save at 10% Loss change', value=1.8, minimum=0.0, maximum=3.0, step=0.1, info="Saves checkpoints at (or bellow) this loss and then each time loss falls by at least 10% This works independently from 'Save every n steps'")
with gr.Row():
save_chackpoint_now = gr.Button('Queue Checkpoint Now')
with gr.Accordion(label ='Stops (can be changed during training)',open = True):
with gr.Row():
with gr.Column():
stop_at_loss = gr.Slider(label='Stop at loss', minimum=0.0, maximum=3.0, step=0.1, value=0.00, info='If non 0 the process will automatically stop once the desired loss value is reached.')
with gr.Column():
stop_at_epoch = gr.Slider(label='Stop at Epoch', minimum=0, maximum=20, step=1, value=0, info='If non 0 the process will stop early once the set epoch is reached.')
with gr.Accordion(label='Advanced Options', open=True):
with gr.Row():
with gr.Column():
warmup_steps = gr.Number(label='Warmup Steps', value=100, info='Number of max steps used for a linear warmup. Reduces early over-fitting by the first training blocks. Value has precedent over Warmup Ratio. Aligns to the closest multiple of graddient accumulation')
warmup_ratio = gr.Slider(label='Warmup Ratio', minimum=0.0, maximum=0.2, step=0.025, value=0.0, info='Ratio of total training steps that will be used for a linear warmup. It applies only if Warmup Step is 0.')
neft_noise_alpha = gr.Slider(label='NEFtune noise scale', minimum=0.0, maximum=15, step=1, value=0.0, info='Add noise to the training to improve generalization. [0 - OFF, Starting value to experiment: 5]')
training_projection = gr.Radio(value = train_choices[4], label='LLaMA Target Projections', info='Change the targets (LORA is typically q-v)', choices=train_choices)
with gr.Accordion(label ='Continued Pretraining',open = False):
with gr.Row():
lora_target_linear = gr.Checkbox(label='All Linear Targets', value=False, info='Use all linear targets in the model')
lora_modulessave = gr.Checkbox(label='Train Head', value=False, info='Train lm_head and embed_tokens')
gr.Markdown('If you use Train Head, you should use 8-bit AdamW optimizer (paged_adamw_8bit), or your puny VRAM will explode. With 4-bit BnB and Rank 16 you COULD pretrain 8B model on 24GB VRAM.')
use_grad_checkpoint = gr.Checkbox(label='Use Gradient Checkpoint', value=False, info='Reduces memory usage but increase computation time')
lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')
optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad','adamw_8bit','paged_adamw_8bit'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.', elem_classes=['slim-dropdown'])
with gr.Column():
train_only_after = gr.Textbox(label='Train Only After', value='', info='Only consider text *after* this string in any given chunk for training. For Alpaca datasets, use "### Response:" to only train the response and ignore the input.')
add_bos_token = gr.Checkbox(label='Add BOS token', value=True, info="Adds BOS token for each dataset item")
add_eos_token = gr.Checkbox(label='Add EOS token', value=False, info="Adds EOS token for each dataset item")
add_eos_token_type = gr.Dropdown(label='EOS placement (Text file)', choices=['Every Block', 'Hard Cut Blocks Only'], value='Every Block', info='', allow_custom_value = False)
group_by_length = gr.Checkbox(label='Group Samples by Length', value=False, info='Group together samples of roughly the same length in the training dataset.')
eliminate_long_blocks = gr.Checkbox(label='Eliminate cutoff blocks', value=False, info='Instead of just trimming blocks at cutoff, eliminate them from dataset alltogether if they are too long.')
higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.')
report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
# for future
#with gr.Accordion(label='Dynamic Scheduler', open = False):
# ds_min_epochs = gr.Number(label='Minimum Epochs', value='1', info='Minimum epochs that will be always performed before ramp down can be triggered')
# ds_max_epochs = gr.Number(label='Maximum Epochs (fallback)', value='50', info='Maximum Epochs before the training will bail out completely (should be a large number)')
# ds_loss_trigger = gr.Slider(label='Trigger Loss', minimum=0.0, maximum=2.8, step=0.1, value=1.6, info='Loss at which the ramp down schedule will be triggered')
# ds_loss_rolling_window = gr.Number(label='Loss rolling average', value='4', info='Calculate loss by averaging last x numbers to avoid jumps and noise')
# ds_epochs_to_ramp = gr.Slider(label='Ramp down ratio', minimum=0.0, maximum=2.0, step=0.1, value=1.00, info='How long the ramp down will last relative to ellapsed steps (before trigger)')
# gr.Markdown('These are settings for FP_dynamic_loss_trigger scheduler. The scheduler will do warm up, then hold constant untill a loss falls under Trigger Loss, then it will commence linear ramp down schedule and stop. The length of ramp down is set by Ramp down ratio where (ramp down steps) = ratio * (elapsed steps). (The time to completition shown will be very high untill ramp down is triggered.)')
with gr.Column():
with gr.Tab(label='JSON Dataset'):
with gr.Row():
with gr.Column():
with gr.Row():
dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The flexible dataset JSON file to use for training.', allow_custom_value=True, elem_classes=['slim-dropdown'])
create_refresh_button(dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button')
with gr.Row():
eval_dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', allow_custom_value=True, elem_classes=['slim-dropdown'])
create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button')
with gr.Column():
with gr.Row():
format = gr.Dropdown(choices=get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the JSON dataset input.', elem_classes=['slim-dropdown'])
create_refresh_button(format, lambda: None, lambda: {'choices': get_datasets('training/formats', 'json')}, 'refresh-button')
with gr.Row():
eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.')
with gr.Tab(label='JSONL Dataset'):
with gr.Row():
with gr.Column():
with gr.Row():
datasetJSONL = gr.Dropdown(choices=get_datasets('training/datasets', 'jsonl'), value='None', label='JSONL Dataset', info='JSONL dataset file to use for training. See OpenAI documentation.', allow_custom_value=True, elem_classes=['slim-dropdown'])
create_refresh_button(datasetJSONL, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'jsonl')}, 'refresh-button')
with gr.Row():
eval_datasetJSONL = gr.Dropdown(choices=get_datasets('training/datasets', 'jsonl'), value='None', label='JSONL Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', allow_custom_value=True, elem_classes=['slim-dropdown'])
create_refresh_button(eval_datasetJSONL, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'jsonl')}, 'refresh-button')
with gr.Column():
with gr.Row():
gr.Markdown('The format will be chosen automatically from the chat template in tokenizer. If the tokenizer doesn\'t have chat template defined (legacy), select the correct template in the WebUI [Parameters - Instruction template]')
with gr.Row():
eval_stepsJSONL = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation JSONL dataset is given, test it every time this many steps pass.')
with gr.Tab(label="Text file"):
with gr.Row():
raw_text_file = gr.Dropdown(choices=get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The text file to use for training.', allow_custom_value=True, elem_classes=['slim-dropdown'])
create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'txt')}, 'refresh-button')
with gr.Row():
with gr.Column():
precize_slicing_overlap = gr.Checkbox(label='Add Overlapping blocks', value = True)
sliding_window = gr.Checkbox(label='DEMENTOR Long-form Learning by FP (Highly Experimental, use low epochs)', value = False, info='Deep Memorization Enforcement Through Overlapping and Repetition. (I named it, so shush). Special process for learning long-form text using low amount of epochs.')
#debug_slicer = gr.Checkbox(label='Dump sentencelist.json to logs', value = non_serialized_params['debug_slicer'], info='Debug Slicer')
with gr.Column():
hard_cut_string = gr.Textbox(label='Hard Cut String', value='\\n\\n\\n', info='String that indicates a cut between logical blocks of text (ex. Ideas or Chapters). Helps prevent unwanted overlap between unrelated ideas.')
min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Text blocks that have less or equal characters than this number.')
with gr.Tab(label="Hybrid"):
hybrid_training = gr.Checkbox(label='Hybrid Training (Experimental)', value = False, info = 'Train using Raw text file AND JSON or JSONL dataset at the same time.')
with gr.Row():
hybrid_data_ratio = gr.Slider(value = 100, minimum=0, maximum=100,label='Percentage of Dataset used')
hybrid_text_ratio = gr.Slider(value = 100, minimum=0, maximum=100,label='Percentage of Text file used')
gr.Markdown('This is an experimental hybrid training using both instruct and non-instruct data at once. You need to select Raw Text file AND JSON or JSONL dataset.\n\nOptionally you can set a percentage of dataset / text to dial the correct model response.')
with gr.Tab(label="URL"):
with gr.Row():
with gr.Column():
download_file_url = gr.Textbox(label='Download JSON or txt file to datasets (or formats) folder', value='',info='The URL of a file to download. If on github, make sure you get url of the raw file (https://raw.githubusercontent.com/...). If huggin face, make sure the url has /resolve/ in it not /blob/')
with gr.Row():
download_check_overwrite = gr.Checkbox(label='Overwrite', value=False, info='Overwrite if file exist')
download_folder = gr.Radio(label="Destination", value='training/datasets', choices=['training/datasets', 'training/formats'], interactive=True)
download_button = gr.Button('Download')
download_status = gr.Textbox(label='Download Status', value='', interactive=False)
with gr.Tab(label="Tools"):
with gr.Row():
with gr.Column():
split_dataset_perc = gr.Number(label='Evaluation dataset split (percentage)', value=10, info='Splits JSON dataset into _train and _eval files by the split percentage. Make sure the JSON is selected in the Formatted Dataset tab first.')
split_dataset_do = gr.Button('Split dataset')
with gr.Column():
convert_system = gr.Textbox(label = 'Convert JSON to JSONL', info = 'Select JSON in JSON Dataset tab and add System Message:', value='You are a helpful AI assistant.', lines=2)
convert_do = gr.Button('Convert JSON to JSONL')
with gr.Row():
with gr.Column():
convert_system2 = gr.Textbox(label = 'Simple TXT to JSONL conversion', info = 'Select TXT in Text File tab. Each item in txt should be separated by at least 3 empty lines. Enter system message:', value='You are a helpful AI assistant.', lines=1)
convert_prompt2 = gr.Textbox(label = 'Prompt', info = 'Prompt that will be inserted for every item', value='Write me a limerick.', lines=1)
convert_do2 = gr.Button('Convert TXT to JSONL')
with gr.Column():
dump_dataset = gr.Checkbox(label='Dump Training Dataset', value=False, info='Just before training begins, decode and dump the entire dataset into JSON file in /logs/')
dump_dataset_remove_s = gr.Checkbox(label='Clean up dump dataset', value=True, info='Removes BOS and EOS form the dump dataset')
with gr.Row():
with gr.Column():
with gr.Row():
cutoff_len = gr.Slider(label='Maximum context length (Cutoff)', minimum=32, maximum=4096, value=256, step=32, info='The maximum length of a chunk (in tokens). Applies to both JSON dataset and text files. Higher values require much more VRAM.')
with gr.Row():
with gr.Column():
check_dataset_btn = gr.Button('Verify Dataset/Text File and suggest data entries')
check_dataset_txt = gr.Textbox(label='Dataset info', value='')
with gr.Row():
start_button = gr.Button("Start LoRA Training", variant='primary')
stop_button = gr.Button("Interrupt")
with gr.Accordion(label="Graph", open=True):
with gr.Row():
# show_actions_button = False - we use old gradio
plot_graph = gr.LinePlot(x="epoch", y="value", title="Loss Metrics", overlay_point=True, tooltip=["epoch", "value"], x_lim=[0, 1], y_lim=[0, 3.5], width=500, height=250)
output = gr.Markdown(value="Ready")
with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'):
with gr.Row():
with gr.Column():
models = gr.Dropdown(utils.get_available_models(), label='Models', multiselect=True)
evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.')
with gr.Row():
with gr.Column():
stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
with gr.Column():
max_length = gr.Number(label='max_length', precision=0, step=256, value=0, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
with gr.Row():
start_current_evaluation = gr.Button("Evaluate loaded model")
start_evaluation = gr.Button("Evaluate selected models")
stop_evaluation = gr.Button("Interrupt")
with gr.Column():
evaluation_log = gr.Markdown(value='')
evaluation_table = gr.Dataframe(value=generate_markdown_table(), interactive=True)
with gr.Row():
save_comments = gr.Button('Save comments', elem_classes="small-button")
refresh_table = gr.Button('Refresh the table', elem_classes="small-button")
# Training events
all_params = [lora_name, always_override, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, raw_text_file, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after, stop_at_loss, add_eos_token, min_chars, report_to, precize_slicing_overlap, add_eos_token_type, save_steps_under_loss, add_bos_token, training_projection,sliding_window,warmup_ratio,grad_accumulation, neft_noise_alpha,group_by_length,eliminate_long_blocks,lora_target_linear, stop_at_epoch, datasetJSONL, eval_datasetJSONL, eval_stepsJSONL, hybrid_training, hybrid_data_ratio, hybrid_text_ratio,lora_RS,lora_RS_alpha,lora_modulessave,use_grad_checkpoint]
def fix_old_version(batch_size_val,micro_batch_size_val, grad_accumulation_val):
if batch_size_val>0:
gradient_acc = batch_size_val // micro_batch_size_val
print(f"Using Old version of Batch Size ({batch_size_val}) to set Gradient Accumulation: {gradient_acc}")
return gradient_acc
return grad_accumulation_val
copy_from.change(partial(do_copy_params, all_params= all_params), copy_from, all_params).then(fix_old_version,[batch_size,micro_batch_size, grad_accumulation],grad_accumulation)
start_button.click(do_train, all_params, [output,plot_graph])
stop_button.click(do_interrupt, None, None, queue=False)
higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha])
def trigger_stop_at_loss(stop_at_loss_value):
non_serialized_params.update({"stop_at_loss": stop_at_loss_value})
if non_serialized_params['training_loop']:
print(f"Queue: [Stop at loss Change] to {stop_at_loss_value}")
def trigger_stop_at_epoch(stop_at_epoch_value):
non_serialized_params.update({"stop_at_epoch": stop_at_epoch_value})
if non_serialized_params['training_loop']:
print(f"Queue: [Stop at Epoch Change] to {stop_at_epoch_value}")
stop_at_loss.change(trigger_stop_at_loss, stop_at_loss, None)
stop_at_epoch.change(trigger_stop_at_epoch, stop_at_epoch, None)
def trigger_save_checkpoint():
non_serialized_params.update({"save_checkpoint_now": True})
if non_serialized_params['training_loop']:
print("Queue: [Save checkpoint] Checkpoint will be saved after the current step is finished.")
else:
print("Use during the training to save the checkpoint at any time.")
def update_button():
return gr.Button.update('[Checkpoint in Queue]', variant='stop', interactive=True)
def update_button2():
time.sleep(1.0)
return gr.Button.update('Queue Checkpoint Now', variant='secondary',interactive = True)
save_chackpoint_now.click(trigger_save_checkpoint, None, None).then(update_button, None,save_chackpoint_now).then(update_button2, None,save_chackpoint_now)
dataset_calc_params = [save_steps,micro_batch_size, epochs, cutoff_len, dataset, format, raw_text_file, warmup_steps, hard_cut_string, min_chars, precize_slicing_overlap,sliding_window,warmup_ratio,grad_accumulation, datasetJSONL, hybrid_training, hybrid_data_ratio, hybrid_text_ratio]
def check_dataset(save_steps:int, micro_batch_size: int, epochs: int, cutoff_len: int, dataset:str, format:str, raw_text_file:str, warmup_steps:int, hard_cut_string:str, min_chars:int, precize_slicing_overlap:bool,sliding_window:bool,warmup_ratio:float,grad_accumulation:int, datasetJSONL:str, hybrid_training:bool, hybrid_data_ratio:int, hybrid_text_ratio:int):
result = "Specify JSON dastaset or Text file"
total_blocks = 0
if shared.tokenizer is None:
yield "Tokenizer is not available. Please Load some Model first."
return
# hybrid training hybrid_training
raw_text_used = False
hybrid_text_train_data = None
max_length_tokens = 0
hybrid_total_text_blocks = 0
if hybrid_training == True:
print(f" === {RED}Hybrid Training{RESET} ===")
if raw_text_file not in ['None', '']:
if datasetJSONL not in ['None', '']:
print(f" - Raw text + JSONL")
elif dataset not in ['None', '']:
print(f" - Raw text + JSON")
else:
print(f" - {RED}Error:{RESET} for Hybrid training you need Raw text AND JSONL or JSON dataset")
yield "Missing dataset and raw file for hybrid training, cannot continue."
return
else:
print(f" - {RED}Error:{RESET} for Hybrid training you need JSONL or JSON dataset AND Raw text file.")
yield "Missing dataset and raw file for hybrid training, cannot continue."
return
if raw_text_file not in ['None', '']:
logger.info("Loading Text file...")
fullpath = clean_path('training/datasets', f'{raw_text_file}')
fullpath = Path(fullpath)
if fullpath.is_dir():
logger.info('Training path directory {}'.format(raw_text_file))
raw_text = ""
file_paths = sorted(fullpath.glob('*.txt'), key=lambda path: natural_keys(path.name))
for file_path in file_paths:
if file_path.is_file():
with file_path.open('r', encoding='utf-8') as file:
raw_text += file.read().replace('\r', '')
logger.info(f"Loaded training file: {file_path.name}")
else:
try:
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
raw_text = file.read().replace('\r', '')
except:
yield f"{raw_text_file}.txt doesn't seem to exsist anymore... check your training/datasets folder"
return
if min_chars<0:
min_chars = 0
EOS_token_str = '</s>'
BOS_token_str = '<s>'
if hasattr(shared.tokenizer, 'bos_token'):
BOS_token_str = shared.tokenizer.bos_token
else:
print(f" - No {RED}BOS{RESET} token defined in tokenizer, using default")
if hasattr(shared.tokenizer, 'eos_token'):
EOS_token_str = shared.tokenizer.eos_token
else:
print(f" - No {RED}EOS{RESET} token defined in tokenizer, using default")
print(f"Tokenizer BOS token: {GREEN}{BOS_token_str}{RESET}, EOS token: {RED}{EOS_token_str}{RESET}")
# == New more precise slicing on sentence boundary ==
if sliding_window:
text_chunks = sliding_block_cut(raw_text, min_chars, False, cutoff_len, hard_cut_string,non_serialized_params['debug_slicer'],EOS_token_str,BOS_token_str)
else:
text_chunks = precise_cut(raw_text, precize_slicing_overlap, min_chars, False, cutoff_len, hard_cut_string,non_serialized_params['debug_slicer'],EOS_token_str,BOS_token_str)
total_blocks = len(text_chunks)
hybrid_total_text_blocks = total_blocks
if hybrid_training==False:
raw_text_used = True
max_length = 0
max_text = ''
for example in text_chunks:
if len(example) > max_length:
max_length = len(example)
max_text = example
input_ids = shared.tokenizer.encode(max_text, truncation=True, max_length=8192)
result = f"Text: ({raw_text_file}.txt) has {total_blocks} blocks (Block Size {cutoff_len} tokens)"
result += f"\nLongest Plain Text Block: {len(input_ids)+1}"
if hybrid_training == True:
num_text_to_keep = int(total_blocks * float(hybrid_text_ratio) / 100.0)
result += f"\nUsing {hybrid_text_ratio}% of text: ({num_text_to_keep}/{total_blocks}) blocks"
hybrid_total_text_blocks = num_text_to_keep
#no suggestion for plaintext as it is set by cutoff_len anyway
max_length_tokens = 0
del text_chunks
# datasets
if raw_text_used == False:
data = None
format_data: dict[str, str] = {}
format_text = ''
if datasetJSONL not in ['None', '']:
logger.info("Loading JSONL datasets...")
with open(clean_path('training/datasets', f'{datasetJSONL}.jsonl'), 'r', encoding='utf-8-sig') as dataFile:
loaded_JSONLdata = json.load(dataFile)
chat_template = shared.tokenizer.chat_template
format_text = "Template: [Embedded]"
if shared.tokenizer.chat_template is None or shared.tokenizer.chat_template =='':
print(f"{RED}Missing chat template in tokenizer. Using instruction_template instead{RESET}")
shared.tokenizer.chat_template = shared.persistent_interface_state['instruction_template_str']
format_text = "Template: [Missing] << using instruction template instead"
logger.info("Applying chat template")
data_list = [{"jsonl": shared.tokenizer.apply_chat_template(entry["messages"], tokenize=False, add_generation_prompt=False)} for entry in loaded_JSONLdata]
shared.tokenizer.chat_template = chat_template
data = DatasetDict()
data['train'] = Dataset.from_list(data_list)
format_data = {"jsonl": "%jsonl%"}
else:
if dataset in ['None', '']:
yield "Select dataset or text file."
return
if format in ['None', '']:
yield "Select format choice for dataset."
return
if shared.tokenizer.pad_token_id is None:
print("Missing pad ID - setting to 0")
shared.tokenizer.pad_token_id = 0
with open(clean_path('training/formats', f'{format}.json'), 'r', encoding='utf-8-sig') as formatFile:
format_data: dict[str, str] = json.load(formatFile)
format_text = f'Format: [JSON] {format}'
logger.info("Loading JSON datasets...")
data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
def generate_prompt(data_point: dict[str, str]):
for options, data in format_data.items():
if set(options.split(',')) == set(x[0] for x in data_point.items() if (type(x[1]) is str and len(x[1].strip()) > 0)):
for key, val in data_point.items():
if type(val) is str:
data = data.replace(f'%{key}%', val)
return data
raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"')
def tokenize_dummy(prompt):
input_ids = shared.tokenizer.encode(prompt, truncation=True, max_length=8192)
labels = [1] * len(input_ids)
input_ids = torch.tensor(input_ids)
pad_token_id = shared.tokenizer.pad_token_id
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": input_ids.ne(pad_token_id),
}
def generate_and_tokenize_prompt(data_point):
prompt = generate_prompt(data_point)
return tokenize_dummy(prompt)
data_keys = []
if data:
if 'train' in data: # Check if the 'train' split exists in the dataset
data_keys = list(data['train'][0].keys())
print("Data Keys:", data_keys)
else:
print("The dataset is empty.")
if shared.tokenizer.pad_token_id is None:
print("Missing pad ID - setting to 0")
shared.tokenizer.pad_token_id = 0
train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
total_blocks = train_data.num_rows
max_length = 0
second_max_length = 0
for example in train_data:
length = len(example['input_ids'])
if length > max_length:
second_max_length = max_length
max_length = length
elif length > second_max_length:
second_max_length = length
max_length_tokens = max_length
if hybrid_training:
result = result+'\n'
else:
result = ''
result += f"Dataset: ({dataset}.json) has {total_blocks} blocks @ length = {cutoff_len} tokens\nKeys: {data_keys} {format_text}"
result += f"\nLongest Data Block: {max_length_tokens} tokens. Second Longest Block: {second_max_length} tokens."
if hybrid_training == True:
num_data_to_keep = int(total_blocks * float(hybrid_data_ratio) / 100.0)
result += f"\nUsing {hybrid_data_ratio}% of dataset: ({num_data_to_keep}/{total_blocks}) blocks"
total_blocks = num_data_to_keep
#for options, data in format_data.items():
# format_keys = options.split(',')
# result += f"{format_keys}, "
#result = result.rstrip()
#result = result.rstrip(',')
if total_blocks>0:
if hybrid_training == True:
total_blocks = hybrid_total_text_blocks + total_blocks
result += f"\n[Total number of Hybrid blocks: {total_blocks}]"
number_ofSteps = int(math.ceil(total_blocks / micro_batch_size) * epochs)
num_stepsPer_epoch = int(math.ceil(number_ofSteps/epochs))
min_warm = math.ceil(100 / grad_accumulation)
warmup_steps_suggest = min(int(min_warm*grad_accumulation), int(math.ceil(number_ofSteps * 0.1)))
warmup_steps_suggest = min(warmup_steps_suggest,num_stepsPer_epoch)
save_each_n_min = int(math.ceil(number_ofSteps/10))
save_each_n_max = int(math.ceil(number_ofSteps/5))
gradient_accumulation_max = int(total_blocks)//micro_batch_size
result += f"\n[Batch Size: {micro_batch_size}, Epochs: {epochs}, Gradient Accumulation: {grad_accumulation}]\n"
result += f"Total number of steps: {number_ofSteps}\n"
result += f"Steps per each Epoch: {num_stepsPer_epoch}\n"
result += f"Suggestions:\n"
if max_length_tokens>0:
next_max_multiple = ((max_length_tokens + 31) // 32) * 32
result += f"Maximum context length: {next_max_multiple} (Current: {cutoff_len})\n"
result += f"Checkpoints: Save every {save_each_n_min} - {save_each_n_max} steps (Current: {int(save_steps)})\n"
result += f"Warmup steps: {warmup_steps_suggest} (Current: {int(warmup_steps)})"
if gradient_accumulation_max < grad_accumulation:
result += f"\n\nWARNING: Gradient Accumulation {grad_accumulation} is too high: It should be below {gradient_accumulation_max}"
result = result.strip()
yield result
return
check_dataset_btn.click(check_dataset, dataset_calc_params ,check_dataset_txt)
# Evaluation events. For some reason, the interrupt event
# doesn't work with the .then() syntax, so I write them one
# by one in this ugly but functional way.
ev = start_evaluation.click(calculate_perplexity, [models, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
start_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False)
start_current_evaluation.click(lambda: ['current model'], None, tmp)
ev_cur = start_current_evaluation.click(calculate_perplexity, [tmp, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
start_current_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False)
stop_evaluation.click(None, None, None, cancels=[ev, ev_cur], queue=False)
refresh_table.click(generate_markdown_table, None, evaluation_table, show_progress=True)
save_comments.click(
save_past_evaluations, evaluation_table, None).then(
lambda: "Comments saved.", None, evaluation_log, show_progress=False)
def reload_lora():
return gr.Dropdown.update(choices=get_available_loras_local(non_serialized_params['Lora_sortedByTime']))
# nonserialized items
sort_byTime.change(lambda x: non_serialized_params.update({"Lora_sortedByTime": x}), sort_byTime, None).then(reload_lora,None,copy_from)
#debug_slicer.change(lambda x: non_serialized_params.update({"debug_slicer": x}), debug_slicer, None)
def update_dataset():
return gr.update(choices=get_datasets('training/datasets', 'json')), gr.update(choices=get_datasets('training/datasets', 'txt'))
download_button.click(download_file_from_url, [download_file_url,download_check_overwrite,download_folder] , download_status).then(update_dataset,None,[dataset , raw_text_file])
def update_datasetJSON():
return gr.update(choices=get_datasets('training/datasets', 'json')), gr.update(choices=get_datasets('training/datasets', 'json'))
def split_dataset(dataset, split_dataset_perc):
if dataset == 'None' or dataset == '':
print("No dataset selected in Formatted Datasets")
return
# Load the original JSON data
logger.info("Splitting JSON datasets 90/10...")
dataset_json_new = f'{dataset}_train.json'
eval_json_new = f'{dataset}_eval.json'
dataset_json = f'{dataset}.json'
with open(clean_path('training/datasets', dataset_json), 'r', encoding='utf-8-sig') as f:
data = json.load(f)
# Define the split ratio (e.g., 80% for training, 20% for evaluation)
split_ratio = 1.0 - float(split_dataset_perc)/100.0
total_samples = len(data)
split_index = int(total_samples * split_ratio)
print(f" + training: {split_index} blocks")
print(f" + eval: {total_samples - split_index} blocks")
# Shuffle the data to ensure randomness
random.shuffle(data)
# Split the data into training and evaluation sets
train_data = data[:split_index]
eval_data = data[split_index:]
# Save the training data to a new JSON file
with open(clean_path('training/datasets', dataset_json_new), 'w', encoding='utf-8') as f:
json.dump(train_data, f, indent=2)
# Save the evaluation data to a new JSON file
with open(clean_path('training/datasets', eval_json_new), 'w', encoding='utf-8') as f:
json.dump(eval_data, f, indent=2)
def select_dataset(dataset):
dataset_json_new = f'{dataset}_train.json'
eval_json_new = f'{dataset}_eval.json'
path1 = clean_path('training/datasets', dataset_json_new)
path2 = clean_path('training/datasets', eval_json_new)
returnA = 'None'
returnB = 'None'
if Path(path1).is_file():
print(f"{dataset_json_new} file selected for training")
returnA = dataset_json_new.replace('.json', '')
if Path(path2).is_file():
print(f"{eval_json_new} file selected for evaluation")
returnB = eval_json_new.replace('.json', '')
return returnA, returnB
split_dataset_do.click(split_dataset,[dataset,split_dataset_perc],None).then(update_datasetJSON, None,[dataset, eval_dataset]).then(select_dataset, dataset,[dataset,eval_dataset])
def update_datasetJSONL():
return gr.update(choices=get_datasets('training/datasets', 'jsonl')),gr.update(choices=get_datasets('training/datasets', 'jsonl'))
def update_datasetJSON():
return gr.update(choices=get_datasets('training/datasets', 'json')),gr.update(choices=get_datasets('training/datasets', 'json'))
def convert_json_to_jsonl(dataset, system_text):
if dataset == 'None' or dataset == '':
print("No dataset selected in Formatted Datasets")
return
dataset_json_new = f'{dataset}.jsonl'
dataset_json = f'{dataset}.json'
with open(clean_path('training/datasets', dataset_json), 'r', encoding='utf-8-sig') as f:
data = json.load(f)
print(f"Converting {dataset_json}...")
converted_data = []
for entry in data:
if system_text == '':
converted_entry = {
"messages": [
{"role": "user", "content": entry["instruction"]},
{"role": "assistant", "content": entry["output"]}
]
}
else:
converted_entry = {
"messages": [
{"role": "system", "content": system_text},
{"role": "user", "content": entry["instruction"]},
{"role": "assistant", "content": entry["output"]}
]
}
converted_data.append(converted_entry)
print(f"Saving {dataset_json_new}")
with open(clean_path('training/datasets', dataset_json_new), 'w') as outfile:
json.dump(converted_data, outfile, indent=2)
def convert_text_to_jsonl(textfile, system_text, prompt):
if textfile == 'None' or textfile == '':
print("No plain text selected in tab Text file")
return
dataset_json_new = f'{textfile}.jsonl'
dataset_txt = f'{textfile}.txt'
with open(clean_path('training/datasets', dataset_txt), 'r', encoding='utf-8-sig') as f:
text = f.read().replace('\r', '')
text_list = text.split("\n\n\n")
print(f"Converting {dataset_txt}...")
converted_data = []
for entry in text_list:
entry = entry.strip()
if entry!='':
converted_entry = {
"messages": [
{"role": "system", "content": system_text},
{"role": "user", "content": prompt},
{"role": "assistant", "content": entry}
]
}
converted_data.append(converted_entry)
print(f"Saving {dataset_json_new}")
with open(clean_path('training/datasets', dataset_json_new), 'w') as outfile:
json.dump(converted_data, outfile, indent=2)
def select_datasetJSONL(dataset):
dataset_json_new = f'{dataset}.jsonl'
pathJSONL = clean_path('training/datasets', dataset_json_new)
returnA = 'None'
returnB = 'None'
if Path(pathJSONL).is_file():
print(f"{dataset_json_new} file selected for training")
returnB = dataset_json_new.replace('.jsonl', '')
return returnA, returnB
def select_datasetJSON(dataset):
dataset_json_new = f'{dataset}.json'
pathJSON = clean_path('training/datasets', dataset_json_new)
return_to_clear = 'None'
return_to_set = 'None'
if Path(pathJSON).is_file():
print(f"{dataset_json_new} file selected for training")
return_to_set = dataset_json_new.replace('.json', '')
return return_to_clear, return_to_set
convert_do.click(convert_json_to_jsonl,[dataset,convert_system],None).then(update_datasetJSONL, None,[datasetJSONL,eval_datasetJSONL]).then(select_datasetJSONL, dataset,[dataset,datasetJSONL])
convert_do2.click(convert_text_to_jsonl,[raw_text_file,convert_system2,convert_prompt2],None).then(update_datasetJSONL, None,[datasetJSONL,eval_datasetJSONL]).then(select_datasetJSONL, raw_text_file,[raw_text_file,datasetJSONL])
dump_dataset.change(lambda x: non_serialized_params.update({"dump_dataset": x}), dump_dataset, None)
dump_dataset_remove_s.change(lambda x: non_serialized_params.update({"dump_dataset_remove_s": x}), dump_dataset_remove_s, None)
def get_datasets(path: str, ext: str):
# include subdirectories for raw txt files to allow training from a subdirectory of txt files
if ext == "txt":
return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('*.txt')) + list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
def do_interrupt():
global WANT_INTERRUPT
WANT_INTERRUPT = True
def reload_model_local():
try:
modelname = shared.model_name
unload_model()
shared.model_name = modelname
if shared.model_name != '':
shared.model, shared.tokenizer = load_model(shared.model_name, shared.args.loader)
if shared.model is not None:
print(f"Successfully reloaded `{shared.model_name}`.")
else:
print(f"Failed to reload `{shared.model_name}`.")
except:
exc = traceback.format_exc()
logger.error('Failed to load the model.')
print(exc)
def do_copy_params(lora_name: str, all_params):
if lora_name:
f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json"
if Path(f_name).is_file():
with open(f_name, 'r', encoding='utf-8') as format_file:
params: dict[str, str] = json.load(format_file)
else:
params = {}
else:
params = {}
result = list()
for i in range(0, len(PARAMETERS)):
key = PARAMETERS[i]
if key in params:
result.append(params[key])
else:
result.append(all_params[i])
return result
def change_rank_limit(use_higher_ranks: bool):
mult = 2 if use_higher_ranks else 1
return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"}
def clean_path(base_path: str, path: str):
"""Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
path = path.replace('\\', '/').replace('..', '_')
if base_path is None:
return path
return f'{Path(base_path).absolute()}/{path}'
def backup_adapter(input_folder):
# Get the creation date of the file adapter_model.bin
try:
adapter_file = Path(f"{input_folder}/adapter_model.bin")
if adapter_file.is_file():
logger.info("Backing up existing LoRA adapter...")
creation_date = datetime.fromtimestamp(adapter_file.stat().st_ctime)
creation_date_str = creation_date.strftime("Backup-%Y-%m-%d")
# Create the new subfolder
subfolder_path = Path(f"{input_folder}/{creation_date_str}")
subfolder_path.mkdir(parents=True, exist_ok=True)
# Check if the file already exists in the subfolder
backup_adapter_file = Path(f"{input_folder}/{creation_date_str}/adapter_model.bin")
if backup_adapter_file.is_file():
print(" - Backup already exists. Skipping backup process.")
return
# Copy existing files to the new subfolder
existing_files = Path(input_folder).iterdir()
for file in existing_files:
if file.is_file():
shutil.copy2(file, subfolder_path)
except Exception as e:
print("An error occurred in backup_adapter:", str(e))
def calc_trainable_parameters(model):
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
return trainable_params, all_param
def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str, precize_slicing_overlap: bool, add_eos_token_type: str, save_steps_under_loss: float, add_bos_token: bool, training_projection: str,sliding_window:bool,warmup_ratio:float, grad_accumulation: int,neft_noise_alpha:float, group_by_length:bool,eliminate_long_blocks:bool,lora_target_linear:bool, stop_at_epoch: float, datasetJSONL:str, eval_datasetJSONL:str, eval_stepsJSONL:int, hybrid_training:bool, hybrid_data_ratio:int, hybrid_text_ratio:int,lora_RS:bool,lora_RS_alpha:int,lora_modulessave:bool,use_grad_checkpoint:bool):
if shared.args.monkey_patch:
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
replace_peft_model_with_int4_lora_model
)
replace_peft_model_with_int4_lora_model()
global train_log_graph
global WANT_INTERRUPT
global mapped_prompts
mapped_prompts = 0
WANT_INTERRUPT = False
statistics['loss'] = []
statistics['loss'].append({'epoch': 0, 'value': 0})
zero_pd = pd.DataFrame(statistics['loss'])
# == Input validation / processing ==
yield "Preparing the input...", zero_pd
lora_file_path = clean_path(None, lora_name)
if lora_file_path.strip() == '':
yield "Missing or invalid LoRA file name input.", zero_pd
return
lora_file_path = f"{Path(shared.args.lora_dir)}/{lora_file_path}"
actual_lr = float(learning_rate)
model_type = type(shared.model).__name__
if model_type in MODEL_CLASSES:
model_id = MODEL_CLASSES[model_type]
else:
model_id = "llama"
if model_type == "PeftModelForCausalLM":
if len(shared.lora_names) > 0:
yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*", zero_pd
logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
else:
yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*", zero_pd