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implement initial Raw Text File Input

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also bump default Rank & Alpha for values that will make sense in testing if you don't know what you're doing and leave the defaults.
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Alex "mcmonkey" Goodwin 2023-03-27 22:15:32 -07:00
parent b749952fe3
commit 2e08af4edf
1 changed files with 76 additions and 40 deletions
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116
modules/training.py
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@ -7,7 +7,7 @@ from pathlib import Path
import gradio as gr
import torch
import transformers
from datasets import load_dataset
from datasets import Dataset, load_dataset
from peft import (LoraConfig, get_peft_model, get_peft_model_state_dict,
prepare_model_for_int8_training)
@ -18,8 +18,8 @@ CURRENT_STEPS = 0
MAX_STEPS = 0
CURRENT_GRADIENT_ACCUM = 1
def get_json_dataset(path: str):
return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path(path).glob('*.json'))), key=str.lower)
def get_dataset(path: str, ext: str):
return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path(path).glob(f'*.{ext}'))), key=str.lower)
def create_train_interface():
with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
@ -40,20 +40,26 @@ def create_train_interface():
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.')
cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.')
with gr.Row():
dataset = gr.Dropdown(choices=get_json_dataset('training/datasets'), value='None', label='Dataset', info='The dataset file to use for training.')
ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': get_json_dataset('training/datasets')}, 'refresh-button')
eval_dataset = gr.Dropdown(choices=get_json_dataset('training/datasets'), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.')
ui.create_refresh_button(eval_dataset, lambda : None, lambda : {'choices': get_json_dataset('training/datasets')}, 'refresh-button')
format = gr.Dropdown(choices=get_json_dataset('training/formats'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
ui.create_refresh_button(format, lambda : None, lambda : {'choices': get_json_dataset('training/formats')}, 'refresh-button')
with gr.Tab(label="Formatted Dataset"):
with gr.Row():
dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.')
ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
eval_dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.')
ui.create_refresh_button(eval_dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
format = gr.Dropdown(choices=get_dataset('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
ui.create_refresh_button(format, lambda : None, lambda : {'choices': get_dataset('training/formats', 'json')}, 'refresh-button')
with gr.Tab(label="Raw Text File"):
with gr.Row():
raw_text_file = gr.Dropdown(choices=get_dataset('training/datasets', 'txt'), value='None', label='Text File', info='The raw text file to use for training.')
ui.create_refresh_button(raw_text_file, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'txt')}, 'refresh-button')
overlap_len = gr.Slider(label='Overlap Length', minimum=0,maximum=512, value=32, step=8, info='Overlap length - ie how many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length above)')
with gr.Row():
start_button = gr.Button("Start LoRA Training")
stop_button = gr.Button("Interrupt")
output = gr.Markdown(value="Ready")
start_button.click(do_train, [lora_name, micro_batch_size, batch_size, epochs, learning_rate, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format], [output])
start_button.click(do_train, [lora_name, micro_batch_size, batch_size, epochs, learning_rate, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, raw_text_file, overlap_len], [output])
stop_button.click(do_interrupt, [], [], cancels=[], queue=False)
def do_interrupt():
@ -84,7 +90,8 @@ def clean_path(base_path: str, path: str):
return path
return f'{Path(base_path).absolute()}/{path}'
def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: float, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str):
def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lora_rank: int,
lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, raw_text_file: str, overlap_len: int):
global WANT_INTERRUPT, CURRENT_STEPS, MAX_STEPS, CURRENT_GRADIENT_ACCUM
WANT_INTERRUPT = False
CURRENT_STEPS = 0
@ -93,20 +100,17 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
# == Input validation / processing ==
yield "Prepping..."
lora_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}"
if dataset is None:
return "**Missing dataset choice input, cannot continue.**"
if format is None:
return "**Missing format choice input, cannot continue.**"
actual_lr = float(learning_rate)
if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
yield f"Cannot input zeroes."
return
gradient_accumulation_steps = batch_size // micro_batch_size
CURRENT_GRADIENT_ACCUM = gradient_accumulation_steps
actual_lr = float(learning_rate)
shared.tokenizer.pad_token = 0
shared.tokenizer.padding_side = "left"
# == Prep the dataset, format, etc ==
with open(clean_path('training/formats', f'{format}.json'), 'r') as formatFile:
format_data: dict[str, str] = json.load(formatFile)
def tokenize(prompt):
result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length")
return {
@ -114,27 +118,55 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
"attention_mask": result["attention_mask"][:-1],
}
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 len(x[1].strip()) > 0):
for key, val in data_point.items():
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 generate_and_tokenize_prompt(data_point):
prompt = generate_prompt(data_point)
return tokenize(prompt)
print("Loading datasets...")
data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
train_data = data['train'].shuffle().map(generate_and_tokenize_prompt)
if eval_dataset == 'None':
# == Prep the dataset, format, etc ==
if raw_text_file is not None:
print("Loading raw text file dataset...")
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r') as file:
raw_text = file.read()
tokens = shared.tokenizer.encode(raw_text)
del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
tokens = list(split_chunks(tokens, cutoff_len - overlap_len))
for i in range(1, len(tokens)):
tokens[i] = tokens[i - 1][-overlap_len:] + tokens[i]
text_chunks = [shared.tokenizer.decode(x) for x in tokens]
del tokens
data = Dataset.from_list([tokenize(x) for x in text_chunks])
train_data = data.shuffle()
eval_data = None
del text_chunks
else:
eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
eval_data = eval_data['train'].shuffle().map(generate_and_tokenize_prompt)
with open(clean_path('training/formats', f'{format}.json'), 'r') as formatFile:
format_data: dict[str, str] = json.load(formatFile)
if dataset is None:
yield "**Missing dataset choice input, cannot continue.**"
return
if format is None:
yield "**Missing format choice input, cannot continue.**"
return
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 len(x[1].strip()) > 0):
for key, val in data_point.items():
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 generate_and_tokenize_prompt(data_point):
prompt = generate_prompt(data_point)
return tokenize(prompt)
print("Loading JSON datasets...")
data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
train_data = data['train'].shuffle().map(generate_and_tokenize_prompt)
if eval_dataset == 'None':
eval_data = None
else:
eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
eval_data = eval_data['train'].shuffle().map(generate_and_tokenize_prompt)
# == Start prepping the model itself ==
if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
@ -229,3 +261,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
else:
print("Training complete!")
yield f"Done! LoRA saved to `{lora_name}`"
def split_chunks(arr, step):
for i in range(0, len(arr), step):
yield arr[i:i + step]