153 lines
4.1 KiB
Python
153 lines
4.1 KiB
Python
import os
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import sys
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sys.path.insert(0, './repository/transformers/src')
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sys.path.insert(0, './repository/GPTQ-for-LLaMa')
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sys.path.insert(0, './repository/peft/src')
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import peft
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import peft.tuners.lora
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assert peft.tuners.lora.is_gptq_available()
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import time
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import torch
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import transformers
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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import accelerate
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from modelutils import find_layers
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from autograd_4bit import make_quant_for_4bit_autograd
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from autograd_4bit import load_llama_model_4bit_low_ram
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from datasets import load_dataset, Dataset
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import json
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from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model, get_peft_model_state_dict, PeftModel
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# Parameters
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DATA_PATH = "./data.txt"
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OUTPUT_DIR = "alpaca_lora"
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lora_path_old = ''
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config_path = './llama-13b-4bit/'
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model_path = './llama-13b-4bit.pt'
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MICRO_BATCH_SIZE = 1
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BATCH_SIZE = 2
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GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
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EPOCHS = 3
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LEARNING_RATE = 2e-4
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CUTOFF_LEN = 256
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LORA_R = 8
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LORA_ALPHA = 16
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LORA_DROPOUT = 0.05
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VAL_SET_SIZE = 0
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TARGET_MODULES = [
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"q_proj",
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"v_proj",
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]
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GRADIENT_CHECKPOINTING = False
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GRADIENT_CHECKPOINTING_RATIO = 1
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warmup_steps = 50
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save_steps = 50
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save_total_limit = 3
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logging_steps = 10
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# Load Basic Model
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model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path)
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# Config Lora
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config = LoraConfig(
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r=LORA_R,
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lora_alpha=LORA_ALPHA,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=LORA_DROPOUT,
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bias="none",
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task_type="CAUSAL_LM",
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)
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if lora_path_old == '':
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model = get_peft_model(model, config)
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else:
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model = PeftModel.from_pretrained(model, lora_path_old)
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print(lora_path_old, 'loaded')
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# Scales to half
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print('Fitting 4bit scales and zeros to half')
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for n, m in model.named_modules():
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if '4bit' in str(type(m)):
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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# Set tokenizer
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tokenizer.pad_token_id = 0
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# Load Data
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with open(DATA_PATH, 'r', encoding='utf8') as file:
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txt = file.read()
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txt = txt.replace('\r\n', '\n')
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rows = [r for r in txt.split('\n') if r != '']
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data = Dataset.from_dict({"input": rows})
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exceed_count = 0
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def tokenize(prompt):
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# there's probably a way to do this with the tokenizer settings
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# but again, gotta move fast
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global exceed_count
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prompt = prompt['input']
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result = tokenizer(
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prompt,
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truncation=True,
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max_length=CUTOFF_LEN + 1,
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padding="max_length",
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)
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d = {
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"input_ids": result["input_ids"][:-1],
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"attention_mask": result["attention_mask"][:-1],
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}
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if sum(d['attention_mask']) >= CUTOFF_LEN:
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exceed_count += 1
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return d
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data = data.shuffle().map(lambda x: tokenize(x))
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print('Train Data: {:.2f}%'.format(exceed_count / len(data) * 100), 'outliers')
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train_data = data
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# Use gradient checkpointing
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if GRADIENT_CHECKPOINTING:
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print('Applying gradient checkpointing ...')
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from gradient_checkpointing import apply_gradient_checkpointing
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apply_gradient_checkpointing(model, checkpoint_ratio=GRADIENT_CHECKPOINTING_RATIO)
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trainer = transformers.Trainer(
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model=model,
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train_dataset=train_data,
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args=transformers.TrainingArguments(
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per_device_train_batch_size=MICRO_BATCH_SIZE,
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gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
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warmup_steps=warmup_steps,
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num_train_epochs=EPOCHS,
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learning_rate=LEARNING_RATE,
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fp16=True,
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logging_steps=logging_steps,
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evaluation_strategy="no",
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save_strategy="steps",
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eval_steps=None,
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save_steps=save_steps,
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output_dir=OUTPUT_DIR,
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save_total_limit=save_total_limit,
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load_best_model_at_end=False
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),
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data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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# Set Model dict
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old_state_dict = model.state_dict
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model.state_dict = (
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lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
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).__get__(model, type(model))
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# Run Trainer
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trainer.train()
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print('Train completed.')
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# Save Model
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model.save_pretrained(OUTPUT_DIR)
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print('Model Saved.')
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