mirror of
https://github.com/tloen/alpaca-lora.git
synced 2024-10-01 01:05:56 -04:00
239 lines
8.2 KiB
Python
239 lines
8.2 KiB
Python
import os
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import sys
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from typing import List
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import fire
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import torch
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import torch.nn as nn
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import bitsandbytes as bnb
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from datasets import load_dataset
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import transformers
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assert (
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"LlamaTokenizer" in transformers._import_structure["models.llama"]
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), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
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from transformers import LlamaForCausalLM, LlamaTokenizer
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from peft import (
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prepare_model_for_int8_training,
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LoraConfig,
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get_peft_model,
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get_peft_model_state_dict,
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set_peft_model_state_dict,
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)
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def train(
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# model/data params
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base_model: str = "", # the only required argument
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data_path: str = "./alpaca_data_cleaned.json",
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output_dir: str = "./lora-alpaca",
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# training hyperparams
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batch_size: int = 128,
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micro_batch_size: int = 4,
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num_epochs: int = 3,
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learning_rate: float = 3e-4,
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cutoff_len: int = 256,
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val_set_size: int = 2000,
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# lora hyperparams
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lora_r: int = 8,
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lora_alpha: int = 16,
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lora_dropout: float = 0.05,
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lora_target_modules: List[str] = [
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"q_proj",
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"v_proj",
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],
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# llm hyperparams
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train_on_inputs: bool = True, # if False, masks out inputs in loss
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group_by_length: bool = False, # faster, but produces an odd training loss curve,
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resume_from_checkpoint: str = None, # either training checkpoint or final adapter
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):
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print(
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f"Training Alpaca-LoRA model with params:\n"
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f"base_model: {base_model}\n"
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f"data_path: {data_path}\n"
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f"output_dir: {output_dir}\n"
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f"batch_size: {batch_size}\n"
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f"micro_batch_size: {micro_batch_size}\n"
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f"num_epochs: {num_epochs}\n"
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f"learning_rate: {learning_rate}\n"
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f"cutoff_len: {cutoff_len}\n"
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f"val_set_size: {val_set_size}\n"
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f"lora_r: {lora_r}\n"
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f"lora_alpha: {lora_alpha}\n"
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f"lora_dropout: {lora_dropout}\n"
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f"lora_target_modules: {lora_target_modules}\n"
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f"train_on_inputs: {train_on_inputs}\n"
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f"group_by_length: {group_by_length}\n"
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f"resume_from_checkpoint: {resume_from_checkpoint}\n"
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)
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assert (
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base_model
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), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
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gradient_accumulation_steps = batch_size // micro_batch_size
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device_map = "auto"
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world_size = int(os.environ.get("WORLD_SIZE", 1))
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ddp = world_size != 1
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if ddp:
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device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
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gradient_accumulation_steps = gradient_accumulation_steps // world_size
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model = LlamaForCausalLM.from_pretrained(
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base_model,
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load_in_8bit=True,
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device_map=device_map,
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)
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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tokenizer.pad_token_id = 0 # unk. we want this to be different from the eos token
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tokenizer.padding_side = "left" # Allow batched inference
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def tokenize(prompt, add_eos_token=True):
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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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result = tokenizer(
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prompt,
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truncation=True,
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max_length=cutoff_len,
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padding=False,
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return_tensors=None,
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)
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if (
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result["input_ids"][-1] != tokenizer.eos_token_id
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and len(result["input_ids"]) < cutoff_len
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and add_eos_token
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):
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result["input_ids"].append(tokenizer.eos_token_id)
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result["attention_mask"].append(1)
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result["labels"] = result["input_ids"].copy()
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return result
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def generate_and_tokenize_prompt(data_point):
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full_prompt = generate_prompt(data_point)
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tokenized_full_prompt = tokenize(full_prompt)
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if not train_on_inputs:
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user_prompt = generate_prompt({**data_point, "output": ""})
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tokenized_user_prompt = tokenize(user_prompt, add_eos_token=False)
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user_prompt_len = len(tokenized_user_prompt["input_ids"])
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tokenized_full_prompt["labels"] = [
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-100
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] * user_prompt_len + tokenized_full_prompt["labels"][
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user_prompt_len:
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] # could be sped up, probably
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return tokenized_full_prompt
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model = prepare_model_for_int8_training(model)
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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=lora_target_modules,
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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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model = get_peft_model(model, config)
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data = load_dataset("json", data_files=data_path)
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if resume_from_checkpoint:
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# Check the available weights and load them
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checkpoint_name = os.path.join(
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resume_from_checkpoint, "pytorch_model.bin"
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) # Full checkpoint
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if not os.path.exists(checkpoint_name):
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checkpoint_name = os.path.join(
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resume_from_checkpoint, "adapter_model.bin"
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) # only LoRA model - LoRA config above has to fit
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resume_from_checkpoint = False # So the trainer won't try loading its state
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# The two files above have a different name depending on how they were saved, but are actually the same.
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if os.path.exists(checkpoint_name):
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print(f"Restarting from {checkpoint_name}")
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adapters_weights = torch.load(checkpoint_name)
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model = set_peft_model_state_dict(model, adapters_weights)
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model.print_trainable_parameters() # Be more transparent about the % of trainable params.
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if val_set_size > 0:
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train_val = data["train"].train_test_split(
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test_size=val_set_size, shuffle=True, seed=42
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)
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train_data = train_val["train"].shuffle().map(generate_and_tokenize_prompt)
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val_data = train_val["test"].shuffle().map(generate_and_tokenize_prompt)
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else:
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train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
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val_data = None
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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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eval_dataset=val_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=100,
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num_train_epochs=num_epochs,
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learning_rate=learning_rate,
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fp16=True,
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logging_steps=10,
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evaluation_strategy="steps" if val_set_size > 0 else "no",
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save_strategy="steps",
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eval_steps=200 if val_set_size > 0 else None,
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save_steps=200,
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output_dir=output_dir,
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save_total_limit=3,
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load_best_model_at_end=True if val_set_size > 0 else False,
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ddp_find_unused_parameters=False if ddp else None,
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group_by_length=group_by_length,
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),
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data_collator=transformers.DataCollatorForSeq2Seq(
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tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
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),
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)
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model.config.use_cache = False
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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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if torch.__version__ >= "2" and sys.platform != "win32":
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model = torch.compile(model)
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trainer.train(resume_from_checkpoint=resume_from_checkpoint)
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model.save_pretrained(output_dir)
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print("\n If there's a warning about missing keys above, please disregard :)")
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def generate_prompt(data_point):
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# sorry about the formatting disaster gotta move fast
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if data_point["input"]:
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{data_point["instruction"]}
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### Input:
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{data_point["input"]}
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### Response:
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{data_point["output"]}"""
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else:
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{data_point["instruction"]}
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### Response:
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{data_point["output"]}"""
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if __name__ == "__main__":
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fire.Fire(train)
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