Delete finetune.py

finetune.py

@@ -1,283 +0,0 @@
-import os
-import sys
-from typing import List
-
-import fire
-import torch
-import transformers
-from datasets import load_dataset
-
-"""
-Unused imports:
-import torch.nn as nn
-import bitsandbytes as bnb
-"""
-
-from peft import (
-    LoraConfig,
-    get_peft_model,
-    get_peft_model_state_dict,
-    prepare_model_for_int8_training,
-    set_peft_model_state_dict,
-)
-from transformers import LlamaForCausalLM, LlamaTokenizer
-
-from utils.prompter import Prompter
-
-
-def train(
-    # model/data params
-    base_model: str = "./hf_ckpt",  # the only required argument
-    data_path: str = "ayuan0324/ocean_only",
-    output_dir: str = "./lora-alpaca",
-    # training hyperparams
-    batch_size: int = 128,
-    micro_batch_size: int = 4,
-    num_epochs: int = 3,
-    learning_rate: float = 1e-4,
-    cutoff_len: int = 512,
-    val_set_size: int = 2000,
-    # lora hyperparams
-    lora_r: int = 8,
-    lora_alpha: int = 16,
-    lora_dropout: float = 0.05,
-    lora_target_modules: List[str] = [
-        "q_proj",
-        "v_proj",
-    ],
-    # llm hyperparams
-    train_on_inputs: bool = True,  # if False, masks out inputs in loss
-    add_eos_token: bool = False,
-    group_by_length: bool = False,  # faster, but produces an odd training loss curve
-    # wandb params
-    wandb_project: str = "",
-    wandb_run_name: str = "",
-    wandb_watch: str = "",  # options: false | gradients | all
-    wandb_log_model: str = "",  # options: false | true
-    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
-    prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
-):
-    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
-        print(
-            f"Training Alpaca-LoRA model with params:\n"
-            f"base_model: {base_model}\n"
-            f"data_path: {data_path}\n"
-            f"output_dir: {output_dir}\n"
-            f"batch_size: {batch_size}\n"
-            f"micro_batch_size: {micro_batch_size}\n"
-            f"num_epochs: {num_epochs}\n"
-            f"learning_rate: {learning_rate}\n"
-            f"cutoff_len: {cutoff_len}\n"
-            f"val_set_size: {val_set_size}\n"
-            f"lora_r: {lora_r}\n"
-            f"lora_alpha: {lora_alpha}\n"
-            f"lora_dropout: {lora_dropout}\n"
-            f"lora_target_modules: {lora_target_modules}\n"
-            f"train_on_inputs: {train_on_inputs}\n"
-            f"add_eos_token: {add_eos_token}\n"
-            f"group_by_length: {group_by_length}\n"
-            f"wandb_project: {wandb_project}\n"
-            f"wandb_run_name: {wandb_run_name}\n"
-            f"wandb_watch: {wandb_watch}\n"
-            f"wandb_log_model: {wandb_log_model}\n"
-            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
-            f"prompt template: {prompt_template_name}\n"
-        )
-    assert (
-        base_model
-    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
-    gradient_accumulation_steps = batch_size // micro_batch_size
-
-    prompter = Prompter(prompt_template_name)
-
-    device_map = "auto"
-    world_size = int(os.environ.get("WORLD_SIZE", 1))
-    ddp = world_size != 1
-    if ddp:
-        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
-        gradient_accumulation_steps = gradient_accumulation_steps // world_size
-
-    # Check if parameter passed or if set within environ
-    use_wandb = len(wandb_project) > 0 or (
-        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
-    )
-    # Only overwrite environ if wandb param passed
-    if len(wandb_project) > 0:
-        os.environ["WANDB_PROJECT"] = wandb_project
-    if len(wandb_watch) > 0:
-        os.environ["WANDB_WATCH"] = wandb_watch
-    if len(wandb_log_model) > 0:
-        os.environ["WANDB_LOG_MODEL"] = wandb_log_model
-
-    model = LlamaForCausalLM.from_pretrained(
-        base_model,
-        load_in_8bit=True,
-        torch_dtype=torch.float16,
-        device_map=device_map,
-    )
-
-    tokenizer = LlamaTokenizer.from_pretrained(base_model)
-
-    tokenizer.pad_token_id = (
-        0  # unk. we want this to be different from the eos token
-    )
-    tokenizer.padding_side = "left"  # Allow batched inference
-
-    def tokenize(prompt, add_eos_token=True):
-        # there's probably a way to do this with the tokenizer settings
-        # but again, gotta move fast
-        result = tokenizer(
-            prompt,
-            truncation=True,
-            max_length=cutoff_len,
-            padding=False,
-            return_tensors=None,
-        )
-        if (
-            result["input_ids"][-1] != tokenizer.eos_token_id
-            and len(result["input_ids"]) < cutoff_len
-            and add_eos_token
-        ):
-            result["input_ids"].append(tokenizer.eos_token_id)
-            result["attention_mask"].append(1)
-
-        result["labels"] = result["input_ids"].copy()
-
-        return result
-
-    def generate_and_tokenize_prompt(data_point):
-        full_prompt = prompter.generate_prompt(
-            data_point["instruction"],
-            data_point["input"],
-            data_point["output"],
-        )
-        tokenized_full_prompt = tokenize(full_prompt)
-        if not train_on_inputs:
-            user_prompt = prompter.generate_prompt(
-                data_point["instruction"], data_point["input"]
-            )
-            tokenized_user_prompt = tokenize(
-                user_prompt, add_eos_token=add_eos_token
-            )
-            user_prompt_len = len(tokenized_user_prompt["input_ids"])
-
-            if add_eos_token:
-                user_prompt_len -= 1
-
-            tokenized_full_prompt["labels"] = [
-                -100
-            ] * user_prompt_len + tokenized_full_prompt["labels"][
-                user_prompt_len:
-            ]  # could be sped up, probably
-        return tokenized_full_prompt
-
-    model = prepare_model_for_int8_training(model)
-
-    config = LoraConfig(
-        r=lora_r,
-        lora_alpha=lora_alpha,
-        target_modules=lora_target_modules,
-        lora_dropout=lora_dropout,
-        bias="none",
-        task_type="CAUSAL_LM",
-    )
-    model = get_peft_model(model, config)
-
-    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
-        data = load_dataset("json", data_files=data_path)
-    else:
-        data = load_dataset(data_path)
-
-    if resume_from_checkpoint:
-        # Check the available weights and load them
-        checkpoint_name = os.path.join(
-            resume_from_checkpoint, "pytorch_model.bin"
-        )  # Full checkpoint
-        if not os.path.exists(checkpoint_name):
-            checkpoint_name = os.path.join(
-                resume_from_checkpoint, "adapter_model.bin"
-            )  # only LoRA model - LoRA config above has to fit
-            resume_from_checkpoint = (
-                False  # So the trainer won't try loading its state
-            )
-        # The two files above have a different name depending on how they were saved, but are actually the same.
-        if os.path.exists(checkpoint_name):
-            print(f"Restarting from {checkpoint_name}")
-            adapters_weights = torch.load(checkpoint_name)
-            set_peft_model_state_dict(model, adapters_weights)
-        else:
-            print(f"Checkpoint {checkpoint_name} not found")
-
-    model.print_trainable_parameters()  # Be more transparent about the % of trainable params.
-
-    if val_set_size > 0:
-        train_val = data["train"].train_test_split(
-            test_size=val_set_size, shuffle=True, seed=42
-        )
-        train_data = (
-            train_val["train"].shuffle().map(generate_and_tokenize_prompt)
-        )
-        val_data = (
-            train_val["test"].shuffle().map(generate_and_tokenize_prompt)
-        )
-    else:
-        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
-        val_data = None
-
-    if not ddp and torch.cuda.device_count() > 1:
-        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
-        model.is_parallelizable = True
-        model.model_parallel = True
-
-    trainer = transformers.Trainer(
-        model=model,
-        train_dataset=train_data,
-        eval_dataset=val_data,
-        args=transformers.TrainingArguments(
-            per_device_train_batch_size=micro_batch_size,
-            gradient_accumulation_steps=gradient_accumulation_steps,
-            warmup_steps=100,
-            num_train_epochs=num_epochs,
-            learning_rate=learning_rate,
-            fp16=True,
-            logging_steps=10,
-            optim="adamw_torch",
-            evaluation_strategy="steps" if val_set_size > 0 else "no",
-            save_strategy="steps",
-            eval_steps=200 if val_set_size > 0 else None,
-            save_steps=200,
-            output_dir=output_dir,
-            save_total_limit=3,
-            load_best_model_at_end=True if val_set_size > 0 else False,
-            ddp_find_unused_parameters=False if ddp else None,
-            group_by_length=group_by_length,
-            report_to="wandb" if use_wandb else None,
-            run_name=wandb_run_name if use_wandb else None,
-        ),
-        data_collator=transformers.DataCollatorForSeq2Seq(
-            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
-        ),
-    )
-    model.config.use_cache = False
-
-    old_state_dict = model.state_dict
-    model.state_dict = (
-        lambda self, *_, **__: get_peft_model_state_dict(
-            self, old_state_dict()
-        )
-    ).__get__(model, type(model))
-
-    if torch.__version__ >= "2" and sys.platform != "win32":
-        model = torch.compile(model)
-
-    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-
-    model.save_pretrained(output_dir)
-
-    print(
-        "\n If there's a warning about missing keys above, please disregard :)"
-    )
-
-
-if __name__ == "__main__":
-    fire.Fire(train)