alpaca-bloom-560m-en

Bloom 560M Finetuned on Instructions

Credit

Code 99.99% copied from

https://github.com/bofenghuang/vigogne

https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0

Inference Code

from peft import PeftModel
from transformers import PreTrainedTokenizer, PreTrainedModel, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModelForCausalLM, LoraConfig
from typing import Optional
from transformers import GenerationConfig
import torch

PROMPT_DICT = {
    "prompt_input": (
        "Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:\n"
    ),
}


def get_model(model_name_or_path: str, load_in_8bit: bool = True, device_map="auto",
              cpu: bool = False) -> PreTrainedModel:
    if cpu:
        model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map=device_map,
                                                     low_cpu_mem_usage=True)
    else:
        model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=load_in_8bit,
                                                     device_map=device_map, torch_dtype=torch.float16)

    return model


def get_peft_model(model: PreTrainedModel, lora_model_name_or_path: Optional[str] = None) -> PeftModelForCausalLM:
    model = PeftModel.from_pretrained(model, lora_model_name_or_path, torch_dtype=torch.float16)

    return model


def get_tokenizer(model_name_or_path: str, max_input_len: int) -> PreTrainedTokenizer:
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=max_input_len,
                                              padding_side="right", use_fast=False)

    return tokenizer


def get_llm_inference_model(base_model_name_or_path: str, lora_model_name_or_path: str, load_in_8bit: bool,
                            device_map) -> PeftModel:
    cpu = True if not torch.cuda.is_available() else False

    model = get_model(base_model_name_or_path, load_in_8bit, device_map, cpu=cpu)

    model = get_peft_model(model, lora_model_name_or_path=lora_model_name_or_path)

    if not load_in_8bit:
        model.half()

    model.eval()

    if torch.__version__ >= "2":
        model = torch.compile(model)

    return model


def generate_prompt(example):
    return (
        PROMPT_DICT["prompt_input"].format_map(example)
        if example["input"]
        else PROMPT_DICT["prompt_no_input"].format_map(example)
    )


def infer(instruction: str, input_text: Optional[str] = None, temperature: float = 0.1, top_p: float = 0.95,
          max_new_tokens: int = 512, early_stopping: bool = True, do_sample: bool = True,
          repetition_penalty: float = 2.5) -> str:
    prompt = generate_prompt({"instruction": instruction, "input": input_text})

    tokenized_inputs = tokenizer(prompt, return_tensors="pt")

    device = "cuda" if torch.cuda.is_available() else "cpu"

    input_ids = tokenized_inputs["input_ids"].to(device)

    generation_config = GenerationConfig(temperature=temperature, top_p=top_p, do_sample=do_sample,
                                         repetition_penalty=repetition_penalty, early_stopping=early_stopping)

    with torch.inference_mode():
        generation_output = model.generate(input_ids=input_ids, generation_config=generation_config,
                                           return_dict_in_generate=True, max_new_tokens=max_new_tokens)

    output = generation_output.sequences[0]

    output = tokenizer.decode(output, skip_special_tokens=True)

    return output.split("### Response:")[1].strip()


base_model_name_or_path = "bigscience/bloom-560m"

lora_model_name_or_path = "crayon-coe/alpaca-bloom-560m-en"

model = get_llm_inference_model(base_model_name_or_path, lora_model_name_or_path, True, "auto")

tokenizer = get_tokenizer(base_model_name_or_path, 512)

context = "Write a letter expressing your love for computers"

output = infer(context)

print(output)

# Output
# I am so grateful to have been able access this wonderful computer system and its amazing features, which I can now use daily with ease. 
# 
# My heartfelt thanks go out in advance of all my friends who are using it as well. 
# Thank you again!

Training Parameters

{
    "max_input_len": 512,
    "load_in_8bit": True,
    "model_name_or_path": "bigscience/bloom-560m",
    "device_map": "auto",
    "bias": "none",
    "lora_dropout": 0.05,
    "lora_alpha": 32,
    "target_modules": ["query_key_value"],
    "task_type": "CAUSAL_LM",
    "lora_r": 16,
    "pad_to_multiple_of": 8,
    "num_train_epochs": 3,
    "learning_rate": 0.0003,
    "gradient_accumulation_steps": 16,
    "per_device_train_batch_size": 8,
    "val_set_size": 500,
    "save_steps": 200,
    "eval_steps": 200,
    "evaluation_strategy": "steps",
    "save_strategy": "steps"
}

Note: If failing, you might need to add offload_folder="some folder name" when getting the PeftModel.

Training Code

# coding=utf-8
# Code 99.99% copied and adapted from:
#    https://github.com/bofenghuang/vigogne
#    https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0


import os
import sys
from dataclasses import dataclass
from typing import Dict, List, Optional, Sequence

import bitsandbytes as bnb
import fire
import torch
import transformers
from datasets import load_dataset
from peft import LoraConfig, TaskType, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"

PROMPT_DICT = {
    "prompt_input": (
        "Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:\n"
    ),
}


def generate_prompt(example):
    return (
        PROMPT_DICT["prompt_input"].format_map(example)
        if example["input"]
        else PROMPT_DICT["prompt_no_input"].format_map(example)
    )


# Modified from: https://github.com/bofenghuang/stanford_alpaca/blob/eb5b171d9b103a12a8e14e0edca9cbc45fe1d512/train.py#L166-L182
# Almost same to transformers.DataCollatorForSeq2Seq
@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        # dtype = torch.long
        # input_ids, labels = tuple([torch.LongTensor(instance[key]) for instance in instances] for key in ("input_ids", "labels"))
        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))

        if self.pad_to_multiple_of is not None:
            max_length_index, max_length = max(enumerate([len(input_ids_) for input_ids_ in input_ids]),
                                               key=lambda x: x[1])
            # int(math.ceil
            n_padding = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of - max_length
            # Pad the longest example to pad_to_multiple_of * N
            input_ids[max_length_index].extend([self.tokenizer.pad_token_id] * n_padding)
            labels[max_length_index].extend([IGNORE_INDEX] * n_padding)

        input_ids = [torch.LongTensor(input_ids_) for input_ids_ in input_ids]
        labels = [torch.LongTensor(labels_) for labels_ in labels]

        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True,
                                                    padding_value=self.tokenizer.pad_token_id)
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)

        return dict(input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id))


def train(model_name_or_path: str, output_dir: str, data_path: str, val_set_size: int = 500,
          model_max_length: int = 512, lora_r: int = 16, lora_alpha: int = 32, lora_dropout: float = 0.05,
          target_modules: List[str] = ["query_key_value"], num_train_epochs: int = 3, learning_rate: float = 0.0001,
          per_device_train_batch_size: int = 8, gradient_accumulation_steps: int = 16, **kwargs):
    device_map = "auto"

    model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=device_map)

    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=model_max_length,
                                              padding_side="right", use_fast=False)

    model = prepare_model_for_int8_training(model)

    lora_config = LoraConfig(r=lora_r, lora_alpha=lora_alpha, target_modules=target_modules, lora_dropout=lora_dropout,
                             bias="none", task_type=TaskType.CAUSAL_LM)

    model = get_peft_model(model, lora_config)

    model.print_trainable_parameters()

    # Load data
    data = load_dataset("json", data_files=data_path)

    def preprocess_function(example):
        # Format prompt
        user_prompt = generate_prompt(example)

        # Get prompt length for masking
        len_user_prompt_tokens = len(tokenizer(user_prompt, truncation=True)["input_ids"])

        input_ids = tokenizer(user_prompt + example["output"] + tokenizer.eos_token, truncation=True)["input_ids"]
        labels = [IGNORE_INDEX] * len_user_prompt_tokens + input_ids[len_user_prompt_tokens:]

        return {"input_ids": input_ids, "labels": labels}

    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(preprocess_function, remove_columns=data["train"].column_names)
        val_data = train_val["test"].map(preprocess_function, remove_columns=data["train"].column_names)
    else:
        train_data = data["train"].shuffle().map(preprocess_function, remove_columns=data["train"].column_names)
        val_data = None

    trainer = transformers.Trainer(
        model=model,
        train_dataset=train_data,
        eval_dataset=val_data,
        args=transformers.TrainingArguments(
            per_device_train_batch_size=per_device_train_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            num_train_epochs=num_train_epochs,
            learning_rate=learning_rate,
            fp16=True,
            output_dir=output_dir,
            load_best_model_at_end=True if val_set_size > 0 else False,
            **kwargs,
        ),
        data_collator=DataCollatorForSupervisedDataset(tokenizer=tokenizer, pad_to_multiple_of=8),
    )
    print(trainer.args)

    # Silence the warnings. Please re-enable for inference!
    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()

    model.save_pretrained(output_dir)


if __name__ == "__main__":
    fire.Fire(train)