falcon_peft.py

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from peft import LoraConfig
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
)
from peft.tuners.lora import LoraLayer

from trl import SFTTrainer


########################################################################
# This is a fully working simple example to use trl's RewardTrainer.
#
# This example fine-tunes any causal language model (GPT-2, GPT-Neo, etc.)
# by using the RewardTrainer from trl, we will leverage PEFT library to finetune
# adapters on the model.
#
########################################################################


# Define and parse arguments.


@dataclass
class ScriptArguments:
    """
    These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train.
    """

    local_rank: Optional[int] = field(default=-1, metadata={"help": "Used for multi-gpu"})

    per_device_train_batch_size: Optional[int] = field(default=4)
    per_device_eval_batch_size: Optional[int] = field(default=1)
    gradient_accumulation_steps: Optional[int] = field(default=4)
    learning_rate: Optional[float] = field(default=2e-4)
    max_grad_norm: Optional[float] = field(default=0.3)
    weight_decay: Optional[int] = field(default=0.001)
    lora_alpha: Optional[int] = field(default=16)
    lora_dropout: Optional[float] = field(default=0.1)
    lora_r: Optional[int] = field(default=64)
    max_seq_length: Optional[int] = field(default=512)
    model_name: Optional[str] = field(
        default="tiiuae/falcon-7b",
        metadata={
            "help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc."
        },
    )
    dataset_name: Optional[str] = field(
        default="timdettmers/openassistant-guanaco",
        metadata={"help": "The preference dataset to use."},
    )
    use_4bit: Optional[bool] = field(
        default=True,
        metadata={"help": "Activate 4bit precision base model loading"},
    )
    use_nested_quant: Optional[bool] = field(
        default=False,
        metadata={"help": "Activate nested quantization for 4bit base models"},
    )
    bnb_4bit_compute_dtype: Optional[str] = field(
        default="float16",
        metadata={"help": "Compute dtype for 4bit base models"},
    )
    bnb_4bit_quant_type: Optional[str] = field(
        default="nf4",
        metadata={"help": "Quantization type fp4 or nf4"},
    )
    num_train_epochs: Optional[int] = field(
        default=1,
        metadata={"help": "The number of training epochs for the reward model."},
    )
    fp16: Optional[bool] = field(
        default=False,
        metadata={"help": "Enables fp16 training."},
    )
    bf16: Optional[bool] = field(
        default=False,
        metadata={"help": "Enables bf16 training."},
    )
    packing: Optional[bool] = field(
        default=False,
        metadata={"help": "Use packing dataset creating."},
    )
    gradient_checkpointing: Optional[bool] = field(
        default=True,
        metadata={"help": "Enables gradient checkpointing."},
    )
    optim: Optional[str] = field(
        default="paged_adamw_32bit",
        metadata={"help": "The optimizer to use."},
    )
    lr_scheduler_type: str = field(
        default="constant",
        metadata={"help": "Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis"},
    )
    max_steps: int = field(default=10000, metadata={"help": "How many optimizer update steps to take"})
    warmup_ratio: float = field(default=0.03, metadata={"help": "Fraction of steps to do a warmup for"})
    group_by_length: bool = field(
        default=True,
        metadata={
            "help": "Group sequences into batches with same length. Saves memory and speeds up training considerably."
        },
    )
    save_steps: int = field(default=10, metadata={"help": "Save checkpoint every X updates steps."})
    logging_steps: int = field(default=10, metadata={"help": "Log every X updates steps."})


parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]


def create_and_prepare_model(args):
    compute_dtype = getattr(torch, args.bnb_4bit_compute_dtype)

    bnb_config = BitsAndBytesConfig(
        load_in_4bit=args.use_4bit,
        bnb_4bit_quant_type=args.bnb_4bit_quant_type,
        bnb_4bit_compute_dtype=compute_dtype,
        bnb_4bit_use_double_quant=args.use_nested_quant,
    )

    if compute_dtype == torch.float16 and args.use_4bit:
        major, _ = torch.cuda.get_device_capability()
        if major >= 8:
            print("=" * 80)
            print("Your GPU supports bfloat16, you can accelerate training with the argument --bf16")
            print("=" * 80)

    device_map = {"": 0}

    model = AutoModelForCausalLM.from_pretrained(
        args.model_name, quantization_config=bnb_config, device_map=device_map, trust_remote_code=True
    )

    peft_config = LoraConfig(
        lora_alpha=args.lora_alpha,
        lora_dropout=args.lora_dropout,
        r=args.lora_r,
        bias="none",
        task_type="CAUSAL_LM",
        target_modules=[
            "query_key_value",
            "dense",
            "dense_h_to_4h",
            "dense_4h_to_h",
        ],  # , "word_embeddings", "lm_head"],
    )

    tokenizer = AutoTokenizer.from_pretrained(args.model_name, trust_remote_code=True)
    tokenizer.pad_token = tokenizer.eos_token

    return model, peft_config, tokenizer


training_arguments = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=script_args.per_device_train_batch_size,
    gradient_accumulation_steps=script_args.gradient_accumulation_steps,
    optim=script_args.optim,
    save_steps=script_args.save_steps,
    logging_steps=script_args.logging_steps,
    learning_rate=script_args.learning_rate,
    fp16=script_args.fp16,
    bf16=script_args.bf16,
    max_grad_norm=script_args.max_grad_norm,
    max_steps=script_args.max_steps,
    warmup_ratio=script_args.warmup_ratio,
    group_by_length=script_args.group_by_length,
    lr_scheduler_type=script_args.lr_scheduler_type,
)

model, peft_config, tokenizer = create_and_prepare_model(script_args)
model.config.use_cache = False
dataset = load_dataset(script_args.dataset_name, split="train")

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    peft_config=peft_config,
    dataset_text_field="text",
    max_seq_length=script_args.max_seq_length,
    tokenizer=tokenizer,
    args=training_arguments,
    packing=script_args.packing,
)


for name, module in trainer.model.named_modules():
    if isinstance(module, LoraLayer):
        if script_args.bf16:
            module = module.to(torch.bfloat16)
    if "norm" in name:
        module = module.to(torch.float32)
    if "lm_head" in name or "embed_tokens" in name:
        if hasattr(module, "weight"):
            if script_args.bf16 and module.weight.dtype == torch.float32:
                module = module.to(torch.bfloat16)

trainer.train()