LLM: add alpaca qlora finetuning example (#9276)
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# Alpaca QLoRA Finetuning (experimental support)
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This example ports [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/tree/main) to BigDL-LLM QLoRA on [Intel GPUs](../../README.md).
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### 0. Requirements
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To run this example with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../README.md#requirements) for more information.
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### 1. Install
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```bash
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conda create -n llm python=3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install transformers==4.34.0
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pip install fire datasets peft==0.5.0
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pip install accelerate==0.23.0
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Finetuning LLaMA-2-7B on a single Arc:
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Example usage:
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```
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python ./alpaca_qlora_finetuning.py \
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--base_model "meta-llama/Llama-2-7b-hf" \
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--data_path "yahma/alpaca-cleaned" \
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--output_dir "./bigdl-qlora-alpaca"
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```
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**Note**: You could also specify `--base_model` to the local path of the huggingface model checkpoint folder and `--data_path` to the local path of the dataset JSON file.
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#### Sample Output
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```log
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{'loss': 1.9231, 'learning_rate': 2.9999945367033285e-05, 'epoch': 0.0}
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{'loss': 1.8622, 'learning_rate': 2.9999781468531096e-05, 'epoch': 0.01}
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{'loss': 1.9043, 'learning_rate': 2.9999508305687345e-05, 'epoch': 0.01}
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{'loss': 1.8967, 'learning_rate': 2.999912588049185e-05, 'epoch': 0.01}
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{'loss': 1.9658, 'learning_rate': 2.9998634195730358e-05, 'epoch': 0.01}
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{'loss': 1.8386, 'learning_rate': 2.9998033254984483e-05, 'epoch': 0.02}
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{'loss': 1.809, 'learning_rate': 2.999732306263172e-05, 'epoch': 0.02}
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{'loss': 1.8552, 'learning_rate': 2.9996503623845395e-05, 'epoch': 0.02}
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1%|█ | 8/1164 [xx:xx<xx:xx:xx, xx s/it]
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```
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/tloen/alpaca-lora/blob/main/finetune.py
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#
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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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 transformers
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from datasets import load_dataset
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import accelerate
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from transformers import LlamaTokenizer
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from peft import (
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LoraConfig,
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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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from utils.prompter import Prompter
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import intel_extension_for_pytorch as ipex
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from bigdl.llm.transformers import AutoModelForCausalLM
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# import them from bigdl.llm.transformers.qlora to get a BigDL-LLM compatible Peft model
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from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training
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def train(
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# model/data params
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base_model: str = "meta-llama/Llama-2-7b-hf", # the only required argument, default to be "meta-llama/Llama-2-7b-hf"
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saved_low_bit_model: str = None, # optional, the path to the saved model with bigdl-llm low-bit optimization
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data_path: str = "yahma/alpaca-cleaned",
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output_dir: str = "./bigdl-qlora-alpaca",
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# training hyperparams
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batch_size: int = 128,
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micro_batch_size: int = 2, # default to be 2, limited by GPU memory
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num_epochs: int = 3,
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learning_rate: float = 3e-5, # default to be 3e-5 to avoid divergence
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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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"k_proj",
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"o_proj",
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"up_proj",
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"down_proj",
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"gate_proj"
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], # according to the QLoRA paper (https://arxiv.org/pdf/2305.14314.pdf), it's suggested to fine tune all linear layers
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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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add_eos_token: bool = False,
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group_by_length: bool = False, # faster, but produces an odd training loss curve
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# wandb params
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wandb_project: str = "",
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wandb_run_name: str = "",
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wandb_watch: str = "", # options: false | gradients | all
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wandb_log_model: str = "", # options: false | true
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resume_from_checkpoint: str = None, # either training checkpoint or final adapter
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prompt_template_name: str = "alpaca", # The prompt template to use, will default to alpaca.
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gradient_checkpointing: bool = False,
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deepspeed: str = None,
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):
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if int(os.environ.get("LOCAL_RANK", 0)) == 0:
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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"add_eos_token: {add_eos_token}\n"
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f"group_by_length: {group_by_length}\n"
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f"wandb_project: {wandb_project}\n"
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f"wandb_run_name: {wandb_run_name}\n"
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f"wandb_watch: {wandb_watch}\n"
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f"wandb_log_model: {wandb_log_model}\n"
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f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
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f"prompt template: {prompt_template_name}\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='huggyllama/llama-7b'"
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gradient_accumulation_steps = batch_size // micro_batch_size
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prompter = Prompter(prompt_template_name)
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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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# Check if parameter passed or if set within environ
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use_wandb = len(wandb_project) > 0 or (
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"WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
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)
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# Only overwrite environ if wandb param passed
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if len(wandb_project) > 0:
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os.environ["WANDB_PROJECT"] = wandb_project
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if len(wandb_watch) > 0:
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os.environ["WANDB_WATCH"] = wandb_watch
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if len(wandb_log_model) > 0:
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os.environ["WANDB_LOG_MODEL"] = wandb_log_model
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if saved_low_bit_model is not None:
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# Load the low bit optimized model if provide the saved path
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model = AutoModelForCausalLM.load_low_bit(
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saved_low_bit_model,
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optimize_model=False,
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torch_dtype=torch.bfloat16,
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modules_to_not_convert=["lm_head"],
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)
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else:
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# Load the base model from a directory or the HF Hub to 4-bit NormalFloat format
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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load_in_low_bit="nf4", # According to the QLoRA paper, using "nf4" could yield better model quality than "int4"
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optimize_model=False,
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torch_dtype=torch.bfloat16,
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# device_map=device_map,
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modules_to_not_convert=["lm_head"],
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)
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print(f"Model loaded on rank {os.environ.get('LOCAL_RANK')}")
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model = model.to(f'xpu:{os.environ.get("LOCAL_RANK", 0)}')
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print(f"Model moved to rank {os.environ.get('LOCAL_RANK')}")
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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print(f"Tokenizer loaded on rank {os.environ.get('LOCAL_RANK')}")
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tokenizer.pad_token_id = (
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0 # unk. we want this to be different from the eos token
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)
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tokenizer.padding_side = "left" # Allow batched inference
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print(model)
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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 = prompter.generate_prompt(
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data_point["instruction"],
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data_point["input"],
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data_point["output"],
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)
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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 = prompter.generate_prompt(
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data_point["instruction"], data_point["input"]
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)
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tokenized_user_prompt = tokenize(
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user_prompt, add_eos_token=add_eos_token
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)
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user_prompt_len = len(tokenized_user_prompt["input_ids"])
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if add_eos_token:
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user_prompt_len -= 1
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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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# Prepare a BigDL-LLM compatible Peft model
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model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=gradient_checkpointing)
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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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if data_path.endswith(".json") or data_path.endswith(".jsonl"):
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data = load_dataset("json", data_files=data_path)
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else:
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data = load_dataset(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 = (
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False # So the trainer won't try loading its state
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)
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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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set_peft_model_state_dict(model, adapters_weights)
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else:
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print(f"Checkpoint {checkpoint_name} not found")
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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 = (
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train_val["train"].shuffle().map(generate_and_tokenize_prompt)
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)
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val_data = (
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train_val["test"].shuffle().map(generate_and_tokenize_prompt)
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)
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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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# Unused
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# if not ddp and torch.cuda.device_count() > 1:
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# # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
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# model.is_parallelizable = True
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# model.model_parallel = True
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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_ratio=0.03,
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# warmup_steps=100,
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max_grad_norm=0.3,
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num_train_epochs=num_epochs,
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learning_rate=learning_rate,
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lr_scheduler_type="cosine",
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bf16=True, # ensure training more stable
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logging_steps=1,
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optim="adamw_torch",
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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=100 if val_set_size > 0 else None,
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save_steps=100,
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output_dir=output_dir,
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save_total_limit=100,
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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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report_to="wandb" if use_wandb else None,
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run_name=wandb_run_name if use_wandb else None,
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gradient_checkpointing=gradient_checkpointing,
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ddp_backend="ccl",
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deepspeed=deepspeed,
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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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trainer.train(resume_from_checkpoint=resume_from_checkpoint)
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model.save_pretrained(output_dir)
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print(
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"\n If there's a warning about missing keys above, please disregard :)"
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)
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if __name__ == "__main__":
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fire.Fire(train)
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@ -0,0 +1,7 @@
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{
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"//": "This file is copied from https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca.json",
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"description": "Template used by Alpaca-LoRA.",
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"prompt_input": "Below is an 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",
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"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",
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"response_split": "### Response:"
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}
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{
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"//": "This file is copied from https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca_legacy.json",
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"description": "Legacy template, used by Original Alpaca repository.",
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"prompt_input": "Below is an 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:",
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"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:",
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"response_split": "### Response:"
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}
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@ -0,0 +1,7 @@
|
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{
|
||||
"//": "This file is copied from https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca_short.json",
|
||||
"description": "A shorter template to experiment with.",
|
||||
"prompt_input": "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
|
||||
"prompt_no_input": "### Instruction:\n{instruction}\n\n### Response:\n",
|
||||
"response_split": "### Response:"
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
"//": "This file is copied from https://github.com/tloen/alpaca-lora/blob/main/templates/vigogne.json",
|
||||
"description": "French template, used by Vigogne for finetuning.",
|
||||
"prompt_input": "Ci-dessous se trouve une instruction qui décrit une tâche, associée à une entrée qui fournit un contexte supplémentaire. Écrivez une réponse qui complète correctement la demande.\n\n### Instruction:\n{instruction}\n\n### Entrée:\n{input}\n\n### Réponse:\n",
|
||||
"prompt_no_input": "Ci-dessous se trouve une instruction qui décrit une tâche. Écrivez une réponse qui complète correctement la demande.\n\n### Instruction:\n{instruction}\n\n### Réponse:\n",
|
||||
"response_split": "### Réponse:"
|
||||
}
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
#
|
||||
# Copyright 2016 The BigDL Authors.
|
||||
#
|
||||
# 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.
|
||||
#
|
||||
# Some parts of this file is adapted from
|
||||
# https://github.com/tloen/alpaca-lora/blob/main/utils/prompter.py
|
||||
#
|
||||
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
|
||||
|
||||
# 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.
|
||||
|
||||
|
||||
import json
|
||||
import os.path as osp
|
||||
from typing import Union
|
||||
from bigdl.llm.utils.common import invalidInputError
|
||||
|
||||
|
||||
class Prompter(object):
|
||||
__slots__ = ("template", "_verbose")
|
||||
|
||||
def __init__(self, template_name: str = "", verbose: bool = False):
|
||||
self._verbose = verbose
|
||||
if not template_name:
|
||||
# Enforce the default here, so the constructor can be called with '' and will not break.
|
||||
template_name = "alpaca"
|
||||
file_name = osp.join("templates", f"{template_name}.json")
|
||||
if not osp.exists(file_name):
|
||||
invalidInputError(False, f"Can't read {file_name}")
|
||||
with open(file_name) as fp:
|
||||
self.template = json.load(fp)
|
||||
if self._verbose:
|
||||
print(
|
||||
f"Using prompt template {template_name}: {self.template['description']}"
|
||||
)
|
||||
|
||||
def generate_prompt(
|
||||
self,
|
||||
instruction: str,
|
||||
input: Union[None, str]=None,
|
||||
label: Union[None, str]=None,
|
||||
) -> str:
|
||||
# returns the full prompt from instruction and optional input
|
||||
# if a label (=response, =output) is provided, it's also appended.
|
||||
if input:
|
||||
res = self.template["prompt_input"].format(
|
||||
instruction=instruction, input=input
|
||||
)
|
||||
else:
|
||||
res = self.template["prompt_no_input"].format(
|
||||
instruction=instruction
|
||||
)
|
||||
if label:
|
||||
res = f"{res}{label}"
|
||||
if self._verbose:
|
||||
print(res)
|
||||
return res
|
||||
|
||||
def get_response(self, output: str) -> str:
|
||||
return output.split(self.template["response_split"])[1].strip()
|
||||
Loading…
Reference in a new issue