4ceefc9b18
* test support bitsandbytesconfig * update style * update cpu example * update example * update readme * update unit test * use bfloat16 * update logic * use int4 * set defalut bnb_4bit_use_double_quant * update * update example * update model.py * update * support lora example
98 lines
3.9 KiB
Python
98 lines
3.9 KiB
Python
#
|
|
# 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.
|
|
#
|
|
|
|
import torch
|
|
import os
|
|
|
|
import transformers
|
|
from transformers import LlamaTokenizer
|
|
import intel_extension_for_pytorch as ipex
|
|
from peft import LoraConfig
|
|
from transformers import BitsAndBytesConfig
|
|
from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training
|
|
from bigdl.llm.transformers import AutoModelForCausalLM
|
|
from datasets import load_dataset
|
|
import argparse
|
|
|
|
if __name__ == "__main__":
|
|
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-hf",
|
|
help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--dataset', type=str, default="Abirate/english_quotes")
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
dataset_path = args.dataset
|
|
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
data = load_dataset(dataset_path)
|
|
data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
|
|
|
|
bnb_config = BitsAndBytesConfig(
|
|
load_in_4bit=True,
|
|
bnb_4bit_use_double_quant=False,
|
|
bnb_4bit_quant_type="nf4",
|
|
bnb_4bit_compute_dtype=torch.bfloat16
|
|
)
|
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
|
quantization_config=bnb_config, )
|
|
|
|
# below is also supported
|
|
# model = AutoModelForCausalLM.from_pretrained(model_path,
|
|
# load_in_low_bit="nf4",
|
|
# optimize_model=False,
|
|
# torch_dtype=torch.bfloat16,
|
|
# modules_to_not_convert=["lm_head"],)
|
|
model = model.to('xpu')
|
|
# Enable gradient_checkpointing if your memory is not enough,
|
|
# it will slowdown the training speed
|
|
# model.gradient_checkpointing_enable()
|
|
model = prepare_model_for_kbit_training(model)
|
|
config = LoraConfig(
|
|
r=8,
|
|
lora_alpha=32,
|
|
target_modules=["q_proj", "k_proj", "v_proj"],
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
model = get_peft_model(model, config)
|
|
|
|
tokenizer.pad_token_id = 0
|
|
tokenizer.padding_side = "left"
|
|
trainer = transformers.Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=transformers.TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps= 1,
|
|
warmup_steps=20,
|
|
max_steps=200,
|
|
learning_rate=2e-5,
|
|
save_steps=100,
|
|
bf16=True, # bf16 is more stable in training
|
|
logging_steps=20,
|
|
output_dir="outputs",
|
|
optim="adamw_hf", # paged_adamw_8bit is not supported yet
|
|
# gradient_checkpointing=True, # can further reduce memory but slower
|
|
),
|
|
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
|
|
result = trainer.train()
|
|
print(result)
|