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LLM: remove CPU english_quotes dataset and update docker example (#10399)

Browse source 
* update dataset

* update readme

* update docker cpu

* update xpu docker
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Wang, Jian4 2024-03-18 10:45:14 +08:00 committed by GitHub
parent 399843faf0
commit 1de13ea578
10 changed files with 92 additions and 217 deletions
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30
docker/llm/README.md
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@ -467,7 +467,7 @@ docker build \
### 2. Prepare Base Model, Data and Container ### 2. Prepare Base Model, Data and Container
Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below: Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
```bash ```bash
export BASE_MODE_PATH=your_downloaded_base_model_path export BASE_MODE_PATH=your_downloaded_base_model_path
@ -483,7 +483,7 @@ docker run -itd \
-e http_proxy=${HTTP_PROXY} \ -e http_proxy=${HTTP_PROXY} \
-e https_proxy=${HTTPS_PROXY} \ -e https_proxy=${HTTPS_PROXY} \
-v $BASE_MODE_PATH:/model \ -v $BASE_MODE_PATH:/model \
-v $DATA_PATH:/data/english_quotes \ -v $DATA_PATH:/data/alpaca-cleaned \
--shm-size="16g" \ --shm-size="16g" \
intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT
``` ```
@ -524,17 +524,17 @@ bash start-qlora-finetuning-on-xpu.sh
After minutes, it is expected to get results like: After minutes, it is expected to get results like:
```bash ```bash
{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03} {'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06} {'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1} {'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13} {'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16} {'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19} {'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22} {'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26} {'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29} {'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32} {'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32} {'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [03:24<00:00, 1.02s/it] 100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00, 2.18s/it]
TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32}) TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
``` ```

3
docker/llm/finetune/qlora/cpu/docker/Dockerfile
View file

@ -36,6 +36,7 @@ RUN mkdir -p /bigdl/data && mkdir -p /bigdl/model && \
pip install datasets transformers==4.35.0 && \ pip install datasets transformers==4.35.0 && \
pip install fire peft==0.5.0 && \ pip install fire peft==0.5.0 && \
pip install accelerate==0.23.0 && \ pip install accelerate==0.23.0 && \
pip install bitsandbytes && \
# install basic dependencies # install basic dependencies
apt-get update && apt-get install -y curl wget gpg gpg-agent software-properties-common libunwind8-dev && \ apt-get update && apt-get install -y curl wget gpg gpg-agent software-properties-common libunwind8-dev && \
# get qlora example code # get qlora example code
@ -43,6 +44,8 @@ RUN mkdir -p /bigdl/data && mkdir -p /bigdl/model && \
cd /bigdl && \ cd /bigdl && \
git clone https://github.com/intel-analytics/BigDL.git && \ git clone https://github.com/intel-analytics/BigDL.git && \
mv BigDL/python/llm/example/CPU/QLoRA-FineTuning/* . && \ mv BigDL/python/llm/example/CPU/QLoRA-FineTuning/* . && \
mkdir -p /GPU/LLM-Finetuning && \
mv BigDL/python/llm/example/GPU/LLM-Finetuning/common /GPU/LLM-Finetuning/common && \
rm -r BigDL && \ rm -r BigDL && \
chown -R mpiuser /bigdl chown -R mpiuser /bigdl

62
docker/llm/finetune/qlora/cpu/docker/README.md
View file

@ -40,7 +40,7 @@ docker build \
### 2. Prepare Base Model, Data and Container ### 2. Prepare Base Model, Data and Container
Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below: Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
```bash ```bash
export BASE_MODE_PATH=your_downloaded_base_model_path export BASE_MODE_PATH=your_downloaded_base_model_path
@ -54,7 +54,7 @@ docker run -itd \
-e http_proxy=${HTTP_PROXY} \ -e http_proxy=${HTTP_PROXY} \
-e https_proxy=${HTTPS_PROXY} \ -e https_proxy=${HTTPS_PROXY} \
-v $BASE_MODE_PATH:/bigdl/model \ -v $BASE_MODE_PATH:/bigdl/model \
-v $DATA_PATH:/bigdl/data/english_quotes \ -v $DATA_PATH:/bigdl/data/alpaca-cleaned \
intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
``` ```
@ -92,19 +92,19 @@ bash start-qlora-finetuning-on-cpu.sh
After minutes, it is expected to get results like: After minutes, it is expected to get results like:
```bash ```bash
{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03} {'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06} {'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1} {'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13} {'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16} {'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19} {'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22} {'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26} {'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29} {'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32} {'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32} {'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [xx:xx<xx:xx, xxxs/it] 100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00, 2.18s/it]
TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32}) TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
``` ```
### 4. Merge the adapter into the original model ### 4. Merge the adapter into the original model
@ -117,38 +117,6 @@ python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --
Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference. Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.
### 5. Use BigDL-LLM to verify the fine-tuning effect
Train more steps and try input sentence like `['quote'] -> [?]` to verify. For example, using `“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ` to inference.
BigDL-LLM llama2 example [link](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2). Update the `LLAMA2_PROMPT_FORMAT = "{prompt}"`.
```bash
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt "“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:" --n-predict 20
```
#### Sample Output
Base_model output
```log
Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: 💻 Fine-tuning a language model on a powerful device like an Intel CPU
```
Merged_model output
```log
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ['bigdl'] ['deep-learning'] ['distributed-computing'] ['intel'] ['optimization'] ['training'] ['training-speed']
```
### 4. Start Multi-Porcess Fine-Tuning in One Docker ### 4. Start Multi-Porcess Fine-Tuning in One Docker
<img src="https://github.com/Uxito-Ada/BigDL/assets/60865256/f25c43b3-2b24-4476-a0fe-804c0ef3c36c" height="240px"><br> <img src="https://github.com/Uxito-Ada/BigDL/assets/60865256/f25c43b3-2b24-4476-a0fe-804c0ef3c36c" height="240px"><br>

87
docker/llm/finetune/qlora/cpu/docker/qlora_finetuning_cpu.py
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@ -1,87 +0,0 @@
#
# 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
from peft import LoraConfig
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)
def merge(row):
row['prediction'] = row['quote'] + ' ->: ' + str(row['tags'])
return row
data = data.map(lambda samples: tokenizer(samples["prediction"]), batched=True)
model = AutoModelForCausalLM.from_pretrained(model_path,
load_in_low_bit="sym_int4",
optimize_model=False,
torch_dtype=torch.float16,
modules_to_not_convert=["lm_head"],)
model = model.to('cpu')
# model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=False)
model.enable_input_require_grads()
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-4,
save_steps=100,
bf16=True,
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)

4
docker/llm/finetune/qlora/cpu/docker/start-qlora-finetuning-on-cpu.sh
View file

@ -11,9 +11,9 @@ then
MODEL_PARAM="--repo-id-or-model-path ./model" # otherwise, default to download from HF repo MODEL_PARAM="--repo-id-or-model-path ./model" # otherwise, default to download from HF repo
fi fi
if [ -d "./data/english_quotes" ]; if [ -d "./data/alpaca-cleaned" ];
then then
DATA_PARAM="--dataset ./data/english_quotes" # otherwise, default to download from HF dataset DATA_PARAM="--dataset ./data/alpaca-cleaned" # otherwise, default to download from HF dataset
fi fi
if [ "$STANDALONE_DOCKER" = "TRUE" ] if [ "$STANDALONE_DOCKER" = "TRUE" ]

3
docker/llm/finetune/qlora/xpu/docker/Dockerfile
View file

@ -34,6 +34,9 @@ RUN curl -fsSL https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-P
pip install git+https://github.com/huggingface/transformers.git@${TRANSFORMERS_COMMIT_ID} && \ pip install git+https://github.com/huggingface/transformers.git@${TRANSFORMERS_COMMIT_ID} && \
pip install peft==0.5.0 datasets accelerate==0.23.0 && \ pip install peft==0.5.0 datasets accelerate==0.23.0 && \
pip install bitsandbytes scipy && \ pip install bitsandbytes scipy && \
git clone https://github.com/intel-analytics/BigDL.git && \
mv BigDL/python/llm/example/GPU/LLM-Finetuning/common /common && \
rm -r BigDL && \
wget https://raw.githubusercontent.com/intel-analytics/BigDL/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/simple-example/qlora_finetuning.py wget https://raw.githubusercontent.com/intel-analytics/BigDL/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/simple-example/qlora_finetuning.py
COPY ./start-qlora-finetuning-on-xpu.sh /start-qlora-finetuning-on-xpu.sh COPY ./start-qlora-finetuning-on-xpu.sh /start-qlora-finetuning-on-xpu.sh

30
docker/llm/finetune/qlora/xpu/docker/README.md
View file

@ -25,7 +25,7 @@ docker build \
### 2. Prepare Base Model, Data and Container ### 2. Prepare Base Model, Data and Container
Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below: Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
```bash ```bash
export BASE_MODE_PATH=your_downloaded_base_model_path export BASE_MODE_PATH=your_downloaded_base_model_path
@ -41,7 +41,7 @@ docker run -itd \
-e http_proxy=${HTTP_PROXY} \ -e http_proxy=${HTTP_PROXY} \
-e https_proxy=${HTTPS_PROXY} \ -e https_proxy=${HTTPS_PROXY} \
-v $BASE_MODE_PATH:/model \ -v $BASE_MODE_PATH:/model \
-v $DATA_PATH:/data/english_quotes \ -v $DATA_PATH:/data/alpaca-cleaned \
--shm-size="16g" \ --shm-size="16g" \
intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT
``` ```
@ -82,17 +82,17 @@ bash start-qlora-finetuning-on-xpu.sh
After minutes, it is expected to get results like: After minutes, it is expected to get results like:
```bash ```bash
{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03} {'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06} {'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1} {'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13} {'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16} {'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19} {'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22} {'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26} {'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29} {'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32} {'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32} {'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [03:24<00:00, 1.02s/it] 100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00, 2.18s/it]
TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32}) TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
``` ```

4
docker/llm/finetune/qlora/xpu/docker/start-qlora-finetuning-on-xpu.sh
View file

@ -9,9 +9,9 @@ then
MODEL_PARAM="--repo-id-or-model-path ./model" # otherwise, default to download from HF repo MODEL_PARAM="--repo-id-or-model-path ./model" # otherwise, default to download from HF repo
fi fi
if [ -d "./data/english_quotes" ]; if [ -d "./data/alpaca-cleaned" ];
then then
DATA_PARAM="--dataset ./data/english_quotes" # otherwise, default to download from HF dataset DATA_PARAM="--dataset ./data/alpaca-cleaned" # otherwise, default to download from HF dataset
fi fi
python qlora_finetuning.py $MODEL_PARAM $DATA_PARAM python qlora_finetuning.py $MODEL_PARAM $DATA_PARAM

52
python/llm/example/CPU/QLoRA-FineTuning/README.md
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@ -38,19 +38,19 @@ python ./qlora_finetuning_cpu.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH -
#### Sample Output #### Sample Output
```log ```log
{'loss': 2.5668, 'learning_rate': 0.0002, 'epoch': 0.03} {'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.6988, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06} {'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.3073, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1} {'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 1.3495, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13} {'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 1.1746, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16} {'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 1.0794, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19} {'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 1.2214, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22} {'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 1.1698, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26} {'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 1.2044, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29} {'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 1.1516, 'learning_rate': 0.0, 'epoch': 0.32} {'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.3923714351654053, 'epoch': 0.32} {'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [xx:xx<xx:xx, xxxs/it] 100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00, 2.18s/it]
TrainOutput(global_step=200, training_loss=1.3923714351654053, metrics={'train_runtime': xx, 'train_samples_per_second': xx, 'train_steps_per_second': xx, 'train_loss': 1.3923714351654053, 'epoch': 0.32}) TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
``` ```
### 3. Merge the adapter into the original model ### 3. Merge the adapter into the original model
@ -60,29 +60,3 @@ python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --
``` ```
Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference. Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.
### 4. Use BigDL-LLM to verify the fine-tuning effect
Train more steps and try input sentence like `['quote'] -> [?]` to verify. For example, using `“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ` to inference.
BigDL-LLM llama2 example [link](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2). Update the `LLAMA2_PROMPT_FORMAT = "{prompt}"`.
```bash
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt "“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:" --n-predict 20
```
#### Sample Output
Base_model output
```log
Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: 💻 Fine-tuning a language model on a powerful device like an Intel CPU
```
Merged_model output
```log
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ['bigdl'] ['deep-learning'] ['distributed-computing'] ['intel'] ['optimization'] ['training'] ['training-speed']
```

32
python/llm/example/CPU/QLoRA-FineTuning/qlora_finetuning_cpu.py
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@ -27,25 +27,37 @@ from datasets import load_dataset
import argparse import argparse
from bigdl.llm.utils.isa_checker import ISAChecker from bigdl.llm.utils.isa_checker import ISAChecker
current_dir = os.path.dirname(os.path.realpath(__file__))
common_util_path = os.path.join(current_dir, '..', '..', 'GPU', 'LLM-Finetuning')
import sys
sys.path.append(common_util_path)
from common.utils import Prompter, get_train_val_data
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model') 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", 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' 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') ', or the path to the huggingface checkpoint folder')
parser.add_argument('--dataset', type=str, default="Abirate/english_quotes") parser.add_argument('--dataset', type=str, default="yahma/alpaca-cleaned")
args = parser.parse_args() args = parser.parse_args()
model_path = args.repo_id_or_model_path model_path = args.repo_id_or_model_path
dataset_path = args.dataset dataset_path = args.dataset
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
if dataset_path.endswith(".json") or dataset_path.endswith(".jsonl"):
data = load_dataset("json", data_files=dataset_path)
else:
data = load_dataset(dataset_path) data = load_dataset(dataset_path)
def merge(row):
row['prediction'] = row['quote'] + ' ->: ' + str(row['tags']) # For illustration purpose, only use part of data to train
return row data = data["train"].train_test_split(train_size=0.1, shuffle=False)
data['train'] = data['train'].map(merge)
# use the max_length to reduce memory usage, should be adjusted by different datasets # Data processing
data = data.map(lambda samples: tokenizer(samples["prediction"], max_length=256), batched=True) prompter = Prompter("alpaca")
train_data, _ = get_train_val_data(data, tokenizer, prompter, train_on_inputs=True,
add_eos_token=False, cutoff_len=256, val_set_size=0, seed=42)
bnb_config = BitsAndBytesConfig( bnb_config = BitsAndBytesConfig(
load_in_4bit=True, load_in_4bit=True,
@ -85,7 +97,7 @@ if __name__ == "__main__":
trainer = transformers.Trainer( trainer = transformers.Trainer(
model=model, model=model,
train_dataset=data["train"], train_dataset=train_data,
args=transformers.TrainingArguments( args=transformers.TrainingArguments(
per_device_train_batch_size=4, per_device_train_batch_size=4,
gradient_accumulation_steps=1, gradient_accumulation_steps=1,
@ -100,7 +112,9 @@ if __name__ == "__main__":
# gradient_checkpointing=True, # can further reduce memory but slower # gradient_checkpointing=True, # can further reduce memory but slower
), ),
# Inputs are dynamically padded to the maximum length of a batch # Inputs are dynamically padded to the maximum length of a batch
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False), data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
) )
model.config.use_cache = False # silence the warnings. Please re-enable for inference! model.config.use_cache = False # silence the warnings. Please re-enable for inference!
result = trainer.train() result = trainer.train()