INT4 W4A16#
vLLM supports quantizing weights to INT4 for memory savings and inference acceleration. This quantization method is particularly useful for reducing model size and maintaining low latency in workloads with low queries per second (QPS).
Please visit the HF collection of quantized INT4 checkpoints of popular LLMs ready to use with vLLM.
Note
INT4 computation is supported on NVIDIA GPUs with compute capability > 8.0 (Ampere, Ada Lovelace, Hopper, Blackwell).
Prerequisites#
To use INT4 quantization with vLLM, you’ll need to install the llm-compressor library:
pip install llmcompressor
Quantization Process#
The quantization process involves four main steps:
Loading the model
Preparing calibration data
Applying quantization
Evaluating accuracy in vLLM
1. Loading the Model#
Load your model and tokenizer using the standard transformers
AutoModel classes:
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
2. Preparing Calibration Data#
When quantizing weights to INT4, you need sample data to estimate the weight updates and calibrated scales.
It’s best to use calibration data that closely matches your deployment data.
For a general-purpose instruction-tuned model, you can use a dataset like ultrachat
:
from datasets import load_dataset
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048
# Load and preprocess the dataset
ds = load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft")
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
return {"text": tokenizer.apply_chat_template(example["messages"], tokenize=False)}
ds = ds.map(preprocess)
def tokenize(sample):
return tokenizer(sample["text"], padding=False, max_length=MAX_SEQUENCE_LENGTH, truncation=True, add_special_tokens=False)
ds = ds.map(tokenize, remove_columns=ds.column_names)
3. Applying Quantization#
Now, apply the quantization algorithms:
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
# Configure the quantization algorithms
recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])
# Apply quantization
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)
# Save the compressed model
SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16-G128"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
This process creates a W4A16 model with weights quantized to 4-bit integers.
4. Evaluating Accuracy#
After quantization, you can load and run the model in vLLM:
from vllm import LLM
model = LLM("./Meta-Llama-3-8B-Instruct-W4A16-G128")
To evaluate accuracy, you can use lm_eval
:
$ lm_eval --model vllm \
--model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A16-G128",add_bos_token=true \
--tasks gsm8k \
--num_fewshot 5 \
--limit 250 \
--batch_size 'auto'
Note
Quantized models can be sensitive to the presence of the bos
token. Make sure to include the add_bos_token=True
argument when running evaluations.
Best Practices#
Start with 512 samples for calibration data, and increase if accuracy drops
Ensure the calibration data contains a high variety of samples to prevent overfitting towards a specific use case
Use a sequence length of 2048 as a starting point
Employ the chat template or instruction template that the model was trained with
If you’ve fine-tuned a model, consider using a sample of your training data for calibration
Tune key hyperparameters to the quantization algorithm:
dampening_frac
sets how much influence the GPTQ algorithm has. Lower values can improve accuracy, but can lead to numerical instabilities that cause the algorithm to fail.actorder
sets the activation ordering. When compressing the weights of a layer weight, the order in which channels are quantized matters. Settingactorder="weight"
can improve accuracy without added latency.
The following is an example of an expanded quantization recipe you can tune to your own use case:
from compressed_tensors.quantization import (
QuantizationArgs,
QuantizationScheme,
QuantizationStrategy,
QuantizationType,
)
recipe = GPTQModifier(
targets="Linear",
config_groups={
"config_group": QuantizationScheme(
targets=["Linear"],
weights=QuantizationArgs(
num_bits=4,
type=QuantizationType.INT,
strategy=QuantizationStrategy.GROUP,
group_size=128,
symmetric=True,
dynamic=False,
actorder="weight",
),
),
},
ignore=["lm_head"],
update_size=NUM_CALIBRATION_SAMPLES,
dampening_frac=0.01
)
Troubleshooting and Support#
If you encounter any issues or have feature requests, please open an issue on the vllm-project/llm-compressor
GitHub repository. The full INT4 quantization example in llm-compressor
is available here.