INT8 W4A8

vLLM supports quantizing weights to INT4 and activations to INT8 for memory savings and inference acceleration. This quantization method is particularly useful for reducing model size while maintaining good performance.

Prerequisites

To use INT8 W4A8 quantization with vLLM, you'll need to install the llm-compressor library.

bash

(venv-llm-compressor) pip install llmcompressor

Additionally, install vllm and lm-evaluation-harness for evaluation:

bash

(venv-vllm) pip install vllm "lm-eval[api]>=0.4.12"

Please use separate environments for vLLM and llm-compressor as they might not work together.

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:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

2. Preparing Calibration Data

When quantizing activations to INT8 and weights to INT4, you need sample data to estimate the activation 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:

python

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.

The following recipes create W4A8 models (int4 weights, int8 activations). On Arm® CPUs, this is accelerated through KleidiAI.

Use groupwise for best accuracy, and channelwise for best inference performance.

=== "Groupwise"

```python
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier

# Configure the quantization algorithms
recipe = [
    GPTQModifier(
        targets="Linear",
        scheme="W4A8",
        ignore=["lm_head"],
        dampening_frac=0.01
    ),
]

# 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: Meta-Llama-3-8B-Instruct-W4A8-G128-Dynamic-Per-Token
SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A8-G128-Dynamic-Per-Token"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
```

=== "Channelwise"

```python
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from compressed_tensors.quantization import QuantizationStrategy, QuantizationType

scheme = {
    "targets": ["Linear"],
    "weights": {
        "num_bits": 4,
        "type": QuantizationType.INT,
        "strategy": QuantizationStrategy.CHANNEL,
        "symmetric": True,
        "dynamic": False,
        "group_size": None,
    },
    "input_activations": {
        "num_bits": 8,
        "type": QuantizationType.INT,
        "strategy": QuantizationStrategy.TOKEN,
        "dynamic": True,
        "symmetric": False,
        "observer": None,
    },
    "output_activations": None,
}

recipe = [
    GPTQModifier(
        targets="Linear",
        config_groups={"group_0": scheme},
        ignore=["lm_head"],
        dampening_frac=0.01,
    ),
]

oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)

# Save the compressed model: Meta-Llama-3-8B-Instruct-W4A8-Channelwise-Dynamic-Per-Token
SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A8-Channelwise-Dynamic-Per-Token"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
```

4. Evaluating Accuracy

=== "Groupwise"

After quantization, you can load and run the model in vLLM:

```python
from vllm import LLM

llm = LLM("./Meta-Llama-3-8B-Instruct-W4A8-G128-Dynamic-Per-Token")
```

To evaluate accuracy, you can use `lm_eval`:

```bash
lm_eval --model vllm \
    --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A8-G128-Dynamic-Per-Token",add_bos_token=true \
    --tasks gsm8k \
    --num_fewshot 5 \
    --limit 250 \
    --batch_size 'auto'
```

=== "Channelwise"

After quantization, you can load and run the model in vLLM:

```python
from vllm import LLM

llm = LLM("./Meta-Llama-3-8B-Instruct-W4A8-Channelwise-Dynamic-Per-Token")
```

To evaluate accuracy, you can use `lm_eval`:

```bash
lm_eval --model vllm \
    --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A8-Channelwise-Dynamic-Per-Token",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 (increase if accuracy drops)
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

Troubleshooting and Support

If you encounter any issues or have feature requests, please open an issue on the vllm-project/llm-compressor GitHub repository.