| Roadmap | Examples | Issues: Help Wanted |
AutoAWQ is an easy-to-use package for 4-bit quantized models. AutoAWQ speeds up models by 2x while reducing memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. AutoAWQ was created and improved upon from the original work from MIT.
Latest News 🔥
- [2023/09] 1.6x-2.5x speed boost on fused models (now including MPT and Falcon).
- [2023/09] Multi-GPU support, bug fixes, and better benchmark scripts available
- [2023/08] PyPi package released and AutoModel class available
Requirements:
- Compute Capability 8.0 (sm80). Ampere and later architectures are supported.
- CUDA Toolkit 11.8 and later.
Install:
- Use pip to install awq
pip install autoawq
CUDA dependencies can be hard to manage sometimes. It is recommended to use conda with AutoAWQ:
conda create --name autoawq python=3.10 -y
conda activate autoawq
conda install pytorch=2.0.1 torchvision torchaudio cudatoolkit=11.8 -c pytorch -c nvidia
pip install autoawq
Build AutoAWQ from scratch
Build time can take 10 minutes. Download your model while you install AutoAWQ.
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip install -e .
The detailed support list:
| Models | Sizes |
|---|---|
| LLaMA-2 | 7B/13B/70B |
| LLaMA | 7B/13B/30B/65B |
| Vicuna | 7B/13B |
| MPT | 7B/30B |
| Falcon | 7B/40B |
| OPT | 125m/1.3B/2.7B/6.7B/13B/30B |
| Bloom | 560m/3B/7B/ |
| GPTJ | 6.7B |
Under examples, you can find examples of how to quantize, run inference, and benchmark AutoAWQ models.
There are two versions of AWQ: GEMM and GEMV. Both names relate to how matrix multiplication runs under the hood. We suggest the following:
- GEMV (quantized): Best for small context, batch size 1, highest number of tokens/s.
- GEMM (quantized): Best for larger context, up to batch size 8, faster than GEMV on batch size > 1, slower than GEMV on batch size = 1.
- FP16 (non-quantized): Best for large batch sizes of 8 or larger, highest throughput. We recommend TGI or vLLM.
Quantization
Expect this to take 10-15 minutes on smaller 7B models, and around 1 hour for 70B models.
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_path = 'lmsys/vicuna-7b-v1.5'
quant_path = 'vicuna-7b-v1.5-awq'
quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4 }
# Load model
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# Quantize
model.quantize(tokenizer, quant_config=quant_config)
# Save quantized model
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)Inference
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
quant_path = "casperhansen/vicuna-7b-v1.5-awq"
quant_file = "awq_model_w4_g128.pt"
# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, quant_file, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: {prompt}
ASSISTANT:"""
tokens = tokenizer(
prompt_template.format(prompt="How are you today?"),
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_new_tokens=512
)AutoAWQForCausalLM.from_quantized
quant_path: Path to folder containing model files.quant_filename: The filename to model weights orindex.jsonfile.max_new_tokens: The max sequence length, used to allocate kv-cache for fused models.fuse_layers: Whether or not to use fused layers.batch_size: The batch size to initialize the AWQ model with.
- Note: Blazing fast generation, slow context processing
- GPU: NVIDIA GeForce RTX 3090
- Version: GEMV
- Command:
python examples/benchmark.py --model_path casperhansen/vicuna-7b-v1.5-awq-gemv
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|---|---|---|---|---|---|
| 1 | 32 | 32 | 231.393 | 153.632 | 4.66 GB (19.68%) |
| 1 | 64 | 64 | 233.909 | 154.475 | 4.66 GB (19.68%) |
| 1 | 128 | 128 | 233.145 | 152.133 | 4.66 GB (19.68%) |
| 1 | 256 | 256 | 228.562 | 147.692 | 4.67 GB (19.72%) |
| 1 | 512 | 512 | 228.914 | 139.179 | 4.80 GB (20.26%) |
| 1 | 1024 | 1024 | 227.393 | 125.058 | 5.56 GB (23.48%) |
| 1 | 2048 | 2048 | 225.736 | 123.228 | 8.08 GB (34.09%) |
- Note: Fast generation, fast context processing
- GPU: NVIDIA GeForce RTX 3090
- Version: GEMM
- Command:
python examples/benchmark.py --model_path casperhansen/vicuna-7b-v1.5-awq
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|---|---|---|---|---|---|
| 1 | 32 | 32 | 521.444 | 126.51 | 4.55 GB (19.21%) |
| 1 | 64 | 64 | 2618.88 | 125.428 | 4.57 GB (19.31%) |
| 1 | 128 | 128 | 2808.09 | 123.865 | 4.61 GB (19.44%) |
| 1 | 256 | 256 | 2807.46 | 120.779 | 4.67 GB (19.72%) |
| 1 | 512 | 512 | 2769.9 | 115.08 | 4.80 GB (20.26%) |
| 1 | 1024 | 1024 | 2640.95 | 105.493 | 5.56 GB (23.48%) |
| 1 | 2048 | 2048 | 2341.36 | 104.188 | 8.08 GB (34.09%) |
- Note: Blazing fast generation, slow context processing
- GPU: NVIDIA GeForce RTX 3090
- Command:
python examples/benchmark.py --model_path casperhansen/mpt-7b-8k-chat-awq-gemv - Version: GEMV
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|---|---|---|---|---|---|
| 1 | 32 | 32 | 187.332 | 136.765 | 3.65 GB (15.42%) |
| 1 | 64 | 64 | 241.026 | 136.476 | 3.67 GB (15.48%) |
| 1 | 128 | 128 | 239.44 | 137.599 | 3.70 GB (15.61%) |
| 1 | 256 | 256 | 233.184 | 137.02 | 3.76 GB (15.88%) |
| 1 | 512 | 512 | 233.082 | 135.633 | 3.89 GB (16.41%) |
| 1 | 1024 | 1024 | 231.504 | 122.197 | 4.40 GB (18.57%) |
| 1 | 2048 | 2048 | 228.307 | 121.468 | 5.92 GB (24.98%) |
- Note: Fast generation, fast context processing
- GPU: NVIDIA GeForce RTX 3090
- Version: GEMM
- Command:
python examples/benchmark.py --model_path casperhansen/mpt-7b-8k-chat-awq
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|---|---|---|---|---|---|
| 1 | 32 | 32 | 557.714 | 118.567 | 3.65 GB (15.42%) |
| 1 | 64 | 64 | 2752.9 | 120.772 | 3.67 GB (15.48%) |
| 1 | 128 | 128 | 2982.67 | 119.52 | 3.70 GB (15.61%) |
| 1 | 256 | 256 | 3009.16 | 116.911 | 3.76 GB (15.88%) |
| 1 | 512 | 512 | 2901.91 | 111.607 | 3.95 GB (16.68%) |
| 1 | 1024 | 1024 | 2718.68 | 102.623 | 4.40 GB (18.57%) |
| 1 | 2048 | 2048 | 2363.61 | 101.368 | 5.92 GB (24.98%) |
- Note: Fast generation, fast context processing
- GPU: NVIDIA GeForce RTX 3090
- Command:
python examples/benchmark.py --model_path casperhansen/falcon-7b-awq --quant_file awq_model_w4_g64.pt - Version: GEMM
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|---|---|---|---|---|---|
| 1 | 32 | 32 | 466.826 | 95.1413 | 4.47 GB (18.88%) |
| 1 | 64 | 64 | 1920.61 | 94.5963 | 4.48 GB (18.92%) |
| 1 | 128 | 128 | 2406.1 | 94.793 | 4.48 GB (18.92%) |
| 1 | 256 | 256 | 2521.08 | 94.1144 | 4.48 GB (18.92%) |
| 1 | 512 | 512 | 2478.28 | 93.4123 | 4.48 GB (18.92%) |
| 1 | 1024 | 1024 | 2256.22 | 94.0237 | 4.69 GB (19.78%) |
| 1 | 2048 | 2048 | 1831.71 | 94.2032 | 6.83 GB (28.83%) |
If you find AWQ useful or relevant to your research, you can cite their paper:
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}