# AWQ [Activation-aware Weight Quantization (AWQ)](https://hf.co/papers/2306.00978) preserves a small fraction of the weights that are important for LLM performance to compress a model to 4-bits with minimal performance degradation. There are several libraries for quantizing models with the AWQ algorithm, such as [llm-awq](https://github.com/mit-han-lab/llm-awq), [autoawq](https://github.com/casper-hansen/AutoAWQ) or [optimum-intel](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc). Transformers supports loading models quantized with the llm-awq and autoawq libraries. This guide will show you how to load models quantized with autoawq, but the process is similar for llm-awq quantized models. Run the command below to install autoawq ```bash pip install autoawq ``` > [!WARNING] > AutoAWQ downgrades Transformers to version 4.47.1. If you want to do inference with AutoAWQ, you may need to reinstall your Transformers' version after installing AutoAWQ. Identify an AWQ-quantized model by checking the `quant_method` key in the models [config.json](https://huggingface.co/TheBloke/zephyr-7B-alpha-AWQ/blob/main/config.json) file. ```json { "_name_or_path": "/workspace/process/huggingfaceh4_zephyr-7b-alpha/source", "architectures": [ "MistralForCausalLM" ], ... ... ... "quantization_config": { "quant_method": "awq", "zero_point": true, "group_size": 128, "bits": 4, "version": "gemm" } } ``` Load the AWQ-quantized model with [`~PreTrainedModel.from_pretrained`]. This automatically sets the other weights to fp16 by default for performance reasons. Use the `torch_dtype` parameter to load these other weights in a different format. If the model is loaded on the CPU, use the `device_map` parameter to move it to a GPU. ```py from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( "TheBloke/zephyr-7B-alpha-AWQ", torch_dtype=torch.float32, device_map="cuda:0" ) ``` Use `attn_implementation` to enable [FlashAttention2](../perf_infer_gpu_one#flashattention-2) to further accelerate inference. ```py from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "TheBloke/zephyr-7B-alpha-AWQ", attn_implementation="flash_attention_2", device_map="cuda:0" ) ``` ## Fused modules Fused modules offer improved accuracy and performance. They are supported out-of-the-box for AWQ modules for [Llama](https://huggingface.co/meta-llama) and [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) architectures, but you can also fuse AWQ modules for unsupported architectures. > [!WARNING] > Fused modules cannot be combined with other optimization techniques such as FlashAttention2. Create an [`AwqConfig`] and set the parameters `fuse_max_seq_len` and `do_fuse=True` to enable fused modules. The `fuse_max_seq_len` parameter is the total sequence length and it should include the context length and the expected generation length. Set it to a larger value to be safe. The example below fuses the AWQ modules of the [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) model. ```python import torch from transformers import AwqConfig, AutoModelForCausalLM quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, do_fuse=True, ) model = AutoModelForCausalLM.from_pretrained( "TheBloke/Mistral-7B-OpenOrca-AWQ", quantization_config=quantization_config ).to(0) ``` The [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) model was benchmarked with `batch_size=1` with and without fused modules.
Unfused module
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) | |-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------| | 1 | 32 | 32 | 60.0984 | 38.4537 | 4.50 GB (5.68%) | | 1 | 64 | 64 | 1333.67 | 31.6604 | 4.50 GB (5.68%) | | 1 | 128 | 128 | 2434.06 | 31.6272 | 4.50 GB (5.68%) | | 1 | 256 | 256 | 3072.26 | 38.1731 | 4.50 GB (5.68%) | | 1 | 512 | 512 | 3184.74 | 31.6819 | 4.59 GB (5.80%) | | 1 | 1024 | 1024 | 3148.18 | 36.8031 | 4.81 GB (6.07%) | | 1 | 2048 | 2048 | 2927.33 | 35.2676 | 5.73 GB (7.23%) |
Fused module
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) | |-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------| | 1 | 32 | 32 | 81.4899 | 80.2569 | 4.00 GB (5.05%) | | 1 | 64 | 64 | 1756.1 | 106.26 | 4.00 GB (5.05%) | | 1 | 128 | 128 | 2479.32 | 105.631 | 4.00 GB (5.06%) | | 1 | 256 | 256 | 1813.6 | 85.7485 | 4.01 GB (5.06%) | | 1 | 512 | 512 | 2848.9 | 97.701 | 4.11 GB (5.19%) | | 1 | 1024 | 1024 | 3044.35 | 87.7323 | 4.41 GB (5.57%) | | 1 | 2048 | 2048 | 2715.11 | 89.4709 | 5.57 GB (7.04%) | The speed and throughput of fused and unfused modules were also tested with the [optimum-benchmark](https://github.com/huggingface/optimum-benchmark) library.
generate throughput per batch size
forward peak memory/batch size
forward latency per batch size
generate throughput/batch size
 For architectures that don't support fused modules, create an [`AwqConfig`] and define a custom fusing mapping in `modules_to_fuse` to determine which modules need to be fused. The example below fuses the AWQ modules of the [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ) model. ```python import torch from transformers import AwqConfig, AutoModelForCausalLM quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, modules_to_fuse={ "attention": ["q_proj", "k_proj", "v_proj", "o_proj"], "layernorm": ["ln1", "ln2", "norm"], "mlp": ["gate_proj", "up_proj", "down_proj"], "use_alibi": False, "num_attention_heads": 56, "num_key_value_heads": 8, "hidden_size": 7168 } ) model = AutoModelForCausalLM.from_pretrained( "TheBloke/Yi-34B-AWQ", quantization_config=quantization_config ).to(0) ``` The parameter `modules_to_fuse` should include the following keys. - `"attention"`: The names of the attention layers to fuse in the following order: query, key, value and output projection layer. If you don't want to fuse these layers, pass an empty list. - `"layernorm"`: The names of all the LayerNorm layers you want to replace with a custom fused LayerNorm. If you don't want to fuse these layers, pass an empty list. - `"mlp"`: The names of the MLP layers you want to fuse into a single MLP layer in the order: (gate (dense, layer, post-attention) / up / down layers). - `"use_alibi"`: If your model uses ALiBi positional embedding. - `"num_attention_heads"`: The number of attention heads. - `"num_key_value_heads"`: The number of key value heads that should be used to implement Grouped Query Attention (GQA). | parameter value | attention | |---|---| | `num_key_value_heads=num_attention_heads` | Multi-Head Attention | | `num_key_value_heads=1` | Multi-Query Attention | | `num_key_value_heads=...` | Grouped Query Attention | - `"hidden_size"`: The dimension of the hidden representations. ## ExLlamaV2 [ExLlamaV2](https://github.com/turboderp/exllamav2) kernels support faster prefill and decoding. Run the command below to install the latest version of autoawq with ExLlamaV2 support. ```bash pip install git+https://github.com/casper-hansen/AutoAWQ.git ``` Set `version="exllama"` in [`AwqConfig`] to enable ExLlamaV2 kernels. > [!TIP] > ExLlamaV2 is supported on AMD GPUs. ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig quantization_config = AwqConfig(version="exllama") model = AutoModelForCausalLM.from_pretrained( "TheBloke/Mistral-7B-Instruct-v0.1-AWQ", quantization_config=quantization_config, device_map="auto", ) ``` ## CPU [Intel Extension for PyTorch (IPEX)](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/) is designed to enable performance optimizations on Intel hardware. Run the command below to install the latest version of autoawq with IPEX support. ```bash pip install intel-extension-for-pytorch # for IPEX-GPU refer to https://intel.github.io/intel-extension-for-pytorch/xpu/2.5.10+xpu/ pip install git+https://github.com/casper-hansen/AutoAWQ.git ``` Set `version="ipex"` in [`AwqConfig`] to enable ExLlamaV2 kernels. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig device = "cpu" # set to "xpu" for Intel GPU quantization_config = AwqConfig(version="ipex") model = AutoModelForCausalLM.from_pretrained( "TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", quantization_config=quantization_config, device_map=device, ) ``` ## Resources Run the AWQ demo [notebook](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY#scrollTo=Wwsg6nCwoThm) for more examples of how to quantize a model, push a quantized model to the Hub, and more.