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# AWQ

<Tip>

Try AWQ quantization with this [notebook](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY)!

</Tip>

[Activation-aware Weight Quantization (AWQ)](https://hf.co/papers/2306.00978) doesn't quantize all the weights in a model, and instead, it preserves a small percentage of weights that are important for LLM performance. This significantly reduces quantization loss such that you can run models in 4-bit precision without experiencing any 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.

Make sure you have autoawq installed:

```bash
pip install autoawq
```

AWQ-quantized models can be identified by checking the `quantization_config` attribute in the model's [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"
  }
}
```

A quantized model is loaded with the [`~PreTrainedModel.from_pretrained`] method. If you loaded your model on the CPU, make sure to move it to a GPU device first. Use the `device_map` parameter to specify where to place the model:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TheBloke/zephyr-7B-alpha-AWQ"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0")
```

Loading an AWQ-quantized model automatically sets other weights to fp16 by default for performance reasons. If you want to load these other weights in a different format, use the `torch_dtype` parameter:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TheBloke/zephyr-7B-alpha-AWQ"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
```

AWQ quantization can also be combined with [FlashAttention-2](../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 offers improved accuracy and performance and it is 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.

<Tip warning={true}>

Fused modules cannot be combined with other optimization techniques such as FlashAttention-2.

</Tip>

<hfoptions id="fuse">
<hfoption id="supported architectures">

To enable fused modules for supported architectures, create an [`AwqConfig`] and set the parameters `fuse_max_seq_len` and `do_fuse=True`. The `fuse_max_seq_len` parameter is the total sequence length and it should include the context length and the expected generation length. You can set it to a larger value to be safe.

For example, to fuse 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

model_id = "TheBloke/Mistral-7B-OpenOrca-AWQ"

quantization_config = AwqConfig(
    bits=4,
    fuse_max_seq_len=512,
    do_fuse=True,
)

model = AutoModelForCausalLM.from_pretrained(model_id, 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.

<figcaption class="text-center text-gray-500 text-lg">Unfused module</figcaption>

|   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%) |

<figcaption class="text-center text-gray-500 text-lg">Fused module</figcaption>

|   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.

<div class="flex gap-4">
  <div>
    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_forward_memory_plot.png" alt="generate throughput per batch size" />
    <figcaption class="mt-2 text-center text-sm text-gray-500">forward peak memory/batch size</figcaption>
  </div>
  <div>
    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_generate_throughput_plot.png" alt="forward latency per batch size" />
    <figcaption class="mt-2 text-center text-sm text-gray-500">generate throughput/batch size</figcaption>
  </div>
</div>

</hfoption>
<hfoption id="unsupported architectures">

For architectures that don't support fused modules yet, you need to create a custom fusing mapping to define which modules need to be fused with the `modules_to_fuse` parameter. For example, to fuse 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

model_id = "TheBloke/Yi-34B-AWQ"

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(model_id, quantization_config=quantization_config).to(0)
```

The parameter `modules_to_fuse` should include:

- `"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). If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA), otherwise GQA is used.
- `"hidden_size"`: The dimension of the hidden representations.

</hfoption>
</hfoptions>



## ExLlama-v2 support

Recent versions of `autoawq` supports ExLlama-v2 kernels for faster prefill and decoding. To get started, first install the latest version of `autoawq` by running:

```bash
pip install git+https://github.com/casper-hansen/AutoAWQ.git
```

Get started by passing an `AwqConfig()` with `version="exllama"`.

```python
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",
)

input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cuda")
output = model(input_ids)
print(output.logits)

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-AWQ")
input_ids = tokenizer.encode("How to make a cake", return_tensors="pt").to(model.device)
output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=50256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

<Tip warning={true}>

Note this feature is supported on AMD GPUs.

</Tip>


## CPU support

Recent versions of `autoawq` supports CPU with ipex op optimizations. To get started, first install the latest version of `autoawq` by running:

```bash
pip install intel-extension-for-pytorch
pip install git+https://github.com/casper-hansen/AutoAWQ.git
```

Get started by passing an `AwqConfig()` with `version="ipex"`.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig

quantization_config = AwqConfig(version="ipex")

model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ",
    quantization_config=quantization_config,
    device_map="cpu",
)

input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cpu")
output = model(input_ids)
print(output.logits)

tokenizer = AutoTokenizer.from_pretrained("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ")
input_ids = tokenizer.encode("How to make a cake", return_tensors="pt")
pad_token_id = tokenizer.eos_token_id
output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=pad_token_id)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

<Tip warning={true}>

Note this feature is supported on Intel CPUs.

</Tip>