Description:

autoawq 0.2.6

AutoAWQ

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AutoAWQ is an easy-to-use package for 4-bit quantized models. AutoAWQ speeds up models by 3x and reduces 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 🔥

[2024/06] CPU inference support (x86) - thanks Intel. Cohere and Phi3 support.
[2024/04] StableLM and StarCoder2 support.
[2024/03] Gemma support.
[2024/02] PEFT-compatible training in FP16.
[2024/02] AMD ROCm support through ExLlamaV2 kernels.
[2024/01] Export to GGUF, ExLlamaV2 kernels, 60% faster context processing.
[2023/12] Mixtral, LLaVa, QWen, Baichuan model support.
[2023/11] AutoAWQ inference has been integrated into 🤗 transformers. Now includes CUDA 12.1 wheels.
[2023/10] Mistral (Fused Modules), Bigcode, Turing support, Memory Bug Fix (Saves 2GB VRAM)
[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

Install
Prerequisites

NVIDIA:

Your NVIDIA GPU(s) must be of Compute Capability 7.5. Turing and later architectures are supported.
Your CUDA version must be CUDA 11.8 or later.


AMD:

Your ROCm version must be ROCm 5.6 or later.



Install from PyPi
To install the newest AutoAWQ from PyPi, you need CUDA 12.1 installed.
pip install autoawq

Build from source
For CUDA 11.8, ROCm 5.6, and ROCm 5.7, you can install wheels from the release page:
pip install autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl

Or from the main branch directly:
pip install autoawq@https://github.com/casper-hansen/AutoAWQ.git

Or by cloning the repository and installing from source:
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip install -e .

All three methods will install the latest and correct kernels for your system from AutoAWQ_Kernels.
If your system is not supported (i.e. not on the release page), you can build the kernels yourself by following the instructions in AutoAWQ_Kernels and then install AutoAWQ from source.
Usage
Under examples, you can find examples of how to quantize, run inference, and benchmark AutoAWQ models.
INT4 GEMM vs INT4 GEMV vs FP16
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): 20% faster than GEMM, only batch size 1 (not good for large context).
GEMM (quantized): Much faster than FP16 at batch sizes below 8 (good with large contexts).
FP16 (non-quantized): Recommended for highest throughput: vLLM.

Compute-bound vs Memory-bound
At small batch sizes with small 7B models, we are memory-bound. This means we are bound by the bandwidth our GPU has to push around the weights in memory, and this is essentially what limits how many tokens per second we can generate. Being memory-bound is what makes quantized models faster because your weights are 3x smaller and can therefore be pushed around in memory much faster. This is different from being compute-bound where the main time spent during generation is doing matrix multiplication.
In the scenario of being compute-bound, which happens at higher batch sizes, you will not gain a speed-up using a W4A16 quantized model because the overhead of dequantization will slow down the overall generation. This happens because AWQ quantized models only store the weights in INT4 but perform FP16 operations during inference, so we are essentially converting INT4 -> FP16 during inference.
Fused modules
Fused modules are a large part of the speedup you get from AutoAWQ. The idea is to combine multiple layers into a single operation, thus becoming more efficient. Fused modules represent a set of custom modules that work separately from Huggingface models. They are compatible with model.generate() and other Huggingface methods, which comes with some inflexibility in how you can use your model if you activate fused modules:

Fused modules are activated when you use fuse_layers=True.
A custom cache is implemented. It preallocates based on batch size and sequence length.

You cannot change the sequence length after you have created your model.
Reference: AutoAWQForCausalLM.from_quantized(max_seq_len=seq_len, batch_size=batch_size)


The main accelerator in the fused modules comes from FasterTransformer, which is only compatible with Linux.
The past_key_values from model.generate() are only dummy values, so they cannot be used after generation.

Examples
More examples can be found in the examples directory.

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, "version": "GEMM" }

# 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 = "TheBloke/zephyr-7B-beta-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>"""

prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"

tokens = tokenizer(
prompt_template.format(prompt=prompt),
return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_seq_len=512
)


Benchmarks
These benchmarks showcase the speed and memory usage of processing context (prefill) and generating tokens (decoding). The results include speed at various batch sizes and different versions of AWQ kernels. We have aimed to test models fairly using the same benchmarking tool that you can use to reproduce the results. Do note that speed may vary not only between GPUs but also between CPUs. What matters most is a GPU with high memory bandwidth and a CPU with high single core clock speed.

Tested with AutoAWQ version 0.1.6
GPU: RTX 4090 (AMD Ryzen 9 7950X)
Command: python examples/benchmark.py --model_path <hf_model> --batch_size 1
🟢 for GEMV, 🔵 for GEMM, 🔴 for avoid using




Model Name
Size
Version
Batch Size
Prefill Length
Decode Length
Prefill tokens/s
Decode tokens/s
Memory (VRAM)




Vicuna
7B
🟢GEMV
1
64
64
639.65
198.848
4.50 GB (19.05%)


Vicuna
7B
🟢GEMV
1
2048
2048
1123.63
133.191
6.15 GB (26.02%)


...
...
...
...
...
...
...
...
...


Mistral
7B
🔵GEMM
1
64
64
1093.35
156.317
4.35 GB (18.41%)


Mistral
7B
🔵GEMM
1
2048
2048
3897.02
114.355
5.55 GB (23.48%)


Mistral
7B
🔵GEMM
8
64
64
4199.18
1185.25
4.35 GB (18.41%)


Mistral
7B
🔵GEMM
8
2048
2048
3661.46
829.754
16.82 GB (71.12%)


...
...
...
...
...
...
...
...
...


Mistral
7B
🟢GEMV
1
64
64
531.99
188.29
4.28 GB (18.08%)


Mistral
7B
🟢GEMV
1
2048
2048
903.83
130.66
5.55 GB (23.48%)


Mistral
7B
🔴GEMV
8
64
64
897.87
486.46
4.33 GB (18.31%)


Mistral
7B
🔴GEMV
8
2048
2048
884.22
411.893
16.82 GB (71.12%)


...
...
...
...
...
...
...
...
...


TinyLlama
1B
🟢GEMV
1
64
64
1088.63
548.993
0.86 GB (3.62%)


TinyLlama
1B
🟢GEMV
1
2048
2048
5178.98
431.468
2.10 GB (8.89%)


...
...
...
...
...
...
...
...
...


Llama 2
13B
🔵GEMM
1
64
64
820.34
96.74
8.47 GB (35.83%)


Llama 2
13B
🔵GEMM
1
2048
2048
2279.41
73.8213
10.28 GB (43.46%)


Llama 2
13B
🔵GEMM
3
64
64
1593.88
286.249
8.57 GB (36.24%)


Llama 2
13B
🔵GEMM
3
2048
2048
2226.7
189.573
16.90 GB (71.47%)


...
...
...
...
...
...
...
...
...


MPT
7B
🔵GEMM
1
64
64
1079.06
161.344
3.67 GB (15.51%)


MPT
7B
🔵GEMM
1
2048
2048
4069.78
114.982
5.87 GB (24.82%)


...
...
...
...
...
...
...
...
...


Falcon
7B
🔵GEMM
1
64
64
1139.93
133.585
4.47 GB (18.92%)


Falcon
7B
🔵GEMM
1
2048
2048
2850.97
115.73
6.83 GB (28.88%)


...
...
...
...
...
...
...
...
...


CodeLlama
34B
🔵GEMM
1
64
64
681.74
41.01
19.05 GB (80.57%)


CodeLlama
34B
🔵GEMM
1
2048
2048
1072.36
35.8316
20.26 GB (85.68%)


...
...
...
...
...
...
...
...
...


DeepSeek
33B
🔵GEMM
1
64
64
1160.18
40.29
18.92 GB (80.00%)


DeepSeek
33B
🔵GEMM
1
2048
2048
1012.1
34.0093
19.87 GB (84.02%)



Multi-GPU
GPU: 2x NVIDIA GeForce RTX 4090



Model
Size
Version
Batch Size
Prefill Length
Decode Length
Prefill tokens/s
Decode tokens/s
Memory (VRAM)




Mixtral
46.7B
🔵GEMM
1
32
32
149.742
93.406
25.28 GB (53.44%)


Mixtral
46.7B
🔵GEMM
1
64
64
1489.64
93.184
25.32 GB (53.53%)


Mixtral
46.7B
🔵GEMM
1
128
128
2082.95
92.9444
25.33 GB (53.55%)


Mixtral
46.7B
🔵GEMM
1
256
256
2428.59
91.5187
25.35 GB (53.59%)


Mixtral
46.7B
🔵GEMM
1
512
512
2633.11
89.1457
25.39 GB (53.67%)


Mixtral
46.7B
🔵GEMM
1
1024
1024
2598.95
84.6753
25.75 GB (54.44%)


Mixtral
46.7B
🔵GEMM
1
2048
2048
2446.15
77.0516
27.98 GB (59.15%)


Mixtral
46.7B
🔵GEMM
1
4096
4096
1985.78
77.5689
34.65 GB (73.26%)



CPU

CPU: INTEL(R) XEON(R) PLATINUM 8592+ with 8-channel 4800MT/s memory.
Command: python examples/benchmark.py --model_path <hf_model> --batch_size 1




Model
Size
Batch Size
Prefill Length
Decode Length
Prefill tokens/s
Decode tokens/s
Memory (RAM)




Mixtral
7B
1
64
64
389.24
16.01
5.59 GB (0.02%)


Mixtral
7B
1
2048
2048
1412
17.76
6.29 GB (0.03%)


Vicuna
7B
1
64
64
346
18.13
8.18 GB (0.03%)


Vicuna
7B
1
2048
2048
1023.4
18.18
8.80 GB (0.04%)


LLaMA2
13B
1
64
64
160.24
9.87
14.65 GB (0.06%)


LLaMA2
13B
1
2048
2048
592.35
9.93
16.87 GB (0.07%)


Mosaicml
7B
1
64
64
433.17
18.79
4.60 GB (0.02%)


Mosaicml
7B
1
2048
2048
404.25
19.91
4.75 GB (0.02%)


Falcon
7B
1
64
64
303.16
14.41
5.18 GB (0.02%)


Falcon
7B
1
2048
2048
634.57
15.55
5.80 GB (0.02%)


CodeLlama
34B
1
64
64
153.73
4.23
29.00 GB (0.12%)


CodeLlama
34B
1
2048
2048
274.25
4.38
35.21 GB (0.15%)


Deepseek-coder
33B
1
64
64
83.08
4.07
22.16 GB (0.09%)


Deepseek-coder
33B
1
2048
2048
296.04
4.33
37.05 GB



Reference
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}
}

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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