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qwen-fast

针对gpt-fast项目做了一点修改，支持qwen的推理，原始的完整版README放在下面。需要注意一下几点

  1. 因为硬件限制(RTX 2080)，我仅针对qwen_1.8b做了测试，在使用torch.compile前后，性能提升明显（50 token/s vs 100token/s）是原来的两倍。考虑到千问系列模型的架构是一样的，只需要修改模型的信息，就可以运行。
  2. 为了尽可能和原始代码保存一致，有一些地方的代码写的不是很优雅，特别是模型转换的脚本。
  3. 我没有测试量化方法，理论上应该不会存在问题。
  4. 请使用base模型进行测试。chat版本的模型经过了固定格式的微调，在使用此原始代码进行测试时生成的格式是有问题的，因此不如base模型来得直观。
  5. 由于tokenizer不同，我还是通过transformer来读取，因此也要安装这个库，不过为了达到原文的教学目的，也不是很要紧。

gpt-fast

Simple and efficient pytorch-native transformer text generation.

Featuring:

  1. Very low latency
  2. <1000 lines of python
  3. No dependencies other than PyTorch and sentencepiece
  4. int8/int4 quantization
  5. Speculative decoding
  6. Tensor parallelism
  7. Supports Nvidia and AMD GPUs

This is NOT intended to be a "framework" or "library" - it is intended to show off what kind of performance you can get with native PyTorch :) Please copy-paste and fork as you desire.

For an in-depth walkthrough of what's in this codebase, see this blog post.

Installation

Download PyTorch nightly Install sentencepiece and huggingface_hub

pip install sentencepiece huggingface_hub

To download llama models, go to https://huggingface.co/meta-llama/Llama-2-7b and go through steps to obtain access. Then login with huggingface-cli login

Downloading Weights

Models tested/supported

openlm-research/open_llama_7b
meta-llama/Llama-2-7b-chat-hf
meta-llama/Llama-2-13b-chat-hf
meta-llama/Llama-2-70b-chat-hf
codellama/CodeLlama-7b-Python-hf
codellama/CodeLlama-34b-Python-hf

For example, to convert Llama-2-7b-chat-hf

export MODEL_REPO=meta-llama/Llama-2-7b-chat-hf
./scripts/prepare.sh $MODEL_REPO

Benchmarks

Benchmarks run on an A100-80GB, power limited to 330W.

Model Technique Tokens/Second Memory Bandwidth (GB/s)
Llama-2-7B Base 104.9 1397.31
8-bit 155.58 1069.20
4-bit (G=32) 196.80 862.69
Llama-2-70B Base OOM
8-bit 19.13 1322.58
4-bit (G=32) 25.25 1097.66

Speculative Sampling

Verifier: Llama-70B (int4), Draft: Llama-7B (int4): 48.4 tok/s

Tensor Parallelism

Model Number of GPUs Tokens/Second Memory Bandwidth (GB/s)
Llama-2-7B 1 104.9 1397.31
2 136.27 954.01
4 168.78 635.09
8 179.27 395.85
Llama-2-70B 1 OOM
2 20.53 1426.41
4 34.15 1204.62
8 47.25 858.28

AMD

Benchmarks run on one GCD of a MI-250x.

Model Technique Tokens/Second Memory Bandwidth (GB/s)
Llama-2-7B Base 76.33 1028.70
8-bit 101.86 700.06

Generate Text

Model definition in model.py, generation code in generate.py.

python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --prompt "Hello, my name is"

To squeeze out a little bit more performance, you can also compile the prefill with --compile_prefill. This will increase compilation times though.

Quantization

Int8 Weight-Only Quantization

To generate this version of the model

# Spits out model at checkpoints/$MODEL_REPO/model_int8.pth
python quantize.py --checkpoint_path checkpoints/$MODEL_REPO/model.pth --mode int8

To run with int8, just pass the int8 checkpoint to generate.py.

python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model_int8.pth

Int4 Weight-Only Quantization

To generate int4 version of model

# Spits out model at checkpoints/$MODEL_REPO/model_int4.g32.pth
python quantize.py --checkpoint_path checkpoints/$MODEL_REPO/model.pth --mode int4 --groupsize 32

To run with int4, just pass the int4 checkpoint to generate.py.

python generate.py --checkpoint_path checkpoints/$MODEL_REPO/model_int4.g32.pth --compile

Speculative Sampling

To generate with speculative sampling (DRAFT_MODEL_REPO should point to a smaller model compared with MODEL_REPO).

In this example, the "smaller" model is just the int8 quantized version of the model.

export DRAFT_MODEL_REPO=meta-llama/Llama-2-7b-chat-hf
python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --draft_checkpoint_path checkpoints/$DRAFT_MODEL_REPO/model_int8.pth

Note: Running on an A100 80GB, albeit power-limited to 330 watts. Empirically, seems like peak bandwidth is about 1700 GB/s.

Tensor Parallelism

torchrun --standalone --nproc_per_node=2 generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth

Experimental

Evaluation

We use the EleutherAI evaluation harness to evaluate our model accuracy. To evaluate the accuracy, make sure the evaluation harness is installed and pass your model checkpoint and desired tasks to eval.py.

python eval.py --checkpoint_path checkpoints/$MODEL_REPO/model.pth --compile --tasks hellaswag winogrande

Note: Generative tasks are currently not supported for gpt-fast

Installation Instructions for the evaluation harness: https://github.com/EleutherAI/lm-evaluation-harness/tree/master#install

GPTQ

We have a pure pytorch implementation of GPTQ that utilizes torch._dynamo.export to access the model structure. You can generate a GPTQ quantized version of int4 quantization by using the same command to quantize it but adding 'gptq' to the quantization mode i.e.

# Spits out model at checkpoints/$MODEL_REPO/model_int4-gptq.g32.pth
python quantize.py --mode int4-gptq --calibration_tasks wikitext --calibration_seq_length 2048

You can then eval or generate text with this model in the same way as above.

License

gpt-fast is released under the BSD 3 license.

Acknowledgements

Thanks to:

  • Lightning AI for supporting pytorch and work in flash attention, int8 quantization, and LoRA fine-tuning.
  • GGML for driving forward fast, on device inference of LLMs
  • Karpathy for spearheading simple, interpretable and fast LLM implementations
  • MLC-LLM for pushing 4-bit quantization performance on heterogenous hardware