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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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tags: |
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- LLM |
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- tensorRT |
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- ChatGLM |
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--- |
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## Model Card for lyraChatGLM |
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lyraChatGLM is currently the **fastest ChatGLM-6B** available. To the best of our knowledge, it is the **first accelerated version of ChatGLM-6B**. |
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The inference speed of lyraChatGLM has achieved **10x** acceleration upon the ealry original version. We are still working hard to further improve the performance. |
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Among its main features are: |
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- weights: original ChatGLM-6B weights released by THUDM. |
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- device: lyraChatGLM is mainly based on TensorRT compiled for SM=80 (A100, for example). |
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- batch_size: compiled with dynamic batch size, max batch_size = 8 |
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## Speed |
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### test environment |
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- device: Nvidia A100 40G |
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- batch size: 8 |
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**Since early chatGLM version didn't suport batch inference, `original` in below table was measured on batch_size=1** |
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**According to [this discussion](https://huggingface.co/TMElyralab/lyraChatGLM/discussions/6), this bug has been fixed and the speed on batch_size=8 reachs up to 137 tokens/s. We will evaluate and update the latest performance.** |
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|version|speed| |
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|:-:|:-:| |
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|original|30 tokens/s| |
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|lyraChatGLM|310 tokens/s| |
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## Model Sources |
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- **Repository:** https://huggingface.co/THUDM/chatglm-6b |
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## Try Demo in 2 fast steps |
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``` bash |
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#step 1 |
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git clone https://huggingface.co/TMElyralab/lyraChatGLM |
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cd lyraChatGLM |
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#step 2 |
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docker run --gpus=1 --rm --net=host -v ${PWD}:/workdir yibolu96/lyra-chatglm-env:0.0.1 python3 /workdir/demo.py |
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``` |
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## Uses |
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```python |
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from transformers import AutoTokenizer |
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from lyraChatGLM import GLM6B, FasterChatGLM |
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import os |
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current_workdir = os.path.dirname(__file__) |
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MAX_OUT_LEN = 100 |
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chatglm6b_dir = os.path.join(current_workdir, "models") |
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tokenizer = AutoTokenizer.from_pretrained(chatglm6b_dir, trust_remote_code=True) |
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input_str = ["为什么我们需要对深度学习模型加速？", ] |
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inputs = tokenizer(input_str, return_tensors="pt", padding=True) |
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input_ids = inputs.input_ids.to('cuda:0') |
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plan_path = os.path.join(current_workdir, "models/glm6b-bs8.ftm") |
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# kernel for chat model. |
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kernel = GLM6B(plan_path=plan_path, |
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batch_size=1, |
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num_beams=1, |
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use_cache=True, |
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num_heads=32, |
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emb_size_per_heads=128, |
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decoder_layers=28, |
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vocab_size=150528, |
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max_seq_len=MAX_OUT_LEN) |
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chat = FasterChatGLM(model_dir=chatglm6b_dir, kernel=kernel).half().cuda() |
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# generate |
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sample_output = chat.generate(inputs=input_ids, max_length=MAX_OUT_LEN) |
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# de-tokenize model output to text |
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res = tokenizer.decode(sample_output[0], skip_special_tokens=True) |
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print(res) |
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``` |
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## Demo output |
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### input |
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为什么我们需要对深度学习模型加速? 。 |
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### output |
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为什么我们需要对深度学习模型加速? 深度学习模型的训练需要大量计算资源,特别是在训练模型时,需要大量的内存、GPU(图形处理器)和其他计算资源。因此,训练深度学习模型需要一定的时间,并且如果模型不能快速训练,则可能会导致训练进度缓慢或无法训练。 |
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以下是一些原因我们需要对深度学习模型加速: |
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1. 训练深度神经网络需要大量的计算资源,特别是在训练深度神经网络时,需要更多的计算资源,因此需要更快的训练速度。 |
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### TODO： |
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We plan to implement a FasterTransformer version to publish a much faster release. Stay tuned! |
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## Citation |
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``` bibtex |
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@Misc{lyraChatGLM2023, |
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author = {Kangjian Wu, Zhengtao Wang, Yibo Lu, Bin Wu}, |
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title = {lyraChatGLM: Accelerating ChatGLM by 10x+}, |
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howpublished = {\url{https://huggingface.co/TMElyralab/lyraChatGLM}}, |
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year = {2023} |
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} |
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``` |
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## Report bug |
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- start a discussion to report any bugs!--> https://huggingface.co/TMElyralab/lyraChatGLM/discussions |
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- report bug with a `[bug]` mark in the title. |
