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https://github.com/NolanoOrg/cformers
SoTA Transformers with C-backend for fast inference on your CPU.
https://github.com/NolanoOrg/cformers
Last synced: about 1 month ago
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SoTA Transformers with C-backend for fast inference on your CPU.
- Host: GitHub
- URL: https://github.com/NolanoOrg/cformers
- Owner: NolanoOrg
- License: mit
- Created: 2023-03-19T15:00:39.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-12-09T18:07:48.000Z (about 1 year ago)
- Last Synced: 2024-08-03T01:12:56.056Z (4 months ago)
- Language: C
- Homepage:
- Size: 169 KB
- Stars: 312
- Watchers: 22
- Forks: 29
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-transformer-nlp - Cformers - SoTA Transformers with C-backend for fast inference on your CPU. (Transformer Implementations By Communities / Other)
README
# Cformers
SoTA Transformers with C-backend for fast inference on your CPU.
## Introduction
We identify three pillers to enable fast inference of SoTA AI models on your CPU:
1. Fast C/C++ LLM inference kernels for CPU.
2. Machine Learning Research & Exploration front - Compression through quantization, sparsification, training on more data, collecting data and training instruction & chat models.
3. Easy to use API for fast AI inference in dynamically typed language like Python.
This project aims to address the third using LLaMa.cpp and GGML.
## Guiding Principles
- Inference Speed! Focus on inference, not training.
- Precompressed models.
- Minimal setup required - soon `pip install cformers` should be good to get started.
- Easily switch between models and quantization types.
- Support variety of prompts.
And most importantly:
- You, the users, get to decide which direction we take this project.
## Usage
Setup
```bash
pip install transformers wget
git clone https://github.com/nolanoOrg/cformers.git
cd cformers/cformers/cpp && make && cd ..
```
Usage:
```python
from interface import AutoInference as AI
ai = AI('EleutherAI/gpt-j-6B')
x = ai.generate('def parse_html(html_doc):', num_tokens_to_generate=500)
print(x['token_str'])
```
OR
```python
from interface import AutoInference as AI
ai = AI('OpenAssistant/oasst-sft-1-pythia-12b')
x = ai.generate("<|prompter|>What's the Earth total population<|endoftext|><|assistant|>", num_tokens_to_generate=100)
print(x['token_str'])
```
OR
```bash
python chat.py
```
chat.py accepts the following parameteres:
- ``` -t 100 ``` Number of tokens to generate
- ```-p Tell me a joke``` for a single prompt interaction
- ```-m pythia``` to load one of the available (bloom, pythia or gptj )
We are working on adding support for `pip install cformers.`
Following Architectures are supported:
- GPT-J
- BLOOM
- GPT-NeoX/Pythia/Open-Assistant/Open-Chat-Kit
- CodeGen
Currently following huggingface models are supported:
- EleutherAI/gpt-j-6B
- bigscience/bloom-560m
- bigscience/bloom-1b1
- bigscience/bloom-1b7
- bigscience/bloom-3b
- BigScience/bloom-7b1
- OpenAssistant/oasst-sft-1-pythia-12b
- togethercomputer/GPT-NeoXT-Chat-Base-20B (thanks to @[HCBlackFox](https://github.com/HCBlackFox))
- Salesforce/codegen-350M-mono
- Salesforce/codegen-2B-mono
- Salesforce/codegen-6B-mono
- Salesforce/codegen-16B-mono
- gpt2 (thanks to @[kamalojasv181](https://github.com/kamalojasv181))
We need to quantize and upload remaining models based on the supported architectures on huggingface. We would appreciate your help in this regard.
## Coming Soon:
Features:
- [X] Switch between models
- [ ] Chat-mode (interactive mode)
- [ ] Various tools to support Prompt-engineering, chaining, saving and sharing.
Code-base restructuring:
- [ ] Switch to Pybind11 rather than Subprocess - expected speedup: 3-4x
- [ ] Restructure the codebase to reuse.
- [ ] Somehow create llama.cpp as a git-submodule/dependency.
## Models
For now, we are focussing on AutoRegressive-style generative models.
- [x] GPT-J
- [x] BLOOM
- [x] GPT-NeoX/Pythia/Open-Assistant/Open-Chat-Kit (Architecture supported, need to quantize and upload models.)
- [X] CodeGen
- [ ] LLaMa & Alpaca
- [ ] OPT & Galactica
- [ ] T5
- [ ] RWKV
- [x] GPT-2
- And more (including multimodal)...
## Quantization types:
- [x] Int4 with fixed zero-offset
- [ ] Int4 with variable zero-offset
- [ ] GPTQ-Int4 with fixed zero-offset
- [ ] GPTQ-Int4 with variable zero-offset
- Int3 quantization, proxy quantization and binning.
## Contributions
We encourage contributions from the community.
### Providing feedback:
- Let us know what features you want, what models you want to use.
- Reporting bugs, raising issues and sending Pull Requests.
### Easy first issues:
Following are some easy first issues ways in which you can help improve CTransformers:
- Pick an existing HF model, quantize it, upload to HF and add it to the mapping in `ctransformers/map_model_to_url.py`
- Add support for new models.
- Add support for new quantization types.
### Issues on Machine Learning side (some are exploratory):
- Try out GPTQ on these models and upload the resulting models to HF.
- Benchmark the quantized models. [#2](https://github.com/NolanoOrg/cformers/issues/2)
- Can we merge Query and Key Matrices for GPT-J/LLaMa? [#3](https://github.com/NolanoOrg/cformers/issues/3)
- Explore CALM (Confident Adaptive Language Modelling) with 4-bit precision models [#4](https://github.com/NolanoOrg/cformers/issues/4)
- Saving Keys and Values in memory at lower precision (refer FlexGen) [#6](https://github.com/NolanoOrg/cformers/issues/6)
- Try out other quantization techniques like proxy quantization, etc.
- Explore SparseGPT [#5](https://github.com/NolanoOrg/cformers/issues/5)
- Explore Quantization of Multimodal Models
### Non-Python side
If you are allergic to Python, you can:
- Port support for fast loading here: https://github.com/ggerganov/llama.cpp/issues/91#issuecomment-1473271638
You can also contribute to LLaMa.cpp and we will port those niceties here.
- Add support for greater than 32 bin/group size int4 quantized weights with GGML/LLaMa.cpp (A potential pitfalls - the intermediate representation may not be losslessly grouppable to >32 bin size, only weight matrix may be grouppable to >32 bin size, etc.)
- Speed up quantized matrix multiplication in GGML/LLaMa.cpp
- Add Int3 and Int2 quantization support to GGML/LLaMa.cpp
- Add fast Ampere-sparse quantized matrix multiplication functions in GGML/LLaMa.cpp
## Misc. Notes
Our interface is still limited to generation. We are working to support other features:
- [ ] Allow stopping-generation midway
- [ ] Anti-prompt for conversation models
- [ ] Returning embeddings and/or logits.
- [ ] [Dev] Switch to pybindings over the C++ kernels and calling them from Python.
- [ ] [Dev] Re-use the code in main.cpp
We would love to hear from you various ways in which we can speed up and improve the interface.
## License
MIT License
## Communication and Support
Discord: https://discord.gg/HGujTPQtR6