base on 0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch with Llama(2) Architecture # 0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch with Llama(2) Architecture   - Paper Link: https://arxiv.org/pdf/2310.11453.pdf ## Prepare Dev env ```bash # Clone this repo git clone https://github.com/beomi/bitnet-transformers cd bitnet-transformers # Install requirements pip install -r clm_requirements.txt # Clone transformers repo git clone https://github.com/huggingface/transformers pip install -e transformers # Update Llama(2) model rm ./transformers/src/transformers/models/llama/modeling_llama.py ln -s $(pwd)/bitnet_llama/modeling_llama.py ./transformers/src/transformers/models/llama/modeling_llama.py ``` We'll overwrite `bitnet_llama/modeling_llama.py` into `transformers`. Since the file is linked, any changes made to the file will be reflected in the `transformers` repo. ## Train Wikitext-103  > You can track metrics via wandb ```bash ./train_wikitext.sh ``` ## GPU Mem Usage Comparison **Train Config** - Batch size: 1 - Gradient accumulation: 1 - Seq length: 2048 - Model: `LLamaForCausalLM` with `BitLinear` layer - Model size: 47,452,672 (47.5M) **Original LLAMA - 16bit** - Uses **250MB** GPU memory for Model weights **BitLLAMA - Mixed 16bit** - Uses **200MB** GPU memory for Model weights - Use bf16(or fp16) to store model weights - Use int8 to store `-1`/`1` 1-bit weights - Use more memory when training than original LLAMA: It saves 1-bit weight and 16bit weight together **BitLLAMA - 8bit** - Uses **100MB** GPU memory for Model weights - Use bf16(or fp16) on-the-fly when needed - Use 8bit to save 1-bit BitLinear weight & other weights **BitLLAMA - 1bit** - Use bf16(or fp16) on-the-fly when needed - Use 1bit to save 1-bit weight ```bash TBD ``` ## Todo - [x] Add `BitLinear` layer - [x] Add `LLamaForCausalLM` model with `BitLinear` layer - [x] Update `.save_pretrained` method (for 1-bit weight saving) - [x] Add sample code for LM training - [ ] Update `BitLinear` layer to use 1-bit weight - [ ] Use uint8 instead of bfloat16 - [ ] Use custom cuda kernel for 1-bit weight ", Assign "at most 3 tags" to the expected json: {"id":"8186","tags":[]} "only from the tags list I provide: []" returns me the "expected json"