tilearn-llm 0.6.0

TILEARN for LLM

Tilearn.llm使用说明

1. CUDA Kernel（以LLAMA为例）

支持显卡：Ampere, Ada, or Hopper GPUs (e.g., A100, A800, H100, H800)

新版本

新版本Dependencies: pytorch >= 2.0.0

该版本完全兼容huggingface接口，不需要额外的转模型操作

LLAMA1/LLAMA2 A800 16GPU seq=1024相比deepspeed zero2训练加速约20%

cuda kernel使用方法-启动脚本修改如下

### TIACC CUDA Kernel
### Open: TIACC_TRAINING_CUDA_KERNEL=1
### Close: TIACC_TRAINING_CUDA_KERNEL=0
export TIACC_TRAINING_CUDA_KERNEL=1

cuda kernel使用方法-代码修改如下

### TIACC
TIACC_TRAINING_CUDA_KERNEL = int(os.getenv('TIACC_TRAINING_CUDA_KERNEL', '0'))
if TIACC_TRAINING_CUDA_KERNEL == 1:
    from tilearn.llm.transformers import LlamaForCausalLM

### 模型接口与标准huggingface一致
model = LlamaForCausalLM.from_pretrained(...)

### TIACC
TIACC_TRAINING_CUDA_KERNEL = int(os.getenv('TIACC_TRAINING_CUDA_KERNEL', '0'))
if TIACC_TRAINING_CUDA_KERNEL == 1:
    from tilearn.llm.transformers import AutoModelForCausalLM

### 模型接口与标准huggingface一致
model = AutoModelForCausalLM.from_pretrained(...)

2. Static Zero

适用场景：在deepspeed zero1、zero2、zero3、offload、int8等不同优化状态间切换

启动脚本修改如下

### TIACC STATIC ZERO
### Open: TIACC_TRAINING_CUDA_KERNEL='O2' 
### support 'O2' / 'O2.5' / 'O3' / 'O3.5' / 'O3_Q8'(doing)
### Close: TIACC_TRAINING_CUDA_KERNEL='None'
export TIACC_TRAINING_STATIC_ZERO='None' #'O2'

代码修改如下

from transformers import HfArgumentParser

TIACC_TRAINING_STATIC_ZERO = os.getenv('TIACC_TRAINING_STATIC_ZERO', 'None')
if TIACC_TRAINING_STATIC_ZERO != 'None':
    from tilearn.llm.transformers import TrainingArguments
	
### 接口与标准huggingface一致
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))