You are viewing main version, which requires installation from source. If you'd like
regular pip install, checkout the latest stable version (v4.42.0).
FBGEMM FP8
With FBGEMM FP8 quantization method, you can quantize your model in FP8 (W8A8):
- the weights will be quantized in 8bit (FP8) per channel
- the activation will be quantized in 8bit (FP8) per token
It relies on the FBGEMM library which provides efficient low-precision general matrix multiplication for small batch sizes and support for accuracy-loss minimizing techniques such as row-wise quantization and outlier-aware quantization.
You need a GPU with compute capability>=9 (e.g. H100)
Before you begin, make sure the following libraries are installed with their latest version:
pip install --upgrade accelerate fbgemm-gpu torch
If you are having issues with fbgemm-gpu and torch library, you might need to install the nighlty release. You can follow the instruction here
from transformers import FbgemmFp8Config, AutoModelForCausalLM, AutoTokenizer
model_name = "meta-llama/Meta-Llama-3-8B"
quantization_config = FbgemmFp8Config()
quantized_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
output = quantized_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))A quantized model can be saved via “saved_pretrained” and be reused again via the “from_pretrained”.
quant_path = "/path/to/save/quantized/model"
model.save_pretrained(quant_path)
model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")