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TorchAO
TorchAO is an architecture optimization library for PyTorch, it provides high performance dtypes, optimization techniques and kernels for inference and training, featuring composability with native PyTorch features like torch.compile, FSDP etc.. Some benchmark numbers can be found here
Before you begin, make sure the following libraries are installed with their latest version:
pip install --upgrade torch torchao
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
model_name = "meta-llama/Meta-Llama-3-8B"
# We support int4_weight_only, int8_weight_only and int8_dynamic_activation_int8_weight
# More examples and documentations for arguments can be found in https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
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")
# compile the quantizd model to get speedup
import torchao
torchao.quantization.utils.recommended_inductor_config_setter()
quantized_model = torch.compile(quantized_model, mode="max-autotune")
output = quantized_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))torchao quantization is implemented with tensor subclasses, currently it does not work with huggingface serialization, both the safetensor option and non-safetensor option, we’ll update here with instructions when it’s working.
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