| `torch.float16``) | |
| To load and run a model using Flash Attention 2, refer to the snippet below: | |
| thon | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| device = "cuda" # the device to load the model onto | |
| model = AutoModelForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder", torch_dtype=torch.float16, attn_implementation="flash_attention_2") | |
| tokenizer = AutoTokenizer.from_pretrained("bigcode/gpt_bigcode-santacoder") | |
| prompt = "def hello_world():" | |
| model_inputs = tokenizer([prompt], return_tensors="pt").to(device) | |
| model.to(device) | |
| generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False) | |
| tokenizer.batch_decode(generated_ids)[0] | |
| 'def hello_world():\n print("hello world")\n\nif name == "main":\n print("hello world")\n<|endoftext|>' | |
| Expected speedups | |
| Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using bigcode/starcoder checkpoint and the Flash Attention 2 version of the model using two different sequence lengths. |