PEFT Multi LoRA Inference

!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git@main
!pip install huggingface_hub
!pip install bitsandbytes
!pip install SentencePiece

import os
import torch

os.environ["CUDA_VISIBLE_DEVICES"] = "0" # force using CUDA device 0
os.environ["ZE_AFFINITY_MASK"] = "0"  # force using Intel XPU device 0

from huggingface_hub import notebook_login


notebook_login()

from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig, BitsAndBytesConfig

model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map="auto", use_auth_token=True)

%%time
model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b", adapter_name="eng_alpaca")

%%time
model.load_adapter("22h/cabrita-lora-v0-1", adapter_name="portuguese_alpaca")

model

device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"

model.to(device)

def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""


def evaluate(
    instruction,
    input=None,
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=256,
    **kwargs,
):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to(device)
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        no_repeat_ngram_size=3,
        **kwargs,
    )

    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Response:")[1].strip()

%%time
model.set_adapter("eng_alpaca")

instruction = "Tell me about alpacas."

print(evaluate(instruction))

%%time
model.set_adapter("portuguese_alpaca")

instruction = "Invente uma desculpa criativa pra dizer que não preciso ir à festa."

print(evaluate(instruction))

with model.disable_adapter():
    instruction = "Invente uma desculpa criativa pra dizer que não preciso ir à festa."

    print(evaluate(instruction))