Running alpacoom model on MPS (Apple Silicon) using HuggingFace Transformers and Peft

alpacoom-mps.py

import os
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import sys

os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"


peft_model_id = "mrm8488/Alpacoom"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False).to("mps")
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1")

model = PeftModel.from_pretrained(model, peft_model_id).to("mps")
model.eval()

# Based on the inference code by `tloen/alpaca-lora`
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 generate(
        instruction,
        input=None,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs,
):
    prompt = generate_prompt(instruction, input)
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("mps")
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **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=256,
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Response:")[1].strip().split("Below")[0]

instruction = sys.argv[1]

print("Instruction:", instruction)
print("Response:", generate(instruction, sys.argv[2]))