home-llm/evaluate.py

#!/usr/bin/env python3

import argparse, os, re, json
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from tqdm import tqdm

CTX_SIZE = 2048

def tokenize(tokenizer, prompt):
    return tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=CTX_SIZE)

def generate(model, tokenizer, prompt):
    inputs = tokenize(tokenizer, prompt)
    with torch.no_grad():
        outputs = model.generate(**inputs)
    text = tokenizer.batch_decode(outputs)
    return text

def main():
    parser = argparse.ArgumentParser(description="Evaluate the function calling for a model")
    parser.add_argument("model")
    parser.add_argument("--dataset_file", default="./data/home_assistant_test.json")
    parser.add_argument("--split", default="<|im_start|>assistant")
    parser.add_argument("--batch-size", default=8)

    args = parser.parse_args()
    model_folder = f"./models/{args.model}"
    split = args.split

    dataset = load_dataset("json", data_files={ "train": args.dataset_file })["train"]

    # filter out examples that are status requests
    dataset = dataset.filter(lambda example: "```homeassistant" in example["text"])

    service_call_regex = re.compile(r"```homeassistant\n([\S \t\n]*?)```")

    torch.set_default_device("cuda")
    print(f"Loading model from {model_folder}...")
    trained_model = AutoModelForCausalLM.from_pretrained(model_folder, trust_remote_code=True, torch_dtype=torch.bfloat16) #, code_revision="834565c23f9b28b96ccbeabe614dd906b6db551a")
    trained_tokenizer = AutoTokenizer.from_pretrained(model_folder, trust_remote_code=True, padding_side='left')

    trained_model.generation_config = GenerationConfig(
        max_new_tokens=128,
        use_cache=True,
        do_sample=True,
        temperature=0.1,
        top_k=40,
        top_p=1.0,
        repetition_penalty=1.15,
        eos_token_id=trained_model.config.eos_token_id,
        pad_token_id=trained_model.config.pad_token_id,
    )

    print("Evaluating...")
    batch_size = int(args.batch_size)
    correct_answers = 0
    total_answers = 0
    color_mismatches = 0

    failed_examples = []
    with tqdm(total=len(dataset), desc="Accuracy") as pbar:
        for batch_start in range(0, len(dataset), batch_size):
            batch = dataset[batch_start:batch_start + batch_size]
            prompts = [ example.split(split)[0] + split for example in batch["text"] ]
            expected_responses = [ example.split(split)[1] for example in batch["text"] ]
            output = generate(trained_model, trained_tokenizer, prompts)

            for model_output, expected_response in zip(output, expected_responses):
                response = model_output.replace(trained_tokenizer.pad_token, "").replace(trained_tokenizer.eos_token, "").split(split)[1]

                expected_service_calls = []

                for block in service_call_regex.findall(expected_response.strip()):
                    for line in block.split("\n"):
                        if len(line) == 0:
                            continue
                        expected_service_calls.append(json.loads(line))
                        total_answers = total_answers + 1
                        
                for block in service_call_regex.findall(response.strip()):
                    for line in block.split("\n"):
                        if len(line) == 0:
                            continue
                        try:
                            json_output = json.loads(line)
                        except:
                            failed_examples.append({ "expected": expected_response, "actual": response, "invalid_json": True })
                            continue

                        if json_output in expected_service_calls:
                            expected_service_calls.pop(expected_service_calls.index(json_output))
                            correct_answers = correct_answers + 1
                        elif "rgb_color" in json_output:
                            for sc in expected_service_calls:
                                sc = { **sc }
                                json_output_copy = { **json_output }
                                if not "rgb_color" in sc:
                                    continue
                                del sc["rgb_color"]
                                del json_output_copy["rgb_color"]
                                if sc == json_output_copy:
                                    correct_answers = correct_answers + 1
                                    color_mismatches = color_mismatches + 1
                                else:
                                    failed_examples.append({ "expected": expected_response, "actual": response })
                        else:
                            failed_examples.append({ "expected": expected_response, "actual": response })

            pbar.update(batch_size)
            pbar.set_description(f"Accuracy: {correct_answers/total_answers*100:.2f}% ({correct_answers}/{total_answers})")

    accuracy = correct_answers/total_answers
    print(f"Final Accuracy Rating: {accuracy*100:.2f}%")
    print(f"Color Mismatches: {color_mismatches}")

    with open(os.path.join(model_folder, "eval_results.json"), "w") as f:
        json.dump({
            "possible_answers": total_answers,
            "correct_answers": correct_answers,
            "accuracy": accuracy,
            "color_mismatches": color_mismatches,
            "failed_examples": failed_examples,
        }, f, indent=4)


if __name__ == "__main__":
    main()