nanochat-omni/scripts/chat_eval.py

"""
Evaluate the Chat model.
All the generic code lives here, and all the evaluation-specific
code lives in nanochat directory and is imported from here.

Example runs:
python -m scripts.chat_eval -a ARC-Easy
torchrun --nproc_per_node=8 -m scripts.chat_eval -- -a ARC-Easy
"""

import argparse
from functools import partial
import torch
import torch.distributed as dist

from nanochat.common import compute_init, compute_cleanup, get_dist_info, print0, autodetect_device_type
from nanochat.checkpoint_manager import load_model
from nanochat.engine import Engine

from tasks.humaneval import HumanEval
from tasks.mmlu import MMLU
from tasks.arc import ARC
from tasks.gsm8k import GSM8K
from tasks.spellingbee import SpellingBee

# -----------------------------------------------------------------------------
# Generative evaluation loop (we go one problem at a time, sample, evaluate)

def run_generative_eval(task_object, tokenizer, model, engine, num_samples, max_new_tokens, temperature, top_k, max_problems=None):

    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
    device = model.get_device()

    num_problems = len(task_object) if max_problems is None else min(len(task_object), max_problems)

    # Run the evaluation
    num_passed, total = 0, 0
    for i in range(ddp_rank, num_problems, ddp_world_size):
        conversation = task_object[i]

        # Tokenize the prompt
        encoded_prompt = tokenizer.render_for_completion(conversation)
        # Get the completions
        results, _ = engine.generate_batch(
            encoded_prompt,
            num_samples=num_samples,
            max_tokens=max_new_tokens,
            temperature=temperature,
            top_k=top_k,
        )
        # Decode the completions as text
        prefix_length = len(encoded_prompt)
        completions = [tokenizer.decode(result_tokens[prefix_length:]) for result_tokens in results]
        # Evaluate success criteria
        outcomes = [task_object.evaluate(conversation, completion) for completion in completions]
        passed = any(outcomes)

        # Keep stats
        total += 1
        num_passed += int(passed)

        # Logging (overwrite the same line in the console)
        print(f"\r\033[KRank {ddp_rank} | {num_passed}/{total} ({100*num_passed/total:.2f}%)", end='', flush=True)

    # Finish the in-place progress line with a newline before final summary
    print()

    # Aggregate results across all ranks
    if ddp:
        num_passed_tensor = torch.tensor([num_passed], dtype=torch.long, device=device)
        total_tensor = torch.tensor([total], dtype=torch.long, device=device)
        dist.all_reduce(num_passed_tensor, op=dist.ReduceOp.SUM)
        dist.all_reduce(total_tensor, op=dist.ReduceOp.SUM)
        num_passed = num_passed_tensor.item()
        total = total_tensor.item()

    print0("=" * 50)
    print0(f"Final: {num_passed}/{total} ({100*num_passed/total:.2f}%)")

    # Return the accuracy
    return num_passed/total

# -----------------------------------------------------------------------------
# Categorical evaluation loop
# A lot easier because we don't have to sample. Therefore, we can actually go
# batches at a time and just check the logits for correct answer choices.

def run_categorical_eval(task_object, tokenizer, model, batch_size, max_problems=None):

    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
    device = model.get_device()
    bos = tokenizer.get_bos_token_id() # use BOS as pad token is ok, these positions are ignored

    # We'll process batches of independent problems at a time because there is no sampling needed
    num_problems = len(task_object) if max_problems is None else min(len(task_object), max_problems)
    ceil_div = lambda x, y: -(-x // y)
    num_batches = ceil_div(num_problems, batch_size)

    # Run the evaluation
    letter_to_id_cache = {} # many letters will repeat often, let's save the tokenizer some work
    num_passed, total = 0, 0
    for i in range(ddp_rank, num_batches, ddp_world_size):
        i0, i1 = i * batch_size, min((i + 1) * batch_size, num_problems)

        # Prepare the batch of problems. They might all be of different length, so we pad/collate them.
        conversations = [task_object[ii] for ii in range(i0, i1)]
        prompt_ids = [tokenizer.render_for_completion(conversation) for conversation in conversations] # TODO: remake the way this works
        max_length = max(len(ids) for ids in prompt_ids)
        answer_time_positions = [len(ids) - 1 for ids in prompt_ids] # where the last token is (and the predicted answer)
        padded_prompt_ids = [ids + [bos] * (max_length - len(ids)) for ids in prompt_ids]
        prompt_ids = torch.tensor(padded_prompt_ids, dtype=torch.long, device=device)

        # Get the logits for the whole batch of conversations in parallel (efficiency win here)
        with torch.no_grad():
            logits = model(prompt_ids) # (B, T, V)

        # Focus on the available answer on just the letters corresponding to choices
        # Note that this helps the evaluation a lot because it specifically narrows the focus to only the available letters
        # The much harder alternative would be to just generate from the Assistant and check if it responded with the correct
        # letter (e.g. A, B, C, D), but evaluations typically make the task easier in this way.
        for idx, conversation in enumerate(conversations):
            # get the token ids of all the available letters of this problem
            letters = conversation['letters']
            letter_ids = []
            for letter in letters:
                if not letter in letter_to_id_cache:
                    encoded_letter = tokenizer.encode(letter)
                    assert len(encoded_letter) == 1, "Each letter must be a single token"
                    letter_to_id_cache[letter] = encoded_letter[0]
                letter_ids.append(letter_to_id_cache[letter])
            # focus logits just down to the answer position and the available letters of the answer
            answer_pos = answer_time_positions[idx]
            focus_logits = logits[idx, answer_pos, letter_ids]
            # get the argmax letter (the predicted answer)
            argmax_letter_id = focus_logits.argmax(dim=-1).item()
            predicted_letter = letters[argmax_letter_id]
            # evaluate the outcome
            outcome = task_object.evaluate(conversation, predicted_letter)
            num_passed += int(outcome)
            total += 1

    # Aggregate results across all ranks
    if ddp:
        num_passed_tensor = torch.tensor([num_passed], dtype=torch.long, device=device)
        total_tensor = torch.tensor([total], dtype=torch.long, device=device)
        dist.all_reduce(num_passed_tensor, op=dist.ReduceOp.SUM)
        dist.all_reduce(total_tensor, op=dist.ReduceOp.SUM)
        num_passed = num_passed_tensor.item()
        total = total_tensor.item()

    average = num_passed/total
    print0(f"Final: {num_passed}/{total} ({100*average:.2f}%)")
    return average

# -----------------------------------------------------------------------------

def run_chat_eval(task_name, model, tokenizer, engine,
                   batch_size=1, num_samples=1, max_new_tokens=512, temperature=0.0, top_k=50,
                   max_problems=None):
    # Create the evaluation object
    task_module = {
        'HumanEval': HumanEval,
        'MMLU': partial(MMLU, subset="all", split="test"),
        'ARC-Easy': partial(ARC, subset="ARC-Easy", split="test"),
        'ARC-Challenge': partial(ARC, subset="ARC-Challenge", split="test"),
        'GSM8K': partial(GSM8K, subset="main", split="test"),
        'SpellingBee': partial(SpellingBee, size=256, split="test"),
    }[task_name]
    task_object = task_module()
    # Run the evaluation
    if task_object.eval_type == 'generative':
        acc = run_generative_eval(task_object, tokenizer, model, engine, num_samples, max_new_tokens, temperature, top_k, max_problems=max_problems)
    elif task_object.eval_type == 'categorical':
        acc = run_categorical_eval(task_object, tokenizer, model, batch_size, max_problems=max_problems)
    else:
        raise ValueError(f"Unsupported task evaluation type: {task_object.eval_type}")
    return acc

# -----------------------------------------------------------------------------
if __name__ == "__main__":

    # Parse command-line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('-i', '--source', type=str, required=True, help="Source of the model: sft|rl")
    parser.add_argument('-a', '--task-name', type=str, default=None, help="Task name. Default = all tasks. Use | to split multiple tasks.")
    parser.add_argument('-t', '--temperature', type=float, default=0.0)
    parser.add_argument('-m', '--max-new-tokens', type=int, default=512)
    parser.add_argument('-n', '--num-samples', type=int, default=1)
    parser.add_argument('-k', '--top-k', type=int, default=50)
    parser.add_argument('-b', '--batch-size', type=int, default=8, help='Batch size for categorical evaluation')
    parser.add_argument('-g', '--model-tag', type=str, default=None, help='Model tag to load')
    parser.add_argument('-s', '--step', type=int, default=None, help='Step to load')
    parser.add_argument('-x', '--max-problems', type=int, default=None, help='Max problems to evaluate')
    parser.add_argument('--device-type', type=str, default='', choices=['cuda', 'cpu', 'mps'], help='Device type for evaluation: cuda|cpu|mps. empty => autodetect')
    args = parser.parse_args()

    device_type = autodetect_device_type() if args.device_type == "" else args.device_type
    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)

    model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
    engine = Engine(model, tokenizer)

    # Get the tasks to evaluate on
    all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
    baseline_accuracies = {
        'ARC-Easy': 0.25, # multiple choice 1 of 4 => 25%
        'ARC-Challenge': 0.25, # multiple choice 1 of 4 => 25%
        'MMLU': 0.25, # multiple choice 1 of 4 => 25%
        'GSM8K': 0.0, # open-ended => 0%
        'HumanEval': 0.0, # open-ended => 0%
        'SpellingBee': 0.0, # open-ended => 0%
    }
    task_names = all_tasks if args.task_name is None else args.task_name.split('|')

    # Run all the task evaluations sequentially
    results = {}
    for task_name in task_names:
        acc = run_chat_eval(
            task_name,
            model, tokenizer, engine,
            batch_size=args.batch_size,
            num_samples=args.num_samples,
            max_new_tokens=args.max_new_tokens,
            temperature=args.temperature,
            top_k=args.top_k,
            max_problems=args.max_problems,
        )
        results[task_name] = acc
        print0(f"{task_name} accuracy: {100 * acc:.2f}%")

    # Log to report
    from nanochat.report import get_report
    all_tasks_were_evaluated = all(task_name in results for task_name in all_tasks)
    # calculate the ChatCORE metric if we can (similar to CORE, it's the mean centered accuracy)
    # this way, ChatCORE ranges from 0 (at random baseline) to 1 (peak performance)
    chatcore_metric_dict = {}
    if all_tasks_were_evaluated:
        centered_mean = 0
        for task_name, acc in results.items():
            baseline_acc = baseline_accuracies.get(task_name, 0.0)
            centered_acc = (acc - baseline_acc) / (1.0 - baseline_acc)
            centered_mean += centered_acc
        chatcore_metric = centered_mean / len(results)
        chatcore_metric_dict = {"ChatCORE metric": chatcore_metric}
    get_report().log(section="Chat evaluation " + args.source, data=[
        vars(args), # CLI args
        results,
        chatcore_metric_dict,
    ])

    compute_cleanup()