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karpathy
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"""
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Evlauate the CORE metric for a given model.
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Run on a single GPU:
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python base_eval.py
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Run with torchrun on e.g. 8 GPUs:
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torchrun --nproc_per_node=8 base_eval.py
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The script will print the CORE metric to the console.
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"""
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import os
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import sys
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import time
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import json
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import random
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import yaml
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import pandas as pd
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import torch
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from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir
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from nanochat.tokenizer import HuggingFaceTokenizer
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from nanochat.checkpoint_manager import load_model
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from nanochat.core_eval import evaluate_task
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# -----------------------------------------------------------------------------
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# nanoChat specific function dealing with I/O etc.
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def evaluate_model(model, tokenizer, device, max_per_task=-1):
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"""
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Evaluate a base model on the CORE benchmark.
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- max_per_task: crop the data to this many examples per task for testing (-1 = disable)
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TODO: clean up this function, delete the need for all the files, for pandas dependency, etc.
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"""
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# Load config and task metadata
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base_dir = get_base_dir()
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eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
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config_path = os.path.join(eval_bundle_dir, "core.yaml")
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data_base_path = os.path.join(eval_bundle_dir, "eval_data")
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eval_meta_data = os.path.join(eval_bundle_dir, "eval_meta_data.csv")
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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tasks = config['icl_tasks']
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eval_metadata = pd.read_csv(eval_meta_data)
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# Evaluate each task
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results = {}
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centered_results = {}
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for task in tasks:
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start_time = time.time()
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label = task['label']
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task_meta = {
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'task_type': task['icl_task_type'],
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'dataset_uri': task['dataset_uri'],
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'num_fewshot': task['num_fewshot'][0],
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'continuation_delimiter': task.get('continuation_delimiter', ' ')
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}
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print0(f"Evaluating: {label} ({task_meta['num_fewshot']}-shot, type: {task_meta['task_type']})... ", end='')
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# Load data for this task
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data_path = os.path.join(data_base_path, task_meta['dataset_uri'])
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with open(data_path, 'r') as f:
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data = [json.loads(line.strip()) for line in f]
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# shuffle the data because in many cases it appears ordered but we want
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# the abillity to only run a subset of the data for debugging purposes etc.
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shuffle_rng = random.Random(1337)
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shuffle_rng.shuffle(data)
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if max_per_task > 0:
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data = data[:max_per_task]
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# run the evaluation for this task
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accuracy = evaluate_task(model, tokenizer, data, device, task_meta)
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results[label] = accuracy
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row = eval_metadata[eval_metadata["Eval Task"] == label]
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random_baseline = row["Random baseline"].values[0]
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centered_result = (accuracy - 0.01 * random_baseline) / (1.0 - 0.01 * random_baseline)
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centered_results[label] = centered_result
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end_time = time.time()
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print0(f"accuracy: {accuracy:.4f} | centered: {centered_result:.4f} | time: {end_time - start_time:.2f}s")
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core_metric = sum(centered_results.values()) / len(centered_results)
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out = {
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"results": results,
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"centered_results": centered_results,
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"core_metric": core_metric
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}
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return out
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# -----------------------------------------------------------------------------
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# HuggingFace loading utilities and light wrappers for a model
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class ModelWrapper:
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"""Lightweight wrapper for a HuggingFace model"""
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def __init__(self, model, max_seq_len=None):
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self.model = model
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self.max_seq_len = max_seq_len
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def __call__(self, input_ids):
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outputs = self.model(input_ids)
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logits = outputs.logits
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return logits
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def load_hf_model(hf_path: str, device):
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print0(f"Loading model from: {hf_path}")
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# Load the model
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(hf_path)
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model.to(device)
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model.eval()
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max_seq_len = 1024 if "openai-community/gpt2" in hf_path else None
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model = ModelWrapper(model, max_seq_len=max_seq_len)
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# Load the tokenizer
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tokenizer = HuggingFaceTokenizer.from_pretrained(hf_path)
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return model, tokenizer
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# -----------------------------------------------------------------------------
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def main():
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assert len(sys.argv) in [1, 2], "Usage: python base_eval.py [hf_path]"
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# distributed / precision setup
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ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
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autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
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# Load model and tokenizer from command line or from file system
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if len(sys.argv) >= 2:
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# atm assume that if a path is given, it's a huggingface model path
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hf_path = sys.argv[1]
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print0(f"Loading huggingface model from: {hf_path}")
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model, tokenizer = load_hf_model(hf_path, device)
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model_name = hf_path # just for logging
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model_slug = hf_path.replace("/", "-") # for the output csv file
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else:
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# load a local model from the file system
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model, tokenizer, meta = load_model("base", device, phase="eval")
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model_name = f"base_model (step {meta['step']})" # just for logging
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model_slug = f"base_model_{meta['step']:06d}" # for the output csv file
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# Evaluate the model
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with autocast_ctx:
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out = evaluate_model(model, tokenizer, device)
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# Write out the results to a csv file
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core_metric = None
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centered_results = {}
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if ddp_rank == 0:
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base_dir = get_base_dir()
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output_csv_path = os.path.join(base_dir, "base_eval", f"{model_slug}.csv")
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os.makedirs(os.path.dirname(output_csv_path), exist_ok=True)
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results = out["results"]
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centered_results = out["centered_results"]
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core_metric = out["core_metric"]
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with open(output_csv_path, 'w') as f:
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f.write(f"{'Task':<35}, {'Accuracy':<10}, {'Centered':<10}\n")
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for label in results:
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f.write(f"{label:<35}, {results[label]:<10.6f}, {centered_results[label]:<10.6f}\n")
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f.write(f"{'CORE':<35}, {'':<10}, {core_metric:<10.6f}\n")
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# Print the content of the csv file to console too
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print0("="*80)
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print0(f"Model: {model_name}")
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print0("="*80)
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with open(output_csv_path, 'r') as f:
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print0(f.read())
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# Log to report
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from nanochat.report import get_report
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get_report().log(section="Base model evaluation", data=[
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{
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"Model": model_name,
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"CORE metric": core_metric,
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},
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centered_results, # the full table
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])
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compute_cleanup()
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if __name__ == "__main__":
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main()
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