mirror of
https://github.com/karpathy/nanochat.git
synced 2025-12-06 12:22:18 +00:00
209 lines
8.1 KiB
Python
209 lines
8.1 KiB
Python
"""
|
|
Evaluate the CORE metric for a given model.
|
|
|
|
Examples:
|
|
|
|
Run on a single GPU to evaluate a local nanoChat model:
|
|
python base_eval.py --model_tag=my_run
|
|
|
|
Run with torchrun on e.g. 8 GPUs:
|
|
torchrun --nproc_per_node=8 base_eval.py --model_tag=my_run
|
|
|
|
Evaluate a HuggingFace model:
|
|
python base_eval.py --hf_path=openai-community/gpt2
|
|
|
|
Configuration parameters:
|
|
- model_tag: Model tag for local nanoChat model (optional)
|
|
- step: Specific checkpoint step to evaluate (optional)
|
|
- hf_path: Path to HuggingFace model (if set, loads from HF instead of local)
|
|
|
|
The script will print the CORE metric to the console.
|
|
"""
|
|
import os
|
|
import sys
|
|
import time
|
|
import json
|
|
import random
|
|
import yaml
|
|
from contextlib import nullcontext
|
|
|
|
import pandas as pd
|
|
import torch
|
|
|
|
from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type
|
|
from nanochat.tokenizer import HuggingFaceTokenizer
|
|
from nanochat.checkpoint_manager import load_model
|
|
from nanochat.core_eval import evaluate_task
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# Configuration
|
|
model_tag = None # optional model tag for the output directory name
|
|
step = None # optional model step for the output directory name
|
|
hf_path = None # optional HuggingFace model path (if set, will load from HF instead of local)
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# nanoChat specific function dealing with I/O etc.
|
|
|
|
def evaluate_model(model, tokenizer, device, max_per_task=-1):
|
|
"""
|
|
Evaluate a base model on the CORE benchmark.
|
|
- max_per_task: crop the data to this many examples per task for testing (-1 = disable)
|
|
TODO: clean up this function, delete the need for all the files, for pandas dependency, etc.
|
|
"""
|
|
# Load config and task metadata
|
|
base_dir = get_base_dir()
|
|
eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
|
|
config_path = os.path.join(eval_bundle_dir, "core.yaml")
|
|
data_base_path = os.path.join(eval_bundle_dir, "eval_data")
|
|
eval_meta_data = os.path.join(eval_bundle_dir, "eval_meta_data.csv")
|
|
with open(config_path, 'r') as f:
|
|
config = yaml.safe_load(f)
|
|
tasks = config['icl_tasks']
|
|
eval_metadata = pd.read_csv(eval_meta_data)
|
|
|
|
# Evaluate each task
|
|
results = {}
|
|
centered_results = {}
|
|
for task in tasks:
|
|
start_time = time.time()
|
|
label = task['label']
|
|
task_meta = {
|
|
'task_type': task['icl_task_type'],
|
|
'dataset_uri': task['dataset_uri'],
|
|
'num_fewshot': task['num_fewshot'][0],
|
|
'continuation_delimiter': task.get('continuation_delimiter', ' ')
|
|
}
|
|
print0(f"Evaluating: {label} ({task_meta['num_fewshot']}-shot, type: {task_meta['task_type']})... ", end='')
|
|
|
|
# Load data for this task
|
|
data_path = os.path.join(data_base_path, task_meta['dataset_uri'])
|
|
with open(data_path, 'r') as f:
|
|
data = [json.loads(line.strip()) for line in f]
|
|
|
|
# shuffle the data because in many cases it appears ordered but we want
|
|
# the abillity to only run a subset of the data for debugging purposes etc.
|
|
shuffle_rng = random.Random(1337)
|
|
shuffle_rng.shuffle(data)
|
|
if max_per_task > 0:
|
|
data = data[:max_per_task]
|
|
|
|
# run the evaluation for this task
|
|
accuracy = evaluate_task(model, tokenizer, data, device, task_meta)
|
|
|
|
results[label] = accuracy
|
|
row = eval_metadata[eval_metadata["Eval Task"] == label]
|
|
random_baseline = row["Random baseline"].values[0]
|
|
centered_result = (accuracy - 0.01 * random_baseline) / (1.0 - 0.01 * random_baseline)
|
|
centered_results[label] = centered_result
|
|
end_time = time.time()
|
|
print0(f"accuracy: {accuracy:.4f} | centered: {centered_result:.4f} | time: {end_time - start_time:.2f}s")
|
|
|
|
core_metric = sum(centered_results.values()) / len(centered_results)
|
|
out = {
|
|
"results": results,
|
|
"centered_results": centered_results,
|
|
"core_metric": core_metric
|
|
}
|
|
return out
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# HuggingFace loading utilities and light wrappers for a model
|
|
|
|
class ModelWrapper:
|
|
"""Lightweight wrapper for a HuggingFace model"""
|
|
def __init__(self, model, max_seq_len=None):
|
|
self.model = model
|
|
self.max_seq_len = max_seq_len
|
|
|
|
def __call__(self, input_ids):
|
|
outputs = self.model(input_ids)
|
|
logits = outputs.logits
|
|
return logits
|
|
|
|
def load_hf_model(hf_path: str, device):
|
|
print0(f"Loading model from: {hf_path}")
|
|
# Load the model
|
|
from transformers import AutoModelForCausalLM
|
|
model = AutoModelForCausalLM.from_pretrained(hf_path)
|
|
model.to(device)
|
|
model.eval()
|
|
max_seq_len = 1024 if "openai-community/gpt2" in hf_path else None
|
|
model = ModelWrapper(model, max_seq_len=max_seq_len)
|
|
# Load the tokenizer
|
|
tokenizer = HuggingFaceTokenizer.from_pretrained(hf_path)
|
|
return model, tokenizer
|
|
|
|
# -----------------------------------------------------------------------------
|
|
def main():
|
|
import argparse
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--hf-path', type=str, default=None, help='HuggingFace model path to evaluate')
|
|
parser.add_argument('--max-per-task', type=int, default=-1, help='Max examples per task to evaluate (-1 = disable)')
|
|
parser.add_argument('--model-tag', type=str, default=None, help='Model tag to evaluate')
|
|
parser.add_argument('--step', type=int, default=None, help='Model step to evaluate')
|
|
args = parser.parse_args()
|
|
model_tag = args.model_tag
|
|
step = args.step
|
|
|
|
# distributed / precision setup
|
|
device_type = autodetect_device_type()
|
|
ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
|
|
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
|
|
|
|
# Load model and tokenizer from command line or from file system
|
|
if args.hf_path is not None:
|
|
# atm assume that if a path is given, it's a huggingface model path
|
|
hf_path = args.hf_path
|
|
print0(f"Loading huggingface model from: {hf_path}")
|
|
|
|
model, tokenizer = load_hf_model(hf_path, device)
|
|
model_name = hf_path # just for logging
|
|
model_slug = hf_path.replace("/", "-") # for the output csv file
|
|
else:
|
|
# Load a local nanoChat model from the file system
|
|
model, tokenizer, meta = load_model("base", device, phase="eval", model_tag=model_tag, step=step)
|
|
model_name = f"base_model (step {meta['step']})" # just for logging
|
|
model_slug = f"base_model_{meta['step']:06d}" # for the output csv file
|
|
print0(f"Loaded model with model_tag: {model_tag}, step: {meta['step']}")
|
|
|
|
# Evaluate the model
|
|
with autocast_ctx:
|
|
out = evaluate_model(model, tokenizer, device, max_per_task=args.max_per_task)
|
|
|
|
# Write out the results to a csv file
|
|
core_metric = None
|
|
centered_results = {}
|
|
if ddp_rank == 0:
|
|
base_dir = get_base_dir()
|
|
output_csv_path = os.path.join(base_dir, "base_eval", f"{model_slug}.csv")
|
|
os.makedirs(os.path.dirname(output_csv_path), exist_ok=True)
|
|
results = out["results"]
|
|
centered_results = out["centered_results"]
|
|
core_metric = out["core_metric"]
|
|
with open(output_csv_path, 'w') as f:
|
|
f.write(f"{'Task':<35}, {'Accuracy':<10}, {'Centered':<10}\n")
|
|
for label in results:
|
|
f.write(f"{label:<35}, {results[label]:<10.6f}, {centered_results[label]:<10.6f}\n")
|
|
f.write(f"{'CORE':<35}, {'':<10}, {core_metric:<10.6f}\n")
|
|
# Print the content of the csv file to console too
|
|
print0("="*80)
|
|
print0(f"Model: {model_name}")
|
|
print0("="*80)
|
|
with open(output_csv_path, 'r') as f:
|
|
print0(f.read())
|
|
|
|
# Log to report
|
|
from nanochat.report import get_report
|
|
get_report(exp_name=model_tag).log(section="Base model evaluation", data=[
|
|
{
|
|
"Model": model_name,
|
|
"CORE metric": core_metric,
|
|
},
|
|
centered_results, # the full table
|
|
])
|
|
|
|
compute_cleanup()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|