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https://github.com/karpathy/nanochat.git
synced 2025-12-06 12:22:18 +00:00
fix: inference_mode, csv metadata, typo, DDP comment
This commit is contained in:
Dipesh Babu
parent
1ccbaf4416
commit
a3e1352f6b
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@ -1,5 +1,5 @@
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"""
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Evlauate the CORE metric for a given model.
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Evaluate 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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@ -17,7 +17,7 @@ import random
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import yaml
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from contextlib import nullcontext
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import pandas as pd
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import csv
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import torch
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from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type
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@ -28,6 +28,7 @@ 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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@ -43,7 +44,12 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
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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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eval_metadata = {}
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with open(eval_meta_data, "r", newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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eval_metadata[row["Eval Task"]] = row
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# Evaluate each task
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results = {}
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@ -57,7 +63,8 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
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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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print0(
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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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@ -72,15 +79,24 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
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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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# eval should be grad-free for stability/perf
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with torch.inference_mode():
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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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# row = eval_metadata[eval_metadata["Eval Task"] == label]
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# random_baseline = row["Random baseline"].values[0]
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row = eval_meta_data.get(label)
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if row is None or "Random baseline" not in row:
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raise KeyError(
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f"Missing 'Random baseline' for task '{label}' in {eval_meta_data}")
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random_baseline = float(row["Random baseline"])
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centered_result = (accuracy - 0.01 * random_baseline) / \
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(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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print0(
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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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@ -93,8 +109,10 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
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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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@ -104,6 +122,7 @@ class ModelWrapper:
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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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@ -118,17 +137,23 @@ def load_hf_model(hf_path: str, device):
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return model, tokenizer
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# -----------------------------------------------------------------------------
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--hf-path', type=str, default=None, help='HuggingFace model path to evaluate')
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parser.add_argument('--max-per-task', type=int, default=-1, help='Max examples per task to evaluate (-1 = disable)')
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parser.add_argument('--hf-path', type=str, default=None,
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help='HuggingFace model path to evaluate')
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parser.add_argument('--max-per-task', type=int, default=-1,
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help='Max examples per task to evaluate (-1 = disable)')
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args = parser.parse_args()
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# distributed / precision setup
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device_type = autodetect_device_type()
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ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
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autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
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ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(
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device_type)
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autocast_ctx = torch.amp.autocast(
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device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
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# Load model and tokenizer from command line or from file system
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if args.hf_path is not None:
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@ -136,24 +161,27 @@ def main():
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hf_path = args.hf_path
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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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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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model_name = f"base_model (step {meta['step']})" # just for logging
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# for the output csv file
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model_slug = f"base_model_{meta['step']:06d}"
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# Evaluate the model
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with autocast_ctx:
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out = evaluate_model(model, tokenizer, device, max_per_task=args.max_per_task)
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out = evaluate_model(model, tokenizer, device,
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max_per_task=args.max_per_task)
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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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output_csv_path = os.path.join(
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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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@ -161,7 +189,8 @@ def main():
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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(
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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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@ -177,10 +206,11 @@ def main():
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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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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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