many small tweaks. base, eval, core work now i think

scripts/base_eval.py

@@ -15,6 +15,7 @@ import time
 import json
 import random
 import yaml
+from contextlib import nullcontext
 
 import pandas as pd
 import torch
@@ -118,18 +119,21 @@ def load_hf_model(hf_path: str, device):
 
 # -----------------------------------------------------------------------------
 def main():
-    assert len(sys.argv) in [1, 2], "Usage: python base_eval.py [hf_path]"
+    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)')
+    args = parser.parse_args()
 
     # distributed / precision setup
     device_type = autodetect_device_type()
     ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
-    dtype = torch.bfloat16 if device_type == "cuda" else torch.float32 # use fp32 on CPU|MPS
-    autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=dtype)
+    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 len(sys.argv) >= 2:
+    if args.hf_path is not None:
         # atm assume that if a path is given, it's a huggingface model path
-        hf_path = sys.argv[1]
+        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
@@ -142,7 +146,7 @@ def main():
 
     # Evaluate the model
     with autocast_ctx:
-        out = evaluate_model(model, tokenizer, device)
+        out = evaluate_model(model, tokenizer, device, max_per_task=args.max_per_task)
 
     # Write out the results to a csv file
     core_metric = None