Add script to extract projector

scripts/extract_mm_projector.py

@@ -0,0 +1,47 @@
+"""
+This is just a utility that I use to extract the projector for quantized models.
+It is NOT necessary at all to train, or run inference/serve demos.
+Use this script ONLY if you fully understand its implications.
+"""
+
+
+import os
+import argparse
+import torch
+import json
+from collections import defaultdict
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Extract MMProjector weights')
+    parser.add_argument('--model-path', type=str, help='model folder')
+    parser.add_argument('--output', type=str, help='output file')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    keys_to_match = ['mm_projector']
+    ckpt_to_key = defaultdict(list)
+    try:
+        model_indices = json.load(open(os.path.join(args.model_path, 'pytorch_model.bin.index.json')))
+        for k, v in model_indices['weight_map'].items():
+            if any(key_match in k for key_match in keys_to_match):
+                ckpt_to_key[v].append(k)
+    except FileNotFoundError:
+        # Smaller models or model checkpoints saved by DeepSpeed.
+        v = 'pytorch_model.bin'
+        for k in torch.load(os.path.join(args.model_path, v), map_location='cpu').keys():
+            if any(key_match in k for key_match in keys_to_match):
+                ckpt_to_key[v].append(k)
+
+    loaded_weights = {}
+
+    for ckpt_name, weight_keys in ckpt_to_key.items():
+        ckpt = torch.load(os.path.join(args.model_path, ckpt_name), map_location='cpu')
+        for k in weight_keys:
+            loaded_weights[k] = ckpt[k]
+
+    torch.save(loaded_weights, args.output)