Deprecate torch dumps, move to safetensors (they load even faster)

README.md

@@ -112,14 +112,6 @@ After downloading the model, follow these steps:
 python download-model.py EleutherAI/gpt-j-6B --text-only
 ```
 
-#### Converting to pytorch (optional)
-
-The script `convert-to-torch.py` allows you to convert models to .pt format, which can be a lot faster to load to the GPU:
-
-    python convert-to-torch.py models/model-name
-
-The output model will be saved to `torch-dumps/model-name.pt`. When you load a new model, the web UI first looks for this .pt file; if it is not found, it loads the model as usual from `models/model-name`. 
-
 ## Starting the web UI
 
     conda activate textgen

convert-to-safetensors.py

@@ -0,0 +1,40 @@
+'''
+
+Converts a transformers model to safetensors format and shards it.
+
+This makes it faster to load (because of safetensors) and lowers its RAM usage
+while loading (because of sharding).
+
+Based on the original script by 81300:
+
+https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
+
+'''
+
+from pathlib import Path
+from sys import argv
+
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+import argparse
+ 
+parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
+parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
+parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
+parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
+parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    path = Path(args.MODEL)
+    model_name = path.name
+
+    print(f"Loading {model_name}...")
+    model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
+    tokenizer = AutoTokenizer.from_pretrained(path)
+
+    out_folder = args.output or Path(f"models/{model_name}_safetensors")
+    print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
+    model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
+    tokenizer.save_pretrained(out_folder)

convert-to-torch.py

@@ -1,22 +0,0 @@
-'''
-Converts a transformers model to .pt, which is faster to load.
- 
-Example:
-python convert-to-torch.py models/opt-1.3b
- 
-The output will be written to torch-dumps/name-of-the-model.pt
-'''
-
-from pathlib import Path
-from sys import argv
-
-import torch
-from transformers import AutoModelForCausalLM
- 
-path = Path(argv[1])
-model_name = path.name
-
-print(f"Loading {model_name}...")
-model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
-print(f"Model loaded.\nSaving to torch-dumps/{model_name}.pt")
-torch.save(model, Path(f"torch-dumps/{model_name}.pt"))

server.py

@@ -108,10 +108,7 @@ def load_model(model_name):
 
     # Default settings
     if not (args.cpu or args.load_in_8bit or args.auto_devices or args.disk or args.gpu_memory is not None or args.cpu_memory is not None or args.deepspeed):
-        if Path(f"torch-dumps/{model_name}.pt").exists():
-            print("Loading in .pt format...")
-            model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
-        elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
+        if model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
         else:
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16).cuda()
@@ -425,7 +422,7 @@ def update_extensions_parameters(*kwargs):
                     i += 1
 
 def get_available_models():
-    return sorted(set([item.replace('.pt', '') for item in map(lambda x : str(x.name), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*'))) if not item.endswith('.txt')]), key=str.lower)
+    return sorted([item.name for item in list(Path('models/').glob('*')) if not item.name.endswith('.txt')], key=lambda x: x.lower())
 
 def get_available_presets():
     return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)