Add --auto-devices and --load-in-8bit options for #4

README.md

@@ -85,13 +85,15 @@ Then browse to
 Optionally, you can use the following command-line flags:
 
 ```
--h, --help     show this help message and exit
---model MODEL  Name of the model to load by default.
---notebook     Launch the webui in notebook mode, where the output is written
-               to the same text box as the input.
---chat         Launch the webui in chat mode.
---cpu          Use the CPU to generate text.
---listen       Make the webui reachable from your local network.
+-h, --help      show this help message and exit
+--model MODEL   Name of the model to load by default.
+--notebook      Launch the webui in notebook mode, where the output is written to the same text
+                box as the input.
+--chat          Launch the webui in chat mode.
+--cpu           Use the CPU to generate text.
+--auto-devices  Automatically split the model across the available GPU(s) and CPU.
+--load-in-8bit  Load the model with 8-bit precision.
+--listen        Make the webui reachable from your local network.
 ```
 
 ## Presets

server.py

@@ -17,6 +17,8 @@ parser.add_argument('--model', type=str, help='Name of the model to load by defa
 parser.add_argument('--notebook', action='store_true', help='Launch the webui in notebook mode, where the output is written to the same text box as the input.')
 parser.add_argument('--chat', action='store_true', help='Launch the webui in chat mode.')
 parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
+parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
+parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
 parser.add_argument('--listen', action='store_true', help='Make the webui reachable from your local network.')
 args = parser.parse_args()
 loaded_preset = None
@@ -28,22 +30,44 @@ def load_model(model_name):
     print(f"Loading {model_name}...")
     t0 = time.time()
 
-    # Loading the model
-    if not args.cpu and 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')):
-        model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
-    elif model_name in ['flan-t5', 't5-large']:
-        if args.cpu:
-            model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}"))
-        else:
+    # Default settings
+    if not (args.cpu or args.auto_devices or args.load_in_8bit):
+        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')):
+            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
+        elif model_name in ['flan-t5', 't5-large']:
             model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
+        else:
+            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
+
+    # Custom
     else:
+        settings = ["low_cpu_mem_usage=True"]
+        cuda = ""
+
+        if model_name in ['flan-t5', 't5-large']:
+            command = f"T5ForConditionalGeneration.from_pretrained"
+        else:
+            command = "AutoModelForCausalLM.from_pretrained"
+
         if args.cpu:
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float32)
+            settings.append("torch_dtype=torch.float32")
         else:
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
+            if args.load_in_8bit:
+                settings.append("device_map='auto'")
+                settings.append("load_in_8bit=True")
+            else:
+                settings.append("torch_dtype=torch.float16")
+                if args.auto_devices:
+                    settings.append("device_map='auto'")
+                else:
+                    cuda = ".cuda()"
+
+        settings = ', '.join(settings)
+        command = f"{command}(Path(f'models/{model_name}'), {settings}){cuda}"
+        model = eval(command)
 
     # Loading the tokenizer
     if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists():