Reuse disk-cache-dir parameter for flexgen

server.py

@@ -42,7 +42,7 @@ parser.add_argument('--load-in-8bit', action='store_true', help='Load the model
 parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
 parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
 parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
-parser.add_argument('--disk-cache-dir', type=str, help='Directory to save the disk cache to. Defaults to "cache/".')
+parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
 parser.add_argument('--gpu-memory', type=int, help='Maximum GPU memory in GiB to allocate. This is useful if you get out of memory errors while trying to generate text. Must be an integer number.')
 parser.add_argument('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
 parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
@@ -122,7 +122,7 @@ def load_model(model_name):
     elif args.flexgen:
         gpu = TorchDevice("cuda:0")
         cpu = TorchDevice("cpu")
-        disk = TorchDisk("cache")
+        disk = TorchDisk(args.disk_cache_dir)
         env = Env(gpu=gpu, cpu=cpu, disk=disk, mixed=TorchMixedDevice([gpu, cpu, disk]))
 
         # Offloading policy
@@ -178,7 +178,7 @@ def load_model(model_name):
                 print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
                 params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{args.cpu_memory or '99'}GiB'}}")
             if args.disk:
-                params.append(f"offload_folder='{args.disk_cache_dir or 'cache'}'")
+                params.append(f"offload_folder='{args.disk_cache_dir}'")
 
         command = f"{command}(Path(f'models/{model_name}'), {', '.join(set(params))})"
         model = eval(command)