Merge branch 'main' into add-train-lora-tab

.github/workflows/stale.yml

@@ -13,7 +13,7 @@ jobs:
       - uses: actions/stale@v5
         with:
           stale-issue-message: ""
-          close-issue-message: "This issue has been closed due to inactivity for 30 days. If you believe it is still relevant, you can reopen it (if you are the author) or leave a comment below."
+          close-issue-message: "This issue has been closed due to inactivity for 30 days. If you believe it is still relevant, please leave a comment below."
           days-before-issue-stale: 30
           days-before-issue-close: 0
           stale-issue-label: "stale"

README.md

@@ -199,11 +199,14 @@ Optionally, you can use the following command-line flags:
 | `--no-stream`    | Don't stream the text output in real time. |
 | `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag.|
 |  `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
+|  `--model-dir MODEL_DIR`                    | Path to directory with all the models |
+|  `--lora-dir LORA_DIR`                      | Path to directory with all the loras |
+|  `--verbose`                                | Print the prompts to the terminal. |
 |  `--listen`                                 | Make the web UI reachable from your local network. |
 |  `--listen-port LISTEN_PORT`                | The listening port that the server will use. |
 |  `--share`                                  | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
 |  `--auto-launch`                            | Open the web UI in the default browser upon launch. |
-| `--verbose`      | Print the prompts to the terminal. |
+|  `--gradio-auth-path GRADIO_AUTH_PATH`      | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
 
 Out of memory errors? [Check the low VRAM guide](https://github.com/oobabooga/text-generation-webui/wiki/Low-VRAM-guide).
 

modules/LoRA.py

@@ -32,7 +32,7 @@ def add_lora_to_model(lora_name):
             elif shared.args.load_in_8bit:
                 params['device_map'] = {'': 0}
             
-        shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params)
+        shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_name}"), **params)
         if not shared.args.load_in_8bit and not shared.args.cpu:
             shared.model.half()
             if not hasattr(shared.model, "hf_device_map"):

modules/models.py

@@ -46,9 +46,9 @@ def load_model(model_name):
     # Default settings
     if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
         if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
+            model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
         else:
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
+            model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
             if torch.has_mps:
                 device = torch.device('mps')
                 model = model.to(device)
@@ -76,11 +76,11 @@ def load_model(model_name):
                             num_bits=4, group_size=64,
                             group_dim=2, symmetric=False))
 
-        model = OptLM(f"facebook/{shared.model_name}", env, "models", policy)
+        model = OptLM(f"facebook/{shared.model_name}", env, shared.args.model_dir, policy)
 
     # DeepSpeed ZeRO-3
     elif shared.args.deepspeed:
-        model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
+        model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
         model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
         model.module.eval() # Inference
         print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
@@ -89,8 +89,8 @@ def load_model(model_name):
     elif shared.is_RWKV:
         from modules.RWKV import RWKVModel, RWKVTokenizer
 
-        model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
-        tokenizer = RWKVTokenizer.from_pretrained(Path('models'))
+        model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
+        tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir))
 
         return model, tokenizer
 
@@ -142,7 +142,7 @@ def load_model(model_name):
             if shared.args.disk:
                 params["offload_folder"] = shared.args.disk_cache_dir
 
-        checkpoint = Path(f'models/{shared.model_name}')
+        checkpoint = Path(f'{shared.args.model_dir}/{shared.model_name}')
 
         if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
             config = AutoConfig.from_pretrained(checkpoint)
@@ -159,10 +159,10 @@ def load_model(model_name):
         model = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
 
     # Loading the tokenizer
-    if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists():
-        tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
+    if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
+        tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
     else:
-        tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/"))
+        tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"))
     tokenizer.truncation_side = 'left'
 
     print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")

modules/shared.py

@@ -107,11 +107,14 @@ parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile t
 parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.')
 parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
 parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
+parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models")
+parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
+parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
 parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
 parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
 parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
 parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
-parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
+parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None)
 args = parser.parse_args()
 
 # Provisional, this will be deleted later

requirements.txt

@@ -1,12 +1,12 @@
-accelerate==0.17.1
-bitsandbytes==0.37.1
+accelerate==0.18.0
+bitsandbytes==0.37.2
 flexgen==0.1.7
 gradio==3.23.0
 markdown
 numpy
 peft==0.2.0
 requests
-rwkv==0.7.0
+rwkv==0.7.1
 safetensors==0.3.0
 sentencepiece
 tqdm

server.py

@@ -31,9 +31,9 @@ if settings_file is not None:
 
 def get_available_models():
     if shared.args.flexgen:
-        return sorted([re.sub('-np$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('-np')], key=str.lower)
+        return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower)
     else:
-        return sorted([re.sub('.pth$', '', item.name) for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
+        return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 def get_available_presets():
     return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
@@ -55,7 +55,7 @@ def get_available_softprompts():
     return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower)
 
 def get_available_loras():
-    return ['None'] + sorted([item.name for item in list(Path('loras/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
+    return ['None'] + sorted([item.name for item in list(Path('shared.args.lora_dir').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 def unload_model():
     shared.model = shared.tokenizer = None
@@ -499,12 +499,21 @@ def create_interface():
         if shared.args.extensions is not None:
             extensions_module.create_extensions_block()
 
+    # Authentication
+    auth = None
+    if shared.args.gradio_auth_path is not None:
+        gradio_auth_creds = []
+        with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
+            for line in file.readlines():
+                gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
+        auth = [tuple(cred.split(':')) for cred in gradio_auth_creds]
+
     # Launch the interface
     shared.gradio['interface'].queue()
     if shared.args.listen:
-        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
+        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
     else:
-        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
+        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
 
 create_interface()