Utils: Support safetensors aria2 download

utils.py

@@ -193,23 +193,6 @@ def num_layers(config):
 from flask_socketio import emit
             
 def _download_with_aria2(aria2_config: str, total_length: int, directory: str = ".", user_agent=None, force_download=False, use_auth_token=None):
-    class Send_to_socketio(object):
-        def write(self, bar):
-            bar = bar.replace("\r", "").replace("\n", "")
-            
-            if bar != "" and [ord(num) for num in bar] != [27, 91, 65]: #No idea why we're getting the 27, 1, 65 character set, just killing to so we can move on
-                try:
-                    print('\r' + bar, end='')
-                    try:
-                        socketio.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True, room="UI_1")
-                    except:
-                        pass
-                    eventlet.sleep(seconds=0)
-                except:
-                    pass
-        def flush(self):
-            pass
-    
     import transformers
     aria2_port = 6799 if koboldai_vars is None else koboldai_vars.aria2_port
     lengths = {}
@@ -244,7 +227,7 @@ def _download_with_aria2(aria2_config: str, total_length: int, directory: str =
                     if k not in visited:
                         lengths[k] = (v[1], v[1])
                 if bar is None:
-                    bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000, file=Send_to_socketio())
+                    bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000, file=UIProgressBarFile())
                     koboldai_vars.status_message = "Download Model"
                     koboldai_vars.total_download_chunks = sum(v[1] for v in lengths.values())
                 koboldai_vars.downloaded_chunks = sum(v[0] for v in lengths.values())
@@ -280,11 +263,9 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
                 raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
     _cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
     _revision = koboldai_vars.revision if koboldai_vars.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
-    sharded = False
     headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
     if use_auth_token:
         headers["authorization"] = f"Bearer {use_auth_token}"
-
     storage_folder = os.path.join(_cache_dir, huggingface_hub.file_download.repo_folder_name(repo_id=pretrained_model_name_or_path, repo_type="model"))
     os.makedirs(storage_folder, exist_ok=True)
 
@@ -294,25 +275,37 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
         except ValueError:
             return False
         return True
-    while True:  # Try to get the huggingface.co URL of the model's pytorch_model.bin or pytorch_model.bin.index.json file
+
-        try:
+    filename = None
-            filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
+
-        except AttributeError:
+    # NOTE: For now sharded Safetensors models are not supported. Haven't seen
-            return
+    # one of these out in the wild yet, probably due to how Safetensors has a
-        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
+    # lot of benifits of sharding built in
-        if is_cached(filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
+    for possible_filename in [
+        transformers.modeling_utils.SAFE_WEIGHTS_NAME,
+        transformers.modeling_utils.WEIGHTS_INDEX_NAME,
+        transformers.modeling_utils.WEIGHTS_NAME
+    ]:
+        # Try to get the huggingface.co URL of the model's weights file(s)
+        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, possible_filename, revision=_revision)
+
+        if is_cached(possible_filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
+            filename = possible_filename
             break
-        if sharded:
+
+    if not filename:
         return
+
+    if filename not in [transformers.modeling_utils.WEIGHTS_INDEX_NAME]:
+        # If the model isn't sharded, theres only one file to download
+        filenames = [filename]
     else:
-            sharded = True
+        # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
-    if not sharded:  # If the model has a pytorch_model.bin file, that's the only file to download
-        filenames = [transformers.modeling_utils.WEIGHTS_NAME]
-    else:  # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
         map_filename = huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, use_auth_token=use_auth_token, user_agent=user_agent, revision=revision)
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
+
     urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
     if not force_download:
         urls = [u for u, n in zip(urls, filenames) if not is_cached(n)]