Merge branch 'Model_Plugins' of https://github.com/ebolam/KoboldAI into Model_Plugins

environments/huggingface.yml

@@ -32,7 +32,7 @@ dependencies:
     - flask-ngrok
     - flask-cors
     - lupa==1.10
-    - transformers==4.29.*
+    - transformers==4.28.*
     - huggingface_hub==0.12.1
     - safetensors==0.3.1
     - accelerate==0.18.0

environments/rocm.yml

@@ -30,7 +30,7 @@ dependencies:
     - flask-ngrok
     - flask-cors
     - lupa==1.10
-    - transformers==4.29.*
+    - transformers==4.28.*
     - huggingface_hub==0.12.1
     - safetensors==0.3.1
     - accelerate==0.18.0

modeling/inference_models/hf_torch.py

@@ -536,10 +536,9 @@ class HFTorchInferenceModel(HFInferenceModel):
                                 last_storage_key = storage_key
                                 if isinstance(f, zipfile.ZipExtFile):
                                     f.close()
-                                try:
+                                ziproot = z.namelist()[0].split("/")[0]
-                                    f = z.open(f"archive/data/{storage_key}")
+                                f = z.open(f"{ziproot}/data/{storage_key}")
-                                except:
+                                
-                                    f = z.open(f"{zipfolder}/data/{storage_key}")
                                 current_offset = 0
                             if current_offset != model_dict[key].seek_offset:
                                 f.read(model_dict[key].seek_offset - current_offset)

modeling/lazy_loader.py

@@ -196,6 +196,8 @@ class RestrictedUnpickler(pickle.Unpickler):
             return collections.OrderedDict
         elif module == "torch._utils" and name == "_rebuild_tensor_v2":
             return torch._utils._rebuild_tensor_v2
+        elif module == "torch._tensor" and name == "_rebuild_from_type_v2":
+            return torch._tensor._rebuild_from_type_v2
         elif module == "torch" and name in (
             "DoubleStorage",
             "FloatStorage",
@@ -207,6 +209,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             "ByteStorage",
             "BoolStorage",
             "BFloat16Storage",
+            "Tensor",
         ):
             return getattr(torch, name)
         elif module == "numpy.core.multiarray" and name == "scalar":
@@ -219,7 +222,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             # Forbid everything else.
             qualified_name = name if module == "__builtin__" else f"{module}.{name}"
             raise pickle.UnpicklingError(
-                f"`{qualified_name}` is forbidden; the model you are loading probably contains malicious code"
+                f"`{qualified_name}` is forbidden; the model you are loading probably contains malicious code. If you think this is incorrect ask the developer to unban the ability for {module} to execute {name}"
             )
 
     def load(self, *args, **kwargs):

requirements.txt

@@ -1,4 +1,4 @@
-transformers==4.29.*
+transformers==4.28.*
 huggingface_hub==0.12.1
 Flask==2.2.3
 Flask-SocketIO==5.3.2

requirements_mtj.txt

@@ -5,7 +5,7 @@ requests
 dm-haiku==0.0.9
 jax==0.3.25
 jaxlib==0.3.25
-transformers==4.29.*
+transformers==4.28.*
 chex == 0.1.5
 huggingface_hub==0.12.1
 progressbar2