Use separate llama-cpp-python packages for GGML support

modules/llamacpp_hf.py

@@ -9,23 +9,38 @@ from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from modules import RoPE, shared
 from modules.logging_colors import logger
+from modules.utils import is_gguf
 
 import llama_cpp
 
+try:
+    import llama_cpp_ggml
+except:
+    llama_cpp_ggml = llama_cpp
+
 if torch.cuda.is_available() and not torch.version.hip:
     try:
         import llama_cpp_cuda
     except:
         llama_cpp_cuda = None
+    try:
+        import llama_cpp_ggml_cuda
+    except:
+        llama_cpp_ggml_cuda = llama_cpp_cuda
 else:
     llama_cpp_cuda = None
+    llama_cpp_ggml_cuda = None
 
 
-def llama_cpp_lib():
-    if shared.args.cpu or llama_cpp_cuda is None:
-        return llama_cpp
+def llama_cpp_lib(model_file: Union[str, Path] = None):
+    if model_file is not None:
+        gguf_model = is_gguf(model_file)
     else:
-        return llama_cpp_cuda
+        gguf_model = True
+    if shared.args.cpu or llama_cpp_cuda is None:
+        return llama_cpp if gguf_model else llama_cpp_ggml
+    else:
+        return llama_cpp_cuda if gguf_model else llama_cpp_ggml_cuda
 
 
 class LlamacppHF(PreTrainedModel):
@@ -165,7 +180,7 @@ class LlamacppHF(PreTrainedModel):
         if path.is_file():
             model_file = path
         else:
-            model_file = list(path.glob('*.gguf*'))[0]
+            model_file = (list(path.glob('*.gguf*')) + list(path.glob('*ggml*.bin')))[0]
 
         logger.info(f"llama.cpp weights detected: {model_file}\n")
 
@@ -193,7 +208,7 @@ class LlamacppHF(PreTrainedModel):
             'logits_all': True,
         }
 
-        Llama = llama_cpp_lib().Llama
+        Llama = llama_cpp_lib(model_file).Llama
         model = Llama(**params)
 
         return LlamacppHF(model)

modules/llamacpp_model.py

@@ -1,5 +1,7 @@
 import re
 from functools import partial
+from pathlib import Path
+from typing import Union
 
 import torch
 
@@ -7,23 +9,38 @@ from modules import RoPE, shared
 from modules.callbacks import Iteratorize
 from modules.logging_colors import logger
 from modules.text_generation import get_max_prompt_length
+from modules.utils import is_gguf
 
 import llama_cpp
 
+try:
+    import llama_cpp_ggml
+except:
+    llama_cpp_ggml = llama_cpp
+
 if torch.cuda.is_available() and not torch.version.hip:
     try:
         import llama_cpp_cuda
     except:
         llama_cpp_cuda = None
+    try:
+        import llama_cpp_ggml_cuda
+    except:
+        llama_cpp_ggml_cuda = llama_cpp_cuda
 else:
     llama_cpp_cuda = None
+    llama_cpp_ggml_cuda = None
 
 
-def llama_cpp_lib():
-    if shared.args.cpu or llama_cpp_cuda is None:
-        return llama_cpp
+def llama_cpp_lib(model_file: Union[str, Path] = None):
+    if model_file is not None:
+        gguf_model = is_gguf(model_file)
     else:
-        return llama_cpp_cuda
+        gguf_model = True
+    if shared.args.cpu or llama_cpp_cuda is None:
+        return llama_cpp if gguf_model else llama_cpp_ggml
+    else:
+        return llama_cpp_cuda if gguf_model else llama_cpp_ggml_cuda
 
 
 def ban_eos_logits_processor(eos_token, input_ids, logits):
@@ -41,8 +58,8 @@ class LlamaCppModel:
     @classmethod
     def from_pretrained(self, path):
 
-        Llama = llama_cpp_lib().Llama
-        LlamaCache = llama_cpp_lib().LlamaCache
+        Llama = llama_cpp_lib(str(path)).Llama
+        LlamaCache = llama_cpp_lib(str(path)).LlamaCache
 
         result = self()
         cache_capacity = 0

modules/models.py

@@ -241,7 +241,7 @@ def llamacpp_loader(model_name):
     if path.is_file():
         model_file = path
     else:
-        model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*.gguf*'))[0]
+        model_file = (list(Path(f'{shared.args.model_dir}/{model_name}').glob('*.gguf*')) + list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin')))[0]
 
     logger.info(f"llama.cpp weights detected: {model_file}")
     model, tokenizer = LlamaCppModel.from_pretrained(model_file)

modules/models_settings.py

@@ -24,9 +24,9 @@ def infer_loader(model_name):
         loader = None
     elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
         loader = 'AutoGPTQ'
-    elif len(list(path_to_model.glob('*.gguf*'))) > 0:
+    elif len(list(path_to_model.glob('*.gguf*')) + list(path_to_model.glob('*ggml*.bin'))) > 0:
         loader = 'llama.cpp'
-    elif re.match(r'.*\.gguf', model_name.lower()):
+    elif re.match(r'.*\.gguf|.*ggml.*\.bin', model_name.lower()):
         loader = 'llama.cpp'
     elif re.match(r'.*rwkv.*\.pth', model_name.lower()):
         loader = 'RWKV'

modules/utils.py

@@ -2,6 +2,7 @@ import os
 import re
 from datetime import datetime
 from pathlib import Path
+from typing import Union
 
 from modules import shared
 from modules.logging_colors import logger
@@ -124,3 +125,11 @@ def get_datasets(path: str, ext: str):
 
 def get_available_chat_styles():
     return sorted(set(('-'.join(k.stem.split('-')[1:]) for k in Path('css').glob('chat_style*.css'))), key=natural_keys)
+
+# Determines if a llama.cpp model is in GGUF format
+# Copied from ctransformers utils.py
+def is_gguf(path: Union[str, Path]) -> bool:
+    path = str(Path(path).resolve())
+    with open(path, "rb") as f:
+        magic = f.read(4)
+    return magic == "GGUF".encode()