Bump llama-cpp-python to 0.2.18 (#4611)

modules/llamacpp_model.py

@@ -1,6 +1,7 @@
 import re
 from functools import partial
 
+import llama_cpp
 import numpy as np
 import torch
 
@@ -9,23 +10,6 @@ from modules.callbacks import Iteratorize
 from modules.logging_colors import logger
 from modules.text_generation import get_max_prompt_length
 
-try:
-    import llama_cpp
-except:
-    llama_cpp = None
-
-try:
-    import llama_cpp_cuda
-except:
-    llama_cpp_cuda = None
-
-
-def llama_cpp_lib():
-    if (shared.args.cpu and llama_cpp is not None) or llama_cpp_cuda is None:
-        return llama_cpp
-    else:
-        return llama_cpp_cuda
-
 
 def ban_eos_logits_processor(eos_token, input_ids, logits):
     logits[eos_token] = -float('inf')
@@ -50,10 +34,6 @@ class LlamaCppModel:
 
     @classmethod
     def from_pretrained(self, path):
-
-        Llama = llama_cpp_lib().Llama
-        LlamaCache = llama_cpp_lib().LlamaCache
-
         result = self()
         cache_capacity = 0
         if shared.args.cache_capacity is not None:
@@ -74,7 +54,6 @@ class LlamaCppModel:
         params = {
             'model_path': str(path),
             'n_ctx': shared.args.n_ctx,
-            'seed': int(shared.args.llama_cpp_seed),
             'n_threads': shared.args.threads or None,
             'n_threads_batch': shared.args.threads_batch or None,
             'n_batch': shared.args.n_batch,
@@ -88,9 +67,9 @@ class LlamaCppModel:
             'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
         }
 
-        result.model = Llama(**params)
+        result.model = llama_cpp.Llama(**params)
         if cache_capacity > 0:
-            result.model.set_cache(LlamaCache(capacity_bytes=cache_capacity))
+            result.model.set_cache(llama_cpp.LlamaCache(capacity_bytes=cache_capacity))
 
         # This is ugly, but the model and the tokenizer are the same object in this library.
         return result, result
@@ -114,13 +93,13 @@ class LlamaCppModel:
         if string != self.grammar_string:
             self.grammar_string = string
             if string.strip() != '':
-                self.grammar = llama_cpp_lib().LlamaGrammar.from_string(string)
+                self.grammar = llama_cpp.LlamaGrammar.from_string(string)
             else:
                 self.grammar = None
 
     def generate(self, prompt, state, callback=None):
 
-        LogitsProcessorList = llama_cpp_lib().LogitsProcessorList
+        LogitsProcessorList = llama_cpp.LogitsProcessorList
 
         prompt = prompt if type(prompt) is str else prompt.decode()
 
@@ -144,15 +123,16 @@ class LlamaCppModel:
             max_tokens=state['max_new_tokens'],
             temperature=state['temperature'],
             top_p=state['top_p'],
-            top_k=state['top_k'],
-            repeat_penalty=state['repetition_penalty'],
-            presence_penalty=state['presence_penalty'],
             frequency_penalty=state['frequency_penalty'],
+            presence_penalty=state['presence_penalty'],
+            repeat_penalty=state['repetition_penalty'],
+            top_k=state['top_k'],
+            stream=True,
+            seed=int(state['seed']) if state['seed'] != -1 else None,
             tfs_z=state['tfs'],
             mirostat_mode=int(state['mirostat_mode']),
             mirostat_tau=state['mirostat_tau'],
             mirostat_eta=state['mirostat_eta'],
-            stream=True,
             logits_processor=logit_processors,
             grammar=self.grammar
         )