General improvements

modules/llamacpp_model.py

@@ -1,10 +1,10 @@
-import os
 from pathlib import Path
-import modules.shared as shared
-from modules.callbacks import Iteratorize
 
 import llamacpp
 
+import modules.shared as shared
+from modules.callbacks import Iteratorize
+
 
 class LlamaCppTokenizer:
     """A thin wrapper over the llamacpp tokenizer"""
@@ -37,19 +37,19 @@ class LlamaCppModel:
 
         result = self()
         result.model = _model
+        result.params = params
 
         tokenizer = LlamaCppTokenizer.from_model(_model)
         return result, tokenizer
 
-    # TODO: Allow passing in params for each inference
-    def generate(self, context="", num_tokens=10, callback=None):
-        # params = self.params
-        # params.n_predict = token_count
-        # params.top_p = top_p
-        # params.top_k = top_k
-        # params.temp = temperature
-        # params.repeat_penalty = repetition_penalty
-        # params.repeat_last_n = repeat_last_n
+    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None):
+        params = self.params
+        params.n_predict = token_count
+        params.top_p = top_p
+        params.top_k = top_k
+        params.temp = temperature
+        params.repeat_penalty = repetition_penalty
+        #params.repeat_last_n = repeat_last_n
 
         # model.params = params
         self.model.add_bos()
@@ -58,7 +58,7 @@ class LlamaCppModel:
         output = ""
         is_end_of_text = False
         ctr = 0
-        while ctr < num_tokens and not is_end_of_text:
+        while ctr < token_count and not is_end_of_text:
             if self.model.has_unconsumed_input():
                 self.model.ingest_all_pending_input()
             else:
@@ -68,14 +68,13 @@ class LlamaCppModel:
                 is_end_of_text = token == self.model.token_eos()
                 if callback:
                     callback(text)
-                output += text
                 ctr += 1
 
         return output
 
     def generate_with_streaming(self, **kwargs):
         with Iteratorize(self.generate, kwargs, callback=None) as generator:
-            reply = kwargs['context']
+            reply = ''
             for token in generator:
                 reply += token
                 yield reply

modules/text_generation.py

@@ -22,7 +22,7 @@ def get_max_prompt_length(tokens):
     return max_length
 
 def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
-    if shared.is_RWKV or shared.is_llamacpp:
+    if any((shared.is_RWKV, shared.is_llamacpp)):
         input_ids = shared.tokenizer.encode(str(prompt))
         input_ids = np.array(input_ids).reshape(1, len(input_ids))
         return input_ids
@@ -116,7 +116,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
 
     # These models are not part of Hugging Face, so we handle them
     # separately and terminate the function call earlier
-    if shared.is_RWKV:
+    if any((shared.is_RWKV, shared.is_llamacpp)):
         try:
             if shared.args.no_stream:
                 reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
@@ -142,24 +142,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             input_ids = encode(question)
             print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
             return
-    elif shared.is_llamacpp:
-        try:
-            if shared.args.no_stream:
-                reply = shared.model.generate(context=question, num_tokens=max_new_tokens)
-                yield formatted_outputs(reply, shared.model_name)
-            else:
-                if not (shared.args.chat or shared.args.cai_chat):
-                    yield formatted_outputs(question, shared.model_name)
-                for reply in shared.model.generate_with_streaming(context=question, num_tokens=max_new_tokens):
-                    yield formatted_outputs(reply, shared.model_name)
-        except Exception as e:
-            print(e)
-        finally:
-            t1 = time.time()
-            output = encode(reply)[0]
-            input_ids = encode(question)
-            print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
-            return
 
     input_ids = encode(question, max_new_tokens)
     original_input_ids = input_ids

requirements.txt

@@ -2,6 +2,7 @@ accelerate==0.18.0
 bitsandbytes==0.37.2
 flexgen==0.1.7
 gradio==3.23.0
+llamacpp==0.1.10
 markdown
 numpy
 peft==0.2.0
@@ -11,5 +12,4 @@ safetensors==0.3.0
 sentencepiece
 tqdm
 datasets
-llamacpp>=0.1.9
 git+https://github.com/huggingface/transformers