修复fastllm内存问题，修改repeat_penalty默认值

package/fastllm_pytools/llm.py

@@ -34,7 +34,8 @@ fastllm_lib.fetch_response_logits_llm_model.restype = ctypes.c_int
 fastllm_lib.response_str_llm_model.argtypes = [ctypes.c_int, ctypes.c_char_p,
                                                ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
                                                ctypes.c_float, ctypes.c_float, ctypes.c_bool]
-fastllm_lib.response_str_llm_model.restype = ctypes.c_char_p
+# fastllm_lib.response_str_llm_model.restype = ctypes.c_char_p
+fastllm_lib.response_str_llm_model.restype = ctypes.POINTER(ctypes.c_char)
 
 fastllm_lib.launch_response_str_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p,
                                                      ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
@@ -42,20 +43,24 @@ fastllm_lib.launch_response_str_llm_model.argtype = [ctypes.c_int, ctypes.c_char
 fastllm_lib.launch_response_str_llm_model.restype = ctypes.c_int
 
 fastllm_lib.fetch_response_str_llm_model.argtypes = [ctypes.c_int, ctypes.c_int]
-fastllm_lib.fetch_response_str_llm_model.restype = ctypes.c_char_p
+# fastllm_lib.fetch_response_str_llm_model.restype = ctypes.c_char_p
+fastllm_lib.fetch_response_str_llm_model.restype = ctypes.POINTER(ctypes.c_char)
 
 fastllm_lib.make_history_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.c_char_p, ctypes.c_char_p]
-fastllm_lib.make_history_llm_model.restype = ctypes.c_char_p
+# fastllm_lib.make_history_llm_model.restype = ctypes.c_char_p
+fastllm_lib.make_history_llm_model.restype = ctypes.POINTER(ctypes.c_char)
 
 fastllm_lib.make_input_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.c_char_p]
-fastllm_lib.make_input_llm_model.restype = ctypes.c_char_p
+# fastllm_lib.make_input_llm_model.restype = ctypes.c_char_p
+fastllm_lib.make_input_llm_model.restype = ctypes.POINTER(ctypes.c_char)
 
 fastllm_lib.add_tokenizer_word_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p, ctypes.c_float, ctypes.c_int]
 
 fastllm_lib.set_device_map.argtype = [ctypes.c_int, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p]
 
 fastllm_lib.get_llm_model_type.argtype = [ctypes.c_int]
-fastllm_lib.get_llm_model_type.restype = ctypes.c_char_p
+# fastllm_lib.get_llm_model_type.restype = ctypes.c_char_p
+fastllm_lib.get_llm_model_type.restype = ctypes.POINTER(ctypes.c_char)
 
 fastllm_lib.response_batch_str_llm_model.argtypes = [ctypes.c_int, ctypes.POINTER(ctypes.c_char_p), ctypes.c_int,
                                                      ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
@@ -67,6 +72,11 @@ fastllm_lib.response_batch_tokens_llm_model.argtypes = [ctypes.c_int, ctypes.c_i
                                                         ctypes.c_float, ctypes.c_float, ctypes.c_bool]
 fastllm_lib.response_batch_tokens_llm_model.restype = ctypes.POINTER(ctypes.c_char_p)
 
+fastllm_lib.freeChars.argtype = [ctypes.POINTER(ctypes.c_char)]
+# fastllm_lib.freeChars.restype = ctypes.c_char_p
+
+fastllm_lib.freeCharArray.argtype = [ctypes.POINTER(ctypes.c_char_p)]
+
 def set_cpu_threads(threads: int):
     fastllm_lib.set_cpu_threads(threads);
 
@@ -134,7 +144,9 @@ class model:
         # 不做成自动缓存是为了避免在多线程调用的时候对缓存dict加锁，同时也为不同场景提供选择空间
         self.tokenizer_decode_token_cache = None
 
-        self.model_type = fastllm_lib.get_llm_model_type(self.model).decode()
+        model_type_ptr = fastllm_lib.get_llm_model_type(self.model)
+        self.model_type = ctypes.string_at(model_type_ptr).decode()
+        fastllm_lib.freeChars(model_type_ptr)
         # print("model_type:", self.model_type)
 
     def get_prompt(self,
@@ -144,8 +156,13 @@ class model:
             history = [];
         prompt = "";
         for i, (old_query, response) in enumerate(history):
-            prompt = fastllm_lib.make_history_llm_model(self.model, prompt.encode(), i, old_query.encode(), response.encode()).decode();
-        prompt = fastllm_lib.make_input_llm_model(self.model, prompt.encode(), len(history), query.encode()).decode();
+            history_ptr = fastllm_lib.make_history_llm_model(self.model, prompt.encode(), i, old_query.encode(), response.encode())
+            prompt = ctypes.string_at(history_ptr).decode()
+            fastllm_lib.freeChars(history_ptr)
+        
+        input_ptr = fastllm_lib.make_input_llm_model(self.model, prompt.encode(), len(history), query.encode())
+        prompt = ctypes.string_at(input_ptr).decode()
+        fastllm_lib.freeChars(input_ptr)
         return prompt;
 
     def save(self, path : str):
@@ -241,7 +258,7 @@ class model:
     def response(self,
                  query: str,
                  history: List[Tuple[str, str]] = None,
-                 max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0) -> str:
+                 max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1) -> str:
         ret = "";
         for i in self.stream_response(query = query,
                                       history = history,
@@ -257,7 +274,7 @@ class model:
     def stream_response(self,
                         query: str,
                         history: List[Tuple[str, str]] = None,
-                        max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0,
+                        max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1,
                         one_by_one = True):
         prompt = query if self.direct_query else self.get_prompt(query, history);
         handle = fastllm_lib.launch_response_str_llm_model(self.model, prompt.encode(),
@@ -267,10 +284,13 @@ class model:
         ret = b'';
         fail_cnt = 0;
         while True:
-            ret += fastllm_lib.fetch_response_str_llm_model(self.model, handle);
+            # ret += fastllm_lib.fetch_response_str_llm_model(self.model, handle);
+            ret_chararry = fastllm_lib.fetch_response_str_llm_model(self.model, handle);
+            ret += ctypes.string_at(ret_chararry)
+            fastllm_lib.freeChars(ret_chararry)
             cur = "";
             try:
-                cur = ret.decode();
+                cur = ret.decode()
                 ret = b'';
             except:
                 fail_cnt += 1;
@@ -289,7 +309,7 @@ class model:
 
     def stream_response_raw(self,
                             input_tokens: List[int],
-                            max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0,
+                            max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1,
                             one_by_one = True
                             ):
         handle = fastllm_lib.launch_response_llm_model(self.model, len(input_tokens),
@@ -315,7 +335,7 @@ class model:
                 yield total_bytes
 
     def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192,
-             do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0, **kwargs):
+             do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1, **kwargs):
         if self.model_type  != "chatglm3":
             if (not(history)):
                 history = [];
@@ -356,7 +376,7 @@ class model:
                 return response, new_history
 
     def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values = None,
-                    max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0,
+                    max_length: int = 8192, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1,
                     return_past_key_values = False, **kwargs) -> str:
         if self.model_type  != "chatglm3":
             if (not(history)):
@@ -445,7 +465,7 @@ class model:
 
     def response_batch(self, querys: List[str],
                        historys: List[List[Tuple[str, str]]] = None,
-                       max_length: int = 1024, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0,
+                       max_length: int = 1024, do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1,
                        **kwargs) -> List[str]:
         query_size = len(querys)
         if (not(historys)):
@@ -463,10 +483,11 @@ class model:
             response = ctypes.string_at(outputs[i]).decode()
             responses.append(response)
             historys[i] = historys[i] + [(querys[i], response)]
+        fastllm_lib.freeCharArray(outputs)
         return responses, historys
 
     def chat_batch(self, tokenizer, querys: List[str], historys: List[List[Tuple[str, str]]] = None, max_length: int = 1024,
-                   do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.0, **kwargs):
+                   do_sample = True, top_p = 0.8, top_k = 1, temperature = 1.0, repeat_penalty = 1.1, **kwargs):
         query_size = len(querys)
         if (not(historys)):
             historys = [[] for _ in range(query_size)]
@@ -490,6 +511,7 @@ class model:
                 response = ctypes.string_at(outputs[i]).decode()
                 responses.append(response)
                 historys[i] = historys[i] + [(querys[i], response)]
+            fastllm_lib.freeCharArray(outputs)
             return responses, historys