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import ctypes;
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import math
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import os;
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import threading
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from typing import Optional, Tuple, Union, List, Callable, Dict, Any;
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from copy import deepcopy
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import platform
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if platform.system() == 'Windows':
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fastllm_lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.split(os.path.realpath(__file__))[0], "fastllm_tools.dll"))
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else:
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fastllm_lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.split(os.path.realpath(__file__))[0], "libfastllm_tools.so"))
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fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
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fastllm_lib.create_llm_model.restype = ctypes.c_int
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fastllm_lib.token_decode.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p]
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fastllm_lib.token_decode.restype = ctypes.c_int
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fastllm_lib.token_encode_string.argtypes = [ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_int)]
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fastllm_lib.token_encode_string.restype = ctypes.c_int
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fastllm_lib.launch_response_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_void_p,
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ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
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ctypes.c_float, ctypes.c_float, ctypes.c_bool]
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fastllm_lib.launch_response_llm_model.restype = ctypes.c_int
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fastllm_lib.fetch_response_llm_model.argtypes = [ctypes.c_int, ctypes.c_int]
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fastllm_lib.fetch_response_llm_model.restype = ctypes.c_int
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fastllm_lib.fetch_response_logits_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_float)]
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fastllm_lib.fetch_response_logits_llm_model.restype = ctypes.c_int
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fastllm_lib.response_str_llm_model.argtypes = [ctypes.c_int, ctypes.c_char_p,
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ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
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ctypes.c_float, ctypes.c_float, ctypes.c_bool]
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fastllm_lib.response_str_llm_model.restype = ctypes.c_char_p
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fastllm_lib.launch_response_str_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p,
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ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
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ctypes.c_float, ctypes.c_float, ctypes.c_bool]
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fastllm_lib.launch_response_str_llm_model.restype = ctypes.c_int
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fastllm_lib.fetch_response_str_llm_model.argtypes = [ctypes.c_int, ctypes.c_int]
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fastllm_lib.fetch_response_str_llm_model.restype = ctypes.c_char_p
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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]
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fastllm_lib.make_history_llm_model.restype = ctypes.c_char_p
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fastllm_lib.make_input_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.c_char_p]
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fastllm_lib.make_input_llm_model.restype = ctypes.c_char_p
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fastllm_lib.add_tokenizer_word_llm_model.argtype = [ctypes.c_int, ctypes.c_char_p, ctypes.c_float, ctypes.c_int]
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fastllm_lib.set_device_map.argtype = [ctypes.c_int, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p]
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fastllm_lib.get_llm_model_type.argtype = [ctypes.c_int]
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fastllm_lib.get_llm_model_type.restype = ctypes.c_char_p
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fastllm_lib.response_batch_str_llm_model.argtypes = [ctypes.c_int, ctypes.POINTER(ctypes.c_char_p), ctypes.c_int,
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ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
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ctypes.c_float, ctypes.c_float, ctypes.c_bool]
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fastllm_lib.response_batch_str_llm_model.restype = ctypes.POINTER(ctypes.c_char_p)
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fastllm_lib.response_batch_tokens_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_int), ctypes.POINTER(ctypes.c_int),
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ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
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ctypes.c_float, ctypes.c_float, ctypes.c_bool]
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fastllm_lib.response_batch_tokens_llm_model.restype = ctypes.POINTER(ctypes.c_char_p)
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def set_cpu_threads(threads: int):
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fastllm_lib.set_cpu_threads(threads);
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def get_cpu_threads() -> int:
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return fastllm_lib.get_cpu_threads();
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def print_ins_info():
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fastllm_lib.print_cpu_ins();
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def set_cpu_kvcache(cpu_kvcache):
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fastllm_lib.set_kvcache_in_cpu(ctypes.c_bool(cpu_kvcache));
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def get_cpu_kvcache():
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return fastllm_lib.get_kvcache_in_cpu();
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def set_cpu_low_mem(low_mem):
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fastllm_lib.set_cpu_low_mem(ctypes.c_bool(low_mem));
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def get_cpu_low_mem():
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return fastllm_lib.get_cpu_low_mem();
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def set_device_map(device_map):
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devices = [];
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values = [];
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if (isinstance(device_map, str)):
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devices.append(device_map);
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values.append(1);
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elif (isinstance(device_map, list)):
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devices = [str(x) for x in device_map];
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values = [1 for x in device_map];
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elif (isinstance(device_map, dict)):
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devices = [str(x) for x in device_map.keys()];
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values = [int(device_map[x]) for x in device_map.keys()];
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else:
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print("set_device_map error.");
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return;
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device_str = ''.join(devices);
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device_len = [len(x) for x in devices];
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fastllm_lib.set_device_map(len(device_len),
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(ctypes.c_int * len(device_len))(*device_len),
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device_str.encode(),
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(ctypes.c_int * len(values))(*values));
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def from_hf(model,
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tokenizer = None,
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dtype = "float16"):
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from fastllm_pytools import hf_model;
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return hf_model.create(model, tokenizer, dtype = dtype);
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class model:
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def __init__ (self, path : str,
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id : int = -99999):
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if (id != -99999):
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self.model = id;
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else:
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self.model = fastllm_lib.create_llm_model(path.encode());
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self.direct_query = False;
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# 为了减少重复申请释放buffer对象而使用的线程局部存储区对象池
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self.thread_local_obj = threading.local()
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self.thread_local_obj.tokenizer_encode_string__output_buffer = None
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self.thread_local_obj.tokenizer_decode_token__output_buffer = None
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# tokenizer_decode_token 输出结果的静态缓存，手工触发构建
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# 由于token数量有限且不太多，所以缓存该结果来减少调用较为适合。
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# 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
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self.tokenizer_decode_token_cache = None
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self.model_type = fastllm_lib.get_llm_model_type(self.model).decode()
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# print("model_type:", self.model_type)
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def get_prompt(self,
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query: str,
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history: List[Tuple[str, str]] = None) -> str:
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if (not(history)):
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history = [];
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prompt = "";
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for i, (old_query, response) in enumerate(history):
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prompt = fastllm_lib.make_history_llm_model(self.model, prompt.encode(), i, old_query.encode(), response.encode()).decode();
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prompt = fastllm_lib.make_input_llm_model(self.model, prompt.encode(), len(history), query.encode()).decode();
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return prompt;
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def save(self, path : str):
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fastllm_lib.save_llm_model(self.model, path.encode());
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def eval(self):
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pass;
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def build_tokenizer_decode_token_cache(self):
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if self.tokenizer_decode_token_cache is not None:
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return
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cache_dict = dict()
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vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model)
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for token_id in range(vocab_size):
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cache_dict[token_id] = self.tokenizer_decode_token(token_id)
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self.tokenizer_decode_token_cache = cache_dict
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def tokenizer_encode_string(self, content: str) -> List[int]:
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output_buffer_init_len = 1024
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if self.thread_local_obj.tokenizer_encode_string__output_buffer is None:
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self.thread_local_obj.tokenizer_encode_string__output_buffer = (ctypes.c_int * output_buffer_init_len)()
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buffer = self.thread_local_obj.tokenizer_encode_string__output_buffer
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buffer_len = len(buffer)
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result_len = fastllm_lib.token_encode_string(self.model, content.encode(), buffer_len, buffer)
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if result_len > buffer_len:
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if result_len > 10240:
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# 要处理的数据过长，使用一次性的buffer
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temp_buffer = (ctypes.c_int * result_len)()
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ret = fastllm_lib.token_encode_string(self.model, content.encode(), result_len, temp_buffer)
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return [i for i in temp_buffer]
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else:
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# 扩展buffer大小
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new_buffer_len = round(math.ceil(result_len / 1024.0)) * 1024
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buffer = (ctypes.c_int * new_buffer_len)()
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self.thread_local_obj.tokenizer_encode_string__output_buffer = buffer
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result_len = fastllm_lib.token_encode_string(self.model, content.encode(), new_buffer_len, buffer)
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return [buffer[i] for i in range(result_len)]
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def tokenizer_decode_token(self, token_id: int) -> bytes:
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if self.tokenizer_decode_token_cache is not None:
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cache_result = self.tokenizer_decode_token_cache.get(token_id)
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if cache_result is not None:
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return cache_result
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output_buffer_init_len = 256
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if self.thread_local_obj.tokenizer_decode_token__output_buffer is None:
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self.thread_local_obj.tokenizer_decode_token__output_buffer = ctypes.create_string_buffer(output_buffer_init_len)
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buffer = self.thread_local_obj.tokenizer_decode_token__output_buffer
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ret = fastllm_lib.token_decode(self.model, token_id, len(buffer), buffer)
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if ret > 0:
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# buffer长度不够，扩展buffer大小
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new_buffer_len = round(math.ceil(ret / 16.0)) * 16
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buffer = ctypes.create_string_buffer(new_buffer_len)
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self.thread_local_obj.tokenizer_decode_token__output_buffer = buffer
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ret = fastllm_lib.token_decode(self.model, token_id, len(buffer), buffer)
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assert ret == 0
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buffer_bytes = buffer.raw
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result_len = len(buffer_bytes)
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for i in range(len(buffer_bytes)):
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if buffer_bytes[i] == 0:
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result_len = i
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break
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return buffer_bytes[:result_len]
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def response_logits(self,
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query: str,
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history: List[Tuple[str, str]] = None,
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tokenizer = None) -> str:
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prompt = query if self.direct_query else self.get_prompt(query, history);
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if (tokenizer == None):
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handle = fastllm_lib.launch_response_str_llm_model(self.model, prompt.encode(),
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ctypes.c_int(1), ctypes.c_bool(False), ctypes.c_float(1), ctypes.c_int(1),
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ctypes.c_float(1), ctypes.c_float(1), ctypes.c_bool(True));
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else:
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input = tokenizer.encode(prompt);
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handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
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1, False, 1, 1, 1, 1, True);
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vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model);
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logits = list(range(vocab_size))
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array = (ctypes.c_float * (vocab_size * 4))(*logits);
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ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array);
|
|
|
|
|
|
out = list(array)[:vocab_size];
|
|
|
|
|
|
while (ret != -1):
|
|
|
|
|
|
ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array);
|
|
|
|
|
|
return out;
|
|
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
|
ret = "";
|
|
|
|
|
|
for i in self.stream_response(query = query,
|
|
|
|
|
|
history = history,
|
|
|
|
|
|
max_length = max_length,
|
|
|
|
|
|
do_sample = do_sample,
|
|
|
|
|
|
top_p = top_p, top_k = top_k,
|
|
|
|
|
|
temperature = temperature,
|
|
|
|
|
|
repeat_penalty = repeat_penalty,
|
|
|
|
|
|
one_by_one = True):
|
|
|
|
|
|
ret += i;
|
|
|
|
|
|
return ret;
|
|
|
|
|
|
|
|
|
|
|
|
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,
|
|
|
|
|
|
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(),
|
|
|
|
|
|
ctypes.c_int(max_length), ctypes.c_bool(do_sample), ctypes.c_float(top_p), ctypes.c_int(top_k),
|
|
|
|
|
|
ctypes.c_float(temperature), ctypes.c_float(repeat_penalty), ctypes.c_bool(False));
|
|
|
|
|
|
res = "";
|
|
|
|
|
|
ret = b'';
|
|
|
|
|
|
fail_cnt = 0;
|
|
|
|
|
|
while True:
|
|
|
|
|
|
ret += fastllm_lib.fetch_response_str_llm_model(self.model, handle);
|
|
|
|
|
|
cur = "";
|
|
|
|
|
|
try:
|
|
|
|
|
|
cur = ret.decode();
|
|
|
|
|
|
ret = b'';
|
|
|
|
|
|
except:
|
|
|
|
|
|
fail_cnt += 1;
|
|
|
|
|
|
if (fail_cnt == 20):
|
|
|
|
|
|
break;
|
|
|
|
|
|
else:
|
|
|
|
|
|
continue;
|
|
|
|
|
|
fail_cnt = 0;
|
|
|
|
|
|
if (cur == "<flmeos>"):
|
|
|
|
|
|
break;
|
|
|
|
|
|
if one_by_one:
|
|
|
|
|
|
yield cur;
|
|
|
|
|
|
else:
|
|
|
|
|
|
res += cur;
|
|
|
|
|
|
yield res;
|
|
|
|
|
|
|
|
|
|
|
|
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,
|
|
|
|
|
|
one_by_one = True
|
|
|
|
|
|
):
|
|
|
|
|
|
handle = fastllm_lib.launch_response_llm_model(self.model, len(input_tokens),
|
|
|
|
|
|
(ctypes.c_int * len(input_tokens))(*input_tokens),
|
|
|
|
|
|
ctypes.c_int(max_length), ctypes.c_bool(do_sample), ctypes.c_float(top_p), ctypes.c_int(top_k),
|
|
|
|
|
|
ctypes.c_float(temperature), ctypes.c_float(repeat_penalty), ctypes.c_bool(False))
|
|
|
|
|
|
|
|
|
|
|
|
# 可能遇到长尾char需要多个token才能够生成，所以只返回bytes，string.decode策略交给外部
|
|
|
|
|
|
# 方便统计输出token数量，和控制不完整utf8时候解码的逻辑
|
|
|
|
|
|
|
|
|
|
|
|
total_bytes = b''
|
|
|
|
|
|
while True:
|
|
|
|
|
|
cur_token = fastllm_lib.fetch_response_llm_model(self.model, handle)
|
|
|
|
|
|
if cur_token == -1:
|
|
|
|
|
|
break
|
|
|
|
|
|
|
|
|
|
|
|
cur_bytes = self.tokenizer_decode_token(cur_token)
|
|
|
|
|
|
|
|
|
|
|
|
if one_by_one:
|
|
|
|
|
|
yield cur_bytes
|
|
|
|
|
|
else:
|
|
|
|
|
|
total_bytes += cur_bytes
|
|
|
|
|
|
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):
|
|
|
|
|
|
if self.model_type != "chatglm3":
|
|
|
|
|
|
if (not(history)):
|
|
|
|
|
|
history = [];
|
|
|
|
|
|
prompt = query if self.direct_query else self.get_prompt(query, history);
|
|
|
|
|
|
input = tokenizer.encode(prompt);
|
|
|
|
|
|
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
|
|
|
|
|
|
False);
|
|
|
|
|
|
|
|
|
|
|
|
result = [];
|
|
|
|
|
|
while True:
|
|
|
|
|
|
cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
|
|
|
|
|
|
if (cur == -1):
|
|
|
|
|
|
break;
|
|
|
|
|
|
result.append(cur);
|
|
|
|
|
|
response = tokenizer.decode(result);
|
|
|
|
|
|
history = history + [(query, response)];
|
|
|
|
|
|
return response, history;
|
|
|
|
|
|
else:
|
|
|
|
|
|
if history is None:
|
|
|
|
|
|
history = []
|
|
|
|
|
|
role = "user"
|
|
|
|
|
|
input = self.build_chatglm3_input(tokenizer, query, history=history, role=role)
|
|
|
|
|
|
history.append({"role": role, "content": query})
|
|
|
|
|
|
|
|
|
|
|
|
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
|
|
|
|
|
|
False);
|
|
|
|
|
|
tokens = [];
|
|
|
|
|
|
while True:
|
|
|
|
|
|
cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
|
|
|
|
|
|
if (cur == -1):
|
|
|
|
|
|
break;
|
|
|
|
|
|
tokens.append(cur);
|
|
|
|
|
|
response = tokenizer.decode(tokens);
|
|
|
|
|
|
if response and response[-1] != "<EFBFBD>":
|
|
|
|
|
|
response, new_history = self.process_chatglm3_response(response, history)
|
|
|
|
|
|
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,
|
|
|
|
|
|
return_past_key_values = False, **kwargs) -> str:
|
|
|
|
|
|
if self.model_type != "chatglm3":
|
|
|
|
|
|
if (not(history)):
|
|
|
|
|
|
history = [];
|
|
|
|
|
|
prompt = query if self.direct_query else self.get_prompt(query, history);
|
|
|
|
|
|
input = tokenizer.encode(prompt);
|
|
|
|
|
|
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
|
|
|
|
|
|
False);
|
|
|
|
|
|
tokens = [];
|
|
|
|
|
|
while True:
|
|
|
|
|
|
cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
|
|
|
|
|
|
if (cur == -1):
|
|
|
|
|
|
break;
|
|
|
|
|
|
tokens.append(cur);
|
|
|
|
|
|
response = tokenizer.decode(tokens);
|
|
|
|
|
|
new_history = history + [(query, response)];
|
|
|
|
|
|
if return_past_key_values:
|
|
|
|
|
|
yield response, new_history, None;
|
|
|
|
|
|
else:
|
|
|
|
|
|
yield response, new_history;
|
|
|
|
|
|
else:
|
|
|
|
|
|
if history is None:
|
|
|
|
|
|
history = []
|
|
|
|
|
|
role = "user"
|
|
|
|
|
|
input = self.build_chatglm3_input(tokenizer, query, history=history, role=role)
|
|
|
|
|
|
history.append({"role": role, "content": query})
|
|
|
|
|
|
|
|
|
|
|
|
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
|
|
|
|
|
|
False);
|
|
|
|
|
|
tokens = [];
|
|
|
|
|
|
while True:
|
|
|
|
|
|
cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
|
|
|
|
|
|
if (cur == -1):
|
|
|
|
|
|
break;
|
|
|
|
|
|
tokens.append(cur);
|
|
|
|
|
|
response = tokenizer.decode(tokens);
|
|
|
|
|
|
if response and response[-1] != "<EFBFBD>":
|
|
|
|
|
|
response, new_history = self.process_chatglm3_response(response, history)
|
|
|
|
|
|
if return_past_key_values:
|
|
|
|
|
|
yield response, new_history, past_key_values
|
|
|
|
|
|
else:
|
|
|
|
|
|
yield response, new_history
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def set_adapter(self, name: str):
|
|
|
|
|
|
fastllm_lib.set_adapter(self.model, str(name).encode())
|
|
|
|
|
|
|
|
|
|
|
|
def disable_adapter(self):
|
|
|
|
|
|
fastllm_lib.disable_adapter(self.model)
|
|
|
|
|
|
|
|
|
|
|
|
def process_chatglm3_response(self, output, history):
|
|
|
|
|
|
content = ""
|
|
|
|
|
|
history = deepcopy(history)
|
|
|
|
|
|
for response in output.split("<|assistant|>"):
|
|
|
|
|
|
metadata, content = response.split("\n", maxsplit=1)
|
|
|
|
|
|
if not metadata.strip():
|
|
|
|
|
|
content = content.strip()
|
|
|
|
|
|
history.append({"role": "assistant", "metadata": metadata, "content": content})
|
|
|
|
|
|
content = content.replace("[[训练时间]]", "2023年")
|
|
|
|
|
|
else:
|
|
|
|
|
|
history.append({"role": "assistant", "metadata": metadata, "content": content})
|
|
|
|
|
|
if history[0]["role"] == "system" and "tools" in history[0]:
|
|
|
|
|
|
content = "\n".join(content.split("\n")[1:-1])
|
|
|
|
|
|
def tool_call(**kwargs):
|
|
|
|
|
|
return kwargs
|
|
|
|
|
|
parameters = eval(content)
|
|
|
|
|
|
content = {"name": metadata.strip(), "parameters": parameters}
|
|
|
|
|
|
else:
|
|
|
|
|
|
content = {"name": metadata.strip(), "content": content}
|
|
|
|
|
|
return content, history
|
|
|
|
|
|
|
|
|
|
|
|
def build_chatglm3_input(self, tokenizer, query, history=None, role="user"):
|
|
|
|
|
|
if history is None:
|
|
|
|
|
|
history = []
|
|
|
|
|
|
input_ids = []
|
|
|
|
|
|
for item in history:
|
|
|
|
|
|
content = item["content"]
|
|
|
|
|
|
if item["role"] == "system" and "tools" in item:
|
|
|
|
|
|
content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False)
|
|
|
|
|
|
input_ids.extend(tokenizer.build_single_message(item["role"], item.get("metadata", ""), content))
|
|
|
|
|
|
input_ids.extend(tokenizer.build_single_message(role, "", query))
|
|
|
|
|
|
input_ids.extend([tokenizer.get_command("<|assistant|>")])
|
|
|
|
|
|
return input_ids
|
|
|
|
|
|
|
|
|
|
|
|
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,
|
|
|
|
|
|
**kwargs) -> List[str]:
|
|
|
|
|
|
query_size = len(querys)
|
|
|
|
|
|
if (not(historys)):
|
|
|
|
|
|
historys = [[] for _ in range(query_size)]
|
|
|
|
|
|
inputs = (ctypes.c_char_p * query_size)()
|
|
|
|
|
|
for i, query in enumerate(querys):
|
|
|
|
|
|
prompt = query if self.direct_query else self.get_prompt(query, historys[i])
|
|
|
|
|
|
inputs[i] = ctypes.c_char_p(prompt.encode())
|
|
|
|
|
|
|
|
|
|
|
|
outputs = fastllm_lib.response_batch_str_llm_model(self.model, inputs, query_size,
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty, False)
|
|
|
|
|
|
|
|
|
|
|
|
responses = []
|
|
|
|
|
|
for i in range(query_size):
|
|
|
|
|
|
response = ctypes.string_at(outputs[i]).decode()
|
|
|
|
|
|
responses.append(response)
|
|
|
|
|
|
historys[i] = historys[i] + [(querys[i], response)]
|
|
|
|
|
|
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):
|
|
|
|
|
|
query_size = len(querys)
|
|
|
|
|
|
if (not(historys)):
|
|
|
|
|
|
historys = [[] for _ in range(query_size)]
|
|
|
|
|
|
|
|
|
|
|
|
inputs = []
|
|
|
|
|
|
inputs_len = []
|
|
|
|
|
|
for i, query in enumerate(querys):
|
|
|
|
|
|
prompt = query if self.direct_query else self.get_prompt(query, historys[i])
|
|
|
|
|
|
input = tokenizer.encode(prompt);
|
|
|
|
|
|
inputs.extend(input)
|
|
|
|
|
|
inputs_len.append(len(input))
|
|
|
|
|
|
|
|
|
|
|
|
outputs = fastllm_lib.response_batch_tokens_llm_model(self.model, query_size,
|
|
|
|
|
|
(ctypes.c_int * len(inputs_len))(*inputs_len),
|
|
|
|
|
|
(ctypes.c_int * len(inputs))(*inputs),
|
|
|
|
|
|
max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
|
|
|
|
|
|
False)
|
|
|
|
|
|
|
|
|
|
|
|
responses = []
|
|
|
|
|
|
for i in range(query_size):
|
|
|
|
|
|
response = ctypes.string_at(outputs[i]).decode()
|
|
|
|
|
|
responses.append(response)
|
|
|
|
|
|
historys[i] = historys[i] + [(querys[i], response)]
|
|
|
|
|
|
return responses, historys
|
|
|
|
|
|
|
|
|
|
|
|
|
