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README.md
View file @ f4100f9b
# Baichuan-13B_CPP # Baichuan-13B_CPP
## 论文
`Baichuan 2: Open Large-scale Language Models`
https://arxiv.org/abs/2309.10305
## 模型结构 ## 模型结构
Baichuan系列模型是由百川智能开发的开源大规模预训练模型，包含7B和13B等规模。其中，Baichuan-7B在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。Baichuan-13B是由百川智能继Baichuan-7B之后开发的包含130亿参数模型，它在高质量的语料上训练了1.4万亿tokens，超过LLaMA-13B 40%，是当前开源 13B 尺寸下训练数据量最多的模型。此外，百川智能还发布了对齐模型（Baichuan-13B-Chat），具有很强的对话能力。 Baichuan系列模型是由百川智能开发的开源大规模预训练模型，包含7B和13B等规模。其中，Baichuan-7B在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。Baichuan-13B是由百川智能继Baichuan-7B之后开发的包含130亿参数模型，它在高质量的语料上训练了1.4万亿tokens，超过LLaMA-13B 40%，是当前开源 13B 尺寸下训练数据量最多的模型。此外，百川智能还发布了对齐模型（Baichuan-13B-Chat），具有很强的对话能力。
模型具体参数： 模型具体参数：
| 模型名称 | 隐含层维度 | 层数 | 头数 | 词表大小 | 总参数量 | 训练数据(tokens) | 位置编码 | 最大长 | | 模型名称 | 隐含层维度 | 层数 | 头数 | 词表大小 | 总参数量 | 位置编码 | 最大长 |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Baichuan-13B | 5,120 | 40 | 40 | 64,000 | 13,264,901,120 | 1.4万亿 | ALiBi | 4096 | | Baichuan-13B | 5,120 | 40 | 40 | 64,000 | 13,264,901,120 | ALiBi | 4096 |
## 算法原理 ## 算法原理
Baichuan整体模型基于标准的Transformer结构，采用了和LLaMA一样的模型设计。Baichuan-13B使用了ALiBi线性偏置技术，相对于Rotary Embedding计算量更小，对推理性能有显著提升。 Baichuan整体模型基于标准的Transformer结构，采用了和LLaMA一样的模型设计。Baichuan-13B使用了ALiBi线性偏置技术，相对于Rotary Embedding计算量更小，对推理性能有显著提升。
...@@ -109,6 +115,6 @@ chmod +x benchmark ...@@ -109,6 +115,6 @@ chmod +x benchmark
- https://developer.hpccube.com/codes/modelzoo/baichuan-13b_cpp - https://developer.hpccube.com/codes/modelzoo/baichuan-13b_cpp
## 参考 ## 参考资料
- [https://github.com/baichuan-inc/Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) - [https://github.com/baichuan-inc/Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B)
\ No newline at end of file
package/fastllm_pytools/hf_model.py
View file @ f4100f9b
from fastllm_pytools import llm from fastllm_pytools import llm;
import torch import torch;
import ctypes import ctypes;
import numpy as np import numpy as np;
fastllm_data_type_dict = { fastllm_data_type_dict = {
"int4": 8, "int4": 8,
...@@ -22,60 +22,61 @@ def create(model, ...@@ -22,60 +22,61 @@ def create(model,
history_sep = None, history_sep = None,
dtype = "float16"): dtype = "float16"):
if (dtype not in fastllm_data_type_dict): if (dtype not in fastllm_data_type_dict):
print("dtype should in ", list(fastllm_data_type_dict.keys())) print("dtype should in ", list(fastllm_data_type_dict.keys()));
exit(0) exit(0);
# 0.1 model info # 0.1 model info
modelInfo = model.config.__dict__ modelInfo = model.config.__dict__
if model.generation_config is not None: if model.generation_config is not None:
modelInfo.update(model.generation_config.__dict__) modelInfo.update(model.generation_config.__dict__)
if (pre_prompt): if (pre_prompt):
modelInfo["pre_prompt"] = pre_prompt modelInfo["pre_prompt"] = pre_prompt;
if (user_role): if (user_role):
modelInfo["user_role"] = user_role modelInfo["user_role"] = user_role;
if (bot_role): if (bot_role):
modelInfo["bot_role"] = bot_role modelInfo["bot_role"] = bot_role;
if (history_sep): if (history_sep):
modelInfo["history_sep"] = history_sep modelInfo["history_sep"] = history_sep;
if (modelInfo["model_type"] == "baichuan" and hasattr(model, "model") and hasattr(model.model, "get_alibi_mask")): if (modelInfo["model_type"] == "baichuan" and hasattr(model, "model") and hasattr(model.model, "get_alibi_mask")):
# Baichuan 2代 # Baichuan 2代
modelInfo["use_alibi"] = "1" modelInfo["use_alibi"] = "1";
modelInfo["pre_prompt"] = "" modelInfo["pre_prompt"] = "";
modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + "> ") if hasattr(model.generation_config, "user_token_id") else "" modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + "> ") if hasattr(model.generation_config, "user_token_id") else "";
modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "" modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "";
modelInfo["history_sep"] = "" modelInfo["history_sep"] = "";
if (modelInfo["model_type"] == "qwen"): if (modelInfo["model_type"] == "qwen"):
if modelInfo["chat_format"] == "chatml": if modelInfo["chat_format"] == "chatml":
modelInfo["im_end_id"] = tokenizer.im_end_id modelInfo["im_end_id"] = tokenizer.im_end_id
modelInfo["im_start_id"] = tokenizer.im_start_id modelInfo["im_start_id"] = tokenizer.im_start_id
weight_type_dict = {} weight_type_dict = {};
module_dict = {} module_dict = {};
weight_bits = {} weight_bits = {};
for key, m in model.named_modules(): for key, m in model.named_modules():
if (str(type(m)).find("QuantizedLinear") != -1): if (str(type(m)).find("QuantizedLinear") != -1):
weight_type_dict[key + ".weight"] = "QuantizedLinear" weight_type_dict[key + ".weight"] = "QuantizedLinear";
weight_bits[key + ".weight"] = m.weight_bit_width weight_bits[key + ".weight"] = m.weight_bit_width;
if (isinstance(m, torch.nn.Linear)): if (isinstance(m, torch.nn.Linear)):
weight_type_dict[key + ".weight"] = "linear" weight_type_dict[key + ".weight"] = "linear";
module_dict[key + ".weight"] = m module_dict[key + ".weight"] = m;
if (isinstance(m, torch.nn.Embedding)): if (isinstance(m, torch.nn.Embedding)):
weight_type_dict[key] = "embedding" weight_type_dict[key] = "embedding";
peft_config = {} peft_config = {}
active_adapter = "" active_adapter = ""
if hasattr(model, "peft_config"): if hasattr(model, "peft_config"):
peft_config = model.peft_config peft_config = model.peft_config
if hasattr(model, "active_adapter"): if hasattr(model, "active_adapter") and isinstance(model.active_adapter, str):
# in transformers >= 4.33.0, active_adapter is a funtion in model, ignore it now
active_adapter = model.active_adapter active_adapter = model.active_adapter
model = model.cpu() model = model.cpu();
dict = model.state_dict() dict = model.state_dict();
model_type = model.config.__dict__["model_type"] model_type = model.config.__dict__["model_type"];
model = llm.fastllm_lib.create_empty_llm_model(model_type.encode()) model = llm.fastllm_lib.create_empty_llm_model(model_type.encode());
for it in modelInfo.keys(): for it in modelInfo.keys():
llm.fastllm_lib.add_dict_llm_model(model, str(it).encode(), str(modelInfo[it]).encode()) llm.fastllm_lib.add_dict_llm_model(model, str(it).encode(), str(modelInfo[it]).encode());
for adapter_name in peft_config.keys(): for adapter_name in peft_config.keys():
adapter_dict = peft_config[adapter_name].__dict__ adapter_dict = peft_config[adapter_name].__dict__
...@@ -90,39 +91,39 @@ def create(model, ...@@ -90,39 +91,39 @@ def create(model,
if modelInfo["model_type"] == "qwen": if modelInfo["model_type"] == "qwen":
pass pass
else: else:
tokenizer = tokenizer.tokenizer tokenizer = tokenizer.tokenizer;
if (hasattr(tokenizer, "sp_model")): if (hasattr(tokenizer, "sp_model")):
piece_size = tokenizer.sp_model.piece_size() piece_size = tokenizer.sp_model.piece_size();
for i in range(piece_size): for i in range(piece_size):
llm.fastllm_lib.add_tokenizer_word_llm_model(model, tokenizer.sp_model.id_to_piece(i).encode(), llm.fastllm_lib.add_tokenizer_word_llm_model(model, tokenizer.sp_model.id_to_piece(i).encode(),
i, ctypes.c_float(tokenizer.sp_model.get_score(i))) i, ctypes.c_float(tokenizer.sp_model.get_score(i)));
else: else:
vocab = tokenizer.get_vocab() vocab = tokenizer.get_vocab();
for v in vocab.keys(): for v in vocab.keys():
if (modelInfo["model_type"] == "moss"): if (modelInfo["model_type"] == "moss"):
vv = [(ord(c) if c not in tokenizer.byte_decoder else tokenizer.byte_decoder[c]) for c in v] vv = [(ord(c) if c not in tokenizer.byte_decoder else tokenizer.byte_decoder[c]) for c in v];
llm.fastllm_lib.add_tokenizer_word_llm_model(model, vv, vocab[v], ctypes.c_float(1.0)) llm.fastllm_lib.add_tokenizer_word_llm_model(model, vv, vocab[v], ctypes.c_float(1.0));
elif (modelInfo["model_type"] == "qwen"): elif (modelInfo["model_type"] == "qwen"):
llm.fastllm_lib.add_tokenizer_word_llm_model(model, v, vocab[v], ctypes.c_float(1.0)) llm.fastllm_lib.add_tokenizer_word_llm_model(model, v, vocab[v], ctypes.c_float(1.0));
else: else:
llm.fastllm_lib.add_tokenizer_word_llm_model(model, v.encode(), vocab[v], ctypes.c_float(1.0)) llm.fastllm_lib.add_tokenizer_word_llm_model(model, v.encode(), vocab[v], ctypes.c_float(1.0));
tot = 0 tot = 0;
for key in dict: for key in dict:
ori_data_type = 0 ori_data_type = 0;
ori_np_data_type = np.float32 ori_np_data_type = np.float32;
cur_weight_type = 0 cur_weight_type = 0;
if (key in weight_type_dict and weight_type_dict[key] in fastllm_weight_type_dict): if (key in weight_type_dict and weight_type_dict[key] in fastllm_weight_type_dict):
cur_weight_type = fastllm_weight_type_dict[weight_type_dict[key]] cur_weight_type = fastllm_weight_type_dict[weight_type_dict[key]];
to_data_type = 0 to_data_type = 0;
if (cur_weight_type == 1): if (cur_weight_type == 1):
to_data_type = fastllm_data_type_dict[dtype] to_data_type = fastllm_data_type_dict[dtype];
if (to_data_type == 7): if (to_data_type == 7):
ori_data_type = 7 ori_data_type = 7;
ori_np_data_type = np.float16 ori_np_data_type = np.float16;
elif (cur_weight_type == 2): elif (cur_weight_type == 2):
# TODO bfloat # TODO bfloat
to_data_type = 0 to_data_type = 0;
weight_name = key weight_name = key
if peft_config is not None: if peft_config is not None:
...@@ -133,19 +134,19 @@ def create(model, ...@@ -133,19 +134,19 @@ def create(model,
(ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)), (ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)),
weight_bits[key], weight_bits[key],
dict[key + "_scale"].numpy().astype(np.float32).ctypes.data_as(ctypes.c_void_p), dict[key + "_scale"].numpy().astype(np.float32).ctypes.data_as(ctypes.c_void_p),
dict[key].numpy().ctypes.data_as(ctypes.c_void_p)) dict[key].numpy().ctypes.data_as(ctypes.c_void_p));
else: else:
llm.fastllm_lib.add_weight_llm_model(model, weight_name.encode(), llm.fastllm_lib.add_weight_llm_model(model, weight_name.encode(),
len(dict[key].shape), len(dict[key].shape),
(ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)), (ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)),
to_data_type, cur_weight_type, ori_data_type, to_data_type, cur_weight_type, ori_data_type,
dict[key].numpy().astype(ori_np_data_type).ctypes.data_as(ctypes.c_void_p)) dict[key].numpy().astype(ori_np_data_type).ctypes.data_as(ctypes.c_void_p));
tot += 1 tot += 1;
print("convert (", tot, "/", len(dict), end = " )\r") print("convert (", tot, "/", len(dict), end = " )\r");
print("") print("");
llm.fastllm_lib.init_params_llm_model(model) llm.fastllm_lib.init_params_llm_model(model);
llm.fastllm_lib.warmup_llm_model(model) llm.fastllm_lib.warmup_llm_model(model);
ret = llm.model("", id = model) ret = llm.model("", id = model);
return ret return ret;
package/fastllm_pytools/llm.py
View file @ f4100f9b
import ctypes import ctypes;
import os import math
from typing import Optional, Tuple, Union, List, Callable, Dict, Any import os;
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, Any;
import platform import platform
if platform.system() == 'Windows': if platform.system() == 'Windows':
...@@ -11,6 +13,12 @@ else: ...@@ -11,6 +13,12 @@ else:
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p] fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p]
fastllm_lib.token_decode.restype = ctypes.c_int
fastllm_lib.token_encode_string.argtypes = [ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_int)]
fastllm_lib.token_encode_string.restype = ctypes.c_int
fastllm_lib.launch_response_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_void_p, fastllm_lib.launch_response_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_void_p,
ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int, ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
ctypes.c_float, ctypes.c_float, ctypes.c_bool] ctypes.c_float, ctypes.c_float, ctypes.c_bool]
...@@ -46,106 +54,178 @@ fastllm_lib.add_tokenizer_word_llm_model.argtype = [ctypes.c_int, ctypes.c_char_ ...@@ -46,106 +54,178 @@ fastllm_lib.add_tokenizer_word_llm_model.argtype = [ctypes.c_int, ctypes.c_char_
fastllm_lib.set_device_map.argtype = [ctypes.c_int, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p] fastllm_lib.set_device_map.argtype = [ctypes.c_int, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p]
def set_cpu_threads(threads: int): def set_cpu_threads(threads: int):
fastllm_lib.set_cpu_threads(threads) fastllm_lib.set_cpu_threads(threads);
def get_cpu_threads() -> int: def get_cpu_threads() -> int:
return fastllm_lib.get_cpu_threads() return fastllm_lib.get_cpu_threads();
def print_ins_info(): def print_ins_info():
fastllm_lib.print_cpu_ins() fastllm_lib.print_cpu_ins();
def set_cpu_kvcache(cpu_kvcache): def set_cpu_kvcache(cpu_kvcache):
fastllm_lib.set_kvcache_in_cpu(ctypes.c_bool(cpu_kvcache)) fastllm_lib.set_kvcache_in_cpu(ctypes.c_bool(cpu_kvcache));
def get_cpu_kvcache(): def get_cpu_kvcache():
return fastllm_lib.get_kvcache_in_cpu() return fastllm_lib.get_kvcache_in_cpu();
def set_cpu_low_mem(low_mem): def set_cpu_low_mem(low_mem):
fastllm_lib.set_cpu_low_mem(ctypes.c_bool(low_mem)) fastllm_lib.set_cpu_low_mem(ctypes.c_bool(low_mem));
def get_cpu_low_mem(): def get_cpu_low_mem():
return fastllm_lib.get_cpu_low_mem() return fastllm_lib.get_cpu_low_mem();
def set_device_map(device_map): def set_device_map(device_map):
devices = [] devices = [];
values = [] values = [];
if (isinstance(device_map, str)): if (isinstance(device_map, str)):
devices.append(device_map) devices.append(device_map);
values.append(1) values.append(1);
elif (isinstance(device_map, list)): elif (isinstance(device_map, list)):
devices = [str(x) for x in device_map] devices = [str(x) for x in device_map];
values = [1 for x in device_map] values = [1 for x in device_map];
elif (isinstance(device_map, dict)): elif (isinstance(device_map, dict)):
devices = [str(x) for x in device_map.keys()] devices = [str(x) for x in device_map.keys()];
values = [int(device_map[x]) for x in device_map.keys()] values = [int(device_map[x]) for x in device_map.keys()];
else: else:
print("set_device_map error.") print("set_device_map error.");
return return;
device_str = ''.join(devices) device_str = ''.join(devices);
device_len = [len(x) for x in devices] device_len = [len(x) for x in devices];
fastllm_lib.set_device_map(len(device_len), fastllm_lib.set_device_map(len(device_len),
(ctypes.c_int * len(device_len))(*device_len), (ctypes.c_int * len(device_len))(*device_len),
device_str.encode(), device_str.encode(),
(ctypes.c_int * len(values))(*values)) (ctypes.c_int * len(values))(*values));
def from_hf(model, def from_hf(model,
tokenizer = None, tokenizer = None,
dtype = "float16"): dtype = "float16"):
from fastllm_pytools import hf_model from fastllm_pytools import hf_model;
return hf_model.create(model, tokenizer, dtype = dtype) return hf_model.create(model, tokenizer, dtype = dtype);
class model: class model:
def __init__ (self, path : str, def __init__ (self, path : str,
id : int = -99999): id : int = -99999):
if (id != -99999): if (id != -99999):
self.model = id self.model = id;
else: else:
self.model = fastllm_lib.create_llm_model(path.encode()) self.model = fastllm_lib.create_llm_model(path.encode());
self.direct_query = False self.direct_query = False;
# 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
self.thread_local_obj = threading.local()
self.thread_local_obj.tokenizer_encode_string__output_buffer = None
self.thread_local_obj.tokenizer_decode_token__output_buffer = None
# tokenizer_decode_token 输出结果的静态缓存，手工触发构建
# 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。
# 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
def get_prompt(self, def get_prompt(self,
query: str, query: str,
history: List[Tuple[str, str]] = None) -> str: history: List[Tuple[str, str]] = None) -> str:
if (not(history)): if (not(history)):
history = [] history = [];
prompt = "" prompt = "";
for i, (old_query, response) in enumerate(history): 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_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() prompt = fastllm_lib.make_input_llm_model(self.model, prompt.encode(), len(history), query.encode()).decode();
return prompt return prompt;
def save(self, path : str): def save(self, path : str):
fastllm_lib.save_llm_model(self.model, path.encode()) fastllm_lib.save_llm_model(self.model, path.encode());
def eval(self): def eval(self):
pass pass;
def build_tokenizer_decode_token_cache(self):
if self.tokenizer_decode_token_cache is not None:
return
cache_dict = dict()
vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model)
for token_id in range(vocab_size):
cache_dict[token_id] = self.tokenizer_decode_token(token_id)
self.tokenizer_decode_token_cache = cache_dict
def tokenizer_encode_string(self, content: str) -> List[int]:
output_buffer_init_len = 1024
if self.thread_local_obj.tokenizer_encode_string__output_buffer is None:
self.thread_local_obj.tokenizer_encode_string__output_buffer = (ctypes.c_int * output_buffer_init_len)()
buffer = self.thread_local_obj.tokenizer_encode_string__output_buffer
buffer_len = len(buffer)
result_len = fastllm_lib.token_encode_string(self.model, content.encode(), buffer_len, buffer)
if result_len > buffer_len:
if result_len > 10240:
# 要处理的数据过长，使用一次性的buffer
temp_buffer = (ctypes.c_int * result_len)()
ret = fastllm_lib.token_encode_string(self.model, content.encode(), result_len, temp_buffer)
return [i for i in temp_buffer]
else:
# 扩展buffer大小
new_buffer_len = round(math.ceil(result_len / 1024.0)) * 1024
buffer = (ctypes.c_int * new_buffer_len)()
self.thread_local_obj.tokenizer_encode_string__output_buffer = buffer
result_len = fastllm_lib.token_encode_string(self.model, content.encode(), new_buffer_len, buffer)
return [buffer[i] for i in range(result_len)]
def tokenizer_decode_token(self, token_id: int) -> bytes:
if self.tokenizer_decode_token_cache is not None:
cache_result = self.tokenizer_decode_token_cache.get(token_id)
if cache_result is not None:
return cache_result
output_buffer_init_len = 256
if self.thread_local_obj.tokenizer_decode_token__output_buffer is None:
self.thread_local_obj.tokenizer_decode_token__output_buffer = ctypes.create_string_buffer(output_buffer_init_len)
buffer = self.thread_local_obj.tokenizer_decode_token__output_buffer
ret = fastllm_lib.token_decode(self.model, token_id, len(buffer), buffer)
if ret > 0:
# buffer长度不够，扩展buffer大小
new_buffer_len = round(math.ceil(ret / 16.0)) * 16
buffer = ctypes.create_string_buffer(new_buffer_len)
self.thread_local_obj.tokenizer_decode_token__output_buffer = buffer
ret = fastllm_lib.token_decode(self.model, token_id, len(buffer), buffer)
assert ret == 0
buffer_bytes = buffer.raw
result_len = len(buffer_bytes)
for i in range(len(buffer_bytes)):
if buffer_bytes[i] == 0:
result_len = i
break
return buffer_bytes[:result_len]
def response_logits(self, def response_logits(self,
query: str, query: str,
history: List[Tuple[str, str]] = None, history: List[Tuple[str, str]] = None,
tokenizer = None) -> str: tokenizer = None) -> str:
prompt = query if self.direct_query else self.get_prompt(query, history) prompt = query if self.direct_query else self.get_prompt(query, history);
if (tokenizer == None): if (tokenizer == None):
handle = fastllm_lib.launch_response_str_llm_model(self.model, prompt.encode(), handle = fastllm_lib.launch_response_str_llm_model(self.model, prompt.encode(),
ctypes.c_int(1), ctypes.c_bool(False), ctypes.c_float(1), ctypes.c_int(1), ctypes.c_int(1), ctypes.c_bool(False), ctypes.c_float(1), ctypes.c_int(1),
ctypes.c_float(1), ctypes.c_float(1), ctypes.c_bool(True)) ctypes.c_float(1), ctypes.c_float(1), ctypes.c_bool(True));
else: else:
input = tokenizer.encode(prompt) input = tokenizer.encode(prompt);
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input), handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
1, False, 1, 1, 1, 1, True) 1, False, 1, 1, 1, 1, True);
vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model) vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model);
logits = list(range(vocab_size)) logits = list(range(vocab_size))
array = (ctypes.c_float * (vocab_size * 4))(*logits) array = (ctypes.c_float * (vocab_size * 4))(*logits);
ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array) ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array);
out = list(array)[:vocab_size] out = list(array)[:vocab_size];
while (ret != -1): while (ret != -1):
ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array) ret = fastllm_lib.fetch_response_logits_llm_model(self.model, handle, array);
return out return out;
def response(self, def response(self,
query: str, query: str,
history: List[Tuple[str, str]] = None, 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 = 1.0) -> str:
ret = "" ret = "";
for i in self.stream_response(query = query, for i in self.stream_response(query = query,
history = history, history = history,
max_length = max_length, max_length = max_length,
...@@ -154,84 +234,111 @@ class model: ...@@ -154,84 +234,111 @@ class model:
temperature = temperature, temperature = temperature,
repeat_penalty = repeat_penalty, repeat_penalty = repeat_penalty,
one_by_one = True): one_by_one = True):
ret += i ret += i;
return ret return ret;
def stream_response(self, def stream_response(self,
query: str, query: str,
history: List[Tuple[str, str]] = None, 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 = 1.0,
one_by_one = True): one_by_one = True):
prompt = query if self.direct_query else self.get_prompt(query, history) prompt = query if self.direct_query else self.get_prompt(query, history);
handle = fastllm_lib.launch_response_str_llm_model(self.model, prompt.encode(), 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_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)) ctypes.c_float(temperature), ctypes.c_float(repeat_penalty), ctypes.c_bool(False));
res = "" res = "";
ret = b'' ret = b'';
fail_cnt = 0 fail_cnt = 0;
while True: while True:
ret += fastllm_lib.fetch_response_str_llm_model(self.model, handle) ret += fastllm_lib.fetch_response_str_llm_model(self.model, handle);
cur = "" cur = "";
try: try:
cur = ret.decode() cur = ret.decode();
ret = b'' ret = b'';
except: except:
fail_cnt += 1 fail_cnt += 1;
if (fail_cnt == 20): if (fail_cnt == 20):
break break;
else: else:
continue continue;
fail_cnt = 0 fail_cnt = 0;
if (cur == "<flmeos>"): 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 break
cur_bytes = self.tokenizer_decode_token(cur_token)
if one_by_one: if one_by_one:
yield cur yield cur_bytes
else: else:
res += cur total_bytes += cur_bytes
yield res yield total_bytes
def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, 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 = 1.0, **kwargs):
if (not(history)): if (not(history)):
history = [] history = [];
prompt = query if self.direct_query else self.get_prompt(query, history) prompt = query if self.direct_query else self.get_prompt(query, history);
input = tokenizer.encode(prompt) input = tokenizer.encode(prompt);
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input), 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, max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
False) False);
result = [] result = [];
while True: while True:
cur = fastllm_lib.fetch_response_llm_model(self.model, handle) cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
if (cur == -1): if (cur == -1):
break break;
result.append(cur) result.append(cur);
response = tokenizer.decode(result) response = tokenizer.decode(result);
history = history + [(query, response)] history = history + [(query, response)];
return response, history return response, history;
def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values = None, 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 = 1.0,
return_past_key_values = False, **kwargs) -> str: return_past_key_values = False, **kwargs) -> str:
if (not(history)): if (not(history)):
history = [] history = [];
prompt = query if self.direct_query else self.get_prompt(query, history) prompt = query if self.direct_query else self.get_prompt(query, history);
input = tokenizer.encode(prompt) input = tokenizer.encode(prompt);
handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input), 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, max_length, do_sample, top_p, top_k, temperature, repeat_penalty,
False) False);
tokens = [] tokens = [];
while True: while True:
cur = fastllm_lib.fetch_response_llm_model(self.model, handle) cur = fastllm_lib.fetch_response_llm_model(self.model, handle);
if (cur == -1): if (cur == -1):
break break;
tokens.append(cur) tokens.append(cur);
response = tokenizer.decode(tokens) response = tokenizer.decode(tokens);
new_history = history + [(query, response)] new_history = history + [(query, response)];
if return_past_key_values: if return_past_key_values:
yield response, new_history, None yield response, new_history, None;
else: else:
yield response, new_history yield response, new_history;
def set_adapter(self, name: str): def set_adapter(self, name: str):
fastllm_lib.set_adapter(self.model, str(name).encode()) fastllm_lib.set_adapter(self.model, str(name).encode())
......
package/fastllm_pytools/torch2flm.py
View file @ f4100f9b
...@@ -21,8 +21,8 @@ fastllm_weight_type_dict = { ...@@ -21,8 +21,8 @@ fastllm_weight_type_dict = {
"embedding": 2 "embedding": 2
} }
v = np.random.randint(-127, 127, [10, 20]) v = np.random.randint(-127, 127, [10, 20]);
temp = v temp = v;
c_max = np.expand_dims(np.abs(v).max(axis = -1), -1) c_max = np.expand_dims(np.abs(v).max(axis = -1), -1)
c_scale = c_max / 127.0 c_scale = c_max / 127.0
v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8) v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8)
...@@ -34,23 +34,31 @@ def write_int8(fo, v): ...@@ -34,23 +34,31 @@ def write_int8(fo, v):
fo.write(struct.pack('i', 3)) fo.write(struct.pack('i', 3))
fo.write(struct.pack('i', 0)) fo.write(struct.pack('i', 0))
for i in range(c_max.shape[0]): for i in range(c_max.shape[0]):
fo.write(struct.pack('f', -c_max[i][0])) fo.write(struct.pack('f', -c_max[i][0]));
fo.write(struct.pack('f', c_max[i][0])) fo.write(struct.pack('f', c_max[i][0]));
fo.write(v.data) fo.write(v.data)
def write_int4(fo, v): def write_int4(fo, v):
c_min = np.expand_dims(-np.abs(v).max(axis = -1), -1) # c_min = np.expand_dims(-np.abs(v).max(axis = -1), -1)
c_max = np.expand_dims(np.abs(v).max(axis = -1), -1) # c_max = np.expand_dims(np.abs(v).max(axis = -1), -1)
c_scale = c_max / 7.0 # c_scale = c_max / 7.0
c_min = c_scale * -8.0 # c_min = c_scale * -8.0
c_min = np.expand_dims(v.min(axis = -1), -1)
c_max = np.expand_dims(v.max(axis = -1), -1)
c_scale = (c_max - c_min) / 15.0
c_zero = np.round(0.0 - c_min / c_scale)
c_zero = c_zero.clip(0, 15)
c_min = -c_scale * c_zero
v = (v - c_min) / c_scale v = (v - c_min) / c_scale
v = (v + 0.5).astype(np.int8).clip(0, 15).astype(np.uint8) v = (v + 0.5).astype(np.int8).clip(0, 15).astype(np.uint8)
v = v[:, 0::2] * 16 + v[:, 1::2] v = v[:, 0::2] * 16 + v[:, 1::2]
fo.write(struct.pack('i', 8)) fo.write(struct.pack('i', 8))
fo.write(struct.pack('i', 0)) fo.write(struct.pack('i', 0))
for i in range(c_min.shape[0]): for i in range(c_min.shape[0]):
fo.write(struct.pack('f', c_min[i][0])) fo.write(struct.pack('f', c_min[i][0]));
fo.write(struct.pack('f', c_max[i][0])) fo.write(struct.pack('f', c_max[i][0]));
fo.write(v.data) fo.write(v.data)
def tofile(exportPath, def tofile(exportPath,
...@@ -91,8 +99,14 @@ def tofile(exportPath, ...@@ -91,8 +99,14 @@ def tofile(exportPath,
# Baichuan 2代 # Baichuan 2代
modelInfo["use_alibi"] = "1" modelInfo["use_alibi"] = "1"
modelInfo["pre_prompt"] = "" modelInfo["pre_prompt"] = ""
modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + "> ") if hasattr(model.generation_config, "user_token_id") else "" modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + ">") if hasattr(model.generation_config, "user_token_id") else "";
modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "" modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "";
modelInfo["history_sep"] = ""
if (modelInfo["model_type"] == "baichuan" and modelInfo["vocab_size"] == 125696):
# Baichuan 2代 7B
modelInfo["pre_prompt"] = ""
modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + ">") if hasattr(model.generation_config, "user_token_id") else "";
modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "";
modelInfo["history_sep"] = "" modelInfo["history_sep"] = ""
if modelInfo["model_type"] == "qwen": if modelInfo["model_type"] == "qwen":
if modelInfo["chat_format"] == "chatml": if modelInfo["chat_format"] == "chatml":
...@@ -140,10 +154,10 @@ def tofile(exportPath, ...@@ -140,10 +154,10 @@ def tofile(exportPath,
for v in vocab.keys(): for v in vocab.keys():
if (modelInfo['model_type'] == "qwen"): if (modelInfo['model_type'] == "qwen"):
s = v s = v
elif (modelInfo["model_type"] == "moss"):
s = [(ord(c) if c not in tokenizer.byte_decoder else tokenizer.byte_decoder[c]) for c in v]
else: else:
s = v.encode() s = v.encode()
if (modelInfo["model_type"] == "moss"):
s = [(ord(c) if c not in tokenizer.byte_decoder else tokenizer.byte_decoder[c]) for c in v]
fo.write(struct.pack('i', len(s))) fo.write(struct.pack('i', len(s)))
for c in s: for c in s:
fo.write(struct.pack('i', c)) fo.write(struct.pack('i', c))
......
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