add 72B and 1.8B Qwen models, add Ascend 910 and Hygon DCU support, add docker support

dcu-support/package/fastllm_pytools/__init__.py

@@ -0,0 +1 @@
+__all__ = ["llm"]

dcu-support/package/fastllm_pytools/hf_model.py

@@ -0,0 +1,154 @@
+from fastllm_pytools import llm;
+import torch;
+import ctypes;
+import numpy as np;
+
+fastllm_data_type_dict = {
+    "int4": 8,
+    "int8": 3,
+    "float16": 7
+}
+fastllm_weight_type_dict = {
+    "linear": 1,
+    "embedding": 2,
+    "QuantizedLinear": 111
+}
+
+def create(model,
+           tokenizer = None,
+           pre_prompt = None,
+           user_role = None,
+           bot_role = None,
+           history_sep = None,
+           dtype = "float16"):
+    if (dtype not in fastllm_data_type_dict):
+        print("dtype should in ", list(fastllm_data_type_dict.keys()));
+        exit(0);
+
+    # 0.1 model info
+    if model.config.model_type == "chatglm" and model.config.transformers_version == "4.30.2":
+        model.config.model_type = "chatglm3"
+    modelInfo = model.config.__dict__
+    if model.generation_config is not None:
+        modelInfo.update(model.generation_config.__dict__)
+    if (pre_prompt):
+        modelInfo["pre_prompt"] = pre_prompt;
+    if (user_role):
+        modelInfo["user_role"] = user_role;
+    if (bot_role):
+        modelInfo["bot_role"] = bot_role;
+    if (history_sep):
+        modelInfo["history_sep"] = history_sep;
+    if (modelInfo["model_type"] == "baichuan" and hasattr(model, "model") and hasattr(model.model, "get_alibi_mask")):
+        # Baichuan 2代
+        modelInfo["use_alibi"] = "1";
+        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"] = "";
+    if (modelInfo["model_type"] == "qwen"):
+        if modelInfo["chat_format"] == "chatml":
+            modelInfo["im_end_id"] = tokenizer.im_end_id
+            modelInfo["im_start_id"] = tokenizer.im_start_id
+
+
+    weight_type_dict = {};
+    module_dict = {};
+    weight_bits = {};
+    for key, m in model.named_modules():
+        if (str(type(m)).find("QuantizedLinear") != -1):
+            weight_type_dict[key + ".weight"] = "QuantizedLinear";
+            weight_bits[key + ".weight"] = m.weight_bit_width;
+        if (isinstance(m, torch.nn.Linear)):
+            weight_type_dict[key + ".weight"] = "linear";
+            module_dict[key + ".weight"] = m;
+        if (isinstance(m, torch.nn.Embedding)):
+            weight_type_dict[key] = "embedding";
+
+    peft_config = {}
+    active_adapter = ""
+    if hasattr(model, "peft_config"):
+        peft_config = model.peft_config
+    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
+
+    model = model.cpu();
+    dict = model.state_dict();
+    model_type = model.config.__dict__["model_type"];
+    model = llm.fastllm_lib.create_empty_llm_model(model_type.encode());
+    for it in modelInfo.keys():
+        llm.fastllm_lib.add_dict_llm_model(model, str(it).encode(), str(modelInfo[it]).encode());
+
+    for adapter_name in peft_config.keys():
+        adapter_dict = peft_config[adapter_name].__dict__
+        for it in adapter_dict.keys():
+            llm.fastllm_lib.add_adapter_dict_llm_model(model, str(adapter_name).encode(), str(it).encode(), str(adapter_dict[it]).encode())
+    if len(active_adapter) != 0:
+        llm.fastllm_lib.set_adapter(model, str(active_adapter).encode())
+
+    # 1. vocab
+    if (tokenizer):
+        if (hasattr(tokenizer, "tokenizer")):
+            if modelInfo["model_type"] == "qwen":
+                pass
+            else:
+                tokenizer = tokenizer.tokenizer;
+        if (hasattr(tokenizer, "sp_model")):
+            piece_size = tokenizer.sp_model.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(),
+                                                             i, ctypes.c_float(tokenizer.sp_model.get_score(i)));
+        else:
+            vocab = tokenizer.get_vocab();
+            for v in vocab.keys():
+                if (modelInfo["model_type"] == "moss"):
+                    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));
+                elif (modelInfo["model_type"] == "qwen"):
+                    llm.fastllm_lib.add_tokenizer_word_llm_model(model, v, vocab[v], ctypes.c_float(1.0));
+                else:
+                    llm.fastllm_lib.add_tokenizer_word_llm_model(model, v.encode(), vocab[v], ctypes.c_float(1.0));
+    tot = 0;
+    for key in dict:
+        ori_data_type = 0;
+        ori_np_data_type = np.float32;
+        cur_weight_type = 0;
+        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]];
+        to_data_type = 0;
+
+        if (cur_weight_type == 1):
+            to_data_type = fastllm_data_type_dict[dtype];
+            if (to_data_type == 7):
+                ori_data_type = 7;
+                ori_np_data_type = np.float16;
+        elif (cur_weight_type == 2):
+            # TODO bfloat
+            to_data_type = 0;
+
+        weight_name = key
+        if peft_config is not None:
+            weight_name = weight_name.replace('base_model.model.', '')
+        if (cur_weight_type == 111):
+            llm.fastllm_lib.add_qlinear_weight_llm_model(model, weight_name.encode(),
+                                                 len(dict[key].shape),
+                                                 (ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)),
+                                                 weight_bits[key],
+                                                 dict[key + "_scale"].numpy().astype(np.float32).ctypes.data_as(ctypes.c_void_p),
+                                                 dict[key].numpy().ctypes.data_as(ctypes.c_void_p));
+        else:
+            llm.fastllm_lib.add_weight_llm_model(model, weight_name.encode(),
+                                             len(dict[key].shape),
+                                             (ctypes.c_int * len(dict[key].shape))(*list(dict[key].shape)),
+                                             to_data_type, cur_weight_type, ori_data_type,
+                                             dict[key].numpy().astype(ori_np_data_type).ctypes.data_as(ctypes.c_void_p));
+        tot += 1;
+        print("convert (", tot, "/", len(dict), end = " )\r");
+
+    print("");
+    llm.fastllm_lib.init_params_llm_model(model);
+    llm.fastllm_lib.warmup_llm_model(model);
+    ret = llm.model("", id = model);
+    return ret;
+

dcu-support/package/fastllm_pytools/llm.py

@@ -0,0 +1,495 @@
+import ctypes;
+import math
+import os;
+import threading
+from typing import Optional, Tuple, Union, List, Callable, Dict, Any;
+from copy import deepcopy
+
+import platform
+if platform.system() == 'Windows':
+    fastllm_lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.split(os.path.realpath(__file__))[0], "fastllm_tools.dll"))
+else:
+    fastllm_lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.split(os.path.realpath(__file__))[0], "libfastllm_tools.so"))
+
+fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
+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,
+                                                  ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
+                                                  ctypes.c_float, ctypes.c_float, ctypes.c_bool]
+fastllm_lib.launch_response_llm_model.restype = ctypes.c_int
+
+fastllm_lib.fetch_response_llm_model.argtypes = [ctypes.c_int, ctypes.c_int]
+fastllm_lib.fetch_response_llm_model.restype = ctypes.c_int
+
+fastllm_lib.fetch_response_logits_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_float)]
+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.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,
+                                                     ctypes.c_float, ctypes.c_float, ctypes.c_bool]
+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.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_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.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.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,
+                                                     ctypes.c_float, ctypes.c_float, ctypes.c_bool]
+fastllm_lib.response_batch_str_llm_model.restype = ctypes.POINTER(ctypes.c_char_p)
+
+fastllm_lib.response_batch_tokens_llm_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_int), ctypes.POINTER(ctypes.c_int),
+                                                        ctypes.c_int, ctypes.c_bool, ctypes.c_float, ctypes.c_int,
+                                                        ctypes.c_float, ctypes.c_float, ctypes.c_bool]
+fastllm_lib.response_batch_tokens_llm_model.restype = ctypes.POINTER(ctypes.c_char_p)
+
+def set_cpu_threads(threads: int):
+    fastllm_lib.set_cpu_threads(threads);
+
+def get_cpu_threads() -> int:
+    return fastllm_lib.get_cpu_threads();
+
+def print_ins_info():
+    fastllm_lib.print_cpu_ins();
+
+def set_cpu_kvcache(cpu_kvcache):
+    fastllm_lib.set_kvcache_in_cpu(ctypes.c_bool(cpu_kvcache));
+
+def get_cpu_kvcache():
+    return fastllm_lib.get_kvcache_in_cpu();
+
+def set_cpu_low_mem(low_mem):
+    fastllm_lib.set_cpu_low_mem(ctypes.c_bool(low_mem));
+
+def get_cpu_low_mem():
+    return fastllm_lib.get_cpu_low_mem();
+
+def set_device_map(device_map):
+    devices = [];
+    values = [];
+    if (isinstance(device_map, str)):
+        devices.append(device_map);
+        values.append(1);
+    elif (isinstance(device_map, list)):
+        devices = [str(x) for x in device_map];
+        values = [1 for x in device_map];
+    elif (isinstance(device_map, dict)):
+        devices = [str(x) for x in device_map.keys()];
+        values = [int(device_map[x]) for x in device_map.keys()];
+    else:
+        print("set_device_map error.");
+        return;
+    device_str = ''.join(devices);
+    device_len = [len(x) for x in devices];
+    fastllm_lib.set_device_map(len(device_len),
+                               (ctypes.c_int * len(device_len))(*device_len),
+                               device_str.encode(),
+                               (ctypes.c_int * len(values))(*values));
+def from_hf(model,
+            tokenizer = None,
+            dtype = "float16"):
+    from fastllm_pytools import hf_model;
+    return hf_model.create(model, tokenizer, dtype = dtype);
+
+class model:
+    def __init__ (self, path : str,
+                  id : int = -99999):
+        if (id != -99999):
+            self.model = id;
+        else:
+            self.model = fastllm_lib.create_llm_model(path.encode());
+        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
+
+        self.model_type = fastllm_lib.get_llm_model_type(self.model).decode()
+        # print("model_type:", self.model_type)
+
+    def get_prompt(self,
+                   query: str,
+                   history: List[Tuple[str, str]] = None) -> str:
+        if (not(history)):
+            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();
+        return prompt;
+
+    def save(self, path : str):
+        fastllm_lib.save_llm_model(self.model, path.encode());
+
+    def eval(self):
+        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,
+                        query: str,
+                        history: List[Tuple[str, str]] = None,
+                        tokenizer = None) -> str:
+        prompt = query if self.direct_query else self.get_prompt(query, history);
+        if (tokenizer == None):
+            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_float(1), ctypes.c_float(1), ctypes.c_bool(True));
+        else:
+            input = tokenizer.encode(prompt);
+            handle = fastllm_lib.launch_response_llm_model(self.model, len(input), (ctypes.c_int * len(input))(*input),
+                                                           1, False, 1, 1, 1, 1, True);
+        vocab_size = fastllm_lib.get_tokenizer_vocab_size(self.model);
+        logits = list(range(vocab_size))
+        array = (ctypes.c_float * (vocab_size * 4))(*logits);
+        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
+
+

dcu-support/package/fastllm_pytools/torch2flm.py

@@ -0,0 +1,218 @@
+import struct
+import numpy as np
+import torch
+
+def writeString(fo, s):
+    fo.write(struct.pack('i', len(s)))
+    fo.write(s.encode())
+
+def writeKeyValue(fo, key, value):
+    writeString(fo, key)
+    writeString(fo, value)
+
+fastllm_data_type_dict = {
+    "int4": 8,
+    "int8": 3,
+    "float16": 7,
+    "float32": 0,
+}
+fastllm_weight_type_dict = {
+    "linear": 1,
+    "embedding": 2
+}
+
+v = np.random.randint(-127, 127, [10, 20]);
+temp = v;
+c_max = np.expand_dims(np.abs(v).max(axis = -1), -1)
+c_scale = c_max / 127.0
+v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8)
+
+def write_int8(fo, v):
+    c_max = np.expand_dims(np.abs(v).max(axis = -1), -1).clip(0.1, 1e100)
+    c_scale = c_max / 127.0
+    v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8)
+    fo.write(struct.pack('i', 3))
+    fo.write(struct.pack('i', 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(v.data)
+
+def write_int4(fo, v):
+    # 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_scale = c_max / 7.0
+    # 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 + 0.5).astype(np.int8).clip(0, 15).astype(np.uint8)
+    v = v[:, 0::2] * 16 + v[:, 1::2]
+    fo.write(struct.pack('i', 8))
+    fo.write(struct.pack('i', 0))
+    for i in range(c_min.shape[0]):
+        fo.write(struct.pack('f', c_min[i][0]));
+        fo.write(struct.pack('f', c_max[i][0]));
+    fo.write(v.data)
+
+def tofile(exportPath,
+           model,
+           tokenizer = None,
+           pre_prompt = None,
+           user_role = None,
+           bot_role = None,
+           history_sep = None,
+           dtype = "float16"):
+    if (dtype not in fastllm_data_type_dict):
+        print("dtype should in ", list(fastllm_data_type_dict.keys()))
+        exit(0)
+
+    dict = model.state_dict()
+    fo = open(exportPath, "wb")
+
+    # 0. version id
+    fo.write(struct.pack('i', 2))
+
+    # 0.1 model info
+    if model.config.model_type == "chatglm" and model.config.transformers_version == "4.30.2":
+        model.config.model_type = "chatglm3"
+    modelInfo = model.config.__dict__
+    if model.generation_config is not None:
+        modelInfo.update(model.generation_config.__dict__)
+    if ("model_type" not in modelInfo):
+        print("unknown model_type.")
+        exit(0)
+
+    if (pre_prompt):
+        modelInfo["pre_prompt"] = pre_prompt
+    if (user_role):
+        modelInfo["user_role"] = user_role
+    if (bot_role):
+        modelInfo["bot_role"] = bot_role
+    if (history_sep):
+        modelInfo["history_sep"] = history_sep
+    if (modelInfo["model_type"] == "baichuan" and hasattr(model, "model") and hasattr(model.model, "get_alibi_mask")):
+        # Baichuan 2代
+        modelInfo["use_alibi"] = "1"
+        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"] = ""
+    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"] = ""
+    if modelInfo["model_type"] == "qwen":
+        if modelInfo["chat_format"] == "chatml":
+            modelInfo["im_end_id"] = tokenizer.im_end_id
+            modelInfo["im_start_id"] = tokenizer.im_start_id
+
+    modelInfo["tokenizer_use_score"] = "1" # 分词带分数
+
+    if hasattr(model, "peft_config"):
+        adapter_size = len(model.peft_config)
+        modelInfo["peft_size"] = adapter_size
+
+    fo.write(struct.pack('i', len(modelInfo)))
+    for it in modelInfo.keys():
+        writeKeyValue(fo, str(it), str(modelInfo[it]))
+
+    if hasattr(model, "peft_config"):
+        for adapter_name in model.peft_config.keys():
+            adapter_dict = model.peft_config[adapter_name].__dict__
+            writeString(fo, adapter_name)
+            fo.write(struct.pack('i', len(adapter_dict)))
+            for it in adapter_dict.keys():
+                writeKeyValue(fo, str(it), str(adapter_dict[it]))
+
+    # 1. vocab
+    if (tokenizer):
+        if (hasattr(tokenizer, "tokenizer")):
+            if (modelInfo['model_type'] == "qwen"):
+                pass
+            else:
+                tokenizer = tokenizer.tokenizer
+        if (hasattr(tokenizer, "sp_model")):
+            piece_size = tokenizer.sp_model.piece_size()
+            fo.write(struct.pack('i', piece_size))
+            for i in range(piece_size):
+                s = tokenizer.sp_model.id_to_piece(i).encode()
+                fo.write(struct.pack('i', len(s)))
+                for c in s:
+                    fo.write(struct.pack('i', c))
+                fo.write(struct.pack('i', i))
+                fo.write(struct.pack('f', float(tokenizer.sp_model.get_score(i))))
+        else:
+            vocab = tokenizer.get_vocab()
+            fo.write(struct.pack('i', len(vocab)))
+            for v in vocab.keys():
+                if (modelInfo['model_type'] == "qwen"):
+                    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:
+                    s = v.encode()
+                fo.write(struct.pack('i', len(s)))
+                for c in s:
+                    fo.write(struct.pack('i', c))
+                fo.write(struct.pack('i', vocab[v]))
+                fo.write(struct.pack('f', 1.0))
+    else:
+        fo.write(struct.pack('i', 0))
+
+    weight_type_dict = {}
+    module_dict = {}
+    for key, m in model.named_modules():
+        if (isinstance(m, torch.nn.Linear)):
+            weight_type_dict[key + ".weight"] = "linear"
+            module_dict[key + ".weight"] = m
+        if (isinstance(m, torch.nn.Embedding)):
+            weight_type_dict[key] = "embedding"
+
+    # 2. weight
+    fo.write(struct.pack('i', len(dict)))
+    tot = 0
+    for key in dict:
+        ori_data_type = 0
+        ori_np_data_type = np.float32
+        cur_weight_type = 0
+        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]]
+        to_data_type = 0
+        if (cur_weight_type == 1):
+            to_data_type = fastllm_data_type_dict[dtype]
+            if (to_data_type == 7):
+                ori_data_type = 7
+                ori_np_data_type = np.float16
+
+        cur = dict[key].numpy().astype(ori_np_data_type)
+        
+        if hasattr(model, "peft_config"):
+            weight_name = key.replace('base_model.model.', '')
+            fo.write(struct.pack('i', len(weight_name)))
+            fo.write(weight_name.encode())
+        else:
+            fo.write(struct.pack('i', len(key)))
+            fo.write(key.encode())
+        fo.write(struct.pack('i', len(cur.shape)))
+        for i in cur.shape:
+            fo.write(struct.pack('i', i))
+        if (to_data_type == 3):
+            write_int8(fo, cur)
+        elif (to_data_type == 8):
+            write_int4(fo, cur)
+        else:
+            fo.write(struct.pack('i', to_data_type))
+            fo.write(cur.data)
+        tot += 1
+        print("output (", tot, "/", len(dict), end = " )\r")
+    print("\nfinish.")
+    fo.close()