初始化项目，由ModelHub XC社区提供模型

Model: Tele-AI/TeleChat2-3B
Source: Original Platform

generation_utils.py

@@ -0,0 +1,162 @@
+from typing import Optional
+from collections import deque
+from queue import Queue
+import copy
+
+
+class History:
+
+    def __init__(self, tokenizer, history):
+        '''
+        init from a list of dict
+        '''
+        # use deque to meet some special situation
+        self.input_history = deque()
+        self.tokenizer = tokenizer
+        if history:
+            self._transfer_from_list(history)
+
+    def _transfer_from_list(self, history):
+        for message in history:
+            content = message.get("content")
+            # the token result may not be equal to the result model gen
+            message.update(self.tokenizer(content))
+            self.input_history.append(message)
+
+    def append(self, message):
+        content = message.get("content")
+        if "input_ids" not in message or "attention_mask" not in message:
+            message.update(self.tokenizer(content))
+        self.input_history.append(message)
+
+    def append_left(self, message):
+        content = message.get("content")
+        if "input_ids" not in message or "attention_mask" not in message:
+            message.update(self.tokenizer(content))
+        self.input_history.appendleft(message)
+
+    def pop(self):
+        x = self.input_history.pop()
+        return x
+
+    def pop_left(self):
+        x = self.pop_left()
+        return x
+
+    def update(self, message):
+        self.input_history.pop()
+        self.append(message)
+
+    def __len__(self):
+        return self.input_history.__len__()
+
+    def __str__(self):
+        return self.input_history.__str__()
+
+    def __copy__(self):
+        new_instance = type(self)(self.tokenizer, [])
+        new_instance.input_history = copy.copy(self.input_history)
+        return new_instance
+
+    def __deepcopy__(self, memodict={}):
+        new_instance = type(self)(self.tokenizer, [])
+        new_instance.input_history = copy.deepcopy(self.input_history)
+        return new_instance
+
+
+class TelechatIterTextStreamer:
+    """
+    With reference to the TextIterStreamers in transformers, we have rewritten this class
+    """
+
+    def __init__(
+            self, tokenizer, history: History = None, skip_prompt: bool = False, timeout: Optional[float] = None,
+            **decode_kwargs
+    ):
+
+        self.tokenizer = tokenizer
+        self.history = history
+        self.skip_prompt = skip_prompt
+        self.timeout = timeout
+        self.decode_kwargs = decode_kwargs
+
+        self.text_queue = Queue()
+        self.cache_time = 0
+        self.text_until = ""
+        self.token_until = []
+        self.stop_signal = None
+        self.next_tokens_are_prompt = True
+
+        self.history.append({"role": "bot", "content": self.text_until})
+
+    def put(self, value):
+        """
+        put printable text into queue
+        """
+        if len(value.shape) > 1 and value.shape[0] > 1:
+            raise ValueError("TextStreamer only supports batch size 1")
+        elif len(value.shape) > 1:
+            value = value[0]
+
+        if self.skip_prompt and self.next_tokens_are_prompt:
+            self.next_tokens_are_prompt = False
+            return
+
+        if value[-1] == self.tokenizer.eos_token_id:
+            return
+
+        # there may be some smart way to decode.
+        self.token_until.extend(value.tolist())
+        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
+
+
+        if self._is_printable(text) or self.cache_time >= 6:
+            output_text = text[len(self.text_until):]
+            self.text_until = text
+
+        else:
+            self.cache_time+=1
+            return
+
+        self.on_finalized_text(output_text)
+
+    def end(self):
+        """Flushes any remaining cache and prints a newline to stdout."""
+        # Flush the cache, if it exists
+        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
+        output_text = text[len(self.text_until):]
+        self.text_until = text
+        self.on_finalized_text(output_text, stream_end=True)
+        self.clear_cache()
+
+    def clear_cache(self):
+        self.cache_time = 0
+        self.token_until = []
+        self.text_until = ""
+        self.history = None
+        self.next_tokens_are_prompt = True
+
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+        """Put the text tuple in the queue."""
+        self.history.update({"role": "bot", "content": self.text_until, "input_ids": self.token_until,
+                             "attention_mask": [1] * len(self.token_until)})
+        self.text_queue.put((text, self.history), timeout=self.timeout)
+        if stream_end:
+            self.text_queue.put((self.stop_signal, self.history), timeout=self.timeout)
+
+    @staticmethod
+    def _is_printable(cp):
+        """Checks whether tokens can be decoded or not"""
+        if "<EFBFBD>" in cp:
+            return False
+        return True
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        value_now, history_until = self.text_queue.get(timeout=self.timeout)
+        if value_now == self.stop_signal:
+            raise StopIteration()
+        else:
+            return value_now, history_until