THUDM · lwh9346 · Apr 25, 2023 · Apr 26, 2023
diff --git a/stream_cli_demo.py b/stream_cli_demo.py
@@ -0,0 +1,65 @@
+import os
+from transformers import AutoTokenizer, AutoModel
+import signal
+import platform
+from stream_utils import ChatGLMStreamDecoder
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "THUDM/chatglm-6b", trust_remote_code=True)
+stream_decoder = ChatGLMStreamDecoder(tokenizer.sp_tokenizer.text_tokenizer.sp)
+model = AutoModel.from_pretrained(
+    "THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+model = model.eval()
+
+os_name = platform.system()
+clear_command = 'cls' if os_name == 'Windows' else 'clear'
+stop_stream = False
+
+
+def signal_handler(signal, frame):
+    global stop_stream
+    stop_stream = True
+
+
+def main():
+    history = []
+    global stop_stream
+    print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
+    while True:
+        query = input("\n用户：")
+        if query.strip() == "stop":
+            break
+        if query.strip() == "clear":
+            history = []
+            stream_decoder.end()
+            stream_decoder.get()
+            os.system(clear_command)
+            print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
+            continue
+        gen_kwargs = {"max_length": 2048, "do_sample": True, "top_p": 0.7,
+                      "temperature": 0.95, "logits_processor": None}
+        if not history:
+            prompt = query
+        else:
+            prompt = "".join([f"[Round {i}]\n问：{q}\n答：{r}\n" for i, (q, r) in enumerate(
+                history)] + [f"[Round {len(history)}]\n问：{query}\n答："])
+        inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+        print("\nChatGLM-6B：", end="")
+        response = []
+        for outputs in model.stream_generate(**inputs, **gen_kwargs):
+            stream_decoder.put([int(outputs[0][-1])])
+            new_resp = stream_decoder.get().replace("<n>", "\n")
+            response.append(new_resp)
+            print(new_resp, end="")
+        # end of line
+        stream_decoder.end()
+        new_resp = stream_decoder.get().replace("<n>", "\n")
+        response.append(new_resp)
+        print(new_resp)
+        response = "".join(response)
+        history.append((query, response))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/stream_utils.py b/stream_utils.py
@@ -0,0 +1,205 @@
+import sentencepiece as spm
+from typing import Tuple
+import re
+import unittest
+
+# python implantation of https://github.com/google/sentencepiece/blob/master/src/sentencepiece_processor.cc
+
+
+def DecodeSentencePiece(piece: str, id: int, is_bos_ws: bool, sp: spm.SentencePieceProcessor, add_dummy_prefix=True, remove_extra_whitespaces=False) -> Tuple[str, bool]:
+    '''
+    Returns decoded piece and a boolean indicating if the function has consumed
+    a bos whitespace token (a piece starting with a kSpaceSymbol). This is used
+    to strip only the first whitespace token from the decoded sequence for
+    add_dummy_prefix.
+    '''
+    if sp.IsControl(id):  # <s>, </s>
+        return "", False  # invisible symbol.
+    elif sp.IsUnknown(id):
+        if sp.IdToPiece(id) == piece:  # <unk>
+            return SPStreamDecoder.DefaultUnknownSymbol, False
+        else:  # return piece when piece is not <unk>.
+            return piece, False
+    has_bos_ws = False  # whether the token starts with a kSpaceSymbol
+    # Consume if the current position is bos and
+    # piece starts with kSpaceSymbol.
+    if is_bos_ws and (add_dummy_prefix or remove_extra_whitespaces):
+        t = piece.removeprefix(SPStreamDecoder.SpaceSymbol)
+        has_bos_ws = t != piece
+        piece = t
+        # if we are removing extra whitespace, we remove all leading whitespace
+        if remove_extra_whitespaces:
+            has_bos_ws = False
+    return piece.replace(SPStreamDecoder.SpaceSymbol, " "), has_bos_ws
+
+
+def ProcessBytePieces(pieces: list[str]) -> str:
+    '''
+    Modified version of original code
+    '''
+    if len(pieces) == 0:
+        return ""
+    surfaces = ""
+    # Constructs byte sequence.
+    bytes_ = bytes([int(piece[1:-1], base=16) for piece in pieces])
+    # Set surfaces of `bytes` for each Unicode character.
+    while len(bytes_) > 0:
+        try:
+            surfaces += bytes_.decode('utf-8')
+            break
+        except UnicodeDecodeError as e:
+            # The byte piece at `e.start` is structurally invalid. Map it to
+            # REPLACEMENT CHARACTER (U+FFFD).
+            surfaces += bytes_[:e.start].decode('utf-8')
+            surfaces += SPStreamDecoder.ReplacementCharacter
+            bytes_ = bytes_[e.end:]
+            continue
+    return surfaces
+
+
+class SPStreamDecoder:
+    SpaceSymbol = chr(0x2581)
+    DefaultUnknownSymbol = chr(0x2047)
+    ReplacementCharacter = chr(0xFFFD)
+
+    def __init__(self, sp: spm.SentencePieceProcessor, remove_extra_whitespaces=False, add_dummy_prefix=True) -> None:
+        self._sp = sp
+        self._bos_ws_seen = False
+        # 'is_bos_ws': whether we expect a bos ws token to consume.
+        self._is_bos_ws = True
+        self._nothing_decoded = True
+        self._ids = []
+        self._decoded = ""
+        self._ending = False
+        self.remove_extra_whitespaces = remove_extra_whitespaces
+        self.add_dummy_prefix = add_dummy_prefix
+
+    def put(self, ids: list[int]) -> None:
+        self._ending = False
+        self._ids += ids
+        self._decode(eos=False)
+
+    def end(self) -> None:
+        self._decode(eos=True)
+        self._is_bos_ws = True
+        self._bos_ws_seen = False
+        self._nothing_decoded = True
+        self._ending = True
+        self._ids = []
+
+    def _decode(self, eos=False) -> None:
+        pieces = [self._sp.IdToPiece(i) for i in self._ids]
+        consumed = 0
+        byte_pieces = []
+        for i, piece in enumerate(pieces):
+            if not self._sp.IsByte(self._ids[i]):
+                self._decoded += ProcessBytePieces(byte_pieces)
+                consumed += len(byte_pieces)
+                if len(self._decoded) > 0:
+                    self._nothing_decoded = False
+                byte_pieces = []
+                # if we have seen a bos_ws token or any non-empty token
+                if self._bos_ws_seen or (not self._nothing_decoded):
+                    self._is_bos_ws = False
+                decoded, self._bos_ws_seen = DecodeSentencePiece(
+                    piece, self._ids[i], self._is_bos_ws, self._sp)
+                self._decoded += decoded
+                consumed += 1
+                if len(self._decoded) > 0:
+                    self._nothing_decoded = False
+            else:
+                byte_pieces.append(piece)
+        if eos:
+            self._decoded += ProcessBytePieces(byte_pieces)
+        else:
+            self._ids = self._ids[consumed:]
+
+    def get(self) -> str:
+        t = self._decoded
+        self._decoded = ""
+        return t
+
+
+class ChatGLMStreamDecoder(SPStreamDecoder):
+
+    def get(self) -> str:
+        # if prefix of special tokens found, wait till it's impossible or end of decode
+        if "[" in self._decoded and len(self._decoded)-self._decoded.index("[") < 8 and not self._ending:
+            return ""
+        if "<" in self._decoded and len(self._decoded)-self._decoded.index("<") < 12 and not self._ending:
+            return ""
+        self._ending = False
+        t = self._decoded
+        self._decoded = ""
+        t = t.replace("<n>", "\n")
+        t = t.replace("[[训练时间]]", "2023年")
+        punkts = [
+            [",", "，"],
+            ["!", "！"],
+            [":", "："],
+            [";", "；"],
+            ["\?", "？"],
+        ]
+        for item in punkts:
+            t = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], t)
+            t = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], t)
+        # for i in range(max_len, 1, -1):
+        #    t = t.replace(f"<|blank_{i}|>", " " * i)
+        for blank_token in re.findall(r"<\|blank_\d+\|>", t):
+            t = t.replace(blank_token, " " *
+                          int(re.search(r"\d+", blank_token)[0]))
+        return t
+
+
+class ChatGLMStreamDecoderTest(unittest.TestCase):
+    def test_ChatGLM_StreamDecoder(self):
+        from transformers import AutoTokenizer, AutoModel
+        test_strings = [
+            "你好👋",  # multi-byte encoding
+            "Hello this is ChatGLM!",  # normal text
+            "你好👋 This is ChatGLM!",  # multi-byte encoding with tail
+            "!?.,！？。，",  # punctuations
+            "A\nB",  # "<n>" -> "\n"
+            "[[训练时间]]",  # training time token
+            "[[训练时间]123",  # broken training time token
+            "1        1",  # blank token. Note: It's hard to match the results of strip(), so add leading and tailing "1"
+            "<|blank_8|123",  # broken blank token
+        ]
+        tokenizer = AutoTokenizer.from_pretrained(
+            "THUDM/chatglm-6b", trust_remote_code=True)
+        model = AutoModel.from_pretrained(
+            "THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+        model = model.eval()
+        encoded_ids = [tokenizer(x)['input_ids'] for x in test_strings]
+        stream_decoder = ChatGLMStreamDecoder(
+            tokenizer.sp_tokenizer.text_tokenizer.sp)
+        # original output
+        expected_outputs = [model.process_response(
+            tokenizer.decode(x)) for x in encoded_ids]
+        # decode token by token
+        decoded_strings_stream_token_by_token = [None for _ in test_strings]
+        for i in range(len(test_strings)):
+            res = []
+            for t in encoded_ids[i]:
+                stream_decoder.put([t])
+                res.append(stream_decoder.get())
+            stream_decoder.end()
+            res.append(stream_decoder.get())
+            res = "".join(res)
+            decoded_strings_stream_token_by_token[i] = res
+        # decode all at once
+        decoded_strings_stream = [None for _ in test_strings]
+        for i in range(len(test_strings)):
+            stream_decoder.put(encoded_ids[i])
+            stream_decoder.end()
+            decoded_strings_stream[i] = stream_decoder.get()
+        for i in range(len(test_strings)):
+            print(
+                f"Stream decoder test{i}: expected: '{expected_outputs[i]}', token_by_token: '{decoded_strings_stream_token_by_token[i]}', all at once: '{decoded_strings_stream[i]}'")
+            self.assertEqual(
+                expected_outputs[i], decoded_strings_stream_token_by_token[i])
+            self.assertEqual(expected_outputs[i], decoded_strings_stream[i])
+
+
+if __name__ == "__main__":
+    unittest.main()