Refine chat output format.
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parent
1b8007e1c3
commit
245d251663
47
qwen/demo.py
47
qwen/demo.py
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@ -22,6 +22,34 @@ config, kwargs = AutoConfig.from_pretrained(
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)
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model = QWenLMHeadModel(config)
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print(model)
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# QWenLMHeadModel(
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# (transformer): QWenModel(
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# (wte): Embedding(151936, 2048)
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# (drop): Dropout(p=0.0, inplace=False)
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# (rotary_emb): RotaryEmbedding()
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# (h): ModuleList(
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# (0-23): 24 x QWenBlock(
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# (ln_1): RMSNorm()
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# (attn): QWenAttention(
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# (c_attn): Linear(in_features=2048, out_features=6144, bias=True)
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# (c_proj): Linear(in_features=2048, out_features=2048, bias=False)
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# (attn_dropout): Dropout(p=0.0, inplace=False)
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# )
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# (ln_2): RMSNorm()
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# (mlp): QWenMLP(
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# (w1): Linear(in_features=2048, out_features=5504, bias=False)
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# (w2): Linear(in_features=2048, out_features=5504, bias=False)
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# (c_proj): Linear(in_features=5504, out_features=2048, bias=False)
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# )
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# )
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# )
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# (ln_f): RMSNorm()
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# )
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# (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
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# )
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = model.from_pretrained(
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@ -36,10 +64,21 @@ model = model.from_pretrained(
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# )
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# 第一轮对话
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response, history = model.chat(tokenizer, "你好", history=None)
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print(response)
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response, history, decode_tokens = model.chat(tokenizer, "你好", "莎是现代汉语的男性的名字，出自《诗经》中的“采采卷", history=None)
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print(decode_tokens)
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# 你好！很高兴为你提供帮助。
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# 第二轮对话
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response, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=history)
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print(response)
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# response, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=None)
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# print(response)
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# <|im_start|>system
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# You are a helpful assistant.<|im_end|>
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# <|im_start|>user
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# 你好<|im_end|>
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# <|im_start|>assistant
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# 莎士比亚是头一个使用“你好”这个词的文学家，他在《哈姆雷特》中写道：“你是谁？你在哪儿？
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# ”他的这一段话，通常被认为是最早的使用“你好”这个词的文学记载。这句话在英国语中非常常见，
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# 特别是在正式或礼貌的情况下。<|im_end|><|endoftext|>
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@ -413,6 +413,7 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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self,
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tokenizer: PreTrainedTokenizer,
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query: str,
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query_assistant: str,
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history: Optional[HistoryType],
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system: str = "You are a helpful assistant.",
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stop_words_ids: Optional[List[List[int]]] = None,
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@ -435,13 +436,13 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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raw_text, context_tokens = make_context(
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tokenizer,
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query,
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query_assistant,
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history=history,
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system=system,
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max_window_size=max_window_size,
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chat_format=generation_config.chat_format,
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max_window_size=max_window_size
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)
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stop_words_ids.extend(get_stop_words_ids(generation_config.chat_format, tokenizer))
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stop_words_ids.extend(get_stop_words_ids(tokenizer))
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input_ids = torch.tensor([context_tokens]).to(self.device)
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outputs = self.generate(
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input_ids,
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@ -449,17 +450,15 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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generation_config=generation_config,
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**kwargs,
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)
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response = decode_tokens(
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decoded, response, end_reason = decode_tokens(
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outputs[0],
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tokenizer,
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raw_text_len=len(raw_text),
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context_length=len(context_tokens),
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chat_format=generation_config.chat_format,
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verbose=False,
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errors="replace",
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)
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history.append((query, response))
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return response, history
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return response, history, decoded
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def generate(
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self,
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@ -106,196 +106,101 @@ def get_batch(context_tokens: torch.LongTensor, eod_id: int):
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return tokens, attention_mask, position_ids
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def get_stop_words_ids(chat_format, tokenizer):
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if chat_format == "raw":
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stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
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elif chat_format == "chatml":
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stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
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else:
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raise NotImplementedError(f"Unknown chat format {chat_format!r}")
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def get_stop_words_ids(tokenizer):
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stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
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return stop_words_ids
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def make_context(
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tokenizer: PreTrainedTokenizer,
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query: str,
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query_assistant: str = "",
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history: List[Tuple[str, str]] = None,
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system: str = "",
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max_window_size: int = 6144,
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chat_format: str = "chatml",
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max_window_size: int = 6144
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):
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if history is None:
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history = []
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if chat_format == "chatml":
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im_start, im_end = "<|im_start|>", "<|im_end|>"
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im_start_tokens = [tokenizer.im_start_id]
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im_end_tokens = [tokenizer.im_end_id]
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nl_tokens = tokenizer.encode("\n")
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im_start, im_end = "<|im_start|>", "<|im_end|>"
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im_start_tokens = [tokenizer.im_start_id]
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im_end_tokens = [tokenizer.im_end_id]
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nl_tokens = tokenizer.encode("\n")
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def _tokenize_str(role, content):
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return f"{role}\n{content}", tokenizer.encode(
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role, allowed_special=set()
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) + nl_tokens + tokenizer.encode(content, allowed_special=set())
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def _tokenize_str(role, content):
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return f"{role}\n{content}", tokenizer.encode(
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role, allowed_special=set()
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) + nl_tokens + tokenizer.encode(content, allowed_special=set())
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system_text, system_tokens_part = _tokenize_str("system", system)
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system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
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system_text, system_tokens_part = _tokenize_str("system", system)
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system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
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assistant_tokens = tokenizer.encode(query_assistant, allowed_special=set())
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raw_text = ""
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context_tokens = []
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raw_text = ""
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context_tokens = []
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for turn_query, turn_response in reversed(history):
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query_text, query_tokens_part = _tokenize_str("user", turn_query)
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query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
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response_text, response_tokens_part = _tokenize_str(
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"assistant", turn_response
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)
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response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
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next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
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prev_chat = (
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f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
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)
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current_context_size = (
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len(system_tokens) + len(next_context_tokens) + len(context_tokens)
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)
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if current_context_size < max_window_size:
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context_tokens = next_context_tokens + context_tokens
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raw_text = prev_chat + raw_text
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else:
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break
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context_tokens = system_tokens + context_tokens
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raw_text = f"{im_start}{system_text}{im_end}" + raw_text
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context_tokens += (
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nl_tokens
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+ im_start_tokens
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+ _tokenize_str("user", query)[1]
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+ im_end_tokens
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+ nl_tokens
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+ im_start_tokens
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+ tokenizer.encode("assistant")
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+ nl_tokens
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for turn_query, turn_response in reversed(history):
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query_text, query_tokens_part = _tokenize_str("user", turn_query)
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query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
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response_text, response_tokens_part = _tokenize_str(
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"assistant", turn_response
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)
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raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
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response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
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elif chat_format == "raw":
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raw_text = query
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context_tokens = tokenizer.encode(raw_text)
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else:
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raise NotImplementedError(f"Unknown chat format {chat_format!r}")
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next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
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prev_chat = (
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f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
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)
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return raw_text, context_tokens
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def _decode_default(
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tokens: List[int],
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*,
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stop_words: List[str],
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eod_words: List[str],
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tokenizer: PreTrainedTokenizer,
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raw_text_len: int,
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verbose: bool = False,
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return_end_reason: bool = False,
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errors: str='replace',
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):
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trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
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if verbose:
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print("\nRaw Generate: ", trim_decode_tokens)
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end_reason = f"Gen length {len(tokens)}"
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for stop_word in stop_words:
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trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
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for eod_word in eod_words:
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if eod_word in trim_decode_tokens:
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end_reason = f"Gen {eod_word!r}"
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trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
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trim_decode_tokens = trim_decode_tokens.strip()
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if verbose:
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print("\nEnd Reason:", end_reason)
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print("\nGenerate: ", trim_decode_tokens)
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if return_end_reason:
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return trim_decode_tokens, end_reason
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else:
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return trim_decode_tokens
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def _decode_chatml(
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tokens: List[int],
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*,
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stop_words: List[str],
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eod_token_ids: List[int],
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tokenizer: PreTrainedTokenizer,
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raw_text_len: int,
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context_length: int,
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verbose: bool = False,
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return_end_reason: bool = False,
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errors: str='replace'
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):
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end_reason = f"Gen length {len(tokens)}"
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eod_token_idx = context_length
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for eod_token_idx in range(context_length, len(tokens)):
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if tokens[eod_token_idx] in eod_token_ids:
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end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
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current_context_size = (
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len(system_tokens) + len(next_context_tokens) + len(context_tokens)
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)
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if current_context_size < max_window_size:
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context_tokens = next_context_tokens + context_tokens
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raw_text = prev_chat + raw_text
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else:
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break
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trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
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if verbose:
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print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
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print("\nRaw Generate:", trim_decode_tokens)
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print("\nEnd Reason:", end_reason)
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for stop_word in stop_words:
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trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
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trim_decode_tokens = trim_decode_tokens.strip()
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if verbose:
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print("\nGenerate:", trim_decode_tokens)
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if return_end_reason:
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return trim_decode_tokens, end_reason
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else:
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return trim_decode_tokens
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context_tokens = system_tokens + context_tokens
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raw_text = f"{im_start}{system_text}{im_end}" + raw_text
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context_tokens += (
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nl_tokens
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+ im_start_tokens
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+ _tokenize_str("user", query)[1]
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+ im_end_tokens
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+ nl_tokens
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+ im_start_tokens
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+ tokenizer.encode("assistant")
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+ nl_tokens
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+ assistant_tokens
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)
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raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n{query_assistant}"
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return raw_text, context_tokens
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def decode_tokens(
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tokens: Union[torch.LongTensor, TokensType],
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tokenizer: PreTrainedTokenizer,
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raw_text_len: int,
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context_length: int,
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chat_format: str,
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verbose: bool = False,
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return_end_reason: bool = False,
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errors: str="replace",
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) -> str:
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if torch.is_tensor(tokens):
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tokens = tokens.cpu().numpy().tolist()
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if chat_format == "chatml":
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return _decode_chatml(
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tokens,
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stop_words=[],
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eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
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tokenizer=tokenizer,
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raw_text_len=raw_text_len,
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context_length=context_length,
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verbose=verbose,
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return_end_reason=return_end_reason,
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errors=errors,
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)
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elif chat_format == "raw":
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return _decode_default(
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tokens,
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stop_words=["<|endoftext|>"],
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eod_words=["<|endoftext|>"],
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tokenizer=tokenizer,
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raw_text_len=raw_text_len,
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verbose=verbose,
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return_end_reason=return_end_reason,
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errors=errors,
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)
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else:
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raise NotImplementedError(f"Unknown chat format {chat_format!r}")
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end_reason = f"Gen length {len(tokens)}"
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eod_token_idx = context_length
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for eod_token_idx in range(context_length, len(tokens)):
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if tokens[eod_token_idx] in [tokenizer.im_start_id, tokenizer.im_end_id]:
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end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
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break
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decoded = tokenizer.decode(tokens, errors=errors)
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decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)
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trim_decode_tokens = decode_tokens[raw_text_len:]
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trim_decode_tokens = trim_decode_tokens.strip()
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return decoded, trim_decode_tokens, end_reason
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class StopWordsLogitsProcessor(LogitsProcessor):
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