test_rh

README.md

@@ -74,17 +74,22 @@ For more instructions, including how to run CLI and web demos, and model quantiz
 
 ## 引用
 
-如果你觉得我们的工作有帮助的话，请考虑引用下列论文。
-
-If you find our work helpful, please consider citing the following paper.
+如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，尽情期待～
 
 ```
-@misc{glm2024chatglm,
-      title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, 
-      author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
-      year={2024},
-      eprint={2406.12793},
-      archivePrefix={arXiv},
-      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+@article{zeng2022glm,
+  title={Glm-130b: An open bilingual pre-trained model},
+  author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
+  journal={arXiv preprint arXiv:2210.02414},
+  year={2022}
+}
+```
+```
+@inproceedings{du2022glm,
+  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
+  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
+  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  pages={320--335},
+  year={2022}
 }
 ```

modeling_chatglm.py

@@ -11,14 +11,12 @@ import torch.utils.checkpoint
 import torch.nn.functional as F
 from torch import nn
 from torch.nn import CrossEntropyLoss, LayerNorm
-from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
 from torch.nn.utils import skip_init
 from typing import Optional, Tuple, Union, List, Callable, Dict, Any
 
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
 )
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import logging
@@ -897,7 +895,6 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
             past_key_values: Optional[torch.Tensor] = None,
             attention_mask: Optional[torch.Tensor] = None,
             position_ids: Optional[torch.Tensor] = None,
-            use_cache: Optional[bool] = None,
             is_first_forward: bool = True,
             **kwargs
     ) -> dict:
@@ -905,16 +902,14 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
         if position_ids is None:
             position_ids = self.get_position_ids(input_ids, device=input_ids.device)
         if not is_first_forward:
-            if past_key_values is not None:
-                position_ids = position_ids[..., -1:]
-                input_ids = input_ids[:, -1:]
+            position_ids = position_ids[..., -1:]
+            input_ids = input_ids[:, -1:]
         return {
             "input_ids": input_ids,
             "past_key_values": past_key_values,
             "position_ids": position_ids,
             "attention_mask": attention_mask,
-            "return_last_logit": True,
-            "use_cache": use_cache
+            "return_last_logit": True
         }
 
     def forward(
@@ -1091,7 +1086,6 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
             generation_config = self.generation_config
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)
-        model_kwargs["use_cache"] = generation_config.use_cache
         bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
 
         if isinstance(eos_token_id, int):
@@ -1197,89 +1191,3 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
         self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
                                             **kwargs)
         return self
-
-
-class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
-    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
-
-        self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
-        if config.classifier_dropout is not None:
-            self.dropout = nn.Dropout(config.classifier_dropout)
-        else:
-            self.dropout = None
-        self.config = config
-
-        if self.config.quantization_bit:
-            self.quantize(self.config.quantization_bit, empty_init=True)
-
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            full_attention_mask: Optional[torch.Tensor] = None,
-            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-            inputs_embeds: Optional[torch.LongTensor] = None,
-            labels: Optional[torch.LongTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            attention_mask=attention_mask,
-            full_attention_mask=full_attention_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = transformer_outputs[0]
-        pooled_hidden_states = hidden_states[-1]
-        if self.dropout is not None:
-            pooled_hidden_states = self.dropout(pooled_hidden_states)
-        logits = self.classifier_head(pooled_hidden_states)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze().float(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits.float(), labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
-
-        if not return_dict:
-            output = (logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )

tokenization_chatglm.py

@@ -65,7 +65,8 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
 
     model_input_names = ["input_ids", "attention_mask", "position_ids"]
 
-    def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
+    def __init__(self, vocab_file, padding_side="left", **kwargs):
+        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=False, **kwargs)
         self.name = "GLMTokenizer"
 
         self.vocab_file = vocab_file
@@ -75,7 +76,6 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
             "<eos>": self.tokenizer.eos_id,
             "<pad>": self.tokenizer.pad_id
         }
-        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
 
     def get_command(self, token):
         if token in self.special_tokens: