Fix use_cache=False

modeling_chatglm.py

@@ -11,12 +11,14 @@ 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
@@ -895,6 +897,7 @@ 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:
@@ -902,14 +905,16 @@ 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:
-            position_ids = position_ids[..., -1:]
-            input_ids = input_ids[:, -1:]
+            if past_key_values is not None:
+                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
+            "return_last_logit": True,
+            "use_cache": use_cache
         }
 
     def forward(
@@ -1086,6 +1091,7 @@ 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):
@@ -1191,3 +1197,89 @@ 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,
+        )