Refine model of qwen.

qwen/demo.py

@@ -54,8 +54,8 @@ print(model)
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 model = model.from_pretrained(model_dir).cuda()
 
-# model = model.eval()
+model = model.eval()
-model = model.train()  # control by @torch.no_grad()
+# model = model.train()  # control by @torch.no_grad()
 
 # 可指定不同的生成长度、top_p等相关超参
 # model.generation_config = GenerationConfig.from_pretrained(

qwen/modeling_qwen.py

@@ -40,7 +40,6 @@ from safetensors import safe_open
 from safetensors.torch import load_file as safe_load_file
 from safetensors.torch import save_file as safe_save_file
 
-
 import sys
 
 sys.path.append("..")
@@ -235,45 +234,16 @@ class QWenModel(QWenPreTrainedModel):
             eps=config.layer_norm_epsilon,
         )
 
-    def get_ntk_alpha(self, true_seq_len):
-        context_value = math.log(true_seq_len / self.seq_length, 2) + 1
-        ntk_alpha = 2 ** math.ceil(context_value) - 1
-        ntk_alpha = max(ntk_alpha, 1)
-        return ntk_alpha
-
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
     ):
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
         input_shape = input_ids.size()
         input_ids = input_ids.view(-1, input_shape[-1])
         batch_size = input_ids.shape[0]
-        elif inputs_embeds is not None:
+        hidden_states = self.wte(input_ids)
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size = inputs_embeds.shape[0]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-        hidden_states = inputs_embeds
-
         kv_seq_len = hidden_states.size()[1]
-
+        rotary_pos_emb_list = [self.rotary_emb(kv_seq_len, ntk_alpha=1.0)]
-        if self.training or not self.use_dynamic_ntk:
-            ntk_alpha_list = [1.0]
-        elif kv_seq_len != hidden_states.size()[1]:
-            ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
-        else:
-            ntk_alpha_list = []
-            ntk_alpha = self.get_ntk_alpha(kv_seq_len)
-            ntk_alpha_list.append(ntk_alpha)
-        self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
-        rotary_pos_emb_list = [self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list]
 
         hidden_states = self.drop(hidden_states)
         output_shape = input_shape + (hidden_states.size(-1),)
@@ -296,19 +266,17 @@ class QWenLMHeadModel(nn.Module):
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.generation_config = GenerationConfig.from_model_config(config)
 
-    def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, **kwargs):
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
         model_inputs = {"input_ids": input_ids}
         return model_inputs
 
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         transformer_outputs = self.transformer(
             input_ids,
-            inputs_embeds=inputs_embeds,
         )
         hidden_states = transformer_outputs[0]
 
@@ -520,7 +488,7 @@ class RotaryEmbedding(torch.nn.Module):
         self._rotary_pos_emb_cache = None
         self._seq_len_cached = 0
         self._ntk_alpha_cached = 1.0
-        self._ntk_alpha_cached_list = [1.0]
+        # self._ntk_alpha_cached_list = [1.0]
 
     def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
         if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached: