fix: typo genrate -> generate

eagle/model/cnets.py

@@ -667,7 +667,7 @@ def reset_kv(self):
         self.stable_kv = None
 
     @torch.no_grad()
-    def topK_genrate(self, hidden_states, input_ids, head, logits_processor):
+    def topK_generate(self, hidden_states, input_ids, head, logits_processor):
 
         input_ids = input_ids.to(hidden_states.device)
         total_tokens = self.total_tokens

eagle/model/cnets1.py

@@ -670,7 +670,7 @@ def reset_kv(self):
         self.stable_kv = None
 
     @torch.no_grad()
-    def topK_genrate(self, hidden_states, input_ids, head, logits_processor):
+    def topK_generate(self, hidden_states, input_ids, head, logits_processor):
 
         input_ids = input_ids.to(hidden_states.device)
         total_tokens = self.total_tokens

eagle/model/utils.py

@@ -223,7 +223,7 @@ def initialize_tree0(input_ids, model, past_key_values, logits_processor):
     #     input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
     #     # Clone the output hidden states
     #
-    #     draft_tokens, retrieve_indices,tree_mask,tree_position_ids = self.ea_layer.topK_genrate(hidden_states, input_ids, self.base_model.lm_head)
+    #     draft_tokens, retrieve_indices,tree_mask,tree_position_ids = self.ea_layer.topK_generate(hidden_states, input_ids, self.base_model.lm_head)
     #     if output_orig:
     #         return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, outputs, orig, hidden_states, token
     #     return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, hidden_states, token
@@ -250,7 +250,7 @@ def initialize_tree(input_ids, model, past_key_values, logits_processor):
         if outputs["hidden_states"][0].device != ea_device:
             outputs["hidden_states"] = [x.to(ea_device) for x in outputs["hidden_states"]]
         hidden_states=torch.cat(outputs["hidden_states"],dim=-1)
-    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_genrate(hidden_states, input_ids, model.base_model.lm_head,logits_processor)
+    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_generate(hidden_states, input_ids, model.base_model.lm_head,logits_processor)
     return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, orig, hidden_states, token
 
 
@@ -463,7 +463,7 @@ def update_inference_inputs(
         token = torch.argmax(prob)
         token = token[None, None]
     # hidden_state = torch.cat((hidden_state, accept_hidden_state_new), dim=1)
-    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_genrate(accept_hidden_state_new,
+    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_generate(accept_hidden_state_new,
                                               input_ids=torch.cat((input_ids, token.to(input_ids.device)), dim=1),
                                               head=model.base_model.lm_head,logits_processor=logits_processor)
 

eagle/modeling_eagle.py

@@ -860,7 +860,7 @@ def sample(self, logits, logits_processor, k=1):
         return sampled_indices, sampled_probs, probabilities
 
     @torch.no_grad()
-    def topK_genrate(self, hidden_states, input_ids, head, logits_processor, max_length=4, use_cache=True,
+    def topK_generate(self, hidden_states, input_ids, head, logits_processor, max_length=4, use_cache=True,
                      attention_mask=None, len_posi=None, ):
         top_k = 5
         bs = input_ids.shape[0]
@@ -1272,7 +1272,7 @@ def initialize_tree(input_ids, model, logits_processor, attention_mask=None):
         token = token[:, None]
     input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
 
-    tree_logits = model.ea_layer.topK_genrate(hidden_states, input_ids, model.base_model.lm_head, logits_processor,
+    tree_logits = model.ea_layer.topK_generate(hidden_states, input_ids, model.base_model.lm_head, logits_processor,
                                            attention_mask=attention_mask)
 
 
@@ -1543,7 +1543,7 @@ def update_inference_inputs(
 
 
 
-    tree_logits = model.ea_layer.topK_genrate(draft_hidden,
+    tree_logits = model.ea_layer.topK_generate(draft_hidden,
                                               input_ids=draft_input_ids,
                                               head=model.base_model.lm_head, logits_processor=logits_processor,attention_mask=attention_mask,len_posi=input_ids.shape[1])
 

eagle/testbug/model/cnets.py

@@ -569,7 +569,7 @@ def sample(self,logits, logits_processor,k=1, replacement=False):
 
 
     @torch.no_grad()
-    def topK_genrate(self, hidden_states, input_ids, head, logits_processor,max_length=4, use_cache=True):
+    def topK_generate(self, hidden_states, input_ids, head, logits_processor,max_length=4, use_cache=True):
         input_ids = input_ids[:, 1:]
         ss_token,ss_prob,ss_op = [],[],[]
         len_posi=input_ids.shape[1]

eagle/testbug/model/ea_model.py

@@ -92,7 +92,7 @@ def forward(
             input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
             # Clone the output hidden states
 
-            ea_logits = self.ea_layer.topK_genrate(hidden_states, input_ids, None, logits_processor)
+            ea_logits = self.ea_layer.topK_generate(hidden_states, input_ids, None, logits_processor)
             if output_orig:
                 return ea_logits, outputs, orig, hidden_states, token
             return ea_logits, hidden_states, token

eagle/testbug/model/ea_modelbs.py

@@ -140,7 +140,7 @@ def forward(
             input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
             # Clone the output hidden states
 
-            ea_logits = self.ea_layer.topK_genrate(hidden_states, input_ids, self.base_model.lm_head, logits_processor,attention_mask=attention_mask)
+            ea_logits = self.ea_layer.topK_generate(hidden_states, input_ids, self.base_model.lm_head, logits_processor,attention_mask=attention_mask)
             if output_orig:
                 return ea_logits, outputs, orig, hidden_states, token
             return ea_logits, hidden_states, token

eagle/testbug/model/utils.py

@@ -441,7 +441,7 @@ def update_inference_inputs(
         token = torch.argmax(prob)
         token = token[None, None]
     # hidden_state = torch.cat((hidden_state, accept_hidden_state_new), dim=1)
-    tree_logits = model.ea_layer.topK_genrate(None,
+    tree_logits = model.ea_layer.topK_generate(None,
                                               input_ids=torch.cat((input_ids, token.to(input_ids.device)), dim=1),
                                               head=None, logits_processor=logits_processor)