init v0.11.0rc0

vllm_ascend/ops/vocab_parallel_embedding.py

@@ -97,6 +97,7 @@ class AscendVocabParallelEmbedding(VocabParallelEmbedding):
 
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
         # Divide the weight matrix along the vocaburaly dimension.
         self.num_added_embeddings = self.num_embeddings - self.org_vocab_size
         self.num_embeddings_per_partition = divide(self.num_embeddings_padded,
@@ -252,3 +253,16 @@ class AscendLogitsProcessor(LogitsProcessor):
             logits = logits[..., :self.org_vocab_size]
 
         return logits
+
+    def forward(
+        self,
+        lm_head: VocabParallelEmbedding,
+        hidden_states: torch.Tensor,
+        # keep this for version compatibility
+        sampling_metadata=None,  # type: ignore
+        embedding_bias: Optional[torch.Tensor] = None,
+    ) -> Optional[torch.Tensor]:
+        return LogitsProcessor.forward(self,
+                                       lm_head,
+                                       hidden_states,
+                                       embedding_bias=embedding_bias)