[Model Runner][Performance] Cache the jugement result of is_encoder_decoder to decrease framework overhead (vllm-project#138)

In Model Runner, is_encoder_decoder is exacted from model_config to
determin whether vllm is running for enc-dec models. Obtaining this
status requires a long call stack, and the CPU overhead is high. So this
PR cache this status in __init__ of ModelInputForNPUBuilder.

Signed-off-by: hw_whx <wanghexiang7@huawei.com>
Co-authored-by: hw_whx <wanghexiang7@huawei.com>

Author: whx-sjtu

vllm_ascend/model_runner.py

@@ -353,6 +353,7 @@ def __init__(self,
         self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
         self.finished_requests_ids = finished_requests_ids
         self.decode_only = True
+        self.is_encoder_decoder = self.runner.model_config.is_encoder_decoder
 
         # Attention metadata inputs.
         self.attn_metadata_builder = self.attn_backend.make_metadata_builder(
@@ -423,7 +424,7 @@ def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
 
         encoder_seq_len = 0
 
-        if self.runner.model_config.is_encoder_decoder:
+        if self.is_encoder_decoder:
             encoder_seq_len = seq_group_metadata.encoder_seq_data.get_len()
 
         inter_data = self.init_cached_inter_data(
@@ -560,7 +561,7 @@ def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int,
             context_len = seq_data.get_num_computed_tokens()
             seq_len = min(seq_len, context_len + token_chunk_size)
         elif self.runner.scheduler_config.is_multi_step or \
-            self.runner.model_config.is_encoder_decoder:
+            self.is_encoder_decoder:
             context_len = seq_len - 1
         else:
             context_len = seq_data.get_num_computed_tokens()