EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

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

Browse Source 
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>
This commit is contained in:
whx
2025-02-21 22:43:11 +08:00
committed by GitHub
parent d21b3be685
commit 386817b4d1
1 changed files with 3 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
vllm_ascend/model_runner.py
View File

@@ -353,6 +353,7 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
self.finished_requests_ids = finished_requests_ids self.finished_requests_ids = finished_requests_ids
self.decode_only = True self.decode_only = True
self.is_encoder_decoder = self.runner.model_config.is_encoder_decoder
# Attention metadata inputs. # Attention metadata inputs.
self.attn_metadata_builder = self.attn_backend.make_metadata_builder( self.attn_metadata_builder = self.attn_backend.make_metadata_builder(
@@ -423,7 +424,7 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
encoder_seq_len = 0 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() encoder_seq_len = seq_group_metadata.encoder_seq_data.get_len()
inter_data = self.init_cached_inter_data( inter_data = self.init_cached_inter_data(
@@ -560,7 +561,7 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
context_len = seq_data.get_num_computed_tokens() context_len = seq_data.get_num_computed_tokens()
seq_len = min(seq_len, context_len + token_chunk_size) seq_len = min(seq_len, context_len + token_chunk_size)
elif self.runner.scheduler_config.is_multi_step or \ 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 context_len = seq_len - 1
else: else:
context_len = seq_data.get_num_computed_tokens() context_len = seq_data.get_num_computed_tokens()