formating

vllm/worker/hpu_model_runner.py

@@ -43,7 +43,8 @@
 from vllm.model_executor.models import supports_multimodal
 from vllm.model_executor.sampling_metadata import SequenceGroupToSample
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
-                             MultiModalKwargs, MultiModalRegistry, MultiModalPlaceholderMap)
+                             MultiModalKwargs, MultiModalPlaceholderMap,
+                             MultiModalRegistry)
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (CompletionSequenceGroupOutput, IntermediateTensors,
                            Logprob, SequenceData, SequenceGroupMetadata,
@@ -794,9 +795,9 @@ def _prepare_prompt(
         prefix_block_tables: List[List[int]] = []
         multi_modal_kwargs_list: List[MultiModalKwargs] = []
         multi_modal_placeholder_maps: Dict[
-            str,
-            MultiModalPlaceholderMap] = collections.defaultdict(MultiModalPlaceholderMap)
-        
+            str, MultiModalPlaceholderMap] = collections.defaultdict(
+                MultiModalPlaceholderMap)
+
         if len(seq_group_metadata_list) == 0:
             return PreparePromptMetadata.empty()
 
@@ -853,8 +854,8 @@ def _prepare_prompt(
             # is always the first token in the sequence.
             input_positions.append(list(range(context_len, seq_len)))
 
-             mm_data = seq_group_metadata.multi_modal_data
-             if mm_data:
+            mm_data = seq_group_metadata.multi_modal_data
+            if mm_data:
                 mm_kwargs = self.multi_modal_input_mapper(mm_data)
                 multi_modal_kwargs_list.append(mm_kwargs)
 
@@ -865,10 +866,7 @@ def _prepare_prompt(
                 mm_data, placeholder_maps = MultiModalPlaceholderMap \
                     .from_seq_group(seq_group_metadata, positions_range)
 
-                print(mm_data)
-
-                if self.mm_registry.has_processor(
-                        self.model_config):
+                if self.mm_registry.has_processor(self.model_config):
                     mm_kwargs = mm_data
                 else:
                     mm_kwargs = self.multi_modal_input_mapper(