[core] separate builder init and builder prepare for each batch (#12253)

Signed-off-by: youkaichao <youkaichao@gmail.com>

vllm/attention/backends/abstract.py

@@ -65,11 +65,6 @@ class AttentionBackend(ABC):
     def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
         raise NotImplementedError
 
-    @classmethod
-    def make_metadata_builder(cls, *args,
-                              **kwargs) -> "AttentionMetadataBuilder":
-        return cls.get_builder_cls()(*args, **kwargs)
-
     @staticmethod
     @abstractmethod
     def get_kv_cache_shape(
@@ -214,6 +209,12 @@ class AttentionMetadataBuilder(ABC, Generic[T]):
 
     @abstractmethod
     def __init__(self, input_builder: "ModelRunnerInputBuilderBase") -> None:
+        """Create the builder, remember some configuration and parameters."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def prepare(self) -> None:
+        """Prepare for one batch."""
         raise NotImplementedError
 
     @abstractmethod

vllm/attention/backends/flash_attn.py

@@ -375,6 +375,12 @@ class FlashAttentionMetadataBuilder(
         AttentionMetadataBuilder[FlashAttentionMetadata]):
 
     def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+        self.sliding_window = input_builder.sliding_window
+        self.block_size = input_builder.block_size
+
+    def prepare(self):
         self.slot_mapping: List[int] = []
         self.prefill_seq_lens: List[int] = []
         self.context_lens: List[int] = []
@@ -388,11 +394,6 @@ class FlashAttentionMetadataBuilder(
         self.num_decode_tokens = 0
         self.has_prefix_cache_hit = False
 
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-        self.sliding_window = input_builder.sliding_window
-        self.block_size = input_builder.block_size
-
     def _add_seq_group(
             self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
             chunked_prefill_enabled: bool, prefix_cache_hit: bool):

vllm/attention/backends/flashinfer.py

@@ -488,6 +488,14 @@ class FlashInferMetadata(AttentionMetadata):
 class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
 
     def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+
+        self.sliding_window = input_builder.sliding_window
+        self.block_size = input_builder.block_size
+
+    def prepare(self):
         self.slot_mapping: List[int] = []
         self.prefill_seq_lens: List[int] = []
         self.context_lens: List[int] = []
@@ -500,12 +508,6 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         self.num_prefill_tokens = 0
         self.num_decode_tokens = 0
 
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-
-        self.sliding_window = input_builder.sliding_window
-        self.block_size = input_builder.block_size
-
         # Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
         # for the precise definition of the following fields.
         # An example:

vllm/attention/backends/placeholder_attn.py

@@ -253,6 +253,11 @@ class PlaceholderAttentionMetadataBuilder(
         AttentionMetadataBuilder[PlaceholderAttentionMetadata]):
 
     def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+
+    def prepare(self):
         self.prefill_seq_lens: List[int] = []
         self.context_lens: List[int] = []
         self.curr_seq_lens: List[int] = []
@@ -263,9 +268,6 @@ class PlaceholderAttentionMetadataBuilder(
         self.num_prefill_tokens = 0
         self.num_decode_tokens = 0
 
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-
     def _add_seq_group(
             self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
             chunked_prefill_enabled: bool):

vllm/attention/backends/torch_sdpa.py

@@ -282,7 +282,10 @@ class TorchSDPAMetadataBuilder(AttentionMetadataBuilder[TorchSDPAMetadata]):
 
     def __init__(self, input_builder: ModelInputForCPUBuilder) -> None:
         self.chunked_prefill = input_builder.chunked_prefill
-        self.input_data = input_builder.input_data
+        self.input_builder = input_builder
+
+    def prepare(self):
+        self.input_data = self.input_builder.input_data
 
     def build(self, seq_lens: List[int], query_lens: List[int],
               cuda_graph_pad_size: int, batch_size: int) -> TorchSDPAMetadata:

vllm/attention/backends/utils.py

@@ -122,6 +122,13 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
     _metadata_cls: Type[TAttentionMetadata]
 
     def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+
+        self.sliding_window = input_builder.sliding_window
+        self.block_size = input_builder.block_size
+
+    def prepare(self):
         self.slot_mapping: List[int] = []
         self.prefill_seq_lens: List[int] = []
         self.context_lens: List[int] = []
@@ -134,12 +141,6 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
         self.num_prefill_tokens = 0
         self.num_decode_tokens = 0
 
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-
-        self.sliding_window = input_builder.sliding_window
-        self.block_size = input_builder.block_size
-
     def _add_seq_group(
             self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
             chunked_prefill_enabled: bool):

vllm/worker/cpu_model_runner.py

@@ -144,9 +144,7 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
                  runner: "CPUModelRunner",
                  finished_requests_ids: Optional[List[str]] = None) -> None:
         super().__init__()
-        self.seq_group_metadata_list: List[SequenceGroupMetadata] = []
         self.runner = runner
-
         self.chunked_prefill = (runner.scheduler_config.chunked_prefill_enabled
                                 or runner.cache_config.enable_prefix_caching)
         self.model_input_cls = self.runner._model_input_cls
@@ -156,10 +154,17 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
         self.device = self.runner.device
         self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
         self.enable_lora = self.runner.lora_config is not None
+        if self.runner.attn_backend is not None:
+            # spec decode (e.g. Medusa) does not have atten backend
+            attn_backend = self.runner.attn_backend
+            self.att_metadata_builder = attn_backend.get_builder_cls()(self)
+
+    def prepare(self,
+                finished_requests_ids: Optional[List[str]] = None) -> None:
+        self.seq_group_metadata_list: List[SequenceGroupMetadata] = []
         self.input_data = ModelInputForCPUBuilder.ModelInputData(
             self.runner.model_config.uses_mrope)
-        self.att_metadata_builder = self.runner.attn_backend.get_builder_cls()(
-            self)
+        self.att_metadata_builder.prepare()
 
     def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
         self.seq_group_metadata_list.append(seq_group_metadata)
@@ -431,6 +436,7 @@ class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
     """
     _model_input_cls: Type[TModelInputForCPU]
     _builder_cls: Type[ModelInputForCPUBuilder]
+    builder: ModelInputForCPUBuilder
 
     def __init__(
         self,
@@ -477,6 +483,10 @@ class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
         # Set after load_model.
         self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
 
+        if hasattr(self, "_builder_cls"):
+            # multi-step model runner does not have `_builder_cls`
+            self.builder = self._builder_cls(weakref.proxy(self))
+
     def load_model(self) -> None:
         self.model = get_model(vllm_config=self.vllm_config)
 
@@ -522,10 +532,10 @@ class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
         metadata for possible additional steps, e.g., sampling.
 
         """
-        builder = self._builder_cls(weakref.proxy(self), finished_requests_ids)
-        builder.set_seq_group_list(seq_group_metadata_list)
+        self.builder.prepare(finished_requests_ids)
+        self.builder.set_seq_group_list(seq_group_metadata_list)
 
-        return builder.build()  # type: ignore
+        return self.builder.build()  # type: ignore
 
     # sampler property will be used by spec_decode_worker
     @property

vllm/worker/model_runner.py

@@ -457,17 +457,13 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         self.enable_prompt_adapter = (self.runner.prompt_adapter_config
                                       is not None)
         self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
-        self.finished_requests_ids = finished_requests_ids
         self.decode_only = True
 
-        # Intermediate data (data in CPU before going to GPU) for
-        # the current sequence group.
-        self.inter_data_list: List[
-            ModelInputForGPUBuilder.InterDataForSeqGroup] = []
-
         # Attention metadata inputs.
-        self.attn_metadata_builder = self.attn_backend.make_metadata_builder(
-            weakref.proxy(self))
+        if self.attn_backend is not None:
+            # spec decode (e.g. Medusa) does not have atten backend
+            self.attn_metadata_builder = self.attn_backend.get_builder_cls()(
+                weakref.proxy(self))
 
         # Engine/Model configurations.
         self.chunked_prefill_enabled = (
@@ -479,6 +475,17 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             self.block_aligned_sliding_window = \
                 self.sliding_window_blocks * self.block_size
 
+    def prepare(self,
+                finished_requests_ids: Optional[List[str]] = None) -> None:
+        self.finished_requests_ids = finished_requests_ids
+
+        # Intermediate data (data in CPU before going to GPU) for
+        # the current sequence group.
+        self.inter_data_list: List[
+            ModelInputForGPUBuilder.InterDataForSeqGroup] = []
+
+        self.attn_metadata_builder.prepare()
+
     def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int,
                       seq_group_metadata: SequenceGroupMetadata):
         """Compute context length, sequence length and tokens
@@ -993,6 +1000,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
     """
     _model_input_cls: Type[TModelInputForGPU]
     _builder_cls: Type[ModelInputForGPUBuilder]
+    builder: ModelInputForGPUBuilder
 
     def __init__(
         self,
@@ -1093,6 +1101,10 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
               SamplingMetadataCache() \
                 if self.parallel_config.pipeline_parallel_size == 1 else None
 
+        if hasattr(self, "_builder_cls"):
+            # multi-step model runner does not have `_builder_cls`
+            self.builder = self._builder_cls(weakref.proxy(self))
+
     def load_model(self) -> None:
         logger.info("Starting to load model %s...", self.model_config.model)
         with DeviceMemoryProfiler() as m:
@@ -1226,13 +1238,13 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
 
         If cuda graph is required, this API automatically pads inputs.
         """
-        builder = self._builder_cls(weakref.proxy(self), finished_requests_ids)
+        self.builder.prepare(finished_requests_ids)
         for seq_group_metadata in seq_group_metadata_list:
-            builder.add_seq_group(seq_group_metadata)
+            self.builder.add_seq_group(seq_group_metadata)
 
-        builder.reset_cached_inter_data()
+        self.builder.reset_cached_inter_data()
 
-        return builder.build()  # type: ignore
+        return self.builder.build()  # type: ignore
 
     @contextmanager
     def set_in_profile_run(self):

vllm/worker/model_runner_base.py

@@ -200,6 +200,11 @@ class ModelRunnerInputBuilderBase(ABC, Generic[T]):
     """A builder to create ModelRunnerInputBase objects.
   """
 
+    @abstractmethod
+    def prepare(self,
+                finished_requests_ids: Optional[List[str]] = None) -> None:
+        raise NotImplementedError
+
     @abstractmethod
     def add_seq_group(self, seq_group_metadata):
         """TBA"""

vllm/worker/xpu_model_runner.py

@@ -113,7 +113,6 @@ class ModelInputForXPUBuilder(ModelRunnerInputBuilderBase[ModelInputForXPU]):
                  runner: "XPUModelRunner",
                  finished_requests_ids: Optional[List[str]] = None) -> None:
         super().__init__()
-        self.seq_group_metadata_list: List[SequenceGroupMetadata] = []
         self.runner = runner
         self.model_input_cls = self.runner._model_input_cls
         self.attn_backend = self.runner.attn_backend
@@ -121,6 +120,10 @@ class ModelInputForXPUBuilder(ModelRunnerInputBuilderBase[ModelInputForXPU]):
         self.block_size = self.runner.block_size
         self.device = self.runner.device
 
+    def prepare(self,
+                finished_requests_ids: Optional[List[str]] = None) -> None:
+        self.seq_group_metadata_list: List[SequenceGroupMetadata] = []
+
     def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
         self.seq_group_metadata_list.append(seq_group_metadata)
 
@@ -408,6 +411,8 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPUWithSamplingMetadata]):
               SamplingMetadataCache() \
                 if self.parallel_config.pipeline_parallel_size == 1 else None
 
+        self.builder = self._builder_cls(weakref.proxy(self))
+
     def load_model(self) -> None:
         with DeviceMemoryProfiler() as m:
             self.model = get_model(vllm_config=self.vllm_config)
@@ -517,7 +522,8 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPUWithSamplingMetadata]):
         metadata for possible additional steps, e.g., sampling.
 
         """
-        builder = self._builder_cls(weakref.proxy(self), finished_requests_ids)
+        builder = self.builder
+        builder.prepare(finished_requests_ids)
         for seq_group_metadata in seq_group_metadata_list:
             builder.add_seq_group(seq_group_metadata)