[Core] Refactor Worker and ModelRunner to consolidate control plane communication

tests/worker/test_model_input.py

@@ -0,0 +1,152 @@
+import dataclasses
+from typing import List, Tuple, Type
+
+import torch
+
+from vllm.attention import AttentionMetadata
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.worker.embedding_model_runner import (
+    ModelInputForGPUWithPoolingMetadata)
+from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
+
+
+class MockAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        raise NotImplementedError
+
+    @staticmethod
+    def get_impl_cls():
+        raise NotImplementedError
+
+    @staticmethod
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return AttentionMetadata
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        raise NotImplementedError
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
+    ) -> None:
+        pass
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: torch.Tensor,
+    ) -> None:
+        pass
+
+
+def test_model_runner_input():
+    sampling_metadata = SamplingMetadata(
+        ["seq_group"],
+        "selected_token_indices",
+        "categorized_sample_indices",
+        "num_prompts",
+    )
+    attn_metadata = AttentionMetadata(
+        num_prefills=1,
+        num_prefill_tokens=2,
+        num_decode_tokens=3,
+        slot_mapping=torch.zeros(1),
+    )
+    model_input = ModelInputForGPUWithSamplingMetadata(
+        input_tokens=torch.ones(10),
+        input_positions=torch.ones(10),
+        sampling_metadata=sampling_metadata,
+        attn_metadata=attn_metadata)
+
+    assert isinstance(model_input, ModelInputForGPUWithSamplingMetadata)
+
+    # Test round trip serialization.
+    tensor_dict = model_input.as_broadcastable_tensor_dict()
+    attn_backend = MockAttentionBackend()
+    received_model_input = (
+        ModelInputForGPUWithSamplingMetadata.from_broadcasted_tensor_dict(
+            tensor_dict, attn_backend=attn_backend))
+    # Check that received copy has correct values.
+    assert isinstance(received_model_input,
+                      ModelInputForGPUWithSamplingMetadata)
+    assert received_model_input.input_tokens is not None
+    assert (
+        received_model_input.input_tokens == model_input.input_tokens).all()
+    assert received_model_input.input_positions is not None
+    assert (received_model_input.input_positions == model_input.input_positions
+            ).all()
+    assert received_model_input.multi_modal_kwargs is None
+    assert (received_model_input.multi_modal_kwargs ==
+            model_input.multi_modal_kwargs)
+    assert received_model_input.lora_requests is None
+    assert received_model_input.lora_requests == model_input.lora_requests
+    assert received_model_input.lora_mapping is None
+    assert received_model_input.lora_mapping == model_input.lora_mapping
+    for field in dataclasses.fields(AttentionMetadata):
+        assert getattr(received_model_input.attn_metadata, field.name,
+                       None) == getattr(attn_metadata, field.name, None)
+    # For sampling metadata, only selected_token_indices is copied.
+    assert (received_model_input.sampling_metadata.selected_token_indices ==
+            sampling_metadata.selected_token_indices)
+    assert received_model_input.sampling_metadata.seq_groups is None
+
+
+def test_embedding_model_runner_input():
+    pooling_metadata = PoolingMetadata(
+        seq_groups=[[0]],
+        seq_data={},
+        prompt_lens=[1],
+    )
+    attn_metadata = AttentionMetadata(
+        num_prefills=1,
+        num_prefill_tokens=2,
+        num_decode_tokens=3,
+        slot_mapping=torch.zeros(1),
+    )
+    model_input = ModelInputForGPUWithPoolingMetadata(
+        input_tokens=torch.ones(10),
+        input_positions=torch.ones(10),
+        pooling_metadata=pooling_metadata,
+        attn_metadata=attn_metadata)
+
+    assert isinstance(model_input, ModelInputForGPUWithPoolingMetadata)
+
+    # Test round trip serialization.
+    tensor_dict = model_input.as_broadcastable_tensor_dict()
+    attn_backend = MockAttentionBackend()
+    received_model_input = (
+        ModelInputForGPUWithPoolingMetadata.from_broadcasted_tensor_dict(
+            tensor_dict, attn_backend=attn_backend))
+    # Check that received copy has correct values.
+    assert isinstance(received_model_input,
+                      ModelInputForGPUWithPoolingMetadata)
+    assert received_model_input.input_tokens is not None
+    assert (
+        received_model_input.input_tokens == model_input.input_tokens).all()
+    assert received_model_input.input_positions is not None
+    assert (received_model_input.input_positions == model_input.input_positions
+            ).all()
+    assert received_model_input.multi_modal_kwargs is None
+    assert (received_model_input.multi_modal_kwargs ==
+            model_input.multi_modal_kwargs)
+    assert received_model_input.lora_requests is None
+    assert received_model_input.lora_requests == model_input.lora_requests
+    assert received_model_input.lora_mapping is None
+    assert received_model_input.lora_mapping == model_input.lora_mapping
+    for field in dataclasses.fields(AttentionMetadata):
+        assert getattr(received_model_input.attn_metadata, field.name,
+                       None) == getattr(attn_metadata, field.name, None)
+    # Pooling metadata is not broadcast.
+    assert received_model_input.pooling_metadata is None

tests/worker/test_model_runner.py

@@ -61,12 +61,13 @@ def test_prepare_prompt(batch_size):
         expected_selected_token_indices.append(selected_token_start_idx +
                                                seq_len - 1)
         selected_token_start_idx += seq_len
-    model_input = model_runner._prepare_model_input(seq_group_metadata_list)
+    model_input = model_runner._prepare_model_input_tensors(
+        seq_group_metadata_list)
     input_tokens = model_input.input_tokens
     input_positions = model_input.input_positions
     attn_metadata = model_input.attn_metadata
     return_seq_lens = model_input.seq_lens
-    slot_mapping = model_input.slot_mapping
+    slot_mapping = attn_metadata.slot_mapping
     assert return_seq_lens == seq_lens
     assert len(slot_mapping) == len(input_tokens)
 
@@ -174,10 +175,11 @@ def test_prepare_decode_cuda_graph(batch_size):
         assert seq_group_metadata.token_chunk_size == 1
         seq_group_metadata_list.append(seq_group_metadata)
 
-    model_input = model_runner._prepare_model_input(seq_group_metadata_list)
+    model_input = model_runner._prepare_model_input_tensors(
+        seq_group_metadata_list)
     input_tokens, input_positions, attn_metadata, slot_mapping = (
         model_input.input_tokens, model_input.input_positions,
-        model_input.attn_metadata, model_input.slot_mapping)
+        model_input.attn_metadata, model_input.attn_metadata.slot_mapping)
     assert len(slot_mapping) == len(input_tokens)
 
     expected_bs = _get_graph_batch_size(len(seq_group_metadata_list))
@@ -259,32 +261,29 @@ def test_empty_seq_group():
         enforce_eager=False,
     )
     seq_group_metadata_list: List[SequenceGroupMetadata] = []
-    model_input = model_runner._prepare_model_input(seq_group_metadata_list)
-    input_tokens, input_positions, attn_metadata, slot_mapping = (
+    model_input = model_runner._prepare_model_input_tensors(
+        seq_group_metadata_list)
+    input_tokens, input_positions, attn_metadata = (
         model_input.input_tokens,
         model_input.input_positions,
         model_input.attn_metadata,
-        model_input.slot_mapping,
     )
-    assert len(input_tokens) == 0
-    assert len(input_positions) == 0
+    assert input_tokens is None
+    assert input_positions is None
     assert attn_metadata is None
-    assert len(slot_mapping) == 0
-
-    model_input = model_runner._prepare_model_input(seq_group_metadata_list)
-    (input_tokens, input_positions, attn_metadata, slot_mapping,
-     return_seq_lens) = (
-         model_input.input_tokens,
-         model_input.input_positions,
-         model_input.attn_metadata,
-         model_input.slot_mapping,
-         model_input.seq_lens,
-     )
-    assert len(input_tokens) == 0
-    assert len(input_positions) == 0
+
+    model_input = model_runner._prepare_model_input_tensors(
+        seq_group_metadata_list)
+    (input_tokens, input_positions, attn_metadata, return_seq_lens) = (
+        model_input.input_tokens,
+        model_input.input_positions,
+        model_input.attn_metadata,
+        model_input.seq_lens,
+    )
+    assert input_tokens is None
+    assert input_positions is None
     assert attn_metadata is None
-    assert len(slot_mapping) == 0
-    assert len(return_seq_lens) == 0
+    assert return_seq_lens is None
 
 
 @pytest.fixture
@@ -353,8 +352,12 @@ def test_hybrid_batches(batch_size, enforce_eager, distributed_init):
         seq_group_metadata_list.append(seq_group_metadata)
         decode_metadata_list.append(seq_group_metadata)
 
-    (input_tokens, input_positions, attn_metadata, _, _, _,
-     _) = model_runner.prepare_input_tensors(seq_group_metadata_list)
+    model_input = model_runner.prepare_model_input(seq_group_metadata_list)
+    (input_tokens, input_positions, attn_metadata) = (
+        model_input.input_tokens,
+        model_input.input_positions,
+        model_input.attn_metadata,
+    )
 
     prefill_meta_actual = attn_metadata.prefill_metadata
     decode_meta_actual = attn_metadata.decode_metadata
@@ -367,7 +370,7 @@ def test_hybrid_batches(batch_size, enforce_eager, distributed_init):
 
     # Verify attn metadata is consistent. We don't need to test individual
     # values here because they are tested above.
-    attn_metadata = model_runner._prepare_model_input(
+    attn_metadata = model_runner._prepare_model_input_tensors(
         seq_group_metadata_list).attn_metadata
 
     for attr_expected, attr_actual in zip(vars(attn_metadata.prefill_metadata),

vllm/attention/backends/abstract.py

@@ -21,9 +21,13 @@ def get_impl_cls() -> Type["AttentionImpl"]:
 
     @staticmethod
     @abstractmethod
-    def make_metadata(*args, **kwargs) -> "AttentionMetadata":
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
         raise NotImplementedError
 
+    @classmethod
+    def make_metadata(cls, *args, **kwargs) -> "AttentionMetadata":
+        return cls.get_metadata_cls()(*args, **kwargs)
+
     @staticmethod
     @abstractmethod
     def get_kv_cache_shape(

vllm/attention/backends/blocksparse_attn.py

@@ -90,8 +90,8 @@ def get_impl_cls() -> Type["BlocksparseFlashAttentionImpl"]:
         return BlocksparseFlashAttentionImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "BlocksparseFlashAttentionMetadata":
-        return BlocksparseFlashAttentionMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return BlocksparseFlashAttentionMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/flash_attn.py

@@ -25,8 +25,8 @@ def get_impl_cls() -> Type["FlashAttentionImpl"]:
         return FlashAttentionImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "FlashAttentionMetadata":
-        return FlashAttentionMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return FlashAttentionMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/flashinfer.py

@@ -22,8 +22,8 @@ def get_impl_cls() -> Type["FlashInferImpl"]:
         return FlashInferImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "FlashInferMetadata":
-        return FlashInferMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return FlashInferMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/ipex_attn.py

@@ -25,8 +25,8 @@ def get_impl_cls() -> Type["IpexAttnBackendImpl"]:
         return IpexAttnBackendImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "IpexAttnMetadata":
-        return IpexAttnMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["IpexAttnMetadata"]:
+        return IpexAttnMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/pallas.py

@@ -16,8 +16,8 @@ def get_impl_cls() -> Type["PallasAttentionBackendImpl"]:
         return PallasAttentionBackendImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "PallasMetadata":
-        return PallasMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["PallasMetadata"]:
+        return PallasMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/rocm_flash_attn.py

@@ -25,8 +25,8 @@ def get_impl_cls() -> Type["ROCmFlashAttentionImpl"]:
         return ROCmFlashAttentionImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "ROCmFlashAttentionMetadata":
-        return ROCmFlashAttentionMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return ROCmFlashAttentionMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/torch_sdpa.py

@@ -31,8 +31,8 @@ def get_impl_cls() -> Type["TorchSDPABackendImpl"]:
         return TorchSDPABackendImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "TorchSDPAMetadata":
-        return TorchSDPAMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return TorchSDPAMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/attention/backends/xformers.py

@@ -28,8 +28,8 @@ def get_impl_cls() -> Type["XFormersImpl"]:
         return XFormersImpl
 
     @staticmethod
-    def make_metadata(*args, **kwargs) -> "XFormersMetadata":
-        return XFormersMetadata(*args, **kwargs)
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return XFormersMetadata
 
     @staticmethod
     def get_kv_cache_shape(

vllm/executor/distributed_gpu_executor.py

@@ -64,8 +64,8 @@ def initialize_cache(self, num_gpu_blocks: int,
                           num_cpu_blocks=num_cpu_blocks)
 
     def execute_model(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        self, execute_model_req: ExecuteModelRequest
+    ) -> Optional[List[SamplerOutput]]:
         if self.parallel_worker_tasks is None:
             self.parallel_worker_tasks = self._run_workers(
                 "start_worker_execution_loop",
@@ -79,7 +79,7 @@ def stop_remote_worker_execution_loop(self) -> None:
         if self.parallel_worker_tasks is None:
             return
 
-        self._driver_execute_model()
+        self._driver_execute_model(execute_model_req=None)
         parallel_worker_tasks = self.parallel_worker_tasks
         self.parallel_worker_tasks = None
         # Ensure that workers exit model loop cleanly
@@ -123,13 +123,13 @@ def save_sharded_state(
 
     @abstractmethod
     def _driver_execute_model(
-        self,
-        execute_model_req: Optional[ExecuteModelRequest] = None
-    ) -> List[SamplerOutput]:
+        self, execute_model_req: Optional[ExecuteModelRequest]
+    ) -> Optional[List[SamplerOutput]]:
         """Run execute_model in the driver worker.
 
-        Passing None will cause the driver to stop the model execution
-        loop running in each of the remote workers.
+        Passing None will cause the driver to stop the model execution loop
+        running in each of the remote workers. In this case, this method
+        returns None. Otherwise, this method returns the model output.
         """
         raise NotImplementedError