[Misc] Simplify PoolerOutput and move to v1/outputs

vllm/executor/executor_base.py

@@ -15,10 +15,10 @@
 from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, PoolerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.tasks import SupportedTask
 from vllm.utils import make_async
-from vllm.v1.outputs import SamplerOutput
+from vllm.v1.outputs import PoolerOutput, SamplerOutput
 from vllm.worker.worker_base import WorkerBase
 
 logger = init_logger(__name__)

vllm/model_executor/layers/pooler.py

@@ -16,9 +16,9 @@
 from vllm.logger import init_logger
 from vllm.model_executor.models.adapters import _load_st_projector
 from vllm.pooling_params import PoolingParams
-from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
 from vllm.tasks import PoolingTask
-from vllm.utils import current_stream, resolve_obj_by_qualname
+from vllm.utils import resolve_obj_by_qualname
+from vllm.v1.outputs import PoolerOutput
 from vllm.v1.pool.metadata import PoolingCursor, PoolingMetadata
 
 logger = init_logger(__name__)
@@ -190,19 +190,6 @@ def get_cross_encoder_activation_function(config: PretrainedConfig):
     return PoolerClassify()
 
 
-def build_output(
-    all_data: Union[torch.Tensor, list[torch.Tensor]], ) -> PoolerOutput:
-    # Pooling models D2H & synchronize occurs here
-    if isinstance(all_data, list):
-        all_data = [d.to("cpu", non_blocking=True) for d in all_data]
-    else:
-        all_data = all_data.to("cpu", non_blocking=True)
-    current_stream().synchronize()
-
-    all_outputs = [PoolingSequenceGroupOutput(data) for data in all_data]
-    return PoolerOutput(outputs=all_outputs)
-
-
 class PoolingMethod(nn.Module, ABC):
 
     @staticmethod
@@ -556,7 +543,7 @@ def forward(
     ) -> PoolerOutput:
         pooled_data = self.pooling(hidden_states, pooling_metadata)
         pooled_data = self.head(pooled_data, pooling_metadata)
-        return build_output(pooled_data)
+        return pooled_data
 
 
 class StepPooler(Pooler):
@@ -607,7 +594,7 @@ def forward(
     ) -> PoolerOutput:
         pooled_data = self.extract_states(hidden_states, pooling_metadata)
         pooled_data = self.head(pooled_data, pooling_metadata)
-        return build_output(pooled_data)
+        return pooled_data
 
 
 class ClassifierPooler(Pooler):
@@ -678,7 +665,7 @@ def forward(
             ]
 
         # scores shape: [batchsize, num_labels]
-        return build_output(scores)
+        return scores
 
 
 class DispatchPooler(Pooler):
@@ -708,7 +695,7 @@ def forward(
     ) -> PoolerOutput:
         poolers_by_task = self.poolers_by_task
 
-        outputs = list[PoolingSequenceGroupOutput]()
+        outputs = list[torch.Tensor]()
         offset = 0
         for task, group in groupby(get_tasks(pooling_metadata)):
             if not (pooler := poolers_by_task.get(task)):
@@ -722,10 +709,10 @@ def forward(
                 pooling_metadata[offset:offset + num_items],
             )
 
-            outputs.extend(group_output.outputs)
+            outputs.extend(group_output)
             offset += num_items
 
-        return PoolerOutput(outputs)
+        return outputs
 
     def extra_repr(self) -> str:
         s = f"supported_task={self.get_supported_tasks()}"

vllm/model_executor/models/gritlm.py

@@ -12,12 +12,12 @@
 from vllm.model_executor.layers.pooler import (DispatchPooler, Pooler,
                                                PoolerHead, PoolerNormalize,
                                                PoolingParamsUpdate,
-                                               build_output, get_prompt_lens,
+                                               get_prompt_lens,
                                                get_prompt_token_ids)
 from vllm.model_executor.models.llama import LlamaForCausalLM
-from vllm.sequence import PoolerOutput
 from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
+from vllm.v1.outputs import PoolerOutput
 from vllm.v1.pool.metadata import PoolingMetadata
 
 from .interfaces_base import default_pooling_type
@@ -212,7 +212,7 @@ def forward(
     ) -> PoolerOutput:
         pooled_data = self.pooling(hidden_states, pooling_metadata)
         pooled_data = self.head(pooled_data, pooling_metadata)
-        return build_output(pooled_data)
+        return pooled_data
 
 
 @default_pooling_type("MEAN")

vllm/sequence.py

@@ -11,7 +11,6 @@
     from vllm.v1.worker.kv_connector_model_runner_mixin import (
         KVConnectorOutput)
 else:
-    LoRARequest = Any
     KVConnectorOutput = Any
 
 VLLM_TOKEN_ID_ARRAY_TYPE = "l"
@@ -48,29 +47,6 @@ class RequestMetrics:
     model_execute_time: Optional[float] = None
 
 
-class PoolingSequenceGroupOutput(
-        msgspec.Struct,
-        omit_defaults=True,  # type: ignore[call-arg]
-        array_like=True,  # type: ignore[call-arg]
-):
-    """The model output associated with a pooling sequence group."""
-    # Annotated as Any to be compatible with msgspec
-    # The actual type is in SequenceGroup.pooled_data
-    data: Any
-
-    def get_data_nbytes(self) -> int:
-        data: torch.Tensor = self.data
-        return data.nbytes
-
-    def __repr__(self) -> str:
-        return f"PoolingSequenceGroupOutput(data={self.data}"
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, PoolingSequenceGroupOutput):
-            raise NotImplementedError()
-        return self.data == other.data
-
-
 # cannot use msgspec.Struct here because Dynamo does not support it
 @dataclass
 class IntermediateTensors:
@@ -119,30 +95,6 @@ def __repr__(self) -> str:
         return f"IntermediateTensors(tensors={self.tensors})"
 
 
-class PoolerOutput(
-        msgspec.Struct,
-        omit_defaults=True,  # type: ignore[call-arg]
-        array_like=True):  # type: ignore[call-arg]
-    """The output from a pooling operation in the pooling model."""
-    outputs: list[PoolingSequenceGroupOutput]
-
-    def get_data_nbytes(self) -> int:
-        return sum(o.get_data_nbytes() for o in self.outputs)
-
-    def __getitem__(self, idx: int) -> PoolingSequenceGroupOutput:
-        return self.outputs[idx]
-
-    def __setitem__(self, idx: int, value: PoolingSequenceGroupOutput):
-        self.outputs[idx] = value
-
-    def __len__(self):
-        return len(self.outputs)
-
-    def __eq__(self, other: object):
-        return isinstance(other,
-                          self.__class__) and self.outputs == other.outputs
-
-
 class ExecuteModelRequest(
         msgspec.Struct,
         array_like=True,  # type: ignore[call-arg]

vllm/v1/outputs.py

@@ -3,7 +3,7 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, NamedTuple, Optional
+from typing import TYPE_CHECKING, NamedTuple, Optional, Union
 
 import torch
 
@@ -65,6 +65,11 @@ def empty_cpu(num_positions: int,
         )
 
 
+# [num_reqs, <dynamic>]
+# The shape of each element depends on the pooler used
+PoolerOutput = Union[torch.Tensor, list[torch.Tensor]]
+
+
 @dataclass
 class SamplerOutput:
 

vllm/v1/worker/gpu_model_runner.py

@@ -52,13 +52,14 @@
 from vllm.multimodal.utils import group_mm_kwargs_by_modality
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingType
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         GiB_bytes, cdiv, check_use_alibi, get_dtype_size,
                         is_pin_memory_available,
                         length_from_prompt_token_ids_or_embeds, round_up,
                         supports_dynamo)
+from vllm.utils.jsontree import json_map_leaves
 from vllm.v1.attention.backends.flash_attn import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
@@ -79,7 +80,7 @@
 # yapf: enable
 from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput,
                              DraftTokenIds, LogprobsLists, LogprobsTensors,
-                             ModelRunnerOutput, SamplerOutput)
+                             ModelRunnerOutput, PoolerOutput, SamplerOutput)
 from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
 from vllm.v1.sample.metadata import SamplingMetadata
@@ -1823,15 +1824,22 @@ def _pool(
                                               device=hidden_states.device)
         seq_lens_cpu = self.seq_lens.cpu[:self.input_batch.num_reqs]
 
-        # Pooling models D2H & synchronize occurs in pooler.py:build_output
-        raw_pooler_output = self.model.pooler(
-            hidden_states=hidden_states, pooling_metadata=pooling_metadata)
+        model = cast(VllmModelForPooling, self.model)
+        raw_pooler_output: PoolerOutput = model.pooler(
+            hidden_states=hidden_states,
+            pooling_metadata=pooling_metadata,
+        )
+        raw_pooler_output = json_map_leaves(
+            lambda x: x.to("cpu", non_blocking=True),
+            raw_pooler_output,
+        )
+        self._sync_device()
 
         pooler_output: list[Optional[torch.Tensor]] = []
         for raw_output, seq_len, prompt_len in zip(
                 raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
 
-            output = raw_output.data if seq_len == prompt_len else None
+            output = raw_output if seq_len == prompt_len else None
             pooler_output.append(output)
 
         return ModelRunnerOutput(
@@ -3233,7 +3241,7 @@ def _dummy_pooler_run(
         for task in self.get_supported_pooling_tasks():
             # Run a full batch with each task to ensure none of them OOMs
             output = self._dummy_pooler_run_task(hidden_states, task)
-            output_size[task] = output.get_data_nbytes()
+            output_size[task] = sum(o.nbytes for o in output)
             del output  # Allow GC
 
         max_task = max(output_size.items(), key=lambda x: x[1])[0]