[Hardware] add platform-specific request validation api (vllm-project#16291)

Signed-off-by: Joe Runde <[email protected]>
Signed-off-by: Yang Wang <[email protected]>

Author: joerunde

vllm/platforms/cpu.py

@@ -180,7 +180,3 @@ def get_device_communicator_cls(cls) -> str:
         Get device specific communicator class for distributed communication.
         """
         return "vllm.distributed.device_communicators.cpu_communicator.CpuCommunicator"  # noqa
-
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True

vllm/platforms/cuda.py

@@ -308,10 +308,6 @@ def supports_fp8(cls) -> bool:
     def supports_v1(cls, model_config: ModelConfig) -> bool:
         return True
 
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True
-
     @classmethod
     def use_custom_allreduce(cls) -> bool:
         return True

vllm/platforms/hpu.py

@@ -92,7 +92,3 @@ def get_punica_wrapper(cls) -> str:
     @classmethod
     def get_device_communicator_cls(cls) -> str:
         return "vllm.distributed.device_communicators.hpu_communicator.HpuCommunicator"  # noqa
-
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True

vllm/platforms/interface.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-
 import enum
 import platform
 import random
@@ -9,14 +8,21 @@
 import numpy as np
 import torch
 
+from vllm.inputs import PromptType
 from vllm.logger import init_logger
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig, VllmConfig
+    from vllm.lora.request import LoRARequest
+    from vllm.pooling_params import PoolingParams
+    from vllm.sampling_params import SamplingParams
     from vllm.utils import FlexibleArgumentParser
 else:
     ModelConfig = None
     VllmConfig = None
+    LoRARequest = None
+    PoolingParams = None
+    SamplingParams = None
     FlexibleArgumentParser = None
 
 logger = init_logger(__name__)
@@ -379,20 +385,21 @@ def supports_v1(cls, model_config: ModelConfig) -> bool:
         """
         return False
 
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        """
-        Returns whether the current platform can support structured output.
-        """
-        return False
-
     @classmethod
     def use_custom_allreduce(cls) -> bool:
         """
         Returns if custom allreduce is supported on the current platform
         """
         return False
 
+    @classmethod
+    def validate_request(
+        cls,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+    ) -> None:
+        """Raises if this request is unsupported on this platform"""
+
 
 class UnspecifiedPlatform(Platform):
     _enum = PlatformEnum.UNSPECIFIED

vllm/platforms/neuron.py

@@ -67,7 +67,3 @@ def get_device_communicator_cls(cls) -> str:
     @classmethod
     def use_all_gather(cls) -> bool:
         return True
-
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True

vllm/platforms/rocm.py

@@ -303,10 +303,6 @@ def supports_v1(cls, model_config: ModelConfig) -> bool:
         # V1 support on AMD gpus is experimental
         return True
 
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True
-
     @classmethod
     def use_custom_allreduce(cls) -> bool:
         # We only enable custom allreduce for MI300 series

vllm/platforms/tpu.py

@@ -1,19 +1,26 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, Union
 
 import torch
 
 import vllm.envs as envs
+from vllm.inputs import PromptType
 from vllm.logger import init_logger
 
 from .interface import Platform, PlatformEnum, _Backend
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig, VllmConfig
+    from vllm.lora.request import LoRARequest
+    from vllm.pooling_params import PoolingParams
+    from vllm.sampling_params import SamplingParams
 else:
     ModelConfig = None
     VllmConfig = None
+    LoRARequest = None
+    PoolingParams = None
+    SamplingParams = None
 
 logger = init_logger(__name__)
 
@@ -135,6 +142,13 @@ def supports_v1(cls, model_config: ModelConfig) -> bool:
         return True
 
     @classmethod
-    def supports_structured_output(cls) -> bool:
-        # Structured output is not supported on TPU.
-        return False
+    def validate_request(
+        cls,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+    ) -> None:
+        """Raises if this request is unsupported on this platform"""
+        if isinstance(params,
+                      SamplingParams) and params.guided_decoding is not None:
+            raise ValueError("Structured output is not supported on "
+                             f"{cls.device_name}.")

vllm/platforms/xpu.py

@@ -140,7 +140,3 @@ def device_support_bf16(cls) -> bool:
     @classmethod
     def get_device_communicator_cls(cls) -> str:
         return "vllm.distributed.device_communicators.xpu_communicator.XpuCommunicator"  # noqa
-
-    @classmethod
-    def supports_structured_output(cls) -> bool:
-        return True

vllm/v1/engine/processor.py

@@ -141,11 +141,6 @@ def _validate_structured_output(self, params: SamplingParams) -> None:
         else:
             params.guided_decoding.backend = engine_level_backend
 
-        from vllm.platforms import current_platform
-        if not current_platform.supports_structured_output():
-            raise ValueError("Structured output is not supported on "
-                             f"{current_platform.device_name}.")
-
         # Request content validation
         if engine_level_backend.startswith("xgrammar"):
             # xgrammar with no fallback
@@ -187,6 +182,11 @@ def process_inputs(
         # TODO(woosuk): Support pooling models.
         # TODO(woosuk): Support encoder-decoder models.
 
+        from vllm.platforms import current_platform
+        current_platform.validate_request(
+            prompt=prompt,
+            params=params,
+        )
         self._validate_lora(lora_request)
         self._validate_params(params)
         if priority != 0: