[gpt-oss] Add gpt-oss bf16 support

vllm/spec_decode/proposer_worker_base.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from typing import List, Optional, Set, Tuple
+
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
+from vllm.spec_decode.interfaces import SpeculativeProposer
+from vllm.worker.worker_base import LoRANotSupportedWorkerBase
+
+
+class ProposerWorkerBase(LoRANotSupportedWorkerBase, SpeculativeProposer):
+    """Interface for proposer workers"""
+
+    @abstractmethod
+    def sampler_output(
+        self,
+        execute_model_req: ExecuteModelRequest,
+        sample_len: int,
+        # A set containing all sequence IDs that were assigned bonus tokens
+        # in their last forward pass. This set is used to backfill the KV cache
+        # with the key-value pairs of the penultimate token in the sequences.
+        # This parameter is only used by the MultiStepWorker, which relies on
+        # the KV cache for token generation. It is not used by workers that
+        # do not utilize the KV cache.
+        seq_ids_with_bonus_token_in_last_step: Set[int]
+    ) -> Tuple[Optional[List[SamplerOutput]], bool]:
+        raise NotImplementedError
+
+    def set_include_gpu_probs_tensor(self) -> None:
+        """Implementation optional"""
+        pass
+
+    def set_should_modify_greedy_probs_inplace(self) -> None:
+        """Implementation optional"""
+        pass
+
+
+class NonLLMProposerWorkerBase(ProposerWorkerBase, ABC):
+    """Proposer worker which does not use a model with kvcache"""
+
+    def execute_model(
+        self,
+        execute_model_req: Optional[ExecuteModelRequest] = None
+    ) -> List[SamplerOutput]:
+        """get_spec_proposals is used to get the proposals"""
+        return []
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """This is never called on the proposer, only the target model"""
+        raise NotImplementedError
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        pass
+
+    def get_cache_block_size_bytes(self) -> int:
+        return 0