[gpt-oss] Add gpt-oss bf16 support

vllm/spec_decode/interfaces.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import List, Optional, Set, Union
+
+import torch
+
+from vllm.sequence import ExecuteModelRequest, PromptLogprobs
+from vllm.worker.worker_base import WorkerBase
+
+
+@dataclass
+class SpeculativeProposals:
+    """Datastructure used to represent proposal tokens from some proposer. It
+    also tracks how many speculative tokens each sequence has.
+    """
+
+    # Speculative proposal tokens.
+    proposal_token_ids: torch.Tensor
+
+    # Probabilities of the proposal tokens according to the proposer.
+    proposal_probs: torch.Tensor
+
+    # The valid length of each proposal; can be zero.
+    proposal_lens: torch.Tensor
+
+    # A flag to mark that there's no available proposals
+    no_proposals: bool = False
+
+    def __repr__(self):
+        return (f"SpeculativeProposals("
+                f"proposal_token_ids={self.proposal_token_ids}, "
+                f"proposal_probs={self.proposal_probs.shape}, "
+                f"proposal_lens={self.proposal_lens})")
+
+
+@dataclass
+class SpeculativeScores:
+    """Datastructure used to represent the scores of speculative tokens
+    according to the scoring model.
+    """
+
+    # Probabilities of the speculative tokens according to the scoring model.
+    probs: torch.Tensor
+
+    # Log-probabilities of the speculative tokens according to the scoring
+    # model. These values can be used to generate Logprob objects that are
+    # returned to the user.
+    logprobs: torch.Tensor
+
+    # Token ids sampled from the scoring model. Used for speculative bonus
+    # tokens and also non-speculative normal decoding.
+    token_ids: torch.Tensor
+
+    # Optional last hidden states from the scoring model.
+    hidden_states: Optional[torch.Tensor] = None
+
+    # Scoring model may also return logprobs for prompt tokens
+    # for each request, when chunked prefill is enabled.
+    prompt_logprobs: Optional[List[PromptLogprobs]] = None
+
+    def __repr__(self):
+        return (f"SpeculativeScores("
+                f"probs={self.probs.shape}, "
+                f"token_ids={self.token_ids.shape})")
+
+
+class SpeculativeProposer(ABC):
+
+    @abstractmethod
+    def get_spec_proposals(
+        self,
+        execute_model_req: ExecuteModelRequest,
+        # If set, this contains all sequence IDs that were assigned
+        # bonus tokens in their last forward pass.
+        seq_ids_with_bonus_token_in_last_step: Set[int],
+    ) -> SpeculativeProposals:
+        raise NotImplementedError
+
+
+class SpeculativeScorer(ABC):
+
+    def __init__(self, scorer_worker: WorkerBase,
+                 device: Union[torch.device, str], vocab_size: int):
+        self._scorer_worker = scorer_worker
+        if isinstance(device, torch.device):
+            device = device.type
+        self._device = device
+        self._vocab_size = vocab_size
+
+    @abstractmethod
+    def score_proposals(
+        self,
+        execute_model_req: ExecuteModelRequest,
+        proposals: SpeculativeProposals,
+    ) -> SpeculativeScores:
+        raise NotImplementedError