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[Misc] Separate adaptive verification config validation (#54108)

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Signed-off-by: Nick Hill <nickhill123@gmail.com>
This commit is contained in:
Nick Hill
2026-08-27 20:03:01 -06:00
committed by GitHub
parent 5bbc58c0ad
commit 8cfba696ee
1 changed files with 36 additions and 30 deletions
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vllm/config/vllm.py
+36 -30
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@@ -1607,6 +1607,7 @@ class VllmConfig:
self._validate_v1_model_runner()
self._validate_batch_sharded_sampling()
self._validate_adaptive_verification()
# Re-compute compile ranges after platform-specific config updates
# (e.g., XPU may lower max_num_batched_tokens when MLA is enabled)
@@ -2444,34 +2445,6 @@ class VllmConfig:
):
unsupported.append("EAGLE3 with pipeline parallelism")
if (
speculative_config.enable_adaptive_verification
and self.lora_config is not None
):
# The per-token LoRA mapping is built from CPU placeholder boundaries,
# while the trimmed batch's true boundaries are decided on the GPU.
unsupported.append("adaptive verification with LoRA")
if (
speculative_config.enable_adaptive_verification
and self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
):
# The draft budget divides by step costs profiled from captured
# cudagraphs; eager execution captures none.
unsupported.append(
"adaptive verification with enforce_eager/cudagraph_mode=none"
)
if (
speculative_config.enable_adaptive_verification
and self.parallel_config.pipeline_parallel_size > 1
):
# Cost curves and confidences currently only exist on the last PP rank;
# earlier ranks would diverge on the trimmed batch shape.
# TODO: we should be able to support adaptive verification with PP by
# broadcasting the cost curves and confidences to all ranks.
unsupported.append("adaptive verification with pipeline parallelism")
if self.parallel_config.enable_dbo:
unsupported.append("dual batch overlap")
@@ -2503,8 +2476,11 @@ class VllmConfig:
unsupported.append("prefill context parallel")
# DSpark is implemented only by the V2 GPU model runner.
if self.speculative_config and self.speculative_config.method == "dspark":
unsupported.append("dspark speculative decoding")
if self.speculative_config:
if self.speculative_config.method == "dspark":
unsupported.append("dspark speculative decoding")
if self.speculative_config.enable_adaptive_verification:
unsupported.append("adaptive draft verification")
# Mixed sliding/full DFlash drafts need multiple KV groups (V2 only).
if self._dflash_needs_multi_kv_group():
@@ -2524,6 +2500,36 @@ class VllmConfig:
return unsupported
def _validate_adaptive_verification(self) -> None:
spec_config = self.speculative_config
if not spec_config or not spec_config.enable_adaptive_verification:
return
if self.lora_config is not None:
# The per-token LoRA mapping is built from CPU placeholder boundaries,
# while the trimmed batch's true boundaries are decided on the GPU.
raise ValueError(
"Adaptive verification is not currently compatible with LoRA"
)
if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
# The draft budget divides by step costs profiled from captured
# cudagraphs; eager execution captures none.
raise ValueError(
"Adaptive verification is not currently compatible with "
"enforce_eager/cudagraph_mode=none"
)
if self.parallel_config.pipeline_parallel_size > 1:
# Cost curves and confidences currently only exist on the last PP rank;
# earlier ranks would diverge on the trimmed batch shape.
# TODO: we should be able to support adaptive verification with PP by
# broadcasting the cost curves and confidences to all ranks.
raise ValueError(
"Adaptive verification is not currently compatible "
"with pipeline parallelism"
)
def _validate_batch_sharded_sampling(self) -> None:
"""Validate `enable_batch_sharded_sampling` against the rest of the config."""
if not self.parallel_config.enable_batch_sharded_sampling: