vllm.v1.worker.gpu.spec_decode.dflash.cudagraph ¶

class DFlashCudaGraphManager(CudaGraphManager):
    """DFlash CudaGraphManager for the parallel-drafting query forward,
    building its own attention metadata from scratch."""

    def __init__(self, *args, causal: bool = False, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.causal = causal

    def capture(
        self,
        forward_fn: Callable,
        input_buffers: InputBuffers,
        block_tables: BlockTables,
        attn_groups: list[list[AttentionGroup]],
        kv_cache_config: KVCacheConfig,
        max_model_len: int,
        progress_bar_desc: str = "Capturing CUDA graphs",
    ) -> None:
        def create_forward_fn(
            desc: BatchExecutionDescriptor,
            warmup: bool,
        ) -> tuple[Callable[[CUDAGraphMode], None], AttentionState]:
            num_tokens = desc.num_tokens
            num_reqs = desc.num_reqs or min(num_tokens, self.max_num_reqs)
            num_tokens_across_dp = (
                torch.full((self.dp_size,), num_tokens, dtype=torch.int32, device="cpu")
                if self.dp_size > 1
                else None
            )
            attn_state = _prepare_dflash_inputs_to_capture(
                num_reqs,
                num_tokens,
                input_buffers,
                block_tables,
                attn_groups,
                kv_cache_config,
                max_model_len,
                skip_attn=(desc.cg_mode == CUDAGraphMode.PIECEWISE),
                causal=self.causal,
            )
            attn_metadata, slot_mappings = attn_state

            fwd = lambda cg_mode: forward_fn(
                num_reqs,
                num_tokens,
                attn_metadata,
                slot_mappings,
                num_tokens_across_dp,
                cg_mode,
            )
            return fwd, attn_state

        super().capture(create_forward_fn, progress_bar_desc)