class EagleCudaGraphManager(CudaGraphManager):
"""CudaGraphManager for Eagle speculative decoding."""
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
cudagraph_mode: CUDAGraphMode,
decode_query_len: int,
):
super().__init__(vllm_config, device, cudagraph_mode, decode_query_len)
# Use a dedicated pool for Eagle to avoid memory overlap with the main
# model's cudagraph. The base class uses a shared global pool, but Eagle's
# internal allocations (e.g., gumbel_sample temporaries) can conflict with
# the main model's allocations when sharing the same pool.
if cudagraph_mode:
self.pool = torch.cuda.graph_pool_handle()
def capture(
self,
forward_fn: Callable,
model_state: ModelState,
input_buffers: InputBuffers,
block_tables: BlockTables,
attn_groups: list[list[AttentionGroup]],
kv_cache_config: KVCacheConfig,
progress_bar_desc: str = "Capturing CUDA graphs",
) -> None:
"""Capture CUDA graphs for Eagle."""
def create_forward_fn(
desc: BatchExecutionDescriptor,
) -> Callable[[CUDAGraphMode], None]:
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_metadata, slot_mappings = prepare_inputs_to_capture(
num_reqs,
num_tokens,
model_state,
input_buffers,
block_tables,
attn_groups,
kv_cache_config,
)
return lambda cg_mode: forward_fn(
num_reqs,
num_tokens,
attn_metadata,
slot_mappings,
num_tokens_across_dp,
cg_mode,
)
super().capture(create_forward_fn, progress_bar_desc)