mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-02-16 07:53:55 +08:00
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from abc import ABC, abstractmethod
|
|
from typing import List, Optional, final
|
|
|
|
from ..pyexecutor.llm_request import LlmRequest
|
|
from ..pyexecutor.resource_manager import ResourceManager
|
|
from ..pyexecutor.scheduler import ScheduledRequests
|
|
|
|
|
|
class Drafter(ABC):
|
|
"""Abstract base class for all drafter implementations."""
|
|
|
|
def __init__(self, max_concurrency: Optional[int] = None) -> None:
|
|
self.max_concurrency = max_concurrency
|
|
|
|
@abstractmethod
|
|
def prepare_draft_tokens(
|
|
self,
|
|
scheduled_requests: ScheduledRequests,
|
|
resource_manager: Optional[ResourceManager] = None,
|
|
) -> None:
|
|
"""
|
|
Prepare the drafter tokens for the forward computation this step.
|
|
|
|
Args:
|
|
scheduled_requests: The scheduled requests for this iteration
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
@final
|
|
def should_use_spec_decode(self, requests: List[LlmRequest],
|
|
max_batch_size: int, max_num_tokens: int,
|
|
max_draft_len: int) -> bool:
|
|
"""
|
|
You probably don't want to override this. ModelEngine
|
|
assumes that speculation is always on if max_concurrency
|
|
is not specified by the user's spec config.
|
|
"""
|
|
|
|
# Inputs typically validated upstream: max_batch_size>0, max_num_tokens>0, max_draft_len>=0
|
|
|
|
if self.max_concurrency is None:
|
|
return True
|
|
|
|
# Defensive guards; keep behavior explicit for zero/empty cases
|
|
if not requests or max_batch_size <= 0 or max_num_tokens <= 0:
|
|
return False
|
|
|
|
tokens_per_request = 1 + max_draft_len
|
|
token_cap = max_num_tokens // tokens_per_request
|
|
if token_cap <= 0:
|
|
return False
|
|
|
|
num_effective_requests = min(len(requests), max_batch_size, token_cap)
|
|
return num_effective_requests <= self.max_concurrency
|