mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-02-01 16:51:11 +08:00
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
from abc import ABC, abstractmethod
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from typing import List, Optional, final
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from ..pyexecutor.llm_request import LlmRequest, get_draft_token_length
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from ..pyexecutor.resource_manager import ResourceManager
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from ..pyexecutor.scheduler import ScheduledRequests
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class Drafter(ABC):
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"""Abstract base class for all drafter implementations."""
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def __init__(self, max_concurrency: Optional[int] = None) -> None:
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self.max_concurrency = max_concurrency
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@abstractmethod
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def prepare_draft_tokens(
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self,
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scheduled_requests: ScheduledRequests,
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resource_manager: Optional[ResourceManager] = None,
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) -> None:
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"""
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Prepare the drafter tokens for the forward computation this step.
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Args:
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scheduled_requests: The scheduled requests for this iteration
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"""
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raise NotImplementedError
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@final
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def should_use_spec_decode(self, requests: List[LlmRequest],
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max_batch_size: int, max_num_tokens: int,
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max_draft_len: int) -> bool:
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"""
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You probably don't want to override this. ModelEngine
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assumes that speculation is always on if max_concurrency
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is not specified by the user's spec config.
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"""
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# Inputs typically validated upstream: max_batch_size>0, max_num_tokens>0, max_draft_len>=0
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if self.max_concurrency is None:
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return True
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# Defensive guards; keep behavior explicit for zero/empty cases
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if not requests or max_batch_size <= 0 or max_num_tokens <= 0:
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return False
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tokens_per_request = 1 + max_draft_len
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token_cap = max_num_tokens // tokens_per_request
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if token_cap <= 0:
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return False
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num_effective_requests = min(len(requests), max_batch_size, token_cap)
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return num_effective_requests <= self.max_concurrency
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@final
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def pad_draft_tokens_for_cuda_graph(
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self, scheduled_requests: ScheduledRequests) -> None:
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"""
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Pad draft tokens to the max draft length for CUDA graph compatibility.
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Args:
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scheduled_requests: The scheduled requests to pad
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"""
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for req in scheduled_requests.generation_requests:
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max_draft_tokens = self.max_draft_tokens
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num_draft_tokens = get_draft_token_length(req)
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req.py_draft_tokens.extend(
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0 for _ in range(max_draft_tokens - num_draft_tokens))
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