[TRTLLM-10325][feat] Refactor speculative decoding workers#10768
[TRTLLM-10325][feat] Refactor speculative decoding workers#10768mikeiovine merged 2 commits intoNVIDIA:mainfrom
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Signed-off-by: Guiju Zhang <7135567+cascade812@users.noreply.github.com>
Signed-off-by: Guiju Zhang <7135567+cascade812@users.noreply.github.com>
📝 WalkthroughWalkthroughThis PR refactors speculative decoding logic by extracting repeated inline operations into dedicated helper methods. Eight new methods are added to the Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes 🚥 Pre-merge checks | ✅ 2 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
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🤖 Fix all issues with AI agents
In `@tensorrt_llm/_torch/speculative/interface.py`:
- Around line 433-455: The _apply_force_accepted_tokens function should guard
against negative force_num_accepted_tokens to avoid producing zeros (which later
cause index errors when using num_accepted_tokens - 1); update the logic that
computes force_total_tokens (and/or the conditional that checks
self.force_num_accepted_tokens) to ignore or clamp negative/zero values (e.g.,
treat any self.force_num_accepted_tokens <= 0 as "no-op" or clamp the forced
value to at least 1) before assigning to num_accepted_tokens[num_contexts:],
keeping the existing min(...) cap with self.max_draft_len + 1 and referring to
self.force_num_accepted_tokens, force_total_tokens, num_accepted_tokens, and
num_contexts to locate the change.
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…VIDIA#10768)" This reverts commit 8cf8fbb.
Description
Refactor
Eagle3OneModelWorkerandMTPWorkerto move shared code intoSpecWorkerBase. This will improve maintainability, reduce code duplication, and ensure critical correctness logic is centralized.Test Coverage
PR Checklist
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