Revert "Add SCV graph replay and adaptive controller for NWOR staging" #4
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yuz207
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Oct 19, 2025
This commit implements five correctness-preserving optimizations that reduce GPU-CPU synchronization overhead in speculative decoding paths without changing behavior. Estimated total speedup: 5-11ms per decode step. Optimization #1: Batch mask sum operations (⭐⭐⭐) - Before: N GPU-CPU syncs (one per request) via .sum().item() in loop - After: Single batched sync via torch.stack().cpu() for all requests - Impact: Reduces 4-8ms overhead to ~0.5ms for typical batch sizes - Locations: Lines 2712-2740 (SCV path), 2757-2829 (fallback path) - Safety: Guards against empty sum_tensors to prevent stacking errors Optimization #2: Eliminate CPU transfer in SCV cache key (⭐⭐⭐) - Before: cu_int32.cpu().tolist() forces GPU->CPU sync on every SCV call - After: Use itertools.accumulate() to compute cumsum directly on CPU - Impact: Removes 0.5-2ms overhead per SCV call, even for cache hits - Location: Lines 2893-2900 - Safety: Uses spec_decode_metadata.num_draft_tokens (already CPU list) Optimization #3: Combine device/dtype conversions (⭐⭐) - Before: Two sequential .to() calls launch two separate kernels - After: Single .to(device=..., dtype=...) launches one combined kernel - Impact: 2x faster conversions (~0.3ms saved) - Locations: Lines 2749-2750, 2882-2883 - Safety: PyTorch API guarantees identical behavior for combined .to() Optimization #4: Hoist device/dtype checks outside loop (⭐⭐) - Before: Per-request device/dtype checks and conversions inside loop - After: Single conversion before loop (tensor slices inherit properties) - Impact: Eliminates 0.1-0.5ms per-request overhead - Location: Lines 2771-2772 (moved from inside loop at 2782-2785) - Safety: PyTorch guarantees all rows share parent tensor's device/dtype Optimization #5: Cache _nwor_debug lookup (⭐) - Before: Duplicate getattr() calls at lines 2640 and 2644 - After: Single lookup cached in local variable - Impact: Negligible performance, cleaner code - Location: Line 2639 - Safety: Trivial refactor with identical semantics All optimizations maintain exact correctness while eliminating redundant GPU-CPU synchronization points and duplicate kernel launches. No changes to NWOR/SCV algorithms or numerical results.
yuz207
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Oct 24, 2025
…dexing - Replace commit_draft_layer with restore_rejected_drafts for CoW semantics * Accepted tokens already in cache from reshape_and_cache_flash (no extra work) * Rejected tokens restored from log buffers * Handle FP8 per-token scales in restoration - Make torch.cuda.synchronize() conditional via VLLM_NWOR_DEBUG_SYNC (ISSUE #6) - Fix fallback indexing bug (ISSUE #4): * Map mask indices to batch positions via _draft_positions * Prevents silent corruption when kernel fallback is triggered This completes the Python-side CoW implementation. CUDA kernel restore_rejected_drafts will be added next.
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Reverts #2