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d8493bd70f
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phase 12: implement real continuous batching scheduler
Rewrote engine.rs from scratch:
- Scheduler loop: admit → prefill → decode → finish → check new requests
- Multiple sequences run concurrently (max_batch_size configurable)
- Each sequence has independent GpuKVCache
- Non-blocking try_recv() for new requests during decode iterations
- Dynamic join: new requests enter batch immediately, don't wait for others
Verified with concurrent test (tools/test_concurrent.py):
- 3 concurrent requests: wall_time=3.8s, concurrency_ratio=2.82x ✓
- 5 concurrent requests: wall_time=6.1s, concurrency_ratio=4.04x ✓
- All outputs are coherent and correct
Design doc (docs/12-continuous-batching.md) fully rewritten with:
- Detailed scheduler loop pseudocode
- Data structures (Sequence, Scheduler)
- Acceptance criteria with specific test cases
- Clear separation from Phase 13 (HTTP layer)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-22 13:44:26 +08:00 |
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7d05ececa0
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docs: split Phase 12 and Phase 13 into separate design documents
- docs/12-continuous-batching.md: scheduler, sequence management,
batching strategy (currently single-request, expandable)
- docs/13-http-api.md: HTTP server, OpenAI-compatible API,
axum architecture, SSE streaming (TODO)
Phase 12 = WHAT to compute (scheduling decisions)
Phase 13 = HOW to expose it (HTTP protocol layer)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-22 13:15:27 +08:00 |
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