ViperEkura/AstrAI
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962c10c52b78f604879ac2c5eb72b7c241c2fab4
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8 Commits
Author SHA1 Message Date
ViperEkura 1cf7d6c76b perf: fill steady-state decode input ids via d2d copy 
- add InferenceWorkspace.fill_input_ids_from_device copying device tokens straight into the fixed-address input_ids buffer
- cache each decode step's sampled tokens on-device in DecodeSteadyState.last_tokens; when the task signature is unchanged the next step reuses them, replacing the tolist -> python list -> elementwise host fill -> pageable h2d round-trip
- _sample_logits returns (host payload, device tokens); prefill discards the device tensor
- signature change (task join/leave/first decode) still takes the host path; both dispatch paths covered by tests

Benchmark: NVIDIA L20, BF16, 1B model + 0.11B test model (4 layers, hidden 512), contiguous KV cache, CUDA Graph, greedy, prompt 512, generation 256, engine decode via scripts/tools/benchmark.py (alternating A/B, 2-4 paired runs)
- 0.11B batch 32: 21429 -> 24415 tok/s mean (1.14x, +13.9%), 4/4 paired runs faster
- 1B batch 32: 4242 -> 4388 tok/s (1.034x, +3.4%), 7.54 -> 7.29 ms/step
- batch 1: no measurable change (<0.5%)
 2026-08-31 14:24:51 +08:00
ViperEkura a29bdfae46 refactor: rework attention backend resolution 
- explicit attn_backend() context wins over ASTR_BACKEND env
- polymorphic available()/supports_call() replace isinstance dispatch
- cache singleton backend instances to avoid hot-path allocation
- training (fwd=None) resolves cuda > flash > torch by capability
- flash dense supports mask-free calls only; masked training falls back to torch
 2026-08-23 14:47:02 +08:00
ViperEkura 6ac3b51496 refactor: separate extension ops and backends 2026-08-16 21:15:52 +08:00
ViperEkura 3406157431 refactor: standardize packed 3d inference 
- keep training attention on dense 4d tensors
- use packed 3d tensors with KV cache for inference
- extend CUDA rotary embedding to packed 3d inputs
- adapt torch, CUDA and FlashAttention backend dispatch
 2026-08-16 13:24:02 +08:00
ViperEkura 47b3ed4e44 feat: propagate attention backend across scheduler threads 
- InferenceEngine/Scheduler accept an explicit backend
- capture request-level attn_backend context onto Task
- split prefill/decode batches by backend instance
- ASTR_BACKEND env overrides ContextVar as process-wide policy
- report resolved backend and CUDA-graph state in benchmark
 2026-08-09 13:32:40 +08:00
ViperEkura c1d05ae11d perf: benchmark decode via real inference engine 
- route decode benchmark through InferenceEngine generate path
- add enable_cuda_graph toggle to engine, scheduler, and executor
- make benchmark --cuda-graph/--no-cuda-graph control the toggle
- hoist local time imports to module top
 2026-08-09 11:47:14 +08:00
ViperEkura be90dfe2bd fix: isolate continuous batch decode state 
- Match steady-state metadata to the active task IDs
- Rebuild request mappings for cached prefix pages
- Add regressions for batch refill and prefix reuse
 2026-08-09 01:01:41 +08:00
ViperEkura 0c1b7664c1 refactor: split infer core into subpackages by concern 
- Eliminate core/ directory into cache/, runtime/, network/ subpackages plus flat modules
- Split cache.py (647 lines) into cache/{buffer,strategy,pool}.py by layer
- Add explicit ContiguousStrategy, make AllocationStrategy a real ABC
- Move TaskCacheState to cache/strategy.py, drop string forward references
- Rename api/ to network/, server.py to app.py
- Move sample.py into runtime/ alongside executor and graph
- Simplify TaskCacheManager.__init__ to single pool param
- Expose pool.strategy and pool.req_pool as public properties
- Fix KVCache import in attention_backend.py (TYPE_CHECKING guard)
- Fix steady-state decode reading uninitialized position_ids on first step
 2026-08-08 23:43:05 +08:00