- Remove unused local variable b in attention_backend.py
- Remove unused variable rank0_sd in test_broadcast_state_dict.py
- Remove unused imports across test files
- New CudaGraphContext class: warmup -> capture -> replay lifecycle
- One graph per batch_size key, all inputs at fixed workspace addresses
- Added position_ids buffer to InferenceWorkspace (required for graph capture)
- Graph only activates when CUDA backend is the current backend
- Default off (opt-in) due to slight numerical divergence in graph replay
- Sampling stays outside the graph (torch.multinomial uses mutable RNG)
- Resolved circular import: KVCache -> TYPE_CHECKING in attention_backend.py
- Replace per-.cu-file static cached tensors with workspace-managed pre-allocated buffers
- InferenceWorkspace now owns decode_o_part / decode_ml_part (mirrors FlashInfer's workspace pattern)
- KVCache carries the buffers through the backend -> C++ kernel chain
- C++ kernels accept optional pre-allocated buffers; fallback to alloc_split_partials for backward compat
- Pre-allocates once at Executor init, zero allocation in the decode hot loop
- Prerequisite for CUDA-graph capture (all kernel addresses are stable)
- Preload V into shared memory alongside K to eliminate per-element KV address lookups in the inner softmax/accum loop (doubles smem)
- Cache split-KV partial tensors (o_part, ml_part) with static tensors instead of per-call allocation in both decode and paged-decode paths
- Force is_causal=True in CUDA decode backend (decode is always causal)
- add FlashAttnBackend (ATTN_BACKEND.FLASH) using flash_attn_func with KV-cache gather + GQA, mirroring TorchNativeBackend
- add flash_attn_available() probe gated on compute capability plus a real-kernel smoke test, cached at first use
- lazy-import flash-attn via importlib so it stays an optional dependency, raising clear errors when unusable
- add 'flash' optional extra (flash-attn>=2.6) and export the new backend
- hoist prefill qo_indptr into the workspace so CudaBackend.fwd_prefill does not rebuild it per layer
- cache has_freq in SamplingBatchInfo to drop the per-step GPU any() sync
- drop pin_memory host staging for input_ids; sync copy suffices for a small batch
- bind_tasks builds kv_indptr (prefix sum of seq_lens) a single time
- fwd_decode/fwd_prefill reuse it instead of rebuilding per layer
- Removes 24 cumsum launches per decode step (was ~1ms/step at B=4)
- Decode B=4: 9.60 -> 7.82 ms/step (-18.5%), +22.8% tok/s
- CudaBackend.fwd_decode passes attn_mask directly instead of kv_cache.decode_mask
- TorchNativeBackend derives pos_mask from attn_mask[:,0,0] on decode
- Drop decode_mask and page_table fields from KVCache and bind_tasks
- alloc_split_partials now uses torch::empty: the split kernel writes every slot it owns, so the per-call zeros/full memset was pure overhead (2 kernels per layer per step)
- decode split-KV MMA kernels now run a true multi-stage cp.async pipeline (wait_group<STAGES-1> instead of wait_group<0>), keeping STAGES-1 tile loads in flight; the old wait_group<0> serialized load and compute so deeper STAGES made no difference
- add a fallback path when ntiles < STAGES to avoid a race on the last tile
- Precompute page_table and decode_mask on KVCache once per step in PagePool.bind_tasks, instead of per-layer in CudaBackend/TorchNativeBackend
- Skip frequency penalty history tensor construction when all penalties are 0 in Executor.execute_decode
- Omit FrequencyPenaltyStrategy from sampling pipeline when penalty is 0
- Deduplicate get_active_tasks calls in scheduler loop (3 to 1), remove redundant sorted() on decode tasks
- Benchmark (L20, bf16, CUDA backend): B=1 9.48->9.40ms (+1%), B=4 10.73->9.89ms (+8.6%), B=8 10.77->10.13ms (+6.4%)
- CudaBackend: paged decode via attn_paged_decode, prefill via attn_prefill
- Decode uses req_to_token as page_table with page_size=1
- Falls back to TorchNativeBackend when kernel unavailable
- Rename forward_decode/forward_extend to fwd_decode/fwd_prefill
- Register ATTN_BACKEND.CUDA in _BACKEND_REGISTRY
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