- cast gradients and inputs to weight.dtype instead of hardcoded bf16
- single code path covers bf16 and fp32 models, no branch needed
- gradient dtype now matches the leaf parameter dtype exactly
- register yaml 1.2 float resolver so scientific notation (2e-5) becomes float, not str
- replaces the decimal-point workaround in train configs
- add containerized training doc under docs/developer
- rewrite docker.sh with gpu default and --no-gpu override
- inject host uid/gid via ASTRAI_UID/GID in train.sh compose()
- filter readonly UID/GID lines when sourcing .env.train
- build image user via USER_UID/USER_GID args matching host uid/gid
- pass all GPUs (count: all) and filter by CUDA_VISIBLE_DEVICES inside the container
- forward NCCL vars through compose environment
- add a GPU trainer Compose profile with mounted data, models, and checkpoints
- add host commands for preflight, lifecycle, logs, status, and checkpoint cleanup
- resume from the latest complete checkpoint with external config or CLI arguments
- rename output pointer field o to o_ptr for consistency with q_ptr/k_ptr/v_ptr
- regroup AttentionParams fields by responsibility and fix misleading comments
- drop unused max_seq_len/total_q fields and paged decode max_seq_len arg
- drop redundant group_size param from decode launchers (computed from p)
- rename q_stride_* to q_*_stride to match mask stride convention
- rename mask_q_stride to mask_l_stride for consistent l-dim naming
- merge k/v and k_cache/v_cache into k_ptr/v_ptr; rename q to q_ptr
- KVSource policy selects contiguous vs paged mode at compile time
- fix scalar prefill head_dim=32 out-of-bounds via G=4 dispatch
- fix MMA decode 4D mask head indexing and invalid-row mask access
- add q_head/kv_head divisibility and head-dim contiguity checks
- validate split-KV scratch and decode out_buf layout in bindings
- set max dynamic shared memory for scalar decode D=256
- cover scalar prefill D=32 in pure C test
- 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
- Replace C++20 explicit lambda template parameters with file-scope structs (DecodeDispatch/PrefillDispatch etc.)
- Remove unused gs variable in run_decode_test
- Tighten paged test atol/rtol from 0.02 to 0.01 to match contiguous tests
- Match steady-state metadata to the active task IDs
- Rebuild request mappings for cached prefix pages
- Add regressions for batch refill and prefix reuse
- 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
- TaskCacheRegistry -> TaskCacheManager (independent, held by scheduler)
- TaskCacheState co-locates 5 parallel dicts into one dataclass
- AllocationStrategy base class + PagedStrategy subclass (page_size is a parameter)
- _rollback() helper for unified cleanup (no duplicate free paths)
- Task._kv_len + prefill_done property (explicit, no output_tokens proxy)
- Steady-state detection single-sourced in TaskCacheManager.bind()
- PagePool is now pure physical layer (no task knowledge)
- Removed dead _page_to_hash dict in RadixCache
- drop Executor unused tokenizer field, _head_dim, stale metrics docstring
- unify greedy sampling via SamplingPipeline.sample, drop top-level duplicate
- drop Task.flush_remaining no-op and unreachable prompt-length branch
- drop ProtocolHandler redundant chunks list (reuse body)
- fix page_size=1 token-slot leak on task_free
- clear _task_pages/_task_slots on alloc-failure paths
- reset _bind_state on task_free to avoid stale steady-state reuse
- remove unreachable contiguous branches in paged-only helpers
- StreamingSeqDataset yields windows sequentially through each shard
- Shard-level shuffle, distributed and multi-worker shard partitioning
- __len__ returns total window count for scheduler total_steps
- Better OS page-cache locality than random-access map-style datasets
- cudaEvent.elapsed_time gives microsecond precision vs perf_counter
- cudaEvent measures actual GPU execution, not just kernel launch
- falls back to time.perf_counter on CPU-only devices
- 64-token prefill forward triggers cuBLAS auto-tuning at init
- reduces first-chat prefill from ~520ms to ~27ms
- warmup decode also drops from ~215ms to ~71ms
- move setup_logging to astrai/logging.py
- timed() now uses logger.isEnabledFor(DEBUG) instead of separate env var
- enable ASTR_LOG_LEVEL=DEBUG to see per-step timing logs
- call setup_logging() in stream_chat.py
- extract TaskTiming + MetricsCollector out of Task/TaskManager
- unify prefill/decode timing into single record() context manager
- expose avg_ttft_ms, avg_decode_tps, avg_e2e_latency_ms via /stats
- all three release builds (pure, cu128, cu130) produce the same .whl filename, causing uploads to overwrite each other
- append the CUDA tag as a local version label (e.g. +cu128, +cu130)
- move JSONL transform auto-creation from JsonlStore.load to DatasetFactory.load via _build_jsonl_transform helper
- remove TokenizeTransform and PipelineConfig imports from storage module
- JsonlStore.load now requires explicit transform= for eager mode
- DatasetFactory.load remains the public API with identical convenience behavior
- add _BindState dataclass and _is_steady_increment() to cache.py
- replace _bind_sig/_bind_seq_lens dual fields with single _bind_state
- replace DecodeSteadyState bare tuple with named dataclass
- use _is_steady_increment() in both PagePool.bind_tasks and Executor.execute_decode
- merge _generate_streaming/_generate_non_streaming into single _generate() with stream flag
- delete dead GenerationRequest class and generate_with_request method
- inline _next_token helper into generate_async
- replace flash-attn double-checked locking with functools.lru_cache
- extract _write_and_gather_kv helper shared by TorchNative/FlashAttn backends
- inline _kv_cache_is_contiguous into its sole call site in FlashAttnBackend
- change default backend priority from flash>cuda>torch to cuda>flash>torch
- add ASTR_BACKEND env var to override default backend at resolve time
- add supports_graph() static method to AttentionBackend ABC, override in CudaBackend
- replace isinstance(get_backend(), CudaBackend) with get_backend().supports_graph() in executor
- add torch.cuda.is_available() guard to CudaBackend.supports()
- Pre-allocate decode_out in InferenceWorkspace so attn_paged_decode does not call torch::empty inside graph capture
- Run live forward before graph capture to get valid output (graph pool memory is zeroed after capture block exits)
- Greedy generation with graph replay is bit-exact across all batch sizes
- _warmup_cuda_graphs pre-captures graphs at init for power-of-two batch sizes
- Graph enabled only when CudaBackend + supported head_dim + warmup succeeds
- Decode speedups vs no-graph: B=1 2.09x, B=4 1.80x, B=8 1.94x, B=16 1.76x
- Pre-allocate decode_out in InferenceWorkspace so attn_paged_decode does not call torch::empty inside graph capture
- Wire decode_out through KVCache, PagePool.bind_tasks, and CudaBackend.fwd_decode
- Run live forward before graph capture to get valid output (graph pool memory is zeroed after capture block exits)
- Greedy generation with graph replay is bit-exact across all batch sizes
- Decode speedups vs no-graph: B=1 2.09x, B=4 1.80x, B=8 1.94x, B=16 1.76x
- Decode with contiguous cache uses flash_attn_with_kvcache instead of materializing full KV via gather + flash_attn_func
- _backend_supports allows FlashAttnBackend for decode (q_len==1) even with explicit mask
- Decode speedups vs TorchNative (B=1,4,8,16 mean): cuda 1.55x, flash 1.40x, torch_native 1.00x
- Read K/V directly from flat pool via cache_batch_idx + cache_seqlens, zero-copy view reshape
- PagedKV::host_kv_len now returns max_context_len instead of max_seq_len
- Eliminates grid-z instability for CUDA graph capture/replay
- Restore skip_no_kernel re-export accidentally removed by ruff --fix
- 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
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