- serve.sh/train.sh no longer pass --build on up/run; the build subcommand is the only path that rebuilds
- compose services pin image: astrai:latest so run reuses the existing image instead of triggering a rebuild
- runtime parsers leave CUDA_VISIBLE_DEVICES unset for gpu.devices: all; an empty string hid every GPU inside the container
- server service reserves count: all GPUs so CUDA_VISIBLE_DEVICES performs the only filtering, matching the trainer
- wrapper compose() strips an empty host CUDA_VISIBLE_DEVICES before invoking docker compose
- mm_fp8 accepts 3D operands through the same signature: grid.z slices by batch strides, size-1 batches broadcast (stride 0), inner .t() views fold into the layout tag at zero copy
- fix _LinearFp8 backward crash on 3D [B,L,d] training inputs (flatten before mm_fp8, reduce grad_b over leading dims)
- expose kRasterGroup/kStreamOut as template knobs; drop the 64x128 mid CTA and staged crosswise-B path from dispatch (direct wins everywhere re-measured, including DRAM-streamed B)
- dispatch thresholds grounded in fresh sweeps: m<=64 -> 64x64 CTA (+27% at 64x8192x2048), small-CTA crossover at SM*14/3 total tiles (+13% at 96 tiles), threshold counts batch x per-matrix tiles (+31% at 64x512^3 bmm, +25% at 8x1024x2048)
- remove scripts/tools/bench_fp8_gemm.py (superseded by csrc/tests/fp8_sweep.cu for kernel-level tuning)
Benchmark: NVIDIA L20, E4M3, NT pre-quantized, median of 100-200 iters
- 64x8192x2048: 29.1 -> 22.8 us (94 TF/s)
- 1024x1536x2048: 67.4 -> 59.6 us (108 TF/s)
- bmm 64x512^3: 139.8 -> 106.7 us; bmm 8x1024x2048: 186 TF/s
- regression-free: 4096^3 192 TF/s, 8192^3 200 TF/s, 512^3 unchanged
- parameterize warp tile (WarpM/WarpN) in Fp8GemmTraits; MMA loops, fragment arrays and epilogue scale with kMt/kNt instead of the fixed 64x32/4x4, enabling cuBLAS-style 64x64 CTAs of 32x32 warps
- dispatch by output tiling (grid-searched via csrc/tests/fp8_sweep.cu): fewer than 48 output tiles take 64x64/32x32 with a lean ring (4 CTAs/SM fill the wave-quantization gap: 512^3 goes 16 -> 64 CTAs); larger shapes keep 128x128 with the kStages+1 ring
- kStages+1 canonic ring rotation drops the post-compute barrier on the congruous path (one __syncthreads per k-tile); LeanRing keeps the kStages ring for the small CTA; direct-crosswise operands always rotate kStages+1 (their prefetch issues right after barrier 1 and would race a lean ring - caught by the pure C layout suite)
- stage the bf16 epilogue through the reclaimed operand smem: swizzled scatter + barrier + coalesced 16B copy-out replaces 8 disjoint 16B per-warp segments (~50% write efficiency before)
- hoist per-lane ldmatrix swizzle offsets out of the mainloop (stage-relative table + ring-base add) so the innermost loop stops recomputing IMAD/LOP3 address chains
- bypass the torch.library dispatch for real CUDA tensors in quantize/mm_fp8 wrappers (~5us/call, ~40% of a 512-wide call's wall time); fake/subclass tensors keep the custom_op route
vs the previous kernel + python path, wall clock on NT squares: 512^3 52 -> 13us (4.0x, 5.2 -> 20.5 TF, now 1.36x cuBLAS _scaled_mm), 1024^3 1.05x, 2048^3 1.02x (46.9 -> 48.2 TF kernel-only); correctness: 4 layouts x 6 shapes pure C suite PASS, 588 pytest PASS
- Add server.py --config serve.yaml; explicit CLI flags override YAML
- Add scripts/serve.sh and serve_runtime.py for the Compose lifecycle
- Template server/cpu ports and param mounts in docker-compose.yml
- Document schema in docs/developer/docker-serving.md and params guide
- Add tests for runtime parsing and server CLI merge logic
- Add astrai.serialization.hf_adapter mapping LLaMA-style HF keys to AstrAI names (input_layernorm, gate_proj, MoE experts/shared_experts) with config aliases for dense and MoE (Mixtral/DeepSeek-V3) layouts; reject biased projections, mismatched head_dim and MLA
- Give AutoModel.from_pretrained weights_format=auto|astrai|hf with auto-detection; read sharded safetensors via model.safetensors.index.json
- Adapt preloaded weights/config in train_context and benchmark CLI
- avoid constructing model_fn more than once when reading config
- keep inference package exports focused on public entry points
- rename extra strategy arguments to strategy_kwargs
- 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
- 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
- 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
- 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
- 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
- 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
- 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)
- adapt bind_tasks to workspace API and reuse a stable workspace
- drop required checkpoint, randomize default 1B GQA preset
- add config override flag for arbitrary model architectures
- Drop hardcoded CACHE_MAX_SEQ=2048 which overflowed at long prompts
- Size prefill pool to prompt_length and decode pool to prompt+5+gen*num_trials
- Unblocks decode/prefill benchmark at prompt 4096+ (was KV cache index OOB)
- add GradSNRTracker to metric_util.py computing SNR = E[g]^2 / Var(g) via per-parameter EMA moments
- add grad_snr_tracker field to TrainContext (instantiated by default)
- register grad_snr in MetricCallback, update tracker on each optimizer step before metrics are recorded
- add grad_snr to default --metrics in train.py CLI
- implement Mano (v2) with axis-rotating tangent projection and manifold normalization, replacing Newton-Schulz iteration
- composite ManoAdamW reuses partition_optimizer_parameters and composite helpers
- register mano_adamw in OptimizerFactory, export Mano and ManoAdamW
- add --mano_momentum and --mano_nesterov CLI options in Optimizer group
- add mano_adamw hyperparameters branch in train.py
- document mano_adamw in params.md
- add tests for single-step projection, axis alternation, factory registration, closure, and resume
- add GroupedOption/GroupedCommand (no third-party dep) that tags each option with a group label and renders help in labeled sections
- add opt() shorthand wrapping click.option with cls=GroupedOption
- tag all ~55 options into 10 groups aligned with params.md chapters
- Remove H5Store, H5Writer, save_h5/load_h5 and h5py dependency
- MmapStore (bin) is the sole pre-tokenized storage backend
- Move setup_logging after imports to fix E402 in __init__.py
- Clean up unused imports across test files
- Move inline test imports to file top
- Remove --max_tokens, let scheduler use max_seq_len - prompt_len
- Rename --cache_len to --max_seq_len to match engine naming
- Unify sampling defaults to 0.8/50/0.95
- setup_logging(): attach handler only to astrai logger, not root
- all astrai.* sub-module loggers inherit automatically
- controlled by ASTR_LOG_LEVEL env var (default INFO)
- called in if __name__ == '__main__' of each CLI script
- Replace argparse with click in all scripts (train, server, generate,
preprocess, benchmark)
- Add --config YAML support to train.py with CLI flag override
- Add --dry-run mode to validate config before training
- Add type annotations throughout benchmark.py
- Unify docstring format across all commands
- Remove redundant deps httpx, requests, pyyaml, rich from pyproject.toml
- Net -346 lines while adding YAML config support
- New FSDP2Executor registers as 'fsdp2' in ExecutorFactory, using per-module fully_shard() instead of FSDP1 FlatParameter wrapper
- FSDP2 preserves original Parameter objects as DTensors, eliminating use_orig_params=True hack
- FSDP2Executor implements _no_sync via set_requires_gradient_sync, clip_grad_norm via unshard, unwrap_model via DTensor.full_tensor
- Drop **_extra/**_ddp_only_kwargs fallbacks in BaseExecutor/FSDPExecutor/FSDP2Executor, replaced by parallel_mode-aware executor_kwargs dispatch in train.py (ddp-only kwargs only passed when parallel_mode=ddp)
- Export FSDP2Executor in astrai.parallel.__init__
- TrainConfig.max_grad_norm defaults to None
- executor.clip_grad_norm returns grad norm without clipping when None
- train.py --max_grad_norm defaults to None
- StreamStore: fetch(begin, end, key) for stream access (SEQ/SFT)
- RecordStore: mixin with fetch_record(i, key) for record access
- H5Store/MmapStore/JsonlStore now dual-inherit both (C3 MRO)
- JsonlStore supports lazy mode via processor= (no TokenizeTransform)
- RecordDataset base class holds processor, DPO/GRPO simplified
- dpo_tokenize pure function for on-the-fly JSONL tokenisation
- DatasetFactory builds processor for jsonl+record datasets
- train.py passes tokenizer_path=param_path uniformly
- progress: len(dataset) returns sample count (stream=windows, record=records)
- json no longer auto-detected as jsonl format
- Store gains fetch_record/num_records alongside stream fetch/__len__
- save_bin/load_bin support per-record offsets via record_keys param
- H5Store/MmapStore/JsonlStore all support dual stream+record access
- DPODataset/GRPODataset use fetch_record, no cross-record concat
- dpo_collate_fn + collate_fn wired through TrainConfig
- fixes attention context leakage in DPO from windowed concatenation
- stream_chat: default empty system_prompt, single-turn mode
- generate_batch: drop hardcoded system role
- generate.py: preserve original fields in messages branch
and use response_key for the output column name
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