- 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- remove scripts/tools/bench_fp8_gemm.py (superseded by csrc/tests/fp8_sweep.cu for kernel-level tuning)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
- split quantize into quantize.cuh, templated on input type (bf16/fp16/fp32)
- rename pybind entry quantize_bf16 to quantize; validate the fmt enum
- fix fp8x2 packing: one 32-bit word packs two pairs (halves were dropped)
- drop the dead OutFp8 template param; GEMM output is always bf16
- fp8_state.reset() restores recipe/format defaults too (test state leak)
- rewrite tests for the two-primitive API with fp32-domain amax references
- 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
- scripts/train.sh load_infra() parses the top-level infra: section of TRAIN_CONFIG_FILE
- exports TRAIN_JOB_NAME/DATA/MODEL/CHECKPOINT_DIR/TRAIN_GPU_COUNT/CUDA_VISIBLE_DEVICES
- infra overrides .env.train via compose interpolation precedence; keys absent fall back
- train.yaml is now the single per-job config: host mounts, GPU filter, and hyperparameters
- requires host python3 with PyYAML when TRAIN_CONFIG_FILE is set; errors fail fast
- docs: docker-training.md documents the infra overrides and precedence
- 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
- merge AttentionParams and PagedAttentionParams into one struct
- add attn_kv_source.cuh with ContigKV/PagedKV addressing policies
- template prefill/decode kernels (MMA + scalar) on the KV policy, deleting the four duplicated attn_paged_*.cuh variants
- template dispatcher launchers on KV; single combine kernel
- verify: all correctness tests pass and SASS matches baseline (no perf regression)
- Fix rotary docs to describe cos/sin freqs_cis table, not complex buffer
- Replace attn_prefill with attn_paged_prefill for the CudaBackend path
- Register attn_paged_prefill in kernel overview, layout, and module list
- Add qo_indptr and InferenceWorkspace to architecture class diagram
- Add FrequencyPenaltyStrategy to sampling design patterns
- Thread a cudaStream_t through attn dispatchers onto torch's current stream
- Scope the device guard to the entry function so kernels run on tensor device
- DISPATCH_HEAD_DIM now forwards varargs so stream reaches each dispatch
- Parallelize CPU reference kernels with OpenMP (paged test 31s -> 7s)
- Merge decode/prefill standalone tests into attn_test.cu with correctness tables
- Drop bench error column (CPU ref too slow at large sizes)
- Update cuda_kernels.md for the merged test layout
- Converts detached strategy metrics before returning loss output
- Removes redundant item conversion from the trainer loop
- Updates the documented contract and regression tests
- Propagates MoE load-balancing loss through model outputs
- Logs task, auxiliary, and weighted losses across strategies
- Computes only explicitly requested callback metrics
- Preserves tensor compute_loss API and adds regression tests
- 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 astrai/optim/composite.py with shared step/zero_grad/state_dict/param_groups helpers and OptimizerFactory
- rename MuonMix to MuonAdamW (matches registered name muon_adamw) and file to muon_adamw.py
- use @OptimizerFactory.register decorator in each optimizer module instead of post-import registration in __init__
- fix closure being invoked once per sub-optimizer in MuonAdamW.step (now exactly once via composite_step)
- NoraNAdamW.step now forwards closure correctly
- Replace all buffer[layer_id][loc] double indexing with buffer[layer_id, loc] single advanced indexing in cache.py and attention.py
- Revert KVStorage buffers back to 4D [n_layers, size, n_kv_heads, head_dim], remove leftover 3D reshape/view in MLA path
- Update docs/guides/inference.md, docs/developer/internals.md, docs/developer/architecture.md to reflect new PagePool/KVStorage/ReqToTokenPool/KVCache classes
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