v0.3.2a0 (beta)

This release replaces the minimind kernel with a native one, compiles the MLX training step, adds MLX activation recomputation, and keeps tool-call tags that a custom tokenizer registers as special tokens. README "Native kernel (0.3.2)" has the migration reasons and the measured comparison.

Changed

• chronos/model/kernel.py replaces the minimind kernel (config base, RoPE/YaRN, gated FFN, dense attention, trainer utilities).
  • import chronos no longer clones anything or changes sys.path, and works offline.
  • Config field names and defaults, RoPE tables and FFN parameter names are unchanged. Logits are bit-identical on out/chronos_256_moe.pth and the 50M en→zh checkpoint (full, padded and prefill+decode paths).
  • All layers share one FP32 RoPE table per (dim, base, scaling, device): 3.50 → 0.50 MiB at the 50M shape, 216 → 24 MiB at the 768H base shape.
  • Resume state is written to chronos_<H>_moe_resume.pth; the release checkpoint keeps its dtype.
• chronos.deps is a shim. get_tokenizer_path() returns the bundled legacy 6400-token tokenizer (CHRONOS_DEFAULT_TOKENIZER overrides it); ensure_minimind() is a no-op with a deprecation warning.
• tools/compare_minimind_chronos*.py use minimind only as an external baseline, from --minimind-dir or CHRONOS_MINIMIND_DIR. Model bundles no longer ship third_party/minimind.
• benchmark_compare.py compares Chronos with a dense GQA baseline built from the same kernel (result key baseline, formerly minimind).
• The preset "MiniMind-MoE (default)" is now "Chronos-v2 legacy (768H, 6400 vocab)", and MINIMIND_MOE_DEFAULTS is now BASE_MOE_DEFAULTS. The old names remain as aliases.
• MLX training micro steps are compiled by default (chronos longrun, and run_mlx_stage pretrain and SFT). CHRONOS_MLX_COMPILE=auto|on|off or a recipe's extra.mlx_compile switches it.

Added

• chronos.mlx.training.compiled: one mx.compile graph per signature for loss and gradients.
  • The recurrent depth is sampled once per step outside the trace. Depth, the ACT switch, shapes and plain config values select the graph.
  • Weights and the RNG state are compile inputs and outputs, so dropout and the cache-miss simulation consume the RNG stream as in eager mode.
  • Router probabilities, loss components and recurrent diagnostics are returned from the graph and written back to the modules.
  • Multimodal batches, lazy experts and signatures that fail to trace run eagerly; the reason is logged once.
  • Measured on real en→zh shards with the 50M config (M5 Max, bf16, shared GPU): 1.08× (SFT) and 1.18× (pretrain) per step, loss curves within 2e-3 of eager.
• chronos.mlx.checkpointing: MLX activation recomputation for activation_checkpointing=true.
  • Each block call is one recomputed region. Dropout masks, the cache-miss draws and routing indices are recorded in the forward pass and replayed during recomputation.
  • Region outputs are packed per dtype. mlx 0.32.4 keeps the gradient of only one copy when a checkpointed function returns the same graph node twice, and a full-length cache slice is such a copy.
  • Measured peak memory: 5.62 → 3.50 GB (SFT) and 6.07 → 3.62 GB (pretrain), at 17% and 21% more time per step than the compiled step alone. It stays off by default at 50M; the planner's memory-constrained tiers enable it.
• chronos.tokenizer.visible.decode_visible: hides control tokens and keeps <tool_call>, </tool_call>, <tools> and </tools> as text, for HF tokenizers and ChronosTokenizer. Serving, the workbench and GRPO rewards use it.
• Tests: tests/test_native_kernel.py, tests/test_tool_tags_special_tokens.py, tests/test_mlx_checkpointing.py, and per-micro-step exactness of the compiled and recomputed steps inside the LongRun loop (tests/test_longrun.py).

Fixed

• Tool calls were lost when a custom HF tokenizer registered <tool_call> as a special token: decoding with skip_special_tokens=True removed the tags, so the parser saw bare JSON.
• Dense mode (use_hybrid_attention=False) crashed on Torch in every recurrent mode, and on MLX with an int32 overflow (a 2**63 window). It now runs dense causal GQA; prefill+decode matches the full forward within 1e-6.
• The Torch pretrain save rewrote the BF16 release checkpoint as FP16.
• Trainer log lines did not reach the Web UI.
• RoPE tables: with many torch threads, a cached table's prefix differed by one ulp from a shorter table (measured with 18 threads on torch 2.10). The tables are now built single-threaded.
• tools/compare_minimind_chronos*.py used chronos.deps without importing it.