go-mlx/TODO.md

TODO.md — go-mlx Task Queue

Dispatched from core/go orchestration. Pick up tasks in order.

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Phase 1: Standalone Package Hardening

• Verify go generate → test round-trip — ✅ 29/29 tests pass. CMake 3.24+, AppleClang 17.0.0, macOS SDK 26.2. Build takes ~2min on M3 Ultra.
• Add missing tests for core operations — ✅ 86 new tests across 4 files: array_test.go (25), ops_test.go (44), nn_test.go (8), fast_test.go (9). Covers: all scalar/array creation, shape ops, element-wise arithmetic, math functions, matrix ops, reductions, indexing, slicing, fused kernels (RMSNorm, LayerNorm, RoPE, SDPA), Linear, Embedding, RepeatKV. Found non-contiguous view bug in Floats()/DataInt32() — see FINDINGS.md.
• Add missing tests for model/tokenizer/sample/cache — model/, tokenizer/, sample/, cache/ have zero test files. Priority: tokenizer (BPE round-trip), sample (temperature/top-k), cache (KV append/trim).
• Benchmark suite — No benchmarks exist. Add: MatMul (various sizes), Softmax, model.Forward (single token), tokenizer.Encode/Decode, full Generate (tokens/sec). Baseline on M3 Ultra.

Phase 2: Model Support

• Gemma3-1B inference validation — The go-i18n Phase 2a needs 1B model inference for domain classification at ~5K sentences/sec. Validate Gemma3-1B loads and generates correctly via model.LoadModel() + model.Generate(). Report tokens/sec.
• Model loading robustness — Test with missing files, corrupted safetensors, wrong dtype. Currently no error handling tests for io.go.
• Add Llama model support — Only Gemma3 and Qwen3 exist. Llama architecture would cover Meta's model family (Llama 3, CodeLlama).

Phase 3: Training Pipeline

• LoRA fine-tuning end-to-end — lora.go has the adapter but no integration test showing: load base model → apply LoRA → train on small dataset → save adapter → reload. Critical for LEM Lab.
• Gradient checkpointing — grad.go has VJP but large models will OOM without checkpointing. Add selective recomputation.
• Mixed precision training — MLX supports BFloat16/Float16. Add dtype selection for training (currently inference uses model's native dtype).

Phase 4: API Polish

• Error handling audit — checkError() only logs. Should return errors to callers instead of silent logging. The C error handler stores last error but Go code doesn't propagate it.
• Memory management audit — Array finalizers use runtime.SetFinalizer. Verify no leaks under sustained inference (1000+ tokens). Check C-side deallocation.
• Documentation — Public API has minimal godoc. Add examples for common workflows: load model, generate text, fine-tune with LoRA.

Phase 5: Ecosystem Integration (Virgil wishlist)

• Streaming token iterator — model.Generate() should return an iter.Seq[Token] (or channel) for token-by-token streaming. LEM Lab chat UI needs this for real-time output. Currently the generate path is batch-only.
• Batch inference API — go-i18n Phase 2a wants ~5K sentences/sec through Gemma3-1B. Single-request inference won't hit that. Add a batch API that processes N prompts concurrently on the GPU. Even batching 8-16 at a time would help throughput significantly.
• Inference metrics — Expose tokens/sec, peak memory, GPU utilisation as structured data. LEM Lab dashboard and go-ai scoring engine both want this. A simple Stats struct returned alongside generation results.
• Model quantisation awareness — MLX supports 4-bit and 8-bit quantised models. The model.LoadModel() path should detect and handle quantised safetensors. This matters for running 27B models on 32GB Macs.
• Embed-friendly model loading — For the Core native app (FrankenPHP + Wails), models are on disk but the path discovery needs to be clean. Add model.Discover(baseDir) that scans for available models and returns metadata (name, params, quant level, size).

Phase 6: Go 1.26 Modernisation

• Evaluate Go 1.26 features — ✅ Documented in FINDINGS.md. Key wins: CGO ~30% faster (free), Green Tea GC default (10-40% less overhead, helps Array finalisers), slice stack alloc. Range-over-func already stable since 1.23.
• Range-over-func for Array — If 1.26 stabilises range-over-func, Array.Iter() returning iter.Seq[float32] (or typed variant) would be cleaner than the current index-based access. Measure overhead vs direct C pointer access.

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Upstream Dependencies

• go-i18n Phase 2a is blocked on this package providing working Gemma3-1B inference
• go-ai/ml/backend_mlx.go imports this package — after split, go-ai needs replace directive or published module

Workflow

1. Virgil in core/go writes tasks here after research
2. This repo's session picks up tasks in phase order
3. Mark [x] when done, note commit hash
4. New discoveries → add tasks, flag in FINDINGS.md