97dcdfb54a
The blocker for LATO.2 on Apple Silicon is one op, not the whole setup.sh --all CUDA stack. Measured: 5 of 7 checkpoints are fully dense, and every SparseConv3d in the model is constructed stride=1/padding=None, which upstream dispatches to spconv's SubMConv3d. No strided or inverse sparse conv is ever instantiated. - SubMConv3d in pure MLX via a sorted-key indice map (27 lookups/voxel vectorised, cached per coordinate set the way spconv uses indice_key) - SparseTensor container + subdivide upsampling - Converter handles the 5-D layout collision: spconv KRSC [O,kz,ky,kx,I] vs torch Conv3d [O,I,kz,ky,kx]. Rank alone is ambiguous; misreading it silently mangles the voxel encoder. - 7/7 tests pass vs an independent naive reference, max err 3e-7. spconv has no Metal build so there is no upstream oracle; the reference shares no indexing code. Kernel orientation (feats[c+d] vs feats[c-d]) remains unverified and is silent when wrong; --flip-kernel builds the mirror for an end-to-end A/B.
79 lines
3.4 KiB
Markdown
79 lines
3.4 KiB
Markdown
# lato.2_mrp_mlx
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An MLX port of [LATO.2](https://github.com/LoHhhha/LATO.2) — factorised 3D mesh generation
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(vertex flow, then connectivity flow) — so it runs natively on Apple Silicon.
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## Why
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LATO.2 generates meshes to a **controllable vertex budget**, which is the interesting part:
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it sidesteps the generate-dense-then-decimate loop that TRELLIS-style pipelines force on
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you. But upstream inherits TRELLIS.2's `setup.sh` and hard-requires CUDA:
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```python
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# upstream modules/sparse/__init__.py
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BACKEND = 'spconv' # accepts only ['spconv', 'torchsparse'] — both CUDA-only
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ATTN = 'flash_attn' # accepts only ['xformers', 'flash_attn'] — both CUDA-only
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```
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No SDPA fallback, no MPS path. Neither sparse backend has a Metal build.
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## The scope, once measured
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The CUDA surface is far smaller than `setup.sh --all` implies. Of the seven released
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checkpoints, five are entirely dense. Sparse code touches only `vertex_autoencoder` and
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`vertex_structured_flow`, and **every** `SparseConv3d` in the model is constructed with the
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defaults `stride=1, padding=None` — which upstream dispatches to `SubMConv3d`.
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So the whole blocker is one operation: **submanifold 3×3×3 convolution**.
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| Upstream | Here |
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|---|---|
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| `spconv.SubMConv3d` | `lato_mlx/sparse/conv.py` — gather/scatter over a sorted-key indice map |
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| `SparseInverseConv3d` | never instantiated upstream; not needed |
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| strided sparse conv | never used; upsampling is `SparseSubdivide` (coord expansion ×8) |
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| `nn.Conv3d` (voxel encoder) | dense, maps to `mlx.nn.Conv3d` |
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| `flash_attn` / `xformers` | `attn_mode="full"` everywhere → plain SDPA |
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## Status
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- [x] `SparseTensor` container + `subdivide`
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- [x] `SubMConv3d` (k=3 and k=1) — **7/7 correctness tests pass**, max err 3e-7 vs an
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independent naive reference
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- [x] Weight converter, all 7 checkpoints → MLX safetensors (3.3 GB)
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- [ ] Remaining sparse ops: `SparseLinear`, `SparseGroupNorm32`, activations, attention
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- [ ] Model graphs: V-VAE, V-Flow, T-VAE, T-Flow, encoders
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- [ ] End-to-end inference
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- [ ] Fleet benchmark (m1max / m2max / m4pro / m1ultra / m3ultra)
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## Correctness
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`spconv` cannot be installed here — that is the reason this port exists — so there is no
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numerical diff against upstream. Instead `tests/test_sparse.py` checks the vectorised
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implementation against a deliberately naive one written straight from the definition
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(dict lookup, per-voxel loop). They share no indexing code, so an off-by-one cannot hide
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in both. Tests also cover batch isolation, isolated voxels, and indice-map hit rates.
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**One assumption remains unverified**: whether spconv gathers `feats[c+d]`
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(cross-correlation — the deep-learning convention, and what this implements) or
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`feats[c-d]`. A flipped kernel is numerically silent. It gets settled end-to-end: the
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V-VAE is an autoencoder, so a clean reconstruction confirms the orientation. Run the
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converter with `--flip-kernel` to test the alternative without touching code.
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## Use
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```bash
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uv venv --python 3.12 .venv
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VIRTUAL_ENV=.venv uv pip install mlx numpy torch trimesh
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hf download 0x4c48/LATO.2 --local-dir ckpt # 3.3 GB upstream weights
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.venv/bin/python -m lato_mlx.convert --ckpt ckpt --out weights
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.venv/bin/python tests/test_sparse.py
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```
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Upstream source is vendored read-only under `upstream/LATO.2` for reference.
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## Licence
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Upstream LATO.2 is MIT (Copyright the LATO.2 authors); its sparse module carries
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Microsoft and VAST-AI-Research copyright, also MIT. This port is MIT on the same terms.
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