8981919ee3
Two silent bugs, both found by diffing against upstream running on CPU torch. The flow models have no sparse conv, so upstream is runnable here with flash-attn swapped for SDPA - a real numerical oracle, unlike the sparse path. 1. rope_phases ships as COMPLEX64 (torch.polar), so the rotation is a complex multiply and cos/sin are the phase's real/imag parts. Taking cos() of a complex phase was completely wrong. Verified against torch's view_as_complex formulation to 1.2e-7. 2. Upstream applies qk RMS norm BEFORE RoPE; I had it reversed. They do not commute - RMS applies a per-component gain, RoPE rotates within pairs. Reversed, one block still correlated 0.9998, which compounded to 0.84 across 30 blocks. After both: SparseStructureFlowModel matches upstream at correlation 1.00000000, max abs diff 1.2e-5, on the real 1.3B checkpoint.
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
"""MLX implementation of the TRELLIS-lineage sparse module.
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Microsoft's TRELLIS.2 sparse stack has been inherited, near-verbatim, by a growing
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family of 3D generation models — LATO.2 and TencentARC's Pixal3D among them. All of
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them hard-require spconv or torchsparse, neither of which has a Metal build, and that
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single dependency is what keeps the whole lineage off Apple Silicon.
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The blocker is one operation: submanifold 3x3x3 convolution. Every SparseConv3d in
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these models is constructed `stride=1, padding=None`, which spconv dispatches to
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SubMConv3d. Implement that in MLX and the rest is ordinary linear/norm/attention work.
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Packaged separately from any one model so each port depends on a tested core rather
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than vendoring its own copy.
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"""
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from .conv import SubMConv3d, build_indice_map
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from .dit import (
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ProjectAttention,
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DiTAttention,
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ModulatedTransformerCrossBlock,
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MultiHeadRMSNorm,
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TimestepEmbedder,
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apply_rope,
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)
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from .tensor import SparseTensor, VarLenTensor, downsample, subdivide, upsample
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from .ops import (
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LayerNorm32,
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SparseFeedForwardNet,
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SparseGELU,
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SparseGroupNorm32,
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SparseLinear,
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SparseMultiHeadAttention,
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SparseResBlock,
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SparseSiLU,
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SparseTransformerBlock,
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SparseTransformerCrossBlock,
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)
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__all__ = [
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"SparseTensor", "VarLenTensor", "subdivide", "downsample", "upsample",
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"SubMConv3d", "build_indice_map",
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"SparseLinear", "LayerNorm32", "SparseGroupNorm32", "SparseSiLU", "SparseGELU",
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"SparseResBlock", "SparseFeedForwardNet", "SparseMultiHeadAttention",
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"SparseTransformerBlock", "SparseTransformerCrossBlock",
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# DiT / flow-model pieces
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"MultiHeadRMSNorm", "apply_rope", "TimestepEmbedder", "DiTAttention",
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"ModulatedTransformerCrossBlock", "ProjectAttention",
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]
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__version__ = "0.1.0"
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