a44a0d7360
ss_flow ships rope_phases precomputed; the SLAT flows do not, because their positions are the input SparseTensor's own coordinates and vary per input. rope_phases_from_coords reproduces RotaryPositionEmbedder, including the pad detail: 3*21=63 frequencies fall one short of head_dim//2=64 and upstream right-pads with 1+0j, so dropping it would shift every later pair by a slot. Verified against ss_flow's own shipped tensor to 9.6e-7 - a real ground truth, since the same embedder produced it.
51 lines
1.8 KiB
Python
51 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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rope_phases_from_coords,
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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", "rope_phases_from_coords", "TimestepEmbedder", "DiTAttention",
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"ModulatedTransformerCrossBlock", "ProjectAttention",
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]
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__version__ = "0.1.0"
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