d48864033e
102/102 encoder params load with 0 missing and 0 unmapped. A synthetic voxelised sphere shell (16,934 voxels) encodes to 56 latent voxels in 135ms on m3ultra, and the latent comes out mean +0.05 / std 0.92 - the approximately unit-normal distribution a KL-trained VAE should produce, which is decent evidence the graph and the sparse conv path are right. Architecture is inferred from tensor shapes, not constructor defaults: upstream defaults latent_dim to 8 but the released weights say 32, and attn_mode/pe_mode defaults are likewise overridden by the trained config. infer_config() reads it off the checkpoint. Also added SparseDownsample. Upstream's docstring says average pooling but the code passes reduce='amax' - following the code. Kernel orientation: tried latent statistics as a cheap discriminator and it does NOT work. The flip is not a no-op (max delta 3.53) but both orientations give a plausible near-unit-normal latent (std 0.919 vs 0.945). Recorded as a negative result; it needs the decoder and reconstruction quality to settle.
230 lines
8.4 KiB
Python
230 lines
8.4 KiB
Python
"""LATO.2 Vertex VAE encoder in MLX.
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Architecture read off the released weights rather than constructor defaults, because
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upstream's defaults (attn_mode="swin", pe_mode="ape", qk_rms_norm) are overridden by the
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config the checkpoint was actually trained with. What the tensors say:
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encoder.input_layer1 [32, 64] SparseLinear 64 -> 32
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encoder.downsample.0..3 SparseResBlock, each ×2 downsample
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32->64, 64->128, 128->256, 256->512
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encoder.self_attn.input_layer [512, 512]
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encoder.self_attn.blocks.0..7 8 transformer blocks, 512ch, 8 heads
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(to_qkv is [1536,512] = 3*512)
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out_layer [2*latent] SparseLinear -> mean/logvar
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No rope, no qk_rms_norm, and no positional-embedding tensors exist in the checkpoint,
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so those upstream branches are inert here. Transformer norms are non-affine; the
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ResBlock's norm1 is affine and norm2 is not.
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`downsample` is max-pooling (upstream passes reduce="amax" despite saying "average
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pooling" in its docstring).
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Optional
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import mlx.core as mx
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import mlx.nn as nn
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from ..sparse.ops import (
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LayerNorm32,
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SparseLinear,
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SparseResBlock,
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SparseTransformerBlock,
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)
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from ..sparse.tensor import SparseTensor, downsample
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class DownResBlock(nn.Module):
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"""SparseResBlock preceded by a ×2 max-pool, as upstream's downsample=True does."""
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def __init__(self, channels: int, out_channels: int):
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super().__init__()
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self.block = SparseResBlock(channels, out_channels)
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def __call__(self, x: SparseTensor) -> SparseTensor:
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return self.block(downsample(x, 2))
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class SparseTransformerBase(nn.Module):
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def __init__(self, channels: int, num_blocks: int, num_heads: int, mlp_ratio=4.0):
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super().__init__()
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self.input_layer = SparseLinear(channels, channels)
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self.blocks = [
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SparseTransformerBlock(channels, num_heads, mlp_ratio)
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for _ in range(num_blocks)
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]
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def __call__(self, x: SparseTensor) -> SparseTensor:
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x = self.input_layer(x)
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for blk in self.blocks:
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x = blk(x)
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return x
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class VertexVAEEncoder(nn.Module):
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def __init__(
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self,
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in_channels: int = 64,
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model_channels: int = 512,
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num_downsample: int = 4,
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num_blocks: int = 8,
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num_head_channels: int = 64,
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latent_dim: int = 8,
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):
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super().__init__()
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self.model_channels = model_channels
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self.latent_dim = latent_dim
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self.input_layer1 = SparseLinear(in_channels, model_channels >> num_downsample)
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# upstream builds these with i = num_downsample-1 .. 0, so list index 0 is the
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# narrowest stage (32 -> 64) and the last is 256 -> 512.
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self.downsample = [
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DownResBlock(model_channels >> (i + 1), model_channels >> i)
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for i in range(num_downsample - 1, -1, -1)
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]
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self.self_attn = SparseTransformerBase(
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model_channels, num_blocks, model_channels // num_head_channels
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)
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self.final_norm = LayerNorm32(model_channels, affine=False, eps=1e-5)
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self.out_layer = SparseLinear(model_channels, latent_dim * 2)
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def __call__(self, x: SparseTensor) -> SparseTensor:
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x = self.input_layer1(x)
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for blk in self.downsample:
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x = blk(x)
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h = self.self_attn(x)
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h = h.replace(self.final_norm(h.feats))
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return self.out_layer(h)
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def encode(self, x: SparseTensor, sample: bool = False) -> SparseTensor:
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"""Returns the latent. `sample=False` takes the posterior mode (deterministic)."""
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h = self(x)
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mean = h.feats[:, : self.latent_dim]
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if not sample:
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return h.replace(mean)
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logvar = mx.clip(h.feats[:, self.latent_dim :], -30.0, 20.0)
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std = mx.exp(0.5 * logvar)
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return h.replace(mean + std * mx.random.normal(mean.shape))
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# ------------------------------------------------------------------ loading
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# checkpoint key -> module path. Upstream nests the spconv module one level deeper
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# (`conv1.conv.weight`), and wraps Linear inside our SparseLinear (`.linear.`).
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def _remap(key: str) -> Optional[str]:
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k = key
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if not k.startswith(("encoder.", "out_layer.")):
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return None # decoder / expander parts, not part of the encoder graph
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k = k.replace("encoder.", "", 1)
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if k.startswith("downsample."):
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parts = k.split(".")
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idx = parts[1]
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rest = ".".join(parts[2:])
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rest = rest.replace("conv1.conv.", "conv1.").replace("conv2.conv.", "conv2.")
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if rest.startswith("skip_connection."):
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rest = rest.replace("skip_connection.", "skip_connection.linear.")
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return f"downsample.{idx}.block.{rest}"
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if k.startswith("self_attn.input_layer."):
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return k.replace("self_attn.input_layer.", "self_attn.input_layer.linear.")
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if k.startswith("self_attn.blocks."):
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return k.replace(".mlp.mlp.0.", ".mlp.mlp_0.").replace(".mlp.mlp.2.", ".mlp.mlp_2.")
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if k.startswith("input_layer1."):
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return k.replace("input_layer1.", "input_layer1.linear.")
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if key.startswith("out_layer."):
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return key.replace("out_layer.", "out_layer.linear.")
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return k
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def infer_config(w: dict) -> dict:
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"""Derive the architecture from tensor shapes.
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Upstream's constructor defaults do not describe the released checkpoint (latent_dim
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defaults to 8 but the weights say 32), so the shapes are the only trustworthy source.
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"""
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n_down = len({k.split(".")[2] for k in w if k.startswith("encoder.downsample.")})
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n_blocks = len({k.split(".")[3] for k in w if k.startswith("encoder.self_attn.blocks.")})
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in_ch = w["encoder.input_layer1.weight"].shape[1]
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model_ch = w["encoder.self_attn.input_layer.weight"].shape[0]
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latent_dim = w["out_layer.weight"].shape[0] // 2
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return {
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"in_channels": in_ch,
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"model_channels": model_ch,
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"num_downsample": n_down,
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"num_blocks": n_blocks,
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"latent_dim": latent_dim,
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}
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def load_encoder(weights_path: str | Path, **kw) -> tuple:
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"""Build the encoder and load converted weights. Returns (model, report)."""
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w = mx.load(str(weights_path))
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cfg = infer_config(w)
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cfg.update(kw) # explicit args win
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model = VertexVAEEncoder(**cfg)
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flat = dict(_flatten(model.parameters()))
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mapped, missing, unused = {}, [], []
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for k, v in w.items():
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m = _remap(k)
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if m is None:
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continue
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if m in flat:
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if flat[m].shape != v.shape:
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raise ValueError(f"shape mismatch {k} -> {m}: {flat[m].shape} vs {v.shape}")
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mapped[m] = v
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else:
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unused.append(f"{k} -> {m}")
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missing = [k for k in flat if k not in mapped]
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if mapped:
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model.update(_unflatten(mapped))
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return model, {
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"loaded": len(mapped),
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"params": len(flat),
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"missing": missing,
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"unmapped": unused,
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}
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def _flatten(tree, prefix=""):
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if isinstance(tree, dict):
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for k, v in tree.items():
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yield from _flatten(v, f"{prefix}{k}.")
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elif isinstance(tree, list):
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for i, v in enumerate(tree):
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yield from _flatten(v, f"{prefix}{i}.")
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elif isinstance(tree, mx.array):
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yield prefix[:-1], tree
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def _unflatten(flat: dict):
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root: dict = {}
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for key, val in flat.items():
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parts = key.split(".")
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node = root
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for i, p in enumerate(parts[:-1]):
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nxt = parts[i + 1]
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default = [] if nxt.isdigit() else {}
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if isinstance(node, list):
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idx = int(p)
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while len(node) <= idx:
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node.append({})
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if not isinstance(node[idx], (dict, list)) or (
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isinstance(default, list) and not isinstance(node[idx], list)
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):
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node[idx] = default
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node = node[idx]
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else:
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if p not in node or not isinstance(node[p], (dict, list)):
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node[p] = default
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node = node[p]
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if isinstance(node, list):
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idx = int(parts[-1])
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while len(node) <= idx:
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node.append(None)
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node[idx] = val
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else:
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node[parts[-1]] = val
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return root
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