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226fe2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | from __future__ import annotations
import argparse, json, os, sys, time, math, gc
from pathlib import Path
# The wrapper below uses standard torch SDPA and dense tensors; sparse IO shells
# stay native (spconv) around this core.
os.environ.setdefault("ATTN_BACKEND", "sdpa")
os.environ.setdefault("SPARSE_ATTN_BACKEND", "flash_attn")
os.environ.setdefault("SPCONV_ALGO", "native")
import torch
import torch.nn as nn
import torch.nn.functional as F
MODEL_ID = "VAST-AI/AniGen"
def ln32(mod: nn.LayerNorm, x: torch.Tensor) -> torch.Tensor:
w = mod.weight.float() if mod.weight is not None else None
b = mod.bias.float() if mod.bias is not None else None
return F.layer_norm(x.float(), mod.normalized_shape, w, b, mod.eps).to(x.dtype)
def rms(mod, x: torch.Tensor) -> torch.Tensor:
# x [..., H, D]
dtype = x.dtype
y = F.normalize(x.float(), dim=-1)
gamma = mod.gamma.float()
return (y * gamma * mod.scale).to(dtype)
def attention(mod, x: torch.Tensor, context: torch.Tensor | None = None) -> torch.Tensor:
# Batch=1 production path. Standard SDPA exports to ONNX and TRT can fuse it.
if mod._type == "self":
qkv = F.linear(x, mod.to_qkv.weight, mod.to_qkv.bias)
B, N, _ = qkv.shape
qkv = qkv.reshape(B, N, 3, mod.num_heads, -1)
q, k, v = qkv.unbind(dim=2)
else:
q = F.linear(x, mod.to_q.weight, mod.to_q.bias)
kv = F.linear(context, mod.to_kv.weight, mod.to_kv.bias)
B, N, _ = q.shape
q = q.reshape(B, N, mod.num_heads, -1)
kv = kv.reshape(B, kv.shape[1], 2, mod.num_heads, -1)
k, v = kv.unbind(dim=2)
if mod.qk_rms_norm:
q = rms(mod.q_rms_norm, q)
k = rms(mod.k_rms_norm, k)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
y = F.scaled_dot_product_attention(q, k, v)
y = y.permute(0, 2, 1, 3).reshape(x.shape[0], x.shape[1], mod.channels)
return F.linear(y, mod.to_out.weight, mod.to_out.bias)
def mlp(sparse_ffn, x: torch.Tensor) -> torch.Tensor:
l1 = sparse_ffn.mlp[0]
act = sparse_ffn.mlp[1]
l2 = sparse_ffn.mlp[2]
y = F.linear(x, l1.weight, l1.bias)
y = F.gelu(y, approximate=getattr(act, "approximate", "none"))
return F.linear(y, l2.weight, l2.bias)
def mod_cross(block, x: torch.Tensor, modvec: torch.Tensor, context: torch.Tensor) -> torch.Tensor:
if block.share_mod:
parts = modvec.chunk(6, dim=1)
else:
parts = block.adaLN_modulation(modvec).chunk(6, dim=1)
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = parts
h = ln32(block.norm1, x)
h = h * (1 + scale_msa[:, None, :]) + shift_msa[:, None, :]
h = attention(block.self_attn, h)
x = x + h * gate_msa[:, None, :]
h = ln32(block.norm2, x)
if block.norm_for_context:
context = ln32(block.context_norm, context)
h = attention(block.cross_attn, h, context)
x = x + h
h = ln32(block.norm3, x)
h = h * (1 + scale_mlp[:, None, :]) + shift_mlp[:, None, :]
h = mlp(block.mlp, h)
return x + h * gate_mlp[:, None, :]
class SLatFlowCore(nn.Module):
def __init__(self, model):
super().__init__()
# Register the original blocks so ONNX sees all weights as initializers.
self.blocks = model.blocks
self.blocks_skin = model.blocks_vert_skin
self.blocks_skl = model.blocks_skl
self.adapters = model.adapter_geo_to_skin
def forward(self, geo, skin, skl, mod_geo, mod_skin, mod_skl, cond):
for b_geo, b_skin, b_skl, adapter in zip(self.blocks, self.blocks_skin, self.blocks_skl, self.adapters):
f_geo, f_skin, f_skl = geo, skin, skl
geo = mod_cross(b_geo, f_geo, mod_geo, cond)
skin = mod_cross(b_skin, f_skin, mod_skin, f_skl) + F.linear(f_geo, adapter.weight, adapter.bias)
skl = mod_cross(b_skl, f_skl, mod_skl, f_skin)
return geo, skin, skl
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default=os.environ.get("CF_ONNX_OUT", "/tmp/cf-slat-core"))
ap.add_argument("--model-root", default=os.environ.get("ANIGEN_MODEL_ROOT", "/tmp/anigen-model"))
ap.add_argument("--app-root", default=os.environ.get("ANIGEN_APP_ROOT", "/home/user/app"))
a = ap.parse_args()
out = Path(a.out); root = Path(a.model_root); app = Path(a.app_root)
out_dir = out / "onnx/anigen/slat-flow-core"; out_dir.mkdir(parents=True, exist_ok=True)
sys.path.insert(0, str(app))
from huggingface_hub import snapshot_download
snapshot_download(MODEL_ID, token=os.environ.get("HF_TOKEN"), local_dir=root,
allow_patterns=["ckpts/anigen/slat_flow_auto/config.json", "ckpts/anigen/slat_flow_auto/ckpts/**"])
os.chdir(root)
from anigen.utils.model_utils import load_model_from_path
model, cfg = load_model_from_path(str(root / "ckpts/anigen/slat_flow_auto"), model_name_in_config="denoiser", device="cuda")
model.eval()
core = SLatFlowCore(model).cuda().eval()
# Small sample lengths for export; sequence axes are dynamic in the ONNX graph.
geo = torch.zeros((1, 256, 1024), device="cuda", dtype=torch.float16)
skin = torch.zeros((1, 256, 512), device="cuda", dtype=torch.float16)
skl = torch.zeros((1, 128, 512), device="cuda", dtype=torch.float16)
mod_geo = torch.zeros((1, 1024), device="cuda", dtype=torch.float16)
mod_skin = torch.zeros((1, 512), device="cuda", dtype=torch.float16)
mod_skl = torch.zeros((1, 512), device="cuda", dtype=torch.float16)
cond = torch.zeros((1, 1374, 1024), device="cuda", dtype=torch.float16)
path = out_dir / "model.onnx"
started = time.time()
# Legacy exporter is used here because dynamic_axes is mature for variable token counts.
with torch.inference_mode():
torch.onnx.export(
core, (geo, skin, skl, mod_geo, mod_skin, mod_skl, cond), str(path),
input_names=["geo", "skin", "skl", "mod_geo", "mod_skin", "mod_skl", "cond"],
output_names=["geo_out", "skin_out", "skl_out"],
dynamic_axes={
"geo": {1: "n_geo"}, "skin": {1: "n_geo"}, "skl": {1: "n_skl"},
"geo_out": {1: "n_geo"}, "skin_out": {1: "n_geo"}, "skl_out": {1: "n_skl"},
},
opset_version=18,
do_constant_folding=True,
external_data=True,
dynamo=False,
)
export_s = time.time() - started
import onnx
onnx.checker.check_model(str(path))
meta = {
"component": "anigen-slat-flow-transformer-core",
"source": MODEL_ID,
"checkpoint": "ckpts/anigen/slat_flow_auto",
"opset": 18,
"precision": "fp16",
"dynamic": {"n_geo": [128, 4096, 16384], "n_skl": [16, 1024, 8192]},
"cond": [1, 1374, 1024],
"native_shell": ["SparseConv3d", "SparseDownsample", "SparseUpsample"],
"export_seconds": round(export_s, 3),
"torch": torch.__version__, "cuda": torch.version.cuda, "gpu": torch.cuda.get_device_name(0),
}
(out_dir / "export_meta.json").write_text(json.dumps(meta, indent=2))
print("SLAT_FLOW_CORE_EXPORTED", json.dumps(meta), flush=True)
del core, model; gc.collect(); torch.cuda.empty_cache()
if __name__ == "__main__":
main()
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