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"format": "companion-forge-custom-ops/v2",
"onnx_domain": "com.companionforge",
"onnx_opset": 1,
"tensorrt_plugin_namespace": "companionforge",
"tensorrt_plugin_version": "1",
"target": {
"tensorrt": "11.2.1.2",
"gpu": "NVIDIA L4",
"compute_capability": "8.9"
},
"operators": [
{
"name": "SparseConv3D",
"onnx": "production",
"tensorrt": "production",
"semantics": "AniGen SubMConv3d / spconv Native, fixed output cardinality N",
"validation": "bench/custom_ops_validation.json"
},
{
"name": "SparseWindowAttention",
"onnx": "production",
"tensorrt": "production",
"semantics": "AniGen shifted-window partition + FlashAttention varlen",
"validation": "bench/custom_ops_validation.json"
},
{
"name": "SparseDownsample",
"onnx": "production",
"tensorrt": "production",
"semantics": "coordinate quantization + unique + scatter mean + inverse map",
"validation": "bench/custom_ops_validation.json"
},
{
"name": "SparseUpsample",
"onnx": "production",
"tensorrt": "production",
"semantics": "inverse-map gather to target sparse coordinates",
"validation": "bench/custom_ops_validation.json"
},
{
"name": "SparseSubdivide",
"onnx": "production",
"tensorrt": "production",
"semantics": "N sparse voxels to 8N child voxels",
"validation": "bench/custom_ops_validation.json"
},
{
"name": "MeshTopologyExtract",
"onnx": "production",
"tensorrt": "production",
"semantics": "FlexiCubes dense topology extraction with TensorRT data-dependent outputs",
"validation": "bench/mesh_topology_validation.json"
},
{
"name": "SparseMeshTopologyExtract",
"onnx": "production",
"tensorrt": "production",
"semantics": "SLat DAE sparse cube features to FlexiCubes mesh, vertex attributes and skin features",
"profile": {
"sparse_cubes": [
128,
65536,
524288
],
"resolution": 256
},
"validation": "bench/sparse_mesh_topology_validation.json",
"input_precision": "fp32",
"output_precision": {
"vertices": "fp32",
"attrs": "fp32",
"skin": "fp32",
"faces": "int32"
}
}
],
"implementation": {
"kind": "TensorRT Python/JIT IPluginV3",
"runtime_registration": "plugins/tensorrt/companion_sparse_trt.py",
"note": "Plans require plugin registration before deserialization. The ONNX ABI is stable and can later be backed by AOT C++/CUDA implementations without changing model graphs."
}
} |