add converter for aten.linalg_cross (torch.cross / torc… - #4485
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/linalg.py 2026-08-13 20:17:38.528572+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/linalg.py 2026-08-13 20:17:59.647057+00:00
@@ -29,18 +29,14 @@
a0, a1, a2 = (component(input, i, "a") for i in range(3))
b0, b1, b2 = (component(other, i, "b") for i in range(3))
def mul(lhs: TRTTensor, rhs: TRTTensor, label: str) -> TRTTensor:
- return impl.elementwise.mul(
- ctx, target, source_ir, f"{name}_{label}", lhs, rhs
- )
+ return impl.elementwise.mul(ctx, target, source_ir, f"{name}_{label}", lhs, rhs)
def sub(lhs: TRTTensor, rhs: TRTTensor, label: str) -> TRTTensor:
- return impl.elementwise.sub(
- ctx, target, source_ir, f"{name}_{label}", lhs, rhs
- )
+ return impl.elementwise.sub(ctx, target, source_ir, f"{name}_{label}", lhs, rhs)
r0 = sub(mul(a1, b2, "a1b2"), mul(a2, b1, "a2b1"), "r0")
r1 = sub(mul(a2, b0, "a2b0"), mul(a0, b2, "a0b2"), "r1")
r2 = sub(mul(a0, b1, "a0b1"), mul(a1, b0, "a1b0"), "r2")
…h.linalg.cross) torch.cross resolves its optional dim and delegates to aten.linalg_cross under decomposition, so a single converter for aten.linalg_cross.default covers both public APIs. Neither had a converter, so models using either op fell back to PyTorch or errored depending on partitioning settings. impl.linalg.cross splits each operand into its 3 components along dim via ISelect (gather), computes the cross product with elementwise mul/sub, and concatenates the 3 result components back along dim. A capability_validator rejects the case where dim's size isn't statically known to be 3, since the converter relies on that precondition. Fixes #3724
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Addresses #3724