The slice indices must be rank-1 and have the same number of elements of the
rank of the operand. Give reasonable error messages for violations of these
requirements instead of a misleading error message that the types of the
indices don't all match.
PiperOrigin-RevId: 340660822
XLA HLO concat does not accept scalars, so fail verification if this occurs. Avoids segfault when accessing an empty output shape.
PiperOrigin-RevId: 337618167
- Use MLIR provided constraints for HLO_ScalarIntTensor and HLO_DimensionTensor.
- Update unit tests to expect new error messages.
PiperOrigin-RevId: 333313131
HLO requires that the element types match for all start index parameters. Right now we don't catch this invalid case until export, so adding a check in the verifier so that we catch this sooner.
This also requires a small tweak to the TF InplaceUpdate op lowering.
PiperOrigin-RevId: 325463796
For input and output dimensions which must match, we shouldn't fail in the case where one dim is dynamic and the other is static. This is insufficient information to conclude a dimension mismatch.
PiperOrigin-RevId: 325344738
mhlo.get_tuple_element supports extracting a mhlo.token type from a tuple. This updates the creation of tuples to allow for mhlo.token typed operands.
PiperOrigin-RevId: 324628663
Constants of unknown shape cannot be materialized. In most cases, one likely wants to use a scalar constant and rely on broadcasting instead.
PiperOrigin-RevId: 324252475
Also add a localized `mlir-hlo-opt` binary for the testing of
tensorflow/compiler/mlir/hlo/... ; this directory is intended to be self-contained
and depend only on MLIR.
PiperOrigin-RevId: 319878984