This is in preparation for making it also support/be a parent class of MemRefType. MemRefs have similar shape/rank/element semantics and it would be useful to be able to use these same utilities for them.
This CL should not change any semantics and only change variables, types, string literals, and comments. In follow-up CLs I will prepare all callers to handle MemRef types or remove their dependence on ShapedType.
Discussion/Rationale in https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/cHLoyfGu8y8
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PiperOrigin-RevId: 248476449
The previous approach is too restrictive; we end up forbidding all dialect-specific
types as element types. Changed to not consider element types entirely.
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PiperOrigin-RevId: 247486537
TensorFlow comparison ops like tf.Less supports broadcast behavior but the result
type have different element types as the input types. Extend broadcastable trait
to allow such cases. Added tf.Less to demonstrate it.
PiperOrigin-RevId: 237846127
So that we can use this function to deduce broadcasted shapes elsewhere.
Also added support for unknown dimensions, by following TensorFlow behavior.
PiperOrigin-RevId: 237846065
* Add common broadcastable binary adder in TF ops and use for a few ops;
- Adding Sub, Mul here
* Change the prepare lowering to use TF variants;
* Add some more legalization patterns;
PiperOrigin-RevId: 233310952
That allows TensorFlow Add and Div ops to use Broadcastable op trait instead of
more restrictive SameValueType op trait.
That in turn allows TensorFlow ops to be registered by defining GET_OP_LIST and
including the generated ops file. Currently, tf-raise-control-flow pass tests
are using dynamic shapes in tf.Add op and AddOp can't be registered without
supporting the dynamic shapes.
TESTED with unit tests
PiperOrigin-RevId: 232927998
We also need the broadcast logic in the TensorFlow dialect. Move it to a
Dialect/ directory for a broader scope. This Dialect/ directory is intended
for code not in core IR, but can potentially be shared by multiple dialects.
Apart from fixing TensorFlow op TableGen to use this trait, this CL only
contains mechanical code shuffling.
PiperOrigin-RevId: 229563911
