Commit Graph

7 Commits

Author SHA1 Message Date
River Riddle 6a0555a875 Refactor SplatElementsAttr to inherit from DenseElementsAttr as opposed to being a separate Attribute type. DenseElementsAttr provides a better internal representation for splat values as well as better API for accessing elements.
PiperOrigin-RevId: 253138287
2019-06-19 23:01:52 -07:00
River Riddle b790a2f396 Remove the explicit attribute kinds for DenseIntElementsAttr and DenseFPElementsAttr in favor of just one DenseElementsAttr. Now that attribute has the ability to define 'classof(Attribute attr)' methods, these derived classes can just be specializations of the main attribute class.
PiperOrigin-RevId: 251948820
2019-06-09 16:22:05 -07:00
Geoffrey Martin-Noble 090662c5f3 Rename VectorOrTensorType to ShapedType
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
2019-05-20 13:43:58 -07:00
Stella Laurenzo d4dcf7de9e Move Quantization -> Dialect/QuantOps, FxpMathOps -> Dialect/FxpMathOps.
Adding the additional layer of directory was discussed offline and matches the Target/ tree. The names match the defacto convention we seem to be following where the C++ namespace is ^(.+)Ops/$ matched against the directory name.

    This is in preparation for patching the Quantizer into this tree, which would have been confusing without moving the Quantization dialect to its more proper home. It is left to others to move other dialects if desired.

    Tested:
      ninja check-mlir

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PiperOrigin-RevId: 248171982
2019-05-20 13:41:55 -07:00
Jacques Pienaar 1273af232c Add build files and update README.
* Add initial version of build files;
    * Update README with instructions to download and build MLIR from github;

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PiperOrigin-RevId: 241102092
2019-03-30 11:23:22 -07:00
Lei Zhang eeadfbc170 Change getBroadcastedShape() to return result shape via parameter
This is a more efficient way than returning SmallVector directly.

PiperOrigin-RevId: 239407024
2019-03-29 17:25:38 -07:00
Lei Zhang 7972dcef84 Pull shape broadcast out as a stand-alone utility function
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
2019-03-29 17:12:11 -07:00