By adding support for complex types to GetScalarOfType and using appropriate
choice of limits for initial values in the unsorted segment reduction ops.
PiperOrigin-RevId: 327061577
This method no longer needs to be explicitly defined by the user, so all of the existing definitions are dead and can be removed.
PiperOrigin-RevId: 326350650
This allows specifying a constant whose shape is only known when operand shape is. Also use it to update tf.Acos legalization.
PiperOrigin-RevId: 325860604
This adds the XlaBuilder RngBitGenerator to the MHLO dialect. The op is currently represented very directly using int attribute for random algorithm and direct import/export.
PiperOrigin-RevId: 325814134
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
- Use FuncOp::getArguments() and Region::getArguments() and friends where possible
instead of going through the front() block.
PiperOrigin-RevId: 325352975
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
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325228073
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325079842
The transformation of unranked to ranked operations no longer generates cast
operations for shapes and sizes. Instead, we use the newly introduced support
for extent tensor and index types directly.
PiperOrigin-RevId: 325057440
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325056915
Instead, we invoke multiple test tools in a row in end to end tests now. For hlo dialects and passes, we use mlir-hlo-opt explicitly.
PiperOrigin-RevId: 324989884
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
Added support for HLO ops bitcast-convert, sort and while in MlirHloBuilder and enabled tests for NonMaxSuppressionV4 and SelfAdjointEigV2Op using these ops.
PiperOrigin-RevId: 324360651
This is required before exporting HLO dialect ops with standard dialect constant to XLA.
Also, sink constants for sort op as well. Added a TODO to generalize this pass to handle more ops and non-const values defined outside.
PiperOrigin-RevId: 324301911
XLA implementation has this limitation and always uses 32 bit result for this instruction. This will cause mismatch between the result type in MLIR and XLA at the time of export.
This should be resolved once we have a special dialect mapping directly to HLOInstructionProto. Another option until then could be to introduce a pass to legalize mhlo itself to match XLA semantics.
PiperOrigin-RevId: 324286936
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
Richard Uhler
A. Unique TensorFlower
Jacques Pienaar
River Riddle
Robert Suderman
Mehdi Amini
Alexander Belyaev
Benjamin Kramer
Andy Ly
Geoffrey Martin-Noble
Lucy Fox
Rahul Joshi
Stephan Herhut
Smit Hinsu
Tim Shen