This is consistent with the design of LMHLO FusionOp, and it simplifies the
usage. Before the change, those redundant operands ended up unused as all sub-regions can already capture needed buffers.
PiperOrigin-RevId: 362381155
This is the same as iota, but instead of taking the dimensions from the result
tensor we use the supplied shape extents tensor.
PiperOrigin-RevId: 362298548
This is an annoying edge case because the collapse->expand lowering expects at
least R1 or it will produce invalid linalg reshapes. Using the direct lowering
works fine.
PiperOrigin-RevId: 362269199
- Extract verification of source target pairs attached to collective permute into a common
helper function and use that to verify both MHLO and LMHLO variants.
- Change MlirGpuTestBase::ParseMlirModule to allow returning back a failure, and use
that to update the mlir_gpu_compile_test to check the new behavior.
PiperOrigin-RevId: 362156962
For now, the pass only reifies the required shape computations. Moving
broadcasts will follow to allow for fusion across them.
PiperOrigin-RevId: 362033715
Return nan at zeta poles or inf where the limit is defined. Also test the kernel
based on the series representation of zeta.
PiperOrigin-RevId: 361993482
For binary ops, we already special-case rank 0 vs rank 1, and same shape. So we
don't need to special-case a maximum rank of 1.
PiperOrigin-RevId: 360891955
For binary ops, we already special-case rank 0 vs rank 1, and same shape. So we
don't need to special-case a maximum rank of 1.
PiperOrigin-RevId: 360881387
The linalg named ops are now type polymorphic, so the type-monomorphic
varieties are redundant (and will be deleted soon).
PiperOrigin-RevId: 360509010
Previously this would be too strict and fail if dynamic and static dims were
compared. Dynamic/unknown are treated as "maybe equal" to a static value without further info, so at this layer don't flag as invalid unless truly are.
PiperOrigin-RevId: 360189086
This op is useful for rank specialization of broadcasts. Kernel Generator
needs to generate one kernel for each rank, so if we can minimize the rank
of the broadcast shape, we can support more cases with the same number of
special-cased kernels.
PiperOrigin-RevId: 360137827
This pattern only works for normal convolutions. It does not work for depthwise
convolutions. The Linalg conv ops are defined with static rank, so it only
supports 1d/2d/3d cases, which are the most typical cases.
This also refactors out the same check in lmhlo.conv lowering.
PiperOrigin-RevId: 359503527
- XLA:HLO -> LMHLO conversion drops all token arguments and return values, however
custom calls that users write still expect to get buffer pointers for these token types.
- To be able to support this, add an optional call target argument mapping attribute to
LMHLO custom calls. When this attribute is present, it indicates the number of
arguments and returns that the custom call expects and also indicates which LMHLO
arg() or output() maps to which arg or result number of the custom call.
PiperOrigin-RevId: 358826664
This just blows up everything to ranked (up to 6) and is probably quite slow.
This is sufficient to make kernelgen compile SelectV2.
PiperOrigin-RevId: 358777728
Verification of HLO_BroadcastInDimOp was previously failing or crashing if the
operand had a dynamic shape or was unranked. Update the verification code to
allow the operand to be unranked or have dynamic shape.
PiperOrigin-RevId: 358056793
A shape that contains exactly one element is effectively a scalar. This leads
to a speedup in cases where we have a binary op with one operand that is
effectively a scalar, because we can use the fast path.
PiperOrigin-RevId: 357515552
Also remove BitcastOp. XLA bitcast requires the input buffer to alias the output buffer, which makes bitcast always a no-op.
PiperOrigin-RevId: 356884383
This is being done by just removing the approximation and lowering to atan2 lib calls later to make the implementation the same as XLA. Note that if the approximation is brought back later, it can be fixed by changing the IR checking `less-than(X, 0)` to `less-than(copysign(X, 1), 0)`
PiperOrigin-RevId: 356253941
Tim Shen
Hanhan Wang
Benjamin Kramer
Rahul Joshi
A. Unique TensorFlower
Stephan Herhut
Adrian Kuegel
Geoffrey Martin-Noble
Jacques Pienaar
Richard Uhler
Alexander Belyaev
Tres Popp
Mahesh Ravishankar