The first part of the Linalg tutorial introduces:
1. the RangeType and ViewType;
2. operations on those, namely RangeOp, ViewOp and SliceOp;
3. programmatic examples to test MLIR construction involving these types, ops and affine.for loops (with a mock custom op called "some_consumer").
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PiperOrigin-RevId: 241409949
This is making up for some differences in standard library and linker flags.
It also get rid of the requirement to build with RTTI.
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PiperOrigin-RevId: 241348845
This CL adds EnumAttr as a general mechanism for modelling enum attributes. Right now
it is using StringAttr under the hood since MLIR does not have native support for enum
attributes.
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PiperOrigin-RevId: 241334043
Example:
func @unknown_std_op() {
%0 = "std.foo_bar_op"() : () -> index
return
}
Will result in:
error: unregistered operation 'std.foo_bar_op' found in dialect ('std') that does not allow unknown operations
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PiperOrigin-RevId: 241266009
have no standard ops for working with these yet, this is simply enough to
represent and round trip them in the printer and parser.
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PiperOrigin-RevId: 241102728
This CL allows the programmatic control of the target hardware vector size when creating a MaterializeVectorsPass.
This is useful for registering passes for the tutorial.
PiperOrigin-RevId: 240996136
A integer number can be specified in the pattern definition and used as the
adjustment to the default benefit score in the generated rewrite pattern C++
definition.
PiperOrigin-RevId: 240994192
This CL removes the reliance of the vectorize pass on the specification of a `fastestVaryingDim` parameter. This parameter is a restriction meant to more easily target a particular loop/memref combination for vectorization and is mainly used for testing.
This also had the side-effect of restricting vectorization patterns to only the ones in which all memrefs were contiguous along the same loop dimension. This simple restriction prevented matmul to vectorize in 2-D.
this CL removes the restriction and adds the matmul test which vectorizes in 2-D along the parallel loops. Support for reduction loops is left for future work.
PiperOrigin-RevId: 240993827
These fail with:
could not convert ‘module’ from ‘llvm::orc::ThreadSafeModule’ to
‘llvm::Expected<llvm::orc::ThreadSafeModule>’
PiperOrigin-RevId: 240892583
Most of the tests have been ported to be unit-tests and this pass is problematic in the way it depends on TableGen-generated files. This pass is also non-deterministic during multi-threading and a blocker to turning it on by default.
PiperOrigin-RevId: 240889154
Originally, the conversion to the LLVM IR dialect had been implemented as pass.
The common conversion infrastructure was factored into DialectConversion from
which the conversion pass inherited. The conversion being a pass is
undesirable for callers that only need the conversion done, for example as a
part of sequence of conversions or outside the pass manager infrastructure.
Split the LLVM IR Dialect conversion into the conversion proper and the
conversion pass, where the latter contains the former instead of inheriting.
NFC.
PiperOrigin-RevId: 240874740
Dialect conversion currently clones the operations that did not match any
pattern. This includes cloning any regions that belong to these operations.
Instead, apply conversion recursively to the nested regions.
Note that if an operation matched one of the conversion patterns, it is up to
the pattern rewriter to fill in the regions of the converted operation. This
may require calling back to the converter and is left for future work.
PiperOrigin-RevId: 240872410
Implicit conversion don't play nicely in expressions such as:
`C() = A(i) * B(i)`.
Make `C()` return an IndexedValue instead of casting to ValueHandle.
This prevents double capture errors and is useful for the tutorial.
PiperOrigin-RevId: 240863223
Example:
%call:2 = call @multi_return() : () -> (f32, i32)
use(%calltensorflow/mlir#0, %calltensorflow/mlir#1)
This cl also adds parser support for uniquely named result values. This means that a test writer can now write something like:
%foo, %bar = call @multi_return() : () -> (f32, i32)
use(%foo, %bar)
Note: The printer will still print the collapsed form.
PiperOrigin-RevId: 240860058
Nicolas Vasilache