This allows client to be able to reuse the same logic to setup a module
for the ExecutionEngine without instanciating one. One use case is running
the optimization pipeline but not JIT-ing.
--
PiperOrigin-RevId: 242614380
TensorContractionBase has become too unwieldy with all the CRTP manipulation once less trivial transformations are implemented.
This CL drops CRTP for inheritance and uses the same name comparison trick to figure out what to cast into.
As a byproduct, all the -inl.h files disappear.
To maintain the separation between directories, a LINALG_STEP variable is introduced
--
PiperOrigin-RevId: 242546977
This dialect does not have a global constructor and has to be registered
manually in `main`. Also fix the way it is exercised in the test.
--
PiperOrigin-RevId: 242434886
For some reason, the OSS build on macOS was not happy with the initialization
syntax and was attempting to call a copy constructor. Hotfix it to use a
different syntax pending further investigation.
--
PiperOrigin-RevId: 242432634
making the IR dumps much nicer.
This is part 2/3 of the path to making dialect types more nice. Part 3/3 will
slightly generalize the set of characters allowed in pretty types and make it
more principled.
--
PiperOrigin-RevId: 242249955
* dyn_cast_or_null
- This will first check if the operation is null before trying to 'dyn_cast':
Value *v = ...;
if (auto forOp = dyn_cast_or_null<AffineForOp>(v->getDefiningOp()))
...
* isa_nonnull
- This will first check if the pointer is null before trying to 'isa':
Value *v = ...;
if (isa_nonnull<AffineForOp>(v->getDefiningOp());
...
--
PiperOrigin-RevId: 242171343
Use MLIR's ExecutionEngine to demonstrate how one can implement a simple
JIT-compiler and executor after fully lowering the Linalg dialect to the LLVM
IR dialect, using the direct conversion (not going through standard
loads/stores).
--
PiperOrigin-RevId: 242127690
This CL adds declarative tiling support in the linalg dialect by providing:
1. loop tiling on linalg ops by simply calling into mlir::tile
2. view tiling on linalg ops by:
a. computing the subview between for each tile dimension based on the loop tile size and the mapping of loops to operand ranges.
b. declaring that the tiled form of a tensorcontraction is the same tensorcontraction on subviews, which essentially gives us a recursive form.
Point 2.b is potentially subject to change in the future.
--
PiperOrigin-RevId: 242058658
This CL adds the last bit to convert from linalg.LoadOp and linalg.StoreOp to the affine dialect, as well as a unit test to exercise the conversion.
--
PiperOrigin-RevId: 242045826
Load and Store Linalg operations are converter to their LLVM IR counterparts
preceded by a sequence of operations that recover the effective address of the
accessed element. The address is computed given the subscripts and the view
descriptor as
base_pointer + base_offset + SUM_i subscript_i * stride_i.
Manual testing shows that the resulting LLVM IR for the matrix multiplication
example can be compiled and executed, producing correct results.
--
PiperOrigin-RevId: 241889003
Lei Zhang