Enable reusing the real mlir-opt main from unit tests and in case where
additional initialization needs to happen before main is invoked (e.g., when
using different command line flag libraries).
PiperOrigin-RevId: 254764575
Now that Locations are Attributes they contain a direct reference to the MLIRContext, i.e. the context can be directly accessed from the given location instead of being explicitly passed in.
PiperOrigin-RevId: 254568329
Conversions from dialect A to dialect B depend on both A and B. Therefore, it
is reasonable for them to live in a separate library that depends on both
DialectA and DialectB library, and does not forces dependees of DialectA or
DialectB to also link in the conversion. Create the directory layout for the
conversions and move the Standard to LLVM dialect conversion as the first
example.
PiperOrigin-RevId: 253312252
* 'get' methods that allow constructing from an ArrayRef of integer or floating point values.
* A 'reshape' method to allow for changing the shape without changing the underlying data.
PiperOrigin-RevId: 252067898
* Add a getCurrentLocation that returns the location directly.
* Add parseOperandList/parseTrailingOperandList overloads without the required operand count.
PiperOrigin-RevId: 251585488
To accomplish this, moving forward users will need to provide a legalization target that defines what operations are legal for the conversion. A target can mark an operation as legal by providing a specific legalization action. The initial actions are:
* Legal
- This action signals that every instance of the given operation is legal,
i.e. any combination of attributes, operands, types, etc. is valid.
* Dynamic
- This action signals that only some instances of a given operation are legal. This
allows for defining fine-tune constraints, like say std.add is only legal when
operating on 32-bit integers.
An example target is shown below:
struct MyTarget : public ConversionTarget {
MyTarget(MLIRContext &ctx) : ConversionTarget(ctx) {
// All operations in the LLVM dialect are legal.
addLegalDialect<LLVMDialect>();
// std.constant op is always legal on this target.
addLegalOp<ConstantOp>();
// std.return op has dynamic legality constraints.
addDynamicallyLegalOp<ReturnOp>();
}
/// Implement the custom legalization handler to handle
/// std.return.
bool isLegal(Operation *op) override {
// Process the dynamic handling for a std.return op (and any others that were
// marked "dynamic").
...
}
};
PiperOrigin-RevId: 251289374
These were just introduced by a previous CL moving MemRef getRank to return int64_t. size_t could be smaller than 64 bits and in equals comparisons, signed vs unsigned doesn't matter. In these cases, we know right now that the particular int64_t is not larger than max size_t (because it currently comes directly from a size() call), the alternative cast plus equals comparison is always safe, so we might as well do it that way and no longer require reasoning deeper into the callstack.
We are already assuming that size() calls fit into int64_t in a number of other cases like the aforementioned getRank() (since exabytes of RAM are rare). If we want to avoid this assumption we will have to come up with a principled way to do it throughout.
--
PiperOrigin-RevId: 250980297
* the 'empty' method should be used to check for emptiness instead of 'size'
* using decl 'CapturableHandle' is unused
* redundant get() call on smart pointer
* using decl 'apply' is unused
* using decl 'ScopeGuard' is unused
--
PiperOrigin-RevId: 250623863
Using ArrayRef introduces issues with the order of evaluation between a constructor and
the arguments of the subsequent calls to the `operator()`.
As a consequence the order of captures is not well-defined can go wrong with certain compilers (e.g. gcc-6.4).
This CL fixes the issue by using lambdas in lieu of ArrayRef.
--
PiperOrigin-RevId: 249114775
Originally, ExecutionEngine was created before MLIR had a proper pass
management infrastructure or an LLVM IR dialect (using the LLVM target
directly). It has been running a bunch of lowering passes to convert the input
IR from Standard+Affine dialects to LLVM IR and, later, to the LLVM IR dialect.
This is no longer necessary and is even undesirable for compilation flows that
perform their own conversion to the LLVM IR dialect. Drop this integration and
make ExecutionEngine accept only the LLVM IR dialect. Users of the
ExecutionEngine can call the relevant passes themselves.
--
PiperOrigin-RevId: 249004676
This means that we can now do something like:
ctx->getRegisteredDialect<LLVMDialect>();
as opposed to:
static_cast<LLVMDialect *>(ctx->getRegisteredDialect("llvm");
--
PiperOrigin-RevId: 247989896
This CL implements the previously unsupported parsing for Range, View and Slice operations.
A pass is introduced to lower to the LLVM.
Tests are moved out of C++ land and into mlir/test/Examples.
This allows better fitting within standard developer workflows.
--
PiperOrigin-RevId: 245796600
This CL starts implementing a Linalg dialect with the objective of supporting
optimizing compilation of loops and library calls for a subset of common linear
algebra operations.
This CL starts by simply adding a linalg.range type and an operation with the
proper roundtripping test.
--
PiperOrigin-RevId: 244189468
other characters within the <>'s now that we can. This will allow quantized
types to use the pretty syntax (among others) after a few changes.
--
PiperOrigin-RevId: 243521268
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
Nicolas Vasilache