This part of the tutorial is now covered by a new flow in Toy. This also removes a point of confusion as there is also a proper Linalg dialect.
PiperOrigin-RevId: 275338933
This chapters introduces the notion of a full conversion, and adds support for lowering down to the LLVM dialect, LLVM IR, and thus code generation.
PiperOrigin-RevId: 275337786
This chapter adds a partial lowering of toy operations, all but PrintOp, to a combination of the Affine and Std dialects. This chapter focuses on introducing the conversion framework, the benefits of partial lowering, and how easily dialects may co-exist in the IR.
PiperOrigin-RevId: 275150649
The GenericCallOp needed to have the CallOpInterface to be picked up by the inliner. This also adds a CastOp to perform shape casts that are generated during inlining. The casts generated by the inliner will be folded away after shape inference.
PiperOrigin-RevId: 275150438
This change performs general cleanups of the implementation of ch.4 and fixes some bugs. For example, the operations currently don't inherit from the shape inference interface.
PiperOrigin-RevId: 275089914
This Chapter now introduces and makes use of the Interface concept
in MLIR to demonstrate ShapeInference.
END_PUBLIC
Closestensorflow/mlir#191
PiperOrigin-RevId: 275085151
This change refactors the toyc driver to be much cleaner and easier to extend. It also cleans up a few comments in the combiner.
PiperOrigin-RevId: 274973808
This is using Table-driven Declarative Rewrite Rules (DRR), the previous
version of the tutorial only showed the C++ patterns.
Closestensorflow/mlir#187
PiperOrigin-RevId: 274852321
This effectively rewrites Ch.2 to introduce dialects, operations, and registration instead of deferring to Ch.3. This allows for introducing the best practices up front(using ODS, registering operations, etc.), and limits the opaque API to the chapter document instead of the code.
PiperOrigin-RevId: 274724289
This function-like operation allows one to define functions that have wrapped
LLVM IR function type, in particular variadic functions. The operation was
added in parallel to the existing lowering flow, this commit only switches the
flow to use it.
Using a custom function type makes the LLVM IR dialect type system more
consistent and avoids complex conversion rules for functions that previously
had to use the built-in function type instead of a wrapped LLVM IR dialect type
and perform conversions during the analysis.
PiperOrigin-RevId: 273910855
This makes the name of the conversion pass more consistent with the naming
scheme, since it actually converts from the Loop dialect to the Standard
dialect rather than working with arbitrary control flow operations.
PiperOrigin-RevId: 272612112
This CL finishes the implementation of the lowering part of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Strided memrefs correspond conceptually to the following templated C++ struct:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t offset;
int64_t sizes[Rank];
int64_t strides[Rank];
};
```
The linearization procedure for address calculation for strided memrefs is the same as for linalg views:
`base_offset + SUM_i index_i * stride_i`.
The following CL will unify Linalg and Standard by removing !linalg.view in favor of strided memrefs.
PiperOrigin-RevId: 272033399
The strided MemRef RFC discusses a normalized descriptor and interaction with library calls (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Lowering of nested LLVM structs as value types does not play nicely with externally compiled C/C++ functions due to ABI issues.
Solving the ABI problem generally is a very complex problem and most likely involves taking
a dependence on clang that we do not want atm.
A simple workaround is to pass pointers to memref descriptors at function boundaries, which this CL implement.
PiperOrigin-RevId: 271591708
The RFC for unifying Linalg and Affine compilation passes into an end-to-end flow with a predictable ABI and linkage to external function calls raised the question of why we have variable sized descriptors for memrefs depending on whether they have static or dynamic dimensions (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
This CL standardizes the ABI on the rank of the memrefs.
The LLVM struct for a memref becomes equivalent to:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t sizes[Rank];
};
```
PiperOrigin-RevId: 270947276
The helper functions makePositionAttr() and positionAttr() were originally
introduced in the lowering-to-LLVM-dialect pass to construct integer array
attributes that are used for static positions in extract/insertelement.
Constructing an integer array attribute being fairly common, a utility function
Builder::getI64ArrayAttr was later introduced into the Builder API. Drop
makePositionAttr and similar homegrown functions and use that API instead.
PiperOrigin-RevId: 269295836
This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used:
// Pass manager for the top-level module.
PassManager pm(ctx);
// Nest a pipeline operating on FuncOp.
OpPassManager &fpm = pm.nest<FuncOp>();
fpm.addPass(...);
// Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp
OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>();
// Nest a pipeline on FuncOps inside of the spirv::ModuleOp.
OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>();
To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation.
/// Pass to verify an operation and signal failure if necessary.
class VerifierPass : public OperationPass<VerifierPass> {
void runOnOperation() override {
Operation *op = getOperation();
if (failed(verify(op)))
signalPassFailure();
markAllAnalysesPreserved();
}
};
PiperOrigin-RevId: 266840344
This pass class generalizes the current functionality between FunctionPass and ModulePass, and allows for operating on any operation type. The pass manager currently only supports OpPasses operating on FuncOp and ModuleOp, but this restriction will be relaxed in follow-up changes. A utility class OpPassBase<OpT> allows for generically referring to operation specific passes: e.g. FunctionPassBase == OpPassBase<FuncOp>.
PiperOrigin-RevId: 266442239
This change refactors and cleans up the implementation of the operation walk methods. After this refactoring is that the explicit template parameter for the operation type is no longer needed for the explicit op walks. For example:
op->walk<AffineForOp>([](AffineForOp op) { ... });
is now accomplished via:
op->walk([](AffineForOp op) { ... });
PiperOrigin-RevId: 266209552
- extend canonicalizeMapAndOperands to propagate constant operands into
the map's expressions (and thus drop those operands).
- canonicalizeMapAndOperands previously only dropped duplicate and
unused operands; however, operands that were constants were
retained.
This change makes IR maps/expressions generated by various
utilities/passes even simpler; also makes some of the test checks more
accurate and simpler -- for eg., 0' instead of symbol(%{{.*}}).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#107
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/107 from bondhugula:canonicalize-maps c889a51486d14fbf7db489f224f881e7e1ff7d72
PiperOrigin-RevId: 266085289
The code and documentation for this chapter of the tutorial have been updated to follow the new flow. The toy 'array' type has been replaced by usages of the MLIR tensor type. The code has also been cleaned up and modernized.
Closestensorflow/mlir#101
PiperOrigin-RevId: 265744086
Change the use of 'array' to 'tensor' to reflect the new flow that the tutorial will follow. Also tidy up some of the documentation, code comments, and fix a few out-dated links.
PiperOrigin-RevId: 265174676
Switch to C++14 standard method as llvm::make_unique has been removed (
https://reviews.llvm.org/D66259). Also mark some targets as c++14 to ease next
integrates.
PiperOrigin-RevId: 263953918
All 'getValue' variants now require that the index is valid, queryable via 'isValidIndex'. 'getSplatValue' now requires that the attribute is a proper splat. This allows for querying these methods on DenseElementAttr with all possible value types; e.g. float, int, APInt, etc. This also allows for removing unnecessary conversions to Attribute that really want the underlying value.
PiperOrigin-RevId: 263437337
Since raw pointers are always passed around for IR construct without
implying any ownership transfer, it can be error prone to have implicit
ownership transferred the same way.
For example this code can seem harmless:
Pass *pass = ....
pm.addPass(pass);
pm.addPass(pass);
pm.run(module);
PiperOrigin-RevId: 263053082
There are currently several different terms used to refer to a parent IR unit in 'get' methods: getParent/getEnclosing/getContaining. This cl standardizes all of these methods to use 'getParent*'.
PiperOrigin-RevId: 262680287
This will allow for reusing the same pattern list, which may be costly to continually reconstruct, on multiple invocations.
PiperOrigin-RevId: 262664599
The entry block is often used recently after insertion. This removes the need to perform an additional lookup in such cases.
PiperOrigin-RevId: 262265671
This CL modifies the LowerLinalgToLoopsPass to use RewritePattern.
This will make it easier to inline Linalg generic functions and regions when emitting to loops in a subsequent CL.
PiperOrigin-RevId: 261894120
Many LLVM transformations benefits from knowing the targets. This enables optimizations,
especially in a JIT context when the target is (generally) well-known.
Closestensorflow/mlir#49
PiperOrigin-RevId: 261840617
This allows for proper forward declaration, as opposed to leaking the internal implementation via a using directive. This also allows for all pattern building to go through 'insert' methods on the OwningRewritePatternList, replacing uses of 'push_back' and 'RewriteListBuilder'.
PiperOrigin-RevId: 261816316
This cl enforces that the conversion of the type signatures for regions, and thus their entry blocks, is handled via ConversionPatterns. A new hook 'applySignatureConversion' is added to the ConversionPatternRewriter to perform the desired conversion on a region. This also means that the handling of rewriting the signature of a FuncOp is moved to a pattern. A default implementation is provided via 'mlir::populateFuncOpTypeConversionPattern'. This removes the hacky implicit 'dynamically legal' status of FuncOp that was present previously, and leaves it up to the user to decide when/how to convert the signature of a function.
PiperOrigin-RevId: 259161999
