In Standard to LLVM dialect conversion, the binary op conversion pattern
implicitly assumed some operands were of LLVM IR dialect type. This is not
necessarily true, for example if the Ops that produce those operands did not
match the existing convresion patterns. Check if all operands are of LLVM IR
dialect type and if not, fail to patch the binary op pattern.
Closestensorflow/mlir#168
PiperOrigin-RevId: 274063207
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 also adds coverage with a missing test, which uncovered a bug in the conditional for testing whether an offset is dynamic or not.
PiperOrigin-RevId: 272505798
A recent ABI compatibility change affected the conversion from standard
CallOp/CallIndirectOp to LLVM::CallOp by changing its signature. In order to
analyze the signature, the code was looking up the callee symbol in the module.
This is incorrect since, during the conversion, the module may contain both the
original and the converted function op that have the same symbol name. There is
no strict guarantee on which of the two symbols will be found by the lookup.
The conversion was not failing because the type legalizer converts the LLVM
types to themselves making the original and the converted function signatures
ultimately produce the same type.
Instead of looking up the function signature to get the list of result types,
use the types of the CallOp/CallIndirectOp results which must match those of
the function in valid IR. These types are guaranteed to be the original,
unconverted types when converting the operation. Furthermore, this avoids the
need to perform a lookup of a symbol name in the module which may be expensive.
Finally, propagate attributes as-is from the original op to the converted op
since they share the attribute name for the callee of direct calls and the rest
of attributes are not affected by the conversion. This removes the need for
additional contorsions between direct and indirect calls to extract the name of
the optional callee attribute only to insert it back. This also prevents the
conversion from unintentionally dropping the other attributes of the op.
PiperOrigin-RevId: 272218871
This CL finishes the implementation of the Linalg + Affine type unification of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
As a consequence, the !linalg.view type, linalg::DimOp, linalg::LoadOp and linalg::StoreOp can now disappear and Linalg can use standard types everywhere.
PiperOrigin-RevId: 272187165
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
- introduce splat op in standard dialect (currently for int/float/index input
type, output type can be vector or statically shaped tensor)
- implement LLVM lowering (when result type is 1-d vector)
- add constant folding hook for it
- while on Ops.cpp, fix some stale names
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#141
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/141 from bondhugula:splat 48976a6aa0a75be6d91187db6418de989e03eb51
PiperOrigin-RevId: 270965304
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
This adds sign- and zero-extension and truncation of integer types to the
standard dialects. This allows to perform integer type conversions without
having to go to the LLVM dialect and introduce custom type casts (between
standard and LLVM integer types).
Closestensorflow/mlir#134
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/134 from ombre5733:sext-zext-trunc-in-std c7657bc84c0ca66b304e53ec03797e09152e4d31
PiperOrigin-RevId: 270479722
- the list of passes run by mlir-cpu-runner included -lower-affine and
-lower-to-llvm but was missing -lower-to-cfg (because -lower-affine at
some point used to lower straight to CFG); add -lower-to-cfg in
between. IR with affine ops can now be run by mlir-cpu-runner.
- update -lower-to-cfg to be consistent with other passes (create*Pass methods
were changed to return unique ptrs, but -lower-to-cfg appears to have been
missed).
- mlir-cpu-runner was unable to parse custom form of affine op's - fix
link options
- drop unnecessary run options from test/mlir-cpu-runner/simple.mlir
(none of the test cases had loops)
- -convert-to-llvmir was changed to -lower-to-llvm at some point, but the
create pass method name wasn't updated (this pass converts/lowers to LLVM
dialect as opposed to LLVM IR). Fix this.
(If we prefer "convert", the cmd-line options could be changed to
"-convert-to-llvm/cfg" then.)
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#115
PiperOrigin-RevId: 266666909
This CL allows binary operations on n-D vector types to be lowered to LLVMIR by performing an (n-1)-D extractvalue, 1-D vector operation and an (n-1)-D insertvalue.
PiperOrigin-RevId: 264339118
The linalg.view type used to be lowered to a struct containing a data pointer, offset, sizes/strides information. This was problematic when passing to external functions due to ABI, struct padding and alignment issues.
The linalg.view type is now lowered to LLVMIR as a *pointer* to a struct containing the data pointer, offset and sizes/strides. This simplifies the interfacing with external library functions and makes it trivial to add new functions without creating a shim that would go from a value type struct to a pointer type.
The consequences are that:
1. lowering explicitly uses llvm.alloca in lieu of llvm.undef and performs the proper llvm.load/llvm.store where relevant.
2. the shim creation function `getLLVMLibraryCallDefinition` disappears.
3. views are passed by pointer, scalars are passed by value. In the future, other structs will be passed by pointer (on a per-need basis).
PiperOrigin-RevId: 264183671
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
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
This CL is step 3/n towards building a simple, programmable and portable vector abstraction in MLIR that can go all the way down to generating assembly vector code via LLVM's opt and llc tools.
This CL adds support for converting MLIR n-D vector types to (n-1)-D arrays of 1-D LLVM vectors and a conversion VectorToLLVM that lowers the `vector.extractelement` and `vector.outerproduct` instructions to the proper mix of `llvm.vectorshuffle`, `llvm.extractelement` and `llvm.mulf`.
This has been independently verified to produce proper avx2 code.
Input:
```
func @vec_1d(%arg0: vector<4xf32>, %arg1: vector<8xf32>) -> vector<8xf32> {
%2 = vector.outerproduct %arg0, %arg1 : vector<4xf32>, vector<8xf32>
%3 = vector.extractelement %2[0 : i32]: vector<4x8xf32>
return %3 : vector<8xf32>
}
```
Command:
```
mlir-opt vector-to-llvm.mlir -vector-lower-to-llvm-dialect --disable-pass-threading | mlir-opt -lower-to-cfg -lower-to-llvm | mlir-translate --mlir-to-llvmir | opt -O3 | llc -O3 -march=x86-64 -mcpu=haswell -mattr=fma,avx2
```
Output:
```
vec_1d: # @vec_1d
# %bb.0:
vbroadcastss %xmm0, %ymm0
vmulps %ymm1, %ymm0, %ymm0
retq
```
PiperOrigin-RevId: 262895929
This will allow for reusing the same pattern list, which may be costly to continually reconstruct, on multiple invocations.
PiperOrigin-RevId: 262664599
This adds support for fcmp to the LLVM dialect and adds any necessary lowerings, as well as support for EDSCs.
Closestensorflow/mlir#69
PiperOrigin-RevId: 262475255
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
Conversion from integers (window or input size, padding etc) to floating point is required to express many ML kernels, for example average pooling.
PiperOrigin-RevId: 259575284
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
This specific PatternRewriter will allow for exposing hooks in the future that are only useful for the conversion framework, e.g. type conversions.
PiperOrigin-RevId: 258818122
This cl begins a large refactoring over how signature types are converted in the DialectConversion infrastructure. The signatures of blocks are now converted on-demand when an operation held by that block is being converted. This allows for handling the case where a region is created as part of a pattern, something that wasn't possible previously.
This cl also generalizes the region signature conversion used by FuncOp to work on any region of any operation. This generalization allows for removing the 'apply*Conversion' functions that were specific to FuncOp/ModuleOp. The implementation currently uses a new hook on TypeConverter, 'convertRegionSignature', but this should ideally be removed in favor of using Patterns. That depends on adding support to the PatternRewriter used by ConversionPattern to allow applying signature conversions to regions, which should be coming in a followup.
PiperOrigin-RevId: 258645733
Users generally want several different modes of conversion. This cl refactors DialectConversion to provide two:
* Partial (applyPartialConversion)
- This mode allows for illegal operations to exist in the IR, and does not fail if an operation fails to be legalized.
* Full (applyFullConversion)
- This mode fails if any operation is not properly legalized to the conversion target. This allows for ensuring that the IR after a conversion only contains operations legal for the target.
PiperOrigin-RevId: 258412243
These methods don't compose well with the rest of conversion framework, and create artificial breaks in conversion. Replace these methods with two(populateAffineToStdConversionPatterns and populateLoopToStdConversionPatterns respectively) that populate a list of patterns to perform the same behavior.
PiperOrigin-RevId: 258219277
Due to the absence of ODS support for enum attributes, the implementation of
the LLVM dialect `icmp` operation was reusing the comparison predicate from the
Standard dialect, creating an avoidable library dependency. With ODS support
and ICmpPredicate attribute recently introduced, the dependency is no longer
justified. Update the Standard to LLVM convresion to also convert the
CmpIPredicate into LLVM::ICmpPredicate and remove the unnecessary includes.
Note that the MLIRLLVMIR library did not explicitly depend on MLIRStandardOps,
requiring dependees of MLIRLLVMIR to also depend on MLIRStandardOps, which
should no longer be the case.
PiperOrigin-RevId: 258148456
This CL splits the lowering of affine to LLVM into 2 parts:
1. affine -> std
2. std -> LLVM
The conversions mostly consists of splitting concerns between the affine and non-affine worlds from existing conversions.
Short-circuiting of affine `if` conditions was never tested or exercised and is removed in the process, it can be reintroduced later if needed.
LoopParametricTiling.cpp is updated to reflect the newly added ForOp::build.
PiperOrigin-RevId: 257794436
This allows for the attribute to hold symbolic references to other operations than FuncOp. This also allows for removing the dependence on FuncOp from the base Builder.
PiperOrigin-RevId: 257650017
There is already a more general 'getParentOfType' method, and 'getModule' is likely to be misused as functions get placed within different regions than ModuleOp.
PiperOrigin-RevId: 257442243
Modules can now contain more than just Functions, this just updates the iteration API to reflect that. The 'begin'/'end' methods have also been updated to iterate over opaque Operations.
PiperOrigin-RevId: 257099084
These methods assume that a function is a valid builtin top-level operation, and removing these methods allows for decoupling FuncOp and IR/. Utility "getParentOfType" methods have been added to Operation/OpState to allow for querying the first parent operation of a given type.
PiperOrigin-RevId: 257018913
Extend the LLVM lowering pass to accept callbacks that construct an instance of
(a subclass of) LLVMTypeConverter and populate a list of conversion patterns.
These callbacks will be called when the pass processes a module and their
results will be used to set up the dialect conversion infrastructure. Clients
can now provide additional conversion patterns to avoid the need of
materializing type conversions between LLVM and other types.
PiperOrigin-RevId: 256532415
As with Functions, Module will soon become an operation, which are value-typed. This eases the transition from Module to ModuleOp. A new class, OwningModuleRef is provided to allow for owning a reference to a Module, and will auto-delete the held module on destruction.
PiperOrigin-RevId: 256196193
Move the data members out of Function and into a new impl storage class 'FunctionStorage'. This allows for Function to become value typed, which will greatly simplify the transition of Function to FuncOp(given that FuncOp is also value typed).
PiperOrigin-RevId: 255983022
