These properties were useful for a few things before traits had a better integration story, but don't really carry their weight well these days. Most of these properties are already checked via traits in most of the code. It is better to align the system around traits, and improve the performance/cost of traits in general.
Differential Revision: https://reviews.llvm.org/D96088
Add a build method that pads the source with a scalar value.
Reviewed By: nicolasvasilache, antiagainst
Differential Revision: https://reviews.llvm.org/D96343
Replace MlirDialectRegistrationHooks with MlirDialectHandle, which under-the-hood is an opaque pointer to MlirDialectRegistrationHooks. Then we expose the functionality previously directly on MlirDialectRegistrationHooks, as functions which take the opaque MlirDialectHandle struct. This makes the actual structure of the registration hooks an implementation detail, and happens to avoid this issue: https://llvm.discourse.group/t/strange-swift-issues-with-dialect-registration-hooks/2759/3
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D96229
The patch extends the runner utils by verification methods that compare two memrefs. The methods compare the content of the two memrefs and print success if the data is identical up to a small numerical error. The methods are meant to simplify the development of integration tests that for example compare optimized and unoptimized code paths (cf. the updates to the linalg matmul integration tests).
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D96326
This commit defines linalg.depthwise_conv_2d_nhwc for depthwise
2-D convolution with NHWC input/output data format.
This op right now only support channel multiplier == 1, which is
the most common case.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94966
Indexing maps for named ops can reference attributes so that
we can synthesize the indexing map dynamically. This supports
cases like strides for convolution ops. However, it does cause
an issue: now the indexing_maps() function call is dependent
on those attributes.
Linalg ops inherit LinalgOpInterfaceTraits, which calls
verifyStructuredOpInterface() to verify the interface.
verifyStructuredOpInterface() further calls indexing_maps().
Note that trait verification is done before the op itself,
where ODS generates the verification for those attributes.
So we can have indexing_maps() referencing non-existing or
invalid attribute, before the ODS-generated verification
kick in.
There isn't a dependency handling mechansim for traits.
This commit adds new interface methods to query whether an
op hasDynamicIndexingMaps() and then perform
verifyIndexingMapRequiredAttributes() in
verifyStructuredOpInterface() to handle the dependency issue.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D96297
This revision fixes the fact that the padding transformation did not have enough information to set the proper type for the padding value.
Additionally, the verifier for Yield in the presence of PadTensorOp is fixed to properly report incorrect number of results or operands. Previously, the error would be silently ignored which made the core issue difficult to debug.
Differential Revision: https://reviews.llvm.org/D96264
After the LLVM dialect types were ported to use built-in types, the parser kept
supporting the old syntax for LLVM dialect types to produce built-in types for
compatibility. Drop this support.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D96275
Following up on https://reviews.llvm.org/D94360, this patch splits the
serialization code into multiple source files to provide a better
structure and allow parallel compilation.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D95855
This reverts commit 511dd4f438 along with
a couple fixes.
Original message:
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Phabricator: https://reviews.llvm.org/D96111
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D96111
This will allow to use `NativeOpTrait` and Operations
declared outside of `mlir` namespace.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D96128
This new invoke will pack a list of argument before calling the
`invokePacked` method. It accepts returned value as output argument
wrapped in `ExecutionEngine::Result<T>`, and delegate the packing of
arguments to a trait to allow for customization for some types.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95961
These patterns move vector.bitcast ops to be before
insert ops or after extract ops where suitable.
With them, bitcast will happen on smaller vectors
and there are more chances to share extract/insert
ops.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D96040
Those types are not needed any longer since LLVM dialect
has migrated to using MLIR's I1, I8, I32 types directly.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D96127
This patch introduces a few more straightforward patterns
to convert vector ops operating on 1-4 element vectors
to their corresponding SPIR-V counterparts.
This patch also enables converting vector<1xT> to T.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D96042
This patch adds patterns to use vector.shape_cast to cast
away leading 1-dimensions from a few vector operations.
It allows exposing more canonical forms of vector.transfer_read,
vector.transfer_write, vector_extract_strided_slice, and
vector.insert_strided_slice. With this, we can have more
opportunity to cancelling extract/insert ops or forwarding
write/read ops.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D95873
Historically, Linalg To LLVM conversion subsumed numerous other conversions,
including (affine) loop lowerings to CFG and conversions from the Standard and
Vector dialects to the LLVM dialect. This was due to the insufficient support
for partial conversions in the infrastructure that essentially required
conversions that involve type change (in this case, !linalg.range to
!llvm.struct) to be performed in a single conversion sweep. This is no longer
the case so remove the subsumed conversions and run them as separate passes
when necessary.
Depends On D95317
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D96008
The `AffineMap` class follows the same semantic as Type and Attribute.
It is immutable object, so it make sence to mark its methods as const.
Also part of its API is already marked as const, this change just make the API consistent.
Reviewed By: ftynse, bondhugula
Differential Revision: https://reviews.llvm.org/D96026
This makes ignoring a result explicit by the user, and helps to prevent accidental errors with dropped results. Marking LogicalResult as no discard was always the intention from the beginning, but got lost along the way.
Differential Revision: https://reviews.llvm.org/D95841
- attribute-dict production is redundant with dictionary-attribute
- definitions of attribute aliases were part of the same production as
uses of attribute aliases
- `std.dim` now accepts the dimension number as an operand, so the
example is out of date. Use the predicate of std.cmpi as a better
example.
Differential Revision: https://reviews.llvm.org/D96076
This reverts commit 953086ddbb because
it breaks GCC 5 build:
error: could not convert '(const char*)""' from 'const char*' to 'llvm::StringLiteral'
static ::llvm::StringLiteral getDialectNamespace() { return ""; }
Use `StringLiteral` for function return type if it is known to return
constant string literals only.
This will make it visible to API users, that such values can be safely
stored, since they refers to constant data, which will never be deallocated.
`StringRef` is general is not safe to store for a long term,
since it might refer to temporal data allocated in heap.
Reviewed By: mehdi_amini, bkramer
Differential Revision: https://reviews.llvm.org/D95945
To allow it usage for Operation classes defined outside of `mlir` namespace.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D95952
* Introduce separate `RankedTensorOf` class. Use it as base class for `AnyRankedTensor`.
* Add C++ class specification (`::mlir::MemRefType`) to `MemRefRankOf` and `StaticShapeMemRefOf`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95936
This revision takes advantage of recent extensions to vectorization to refactor contraction detection into a bona fide Linalg interface.
The mlit-linalg-ods-gen parser is extended to support adding such interfaces.
The detection that was originally enabling vectorization is refactored to serve as both a test on a generic LinalgOp as well as to verify ops that declare to conform to that interface.
This is plugged through Linalg transforms and strategies but it quickly becomes evident that the complexity and rigidity of the C++ class based templating does not pay for itself.
Therefore, this revision changes the API for vectorization patterns to get rid of templates as much as possible.
Variadic templates are relegated to the internals of LinalgTransformationFilter as much as possible and away from the user-facing APIs.
It is expected other patterns / transformations will follow the same path and drop as much C++ templating as possible from the class definition.
Differential revision: https://reviews.llvm.org/D95973
Historically, the Vector to LLVM dialect conversion subsumed the Standard to
LLVM dialect conversion patterns. This was necessary because the conversion
infrastructure did not have sufficient support for reconciling type
conversions. This support is now available. Only keep the patterns related to
the Vector dialect in the Vector to LLVM conversion and require type casts
operations to be inserted if necessary. These casts will be removed by
following conversions if possible. Update integration tests to also run the
Standard to LLVM conversion.
There is a significant amount of test churn, which is due to (a) unnecessarily
strict tests in VectorToLLVM and (b) many patterns actually targeting Standard
dialect ops instead of LLVM dialect ops leading to tests actually exercising a
Vector->Standard->LLVM conversion. This churn is a good illustration of the
reason to make the conversion partial: now the tests only check the code in the
Vector to LLVM conversion and will not be randomly broken by changes in
Standard to LLVM conversion.
Arguably, it may be possible to extract Vector to Standard patterns into a
separate pass, but given the ongoing splitting of the Standard dialect, such
pass will be short-lived and will require further refactoring.
Depends On D95626
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D95685
In dialect conversion infrastructure, source materialization applies as part of
the finalization procedure to results of the newly produced operations that
replace previously existing values with values having a different type.
However, such operations may be created to replace operations created in other
patterns. At this point, it is possible that the results of the _original_
operation are still in use and have mismatching types, but the results of the
_intermediate_ operation that performed the type change are not in use leading
to the absence of source materialization. For example,
%0 = dialect.produce : !dialect.A
dialect.use %0 : !dialect.A
can be replaced with
%0 = dialect.other : !dialect.A
%1 = dialect.produce : !dialect.A // replaced, scheduled for removal
dialect.use %1 : !dialect.A
and then with
%0 = dialect.final : !dialect.B
%1 = dialect.other : !dialect.A // replaced, scheduled for removal
%2 = dialect.produce : !dialect.A // replaced, scheduled for removal
dialect.use %2 : !dialect.A
in the same rewriting, but only the %1->%0 replacement is currently considered.
Change the logic in dialect conversion to look up all values that were replaced
by the given value and performing source materialization if any of those values
is still in use with mismatching types. This is performed by computing the
inverse value replacement mapping. This arguably expensive manipulation is
performed only if there were some type-changing replacements. An alternative
could be to consider all replaced operations and not only those that resulted
in type changes, but it would harm pattern-level composability: the pattern
that performed the non-type-changing replacement would have to be made aware of
the type converter in order to call the materialization hook.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95626
This revision defines a Linalg contraction in general terms:
1. Has 2 input and 1 output shapes.
2. Has at least one reduction dimension.
3. Has only projected permutation indexing maps.
4. its body computes `u5(u1(c) + u2(u3(a) * u4(b)))` on some field
(AddOpType, MulOpType), where u1, u2, u3, u4 and u5 represent scalar unary
operations that may change the type (e.g. for mixed-precision).
As a consequence, when vectorization of such an op occurs, the only special
behavior is that the (unique) MulOpType is vectorized into a
`vector.contract`. All other ops are handled in a generic fashion.
In the future, we may wish to allow more input arguments and elementwise and
constant operations that do not involve the reduction dimension(s).
A test is added to demonstrate the proper vectorization of matmul_i8_i8_i32.
Differential revision: https://reviews.llvm.org/D95939
This separation improves the layering and paves the way for more interfaces coming up in the future.
Differential revision: https://reviews.llvm.org/D95941
We could extend this with an interface to allow dialect to perform a type
conversion, but that would make the folder creating operation which isn't
the case at the moment, and isn't necessarily always desirable.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95991
The AsyncRuntime declares prototypes for extern "C" functions inside a
namespace in the header, but not inside that namespace in the
definition. This causes Visual Studio to treat them as different
entities and thus the dllexport is ignored for the definitions.
Using the same namespace fixes this issue.
Secondly, this commit moves the dllexport to be consistent with the
JITs expectation.
This is an update to https://reviews.llvm.org/D95386 that fixes the
compile issues in old versions of Visual studio.
Differential Revision: https://reviews.llvm.org/D95933
We should be check whether lb + step >= ub to determine
whether this is a single iteration. Previously we were
checking lb + lb >= ub.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95440
Add the conversion pattern for vector.bitcast to lower it to
the LLVM Dialect.
Reviewed By: ThomasRaoux, aartbik
Differential Revision: https://reviews.llvm.org/D95579
This revision adds two new classes, RewriterBase and IRRewriter. RewriterBase is a new shared base class between IRRewriter and PatternRewriter. PatternRewriter will continue to be the base class used to perform rewrites within a rewrite pattern. IRRewriter on the other hand, is a new class that allows for tracking IR rewrites from outside of a rewrite pattern. In this revision all of the old API from PatternRewriter is moved to RewriterBase, but the distinction between IRRewriter and PatternRewriter is kept on the chance that a necessary API divergence happens in the future.
Currently if you want to have some utility that transforms a piece of IR and share it between pattern and non-pattern code, you have to duplicate it. This revision enables the creation of utilities that can be invoked from rewrite patterns and normal transformation code:
```c++
void someSharedUtility(RewriterBase &rewriter, ...) {
// Some interesting IR mutation here.
}
// Some RewritePattern
LogicalResult MyPattern::matchAndRewrite(Operation *op, PatternRewriter &rewriter) {
...
someSharedUtility(rewriter, ...);
...
}
// Some Pass
void MyPass::runOnOperation() {
...
IRRewriter rewriter(...);
someSharedUtility(rewriter, ...);
}
```
Differential Revision: https://reviews.llvm.org/D94638
The MLIR Async runtime uses different namespacing for the header file,
and the definitions of its C API. The header file places the extern "C"
functions inside namespace mlir::runtime, and the definitions are not
in a namespace. This causes issues in cl.exe. It treats the declaration
and definition as different, and thus does not apply dllexport to the
definition, which leads to the mlir_async_runtime.dll containing no
definitions, and the mlir_async_runtime.lib not being generated.
This patch moves the namespace to cover the definitions, and thus
generates the dll correctly on Windows with cl.exe.
This was tested with Visual Studio C++ 19.28.29336.
Differential Revision: https://reviews.llvm.org/D95386
Add the necessary bits to CMakeLists to make it possible to configure
MLIR against installed LLVM, and build it with minimal need for LLVM
source tree. The latter is only necessary to run unittests, and if it
is missing then unittests are skipped with a warning.
This change includes the necessary changes to tests, in particular
adding some missing substitutions and defining missing variables
for lit.site.cfg.py substitution.
Reviewed By: stephenneuendorffer
Differential Revision: https://reviews.llvm.org/D85464
Co-authored-by: Isuru Fernando <isuruf@gmail.com>
The __resume function trips up LLVM's 'X86 DAG->DAG Instruction Selection' unless optimizations are disabled.
Only adding the __resume function when it's needed allows lowering through AsyncToLLVM and LLVM without '-O0' as long as the coroutine functionality is not used.
Reviewed By: ezhulenev
Differential Revision: https://reviews.llvm.org/D95868
It will allow to perform additional manipulation with the newly created Operation.
For example, custom attributes propagation/changes.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95525
This makes the generated code independent from actual namespace of its users.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95520
We should be check whether lb + step >= ub to determine
whether this is a single iteration. Previously we were
checking lb + lb >= ub.
Differential Revision: https://reviews.llvm.org/D95440
Fix a bug that was introduced where calling the codegen strategy with actual concrete C++ Op types did not trigger the expected behavior.
Also introduce a test for the behavior that was missing.
Differential Revision: https://reviews.llvm.org/D95863
This revision unifies Linalg vectorization and paves the way for vectorization of Linalg ops with mixed-precision operations.
The new algorithm traverses the ops in the linalg block in order and avoids recursion.
It uses a BlockAndValueMapping to keep track of vectorized operations.
The revision makes the following modifications but is otherwise NFC:
1. vector.transfer_read are created eagerly and may appear in a different order than the original order.
2. a more progressive vectorization to vector.contract results in only the multiply operation being converted to `vector.contract %a, %b, %zero`, where `%zero` is a
constant of the proper type. Later vector canonicalizations are assumed to rewrite vector.contract %a, %b, %zero + add to a proper accumulate form.
Differential revision: https://reviews.llvm.org/D95797
In dialect conversion, signature conversions essentially perform block argument
replacement and are added to the general value remapping. However, the replaced
values were not tracked, so if a signature conversion was rolled back, the
construction of operand lists for the following patterns could have obtained
block arguments from the mapping and give them to the pattern leading to
use-after-free. Keep track of signature conversions similarly to normal block
argument replacement, and erase such replacements from the general mapping when
the conversion is rolled back.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95688
[s|z]exti ops do not have the same operand and result type.
As a consequence, the lowering of the n-D vector form needs to be relaxed a bit.
This revision additionally performs a few NFC renamings of variables to make them more intuitive.
Differential Revision: https://reviews.llvm.org/D95760
Comitted log, exp, maximum, minimum, comparison, ceil and floor conversions from TOSA to LinAlg. Support for signless integer and floating point.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D95839
Add printer and parser hooks for a custom directive that allows
parsing and printing of idioms that can represent a list of values
each of which is either an integer or an SSA value. For example in
`subview %source[%offset_0, 1] [4, %size_1] [%stride_0, 3]`
each of the list (which represents offset, size and strides) is a mix
of either statically know integer values or dynamically computed SSA
values. Since this is used in many places adding a custom directive to
parse/print this idiom allows using assembly format on operations
which use this idiom.
Differential Revision: https://reviews.llvm.org/D95773
Support OpImageType in SPIRV Dialect.
This change doesn't support operand AccessQualifier since
it is optinal and only enables under Kernel capability.
co-authored-by: Alan Liu <alanliu.yf@gmail.com>
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D95580
Support OpImageType in SPIRV Dialect.
This change doesn't support operand AccessQualifier since
it is optinal and only enables under Kernel capability.
co-authored-by: Alan Liu <alanliu.yf@gmail.com>
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D95580
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
This is as same as D95615 but fixing one dep in CMakeLists.txt
Different from D95671, the fix was applied to run target.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D95785
This reverts commit d9b953d84b.
This commit resulted in build bot failures and the author is away from a
computer, so I am reverting on their behalf until they have a chance to
look into this.
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95671
* Fixing missing `type` keyword in alias print
* Add test for large tuple type alias & rerun output to verify printed
form can be parsed (which caught the above).
The result values of vp2intersect are vectors of bits, i.e.,
vector<8xi1> or vector<16xi8> (instead of i8 or i16).
Differential Revision: https://reviews.llvm.org/D95678
Tuples can occupy quite a lot of space, instead of printing out tuple type
everywhere, just use the type alias if larger (arbitrarily chose a bound for
now).
Differential Revision: https://reviews.llvm.org/D95707
Update ElementsAttr::isValidIndex to handle ElementsAttr with a scalar. Scalar will have rank 0.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95663
Currently, for a scf.parallel (i,j,k) after the loop collapsing to 1D is done, the
IVs would be traversed as for an scf.parallel(k,j,i).
Differential Revision: https://reviews.llvm.org/D95693
Previously, CMake would find any version of Python3. However, the project
claims to require 3.6 or greater, and 3.6 features are being used.
Reviewed By: yln
Differential Revision: https://reviews.llvm.org/D95635
The library is not actually static when BUILD_SHARED_LIBS is on, and tests need to explicitly load it already. Also, the shared objects it was linked to did not use any symbols from it and it was therefore never linked to it.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95612
This segfault could occur from out of bounds accesses when simplifying
tensor.extract with a constant index and a tensor created by
tensor.from_elements.
This IR is not necesarilly invalid as it might conditionally be
never executed.
Differential Revision: https://reviews.llvm.org/D95535
This class is looking up a dialect prefix on the identifier on initialization
and keeping a pointer to the Dialect when found.
The NamedAttribute key is now a DialectIdentifier.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D95418
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95615
Expand existing one to handle the common case for verifying compatible
is existing and inferred. This considers arrays equivalent if they they
have the same size and pairwise compatible elements.
Rationale:
Providing an output tensor, even if one is not used as input to
the kernel provides the right pattern for using lingalg sparse
kernels (in contrast with reusing a tensor just to provide the shape).
This prepares proper bufferization that will follow.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D95587
It is no longer necessary to also convert other "standard" ops along with the
complex dialect: the element types are now built-in integers or floating point
types, and the top-level cast between complex and struct is automatically
inserted and removed in progressive lowering.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D95625
Depending on the headers only is fine, but we do not want to use any symbols from LLVMSupport. If we do, static registration of cl options is linked in as well, and loading multiple such libraries in the cuda/rocm-runner fails because the same cl options are registered multiple times.
The cuda/rocm-runners also depend on LLVMSupport, so one could think that already loading a single such library would fail. It does not because the map of cl options is not shared between the runner and the loaded libraries (but it is shared across all loaded libraries, presumably because it has external linkage, in contrast to the static registration which has internal linkage).
This change is a preparation step for dynamically loading the mlir_async_runtime.so and cuda-runtime-wrappers.so in the same test. The async runtime depends on LLVMSupport in a more fundamental way (llvm::ThreadPool), and as explained above there can only be one.
This change also switches to add_mlir_library to make it consistent with the other runner_utils libraries.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D95613
This revision creates a build method of PadTensorOp which can be mapped to
SimplePad op. The verifier is updated to accept a static custom result type,
which has the same semantic as SimplePadOp.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95555
The subview verifier in the rank-reduced case is plainly skipping verification
when the resulting type is a memref with empty affine map. This is generally incorrect.
Instead, form the actual expected rank-reduced MemRefType that takes into account the projections of 1's dimensions. Then, check the canonicalized expected rank-reduced type against the canonicalized candidate type.
Differential Revision: https://reviews.llvm.org/D95316
This revision adds a layer of SFINAE to the composable codegen strategy so it does
not have to require statically defined ops but instead can also be used with OpInterfaces, Operation* and an op name string.
A linalg.matmul_i8_i8_i32 is added to the .tc spec to demonstrate how all this works end to end.
Differential Revision: https://reviews.llvm.org/D95600
This revision improves the usage of the codegen strategy by adding a few flags that
make it easier to control for the CLI.
Usage of ModuleOp is replaced by FuncOp as this created issues in multi-threaded mode.
A simple benchmarking capability is added for linalg.matmul as well as linalg.matmul_column_major.
This latter op is also added to linalg.
Now obsolete linalg integration tests that also take too long are deleted.
Correctness checks are still missing at this point.
Differential revision: https://reviews.llvm.org/D95531
Fixes a few small issues in the docs. It seems one of the examples was missing
the expected MLIR output due to a copy-paste typo.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95599
This prevents needless reinitialization for clients that want to reuse a pass manager multiple times. A new `getRegisryHash` function is exposed by the context to give a rough indicator of when the context registry has changed.
Differential Revision: https://reviews.llvm.org/D95493
`emplace???` functions running concurrently can set the ready flag and then pending awaiter will never be executed
Differential Revision: https://reviews.llvm.org/D95517
OffsetSizeAndStrideOpInterface now have the ability to specify only a leading subset of
offset, sizes, strides operands/attributes.
The size of that leading subset must be limited by the corresponding entry in `getArrayAttrMaxRanks` to avoid overflows.
Missing trailing dimensions are assumed to span the whole range (i.e. [0 .. dim)).
This brings more natural semantics to slice-like op on top of subview and is a simplifies to removing all uses of SliceOp in dependent projects.
Differential revision: https://reviews.llvm.org/D95441
The current context is thread-local state, and in preparation of GPU async execution (on multiple threads) we need to set the context before calling API that create resources.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D94495
This follows up on the introduction of C API for the same object and is similar
to AffineExpr and AffineMap.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D95437
Depends On D95000
Move async.execute outlining and async -> async.runtime lowering into the separate Async transformation pass
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D95311
Adds vp2intersect to the AVX512 dialect and defines a lowering to the
LLVM dialect.
Author: Matthias Springer <springerm@google.com>
Differential Revision: https://reviews.llvm.org/D95301
The `getCapsule` and `createFromCapsule` comments incorrectly state the `PyMlirContext` and `MlirContext` in `PyLocation`, `PyAttribute`, and `PyType` classes.
Differential Revision: https://reviews.llvm.org/D95413
Instead of using llvm.call operations to call LLVM coro intrinsics use Coro operations from the LLVM dialect.
(This was reviewed as a part of https://reviews.llvm.org/D94923 but was lost in arc land from local branch)
Differential Revision: https://reviews.llvm.org/D95405
[NFC] No new functionality, mostly a cleanup and one more abstraction level between Async and LLVM IR.
Instead of lowering from Async to LLVM coroutines and Async Runtime API in one shot, do it progressively via async.coro and async.runtime operations.
1. Lower from async to async.runtime/coro (e.g. async.execute to function with coro setup and runtime calls)
2. Lower from async.runtime/coro to LLVM intrinsics and runtime API calls
Intermediate coro/runtime operations will allow to run transformations on a higher level IR and do not try to match IR based on the LLVM::CallOp properties.
Although async.coro is very close to LLVM coroutines, it is not exactly the same API, instead it is optimized for usability in async lowering, and misses a lot of details that are present in @llvm.coro intrinsic.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D94923
This patch adds support for producer-consumer fusion scenarios with
multiple producer stores to the AffineLoopFusion pass. The patch
introduces some changes to the producer-consumer algorithm, including:
* For a given consumer loop, producer-consumer fusion iterates over its
producer candidates until a fixed point is reached.
* Producer candidates are gathered beforehand for each iteration of the
consumer loop and visited in reverse program order (not strictly guaranteed)
to maximize the number of loops fused per iteration.
In general, these changes were needed to simplify the multi-store producer
support and remove some of the workarounds that were introduced in the past
to support more fusion cases under the single-store producer limitation.
This patch also preserves the existing functionality of AffineLoopFusion with
one minor change in behavior. Producer-consumer fusion didn't fuse scenarios
with escaping memrefs and multiple outgoing edges (from a single store).
Multi-store producer scenarios will usually (always?) have multiple outgoing
edges so we couldn't fuse any with escaping memrefs, which would greatly limit
the applicability of this new feature. Therefore, the patch enables fusion for
these scenarios. Please, see modified tests for specific details.
Reviewed By: andydavis1, bondhugula
Differential Revision: https://reviews.llvm.org/D92876
This revision starts evolving the APIs to manipulate ops with offsets, sizes and operands towards a ValueOrAttr abstraction that is already used in folding under the name OpFoldResult.
The objective, in the future, is to allow such manipulations all the way to the level of ODS to avoid all the genuflexions involved in distinguishing between values and attributes for generic constant foldings.
Once this evolution is accepted, the next step will be a mechanical OpFoldResult -> ValueOrAttr.
Differential Revision: https://reviews.llvm.org/D95310
This revision addresses a remaining comment that was overlooked in https://reviews.llvm.org/D95243:
the pad hoisting transformation is made to additionally bail out on side effecting ops other than LoopLikeOps.
This transformation anchors on a padding op whose result is only used as an input
to a Linalg op and pulls it out of a given number of loops.
The result is a packing of padded tailes of ops that is amortized just before
the outermost loop from which the pad operation is hoisted.
Differential revision: https://reviews.llvm.org/D95243
This revision allows the base Linalg tiling pattern to optionally require padding to
a constant bounding shape.
When requested, a simple analysis is performed, similar to buffer promotion.
A temporary `linalg.simple_pad` op is added to model padding for the purpose of
connecting the dots. This will be replaced by a more fleshed out `linalg.pad_tensor`
op when it is available.
In the meantime, this temporary op serves the purpose of exhibiting the necessary
properties required from a more fleshed out pad op, to compose with transformations
properly.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95149
* As discussed, fixes the ordering or (operands, results) -> (results, operands) in various `create` like methods.
* Fixes a syntax error in an ODS accessor method.
* Removes the linalg example in favor of a test case that exercises the same.
* Fixes FuncOp visibility to properly use None instead of the empty string and defaults it to None.
* Implements what was documented for requiring that trailing __init__ args `loc` and `ip` are keyword only.
* Adds a check to `InsertionPoint.insert` so that if attempting to insert past the terminator, an exception is raised telling you what to do instead. Previously, this would crash downstream (i.e. when trying to print the resultant module).
* Renames `_ods_build_default` -> `build_generic` and documents it.
* Removes `result` from the list of prohibited words and for single-result ops, defaults to naming the result `result`, thereby matching expectations and what is already implemented on the base class.
* This was intended to be a relatively small set of changes to be inlined with the broader support for ODS generating the most specific builder, but it spidered out once actually testing various combinations, so rolling up separately.
Differential Revision: https://reviews.llvm.org/D95320
* Adds a flag to MlirOperationState to enable result type inference using the InferTypeOpInterface.
* I chose this level of implementation for a couple of reasons:
a) In the creation flow is naturally where generated and custom builder code will be invoking such a thing
b) it is a bit more efficient to share the data structure and unpacking vs having a standalone entry-point
c) we can always decide to expose more of these interfaces with first-class APIs, but that doesn't preclude that we will always want to use this one in this way (and less API surface area for common things is better for API stability and evolution).
* I struggled to find an appropriate way to test it since we don't link the test dialect into anything CAPI accessible at present. I opted instead for one of the simplest ops I found in a regular dialect which implements the interface.
* This does not do any trait-based type selection. That will be left to generated tablegen wrappers.
Differential Revision: https://reviews.llvm.org/D95283
Fusion of generic/indexed_generic operations with tensor_reshape by
expansion when the latter just adds/removes unit-dimensions is
disabled since it just adds unit-trip count loops.
Differential Revision: https://reviews.llvm.org/D94626
This revision adds support for using either operand or result types to anchor an optional group. It also removes the arbitrary restriction that type directives must refer to variables in the same group, which is overly limiting for a declarative format syntax.
Fixes PR#48784
Differential Revision: https://reviews.llvm.org/D95109
representing dependence from producer result to consumer.
With Linalg on tensors the dependence between operations can be from
the result of the producer to the consumer. This change just does a
NFC refactoring of the LinalgDependenceGraphElem to allow representing
both OpResult and OpOperand*.
Differential Revision: https://reviews.llvm.org/D95208
spv.Ordered/spv.Unordered are meant for OpenCL Kernel capability.
For Vulkan Shader capability, we should use spv.IsNan to check
whether a number is NaN.
Add a new pattern for converting `std.cmpf ord|uno` to spv.IsNan
and bumped the pattern converting to spv.Ordered/spv.Unordered
to a higher benefit. The SPIR-V target environment will properly
select between these two patterns.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D95237
Previously we only autogen the availability for ops that are
direct instantiating `SPV_Op` and expected other subclasses of
`SPV_Op` to define aggregated availability for all ops. This is
quite error prone and we can miss capabilities for certain ops.
Also it's arguable to have multiple levels of subclasses and try
to deduplicate too much: having the availability directly in the
op can be quite explicit and clear. A few extra lines of
declarative code is fine.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D95236
This PR only has coro intrinsics needed for the Async to LLVM lowering. Will add other intrinsics as needed in the followup PRs.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D95143
- Fix arguments name for subview and subtensor.
- Fix a typo in a comment of subtensor's method.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95211
With this, we have complete support for finding integer sample points in FlatAffineConstraints.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95047
- Extend spirv::ConstantOp::getZero/One to handle float, vector of int, and vector of float.
- Refactor ZeroExtendI1Pattern to use getZero/One methods.
- Add one more test for lowering std.zexti which extends vector<4xi1> to vector<4xi64>.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D95120
Add factory to create streams for logging the reproducer. Allows for more general logging (beyond file) and logging the configuration/module separately (logged in order, configuration before module).
Also enable querying filename of ToolOutputFile.
Differential Revision: https://reviews.llvm.org/D94868
This extracts the implementation of getType, setType, and getBody from
FunctionSupport.h into the mlir::impl namespace and defines them
generically in FunctionSupport.cpp. This allows them to be used
elsewhere for any FunctionLike ops that use FunctionType for their
type signature.
Using the new helpers, FuncOpSignatureConversion is generalized to
work with all such FunctionLike ops. Convenience helpers are added to
configure the pattern for a given concrete FunctionLike op type.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D95021
Define OrderedOp and UnorderedOp instructions in SPIR-V and convert
cmpf operations with `ord` and `uno` tag to these instructions
respectively.
Differential Revision: https://reviews.llvm.org/D95098
The SPIR-V spec uses OpSpecConstantOp. Using an inconsistent name
makes the dialect generation scripts fail. Update to use the right
operation name, and fix the auto generation scripts as well.
Differential Revision: https://reviews.llvm.org/D95097
I attempted to write a test case for this, but the situations in which the kind is used for RegionDirective and ResultsDirective have zero overlap; meaning that there isn't a situation in which sharing the kind creates a conflict.
Differential Revision: https://reviews.llvm.org/D94988
Having this function in a public scope is helpful to register dialects that are
defined at runtime, and thus that need a runtime-defined TypeID.
Also, a similar function in DialectRegistry, insert(TypeID, StringRef, ...), has
a public scope.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D95091
An `unrealized_conversion_cast` operation represents an unrealized conversion
from one set of types to another, that is used to enable the inter-mixing of
different type systems. This operation should not be attributed any special
representational or execution semantics, and is generally only intended to be
used to satisfy the temporary intermixing of type systems during the conversion
of one type system to another.
This operation was discussed in the following RFC(and ODM):
https://llvm.discourse.group/t/open-meeting-1-14-dialect-conversion-and-type-conversion-the-question-of-cast-operations/
Differential Revision: https://reviews.llvm.org/D94832
A cast-like operation is one that converts from a set of input types to a set of output types. The arity of the inputs may be from 0-N, whereas the arity of the outputs may be anything from 1-N. Cast-like operations are removable in cases where they produce a "no-op", i.e when the input types and output types match 1-1.
Differential Revision: https://reviews.llvm.org/D94831
Rationale:
Since I made the argument that metadata helps with extra
verification checks, I better actually do that ;-)
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D95072
Resumed coroutine potentially can deallocate the token/value/group and destroy the mutex before the std::unique_ptr destructor.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D95037
Like SubView, SubTensor/SubTensorInsertOp are allowed to have rank-reducing/expanding semantics. In the case of SubTensorInsertOp , the rank of offsets/sizes/strides should be the rank of the destination tensor.
Also, add a builder flavor for SubTensorOp to return a rank-reduced tensor.
Differential Revision: https://reviews.llvm.org/D95076
The patch adapts the rocm runtime wrapper due to subtle differences between the cuda and the rocm/hip runtime api.
Reviewed By: csigg
Differential Revision: https://reviews.llvm.org/D95027
This patch adds support for producer-consumer fusion scenarios with
multiple producer stores to the AffineLoopFusion pass. The patch
introduces some changes to the producer-consumer algorithm, including:
* For a given consumer loop, producer-consumer fusion iterates over its
producer candidates until a fixed point is reached.
* Producer candidates are gathered beforehand for each iteration of the
consumer loop and visited in reverse program order (not strictly guaranteed)
to maximize the number of loops fused per iteration.
In general, these changes were needed to simplify the multi-store producer
support and remove some of the workarounds that were introduced in the past
to support more fusion cases under the single-store producer limitation.
This patch also preserves the existing functionality of AffineLoopFusion with
one minor change in behavior. Producer-consumer fusion didn't fuse scenarios
with escaping memrefs and multiple outgoing edges (from a single store).
Multi-store producer scenarios will usually (always?) have multiple outgoing
edges so we couldn't fuse any with escaping memrefs, which would greatly limit
the applicability of this new feature. Therefore, the patch enables fusion for
these scenarios. Please, see modified tests for specific details.
Reviewed By: andydavis1, bondhugula
Differential Revision: https://reviews.llvm.org/D92876
Add a check if regions do not implement the RegionBranchOpInterface. This is not
allowed in the current deallocation steps. Furthermore, we handle edge-cases,
where a single region is attached and the parent operation has no results.
This fixes: https://bugs.llvm.org/show_bug.cgi?id=48575
Differential Revision: https://reviews.llvm.org/D94586
The runtime-wrappers depend on LLVMSupport, pulling in static initialization code (e.g. command line arguments). Dynamically loading multiple such libraries results in ODR violoations.
So far this has not been an issue, but in D94421, I would like to load both the async-runtime and the cuda-runtime-wrappers as part of a cuda-runner integration test. When doing this, code that asserts that an option category is only registered once fails (note that I've only experienced this in Google's bazel where the async-runtime depends on LLVMSupport, but a similar issue would happen in cmake if more than one runtime-wrapper starts to depend on LLVMSupport).
The underlying issue is that we have a mix of static and dynamic linking. If all dependencies were loaded as shared objects (i.e. if LLVMSupport was linked dynamically to the runtime wrappers), each dependency would only get loaded once. However, linking dependencies dynamically would require special attention to paths (one could dynamically load the dependencies first given explicit paths). The simpler approach seems to be to link all dependencies statically into a single shared object.
This change basically applies the same logic that we have in the c_runner_utils: we have a shared object target that can be loaded dynamically, and we have a static library target that can be linked to other runtime-wrapper shared object targets.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D94399
Use cases with 16- or even 8-bit pointer/index structures have been identified.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D95015
* Matches how all of the other shaped types are declared.
* No super principled reason fro this ordering beyond that it makes the one that was different be like the rest.
* Also matches ordering of things like ndarray, et al.
Reviewed By: ftynse, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94812
* This isn't exclusive with other mechanisms for more ODS centric op definitions, but based on discussions, we feel that we will always benefit from a python escape hatch, and that is the most natural way to write things that don't fit the mold.
* I suspect this facility needs further tweaking, and once it settles, I'll document it and add more tests.
* Added extensions for linalg, since it is unusable without them and continued to evolve my e2e example.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D94752
* This allows us to hoist trait level information for regions and sized-variadic to class level attributes (_ODS_REGIONS, _ODS_OPERAND_SEGMENTS, _ODS_RESULT_SEGMENTS).
* Eliminates some splicey python generated code in favor of a native helper for it.
* Makes it possible to implement custom, variadic and region based builders with one line of python, without needing to manually code access to the segment attributes.
* Needs follow-on work for region based callbacks and support for SingleBlockImplicitTerminator.
* A follow-up will actually add ODS support for generating custom Python builders that delegate to this new method.
* Also includes the start of an e2e sample for constructing linalg ops where this limitation was discovered (working progressively through this example and cleaning up as I go).
Differential Revision: https://reviews.llvm.org/D94738
This commit adds a new trait that can be attached to ops that have
signed semantics.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D94896
cmake_minimum_required(VERSION) calls cmake_policy(VERSION),
which sets all policies up to VERSION to NEW.
LLVM started requiring CMake 3.13 last year, so we can remove
a bunch of code setting policies prior to 3.13 to NEW as it
no longer has any effect.
Reviewed By: phosek, #libunwind, #libc, #libc_abi, ldionne
Differential Revision: https://reviews.llvm.org/D94374
In prehistorical times, AffineApplyOp was allowed to produce multiple values.
This allowed the creation of intricate SSA use-def chains.
AffineApplyNormalizer was originally introduced as a means of reusing the AffineMap::compose method to write SSA use-def chains.
Unfortunately, symbols that were produced by an AffineApplyOp needed to be promoted to dims and reordered for the mathematical composition to be valid.
Since then, single result AffineApplyOp became the law of the land but the original assumptions were not revisited.
This revision revisits these assumptions and retires AffineApplyNormalizer.
Differential Revision: https://reviews.llvm.org/D94920
* Development setup recommendations.
* Test updates to match what we actually do.
* Update cmake variable `PYTHON_EXECUTABLE` -> `Python3_EXECUTABLE` to match the upgrade to python3 repo wide.
This patch adds support for checking if two PresburgerSets are equal. In particular, one can check if two FlatAffineConstraints are equal by constructing PrebsurgerSets from them and comparing these.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D94915
Use cross-compilation approach for `mlir-linalg-ods-gen` application
similar to TblGen tools.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94598
Added the ability to read (an extended version of) the FROSTT
file format, so that we can now read in sparse tensors of arbitrary
rank. Generalized the API to deal with more than two dimensions.
Also added the ability to sort the indices of sparse tensors
lexicographically. This is an important step towards supporting
auto gen of initialization code, since sparse storage formats
are easier to initialize if the indices are sorted. Since most
external formats don't enforce such properties, it is convenient
to have this ability in our runtime support library.
Lastly, the re-entrant problem of the original implementation
is fixed by passing an opaque object around (rather than having
a single static variable, ugh!).
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94852
The operantion is an identity if the values yielded by the operation
is the argument of the basic block of that operation. Add this missing check.
Differential Revision: https://reviews.llvm.org/D94819
This commit adds support to generate an additional builder for
each named op that has attributes. This gives better experience
when creating the named ops.
Along the way adds support for i64.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D94733
This is a very minor improvement during iteration graph construction.
If the first attempt considering the dimension order of all tensors fails,
a second attempt is made using the constraints of sparse tensors only.
Dense tensors prefer dimension order (locality) but provide random access
if needed, enabling the compilation of more sparse kernels.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D94709
With the recent changes to linalg on tensor semantics, the tiling
operations works out-of-the-box for generic operations. Add a test to
verify that and some minor refactoring.
Differential Revision: https://reviews.llvm.org/D93077
Add canonicalization to replace use of the result of a linalg
operation on tensors in a dim operation, to use one of the operands of
the linalg operations instead. This allows the linalg op itself to be
deleted when all its non-dim uses are removed (say through tiling, etc.)
Differential Revision: https://reviews.llvm.org/D93076
linalg.generic/indexed_generic operations on tensors whose body is
just yielding the (non-induction variable) arguments of the operation
can be canonicalized by replacing uses of the result with the
corresponding arguments.
Differential Revision: https://reviews.llvm.org/D94581
The standard and gpu dialect both have `alloc` operations which use the
memory effect `MemAlloc`. In both cases, it is specified on both the
operation itself and on the result. This results in two memory effects
being created for these operations. When `MemAlloc` is defined on an
operation, it represents some background effect which the compiler
cannot reason about, and inhibits the ability of the compiler to
remove dead `std.alloc` operations. This change removes the uneeded
`MemAlloc` effect from these operations and leaves the effect on the
result, which allows dead allocs to be erased.
There is the same problem, but to a lesser extent, with MemFree, MemRead
and MemWrite. Over-specifying these traits is not currently inhibiting
any optimization.
Differential Revision: https://reviews.llvm.org/D94662
This spilts out BufferDeallocationInternals.md, since buffer
deallocation is not part of bufferization per se.
Differential Revision: https://reviews.llvm.org/D94351
TosaToLinalg was depending on its header file indirectly through
Passes.h rather than directly. This removes that indirection.
Differential Revision: https://reviews.llvm.org/D94706
This revision adds a new `replaceOpWithIf` hook that replaces uses of an operation that satisfy a given functor. If all uses are replaced, the operation gets erased in a similar manner to `replaceOp`. DialectConversion support will be added in a followup as this requires adjusting how replacements are tracked there.
Differential Revision: https://reviews.llvm.org/D94632
In the overwhelmingly common case, enum attribute case strings represent valid identifiers in MLIR syntax. This revision updates the format generator to format as a keyword in these cases, removing the need to wrap values in a string. The parser still retains the ability to parse the string form, but the printer will use the keyword form when applicable.
Differential Revision: https://reviews.llvm.org/D94575
This is a variant of TypesMatchWith that provides support for variadic arguments. This is necessary because ranges generally can't use the default operator== comparators for checking equality.
Differential Revision: https://reviews.llvm.org/D94574
Initial commit to add support for lowering from TOSA to Linalg. The focus is on
the essential infrastructure for these lowerings and integration with existing
passes.
Includes lowerings for a subset of operations including:
abs, add, sub, pow, and, or, xor, left shift, right shift, tanh
Lit tests are used to validate correctness.
Differential Revision: https://reviews.llvm.org/D94247
With this, we have complete support for emptiness checks. This also paves the way for future support to check if two FlatAffineConstraints are equal.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D94272
Similar to the parallelization strategies, the vectorization strategies
provide control on what loops should be vectorize. Unlike the parallel
strategies, only innermost loops are considered, but including reductions,
with the control of vectorizing dense loops only or dense and sparse loops.
The vectorized loops are always controlled by a vector mask to avoid
overrunning the iterations, but subsequent vector operation folding removes
redundant masks and replaces the operations with more efficient counterparts.
Similarly, we will rely on subsequent loop optimizations to further optimize
masking, e.g. using an unconditional full vector loop and scalar cleanup loop.
The current strategy already demonstrates a nice interaction between the
sparse compiler and all prior optimizations that went into the vector dialect.
Ongoing discussion at:
https://llvm.discourse.group/t/mlir-support-for-sparse-tensors/2020/10
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D94551
This corrects the last 2 issues caught by tests when causing dialect
conversion rollbacks to occur.
Differential Revision: https://reviews.llvm.org/D94623
This commit adds support for parsing attribute uses in indexing
maps. These attribute uses are represented as affine symbols in
the resultant indexing maps because we can only know their
concrete value (which are coming from op attributes and are
constants) for specific op instances. The `indxing_maps()`
calls are synthesized to read these attributes and create affine
constants to replace the placeholder affine symbols and simplify.
Depends on D94240
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94335
An invalid permutation will trigger a C++ assertion when attempting to create an AffineMap from the permutation.
This patch adds an `isPermutation` function to check the given permutation before creating the AffineMap.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D94492
This avoids large source files and gives a better structure. It also
allows leveraging compilation parallelism.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D94360
With this, now we can specify a list of attributes on named ops
generated from the spec. The format is defined as
```
attr-id ::= bare-id (`?`)?
attr-typedef ::= type (`[` `]`)?
attr-def ::= attr-id `:` attr-typedef
tc-attr-def ::= `attr` `(` attr-def-list `)`
tc-def ::= `def` bare-id
`(`tensor-def-list`)` `->` `(` tensor-def-list`)`
(tc-attr-def)?
```
For example,
```
ods_def<SomeCppOp>
def some_op(...) -> (...)
attr(
f32_attr: f32,
i32_attr: i32,
array_attr : f32[],
optional_attr? : f32
)
```
where `?` means optional attribute and `[]` means array type.
Reviewed By: hanchung, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94240
This commit moves dangling ops in the main ops.td file to the proper
file matching their categories. This makes ops.td as purely including
all category files.
Differential Revision: https://reviews.llvm.org/D94413
Use TableGen and information in ACC.td for the Default enum in the OpenACC dialect.
This patch generalize what was done for OpenMP for directives.
Follow up patch after D93576
Reviewed By: kiranchandramohan
Differential Revision: https://reviews.llvm.org/D93710
This revision uniformizes fusion APIs to allow passing OpOperand, OpResult and adds a finer level of control fusion.
Differential Revision: https://reviews.llvm.org/D94493
Continue the convergence between LLVM dialect and built-in types by using the
built-in vector type whenever possible, that is for fixed vectors of built-in
integers and built-in floats. LLVM dialect vector type is still in use for
pointers, less frequent floating point types that do not have a built-in
equivalent, and scalable vectors. However, the top-level `LLVMVectorType` class
has been removed in favor of free functions capable of inspecting both built-in
and LLVM dialect vector types: `LLVM::getVectorElementType`,
`LLVM::getNumVectorElements` and `LLVM::getFixedVectorType`. Additional work is
necessary to design an implemented the extensions to built-in types so as to
remove the `LLVMFixedVectorType` entirely.
Note that the default output format for the built-in vectors does not have
whitespace around the `x` separator, e.g., `vector<4xf32>` as opposed to the
LLVM dialect vector type format that does, e.g., `!llvm.vec<4 x fp128>`. This
required changing the FileCheck patterns in several tests.
Reviewed By: mehdi_amini, silvas
Differential Revision: https://reviews.llvm.org/D94405
River Riddle