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
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
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
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
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
- 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
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
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
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
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
Alex Zinenko