Conversion to LLVM becomes confusing and incorrect if someone tries to lower
STD -> LLVM and only then GPULaunchFuncOp to LLVM separately. Although it is
technically allowed now, it works incorrectly because of the argument
promotion. The correct way to use this conversion pattern is to add to the
STD->LLVM patterns before running the pass.
Differential Revision: https://reviews.llvm.org/D88147
This tweaks the generated code for parsing attributes with a custom
directive to call `addAttribute` on the `OperationState` directly,
and adds a newline after this call. Previously, the generated code
would call `addAttribute` on the `OperationState` field `attributes`,
which has no such method and fails to compile. Furthermore, the lack
of newline would generate code with incorrectly formatted single line
`if` statements. Added tests for parsing and printing attributes with
a custom directive.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D87860
- Use TypeRange instead of ArrayRef<Type> where possible.
- Change some of the custom builders to also use TypeRange
Differential Revision: https://reviews.llvm.org/D87944
- Change the default builders to use TypeRange instead of ArrayRef<Type>
- Custom builders defined in LinalgStructuredOps now conflict with the default
separate param ones, but the default collective params one is still needed. Resolve
this by replicating the collective param builder as a custom builder and skipping
the generation of default builders for these ops.
Differential Revision: https://reviews.llvm.org/D87926
Blocks in a region and operations in a block are organized in a linked list.
The C API only provides functions to append or to insert elements at the
specified numeric position in the list. The latter is expensive since it
requires to traverse the list. Add insert before/after functionality with low
cost that relies on the iplist elements being convertible to iterators.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D88148
* The API is a bit more verbose than I feel like it needs to be. In a follow-up I'd like to abbreviate some things and look in to creating aliases for common accessors.
* There is a lingering lifetime hazard between the module and newly added operations. We have the facilities now to solve for this but I will do that in a follow-up.
* We may need to craft a more limited API for safely referencing successors when creating operations. We need more facilities to really prove that out and should defer for now.
Differential Revision: https://reviews.llvm.org/D87996
* Removes the half-completed prior attempt at region/block mutation in favor of new approach to ownership.
* Will re-add mutation more correctly in a follow-on.
* Eliminates the detached state on blocks and regions, simplifying the ownership hierarchy.
* Adds both iterator and index based access at each level.
Differential Revision: https://reviews.llvm.org/D87982
* Fixes a rather egregious bug with respect to the inability to return arbitrary objects from py::init (was causing aliasing of multiple py::object -> native instance).
* Makes Modules and Operations referencable types so that they can be reliably depended on.
* Uniques python operation instances within a context. Opens the door for further accounting.
* Next I will retrofit region and block to be dependent on the operation, and I will attempt to model the API to avoid detached regions/blocks, which will simplify things a lot (in that world, only operations can be detached).
* Added quite a bit of test coverage to check for leaks and reference issues.
* Supercedes: https://reviews.llvm.org/D87213
Differential Revision: https://reviews.llvm.org/D87958
A sequence of two reshapes such that one of them is just adding unit
extent dims can be folded to a single reshape.
Differential Revision: https://reviews.llvm.org/D88057
Instead of performing a transformation, such pass yields a new pass pipeline
to run on the currently visited operation.
This feature can be used for example to implement a sub-pipeline that
would run only on an operation with specific attributes. Another example
would be to compute a cost model and dynamic schedule a pipeline based
on the result of this analysis.
Discussion: https://llvm.discourse.group/t/rfc-dynamic-pass-pipeline/1637
Recommit after fixing an ASAN issue: the callback lambda needs to be
allocated to a temporary to have its lifetime extended to the end of the
current block instead of just the current call expression.
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D86392
The OpBuilder is required to start with OpBuilder and OperationState, so remove
the need for the user to specify it. To make it simpler to update callers,
retain the legacy behavior for now and skip injecting OpBuilder/OperationState
when params start with OpBuilder.
Related to bug 47442.
Differential Revision: https://reviews.llvm.org/D88050
This reverts commit 385c3f43fc.
Test mlir/test/Pass:dynamic-pipeline-fail-on-parent.mlir.test fails
when run with ASAN:
ERROR: AddressSanitizer: stack-use-after-scope on address ...
Reviewed By: bkramer, pifon2a
Differential Revision: https://reviews.llvm.org/D88079
Change the indexing map to iterate over the (b, x0, x1, z0, z1, q, k) instead of (b, x0, x1, k, q, z0, z1) to evaluate the convolution expression:
Y[b, x0, x1, k] = sum(W[z0, z1, q, k] * X[b, x0 + z0, x1 + z1, q], z0, z1, q)
This allows llvm auto vectorize to work and has better locality resulting significant performance improvments
Differential Revision: https://reviews.llvm.org/D87781
Instead of performing a transformation, such pass yields a new pass pipeline
to run on the currently visited operation.
This feature can be used for example to implement a sub-pipeline that
would run only on an operation with specific attributes. Another example
would be to compute a cost model and dynamic schedule a pipeline based
on the result of this analysis.
Discussion: https://llvm.discourse.group/t/rfc-dynamic-pass-pipeline/1637
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D86392
This patch adds a utility based on SuperVectorizer to vectorize an
affine loop nest using a given vectorization strategy. This strategy allows
targeting specific loops for vectorization instead of relying of the
SuperVectorizer analysis to choose the right loops to vectorize.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85869
Add attributes for the async, wait and self clauses. These clauses can be present without
values. When this is the case they are modelled with an attribute instead of operands.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87991
This adds support for the interface and provides unambigious information
on the control flow as it is unconditional on any runtime values.
The code is tested through confirming that buffer-placement behaves as
expected.
Differential Revision: https://reviews.llvm.org/D87894
This crash only happens when a function pass is followed by a module
pass. In this case the splitting of the pass pipeline didn't handle
properly the verifier passes and ended up with an odd number of pass in
the pipeline, breaking an assumption of the local crash reproducer
executor and hitting an assertion.
Differential Revision: https://reviews.llvm.org/D88000
Vendor/device information are not resource limits. Moving to
target environment directly for better organization.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D87911
* Per thread https://llvm.discourse.group/t/revisiting-ownership-and-lifetime-in-the-python-bindings/1769
* Reworks contexts so it is always possible to get back to a py::object that holds the reference count for an arbitrary MlirContext.
* Retrofits some of the base classes to automatically take a reference to the context, elimintating keep_alives.
* More needs to be done, as discussed, when moving on to the operations/blocks/regions.
Differential Revision: https://reviews.llvm.org/D87886
I realized when using this that one can't get very good error messages
without an additional message attribute.
Differential Revision: https://reviews.llvm.org/D87875
constBuilderCall was not defined for TypeArrayAttr, resulting in tblgen not emitting the correct code when TypeArrayAttr is used with a default valued attribute.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D87907
Add missing operands to represent copyin with readonly modifier, copyout with zero modifier
and create with zero modifier.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87874
Following patch D87712, this patch switch AnyInteger for operands gangNum, gangStatic,
workerNum, vectoreLength and tileOperands to Index and AnyInteger.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87848
This revision allows representing a reduction at the level of linalg on tensors for named ops. When a structured op has a reduction and returns tensor(s), new conventions are added and documented.
As an illustration, the syntax for a `linalg.matmul` writing into a buffer is:
```
linalg.matmul ins(%a, %b : memref<?x?xf32>, tensor<?x?xf32>)
outs(%c : memref<?x?xf32>)
```
, whereas the syntax for a `linalg.matmul` returning a new tensor is:
```
%d = linalg.matmul ins(%a, %b : tensor<?x?xf32>, memref<?x?xf32>)
init(%c : memref<?x?xf32>)
-> tensor<?x?xf32>
```
Other parts of linalg will be extended accordingly to allow mixed buffer/tensor semantics in the presence of reductions.
This op is a catch-all for creating witnesses from various random kinds
of constraints. In particular, I when dealing with extents directly,
which are of `index` type, one can directly use std ops for calculating
the predicates, and then use cstr_require for the final conversion to a
witness.
Differential Revision: https://reviews.llvm.org/D87871
- Change OpClass new method addition to find and eliminate any existing methods that
are made redundant by the newly added method, as well as detect if the newly added
method will be redundant and return nullptr in that case.
- To facilitate that, add the notion of resolved and unresolved parameters, where resolved
parameters have each parameter type known, so that redundancy checks on methods
with same name but different parameter types can be done.
- Eliminate existing code to avoid adding conflicting/redundant build methods and rely
on this new mechanism to eliminate conflicting build methods.
Fixes https://bugs.llvm.org/show_bug.cgi?id=47095
Differential Revision: https://reviews.llvm.org/D87059
Add support to tile affine.for ops with parametric sizes (i.e., SSA
values). Currently supports hyper-rectangular loop nests with constant
lower bounds only. Move methods
- moveLoopBody(*)
- getTileableBands(*)
- checkTilingLegality(*)
- tilePerfectlyNested(*)
- constructTiledIndexSetHyperRect(*)
to allow reuse with constant tile size API. Add a test pass -test-affine
-parametric-tile to test parametric tiling.
Differential Revision: https://reviews.llvm.org/D87353
Add support for return values in affine.for yield along the same lines
as scf.for and affine.parallel.
Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D87437
Fold the operation if the source is a scalar constant or splat constant.
Update transform-patterns-matmul-to-vector.mlir because the broadcast ops are folded in the conversion.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D87703
This patch change the type of operands async, wait, numGangs, numWorkers and vectorLength from index
to AnyInteger to fit with acc.loop and the OpenACC specification.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87712
Adds a pattern that replaces a chain of two tensor_cast operations by a single tensor_cast operation if doing so will not remove constraints on the shapes.
C API test uses FileCheck comments inside C code and needs to
temporarily switch off clang-format to prevent it from messing with
FileCheck directives. A recently landed commit forgot to turn it back on
after a block of FileCheck comments. Fix that.
ConvOp vectorization supports now only convolutions of static shapes with dimensions
of size either 3(vectorized) or 1(not) as underlying vectors have to be of static
shape as well. In this commit we add support for convolutions of any size as well as
dynamic shapes by leveraging existing matmul infrastructure for tiling of both input
and kernel to sizes accepted by the previous version of ConvOp vectorization.
In the future this pass can be extended to take "tiling mask" as a user input which
will enable vectorization of user specified dimensions.
Differential Revision: https://reviews.llvm.org/D87676
This patch provides C API for MLIR affine map.
- Implement C API for AffineMap class.
- Add Utils.h to include/mlir/CAPI/, and move the definition of the CallbackOstream to Utils.h to make sure mlirAffineMapPrint work correct.
- Add TODO for exposing the C API related to AffineExpr and mutable affine map.
Differential Revision: https://reviews.llvm.org/D87617
Add missing operands to represent copin with readonly modifier, copyout with zero
modifier, create with zero modifier and default clause.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87733
Numerous MLIR functions return instances of `StringRef` to refer to a
non-owning fragment of a string (usually owned by the context). This is a
relatively simple class that is defined in LLVM. Provide a simple wrapper in
the MLIR C API that contains the pointer and length of the string fragment and
use it for Standard attribute functions that return StringRef instead of the
previous, callback-based mechanism.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D87677
Add a verifier for the loop op in the OpenACC dialect. Check basic restriction
from 2.9 Loop construct from the OpenACC 3.0 specs.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87546
This patch adds the missing print for the vector_length in the parallel operation.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87630
This add canonicalizer for
- extracting an element from a dynamic_tensor_from_elements
- propagating constant operands to the type of dynamic_tensor_from_elements
Differential Revision: https://reviews.llvm.org/D87525
When packing function results into a structure during the standard-to-llvm
dialect conversion, do not assume the conversion was successful and propagate
nullptr as error state.
Fixes PR45184.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D87605
Now backends spell out which namespace they want to be in, instead of relying on
clients #including them inside already-opened namespaces. This also means that
cppNamespaces should be fully qualified, and there's no implicit "::mlir::"
prepended to them anymore.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D86811
Added support to the Std dialect cast operations to do casts in vector types when feasible.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87410
Type converter may fail and return nullptr on unconvertible types. The function
conversion did not include a check and was attempting to use a nullptr type to
construct an LLVM function, leading to a crash. Add a check and return early.
The rest of the call stack propagates errors properly.
Fixes PR47403.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D87075
This introduces a builder for the more general case that supports zero
elements (where the element type can't be inferred from the ValueRange,
since it might be empty).
Also, fix up some cases in ShapeToStandard lowering that hit this. It
happens very easily when dealing with shapes of 0-D tensors.
The SameOperandsAndResultElementType is redundant with the new
TypesMatchWith and prevented having zero elements.
Differential Revision: https://reviews.llvm.org/D87492
Addressed some CR issues pointed out in D87111. Formatting and other nits.
The original Diff D87111 - Add an option for unrolling loops up to a factor.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D87313
This revision refactors and cleans up a bunch of things to simplify StructuredOpInterface
before work can proceed on Linalg on tensors:
- break out pieces of the StructuredOps trait that are part of the StructuredOpInterface,
- drop referenceIterators and referenceIndexingMaps that end up being more confusing than useful,
- drop NamedStructuredOpTrait
Previously only the input type was printed, and the parser applied it to
both input and output, creating an invalid transpose. Print and parse
both types, and verify that they match.
Differential Revision: https://reviews.llvm.org/D87462
This patch adds a new named structured op to accompany linalg.matmul and
linalg.matvec. We needed it for our codegen, so I figured it would be useful
to add it to Linalg.
Reviewed By: nicolasvasilache, mravishankar
Differential Revision: https://reviews.llvm.org/D87292
Rationale:
After some discussion we decided that it is safe to assume 32-bit
indices for all subscripting in the vector dialect (it is unlikely
the dialect will be used; or even work; for such long vectors).
So rather than detecting specific situations that can exploit
32-bit indices with higher parallel SIMD, we just optimize it
by default, and let users that don't want it opt-out.
Reviewed By: nicolasvasilache, bkramer
Differential Revision: https://reviews.llvm.org/D87404
I was having a lot of trouble parsing the messages. In particular, the
messages like:
```
<stdin>:3:8: error: 'scf.if' op along control flow edge from Region #0 to scf.if source #1 type '!npcomprt.tensor' should match input #1 type 'tensor<?xindex>'
```
In particular, one thing that kept catching me was parsing the "to scf.if
source #1 type" as one thing, but really it is
"to parent results: source type #1".
Differential Revision: https://reviews.llvm.org/D87334
This commit specifies reduction dimensions for ConvOps. This prevents
running reduction loops in parallel and enables easier detection of kernel dimensions
which we will need later on.
Differential Revision: https://reviews.llvm.org/D87288
The current BufferPlacement transformation cannot handle loops properly. Buffers
passed via backedges will not be freed automatically introducing memory leaks.
This CL adds support for loops to overcome these limitations.
Differential Revision: https://reviews.llvm.org/D85513
Take advantage of the new `dynamic_tensor_from_elements` operation in `std`.
Instead of stack-allocated memory, we can now lower directly to a single `std`
operation.
Differential Revision: https://reviews.llvm.org/D86935
Currently, there is no option to allow for unrolling a loop up to a specific factor (specified by the user).
The code for doing that is there and there are benefits when unrolling is done to smaller loops (smaller than the factor specified).
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D87111
This replaces the select chain for edge-padding with an scf.if that
performs the memory operation when the index is in bounds and uses the
pad value when it's not. For transfer_write the same mechanism is used,
skipping the store when the index is out of bounds.
The integration test has a bunch of cases of how I believe this should
work.
Differential Revision: https://reviews.llvm.org/D87241
In this commit a new way of convolution ops lowering is introduced.
The conv op vectorization pass lowers linalg convolution ops
into vector contractions. This lowering is possible when conv op
is first tiled by 1 along specific dimensions which transforms
it into dot product between input and kernel subview memory buffers.
This pass converts such conv op into vector contraction and does
all necessary vector transfers that make it work.
Differential Revision: https://reviews.llvm.org/D86619
With `dynamic_tensor_from_elements` tensor values of dynamic size can be
created. The body of the operation essentially maps the index space to tensor
elements.
Declare SCF operations in the `scf` namespace to avoid name clash with the new
`std.yield` operation. Resolve ambiguities between `linalg/shape/std/scf.yield`
operations.
Differential Revision: https://reviews.llvm.org/D86276
Vector to SCF conversion still had issues due to the interaction with the natural alignment derived by the LLVM data layout. One traditional workaround is to allocate aligned. However, this does not always work for vector sizes that are non-powers of 2.
This revision implements a more portable mechanism where the intermediate allocation is always a memref of elemental vector type. AllocOp is extended to use the natural LLVM DataLayout alignment for non-scalar types, when the alignment is not specified in the first place.
An integration test is added that exercises the transfer to scf.for + scalar lowering with a 5x5 transposition.
Differential Revision: https://reviews.llvm.org/D87150
* Resolves todos from D87091.
* Also modifies PyConcreteAttribute to follow suite (should be useful for ElementsAttr and friends).
* Adds a test to ensure that the ShapedType base class functions as expected.
Differential Revision: https://reviews.llvm.org/D87208
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Shaped Type, Tensor Type and MemRef Type subclasses.
The Tensor Type and MemRef Type are bound as ranked and unranked separately.
This patch adds the ***GetChecked C API to make sure the python side can get a valid type or a nullptr.
Shaped type is not a kind of standard types, it is the base class for vectors, memrefs and tensors, this patch binds the PyShapedType class as the base class of Vector Type, Tensor Type and MemRef Type subclasses.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D87091
Historically, the operations in the MLIR's LLVM dialect only checked that the
operand are of LLVM dialect type without more detailed constraints. This was
due to LLVM dialect types wrapping LLVM IR types and having clunky verification
methods. With the new first-class modeling, it is possible to define type
constraints similarly to other dialects and use them to enforce some
correctness rules in verifiers instead of having LLVM assert during translation
to LLVM IR. This hardening discovered several issues where MLIR was producing
LLVM dialect operations that cannot exist in LLVM IR.
Depends On D85900
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85901
When allowed, use 32-bit indices rather than 64-bit indices in the
SIMD computation of masks. This runs up to 2x and 4x faster on
a number of AVX2 and AVX512 microbenchmarks.
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D87116
Sizes of tiles (subviews) are bigger by 1 than they should. Let's consider
1D convolution without batches or channels. Furthermore let m iterate over
the output and n over the kernel then input is accessed with m + n. In tiling
subview sizes for convolutions are computed by applying requested tile size
together with kernel size to the above mentioned expression thus let's say
for tile size of 2 the subview size is 2 + size(n), which is bigger by one
than it should since we move kernel only once. The problem behind it is that
range is not turned into closed interval before the composition. This commit
fixes the problem by turning ranges first into closed intervals by substracting
1 and after the composition back to half open by adding 1.
Differential Revision: https://reviews.llvm.org/D86638
This patch adds basic support for vectorization of uniform values to SuperVectorizer.
For now, only invariant values to the target vector loops are considered uniform. This
enables the vectorization of loops that use function arguments and external definitions
to the vector loops. We could extend uniform support in the future if we implement some
kind of divergence analysis algorithm.
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D86756
Make use of affine memory op interfaces in AffineLoopInvariantCodeMotion so
that it can also work on affine.vector_load and affine.vector_store ops.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D86986
Make sure that memory ops that are defined inside the loop are registered
as such in 'defineOp'. In the test provided, the 'mulf' op was hoisted
outside the loop nest even when its 'affine.load' operand was not.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D86982
In this PR, the users of BufferPlacement can configure
BufferAssginmentTypeConverter. These new configurations would give the user more
freedom in the process of converting function signature, and return and call
operation conversions.
These are the new features:
- Accepting callback functions for decomposing types (i.e. 1 to N type
conversion such as unpacking tuple types).
- Defining ResultConversionKind for specifying whether a function result
with a certain type should be appended to the function arguments list or
should be kept as function result. (Usage:
converter.setResultConversionKind<MemRefType>(AppendToArgumentList))
- Accepting callback functions for composing or decomposing values (i.e. N
to 1 and 1 to N value conversion).
Differential Revision: https://reviews.llvm.org/D85133
This reverts commit 94f5d24877 because
of failing the following tests:
MLIR :: Dialect/Linalg/tensors-to-buffers.mlir
MLIR :: Transforms/buffer-placement-preparation-allowed-memref-results.mlir
MLIR :: Transforms/buffer-placement-preparation.mlir
Added 128 byte alignment to alloc ops created in VectorToSCF pass.
128b alignment was already introduced to this pass but not to all alloc
ops. This commit changes that by adding 128b alignment to the remaining ops.
The point of specifying alignment is to prevent possible memory alignment errors
on weakly tested architectures.
Differential Revision: https://reviews.llvm.org/D86454
In this PR, the users of BufferPlacement can configure
BufferAssginmentTypeConverter. These new configurations would give the user more
freedom in the process of converting function signature, and return and call
operation conversions.
These are the new features:
- Accepting callback functions for decomposing types (i.e. 1 to N type
conversion such as unpacking tuple types).
- Defining ResultConversionKind for specifying whether a function result
with a certain type should be appended to the function arguments list or
should be kept as function result. (Usage:
converter.setResultConversionKind<MemRefType>(AppendToArgumentList))
- Accepting callback functions for composing or decomposing values (i.e. N
to 1 and 1 to N value conversion).
Differential Revision: https://reviews.llvm.org/D85133
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Complex Type, Vector Type and Tuple Type subclasses.
For the convenience of type checking, this patch defines a `mlirTypeIsAIntegerOrFloat` function to check whether the given type is an integer or float type.
These three subclasses in this patch have similar binding strategy:
- The function pointer `isaFunction` points to `mlirTypeIsA***`.
- The `mlir***TypeGet` C API is bound with the `get_***` method in the python side.
- The Complex Type and Vector Type check whether the given type is an integer or float type.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D86785
Unsigned and Signless attributes use uintN_t and signed attributes use intN_t, where N is the fixed width. The 1-bit variants use bool.
Differential Revision: https://reviews.llvm.org/D86739
This patch add the missing operands to the acc.loop operation. Only the device_type
information is not part of the operation for now.
Reviewed By: rriddle, kiranchandramohan
Differential Revision: https://reviews.llvm.org/D86753
This adds some initial support for regions and does not support formatting the specific arguments of a region. For now this can be achieved by using a custom directive that formats the arguments and then parses the region.
Differential Revision: https://reviews.llvm.org/D86760
Symbol names are a special form of StringAttr that get treated specially in certain areas, such as formatting. This revision adds a special derived attr for them in ODS and adds support in the assemblyFormat for formatting them properly.
Differential Revision: https://reviews.llvm.org/D86759
This revision adds support for custom directives to the declarative assembly format. This allows for users to use C++ for printing and parsing subsections of an otherwise declaratively specified format. The custom directive is structured as follows:
```
custom-directive ::= `custom` `<` UserDirective `>` `(` Params `)`
```
`user-directive` is used as a suffix when this directive is used during printing and parsing. When parsing, `parseUserDirective` will be invoked. When printing, `printUserDirective` will be invoked. The first parameter to these methods must be a reference to either the OpAsmParser, or OpAsmPrinter. The type of rest of the parameters is dependent on the `Params` specified in the assembly format.
Differential Revision: https://reviews.llvm.org/D84719
* This is just enough to create regions/blocks and iterate over them.
* Does not yet implement the preferred iteration strategy (python pseudo containers).
* Refinements need to come after doing basic mappings of operations and values so that the whole hierarchy can be used.
Differential Revision: https://reviews.llvm.org/D86683
The tensor_reshape op was only fusible only if it is a collapsing case. Now we
propagate the op to all the operands so there is a further chance to fuse it
with generic op. The pre-conditions are:
1) The producer is not an indexed_generic op.
2) All the shapes of the operands are the same.
3) All the indexing maps are identity.
4) All the loops are parallel loops.
5) The producer has a single user.
It is possible to fuse the ops if the producer is an indexed_generic op. We
still can compute the original indices. E.g., if the reshape op collapses the d0
and d1, we can use DimOp to get the width of d1, and calculate the index
`d0 * width + d1`. Then replace all the uses with it. However, this pattern is
not implemented in the patch.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86314
The prior diff that introduced `addAffineIfOpDomain` missed appending
constraints from the ifOp domain. This revision fixes this problem.
Differential Revision: https://reviews.llvm.org/D86421
Adding a conversion pattern for the parallel Operation. This will
help the conversion of parallel operation with standard dialect to
parallel operation with llvm dialect. The type conversion of the block
arguments in a parallel region are controlled by the pattern for the
parallel Operation. Without this pattern, a parallel Operation with
block arguments cannot be converted from standard to LLVM dialect.
Other OpenMP operations without regions are marked as legal. When
translation of OpenMP operations with regions are added then patterns
for these operations can also be added.
Also uses all the standard to llvm patterns. Patterns of other dialects
can be added later if needed.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D86273
When dealing with dialects that will results in function calls to
external libraries, it is important to be able to handle maps as some
dialects may require mapped data. Before this patch, the detection of
whether normalization can apply or not, operations are compared to an
explicit list of operations (`alloc`, `dealloc`, `return`) or to the
presence of specific operation interfaces (`AffineReadOpInterface`,
`AffineWriteOpInterface`, `AffineDMAStartOp`, or `AffineDMAWaitOp`).
This patch add a trait, `MemRefsNormalizable` to determine if an
operation can have its `memrefs` normalized.
This trait can be used in turn by dialects to assert that such
operations are compatible with normalization of `memrefs` with
nontrivial memory layout specification. An example is given in the
literal tests.
Differential Revision: https://reviews.llvm.org/D86236
This patch allows to pass the gpu module name to SPIR-V
module during conversion. This has many benefits as we can lookup
converted to SPIR-V kernel in the symbol table.
In order to avoid symbol conflicts, `"__spv__"` is added to the
gpu module name to form the new one.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86384
This patch introduces a hook to encode descriptor set
and binding number into `spv.globalVariable`'s symbolic name. This
allows to preserve this information, and at the same time legalize
the global variable for the conversion to LLVM dialect.
This is required for `mlir-spirv-cpu-runner` to convert kernel
arguments into LLVM.
Also, a couple of some nits added:
- removed unused comment
- changed to a capital letter in the comment
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86515
This patch adds an optional name to SPIR-V module.
This will help with lowering from GPU dialect (so that we
can pass the kernel module name) and will be more naturally
aligned with `GPUModuleOp`/`ModuleOp`.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86386
As discussed in D86576, noundef attribute is removed from masked store/load/gather/scatter's
pointer operands.
Reviewed By: efriedma
Differential Revision: https://reviews.llvm.org/D86656
This patch adds NoUndef to Intrinsics.td.
The attribute is attached to llvm.assume's operand, because llvm.assume(undef)
is UB.
It is attached to pointer operands of several memory accessing intrinsics
as well.
This change makes ValueTracking::getGuaranteedNonPoisonOps' intrinsic check
unnecessary, so it is removed.
Reviewed By: jdoerfert
Differential Revision: https://reviews.llvm.org/D86576
The PDL Interpreter dialect provides a lower level abstraction compared to the PDL dialect, and is targeted towards low level optimization and interpreter code generation. The dialect operations encapsulates low-level pattern match and rewrite "primitives", such as navigating the IR (Operation::getOperand), creating new operations (OpBuilder::create), etc. Many of the operations within this dialect also fuse branching control flow with some form of a predicate comparison operation. This type of fusion reduces the amount of work that an interpreter must do when executing.
An example of this representation is shown below:
```mlir
// The following high level PDL pattern:
pdl.pattern : benefit(1) {
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite %root {
pdl.replace %root with (%inputOperand)
}
}
// May be represented in the interpreter dialect as follows:
module {
func @matcher(%arg0: !pdl.operation) {
pdl_interp.check_operation_name of %arg0 is "foo.op" -> ^bb2, ^bb1
^bb1:
pdl_interp.return
^bb2:
pdl_interp.check_operand_count of %arg0 is 1 -> ^bb3, ^bb1
^bb3:
pdl_interp.check_result_count of %arg0 is 1 -> ^bb4, ^bb1
^bb4:
%0 = pdl_interp.get_operand 0 of %arg0
pdl_interp.is_not_null %0 : !pdl.value -> ^bb5, ^bb1
^bb5:
%1 = pdl_interp.get_result 0 of %arg0
pdl_interp.is_not_null %1 : !pdl.value -> ^bb6, ^bb1
^bb6:
pdl_interp.record_match @rewriters::@rewriter(%0, %arg0 : !pdl.value, !pdl.operation) : benefit(1), loc([%arg0]), root("foo.op") -> ^bb1
}
module @rewriters {
func @rewriter(%arg0: !pdl.value, %arg1: !pdl.operation) {
pdl_interp.replace %arg1 with(%arg0)
pdl_interp.return
}
}
}
```
Differential Revision: https://reviews.llvm.org/D84579
Provides fast, generic way of setting a mask up to a certain
point. Potential use cases that may benefit are create_mask
and transfer_read/write operations in the vector dialect.
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D86501
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Index Type, Floating Point Type and None Type subclasses.
These three subclasses share the same binding strategy:
- The function pointer `isaFunction` points to `mlirTypeIsA***`.
- The `mlir***TypeGet` C API is bound with the `***Type` constructor in the python side.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86466
* Generic mlir.ir.Attribute class.
* First standard attribute (mlir.ir.StringAttr), following the same pattern as generic vs standard types.
* NamedAttribute class.
Differential Revision: https://reviews.llvm.org/D86250
Binding MemRefs of f16 needs special handling as the type is not supported on
CPU. There was a bug in the type used.
Differential Revision: https://reviews.llvm.org/D86328
Add a folder to the affine.parallel op so that loop bounds expressions are canonicalized.
Additionally, a new AffineParallelNormalizePass is added to adjust affine.parallel ops so that the lower bound is always 0 and the upper bound always represents a range with a step size of 1.
Differential Revision: https://reviews.llvm.org/D84998
Removed the Standard to LLVM conversion patterns that were previously
pulled in for testing purposes. This helps to separate the conversion
to LLVM dialect of the MLIR module with both SPIR-V and Standard
dialects in it (particularly helpful for SPIR-V cpu runner). Also,
tests were changed accordingly.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86285
- This utility to merge a block anywhere into another one can help inline single
block regions into other blocks.
- Modified patterns test to use the new function.
Differential Revision: https://reviews.llvm.org/D86251
Add the unsigned complements to the existing FPToSI and SIToFP operations in the
standard dialect, with one-to-one lowerings to the corresponding LLVM operations.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85557
PDL presents a high level abstraction for the rewrite pattern infrastructure available in MLIR. This abstraction allows for representing patterns transforming MLIR, as MLIR. This allows for applying all of the benefits that the general MLIR infrastructure provides, to the infrastructure itself. This means that pattern matching can be more easily verified for correctness, targeted by frontends, and optimized.
PDL abstracts over various different aspects of patterns and core MLIR data structures. Patterns are specified via a `pdl.pattern` operation. These operations contain a region body for the "matcher" code, and terminate with a `pdl.rewrite` that either dispatches to an external rewriter or contains a region for the rewrite specified via `pdl`. The types of values in `pdl` are handle types to MLIR C++ types, with `!pdl.attribute`, `!pdl.operation`, and `!pdl.type` directly mapping to `mlir::Attribute`, `mlir::Operation*`, and `mlir::Value` respectively.
An example pattern is shown below:
```mlir
// pdl.pattern contains metadata similarly to a `RewritePattern`.
pdl.pattern : benefit(1) {
// External input operand values are specified via `pdl.input` operations.
// Result types are constrainted via `pdl.type` operations.
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite(%root) {
pdl.replace %root with (%inputOperand)
}
}
```
This is a culmination of the work originally discussed here: https://groups.google.com/a/tensorflow.org/g/mlir/c/j_bn74ByxlQ
Differential Revision: https://reviews.llvm.org/D84578
Provide C API for MLIR standard attributes. Since standard attributes live
under lib/IR in core MLIR, place the C APIs in the IR library as well (standard
ops will go in a separate library).
Affine map and integer set attributes are only exposed as placeholder types
with IsA support due to the lack of C APIs for the corresponding types.
Integer and floating point attribute APIs expecting APInt and APFloat are not
exposed pending decision on how to support APInt and APFloat.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86143
* The binding for Type is trivial and should be non-controversial.
* The way that I define the IntegerType should serve as a pattern for what I want to do next.
* I propose defining the rest of the standard types in this fashion and then generalizing for dialect types as necessary.
* Essentially, creating/accessing a concrete Type (vs interacting with the string form) is done by "casting" to the concrete type (i.e. IntegerType can be constructed with a Type and will throw if the cast is illegal).
* This deviates from some of our previous discussions about global objects but I think produces a usable API and we should go this way.
Differential Revision: https://reviews.llvm.org/D86179
If Memref has rank > 1 this pass emits N-1 loops around
TransferRead op and transforms the op itself to 1D read. Since vectors
must have static shape while memrefs don't the pass emits if condition
to prevent out of bounds accesses in case some memref dimension is smaller
than the corresponding dimension of targeted vector. This logic is fine
but authors forgot to apply `permutation_map` on loops upper bounds and
thus if condition compares induction variable to incorrect loop upper bound
(dimension of the memref) in case `permutation_map` is not identity map.
This commit aims to fix that.
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This greatly simplifies a large portion of the underlying infrastructure, allows for lookups of singleton classes to be much more efficient and always thread-safe(no locking). As a result of this, the dialect symbol registry has been removed as it is no longer necessary.
For users broken by this change, an alert was sent out(https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types) that helps prevent a majority of the breakage surface area. All that should be necessary, if the advice in that alert was followed, is removing the kind passed to the ::get methods.
Differential Revision: https://reviews.llvm.org/D86121
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
LinalgDistribution options to allow more general distributions.
Changing the signature of the callback to send in the ranges for all
the parallel loops and expect a vector with the Value to use for the
processor-id and number-of-processors for each of the parallel loops.
Differential Revision: https://reviews.llvm.org/D86095
There should be an equivalent std.floor op to std.ceil. This includes
matching lowerings for SPIRV, NVVM, ROCDL, and LLVM.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85940
Create a reduction pass that accepts an optimization pass as argument
and only replaces the golden module in the pipeline if the output of the
optimization pass is smaller than the input and still exhibits the
interesting behavior.
Add a -test-pass option to test individual passes in the MLIR Reduce
tool.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D84783
This patch adds more op/type conversion support
necessary for `spirv-runner`:
- EntryPoint/ExecutionMode: currently removed since we assume
having only one kernel function in the kernel module.
- StorageBuffer storage class is now supported. We are not
concerned with multithreading so this is fine for now.
- Type conversion enhanced, now regular offsets and strides
for structs and arrays are supported (based on
`VulkanLayoutUtils`).
- Support of `spc.AccessChain` that is modelled with GEP op
in LLVM dialect.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86109
When the operand to the linalg.tensor_reshape op is a splat constant,
the result can be replaced with a splat constant of the same value but
different type.
Differential Revision: https://reviews.llvm.org/D86117
Provide C API for MLIR standard types. Since standard types live under lib/IR
in core MLIR, place the C APIs in the IR library as well (standard ops will go
into a separate library). This also defines a placeholder for affine maps that
are necessary to construct a memref, but are not yet exposed to the C API.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86094
According to the LLVM Language Reference, 'cmpxchg' accepts integer or pointer
types. Several MLIR tests were using it with floats as it appears possible to
programmatically construct and print such an instruction, but it cannot be
parsed back. Use integers instead.
Depends On D85899
Reviewed By: flaub, rriddle
Differential Revision: https://reviews.llvm.org/D85900
Legacy implementation of the LLVM dialect in MLIR contained an instance of
llvm::Module as it was required to parse LLVM IR types. The access to the data
layout of this module was exposed to the users for convenience, but in practice
this layout has always been the default one obtained by parsing an empty layout
description string. Current implementation of the dialect no longer relies on
wrapping LLVM IR types, but it kept an instance of DataLayout for
compatibility. This effectively forces a single data layout to be used across
all modules in a given MLIR context, which is not desirable. Remove DataLayout
from the LLVM dialect and attach it as a module attribute instead. Since MLIR
does not yet have support for data layouts, use the LLVM DataLayout in string
form with verification inside MLIR. Introduce the layout when converting a
module to the LLVM dialect and keep the default "" description for
compatibility.
This approach should be replaced with a proper MLIR-based data layout when it
becomes available, but provides an immediate solution to compiling modules with
different layouts, e.g. for GPUs.
This removes the need for LLVMDialectImpl, which is also removed.
Depends On D85650
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D85652
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
The convresion of memref cast operaitons from the Standard dialect to the LLVM
dialect has been emitting bitcasts from a struct type to itself. Beyond being
useless, such casts are invalid as bitcast does not operate on aggregate types.
This kept working by accident because LLVM IR bitcast construction API skips
the construction if types are equal before it verifies that the types are
acceptable in a bitcast. Do not emit such bitcasts, the memref cast that only
adds/erases size information is in fact a noop on the current descriptor as it
always contains dynamic values for all sizes.
Reviewed By: pifon2a
Differential Revision: https://reviews.llvm.org/D85899
Masked loading/storing in various forms can be optimized
into simpler memory operations when the mask is all true
or all false. Note that the backend does similar optimizations
but doing this early may expose more opportunities for further
optimizations. This further prepares progressively lowering
transfer read and write into 1-D memory operations.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D85769
This exercises the corner case that was fixed in
https://reviews.llvm.org/rG8979a9cdf226066196f1710903d13492e6929563.
The bug can be reproduced when there is a @callee with a custom type argument and @caller has a producer of this argument passed to the @callee.
Example:
func @callee(!test.test_type) -> i32
func @caller() -> i32 {
%arg = "test.type_producer"() : () -> !test.test_type
%out = call @callee(%arg) : (!test.test_type) -> i32
return %out : i32
}
Even though there is a type conversion for !test.test_type, the output IR (before the fix) contained a DialectCastOp:
module {
llvm.func @callee(!llvm.ptr<i8>) -> !llvm.i32
llvm.func @caller() -> !llvm.i32 {
%0 = llvm.mlir.null : !llvm.ptr<i8>
%1 = llvm.mlir.cast %0 : !llvm.ptr<i8> to !test.test_type
%2 = llvm.call @callee(%1) : (!test.test_type) -> !llvm.i32
llvm.return %2 : !llvm.i32
}
}
instead of
module {
llvm.func @callee(!llvm.ptr<i8>) -> !llvm.i32
llvm.func @caller() -> !llvm.i32 {
%0 = llvm.mlir.null : !llvm.ptr<i8>
%1 = llvm.call @callee(%0) : (!llvm.ptr<i8>) -> !llvm.i32
llvm.return %1 : !llvm.i32
}
}
Differential Revision: https://reviews.llvm.org/D85914
-- This commit handles the returnOp in memref map layout normalization.
-- An initial filter is applied on FuncOps which helps us know which functions can be
a suitable candidate for memref normalization which doesn't lead to invalid IR.
-- Handles memref map normalization for external function assuming the external function
is normalizable.
Differential Revision: https://reviews.llvm.org/D85226
Provide printing functions for most IR objects in C API (except Region that
does not have a `print` function, and Module that is expected to be printed as
Operation instead). The printing is based on a callback that is called with
chunks of the string representation and forwarded user-defined data.
Reviewed By: stellaraccident, Jing, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85748
This patch adds the translation of the proc_bind clause in a
parallel operation.
The values that can be specified for the proc_bind clause are
specified in the OMP.td tablegen file in the llvm/Frontend/OpenMP
directory. From this single source of truth enumeration for
proc_bind is generated in llvm and mlir (used in specification of
the parallel Operation in the OpenMP dialect). A function to return
the enum value from the string representation is also generated.
A new header file (DirectiveEmitter.h) containing definitions of
classes directive, clause, clauseval etc is created so that it can
be used in mlir as well.
Reviewers: clementval, jdoerfert, DavidTruby
Differential Revision: https://reviews.llvm.org/D84347
Inital conversion of `spv._address_of` and `spv.globalVariable`.
In SPIR-V, the global returns a pointer, whereas in LLVM dialect
the global holds an actual value. This difference is handled by
`spv._address_of` and `llvm.mlir.addressof`ops that both return
a pointer. Moreover, only current invocation is in conversion's
scope.
Reviewed By: antiagainst, mravishankar
Differential Revision: https://reviews.llvm.org/D84626
Now that LLVM dialect types are implemented directly in the dialect, we can use
MLIR hooks for verifying type construction invariants. Implement the verifiers
and use them in the parser.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85663
Linalg to processors.
This changes adds infrastructure to distribute the loops generated in
Linalg to processors at the time of generation. This addresses use
case where the instantiation of loop is done just to distribute
them. The option to distribute is added to TilingOptions for now and
will allow specifying the distribution as a transformation option,
just like tiling and promotion are specified as options.
Differential Revision: https://reviews.llvm.org/D85147
- Fix ODS framework to suppress build methods that infer result types and are
ambiguous with collective variants. This applies to operations with a single variadic
inputs whose result types can be inferred.
- Extended OpBuildGenTest to test these kinds of ops.
Differential Revision: https://reviews.llvm.org/D85060
This diff attempts to resolve the TODO in `getOpIndexSet` (formerly
known as `getInstIndexSet`), which states "Add support to handle IfInsts
surronding `op`".
Major changes in this diff:
1. Overload `getIndexSet`. The overloaded version considers both
`AffineForOp` and `AffineIfOp`.
2. The `getInstIndexSet` is updated accordingly: its name is changed to
`getOpIndexSet` and its implementation is based on a new API `getIVs`
instead of `getLoopIVs`.
3. Add `addAffineIfOpDomain` to `FlatAffineConstraints`, which extracts
new constraints from the integer set of `AffineIfOp` and merges it to
the current constraint system.
4. Update how a `Value` is determined as dim or symbol for
`ValuePositionMap` in `buildDimAndSymbolPositionMaps`.
Differential Revision: https://reviews.llvm.org/D84698
This patch also fixes a minor issue that shape.rank should allow
returning !shape.size. The dialect doc has such an example for
shape.rank.
Differential Revision: https://reviews.llvm.org/D85556
This reverts commit 9f24640b7e.
We hit some dead-locks on thread exit in some configurations: TLS exit handler is taking a lock.
Temporarily reverting this change as we're debugging what is going on.
This revision aims to provide a new API, `checkTilingLegality`, to
verify that the loop tiling result still satisifes the dependence
constraints of the original loop nest.
Previously, there was no check for the validity of tiling. For instance:
```
func @diagonal_dependence() {
%A = alloc() : memref<64x64xf32>
affine.for %i = 0 to 64 {
affine.for %j = 0 to 64 {
%0 = affine.load %A[%j, %i] : memref<64x64xf32>
%1 = affine.load %A[%i, %j - 1] : memref<64x64xf32>
%2 = addf %0, %1 : f32
affine.store %2, %A[%i, %j] : memref<64x64xf32>
}
}
return
}
```
You can find more information about this example from the Section 3.11
of [1].
In general, there are three types of dependences here: two flow
dependences, one in direction `(i, j) = (0, 1)` (notation that depicts a
vector in the 2D iteration space), one in `(i, j) = (1, -1)`; and one
anti dependence in the direction `(-1, 1)`.
Since two of them are along the diagonal in opposite directions, the
default tiling method in `affine`, which tiles the iteration space into
rectangles, will violate the legality condition proposed by Irigoin and
Triolet [2]. [2] implies two tiles cannot depend on each other, while in
the `affine` tiling case, two rectangles along the same diagonal are
indeed dependent, which simply violates the rule.
This diff attempts to put together a validator that checks whether the
rule from [2] is violated or not when applying the default tiling method
in `affine`.
The canonical way to perform such validation is by examining the effect
from adding the constraint from Irigoin and Triolet to the existing
dependence constraints.
Since we already have the prior knowlegde that `affine` tiles in a
hyper-rectangular way, and the resulting tiles will be scheduled in the
same order as their respective loop indices, we can simplify the
solution to just checking whether all dependence components are
non-negative along the tiling dimensions.
We put this algorithm into a new API called `checkTilingLegality` under
`LoopTiling.cpp`. This function iterates every `load`/`store` pair, and
if there is any dependence between them, we get the dependence component
and check whether it has any negative component. This function returns
`failure` if the legality condition is violated.
[1]. Bondhugula, Uday. Effective Automatic parallelization and locality optimization using the Polyhedral model. https://dl.acm.org/doi/book/10.5555/1559029
[2]. Irigoin, F. and Triolet, R. Supernode Partitioning. https://dl.acm.org/doi/10.1145/73560.73588
Differential Revision: https://reviews.llvm.org/D84882
Implement the Reduction Tree Pass framework as part of the MLIR Reduce tool. This is a parametarizable pass that allows for the implementation of custom reductions passes in the tool.
Implement the FunctionReducer class as an example of a Reducer class parameter for the instantiation of a Reduction Tree Pass.
Create a pass pipeline with a Reduction Tree Pass with the FunctionReducer class specified as parameter.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D83969
This also beefs up the test coverage:
- Make unranked memref testing consistent with ranked memrefs.
- Add testing for the invalid element type cases.
This is not quite NFC: index types are now allowed in unranked memrefs.
Differential Revision: https://reviews.llvm.org/D85541
This simple patch translates the num_threads and if clauses of the parallel
operation. Also includes test cases.
A minor change was made to parsing of the if clause to parse AnyType and
return the parsed type. Updates to test cases also.
Reviewed by: SouraVX
Differential Revision: https://reviews.llvm.org/D84798
This revision refactors the default definition of the attribute and type `classof` methods to use the TypeID of the concrete class instead of invoking the `kindof` method. The TypeID is already used as part of uniquing, and this allows for removing the need for users to define any of the type casting utilities themselves.
Differential Revision: https://reviews.llvm.org/D85356
This class allows for defining thread local objects that have a set non-static lifetime. This internals of the cache use a static thread_local map between the various different non-static objects and the desired value type. When a non-static object destructs, it simply nulls out the entry in the static map. This will leave an entry in the map, but erase any of the data for the associated value. The current use cases for this are in the MLIRContext, meaning that the number of items in the static map is ~1-2 which aren't particularly costly enough to warrant the complexity of pruning. If a use case arises that requires pruning of the map, the functionality can be added.
This is especially useful in the context of MLIR for implementing thread-local caching of context level objects that would otherwise have very high lock contention. This revision adds a thread local cache in the MLIRContext for attributes, identifiers, and types to reduce some of the locking burden. This led to a speedup of several hundred miliseconds when compiling a conversion pass on a very large mlir module(>300K operations).
Differential Revision: https://reviews.llvm.org/D82597
This change adds initial support needed to generate OpenCL compliant SPIRV.
If Kernel capability is declared then memory model becomes OpenCL.
If Addresses capability is declared then addressing model becomes Physical64.
Additionally for Kernel capability interface variable ABI attributes are not
generated as entry point function is expected to have normal arguments.
Differential Revision: https://reviews.llvm.org/D85196
This revision adds a folding pattern to replace affine.min ops by the actual min value, when it can be determined statically from the strides and bounds of enclosing scf loop .
This matches the type of expressions that Linalg produces during tiling and simplifies boundary checks. For now Linalg depends both on Affine and SCF but they do not depend on each other, so the pattern is added there.
In the future this will move to a more appropriate place when it is determined.
The canonicalization of AffineMinOp operations in the context of enclosing scf.for and scf.parallel proceeds by:
1. building an affine map where uses of the induction variable of a loop
are replaced by `%lb + %step * floordiv(%iv - %lb, %step)` expressions.
2. checking if any of the results of this affine map divides all the other
results (in which case it is also guaranteed to be the min).
3. replacing the AffineMinOp by the result of (2).
The algorithm is functional in simple parametric tiling cases by using semi-affine maps. However simplifications of such semi-affine maps are not yet available and the canonicalization does not succeed yet.
Differential Revision: https://reviews.llvm.org/D82009
Using a shuffle for the last recursive step in progressive lowering not only
results in much more compact IR, but also more efficient code (since the
backend is no longer confused on subvector aliasing for longer vectors).
E.g. the following
%f = vector.shape_cast %v0: vector<1024xf32> to vector<32x32xf32>
yields much better x86-64 code that runs 3x faster than the original.
Reviewed By: bkramer, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85482
This patch moves the registration to a method in the MLIRContext: getOrCreateDialect<ConcreteDialect>()
This method requires dialect to provide a static getDialectNamespace()
and store a TypeID on the Dialect itself, which allows to lazyily
create a dialect when not yet loaded in the context.
As a side effect, it means that duplicated registration of the same
dialect is not an issue anymore.
To limit the boilerplate, TableGen dialect generation is modified to
emit the constructor entirely and invoke separately a "init()" method
that the user implements.
Differential Revision: https://reviews.llvm.org/D85495
This new pattern mixes vector.transpose and direct lowering to vector.reduce.
This allows more progressive lowering than immediately going to insert/extract and
composes more nicely with other canonicalizations.
This has 2 use cases:
1. for very wide vectors the generated IR may be much smaller
2. when we have a custom lowering for transpose ops we can target it directly
rather than rely LLVM
Differential Revision: https://reviews.llvm.org/D85428
When any of the memrefs in a structured linalg op has a zero dimension, it becomes dead.
This is consistent with the fact that linalg ops deduce their loop bounds from their operands.
Note however that this is not the case for the `tensor<0xelt_type>` which is a special convention
that must be lowered away into either `memref<elt_type>` or just `elt_type` before this
canonicalization can kick in.
Differential Revision: https://reviews.llvm.org/D85413
The intrinsics were already supported and vector.transfer_read/write lowered
direclty into these operations. By providing them as individual ops, however,
clients can used them directly, and it opens up progressively lowering transfer
operations at higher levels (rather than direct lowering to LLVM IR as done now).
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D85357
Previous type model in the LLVM dialect did not support identified structure
types properly and therefore could use stateless translations implemented as
free functions. The new model supports identified structs and must keep track
of the identified structure types present in the target context (LLVMContext or
MLIRContext) to avoid creating duplicate structs due to LLVM's type
auto-renaming. Expose the stateful type translation classes and use them during
translation, storing the state as part of ModuleTranslation.
Drop the test type translation mechanism that is no longer necessary and update
the tests to exercise type translation as part of the main translation flow.
Update the code in vector-to-LLVM dialect conversion that relied on stateless
translation to use the new class in a stateless manner.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85297
Per Vulkan's SPIR-V environment spec: "While the OpSRem and OpSMod
instructions are supported by the Vulkan environment, they require
non-negative values and thus do not enable additional functionality
beyond what OpUMod provides."
The `getOffsetForBitwidth` function is used for lowering std.load
and std.store, whose indices are of `index` type and cannot be
negative. So we should be okay to use spv.UMod directly here to
be exact. Also made the comment explicit about the assumption.
Differential Revision: https://reviews.llvm.org/D83714
If Int16 is not available, 16-bit integers inside Workgroup storage
class should be emulated via 32-bit integers. This was previously
broken because the capability querying logic was incorrectly
intercepting all storage classes where it meant to only handle
interface storage classes. Adjusted where we return to fix this.
Differential Revision: https://reviews.llvm.org/D85308
Introduce an initial version of C API for MLIR core IR components: Value, Type,
Attribute, Operation, Region, Block, Location. These APIs allow for both
inspection and creation of the IR in the generic form and intended for wrapping
in high-level library- and language-specific constructs. At this point, there
is no stability guarantee provided for the API.
Reviewed By: stellaraccident, lattner
Differential Revision: https://reviews.llvm.org/D83310
This dialect was introduced during the bring-up of the new LLVM dialect type
system for testing purposes. The main LLVM dialect now uses the new type system
and the test dialect is no longer necessary, so remove it.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85224
Handle the case where the ViewOp takes in a memref that has
an memory space.
Reviewed By: ftynse, bondhugula, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85048
This patch introduces a conversion of `spv.loop` to LLVM dialect.
Similarly to `spv.selection`, op's control attributes are not mapped
to LLVM yet and therefore the conversion fails if the loop control is
not `None`. Also, all blocks within the loop should be reachable in
order for conversion to succeed.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D84245
Always define a remapping for the memref replacement (`indexRemap`)
with the proper number of inputs, including all the `outerIVs`, so that
the number of inputs and the operands provided for the map don't mismatch.
Reviewed By: bondhugula, andydavis1
Differential Revision: https://reviews.llvm.org/D85177
Introduces the expand and compress operations to the Vector dialect
(important memory operations for sparse computations), together
with a first reference implementation that lowers to the LLVM IR
dialect to enable running on CPU (and other targets that support
the corresponding LLVM IR intrinsics).
Reviewed By: reidtatge
Differential Revision: https://reviews.llvm.org/D84888
Simplify semi-affine expression for the operations like ceildiv,
floordiv and modulo by any given symbol by checking divisibilty by that
symbol.
Some properties used in simplification are:
1) Commutative property of the floordiv and ceildiv:
((expr1 floordiv expr2) floordiv expr3 ) = ((expr1 floordiv expr3) floordiv expr2)
((expr1 ceildiv expr2) ceildiv expr3 ) = ((expr1 ceildiv expr3) ceildiv expr2)
While simplification if operations are different no simplification is
possible as there is no property that simplify expressions like these:
((expr1 ceildiv expr2) floordiv expr3) or ((expr1 floordiv expr2)
ceildiv expr3).
2) If both expr1 and expr2 are divisible by the expr3 then:
(expr1 % expr2) / expr3 = ((expr1 / expr3) % (expr2 / expr3))
where / is divide symbol.
3) If expr1 is divisible by expr2 then expr1 % expr2 = 0.
Signed-off-by: Yash Jain <yash.jain@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D84920
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = linalg.fill(%extra_alloc, %pad)
%3 = subview %view [...][...][...]
linalg.copy(%3, %alloc)
memref_cast %extra_alloc: memref<B...> to memref<A...>
scf.yield %4 : memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
A new first-party modeling for LLVM IR types in the LLVM dialect has been
developed in parallel to the existing modeling based on wrapping LLVM `Type *`
instances. It resolves the long-standing problem of modeling identified
structure types, including recursive structures, and enables future removal of
LLVMContext and related locking mechanisms from LLVMDialect.
This commit only switches the modeling by (a) renaming LLVMTypeNew to LLVMType,
(b) removing the old implementaiton of LLVMType, and (c) updating the tests. It
is intentionally minimal. Separate commits will remove the infrastructure built
for the transition and update API uses where appropriate.
Depends On D85020
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85021
With new LLVM dialect type modeling, the dialect types no longer wrap LLVM IR
types. Therefore, they need to be translated to and from LLVM IR during export
and import. Introduce the relevant functionality for translating types. It is
currently exercised by an ad-hoc type translation roundtripping test that will
be subsumed by the actual translation test when the type system transition is
complete.
Depends On D84339
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D85019
This is a first patch that sweeps over tests to fix
indentation (tabs to spaces). It also adds label checks and
removes redundant matching of `%{{.*}} = `.
The following tests have been fixed:
- arithmetic-ops-to-llvm
- bitwise-ops-to-llvm
- cast-ops-to-llvm
- comparison-ops-to-llvm
- logical-ops-to-llvm (renamed to match the rest)
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85181
Unit attributes are given meaning by their existence, and thus have no meaningful value beyond "is it present". As such, in the format of an operation unit attributes are generally used to guard the printing of other elements and aren't generally printed themselves; as the presence of the group when parsing means that the unit attribute should be added. This revision adds support to the declarative format for eliding unit attributes in situations where they anchor an optional group, but aren't the first element.
For example,
```
let assemblyFormat = "(`is_optional` $unit_attr^)? attr-dict";
```
would print `foo.op is_optional` when $unit_attr is present, instead of the current `foo.op is_optional unit`.
Differential Revision: https://reviews.llvm.org/D84577
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = vector.transfer_read %view[...], %pad : memref<A...>, vector<...>
%3 = vector.type_cast %extra_alloc : memref<...> to
memref<vector<...>> store %2, %3[] : memref<vector<...>> %4 =
memref_cast %extra_alloc: memref<B...> to memref<A...> scf.yield %4 :
memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
Differential Revision: https://reviews.llvm.org/D84631
This reverts commit 35b65be041.
Build is broken with -DBUILD_SHARED_LIBS=ON with some undefined
references like:
VectorTransforms.cpp:(.text._ZN4llvm12function_refIFvllEE11callback_fnIZL24createScopedInBoundsCondN4mlir25VectorTransferOpInterfaceEE3$_8EEvlll+0xa5): undefined reference to `mlir::edsc::op::operator+(mlir::Value, mlir::Value)'
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This model makes thread-safe type manipulation hard and is being
progressively replaced with a cleaner MLIR model that replicates the type
system. Introduce a set of classes reflecting the LLVM IR type system in MLIR
instead of wrapping the existing types. These are currently introduced as
separate classes without affecting the dialect flow, and are exercised through
a test dialect. Once feature parity is reached, the old implementation will be
gradually substituted with the new one.
Depends On D84171
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D84339
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = vector.transfer_read %view[...], %pad : memref<A...>, vector<...>
%3 = vector.type_cast %extra_alloc : memref<...> to
memref<vector<...>> store %2, %3[] : memref<vector<...>> %4 =
memref_cast %extra_alloc: memref<B...> to memref<A...> scf.yield %4 :
memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
Differential Revision: https://reviews.llvm.org/D84631
This patch handles loopControl and selectionControl in parsing and
printing. In order to reuse the functionality, and avoid handling cases when
`{` of the region is parsed as a dictionary attribute, `control` keyword was
introduced.`None` is a default control attribute. This functionality can be
later extended to `spv.func`.
Also, loopControl and selectionControl can now be (de)serialized.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84175
This is an operation that can returns a new ValueShape with a different shape. Useful for composing shape function calls and reusing existing shape transfer functions.
Just adding the op in this change.
Differential Revision: https://reviews.llvm.org/D84217
This change allow CooperativeMatrix Load/Store operations to use pointer type
that may not match the matrix element type. This allow us to declare buffer
with a larger type size than the matrix element type. This follows SPIR-V spec
and this is needed to be able to use cooperative matrix in combination with
shared local memory efficiently.
Differential Revision: https://reviews.llvm.org/D84993
In a context in which `shape.broadcast` is known not to produce an error value,
we want it to operate solely on extent tensors. The operation's behavior is
then undefined in the error case as the result type cannot hold this value.
Differential Revision: https://reviews.llvm.org/D84933
Replaced definition of named ND ConvOps with tensor comprehension
syntax which reduces boilerplate code significantly. Furthermore,
new ops to support TF convolutions added (without strides and dilations).
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84628
Now that we can have a memref of index type, we no longer need to materialize shapes in i64 and then index_cast.
Differential Revision: https://reviews.llvm.org/D84938
-- Introduces a pass that normalizes the affine layout maps to the identity layout map both within and across functions by rewriting function arguments and call operands where necessary.
-- Memref normalization is now implemented entirely in the module pass '-normalize-memrefs' and the limited intra-procedural version has been removed from '-simplify-affine-structures'.
-- Run using -normalize-memrefs.
-- Return ops are not handled and would be handled in the subsequent revisions.
Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D84490
When lowering to the standard dialect, we currently support only the extent
tensor variant of the shape.rank operation. This change lets the conversion
pattern fail in a well-defined manner.
Differential Revision: https://reviews.llvm.org/D84852
This patch introduces new intrinsics in LLVM dialect:
- `llvm.intr.floor`
- `llvm.intr.maxnum`
- `llvm.intr.minnum`
- `llvm.intr.smax`
- `llvm.intr.smin`
These intrinsics correspond to SPIR-V ops from GLSL
extended instruction set (`spv.GLSL.Floor`, `spv.GLSL.FMax`,
`spv.GLSL.FMin`, `spv.GLSL.SMax` and `spv.GLSL.SMin`
respectively). Also conversion patterns for them were added.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84661
This is a second patch on conversion of GLSL ops to LLVM dialect.
It introduces patterns to convert `spv.InverseSqrt` and `spv.Tanh`.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84633
This is the first patch that adds support for GLSL extended
instruction set ops. These are direct conversions, apart from `spv.Tan`
that is lowered to `sin() / cos()`.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84627
This commit is part of a greater project which aims to add
full end-to-end support for convolutions inside mlir. The
reason behind having conv ops for each rank rather than
having one generic ConvOp is to enable better optimizations
for every N-D case which reflects memory layout of input/kernel
buffers better and simplifies code as well. We expect plain linalg.conv
to be progressively retired.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D83879
The lowering does not support all types for its source operations. This change
makes the patterns fail in a well-defined manner.
Differential Revision: https://reviews.llvm.org/D84443
Operating on indices and extent tensors directly, the type conversion is no
longer needed for the supported cases.
Differential Revision: https://reviews.llvm.org/D84442
This adds conversions for const_size and to_extent_tensor. Also, cast-like operations are now folded away if the source and target types are the same.
Differential Revision: https://reviews.llvm.org/D84745
Conversion of `spv.BranchConditional` now supports branch weights
that are mapped to weights vector in `llvm.cond_br`.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84657
Added a check for 'Function' storage class in `spv.globalVariable`
verifier since it only can be used with `spv.Variable`.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84731
This patch adds support of Volatile and Nontemporal
memory accesses to `spv.Load` and `spv.Store`. These attributes are
modelled with a `volatile` and `nontemporal` flags.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84739
- Initiate the unit test with a test that tests variants of build() methods
generated for ops with variadic operands and results.
- The intent is to migrate unit .td tests in mlir/test/mlir-tblgen that check for
generated C++ code to these unit tests which test both that the generated code
compiles and also is functionally correct.
Differential Revision: https://reviews.llvm.org/D84074
functions.
This allows using command line flags to lowere from GPU to SPIR-V. The
pass added is only for testing/example purposes. Most uses cases will
need more fine-grained control on setting workgroup sizes for kernel
functions.
Differential Revision: https://reviews.llvm.org/D84619
The MemRefDataFlow pass does store to load forwarding
only for affine store/loads. This patch updates the pass
to use affine read/write interface which enables vector
forwarding.
Reviewed By: dcaballe, bondhugula, ftynse
Differential Revision: https://reviews.llvm.org/D84302
For the purpose of vector transforms, the Tablegen-based infra is subsumed by simple C++ pattern application. Deprecate declarative transforms whose complexity does not pay for itself.
Differential Revision: https://reviews.llvm.org/D84753
Do not return error code, instead return created resource handles or void. Error reporting is done by the library function.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D84660
The current transformation to shape.reduce does not support tensor values.
This adds the required changes to make that work, including fixing the builder
for shape.reduce.
Differential Revision: https://reviews.llvm.org/D84744
- replace DotOp, now that DRR rules have been dropped.
- Capture arguments mismatch in the parser. The number of parsed arguments must
equal the number of expected arguments.
Reviewed By: ftynse, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D82952
The LowerAffine psas was a FunctionPass only for legacy
reasons. Making this Op-agnostic allows it to be used from command
line when affine expressions are within operations other than
`std.func`.
Differential Revision: https://reviews.llvm.org/D84590
linalg.indexed_generic (consumer) with tensor arguments.
The implementation of fusing std.constant producer with a
linalg.indexed_generic consumer was already in place. It is exposed
with this change. Also cleaning up some of the patterns that implement
the fusion to not be templated, thereby avoiding lot of conditional
checks for calling the right instantiation.
Differential Revision: https://reviews.llvm.org/D84566
Introduce support for mutable storage in the StorageUniquer infrastructure.
This makes MLIR have key-value storage instead of just uniqued key storage. A
storage instance now contains a unique immutable key and a mutable value, both
stored in the arena allocator that belongs to the context. This is a
preconditio for supporting recursive types that require delayed initialization,
in particular LLVM structure types. The functionality is exercised in the test
pass with trivial self-recursive type. So far, recursive types can only be
printed in parsed in a closed type system. Removing this restriction is left
for future work.
Differential Revision: https://reviews.llvm.org/D84171
This patch introduces 2 new optional attributes to `llvm.load`
and `llvm.store` ops: `volatile` and `nontemporal`. These attributes
are translated into proper LLVM as a `volatile` marker and a metadata node
respectively. They are also helpful with SPIR-V to LLVM dialect conversion
since they are the mappings for `Volatile` and `NonTemporal` Memory Operands.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D84396
This diff provides a concrete test case for the error that will be raised when the iteration space is non hyper-rectangular.
The corresponding emission method for this error message has been changed as well.
Differential Revision: https://reviews.llvm.org/D84531
Previous changes generalized some of the operands and results. Complete
a larger group of those to simplify progressive lowering. Also update
some of the declarative asm form due to generalization. Tried to keep it
mostly mechanical.
Based on https://reviews.llvm.org/D84439 but less restrictive, else we
don't allow shape_of to be able to produce a ranked output and doesn't
allow for iterative refinement here. We can consider making it more
restrictive later.
This patch introduces conversion pattern for `spv.Store` and `spv.Load`.
Only op with `Function` Storage Class is supported at the moment
because `spv.GlobalVariable` has not been introduced yet. If the op
has memory access attribute, then there are the following cases.
If the access is `Aligned`, add alignment to the op builder. Otherwise
the conversion fails as other cases are not supported yet because they
require additional attributes for `llvm.store`/`llvm.load` ops: e.g.
`volatile` and `nontemporal`.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84236
The patch introduces the conversion pattern for function-level
`spv.Variable`. It is modelled as `llvm.alloca` op. If initialized, then
additional store instruction is used. Note that there is no initialization
for arrays and structs since constants of these types are not supported in
LLVM dialect yet. Also, at the moment initialisation is only possible via
`spv.constant` (since `spv.GlobalVariable` conversion is not implemented
yet).
The input code has some scoping is not taken into account and will be
addressed in a different patch.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D84224
This concerns `from/to_extent_tensor`, `size_to_index`, `index_to_size`, and
`const_size` conversion patterns. The new lowering will work directly on indices
and extent tensors. The shape and size values will allow for error values but
are not yet supported by the dialect conversion.
Differential Revision: https://reviews.llvm.org/D84436
The operation `shape.shape_of` now returns an extent tensor `tensor<?xindex>` in
cases when no error are possible. All consuming operation will eventually accept
both, shapes and extent tensors.
Differential Revision: https://reviews.llvm.org/D84160
The default lowering of `assert` calls `abort` in case the assertion is
violated. The failure message is ignored but should be used by custom lowerings
that can assume more about their environment.
Differential Revision: https://reviews.llvm.org/D83886
The operation `shape.const_shape` was used for constants of type shape only.
We can now also use it to create constant extent tensors.
Differential Revision: https://reviews.llvm.org/D84157
This patch introduces branch weights metadata to `llvm.cond_br` op in
LLVM Dialect. It is modelled as optional `ElementsAttr`, for example:
```
llvm.cond_br %cond weights(dense<[1, 3]> : vector<2xi32>), ^bb1, ^bb2
```
When exporting to proper LLVM, this attribute is transformed into metadata
node. The test for metadata creation is added to `../Target/llvmir.mlir`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D83658
This revision adds support for much deeper type conversion integration into the conversion process, and enables auto-generating cast operations when necessary. Type conversions are now largely automatically managed by the conversion infra when using a ConversionPattern with a provided TypeConverter. This removes the need for patterns to do type cast wrapping themselves and moves the burden to the infra. This makes it much easier to perform partial lowerings when type conversions are involved, as any lingering type conversions will be automatically resolved/legalized by the conversion infra.
To support this new integration, a few changes have been made to the type materialization API on TypeConverter. Materialization has been split into three separate categories:
* Argument Materialization: This type of materialization is used when converting the type of block arguments when calling `convertRegionTypes`. This is useful for contextually inserting additional conversion operations when converting a block argument type, such as when converting the types of a function signature.
* Source Materialization: This type of materialization is used to convert a legal type of the converter into a non-legal type, generally a source type. This may be called when uses of a non-legal type persist after the conversion process has finished.
* Target Materialization: This type of materialization is used to convert a non-legal, or source, type into a legal, or target, type. This type of materialization is used when applying a pattern on an operation, but the types of the operands have not yet been converted.
Differential Revision: https://reviews.llvm.org/D82831
The `makeTiledViews` did not use the sizes of the tiled views based on
the result of the loop bound inference computation. This manifested as
an error in computing tile sizes with convolution where not all the
result expression of concatenated affine maps are simple
AffineDimExpr.
Differential Revision: https://reviews.llvm.org/D84366
linalg.conv does not support memrefs with rank smaller than 3 as stated here:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/nn/convolution
However it does not verify it and thus crashes with "LLVM ERROR: out of memory"
error for 1D case and "nWin > 0 && "expected at least one window dimension"" assertion
for 2D case. This commit adds check for that in the verification method.
Differential Revision: https://reviews.llvm.org/D84317
Loop bound inference is right now very limited as it supports only permutation maps and thus
it is impossible to implement convolution with linalg.generic as it requires more advanced
loop bound inference. This commits solves it for the convolution case.
Depends On D83158
Differential Revision: https://reviews.llvm.org/D83191
The underlying infrastructure supports this already, just add the
pattern matching for linalg.generic.
Differential Revision: https://reviews.llvm.org/D84335
AllocOp is updated in normalizeMemref(AllocOp allocOp), but, when the
AllocOp has `alignment` attribute, it was ignored and updated AllocOp
does not have `alignment` attribute. This patch fixes it.
Differential Revision: https://reviews.llvm.org/D83656
Introduces the scatter/gather operations to the Vector dialect
(important memory operations for sparse computations), together
with a first reference implementation that lowers to the LLVM IR
dialect to enable running on CPU (and other targets that support
the corresponding LLVM IR intrinsics).
The operations can be used directly where applicable, or can be used
during progressively lowering to bring other memory operations closer to
hardware ISA support for a gather/scatter. The semantics of the operation
closely correspond to those of the corresponding llvm intrinsics.
Note that the operation allows for a dynamic index vector (which is
important for sparse computations). However, this first reference
lowering implementation "serializes" the address computation when
base + index_vector is converted to a vector of pointers. Exploring
how to use SIMD properly during these step is TBD. More general
memrefs and idiomatic versions of striding are also TBD.
Reviewed By: arpith-jacob
Differential Revision: https://reviews.llvm.org/D84039
This patch introduces conversion pattern for `spv.selection` op.
The conversion can only be applied to selection with all blocks being
reachable. Moreover, selection with control attributes "Flatten" and
"DontFlatten" is not supported.
Since the `PatternRewriter` hook for block merging has not been implemented
for `ConversionPatternRewriter`, merge and continue blocks are kept
separately.
Reviewed By: antiagainst, ftynse
Differential Revision: https://reviews.llvm.org/D83860
This patch introduces conversion for `spv.Branch` and `spv.BranchConditional`
ops. Branch weigths for `spv.BranchConditional` are not supported at the
moment, and conversion in this case fails.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83784
This commit adds functionality needed for implementation of convolutions with
linalg.generic op. Since linalg.generic right now expects indexing maps to be
just permutations, offset indexing needed in convolutions is not possible.
Therefore in this commit we address the issue by adding support for symbols inside
indexing maps which enables more advanced indexing. The upcoming commit will
solve the problem of computing loop bounds from such maps.
Differential Revision: https://reviews.llvm.org/D83158
Linkage support is already present in the LLVM dialect, and is being translated
for globals other than functions. Translation support has been missing for
functions because their conversion goes through a different code path than
other globals.
Differential Revision: https://reviews.llvm.org/D84149
Summary: The logic was conservative but inverted: cases that should remain unmasked became 1-D masked.
Differential Revision: https://reviews.llvm.org/D84051
Summary: Vector contract patterns were only parameterized by a `vectorTransformsOptions`. As a result, even if an mlir file was containing several occurrences of `vector.contract`, all of them would be lowered in the same way. More granularity might be required . This Diff adds a `constraint` argument to each of these patterns which allows the user to specify with more precision on which `vector.contract` should each of the lowering apply.
Differential Revision: https://reviews.llvm.org/D83960
- Added more default values for `attributes` parameter for 2 more build methods
- Extend the op-decls.td unit test to test these build methods.
Differential Revision: https://reviews.llvm.org/D83839
Lower `shape.shape_eq` to the `scf` (and `std`) dialect. For now, this lowering
is limited to extent tensor operands.
Differential Revision: https://reviews.llvm.org/D82530
To make it clear when shape error values cannot occur the shape operations can
operate on extent tensors. This change updates the lowering for `shape.reduce`
accordingly.
Differential Revision: https://reviews.llvm.org/D83944
Some dialects have semantics which is not well represented by common
SSA structures with dominance constraints. This patch allows
operations to declare the 'kind' of their contained regions.
Currently, two kinds are allowed: "SSACFG" and "Graph". The only
difference between them at the moment is that SSACFG regions are
required to have dominance, while Graph regions are not required to
have dominance. The intention is that this Interface would be
generated by ODS for existing operations, although this has not yet
been implemented. Presumably, if someone were interested in code
generation, we might also have a "CFG" dialect, which defines control
flow, but does not require SSA.
The new behavior is mostly identical to the previous behavior, since
registered operations without a RegionKindInterface are assumed to
contain SSACFG regions. However, the behavior has changed for
unregistered operations. Previously, these were checked for
dominance, however the new behavior allows dominance violations, in
order to allow the processing of unregistered dialects with Graph
regions. One implication of this is that regions in unregistered
operations with more than one op are no longer CSE'd (since it
requires dominance info).
I've also reorganized the LangRef documentation to remove assertions
about "sequential execution", "SSA Values", and "Dominance". Instead,
the core IR is simply "ordered" (i.e. totally ordered) and consists of
"Values". I've also clarified some things about how control flow
passes between blocks in an SSACFG region. Control Flow must enter a
region at the entry block and follow terminator operation successors
or be returned to the containing op. Graph regions do not define a
notion of control flow.
see discussion here:
https://llvm.discourse.group/t/rfc-allowing-dialects-to-relax-the-ssa-dominance-condition/833/53
Differential Revision: https://reviews.llvm.org/D80358
- Add function `verifyTypes` that Op's can call to do type checking verification
along the control flow edges described the Op's RegionBranchOpInterface.
- We cannot rely on the verify methods on the OpInterface because the interface
functions assume valid Ops, so they may crash if invoked on unverified Ops.
(For example, scf.for getSuccessorRegions() calls getRegionIterArgs(), which
dereferences getBody() block. If the scf.for is invalid with no body, this
can lead to a segfault). `verifyTypes` can be called post op-verification to
avoid this.
Differential Revision: https://reviews.llvm.org/D82829
This folds shape.broadcast where at least one operand is a scalar to the
other operand.
Also add an assemblyFormat for shape.broadcast and shape.concat.
Differential Revision: https://reviews.llvm.org/D83854
Add `shape.shape_eq` operation to the shape dialect.
The operation allows to test shapes and extent tensors for equality.
Differential Revision: https://reviews.llvm.org/D82528
In 2b3c505, the pointer arguments for the matrix load and store
intrinsics was changed to always be the element type of the vector
argument.
This patch updates the MatrixBuilder to not add the pointer type to the
overloaded types and adjusts the clang/mlir tests.
This should fix a few build failures on GreenDragon, including
http://green.lab.llvm.org/green/job/test-suite-verify-machineinstrs-x86_64-O0-g/7891/
Summary:
linalg.copy + linalg.fill can be used to create a padded local buffer.
The `masked` attribute is only valid on this padded buffer.
When forwarding to vector.transfer ops, the attribute must be reset
conservatively.
Differential Revision: https://reviews.llvm.org/D83782
This adds a `parseOptionalAttribute` method to the OpAsmParser that allows for parsing optional attributes, in a similar fashion to how optional types are parsed. This also enables the use of attribute values as the first element of an assembly format optional group.
Differential Revision: https://reviews.llvm.org/D83712
Up until now, there has been an implicit agreement that when an operation is marked as
"erased" all uses of that operation's results are guaranteed to be removed during conversion. How this works in practice is that there is either an assert/crash/asan failure/etc. This revision adds support for properly detecting when an erased operation has dangling users, emits and error and fails the conversion.
Differential Revision: https://reviews.llvm.org/D82830
- Arguments of the first block of a region are considered region arguments.
- Add API on Region class to deal with these arguments directly instead of
using the front() block.
- Changed several instances of existing code that can use this API
- Fixes https://bugs.llvm.org/show_bug.cgi?id=46535
Differential Revision: https://reviews.llvm.org/D83599
This patch introduces lowering of the OpenMP parallel operation to LLVM
IR using the OpenMPIRBuilder.
Functions topologicalSort and connectPhiNodes are generalised so that
they work with operations also. connectPhiNodes is also made static.
Lowering works for a parallel region with multiple blocks. Clauses and
arguments of the OpenMP operation are not handled.
Reviewed By: rriddle, anchu-rajendran
Differential Revision: https://reviews.llvm.org/D81660
Summary: The native alignment may generally not be used when lowering a vector.transfer to the underlying load/store operation. This revision fixes the unmasked load/store alignment to match that of the masked path.
Differential Revision: https://reviews.llvm.org/D83684
- Provide default value for `ArrayRef<NamedAttribute> attributes` parameter of
the collective params build method.
- Change the `genSeparateArgParamBuilder` function to not generate build methods
that may be ambiguous with the new collective params build method.
- This change should help eliminate passing empty NamedAttribue ArrayRef when the
collective params build method is used
- Extend op-decl.td unit test to make sure the ambiguous build methods are not
generated.
Differential Revision: https://reviews.llvm.org/D83517
Per the Vulkan's SPIR-V environment spec, "for the OpSRem and OpSMod
instructions, if either operand is negative the result is undefined."
So we cannot directly use spv.SRem/spv.SMod if either operand can be
negative. Emulate it via spv.UMod.
Because the emulation uses spv.SNegate, this commit also defines
spv.SNegate.
Differential Revision: https://reviews.llvm.org/D83679
Summary:
These are semantically equivalent, but fmuladd allows decaying the op
into fmul+fadd if there is no fma instruction available. llvm.fma lowers
to scalar calls to libm fmaf, which is a lot slower.
Reviewers: nicolasvasilache, aartbik, ftynse
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D83666
Introduce pass to convert parallel affine.for op into 1-D affine.parallel op.
Run using --affine-parallelize. Removes test-detect-parallel: pass for checking
parallel affine.for ops.
Signed-off-by: Yash Jain <yash.jain@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D83193
- Create a pass that generates bugs based on trivially defined behavior for the purpose of testing the MLIR Reduce Tool.
- Implement the functionality inside the pass to crash mlir-opt in the presence of an operation with the name "crashOp".
- Register the pass as a test pass in the mlir-opt tool.
Reviewed by: jpienaar
Differential Revision: https://reviews.llvm.org/D83422
Improve the logic deciding if it is safe to hoist vector transfer read/write
out of the loop. Change the logic to prevent hoisting operations if there are
any unknown access to the memref in the loop no matter where the operation is.
For other transfer read/write in the loop check if we can prove that they
access disjoint memory and ignore them in this case.
Differential Revision: https://reviews.llvm.org/D83538
This revision folds vector.transfer operations by updating the `masked` bool array attribute when more unmasked dimensions can be discovered.
Differential revision: https://reviews.llvm.org/D83586
We temporarily had separate OUTER lowering (for matmat flavors) and
AXPY lowering (for matvec flavors). With the new generalized
"vector.outerproduct" semantics, these cases can be merged into
a single lowering method. This refactoring will simplify future
decisions on cost models and lowering heuristics.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83585
This specialization allows sharing more code where an AXPY follows naturally
in cases where an OUTERPRODUCT on a scalar would be generated.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83453
TransposeOp are often followed by ExtractOp.
In certain cases however, it is unnecessary (and even detrimental) to lower a TransposeOp to either a flat transpose (llvm.matrix intrinsics) or to unrolled scalar insert / extract chains.
Providing foldings of ExtractOp mitigates some of the unnecessary complexity.
Differential revision: https://reviews.llvm.org/D83487
This revision adds support for vectorizing named and generic contraction ops to vector.contract. Cases in which the memref is 0-D are special cased to emit std.load/std.store instead of vector.transfer. Relevant tests are added.
Differential revision: https://reviews.llvm.org/D83307
This patch introduces type conversion for SPIR-V structs. Since
handling offset case requires thorough testing, it was left out
for now. Hence, only structs with no offset are currently
supported. Also, structs containing member decorations cannot
be translated.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83403
This commit augments spv.CopyMemory's implementation to support 2 memory
access operands. Hence, more closely following the spec. The following
changes are introduces:
- Customize logic for spv.CopyMemory serialization and deserialization.
- Add 2 additional attributes for source memory access operand.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83241
Summary:
* Native '_mlir' extension module.
* Python mlir/__init__.py trampoline module.
* Lit test that checks a message.
* Uses some cmake configurations that have worked for me in the past but likely needs further elaboration.
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D83279
This patch adds type conversion for 4 SPIR-V types: array, runtime array, pointer
and struct. This conversion is integrated using a separate function
`populateSPIRVToLLVMTypeConversion()` that adds new type conversions. At the moment,
this is a basic skeleton that allows to perfom conversion from SPIR-V array,
runtime array and pointer types to LLVM typesystem. There is no support of array
strides or storage classes. These will be supported on the case by case basis.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83399
Summary:
Added canonicalization and folding was:
- Folding when either input is an attribute indicating a scalar input
which can always be broadcasted.
- Canonicalization where it can be determined that either input shape is
a scalar.
- Canonicalization where the partially specified input shapes can be
proven to be broadcastable always.
Differential Revision: https://reviews.llvm.org/D83194
Summary:
* This allows these flags to be passed on the command line with normal CMake bool-interpreted values like ON/OFF instead of requiring 0/1.
* As-is, if passing ON/OFF, these will cause a parse error in lit.site.cfg.py because Python tries to interpret the string literally.
Reviewers: stephenneuendorffer
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D83451
Depending on where the 0 dimension is within the shape, the parser will currently reject .mlir generated by the printer.
Differential Revision: https://reviews.llvm.org/D83445
This patch adds conversion patterns for `spv.BitFieldSExtract` and `spv.BitFieldUExtract`.
As in the patch for `spv.BitFieldInsert`, `offset` and `count` have to be broadcasted in
vector case and casted to match the type of the base.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D82640
This patch introduces 3 new direct conversions for SPIR-V ops:
- `spv.Select`
- `spv.Undef`
- `spv.FMul` that was skipped in the patch with arithmetic ops
Differential Revision: https://reviews.llvm.org/D83291
Create the framework and testing environment for MLIR Reduce - a tool
with the objective to reduce large test cases into smaller ones while
preserving their interesting behavior.
Implement the functionality to parse command line arguments, parse the
MLIR test cases into modules and run the interestingness tests on
the modules.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D82803
This revision adds foldings for ExtractOp operations that come from previous InsertOp.
InsertOp have cumulative semantic where multiple chained inserts are necessary to produce the final value from which the extracts are obtained.
Additionally, TransposeOp may be interleaved and need to be tracked in order to follow the producer consumer relationships and properly compute positions.
Differential revision: https://reviews.llvm.org/D83150
An operation can specify that an operation or result type matches the
type of another operation, result, or attribute via the `AllTypesMatch`
or `TypesMatchWith` constraints.
Use these constraints to also automatically resolve types in the
automatically generated assembly parser.
This way, only the attribute needs to be listed in `assemblyFormat`,
e.g. for constant operations.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D78434
Haruki Imai