The example was still using the -now- removed sparse_tensor.init_tensor.
Also, I made the input operands of the matrix multiplication sparse too
(since it looks a bit strange to multiply two dense matrices into a sparse).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D126897
In strict mode, only the new inserted operation is allowed to add to the
worklist. Before this change, it would add the users of a replaced op
and it didn't check if the users are allowed to be pushed into the
worklist
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D126899
Prior to this patch, the lowering of memref.reshape operations to the
LLVM dialect failed if the shape argument had a static shape with
dynamic dimensions. This patch adds the necessary support so that when
the shape argument has dynamic values, the lowering probes the dimension
at runtime to set the size in the `MemRefDescriptor` type. This patch
also computes the stride for dynamic dimensions by deriving it from the
sizes of the inner dimensions.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126604
These ops complement the tiling/padding transformations by transforming
higher-level named structured operations such as depthwise convolutions into
lower-level and/or generic equivalents that are better handled by some
downstream transformations.
Differential Revision: https://reviews.llvm.org/D126698
This enabled opaque pointers by default in LLVM. The effect of this
is twofold:
* If IR that contains *neither* explicit ptr nor %T* types is passed
to tools, we will now use opaque pointer mode, unless
-opaque-pointers=0 has been explicitly passed.
* Users of LLVM as a library will now default to opaque pointers.
It is possible to opt-out by calling setOpaquePointers(false) on
LLVMContext.
A cmake option to toggle this default will not be provided. Frontends
or other tools that want to (temporarily) keep using typed pointers
should disable opaque pointers via LLVMContext.
Differential Revision: https://reviews.llvm.org/D126689
Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no longer needed.
Differential Revision: https://reviews.llvm.org/D126180
The trick of using an empty token in the `FOREVERY_O` x-macro relies on preprocessor behavior which is only standard since C99 6.10.3/4 and C++11 N3290 16.3/4 (whereas it was undefined behavior up through C++03 16.3/10). Since the `ExecutionEngine/SparseTensorUtils.cpp` file is required to be compile-able under C++98 compatibility mode (unlike the C++11 used elsewhere in MLIR), we shouldn't rely on that behavior.
Also, using a non-empty suffix helps improve uniformity of the API, since all other primary/overhead suffixes are also non-empty. I'm using the suffix `0` since that's the value used by the `SparseTensorEncoding` attribute for indicating the index overhead-type.
Depends On D126720
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126724
Ctlz is an intrinsic in LLVM but does not have equivalent operations in SPIR-V.
Including a decomposition gives an alternative path for these platforms.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D126261
There is no direct ctypes for MLIR's complex (and thus np.complex128
and np.complex64) yet, causing the mlir python binding methods for
memrefs to crash. This revision fixes this by passing complex arrays
as tuples of floats, correcting at the boundaries for the proper view.
NOTE: some of these changes (4 -> 2) were forced by the new "linting"
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D126422
This fillRow(..., 0) is redundant because when the size of the tableau is
consistent, the resize always creates a new row, which is zero-initialized.
Also added asserts throughout to ensure the dimensions of the tableau remain
consistent.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D126709
Fix the warning: comparison of unsigned expression in ‘>= 0’ is always
true.
Reviewed By: kiranchandramohan, shraiysh
Differential Revision: https://reviews.llvm.org/D126784
This reverts commit 9b7193f852.
This is an older branch that was committed by mistake and does not include addressed review comments, an updated version will come next.
By defining the `{primary,overhead}TypeFunctionSuffix` functions via the same x-macros used to generate the runtime library's functions themselves, this helps avoid bugs from typos or things getting out of sync.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D126720
The previous macro definition using `{...}` would fail to compile when the callsite uses a semicolon followed by an else-statement (i.e., `if (...) FATAL(...); else ...;`). Replacing the simple braces with `do{...}while(0)` (n.b., semicolon not included in the macro definition) enables callsites to use the semicolon plus else-statement syntax without problems. The new definition now requires the semicolon at all callsites, but since it was already being called that way nothing changes.
For more explanation, see <https://gcc.gnu.org/onlinedocs/cpp/Swallowing-the-Semicolon.html>
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126514
The primary goal of this change is to define readSparseTensorShape. Whereas the SparseTensorFile class is merely introduced as a way to reduce code duplication along the way.
Depends On D126106
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126233
Remove boilerplate examples and add a text at the dialect level to describe
what kind of operands the operations accept (i.e., scalar, tensor or vector).
Left a shorter sentence describing the input operands for each operation as
this redundancy is convenient when browsing the documentation using the
website.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126648
This patch supports to convert the llvm intrinsic to the corresponding op. It still leaves some intrinsics to be handled specially.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126639
The current translation uses the old "ugly"/"raw" form which used PDLValue for the arguments
and results. This commit updates the C++ generation to use the recently added sugar that
allows for directly using the desired types for the arguments and result of PDL functions.
In addition, this commit also properly imports the C++ class for ODS operations, constraints,
and interfaces. This allows for a much more convienent C++ API than previously granted
with the raw/low-level types.
Differential Revision: https://reviews.llvm.org/D124817
We were currently only completing on the first operand because
the completion check was outside of the parse loop.
Differential Revision: https://reviews.llvm.org/D124784
This commit adds a new PDLL specific LSP command, pdll.viewOutput, that
allows for viewing the intermediate outputs of a given PDLL file. The available
intermediate forms currently mirror those in mlir-pdll, namely: AST, MLIR, CPP.
This is extremely useful for a developer of PDLL, as it simplifies various testing,
and is also quite useful for users as they can easily view what is actually being
generated for their PDLL files.
This new command is added to the vscode client, and is available in the right
client context menu of PDLL files, or via the vscode command palette.
Differential Revision: https://reviews.llvm.org/D124783
This allows for the results of operations to be inferred in certain contexts,
and matches the support in PDL for result type inference. The main two
initial circumstances are when used as a replacement of another operation,
or when the operation being created implements InferTypeOpInterface.
Differential Revision: https://reviews.llvm.org/D124782
Python bindings for extensions of the Transform dialect are defined in separate
Python source files that can be imported on-demand, i.e., that are not imported
with the "main" transform dialect. This requires a minor addition to the
ODS-based bindings generator. This approach is consistent with the current
model for downstream projects that are expected to bundle MLIR Python bindings:
such projects can include their custom extensions into the bundle similarly to
how they include their dialects.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D126208
Vectorization is a key transformation to achieve high performance on most
architectures. In the transform dialect, vectorization is implemented as a
parameterizable transform op. It currently applies to a scope of payload IR
delimited by some isolated-from-above op, mainly because several enabling
transformations (such as affine simplification) are needed to perform
vectorization and these transformation would apply to ops other than the "main"
computational payload op. A separate "navigation" transform op that obtains the
isolated-from-above ancestor of an op is introduced in the core transform
dialect. Even though it is currently only useful for vectorization,
isolated-from-above ops are a common anchor for transformations (usually
implemented as passes) that is likely to be reused in the future.
Depends On D126374
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D126542
Add ops to the structured transform extension of the transform dialect that
perform interchange, padding and scalarization on structured ops. Along with
tiling that is already defined, this provides a minimal set of transformations
necessary to build vectorizable code for a single structured op.
Define two helper traits: one that implements TransformOpInterface by applying
a function to each payload op independently and another that provides a simple
"functional-style" producer/consumer list of memory effects for the transform
ops.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D126374
I don't see a point here in the lit tests here since sqrt, mul and other ops
expand as well. I just added "smoke" tests to verify that the conversion works
and does not create any illegal ops.
I will create a patch that adds a simple integration test to
mlir/test/Integration/Dialect/ComplexOps/ that will compare the values.
Differential Revision: https://reviews.llvm.org/D126539
This patch adds support for applying a relation on domain/range of a relation.
Reviewed By: arjunp, ftynse
Differential Revision: https://reviews.llvm.org/D126339
Now that analysis and bufferization are better separated, post-analysis steps are no longer needed. Users can directly interleave analysis and bufferization as needed.
Differential Revision: https://reviews.llvm.org/D126571
The semicolons were introduced in D126105 in order to correct clang-format, but I forgot this file must be compiled as C++98 rather than C++11.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126561
This supports the operation conversion for threadprivate directive. The
support for memref type conversion is not implemented.
Reviewed By: kiranchandramohan, shraiysh
Differential Revision: https://reviews.llvm.org/D124610
This patch fixes the following compiler error:
error: declaration of ‘mlir::LLVM::cconv::CConv mlir::LLVM::detail::CConvAttrStorage::CConv’ changes meaning of ‘CConv’ [-fpermissive]
CConv as a member variable name was shadowing CConv as an enumeration,
hence the compiler error.
Reviewed By: ftynse, alexbatashev
Differential Revision: https://reviews.llvm.org/D126530
This essentially builds an index for the parsed records and record values (fields).
This covers quite a few cases, but is limited by the currently lackluster location
tracking in tablegen. A followup will work on plumbing more locations through
tablegen, which should greatly improve what we can do here.
Differential Revision: https://reviews.llvm.org/D125443
This allows for following links to include files. This support is effectively
identical to the logic in the PDLL language server, and code is shared as
much as possible.
Differential Revision: https://reviews.llvm.org/D125442
This provides a format for externally specifying the include directories
for a source file. The format of the tablegen database is exactly the
same as that for PDLL, namely it includes the absolute source file name and
the set of include directories. The database format is shared to simplify
the infra, and also because the format itself is general enough to share. Even
if we desire to expand in the future to contain the actual compilation command,
nothing there is specific enough that we would need two different formats.
As with PDLL, support for generating the database is added to our mlir_tablegen
cmake command.
Differential Revision: https://reviews.llvm.org/D125441
This patch adds support for Calling Convention attribute in LLVM
dialect, including enums, custom syntax and import from LLVM IR.
Additionally fix import of dso_local attribute.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126161
This is a followup to D126105 to move functions in SparseTensorUtils.cpp to match their locations in SparseTensorUtils.h
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126106
This change makes the public API of SparseTensorUtils.cpp explicit, whereas before the publicity of these functions was only implicit. Implicit publicity is sufficient for mlir-opt to generate calls to these functions, but it's not enough to enable C/C++ code to call them directly in the usual way (i.e., without going through codegen). Thus, leaving the publicity implicit prevents development of other tools (e.g., microbenchmarks).
In addition this change also marks the functions MLIR_CRUNNERUTILS_EXPORT, which is required by the JIT under certain configurations (albeit not for anything in our test suite).
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126105
The Transform dialect uses the side effect modeling mechanism to record the
effects of the transform ops on the mapping between Transform IR values and
Payload IR ops. Introduce a checker pass that warns if a Transform IR value is
used after it has been freed (consumed). This pass is mostly intended as a
debugging aid in addition to the verification/assertion mechanisms in the
transform interpreter. It reports all potential use-after-free situations.
The implementation makes a series of simplifying assumptions to be simple and
conservative. A more advanced implementation would rely on the data flow-like
analysis associated with a side-effect resource rather than a value, which is
currently not supported by the analysis infrastructure.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D126381
If we don't specify the result index while matching operand with the
result of certain operation, it's supposed to match all the results of
the operation with the operand. For registered op, it's easy to do that
by either indexing with number or name. For unregistered op, this commit
enables the numeric result indexing for this use case.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D126330
This commit fixes `Tensor_InsertSliceOp` `sizes` inputs/attributes
description.
Before this commit, the description says the `sizes` inputs/attributes
denote the size of the return type. But according to the
`InsertSliceOpConstantArgumentFolder` in
`lib/Dialect/Tensor/IR/TensorOps.cpp`, the `sizes` inputs/attributes
actually denote the size of the source type.
I had an off-line discussion with the authors of `TensorOps.td` and
`TensorOps.cpp`. We concluded that it was a typo in the Op description.
This commit updates the Op description to match the actual usage.
Differential Revision: https://reviews.llvm.org/D126264
Using 64-bit integer/float type in interface storage classes would
require Int64/Float64 capability, per the Vulkan spec:
```
shaderInt64 specifies whether 64-bit integers (signed and unsigned) are
supported in shader code. If this feature is not enabled, 64-bit integer
types must not be used in shader code. This also specifies whether
shader modules can declare the Int64 capability. Declaring and using
64-bit integers is enabled for all storage classes that SPIR-V allows
with the Int64 capability.
```
This is different from, say, 16-bit element types, where:
```
shaderInt16 specifies whether 16-bit integers (signed and unsigned) are
supported in shader code. If this feature is not enabled, 16-bit integer
types must not be used in shader code. This also specifies whether
shader modules can declare the Int16 capability. However, this only
enables a subset of the storage classes that SPIR-V allows for the Int16
SPIR-V capability: Declaring and using 16-bit integers in the Private,
Workgroup (for non-Block variables), and Function storage classes is
enabled, while declaring them in the interface storage classes (e.g.,
UniformConstant, Uniform, StorageBuffer, Input, Output, and
PushConstant) is not enabled.
```
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D126256
These attributes can carry useful information, e.g., pipelines
might use them to organize and chain patterns.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D126320
This patch adds support for obtaining a set corresponding to the domain/range
of the relation.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126326
This patch makes sure that the address dereferences to value in
omp.atomic.write operation.
Reviewed By: kiranchandramohan, peixin
Differential Revision: https://reviews.llvm.org/D126272
This suppresse annoying warning when building mlir.
```
warning: base class ‘class mlir::PassWrapper<{anonymous}::TestStatisticPass,
mlir::OperationPass<void> >’ should be explicitly initialized in the copy constructor [-Wextra]
```
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D126209
This commit breaks down diagnostic string literals so that the attribute
name and enumurator names can be shared with the stringify utility
function and the "expected ", " to be one of ", and ", " can be shared
between different enum-related diagnostic.
Differential Revision: https://reviews.llvm.org/D125938
Add lowering for cases where the reduction dimension is fully unrolled.
It is common to unroll the reduction dimension, therefore we would want
to lower the contractions to an elementwise vector op in this case.
Differential Revision: https://reviews.llvm.org/D126120
Aart Bik