The TensorConstantOp bufferize conversion pattern has a bug that
makes it incorrect in the case of vectors whose alignment is not
the natural alignment. Circumvent it temporarily by using a power of 2.
Differential Revision: https://reviews.llvm.org/D89265
This revision introduces support for buffer allocation for any named linalg op.
To avoid template instantiating many ops, a new ConversionPattern is created to capture the LinalgOp interface.
Some APIs are updated to remain consistent with MLIR style:
`OwningRewritePatternList * -> OwningRewritePatternList &`
`BufferAssignmentTypeConverter * -> BufferAssignmentTypeConverter &`
Differential revision: https://reviews.llvm.org/D89226
This revision also inserts an end-to-end test that lowers tensors to buffers all the way to executable code on CPU.
Differential revision: https://reviews.llvm.org/D88998
Current setup for conv op vectorization does not enable user to specify tile
sizes as well as dimensions for vectorization. In this commit we change that by
adding tile sizes as pass arguments. Every dimension with corresponding tile
size > 1 is automatically vectorized.
Differential Revision: https://reviews.llvm.org/D88533
Recently, restrictions on vector reductions were made more relaxed by
accepting any width signless integer and floating-point. This CL relaxes
the restriction even more by including unsigned and signed integers.
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D88442
(1) simplify integer printing logic by always using 64-bit print
(2) add index support (since vector<16xindex> is planned to be added)
(3) adjust naming convention print_x -> printX
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D88436
This generalizes printing beyond just i1,i32,i64 and also accounts
for signed and unsigned interpretation in the output.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D88290
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.
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 commit introduces end-to-end integration tests for
convolutions that test multiple ways of ConvOps lowering.
Differential Revision: https://reviews.llvm.org/D87277
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
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
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
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
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
Added a "clone" of the 1D vector's test_transfer_read and added a second dimensionality. The test is not as generic as I would like it to be, because more generic versions appear to break the compiler or the runtime at this stage. As bug are fixed, I will be happy to add another more complete test.
Differential Revision: https://reviews.llvm.org/D83096
Integration test that illustrates the gather operation with a
real-world operation expressed in mostly the Vector dialect.
Uses jagged diagonal storage.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D84571
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
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
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
Summary:
Two integration tests focused on i1 vectors, which exposed omissions
in the llvm backend which have since then been fixed. Note that this also
exposed an inaccuracy for print_i1 which has been fixed in this CL:
for a pure C ABI, int should be used rather than bool.
Reviewers: nicolasvasilache, ftynse, reidtatge, andydavis1, bkramer
Reviewed By: bkramer
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81957
Summary:
This CL introduces an integration test directory for MLIR in general, with
vector dialect integration tests in particular as a first working suite. To
run all the integration tests (and currently just the vector suite):
$ cmake --build . --target check-mlir-integration
[0/1] Running the MLIR integration tests
Testing Time: 0.24s
Passed: 22
The general call is to contribute to this integration test directory with more
tests and other suites, running end-to-end examples that may be too heavy for
the regular test directory, but should be tested occasionally to verify the
health of MLIR.
Background discussion at:
https://llvm.discourse.group/t/vectorops-rfc-add-suite-of-integration-tests-for-vector-dialect-operations/1213/
Reviewers: nicolasvasilache, reidtatge, andydavis1, rriddle, ftynse, mehdi_amini, jpienaar, stephenneuendorffer
Reviewed By: nicolasvasilache, stephenneuendorffer
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81626
Nicolas Vasilache