TOSAs depthwise_conv2d operation includes a reshape to include the implicit x1 dimension.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D126212
We were custom counting per bit for the clz instruction. Math dialect
now has an intrinsic to do this in one instruction. Migrated to this
instruction and fixed a minor bug math-to-llvm for the intrinsic.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D125592
In addition, fixed a small bug with padding incorrectly inferring output shape for dynaic inputs in convolution
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D121872
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
These passes generally don't rely on any special aspects of FuncOp, and moving allows
for these passes to be used in many more situations. The passes that obviously weren't
relying on invariants guaranteed by a "function" were updated to be generic pass, the
rest were updated to be FunctionOpinterface InterfacePasses.
The test updates are NFC switching from implicit nesting (-pass -pass2) form to
the -pass-pipeline form (generic passes do not implicitly nest as op-specific passes do).
Differential Revision: https://reviews.llvm.org/D121190
The Func has a large number of legacy dependencies carried over from the old
Standard dialect, which was pervasive and contained a large number of varied
operations. With the split of the standard dialect and its demise, a lot of lingering
dead dependencies have survived to the Func dialect. This commit removes a
large majority of then, greatly reducing the dependence surface area of the
Func dialect.
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
BlockArguments gained the ability to have locations attached a while ago, but they
have always been optional. This goes against the core tenant of MLIR where location
information is a requirement, so this commit updates the API to require locations.
Fixes#53279
Differential Revision: https://reviews.llvm.org/D117633
The only benefit of FunctionPass is that it filters out function
declarations. This isn't enough to justify carrying it around, as we can
simplify filter out declarations when necessary within the pass. We can
also explore with better scheduling primitives to filter out declarations
at the pipeline level in the future.
The definition of FunctionPass is left intact for now to allow time for downstream
users to migrate.
Differential Revision: https://reviews.llvm.org/D117182
Adding the optional decompositions have been verified to improve memory
usage on common models. Added the decomposition to the default tosa to linalg
passes.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D117175
Dynamic batch for rescale, gather, max_pool, avg_pool, conv2D and depthwise_conv2D. Split helper functions into a separate header file.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D117031
Moved all TOSA decomposition patterns so that they can be optionally populated
and used by external rewrites. This avoids decomposing TOSa operations when
backends may benefit from the non-decomposed version.
Reviewed By: rsuderman, mehdi_amini
Differential Revision: https://reviews.llvm.org/D116526
Includes dependency fix that resulted in canonicalizer pass not linking in.
Linalg named ops lowering are moved to a separate pass. This allows TOSA
canonicalizers to run between named-ops lowerings and the general TOSA
lowerings. This allows the TOSA canonicalizers to run between lowerings.
Differential Revision: https://reviews.llvm.org/D116057
This reverts commit 313de31fbb.
There is a missing CMake dependency, building with shared libraries is
broken:
55.509 [45/4/3061] Linking CXX shared library lib/libMLIRTosaToLinalg.so.14git
FAILED: lib/libMLIRTosaToLinalg.so.14git
...
TosaToLinalgPass.cpp: undefined reference to `mlir::createCanonicalizerPass()'
Linalg named ops lowering are moved to a separate pass. This allows TOSA
canonicalizers to run between named-ops lowerings and the general TOSA
lowerings. This allows the TOSA canonicalizers to run between lowerings.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D116057
It is possible for the shift value to exceed the number of bits. In these
cases we can just multiply by zero. This is relatively rare occurence but
should be handled.
Reviewed By: not-jenni
Differential Revision: https://reviews.llvm.org/D115779
After removing the range type, Linalg does not define any type. The revision thus consolidates the LinalgOps.h and LinalgTypes.h into a single Linalg.h header. Additionally, LinalgTypes.cpp is renamed to LinalgDialect.cpp to follow the convention adopted by other dialects such as the tensor dialect.
Depends On D115727
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D115728
This revision reintroduces tensor.insert_slice verification which seems
to have vanished over time: a verifier was initially introduced in cf9503c1b7
but for some reason the invalid.mlir was not properly updated; as time passed the verifier was not called anymore and later the code was deleted.
As a consequence, a non-negligible portion of tests has run astray using invalid
tensor.insert_slice semantics and needed to be fixed.
Also, extract isRankReducedType from TensorOps for better reuse
Originally, this facility was used by both tensor and memref forms but
it got copied around as dialects were split.
Differential Revision: https://reviews.llvm.org/D114715
Transpose convolution decomposition is now performed in a separate pass. This
allows padding / constant propagation to be performed at the TOSA level. It
also adds support for striding when there is no dilation.
Differential Revision: https://reviews.llvm.org/D114409
Split tosa.reshape into three individual lowerings: collapse, expand and a
combination of both. Add simple dynamic shape support.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D113936
Names should be consistent across all operations otherwise painful bugs will surface.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D113762
New TOSA pad operation can support explicitly specifying the pad value. Added
lowering to linalg that uses the explicit value.
Differential Revision: https://reviews.llvm.org/D113515
Specification specified the output type for quantized average pool should be
an i32. Only accumulator should be an i32, result type should match the input
type.
Caused in https://reviews.llvm.org/D111590
Reviewed By: sjarus, GMNGeoffrey
Differential Revision: https://reviews.llvm.org/D112484
This is the only lowering to Linalg Tosa has, so it's needlessly
verbose. Likely this was a carry over from IREE's usage where we
originally lowered to linalg on buffers (the only linalg that existed at
the time), so the everything on tensors needed the suffix. We're dropping
it in IREE also, having transitioned entirely to using Linalg on
tensors.
Reviewed By: sjarus
Differential Revision: https://reviews.llvm.org/D111911
Part of the arith update broke UiToFp32. Fixed the lowering and included a new
test to detect a regression.
Differential Revision: https://reviews.llvm.org/D111772
Precursor: https://reviews.llvm.org/D110200
Removed redundant ops from the standard dialect that were moved to the
`arith` or `math` dialects.
Renamed all instances of operations in the codebase and in tests.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D110797
Average pool assumed the same input/output type. Result type for integers
is always an i32, should be updated appropriately.
Reviewed By: GMNGeoffrey
Differential Revision: https://reviews.llvm.org/D111590
The discussion in https://reviews.llvm.org/D110425 demonstrated that "packing"
may be a confusing term to define the behavior of this op in presence of the
attribute. Instead, indicate the intended effect of preventing the folder from
being applied.
Reviewed By: nicolasvasilache, silvas
Differential Revision: https://reviews.llvm.org/D111046
This has been a TODO for a long time, and it brings about many advantages (namely nice accessors, and less fragile code). The existing overloads that accept ArrayRef are now treated as deprecated and will be removed in a followup (after a small grace period). Most of the upstream MLIR usages have been fixed by this commit, the rest will be handled in a followup.
Differential Revision: https://reviews.llvm.org/D110293
Initially, the padding transformation and the related operation were only used
to guarantee static shapes of subtensors in tiled operations. The
transformation would not insert the padding operation if the shapes were
already static, and the overall code generation would actively remove such
"noop" pads. However, this transformation can be also used to pack data into
smaller tensors and marshall them into faster memory, regardless of the size
mismatches. In context of expert-driven transformation, we should assume that,
if padding is requested, a potentially padded tensor must be always created.
Update the transformation accordingly. To do this, introduce an optional
`packing` attribute to the `pad_tensor` op that serves as an indication that
the padding is an intentional choice (as opposed to side effect of type
normalization) and should be left alone by cleanups.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D110425
natashaknk