The change of https://reviews.llvm.org/D121513#3411651
has caused a build error when building with clang:
/mnt/vss/_work/1/llvm-project/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp:599:26: error: extra ';' outside of a function is incompatible with C++98 [-Werror,-Wc++98-compat-extra-semi]
ReduceFolder(ReduceAllOp);
Reviewed By: hpmorgan, Mogball
Differential Revision: https://reviews.llvm.org/D122599
- Adds default implementations of `isDefinedOutsideOfLoop` and `moveOutOfLoop` since 99% of all implementations of these functions were identical
- `moveOutOfLoop` takes one operation and doesn't return anything anymore. 100% of all implementations of this function would always return `success` and uses would either respond with a pass failure or an `llvm_unreachable`.
insert is soft deprecated, so remove all references so it's less likely
to be used and can be easily removed in the future.
Differential Revision: https://reviews.llvm.org/D120021
The Utils.cpp file in StandardOps essentially just contains utilities for interacting with arithmetic
operations, and at this point makes more sense as a utility file for the arithemtic dialect.
Differential Revision: https://reviews.llvm.org/D118280
OwningRewritePatternList has been deprecated for ~10 months now, we can remove
the leftover using directives at this point.
Differential Revision: https://reviews.llvm.org/D118287
When 2 clamp ops are in a row, they can be canonicalized into a single clamp
that uses the most constrained range
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D117934
When the min/max are the total range of the value, it is a no-op as the values
are already restricted to that range.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D117625
Each attribute has two accessor: one suffixed with `Attr` which returns the attribute itself
and one without the suffix which unwrap the attribute.
For example for a StringAttr attribute with a field named `kind`, we'll generate:
StringAttr getKindAttr();
StringRef getKind();
Differential Revision: https://reviews.llvm.org/D116466
TOSA's canonicalizers that change dense operations should be moved to a
seperate optimization pass to avoid canonicalizing to operations not supported
for relevant backends.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D115890
When the input and output of a pool2d op are both 1x1, it can be canonicalized to a no-op
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D115908
Slight rename and better variable type usage in tosa.conv2d to
tosa.fully_connected lowering. Included disabling pass for padded
convolutions.
Reviewed By: not-jenni
Differential Revision: https://reviews.llvm.org/D115776
Wrong type was used for the result type in the tosa.conv_2d canonicalization.
The type should match the result element type should match the result type
not the input element type.
Differential Revision: https://reviews.llvm.org/D115463
Quantized case needs to include zero-point corrections before the tosa.mul.
Disabled for the quantized use-case.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D115264
For a 1x1 weight and stride of 1, the input/weight can be reshaped and
multiplied elementwise then reshaped back
Reviewed By: rsuderman, KoolJBlack
Differential Revision: https://reviews.llvm.org/D115207
Fixed the tosa.conv2d to tosa.fully_connected canonicalization for incorrect
output channels. Included uptes to tests to include checks for the result
shapes during canonicalization.
This allows conv2d to transform to the simpler fully_connected operation.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D115170
This reverts commit 13bdb7ab4a. The commit introduced/uncovered an unintended bug in models containing Conv2D.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D115079
For a 1x1 weight and stride of 1, the input/weight can be reshaped and passed into a fully connected op then reshaped back
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D114757
Padding now can explicitly specify the padding value when non-zero is wanted.
This also includes bypassing pads when the pad does nothing.
Differential Revision: https://reviews.llvm.org/D113611
Floating point optimization can produce incorrect numerical resutls for
-0.0 + 0.0 optimization as result needs to be -0.0.
Reviewed By: eric-k256
Differential Revision: https://reviews.llvm.org/D114127
Transpose conv2d shape inference was incorrect, tests did not properly validate
that the shape inference was executing. Corrected shape inference, and extended
tests to actually execute.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D114026
Multiply by one can be removed during canonicalization. This optimizes away unneeded operations.
Differential Revision: https://reviews.llvm.org/D113807
There are several aspects of the API that either aren't easy to use, or are
deceptively easy to do the wrong thing. The main change of this commit
is to remove all of the `getValue<T>`/`getFlatValue<T>` from ElementsAttr
and instead provide operator[] methods on the ranges returned by
`getValues<T>`. This provides a much more convenient API for the value
ranges. It also removes the easy-to-be-inefficient nature of
getValue/getFlatValue, which under the hood would construct a new range for
the type `T`. Constructing a range is not necessarily cheap in all cases, and
could lead to very poor performance if used within a loop; i.e. if you were to
naively write something like:
```
DenseElementsAttr attr = ...;
for (int i = 0; i < size; ++i) {
// We are internally rebuilding the APFloat value range on each iteration!!
APFloat it = attr.getFlatValue<APFloat>(i);
}
```
Differential Revision: https://reviews.llvm.org/D113229
Add pad_const field to tosa.pad.
Add builders to enable optional construction of pad_const in pad op.
Update documentation of tosa.clamp to match spec wording.
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D113322
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 canonicalizer replaces reshapes of constant tensors that contain the updated shape (skipping the reshape operation).
Differential Revision: https://reviews.llvm.org/D112038
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
Should have verified the perm length and input rank were the same before
inferring shape. Caused a crash with invalid IR.
Differential Revision: https://reviews.llvm.org/D110674
For such cases, the type of the constant DenseElementsAttr is
different from the transpose op return type.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D110446
* If the input is a constant splat value, we just
need to reshape it.
* If the input is a general constant with one user,
we can also constant fold it, without bloating
the IR.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D110439
TosaOp defintion had an artificial constraint that the input/output types
needed to be ranked to invoke the quantization builder. This is correct as an
unranked tensor could still be quantized.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D109863
Tosa.while shape inference requires repeatedly running shape inference across
the body of the loop until the types become static as we do not know the number
of iterations required by the loop body. Once the least specific arguments are
known they are propagated to both regions.
To determine the final end type, the least restrictive types are determined
from all yields.
Differential Revision: https://reviews.llvm.org/D108801
Some folding cases are trivial to fold away, specifically no-op cases where
an operation's input and output are the same. Canonicalizing these away
removes unneeded operations.
The current version includes tensor cast operations to resolve shape
discreprencies that occur when an operation's result type differs from the
input type. These are resolved during a tosa shape propagation pass.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D107321
This enables querying shapes/values as shapes without mutating the IR
directly (e.g., towards enabling doing inference in analysis &
application steps, inferring function shape with constant from callsite,
...). Add a new ShapeAdaptor that abstracts over whether shape is from
Type or ShapedTypeComponents or DenseIntElementsAttribute. This adds new
accessors to ValueShapeRange to get Shape and value as shape, but
doesn't restrict or remove the previous way of accessing Type via the
Value for now, that does mean a less refined shape could be accidentally
queried and will be restricted in follow up.
Currently restricted Value query to what can be represented as Shape. So
only supports cases where constant subgraph evaluation's output is a
shape. I had considered making it more general, but without TBD extern
attribute concept or some such a user cannot today uniformly avoid
overhead.
Update TOSA ops and also the shape inference pass.
Differential Revision: https://reviews.llvm.org/D107768
We can propagate the shape from tosa.cond_if operands into the true/false
regions then through the connected blocks. Then, using the tosa.yield ops
we can determine what all possible return types are.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D105940
Handles shape inference for identity, cast, and rescale. These were missed
during the initialy elementwise work. This includes resize shape propagation
which includes both attribute and input type based propagation.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D105845
Make broadcastable needs the output shape to determine whether the operation
includes additional broadcasting. Include some canonicalizations for TOSA
to remove unneeded reshape.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D106846
Retaining old interface and should be constructable as previous, change would have been NFC except it this doesn't implicitly work with OpAdaptor generated in C++14.
Differential Revision: https://reviews.llvm.org/D106772
Added shape inference handles cases for convolution operations. This includes
conv2d, conv3d, depthwise_conv2d, and transpose_conv2d. With transpose conv
we use the specified output shape when possible however will shape propagate
if the output shape attribute has dynamic values.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D105645
Pool operations perform the same shape propagation. Included the shape
propagation and tests for these avg_pool2d and max_pool2d.
Differential Revision: https://reviews.llvm.org/D105665
lipracer