This patch adds better functions for parsing MultiAffineFunctions and
PWMAFunctions in Presburger unittests.
A PWMAFunction can now be parsed as:
```
PWMAFunction result = parsePWMAF({
{"(x, y) : (x >= 10, x <= 20, y >= 1)", "(x, y) -> (x + y)"},
{"(x, y) : (x >= 21)", "(x, y) -> (x + y)"},
{"(x, y) : (x <= 9)", "(x, y) -> (x - y)"},
{"(x, y) : (x >= 10, x <= 20, y <= 0)", "(x, y) -> (x - y)"},
});
```
which is much more readable than the old format since the output can be
described as an AffineMap, instead of coefficients.
This patch also adds support for parsing divisions in MultiAffineFunctions
and PWMAFunctions which was previously not possible.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D133654
Add BUILD.bazel files for most of the MLIR tests and lit tests itself.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D133455
This change adds a set of utilities to replace the result of a
`tensor.collapse_shape -> tensor.extract_slice` chain with the
equivalent result formed by aggregating slices of the
`tensor.collapse_shape` source. In general, it is not possible to
commute `extract_slice` and `collapse_shape` if linearized dimensions
are sliced. The i-th dimension of the `tensor.collapse_shape`
result is a "linearized sliced dimension" if:
1) Reassociation indices of tensor.collapse_shape in the i'th position
is greater than size 1 (multiple dimensions of the input are collapsed)
2) The i-th dimension is sliced by `tensor.extract_slice`.
We can work around this by stitching together the result of
`tensor.extract_slice` by iterating over any linearized sliced dimensions.
This is equivalent to "tiling" the linearized-and-sliced dimensions of
the `tensor.collapse_shape` operation in order to manifest the result
tile (the result of the `tensor.extract_slice`). The user of the
utilities must provide the mechanism to create the tiling (e.g. a loop).
In the tests, it is demonstrated how to apply the utilities using either
`scf.for` or `scf.foreach_thread`.
The below example illustrates the pattern using `scf.for`:
```
%0 = linalg.generic ... -> tensor<3x7x11x10xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]] : ... to tensor<341x10xf32>
%2 = tensor.extract_slice %1 [13, 0] [10, 10] [2, 1] : .... tensor<10x10xf32>
```
We can construct %2 by generating the following IR:
```
%dest = linalg.init_tensor() : tensor<10x10xf32>
%2 = scf.for %iv = %c0 to %c10 step %c1 iter_args(%arg0) -> tensor<10x10xf32> {
// Step 1: Map this output idx (%iv) to a multi-index for the input (%3):
%linear_index = affine.apply affine_map<(d0)[]->(d0*2 + 11)>(%iv)
%3:3 = arith.delinearize_index %iv into (3, 7, 11)
// Step 2: Extract the slice from the input
%4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
%5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
tensor<1x1x1x10xf32> into tensor<1x10xf32>
// Step 3: Insert the slice into the destination
%6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
tensor<1x10xf32> into tensor<10x10xf32>
scf.yield %6 : tensor<10x10xf32>
}
```
The pattern was discussed in the RFC here: https://discourse.llvm.org/t/rfc-tensor-extracting-slices-from-tensor-collapse-shape/64034
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129699
CombiningKind was implemented before EnumAttr, so it reimplements the same behaviour with the custom code. Except for a few places, EnumAttr is a drop-in replacement.
Reviewed By: nicolasvasilache, pifon2a
Differential Revision: https://reviews.llvm.org/D133343
This adds a `write_bytecode` method to the Operation class.
The method takes a file handle and writes the binary blob to it.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D133210
This method allows to declare regions as "repetitive" even if the parent op does not implement the RegionBranchOpInterface.
This is needed to support loop-like ops that have parallel semantics but do not branch between regions.
Differential Revision: https://reviews.llvm.org/D133113
This adds division and power implementations to UInt. Modulo and
division are handled by the same function. These are necessary for some
higher order mathematics, often involving large floating point numbers.
Reviewed By: sivachandra, lntue
Differential Revision: https://reviews.llvm.org/D132184
builtin_wrappers contains the wrappers for the clz builtins, which do
not depend on anything in fputil. This patch moves the file out of
FPUtil. The location is updated as appropriate.
Reviewed By: lntue
Differential Revision: https://reviews.llvm.org/D133035
This matches the change made to cmake, mostly. I've left the process of
splitting the inclusions for all the math functions for a later time.
Reviewed By: lntue, sivachandra
Differential Revision: https://reviews.llvm.org/D133073
Printing support enables the production of more useful error messages in unit testing e.g. when using matchers such as `UnorderedElementsAre()` to inspect the contents of a `StringMap`.
Reviewed By: gribozavr2, sgatev, ymandel
Differential Revision: https://reviews.llvm.org/D132747
It's only used from there, and this lets us remove the dependency from Analysis
to the Arith dialect.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D132928
TLite is a lightweight, statically linkable[1], model evaluator, supporting a
subset of what the full tensorflow library does, sufficient for the
types of scenarios we envision having. It is also faster.
We still use saved models as "source of truth" - 'release' mode's AOT
starts from a saved model; and the ML training side operates in terms of
saved models.
Using TFLite solves the following problems compared to using the full TF
C API:
- a compiler-friendly implementation for runtime-loadable (as opposed
to AOT-embedded) models: it's statically linked; it can be built via
cmake;
- solves an issue we had when building the compiler with both AOT and
full TF C API support, whereby, due to a packaging issue on the TF
side, we needed to have the pip package and the TF C API library at
the same version. We have no such constraints now.
The main liability is it supporting a subset of what the full TF
framework does. We do not expect that to cause an issue, but should that
be the case, we can always revert back to using the full framework
(after also figuring out a way to address the problems that motivated
the move to TFLite).
Details:
This change switches the development mode to TFLite. Models are still
expected to be placed in a directory - i.e. the parameters to clang
don't change; what changes is the directory content: we still need
an `output_spec.json` file; but instead of the saved_model protobuf and
the `variables` directory, we now just have one file, `model.tflite`.
The change includes a utility showing how to take a saved model and
convert it to TFLite, which it uses for testing.
The full TF implementation can still be built (not side-by-side). We
intend to remove it shortly, after patching downstream dependencies. The
build behavior, however, prioritizes TFLite - i.e. trying to enable both
full TF C API and TFLite will just pick TFLite.
[1] thanks to @petrhosek's changes to TFLite's cmake support and its deps!
Currently, buffer deallocation considers arith.select to be
non-aliasing, which results in deallocs being inserted incorrectly. Since
arith.select doesn't implement any useful interfaces, this change just handles
it explicitly. Eventually this should probably be fixed properly, if this pass
is going to be used long term.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D132460
Groverkss