This commit renames the patterns used to match on lists of constant
values to `m_TorchListOfConstant{valueType}s`. This is needed to avoid
ambiguity for when `valueType` has `Optional` in it. In particular, it
makes it clear whether the values in the list are optional or the list
itself is optional.
This commit makes the following changes needed to update bump LLVM:
- Replace `linalg.init_tensor` with `tensor.empty` (see:
https://reviews.llvm.org/D135129)
- Replace `NoSideEffect` with `Pure` (see
https://reviews.llvm.org/D135505)
- Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp`
with `getBody`
- Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp`
conversion pattern. The result type of `tosa::ReduceSumOp` must have
the same rank as the input type. (see:
https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum)
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
This commit removes the `weight` tensor from the inputs of one of the
`linalg.generic` ops generated by the `aten.convolution` linalg
lowering, since the indexed values are not actually used by the body
of the `linalg.generic`. Moreover, in general the `weight` tensor does
not have the same shape as the output tensor of the `linalg.generic`,
so both tensors being indexed by the same indexing maps is wrong.
Summary of changes:
- Updated references to the Arith dialect
(https://reviews.llvm.org/D134762)
- Switched to prefixed accessors for MemRef dialect
(https://reviews.llvm.org/D134995)
- Fixed warnings about signed/unsigned comparisons, ignored return
values, and unused variables
This commit adds decomposition of `aten.linear` op. Due to limited
support at tosa backend in case of dynamic dimensions, this
decomposition is currently disabled for tosa backend.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit lowers `aten.matmul` to `linalg.BatchMatmul` under the
following conditions:
1. The result of matrix multiplication must have batch dimensions,
i.e., rank greater than 2.
2. The resultant matrix must have at most 1 dynamic batch dimension.
It also handles broadcasting of batch dimensions when batch dimensions
of the matrices are broadcastable.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This helps keep things organized and also exposes more parallelism to
the build system. It seems though that most of the compile time is
actually spent in the headers though, so the wall time doesn't decrease
as much as I had hoped (and now that the headers are being included
multiple times, the cpu time actually increases a lot, sadly -- will try
to dig into this).
Gaurav Shukla
Ramiro Leal-Cavazos
George Petterson
Ashay Rane
Maksim Levental
Vivek Khandelwal
Sean Silva