On my local machine, `unzip` didn't exist (producing a "command not
found" error), but CMake ignored the error. Although the build did
succeed (because it found a previously-built version of libtorch), it
seems better to abort builds on such failures, so this patch checks the
return code of all external process invocations.
Along similar lines, this patch also updates the shell scripts in
`build_tools` to extensively use double-quoting to prevent unintentional
word splitting or globbing. Since some of the scripts execute `rm`
while using shell variables, this patch also adds the preamble `set -u`
to abort execution if an undefined variable is referenced, so that we
reduce the chances of executing `rm -rf /` if the path expression
happens to refer to an undefined variable.
Add an option to cache libtorch/ releases if you don't want to
download the latest. Add an option to enable source builds.
TESTS:
macOS: verify with / without cache downloads
verify source builds -- shared and static
Linux: Build Tests and Release builds
A previous fix to the handling of size-1 dims in
`aten.view` (https://github.com/llvm/torch-mlir/pull/962) resulted in
the wrong grouping of dimensions when size-1 dims where between two
dims of size greater than 1. This commit fixes that.
TorchScript nodes like `prim::Load` and `prim::Store` aren't supported
in torch-mlir because they can't be lowered to backends, but such nodes
can occur in the TorchScript IR.
This patch adds a rudimentary translation from such nodes to
corresponding ops in the Torch dialect. Since we expected such nodes to
go away during lowering because of the SymbolDCE pass, this patch does
not add code to lower these ops beyond the Torch dialect.
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 commit fixes the shape function for `index.Tensor`, adding
support for multiple index tensors and `None`s in the indices
list. This commit also adds support for input tensors of rank greater
than 1. The lowering for `index.Tensor` still has the the limitation
that only a single index tensor along the first dimension of the input
tensor is supported.
Prior to this patch, the torch dialect included `AtenTriuOp` for
computing the upper triangular part of the input matrix, but there was
no code for lowering the op to the linalg dialect.
This patch adds code to generate a `linalg.generic` operation that
compares indices (computed using `linalg.index`) to choose between zero
or the original value (using `arith.select`). The lowering fails if the
number of dimensions are less than two. This patch also adds a few
end-to-end tests.
The MacOS builders are having linking trouble with the extension library.
Until it's fixed, all support for op extensions is disabled. It should be
easy to restore once the issue is resolved.
The function `AffineMap::inferFromExprList` does not work if the first
vector of expressions is empty, because it uses these expressions to
obtain the context. This prevented `aten.permute` from working for
inputs of 0-rank. This commit adds support for 0-rank inputs.
PyTorch allows new operators to be registered dynamically in modules.
Torch-mlir already makes it fairly straightforward to add support for
new operators, and this commit just extends that support to allow new
PyTorch ops to come from a external module.
This does *not* allow ops to be dynamically loaded into torch-mlir.
Torch-mlir must still be compiled with support built-in.
Add a `_torch_mlir_custom_op_example` subpackage to `torch_mlir` which
registers an demonstration op. It will not be imported by default when
importing torch_mlir. It's strictly for testing and documentation.
Adds an end-to-end test for the `torch_mlir_custom_op_example::identity` op.
With all these changes, we should now be actively testing PyTorch extension
support with all future patches.
Now that upstream exposes them nicely, we can use them.
I noticed that we had added stuff into the upstream_shape_helpers.py
file (which was supposed to stay pristine), so some more shape functions
need to be upstreamed.
Going forward, all shape functions should be upstreamed similar to
https://github.com/pytorch/pytorch/pull/76889 instead of added in this
file.
This commit adds lowering of `aten.div.Tensor_mode` op.
This commit also fixes formatting for the test file elementwise.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit decomposes `aten.baddbmm` op into `aten.bmm`,
`aten.mul.Scalar`, and `aten.add.Tensor` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
use_tracing=True was behaving unexpectedly because the handling of
single arguments was happening after the torch.jit.trace call.
This also fixes the check to specifically test for a torch.Tensor or
TensorPlaceholder so that both lists and tuples would be correctly
handled.
Quinn Dawkins
Ashay Rane
powderluv
Tanyo Kwok
Ramiro Leal-Cavazos
Suraj Sudhir
Sean Silva
JakopinA
Gaurav Shukla
erman-gurses
Maksim Levental
Vivek Khandelwal
Albert Sandru
Bob Adolf
Henry Tu
Jae Hoon (Antonio) Kim
Vidush Singhal