lowering.
This commit addresses the remaining comments on lowering of
slice_scatter and select_scatter.
Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
`aten.select_scatter` op.
This commit adds:
1. Lowering of `aten.slice_scatter` op into `tensor.insert_slice`
op.
2. Decomposes the `aten.select_scatter` op into `aten.slice_scater`
op.
Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
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.
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.
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 adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
When compiling without assertions (i.e. in `NDEBUG` mode), a handful of
statements turn to NOPs, which results in warnings such as missing
return statement or unused variables and function. This patch replaces
such statements with `llvm_unreachable()`, which informs the compiler
about program termination regardless of the `NDEBUG` mode. This also
enables torch-mlir to be compiled using the flags `-Wall`, `-Wextra`,
`-Wpedantic`, and `-Werror`.
This patch adds support for the torch.linalg.vector_norm op to the torch
dialect, including the necessary shape function. It also extends the
conversion of reduction operators to support lowering of
AtenLinalgVectorNormOp, in addition to adding a handful of end-to-end
tests to validate the lowering.
There exist several opportunities to make this lowering optimal and
robust. For instance, in its current form, the translation does not
support ord = 0, +inf, or -inf. For L1 norms, we don't need to raise
each element to the power 1.0. Similarly, L2 norms could benefit from
strength reduction. Since the canonicalization pass is not able to
apply these optimizations, we should consider applying them during the
linalg lowering itself.
This commit adds support for aten.max_pool2d, aten.max_pool2d_with_indices,
and aten.avg_pool2d op for the cases where ceil_mode = true.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The preserve memory specifies that `If any of the input tensors is in channels_last format,
operator output should be in channels_last format` and hence can be
added as is in aten_empty_like op.
This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`,
`aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py and scalar_comparison.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds lowering of `aten.masked_fill.Scalar` op.
This commit also fixes the formatting of the file constant_alloc.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
1. This commit adds lowering of "while-like" prim loop to scf.while
operation.
2. Adds lowering of "for-like" prim loops to scf.for operation.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
This commit adds lowering of `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
Suraj Sudhir
Prateek Gupta
Andrew Cain
Ramiro Leal-Cavazos
Gaurav Shukla
Ashay Rane
Maksim Levental
Vivek Khandelwal
Albert Sandru
Bob Adolf
Vidush Singhal
Prashant Kumar
gpetters94
Sean Silva