The current padding operation was not functional for dynamic shapes.
Updated and enabled tests so that onnx.pad tests pass.
Work TBD for reflection padding.
Set PyTorch and TorchVision version to nightly release 2024-03-07.
This commit also removes the deprecated constraints API:
342e7929b8
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
We can support `onnx.Size` by requesing the size of each dimensions and
taking the product of the results, then packing it into a tensor.
---------
Co-authored-by: Scott Todd <scott.todd0@gmail.com>
This mostly copy-pastes the reduce minimum implementation to reduce max
to improve test coverage. We also improve the aten lowering for min/max
dim for unsigned types.
The addition of an e2e test is actually provided in the Shark-Testsuite.
This adds 2 test cases for the gridsampler e2e test.
Also as intended there were some items found which needed correction, so
the Gridsampler op has also a change.
`getRawBuffer` expects a densely packed vector of `i1` values however
`onnx` does not densely pack the values. Include code to handle the
packing / unpacking.
A handful of operations are commonly used in shape calculations (slice,
concat, broadcast). Added these additional folders to better propagate
simple shape computations.
This is the lowering of gridsampler from onnx to torch using our prior
implementation of AtenGridSamplerOp.
Here are several checks for cornercases implemented. We may decide to
have part of these checks in AtenGridSamplerOp instead of the onnx
lowering portion.
Finish supporting importing the vast majority of `onnx` operations. This
includes:
- region support
- region value inherentance
- `torch.string` support
- `torch.list` support
- `torch.optional` support
A bunch of small fixes are interlinked and trigger crashes if not
addressed as a group. This includes:
- aten view when expand from a rank-0 tensor
- slice folder with negative indices
- `aten._shape_as_tensor` folder on a rank-0 tensor
- `aten.cat` of a tensor with a length-0 tensor
Torch lowering only supported the most recent version. Refactored the
lowering so more easily handle default values and optional operands /
attributes.
We collapsed and broadcasted scatter indices to a single element
version. We should instead upport `tm_tensor.scatter`s support for
multiple indices and the implicitly broadcasted behavior. This avoids
the serialization and materializing a needlessly large indices tensor.
Also note that we are in the process of proposing SparseTensorMetadata
to PyTorch FX graph export (see
https://github.com/pytorch/pytorch/pull/117907). This will hopefully
eventually replace the current data structures in torch-mlir.
There is no reason to treat `ConstantOfShape` as a specialized import
any as there exists a onnx-to-torch equivalent. Dropping the import
coding and adding support for resource conversion substantially
increases test coverage for dynamically shaped tests.
According to the [official TOSA
spec](https://www.mlplatform.org/tosa/tosa_spec.html#_cast), `tosa.cast`
allows a cast from `fp32` to `fp16`. We were not previously accounting
for this in the `TorchToTosa` lowering.
Also did a tiny bit of cleanup in the code to make it easier to spot
which conversions are currently allowed.
---------
Co-authored-by: Srinath Avadhanula <srinath.avadhanula@getcruise.com>
This enables better re-use in downstreams which use different func
implementations and should have no impact on those that don't except in
opt pipelines if using the old form. With interfaces, explicit pipelines
via `--pass-pipeline=` must be used.
Simple folder for limited size aten tensor operations. This is primarily
useful for shape computation folding as they unfortunately can use
`aten` operators. Add, sub, mul are common examples of these folders.
Onnx slice lowering used arange needlessly instead of directly
constructing the constant dimension values. This makes lowerings to
linalg struggle as multiple folders are required to get what is a
constant index value.
As of https://github.com/pytorch/pytorch/pull/118969, `ExportedProgram`
has the long awaited fixes to correctly categorize various things
relating to parameters, buffers, mutated inputs and constants.
With this additional modeling, we are finally able to implement
(safely/soundly) the mutable semantics that were attempted on the
TorchScript path. The difference is that on that path, we had to
conservatively treat everything as mutable and run some dodgy heuristics
(which have been the cause of many bugs relating to
"MaximizeValueSemantics") to try to get back to an immutable state.
The new model supports mutability at the graph edges, allowing both user
inputs and buffers to be mutated (there is some more support than that,
but that is all I fully tracked through to implementation).
Therefore, when we receive programs like this, we now can selectively
enable mutation at the edges. This happens to be the mutability model
that IREE supports, which I expect to be a primary beneficiary. However,
there is nothing stopping anyone else from handling the `!torch.tensor`
types and the existing copy/overwrite ops that will be selectively
added.
Since this relies on API changes that will not release until 2.3, I'm
being a bit cautious about not refactoring existing facilities.
We can route the torch tests via `onnx` using the `torch.onnx.export`
tooling. We can then reimport, lower to torch, and compile to linalg to
validate the onnx path is working correctly.
The current implementation exposes some failures in the `onnx` path so
we cannot enable the onnx test suite yet due to segmentation faults.
This commit adds the OnnxToTorch lowering for cosh, acosh, asin, asinh,
and atanh op.
This commit also adds the TorchToLinalg lowering for acosh, asin, asinh,
and atanh op.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Some operations include a backend matcher for specialized operations. We
map these back to generics so they appropriately match to the high
performance versions. This is done for the attention operation.
Fixes https://github.com/llvm/torch-mlir/issues/2866
Some backends / downstream projects expect that a "fully converted"
program has no remaining ops or attributes from the original dialect(s).
This test exposes issues that need fixing
(1) propagate sparsity into the FX graph (over elt-wise) (2) batched
dimensions need a new "dense(batch)" format
This commit adds the OnnxToTorch support for Mean, IsInf, IsNaN, and
PRelu ops. All high priority ops were taken so went with these. The non
trivial ones are Mean and IsInf which might require extra review
---------
Co-authored-by: MaheshRavishankar <mravisha@amd.com>
Various improvements on sparsity metadata:
(1) define single data structure for all sparsity related metadata
(2) handle batched dense dimensions, as well as dense subtensor
dimensions
(3) refine sparsity propagation for deeper networks
This PR introduces a sparse_jit wrapper that can run simple models with
sparse tensor inputs end-to-end. The implementation shows all required
components on modifying sparse tensor types with a 1:N relation on the
call sites. Two tests shows that the JIT runs end-to-end while computing
the correct results.
More details to follow (generalizing to COO and different ranks, as well
as support for *output* sparse tensors), but the general concepts are
all here now.
**_Update: Thanks to Rob, bump to proper LLVM/MLIR hash is done!_**
_**NOTE that all parameter passing changes are nicely done "downstream"
in MLIR, so very little changes are required in torch-mlir code
proper**_
---------
Co-authored-by: Franz Haniel <77495327+frafranz@users.noreply.github.com>
Co-authored-by: Franz Haniel <franz.haniel@amd.com>
This PR contains three commits to update the validation checks in the
ONNX -> Torch conversion pass for the AveragePool, Pad, and Slice operators:
> onnx: fix preconditions for lowering AveragePool ops
>
> The `pads` attribute of the AveragePool operator specifies the value to
> pad at both the beginning as well as the end of the axis (see
> https://onnx.ai/onnx/operators/onnx__AveragePool.html#attributes), so
> the size of this attribute should be twice the rank of the input tensor.
> However, our TorchOnnxToTorch bails out early since it incorrectly
> compares the pads attribute with the rank (not twice the rank) of the
> input tensor.
>
> This patch fixes the code to match the spec and adds a lit test.
> onnx: allow optional constant value for Pad operator
>
> The `constant_value` input of the onnx.Pad operator is optional (see
> https://onnx.ai/onnx/operators/onnx__Pad.html#inputs), but the
existing
> logic for lowering the operator into the Torch dialect assumes that it
> is mandatory.
>
> This patch makes the attribute optional and constructs a default value
> (a list of zeros the size of the input tensor) if the attribute was not
> specified.
> onnx: fix checks for axes and steps inputs of Slice operator
>
> The ONNX Spec for the Slice operator allows the `starts` and `ends`
> inputs to have fewer indices that the dimensions of the `data` tensor
> (see https://onnx.ai/onnx/operators/onnx__Slice.html), but our code
> expects these inputs to be as many as the `data` tensor's dimensions.
>
> More precisely, the spec requires that the `starts` and `ends` inputs
> are only as long as the `axes` input, but since the `axes` input is
> optional, the default type for the `axes` input has to match the type
> for the `starts` and `ends` inputs. Moreover, the number of indices in
> the `steps` input also has to match those in the `axes` inputs (instad
> of matching the dimensions of the `data` input).
>
> This patch fixes the checks in the TorchOnnxToTorch conversion so that
> they match the ONNX spec.
This commit modifies the OnnxToTorch lowering of Onnx.Reshape op by
creating the result shape list for the aten.reshape using the result
shape values inferred from the op's result shape.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Folds aten::index_select ops under the following conditions:
1. If the input and output are the same shape, the indexing operation is
a NOP, so just return the input.
2. If the input has shape <1x1x...xNx...x1> (all 1's except for one
dim), and the output shape is <1x1x...x1> (all 1's), then there is a
single index, so extract the single element value and return a tensor
with that value.
---------
Co-authored-by: Dave Liddell <dliddell@xilinx.com>
Andreas Falkenberg
Rob Suderman
Scott Todd
Vivek Khandelwal
Chi_Liu
Aart Bik
Peiming Liu
Sambhav Jain
Srinath Avadhanula
Stella Laurenzo
aldesilv
saienduri
Ashay Rane
Dave Liddell
Xida Ren (Cedar)