There were two issues related to `ignore_index` being set
(1) the onnx-to-linalg pass as not reading the value correctly (2) the
mean pass was not considering the `ignore_index` value
For (2) when taking the mean we need to know how many of the values were
considered in the sum and therefore we cannot divide by the total number
of elements. Adding a summation across the total number should correct
this issue.
This adds an onnx->torch conversion for onnx.RoiAlign into
torchvision.roi_align or torchvision.roi_pool, and adds those two
torchvision ops to torch-mlir.
There is currently no int16 quantization support in torch. This patch
adds a new mlir type to correspond to the missing "torch.qint16" type,
and enables lowering of quantization-related onnx ops using int16 types.
In follow-up patches, custom quantization logic for ops like
aten.matmul/aten.mm/aten.convolution may need to be revisited to allow
support for qint16. The passes in FuseQuantizedOps.cpp may also need
slight modifications.
This commit adds the lowering for SequenceAt, SequenceEmpty,
SequenceInsert, SequenceErase op
Signed-Off By: Vivek Khandelwal<vivekkhandelwal1424@gmail.com>
Updates:
- some unsupported modes are now going to report a match failure for
unsupported coordinate transformation modes.
- fixes a bug that was introduced in the last patch for resize (my
bad...)
- uses actual x and y coordinates for computing weights in bilinear
interpolation (rather than eps modified values)
- slightly simplifies the bilinear interpolation payload for readability
and performance
- passes coordinate transformation mode information from an onnx.Resize
op to the mode string for the aten._interpolate op. This allows us to
perform custom logic in the torch->linalg lowering to support
onnx.Resize options without losing the default behaviors of the
interpolate op.
The old lowering only had logic for 2d (i.e. images). this patch allows
interpolation for n spatial dims, which is required for some 3d vision
models such as
- onnx/models/pytorch-3dunet_vaiq_int8
which successfully compiles and runs with this patch.
As mentioned in issue #3290 , the difference between onnx.Transpose in
versions 1 and 13 is minimal, and therefore should be supported with the
same conversion pattern.
This commit adds the OnnxToTorch support for ReduceSumSquare ops.
---------
Co-authored-by: Ubuntu <archana@archana-cpu.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
This commit also cleans up the OnnxToTorch lowering for the Squeeze and
Unsqueeze op and adds the support for handling edge cases.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Version number was set too high. Lowered to support more cases allows
more tests to pass.
Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
Previous implementation erroneously mixed up num_outputs with
slice_size. New version correctly computs the slice size and directly
performs slicing rather than leveraging `aten.split.tensor`. This is due
to `onnx` supporting a fixed number of splits making the size
computation more easily computeable when lowering to `aten` rather than
deferring to `aten.split.tensor`.
---------
Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
This PR only performs a lit test. In lieu of an e2e test, https://github.com/nod-ai/SHARK-TestSuite/pull/142 makede sure that the lowering works & the numbers check out.
Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
Shapes can be processed as tensors to represent the set of dimensions.
As reshapes take a list of scalars this can result in a single dynamic
dimension blocking the adjacent static dimensions.
This pass attempts to de-couple tensor computations related to shapes
and propagate values to better support lowering scalar tensor
computations.
Rob Suderman
Vinayak Dev
pdhirajkumarprasad
jinchen
Xida Ren (Cedar)
Gaurav Shukla
zjgarvey
Vivek Khandelwal
Suraj Sudhir
Matthias Gehre
aldesilv
Surya Jasper
RattataKing
Andrew Woloszyn
Archana Ramalingam
penguin_wwy