This implements the Onnx.NegativeLogLikelihoodLoss op using the
signature provided
[here](https://onnx.ai/onnx/operators/onnx__NegativeLogLikelihoodLoss.html)
by replacing it with a `NLLLossForward` op.
Additionally, I included a helper function `get_loss_reduction_enum` to
convert from a string `reduction` parameter to the corresponding
intended integer value since this is an operation that will be reused
for any loss function module. This differs from `get_reduction_enum` in
`TorchUpstream.cpp` which handles the `reduce` parameter from
`scatter_reduce` type operations.
Issues was found here https://github.com/nod-ai/SHARK-Turbine/issues/643
- [ONNX] Fix padding attributes for onnx.AveragePool
- [Linalg] Add countIncludePad false support for AtenAvgPool1/2dOp
- [Linalg] Add an avg_pool2d countIncludePad False e2e tests
- [Linalg] Fix conflict with AtenAvgPool3dOp
- [Linalg] Fix e2e crash with AtenAvgPool1dOp
- [Linalg] Add dynamic dim support for AtenAvgPool2dOp
- [Linalg] Fix AvgPool2dDivisorOverrideModule crash
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>
Supports asymmetric padding by performing a torch.nn.functional.pad on
the input before performing the convolution.
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
This commit also adds the Torch declaration for aten.max_unpool2d and
aten.max_unpool3d op. The TorchToLinalg lowering for the same will be
added in a follow-up commit.
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.
This PR fixes the bugs for `Torch::AtenOneHotOp` by:
1) Using `Torch::kUnknownSize` as the default value for `numClasses` in
the pattern matching stage in `DecomposeAtenOneHotOp`
2) Adding `AtenIntScalarOp` to the patterns in `TorchToArith`
3) Handling both `int` and `float` types for `off` and `on` values in
`TorchOnnxToTorch` conversion
It also includes:
1) A new test in `TorchToArith/basic.mlir`, for `torch.aten.Int.Scalar`,
and
2) A new test in `decompose-complex-ops.mlir`, for `torch.aten.one_hot`
**Dependencies**
This PR is dependent on #3334.
This commit fixes the bugs for the `onnx.OneHot` operator by:
1) Converting negative indices to non-negative indices
2) Handling both `int` and `float` types for `off` and `on` values
3) Using the correct result type
It also includes a new unit test.
This commit fixes the onnx.MaxPool op lowering which was lacking the
indices result support.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
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.
Chi_Liu
Vivek Khandelwal
Umang Yadav
Manupa Karunaratne
Arham Khan
Phaneesh Barwaria
Vinayak Dev
Surya Jasper
Suraj Sudhir
zjgarvey
Matthias Gehre
Yuanqiang Liu
aldesilv
Rob Suderman
Xida Ren (Cedar)
Angel Zhang
RattataKing
lialan
Andrew Woloszyn
NeverRaR
Archana Ramalingam
Andreas Falkenberg
jinchen