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.
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.
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.
* Enables assume_strict_symbolic_shapes on fx_importer imported
programs, indicating strict shape semantics.
* Reworks the view->reshape lowering to take advantage of strict mode
and do one of:
* Collapse to 0D
* Flatten/Unflatten when there is an inferred dim.
* Fallback to tensor.reshape
* Splits some test cases up and adds an attribute to control the old
pattern (so new corners can be tested in strict mode in isolation).
* Dynamic inferred mode needs upstream work to generalize expand_shape
(so that case is suppressed here).
* Deletes the assert from the existing tensor.reshape lowering if strict
shape mode is enabled (since the condition it is dynamically asserting
cannot happen).
This commit adds the OnnxToTorch support for ReduceSumSquare ops.
---------
Co-authored-by: Ubuntu <archana@archana-cpu.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
This is probably a decent PR for learning about blocks and regions.
If you're here to learn about that, consider also looking at
lib/Conversion/TorchToSCF/TorchToSCF.cpp
While this doesn't include an e2e test, it is tested downstream in
https://github.com/nod-ai/SHARK-TestSuite/blob/main/e2eshark/onnx/operators/If/model.py
---------
Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
This is part 1 of ~3, formatting all miscellaneous text files and CPP files matched by a first run of pre-commit. These tend to be low change-traffic and are likely not disruptive.
Subsequent patches will format Python files and remaining CPP files.
Sparse tensor conversions are represented by special aten operators.
This PR ensures the conversions are recognized (instead of failing the
full torch aten lowering to linalg).
All e2e iree tests compiled, but they have the run issue of mismatch of
dtype like the following
```
expected:
1x1x2x2xsi32=[[[12 16][24 28]]]
actual:
1x1x2x2xi32=[[[12 16][24 28]]]
```
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>
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>
We can map to `tensor.reshape` for handling multiple output dynamic
shapes. Later we can perform a more complex analysis for indentifying
expand/collapse cases from the tensor.reshape.
Initially we planned to handle this identification at the `torch` level
however it will be easier to handle once converted to core
mlir-dialects.
Align corner modes which select what the corners mean.
Either the center of the corner points or the edges of the edge points.
---------
Co-authored-by: Rob Suderman <rob.suderman@gmail.com>
1. onnx.MatMulInteger now converts to aten.matmul instead of aten.mm
2. aten.matmul, for ranks >=2, now allows quantized inputs and will
lower to linalg::quantized_matmul or linalg::quantized_batch_matmul.
3. added AtenMatmulOp to the FuseQuantizeOps rewrite patters
QuantizeOperands, QuantizeTransposedOperands, and QuantizeAccumulator
4. added several tests, including some to test AtenMmOp with varying
quantization signed-ness.
5. a quantized matmul mat-vec test is added to verify the failure to
lower to linalg; cleaned of out-of-date code related to common
torch-mlir lowering xfails.
6. in debugging a real model with quantized matmuls, I found a bug on
the scalarize-shapes pass which resulted from the aten.full op folder
returning an incompatible result type. This is fixed by the small change
here to
[lib/Dialect/Torch/IR/TorchOps.cpp](https://github.com/llvm/torch-mlir/compare/main...zjgarvey:torch-mlir:MatMulIntegerFix?expand=1#diff-dc8ed165c207918e606490eee3984b1ad51d7034e6aac36fc046bf47f6f03f4f).
…ute_reshape_shape
as that `aten.view` support at most one `-1` in dim list. The original
calculation of `numel` is wrong when there is a `-1` in dim list.
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>
This commit also cleans up the OnnxToTorch lowering for the ReduceMean
op and adds the support for handling edge cases.
Signed-Off By: Vivek Khandelwal vivekkhandelwal1424@gmail.com
The `convertTensorToElementType` function expects it's argument to have
a valid tensor type that is not `Torch::NoneType`. This PR checks that
the bias tensor is not of type `Torch::NoneType` before calling
`convertTensorToElementType` on the bias tensor argument in the
`matchAndRewrite` member function of the `ConvertAtenConvolutionOp`
class.
Now there no lowing for `aten.Int.bool` in `convert-torch-to-arith`
pass. this PR add this support.
Below is the UT.
```
func.func @torch.aten.Int.bool(%arg0: !torch.bool) -> !torch.int {
%0 = torch.aten.Int.bool %arg0 : !torch.bool -> !torch.int
return %0 : !torch.int
}
```
This commit adds the OnnxToTorch lowering for the Mish, Softplus,
HardSwish, Trilu, ThresholdedRelu op
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
This adds support for converting DynamicQuantizeLinear from torch-onnx
to torch.
I could not get an e2e test to pass, since there seems to be some issues
with uint8 casting somewhere lower in the pipeline. For example
compiling with IREE for llvm-cpu, I would get either the correct zero
point (if zp < 128) or the correct zero-point minus 256 (if zp >= 128).
The output tensor seems to always return a tensor of zeros, which also
occurs when running uint8 examples through QuantizeLinear.
Edit: the first problem can be resolved by casting the output back to
uint8 on output, the second problem is resolved with PR #3018
Reduce mean lowerings did not succesfully lower to `linalg` via torched.
There were two separate paths that could be consolidated to a single
simpler pass. This resulted in a significant improvement in test
coverage.
If the broadcast shape is length-1 at a dim while `?` in the input dim
then we need to broadcast to the dynamic dim. This is equivalent to
taking a max of two dimensions.
This folds small version of the tensor-scalar comparison operators as
they are commonly used for shape computations. This includes le, lt, ge,
gt, eq, and ne.
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.
Manupa Karunaratne
Arham Khan
Vivek Khandelwal
Umang Yadav
Vinayak Dev
Surya Jasper
zjgarvey
Matthias Gehre
aldesilv
Suraj Sudhir
Xida Ren (Cedar)
Angel Zhang
Yuanqiang Liu
RattataKing
lialan
Wu Yuan
Andrew Woloszyn
NeverRaR
Archana Ramalingam
Stella Laurenzo
Andreas Falkenberg
jinchen
penguin_wwy
Aart Bik
Benoit Jacob
Rob Suderman
Thomas Dietert
Xinan Jiang(姜曦楠)
Gaurav Shukla
Xida Ren (Cedar)
Pavani Chowdary