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
}
```
Fix bug of DecomposeAtenSelectIntOp. Because it may use resultTy when
resultTy has not been inferred.
```
auto resultTy = op.getType().cast<BaseTensorType>();
if (sliceTy.getSizes().size() == resultTy.getSizes().size()) {
rewriter.replaceOp(op, slice);
return success();
}
```
So I add restriction.
This was found while tracing backwards graphs: the convolution_backwards
op will return None if the first result is not needed. Confirmed by
defining a custom op with a `Tensor` return signature and having its
meta kernel return None.
Two e2e tests (AdaptiveAveragePool1/2dUnitOutputSizeDynamic) were
failing due to numerics. This was as a result of passing -1 as the
kernel size in the lowering for the corresponding onnx op
GlobalAveragePool.
https://github.com/llvm/torch-mlir/pull/3055 adds
`lib/Dialect/Torch/Transforms/ScalarizeShapes.cpp`, which depends on
`torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h`.
```
ERROR: /root/.cache/bazel/_bazel_root/b89349c08f7224396763d14fe35cba11/external/torch-mlir/BUILD.bazel:170:11: Compiling lib/Dialect/Torch/Transforms/ScalarizeShapes.cpp failed: (Exit 1): clang failed: error executing command /usr/lib/llvm-16/bin/clang -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -Wunused-but-set-parameter -Wno-free-nonheap-object -fcolor-diagnostics -fno-omit-frame-pointer ... (remaining 101 arguments skipped)
Use --sandbox_debug to see verbose messages from the sandbox and retain the sandbox build root for debugging
external/torch-mlir/lib/Dialect/Torch/Transforms/ScalarizeShapes.cpp:18:10: fatal error: 'torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h' file not found
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1 error generated.
Target @torch-mlir//:torch-mlir-opt failed to build
```
This PR adds the dependency and brings bazel builds back to green.
CI:
https://github.com/sjain-stanford/torch-mlir/actions/runs/8445558053/job/23132941876
* Also adds the basic scaffolding for handling more of these, which will
be needed for cond, while, etc.
* Refactors some of the support in the generic OpOverload emitter so it
can be shared with these other special forms.
This has been on my list for a while, but it just so happens that as
part of upgrading to PyTorch 2.3 and a pure upstream flow in Turbine, we
were using a feature that required integration with auto_functionalized.
This is perhaps the "weirdest" of the higher-order ops and a poor place
to start, but needs must. We have testing for this in Turbine.
Full support in Turbine has an entire custom ops facility. I've reduced
this down to a unit test in torch-mlir.
Reshaping tensors depend on directly matching individual dimensions to
their corresponding dim in the `torch.view` reshape dimensions. This
involves decoupling dynamic dimensions from their static counterparts
and support cleanup / canonicalization.
This commit adds the OnnxToTorch lowering for the Mish, Softplus,
HardSwish, Trilu, ThresholdedRelu op
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
The previous conversions for AtenAdaptiveAvgPool1dOp and
AtenAdaptiveMaxPool2dOp are refactored into a general templated
conversion that works for all of the AtenAdaptive...PoolNdOp's.
New support is added for the following ops:
1. AtenAdaptiveMaxPool1d
2. AtenAdaptiveMaxPool3d
3. AtenAdaptiveAvgPool3d
Support is also provided for passing inputs without batch dimensions.
For example, applying adaptive_avg_pool2d to an input tensor of rank 3.
After [pytorch #118162](https://github.com/pytorch/pytorch/pull/118162)
gets down to torch-mlir, I'll add a test for AdaptiveMaxPool1d with
return_indices (which will pass with that upstream fix).
---------
Co-authored-by: James Newling <james.newling@gmail.com>
At some point, this op became kwarg-only instead of arg/kwarg.
Discovered when upgrading to PyTorch 2.3.
Also adds a test as this was untested in-tree (was caught out of tree).
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
Added support for dynamic shapes in `flattenusingints` op in tosa
dialect. Due to this some Argmax tests pass
This PR fixes this issue https://github.com/llvm/torch-mlir/issues/3004
The following tests pass after this PR
```
1. "ArgmaxIntModule_basic"
2. "ArgmaxIntModule_multiple_maxs"
3. "ArgmaxModule_basic"
```
The only difference between version 7 and newer versions is support for
different data types. We should allow this pattern to match as early as
7. Earlier versions have a more manual broadcast specification through
attributes, so I did not include those versions.
See: [onnx.Div
docs](https://onnx.ai/onnx/operators/onnx__Div.html#l-onnx-doc-divl)
miheer vaidya
Xinan Jiang(姜曦楠)
Vivek Khandelwal
Stella Laurenzo
Gaurav Shukla
Xinyu Yang
Jiawei Wu
zjgarvey
Xida Ren (Cedar)
Sambhav Jain
Yuanqiang Liu
saienduri
Rob Suderman
schnkmwt
penguin_wwy
jinchen
Aart Bik
Abhishek-TyRnT
Pavani Chowdary
Nithin Meganathan
ptrifunovic98
aldesilv
Devjiu