(1) test full pytorch output for eltwise
(2) use "random" input for LIF, to get general sparse tensor
(3) introduce way to get true sparsity into network (needs backend fix
first)
…cation and sparse tensors.
**NOTE**: This PR _doges_ the issue in buffer-deallocation pass instead
of resolving it. In the future, we need to fix the bug in
buffer-deallocation pass when handling code generated by sparse
compiler.
While waiting for the full resolution of feature request
https://github.com/pytorch/pytorch/issues/117188
(which will propagate sparsity the right way in upstream PyTorch for all
FX Graphs), this minor change allows us to start testing sparsity
"within" a network, rather than just the parameters. Feel free to add
your own rules for testing (but within reason for what will be done
upstream).
Note, two TODOs need to be addressed to work around some pending issues
to make the JIT execution work.
This commit adds the OnnxToTorch support for ReduceSumSquare ops.
---------
Co-authored-by: Ubuntu <archana@archana-cpu.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
While playing with TorchDynamo on ResNet18. I notice following issues:
- `prims.convert_element_type` can’t be canonicalized even if the input
and the output share the same type
- `aten.max_pool2d_with_indices` is always used instead of
`aten.max_pool2d`, even if the second returned output (indices) has no
user
This PR fixes above issues by adding a folder to the
PrimsConvertElementTypeOp and a canonicalizer to the
AtenMaxPool2dWithIndicesOp
Lit test:
`cmake --build build --target check-torch-mlir-all`
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
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>
I spent a little while debugging numerics issues with some tests similar
to the ones in quantized_models.py, only to find that pytorch's
quantized conv transpose is catastrophically inaccurate. I'll upstream
the issue and only leave the tests here which are of the form quantize
-> dequantize -> op.
For some sparse programs (and I am sure other not-seen corner cases for
dense), some passes were missing in the reference pipeline, eventually
resulting in e.g. a unresolved unrealized cast issue. This PR adds some
very obvious missing passes to avoid this situation.
Fixes https://github.com/llvm/torch-mlir/issues/3258
In addition disabling the LTC builds since they are already covered in
CI (build_posix.sh) and I am not aware of a consumer of this flow in the
binary releases of torch-mlir (the main dependency there is from
source).
penguin_wwy
Vivek Khandelwal
Angel Zhang
Peiming Liu
Aart Bik
Yuanqiang Liu
aldesilv
Benoit Jacob
zjgarvey
Jiawei Wu
Xinyu Yang
Vinayak Dev
Xida Ren (Cedar)
Rob Suderman
Archana Ramalingam
Ze Zhang
Prashant Kumar
Sambhav Jain
jinchen
Jae Hoon (Antonio) Kim