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)
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.
We can support `onnx.Size` by requesing the size of each dimensions and
taking the product of the results, then packing it into a tensor.
---------
Co-authored-by: Scott Todd <scott.todd0@gmail.com>
This mostly copy-pastes the reduce minimum implementation to reduce max
to improve test coverage. We also improve the aten lowering for min/max
dim for unsigned types.
The addition of an e2e test is actually provided in the Shark-Testsuite.
This adds 2 test cases for the gridsampler e2e test.
Also as intended there were some items found which needed correction, so
the Gridsampler op has also a change.
Current implementation depends on using `aten.view` which has issues
inferring tensor collapse/expand operations during the lowering to
`linalg`. Using flatten and unsqueeze better infers what the later
reshape behavior.
Add e2d support for `aten.linalg_norm` by decompose it to
`aten.linalg_vector_norm`.
Lowering to `aten.linalg_matrix_norm` is still unsupported.
To Test:
`python -m e2e_testing.main -v`
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
`getRawBuffer` expects a densely packed vector of `i1` values however
`onnx` does not densely pack the values. Include code to handle the
packing / unpacking.
A handful of operations are commonly used in shape calculations (slice,
concat, broadcast). Added these additional folders to better propagate
simple shape computations.
Existing lowering via aten.view does not work as well for dynamic shapes
as the lowering to tensor.expand must re-infer dynamic shape matching.
Better to directly lower.
This is the lowering of gridsampler from onnx to torch using our prior
implementation of AtenGridSamplerOp.
Here are several checks for cornercases implemented. We may decide to
have part of these checks in AtenGridSamplerOp instead of the onnx
lowering portion.
Finish supporting importing the vast majority of `onnx` operations. This
includes:
- region support
- region value inherentance
- `torch.string` support
- `torch.list` support
- `torch.optional` support
A bunch of small fixes are interlinked and trigger crashes if not
addressed as a group. This includes:
- aten view when expand from a rank-0 tensor
- slice folder with negative indices
- `aten._shape_as_tensor` folder on a rank-0 tensor
- `aten.cat` of a tensor with a length-0 tensor
The decomposition only suports a NCHW lowering however the operation can
support arbitrary spatial dimensions. Updated the lowering to better
support spatial dimensions.
The corrective transpose at the end is computed incorrectly. Is it
actually computin the inverse transpose. Inverting the permutations
fixes the issue.
Torch lowering only supported the most recent version. Refactored the
lowering so more easily handle default values and optional operands /
attributes.
Added Support for float dtype in in torch.arange in TOSA Dialect
This resolves the following issue :-
https://github.com/llvm/torch-mlir/issues/2762
The following test cases are passing after this change
1. ArangeDtypeIntModule_basic
2. ArangeFloatModule_basic
3. ArangeNegativeStartFloatModule_basic
4. ArangeStartFloatModule_basic
5. ArangeStartNegativeStepFloatModule_basic
6. ArangeStartOutDtypeModule_basic
7. ArangeStartStepFloatModule_basic
---------
Co-authored-by: James Newling <james.newling@gmail.com>
We collapsed and broadcasted scatter indices to a single element
version. We should instead upport `tm_tensor.scatter`s support for
multiple indices and the implicitly broadcasted behavior. This avoids
the serialization and materializing a needlessly large indices tensor.
Rob Suderman
Xida Ren (Cedar)
zjgarvey
jinchen
Abhishek-TyRnT
Yuanqiang Liu
Pavani Chowdary
Xinan Jiang(姜曦楠)
Nithin Meganathan
ptrifunovic98
aldesilv
Andreas Falkenberg
Scott Todd
Ze Zhang
Chi_Liu
Vivek Khandelwal
mmakevic