* [TOSA] Fix conversion for depthwise convolutions
* Add e2e tests for depthwise and grouped convolutions
Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
This commit updates the `llvm-project` and `mlir-hlo` submodules to
commits:
llvm-project: a3f2751f782f3cdc6ba4790488ec20163a40ac37
mlir-hlo: 97c7e4b4506c3a2441c923e592833f45da439009
Changes made:
- Rename `getSuccessorEntryOperands` with `getEntrySuccessorOperands`
and remove `operands` from
`getSuccessorRegions` (https://reviews.llvm.org/D157506)
- Make `TypeConverter` a `const` (https://reviews.llvm.org/D157601)
It's actually fine to not check the rank of the indices, because the conversion anyways flattens the index tensor to be (1, numElements) before applying tosa::gather, and then anyways reshapes the output tensor to the output shape of the aten.embedding.
* RecomposeComplexOps: Remove dead slice op
* lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors
* lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none
* lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max
* More tests for aten.split.Tensor
check the return type of the division to figure out whether to use
the floating point implementation of a division or to use the integer.
the issue rose from the fact that the inputs are all integer but the
result was casted to floating point. The conversion then chose to
use the integer implementation of division which is not legal in tosa
when all the inputs get casted to floating point.
fix(TorchToLinalg): AtenDivScalarOp
upcast self operand as well if applicable, the self operand must also
be casted to float as it can be an integer.
Lowering torch operations that allow different compatible data types
in its operands to tosa end up generating invalid tosa IR with mixed
data types. In tosa spec, certain operations (generally element-wise
operations) require all operands to have the same data type.
Add wrapper functions for those element-wise tosa ops to perform op
creation with type conversion if necessary.
Add support for lowering torch.aten.cat to tosa.concat
* add support for aten cat to tosa
---------
Co-authored-by: yifei <y.zhou@xilinx.com>
Co-authored-by: Lisa Liu <lingl@xilinx.com>
When the user does not specify the `stride` value in 2d pooling ops,
`stride` is given the value of an empty list. However, the current
lowerings for pooling ops assumed that the `stride` operand would
always be a list of two ints, leading to crashes when that was not the
case. This commit fixes the crashes by setting the value of `stride`
to `kernel_size` when `stride` is the empty list, since this is the
default `stride` value specified in PyTorch docs. See:
https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d
-- In Python we have the concept of negative dimension indexing.
-- We would want to normalize such dimensions to be +ve and within the
expected range instead.
-- This commit takes care of a few remaining set of Ops and their
lowerings by applying `toPositiveDim` and `isValidDim` to the
extracted integer `dim` value.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
`TorchToTMTensor` depends on `TorchMLIRTorchUtils` for
`mlir::torch::torch_upstream::get_reduction_enum`.
`TorchMLIRTorchConversionPasses` depends on multiple libs for both tblgen'd
headers and definitions. Test with `ninja TorchMLIRTorchConversionPasses` from
a clean build.
* implemented ceil_mode== true support for lowering aten.max_pool2d to tosa
* add e2e test for lowering aten.max_pool2d to tosa with ceil_mode=true
---------
Co-authored-by: Lisa Liu <lingl@xilinx.com>
* implemented lowering torch.aten.constant_pad_nd to tosa
* add constant_pad_nd e2e tests to TOSA_PASS_SET
* add PadModule_basic & PadWithNoneValModule_basic to TOSA_PASS_SET
---------
Co-authored-by: Lisa Liu <lingl@xilinx.com>
Credit to @vivekkhandelwal1 for finding the necessary changes.
Summary of changes:
- Switch Tosa_IntArrayAttr[N], Tosa_IntArrayAttrUpto[N] to DenseI64ArrayAttr.
- Replace kNoIterationLimit with kNoLimit. (https://reviews.llvm.org/D140525)
- Add dependency on MhloPasses when MHLO is enabled
- Specify result type when using mhlo::DotOp
Summary of changes:
- LLVM now includes <optional> instead of "llvm/ADT/Optional.h" in most
(although not all) places
(https://reviews.llvm.org/rG541ef3d61e9341cd38420c0dbca9250c4d0ea04c).
This patch replaces the affected instances of `llvm::Optional` with
`std::optional`.
- In the usages of llvm::Optional that remain, llvm::Optional::value()
is deprecated, so this patch replaces them with a dereference.
Summary of changes:
- Replace `llvm::None` with `std::nullopt`, since the former is deprecated
(https://reviews.llvm.org/D139763)
- Use setter for symbol visibility instead of passing string attribute when
creating FuncOp
Simon Camphausen
Ramiro Leal-Cavazos
Jiawei Wu
Matthias Gehre
Chi_Liu
Tiago Trevisan Jost
Maksim Levental
Christopher McGirr
Yuanqiang Liu
TatWai Chong
yifei410
Abhishek Varma
Alexandre Rames
Michael Feliz
lisaliu1
Priya Savithiri
Vivek Khandelwal
Jiahao Li
Ashay Rane
Raghavan Raman
Srirammaswamy
Chi_Liu
Tanyo Kwok