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>
* 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.
A circular dependency was introduced in e7edcc62fd.
Specifically, the `makeShapeLLVMCompatible` and `makeShapeTorchCompatible` utilities were being called from `lib/Dialect/Torch/IR/TorchTypes.cpp` and `lib/Dialect/Torch/IR/TorchOps.cpp` defined under the `:TorchMLIRTorchDialect` bazel target, leading it to take a dependency on `:TorchMLIRConversionUtils` which already depends on `:TorchMLIRTorchDialect`, hence creating a circular dependency.
This commit resolves the same by moving said utilities from `lib/Conversion/Utils/Utils.cpp` to `lib/Dialect/Torch/Utils/Utils.cpp`. Please LMK if there's a better way to fix this and I will update the code.
This commit also adds the required targets to support building the new conversions from Torch to ML Program dialect that was introduced in f416953600.
Bazel build GHA triggered manually to verify: https://github.com/sjain-stanford/torch-mlir/actions/runs/3645944517
- Support for non-prefixed accessors has been removed. See:
https://reviews.llvm.org/D136727
- Rename `operands` to `methodOperands` in `prim.CallMethod` since the
name `operands` overlaps with a builtin method name. See:
https://reviews.llvm.org/D136727
- Add passes in refbackend to lower memref.subview. See:
https://reviews.llvm.org/D136377
- Replace `CopyToValueTensorOps` first in `RewriteViewLikeSubgraph` in
maximize-value-semantics.
The current implementation of the `RewriteViewLikeSubgraph` pass in
maximize-value-semantics creates temporarily invalid IR. In
particular, given a forward slice starting from a
`CopyToNonValueTensorOp` and ending in `CopyToValueTensorOp`s, the
pass first replaces all uses of the `CopyToNonValueTensorOp` with
its operand, which results in all the `CopyToValueTensorOp` users
having their operand have type `!torch.vtensor`, which is invalid.
The correct way to do things is to first replace all the
`CopyToValueTensorOp`s with their operand, and then replace all uses
of the `CopyToNonValueTensorOp` with its operand.
This only started failing now because the generated accessor
`getOperand` for the `CopyToValueTensorOp` now returns a
`TypedValue<NonValueTensorType>`, which has an assert checking that
the value returned is of the expected type.
Summary of changes:
- Replace call to `MemoryEffectOpInterface::hasNoEffect`
with `isMemoryEffectFree`.
- Make fix for the dynamic dims, since
`kDynamicSize` value changed to
`std::numeric_limits<int64_t>::min()` from `-1` in llvm
- `makeShapeLLVMCompatible` and `makeShapeTorchCompatible`
utilities convert shapes in order to remain consistent
with the Torch and MLIR semantics.
- Update tags
llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
This commit renames the patterns used to match on lists of constant
values to `m_TorchListOfConstant{valueType}s`. This is needed to avoid
ambiguity for when `valueType` has `Optional` in it. In particular, it
makes it clear whether the values in the list are optional or the list
itself is optional.
This commit removes almost all of the valsem ops, since the value
semantics version of the ops now exist in PyTorch. The only op missing
is `aten.bernoulli_.float`. In addition, this commit also simplifies
the implementation of `aten.fill.Scalar` by moving it to the pattern
that converts elementwise ops.
This commit makes the following changes needed to update bump LLVM:
- Replace `linalg.init_tensor` with `tensor.empty` (see:
https://reviews.llvm.org/D135129)
- Replace `NoSideEffect` with `Pure` (see
https://reviews.llvm.org/D135505)
- Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp`
with `getBody`
- Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp`
conversion pattern. The result type of `tosa::ReduceSumOp` must have
the same rank as the input type. (see:
https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum)
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
Matthias Gehre
Chi_Liu
Tiago Trevisan Jost
Maksim Levental
Christopher McGirr
Yuanqiang Liu
TatWai Chong
yifei410
Ramiro Leal-Cavazos
Abhishek Varma
Michael Feliz
lisaliu1
Priya Savithiri
Vivek Khandelwal
Jiahao Li
Ashay Rane
Raghavan Raman
Chi_Liu
Tanyo Kwok
Sambhav Jain
Chi_Liu