-- 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>
-- This commit adds e2e support for atend.sort op.
-- 1. Adds aten.sort op in torch dialect.
-- 2. Adds tm_tensor.sort op in TMTensor dialect.
-- 3. Adds lowering of aten.sort -> tm_tensor.sort.
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.
-- This commit adds e2e support for aten.randint by decomposing it into
an aten.randint.low by setting low=0.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
This commits adds the support for cases for index_put_op:
1.) where index is a 2-d tensor.
2.) where indices is a list of tensors and none, with exactly
2 non none tensors along the consecutive dimensions.
This commit also adds a utility to compute the broadcast shape
given the two input tensors.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit also adds the support for non-unit output padding in the
case of transposed convolution.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
The ops `aten.convolution_overrideable` and
`aten.convolution_backward_overrideable` are currently not e2e tested
in Torch-MLIR. Moreover, there is no way to add e2e tests for them
because the ops cannot be called using the CPU backend (this also
prevents adding tested dtype functions for these ops). Since these two
ops are not expected to ever appear in PyTorch traces obtained through
standard means (https://github.com/pytorch/pytorch/issues/97481),
Torch-MLIR should not have to worry about them.
The `RecomposeComplexOps` pass currently does not have a TableGen
declaration and it is using the base class of `DecomposeComplexOps`,
which causes `--mlir-print-ir-after-all` to create wrong pass
labels. This commit fixes that as well as some minor typos in the name
of the pass.
To keep things simple in shape functions, `Scalar` inputs are
considered `float`s. This means that when inserting the shape
functions into the IR, we must cast any `!torch.number`s into `float`s
so that the operand type matches the expected type in the shape
function. This commit adds the cast from `Scalar` to `float`.
There are several ops that have their shape function upstream and had
not been updated in Torch-MLIR to use the upstream version. This
commit updates those shape function. In addition, TODOs have been
added for shape functions that should be upstream but are not.
The original design for the dtype functions outlined in
https://github.com/llvm/torch-mlir/issues/1462 was unable to properly
handle ops that take optional tensors as an input when the optional
tensor has a value of None. By the time the op gets imported into
torch-mlir, if an optional value is None, all information about the
original type is lost from the op type signature, preventing
torch-mlir from knowing if a value of None was from an optional tensor
or not, which was crucial in the original design since each tensor
argument must be turned into two separate arguments for the dtype
function.
This commit changes the interface to dtype functions such that each
tensor turns into a tuple of two ints, the first representing the rank
of the tensor and the second the dtype of the tensor. Since now there
is a one-to-one correspondence between the operands of an op and the
operands of its dtype function, there is no ambiguity about which
operand of the op corresponds with which operand of the dtype
function.
To test the implementation, this commit defines dtype function for
convolution op, which takes one optional tensor as an argument.
* 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>
* LowerToBackendContract: Explicitly error out on unimplemented operator
But only reject torch.operator when results are invalid.
Otherwise it might be a custom op that the backend supports.
This commit adds a check that `defaultDtype` exists in the RefineTypes
handling of `AtenSumOp` before accessing the method `isInteger`, which
crashes the program is `defaultDtype` is null.
The handling of `defaultDtype` is the same as the one used for the
`AtenSumDimIntListOp`.
* 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>
Currently, the op `torch.tensor_static_info_cast` will not get
canonicalized away if the result type has any shape or dtype
information. This is because `isValidSubtype` only returns true when
the tensor types being compared are exactly the same or the supertype
has no shape and dtype information. Being unable to canonicalize away
the `torch.tensor_static_info_cast` gets in the way of further
optimizations, such as shape propagation.
This commit improves `isValidSubtype` by adding logic that compares
the shapes and dtypes of the two tensor types to determine of one type
is indeed a valid subtype of the other.
Fixes https://github.com/llvm/torch-mlir/issues/1926
The current implementation of `getScalarValue` does not check that the
input to a `ValueTensorLiteralOp` is an i64 before extracting the
value, and it does not check that the result type of the
`PrimNumToTensorScalarOp` is also an i64. This leads to crashes or
invalid IR generated when the `input` is something other than an i64
tensor or `!torch.int`.
This commit addresses those issues. In addition, the function
`getScalarValue` is renamed to `getScalarIntValue` to make it clear
that it *only* extracts scalar integers.
The data-flow analysis does not always propagate information to the
entire graph. This results in some lattice elements being
uninitialized. Currently the lattice elements are not checked to see
if they are uninitialized before rewriting the graph, potentially
resulting in invalid IR (see
https://github.com/llvm/torch-mlir/issues/1896).
This commit adds handling for uninitialized lattice elements.
Set PyTorch and TorchVision version to nightly release 2023-02-27.
This commit also adds the lowering for aten.add and aten.Float.Scalar op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Chi_Liu
Abhishek Varma
Zhekun Zhang
Alexandre Rames
Yuanqiang Liu
Vivek Khandelwal
Alexandre Rames
Ramiro Leal-Cavazos
Ziheng Jiang
Maksim Levental
Michael Feliz
lisaliu1
Sean Silva
Matthias Gehre
Jiahao Li
gpetters94
Roll PyTorch Action
Priya Savithiri
Zachary Cetinic