* add support for mhlo
* Add Test for torch.ne
* fix torch.ne shape/add static test case
* add support for static torch.ne
---------
Co-authored-by: root <root@n31-177-039.byted.org>
The `copy_` op being replaced by `RecomposeSliceCopy_` operates on a
subset of the tensor being mutated, while the `index_put` op being
used to replace the `copy_` op operates on the entire tensor being
mutated. This means that the result type of the `index_put` should be
the type of the input to `index_put` and we need to make sure that
`copy_` does not have users before replacing to avoid type conflicts.
This commit also fixes the result type used for the
`AtenArangeStartStepOp`, and an off-by-1 error when creating the
indices vector.
Lastly, this commit also clamps the `end` value from the slice to the
size of the dimension.
Before inlining a global slot, the users of the global slot are
checked to see if they are `ReadOnly` or `MemoryEffectFree` to make
sure that the global slot is not being mutated. Because the op
`copy.to_vtensor` currently does not have the `ReadOnly` trait, if a
global slot is passed to `copy.to_vtensor`, the pass
`InlineGlobalSlots` will fail.
The op `copy.to_vtensor` is `ReadOnly`, since it does not modify the
contents of the input tensor; it simply makes a new copy. This commit
adds the trait as well as an e2e test that generates the case of a
global slot being passed to a `copy.to_vtensor`.
* feat: split pytorch requirements into stable and nightly
* fix: add true to tests to see full output
* refactor: add comments to explain true statement
* feat: move some tests to experimental mode
* refactor: refactor pipeline into more fine grained difference
* feat: add version differentiation for some tests
* feat: activate more configs
* refactor: change implementation to use less requirement files
* refactor: remove contraints used for testing
* fix: revert some requirement file names
* refactor: remove unnecessary ninja install
* fix: fix version parsing
* refactor: remove dependency on torchvision in main requirements file
* refactor: remove index url
* style: remove unnecesary line switch
* fix: readd index url
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
Bool tensors are represented in TorchScript as an array of
`int8_t`s. However, when importing them into Torch-MLIR, the importer
was assuming the array had `int32_t` elements, leading to the importer
reading into memory that was out of bounds. This commit fixes the
casting of the bool tensor.
The current decomposition for `aten.randn.generator` does not specify
the `dtype` argument of the empty tensors created to store the random
values. This leads to invalid IR when the output type of the `randn`
op is not the default PyTorch dtype.
-- 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>
-- 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>
* 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>
Matthias Gehre
JianzheXiao
Yuanqiang Liu
Vivek Khandelwal
Ramiro Leal-Cavazos
Zhekun Zhang
maxbartel
Gaurav Shukla
gpetters94
Maksim Levental
Prashant Kumar
rahul shrivastava
yifei410
Ze Zhang
Abhishek Varma
Chi_Liu
lisaliu1
Jiahao Li
Ziheng Jiang