This commit adds the torch-onnx-to-torch-backend pipeline which
converts the Torch Onnx IR to Torch Backend IR.
This commit also moves the `ScalarizeShapes` pass from the
`torch-backend-to-linalg-on-tensors-backend-pipeline` to the
`torch-onnx-to-torch-backend` pipeline since the primary goal of
this pass is to scalarize the shapes in the IR coming from the
Onnx models.
Set PyTorch and TorchVision version to nightly release 2024-10-15.
Tracker issue for the failing tests added to xfail_set in this PR.
Issue: https://github.com/llvm/torch-mlir/issues/3796
This commit disables the failing sparse tensor tests since they are not
maintained on day-to-day basis and blocks the roll PyTorch update for now.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Some ops were failing to infer the static component of partially dynamic
shapes, and the cause was a missing aten.slice.t pattern.
The lit test included here is an IR dump created before
DropAbstractInterpCalculations for an unflatten op that was failing to
infer shapes before the change.
-Adds patterns for propagating shapes through AtenWhereSelf and
AtenEqTensor
-Adds fold pattern for a rank0 squeezeDim of a full op
-Adds support for getting a list from a splat ValueTensorLiteralOp for
materializing scalar comparisons in where.self and eq.tensor
With a bit of hammering, these changes should unblock several IREE
inference failures.
This was preventing dynamic dims in an ONNX model from being reified (causing the generation of `tensor.cast`s and preventing fusion in iree):
```mlir
%2 = torch.vtensor.literal(dense<[4, 256]> : tensor<2xsi64>) : !torch.vtensor<[2],si64>]
%7 = torch.prim.ListConstruct %int2 : (!torch.int) -> !torch.list<int>
%8 = torch.aten.reshape %2, %7 : !torch.vtensor<[2],si64>, !torch.list<int> -> !torch.vtensor<[2],si64>
//... chain of foldable ops linking %2 to the `shape` operand of a `torch.aten.broadcast_to ... -> !torch.vtensor<[?,?],si64>`
```
This patch adds two things:
1. support for folding scalar patterns like [1]---squeeze--->[]
---unsqueeze--->[1].
2. a canonicalizer for aten.view that applies when we can statically or
dynamically (through the scalarized view shapes) infer that it is a
flatten or unflatten op in the last dim.
I'm not sure if this is the right place to be adding such a view
canonicalizer. Catastrophically, there is a decomposition from flatten
and unflatten into aten.view. Until this gets deleted (and it definitely
should be deleted), I felt like this would be an appropriate temporary
home. We run scalarize shapes after lowering to the backend contract
(i.e., decomposing), and scalarize shapes is required to be able to
infer dynamic dims coming from size int ops.
# Description
Implementation of the op for `torch.aten.unfold`: [TorchToLinalg Op
Support #347](https://github.com/nod-ai/SHARK-ModelDev/issues/849)
Documentation of op can be found here: [PyTorch
Docs](https://pytorch.org/docs/stable/generated/torch.Tensor.unfold.html)
For this op, we apply a sliding window of some `size` along a single
`dimension`, with `step` in between iterations.
`Declaration: aten::unfold(Tensor(a) self, int dimension, int size, int
step) -> Tensor(a)`
The resulting `unfolded` tensor modifies the shape of `dimension` to be
equal to the number of blocks that the sliding windows extracts/inserts,
with an additional dimension of `size` appended (the number of cols of
the output tensor directly translates from the size of the sliding
window).
So if we had a tensor of rank 3 (A x B x C), with dimension = 1, size =
2 and step = 2:
(A x B x C) |=> (A x (B - size) // step + 1 x C x size)
After extracting the window from the input tensor, we insert the (1 x
size) slice into the output tensor. We can make this simpler by mapping
the output indices from the input indices, like they do in the official
implementation:
[PyTorch
Code](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/lowering.py#L1694)
The op can be valid with no attached shape symbols if they are not
required by the corresponding affine map. Fix the verifier to consider
number of arguments for both.
Instead of
`Unhandled type in getScalarTypeForType`
You now get
Unhandled type in getScalarTypeForType: (type name)
Type properties:
Is integer: yes
Bit width:
...
The root cause is https://github.com/llvm/torch-mlir/issues/3720, at
least for unsigned integer issues.
Fixes https://github.com/iree-org/iree/issues/18562.
During canonicalization pass on `AtenUnflattenIntOp`, if the second dim
was statically equal to one, we would create an `AtenAddIntOp` to add
one to the dimension obtained from `op.getDim()`. This, when passed into
`Torch::unsqueezeTensor()`, would make it get interpreted as
non-constant, which would lead to MLIR failing an assertion when
`UnsqueezeOp` would later get lowered into `ExpandShapeOp`, as the
output of the `UnsqueezeOp` would consist of only dynamic dims.
This patch fixes this behavior, by extracting the integer value from the
dim if it was constant, and then emitting a `ConstantIntOp` from
(dim+1). This creates an output with static shape.
Bump forward and refactor inline global slots to no longer track via
symlinks. This appears to make the tests past until we manage to remove
torchscript work.
As titled, create a new decomposition for `aten.fmod.Tensor` to
`aten.div`, `aten.trunc`, `aten.mul` and `aten.sub`. Note that we only
use `aten.trunc` for floating point operations. This further gets
decomposed to `aten.where` etc. by other existing decompositions.
This decomposition now makes TOSA pass for a simple model with
`aten.fmod` while it makes `stablehlo` fail. For now, we disallow this
decomposition for `stablehlo`
---------
Co-authored-by: Srinath Avadhanula <srinath.avadhanula@getcruise.com>
Addresses an issue in <https://github.com/llvm/torch-mlir/issues/3651>
where some unflatten ops generated from onnx models weren't propagating
static shape information. It may be necessary to add further
optimizations for the more general case when some static information is
present in the unflatten (or possibly reshape/view) op's `sizes` list,
but not reflected in the output shape. These ops will only successfully
infer shapes if the `sizes` list is gotten from a list of constant ints
(with possibly one -1). A common example where this fails is when some
of the `sizes` are determined from `aten.size.int` ops on dynamic
tensors, and other `sizes` are known statically.
This PR includes:
- a canonicalizer for `aten.unflatten.int` which converts to
`aten.unsqueeze` when it is expanding one dim to two, and one of the new
dims is statically 1.
- an improvement to the folder for `aten.__or__.bool` which does not
rely on *both* operands being static.
This PR add `floordiv` to the `PY_BUILTIN_TO_TORCH_OP`. For
`aten.mul.int` and `aten.floordiv.int` ops, we add new Canonicalization
Patterns as follow:
```
%1 = torch.aten.mul.int %input, %const-5
%2 = torch.aten.mul.int %1, %const-6
```
Will be replaced by
`torch.aten.mul.int %input, %const-30`
And
```
%1 = torch.aten.mul.int %input, %const-5
%2 = torch.aten.floordiv.int %1, %const-5
```
Will directly return `%input`
This PR also relaxes the `float` type constraint in TorchToTosa for the
`AtenRsubScalarOp` conversion.
To test:
`cmake --build build --target check-torch-mlir-all`
This commit adds the shape info for the tensors created during the
decomposition of GroupNorm op.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Set PyTorch and TorchVision version to nightly release 2024-08-18.
This commit also updates the `scaled_dot_product_attention` op.
A new attribute `enable_gqa` has been added. As of now, only the
default value for the same is supported.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
The einsum lowering was missing the behavior for duplicate indices in
the equation. This amounts to a diagonalization along duplicate pairs of
indices in the equation.
This patch adds basic support for lowering graphs with per-channel
quantization. Per-channel quantized ops have to be excluded from
`FuseQuantizedOps` for now but can be used in QDQ quantized form.
Using this patch, we're able to import and execute (on the linalg
backend) graphs with per-channel quantization applied using the "new"
PyTorch 2.0 Export Quantization.
The static uneven divisible AdaptiveAvgPool2d means that although the
input size is not an integer multiple of ouput size, but the kernel and
stride size can also be fixed (not dynamic). The derivation logic of
kernel and stride size is consistent with
torch/_decomp/decomposations.py:adaptive_avg_pool2d as described in the
following:
1. Stride Size
Firstly , derive the start index in each reduce operation according to
the output size (`n`), `start_index = ([0, 1, ..., n - 1] * input_size)
// output_size`. For each index `k`, if `k * (input_size % output_size)
< output_size`, then the current and previous stride keeps the same as
`input_size // output_size`. So suppose `(n-1) * (input_size %
output_size) < output_size`, the stride in the whole AdaptiveAvgPool2d
process keeps static, as `input_size // output_size`.
2. Kernel Size
torch/_decomp/decomposations.py:adaptive_avg_pool2d calculates a static
kernel size when the input/output sizes satisfy either of the two
conditions, `input_size % output_size == 0` or `output_size %
(input_size % output_size) == 0`. Here if `input_size % output_size ==
0`, then the kernel size equals `input_size // output_size`, otherwise
`input_size // output_size + 1.`
Vivek Khandelwal
yyp0
zjgarvey
Marius Brehler
Ian Wood
Stephen Baione
Prathamesh Tagore
Xida Ren (Cedar)
Vinayak Dev
Yuanqiang Liu
Branko Trifkovic
Rob Suderman
Srinath Avadhanula
Ze Zhang
lingzhiz1998
pkapris-syrmia
Felix Schneider
Chi_Liu
Rob Suderman
Ivan Butygin