This patch add a test for 638ef14, which use `linalg.broadcast` instead
of `generic` for convolution bias.
Co-authored-by: Rongsheng Gao <gaorongsheng@huawei.com>
Supports the result with dynamic shape and scalar indices like
```
func.func @test_gather_scalar(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32> attributes {torch.onnx_meta.opset_version = 13 : si64} {
%0 = torch.operator "onnx.Gather"(%arg0, %arg1) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3,4,5],f32>, !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
```
`Torch::AtenSqueezeOp` is referring to the result shape, so it will
failed on lowering if the result shape is dynamic.
The `axis` attribute is optionally available. Added support by computing
the pad based on the axis values.
---------
Signed-off-by: Rob Suderman <rob.suderman@gmail.com>
- This PR adds new (and equivalent) more tensorized impl of
MelWeightMatrix which lowers all the way to linalg.
- [Ref Pytorch
Impl](https://gist.github.com/PhaneeshB/4e6dfcded3007b1b686fbe28f07a67cd)
- Thanks to @rsuderman for pointing out the difficulties [earlier
impl](#3503) posed during lowering to linalg and also for providing a
better numpy impl 🙏
This commit adds the shape info for the tensors created during the
decomposition of GroupNorm op.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
This commit extends the OnnxToTorch lowering for BatchNormalization op
for supporting the case when training=True.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
The saga of aligning onnx and torch padding conventions continues.
```python
onnx_pads = [low_x, low_y, low_z, high_x, high_y, high_z]
torch_pads = [low_z, high_z, low_y, high_y, low_x, high_x]
```
So not only is the lexicographical ordering hierarchy swapped (low/high
x spatial-dim -> spatial-dim x low/high) but the ordering in the the
spatial-dim specification is also reversed.
This patch properly reverses the pad ordering (and actually uses the
`shuffledPadding` to pad).
The pattern `m_OnnxListOfConstantInts` previously only checked if the
attr inside an `onnx.Constant` op is a `DenseResourceElementsAttr`, but
didn't handle `ElementsAttr`'s. This patch adds support for
`ElementsAttr` and provides an example of it's use via a lit test for
`onnx.Unsqueeze`.
`onnx.Shape` can select only a subset of indices using attributes. Add
support for these attributes.
---------
Co-authored-by: zjgarvey <47986913+zjgarvey@users.noreply.github.com>
Following up from the discussion in
<https://github.com/llvm/torch-mlir/pull/3550>, I've edited the lowering
to prevent OOB extracts in a more direct fashion (i.e., just clamping
directly).
I don't think this affects the lit tests at all, but I've tested the
changes in our external test suite at
<https://github.com/nod-ai/SHARK-TestSuite/tree/main/>. I found the
issue when I was unexpectedly getting `nan`'s along the output image
border for a resize test there.
Change linalg.matmul_unsigned to linalg.matmul with unsigned type_fn
Signed-off-by: Max Dawkins <max.dawkins@gmail.com>
Co-authored-by: Max Dawkins <max.dawkins@gmail.com>
This PR adds a conversion in the TorchOnnxToTorch pass for the ONNX
Multinomial operation. It also adds a TorchToLinalg lowering for the
`aten.Multinomial` op and does a light refactor of some repeated code
that generates random floating point numbers in
`TorchToLinalg/Random.cpp`.
This patch adds a few misc pad op related changes:
1. Addresses issue <https://github.com/llvm/torch-mlir/issues/3457>
2. Addresses issue <https://github.com/llvm/torch-mlir/issues/3442>
3. Fixes the padding order for asymmetrically padded onnx.Conv ops
4. Enables passing quantization through those onnx.Conv op pre-paddings
5. Modifies the torch-to-linalg lowering of AtenReplicationPad2d op to
enable support for input rank != 4
Unfortunately, even with all of these changes, the e2e tests for the
ReplicationPad2d still fail the onnx config, since the torch export
procedure for rearranging the pad order is complicated enough that the
padding ints end up not being able to fold back to constants.
The LpNormalization lowering was previously just computing the norm,
which is incorrect. This computes the norm then divides the input tensor
by it's norm.
I've tested this against some simple onnx models locally. I'll look into
adding a test case for this in an external test suite.
Fix the pad tensor rearrangement such that we change the representation
from [x1_begin, x2_begin, ..., x1_end, x2_end,...] to [xn_begin, xn_end,
...., x2_begin, x2_end, x1_begin, x1_end] where x1, x2 .. xn are the
dimensions of the pads tensor argument.
---------
Co-authored-by: zjgarvey <zjgarvey@gmail.com>
Co-authored-by: zjgarvey <47986913+zjgarvey@users.noreply.github.com>
Addresses an issue with onnx.Gather lowering to linalg:
<https://github.com/nod-ai/SHARK-Turbine/issues/242>
The builder for tensor.expand_shape, without an explicitly provided
output shape, fails to infer an output shape in the case of multiple
dynamic reassociation dims. I tried adding the output shape explicitly
for tensor.expand_shape, but ran into compilation issues later on (see
<https://github.com/iree-org/iree/issues/17760>).
This PR adds support by lowering this op to tensor.reshape when multiple
dynamic reassociation dims are provided.
- Adds limited support for lowering onnx.Loop to primLoopOp
- lower in the pipeline`torch-to-scf` there is a check to see if loop is
for like. A primLoopOp is for like when the input condition is a
`trueBoolConstant`. To adapt the onnx to torch lowering to take
advantage of it, the implementation checks for specific op patterns in
the loodBody region and decides if loop is for like and uses the right
input condition op.
- to adapt the onnxLoopBody to torchLoopBody, we need to adapt the input
block arguments and set the correct output condition variable in the
loop body.
- scanOutput variables are currently not supported.
Before this PR, a statically shaped aten.convolution would generate
dynamically shaped linalg IR, and even `-canonicalize` would not be able
to fold it back into static shapes. This PR ensure that shape
calculations are folded on construction to directly generate statically
shaped linalg IR.
We achieve that by ensuring that `arith` ops involved in computing
shapes are created via `createOrFold`, so that later uses of
`getAsOpFoldResult` see constants instead of those ops.
For example
```
module {
func.func @forward(%arg0: !torch.vtensor<[32,336,112,112],f32>,
%arg1: !torch.vtensor<[336,168,3,3],f32>,
%arg2: !torch.vtensor<[336],f32>)
-> !torch.vtensor<[32,336,56,56],f32> {
%false = torch.constant.bool false
%int2 = torch.constant.int 2
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct : () -> !torch.list<int>
%3 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %0, %0, %false, %2, %int2
: !torch.vtensor<[32,336,112,112],f32>, !torch.vtensor<[336,168,3,3],f32>, !torch.vtensor<[336],f32>, !torch.list<int>,
!torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int
-> !torch.vtensor<[32,336,56,56],f32>
return %3 : !torch.vtensor<[32,336,56,56],f32>
}
}
```
would result in
```
[...]
%padded = tensor.pad %2 low[%14, %15, %16, %17] high[%14, %15, %16, %17] {
^bb0(%arg3: index, %arg4: index, %arg5: index, %arg6: index):
tensor.yield %cst : f32
} : tensor<32x336x112x112xf32> to tensor<?x?x?x?xf32>
[...]
%45 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
ins(%expanded, %expanded_37 : tensor<?x2x?x?x?xf32>, tensor<2x168x168x3x3xf32>)
outs(%expanded_44 : tensor<32x2x168x?x?xf32>) -> tensor<32x2x168x?x?xf32>
[...]
```
and with this PR all shapes are static.
This adds a torchvision op to torch-mlir and a path from onnx.DeformConv
to torchvision.deform_conv2d.
I'm not implementing the torch->linalg lowering for the torchvision op
yet, but posting this PR to get feedback on some of the choices being
made here and to flesh out the onnx frontend a bit.
justin-ngo-arm
Longsheng Mou
jinchen
Muhammad Abubakar
Rob Suderman
Phaneesh Barwaria
Vivek Khandelwal
Ian Wood
aldesilv
zjgarvey
Marius Brehler
Rob Suderman
Gaurav Shukla
Vinayak Dev
pdhirajkumarprasad
Yuanqiang Liu
Arham Khan
Sagar Kulkarni
Matthias Gehre
Suraj Sudhir