Commit Graph

1808 Commits (main)

Author SHA1 Message Date
justin-ngo-arm 14ef05a292
[TOSA] Extend Torch to TOSA reduction ops legalization (#3710)
- Add Torch to TOSA legalization for the following reduction ops:
  + aten.min.dim
  + aten.min
  + aten.max
  + aten.prod
  + aten.prod.dim_int
  + aten.all.dim
- Add dtype casting support for reduce sum and prod ops
- Extend aten.max.dim legalization to a template to support aten.min.dim
legalization
- Update end-to-end tests sets in xfail_sets.py

Signed-off-by: Justin Ngo <justin.ngo@arm.com>
Change-Id: I854dd6c0c55e570c1fb7242f20c85cf64d6e7fe0

Signed-off-by: Justin Ngo <justin.ngo@arm.com>
2024-09-16 12:40:24 -07:00
Srinath Avadhanula bc70c50373
Delete unnecessary linalg conversion for aten.fmod (#3707)
Follow up cleanup for [this
PR](https://github.com/llvm/torch-mlir/pull/3689), which introduced a
decomposition for `aten.fmod.Tensor`. This means that the lowering for
this operator in linalg is no longer needed.

Thanks to @vivekkhandelwal1 for pointing this out.

---------

Co-authored-by: Srinath Avadhanula <srinath.avadhanula@getcruise.com>
2024-09-13 09:39:58 -07:00
Yuanqiang Liu 7b94ced39a
[Stablehlo] fix aten compare ops' promote rules (#3709)
previous PR(https://github.com/llvm/torch-mlir/pull/3702)
2024-09-13 18:48:41 +08:00
zjgarvey d61986cfcf
Add Decompostion for `Aten_SafeSoftmaxOp` (#3708)
Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-09-12 16:58:10 -05:00
yyp0 edf725ef42
[Torch] add AtenAsStridedOp in torch dialect (#3706) 2024-09-12 19:07:11 +08:00
Yuanqiang Liu 3f07077ff9
[Torch] enhance fold of aten.alias (#3705) 2024-09-12 17:04:57 +08:00
Branko Trifkovic 1c4b9d6a0e
Implement lowering of torch.aten.hstack (#3563) 2024-09-11 16:41:47 +05:30
Rob Suderman 6934ab81b0
Bump llvm/llvm-project@b6603e1bf1 (#3697)
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.
2024-09-10 08:57:15 -07:00
giacs-epic b35675a78e
[onnx] Add support for `auto_pad` in `onnx.Conv` (#3670)
Add logic for `auto_pad` attribute in the conversion of `onnx.Conv`
torch dialect.
Add lit tests covering different configurations of `auto_pad`.
2024-09-10 20:31:53 +05:30
rohan-tan-bhowmik e86f56bc76
[Torch] [TMTensor] Added mask and is_causal support for torch.aten.scaled_dot_product_attention (#3690)
Enabled mask and is_causal parameters for torch.aten.scaled_dot_product
attention + relevant comments + tests.

The tests added highlight the new capabilities introduced in this PR,
including:

Attention with F16 mask
Attention with Boolean mask
Causal attention with same Q K V shapes
Causal attention without Q K V shapes

Made sure that one cannot input both mask and is_causal.
2024-09-09 15:51:41 -07:00
Srinath Avadhanula 0a788e0467
Decompose aten.fmod into aten.mul,sub,div etc. (#3689)
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>
2024-09-09 09:00:11 -07:00
Felix Schneider df6098e43d
[TorchToLinalg] Use `linalg.transpose` instead of `generic` when lowering `aten.T` (#3660)
The lowering pattern for `aten.T` uses transposition implemented via
`linalg.generic`. For downstream passes it is advantageous to use named
ops wherever possible, so this patch changes the lowering to use
`linalg.transpose` instead.
2024-09-07 08:09:10 +02:00
Branko Trifkovic 70d5730c87
[LINALG] Implement lowering of torch.aten.rot90 (#3551) 2024-09-06 10:36:17 +05:30
justin-ngo-arm d4b5e05ac1
[TOSA] Add Torch to Tosa Legalization for torch.tril (#3678)
Change-Id: Ie5ba31a27394c3adcea00266a9d562862dbd8b08

Signed-off-by: Justin Ngo <justin.ngo@arm.com>
2024-09-05 11:27:29 -07:00
zjgarvey 295bf418a4
Add a canonicalization pattern for `aten.unflatten.int` (#3656)
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.
2024-09-03 16:38:20 -07:00
Ze Zhang b3942ff984
Add canonicalize pattern for aten.mul.int and aten.floordiv.int (#3680)
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`
2024-09-03 09:13:59 -07:00
Vivek Khandelwal 567ed44fd0
[MLIR][TORCH] Add E2E support for aten.polar op (#3671)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-09-03 10:51:03 +05:30
jinchen fd759e4b1f
Fix onnx.Gather lowering with dynamic shapes (#3675)
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.
2024-08-29 17:02:16 -07:00
lingzhiz1998 5bc59ce1fa
[TorchToLinalg] Support lowering MaxPool3dWithIndices (#3652)
Support torch.MaxPool3dWithIndices lowering to linalg backend.
2024-08-27 14:14:25 -05:00
Xida Ren (Cedar) eb7bf78a9c
Add RestructureNonConstantAxes pass to address reduce op tests failing on non constant axes (#3600) 2024-08-26 14:06:06 -07:00
Felix Schneider 638ef14512
[TorchToLinalg] Use `linalg.broadcast` instead of `generic` for conv bias (#3661)
The current implementation uses a `linalg.generic` to broadcast the bias
tensor for the lowering of convolutions. This is suboptimal for later
pattern matching. This patch changes it to use the respective named op,
`linalg.broadcast`, instead.
2024-08-26 20:29:11 +02:00
Rob Suderman f9766c89f6
[onnx] Handle `torch.aten` for inner product case (#3634)
The following case was failing to lower for einsum. This fixes up the
inner product issue.
2024-08-24 11:41:25 -07:00
Rob Suderman 6cf139687d
[onnx] Support for optional `axis` attribute for `onnx.Pad` (#3635)
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>
2024-08-24 11:41:08 -07:00
Rob Suderman b3b8e2e96a
[torch] Fix lowerings of rshift and lshift (#3665)
I missed adding second operand conversion and adding them to the set of
rewrite patterns.
2024-08-24 03:27:18 +00:00
Rob Suderman 9a4c8c606c
[torch] Add `torch.aten.view.dtype` to op list (#3664)
Support dtype conversion between types. This is useful for bitcasting
buffers between differing bit depths.
2024-08-23 19:02:53 -07:00
Phaneesh Barwaria 9a6fe58a02
onnx.MelWeightMatrix Onnx to Torch to Linalg (#3659)
- 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 🙏
2024-08-22 08:55:03 -07:00
Vivek Khandelwal fcc5f444cd
MLIR][TORCH] Fix GroupNorm decomposition by adding shape info (#3658)
This commit adds the shape info for the tensors created during the
decomposition of GroupNorm op.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-08-22 21:20:40 +05:30
lingzhiz1998 7f886cc270
[TorchToLinalg] Support torch.isclose lower to linalg (#3631) 2024-08-21 11:55:54 +08:00
Ian Wood a24114efa3
[TorchToLinalg] remove `extract_slice` grid_sample lowering (#3483)
Instead of using extract_slice for grid sampler, use affine constants to access the X and Y values in the generic op's region.
2024-08-20 14:23:43 -07:00
zjgarvey f66908f190
[TorchToLinalg] address a dtype mismatch in `aten.multinomial` lowering (#3630)
Resolves <https://github.com/llvm/torch-mlir/issues/3628>
Unblocks a compile failure for one of the MiGraphx models
(`AgentModel`).
2024-08-20 15:14:48 -05:00
Vivek Khandelwal 0a86deb59a
build: manually update PyTorch version (#3627)
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>
2024-08-19 12:03:56 +05:30
Rob Suderman 78deb175b3
[onnx] Fix shortcircuit path (#3633)
The implementation was short circuiting the second result. Updated to
guarantee we do not short circuit.
2024-08-16 09:23:47 -07:00
Rob Suderman 3a599bec80
[onnx] Fix onnx.ThresholdedRelu crash (#3638)
Result type was not fetched causing a crash on construction
2024-08-16 09:23:38 -07:00
Rob Suderman f09cb766dc
[onnx] Fix `torch` lowering for determinant (#3639)
The determinant lowering had some extract / insert shape mismatches.
Replumbed shape manipulations to correctly implement the determinant
operation.
2024-08-15 15:41:50 -07:00
yyp0 43e3118eb9
[Stablehlo] use stablehlo specs lowering AtenSliceScatterOp (#3592) 2024-08-15 20:06:29 +08:00
Yevhenii Havrylko 64b0d4aed3
Add missing dependency to TorchMLIRRefBackend target (#3107)
Discovered in https://github.com/llvm/torch-mlir/issues/3104
Most likely when building with stablehlo, while waiting for it missing
dependency was generated to location shared with another dependency.
2024-08-14 23:41:51 +08:00
pkapris-syrmia 23ec5399e5
Implement lowering of aten.atleast_2d (#3546)
This operator is needed to implement aten.vstack, which will be
submitted in a subsequent PR
2024-08-14 18:52:31 +05:30
Branko Trifkovic da877a781e
Added support for integer to complex conversion (#3604) 2024-08-14 18:13:00 +05:30
pkapris-syrmia 10fe5d08d1
Implement lowering for torch.aten.rad2deg (#3586) 2024-08-14 16:37:28 +05:30
Vivek Khandelwal 4a0bed0ce0
[ONNX] Add training mode support for BatchNormalization op (#3597)
This commit extends the OnnxToTorch lowering for BatchNormalization op
for supporting the case when training=True.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-08-14 10:46:38 +05:30
Rob Suderman 2511cf46b4
[onnx] Fix `onnx.RNN` for layout attribute (#3620)
The `layout` attribute was not considered for the `onnx.RNN` operation.
Added support for the attribute to transpose the inputs / outputs of the
RNN when valid.
2024-08-13 14:34:25 -07:00
Rob Suderman af67f9efb0
[onnx] Support integer types for `onnx.Pow` (#3626)
Pow is not support for the `torch` operator. Add casting for integer
types.
2024-08-13 09:39:04 -07:00
Rob Suderman 39307f0462
[onnx] Fix `onnx.Gather` for bad expansion (#3625)
A case where unsqueeze was require was missed causing compilation
failures.
2024-08-13 09:38:55 -07:00
Rob Suderman 9ab93436c4
[torch] Support diagonal `einsum.Diagonal` (#3618)
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.
2024-08-13 09:38:43 -07:00
pkapris-syrmia d11d6f6fea
[TorchToLinalg] Fix torch.aten.remainder for negative operands (#3581)
Closes #3575

The PyTorch remainder operator is meant to compute the Python modulus
operator entrywise:

https://pytorch.org/docs/stable/generated/torch.remainder.html#torch.remainder

In python the modulus operator is meant to always return a result with
the same sign as the divisor:

https://docs.python.org/3/reference/expressions.html#binary-arithmetic-operations

In other words, torch.aten.remainder should return a Python-style
modulus instead of a C-style modulus. However the remainder operator was
simply translated into arith.ModSI or arith.ModF, which both effectively
compute the C-style modulus. Now the lowering has been modified so that
the modulus operator works properly with negative numbers, both in the
dividend, and the divisor.
2024-08-13 21:17:21 +05:30
Yuanqiang Liu c5b3cf299a
[Torch] emit upsample_nearest1d/2d/vec, and add shape/dtype functions (#3629) 2024-08-13 19:14:24 +08:00
aldesilv a4ba02eef5
[ONNX] add support for tfidfvectorizer (#3553)
1-d/2-d input and output
implemented based on the description and example test cases in
https://github.com/onnx/onnx/blob/main/docs/Operators.md#TfIdfVectorizer
and some notes from

https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_tfidf_vectorizer.py#L128

---------

Co-authored-by: zjgarvey <zjgarvey@gmail.com>
2024-08-12 18:10:11 -05:00
Rob Suderman d3695a97a0
[onnx] Fix `onnx.Hardmax` lowering to torch (#3624)
The lowering to torch makes assumption about the dimensions / types of
reduce max and onehot. We need to correct for expected torch behavior.
2024-08-12 11:19:02 -07:00
Phaneesh Barwaria 026dfade64
onnx.MelWeightMatrix TorchOnnxToTorch (#3503)
Just uploading what I have till now

[Gist](https://gist.github.com/PhaneeshB/761f75f5522d9f4a40ef949a328e93fe)
of pytorch impl that I'm following to implement the OnnxToTorch lowering

Additional Details - (also pasted as comment in gist)
[Op
Description](https://github.com/onnx/onnx/blob/main/docs/Operators.md#melweightmatrix)
in Onnx Documentation

[Example](https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-93)
Used the same example in this file.
the Expected output is shown in the example

[Reference Onnx
Impl](4c3ed5e08b/onnx/reference/ops/op_mel_weight_matrix.py (L13))
- This is the base for the above code.
2024-08-12 21:18:29 +05:30
Felix Schneider 0314188dbe
[torch] Basic support for per-channel quantized graphs (#3623)
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.
2024-08-10 15:51:09 +02:00
Rob Suderman 44266ab0c4
[onnx] Support `fp8` for `onnx.QuantizeLinear` (#3619)
We need to directly decompose quantize linear for `fp8` types as the
equivalent torch operations do not support the operation.
2024-08-09 12:32:46 -07:00
Rob Suderman 8358e8c255
[onnx] Add support for `fp8` `onnx.DequantizeLinear` (#3617)
Fp8 needs a slightly different path for dequantization as the `torch`
dequantize operation does not support `fp8` types.
2024-08-08 16:20:53 -07:00
Rob Suderman 880e64bbbb
[onnx] `onnx.Split` may not have `num_outputs` which can be inferred (#3608)
The attribute does not exist in all variants of the operation. It can be
inferred from the number of results so we should just do that.
2024-08-08 16:17:38 -07:00
Rob Suderman fd98476f77
[torch] Unpacking sometimes misses shape inference (#3609)
It is possible that the unpacked tensor does not match the same inferred
shapes. This is pretty common when ingesting form the `onnx` frontend.
2024-08-08 16:17:31 -07:00
Rob Suderman 4350672685
[torch] Add integer support for pooling operations (#3610)
If we pass an integer type to the pooling operation we incorrectly pad
with an integer value with causes downstream compilation failures.
2024-08-07 21:42:10 -07:00
zjgarvey 7f2a17e757
[ONNX] fix padding for `onnx.MaxPool` (#3611)
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).
2024-08-07 20:34:00 -07:00
Rob Suderman 6c33ab024e
[onnx] `onnx.CenterCropPad` used an incorrect type for toScalar (#3605)
To scalar should have a rank-0 tensor type not rank-1 with length 1.
Changing allows proper compilation.
2024-08-07 20:33:33 -07:00
Rob Suderman 59a4c6fda4
[onnx] Fix transposition code for `onnx.OneHot` (#3606)
The post onehot transposition code was unexercised. Fixed the test and
transformation to check use.
2024-08-07 18:20:26 -07:00
Marius Brehler 341f415b1e
[onnx] Fix lowering `onnx.Shrink` to Torch (#3603)
This fixes the result type of the `torch.aten.lt.Scalar` and
`torch.aten.ge.Scalar` ops created during the lowering of `onnx.Shrink`
to Torch.
2024-08-07 21:25:14 +02:00
Rob Suderman 18139994e8
[onnx] Fix edge condition for `onnx.ReduceMax` (#3598)
For length-0 on `onnx.ReduceMax` the length 0 case was incorrect due to
a copy paste error.
2024-08-07 10:32:28 -07:00
zjgarvey 8d95fe9eeb
[TorchToArith] Add a lowering for `torch.add.float_int` (#3594) 2024-08-07 11:55:27 -05:00
Chi_Liu a51b4e014a
[Torch] Disable 1-d quantized convolution (#3601)
To fix https://github.com/nod-ai/SHARK-Turbine/issues/253#issuecomment-2271815640
Prevent fusion for 1d convolution ops and just do it as an f32 conv
since there isn't a linalg named op for quantized 1-d convolution yet.  
Get 24 onnx eca* models passed in iree-comiple.
2024-08-07 09:01:16 -07:00
Branko Trifkovic 2d6bfb2dec
[LINALG] Added support for conversion from float to complex. (#3595) 2024-08-07 12:36:48 +05:30
Rob Suderman b48e55c2f7
[onnx] Handle negative indices for `onnx.GatherElements` (#3599)
Add a check for negative indices and offset appropriately for
`onnx.GatherElements`.
2024-08-06 18:54:01 -07:00
Rob Suderman b1a232222f
[onnx] Fix `onnx.Shape` to include `start` and `end` processing (#3580)
`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>
2024-08-05 13:56:07 -07:00
Gaurav Shukla 839fe90f86
[MLIR][ONNX] Add support for onnx.scan op (#3516)
This commit lowers onnx.scan op to torch.prim.Loop op and adds the
lowering in the onnx pipeline.

Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
2024-08-05 15:37:26 +05:30
Rob Suderman 7e7af67080
Avoid warnings-as-errors build failure (#3588)
Lambda needs a return value to avoid a build failure.
2024-08-02 12:27:31 -07:00
zjgarvey d0933b0eb6
[TorchToLinalg] Fix possible OOB access in Interpolate lowering (#3570)
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.
2024-08-02 13:55:37 -05:00
zjgarvey 79ae0afc2f
[TorchToLinalg] Simplify QuantizePerTensor lowering (#3576)
Uses arith::MaximumFOp and arith::MinimumFOp instead of comparison and
select ops to improve readability of IR.
2024-08-02 13:40:52 -05:00
Rob Suderman f7b5c13870
Change linalg.matmul_unsigned to linalg.matmul with unsigned type_fn (#3587)
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>
2024-08-02 11:32:24 -07:00
Rob Suderman d273bdfabf
[onnx] Fix default `alpha` for `onnx.Elu` (#3583)
We were defaulting to `0.0` for `onnx.Elu` when it is supposed to be
`1.0`.
2024-08-02 09:29:17 -07:00
Rob Suderman 3d33c5a206
[onnx] Fix `onnx.ScatterElements` for negative indices (#3582)
We need to adjust for negative scatter indice values. Added
materializing out the inbounds adjustment.
2024-08-02 09:01:10 -07:00
Rob Suderman 306ed62edd
[onnx][torch] Fix `onnx.SoftmaxCrossEntropyLoss` for ignore index (#3585)
There were two issues related to `ignore_index` being set

(1) the onnx-to-linalg pass as not reading the value correctly (2) the
mean pass was not considering the `ignore_index` value

For (2) when taking the mean we need to know how many of the values were
considered in the sum and therefore we cannot divide by the total number
of elements. Adding a summation across the total number should correct
this issue.
2024-08-02 09:00:56 -07:00
yyp0 22cd4441e7
[Torch] Add support for static uneven divisible AdaptiveAvgPool2d (#3566)
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.`
2024-08-01 11:37:53 +08:00
Jiawei Wu edc87fc577
[stablehlo] support dynamic-shaped index in stablehlo conversion for aten.index-like ops (#3322)
For now, at most one dynamic dim of index tensors in
aten.index/aten.index_put-like op is supported.
2024-08-01 10:41:09 +08:00
Rob Suderman 7f475e174e
Add extf-trunc f32-f64-f32 ellision (#3579)
Torch has all scalars represented as i64 and f64 types which results in
extraneous trunc-extf commands. We can rework this by elliding
widen-narrow cases away.
2024-07-31 16:50:00 -07:00
Jiawei Wu 7b2902f6e2
[stablehlo]: fix aten.index_put_hacked_twin lowering to StableHlo (#3572)
Current StableHlo lowering strategy works well when `src` tensor's rank
is no bigger than `dst` tensor's. The new patch make it succeed in other
cases. The following is an example.
```
%190 = torch.prim.ListConstruct %arg4 : (!torch.vtensor<[1,1024],si64>) -> !torch.list<vtensor>
%191 = torch.aten.index_put.hacked_twin %189, %190, %186, %true : !torch.vtensor<[1024,768],f32>, !torch.list<vtensor>, !torch.vtensor<[1,1024,768],f32>, !torch.bool -> !torch.vtensor<[1024,768],f32>
```
2024-07-31 22:33:57 +08:00
yyp0 f49b9c14f1
[Torch] Add support for Aten__Or__BoolOp (#3574) 2024-07-31 17:23:53 +08:00
Suraj Sudhir d3efab984b
[TOSA] Fix Tensor.hacked_twin to support diff size indexes (#3547)
- Broadcasts index list tensors
- Adds torch.nn.Unfold test

Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2024-07-30 14:32:05 -07:00
Ivan Butygin 8bd1b9751f
`max_unpool3d` linalg lowering (#3536)
An attempt of  `aten.max_unpool3d` to linalg lowering.
There are known issues with this implementation (see comment in code).
2024-07-30 20:59:17 +03:00
zjgarvey f1c74e1431
[TorchToLinalg] add support for depthwise qconv (#3564)
- Adds support for lowering depthwise + quantized convolution ops to
linalg::DepthwiseConv2DNhwcHwcQOp
- Changed the variable name for groupSize (which is really C/G) to the
more appropriate numGroups (G).
- Discovered in e2e testing that linalg does not accept (Cin = groups &&
Cout = K*groups for K>1) as a "depthwise" conv, so this also updates the
case-checking to reflect this issue.
2024-07-29 12:25:07 -07:00
zjgarvey 50d6ce225f
Align Quantization Rounding Scheme with ONNX/Pytorch (#3569)
Pytorch and ONNX apparently round to nearest, ties go to nearest even,
but we were using `math::round` for the torch-to-linalg conversion of
`quantize_per_tensor`, which rounds away from zero on ties.
2024-07-29 12:24:46 -07:00
Vinayak Dev 30c4d2f2b8
[torch] Add OnnxToTorch lowering for Onnx.Unique op (#3523)
Adds OnnxToTorch Lowering for the `Onnx.Unique` op.
2024-07-29 17:32:44 +05:30
pdhirajkumarprasad a211ccbcff
Implementation of SplitToSequence ops lowering (#3509)
Added support for splitToSequence ops lowering
Added test case with filecheck
2024-07-29 15:44:22 +05:30
Vivek Khandelwal b6e4725259
[ONNX] Add OnnxToTorch lowering for NonMaxSuppression op (#3501)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-07-26 21:01:27 +05:30
yyp0 ea60d72489
[Torch] Add AtenMaskedFillTensorOp support (#3561) 2024-07-26 15:32:13 +08:00
Vivek Khandelwal 15cf7106c4
[ONNX] Reduce Onnx.Flatten op version (#3560)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-07-24 21:27:20 +05:30
Yuanqiang Liu 003b06dfa1
[Torch] enhance naryFolderHelper to support mixed dtypes (#3559)
* so that it could support like `i64 + f64 => f64`.
* also unify `aten.log`'s folder code to use `naryFolderHelper`.
2024-07-24 17:54:59 +08:00
Yuanqiang Liu aad1604046
[Torch] enhance fold of aten.squeeze.dim (#3558) 2024-07-24 14:13:48 +08:00
Ze Zhang d1e172f418
Register fake_quantize_cachemask ops and add their decompose patterns (#3556)
Test:

`cmake --build build --target check-torch-mlir-all`
2024-07-23 11:33:12 -07:00
Yuanqiang Liu 21ad890009
[Torch] enhance fold of aten.slice.Tensor (#3557)
so that it could support folding slice with any static shape.
2024-07-23 22:53:03 +08:00
Yuanqiang Liu 78846425e2
[Torch] add constriants when decompose aten.split_with_sizes (#3555) 2024-07-23 10:34:29 +08:00
Vivek Khandelwal 22c9008bb9
build: Update Roll PyTorch version (#3548)
This commit also updates the PyTorch and Torchvision nightly links since
they are now moved to a different location.

PyTorch Nightly: https://download.pytorch.org/whl/nightly/cpu/torch/
Torchvision Nightly:
https://download.pytorch.org/whl/nightly/cpu/torchvision/

Disables dtype checks for some ops, tracked by https://github.com/llvm/torch-mlir/issues/3552

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-07-19 21:38:57 +05:30
bosko-syrmia 2cdf3deae3
implement lowering of torch.aten._linalg_slogdet (#3524) 2024-07-19 11:24:43 +05:30
Branko Trifkovic c7d972ed58
Implement lowering of torch.aten.tril_indices (#3517) 2024-07-18 18:38:12 +05:30
jinchen f0ce1e94ce
[ONNX] Add OnnxToTorch support for SequenceMap (#3535) 2024-07-17 14:25:09 -07:00
pkapris-syrmia fde286f491
Implement lowering for torch.aten.hann_window.periodic (#3502) 2024-07-17 18:21:23 +05:30
pkapris-syrmia b59efc75f3
Implement lowering of torch.aten.atleast_1d (#3498)
This operator is necessary in order to implement torch.aten.vstack.
Which will be added in a future PR.
2024-07-17 18:20:30 +05:30
Arham Khan 574143448b
[E2E][ONNX] torch.multinomial (#3404)
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`.
2024-07-16 23:09:39 +05:30
rohan-tan-bhowmik 0791a8860c
[Torch] Implements TorchToLinalg lowering of torch.ops.aten._weight_norm_interface (#3538)
Resolves https://github.com/nod-ai/SHARK-Turbine/issues/757.

Adds TorchToLinalg lowering for `Aten_WeightNormInterfaceOp`.

---------

Co-authored-by: Ubuntu <rbhowmik@RohanBhowmikVM.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
2024-07-16 23:09:12 +05:30
Yuanqiang Liu 714270a922
[Stablehlo] legalize deprecated ops to stablehlo ops (#3543) 2024-07-17 00:05:11 +08:00
Xinyu Yang e5d1677894
[Torch] Eliminate getWithLeastStaticInformation in DecomposeAtenLinspaceOp and DecomposeAtenFakeQuantizePerTensorAffineOp (#3539)
as title
2024-07-15 10:02:36 +08:00
Yuanqiang Liu 5e4f00acb1
[Torch] add support for aten.scatter_add (#3534) 2024-07-12 09:15:42 +08:00
zjgarvey 0fb8b017d8
Adds misc fixes for some padding related issues (#3528)
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.
2024-07-11 20:01:45 -05:00
Yuanqiang Liu b38585e077
[Torch Dialect] fix aten.nan_to_num's decomposition when inf=None (#3530)
also add shape infer in decomposition, see
https://github.com/llvm/torch-mlir/issues/3312
2024-07-11 08:46:40 +08:00
Xida Ren (Cedar) 5342aa70cf
Support onnx.GRU and onnx.RNN (#3447) 2024-07-10 14:04:17 -04:00
Yuanqiang Liu 5bee9aac63
[Stablehlo] simplify promoteType (#3525)
only provide `outElementType` when promoteType
2024-07-10 10:52:19 +08:00
zjgarvey dcb48dd46c
[ONNX] Fix LpNormalization Lowering (#3521)
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.
2024-07-09 15:42:26 -05:00
Gaurav Shukla 0b46d1110a
[MLIR][ONNX] Add support for onnx.ScatterND (#3479)
This commit adds support for onnx.ScatterND op in the onnx pipeline.

Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
2024-07-08 13:27:14 +05:30
Matthias Gehre 6ea6a6c2fe
TorchOnnxToTorch: Fix stack-use-after-free (#3480)
We used to move the SmallVector into an ArrayRef and then the
SmallVector left the scope.

Found by asan.
2024-07-08 09:20:09 +02:00
Yuanqiang Liu 3225f20ab1
[Stablehlo] use index type as dim size, avoid to generate index_cast (#3526)
For example, the original IR is:
```
module attributes {torch.debug_module_name = "Matmul3D"} {
  func.func @forward(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
    %c0 = arith.constant 0 : index
    %c1 = arith.constant 1 : index
    %c2 = arith.constant 2 : index
    %dim = tensor.dim %arg1, %c0 : tensor<?x?x?xf32>
    %0 = arith.index_cast %dim : index to i64
    %dim_0 = tensor.dim %arg1, %c1 : tensor<?x?x?xf32>
    %1 = arith.index_cast %dim_0 : index to i64
    %dim_1 = tensor.dim %arg1, %c2 : tensor<?x?x?xf32>
    %2 = arith.index_cast %dim_1 : index to i64
    %from_elements = tensor.from_elements %0, %1, %2 : tensor<3xi64>
    %3 = stablehlo.dynamic_broadcast_in_dim %arg1, %from_elements, dims = [0, 1, 2] : (tensor<?x?x?xf32>, tensor<3xi64>) -> tensor<?x?x?xf32>
    %4 = stablehlo.dot_general %arg0, %3, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
    return %4 : tensor<?x?x?xf32>
  }
}
```
After using IndexType, the IR is:
```
module attributes {torch.debug_module_name = "Matmul3D"} {
  func.func @forward(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
    %c0 = arith.constant 0 : index
    %c1 = arith.constant 1 : index
    %c2 = arith.constant 2 : index
    %dim = tensor.dim %arg1, %c0 : tensor<?x?x?xf32>
    %dim_0 = tensor.dim %arg1, %c1 : tensor<?x?x?xf32>
    %dim_1 = tensor.dim %arg1, %c2 : tensor<?x?x?xf32>
    %from_elements = tensor.from_elements %dim, %dim_0, %dim_1 : tensor<3xindex>
    %0 = stablehlo.dynamic_broadcast_in_dim %arg1, %from_elements, dims = [0, 1, 2] : (tensor<?x?x?xf32>, tensor<3xindex>) -> tensor<?x?x?xf32>
    %1 = stablehlo.dot_general %arg0, %0, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
    return %1 : tensor<?x?x?xf32>
  }
}
```

The benefits of using IndexType on shape tensor:
* simplify the IR, avoid to generate `arith.index_cast`
* let backend compiler have a chance to decide the index width of shape
tensor
* let stablehlo backend have a chance to serialize dynamic shape IR by
[shape_legalize_to_stablehlo](https://github.com/openxla/stablehlo/blob/main/stablehlo/tests/shape_legalize_to_stablehlo.mlir)
2024-07-07 18:03:03 +08:00
Ze Zhang d466d5b809
Register fake_quantize related ops (#3522)
Register `aten.fake_quantize_per_channel_affine` and
`aten.fake_quantize_per_tensor_affine.tensor_qparams` ops

---------

Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-07-05 11:02:03 -07:00
Sagar Kulkarni 0fe74845da
[ONNX] Fix bug in ONNXToTorch PadOp's pads tensor rearrangement (#3485)
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>
2024-07-03 15:02:49 -05:00
Scott Todd ca0e906675
Fix `uint64_t` type. (#3519)
`u_int64_t` is nonstandard and does not exist in MSVC.
2024-07-02 16:06:20 +00:00
Yuanqiang Liu f1e3701caf
[Stablehlo] fix compareOp with scalar's lowering (#3518)
* use lhs tensor's element type as compute type when rhs is scalar.
* previously `a != 1.0`(a is a fp32 tensor) will lowering to `%6 =
stablehlo.compare EQ, %4, %5, FLOAT : (tensor<2x5xf64>, tensor<2x5xf64>)
-> tensor<2x5xi1>`
* now it will lowering to `%6 = stablehlo.compare EQ, %4, %5, FLOAT :
(tensor<2x5xf32>, tensor<2x5xf32>) -> tensor<2x5xi1>`
2024-07-02 15:31:06 +08:00
Yuanqiang Liu e2fbded49c
[Torch Dialect] improve argmax/argmin's decomposition to support keep… (#3514)
…dim=True when dim=None
2024-07-02 09:08:57 +08:00
Yuanqiang Liu 0e71a192d8
[Torch] support decomposition of aten.aminmax (#3513)
* unify decompisition of `aten.amax` and `aten.amin`
* support `aten.amax` with `dim=()`
2024-06-29 21:44:05 +08:00
Yuanqiang Liu f9fc741eef
[Stablehlo] support aten.any.dim, aten.min.dim (#3500)
* refactor `TorchToStablehlo/Reduction.cpp`
* add `ConvertAtenReduceWithIndicesOp` patterns
2024-06-29 16:53:33 +08:00
jinchen 3915db0a86
[ONNX] Add OnnxToTorch support for CenterCropPad (#3496) 2024-06-28 12:47:29 -07:00
zjgarvey af236dab66
Add support for multiple dynamic reassociation dims for unflatten.int (#3504)
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.
2024-06-28 09:59:51 -07:00
Max191 a1c4089e71
Fix unused variable warning from assertion variable (#3512)
Inlines a variable into an assertion that is not used elsewhere to fix
build warnings.
2024-06-28 12:20:29 -04:00
Jiawei Wu f75cbb4df9
[torch dialect] emit aten.fmax/fmin and add decomposition patterns (#3510) 2024-06-29 00:07:55 +08:00
Phaneesh Barwaria 5a627c46b7
onnx.DFT basic support (#3463)
- adds support for DFT v20 on the FFT and IFFT path
- adds required skeleton code for IFFT ops to be recognised in TMlir
2024-06-28 20:08:43 +05:30
Christopher McGirr 7e6d76e997
[Torch] Fix torch.constant.int operation parsing (#3476)
Due to the custom operation parser, the print and parser were expecting
two different forms.

One having the dictionary before the value and the other after.
Following the format of the other constants ops, the constant.int will
follow the `value attr-dict` format. Updated the parser accordingly.
2024-06-28 16:06:52 +02:00
Aart Bik 1f73895f93
[torch-mlir] bump to llvm/llvm-project@9b78ddf3b2 (#3491)
This bump triggered an upstream assert. Includes a WAR for #3506.

Also includes several things I needed to do to repro:

* When TORCH_MLIR_TEST_CONCURRENCY=1, test runs will be printed.
* Added TORCH_MLIR_TEST_VERBOSE=1 handling to enable verbose mode
(useful on CI).

---------

Co-authored-by: Stella Laurenzo <stellaraccident@gmail.com>
2024-06-27 19:28:02 -07:00
jinchen 6d0ca499e6
[ONNX] Add OnnxToTorch support for ReverseSequence (#3495) 2024-06-27 14:33:41 -07:00
Phaneesh Barwaria 39d1332008
add onnx loop support (#3408)
- 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.
2024-06-27 17:08:44 +05:30
Matthias Gehre 6678e1a256
TorchToLinalg: Try folding shape computations to keep static shapes when possible (#3475)
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.
2024-06-27 08:43:10 +02:00
Suraj Sudhir 6eebe61bfe
[Tosa] Conversion from torch.__interpolate to tosa.resize() (#3488)
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2024-06-26 09:10:14 -07:00
Ramiro Leal-Cavazos e29191bd08
[LINALG] Broadcast `values` to shape of slize in `index_put` (#3487)
The `index_put` operation, `input[indices] = values`, allows for the
values to be any shape that is broadcastable to the slice
`input[indices]`. This commit adds broadcasting support to the Linalg
lowering of `IndexPutHackedTwinOp`.

Fixes: #3465
2024-06-26 08:59:49 +00:00
zjgarvey d2bc70f188
[TorchToLinalg][ONNX] Add Basic Determinant Support (#3481)
This adds support for a few ops:

- torch.linalg_det
- torch._linalg_det (if the LU and pivot returns are unused)
- onnx.Det

An scf loop is used, since the row reduction algorithm applied here has
some loop-carried dependencies.
The current support being added here is very basic, and only works if no
permutations are required during row reduction, and assumes the matrices
are non-singular.
2024-06-25 13:34:19 -05:00
zjgarvey 368fabf0c1
[ONNX] Basic Support for DeformConv (#3469)
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.
2024-06-25 12:16:51 -05:00
zjgarvey e346c911f7
[ONNX] Add basic support for RoiAlign (#3493)
This adds an onnx->torch conversion for onnx.RoiAlign into
torchvision.roi_align or torchvision.roi_pool, and adds those two
torchvision ops to torch-mlir.
2024-06-25 11:02:45 -05:00
Vinayak Dev 02340408b7
[torch] Add OnnxToTorch lowering for Onnx.STFT op (#3492)
Adds OnnxToTorch lowering for `Onnx.STFT` op.
2024-06-25 19:00:45 +05:30
Vivek Khandelwal 3c3fbe4680
[ONNX] Add OnnxToTorch lowering for Onnx.Upsample Op (#3371)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-06-25 12:58:31 +05:30
Chi_Liu fc19709daa
[ONNX] Add averagepool dilations support (#3490)
- To fix dilations issue: https://github.com/llvm/torch-mlir/issues/3428
- Test by: https://github.com/nod-ai/SHARK-TestSuite/pull/268
2024-06-21 17:24:57 -07:00
Branko Trifkovic 98c6971a01
Implement lowering of torch.aten.triu_indices (#3451)
Closes
[nod-ai/SHARK-Turbine/issues/709](https://github.com/nod-ai/SHARK-Turbine/issues/709)

---------

Co-authored-by: Branko Trifkovic <branko.trifkovic@syrmia.com>
2024-06-21 16:16:38 -07:00
Matthias Gehre acd57a3520
Support fake_quantize_per_tensor_affine_cachemask (#3477)
Add a new op with shape/dtypes and decompose into
`fake_quantize_per_tensor_affine` when the second result is unused.

The xfail_set change is on ONNX because torch cannot export this op to
ONNX.
2024-06-21 07:15:31 +00:00
Vivek Khandelwal 83bfb6fb19
[ONNX] Add OnnxToTorch lowering for OptionalHasElement op (#3472)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-06-21 11:19:00 +05:30
Vivek Khandelwal d29ad4dfbd
[ONNX] Fix Onnx.Hardsigmoid lowering (#3239)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-06-21 11:18:14 +05:30
zjgarvey 694210f429
[TorchToLinalg] Fix Quantized Convolution Accumulator Type (#3459)
1. truncates zero-points to i32
2. modifies the default accumulator type for i8 from i64 to i32. 
3. now uses the input dtype to infer accumulator dtype.
2024-06-20 13:54:20 -07:00
Xinyu Yang c7d52f63b4
[stablehlo] add aten::_int_mm lowering (#3474)
as title
2024-06-20 16:10:31 +08:00
Vivek Khandelwal 822d763308
[ONNX] Add OnnxToTorch lowering for Optional, OptionalGetElement op (#3467)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-06-18 19:40:18 +05:30
Branko Trifkovic 676fa8cc09
Implement lowering of torch.aten.renorm (#3388)
Closes
[nod-ai/SHARK-Turbine/issues/689](https://github.com/nod-ai/SHARK-Turbine/issues/689)

---------

Co-authored-by: Branko Trifkovic <branko.trifkovic@syrmia.com>
2024-06-17 10:40:57 -07:00
Umang Yadav 59bade3376
[ONNX] Add missing "Abs" in GlobalLpPool (#3460)
Taking `abs` is required to mimic same logic as onnx/onnxruntime. 
Without `abs`, it wouldn't produce correct results for negative values. 

Reference code : 

f5b6f6dc26/onnxruntime/core/providers/cpu/nn/pool_functors.h (L604)


375c161c67/onnx/reference/ops/op_lp_pool.py (L31)
2024-06-17 11:17:16 +05:30
ptrifunovic98 4555629246
Implement lowering of torch.aten.kthvalue (#3360)
Closes
[nod-ai/SHARK-Turbine#620](https://github.com/nod-ai/SHARK-Turbine/issues/620)
2024-06-15 11:18:39 +05:30
Manupa Karunaratne d2b663ece7
Add onnx op LRN lowering (#3432)
This commit adds support for lowering
Onnx LRN op to aten.
2024-06-14 16:44:43 +00:00
Arham Khan 09c988046c
[ONNX] Add OnnxToTorch lowering for Onnx.NegativeLogLikelihoodLoss Op (#3380)
This implements the Onnx.NegativeLogLikelihoodLoss op using the
signature provided
[here](https://onnx.ai/onnx/operators/onnx__NegativeLogLikelihoodLoss.html)
by replacing it with a `NLLLossForward` op.

Additionally, I included a helper function `get_loss_reduction_enum` to
convert from a string `reduction` parameter to the corresponding
intended integer value since this is an operation that will be reused
for any loss function module. This differs from `get_reduction_enum` in
`TorchUpstream.cpp` which handles the `reduce` parameter from
`scatter_reduce` type operations.
2024-06-14 22:01:11 +05:30
Vivek Khandelwal 2ea2bc3948
[ONNX] Add OnnxToTorch Lowering for GroupNormalization op (#3458)
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-06-14 16:18:53 +00:00
Umang Yadav 04c6479350
[ONNX] Add onnx parser for LpPool operator (#3449)
Similar to https://github.com/llvm/torch-mlir/pull/3435

Solves https://github.com/nod-ai/SHARK-Turbine/issues/728
2024-06-14 21:41:18 +05:30