695458daea
Fix ArgAnnotation with boolean flag which instructs value semantics ( #3238 )
2024-04-28 02:24:55 +08:00
4fbe77a051
[dynamo] Verify the default value is passed by kwargs ( #2998 )
2024-04-28 02:18:33 +08:00
f173a06fa7
[Torch] emit aten.ne.str and add folder ( #3242 )
2024-04-28 00:58:50 +08:00
944a6df611
Extract the Python APIs in the pt1 dir back to the root ( #3237 )
2024-04-27 18:27:37 +08:00
9a12a093a6
[onnx] Support `onnx.OneHot` lowering to `torch` ( #3196 )
...
[onnx] Support `onnx.OneHot` lowering to `torch`
Leverage the `aten.onehot` implementation along with `aten.transpose`
and `aten.where.scalar`.
2024-04-26 12:08:15 -07:00
ac85338491
[Stablehlo] Support AtenPowScalarOp, AtenTanOp, AtenAsinhOp, AtenAcoshOp, AtenAtanhOp, Atan2Op ( #3233 )
2024-04-26 15:47:44 +08:00
634a796933
[Torch] fold aten.log ( #3223 )
2024-04-26 10:10:02 +08:00
122eb69a98
[stablehlo] add aten left/right shift op conversion support ( #3234 )
2024-04-26 09:20:49 +08:00
cd33d8b011
[onnx] Update DefaultDomainGtoP.cpp gridsampler ( #3228 )
...
Gridsampler
In onnx the interpolation mode is called 'linear' whereas in pytorch it
is called 'bilinear'. This led to the problem that everything other than
'bilinear' was rejected. It needed to be changed to linear.
2024-04-25 18:07:05 -07:00
ac11ec796d
[MLIR][ONNX] Add OnnxToTorch support for ReduceLogSum Op ( #3229 )
...
This commit adds the OnnxToTorch support for ReduceLogSum op
2024-04-25 19:37:57 -04:00
2eac8a992f
[torch-mlir][sparse] sparse tensor dialect is a legal dialect ( #3227 )
2024-04-26 02:36:42 +08:00
b0ba3def93
[Torch] support AtenScalarImplicitOp canonicalize with float ( #3231 )
2024-04-26 02:36:13 +08:00
4361178caa
[torch-mlir][sparse] recognize sparse tensor conversion ( #3226 )
...
Sparse tensor conversions are represented by special aten operators.
This PR ensures the conversions are recognized (instead of failing the
full torch aten lowering to linalg).
2024-04-26 02:32:07 +08:00
9e2fe47c5d
build: manually update PyTorch version ( #3210 )
...
Set PyTorch and TorchVision version to nightly release 2024-04-22.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-04-25 08:53:10 -07:00
7030eacb76
[stablehlo] Support aten.any and aten.all lowering ( #3217 )
2024-04-25 11:15:52 +08:00
7be22bb260
Update add_ops.md to link torch mlir get started instructions prominently ( #3222 )
2024-04-24 17:03:41 +00:00
678c03b762
Fix nan issue for fp16 torch.randn/randn_like in ConvertAtenUniformOp ( #3184 )
...
For ops that use ConvertAtenUniformOp (e.g. torch.randn/randn_like),
fp16 datatype returns nan values. Trying to lower [this
repro](https://gist.github.com/aviator19941/1c65e658241dea6906ca423f9abaee69 )
will result in nan's, this PR fixes the issue.
2024-04-24 12:28:08 +05:30
fab2696489
[Torch] support aten.trunc ( #3219 )
...
decompose `trunc(x)` to `sign(x) * floor(abs(x))`
2024-04-24 14:32:33 +08:00
e18bf42d0e
[stablehlo] Support ConstantPadNdOp in stablehlo ( #3211 )
...
as title
2024-04-24 14:15:11 +08:00
dc470e65c8
add torch.qint32 to dtype-spec in TorchTypes.td ( #3206 )
2024-04-24 11:49:26 +08:00
8a1dbbd597
[torchscript] export extra library file name to user ( #3203 )
...
* so that it could be specified by user.
2024-04-24 11:34:02 +08:00
f77d88390a
[onnx] handle dynamic padSize tensor in onnx.Pad ( #3214 )
...
- Fix pad size to data_rank for dynamic paddingSize Tensor.
- This fix is in accordance with [input
specification](https://onnx.ai/onnx/operators/onnx__Pad.html#inputs ) for
onnx.Pad
- Impl will need to be updated for dynamic padSize when support for
`axes` is added.
2024-04-24 11:31:37 +08:00
42b9eccdb3
[Stablehlo] Fix AtenSumDimIntListOp when dim==None ( #3216 )
...
as titile
2024-04-24 11:25:46 +08:00
4da3d714cc
[Torch] Support AtenProdOp on linalg and stablehlo ( #3215 )
2024-04-24 11:14:04 +08:00
a8ba865fca
[torch] Adds Quantization Support for `aten.relu` ( #3177 )
...
A choice was made to quantize the return type of Relu with a scale and
zero point copied from the input's quantization scheme. With this
choice, the torch-to-linalg conversion of quantized Relu essentially
computes max(input, zeroPoint) in the elementwise payload.
2024-04-23 11:01:36 -07:00
09d42044b4
Support select_last_index attribute of onnx argmin op ( #3212 )
...
The tests listed in https://github.com/nod-ai/SHARK-Turbine/issues/648
all compiled, and the values of results match, but having runtime issue
of dtype mismatch of i/si.
2024-04-23 10:43:38 -07:00
61e6312c87
Support select_last_index attribute of onnx argmax op ( #3192 )
...
The tests listed in https://github.com/nod-ai/SHARK-Turbine/issues/635
all compiled, but having run issue of dtype mismatch of i/si.
2024-04-23 10:16:08 -07:00
ddb29c2c02
[onnx] Add OnnxToTorch support for `onnx.ConvInteger` ( #3179 )
...
All e2e iree tests compiled, but they have the run issue of mismatch of
dtype like the following
```
expected:
1x1x2x2xsi32=[[[12 16][24 28]]]
actual:
1x1x2x2xi32=[[[12 16][24 28]]]
```
2024-04-23 09:42:02 -07:00
db3842f2e8
[Stablehlo] support lowering sinh & cosh to stablehlo ( #3213 )
2024-04-23 19:54:58 +08:00
c1967b607f
[Stablehlo] add AtenLog10Op, AtenLog2Op lowering to stablehlo ( #3208 )
2024-04-23 19:06:55 +08:00
1f8123b5f0
[Stablehlo] support unary ops which promote to floating point ( #3209 )
...
* promote input to output element-type when lowering to stablehlo, so
that it could satisfy stablehlo's type constraints.
* split promote-to-fp unary ops from fp-only unary ops.
2024-04-23 17:57:12 +08:00
797e4cd395
[Stablehlo] lowering asin, acos, atan ( #3207 )
...
* lowering asin, acos and atan to chlo ops.
2024-04-23 16:24:53 +08:00
cff2f084d4
[torch] Add OnnxToTorch lowering for `onnx.ReduceL2` ( #3175 )
...
Adds OnnxToTorch lowering for the ReduceL2 op.
2024-04-23 02:03:05 -04:00
3c252cdd44
[onnx] Add `onnx-to-torch` lowering for random ops ( #3193 )
...
This commit adds the OnnxToTorch lowering for Onnx's RandomNormal, RandomNormalLike, RandomUniform, and RandomUniformLike op.
2024-04-22 22:28:07 +05:30
6abc7371c8
[MLIR][TORCH] Fix OnnxToLinalg lowering issue for Squeeze and Unsqueeze op ( #2991 )
...
This commit also cleans up the OnnxToTorch lowering for the Squeeze and
Unsqueeze op and adds the support for handling edge cases.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-04-22 08:52:42 +00:00
e5bdd71baf
[Torch] Emit and decompose prims.iota op ( #3132 )
2024-04-21 19:45:01 -07:00
a60e84e5ee
[stablehlo] add aten.expm1 op conversion support ( #3199 )
2024-04-21 19:20:49 -07:00
8222637159
[onnx] Extend op version number of `onnx.ScatterElements` ( #3195 )
...
Version number was set too high. Lowered to support more cases allows
more tests to pass.
Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
2024-04-21 12:32:18 -04:00
733cace1df
[onnx] Fix `onnx.split` by directly handling slicing ( #3194 )
...
Previous implementation erroneously mixed up num_outputs with
slice_size. New version correctly computs the slice size and directly
performs slicing rather than leveraging `aten.split.tensor`. This is due
to `onnx` supporting a fixed number of splits making the size
computation more easily computeable when lowering to `aten` rather than
deferring to `aten.split.tensor`.
---------
Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
2024-04-21 12:31:56 -04:00
b6b01602d3
[stablehlo] add aten.fmod.Tensor op conversion support ( #3198 )
2024-04-21 08:39:36 +08:00
ea0ecb67be
[stablehlo] add aten.remainder.Tensor op conversion support ( #3197 )
2024-04-21 00:03:37 +08:00
b01245c0e8
[onnx] Fix `onnx.Not` for non-bool inputs ( #3187 )
...
Need to perform a bool cast to support `onnx.Not` on non-bool inputs.
2024-04-19 11:32:24 -07:00
790a697245
[Torch] Add folder for AtenIntOp, AtenFloatOp ( #3189 )
...
See unit test below:
```
// CHECK-LABEL: func.func @torch.aten.tensor.float(
// CHECK-NEXT: torch.vtensor.literal(dense<1.000000e+01> : tensor<f32>) : !torch.vtensor<[],f32>
func.func @torch.aten.tensor.float() -> !torch.vtensor<[],f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%float1.000000e01 = torch.constant.float 1.000000e+01
%67 = torch.aten.tensor.float %float1.000000e01, %none, %none, %false : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],f32>
return %67 : !torch.vtensor<[],f32>
}
// CHECK-LABEL: func.func @torch.aten.tensor.int(
// CHECK-NEXT: torch.vtensor.literal(dense<45> : tensor<si32>) : !torch.vtensor<[],si32>
func.func @torch.aten.tensor.int() -> !torch.vtensor<[],si32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int45 = torch.constant.int 45
%67 = torch.aten.tensor.int %int45, %none, %none, %false : !torch.int, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],si32>
return %67 : !torch.vtensor<[],si32>
}
```
2024-04-19 22:17:06 +08:00
5a98c72c7f
[StableHLO] Fix aten.clamp.Tensor in FxImporter2StableHLO ( #3190 )
...
The FX importer will pass static shapes to the Torch dialect, so it
needs to generate a StableHLO that satisfies shape inference.
2024-04-19 17:08:29 +08:00
0a6073414d
[FxImporter] Add fx importer to stablehlo e2e test config ( #3183 )
2024-04-18 21:29:17 -07:00
6c4f7deebb
[stablehlo] add aten.clamp.Tensor op conversion support ( #3185 )
2024-04-19 10:55:27 +08:00
be742a937d
[onnx] Update the failure triage for onnx ( #3186 )
...
Reclassifying what the source of failures are for various bugs so we can
reprioritize what failures are common.
2024-04-18 14:58:13 -07:00
0e77de996a
[torch] Add support for `torch.view` with dynamic shapes ( #3164 )
...
We can map to `tensor.reshape` for handling multiple output dynamic
shapes. Later we can perform a more complex analysis for indentifying
expand/collapse cases from the tensor.reshape.
Initially we planned to handle this identification at the `torch` level
however it will be easier to handle once converted to core
mlir-dialects.
2024-04-18 11:47:19 -07:00
4c21e20caa
[torch] Support rank-0 index for torch index select ( #3182 )
...
Need to perform an expand in the case where the indices is rank-0.
2024-04-18 11:32:31 -07:00
6e5630d696
build: manually update PyTorch version ( #3170 )
...
Set PyTorch and TorchVision version to nightly release 2024-04-16.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-04-18 12:55:10 +05:30
Yuanqiang Liu
penguin_wwy
Rob Suderman
Xinyu Yang
Andreas Falkenberg
Archana Ramalingam
Aart Bik
Vivek Khandelwal
Xida Ren (Cedar)
Avinash Sharma
Phaneesh Barwaria
zjgarvey
jinchen
Vinayak Dev