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Author SHA1 Message Date
Yuanqiang Liu 208e5fac64 emit aten.upsample_bilinear2d 2024-09-14 16:09:21 +08:00
yyp0 a13f65026a [Torch] add AtenAsStridedOp in torch dialect (#3706) 2024-09-13 09:45:38 +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
pkapris-syrmia 10fe5d08d1
Implement lowering for torch.aten.rad2deg (#3586) 2024-08-14 16:37:28 +05:30
rohan-tan-bhowmik 1c16de147a
Minor change in TMTensorOps.td (#3602) 
Fixed a little programming choice style that bothered me.
 2024-08-14 16:33:49 +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
zjgarvey c8efc201f4
[Onnx] expand support for constant matching (#3607) 
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`.
 2024-08-07 19:35:34 -05:00
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
yyp0 f49b9c14f1
[Torch] Add support for Aten__Or__BoolOp (#3574) 2024-07-31 17:23:53 +08:00
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Xinyu Yang 6f94c7b0aa
[Torch] Add support for Meshgrid (#3462) 2024-06-14 23:59:08 +08:00
Vinayak Dev 39d882f7c9
[torch] Add OnnxToTorch lowering for the Col2Im op (#3424) 
Adds OnnxToTorch lowering for the `onnx.Col2Im` op.
 2024-06-13 08:42:06 +00:00
zjgarvey de28c8540b
[ONNX] add int16 quantization support (#3446) 
There is currently no int16 quantization support in torch. This patch
adds a new mlir type to correspond to the missing "torch.qint16" type,
and enables lowering of quantization-related onnx ops using int16 types.

In follow-up patches, custom quantization logic for ops like
aten.matmul/aten.mm/aten.convolution may need to be revisited to allow
support for qint16. The passes in FuseQuantizedOps.cpp may also need
slight modifications.
 2024-06-12 10:37:22 +05:30
Vivek Khandelwal d35b6b412a
[ONNX] Add OnnxToTorch Lowering for Sequence Ops (#3425) 
This commit adds the lowering for SequenceAt, SequenceEmpty,
SequenceInsert, SequenceErase op

Signed-Off By: Vivek Khandelwal<vivekkhandelwal1424@gmail.com>
 2024-06-08 09:58:11 +05:30
Rob Suderman 75af64fc12
[torch] Add support for f8 types for linalg conversion (#3436) 
Linalg conversion requires mapping for f8 types
 2024-06-07 13:59:38 -07:00
Sambhav Jain d0a818a03e
Representing Symbolic Shape Expressions in Torch Dialect (#3372) 
Torch Dialect with symbolic shape expressions:
```ll
module {                                                                                                                                                                                                     
  func.func @main(%arg0: !torch.vtensor<[?,?,3],f32>, %arg1: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {                                                                                   
    %0 = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int                                                                                                                                    
    %1 = torch.symbolic_int "s1" {min_val = 0, max_val = 100} : !torch.int                                                                                                                                   
    %2 = torch.symbolic_int "s3" {min_val = 0, max_val = 50} : !torch.int                                                                                                                                    
    
    torch.bind_symbolic_shape %arg0, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                          
    torch.bind_symbolic_shape %arg1, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                          
    
    %3 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>                                                                                                                  
    torch.bind_symbolic_shape %3, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                             
    
    %4 = torch.aten.sigmoid %arg1 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>                                                                                                               
    torch.bind_symbolic_shape %4, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                             
    
    %5 = torch.prim.ListConstruct %3, %3, %4 : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.list<vtensor>                                               
    %int1 = torch.constant.int 1                                                                                                                                                                             
    %6 = torch.aten.cat %5, %int1 : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[?,?,3],f32>                                                                                                          
    torch.bind_symbolic_shape %6, [%0, %1, %2], #affine_map<()[s0, s1, s2] -> (s0, s1 * 2 + s2, 3)> : !torch.vtensor<[?,?,3],f32>                                                                            
    
    return %6 : !torch.vtensor<[?,?,3],f32>                                                                                                                                                                  
  }                                                                                                                                                                                                          
}              
```

For reference, this is the TorchDynamo exported program with symbolic
shape expressions that the above Torch dialect program is imported from:
```py
ExportedProgram:                                                                                                                                                                                             
    class GraphModule(torch.nn.Module):                                                                                                                                                                      
        def forward(self, x: "f32[s0, s1, 3]", y: "f32[s0, s3, 3]"):                                                                                                                                         
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:31 in forward, code: a = torch.tanh(x)                                        
            tanh: "f32[s0, s1, 3]" = torch.ops.aten.tanh.default(x);  x = None                                                                                                                               
                                                                                                                                                                                                             
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:32 in forward, code: b = torch.sigmoid(y)                                     
            sigmoid: "f32[s0, s3, 3]" = torch.ops.aten.sigmoid.default(y);  y = None                                                                                                                         
                                                                                                                                                                                                             
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:33 in forward, code: return torch.cat((a, a, b), dim=1)                       
            cat: "f32[s0, 2*s1 + s3, 3]" = torch.ops.aten.cat.default([tanh, tanh, sigmoid], 1);  tanh = sigmoid = None                                                                                      
            return (cat,)                                                                                                                                                                                    
                                                                                                                                                                                                             
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='cat'), target=None)])                                               
Range constraints: {s0: ValueRanges(lower=5, upper=10, is_bool=False), s1: ValueRanges(lower=0, upper=100, is_bool=False), s3: ValueRanges(lower=0, upper=50, is_bool=False)} 
```

Huge credit to @stellaraccident for the inputs that helped evaluate the
various design options and arrive at the representation of choice.


- [x] Op definitions for symbolic_int and bind_symbolic_shape ops
- [x] fx_importer updates to import range constraints + create
symbolic_int ops
- [x] fx_importer changes for AffineMapAttr building + adding
bind_symbolic_shape ops
- [x] custom printer/parser for inlined AffineMap expressions in mlir
assembly
- [x] Dialect lit test
- [x] fx_importer python lit tests
- [ ] Cleanup pass to remove these ops (can add in a follow-on)
 2024-06-07 04:04:03 -07:00
Vivek Khandelwal 72837fbb3d
build: manually update PyTorch version (#3340) 
Set PyTorch and TorchVision version to nightly release 2024-05-14.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-06 22:23:40 +05:30
Vivek Khandelwal 661be2d5b0
[MLIR][Torch] Add TorchToLinalg lowering for AtenAvgPool3dOp (#3030) 
This commit also fixes the average pool op' test failing for
OnnxToLinalg lowering.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-04 22:12:34 +05:30
Vivek Khandelwal 35dd8c52cd
[ONNX] Add OnnxToTorch Lowering for MaxUnpool op (#3413) 
This commit also adds the Torch declaration for aten.max_unpool2d and
aten.max_unpool3d op. The TorchToLinalg lowering for the same will be
added in a follow-up commit.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-04 21:09:53 +05:30
Yuanqiang Liu 50f7103098
[Stablehlo] support uint8 (#3367) 
Support lowering unsigned integer type to stablehlo as discussed in
https://github.com/llvm/torch-mlir/pull/2184.

The things I do in this PR:
1. create `setupBackendTypeConversionForStablehlo()`,
`createFuncBackendTypeConversionForStablehloPass` and
`createFinalizingBackendTypeConversionForStablehloPass`.
2. remove `InferTypeOpInterface` from `torch_c.to_builtin_tensor`,
because it's different result type between linalg backend and stablehlo
backend:
```
// linalg backend
func.func @forward(%arg0: !torch.vtensor<[3],ui8>) -> tensor<3xf32> {
    %c = torch_c.to_builtin_tensor %arg0 : (!torch.vtensor<[3], ui8> -> tensor<3xi8>
    %0 = tensor.empty() : tensor<3xf32>
    %1 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%arg0 : tensor<3xi8>) outs(%0 : tensor<3xf32>) {
    ^bb0(%in: i8, %out: f32):
      %2 = arith.uitofp %in : i8 to f32
      linalg.yield %2 : f32
    } -> tensor<3xf32>
    return %1 : tensor<3xf32>
}
// stablehlo backend
func.func @forward(%arg0: !torch.vtensor<[3],ui8>) -> tensor<3xf32> {
    %c = torch_c.to_builtin_tensor %arg0 : (!torch.vtensor<[3], ui8> -> tensor<3xui8>
    %0 = stablehlo.convert %arg0 : (tensor<3xui8> -> tensor<3xf32>
    return %0 : tensor<3xf32>
}
```
3. fix stablehlo and linalg's conversion
 2024-06-04 09:04:59 +08:00