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1586 Commits (5a627c46b76f8cdc737aef3bda1b910836e33d88)

Author SHA1 Message Date
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
Xinyu Yang 6f94c7b0aa
[Torch] Add support for Meshgrid (#3462) 2024-06-14 23:59:08 +08:00
Phaneesh Barwaria 919b599ebe
onnx.MaxPool add atenMaxPool1d lowering support (#3452) 
fixes #3422
 2024-06-13 15:37:11 +05:30
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
Surya Jasper de7f058a0e
[MLIR][ONNX] Add OnnxToTorch support for MaxRoiPool Op (#3395) 
This PR adds OnnxToTorch support for MaxRoiPool op
 2024-06-13 10:46:14 +05:30
Umang Yadav 9b76a2e3eb
[ONNX] add onnx lowering for global lp pool operator (#3435) 
Solves https://github.com/nod-ai/SHARK-Turbine/issues/727

Uses AvgPool to implement GlobalLpPool similar to this
https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_lp_pool.py

cc: @vivekkhandelwal1
 2024-06-13 10:37:08 +05:30
Lei Zhang 77d7f64472
Update to llvm/llvm-proect@27ac46e6be (2024-6-12) (#3454) 
This would require to bump stablehlo at the same time.
 2024-06-12 19:34:01 -07:00
Chi_Liu ae6f5e8251
[ONNX] Fix AveragePool attributes support (#3235) 
Issues was found here https://github.com/nod-ai/SHARK-Turbine/issues/643
    - [ONNX] Fix padding attributes for onnx.AveragePool
    - [Linalg] Add countIncludePad false support for AtenAvgPool1/2dOp
    - [Linalg] Add an avg_pool2d countIncludePad False e2e tests
    - [Linalg] Fix conflict with AtenAvgPool3dOp
    - [Linalg] Fix e2e crash with AtenAvgPool1dOp
    - [Linalg] Add dynamic dim support for AtenAvgPool2dOp
    - [Linalg] Fix AvgPool2dDivisorOverrideModule crash
 2024-06-12 12:16:43 -07:00
Suraj Sudhir 41d04a8995
[onnx] Resize supports default-valued attributes (#3450) 
Handles onnx exporters emitting default-valued attributes.

Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
 2024-06-12 09:23:42 -07:00
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
zjgarvey 7cd3368b20
[ONNX] Fix resize ceil numerics and add half_pixel_symmetric support (#3443) 
This patch fixes several failing tests in our [external test
suite](https://github.com/nod-ai/SHARK-TestSuite/tree/main/iree_tests/onnx/node/generated),
and addresses some of the issues discussed in #3420
 2024-06-11 22:35:50 -05:00
Matthias Gehre e07a0bfc54
onnx.resize: Add support for coordTfMode "half_pixel" (#3441) 
half_pixel is also the default mode used by ONNX, see
https://onnx.ai/onnx/operators/onnx__Resize.html
 2024-06-10 20:59:29 +02:00
Aart Bik d77bab37d1
[torch-mlir][sparse] re-enable all sparse tests (#3444) 
this fixes the following issue:

https://github.com/llvm/torch-mlir/issues/3418
 2024-06-10 11:19:32 -07:00
Vivek Khandelwal 5bc626465b
[ONNX] Lower Onnx.Concat lowering version (#3437) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-09 12:07:20 +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
Yuanqiang Liu 689efc8917
[Torch] fix toBuiltinTensor() (#3415) 
* Let `toBuiltinTensor()` reflects the original dtype of
`!torch.vtensor`.
* Backend handles dtype conversion themselves.
 2024-06-08 09:36:32 +08:00
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
aldesilv f794582b18
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421) 2024-06-07 14:04:11 -05:00
Vivek Khandelwal 1a9c0a35a9
[Onnx] Add Onnx->Torch lowering for Onnx.Shrink Op (#3385) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-07 22:47:27 +05:30
Suraj Sudhir 1c2778dd56
[ONNX] Conv op adds support for asymmetric padding. (#3426) 
Supports asymmetric padding by performing a torch.nn.functional.pad on
the input before performing the convolution.

Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
 2024-06-07 09:54:39 -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
Xinyu Yang 431d98b405
[Stablehlo] Add lowering of GridSampler Op (#3084) 
Inspired by PyTorch decompositions.py.
See
ec58f1f74e/torch/_decomp/decompositions.py (L3923-L4086)
Only support paddingMode=0 or 1 and interpolationMode=0 or 1
 2024-06-07 16:06:07 +08:00
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