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803 Commits (18139994e807d262f52a13b2c8e1b3edfa45ffa0)

Author SHA1 Message Date
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
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
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
yyp0 f49b9c14f1
[Torch] Add support for Aten__Or__BoolOp (#3574) 2024-07-31 17:23:53 +08:00
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
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
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
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
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
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 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
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
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
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
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
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
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
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
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