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Author SHA1 Message Date
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
Rob Suderman 7f188eb824
Add f8 types to fx importer (#3434) 
Missing types for tracing float8 types.
 2024-06-07 13:58:18 -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 94838ca44d
[Bazel] Add BuiltinDialectTdFiles dep to MLIRTorchOpsIncGen (#3430) 
This is needed after https://github.com/llvm/torch-mlir/pull/3372.
 2024-06-07 05:02:17 -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
penguin_wwy d59d0b6e5a
[Linalg] Promote type for compare tensor op (#3416) 2024-06-04 16:05:39 -07:00
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
penguin_wwy 89f7d24fdc
[Bazel] Fix bazel deps (#3414) 
#3367 and #3364 introduced new dependencies, causing the [Bazel
workflow](https://github.com/llvm/torch-mlir/actions/workflows/bazelBuildAndTest.yml)
to fail. These need to be fixed in Bazel.
 2024-06-04 15:50:29 +08:00
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
zjgarvey 56d21cba62
Link necessary op interface implementations (#3364) 
This patch adds two `memref` passes to `torch-mlir-opt`, which already
occur in the pass pipeline
`torch-backend-to-linalg-on-tensors-backend-pipeline`. Additionally,
necessary op interface external models are included to address issue
#3352.
 2024-06-03 19:43:28 -05:00
Rob Suderman 0a6861b1e8
Add conversion operation for bool resolved_literal (#3410) 
Resolving `bool` literals can result in a type change to uint8. This
needs to be converted back to the expected type before returning to the
wrapped `torch` operators.
 2024-06-03 14:43:38 -07:00
Xida Ren (Cedar) 11c3281a8a
Fix reducesum onnx lit test to linalg lowering fails (#3218) 
fixes https://github.com/nod-ai/SHARK-Turbine/issues/653

---------

Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
 2024-06-03 16:36:09 -04:00
Xida Ren (Cedar) 948981a773
Update development.md to use ld.lld (#3412) 
@kuhar mentioned in the previous PR that we should use ld.lld. I kept
using ld because for my LLD version, it worked.

After updating to a new LLD version, that became necessary.
 2024-06-03 14:10:48 -04:00
zjgarvey 8995c90879
[TorchToLinalg] add support for quantized group conv (#3341) 
This addresses 7 of the model failures I'm seeing in the test suite. See
[Shark-Turbine issue
#566](https://github.com/nod-ai/SHARK-Turbine/issues/566).

Need the op ```linalg.conv_2d_ngchw_gfchw_q``` to be added upstream
before merging this. See [llvm-project PR #92136
](https://github.com/llvm/llvm-project/pull/92136).

A small additional expansion to operand quantization is included in this
patch to address a model failure that occurs when unblocking the
quantized group convolutions in one of these onnx models.
 2024-06-03 21:57:44 +05:30
Vivek Khandelwal 6382dbbcc0
[ONNX] Add OnnxToTorch lowering for SpaceToDepth op (#3393) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-03 20:29:39 +05:30
Xinyu Yang 285b087a5d
[Torch] Emit rrelu and decompose it (#3250) 
as title
 2024-06-03 19:25:52 +08:00
Xinyu Yang 267052df2a
[Torch] decompose AtenLerpTensorOp (#3251) 
as title
 2024-06-03 15:25:09 +08:00
Xinyu Yang 23b53050de
[Torch]Support conv_transpose1d and conv_transpose3d (#3286) 
1. Support conv_transpose1d and conv_transpose3d
2. Fix bugs of convertTransposedConv func in
lib/Conversion/TorchToStablehlo/Linear.cpp
 2024-06-03 15:11:12 +08:00
Rob Suderman 617b00b983
[NFC] Fix member cast change to global for landing collision (#3407) 
A PR landed when moving away from a deprecated cast function. Updated
the corresponding lines to pass.
 2024-05-31 17:31:24 +00:00
zjgarvey 878ba72c65
Bump LLVM to llvm/llvm-project@6127f15 (#3396) 
Signed-off-by: zjgarvey <zjgarvey@gmail.com>
 2024-05-31 17:49:20 +01:00
zjgarvey 8952377603
[Onnx] reduce MatMul OpsetVersion to 1 (#3403) 
Resolves #3324
 2024-05-31 22:17:56 +05:30
Surya Jasper fc100a117d
[MLIR][ONNX] Add OnnxToTorch support for Scatter Op (#3400) 
This PR adds OnnxToTorch support for Scatter op
 2024-05-31 07:36:48 +00:00
Rob Suderman afca88a058
[NFC] Change to *cast instead of .*cast variants (#3405) 
Member casts have been deprecated. Changing over a bunch of the member
cast calls to the global templated variants to remove deprecation
warnings.
 2024-05-30 23:45:13 -07:00
Yuanqiang Liu 4e05e2cd1e
[Torch] support recompose of aten.split.with_sizes and aten.tensor_sp… (#3401) 
…lit.sections

* support recompose to aten.split.with_sizes and
aten.tensor_split.sections
* fix recompose of aten.chunk
 2024-05-31 09:56:47 +08:00
zjgarvey 074098d20c
Modifies onnx resize lowering to fix numerical issues (#3381) 
Updates:

- some unsupported modes are now going to report a match failure for
unsupported coordinate transformation modes.
- fixes a bug that was introduced in the last patch for resize (my
bad...)
- uses actual x and y coordinates for computing weights in bilinear
interpolation (rather than eps modified values)
- slightly simplifies the bilinear interpolation payload for readability
and performance
- passes coordinate transformation mode information from an onnx.Resize
op to the mode string for the aten._interpolate op. This allows us to
perform custom logic in the torch->linalg lowering to support
onnx.Resize options without losing the default behaviors of the
interpolate op.
 2024-05-30 20:34:37 -04:00
Vivek Khandelwal d7b8f00d01
[ONNX] Add OnnxToTorch Lowering for LpNormalization op (#3397) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-05-30 23:05:26 +05:30
penguin_wwy e4be197efd
[FxImporter] Fix transpose rank zero (#3382) 2024-05-30 14:31:18 +08:00
penguin_wwy 1f544c37d0
[NFC] Remove unused header files (#3386) 2024-05-30 14:30:36 +08:00
Xida Ren (Cedar) 23d2d66a59
Fix error when attempting to read elided onnx constants (#3398) 
Co-authored-by: zjgarvey <zjgarvey@gmail.com>
 2024-05-29 16:56:23 -07:00
penguin_wwy a5d3b546f8
[FxImporter] Fix embedding bag (#3387) 2024-05-29 14:46:21 +08:00
Yuanqiang Liu e0a5adb1db
[Torch] fix aten.linear's decomposition (#3391) 
* support aten.linear with more rank.
 2024-05-27 15:49:50 +08:00
Yuanqiang Liu 05929f9171
enhance verbose option in e2e_testing (#3390) 
so that `python3 e2e_testing/main.py -v` would print intermediate IR.
 2024-05-27 08:01:07 +08:00
Yuanqiang Liu 28aeb047c1
[Stablehlo] fix crashing on AtenEmbeddingBagSumExample_basic (#3389) 2024-05-26 12:34:56 +08:00
zjgarvey 27169dcda9
Replace some depreciated uses of cast (#3343) 
Contributing towards #3299
 2024-05-23 09:01:47 -07:00
Yuanqiang Liu 5bb1a65ec9
[Stablehlo] refactor reduction lowering and support aten.amin (#3383) 
* implement detailed lowering template pattern
`ConvertAtenReduceAllDimsOp` and `ConvertAtenReduceKeepDimOp`
* support `aten.amin`'s lowering.
 2024-05-23 20:40:20 +08:00
Gaurav Shukla 43f961eca4
[MLIR] Fix 64-bit product during aten.view lowering (#3378) 
std::accumulate needs 64-bit init value to perform 64-bit arithmetic on
a list of integers.

Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
 2024-05-23 08:59:28 +05:30
penguin_wwy d924d0047f
[FxImporter] Fix primitive type in return (#3379) 2024-05-23 09:55:33 +08:00
Angel Zhang 2e194e13d6
[Torch] Fix bugs for `Torch::AtenOneHotOp` (#3350) 
This PR fixes the bugs for `Torch::AtenOneHotOp` by:

1) Using `Torch::kUnknownSize` as the default value for `numClasses` in
   the pattern matching stage in `DecomposeAtenOneHotOp`
2) Adding `AtenIntScalarOp` to the patterns in `TorchToArith`
3) Handling both `int` and `float` types for `off` and `on` values in
`TorchOnnxToTorch` conversion

It also includes:

1) A new test in `TorchToArith/basic.mlir`, for `torch.aten.Int.Scalar`,
and
2) A new test in `decompose-complex-ops.mlir`, for `torch.aten.one_hot`

**Dependencies**

This PR is dependent on #3334.
 2024-05-22 17:19:08 +00:00
Yuanqiang Liu f4bfe3f948
Bump llvm and stablehlo (#3377) 
* bump llvm to 1e5f29af81a5f6fda308074f6345b9fba4faa71c
* bump stablehlo to c44d9af8d4879adccf1054cb61a53377ae5898cb
 2024-05-22 23:28:45 +08:00
Xinyu Yang 4d7cdba4bf
[Torch] eliminate "getWithLeastStaticInformation" in DecomposeAtenTriuOp (#3330) 
I am trying to eliminate 'getWithLeastStaticInformation' in
DecomposeAtenTriuOp. Could you provide me with some suggestions?
@qingyunqu @zjgarvey 
See issue https://github.com/llvm/torch-mlir/issues/3312
 2024-05-22 23:16:57 +08:00
penguin_wwy 972d47b586
[FxImporter] Fix constant bool tensor (#3375) 2024-05-22 22:59:01 +08:00
Angel Zhang 52be4bdc18
[ONNX] Fix bugs for the `onnx.OneHot` operator (#3334) 
This commit fixes the bugs for the `onnx.OneHot` operator by:

1) Converting negative indices to non-negative indices
2) Handling both `int` and `float` types for `off` and `on` values
3) Using the correct result type

It also includes a new unit test.
 2024-05-22 08:32:00 -04:00
Sambhav Jain 6e485574e5
[Pipeline] Use dedicated simplification pipeline for TorchDynamo frontend (#3376) 
Discord Thread:
https://discord.com/channels/636084430946959380/1238330633328005243

## Context: 

[This](https://github.com/llvm/torch-mlir/blob/main/python/torch_mlir/fx.py#L61)
was updated to support e2e tests for the TorchDynamo frontend in
Torch-MLIR, where we run FX decompositions and import the FX IR to
generate Torch dialect, followed by
`torch-function-to-torch-backend-pipeline`, skipping only the shape/type
refinement for now. However, we should be able to skip many of the torch
simplification passes, as depicted in the [frontend
roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/images/roadmap_frontend.png).

Based on IREE's TorchDynamo
[pipeline](https://github.com/iree-org/iree/blob/main/compiler/plugins/input/Torch/InputConversion/Passes.cpp#L29),
the only two passes we seem to require are: `ReduceOpVariantsPass` and
`DecomposeComplexOpsPass`. This is inline with our findings as well
based on initial exploration.

This PR creates a dedicated frontend simplification pipeline for
TorchDynamo / FX Importer which calls only `ReduceOpVariantsPass` and
`DecomposeComplexOpsPass`. We rely on the e2e fx_importer tests to
ensure we're not regressing by removing many of the passes that were
historically needed for TorchScript.

One notable change here is that we do not call the
`LowerToBackendContractPass` anymore, which used to call
`TorchSimplificationPipeline` iteratively until VerifyBackendContract
was clean. Some of this was required for the shape/type refinement to
converge, which seems a non-issue for Dynamo frontend. Do we anticipate
this (the iterative invocation of TorchSimplificationPipeline followed
by VerifyBackendContract) to be worth retaining in the Dynamo frontend
pipeline? If so, I can make those changes, PLMK.
 2024-05-22 05:23:18 -07:00
Aart Bik 560ca24771
[torch-mlir][sparse] replace xavier with ones initialization (#3374) 
ensures stability of results between different set ups
 2024-05-21 17:12:55 -07:00
RattataKing fcf48872b3
[ONNX] Implement Softsign op (#3373) 2024-05-21 12:10:26 -07:00