llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
2,930 Commits
47 Branches
1195 Tags
71 MiB
Commit Graph

2930 Commits (7f188eb824774753ebd169786aa4cc45e99b977c)
 

Author SHA1 Message Date
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
penguin_wwy c2c1c2cfa4
[FxImporter] Fix failed e2e case (#3365) 2024-05-22 00:20:54 +08:00