Commit Graph

162 Commits (46c0f3cad0107da08100a1f7e7b3dad1aec98983)

Author SHA1 Message Date
Yuanqiang Liu 46c0f3cad0
[Torch] emit aten.log_sigmoid and decompose it to log(sigmoid) (#3246) 2024-04-28 11:47:43 +08:00
Stella Laurenzo 6877302504
[NFC reformat] Applies pre-commit formatting to Python files. (#3244)
This is a large change because prior to this point, Python files in the
project were not consistently formatted. This reformats them all with
black defaults.

Based on experience with prior projects, if you have a dev/long-term
branch with Python patches, you can minimize merge conflicts prior to
rebasing to include this commit by running `black` on your modified
Python files, squashing, and then rebasing/merging.
2024-04-27 14:16:31 -07:00
penguin_wwy 4fbe77a051
[dynamo] Verify the default value is passed by kwargs (#2998) 2024-04-28 02:18:33 +08:00
Rob Suderman 9a12a093a6
[onnx] Support `onnx.OneHot` lowering to `torch` (#3196)
[onnx] Support `onnx.OneHot` lowering to `torch`

Leverage the `aten.onehot` implementation along with `aten.transpose`
and `aten.where.scalar`.
2024-04-26 12:08:15 -07:00
Xinyu Yang ac85338491
[Stablehlo] Support AtenPowScalarOp, AtenTanOp, AtenAsinhOp, AtenAcoshOp, AtenAtanhOp, Atan2Op (#3233) 2024-04-26 15:47:44 +08:00
penguin_wwy 122eb69a98
[stablehlo] add aten left/right shift op conversion support (#3234) 2024-04-26 09:20:49 +08:00
Xinyu Yang 7030eacb76
[stablehlo] Support aten.any and aten.all lowering (#3217) 2024-04-25 11:15:52 +08:00
Yuanqiang Liu fab2696489
[Torch] support aten.trunc (#3219)
decompose `trunc(x)` to `sign(x) * floor(abs(x))`
2024-04-24 14:32:33 +08:00
Xinyu Yang e18bf42d0e
[stablehlo] Support ConstantPadNdOp in stablehlo (#3211)
as title
2024-04-24 14:15:11 +08:00
Xinyu Yang 42b9eccdb3
[Stablehlo] Fix AtenSumDimIntListOp when dim==None (#3216)
as titile
2024-04-24 11:25:46 +08:00
Xinyu Yang 4da3d714cc
[Torch] Support AtenProdOp on linalg and stablehlo (#3215) 2024-04-24 11:14:04 +08:00
zjgarvey a8ba865fca
[torch] Adds Quantization Support for `aten.relu` (#3177)
A choice was made to quantize the return type of Relu with a scale and
zero point copied from the input's quantization scheme. With this
choice, the torch-to-linalg conversion of quantized Relu essentially
computes max(input, zeroPoint) in the elementwise payload.
2024-04-23 11:01:36 -07:00
Yuanqiang Liu db3842f2e8
[Stablehlo] support lowering sinh & cosh to stablehlo (#3213) 2024-04-23 19:54:58 +08:00
Xinyu Yang c1967b607f
[Stablehlo] add AtenLog10Op, AtenLog2Op lowering to stablehlo (#3208) 2024-04-23 19:06:55 +08:00
Yuanqiang Liu 1f8123b5f0
[Stablehlo] support unary ops which promote to floating point (#3209)
* promote input to output element-type when lowering to stablehlo, so
that it could satisfy stablehlo's type constraints.
* split promote-to-fp unary ops from fp-only unary ops.
2024-04-23 17:57:12 +08:00
Yuanqiang Liu 797e4cd395
[Stablehlo] lowering asin, acos, atan (#3207)
* lowering asin, acos and atan to chlo ops.
2024-04-23 16:24:53 +08:00
Vinayak Dev cff2f084d4
[torch] Add OnnxToTorch lowering for `onnx.ReduceL2` (#3175)
Adds OnnxToTorch lowering for the ReduceL2 op.
2024-04-23 02:03:05 -04:00
Vivek Khandelwal 3c252cdd44
[onnx] Add `onnx-to-torch` lowering for random ops (#3193)
This commit adds the OnnxToTorch lowering for Onnx's RandomNormal, RandomNormalLike, RandomUniform, and RandomUniformLike op.
2024-04-22 22:28:07 +05:30
Vivek Khandelwal 6abc7371c8
[MLIR][TORCH] Fix OnnxToLinalg lowering issue for Squeeze and Unsqueeze op (#2991)
This commit also cleans up the OnnxToTorch lowering for the Squeeze and
Unsqueeze op and adds the support for handling edge cases.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-04-22 08:52:42 +00:00
penguin_wwy e5bdd71baf
[Torch] Emit and decompose prims.iota op (#3132) 2024-04-21 19:45:01 -07:00
penguin_wwy a60e84e5ee
[stablehlo] add aten.expm1 op conversion support (#3199) 2024-04-21 19:20:49 -07:00
Rob Suderman 8222637159
[onnx] Extend op version number of `onnx.ScatterElements` (#3195)
Version number was set too high. Lowered to support more cases allows
more tests to pass.

Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
2024-04-21 12:32:18 -04:00
Rob Suderman 733cace1df
[onnx] Fix `onnx.split` by directly handling slicing (#3194)
Previous implementation erroneously mixed up num_outputs with
slice_size. New version correctly computs the slice size and directly
performs slicing rather than leveraging `aten.split.tensor`. This is due
to `onnx` supporting a fixed number of splits making the size
computation more easily computeable when lowering to `aten` rather than
deferring to `aten.split.tensor`.

---------

Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>
2024-04-21 12:31:56 -04:00
penguin_wwy b6b01602d3
[stablehlo] add aten.fmod.Tensor op conversion support (#3198) 2024-04-21 08:39:36 +08:00
penguin_wwy ea0ecb67be
[stablehlo] add aten.remainder.Tensor op conversion support (#3197) 2024-04-21 00:03:37 +08:00
Rob Suderman b01245c0e8
[onnx] Fix `onnx.Not` for non-bool inputs (#3187)
Need to perform a bool cast to support `onnx.Not` on non-bool inputs.
2024-04-19 11:32:24 -07:00
Xinyu Yang 790a697245
[Torch] Add folder for AtenIntOp, AtenFloatOp (#3189)
See unit test below:
```
// CHECK-LABEL:   func.func @torch.aten.tensor.float(
// CHECK-NEXT: torch.vtensor.literal(dense<1.000000e+01> : tensor<f32>) : !torch.vtensor<[],f32>
func.func @torch.aten.tensor.float() -> !torch.vtensor<[],f32> {
  %none = torch.constant.none
  %false = torch.constant.bool false
  %float1.000000e01 = torch.constant.float 1.000000e+01
  %67 = torch.aten.tensor.float %float1.000000e01, %none, %none, %false : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],f32>
  return %67 : !torch.vtensor<[],f32>
}

// CHECK-LABEL:   func.func @torch.aten.tensor.int(
// CHECK-NEXT: torch.vtensor.literal(dense<45> : tensor<si32>) : !torch.vtensor<[],si32>
func.func @torch.aten.tensor.int() -> !torch.vtensor<[],si32> {
  %none = torch.constant.none
  %false = torch.constant.bool false 
  %int45 = torch.constant.int 45
  %67 = torch.aten.tensor.int %int45, %none, %none, %false : !torch.int, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],si32>
  return %67 : !torch.vtensor<[],si32>
}

```
2024-04-19 22:17:06 +08:00
penguin_wwy 5a98c72c7f
[StableHLO] Fix aten.clamp.Tensor in FxImporter2StableHLO (#3190)
The FX importer will pass static shapes to the Torch dialect, so it
needs to generate a StableHLO that satisfies shape inference.
2024-04-19 17:08:29 +08:00
penguin_wwy 0a6073414d
[FxImporter] Add fx importer to stablehlo e2e test config (#3183) 2024-04-18 21:29:17 -07:00
penguin_wwy 6c4f7deebb
[stablehlo] add aten.clamp.Tensor op conversion support (#3185) 2024-04-19 10:55:27 +08:00
Rob Suderman be742a937d
[onnx] Update the failure triage for onnx (#3186)
Reclassifying what the source of failures are for various bugs so we can
reprioritize what failures are common.
2024-04-18 14:58:13 -07:00
Rob Suderman 0e77de996a
[torch] Add support for `torch.view` with dynamic shapes (#3164)
We can map to `tensor.reshape` for handling multiple output dynamic
shapes. Later we can perform a more complex analysis for indentifying
expand/collapse cases from the tensor.reshape.

Initially we planned to handle this identification at the `torch` level
however it will be easier to handle once converted to core
mlir-dialects.
2024-04-18 11:47:19 -07:00
Xinyu Yang d4313eed4a
[Torch] Add decomposition of RepeatInterleaveSelfInt Op (#3075)
Decomposition RepeatInterleaveSelfInt with following ops:
```python

def my_repeat_interleave(input, repeats, dim=None):
    if dim is None:
        # Flatten the input and then repeat
        return input.flatten().unsqueeze(-1).tile((1, repeats)).flatten()
    else:
        # Calculate the shape after repeat
        expanded_shape = list(input.shape)
        expanded_shape[dim] *= repeats
        # Repeat the tensor along the specified dimension
        repeat_shape = [1] * (input.dim() + 1)
        repeat_shape[dim + 1] = repeats
        input = input.unsqueeze(-1)

        # Tile and then reshape
        tiled = torch.tile(input, repeat_shape)
        # Rearrange and reshape
        repeated = tiled.reshape(*expanded_shape)
    return repeated

```

I passed the tests of stablehlo and linalg. When testing onnx, strange
things happened.
In torch-mlir's CI **torch_nightly** and my own
environment(torch==2.4.0.dev20240318+cpu), it can **pass the pass**.
In torch-mlir's CI  **torch_stable**, it **failed**.
The test case is `RepeatInterleaveSelfIntNoDimModule_basic`, the result
shape should be [120].
```python
class RepeatInterleaveSelfIntNoDimModule(torch.nn.Module):

    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([3, 4, 5], torch.float32, True),
    ])
    def forward(self, x):
        return x.repeat_interleave(2)


@register_test_case(module_factory=lambda: RepeatInterleaveSelfIntNoDimModule())
def RepeatInterleaveSelfIntNoDimModule_basic(module, tu: TestUtils):
    module.forward(tu.rand(3, 4, 5))
```
The error log is as follows:
```
  Unexpected outcome summary: (onnx)
  
  ****** Failed tests - 1 tests
      FAIL - "RepeatInterleaveSelfIntNoDimModule_basic"
          @ trace item #0 - call to "forward"
          @ output of call to "forward"
          ERROR: shape (torch.Size([6, 4, 5])) is not equal to golden shape (torch.Size([120]))
```

@rsuderman 
Would you please help me check what's wrong with my PR? Thanks a lot.
2024-04-18 06:27:51 +08:00
penguin_wwy 3aa81f78d8
[FxImporter] Replace local_scalar_dense in fx_importer (#3180) 2024-04-17 22:45:47 +08:00
Xinyu Yang d2ba956e69
[Torch] Support Aten_CastLongOp. (#3160)
By canonicalize Aten_CastLongOp into AtenToDtypeOp
2024-04-17 21:58:32 +08:00
penguin_wwy e4b11a0ab4
[FxImporter] Fix fx importer test config and clean xfail set (#3176) 2024-04-16 22:36:07 -07:00
penguin_wwy 398aeeec87
[FxImporter] Fix kwarg operands in fx importer (#3166)
Remove the `kwarg_only` limitation, for example
```
torch.add(x, 3.0, alpha=2)
```
compiled to
```
%0 = torch.aten.add.Scalar %arg0, %float3.000000e00, %int1
```
fix to
```
%0 = torch.aten.add.Scalar %arg0, %float3.000000e00, %int2
```
2024-04-16 13:17:05 -07:00
zjgarvey 7a1ad0d7c0
[TorchToLinalg] Adds Support for Remaining Quantized Matmul Cases (#3167)
The new cases added for quantized matmuls are:

1. vec-vec
2. vec-mat
3. mat-vec

each of which are now lowered to expand(s), quantized_matmul, and
collapse.
2024-04-16 09:28:28 -07:00
Vinayak Dev a0232e9ebd
[MLIR][TORCH] Add OnnxToTorch lowering for ReduceL1 Op (#3146)
Adds OnnxToTorch Lowering for the ReduceL1 op.
2024-04-16 12:24:46 +05:30
penguin_wwy 10b6062d41
[CI] Enable the tests for fx_importer in the CI (#3168)
Replace the torchdynamo e2e with the fx_importer e2e
2024-04-15 21:20:23 -07:00
Xinyu Yang ae4724763a
[Stablehlo] Enhance broadcast pattern in matmul Ops (#3161)
To pass test "MatmulStaticBroadcast_basic" in stablehlo:
```python
class MatmulStaticBroadcast(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([4, 1, 6, 7], torch.float32, True),
        ([8, 1, 5, 7, 6], torch.float32, True),
    ])
    def forward(self, lhs, rhs):
        return torch.matmul(lhs, rhs)


@register_test_case(module_factory=lambda: MatmulStaticBroadcast())
def MatmulStaticBroadcast_basic(module, tu: TestUtils):
    module.forward(tu.rand(4, 1, 6, 7), tu.rand(8, 1, 5, 7, 6))
```
2024-04-16 10:10:36 +08:00
zjgarvey 5e564b5864
Adds Some Quantization Support for AtenMatmulOp (#3147)
1. onnx.MatMulInteger now converts to aten.matmul instead of aten.mm
2. aten.matmul, for ranks >=2, now allows quantized inputs and will
lower to linalg::quantized_matmul or linalg::quantized_batch_matmul.
3. added AtenMatmulOp to the FuseQuantizeOps rewrite patters
QuantizeOperands, QuantizeTransposedOperands, and QuantizeAccumulator
4. added several tests, including some to test AtenMmOp with varying
quantization signed-ness.
5. a quantized matmul mat-vec test is added to verify the failure to
lower to linalg; cleaned of out-of-date code related to common
torch-mlir lowering xfails.
6. in debugging a real model with quantized matmuls, I found a bug on
the scalarize-shapes pass which resulted from the aten.full op folder
returning an incompatible result type. This is fixed by the small change
here to
[lib/Dialect/Torch/IR/TorchOps.cpp](https://github.com/llvm/torch-mlir/compare/main...zjgarvey:torch-mlir:MatMulIntegerFix?expand=1#diff-dc8ed165c207918e606490eee3984b1ad51d7034e6aac36fc046bf47f6f03f4f).
2024-04-15 16:06:47 -07:00
IanWood1 5708ee7ec9
Added 2 Ops: Floor divide scalar and Floor divide scalar mode (#3156)
- Added linalg lowering for `AtenFloorDivideScalarOp`
  - Needed `AtenDivScalarModeOp` for the decomp.
- Added linalg lowering for `AtenDivScalarModeOp`
- Moved linalg payload logic to `createDivModePayload()` since the logic
was nearly identical for both `AtenDivScalarModeOp` and
`AtenDivTensorModeOp`. Just a template function
 -  Added `AtenDivScalarModeOp` lowering for stablehlo
 

Pytorch's
[`torch.floor_divide()`](https://pytorch.org/docs/stable/generated/torch.floor_divide.html)
in a previous version (for a reason unknown to me) preformed a
truncation instead of "floor". The already implemented op
`AtenFloorDivideTensorOp` was done before this change. However, this
wasn't caught because our testcases only tested positive floor division.
I changed this to floor as well as adding a few test cases.
2024-04-15 13:45:10 -07:00
penguin_wwy 45eaeaaf36
[FxImporter] Add FxImporter config in e2e-test (#3151) 2024-04-12 16:07:56 -07:00
Xinan Jiang(姜曦楠) 71d90788d3
[MLIR][TORCH] Support parallel dimemsions expand/collapse (#3051)
This PR support `aten.view` with unique unknown dimension both in input
shape and output shape while the pass convert-torch-to-linalg that
lowing `aten.view` to `tensor.collapse_shape` or `tensor.expand_shape`.

Below is an example
```
func.func @test_reshape(%arg0: !torch.vtensor<[1,?,50,16],f32>) -> !torch.vtensor<[1,?,16],f32> attributes {torch.assume_strict_symbolic_shapes, torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
  %int1 = torch.constant.int 1
  %int-1 = torch.constant.int -1
  %int16 = torch.constant.int 16
  %0 = torch.prim.ListConstruct %int1, %int-1, %int16 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
  %1 = torch.aten.view %arg0, %0 : !torch.vtensor<[1,?,50,16],f32>, !torch.list<int> -> !torch.vtensor<[1,?,16],f32>
  return %1 : !torch.vtensor<[1,?,16],f32>
}
```
2024-04-11 10:43:03 -07:00
Rob Suderman a1fe307a76
[torch] Support implicit batch for index_put (#3128)
If there is only a single value scattered there can be an implicit batch
dimension. This includes a check for the implicit batch dimension when
reshaping the update tensor. It includes an e2e test to verify
correctness.
2024-04-11 10:18:03 -07:00
Xinyu Yang 308c45e61a
[Torch] Fix PrimListUnpackOp::getCanonicalizationPatterns (#3140)
Fix the case PrimListUnpackOp's result num is not equal to PrimList
length.
See the following example:
```python
    def forward(self, x):
        if len(x.shape) == 5:
            b0, t, c0, h0, w0 = x.shape
            b, c, h, w = torch.mul(b0, t), c0, h0, w0
        else:
            b1, c1, h1, w1 = x.shape
            b, c, h, w = b1, c1, h1, w1
        res = torch.reshape(x, [b, c, h, w])
        return res
```
Without this fix, the following error message will occur:
```
/root/torch-mlir/externals/llvm-project/mlir/lib/IR/PatternMatch.cpp:118: virtual void mlir::RewriterBase::replaceOp(mlir::Operation *, mlir::ValueRange): Assertion `op->getNumResults() == newValues.size() && "incorrect # of replacement values"' failed.
```
2024-04-11 19:48:49 +08:00
Xinyu Yang 6524838bcb
[Torch] Add general AdaptiveAvgPool2dOp decompose support (#3111)
Previously, it could only handle the situations where outputsize == (1,
1) or outputsize == (input_H, input_W). Now it supports all situations
where input_H % output_H== 0 && input_W % output_W == 0
2024-04-11 17:02:59 +08:00
zjgarvey aa5e150313
Adds Some uint8 Quantization Fixes (#3122)
1. Changes the linalg lowering for dequantization ops to always sign
cast to float to prevent misrepresenting uint32 overflow on subtraction
with zero point.
2. Adds a basic quantized model test which only quantizes and
dequantizes and now passes with these changes in linalg and onnx
configs.
3. Changes the aten.mm lowering to allow mismatched quantized types. 
4. If a quantized matmul arg is uint8, we shift by 128 to faithfully
represent the quantization as a signed i8 quantization. This worked fine
in the AtenMmOp lowering, but I'd be happy to move it to a rewrite in
FuseQuantizedOps.cpp instead if that seems more appropriate.

With the changes 3 and 4, the QuantizedMLP_basic and
QuantizedSingleLayer_basic e2e tests now passes with the onnx config.
2024-04-10 12:36:58 -07:00
Xinyu Yang 42a16fa912
[Torch] Support Aten_CastFloatOp. (#3115)
By canonicalize Aten_CastFloatOp into AtenToDtypeOp
2024-04-09 11:06:53 +08:00