# This is a combination of 22 commits.

# This is the 1st commit message:

[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (llvm#2340)

[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
# This is the commit message llvm#2:

update PyTorch version to 2.1.0.dev20230729 (llvm#2354)

- torch version: 2.1.0.dev20230729
 - torch commit hash: b638df0afb83572724032c824c64e481bb4499a0
 - torchvision version: 0.16.0.dev20230729

Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
# This is the commit message llvm#3:

update PyTorch version to 2.1.0.dev20230730 (llvm#2356)

- torch version: 2.1.0.dev20230730
 - torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742
 - torchvision version: 0.16.0.dev20230730

Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
# This is the commit message llvm#4:

update PyTorch version to 2.1.0.dev20230731 (llvm#2359)

- torch version: 2.1.0.dev20230731
 - torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7
 - torchvision version: 0.16.0.dev20230731

Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
# This is the commit message llvm#5:

LTC->MLIR Debug Info support (llvm#1922)

* LTC->MLIR Debug Info support

* SW-95317 Propagate Lazy->Jit->MLIR scope name.

* Enhance location information based on op names

Currently, the location information attached to the ops just considers
the filename, line number and column number. Attaching operation name
would help identify the type of computation by just looking at the
profile of execution.

* Update locations logic; updated debug-info.py test

* Use {scope}/{op_name} format to track names by default

---------

Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net>
Co-authored-by: Mark Browning <mark@cerebras.net>
Co-authored-by: Vimal Patel <vimal@polymagelabs.com>
# This is the commit message llvm#6:

build: update llvm tag to 4189584

Summary of changes:
- Update tags
  llvm: 4189584
  mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>

# This is the commit message llvm#7:

update PyTorch version to 2.1.0.dev20230802 (llvm#2366)

- torch version: 2.1.0.dev20230802
 - torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e
 - torchvision version: 0.16.0.dev20230802

Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
# This is the commit message llvm#8:

Change Python version from 3.10 to 3.11 in installation instructions (llvm#2370)


# This is the commit message llvm#9:

Add CITATION file (llvm#2371)


# This is the commit message llvm#10:

Add packaging as an install dependency (llvm#2369)

Needed by `torch_mlir._version`. Resolves llvm#2368.
# This is the commit message llvm#11:

[Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (llvm#2358)

* [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op
# This is the commit message llvm#12:

update PyTorch version to 2.1.0.dev20230803 (llvm#2372)

- torch version: 2.1.0.dev20230803
 - torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e
 - torchvision version: 0.16.0.dev20230803

Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
# This is the commit message llvm#13:

Prevent failed stable CI job from cancelling nightly jobs (llvm#2373)

The CI jobs that use stable PyTorch are currently not required to pass
in order for a patch to get merged in `main`. This commit makes sure
that if a CI job for stable PyTorch fails, it does not cancel the
other required jobs.
# This is the commit message llvm#14:

[Torch Dialect] emit aten.tile op and decompose it into aten.repeat (llvm#2355)


# This is the commit message llvm#15:

update

# This is the commit message llvm#16:

update xfail sets

# This is the commit message llvm#17:

update xfail_sets

# This is the commit message llvm#18:

update

# This is the commit message llvm#19:

fix xfail_sets

# This is the commit message llvm#20:

update:

# This is the commit message llvm#21:

update

# This is the commit message llvm#22:

update:

.github/workflows/buildAndTest.yml

@@ -22,6 +22,7 @@ concurrency:
 # macos  - arm64  - llvm in-tree     - pytorch binary - build only    # cross compile, can't test arm64
 jobs:
   build-test:
+    continue-on-error: ${{ matrix.torch-version == 'stable' }}
     strategy:
       fail-fast: true
       matrix:

CITATION.cff

@@ -0,0 +1,19 @@
+cff-version: 1.2.0
+title: Torch-MLIR
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - name: LLVM
+repository-code: 'https://github.com/llvm/torch-mlir'
+abstract: >-
+  The Torch-MLIR project aims to provide first class support
+  from the PyTorch ecosystem to the MLIR ecosystem.
+keywords:
+  - Compiler
+  - PyTorch
+  - MLIR
+license:
+  - Apache-2.0 with LLVM Exceptions
+  - BSD

README.md

@@ -43,17 +43,17 @@ We have few paths to lower down to the Torch MLIR Dialect.
 
 ## Install torch-mlir snapshot
 
-At the time of writing, we release pre-built snapshot of torch-mlir for Python 3.10 on Linux and macOS.
+At the time of writing, we release pre-built snapshot of torch-mlir for Python 3.11 on Linux and macOS.
 
-If you have Python 3.10, the following commands initialize a virtual environment.
+If you have Python 3.11, the following commands initialize a virtual environment.
 ```shell
-python3.10 -m venv mlir_venv
+python3.11 -m venv mlir_venv
 source mlir_venv/bin/activate
 ```
 
-Or, if you want to switch over multiple versions of Python using conda, you can create a conda environment with Python 3.10.
+Or, if you want to switch over multiple versions of Python using conda, you can create a conda environment with Python 3.11.
 ```shell
-conda create -n torch-mlir python=3.10
+conda create -n torch-mlir python=3.11
 conda activate torch-mlir
 python -m pip install --upgrade pip
 ```

e2e_testing/main.py

@@ -31,6 +31,7 @@
     LINALG_XFAIL_SET,
     MAKE_FX_TOSA_PASS_SET,
     STABLEHLO_PASS_SET,
+    STABLEHLO_CRASHING_SET,
     TOSA_PASS_SET,
     LTC_XFAIL_SET,
     TORCHDYNAMO_XFAIL_SET,
@@ -101,7 +102,7 @@ def main():
     elif args.config == "stablehlo":
         config = StablehloBackendTestConfig(LinalgOnTensorsStablehloBackend())
         xfail_set = all_test_unique_names - STABLEHLO_PASS_SET
-        crashing_set = set()
+        crashing_set = STABLEHLO_CRASHING_SET
     elif args.config == "native_torch":
         config = NativeTorchTestConfig()
         xfail_set = set()

e2e_testing/xfail_sets.py

@@ -310,6 +310,34 @@
 }
 
 STABLEHLO_PASS_SET = {
+    "TileBigDimsSizeModule_basic",
+    "TileSmallDimsSizeModule_basic",
+    "AddIntModule_basic",
+    "AtenIntBoolOpModule_basic",
+    "AtenIntTensorByteDtypeModule_basic",
+    "AtenIntTensorCharDtypeModule_basic",
+    "BoolFloatFalseModule_basic",
+    "BoolFloatTrueModule_basic",
+    "BoolIntFalseModule_basic",
+    "BoolIntTrueModule_basic",
+    "CeilFloatModule_basic",
+    "DivFloatModule_basic",
+    "DivIntModule_basic",
+    "EqIntModule_basic",
+    "GeFloatIntModule_basic",
+    "GeFloatModule_basic",
+    "GeIntModule_basic",
+    "GtFloatIntModule_basic",
+    "GtIntModule_basic",
+    "MulIntModule_basic",
+    "NeFloatIntModule_basic",
+    "NeIntModule_basic",
+    "SqrtIntModule_basic",
+    "SubFloatModule_basic",
+    "SubIntModule_basic",
+    "TensorToBoolZeroRank_basic",
+    "TensorToIntZeroRank_basic",
+    "TensorToFloatZeroRank_basic",
     "AliasModule_basic",
     "TensorIntModule_basic",
     "AllBoolFalseModule_basic",
@@ -352,6 +380,7 @@
     "MaskedFillScalarIntValueStaticModule_basic",
     "MaskedFillScalarFloatValueStaticModule_basic",
     "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
+    "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
     "AddSizeIntModule_basic",
     "AddSizeIntNegDimModule_basic",
     "ArangeDtypeFloatModule_basic",
@@ -753,6 +782,7 @@
     "ReshapeExpandModule_basic",
     "RollModule_basic",
     "TestMultipleTensorReturn_basic",
+    "AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
     "BaddbmmStaticModule_basic",
     "BaddbmmBroadcast1DInputModule_basic",
@@ -826,9 +856,23 @@
     "TupleModule_basic",
 }
 
+STABLEHLO_CRASHING_SET = {
+    # These e2e tests crash because currently mlir-hlo's shape-component-analysis
+    # only support exact one index in tensor::ExtractOp when it's related with 
+    # some tensors' shape. REF:
+    # https://github.com/tensorflow/mlir-hlo/blob/master/mhlo/analysis/shape_component_analysis.cc#L586
+    # FIXME if upstream mlir-hlo fix this.
+    "ViewCollapseDynamicWithAtenSizeIntModule_basic",
+    "UnsafeViewCollapseDynamicWithAtenSizeIntModule_basic",
+
+    "Aten_EmbeddingBagExample_basic"
+}
+
 # Write the TOSA set as a "passing" set as it is very early in development
 # and very few tests work yet.
 TOSA_PASS_SET = {
+    "TileBigDimsSizeModule_basic",
+    "TileSmallDimsSizeModule_basic",
     "IndexPutImpl2DNoneIndexStaticModule_basic",
     "AliasModule_basic",
     "MaxPool2dEmptyStrideStaticModule_basic",
@@ -1142,6 +1186,11 @@
     "TupleModule_basic",
     "NumpyTRank0Module_basic",
     "Permute0RankModule_basic",
+    "Add_Module_basic",
+    "SoftmaxIntModule_basic",
+    "SoftmaxIntNegDimModule_basic",
+    "_LogSoftmaxModule_basic",
+    "_SoftmaxModule_basic",
 }
 
 MAKE_FX_TOSA_PASS_SET = (TOSA_PASS_SET | {
@@ -1150,6 +1199,8 @@
     "SliceWholeTensorModule_basic",
     "TensorFloatModule_basic",
     "TensorIntModule_basic",
+    "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
+    "AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
 }) - {
 ### Test failing in make_fx_tosa but not in tosa
 
@@ -1172,6 +1223,8 @@
     # Unimplemented operator 'aten._index_put_impl_.hacked_twin'
     "IndexPutImpl1DFloatNonAccumulateModule_basic",
     "IndexPutImpl1DIntNonAccumulateModule_basic",
+    # RuntimeError: The size of tensor a (7) must match the size of tensor b (3) at non-singleton dimension 1
+    "Add_Module_basic",
 }
 
 if torch_version_for_comparison() < version.parse("2.1.0.dev"):
@@ -1190,6 +1243,8 @@
     "_ConvolutionDeprecated2DBenchmarkModule_basic",
     "_ConvolutionDeprecated2DCudnnModule_basic",
     "_ConvolutionDeprecated2DDeterministicModule_basic",
+    "AdaptiveAvgPool1dNonUnitOutputSizeDynamicModule_basic",
+    "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool2dNonUnitOutputSizeDynamicModule_basic",
     "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
     "AddIntModule_basic",

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -5250,6 +5250,55 @@ def Torch_AtenScatter_ValueOp : Torch_Op<"aten.scatter_.value", [
   }];
 }
 
+def Torch_AtenMaskedScatterOp : Torch_Op<"aten.masked_scatter", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::masked_scatter : (Tensor, Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$mask,
+    AnyTorchTensorType:$source
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenMaskedScatterOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 3, 1);
+    }
+    void AtenMaskedScatterOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 3, 1);
+    }
+  }];
+}
+
+def Torch_AtenMaskedScatter_Op : Torch_Op<"aten.masked_scatter_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::masked_scatter_ : (Tensor, Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$mask,
+    AnyTorchTensorType:$source
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenMaskedScatter_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 3, 1);
+    }
+    void AtenMaskedScatter_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 3, 1);
+    }
+  }];
+}
+
 def Torch_AtenAdaptiveAvgPool2dOp : Torch_Op<"aten.adaptive_avg_pool2d", [
     AllowsTypeRefinement,
     HasValueSemantics,
@@ -5274,6 +5323,30 @@ def Torch_AtenAdaptiveAvgPool2dOp : Torch_Op<"aten.adaptive_avg_pool2d", [
   }];
 }
 
+def Torch_AtenAdaptiveAvgPool1dOp : Torch_Op<"aten.adaptive_avg_pool1d", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::adaptive_avg_pool1d : (Tensor, int[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchListOfTorchIntType:$output_size
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenAdaptiveAvgPool1dOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenAdaptiveAvgPool1dOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
 def Torch_AtenTopkOp : Torch_Op<"aten.topk", [
     AllowsTypeRefinement,
     HasValueSemantics,
@@ -7697,6 +7770,33 @@ def Torch_AtenRepeatOp : Torch_Op<"aten.repeat", [
   }];
 }
 
+<<<<<<< HEAD
+def Torch_AtenTileOp : Torch_Op<"aten.tile", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::tile : (Tensor, int[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchListOfTorchIntType:$dims
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenTileOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenTileOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
+=======
+>>>>>>> ad24482e (update)
 def Torch_AtenReshapeOp : Torch_Op<"aten.reshape", [
     AllowsTypeRefinement,
     ReadOnly

lib/Conversion/TorchToStablehlo/Basic.cpp

@@ -232,6 +232,51 @@ class ConvertAtenConstPatternOp : public OpConversionPattern<AtenOpT> {
 
 } // namespace
 
+namespace {
+// Casts a tensor of exactly one element to an elemental type.
+// Many codes borrowed from
+// `lib/Conversion/TorchToLinalg/TensorScalarInterop.cpp`
+template <typename AtenOpT>
+class ConvertAtenTensorToScalarLikeOp : public OpConversionPattern<AtenOpT> {
+public:
+  using OpConversionPattern<AtenOpT>::OpConversionPattern;
+  using OpAdaptor = typename AtenOpT::Adaptor;
+  LogicalResult
+  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    auto inputType =
+        adaptor.getA().getType().template dyn_cast<RankedTensorType>();
+    if (!inputType)
+
+      op.emitError("only Tensor types supported in StableHLO");
+    auto outType =
+        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
+            op.getType());
+    Location loc = op.getLoc();
+    Value input = adaptor.getA();
+    SmallVector<Value> inputSizes = getTensorSizes(rewriter, loc, input);
+    int64_t inputRank = inputSizes.size();
+    Type inputDtype =
+        op.getA().getType().template cast<BaseTensorType>().getDtype();
+
+    Value constantOne =
+        rewriter.create<arith::ConstantOp>(loc, rewriter.getI64IntegerAttr(1));
+    for (int64_t i = 0; i < inputRank; i++)
+      checkDimEqualHelper(rewriter, loc, inputSizes[i], constantOne);
+
+    Value constantZero =
+        rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(0));
+    SmallVector<Value> indices(inputRank, constantZero);
+    Value result = rewriter.create<tensor::ExtractOp>(loc, input, indices);
+    Type resultType =
+        this->getTypeConverter()->convertType(op->getResult(0).getType());
+    rewriter.replaceOp(op, convertScalarToDtype(rewriter, loc, result,
+                                                resultType, inputDtype));
+    return success();
+  }
+};
+} // namespace
+
 // The binary broadcast patterns
 namespace {
 template <typename AtenOpT, typename ChloOpT>
@@ -1662,6 +1707,16 @@ void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
   INSERT_CONSTANT_FILL_PATTERN(AtenZerosOp, 0);
 #undef INSERT_CONSTANT_FILL_PATTERN
 
+#define INSERT_TENSOR_TO_SCALAR_PATTERN(AtenOp)                                \
+  target.addIllegalOp<AtenOp>();                                               \
+  patterns.add<ConvertAtenTensorToScalarLikeOp<AtenOp>>(typeConverter,         \
+                                                        context)
+
+  INSERT_TENSOR_TO_SCALAR_PATTERN(AtenIntTensorOp);
+  INSERT_TENSOR_TO_SCALAR_PATTERN(AtenFloatTensorOp);
+  INSERT_TENSOR_TO_SCALAR_PATTERN(AtenBoolTensorOp);
+#undef INSERT_TENSOR_TO_SCALAR_PATTERN
+
 #define INSERT_BINARY_ADDSUB_PATTERN(AtenOp, ChloOp)                           \
   target.addIllegalOp<AtenOp>();                                               \
   patterns.add<ConvertAtenAddSubOp<AtenOp, ChloOp>>(typeConverter, context)