[AutoBump] Merge with fixes of 1d48596 (May 10)

docs/development.md

@@ -13,9 +13,6 @@ While this is running, you can already setup the Python venv and dependencies in
 
 ## Setup your Python VirtualEnvironment and Dependencies
 
-Also, ensure that you have the appropriate `python-dev` package installed
-to access the Python development libraries / headers.
-
 ```shell
 python -m venv mlir_venv
 source mlir_venv/bin/activate
@@ -26,6 +23,15 @@ python -m pip install -r requirements.txt
 python -m pip install -r torchvision-requirements.txt
 ```
 
+Also, ensure that you have the appropriate `python-dev` package installed
+to access the Python development libraries / headers. For example, you can install
+it with the following `apt` command on Ubuntu/Debian.
+
+```shell
+sudo apt install python3-dev
+```
+
+
 ## (Optional) Set up pre-commit
 
 This project uses [pre-commit](https://pre-commit.com/) in its CI. You can

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -651,7 +651,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
           result = rewriter.create<Torch::AtenMaximumOp>(
               binder.getLoc(), resultType, result, operands[i]);
         }
-        rewriter.replaceOp(binder.op, result.getDefiningOp());
+        rewriter.replaceOp(binder.op, result);
         return success();
       });
   patterns.onOp(
@@ -667,7 +667,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
           result = rewriter.create<Torch::AtenMinimumOp>(
               binder.getLoc(), resultType, result, operands[i]);
         }
-        rewriter.replaceOp(binder.op, result.getDefiningOp());
+        rewriter.replaceOp(binder.op, result);
         return success();
       });
   patterns.onOp("Neg", 1,

lib/Conversion/TorchToLinalg/Pooling.cpp

@@ -216,6 +216,12 @@ namespace {
 
 template <typename T> struct DimensionTraits {};
 
+template <> struct DimensionTraits<AtenMaxPool1dOp> {
+  static constexpr int64_t Dim = 1;
+  // unused const variable warning suppression:
+  static_assert(Dim == Dim);
+};
+
 template <> struct DimensionTraits<AtenMaxPool2dOp> {
   static constexpr int64_t Dim = 2;
   // unused const variable warning suppression:
@@ -359,7 +365,24 @@ class ConvertAtenMaxPoolOp : public OpConversionPattern<OpTy> {
 
     Type elementType = cast<RankedTensorType>(self.getType()).getElementType();
 
-    if constexpr (Dim == 2) {
+    if constexpr (Dim == 1) {
+      SmallVector<Value, 4> outTensorShape;
+      Value maxPool1d, paddedInput;
+      TypedAttr smallestFPValueAttr = rewriter.getFloatAttr(
+          elementType,
+          APFloat::getInf(
+              cast<mlir::FloatType>(elementType).getFloatSemantics(),
+              /*Negative=*/true));
+      if (failed(createPoolingOp<linalg::PoolingNcwMaxOp>(
+              op, rewriter, self, /*supportNonFPInput=*/true, ceilMode,
+              /*dimensionality=*/1, kernelSizeIntValues, strideInts,
+              paddingInts, dilationInts, smallestFPValueAttr, outTensorShape,
+              paddedInput, maxPool1d)))
+        return rewriter.notifyMatchFailure(op, "unable to compute maxpool1d");
+      Type newResultType = this->getTypeConverter()->convertType(op.getType());
+      rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, maxPool1d);
+      return success();
+    } else if constexpr (Dim == 2) {
       SmallVector<Value, 4> outTensorShape;
       // `maxpool2d` contains the result of maxpool2d operation over the input.
       Value maxPool2d, paddedInput;
@@ -1123,8 +1146,10 @@ void mlir::torch::torch_to_linalg::populatePoolingPatternsAndLegality(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     ConversionTarget &target) {
   MLIRContext *context = patterns.getContext();
+  target.addIllegalOp<AtenMaxPool1dOp>();
   target.addIllegalOp<AtenMaxPool2dOp>();
   target.addIllegalOp<AtenMaxPool3dOp>();
+  patterns.add<ConvertAtenMaxPoolOp<AtenMaxPool1dOp>>(typeConverter, context);
   patterns.add<ConvertAtenMaxPoolOp<AtenMaxPool2dOp>>(typeConverter, context);
   patterns.add<ConvertAtenMaxPoolOp<AtenMaxPool3dOp>>(typeConverter, context);
 

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10696,6 +10696,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.max_pool1d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.bool) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.max_pool2d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.bool) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"

projects/pt1/e2e_testing/xfail_sets.py

@@ -1124,6 +1124,8 @@
     "Matmul_matvec",
     "Matmul_vecmat",
     "MatmulStaticBroadcast_basic",
+    "MaxPool1dStaticModule_basic",
+    "MaxPool1dEmptyStrideStaticModule_basic",
     "MaxPool2dStaticModule_basic",
     "MaxPool2dEmptyStrideStaticModule_basic",
     "MaxPool3dStaticModule_basic",
@@ -2082,6 +2084,9 @@
         "CumsumStaticNegativeDimModule_basic",
         "CumsumInputDtypeInt32Module_basic",
         "EyeStaticModule_basic",
+        "MaxPool1dEmptyStrideStaticModule_basic",
+        "MaxPool1dStaticCeilModeTrueModule_basic",
+        "MaxPool1dStaticModule_basic",
         "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
         "AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
         "AdaptiveAvgPool1dStaticEvenMultiple_basic",
@@ -2552,6 +2557,11 @@
     "LinalgNormKeepDimComplexModule_basic",
     "LinalgVectorNormComplexModule_basic",
     "LogSoftmaxBackwardModule_basic",
+    "MaxPool1dCeilModeTrueModule_basic",
+    "MaxPool1dEmptyStrideStaticModule_basic",
+    "MaxPool1dModule_basic",
+    "MaxPool1dStaticCeilModeTrueModule_basic",
+    "MaxPool1dStaticModule_basic",
     "MaxPool2dCeilModeTrueModule_basic",
     "MaxPool2dModule_basic",
     "MaxPool2dWithIndicesAllOnesModule_basic",
@@ -2867,12 +2877,6 @@
     "SliceCopyMax_Module_basic",
 }
 
-if torch_version_for_comparison() >= version.parse("2.4.0.dev"):
-    ONNX_XFAIL_SET = ONNX_XFAIL_SET | {
-        # ERROR: Found dtype (torch.float64) but expected (torch.float32)
-        "ReduceL1NormWithDTypeModule_basic",
-    }
-
 if torch_version_for_comparison() < version.parse("2.3.0.dev"):
     ONNX_XFAIL_SET = ONNX_XFAIL_SET | {
         # ERROR: shape (torch.Size([6, 4, 5])) is not equal to golden shape (torch.Size([120]))

projects/pt1/examples/example-requirements.txt

@@ -0,0 +1,4 @@
+datasets
+transformers
+requests
+pillow

projects/pt1/examples/torchscript_stablehlo_backend_resnet.py

@@ -11,6 +11,6 @@
     model, data, output_type=torchscript.OutputType.STABLEHLO, use_tracing=False
 )
 with open(out_stablehlo_mlir_path, "w", encoding="utf-8") as outf:
-    outf.write(str(module))
+    outf.write(module.operation.get_asm())
 
 print(f"StableHLO IR of resent18 successfully written into {out_stablehlo_mlir_path}")

projects/pt1/examples/torchscript_stablehlo_backend_tinybert.py

@@ -25,6 +25,6 @@ def forward(self, data):
     model, data, output_type=torchscript.OutputType.STABLEHLO, use_tracing=True
 )
 with open(out_stablehlo_mlir_path, "w", encoding="utf-8") as outf:
-    outf.write(str(module))
+    outf.write(module.operation.get_asm())
 
 print(f"StableHLO IR of tiny bert successfully written into {out_stablehlo_mlir_path}")

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -2705,6 +2705,11 @@ def aten〇masked_select〡dtype(self_rank_dtype: Tuple[int, int], mask_rank_dty
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5)], kernel_size=[2]))
+def aten〇max_pool1d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0,), dilation: List[int] = (1,), ceil_mode: bool = False) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7)], kernel_size=[2, 2]))
 def aten〇max_pool2d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0, 0,), dilation: List[int] = (1, 1,), ceil_mode: bool = False) -> int:
     self_rank, self_dtype = self_rank_dtype