Implement lowering for torch.aten.hann_window.periodic (#3502)

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

@@ -12570,6 +12570,34 @@ def Torch_AtenBaddbmm_Op : Torch_Op<"aten.baddbmm_", [
   }];
 }
 
+def Torch_AtenHannWindowPeriodicOp : Torch_Op<"aten.hann_window.periodic", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::hann_window.periodic : (int, bool, int?, int?, Device?, bool?) -> (Tensor)`";
+  let arguments = (ins
+    Torch_IntType:$window_length,
+    Torch_BoolType:$periodic,
+    AnyTorchOptionalIntType:$dtype,
+    AnyTorchOptionalIntType:$layout,
+    AnyTorchOptionalDeviceType:$device,
+    AnyTorchOptionalBoolType:$pin_memory
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenHannWindowPeriodicOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 6, 1);
+    }
+    void AtenHannWindowPeriodicOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 6, 1);
+    }
+  }];
+}
+
 def Torch_AtenFftFftOp : Torch_Op<"aten.fft_fft", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6619,6 +6619,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.hann_window.periodic\"(%arg0: !torch.int, %arg1: !torch.bool, %arg2: !torch.optional<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<Device>, %arg5: !torch.optional<bool>) -> !torch.list<int> {\n"
+"    %0 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.hardshrink\"(%arg0: !torch.list<int>, %arg1: !torch.float) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -10786,6 +10790,26 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.hann_window.periodic\"(%arg0: !torch.int, %arg1: !torch.bool, %arg2: !torch.optional<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<Device>, %arg5: !torch.optional<bool>) -> !torch.int {\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int6 = torch.constant.int 6\n"
+"    %none = torch.constant.none\n"
+"    %0 = torch.aten.__is__ %arg2, %none : !torch.optional<int>, !torch.none -> !torch.bool\n"
+"    %1 = torch.prim.If %0 -> (!torch.int) {\n"
+"      torch.prim.If.yield %int6 : !torch.int\n"
+"    } else {\n"
+"      %3 = torch.prim.unchecked_cast %arg2 : !torch.optional<int> -> !torch.int\n"
+"      torch.prim.If.yield %3 : !torch.int\n"
+"    }\n"
+"    %2 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%1) : (!torch.int) -> !torch.bool\n"
+"    torch.prim.If %2 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.hardshrink\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number) -> !torch.int {\n"
 "    %int4 = torch.constant.int 4\n"
 "    %int11 = torch.constant.int 11\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -8128,6 +8128,71 @@ public:
 };
 } // namespace
 
+namespace {
+// Decompose `aten.hann_window` into `aten.arange.start`, `aten.mul.Scalar`,
+// `aten.sin` and `aten.square` or into `aten.ones` in the trivial case
+class DecomposeAtenHannWindowPeriodicOp
+    : public OpRewritePattern<AtenHannWindowPeriodicOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenHannWindowPeriodicOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    MLIRContext *context = op.getContext();
+    Type opType = op.getType();
+
+    Value opWindowLength = op.getWindowLength();
+    Value opDtype = op.getDtype();
+    Value opLayout = op.getLayout();
+    Value opDevice = op.getDevice();
+    Value opPinMemory = op.getPinMemory();
+
+    int64_t window_length;
+    if (!matchPattern(opWindowLength, m_TorchConstantInt(&window_length)) ||
+        window_length <= 0)
+      return rewriter.notifyMatchFailure(
+          op, "Expected a constant integer greater than zero");
+    bool periodic;
+    if (!matchPattern(op.getPeriodic(), m_TorchConstantBool(&periodic)))
+      return rewriter.notifyMatchFailure(
+          op, "Expected a constant boolean value for periodic");
+
+    if (window_length == 1) {
+      Value one =
+          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
+      SmallVector<Value> sizes({one});
+      Value sizeList = rewriter.create<PrimListConstructOp>(
+          loc, ListType::get(IntType::get(context)), sizes);
+      rewriter.replaceOpWithNewOp<AtenOnesOp>(op, opType, sizeList, opDtype,
+                                              opLayout, opDevice, opPinMemory);
+      return success();
+    }
+
+    Value zero =
+        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
+
+    Value arange = rewriter.create<AtenArangeStartOp>(
+        loc, opType, zero, op.getWindowLength(), opDtype, opLayout, opDevice,
+        opPinMemory);
+
+    double denominator = !periodic ? window_length - 1 : window_length;
+
+    double piOverDenominator = 3.14159 / denominator;
+
+    Value cstFactor = rewriter.create<ConstantFloatOp>(
+        loc, rewriter.getF64FloatAttr(piOverDenominator));
+
+    Value fraction =
+        rewriter.create<AtenMulScalarOp>(loc, opType, arange, cstFactor);
+    Value sine = rewriter.create<AtenSinOp>(loc, opType, fraction);
+
+    rewriter.replaceOpWithNewOp<AtenSquareOp>(op, opType, sine);
+
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 // Decompose `aten.scatter.value` op into `aten.scatter.src` op.
 class DecomposeAtenScatterValueOp
@@ -8989,6 +9054,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenCrossEntropyLossOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenVarMeanDimOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTopkOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenHannWindowPeriodicOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenScalarTensor>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenScatterValueOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenSgnOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -540,6 +540,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenCrossEntropyLossOp>();
   target.addIllegalOp<AtenVarMeanDimOp>();
   target.addIllegalOp<AtenTopkOp>();
+  target.addIllegalOp<AtenHannWindowPeriodicOp>();
   target.addIllegalOp<AtenScalarTensorOp>();
   target.addIllegalOp<AtenScatterValueOp>();
   target.addIllegalOp<AtenTypeAsOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -880,6 +880,8 @@ STABLEHLO_PASS_SET = {
     "ArgmaxModule_with_dim",
     "AtenComplex64Module_basic",
     "AtenFloatScalarModule_basic",
+    "AtenHannWindowPeriodicFalseModule_basic",
+    "AtenHannWindowPeriodicTrueModule_basic",
     "AtenIntBoolOpConstFalseModule_basic",
     "AtenIntBoolOpConstTrueModule_basic",
     "AtenIntBoolOpModule_basic",
@@ -2839,6 +2841,8 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
     "AtenEyeMModuleInt2D_basic",
     "AtenEyeModuleInt2D_basic",
     "AtenFloatScalarModule_basic",
+    "AtenHannWindowPeriodicTrueModule_basic",
+    "AtenHannWindowPeriodicFalseModule_basic",
     "AtenInstanceNormModule_basic",
     "AtenIntBoolOpConstFalseModule_basic",
     "AtenIntBoolOpConstTrueModule_basic",

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

@@ -290,6 +290,9 @@ def aten〇log〡shape(self: List[int]) -> List[int]:
 def aten〇log_sigmoid〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇hann_window〇periodic〡shape(window_length: int, periodic: bool, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
+    return [window_length]
+
 def aten〇hardshrink〡shape(self: List[int], lambd: float = 0.5) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2444,6 +2447,15 @@ def aten〇log_sigmoid〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     assert not self_dtype == torch.bool
     return self_dtype
 
+@check_dtype_function([Invocation(10, False),                      Invocation(10, True),
+                       Invocation(10, False, dtype=torch.float32), Invocation(10, True, dtype=torch.float32),
+                       Invocation(10, False, dtype=torch.float64), Invocation(10, True, dtype=torch.float64)])
+def aten〇hann_window〇periodic〡dtype(window_length: int, periodic: bool, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> int:
+    result_dtype = torch.float32 if dtype is None else dtype
+    assert is_float_dtype(result_dtype)
+    return result_dtype
+
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, lambd=0.5))
 def aten〇hardshrink〡dtype(self_rank_dtype: Tuple[int, int], lambd: Union[int, float, complex] = 0.5) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -920,6 +920,9 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit_with_mutating_variants(
         "aten::baddbmm : (Tensor, Tensor, Tensor, Scalar, Scalar) -> (Tensor)"
     )
+    emit(
+        "aten::hann_window.periodic : (int, bool, int?, int?, Device?, bool?) -> (Tensor)"
+    )
     emit("aten::fft_fft : (Tensor, int?, int, str?) -> (Tensor)")
     emit("aten::fft_ifft : (Tensor, int?, int, str?) -> (Tensor)")
     emit("aten::fmod.Tensor : (Tensor, Tensor) -> (Tensor)")

projects/pt1/python/torch_mlir_e2e_test/test_suite/__init__.py

@@ -41,6 +41,7 @@ def register_all_tests():
     from . import elementwise_comparison
     from . import squeeze
     from . import slice_like
+    from . import spectral
     from . import nll_loss
     from . import index_select
     from . import linalg_algorithms

projects/pt1/python/torch_mlir_e2e_test/test_suite/spectral.py

@@ -0,0 +1,53 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+import torch
+
+from torch_mlir_e2e_test.framework import TestUtils
+from torch_mlir_e2e_test.registry import register_test_case
+from torch_mlir_e2e_test.annotations import annotate_args, export
+
+# ==============================================================================
+
+
+class AtenHannWindowPeriodicFalseModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        return torch.ops.aten.hann_window(20, False)
+
+
+@register_test_case(module_factory=lambda: AtenHannWindowPeriodicFalseModule())
+def AtenHannWindowPeriodicFalseModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+# ==============================================================================
+
+
+class AtenHannWindowPeriodicTrueModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        return torch.ops.aten.hann_window(20, True)
+
+
+@register_test_case(module_factory=lambda: AtenHannWindowPeriodicTrueModule())
+def AtenHannWindowPeriodicTrueModule_basic(module, tu: TestUtils):
+    module.forward()