support aten._trilinear and improve einsum decomposition

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

@@ -14203,6 +14203,36 @@ def Torch_AtenGridSamplerOp : Torch_Op<"aten.grid_sampler", [
   }];
 }
 
+def Torch_Aten_TrilinearOp : Torch_Op<"aten._trilinear", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::_trilinear : (Tensor, Tensor, Tensor, int[], int[], int[], int[], int) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$i1,
+    AnyTorchTensorType:$i2,
+    AnyTorchTensorType:$i3,
+    AnyTorchListOfTorchIntType:$expand1,
+    AnyTorchListOfTorchIntType:$expand2,
+    AnyTorchListOfTorchIntType:$expand3,
+    AnyTorchListOfTorchIntType:$sumdim,
+    Torch_IntType:$unroll_dim
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult Aten_TrilinearOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 8, 1);
+    }
+    void Aten_TrilinearOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 8, 1);
+    }
+  }];
+}
+
 def Torch_Aten__Contains__StrOp : Torch_Op<"aten.__contains__.str", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -8860,6 +8860,112 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.linear(%arg0, %arg1, %arg2) : (!torch.list<int>, !torch.list<int>, !torch.optional<list<int>>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten._trilinear\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.list<int>, %arg7: !torch.int) -> !torch.list<int> {\n"
+"    %int3 = torch.constant.int 3\n"
+"    %int-1 = torch.constant.int -1\n"
+"    %str = torch.constant.str \"AssertionError: number of dimensions must match\"\n"
+"    %str_0 = torch.constant.str \"expand dimension {} is out of bounds for input of shape {}\"\n"
+"    %true = torch.constant.bool true\n"
+"    %none = torch.constant.none\n"
+"    %str_1 = torch.constant.str \"AssertionError: \"\n"
+"    %str_2 = torch.constant.str \"unroll_dim must be in [0, {}]\"\n"
+"    %false = torch.constant.bool false\n"
+"    %int0 = torch.constant.int 0\n"
+"    %int1 = torch.constant.int 1\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.len.t %arg3 : !torch.list<int> -> !torch.int\n"
+"    %2 = torch.aten.add.int %0, %1 : !torch.int, !torch.int -> !torch.int\n"
+"    %3 = torch.aten.ge.int %arg7, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    %4 = torch.prim.If %3 -> (!torch.bool) {\n"
+"      %23 = torch.aten.lt.int %arg7, %2 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %23 : !torch.bool\n"
+"    } else {\n"
+"      torch.prim.If.yield %false : !torch.bool\n"
+"    }\n"
+"    torch.prim.If %4 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      %23 = torch.aten.sub.int %2, %int1 : !torch.int, !torch.int -> !torch.int\n"
+"      %24 = torch.aten.format(%str_2, %23) : !torch.str, !torch.int -> !torch.str\n"
+"      %25 = torch.aten.add.str %str_1, %24 : !torch.str, !torch.str -> !torch.str\n"
+"      torch.prim.RaiseException %25, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %5 = call @__torch__.torch.jit._shape_functions._copy(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    %6 = call @__torch__.torch.jit._shape_functions._copy(%arg1) : (!torch.list<int>) -> !torch.list<int>\n"
+"    %7 = call @__torch__.torch.jit._shape_functions._copy(%arg2) : (!torch.list<int>) -> !torch.list<int>\n"
+"    %8 = torch.prim.ListConstruct %5, %6, %7 : (!torch.list<int>, !torch.list<int>, !torch.list<int>) -> !torch.list<list<int>>\n"
+"    %9 = torch.prim.ListConstruct %arg3, %arg4, %arg5 : (!torch.list<int>, !torch.list<int>, !torch.list<int>) -> !torch.list<list<int>>\n"
+"    torch.prim.Loop %int3, %true, init() {\n"
+"    ^bb0(%arg8: !torch.int):\n"
+"      %23 = torch.aten.__getitem__.t %9, %arg8 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"      %24 = torch.aten.__getitem__.t %8, %arg8 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"      %25 = torch.aten.len.t %24 : !torch.list<int> -> !torch.int\n"
+"      %26 = torch.aten.len.t %23 : !torch.list<int> -> !torch.int\n"
+"      torch.prim.Loop %26, %true, init() {\n"
+"      ^bb0(%arg9: !torch.int):\n"
+"        %27 = torch.aten.__getitem__.t %23, %arg9 : !torch.list<int>, !torch.int -> !torch.int\n"
+"        %28 = torch.aten.le.int %27, %25 : !torch.int, !torch.int -> !torch.bool\n"
+"        torch.prim.If %28 -> () {\n"
+"          torch.prim.If.yield\n"
+"        } else {\n"
+"          %30 = torch.aten.__getitem__.t %8, %arg8 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"          %31 = torch.aten.format(%str_0, %27, %30) : !torch.str, !torch.int, !torch.list<int> -> !torch.str\n"
+"          %32 = torch.aten.add.str %str_1, %31 : !torch.str, !torch.str -> !torch.str\n"
+"          torch.prim.RaiseException %32, %none : !torch.str, !torch.none\n"
+"          torch.prim.If.yield\n"
+"        }\n"
+"        %29 = torch.aten.__getitem__.t %8, %arg8 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"        torch.aten.insert.t %29, %27, %int1 : !torch.list<int>, !torch.int, !torch.int\n"
+"        torch.prim.Loop.condition %true, iter()\n"
+"      } : (!torch.int, !torch.bool) -> ()\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %10 = torch.aten.len.t %5 : !torch.list<int> -> !torch.int\n"
+"    %11 = torch.aten.len.t %6 : !torch.list<int> -> !torch.int\n"
+"    %12 = torch.aten.eq.int %10, %11 : !torch.int, !torch.int -> !torch.bool\n"
+"    %13 = torch.prim.If %12 -> (!torch.bool) {\n"
+"      %23 = torch.aten.len.t %6 : !torch.list<int> -> !torch.int\n"
+"      %24 = torch.aten.len.t %7 : !torch.list<int> -> !torch.int\n"
+"      %25 = torch.aten.eq.int %23, %24 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %25 : !torch.bool\n"
+"    } else {\n"
+"      torch.prim.If.yield %false : !torch.bool\n"
+"    }\n"
+"    torch.prim.If %13 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %14 = call @__torch__.torch.jit._shape_functions.broadcast_three(%5, %6, %7) : (!torch.list<int>, !torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
+"    %15 = torch.prim.ListConstruct %false : (!torch.bool) -> !torch.list<bool>\n"
+"    %16 = torch.aten.len.t %14 : !torch.list<int> -> !torch.int\n"
+"    %17 = torch.operator \"aten.mul.left_t\"(%15, %16) : (!torch.list<bool>, !torch.int) -> !torch.list<bool> \n"
+"    %18 = torch.aten.len.t %arg6 : !torch.list<int> -> !torch.int\n"
+"    torch.prim.Loop %18, %true, init() {\n"
+"    ^bb0(%arg8: !torch.int):\n"
+"      %23 = torch.aten.__getitem__.t %arg6, %arg8 : !torch.list<int>, !torch.int -> !torch.int\n"
+"      %24 = torch.aten._set_item.t %17, %23, %true : !torch.list<bool>, !torch.int, !torch.bool -> !torch.list<bool>\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %19 = torch.aten.len.t %14 : !torch.list<int> -> !torch.int\n"
+"    %20 = torch.aten.sub.int %19, %int1 : !torch.int, !torch.int -> !torch.int\n"
+"    %21 = torch.aten.__range_length %20, %int-1, %int-1 : !torch.int, !torch.int, !torch.int -> !torch.int\n"
+"    %22 = torch.prim.Loop %21, %true, init(%14) {\n"
+"    ^bb0(%arg8: !torch.int, %arg9: !torch.list<int>):\n"
+"      %23 = torch.aten.__derive_index %arg8, %20, %int-1 : !torch.int, !torch.int, !torch.int -> !torch.int\n"
+"      %24 = torch.aten.__getitem__.t %17, %23 : !torch.list<bool>, !torch.int -> !torch.bool\n"
+"      %25 = torch.prim.If %24 -> (!torch.list<int>) {\n"
+"        %26 = func.call @__torch__.torch.jit._shape_functions._reduce_along_dim(%arg9, %23, %false) : (!torch.list<int>, !torch.int, !torch.bool) -> !torch.list<int>\n"
+"        torch.prim.If.yield %26 : !torch.list<int>\n"
+"      } else {\n"
+"        torch.prim.If.yield %arg9 : !torch.list<int>\n"
+"      }\n"
+"      torch.prim.Loop.condition %true, iter(%25 : !torch.list<int>)\n"
+"    } : (!torch.int, !torch.bool, !torch.list<int>) -> !torch.list<int>\n"
+"    return %22 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.scaled_dot_product_attention\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.optional<list<int>>, %arg4: !torch.float, %arg5: !torch.bool, %arg6: !torch.optional<float>, %arg7: !torch.bool) -> !torch.list<int> {\n"
 "    %int-1 = torch.constant.int -1\n"
 "    %0 = torch.aten.__getitem__.t %arg2, %int-1 : !torch.list<int>, !torch.int -> !torch.int\n"
@@ -15285,6 +15391,15 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
 "    return %4 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten._trilinear\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.list<int>, %arg7: !torch.int) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %2:2 = torch.prim.TupleUnpack %arg2 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %3 = torch.prim.ListConstruct %0#0, %1#0, %2#0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<optional<int>>\n"
+"    %4 = torch.prim.ListConstruct %0#1, %1#1, %2#1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
+"    %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%3, %4) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %5 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.cat\"(%arg0: !torch.list<tuple<int, int>>, %arg1: !torch.int) -> !torch.int {\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -9,6 +9,7 @@
 
 #include "PassDetail.h"
 
+#include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/BuiltinDialect.h"
 #include "mlir/Transforms/DialectConversion.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
@@ -399,9 +400,9 @@ static Value collapseDimForMatmul(PatternRewriter &rewriter, Location loc,
   auto inputType = cast<ValueTensorType>(input.getType());
   auto inputRank = batchDimsLength + contractingDimsLength + otherDimsLength +
                    reduceDimsLength;
-  SmallVector<Value> inputShapeTensor;
+  SmallVector<OpFoldResult> inputShapeTensor;
   for (auto i = 0; i < inputRank; ++i) {
-    inputShapeTensor.emplace_back(rewriter.create<AtenSizeIntOp>(
+    inputShapeTensor.emplace_back(rewriter.createOrFold<AtenSizeIntOp>(
         loc, input,
         rewriter.create<Torch::ConstantIntOp>(loc,
                                               rewriter.getI64IntegerAttr(i))));
@@ -412,13 +413,23 @@ static Value collapseDimForMatmul(PatternRewriter &rewriter, Location loc,
       rewriter.create<Torch::ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
   auto dimOffset = 0;
 
+  auto materializeIntFold = [&](OpFoldResult thing) {
+    if (auto attr = dyn_cast<mlir::Attribute>(thing)) {
+      Value result = rewriter.create<Torch::ConstantIntOp>(
+          loc, cast<mlir::IntegerAttr>(attr));
+      return result;
+    }
+    return cast<mlir::Value>(thing);
+  };
+
   auto appendDims = [&](int64_t dimLength) {
-    Value prod = constOne;
+    OpFoldResult prod = getAsOpFoldResult(constOne);
     for (auto i = 0; i < dimLength; ++i) {
-      prod = rewriter.create<AtenMulIntOp>(loc, prod,
-                                           inputShapeTensor[i + dimOffset]);
+      prod = rewriter.createOrFold<AtenMulIntOp>(
+          loc, materializeIntFold(prod),
+          materializeIntFold(inputShapeTensor[i + dimOffset]));
     }
-    outShapeTensor.emplace_back(prod);
+    outShapeTensor.emplace_back(materializeIntFold(prod));
     dimOffset += dimLength;
   };
 
@@ -570,21 +581,32 @@ static LogicalResult performMatmul(PatternRewriter &rewriter, Location loc,
   Type outputDType = lhsType.hasDtype() ? lhsType.getOptionalDtype()
                                         : rhsType.getOptionalDtype();
 
+  auto materializeIntFold = [&](OpFoldResult thing) {
+    if (auto attr = dyn_cast<mlir::Attribute>(thing)) {
+      Value result = rewriter.create<Torch::ConstantIntOp>(
+          loc, cast<mlir::IntegerAttr>(attr));
+      return result;
+    }
+    return cast<mlir::Value>(thing);
+  };
+
   llvm::SmallDenseMap<char, Value> lhsDimShapeMap;
   for (size_t idx = 0; idx < lhsTokens.size(); ++idx) {
     char d = lhsTokens[idx];
-    lhsDimShapeMap[d] = rewriter.create<AtenSizeIntOp>(
+    OpFoldResult lhsFold = rewriter.createOrFold<AtenSizeIntOp>(
         loc, lhs,
         rewriter.create<Torch::ConstantIntOp>(loc,
                                               rewriter.getI64IntegerAttr(idx)));
+    lhsDimShapeMap[d] = materializeIntFold(lhsFold);
   }
   llvm::SmallDenseMap<char, Value> rhsDimShapeMap;
   for (size_t idx = 0; idx < rhsTokens.size(); ++idx) {
     char d = rhsTokens[idx];
-    rhsDimShapeMap[d] = rewriter.create<AtenSizeIntOp>(
+    OpFoldResult rhsFold = rewriter.createOrFold<AtenSizeIntOp>(
         loc, rhs,
         rewriter.create<Torch::ConstantIntOp>(loc,
                                               rewriter.getI64IntegerAttr(idx)));
+    rhsDimShapeMap[d] = materializeIntFold(rhsFold);
   }
 
   // parse batch, contracting, other, reduce dims of lhs and rhs
@@ -604,8 +626,9 @@ static LogicalResult performMatmul(PatternRewriter &rewriter, Location loc,
       bool lhsContains = lhsDimShapeMap.count(d) > 0;
       bool rhsContains = rhsDimShapeMap.count(d) > 0;
       if (lhsContains && rhsContains) {
-        outDimShapeMap[d] = rewriter.create<Torch::PrimMaxIntOp>(
+        OpFoldResult out = rewriter.createOrFold<Torch::PrimMaxIntOp>(
             loc, lhsDimShapeMap[d], rhsDimShapeMap[d]);
+        outDimShapeMap[d] = materializeIntFold(out);
       } else if (lhsContains) {
         outDimShapeMap[d] = lhsDimShapeMap[d];
       } else if (rhsContains) {
@@ -1973,6 +1996,125 @@ class DecomposeAtenEinsumOp : public OpRewritePattern<AtenEinsumOp> {
 };
 } // namespace
 
+namespace {
+// Trilinear einstein sum, decomposed to:
+// (i1.unsqueeze(expand1) * i2.unsqueeze(expand2) * i3.unsqueeze(expand3))
+//    .sum(sumdim)
+// The unrollDim operand does not impact the output of the operation, so
+// it is ignored.
+
+class DecomposeAten_TrilinearOp : public OpRewritePattern<Aten_TrilinearOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(Aten_TrilinearOp op,
+                                PatternRewriter &rewriter) const override {
+
+    Location loc = op.getLoc();
+
+    Value input1 = op.getI1();
+    Value input2 = op.getI2();
+    Value input3 = op.getI3();
+
+    // Expansions
+    SmallVector<int64_t> expand1;
+    SmallVector<int64_t> expand2;
+    SmallVector<int64_t> expand3;
+    if (!matchPattern(op.getExpand1(), m_TorchListOfConstantInts(expand1))) {
+      return rewriter.notifyMatchFailure(op, "expand1 should be constant");
+    }
+    if (!matchPattern(op.getExpand2(), m_TorchListOfConstantInts(expand2))) {
+      return rewriter.notifyMatchFailure(op, "expand2 should be constant");
+    }
+    if (!matchPattern(op.getExpand3(), m_TorchListOfConstantInts(expand3))) {
+      return rewriter.notifyMatchFailure(op, "expand3 should be constant");
+    }
+
+    SmallVector<int64_t> sumDim;
+    if (!matchPattern(op.getSumdim(), m_TorchListOfConstantInts(sumDim))) {
+      return rewriter.notifyMatchFailure(op, "sumDim should be constant");
+    }
+
+    // Check if there are any dimensions that intersect between expand1,
+    // expand2, and expand3.
+    int64_t totalDims =
+        cast<BaseTensorType>(input1.getType()).getSizes().size() +
+        expand1.size();
+    if (sharedExpandDims(totalDims, expand1, expand2, expand3, sumDim)) {
+      // pytorch issue filed: https://github.com/pytorch/pytorch/issues/138353
+      // TODO: Remove warning when issue gets resolved.
+      op->emitWarning("aten::_trilinear implementation in this case is "
+                      "non-functional (returns an empty dimension). We will "
+                      "intentionally deviate from this behavior.");
+    }
+
+    // Apply unsqueeze to respective input tensors at the specified dimensions
+    SmallVector<int64_t> sortedExpand1 = expand1;
+    std::sort(sortedExpand1.begin(), sortedExpand1.end());
+    for (auto expand : sortedExpand1) {
+      Value expandDim = rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(expand));
+      input1 = *unsqueezeTensor(rewriter, op, input1, expandDim);
+    }
+    SmallVector<int64_t> sortedExpand2 = expand2;
+    std::sort(sortedExpand2.begin(), sortedExpand2.end());
+    for (auto expand : sortedExpand2) {
+      Value expandDim = rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(expand));
+      input2 = *unsqueezeTensor(rewriter, op, input2, expandDim);
+    }
+    SmallVector<int64_t> sortedExpand3 = expand3;
+    std::sort(sortedExpand3.begin(), sortedExpand3.end());
+    for (auto expand : sortedExpand3) {
+      Value expandDim = rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(expand));
+      input3 = *unsqueezeTensor(rewriter, op, input3, expandDim);
+    }
+
+    // Apply multiplication operation.
+    auto mul1 =
+        rewriter.create<AtenMulTensorOp>(loc, op.getType(), input1, input2);
+    auto mul2 =
+        rewriter.create<AtenMulTensorOp>(loc, op.getType(), mul1, input3);
+
+    // Apply sum operation.
+    // Parse sumDim in descending order to avoid any issues with the
+    // dimensions being removed.
+    Value result = mul2;
+    SmallVector<int64_t> sortedSumDims = sumDim;
+    std::sort(sortedSumDims.rbegin(), sortedSumDims.rend());
+    for (int64_t dim : sortedSumDims) {
+      Value dimValue = rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(dim));
+      result =
+          createSumAlongDimension(rewriter, loc, op, result, dimValue, false);
+    }
+
+    rewriter.replaceOp(op, result);
+    return success();
+  }
+
+private:
+  // Determine if there are any dimensions that intersect between expand1,
+  // expand2, and expand3.
+  bool sharedExpandDims(const int64_t &totalDims,
+                        const SmallVector<int64_t> &expand1,
+                        const SmallVector<int64_t> &expand2,
+                        const SmallVector<int64_t> &expand3,
+                        const SmallVector<int64_t> &sumDim) const {
+    for (int64_t i = 0; i < totalDims; ++i) {
+      if (!contains(sumDim, i) && contains(expand1, i) &&
+          contains(expand2, i) && contains(expand3, i)) {
+        return true;
+      }
+    }
+    return false;
+  }
+  bool contains(const SmallVector<int64_t> &vec, int64_t value) const {
+    return std::find(vec.begin(), vec.end(), value) != vec.end();
+  }
+};
+} // namespace
+
 namespace {
 // Calculate the trace of the input tensor as the sum over its diagonal
 // elements. This computation is performed as:
@@ -10060,6 +10202,7 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast1dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast2dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEinsumOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAten_TrilinearOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTraceOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenHardswishOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenSoftplusOp>(patterns);