[linalg] Fix bug for conversion of complex dtype (#3269)

The conversion of complex type wasn't supported or checked; the support
and required tests were added.

Fixes:
https://github.com/iree-org/iree/issues/17226#issuecomment-2087779158

lib/Conversion/Utils/Utils.cpp

@@ -10,6 +10,7 @@
 #include "torch-mlir/Conversion/Utils/Utils.h"
 
 #include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Complex/IR/Complex.h"
 #include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
@@ -349,6 +350,26 @@ Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar, Type dtype,
     return b.create<arith::ExtSIOp>(loc, dtype, scalar);
   }
 
+  if (auto dtypeComplex = dyn_cast<mlir::ComplexType>(dtype)) {
+    if (auto scalarComplex = dyn_cast<mlir::ComplexType>(scalarType)) {
+      auto dtypeElemType = dtypeComplex.getElementType();
+
+      // Extract the real and imaginary parts of the scalar.
+      // Cast them to the target element type, and create a new complex
+      // value with the target complex type.
+      Value realVal = b.create<complex::ReOp>(loc, scalar);
+      Value imgVal = b.create<complex::ImOp>(loc, scalar);
+
+      realVal = convertScalarToDtype(b, loc, realVal, dtypeElemType);
+      imgVal = convertScalarToDtype(b, loc, imgVal, dtypeElemType);
+
+      return b.create<complex::CreateOp>(loc, dtypeComplex, realVal, imgVal);
+    }
+    mlir::emitError(loc) << "unsupported scalar type for convertScalarToDtype "
+                         << scalarType << "(scalar type) -> " << dtype
+                         << "(dtype)";
+  }
+
   llvm_unreachable("convertScalarToDtype should handle all the types");
 }
 

projects/pt1/e2e_testing/xfail_sets.py

@@ -575,6 +575,7 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
     "ElementwiseErfIntModule_basic",
     "ElementwiseLogitModule_basic",
     "ElementwiseMulTensorComplexModule_basic",
+    "ElementwiseMulTensorComplexDiffModule_basic",
     "ElementwiseQuantizePerTensorModule_basic",
     "ElementwiseQuantizePerTensorUIntModule_basic",
     "ElementwiseReciprocalIntModule_basic",
@@ -2314,6 +2315,7 @@ ONNX_XFAIL_SET = {
     "ElementwiseExpm1Module_basic",
     "ElementwiseFmodTensor_Int_basic",
     "ElementwiseMulTensorComplexModule_basic",
+    "ElementwiseMulTensorComplexDiffModule_basic",
     "ElementwiseOrTensorModule_basic",
     "ElementwiseOrTensorStaticShapeModule_basic",
     "ElementwiseQuantizePerTensorModule_basic",

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

@@ -1839,6 +1839,34 @@ def ElementwiseMulTensorComplexModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+# torch.complex32 is not supported by the refbackend.
+class ElementwiseMulTensorComplexDiffModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1], torch.complex64, True),
+            ([-1], torch.complex128, True),
+        ]
+    )
+    def forward(self, a, b):
+        return torch.mul(a, b)
+
+
+@register_test_case(module_factory=lambda: ElementwiseMulTensorComplexDiffModule())
+def ElementwiseMulTensorComplexDiffModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(4, high=10).type(torch.complex64),
+        tu.randint(4, high=10).type(torch.complex128),
+    )
+
+
+# ==============================================================================
+
+
 class ElementwiseMishModule(torch.nn.Module):
     def __init__(self):
         super().__init__()