Fix creation of empty tensor in decomposition for randn ops (#2043)

The current decomposition for `aten.randn.generator` does not specify
the `dtype` argument of the empty tensors created to store the random
values. This leads to invalid IR when the output type of the `randn`
op is not the default PyTorch dtype.

lib/Conversion/TorchToLinalg/TensorConstructors.cpp

@@ -226,7 +226,8 @@ public:
         typeConverter->convertType(op.getType()).cast<RankedTensorType>();
     Type resultElementType;
     if (op.getDtype().getType().isa<Torch::NoneType>()) {
-      resultElementType = resultType.getElementType();
+      resultElementType = getDefaultDtypeForTorchScalar(
+          Torch::FloatType::get(op->getContext()));
     } else {
       int64_t dtypeInt;
       if (!matchPattern(op.getDtype(), m_TorchConstantInt(&dtypeInt)))

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3716,8 +3716,14 @@ public:
   LogicalResult matchAndRewrite(AtenRandnGeneratorOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
-    Type resultType = op.getType();
+    auto resultType = op.getType().cast<BaseTensorType>();
 
+    if (!resultType.hasDtype()) {
+      return rewriter.notifyMatchFailure(
+          op, "expected result type to have a dtype");
+    }
+
+    Value dtype = getDtypeIntValueForType(rewriter, loc, resultType.getDtype());
     Value none = rewriter.create<ConstantNoneOp>(loc);
     Value low = rewriter.create<Torch::ConstantFloatOp>(
         loc, rewriter.getF64FloatAttr((double)0.0));
@@ -3729,11 +3735,13 @@ public:
         loc, rewriter.getF64FloatAttr((double)(2.0 * 3.14159)));
 
     Value emptyTensorA = rewriter.create<AtenEmptyMemoryFormatOp>(
-        loc, resultType, op.getSize(), /*dtype=*/none, /*layout=*/op.getLayout(),
+        loc, resultType, op.getSize(), /*dtype=*/dtype,
+        /*layout=*/op.getLayout(),
         /*device=*/op.getDevice(), /*pin_memory=*/op.getPinMemory(),
         /*memory_format=*/none);
     Value emptyTensorB = rewriter.create<AtenEmptyMemoryFormatOp>(
-        loc, resultType, op.getSize(), /*dtype=*/none, /*layout=*/op.getLayout(),
+        loc, resultType, op.getSize(), /*dtype=*/dtype,
+        /*layout=*/op.getLayout(),
         /*device=*/op.getDevice(), /*pin_memory=*/op.getPinMemory(),
         /*memory_format=*/none);
 

python/torch_mlir_e2e_test/test_suite/rng.py

@@ -400,6 +400,30 @@ def RandnGeneratorModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+
+class RandnGeneratorF64Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        a = torch.ops.aten.randn([4, 512, 1024], generator=None, dtype=torch.float64)
+        std = torch.std(a)
+        return std
+
+
+@register_test_case(module_factory=lambda: RandnGeneratorF64Module())
+def RandnGeneratorF64Module_basic(module, tu: TestUtils):
+    module.forward()
+
+
+# ==============================================================================
+
+
 class RandnLikeModule(torch.nn.Module):
     def __init__(self):
         super().__init__()