Fix bug in NumToTensor handling of float values

This commit fixes a type promotion bug when NumToTensor was given a
float as an argument. In particular, the rules for type promotion of a
scalar vary depending on if the scalar is part of a tensor op or
not. NumToTensor falls under the second category, but it was being
treated as part of the first category.

e2e_testing/torchscript/basic.py

@@ -630,7 +630,7 @@ def LogSoftmaxIntModule_basic(module, tu: TestUtils):
     module.forward(torch.randn(3, 2, 4).double())
 
 
-class NumToTensorModule(torch.nn.Module):
+class NumToTensorIntModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
 
@@ -642,8 +642,26 @@ class NumToTensorModule(torch.nn.Module):
     def forward(self):
         return torch.ops.prim.NumToTensor(1)
 
-@register_test_case(module_factory=lambda: NumToTensorModule())
+@register_test_case(module_factory=lambda: NumToTensorIntModule())
-def NumToTensorModule_basic(module, tu: TestUtils):
+def NumToTensorIntModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+class NumToTensorFloatModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+
+    def forward(self):
+        return torch.ops.prim.NumToTensor(1.0)
+
+
+@register_test_case(module_factory=lambda: NumToTensorFloatModule())
+def NumToTensorFloatModule_basic(module, tu: TestUtils):
     module.forward()
 
 

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -1290,7 +1290,19 @@ ChangeResult TypeAnalyzer::visitNumToTensorOp(PrimNumToTensorScalarOp op) {
   auto knowledge =
       ValueKnowledge::getNotNonePessimisticValueState(op->getContext());
   knowledge.hasSizes = true;
-  knowledge.dtype = getDefaultDtypeForTorchScalar(op.a().getType());
+
+  // The resulting type from converting a Scalar into a Tensor is different
+  // if the scalar is part of a tensor operation (such as AtenMulScalar) or
+  // not. In the former case, the type promotion rules are captured by the
+  // `getDefaultDtypeForTorchScalar` helper above. The latter case follows the
+  // rules in https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/ScalarOps.h.
+  // `NumToTensor` falls in the latter case.
+  Type type = op.a().getType();
+  if (type.isa<Torch::FloatType>())
+    knowledge.dtype = Float64Type::get(op.getContext());
+  else if (type.isa<Torch::IntType>())
+    knowledge.dtype = IntegerType::get(op.getContext(), 64, IntegerType::Signed);
+
   return getLatticeElement(op.getResult()).join(knowledge);
 }