diff --git a/csrc/dynamic_transform.cpp b/csrc/dynamic_transform.cpp
index c82520e8a11..404506ac3f8 100644
--- a/csrc/dynamic_transform.cpp
+++ b/csrc/dynamic_transform.cpp
@@ -627,20 +627,40 @@ void DynamicTransformConcretizer::mutate(TensorView* tv) {
 
       // Determine the output IterType
       IterType iter_type = IterType::Symbolic;
-      for (auto inp_id : ir_utils::filterByType<IterDomain>(expr->inputs())) {
+      const auto input_ids =
+          ir_utils::filterByType<IterDomain>(expr->inputs()).vector();
+      for (auto i : c10::irange(input_ids.size())) {
+        auto inp_id = input_ids.at(i);
         auto updated_id = maybeMutated(inp_id)->as<IterDomain>();
-        iter_type = ops::promoteIterType(iter_type, updated_id->getIterType());
+        TORCH_CHECK(
+            updated_id == inp_id || !updated_id->isSymbolic(),
+            "Mutated IterDomains between root and rfactor should not be symbolic");
+        if (i == 0) {
+          // ops::promoteIterType will favor Symbolic if it encounters it
+          // alongside Broadcast. This is preferable at fusion definition, but
+          // here we are propagating, and if we only see Broadcast in some
+          // dimension, then we should not retain Symbolic. To work around this,
+          // we always overwrite Symbolic with the first concrete IterType we
+          // encounter.
+          iter_type = updated_id->getIterType();
+        } else {
+          iter_type =
+              ops::promoteIterType(iter_type, updated_id->getIterType());
+        }
       }
-      TORCH_INTERNAL_ASSERT(
-          iter_type != IterType::Symbolic,
-          "Failed to concretize an output IterType for expression: ",
-          expr->toString());
-
       // Update the IterType of each output
       for (auto out_id : ir_utils::filterByType<IterDomain>(expr->outputs())) {
         if (!out_id->isSymbolic()) {
           continue;
         }
+
+        // If out_id is Symbolic, we need to concretize it here. If we did not
+        // yet determine its IterType, then we've missed our chance.
+        TORCH_INTERNAL_ASSERT(
+            iter_type != IterType::Symbolic,
+            "Failed to concretize an output IterType for expression: ",
+            expr->toString());
+
         auto concretized_out_id =
             IterDomainBuilder(maybeMutated(out_id)->as<IterDomain>())
                 .iter_type(iter_type)
diff --git a/csrc/ir/nodes.cpp b/csrc/ir/nodes.cpp
index fa96cff28ba..db38a2a7737 100644
--- a/csrc/ir/nodes.cpp
+++ b/csrc/ir/nodes.cpp
@@ -2783,6 +2783,19 @@ IterDomain* IterDomain::resize(
       "Expansion factor must be an integer scalar: ",
       right_expansion->toString());
 
+  if (left_expansion->isConstInt() && right_expansion->isConstInt()) {
+    auto left = left_expansion->evaluateInt();
+    auto right = right_expansion->evaluateInt();
+    if (left == 0 && right == 0) {
+      // This is a trivial resize. Check that we are not changing the IterType,
+      // then return the input.
+      TORCH_CHECK(
+          !iter_type_opt.has_value() ||
+              iter_type_opt.value() == in->getIterType(),
+          "If IterType is specified in pad with zero expansion then it must match input");
+      return in;
+    }
+  }
   TORCH_CHECK(
       in->getIterType() == IterType::Iteration ||
           in->getIterType() == IterType::Broadcast ||
@@ -2823,12 +2836,13 @@ IterDomain* IterDomain::resize(
   if (iter_type_opt.has_value()) {
     iter_type = iter_type_opt.value();
   } else if (left_expansion->isConstInt() && right_expansion->isConstInt()) {
+    auto left = left_expansion->evaluateInt();
+    auto right = right_expansion->evaluateInt();
     if (resized_id_size->isConstInt()) {
       // Means input extent is also known
       auto out_extent = resized_id_size->evaluateInt();
       iter_type = out_extent == 1 ? IterType::Broadcast : IterType::Iteration;
-    } else if (
-        left_expansion->evaluateInt() + right_expansion->evaluateInt() > 1) {
+    } else if (left + right > 1) {
       // Input extent is non-negative, so we know out_extent > 1
       iter_type = IterType::Iteration;
     }
diff --git a/test/test_resize.cpp b/test/test_resize.cpp
index 9baaa8d95f1..b3c58077746 100644
--- a/test/test_resize.cpp
+++ b/test/test_resize.cpp
@@ -483,6 +483,46 @@ TEST_F(ResizeTest, FusionResizePadScheduler4) {
       __FILE__);
 }
 
+// Pad a broadcast
+// See https://github.com/NVIDIA/Fuser/issues/798
+TEST_F(ResizeTest, FusionResizePadBroadcastInput) {
+  auto fusion = std::make_unique<Fusion>();
+  FusionGuard fg(fusion.get());
+
+  // IterTypes are {Broadcast, Iteration}
+  auto tv0 = makeConcreteTensor({1, -1});
+  fusion->addInput(tv0);
+
+  // trivial pad of broadcast dimension
+  auto tv1 =
+      pad(tv0,
+          {fusion->oneVal(),
+           fusion->zeroVal(),
+           fusion->zeroVal(),
+           fusion->zeroVal()});
+  fusion->addOutput(tv1);
+
+  std::vector<int64_t> shape({1, 2});
+
+  auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
+
+  auto t0 = at::randn(shape, options);
+  std::vector<c10::IValue> aten_inputs({t0});
+
+  FusionExecutorCache executor_cache(std::move(fusion));
+  auto cg_outputs = executor_cache.runFusionWithInputs(aten_inputs);
+
+  auto t1 = at::pad(t0, {1, 0, 0, 0});
+
+  testValidate(
+      executor_cache.fusion(),
+      cg_outputs,
+      aten_inputs,
+      {t1},
+      __LINE__,
+      __FILE__);
+}
+
 // Trivial cat
 TEST_F(ResizeTest, FusionResizeCat1) {
   Fusion fusion;