diff --git a/extension/pybindings/pybindings.cpp b/extension/pybindings/pybindings.cpp
index dbb5bf0345b..a2a65787cb4 100644
--- a/extension/pybindings/pybindings.cpp
+++ b/extension/pybindings/pybindings.cpp
@@ -147,19 +147,19 @@ void setup_output_storage(
   }
   for (size_t i = 0; i < output_storages.size(); ++i) {
     if (output_storages[i].size() == 0) {
-      // Skip empty output storages, this would happen for non-tensor outputs.
+      // Skip empty output storages, this would happen for non-tensor outputs
+      // and memory planned outputs.
       continue;
     }
     Error output_status = method.set_output_data_ptr(
         output_storages[i].data(), output_storages[i].size(), i);
-    // InvalidState can be the status if outputs are already memory planned.
-    // That's fine and we don't need to alert the user to that error.
-    if (output_status != Error::Ok && output_status != Error::InvalidState) {
-      ET_LOG(
-          Error,
-          "Cannot set_output_data_ptr(): 0x%" PRIx32,
-          static_cast<uint32_t>(output_status));
-    }
+    // We already should be skipping non-tensor outputs, and memory planned
+    // outputs so any error is real.
+    THROW_IF_ERROR(
+        output_status,
+        "set_output_data_ptr failed for output %zu with error 0x%" PRIx32,
+        i,
+        static_cast<uint32_t>(output_status));
   }
 }
 
@@ -890,26 +890,34 @@ struct PyModule final {
 
   std::vector<std::vector<uint8_t>> make_output_storages(const Method& method) {
     const auto num_outputs = method.outputs_size();
-    // These output storages will not be used if the ExecuTorch program already
-    // pre-allocated output space. That is represented by an error from
-    // set_output_data_ptr.
-    std::vector<std::vector<uint8_t>> output_storages(num_outputs);
+    // Create a buffer for each output tensor. Memory planned outputs and non
+    // tensor outputs get an empty buffer in this list which is ignored later.
+    std::vector<std::vector<uint8_t>> output_storages;
+    output_storages_.reserve(num_outputs);
+    auto meta = method.method_meta();
     for (size_t i = 0; i < num_outputs; ++i) {
+      auto output_type = meta.output_tag(i);
+      THROW_IF_ERROR(
+          output_type.error(), "Failed to get output type for output %zu", i);
+      if (output_type.get() != Tag::Tensor) {
+        // Skip allocating storage for non-tensor outputs.
+        output_storages.emplace_back();
+        continue;
+      }
       const auto& output_tensor_meta =
           method.method_meta().output_tensor_meta(i);
-      if (!output_tensor_meta.ok()) {
-        // If the output isn't a tensor it won't have a tensor meta.
-        ET_LOG(
-            Error,
-            "Tensor meta doesn't exist for output %zu, error is 0x%" PRIx32
-            ", skipping allocating storage",
-            i,
-            static_cast<uint32_t>(output_tensor_meta.error()));
-        output_storages[i] = std::vector<uint8_t>();
+      THROW_IF_ERROR(
+          output_tensor_meta.error(),
+          "Failed to get output tensor meta for output %zu",
+          i);
+      if (output_tensor_meta.get().is_memory_planned()) {
+        // Skip allocating storage for planned memory outputs.
+        output_storages.emplace_back();
         continue;
       }
+      // Allocate storage for the output tensor.
       const size_t output_size = output_tensor_meta.get().nbytes();
-      output_storages[i] = std::vector<uint8_t>(output_size);
+      output_storages.emplace_back(output_size);
     }
     return output_storages;
   }