diff --git a/autoparallel/propagation_rules.py b/autoparallel/propagation_rules.py
index abba8964..26efa2bf 100644
--- a/autoparallel/propagation_rules.py
+++ b/autoparallel/propagation_rules.py
@@ -379,7 +379,7 @@ def factory_rule(mesh, op_schema: OpSchema) -> OpStrategy:
     This util applies to any factory function that takes 'size' as the first argument,
     and supports Replication and Shard placements all at zero cost.
     """
-    assert isinstance(op_schema.args_schema[0], torch.Size)
+    assert isinstance(op_schema.args_schema[0], (torch.Size, list))
     shape = op_schema.args_schema[0]
     x = torch.empty(shape, device="meta")
     stride = x.stride()
@@ -408,7 +408,7 @@ def factory_rule(mesh, op_schema: OpSchema) -> OpStrategy:
     for strategy_comb in strategy_combs:
         spec_list = [DTensorSpec(mesh, specs) for specs in zip(*strategy_comb)]
         output_specs = spec_list[0]
-        output_specs.tensor_meta = TensorMeta(shape, stride, dtype)
+        output_specs.tensor_meta = TensorMeta(shape, stride, dtype)  # type: ignore[arg-type]
 
         if not is_tensor_shardable(shape, output_specs):
             continue
@@ -421,8 +421,13 @@ def factory_rule(mesh, op_schema: OpSchema) -> OpStrategy:
             * len(strategy_combs)
         ]
 
+        # NOTE: why do we have input_specs for constructor nodes, given that they have no inputs?
+        # This is because the optimizer code expects to see input_specs for all nodes, and it
+        # uses the input_specs to determine the sharding of the output.  So we have to give it
+        # something, even though it is in principle not needed.
         strategy = OpSpec(
             output_specs=output_specs,
+            input_specs=[output_specs],
             redistribute_cost=redistribute_cost,
         )
         all_strategies.append(strategy)
diff --git a/autoparallel/utils.py b/autoparallel/utils.py
index 96ecbfe4..2627cb1d 100644
--- a/autoparallel/utils.py
+++ b/autoparallel/utils.py
@@ -73,9 +73,6 @@ def propagate_tensor_meta(op, user_args, user_kwargs, out_strat):
                         else:
                             assert tm is None
         if strat.input_specs is None:
-            if op in TENSOR_FACTORY_OPS:
-                # there isn't an input spec bc the op has no input!
-                continue
 
             supported_ops = {
                 torch.ops.prims.convert_element_type.default,
@@ -88,9 +85,18 @@ def propagate_tensor_meta(op, user_args, user_kwargs, out_strat):
             )
             strat.input_specs = (strat.output_specs,)
             assert strat.redistribute_cost is None
-        assert len(tensor_metas) == len(
-            strat.input_specs
-        ), f"{op}, {len(tensor_metas)}, {len(strat.input_specs)}"
+        # NOTE: this invariant wrt factory ops is something I believe
+        # I'll keep for the solver, so we need to have some consistency here
+        # i.e., even though factory ops don't have inputs, we do put an
+        # input spec for it which is equal to the output spec
+        if op in TENSOR_FACTORY_OPS:
+            assert len(tensor_metas) == 0, f"{op}, {len(tensor_metas)}"
+            assert len(strat.input_specs) == 1, f"{op}, {len(strat.input_specs)}"
+        else:
+            assert len(tensor_metas) == len(
+                strat.input_specs
+            ), f"{op}, {len(tensor_metas)}, {len(strat.input_specs)}"
+
         for tm, ispec in zip(tensor_metas, strat.input_specs):
             if ispec.tensor_meta is None:
                 ispec.tensor_meta = tm