diff --git a/jax/experimental/shard_map.py b/jax/experimental/shard_map.py
index e4db2f7ec358..b6cf69d218bf 100644
--- a/jax/experimental/shard_map.py
+++ b/jax/experimental/shard_map.py
@@ -663,22 +663,22 @@ def _shard_map_lowering_shardy(
     # Nested `ManualComputationOp`s cannot refer to axes that are already
     # manual. So figure out what axes are free thus far and get the new axis
     # context.
-    free_axis = frozenset(mesh.axis_names) - ctx.module_context.axis_context.manual_axes
-    new_axis_context = sharding_impls.SPMDAxisContext(mesh, free_axis - auto)
+    free_axes = frozenset(mesh.axis_names) - ctx.module_context.axis_context.manual_axes
+    new_axis_context = sharding_impls.SPMDAxisContext(mesh, free_axes - auto)
   else:
     new_axis_context = sharding_impls.SPMDAxisContext(
         mesh, frozenset(mesh.axis_names) - auto)
   sub_ctx = ctx.module_context.replace(axis_context=new_axis_context)
   args = (*ctx.dim_var_values, *in_nodes)
-
   manual_axes = sub_ctx.axis_context.manual_axes
   mesh_shape = mesh.shape
   manual_axes_size = np.prod([mesh_shape[a] for a in manual_axes])
   if manual_axes_size == 1:
     # No need for a `ManualComputationOp` if all manual axes are size 1.
-    out_nodes, _ = mlir.jaxpr_subcomp(
-        sub_ctx, jaxpr, ctx.name_stack, mlir.TokenSet(), (), *args,
-        dim_var_values=ctx.dim_var_values)
+    with core.extend_axis_env_nd(tuple(mesh.shape.items())):
+      out_nodes, _ = mlir.jaxpr_subcomp(
+          sub_ctx, jaxpr, ctx.name_stack, mlir.TokenSet(), (), *args,
+          dim_var_values=ctx.dim_var_values)
     return out_nodes
 
   in_shardings = sdy.TensorShardingPerValueAttr.get(map(