Allow marked axis in einx.experimental.shard only as leftmost member …

…of composition

einx/experimental/op/shard.py

@@ -12,6 +12,15 @@
 tnp = einx.tracer.import_("numpy", as_="np")
 
 
+def _is_composed(expr):
+    node = expr
+    while node is not None:
+        if isinstance(node, einx.expr.stage3.Composition):
+            return True
+        node = node.parent
+    return False
+
+
 @einx.jit(
     trace=lambda t, c: lambda expr_in, tensor_in, expr_out, backend=None: c(
         expr_in,
@@ -26,6 +35,19 @@ def shard_stage3(expr_in, tensor_in, expr_out, mesh=None, backend=None):
         for expr in root.all():
             if isinstance(expr, einx.expr.stage3.Concatenation):
                 raise ValueError("Concatenation not allowed")
+            if isinstance(expr, einx.expr.stage3.Marker):
+                child = expr
+                while child.parent is not None:
+                    if (
+                        isinstance(child.parent, einx.expr.stage3.List)
+                        and _is_composed(child.parent)
+                        and child is not child.parent.children[0]
+                    ):
+                        raise ValueError(
+                            "If device axes are used within a composition they "
+                            "must appear as the left-most member of the composition"
+                        )
+                    child = child.parent
 
     # Call tensor factories
     tensor_in = einx.tracer.call_factory(tensor_in, expr_in.shape, backend=backend)
@@ -59,9 +81,10 @@ def shard_stage3(expr_in, tensor_in, expr_out, mesh=None, backend=None):
     # Construct partition spec
     axes = tuple(axis for axis in expr_in if isinstance(axis, einx.expr.stage3.Axis))
     partition_spec = [axis.name if einx.expr.stage3.is_marked(axis) else None for axis in axes]
+    partition_spec = tP(*partition_spec)
 
     # Shard tensor
-    sharding = tNamedSharding(mesh, tP(*partition_spec))
+    sharding = tNamedSharding(mesh, partition_spec)
     tensor_in = tjax.device_put(tensor_in, sharding)
 
     # Unflatten output expressions
@@ -203,7 +226,7 @@ def shard(
         representation of the operation.
     """
     if backend.name != "jax":
-        raise NotImplementedError("einx.shard is currently only supported for Jax")
+        raise NotImplementedError("einx.experimental.shard is currently only supported for Jax")
     expr_in, expr_out = parse(
         description, einx.tracer.get_shape(tensor), mesh=mesh, cse=cse, **parameters
     )