isinstance -> subclass to better support functionality in typing module (#112)

lib/python/ray/arrays/distributed/linalg.py

@@ -176,7 +176,7 @@ def qr(a):
       for r in range(i, a.num_blocks[0]):
         y_ri = y_val.objrefs[r - i, 0]
         W_rcs.append(qr_helper2(y_ri, a_work.objrefs[r, c]))
-      W_c = ra.linalg.sum_list(*W_rcs)
+      W_c = ra.sum_list(*W_rcs)
       for r in range(i, a.num_blocks[0]):
         y_ri = y_val.objrefs[r - i, 0]
         A_rc = qr_helper1(a_work.objrefs[r, c], y_ri, t, W_c)

lib/python/ray/arrays/remote/core.py

@@ -1,8 +1,8 @@
-from typing import List
+from typing import List, Any
 import numpy as np
 import ray
 
-__all__ = ["zeros", "zeros_like", "ones", "eye", "dot", "vstack", "hstack", "subarray", "copy", "tril", "triu", "diag", "transpose", "add", "subtract", "sum", "shape"]
+__all__ = ["zeros", "zeros_like", "ones", "eye", "dot", "vstack", "hstack", "subarray", "copy", "tril", "triu", "diag", "transpose", "add", "subtract", "sum", "shape", "sum_list"]
 
 @ray.remote([List[int], str, str], [np.ndarray])
 def zeros(shape, dtype_name="float", order="C"):
@@ -75,3 +75,9 @@ def sum(x, axis=-1):
 @ray.remote([np.ndarray], [tuple])
 def shape(a):
   return np.shape(a)
+
+# We use Any to allow different numerical types as well as numpy arrays.
+# TODO(rkn):this isn't in the numpy API, so be careful about exposing this.
+@ray.remote([Any], [Any])
+def sum_list(*xs):
+  return np.sum(xs, axis=0)

lib/python/ray/arrays/remote/linalg.py

@@ -86,7 +86,3 @@ def matrix_rank(M):
 @ray.remote([np.ndarray], [np.ndarray])
 def multi_dot(*a):
   raise NotImplementedError
-
-@ray.remote([np.ndarray], [np.ndarray])
-def sum_list(*xs):
-  return np.sum(xs, axis=0)

lib/python/ray/worker.py

@@ -164,7 +164,7 @@ def check_return_values(function, result):
     if len(result) != len(function.return_types):
       raise Exception("The @remote decorator for function {} has {} return values with types {}, but {} returned {} values.".format(function.__name__, len(function.return_types), function.return_types, function.__name__, len(result)))
     for i in range(len(result)):
-      if (not isinstance(result[i], function.return_types[i])) and (not isinstance(result[i], ray.lib.ObjRef)):
+      if (not issubclass(type(result[i]), function.return_types[i])) and (not isinstance(result[i], ray.lib.ObjRef)):
         raise Exception("The {}th return value for function {} has type {}, but the @remote decorator expected a return value of type {} or an ObjRef.".format(i, function.__name__, type(result[i]), function.return_types[i]))
 
 # helper method, this should not be called by the user
@@ -187,7 +187,7 @@ def check_arguments(function, args):
       # TODO(rkn): When we have type information in the ObjRef, do type checking here.
       pass
     else:
-      if not isinstance(arg, expected_type): # TODO(rkn): This check doesn't really work, e.g., isinstance([1,2,3], typing.List[str]) == True
+      if not issubclass(type(arg), expected_type): # TODO(rkn): This check doesn't really work, e.g., issubclass(type([1, 2, 3]), typing.List[str]) == True
         raise Exception("Argument {} for function {} has type {} but an argument of type {} was expected.".format(i, function.__name__, type(arg), expected_type))
 
 # helper method, this should not be called by the user
@@ -219,7 +219,7 @@ def get_arguments_for_execution(function, args, worker=global_worker):
       # pass the argument by value
       argument = arg
 
-    if not isinstance(argument, expected_type):
+    if not issubclass(type(argument), expected_type):
       raise Exception("Argument {} for function {} has type {} but an argument of type {} was expected.".format(i, function.__name__, type(argument), expected_type))
     arguments.append(argument)
   return arguments