Simplify by using dtype_utils.is_valid_dtype in computation_types.py …

…instead of a local list of valid dtypes.

This also results in code like `tff.types.TensorStype(tf.string)` now raising an error (the intended behavior), and must be changed to `tff.types.TensorStype(np.str_)`.

PiperOrigin-RevId: 662644692

RELEASE.md

@@ -19,6 +19,11 @@ and this project adheres to
 *   Added some TFF executor classes to the public API (CPPExecutorFactory,
     ResourceManagingExecutorFactory, RemoteExecutor, RemoteExecutorGrpcStub).
 
+### Fixed
+
+*   A bug where `tf.string` was mistakenly allowed as a dtype to
+    `tff.types.TensorType`. This now must be `np.str_`.
+
 ### Changed
 
 *   `tff.Computation` and `tff.framework.ConcreteComputation` to be able to

tensorflow_federated/python/core/impl/types/BUILD

@@ -58,6 +58,7 @@ py_library(
     srcs = ["computation_types.py"],
     deps = [
         ":array_shape",
+        ":dtype_utils",
         ":placements",
         "//tensorflow_federated/python/common_libs:py_typecheck",
         "//tensorflow_federated/python/common_libs:structure",

tensorflow_federated/python/core/impl/types/computation_types.py

@@ -31,6 +31,7 @@
 from tensorflow_federated.python.common_libs import py_typecheck
 from tensorflow_federated.python.common_libs import structure
 from tensorflow_federated.python.core.impl.types import array_shape
+from tensorflow_federated.python.core.impl.types import dtype_utils
 from tensorflow_federated.python.core.impl.types import placements
 
 T = TypeVar('T')
@@ -307,30 +308,6 @@ def _is_dtype_like(obj: object) -> TypeGuard[_DtypeLike]:
     return isinstance(obj, np.dtype)
 
 
-_ALLOWED_NP_DTYPES = [
-    np.bool_,
-    np.bytes_,
-    np.complex128,
-    np.complex64,
-    np.float16,
-    np.float32,
-    np.float64,
-    np.int16,
-    np.int32,
-    np.int64,
-    np.int8,
-    np.str_,
-    np.uint16,
-    np.uint32,
-    np.uint64,
-    np.uint8,
-]
-
-
-def _is_allowed_np_dtype(dtype: np.dtype) -> bool:
-  return dtype in _ALLOWED_NP_DTYPES
-
-
 def _is_array_shape_like(
     obj: object,
 ) -> TypeGuard[Union[array_shape._ArrayShapeLike]]:
@@ -359,13 +336,12 @@ def _to_dtype(dtype: _DtypeLike) -> np.dtype:
   """
   if isinstance(dtype, np.dtype):
     dtype = dtype.type
-  if dtype == np.bytes_:
+  if dtype is np.bytes_:
     dtype = np.str_
-  dtype = np.dtype(dtype)
 
-  if not _is_allowed_np_dtype(dtype):
+  if not dtype_utils.is_valid_dtype(dtype):
     raise NotImplementedError(f'Unexpected `dtype` found: {dtype}.')
-  return dtype
+  return np.dtype(dtype)
 
 
 class TensorType(Type, metaclass=_Intern):

tensorflow_federated/python/learning/metrics/aggregator_test.py

@@ -621,7 +621,7 @@ def result(self):
       @federated_computation.federated_computation(
           computation_types.FederatedType(
               collections.OrderedDict(
-                  custom_sum=computation_types.TensorType(tf.string),
+                  custom_sum=computation_types.TensorType(np.str_),
               ),
               placements.CLIENTS,
           )