Issue #339. with tn.DefaultBackend(backend): support

tensornetwork/__init__.py

@@ -10,12 +10,8 @@
 from tensornetwork.version import __version__
 from tensornetwork.visualization.graphviz import to_graphviz
 from tensornetwork import contractors
-from tensornetwork import config
 from typing import Text, Optional, Type, Union
 from tensornetwork.utils import load_nodes, save_nodes
 from tensornetwork.matrixproductstates.finite_mps import FiniteMPS
 from tensornetwork.matrixproductstates.infinite_mps import InfiniteMPS
-
-
-def set_default_backend(backend: Union[Text, BaseBackend]) -> None:
-  config.default_backend = backend
+from tensornetwork.backend_contextmanager import DefaultBackend, set_default_backend

tensornetwork/backend_contextmanager.py

@@ -0,0 +1,41 @@
+from typing import Text, Union
+from tensornetwork.backends.base_backend import BaseBackend
+
+class DefaultBackend():
+  """Context manager for setting up backend for nodes"""
+
+  def __init__(self, backend: Union[Text, BaseBackend]) -> None:
+    if not isinstance(backend, (Text, BaseBackend)):
+      raise ValueError("Item passed to DefaultBackend "
+                       "must be Text or BaseBackend")
+    self.backend = backend
+
+  def __enter__(self):
+    _default_backend_stack.stack.append(self)
+
+  def __exit__(self, exc_type, exc_val, exc_tb):
+    _default_backend_stack.stack.pop()
+
+class _DefaultBackendStack():
+  """A stack to keep track default backends context manager"""
+
+  def __init__(self):
+    self.stack = []
+    self.default_backend = "numpy"
+
+  def get_current_backend(self):
+    return self.stack[-1].backend if self.stack else self.default_backend
+
+_default_backend_stack = _DefaultBackendStack()
+
+def get_default_backend():
+  return _default_backend_stack.get_current_backend()
+
+def set_default_backend(backend: Union[Text, BaseBackend]) -> None:
+  if _default_backend_stack.stack:
+    raise AssertionError("The default backend should not be changed "
+                         "inside the backend context manager")
+  if not isinstance(backend, (Text, BaseBackend)):
+    raise ValueError("Item passed to set_default_backend "
+                     "must be Text or BaseBackend")
+  _default_backend_stack.default_backend = backend

tensornetwork/backends/backend_factory.py

@@ -19,7 +19,6 @@
 from tensornetwork.backends.shell import shell_backend
 from tensornetwork.backends.pytorch import pytorch_backend
 from tensornetwork.backends import base_backend
-import tensornetwork.config as config_file
 
 _BACKENDS = {
     "tensorflow": tensorflow_backend.TensorFlowBackend,

tensornetwork/config.py

@@ -11,5 +11,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-default_backend = "numpy"

tensornetwork/ncon_interface.py

@@ -16,7 +16,7 @@
 import warnings
 from typing import Any, Sequence, List, Optional, Union, Text, Tuple, Dict
 from tensornetwork import network_components
-from tensornetwork import config
+from tensornetwork.backend_contextmanager import get_default_backend
 from tensornetwork.backends import backend_factory
 Tensor = Any
 
@@ -67,8 +67,8 @@ def ncon(tensors: Sequence[Union[network_components.BaseNode, Tensor]],
         structure.
       con_order: List of edge labels specifying the contraction order.
       out_order: List of edge labels specifying the output order.
-      backend: String specifying the backend to use. Defaults to 
-        `tensornetwork.config.default_backend`.
+      backend: String specifying the backend to use. Defaults to
+        `tensornetwork.backend_contextmanager.get_default_backend`.
 
     Returns:
       The result of the contraction. The result is returned as a `Node`
@@ -78,7 +78,7 @@ def ncon(tensors: Sequence[Union[network_components.BaseNode, Tensor]],
   if backend and (backend not in backend_factory._BACKENDS):
     raise ValueError("Backend '{}' does not exist".format(backend))
   if backend is None:
-    backend = config.default_backend
+    backend = get_default_backend()
 
   are_nodes = [isinstance(t, network_components.BaseNode) for t in tensors]
   nodes = {t for t in tensors if isinstance(t, network_components.BaseNode)}

tensornetwork/network_components.py

@@ -21,10 +21,10 @@
 import h5py
 
 #pylint: disable=useless-import-alias
-import tensornetwork.config as config
 from tensornetwork import ops
 from tensornetwork.backends import backend_factory
 from tensornetwork.backends.base_backend import BaseBackend
+from tensornetwork.backend_contextmanager import get_default_backend
 
 string_type = h5py.special_dtype(vlen=str)
 Tensor = Any
@@ -525,8 +525,8 @@ def __init__(self,
     """Create a node.
 
     Args:
-      tensor: The concrete that is represented by this node, or a `BaseNode` 
-        object. If a tensor is passed, it can be 
+      tensor: The concrete that is represented by this node, or a `BaseNode`
+        object. If a tensor is passed, it can be
         be either a numpy array or the tensor-type of the used backend.
         If a `BaseNode` is passed, the passed node has to have the same \
         backend as given by `backend`.
@@ -543,7 +543,7 @@ def __init__(self,
       backend = tensor.backend
       tensor = tensor.tensor
     if not backend:
-      backend = config.default_backend
+      backend = get_default_backend()
     if isinstance(backend, BaseBackend):
       backend_obj = backend
     else:
@@ -633,13 +633,13 @@ def __init__(self,
       backend: An optional backend for the node. If `None`, a default
         backend is used
       dtype: The dtype used to initialize a numpy-copy node.
-        Note that this dtype has to be a numpy dtype, and it has to be 
+        Note that this dtype has to be a numpy dtype, and it has to be
         compatible with the dtype of the backend, e.g. for a tensorflow
         backend with a tf.Dtype=tf.floa32, `dtype` has to be `np.float32`.
     """
 
     if not backend:
-      backend = config.default_backend
+      backend = get_default_backend()
     backend_obj = backend_factory.get_backend(backend)
 
     self.rank = rank
@@ -1092,14 +1092,14 @@ def disconnect(self,
                  edge2_name: Optional[Text] = None) -> Tuple["Edge", "Edge"]:
     """
     Break an existing non-dangling edge.
-    This updates both Edge.node1 and Edge.node2 by removing the 
+    This updates both Edge.node1 and Edge.node2 by removing the
     connecting edge from `Edge.node1.edges` and `Edge.node2.edges`
     and adding new dangling edges instead
     Args:
       edge1_name: A name for the new dangling edge at `self.node1`
       edge2_name: A name for the new dangling edge at `self.node2`
     Returns:
-      (new_edge1, new_edge2): The new `Edge` objects of 
+      (new_edge1, new_edge2): The new `Edge` objects of
         `self.node1` and `self.node2`
     """
     if self.is_dangling():
@@ -1155,7 +1155,7 @@ def get_parallel_edges(edge: Edge) -> Set[Edge]:
     edge: The given edge.
 
   Returns:
-    A `set` of all of the edges parallel to the given edge 
+    A `set` of all of the edges parallel to the given edge
     (including the given edge).
   """
   return get_shared_edges(edge.node1, edge.node2)
@@ -1389,8 +1389,8 @@ def split_edge(edge: Edge,
                shape: Tuple[int, ...],
                new_edge_names: Optional[List[Text]] = None) -> List[Edge]:
   """Split an `Edge` into multiple edges according to `shape`. Reshapes
-  the underlying tensors connected to the edge accordingly. 
-  
+  the underlying tensors connected to the edge accordingly.
+
   This method acts as the inverse operation of flattening edges and
   distinguishes between the following edge cases when adding new edges:
     1) standard edge connecting two different nodes: reshape node dimensions
@@ -1772,7 +1772,7 @@ def disconnect(edge,
                edge2_name: Optional[Text] = None) -> Tuple[Edge, Edge]:
   """
   Break an existing non-dangling edge.
-  This updates both Edge.node1 and Edge.node2 by removing the 
+  This updates both Edge.node1 and Edge.node2 by removing the
   connecting edge from `Edge.node1.edges` and `Edge.node2.edges`
   and adding new dangling edges instead
   """
@@ -1894,9 +1894,9 @@ def outer_product_final_nodes(nodes: Iterable[BaseNode],
                               edge_order: List[Edge]) -> BaseNode:
   """Get the outer product of `nodes`
 
-  For example, if there are 3 nodes remaining in `nodes` with 
+  For example, if there are 3 nodes remaining in `nodes` with
   shapes :math:`(2, 3)`, :math:`(4, 5, 6)`, and :math:`(7)`
-  respectively, the newly returned node will have shape 
+  respectively, the newly returned node will have shape
   :math:`(2, 3, 4, 5, 6, 7)`.
 
   Args:

tensornetwork/network_operations.py

@@ -19,7 +19,6 @@
 import numpy as np
 
 #pylint: disable=useless-import-alias
-import tensornetwork.config as config
 #pylint: disable=line-too-long
 from tensornetwork.network_components import BaseNode, Node, CopyNode, Edge, disconnect
 from tensornetwork.backends import backend_factory

tensornetwork/tests/backend_contextmanager_test.py

@@ -0,0 +1,46 @@
+import tensornetwork as tn
+from tensornetwork.backend_contextmanager import _default_backend_stack
+import pytest
+import numpy as np
+
+def test_contextmanager_simple():
+  with tn.DefaultBackend("tensorflow"):
+    a = tn.Node(np.ones((10,)))
+    b = tn.Node(np.ones((10,)))
+  assert a.backend.name == b.backend.name
+
+def test_contextmanager_default_backend():
+  tn.set_default_backend("pytorch")
+  with tn.DefaultBackend("numpy"):
+    assert _default_backend_stack.default_backend == "pytorch"
+
+def test_contextmanager_interruption():
+  tn.set_default_backend("pytorch")
+  with pytest.raises(AssertionError):
+    with tn.DefaultBackend("numpy"):
+      tn.set_default_backend("tensorflow")
+
+def test_contextmanager_nested():
+  with tn.DefaultBackend("tensorflow"):
+    a = tn.Node(np.ones((10,)))
+    assert a.backend.name == "tensorflow"
+    with tn.DefaultBackend("numpy"):
+      b = tn.Node(np.ones((10,)))
+      assert b.backend.name == "numpy"
+    c = tn.Node(np.ones((10,)))
+    assert c.backend.name == "tensorflow"
+  d = tn.Node(np.ones((10,)))
+  assert d.backend.name == "numpy"
+
+def test_contextmanager_wrong_item():
+  a = tn.Node(np.ones((10,)))
+  with pytest.raises(ValueError):
+    with tn.DefaultBackend(a): # pytype: disable=wrong-arg-types
+      pass
+
+def test_contextmanager_BaseBackend():
+  tn.set_default_backend("pytorch")
+  a = tn.Node(np.ones((10,)))
+  with tn.DefaultBackend(a.backend):
+    b = tn.Node(np.ones((10,)))
+  assert b.backend.name == "pytorch"

tensornetwork/tests/tensornetwork_test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import tensornetwork as tn
+from tensornetwork.backend_contextmanager import _default_backend_stack
 import pytest
 import numpy as np
 import tensorflow as tf
@@ -522,7 +523,7 @@ def test_set_node2(backend):
 
 def test_set_default(backend):
   tn.set_default_backend(backend)
-  assert tn.config.default_backend == backend
+  assert _default_backend_stack.default_backend == backend
   a = tn.Node(np.eye(2))
   assert a.backend.name == backend