move build_and_reuse_placeholder to input_source

docs/modules/graph_builder.rst

 tensorpack.graph_builder package
 ================================
 
+These are some useful functions if you need to write your own trainers.
+Note that they may not be well maintained.
+
 .. automodule:: tensorpack.graph_builder
     :members:
     :undoc-members:

tensorpack/graph_builder/model_desc.py

@@ -17,35 +17,6 @@ TensorSpec = backport_tensor_spec()
 __all__ = ['InputDesc', 'ModelDesc', 'ModelDescBase']
 
 
-def build_or_reuse_placeholder(tensor_spec):
-    """
-    Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
-
-    Args:
-        tensor_spec (tf.TensorSpec):
-
-    Returns:
-        tf.Tensor:
-    """
-    g = tfv1.get_default_graph()
-    name = tensor_spec.name
-    try:
-        tensor = g.get_tensor_by_name(name + ':0')
-        assert "Placeholder" in tensor.op.type, "Tensor {} exists but is not a placeholder!".format(name)
-        assert tensor_spec.is_compatible_with(tensor), \
-            "Tensor {} exists but is not compatible with the signature!".format(tensor)
-        if tensor.shape == tensor_spec.shape:
-            # It might be desirable to use a placeholder of a different shape in some tower
-            # (e.g., a less specific shape)
-            return tensor
-    except KeyError:
-        pass
-    with tfv1.name_scope(None):   # clear any name scope it might get called in
-        ret = tfv1.placeholder(
-            tensor_spec.dtype, shape=tensor_spec.shape, name=tensor_spec.name)
-    return ret
-
-
 class InputDesc(
         namedtuple('InputDescTuple', ['type', 'shape', 'name'])):
     """
@@ -65,7 +36,7 @@ class InputDesc(
             shape (tuple):
             name (str):
         """
-        # TODO mark deprecated
+        log_deprecated("InputDesc", "Use tf.TensorSpec instead!", "2020-03-01")
         assert isinstance(type, tf.DType), type
         return tf.TensorSpec(shape=shape, dtype=type, name=name)
 

tensorpack/graph_builder/utils.py

@@ -13,7 +13,7 @@ from ..tfutils.varreplace import custom_getter_scope
 from ..utils import logger
 from ..utils.argtools import call_only_once
 
-__all__ = ["LeastLoadedDeviceSetter"]
+__all__ = ["LeastLoadedDeviceSetter", "allreduce_grads", "aggregate_grads"]
 
 
 """

tensorpack/input_source/input_source.py

@@ -18,8 +18,7 @@ from ..tfutils.summary import add_moving_summary
 from ..tfutils.tower import get_current_tower_context
 from ..utils import logger
 from ..utils.concurrency import ShareSessionThread
-from .input_source_base import InputSource
-from ..graph_builder.model_desc import build_or_reuse_placeholder
+from .input_source_base import InputSource, build_or_reuse_placeholder
 
 try:
     from tensorflow.python.ops.data_flow_ops import StagingArea

tensorpack/input_source/input_source_base.py

@@ -12,11 +12,40 @@ from ..callbacks.base import CallbackFactory
 from ..tfutils.common import get_op_tensor_name
 from ..utils import logger
 from ..utils.argtools import call_only_once, memoized_method
-from ..graph_builder.model_desc import build_or_reuse_placeholder
+from ..compat import tfv1
 
 __all__ = ['InputSource', 'remap_input_source']
 
 
+def build_or_reuse_placeholder(tensor_spec):
+    """
+    Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
+
+    Args:
+        tensor_spec (tf.TensorSpec):
+
+    Returns:
+        tf.Tensor:
+    """
+    g = tfv1.get_default_graph()
+    name = tensor_spec.name
+    try:
+        tensor = g.get_tensor_by_name(name + ':0')
+        assert "Placeholder" in tensor.op.type, "Tensor {} exists but is not a placeholder!".format(name)
+        assert tensor_spec.is_compatible_with(tensor), \
+            "Tensor {} exists but is not compatible with the signature!".format(tensor)
+        if tensor.shape == tensor_spec.shape:
+            # It might be desirable to use a placeholder of a different shape in some tower
+            # (e.g., a less specific shape)
+            return tensor
+    except KeyError:
+        pass
+    with tfv1.name_scope(None):   # clear any name scope it might get called in
+        ret = tfv1.placeholder(
+            tensor_spec.dtype, shape=tensor_spec.shape, name=tensor_spec.name)
+    return ret
+
+
 def get_tensors_inputs(placeholders, tensors, names):
     """
     Args: