Merge pull request #184 from NREL/gb/allowed_const

added kwarg to fwp to allow for constant tensor output e.g., in the c…

sup3r/pipeline/forward_pass.py

@@ -610,7 +610,8 @@ def __init__(self,
  exo_kwargs=None,
  bias_correct_method=None,
  bias_correct_kwargs=None,
- max_nodes=None):
+ max_nodes=None,
+ allowed_const=False):
  """Use these inputs to initialize data handlers on different nodes and
  to define the size of the data chunks that will be passed through the
  generator.
@@ -715,6 +716,15 @@ def __init__(self,
  max_nodes : int | None
  Maximum number of nodes to distribute spatiotemporal chunks across.
  If None then a node will be used for each temporal chunk.
+ allowed_const : list | bool
+ Tensorflow has a tensor memory limit of 2GB (result of protobuf
+ limitation) and when exceeded can return a tensor with a
+ constant output. sup3r will raise a ``MemoryError`` in response. If
+ your model is allowed to output a constant output, set this to True
+ to allow any constant output or a list of allowed possible constant
+ outputs. For example, a precipitation model should be allowed to
+ output all zeros so set this to ``[0]``. For details on this limit:
+ https://github.com/tensorflow/tensorflow/issues/51870
  """
  self._input_handler_kwargs = input_handler_kwargs or {}
  target = self._input_handler_kwargs.get('target', None)
@@ -749,6 +759,7 @@ def __init__(self,
  self._lr_lat_lon = None
  self._init_handler = None
  self._handle_features = None
+ self.allowed_const = allowed_const
 
  self._single_ts_files = self._input_handler_kwargs.get(
  'single_ts_files', None)
@@ -1779,9 +1790,20 @@ def _constant_output_check(self, out_data):
  out_data : ndarray
  Forward pass output corresponding to the given chunk index
  """
+
+ allowed_const = self.strategy.allowed_const
+ if allowed_const is True:
+ return
+ elif allowed_const is False:
+ allowed_const = []
+ elif not isinstance(allowed_const, (list, tuple)):
+ allowed_const = [allowed_const]
+
  for i, f in enumerate(self.output_features):
  msg = f'All spatiotemporal values are the same for {f} output!'
- if np.all(out_data[0, 0, 0, i] == out_data[..., i]):
+ value0 = out_data[0, 0, 0, i]
+ all_same = (value0 == out_data[..., i]).all()
+ if all_same and value0 not in allowed_const:
  self.strategy.failed_chunks = True
  logger.error(msg)
  raise MemoryError(msg)