# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # 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. # ============================================================================== """smart_cond and related utilities.""" from tensorflow.python.framework import tensor from tensorflow.python.framework import tensor_util from tensorflow.python.ops import cond from tensorflow.python.ops import control_flow_case from tensorflow.python.util.tf_export import tf_export @tf_export("__internal__.smart_cond.smart_cond", v1=[]) def smart_cond(pred, true_fn=None, false_fn=None, name=None): """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. If `pred` is a bool or has a constant value, we return either `true_fn()` or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. Args: pred: A scalar determining whether to return the result of `true_fn` or `false_fn`. true_fn: The callable to be performed if pred is true. false_fn: The callable to be performed if pred is false. name: Optional name prefix when using `tf.cond`. Returns: Tensors returned by the call to either `true_fn` or `false_fn`. Raises: TypeError: If `true_fn` or `false_fn` is not callable. """ if not callable(true_fn): raise TypeError(f"Argument `true_fn` must be callable. Received {true_fn}") if not callable(false_fn): raise TypeError( f"Argument `false_fn` must be callable. Received {false_fn}") pred_value = smart_constant_value(pred) if pred_value is not None: if pred_value: return true_fn() else: return false_fn() else: return cond.cond(pred, true_fn=true_fn, false_fn=false_fn, name=name) def smart_constant_value(pred): """Return the bool value for `pred`, or None if `pred` had a dynamic value. Args: pred: A scalar, either a Python bool or tensor. Returns: True or False if `pred` has a constant boolean value, None otherwise. Raises: TypeError: If `pred` is not a Tensor or bool. """ if isinstance(pred, tensor.Tensor): pred_value = tensor_util.constant_value(pred) # TODO(skyewm): consider folding this into tensor_util.constant_value. # pylint: disable=protected-access if pred_value is None: pred_value = tensor_util.try_evaluate_constant(pred) # pylint: enable=protected-access elif pred in {0, 1}: # Accept 1/0 as valid boolean values pred_value = bool(pred) elif isinstance(pred, bool): pred_value = pred else: raise TypeError("Argument `pred` must be a Tensor, or a Python bool, or 1 " f"or 0. Received: pred={pred} of type " f"{type(pred).__name__}") return pred_value def smart_case(pred_fn_pairs, default=None, exclusive=False, name="smart_case"): """Like tf.case, except attempts to statically evaluate predicates. If any predicate in `pred_fn_pairs` is a bool or has a constant value, the associated callable will be called or omitted depending on its value. Otherwise this functions like tf.case. Args: pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor and a callable which returns a list of tensors. default: Optional callable that returns a list of tensors. exclusive: True iff at most one predicate is allowed to evaluate to `True`. name: A name for this operation (optional). Returns: The tensors returned by the first pair whose predicate evaluated to True, or those returned by `default` if none does. Raises: TypeError: If `pred_fn_pairs` is not a list/dictionary. TypeError: If `pred_fn_pairs` is a list but does not contain 2-tuples. TypeError: If `fns[i]` is not callable for any i, or `default` is not callable. """ return control_flow_case._case_helper( # pylint: disable=protected-access smart_cond, pred_fn_pairs, default, exclusive, name, allow_python_preds=True)