diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/rev_block_lib.py')
-rw-r--r-- | tensorflow/contrib/layers/python/layers/rev_block_lib.py | 26 |
1 files changed, 18 insertions, 8 deletions
diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib.py b/tensorflow/contrib/layers/python/layers/rev_block_lib.py index 0e35b1aa8b..dad3da3748 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib.py @@ -514,15 +514,15 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): original_vars = set(tape.watched_variables()) # Backward pass - def grad_fn(*output_grads, **kwargs): + def _grad_fn(output_grads, variables=None): """Recompute outputs for gradient computation.""" - variables = [] + variables = variables or [] if original_vars: - variables = kwargs["variables"] - if set(variables) != original_vars: - raise ValueError(_WRONG_VARS_ERR) - del kwargs - inputs = list(args) + assert variables, ("Fn created variables but the variables were not " + "passed to the gradient fn.") + if set(variables) != original_vars: + raise ValueError(_WRONG_VARS_ERR) + inputs = [array_ops.identity(x) for x in list(args)] # Recompute outputs with framework_ops.control_dependencies(output_grads): if use_data_dep_: @@ -538,7 +538,7 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): if original_vars != recompute_vars: raise ValueError(_WRONG_VARS_ERR) - if not (isinstance(outputs, list) or isinstance(outputs, tuple)): + if not isinstance(outputs, (list, tuple)): outputs = [outputs] outputs = list(outputs) grads = gradients_impl.gradients(outputs, inputs + variables, @@ -554,6 +554,16 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): grad_vars = grads[len(inputs):] return grad_inputs, grad_vars + # custom_gradient inspects the signature of the function to determine + # whether the user expects variables passed in the grad_fn. If the function + # created variables, the grad_fn should accept the "variables" kwarg. + if original_vars: + def grad_fn(*output_grads, **kwargs): + return _grad_fn(output_grads, kwargs["variables"]) + else: + def grad_fn(*output_grads): + return _grad_fn(output_grads) + return outputs, grad_fn return fn_with_recompute(*args) |