1409 lines
59 KiB
Python
1409 lines
59 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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"""while_v2 and gradient.
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This is a version of while_loop that emits a single While op, as well as the
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gradient function for While ops produced by while_loop. This will eventually
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replace the current tf.while_loop implementation once it reaches feature and
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performance parity.
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"""
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import collections
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from tensorflow.core.framework import attr_value_pb2
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from tensorflow.python.client import pywrap_tf_session as c_api
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from tensorflow.python.eager import backprop_util
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from tensorflow.python.framework import auto_control_deps_utils as acd
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import func_graph as func_graph_module
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from tensorflow.python.framework import indexed_slices
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor as tensor_lib
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import control_flow_util as util_v1
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from tensorflow.python.ops import control_flow_util_v2 as util
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from tensorflow.python.ops import default_gradient
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from tensorflow.python.ops import gen_functional_ops
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from tensorflow.python.ops import gen_resource_variable_ops
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from tensorflow.python.ops import gradients_util
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from tensorflow.python.ops import handle_data_util
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from tensorflow.python.ops import list_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import tensor_array_ops
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from tensorflow.python.ops import while_v2_indexed_slices_rewriter
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from tensorflow.python.util import compat
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from tensorflow.python.util import nest
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from tensorflow.python.util import object_identity
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from tensorflow.python.util import variable_utils
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# pylint: disable=protected-access
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def while_loop(cond,
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body,
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loop_vars,
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shape_invariants=None,
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parallel_iterations=10,
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maximum_iterations=None,
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name=None,
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return_same_structure=True,
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back_prop=True):
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"""Like tf.while_loop, except emits a single While op."""
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loop_vars = variable_utils.convert_variables_to_tensors(loop_vars)
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# Keep the original loop_vars around to know which args were TensorArrays.
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orig_loop_vars = loop_vars
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flat_orig_loop_vars = nest.flatten(orig_loop_vars, expand_composites=True)
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# Cache its length since we use it at multiple places below.
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len_orig_loop_vars = len(orig_loop_vars)
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# Convert TensorArrays to their flow variables. These get converted back to
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# TensorArrays before calling `cond` and `body`. See `wrapped_cond` and
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# `wrapped_body` below.
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loop_vars = _tensor_array_to_flow(loop_vars)
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loop_vars = nest.map_structure(
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indexed_slices.internal_convert_to_tensor_or_indexed_slices,
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loop_vars,
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expand_composites=True)
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# `loop_vars_signature` is a structure of TypeSpecs and has the same
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# structure with the `orig_loop_vars`. If `shape_invariants` is not None, its
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# shape information comes from `shape_invariants` instead of `orig_loop_vars`.
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# It is used to pack flattened vars into structured vars.
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if shape_invariants is not None:
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loop_vars_signature = nest.map_structure(
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control_flow_ops._shape_invariant_to_type_spec,
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loop_vars, shape_invariants)
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else:
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loop_vars_signature = nest.map_structure(
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control_flow_ops._shape_invariant_to_type_spec, loop_vars)
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flat_shape_invariants = nest.map_structure(
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lambda spec: spec.shape,
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nest.flatten(loop_vars_signature, expand_composites=True))
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if not name:
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name = "while"
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with ops.name_scope(name) as scope:
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with ops.name_scope(None):
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cond_name = util.unique_fn_name(scope, "cond")
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body_name = util.unique_fn_name(scope, "body")
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maximum_iterations_loop_var = _build_maximum_iterations_loop_var(
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maximum_iterations)
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loop_counter = constant_op.constant(
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0,
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dtype=maximum_iterations_loop_var.dtype
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if maximum_iterations is not None else None,
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name="loop_counter")
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# Add loop counter needed for computing gradients.
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loop_vars = [loop_counter, maximum_iterations_loop_var] + list(loop_vars)
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func_graph_signature = (
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[tensor_spec.TensorSpec.from_tensor(loop_counter),
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tensor_spec.TensorSpec.from_tensor(maximum_iterations_loop_var)] +
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list(loop_vars_signature))
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# Automatic control dependencies are added in defuns, but not in v1
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# graphs. Propagate that behavior here.
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add_control_dependencies = ops.get_default_graph()._add_control_dependencies
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def wrapped_cond(loop_counter, maximum_iterations_arg, *args):
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"""Extra `cond` wrapper that can handle the extra counter loop_var."""
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# Convert the flow variables in `args` to TensorArrays. `args` should
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# already have the same structure as `orig_loop_vars` but currently there
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# is no nest.zip so we call `_pack_sequence_as` which flattens `args`,
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# converts flows in `args` to TensorArrays and packs it into the
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# structure of `loop_vars_signature`.
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pred = cond(
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*_pack_sequence_as(loop_vars_signature, flat_orig_loop_vars, args))
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if (tensor_util.is_tf_type(pred) and
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(pred.shape.dims is None or pred.shape.dims)):
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pred = array_ops.squeeze_v2(pred)
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if maximum_iterations is None:
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return pred
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else:
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return math_ops.logical_and(
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loop_counter < maximum_iterations_arg, pred)
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cond_graph = func_graph_module.func_graph_from_py_func(
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cond_name,
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wrapped_cond,
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[], # We provide signature instead of args.
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{},
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signature=func_graph_signature,
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func_graph=util.WhileCondFuncGraph(
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cond_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
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add_control_dependencies=add_control_dependencies)
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def wrapped_body(loop_counter, maximum_iterations_arg, *args):
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"""Loop body augmented with counter update.
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Args:
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loop_counter: Loop counter which needs to be incremented in the body.
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maximum_iterations_arg: Maximum iterations of the loop.
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*args: List of args
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Returns:
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A list of tensors the same length as args.
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"""
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# The function was created with a signature rather than tensors, so
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# internal placeholders were created without handle data.
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_copy_handle_data(nest.flatten(loop_vars[2:], expand_composites=True),
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nest.flatten(args, expand_composites=True))
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# Capture the tensors already captured in cond_graph so that they appear
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# in the same order in body_graph.external_captures.
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for t in cond_graph.external_captures:
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ops.get_default_graph().capture(t)
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# Convert the flow variables in `args` to TensorArrays. `args` should
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# already have the same structure as `orig_loop_vars` but currently there
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# is no nest.zip so we call `_pack_sequence_as` which flattens `args`,
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# converts flows in `args` to TensorArrays and packs it into the
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# structure of `loop_vars_signature`.
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outputs = body(
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*_pack_sequence_as(loop_vars_signature, flat_orig_loop_vars, args))
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if not nest.is_nested(outputs):
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outputs = [outputs]
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try:
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# The legacy while_loop considers list and tuple to be the same
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# structure.
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nest.assert_same_structure(outputs, orig_loop_vars, check_types=False,
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expand_composites=True)
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except ValueError:
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# Traditionally we consider variables and tensors to be the same
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# structure.
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vars1 = variable_utils.convert_variables_to_tensors(outputs)
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vars2 = variable_utils.convert_variables_to_tensors(orig_loop_vars)
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nest.assert_same_structure(vars1, vars2, check_types=False,
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expand_composites=True)
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outputs = _tensor_array_to_flow(outputs)
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# TODO(srbs): Update lowering code to create _Enter nodes with
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# is_constant=True for inputs that are directly passed to outputs.
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return [loop_counter + 1, maximum_iterations_arg] + list(outputs)
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body_graph = func_graph_module.func_graph_from_py_func(
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body_name,
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wrapped_body,
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[], # We provide signature instead of args.
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{},
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signature=func_graph_signature,
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func_graph=util.WhileBodyFuncGraph(
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body_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
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add_control_dependencies=add_control_dependencies)
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# Add external captures of body to the list of loop vars.
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# Note that external tensors will be treated as loop invariants, i.e.,
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# the value of that tensor in each iteration is the same as it was at the
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# beginning of the loop execution.
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deferred_external_captures = nest.flatten(
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[c() for c in body_graph.deferred_external_captures],
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expand_composites=True)
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loop_vars = (
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loop_vars + body_graph.external_captures + deferred_external_captures)
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# TODO(srbs): Update lowering code to create _Enter nodes with
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# is_constant=True for inputs that are directly passed to outputs.
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body_graph.outputs.extend(body_graph.internal_captures)
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body_graph.outputs.extend(body_graph.deferred_internal_captures)
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# Capture the extra `external_captures` of `body_graph` in `cond_graph` so
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# that it expects to receive those as arguments.
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with cond_graph.as_default():
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num_cond_captures = len(cond_graph.external_captures)
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assert (cond_graph.external_captures ==
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body_graph.external_captures[:num_cond_captures])
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_duplicate_body_captures_in_cond(
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cond_graph, body_graph.external_captures[num_cond_captures:] +
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deferred_external_captures)
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# Make sure that the shapes of the loop outputs are compatible with the
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# shape invariants, or the shapes of the loop vars if the invariants are not
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# specified.
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num_flattened_outputs = len(nest.flatten(orig_loop_vars,
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expand_composites=True))
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# First var is loop counter and second var is maximum_iterations.
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first_loop_var_index = 2
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_check_shapes_compat(
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body_graph.outputs[first_loop_var_index:first_loop_var_index +
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num_flattened_outputs],
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flat_shape_invariants,
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nest.flatten(loop_vars[first_loop_var_index:first_loop_var_index +
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len_orig_loop_vars], expand_composites=True))
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num_original_outputs = len(body_graph.outputs)
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if back_prop and util.output_all_intermediates():
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# Export all tensors in the loop body that may be needed for gradient
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# computation. We do this by accumulating the intermediate values in
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# TensorLists.
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intermediate_tensors = _get_intermediates(body_graph)
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for intermediate_tensor in intermediate_tensors:
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tensor_list = list_ops.empty_tensor_list(
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element_dtype=intermediate_tensor.dtype,
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element_shape=intermediate_tensor.shape,
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max_num_elements=maximum_iterations)
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loop_vars.append(tensor_list)
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with cond_graph.as_default():
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# Add a placeholder to cond_graph's inputs corresponding to the
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# tensor_list.
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cond_graph.capture(tensor_list)
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with body_graph.as_default():
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# Push the intermediate tensor to the tensor list. This captures the
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# `tensor_list` as well.
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appended_tensor_list = list_ops.tensor_list_push_back(
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tensor_list, intermediate_tensor)
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# Add this modified tensor list to the list of outputs.
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body_graph.outputs.append(appended_tensor_list)
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flattened_loop_vars = nest.flatten(loop_vars, expand_composites=True)
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_check_num_inputs_outputs(cond_graph, body_graph,
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len(flattened_loop_vars))
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_check_inputs_outputs_types_match(body_graph, flattened_loop_vars)
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with ops.control_dependencies(
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list(cond_graph.function_captures.control) + list(
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body_graph.function_captures.control)):
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output_shapes = [t.shape for t in body_graph.outputs]
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orig_loop_vars_range = slice(first_loop_var_index,
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first_loop_var_index + num_flattened_outputs)
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output_shapes[orig_loop_vars_range] = flat_shape_invariants
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outputs = _build_while_op(
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flattened_loop_vars,
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cond_graph,
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body_graph,
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output_shapes=output_shapes,
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parallel_iterations=parallel_iterations,
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name=scope,
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num_original_outputs=num_original_outputs)
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if not ops.get_default_graph().building_function:
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# In V1 graph mode, return identities for each output of the While op,
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# rather than the output of the While op directly. This makes pruning work
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# if the output of while_loop() is fetched: the lowering pass converts the
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# While outputs into IdentityN outputs, which if fetched will cause all
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# ops in the body to be run (since it takes all exit ops as input). After
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# lowering, each output identity op will end up with only the appropriate
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# exit op as input.
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outputs = tuple(array_ops.identity(t) for t in outputs)
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output_loop_vars = outputs[first_loop_var_index:first_loop_var_index +
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num_flattened_outputs]
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if not back_prop:
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output_loop_vars = [array_ops.stop_gradient(t) for t in output_loop_vars]
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outputs = _pack_sequence_as(
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loop_vars_signature, flat_orig_loop_vars, output_loop_vars)
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if return_same_structure:
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return outputs
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flattened_outputs = nest.flatten(outputs, expand_composites=True)
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if len(flattened_outputs) == 1:
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return flattened_outputs[0]
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else:
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return outputs
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@ops.RegisterGradient("StatelessWhile")
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@ops.RegisterGradient("While")
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def _WhileGrad(op: ops.Operation, *grads): # pylint: disable=invalid-name
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"""The gradient of a While op produced by while_loop."""
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# Note that op is not always the same as while_op because the gradient tape,
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# for eager mode compatibility, forgets information about the proper op. Since
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# the loop cannot run in eager mode, however, we can safely introspect into
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# the graph here.
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while_op = op.outputs[0].op
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cond_graph = _get_graph(while_op, "cond", "_cond_graph")
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body_graph = _get_graph(while_op, "body", "_body_graph")
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orig_num_params = len(body_graph.outputs)
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maximum_iterations = op.inputs[1]
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parallel_iterations = op.get_attr("parallel_iterations")
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try:
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num_original_outputs = while_op.get_attr("_num_original_outputs")
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except: # pylint: disable=bare-except
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num_original_outputs = len(while_op.outputs)
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num_intermediates = len(while_op.outputs) - num_original_outputs
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grads = [
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_preprocess_grad(grad, body_out, while_in, while_out) # pylint: disable=g-complex-comprehension
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for grad, body_out, while_in, while_out in zip(
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grads[:num_original_outputs],
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body_graph.outputs[:num_original_outputs],
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while_op.inputs[:num_original_outputs],
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while_op.outputs[:num_original_outputs])
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] + [None] * num_intermediates
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# Skip gradients with respect to the captures whenever possible.
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if getattr(op, "skip_input_indices", None) is not None:
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captures_start_index = (
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len(body_graph.inputs) - len(body_graph.internal_captures))
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for i in op.skip_input_indices:
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if i >= captures_start_index:
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grads[i] = None
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# We compute the gradient for the sub-graph between trainable ys and xs
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# with non-None incoming gradients. We later pad the None's to the list of
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# outputs.
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ys, xs, non_none_grads = zip(*[(y, x, grad) for (y, x, grad) in zip(
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body_graph.outputs, body_graph.inputs, grads) if grad is not None])
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body_grad_graph, args = _create_grad_func(
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ys, xs, non_none_grads, cond_graph, body_graph,
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util.unique_grad_fn_name(body_graph.name), op, maximum_iterations)
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if body_grad_graph.while_op_needs_rewrite:
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# Modify 'op' to output the intermediate accumulators needed by the grad
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# function.
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# NOTE(skyewm): if there are any active sessions, this modification to `op`
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# may make them unrunnable!
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cond_graph.name += "_rewritten"
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body_graph.name += "_rewritten"
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# `body_grad_graph.extra_inputs` here is equivalent to skimming off the new
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# `body_graph.external_captures` added during `_create_grad_func`.
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new_inputs = body_grad_graph.extra_inputs
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new_outputs = body_graph.outputs[orig_num_params:]
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while_op._set_func_attr("cond", util.create_new_tf_function(cond_graph))
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while_op._set_func_attr("body", util.create_new_tf_function(body_graph))
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if len(body_graph.output_types) != len(while_op.inputs) + len(new_inputs):
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# Continuing leads to an invalid graph with disconnected inputs.
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raise AssertionError(
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"Inputs and outputs constructed for the forward op of a While "
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"gradient don't match with 'output_types' at "
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f"{len(body_graph.output_types)},'inputs' at length "
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f"{len(while_op.inputs)}, and 'new_inputs' at length "
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f"{len(new_inputs)}. This doesn't make sense, please file a bug.")
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while_op._set_type_list_attr("T", body_graph.output_types)
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while_op._set_shape_list_attr("output_shapes", body_graph.output_shapes)
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while_op._add_while_inputs(new_inputs)
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while_op._add_outputs([t.dtype for t in new_outputs],
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[t.shape for t in new_outputs])
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_copy_handle_data(new_outputs, while_op.outputs[orig_num_params:])
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# Do not ignore grads wrt extra outputs when computing higher order
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# derivatives.
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while_op._set_attr("_num_original_outputs",
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attr_value_pb2.AttrValue(i=len(while_op.outputs)))
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captured_inputs = _resolve_grad_captures(body_graph, body_grad_graph,
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while_op)
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loop_vars = args + captured_inputs
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# This modifies body_grad_graph.
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loop_vars = while_v2_indexed_slices_rewriter.rewrite_grad_indexed_slices(
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grads, body_grad_graph, loop_vars, while_op.inputs)
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def grad_cond(counter, unused_maximum_iterations_arg, forward_loop_iters,
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*unused_args):
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return counter < forward_loop_iters
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grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name)
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cond_grad_graph = func_graph_module.func_graph_from_py_func(
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grad_cond_name, grad_cond, loop_vars, {},
|
|
func_graph=util.WhileCondFuncGraph(grad_cond_name))
|
|
|
|
_check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars))
|
|
|
|
outputs = _build_while_op(
|
|
loop_vars,
|
|
cond_grad_graph,
|
|
body_grad_graph,
|
|
output_shapes=[t.shape for t in body_grad_graph.outputs],
|
|
parallel_iterations=parallel_iterations,
|
|
name="%s_grad" % while_op.name,
|
|
num_original_outputs=len(body_grad_graph.outputs))
|
|
|
|
# See comment in while_loop.
|
|
outputs = [array_ops.identity(t) for t in outputs]
|
|
return _get_structured_grad_output(outputs, grads, body_grad_graph)
|
|
|
|
|
|
def _build_while_op(loop_vars, cond_graph, body_graph, output_shapes,
|
|
parallel_iterations, name, num_original_outputs):
|
|
"""Builds the functional StatelessWhile/While op."""
|
|
cond_stateful_ops = [
|
|
op for op in cond_graph.get_operations() if op._is_stateful
|
|
]
|
|
body_stateful_ops = [
|
|
op for op in body_graph.get_operations() if op._is_stateful
|
|
]
|
|
if (cond_stateful_ops or body_stateful_ops):
|
|
op_fn = gen_functional_ops._while
|
|
else:
|
|
op_fn = gen_functional_ops.stateless_while
|
|
|
|
def _make_op(inputs):
|
|
while_op, tensors = util.get_op_and_outputs(op_fn(
|
|
inputs,
|
|
util.create_new_tf_function(cond_graph),
|
|
util.create_new_tf_function(body_graph),
|
|
output_shapes=output_shapes,
|
|
parallel_iterations=parallel_iterations,
|
|
name=name))
|
|
_copy_handle_data(body_graph.outputs, tensors)
|
|
util.maybe_set_lowering_attr(while_op)
|
|
util.maybe_propagate_compile_time_consts_in_xla(while_op)
|
|
_set_read_only_resource_inputs_attr(while_op, [cond_graph, body_graph])
|
|
# This is needed so we do not compute derivative wrt these extra outputs.
|
|
while_op._set_attr("_num_original_outputs",
|
|
attr_value_pb2.AttrValue(i=num_original_outputs))
|
|
# The while op may be created inside a tf.function, in which case ops
|
|
# needs to capture "through" it when taking gradients; outer_graph is used
|
|
# as a sanity check that capturing only happens from parent to child.
|
|
cond_graph.outer_graph = ops.get_default_graph()
|
|
body_graph.outer_graph = ops.get_default_graph()
|
|
while_op._cond_graph = cond_graph
|
|
while_op._body_graph = body_graph
|
|
return tensors
|
|
return util.run_as_function_for_tape_gradients(_make_op, loop_vars)
|
|
|
|
|
|
def _get_intermediates(func_graph):
|
|
"""Returns all tensors in `func_graph` that should be accumulated."""
|
|
# We currently accumulate output tensors of most ops in the function and rely
|
|
# on the pruning pass to get rid of the unused accumulators at runtime.
|
|
# However, this can bloat the GraphDef and make debugging harder so we perform
|
|
# some optimizations.
|
|
#
|
|
# Optimization we currently perform:
|
|
# 1. We do not accumulate tensors which already have an accumulator
|
|
# in the loop body.
|
|
# 2. We do not accumulate outputs of Identity nodes. When building the
|
|
# FuncGraph, we add an Identity node for each output (see
|
|
# `AutomaticControlDependencies.mark_as_return`). Accumulating outputs
|
|
# of all these nodes bloats the GraphDef quite a bit so we remove those.
|
|
# Since the gradient of an Identity node does not rely on its forward op's
|
|
# input this is safe to do.
|
|
#
|
|
# Other possible optimizations:
|
|
# 1. Only accumulate tensors that will be required by the backward pass.
|
|
# This will require running the gradient pass and hence would increase the
|
|
# graph building time for the forward pass.
|
|
# 2. Do not accumulate Const nodes created inside the loop body.
|
|
# 3. Do not accumulate loop vars that are returned as-is just like captured
|
|
# tensors.
|
|
intermediates = []
|
|
reverse_captures = dict((v.ref(), k) for k, v in func_graph.captures)
|
|
|
|
for op in func_graph.get_operations():
|
|
if op.type == "Identity":
|
|
continue
|
|
# Accumulating mutexes can cause deadlock.
|
|
if op.type == "MutexLock":
|
|
continue
|
|
for o in op.outputs:
|
|
if (o is not func_graph.inputs[0] and # Loop counter.
|
|
o.dtype != dtypes.resource and # Do not accumulate resource tensors.
|
|
_get_accumulator(o) is None and # Has existing accumulator.
|
|
o.ref() not in reverse_captures
|
|
): # Captured value, hence loop invariant.
|
|
intermediates.append(o)
|
|
return intermediates
|
|
|
|
|
|
def _preprocess_grad(grad, body_graph_output, while_op_input, while_op_output):
|
|
"""Returns the initial gradient to be used for a given output tensor.
|
|
|
|
Args:
|
|
grad: the original gradient Tensor passed to the gradient function.
|
|
body_graph_output: the corresponding Tensor in the body graph.
|
|
while_op_input: the corresponding Tensor input of the While op.
|
|
while_op_output: the corresponding Tensor output of the While op.
|
|
|
|
Returns:
|
|
A Tensor or None.
|
|
"""
|
|
# Set the incoming gradient of non-trainable inputs to None. It is possible
|
|
# that we receive non-None gradients for non-trainable types in nested while
|
|
# loops because we accumulate outputs of the inner while as variant tensors
|
|
# which are trainable and hence receive zeros_like tensors in the gradient
|
|
# pass. The non-trainable tensors then receive the popped zeros tensor from
|
|
# this zeros variant. The gradient for the loop vars corresponding to these
|
|
# tensors is None or zeros (this happens only if the loop var is accumulated
|
|
# as well) in _grad_fn so we reset these.
|
|
# TODO(b/118712257): Remove once we can handle None output grads in _grad_fn.
|
|
if not _is_trainable(body_graph_output):
|
|
return None
|
|
|
|
# GradientTape initializes resource and variant grads as None instead of
|
|
# zeros. Set to zeros so _GradientsHelper computes the gradients instead of
|
|
# returning None.
|
|
# TODO(b/143286622): The supports_default_grad check is needed
|
|
# because While op emits non-differentiable resource tensors
|
|
# as outputs. Remove this check when that is not the case.
|
|
# Note: We use `while_op_input` instead of `while_op_output` for the call
|
|
# to `supports_default_grad` because `while_op_output` may be missing
|
|
# handle_data if the While is in a restored saved model.
|
|
if (while_op_output.dtype in (dtypes.resource, dtypes.variant) and
|
|
default_gradient.supports_default_grad(while_op_input) and grad is None):
|
|
return _zeros_like(while_op_input, while_op_output)
|
|
|
|
# Convert IndexedSlices to dense tensors since it is unlikely that downstream
|
|
# gradient functions with properly handle indexed slices. This is similar to
|
|
# what we do in tf.function gradients.
|
|
if isinstance(grad, indexed_slices.IndexedSlices):
|
|
return ops.convert_to_tensor(grad)
|
|
|
|
return grad
|
|
|
|
|
|
# TODO(skyewm): make this return constants if op_output's shape is fully
|
|
# defined (this can be done by checking the "shape" attr of resource vars).
|
|
def _zeros_like(op_input, op_output):
|
|
"""Like array_ops.zeros_like() but also accepts resource var handles."""
|
|
if op_output.dtype == dtypes.resource:
|
|
# Note: We use `op_input` instead of `op_output` to get the zeros dtype
|
|
# because `op_output` may be missing handle_data if the While is in a
|
|
# restored saved model.
|
|
return array_ops.zeros(
|
|
gen_resource_variable_ops.variable_shape(op_output),
|
|
dtype=default_gradient.get_zeros_dtype(op_input))
|
|
return array_ops.zeros_like(op_output)
|
|
|
|
|
|
def _is_trainable(tensor):
|
|
"""Returns whether the given tensor is trainable."""
|
|
if not backprop_util.IsTrainable(tensor):
|
|
return False
|
|
|
|
# Special case: untrainable accumulator output. The gradients algorithm
|
|
# doesn't know about tensor lists of untrainable elements. In theory the
|
|
# tensor list gradient functions should return None as appropriate, but
|
|
# because we can't return None from the gradient function we filter out
|
|
# untrainable accumulator output here to avoid computing the gradient at all.
|
|
if tensor.op.type == "TensorListPopBack" and tensor.value_index == 0:
|
|
assert tensor.dtype == dtypes.variant
|
|
element_type = tensor.op.get_attr("element_dtype")
|
|
return backprop_util.IsTrainable(element_type)
|
|
|
|
return True
|
|
|
|
|
|
def _get_graph(while_op, func_attr_name, attr_graph_name):
|
|
"""Returns `FuncGraph` for the given function attribute.
|
|
|
|
Args:
|
|
while_op: The While Operation.
|
|
func_attr_name: string
|
|
attr_graph_name: cached forward graph name
|
|
|
|
Returns:
|
|
`FuncGraph`
|
|
"""
|
|
func_graph = getattr(while_op, attr_graph_name, None)
|
|
if func_graph is None:
|
|
# TODO(srbs): Handle TensorShapeProto in function_def_to_graph.input_shapes.
|
|
input_shapes = [
|
|
tensor_shape.TensorShape(s) for s in while_op.get_attr("output_shapes")
|
|
]
|
|
func_name = while_op.get_attr(func_attr_name).name
|
|
func_graph = util.get_func_graph(while_op, input_shapes, func_name)
|
|
func_graph._while = while_op
|
|
return func_graph
|
|
|
|
|
|
def _create_grad_func(ys, xs, grads, cond_graph, body_graph, name, while_op,
|
|
maximum_iterations):
|
|
"""Builds and returns the gradient FuncGraph of `func_graph` and its args.
|
|
|
|
The returned grad_func_graph must be called with the returned
|
|
args + grad_func_graph.captures.
|
|
|
|
Args:
|
|
ys: A `Tensor` or list of tensors to be differentiated.
|
|
xs: A `Tensor` or list of tensors to be used for differentiation.
|
|
grads: The incoming grads for `ys`.
|
|
cond_graph: FuncGraph for the forward cond function.
|
|
body_graph: FuncGraph for the forward body function.
|
|
name: Name of the returned gradient function.
|
|
while_op: The forward While op.
|
|
maximum_iterations: Tensor. The maximum number of iterations.
|
|
|
|
Returns:
|
|
2-tuple of (grad_func_graph, args).
|
|
"""
|
|
assert len(ys) == len(grads)
|
|
|
|
total_iters = while_op.outputs[0]
|
|
counter = constant_op.constant(
|
|
0, dtype=total_iters.dtype, name="grad_counter")
|
|
|
|
# Build frozen sets so that we do not have linear time lookups in
|
|
# `_is_loop_invariant`. Note: `body_graph.inputs` and `body_graph.outputs`
|
|
# may get updated during gradient computation because we add accumulators to
|
|
# the forward op. However, those are not loop invariants so wouldn't affect
|
|
# the output of `_is_loop_invariant`. Also we would never attempt to capture
|
|
# those accumulators so `_is_loop_invariant` should never receive those new
|
|
# tensors as args.
|
|
body_graph_inputs = object_identity.ObjectIdentitySet(body_graph.inputs)
|
|
body_graph_outputs = object_identity.ObjectIdentitySet(body_graph.outputs)
|
|
|
|
args = [counter, maximum_iterations, total_iters] + list(grads)
|
|
# Note: The returned function does not have `args` in the list of
|
|
# `external_captures`.
|
|
grad_func_graph = func_graph_module.func_graph_from_py_func(
|
|
name,
|
|
lambda *args: _grad_fn(ys, xs, args, body_graph),
|
|
args, {},
|
|
func_graph=_WhileBodyGradFuncGraph(name, cond_graph, body_graph,
|
|
maximum_iterations, while_op,
|
|
body_graph_inputs, body_graph_outputs))
|
|
|
|
# Update the list of outputs with tensors corresponding to the captured
|
|
# tensors. We capture 3 types of tensors when building the grad fn:
|
|
# 1. Accumulators for forward graph intermediates which are not loop
|
|
# invariants. The outputs corresponding to these are populated in
|
|
# `internal_capture_to_output` by `_WhileBodyGradFuncGraph`.
|
|
# 2. Resources, which are output as is.
|
|
# 3. Forward graph loop invariants, which are output as is.
|
|
for external_capture, internal_capture in grad_func_graph.captures:
|
|
if (ops.tensor_id(internal_capture)
|
|
in grad_func_graph.internal_capture_to_output):
|
|
new_output = grad_func_graph.internal_capture_to_output[ops.tensor_id(
|
|
internal_capture)]
|
|
else:
|
|
raise ValueError(
|
|
f"Tensor {str(internal_capture)} which captures "
|
|
f"{str(external_capture)} is in list of "
|
|
f"internal_captures but not in internal_capture_to_output.")
|
|
grad_func_graph.outputs.append(new_output)
|
|
grad_func_graph.structured_outputs.append(new_output)
|
|
|
|
return grad_func_graph, args
|
|
|
|
|
|
def _grad_fn(ys, xs, args, func_graph):
|
|
"""Computes the gradient of `func_graph` in the current graph.
|
|
|
|
This function builds the gradient graph of the corresponding forward-pass
|
|
`func_graph` by differentiating `func_graph`'s outputs w.r.t. its inputs.
|
|
|
|
Args:
|
|
ys: A `Tensor` or list of tensors to be differentiated.
|
|
xs: A `Tensor` or list of tensors to be used for differentiation.
|
|
args: The input arguments.
|
|
args[0] - Loop counter
|
|
args[1] - Total number of iterations.
|
|
args[2] - maximum_iterations.
|
|
args[3:] - Incoming gradients for `ys`.
|
|
func_graph: function.FuncGraph. The corresponding forward-pass function.
|
|
|
|
Returns:
|
|
The output gradient Tensors.
|
|
"""
|
|
grad_ys = args[3:]
|
|
|
|
# Build the gradient graph. Note that this builds the gradient computation of
|
|
# func_graph in the current graph, which requires capturing tensors from
|
|
# func_graph. The captured func_graph tensors are resolved to external tensors
|
|
# after the forward While op has been rewritten in _resolve_grad_captures.
|
|
# TODO(srbs): Mark GradientsHelper as public?
|
|
grad_outs = gradients_util._GradientsHelper(
|
|
ys, xs, grad_ys=grad_ys, src_graph=func_graph,
|
|
unconnected_gradients="zero")
|
|
|
|
# TODO(b/118712257): Handle the case when grad_outs has None's e.g. when there
|
|
# is a tf.StopGradient in the loop body.
|
|
assert all(g is not None for g in grad_outs)
|
|
counter = args[0]
|
|
maximum_iterations = args[1]
|
|
total_iters = args[2]
|
|
return [counter + 1, maximum_iterations, total_iters] + grad_outs
|
|
|
|
|
|
def _resolve_grad_captures(body_graph, body_grad_graph, while_op):
|
|
"""Returns the tensors to pass as captured inputs to `body_grad_graph`.
|
|
|
|
`body_grad_graph` may have external references to:
|
|
1. Its outer graph containing the input gradients. These are left as-is.
|
|
2. Accumulators captured from the forward-pass graph. These should have been
|
|
added as `while_op` outputs after the gradient graph was built. We replace
|
|
these with the corresponding output of `while_op`, i.e. a tensor in
|
|
`body_graph.outer_graph`. In the case of nested control flow or functions,
|
|
the gradient logic handling `body_grad_graph.outer_graph` will make sure
|
|
the tensor from `body_graph.outer_graph` is also correctly captured.
|
|
|
|
Args:
|
|
body_graph: FuncGraph. The forward-pass body function.
|
|
body_grad_graph: FuncGraph. The body gradients function.
|
|
while_op: The forward-pass While Operation calling `body_graph`.
|
|
|
|
Returns:
|
|
A list of input tensors to be passed as the captured inputs to
|
|
`body_grad_graph`.
|
|
"""
|
|
new_capture_inputs = []
|
|
for t in body_grad_graph.external_captures:
|
|
# Resolve tensors captured from the forward graph to the outputs of the
|
|
# forward while_op.
|
|
if t.graph == body_graph:
|
|
# Captured accumulator or loop invariant.
|
|
for i, output in enumerate(t.graph.outputs):
|
|
if output is t:
|
|
t = while_op.outputs[i]
|
|
break
|
|
|
|
# Note: We rely on the capturing logic of the gradient While op graph to
|
|
# correctly capture the tensors in `body_graph.outer_graph`. Both cond_v2
|
|
# and while_v2 handle this while building their gradient functions.
|
|
assert t.graph == body_graph.outer_graph
|
|
|
|
new_capture_inputs.append(t)
|
|
return new_capture_inputs
|
|
|
|
|
|
def _get_structured_grad_output(outputs, grads, body_grad_graph):
|
|
"""Returns the values that should be returned from the while grad function.
|
|
|
|
Args:
|
|
outputs: the raw Tensor outputs of the grad While op.
|
|
grads: the input gradients to the gradient function.
|
|
body_grad_graph: _WhileBodyGradFuncGraph.
|
|
|
|
Returns:
|
|
A list of gradient values. May include Nones.
|
|
"""
|
|
result = []
|
|
# outputs[0] is the loop counter.
|
|
# outputs[1] is maximum_iterations.
|
|
# outputs[2] is the total number of loop iterations.
|
|
outputs_idx = 3
|
|
structured_outputs_idx = 3
|
|
for g in grads:
|
|
# Set None as the output gradient for tensors with None input gradient.
|
|
if g is None:
|
|
result.append(None)
|
|
continue
|
|
output = body_grad_graph.structured_outputs[structured_outputs_idx]
|
|
structured_outputs_idx += 1
|
|
if isinstance(output, indexed_slices.IndexedSlices):
|
|
# TODO(skyewm): is there a more robust way to determine the order of
|
|
# flattened IndexedSlices components?
|
|
result.append(indexed_slices.IndexedSlices(
|
|
values=outputs[outputs_idx],
|
|
indices=outputs[outputs_idx + 1],
|
|
dense_shape=outputs[outputs_idx + 2]))
|
|
outputs_idx += 3
|
|
else:
|
|
assert isinstance(output, tensor_lib.Tensor)
|
|
result.append(outputs[outputs_idx])
|
|
outputs_idx += 1
|
|
|
|
return result
|
|
|
|
|
|
def _get_accumulator(tensor):
|
|
r"""Returns TensorList if any containing accumulated values of tensor.
|
|
|
|
We try to find a pattern of the form:
|
|
|
|
input_tl tensor
|
|
\ /
|
|
(TensorListPushBack)
|
|
|
|
|
output_tl
|
|
|
|
which satisfies the following conditions:
|
|
|
|
1. input_tl must be in tensor.graph.inputs.
|
|
2. output_tl or Identity(output_tl) must be in tensor.graph.outputs.
|
|
3. tensor.graph.input_index(input_tl) == tensor.graph.output_index(output_t).
|
|
|
|
output_tl or Identity(output_tl) (whichever is in tensor.graph.outputs) is
|
|
returned if such a pattern is found else None is returned.
|
|
|
|
Args:
|
|
tensor: The Tensor to be accumulated.
|
|
|
|
Returns:
|
|
A variant tensor in the same graph as `tensor` or None if no accumulator is
|
|
found.
|
|
"""
|
|
assert isinstance(tensor.graph, func_graph_module.FuncGraph)
|
|
|
|
def get_func_graph_output(t):
|
|
"""Returns t or Identity(t) whichever exists in graph outputs else None."""
|
|
for output in tensor.graph.outputs:
|
|
if output is t:
|
|
return t
|
|
# tf.defun adds an Identity for each output, check whether that is the case.
|
|
identity_op = t.consumers()[0]
|
|
if (identity_op.type == "Identity" and
|
|
any(identity_op.outputs[0] is t for t in tensor.graph.outputs)):
|
|
return identity_op.outputs[0]
|
|
return None
|
|
|
|
for consumer in tensor.consumers():
|
|
# Find the consumer that is a TensorListPushBack node whose TensorList input
|
|
# is in the list of function inputs.
|
|
if consumer.type != "TensorListPushBack":
|
|
continue
|
|
|
|
accum_input_idx = -1
|
|
for accum_input_idx, inp in enumerate(tensor.graph.inputs):
|
|
if inp is consumer.inputs[0]:
|
|
break
|
|
else:
|
|
continue
|
|
|
|
output = get_func_graph_output(consumer.outputs[0])
|
|
if output is None:
|
|
# The TensorList output of `consumer` is not in the list of function
|
|
# outputs.
|
|
continue
|
|
|
|
for accum_output_idx, out in enumerate(tensor.graph.outputs):
|
|
if out is output:
|
|
if accum_input_idx == accum_output_idx:
|
|
return output
|
|
break
|
|
|
|
return None
|
|
|
|
|
|
OptimizedReductionOpsCacheKey = collections.namedtuple(
|
|
"OptimizedReductionOpsCacheKey", [
|
|
"op_type",
|
|
"inputs",
|
|
"dtypes",
|
|
"input_types",
|
|
"name",
|
|
"attrs",
|
|
"op_def",
|
|
"compute_device",
|
|
])
|
|
|
|
|
|
class _WhileBodyGradFuncGraph(util.WhileBodyFuncGraph):
|
|
"""FuncGraph for the gradient function of the body of a While op.
|
|
|
|
Contains the logic for capturing the tensors from the body of the forward
|
|
While op which is as follows:
|
|
1. If the tensor is of resource type (these are not accumulated):
|
|
a. Ensure that the tensor is a loop invariant, i.e., it exists in both loop
|
|
inputs and outputs at the same index.
|
|
b. Lookup the corresponding resource tensor in the forward outer graph and
|
|
try to capture that.
|
|
2. If the tensor is not of resource type:
|
|
a. Create an accumulator for that tensor and output it from the forward
|
|
pass. Note this also requires adding it as an input to the forward pass.
|
|
b. Capture the accumulator from the forward pass in this FuncGraph. This
|
|
will later be resolved to the correct output of the forward While op.
|
|
c. Pop a value from the captured placeholder and use it as the captured
|
|
value for the forward pass tensor.
|
|
|
|
This only allows capturing tensors in the forward graph. A ValueError is
|
|
raised if an attempt is made to capture a tensor not in the forward graph.
|
|
To manually capture a tensor that is not in the forward graph, call `capture`
|
|
with `allowlisted=True`.
|
|
|
|
Note: The `captures` dict does not contain the forward tensor since it is not
|
|
directly captured. It contains the accumulator corresponding to this forward
|
|
tensor.
|
|
|
|
Attributes:
|
|
while_op_needs_rewrite: True if any non-resource intermediates were
|
|
captured, meaning the forward While op needs to be rewritten to output the
|
|
corresponding accumulators.
|
|
extra_inputs: list of EmptyTensorList tensors to be used as initial input to
|
|
the new accumulators in the forward graph. It may also contain external
|
|
captures of the custom gradient function.
|
|
internal_capture_to_output: dict from a tensor_id(captured placeholder) to
|
|
the corresponding tensor that needs to be added to the list of outputs.
|
|
For instance, when capturing an accumulator TensorList this contains the
|
|
TensorList obtained after popping a tensor from the list. Other entries
|
|
in this dict are expected, though not enforced, to be identities.
|
|
This dict is needed because these output tensors need to be added to
|
|
FuncGraph.outputs "after" the tensors returned from the gradient function.
|
|
"""
|
|
|
|
def __init__(self, name, forward_cond_graph, forward_body_graph,
|
|
maximum_iterations, forward_while_op, body_graph_inputs,
|
|
body_graph_outputs):
|
|
super(_WhileBodyGradFuncGraph, self).__init__(name)
|
|
self.extra_inputs = []
|
|
self.internal_capture_to_output = {}
|
|
# FuncGraph for the body of the forward While op.
|
|
self._forward_graph = forward_body_graph
|
|
# FuncGraph for the cond of the forward While op.
|
|
self._forward_cond_graph = forward_cond_graph
|
|
self._maximum_iterations = maximum_iterations
|
|
self._forward_while_op = forward_while_op
|
|
# Dict from forward intermediate tensor to its indirectly captured tensor
|
|
# in this graph. Indirect capturing happens in two ways:
|
|
# 1. For non-resource tensors we capture their accumulators from the forward
|
|
# outer graph and pop values from that accumulator inside this graph
|
|
# using TensorListPopBack.
|
|
# 2. For resource tensors we directly capture their corresponding tensor
|
|
# in the forward outer graph.
|
|
self._indirect_captures = {}
|
|
|
|
@property
|
|
def while_op_needs_rewrite(self):
|
|
return self.extra_inputs
|
|
|
|
def _create_op_internal(
|
|
self,
|
|
op_type,
|
|
inputs,
|
|
dtypes=None, # pylint: disable=redefined-outer-name
|
|
input_types=None,
|
|
name=None,
|
|
attrs=None,
|
|
op_def=None,
|
|
compute_device=True):
|
|
# For a reduction op, if op is in the gradient body graph and its input is
|
|
# from the forward graph, moving op to the forward graph means we would
|
|
# store the tensor after the reduction as opposed to the tensor before
|
|
# reduction, and therefore could significantly reduce memory consumption.
|
|
# For now, we do this only for a few ops.
|
|
#
|
|
# We don't do this if any input tensor has already been accumulated. This
|
|
# can happen if we output all intermediates in the forward pass.
|
|
#
|
|
# If in XLA context, do not move constant ops to forward pass as pushing to
|
|
# and popping from a TensorList removes the constant property of an op and
|
|
# breaks XLA compilation, which requires certain inputs to be compile-time
|
|
# constant for certain ops.
|
|
#
|
|
# This optimization is currently also disabled when under a persistent tape,
|
|
# since it leads to an unbounded number of side outputs. With caching it may
|
|
# be possible to re-enable it.
|
|
optimized_reduction_ops = {
|
|
"Shape", "Size", "Rank", "TensorListElementShape", "TensorListLength"
|
|
}
|
|
if (op_type in optimized_reduction_ops and
|
|
not util.output_all_intermediates() and
|
|
all(input.graph is self._forward_graph for input in inputs) and
|
|
all(_get_accumulator(input) is None for input in inputs) and
|
|
not util_v1.GraphOrParentsInXlaContext(self._forward_graph) and
|
|
not util.graph_wrapped_for_higher_order_tape_gradients(
|
|
self._forward_graph)):
|
|
return self._move_op_to_forward_graph(
|
|
op_type,
|
|
inputs,
|
|
dtypes=dtypes,
|
|
input_types=input_types,
|
|
name=name,
|
|
attrs=attrs,
|
|
op_def=op_def,
|
|
compute_device=compute_device)
|
|
|
|
return super(_WhileBodyGradFuncGraph, self)._create_op_internal(
|
|
op_type,
|
|
inputs,
|
|
dtypes=dtypes,
|
|
input_types=input_types,
|
|
name=name,
|
|
attrs=attrs,
|
|
op_def=op_def,
|
|
compute_device=compute_device)
|
|
|
|
def _move_op_to_forward_graph(
|
|
self,
|
|
op_type,
|
|
inputs,
|
|
dtypes=None, # pylint: disable=redefined-outer-name
|
|
input_types=None,
|
|
name=None,
|
|
attrs=None,
|
|
op_def=None,
|
|
compute_device=True):
|
|
# We have a cache of reduction ops that have already been moved to the
|
|
# forward graph, and we will check it first to avoid moving an op twice.
|
|
if not hasattr(self._forward_graph, "_optimized_reduction_ops_cache"):
|
|
self._forward_graph._optimized_reduction_ops_cache = {}
|
|
cache_key = self._get_optimized_reduction_ops_cache_key(
|
|
op_type, inputs, dtypes, input_types, name, attrs, op_def,
|
|
compute_device)
|
|
cached_op = self._forward_graph._optimized_reduction_ops_cache.get(
|
|
cache_key)
|
|
if cached_op is not None:
|
|
# This op has already been moved to the forward graph and we have it in
|
|
# the cache.
|
|
return cached_op
|
|
|
|
with self._forward_graph.as_default():
|
|
# `name` was built using name_scope stack of gradient graph and may not
|
|
# be unique in the forward graph. `Graph.create_op` does not uniquify
|
|
# names which are name scopes i.e. end in `/`. To ensure that the op
|
|
# created gets a unique name in the forward graph we get rid of the
|
|
# trailing slash.
|
|
name = ops.name_from_scope_name(name)
|
|
result = self._forward_graph._create_op_internal(
|
|
op_type,
|
|
inputs,
|
|
dtypes=dtypes,
|
|
input_types=input_types,
|
|
name=name,
|
|
attrs=attrs,
|
|
op_def=op_def,
|
|
compute_device=compute_device)
|
|
|
|
# Store the op we just moved to the forward graph so that it does
|
|
# not need to be added there again.
|
|
self._forward_graph._optimized_reduction_ops_cache[cache_key] = result
|
|
return result
|
|
|
|
def _get_optimized_reduction_ops_cache_key(
|
|
self,
|
|
op_type,
|
|
inputs,
|
|
dtypes=None, # pylint: disable=redefined-outer-name
|
|
input_types=None,
|
|
name=None,
|
|
attrs=None,
|
|
op_def=None,
|
|
compute_device=True):
|
|
# We need all elements of CacheKey to be hashable.
|
|
inputs = tuple(map(lambda t: t.ref(), inputs))
|
|
|
|
if dtypes is not None:
|
|
dtypes = tuple(dtypes)
|
|
|
|
if input_types is not None:
|
|
input_types = tuple(input_types)
|
|
|
|
if attrs is not None:
|
|
hashable_attrs = []
|
|
for attr_name, attr_value in sorted(attrs.items()):
|
|
hashable_attrs.append((attr_name, attr_value.SerializeToString()))
|
|
attrs = tuple(hashable_attrs)
|
|
|
|
if op_def is not None:
|
|
op_def = op_def.SerializeToString()
|
|
|
|
return OptimizedReductionOpsCacheKey(op_type, inputs, dtypes, input_types,
|
|
name, attrs, op_def, compute_device)
|
|
|
|
def _capture_helper(self, tensor, name):
|
|
"""Implements the capturing described in the class docstring."""
|
|
captured_tensor = self._indirect_captures.get(ops.tensor_id(tensor))
|
|
if captured_tensor is not None:
|
|
return captured_tensor
|
|
|
|
if tensor.graph is not self._forward_graph:
|
|
already_captured = id(tensor) in self.function_captures.by_val_internal
|
|
captured_tensor = super(_WhileBodyGradFuncGraph, self)._capture_helper(
|
|
tensor, name)
|
|
if not already_captured:
|
|
# Adds the captured tensor to the list of outputs so that the input
|
|
# and output signatures match.
|
|
self.internal_capture_to_output[ops.tensor_id(
|
|
captured_tensor)] = captured_tensor
|
|
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
|
|
return captured_tensor
|
|
|
|
while tensor.op.type == "Identity":
|
|
# We do not accumulate the output of identity nodes so we try to capture
|
|
# the input of the Identity node instead.
|
|
tensor = tensor.op.inputs[0]
|
|
|
|
captured_tensor = self._indirect_captures.get(ops.tensor_id(tensor))
|
|
if captured_tensor is not None:
|
|
return captured_tensor
|
|
|
|
# No need to accumulate loop invariants. Capture them directly.
|
|
# The captured tensor gets resolved to the corresponding while output in
|
|
# `_resolve_grad_captures`.
|
|
if _is_loop_invariant(tensor, self._forward_graph.inputs,
|
|
self._forward_graph.outputs):
|
|
captured_tensor = super(_WhileBodyGradFuncGraph,
|
|
self)._capture_helper(tensor, name)
|
|
# Add to `internal_capture_to_output` so that this gets added to the list
|
|
# of outputs.
|
|
self.internal_capture_to_output[ops.tensor_id(
|
|
captured_tensor)] = captured_tensor
|
|
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
|
|
return captured_tensor
|
|
|
|
# Do not accumulate Const nodes. Instead copy them directly in the backward
|
|
# graph.
|
|
# TODO(srbs): This just checks for `Const` nodes. Consider checking for
|
|
# graph compile time consts in general.
|
|
# TODO(srbs): Consider making this a loop input.
|
|
if constant_op.is_constant(tensor):
|
|
real_value = constant_op.constant(
|
|
tensor_util.constant_value(tensor), dtype=tensor.dtype)
|
|
self._indirect_captures[ops.tensor_id(tensor)] = real_value
|
|
return real_value
|
|
|
|
# Resource tensors are not accumulated and handled specially.
|
|
if tensor.dtype == dtypes.resource:
|
|
return self._resource_capture_helper(tensor)
|
|
|
|
# Create or find an existing accumulator output for `tensor` in the forward
|
|
# graph, and fetch from this accumulator in the gradient graph to get the
|
|
# raw intermediate value.
|
|
accumulator = _get_accumulator(tensor)
|
|
if accumulator is None:
|
|
# Create the initial empty tensor list.
|
|
#
|
|
# Note: We clear the control dependencies to avoid a cycle in case a
|
|
# control tensor has an input path to an output of the forward While.
|
|
#
|
|
# E.g.:
|
|
# x = tf.while_loop(...)
|
|
# y = f(x)
|
|
# with tf.control_dependencies([y]):
|
|
# tf.gradients(y, x)
|
|
#
|
|
# Since the EmptyTensorList is fed back into the forward While, not
|
|
# removing the control edge would cause a cycle.
|
|
with self._forward_graph.outer_graph.as_default():
|
|
with util.clear_control_inputs():
|
|
tensor_list = list_ops.empty_tensor_list(
|
|
element_dtype=tensor.dtype,
|
|
element_shape=tensor.shape,
|
|
max_num_elements=self._maximum_iterations,
|
|
name=_build_accumulator_name(tensor))
|
|
self.extra_inputs.append(tensor_list)
|
|
|
|
# Push the intermediate tensor to the tensor list. This captures
|
|
# `tensor_list`.
|
|
with self._forward_graph.as_default():
|
|
accumulator = list_ops.tensor_list_push_back(tensor_list, tensor)
|
|
# Add the modified tensor list to the list of outputs. This output will be
|
|
# all the accumulated values.
|
|
self._forward_graph.outputs.append(accumulator)
|
|
|
|
# Capture in the cond graph as well so the forward cond and body inputs
|
|
# match.
|
|
with self._forward_cond_graph.as_default():
|
|
self._forward_cond_graph.capture(tensor_list)
|
|
|
|
# Capture the accumulator tensor list in the gradient graph directly from
|
|
# the forward graph -- we'll later modify this to capture the final list
|
|
# output by the forward While op instead.
|
|
captured_accumulator = super(_WhileBodyGradFuncGraph, self)._capture_helper(
|
|
accumulator, name)
|
|
|
|
# Pop the intermediate value from the tensor list in the gradient graph.
|
|
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
|
|
captured_accumulator, element_dtype=tensor.dtype)
|
|
|
|
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
|
|
self.internal_capture_to_output[ops.tensor_id(
|
|
captured_accumulator)] = new_tensor_list
|
|
return captured_tensor
|
|
|
|
def _resource_capture_helper(self, tensor):
|
|
"""Returns the captured resource tensor.
|
|
|
|
Resource-type tensors are not accumulated. If a resource tensor exists in
|
|
the loop body it must either be a loop input or an output of a nested While
|
|
op inside the loop body which had captured the external resource.
|
|
|
|
Args:
|
|
tensor: the external resource Tensor to be captured.
|
|
|
|
Returns:
|
|
Tensor in this graph.
|
|
"""
|
|
assert tensor.dtype == dtypes.resource
|
|
|
|
forward_graph_input_names = [t.name for t in self._forward_graph.inputs]
|
|
forward_graph_name_to_opdef = {
|
|
op.name: op.node_def for op in self._forward_graph.get_operations()}
|
|
index = util.resource_input_index(
|
|
tensor.name, forward_graph_input_names,
|
|
forward_graph_name_to_opdef,
|
|
self._forward_graph._functions)
|
|
|
|
input_placeholder = self._forward_graph.inputs[index]
|
|
tensor_in_outer_graph = self._forward_graph._while.inputs[index]
|
|
|
|
assert input_placeholder.dtype == dtypes.resource
|
|
assert tensor_in_outer_graph.dtype == dtypes.resource
|
|
# This must be a loop invariant. However, infrastructure
|
|
# (e.g. tf.vectorized_map) may insert identity nodes, function calls, conds,
|
|
# etc. which take and return the resource tensor unmodified; this means that
|
|
# the Python objects may differ.
|
|
if index != util.resource_input_index(
|
|
self._forward_graph.outputs[index].name, forward_graph_input_names,
|
|
forward_graph_name_to_opdef,
|
|
self._forward_graph._functions):
|
|
raise AssertionError(
|
|
f"Resource tensors must be loop invariants {tensor_in_outer_graph}")
|
|
|
|
self._indirect_captures[ops.tensor_id(tensor)] = self.capture(
|
|
tensor_in_outer_graph)
|
|
return self._indirect_captures[ops.tensor_id(tensor)]
|
|
|
|
|
|
def _check_shapes_compat(flat_output_tensors, flat_shape_invariants,
|
|
flat_input_tensors):
|
|
for (t, shape, input_t) in zip(flat_output_tensors, flat_shape_invariants,
|
|
flat_input_tensors):
|
|
if not control_flow_ops._ShapeLessThanOrEqual(t.shape, shape):
|
|
raise ValueError(
|
|
f"Input tensor `{input_t.name}` enters the loop with shape {shape}, "
|
|
f"but has shape {t.shape} after one iteration. To allow the shape to "
|
|
"vary across iterations, use the `shape_invariants` argument of "
|
|
"tf.while_loop to specify a less-specific shape.")
|
|
|
|
|
|
def _check_num_inputs_outputs(cond_graph, body_graph, num_flattened_loop_vars):
|
|
"""Checks the number of inputs/outputs of `cond_graph` and `body_graph`."""
|
|
assert len(cond_graph.inputs) == num_flattened_loop_vars, (
|
|
"cond_graph takes %d inputs; Expected: %d" % (len(cond_graph.inputs),
|
|
num_flattened_loop_vars))
|
|
assert len(cond_graph.outputs) == 1, (
|
|
"cond_graph has %d outputs; Expected: 1" % len(cond_graph.outputs))
|
|
assert len(body_graph.inputs) == num_flattened_loop_vars, (
|
|
"body_graph takes %d inputs; Expected: %d" % (len(body_graph.inputs),
|
|
num_flattened_loop_vars))
|
|
assert len(body_graph.outputs) == num_flattened_loop_vars, (
|
|
"body_graph has %d outputs; Expected: %d" % (len(body_graph.outputs),
|
|
num_flattened_loop_vars))
|
|
|
|
|
|
def _check_inputs_outputs_types_match(body_graph, flattened_loop_vars):
|
|
for inp, out, loop_var in zip(body_graph.inputs, body_graph.outputs,
|
|
flattened_loop_vars):
|
|
if inp.dtype != out.dtype:
|
|
raise TypeError(
|
|
f"Loop var {loop_var.name} enters the loop with type {inp.dtype} "
|
|
f"but has type {out.dtype} after 1 iteration. {loop_var.name} type "
|
|
"should remain constant.")
|
|
|
|
|
|
def _build_cond_placeholders_name_prefix(cond_graph):
|
|
return cond_graph.unique_name(cond_graph.name + "___redundant_placeholder")
|
|
|
|
|
|
def _duplicate_body_captures_in_cond(cond_graph, body_graph_captures):
|
|
"""Creates placeholders for body captures in cond_graph.
|
|
|
|
This is needed to match signatures of cond and body graphs.
|
|
|
|
Args:
|
|
cond_graph: cond branch graph
|
|
body_graph_captures: Tensors which were captured when building the
|
|
`body_graph`.
|
|
"""
|
|
types = [t.dtype.as_datatype_enum for t in body_graph_captures]
|
|
# TODO(srbs): Providing a unique prefix does not ensure that there is no
|
|
# conflict between the placeholder names and existing nodes in the graph.
|
|
# However passing a list of strings may not be performant.
|
|
# Ideally we should move `Graph.unique_name` to C++ or make
|
|
# `Graph._names_in_use` a trie so that we can find a unique prefix.
|
|
# TODO(b/143286622): This should not be required once captures are separated
|
|
# from regular loop vars.
|
|
with cond_graph._c_graph.get() as c_graph:
|
|
placeholders = c_api.TF_CreatePlaceholders(
|
|
c_graph, types,
|
|
compat.as_str(_build_cond_placeholders_name_prefix(cond_graph)))
|
|
placeholder_ops = [
|
|
ops.Operation._from_c_op(ph.oper, cond_graph) for ph in placeholders
|
|
]
|
|
|
|
tensors = []
|
|
for op in placeholder_ops:
|
|
tensors.append(op.outputs[0])
|
|
|
|
# Update `cond_graph._captures` and `cond_graph.inputs` to contain the
|
|
# newly created placeholders.
|
|
tuples = zip(body_graph_captures, tensors)
|
|
keys = [id(t) for t in body_graph_captures]
|
|
for k, v in zip(keys, tuples):
|
|
cond_graph._function_captures.add_or_replace(
|
|
key=k,
|
|
external=v[0],
|
|
internal=v[1],
|
|
is_by_ref=False)
|
|
cond_graph.inputs.extend(tensors)
|
|
|
|
|
|
def _copy_handle_data(src_tensors, tgt_tensors):
|
|
for src_t, tgt_t in zip(src_tensors, tgt_tensors):
|
|
handle_data_util.copy_handle_data(src_t, tgt_t)
|
|
|
|
|
|
def _pack_sequence_as(loop_vars_signature, flat_orig_loop_vars, loop_vars):
|
|
"""Like `nest.pack_sequence_as` but also replaces flows with TensorArrays."""
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def flow_to_tensor_array(flow, ta): # pylint: disable=missing-docstring
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return (tensor_array_ops.build_ta_with_new_flow(ta, flow) if isinstance( # pylint: disable=g-long-ternary
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ta, tensor_array_ops.TensorArray) else flow)
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flattened_loop_vars = [
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flow_to_tensor_array(*z)
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for z in zip(nest.flatten(loop_vars, expand_composites=True),
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flat_orig_loop_vars)
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]
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return nest.pack_sequence_as(loop_vars_signature, flattened_loop_vars,
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expand_composites=True)
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def _tensor_array_to_flow(loop_vars):
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def f(maybe_ta):
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if isinstance(maybe_ta, tensor_array_ops.TensorArray):
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return maybe_ta.flow
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return maybe_ta
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return nest.map_structure(f, loop_vars, expand_composites=True)
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def _build_maximum_iterations_loop_var(maximum_iterations):
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if maximum_iterations is None:
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# Default value for max_num_elements to EmptyTensorList meaning that the
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# list size is unbounded.
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maximum_iterations = -1
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# EmptyTensorList expects `max_num_elements` to be of type int32.
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return ops.convert_to_tensor(
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maximum_iterations, dtype=dtypes.int32, name="maximum_iterations")
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def _build_accumulator_name(tensor):
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# Tensor name may be of the form "pow/y:0". Name scope does not allow ":".
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return "{}/accumulator".format(tensor.name).replace(":", "_")
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def _is_loop_invariant(tensor, inputs, outputs):
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return (any(tensor is t for t in inputs) and
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any(tensor is t for t in outputs))
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def _set_read_only_resource_inputs_attr(op: ops.Operation, branch_graphs):
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"""Sets the list of resource inputs which are read-only.
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This is used by AutomaticControlDependencies.
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Args:
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op: While Operation.
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branch_graphs: List of branch FuncGraphs.
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"""
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read_only_indices = set(range(len(op.inputs)))
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for branch_graph in branch_graphs:
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if not read_only_indices:
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break
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branch_read_only_indices = acd.get_read_only_resource_input_indices_graph(
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branch_graph)
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read_only_indices = read_only_indices.intersection(branch_read_only_indices)
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ops.set_int_list_attr(op, acd.READ_ONLY_RESOURCE_INPUTS_ATTR,
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sorted(read_only_indices))
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# pylint: enable=protected-access
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