/
batch_ops_utils.py
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/
batch_ops_utils.py
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# Copyright 2017 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.
# ==============================================================================
"""Utility for batching remote OPs together to reduce RPC overhead."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
class ScheduledOp(object):
"""Represents a scheduled remote operation."""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def batching_key(self):
"""Returns the key for batching operations."""
@abc.abstractmethod
def batch_runner_fn(self):
"""Returns the function that executes the operation on the batch."""
class ScheduledStampedResourceOp(ScheduledOp):
"""Wrapper class for batched operations on stamped resources."""
def __init__(self, resource_handle, op, **kwargs):
self.resource_handle = resource_handle
self.op = op
self.args = kwargs
def batching_key(self):
# We want to group the same operations on the same device and run them in
# one batch. So we use (device, operation) as the key.
return self.resource_handle.device, self.op
def batch_runner_fn(self):
return _scheduled_stamp_resource_op_runner
def _move_tensors(tensors, device):
"""Moves a list of tensors to a device by concatenating/splitting them."""
# Reset the device setting to avoid weird interactions with device merging
# logic.
zero = constant_op.constant(0, dtype=dtypes.int32)
with ops.device(None):
if all(tensor.shape == tensor_shape.scalar() for tensor in tensors):
with ops.device(tensors[0].device):
values = array_ops.stack(tensors)
with ops.device(device):
return array_ops.unstack(values)
else:
with ops.device(tensors[0].device):
sizes = array_ops.stack(array_ops.shape_n(tensors))[:, 0]
values = array_ops.concat(tensors, axis=zero)
with ops.device(device):
sizes = array_ops.unstack(sizes)
return list(array_ops.split(values, sizes, axis=zero))
def _scheduled_stamp_resource_op_runner(batch, stamp):
"""Runs a batch operation on a stamped resource."""
if not batch:
return
arg_keys = set(batch[0].args.keys())
grouped_args = collections.OrderedDict()
resource_handles = []
# Check that the set of arguments is the same across all the scheduled ops.
for op in batch:
if set(op.args.keys()) != arg_keys:
raise ValueError("Mismatching arguments: %s, %s.", op.args, arg_keys)
for key in arg_keys:
grouped_args.setdefault(key, []).append(op.args[key])
resource_handles.append(op.resource_handle)
# Move all the inputs to the op device in one RPC.
grouped_args = collections.OrderedDict(
(k, _move_tensors(v, resource_handles[0].device))
for k, v in sorted(grouped_args.items()))
with ops.device(resource_handles[0].device):
return batch[0].op(resource_handles, stamp, **grouped_args)
def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device):
"""Given a dictionary of ops for each handler, runs them in batch."""
batched_ops = collections.OrderedDict()
# Group the ops by their batching_key. Ops that share the same batching key
# can be executed together.
for handler in per_handler_ops.keys():
for op in per_handler_ops[handler]:
key = (op.batching_key(), op.batch_runner_fn())
batched_ops.setdefault(key, []).append(op)
op_results = {}
for batch in batched_ops.values():
# Run each of the batched ops using its runner.
results = batch[0].batch_runner_fn()(batch, stamp)
# If the result is a tuple, move each entry in the tuple in one RPC.
if isinstance(results, tuple):
results = tuple(
_move_tensors(result, worker_device) for result in results)
# Once all the results are on the worker, create individual tuple for
# each scheduled op request.
for i in range(len(batch)):
op_results[batch[i]] = tuple(result[i] for result in results)
# If the result is a tuple, it didn't have any outputs, so use the
# `ops.Operation` as the result for all the scheduled ops.
elif isinstance(results, ops.Operation):
for i in range(len(batch)):
op_results[batch[i]] = results
else:
raise ValueError("Unknown type of result %s.", results)
handler_results = collections.defaultdict(list)
# Dispatch the results of the ScheduledOps to the handlers that requested
# them.
for handler in per_handler_ops.keys():
for op in per_handler_ops[handler]:
handler_results[handler].append(op_results[op])
return handler_results