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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.
# ======================================
"""Defines the `Topology` class, that describes a TPU fabric topology."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.tpu.proto import topology_pb2
class Topology(object):
"""Describes a set of TPU devices.
Represents both the shape of the physical mesh, and the mapping between
TensorFlow TPU devices to physical mesh coordinates.
"""
def __init__(self, serialized=None, mesh_shape=None, device_coordinates=None):
"""Builds a Topology object.
If `serialized` is not `None`, the topology is parsed from `serialized` and
the other arguments are ignored. Otherwise, the topology is computed from
`mesh_shape` and `device_coordinates`.
Args:
serialized: A serialized `TopologyProto`, or `None`. If not `None`, the
serialized proto is parsed to discover the topology.
mesh_shape: A sequence of 3 positive integers, or `None`. If not `None`,
the shape of the TPU topology, in number of cores. Ignored if
`serialized` is not `None`.
device_coordinates: A rank 3 numpy array that describes the mapping from
TensorFlow TPU devices to TPU fabric coordinates, or `None`. Ignored
if `serialized is not `None`.
Raises:
ValueError: If `serialized` does not describe a well-formed topology.
ValueError: If `serialized` is `None` and `mesh_shape` is not a sequence
of 3 positive integers.
ValueError: If `serialized` is `None` and `device_coordinates` is not a
rank 3 numpy int32 array that describes a valid coordinate mapping.
"""
if serialized:
self._serialized = serialized
self._parse_topology(serialized)
else:
self._mesh_shape = np.asarray(mesh_shape, dtype=np.int32)
self._device_coordinates = np.asarray(device_coordinates, np.int32)
if len(self._mesh_shape) != 3 or any(self._mesh_shape < 1):
raise ValueError("`mesh_shape` must be a sequence of 3 positive "
"entries; got {}".format(self._mesh_shape))
if (len(self._device_coordinates.shape) != 3 or
self._device_coordinates.shape[2] != len(self._mesh_shape)):
raise ValueError("`device_coordinates` must be a rank 3 int32 array "
"with minor dimension equal to the mesh shape rank")
def _parse_topology(self, serialized):
"""Parses a serialized `TopologyProto` into `self`."""
proto = topology_pb2.TopologyProto()
proto.ParseFromString(serialized)
self._mesh_shape = np.array(proto.mesh_shape, dtype=np.int32)
if len(self._mesh_shape) != 3 or any(self._mesh_shape < 1):
raise ValueError("`mesh_shape` must be a vector of size 3 with positive "
"entries; got {}".format(self._mesh_shape))
if proto.num_tasks < 0:
raise ValueError("`num_tasks` must be >= 0; got {}".format(
proto.num_tasks))
if proto.num_tpu_devices_per_task < 0:
raise ValueError("`num_tpu_devices_per_task` must be >= 0; got {}".format(
proto.num_tpu_devices_per_task))
expected_coordinates_size = (
proto.num_tasks * proto.num_tpu_devices_per_task * len(
proto.mesh_shape))
if len(proto.device_coordinates) != expected_coordinates_size:
raise ValueError("`device_coordinates` must have shape num_tasks ({}) * "
"num_tpu_devices_per_task ({}) * len(mesh_shape) ({}); "
"got shape {}".format(proto.num_tasks,
proto.num_tpu_devices_per_task,
proto.mesh_shape,
len(proto.device_coordinates)))
coords = np.array(proto.device_coordinates, dtype=np.int32)
if any(coords < 0):
raise ValueError("`device_coordinates` must be >= 0")
coords = coords.reshape((proto.num_tasks, proto.num_tpu_devices_per_task,
len(proto.mesh_shape)))
self._device_coordinates = coords
@property
def mesh_shape(self):
"""A rank 1 int32 array describing the shape of the TPU topology."""
return self._mesh_shape
@property
def device_coordinates(self):
"""Describes the mapping from TPU devices to topology coordinates.
Returns:
A rank 3 int32 array with shape `[tasks, devices, axis]`.
`tasks` is the number of tasks in the TPU cluster, `devices` is the number
of TPU devices per task, and `axis` is the number of axes in the TPU
cluster topology. Each entry gives the `axis`-th coordinate in the
topology of a task/device pair. TPU topologies are 3-dimensional, with
dimensions `(x, y, core number)`.
"""
return self._device_coordinates
def serialized(self):
"""Returns the serialized form of the topology."""
if self._serialized is None:
proto = topology_pb2.TopologyProto()
proto.mesh_shape[:] = list(self._mesh_shape)
proto.num_tasks = self._device_coordinates.shape[0]
proto.num_tpu_devices_per_task = self._device_coordinates.shape[1]
proto.device_coordinates = list(self._device_coordinates.flatten())
self._serialized = proto.SerializeToString()
return self._serialized
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