tensorflow/python/tpu/device_assignment.py - platform/external/tensorflow - Git at Google

 # 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.
 # ======================================
 """Library of TPU helper functions."""

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import math
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin

 from tensorflow.python.tpu.topology import Topology
 from tensorflow.python.util.tf_export import tf_export


 SINGLE_CORE_ASSIGNMENT = [[[0, 0, 0]]]


 def _compute_task_and_cores_to_replicas(core_assignment, topology):
   """Computes a nested dict which maps task and logical core to replicas."""
   task_and_cores_to_replicas = {}
   for replica in xrange(core_assignment.shape[0]):
     for logical_core in xrange(core_assignment.shape[1]):
       coordinates = core_assignment[replica, logical_core, :]
       task_id = topology.task_ordinal_at_coordinates(coordinates)
       if task_id not in task_and_cores_to_replicas:
         task_and_cores_to_replicas[task_id] = {}
       if logical_core not in task_and_cores_to_replicas[task_id]:
         task_and_cores_to_replicas[task_id][logical_core] = set()

       task_and_cores_to_replicas[task_id][logical_core].add(replica)

   task_to_sorted_replica_id = {}

   for task, core_to_replicas in task_and_cores_to_replicas.items():
     core_to_sorted_replicas = {}
     for core, replicas in core_to_replicas.items():
       core_to_sorted_replicas[core] = sorted(replicas)

     task_to_sorted_replica_id[task] = core_to_sorted_replicas
   return task_to_sorted_replica_id


 @tf_export("tpu.experimental.DeviceAssignment")
 class DeviceAssignment(object):
   """Mapping from logical cores in a computation to the physical TPU topology.

   Prefer to use the `DeviceAssignment.build()` helper to construct a
   `DeviceAssignment`; it is easier if less flexible than constructing a
   `DeviceAssignment` directly.
   """

   def __init__(self, topology, core_assignment):
     """Constructs a `DeviceAssignment` object.

     Args:
       topology: A `Topology` object that describes the physical TPU topology.
       core_assignment: A logical to physical core mapping, represented as a
         rank 3 numpy array. See the description of the `core_assignment`
         property for more details.

     Raises:
       ValueError: If `topology` is not `Topology` object.
       ValueError: If `core_assignment` is not a rank 3 numpy array.
     """
     if not isinstance(topology, Topology):
       raise ValueError("topology must be a Topology object, got {}".format(
           type(topology)))
     core_assignment = np.asarray(core_assignment, dtype=np.int32)

     self._topology = topology

     if core_assignment.ndim != 3:
       raise ValueError("core_assignment must be a rank 3 numpy array, "
                        "got shape {}".format(core_assignment.shape))

     self._num_replicas = core_assignment.shape[0]
     self._num_cores_per_replica = core_assignment.shape[1]

     if core_assignment.shape[-1] != topology.mesh_rank:
       raise ValueError(
           "minor dimension of core_assignment must have size equal to topology "
           "rank ({}), got shape {}".format(topology.mesh_rank,
                                            core_assignment.shape))

     self._core_assignment = core_assignment
     self._task_and_cores_to_replicas = _compute_task_and_cores_to_replicas(
         self._core_assignment, topology)

   @property
   def topology(self):
     """A `Topology` that describes the TPU topology."""
     return self._topology

   @property
   def num_cores_per_replica(self):
     """The number of cores per replica."""
     return self._num_cores_per_replica

   @property
   def num_replicas(self):
     """The number of replicas of the computation."""
     return self._num_replicas

   @property
   def core_assignment(self):
     """The logical to physical core mapping.

     Returns:
       An integer numpy array of rank 3, with shape
       `[num_replicas, num_cores_per_replica, topology_rank]`. Maps
       (replica, logical core) pairs to physical topology coordinates.
     """
     return self._core_assignment

   def coordinates(self, replica, logical_core):
     """Returns the physical topology coordinates of a logical core."""
     return tuple(self.core_assignment[replica, logical_core, :])

   def lookup_replicas(self, task_id, logical_core):
     """Lookup replica ids by task number and logical core.

     Args:
       task_id: TensorFlow task number.
       logical_core: An integer, identifying a logical core.
     Returns:
       A sorted list of the replicas that are attached to that task and
       logical_core.
     Raises:
       ValueError: If no replica exists in the task which contains the logical
       core.
     """
     try:
       return self._task_and_cores_to_replicas[task_id][logical_core]
     except KeyError:
       raise ValueError(
           "Can not find any replica in task: {} contains logical_core: {} ".
           format(task_id, logical_core))

   def tpu_ordinal(self, replica=0, logical_core=0):
     """Returns the ordinal of the TPU device assigned to a logical core."""
     coordinates = self.coordinates(replica, logical_core)
     return self._topology.tpu_device_ordinal_at_coordinates(coordinates)

   def host_device(self, replica=0, logical_core=0, job=None):
     """Returns the CPU device attached to a logical core."""
     coordinates = self.coordinates(replica, logical_core)
     return self._topology.cpu_device_name_at_coordinates(coordinates, job=job)

   def tpu_device(self, replica=0, logical_core=0, job=None):
     """Returns the name of the TPU device assigned to a logical core."""
     coordinates = self.coordinates(replica, logical_core)
     return self._topology.tpu_device_name_at_coordinates(coordinates, job=job)

   @staticmethod
   def build(topology,
             computation_shape=None,
             computation_stride=None,
             num_replicas=1):
     return device_assignment(topology, computation_shape, computation_stride,
                              num_replicas)


 def device_assignment(topology,
                       computation_shape=None,
                       computation_stride=None,
                       num_replicas=1):
   """Computes a device_assignment of a computation across a TPU topology.

   Attempts to choose a compact grid of cores for locality.

   Returns a `DeviceAssignment` that describes the cores in the topology assigned
   to each core of each replica.

   `computation_shape` and `computation_stride` values should be powers of 2 for
   optimal packing.

   Args:
     topology: A `Topology` object that describes the TPU cluster topology.
       To obtain a TPU topology, evaluate the `Tensor` returned by
       `initialize_system` using `Session.run`. Either a serialized
       `TopologyProto` or a `Topology` object may be passed. Note: you must
       evaluate the `Tensor` first; you cannot pass an unevaluated `Tensor` here.
     computation_shape: A rank 1 int32 numpy array with size equal to the
       topology rank, describing the shape of the computation's block of cores.
       If None, the `computation_shape` is `[1] * topology_rank`.
     computation_stride: A rank 1 int32 numpy array of size `topology_rank`,
       describing the inter-core spacing of the `computation_shape` cores in the
       TPU topology. If None, the `computation_stride` is `[1] * topology_rank`.
     num_replicas: The number of computation replicas to run. The replicas will
       be packed into the free spaces of the topology.

   Returns:
     A DeviceAssignment object, which describes the mapping between the logical
     cores in each computation replica and the physical cores in the TPU
     topology.

   Raises:
     ValueError: If `topology` is not a valid `Topology` object.
     ValueError: If `computation_shape` or `computation_stride` are not 1D int32
       numpy arrays with shape [3] where all values are positive.
     ValueError: If computation's replicas cannot fit into the TPU topology.
   """
   # Deserialize the Topology proto, if it is a string.
   if isinstance(topology, bytes):
     topology = Topology(serialized=topology)

   if not isinstance(topology, Topology):
     raise ValueError("`topology` is not a Topology object; got {}".format(
         type(topology)))

   topology_rank = len(topology.mesh_shape)
   mesh_shape = topology.mesh_shape
   if computation_shape is None:
     computation_shape = np.array([1] * topology_rank, dtype=np.int32)
   else:
     computation_shape = np.asarray(computation_shape, dtype=np.int32)

   if computation_stride is None:
     computation_stride = np.array([1] * topology_rank, dtype=np.int32)
   else:
     computation_stride = np.asarray(computation_stride, dtype=np.int32)

   if computation_shape.shape != (topology_rank,):
     raise ValueError("computation_shape must have shape [{}]; got {}".format(
         topology_rank, computation_shape.shape))
   if computation_stride.shape != (topology_rank,):
     raise ValueError("computation_stride must have shape [{}]; got {}".format(
         topology_rank, computation_stride.shape))

   if any(computation_shape < 1):
     raise ValueError(
         "computation_shape must be positive; got computation_shape={}".format(
             computation_shape))
   if any(computation_stride < 1):
     raise ValueError(
         "computation_stride must be positive; got computation_stride={}".format(
             computation_stride))

   # Computes the physical size of one computation instance.
   computation_footprint = computation_shape * computation_stride
   if any(computation_footprint > mesh_shape):
     raise ValueError(
         "computation footprint {} does not fit in TPU topology shape {}".format(
             computation_footprint, mesh_shape))

   # Computes how many copies of the computation footprint fit in the mesh.
   block_counts = mesh_shape // computation_footprint

   replica_counts = block_counts * computation_stride
   max_replicas = np.prod(replica_counts)
   if num_replicas > max_replicas:
     raise ValueError(
         "requested {} replicas but only {} replicas with shape {} and "
         "computation_stride {} fit in a TPU mesh of shape {}".format(
             num_replicas, max_replicas, computation_shape, computation_stride,
             mesh_shape))

   def ceil_of_ratio(n, m):
     return (n + m - 1) // m

   replica_shape = [0] * topology_rank
   if num_replicas > 0:
     remaining_replicas = num_replicas
     remaining_dims = topology_rank

     # Choose dimensions as close to an equal cube as possible, in order of
     # increasing dimension size. By visiting dimensions in increasing size, we
     # assign the most constrained dimension first, so we won't make infeasible
     # choices.
     #
     # As a secondary sort order, visit the dimensions in reverse order. This
     # means we try to use both cores on the same chip in preference to two cores
     # on different chips.
     for x, ni in sorted(((x, -i) for (i, x) in enumerate(replica_counts))):
       i = -ni
       target_size = int(math.ceil(remaining_replicas**(1.0 / remaining_dims)))
       replica_shape[i] = min(target_size, x)
       remaining_replicas = ceil_of_ratio(remaining_replicas, replica_shape[i])
       remaining_dims -= 1

     assert remaining_replicas == 1 and remaining_dims == 0

   # Assigns an offset to each replica such that no two replicas overlap.
   replica_offsets = np.full([num_replicas, topology_rank], -1, dtype=np.int32)
   for replica in xrange(num_replicas):
     # Chooses a replica number in each axis.
     t = replica
     pos = []
     for dim in replica_shape[::-1]:
       pos.append(t % dim)
       t //= dim
     replica_pos = np.array(pos[::-1], dtype=np.int32)

     # Determines where that replica starts in each axis.
     outer = replica_pos // computation_stride
     inner = replica_pos % computation_stride
     replica_offsets[replica, :] = outer * computation_footprint + inner

   # Computes a complete logical core -> physical core mapping for each replica.
   indices = [
       np.arange(0, computation_shape[i] * computation_stride[i],
                 computation_stride[i]) for i in xrange(topology_rank)
   ]
   indices = np.concatenate(
       [i[..., np.newaxis] for i in np.meshgrid(*indices, indexing="ij")],
       axis=-1)
   indices = indices.reshape((-1, topology_rank))
   assignment = indices + replica_offsets[:, np.newaxis, :]
   return DeviceAssignment(topology, core_assignment=assignment)
	# 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.
	# ======================================
	"""Library of TPU helper functions."""

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import math
	import numpy as np
	from six.moves import xrange # pylint: disable=redefined-builtin

	from tensorflow.python.tpu.topology import Topology
	from tensorflow.python.util.tf_export import tf_export


	SINGLE_CORE_ASSIGNMENT = [[[0, 0, 0]]]


	def _compute_task_and_cores_to_replicas(core_assignment, topology):
	"""Computes a nested dict which maps task and logical core to replicas."""
	task_and_cores_to_replicas = {}
	for replica in xrange(core_assignment.shape[0]):
	for logical_core in xrange(core_assignment.shape[1]):
	coordinates = core_assignment[replica, logical_core, :]
	task_id = topology.task_ordinal_at_coordinates(coordinates)
	if task_id not in task_and_cores_to_replicas:
	task_and_cores_to_replicas[task_id] = {}
	if logical_core not in task_and_cores_to_replicas[task_id]:
	task_and_cores_to_replicas[task_id][logical_core] = set()

	task_and_cores_to_replicas[task_id][logical_core].add(replica)

	task_to_sorted_replica_id = {}

	for task, core_to_replicas in task_and_cores_to_replicas.items():
	core_to_sorted_replicas = {}
	for core, replicas in core_to_replicas.items():
	core_to_sorted_replicas[core] = sorted(replicas)

	task_to_sorted_replica_id[task] = core_to_sorted_replicas
	return task_to_sorted_replica_id


	@tf_export("tpu.experimental.DeviceAssignment")
	class DeviceAssignment(object):
	"""Mapping from logical cores in a computation to the physical TPU topology.

	Prefer to use the `DeviceAssignment.build()` helper to construct a
	`DeviceAssignment`; it is easier if less flexible than constructing a
	`DeviceAssignment` directly.
	"""

	def __init__(self, topology, core_assignment):
	"""Constructs a `DeviceAssignment` object.

	Args:
	topology: A `Topology` object that describes the physical TPU topology.
	core_assignment: A logical to physical core mapping, represented as a
	rank 3 numpy array. See the description of the `core_assignment`
	property for more details.

	Raises:
	ValueError: If `topology` is not `Topology` object.
	ValueError: If `core_assignment` is not a rank 3 numpy array.
	"""
	if not isinstance(topology, Topology):
	raise ValueError("topology must be a Topology object, got {}".format(
	type(topology)))
	core_assignment = np.asarray(core_assignment, dtype=np.int32)

	self._topology = topology

	if core_assignment.ndim != 3:
	raise ValueError("core_assignment must be a rank 3 numpy array, "
	"got shape {}".format(core_assignment.shape))

	self._num_replicas = core_assignment.shape[0]
	self._num_cores_per_replica = core_assignment.shape[1]

	if core_assignment.shape[-1] != topology.mesh_rank:
	raise ValueError(
	"minor dimension of core_assignment must have size equal to topology "
	"rank ({}), got shape {}".format(topology.mesh_rank,
	core_assignment.shape))

	self._core_assignment = core_assignment
	self._task_and_cores_to_replicas = _compute_task_and_cores_to_replicas(
	self._core_assignment, topology)

	@property
	def topology(self):
	"""A `Topology` that describes the TPU topology."""
	return self._topology

	@property
	def num_cores_per_replica(self):
	"""The number of cores per replica."""
	return self._num_cores_per_replica

	@property
	def num_replicas(self):
	"""The number of replicas of the computation."""
	return self._num_replicas

	@property
	def core_assignment(self):
	"""The logical to physical core mapping.

	Returns:
	An integer numpy array of rank 3, with shape
	`[num_replicas, num_cores_per_replica, topology_rank]`. Maps
	(replica, logical core) pairs to physical topology coordinates.
	"""
	return self._core_assignment

	def coordinates(self, replica, logical_core):
	"""Returns the physical topology coordinates of a logical core."""
	return tuple(self.core_assignment[replica, logical_core, :])

	def lookup_replicas(self, task_id, logical_core):
	"""Lookup replica ids by task number and logical core.

	Args:
	task_id: TensorFlow task number.
	logical_core: An integer, identifying a logical core.
	Returns:
	A sorted list of the replicas that are attached to that task and
	logical_core.
	Raises:
	ValueError: If no replica exists in the task which contains the logical
	core.
	"""
	try:
	return self._task_and_cores_to_replicas[task_id][logical_core]
	except KeyError:
	raise ValueError(
	"Can not find any replica in task: {} contains logical_core: {} ".
	format(task_id, logical_core))

	def tpu_ordinal(self, replica=0, logical_core=0):
	"""Returns the ordinal of the TPU device assigned to a logical core."""
	coordinates = self.coordinates(replica, logical_core)
	return self._topology.tpu_device_ordinal_at_coordinates(coordinates)

	def host_device(self, replica=0, logical_core=0, job=None):
	"""Returns the CPU device attached to a logical core."""
	coordinates = self.coordinates(replica, logical_core)
	return self._topology.cpu_device_name_at_coordinates(coordinates, job=job)

	def tpu_device(self, replica=0, logical_core=0, job=None):
	"""Returns the name of the TPU device assigned to a logical core."""
	coordinates = self.coordinates(replica, logical_core)
	return self._topology.tpu_device_name_at_coordinates(coordinates, job=job)

	@staticmethod
	def build(topology,
	computation_shape=None,
	computation_stride=None,
	num_replicas=1):
	return device_assignment(topology, computation_shape, computation_stride,
	num_replicas)


	def device_assignment(topology,
	computation_shape=None,
	computation_stride=None,
	num_replicas=1):
	"""Computes a device_assignment of a computation across a TPU topology.

	Attempts to choose a compact grid of cores for locality.

	Returns a `DeviceAssignment` that describes the cores in the topology assigned
	to each core of each replica.

	`computation_shape` and `computation_stride` values should be powers of 2 for
	optimal packing.

	Args:
	topology: A `Topology` object that describes the TPU cluster topology.
	To obtain a TPU topology, evaluate the `Tensor` returned by
	`initialize_system` using `Session.run`. Either a serialized
	`TopologyProto` or a `Topology` object may be passed. Note: you must
	evaluate the `Tensor` first; you cannot pass an unevaluated `Tensor` here.
	computation_shape: A rank 1 int32 numpy array with size equal to the
	topology rank, describing the shape of the computation's block of cores.
	If None, the `computation_shape` is `[1] * topology_rank`.
	computation_stride: A rank 1 int32 numpy array of size `topology_rank`,
	describing the inter-core spacing of the `computation_shape` cores in the
	TPU topology. If None, the `computation_stride` is `[1] * topology_rank`.
	num_replicas: The number of computation replicas to run. The replicas will
	be packed into the free spaces of the topology.

	Returns:
	A DeviceAssignment object, which describes the mapping between the logical
	cores in each computation replica and the physical cores in the TPU
	topology.

	Raises:
	ValueError: If `topology` is not a valid `Topology` object.
	ValueError: If `computation_shape` or `computation_stride` are not 1D int32
	numpy arrays with shape [3] where all values are positive.
	ValueError: If computation's replicas cannot fit into the TPU topology.
	"""
	# Deserialize the Topology proto, if it is a string.
	if isinstance(topology, bytes):
	topology = Topology(serialized=topology)

	if not isinstance(topology, Topology):
	raise ValueError("`topology` is not a Topology object; got {}".format(
	type(topology)))

	topology_rank = len(topology.mesh_shape)
	mesh_shape = topology.mesh_shape
	if computation_shape is None:
	computation_shape = np.array([1] * topology_rank, dtype=np.int32)
	else:
	computation_shape = np.asarray(computation_shape, dtype=np.int32)

	if computation_stride is None:
	computation_stride = np.array([1] * topology_rank, dtype=np.int32)
	else:
	computation_stride = np.asarray(computation_stride, dtype=np.int32)

	if computation_shape.shape != (topology_rank,):
	raise ValueError("computation_shape must have shape [{}]; got {}".format(
	topology_rank, computation_shape.shape))
	if computation_stride.shape != (topology_rank,):
	raise ValueError("computation_stride must have shape [{}]; got {}".format(
	topology_rank, computation_stride.shape))

	if any(computation_shape < 1):
	raise ValueError(
	"computation_shape must be positive; got computation_shape={}".format(
	computation_shape))
	if any(computation_stride < 1):
	raise ValueError(
	"computation_stride must be positive; got computation_stride={}".format(
	computation_stride))

	# Computes the physical size of one computation instance.
	computation_footprint = computation_shape * computation_stride
	if any(computation_footprint > mesh_shape):
	raise ValueError(
	"computation footprint {} does not fit in TPU topology shape {}".format(
	computation_footprint, mesh_shape))

	# Computes how many copies of the computation footprint fit in the mesh.
	block_counts = mesh_shape // computation_footprint

	replica_counts = block_counts * computation_stride
	max_replicas = np.prod(replica_counts)
	if num_replicas > max_replicas:
	raise ValueError(
	"requested {} replicas but only {} replicas with shape {} and "
	"computation_stride {} fit in a TPU mesh of shape {}".format(
	num_replicas, max_replicas, computation_shape, computation_stride,
	mesh_shape))

	def ceil_of_ratio(n, m):
	return (n + m - 1) // m

	replica_shape = [0] * topology_rank
	if num_replicas > 0:
	remaining_replicas = num_replicas
	remaining_dims = topology_rank

	# Choose dimensions as close to an equal cube as possible, in order of
	# increasing dimension size. By visiting dimensions in increasing size, we
	# assign the most constrained dimension first, so we won't make infeasible
	# choices.
	#
	# As a secondary sort order, visit the dimensions in reverse order. This
	# means we try to use both cores on the same chip in preference to two cores
	# on different chips.
	for x, ni in sorted(((x, -i) for (i, x) in enumerate(replica_counts))):
	i = -ni
	target_size = int(math.ceil(remaining_replicas**(1.0 / remaining_dims)))
	replica_shape[i] = min(target_size, x)
	remaining_replicas = ceil_of_ratio(remaining_replicas, replica_shape[i])
	remaining_dims -= 1

	assert remaining_replicas == 1 and remaining_dims == 0

	# Assigns an offset to each replica such that no two replicas overlap.
	replica_offsets = np.full([num_replicas, topology_rank], -1, dtype=np.int32)
	for replica in xrange(num_replicas):
	# Chooses a replica number in each axis.
	t = replica
	pos = []
	for dim in replica_shape[::-1]:
	pos.append(t % dim)
	t //= dim
	replica_pos = np.array(pos[::-1], dtype=np.int32)

	# Determines where that replica starts in each axis.
	outer = replica_pos // computation_stride
	inner = replica_pos % computation_stride
	replica_offsets[replica, :] = outer * computation_footprint + inner

	# Computes a complete logical core -> physical core mapping for each replica.
	indices = [
	np.arange(0, computation_shape[i] * computation_stride[i],
	computation_stride[i]) for i in xrange(topology_rank)
	]
	indices = np.concatenate(
	[i[..., np.newaxis] for i in np.meshgrid(*indices, indexing="ij")],
	axis=-1)
	indices = indices.reshape((-1, topology_rank))
	assignment = indices + replica_offsets[:, np.newaxis, :]
	return DeviceAssignment(topology, core_assignment=assignment)