o 2h(@s\dZddlZddlmZddlmZddlmZddZ dd Z ed Gd d d e Z dS) zCDefines the `Topology` class, that describes a TPU fabric topology.N) topology_pb2) numpy_compat) tf_exportcCs"|dur d||fSd|||fS)z@Returns the device name for the TPU `device` on `task` of `job`.Nz/task:%d/device:TPU:%dz/job:%s/task:%d/device:TPU:%d)jobtaskdevicerrY/var/www/html/chatgem/venv/lib/python3.10/site-packages/tensorflow/python/tpu/topology.py_tpu_device_names r cCs|durd|Sd||fS)z>Returns the device name for the CPU device on `task` of `job`.Nz/task:%d/device:CPU:0z/job:%s/task:%d/device:CPU:0r)rrrrr _tpu_host_device_name s r ztpu.experimental.Topologyc@seZdZdZdddZddZddZed d Zed d Z ed dZ eddZ ddZ ddZ d ddZd ddZeddZeddZddZdS)!TopologyzDescribes a set of TPU devices. Represents both the shape of the physical mesh, and the mapping between TensorFlow TPU devices to physical mesh coordinates. Nc Cs||_|r ||nUtj|tjd|_tj|tjd|_t|jdks+t |jdkr4t d|jdt|jj dksG|jj dt|jkr`t d |jj t|jj |jj d|jt|j| \|_|_t|jd k|_d S) a(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 4 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`. If specified, array is 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 4-dimensional, with dimensions `(x, y, z, core number)`. This arg is 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 4 positive integers. ValueError: If `serialized` is `None` and `device_coordinates` is not a rank 3 numpy int32 array that describes a valid coordinate mapping. dtypezG`mesh_shape` must be a sequence of 4 positive entries; got `mesh_shape=`z`device_coordinates` must be a rank 3 int32 array with minor dimension equal to the `mesh_shape` rankgot device_coordinates.shape={} len(device_coordinates.shape)={} device_coordinates.shape[2]={} mesh_shape={}, len(mesh_shape)={}rN) _serialized_parse_topologyr np_asarraynpint32 _mesh_shape_device_coordinateslenany ValueErrorshapeformat_invert_topology_topology_tasks_topology_devicesargwhere_missing_devices)self serialized mesh_shapedevice_coordinatesrrr __init__0s.    zTopology.__init__cCst}||tj|jtjd|_t|jdks!t |jdkr)t d |j|j dkr6t d |j |j dkrCt d |j |j |j t|j}t|j|kret d |j |j |jt|jtj|jtjd}t |dkr{t d |||j |j t|jf}||_d S) z0Parses a serialized `TopologyProto` into `self`.r rrzE`mesh_shape` must be a vector of size 4 with positive entries; got {}rz `num_tasks` must be >= 0; got {}z/`num_tpu_devices_per_task` must be >= 0; got {}zx`device_coordinates` must have shape num_tasks ({}) * num_tpu_devices_per_task ({}) * len(mesh_shape) ({}); got shape {}z7All values in `device_coordinates` must be >= 0, got {}N)r TopologyProtoParseFromStringrarrayr'rrrrrr num_tasksnum_tpu_devices_per_taskr(reshaper)r%r&protoexpected_coordinates_sizecoordsrrr rksJ       zTopology._parse_topologyc Cstjt|jdtjd}tjt|jdtjd}t|jjdD]+}t|jjdD] }|j||ddf\}}}}||||||f<||||||f<q*q ||fS)zEInverts a [task,device,axis] topology to [x,y,z] -> task/device maps.r rrN)rfulllistr'rranger(r) r%tasksdevicesrrxyzcorerrr r szTopology._invert_topologycC|jS)z>A rank 1 int32 array describing the shape of the TPU topology.)rr%rrr r'zTopology.mesh_shapecCs t|jS)z-Returns the number of dimensions in the mesh.)rrr>rrr mesh_ranks zTopology.mesh_rankcCr=)aDescribes 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 4-dimensional, with dimensions `(x, y, z, core number)`. )rr>rrr r(s zTopology.device_coordinatescCr=)z$Array of indices of missing devices.)r$r>rrr missing_devicesr?zTopology.missing_devicescC|jt|S)aCReturns the TensorFlow task number attached to `device_coordinates`. Args: device_coordinates: An integer sequence describing a device's physical coordinates in the TPU fabric. Returns: Returns the TensorFlow task number that contains the TPU device with those physical coordinates. )r!tupler%r(rrr task_ordinal_at_coordinates z$Topology.task_ordinal_at_coordinatescCrB)aYReturns the TensorFlow device number at `device_coordinates`. Args: device_coordinates: An integer sequence describing a device's physical coordinates in the TPU fabric. Returns: Returns the TensorFlow device number within the task corresponding to attached to the device with those physical coordinates. )r"rCrDrrr !tpu_device_ordinal_at_coordinatesrFz*Topology.tpu_device_ordinal_at_coordinatescCst||jt|S)z2Returns the CPU device attached to a logical core.)r r!rCr%r(rrrr cpu_device_name_at_coordinatessz'Topology.cpu_device_name_at_coordinatescCs t||jt||jt|S)z>Returns the name of the TPU device assigned to a logical core.)r r!rCr"rHrrr tpu_device_name_at_coordinatess  z'Topology.tpu_device_name_at_coordinatescC |jjdS)z8Returns the number of TensorFlow tasks in the TPU slice.rrrr>rrr r- zTopology.num_taskscCrK)zrrr num_tpus_per_taskrMzTopology.num_tpus_per_taskcCsh|jdur1t}t|j|jdd<|jjd|_|jjd|_ |j t|j | |_|jS)z,Returns the serialized form of the topology.Nrr)rrr*r5rr'rrr-r.r(extendflattenSerializeToString)r%r0rrr r&s  zTopology.serialized)NNN)N)__name__ __module__ __qualname____doc__r)rr propertyr'r@r(rArErGrIrJr-rNr&rrrr r (s, ;%        r ) rUnumpyrtensorflow.core.protobuf.tpurtensorflow.python.utilr tensorflow.python.util.tf_exportrr r objectr rrrr s