package xrt

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Used in: XLAComputationConfig

• repeated DeviceAssignment.ComputationDevice computation_devices = 1

  As many ComputationDevice as many there are computations (number of cores per replica).

Used in: DeviceAssignment

• repeated ComputationDevice.DeviceMeshCoordinates replica_devices = 1

  As many replicas as there are in the replicated computation.

Used in: ComputationDevice

• repeated int32 value = 1

  The mesh coordinates for the device. Usually (X, Y, Core), in the order in which they are returned in the TopologyProto. X = value(0) Y = value(1) Core = value(2)

Literal to allocate space for, and transfer to, device memory.

• int32 device_ordinal = 1

• optional xla.LiteralProto value = 2

Options and XLA computation for a compilation.

• optional XLAComputationConfig config = 1

• optional xla.HloSnapshot hlo_snapshot = 2

Options for an XLA compilation.

Used in: XLAComputation

• int32 num_replicas = 1

  The number of replicas the computation will be run on. If this is default (0) it is interpreted as 1.

• int32 num_cores_per_replica = 2

  The number of "model-parallel" cores per replica. If this is default (0) it is interpreted as 1.

• optional diplomacy.tensorflow.tf2xla.HostComputeMetadata host_compute_metadata = 3

  Optional metadata about host sends and recvs.

• optional xla.ProgramShapeProto program_shape = 4

  The arg/result shapes for the whole computation.

• repeated xla.ProgramShapeProto per_core_program_shape = 5

  The arg/result shapes for each core of a model-parallel computation. per_core_args_and_result_shapes is optional for a single-core computation.

• optional DeviceAssignment device_assignment = 6

  Describes how replicated computation instances should be assigned to devices. There are num_cores_per_replica computations, and each one will be sent and executed to the set of replica device numbers described in the DeviceAssignment proto.

• optional xla.DebugOptions debug_options = 7

  The debugging options to be passed to the XLA compilation process.

Node in a tree describing a tuple constructed from input handles. A node is an internal node if tuples is non-empty, in which case input_index and release_input_handle are ignored. Otherwise a node is a leaf node. Each leaf XLATupleNode is the index of an input which corresponds to a handle that will be grafted onto the output tuple at that location. If release_input_handle is true that input handle will be released and become invalid. Inputs may be repeated in which case leaves of the output tuple will alias. If an input is repeated, release_input_handle must be false for every leaf where that input appears. For example, if input 0 has shape {} and input 1 has shape {2,3} then the XLATupleNode with structure {1,{0,1}} corresponds to a tuple with shape {{2,3},{{},{2,3}}}.

• int32 input_index = 1

• bool release_input_handle = 2

• repeated XLATupleNode tuples = 3

Options for an XLA execution.

• int32 device_ordinal = 1

  Local device to run on. This is present because the execute Op may be placed on a device such as CPU or TPU_SYSTEM that logically manages multiple cores.

• int32 core_index_in_replica = 2

  Which model-parallel computation to run from the compiled bundle.

• string execution_instance_key = 3

  Optional key to disambiguate between executions. This is only needed if multiple host send/recvs may be outstanding concurrently with executions.

• uint32 rng_seed = 4

  If non-zero, rng_seed to reset the core with.

• bool release_input_handles = 5

  If true, release allocation handles on the inputs after running.

• bool release_compilation_handle = 6

  If true, release the handle to the computation after running.

• bool return_exploded_tuple = 7

  If set to true, and the result shape is a tuple, then instead of returning a single tuple allocation the execution will return a vector of allocations, one for each of the first-level elements of the result tuple.