configuration.yaml

---
variables:
  pjrt_variables:
    PJRT_DEVICE:
      description:
        - Indicates which device is being used with PJRT. It can be either CPU,
          TPU, or CUDA
      type: string
    PJRT_SELECT_DEFAULT_DEVICE:
      description:
       - Whether or not to select a default PJRT device based on the environment
         if the runtime is not already configured.
    PJRT_CPU_ASYNC_CLIENT:
      description:
        - Whether or not to create an async PJRT client for the CPU device(s).
      type: bool
      default_value: false
    PJRT_GPU_ASYNC_CLIENT:
      description:
        - Whether or not to create an async PJRT client for the GPU device(s).
      type: bool
      default_value: false
    PJRT_TPU_MAX_INFLIGHT_COMPUTATIONS:
      description:
        - Max inflight computations that the PJRT client can handle for TPU.
      type: int
      default_value: 32
  build_variables:
    DEBUG:
      description:
        - Whether or not to build pytorch/xla in the debug mode.
      type: bool
      default_value: false
    GRPC_VERBOSITY:
      description:
        - Verbosity level for GRPC, e.g. INFO, ERROR, etc.
      type: string
      default_value: "ERROR"
    XLA_CUDA:
      description:
        - Build the xla client with CUDA enabled.
      type: bool
      default_value: false
    GIT_VERSIONED_XLA_BUILD:
      description:
        - Creates a versioned build. In particular, appends a git sha to the
          version number string
      type: bool
      default_value: false
    COMPILE_PARALLEL:
      description:
        - Enabled parallel compile for PyTorch/XLA build.
      type: bool
      default_value: true
    BUILD_CPP_TESTS:
      description:
        - Whether or not to build the cpp tests.
      type: bool
      default_value: true
    BUNDLE_LIBTPU:
      description:
        - Whether or not to include libtpu in the final wheel.
      type: bool
      default_value: false
    ALLOW_MULTIPLE_LIBTPU_LOAD:
      description:
        - Allow for multiple processes to load libtpu at the same time.
      type: bool
      default_value: true
    PT_XLA_DEBUG:
      description:
        - Used to automatically analyze the metrics report and provide a
          summary.
      type: bool
      default_value: false
    TPUVM_MODE:
      description:
        - Include some additional TPUVM features and code when building the
          third party tensorflow.
      type: bool
      default_value: false
    TORCH_XLA_VERSION:
      description:
        - Specifies the version of PyTorch/XLA, rather than the hard-coded
          version in setup.py; used when we're building binaries for
          distribution. Should be parseable as a version number, e.g. 1.14
      type: string
      default_value: "1.14"
    TORCH_XLA_PACKAGE_NAME:
      description:
        - Allows the developer to change the package name to something other
          than torch_xla.
      type: string
      default_value: "torch_xla"
    TPU_ML_PLATFORM:
      description:
        - Name of the ML platform being used on TPU, e.g. PyTorch/XLA,
          Tensorflow, or JAX.
      type: string
      default_value: "PyTorch/XLA"
    XLA_BAZEL_VERBOSE:
      description:
        - Turn on verbose messages during the bazel build of the xla/xrt client.
      type: bool
      default_value: false
  feature_variables:
    XLA_SYNC_WAIT:
      description:
        - Forces the XLA tensor sync operation to wait for its completion,
          before moving to the next step.
      type: bool
      default_value: false
    XLA_NO_SPECIAL_SCALARS:
      description:
        - When set to false, this will route some tensor values to constant
          scalars.
      type: bool
      default_value: false
    XLA_TENSOR_UPDATE_SYNC:
      description:
        - Used to decide whether or not to sync update in
          XLANativeFunctions::_copy_from.
      type: bool
      default_value: true
    XLA_IO_THREAD_POOL_SIZE:
      description:
        - Number of threads for the IO thread pool in the XLA client. Defaults
          to std::thread::hardware_concurrency().
      type: int
    XLA_TENSOR_ALLOCATOR_MAXSIZE:
      description:
        - Max cache size to be used by TensorAllocator in XRT. We only cache
          blocks smaller than this number, measured in bytes.
      type: int
      default_value: 1000000000
    XLA_IR_SHAPE_CACHE_SIZE:
      description:
        - Size for the shape cache used by XLA.
      type: int
      default_value: 12288
    XLA_DEVDATA_CACHE_SIZE:
      description:
        - Max cache size for XLA Data cache.
      type: int
      default_value: 128
    XLA_TRIM_GRAPH_CHECK_FREQUENCY:
      description:
        - Frequency to check and, if applicable, trim the IR graph.
      type: int
      default_value: 5000
    XLA_DENSE_GATHER_FACTOR:
      description:
        - Used as a threshold for when we should use dense gather. We multiply
          the factor by 10, and compare it to the number of input elements. If
          there are more input elements, we'll use sparse gather.
      type: int
      default_value: 8192
    XLA_DENSE_SCATTER_FACTOR:
      description:
        - Used as a threshold to determine when to use dense scatter. If the
          dense scatter factor times the number of index elements is greater
          than or equal to the number of input elements, we use dense scatter
      type: int
      default_value: 100
    XLA_RESIZE_SPLIT_FACTOR:
      description:
        - Used as a threshold to determine when the resize is too large to be
          done all at once, in which case we do one dimension at a time.
      type: float
      default_value: 3.0
    XLA_MAX_PADDING_FACTOR:
      description:
        - Used as a threshold to determine whether to use a sorted or
          descending layout for shape.
      type: float
      default_value: 1.25
    XLA_LAYOUTS:
      description:
        - A list of key/value pairs of the format "k1=v1;k2=v2". Keys are
          Shapes and values are layouts.
      type: string
      default_value: ""
    XLA_RNG_BIT_GENERATOR:
      description:
        - String name of the bit generator type, which can be either default,
          philox, or three_fry. No default value because in that case there's
          special behavior.
      type: string
    XLA_EXPERIMENTAL:
      description:
        - Used to enable experimental features. Representing a list separated
          by ":".
      type: string
      default_value: ""
    XLA_FLAGS:
      description:
        - List of flags used by XLA, separated by " ". This is only referenced
          in PyTorch/XLA to add flags to the list.
      type: string
      default_value: ""
    DISABLE_NUMERIC_CC_TOKEN:
      description:
        - Whether or not to skip modifying the existing token based on the
          XlaOp when creating a new token. When disabled, the same token is
          used for every XlaOp.
      type: bool
      default_value: false
    XLA_USE_SPMD:
      description:
        - Deprecated. Whether or not to use the SPMD virtual device optimization.
          Use `torch_xla.runtime.use_spmd()` instead.
      type: bool
      default_value: false
    SPLIT_EXECUTOR_CACHE_SIZE:
      description:
        - Compiler cache size for the op by op executor.
      type: int
      default_value: 2048
    XLA_USE_DUMMY_STORE:
      description:
        - If set to true, and user skips store based barrier by
          setting TORCH_DIST_INIT_BARRIER=0, the `pjrt_rendezvous_handler`
          will create a DummyStore to replace TCPStore to save open file
          descriptors.
      type: bool
      default_value: false
  device_variables:
    TPU_NUM_DEVICES:
      description:
        - Number of TPU devices being used by this instance of XRT.
      type: int
      default_value: 8
    CPU_NUM_DEVICES:
      description:
        - Number of CPU devices being used by this instance of XRT.
      type: int
    GPU_NUM_DEVICES:
      description:
        - Number of GPU devices being used by this instance of XRT.
      type: int
  debug_variables:
    XLA_FNTRACKER_FILE:
      description:
        - If set, the path to a file where output from the function tracker
          should be written.
      type: string
      default_value: ""
    XLA_FNTRACKER_LIST:
      description:
        - Tags for the tracker context, which tell the function tracker which
          functions to track.
      type: string
      default_value: ""
    XLA_METRICS_SAMPLES:
      description:
        - Max samples to use for any metric.
      type: int
      default_value: 1024
    XLA_COMPILE_TIME_THRESHOLD:
      description:
        - Threshold that determines when we log a slow compilation to the hlo
          folder. Defaults to std::numeric_limits<double>::max().
      type: int
    XLA_FNTRACKER_LEVEL:
      description:
        - Level for the tracker context. When tracking functions, only
          functions with a level less than or equal to this level will get
          tracked. Defaults to std::numeric_limits<int>::max().
      type: int
    XLA_SAVE_TENSORS_FILE:
      description:
        - The path to a file which will be used to dump the IR graphs during
          execution. Note that the file can become really big if the option is
          left enabled and the PyTorch program let run for long time. The
          graphs are appended to the file, so to have a clean sheet from run to
          run, the file should be explicitly removed.
      type: string
      default_value: ""
    XLA_SAVE_TENSORS_FMT:
      description:
        - The format of the graphs stored within the XLA_SAVE_TENSORS_FILE
          file. Can be text (the default), dot (the Graphviz format) or hlo.
      type: string
      default_value: "text"
    XLA_METRICS_FILE:
      description:
        - If set, the path to a local file where the internal metrics will be
          saved at every step. Metrics will be appended to the file, if already
          existing. Internally defaults to "None".
      type: string
    XLA_SAVE_HLO_FILE:
      description:
        - If set, the path to a local file where, in case of
          compilation/execution error, the offending HLO graph will be saved.
      type: string
      default_value: ""
    XLA_SLOW_COMPILE_HLO_FOLDER:
      description:
        - Folder name to save HLO files to, when the compile time is above a
          certain threshold.
      type: string
      default_value: ""
    XLA_TEST_DUMP_GRAPHS:
      description:
        - Type of graph to print to std::cerr. It can be empty, text, hlo, or
          dot.
      type: string
      default_value: ""
    PT_XLA_DEBUG_FILE:
      description:
        - If set, filepath used for printing out reports.
      type: string
      default_value: ""
    TF_CPP_VMODULE:
      description:
        - Environment variable used for TF VLOGs and takes the form of
          TF_CPP_VMODULE=name=value,.... Note that for VLOGs you must set
          TF_CPP_MIN_LOG_LEVEL=0. For PyTorch/XLA using a configuration like
          TF_CPP_VMODULE=tensor=5 would enable logging.
      type: string
    TF_CPP_MIN_LOG_LEVEL:
      description:
        - Level to print messages for. TF_CPP_MIN_LOG_LEVEL=0 will turn on INFO
          logging, TF_CPP_MIN_LOG_LEVEL=1 WARNING and so on. Our PyTorch/XLA
          TF_VLOG uses tensorflow::INFO level by default so to see VLOGs set
          TF_CPP_MIN_LOG_LEVEL=0.
      type: int
      default_value: 1
    TF_CPP_LOG_THREAD_ID:
      description:
        - If set to true, the TF logs will show the thread ID helping with
          debugging multithreaded processes.
      type: bool
      default_value: false
    TORCH_TEST_DEVICES:
      description:
        - Provided by the upstream and used to test new device types.
      type: string
    XLA_IR_DEBUG:
      description:
        - Enables the Python stack trace to be captured where creating IR
          nodes, hence allowing to understand which PyTorch operation was
          responsible for generating the IR.
      type: bool
      default_value: false
    XLA_HLO_DEBUG:
      description:
        - Enables the Python stack frame captured when XLA_IR_DEBUG is active,
          to be propagated to the XLA HLO metadata.
      type: bool
      default_value: false
    XLA_TEST_DUMP_METRICS:
      description:
        - Controls whether or not metrics are dumped in cpp test tear down.
      type: bool
      default_value: false
    XLA_TEST_DUMP_TENSORS:
      description:
        - Whether or not to print tensors to std::cerr in CPP tests.
      type: bool
      default_value: false
    XLA_DUMP_HLO_GRAPH:
      description:
        - If set to true in case of a compilation or execution error the
          offending HLO graph will be dumped as part of the runtime error
          raised by xla_util.cc.
      type: bool
      default_value: false
    XLA_DUMP_FATAL_STACK:
      description:
        - Installs the stack trace handler upon XLA client creation.
      type: bool
      default_value: false
    XLA_METRICS_PERCENTILES:
      description:
        - List of metrics percentiles to record.
      type: string
      default_value: "0.01:0.05:0.1:0.2:0.5:0.8:0.9:0.95:0.99"
    XLA_RELEASE_GIL_DURING_TRANSFER:
      description:
        - Release Python's GIL when transferring data from the runtime.
      type: bool
      default_value: true
    XLA_STABLEHLO_COMPILE:
      description:
        - Pass StableHLO to XLA PjRt client for compilation. This compilation
          flag is experimental. The default_value will be set to true when
          StableHLO workflow is mature.
      type: bool
      default_value: false
    XLA_DUMP_POST_OPTIMIZATIONS:
      description:
        - Dump the HLO graph after optimizations. You need to use it together
          with XLA_SAVE_TENSORS_FMT='hlo' and XLA_SAVE_TENSORS_FILE='your/location'.
      type: bool
      default_value: false
    XLA_DISABLE_FUNCTIONALIZATION:
      description:
        - Setting this to true will disable functionalization, which is a dispatcher
          pass in PyTorch that remove views and mutations to produce functional graphs.
          This flag's main purpose is to A/B test the impact of functionalization in
          your code.
      type: bool
      default_value: false
    XLA_FALLBACK_CPU:
      description:
        - Forces CPU OpenXLA fallback. By default, PyTorch/XLA will run any operation
          that doesn't have a lowering using PyTorch CUDA as fallback. Setting this
          flag will force PyTorch/XLA to use PyTorch CPU as fallback.