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884 lines
32 KiB
884 lines
32 KiB
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Superset of ``torch.distributed.launch``.
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``torchrun`` provides a superset of the functionality as ``torch.distributed.launch``
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with the following additional functionalities:
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1. Worker failures are handled gracefully by restarting all workers.
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2. Worker ``RANK`` and ``WORLD_SIZE`` are assigned automatically.
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3. Number of nodes is allowed to change between minimum and maximum sizes (elasticity).
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.. note:: ``torchrun`` is a python
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`console script <https://packaging.python.org/en/latest/specifications/entry-points/#use-for-scripts>`_
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to the main module
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`torch.distributed.run <https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py>`_
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declared in the ``entry_points`` configuration in
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`setup.py <https://github.com/pytorch/pytorch/blob/master/setup.py>`_.
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It is equivalent to invoking ``python -m torch.distributed.run``.
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Transitioning from torch.distributed.launch to torchrun
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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``torchrun`` supports the same arguments as ``torch.distributed.launch`` **except**
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for ``--use-env`` which is now deprecated. To migrate from ``torch.distributed.launch``
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to ``torchrun`` follow these steps:
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1. If your training script is already reading ``local_rank`` from the ``LOCAL_RANK`` environment variable.
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Then you need simply omit the ``--use-env`` flag, e.g.:
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+--------------------------------------------------------------------+--------------------------------------------+
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| ``torch.distributed.launch`` | ``torchrun`` |
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+====================================================================+============================================+
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| .. code-block:: shell-session | .. code-block:: shell-session |
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| $ python -m torch.distributed.launch --use-env train_script.py | $ torchrun train_script.py |
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+--------------------------------------------------------------------+--------------------------------------------+
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2. If your training script reads local rank from a ``--local-rank`` cmd argument.
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Change your training script to read from the ``LOCAL_RANK`` environment variable as
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demonstrated by the following code snippet:
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+-------------------------------------------------------+----------------------------------------------------+
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| ``torch.distributed.launch`` | ``torchrun`` |
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+=======================================================+====================================================+
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| .. code-block:: python | .. code-block:: python |
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| import argparse | import os |
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| parser = argparse.ArgumentParser() | local_rank = int(os.environ["LOCAL_RANK"]) |
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| parser.add_argument("--local-rank", type=int) | |
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| args = parser.parse_args() | |
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| local_rank = args.local_rank | |
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+-------------------------------------------------------+----------------------------------------------------+
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The aformentioned changes suffice to migrate from ``torch.distributed.launch`` to ``torchrun``.
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To take advantage of new features such as elasticity, fault-tolerance, and error reporting of ``torchrun``
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please refer to:
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* :ref:`elastic_train_script` for more information on authoring training scripts that are ``torchrun`` compliant.
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* the rest of this page for more information on the features of ``torchrun``.
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Usage
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--------
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Single-node multi-worker
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++++++++++++++++++++++++++++++
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::
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torchrun
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--standalone
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--nnodes=1
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--nproc-per-node=$NUM_TRAINERS
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YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
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Stacked single-node multi-worker
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+++++++++++++++++++++++++++++++++++
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To run multiple instances (separate jobs) of single-node, multi-worker on the
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same host, we need to make sure that each instance (job) is
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setup on different ports to avoid port conflicts (or worse, two jobs being merged
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as a single job). To do this you have to run with ``--rdzv-backend=c10d``
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and specify a different port by setting ``--rdzv-endpoint=localhost:$PORT_k``.
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For ``--nodes=1``, its often convenient to let ``torchrun`` pick a free random
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port automatically instead of manually assigning different ports for each run.
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::
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torchrun
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--rdzv-backend=c10d
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--rdzv-endpoint=localhost:0
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--nnodes=1
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--nproc-per-node=$NUM_TRAINERS
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YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
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Fault tolerant (fixed sized number of workers, no elasticity, tolerates 3 failures)
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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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::
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torchrun
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--nnodes=$NUM_NODES
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--nproc-per-node=$NUM_TRAINERS
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--max-restarts=3
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--rdzv-id=$JOB_ID
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--rdzv-backend=c10d
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--rdzv-endpoint=$HOST_NODE_ADDR
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YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
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``HOST_NODE_ADDR``, in form <host>[:<port>] (e.g. node1.example.com:29400), specifies the node and
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the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
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node in your training cluster, but ideally you should pick a node that has a high bandwidth.
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.. note::
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If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
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Elastic (``min=1``, ``max=4``, tolerates up to 3 membership changes or failures)
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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::
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torchrun
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--nnodes=1:4
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--nproc-per-node=$NUM_TRAINERS
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--max-restarts=3
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--rdzv-id=$JOB_ID
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--rdzv-backend=c10d
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--rdzv-endpoint=$HOST_NODE_ADDR
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YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
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``HOST_NODE_ADDR``, in form <host>[:<port>] (e.g. node1.example.com:29400), specifies the node and
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the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
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node in your training cluster, but ideally you should pick a node that has a high bandwidth.
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.. note::
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If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
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Note on rendezvous backend
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------------------------------
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For multi-node training you need to specify:
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1. ``--rdzv-id``: A unique job id (shared by all nodes participating in the job)
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2. ``--rdzv-backend``: An implementation of
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:py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler`
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3. ``--rdzv-endpoint``: The endpoint where the rendezvous backend is running; usually in form
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``host:port``.
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Currently ``c10d`` (recommended), ``etcd-v2``, and ``etcd`` (legacy) rendezvous backends are
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supported out of the box. To use ``etcd-v2`` or ``etcd``, setup an etcd server with the ``v2`` api
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enabled (e.g. ``--enable-v2``).
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.. warning::
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``etcd-v2`` and ``etcd`` rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
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server. Our tests use etcd v3.4.3.
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.. warning::
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For etcd-based rendezvous we recommend using ``etcd-v2`` over ``etcd`` which is functionally
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equivalent, but uses a revised implementation. ``etcd`` is in maintenance mode and will be
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removed in a future version.
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Definitions
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--------------
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1. ``Node`` - A physical instance or a container; maps to the unit that the job manager works with.
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2. ``Worker`` - A worker in the context of distributed training.
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3. ``WorkerGroup`` - The set of workers that execute the same function (e.g. trainers).
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4. ``LocalWorkerGroup`` - A subset of the workers in the worker group running on the same node.
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5. ``RANK`` - The rank of the worker within a worker group.
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6. ``WORLD_SIZE`` - The total number of workers in a worker group.
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7. ``LOCAL_RANK`` - The rank of the worker within a local worker group.
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8. ``LOCAL_WORLD_SIZE`` - The size of the local worker group.
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9. ``rdzv_id`` - A user-defined id that uniquely identifies the worker group for a job. This id is
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used by each node to join as a member of a particular worker group.
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9. ``rdzv_backend`` - The backend of the rendezvous (e.g. ``c10d``). This is typically a strongly
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consistent key-value store.
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10. ``rdzv_endpoint`` - The rendezvous backend endpoint; usually in form ``<host>:<port>``.
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A ``Node`` runs ``LOCAL_WORLD_SIZE`` workers which comprise a ``LocalWorkerGroup``. The union of
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all ``LocalWorkerGroups`` in the nodes in the job comprise the ``WorkerGroup``.
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Environment Variables
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----------------------
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The following environment variables are made available to you in your script:
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1. ``LOCAL_RANK`` - The local rank.
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2. ``RANK`` - The global rank.
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3. ``GROUP_RANK`` - The rank of the worker group. A number between 0 and ``max_nnodes``. When
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running a single worker group per node, this is the rank of the node.
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4. ``ROLE_RANK`` - The rank of the worker across all the workers that have the same role. The role
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of the worker is specified in the ``WorkerSpec``.
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5. ``LOCAL_WORLD_SIZE`` - The local world size (e.g. number of workers running locally); equals to
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``--nproc-per-node`` specified on ``torchrun``.
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6. ``WORLD_SIZE`` - The world size (total number of workers in the job).
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7. ``ROLE_WORLD_SIZE`` - The total number of workers that was launched with the same role specified
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in ``WorkerSpec``.
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8. ``MASTER_ADDR`` - The FQDN of the host that is running worker with rank 0; used to initialize
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the Torch Distributed backend.
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9. ``MASTER_PORT`` - The port on the ``MASTER_ADDR`` that can be used to host the C10d TCP store.
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10. ``TORCHELASTIC_RESTART_COUNT`` - The number of worker group restarts so far.
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11. ``TORCHELASTIC_MAX_RESTARTS`` - The configured maximum number of restarts.
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12. ``TORCHELASTIC_RUN_ID`` - Equal to the rendezvous ``run_id`` (e.g. unique job id).
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13. ``PYTHON_EXEC`` - System executable override. If provided, the python user script will
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use the value of ``PYTHON_EXEC`` as executable. The `sys.executable` is used by default.
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Deployment
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------------
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1. (Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
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passed as ``--rdzv-endpoint`` to the launcher script)
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2. Single-node multi-worker: Start the launcher on the host to start the agent process which
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creates and monitors a local worker group.
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3. Multi-node multi-worker: Start the launcher with the same arguments on all the nodes
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participating in training.
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When using a job/cluster manager the entry point command to the multi-node job should be this
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launcher.
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Failure Modes
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---------------
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1. Worker failure: For a training job with ``n`` workers, if ``k<=n`` workers fail all workers
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are stopped and restarted up to ``max_restarts``.
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2. Agent failure: An agent failure results in a local worker group failure. It is up to the job
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manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
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are supported by the agent.
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3. Node failure: Same as agent failure.
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Membership Changes
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--------------------
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1. Node departure (scale-down): The agent is notified of the departure, all existing workers are
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stopped, a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
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``WORLD_SIZE``.
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2. Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
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a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
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``WORLD_SIZE``.
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Important Notices
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--------------------
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1. This utility and multi-process distributed (single-node or
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multi-node) GPU training currently only achieves the best performance using
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the NCCL distributed backend. Thus NCCL backend is the recommended backend to
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use for GPU training.
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2. The environment variables necessary to initialize a Torch process group are provided to you by
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this module, no need for you to pass ``RANK`` manually. To initialize a process group in your
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training script, simply run:
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::
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>>> # xdoctest: +SKIP("stub")
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>>> import torch.distributed as dist
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>>> dist.init_process_group(backend="gloo|nccl")
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3. In your training program, you can either use regular distributed functions
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or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
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training program uses GPUs for training and you would like to use
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:func:`torch.nn.parallel.DistributedDataParallel` module,
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here is how to configure it.
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::
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local_rank = int(os.environ["LOCAL_RANK"])
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model = torch.nn.parallel.DistributedDataParallel(model,
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device_ids=[local_rank],
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output_device=local_rank)
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Please ensure that ``device_ids`` argument is set to be the only GPU device id
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that your code will be operating on. This is generally the local rank of the
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process. In other words, the ``device_ids`` needs to be ``[int(os.environ("LOCAL_RANK"))]``,
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and ``output_device`` needs to be ``int(os.environ("LOCAL_RANK"))`` in order to use this
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utility
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4. On failures or membership changes ALL surviving workers are killed immediately. Make sure to
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checkpoint your progress. The frequency of checkpoints should depend on your job's tolerance
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for lost work.
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5. This module only supports homogeneous ``LOCAL_WORLD_SIZE``. That is, it is assumed that all
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nodes run the same number of local workers (per role).
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6. ``RANK`` is NOT stable. Between restarts, the local workers on a node can be assigned a
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different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
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ranks or some correlation between ``RANK`` and ``LOCAL_RANK``.
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7. When using elasticity (``min_size!=max_size``) DO NOT hard code assumptions about
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``WORLD_SIZE`` as the world size can change as nodes are allowed to leave and join.
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8. It is recommended for your script to have the following structure:
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::
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def main():
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load_checkpoint(checkpoint_path)
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initialize()
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train()
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def train():
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for batch in iter(dataset):
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train_step(batch)
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if should_checkpoint:
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save_checkpoint(checkpoint_path)
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9. (Recommended) On worker errors, this tool will summarize the details of the error
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(e.g. time, rank, host, pid, traceback, etc). On each node, the first error (by timestamp)
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is heuristically reported as the "Root Cause" error. To get tracebacks as part of this
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error summary print out, you must decorate your main entrypoint function in your
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training script as shown in the example below. If not decorated, then the summary
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will not include the traceback of the exception and will only contain the exitcode.
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For details on torchelastic error handling see: https://pytorch.org/docs/stable/elastic/errors.html
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::
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from torch.distributed.elastic.multiprocessing.errors import record
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@record
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def main():
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# do train
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pass
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if __name__ == "__main__":
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main()
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"""
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import logging
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import os
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import sys
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import uuid
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import importlib.metadata as metadata
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from argparse import REMAINDER, ArgumentParser
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from typing import Callable, List, Tuple, Type, Union, Optional, Set
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import torch
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from torch.distributed.argparse_util import check_env, env
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from torch.distributed.elastic.multiprocessing import DefaultLogsSpecs, LogsSpecs, Std
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from torch.distributed.elastic.multiprocessing.errors import record
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from torch.distributed.elastic.rendezvous.utils import _parse_rendezvous_config
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from torch.distributed.elastic.utils import macros
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from torch.distributed.elastic.utils.logging import get_logger
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from torch.distributed.launcher.api import LaunchConfig, elastic_launch
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from torch.utils.backend_registration import _get_custom_mod_func
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log = get_logger(__name__)
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def get_args_parser() -> ArgumentParser:
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"""Parse the command line options."""
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parser = ArgumentParser(description="Torch Distributed Elastic Training Launcher")
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#
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# Worker/node size related arguments.
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#
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parser.add_argument(
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"--nnodes",
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action=env,
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type=str,
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default="1:1",
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help="Number of nodes, or the range of nodes in form <minimum_nodes>:<maximum_nodes>.",
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)
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parser.add_argument(
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"--nproc-per-node",
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"--nproc_per_node",
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action=env,
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type=str,
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default="1",
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help="Number of workers per node; supported values: [auto, cpu, gpu, int].",
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)
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#
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# Rendezvous related arguments
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#
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parser.add_argument(
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"--rdzv-backend",
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"--rdzv_backend",
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action=env,
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type=str,
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default="static",
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help="Rendezvous backend.",
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)
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parser.add_argument(
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"--rdzv-endpoint",
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"--rdzv_endpoint",
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action=env,
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type=str,
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default="",
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help="Rendezvous backend endpoint; usually in form <host>:<port>.",
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)
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parser.add_argument(
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"--rdzv-id",
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"--rdzv_id",
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action=env,
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type=str,
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default="none",
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help="User-defined group id.",
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)
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parser.add_argument(
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"--rdzv-conf",
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"--rdzv_conf",
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action=env,
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type=str,
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default="",
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help="Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...).",
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)
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parser.add_argument(
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"--standalone",
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action=check_env,
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help="Start a local standalone rendezvous backend that is represented by a C10d TCP store "
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"on a free port. Useful when launching single-node, multi-worker job. If specified "
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"--rdzv-backend, --rdzv-endpoint, --rdzv-id are auto-assigned and any explicitly set values "
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"are ignored.",
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)
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#
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# User-code launch related arguments.
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#
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parser.add_argument(
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"--max-restarts",
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"--max_restarts",
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action=env,
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type=int,
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default=0,
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help="Maximum number of worker group restarts before failing.",
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)
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parser.add_argument(
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"--monitor-interval",
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"--monitor_interval",
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action=env,
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type=float,
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default=5,
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help="Interval, in seconds, to monitor the state of workers.",
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)
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parser.add_argument(
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"--start-method",
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"--start_method",
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action=env,
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type=str,
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default="spawn",
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choices=["spawn", "fork", "forkserver"],
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help="Multiprocessing start method to use when creating workers.",
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)
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parser.add_argument(
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"--role",
|
|
action=env,
|
|
type=str,
|
|
default="default",
|
|
help="User-defined role for the workers.",
|
|
)
|
|
parser.add_argument(
|
|
"-m",
|
|
"--module",
|
|
action=check_env,
|
|
help="Change each process to interpret the launch script as a Python module, executing "
|
|
"with the same behavior as 'python -m'.",
|
|
)
|
|
parser.add_argument(
|
|
"--no-python",
|
|
"--no_python",
|
|
action=check_env,
|
|
help="Skip prepending the training script with 'python' - just execute it directly. Useful "
|
|
"when the script is not a Python script.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--run-path",
|
|
"--run_path",
|
|
action=check_env,
|
|
help="Run the training script with runpy.run_path in the same interpreter."
|
|
" Script must be provided as an abs path (e.g. /abs/path/script.py)."
|
|
" Takes precedence over --no-python.",
|
|
)
|
|
parser.add_argument(
|
|
"--log-dir",
|
|
"--log_dir",
|
|
action=env,
|
|
type=str,
|
|
default=None,
|
|
help="Base directory to use for log files (e.g. /var/log/torch/elastic). The same "
|
|
"directory is re-used for multiple runs (a unique job-level sub-directory is created with "
|
|
"rdzv_id as the prefix).",
|
|
)
|
|
parser.add_argument(
|
|
"-r",
|
|
"--redirects",
|
|
action=env,
|
|
type=str,
|
|
default="0",
|
|
help="Redirect std streams into a log file in the log directory (e.g. [-r 3] redirects "
|
|
"both stdout+stderr for all workers, [-r 0:1,1:2] redirects stdout for local rank 0 and "
|
|
"stderr for local rank 1).",
|
|
)
|
|
parser.add_argument(
|
|
"-t",
|
|
"--tee",
|
|
action=env,
|
|
type=str,
|
|
default="0",
|
|
help="Tee std streams into a log file and also to console (see --redirects for format).",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--local-ranks-filter",
|
|
"--local_ranks_filter",
|
|
action=env,
|
|
type=str,
|
|
default="",
|
|
help="Only show logs from specified ranks in console (e.g. [--local_ranks_filter=0,1,2] will "
|
|
"only show logs from rank 0, 1 and 2). This will only apply to stdout and stderr, not to"
|
|
"log files saved via --redirect or --tee",
|
|
)
|
|
|
|
#
|
|
# Backwards compatible parameters with caffe2.distributed.launch.
|
|
#
|
|
|
|
parser.add_argument(
|
|
"--node-rank",
|
|
"--node_rank",
|
|
type=int,
|
|
action=env,
|
|
default=0,
|
|
help="Rank of the node for multi-node distributed training.",
|
|
)
|
|
parser.add_argument(
|
|
"--master-addr",
|
|
"--master_addr",
|
|
default="127.0.0.1",
|
|
type=str,
|
|
action=env,
|
|
help="Address of the master node (rank 0) that only used for static rendezvous. It should "
|
|
"be either the IP address or the hostname of rank 0. For single node multi-proc training "
|
|
"the --master-addr can simply be 127.0.0.1; IPv6 should have the pattern "
|
|
"`[0:0:0:0:0:0:0:1]`.",
|
|
)
|
|
parser.add_argument(
|
|
"--master-port",
|
|
"--master_port",
|
|
default=29500,
|
|
type=int,
|
|
action=env,
|
|
help="Port on the master node (rank 0) to be used for communication during distributed "
|
|
"training. It is only used for static rendezvous.",
|
|
)
|
|
parser.add_argument(
|
|
"--local-addr",
|
|
"--local_addr",
|
|
default=None,
|
|
type=str,
|
|
action=env,
|
|
help="Address of the local node. If specified, will use the given address for connection. "
|
|
"Else, will look up the local node address instead. Else, it will be default to local "
|
|
"machine's FQDN.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--logs-specs",
|
|
"--logs_specs",
|
|
default=None,
|
|
type=str,
|
|
help="torchrun.logs_specs group entrypoint name, value must be type of LogsSpecs. "
|
|
"Can be used to override custom logging behavior.",
|
|
)
|
|
|
|
#
|
|
# Positional arguments.
|
|
#
|
|
|
|
parser.add_argument(
|
|
"training_script",
|
|
type=str,
|
|
help="Full path to the (single GPU) training program/script to be launched in parallel, "
|
|
"followed by all the arguments for the training script.",
|
|
)
|
|
|
|
# Rest from the training program.
|
|
parser.add_argument("training_script_args", nargs=REMAINDER)
|
|
|
|
return parser
|
|
|
|
|
|
def parse_args(args):
|
|
parser = get_args_parser()
|
|
return parser.parse_args(args)
|
|
|
|
|
|
def parse_min_max_nnodes(nnodes: str):
|
|
arr = nnodes.split(":")
|
|
|
|
if len(arr) == 1:
|
|
min_nodes = max_nodes = int(arr[0])
|
|
elif len(arr) == 2:
|
|
min_nodes = int(arr[0])
|
|
max_nodes = int(arr[1])
|
|
else:
|
|
raise RuntimeError(f'nnodes={nnodes} is not in "MIN:MAX" format') # noqa: E231
|
|
|
|
return min_nodes, max_nodes
|
|
|
|
|
|
def determine_local_world_size(nproc_per_node: str):
|
|
try:
|
|
logging.info("Using nproc_per_node=%s.", nproc_per_node)
|
|
return int(nproc_per_node)
|
|
except ValueError as e:
|
|
if nproc_per_node == "cpu":
|
|
num_proc = os.cpu_count()
|
|
device_type = "cpu"
|
|
elif nproc_per_node == "gpu":
|
|
if not torch.cuda.is_available():
|
|
raise ValueError("Cuda is not available.") from e
|
|
device_type = "gpu"
|
|
num_proc = torch.cuda.device_count()
|
|
elif nproc_per_node == torch._C._get_privateuse1_backend_name():
|
|
if not _get_custom_mod_func("is_available")():
|
|
raise ValueError(f"{nproc_per_node} is not available.") from e
|
|
device_type = nproc_per_node
|
|
num_proc = _get_custom_mod_func("device_count")()
|
|
elif nproc_per_node == "auto":
|
|
if torch.cuda.is_available():
|
|
num_proc = torch.cuda.device_count()
|
|
device_type = "gpu"
|
|
elif hasattr(torch, torch._C._get_privateuse1_backend_name()) and \
|
|
_get_custom_mod_func("is_available")():
|
|
num_proc = _get_custom_mod_func("device_count")()
|
|
device_type = torch._C._get_privateuse1_backend_name()
|
|
else:
|
|
num_proc = os.cpu_count()
|
|
device_type = "cpu"
|
|
else:
|
|
raise ValueError(f"Unsupported nproc_per_node value: {nproc_per_node}") from e
|
|
|
|
log.info(
|
|
"Using nproc_per_node=%s,"
|
|
" setting to %s since the instance "
|
|
"has %s %s",
|
|
nproc_per_node, num_proc, os.cpu_count(), device_type
|
|
)
|
|
return num_proc
|
|
|
|
|
|
def get_rdzv_endpoint(args):
|
|
if args.rdzv_backend == "static" and not args.rdzv_endpoint:
|
|
return f"{args.master_addr}:{args.master_port}" # noqa: E231
|
|
return args.rdzv_endpoint
|
|
|
|
|
|
def get_use_env(args) -> bool:
|
|
"""
|
|
Retrieve ``use_env`` from the args.
|
|
|
|
``use_env`` is a legacy argument, if ``use_env`` is False, the
|
|
``--node-rank`` argument will be transferred to all worker processes.
|
|
``use_env`` is only used by the ``torch.distributed.launch`` and will
|
|
be deprecated in future releases.
|
|
"""
|
|
if not hasattr(args, "use_env"):
|
|
return True
|
|
return args.use_env
|
|
|
|
|
|
def _get_logs_specs_class(logs_specs_name: Optional[str]) -> Type[LogsSpecs]:
|
|
"""
|
|
Attemps to load `torchrun.logs_spec` entrypoint with key of `logs_specs_name` param.
|
|
Provides plugin mechanism to provide custom implementation of LogsSpecs.
|
|
|
|
Returns `DefaultLogsSpecs` when logs_spec_name is None.
|
|
Raises ValueError when entrypoint for `logs_spec_name` can't be found in entrypoints.
|
|
"""
|
|
logs_specs_cls = None
|
|
if logs_specs_name is not None:
|
|
eps = metadata.entry_points()
|
|
if hasattr(eps, "select"): # >= 3.10
|
|
group = eps.select(group="torchrun.logs_specs")
|
|
if group.select(name=logs_specs_name):
|
|
logs_specs_cls = group[logs_specs_name].load()
|
|
|
|
elif specs := eps.get("torchrun.logs_specs"): # < 3.10
|
|
if entrypoint_list := [ep for ep in specs if ep.name == logs_specs_name]:
|
|
logs_specs_cls = entrypoint_list[0].load()
|
|
|
|
if logs_specs_cls is None:
|
|
raise ValueError(f"Could not find entrypoint under 'torchrun.logs_specs[{logs_specs_name}]' key")
|
|
|
|
logging.info("Using logs_spec '%s' mapped to %s", logs_specs_name, str(logs_specs_cls))
|
|
else:
|
|
logs_specs_cls = DefaultLogsSpecs
|
|
|
|
return logs_specs_cls
|
|
|
|
|
|
def config_from_args(args) -> Tuple[LaunchConfig, Union[Callable, str], List[str]]:
|
|
# If ``args`` not passed, defaults to ``sys.argv[:1]``
|
|
min_nodes, max_nodes = parse_min_max_nnodes(args.nnodes)
|
|
assert 0 < min_nodes <= max_nodes
|
|
assert args.max_restarts >= 0
|
|
|
|
if hasattr(args, "master_addr") and args.rdzv_backend != "static" and not args.rdzv_endpoint:
|
|
log.warning(
|
|
"master_addr is only used for static rdzv_backend and when rdzv_endpoint "
|
|
"is not specified."
|
|
)
|
|
|
|
nproc_per_node = determine_local_world_size(args.nproc_per_node)
|
|
if "OMP_NUM_THREADS" not in os.environ and nproc_per_node > 1:
|
|
omp_num_threads = 1
|
|
log.warning(
|
|
"\n*****************************************\n"
|
|
"Setting OMP_NUM_THREADS environment variable for each process to be "
|
|
"%s in default, to avoid your system being overloaded, "
|
|
"please further tune the variable for optimal performance in "
|
|
"your application as needed. \n"
|
|
"*****************************************",
|
|
omp_num_threads
|
|
)
|
|
# This env variable will be passed down to the subprocesses
|
|
os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
|
|
|
|
log_line_prefix_template = os.getenv("TORCHELASTIC_LOG_LINE_PREFIX_TEMPLATE")
|
|
|
|
rdzv_configs = _parse_rendezvous_config(args.rdzv_conf)
|
|
|
|
if args.rdzv_backend == "static":
|
|
rdzv_configs["rank"] = args.node_rank
|
|
|
|
rdzv_endpoint = get_rdzv_endpoint(args)
|
|
|
|
ranks: Optional[Set[int]] = None
|
|
if args.local_ranks_filter:
|
|
try:
|
|
ranks = set(map(int, args.local_ranks_filter.split(",")))
|
|
assert ranks
|
|
except Exception as e:
|
|
raise Exception(
|
|
"--local_ranks_filter must be a comma-separated list of integers e.g. --local_ranks_filter=0,1,2"
|
|
) from e
|
|
|
|
logs_specs_cls: Type[LogsSpecs] = _get_logs_specs_class(args.logs_specs)
|
|
logs_specs = logs_specs_cls(
|
|
log_dir=args.log_dir,
|
|
redirects=Std.from_str(args.redirects),
|
|
tee=Std.from_str(args.tee),
|
|
local_ranks_filter=ranks,
|
|
)
|
|
|
|
config = LaunchConfig(
|
|
min_nodes=min_nodes,
|
|
max_nodes=max_nodes,
|
|
nproc_per_node=nproc_per_node,
|
|
run_id=args.rdzv_id,
|
|
role=args.role,
|
|
rdzv_endpoint=rdzv_endpoint,
|
|
rdzv_backend=args.rdzv_backend,
|
|
rdzv_configs=rdzv_configs,
|
|
max_restarts=args.max_restarts,
|
|
monitor_interval=args.monitor_interval,
|
|
start_method=args.start_method,
|
|
log_line_prefix_template=log_line_prefix_template,
|
|
local_addr=args.local_addr,
|
|
logs_specs=logs_specs,
|
|
)
|
|
|
|
with_python = not args.no_python
|
|
cmd: Union[Callable, str]
|
|
cmd_args = []
|
|
use_env = get_use_env(args)
|
|
if args.run_path:
|
|
cmd = run_script_path
|
|
cmd_args.append(args.training_script)
|
|
else:
|
|
if with_python:
|
|
cmd = os.getenv("PYTHON_EXEC", sys.executable)
|
|
cmd_args.append("-u")
|
|
if args.module:
|
|
cmd_args.append("-m")
|
|
cmd_args.append(args.training_script)
|
|
else:
|
|
if args.module:
|
|
raise ValueError(
|
|
"Don't use both the '--no-python' flag"
|
|
" and the '--module' flag at the same time."
|
|
)
|
|
cmd = args.training_script
|
|
if not use_env:
|
|
cmd_args.append(f"--local-rank={macros.local_rank}")
|
|
cmd_args.extend(args.training_script_args)
|
|
|
|
return config, cmd, cmd_args
|
|
|
|
|
|
def run_script_path(training_script: str, *training_script_args: str):
|
|
"""
|
|
Run the provided `training_script` from within this interpreter.
|
|
|
|
Usage: `script_as_function("/abs/path/to/script.py", "--arg1", "val1")`
|
|
"""
|
|
import runpy
|
|
import sys
|
|
|
|
sys.argv = [training_script] + [*training_script_args]
|
|
runpy.run_path(sys.argv[0], run_name="__main__")
|
|
|
|
|
|
def run(args):
|
|
if args.standalone:
|
|
args.rdzv_backend = "c10d"
|
|
args.rdzv_endpoint = "localhost:0"
|
|
args.rdzv_id = str(uuid.uuid4())
|
|
log.info(
|
|
"\n**************************************\n"
|
|
"Rendezvous info:\n"
|
|
"--rdzv-backend=%s "
|
|
"--rdzv-endpoint=%s "
|
|
"--rdzv-id=%s\n"
|
|
"**************************************\n",
|
|
args.rdzv_backend, args.rdzv_endpoint, args.rdzv_id
|
|
)
|
|
|
|
config, cmd, cmd_args = config_from_args(args)
|
|
elastic_launch(
|
|
config=config,
|
|
entrypoint=cmd,
|
|
)(*cmd_args)
|
|
|
|
|
|
@record
|
|
def main(args=None):
|
|
args = parse_args(args)
|
|
run(args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|