Source code for torchx.components.dist
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
For distributed training, TorchX relies on the scheduler's gang scheduling
capabilities to schedule ``n`` copies of nodes. Once launched, the application
is expected to be written in a way that leverages this topology, for instance,
with PyTorch's
`DDP <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`_.
You can express a variety of node topologies with TorchX by specifying multiple
:py:class:`torchx.specs.Role` in your component's AppDef. Each role maps to
a homogeneous group of nodes that performs a "role" (function) in the overall
training. Scheduling-wise, TorchX launches each role as a sub-gang.
A DDP-style training job has a single role: trainers. Whereas a
training job that uses parameter servers will have two roles: parameter server, trainer.
You can specify different entrypoint (executable), num replicas, resource requirements,
and more for each role.
DDP Builtin
----------------
DDP-style trainers are common and easy to templetize since they are homogeneous
single role AppDefs, so there is a builtin: ``dist.ddp``. Assuming your DDP
training script is called ``main.py``, launch it as:
.. code:: shell-session
# locally, 1 node x 4 workers
$ torchx run -s local_cwd dist.ddp -j 1x4 --script main.py
# locally, 2 node x 4 workers (8 total)
$ torchx run -s local_cwd dist.ddp -j 2x4 --script main.py
# remote (optionally pass --rdzv_port to use a different master port than the default 29500)
$ torchx run -s kubernetes -cfg queue=default dist.ddp \\
-j 2x4 \\
--script main.py
# remote -- elastic/autoscaling with 2 minimum and max 5 nodes with 8
# workers each
$ torchx run -s kubernetes dist.ddp -j 2:5x8 --script main.py
Note that the only difference compared to the local launch is the scheduler (``-s``).
The ``dist.ddp`` builtin uses ``torchelastic`` (more specifically ``torch.distributed.run``)
under the hood. Read more about torchelastic `here <https://pytorch.org/docs/stable/elastic/run.html>`_.
Components APIs
-----------------
"""
import os
import re
import shlex
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Tuple
import torchx
import torchx.specs as specs
from torchx.components.structured_arg import StructuredJArgument, StructuredNameArgument
from torchx.specs import macros
_TORCH_DEBUG_FLAGS: Dict[str, str] = {
"CUDA_LAUNCH_BLOCKING": "1",
"NCCL_DESYNC_DEBUG": "1",
"TORCH_DISTRIBUTED_DEBUG": "DETAIL",
"TORCH_SHOW_CPP_STACKTRACES": "1",
}
"""
These are commonly set environment variables to debug PyTorch execution.
* ``CUDA_LAUNCH_BLOCKING``: Read more `here <https://docs.nvidia.com/cuda/cuda-gdb/index.html#set-cuda-launch-blocking>`__.
* ``NCCL_DESYNC_DEBUG``
* ``TORCH_DISTRIBUTED_DEBUG``: Read more `here <https://pytorch.org/docs/stable/distributed.html#torch-distributed-debug>`__.
* ``TORCH_SHOW_CPP_STACKTRACES``: Read more `here <https://pytorch.org/docs/stable/distributed.html#torch-distributed-debug>`__.
"""
def spmd(
*args: str,
script: Optional[str] = None,
m: Optional[str] = None,
image: str = torchx.IMAGE,
name: str = "/",
h: str = "gpu.small",
j: str = "1x1",
env: Optional[Dict[str, str]] = None,
max_retries: int = 0,
mounts: Optional[List[str]] = None,
debug: bool = False,
) -> specs.AppDef:
"""
Usage (by script): torchx run spmd -j 2x8 -h aws_p4d.24xlarge --name my_experiment/trial_1 --script path/to/my/trainer.py -foo bar
Usage (by module): torchx run spmd -j 2x8 -h aws_p4d.24xlarge --name my_experiment/trial_1 -m path.to.my.trainer -foo bar
Usage (infer GPU count): torchx run spmd -j 2 -h p4d.24xlarge ... (same as -j 2x8)
Creates a torchx.specs.AppDef (Job Definition) for a Single-Process-Multiple-Data (SPMD)
style application. See: https://en.wikipedia.org/wiki/Single_program,_multiple_data.
SPMD launches `n x m` (set via the `-j nxm` option) copies of the same program,
where `n` is the number of nodes (hosts) and `m` is the number of processes on each node.
If you have a distributed PyTorch script (DDP, FSDP, RPC) use this component to launch
the distributed application. You can also use `-j 1x1` to launch a single process application
which would be equivalent to launching with regular `python` except that your application
can safely call `torch.distributed.init_process_group(backend)`.
Note: For multi-node distributed runs, the hosts MUST have a network route to each other
AND port 29500 should be open on all hosts. Please check your security group settings.
Args:
args: the arguments to the main module or script (e.g. my/trainer.py -foo bar)
(for docker based runs) the script path must be relative to the WORKDIR of the image
script:
m: the main module name (e.g. my.module.trainer). When this option is used, the `script_args` are passed
as the arguments to the main module). Invoking my module is useful when the relative/absolute path
of the main script is unknown w.r.t the WORKDIR of the image. Use this option when it makes sense to
invoke the main script via `python -m <MAIN.MODULE>`.
image: the base docker image of the workspace, if workspace is disabled, then the image of the job
name: ``{experimentname}/{runname}`` or ``{experimentname}/`` or ``/{runname}`` or ``{runname}``
h: the type of host to run on (e.g. aws_p4d.24xlarge). Must be one of the registered named resources
j: {nnodes}x{nproc_per_node}. For GPU hosts omitting nproc_per_node will infer it from the GPU count on the host
env: environment variables to be passed to the run (e.g. ENV1=v1,ENV2=v2,ENV3=v3)
max_retries: the number of scheduler retries allowed
rdzv_port: the port on rank0's host to use for hosting the c10d store used for rendezvous.
Only takes effect when running multi-node. When running single node, this parameter
is ignored and a random free port is chosen.
mounts: (for docker based runs only) mounts to mount into the worker environment/container
(ex. type=<bind/volume>,src=/host,dst=/job[,readonly]).
debug: whether to run with preset debug flags enabled
"""
if env is None:
env = {}
return ddp(
*args,
script=script,
m=m,
image=image,
name=name,
h=h,
j=str(StructuredJArgument.parse_from(h, j)),
env=env,
max_retries=max_retries,
mounts=mounts,
debug=debug,
)
[docs]def ddp(
*script_args: str,
script: Optional[str] = None,
m: Optional[str] = None,
image: str = torchx.IMAGE,
name: str = "/",
h: Optional[str] = None,
cpu: int = 2,
gpu: int = 0,
memMB: int = 1024,
j: str = "1x2",
env: Optional[Dict[str, str]] = None,
max_retries: int = 0,
rdzv_port: int = 29500,
rdzv_backend: str = "c10d",
mounts: Optional[List[str]] = None,
debug: bool = False,
) -> specs.AppDef:
"""
Distributed data parallel style application (one role, multi-replica).
Uses `torch.distributed.run <https://pytorch.org/docs/stable/distributed.elastic.html>`_
to launch and coordinate PyTorch worker processes. Defaults to using ``c10d`` rendezvous backend
on rendezvous_endpoint ``$rank_0_host:$rdzv_port``. Note that ``rdzv_port`` parameter is ignored
when running on single node, and instead we use port 0 which instructs torchelastic to chose
a free random port on the host.
Note: (cpu, gpu, memMB) parameters are mutually exclusive with ``h`` (named resource) where
``h`` takes precedence if specified for setting resource requirements.
See `registering named resources <https://pytorch.org/torchx/latest/advanced.html#registering-named-resources>`_.
Args:
script_args: arguments to the main module
script: script or binary to run within the image
m: the python module path to run
image: image (e.g. docker)
name: job name override in the following format: ``{experimentname}/{runname}`` or ``{experimentname}/`` or ``/{runname}`` or ``{runname}``.
Uses the script or module name if ``{runname}`` not specified.
cpu: number of cpus per replica
gpu: number of gpus per replica
memMB: cpu memory in MB per replica
h: a registered named resource (if specified takes precedence over cpu, gpu, memMB)
j: [{min_nnodes}:]{nnodes}x{nproc_per_node}, for gpu hosts, nproc_per_node must not exceed num gpus
env: environment varibles to be passed to the run (e.g. ENV1=v1,ENV2=v2,ENV3=v3)
max_retries: the number of scheduler retries allowed
rdzv_port: the port on rank0's host to use for hosting the c10d store used for rendezvous.
Only takes effect when running multi-node. When running single node, this parameter
is ignored and a random free port is chosen.
rdzv_backend: the rendezvous backend to use. Only takes effect when running multi-node.
mounts: mounts to mount into the worker environment/container (ex. type=<bind/volume>,src=/host,dst=/job[,readonly]).
See scheduler documentation for more info.
debug: whether to run with preset debug flags enabled
"""
if (script is None) == (m is None):
raise ValueError("exactly one of --script and -m must be specified")
# nnodes: number of nodes or minimum nodes for elastic launch
# max_nnodes: maximum number of nodes for elastic launch
# nproc_per_node: number of processes on each node
min_nnodes, max_nnodes, nproc_per_node, nnodes_rep = parse_nnodes(j)
if max_nnodes == 1:
# using port 0 makes elastic chose a free random port which is ok
# for single-node jobs since all workers run under a single agent
# When nnodes is 0 and max_nnodes is 1, it's stil a single node job
# but pending until the resources become available
rdzv_endpoint = "localhost:0"
else:
# for multi-node, rely on the rank0_env environment variable set by
# the schedulers (see scheduler implementation for the actual env var this maps to)
# some schedulers (e.g. aws batch) make the rank0's ip-addr available on all BUT on rank0
# so default to "localhost" if the env var is not set or is empty
# rdzv_endpoint bash resolves to something to the effect of
# ${TORCHX_RANK0_HOST:=localhost}:29500
# use $$ in the prefix to escape the '$' literal (rather than a string Template substitution argument)
rdzv_endpoint = _noquote(f"$${{{macros.rank0_env}:=localhost}}:{rdzv_port}")
if env is None:
env = {}
argname = StructuredNameArgument.parse_from(
name=name,
m=m,
script=script,
)
env["TORCHX_TRACKING_EXPERIMENT_NAME"] = argname.experiment_name
env.setdefault("LOGLEVEL", os.getenv("LOGLEVEL", "WARNING"))
if debug:
env.update(_TORCH_DEBUG_FLAGS)
cmd = [
"torchrun",
"--rdzv_backend",
rdzv_backend,
"--rdzv_endpoint",
rdzv_endpoint,
"--rdzv_id",
f"{macros.app_id}",
"--nnodes",
nnodes_rep,
"--nproc_per_node",
str(nproc_per_node),
"--tee",
"3",
"--role",
"",
]
# TODO 'node_rank' is made optional as it currently does not work with the AWS Batch scheduler.
# node_rank is only used when rdzv_backend is 'static'
if rdzv_backend == "static":
cmd += ["--node_rank", f"{macros.replica_id}"]
if script is not None:
cmd += [script]
elif m is not None:
cmd += ["-m", m]
cmd += script_args
return specs.AppDef(
name=argname.run_name,
roles=[
specs.Role(
name=get_role_name(script, m),
image=image,
min_replicas=min_nnodes,
entrypoint="bash",
num_replicas=int(max_nnodes),
resource=specs.resource(cpu=cpu, gpu=gpu, memMB=memMB, h=h),
args=["-c", _args_join(cmd)],
env=env,
port_map={
"c10d": rdzv_port,
},
max_retries=max_retries,
mounts=specs.parse_mounts(mounts) if mounts else [],
)
],
)
def get_role_name(script: Optional[str], m: Optional[str]) -> str:
if script:
# script name/module no extension
role_name = Path(script).stem
elif m:
role_name = m.rpartition(".")[2]
else:
raise ValueError("failed to compute role_name")
return role_name
def _args_join(args: Iterable[str]) -> str:
"""
_args_join is like shlex.join but if the argument is wrapped in _noquote
it'll not quote that argument.
"""
quoted = [arg if isinstance(arg, _noquote) else shlex.quote(arg) for arg in args]
return " ".join(quoted)
class _noquote(str):
"""
_noquote is a wrapper around str that indicates that the argument shouldn't
be passed through shlex.quote.
"""
pass
def parse_nnodes(j: str) -> Tuple[int, int, int, str]:
"""
parse_nnodes converts a node and process string into the individual
components. Format is ``[[<min_replicas>:]<replicas>x]<num processes>``.
"""
if re.match("^\\d+:\\d+x\\d+$", j): # match 2:4x1
nnodes_rep, nproc_per_node = j.split("x")
min_nnodes, max_nnodes = nnodes_rep.split(":")
elif re.match("^\\d+x\\d+$", j): # match 2x1
min_nnodes, nproc_per_node = j.split("x")
max_nnodes = min_nnodes
nnodes_rep = min_nnodes
elif re.match("^\\d+$", j): # match 2
min_nnodes = "1"
max_nnodes = min_nnodes
nnodes_rep = min_nnodes
nproc_per_node = j
else:
raise ValueError(
f"Invalid format for -j, usage example: 1:2x4 or 1x4 or 4. Given: {j}"
)
return int(min_nnodes), int(max_nnodes), int(nproc_per_node), nnodes_rep
