import os
import sys
import tempfile
import torch
from .state import AcceleratorState, PartialState
from .utils import (
PrecisionType,
PrepareForLaunch,
are_libraries_initialized,
check_cuda_p2p_ib_support,
is_mps_available,
patch_environment,
)
def test_launch():
"Verify a `PartialState` can be initialized."
_ = PartialState()
def notebook_launcher(
function,
args=(),
num_processes=None,
mixed_precision="no",
use_port="29500",
master_addr="127.0.0.1",
node_rank=0,
num_nodes=1,
):
"""
Launches a training function, using several processes or multiple nodes if it's possible in the current environment
(TPU with multiple cores for instance).
To use this function absolutely zero calls to a CUDA device must be made in the notebook session before calling. If
any have been made, you will need to restart the notebook and make sure no cells use any CUDA capability.
Setting `ACCELERATE_DEBUG_MODE="1"` in your environment will run a test before truly launching to ensure that none
of those calls have been made.
Args:
function (`Callable`):
The training function to execute. If it accepts arguments, the first argument should be the index of the
process run.
args (`Tuple`):
Tuple of arguments to pass to the function (it will receive `*args`).
num_processes (`int`, *optional*):
The number of processes to use for training. Will default to 8 in Colab/Kaggle if a TPU is available, to
the number of GPUs available otherwise.
mixed_precision (`str`, *optional*, defaults to `"no"`):
If `fp16` or `bf16`, will use mixed precision training on multi-GPU.
use_port (`str`, *optional*, defaults to `"29500"`):
The port to use to communicate between processes when launching a multi-GPU training.
master_addr (`str`, *optional*, defaults to `"127.0.0.1"`):
The address to use for communication between processes.
node_rank (`int`, *optional*, defaults to 0):
The rank of the current node.
num_nodes (`int`, *optional*, defaults to 1):
The number of nodes to use for training.
Example:
```python
# Assume this is defined in a Jupyter Notebook on an instance with two GPUs
from accelerate import notebook_launcher
def train(*args):
# Your training function here
...
notebook_launcher(train, args=(arg1, arg2), num_processes=2, mixed_precision="fp16")
```
"""
# Are we in a google colab or a Kaggle Kernel?
in_colab = False
in_kaggle = False
if any(key.startswith("KAGGLE") for key in os.environ.keys()):
in_kaggle = True
elif "IPython" in sys.modules:
in_colab = "google.colab" in str(sys.modules["IPython"].get_ipython())
try:
mixed_precision = PrecisionType(mixed_precision.lower())
except ValueError:
raise ValueError(
f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."
)
if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME", None) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state) > 0:
raise ValueError(
"To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
"your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`."
)
if num_processes is None:
num_processes = 8
launcher = PrepareForLaunch(function, distributed_type="TPU")
print(f"Launching a training on {num_processes} TPU cores.")
xmp.spawn(launcher, args=args, nprocs=num_processes, start_method="fork")
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("Launching training on one GPU.")
else:
print("Launching training on one CPU.")
function(*args)
else:
if num_processes is None:
raise ValueError(
"You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."
)
if node_rank >= num_nodes:
raise ValueError("The node_rank must be less than the number of nodes.")
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state) > 0:
raise ValueError(
"To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
"inside your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`."
)
# Check for specific libraries known to initialize CUDA that users constantly use
problematic_imports = are_libraries_initialized("bitsandbytes")
if len(problematic_imports) > 0:
err = (
"Could not start distributed process. Libraries known to initialize CUDA upon import have been "
"imported already. Please keep these imports inside your training function to try and help with this:"
)
for lib_name in problematic_imports:
err += f"\n\t* `{lib_name}`"
raise RuntimeError(err)
patched_env = dict(
nproc=num_processes,
node_rank=node_rank,
world_size=num_nodes * num_processes,
master_addr=master_addr,
master_port=use_port,
mixed_precision=mixed_precision,
)
# Check for CUDA P2P and IB issues
if not check_cuda_p2p_ib_support():
patched_env["nccl_p2p_disable"] = "1"
patched_env["nccl_ib_disable"] = "1"
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(**patched_env):
# First dummy launch
if os.environ.get("ACCELERATE_DEBUG_MODE", "false").lower() == "true":
launcher = PrepareForLaunch(test_launch, distributed_type="MULTI_GPU")
try:
start_processes(launcher, args=(), nprocs=num_processes, start_method="fork")
except ProcessRaisedException as e:
err = "An issue was found when verifying a stable environment for the notebook launcher."
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
f"{err}"
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic and causing CUDA to be initialized."
) from e
else:
raise RuntimeError(f"{err} The following error was raised: {e}") from e
# Now the actual launch
launcher = PrepareForLaunch(function, distributed_type="MULTI_GPU")
print(f"Launching training on {num_processes} GPUs.")
try:
start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic and causing CUDA to be initialized."
) from e
else:
raise RuntimeError(f"An issue was found when launching the training: {e}") from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
print("Launching training on MPS.")
elif torch.cuda.is_available():
print("Launching training on one GPU.")
else:
print("Launching training on CPU.")
function(*args)
def debug_launcher(function, args=(), num_processes=2):
"""
Launches a training function using several processes on CPU for debugging purposes.
This function is provided for internal testing and debugging, but it's not intended for real trainings. It will
only use the CPU.
Args:
function (`Callable`):
The training function to execute.
args (`Tuple`):
Tuple of arguments to pass to the function (it will receive `*args`).
num_processes (`int`, *optional*, defaults to 2):
The number of processes to use for training.
"""
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=num_processes,
master_addr="127.0.0.1",
master_port="29500",
accelerate_mixed_precision="no",
accelerate_debug_rdv_file=tmp_file.name,
accelerate_use_cpu="yes",
):
launcher = PrepareForLaunch(function, debug=True)
start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")