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"""
utility helpers for distributed checks
"""
import os
from contextlib import contextmanager
import torch
import torch.distributed as dist
from accelerate import Accelerator
accelerate = None # pylint: disable=invalid-name
def load_accelerate():
global accelerate # pylint: disable=global-statement
accelerate = Accelerator()
def is_distributed():
"""
Check if distributed training is initialized.
"""
global accelerate # pylint: disable=global-statement
if not accelerate:
accelerate = Accelerator()
return dist.is_available() and dist.is_initialized()
def barrier():
"""
Acts as a barrier to wait for all processes. This ensures that all processes
reach the barrier before proceeding further.
"""
if is_distributed():
dist.barrier()
def is_main_process():
"""
Check if the current process is the main process.
If not in distributed mode, always return True.
"""
if not is_distributed():
return True
return dist.get_rank() == 0
def get_world_size():
return int(os.getenv("WORLD_SIZE", "1"))
@contextmanager
def zero_first(is_main):
"""
runs the wrapped context so that rank 0 runs first before other ranks
"""
if not is_main: # other ranks wait first
barrier()
yield
if is_main: # then rank 0 waits after it has run the context
barrier()
def gather_scalar_from_all_ranks(fn, world_size=1): # pylint: disable=invalid-name
"""
Run a callable 'fn' on all ranks and gather the results on the specified rank.
Args:
- fn (callable): A function that computes the value. This should not have any side effects.
- rank (int, optional): The rank that gathers the values. Default is 0.
- world_size (int, optional): Total number of processes in the current distributed setup.
Returns:
- A list of computed values from all ranks if on the gathering rank, otherwise None.
"""
value_scalar = fn()
if not is_distributed():
return [value_scalar]
value_tensor = torch.tensor(value_scalar, device=dist.get_rank()).float()
if not is_main_process():
dist.gather(value_tensor, dst=0)
else:
gathered_tensors = [torch.zeros_like(value_tensor) for _ in range(world_size)]
dist.gather(value_tensor, gather_list=gathered_tensors, dst=0)
# Convert tensors back to their original type (int or float)
gathered_values = []
for tensor in gathered_tensors:
if tensor == tensor.int():
gathered_values.append(int(tensor.item()))
else:
gathered_values.append(float(tensor.item()))
return gathered_values
return None
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