from mpi4py import MPI
import os, subprocess, sys
import numpy as np
[docs]def mpi_fork(n, bind_to_core=False):
"""
Re-launches the current script with workers linked by MPI.
Also, terminates the original process that launched it.
Taken almost without modification from the Baselines function of the
`same name`_.
.. _`same name`: https://github.com/openai/baselines/blob/master/baselines/common/mpi_fork.py
Args:
n (int): Number of process to split into.
bind_to_core (bool): Bind each MPI process to a core.
"""
if n<=1:
return
if os.getenv("IN_MPI") is None:
env = os.environ.copy()
env.update(
MKL_NUM_THREADS="1",
OMP_NUM_THREADS="1",
IN_MPI="1"
)
args = ["mpirun", "-np", str(n)]
if bind_to_core:
args += ["-bind-to", "core"]
args += [sys.executable] + sys.argv
subprocess.check_call(args, env=env)
sys.exit()
def msg(m, string=''):
print(('Message from %d: %s \t '%(MPI.COMM_WORLD.Get_rank(), string))+str(m))
[docs]def proc_id():
"""Get rank of calling process."""
return MPI.COMM_WORLD.Get_rank()
def allreduce(*args, **kwargs):
return MPI.COMM_WORLD.Allreduce(*args, **kwargs)
[docs]def num_procs():
"""Count active MPI processes."""
return MPI.COMM_WORLD.Get_size()
def broadcast(x, root=0):
MPI.COMM_WORLD.Bcast(x, root=root)
def mpi_op(x, op):
x, scalar = ([x], True) if np.isscalar(x) else (x, False)
x = np.asarray(x, dtype=np.float32)
buff = np.zeros_like(x, dtype=np.float32)
allreduce(x, buff, op=op)
return buff[0] if scalar else buff
def mpi_sum(x):
return mpi_op(x, MPI.SUM)
[docs]def mpi_avg(x):
"""Average a scalar or vector over MPI processes."""
return mpi_sum(x) / num_procs()
[docs]def mpi_statistics_scalar(x, with_min_and_max=False):
"""
Get mean/std and optional min/max of scalar x across MPI processes.
Args:
x: An array containing samples of the scalar to produce statistics
for.
with_min_and_max (bool): If true, return min and max of x in
addition to mean and std.
"""
x = np.array(x, dtype=np.float32)
global_sum, global_n = mpi_sum([np.sum(x), len(x)])
mean = global_sum / global_n
global_sum_sq = mpi_sum(np.sum((x - mean)**2))
std = np.sqrt(global_sum_sq / global_n) # compute global std
if with_min_and_max:
global_min = mpi_op(np.min(x) if len(x) > 0 else np.inf, op=MPI.MIN)
global_max = mpi_op(np.max(x) if len(x) > 0 else -np.inf, op=MPI.MAX)
return mean, std, global_min, global_max
return mean, std