nvidia_gpu_pjit.py

# [email protected]

# `jax.distributed.initialize` is available in jax-0.2.25.
# $ pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html  # Note: wheels only available on linux.

# Run this script on 2 GPU nodes, assuming 10.128.0.6 is the master node
# python nvidia_gpu_pjit.py --server_addr="10.128.0.6:1456" --num_hosts=2 --host_idx=0
# python nvidia_gpu_pjit.py --server_addr="10.128.0.6:1456" --num_hosts=2 --host_idx=1
from absl import app
from absl import flags

import jax
import jax.numpy as jnp
import numpy as np
from jax.experimental.pjit import pjit, PartitionSpec as P
from jax.experimental import maps
 
flags.DEFINE_string('server_addr', '', help='server ip addr')
flags.DEFINE_integer('num_hosts', 1, help='num of hosts' )
flags.DEFINE_integer('host_idx', 0, help='index of current host' )
FLAGS = flags.FLAGS
 
 
def main(argv):
  jax.distributed.initialize(FLAGS.server_addr, FLAGS.num_hosts, FLAGS.host_idx)
  print('global devices=', jax.devices())
  print('local devices=', jax.local_devices())

  def f(x, w):
   return jnp.einsum('blm,md->bld', x, w)

  x = jnp.ones((2, 4, 20))
  w = jnp.ones((20, 4))
  print(f(x, w).shape)
  
  # Model parallelism via pjit
  n = jax.device_count()
  mesh_shape = (n,)
  device_mesh = np.array(jax.devices()).reshape(mesh_shape)
  with maps.Mesh(device_mesh, ('mdl',)):
    result = pjit(f, in_axis_resources=(P(None, None, 'mdl'), P('mdl', None)), out_axis_resources=None)(x, w)
    print(result)
    
  # result is replicated on each chip
  print('print shapes of result on each chip locally')
  for i in range(len(result.device_buffers)):
    print(result.device_buffers[i].shape)
 
if __name__ == '__main__':
  app.run(main)