Support SPMD through the xla:// init_method

test/pjrt/test_runtime_tpu.py

@@ -238,6 +238,13 @@ def test_execute_time_metric(self):
           f"Expected exectue time of {i} to take more than "
           f"{expected_time_seconds} seconds, got {v / 1e9} seconds")
 
+  @mock.patch('torch_xla._internal.tpu.get_worker_ips')
+  def test_master_ip_discovery(self, patched_get_worker_ips):
+    # A basic test to verify the non-SPMD codepath returns the correct IP. Two
+    # IPs are needed to avoid the short-circuit return of localhost.
+    patched_get_worker_ips.return_value = ['10.0.0.1', '10.0.0.2']
+    self.assertTrue(xr.get_master_ip(), '10.0.0.1')
+
 
 if __name__ == '__main__':
   absltest.main()

test/spmd/test_xla_distributed_checkpoint.py

@@ -477,6 +477,15 @@ def test_manager_async_step_tracking(self, tmpdir):
             torch.allclose(v, new_state_dict[k])
             for k, v in state_dict.items()))
 
+  @unittest.skipUnless(xr.device_type() == 'TPU',
+                       'TPU required for worker IP discovery')
+  @unittest.mock.patch('torch_xla._internal.tpu.get_worker_ips')
+  def test_master_ip_discovery(self, patched_get_worker_ips):
+    # A basic test to verify the SPMD codepath returns the correct IP. Two IPs
+    # are needed to avoid the short-circuit return of localhost.
+    patched_get_worker_ips.return_value = ['10.0.0.1', '10.0.0.2']
+    self.assertTrue(xr.get_master_ip(), '10.0.0.1')
+
 
 if __name__ == '__main__':
   test = unittest.main()

torch_xla/_internal/rendezvous.py

@@ -28,7 +28,6 @@ def pjrt_rendezvous_handler(url: str,
     ) == 'TPU' else 'localhost'
 
   master_port = xu.getenv_as('MASTER_PORT', int, 12355)
-  world_size = xr.world_size()
   with _store_lock:
     global _store
     if not _store:
@@ -44,4 +43,8 @@ def pjrt_rendezvous_handler(url: str,
             xr.process_count(),
             is_master=xr.process_index() == 0)
 
-  yield (_store, xr.global_ordinal(), world_size)
+  # In SPMD, the world size and rank are determined by the process count and
+  # index, while in multiprocess they are based on the device count and ordinal.
+  world_size = xr.process_count() if xr.is_spmd() else xr.world_size()
+  rank = xr.process_index() if xr.is_spmd() else xr.global_ordinal()
+  yield (_store, rank, world_size)

torch_xla/_internal/tpu.py

@@ -1,5 +1,6 @@
 import functools
 import glob
+from ipaddress import ip_address
 import operator
 import os
 import pathlib
@@ -10,6 +11,7 @@
 import yaml
 
 import torch
+import torch_xla
 import torch_xla.utils.utils as xu
 import torch_xla.core.xla_env_vars as xenv
 import torch_xla.core.xla_model as xm
@@ -268,23 +270,49 @@ def configure_topology(local_rank: int,
 
 
 def discover_master_worker_ip(use_localhost: bool = True) -> str:
-  """Find the IP of the TPU host with TPU:0.
+  """Find the IP of the master TPU host.
+
+  In multiprocess, this is the host with TPU:0.
+  In SPMD mode, this is the host running process 0.
 
   TPU device IDs are nondeterministic and independent from Cloud TPU worker IDs.
 
   Args:
     use_localhost: if there is only one TPU host, return 'localhost` instead
       of that host's internal IP.
   """
+  import torch_xla.runtime as xr
   worker_ips = get_worker_ips()
   if len(worker_ips) == 1:
     return 'localhost'
 
   tpu_env = get_tpu_env()
   current_worker_id = int(tpu_env[xenv.WORKER_ID])
+  if xr.is_spmd():
+    return _spmd_find_master_ip(worker_ips[current_worker_id])
+
   t = torch.tensor([current_worker_id], device=xm.xla_device())
   xm.collective_broadcast([t])
   xm.mark_step()
 
   master_worker_id = int(t.cpu())
   return worker_ips[master_worker_id]
+
+
+def _spmd_find_master_ip(current_worker_ip: str) -> str:
+  import torch_xla.runtime as xr
+  import torch_xla.experimental.xla_sharding as xs
+  from_cpu_shards = torch_xla._XLAC._global_tensor_from_cpu_shards
+  ip_int = int(ip_address(current_worker_ip))
+  n_dev = xr.global_runtime_device_count()
+  local_ndev = len(torch_xla._XLAC._xla_get_runtime_devices())
+  # Create a global (n_dev x 2) tensor containing all process indices and IPs,
+  # and find the process 0 IP as the master IP.
+  shard = torch.LongTensor([[xr.process_index(), ip_int]])
+  op_sharding = xs.Mesh(range(n_dev), (n_dev, 1)).get_op_sharding((0, 1))
+  global_tensor = from_cpu_shards([shard] * local_ndev, op_sharding).cpu()
+  # Process 0 may not control device 0, so we must do a linear search.
+  for proc, ip in global_tensor.tolist():
+    if proc == 0:
+      return str(ip_address(ip))
+  raise RuntimeError('Could not find IP of host running process 0')

torch_xla/runtime.py

@@ -242,3 +242,14 @@ def is_spmd():
   """Returns if SPMD is set for execution."""
   # TODO(yeounoh) replace this when we fully deprecate the flag.
   return xu.check_env_flag('XLA_USE_SPMD')
+
+
+@requires_pjrt
+def get_master_ip() -> str:
+  """Retrieve the master worker IP for the runtime. This calls into
+  backend-specific discovery APIs.
+
+  Returns master worker's IP address as a string."""
+  if device_type() == 'TPU':
+    return tpu.discover_master_worker_ip()
+  raise RuntimeError(f'IP discovery not supported for device: {device_type()}')