Support synchronous saving and loading in CheckpointManager

test/spmd/test_xla_distributed_checkpoint.py

@@ -1,3 +1,4 @@
+import functools
 import os
 import sys
 import tempfile
@@ -15,11 +16,23 @@
     create_default_local_save_plan,
     create_default_global_save_plan,
 )
-from torch_xla.experimental.distributed_checkpoint import SPMDLoadPlanner, SPMDSavePlanner
+from torch_xla.experimental.distributed_checkpoint import SPMDLoadPlanner, SPMDSavePlanner, CheckpointManager
 from torch_xla.experimental.distributed_checkpoint._helpers import (
     _sharded_cpu_state_dict, _CpuShards, _is_sharded_tensor)
 
 
+# Wrapper to manage a temporary directory for the wrapped test
+def run_with_tmpdir(f):
+
+  @functools.wraps(f)
+  def run(*args, **kwargs):
+    with tempfile.TemporaryDirectory() as tmpdir:
+      kwargs.setdefault('tmpdir', tmpdir)
+      f(*args, **kwargs)
+
+  return run
+
+
 class DistributedCheckpointTestBase(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
@@ -319,6 +332,87 @@ def test_sharded_cpu_state_dict(self):
         self.assertTrue(param.device == torch.device("cpu"))
 
 
+class CheckpointManagerTest(DistributedCheckpointTestBase):
+
+  def setUp(self):
+    super().setUp()
+    # Initialize the a minimal process group
+    dist.init_process_group(
+        backend='gloo', init_method='tcp://127.1:8932', world_size=1, rank=0)
+
+  def tearDown(self):
+    super().tearDown()
+    # Destroy the CPU process group after the test
+    dist.destroy_process_group()
+
+  @run_with_tmpdir
+  def test_manager_checkpointing(self, tmpdir):
+    chkpt_mgr = CheckpointManager(tmpdir, save_interval=10)
+    state_dict = self._get_sharded_model().state_dict()
+
+    # Take a checkpoint on step 0
+    self.assertTrue(chkpt_mgr.save(0, state_dict))
+
+    # Load the checkpoint into a new state_dict
+    new_state_dict = self._get_sharded_model().state_dict()
+    self.assertFalse(
+        any(
+            torch.allclose(v, new_state_dict[k])
+            for k, v in state_dict.items()))
+    chkpt_mgr.restore(0, new_state_dict)
+    self.assertTrue(
+        all(
+            torch.allclose(v, new_state_dict[k])
+            for k, v in state_dict.items()))
+
+  @run_with_tmpdir
+  def test_manager_step_tracking(self, tmpdir):
+    chkpt_mgr = CheckpointManager(tmpdir, save_interval=10)
+    state_dict = self._get_sharded_model().state_dict()
+
+    # No steps are being tracked initially
+    self.assertEqual(chkpt_mgr.all_steps(), [])
+
+    # Steps not divisible by 10 should not be saved
+    for step in range(1, 10):
+      self.assertFalse(chkpt_mgr.save(step, state_dict))
+      self.assertEqual(chkpt_mgr.all_steps(), [])
+
+    # Steps divisible by 10 should be saved
+    saved = set()
+    for step in range(0, 100, 10):
+      self.assertTrue(chkpt_mgr.save(step, state_dict))
+      saved.add(step)
+      self.assertEqual(set(chkpt_mgr.all_steps()), saved)
+
+  @run_with_tmpdir
+  def test_manager_max_to_keep(self, tmpdir):
+    chkpt_mgr = CheckpointManager(tmpdir, save_interval=10, max_to_keep=2)
+    state_dict = self._get_sharded_model().state_dict()
+
+    # No steps are being tracked initially
+    self.assertEqual(chkpt_mgr.all_steps(), [])
+
+    self.assertTrue(chkpt_mgr.save(10, state_dict))
+    self.assertEqual(set(chkpt_mgr.all_steps()), {10})
+
+    self.assertTrue(chkpt_mgr.save(20, state_dict))
+    self.assertEqual(set(chkpt_mgr.all_steps()), {10, 20})
+
+    # The oldest checkpoint should be erased
+    self.assertTrue(chkpt_mgr.save(30, state_dict))
+    self.assertEqual(set(chkpt_mgr.all_steps()), {30, 20})
+
+    # The oldest is selected by creation timestamp, not step
+    self.assertTrue(chkpt_mgr.save(10, state_dict))
+    self.assertEqual(set(chkpt_mgr.all_steps()), {30, 10})
+
+    # The deletion order should persist across executions
+    chkpt_mgr = CheckpointManager(tmpdir, save_interval=10, max_to_keep=2)
+    self.assertTrue(chkpt_mgr.save(20, state_dict))
+    self.assertEqual(set(chkpt_mgr.all_steps()), {20, 10})
+
+
 if __name__ == '__main__':
   test = unittest.main()
   sys.exit(0 if test.result.wasSuccessful() else 1)

torch_xla/experimental/distributed_checkpoint/manager.py

@@ -1,9 +1,35 @@
+import fsspec
+import logging
+import os
+import pickle
+import torch.distributed as dist
 import torch.distributed.checkpoint as dist_cp
+import torch_xla.runtime as xr
 import torch_xla.experimental.distributed_checkpoint as xc
 
-from typing import List, Optional
+from dataclasses import dataclass
+from datetime import datetime
+from collections import deque
+from fsspec.core import url_to_fs
+from os.path import basename
+from typing import Deque, List, Optional, Union
 from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
 
+# TODO(jonbolin): Import path will change
+from torch.distributed.checkpoint._fsspec_filesystem import FsspecReader, FsspecWriter
+
+# File to track manager-specific metadata within each checkpoint path
+_MANAGER_METADATA_FILE = '.manager_metadata'
+
+
+@dataclass
+class _CheckpointMetadata:
+  # The step at which the checkpoint was taken
+  step: int
+
+  # The time at which the checkpoint was taken
+  ts: datetime
+
 
 class CheckpointManager:
   """
@@ -53,22 +79,33 @@ class CheckpointManager:
   https://github.com/google/orbax/blob/efc079c4e5b437782a80138913d322cb3ed365c7/checkpoint/orbax/checkpoint/checkpoint_manager.py
   """
 
+  # The base path to write checkpoints to. Each checkpoint taken by the manager
+  # will be written into a subdirectory of this path, identified by the
+  # checkpoint's step.
+  base_path: Union[str, os.PathLike]
+
+  # The interval to take checkpoints, in steps.
+  save_interval: int
+
+  # The maximum number of checkpoints to keep.
+  max_to_keep: int
+
   def __init__(self,
                path: str,
-               save_period: int,
-               max_to_keep: Optional[int] = -1,
+               save_interval: int,
+               max_to_keep: Optional[int] = 0,
                async_queue_size: Optional[int] = 1):
     """
     Create a checkpoint manager that reads and writes checkpoints into
     the provided directory.
 
     Args:
       path: The base path for the CheckpointManager to write checkpoints into.
-      save_period: The number of steps between saving checkpoints.
+      save_interval: The number of steps between saving checkpoints.
       max_to_keep: The maximum number of checkpoints to be tracked by the
             CheckpointManager. When a new checkpoint will be taken, the
             checkpoint for the lowest tracked step will be deleted.
-            Default: -1, indicating no upper bound on the number of checkpoints.
+            Default: 0, indicating no upper bound on the number of checkpoints.
       async_queue_size: The size of the execution queue which processes async
             checkpoints. This should be a small value to ensure training doesn't
             get too far ahead of the last finished checkpoint, but increasing
@@ -77,14 +114,61 @@ def __init__(self,
             Default: 1, which only allows a single async checkpoint to be
             pending at a time.
     """
-    raise NotImplementedError
+    assert dist.is_initialized(), "A process group is required."
+    assert save_interval > 0, "save_interval must be positive"
+    assert async_queue_size > 0, "async_queue_size must be positive"
+    assert max_to_keep >= 0, "max_to_keep must be non-negative"
+
+    self.base_path = path
+    self.save_interval = save_interval
+    self.max_to_keep = max_to_keep
+
+    self._tracked_chkpts = self._load_tracked_chkpts()
+
+  def _load_tracked_chkpts(self) -> Deque[_CheckpointMetadata]:
+    """
+    Loads a list of all tracked checkpoints from the storage backend.
+    """
+    all_chkpts = []
+    invalid_paths = []
+    fs, raw_path = url_to_fs(self.base_path)
+    for path in fs.ls(raw_path, detail=False):
+      try:
+        with fsspec.open(os.path.join(path, _MANAGER_METADATA_FILE), 'rb') as f:
+          all_chkpts.append(pickle.load(f))
+      except:
+        invalid_paths.append(path)
+
+    if invalid_paths:
+      logging.warning(f'Ignoring invalid checkpoints: {invalid_paths}')
+    return deque(sorted(all_chkpts, key=lambda m: m.ts))
+
+  def _get_path(self, step: int) -> str:
+    return os.path.join(self.base_path, str(step))
+
+  def _delete_chkpt_at_step(self, step):
+    path = self._get_path(step)
+    fs, raw_path = url_to_fs(path)
+    if fs.exists(raw_path):
+      fs.rm(raw_path, recursive=True)
+
+  def _release_oldest_checkpoints(self):
+    """
+    Delete oldest checkpoints until the number of tracked checkpoints is below
+    self.max_to_keep. This operation is only execution on the rank 0 process.
+    """
+    if dist.get_rank() == 0 and self.max_to_keep > 0:
+      while len(self._tracked_chkpts) > self.max_to_keep:
+        oldest_chkpt = self._tracked_chkpts.popleft()
+        self._delete_chkpt_at_step(oldest_chkpt.step)
 
   def should_save(self, step: int) -> bool:
     """
     Returns true if a checkpoint should be saved for the current step or if
     a preemption has been detected.
     """
-    raise NotImplementedError
+    # TODO(jonbolin): Support preemption notice for auto checkpointing
+    return step % self.save_interval == 0
 
   def save(self,
            step,
@@ -101,7 +185,22 @@ def save(self,
     Returns:
       True if a checkpoint was taken and False otherwise.
     """
-    raise NotImplementedError
+    if self.should_save(step) or force:
+      path = self._get_path(step)
+      # Delete any existing checkpoint at the current step.
+      self._delete_chkpt_at_step(step)
+      dist_cp.save_state_dict(
+          state_dict=state_dict,
+          storage_writer=FsspecWriter(path),
+          planner=xc.SPMDSavePlanner(),
+      )
+      metadata = _CheckpointMetadata(step=step, ts=datetime.now())
+      with fsspec.open(os.path.join(path, _MANAGER_METADATA_FILE), 'wb') as f:
+        pickle.dump(metadata, f)
+      self._tracked_chkpts.append(metadata)
+      self._release_oldest_checkpoints()
+      return True
+    return False
 
   def save_async(self,
                  step: int,
@@ -139,10 +238,17 @@ def restore(self, step: int, state_dict: STATE_DICT_TYPE) -> None:
       state_dict: The state dict to restore the checkpoint into. Values are
                   updated in-place within the state_dict.
     """
-    raise NotImplementedError
+    tracked_steps = set(x.step for x in self._tracked_chkpts)
+    assert step in tracked_steps, f'Cannot restore from untracked step {step}. Valid steps are: {tracked_steps}'
+    path = self._get_path(step)
+    dist_cp.load_state_dict(
+        state_dict=state_dict,
+        storage_reader=FsspecReader(path),
+        planner=xc.SPMDLoadPlanner(),
+    )
 
   def all_steps(self) -> List[int]:
     """
     List all steps tracked by the CheckpointManager.
     """
-    raise NotImplementedError
+    return sorted(x.step for x in self._tracked_chkpts)