Integrate TorchFT

**Summary**
This is a WIP TorchFT integration PR.

**Current Issues**

This doesn't work at this moment as there are hanged groups when a new
group joins.

**Issue 1:**
~Group 0 and group 1 will hang during the first `should_commit` after
group 1 applying the pending state_dict from group 0.~

Fixed with: pytorch/torchft#83

**Issue 2:**
~Group 0 and group 1 will pass the `should_commit` but group 0 needs
healing which is wrong and the healing process will cause another hang.~

Fixed with: pytorch/torchft#83

**Issue 3:**
~The byproduct of issue 1 and issue 2: group 1 will continue to print
out~
```
[rank0]:devgpu051:76838:80357 [0] misc/socket.cc:50 NCCL WARN
socketProgress: Connection closed by remote peer
devgpu051.cln3.svc.fbinfra.net<33618>
```

Fixed with pytorch/torchft#91 and several other
fixes.

**Issue 4:**
When there are 3 groups, everyone requests the state dict every step.
***How to reproduce?***
Using the `Reproduce steps` to run 2 groups, then add another group by
modifying the command.

Seems to be fixed, will need more tests.

**Issue 5:**
Hang will happen if using functional collective.
***How to reproduce?***
Pull the latest version of this PR and comment out line 41 and uncomment
line 42 in `torchtitan/utils.py`

**Reproduce steps:**

1. Patch TorchFT with pytorch/torchft#82
2. Execute lighthouse
3. Execute the following command in one terminal:
```
TORCHFT_MANAGER_PORT=29520 REPLICA_GROUP_ID=0 CUDA_VISIBLE_DEVICES=0,1
NGPU=2 ./run_llama_train.sh --training.data_parallel_shard_degree=2
--experimental.enable_torchft --experimental.ft_replica_group_id=0
```
4. Wait 10 seconds, execute following command in another terminal:
```
TORCHFT_MANAGER_PORT=29522 REPLICA_GROUP_ID=1 CUDA_VISIBLE_DEVICES=2,3
NGPU=2 ./run_llama_train.sh --training.data_parallel_shard_degree=2
--experimental.enable_torchft --experimental.ft_replica_group_id=1
```

ghstack-source-id: f07ae76e95c450994ba418c06b9bb064275fc974
Pull Request resolved: #834

Author: fegin

run_train.sh

@@ -19,7 +19,10 @@ if [ $# -ne 0 ]; then
     overrides="$*"
 fi
 
+TORCHFT_LIGHTHOUSE=${TORCHFT_LIGHTHOUSE:-"http://localhost:29510"}
+
 PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True" \
+TORCHFT_LIGHTHOUSE=${TORCHFT_LIGHTHOUSE} \
 torchrun --nproc_per_node=${NGPU} --rdzv_backend c10d --rdzv_endpoint="localhost:0" \
 --local-ranks-filter ${LOG_RANK} --role rank --tee 3 \
 torchtitan/train.py --job.config_file ${CONFIG_FILE} $overrides

tests/unit_tests/test_checkpoint.py

@@ -61,10 +61,18 @@ class DummyJob:
     dump_folder: str = "dummy_folder"
 
 
+@dataclass
+class DummyExperimental:
+    ft_replica_id = 0
+    ft_group_size = 1
+
+
 @dataclass
 class DummyJobConfig:
     checkpoint: DummyCheckpointConfig = field(default_factory=DummyCheckpointConfig)
     job: DummyJob = field(default_factory=DummyJob)
+    experimental: DummyExperimental = field(default_factory=DummyExperimental)
+    ft_manager = None
 
 
 # Dummy instances to supply as constructor arguments.

tests/unit_tests/test_model_converter.py

@@ -22,6 +22,7 @@ def build_parallel_dims(job_config, world_size):
         pp=job_config.experimental.pipeline_parallel_degree,
         world_size=world_size,
         enable_loss_parallel=not job_config.training.disable_loss_parallel,
+        ft_manager=None,
     )
     return parallel_dims
 

torchtitan/components/checkpoint.py

@@ -31,6 +31,7 @@
 from torch.distributed.checkpoint.stateful import Stateful
 from torch.utils.data import DataLoader
 
+from torchtitan.components.ft import FTManager
 from torchtitan.components.optimizer import LRSchedulersContainer, OptimizersContainer
 from torchtitan.config_manager import JobConfig, TORCH_DTYPE_MAP
 from torchtitan.tools.logging import init_logger, logger
@@ -214,6 +215,19 @@ class CheckpointManager:
     3. LR schedulers also index model states like optimizers. Here we flatten the lr_schedulers
     with the assumption that all lr_schedulers have the same state_dict.
 
+    Note: TorchFT checkpointing flow
+
+    There are two types of checkpoints: when TorchFT is enabled: 1) the full perisistent
+    checkpoint, 2) the per-replica checkpoint.
+
+    The full perisistent checkpoint is saved by the replica with
+    ``ft_manager.participating_rank() == 0``. It contains everything including the model,
+    optimizer, lr_scheduler, dataloader, and train_state. Right now the full perisistent
+    checkpoint is loaded by all replicas. However, we can optimize it to only load if
+    there are no other alive replicas.
+
+    The per-replica checkpoint contains only the dataloader and is saved/loaded by all
+    replicas to/from the its own folder. The folder name is prefixed with the ft_replica_id.
 
     Args:
         dataloader (DataLoader): The dataloader used to load the data.
@@ -223,6 +237,7 @@ class CheckpointManager:
         states (Dict[str, Any]): The states that need to be saved, other than the
             previous 4 components.
         job_config (JobConfig): The job config used to configure the checkpointing.
+        ft_manager (Optional[ft.Manager]): The FTManager from TorchFT.
     """
 
     def __init__(
@@ -233,16 +248,41 @@ def __init__(
         lr_schedulers: LRSchedulersContainer,
         states: Dict[str, Any],
         job_config: JobConfig,
+        ft_manager: FTManager,
     ) -> None:
         ckpt_config = job_config.checkpoint
         self.enable_checkpoint = ckpt_config.enable_checkpoint
+        self.ft_manager = ft_manager.manager if ft_manager.enabled else None
+
+        if self.ft_manager:
+            optimizers.init_cache_state_dict()
+
+            def state_dict():
+                ret = {}
+                for k, v in self.states.items():
+                    if k in {
+                        MODEL,
+                        OPTIMIZER,
+                        LR_SCHEDULER,
+                        TRAIN_STATE,
+                    }:
+                        ret[k] = v.state_dict()
+                return ret
+
+            def load_state_dict(state_dict):
+                assert state_dict is not None
+                for k, v in state_dict.items():
+                    self.states[k].load_state_dict(v)
+
+            self.ft_manager.set_state_dict_fns(load_state_dict, state_dict)
+        self.ft_replica_id = job_config.experimental.ft_replica_id
 
         async_mode = ckpt_config.async_mode.lower()
         self.enable_staging = (
             self.enable_checkpoint and async_mode == AsyncMode.ASYNC_WITH_PINNED_MEM
-        )
+        ) or self.ft_manager
 
-        if not self.enable_checkpoint:
+        if not self.enable_checkpoint and self.ft_manager is None:
             return
 
         self.states = states
@@ -254,6 +294,13 @@ def __init__(
                 LR_SCHEDULER: lr_schedulers,
             }
         )
+        self.ft_states = {DATALOADER: dataloader}
+
+        self.staging = False
+        self.sending_to_checkpoint_mp = False
+        self.staging_id = None
+        self.cpu_offload_state_dict = None
+        self.staging_stream = torch.cuda.Stream() if self.enable_staging else None
 
         self.staging = False
         self.sending_to_checkpoint_mp = False
@@ -264,7 +311,7 @@ def __init__(
         self.folder = os.path.join(job_config.job.dump_folder, ckpt_config.folder)
         self.interval = ckpt_config.interval
         async_mode = ckpt_config.async_mode.lower()
-        if async_mode == AsyncMode.ASYNC:
+        if async_mode == AsyncMode.ASYNC or self.ft_manager:
             self.pg = dist.new_group(backend="gloo")
 
         self.keep_latest_k = ckpt_config.keep_latest_k
@@ -339,35 +386,44 @@ def save(self, curr_step: int, force: bool = False) -> None:
             None
         """
 
+        if self.ft_manager:
+            self._ft_save(curr_step)
+
         if not self._should_save(curr_step, force):
             return
 
         begin = time.monotonic()
-        logger.info("Saving the checkpoint (or staging if async is enabled).")
-        checkpoint_id = self._create_checkpoint_id(curr_step)
-        self._async_wait()
-        # This GC is called for async checkpoint as it is useless to do
-        # GC right after async_save -- the CPU memory is not able to be
-        # freed until _async_wait()
-        if force:
-            self._save_last_step(curr_step)
-        elif self.async_mode == AsyncMode.ASYNC_WITH_PINNED_MEM:
-            GarbageCollection.collect("GC collection invoked by checkpointer.")
-            self._async_with_pinned_memory(checkpoint_id)
-        elif self.async_mode == AsyncMode.ASYNC:
-            GarbageCollection.collect("GC collection invoked by checkpointer.")
-            self.async_future = dcp.async_save(
-                self.states, checkpoint_id=checkpoint_id, process_group=self.pg
-            )
-            GarbageCollection.collect("GC collection invoked by checkpointer.")
-        else:
-            save_with_gc(self.states, checkpoint_id=checkpoint_id)
-        self._purge_stale_checkpoints()
+        if not self.ft_manager or self.ft_manager.participating_rank() == 0:
+            logger.info("Saving the checkpoint (or staging if async is enabled).")
+            checkpoint_id = self._create_checkpoint_id(curr_step)
+            self._async_wait()
+            # This GC is called for async checkpoint as it is useless to do
+            # GC right after async_save -- the CPU memory is not able to be
+            # freed until _async_wait()
+            if force:
+                self._save_last_step(curr_step)
+            elif self.async_mode == AsyncMode.ASYNC_WITH_PINNED_MEM:
+                GarbageCollection.collect("GC collection invoked by checkpointer.")
+                self._async_with_pinned_memory(checkpoint_id)
+            elif self.async_mode == AsyncMode.ASYNC:
+                GarbageCollection.collect("GC collection invoked by checkpointer.")
+                self.async_future = dcp.async_save(
+                    self.states, checkpoint_id=checkpoint_id, process_group=self.pg
+                )
+                GarbageCollection.collect("GC collection invoked by checkpointer.")
+            else:
+                save_with_gc(self.states, checkpoint_id=checkpoint_id)
+            self._purge_stale_checkpoints()
 
-        logger.info(
-            "Finished saving the checkpoint (or staging if async is enabled)"
-            f"in {time.monotonic() - begin:.2f} seconds."
-        )
+            logger.info(
+                "Finished saving the checkpoint (or staging if async is enabled)"
+                f"in {time.monotonic() - begin:.2f} seconds."
+            )
+        elif self.ft_manager:
+            logger.info(
+                "Replica %d doesn't save checkpoint.",
+                self.ft_manager.participating_rank(),
+            )
 
     @torch.no_grad()
     def load(self, step: int = -1) -> bool:
@@ -384,6 +440,9 @@ def load(self, step: int = -1) -> bool:
             bool: Whether the checkpoint was loaded successfully.
         """
 
+        if self.ft_manager:
+            self._ft_load()
+
         if not self.enable_checkpoint or not os.path.isdir(self.folder):
             return False
 
@@ -467,10 +526,36 @@ def _find_load_step(self, folder: str = "") -> int:
             return -1
         return max(step_counts)
 
+    def _ft_folder(self) -> str:
+        return os.path.join(self.folder, f"ft-replicat-{self.ft_replica_id}")
+
     def _create_checkpoint_id(self, step: int, folder: str = "") -> str:
         folder = folder if folder else self.folder
         return os.path.join(folder, f"step-{step}")
 
+    def _ft_save(self, step: int) -> None:
+        begin = time.monotonic()
+        self._async_wait()
+        checkpoint_id = self._create_checkpoint_id(step, folder=self._ft_folder())
+        self.async_future = dcp.async_save(
+            self.ft_states, checkpoint_id=checkpoint_id, process_group=self.pg
+        )
+        logger.info(f"Staging ft checkpoint took {time.monotonic() - begin} secs.")
+
+    def _ft_load(self) -> None:
+        step = self._find_load_step(folder=self._ft_folder())
+        if step == -1:
+            return
+
+        begin = time.monotonic()
+        logger.info(f"Loading the FT checkpoint at step {step}.")
+        checkpoint_id = self._create_checkpoint_id(step, folder=self._ft_folder())
+        dcp.load(self.ft_states, checkpoint_id=checkpoint_id)
+        GarbageCollection.collect("GC collection for checkpoint loading.")
+        logger.info(
+            f"Finished loading the ft checkpoint in {time.monotonic() - begin:.2f} seconds."
+        )
+
     def _states_to_load(self, step: int) -> Dict[str, Any]:
         """Determines which states to load for the given step.
 
@@ -491,6 +576,8 @@ def _states_to_load(self, step: int) -> Dict[str, Any]:
         for exclude_key in self.exclude_from_loading:
             if exclude_key not in states:
                 raise ValueError(f"{exclude_key} not found in state_dict.")
+        if self.ft_manager:
+            states_to_load.pop(DATALOADER)
         return states_to_load
 
     def _save_last_step(self, curr_step: int) -> None:
@@ -577,6 +664,7 @@ def _purge_stale_checkpoints(self):
             self.keep_latest_k > 0
             and dist.get_rank() == 0
             and os.path.isdir(self.folder)
+            and (not self.ft_manager or self.ft_manager.participating_rank() == 0)
         ):
             discovered_checkpoints = []
             for filename in os.listdir(self.folder):

torchtitan/components/ft.py

@@ -0,0 +1,78 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import importlib
+from typing import Optional
+
+from torchtitan.config_manager import JobConfig
+
+if importlib.util.find_spec("torchft") is not None:
+    import torchft as ft
+
+    has_torchft = True
+else:
+    has_torchft = False
+
+
+class FTManager:
+    def __init__(
+        self,
+        manager: Optional["ft.Manager"],
+        group_size: int = 1,
+        replica_id: int = 0,
+    ) -> None:
+        self._manager = manager
+        self.group_size = group_size
+        self.replica_id = replica_id
+
+    @property
+    def enabled(self) -> bool:
+        return self._manager is not None
+
+    @property
+    def manager(self) -> "ft.Manager":
+        assert self._manager is not None
+        return self._manager
+
+    def get_dp_rank(self, dp_degree: int, dp_rank: int) -> int:
+        return dp_degree * self.replica_id + dp_rank
+
+    def get_dp_degree(self, dp_degree: int) -> int:
+        return dp_degree * self.group_size
+
+
+def init_ft_manager(job: JobConfig) -> FTManager:
+    """Initialize the FT manager if TorchFT is enabled.
+
+    Args:
+        job (JobConfig): The job configuration.
+
+    Returns:
+        Optional[ft.Manager]: The FT manager if TorchFT is enabled, otherwise None.
+    """
+    if not job.experimental.enable_torchft:
+        return FTManager(None)
+
+    if not has_torchft:
+        raise ImportError("torchft is not installed. Please install it.")
+
+    if job.experimental.ft_min_replica_size < 1:
+        raise ValueError("At least one FT replica is required.")
+
+    pg = ft.ProcessGroupBabyNCCL()
+
+    return FTManager(
+        ft.Manager(
+            pg=pg,
+            min_replica_size=job.experimental.ft_min_replica_size,
+            load_state_dict=None,
+            state_dict=None,
+            use_async_quorum=True,
+            replica_id=f"torchtitan_ft_{job.experimental.ft_replica_id}",
+        ),
+        group_size=job.experimental.ft_group_size,
+        replica_id=job.experimental.ft_replica_id,
+    )