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: 1c5c3953695085b0fe9d02458c074bd63666b2e2
Pull Request resolved: #834

run_llama_train.sh

@@ -19,7 +19,11 @@ if [ $# -ne 0 ]; then
     overrides="$*"
 fi
 
+TORCHFT_MANAGER_PORT=${TORCHFT_MANAGER_PORT:-"29512"}
+
 PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True" \
+TORCHFT_LIGHTHOUSE=http://localhost:29510 \
+TORCHFT_MANAGER_PORT=${TORCHFT_MANAGER_PORT} \
 torchrun --nproc_per_node=${NGPU} --rdzv_backend c10d --rdzv_endpoint="localhost:0" \
 --local-ranks-filter ${LOG_RANK} --role rank --tee 3 \
 train.py --job.config_file ${CONFIG_FILE} $overrides

torchtitan/config_manager.py

@@ -631,6 +631,30 @@ def __init__(self):
             action="store_true",
         )
 
+        self.parser.add_argument(
+            "--experimental.enable_torchft",
+            action="store_true",
+            help="Enable TorchFT integration.",
+        )
+
+        self.parser.add_argument(
+            "--experimental.ft_replica_group_id",
+            type=int,
+            default=-1,
+            help="The TorchFT replicate group ID of this run.",
+        )
+
+        self.parser.add_argument(
+            "--experimental.ft_replica_group_size",
+            type=int,
+            default=-1,
+            help="""
+                The number of TorchFT replicate groups. This number will be used for
+                dataloader to split the dataset across the replicate groups and FSDP
+                dimension.
+            """,
+        )
+
     def to_dict(self):
         return self.args_dict
 

torchtitan/ft.py

@@ -0,0 +1,52 @@
+# 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 dataclasses import dataclass
+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
+
+
+@dataclass
+class FTManager:
+    manager: ft.Manager
+    replicate_group_size: int
+
+
+def init_ft_manager(job: JobConfig) -> Optional[FTManager]:
+    """Initialize the FT manager if TorchFT is enabled.
+
+    Args:
+        job (JobConfig): The job configuration.
+
+    Returns:
+        Optional[FTManager]: The FT manager if TorchFT is enabled, otherwise None.
+    """
+    if not job.experimental.enable_torchft:
+        return None
+
+    if not has_torchft:
+        raise ImportError("torchft is not installed. Please install it.")
+
+    pg = ft.ProcessGroupBabyNCCL()
+    manager = ft.Manager(
+        pg=pg,
+        min_replica_size=1,
+        load_state_dict=None,
+        state_dict=None,
+        use_async_quorum=True,
+        replica_id=f"torchtitan_ft_{job.experimental.ft_replica_group_id}",
+    )
+
+    return FTManager(manager, job.experimental.ft_replica_group_size)

torchtitan/optimizer.py

@@ -6,7 +6,7 @@
 
 import copy
 import functools
-from typing import Any, Callable, Dict, Iterable, List
+from typing import Any, Callable, Dict, Iterable, List, Optional
 
 import torch
 import torch.nn as nn
@@ -177,8 +177,41 @@ def zero_grad(self) -> None:
         pass
 
 
+class FTOptimizersContainer(OptimizersContainer):
+    def __init__(
+        self,
+        model_parts: List[nn.Module],
+        optimizer_kwargs: Dict[str, Any],
+        name: str,
+        ft_manager: Any,
+    ) -> None:
+        import torchft as ft
+
+        super().__init__(model_parts, optimizer_kwargs, name)
+
+        # Force to initialize the optimizer state so that `optim.step()`
+        # won't be called by state_dict() and load_state_dict().
+        _ = {
+            k: v
+            for sd in map(get_optimizer_state_dict, model_parts, self.optimizers)
+            for k, v in sd.items()
+        }
+        self.optimizers = [
+            ft.Optimizer(ft_manager.manager, optim) for optim in self.optimizers
+        ]
+        self.cache_state_dict: Dict[str, Any] = {}
+
+    def init_cache_state_dict(self) -> None:
+        self.cache_state_dict = super().state_dict()
+
+    def state_dict(self) -> Dict[str, Any]:
+        return self.cache_state_dict
+
+
 def build_optimizers(
-    model_parts: List[nn.Module], job_config: JobConfig
+    model_parts: List[nn.Module],
+    job_config: JobConfig,
+    ft_manager: Optional[Any] = None,
 ) -> OptimizersContainer:
     """Create a OptimizersContainer for the given model parts and job config.
 
@@ -213,11 +246,14 @@ def build_optimizers(
         "foreach": not fused,
     }
 
-    return (
-        OptimizersContainer(model_parts, optimizer_kwargs, name)
-        if not optim_in_bwd
-        else OptimizersInBackwardContainer(model_parts, optimizer_kwargs, name)
-    )
+    if optim_in_bwd and ft_manager:
+        raise ValueError("TorchFT is not supported with optimizers in backward.")
+    elif optim_in_bwd:
+        return OptimizersInBackwardContainer(model_parts, optimizer_kwargs, name)
+    elif ft_manager:
+        return FTOptimizersContainer(model_parts, optimizer_kwargs, name, ft_manager)
+    else:
+        return OptimizersContainer(model_parts, optimizer_kwargs, name)
 
 
 class LRSchedulersContainer(Stateful):

torchtitan/parallelisms/parallel_dims.py

@@ -6,6 +6,7 @@
 
 from dataclasses import dataclass
 from functools import cached_property
+from typing import Any, Optional
 
 from torch.distributed.device_mesh import init_device_mesh
 
@@ -24,6 +25,7 @@ class ParallelDims:
     pp: int
     world_size: int
     enable_loss_parallel: bool
+    ft_manager: Optional[Any]
 
     def __post_init__(self):
         self._validate()
@@ -56,13 +58,24 @@ def build_mesh(self, device_type):
             [self.pp, self.dp_replicate, self.dp_shard, self.cp, self.tp],
             ["pp", "dp_replicate", "dp_shard", "cp", "tp"],
         ):
-            if d > 1:
+            if d > 1 or (name == "dp_replicate" and self.ft_manager is not None):
                 dims.append(d)
                 names.append(name)
 
         logger.info(f"Building {len(dims)}-D device mesh with {names}, {dims}")
         names = tuple(names)
-        mesh = init_device_mesh(device_type, dims, mesh_dim_names=names)
+        if self.ft_manager is None:
+            mesh = init_device_mesh(device_type, dims, mesh_dim_names=names)
+        else:
+            from torchft.process_group import ft_init_device_mesh
+
+            mesh = ft_init_device_mesh(
+                device_type=device_type,
+                mesh_shape=dims,
+                mesh_dim_names=names,
+                replicate_dim=names.index("dp_replicate"),
+                manager=self.ft_manager.manager,
+            )
 
         # Create all the submesh here to ensure all required process groups are
         # initialized:
@@ -73,7 +86,7 @@ def build_mesh(self, device_type):
         # Mesh for loss all-reduce
         dp_cp_mesh_dim_names = []
 
-        if self.dp_replicate_enabled:
+        if self.dp_replicate_enabled or ft_manager is not None:
             dp_mesh_dim_names.append("dp_replicate")
             dp_cp_mesh_dim_names.append("dp_replicate")
         if self.dp_shard_enabled:
@@ -101,7 +114,7 @@ def dp_enabled(self):
 
     @property
     def dp_replicate_enabled(self):
-        return self.dp_replicate > 1
+        return self.dp_replicate > 1 or self.ft_manager is not None
 
     @property
     def dp_shard_enabled(self):

torchtitan/utils.py

@@ -5,6 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import contextlib
+import copy
 import gc
 import importlib
 import math
@@ -18,6 +19,7 @@
 import torch
 import torch.distributed._functional_collectives as funcol
 import torch.distributed.distributed_c10d as c10d
+import torchft as ft
 from torch import distributed as dist
 from torch._utils import _get_available_device_type, _get_device_module
 from torch.distributed.device_mesh import DeviceMesh
@@ -38,6 +40,12 @@ def get_device_info():
 
 
 def dist_reduce(x: torch.Tensor, reduceOp: str, mesh: DeviceMesh) -> float:
+    if isinstance(mesh, ft.process_group._FlattenDeviceMesh):
+        x = funcol.all_reduce(
+            x, reduceOp=reduceOp, group=mesh.managed_mesh.replicate_pg
+        )
+        mesh = mesh.managed_mesh.mesh
+
     if isinstance(x, DTensor):
         # functional collectives do not support DTensor inputs
         x = x.full_tensor()
@@ -407,6 +415,16 @@ def clip_grad_norm_(
     if isinstance(total_norm, DTensor):
         # Will reach here if any non-PP parallelism is used.
         # If only using PP, total_norm will be a local tensor.
+        mesh = total_norm._spec.mesh
+        if isinstance(mesh, ft.process_group.ManagedDeviceMesh):
+            # The gradients along the replicated dim has already been reduced.
+            # So we don't need another reducution beforing removing the
+            # replicate dimension
+            local_tensor = total_norm.to_local()
+            placements = list(copy.copy(total_norm._spec.placements))
+            placements.pop(mesh.replicate_dim)
+            total_norm = DTensor.from_local(local_tensor, mesh.mesh, placements)
+
         total_norm = total_norm.full_tensor()
 
     if pp_mesh is not None:

train.py

@@ -16,6 +16,7 @@
 from torchtitan.checkpoint import CheckpointManager, TrainState
 from torchtitan.config_manager import JobConfig
 from torchtitan.float8 import Float8Handler
+from torchtitan.ft import init_ft_manager
 from torchtitan.logging import init_logger, logger
 from torchtitan.metrics import build_device_memory_monitor, build_metric_logger
 from torchtitan.parallelisms import ParallelDims
@@ -42,6 +43,10 @@ def main(job_config: JobConfig):
     # take control of garbage collection to avoid stragglers
     gc_handler = utils.GarbageCollection(gc_freq=job_config.training.gc_freq)
 
+    device = torch.device(f"{device_type}:{int(os.environ['LOCAL_RANK'])}")
+    device_module.set_device(device)
+    ft_manager = init_ft_manager(job_config)
+
     # init distributed
     world_size = int(os.environ["WORLD_SIZE"])
     parallel_dims = ParallelDims(
@@ -52,9 +57,8 @@ def main(job_config: JobConfig):
         pp=job_config.experimental.pipeline_parallel_degree,
         world_size=world_size,
         enable_loss_parallel=not job_config.training.disable_loss_parallel,
+        ft_manager=ft_manager,
     )
-    device = torch.device(f"{device_type}:{int(os.environ['LOCAL_RANK'])}")
-    device_module.set_device(device)
     utils.init_distributed(job_config)
     # initialize device memory monitor and get peak flops for MFU calculation
     device_memory_monitor = build_device_memory_monitor()
@@ -187,7 +191,7 @@ def loss_fn(pred, labels):
     )
 
     # build optimizer after applying parallelisms to the model
-    optimizers = train_spec.build_optimizers_fn(model_parts, job_config)
+    optimizers = train_spec.build_optimizers_fn(model_parts, job_config, ft_manager)
     lr_schedulers = train_spec.build_lr_schedulers_fn(optimizers, job_config)
 
     train_state = TrainState()
@@ -200,6 +204,7 @@ def loss_fn(pred, labels):
         lr_schedulers=lr_schedulers,
         states={"train_state": train_state},
         job_config=job_config,
+        ft_manager=ft_manager,
     )
 
     if job_config.checkpoint.create_seed_checkpoint:
@@ -240,8 +245,6 @@ def loss_fn(pred, labels):
     time_last_log = time.perf_counter()
     device_memory_monitor.reset_peak_stats()
 
-    checkpoint.reset()
-
     # train loop
     logger.info(
         f"Training starts at step {train_state.step + 1}, "

train_configs/llama3_8b.toml

@@ -44,7 +44,6 @@ pipeline_parallel_degree = 1
 [checkpoint]
 enable_checkpoint = false
 folder = "checkpoint"
-interval_type = "steps"
 interval = 500
 model_weights_only = false
 export_dtype = "float32"