set pg names (#1986)

Summary:
- we need to pass the global rank information to pytorch so that the pg
name can include the pg information
- this is necessary to differentiate the default pg's on different
replicas
- these need to different because flight recorder matches collectives
based on pg name as well
- add ft training to experiments folder, we'll move remaining pieces of
ft to this gradually but make new features only available through this
folder

---
[//]: # (BEGIN SAPLING FOOTER)
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* #1988
* #1987
* __->__ #1986

Co-authored-by: Tushar Jain <tushar00jain@users.noreply.github.com>

Author: tushar00jain

torchtitan/distributed/utils.py

@@ -259,7 +259,10 @@ def maybe_enable_amp(
 
 
 def init_distributed(
-    comm_config: CommConfig, enable_cpu_backend: bool = False, base_folder: str = ""
+    comm_config: CommConfig,
+    enable_cpu_backend: bool = False,
+    base_folder: str = "",
+    ranks: list[int] | None = None,
 ):
     def _warn_overwrite_env(env, val):
         if env in os.environ:
@@ -303,6 +306,7 @@ def _get_distributed_backend(enable_cpu_backend):
     torch.distributed.init_process_group(
         backend=_get_distributed_backend(enable_cpu_backend),
         timeout=timedelta(seconds=comm_config.init_timeout_seconds),
+        _ranks=ranks if ranks is not None else [],
     )
 
 

torchtitan/experiments/ft/train.py

@@ -0,0 +1,51 @@
+# 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 os
+
+from torchtitan.distributed import ParallelDims, utils as dist_utils
+from torchtitan.train import main, Trainer
+
+
+class FTTrainer(Trainer):
+    def init_distributed(self) -> ParallelDims:
+        job_config = self.job_config
+
+        # determine the global ranks when fault tolerance is enabled
+        global_ranks = []
+        ft_config = job_config.fault_tolerance
+        if ft_config.enable:
+            group_size = ft_config.group_size
+            replica_id = ft_config.replica_id
+            first_rank = replica_id * group_size
+            last_rank = first_rank + group_size - 1
+            global_ranks = list(range(first_rank, last_rank + 1))
+
+        # init distributed and build meshes
+        dist_utils.init_distributed(
+            job_config.comm,
+            enable_cpu_backend=job_config.training.enable_cpu_offload,
+            base_folder=job_config.job.dump_folder,
+            ranks=global_ranks,
+        )
+
+        world_size = int(os.environ["WORLD_SIZE"])
+        parallelism_config = job_config.parallelism
+
+        return ParallelDims(
+            dp_shard=parallelism_config.data_parallel_shard_degree,
+            dp_replicate=parallelism_config.data_parallel_replicate_degree,
+            cp=parallelism_config.context_parallel_degree,
+            tp=parallelism_config.tensor_parallel_degree,
+            pp=parallelism_config.pipeline_parallel_degree,
+            ep=parallelism_config.expert_parallel_degree,
+            etp=parallelism_config.expert_tensor_parallel_degree,
+            world_size=world_size,
+        )
+
+
+if __name__ == "__main__":
+    main(FTTrainer)