Merge branch 'main' into patch-1

Author: AlannaBurke

intermediate_source/FSDP_tutorial.rst

@@ -73,7 +73,7 @@ Model Initialization
     #  )
 
 We can inspect the nested wrapping with ``print(model)``. ``FSDPTransformer`` is a joint class of `Transformer <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L100>`_ and `FSDPModule
-<​https://docs.pytorch.org/docs/main/distributed.fsdp.fully_shard.html#torch.distributed.fsdp.FSDPModule>`_. The same thing happens to `FSDPTransformerBlock <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L76C7-L76C18>`_. All FSDP2 public APIs are exposed through ``FSDPModule``. For example, users can call ``model.unshard()`` to manually control all-gather schedules. See "explicit prefetching" below for details.
+<https://docs.pytorch.org/docs/main/distributed.fsdp.fully_shard.html#torch.distributed.fsdp.FSDPModule>`_. The same thing happens to `FSDPTransformerBlock <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L76C7-L76C18>`_. All FSDP2 public APIs are exposed through ``FSDPModule``. For example, users can call ``model.unshard()`` to manually control all-gather schedules. See "explicit prefetching" below for details.
 
 **model.parameters() as DTensor**: ``fully_shard`` shards parameters across ranks, and convert ``model.parameters()`` from plain ``torch.Tensor`` to DTensor to represent sharded parameters. FSDP2 shards on dim-0 by default so DTensor placements are `Shard(dim=0)`. Say we have N ranks and a parameter with N rows before sharding. After sharding, each rank will have 1 row of the parameter. We can inspect sharded parameters using ``param.to_local()``.
 

unstable_source/flight_recorder_tutorial.rst

@@ -56,7 +56,7 @@ There are three required environment variables to get the initial version of Fli
   We recommended to set this value at *2000*. The default value is ``2000``.
 - ``TORCH_NCCL_DUMP_ON_TIMEOUT = (true, false)``: Setting this to ``true`` will write out diagnostic files to disk on job timeout.
   If enabled, there will be one file per rank output in the job's running directory. The default value is ``false``.
-- ``TORCH_NCCL_DEBUG_INFO_TEMP_FILE``: Setting the path where the flight recorder will be dumped with file prefix. One file per
+- ``TORCH_FR_DUMP_TEMP_FILE``: Setting the path where the flight recorder will be dumped with file prefix. One file per
   rank. The default value is ``/tmp/nccl_trace_rank_``.
 
 **Optional settings:**