[v1] shorten grad_norm fix comment

Author: mhh111

src/llamafactory/v1/core/base_trainer.py

@@ -279,30 +279,19 @@ def fit(self) -> None:
                     # deepspeed: engine.step() already ran inside backward at the sync boundary
                     grad_norm = self._deepspeed_engine.get_grad_norm()
                 else:
-                    # FSDP2 shards params/grads as DTensors across the fsdp mesh, so each rank only
-                    # holds 1/shard_size of every parameter. `get_total_norm`/`clip_grad_norm_`
-                    # return a *per-rank local shard* norm and `.item()` reads only that local
-                    # partial (== global_norm / sqrt(shard_size)). Using it directly makes the
-                    # reported grad_norm scale as 1/sqrt(dp_size) (e.g. 8xdp mbs1 vs 4xdp mbs2
-                    # differ by sqrt(2)), and -- worse -- makes `clip_grad_norm_` apply the clip
-                    # coefficient per-shard, corrupting the update once clipping is actually on.
-                    # Fix: reduce to the true global norm first (full_tensor all-reduces across the
-                    # fsdp shard mesh), then clip with that scalar via clip_grads_with_norm_.
+                    # FSDP2 shards params/grads across the fsdp mesh, so clip_grad_norm_ returns a
+                    # per-rank local shard norm (global / sqrt(shard_size)): reported grad_norm then
+                    # scales as 1/sqrt(dp_size) and the clip coefficient is applied per-shard. Reduce
+                    # to the true global norm first, then clip with it.
                     from torch.distributed.tensor import DTensor
 
                     grads = [p.grad for p in self.model.parameters() if p.grad is not None]
                     total_norm = torch.nn.utils.get_total_norm(grads)
                     if isinstance(total_norm, DTensor):
-                        # full_tensor already all-reduces across the whole fsdp shard mesh. With the
-                        # default mp_shard_size = world_size this mesh spans CP too, so FSDP's
-                        # reduce-scatter has already summed grads across CP -- no separate CP
-                        # reduction is wanted (it would over-count grad_norm by sqrt(cp_size) and
-                        # also skew the clip coefficient below). CP-on and CP-off both report the
-                        # true global norm this way.
+                        # full_tensor all-reduces across the fsdp mesh (spans CP under default
+                        # mp_shard=world); a separate CP reduce would over-count by sqrt(cp_size).
                         total_norm = total_norm.full_tensor()
-                    # pass the (replicated) global norm as a Tensor -- clip_grads_with_norm_ does
-                    # torch.clamp(max_norm / (total_norm + 1e-6), max=1.0), which rejects a bare
-                    # python float. .item() for reporting only, after clipping.
+                    # pass a Tensor: clip_grads_with_norm_ clamps max_norm / (total_norm + 1e-6).
                     torch.nn.utils.clip_grads_with_norm_(
                         self.model.parameters(), self.args.max_grad_norm, total_norm
                     )