fix: Fix fsdp1 grad clipping and log grad norm

nemo_reinforcer/algorithms/dpo.py

@@ -482,6 +482,7 @@ def dpo_train(
             losses = train_results["loss"]
             metrics = {
                 "loss": train_results["loss"].numpy(),
+                "grad_norm": train_results["grad_norm"].numpy(),
             }
             metrics.update(train_results["all_mb_metrics"])
             for k, v in metrics.items():

nemo_reinforcer/algorithms/grpo.py

@@ -576,6 +576,7 @@ def grpo_train(
         metrics = {
             "loss": train_results["loss"].numpy(),
             "reward": rewards.numpy(),
+            "grad_norm": train_results["grad_norm"].numpy(),
         }
         metrics.update(train_results["all_mb_metrics"])
         for k, v in metrics.items():

nemo_reinforcer/algorithms/sft.py

@@ -486,6 +486,7 @@ def sft_train(
             losses = train_results["loss"]
             metrics = {
                 "loss": train_results["loss"].numpy(),
+                "grad_norm": train_results["grad_norm"].numpy(),
             }
             metrics.update(train_results["all_mb_metrics"])
             for k, v in metrics.items():

nemo_reinforcer/models/policy/dtensor_policy_worker.py

@@ -369,6 +369,7 @@ def train(
                                 total_norm=grad_norm,
                                 dtype=torch.float32,
                             )
+                        grad_norm = torch.tensor([grad_norm])
 
                     # Update parameters
                     self.optimizer.step()

nemo_reinforcer/models/policy/fsdp1_policy_worker.py

@@ -310,10 +310,20 @@ def train(
                         all_mb_metrics.append(loss_metrics)
 
                 # Clip gradients
+                grad_norm = None
                 if not eval_mode:
-                    torch.nn.utils.clip_grad_norm_(
-                        self.model.parameters(), max_norm=self.cfg["max_grad_norm"]
-                    )
+                    if isinstance(self.model, FullyShardedDataParallel):
+                        # when using FSDP1, use FSDP's clip_grad_norm_
+                        # to ensure grad norm is being computed over all parameters
+                        # see https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_
+                        grad_norm = self.model.clip_grad_norm_(
+                            max_norm=self.cfg["max_grad_norm"]
+                        )
+                    else:
+                        grad_norm = torch.nn.utils.clip_grad_norm_(
+                            self.model.parameters(), max_norm=self.cfg["max_grad_norm"]
+                        )
+                    grad_norm = grad_norm.cpu()
 
                     # Update parameters
                     self.optimizer.step()
@@ -336,6 +346,7 @@ def train(
             metrics = {
                 "global_loss": global_loss.cpu(),
                 "local_loss": local_loss.cpu(),
+                "grad_norm": grad_norm,
                 "rank": torch.distributed.get_rank(),
                 "all_mb_metrics": dict(mb_metrics),
             }

nemo_reinforcer/models/policy/hf_policy.py

@@ -177,8 +177,10 @@ def train(
         results = self.worker_group.get_all_worker_results(futures)
 
         # Aggregate the results
-        aggregated_results = {}
-        aggregated_results["loss"] = results[0]["global_loss"]
+        aggregated_results = {
+            "loss": results[0]["global_loss"],
+            "grad_norm": results[0]["grad_norm"],
+        }
 
         # Aggregate metrics across all workers
         all_mb_metrics = defaultdict(list)

tests/unit/algorithms/test_sft.py

@@ -24,7 +24,11 @@
 def mock_components():
     # Create mock components
     policy = MagicMock()
-    policy.train.return_value = {"loss": torch.tensor(0.5), "all_mb_metrics": {}}
+    policy.train.return_value = {
+        "loss": torch.tensor(0.5),
+        "grad_norm": torch.tensor(1.0),
+        "all_mb_metrics": {},
+    }
 
     # Create a proper message log structure with token_ids
     mock_batch = {