fix: prevent division by zero in ClippedPGLossFn calculation

nemo_reinforcer/algorithms/loss_functions.py

@@ -91,7 +91,7 @@ def __call__(
         mask = token_mask * sample_mask.unsqueeze(-1)
 
         lp_error = torch.abs(generation_logprobs - prev_logprobs)  # noqa: F841  (precommit ignore for now)
-        mult_prob_error = ((torch.exp(lp_error) * mask).sum() / mask.sum()).item()
+        mult_prob_error = masked_mean(torch.exp(lp_error), mask).item()
 
         next_token_logits = next_token_logits[:, :-1]  # Remove last position's logits
         next_token_logprobs = torch.nn.functional.log_softmax(next_token_logits, dim=-1)
@@ -124,13 +124,8 @@ def __call__(
         loss1 = -advantages * ratios
         loss2 = -advantages * ratios_clamped
 
-        if mask.sum() > 0:
-            actor_loss = masked_mean(torch.max(loss1, loss2), mask)
-            loss = actor_loss + kl
-        else:
-            # disable this update since there are no valid tokens
-            loss = loss1.view(-1)[0] * 0
-
+        actor_loss = masked_mean(torch.max(loss1, loss2), mask)
+        loss = actor_loss + kl
         with torch.no_grad():
             probs_ratio = masked_mean(ratios.detach(), mask).item()
             probs_ratio_clamped = masked_mean(ratios_clamped.detach(), mask).item()

nemo_reinforcer/algorithms/utils.py

@@ -120,9 +120,7 @@ def wrapper(*args, **kwargs):
 @surpress_user_warnings
 def masked_mean(values, mask, dim=None):
     """Masks values with mask, and computes the mean of the values using the masked values."""
-    if dim is None:
-        return values[mask.bool()].mean()
-    return as_masked_tensor(values, mask.bool()).mean(dim=dim).to_tensor(torch.nan)
+    return (values * mask).sum(dim=dim) / (mask.sum(dim=dim) + 1e-8)
 
 
 def set_seed(seed: int):

tests/unit/algorithms/test_loss_functions.py

@@ -386,3 +386,25 @@ def test_clipped_pg_loss_zero_mask():
 
     # Loss should be exactly zero
     torch.testing.assert_close(loss, torch.tensor(0.0, device=device))
+
+
+def test_masked_mean_all_zeros():
+    """Test masked_mean function with all zeros mask."""
+    values = torch.tensor([1.0, 2.0, 3.0, 4.0])
+    mask = torch.zeros_like(values)
+
+    # All zeros mask should return 0
+    result = masked_mean(values, mask)
+    print(result)
+    torch.testing.assert_allclose(result, torch.tensor(0.0))
+
+    # With check_zero_mask=False
+    mask[0] = 1
+    result = masked_mean(values, mask)
+    torch.testing.assert_allclose(result, torch.tensor(1.0))
+
+    # Case 2: dim is not None
+    values = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
+    mask = torch.zeros_like(values)
+    result = masked_mean(values, mask, dim=1)
+    torch.testing.assert_allclose(result, torch.tensor([0.0, 0.0]))