fix: typecast bf16 to float in MIA

docs/contributing.md

@@ -153,7 +153,7 @@ Follow the steps below to start contributing:
 4. Set up the environment in dev mode after following steps in [Quick Start](../README.md#-quickstart). This installs other packages such as `ruff`, `precommit` etc.
 
    ```bash
-   pip install .[dev]
+   pip install ".[dev]"
    ```
 
 5. Develop the features in your fork/branch.

src/evals/metrics/mia/min_k.py

@@ -17,7 +17,7 @@ def compute_batch_values(self, batch):
 
     def compute_score(self, sample_stats):
         """Score single sample using min-k negative log probs scores attack."""
-        lp = sample_stats.cpu().numpy()
+        lp = sample_stats.float().cpu().numpy()
         if lp.size == 0:
             return 0
 

src/evals/metrics/mia/min_k_plus_plus.py

@@ -30,9 +30,9 @@ def compute_score(self, sample_stats):
 
         # Handle numerical stability
         sigma = torch.clamp(sigma, min=1e-6)
-        scores = (target_prob.cpu().numpy() - mu.cpu().numpy()) / torch.sqrt(
-            sigma
-        ).cpu().numpy()
+        scores = (
+            target_prob.float().cpu().numpy() - mu.float().cpu().numpy()
+        ) / torch.sqrt(sigma).cpu().numpy()
 
         # Take bottom k% as the attack score
         num_k = max(1, int(len(scores) * self.k))