FIX TST NaN issue with HQQ GPU test (#2143)

This test calculates the correlation coefficient of HQQ model outputs.
Although the model outputs are finite, the resulting matrix contains
NaNs. Casting the outputs from 16 to 32 bit precision resolves the
issue.

tests/test_gpu_examples.py

@@ -2917,18 +2917,18 @@ def test_hqq_lora_model_outputs(self):
             output_hqq = model(**inputs).logits
 
         # check that outputs of HQQ are highly correlated; there are outliers, so don't check for equality
-        cc_matrix = torch.corrcoef(torch.stack((output_normal.flatten(), output_hqq.flatten())))
+        cc_matrix = torch.corrcoef(torch.stack((output_normal.float().flatten(), output_hqq.float().flatten())))
         assert cc_matrix.min() > 0.97
 
         # check that outputs are the same after merging
-        cc_matrix = torch.corrcoef(torch.stack((output_normal.flatten(), output_hqq.flatten())))
+        cc_matrix = torch.corrcoef(torch.stack((output_normal.float().flatten(), output_hqq.float().flatten())))
         assert cc_matrix.min() > 0.97
 
         # check outputs are the same after unmerging
         model.unmerge_adapter()
         with torch.inference_mode():
             output_unmerged = model(**inputs).logits
-        cc_matrix = torch.corrcoef(torch.stack((output_normal.flatten(), output_unmerged.flatten())))
+        cc_matrix = torch.corrcoef(torch.stack((output_normal.float().flatten(), output_unmerged.float().flatten())))
         assert cc_matrix.min() > 0.97
 
         # check that the results are the same after saving and loading
@@ -2957,7 +2957,9 @@ def test_hqq_lora_model_outputs(self):
         model = model.merge_and_unload()
         with torch.inference_mode():
             output_merged_unloaded = model(**inputs).logits
-        cc_matrix = torch.corrcoef(torch.stack((output_normal.flatten(), output_merged_unloaded.flatten())))
+        cc_matrix = torch.corrcoef(
+            torch.stack((output_normal.float().flatten(), output_merged_unloaded.float().flatten()))
+        )
         assert cc_matrix.min() > 0.97