add a test for the mask expansion for sequence packing

tests/test_expand_mask.py

@@ -0,0 +1,45 @@
+"""
+Unit tests for the monkey patch for expand mask to handle packed sequences
+"""
+import unittest
+
+import torch
+
+from axolotl.monkeypatch.llama_expand_mask import _expand_mask
+
+
+class TestExpandMask(unittest.TestCase):
+    """
+    Test class for attention mask expansion for packed sequences
+    """
+
+    def test_output(self):
+        mask = torch.tensor([[1, 1, 1, 2], [2, 3, 3, 0]])
+        dtype = torch.float32
+        expected_output = torch.tensor(
+            [
+                [
+                    [
+                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [-3.4028e38, -3.4028e38, -3.4028e38, 0.0000e00],
+                    ]
+                ],
+                [
+                    [
+                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
+                        [-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [-3.4028e38, -3.4028e38, -3.4028e38, -3.4028e38],
+                    ]
+                ],
+            ]
+        )
+
+        # Check that the output matches the expected output
+        self.assertTrue(torch.allclose(_expand_mask(mask, dtype), expected_output))
+
+
+if __name__ == "__main__":
+    unittest.main()