Unit test watermark

server/tests/utils/test_watermark.py

@@ -0,0 +1,54 @@
+# test_watermark_logits_processor.py
+
+import os
+import numpy as np
+import torch
+from text_generation_server.utils.watermark import WatermarkLogitsProcessor
+
+
+GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
+DELTA = os.getenv("WATERMARK_DELTA", 2.0)
+
+
+def test_seed_rng():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    processor._seed_rng(input_ids)
+    assert isinstance(processor.rng, torch.Generator)
+
+
+def test_get_greenlist_ids():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    result = processor._get_greenlist_ids(input_ids, 10, torch.device("cpu"))
+    assert max(result) <= 10
+    assert len(result) == int(10 * 0.5)
+
+
+def test_calc_greenlist_mask():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    greenlist_token_ids = torch.tensor([2, 3])
+    result = processor._calc_greenlist_mask(scores, greenlist_token_ids)
+    assert result.tolist() == [[False, False, False, False], [False, False, True, True]]
+    assert result.shape == scores.shape
+
+
+def test_bias_greenlist_logits():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    green_tokens_mask = torch.tensor(
+        [[False, False, True, True], [False, False, False, True]]
+    )
+    greenlist_bias = 2.0
+    result = processor._bias_greenlist_logits(scores, green_tokens_mask, greenlist_bias)
+    assert np.allclose(result.tolist(), [[0.5, 0.3, 2.2, 2.8], [0.1, 0.2, 0.7, 2.9]])
+    assert result.shape == scores.shape
+
+
+def test_call():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    result = processor(input_ids, scores)
+    assert result.shape == scores.shape