[Test] Add Functional Tests for Penalty Parameters (#11931)

test/srt/run_suite.py

@@ -108,7 +108,7 @@ suites = {
         TestFile("test_no_overlap_scheduler.py", 234),
         TestFile("test_original_logprobs.py", 41),
         TestFile("test_page_size.py", 60),
-        TestFile("test_penalty.py", 41),
+        TestFile("test_penalty.py", 82),
         TestFile("test_priority_scheduling.py", 130),
         TestFile("test_pytorch_sampling_backend.py", 66),
         TestFile("test_radix_attention.py", 105),
@@ -266,7 +266,7 @@ suite_amd = {
         TestFile("test_mla_deepseek_v3.py", 221),
         TestFile("test_no_chunked_prefill.py", 108),
         TestFile("test_page_size.py", 60),
-        TestFile("test_penalty.py", 41),
+        TestFile("test_penalty.py", 180),
         TestFile("test_pytorch_sampling_backend.py", 66),
         TestFile("test_radix_attention.py", 105),
         TestFile("test_reasoning_parser.py", 5),

test/srt/test_penalty.py

@@ -1,5 +1,6 @@
 import json
 import random
+import re
 import unittest
 from concurrent.futures import ThreadPoolExecutor
 
@@ -16,7 +17,6 @@ from sglang.test.test_utils import (
 
 
 class TestPenalty(CustomTestCase):
-
     @classmethod
     def setUpClass(cls):
         cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
@@ -32,6 +32,7 @@ class TestPenalty(CustomTestCase):
         kill_process_tree(cls.process.pid)
 
     def run_decode(self, sampling_params):
+        """Helper method for basic decode tests."""
         return_logprob = True
         top_logprobs_num = 5
         return_text = True
@@ -57,6 +58,75 @@ class TestPenalty(CustomTestCase):
         print(json.dumps(response.json()))
         print("=" * 100)
 
+    def run_generate_with_prompt(self, prompt, sampling_params, max_tokens=100):
+        """Helper method to generate text with a specific prompt and parameters."""
+        sampling_params.setdefault("temperature", 0.05)
+        sampling_params.setdefault("top_p", 1.0)
+
+        response = requests.post(
+            self.base_url + "/v1/chat/completions",
+            json={
+                "model": self.model,
+                "messages": [{"role": "user", "content": prompt}],
+                "max_tokens": max_tokens,
+                **sampling_params,
+            },
+        )
+        self.assertEqual(response.status_code, 200)
+        result = response.json()
+        content = result["choices"][0]["message"]["content"]
+        return content
+
+    def count_word_repetitions(self, text, word):
+        """Count how many times a specific word appears in the text."""
+        return len(re.findall(r"\b" + re.escape(word) + r"\b", text.lower()))
+
+    def _test_penalty_effect(
+        self,
+        prompt,
+        baseline_params,
+        penalty_params,
+        target_word,
+        expected_reduction=True,
+        max_tokens=50,
+    ):
+        """Generic test for penalty effects."""
+        # Run multiple iterations to get more reliable results
+        baseline_counts = []
+        penalty_counts = []
+
+        for i in range(5):
+            baseline_output = self.run_generate_with_prompt(
+                prompt, baseline_params, max_tokens
+            )
+            penalty_output = self.run_generate_with_prompt(
+                prompt, penalty_params, max_tokens
+            )
+
+            baseline_count = self.count_word_repetitions(baseline_output, target_word)
+            penalty_count = self.count_word_repetitions(penalty_output, target_word)
+
+            baseline_counts.append(baseline_count)
+            penalty_counts.append(penalty_count)
+
+        # Calculate averages
+        avg_baseline = sum(baseline_counts) / len(baseline_counts)
+        avg_penalty = sum(penalty_counts) / len(penalty_counts)
+
+        if expected_reduction:
+            # Simple check: penalty should reduce repetition
+            self.assertLess(
+                avg_penalty,
+                avg_baseline,
+                f"Penalty should reduce '{target_word}' repetition: {avg_baseline:.1f} → {avg_penalty:.1f}",
+            )
+        else:
+            self.assertGreater(
+                avg_penalty,
+                avg_baseline,
+                f"Negative penalty should increase '{target_word}' repetition",
+            )
+
     def test_default_values(self):
         self.run_decode({})
 
@@ -90,6 +160,87 @@ class TestPenalty(CustomTestCase):
         with ThreadPoolExecutor(8) as executor:
             list(executor.map(self.run_decode, args))
 
+    def test_frequency_penalty_reduces_word_repetition(self):
+        """Test frequency penalty using word repetition."""
+        prompt = "Write exactly 10 very small sentences, each containing the word 'data'. Use the word 'data' as much as possible."
+        baseline_params = {"frequency_penalty": 0.0, "repetition_penalty": 1.0}
+        penalty_params = {"frequency_penalty": 1.99, "repetition_penalty": 1.0}
+        self._test_penalty_effect(prompt, baseline_params, penalty_params, "data")
+
+    def test_presence_penalty_reduces_topic_repetition(self):
+        """Test presence penalty using topic repetition."""
+        prompt = "Write the word 'machine learning' exactly 20 times in a row, separated by spaces."
+        baseline_params = {"presence_penalty": 0.0, "repetition_penalty": 1.0}
+        penalty_params = {"presence_penalty": 1.99, "repetition_penalty": 1.0}
+        self._test_penalty_effect(
+            prompt, baseline_params, penalty_params, "machine learning"
+        )
+
+    def test_combined_penalties_reduce_repetition(self):
+        """Test combined penalty effects."""
+        prompt = "Write exactly 10 short sentences, each containing the word 'data'. Use the word 'data' as much as possible."
+        baseline_params = {
+            "frequency_penalty": 0.0,
+            "presence_penalty": 0.0,
+            "repetition_penalty": 1.0,
+        }
+        penalty_params = {
+            "frequency_penalty": 1.99,
+            "presence_penalty": 1.99,
+            "repetition_penalty": 1.99,
+        }
+        self._test_penalty_effect(
+            prompt, baseline_params, penalty_params, "data", max_tokens=100
+        )
+
+    def test_penalty_edge_cases_negative_penalty_values(self):
+        """Test edge cases with negative penalty values."""
+        prompt = "Write the word 'test' exactly 15 times in a row, separated by spaces."
+        baseline_params = {
+            "frequency_penalty": 0.0,
+            "presence_penalty": 0.0,
+            "repetition_penalty": 1.0,
+        }
+        negative_penalty_params = {
+            "frequency_penalty": -0.5,
+            "presence_penalty": -0.25,
+            "repetition_penalty": 1.0,
+        }
+        # Negative penalties should increase repetition (expected_reduction=False)
+        self._test_penalty_effect(
+            prompt,
+            baseline_params,
+            negative_penalty_params,
+            "test",
+            expected_reduction=False,
+            max_tokens=60,
+        )
+
+    def test_penalty_edge_cases_extreme_penalty_values(self):
+        """Test edge cases with extreme penalty values."""
+        prompt = (
+            "Write the word 'extreme' exactly 20 times in a row, separated by spaces."
+        )
+        baseline_params = {
+            "frequency_penalty": 0.0,
+            "presence_penalty": 0.0,
+            "repetition_penalty": 1.0,
+        }
+        extreme_penalty_params = {
+            "frequency_penalty": 2.0,
+            "presence_penalty": 2.0,
+            "repetition_penalty": 2.0,
+        }
+        # Extreme penalties should strongly reduce repetition
+        self._test_penalty_effect(
+            prompt,
+            baseline_params,
+            extreme_penalty_params,
+            "extreme",
+            expected_reduction=True,
+            max_tokens=80,
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=3)