ci: migrate DLLM tests to test/registered/dllm/ (#16420)

test/registered/dllm/test_llada2_mini.py

@@ -0,0 +1,85 @@
+from sglang.test.ci.ci_register import register_cuda_ci
+
+register_cuda_ci(est_time=520, suite="stage-b-test-small-1-gpu")
+
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.send_one import BenchArgs, send_one_prompt
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_amd_ci,
+    is_in_ci,
+    popen_launch_server,
+    write_github_step_summary,
+)
+
+
+class TestLLaDA2Mini(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "inclusionAI/LLaDA2.0-mini"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+
+        other_args = [
+            "--trust-remote-code",
+            "--mem-fraction-static",
+            "0.9",
+            "--max-running-requests",
+            "1",
+            "--attention-backend",
+            "flashinfer",
+            "--dllm-algorithm",
+            "LowConfidence",  # TODO: Add dLLM configurations
+        ]
+
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"{metrics=}")
+
+        self.assertGreater(metrics["accuracy"], 0.88)
+        self.assertGreater(metrics["output_throughput"], 150)
+
+    def test_bs_1_speed(self):
+        args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
+        acc_length, speed = send_one_prompt(args)
+
+        print(f"{speed=:.2f}")
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_bs_1_speed (llada2-mini) with tp1\n"
+                f"{speed=:.2f} token/s\n"
+            )
+            if is_in_amd_ci():
+                self.assertGreater(speed, 10)
+            else:
+                self.assertGreater(speed, 250)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/dllm/test_llada2_mini_amd.py

@@ -0,0 +1,91 @@
+from sglang.test.ci.ci_register import register_amd_ci
+
+register_amd_ci(est_time=520, suite="stage-b-test-small-1-gpu")
+
+"""
+Test LLaDA2 (Diffusion Language Model) on AMD GPUs.
+
+This test verifies that DLLM works on AMD with triton attention backend.
+"""
+
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.send_one import BenchArgs, send_one_prompt
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+    write_github_step_summary,
+)
+
+
+class TestLLaDA2MiniAMD(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "inclusionAI/LLaDA2.0-mini"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+
+        other_args = [
+            "--trust-remote-code",
+            "--mem-fraction-static",
+            "0.9",
+            "--max-running-requests",
+            "1",
+            "--attention-backend",
+            "triton",  # Use triton for AMD instead of flashinfer
+            "--dllm-algorithm",
+            "LowConfidence",
+        ]
+
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        """Test GSM8K accuracy with DLLM on AMD."""
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"{metrics=}")
+
+        # Relaxed thresholds for AMD - may need adjustment
+        self.assertGreater(metrics["accuracy"], 0.80)
+        self.assertGreater(metrics["output_throughput"], 50)
+
+    def test_bs_1_speed(self):
+        """Test single batch inference speed."""
+        args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
+        acc_length, speed = send_one_prompt(args)
+
+        print(f"{speed=:.2f}")
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_bs_1_speed (llada2-mini AMD) with tp1\n"
+                f"{speed=:.2f} token/s\n"
+            )
+            # Relaxed threshold for AMD
+            self.assertGreater(speed, 10)
+
+
+if __name__ == "__main__":
+    unittest.main()