Split test_piecewise_cuda_graph.py to optimize CI resource usage (#15290)

2025-12-17 21:20:21 -08:00
parent 3d42b7e7b0
commit 58c840db6b
4 changed files with 248 additions and 253 deletions
@@ -0,0 +1,250 @@
+import unittest
+
+import torch
+
+from sglang import Engine
+from sglang.lang.chat_template import get_chat_template_by_model_path
+from sglang.srt.utils import kill_process_tree
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_IMAGE_URL,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    SimpleNamespace,
+    popen_launch_server,
+)
+
+
+class TestPiecewiseCudaGraphGPTQ(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "Qwen/Qwen3-30B-A3B-GPTQ-Int4"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=["--enable-piecewise-cuda-graph"],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_mgsm_accuracy(self):
+        num_examples = 1319
+
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mgsm_en",
+            num_examples=num_examples,
+            num_threads=min(num_examples, 1024),
+        )
+
+        metrics = run_eval(args)
+        print(f"MGSM Accuracy: {metrics['score']:.3f}")
+
+        # Expected accuracy: 0.948, allow some variance
+        self.assertGreaterEqual(metrics["score"], 0.92)
+
+
+class TestPiecewiseCudaGraphFP8(CustomTestCase):
+    """Test piecewise CUDA graph with FP8 quantized model"""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "nvidia/Llama-3.1-8B-Instruct-FP8"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--enable-piecewise-cuda-graph",
+                "--quantization",
+                "modelopt_fp8",
+                "--kv-cache-dtype",
+                "bfloat16",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_mgsm_accuracy(self):
+        """Test MGSM accuracy with FP8 model"""
+        num_examples = 1319
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mgsm_en",
+            num_examples=num_examples,
+            num_threads=min(num_examples, 1024),
+        )
+        metrics = run_eval(args)
+        self.assertGreaterEqual(metrics["score"], 0.85)
+        print(f"MGSM Accuracy: {metrics['score']:.3f}")
+
+
+class TestPiecewiseCudaGraphQwen25VL(CustomTestCase):
+    """Test piecewise CUDA graph with Qwen2.5-VL-7B-Instruct model"""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "Qwen/Qwen2.5-VL-7B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--enable-piecewise-cuda-graph",
+                "--piecewise-cuda-graph-compiler",
+                "eager",
+                "--disable-radix-cache",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k_accuracy(self):
+        """Test GSM8K accuracy with 8-shot setting"""
+        num_examples = 2000
+
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mgsm_en",
+            num_examples=num_examples,
+            num_threads=min(num_examples, 1024),
+        )
+
+        metrics = run_eval(args)
+        print(f"GSM8K Accuracy: {metrics['score']:.3f}")
+
+        self.assertGreaterEqual(metrics["score"], 0.70)
+
+
+class TestPiecewiseCudaGraphInternVL25(CustomTestCase):
+    """Test piecewise CUDA graph with InternVL2.5-8B-Instruct model"""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "OpenGVLab/InternVL2_5-8B"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--enable-piecewise-cuda-graph",
+                "--piecewise-cuda-graph-compiler",
+                "eager",
+                "--disable-radix-cache",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k_accuracy(self):
+        """Test GSM8K accuracy with 8-shot setting"""
+        num_examples = 2000
+
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mgsm_en",
+            num_examples=num_examples,
+            num_threads=min(num_examples, 1024),
+        )
+
+        metrics = run_eval(args)
+        print(f"GSM8K Accuracy: {metrics['score']:.3f}")
+
+        self.assertGreaterEqual(metrics["score"], 0.70)
+
+
+class TestPiecewiseCudaGraphQwen25VLEmbedding(CustomTestCase):
+    """Test piecewise CUDA graph with Qwen2.5-VL-3B-Instruct embedding model"""
+
+    def test_embedding(self):
+        model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
+        chat_template = get_chat_template_by_model_path(model_path)
+        text = f"{chat_template.image_token}What is in this picture? Answer: "
+
+        engine = Engine(
+            model_path=model_path,
+            enable_multimodal=True,
+            is_embedding=True,
+            enable_piecewise_cuda_graph=True,
+            piecewise_cuda_graph_compiler="eager",
+        )
+        out = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0]["embedding"]
+        engine.shutdown()
+        self.assertGreater(len(out), 0)
+
+        engine = Engine(
+            model_path=model_path,
+            enable_multimodal=True,
+            is_embedding=True,
+            enable_piecewise_cuda_graph=False,
+        )
+        out_without_pcg = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0][
+            "embedding"
+        ]
+        engine.shutdown()
+        self.assertGreater(len(out_without_pcg), 0)
+
+        self.assertTrue(
+            torch.allclose(torch.tensor(out), torch.tensor(out_without_pcg))
+        )
+
+
+class TestPiecewiseCudaGraphAWQ(CustomTestCase):
+    """Test piecewise CUDA graph with AWQ quantized model"""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "Qwen/QwQ-32B-AWQ"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=["--enable-piecewise-cuda-graph"],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_mgsm_accuracy(self):
+        """Test MGSM accuracy with AWQ model"""
+        num_examples = 1319
+
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mgsm_en",
+            num_examples=num_examples,
+            num_threads=min(num_examples, 1024),
+        )
+
+        metrics = run_eval(args)
+        print(f"MGSM Accuracy: {metrics['score']:.3f}")
+        print(f"Output throughput: {metrics.get('throughput', 'N/A')} token/s")
+
+        # Expected accuracy: 0.680, allow some variance
+        self.assertGreaterEqual(metrics["score"], 0.65)
+
+
+if __name__ == "__main__":
+    unittest.main()