Add tensor.detach() back to update weight util (#8691)

python/sglang/srt/weight_sync/utils.py

@@ -45,7 +45,7 @@ async def update_weights(
         (
             name,
             MultiprocessingSerializer.serialize(
-                _preprocess_tensor_for_update_weights(tensor)
+                _preprocess_tensor_for_update_weights(tensor.detach())
             ),
         )
         for name, tensor in params_batch

test/srt/test_utils_update_weights.py

@@ -1,10 +1,9 @@
 import asyncio
 import os
+import unittest
 
-import pytest
 import torch
 import torch.distributed as dist
-from loguru import logger
 from torch.distributed.device_mesh import init_device_mesh
 from transformers import AutoModelForCausalLM
 
@@ -39,11 +38,29 @@ def setup_single_process_distributed():
         os.environ["LOCAL_RANK"] = "0"
 
 
-class TestUtilsUpdateWeights:
+class TestUtilsUpdateWeights(unittest.TestCase):
     """Test class for utils.update_weights function"""
 
-    @pytest.fixture(scope="class")
-    def setup_distributed(self):
+    @classmethod
+    def setUpClass(cls):
+        """Setup distributed environment and test fixtures for the entire test class"""
+        cls.setup_distributed()
+        cls.setup_test_engine()
+        cls.setup_test_model()
+        cls.setup_device_mesh()
+
+    @classmethod
+    def tearDownClass(cls):
+        """Cleanup after all tests"""
+        if hasattr(cls, "engine") and cls.engine:
+            cls.engine.shutdown()
+
+        # Cleanup distributed
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+    @classmethod
+    def setup_distributed(cls):
         """Setup distributed environment for testing"""
         setup_single_process_distributed()
 
@@ -53,13 +70,15 @@ class TestUtilsUpdateWeights:
                     backend="nccl" if torch.cuda.is_available() else "gloo"
                 )
             except Exception as e:
-                pytest.skip(f"Could not initialize distributed backend: {e}")
+                raise unittest.SkipTest(
+                    f"Could not initialize distributed backend: {e}"
+                )
 
-        rank = dist.get_rank()
-        world_size = dist.get_world_size()
+        cls.rank = dist.get_rank()
+        cls.world_size = dist.get_world_size()
 
         if torch.cuda.is_available():
-            torch.cuda.set_device(rank % torch.cuda.device_count())
+            torch.cuda.set_device(cls.rank % torch.cuda.device_count())
 
         # Set up environment variables
         os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
@@ -68,38 +87,26 @@ class TestUtilsUpdateWeights:
         os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4"
         os.environ["CUDA_MODULE_LOADING"] = "AUTO"
 
-        yield rank, world_size
-
-        # Cleanup
-        if dist.is_initialized():
-            dist.destroy_process_group()
-
-    @pytest.fixture(scope="class")
-    def test_engine(self, setup_distributed):
+    @classmethod
+    def setup_test_engine(cls):
         """Setup test engine"""
-        rank, world_size = setup_distributed
-
-        if rank == 0:
-            os.environ["SGLANG_BLOCK_NONZERO_RANK_CHILDREN"] = "0"
-            engine = AsyncEngine(
+        if cls.rank == 0:
+            cls.engine = AsyncEngine(
                 model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
                 dtype="bfloat16",
                 mem_fraction_static=0.3,
                 enable_memory_saver=True,
-                tp_size=world_size,
-                disable_cuda_graph=True,
+                tp_size=cls.world_size,
+                disable_cuda_graph=False,
             )
-            yield engine
-            engine.shutdown()
-
         else:
-            yield None
+            cls.engine = None
 
-    @pytest.fixture(scope="class")
-    def test_model(self):
+    @classmethod
+    def setup_test_model(cls):
         """Load test model"""
         try:
-            model = AutoModelForCausalLM.from_pretrained(
+            cls.model = AutoModelForCausalLM.from_pretrained(
                 DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
                 device_map="cpu",
                 trust_remote_code=True,
@@ -108,25 +115,20 @@ class TestUtilsUpdateWeights:
                     torch.float16 if torch.cuda.is_available() else torch.float32
                 ),
             )
-            return model
         except Exception as e:
-            pytest.skip(f"Could not load test model: {e}")
+            raise unittest.SkipTest(f"Could not load test model: {e}")
 
-    @pytest.fixture(scope="class")
-    def device_mesh(self, setup_distributed):
+    @classmethod
+    def setup_device_mesh(cls):
         """Create device mesh for testing"""
-        rank, world_size = setup_distributed
-
         if not torch.cuda.is_available():
-            pytest.skip("CUDA not available for device mesh")
+            raise unittest.SkipTest("CUDA not available for device mesh")
 
-        device_mesh_key = "tp"
-        mesh = init_device_mesh(
-            "cuda", (world_size,), mesh_dim_names=(device_mesh_key,)
+        cls.device_mesh_key = "tp"
+        cls.mesh = init_device_mesh(
+            "cuda", (cls.world_size,), mesh_dim_names=(cls.device_mesh_key,)
         )
 
-        return device_mesh_key, mesh
-
     def create_test_params_batch(self, model, num_params=64):
         """Create a batch of test parameters from the model"""
         param_names = []
@@ -143,31 +145,27 @@ class TestUtilsUpdateWeights:
 
         return list(zip(param_names, test_tensors))
 
-    @pytest.mark.asyncio
-    async def test_utils_update_weights(
-        self, setup_distributed, test_engine, test_model, device_mesh
-    ):
+    def test_utils_update_weights(self):
         """Test basic functionality of utils.update_weights"""
-        rank, world_size = setup_distributed
-        device_mesh_key, mesh = device_mesh
 
-        # Create test parameters batch
-        params_batch = self.create_test_params_batch(test_model, num_params=2)
+        async def async_test():
+            # Create test parameters batch
+            params_batch = self.create_test_params_batch(self.model, num_params=2)
 
-        print(
-            f"Rank {rank} testing utils.update_weights with {len(params_batch)} parameters"
-        )
-        # Test the utils.update_weights function
-        result = await update_weights(
-            engine=test_engine,
-            params_batch=params_batch,
-            device_mesh_key=device_mesh_key,
-            device_mesh=mesh,
-            load_format=None,
-        )
+            # Test the utils.update_weights function
+            result = await update_weights(
+                engine=self.engine,
+                params_batch=params_batch,
+                device_mesh_key=self.device_mesh_key,
+                device_mesh=self.mesh,
+                load_format=None,
+            )
 
-        assert "Success" in result
+            self.assertIn("Success", result)
+
+        # Run the async test
+        asyncio.run(async_test())
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    unittest.main()