Extract update_weights from RL Engine to SGLang to keep simplicity and fix torch reduce (#8267)

Co-authored-by: CuiBo 82354186+SuperCB@users.noreply.github.com
Co-authored-by: GeLee 865038696@qq.com
Co-authored-by: 杨睿 yangruipis@163.com

test/srt/run_suite.py

@@ -101,6 +101,7 @@ suites = {
         TestFile("test_triton_sliding_window.py", 250),
         TestFile("test_update_weights_from_disk.py", 114),
         TestFile("test_update_weights_from_tensor.py", 48),
+        TestFile("test_utils_update_weights.py", 48),
         TestFile("test_vertex_endpoint.py", 31),
         TestFile("test_vision_chunked_prefill.py", 175),
         TestFile("test_vlm_input_format.py", 300),

test/srt/test_utils_update_weights.py

@@ -0,0 +1,173 @@
+import asyncio
+import os
+
+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
+
+from sglang.srt.entrypoints.engine import Engine
+from sglang.srt.weight_sync.utils import update_weights
+from sglang.test.test_utils import DEFAULT_SMALL_MODEL_NAME_FOR_TEST
+
+
+class AsyncEngine(Engine):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    async def update_weights_from_tensor(self, update_weights_request):
+        return await self.tokenizer_manager.update_weights_from_tensor(
+            update_weights_request, None
+        )
+
+
+def is_distributed_available():
+    """Check if distributed training environment is available"""
+    required_vars = ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT"]
+    return all(var in os.environ for var in required_vars)
+
+
+def setup_single_process_distributed():
+    """Setup distributed environment for single process testing"""
+    if not is_distributed_available():
+        os.environ["RANK"] = "0"
+        os.environ["WORLD_SIZE"] = "1"
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = "12356"
+        os.environ["LOCAL_RANK"] = "0"
+
+
+class TestUtilsUpdateWeights:
+    """Test class for utils.update_weights function"""
+
+    @pytest.fixture(scope="class")
+    def setup_distributed(self):
+        """Setup distributed environment for testing"""
+        setup_single_process_distributed()
+
+        if not dist.is_initialized():
+            try:
+                dist.init_process_group(
+                    backend="nccl" if torch.cuda.is_available() else "gloo"
+                )
+            except Exception as e:
+                pytest.skip(f"Could not initialize distributed backend: {e}")
+
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+
+        if torch.cuda.is_available():
+            torch.cuda.set_device(rank % torch.cuda.device_count())
+
+        # Set up environment variables
+        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+        os.environ["NCCL_CUMEM_ENABLE"] = "0"
+        os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+        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):
+        """Setup test engine"""
+        rank, world_size = setup_distributed
+
+        if rank == 0:
+            os.environ["SGLANG_BLOCK_NONZERO_RANK_CHILDREN"] = "0"
+            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,
+            )
+            yield engine
+            engine.shutdown()
+
+        else:
+            yield None
+
+    @pytest.fixture(scope="class")
+    def test_model(self):
+        """Load test model"""
+        try:
+            model = AutoModelForCausalLM.from_pretrained(
+                DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
+                device_map="cpu",
+                trust_remote_code=True,
+                low_cpu_mem_usage=True,
+                torch_dtype=(
+                    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}")
+
+    @pytest.fixture(scope="class")
+    def device_mesh(self, setup_distributed):
+        """Create device mesh for testing"""
+        rank, world_size = setup_distributed
+
+        if not torch.cuda.is_available():
+            pytest.skip("CUDA not available for device mesh")
+
+        device_mesh_key = "tp"
+        mesh = init_device_mesh(
+            "cuda", (world_size,), mesh_dim_names=(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 = []
+        test_tensors = []
+
+        # Get first few parameters from the model for testing
+        for i, (name, tensor) in enumerate(model.named_parameters()):
+            if i >= num_params:
+                break
+            param_names.append(name)
+            # Create test tensor with known values, matching original shape and dtype
+            test_tensor = torch.full_like(tensor, 1.5, dtype=tensor.dtype).cuda()
+            test_tensors.append(test_tensor)
+
+        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
+    ):
+        """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)
+
+        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,
+        )
+
+        assert "Success" in result
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])