[core][bugfix] configure env var during import vllm (#12209)

Signed-off-by: youkaichao <youkaichao@gmail.com>

examples/offline_inference/rlhf.py

@@ -19,7 +19,7 @@ from ray.util.placement_group import placement_group
 from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
 from transformers import AutoModelForCausalLM
 
-from vllm import LLM, SamplingParams, configure_as_vllm_process
+from vllm import LLM, SamplingParams
 from vllm.utils import get_ip, get_open_port
 from vllm.worker.worker import Worker
 
@@ -98,12 +98,7 @@ class MyLLM(LLM):
 """
 Start the training process, here we use huggingface transformers 
 as an example to hold a model on GPU 0.
-
-It is important for all the processes outside of vLLM to call
-`configure_as_vllm_process` to set some common environment variables
-the same as vLLM workers.
 """
-configure_as_vllm_process()
 
 train_model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
 train_model.to("cuda:0")

vllm/__init__.py

@@ -1,4 +1,7 @@
 """vLLM: a high-throughput and memory-efficient inference engine for LLMs"""
+import os
+
+import torch
 
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -17,16 +20,10 @@ from vllm.sampling_params import SamplingParams
 
 from .version import __version__, __version_tuple__
 
-
-def configure_as_vllm_process():
-    """
-    set some common config/environment variables that should be set
-    for all processes created by vllm and all processes
-    that interact with vllm workers.
-    """
-    import os
-
-    import torch
+# set some common config/environment variables that should be set
+# for all processes created by vllm and all processes
+# that interact with vllm workers.
+# they are executed whenever `import vllm` is called.
 
 # see https://github.com/NVIDIA/nccl/issues/1234
 os.environ['NCCL_CUMEM_ENABLE'] = '0'
@@ -36,25 +33,6 @@ def configure_as_vllm_process():
 # see https://github.com/vllm-project/vllm/issues/10619
 torch._inductor.config.compile_threads = 1
 
-    from vllm.platforms import current_platform
-
-    if current_platform.is_xpu():
-        # see https://github.com/pytorch/pytorch/blob/43c5f59/torch/_dynamo/config.py#L158
-        torch._dynamo.config.disable = True
-    elif current_platform.is_hpu():
-        # NOTE(kzawora): PT HPU lazy backend (PT_HPU_LAZY_MODE = 1)
-        # does not support torch.compile
-        # Eager backend (PT_HPU_LAZY_MODE = 0) must be selected for
-        # torch.compile support
-        is_lazy = os.environ.get('PT_HPU_LAZY_MODE', '1') == '1'
-        if is_lazy:
-            torch._dynamo.config.disable = True
-            # NOTE(kzawora) multi-HPU inference with HPUGraphs (lazy-only)
-            # requires enabling lazy collectives
-            # see https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html # noqa: E501
-            os.environ['PT_HPU_ENABLE_LAZY_COLLECTIVES'] = 'true'
-
-
 __all__ = [
     "__version__",
     "__version_tuple__",
@@ -80,5 +58,4 @@ __all__ = [
     "AsyncEngineArgs",
     "initialize_ray_cluster",
     "PoolingParams",
-    "configure_as_vllm_process",
 ]

vllm/plugins/__init__.py

@@ -1,6 +1,9 @@
 import logging
+import os
 from typing import Callable, Dict
 
+import torch
+
 import vllm.envs as envs
 
 logger = logging.getLogger(__name__)
@@ -51,6 +54,26 @@ def load_general_plugins():
     if plugins_loaded:
         return
     plugins_loaded = True
+
+    # some platform-specific configurations
+    from vllm.platforms import current_platform
+
+    if current_platform.is_xpu():
+        # see https://github.com/pytorch/pytorch/blob/43c5f59/torch/_dynamo/config.py#L158
+        torch._dynamo.config.disable = True
+    elif current_platform.is_hpu():
+        # NOTE(kzawora): PT HPU lazy backend (PT_HPU_LAZY_MODE = 1)
+        # does not support torch.compile
+        # Eager backend (PT_HPU_LAZY_MODE = 0) must be selected for
+        # torch.compile support
+        is_lazy = os.environ.get('PT_HPU_LAZY_MODE', '1') == '1'
+        if is_lazy:
+            torch._dynamo.config.disable = True
+            # NOTE(kzawora) multi-HPU inference with HPUGraphs (lazy-only)
+            # requires enabling lazy collectives
+            # see https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html # noqa: E501
+            os.environ['PT_HPU_ENABLE_LAZY_COLLECTIVES'] = 'true'
+
     plugins = load_plugins_by_group(group='vllm.general_plugins')
     # general plugins, we only need to execute the loaded functions
     for func in plugins.values():

vllm/worker/worker_base.py

@@ -535,9 +535,6 @@ class WorkerWrapperBase:
         kwargs = all_kwargs[self.rpc_rank]
         enable_trace_function_call_for_thread(self.vllm_config)
 
-        from vllm import configure_as_vllm_process
-        configure_as_vllm_process()
-
         from vllm.plugins import load_general_plugins
         load_general_plugins()