[Core] Fix circular reference which leaked llm instance in local dev env (#4737)

Storing exception frame is extremely prone to circular refernece because it contains the reference to objects.

When tensorizer is not installed, it leaks llm instance because error frame has references to various modules which cause circular reference problem.

I also found spec decoding has a circular reference issue, and I solved it using weakref.proxy.

tests/basic_correctness/test_basic_correctness.py

@@ -3,9 +3,12 @@
 Run `pytest tests/basic_correctness/test_basic_correctness.py`.
 """
 import os
+import weakref
 
 import pytest
 
+from vllm import LLM
+
 MODELS = [
     "facebook/opt-125m",
     "meta-llama/Llama-2-7b-hf",
@@ -13,6 +16,16 @@ MODELS = [
 VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
 
 
+def test_vllm_gc_ed():
+    """Verify vllm instance is GC'ed when it is deleted"""
+    llm = LLM("facebook/opt-125m")
+    weak_llm = weakref.ref(llm)
+    del llm
+    # If there's any circular reference to vllm, this fails
+    # because llm instance is not GC'ed.
+    assert weak_llm() is None
+
+
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [5])

vllm/model_executor/model_loader/tensorizer.py

@@ -19,7 +19,7 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 
-tensorizer_load_fail = None
+tensorizer_error_msg = None
 
 try:
     from tensorizer import (DecryptionParams, EncryptionParams,
@@ -28,7 +28,7 @@ try:
     from tensorizer.utils import (convert_bytes, get_mem_usage,
                                   no_init_or_tensor)
 except ImportError as e:
-    tensorizer_load_fail = e
+    tensorizer_error_msg = str(e)
 
 __all__ = [
     'EncryptionParams', 'DecryptionParams', 'TensorDeserializer',
@@ -254,11 +254,11 @@ class TensorizerAgent:
 
     def __init__(self, tensorizer_config: TensorizerConfig,
                  quant_config: QuantizationConfig, **extra_kwargs):
-        if tensorizer_load_fail is not None:
+        if tensorizer_error_msg is not None:
             raise ImportError(
                 "Tensorizer is not installed. Please install tensorizer "
-                "to use this feature with `pip install vllm[tensorizer]`."
-            ) from tensorizer_load_fail
+                "to use this feature with `pip install vllm[tensorizer]`. "
+                "Error message: {}".format(tensorizer_error_msg))
 
         self.tensorizer_config = tensorizer_config
         self.tensorizer_args = (

vllm/spec_decode/multi_step_worker.py

@@ -1,4 +1,5 @@
 import copy
+import weakref
 from typing import List, Tuple
 
 import torch
@@ -32,7 +33,7 @@ class MultiStepWorker(Worker):
         super().init_device()
 
         self._proposer = Top1Proposer(
-            self,
+            weakref.proxy(self),
             self.device,
             self.vocab_size,
             max_proposal_len=self.max_model_len,

vllm/spec_decode/ngram_worker.py

@@ -1,3 +1,4 @@
+import weakref
 from typing import List, Optional, Tuple
 
 import torch
@@ -37,7 +38,7 @@ class NGramWorker(LoraNotSupportedWorkerBase):
 
         # Current only support Top1Proposer
         self._proposer = Top1Proposer(
-            self,
+            weakref.proxy(self),
             device=self.device,
             vocab_size=self.vocab_size,
         )