Fix CompilationConfig repr

tests/test_config.py

@@ -5,6 +5,7 @@
 
 import pytest
 
+from vllm.compilation.backends import VllmBackend
 from vllm.config import (LoadConfig, ModelConfig, PoolerConfig, VllmConfig,
                          config, get_field)
 from vllm.model_executor.layers.pooler import PoolingType
@@ -43,6 +44,18 @@ def test_config(test_config, expected_error):
         config(test_config)
 
 
+def test_compile_config_repr_succeeds():
+    # setup: VllmBackend mutates the config object
+    config = VllmConfig()
+    backend = VllmBackend(config)
+    backend.configure_post_pass()
+
+    # test that repr(config) succeeds
+    val = repr(config)
+    assert 'VllmConfig' in val
+    assert 'inductor_passes' in val
+
+
 def test_get_field():
 
     @dataclass

vllm/config.py

@@ -647,7 +647,7 @@ def architectures(self) -> list[str]:
     def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                           tokenizer: str) -> None:
         """Pull model/tokenizer from S3 to temporary directory when needed.
-        
+
         Args:
             model: Model name or path
             tokenizer: Tokenizer name or path
@@ -1369,9 +1369,9 @@ def get_diff_sampling_param(self) -> dict[str, Any]:
     def is_encoder_decoder(self) -> bool:
         """Extract the HF encoder/decoder model flag."""
         """
-        For Mllama, VLLM overrides HF's is_encoder_decoder flag and sets it to 
+        For Mllama, VLLM overrides HF's is_encoder_decoder flag and sets it to
         True to enable cross-attention
-        Neuron needs all multimodal data to be in the decoder and does not 
+        Neuron needs all multimodal data to be in the decoder and does not
         need to explicitly enable cross-attention
         """
         if (current_platform.is_neuron()
@@ -1791,7 +1791,7 @@ class is dynamically inherited by the worker class. This is used to inject
     """Global rank in distributed setup."""
 
     enable_multimodal_encoder_data_parallel: bool = False
-    """ Use data parallelism instead of tensor parallelism for vision encoder. 
+    """ Use data parallelism instead of tensor parallelism for vision encoder.
     Only support LLama4 for now"""
 
     @property
@@ -2255,9 +2255,9 @@ class DeviceConfig:
 
     device: SkipValidation[Union[Device, torch.device]] = "auto"
     """Device type for vLLM execution.
-    This parameter is deprecated and will be 
-    removed in a future release. 
-    It will now be set automatically based 
+    This parameter is deprecated and will be
+    removed in a future release.
+    It will now be set automatically based
     on the current platform."""
     device_type: str = field(init=False)
     """Device type from the current platform. This is set in
@@ -3987,19 +3987,24 @@ def compute_hash(self) -> str:
 
     def __repr__(self) -> str:
         exclude = {
-            "static_forward_context",
-            "enabled_custom_ops",
-            "disabled_custom_ops",
-            "compilation_time",
-            "bs_to_padded_graph_size",
-            "pass_config",
-            "traced_files",
+            "static_forward_context": True,
+            "enabled_custom_ops": True,
+            "disabled_custom_ops": True,
+            "compilation_time": True,
+            "bs_to_padded_graph_size": True,
+            "pass_config": True,
+            "traced_files": True,
+            "inductor_compile_config": {
+                "post_grad_custom_post_pass": True,
+            },
         }
         # The cast to string is necessary because Pydantic is mocked in docs
         # builds and sphinx-argparse doesn't know the return type of decode()
         return str(
             TypeAdapter(CompilationConfig).dump_json(
-                self, exclude=exclude, exclude_unset=True).decode())
+                self,
+                exclude=exclude,  # type: ignore[arg-type]
+                exclude_unset=True).decode())
 
     __str__ = __repr__