[Core] Add update_config RPC method

tests/test_config.py

@@ -8,7 +8,7 @@
 
 from vllm.compilation.backends import VllmBackend
 from vllm.config import (LoadConfig, ModelConfig, PoolerConfig, VllmConfig,
-                         config, get_field)
+                         config, get_field, update_config)
 from vllm.model_executor.layers.pooler import PoolingType
 from vllm.platforms import current_platform
 
@@ -79,6 +79,34 @@ def test_get_field():
     assert c.default_factory is MISSING
 
 
+@dataclass
+class _TestNestedConfig:
+    a: _TestConfigFields = field(
+        default_factory=lambda: _TestConfigFields(a=0))
+
+
+def test_update_config():
+    # Simple update
+    config1 = _TestConfigFields(a=0)
+    new_config1 = update_config(config1, {"a": 42})
+    assert new_config1.a == 42
+    # Nonexistent field
+    with pytest.raises(AssertionError):
+        new_config1 = update_config(config1, {"nonexistent": 1})
+    # Nested update with dataclass
+    config2 = _TestNestedConfig()
+    new_inner_config = _TestConfigFields(a=1, c="new_value")
+    new_config2 = update_config(config2, {"a": new_inner_config})
+    assert new_config2.a == new_inner_config
+    # Nested update with dict
+    config3 = _TestNestedConfig()
+    new_config3 = update_config(config3, {"a": {"c": "new_value"}})
+    assert new_config3.a.c == "new_value"
+    # Nested update with invalid type
+    with pytest.raises(AssertionError):
+        new_config3 = update_config(config3, {"a": "new_value"})
+
+
 @pytest.mark.parametrize(
     ("model_id", "expected_runner_type", "expected_task"),
     [

tests/v1/worker/test_gpu_model_runner.py

@@ -433,16 +433,28 @@ def rnd_stride_order():
         assert all(not kv.is_contiguous() for kv in model_runner.kv_caches)
 
 
+def test_update_config(model_runner):
+    # Simple update
+    model_runner.update_config({"load_config": {"load_format": "dummy"}})
+    assert model_runner.load_config.load_format == "dummy"
+    # Raise error on non-existing config
+    with pytest.raises(AssertionError):
+        model_runner.update_config({"do_not_exist_config": "dummy"})
+
+
 def test_load_model_weights_inplace(dist_init, model_runner, model_runner_2):
     # In this test, model_runner loads model + weights in one go, while
     # model_runner_2 loads dummy weights first then load real weights inplace
     model_runner.load_model()
     original_load_format = model_runner_2.load_config.load_format
-    model_runner_2.load_config.load_format = "dummy"
+    model_runner_2.update_config({"load_config": {"load_format": "dummy"}})
     model_runner_2.load_model()  # Initial model loading with dummy weights
     assert str(model_runner.get_model().state_dict()) != str(
         model_runner_2.get_model().state_dict())
-    model_runner_2.load_config.load_format = original_load_format
+    model_runner_2.update_config(
+        {"load_config": {
+            "load_format": original_load_format
+        }})
     model_runner_2.load_model()  # Load real weights inplace
     assert str(model_runner.get_model().state_dict()) == str(
         model_runner_2.get_model().state_dict())

vllm/config.py

@@ -71,6 +71,7 @@
     ConfigType = type[DataclassInstance]
     HfOverrides = Union[dict, Callable[[type], type]]
 else:
+    DataclassInstance = Any
     PlacementGroup = Any
     PretrainedConfig = Any
     ExecutorBase = Any
@@ -87,7 +88,7 @@
                            "vllm.model_executor.models")
 
 logger = init_logger(__name__)
-
+DataclassInstanceT = TypeVar("DataclassInstanceT", bound=DataclassInstance)
 ConfigT = TypeVar("ConfigT", bound=ConfigType)
 
 TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
@@ -4842,3 +4843,21 @@ def get_layers_from_vllm_config(vllm_config: VllmConfig,
         vllm_config.compilation_config.static_forward_context.items()
         if isinstance(layer, layer_type)
     }
+
+
+def update_config(config: DataclassInstanceT,
+                  overrides: dict[str, Any]) -> DataclassInstanceT:
+    processed_overrides = {}
+    for field_name, value in overrides.items():
+        assert hasattr(
+            config, field_name), f"{type(config)} has no field `{field_name}`"
+        current_value = getattr(config, field_name)
+        if is_dataclass(current_value) and not is_dataclass(value):
+            assert isinstance(value, dict), (
+                f"Overrides to {type(config)}.{field_name} must be a dict"
+                f"  or {type(current_value)}, but got {type(value)}")
+            value = update_config(
+                current_value,  # type: ignore[type-var]
+                value)
+        processed_overrides[field_name] = value
+    return replace(config, **processed_overrides)

vllm/v1/worker/gpu_model_runner.py

@@ -20,7 +20,7 @@
 from vllm.attention.layer import Attention
 from vllm.compilation.counter import compilation_counter
 from vllm.config import (CompilationLevel, VllmConfig,
-                         get_layers_from_vllm_config)
+                         get_layers_from_vllm_config, update_config)
 from vllm.distributed.eplb.eplb_state import EplbState
 from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                           has_kv_transfer_group)
@@ -1721,6 +1721,16 @@ def generate_draft_token_ids(
                 draft_token_ids.append(drafter_output.tolist())
         return draft_token_ids
 
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        allowed_config_names = {"load_config", "model_config"}
+        for config_name, config_overrides in overrides.items():
+            assert config_name in allowed_config_names, \
+                f"Config `{config_name}` not supported. " \
+                f"Allowed configs: {allowed_config_names}"
+            config = getattr(self, config_name)
+            new_config = update_config(config, config_overrides)
+            setattr(self, config_name, new_config)
+
     def load_model(self) -> None:
         logger.info("Starting to load model %s...", self.model_config.model)
         with DeviceMemoryProfiler() as m:  # noqa: SIM117

vllm/v1/worker/gpu_worker.py

@@ -3,7 +3,7 @@
 """A GPU worker class."""
 import gc
 import os
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 import torch.distributed
@@ -184,6 +184,9 @@ def load_model(self) -> None:
         with context:
             self.model_runner.load_model()
 
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        self.model_runner.update_config(overrides)
+
     @torch.inference_mode()
     def determine_available_memory(self) -> int:
         """Profiles the peak memory usage of the model to determine how much 

vllm/v1/worker/tpu_model_runner.py

@@ -1,9 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import bisect
+import dataclasses
 import gc
 import time
-from typing import TYPE_CHECKING, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
 from unittest.mock import patch
 
 import numpy as np
@@ -1104,6 +1105,15 @@ def concat_lists(input_lists):
 
         return model_runner_output
 
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        for config_name, config_overrides in overrides.items():
+            try:
+                config = getattr(self, config_name)
+            except AttributeError as exc:
+                raise ValueError(f"Unknown config {config_name}") from exc
+            new_config = dataclasses.replace(config, **config_overrides)
+            setattr(self, config_name, new_config)
+
     def load_model(self) -> None:
         self.device = self.device_config.device
 

vllm/v1/worker/tpu_worker.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """A TPU worker class."""
 import os
-from typing import Optional
+from typing import Any, Optional
 
 import torch
 import torch.distributed
@@ -259,6 +259,9 @@ def add_lora(self, lora_request: LoRARequest) -> bool:
     def load_model(self) -> None:
         self.model_runner.load_model()
 
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        self.model_runner.update_config(overrides)
+
     def compile_or_warm_up_model(self) -> None:
         if not self.model_config.enforce_eager:
             self.model_runner.capture_model()