Move ModelConfig from config/__init__.py to config/model.py (#25252)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/conftest.py

@@ -39,7 +39,8 @@ from vllm import LLM, SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
-from vllm.config import ConvertOption, RunnerOption, _get_and_verify_dtype
+from vllm.config.model import (ConvertOption, RunnerOption,
+                               _get_and_verify_dtype)
 from vllm.connections import global_http_connection
 from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,

tests/distributed/test_pipeline_parallel.py

@@ -14,7 +14,7 @@ from typing import Literal, NamedTuple, Optional
 
 import pytest
 
-from vllm.config import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
+from vllm.config.model import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
 from vllm.logger import init_logger
 from vllm.transformers_utils.config import get_config
 

tests/models/test_initialization.py

@@ -7,7 +7,6 @@ from unittest.mock import patch
 import pytest
 
 from vllm import LLM
-from vllm.config import ModelImpl
 from vllm.engine.llm_engine import LLMEngine as V0LLMEngine
 from vllm.utils import GiB_bytes
 from vllm.v1.core.kv_cache_utils import get_kv_cache_configs
@@ -111,8 +110,8 @@ def can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch,
             # these tests seem to produce leftover memory
             gpu_memory_utilization=0.80,
             load_format="dummy",
-            model_impl=ModelImpl.TRANSFORMERS
-            if model_arch in _TRANSFORMERS_BACKEND_MODELS else ModelImpl.VLLM,
+            model_impl="transformers"
+            if model_arch in _TRANSFORMERS_BACKEND_MODELS else "vllm",
             hf_overrides=hf_overrides_fn,
             max_num_seqs=model_info.max_num_seqs)
 

tests/v1/sample/test_logprobs.py

@@ -3,6 +3,7 @@
 
 import itertools
 from collections.abc import Generator
+from typing import get_args
 
 import pytest
 import torch
@@ -464,7 +465,7 @@ def test_all_logprobs(example_prompts, monkeypatch: pytest.MonkeyPatch):
                 assert len(prompt_logprob) == vocab_size
 
 
-@pytest.mark.parametrize("logprobs_mode", list(LogprobsMode))
+@pytest.mark.parametrize("logprobs_mode", get_args(LogprobsMode))
 def test_logprobs_mode(logprobs_mode: LogprobsMode,
                        monkeypatch: pytest.MonkeyPatch):
     """Test with LLM engine with different logprobs_mode.
@@ -493,14 +494,12 @@ def test_logprobs_mode(logprobs_mode: LogprobsMode,
             for logprobs in output.logprobs:
                 for token_id in logprobs:
                     logprob = logprobs[token_id]
-                    if logprobs_mode in (LogprobsMode.RAW_LOGPROBS,
-                                         LogprobsMode.PROCESSED_LOGPROBS):
+                    if logprobs_mode in ("raw_logprobs", "processed_logprobs"):
                         assert logprob.logprob <= 0
                     if logprob.logprob > 0:
                         positive_values = positive_values + 1
                     total_token_with_logprobs = total_token_with_logprobs + 1
         assert total_token_with_logprobs >= len(results[0].outputs)
-        if logprobs_mode in (LogprobsMode.RAW_LOGITS,
-                             LogprobsMode.PROCESSED_LOGITS):
+        if logprobs_mode in ("raw_logits", "processed_logits"):
             assert positive_values > 0
         del llm

vllm/config/scheduler.py

@@ -3,7 +3,7 @@
 
 import hashlib
 from dataclasses import field
-from typing import TYPE_CHECKING, Any, Literal, Optional, Union
+from typing import Any, Literal, Optional, Union
 
 from pydantic import SkipValidation, model_validator
 from pydantic.dataclasses import dataclass
@@ -15,13 +15,9 @@ from vllm.utils import (DEFAULT_MAX_NUM_BATCHED_TOKENS,
                         MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                         POOLING_MODEL_MAX_NUM_BATCHED_TOKENS)
 
-if TYPE_CHECKING:
-    from vllm.config import RunnerType
-else:
-    RunnerType = Any
-
 logger = init_logger(__name__)
 
+RunnerType = Literal["generate", "pooling", "draft"]
 PreemptionMode = Literal["swap", "recompute"]
 SchedulerPolicy = Literal["fcfs", "priority"]
 

vllm/config/utils.py

@@ -1,8 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import ast
+import inspect
+import textwrap
 from dataclasses import MISSING, Field, field, fields, is_dataclass
-from typing import TYPE_CHECKING, TypeVar
+from typing import TYPE_CHECKING, Any, TypeVar
+
+import regex as re
 
 if TYPE_CHECKING:
     from _typeshed import DataclassInstance
@@ -45,3 +50,96 @@ def get_field(cls: ConfigType, name: str) -> Field:
         return field(default=default)
     raise ValueError(
         f"{cls.__name__}.{name} must have a default value or default factory.")
+
+
+def contains_object_print(text: str) -> bool:
+    """
+    Check if the text looks like a printed Python object, e.g.
+    contains any substring matching the pattern: "at 0xFFFFFFF>"
+    We match against 0x followed by 2-16 hex chars (there's
+    a max of 16 on a 64-bit system).
+
+    Args:
+        text (str): The text to check
+
+    Returns:
+        result (bool): `True` if a match is found, `False` otherwise.
+    """
+    pattern = r'at 0x[a-fA-F0-9]{2,16}>'
+    match = re.search(pattern, text)
+    return match is not None
+
+
+def assert_hashable(text: str) -> bool:
+    if not contains_object_print(text):
+        return True
+    raise AssertionError(
+        f"vLLM tried to hash some configs that may have Python objects ids "
+        f"in them. This is a bug, please file an issue. "
+        f"Text being hashed: {text}")
+
+
+def get_attr_docs(cls: type[Any]) -> dict[str, str]:
+    """
+    Get any docstrings placed after attribute assignments in a class body.
+
+    https://davidism.com/mit-license/
+    """
+
+    def pairwise(iterable):
+        """
+        Manually implement https://docs.python.org/3/library/itertools.html#itertools.pairwise
+
+        Can be removed when Python 3.9 support is dropped.
+        """
+        iterator = iter(iterable)
+        a = next(iterator, None)
+
+        for b in iterator:
+            yield a, b
+            a = b
+
+    try:
+        cls_node = ast.parse(textwrap.dedent(inspect.getsource(cls))).body[0]
+    except (OSError, KeyError, TypeError):
+        # HACK: Python 3.13+ workaround - set missing __firstlineno__
+        # Workaround can be removed after we upgrade to pydantic==2.12.0
+        with open(inspect.getfile(cls)) as f:
+            for i, line in enumerate(f):
+                if f"class {cls.__name__}" in line and ":" in line:
+                    cls.__firstlineno__ = i + 1
+                    break
+        cls_node = ast.parse(textwrap.dedent(inspect.getsource(cls))).body[0]
+
+    if not isinstance(cls_node, ast.ClassDef):
+        raise TypeError("Given object was not a class.")
+
+    out = {}
+
+    # Consider each pair of nodes.
+    for a, b in pairwise(cls_node.body):
+        # Must be an assignment then a constant string.
+        if (not isinstance(a, (ast.Assign, ast.AnnAssign))
+                or not isinstance(b, ast.Expr)
+                or not isinstance(b.value, ast.Constant)
+                or not isinstance(b.value.value, str)):
+            continue
+
+        doc = inspect.cleandoc(b.value.value)
+
+        # An assignment can have multiple targets (a = b = v), but an
+        # annotated assignment only has one target.
+        targets = a.targets if isinstance(a, ast.Assign) else [a.target]
+
+        for target in targets:
+            # Must be assigning to a plain name.
+            if not isinstance(target, ast.Name):
+                continue
+
+            out[target.id] = doc
+
+    return out
+
+
+def is_init_field(cls: ConfigType, name: str) -> bool:
+    return next(f for f in fields(cls) if f.name == name).init

vllm/engine/arg_utils.py

@@ -27,11 +27,11 @@ from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
                          EPLBConfig, HfOverrides, KVEventsConfig,
                          KVTransferConfig, LoadConfig, LogprobsMode,
                          LoRAConfig, MambaDType, MMEncoderTPMode, ModelConfig,
-                         ModelDType, ModelImpl, ObservabilityConfig,
-                         ParallelConfig, PoolerConfig, PrefixCachingHashAlgo,
-                         RunnerOption, SchedulerConfig, SchedulerPolicy,
-                         SpeculativeConfig, StructuredOutputsConfig,
-                         TaskOption, TokenizerMode, VllmConfig, get_attr_docs)
+                         ModelDType, ObservabilityConfig, ParallelConfig,
+                         PoolerConfig, PrefixCachingHashAlgo, RunnerOption,
+                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
+                         StructuredOutputsConfig, TaskOption, TokenizerMode,
+                         VllmConfig, get_attr_docs)
 from vllm.config.multimodal import MMCacheType, MultiModalConfig
 from vllm.config.parallel import ExpertPlacementStrategy
 from vllm.config.utils import get_field
@@ -548,7 +548,6 @@ class EngineArgs:
         model_group.add_argument("--max-logprobs",
                                  **model_kwargs["max_logprobs"])
         model_group.add_argument("--logprobs-mode",
-                                 choices=[f.value for f in LogprobsMode],
                                  **model_kwargs["logprobs_mode"])
         model_group.add_argument("--disable-sliding-window",
                                  **model_kwargs["disable_sliding_window"])
@@ -593,9 +592,7 @@ class EngineArgs:
                                  **model_kwargs["override_generation_config"])
         model_group.add_argument("--enable-sleep-mode",
                                  **model_kwargs["enable_sleep_mode"])
-        model_group.add_argument("--model-impl",
-                                 choices=[f.value for f in ModelImpl],
-                                 **model_kwargs["model_impl"])
+        model_group.add_argument("--model-impl", **model_kwargs["model_impl"])
         model_group.add_argument("--override-attention-dtype",
                                  **model_kwargs["override_attention_dtype"])
         model_group.add_argument("--logits-processors",

vllm/model_executor/model_loader/utils.py

@@ -13,8 +13,7 @@ from torch import nn
 from typing_extensions import assert_never
 
 from vllm.attention import Attention
-from vllm.config import (ModelConfig, ModelImpl, VllmConfig,
-                         set_current_vllm_config)
+from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import QKVCrossParallelLinear
 from vllm.model_executor.layers.quantization.base_config import (
@@ -176,8 +175,8 @@ def get_model_architecture(
     )
 
     if arch == model_config._get_transformers_backend_cls():
-        assert model_config.model_impl != ModelImpl.VLLM
-        if model_config.model_impl == ModelImpl.AUTO:
+        assert model_config.model_impl != "vllm"
+        if model_config.model_impl == "auto":
             logger.warning_once(
                 "%s has no vLLM implementation, falling back to Transformers "
                 "implementation. Some features may not be supported and "

vllm/model_executor/models/registry.py

@@ -19,7 +19,7 @@ from typing import Callable, Optional, TypeVar, Union
 import torch.nn as nn
 import transformers
 
-from vllm.config import (ModelConfig, ModelImpl, iter_architecture_defaults,
+from vllm.config import (ModelConfig, iter_architecture_defaults,
                          try_match_architecture_defaults)
 from vllm.logger import init_logger
 from vllm.transformers_utils.dynamic_module import (
@@ -587,7 +587,7 @@ class _ModelRegistry:
                     if model_module is not None:
                         break
             else:
-                if model_config.model_impl != ModelImpl.TRANSFORMERS:
+                if model_config.model_impl != "transformers":
                     return None
 
                 raise ValueError(
@@ -598,7 +598,7 @@ class _ModelRegistry:
                     "'auto_map' (relevant if the model is custom).")
 
         if not model_module.is_backend_compatible():
-            if model_config.model_impl != ModelImpl.TRANSFORMERS:
+            if model_config.model_impl != "transformers":
                 return None
 
             raise ValueError(
@@ -644,20 +644,20 @@ class _ModelRegistry:
             raise ValueError("No model architectures are specified")
 
         # Require transformers impl
-        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+        if model_config.model_impl == "transformers":
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
             if arch is not None:
                 model_info = self._try_inspect_model_cls(arch)
                 if model_info is not None:
                     return (model_info, arch)
-        elif model_config.model_impl == ModelImpl.TERRATORCH:
+        elif model_config.model_impl == "terratorch":
             model_info = self._try_inspect_model_cls("Terratorch")
             return (model_info, "Terratorch")
 
         # Fallback to transformers impl (after resolving convert_type)
         if (all(arch not in self.models for arch in architectures)
-                and model_config.model_impl == ModelImpl.AUTO
+                and model_config.model_impl == "auto"
                 and getattr(model_config, "convert_type", "none") == "none"):
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
@@ -674,7 +674,7 @@ class _ModelRegistry:
 
         # Fallback to transformers impl (before resolving runner_type)
         if (all(arch not in self.models for arch in architectures)
-                and model_config.model_impl == ModelImpl.AUTO):
+                and model_config.model_impl == "auto"):
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
             if arch is not None:
@@ -695,14 +695,14 @@ class _ModelRegistry:
             raise ValueError("No model architectures are specified")
 
         # Require transformers impl
-        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+        if model_config.model_impl == "transformers":
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
             if arch is not None:
                 model_cls = self._try_load_model_cls(arch)
                 if model_cls is not None:
                     return (model_cls, arch)
-        elif model_config.model_impl == ModelImpl.TERRATORCH:
+        elif model_config.model_impl == "terratorch":
             arch = "Terratorch"
             model_cls = self._try_load_model_cls(arch)
             if model_cls is not None:
@@ -710,7 +710,7 @@ class _ModelRegistry:
 
         # Fallback to transformers impl (after resolving convert_type)
         if (all(arch not in self.models for arch in architectures)
-                and model_config.model_impl == ModelImpl.AUTO
+                and model_config.model_impl == "auto"
                 and getattr(model_config, "convert_type", "none") == "none"):
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
@@ -727,7 +727,7 @@ class _ModelRegistry:
 
         # Fallback to transformers impl (before resolving runner_type)
         if (all(arch not in self.models for arch in architectures)
-                and model_config.model_impl == ModelImpl.AUTO):
+                and model_config.model_impl == "auto"):
             arch = self._try_resolve_transformers(architectures[0],
                                                   model_config)
             if arch is not None:

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -29,15 +29,12 @@ class TopKTopPSampler(nn.Module):
     Implementations may update the logits tensor in-place.
     """
 
-    def __init__(
-            self,
-            logprobs_mode: LogprobsMode = LogprobsMode.RAW_LOGPROBS) -> None:
+    def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs") -> None:
         super().__init__()
         self.logprobs_mode = logprobs_mode
         # flashinfer optimization does not apply if intermediate
         # logprobs/logits after top_k/top_p need to be returned
-        if logprobs_mode not in (LogprobsMode.PROCESSED_LOGITS,
-                                 LogprobsMode.PROCESSED_LOGPROBS
+        if logprobs_mode not in ("processed_logits", "processed_logprobs"
                                  ) and current_platform.is_cuda():
             if is_flashinfer_available:
                 flashinfer_version = flashinfer.__version__
@@ -90,9 +87,9 @@ class TopKTopPSampler(nn.Module):
         """
         logits = self.apply_top_k_top_p(logits, k, p)
         logits_to_return = None
-        if self.logprobs_mode == LogprobsMode.PROCESSED_LOGITS:
+        if self.logprobs_mode == "processed_logits":
             logits_to_return = logits
-        elif self.logprobs_mode == LogprobsMode.PROCESSED_LOGPROBS:
+        elif self.logprobs_mode == "processed_logprobs":
             logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
         probs = logits.softmax(dim=-1, dtype=torch.float32)
         return random_sample(probs, generators), logits_to_return
@@ -115,7 +112,7 @@ class TopKTopPSampler(nn.Module):
                                     "PyTorch-native implementation.")
             return self.forward_native(logits, generators, k, p)
         assert self.logprobs_mode not in (
-            LogprobsMode.PROCESSED_LOGITS, LogprobsMode.PROCESSED_LOGPROBS
+            "processed_logits", "processed_logprobs"
         ), "FlashInfer does not support returning logits/logprobs"
         # flashinfer sampling functions expect contiguous logits.
         # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous

vllm/v1/sample/sampler.py

@@ -60,8 +60,7 @@ class Sampler(nn.Module):
     9. Return the final `SamplerOutput`.
     """
 
-    def __init__(self,
-                 logprobs_mode: LogprobsMode = LogprobsMode.RAW_LOGPROBS):
+    def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs"):
         super().__init__()
         self.topk_topp_sampler = TopKTopPSampler(logprobs_mode)
         self.pin_memory = is_pin_memory_available()
@@ -78,9 +77,9 @@ class Sampler(nn.Module):
         # is used for sampling (after penalties and temperature scaling).
         num_logprobs = sampling_metadata.max_num_logprobs
         if num_logprobs is not None:
-            if self.logprobs_mode == LogprobsMode.RAW_LOGPROBS:
+            if self.logprobs_mode == "raw_logprobs":
                 raw_logprobs = self.compute_logprobs(logits)
-            elif self.logprobs_mode == LogprobsMode.RAW_LOGITS:
+            elif self.logprobs_mode == "raw_logits":
                 raw_logprobs = logits.clone()
 
         # Use float32 for the logits.
@@ -156,9 +155,9 @@ class Sampler(nn.Module):
             if sampling_metadata.all_greedy:
                 processed_logprobs = None
                 if sampling_metadata.max_num_logprobs is not None:
-                    if self.logprobs_mode == LogprobsMode.PROCESSED_LOGITS:
+                    if self.logprobs_mode == "processed_logits":
                         processed_logprobs = logits
-                    elif self.logprobs_mode == LogprobsMode.PROCESSED_LOGPROBS:
+                    elif self.logprobs_mode == "processed_logprobs":
                         processed_logprobs = self.compute_logprobs(logits)
                 return greedy_sampled, processed_logprobs