Move`VllmConfig` from `config/__init__.py` to `config/vllm.py` (#25271)

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

vllm/attention/layer.py

@@ -20,8 +20,7 @@ from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.model_executor.layers.linear import UnquantizedLinearMethod
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.utils.quant_utils import (

vllm/attention/layers/chunked_local_attention.py

@@ -9,7 +9,8 @@ from vllm import envs
 from vllm.attention.backends.abstract import (AttentionBackend,
                                               AttentionMetadata)
 from vllm.attention.selector import get_attn_backend
-from vllm.config import CacheConfig, QuantizationConfig
+from vllm.config import CacheConfig
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata, make_local_attention_virtual_batches,
     subclass_attention_backend)

vllm/config/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
+"""Utility functions for vLLM config dataclasses."""
 import ast
 import inspect
 import textwrap
-from dataclasses import MISSING, Field, field, fields, is_dataclass
-from typing import TYPE_CHECKING, Any, TypeVar
+from dataclasses import MISSING, Field, field, fields, is_dataclass, replace
+from typing import TYPE_CHECKING, Any, Protocol, TypeVar
 
 import regex as re
+from typing_extensions import runtime_checkable
 
 if TYPE_CHECKING:
     from _typeshed import DataclassInstance
-
-    ConfigType = type[DataclassInstance]
 else:
-    ConfigType = type
+    DataclassInstance = Any
 
+ConfigType = type[DataclassInstance]
 ConfigT = TypeVar("ConfigT", bound=ConfigType)
 
 
@@ -143,3 +143,33 @@ def get_attr_docs(cls: type[Any]) -> dict[str, str]:
 
 def is_init_field(cls: ConfigType, name: str) -> bool:
     return next(f for f in fields(cls) if f.name == name).init
+
+
+@runtime_checkable
+class SupportsHash(Protocol):
+
+    def compute_hash(self) -> str:
+        ...
+
+
+class SupportsMetricsInfo(Protocol):
+
+    def metrics_info(self) -> dict[str, str]:
+        ...
+
+
+def update_config(config: ConfigT, overrides: dict[str, Any]) -> ConfigT:
+    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/model_executor/layers/mamba/linear_attn.py

@@ -29,8 +29,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba_utils import (
     MambaStateDtypeCalculator, MambaStateShapeCalculator)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
 

vllm/model_executor/layers/quantization/auto_round.py

@@ -9,9 +9,8 @@ import torch
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types

vllm/model_executor/layers/quantization/bitblas.py

@@ -7,9 +7,8 @@ from packaging import version
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
     BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_NUM_BITS,
     BITBLAS_SUPPORTED_SYM, MINIMUM_BITBLAS_VERSION)

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -13,9 +13,8 @@ from vllm.model_executor.layers.fused_moe.layer import (FusedMoE,
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod,
                                                set_weight_attrs)
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 

vllm/model_executor/layers/quantization/deepspeedfp.py

@@ -9,9 +9,8 @@ import torch.nn.functional as F
 from packaging import version
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.utils import set_weight_attrs
 
 

vllm/model_executor/layers/quantization/gptq.py

@@ -4,7 +4,7 @@
 import enum
 from enum import Enum
 from fractions import Fraction
-from typing import Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@@ -13,7 +13,6 @@ from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.layer import FusedMoE
 from vllm.model_executor.layers.linear import LinearMethodBase
-from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils.gptq_utils import (
@@ -26,6 +25,11 @@ from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
 from vllm.transformers_utils.config import get_safetensors_params_metadata
 from vllm.utils import is_list_of
 
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.quantization import QuantizationMethods
+else:
+    QuantizationMethods = str
+
 
 class GPTQConfig(QuantizationConfig):
     """Config class for GPTQ.

vllm/model_executor/layers/quantization/gptq_bitblas.py

@@ -9,9 +9,8 @@ from torch.nn.parameter import Parameter
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
     BitBLASLinearKernel, MPLinearLayerConfig)
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -43,7 +43,7 @@ logger = init_logger(__name__)
 
 
 def get_moe_quant_method(
-    config: QuantizationConfig,
+    config: "GPTQMarlinConfig",
     layer: torch.nn.Module,
     prefix: str,
     moe_method_cls: type,

vllm/model_executor/layers/quantization/gptq_marlin_24.py

@@ -9,9 +9,8 @@ from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            ChannelQuantScaleParameter,
                                            GroupQuantScaleParameter,

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -14,11 +14,10 @@ from vllm.model_executor.layers.fused_moe import (FusedMoEMethodBase,
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
-from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.layers.quantization.awq import (AWQLinearMethod,
                                                          is_layer_skipped_awq)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
 from vllm.model_executor.layers.quantization.fp8 import (Fp8Config,
                                                          Fp8LinearMethod)
 from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod

vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py

@@ -7,8 +7,7 @@ import torch
 from packaging import version
 
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
     BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_GROUP_SIZES,

vllm/model_executor/layers/quantization/tpu_int8.py

@@ -8,9 +8,8 @@ from torch.nn import Module
 from torch.nn.parameter import Parameter
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
-from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import (QuantizationConfig,
+                                                     QuantizationMethods)
 from vllm.model_executor.parameter import ModelWeightParameter
 
 ACTIVATION_SCHEMES = ["none", "dynamic"]

vllm/model_executor/layers/quantization/utils/gptq_utils.py

@@ -4,21 +4,27 @@ from collections.abc import Mapping
 from copy import deepcopy
 from fractions import Fraction
 from types import MappingProxyType
-from typing import Optional, Union
+from typing import TYPE_CHECKING, Optional, Union
 
 import regex as re
 import torch
 
-from vllm.config import QuantizationConfig
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, UnquantizedEmbeddingMethod)
 
+if TYPE_CHECKING:
+    from ..gptq import GPTQConfig
+    from ..gptq_marlin import GPTQMarlinConfig
+else:
+    GPTQConfig = object
+    GPTQMarlinConfig = object
+
 
 # Match dynamic rules with module name (prefix) and override quantize
 # config if module (prefix) matches a rule
-def override_config(config: QuantizationConfig, prefix: str):
+def override_config(config: Union[GPTQConfig, GPTQMarlinConfig], prefix: str):
     weight_bits = get_dynamic_override(config, prefix, "bits",
                                        config.weight_bits)
     if isinstance(weight_bits, int):
@@ -34,6 +40,7 @@ def override_config(config: QuantizationConfig, prefix: str):
 
     config.pack_factor = Fraction(32, config.weight_bits)  # packed into int32
     if config.get_name() == "gptq_marlin":
+        assert isinstance(config, GPTQMarlinConfig)
         is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym)
         if isinstance(is_sym, bool):
             config.is_sym = is_sym
@@ -45,6 +52,7 @@ def override_config(config: QuantizationConfig, prefix: str):
         config.quant_type = config.TYPE_MAP[(config.weight_bits,
                                              config.is_sym)]
     elif config.get_name() == "gptq":
+        assert isinstance(config, GPTQConfig)
         if config.weight_bits not in [2, 3, 4, 8]:
             raise ValueError(
                 "Currently, only 2/3/4/8-bit weight quantization is "
@@ -52,7 +60,7 @@ def override_config(config: QuantizationConfig, prefix: str):
 
 
 def get_dynamic_override(
-    config: QuantizationConfig,
+    config: Union[GPTQConfig, GPTQMarlinConfig],
     layer_name: str,
     key: Optional[str] = None,
     default_value: Union[int, bool,
@@ -116,7 +124,7 @@ def is_layer_gptq_quantized(
 
 
 def get_linear_quant_method(
-    config: QuantizationConfig,
+    config: Union[GPTQConfig, GPTQMarlinConfig],
     layer: torch.nn.Module,
     prefix: str,
     linear_method_cls: type,

vllm/model_executor/models/aimv2.py

@@ -17,8 +17,7 @@ from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.transformers_utils.configs.ovis import AIMv2Config
 

vllm/model_executor/models/aria.py

@@ -9,13 +9,14 @@ from transformers import AriaConfig, AriaTextConfig, BatchFeature
 from transformers.models.aria.modeling_aria import AriaCrossAttention
 from transformers.models.aria.processing_aria import AriaProcessor
 
-from vllm.config import QuantizationConfig, VllmConfig
+from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)