chore: cleanup quant deps (#12268)

python/sglang/srt/layers/quantization/__init__.py

@@ -7,31 +7,14 @@ from typing import TYPE_CHECKING, Dict, Optional, Type
 
 import torch
 
-try:
-    from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
-    from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
-    from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
-    from vllm.model_executor.layers.quantization.experts_int8 import ExpertsInt8Config
-    from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
-        GPTQMarlin24Config,
-    )
-    from vllm.model_executor.layers.quantization.marlin import MarlinConfig
-    from vllm.model_executor.layers.quantization.qqq import QQQConfig
-    from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
-
-    VLLM_AVAILABLE = True
-except ImportError as e:
-    VLLM_AVAILABLE = False
-    VLLM_IMPORT_ERROR = e
 
 # Define empty classes as placeholders when vllm is not available
 class DummyConfig:
     def override_quantization_method(self, *args, **kwargs):
         return None
 
-    AQLMConfig = BitsAndBytesConfig = CompressedTensorsConfig = DeepSpeedFPConfig = (
+
-        ExpertsInt8Config
+CompressedTensorsConfig = DummyConfig
-    ) = GPTQMarlin24Config = MarlinConfig = QQQConfig = Int8TpuConfig = DummyConfig
 
 from sglang.srt.layers.quantization.auto_round import AutoRoundConfig
 from sglang.srt.layers.quantization.awq import AWQConfig, AWQMarlinConfig
@@ -62,7 +45,7 @@ _is_mxfp_supported = mxfp_supported()
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.topk import TopKOutput
 
-# Base quantization methods that don't depend on vllm
+# Base quantization methods
 BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "fp8": Fp8Config,
     "blockwise_int8": BlockInt8Config,
@@ -102,19 +85,8 @@ elif _is_mxfp_supported and is_hip():
             "mxfp4": Mxfp4Config,
         }
     )
-# VLLM-dependent quantization methods
-VLLM_QUANTIZATION_METHODS = {
-    "aqlm": AQLMConfig,
-    "deepspeedfp": DeepSpeedFPConfig,
-    "tpu_int8": Int8TpuConfig,
-    "marlin": MarlinConfig,
-    "gptq_marlin_24": GPTQMarlin24Config,
-    "bitsandbytes": BitsAndBytesConfig,
-    "qqq": QQQConfig,
-    "experts_int8": ExpertsInt8Config,
-}
 
-QUANTIZATION_METHODS = {**BASE_QUANTIZATION_METHODS, **VLLM_QUANTIZATION_METHODS}
+QUANTIZATION_METHODS = {**BASE_QUANTIZATION_METHODS}
 
 
 def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
@@ -123,50 +95,8 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
             f"Invalid quantization method: {quantization}. "
             f"Available methods: {list(QUANTIZATION_METHODS.keys())}"
         )
-    if quantization in VLLM_QUANTIZATION_METHODS and not VLLM_AVAILABLE:
-        raise ValueError(
-            f"{quantization} quantization requires some operators from vllm. "
-            f"Please install vllm by `pip install vllm==0.9.0.1`\n"
-            f"Import error: {VLLM_IMPORT_ERROR}"
-        )
 
     return QUANTIZATION_METHODS[quantization]
 
 
 original_isinstance = builtins.isinstance
-
-
-def monkey_patch_isinstance_for_vllm_base_layer(reverse: bool = False):
-    """
-    Patch isinstance so that the `get_quant_method` in vllm's QuantizationConfig
-    can recognize sglang layers
-    """
-    if not VLLM_AVAILABLE:
-        return
-
-    if reverse:
-        builtins.isinstance = original_isinstance
-        return
-
-    from vllm.model_executor.layers.fused_moe import FusedMoE
-    from vllm.model_executor.layers.linear import LinearBase
-    from vllm.model_executor.layers.vocab_parallel_embedding import (
-        VocabParallelEmbedding,
-    )
-
-    from sglang.srt.layers.linear import LinearBase as PatchedLinearBase
-    from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE as PatchedFusedMoE
-    from sglang.srt.layers.vocab_parallel_embedding import (
-        VocabParallelEmbedding as PatchedVocabParallelEmbedding,
-    )
-
-    def patched_isinstance(obj, classinfo):
-        if classinfo is LinearBase:
-            return original_isinstance(obj, PatchedLinearBase)
-        if classinfo is FusedMoE:
-            return original_isinstance(obj, PatchedFusedMoE)
-        if classinfo is VocabParallelEmbedding:
-            return original_isinstance(obj, PatchedVocabParallelEmbedding)
-        return original_isinstance(obj, classinfo)
-
-    builtins.isinstance = patched_isinstance

python/sglang/srt/model_executor/model_runner.py

@@ -77,7 +77,6 @@ from sglang.srt.layers.dp_attention import (
     initialize_dp_attention,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.layers.quantization import monkey_patch_isinstance_for_vllm_base_layer
 from sglang.srt.layers.sampler import Sampler
 from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
 from sglang.srt.lora.lora_manager import LoRAManager
@@ -730,7 +729,6 @@ class ModelRunner:
         # Load the model
         # Remove monkey_patch when linear.py quant remove dependencies with vllm
         monkey_patch_vllm_parallel_state()
-        monkey_patch_isinstance_for_vllm_base_layer()
 
         with self.memory_saver_adapter.region(
             GPU_MEMORY_TYPE_WEIGHTS,
@@ -742,7 +740,6 @@ class ModelRunner:
                 device_config=DeviceConfig(self.device, self.gpu_id),
             )
         monkey_patch_vllm_parallel_state(reverse=True)
-        monkey_patch_isinstance_for_vllm_base_layer(reverse=True)
 
         get_offloader().post_init()