Resolve LinearBase circular dependency

sgl-project · Feb 25, 2025 · 3f09b46 · 3f09b46
1 parent 6ce9dbe
commit 3f09b46
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Showing 3 changed files with 92 additions and 67 deletions.
diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
@@ -22,7 +22,7 @@
 from transformers import PretrainedConfig
 
 from sglang.srt.hf_transformers_utils import get_config, get_context_length
-from sglang.srt.layers.quantization import QUANTIZATION_METHODS
+from sglang.srt.layers.quantization import QUANTIZATION_METHODS, get_quantization_config
 from sglang.srt.utils import get_bool_env_var, is_hip
 
 logger = logging.getLogger(__name__)
@@ -218,7 +218,7 @@ def _parse_quant_hf_config(self):
 
     # adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
     def _verify_quantization(self) -> None:
-        supported_quantization = [*QUANTIZATION_METHODS]
+        supported_quantization = QUANTIZATION_METHODS
         rocm_supported_quantization = [
             "awq",
             "gptq",
@@ -253,7 +253,8 @@ def _verify_quantization(self) -> None:
             quant_method = quant_cfg.get("quant_method", "").lower()
 
             # Detect which checkpoint is it
-            for _, method in QUANTIZATION_METHODS.items():
+            for name in QUANTIZATION_METHODS:
+                method = get_quantization_config(name)
                 quantization_override = method.override_quantization_method(
                     quant_cfg, self.quantization
                 )

diff --git a/python/sglang/srt/layers/__init__.py b/python/sglang/srt/layers/__init__.py
@@ -0,0 +1,18 @@
+def patch_vllm_linear_base_isinstance():
+    import builtins
+
+    from vllm.model_executor.layers.linear import LinearBase
+
+    from sglang.srt.layers.linear import LinearBase as PatchedLinearBase
+
+    original_isinstance = builtins.isinstance
+
+    def patched_isinstance(obj, classinfo):
+        if classinfo is LinearBase:
+            return original_isinstance(obj, PatchedLinearBase)
+        return original_isinstance(obj, classinfo)
+
+    builtins.isinstance = patched_isinstance
+
+
+patch_vllm_linear_base_isinstance()
diff --git a/python/sglang/srt/layers/quantization/__init__.py b/python/sglang/srt/layers/quantization/__init__.py
@@ -1,64 +1,88 @@
 # Adapted from https://raw.githubusercontent.com/vllm-project/vllm/v0.5.5/vllm/model_executor/layers/quantization/__init__.py
-from typing import Callable, Dict, Optional, Type
+from typing import Callable, Dict, List, Optional, Type
 
 import torch
-from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
-from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.awq_marlin import (
     AWQMarlinConfig,
     AWQMoEMethod,
 )
-from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
-from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (
-    CompressedTensorsConfig,
-)
-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.fbgemm_fp8 import FBGEMMFp8Config
-from vllm.model_executor.layers.quantization.gguf import GGUFConfig
-from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import GPTQMarlinConfig
-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
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
-from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
-from sglang.srt.layers.quantization.fp8 import Fp8Config
-from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
-from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
-
-QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
-    "aqlm": AQLMConfig,
-    "awq": AWQConfig,
-    "deepspeedfp": DeepSpeedFPConfig,
-    "tpu_int8": Int8TpuConfig,
-    "fp8": Fp8Config,
-    "blockwise_int8": BlockInt8Config,
-    "fbgemm_fp8": FBGEMMFp8Config,
-    "marlin": MarlinConfig,
-    "modelopt": ModelOptFp8Config,
-    "gguf": GGUFConfig,
-    "gptq_marlin_24": GPTQMarlin24Config,
-    "gptq_marlin": GPTQMarlinConfig,
-    "awq_marlin": AWQMarlinConfig,
-    "gptq": GPTQConfig,
-    "compressed-tensors": CompressedTensorsConfig,
-    "bitsandbytes": BitsAndBytesConfig,
-    "qqq": QQQConfig,
-    "experts_int8": ExpertsInt8Config,
-    "w8a8_int8": W8A8Int8Config,
+
+QUANTIZATION_METHODS: List[str] = {
+    "aqlm",
+    "awq",
+    "deepspeedfp",
+    "tpu_int8",
+    "fp8",
+    "blockwise_int8",
+    "fbgemm_fp8",
+    "marlin",
+    "modelopt",
+    "gguf",
+    "gptq_marlin_24",
+    "gptq_marlin",
+    "awq_marlin",
+    "gptq",
+    "compressed-tensors",
+    "bitsandbytes",
+    "qqq",
+    "experts_int8",
+    "w8a8_int8",
 }
 
 
 def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     if quantization not in QUANTIZATION_METHODS:
-        raise ValueError(
-            f"Invalid quantization method: {quantization}. "
-            f"Available methods: {list(QUANTIZATION_METHODS.keys())}"
-        )
-    return QUANTIZATION_METHODS[quantization]
+        raise ValueError(f"Invalid quantization method: {quantization}.")
+
+    from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
+    from vllm.model_executor.layers.quantization.awq import AWQConfig
+    from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
+    from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (
+        CompressedTensorsConfig,
+    )
+    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.fbgemm_fp8 import FBGEMMFp8Config
+    from vllm.model_executor.layers.quantization.gguf import GGUFConfig
+    from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+    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
+
+    from .blockwise_int8 import BlockInt8Config
+    from .fp8 import Fp8Config
+    from .modelopt_quant import ModelOptFp8Config
+    from .w8a8_int8 import W8A8Int8Config
+
+    method_to_config: Dict[str, Type[QuantizationConfig]] = {
+        "aqlm": AQLMConfig,
+        "awq": AWQConfig,
+        "deepspeedfp": DeepSpeedFPConfig,
+        "tpu_int8": Int8TpuConfig,
+        "fp8": Fp8Config,
+        "blockwise_int8": BlockInt8Config,
+        "fbgemm_fp8": FBGEMMFp8Config,
+        "marlin": MarlinConfig,
+        "modelopt": ModelOptFp8Config,
+        "gguf": GGUFConfig,
+        "gptq_marlin_24": GPTQMarlin24Config,
+        "gptq_marlin": GPTQMarlinConfig,
+        "awq_marlin": AWQMarlinConfig,
+        "gptq": GPTQConfig,
+        "compressed-tensors": CompressedTensorsConfig,
+        "bitsandbytes": BitsAndBytesConfig,
+        "qqq": QQQConfig,
+        "experts_int8": ExpertsInt8Config,
+        "w8a8_int8": W8A8Int8Config,
+    }
+
+    return method_to_config[quantization]
 
 
 def gptq_get_quant_method(self, layer, prefix):
@@ -133,23 +157,6 @@ def awq_moe_method_apply(
     )
 
 
-def patch_vllm_linear_base_isinstance():
-    import builtins
-
-    from vllm.model_executor.layers.linear import LinearBase
-
-    from sglang.srt.layers.linear import LinearBase as PatchedLinearBase
-
-    original_isinstance = builtins.isinstance
-
-    def patched_isinstance(obj, classinfo):
-        if classinfo is LinearBase:
-            return original_isinstance(obj, PatchedLinearBase)
-        return original_isinstance(obj, classinfo)
-
-    builtins.isinstance = patched_isinstance
-
-
 def apply_monkey_patches():
     """Apply all monkey patches in one place."""
     from vllm.model_executor.layers.quantization.awq_marlin import AWQMoEMethod
@@ -159,7 +166,6 @@ def apply_monkey_patches():
     setattr(AWQMoEMethod, "apply", awq_moe_method_apply)
 
 
-patch_vllm_linear_base_isinstance()
 # Apply patches when module is imported
 apply_monkey_patches()