[Misc] Add ignored layers for fp8 quantization (#6657)

vllm/model_executor/layers/quantization/fp8.py

@@ -8,12 +8,15 @@ from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   fused_moe)
-from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, apply_fp8_linear, create_per_tensor_scale_param,
     cutlass_fp8_supported, per_tensor_dequantize, requantize_with_max_scale)
@@ -33,6 +36,7 @@ class Fp8Config(QuantizationConfig):
         self,
         is_checkpoint_fp8_serialized: bool = False,
         activation_scheme: str = "dynamic",
+        ignored_layers: Optional[List[str]] = None,
     ) -> None:
         self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
         if is_checkpoint_fp8_serialized:
@@ -42,6 +46,7 @@ class Fp8Config(QuantizationConfig):
             raise ValueError(
                 f"Unsupported activation scheme {activation_scheme}")
         self.activation_scheme = activation_scheme
+        self.ignored_layers = ignored_layers or []
 
     @classmethod
     def get_name(cls) -> str:
@@ -64,14 +69,18 @@ class Fp8Config(QuantizationConfig):
         quant_method = cls.get_from_keys(config, ["quant_method"])
         is_checkpoint_fp8_serialized = ("fp8" in quant_method)
         activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
         return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
-                   activation_scheme=activation_scheme)
+                   activation_scheme=activation_scheme,
+                   ignored_layers=ignored_layers)
 
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
         from vllm.attention.layer import Attention  # Avoid circular import
 
         if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.ignored_layers):
+                return UnquantizedLinearMethod()
             return Fp8LinearMethod(self)
         elif isinstance(layer, FusedMoE):
             return Fp8MoEMethod(self)