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

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -11,6 +11,8 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 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 (
     apply_fp8_linear, create_per_channel_scale_param)
 from vllm.model_executor.utils import set_weight_attrs
@@ -18,14 +20,6 @@ from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
-# Note: this is a hack. We should update each model to register the
-# stacked params and get it from there instead in a future PR.
-# fused_name: List[shard_name]
-_FUSED_LAYER_NAME_MAPPING = {
-    "qkv_proj": ["q_proj", "k_proj", "v_proj"],
-    "gate_up_proj": ["gate_proj", "up_proj"]
-}
-
 
 class FBGEMMFp8Config(QuantizationConfig):
     """Config class for FBGEMM Fp8."""
@@ -62,37 +56,10 @@ class FBGEMMFp8Config(QuantizationConfig):
         input_scale_ub = cls.get_from_keys(config, ["activation_scale_ub"])
         return cls(ignore_list=ignore_list, input_scale_ub=input_scale_ub)
 
-    def _is_layer_skipped(self, prefix: str) -> bool:
-        # prefix: model.layers.0.self_attn.q_proj
-        # proj_name: q_proj
-        proj_name = prefix.split(".")[-1]
-        if proj_name in _FUSED_LAYER_NAME_MAPPING:
-            shard_prefixes = [
-                prefix.replace(proj_name, shard_proj_name)
-                for shard_proj_name in _FUSED_LAYER_NAME_MAPPING[proj_name]
-            ]
-
-            is_skipped = None
-            for shard_prefix in shard_prefixes:
-                is_shard_skipped = shard_prefix in self.ignore_list
-
-                if is_skipped is None:
-                    is_skipped = is_shard_skipped
-                elif is_shard_skipped != is_skipped:
-                    raise ValueError(
-                        f"Detected some but not all shards of {prefix} "
-                        "are quantized. All shards of fused layers "
-                        "to have the same precision.")
-        else:
-            is_skipped = prefix in self.ignore_list
-
-        assert is_skipped is not None
-        return is_skipped
-
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
         if isinstance(layer, LinearBase):
-            if self._is_layer_skipped(prefix):
+            if is_layer_skipped(prefix, self.ignore_list):
                 return UnquantizedLinearMethod()
             return FBGEMMFp8LinearMethod(self)
         return None