[Bugfix]fix mixed bits and visual language model quantization in AutoRound (#21802)

Signed-off-by: Wenhua Cheng <wenhua.cheng@intel.com>

vllm/model_executor/layers/quantization/auto_round.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from fractions import Fraction
-from typing import Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import torch
 
@@ -16,6 +16,9 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
+if TYPE_CHECKING:
+    from vllm.model_executor.models.utils import WeightsMapper
+
 logger = init_logger(__name__)
 
 
@@ -28,7 +31,13 @@ class AutoRoundConfig(QuantizationConfig):
     SUPPORTED_DTYPES = {"int"}
     SUPPORTED_FORMATS = {"auto_round:auto_gptq", "auto_round:auto_awq"}
     SUPPORTED_BACKENDS = {
-        "auto", "gptq", "gptq:marlin", "awq", "awq:marlin", "marlin", "ipex"
+        "auto",
+        "gptq",
+        "gptq:marlin",
+        "awq",
+        "awq:marlin",
+        "marlin",
+        "ipex",
     }
 
     def __init__(
@@ -109,26 +118,70 @@ class AutoRoundConfig(QuantizationConfig):
         )
 
     def get_layer_config(self, layer, layer_name: str):
-        # Priority: extra_config > block_name_to_quantize > type fallback
-        if self.extra_config and layer_name in self.extra_config:
-            cfg = self.extra_config[layer_name]
-            return cfg.get("bits", self.weight_bits), cfg.get(
-                "group_size", self.group_size), cfg.get("sym", self.sym)
 
-        quantized = True
+        def get_config(name: str, quantized: bool = True):
+            cfg = self.extra_config.get(name, {}) if self.extra_config else {}
+            return (
+                cfg.get("bits", self.weight_bits if quantized else 16),
+                cfg.get("group_size", self.group_size if quantized else -1),
+                cfg.get("sym", self.sym if quantized else True),
+            )
+
+        # 1. Exact match from config
+        if self.extra_config and layer_name in self.extra_config:
+            return get_config(layer_name)
+
+        # 2. Determine whether layer should be quantized
+        quantized = not isinstance(layer, ParallelLMHead)
         if self.block_name_to_quantize:
             quantized = any(
                 layer_name.startswith(name)
                 for name in self.block_name_to_quantize)
-        elif isinstance(layer, ParallelLMHead):
-            quantized = False
 
-        return (self.weight_bits, self.group_size,
-                self.sym) if quantized else (16, -1, True)
+        # 3. Handle fused MoE
+        if self.extra_config and "fusedmoe" in layer.__class__.__name__.lower(
+        ):
+            moe_configs = [
+                get_config(name, quantized) for name in self.extra_config
+                if name.startswith(layer_name)
+            ]
+            if moe_configs:
+                if len(set(moe_configs)) == 1:
+                    return moe_configs[0]
+                raise ValueError(f"Fused MoE layer '{layer_name}' requires "
+                                 f"consistent quant config for all sub-layers")
+
+        # 4. Handle fused QKV or other patterns
+        if self.extra_config:
+            for fusion_key, sub_keys in self.packed_modules_mapping.items():
+                if fusion_key in layer_name and layer_name.count(
+                        fusion_key) == 1:
+                    sub_names = [
+                        layer_name.replace(fusion_key, sub_key)
+                        for sub_key in sub_keys
+                    ]
+                    sub_configs = [
+                        get_config(name, quantized) for name in sub_names
+                    ]
+                    if len(set(sub_configs)) == 1:
+                        return sub_configs[0]
+                    raise ValueError(
+                        f"Fused module '{layer_name}' requires "
+                        f"consistent quant config for {sub_names}")
+
+        # 5. Fallback
+        return get_config(layer_name, quantized)
 
     def check_quantized(self, weight_bits: int) -> bool:
         return weight_bits < 16
 
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        if self.block_name_to_quantize is not None:
+            self.block_name_to_quantize = hf_to_vllm_mapper.apply_list(
+                self.block_name_to_quantize)
+        if self.extra_config is not None:
+            self.extra_config = hf_to_vllm_mapper.apply_dict(self.extra_config)
+
     def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
         from vllm.model_executor.layers.fused_moe import FusedMoE
         from vllm.model_executor.layers.quantization.utils.marlin_utils import (
@@ -141,9 +194,14 @@ class AutoRoundConfig(QuantizationConfig):
             else:
                 return None
 
-        logger.debug("[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
-                     prefix, layer.__class__.__name__, weight_bits, group_size,
-                     sym)
+        logger.debug(
+            "[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
+            prefix,
+            layer.__class__.__name__,
+            weight_bits,
+            group_size,
+            sym,
+        )
         if backend == "auto" or "marlin" in backend:
             AWQ_TYPE_MAP = {
                 4: scalar_types.uint4,
@@ -162,15 +220,19 @@ class AutoRoundConfig(QuantizationConfig):
         if use_marlin:
             from vllm.model_executor.layers.quantization.awq_marlin import (
                 AWQMarlinConfig, AWQMarlinLinearMethod, AWQMoEMethod)
-            quant_args_marlin = AWQMarlinConfig(weight_bits=weight_bits,
-                                                group_size=group_size,
-                                                zero_point=not sym,
-                                                lm_head_quantized=False,
-                                                full_config={},
-                                                modules_to_not_convert=[])
+
+            quant_args_marlin = AWQMarlinConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                zero_point=not sym,
+                lm_head_quantized=False,
+                full_config={},
+                modules_to_not_convert=[],
+            )
         else:
             from vllm.model_executor.layers.quantization.awq import (
                 AWQConfig, AWQLinearMethod)
+
             quant_args = AWQConfig(
                 weight_bits=weight_bits,
                 group_size=group_size,
@@ -182,6 +244,7 @@ class AutoRoundConfig(QuantizationConfig):
                 return AWQMoEMethod(quant_args_marlin)
             from vllm.model_executor.layers.quantization.moe_wna16 import (
                 MoeWNA16Config)
+
             config = {
                 "quant_method": "awq",
                 "bits": weight_bits,
@@ -206,6 +269,7 @@ class AutoRoundConfig(QuantizationConfig):
         from vllm.model_executor.layers.fused_moe import FusedMoE
         from vllm.model_executor.layers.quantization.utils.marlin_utils import (
             check_marlin_supported, check_moe_marlin_supports_layer)
+
         weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
         if not self.check_quantized(weight_bits):
             if isinstance(layer, (LinearBase, ParallelLMHead)):
@@ -213,19 +277,24 @@ class AutoRoundConfig(QuantizationConfig):
             else:
                 return None
 
-        logger.debug("[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
-                     prefix, layer.__class__.__name__, weight_bits, group_size,
-                     sym)
+        logger.debug(
+            "[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
+            prefix,
+            layer.__class__.__name__,
+            weight_bits,
+            group_size,
+            sym,
+        )
         if backend == "auto" or "marlin" in backend:
             GPTQ_TYPE_MAP = {
                 (4, True): scalar_types.uint4b8,
                 (8, True): scalar_types.uint8b128,
             }
-            use_marlin = ((weight_bits, sym) in GPTQ_TYPE_MAP
-                          and check_marlin_supported(
+            use_marlin = (weight_bits,
+                          sym) in GPTQ_TYPE_MAP and check_marlin_supported(
                               GPTQ_TYPE_MAP[(weight_bits, sym)],
                               group_size,
-                              has_zp=not sym))
+                              has_zp=not sym)
             if isinstance(layer, FusedMoE):
                 use_marlin = use_marlin and check_moe_marlin_supports_layer(
                     layer, group_size)
@@ -234,26 +303,33 @@ class AutoRoundConfig(QuantizationConfig):
         if use_marlin:
             from vllm.model_executor.layers.quantization.gptq_marlin import (
                 GPTQMarlinConfig, GPTQMarlinLinearMethod, GPTQMarlinMoEMethod)
-            quant_args_marlin = GPTQMarlinConfig(weight_bits=weight_bits,
-                                                 group_size=group_size,
-                                                 is_sym=sym,
-                                                 lm_head_quantized=False,
-                                                 desc_act=False,
-                                                 dynamic={},
-                                                 full_config={})
+
+            quant_args_marlin = GPTQMarlinConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                is_sym=sym,
+                lm_head_quantized=False,
+                desc_act=False,
+                dynamic={},
+                full_config={},
+            )
         else:
             from vllm.model_executor.layers.quantization.gptq import (
                 GPTQConfig, GPTQLinearMethod)
-            quant_args = GPTQConfig(weight_bits=weight_bits,
-                                    group_size=group_size,
-                                    lm_head_quantized=False,
-                                    desc_act=False,
-                                    dynamic={})
+
+            quant_args = GPTQConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                lm_head_quantized=False,
+                desc_act=False,
+                dynamic={},
+            )
 
         if isinstance(layer, FusedMoE):
             if use_marlin:
                 from vllm.model_executor.layers.quantization.moe_wna16 import (
                     MoeWNA16Config)
+
                 config = {
                     "quant_method": "gptq",
                     "bits": weight_bits,
@@ -282,6 +358,7 @@ class AutoRoundConfig(QuantizationConfig):
                 return None
         from vllm.model_executor.layers.quantization.ipex_quant import (
             IPEXAWQLinearMethod, IPEXConfig, IPEXGPTQLinearMethod)
+
         if isinstance(layer, (LinearBase, ParallelLMHead)):
             if "awq" in self.packing_format:
                 config = IPEXConfig(method="awq",