[VLM] Enable overriding whether post layernorm is used in vision enco…

…der + fix quant args (vllm-project#9217)

Co-authored-by: Isotr0py <2037008807@qq.com>
Signed-off-by: NickLucche <nlucches@redhat.com>

vllm/model_executor/layers/quantization/awq.py

@@ -3,7 +3,8 @@
 import torch
 
 from vllm import _custom_ops as ops
-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)
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
@@ -21,10 +22,12 @@ def __init__(
         weight_bits: int,
         group_size: int,
         zero_point: bool,
+        modules_to_not_convert: Optional[List[str]] = None,
     ) -> None:
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.zero_point = zero_point
+        self.modules_to_not_convert = modules_to_not_convert or []
 
         if self.weight_bits != 4:
             raise ValueError(
@@ -35,7 +38,8 @@ def __init__(
     def __repr__(self) -> str:
         return (f"AWQConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
-                f"zero_point={self.zero_point})")
+                f"zero_point={self.zero_point}, "
+                f"modules_to_not_convert={self.modules_to_not_convert})")
 
     def get_name(self) -> str:
         return "awq"
@@ -61,18 +65,26 @@ def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
         weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
         group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
         zero_point = cls.get_from_keys(config, ["zero_point"])
-        return cls(weight_bits, group_size, zero_point)
+        modules_to_not_convert = cls.get_from_keys_or(
+            config, ["modules_to_not_convert"], None)
+        return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
 
     def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> Optional["AWQLinearMethod"]:
+                         prefix: str) -> Optional["LinearMethodBase"]:
         if isinstance(layer, LinearBase):
+            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
+                return UnquantizedLinearMethod()
             return AWQLinearMethod(self)
         return None
 
     def get_scaled_act_names(self) -> List[str]:
         return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
 
 
+def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
+    return any(module_name in prefix for module_name in modules_to_not_convert)
+
+
 class AWQLinearMethod(LinearMethodBase):
     """Linear method for AWQ.
 

vllm/model_executor/models/blip.py

@@ -122,7 +122,7 @@ def input_processor_for_blip(
 # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa
 class BlipVisionEmbeddings(nn.Module):
 
-    def __init__(self, config: BlipVisionConfig):
+    def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]):
         super().__init__()
 
         self.config = config
@@ -167,9 +167,10 @@ class BlipParallelAttention(nn.Module):
 
     def __init__(
         self,
-        config: BlipVisionConfig,
+        config: Union[BlipVisionConfig, Blip2VisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
-    ):
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -189,11 +190,13 @@ def __init__(
             self.num_heads,
             bias=config.qkv_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv",
         )
         self.projection = RowParallelLinear(
             self.embed_dim,
             self.embed_dim,
             quant_config=quant_config,
+            prefix=f"{prefix}.projection",
         )
 
         self.tp_size = get_tensor_model_parallel_world_size()
@@ -235,9 +238,12 @@ def forward(
 
 class BlipMLP(nn.Module):
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         self.config = config
@@ -246,11 +252,13 @@ def __init__(self,
         self.fc1 = ColumnParallelLinear(config.hidden_size,
                                         config.intermediate_size,
                                         bias=True,
-                                        quant_config=quant_config)
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
         self.fc2 = RowParallelLinear(config.intermediate_size,
                                      config.hidden_size,
                                      bias=True,
-                                     quant_config=quant_config)
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc1(hidden_states)
@@ -262,24 +270,32 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
 class BlipEncoderLayer(nn.Module):
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         # fallback to sdpa attention if tp unavailable
         num_heads = config.num_attention_heads
         tp_size = get_tensor_model_parallel_world_size()
         if USE_XFORMERS_OPS and num_heads % tp_size == 0:
-            self.self_attn = BlipParallelAttention(config,
-                                                   quant_config=quant_config)
+            self.self_attn = BlipParallelAttention(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.self_attn",
+            )
         else:
             # Blip doesn't have SDPA attention implemented in transformers
             # use eager attention instead for cpu backend
             self.self_attn = BlipAttention(config)
         self.layer_norm1 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
-        self.mlp = BlipMLP(config, quant_config=quant_config)
+        self.mlp = BlipMLP(config,
+                           quant_config=quant_config,
+                           prefix=f"{prefix}.mlp")
         self.layer_norm2 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
 
@@ -307,10 +323,13 @@ class BlipEncoder(nn.Module):
         config: BlipConfig
     """
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         self.config = config
@@ -321,8 +340,10 @@ def __init__(self,
             num_hidden_layers = num_hidden_layers_override
 
         self.layers = nn.ModuleList([
-            BlipEncoderLayer(config=config, quant_config=quant_config)
-            for _ in range(num_hidden_layers)
+            BlipEncoderLayer(config=config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
         ])
 
     def forward(self, inputs_embeds: torch.Tensor):
@@ -337,10 +358,15 @@ class BlipVisionModel(nn.Module):
     config_class = BlipVisionConfig
     main_input_name = "pixel_values"
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         tp_size = get_tensor_model_parallel_world_size()
@@ -354,19 +380,24 @@ def __init__(self,
             config=config,
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
         )
 
+        num_hidden_layers = config.num_hidden_layers
         if len(self.encoder.layers) > config.num_hidden_layers:
             raise ValueError(
-                f"The original encoder only has {config.num_hidden_layers} "
+                f"The original encoder only has {num_hidden_layers} "
                 f"layers, but you requested {len(self.encoder.layers)} layers."
             )
-        elif len(self.encoder.layers) == config.num_hidden_layers:
+
+        # If possible, skip post_layernorm to conserve memory
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
             self.post_layernorm = nn.LayerNorm(config.hidden_size,
                                                eps=config.layer_norm_eps)
         else:
-            # post_layernorm is unused when we extract intermediate features
-            # In this case, we can skip it to conserve memory
             self.post_layernorm = None
 
     def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:

vllm/model_executor/models/blip2.py

@@ -490,7 +490,7 @@ def __init__(self,
         self.multimodal_config = multimodal_config
 
         # TODO: Optionally initializes this for supporting embeddings.
-        self.vision_model = BlipVisionModel(config.vision_config)
+        self.vision_model = BlipVisionModel(config.vision_config, quant_config)
 
         self.query_tokens = nn.Parameter(
             torch.zeros(1, config.num_query_tokens,