[Model] Add BNB quantization support for Idefics3 (vllm-project#10310)

Signed-off-by: B-201 <[email protected]>
Co-authored-by: Jee Jee Li <[email protected]>
Signed-off-by: rickyx <[email protected]>

vllm/model_executor/models/idefics3.py

@@ -22,6 +22,7 @@
 from PIL import Image
 from torch import nn
 # Temporary solution for transformers below 4.46.0.
+from transformers import PretrainedConfig as Idefics3Config
 from transformers import ProcessorMixin as Idefics3ImageProcessor
 
 from vllm.attention import AttentionMetadata
@@ -31,6 +32,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.models.module_mapping import MultiModelKeys
@@ -374,12 +376,23 @@ def dummy_data_for_idefics3(
 
 class Idefics3SimpleMLP(nn.Module):
 
-    def __init__(self, config):
+    def __init__(
+        self,
+        config: Idefics3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
         super().__init__()
         input_size = config.vision_config.hidden_size * (config.scale_factor**
                                                          2)
         output_size = config.text_config.hidden_size
-        self.proj = ReplicatedLinear(input_size, output_size, bias=False)
+        self.proj = ReplicatedLinear(
+            input_size,
+            output_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "proj"),
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out, _ = self.proj(x)
@@ -388,10 +401,19 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class Idefics3Connector(nn.Module):
 
-    def __init__(self, config):
+    def __init__(
+        self,
+        config: Idefics3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
         super().__init__()
         self.scale_factor = config.scale_factor
-        self.modality_projection = Idefics3SimpleMLP(config)
+        self.modality_projection = Idefics3SimpleMLP(
+            config,
+            quant_config,
+            prefix=maybe_prefix(prefix, "modality_projection"),
+        )
 
     def pixel_shuffle(self,
                       x: torch.Tensor,
@@ -431,9 +453,15 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.config = config
         self.padding_idx = self.config.text_config.pad_token_id
         self.vocab_size = self.config.text_config.vocab_size
-        self.vision_model = Idefics3VisionTransformer(config.vision_config,
-                                                      quant_config)
-        self.connector = Idefics3Connector(config)
+        self.vision_model = Idefics3VisionTransformer(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"))
+        self.connector = Idefics3Connector(
+            config,
+            quant_config,
+            prefix=maybe_prefix(prefix, "connector"),
+        )
         self.text_model = LlamaModel(
             vllm_config=vllm_config.with_hf_config(config.text_config),
             prefix=maybe_prefix(prefix, "text_model"),
@@ -637,6 +665,32 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
         "gate_up_proj",
         "down_proj",
     ]
+
+    # BitandBytes specific attributes
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+        # vision_model
+        ".fc1.",
+        ".fc2.",
+        ".out_proj.",
+        # connector
+        ".proj.",
+    ]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
+
     embedding_modules = {}
     embedding_padding_modules = []