[Model][LoRA]LoRA support added for MiniCPMV2.6

vllm/model_executor/models/idefics2_vision_model.py

@@ -65,11 +65,10 @@ def __init__(self, config: Idefics2VisionConfig):
         self.position_embedding = nn.Embedding(self.num_positions,
                                                self.embed_dim)
 
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        patch_attention_mask: torch.BoolTensor,
-    ) -> torch.Tensor:
+    def forward(self,
+                pixel_values: torch.FloatTensor,
+                patch_attention_mask: torch.BoolTensor,
+                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
         batch_size, _, max_im_h, max_im_w = pixel_values.shape
         patch_embeds = self.patch_embedding(pixel_values)
         embeddings = patch_embeds.flatten(2).transpose(1, 2)
@@ -84,8 +83,13 @@ def forward(
                                   fill_value=0)
 
         for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
-            nb_patches_h = p_attn_mask[:, 0].sum()
-            nb_patches_w = p_attn_mask[0].sum()
+
+            if tgt_sizes is not None:
+                nb_patches_h = tgt_sizes[batch_idx][0]
+                nb_patches_w = tgt_sizes[batch_idx][1]
+            else:
+                nb_patches_h = p_attn_mask[:, 0].sum()
+                nb_patches_w = p_attn_mask[0].sum()
             fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
             fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
             bucket_coords_h = torch.bucketize(fractional_coords_h,
@@ -287,10 +291,12 @@ def forward(
         self,
         pixel_values,
         patch_attention_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.tensor:
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ) -> torch.Tensor:
         hidden_states = self.embeddings(
             pixel_values=pixel_values,
-            patch_attention_mask=patch_attention_mask)
+            patch_attention_mask=patch_attention_mask,
+            tgt_sizes=tgt_sizes)
         encoder_outputs = self.encoder(hidden_states)
         last_hidden_state = self.post_layernorm(encoder_outputs)
         return last_hidden_state

vllm/model_executor/models/minicpmv.py

@@ -31,14 +31,13 @@
 import torch.types
 from PIL import Image
 from torch import nn
-from torch.nn.init import trunc_normal_
 from transformers import PretrainedConfig
 from typing_extensions import NotRequired
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
-from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.resampler import BaseResampler
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.resampler import (Resampler2,
@@ -106,58 +105,6 @@ class MiniCPMVImagePixelInputs(TypedDict):
 DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
 
 
-class BaseResampler(nn.Module):
-    """
-    A 2D perceiver-resampler network with one cross attention layers by
-        (grid_size**2) learnable queries and 2d sincos pos_emb
-    Outputs:
-        A tensor with the shape of (grid_size**2, embed_dim)
-    """
-
-    def __init__(
-        self,
-        num_queries: int,
-        embed_dim: int,
-        num_heads: int,
-        kv_dim: Optional[int] = None,
-        norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
-    ) -> None:
-        super().__init__()
-
-        self.num_queries = num_queries
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-
-        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
-        trunc_normal_(self.query, std=0.02)
-        if kv_dim is not None and kv_dim != embed_dim:
-            self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
-        else:
-            # Maintain the same return value with ReplicatedLinear.forward
-            self.kv_proj = lambda *args, **kwargs: (
-                nn.Identity()(*args, **kwargs),
-                None,
-            )
-        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
-        self.ln_q = norm_layer(embed_dim)
-        self.ln_kv = norm_layer(embed_dim)
-        self.ln_post = norm_layer(embed_dim)
-        self.proj = nn.Parameter(
-            (embed_dim**-0.5) * torch.randn(embed_dim, embed_dim))
-
-    def _init_weights(self, m: nn.Module) -> None:
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def _repeat(self, query, N: int):
-        return query.unsqueeze(1).repeat(1, N, 1)
-
-
 class Resampler2_5(BaseResampler):
 
     def __init__(
@@ -869,7 +816,35 @@ def is_default_weight_loading(self, name: str) -> bool:
         return "resampler" in name
 
 
-class MiniCPMV2_6(MiniCPMVBaseModel):
+class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+    # LoRA specific attributes
+    supported_lora_modules = [
+        # vision encoder
+        "fc1",
+        "fc2",
+        "out_proj",
+        # language model
+        "qkv_proj",  # same name with vision encoder
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        # resampler
+        "kv_proj",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
 
     def __init__(
         self,
@@ -894,15 +869,8 @@ def init_llm(
                           name="model")
 
     def init_vision_module(self) -> nn.Module:
-        # A custom version of SiglipVisionTransformer, won't work with TP
-        from vllm.model_executor.models.na_vit import SiglipVisionTransformer
 
-        if self.config._attn_implementation == "flash_attention_2":
-            self.config.vision_config._attn_implementation = "flash_attention_2"
-        else:
-            # not support sdpa
-            self.config.vision_config._attn_implementation = "eager"
-        model = SiglipVisionTransformer(self.config.vision_config)
+        model = Idefics2VisionTransformer(self.config.vision_config)
         if self.config.drop_vision_last_layer:
             model.encoder.layers = model.encoder.layers[:-1]
         return model
@@ -928,7 +896,7 @@ def get_vision_embedding(
             pixel_values,
             patch_attention_mask=patch_attn_mask,
             tgt_sizes=tgt_sizes,
-        ).last_hidden_state
+        )
         return vision_embedding
 
     def get_vision_hidden_states(
@@ -960,12 +928,12 @@ def get_vision_hidden_states(
             all_pixel_values.type(dtype),
             patch_attention_mask=patch_attn_mask,
             tgt_sizes=tgt_sizes,
-        ).last_hidden_state
+        )
 
         return self.resampler(vision_embedding, tgt_sizes)
 
     def is_default_weight_loading(self, name: str) -> bool:
-        return "resampler" in name or "vpm" in name
+        return "resampler" in name
 
 
 _SUPPORT_VERSION = {