[Bugfix] Clean up MiniCPM-V (#6939)

Co-authored-by: hezhihui <hzh7269@modelbest.cn>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/source/models/supported_models.rst

@@ -222,9 +222,13 @@ Vision Language Models
     -
   * - :code:`MiniCPM-V`
     - MiniCPM-V
-    - :code:`openbmb/MiniCPM-V-2`, :code:`openbmb/MiniCPM-Llama3-V-2_5`, etc.
+    - :code:`openbmb/MiniCPM-V-2` (see note), :code:`openbmb/MiniCPM-Llama3-V-2_5`, etc.
     -
 
+.. note::
+  For :code:`openbmb/MiniCPM-V-2`, the official repo doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
+  For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
+
 ----
 
 If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.

vllm/model_executor/models/llama.py

@@ -418,11 +418,9 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
-        input_embeds: Optional[torch.Tensor] = None
     ) -> Union[torch.Tensor, IntermediateTensors]:
         model_output = self.model(input_ids, positions, kv_caches,
-                                  attn_metadata, intermediate_tensors,
-                                  input_embeds)
+                                  attn_metadata, intermediate_tensors)
         return model_output
 
     def compute_logits(self, hidden_states: torch.Tensor,

vllm/model_executor/models/minicpm.py

@@ -370,6 +370,7 @@ class MiniCPMModel(nn.Module):
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if inputs_embeds is not None:
@@ -463,11 +464,10 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA):
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
-        input_embeds: Optional[torch.Tensor] = None,
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, kv_caches,
-                                   attn_metadata, input_embeds)
+                                   attn_metadata, intermediate_tensors)
         return hidden_states
 
     def compute_logits(self, hidden_states: torch.Tensor,

vllm/model_executor/models/minicpmv.py

@@ -20,32 +20,34 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Inference-only MiniCPM-V-2 model compatible with HuggingFace weights."""
+"""Inference-only MiniCPM-V model compatible with HuggingFace weights."""
 import math
 import re
 from functools import partial
-from typing import Iterable, List, Optional, Tuple
+from typing import Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 import torch.nn.functional as F
+import torch.types
 from PIL import Image
 from torch import nn
 from torch.nn.init import trunc_normal_
 from transformers.configuration_utils import PretrainedConfig
-from transformers.models.idefics2.modeling_idefics2 import (
-    Idefics2VisionTransformer)
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import SupportsVision
-from vllm.model_executor.models.llama import LlamaForCausalLM
-from vllm.model_executor.models.minicpm import MiniCPMForCausalLM
+from vllm.model_executor.models.llama import LlamaModel
+from vllm.model_executor.models.minicpm import MiniCPMModel
+from vllm.model_executor.models.qwen2 import Qwen2Model
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import (cached_get_image_processor,
@@ -53,12 +55,12 @@ from vllm.multimodal.image import (cached_get_image_processor,
 from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
 
 _KEYS_TO_MODIFY_MAPPING = {
-    "language_model.lm_head": "lm_head",
-    "language_model.model": "language_model",
+    "llm.lm_head": "lm_head",
+    "llm.model": "llm",
 }
 
 
-def get_abs_pos(abs_pos, tgt_size):
+def get_abs_pos(abs_pos: torch.Tensor, tgt_size: torch.Tensor):
     # abs_pos: L, C
     # tgt_size: (H, W)
     # return: M, C
@@ -75,10 +77,10 @@ def get_abs_pos(abs_pos, tgt_size):
 
 
 # https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
-def get_2d_sincos_pos_embed(embed_dim,
-                            grid_size,
-                            cls_token=False,
-                            version=2.0):
+def get_2d_sincos_pos_embed(embed_dim: int,
+                            grid_size: Union[int, Tuple[int, int]],
+                            cls_token: bool = False,
+                            version: Tuple[int, int] = (2, 0)):
     """
     grid_size: int of the grid height and width
     return:
@@ -95,7 +97,7 @@ def get_2d_sincos_pos_embed(embed_dim,
     grid = np.meshgrid(grid_w, grid_h)  # here w goes first
     grid = np.stack(grid, axis=0)
 
-    if version == 2.0:
+    if version == (2, 0):
         grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
         pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
         if cls_token:
@@ -106,7 +108,9 @@ def get_2d_sincos_pos_embed(embed_dim,
     return pos_embed
 
 
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version=2.0):
+def get_2d_sincos_pos_embed_from_grid(embed_dim: int,
+                                      grid: Union[int, Tuple[int, int]],
+                                      version: Tuple[int, int] = (2, 0)):
     assert embed_dim % 2 == 0
 
     # use half of dimensions to encode grid_h
@@ -115,14 +119,16 @@ def get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version=2.0):
     emb_w = get_1d_sincos_pos_embed_from_grid(
         embed_dim // 2, grid[1], version)  # (H*W, D/2) or (H, W, D/2)
 
-    if version == 2.0:
+    if version == (2, 0):
         emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
     else:
         emb = np.concatenate([emb_h, emb_w], axis=-1)  # (H, W, D)
     return emb
 
 
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, version=2.0):
+def get_1d_sincos_pos_embed_from_grid(embed_dim: int,
+                                      pos: int,
+                                      version: Tuple[int, int] = (2, 0)):
     """
     embed_dim: output dimension for each position
     pos: a list of positions to be encoded: size (M,) / (H, W)
@@ -133,7 +139,7 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, version=2.0):
     omega /= embed_dim / 2.
     omega = 1. / 10000**omega  # (D/2,)
 
-    if version == 2.0:
+    if version == (2, 0):
         pos = pos.reshape(-1)  # (M,)
         out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
         emb_sin = np.sin(out)  # (M, D/2)
@@ -158,19 +164,19 @@ class Resampler(nn.Module):
     default_norm_layer = partial(nn.LayerNorm, eps=1e-6)
 
     def __init__(self,
-                 num_queries,
-                 grid_size,
-                 embed_dim,
-                 num_heads,
-                 kv_dim=None,
-                 norm_layer=default_norm_layer,
-                 adaptive=False,
-                 max_size=(70, 70),
-                 version=2.0):
+                 num_queries: int,
+                 grid_size: int,
+                 embed_dim: int,
+                 num_heads: int,
+                 kv_dim: Optional[int] = None,
+                 norm_layer: nn.Module = default_norm_layer,
+                 adaptive: bool = False,
+                 max_size: Tuple[int, int] = (70, 70),
+                 version: Tuple[int, int] = (2, 0)):
         super().__init__()
 
         self.version = version
-        if self.version == 2.0:
+        if self.version == (2, 0):
             self.num_queries = grid_size**2
         else:
             self.num_queries = num_queries
@@ -195,7 +201,7 @@ class Resampler(nn.Module):
         self.proj = nn.Parameter(
             (embed_dim**-0.5) * torch.randn(embed_dim, embed_dim))
 
-        if self.version == 2.0:
+        if self.version == (2, 0):
             self.pos_embed = nn.Parameter(
                 torch.from_numpy(
                     get_2d_sincos_pos_embed(
@@ -206,14 +212,17 @@ class Resampler(nn.Module):
 
         self.apply(self._init_weights)
 
-    def _set_2d_pos_cache(self, max_size, device='cpu'):
+    def _set_2d_pos_cache(self,
+                          max_size: Tuple[int, int],
+                          device: torch.types.Device = 'cpu'):
         pos_embed = torch.from_numpy(
             get_2d_sincos_pos_embed(self.embed_dim,
                                     max_size,
                                     version=self.version)).float().to(device)
         self.register_buffer("pos_embed", pos_embed, persistent=False)
 
-    def _adjust_pos_cache(self, tgt_sizes, device):
+    def _adjust_pos_cache(self, tgt_sizes: torch.Tensor,
+                          device: torch.types.Device):
         max_h = torch.max(tgt_sizes[:, 0])
         max_w = torch.max(tgt_sizes[:, 1])
         if max_h > self.max_size[0] or max_w > self.max_size[1]:
@@ -223,7 +232,7 @@ class Resampler(nn.Module):
             ]
             self._set_2d_pos_cache(self.max_size, device)
 
-    def _init_weights(self, m):
+    def _init_weights(self, m: nn.Module):
         if isinstance(m, nn.Linear):
             trunc_normal_(m.weight, std=.02)
             if isinstance(m, nn.Linear) and m.bias is not None:
@@ -232,7 +241,9 @@ class Resampler(nn.Module):
             nn.init.constant_(m.bias, 0)
             nn.init.constant_(m.weight, 1.0)
 
-    def forward_2_5(self, x, tgt_sizes=None):
+    def forward_2_5(self,
+                    x: torch.Tensor,
+                    tgt_sizes: Optional[torch.Tensor] = None):
         assert x.shape[0] == tgt_sizes.shape[0]
         bs = x.shape[0]
 
@@ -278,7 +289,10 @@ class Resampler(nn.Module):
         x = x @ self.proj
         return x
 
-    def forward_2(self, x, tgt_sizes=None, attn_mask=None):
+    def forward_2(self,
+                  x: torch.Tensor,
+                  tgt_sizes: Optional[torch.Tensor] = None,
+                  attn_mask: Optional[torch.Tensor] = None):
         if self.adaptive:
             pos_embed = torch.Tensor(
                 get_2d_sincos_pos_embed(self.embed_dim,
@@ -302,8 +316,11 @@ class Resampler(nn.Module):
         x = x @ self.proj
         return x
 
-    def forward(self, x, tgt_sizes=None, attn_mask=None):
-        if self.version == 2.0:
+    def forward(self,
+                x: torch.Tensor,
+                tgt_sizes: Optional[torch.Tensor] = None,
+                attn_mask: Optional[torch.Tensor] = None):
+        if self.version == (2, 0):
             return self.forward_2(x, tgt_sizes=tgt_sizes, attn_mask=attn_mask)
         else:
             return self.forward_2_5(x, tgt_sizes=tgt_sizes)
@@ -322,7 +339,7 @@ def dummy_seq_data_for_minicpmv(seq_len: int):
     return SequenceData(token_ids)
 
 
-def dummy_image_for_minicpmv(hf_config):
+def dummy_image_for_minicpmv(hf_config: PretrainedConfig):
     width = height = hf_config.image_size
     image = Image.new("RGB", (width, height), color=0)
     return {"image": image}
@@ -381,7 +398,7 @@ class MiniCPMV(nn.Module, SupportsVision):
 
     def __init__(
         self,
-        config,
+        config: PretrainedConfig,
         multimodal_config: MultiModalConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
@@ -390,30 +407,48 @@ class MiniCPMV(nn.Module, SupportsVision):
         self.config = config
         self.multimodal_config = multimodal_config
 
-        self.version = float(self.config.version)
+        if not hasattr(self.config, "version"):
+            if self.config.hidden_size == 2304 and self.config.query_num == 64:
+                self.version = (2, 0)
+            else:
+                self.version = (2, 5)
+        else:
+            self.version = str(self.config.version).split(".")
+            self.version = tuple([int(x) for x in self.version])
         self.llm = self.init_llm(config, cache_config, quant_config)
         self.vpm = self.init_vision_module()
         param_dtype = torch.get_default_dtype()
         self.vpm.to(dtype=param_dtype)
-        self.vision_dim = self.vpm.embed_dim if self.version == 2.0 \
+        self.vision_dim = self.vpm.embed_dim if self.version == (2, 0) \
             else self.vpm.embeddings.embed_dim
-        self.embed_dim = self.llm.config.hidden_size
+        self.embed_dim = self.config.hidden_size
         self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
         self.resampler.to(device="cuda", dtype=param_dtype)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
-    def init_llm(self, config, cache_config, quant_config):
-        if self.version == 2.0:
-            return MiniCPMForCausalLM(config,
-                                      cache_config=cache_config,
-                                      quant_config=quant_config)
+    def init_llm(self,
+                 config: PretrainedConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None):
+        if self.version == (2, 0):
+            return MiniCPMModel(config,
+                                cache_config=cache_config,
+                                quant_config=quant_config)
+        elif self.version == (2, 5):
+            return LlamaModel(config,
+                              cache_config=cache_config,
+                              quant_config=quant_config)
         else:
-            return LlamaForCausalLM(config,
-                                    cache_config=cache_config,
-                                    quant_config=quant_config)
+            return Qwen2Model(config,
+                              cache_config=cache_config,
+                              quant_config=quant_config)
 
     def init_vision_module(self):
-        if self.version == 2.0:
+        if self.version == (2, 0):
             try:
                 import timm
             except ImportError:
@@ -433,16 +468,30 @@ class MiniCPMV(nn.Module, SupportsVision):
 
             if self.config.drop_vision_last_layer:
                 model.blocks = model.blocks[:-1]
-        else:
+        elif self.version == (2, 5):
+            from transformers.models.idefics2.modeling_idefics2 import (
+                Idefics2VisionTransformer)
             model = Idefics2VisionTransformer(self.config.vision_config)
             if self.config.drop_vision_last_layer:
                 model.encoder.layers = model.encoder.layers[:-1]
+        else:
+            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)
+            if self.config.drop_vision_last_layer:
+                model.encoder.layers = model.encoder.layers[:-1]
         return model
 
-    def init_resampler(self, embed_dim, vision_dim):
+    def init_resampler(self, embed_dim: int, vision_dim: int):
         default_dtype = torch.get_default_dtype()
         torch.set_default_dtype(torch.float16)
-        if self.version == 2.0:
+        if self.version == (2, 0):
             resampler = Resampler(grid_size=int(
                 math.sqrt(self.config.query_num)),
                                   num_queries=None,
@@ -463,11 +512,11 @@ class MiniCPMV(nn.Module, SupportsVision):
         return resampler
 
     def get_vision_embedding(self,
-                             pixel_values,
-                             patch_attn_mask=None,
-                             tgt_sizes=None,
-                             version=2.0):
-        if version == 2.0:
+                             pixel_values: List[List[torch.Tensor]],
+                             patch_attn_mask: Optional[torch.Tensor] = None,
+                             tgt_sizes: Optional[torch.Tensor] = None,
+                             version: Tuple[int, int] = (2, 0)):
+        if version == (2, 0):
             res = []
             dtype = self.vpm.pos_embed.data.dtype
             for pixel_value in pixel_values:
@@ -484,21 +533,32 @@ class MiniCPMV(nn.Module, SupportsVision):
                                                         num_prefix_tokens:]
                 res.append(self.resampler(vision_embedding, tgt_size))
             return torch.vstack(res)
-        else:
+        elif version == (2, 5):
             vision_embedding = self.vpm(
                 pixel_values.type(dtype),
                 patch_attention_mask=patch_attn_mask).last_hidden_state
             vision_embedding = self.resampler(vision_embedding, tgt_sizes)
+        else:
+            vision_embedding = self.vpm(pixel_values.type(dtype),
+                                        patch_attention_mask=patch_attn_mask,
+                                        tgt_sizes=tgt_sizes).last_hidden_state
 
-    def get_image_bounds(self, input_ids):
+    def get_image_bounds(self, input_ids: torch.Tensor):
         tokenizer = cached_get_tokenizer(self.config._name_or_path,
                                          trust_remote_code=True)
-        im_start_token_id = tokenizer.im_start_id
-        im_end_token_id = tokenizer.im_end_id
-        image_start_tokens = torch.where(input_ids == im_start_token_id)[0]
+        if not hasattr(tokenizer, "slice_start_id"):
+            start_cond = input_ids == tokenizer.im_start_id
+            end_cond = input_ids == tokenizer.im_end_id
+        else:
+            start_cond = (input_ids == tokenizer.im_start_id) | (
+                input_ids == tokenizer.slice_start_id)
+            end_cond = (input_ids == tokenizer.im_end_id) | (
+                input_ids == tokenizer.slice_end_id)
+
+        image_start_tokens = torch.where(start_cond)[0]
         image_start_tokens += 1
-        image_end_tokens = torch.where(input_ids == im_end_token_id)[0]
-        valid_image_nums = min(len(image_start_tokens), len(image_end_tokens))
+        image_end_tokens = torch.where(end_cond)[0]
+        valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
         if valid_image_nums == 0:
             return []
         image_bound = torch.hstack([
@@ -508,12 +568,14 @@ class MiniCPMV(nn.Module, SupportsVision):
 
         return image_bound
 
-    def get_vision_hidden_states(self, data):
+    def get_vision_hidden_states(self, data: Dict[str,
+                                                  Union[List[torch.Tensor],
+                                                        torch.Tensor]]):
         if "vision_hidden_states" not in data:
             pixel_values = data["pixel_values"]
             tgt_sizes = data["tgt_sizes"]
             vision_hidden_states = []
-            if self.version == 2.0:
+            if self.version == (2, 0):
                 if pixel_values is not None and len(pixel_values) > 0:
                     vision_hidden_states = self.get_vision_embedding(
                         pixel_values)
@@ -534,17 +596,26 @@ class MiniCPMV(nn.Module, SupportsVision):
                     B, L, _ = all_pixel_values.shape
                     all_pixel_values = all_pixel_values.permute(
                         0, 2, 1).reshape(B, 3, -1, L)
-
                     patch_attn_mask = torch.zeros((B, 1, max_patches),
                                                   dtype=torch.bool,
                                                   device=device)
-                    for i in range(B):
-                        patch_attn_mask[i, :tgt_sizes[i][0] *
-                                        tgt_sizes[i][1]] = True
+                    if self.version == (2, 5):
+                        for i in range(B):
+                            patch_attn_mask[i, :tgt_sizes[i][0] *
+                                            tgt_sizes[i][1]] = True
+                        vision_embedding = self.vpm(
+                            all_pixel_values.type(dtype),
+                            patch_attention_mask=patch_attn_mask
+                        ).last_hidden_state
+                    else:
+                        for i in range(B):
+                            patch_attn_mask[i, 0, :tgt_sizes[i][0] *
+                                            tgt_sizes[i][1]] = True
+                        vision_embedding = self.vpm(
+                            all_pixel_values.type(dtype),
+                            patch_attention_mask=patch_attn_mask,
+                            tgt_sizes=tgt_sizes).last_hidden_state
 
-                    vision_embedding = self.vpm(
-                        all_pixel_values.type(dtype),
-                        patch_attention_mask=patch_attn_mask).last_hidden_state
                     vision_hidden_states = self.resampler(
                         vision_embedding, tgt_sizes)
 
@@ -556,7 +627,8 @@ class MiniCPMV(nn.Module, SupportsVision):
 
         return vision_hidden_states
 
-    def get_embedding(self, data):
+    def get_embedding(self, data: Dict[str, Union[List[torch.Tensor],
+                                                  torch.Tensor]]):
         input_ids = data["input_ids"]
 
         vision_hidden_states = self.get_vision_hidden_states(data)
@@ -565,11 +637,11 @@ class MiniCPMV(nn.Module, SupportsVision):
         else:
             image_bounds = []
 
-        if hasattr(self.llm.config, 'scale_emb'):
-            vlm_embedding = self.llm.model.embed_tokens(
-                input_ids) * self.llm.config.scale_emb
+        if hasattr(self.config, 'scale_emb'):
+            vlm_embedding = self.llm.embed_tokens(
+                input_ids) * self.config.scale_emb
         else:
-            vlm_embedding = self.llm.model.embed_tokens(input_ids)
+            vlm_embedding = self.llm.embed_tokens(input_ids)
         vision_hidden_states = [
             i.type(vlm_embedding.dtype) if isinstance(i, torch.Tensor) else i
             for i in vision_hidden_states
@@ -587,7 +659,9 @@ class MiniCPMV(nn.Module, SupportsVision):
                 vision_hidden_states.view(-1, vision_hidden_states.shape[-1]))
         return vlm_embedding, vision_hidden_states
 
-    def process_multimodal_inputs(self, inputs):
+    def process_multimodal_inputs(self, inputs: Dict[str,
+                                                     Union[List[torch.Tensor],
+                                                           torch.Tensor]]):
         pixel_values = []
         tgt_sizes = []
         for b in range(len(inputs["pixel_values"])):
@@ -613,7 +687,6 @@ class MiniCPMV(nn.Module, SupportsVision):
             "input_ids": input_ids,
             "tgt_sizes": kwargs.pop("tgt_sizes", None),
         }
-
         inputs = self.process_multimodal_inputs(inputs)
 
         vlm_embeddings, vision_hidden_states = self.get_embedding(inputs)
@@ -623,19 +696,21 @@ class MiniCPMV(nn.Module, SupportsVision):
                           kv_caches=kv_caches,
                           attn_metadata=attn_metadata,
                           intermediate_tensors=intermediate_tensors,
-                          input_embeds=vlm_embeddings)
+                          inputs_embeds=vlm_embeddings)
         return output
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        return self.llm.compute_logits(hidden_states, sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
 
     def sample(
         self,
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[SamplerOutput]:
-        next_tokens = self.llm.sample(logits, sampling_metadata)
+        next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
@@ -649,9 +724,9 @@ class MiniCPMV(nn.Module, SupportsVision):
         ]
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
-            #     for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
-            #         if key_to_modify in name:
-            #             name = name.replace(key_to_modify, new_key)
+            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
+                if key_to_modify in name:
+                    name = name.replace(key_to_modify, new_key)
             if "rotary_emb.inv_freq" in name:
                 continue
             if ("rotary_emb.cos_cached" in name

vllm/model_executor/models/na_vit.py

@@ -0,0 +1,804 @@
+import logging
+import math
+import os
+import warnings
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.init import _calculate_fan_in_and_fan_out
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (ModelOutput, is_flash_attn_2_available,
+                                replace_return_docstrings)
+
+logger = logging.getLogger("vllm")
+
+
+# For Siglip: copied from
+#   HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
+# Remove hints as there's little possibility to change these code.
+class SiglipVisionConfig(PretrainedConfig):
+
+    model_type = "siglip_vision_model"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        image_size=224,
+        patch_size=16,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
+                                                                  os.PathLike],
+                        **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs)
+
+        # get the vision config dict if we are loading from SiglipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["vision_config"]
+
+        if "model_type" in config_dict and hasattr(
+                cls,
+                "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                "You are using a model of type %s to "
+                "instantiate a model of type %s. "
+                "This is not supported for all configurations"
+                "of models and can yield errors.", config_dict['model_type'],
+                cls.model_type)
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
+
+SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "google/siglip-base-patch16-224",
+    # See all SigLIP models at https://huggingface.co/models?filter=siglip
+]
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import pad_input  # noqa
+    from flash_attn.bert_padding import index_first_axis, unpad_input
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(
+        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l_ = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l_ - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    if tensor.dtype in [torch.float16, torch.bfloat16]:
+        # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
+        og_dtype = tensor.dtype
+        tensor = tensor.to(torch.float32)
+        tensor.erfinv_()
+        tensor = tensor.to(og_dtype)
+    else:
+        tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.0))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    if tensor.dtype == torch.float16:
+        # The `clamp_` op is not (yet?) defined in float16+cpu
+        tensor = tensor.to(torch.float32)
+        tensor.clamp_(min=a, max=b)
+        tensor = tensor.to(torch.float16)
+    else:
+        tensor.clamp_(min=a, max=b)
+
+
+def trunc_normal_tf_(tensor: torch.Tensor,
+                     mean: float = 0.0,
+                     std: float = 1.0,
+                     a: float = -2.0,
+                     b: float = 2.0) -> torch.Tensor:
+    with torch.no_grad():
+        _trunc_normal_(tensor, 0, 1.0, a, b)
+        tensor.mul_(std).add_(mean)
+
+
+def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == "fan_in":
+        denom = fan_in
+    elif mode == "fan_out":
+        denom = fan_out
+    elif mode == "fan_avg":
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
+    elif distribution == "normal":
+        with torch.no_grad():
+            tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        with torch.no_grad():
+            tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
+
+
+def default_flax_embed_init(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="normal")
+
+
+class SiglipVisionModelOutput(ModelOutput):
+    image_embeds: Optional[torch.FloatTensor] = None
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class SiglipVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches_per_side = self.image_size // self.patch_size
+        self.num_patches = self.num_patches_per_side**2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+
+    def forward(self,
+                pixel_values: torch.FloatTensor,
+                patch_attention_mask: torch.BoolTensor,
+                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
+        batch_size = pixel_values.size(0)
+
+        patch_embeds = self.patch_embedding(pixel_values)
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+        max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
+        max_nb_patches_h, max_nb_patches_w = (max_im_h // self.patch_size,
+                                              max_im_w // self.patch_size)
+        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0,
+                                  1 / self.num_patches_per_side)
+        position_ids = torch.full(
+            size=(
+                batch_size,
+                max_nb_patches_h * max_nb_patches_w,
+            ),
+            fill_value=0,
+        )
+
+        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
+            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,
+                                              boundaries,
+                                              right=True)
+            bucket_coords_w = torch.bucketize(fractional_coords_w,
+                                              boundaries,
+                                              right=True)
+
+            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side +
+                       bucket_coords_w).flatten()
+            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
+
+        position_ids = position_ids.to(self.position_embedding.weight.device)
+
+        embeddings = embeddings + self.position_embedding(position_ids)
+        return embeddings
+
+
+class SiglipAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads (got `embed_dim`: "
+                f"{self.embed_dim} and `num_heads`:"
+                f" {self.num_heads}).")
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        batch_size, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, q_len, self.num_heads,
+                                         self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, q_len, self.num_heads,
+                                     self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, q_len, self.num_heads,
+                                         self.head_dim).transpose(1, 2)
+
+        k_v_seq_len = key_states.shape[-2]
+        attn_weights = torch.matmul(query_states, key_states.transpose(
+            2, 3)) * self.scale
+
+        if attn_weights.size() != (batch_size, self.num_heads, q_len,
+                                   k_v_seq_len):
+            raise ValueError(
+                "Attention weights should be of size "
+                f"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
+                f" {attn_weights.size()}")
+
+        if attention_mask is not None:
+            if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
+                raise ValueError(
+                    "Attention mask should be of size "
+                    f"{(batch_size, 1, q_len, k_v_seq_len)}",
+                    f"but is {attention_mask.size()}")
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights,
+                                             dim=-1,
+                                             dtype=torch.float32).to(
+                                                 query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights,
+                                             p=self.dropout,
+                                             training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (batch_size, self.num_heads, q_len,
+                                  self.head_dim):
+            raise ValueError(
+                "`attn_output` should be of size "
+                f"{(batch_size, self.num_heads, q_len, self.head_dim)}, "
+                "but is"
+                f" {attn_output.size()}")
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class SiglipFlashAttention2(SiglipAttention):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.is_causal = False  # Hack to make sure we don't use a causal mask
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads,
+                                         self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_heads,
+                                     self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_heads,
+                                         self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(
+                kv_seq_len, self.layer_idx)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning(
+                "The input hidden states seems to be "
+                "silently casted in float32, "
+                "this might be related to the fact "
+                "you have upcasted embedding or layer norm layers in float32. "
+                "We will cast back the input in"
+                " %s.", target_dtype)
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(query_states,
+                                                    key_states,
+                                                    value_states,
+                                                    attention_mask,
+                                                    q_len,
+                                                    dropout=dropout_rate)
+
+        attn_output = attn_output.reshape(bsz, q_len,
+                                          self.embed_dim).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights
+
+    def _flash_attention_forward(self,
+                                 query_states,
+                                 key_states,
+                                 value_states,
+                                 attention_mask,
+                                 query_length,
+                                 dropout=0.0,
+                                 softmax_scale=None):
+        causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (query_states, key_states, value_states, indices_q, cu_seq_lens,
+             max_seq_lens) = self._upad_input(query_states, key_states,
+                                              value_states, attention_mask,
+                                              query_length)
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size,
+                                    query_length)
+        else:
+            attn_output = flash_attn_func(query_states,
+                                          key_states,
+                                          value_states,
+                                          dropout,
+                                          softmax_scale=softmax_scale,
+                                          causal=causal)
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask,
+                    query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(
+            attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
+                              head_dim), indices_k)
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
+                                head_dim), indices_k)
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads,
+                                    head_dim), indices_k)
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            (query_layer, indices_q, cu_seqlens_q,
+             max_seqlen_in_batch_q) = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
+class SiglipMLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer
+# with CLIP->Siglip
+class SiglipEncoderLayer(nn.Module):
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self._use_flash_attention_2 = (
+            config._attn_implementation == "flash_attention_2")
+        self.self_attn = (SiglipAttention(config)
+                          if not self._use_flash_attention_2 else
+                          SiglipFlashAttention2(config))
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor]:
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states, )
+
+        if output_attentions:
+            outputs += (attn_weights, )
+
+        return outputs
+
+
+class SiglipPreTrainedModel(PreTrainedModel):
+    config_class = SiglipVisionConfig
+    base_model_prefix = "siglip"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+
+        if isinstance(module, SiglipVisionEmbeddings):
+            width = self.config.hidden_size
+            nn.init.normal_(module.position_embedding.weight,
+                            std=1 / np.sqrt(width))
+        elif isinstance(module, nn.Embedding):
+            default_flax_embed_init(module.weight)
+        elif isinstance(module, SiglipAttention):
+            nn.init.normal_(module.q_proj.weight)
+            nn.init.normal_(module.k_proj.weight)
+            nn.init.normal_(module.v_proj.weight)
+            nn.init.normal_(module.out_proj.weight)
+            nn.init.zeros_(module.q_proj.bias)
+            nn.init.zeros_(module.k_proj.bias)
+            nn.init.zeros_(module.v_proj.bias)
+            nn.init.zeros_(module.out_proj.bias)
+        elif isinstance(module, SiglipMLP):
+            nn.init.normal_(module.fc1.weight)
+            nn.init.normal_(module.fc2.weight)
+            nn.init.normal_(module.fc1.bias, std=1e-6)
+            nn.init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            lecun_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoder
+# with CLIP->Siglip
+class SiglipEncoder(nn.Module):
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([
+            SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)
+        ])
+        self.gradient_checkpointing = False
+
+    # Ignore copy
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        output_attentions = output_attentions if output_attentions is not None \
+                                else self.config.output_attentions
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else
+                                self.config.output_hidden_states)
+        return_dict = return_dict if return_dict is not None \
+                        else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states, )
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1], )
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states, )
+
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, encoder_states, all_attentions]
+                if v is not None)
+        return BaseModelOutput(last_hidden_state=hidden_states,
+                               hidden_states=encoder_states,
+                               attentions=all_attentions)
+
+
+class SiglipVisionTransformer(SiglipPreTrainedModel):
+    config_class = SiglipVisionConfig
+    main_input_name = "pixel_values"
+    _supports_flash_attn_2 = True
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__(config)
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim,
+                                           eps=config.layer_norm_eps)
+        self._use_flash_attention_2 = (
+            config._attn_implementation == "flash_attention_2")
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.embeddings.patch_embedding
+
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling,
+                               config_class=SiglipVisionConfig)
+    def forward(
+        self,
+        pixel_values,
+        patch_attention_mask: Optional[torch.BoolTensor] = None,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+        """
+        output_attentions = output_attentions if output_attentions is not None \
+                                else self.config.output_attentions
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else
+                                self.config.output_hidden_states)
+        return_dict = return_dict if return_dict is not None \
+                        else self.config.use_return_dict
+
+        batch_size = pixel_values.size(0)
+        if patch_attention_mask is None:
+            patch_attention_mask = torch.ones(
+                size=(
+                    batch_size,
+                    pixel_values.size(2) // self.config.patch_size,
+                    pixel_values.size(3) // self.config.patch_size,
+                ),
+                dtype=torch.bool,
+                device=pixel_values.device,
+            )
+
+        hidden_states = self.embeddings(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+            tgt_sizes=tgt_sizes)
+
+        patch_attention_mask = patch_attention_mask.view(batch_size, -1)
+        # The call to `_upad_input` in `_flash_attention_forward` is expensive
+        # So when the `patch_attention_mask` is full of 1s
+        # (i.e. attending to the whole sequence),
+        # avoiding passing the attention_mask,
+        # which is equivalent to attending to the full sequence
+        if not torch.any(~patch_attention_mask):
+            attention_mask = None
+        else:
+            attention_mask = (_prepare_4d_attention_mask(
+                patch_attention_mask, hidden_states.dtype)
+                              if not self._use_flash_attention_2 else
+                              patch_attention_mask)
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        if not return_dict:
+            return (last_hidden_state, None) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=None,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

vllm/model_executor/models/qwen2.py

@@ -342,7 +342,7 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, kv_caches,
-                                   attn_metadata)
+                                   attn_metadata, intermediate_tensors)
         return hidden_states
 
     def compute_logits(self, hidden_states: torch.Tensor,