[Feat/Fix] Refactoring Llava models into single file (#475)

examples/usage/llava/http_llama3_llava_test.py

@@ -22,11 +22,7 @@ import aiohttp
 import requests
 
 from llava.conversation import (
-    default_conversation,
-    conv_templates,
-    SeparatorStyle,
     conv_llava_llama_3,
-    conv_qwen,
 )
 
 
@@ -43,7 +39,8 @@ async def test_concurrent(args):
 
     prompt = "<image>\nPlease generate caption towards this image."
     conv_template = copy.deepcopy(conv_llava_llama_3)
-    conv_template.append_message(role="user", message=prompt)
+    conv_template.append_message(role=conv_template.roles[0], message=prompt)
+    conv_template.append_message(role=conv_template.roles[1], message=None)
     prompt_with_template = conv_template.get_prompt()
     response = []
     for i in range(1):
@@ -74,7 +71,8 @@ def test_streaming(args):
     url = f"{args.host}:{args.port}"
     prompt = "<image>\nPlease generate caption towards this image."
     conv_template = copy.deepcopy(conv_llava_llama_3)
-    conv_template.append_message(role="user", message=prompt)
+    conv_template.append_message(role=conv_template.roles[0], message=prompt)
+    conv_template.append_message(role=conv_template.roles[1], message=None)
     prompt_with_template = conv_template.get_prompt()
     pload = {
         "text": prompt_with_template,

examples/usage/llava/http_qwen_llava_test.py

@@ -22,11 +22,7 @@ import aiohttp
 import requests
 
 from llava.conversation import (
-    default_conversation,
-    conv_templates,
-    SeparatorStyle,
-    conv_llava_llama_3,
-    conv_qwen,
+    conv_qwen
 )
 
 
@@ -43,7 +39,8 @@ async def test_concurrent(args):
 
     prompt = "<image>\nPlease generate caption towards this image."
     conv_template = copy.deepcopy(conv_qwen)
-    conv_template.append_message(role="user", message=prompt)
+    conv_template.append_message(role=conv_template.roles[0], message=prompt)
+    conv_template.append_message(role=conv_template.roles[1], message=None)
     prompt_with_template = conv_template.get_prompt()
     response = []
     for i in range(1):
@@ -74,7 +71,8 @@ def test_streaming(args):
     url = f"{args.host}:{args.port}"
     prompt = "<image>\nPlease generate caption towards this image."
     conv_template = copy.deepcopy(conv_qwen)
-    conv_template.append_message(role="user", message=prompt)
+    conv_template.append_message(role=conv_template.roles[0], message=prompt)
+    conv_template.append_message(role=conv_template.roles[1], message=None)
     prompt_with_template = conv_template.get_prompt()
     pload = {
         "text": prompt_with_template,
@@ -113,5 +111,5 @@ if __name__ == "__main__":
     parser.add_argument("--host", type=str, default="http://127.0.0.1")
     parser.add_argument("--port", type=int, default=30000)
     args = parser.parse_args()
-    # asyncio.run(test_concurrent(args))
+    asyncio.run(test_concurrent(args))
     test_streaming(args)

python/sglang/srt/managers/router/model_runner.py

@@ -421,7 +421,12 @@ def import_model_classes():
         if not ispkg:
             module = importlib.import_module(name)
             if hasattr(module, "EntryClass"):
-                model_arch_name_to_cls[module.EntryClass.__name__] = module.EntryClass
+                entry = module.EntryClass
+                if isinstance(entry, list): # To support multiple model classes in one module
+                    for cls in entry:
+                        model_arch_name_to_cls[cls.__name__] = cls
+                else:
+                    model_arch_name_to_cls[entry.__name__] = entry
     return model_arch_name_to_cls
 
 

python/sglang/srt/models/llava.py

@@ -5,7 +5,7 @@ from typing import List, Iterable, Optional, Tuple
 import numpy as np
 import torch
 from torch import nn
-from transformers import CLIPVisionModel, LlavaConfig
+from transformers import CLIPVisionModel, CLIPVisionConfig, LlavaConfig, Qwen2Config, MistralConfig 
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -18,6 +18,8 @@ from sglang.srt.mm_utils import (
     unpad_image_shape,
 )
 from sglang.srt.models.llama2 import LlamaForCausalLM
+from sglang.srt.models.qwen2 import Qwen2ForCausalLM
+from sglang.srt.models.mistral import MistralForCausalLM
 
 
 class LlavaLlamaForCausalLM(nn.Module):
@@ -287,8 +289,101 @@ class LlavaLlamaForCausalLM(nn.Module):
         return self.image_size // self.patch_size
 
 
-first_call = True
+class LlavaQwenForCausalLM(LlavaLlamaForCausalLM):
+    def __init__(
+        self,
+        config: LlavaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__(config, quant_config=quant_config)
+        self.config = config
+        self.vision_tower = None
+        if getattr(self.config, "vision_config", None) is None:
+            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
 
+        if getattr(self.config, "text_config", None) is None:
+            self.config.text_config = Qwen2Config(self.config._name_or_path)
+
+        self.config.vision_config.hidden_size = config.mm_hidden_size
+        self.config.text_config.hidden_size = config.hidden_size
+
+        if getattr(self.config, "projector_hidden_act", None) is None:
+            self.config.projector_hidden_act = "gelu"
+
+        if getattr(self.config, "image_token_index", None) is None:
+            self.config.image_token_index = 151646
+
+        self.multi_modal_projector = LlavaMultiModalProjector(config)
+        self.language_model = Qwen2ForCausalLM(config, quant_config=quant_config)
+        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
+            self.language_model.model.image_newline = nn.Parameter(
+                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
+            )
+
+    def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
+        new_image_feature_len = self.image_feature_len
+        # now only support spatial_unpad + anyres
+        if self.mm_patch_merge_type.startswith("spatial"):
+            height = width = self.num_patches_per_side
+            if pt_shape[0] > 1:
+                if self.image_aspect_ratio == "anyres":
+                    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
+                        image_size,
+                        self.image_grid_pinpoints,
+                        self.vision_tower.config.image_size,
+                    )
+                if "unpad" in self.mm_patch_merge_type:
+                    h = num_patch_height * height
+                    w = num_patch_width * width
+                    new_h, new_w = unpad_image_shape(h, w, image_size)
+                    new_image_feature_len += new_h * (new_w + 1)
+
+        pad_ids = pad_value * (
+            (new_image_feature_len + len(pad_value)) // len(pad_value)
+        )
+        offset = input_ids.index(self.config.image_token_index)
+        # old_len + pad_len - 1, because we need to remove image_token_id
+        new_input_ids = (
+            input_ids[:offset]
+            + pad_ids[:new_image_feature_len]
+            + input_ids[offset + 1 :]
+        )
+        return new_input_ids, offset
+
+
+class LlavaMistralForCausalLM(LlavaLlamaForCausalLM):
+    def __init__(
+        self,
+        config: LlavaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__(config, quant_config=quant_config)
+        self.config = config
+        self.vision_tower = None
+        if getattr(self.config, "vision_config", None) is None:
+            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
+
+        if getattr(self.config, "text_config", None) is None:
+            self.config.text_config = MistralConfig(self.config._name_or_path)
+
+        self.config.vision_config.hidden_size = config.mm_hidden_size
+        self.config.text_config.hidden_size = config.hidden_size
+
+        if getattr(self.config, "projector_hidden_act", None) is None:
+            self.config.projector_hidden_act = "gelu"
+
+        if getattr(self.config, "image_token_index", None) is None:
+            self.config.image_token_index = 32000
+
+        self.multi_modal_projector = LlavaMultiModalProjector(config)
+        self.language_model = MistralForCausalLM(config, quant_config=quant_config)
+        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
+            self.language_model.model.image_newline = nn.Parameter(
+                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
+            )
+
+
+first_call = True
 
 def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
     batch_size = pixel_values.shape[0]
@@ -319,4 +414,8 @@ def monkey_path_clip_vision_embed_forward():
     )
 
 
-EntryClass = LlavaLlamaForCausalLM
+EntryClass = [
+    LlavaLlamaForCausalLM, 
+    LlavaQwenForCausalLM,
+    LlavaMistralForCausalLM
+]

python/sglang/srt/models/llava_mistral.py

-"""Inference-only LLaVa model compatible with HuggingFace weights."""
-
-from typing import List, Iterable, Optional, Tuple
-
-import numpy as np
-import torch
-from torch import nn
-from transformers import CLIPVisionConfig, CLIPVisionModel, LlavaConfig, MistralConfig
-from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-
-from sglang.srt.managers.router.infer_batch import ForwardMode
-from sglang.srt.managers.router.model_runner import InputMetadata
-from sglang.srt.mm_utils import (
-    get_anyres_image_grid_shape,
-    unpad_image,
-    unpad_image_shape,
-)
-from sglang.srt.models.mistral import MistralForCausalLM
-
-
-class LlavaMistralForCausalLM(nn.Module):
-    def __init__(
-        self,
-        config: LlavaConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.vision_tower = None
-        if getattr(self.config, "vision_config", None) is None:
-            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
-
-        if getattr(self.config, "text_config", None) is None:
-            self.config.text_config = MistralConfig(self.config._name_or_path)
-
-        self.config.vision_config.hidden_size = config.mm_hidden_size
-        self.config.text_config.hidden_size = config.hidden_size
-
-        if getattr(self.config, "projector_hidden_act", None) is None:
-            self.config.projector_hidden_act = "gelu"
-
-        if getattr(self.config, "image_token_index", None) is None:
-            self.config.image_token_index = 32000
-
-        self.multi_modal_projector = LlavaMultiModalProjector(config)
-        self.language_model = MistralForCausalLM(config, quant_config=quant_config)
-        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
-            self.language_model.model.image_newline = nn.Parameter(
-                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
-            )
-
-    def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
-        new_image_feature_len = self.image_feature_len
-        # now only support spatial_unpad + anyres
-        if self.mm_patch_merge_type.startswith("spatial"):
-            height = width = self.num_patches_per_side
-            if pt_shape[0] > 1:
-                if self.image_aspect_ratio == "anyres":
-                    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
-                        image_size,
-                        self.image_grid_pinpoints,
-                        self.vision_tower.config.image_size,
-                    )
-                if "unpad" in self.mm_patch_merge_type:
-                    h = num_patch_height * height
-                    w = num_patch_width * width
-                    new_h, new_w = unpad_image_shape(h, w, image_size)
-                    new_image_feature_len += new_h * (new_w + 1)
-
-        pad_ids = pad_value * (
-            (new_image_feature_len + len(pad_value)) // len(pad_value)
-        )
-        offset = input_ids.index(self.config.image_token_index)
-        # old_len + pad_len - 1, because we need to remove image_token_id
-        new_input_ids = (
-            input_ids[:offset]
-            + pad_ids[:new_image_feature_len]
-            + input_ids[offset + 1 :]
-        )
-        return new_input_ids, offset
-
-    def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
-        image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
-        # NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
-
-        selected_image_feature = image_outputs.hidden_states[self.vision_feature_layer]
-        if self.vision_feature_select_strategy in ["default", "patch"]:
-            selected_image_feature = selected_image_feature[:, 1:]
-        elif self.vision_feature_select_strategy == "full":
-            selected_image_feature = selected_image_feature
-        else:
-            raise ValueError(
-                f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}"
-            )
-        image_features = self.multi_modal_projector(selected_image_feature)
-
-        return image_features
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        positions: torch.Tensor,
-        input_metadata: InputMetadata,
-        pixel_values: Optional[List[Optional[np.array]]] = None,
-        image_sizes: Optional[List[List[int]]] = None,
-        image_offsets: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        if input_metadata.forward_mode == ForwardMode.EXTEND:
-            bs = input_metadata.batch_size
-
-            # Embed text input
-            input_embeds = self.language_model.model.embed_tokens(input_ids)
-
-            # Embed vision input
-            need_vision = (
-                (positions[input_metadata.extend_start_loc] < self.image_feature_len)
-                .cpu()
-                .numpy()
-            )
-            # FIXME: We need to substract the length of the system prompt
-            has_pixel = np.array([pixel_values[i] is not None for i in range(bs)])
-            need_vision = need_vision & has_pixel
-
-            if need_vision.any():
-                pixel_values = [pixel_values[i] for i in range(bs) if need_vision[i]]
-                image_sizes = [image_sizes[i] for i in range(bs) if need_vision[i]]
-
-                ########## Encode Image ########
-
-                if pixel_values[0].ndim == 4:
-                    # llava-hd: BS, num_patch, C=3, H=336, W=336, num_patch obtained from process_images
-                    np.concatenate(pixel_values, axis=0)
-                    # ndim=4
-                    concat_images = torch.tensor(
-                        np.concatenate(pixel_values, axis=0),
-                        device=self.vision_tower.device,
-                    )
-                    image_features = self.encode_images(concat_images)
-                    split_sizes = [image.shape[0] for image in pixel_values]
-                    image_features = torch.split(image_features, split_sizes, dim=0)
-                    # hd image_features: BS, num_patch, 576, 4096
-                else:
-                    # normal pixel: BS, C=3, H=336, W=336
-                    pixel_values = torch.tensor(
-                        np.array(pixel_values), device=self.vision_tower.device
-                    )
-                    image_features = self.encode_images(pixel_values)
-                    # image_features: BS, 576, 4096
-
-                if self.mm_patch_merge_type.startswith("spatial"):
-                    new_image_features = []
-                    for image_idx, image_feature in enumerate(image_features):
-                        if image_feature.shape[0] > 1:
-                            base_image_feature = image_feature[0]
-                            image_feature = image_feature[1:]
-                            height = width = self.num_patches_per_side
-                            assert height * width == base_image_feature.shape[0]
-                            if self.image_aspect_ratio == "anyres":
-                                (
-                                    num_patch_width,
-                                    num_patch_height,
-                                ) = get_anyres_image_grid_shape(
-                                    image_sizes[image_idx],
-                                    self.image_grid_pinpoints,
-                                    self.vision_tower.config.image_size,
-                                )
-                                image_feature = image_feature.view(
-                                    num_patch_height, num_patch_width, height, width, -1
-                                )
-                            else:
-                                raise NotImplementedError()
-                            if "unpad" in self.mm_patch_merge_type:
-                                image_feature = image_feature.permute(
-                                    4, 0, 2, 1, 3
-                                ).contiguous()
-                                image_feature = image_feature.flatten(1, 2).flatten(
-                                    2, 3
-                                )
-                                image_feature = unpad_image(
-                                    image_feature, image_sizes[image_idx]
-                                )
-                                image_feature = torch.cat(
-                                    (
-                                        image_feature,
-                                        self.language_model.model.image_newline[
-                                            :, None, None
-                                        ].expand(*image_feature.shape[:-1], 1),
-                                    ),
-                                    dim=-1,
-                                )
-                                image_feature = image_feature.flatten(1, 2).transpose(
-                                    0, 1
-                                )
-                            else:
-                                image_feature = image_feature.permute(
-                                    0, 2, 1, 3, 4
-                                ).contiguous()
-                                image_feature = image_feature.flatten(0, 3)
-                            image_feature = torch.cat(
-                                (base_image_feature, image_feature), dim=0
-                            )
-                        else:
-                            image_feature = image_feature[0]
-                            if "unpad" in self.mm_patch_merge_type:
-                                image_feature = torch.cat(
-                                    (
-                                        image_feature,
-                                        self.language_model.model.image_newline[None],
-                                    ),
-                                    dim=0,
-                                )
-                        new_image_features.append(image_feature)
-                    image_features = new_image_features
-
-                extend_start_loc_cpu = input_metadata.extend_start_loc.cpu().numpy()
-                pt = 0
-                for i in range(bs):
-                    if not need_vision[i]:
-                        continue
-
-                    start_idx = extend_start_loc_cpu[i]
-                    pad_len, pad_dim = image_features[pt].shape  # 576, 4096
-                    dim = input_embeds.shape[1]
-                    assert (
-                        pad_dim == dim
-                    ), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
-                    # Fill in the placeholder for the image
-                    try:
-                        input_embeds[
-                            start_idx
-                            + image_offsets[i] : start_idx
-                            + image_offsets[i]
-                            + pad_len
-                        ] = image_features[pt]
-                    except RuntimeError as e:
-                        print(f"RuntimeError in llava image encoding: {e}")
-                        print(input_embeds.shape)
-                        print(start_idx, image_offsets[i])
-                    pt += 1
-
-            return self.language_model(
-                input_ids, positions, input_metadata, input_embeds=input_embeds
-            )
-        elif input_metadata.forward_mode == ForwardMode.DECODE:
-            return self.language_model(input_ids, positions, input_metadata)
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        # load clip vision model by cfg['mm_vision_tower']:
-        #   huggingface_name or path_of_clip_relative_to_llava_model_dir
-        vision_path = self.config.mm_vision_tower
-        self.vision_tower = CLIPVisionModel.from_pretrained(
-            vision_path, torch_dtype=torch.float16
-        ).cuda()
-        self.vision_tower.eval()
-
-        self.vision_feature_layer = self.config.mm_vision_select_layer
-        self.vision_feature_select_strategy = self.config.mm_vision_select_feature
-        self.image_size = self.vision_tower.config.image_size
-        self.patch_size = self.vision_tower.config.patch_size
-
-        self.mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
-        self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
-        self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)
-
-        self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
-        if self.vision_feature_select_strategy == "patch":
-            pass
-        elif self.vision_feature_select_strategy == "cls_patch":
-            self.image_feature_len += 1
-        else:
-            raise ValueError(f"Unexpected select feature: {self.select_feature}")
-
-        # load mm_projector
-        projector_weights = {
-            "model.mm_projector.0": "multi_modal_projector.linear_1",
-            "model.mm_projector.2": "multi_modal_projector.linear_2",
-            "model.vision_tower.vision_tower": "vision_tower",  # Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
-        }
-        params_dict = dict(self.named_parameters())
-        weights = list(weights)
-        for name, loaded_weight in weights:
-            # FIXME: why projector weights read two times?
-            if "projector" in name or "vision_tower" in name:
-                for weight_name, param_name in projector_weights.items():
-                    if weight_name in name:
-                        name = name.replace(weight_name, param_name)
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-
-        # load language model
-        self.language_model.load_weights(weights)
-
-        monkey_path_clip_vision_embed_forward()
-
-    @property
-    def num_patches_per_side(self):
-        return self.image_size // self.patch_size
-
-
-first_call = True
-
-
-def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-    batch_size = pixel_values.shape[0]
-
-    # Move this conv layer to CPU to avoid a bug in torch >= 2.1 on A10G.
-    global first_call
-    if first_call:
-        self.patch_embedding.cpu().float()
-        first_call = False
-    pixel_values = pixel_values.to(dtype=torch.float32, device="cpu")
-    patch_embeds = self.patch_embedding(pixel_values).cuda().half()
-
-    patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-    class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-    embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-    embeddings = embeddings + self.position_embedding(self.position_ids)
-    return embeddings
-
-
-def monkey_path_clip_vision_embed_forward():
-    import transformers
-
-    setattr(
-        transformers.models.clip.modeling_clip.CLIPVisionEmbeddings,
-        "forward",
-        clip_vision_embed_forward,
-    )
-
-
-EntryClass = LlavaMistralForCausalLM

python/sglang/srt/models/llava_qwen.py

-"""Inference-only LLaVa model compatible with HuggingFace weights."""
-
-from typing import List, Iterable, Optional, Tuple
-
-import numpy as np
-import torch
-from torch import nn
-from transformers import CLIPVisionConfig, CLIPVisionModel, LlavaConfig, Qwen2Config
-from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-
-from sglang.srt.managers.router.infer_batch import ForwardMode
-from sglang.srt.managers.router.model_runner import InputMetadata
-from sglang.srt.mm_utils import (
-    get_anyres_image_grid_shape,
-    unpad_image,
-    unpad_image_shape,
-)
-from sglang.srt.models.qwen2 import Qwen2ForCausalLM
-
-
-class LlavaQwenForCausalLM(nn.Module):
-    def __init__(
-        self,
-        config: LlavaConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.vision_tower = None
-        if getattr(self.config, "vision_config", None) is None:
-            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
-
-        if getattr(self.config, "text_config", None) is None:
-            self.config.text_config = Qwen2Config(self.config._name_or_path)
-
-        self.config.vision_config.hidden_size = config.mm_hidden_size
-        self.config.text_config.hidden_size = config.hidden_size
-
-        if getattr(self.config, "projector_hidden_act", None) is None:
-            self.config.projector_hidden_act = "gelu"
-
-        if getattr(self.config, "image_token_index", None) is None:
-            self.config.image_token_index = 151646
-
-        self.multi_modal_projector = LlavaMultiModalProjector(config)
-        self.language_model = Qwen2ForCausalLM(config, quant_config=quant_config)
-        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
-            self.language_model.model.image_newline = nn.Parameter(
-                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
-            )
-
-    def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
-        new_image_feature_len = self.image_feature_len
-        # now only support spatial_unpad + anyres
-        if self.mm_patch_merge_type.startswith("spatial"):
-            height = width = self.num_patches_per_side
-            if pt_shape[0] > 1:
-                if self.image_aspect_ratio == "anyres":
-                    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
-                        image_size,
-                        self.image_grid_pinpoints,
-                        self.vision_tower.config.image_size,
-                    )
-                if "unpad" in self.mm_patch_merge_type:
-                    h = num_patch_height * height
-                    w = num_patch_width * width
-                    new_h, new_w = unpad_image_shape(h, w, image_size)
-                    new_image_feature_len += new_h * (new_w + 1)
-
-        pad_ids = pad_value * (
-            (new_image_feature_len + len(pad_value)) // len(pad_value)
-        )
-        offset = input_ids.index(self.config.image_token_index)
-        # old_len + pad_len - 1, because we need to remove image_token_id
-        new_input_ids = (
-            input_ids[:offset]
-            + pad_ids[:new_image_feature_len]
-            + input_ids[offset + 1 :]
-        )
-        return new_input_ids, offset
-
-    def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
-        image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
-        # NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
-
-        selected_image_feature = image_outputs.hidden_states[self.vision_feature_layer]
-        if self.vision_feature_select_strategy in ["default", "patch"]:
-            selected_image_feature = selected_image_feature[:, 1:]
-        elif self.vision_feature_select_strategy == "full":
-            selected_image_feature = selected_image_feature
-        else:
-            raise ValueError(
-                f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}"
-            )
-        image_features = self.multi_modal_projector(selected_image_feature)
-
-        return image_features
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        positions: torch.Tensor,
-        input_metadata: InputMetadata,
-        pixel_values: Optional[List[Optional[np.array]]] = None,
-        image_sizes: Optional[List[List[int]]] = None,
-        image_offsets: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        if input_metadata.forward_mode == ForwardMode.EXTEND:
-            bs = input_metadata.batch_size
-
-            # Embed text input
-            input_embeds = self.language_model.model.embed_tokens(input_ids)
-
-            # Embed vision input
-            need_vision = (
-                (positions[input_metadata.extend_start_loc] < self.image_feature_len)
-                .cpu()
-                .numpy()
-            )
-            # FIXME: We need to substract the length of the system prompt
-            has_pixel = np.array([pixel_values[i] is not None for i in range(bs)])
-            need_vision = need_vision & has_pixel
-
-            if need_vision.any():
-                pixel_values = [pixel_values[i] for i in range(bs) if need_vision[i]]
-                image_sizes = [image_sizes[i] for i in range(bs) if need_vision[i]]
-
-                ########## Encode Image ########
-
-                if pixel_values[0].ndim == 4:
-                    # llava-hd: BS, num_patch, C=3, H=336, W=336, num_patch obtained from process_images
-                    np.concatenate(pixel_values, axis=0)
-                    # ndim=4
-                    concat_images = torch.tensor(
-                        np.concatenate(pixel_values, axis=0),
-                        device=self.vision_tower.device,
-                    )
-                    image_features = self.encode_images(concat_images)
-                    split_sizes = [image.shape[0] for image in pixel_values]
-                    image_features = torch.split(image_features, split_sizes, dim=0)
-                    # hd image_features: BS, num_patch, 576, 4096
-                else:
-                    # normal pixel: BS, C=3, H=336, W=336
-                    pixel_values = torch.tensor(
-                        np.array(pixel_values), device=self.vision_tower.device
-                    )
-                    image_features = self.encode_images(pixel_values)
-                    # image_features: BS, 576, 4096
-
-                if self.mm_patch_merge_type.startswith("spatial"):
-                    new_image_features = []
-                    for image_idx, image_feature in enumerate(image_features):
-                        if image_feature.shape[0] > 1:
-                            base_image_feature = image_feature[0]
-                            image_feature = image_feature[1:]
-                            height = width = self.num_patches_per_side
-                            assert height * width == base_image_feature.shape[0]
-                            if self.image_aspect_ratio == "anyres":
-                                (
-                                    num_patch_width,
-                                    num_patch_height,
-                                ) = get_anyres_image_grid_shape(
-                                    image_sizes[image_idx],
-                                    self.image_grid_pinpoints,
-                                    self.vision_tower.config.image_size,
-                                )
-                                image_feature = image_feature.view(
-                                    num_patch_height, num_patch_width, height, width, -1
-                                )
-                            else:
-                                raise NotImplementedError()
-                            if "unpad" in self.mm_patch_merge_type:
-                                image_feature = image_feature.permute(
-                                    4, 0, 2, 1, 3
-                                ).contiguous()
-                                image_feature = image_feature.flatten(1, 2).flatten(
-                                    2, 3
-                                )
-                                image_feature = unpad_image(
-                                    image_feature, image_sizes[image_idx]
-                                )
-                                image_feature = torch.cat(
-                                    (
-                                        image_feature,
-                                        self.language_model.model.image_newline[
-                                            :, None, None
-                                        ].expand(*image_feature.shape[:-1], 1),
-                                    ),
-                                    dim=-1,
-                                )
-                                image_feature = image_feature.flatten(1, 2).transpose(
-                                    0, 1
-                                )
-                            else:
-                                image_feature = image_feature.permute(
-                                    0, 2, 1, 3, 4
-                                ).contiguous()
-                                image_feature = image_feature.flatten(0, 3)
-                            image_feature = torch.cat(
-                                (base_image_feature, image_feature), dim=0
-                            )
-                        else:
-                            image_feature = image_feature[0]
-                            if "unpad" in self.mm_patch_merge_type:
-                                image_feature = torch.cat(
-                                    (
-                                        image_feature,
-                                        self.language_model.model.image_newline[None],
-                                    ),
-                                    dim=0,
-                                )
-                        new_image_features.append(image_feature)
-                    image_features = new_image_features
-
-                extend_start_loc_cpu = input_metadata.extend_start_loc.cpu().numpy()
-                pt = 0
-                for i in range(bs):
-                    if not need_vision[i]:
-                        continue
-
-                    start_idx = extend_start_loc_cpu[i]
-                    pad_len, pad_dim = image_features[pt].shape  # 576, 4096
-                    dim = input_embeds.shape[1]
-                    assert (
-                        pad_dim == dim
-                    ), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
-                    # Fill in the placeholder for the image
-                    try:
-                        input_embeds[
-                            start_idx
-                            + image_offsets[i] : start_idx
-                            + image_offsets[i]
-                            + pad_len
-                        ] = image_features[pt]
-                    except RuntimeError as e:
-                        print(f"RuntimeError in llava image encoding: {e}")
-                        print(input_embeds.shape)
-                        print(start_idx, image_offsets[i])
-                    pt += 1
-
-            return self.language_model(
-                input_ids, positions, input_metadata, input_embeds=input_embeds
-            )
-        elif input_metadata.forward_mode == ForwardMode.DECODE:
-            return self.language_model(input_ids, positions, input_metadata)
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        # load clip vision model by cfg['mm_vision_tower']:
-        #   huggingface_name or path_of_clip_relative_to_llava_model_dir
-        vision_path = self.config.mm_vision_tower
-        self.vision_tower = CLIPVisionModel.from_pretrained(
-            vision_path, torch_dtype=torch.float16
-        ).cuda()
-        self.vision_tower.eval()
-
-        self.vision_feature_layer = self.config.mm_vision_select_layer
-        self.vision_feature_select_strategy = self.config.mm_vision_select_feature
-        self.image_size = self.vision_tower.config.image_size
-        self.patch_size = self.vision_tower.config.patch_size
-
-        self.mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
-        self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
-        self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)
-
-        self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
-        if self.vision_feature_select_strategy == "patch":
-            pass
-        elif self.vision_feature_select_strategy == "cls_patch":
-            self.image_feature_len += 1
-        else:
-            raise ValueError(f"Unexpected select feature: {self.select_feature}")
-
-        # load mm_projector
-        projector_weights = {
-            "model.mm_projector.0": "multi_modal_projector.linear_1",
-            "model.mm_projector.2": "multi_modal_projector.linear_2",
-            "model.vision_tower.vision_tower": "vision_tower",  # Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
-        }
-        params_dict = dict(self.named_parameters())
-        weights = list(weights)
-        for name, loaded_weight in weights:
-            # FIXME: why projector weights read two times?
-            if "projector" in name or "vision_tower" in name:
-                for weight_name, param_name in projector_weights.items():
-                    if weight_name in name:
-                        name = name.replace(weight_name, param_name)
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-
-        # load language model
-        self.language_model.load_weights(weights)
-
-        monkey_path_clip_vision_embed_forward()
-
-    @property
-    def num_patches_per_side(self):
-        return self.image_size // self.patch_size
-
-
-first_call = True
-
-
-def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-    batch_size = pixel_values.shape[0]
-
-    # Move this conv layer to CPU to avoid a bug in torch >= 2.1 on A10G.
-    global first_call
-    if first_call:
-        self.patch_embedding.cpu().float()
-        first_call = False
-    pixel_values = pixel_values.to(dtype=torch.float32, device="cpu")
-    patch_embeds = self.patch_embedding(pixel_values).cuda().half()
-
-    patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-    class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-    embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-    embeddings = embeddings + self.position_embedding(self.position_ids)
-    return embeddings
-
-
-def monkey_path_clip_vision_embed_forward():
-    import transformers
-
-    setattr(
-        transformers.models.clip.modeling_clip.CLIPVisionEmbeddings,
-        "forward",
-        clip_vision_embed_forward,
-    )
-
-
-EntryClass = LlavaQwenForCausalLM