Yi-VL Model (#112)

python/sglang/srt/models/yivl.py

@@ -0,0 +1,101 @@
+"""Inference-only Yi-VL model."""
+import os
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+from transformers import CLIPVisionModel, LlavaConfig
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
+
+from sglang.srt.models.llava import LlavaLlamaForCausalLM, clip_vision_embed_forward, monkey_path_clip_vision_embed_forward
+
+
+class YiVLForCausalLM(LlavaLlamaForCausalLM):
+    def __init__(self, *args, **kwargs):
+        self.config = kwargs["config"]
+        super().__init__(self.config)
+
+        self.multi_modal_projector = YiVLMultiModalProjector(self.config)
+        self.vision_tower_subfolder = self.config.mm_vision_tower.replace("./", "") # Everything after "./"
+
+    def load_weights(
+        self,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        # We have to use the subfolder of the main model directory (e.g. 01-ai/Yi-VL-6B)
+        self.vision_tower = CLIPVisionModel.from_pretrained(
+            model_name_or_path, torch_dtype=torch.float16, subfolder=self.vision_tower_subfolder
+        ).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
+        # TODO: support TP?
+        projector_weights = {
+            "model.mm_projector.0": "multi_modal_projector.linear_1",
+            "model.mm_projector.1": "multi_modal_projector.ln_1",
+            "model.mm_projector.3": "multi_modal_projector.linear_2",
+            "model.mm_projector.4": "multi_modal_projector.ln_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())
+        for name, loaded_weight in hf_model_weights_iterator(
+            model_name_or_path, cache_dir, load_format, revision
+        ):
+            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(
+            model_name_or_path, cache_dir, load_format, revision
+        )
+
+        monkey_path_clip_vision_embed_forward()
+
+class YiVLMultiModalProjector(nn.Module):
+    def __init__(self, config: LlavaConfig):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size)
+        self.ln_1 = nn.LayerNorm(config.text_config.hidden_size)
+        self.act = nn.GELU()
+        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size)
+        self.ln_2 = nn.LayerNorm(config.text_config.hidden_size)
+
+    def forward(self, image_features):
+        hidden_states = self.linear_1(image_features)
+        hidden_state = self.ln_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        hidden_states = self.ln_2(hidden_states)
+        return hidden_states
+
+EntryClass = YiVLForCausalLM