update

vllm/model_executor/models/rvl.py

@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Mapping
+
+import torch
+import torch.nn as nn
+from transformers.activations import GELUActivation
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict
+
+from .llava_next import (
+    LlavaDummyInputsBuilder,
+    LlavaNextMultiModalProcessor,
+    LlavaNextProcessingInfo,
+)
+from .llava_onevision import LlavaOnevisionForConditionalGeneration
+from .utils import WeightsMapper
+
+
+class RVLProcessingInfo(LlavaNextProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(**kwargs)
+
+
+class RVLDummyInputsBuilder(LlavaDummyInputsBuilder[RVLProcessingInfo]):
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        image_token = "<image>"
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = self.info.get_image_size_with_most_features()
+
+        image_overrides = mm_options.get("image")
+
+        return {
+            "image": self._get_dummy_images(
+                width=target_width,
+                height=target_height,
+                num_images=num_images,
+                overrides=image_overrides,
+            ),
+        }
+
+
+class RVLMultiModalProjector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(config.vision_config.hidden_size, eps=1e-06)
+        self.linear_1 = nn.Linear(
+            config.vision_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True,
+        )
+        self.act = GELUActivation()
+        self.linear_2 = nn.Linear(
+            config.text_config.hidden_size,
+            config.text_config.hidden_size,
+            bias=True,
+        )
+
+    def forward(self, image_feature: torch.Tensor) -> torch.Tensor:
+        image_feature = self.pre_norm(image_feature)
+        hidden_states = self.linear_1(image_feature)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+
+        return hidden_states
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    LlavaNextMultiModalProcessor,
+    info=RVLProcessingInfo,
+    dummy_inputs=RVLDummyInputsBuilder,
+)
+class RForConditionalGeneration(LlavaOnevisionForConditionalGeneration):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers
+            # v4.52
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "model.image_newline": "image_newline",
+            "lm_head.": "language_model.lm_head.",
+        }
+    )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        config = vllm_config.model_config.hf_config
+        self.multi_modal_projector = RVLMultiModalProjector(config)