diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md
index 297d98142..3159d3bd1 100644
--- a/docs/models/supported_models.md
+++ b/docs/models/supported_models.md
@@ -615,6 +615,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `ChameleonForConditionalGeneration` | Chameleon | T + I | `facebook/chameleon-7b`, etc. | | ✅︎ | ✅︎ |
| `Cohere2VisionForConditionalGeneration` | Command A Vision | T + I+ | `CohereLabs/command-a-vision-07-2025`, etc. | | ✅︎ | ✅︎ |
| `DeepseekVLV2ForCausalLM`^ | DeepSeek-VL2 | T + I+ | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ | ✅︎ |
+| `DonutForConditionalGeneration`^ | Donut | T + I | `ByteDance/Dolphin`, `naver-clova-ix/donut-base-finetuned-docvqa`, etc. | | | |
| `Florence2ForConditionalGeneration` | Florence-2 | T + I | `microsoft/Florence-2-base`, `microsoft/Florence-2-large`, etc. | | | |
| `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ | ✅︎ |
| `Gemma3ForConditionalGeneration` | Gemma 3 | T + I+ | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ | ⚠️ |
diff --git a/examples/offline_inference/dolphin.py b/examples/offline_inference/dolphin.py
new file mode 100644
index 000000000..d2ba27cd1
--- /dev/null
+++ b/examples/offline_inference/dolphin.py
@@ -0,0 +1,311 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+import copy
+import os
+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+import regex as re
+from PIL import Image
+from transformers import DonutProcessor
+
+from vllm import LLM, SamplingParams
+from vllm.inputs import ExplicitEncoderDecoderPrompt, TextPrompt, TokensPrompt
+from vllm.multimodal.utils import fetch_image
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+@dataclass
+class ImageDimensions:
+ original_w: int
+ original_h: int
+ padded_w: int
+ padded_h: int
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+def map_to_original_coordinates(
+ x1, y1, x2, y2, dims: ImageDimensions
+) -> tuple[int, int, int, int]:
+ try:
+ top = (dims.padded_h - dims.original_h) // 2
+ left = (dims.padded_w - dims.original_w) // 2
+ orig_x1 = max(0, x1 - left)
+ orig_y1 = max(0, y1 - top)
+ orig_x2 = min(dims.original_w, x2 - left)
+ orig_y2 = min(dims.original_h, y2 - top)
+ if orig_x2 <= orig_x1:
+ orig_x2 = min(orig_x1 + 1, dims.original_w)
+ if orig_y2 <= orig_y1:
+ orig_y2 = min(orig_y1 + 1, dims.original_h)
+ return int(orig_x1), int(orig_y1), int(orig_x2), int(orig_y2)
+ except Exception as e:
+ print(f"map_to_original_coordinates error: {str(e)}")
+ return 0, 0, min(100, dims.original_w), min(100, dims.original_h)
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+def adjust_box_edges(image, boxes: list[list[float]], max_pixels=15, threshold=0.2):
+ if isinstance(image, str):
+ image = cv2.imread(image)
+ img_h, img_w = image.shape[:2]
+ new_boxes = []
+ for box in boxes:
+ best_box = copy.deepcopy(box)
+
+ def check_edge(img, current_box, i, is_vertical):
+ edge = current_box[i]
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ _, binary = cv2.threshold(
+ gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU
+ )
+ if is_vertical:
+ line = binary[current_box[1] : current_box[3] + 1, edge]
+ else:
+ line = binary[edge, current_box[0] : current_box[2] + 1]
+ transitions = np.abs(np.diff(line))
+ return np.sum(transitions) / len(transitions)
+
+ edges = [(0, -1, True), (2, 1, True), (1, -1, False), (3, 1, False)]
+ current_box = copy.deepcopy(box)
+ current_box[0] = min(max(current_box[0], 0), img_w - 1)
+ current_box[1] = min(max(current_box[1], 0), img_h - 1)
+ current_box[2] = min(max(current_box[2], 0), img_w - 1)
+ current_box[3] = min(max(current_box[3], 0), img_h - 1)
+
+ for i, direction, is_vertical in edges:
+ best_score = check_edge(image, current_box, i, is_vertical)
+ if best_score <= threshold:
+ continue
+ for step in range(max_pixels):
+ current_box[i] += direction
+ if i == 0 or i == 2:
+ current_box[i] = min(max(current_box[i], 0), img_w - 1)
+ else:
+ current_box[i] = min(max(current_box[i], 0), img_h - 1)
+ score = check_edge(image, current_box, i, is_vertical)
+ if score < best_score:
+ best_score = score
+ best_box = copy.deepcopy(current_box)
+ if score <= threshold:
+ break
+ new_boxes.append(best_box)
+ return new_boxes
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+def process_coordinates(coords, padded_image, dims: ImageDimensions, previous_box=None):
+ try:
+ x1, y1 = int(coords[0] * dims.padded_w), int(coords[1] * dims.padded_h)
+ x2, y2 = int(coords[2] * dims.padded_w), int(coords[3] * dims.padded_h)
+ x1, y1, x2, y2 = (
+ max(0, min(x1, dims.padded_w - 1)),
+ max(0, min(y1, dims.padded_h - 1)),
+ max(0, min(x2, dims.padded_w)),
+ max(0, min(y2, dims.padded_h)),
+ )
+ if x2 <= x1:
+ x2 = min(x1 + 1, dims.padded_w)
+ if y2 <= y1:
+ y2 = min(y1 + 1, dims.padded_h)
+ new_boxes = adjust_box_edges(padded_image, [[x1, y1, x2, y2]])
+ x1, y1, x2, y2 = new_boxes[0]
+ x1, y1, x2, y2 = (
+ max(0, min(x1, dims.padded_w - 1)),
+ max(0, min(y1, dims.padded_h - 1)),
+ max(0, min(x2, dims.padded_w)),
+ max(0, min(y2, dims.padded_h)),
+ )
+ if x2 <= x1:
+ x2 = min(x1 + 1, dims.padded_w)
+ if y2 <= y1:
+ y2 = min(y1 + 1, dims.padded_h)
+ if previous_box is not None:
+ prev_x1, prev_y1, prev_x2, prev_y2 = previous_box
+ if (x1 < prev_x2 and x2 > prev_x1) and (y1 < prev_y2 and y2 > prev_y1):
+ y1 = prev_y2
+ y1 = min(y1, dims.padded_h - 1)
+ if y2 <= y1:
+ y2 = min(y1 + 1, dims.padded_h)
+ new_previous_box = [x1, y1, x2, y2]
+ orig_x1, orig_y1, orig_x2, orig_y2 = map_to_original_coordinates(
+ x1, y1, x2, y2, dims
+ )
+ return x1, y1, x2, y2, orig_x1, orig_y1, orig_x2, orig_y2, new_previous_box
+ except Exception as e:
+ print(f"process_coordinates error: {str(e)}")
+ orig_x1, orig_y1, orig_x2, orig_y2 = (
+ 0,
+ 0,
+ min(100, dims.original_w),
+ min(100, dims.original_h),
+ )
+ return 0, 0, 100, 100, orig_x1, orig_y1, orig_x2, orig_y2, [0, 0, 100, 100]
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+def prepare_image(image) -> tuple[np.ndarray, ImageDimensions]:
+ try:
+ image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+ original_h, original_w = image_cv.shape[:2]
+ max_size = max(original_h, original_w)
+ top = (max_size - original_h) // 2
+ bottom = max_size - original_h - top
+ left = (max_size - original_w) // 2
+ right = max_size - original_w - left
+ padded_image = cv2.copyMakeBorder(
+ image_cv, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(0, 0, 0)
+ )
+ padded_h, padded_w = padded_image.shape[:2]
+ dimensions = ImageDimensions(
+ original_w=original_w,
+ original_h=original_h,
+ padded_w=padded_w,
+ padded_h=padded_h,
+ )
+ return padded_image, dimensions
+ except Exception as e:
+ print(f"prepare_image error: {str(e)}")
+ h, w = image.height, image.width
+ dimensions = ImageDimensions(original_w=w, original_h=h, padded_w=w, padded_h=h)
+ return np.zeros((h, w, 3), dtype=np.uint8), dimensions
+
+
+# Copied from https://github.com/bytedance/Dolphin/utils/utils.py
+def parse_layout_string(bbox_str):
+ """Parse layout string using regular expressions"""
+ pattern = r"\[(\d*\.?\d+),\s*(\d*\.?\d+),\s*(\d*\.?\d+),\s*(\d*\.?\d+)\]\s*(\w+)"
+ matches = re.finditer(pattern, bbox_str)
+
+ parsed_results = []
+ for match in matches:
+ coords = [float(match.group(i)) for i in range(1, 5)]
+ label = match.group(5).strip()
+ parsed_results.append((coords, label))
+
+ return parsed_results
+
+
+model_id = "ByteDance/Dolphin"
+
+# The input image size for Dolphin is 896 x 896,
+# and the patch_size is 4 x 4.
+# Therefore, the initial number of patches is:
+# Height: 896 / 4 = 224 patches
+# Width: 896 / 4 = 224 patches
+
+# The Dolphin model uses a staged downsampling approach,
+# defined by the "depths": [2, 2, 14, 2] configuration.
+# Before entering stages 2, 3, and 4, a "Patch Merging" operation is performed,
+# which halves the feature map's dimensions (dividing both height and width by 2).
+# Before Stage 2: The size changes from 224 x 224 to (224/2) x (224/2) = 112 x 112.
+# Before Stage 3: The size changes from 112 x 112 to (112/2) x (112/2) = 56 x 56.
+# Before Stage 4: The size changes from 56 x 56 to (56/2) x (56/2) = 28 x 28.
+
+# Because vLLM needs to fill the image features with an encoder_prompt,
+# and the encoder_prompt will have `` tokens added when tokenized,
+# we need to construct an encoder_prompt with a length of 28 x 28 - 1 = 783.
+encoder_prompt = "".join(["0"] * 783)
+sampling_params = SamplingParams(
+ temperature=0.0,
+ max_tokens=2048,
+)
+
+processor = DonutProcessor.from_pretrained(model_id)
+llm = LLM(
+ model=model_id,
+ dtype="float16",
+ max_num_seqs=8,
+ hf_overrides={"architectures": ["DonutForConditionalGeneration"]},
+)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "--image_path", type=str, default=None, help="Path to a local image file."
+)
+args = parser.parse_args()
+
+if args.image_path:
+ if not os.path.exists(args.image_path):
+ raise FileNotFoundError(f"Error: File not found at {args.image_path}")
+ image = Image.open(args.image_path).convert("RGB")
+else:
+ image = fetch_image(
+ "https://huggingface.co/datasets/hf-internal-testing/example-documents/resolve/main/jpeg_images/0.jpg"
+ )
+
+
+prompt = "Parse the reading order of this document. "
+decoder_prompt = f"{prompt}"
+decoder_prompt_tokens = TokensPrompt(
+ prompt_token_ids=processor.tokenizer(decoder_prompt, add_special_tokens=False)[
+ "input_ids"
+ ]
+)
+enc_dec_prompt = ExplicitEncoderDecoderPrompt(
+ encoder_prompt=TextPrompt(prompt=encoder_prompt, multi_modal_data={"image": image}),
+ decoder_prompt=decoder_prompt_tokens,
+)
+layout_outputs = llm.generate(prompts=enc_dec_prompt, sampling_params=sampling_params)
+layout_result_str = layout_outputs[0].outputs[0].text
+print(f"Layout analysis output:\n{layout_result_str}")
+
+padded_image, dims = prepare_image(image)
+layout_results = parse_layout_string(layout_result_str)
+text_table_elements = []
+previous_box = None
+reading_order = 0
+for bbox_coords, label in layout_results:
+ if label == "fig":
+ continue
+ try:
+ x1, y1, x2, y2, orig_x1, orig_y1, orig_x2, orig_y2, previous_box = (
+ process_coordinates(bbox_coords, padded_image, dims, previous_box)
+ )
+ cropped = padded_image[y1:y2, x1:x2]
+ if cropped.size > 0 and cropped.shape[0] > 3 and cropped.shape[1] > 3:
+ pil_crop = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))
+ prompt_ocr = (
+ "Parse the table in the image. "
+ if label == "tab"
+ else "Read text in the image. "
+ )
+ text_table_elements.append(
+ {
+ "crop": pil_crop,
+ "prompt": prompt_ocr,
+ "reading_order": reading_order,
+ }
+ )
+ reading_order += 1
+ except Exception as e:
+ print(f"Error processing bbox (label: {label}): {str(e)}")
+ continue
+
+if text_table_elements:
+ batch_prompts = []
+ for elem in text_table_elements:
+ decoder_prompt_str = f"{elem['prompt']}"
+ decoder_prompt_tokens = TokensPrompt(
+ prompt_token_ids=processor.tokenizer(
+ decoder_prompt_str, add_special_tokens=False
+ )["input_ids"]
+ )
+ enc_dec_prompt = ExplicitEncoderDecoderPrompt(
+ encoder_prompt=TextPrompt(
+ prompt=encoder_prompt, multi_modal_data={"image": elem["crop"]}
+ ),
+ decoder_prompt=decoder_prompt_tokens,
+ )
+ batch_prompts.append(enc_dec_prompt)
+ batch_outputs = llm.generate(prompts=batch_prompts, sampling_params=sampling_params)
+ for i, output in enumerate(batch_outputs):
+ text_table_elements[i]["text"] = output.outputs[0].text.strip()
+
+print("------" * 8)
+text_table_elements.sort(key=lambda x: x["reading_order"])
+for elem in text_table_elements:
+ print(elem.get("text", ""))
diff --git a/examples/offline_inference/encoder_decoder_multimodal.py b/examples/offline_inference/encoder_decoder_multimodal.py
index d27a902ed..655f9f3fc 100644
--- a/examples/offline_inference/encoder_decoder_multimodal.py
+++ b/examples/offline_inference/encoder_decoder_multimodal.py
@@ -13,6 +13,7 @@ from typing import NamedTuple
from vllm import LLM, EngineArgs, PromptType, SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset
+from vllm.multimodal.utils import fetch_image
from vllm.utils import FlexibleArgumentParser
@@ -21,6 +22,50 @@ class ModelRequestData(NamedTuple):
prompts: Sequence[PromptType]
+def run_donut():
+ engine_args = EngineArgs(
+ model="naver-clova-ix/donut-base-finetuned-docvqa",
+ max_num_seqs=2,
+ limit_mm_per_prompt={"image": 1},
+ dtype="float16",
+ hf_overrides={"architectures": ["DonutForConditionalGeneration"]},
+ )
+
+ # The input image size for donut-base-finetuned-docvqa is 2560 x 1920,
+ # and the patch_size is 4 x 4.
+ # Therefore, the initial number of patches is:
+ # Height: 1920 / 4 = 480 patches
+ # Width: 2560 / 4 = 640 patches
+ # The Swin model uses a staged downsampling approach,
+ # defined by the "depths": [2, 2, 14, 2] configuration.
+ # Before entering stages 2, 3, and 4, a "Patch Merging" operation is performed,
+ # which halves the feature map's dimensions (dividing both height and width by 2).
+ # Before Stage 2: The size changes from 480 x 640 to (480/2) x (640/2) = 240 x 320.
+ # Before Stage 3: The size changes from 240 x 320 to (240/2) x (320/2) = 120 x 160.
+ # Before Stage 4: The size changes from 120 x 160 to (120/2) x (160/2) = 60 x 80.
+ # Because vLLM needs to fill the image features with an encoder_prompt,
+ # and the encoder_prompt will have `` tokens added when tokenized,
+ # we need to construct an encoder_prompt with a length of 60 x 80 - 1 = 4799.
+ prompts = [
+ {
+ "encoder_prompt": {
+ "prompt": "".join(["$"] * 4799),
+ "multi_modal_data": {
+ "image": fetch_image(
+ "https://huggingface.co/datasets/hf-internal-testing/example-documents/resolve/main/jpeg_images/0.jpg"
+ ) # noqa: E501
+ },
+ },
+ "decoder_prompt": "What time is the coffee break?", # noqa: E501
+ },
+ ]
+
+ return ModelRequestData(
+ engine_args=engine_args,
+ prompts=prompts,
+ )
+
+
def run_florence2():
engine_args = EngineArgs(
model="microsoft/Florence-2-large",
@@ -118,6 +163,7 @@ def run_whisper():
model_example_map = {
+ "donut": run_donut,
"florence2": run_florence2,
"mllama": run_mllama,
"whisper": run_whisper,
diff --git a/tests/models/multimodal/processing/test_common.py b/tests/models/multimodal/processing/test_common.py
index adc8b2510..a604d11f0 100644
--- a/tests/models/multimodal/processing/test_common.py
+++ b/tests/models/multimodal/processing/test_common.py
@@ -160,6 +160,7 @@ def _test_processing_correctness(
# incorrect token ids. So we need use `add_special_tokens=False` here
# to leave bos_token to be added by the processor.
_ADD_SPECIAL_TOKENS_OVERRIDES = {
+ "donut": False,
"mllama": False,
"ovis": False,
"ovis2_5": False,
@@ -270,6 +271,7 @@ def _test_processing_correctness_one(
"facebook/chameleon-7b",
"CohereLabs/command-a-vision-07-2025",
"deepseek-ai/deepseek-vl2-tiny",
+ "naver-clova-ix/donut-base-finetuned-docvqa",
"microsoft/Florence-2-base",
"adept/fuyu-8b",
"google/gemma-3-4b-it",
diff --git a/tests/models/registry.py b/tests/models/registry.py
index 25dbbd7fa..b34c6f2e5 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -513,6 +513,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
is_available_online=False,
),
# [Encoder-decoder]
+ "DonutForConditionalGeneration": _HfExamplesInfo("naver-clova-ix/donut-base-finetuned-docvqa", # noqa: E501
+ hf_overrides={"architectures": ["DonutForConditionalGeneration"], "model_type": "donut"}, # noqa: E501
+ extras={"dolphin": "ByteDance/Dolphin"}), # noqa: E501
# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
# Therefore, we borrow the BartTokenizer from the original Bart model
"Florence2ForConditionalGeneration": _HfExamplesInfo("microsoft/Florence-2-base", # noqa: E501
diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py
index bbe958351..dbf8d3ba5 100644
--- a/vllm/engine/llm_engine.py
+++ b/vllm/engine/llm_engine.py
@@ -1822,7 +1822,7 @@ class LLMEngine:
assert isinstance(mm_processor, EncDecMultiModalProcessor)
if mm_processor.pad_dummy_encoder_prompt:
- return # Skip encoder length check for Whisper
+ return # Skip encoder length check for Whisper and Donut
if model_config.is_multimodal_model:
suggestion = (
diff --git a/vllm/model_executor/models/donut.py b/vllm/model_executor/models/donut.py
new file mode 100644
index 000000000..b1f6a0af6
--- /dev/null
+++ b/vllm/model_executor/models/donut.py
@@ -0,0 +1,398 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature, NougatProcessor
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.bart import BartParallelLMHead, MBartDecoder
+from vllm.model_executor.models.interfaces import (MultiModalEmbeddings,
+ SupportsMultiModal,
+ SupportsV0Only)
+from vllm.model_executor.models.swin import SwinModel
+from vllm.model_executor.models.utils import (AutoWeightsLoader,
+ _flatten_embeddings, flatten_bn)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+ MultiModalKwargsItems)
+from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (BaseProcessingInfo,
+ EncDecMultiModalProcessor,
+ PromptIndexTargets, PromptInsertion,
+ PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+
+
+class MBartDecoderWrapper(nn.Module):
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+ config = vllm_config.model_config.hf_config
+ cache_config = vllm_config.cache_config
+ quant_config = vllm_config.quant_config
+
+ self.decoder = MBartDecoder(config,
+ cache_config,
+ quant_config=quant_config,
+ prefix=f"{prefix}.decoder")
+
+ def forward(self, *args, **kwargs):
+ return self.decoder(*args, **kwargs)
+
+
+class DonutLanguageForConditionalGeneration(nn.Module, SupportsV0Only):
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+
+ config = vllm_config.model_config.hf_config
+
+ self.config = config
+ self.model = MBartDecoderWrapper(vllm_config=vllm_config,
+ prefix=f"{prefix}.model")
+ embed_scale = math.sqrt(
+ config.d_model) if config.scale_embedding else 1.0
+
+ self.vocab_size = config.vocab_size
+ self.lm_head = BartParallelLMHead(self.vocab_size,
+ config.d_model,
+ embed_scale=embed_scale)
+
+ self.logits_processor = LogitsProcessor(self.vocab_size,
+ config.vocab_size)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ inputs_embeds: torch.Tensor,
+ **kwargs,
+ ) -> torch.Tensor:
+ r"""
+ Args:
+ input_ids
+ torch.Tensor of *decoder* input token ids.
+ positions
+ torch.Tensor of *decoder* position indices.
+ Returns:
+ Output torch.Tensor
+ """
+
+ return self.model(decoder_input_ids=input_ids,
+ decoder_positions=positions,
+ encoder_hidden_states=inputs_embeds)
+
+ def compute_logits(
+ self,
+ hidden_states: torch.Tensor,
+ sampling_metadata: SamplingMetadata,
+ ) -> Optional[torch.Tensor]:
+ logits = self.logits_processor(self.lm_head, hidden_states,
+ sampling_metadata)
+ return logits
+
+ def load_weights(self, weights: Iterable[tuple[str,
+ torch.Tensor]]) -> set[str]:
+ stacked_params_mapping = [
+ # (param_name, shard_name, shard_id)
+ ("qkv_proj", "q_proj", "q"),
+ ("qkv_proj", "k_proj", "k"),
+ ("qkv_proj", "v_proj", "v"),
+ ]
+
+ params_dict = dict(self.named_parameters())
+ loaded_params: set[str] = set()
+ for name, loaded_weight in weights:
+ for (param_name, weight_name, shard_id) in stacked_params_mapping:
+ if weight_name not in name:
+ continue
+ name = name.replace(weight_name, param_name)
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(param, loaded_weight, shard_id)
+ break
+ else:
+ if "final_logits_bias" in name:
+ continue
+ # if self.config.tie_word_embeddings and "embed_tokens" in name:
+ # continue
+ param = params_dict[name]
+ weight_loader = getattr(param, "weight_loader",
+ default_weight_loader)
+ weight_loader(param, loaded_weight)
+ loaded_params.add(name)
+ return loaded_params
+
+
+class DonutImagePixelInputs(TypedDict):
+ type: Literal["pixel_values"]
+ data: torch.Tensor
+ """Shape: (batch_size, num_channel, height, width)"""
+
+
+class DonutProcessingInfo(BaseProcessingInfo):
+
+ def get_hf_config(self):
+ return self.ctx.get_hf_config()
+
+ def get_hf_processor(self):
+ return self.ctx.get_hf_processor()
+
+ def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+ return {"image": 1}
+
+ def get_num_image_tokens(self) -> int:
+ return 1
+
+
+class DonutDummyInputsBuilder(BaseDummyInputsBuilder[DonutProcessingInfo]):
+
+ def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+ return ""
+
+ def get_dummy_mm_data(
+ self,
+ seq_len: int,
+ mm_counts: Mapping[str, int],
+ ) -> MultiModalDataDict:
+ num_images = mm_counts.get("image", 0)
+
+ target_width, target_height = self.info.get_hf_config(
+ ).encoder.image_size
+
+ return {
+ "image":
+ self._get_dummy_images(width=target_width,
+ height=target_height,
+ num_images=num_images)
+ }
+
+
+class DonutMultiModalProcessor(EncDecMultiModalProcessor[DonutProcessingInfo]):
+
+ def _hf_processor_applies_updates(
+ self,
+ prompt_text: str,
+ mm_items: MultiModalDataItems,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ tokenization_kwargs: Mapping[str, object],
+ ) -> bool:
+ return False
+
+ def create_encoder_prompt(
+ self,
+ prompt: Union[str, list[int]],
+ mm_data: MultiModalDataDict,
+ ) -> Union[str, list[int]]:
+ return prompt
+
+ def create_decoder_prompt(
+ self,
+ prompt: Union[str, list[int]],
+ mm_data: MultiModalDataDict,
+ ) -> Union[str, list[int]]:
+ return prompt
+
+ @property
+ def pad_dummy_encoder_prompt(self) -> bool:
+ return True
+
+ def _call_hf_processor(
+ self,
+ prompt: str,
+ mm_data: Mapping[str, object],
+ mm_kwargs: Mapping[str, object],
+ tok_kwargs: Mapping[str, object],
+ ) -> BatchFeature:
+ hf_processor = self.info.get_hf_processor()
+ if mm_data:
+ processed_outputs = super()._call_hf_processor(
+ prompt, mm_data, mm_kwargs, tok_kwargs)
+ if isinstance(hf_processor, NougatProcessor):
+ processed_outputs["input_ids"] = processed_outputs["labels"]
+ else:
+ tokenizer = hf_processor.tokenizer
+ processed_outputs = tokenizer(prompt,
+ add_special_tokens=False,
+ return_tensors="pt")
+ return processed_outputs
+
+ def _get_mm_fields_config(
+ self,
+ hf_inputs: BatchFeature,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ ) -> Mapping[str, MultiModalFieldConfig]:
+ return dict(pixel_values=MultiModalFieldConfig.batched("image"))
+
+ def _get_prompt_updates(
+ self,
+ mm_items: MultiModalDataItems,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ out_mm_kwargs: MultiModalKwargsItems,
+ ) -> Sequence[PromptUpdate]:
+ hf_processor = self.info.get_hf_processor()
+ tokenizer = hf_processor.tokenizer
+ pad_token_id = tokenizer.pad_token_id
+ num_image_tokens = self.info.get_num_image_tokens()
+ image_tokens = [pad_token_id] * num_image_tokens
+
+ return [
+ PromptInsertion(
+ modality="image",
+ target=PromptIndexTargets.start(),
+ insertion=image_tokens,
+ )
+ ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(DonutMultiModalProcessor,
+ info=DonutProcessingInfo,
+ dummy_inputs=DonutDummyInputsBuilder)
+class DonutForConditionalGeneration(nn.Module, SupportsMultiModal,
+ SupportsV0Only):
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+ config = vllm_config.model_config.hf_config
+ processor_config = vllm_config.model_config.hf_image_processor_config
+
+ self.config = config
+ self.vision_config = config.encoder
+ self.processor_config = processor_config
+ self.encoder = SwinModel(config=config.encoder)
+
+ self.decoder = DonutLanguageForConditionalGeneration(
+ vllm_config=vllm_config.with_hf_config(config.decoder),
+ prefix=f"{prefix}.decoder",
+ )
+ self.pad_token_id = config.pad_token_id
+
+ def _validate_pixel_values(
+ self, data: Union[torch.Tensor, list[torch.Tensor]]
+ ) -> Union[torch.Tensor, list[torch.Tensor]]:
+
+ # size = self.processor_config["size"]
+ h, w = self.config.encoder.image_size
+ expected_dims = (3, h, w)
+
+ def _validate_shape(d: torch.Tensor):
+ actual_dims = tuple(d.shape)
+
+ if actual_dims != expected_dims:
+ raise ValueError(
+ "The expected shape of pixel values per batch "
+ f"is {expected_dims}. You supplied {actual_dims}.")
+
+ for d in data:
+ _validate_shape(d)
+
+ return data
+
+ def _parse_and_validate_image_input(self, **kwargs: object):
+ pixel_values: Optional[Union[list[list[torch.Tensor]],
+ list[torch.Tensor],
+ torch.Tensor]] = kwargs.pop(
+ "pixel_values", None)
+ image_embeds: Optional[Union[list[list[torch.Tensor]],
+ list[torch.Tensor],
+ torch.Tensor]] = kwargs.pop(
+ "image_embeds", None)
+
+ if pixel_values is None and image_embeds is None:
+ return None
+
+ if pixel_values is not None and image_embeds is not None:
+ raise ValueError(
+ "Both pixel values and image embeds are provided.")
+
+ if pixel_values is not None:
+ return DonutImagePixelInputs(
+ type="pixel_values",
+ data=self._validate_pixel_values(
+ flatten_bn(pixel_values, concat=True)),
+ )
+
+ if image_embeds is not None:
+ raise NotImplementedError
+
+ raise AssertionError("This line should be unreachable.")
+
+ def _process_image_input(
+ self, image_input: DonutImagePixelInputs) -> torch.Tensor:
+ assert image_input["type"] == "pixel_values"
+ pixel_values = image_input["data"]
+ dtype = next(self.encoder.parameters()).dtype
+ pixel_values = pixel_values.to(dtype)
+ return self.encoder(pixel_values)
+
+ def get_language_model(self) -> torch.nn.Module:
+ return self.decoder
+
+ def get_multimodal_embeddings(
+ self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+ image_input = self._parse_and_validate_image_input(**kwargs)
+ if image_input is None:
+ return None
+ vision_embeddings = self._process_image_input(image_input)
+ return vision_embeddings
+
+ def get_input_embeddings(
+ self,
+ input_ids: torch.Tensor,
+ multimodal_embeddings: MultiModalEmbeddings,
+ ) -> torch.Tensor:
+ return _flatten_embeddings(multimodal_embeddings)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ *,
+ encoder_input_ids: torch.Tensor,
+ encoder_positions: torch.Tensor,
+ **kwargs,
+ ) -> torch.Tensor:
+ r"""
+ Args:
+ input_ids
+ torch.Tensor of *decoder* input token ids.
+ positions
+ torch.Tensor of *decoder* position indices.
+ encoder_input_ids
+ torch.Tensor of *encoder* input token ids.
+ encoder_positions
+ torch.Tensor of *encoder* position indices
+ Returns:
+ Output torch.Tensor
+ """
+
+ inputs_embeds = None
+ if encoder_input_ids.numel() > 0:
+ vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+ inputs_embeds = self.get_input_embeddings(encoder_input_ids,
+ vision_embeddings)
+
+ hidden_states = self.decoder(input_ids,
+ positions,
+ inputs_embeds=inputs_embeds)
+ return hidden_states
+
+ def compute_logits(
+ self,
+ hidden_states: torch.Tensor,
+ sampling_metadata: SamplingMetadata,
+ ) -> Optional[torch.Tensor]:
+ return self.decoder.compute_logits(hidden_states, sampling_metadata)
+
+ def load_weights(self, weights: Iterable[tuple[str,
+ torch.Tensor]]) -> set[str]:
+ loader = AutoWeightsLoader(self)
+ return loader.load_weights(weights)
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index 465c25f09..ebf78771e 100644
--- a/vllm/model_executor/models/registry.py
+++ b/vllm/model_executor/models/registry.py
@@ -252,6 +252,7 @@ _MULTIMODAL_MODELS = {
"Tarsier2ForConditionalGeneration": ("qwen2_vl", "Tarsier2ForConditionalGeneration"), # noqa: E501
"VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"), # noqa: E501
# [Encoder-decoder]
+ "DonutForConditionalGeneration": ("donut", "DonutForConditionalGeneration"),
"Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"), # noqa: E501
"MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"), # noqa: E501
"Llama4ForConditionalGeneration": ("mllama4", "Llama4ForConditionalGeneration"), # noqa: E501
diff --git a/vllm/model_executor/models/swin.py b/vllm/model_executor/models/swin.py
new file mode 100644
index 000000000..30b441f5b
--- /dev/null
+++ b/vllm/model_executor/models/swin.py
@@ -0,0 +1,475 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import SwinConfig
+from transformers.models.swin.modeling_swin import SwinEmbeddings
+from transformers.models.swin.modeling_swin import SwinLayer as HFSwinLayer
+from transformers.models.swin.modeling_swin import SwinPatchMerging
+from transformers.pytorch_utils import meshgrid
+
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+ QKVParallelLinear,
+ RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+class SwinSelfAttention(nn.Module):
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ dim: int,
+ num_heads: int,
+ window_size: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ if dim % num_heads != 0:
+ raise ValueError(
+ f"The hidden size ({dim}) is not a multiple of the number of "
+ f"attention heads ({num_heads})")
+
+ self.num_attention_heads = num_heads
+ self.attention_head_size = int(dim / num_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.window_size = (window_size if isinstance(window_size, Iterable)
+ else (window_size, window_size))
+ self.scale = self.attention_head_size**-0.5
+
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros(
+ (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1),
+ num_heads))
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
+ coords_flatten = torch.flatten(coords, 1)
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:,
+ None, :]
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+ relative_coords[:, :, 0] += self.window_size[0] - 1
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1)
+
+ self.relative_position_index = nn.Parameter(relative_position_index,
+ requires_grad=False)
+
+ self.qkv = QKVParallelLinear(
+ hidden_size=dim,
+ head_size=self.attention_head_size,
+ total_num_heads=self.num_attention_heads,
+ bias=config.qkv_bias,
+ quant_config=quant_config,
+ prefix=f"{prefix}.qkv",
+ )
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+ self.attention_head_size)
+ x = x.view(new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def _get_rel_pos_bias(self) -> torch.Tensor:
+ relative_position_bias = self.relative_position_bias_table[
+ self.relative_position_index.view(-1)]
+ relative_position_bias = relative_position_bias.view(
+ self.window_size[0] * self.window_size[1],
+ self.window_size[0] * self.window_size[1], -1)
+ relative_position_bias = relative_position_bias.permute(
+ 2, 0, 1).contiguous()
+ return relative_position_bias.unsqueeze(0)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor, ...]:
+ batch_size, dim, num_channels = hidden_states.shape
+
+ qkv_output, _ = self.qkv(hidden_states)
+ query_layer, key_layer, value_layer = qkv_output.chunk(3, dim=-1)
+
+ key_layer = self.transpose_for_scores(key_layer)
+ value_layer = self.transpose_for_scores(value_layer)
+ query_layer = self.transpose_for_scores(query_layer)
+
+ attention_scores = self._get_rel_pos_bias()
+ if attention_mask is not None:
+ mask_shape = attention_mask.shape[0]
+ attention_mask_expanded = attention_mask.view(
+ 1, mask_shape, 1, dim,
+ dim).expand(batch_size // mask_shape, mask_shape,
+ self.num_attention_heads, dim, dim)
+ attention_scores = attention_scores + \
+ attention_mask_expanded.unsqueeze(
+ 1).unsqueeze(0)
+ attention_scores = attention_scores.view(-1,
+ self.num_attention_heads,
+ dim, dim)
+
+ context_layer = torch.nn.functional.scaled_dot_product_attention(
+ query_layer,
+ key_layer,
+ value_layer,
+ attn_mask=attention_scores,
+ dropout_p=0.,
+ )
+ attention_probs = None
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (
+ self.all_head_size, )
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ outputs = (context_layer,
+ attention_probs) if output_attentions else (context_layer, )
+
+ return outputs
+
+
+class SwinSelfOutput(nn.Module):
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ dim: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.dense = RowParallelLinear(
+ input_size=dim,
+ output_size=dim,
+ quant_config=quant_config,
+ prefix=f"{prefix}.dense",
+ )
+
+ def forward(self, hidden_states: torch.Tensor,
+ input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states, _ = self.dense(hidden_states)
+
+ return hidden_states
+
+
+class SwinAttention(nn.Module):
+
+ def __init__(self,
+ config: SwinConfig,
+ dim: int,
+ num_heads: int,
+ window_size: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "") -> None:
+ super().__init__()
+ self.self = SwinSelfAttention(config,
+ dim,
+ num_heads,
+ window_size,
+ quant_config=quant_config,
+ prefix=f"{prefix}.self")
+ self.output = SwinSelfOutput(config,
+ dim,
+ quant_config=quant_config,
+ prefix=f"{prefix}.output")
+ self.pruned_heads = set()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(hidden_states, attention_mask, head_mask,
+ output_attentions)
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output, ) + self_outputs[1:]
+ return outputs
+
+
+class SwinIntermediate(nn.Module):
+
+ def __init__(self,
+ config: SwinConfig,
+ dim: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "") -> None:
+ super().__init__()
+ self.dense = ColumnParallelLinear(dim,
+ int(config.mlp_ratio * dim),
+ quant_config=quant_config,
+ prefix=f"{prefix}.dense")
+ self.intermediate_act_fn = get_act_fn(config.hidden_act)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states, _ = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class SwinOutput(nn.Module):
+
+ def __init__(self,
+ config: SwinConfig,
+ dim: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "") -> None:
+ super().__init__()
+ self.dense = RowParallelLinear(int(config.mlp_ratio * dim),
+ dim,
+ quant_config=quant_config,
+ prefix=f"{prefix}.dense")
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states, _ = self.dense(hidden_states)
+ return hidden_states
+
+
+class SwinLayer(HFSwinLayer):
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ dim: int,
+ input_resolution: int,
+ num_heads: int,
+ drop_path_rate: float = 0.0,
+ shift_size: int = 0,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__(
+ config=config,
+ dim=dim,
+ input_resolution=input_resolution,
+ num_heads=num_heads,
+ drop_path_rate=drop_path_rate,
+ shift_size=shift_size,
+ )
+
+ self.attention = SwinAttention(config,
+ dim,
+ num_heads,
+ window_size=self.window_size,
+ quant_config=quant_config,
+ prefix=f"{prefix}.attention")
+ self.intermediate = SwinIntermediate(config,
+ dim,
+ quant_config=quant_config,
+ prefix=f"{prefix}.intermediate")
+ self.output = SwinOutput(config,
+ dim,
+ quant_config=quant_config,
+ prefix=f"{prefix}.output")
+
+
+class SwinStage(nn.Module):
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ dim: int,
+ input_resolution: int,
+ depth: int,
+ num_heads: int,
+ drop_path: list[float],
+ downsample: Optional[SwinPatchMerging] = None,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.dim = dim
+ self.blocks = nn.ModuleList([
+ SwinLayer(config=config,
+ dim=dim,
+ input_resolution=input_resolution,
+ num_heads=num_heads,
+ drop_path_rate=drop_path[layer_idx],
+ shift_size=0 if
+ (layer_idx % 2 == 0) else config.window_size // 2,
+ quant_config=quant_config,
+ prefix=f"{prefix}.blocks.{layer_idx}")
+ for layer_idx in range(depth)
+ ])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution,
+ dim=dim,
+ norm_layer=nn.LayerNorm)
+ else:
+ self.downsample = None
+
+ self.pointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ input_dimensions: tuple[int, int],
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ always_partition: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ height, width = input_dimensions
+ for i, layer_module in enumerate(self.blocks):
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(hidden_states, input_dimensions,
+ layer_head_mask, output_attentions,
+ always_partition)
+
+ hidden_states = layer_outputs[0]
+
+ hidden_states_before_downsampling = hidden_states
+ if self.downsample is not None:
+ height_downsampled, width_downsampled = (height + 1) // 2, (width +
+ 1) // 2
+ output_dimensions = (height, width, height_downsampled,
+ width_downsampled)
+ hidden_states = self.downsample(hidden_states_before_downsampling,
+ input_dimensions)
+ else:
+ output_dimensions = (height, width, height, width)
+
+ stage_outputs = (hidden_states, hidden_states_before_downsampling,
+ output_dimensions)
+
+ if output_attentions:
+ stage_outputs += layer_outputs[1:]
+ return stage_outputs
+
+
+class SwinEncoder(nn.Module):
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ grid_size: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.num_layers = len(config.depths)
+ self.config = config
+ dpr = [
+ x.item() for x in torch.linspace(
+ 0, config.drop_path_rate, sum(config.depths), device="cpu")
+ ]
+ self.layers = nn.ModuleList([
+ SwinStage(config=config,
+ dim=int(config.embed_dim * 2**layer_idx),
+ input_resolution=(grid_size[0] // (2**layer_idx),
+ grid_size[1] // (2**layer_idx)),
+ depth=config.depths[layer_idx],
+ num_heads=config.num_heads[layer_idx],
+ drop_path=dpr[sum(config.depths[:layer_idx]
+ ):sum(config.depths[:layer_idx + 1])],
+ downsample=SwinPatchMerging if
+ (layer_idx < self.num_layers - 1) else None,
+ quant_config=quant_config,
+ prefix=f"{prefix}.layers.{layer_idx}")
+ for layer_idx in range(self.num_layers)
+ ])
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ input_dimensions: tuple[int, int],
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ always_partition: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ for i, layer_module in enumerate(self.layers):
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(hidden_states, input_dimensions,
+ layer_head_mask, output_attentions,
+ always_partition)
+
+ hidden_states = layer_outputs[0]
+ output_dimensions = layer_outputs[2]
+
+ input_dimensions = (output_dimensions[-2], output_dimensions[-1])
+
+ return hidden_states
+
+
+class SwinModel(nn.Module):
+ config_class: SwinConfig
+
+ def __init__(
+ self,
+ config: SwinConfig,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.num_layers = len(config.depths)
+ self.num_features = int(config.embed_dim * 2**(self.num_layers - 1))
+
+ self.embeddings = SwinEmbeddings(config)
+ self.encoder = SwinEncoder(config,
+ self.embeddings.patch_grid,
+ quant_config=quant_config,
+ prefix=f"{prefix}.encoder")
+
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ ) -> tuple[torch.Tensor]:
+ embedding_output, input_dimensions = self.embeddings(pixel_values)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ input_dimensions,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ )
+
+ return encoder_outputs
+
+ def load_weights(self, weights: Iterable[tuple[str,
+ torch.Tensor]]) -> set[str]:
+ stacked_params_mapping = [
+ ("qkv", "query", "q"),
+ ("qkv", "key", "k"),
+ ("qkv", "value", "v"),
+ ]
+ params_dict = dict(self.named_parameters())
+ loaded_params: set[str] = set()
+
+ for name, loaded_weight in weights:
+
+ for (param_name, weight_name, shard_id) in stacked_params_mapping:
+ if weight_name not in name:
+ continue
+ name = name.replace(weight_name, param_name)
+
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(param, loaded_weight, shard_id)
+ break
+ else:
+ param = params_dict[name]
+ weight_loader = getattr(param, "weight_loader",
+ default_weight_loader)
+ weight_loader(param, loaded_weight)
+ loaded_params.add(name)
+ return loaded_params
diff --git a/vllm/multimodal/profiling.py b/vllm/multimodal/profiling.py
index 2da9b4c72..ea2efbdd8 100644
--- a/vllm/multimodal/profiling.py
+++ b/vllm/multimodal/profiling.py
@@ -209,7 +209,7 @@ class MultiModalProfiler(Generic[_I]):
if processor.pad_dummy_encoder_prompt:
num_tokens_to_pad = max(total_len, seq_len) - total_len
encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
- # NOTE: Whisper allows total_len > seq_len.
+ # NOTE: Whisper and Donut allows total_len > seq_len.
elif total_len > seq_len and not envs.VLLM_USE_V1:
# `max_num_batched_tokens` is defined by `SchedulerConfig`
logger.warning_once(
diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py
index 69f8e531e..219857dc7 100644
--- a/vllm/v1/engine/processor.py
+++ b/vllm/v1/engine/processor.py
@@ -389,7 +389,7 @@ class Processor:
assert isinstance(mm_processor, EncDecMultiModalProcessor)
if mm_processor.pad_dummy_encoder_prompt:
- return # Skip encoder length check for Whisper
+ return # Skip encoder length check for Whisper and Donut
if model_config.is_multimodal_model:
suggestion = (