init
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398
model_executor/models/paligemma.py
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398
model_executor/models/paligemma.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Literal, Optional, TypedDict, Union
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import torch
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from torch import nn
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from transformers import BatchFeature, PaliGemmaConfig
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalInputs, MultiModalKwargs)
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from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
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MultiModalDataItems)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptIndexTargets,
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PromptInsertion, PromptUpdate,
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PromptUpdateDetails)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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from .vision import get_vision_encoder_info
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logger = init_logger(__name__)
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class PaliGemmaImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, num_channels, height, width)`"""
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class PaliGemmaImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs,
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PaliGemmaImageEmbeddingInputs]
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class PaliGemmaMultiModalProjector(nn.Module):
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def __init__(self, vision_hidden_size: int, projection_dim: int):
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super().__init__()
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self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True)
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states = self.linear(image_features)
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return hidden_states
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class PaliGemmaProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(PaliGemmaConfig)
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def get_vision_encoder_info(self):
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return get_vision_encoder_info(self.get_hf_config())
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": 1}
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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) -> int:
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vision_encoder_info = self.get_vision_encoder_info()
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return vision_encoder_info.get_num_image_tokens(
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image_width=image_width,
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image_height=image_height,
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)
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class PaliGemmaDummyInputsBuilder(
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BaseDummyInputsBuilder[PaliGemmaProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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return ""
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> MultiModalDataDict:
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hf_config = self.info.get_hf_config()
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vision_config = hf_config.vision_config
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max_image_size = vision_config.image_size
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num_images = mm_counts.get("image", 0)
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return {
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"image":
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self._get_dummy_images(width=max_image_size,
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height=max_image_size,
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num_images=num_images)
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}
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class PaliGemmaMultiModalProcessor(
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BaseMultiModalProcessor[PaliGemmaProcessingInfo]):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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) -> BatchFeature:
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tokenizer = self.info.get_tokenizer()
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if not mm_data:
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prompt_ids = tokenizer.encode(prompt)
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return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
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return super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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)
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(pixel_values=MultiModalFieldConfig.batched("image"))
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargs,
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) -> Sequence[PromptUpdate]:
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hf_config = self.info.get_hf_config()
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image_token_id = hf_config.image_token_index
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tokenizer = self.info.get_tokenizer()
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bos_token_id = tokenizer.bos_token_id
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assert isinstance(bos_token_id, int)
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def get_insertion(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems))
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if isinstance(images, ImageEmbeddingItems):
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num_image_tokens = images.get_feature_size(item_idx)
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else:
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image_size = images.get_image_size(item_idx)
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num_image_tokens = self.info.get_num_image_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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)
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image_tokens = [image_token_id] * num_image_tokens
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return PromptUpdateDetails.select_token_id(
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image_tokens + [bos_token_id],
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embed_token_id=image_token_id,
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)
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# Paligemma 1 and 2 have different tokenizer.add_bos_token
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# Insert <image>*n + <bos> after <bos> for Paligemma 1
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# Insert <image>*n + <bos> for Paligemma 2
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return [
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PromptInsertion(
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modality="image",
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target=PromptIndexTargets.prefix(
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[bos_token_id] if tokenizer.add_bos_token else []),
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insertion=get_insertion,
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)
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]
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def apply(
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self,
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prompt: Union[str, list[int]],
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mm_data: MultiModalDataDict,
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hf_processor_mm_kwargs: Mapping[str, object],
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return_mm_hashes: bool = False,
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) -> MultiModalInputs:
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mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
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return_mm_hashes)
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prompt_token_ids = mm_inputs["prompt_token_ids"]
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tokenizer = self.info.get_tokenizer()
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newline_prompt = "\n"
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newline_token_id = tokenizer.encode(newline_prompt)[-1] # 108
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# Force to add newline at the end of prompt for paligemma's format
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# This step can NOT be replacemented by current PromptUpdate methods
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if len(prompt_token_ids) and prompt_token_ids[-1] != newline_token_id:
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prompt_token_ids.append(newline_token_id)
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mm_inputs["prompt_token_ids"] = prompt_token_ids
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mm_inputs["prompt"] += newline_prompt
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return mm_inputs
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@MULTIMODAL_REGISTRY.register_processor(
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PaliGemmaMultiModalProcessor,
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info=PaliGemmaProcessingInfo,
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dummy_inputs=PaliGemmaDummyInputsBuilder)
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class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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self.vision_tower = SiglipVisionModel(config.vision_config,
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quant_config,
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prefix=maybe_prefix(
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prefix, "vision_tower"))
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self.multi_modal_projector = PaliGemmaMultiModalProjector(
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vision_hidden_size=config.vision_config.hidden_size,
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projection_dim=config.vision_config.projection_dim)
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self.quant_config = quant_config
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if config.text_config.model_type == "gemma":
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config.text_config.architectures = ["GemmaForCausalLM"]
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else:
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config.text_config.architectures = ["Gemma2ForCausalLM"]
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.language_model.logits_processor.scale *= logit_scale
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors)
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def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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actual_dims = tuple(data.shape[1:])
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if actual_dims != expected_dims:
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expected_expr = ("batch_size", *map(str, expected_dims))
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raise ValueError(
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f"The expected shape of pixel values is {expected_expr}. "
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f"You supplied {tuple(data.shape)}.")
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return data
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[PaliGemmaImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None:
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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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pixel_values = flatten_bn(pixel_values, concat=True)
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return PaliGemmaImagePixelInputs(
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type="pixel_values",
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data=self._validate_pixel_values(pixel_values),
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)
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if image_embeds is not None:
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if not isinstance(image_embeds, torch.Tensor):
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raise ValueError("Incorrect type of image embeddings. "
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f"Got type: {type(image_embeds)}")
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image_embeds = flatten_bn(image_embeds, concat=True)
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return PaliGemmaImageEmbeddingInputs(
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type="image_embeds",
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data=image_embeds,
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)
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raise AssertionError("This line should be unreachable.")
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def _image_pixels_to_features(
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self,
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vision_tower: SiglipVisionModel,
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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target_dtype = vision_tower.get_input_embeddings().weight.dtype
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image_features = vision_tower(pixel_values.to(dtype=target_dtype))
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return image_features
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def _process_image_input(
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self,
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image_input: PaliGemmaImageInputs,
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) -> torch.Tensor:
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if image_input["type"] == "image_embeds":
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return image_input["data"]
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assert self.vision_tower is not None
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pixel_values = image_input["data"]
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image_features = self._image_pixels_to_features(
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self.vision_tower,
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pixel_values,
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)
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return self.multi_modal_projector(image_features)
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def get_language_model(self) -> torch.nn.Module:
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return self.language_model
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def get_multimodal_embeddings(
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self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is None:
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return None
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vision_embeddings = self._process_image_input(image_input)
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa
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vision_embeddings = vision_embeddings * (self.config.hidden_size**-0.5)
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return vision_embeddings
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
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) -> torch.Tensor:
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inputs_embeds = self.language_model.get_input_embeddings(input_ids)
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if multimodal_embeddings is not None:
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inputs_embeds = merge_multimodal_embeddings(
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input_ids, inputs_embeds, multimodal_embeddings,
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self.config.image_token_index)
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return inputs_embeds
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def forward(self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs: object) -> IntermediateTensors:
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if intermediate_tensors is not None:
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inputs_embeds = None
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# NOTE: In v1, inputs_embeds is always generated at model runner, this
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# condition is for v0 compatibility.
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elif inputs_embeds is None:
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vision_embeddings = self.get_multimodal_embeddings(**kwargs)
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inputs_embeds = self.get_input_embeddings(input_ids,
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vision_embeddings)
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input_ids = None
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hidden_states = self.language_model.model(input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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return self.language_model.compute_logits(hidden_states,
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sampling_metadata)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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