591 lines
22 KiB
Python
591 lines
22 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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from transformers import AutoModel, PretrainedConfig
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.linear import ReplicatedLinear
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from vllm.model_executor.layers.pooler import DispatchPooler
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.quantization.awq import AWQConfig
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from vllm.model_executor.models.internvl import (
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BaseInternVLDummyInputsBuilder,
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BaseInternVLMultiModalProcessor,
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BaseInternVLProcessingInfo,
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InternVLImageEmbeddingInputs,
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InternVLImageInputs,
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InternVLImagePixelInputs,
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)
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.siglip import SiglipVisionModel
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.processor import cached_image_processor_from_config
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from vllm.transformers_utils.processors.nemotron_vl import (
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LlamaNemotronNanoVLImageProcessor,
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LlamaNemotronNanoVLProcessor,
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LlamaNemotronVLEmbedImageProcessor,
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LlamaNemotronVLEmbedProcessor,
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)
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from vllm.transformers_utils.repo_utils import get_hf_file_to_dict
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from .interfaces import (
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MultiModalEmbeddings,
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SupportsCrossEncoding,
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SupportsLoRA,
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SupportsMultiModal,
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SupportsPP,
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)
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from .interfaces_base import VllmModelForPooling
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from .utils import (
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AutoWeightsLoader,
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WeightsMapper,
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init_vllm_registered_model,
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maybe_prefix,
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)
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class NemotronVLProcessingInfo(BaseInternVLProcessingInfo):
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"""Processing info for Nemotron VL models."""
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def get_image_processor(self, **kwargs: object):
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kwargs = self.ctx.get_merged_mm_kwargs(kwargs)
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orig_processor = cached_image_processor_from_config(
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self.ctx.model_config, **kwargs
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)
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return LlamaNemotronNanoVLImageProcessor(
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image_size=orig_processor.image_size,
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min_dynamic_patch=1,
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max_dynamic_patch=orig_processor.max_num_tiles,
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dynamic_image_size=True,
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use_thumbnail=orig_processor.use_thumbnail,
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)
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def get_hf_processor(self, **kwargs: object) -> LlamaNemotronNanoVLProcessor:
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config = self.get_hf_config()
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vision_config = config.vision_config
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image_processor = self.get_image_processor(**kwargs)
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image_size = image_processor.image_size
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patch_size = vision_config.patch_size
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downsample_ratio = config.downsample_ratio
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image_seq_length = int((image_size // patch_size) ** 2 * (downsample_ratio**2))
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return LlamaNemotronNanoVLProcessor(
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tokenizer=self.get_tokenizer(),
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image_processor=image_processor,
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image_seq_length=image_seq_length,
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)
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@MULTIMODAL_REGISTRY.register_processor(
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BaseInternVLMultiModalProcessor[NemotronVLProcessingInfo],
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info=NemotronVLProcessingInfo,
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dummy_inputs=BaseInternVLDummyInputsBuilder[NemotronVLProcessingInfo],
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)
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class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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if modality.startswith("image"):
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return "<image>"
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raise ValueError("Only image modality is supported")
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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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.model_config = vllm_config.model_config
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self.multimodal_config = multimodal_config
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self._patch_quant_config(config, quant_config)
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image_size = config.force_image_size or config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.patch_size = patch_size
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self.num_image_token = int(
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(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
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)
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self.downsample_ratio = config.downsample_ratio
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self.ps_version = config.ps_version
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with self._mark_tower_model(vllm_config, "image"):
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self.vision_model = self._init_vision_model(
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config,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "vision_model"),
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)
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self.mlp1 = self._init_mlp1(config)
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with self._mark_language_model(vllm_config):
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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.get_text_config(),
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.img_context_token_id = None
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self.visual_token_mask = None
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors
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)
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def _patch_quant_config(
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self, config: PretrainedConfig, quant_config: QuantizationConfig
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):
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# the awq models from OpenGVLab missing `modules_to_not_convert`
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# patch the quant_config to add `modules_to_not_convert` back
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if isinstance(quant_config, AWQConfig):
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text_config = config.get_text_config()
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llm_quant_config = getattr(text_config, "quantization_config", None)
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if (not quant_config.modules_to_not_convert) and (
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llm_quant_config is not None
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):
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quant_config.modules_to_not_convert.append("vision_model")
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def _init_vision_model(
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self,
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config: PretrainedConfig,
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quant_config: QuantizationConfig | None,
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*,
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prefix: str,
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):
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return AutoModel.from_config(
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config.vision_config,
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trust_remote_code=self.model_config.trust_remote_code,
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)
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def _init_mlp1(
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self,
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config: PretrainedConfig,
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vit_hidden_size: int | None = None,
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vision_projection_hidden_size: int | None = None,
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) -> nn.Module:
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if vit_hidden_size is None:
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vit_hidden_size = config.vit_hidden_size
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if vision_projection_hidden_size is None:
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vision_projection_hidden_size = config.projector_hidden_size
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llm_hidden_size = config.get_text_config().hidden_size
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return nn.Sequential(
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nn.LayerNorm(
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vit_hidden_size * int(1 / self.downsample_ratio) ** 2, bias=True
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),
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nn.Linear(
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vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
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vision_projection_hidden_size,
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bias=True,
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),
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nn.GELU(),
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nn.Linear(vision_projection_hidden_size, llm_hidden_size),
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)
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def pixel_shuffle(self, x, scale_factor=0.5):
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n, w, h, c = x.size()
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# N, W, H, C --> N, W, H * scale, C // scale
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x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
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# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
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x = x.permute(0, 2, 1, 3).contiguous()
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x = x.view(
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n,
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int(h * scale_factor),
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int(w * scale_factor),
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int(c / (scale_factor * scale_factor)),
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)
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if self.ps_version == "v1":
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pass
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else:
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x = x.permute(0, 2, 1, 3).contiguous()
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return x
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def _call_vision_model(self, pixel_values: torch.Tensor) -> torch.Tensor:
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"""Call vision model and return embeddings.
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Override this method in subclasses to handle different vision model
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interfaces (e.g., SigLIP vs C-RADIO).
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"""
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vit_embeds = self.vision_model(x=pixel_values).features
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return vit_embeds.to(dtype=torch.bfloat16)
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def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
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# https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1/blob/main/modeling.py#L177
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vit_embeds = self._call_vision_model(pixel_values)
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h = w = int(vit_embeds.shape[1] ** 0.5)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
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vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
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vit_embeds = self.mlp1(vit_embeds)
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return vit_embeds
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def _parse_and_validate_image_input(
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self, **kwargs: object
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) -> InternVLImageInputs | None:
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pixel_values_flat = kwargs.pop("pixel_values_flat", None)
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image_num_patches = kwargs.pop("image_num_patches", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values_flat is None and image_embeds is None:
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return None
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if image_embeds is not None:
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return InternVLImageEmbeddingInputs(
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type="image_embeds",
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data=image_embeds,
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)
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image_token_id = kwargs["image_token_id"]
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if isinstance(image_token_id, torch.Tensor):
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image_token_id = image_token_id.flatten().unique().item()
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assert isinstance(image_token_id, int)
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self.img_context_token_id = image_token_id
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if pixel_values_flat is not None:
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return InternVLImagePixelInputs(
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type="pixel_values",
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pixel_values_flat=pixel_values_flat,
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num_patches=image_num_patches,
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resolve_bindings={
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"h": self.config.force_image_size,
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"w": self.config.force_image_size,
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},
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)
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raise AssertionError("This line should be unreachable.")
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def _process_image_input(
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self,
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image_input: InternVLImageInputs,
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) -> tuple[torch.Tensor, ...]:
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if image_input["type"] == "image_embeds":
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return image_input["data"]
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image_embeds = self.extract_feature(image_input["pixel_values_flat"])
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num_patches = image_input["num_patches"]
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hidden_size = self.config.get_text_config().hidden_size
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# Only one image in the current batch
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if len(num_patches) == 1:
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return (image_embeds.view(-1, hidden_size),)
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# NOTE: Image embeddings are split into separate tensors for each image
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# by the size of each embedding.
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feature_size = image_embeds.shape[1]
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image_embeds = image_embeds.view(-1, hidden_size)
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image_feature_sizes = [
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num_patches * feature_size for num_patches in num_patches
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]
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return image_embeds.split(image_feature_sizes)
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def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
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modalities = {}
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# Preserve the order of modalities if there are multiple of them
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# from the order of kwargs.
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for input_key in kwargs:
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if (
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input_key in ("pixel_values_flat", "image_embeds")
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and "images" not in modalities
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):
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modalities["images"] = self._parse_and_validate_image_input(**kwargs)
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return modalities
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def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
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self.visual_token_mask = None
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def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
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modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
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if not modalities:
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return []
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# The result multimodal_embeddings is tuple of tensors, with each
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# tensor corresponding to a multimodal data item (image).
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multimodal_embeddings: tuple[torch.Tensor, ...] = ()
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# NOTE: It is important to iterate over the keys in this dictionary
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# to preserve the order of the modalities.
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for modality in modalities:
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if modality == "images":
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image_input = modalities["images"]
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image_embeddings = self._process_image_input(image_input)
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multimodal_embeddings += tuple(image_embeddings)
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return multimodal_embeddings
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def embed_input_ids(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: MultiModalEmbeddings | None = None,
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*,
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is_multimodal: torch.Tensor | None = None,
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) -> torch.Tensor:
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if multimodal_embeddings is not None and len(multimodal_embeddings) > 0:
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self._set_visual_token_mask(input_ids)
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# This is to satisfy the type checker for each overload
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if multimodal_embeddings is None or is_multimodal is None:
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return super().embed_input_ids(input_ids)
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return super().embed_input_ids(
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input_ids,
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multimodal_embeddings=multimodal_embeddings,
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is_multimodal=is_multimodal,
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)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs: object,
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) -> IntermediateTensors:
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if intermediate_tensors is not None:
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inputs_embeds = None
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forward_kwargs = {
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"input_ids": input_ids,
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"positions": positions,
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"intermediate_tensors": intermediate_tensors,
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"inputs_embeds": inputs_embeds,
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}
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# Only required if the model is mono-architecture
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if self.visual_token_mask is not None:
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forward_kwargs.update({"visual_token_mask": self.visual_token_mask})
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self.visual_token_mask = None
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hidden_states = self.language_model.model(**forward_kwargs)
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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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) -> torch.Tensor | None:
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return self.language_model.compute_logits(hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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## Ignore registered_buffers
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## see https://huggingface.co/nvidia/C-RADIOv2-H/blob/main/input_conditioner.py#L28 # noqa: E501
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skip_substrs = ["norm_mean", "norm_std"]
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loader = AutoWeightsLoader(self, skip_substrs=skip_substrs)
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return loader.load_weights(weights)
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def get_mm_mapping(self) -> MultiModelKeys:
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"""
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Get the module prefix in multimodal models
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"""
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return MultiModelKeys.from_string_field(
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language_model="language_model",
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connector="mlp1",
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tower_model="vision_model",
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)
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# --------------------------------------------------------
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# LlamaNemotronVL Embedding Model (nvidia/llama-nemotron-embed-vl-1b-v2)
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# Extends LlamaNemotronVLChatModel for embedding/pooling tasks:
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# - SigLIP vision encoder (instead of C-RADIO)
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# - Bidirectional (non-causal) LLaMA language model
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# - Pooler output instead of generative logits
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# --------------------------------------------------------
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class LlamaNemotronVLEmbedProcessingInfo(BaseInternVLProcessingInfo):
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"""Processing info for LlamaNemotronVL embedding model."""
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def get_image_processor(self, **kwargs):
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model_config = self.ctx.model_config
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config = self.get_hf_config()
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processor_config = (
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get_hf_file_to_dict(
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"processor_config.json",
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model_config.model,
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model_config.revision,
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)
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or {}
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)
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min_dynamic_patch = processor_config.get(
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"min_input_tiles",
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getattr(config, "min_dynamic_patch", 1),
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)
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max_dynamic_patch = processor_config.get(
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"max_input_tiles",
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getattr(config, "max_dynamic_patch", 1),
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)
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dynamic_image_size = processor_config.get(
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"dynamic_image_size",
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getattr(config, "dynamic_image_size", True),
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)
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kwargs = self.ctx.get_merged_mm_kwargs(kwargs)
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kwargs.setdefault("image_size", config.force_image_size)
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kwargs.setdefault("min_dynamic_patch", min_dynamic_patch)
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kwargs.setdefault("max_dynamic_patch", max_dynamic_patch)
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kwargs.setdefault("dynamic_image_size", dynamic_image_size)
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kwargs.setdefault("use_thumbnail", True)
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return LlamaNemotronVLEmbedImageProcessor(**kwargs)
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def get_hf_processor(self, **kwargs: object) -> LlamaNemotronVLEmbedProcessor:
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config = self.get_hf_config()
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vision_config = config.vision_config
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image_processor = self.get_image_processor(**kwargs)
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image_size = image_processor.image_size
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patch_size = vision_config.patch_size
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downsample_ratio = config.downsample_ratio
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image_seq_length = int((image_size // patch_size) ** 2 * (downsample_ratio**2))
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return LlamaNemotronVLEmbedProcessor(
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tokenizer=self.get_tokenizer(),
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image_processor=image_processor,
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image_seq_length=image_seq_length,
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)
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@MULTIMODAL_REGISTRY.register_processor(
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BaseInternVLMultiModalProcessor[LlamaNemotronVLEmbedProcessingInfo],
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info=LlamaNemotronVLEmbedProcessingInfo,
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dummy_inputs=BaseInternVLDummyInputsBuilder[LlamaNemotronVLEmbedProcessingInfo],
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)
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class LlamaNemotronVLForEmbedding(LlamaNemotronVLChatModel, VllmModelForPooling):
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"""
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LlamaNemotronVL model for embeddings.
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Inherits from LlamaNemotronVLChatModel and specializes it for embedding tasks:
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- Uses SigLIP vision encoder instead of C-RADIO
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- Uses bidirectional LLaMA (via llm_config) instead of causal LLaMA
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- Adds pooler for embedding output instead of generating logits
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"""
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is_pooling_model = True
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# Weight mapping from checkpoint format to vLLM format
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# Different from parent class due to different vision model structure
|
|
weight_mapper = WeightsMapper(
|
|
orig_to_new_prefix={
|
|
# Language model mapping
|
|
"language_model.layers.": "language_model.model.layers.",
|
|
"language_model.embed_tokens.": "language_model.model.embed_tokens.",
|
|
"language_model.norm.": "language_model.model.norm.",
|
|
# Vision model mapping (SiglipVisionModel has nested vision_model)
|
|
"vision_model.encoder.": "vision_model.vision_model.encoder.",
|
|
"vision_model.embeddings.": "vision_model.vision_model.embeddings.",
|
|
"vision_model.post_layernorm.": "vision_model.vision_model.post_layernorm.",
|
|
}
|
|
)
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
|
|
# Override: get img_context_token_id from config (parent sets None)
|
|
self.img_context_token_id = getattr(config, "img_context_token_id", None)
|
|
|
|
# Initialize pooler for embedding output
|
|
pooler_config = vllm_config.model_config.pooler_config
|
|
assert pooler_config is not None
|
|
self.pooler = DispatchPooler.for_embedding(pooler_config)
|
|
|
|
def _init_vision_model(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config,
|
|
*,
|
|
prefix: str,
|
|
) -> nn.Module:
|
|
"""Override to use SigLIP instead of C-RADIO."""
|
|
return SiglipVisionModel(
|
|
config.vision_config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
use_head=False,
|
|
)
|
|
|
|
def _init_mlp1(self, config: PretrainedConfig) -> nn.Module:
|
|
"""Override to use different MLP structure for embedding model."""
|
|
return super()._init_mlp1(
|
|
config,
|
|
vit_hidden_size=config.vision_config.hidden_size,
|
|
vision_projection_hidden_size=config.get_text_config().hidden_size,
|
|
)
|
|
|
|
def _call_vision_model(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
|
"""Override to handle SigLIP interface."""
|
|
return self.vision_model(pixel_values)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
"""Override to use different weight mapping for SigLIP."""
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights, mapper=self.weight_mapper)
|
|
|
|
|
|
class LlamaNemotronVLForSequenceClassification(
|
|
LlamaNemotronVLForEmbedding, SupportsCrossEncoding
|
|
):
|
|
"""LlamaNemotronVL model variant for sequence classification / reranking."""
|
|
|
|
# Reranker checkpoint places base model weights under `model.*`,
|
|
# while `score.*` remains at the top level.
|
|
weight_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""}) | (
|
|
LlamaNemotronVLForEmbedding.weight_mapper
|
|
)
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
text_config = vllm_config.model_config.hf_config.get_text_config()
|
|
model_config = vllm_config.model_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.score = ReplicatedLinear(
|
|
model_config.get_hidden_size(),
|
|
text_config.num_labels,
|
|
bias=False,
|
|
params_dtype=model_config.head_dtype,
|
|
quant_config=quant_config,
|
|
return_bias=False,
|
|
prefix=maybe_prefix(prefix, "score"),
|
|
)
|
|
|
|
pooler_config = model_config.pooler_config
|
|
assert pooler_config is not None
|
|
self.pooler = DispatchPooler.for_seq_cls(pooler_config, classifier=self.score)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loaded_weights = super().load_weights(weights)
|
|
|
|
# reranker checkpoint omits the inner LM seq-cls head
|
|
# (`language_model.score.*`). It is unused by this outer model, but
|
|
# the default loader expects all parameters to be initialized.
|
|
for name, param in self.named_parameters():
|
|
if not name.startswith("language_model.score.") or name in loaded_weights:
|
|
continue
|
|
|
|
if name.endswith(".weight"):
|
|
torch.nn.init.kaiming_uniform_(param, a=math.sqrt(5))
|
|
elif name.endswith(".bias"):
|
|
torch.nn.init.zeros_(param)
|
|
else:
|
|
torch.nn.init.normal_(param, mean=0.0, std=0.02)
|
|
|
|
loaded_weights.add(name)
|
|
|
|
return loaded_weights
|