wehub-resource-sync
529 lines
18 KiB
Python
529 lines
18 KiB
Python
# 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 Annotated, Literal
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import torch
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import torch.nn as nn
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from transformers import BatchFeature
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.inputs import MultiModalDataDict
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.models.interfaces import (
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MultiModalEmbeddings,
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SupportsMultiModal,
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SupportsPP,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargsItems
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from vllm.multimodal.parse import (
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ImageEmbeddingItems,
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ImageProcessorItems,
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ImageSize,
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MultiModalDataItems,
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)
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from vllm.multimodal.processing import (
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BaseDummyInputsBuilder,
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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InputProcessingContext,
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PromptIndexTargets,
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PromptInsertion,
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PromptUpdate,
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PromptUpdateDetails,
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)
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs import OpenVLAConfig
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from vllm.transformers_utils.processors.openvla import (
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OpenVLAImageProcessor,
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OpenVLAProcessor,
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)
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .module_mapping import MultiModelKeys
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from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
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# openvla/openvla-7b uses 224x224 images with ViT patch size 14, yielding a
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# 16x16 image-token grid.
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_OPENVLA_IMAGE_SIZE = 224
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_OPENVLA_PATCH_SIZE = 14
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_OPENVLA_TIMM_MODEL_IDS = (
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"vit_large_patch14_reg4_dinov2.lvd142m",
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"vit_so400m_patch14_siglip_224",
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)
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_OPENVLA_TIMM_OVERRIDE_ACT_LAYERS = (None, None)
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_OPENVLA_IMAGE_SIZES = (_OPENVLA_IMAGE_SIZE, _OPENVLA_IMAGE_SIZE)
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def _get_num_image_tokens(image_size: int) -> int:
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return (image_size // _OPENVLA_PATCH_SIZE) ** 2
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class OpenVLAImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- c: Number of channels (6)
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- h: Height
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- w: Width
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"""
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type: Literal["pixel_values"] = "pixel_values"
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data: Annotated[torch.Tensor, TensorShape("bn", 6, "h", "w")]
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class PrismaticVisionBackbone(nn.Module):
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"""OpenVLA's fused DINOv2 + SigLIP vision backbone."""
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def __init__(
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self,
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*,
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image_sizes: Sequence[int],
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timm_model_ids: Sequence[str],
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timm_override_act_layers: Sequence[str | None],
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use_fused_vision_backbone: bool,
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) -> None:
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super().__init__()
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if not use_fused_vision_backbone:
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raise ValueError(
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"OpenVLA currently supports only the fused DINOv2 + SigLIP "
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"vision backbone."
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)
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if tuple(image_sizes) != _OPENVLA_IMAGE_SIZES:
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raise ValueError(
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"OpenVLA currently supports only 224x224 image inputs, "
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f"got image_sizes={list(image_sizes)}."
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)
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if tuple(timm_model_ids) != _OPENVLA_TIMM_MODEL_IDS:
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raise ValueError(
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"OpenVLA currently supports only the dinosiglip-vit-so-224px "
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"vision backbone, got "
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f"timm_model_ids={list(timm_model_ids)}."
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)
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if tuple(timm_override_act_layers) != _OPENVLA_TIMM_OVERRIDE_ACT_LAYERS:
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raise ValueError(
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"OpenVLA currently supports only the default timm activation "
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"layers, got "
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f"timm_override_act_layers={list(timm_override_act_layers)}."
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)
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self.image_size = image_sizes[0]
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self.use_fused_vision_backbone = use_fused_vision_backbone
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self.embed_dim = 2176 if use_fused_vision_backbone else 1024
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try:
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import timm
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except ImportError as e:
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raise ImportError(
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"Please install timm to use OpenVLA. OpenVLA verification "
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"used timm==0.9.10."
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) from e
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self.dinov2_featurizer = timm.create_model(
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timm_model_ids[0],
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pretrained=False,
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num_classes=0,
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img_size=self.image_size,
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act_layer=timm_override_act_layers[0],
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)
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self.siglip_featurizer = (
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timm.create_model(
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timm_model_ids[1],
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pretrained=False,
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num_classes=0,
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img_size=self.image_size,
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act_layer=timm_override_act_layers[1],
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)
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if use_fused_vision_backbone
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else None
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)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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if self.dinov2_featurizer is None:
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raise RuntimeError("OpenVLA vision backbone is not initialized.")
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if self.use_fused_vision_backbone and pixel_values.shape[1] != 6:
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raise ValueError(
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"OpenVLA fused DINOv2 + SigLIP backbone expects 6-channel "
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"image inputs: 3 DINOv2-normalized channels followed by 3 "
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"SigLIP-normalized channels, "
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f"got {pixel_values.shape[1]} channels."
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)
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dinov2_pixels = pixel_values[:, :3]
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num_dinov2_blocks = len(self.dinov2_featurizer.blocks)
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dinov2_features = self.dinov2_featurizer.get_intermediate_layers(
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dinov2_pixels, n={num_dinov2_blocks - 2}
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)[0]
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if self.siglip_featurizer is not None:
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siglip_pixels = pixel_values[:, 3:]
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num_siglip_blocks = len(self.siglip_featurizer.blocks)
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siglip_features = self.siglip_featurizer.get_intermediate_layers(
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siglip_pixels, n={num_siglip_blocks - 2}
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)[0]
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return torch.cat([dinov2_features, siglip_features], dim=-1)
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return dinov2_features
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class PrismaticProjector(nn.Module):
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"""Project Prismatic vision features into the language-model hidden size."""
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def __init__(
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self,
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*,
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vision_dim: int,
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text_dim: int,
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use_fused_vision_backbone: bool,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.use_fused_vision_backbone = use_fused_vision_backbone
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if use_fused_vision_backbone:
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intermediate_dim = 4 * vision_dim
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self.fc1 = ColumnParallelLinear(
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vision_dim,
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intermediate_dim,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1",
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)
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self.act_fn1 = get_act_fn("gelu")
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self.fc2 = RowParallelLinear(
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intermediate_dim,
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text_dim,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2",
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)
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self.act_fn2 = get_act_fn("gelu")
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self.fc3 = ReplicatedLinear(
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text_dim,
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text_dim,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc3",
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)
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else:
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self.fc1 = ColumnParallelLinear(
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vision_dim,
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text_dim,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1",
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)
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self.act_fn1 = get_act_fn("gelu")
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self.fc2 = RowParallelLinear(
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text_dim,
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text_dim,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2",
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)
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc1(image_features)
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hidden_states = self.act_fn1(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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if self.use_fused_vision_backbone:
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hidden_states = self.act_fn2(hidden_states)
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hidden_states, _ = self.fc3(hidden_states)
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return hidden_states
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class OpenVLAProcessingInfo(BaseProcessingInfo):
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def __init__(self, ctx: InputProcessingContext) -> None:
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super().__init__(ctx)
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self.hf_processor = OpenVLAProcessor(
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image_processor=OpenVLAImageProcessor(
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image_size=self.get_hf_config().image_sizes[0],
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),
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tokenizer=self.get_tokenizer(),
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)
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def get_hf_config(self) -> OpenVLAConfig:
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return self.ctx.get_hf_config(OpenVLAConfig)
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def get_hf_processor(self, **kwargs: object) -> OpenVLAProcessor:
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return self.hf_processor
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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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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image_size = self.get_hf_config().image_sizes[0]
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return _get_num_image_tokens(image_size)
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def get_image_size_with_most_features(self) -> ImageSize:
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image_size = self.get_hf_config().image_sizes[0]
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return ImageSize(width=image_size, height=image_size)
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def get_mm_max_tokens_per_item(
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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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) -> Mapping[str, int] | None:
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image_size = self.get_hf_config().image_sizes[0]
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return {"image": _get_num_image_tokens(image_size)}
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class OpenVLADummyInputsBuilder(BaseDummyInputsBuilder[OpenVLAProcessingInfo]):
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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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mm_options: Mapping[str, BaseDummyOptions],
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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image_overrides = mm_options.get("image")
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image_size = self.info.get_image_size_with_most_features()
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return {
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"image": self._get_dummy_images(
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width=image_size.width,
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height=image_size.height,
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num_images=num_images,
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overrides=image_overrides,
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)
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}
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class OpenVLAMultiModalProcessor(BaseMultiModalProcessor[OpenVLAProcessingInfo]):
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"""Processor contract for OpenVLA image inputs.
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OpenVLA feeds the same RGB image to DINOv2 and SigLIP after different
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normalizations. The processor exposes this as one 6-channel tensor:
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channels 0-2 are DINOv2-normalized and channels 3-5 are SigLIP-normalized.
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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 _hf_processor_applies_updates(
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self,
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prompt_text: str,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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tokenization_kwargs: Mapping[str, object],
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) -> bool:
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return False
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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: MultiModalKwargsItems,
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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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def get_insertion(item_idx: int) -> PromptUpdateDetails[list[int]]:
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems)
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)
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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,
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embed_token_id=image_token_id,
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)
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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 bos_token_id is not None else []
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),
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insertion=get_insertion,
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)
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]
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@MULTIMODAL_REGISTRY.register_processor(
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OpenVLAMultiModalProcessor,
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info=OpenVLAProcessingInfo,
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dummy_inputs=OpenVLADummyInputsBuilder,
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)
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class OpenVLAForActionPrediction(nn.Module, SupportsMultiModal, SupportsPP):
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"""OpenVLA wrapper with vLLM language-model execution wired in."""
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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 None
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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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self.config = config
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self.multimodal_config = vllm_config.model_config.multimodal_config
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self.image_token_id = config.image_token_index
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self.n_action_bins = config.n_action_bins
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self.num_patches = _get_num_image_tokens(config.image_sizes[0])
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with self._mark_tower_model(vllm_config, "image"):
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self.vision_backbone = PrismaticVisionBackbone(
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image_sizes=config.image_sizes,
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timm_model_ids=config.timm_model_ids,
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timm_override_act_layers=config.timm_override_act_layers,
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use_fused_vision_backbone=config.use_fused_vision_backbone,
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)
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self.projector = PrismaticProjector(
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vision_dim=self.vision_backbone.embed_dim,
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text_dim=config.text_config.hidden_size,
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use_fused_vision_backbone=config.use_fused_vision_backbone,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "projector"),
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)
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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.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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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 get_language_model(self) -> nn.Module:
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return self.language_model
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def _parse_and_validate_image_input(
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self,
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**kwargs: object,
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) -> OpenVLAImagePixelInputs | None:
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pixel_values = kwargs.pop("pixel_values", None)
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if pixel_values is None:
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return None
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return OpenVLAImagePixelInputs(
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type="pixel_values",
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data=pixel_values,
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resolve_bindings={
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"h": self.config.image_sizes[0],
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"w": self.config.image_sizes[0],
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},
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)
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def _process_image_input(
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self,
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image_input: OpenVLAImagePixelInputs,
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) -> torch.Tensor:
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if self.vision_backbone.dinov2_featurizer is None:
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raise RuntimeError("OpenVLA vision backbone is not initialized.")
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pixel_values = image_input["data"].to(
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dtype=self.vision_backbone.dinov2_featurizer.patch_embed.proj.weight.dtype
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)
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vision_features = self.vision_backbone(pixel_values)
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return self.projector(vision_features)
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def embed_multimodal(self, **kwargs: object) -> 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 []
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return self._process_image_input(image_input)
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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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) -> torch.Tensor | IntermediateTensors:
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if intermediate_tensors is not None:
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inputs_embeds = None
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return self.language_model.model(
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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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)
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def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
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return self.language_model.compute_logits(hidden_states)
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def get_mm_mapping(self) -> MultiModelKeys:
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return MultiModelKeys.from_string_field(
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language_model="language_model",
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connector="projector",
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tower_model="vision_backbone",
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)
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def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
|
|
return num_image_tokens
|
|
|
|
def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
|
|
return num_vision_tokens
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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|
def maybe_rename_vision_weights(
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|
weights: Iterable[tuple[str, torch.Tensor]],
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|
) -> Iterable[tuple[str, torch.Tensor]]:
|
|
for name, weight in weights:
|
|
if name.startswith("vision_backbone.featurizer."):
|
|
name = name.replace(
|
|
"vision_backbone.featurizer.",
|
|
"vision_backbone.dinov2_featurizer.",
|
|
1,
|
|
)
|
|
elif name.startswith("vision_backbone.fused_featurizer."):
|
|
name = name.replace(
|
|
"vision_backbone.fused_featurizer.",
|
|
"vision_backbone.siglip_featurizer.",
|
|
1,
|
|
)
|
|
# HF uses .scale_factor, timm uses .gamma
|
|
if ".ls1.scale_factor" in name or ".ls2.scale_factor" in name:
|
|
name = name.replace(".scale_factor", ".gamma")
|
|
yield name, weight
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(maybe_rename_vision_weights(weights))
|