wehub-resource-sync
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""OpenVLA configuration support.
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OpenVLA checkpoints use a custom ``model_type`` and nest the language model
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configuration under ``text_config``. This shim lets vLLM load the checkpoint
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configuration without executing Hugging Face remote code.
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"""
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from typing import Any
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from transformers import LlamaConfig, PretrainedConfig
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class OpenVLAConfig(PretrainedConfig):
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"""Configuration class for OpenVLA models."""
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model_type = "openvla"
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def __init__(
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self,
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timm_model_ids: list[str] | None = None,
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timm_override_act_layers: list[str | None] | None = None,
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image_sizes: list[int] | None = None,
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use_fused_vision_backbone: bool = True,
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image_token_index: int = 32000,
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n_action_bins: int = 256,
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text_config: dict[str, Any] | LlamaConfig | None = None,
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**kwargs: Any,
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) -> None:
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kwargs.setdefault("architectures", ["OpenVLAForActionPrediction"])
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super().__init__(**kwargs)
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self.timm_model_ids = timm_model_ids or [
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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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self.timm_override_act_layers = timm_override_act_layers or [None, None]
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self.image_sizes = image_sizes or [224, 224]
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self.use_fused_vision_backbone = use_fused_vision_backbone
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self.image_token_index = image_token_index
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self.n_action_bins = n_action_bins
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if text_config is None:
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text_config = LlamaConfig(architectures=["LlamaForCausalLM"])
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elif isinstance(text_config, dict):
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text_config = text_config.copy()
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text_config.setdefault("architectures", ["LlamaForCausalLM"])
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text_config = LlamaConfig(**text_config)
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self.text_config = text_config
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