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open-vla-swxm

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Safetensors
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openvla
feature-extraction
robotics
vla
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  1. README.md +99 -0
  2. added_tokens.json +3 -0
  3. config.json +3294 -0
  4. configuration_prismatic.py +140 -0
  5. dataset_statistics.json +127 -0
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  10. model-00004-of-00004.safetensors +3 -0
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  12. modeling_prismatic.py +561 -0
  13. preprocessor_config.json +114 -0
  14. processing_prismatic.py +257 -0
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+ ---
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+ library_name: transformers
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+ tags:
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+ - robotics
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+ - vla
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+ - image-text-to-text
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+ - multimodal
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+ - pretraining
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+ license: mit
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+ language:
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+ - en
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+ pipeline_tag: image-text-to-text
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+ ---
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+
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+ # OpenVLA 7B
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+
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+ OpenVLA 7B (`openvla-7b`) is an open vision-language-action model trained on 970K robot manipulation episodes from the [Open X-Embodiment](https://robotics-transformer-x.github.io/) dataset.
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+ The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning.
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+
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+ All OpenVLA checkpoints, as well as our [training codebase](https://github.com/openvla/openvla) are released under an MIT License.
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+
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+ For full details, please read [our paper](https://arxiv.org/abs/2406.09246) and see [our project page](https://openvla.github.io/).
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+
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+ ## Model Summary
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+
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+ - **Developed by:** The OpenVLA team consisting of researchers from Stanford, UC Berkeley, Google Deepmind, and the Toyota Research Institute.
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+ - **Model type:** Vision-language-action (language, image => robot actions)
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+ - **Language(s) (NLP):** en
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+ - **License:** MIT
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+ - **Finetuned from:** [`prism-dinosiglip-224px`](https://github.com/TRI-ML/prismatic-vlms), a VLM trained from:
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+ + **Vision Backbone**: DINOv2 ViT-L/14 and SigLIP ViT-So400M/14
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+ + **Language Model**: Llama-2
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+ - **Pretraining Dataset:** [Open X-Embodiment](https://robotics-transformer-x.github.io/) -- specific component datasets can be found [here](https://github.com/openvla/openvla).
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+ - **Repository:** [https://github.com/openvla/openvla](https://github.com/openvla/openvla)
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+ - **Paper:** [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/abs/2406.09246)
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+ - **Project Page & Videos:** [https://openvla.github.io/](https://openvla.github.io/)
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+
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+ ## Uses
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+
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+ OpenVLA models take a language instruction and a camera image of a robot workspace as input, and predict (normalized) robot actions consisting of 7-DoF end-effector deltas
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+ of the form (x, y, z, roll, pitch, yaw, gripper). To execute on an actual robot platform, actions need to be *un-normalized* subject to statistics computed on a per-robot,
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+ per-dataset basis. See [our repository](https://github.com/openvla/openvla) for more information.
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+
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+ OpenVLA models can be used zero-shot to control robots for specific combinations of embodiments and domains seen in the Open-X pretraining mixture (e.g., for
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+ [BridgeV2 environments with a Widow-X robot](https://rail-berkeley.github.io/bridgedata/)). They can also be efficiently *fine-tuned* for new tasks and robot setups
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+ given minimal demonstration data; [see here](https://github.com/openvla/openvla/blob/main/scripts/finetune.py).
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+
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+ **Out-of-Scope:** OpenVLA models do not zero-shot generalize to new (unseen) robot embodiments, or setups that are not represented in the pretraining mix; in these cases,
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+ we suggest collecting a dataset of demonstrations on the desired setup, and fine-tuning OpenVLA models instead.
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+
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+ ## Getting Started
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+
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+ OpenVLA 7B can be used to control multiple robots for domains represented in the pretraining mixture out-of-the-box. For example,
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+ here is an example for loading `openvla-7b` for zero-shot instruction following in the [BridgeV2 environments] with a Widow-X robot:
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+
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+ ```python
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+ # Install minimal dependencies (`torch`, `transformers`, `timm`, `tokenizers`, ...)
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+ # > pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
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+ from transformers import AutoModelForVision2Seq, AutoProcessor
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+ from PIL import Image
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+
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+ import torch
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+
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+ # Load Processor & VLA
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+ processor = AutoProcessor.from_pretrained("openvla/openvla-7b", trust_remote_code=True)
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+ vla = AutoModelForVision2Seq.from_pretrained(
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+ "openvla/openvla-7b",
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+ attn_implementation="flash_attention_2", # [Optional] Requires `flash_attn`
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+ torch_dtype=torch.bfloat16,
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+ low_cpu_mem_usage=True,
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+ trust_remote_code=True
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+ ).to("cuda:0")
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+
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+ # Grab image input & format prompt
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+ image: Image.Image = get_from_camera(...)
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+ prompt = "In: What action should the robot take to {<INSTRUCTION>}?\nOut:"
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+
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+ # Predict Action (7-DoF; un-normalize for BridgeV2)
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+ inputs = processor(prompt, image).to("cuda:0", dtype=torch.bfloat16)
80
+ action = vla.predict_action(**inputs, unnorm_key="bridge_orig", do_sample=False)
81
+
82
+ # Execute...
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+ robot.act(action, ...)
84
+ ```
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+
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+ For more examples, including scripts for fine-tuning OpenVLA models on your own robot demonstration datasets, see [our training repository](https://github.com/openvla/openvla).
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+
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+ ## Citation
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+
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+ **BibTeX:**
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+
92
+ ```bibtex
93
+ @article{kim24openvla,
94
+ title={OpenVLA: An Open-Source Vision-Language-Action Model},
95
+ author={{Moo Jin} Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
96
+ journal = {arXiv preprint arXiv:2406.09246},
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+ year={2024}
98
+ }
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+ ```
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3283
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3285
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configuration_prismatic.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ configuration_prismatic.py
3
+
4
+ HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
5
+ Default configuration specifies `siglip-224px+7b`.
6
+ """
7
+
8
+ from typing import Any, Dict, List, Optional
9
+
10
+ from transformers import PretrainedConfig
11
+ from transformers.models.auto import CONFIG_MAPPING
12
+
13
+ # === Utilities for Mapping Prismatic names to HF names ===
14
+ # fmt: off
15
+ VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
16
+ "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
17
+
18
+ "clip-vit-l-336px": [336],
19
+ "siglip-vit-so400m-384px": [384],
20
+
21
+ "dinoclip-vit-l-336px": [336, 336],
22
+ "dinosiglip-vit-so-224px": [224, 224],
23
+ "dinosiglip-vit-so-384px": [384, 384],
24
+ }
25
+ VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
26
+ "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
27
+ "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
28
+
29
+ "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
30
+ "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
31
+
32
+ "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
33
+ "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
34
+
35
+ "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
36
+ "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
37
+ "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
38
+ }
39
+ TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
40
+ "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
41
+ "dinov2-vit-l": [None], "in1k-vit-l": [None],
42
+ "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
43
+ "dinoclip-vit-l-336px": [None, "quick_gelu"],
44
+ "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
45
+ }
46
+
47
+ LLM_BACKBONE_TO_HF_PATH = {
48
+ "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
49
+ "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
50
+
51
+ "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
52
+
53
+ "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
54
+ "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
55
+
56
+ "phi-2-3b": "microsoft/phi-2",
57
+ }
58
+ LLM_BACKBONE_TO_HF_METACLASS = {
59
+ "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
60
+ "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
61
+
62
+ "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
63
+
64
+ "phi-2-3b": "phi",
65
+ }
66
+
67
+ VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
68
+ VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
69
+ # fmt: on
70
+
71
+
72
+ class PrismaticConfig(PretrainedConfig):
73
+ model_type: str = "prismatic"
74
+ is_composition: bool = False
75
+
76
+ def __init__(
77
+ self,
78
+ vision_backbone_id: str = "siglip-vit-so400m",
79
+ llm_backbone_id: str = "vicuna-v15-7b",
80
+ arch_specifier: str = "no-align+gelu-mlp",
81
+ use_fused_vision_backbone: Optional[bool] = None,
82
+ image_resize_strategy: str = "letterbox",
83
+ text_config: Optional[Dict[str, Any]] = None,
84
+ llm_max_length: int = 2048,
85
+ pad_token_id: int = 32000,
86
+ pad_to_multiple_of: int = 64,
87
+ output_projector_states: bool = False,
88
+ **kwargs: str,
89
+ ) -> None:
90
+ if vision_backbone_id not in VALID_VISION_BACKBONES:
91
+ raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
92
+
93
+ if llm_backbone_id not in VALID_LLM_BACKBONES:
94
+ raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
95
+
96
+ # Set Prismatic Configuration Fields
97
+ self.vision_backbone_id = vision_backbone_id
98
+ self.llm_backbone_id = llm_backbone_id
99
+ self.arch_specifier = arch_specifier
100
+ self.output_projector_states = output_projector_states
101
+
102
+ # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
103
+ self.use_fused_vision_backbone = (
104
+ use_fused_vision_backbone
105
+ if use_fused_vision_backbone is not None
106
+ else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
107
+ )
108
+
109
+ self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
110
+ self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
111
+ self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
112
+ self.image_resize_strategy = image_resize_strategy
113
+
114
+ self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
115
+ self.llm_max_length = llm_max_length
116
+ self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
117
+
118
+ # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
119
+ self.text_config = (
120
+ CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
121
+ if text_config is not None
122
+ else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
123
+ )
124
+
125
+ # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
126
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
127
+
128
+
129
+ class OpenVLAConfig(PrismaticConfig):
130
+ model_type: str = "openvla"
131
+
132
+ def __init__(
133
+ self,
134
+ norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
135
+ n_action_bins: int = 256,
136
+ **kwargs: str,
137
+ ) -> None:
138
+ self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
139
+
140
+ super().__init__(**kwargs)
dataset_statistics.json ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "kinova": {
3
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4
+ "mean": [
5
+ -0.00020067453442607075,
6
+ -0.0006858264678157866,
7
+ -0.009424498304724693,
8
+ 0.0,
9
+ 0.0,
10
+ 0.0,
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+ -0.4078544080257416
12
+ ],
13
+ "std": [
14
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15
+ 0.018178345635533333,
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+ 0.0452522449195385,
17
+ 0.0,
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+ 0.0,
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+ 0.0,
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+ ],
22
+ "max": [
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+ 0.10962827503681183,
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+ ],
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+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,561 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ modeling_prismatic.py
3
+
4
+ Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
5
+ from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
6
+ logic in `prismatic.models.vlms.prismatic.py`.
7
+
8
+ Note =>> for the time being, not adding the custom HF "docstring" formatting.
9
+
10
+ References [LLaVa, IDEFICS-2]:
11
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
12
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
13
+ """
14
+
15
+ import logging
16
+ from dataclasses import dataclass
17
+ from functools import partial
18
+ from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
19
+
20
+ import numpy as np
21
+ import timm
22
+ import tokenizers
23
+ import torch
24
+ import torch.nn as nn
25
+ import transformers
26
+ from timm.models.vision_transformer import LayerScale
27
+ from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
28
+ from transformers.modeling_outputs import ModelOutput
29
+
30
+ from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
31
+
32
+ # Get Logger
33
+ logger = logging.getLogger(__name__)
34
+
35
+
36
+ # === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
37
+ IGNORE_INDEX = -100
38
+
39
+
40
+ # === Utility Functions for Monkey-Patching ===
41
+ def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
42
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
43
+ result = fn(*args, **kwargs)
44
+ return result[0] if isinstance(result, tuple) else result
45
+
46
+ return wrapper
47
+
48
+
49
+ # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
50
+ # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
51
+ # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
52
+ def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
53
+ return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
54
+
55
+
56
+ def ls_apply_patch(ls_module: LayerScale):
57
+ ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
58
+ ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
59
+ del ls_module.gamma
60
+
61
+
62
+ # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
63
+ class PrismaticVisionBackbone(nn.Module):
64
+ def __init__(
65
+ self,
66
+ use_fused_vision_backbone: bool,
67
+ image_sizes: List[int],
68
+ timm_model_ids: List[str],
69
+ timm_override_act_layers: List[Optional[str]],
70
+ ) -> None:
71
+ super().__init__()
72
+ self.use_fused_vision_backbone = use_fused_vision_backbone
73
+
74
+ # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
75
+ # =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
76
+ # Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
77
+ assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
78
+ self.featurizer = timm.create_model(
79
+ timm_model_ids[0],
80
+ pretrained=False,
81
+ num_classes=0,
82
+ img_size=image_sizes[0],
83
+ act_layer=timm_override_act_layers[0],
84
+ )
85
+ self.featurizer.forward = unpack_tuple(
86
+ partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
87
+ )
88
+ self.embed_dim = self.featurizer.embed_dim
89
+
90
+ # If `use_fused_vision_backbone` =>> create "beta" featurizer
91
+ if self.use_fused_vision_backbone:
92
+ self.fused_featurizer = timm.create_model(
93
+ timm_model_ids[1],
94
+ pretrained=False,
95
+ num_classes=0,
96
+ img_size=image_sizes[1],
97
+ act_layer=timm_override_act_layers[1],
98
+ )
99
+ self.fused_featurizer.forward = unpack_tuple(
100
+ partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
101
+ )
102
+ self.embed_dim += self.fused_featurizer.embed_dim
103
+
104
+ # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
105
+ for module in self.featurizer.modules():
106
+ if isinstance(module, LayerScale):
107
+ ls_apply_patch(module)
108
+
109
+ if self.use_fused_vision_backbone:
110
+ for module in self.fused_featurizer.modules():
111
+ if isinstance(module, LayerScale):
112
+ ls_apply_patch(module)
113
+
114
+ def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
115
+ """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
116
+ if not self.use_fused_vision_backbone:
117
+ return self.featurizer(pixel_values)
118
+
119
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
120
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
121
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
122
+
123
+ return torch.cat([patches, patches_fused], dim=2)
124
+
125
+
126
+ # === Prismatic Projector (nn.Module) Definitions ===
127
+ class PrismaticProjector(nn.Module):
128
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
129
+ super().__init__()
130
+ self.use_fused_vision_backbone = use_fused_vision_backbone
131
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
132
+
133
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
134
+ if not self.use_fused_vision_backbone:
135
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
136
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
137
+ self.act_fn1 = nn.GELU()
138
+ else:
139
+ initial_projection_dim = 4 * vision_dim
140
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
141
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
142
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
143
+ self.act_fn1 = nn.GELU()
144
+ self.act_fn2 = nn.GELU()
145
+
146
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
147
+ if not self.use_fused_vision_backbone:
148
+ projected_features = self.fc1(img_patches)
149
+ projected_features = self.act_fn1(projected_features)
150
+ projected_features = self.fc2(projected_features)
151
+ else:
152
+ projected_features = self.fc1(img_patches)
153
+ projected_features = self.act_fn1(projected_features)
154
+ projected_features = self.fc2(projected_features)
155
+ projected_features = self.act_fn2(projected_features)
156
+ projected_features = self.fc3(projected_features)
157
+
158
+ return projected_features
159
+
160
+
161
+ # === Main HF Class Definitions ===
162
+ @dataclass
163
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
164
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
165
+
166
+ loss: Optional[torch.FloatTensor] = None
167
+ logits: torch.FloatTensor = None
168
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
169
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
170
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
171
+
172
+ # Additions for VLMs
173
+ projector_features: Optional[torch.FloatTensor] = None
174
+
175
+
176
+ class PrismaticPreTrainedModel(PreTrainedModel):
177
+ config_class: PretrainedConfig = PrismaticConfig
178
+ base_model_prefix: str = "model"
179
+ supports_gradient_checkpointing: bool = True
180
+
181
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
182
+ _skip_keys_device_placement: str = "past_key_values"
183
+ _supports_flash_attn_2: bool = True
184
+
185
+ def _init_weights(self, module: nn.Module) -> None:
186
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
187
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
188
+ # https://github.com/TRI-ML/prismatic-vlms
189
+ std = (
190
+ self.config.initializer_range
191
+ if hasattr(self.config, "initializer_range")
192
+ else self.config.text_config.initializer_range
193
+ )
194
+
195
+ if hasattr(module, "class_embedding"):
196
+ module.class_embedding.data.normal_(mean=0.0, std=std)
197
+
198
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
199
+ module.weight.data.normal_(mean=0.0, std=std)
200
+ if module.bias is not None:
201
+ module.bias.data.zero_()
202
+ elif isinstance(module, nn.Embedding):
203
+ module.weight.data.normal_(mean=0.0, std=std)
204
+ if module.padding_idx is not None:
205
+ module.weight.data[module.padding_idx].zero_()
206
+
207
+ @property
208
+ def _supports_sdpa(self) -> bool:
209
+ """Check LLM supports SDPA Attention"""
210
+ return self.language_model._supports_sdpa
211
+
212
+
213
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
214
+ def __init__(self, config: PrismaticConfig) -> None:
215
+ super().__init__(config)
216
+
217
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
218
+ if config.use_fused_vision_backbone is None:
219
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
220
+
221
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
222
+ raise NotImplementedError(
223
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
224
+ "if you urgently need support for latest TIMM versions."
225
+ )
226
+
227
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
228
+ logger.warning(
229
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
230
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
231
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
232
+ f"use the above versions."
233
+ )
234
+
235
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
236
+ self.vision_backbone = PrismaticVisionBackbone(
237
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
238
+ )
239
+
240
+ # Create Multimodal Projector
241
+ self.projector = PrismaticProjector(
242
+ config.use_fused_vision_backbone,
243
+ vision_dim=self.vision_backbone.embed_dim,
244
+ llm_dim=config.text_config.hidden_size,
245
+ )
246
+
247
+ # Instantiate LLM Backbone
248
+ self.language_model = AutoModelForCausalLM.from_config(
249
+ config.text_config, attn_implementation=config._attn_implementation
250
+ )
251
+ self.vocab_size = config.text_config.vocab_size
252
+ self.pad_token_id = config.pad_token_id
253
+
254
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
255
+ self.post_init()
256
+
257
+ # === `PreTrainedModel` Boilerplate ===
258
+ def get_input_embeddings(self) -> nn.Module:
259
+ return self.language_model.get_input_embeddings()
260
+
261
+ def set_input_embeddings(self, value: nn.Module) -> None:
262
+ self.language_model.set_input_embeddings(value)
263
+
264
+ def get_output_embeddings(self) -> nn.Module:
265
+ return self.language_model.get_output_embeddings()
266
+
267
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
268
+ self.language_model.set_output_embeddings(new_embeddings)
269
+
270
+ def get_decoder(self) -> nn.Module:
271
+ return self.language_model.get_decoder()
272
+
273
+ def set_decoder(self, decoder: nn.Module) -> None:
274
+ self.language_model.set_decoder(decoder)
275
+
276
+ def tie_weights(self) -> None:
277
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
278
+
279
+ def resize_token_embeddings(
280
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
281
+ ) -> nn.Embedding:
282
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
283
+
284
+ # Update config/instance variables
285
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
286
+ self.vocab_size = updated_embeddings.num_embeddings
287
+
288
+ return updated_embeddings
289
+
290
+ # === Core Prismatic VLM `forward()` Logic ===
291
+ def forward(
292
+ self,
293
+ input_ids: Optional[torch.LongTensor] = None,
294
+ attention_mask: Optional[torch.Tensor] = None,
295
+ pixel_values: Optional[torch.FloatTensor] = None,
296
+ labels: Optional[torch.LongTensor] = None,
297
+ inputs_embeds: Optional[torch.FloatTensor] = None,
298
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
299
+ use_cache: Optional[bool] = None,
300
+ output_attentions: Optional[bool] = None,
301
+ output_hidden_states: Optional[bool] = None,
302
+ output_projector_features: Optional[bool] = None,
303
+ return_dict: Optional[bool] = None,
304
+ ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
305
+ """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
306
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
307
+ output_hidden_states = (
308
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
309
+ )
310
+ output_projector_features = output_projector_features if output_projector_features is not None else False
311
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
312
+
313
+ # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
314
+ use_cache = use_cache and not self.training
315
+
316
+ # Instantiate Placeholder for Projector Features
317
+ projected_patch_embeddings = None
318
+
319
+ # Note :: We only support forward passes with the following cases:
320
+ # => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
321
+ # => Unimodal Forward :: (pixel_values is None)
322
+ # => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
323
+
324
+ # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
325
+ if input_ids.shape[1] == 1:
326
+ assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
327
+ assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
328
+ assert labels is None, "Unexpected key `labels` provided during cached generation!"
329
+
330
+ language_model_output = self.language_model(
331
+ input_ids=input_ids,
332
+ attention_mask=None,
333
+ position_ids=None,
334
+ past_key_values=past_key_values,
335
+ inputs_embeds=None,
336
+ labels=None,
337
+ use_cache=use_cache,
338
+ output_attentions=output_attentions,
339
+ output_hidden_states=output_hidden_states,
340
+ return_dict=return_dict,
341
+ )
342
+
343
+ # === Handle Unimodal Forward ===
344
+ elif pixel_values is None:
345
+ assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
346
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
347
+
348
+ language_model_output = self.language_model(
349
+ input_ids=input_ids,
350
+ attention_mask=attention_mask,
351
+ position_ids=None,
352
+ past_key_values=None,
353
+ inputs_embeds=None,
354
+ labels=labels,
355
+ use_cache=use_cache,
356
+ output_attentions=output_attentions,
357
+ output_hidden_states=output_hidden_states,
358
+ return_dict=return_dict,
359
+ )
360
+
361
+ # === Handle Multimodal Forward ===
362
+ elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
363
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
364
+
365
+ # Visual Feature Extraction
366
+ patch_features = self.vision_backbone(pixel_values)
367
+
368
+ # Projection Logic =>> Update Attention Mask
369
+ projected_patch_embeddings = self.projector(patch_features)
370
+ projected_patch_attention_mask = None
371
+ if attention_mask is not None:
372
+ projected_patch_attention_mask = torch.full(
373
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
374
+ fill_value=True,
375
+ dtype=attention_mask.dtype,
376
+ device=attention_mask.device,
377
+ )
378
+
379
+ # Get Input Embeddings (from Language Model Embeddings)
380
+ input_embeddings = self.get_input_embeddings()(input_ids)
381
+
382
+ # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
383
+ multimodal_embeddings = torch.cat(
384
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
385
+ )
386
+ multimodal_attention_mask = None
387
+ if attention_mask is not None:
388
+ multimodal_attention_mask = torch.cat(
389
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
390
+ )
391
+
392
+ # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
393
+ multimodal_labels = None
394
+ if labels is not None:
395
+ projected_patch_labels = torch.full(
396
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
397
+ fill_value=IGNORE_INDEX,
398
+ dtype=labels.dtype,
399
+ device=labels.device,
400
+ )
401
+ multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
402
+
403
+ # Dispatch to Language Model
404
+ language_model_output = self.language_model(
405
+ input_ids=None,
406
+ attention_mask=multimodal_attention_mask,
407
+ position_ids=None,
408
+ past_key_values=None,
409
+ inputs_embeds=multimodal_embeddings,
410
+ labels=multimodal_labels,
411
+ use_cache=use_cache,
412
+ output_attentions=output_attentions,
413
+ output_hidden_states=output_hidden_states,
414
+ return_dict=return_dict,
415
+ )
416
+
417
+ # === Otherwise =>> Assume Invalid! ===
418
+ elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
419
+ raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
420
+
421
+ else:
422
+ raise ValueError(
423
+ "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
424
+ f"=> `input_ids` = {input_ids is not None}\n"
425
+ f"=> `attention_mask` = {attention_mask is not None}\n"
426
+ f"=> `pixel_values` = {pixel_values is not None}\n"
427
+ f"=> `labels` = {labels is not None}\n"
428
+ f"=> `input_embeds` = {inputs_embeds is not None}\n"
429
+ f"=> `past_key_values` = {past_key_values is not None}\n"
430
+ f"=> `use_cache` = {use_cache}"
431
+ )
432
+
433
+ # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
434
+ if not return_dict:
435
+ if output_projector_features and (projected_patch_embeddings is not None):
436
+ return *language_model_output, projected_patch_embeddings
437
+
438
+ return language_model_output
439
+
440
+ return PrismaticCausalLMOutputWithPast(
441
+ loss=language_model_output.loss,
442
+ logits=language_model_output.logits,
443
+ past_key_values=language_model_output.past_key_values,
444
+ hidden_states=language_model_output.hidden_states,
445
+ attentions=language_model_output.attentions,
446
+ projector_features=projected_patch_embeddings,
447
+ )
448
+
449
+ # === GenerationMixin Methods ===
450
+ def prepare_inputs_for_generation(
451
+ self,
452
+ input_ids: Optional[torch.Tensor] = None,
453
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
454
+ inputs_embeds: Optional[torch.FloatTensor] = None,
455
+ pixel_values: Optional[torch.FloatTensor] = None,
456
+ attention_mask: Optional[torch.Tensor] = None,
457
+ **kwargs: str,
458
+ ) -> Dict[str, torch.Tensor]:
459
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
460
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
461
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
462
+ ):
463
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
464
+
465
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
466
+ if past_key_values is not None:
467
+ input_ids = input_ids[:, -1:]
468
+
469
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
470
+ if inputs_embeds is not None and past_key_values is None:
471
+ model_inputs = {"input_embeds": inputs_embeds}
472
+ else:
473
+ model_inputs = {"input_ids": input_ids}
474
+
475
+ # Make sure `pixel_values` are preserved in `model_inputs`
476
+ model_inputs.update(
477
+ {
478
+ "attention_mask": attention_mask,
479
+ "pixel_values": pixel_values,
480
+ "past_key_values": past_key_values,
481
+ "use_cache": kwargs.get("use_cache"),
482
+ }
483
+ )
484
+
485
+ return model_inputs
486
+
487
+ # Defer to Language Model (all handle this differently, with different return types)
488
+ def _reorder_cache(self, *args, **kwargs) -> Any:
489
+ return self.language_model._reorder_cache(*args, **kwargs)
490
+
491
+
492
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
493
+ config_class: PretrainedConfig = OpenVLAConfig
494
+
495
+ def __init__(self, config: OpenVLAConfig) -> None:
496
+ super().__init__(config)
497
+ self.norm_stats = config.norm_stats
498
+
499
+ # Compute action bins
500
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
501
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
502
+
503
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
504
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
505
+
506
+ def predict_action(
507
+ self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs
508
+ ) -> np.ndarray:
509
+ """Thin wrapper around .generate() that decodes predicted actions and unnormalizes them."""
510
+ # We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
511
+ # in order for the predictions to match the training configuration and be accurate.
512
+ input_ids = torch.cat(
513
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
514
+ )
515
+
516
+ # Run VLA inference
517
+ generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
518
+
519
+ # Extract predicted action tokens and translate into (normalized) continuous actions
520
+ predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
521
+ discretized_actions = self.vocab_size - predicted_action_token_ids
522
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
523
+ normalized_actions = self.bin_centers[discretized_actions]
524
+
525
+ # Unnormalize actions
526
+ action_norm_stats = self.get_action_stats(unnorm_key)
527
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
528
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
529
+ actions = np.where(
530
+ mask,
531
+ 0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
532
+ normalized_actions,
533
+ )
534
+
535
+ return actions
536
+
537
+ @staticmethod
538
+ def _check_unnorm_key(norm_stats, unnorm_key):
539
+ if unnorm_key is None:
540
+ assert len(norm_stats) == 1, (
541
+ f"Your model was trained on more than one dataset, "
542
+ f"please pass a `unnorm_key` from the following options to choose the statistics "
543
+ f"used for un-normalizing actions: {norm_stats.keys()}"
544
+ )
545
+ unnorm_key = next(iter(norm_stats.keys()))
546
+
547
+ assert unnorm_key in norm_stats, (
548
+ f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
549
+ f"please choose from: {norm_stats.keys()}"
550
+ )
551
+ return unnorm_key
552
+
553
+ def get_action_dim(self, unnorm_key=None):
554
+ """Dimensionality of the policy's action space."""
555
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
556
+ return len(self.norm_stats[unnorm_key]["action"]["q01"])
557
+
558
+ def get_action_stats(self, unnorm_key=None):
559
+ """Dimensionality of the policy's action space."""
560
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
561
+ return self.norm_stats[unnorm_key]["action"]
preprocessor_config.json ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoImageProcessor": "openvla/openvla-7b--processing_prismatic.PrismaticImageProcessor",
4
+ "AutoProcessor": "openvla/openvla-7b--processing_prismatic.PrismaticProcessor"
5
+ },
6
+ "image_processor_type": "PrismaticImageProcessor",
7
+ "image_resize_strategy": "resize-naive",
8
+ "input_sizes": [
9
+ [
10
+ 3,
11
+ 224,
12
+ 224
13
+ ],
14
+ [
15
+ 3,
16
+ 224,
17
+ 224
18
+ ]
19
+ ],
20
+ "interpolations": [
21
+ "bicubic",
22
+ "bicubic"
23
+ ],
24
+ "means": [
25
+ [
26
+ 0.485,
27
+ 0.456,
28
+ 0.406
29
+ ],
30
+ [
31
+ 0.5,
32
+ 0.5,
33
+ 0.5
34
+ ]
35
+ ],
36
+ "processor_class": "PrismaticProcessor",
37
+ "stds": [
38
+ [
39
+ 0.229,
40
+ 0.224,
41
+ 0.225
42
+ ],
43
+ [
44
+ 0.5,
45
+ 0.5,
46
+ 0.5
47
+ ]
48
+ ],
49
+ "tvf_crop_params": [
50
+ {
51
+ "output_size": [
52
+ 224,
53
+ 224
54
+ ]
55
+ },
56
+ {
57
+ "output_size": [
58
+ 224,
59
+ 224
60
+ ]
61
+ }
62
+ ],
63
+ "tvf_do_letterbox": false,
64
+ "tvf_letterbox_fill": null,
65
+ "tvf_normalize_params": [
66
+ {
67
+ "inplace": false,
68
+ "mean": [
69
+ 0.484375,
70
+ 0.455078125,
71
+ 0.40625
72
+ ],
73
+ "std": [
74
+ 0.228515625,
75
+ 0.2236328125,
76
+ 0.224609375
77
+ ]
78
+ },
79
+ {
80
+ "inplace": false,
81
+ "mean": [
82
+ 0.5,
83
+ 0.5,
84
+ 0.5
85
+ ],
86
+ "std": [
87
+ 0.5,
88
+ 0.5,
89
+ 0.5
90
+ ]
91
+ }
92
+ ],
93
+ "tvf_resize_params": [
94
+ {
95
+ "antialias": true,
96
+ "interpolation": 3,
97
+ "max_size": null,
98
+ "size": [
99
+ 224,
100
+ 224
101
+ ]
102
+ },
103
+ {
104
+ "antialias": true,
105
+ "interpolation": 3,
106
+ "max_size": null,
107
+ "size": [
108
+ 224,
109
+ 224
110
+ ]
111
+ }
112
+ ],
113
+ "use_fused_vision_backbone": true
114
+ }
processing_prismatic.py ADDED
@@ -0,0 +1,257 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ processing_prismatic.py
3
+
4
+ HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
5
+ specifies `siglip-224px+7b`.
6
+ """
7
+
8
+ from typing import Any, ClassVar, List, Optional, Tuple, Union
9
+
10
+ import timm.data
11
+ import torch
12
+ import torchvision.transforms.functional as TVF
13
+ from PIL import Image
14
+ from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
15
+ from transformers import PreTrainedTokenizerBase
16
+ from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
17
+ from transformers.processing_utils import ProcessorMixin
18
+ from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
19
+ from transformers.utils import TensorType
20
+
21
+
22
+ # === Image Processing ===
23
+ def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
24
+ """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
25
+ (w, h), max_wh = image.size, max(image.size)
26
+ horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
27
+ padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
28
+
29
+ return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
30
+
31
+
32
+ class PrismaticImageProcessor(ImageProcessingMixin):
33
+ model_input_names: ClassVar[List[str]] = ["pixel_values"]
34
+
35
+ def __init__(
36
+ self,
37
+ use_fused_vision_backbone: bool = False,
38
+ image_resize_strategy: str = "letterbox",
39
+ input_sizes: Optional[List[Tuple[int, int, int]]] = None,
40
+ interpolations: Optional[List[str]] = None,
41
+ means: Optional[List[Tuple[float, float, float]]] = None,
42
+ stds: Optional[List[Tuple[float, float, float]]] = None,
43
+ **kwargs: str,
44
+ ) -> None:
45
+ """
46
+ Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
47
+ created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
48
+
49
+ @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
50
+ @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
51
+ @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
52
+ @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
53
+ @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
54
+ @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
55
+ """
56
+ self.use_fused_vision_backbone = use_fused_vision_backbone
57
+ self.image_resize_strategy = image_resize_strategy
58
+
59
+ # Handle `None` default values
60
+ input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
61
+ means = [(0.5, 0.5, 0.5)] if means is None else means
62
+ stds = [(0.5, 0.5, 0.5)] if stds is None else stds
63
+
64
+ # TIMM `data_cfg` Parameters
65
+ self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
66
+
67
+ # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
68
+ self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
69
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
70
+
71
+ for idx in range(len(input_sizes)):
72
+ transform = timm.data.create_transform(
73
+ input_size=self.input_sizes[idx],
74
+ interpolation=self.interpolations[idx],
75
+ mean=self.means[idx],
76
+ std=self.stds[idx],
77
+ crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
78
+ crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0`
79
+ is_training=False, # No image augmentations when loading the transform!
80
+ )
81
+
82
+ # [Validation] Ensure appropriate transform structure, expected sizes
83
+ if not (
84
+ isinstance(transform, Compose)
85
+ and (len(transform.transforms) == 4)
86
+ and isinstance(transform.transforms[0], Resize)
87
+ and isinstance(transform.transforms[1], CenterCrop)
88
+ and isinstance(transform.transforms[2], ToTensor)
89
+ and isinstance(transform.transforms[3], Normalize)
90
+ and (transform.transforms[0].size == self.input_sizes[idx][-1])
91
+ and (transform.transforms[1].size == self.input_sizes[idx][-2:])
92
+ ):
93
+ raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
94
+
95
+ # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
96
+ # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
97
+ resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
98
+ self.tvf_resize_params.append(
99
+ {
100
+ "size": resize_t.size,
101
+ "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
102
+ "max_size": None,
103
+ "antialias": True,
104
+ }
105
+ )
106
+ self.tvf_crop_params.append({"output_size": crop_t.size})
107
+ self.tvf_normalize_params.append(
108
+ {
109
+ "mean": norm_t.mean.float().numpy().tolist(),
110
+ "std": norm_t.std.float().numpy().tolist(),
111
+ "inplace": False,
112
+ }
113
+ )
114
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
115
+
116
+ # Handle Prismatic `image_resize_strategy`
117
+ if self.image_resize_strategy == "resize-naive":
118
+ self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
119
+ elif self.image_resize_strategy == "letterbox":
120
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
121
+ elif self.image_resize_strategy == "resize-crop":
122
+ pass
123
+ else:
124
+ raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
125
+
126
+ # Dispatch **kwargs to super()
127
+ super().__init__(**kwargs)
128
+
129
+ def apply_transform(self, img: Image.Image) -> torch.Tensor:
130
+ """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
131
+ if self.tvf_do_letterbox:
132
+ img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
133
+
134
+ # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
135
+ imgs_t = []
136
+ for idx in range(len(self.input_sizes)):
137
+ img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
138
+ img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
139
+ img_idx_t = TVF.to_tensor(img_idx)
140
+ img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
141
+ imgs_t.append(img_idx_t)
142
+
143
+ # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
144
+ img_t = torch.vstack(imgs_t)
145
+
146
+ return img_t
147
+
148
+ def preprocess(
149
+ self,
150
+ images: Union[Image.Image, List[Image.Image]],
151
+ return_tensors: Optional[Union[str, TensorType]] = None,
152
+ **_: str,
153
+ ) -> BatchFeature:
154
+ """
155
+ Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
156
+ explicitly only handle PIL.Image.Image instances for simplicity.
157
+
158
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
159
+ @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
160
+
161
+ @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
162
+ """
163
+ if not isinstance(images, list):
164
+ images = [images]
165
+
166
+ # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
167
+ pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
168
+
169
+ # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
170
+ return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
171
+
172
+ def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
173
+ return self.preprocess(images, **kwargs)
174
+
175
+
176
+ # === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
177
+ # =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
178
+ class PrismaticProcessor(ProcessorMixin):
179
+ attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
180
+ image_processor_class: str = "AutoImageProcessor"
181
+ tokenizer_class: str = "AutoTokenizer"
182
+
183
+ def __init__(
184
+ self,
185
+ image_processor: Optional[ImageProcessingMixin] = None,
186
+ tokenizer: Optional[PreTrainedTokenizerBase] = None,
187
+ ) -> None:
188
+ super().__init__(image_processor, tokenizer)
189
+
190
+ def __call__(
191
+ self,
192
+ text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
193
+ images: Union[Image.Image, List[Image.Image]],
194
+ padding: Union[bool, str, PaddingStrategy] = False,
195
+ truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
196
+ max_length: Optional[int] = None,
197
+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
198
+ ) -> BatchFeature:
199
+ """
200
+ Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
201
+ forwards images to PrismaticImageProcessor.
202
+
203
+ @param text: The (batch) of text to encode; must be a string or list of strings.
204
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
205
+ @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
206
+ @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
207
+ @param max_length: Maximum length (in tokens) to truncate
208
+ @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
209
+
210
+ @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
211
+ """
212
+ pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
213
+ text_inputs = self.tokenizer(
214
+ text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
215
+ )
216
+
217
+ # [Validate] Need same number of images and text inputs!
218
+ if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
219
+ raise ValueError("Batch is malformed; expected same number of images and text inputs!")
220
+
221
+ return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
222
+
223
+ # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
224
+ def batch_decode(
225
+ self,
226
+ sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
227
+ skip_special_tokens: bool = False,
228
+ clean_up_tokenization_spaces: Optional[bool] = None,
229
+ **kwargs: str,
230
+ ) -> List[str]:
231
+ return self.tokenizer.batch_decode(
232
+ sequences=sequences,
233
+ skip_special_tokens=skip_special_tokens,
234
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
235
+ **kwargs,
236
+ )
237
+
238
+ def decode(
239
+ self,
240
+ token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
241
+ skip_special_tokens: bool = False,
242
+ clean_up_tokenization_spaces: Optional[bool] = None,
243
+ **kwargs: str,
244
+ ) -> str:
245
+ return self.tokenizer.decode(
246
+ token_ids=token_ids,
247
+ skip_special_tokens=skip_special_tokens,
248
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
249
+ **kwargs,
250
+ )
251
+
252
+ @property
253
+ def model_input_names(self) -> List[str]:
254
+ tokenizer_input_names = self.tokenizer.model_input_names
255
+ image_processor_input_names = self.image_processor.model_input_names
256
+
257
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
processor_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoProcessor": "processing_prismatic.PrismaticProcessor"
4
+ },
5
+ "processor_class": "PrismaticProcessor"
6
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<PAD>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "unk_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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+ size 499723
tokenizer_config.json ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ },
29
+ "32000": {
30
+ "content": "<PAD>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": false,
35
+ "special": true
36
+ }
37
+ },
38
+ "auto_map": {
39
+ "AutoProcessor": "openvla/openvla-7b--processing_prismatic.PrismaticProcessor"
40
+ },
41
+ "bos_token": "<s>",
42
+ "clean_up_tokenization_spaces": false,
43
+ "eos_token": "</s>",
44
+ "legacy": false,
45
+ "model_max_length": 2048,
46
+ "pad_token": "<PAD>",
47
+ "padding_side": "right",
48
+ "processor_class": "PrismaticProcessor",
49
+ "sp_model_kwargs": {},
50
+ "tokenizer_class": "LlamaTokenizer",
51
+ "unk_token": "<unk>",
52
+ "use_default_system_prompt": false
53
+ }