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  2. config.json +3168 -0
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  12. processing_prismatic.py +252 -0
  13. processor_config.json +6 -0
  14. special_tokens_map.json +30 -0
  15. tokenizer.json +0 -0
  16. tokenizer.model +3 -0
  17. tokenizer_config.json +53 -0
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+ "std": [
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+ ]
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+ }
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+ }
3146
+ },
3147
+ "output_projector_states": false,
3148
+ "pad_to_multiple_of": 64,
3149
+ "pad_token_id": 32000,
3150
+ "text_config": {
3151
+ "model_type": "llama",
3152
+ "pad_token_id": 32000,
3153
+ "torch_dtype": "bfloat16",
3154
+ "vocab_size": 32064
3155
+ },
3156
+ "timm_model_ids": [
3157
+ "vit_large_patch14_reg4_dinov2.lvd142m",
3158
+ "vit_so400m_patch14_siglip_224"
3159
+ ],
3160
+ "timm_override_act_layers": [
3161
+ null,
3162
+ null
3163
+ ],
3164
+ "torch_dtype": "bfloat16",
3165
+ "transformers_version": "4.40.1",
3166
+ "use_fused_vision_backbone": true,
3167
+ "vision_backbone_id": "dinosiglip-vit-so-224px"
3168
+ }
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 +1,377 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ }
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+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,583 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+
105
+ # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
106
+ for module in self.featurizer.modules():
107
+ if isinstance(module, LayerScale):
108
+ ls_apply_patch(module)
109
+
110
+ if self.use_fused_vision_backbone:
111
+ for module in self.fused_featurizer.modules():
112
+ if isinstance(module, LayerScale):
113
+ ls_apply_patch(module)
114
+
115
+ ### Add a seperator token
116
+ self.sep_tok = nn.Embedding(1, self.embed_dim)
117
+
118
+
119
+ def forward(self, pixel_values: torch.Tensor, freeze=False) -> torch.Tensor:
120
+ """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
121
+ if not self.use_fused_vision_backbone:
122
+ return self.featurizer(pixel_values)
123
+ batch_size, num_steps, num_channel, img_size, img_size = pixel_values.shape
124
+ pixel_values = pixel_values.reshape(batch_size*num_steps, 6, img_size, img_size)
125
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
126
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
127
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
128
+ patches = torch.cat([patches, patches_fused], dim=2) ### (batch_size*num_steps, 16*16, embed_dim)
129
+ patches = patches.reshape(batch_size, num_steps, 256, self.embed_dim)
130
+ patches = patches.detach() if freeze else patches
131
+
132
+ ### Add seperator tokens
133
+ sep_tok = self.sep_tok.weight[0]
134
+ separators = sep_tok.expand(batch_size, num_steps, 1, self.embed_dim)
135
+ patches = torch.cat([patches, separators], dim=2)
136
+ patches = patches.reshape(batch_size, -1, self.embed_dim)[:, :-1]
137
+
138
+ return patches
139
+
140
+
141
+
142
+
143
+ # === Prismatic Projector (nn.Module) Definitions ===
144
+ class PrismaticProjector(nn.Module):
145
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
146
+ super().__init__()
147
+ self.use_fused_vision_backbone = use_fused_vision_backbone
148
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
149
+
150
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
151
+ if not self.use_fused_vision_backbone:
152
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
153
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
154
+ self.act_fn1 = nn.GELU()
155
+ else:
156
+ initial_projection_dim = 4 * vision_dim
157
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
158
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
159
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
160
+ self.act_fn1 = nn.GELU()
161
+ self.act_fn2 = nn.GELU()
162
+
163
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
164
+ if not self.use_fused_vision_backbone:
165
+ projected_features = self.fc1(img_patches)
166
+ projected_features = self.act_fn1(projected_features)
167
+ projected_features = self.fc2(projected_features)
168
+ else:
169
+ projected_features = self.fc1(img_patches)
170
+ projected_features = self.act_fn1(projected_features)
171
+ projected_features = self.fc2(projected_features)
172
+ projected_features = self.act_fn2(projected_features)
173
+ projected_features = self.fc3(projected_features)
174
+
175
+ return projected_features
176
+
177
+
178
+ # === Main HF Class Definitions ===
179
+ @dataclass
180
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
181
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
182
+
183
+ loss: Optional[torch.FloatTensor] = None
184
+ logits: torch.FloatTensor = None
185
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
186
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
187
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
188
+
189
+ # Additions for VLMs
190
+ projector_features: Optional[torch.FloatTensor] = None
191
+
192
+
193
+ class PrismaticPreTrainedModel(PreTrainedModel):
194
+ config_class: PretrainedConfig = PrismaticConfig
195
+ base_model_prefix: str = "model"
196
+ supports_gradient_checkpointing: bool = True
197
+
198
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
199
+ _skip_keys_device_placement: str = "past_key_values"
200
+ _supports_flash_attn_2: bool = True
201
+
202
+ def _init_weights(self, module: nn.Module) -> None:
203
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
204
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
205
+ # https://github.com/TRI-ML/prismatic-vlms
206
+ std = (
207
+ self.config.initializer_range
208
+ if hasattr(self.config, "initializer_range")
209
+ else self.config.text_config.initializer_range
210
+ )
211
+
212
+ if hasattr(module, "class_embedding"):
213
+ module.class_embedding.data.normal_(mean=0.0, std=std)
214
+
215
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
216
+ module.weight.data.normal_(mean=0.0, std=std)
217
+ if module.bias is not None:
218
+ module.bias.data.zero_()
219
+ elif isinstance(module, nn.Embedding):
220
+ module.weight.data.normal_(mean=0.0, std=std)
221
+ if module.padding_idx is not None:
222
+ module.weight.data[module.padding_idx].zero_()
223
+
224
+ @property
225
+ def _supports_sdpa(self) -> bool:
226
+ """Check LLM supports SDPA Attention"""
227
+ return self.language_model._supports_sdpa
228
+
229
+
230
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
231
+ def __init__(self, config: PrismaticConfig) -> None:
232
+ super().__init__(config)
233
+
234
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
235
+ if config.use_fused_vision_backbone is None:
236
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
237
+
238
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
239
+ raise NotImplementedError(
240
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
241
+ "if you urgently need support for latest TIMM versions."
242
+ )
243
+
244
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
245
+ logger.warning(
246
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
247
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
248
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
249
+ f"use the above versions."
250
+ )
251
+
252
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
253
+ self.vision_backbone = PrismaticVisionBackbone(
254
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
255
+ )
256
+
257
+ # Create Multimodal Projector
258
+ self.projector = PrismaticProjector(
259
+ config.use_fused_vision_backbone,
260
+ vision_dim=self.vision_backbone.embed_dim,
261
+ llm_dim=config.text_config.hidden_size,
262
+ )
263
+
264
+ # Instantiate LLM Backbone
265
+ self.language_model = AutoModelForCausalLM.from_config(
266
+ config.text_config, attn_implementation=config._attn_implementation
267
+ )
268
+ self.vocab_size = config.text_config.vocab_size
269
+ self.pad_token_id = config.pad_token_id
270
+
271
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
272
+ self.post_init()
273
+
274
+ # === `PreTrainedModel` Boilerplate ===
275
+ def get_input_embeddings(self) -> nn.Module:
276
+ return self.language_model.get_input_embeddings()
277
+
278
+ def set_input_embeddings(self, value: nn.Module) -> None:
279
+ self.language_model.set_input_embeddings(value)
280
+
281
+ def get_output_embeddings(self) -> nn.Module:
282
+ return self.language_model.get_output_embeddings()
283
+
284
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
285
+ self.language_model.set_output_embeddings(new_embeddings)
286
+
287
+ def get_decoder(self) -> nn.Module:
288
+ return self.language_model.get_decoder()
289
+
290
+ def set_decoder(self, decoder: nn.Module) -> None:
291
+ self.language_model.set_decoder(decoder)
292
+
293
+ def tie_weights(self) -> None:
294
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
295
+
296
+ def resize_token_embeddings(
297
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
298
+ ) -> nn.Embedding:
299
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
300
+
301
+ # Update config/instance variables
302
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
303
+ self.vocab_size = updated_embeddings.num_embeddings
304
+
305
+ return updated_embeddings
306
+
307
+ # === Core Prismatic VLM `forward()` Logic ===
308
+ def forward(
309
+ self,
310
+ input_ids: Optional[torch.LongTensor] = None,
311
+ attention_mask: Optional[torch.Tensor] = None,
312
+ pixel_values: Optional[torch.FloatTensor] = None,
313
+ labels: Optional[torch.LongTensor] = None,
314
+ inputs_embeds: Optional[torch.FloatTensor] = None,
315
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
316
+ use_cache: Optional[bool] = None,
317
+ output_attentions: Optional[bool] = None,
318
+ output_hidden_states: Optional[bool] = None,
319
+ output_projector_features: Optional[bool] = None,
320
+ return_dict: Optional[bool] = None,
321
+ freeze_vision_backbone: Optional[bool] = False,
322
+ ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
323
+ """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
324
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
325
+ output_hidden_states = (
326
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
327
+ )
328
+ output_projector_features = output_projector_features if output_projector_features is not None else False
329
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
330
+
331
+ # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
332
+ use_cache = use_cache and not self.training
333
+
334
+ # Instantiate Placeholder for Projector Features
335
+ projected_patch_embeddings = None
336
+
337
+ # Note :: We only support forward passes with the following cases:
338
+ # => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
339
+ # => Unimodal Forward :: (pixel_values is None)
340
+ # => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
341
+
342
+ # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
343
+ if input_ids.shape[1] == 1:
344
+ assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
345
+ assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
346
+ assert labels is None, "Unexpected key `labels` provided during cached generation!"
347
+
348
+ language_model_output = self.language_model(
349
+ input_ids=input_ids,
350
+ attention_mask=None,
351
+ position_ids=None,
352
+ past_key_values=past_key_values,
353
+ inputs_embeds=None,
354
+ labels=None,
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 Unimodal Forward ===
362
+ elif pixel_values is None:
363
+ assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
364
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
365
+
366
+ language_model_output = self.language_model(
367
+ input_ids=input_ids,
368
+ attention_mask=attention_mask,
369
+ position_ids=None,
370
+ past_key_values=None,
371
+ inputs_embeds=None,
372
+ labels=labels,
373
+ use_cache=use_cache,
374
+ output_attentions=output_attentions,
375
+ output_hidden_states=output_hidden_states,
376
+ return_dict=return_dict,
377
+ )
378
+
379
+ # === Handle Multimodal Forward ===
380
+ elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
381
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
382
+
383
+ # Visual Feature Extraction
384
+ patch_features = self.vision_backbone(pixel_values, freeze=freeze_vision_backbone)
385
+
386
+ # Projection Logic =>> Update Attention Mask
387
+ projected_patch_embeddings = self.projector(patch_features)
388
+ projected_patch_attention_mask = None
389
+ if attention_mask is not None:
390
+ projected_patch_attention_mask = torch.full(
391
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
392
+ fill_value=True,
393
+ dtype=attention_mask.dtype,
394
+ device=attention_mask.device,
395
+ )
396
+
397
+ # Get Input Embeddings (from Language Model Embeddings)
398
+ input_embeddings = self.get_input_embeddings()(input_ids)
399
+
400
+ # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
401
+ multimodal_embeddings = torch.cat(
402
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
403
+ )
404
+ multimodal_attention_mask = None
405
+ if attention_mask is not None:
406
+ multimodal_attention_mask = torch.cat(
407
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
408
+ )
409
+
410
+ # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
411
+ multimodal_labels = None
412
+ if labels is not None:
413
+ projected_patch_labels = torch.full(
414
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
415
+ fill_value=IGNORE_INDEX,
416
+ dtype=labels.dtype,
417
+ device=labels.device,
418
+ )
419
+ multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
420
+
421
+ # Dispatch to Language Model
422
+ language_model_output = self.language_model(
423
+ input_ids=None,
424
+ attention_mask=multimodal_attention_mask,
425
+ position_ids=None,
426
+ past_key_values=None,
427
+ inputs_embeds=multimodal_embeddings,
428
+ labels=multimodal_labels,
429
+ use_cache=use_cache,
430
+ output_attentions=output_attentions,
431
+ output_hidden_states=output_hidden_states,
432
+ return_dict=return_dict,
433
+ )
434
+
435
+ # === Otherwise =>> Assume Invalid! ===
436
+ elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
437
+ raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
438
+
439
+ else:
440
+ raise ValueError(
441
+ "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
442
+ f"=> `input_ids` = {input_ids is not None}\n"
443
+ f"=> `attention_mask` = {attention_mask is not None}\n"
444
+ f"=> `pixel_values` = {pixel_values is not None}\n"
445
+ f"=> `labels` = {labels is not None}\n"
446
+ f"=> `input_embeds` = {inputs_embeds is not None}\n"
447
+ f"=> `past_key_values` = {past_key_values is not None}\n"
448
+ f"=> `use_cache` = {use_cache}"
449
+ )
450
+
451
+ # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
452
+ if not return_dict:
453
+ if output_projector_features and (projected_patch_embeddings is not None):
454
+ return *language_model_output, projected_patch_embeddings
455
+
456
+ return language_model_output
457
+
458
+ return PrismaticCausalLMOutputWithPast(
459
+ loss=language_model_output.loss,
460
+ logits=language_model_output.logits,
461
+ past_key_values=language_model_output.past_key_values,
462
+ hidden_states=language_model_output.hidden_states,
463
+ attentions=language_model_output.attentions,
464
+ projector_features=projected_patch_embeddings,
465
+ )
466
+
467
+ # === GenerationMixin Methods ===
468
+ def prepare_inputs_for_generation(
469
+ self,
470
+ input_ids: Optional[torch.Tensor] = None,
471
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
472
+ inputs_embeds: Optional[torch.FloatTensor] = None,
473
+ pixel_values: Optional[torch.FloatTensor] = None,
474
+ attention_mask: Optional[torch.Tensor] = None,
475
+ **kwargs: str,
476
+ ) -> Dict[str, torch.Tensor]:
477
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
478
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
479
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
480
+ ):
481
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
482
+
483
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
484
+ if past_key_values is not None:
485
+ input_ids = input_ids[:, -1:]
486
+
487
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
488
+ if inputs_embeds is not None and past_key_values is None:
489
+ model_inputs = {"input_embeds": inputs_embeds}
490
+ else:
491
+ model_inputs = {"input_ids": input_ids}
492
+
493
+ # Make sure `pixel_values` are preserved in `model_inputs`
494
+ model_inputs.update(
495
+ {
496
+ "attention_mask": attention_mask,
497
+ "pixel_values": pixel_values,
498
+ "past_key_values": past_key_values,
499
+ "use_cache": kwargs.get("use_cache"),
500
+ }
501
+ )
502
+
503
+ return model_inputs
504
+
505
+ # Defer to Language Model (all handle this differently, with different return types)
506
+ def _reorder_cache(self, *args, **kwargs) -> Any:
507
+ return self.language_model._reorder_cache(*args, **kwargs)
508
+
509
+
510
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
511
+ config_class: PretrainedConfig = OpenVLAConfig
512
+
513
+ def __init__(self, config: OpenVLAConfig) -> None:
514
+ super().__init__(config)
515
+ self.norm_stats = config.norm_stats
516
+
517
+ # Compute action bins
518
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
519
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
520
+
521
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
522
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
523
+
524
+ def predict_action(
525
+ self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
526
+ ) -> np.ndarray:
527
+ """Thin wrapper around super().generate() that decodes predicted actions and de-normalizes them."""
528
+
529
+ # We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
530
+ # in order for the predictions to match the training configuration and be accurate.
531
+ input_ids = torch.cat(
532
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
533
+ )
534
+
535
+ # Run VLA inference
536
+ generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
537
+
538
+ # Extract predicted action tokens and translate into (normalized) continuous actions
539
+ predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
540
+ discretized_actions = self.vocab_size - predicted_action_token_ids
541
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
542
+ normalized_actions = self.bin_centers[discretized_actions]
543
+
544
+ # # Unnormalize actions
545
+ action_norm_stats = self.get_action_stats(unnorm_key)
546
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
547
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
548
+ actions = np.where(
549
+ mask,
550
+ 0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
551
+ normalized_actions,
552
+ )
553
+
554
+ return actions
555
+
556
+ @staticmethod
557
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
558
+ if unnorm_key is None and len(norm_stats) != 1:
559
+ raise ValueError(
560
+ f"Your model was trained on more than one dataset. "
561
+ f"Please pass a `unnorm_key` from the following options to choose the statistics used for "
562
+ f"de-normalizing actions: {norm_stats.keys()}"
563
+ )
564
+
565
+ # If None, grab the (singular) dataset in `norm_stats` to use as `unnorm_key`
566
+ unnorm_key = unnorm_key if unnorm_key is not None else next(iter(norm_stats.keys()))
567
+ if unnorm_key not in norm_stats:
568
+ raise ValueError(
569
+ f"The `unnorm_key` you chose ({unnorm_key = }) is not in the available statistics. "
570
+ f"Please choose from: {norm_stats.keys()}"
571
+ )
572
+
573
+ return unnorm_key
574
+
575
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
576
+ """Get the dimensionality of the policy's action space."""
577
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
578
+ return len(self.norm_stats[unnorm_key]["action"]["q01"])
579
+
580
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
581
+ """Get all the logged statistics for the given dataset."""
582
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
583
+ return self.norm_stats[unnorm_key]["action"]
preprocessor_config.json ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
4
+ "AutoProcessor": "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 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
49
+ @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
50
+ @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
51
+ @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
52
+ @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
53
+ @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
54
+ """
55
+ self.use_fused_vision_backbone = use_fused_vision_backbone
56
+ self.image_resize_strategy = image_resize_strategy
57
+
58
+ # Handle `None` default values
59
+ input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
60
+ means = [(0.5, 0.5, 0.5)] if means is None else means
61
+ stds = [(0.5, 0.5, 0.5)] if stds is None else stds
62
+
63
+ # TIMM `data_cfg` Parameters
64
+ self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
65
+
66
+ # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
67
+ self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
68
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
69
+
70
+ for idx in range(len(input_sizes)):
71
+ transform = timm.data.create_transform(
72
+ input_size=self.input_sizes[idx],
73
+ interpolation=self.interpolations[idx],
74
+ mean=self.means[idx],
75
+ std=self.stds[idx],
76
+ crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
77
+ crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0`
78
+ is_training=False, # No image augmentations when loading the transform!
79
+ )
80
+
81
+ # [Validation] Ensure appropriate transform structure, expected sizes
82
+ if not (
83
+ isinstance(transform, Compose)
84
+ and (len(transform.transforms) == 4)
85
+ and isinstance(transform.transforms[0], Resize)
86
+ and isinstance(transform.transforms[1], CenterCrop)
87
+ and isinstance(transform.transforms[2], ToTensor)
88
+ and isinstance(transform.transforms[3], Normalize)
89
+ and (transform.transforms[0].size == self.input_sizes[idx][-1])
90
+ and (transform.transforms[1].size == self.input_sizes[idx][-2:])
91
+ ):
92
+ raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
93
+
94
+ # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
95
+ # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
96
+ resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
97
+ self.tvf_resize_params.append(
98
+ {
99
+ "size": resize_t.size,
100
+ "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
101
+ "max_size": None,
102
+ "antialias": True,
103
+ }
104
+ )
105
+ self.tvf_crop_params.append({"output_size": crop_t.size})
106
+ self.tvf_normalize_params.append(
107
+ {
108
+ "mean": norm_t.mean.float().numpy().tolist(),
109
+ "std": norm_t.std.float().numpy().tolist(),
110
+ "inplace": False,
111
+ }
112
+ )
113
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
114
+
115
+ # Handle Prismatic `image_resize_strategy`
116
+ if self.image_resize_strategy == "resize-naive":
117
+ self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
118
+ elif self.image_resize_strategy == "letterbox":
119
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
120
+ elif self.image_resize_strategy == "resize-crop":
121
+ pass
122
+ else:
123
+ raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
124
+
125
+ # Dispatch **kwargs to super()
126
+ super().__init__(**kwargs)
127
+
128
+ def apply_transform(self, img: Image.Image) -> torch.Tensor:
129
+ """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
130
+ if self.tvf_do_letterbox:
131
+ img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
132
+
133
+ # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
134
+ imgs_t = []
135
+ for idx in range(len(self.input_sizes)):
136
+ img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
137
+ img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
138
+ img_idx_t = TVF.to_tensor(img_idx)
139
+ img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
140
+ imgs_t.append(img_idx_t)
141
+
142
+ # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
143
+ img_t = torch.vstack(imgs_t)
144
+
145
+ return img_t
146
+
147
+ def preprocess(
148
+ self,
149
+ images: Union[Image.Image, List[Image.Image]],
150
+ return_tensors: Optional[Union[str, TensorType]] = None,
151
+ **_: str,
152
+ ) -> BatchFeature:
153
+ """
154
+ Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
155
+ explicitly only handle PIL.Image.Image instances for simplicity.
156
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
157
+ @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
158
+ @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
159
+ """
160
+ if not isinstance(images, list):
161
+ images = [images]
162
+
163
+ # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
164
+ pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
165
+
166
+ # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
167
+ return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
168
+
169
+ def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
170
+ return self.preprocess(images, **kwargs)
171
+
172
+
173
+ # === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
174
+ # =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
175
+ class PrismaticProcessor(ProcessorMixin):
176
+ attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
177
+ image_processor_class: str = "AutoImageProcessor"
178
+ tokenizer_class: str = "AutoTokenizer"
179
+
180
+ def __init__(
181
+ self,
182
+ image_processor: Optional[ImageProcessingMixin] = None,
183
+ tokenizer: Optional[PreTrainedTokenizerBase] = None,
184
+ ) -> None:
185
+ super().__init__(image_processor, tokenizer)
186
+
187
+ def __call__(
188
+ self,
189
+ text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
190
+ images: Union[Image.Image, List[Image.Image]],
191
+ padding: Union[bool, str, PaddingStrategy] = False,
192
+ truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
193
+ max_length: Optional[int] = None,
194
+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
195
+ ) -> BatchFeature:
196
+ """
197
+ Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
198
+ forwards images to PrismaticImageProcessor.
199
+ @param text: The (batch) of text to encode; must be a string or list of strings.
200
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
201
+ @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
202
+ @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
203
+ @param max_length: Maximum length (in tokens) to truncate
204
+ @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
205
+ @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
206
+ """
207
+ pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"].unsqueeze(0)
208
+ text_inputs = self.tokenizer(
209
+ text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
210
+ )
211
+
212
+ # [Validate] Need same number of images and text inputs!
213
+ if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
214
+ raise ValueError("Batch is malformed; expected same number of images and text inputs!")
215
+
216
+ return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
217
+
218
+ # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
219
+ def batch_decode(
220
+ self,
221
+ sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
222
+ skip_special_tokens: bool = False,
223
+ clean_up_tokenization_spaces: Optional[bool] = None,
224
+ **kwargs: str,
225
+ ) -> List[str]:
226
+ return self.tokenizer.batch_decode(
227
+ sequences=sequences,
228
+ skip_special_tokens=skip_special_tokens,
229
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
230
+ **kwargs,
231
+ )
232
+
233
+ def decode(
234
+ self,
235
+ token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
236
+ skip_special_tokens: bool = False,
237
+ clean_up_tokenization_spaces: Optional[bool] = None,
238
+ **kwargs: str,
239
+ ) -> str:
240
+ return self.tokenizer.decode(
241
+ token_ids=token_ids,
242
+ skip_special_tokens=skip_special_tokens,
243
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
244
+ **kwargs,
245
+ )
246
+
247
+ @property
248
+ def model_input_names(self) -> List[str]:
249
+ tokenizer_input_names = self.tokenizer.model_input_names
250
+ image_processor_input_names = self.image_processor.model_input_names
251
+
252
+ 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
3
+ 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,
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+ "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,
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+ "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": "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
+ }