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Openvla-oft-SFT-libero-goal-trajall

Safetensors
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+ }
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+ }
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+ },
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+ "output_projector_states": false,
3149
+ "pad_to_multiple_of": 64,
3150
+ "pad_token_id": 32000,
3151
+ "text_config": {
3152
+ "model_type": "llama",
3153
+ "pad_token_id": 32000,
3154
+ "torch_dtype": "bfloat16",
3155
+ "vocab_size": 32064
3156
+ },
3157
+ "timm_model_ids": [
3158
+ "vit_large_patch14_reg4_dinov2.lvd142m",
3159
+ "vit_so400m_patch14_siglip_224"
3160
+ ],
3161
+ "timm_override_act_layers": [
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+ null,
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+ null
3164
+ ],
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+ "torch_dtype": "bfloat16",
3166
+ "transformers_version": "4.40.1",
3167
+ "use_fused_vision_backbone": true,
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+ "vision_backbone_id": "dinosiglip-vit-so-224px"
3169
+ }
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 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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+ }
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+ }
generation_config.json ADDED
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+ "eos_token_id": 2,
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+ "vision_backbone.fused_featurizer.blocks.8.norm2.weight": "model-00001-of-00004.safetensors",
971
+ "vision_backbone.fused_featurizer.blocks.9.attn.proj.bias": "model-00001-of-00004.safetensors",
972
+ "vision_backbone.fused_featurizer.blocks.9.attn.proj.weight": "model-00001-of-00004.safetensors",
973
+ "vision_backbone.fused_featurizer.blocks.9.attn.qkv.bias": "model-00001-of-00004.safetensors",
974
+ "vision_backbone.fused_featurizer.blocks.9.attn.qkv.weight": "model-00001-of-00004.safetensors",
975
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc1.bias": "model-00001-of-00004.safetensors",
976
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc1.weight": "model-00001-of-00004.safetensors",
977
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.bias": "model-00001-of-00004.safetensors",
978
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.weight": "model-00001-of-00004.safetensors",
979
+ "vision_backbone.fused_featurizer.blocks.9.norm1.bias": "model-00001-of-00004.safetensors",
980
+ "vision_backbone.fused_featurizer.blocks.9.norm1.weight": "model-00001-of-00004.safetensors",
981
+ "vision_backbone.fused_featurizer.blocks.9.norm2.bias": "model-00001-of-00004.safetensors",
982
+ "vision_backbone.fused_featurizer.blocks.9.norm2.weight": "model-00001-of-00004.safetensors",
983
+ "vision_backbone.fused_featurizer.norm.bias": "model-00001-of-00004.safetensors",
984
+ "vision_backbone.fused_featurizer.norm.weight": "model-00001-of-00004.safetensors",
985
+ "vision_backbone.fused_featurizer.patch_embed.proj.bias": "model-00001-of-00004.safetensors",
986
+ "vision_backbone.fused_featurizer.patch_embed.proj.weight": "model-00001-of-00004.safetensors",
987
+ "vision_backbone.fused_featurizer.pos_embed": "model-00001-of-00004.safetensors"
988
+ }
989
+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,2039 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ modeling_prismatic.py
3
+
4
+ Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions.
5
+ Inherits from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained,
6
+ but exactly replicate the logic in `prismatic.models.vlms.prismatic.py`.
7
+ """
8
+
9
+ import logging
10
+ from dataclasses import dataclass
11
+ from functools import partial
12
+ from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
13
+
14
+ import numpy as np
15
+ import timm
16
+ import tokenizers
17
+ import torch
18
+ import torch.nn as nn
19
+ import transformers
20
+ from timm.models.vision_transformer import LayerScale
21
+ from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
22
+ from transformers.modeling_outputs import ModelOutput
23
+
24
+ from .train_utils import (
25
+ get_current_action_mask,
26
+ get_next_actions_mask,
27
+ load_component_state_dict,
28
+ find_checkpoint_file,
29
+ )
30
+ from .constants import (
31
+ ACTION_DIM,
32
+ ACTION_PROPRIO_NORMALIZATION_TYPE,
33
+ ACTION_TOKEN_BEGIN_IDX,
34
+ IGNORE_INDEX,
35
+ NUM_ACTIONS_CHUNK,
36
+ STOP_INDEX,
37
+ NormalizationType,
38
+ )
39
+
40
+ from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
41
+
42
+ # Set up logger
43
+ logger = logging.getLogger(__name__)
44
+
45
+
46
+ # === Utility Functions for Monkey-Patching ===
47
+ def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
48
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
49
+ result = fn(*args, **kwargs)
50
+ return result[0] if isinstance(result, tuple) else result
51
+
52
+ return wrapper
53
+
54
+
55
+ # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
56
+ # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
57
+ # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
58
+ def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
59
+ return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
60
+
61
+
62
+ def ls_apply_patch(ls_module: LayerScale):
63
+ ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
64
+ ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
65
+ del ls_module.gamma
66
+
67
+
68
+ class ProprioProjector(nn.Module):
69
+ """
70
+ Projects proprio state inputs into the LLM's embedding space.
71
+ """
72
+ def __init__(self, llm_dim: int, proprio_dim: int) -> None:
73
+ super().__init__()
74
+ self.llm_dim = llm_dim
75
+ self.proprio_dim = proprio_dim
76
+
77
+ self.fc1 = nn.Linear(self.proprio_dim, self.llm_dim, bias=True)
78
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
79
+ self.act_fn1 = nn.GELU()
80
+
81
+ def forward(self, proprio: torch.Tensor = None) -> torch.Tensor:
82
+ # proprio: (bsz, proprio_dim)
83
+ projected_features = self.fc1(proprio)
84
+ projected_features = self.act_fn1(projected_features)
85
+ projected_features = self.fc2(projected_features)
86
+ return projected_features
87
+
88
+ # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
89
+ class PrismaticVisionBackbone(nn.Module):
90
+ """
91
+ Vision backbone for Prismatic models that handles image feature extraction.
92
+
93
+ Supports both single backbone (e.g., SigLIP) and fused backbone (e.g., SigLIP + DINOv2) configurations.
94
+ For fused backbones, features from both models are concatenated along the feature dimension.
95
+ """
96
+
97
+ def __init__(
98
+ self,
99
+ use_fused_vision_backbone: bool,
100
+ image_sizes: List[int],
101
+ timm_model_ids: List[str],
102
+ timm_override_act_layers: List[Optional[str]],
103
+ ) -> None:
104
+ """
105
+ Initialize the vision backbone.
106
+
107
+ Args:
108
+ use_fused_vision_backbone: Whether to use two backbones and fuse their features
109
+ image_sizes: List of image sizes for each backbone
110
+ timm_model_ids: List of TIMM model IDs to use for each backbone
111
+ timm_override_act_layers: List of activation layer overrides for each backbone
112
+ """
113
+ super().__init__()
114
+ self.use_fused_vision_backbone = use_fused_vision_backbone
115
+ self.num_images_in_input = 1 # Default value, can be overridden later
116
+
117
+ # Validate number of (fused) vision backbones
118
+ if len(timm_model_ids) > 2:
119
+ raise ValueError("Prismatic models only support up to 2 (fused) vision backbones!")
120
+
121
+ # Create primary featurizer
122
+ self.featurizer = self._create_featurizer(
123
+ model_id=timm_model_ids[0], img_size=image_sizes[0], act_layer=timm_override_act_layers[0]
124
+ )
125
+ self.embed_dim = self.featurizer.embed_dim
126
+
127
+ # Create secondary featurizer if using fused backbone
128
+ if self.use_fused_vision_backbone:
129
+ self.fused_featurizer = self._create_featurizer(
130
+ model_id=timm_model_ids[1], img_size=image_sizes[1], act_layer=timm_override_act_layers[1]
131
+ )
132
+ self.embed_dim += self.fused_featurizer.embed_dim
133
+
134
+ # Patch LayerScale modules for HF compatibility
135
+ self._patch_layer_scales()
136
+
137
+ def _create_featurizer(self, model_id: str, img_size: int, act_layer: Optional[str]) -> nn.Module:
138
+ """
139
+ Create a TIMM-based featurizer model with appropriate configurations.
140
+
141
+ Args:
142
+ model_id: The TIMM model ID to load
143
+ img_size: Input image size for the model
144
+ act_layer: Override for the activation layer type
145
+
146
+ Returns:
147
+ A configured featurizer model
148
+ """
149
+ featurizer = timm.create_model(
150
+ model_id,
151
+ pretrained=False,
152
+ num_classes=0,
153
+ img_size=img_size,
154
+ act_layer=act_layer,
155
+ )
156
+
157
+ # Monkey-patch the forward function to extract the second-to-last layer features
158
+ num_blocks = len(featurizer.blocks)
159
+ featurizer.forward = unpack_tuple(partial(featurizer.get_intermediate_layers, n={num_blocks - 2}))
160
+
161
+ return featurizer
162
+
163
+ def _patch_layer_scales(self) -> None:
164
+ """
165
+ Patch all LayerScale modules to be compatible with HF's parameter naming.
166
+
167
+ HF Transformers overwrites parameters with names containing 'gamma',
168
+ so we need to rename and modify the forward method.
169
+ """
170
+ # Patch primary featurizer
171
+ for module in self.featurizer.modules():
172
+ if isinstance(module, LayerScale):
173
+ ls_apply_patch(module)
174
+
175
+ # Patch secondary featurizer if it exists
176
+ if self.use_fused_vision_backbone:
177
+ for module in self.fused_featurizer.modules():
178
+ if isinstance(module, LayerScale):
179
+ ls_apply_patch(module)
180
+
181
+ def get_num_patches(self) -> int:
182
+ """
183
+ Returns the number of vision patches output by the vision backbone.
184
+
185
+ Returns:
186
+ Number of patches per image
187
+ """
188
+ return self.featurizer.patch_embed.num_patches
189
+
190
+ def get_num_images_in_input(self) -> int:
191
+ """
192
+ Returns the number of input images for the vision backbone.
193
+
194
+ Returns:
195
+ Number of images expected in the input
196
+ """
197
+ return self.num_images_in_input
198
+
199
+ def set_num_images_in_input(self, num_images_in_input: int) -> None:
200
+ """
201
+ Sets the number of input images for the vision backbone.
202
+
203
+ Args:
204
+ num_images_in_input: Number of images to expect in the input
205
+ """
206
+ self.num_images_in_input = num_images_in_input
207
+
208
+ def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
209
+ """
210
+ Implements the forward pass for the vision backbone.
211
+
212
+ If `self.use_fused_vision_backbone == True`, uses both SigLIP and DINOv2 transformers to extract visual features
213
+ (otherwise uses SigLIP only). Allows multi-image inputs (but only for fused vision backbone).
214
+
215
+ Args:
216
+ pixel_values (torch.Tensor): Pixels for input image(s), (B, C, H, W).
217
+ """
218
+ if self.num_images_in_input == 1:
219
+ if not self.use_fused_vision_backbone:
220
+ return self.featurizer(pixel_values)
221
+
222
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
223
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
224
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
225
+
226
+ return torch.cat([patches, patches_fused], dim=2)
227
+
228
+ else:
229
+ assert self.use_fused_vision_backbone, "Multi-image inputs require using fused backbone!"
230
+
231
+ # Split `pixel_values` into individual images (each with 6 channels: 3 for SigLIP + 3 for DINOv2)
232
+ images = torch.split(pixel_values, [6] * self.num_images_in_input, dim=1)
233
+
234
+ # Process each image and collect patches
235
+ all_patches = []
236
+ for img in images:
237
+ # Split each image further into two stacks of channels (each with 3 channels)
238
+ img_regular, img_fused = torch.split(img, [3, 3], dim=1)
239
+
240
+ # Get patches from both SigLIP and DINOv2 vision transformers
241
+ patches = self.featurizer(img_regular)
242
+ patches_fused = self.fused_featurizer(img_fused)
243
+
244
+ # Concatenate SigLIP and DINOv2 patches along the hidden dimension
245
+ combined_patches = torch.cat([patches, patches_fused], dim=2)
246
+ all_patches.append(combined_patches)
247
+
248
+ # Concatenate all patches along the patch dimension
249
+ return torch.cat(all_patches, dim=1)
250
+
251
+
252
+ # === Prismatic Projector (nn.Module) Definitions ===
253
+ class PrismaticProjector(nn.Module):
254
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
255
+ super().__init__()
256
+ self.use_fused_vision_backbone = use_fused_vision_backbone
257
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
258
+
259
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
260
+ if not self.use_fused_vision_backbone:
261
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
262
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
263
+ self.act_fn1 = nn.GELU()
264
+ else:
265
+ initial_projection_dim = 4 * vision_dim
266
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
267
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
268
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
269
+ self.act_fn1 = nn.GELU()
270
+ self.act_fn2 = nn.GELU()
271
+
272
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
273
+ if not self.use_fused_vision_backbone:
274
+ projected_features = self.fc1(img_patches)
275
+ projected_features = self.act_fn1(projected_features)
276
+ projected_features = self.fc2(projected_features)
277
+ else:
278
+ projected_features = self.fc1(img_patches)
279
+ projected_features = self.act_fn1(projected_features)
280
+ projected_features = self.fc2(projected_features)
281
+ projected_features = self.act_fn2(projected_features)
282
+ projected_features = self.fc3(projected_features)
283
+
284
+ return projected_features
285
+
286
+
287
+ # === Main HF Class Definitions ===
288
+ @dataclass
289
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
290
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
291
+
292
+ loss: Optional[torch.FloatTensor] = None
293
+ logits: torch.FloatTensor = None
294
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
295
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
296
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
297
+
298
+ # Additions for VLMs
299
+ projector_features: Optional[torch.FloatTensor] = None
300
+
301
+
302
+ class PrismaticPreTrainedModel(PreTrainedModel):
303
+ config_class: PretrainedConfig = PrismaticConfig
304
+ base_model_prefix: str = "model"
305
+ supports_gradient_checkpointing: bool = True
306
+
307
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
308
+ _skip_keys_device_placement: str = "past_key_values"
309
+ _supports_flash_attn_2: bool = True
310
+
311
+ def _init_weights(self, module: nn.Module) -> None:
312
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
313
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
314
+ # https://github.com/TRI-ML/prismatic-vlms
315
+ std = (
316
+ self.config.initializer_range
317
+ if hasattr(self.config, "initializer_range")
318
+ else self.config.text_config.initializer_range
319
+ )
320
+
321
+ if hasattr(module, "class_embedding"):
322
+ module.class_embedding.data.normal_(mean=0.0, std=std)
323
+
324
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
325
+ module.weight.data.normal_(mean=0.0, std=std)
326
+ if module.bias is not None:
327
+ module.bias.data.zero_()
328
+ elif isinstance(module, nn.Embedding):
329
+ module.weight.data.normal_(mean=0.0, std=std)
330
+ if module.padding_idx is not None:
331
+ module.weight.data[module.padding_idx].zero_()
332
+
333
+ @property
334
+ def _supports_sdpa(self) -> bool:
335
+ """Check LLM supports SDPA Attention"""
336
+ return self.language_model._supports_sdpa
337
+
338
+
339
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
340
+ def __init__(self, config: PrismaticConfig) -> None:
341
+ super().__init__(config)
342
+
343
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
344
+ if config.use_fused_vision_backbone is None:
345
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
346
+
347
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
348
+ raise NotImplementedError(
349
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
350
+ "if you urgently need support for latest TIMM versions."
351
+ )
352
+
353
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
354
+ logger.warning(
355
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
356
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
357
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
358
+ f"use the above versions."
359
+ )
360
+
361
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
362
+ self.vision_backbone = PrismaticVisionBackbone(
363
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
364
+ )
365
+
366
+ # Create Multimodal Projector
367
+ self.projector = PrismaticProjector(
368
+ config.use_fused_vision_backbone,
369
+ vision_dim=self.vision_backbone.embed_dim,
370
+ llm_dim=config.text_config.hidden_size,
371
+ )
372
+
373
+ self.proprio_projector = None
374
+ if config.use_proprio:
375
+ self.proprio_projector = ProprioProjector(
376
+ llm_dim=config.text_config.hidden_size,
377
+ proprio_dim=config.proprio_dim
378
+ )
379
+
380
+ # Instantiate LLM Backbone
381
+ self.language_model = AutoModelForCausalLM.from_config(
382
+ config.text_config, attn_implementation=config._attn_implementation
383
+ )
384
+ self.vocab_size = config.text_config.vocab_size
385
+ self.pad_token_id = config.pad_token_id
386
+ self.llm_dim = config.text_config.hidden_size
387
+
388
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
389
+ self.post_init()
390
+
391
+ # === `PreTrainedModel` Boilerplate ===
392
+ def get_input_embeddings(self) -> nn.Module:
393
+ return self.language_model.get_input_embeddings()
394
+
395
+ def set_input_embeddings(self, value: nn.Module) -> None:
396
+ self.language_model.set_input_embeddings(value)
397
+
398
+ def get_output_embeddings(self) -> nn.Module:
399
+ return self.language_model.get_output_embeddings()
400
+
401
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
402
+ self.language_model.set_output_embeddings(new_embeddings)
403
+
404
+ def get_decoder(self) -> nn.Module:
405
+ return self.language_model.get_decoder()
406
+
407
+ def set_decoder(self, decoder: nn.Module) -> None:
408
+ self.language_model.set_decoder(decoder)
409
+
410
+ def tie_weights(self) -> None:
411
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
412
+
413
+ def resize_token_embeddings(
414
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
415
+ ) -> nn.Embedding:
416
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
417
+
418
+ # Update config/instance variables
419
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
420
+ self.vocab_size = updated_embeddings.num_embeddings
421
+
422
+ return updated_embeddings
423
+
424
+ def _replace_input_embeddings(self, input_embeddings, all_actions_mask, noisy_action_features):
425
+ """
426
+ Replace embeddings in input_embeddings at positions where all_actions_mask is True
427
+ with embeddings from noisy_action_features, using vectorized operations.
428
+
429
+ Args:
430
+ input_embeddings: Tensor of shape (B, S, D)
431
+ all_actions_mask: Boolean tensor of shape (B, S)
432
+ noisy_action_features: Tensor of shape (B, K, D) where K is the number of True values in mask per sample
433
+
434
+ Returns:
435
+ Modified input_embeddings tensor
436
+ """
437
+ # Clone input to avoid modifying the original tensor
438
+ new_input_embeddings = input_embeddings.clone()
439
+
440
+ # Create a tensor with the same shape of input_embeddings to hold the noisy action features
441
+ repositioned_noisy_action_features = torch.zeros_like(input_embeddings)
442
+
443
+ # Create batch indices for splicing
444
+ batch_indices = torch.arange(input_embeddings.shape[0], device=input_embeddings.device)
445
+ batch_indices = batch_indices.unsqueeze(1).expand(-1, noisy_action_features.shape[1])
446
+
447
+ # Get indices where mask is True for each sample
448
+ masked_indices = torch.stack([torch.where(mask)[0] for mask in all_actions_mask])
449
+
450
+ # Move the noisy action features into their correct positions
451
+ repositioned_noisy_action_features[batch_indices, masked_indices] = noisy_action_features
452
+
453
+ # Combine original input embeddings and noisy action embeddings using the mask
454
+ new_input_embeddings = torch.where(
455
+ all_actions_mask.unsqueeze(-1), repositioned_noisy_action_features, new_input_embeddings
456
+ )
457
+
458
+ return new_input_embeddings
459
+
460
+ def _process_action_masks(self, labels):
461
+ """Helper to get action masks from labels"""
462
+ current_action_mask = get_current_action_mask(labels)
463
+ next_actions_mask = get_next_actions_mask(labels)
464
+ all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
465
+ return all_actions_mask
466
+
467
+ def _process_vision_features(self, pixel_values, language_embeddings=None, use_film=False):
468
+ """Process vision features with optional FiLM conditioning"""
469
+ if use_film:
470
+ # FiLM: Infuse language inputs into visual features
471
+ patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
472
+ else:
473
+ patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
474
+
475
+ # Project patch embeddings into language embedding space
476
+ return self.projector(patch_features)
477
+
478
+ def _process_proprio_features(self, projected_patch_embeddings, proprio, proprio_projector):
479
+ """Process proprioceptive features and append to vision features"""
480
+ if proprio_projector is not None and proprio is not None:
481
+ # projected_patch_embeddings: (bsz, num_patches * num_images, llm_dim)
482
+ # proprio: (bsz, proprio_dim) or (propro_dim,)
483
+ proprio = proprio.reshape(projected_patch_embeddings.shape[0], -1) # (bsz, proprio_dim)
484
+ proprio_features = proprio_projector(proprio) # (bsz, llm_dim)
485
+ proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim)
486
+ # For simplicity, just append proprio token to the end of projected vision patch tokens
487
+ return torch.cat((projected_patch_embeddings, proprio_features), dim=1)
488
+ return projected_patch_embeddings
489
+
490
+ def _build_multimodal_attention(self, input_embeddings, projected_patch_embeddings, attention_mask):
491
+ """Build multimodal embeddings and attention mask"""
492
+ # Update attention mask
493
+ projected_patch_attention_mask = None
494
+ if attention_mask is not None:
495
+ projected_patch_attention_mask = torch.full(
496
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
497
+ fill_value=True,
498
+ dtype=attention_mask.dtype,
499
+ device=attention_mask.device,
500
+ )
501
+
502
+ # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
503
+ multimodal_embeddings = torch.cat(
504
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
505
+ )
506
+
507
+ multimodal_attention_mask = None
508
+ if attention_mask is not None:
509
+ multimodal_attention_mask = torch.cat(
510
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
511
+ )
512
+
513
+ return multimodal_embeddings, multimodal_attention_mask
514
+
515
+ def _build_multimodal_labels(self, labels, projected_patch_embeddings):
516
+ """Build multimodal labels with IGNORE_INDEX for patch embeddings"""
517
+ if labels is not None:
518
+ projected_patch_labels = torch.full(
519
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
520
+ fill_value=IGNORE_INDEX,
521
+ dtype=labels.dtype,
522
+ device=labels.device,
523
+ )
524
+ return torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
525
+ return None
526
+
527
+ # === Core Prismatic VLM `forward()` Logic ===
528
+ # def forward(
529
+ # self,
530
+ # input_ids: Optional[torch.LongTensor] = None,
531
+ # attention_mask: Optional[torch.Tensor] = None,
532
+ # pixel_values: Optional[torch.FloatTensor] = None,
533
+ # labels: Optional[torch.LongTensor] = None,
534
+ # inputs_embeds: Optional[torch.FloatTensor] = None,
535
+ # past_key_values: Optional[List[torch.FloatTensor]] = None,
536
+ # use_cache: Optional[bool] = None,
537
+ # output_attentions: Optional[bool] = None,
538
+ # output_hidden_states: Optional[bool] = None,
539
+ # output_projector_features: Optional[bool] = None,
540
+ # return_dict: Optional[bool] = None,
541
+ # proprio=None,
542
+ # proprio_projector=None,
543
+ # noisy_actions=None,
544
+ # noisy_action_projector=None,
545
+ # diffusion_timestep_embeddings=None,
546
+ # use_film: bool = False,
547
+ # ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
548
+ # """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
549
+ # output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
550
+ # output_hidden_states = (
551
+ # output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
552
+ # )
553
+ # output_projector_features = output_projector_features if output_projector_features is not None else False
554
+ # return_dict = return_dict if return_dict is not None else self.config.use_return_dict
555
+
556
+ # # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
557
+ # use_cache = use_cache and not self.training
558
+
559
+ # # Instantiate Placeholder for Projector Features
560
+ # projected_patch_embeddings = None
561
+
562
+ # # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
563
+ # if input_ids.shape[1] == 1:
564
+ # assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
565
+ # assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
566
+ # assert labels is None, "Unexpected key `labels` provided during cached generation!"
567
+
568
+ # language_model_output = self.language_model(
569
+ # input_ids=input_ids,
570
+ # attention_mask=None,
571
+ # position_ids=None,
572
+ # past_key_values=past_key_values,
573
+ # inputs_embeds=None,
574
+ # labels=None,
575
+ # use_cache=use_cache,
576
+ # output_attentions=output_attentions,
577
+ # output_hidden_states=output_hidden_states,
578
+ # return_dict=return_dict,
579
+ # )
580
+
581
+ # # === Handle Unimodal Forward ===
582
+ # elif pixel_values is None:
583
+ # assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
584
+ # assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
585
+
586
+ # language_model_output = self.language_model(
587
+ # input_ids=input_ids,
588
+ # attention_mask=attention_mask,
589
+ # position_ids=None,
590
+ # past_key_values=None,
591
+ # inputs_embeds=None,
592
+ # labels=labels,
593
+ # use_cache=use_cache,
594
+ # output_attentions=output_attentions,
595
+ # output_hidden_states=output_hidden_states,
596
+ # return_dict=return_dict,
597
+ # )
598
+
599
+ # # === Handle Multimodal Forward ===
600
+ # elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
601
+ # assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!"
602
+
603
+ # #test
604
+ #
605
+ # #test end
606
+
607
+ # # Get input embeddings (from language model embeddings)
608
+ # input_embeddings = self.get_input_embeddings()(input_ids) # (B, seq_len, D)
609
+
610
+ # # Extract action masks
611
+ # all_actions_mask = self._process_action_masks(labels)
612
+
613
+ # # Extract the language portion of the input embeddings (i.e. remove the action tokens portion)
614
+ # language_embeddings = input_embeddings[~all_actions_mask].reshape(
615
+ # input_embeddings.shape[0], -1, input_embeddings.shape[2]
616
+ # ) # (B, lang_seq_len, llm_dim)
617
+
618
+ # # Get visual features
619
+ # projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
620
+
621
+ # # Add proprioceptive state if provided
622
+ # projected_patch_embeddings = self._process_proprio_features(
623
+ # projected_patch_embeddings, proprio, proprio_projector
624
+ # )
625
+
626
+ # # [Diffusion] Add diffusion timestep embedding if provided
627
+ # if diffusion_timestep_embeddings is not None:
628
+ # # For simplicity, just append diffusion timestep embedding to the end of projected vision patch tokens
629
+ # projected_patch_embeddings = torch.cat(
630
+ # (projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
631
+ # )
632
+
633
+ # # Process action embeddings
634
+ # if noisy_actions is not None:
635
+ # # Get mask corresponding to all action tokens
636
+ # all_actions_mask = self._process_action_masks(labels)
637
+
638
+ # # Reshape noisy actions into individual action tokens
639
+ # # noisy_actions: (B, chunk_len, action_dim) -> (B, chunk_len * action_dim, 1)
640
+ # B = noisy_actions.shape[0]
641
+ # noisy_actions = noisy_actions.reshape(B, -1).unsqueeze(-1)
642
+
643
+ # # Project noisy action tokens into language model embedding space
644
+ # noisy_action_features = noisy_action_projector(noisy_actions) # (B, chunk_len * action_dim, llm_dim)
645
+
646
+ # # Replace embeddings of the action tokens with noisy action embeddings
647
+ # input_embeddings = self._replace_input_embeddings(
648
+ # input_embeddings, all_actions_mask, noisy_action_features
649
+ # )
650
+ # else:
651
+ # # Replace the embeddings of the action tokens with zeros
652
+ # # (Later on, the positional embeddings will be added to them)
653
+ # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
654
+ # input_embeddings = input_embeddings * ~all_actions_mask
655
+
656
+ # # Build multimodal embeddings & attention mask
657
+ # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
658
+ # input_embeddings, projected_patch_embeddings, attention_mask
659
+ # )
660
+
661
+ # # Build labels for multimodal sequence if needed
662
+ # multimodal_labels = self._build_multimodal_labels(labels, projected_patch_embeddings)
663
+
664
+ # # Dispatch to language model
665
+ # language_model_output = self.language_model(
666
+ # input_ids=None,
667
+ # attention_mask=multimodal_attention_mask,
668
+ # position_ids=None,
669
+ # past_key_values=None,
670
+ # inputs_embeds=multimodal_embeddings,
671
+ # labels=multimodal_labels,
672
+ # use_cache=use_cache,
673
+ # output_attentions=output_attentions,
674
+ # output_hidden_states=output_hidden_states,
675
+ # return_dict=return_dict,
676
+ # )
677
+
678
+ # # === Otherwise =>> Assume Invalid! ===
679
+ # elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
680
+ # raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
681
+
682
+ # else:
683
+ # raise ValueError(
684
+ # "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
685
+ # f"=> `input_ids` = {input_ids is not None}\n"
686
+ # f"=> `attention_mask` = {attention_mask is not None}\n"
687
+ # f"=> `pixel_values` = {pixel_values is not None}\n"
688
+ # f"=> `labels` = {labels is not None}\n"
689
+ # f"=> `input_embeds` = {inputs_embeds is not None}\n"
690
+ # f"=> `past_key_values` = {past_key_values is not None}\n"
691
+ # f"=> `use_cache` = {use_cache}"
692
+ # )
693
+
694
+ # # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
695
+ # if not return_dict:
696
+ # if output_projector_features and (projected_patch_embeddings is not None):
697
+ # return *language_model_output, projected_patch_embeddings
698
+
699
+ # return language_model_output
700
+
701
+ # return PrismaticCausalLMOutputWithPast(
702
+ # loss=language_model_output.loss,
703
+ # logits=language_model_output.logits,
704
+ # past_key_values=language_model_output.past_key_values,
705
+ # hidden_states=language_model_output.hidden_states,
706
+ # attentions=language_model_output.attentions,
707
+ # projector_features=projected_patch_embeddings,
708
+ # )
709
+
710
+ # === GenerationMixin Methods ===
711
+ def prepare_inputs_for_generation(
712
+ self,
713
+ input_ids: Optional[torch.Tensor] = None,
714
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
715
+ inputs_embeds: Optional[torch.FloatTensor] = None,
716
+ pixel_values: Optional[torch.FloatTensor] = None,
717
+ attention_mask: Optional[torch.Tensor] = None,
718
+ **kwargs: str,
719
+ ) -> Dict[str, torch.Tensor]:
720
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
721
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
722
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
723
+ ):
724
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
725
+
726
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
727
+ if past_key_values is not None:
728
+ input_ids = input_ids[:, -1:]
729
+
730
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
731
+ if inputs_embeds is not None and past_key_values is None:
732
+ model_inputs = {"input_embeds": inputs_embeds}
733
+ else:
734
+ model_inputs = {"input_ids": input_ids}
735
+
736
+ # Make sure `pixel_values` are preserved in `model_inputs`
737
+ model_inputs.update(
738
+ {
739
+ "attention_mask": attention_mask,
740
+ "pixel_values": pixel_values,
741
+ "past_key_values": past_key_values,
742
+ "use_cache": kwargs.get("use_cache"),
743
+ }
744
+ )
745
+
746
+ return model_inputs
747
+
748
+ # Defer to Language Model (all handle this differently, with different return types)
749
+ def _reorder_cache(self, *args, **kwargs) -> Any:
750
+ return self.language_model._reorder_cache(*args, **kwargs)
751
+
752
+ def _prepare_input_for_action_prediction_verl(self, input_ids, attention_mask):
753
+ """Prepares input for action prediction by adding necessary tokens"""
754
+ # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
755
+ placeholder_action_token_ids = (
756
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
757
+ )
758
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
759
+
760
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
761
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
762
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
763
+
764
+ # Extend the attention mask to fit the new shape of input
765
+ # Note: Only batch size == 1 supported right now
766
+ mask_extension = (
767
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
768
+ .to(attention_mask.device)
769
+ .to(attention_mask.dtype)
770
+ )
771
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
772
+
773
+ return input_ids, attention_mask
774
+
775
+ def _prepare_labels_for_action_prediction_verl(self, labels, input_ids):
776
+ """Creates labels tensor for action prediction if not provided"""
777
+ # Extend labels tensor with fake action labels
778
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
779
+ labels_extension = (
780
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
781
+ * ARBITRARY_ACTION_TOKEN_IDX
782
+ )
783
+ labels = torch.cat([labels, labels_extension], dim=-1)
784
+
785
+ # Replace last label token with stop token
786
+ labels[:, -1] = STOP_INDEX
787
+
788
+ return labels
789
+
790
+ def _verl_discrete_compute_logits(
791
+ self,
792
+ input_embeddings,
793
+ all_actions_mask,
794
+ projected_patch_embeddings,
795
+ attention_mask,
796
+ labels,
797
+ NUM_PATCHES,
798
+ NUM_PROMPT_TOKENS,
799
+ action_head=None,
800
+ ):#contintue!!!!!
801
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
802
+ # Zero out action token embeddings
803
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
804
+ input_embeddings = input_embeddings * ~all_actions_mask
805
+
806
+ # Build multimodal embeddings and attention mask
807
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
808
+ input_embeddings, projected_patch_embeddings, attention_mask
809
+ )
810
+
811
+ # Forward pass through language model
812
+ language_model_output = self.language_model(
813
+ input_ids=None,
814
+ attention_mask=multimodal_attention_mask,
815
+ position_ids=None,
816
+ past_key_values=None,
817
+ inputs_embeds=multimodal_embeddings,
818
+ labels=None,
819
+ use_cache=None,
820
+ output_attentions=False,
821
+ output_hidden_states=False,
822
+ return_dict=True,
823
+ )
824
+
825
+ # Extract hidden states for action tokens
826
+ #last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
827
+ # actions_hidden_states = last_hidden_states[
828
+ # :,
829
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
830
+ # :,
831
+ # ] # (B, act_chunk_len, D)
832
+
833
+ # Handle different prediction methods
834
+ # if action_head is not None:
835
+ # # L1 regression prediction
836
+ # normalized_actions = action_head.predict_action(actions_hidden_states)
837
+ # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
838
+ # normalized_actions = normalized_actions.float().cpu().detach().numpy()
839
+ # else:
840
+ # Discrete token-based prediction
841
+
842
+ compute_logits = language_model_output.logits[
843
+ :,
844
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
845
+ ]
846
+
847
+ return compute_logits
848
+
849
+ # def forward(
850
+ # self,
851
+ # input_ids: Optional[torch.LongTensor] = None,
852
+ # unnorm_key: Optional[str] = None,
853
+ # proprio=None,
854
+ # proprio_projector=None,
855
+ # action_head=None,
856
+ # noisy_action_projector=None,
857
+ # use_film: bool = False,
858
+ # **kwargs: str,
859
+ # ) :
860
+ # """Predict actions from input sequence, with options for different prediction methods.
861
+
862
+ # Args:
863
+ # input_ids: Input token ids
864
+ # unnorm_key: Key for unnormalization statistics
865
+ # proprio: Proprioceptive features
866
+ # proprio_projector: Projector for proprioceptive features
867
+ # action_head: Optional head for L1 regression or diffusion-based prediction
868
+ # noisy_action_projector: Projector for noisy actions in diffusion-based prediction
869
+ # use_film: Whether to use FiLM conditioning
870
+ # **kwargs: Additional arguments including pixel_values and attention_mask
871
+
872
+ # Returns:
873
+ # Tuple of (unnormalized_actions, action_hidden_states)
874
+ # """
875
+ # # If the special empty token ('') does not already appear after the colon (':') token in the prompt
876
+ # # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
877
+ # # if not torch.all(input_ids[:, -1] == 29871):
878
+ # # input_ids = torch.cat(
879
+ # # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
880
+ # # )
881
+ # #print("!!!!!!!!!!!!!!Entering forward!!!!!!!!!!")
882
+ # pixel_values = kwargs["pixel_values"]
883
+ # attention_mask = kwargs["attention_mask"]
884
+
885
+ # # Create fake labels tensor (needed for action mask)
886
+ # labels = input_ids.clone()
887
+ # labels[:] = IGNORE_INDEX
888
+
889
+ # # Get number of tokens in prompt (excluding the start token)
890
+ # NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
891
+
892
+ # # Prepare inputs by adding necessary tokens
893
+ # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask)
894
+
895
+ # #test
896
+ # placeholder_action_token_ids = (
897
+ # torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
898
+ # )
899
+ # input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
900
+
901
+ # # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
902
+ # stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
903
+ # input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
904
+
905
+ # # Extend the attention mask to fit the new shape of input
906
+ # # Note: Only batch size == 1 supported right now
907
+ # mask_extension = (
908
+ # torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
909
+ # .to(attention_mask.device)
910
+ # .to(attention_mask.dtype)
911
+ # )
912
+ # attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
913
+
914
+ # #return input_ids, attention_mask
915
+
916
+ # #test end
917
+
918
+
919
+ # # Update labels tensor for action mask computation later
920
+ # #labels = self._prepare_labels_for_action_prediction_verl(labels, input_ids)
921
+ # #test
922
+
923
+ # ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
924
+ # labels_extension = (
925
+ # torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
926
+ # * ARBITRARY_ACTION_TOKEN_IDX
927
+ # )
928
+ # labels = torch.cat([labels, labels_extension], dim=-1)
929
+
930
+ # # Replace last label token with stop token
931
+ # labels[:, -1] = STOP_INDEX
932
+
933
+ # #return labels
934
+
935
+ # #test ed
936
+
937
+
938
+ # # Get input embeddings and action masks
939
+
940
+
941
+
942
+ # input_embeddings = self.get_input_embeddings()(input_ids)
943
+
944
+
945
+ # #all_actions_mask = self._process_action_masks(labels)
946
+ # #test
947
+ # #current_action_mask = get_current_action_mask(labels)
948
+ # newline_positions = labels != IGNORE_INDEX
949
+
950
+ # # Calculate cumulative sum to identify regions between newlines
951
+ # cumsum = torch.cumsum(newline_positions, dim=1)
952
+
953
+ # # Create the mask
954
+ # mask = (1 <= cumsum) & (cumsum <= ACTION_DIM)
955
+
956
+ # # Extract the action part only
957
+ # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
958
+ # current_action_mask = action_tokens_only_mask * mask
959
+
960
+ # #next_actions_mask = get_next_actions_mask(labels)
961
+ # newline_positions = labels != IGNORE_INDEX
962
+
963
+ # # Calculate cumulative sum to identify regions between newlines
964
+ # cumsum = torch.cumsum(newline_positions, dim=1)
965
+
966
+ # # Create the mask
967
+ # mask = cumsum > ACTION_DIM
968
+
969
+ # # Extract the action part only
970
+ # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
971
+ # next_actions_mask = action_tokens_only_mask * mask
972
+
973
+ # all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
974
+
975
+ # #test end
976
+
977
+ # # Extract language embeddings
978
+ # language_embeddings = input_embeddings[~all_actions_mask].reshape(
979
+ # input_embeddings.shape[0], -1, input_embeddings.shape[2]
980
+ # )
981
+
982
+ # # Process vision features
983
+ # #projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
984
+ # #test
985
+ # if use_film:
986
+ # # FiLM: Infuse language inputs into visual features
987
+ # raise ValueError
988
+ # patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
989
+ # else:
990
+ # patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
991
+
992
+ # projected_patch_embeddings = self.projector(patch_features)
993
+ # #test end
994
+
995
+
996
+ # # Add proprioceptive features if provided
997
+ # use_proprio = proprio_projector is not None and proprio is not None
998
+ # if use_proprio:
999
+ # proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1000
+ # projected_patch_embeddings = self._process_proprio_features(
1001
+ # projected_patch_embeddings, proprio, proprio_projector
1002
+ # )
1003
+
1004
+ # # Use diffusion if provided, otherwise use regression or discrete prediction
1005
+ # use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1006
+
1007
+ # # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1008
+ # NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1009
+ # if use_proprio:
1010
+ # NUM_PATCHES += 1
1011
+ # if use_diffusion:
1012
+ # NUM_PATCHES += 1
1013
+
1014
+ # if use_diffusion:
1015
+ # raise ValueError
1016
+ # # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1017
+ # noise = torch.randn(
1018
+ # size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1019
+ # )
1020
+
1021
+ # # Run diffusion-based prediction
1022
+ # normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1023
+ # input_embeddings,
1024
+ # all_actions_mask,
1025
+ # noise,
1026
+ # action_head,
1027
+ # projected_patch_embeddings,
1028
+ # labels,
1029
+ # attention_mask,
1030
+ # NUM_PATCHES,
1031
+ # NUM_PROMPT_TOKENS,
1032
+ # noisy_action_projector,
1033
+ # )
1034
+ # else:
1035
+ # # Run regression or discrete token-based prediction
1036
+ # # compute_logits = self._verl_discrete_compute_logits(
1037
+ # # input_embeddings,
1038
+ # # all_actions_mask,
1039
+ # # projected_patch_embeddings,
1040
+ # # attention_mask,
1041
+ # # labels,
1042
+ # # NUM_PATCHES,
1043
+ # # NUM_PROMPT_TOKENS,
1044
+ # # action_head,
1045
+ # # )
1046
+
1047
+ # #test
1048
+
1049
+ # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1050
+ # input_embeddings = input_embeddings * ~all_actions_mask
1051
+
1052
+ # # Build multimodal embeddings and attention mask
1053
+ # # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1054
+ # # input_embeddings, projected_patch_embeddings, attention_mask
1055
+ # # )
1056
+ # #test
1057
+
1058
+ # projected_patch_attention_mask = None
1059
+ # if attention_mask is not None:
1060
+ # projected_patch_attention_mask = torch.full(
1061
+ # (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
1062
+ # fill_value=True,
1063
+ # dtype=attention_mask.dtype,
1064
+ # device=attention_mask.device,
1065
+ # )
1066
+
1067
+ # # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
1068
+ # multimodal_embeddings = torch.cat(
1069
+ # [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
1070
+ # )
1071
+
1072
+ # multimodal_attention_mask = None
1073
+ # if attention_mask is not None:
1074
+ # multimodal_attention_mask = torch.cat(
1075
+ # [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
1076
+ # )
1077
+
1078
+ # #return multimodal_embeddings, multimodal_attention_mask
1079
+
1080
+ # #test end
1081
+
1082
+ # # Forward pass through language model
1083
+ # language_model_output = self.language_model(
1084
+ # input_ids=None,
1085
+ # attention_mask=multimodal_attention_mask,
1086
+ # position_ids=None,
1087
+ # past_key_values=None,
1088
+ # inputs_embeds=multimodal_embeddings,
1089
+ # labels=None,
1090
+ # use_cache=None,
1091
+ # output_attentions=False,
1092
+ # output_hidden_states=False,
1093
+ # return_dict=True,
1094
+ # )
1095
+
1096
+
1097
+ # compute_logits = language_model_output.logits[
1098
+ # :,
1099
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1100
+ # ]
1101
+
1102
+ # #test end
1103
+
1104
+ # return compute_logits
1105
+
1106
+ def forward(
1107
+ self,
1108
+ input_ids: Optional[torch.LongTensor] = None,
1109
+ pixel_values=None,
1110
+ attention_mask=None,
1111
+ #labels=None,
1112
+ proprio=None,
1113
+ #proprio_projector=None,
1114
+ action_head=None,
1115
+ noisy_action_projector=None,
1116
+ use_film: bool = False,
1117
+ **kwargs: str,
1118
+ ) :
1119
+ """Predict actions from input sequence, with options for different prediction methods.
1120
+
1121
+ Args:
1122
+ input_ids: Input token ids
1123
+ unnorm_key: Key for unnormalization statistics
1124
+ proprio: Proprioceptive features
1125
+ proprio_projector: Projector for proprioceptive features
1126
+ action_head: Optional head for L1 regression or diffusion-based prediction
1127
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1128
+ use_film: Whether to use FiLM conditioning
1129
+ **kwargs: Additional arguments including pixel_values and attention_mask
1130
+
1131
+ Returns:
1132
+ Tuple of (unnormalized_actions, action_hidden_states)
1133
+ """
1134
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1135
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1136
+ # if not torch.all(input_ids[:, -1] == 29871):
1137
+ # input_ids = torch.cat(
1138
+ # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1139
+ # )
1140
+
1141
+ #pixel_values = kwargs["pixel_values"]
1142
+ #attention_mask = kwargs["attention_mask"]
1143
+
1144
+ # Create fake labels tensor (needed for action mask)
1145
+ labels = input_ids.clone()
1146
+ labels[:] = IGNORE_INDEX
1147
+
1148
+ # # Get number of tokens in prompt (excluding the start token)
1149
+ NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1150
+
1151
+
1152
+ # # Prepare inputs by adding necessary tokens
1153
+ # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask)
1154
+
1155
+ # #test
1156
+ placeholder_action_token_ids = (
1157
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
1158
+ )
1159
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
1160
+
1161
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
1162
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
1163
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
1164
+
1165
+ # Extend the attention mask to fit the new shape of input
1166
+ # Note: Only batch size == 1 supported right now
1167
+ mask_extension = (
1168
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
1169
+ .to(attention_mask.device)
1170
+ .to(attention_mask.dtype)
1171
+ )
1172
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
1173
+
1174
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
1175
+ labels_extension = (
1176
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
1177
+ * ARBITRARY_ACTION_TOKEN_IDX
1178
+ )
1179
+ labels = torch.cat([labels, labels_extension], dim=-1)
1180
+
1181
+ # # Replace last label token with stop token
1182
+ labels[:, -1] = STOP_INDEX
1183
+
1184
+
1185
+ # Get input embeddings and action masks
1186
+
1187
+ #NUM_PROMPT_TOKENS = kwargs["num_prompt_tokens"]
1188
+
1189
+ input_embeddings = self.get_input_embeddings()(input_ids)
1190
+
1191
+
1192
+ #all_actions_mask = self._process_action_masks(labels)
1193
+ #test
1194
+ #current_action_mask = get_current_action_mask(labels)
1195
+ newline_positions = labels != IGNORE_INDEX
1196
+
1197
+ # Calculate cumulative sum to identify regions between newlines
1198
+ cumsum = torch.cumsum(newline_positions, dim=1)
1199
+
1200
+ # Create the mask
1201
+ mask = (1 <= cumsum) & (cumsum <= ACTION_DIM)
1202
+
1203
+ # Extract the action part only
1204
+ action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
1205
+ current_action_mask = action_tokens_only_mask * mask
1206
+
1207
+ #next_actions_mask = get_next_actions_mask(labels)
1208
+ newline_positions = labels != IGNORE_INDEX
1209
+
1210
+ # Calculate cumulative sum to identify regions between newlines
1211
+ cumsum = torch.cumsum(newline_positions, dim=1)
1212
+
1213
+ # Create the mask
1214
+ mask = cumsum > ACTION_DIM
1215
+
1216
+ # Extract the action part only
1217
+ action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
1218
+ next_actions_mask = action_tokens_only_mask * mask
1219
+
1220
+ all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
1221
+
1222
+ #test end
1223
+
1224
+ # Extract language embeddings
1225
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1226
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1227
+ )
1228
+
1229
+ # Process vision features
1230
+ #projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1231
+ #test
1232
+ if use_film:
1233
+ # FiLM: Infuse language inputs into visual features
1234
+ raise ValueError
1235
+ patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
1236
+ else:
1237
+ patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
1238
+
1239
+ projected_patch_embeddings = self.projector(patch_features)
1240
+ #test end
1241
+
1242
+
1243
+ # Add proprioceptive features if provided
1244
+ use_proprio = self.proprio_projector is not None and proprio is not None
1245
+ if use_proprio:
1246
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1247
+ projected_patch_embeddings = self._process_proprio_features(
1248
+ projected_patch_embeddings, proprio, self.proprio_projector
1249
+ )
1250
+
1251
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1252
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1253
+
1254
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1255
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1256
+ if use_proprio:
1257
+ NUM_PATCHES += 1
1258
+ if use_diffusion:
1259
+ NUM_PATCHES += 1
1260
+
1261
+ if use_diffusion:
1262
+ raise ValueError
1263
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1264
+ noise = torch.randn(
1265
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1266
+ )
1267
+
1268
+ # Run diffusion-based prediction
1269
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1270
+ input_embeddings,
1271
+ all_actions_mask,
1272
+ noise,
1273
+ action_head,
1274
+ projected_patch_embeddings,
1275
+ labels,
1276
+ attention_mask,
1277
+ NUM_PATCHES,
1278
+ NUM_PROMPT_TOKENS,
1279
+ noisy_action_projector,
1280
+ )
1281
+ else:
1282
+ # Run regression or discrete token-based prediction
1283
+ # compute_logits = self._verl_discrete_compute_logits(
1284
+ # input_embeddings,
1285
+ # all_actions_mask,
1286
+ # projected_patch_embeddings,
1287
+ # attention_mask,
1288
+ # labels,
1289
+ # NUM_PATCHES,
1290
+ # NUM_PROMPT_TOKENS,
1291
+ # action_head,
1292
+ # )
1293
+
1294
+ #test
1295
+
1296
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1297
+ input_embeddings = input_embeddings * ~all_actions_mask
1298
+
1299
+ # Build multimodal embeddings and attention mask
1300
+ # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1301
+ # input_embeddings, projected_patch_embeddings, attention_mask
1302
+ # )
1303
+ #test
1304
+
1305
+ projected_patch_attention_mask = None
1306
+ if attention_mask is not None:
1307
+ projected_patch_attention_mask = torch.full(
1308
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
1309
+ fill_value=True,
1310
+ dtype=attention_mask.dtype,
1311
+ device=attention_mask.device,
1312
+ )
1313
+
1314
+ # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
1315
+ multimodal_embeddings = torch.cat(
1316
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
1317
+ )
1318
+
1319
+ multimodal_attention_mask = None
1320
+ if attention_mask is not None:
1321
+ multimodal_attention_mask = torch.cat(
1322
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
1323
+ )
1324
+
1325
+ #return multimodal_embeddings, multimodal_attention_mask
1326
+
1327
+ #test end
1328
+
1329
+ # Forward pass through language model
1330
+ language_model_output = self.language_model(
1331
+ input_ids=None,
1332
+ attention_mask=multimodal_attention_mask,
1333
+ position_ids=None,
1334
+ past_key_values=None,
1335
+ inputs_embeds=multimodal_embeddings,
1336
+ labels=None,
1337
+ use_cache=None,
1338
+ output_attentions=False,
1339
+ output_hidden_states=False,
1340
+ return_dict=True,
1341
+ )
1342
+
1343
+
1344
+ compute_logits = language_model_output.logits[
1345
+ :,
1346
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1347
+ ]
1348
+
1349
+ #test end
1350
+
1351
+ return compute_logits
1352
+
1353
+
1354
+
1355
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
1356
+ config_class: PretrainedConfig = OpenVLAConfig
1357
+
1358
+ def __init__(self, config: OpenVLAConfig) -> None:
1359
+ super().__init__(config)
1360
+ self.norm_stats = config.norm_stats
1361
+
1362
+ # Compute action bins
1363
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
1364
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
1365
+
1366
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
1367
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
1368
+
1369
+ def load_proprio_projector_weights(self, checkpoint_path_or_repo_id: str):
1370
+ """
1371
+ Load pre-trained weights for the proprio projector.
1372
+
1373
+ Args:
1374
+ checkpoint_path_or_repo_id: Either a local path to checkpoint file or HF Hub repo ID
1375
+ """
1376
+ if self.proprio_projector is None:
1377
+ raise ValueError("Model was not initialized with use_proprio=True")
1378
+
1379
+ checkpoint_path = find_checkpoint_file(checkpoint_path_or_repo_id, "proprio_projector")
1380
+ state_dict = load_component_state_dict(checkpoint_path)
1381
+ self.proprio_projector.load_state_dict(state_dict)
1382
+
1383
+ def _prepare_input_for_action_prediction(self, input_ids, attention_mask):
1384
+ """Prepares input for action prediction by adding necessary tokens"""
1385
+ # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
1386
+ placeholder_action_token_ids = (
1387
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
1388
+ )
1389
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
1390
+
1391
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
1392
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
1393
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
1394
+
1395
+ # Extend the attention mask to fit the new shape of input
1396
+ # Note: Only batch size == 1 supported right now
1397
+ mask_extension = (
1398
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
1399
+ .to(attention_mask.device)
1400
+ .to(attention_mask.dtype)
1401
+ )
1402
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
1403
+
1404
+ return input_ids, attention_mask
1405
+
1406
+ def _prepare_labels_for_action_prediction(self, labels, input_ids):
1407
+ """Creates labels tensor for action prediction if not provided"""
1408
+ # Extend labels tensor with fake action labels
1409
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
1410
+ labels_extension = (
1411
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
1412
+ * ARBITRARY_ACTION_TOKEN_IDX
1413
+ )
1414
+ labels = torch.cat([labels, labels_extension], dim=-1)
1415
+
1416
+ # Replace last label token with stop token
1417
+ labels[:, -1] = STOP_INDEX
1418
+
1419
+ return labels
1420
+
1421
+ def _unnormalize_actions(self, normalized_actions, unnorm_key=None):
1422
+ """Unnormalize actions using dataset statistics"""
1423
+ action_norm_stats = self.get_action_stats(unnorm_key)
1424
+
1425
+ if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS:
1426
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool))
1427
+ action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"])
1428
+ elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99:
1429
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
1430
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
1431
+ else:
1432
+ raise ValueError("Unsupported action/proprio normalization type detected!")
1433
+
1434
+ actions = np.where(
1435
+ mask,
1436
+ 0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low,
1437
+ normalized_actions,
1438
+ )
1439
+
1440
+ return actions
1441
+
1442
+ def _run_diffusion_prediction(
1443
+ self,
1444
+ input_embeddings,
1445
+ all_actions_mask,
1446
+ noise,
1447
+ action_head,
1448
+ projected_patch_embeddings,
1449
+ labels,
1450
+ attention_mask,
1451
+ NUM_PATCHES,
1452
+ NUM_PROMPT_TOKENS,
1453
+ noisy_action_projector,
1454
+ ):
1455
+ """Run diffusion-based action prediction"""
1456
+ # Set diffusion timestep values
1457
+ action_head.noise_scheduler.set_timesteps(action_head.num_diffusion_steps)
1458
+ # Clone embedding for reuse in each timestep
1459
+ orig_projected_patch_embeddings = projected_patch_embeddings.clone()
1460
+ curr_noisy_actions = noise
1461
+
1462
+ # Reverse diffusion: Iteratively denoise to generate action prediction
1463
+ for t in action_head.noise_scheduler.timesteps:
1464
+ # Get diffusion model's noise prediction (conditioned on VLA latent embedding, current noisy action
1465
+ # embedding, and diffusion timestep embedding)
1466
+ timesteps = torch.Tensor([t]).to(labels.device)
1467
+ diffusion_timestep_embeddings = (
1468
+ action_head.time_encoder(timesteps).to(curr_noisy_actions.dtype).to(curr_noisy_actions.device)
1469
+ ) # (B, llm_dim)
1470
+ diffusion_timestep_embeddings = diffusion_timestep_embeddings.unsqueeze(1) # (B, 1, llm_dim)
1471
+
1472
+ # [Diffusion] Replace the embeddings of the action tokens with noisy actions
1473
+ # (Later on, the positional embeddings will be added to them)
1474
+
1475
+ # For simplicity, append diffusion timestep embedding to the end of projected vision tokens
1476
+ projected_patch_embeddings = torch.cat(
1477
+ (orig_projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
1478
+ )
1479
+
1480
+ # Reshape and project noisy actions into language embedding space
1481
+ B = curr_noisy_actions.shape[0]
1482
+ orig_curr_noisy_actions_shape = curr_noisy_actions.shape
1483
+ curr_noisy_actions = curr_noisy_actions.reshape(B, -1).unsqueeze(-1)
1484
+ noisy_action_features = noisy_action_projector(curr_noisy_actions)
1485
+ curr_noisy_actions = curr_noisy_actions.reshape(orig_curr_noisy_actions_shape)
1486
+
1487
+ # Replace action token embeddings with noisy action embeddings
1488
+ input_embeddings = self._replace_input_embeddings(
1489
+ input_embeddings.clone(), all_actions_mask, noisy_action_features
1490
+ )
1491
+
1492
+ # Build multimodal embeddings and attention mask
1493
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1494
+ input_embeddings, projected_patch_embeddings, attention_mask
1495
+ )
1496
+
1497
+ # Forward pass through language model
1498
+ language_model_output = self.language_model(
1499
+ input_ids=None,
1500
+ attention_mask=multimodal_attention_mask,
1501
+ position_ids=None,
1502
+ past_key_values=None,
1503
+ inputs_embeds=multimodal_embeddings,
1504
+ labels=None,
1505
+ use_cache=None,
1506
+ output_attentions=False,
1507
+ output_hidden_states=True,
1508
+ return_dict=True,
1509
+ )
1510
+
1511
+ # Extract hidden states for action portion of response
1512
+ last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1513
+ actions_hidden_states = last_hidden_states[
1514
+ :,
1515
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1516
+ :,
1517
+ ] # (B, act_chunk_len, D)
1518
+
1519
+ # Predict noise and update noisy actions: x_t -> x_{t-1}
1520
+ noise_pred = action_head.predict_noise(actions_hidden_states)
1521
+ curr_noisy_actions = action_head.noise_scheduler.step(noise_pred, t, curr_noisy_actions).prev_sample
1522
+
1523
+ curr_noisy_actions = curr_noisy_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1524
+
1525
+ # Return final actions
1526
+ return curr_noisy_actions.float().cpu().detach().numpy(), actions_hidden_states
1527
+
1528
+ def _regression_or_discrete_prediction(
1529
+ self,
1530
+ input_embeddings,
1531
+ all_actions_mask,
1532
+ projected_patch_embeddings,
1533
+ attention_mask,
1534
+ labels,
1535
+ NUM_PATCHES,
1536
+ NUM_PROMPT_TOKENS,
1537
+ action_head=None,
1538
+ ):
1539
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
1540
+ # Zero out action token embeddings
1541
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1542
+ input_embeddings = input_embeddings * ~all_actions_mask
1543
+
1544
+ # Build multimodal embeddings and attention mask
1545
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1546
+ input_embeddings, projected_patch_embeddings, attention_mask
1547
+ )
1548
+
1549
+ # Forward pass through language model
1550
+ language_model_output = self.language_model(
1551
+ input_ids=None,
1552
+ attention_mask=multimodal_attention_mask,
1553
+ position_ids=None,
1554
+ past_key_values=None,
1555
+ inputs_embeds=multimodal_embeddings,
1556
+ labels=None,
1557
+ use_cache=None,
1558
+ output_attentions=False,
1559
+ output_hidden_states=True,
1560
+ return_dict=True,
1561
+ )
1562
+
1563
+ # Extract hidden states for action tokens
1564
+ last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1565
+ actions_hidden_states = last_hidden_states[
1566
+ :,
1567
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1568
+ :,
1569
+ ] # (B, act_chunk_len, D)
1570
+
1571
+ # Handle different prediction methods
1572
+ if action_head is not None:
1573
+ # L1 regression prediction
1574
+ normalized_actions = action_head.predict_action(actions_hidden_states)
1575
+ normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1576
+ normalized_actions = normalized_actions.float().cpu().detach().numpy()
1577
+ else:
1578
+ # Discrete token-based prediction
1579
+ predicted_action_token_ids = (
1580
+ language_model_output.logits[
1581
+ :,
1582
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1583
+ ]
1584
+ .argmax(dim=2)
1585
+ .cpu()
1586
+ .numpy()
1587
+ )
1588
+ discretized_actions = self.vocab_size - predicted_action_token_ids
1589
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
1590
+ normalized_actions = self.bin_centers[discretized_actions]
1591
+ normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1592
+
1593
+ return normalized_actions, actions_hidden_states
1594
+
1595
+ def _verl_discrete_prediction(
1596
+ self,
1597
+ input_embeddings,
1598
+ all_actions_mask,
1599
+ projected_patch_embeddings,
1600
+ attention_mask,
1601
+ labels,
1602
+ NUM_PATCHES,
1603
+ NUM_PROMPT_TOKENS,
1604
+ action_head=None,
1605
+ do_sample=True,
1606
+ temperature=1,
1607
+ ):
1608
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
1609
+ # Zero out action token embeddings
1610
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1611
+ input_embeddings = input_embeddings * ~all_actions_mask
1612
+
1613
+ # Build multimodal embeddings and attention mask
1614
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1615
+ input_embeddings, projected_patch_embeddings, attention_mask
1616
+ )
1617
+
1618
+ # Forward pass through language model
1619
+ language_model_output = self.language_model(
1620
+ input_ids=None,
1621
+ attention_mask=multimodal_attention_mask,
1622
+ position_ids=None,
1623
+ past_key_values=None,
1624
+ inputs_embeds=multimodal_embeddings,
1625
+ labels=None,
1626
+ use_cache=None,
1627
+ output_attentions=False,
1628
+ output_hidden_states=False,
1629
+ return_dict=True,
1630
+ )
1631
+
1632
+ # Extract hidden states for action tokens
1633
+ #last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1634
+ # actions_hidden_states = last_hidden_states[
1635
+ # :,
1636
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1637
+ # :,
1638
+ # ] # (B, act_chunk_len, D)
1639
+
1640
+ # Handle different prediction methods
1641
+ # if action_head is not None:
1642
+ # # L1 regression prediction
1643
+ # normalized_actions = action_head.predict_action(actions_hidden_states)
1644
+ # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1645
+ # normalized_actions = normalized_actions.float().cpu().detach().numpy()
1646
+ # else:
1647
+ # Discrete token-based prediction
1648
+
1649
+ #test
1650
+ # NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES
1651
+ # j = torch.arange(language_model_output.logits.shape[1], device=NUM_PROMPT_TOKENS.device)
1652
+ # start = NUM_PROMPT_TOKENS.unsqueeze(1)
1653
+ # end = start + ACTION_DIM * NUM_ACTIONS_CHUNK
1654
+ # mask_2d = (j >= start) & (j < end)
1655
+ # mask = mask_2d.unsqueeze(-1)
1656
+ # actions_masks = mask.expand_as(language_model_output.logits)
1657
+
1658
+
1659
+ NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES
1660
+ batch_size = language_model_output.logits.shape[0]
1661
+ device = language_model_output.logits.device
1662
+
1663
+
1664
+ start_indices = NUM_PROMPT_TOKENS.unsqueeze(1) # [batch_size, 1]
1665
+ position_offsets = torch.arange(ACTION_DIM * NUM_ACTIONS_CHUNK, device=device).unsqueeze(0) # [1, seq_length]
1666
+ seq_indices = start_indices + position_offsets # [batch_size, ACTION_DIM*NUM_ACTIONS_CHUNK]
1667
+ #test end
1668
+ #test add
1669
+ #print("language_model_output",language_model_output.logits.shape[-1])
1670
+ #print("self.vocab_size",self.vocab_size) 32000
1671
+ #topk_values, topk_indices = torch.topk(language_model_output.logits, k=256, dim=-1)
1672
+ #print(topk_indices)
1673
+ #assert language_model_output.logits.shape[-1] == self.vocab_size
1674
+ #test add
1675
+ if do_sample == False:
1676
+ #org
1677
+ # reponse_ids = language_model_output.logits[
1678
+ # :,
1679
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1680
+ # ].argmax(dim=2)
1681
+ #reponse_ids = language_model_output.logits[actions_masks].argmax(dim=2)
1682
+ #org end
1683
+
1684
+ #padding
1685
+ # reponse_ids = language_model_output.logits[
1686
+ # torch.arange(batch_size, device=device).unsqueeze(-1),
1687
+ # seq_indices,
1688
+ # :
1689
+ # ].argmax(dim=2)
1690
+ #padding end
1691
+
1692
+ #padding + only get last 256 token
1693
+ reponse_ids_logits = language_model_output.logits[
1694
+ torch.arange(batch_size, device=device).unsqueeze(-1),
1695
+ seq_indices,
1696
+ :
1697
+ ]
1698
+ start_index = self.vocab_size - 256
1699
+ response_last256 = reponse_ids_logits[..., -256-64:-64] # Shape: [batch_size, seq_len, 256]
1700
+ last256_argmax = response_last256.argmax(dim=-1) # Shape: [batch_size, seq_len]
1701
+ reponse_ids = last256_argmax + start_index # Shape: [batch_size, seq_len]
1702
+ #padding + only get last 256 token end
1703
+
1704
+ predicted_action_token_ids = reponse_ids.cpu().numpy()
1705
+
1706
+ else:
1707
+ assert temperature>0
1708
+ #org
1709
+ # action_logits = language_model_output.logits[
1710
+ # :,
1711
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1712
+ # ]
1713
+ #action_logits = language_model_output.logits[actions_masks]
1714
+ #org end
1715
+
1716
+ action_logits = language_model_output.logits[
1717
+ torch.arange(batch_size, device=device).unsqueeze(-1),
1718
+ seq_indices,
1719
+ :
1720
+ ]
1721
+ # padding
1722
+ # scaled_logits = action_logits / temperature
1723
+ # probs = torch.softmax(scaled_logits, dim=-1)
1724
+ # probs_flat = probs.reshape(-1, probs.shape[-1]) # (B*act_chunk_len, vocab_size)
1725
+ # sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1) # (B*act_chunk_len, 1)
1726
+ # reponse_ids = sampled_indices_flat.view(action_logits.shape[0], -1)
1727
+ # padding end
1728
+
1729
+ #padding + only get last 256 token
1730
+ action_logits_last256 = action_logits[..., -256-64:-64]
1731
+ scaled_logits = action_logits_last256 / temperature
1732
+ probs = torch.softmax(scaled_logits, dim=-1)
1733
+ assert probs.shape[-1] == 256
1734
+ probs_flat = probs.reshape(-1, probs.shape[-1])
1735
+ sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1)
1736
+ original_ids_flat = sampled_indices_flat + (self.vocab_size - 256)
1737
+ reponse_ids = original_ids_flat.view(action_logits.shape[0], -1)
1738
+ #padding + only get last 256 token end
1739
+
1740
+ predicted_action_token_ids = reponse_ids.cpu().numpy()
1741
+
1742
+ discretized_actions = self.vocab_size - predicted_action_token_ids
1743
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
1744
+ normalized_actions = self.bin_centers[discretized_actions]
1745
+ #normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1746
+ normalized_actions = normalized_actions.reshape(-1, ACTION_DIM)
1747
+
1748
+ return normalized_actions, reponse_ids
1749
+ #return normalized_actions, actions_hidden_states
1750
+
1751
+
1752
+
1753
+
1754
+ def predict_action(
1755
+ self,
1756
+ input_ids: Optional[torch.LongTensor] = None,
1757
+ unnorm_key: Optional[str] = None,
1758
+ proprio=None,
1759
+ proprio_projector=None,
1760
+ action_head=None,
1761
+ noisy_action_projector=None,
1762
+ use_film: bool = False,
1763
+ **kwargs: str,
1764
+ ) -> np.ndarray:
1765
+ """Predict actions from input sequence, with options for different prediction methods.
1766
+
1767
+ Args:
1768
+ input_ids: Input token ids
1769
+ unnorm_key: Key for unnormalization statistics
1770
+ proprio: Proprioceptive features
1771
+ proprio_projector: Projector for proprioceptive features
1772
+ action_head: Optional head for L1 regression or diffusion-based prediction
1773
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1774
+ use_film: Whether to use FiLM conditioning
1775
+ **kwargs: Additional arguments including pixel_values and attention_mask
1776
+
1777
+ Returns:
1778
+ Tuple of (unnormalized_actions, action_hidden_states)
1779
+ """
1780
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1781
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1782
+ if not torch.all(input_ids[:, -1] == 29871):
1783
+ input_ids = torch.cat(
1784
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1785
+ )
1786
+
1787
+ pixel_values = kwargs["pixel_values"]
1788
+ attention_mask = kwargs["attention_mask"]
1789
+
1790
+ # Create fake labels tensor (needed for action mask)
1791
+ labels = input_ids.clone()
1792
+ labels[:] = IGNORE_INDEX
1793
+
1794
+ # Get number of tokens in prompt (excluding the start token)
1795
+ NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1796
+
1797
+ # Prepare inputs by adding necessary tokens
1798
+ input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
1799
+
1800
+ # Update labels tensor for action mask computation later
1801
+ labels = self._prepare_labels_for_action_prediction(labels, input_ids)
1802
+
1803
+ # Get input embeddings and action masks
1804
+ input_embeddings = self.get_input_embeddings()(input_ids)
1805
+ all_actions_mask = self._process_action_masks(labels)
1806
+
1807
+ # Extract language embeddings
1808
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1809
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1810
+ )
1811
+
1812
+ # Process vision features
1813
+ projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1814
+
1815
+ # Add proprioceptive features if provided
1816
+ use_proprio = proprio_projector is not None and proprio is not None
1817
+ if use_proprio:
1818
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1819
+ projected_patch_embeddings = self._process_proprio_features(
1820
+ projected_patch_embeddings, proprio, proprio_projector
1821
+ )
1822
+
1823
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1824
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1825
+
1826
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1827
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1828
+ if use_proprio:
1829
+ NUM_PATCHES += 1
1830
+ if use_diffusion:
1831
+ NUM_PATCHES += 1
1832
+
1833
+ if use_diffusion:
1834
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1835
+ noise = torch.randn(
1836
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1837
+ )
1838
+
1839
+ # Run diffusion-based prediction
1840
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1841
+ input_embeddings,
1842
+ all_actions_mask,
1843
+ noise,
1844
+ action_head,
1845
+ projected_patch_embeddings,
1846
+ labels,
1847
+ attention_mask,
1848
+ NUM_PATCHES,
1849
+ NUM_PROMPT_TOKENS,
1850
+ noisy_action_projector,
1851
+ )
1852
+ else:
1853
+ # Run regression or discrete token-based prediction
1854
+ normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction(
1855
+ input_embeddings,
1856
+ all_actions_mask,
1857
+ projected_patch_embeddings,
1858
+ attention_mask,
1859
+ labels,
1860
+ NUM_PATCHES,
1861
+ NUM_PROMPT_TOKENS,
1862
+ action_head,
1863
+ )
1864
+
1865
+ # Unnormalize predicted actions
1866
+ actions = self._unnormalize_actions(normalized_actions, unnorm_key)
1867
+
1868
+ return actions, actions_hidden_states
1869
+
1870
+ def generate_action_verl(
1871
+ self,
1872
+ input_ids: Optional[torch.LongTensor] = None,
1873
+ unnorm_key: Optional[str] = None,
1874
+ proprio=None,
1875
+ # proprio_projector=None,
1876
+ action_head=None,
1877
+ noisy_action_projector=None,
1878
+ use_film: bool = False,
1879
+ **kwargs: str,
1880
+ ) -> np.ndarray:
1881
+ """Predict actions from input sequence, with options for different prediction methods.
1882
+
1883
+ Args:
1884
+ input_ids: Input token ids
1885
+ unnorm_key: Key for unnormalization statistics
1886
+ proprio: Proprioceptive features
1887
+ proprio_projector: Projector for proprioceptive features
1888
+ action_head: Optional head for L1 regression or diffusion-based prediction
1889
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1890
+ use_film: Whether to use FiLM conditioning
1891
+ **kwargs: Additional arguments including pixel_values and attention_mask
1892
+
1893
+ Returns:
1894
+ Tuple of (unnormalized_actions, action_hidden_states)
1895
+ """
1896
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1897
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1898
+ # if not torch.all(input_ids[:, -1] == 29871):
1899
+ # input_ids = torch.cat(
1900
+ # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1901
+ # )
1902
+
1903
+ pixel_values = kwargs["pixel_values"]
1904
+ attention_mask = kwargs["attention_mask"]
1905
+ do_sample = kwargs["do_sample"]
1906
+ temperature = kwargs["temperature"]
1907
+
1908
+ # Create fake labels tensor (needed for action mask)
1909
+ labels = input_ids.clone()
1910
+ labels[:] = IGNORE_INDEX
1911
+
1912
+ # Get number of tokens in prompt (excluding the start token)
1913
+ #NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1914
+ #test
1915
+ padding_idx = kwargs["padding_idx"]
1916
+ num_prompt_tokens = input_ids.ne(padding_idx).sum(dim=1) - 1
1917
+ #test end
1918
+
1919
+
1920
+ # Prepare inputs by adding necessary tokens
1921
+ input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
1922
+
1923
+ # Update labels tensor for action mask computation later
1924
+ labels = self._prepare_labels_for_action_prediction(labels, input_ids)
1925
+
1926
+ #here to convert padding from before to last
1927
+ #test
1928
+ padding_mask = input_ids.ne(padding_idx)
1929
+ assert torch.all(padding_mask==attention_mask.ne(0))
1930
+ #print("in predict_action padding_mask:", padding_mask)
1931
+ padding_mask = padding_mask.int()
1932
+ sorted_indices = torch.argsort(padding_mask, dim=1, descending=True, stable=True)
1933
+ input_ids = torch.gather(input_ids, 1, sorted_indices)
1934
+ attention_mask = torch.gather(attention_mask, 1, sorted_indices)
1935
+ labels = torch.gather(labels, 1, sorted_indices)
1936
+ assert use_film==False
1937
+ #test end
1938
+
1939
+
1940
+ # Get input embeddings and action masks
1941
+ input_embeddings = self.get_input_embeddings()(input_ids)
1942
+ all_actions_mask = self._process_action_masks(labels)
1943
+
1944
+ # Extract language embeddings
1945
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1946
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1947
+ )
1948
+
1949
+ # Process vision features
1950
+ projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1951
+
1952
+ # Add proprioceptive features if provided
1953
+ use_proprio = self.proprio_projector is not None and proprio is not None
1954
+ if use_proprio:
1955
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1956
+ projected_patch_embeddings = self._process_proprio_features(
1957
+ projected_patch_embeddings, proprio, self.proprio_projector
1958
+ )
1959
+
1960
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1961
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1962
+
1963
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1964
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1965
+ if use_proprio:
1966
+ NUM_PATCHES += 1
1967
+ if use_diffusion:
1968
+ NUM_PATCHES += 1
1969
+
1970
+ if use_diffusion:
1971
+ raise ValueError
1972
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1973
+ noise = torch.randn(
1974
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1975
+ )
1976
+
1977
+ # Run diffusion-based prediction
1978
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1979
+ input_embeddings,
1980
+ all_actions_mask,
1981
+ noise,
1982
+ action_head,
1983
+ projected_patch_embeddings,
1984
+ labels,
1985
+ attention_mask,
1986
+ NUM_PATCHES,
1987
+ NUM_PROMPT_TOKENS,
1988
+ noisy_action_projector,
1989
+ )
1990
+ else:
1991
+ # Run regression or discrete token-based prediction
1992
+ normalized_actions, reponse_ids = self._verl_discrete_prediction(
1993
+ input_embeddings,
1994
+ all_actions_mask,
1995
+ projected_patch_embeddings,
1996
+ attention_mask,
1997
+ labels,
1998
+ NUM_PATCHES,
1999
+ num_prompt_tokens,
2000
+ action_head,
2001
+ do_sample=do_sample,
2002
+ temperature=temperature,
2003
+ )
2004
+
2005
+ # Unnormalize predicted actions
2006
+ actions = self._unnormalize_actions(normalized_actions, unnorm_key)
2007
+ #verl add!
2008
+ actions = actions.reshape(-1 ,NUM_ACTIONS_CHUNK, ACTION_DIM)
2009
+ #
2010
+ return actions, reponse_ids
2011
+
2012
+
2013
+
2014
+ @staticmethod
2015
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
2016
+ """Validate and resolve the unnormalization key for action statistics"""
2017
+ if unnorm_key is None:
2018
+ assert len(norm_stats) == 1, (
2019
+ f"Your model was trained on more than one dataset, "
2020
+ f"please pass a `unnorm_key` from the following options to choose the statistics "
2021
+ f"used for un-normalizing actions: {norm_stats.keys()}"
2022
+ )
2023
+ unnorm_key = next(iter(norm_stats.keys()))
2024
+
2025
+ assert unnorm_key in norm_stats, (
2026
+ f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
2027
+ f"please choose from: {norm_stats.keys()}"
2028
+ )
2029
+ return unnorm_key
2030
+
2031
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
2032
+ """Get the dimensionality of the policy's action space."""
2033
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
2034
+ return len(self.norm_stats[unnorm_key]["action"]["min"])
2035
+
2036
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
2037
+ """Get all the logged statistics for the given dataset."""
2038
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
2039
+ 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 +1,257 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
2
+ 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,
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": "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
+ }