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openvla-oft-libero10-traj1-rl

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+ "output_projector_states": false,
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+ "pad_to_multiple_of": 64,
3152
+ "pad_token_id": 32000,
3153
+ "text_config": {
3154
+ "model_type": "llama",
3155
+ "pad_token_id": 32000,
3156
+ "torch_dtype": "bfloat16",
3157
+ "vocab_size": 32064
3158
+ },
3159
+ "timm_model_ids": [
3160
+ "vit_large_patch14_reg4_dinov2.lvd142m",
3161
+ "vit_so400m_patch14_siglip_224"
3162
+ ],
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+ "timm_override_act_layers": [
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+ null,
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+ null
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.40.1",
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+ "use_fused_vision_backbone": true,
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+ "vision_backbone_id": "dinosiglip-vit-so-224px"
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+ }
configuration_prismatic.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ configuration_prismatic.py
3
+
4
+ HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
5
+ Default configuration specifies `siglip-224px+7b`.
6
+ """
7
+
8
+ from typing import Any, Dict, List, Optional
9
+
10
+ from transformers import PretrainedConfig
11
+ from transformers.models.auto import CONFIG_MAPPING
12
+
13
+ # === Utilities for Mapping Prismatic names to HF names ===
14
+ # fmt: off
15
+ VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
16
+ "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
17
+
18
+ "clip-vit-l-336px": [336],
19
+ "siglip-vit-so400m-384px": [384],
20
+
21
+ "dinoclip-vit-l-336px": [336, 336],
22
+ "dinosiglip-vit-so-224px": [224, 224],
23
+ "dinosiglip-vit-so-384px": [384, 384],
24
+ }
25
+ VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
26
+ "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
27
+ "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
28
+
29
+ "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
30
+ "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
31
+
32
+ "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
33
+ "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
34
+
35
+ "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
36
+ "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
37
+ "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
38
+ }
39
+ TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
40
+ "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
41
+ "dinov2-vit-l": [None], "in1k-vit-l": [None],
42
+ "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
43
+ "dinoclip-vit-l-336px": [None, "quick_gelu"],
44
+ "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
45
+ }
46
+
47
+ LLM_BACKBONE_TO_HF_PATH = {
48
+ "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
49
+ "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
50
+
51
+ "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
52
+
53
+ "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
54
+ "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
55
+
56
+ "phi-2-3b": "microsoft/phi-2",
57
+ }
58
+ LLM_BACKBONE_TO_HF_METACLASS = {
59
+ "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
60
+ "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
61
+
62
+ "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
63
+
64
+ "phi-2-3b": "phi",
65
+ }
66
+
67
+ VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
68
+ VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
69
+ # fmt: on
70
+
71
+
72
+ class PrismaticConfig(PretrainedConfig):
73
+ model_type: str = "prismatic"
74
+ is_composition: bool = False
75
+
76
+ def __init__(
77
+ self,
78
+ vision_backbone_id: str = "siglip-vit-so400m",
79
+ llm_backbone_id: str = "vicuna-v15-7b",
80
+ arch_specifier: str = "no-align+gelu-mlp",
81
+ use_fused_vision_backbone: Optional[bool] = None,
82
+ image_resize_strategy: str = "letterbox",
83
+ text_config: Optional[Dict[str, Any]] = None,
84
+ llm_max_length: int = 2048,
85
+ pad_token_id: int = 32000,
86
+ pad_to_multiple_of: int = 64,
87
+ output_projector_states: bool = False,
88
+ **kwargs: str,
89
+ ) -> None:
90
+ if vision_backbone_id not in VALID_VISION_BACKBONES:
91
+ raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
92
+
93
+ if llm_backbone_id not in VALID_LLM_BACKBONES:
94
+ raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
95
+
96
+ # Set Prismatic Configuration Fields
97
+ self.vision_backbone_id = vision_backbone_id
98
+ self.llm_backbone_id = llm_backbone_id
99
+ self.arch_specifier = arch_specifier
100
+ self.output_projector_states = output_projector_states
101
+
102
+ # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
103
+ self.use_fused_vision_backbone = (
104
+ use_fused_vision_backbone
105
+ if use_fused_vision_backbone is not None
106
+ else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
107
+ )
108
+
109
+ self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
110
+ self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
111
+ self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
112
+ self.image_resize_strategy = image_resize_strategy
113
+
114
+ self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
115
+ self.llm_max_length = llm_max_length
116
+ self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
117
+
118
+ # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
119
+ self.text_config = (
120
+ CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
121
+ if text_config is not None
122
+ else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
123
+ )
124
+
125
+ # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
126
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
127
+
128
+
129
+ class OpenVLAConfig(PrismaticConfig):
130
+ model_type: str = "openvla"
131
+
132
+ def __init__(
133
+ self,
134
+ norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
135
+ n_action_bins: int = 256,
136
+ **kwargs: str,
137
+ ) -> None:
138
+ self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
139
+
140
+ super().__init__(**kwargs)
dataset_statistics.json ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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+ }
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+ }
generation_config.json ADDED
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+ "vision_backbone.fused_featurizer.blocks.7.norm2.bias": "model-00001-of-00007.safetensors",
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+ "vision_backbone.fused_featurizer.blocks.7.norm2.weight": "model-00001-of-00007.safetensors",
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+ "vision_backbone.fused_featurizer.blocks.8.attn.proj.bias": "model-00001-of-00007.safetensors",
960
+ "vision_backbone.fused_featurizer.blocks.8.attn.proj.weight": "model-00001-of-00007.safetensors",
961
+ "vision_backbone.fused_featurizer.blocks.8.attn.qkv.bias": "model-00001-of-00007.safetensors",
962
+ "vision_backbone.fused_featurizer.blocks.8.attn.qkv.weight": "model-00001-of-00007.safetensors",
963
+ "vision_backbone.fused_featurizer.blocks.8.mlp.fc1.bias": "model-00001-of-00007.safetensors",
964
+ "vision_backbone.fused_featurizer.blocks.8.mlp.fc1.weight": "model-00001-of-00007.safetensors",
965
+ "vision_backbone.fused_featurizer.blocks.8.mlp.fc2.bias": "model-00001-of-00007.safetensors",
966
+ "vision_backbone.fused_featurizer.blocks.8.mlp.fc2.weight": "model-00001-of-00007.safetensors",
967
+ "vision_backbone.fused_featurizer.blocks.8.norm1.bias": "model-00001-of-00007.safetensors",
968
+ "vision_backbone.fused_featurizer.blocks.8.norm1.weight": "model-00001-of-00007.safetensors",
969
+ "vision_backbone.fused_featurizer.blocks.8.norm2.bias": "model-00001-of-00007.safetensors",
970
+ "vision_backbone.fused_featurizer.blocks.8.norm2.weight": "model-00001-of-00007.safetensors",
971
+ "vision_backbone.fused_featurizer.blocks.9.attn.proj.bias": "model-00001-of-00007.safetensors",
972
+ "vision_backbone.fused_featurizer.blocks.9.attn.proj.weight": "model-00001-of-00007.safetensors",
973
+ "vision_backbone.fused_featurizer.blocks.9.attn.qkv.bias": "model-00001-of-00007.safetensors",
974
+ "vision_backbone.fused_featurizer.blocks.9.attn.qkv.weight": "model-00001-of-00007.safetensors",
975
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976
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc1.weight": "model-00001-of-00007.safetensors",
977
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.bias": "model-00001-of-00007.safetensors",
978
+ "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.weight": "model-00001-of-00007.safetensors",
979
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980
+ "vision_backbone.fused_featurizer.blocks.9.norm1.weight": "model-00001-of-00007.safetensors",
981
+ "vision_backbone.fused_featurizer.blocks.9.norm2.bias": "model-00001-of-00007.safetensors",
982
+ "vision_backbone.fused_featurizer.blocks.9.norm2.weight": "model-00001-of-00007.safetensors",
983
+ "vision_backbone.fused_featurizer.norm.bias": "model-00001-of-00007.safetensors",
984
+ "vision_backbone.fused_featurizer.norm.weight": "model-00001-of-00007.safetensors",
985
+ "vision_backbone.fused_featurizer.patch_embed.proj.bias": "model-00001-of-00007.safetensors",
986
+ "vision_backbone.fused_featurizer.patch_embed.proj.weight": "model-00001-of-00007.safetensors",
987
+ "vision_backbone.fused_featurizer.pos_embed": "model-00001-of-00007.safetensors"
988
+ }
989
+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,1996 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 prismatic.training.train_utils import (
25
+ get_current_action_mask,
26
+ get_next_actions_mask,
27
+ )
28
+ from prismatic.vla.constants import (
29
+ ACTION_DIM,
30
+ ACTION_PROPRIO_NORMALIZATION_TYPE,
31
+ ACTION_TOKEN_BEGIN_IDX,
32
+ IGNORE_INDEX,
33
+ NUM_ACTIONS_CHUNK,
34
+ STOP_INDEX,
35
+ NormalizationType,
36
+ )
37
+
38
+ from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
39
+
40
+ # Set up logger
41
+ logger = logging.getLogger(__name__)
42
+
43
+
44
+ # === Utility Functions for Monkey-Patching ===
45
+ def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
46
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
47
+ result = fn(*args, **kwargs)
48
+ return result[0] if isinstance(result, tuple) else result
49
+
50
+ return wrapper
51
+
52
+
53
+ # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
54
+ # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
55
+ # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
56
+ def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
57
+ return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
58
+
59
+
60
+ def ls_apply_patch(ls_module: LayerScale):
61
+ ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
62
+ ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
63
+ del ls_module.gamma
64
+
65
+
66
+ # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
67
+ class PrismaticVisionBackbone(nn.Module):
68
+ """
69
+ Vision backbone for Prismatic models that handles image feature extraction.
70
+
71
+ Supports both single backbone (e.g., SigLIP) and fused backbone (e.g., SigLIP + DINOv2) configurations.
72
+ For fused backbones, features from both models are concatenated along the feature dimension.
73
+ """
74
+
75
+ def __init__(
76
+ self,
77
+ use_fused_vision_backbone: bool,
78
+ image_sizes: List[int],
79
+ timm_model_ids: List[str],
80
+ timm_override_act_layers: List[Optional[str]],
81
+ ) -> None:
82
+ """
83
+ Initialize the vision backbone.
84
+
85
+ Args:
86
+ use_fused_vision_backbone: Whether to use two backbones and fuse their features
87
+ image_sizes: List of image sizes for each backbone
88
+ timm_model_ids: List of TIMM model IDs to use for each backbone
89
+ timm_override_act_layers: List of activation layer overrides for each backbone
90
+ """
91
+ super().__init__()
92
+ self.use_fused_vision_backbone = use_fused_vision_backbone
93
+ self.num_images_in_input = 1 # Default value, can be overridden later
94
+
95
+ # Validate number of (fused) vision backbones
96
+ if len(timm_model_ids) > 2:
97
+ raise ValueError("Prismatic models only support up to 2 (fused) vision backbones!")
98
+
99
+ # Create primary featurizer
100
+ self.featurizer = self._create_featurizer(
101
+ model_id=timm_model_ids[0], img_size=image_sizes[0], act_layer=timm_override_act_layers[0]
102
+ )
103
+ self.embed_dim = self.featurizer.embed_dim
104
+
105
+ # Create secondary featurizer if using fused backbone
106
+ if self.use_fused_vision_backbone:
107
+ self.fused_featurizer = self._create_featurizer(
108
+ model_id=timm_model_ids[1], img_size=image_sizes[1], act_layer=timm_override_act_layers[1]
109
+ )
110
+ self.embed_dim += self.fused_featurizer.embed_dim
111
+
112
+ # Patch LayerScale modules for HF compatibility
113
+ self._patch_layer_scales()
114
+
115
+ def _create_featurizer(self, model_id: str, img_size: int, act_layer: Optional[str]) -> nn.Module:
116
+ """
117
+ Create a TIMM-based featurizer model with appropriate configurations.
118
+
119
+ Args:
120
+ model_id: The TIMM model ID to load
121
+ img_size: Input image size for the model
122
+ act_layer: Override for the activation layer type
123
+
124
+ Returns:
125
+ A configured featurizer model
126
+ """
127
+ featurizer = timm.create_model(
128
+ model_id,
129
+ pretrained=False,
130
+ num_classes=0,
131
+ img_size=img_size,
132
+ act_layer=act_layer,
133
+ )
134
+
135
+ # Monkey-patch the forward function to extract the second-to-last layer features
136
+ num_blocks = len(featurizer.blocks)
137
+ featurizer.forward = unpack_tuple(partial(featurizer.get_intermediate_layers, n={num_blocks - 2}))
138
+
139
+ return featurizer
140
+
141
+ def _patch_layer_scales(self) -> None:
142
+ """
143
+ Patch all LayerScale modules to be compatible with HF's parameter naming.
144
+
145
+ HF Transformers overwrites parameters with names containing 'gamma',
146
+ so we need to rename and modify the forward method.
147
+ """
148
+ # Patch primary featurizer
149
+ for module in self.featurizer.modules():
150
+ if isinstance(module, LayerScale):
151
+ ls_apply_patch(module)
152
+
153
+ # Patch secondary featurizer if it exists
154
+ if self.use_fused_vision_backbone:
155
+ for module in self.fused_featurizer.modules():
156
+ if isinstance(module, LayerScale):
157
+ ls_apply_patch(module)
158
+
159
+ def get_num_patches(self) -> int:
160
+ """
161
+ Returns the number of vision patches output by the vision backbone.
162
+
163
+ Returns:
164
+ Number of patches per image
165
+ """
166
+ return self.featurizer.patch_embed.num_patches
167
+
168
+ def get_num_images_in_input(self) -> int:
169
+ """
170
+ Returns the number of input images for the vision backbone.
171
+
172
+ Returns:
173
+ Number of images expected in the input
174
+ """
175
+ return self.num_images_in_input
176
+
177
+ def set_num_images_in_input(self, num_images_in_input: int) -> None:
178
+ """
179
+ Sets the number of input images for the vision backbone.
180
+
181
+ Args:
182
+ num_images_in_input: Number of images to expect in the input
183
+ """
184
+ self.num_images_in_input = num_images_in_input
185
+
186
+ def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
187
+ """
188
+ Implements the forward pass for the vision backbone.
189
+
190
+ If `self.use_fused_vision_backbone == True`, uses both SigLIP and DINOv2 transformers to extract visual features
191
+ (otherwise uses SigLIP only). Allows multi-image inputs (but only for fused vision backbone).
192
+
193
+ Args:
194
+ pixel_values (torch.Tensor): Pixels for input image(s), (B, C, H, W).
195
+ """
196
+ if self.num_images_in_input == 1:
197
+ if not self.use_fused_vision_backbone:
198
+ return self.featurizer(pixel_values)
199
+
200
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
201
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
202
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
203
+
204
+ return torch.cat([patches, patches_fused], dim=2)
205
+
206
+ else:
207
+ assert self.use_fused_vision_backbone, "Multi-image inputs require using fused backbone!"
208
+
209
+ # Split `pixel_values` into individual images (each with 6 channels: 3 for SigLIP + 3 for DINOv2)
210
+ images = torch.split(pixel_values, [6] * self.num_images_in_input, dim=1)
211
+
212
+ # Process each image and collect patches
213
+ all_patches = []
214
+ for img in images:
215
+ # Split each image further into two stacks of channels (each with 3 channels)
216
+ img_regular, img_fused = torch.split(img, [3, 3], dim=1)
217
+
218
+ # Get patches from both SigLIP and DINOv2 vision transformers
219
+ patches = self.featurizer(img_regular)
220
+ patches_fused = self.fused_featurizer(img_fused)
221
+
222
+ # Concatenate SigLIP and DINOv2 patches along the hidden dimension
223
+ combined_patches = torch.cat([patches, patches_fused], dim=2)
224
+ all_patches.append(combined_patches)
225
+
226
+ # Concatenate all patches along the patch dimension
227
+ return torch.cat(all_patches, dim=1)
228
+
229
+
230
+ # === Prismatic Projector (nn.Module) Definitions ===
231
+ class PrismaticProjector(nn.Module):
232
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
233
+ super().__init__()
234
+ self.use_fused_vision_backbone = use_fused_vision_backbone
235
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
236
+
237
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
238
+ if not self.use_fused_vision_backbone:
239
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
240
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
241
+ self.act_fn1 = nn.GELU()
242
+ else:
243
+ initial_projection_dim = 4 * vision_dim
244
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
245
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
246
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
247
+ self.act_fn1 = nn.GELU()
248
+ self.act_fn2 = nn.GELU()
249
+
250
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
251
+ if not self.use_fused_vision_backbone:
252
+ projected_features = self.fc1(img_patches)
253
+ projected_features = self.act_fn1(projected_features)
254
+ projected_features = self.fc2(projected_features)
255
+ else:
256
+ projected_features = self.fc1(img_patches)
257
+ projected_features = self.act_fn1(projected_features)
258
+ projected_features = self.fc2(projected_features)
259
+ projected_features = self.act_fn2(projected_features)
260
+ projected_features = self.fc3(projected_features)
261
+
262
+ return projected_features
263
+
264
+
265
+ # === Main HF Class Definitions ===
266
+ @dataclass
267
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
268
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
269
+
270
+ loss: Optional[torch.FloatTensor] = None
271
+ logits: torch.FloatTensor = None
272
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
273
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
274
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
275
+
276
+ # Additions for VLMs
277
+ projector_features: Optional[torch.FloatTensor] = None
278
+
279
+
280
+ class PrismaticPreTrainedModel(PreTrainedModel):
281
+ config_class: PretrainedConfig = PrismaticConfig
282
+ base_model_prefix: str = "model"
283
+ supports_gradient_checkpointing: bool = True
284
+
285
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
286
+ _skip_keys_device_placement: str = "past_key_values"
287
+ _supports_flash_attn_2: bool = True
288
+
289
+ def _init_weights(self, module: nn.Module) -> None:
290
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
291
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
292
+ # https://github.com/TRI-ML/prismatic-vlms
293
+ std = (
294
+ self.config.initializer_range
295
+ if hasattr(self.config, "initializer_range")
296
+ else self.config.text_config.initializer_range
297
+ )
298
+
299
+ if hasattr(module, "class_embedding"):
300
+ module.class_embedding.data.normal_(mean=0.0, std=std)
301
+
302
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
303
+ module.weight.data.normal_(mean=0.0, std=std)
304
+ if module.bias is not None:
305
+ module.bias.data.zero_()
306
+ elif isinstance(module, nn.Embedding):
307
+ module.weight.data.normal_(mean=0.0, std=std)
308
+ if module.padding_idx is not None:
309
+ module.weight.data[module.padding_idx].zero_()
310
+
311
+ @property
312
+ def _supports_sdpa(self) -> bool:
313
+ """Check LLM supports SDPA Attention"""
314
+ return self.language_model._supports_sdpa
315
+
316
+
317
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
318
+ def __init__(self, config: PrismaticConfig) -> None:
319
+ super().__init__(config)
320
+
321
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
322
+ if config.use_fused_vision_backbone is None:
323
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
324
+
325
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
326
+ raise NotImplementedError(
327
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
328
+ "if you urgently need support for latest TIMM versions."
329
+ )
330
+
331
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
332
+ logger.warning(
333
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
334
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
335
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
336
+ f"use the above versions."
337
+ )
338
+
339
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
340
+ self.vision_backbone = PrismaticVisionBackbone(
341
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
342
+ )
343
+
344
+ # Create Multimodal Projector
345
+ self.projector = PrismaticProjector(
346
+ config.use_fused_vision_backbone,
347
+ vision_dim=self.vision_backbone.embed_dim,
348
+ llm_dim=config.text_config.hidden_size,
349
+ )
350
+
351
+ # Instantiate LLM Backbone
352
+ self.language_model = AutoModelForCausalLM.from_config(
353
+ config.text_config, attn_implementation=config._attn_implementation
354
+ )
355
+ self.vocab_size = config.text_config.vocab_size
356
+ self.pad_token_id = config.pad_token_id
357
+ self.llm_dim = config.text_config.hidden_size
358
+
359
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
360
+ self.post_init()
361
+
362
+ # === `PreTrainedModel` Boilerplate ===
363
+ def get_input_embeddings(self) -> nn.Module:
364
+ return self.language_model.get_input_embeddings()
365
+
366
+ def set_input_embeddings(self, value: nn.Module) -> None:
367
+ self.language_model.set_input_embeddings(value)
368
+
369
+ def get_output_embeddings(self) -> nn.Module:
370
+ return self.language_model.get_output_embeddings()
371
+
372
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
373
+ self.language_model.set_output_embeddings(new_embeddings)
374
+
375
+ def get_decoder(self) -> nn.Module:
376
+ return self.language_model.get_decoder()
377
+
378
+ def set_decoder(self, decoder: nn.Module) -> None:
379
+ self.language_model.set_decoder(decoder)
380
+
381
+ def tie_weights(self) -> None:
382
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
383
+
384
+ def resize_token_embeddings(
385
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
386
+ ) -> nn.Embedding:
387
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
388
+
389
+ # Update config/instance variables
390
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
391
+ self.vocab_size = updated_embeddings.num_embeddings
392
+
393
+ return updated_embeddings
394
+
395
+ def _replace_input_embeddings(self, input_embeddings, all_actions_mask, noisy_action_features):
396
+ """
397
+ Replace embeddings in input_embeddings at positions where all_actions_mask is True
398
+ with embeddings from noisy_action_features, using vectorized operations.
399
+
400
+ Args:
401
+ input_embeddings: Tensor of shape (B, S, D)
402
+ all_actions_mask: Boolean tensor of shape (B, S)
403
+ noisy_action_features: Tensor of shape (B, K, D) where K is the number of True values in mask per sample
404
+
405
+ Returns:
406
+ Modified input_embeddings tensor
407
+ """
408
+ # Clone input to avoid modifying the original tensor
409
+ new_input_embeddings = input_embeddings.clone()
410
+
411
+ # Create a tensor with the same shape of input_embeddings to hold the noisy action features
412
+ repositioned_noisy_action_features = torch.zeros_like(input_embeddings)
413
+
414
+ # Create batch indices for splicing
415
+ batch_indices = torch.arange(input_embeddings.shape[0], device=input_embeddings.device)
416
+ batch_indices = batch_indices.unsqueeze(1).expand(-1, noisy_action_features.shape[1])
417
+
418
+ # Get indices where mask is True for each sample
419
+ masked_indices = torch.stack([torch.where(mask)[0] for mask in all_actions_mask])
420
+
421
+ # Move the noisy action features into their correct positions
422
+ repositioned_noisy_action_features[batch_indices, masked_indices] = noisy_action_features
423
+
424
+ # Combine original input embeddings and noisy action embeddings using the mask
425
+ new_input_embeddings = torch.where(
426
+ all_actions_mask.unsqueeze(-1), repositioned_noisy_action_features, new_input_embeddings
427
+ )
428
+
429
+ return new_input_embeddings
430
+
431
+ def _process_action_masks(self, labels):
432
+ """Helper to get action masks from labels"""
433
+ current_action_mask = get_current_action_mask(labels)
434
+ next_actions_mask = get_next_actions_mask(labels)
435
+ all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
436
+ return all_actions_mask
437
+
438
+ def _process_vision_features(self, pixel_values, language_embeddings=None, use_film=False):
439
+ """Process vision features with optional FiLM conditioning"""
440
+ if use_film:
441
+ # FiLM: Infuse language inputs into visual features
442
+ patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
443
+ else:
444
+ patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
445
+
446
+ # Project patch embeddings into language embedding space
447
+ return self.projector(patch_features)
448
+
449
+ def _process_proprio_features(self, projected_patch_embeddings, proprio, proprio_projector):
450
+ """Process proprioceptive features and append to vision features"""
451
+ if proprio_projector is not None and proprio is not None:
452
+ # projected_patch_embeddings: (bsz, num_patches * num_images, llm_dim)
453
+ # proprio: (bsz, proprio_dim) or (propro_dim,)
454
+ proprio = proprio.reshape(projected_patch_embeddings.shape[0], -1) # (bsz, proprio_dim)
455
+ proprio_features = proprio_projector(proprio) # (bsz, llm_dim)
456
+ proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim)
457
+ # For simplicity, just append proprio token to the end of projected vision patch tokens
458
+ return torch.cat((projected_patch_embeddings, proprio_features), dim=1)
459
+ return projected_patch_embeddings
460
+
461
+ def _build_multimodal_attention(self, input_embeddings, projected_patch_embeddings, attention_mask):
462
+ """Build multimodal embeddings and attention mask"""
463
+ # Update attention mask
464
+ projected_patch_attention_mask = None
465
+ if attention_mask is not None:
466
+ projected_patch_attention_mask = torch.full(
467
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
468
+ fill_value=True,
469
+ dtype=attention_mask.dtype,
470
+ device=attention_mask.device,
471
+ )
472
+
473
+ # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
474
+ multimodal_embeddings = torch.cat(
475
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
476
+ )
477
+
478
+ multimodal_attention_mask = None
479
+ if attention_mask is not None:
480
+ multimodal_attention_mask = torch.cat(
481
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
482
+ )
483
+
484
+ return multimodal_embeddings, multimodal_attention_mask
485
+
486
+ def _build_multimodal_labels(self, labels, projected_patch_embeddings):
487
+ """Build multimodal labels with IGNORE_INDEX for patch embeddings"""
488
+ if labels is not None:
489
+ projected_patch_labels = torch.full(
490
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
491
+ fill_value=IGNORE_INDEX,
492
+ dtype=labels.dtype,
493
+ device=labels.device,
494
+ )
495
+ return torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
496
+ return None
497
+
498
+ # === Core Prismatic VLM `forward()` Logic ===
499
+ # def forward(
500
+ # self,
501
+ # input_ids: Optional[torch.LongTensor] = None,
502
+ # attention_mask: Optional[torch.Tensor] = None,
503
+ # pixel_values: Optional[torch.FloatTensor] = None,
504
+ # labels: Optional[torch.LongTensor] = None,
505
+ # inputs_embeds: Optional[torch.FloatTensor] = None,
506
+ # past_key_values: Optional[List[torch.FloatTensor]] = None,
507
+ # use_cache: Optional[bool] = None,
508
+ # output_attentions: Optional[bool] = None,
509
+ # output_hidden_states: Optional[bool] = None,
510
+ # output_projector_features: Optional[bool] = None,
511
+ # return_dict: Optional[bool] = None,
512
+ # proprio=None,
513
+ # proprio_projector=None,
514
+ # noisy_actions=None,
515
+ # noisy_action_projector=None,
516
+ # diffusion_timestep_embeddings=None,
517
+ # use_film: bool = False,
518
+ # ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
519
+ # """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
520
+ # output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
521
+ # output_hidden_states = (
522
+ # output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
523
+ # )
524
+ # output_projector_features = output_projector_features if output_projector_features is not None else False
525
+ # return_dict = return_dict if return_dict is not None else self.config.use_return_dict
526
+
527
+ # # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
528
+ # use_cache = use_cache and not self.training
529
+
530
+ # # Instantiate Placeholder for Projector Features
531
+ # projected_patch_embeddings = None
532
+
533
+ # # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
534
+ # if input_ids.shape[1] == 1:
535
+ # assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
536
+ # assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
537
+ # assert labels is None, "Unexpected key `labels` provided during cached generation!"
538
+
539
+ # language_model_output = self.language_model(
540
+ # input_ids=input_ids,
541
+ # attention_mask=None,
542
+ # position_ids=None,
543
+ # past_key_values=past_key_values,
544
+ # inputs_embeds=None,
545
+ # labels=None,
546
+ # use_cache=use_cache,
547
+ # output_attentions=output_attentions,
548
+ # output_hidden_states=output_hidden_states,
549
+ # return_dict=return_dict,
550
+ # )
551
+
552
+ # # === Handle Unimodal Forward ===
553
+ # elif pixel_values is None:
554
+ # assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
555
+ # assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
556
+
557
+ # language_model_output = self.language_model(
558
+ # input_ids=input_ids,
559
+ # attention_mask=attention_mask,
560
+ # position_ids=None,
561
+ # past_key_values=None,
562
+ # inputs_embeds=None,
563
+ # labels=labels,
564
+ # use_cache=use_cache,
565
+ # output_attentions=output_attentions,
566
+ # output_hidden_states=output_hidden_states,
567
+ # return_dict=return_dict,
568
+ # )
569
+
570
+ # # === Handle Multimodal Forward ===
571
+ # elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
572
+ # assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!"
573
+
574
+ # #test
575
+ #
576
+ # #test end
577
+
578
+ # # Get input embeddings (from language model embeddings)
579
+ # input_embeddings = self.get_input_embeddings()(input_ids) # (B, seq_len, D)
580
+
581
+ # # Extract action masks
582
+ # all_actions_mask = self._process_action_masks(labels)
583
+
584
+ # # Extract the language portion of the input embeddings (i.e. remove the action tokens portion)
585
+ # language_embeddings = input_embeddings[~all_actions_mask].reshape(
586
+ # input_embeddings.shape[0], -1, input_embeddings.shape[2]
587
+ # ) # (B, lang_seq_len, llm_dim)
588
+
589
+ # # Get visual features
590
+ # projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
591
+
592
+ # # Add proprioceptive state if provided
593
+ # projected_patch_embeddings = self._process_proprio_features(
594
+ # projected_patch_embeddings, proprio, proprio_projector
595
+ # )
596
+
597
+ # # [Diffusion] Add diffusion timestep embedding if provided
598
+ # if diffusion_timestep_embeddings is not None:
599
+ # # For simplicity, just append diffusion timestep embedding to the end of projected vision patch tokens
600
+ # projected_patch_embeddings = torch.cat(
601
+ # (projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
602
+ # )
603
+
604
+ # # Process action embeddings
605
+ # if noisy_actions is not None:
606
+ # # Get mask corresponding to all action tokens
607
+ # all_actions_mask = self._process_action_masks(labels)
608
+
609
+ # # Reshape noisy actions into individual action tokens
610
+ # # noisy_actions: (B, chunk_len, action_dim) -> (B, chunk_len * action_dim, 1)
611
+ # B = noisy_actions.shape[0]
612
+ # noisy_actions = noisy_actions.reshape(B, -1).unsqueeze(-1)
613
+
614
+ # # Project noisy action tokens into language model embedding space
615
+ # noisy_action_features = noisy_action_projector(noisy_actions) # (B, chunk_len * action_dim, llm_dim)
616
+
617
+ # # Replace embeddings of the action tokens with noisy action embeddings
618
+ # input_embeddings = self._replace_input_embeddings(
619
+ # input_embeddings, all_actions_mask, noisy_action_features
620
+ # )
621
+ # else:
622
+ # # Replace the embeddings of the action tokens with zeros
623
+ # # (Later on, the positional embeddings will be added to them)
624
+ # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
625
+ # input_embeddings = input_embeddings * ~all_actions_mask
626
+
627
+ # # Build multimodal embeddings & attention mask
628
+ # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
629
+ # input_embeddings, projected_patch_embeddings, attention_mask
630
+ # )
631
+
632
+ # # Build labels for multimodal sequence if needed
633
+ # multimodal_labels = self._build_multimodal_labels(labels, projected_patch_embeddings)
634
+
635
+ # # Dispatch to language model
636
+ # language_model_output = self.language_model(
637
+ # input_ids=None,
638
+ # attention_mask=multimodal_attention_mask,
639
+ # position_ids=None,
640
+ # past_key_values=None,
641
+ # inputs_embeds=multimodal_embeddings,
642
+ # labels=multimodal_labels,
643
+ # use_cache=use_cache,
644
+ # output_attentions=output_attentions,
645
+ # output_hidden_states=output_hidden_states,
646
+ # return_dict=return_dict,
647
+ # )
648
+
649
+ # # === Otherwise =>> Assume Invalid! ===
650
+ # elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
651
+ # raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
652
+
653
+ # else:
654
+ # raise ValueError(
655
+ # "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
656
+ # f"=> `input_ids` = {input_ids is not None}\n"
657
+ # f"=> `attention_mask` = {attention_mask is not None}\n"
658
+ # f"=> `pixel_values` = {pixel_values is not None}\n"
659
+ # f"=> `labels` = {labels is not None}\n"
660
+ # f"=> `input_embeds` = {inputs_embeds is not None}\n"
661
+ # f"=> `past_key_values` = {past_key_values is not None}\n"
662
+ # f"=> `use_cache` = {use_cache}"
663
+ # )
664
+
665
+ # # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
666
+ # if not return_dict:
667
+ # if output_projector_features and (projected_patch_embeddings is not None):
668
+ # return *language_model_output, projected_patch_embeddings
669
+
670
+ # return language_model_output
671
+
672
+ # return PrismaticCausalLMOutputWithPast(
673
+ # loss=language_model_output.loss,
674
+ # logits=language_model_output.logits,
675
+ # past_key_values=language_model_output.past_key_values,
676
+ # hidden_states=language_model_output.hidden_states,
677
+ # attentions=language_model_output.attentions,
678
+ # projector_features=projected_patch_embeddings,
679
+ # )
680
+
681
+ # === GenerationMixin Methods ===
682
+ def prepare_inputs_for_generation(
683
+ self,
684
+ input_ids: Optional[torch.Tensor] = None,
685
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
686
+ inputs_embeds: Optional[torch.FloatTensor] = None,
687
+ pixel_values: Optional[torch.FloatTensor] = None,
688
+ attention_mask: Optional[torch.Tensor] = None,
689
+ **kwargs: str,
690
+ ) -> Dict[str, torch.Tensor]:
691
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
692
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
693
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
694
+ ):
695
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
696
+
697
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
698
+ if past_key_values is not None:
699
+ input_ids = input_ids[:, -1:]
700
+
701
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
702
+ if inputs_embeds is not None and past_key_values is None:
703
+ model_inputs = {"input_embeds": inputs_embeds}
704
+ else:
705
+ model_inputs = {"input_ids": input_ids}
706
+
707
+ # Make sure `pixel_values` are preserved in `model_inputs`
708
+ model_inputs.update(
709
+ {
710
+ "attention_mask": attention_mask,
711
+ "pixel_values": pixel_values,
712
+ "past_key_values": past_key_values,
713
+ "use_cache": kwargs.get("use_cache"),
714
+ }
715
+ )
716
+
717
+ return model_inputs
718
+
719
+ # Defer to Language Model (all handle this differently, with different return types)
720
+ def _reorder_cache(self, *args, **kwargs) -> Any:
721
+ return self.language_model._reorder_cache(*args, **kwargs)
722
+
723
+ def _prepare_input_for_action_prediction_verl(self, input_ids, attention_mask):
724
+ """Prepares input for action prediction by adding necessary tokens"""
725
+ # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
726
+ placeholder_action_token_ids = (
727
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
728
+ )
729
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
730
+
731
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
732
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
733
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
734
+
735
+ # Extend the attention mask to fit the new shape of input
736
+ # Note: Only batch size == 1 supported right now
737
+ mask_extension = (
738
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
739
+ .to(attention_mask.device)
740
+ .to(attention_mask.dtype)
741
+ )
742
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
743
+
744
+ return input_ids, attention_mask
745
+
746
+ def _prepare_labels_for_action_prediction_verl(self, labels, input_ids):
747
+ """Creates labels tensor for action prediction if not provided"""
748
+ # Extend labels tensor with fake action labels
749
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
750
+ labels_extension = (
751
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
752
+ * ARBITRARY_ACTION_TOKEN_IDX
753
+ )
754
+ labels = torch.cat([labels, labels_extension], dim=-1)
755
+
756
+ # Replace last label token with stop token
757
+ labels[:, -1] = STOP_INDEX
758
+
759
+ return labels
760
+
761
+ def _verl_discrete_compute_logits(
762
+ self,
763
+ input_embeddings,
764
+ all_actions_mask,
765
+ projected_patch_embeddings,
766
+ attention_mask,
767
+ labels,
768
+ NUM_PATCHES,
769
+ NUM_PROMPT_TOKENS,
770
+ action_head=None,
771
+ ):#contintue!!!!!
772
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
773
+ # Zero out action token embeddings
774
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
775
+ input_embeddings = input_embeddings * ~all_actions_mask
776
+
777
+ # Build multimodal embeddings and attention mask
778
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
779
+ input_embeddings, projected_patch_embeddings, attention_mask
780
+ )
781
+
782
+ # Forward pass through language model
783
+ language_model_output = self.language_model(
784
+ input_ids=None,
785
+ attention_mask=multimodal_attention_mask,
786
+ position_ids=None,
787
+ past_key_values=None,
788
+ inputs_embeds=multimodal_embeddings,
789
+ labels=None,
790
+ use_cache=None,
791
+ output_attentions=False,
792
+ output_hidden_states=False,
793
+ return_dict=True,
794
+ )
795
+
796
+ # Extract hidden states for action tokens
797
+ #last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
798
+ # actions_hidden_states = last_hidden_states[
799
+ # :,
800
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
801
+ # :,
802
+ # ] # (B, act_chunk_len, D)
803
+
804
+ # Handle different prediction methods
805
+ # if action_head is not None:
806
+ # # L1 regression prediction
807
+ # normalized_actions = action_head.predict_action(actions_hidden_states)
808
+ # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
809
+ # normalized_actions = normalized_actions.float().cpu().detach().numpy()
810
+ # else:
811
+ # Discrete token-based prediction
812
+
813
+ compute_logits = language_model_output.logits[
814
+ :,
815
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
816
+ ]
817
+
818
+ return compute_logits
819
+
820
+ # def forward(
821
+ # self,
822
+ # input_ids: Optional[torch.LongTensor] = None,
823
+ # unnorm_key: Optional[str] = None,
824
+ # proprio=None,
825
+ # proprio_projector=None,
826
+ # action_head=None,
827
+ # noisy_action_projector=None,
828
+ # use_film: bool = False,
829
+ # **kwargs: str,
830
+ # ) :
831
+ # """Predict actions from input sequence, with options for different prediction methods.
832
+
833
+ # Args:
834
+ # input_ids: Input token ids
835
+ # unnorm_key: Key for unnormalization statistics
836
+ # proprio: Proprioceptive features
837
+ # proprio_projector: Projector for proprioceptive features
838
+ # action_head: Optional head for L1 regression or diffusion-based prediction
839
+ # noisy_action_projector: Projector for noisy actions in diffusion-based prediction
840
+ # use_film: Whether to use FiLM conditioning
841
+ # **kwargs: Additional arguments including pixel_values and attention_mask
842
+
843
+ # Returns:
844
+ # Tuple of (unnormalized_actions, action_hidden_states)
845
+ # """
846
+ # # If the special empty token ('') does not already appear after the colon (':') token in the prompt
847
+ # # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
848
+ # # if not torch.all(input_ids[:, -1] == 29871):
849
+ # # input_ids = torch.cat(
850
+ # # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
851
+ # # )
852
+ # #print("!!!!!!!!!!!!!!Entering forward!!!!!!!!!!")
853
+ # pixel_values = kwargs["pixel_values"]
854
+ # attention_mask = kwargs["attention_mask"]
855
+
856
+ # # Create fake labels tensor (needed for action mask)
857
+ # labels = input_ids.clone()
858
+ # labels[:] = IGNORE_INDEX
859
+
860
+ # # Get number of tokens in prompt (excluding the start token)
861
+ # NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
862
+
863
+ # # Prepare inputs by adding necessary tokens
864
+ # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask)
865
+
866
+ # #test
867
+ # placeholder_action_token_ids = (
868
+ # torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
869
+ # )
870
+ # input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
871
+
872
+ # # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
873
+ # stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
874
+ # input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
875
+
876
+ # # Extend the attention mask to fit the new shape of input
877
+ # # Note: Only batch size == 1 supported right now
878
+ # mask_extension = (
879
+ # torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
880
+ # .to(attention_mask.device)
881
+ # .to(attention_mask.dtype)
882
+ # )
883
+ # attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
884
+
885
+ # #return input_ids, attention_mask
886
+
887
+ # #test end
888
+
889
+
890
+ # # Update labels tensor for action mask computation later
891
+ # #labels = self._prepare_labels_for_action_prediction_verl(labels, input_ids)
892
+ # #test
893
+
894
+ # ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
895
+ # labels_extension = (
896
+ # torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
897
+ # * ARBITRARY_ACTION_TOKEN_IDX
898
+ # )
899
+ # labels = torch.cat([labels, labels_extension], dim=-1)
900
+
901
+ # # Replace last label token with stop token
902
+ # labels[:, -1] = STOP_INDEX
903
+
904
+ # #return labels
905
+
906
+ # #test ed
907
+
908
+
909
+ # # Get input embeddings and action masks
910
+
911
+
912
+
913
+ # input_embeddings = self.get_input_embeddings()(input_ids)
914
+
915
+
916
+ # #all_actions_mask = self._process_action_masks(labels)
917
+ # #test
918
+ # #current_action_mask = get_current_action_mask(labels)
919
+ # newline_positions = labels != IGNORE_INDEX
920
+
921
+ # # Calculate cumulative sum to identify regions between newlines
922
+ # cumsum = torch.cumsum(newline_positions, dim=1)
923
+
924
+ # # Create the mask
925
+ # mask = (1 <= cumsum) & (cumsum <= ACTION_DIM)
926
+
927
+ # # Extract the action part only
928
+ # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
929
+ # current_action_mask = action_tokens_only_mask * mask
930
+
931
+ # #next_actions_mask = get_next_actions_mask(labels)
932
+ # newline_positions = labels != IGNORE_INDEX
933
+
934
+ # # Calculate cumulative sum to identify regions between newlines
935
+ # cumsum = torch.cumsum(newline_positions, dim=1)
936
+
937
+ # # Create the mask
938
+ # mask = cumsum > ACTION_DIM
939
+
940
+ # # Extract the action part only
941
+ # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
942
+ # next_actions_mask = action_tokens_only_mask * mask
943
+
944
+ # all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
945
+
946
+ # #test end
947
+
948
+ # # Extract language embeddings
949
+ # language_embeddings = input_embeddings[~all_actions_mask].reshape(
950
+ # input_embeddings.shape[0], -1, input_embeddings.shape[2]
951
+ # )
952
+
953
+ # # Process vision features
954
+ # #projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
955
+ # #test
956
+ # if use_film:
957
+ # # FiLM: Infuse language inputs into visual features
958
+ # raise ValueError
959
+ # patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
960
+ # else:
961
+ # patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
962
+
963
+ # projected_patch_embeddings = self.projector(patch_features)
964
+ # #test end
965
+
966
+
967
+ # # Add proprioceptive features if provided
968
+ # use_proprio = proprio_projector is not None and proprio is not None
969
+ # if use_proprio:
970
+ # proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
971
+ # projected_patch_embeddings = self._process_proprio_features(
972
+ # projected_patch_embeddings, proprio, proprio_projector
973
+ # )
974
+
975
+ # # Use diffusion if provided, otherwise use regression or discrete prediction
976
+ # use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
977
+
978
+ # # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
979
+ # NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
980
+ # if use_proprio:
981
+ # NUM_PATCHES += 1
982
+ # if use_diffusion:
983
+ # NUM_PATCHES += 1
984
+
985
+ # if use_diffusion:
986
+ # raise ValueError
987
+ # # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
988
+ # noise = torch.randn(
989
+ # size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
990
+ # )
991
+
992
+ # # Run diffusion-based prediction
993
+ # normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
994
+ # input_embeddings,
995
+ # all_actions_mask,
996
+ # noise,
997
+ # action_head,
998
+ # projected_patch_embeddings,
999
+ # labels,
1000
+ # attention_mask,
1001
+ # NUM_PATCHES,
1002
+ # NUM_PROMPT_TOKENS,
1003
+ # noisy_action_projector,
1004
+ # )
1005
+ # else:
1006
+ # # Run regression or discrete token-based prediction
1007
+ # # compute_logits = self._verl_discrete_compute_logits(
1008
+ # # input_embeddings,
1009
+ # # all_actions_mask,
1010
+ # # projected_patch_embeddings,
1011
+ # # attention_mask,
1012
+ # # labels,
1013
+ # # NUM_PATCHES,
1014
+ # # NUM_PROMPT_TOKENS,
1015
+ # # action_head,
1016
+ # # )
1017
+
1018
+ # #test
1019
+
1020
+ # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1021
+ # input_embeddings = input_embeddings * ~all_actions_mask
1022
+
1023
+ # # Build multimodal embeddings and attention mask
1024
+ # # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1025
+ # # input_embeddings, projected_patch_embeddings, attention_mask
1026
+ # # )
1027
+ # #test
1028
+
1029
+ # projected_patch_attention_mask = None
1030
+ # if attention_mask is not None:
1031
+ # projected_patch_attention_mask = torch.full(
1032
+ # (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
1033
+ # fill_value=True,
1034
+ # dtype=attention_mask.dtype,
1035
+ # device=attention_mask.device,
1036
+ # )
1037
+
1038
+ # # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
1039
+ # multimodal_embeddings = torch.cat(
1040
+ # [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
1041
+ # )
1042
+
1043
+ # multimodal_attention_mask = None
1044
+ # if attention_mask is not None:
1045
+ # multimodal_attention_mask = torch.cat(
1046
+ # [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
1047
+ # )
1048
+
1049
+ # #return multimodal_embeddings, multimodal_attention_mask
1050
+
1051
+ # #test end
1052
+
1053
+ # # Forward pass through language model
1054
+ # language_model_output = self.language_model(
1055
+ # input_ids=None,
1056
+ # attention_mask=multimodal_attention_mask,
1057
+ # position_ids=None,
1058
+ # past_key_values=None,
1059
+ # inputs_embeds=multimodal_embeddings,
1060
+ # labels=None,
1061
+ # use_cache=None,
1062
+ # output_attentions=False,
1063
+ # output_hidden_states=False,
1064
+ # return_dict=True,
1065
+ # )
1066
+
1067
+
1068
+ # compute_logits = language_model_output.logits[
1069
+ # :,
1070
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1071
+ # ]
1072
+
1073
+ # #test end
1074
+
1075
+ # return compute_logits
1076
+
1077
+ def forward(
1078
+ self,
1079
+ input_ids: Optional[torch.LongTensor] = None,
1080
+ pixel_values=None,
1081
+ attention_mask=None,
1082
+ #labels=None,
1083
+ proprio=None,
1084
+ proprio_projector=None,
1085
+ action_head=None,
1086
+ noisy_action_projector=None,
1087
+ use_film: bool = False,
1088
+ **kwargs: str,
1089
+ ) :
1090
+ """Predict actions from input sequence, with options for different prediction methods.
1091
+
1092
+ Args:
1093
+ input_ids: Input token ids
1094
+ unnorm_key: Key for unnormalization statistics
1095
+ proprio: Proprioceptive features
1096
+ proprio_projector: Projector for proprioceptive features
1097
+ action_head: Optional head for L1 regression or diffusion-based prediction
1098
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1099
+ use_film: Whether to use FiLM conditioning
1100
+ **kwargs: Additional arguments including pixel_values and attention_mask
1101
+
1102
+ Returns:
1103
+ Tuple of (unnormalized_actions, action_hidden_states)
1104
+ """
1105
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1106
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1107
+ # if not torch.all(input_ids[:, -1] == 29871):
1108
+ # input_ids = torch.cat(
1109
+ # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1110
+ # )
1111
+
1112
+ #pixel_values = kwargs["pixel_values"]
1113
+ #attention_mask = kwargs["attention_mask"]
1114
+
1115
+ # Create fake labels tensor (needed for action mask)
1116
+ labels = input_ids.clone()
1117
+ labels[:] = IGNORE_INDEX
1118
+
1119
+ # # Get number of tokens in prompt (excluding the start token)
1120
+ NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1121
+
1122
+
1123
+ # # Prepare inputs by adding necessary tokens
1124
+ # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask)
1125
+
1126
+ # #test
1127
+ placeholder_action_token_ids = (
1128
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
1129
+ )
1130
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
1131
+
1132
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
1133
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
1134
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
1135
+
1136
+ # Extend the attention mask to fit the new shape of input
1137
+ # Note: Only batch size == 1 supported right now
1138
+ mask_extension = (
1139
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
1140
+ .to(attention_mask.device)
1141
+ .to(attention_mask.dtype)
1142
+ )
1143
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
1144
+
1145
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
1146
+ labels_extension = (
1147
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
1148
+ * ARBITRARY_ACTION_TOKEN_IDX
1149
+ )
1150
+ labels = torch.cat([labels, labels_extension], dim=-1)
1151
+
1152
+ # # Replace last label token with stop token
1153
+ labels[:, -1] = STOP_INDEX
1154
+
1155
+
1156
+ # Get input embeddings and action masks
1157
+
1158
+ #NUM_PROMPT_TOKENS = kwargs["num_prompt_tokens"]
1159
+
1160
+ input_embeddings = self.get_input_embeddings()(input_ids)
1161
+
1162
+
1163
+ #all_actions_mask = self._process_action_masks(labels)
1164
+ #test
1165
+ #current_action_mask = get_current_action_mask(labels)
1166
+ newline_positions = labels != IGNORE_INDEX
1167
+
1168
+ # Calculate cumulative sum to identify regions between newlines
1169
+ cumsum = torch.cumsum(newline_positions, dim=1)
1170
+
1171
+ # Create the mask
1172
+ mask = (1 <= cumsum) & (cumsum <= ACTION_DIM)
1173
+
1174
+ # Extract the action part only
1175
+ action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
1176
+ current_action_mask = action_tokens_only_mask * mask
1177
+
1178
+ #next_actions_mask = get_next_actions_mask(labels)
1179
+ newline_positions = labels != IGNORE_INDEX
1180
+
1181
+ # Calculate cumulative sum to identify regions between newlines
1182
+ cumsum = torch.cumsum(newline_positions, dim=1)
1183
+
1184
+ # Create the mask
1185
+ mask = cumsum > ACTION_DIM
1186
+
1187
+ # Extract the action part only
1188
+ action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX
1189
+ next_actions_mask = action_tokens_only_mask * mask
1190
+
1191
+ all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
1192
+
1193
+ #test end
1194
+
1195
+ # Extract language embeddings
1196
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1197
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1198
+ )
1199
+
1200
+ # Process vision features
1201
+ #projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1202
+ #test
1203
+ if use_film:
1204
+ # FiLM: Infuse language inputs into visual features
1205
+ raise ValueError
1206
+ patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
1207
+ else:
1208
+ patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
1209
+
1210
+ projected_patch_embeddings = self.projector(patch_features)
1211
+ #test end
1212
+
1213
+
1214
+ # Add proprioceptive features if provided
1215
+ use_proprio = proprio_projector is not None and proprio is not None
1216
+ if use_proprio:
1217
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1218
+ projected_patch_embeddings = self._process_proprio_features(
1219
+ projected_patch_embeddings, proprio, proprio_projector
1220
+ )
1221
+
1222
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1223
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1224
+
1225
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1226
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1227
+ if use_proprio:
1228
+ NUM_PATCHES += 1
1229
+ if use_diffusion:
1230
+ NUM_PATCHES += 1
1231
+
1232
+ if use_diffusion:
1233
+ raise ValueError
1234
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1235
+ noise = torch.randn(
1236
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1237
+ )
1238
+
1239
+ # Run diffusion-based prediction
1240
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1241
+ input_embeddings,
1242
+ all_actions_mask,
1243
+ noise,
1244
+ action_head,
1245
+ projected_patch_embeddings,
1246
+ labels,
1247
+ attention_mask,
1248
+ NUM_PATCHES,
1249
+ NUM_PROMPT_TOKENS,
1250
+ noisy_action_projector,
1251
+ )
1252
+ else:
1253
+ # Run regression or discrete token-based prediction
1254
+ # compute_logits = self._verl_discrete_compute_logits(
1255
+ # input_embeddings,
1256
+ # all_actions_mask,
1257
+ # projected_patch_embeddings,
1258
+ # attention_mask,
1259
+ # labels,
1260
+ # NUM_PATCHES,
1261
+ # NUM_PROMPT_TOKENS,
1262
+ # action_head,
1263
+ # )
1264
+
1265
+ #test
1266
+
1267
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1268
+ input_embeddings = input_embeddings * ~all_actions_mask
1269
+
1270
+ # Build multimodal embeddings and attention mask
1271
+ # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1272
+ # input_embeddings, projected_patch_embeddings, attention_mask
1273
+ # )
1274
+ #test
1275
+
1276
+ projected_patch_attention_mask = None
1277
+ if attention_mask is not None:
1278
+ projected_patch_attention_mask = torch.full(
1279
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
1280
+ fill_value=True,
1281
+ dtype=attention_mask.dtype,
1282
+ device=attention_mask.device,
1283
+ )
1284
+
1285
+ # Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
1286
+ multimodal_embeddings = torch.cat(
1287
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
1288
+ )
1289
+
1290
+ multimodal_attention_mask = None
1291
+ if attention_mask is not None:
1292
+ multimodal_attention_mask = torch.cat(
1293
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
1294
+ )
1295
+
1296
+ #return multimodal_embeddings, multimodal_attention_mask
1297
+
1298
+ #test end
1299
+
1300
+ # Forward pass through language model
1301
+ language_model_output = self.language_model(
1302
+ input_ids=None,
1303
+ attention_mask=multimodal_attention_mask,
1304
+ position_ids=None,
1305
+ past_key_values=None,
1306
+ inputs_embeds=multimodal_embeddings,
1307
+ labels=None,
1308
+ use_cache=None,
1309
+ output_attentions=False,
1310
+ output_hidden_states=False,
1311
+ return_dict=True,
1312
+ )
1313
+
1314
+
1315
+ compute_logits = language_model_output.logits[
1316
+ :,
1317
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1318
+ ]
1319
+
1320
+ #test end
1321
+
1322
+ return compute_logits
1323
+
1324
+
1325
+
1326
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
1327
+ config_class: PretrainedConfig = OpenVLAConfig
1328
+
1329
+ def __init__(self, config: OpenVLAConfig) -> None:
1330
+ super().__init__(config)
1331
+ self.norm_stats = config.norm_stats
1332
+
1333
+ # Compute action bins
1334
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
1335
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
1336
+
1337
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
1338
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
1339
+
1340
+ def _prepare_input_for_action_prediction(self, input_ids, attention_mask):
1341
+ """Prepares input for action prediction by adding necessary tokens"""
1342
+ # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
1343
+ placeholder_action_token_ids = (
1344
+ torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
1345
+ )
1346
+ input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
1347
+
1348
+ # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
1349
+ stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
1350
+ input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
1351
+
1352
+ # Extend the attention mask to fit the new shape of input
1353
+ # Note: Only batch size == 1 supported right now
1354
+ mask_extension = (
1355
+ torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
1356
+ .to(attention_mask.device)
1357
+ .to(attention_mask.dtype)
1358
+ )
1359
+ attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
1360
+
1361
+ return input_ids, attention_mask
1362
+
1363
+ def _prepare_labels_for_action_prediction(self, labels, input_ids):
1364
+ """Creates labels tensor for action prediction if not provided"""
1365
+ # Extend labels tensor with fake action labels
1366
+ ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
1367
+ labels_extension = (
1368
+ torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
1369
+ * ARBITRARY_ACTION_TOKEN_IDX
1370
+ )
1371
+ labels = torch.cat([labels, labels_extension], dim=-1)
1372
+
1373
+ # Replace last label token with stop token
1374
+ labels[:, -1] = STOP_INDEX
1375
+
1376
+ return labels
1377
+
1378
+ def _unnormalize_actions(self, normalized_actions, unnorm_key=None):
1379
+ """Unnormalize actions using dataset statistics"""
1380
+ action_norm_stats = self.get_action_stats(unnorm_key)
1381
+
1382
+ if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS:
1383
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool))
1384
+ action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"])
1385
+ elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99:
1386
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
1387
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
1388
+ else:
1389
+ raise ValueError("Unsupported action/proprio normalization type detected!")
1390
+
1391
+ actions = np.where(
1392
+ mask,
1393
+ 0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low,
1394
+ normalized_actions,
1395
+ )
1396
+
1397
+ return actions
1398
+
1399
+ def _run_diffusion_prediction(
1400
+ self,
1401
+ input_embeddings,
1402
+ all_actions_mask,
1403
+ noise,
1404
+ action_head,
1405
+ projected_patch_embeddings,
1406
+ labels,
1407
+ attention_mask,
1408
+ NUM_PATCHES,
1409
+ NUM_PROMPT_TOKENS,
1410
+ noisy_action_projector,
1411
+ ):
1412
+ """Run diffusion-based action prediction"""
1413
+ # Set diffusion timestep values
1414
+ action_head.noise_scheduler.set_timesteps(action_head.num_diffusion_steps)
1415
+ # Clone embedding for reuse in each timestep
1416
+ orig_projected_patch_embeddings = projected_patch_embeddings.clone()
1417
+ curr_noisy_actions = noise
1418
+
1419
+ # Reverse diffusion: Iteratively denoise to generate action prediction
1420
+ for t in action_head.noise_scheduler.timesteps:
1421
+ # Get diffusion model's noise prediction (conditioned on VLA latent embedding, current noisy action
1422
+ # embedding, and diffusion timestep embedding)
1423
+ timesteps = torch.Tensor([t]).to(labels.device)
1424
+ diffusion_timestep_embeddings = (
1425
+ action_head.time_encoder(timesteps).to(curr_noisy_actions.dtype).to(curr_noisy_actions.device)
1426
+ ) # (B, llm_dim)
1427
+ diffusion_timestep_embeddings = diffusion_timestep_embeddings.unsqueeze(1) # (B, 1, llm_dim)
1428
+
1429
+ # [Diffusion] Replace the embeddings of the action tokens with noisy actions
1430
+ # (Later on, the positional embeddings will be added to them)
1431
+
1432
+ # For simplicity, append diffusion timestep embedding to the end of projected vision tokens
1433
+ projected_patch_embeddings = torch.cat(
1434
+ (orig_projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
1435
+ )
1436
+
1437
+ # Reshape and project noisy actions into language embedding space
1438
+ B = curr_noisy_actions.shape[0]
1439
+ orig_curr_noisy_actions_shape = curr_noisy_actions.shape
1440
+ curr_noisy_actions = curr_noisy_actions.reshape(B, -1).unsqueeze(-1)
1441
+ noisy_action_features = noisy_action_projector(curr_noisy_actions)
1442
+ curr_noisy_actions = curr_noisy_actions.reshape(orig_curr_noisy_actions_shape)
1443
+
1444
+ # Replace action token embeddings with noisy action embeddings
1445
+ input_embeddings = self._replace_input_embeddings(
1446
+ input_embeddings.clone(), all_actions_mask, noisy_action_features
1447
+ )
1448
+
1449
+ # Build multimodal embeddings and attention mask
1450
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1451
+ input_embeddings, projected_patch_embeddings, attention_mask
1452
+ )
1453
+
1454
+ # Forward pass through language model
1455
+ language_model_output = self.language_model(
1456
+ input_ids=None,
1457
+ attention_mask=multimodal_attention_mask,
1458
+ position_ids=None,
1459
+ past_key_values=None,
1460
+ inputs_embeds=multimodal_embeddings,
1461
+ labels=None,
1462
+ use_cache=None,
1463
+ output_attentions=False,
1464
+ output_hidden_states=True,
1465
+ return_dict=True,
1466
+ )
1467
+
1468
+ # Extract hidden states for action portion of response
1469
+ last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1470
+ actions_hidden_states = last_hidden_states[
1471
+ :,
1472
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1473
+ :,
1474
+ ] # (B, act_chunk_len, D)
1475
+
1476
+ # Predict noise and update noisy actions: x_t -> x_{t-1}
1477
+ noise_pred = action_head.predict_noise(actions_hidden_states)
1478
+ curr_noisy_actions = action_head.noise_scheduler.step(noise_pred, t, curr_noisy_actions).prev_sample
1479
+
1480
+ curr_noisy_actions = curr_noisy_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1481
+
1482
+ # Return final actions
1483
+ return curr_noisy_actions.float().cpu().detach().numpy(), actions_hidden_states
1484
+
1485
+ def _regression_or_discrete_prediction(
1486
+ self,
1487
+ input_embeddings,
1488
+ all_actions_mask,
1489
+ projected_patch_embeddings,
1490
+ attention_mask,
1491
+ labels,
1492
+ NUM_PATCHES,
1493
+ NUM_PROMPT_TOKENS,
1494
+ action_head=None,
1495
+ ):
1496
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
1497
+ # Zero out action token embeddings
1498
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1499
+ input_embeddings = input_embeddings * ~all_actions_mask
1500
+
1501
+ # Build multimodal embeddings and attention mask
1502
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1503
+ input_embeddings, projected_patch_embeddings, attention_mask
1504
+ )
1505
+
1506
+ # Forward pass through language model
1507
+ language_model_output = self.language_model(
1508
+ input_ids=None,
1509
+ attention_mask=multimodal_attention_mask,
1510
+ position_ids=None,
1511
+ past_key_values=None,
1512
+ inputs_embeds=multimodal_embeddings,
1513
+ labels=None,
1514
+ use_cache=None,
1515
+ output_attentions=False,
1516
+ output_hidden_states=True,
1517
+ return_dict=True,
1518
+ )
1519
+
1520
+ # Extract hidden states for action tokens
1521
+ last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1522
+ actions_hidden_states = last_hidden_states[
1523
+ :,
1524
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1525
+ :,
1526
+ ] # (B, act_chunk_len, D)
1527
+
1528
+ # Handle different prediction methods
1529
+ if action_head is not None:
1530
+ # L1 regression prediction
1531
+ normalized_actions = action_head.predict_action(actions_hidden_states)
1532
+ normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1533
+ normalized_actions = normalized_actions.float().cpu().detach().numpy()
1534
+ else:
1535
+ # Discrete token-based prediction
1536
+ predicted_action_token_ids = (
1537
+ language_model_output.logits[
1538
+ :,
1539
+ NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1540
+ ]
1541
+ .argmax(dim=2)
1542
+ .cpu()
1543
+ .numpy()
1544
+ )
1545
+ discretized_actions = self.vocab_size - predicted_action_token_ids
1546
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
1547
+ normalized_actions = self.bin_centers[discretized_actions]
1548
+ normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1549
+
1550
+ return normalized_actions, actions_hidden_states
1551
+
1552
+ def _verl_discrete_prediction(
1553
+ self,
1554
+ input_embeddings,
1555
+ all_actions_mask,
1556
+ projected_patch_embeddings,
1557
+ attention_mask,
1558
+ labels,
1559
+ NUM_PATCHES,
1560
+ NUM_PROMPT_TOKENS,
1561
+ action_head=None,
1562
+ do_sample=True,
1563
+ temperature=1,
1564
+ ):
1565
+ """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
1566
+ # Zero out action token embeddings
1567
+ all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
1568
+ input_embeddings = input_embeddings * ~all_actions_mask
1569
+
1570
+ # Build multimodal embeddings and attention mask
1571
+ multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
1572
+ input_embeddings, projected_patch_embeddings, attention_mask
1573
+ )
1574
+
1575
+ # Forward pass through language model
1576
+ language_model_output = self.language_model(
1577
+ input_ids=None,
1578
+ attention_mask=multimodal_attention_mask,
1579
+ position_ids=None,
1580
+ past_key_values=None,
1581
+ inputs_embeds=multimodal_embeddings,
1582
+ labels=None,
1583
+ use_cache=None,
1584
+ output_attentions=False,
1585
+ output_hidden_states=False,
1586
+ return_dict=True,
1587
+ )
1588
+
1589
+ # Extract hidden states for action tokens
1590
+ #last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
1591
+ # actions_hidden_states = last_hidden_states[
1592
+ # :,
1593
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1594
+ # :,
1595
+ # ] # (B, act_chunk_len, D)
1596
+
1597
+ # Handle different prediction methods
1598
+ # if action_head is not None:
1599
+ # # L1 regression prediction
1600
+ # normalized_actions = action_head.predict_action(actions_hidden_states)
1601
+ # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1602
+ # normalized_actions = normalized_actions.float().cpu().detach().numpy()
1603
+ # else:
1604
+ # Discrete token-based prediction
1605
+
1606
+ #test
1607
+ # NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES
1608
+ # j = torch.arange(language_model_output.logits.shape[1], device=NUM_PROMPT_TOKENS.device)
1609
+ # start = NUM_PROMPT_TOKENS.unsqueeze(1)
1610
+ # end = start + ACTION_DIM * NUM_ACTIONS_CHUNK
1611
+ # mask_2d = (j >= start) & (j < end)
1612
+ # mask = mask_2d.unsqueeze(-1)
1613
+ # actions_masks = mask.expand_as(language_model_output.logits)
1614
+
1615
+
1616
+ NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES
1617
+ batch_size = language_model_output.logits.shape[0]
1618
+ device = language_model_output.logits.device
1619
+
1620
+
1621
+ start_indices = NUM_PROMPT_TOKENS.unsqueeze(1) # [batch_size, 1]
1622
+ position_offsets = torch.arange(ACTION_DIM * NUM_ACTIONS_CHUNK, device=device).unsqueeze(0) # [1, seq_length]
1623
+ seq_indices = start_indices + position_offsets # [batch_size, ACTION_DIM*NUM_ACTIONS_CHUNK]
1624
+ #test end
1625
+ #test add
1626
+ #print("language_model_output",language_model_output.logits.shape[-1])
1627
+ #print("self.vocab_size",self.vocab_size) 32000
1628
+ #topk_values, topk_indices = torch.topk(language_model_output.logits, k=256, dim=-1)
1629
+ #print(topk_indices)
1630
+ #assert language_model_output.logits.shape[-1] == self.vocab_size
1631
+ #test add
1632
+ if do_sample == False:
1633
+ #org
1634
+ # reponse_ids = language_model_output.logits[
1635
+ # :,
1636
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1637
+ # ].argmax(dim=2)
1638
+ #reponse_ids = language_model_output.logits[actions_masks].argmax(dim=2)
1639
+ #org end
1640
+
1641
+ #padding
1642
+ # reponse_ids = language_model_output.logits[
1643
+ # torch.arange(batch_size, device=device).unsqueeze(-1),
1644
+ # seq_indices,
1645
+ # :
1646
+ # ].argmax(dim=2)
1647
+ #padding end
1648
+
1649
+ #padding + only get last 256 token
1650
+ reponse_ids_logits = language_model_output.logits[
1651
+ torch.arange(batch_size, device=device).unsqueeze(-1),
1652
+ seq_indices,
1653
+ :
1654
+ ]
1655
+ start_index = self.vocab_size - 256
1656
+ response_last256 = reponse_ids_logits[..., -256-64:-64] # Shape: [batch_size, seq_len, 256]
1657
+ last256_argmax = response_last256.argmax(dim=-1) # Shape: [batch_size, seq_len]
1658
+ reponse_ids = last256_argmax + start_index # Shape: [batch_size, seq_len]
1659
+ #padding + only get last 256 token end
1660
+
1661
+ predicted_action_token_ids = reponse_ids.cpu().numpy()
1662
+
1663
+ else:
1664
+ assert temperature>0
1665
+ #org
1666
+ # action_logits = language_model_output.logits[
1667
+ # :,
1668
+ # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
1669
+ # ]
1670
+ #action_logits = language_model_output.logits[actions_masks]
1671
+ #org end
1672
+
1673
+ action_logits = language_model_output.logits[
1674
+ torch.arange(batch_size, device=device).unsqueeze(-1),
1675
+ seq_indices,
1676
+ :
1677
+ ]
1678
+ # padding
1679
+ # scaled_logits = action_logits / temperature
1680
+ # probs = torch.softmax(scaled_logits, dim=-1)
1681
+ # probs_flat = probs.reshape(-1, probs.shape[-1]) # (B*act_chunk_len, vocab_size)
1682
+ # sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1) # (B*act_chunk_len, 1)
1683
+ # reponse_ids = sampled_indices_flat.view(action_logits.shape[0], -1)
1684
+ # padding end
1685
+
1686
+ #padding + only get last 256 token
1687
+ action_logits_last256 = action_logits[..., -256-64:-64]
1688
+ scaled_logits = action_logits_last256 / temperature
1689
+ probs = torch.softmax(scaled_logits, dim=-1)
1690
+ assert probs.shape[-1] == 256
1691
+ probs_flat = probs.reshape(-1, probs.shape[-1])
1692
+ sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1)
1693
+ original_ids_flat = sampled_indices_flat + (self.vocab_size - 256)
1694
+ reponse_ids = original_ids_flat.view(action_logits.shape[0], -1)
1695
+ #padding + only get last 256 token end
1696
+
1697
+ predicted_action_token_ids = reponse_ids.cpu().numpy()
1698
+
1699
+ discretized_actions = self.vocab_size - predicted_action_token_ids
1700
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
1701
+ normalized_actions = self.bin_centers[discretized_actions]
1702
+ #normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
1703
+ normalized_actions = normalized_actions.reshape(-1, ACTION_DIM)
1704
+
1705
+ return normalized_actions, reponse_ids
1706
+ #return normalized_actions, actions_hidden_states
1707
+
1708
+
1709
+
1710
+
1711
+ def predict_action(
1712
+ self,
1713
+ input_ids: Optional[torch.LongTensor] = None,
1714
+ unnorm_key: Optional[str] = None,
1715
+ proprio=None,
1716
+ proprio_projector=None,
1717
+ action_head=None,
1718
+ noisy_action_projector=None,
1719
+ use_film: bool = False,
1720
+ **kwargs: str,
1721
+ ) -> np.ndarray:
1722
+ """Predict actions from input sequence, with options for different prediction methods.
1723
+
1724
+ Args:
1725
+ input_ids: Input token ids
1726
+ unnorm_key: Key for unnormalization statistics
1727
+ proprio: Proprioceptive features
1728
+ proprio_projector: Projector for proprioceptive features
1729
+ action_head: Optional head for L1 regression or diffusion-based prediction
1730
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1731
+ use_film: Whether to use FiLM conditioning
1732
+ **kwargs: Additional arguments including pixel_values and attention_mask
1733
+
1734
+ Returns:
1735
+ Tuple of (unnormalized_actions, action_hidden_states)
1736
+ """
1737
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1738
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1739
+ if not torch.all(input_ids[:, -1] == 29871):
1740
+ input_ids = torch.cat(
1741
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1742
+ )
1743
+
1744
+ pixel_values = kwargs["pixel_values"]
1745
+ attention_mask = kwargs["attention_mask"]
1746
+
1747
+ # Create fake labels tensor (needed for action mask)
1748
+ labels = input_ids.clone()
1749
+ labels[:] = IGNORE_INDEX
1750
+
1751
+ # Get number of tokens in prompt (excluding the start token)
1752
+ NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1753
+
1754
+ # Prepare inputs by adding necessary tokens
1755
+ input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
1756
+
1757
+ # Update labels tensor for action mask computation later
1758
+ labels = self._prepare_labels_for_action_prediction(labels, input_ids)
1759
+
1760
+ # Get input embeddings and action masks
1761
+ input_embeddings = self.get_input_embeddings()(input_ids)
1762
+ all_actions_mask = self._process_action_masks(labels)
1763
+
1764
+ # Extract language embeddings
1765
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1766
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1767
+ )
1768
+
1769
+ # Process vision features
1770
+ projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1771
+
1772
+ # Add proprioceptive features if provided
1773
+ use_proprio = proprio_projector is not None and proprio is not None
1774
+ if use_proprio:
1775
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1776
+ projected_patch_embeddings = self._process_proprio_features(
1777
+ projected_patch_embeddings, proprio, proprio_projector
1778
+ )
1779
+
1780
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1781
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1782
+
1783
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1784
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1785
+ if use_proprio:
1786
+ NUM_PATCHES += 1
1787
+ if use_diffusion:
1788
+ NUM_PATCHES += 1
1789
+
1790
+ if use_diffusion:
1791
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1792
+ noise = torch.randn(
1793
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1794
+ )
1795
+
1796
+ # Run diffusion-based prediction
1797
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1798
+ input_embeddings,
1799
+ all_actions_mask,
1800
+ noise,
1801
+ action_head,
1802
+ projected_patch_embeddings,
1803
+ labels,
1804
+ attention_mask,
1805
+ NUM_PATCHES,
1806
+ NUM_PROMPT_TOKENS,
1807
+ noisy_action_projector,
1808
+ )
1809
+ else:
1810
+ # Run regression or discrete token-based prediction
1811
+ normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction(
1812
+ input_embeddings,
1813
+ all_actions_mask,
1814
+ projected_patch_embeddings,
1815
+ attention_mask,
1816
+ labels,
1817
+ NUM_PATCHES,
1818
+ NUM_PROMPT_TOKENS,
1819
+ action_head,
1820
+ )
1821
+
1822
+ # Unnormalize predicted actions
1823
+ actions = self._unnormalize_actions(normalized_actions, unnorm_key)
1824
+
1825
+ return actions, actions_hidden_states
1826
+
1827
+ def generate_action_verl(
1828
+ self,
1829
+ input_ids: Optional[torch.LongTensor] = None,
1830
+ unnorm_key: Optional[str] = None,
1831
+ proprio=None,
1832
+ proprio_projector=None,
1833
+ action_head=None,
1834
+ noisy_action_projector=None,
1835
+ use_film: bool = False,
1836
+ **kwargs: str,
1837
+ ) -> np.ndarray:
1838
+ """Predict actions from input sequence, with options for different prediction methods.
1839
+
1840
+ Args:
1841
+ input_ids: Input token ids
1842
+ unnorm_key: Key for unnormalization statistics
1843
+ proprio: Proprioceptive features
1844
+ proprio_projector: Projector for proprioceptive features
1845
+ action_head: Optional head for L1 regression or diffusion-based prediction
1846
+ noisy_action_projector: Projector for noisy actions in diffusion-based prediction
1847
+ use_film: Whether to use FiLM conditioning
1848
+ **kwargs: Additional arguments including pixel_values and attention_mask
1849
+
1850
+ Returns:
1851
+ Tuple of (unnormalized_actions, action_hidden_states)
1852
+ """
1853
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
1854
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
1855
+ # if not torch.all(input_ids[:, -1] == 29871):
1856
+ # input_ids = torch.cat(
1857
+ # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
1858
+ # )
1859
+
1860
+ pixel_values = kwargs["pixel_values"]
1861
+ attention_mask = kwargs["attention_mask"]
1862
+ do_sample = kwargs["do_sample"]
1863
+ temperature = kwargs["temperature"]
1864
+
1865
+ # Create fake labels tensor (needed for action mask)
1866
+ labels = input_ids.clone()
1867
+ labels[:] = IGNORE_INDEX
1868
+
1869
+ # Get number of tokens in prompt (excluding the start token)
1870
+ #NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
1871
+ #test
1872
+ padding_idx = kwargs["padding_idx"]
1873
+ num_prompt_tokens = input_ids.ne(padding_idx).sum(dim=1) - 1
1874
+ #test end
1875
+
1876
+
1877
+ # Prepare inputs by adding necessary tokens
1878
+ input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
1879
+
1880
+ # Update labels tensor for action mask computation later
1881
+ labels = self._prepare_labels_for_action_prediction(labels, input_ids)
1882
+
1883
+ #here to convert padding from before to last
1884
+ #test
1885
+ padding_mask = input_ids.ne(padding_idx)
1886
+ assert torch.all(padding_mask==attention_mask.ne(0))
1887
+ #print("in predict_action padding_mask:", padding_mask)
1888
+ padding_mask = padding_mask.int()
1889
+ sorted_indices = torch.argsort(padding_mask, dim=1, descending=True, stable=True)
1890
+ input_ids = torch.gather(input_ids, 1, sorted_indices)
1891
+ attention_mask = torch.gather(attention_mask, 1, sorted_indices)
1892
+ labels = torch.gather(labels, 1, sorted_indices)
1893
+ assert use_film==False
1894
+ #test end
1895
+
1896
+
1897
+ # Get input embeddings and action masks
1898
+ input_embeddings = self.get_input_embeddings()(input_ids)
1899
+ all_actions_mask = self._process_action_masks(labels)
1900
+
1901
+ # Extract language embeddings
1902
+ language_embeddings = input_embeddings[~all_actions_mask].reshape(
1903
+ input_embeddings.shape[0], -1, input_embeddings.shape[2]
1904
+ )
1905
+
1906
+ # Process vision features
1907
+ projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
1908
+
1909
+ # Add proprioceptive features if provided
1910
+ use_proprio = proprio_projector is not None and proprio is not None
1911
+ if use_proprio:
1912
+ proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
1913
+ projected_patch_embeddings = self._process_proprio_features(
1914
+ projected_patch_embeddings, proprio, proprio_projector
1915
+ )
1916
+
1917
+ # Use diffusion if provided, otherwise use regression or discrete prediction
1918
+ use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
1919
+
1920
+ # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
1921
+ NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
1922
+ if use_proprio:
1923
+ NUM_PATCHES += 1
1924
+ if use_diffusion:
1925
+ NUM_PATCHES += 1
1926
+
1927
+ if use_diffusion:
1928
+ raise ValueError
1929
+ # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
1930
+ noise = torch.randn(
1931
+ size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
1932
+ )
1933
+
1934
+ # Run diffusion-based prediction
1935
+ normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
1936
+ input_embeddings,
1937
+ all_actions_mask,
1938
+ noise,
1939
+ action_head,
1940
+ projected_patch_embeddings,
1941
+ labels,
1942
+ attention_mask,
1943
+ NUM_PATCHES,
1944
+ NUM_PROMPT_TOKENS,
1945
+ noisy_action_projector,
1946
+ )
1947
+ else:
1948
+ # Run regression or discrete token-based prediction
1949
+ normalized_actions, reponse_ids = self._verl_discrete_prediction(
1950
+ input_embeddings,
1951
+ all_actions_mask,
1952
+ projected_patch_embeddings,
1953
+ attention_mask,
1954
+ labels,
1955
+ NUM_PATCHES,
1956
+ num_prompt_tokens,
1957
+ action_head,
1958
+ do_sample=do_sample,
1959
+ temperature=temperature,
1960
+ )
1961
+
1962
+ # Unnormalize predicted actions
1963
+ actions = self._unnormalize_actions(normalized_actions, unnorm_key)
1964
+ #verl add!
1965
+ actions = actions.reshape(-1 ,NUM_ACTIONS_CHUNK, ACTION_DIM)
1966
+ #
1967
+ return actions, reponse_ids
1968
+
1969
+
1970
+
1971
+ @staticmethod
1972
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
1973
+ """Validate and resolve the unnormalization key for action statistics"""
1974
+ if unnorm_key is None:
1975
+ assert len(norm_stats) == 1, (
1976
+ f"Your model was trained on more than one dataset, "
1977
+ f"please pass a `unnorm_key` from the following options to choose the statistics "
1978
+ f"used for un-normalizing actions: {norm_stats.keys()}"
1979
+ )
1980
+ unnorm_key = next(iter(norm_stats.keys()))
1981
+
1982
+ assert unnorm_key in norm_stats, (
1983
+ f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
1984
+ f"please choose from: {norm_stats.keys()}"
1985
+ )
1986
+ return unnorm_key
1987
+
1988
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
1989
+ """Get the dimensionality of the policy's action space."""
1990
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
1991
+ return len(self.norm_stats[unnorm_key]["action"]["min"])
1992
+
1993
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
1994
+ """Get all the logged statistics for the given dataset."""
1995
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
1996
+ 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
+ }