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+ }
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+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,565 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ modeling_prismatic.py
3
+
4
+ Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
5
+ from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
6
+ logic in `prismatic.models.vlms.prismatic.py`.
7
+
8
+ Note =>> for the time being, not adding the custom HF "docstring" formatting.
9
+
10
+ References [LLaVa, IDEFICS-2]:
11
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
12
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
13
+ """
14
+
15
+ import logging
16
+ from dataclasses import dataclass
17
+ from functools import partial
18
+ from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
19
+
20
+ import numpy as np
21
+ import timm
22
+ import tokenizers
23
+ import torch
24
+ import torch.nn as nn
25
+ import transformers
26
+ from timm.models.vision_transformer import LayerScale
27
+ from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
28
+ from transformers.modeling_outputs import ModelOutput
29
+
30
+ from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
31
+
32
+ # Get Logger
33
+ logger = logging.getLogger(__name__)
34
+
35
+
36
+ # === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
37
+ IGNORE_INDEX = -100
38
+
39
+
40
+ # === Utility Functions for Monkey-Patching ===
41
+ def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
42
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
43
+ result = fn(*args, **kwargs)
44
+ return result[0] if isinstance(result, tuple) else result
45
+
46
+ return wrapper
47
+
48
+
49
+ # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
50
+ # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
51
+ # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
52
+ def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
53
+ return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
54
+
55
+
56
+ def ls_apply_patch(ls_module: LayerScale):
57
+ ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
58
+ ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
59
+ del ls_module.gamma
60
+
61
+
62
+ # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
63
+ class PrismaticVisionBackbone(nn.Module):
64
+ def __init__(
65
+ self,
66
+ use_fused_vision_backbone: bool,
67
+ image_sizes: List[int],
68
+ timm_model_ids: List[str],
69
+ timm_override_act_layers: List[Optional[str]],
70
+ ) -> None:
71
+ super().__init__()
72
+ self.use_fused_vision_backbone = use_fused_vision_backbone
73
+
74
+ # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
75
+ # =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
76
+ # Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
77
+ assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
78
+ self.featurizer = timm.create_model(
79
+ timm_model_ids[0],
80
+ pretrained=False,
81
+ num_classes=0,
82
+ img_size=image_sizes[0],
83
+ act_layer=timm_override_act_layers[0],
84
+ )
85
+ self.featurizer.forward = unpack_tuple(
86
+ partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
87
+ )
88
+ self.embed_dim = self.featurizer.embed_dim
89
+
90
+ # If `use_fused_vision_backbone` =>> create "beta" featurizer
91
+ if self.use_fused_vision_backbone:
92
+ self.fused_featurizer = timm.create_model(
93
+ timm_model_ids[1],
94
+ pretrained=False,
95
+ num_classes=0,
96
+ img_size=image_sizes[1],
97
+ act_layer=timm_override_act_layers[1],
98
+ )
99
+ self.fused_featurizer.forward = unpack_tuple(
100
+ partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
101
+ )
102
+ self.embed_dim += self.fused_featurizer.embed_dim
103
+
104
+ # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
105
+ for module in self.featurizer.modules():
106
+ if isinstance(module, LayerScale):
107
+ ls_apply_patch(module)
108
+
109
+ if self.use_fused_vision_backbone:
110
+ for module in self.fused_featurizer.modules():
111
+ if isinstance(module, LayerScale):
112
+ ls_apply_patch(module)
113
+
114
+ def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
115
+ """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
116
+ if not self.use_fused_vision_backbone:
117
+ return self.featurizer(pixel_values)
118
+
119
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
120
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
121
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
122
+
123
+ return torch.cat([patches, patches_fused], dim=2)
124
+
125
+
126
+ # === Prismatic Projector (nn.Module) Definitions ===
127
+ class PrismaticProjector(nn.Module):
128
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
129
+ super().__init__()
130
+ self.use_fused_vision_backbone = use_fused_vision_backbone
131
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
132
+
133
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
134
+ if not self.use_fused_vision_backbone:
135
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
136
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
137
+ self.act_fn1 = nn.GELU()
138
+ else:
139
+ initial_projection_dim = 4 * vision_dim
140
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
141
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
142
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
143
+ self.act_fn1 = nn.GELU()
144
+ self.act_fn2 = nn.GELU()
145
+
146
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
147
+ if not self.use_fused_vision_backbone:
148
+ projected_features = self.fc1(img_patches)
149
+ projected_features = self.act_fn1(projected_features)
150
+ projected_features = self.fc2(projected_features)
151
+ else:
152
+ projected_features = self.fc1(img_patches)
153
+ projected_features = self.act_fn1(projected_features)
154
+ projected_features = self.fc2(projected_features)
155
+ projected_features = self.act_fn2(projected_features)
156
+ projected_features = self.fc3(projected_features)
157
+
158
+ return projected_features
159
+
160
+
161
+ # === Main HF Class Definitions ===
162
+ @dataclass
163
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
164
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
165
+
166
+ loss: Optional[torch.FloatTensor] = None
167
+ logits: torch.FloatTensor = None
168
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
169
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
170
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
171
+
172
+ # Additions for VLMs
173
+ projector_features: Optional[torch.FloatTensor] = None
174
+
175
+
176
+ class PrismaticPreTrainedModel(PreTrainedModel):
177
+ config_class: PretrainedConfig = PrismaticConfig
178
+ base_model_prefix: str = "model"
179
+ supports_gradient_checkpointing: bool = True
180
+
181
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
182
+ _skip_keys_device_placement: str = "past_key_values"
183
+ _supports_flash_attn_2: bool = True
184
+
185
+ def _init_weights(self, module: nn.Module) -> None:
186
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
187
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
188
+ # https://github.com/TRI-ML/prismatic-vlms
189
+ std = (
190
+ self.config.initializer_range
191
+ if hasattr(self.config, "initializer_range")
192
+ else self.config.text_config.initializer_range
193
+ )
194
+
195
+ if hasattr(module, "class_embedding"):
196
+ module.class_embedding.data.normal_(mean=0.0, std=std)
197
+
198
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
199
+ module.weight.data.normal_(mean=0.0, std=std)
200
+ if module.bias is not None:
201
+ module.bias.data.zero_()
202
+ elif isinstance(module, nn.Embedding):
203
+ module.weight.data.normal_(mean=0.0, std=std)
204
+ if module.padding_idx is not None:
205
+ module.weight.data[module.padding_idx].zero_()
206
+
207
+ @property
208
+ def _supports_sdpa(self) -> bool:
209
+ """Check LLM supports SDPA Attention"""
210
+ return self.language_model._supports_sdpa
211
+
212
+
213
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
214
+ def __init__(self, config: PrismaticConfig) -> None:
215
+ super().__init__(config)
216
+
217
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
218
+ if config.use_fused_vision_backbone is None:
219
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
220
+
221
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
222
+ raise NotImplementedError(
223
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
224
+ "if you urgently need support for latest TIMM versions."
225
+ )
226
+
227
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
228
+ logger.warning(
229
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
230
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
231
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
232
+ f"use the above versions."
233
+ )
234
+
235
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
236
+ self.vision_backbone = PrismaticVisionBackbone(
237
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
238
+ )
239
+
240
+ # Create Multimodal Projector
241
+ self.projector = PrismaticProjector(
242
+ config.use_fused_vision_backbone,
243
+ vision_dim=self.vision_backbone.embed_dim,
244
+ llm_dim=config.text_config.hidden_size,
245
+ )
246
+
247
+ # Instantiate LLM Backbone
248
+ self.language_model = AutoModelForCausalLM.from_config(
249
+ config.text_config, attn_implementation=config._attn_implementation
250
+ )
251
+ self.vocab_size = config.text_config.vocab_size
252
+ self.pad_token_id = config.pad_token_id
253
+
254
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
255
+ self.post_init()
256
+
257
+ # === `PreTrainedModel` Boilerplate ===
258
+ def get_input_embeddings(self) -> nn.Module:
259
+ return self.language_model.get_input_embeddings()
260
+
261
+ def set_input_embeddings(self, value: nn.Module) -> None:
262
+ self.language_model.set_input_embeddings(value)
263
+
264
+ def get_output_embeddings(self) -> nn.Module:
265
+ return self.language_model.get_output_embeddings()
266
+
267
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
268
+ self.language_model.set_output_embeddings(new_embeddings)
269
+
270
+ def get_decoder(self) -> nn.Module:
271
+ return self.language_model.get_decoder()
272
+
273
+ def set_decoder(self, decoder: nn.Module) -> None:
274
+ self.language_model.set_decoder(decoder)
275
+
276
+ def tie_weights(self) -> None:
277
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
278
+
279
+ def resize_token_embeddings(
280
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
281
+ ) -> nn.Embedding:
282
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
283
+
284
+ # Update config/instance variables
285
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
286
+ self.vocab_size = updated_embeddings.num_embeddings
287
+
288
+ return updated_embeddings
289
+
290
+ # === Core Prismatic VLM `forward()` Logic ===
291
+ def forward(
292
+ self,
293
+ input_ids: Optional[torch.LongTensor] = None,
294
+ attention_mask: Optional[torch.Tensor] = None,
295
+ pixel_values: Optional[torch.FloatTensor] = None,
296
+ labels: Optional[torch.LongTensor] = None,
297
+ inputs_embeds: Optional[torch.FloatTensor] = None,
298
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
299
+ use_cache: Optional[bool] = None,
300
+ output_attentions: Optional[bool] = None,
301
+ output_hidden_states: Optional[bool] = None,
302
+ output_projector_features: Optional[bool] = None,
303
+ return_dict: Optional[bool] = None,
304
+ ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
305
+ """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
306
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
307
+ output_hidden_states = (
308
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
309
+ )
310
+ output_projector_features = output_projector_features if output_projector_features is not None else False
311
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
312
+
313
+ # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
314
+ use_cache = use_cache and not self.training
315
+
316
+ # Instantiate Placeholder for Projector Features
317
+ projected_patch_embeddings = None
318
+
319
+ # Note :: We only support forward passes with the following cases:
320
+ # => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
321
+ # => Unimodal Forward :: (pixel_values is None)
322
+ # => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
323
+
324
+ # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
325
+ if input_ids.shape[1] == 1:
326
+ assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
327
+ assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
328
+ assert labels is None, "Unexpected key `labels` provided during cached generation!"
329
+
330
+ language_model_output = self.language_model(
331
+ input_ids=input_ids,
332
+ attention_mask=None,
333
+ position_ids=None,
334
+ past_key_values=past_key_values,
335
+ inputs_embeds=None,
336
+ labels=None,
337
+ use_cache=use_cache,
338
+ output_attentions=output_attentions,
339
+ output_hidden_states=output_hidden_states,
340
+ return_dict=return_dict,
341
+ )
342
+
343
+ # === Handle Unimodal Forward ===
344
+ elif pixel_values is None:
345
+ assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
346
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
347
+
348
+ language_model_output = self.language_model(
349
+ input_ids=input_ids,
350
+ attention_mask=attention_mask,
351
+ position_ids=None,
352
+ past_key_values=None,
353
+ inputs_embeds=None,
354
+ labels=labels,
355
+ use_cache=use_cache,
356
+ output_attentions=output_attentions,
357
+ output_hidden_states=output_hidden_states,
358
+ return_dict=return_dict,
359
+ )
360
+
361
+ # === Handle Multimodal Forward ===
362
+ elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
363
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
364
+
365
+ # Visual Feature Extraction
366
+ patch_features = self.vision_backbone(pixel_values)
367
+
368
+ # Projection Logic =>> Update Attention Mask
369
+ projected_patch_embeddings = self.projector(patch_features)
370
+ projected_patch_attention_mask = None
371
+ if attention_mask is not None:
372
+ projected_patch_attention_mask = torch.full(
373
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
374
+ fill_value=True,
375
+ dtype=attention_mask.dtype,
376
+ device=attention_mask.device,
377
+ )
378
+
379
+ # Get Input Embeddings (from Language Model Embeddings)
380
+ input_embeddings = self.get_input_embeddings()(input_ids)
381
+
382
+ # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
383
+ multimodal_embeddings = torch.cat(
384
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
385
+ )
386
+ multimodal_attention_mask = None
387
+ if attention_mask is not None:
388
+ multimodal_attention_mask = torch.cat(
389
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
390
+ )
391
+
392
+ # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
393
+ multimodal_labels = None
394
+ if labels is not None:
395
+ projected_patch_labels = torch.full(
396
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
397
+ fill_value=IGNORE_INDEX,
398
+ dtype=labels.dtype,
399
+ device=labels.device,
400
+ )
401
+ multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
402
+
403
+ # Dispatch to Language Model
404
+ language_model_output = self.language_model(
405
+ input_ids=None,
406
+ attention_mask=multimodal_attention_mask,
407
+ position_ids=None,
408
+ past_key_values=None,
409
+ inputs_embeds=multimodal_embeddings,
410
+ labels=multimodal_labels,
411
+ use_cache=use_cache,
412
+ output_attentions=output_attentions,
413
+ output_hidden_states=output_hidden_states,
414
+ return_dict=return_dict,
415
+ )
416
+
417
+ # === Otherwise =>> Assume Invalid! ===
418
+ elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
419
+ raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
420
+
421
+ else:
422
+ raise ValueError(
423
+ "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
424
+ f"=> `input_ids` = {input_ids is not None}\n"
425
+ f"=> `attention_mask` = {attention_mask is not None}\n"
426
+ f"=> `pixel_values` = {pixel_values is not None}\n"
427
+ f"=> `labels` = {labels is not None}\n"
428
+ f"=> `input_embeds` = {inputs_embeds is not None}\n"
429
+ f"=> `past_key_values` = {past_key_values is not None}\n"
430
+ f"=> `use_cache` = {use_cache}"
431
+ )
432
+
433
+ # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
434
+ if not return_dict:
435
+ if output_projector_features and (projected_patch_embeddings is not None):
436
+ return *language_model_output, projected_patch_embeddings
437
+
438
+ return language_model_output
439
+
440
+ return PrismaticCausalLMOutputWithPast(
441
+ loss=language_model_output.loss,
442
+ logits=language_model_output.logits,
443
+ past_key_values=language_model_output.past_key_values,
444
+ hidden_states=language_model_output.hidden_states,
445
+ attentions=language_model_output.attentions,
446
+ projector_features=projected_patch_embeddings,
447
+ )
448
+
449
+ # === GenerationMixin Methods ===
450
+ def prepare_inputs_for_generation(
451
+ self,
452
+ input_ids: Optional[torch.Tensor] = None,
453
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
454
+ inputs_embeds: Optional[torch.FloatTensor] = None,
455
+ pixel_values: Optional[torch.FloatTensor] = None,
456
+ attention_mask: Optional[torch.Tensor] = None,
457
+ **kwargs: str,
458
+ ) -> Dict[str, torch.Tensor]:
459
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
460
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
461
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
462
+ ):
463
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
464
+
465
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
466
+ if past_key_values is not None:
467
+ input_ids = input_ids[:, -1:]
468
+
469
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
470
+ if inputs_embeds is not None and past_key_values is None:
471
+ model_inputs = {"input_embeds": inputs_embeds}
472
+ else:
473
+ model_inputs = {"input_ids": input_ids}
474
+
475
+ # Make sure `pixel_values` are preserved in `model_inputs`
476
+ model_inputs.update(
477
+ {
478
+ "attention_mask": attention_mask,
479
+ "pixel_values": pixel_values,
480
+ "past_key_values": past_key_values,
481
+ "use_cache": kwargs.get("use_cache"),
482
+ }
483
+ )
484
+
485
+ return model_inputs
486
+
487
+ # Defer to Language Model (all handle this differently, with different return types)
488
+ def _reorder_cache(self, *args, **kwargs) -> Any:
489
+ return self.language_model._reorder_cache(*args, **kwargs)
490
+
491
+
492
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
493
+ config_class: PretrainedConfig = OpenVLAConfig
494
+
495
+ def __init__(self, config: OpenVLAConfig) -> None:
496
+ super().__init__(config)
497
+ self.norm_stats = config.norm_stats
498
+
499
+ # Compute action bins
500
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
501
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
502
+
503
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
504
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
505
+
506
+ def predict_action(
507
+ self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
508
+ ) -> np.ndarray:
509
+ """Thin wrapper around super().generate() that decodes predicted actions and de-normalizes them."""
510
+
511
+ # We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
512
+ # in order for the predictions to match the training configuration and be accurate.
513
+ input_ids = torch.cat(
514
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
515
+ )
516
+
517
+ # Run VLA inference
518
+ generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
519
+
520
+ # Extract predicted action tokens and translate into (normalized) continuous actions
521
+ predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
522
+ discretized_actions = self.vocab_size - predicted_action_token_ids
523
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
524
+ normalized_actions = self.bin_centers[discretized_actions]
525
+
526
+ # Unnormalize actions
527
+ action_norm_stats = self.get_action_stats(unnorm_key)
528
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
529
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
530
+ actions = np.where(
531
+ mask,
532
+ 0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
533
+ normalized_actions,
534
+ )
535
+
536
+ return actions
537
+
538
+ @staticmethod
539
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
540
+ if unnorm_key is None and len(norm_stats) != 1:
541
+ raise ValueError(
542
+ f"Your model was trained on more than one dataset. "
543
+ f"Please pass a `unnorm_key` from the following options to choose the statistics used for "
544
+ f"de-normalizing actions: {norm_stats.keys()}"
545
+ )
546
+
547
+ # If None, grab the (singular) dataset in `norm_stats` to use as `unnorm_key`
548
+ unnorm_key = unnorm_key if unnorm_key is not None else next(iter(norm_stats.keys()))
549
+ if unnorm_key not in norm_stats:
550
+ raise ValueError(
551
+ f"The `unnorm_key` you chose ({unnorm_key = }) is not in the available statistics. "
552
+ f"Please choose from: {norm_stats.keys()}"
553
+ )
554
+
555
+ return unnorm_key
556
+
557
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
558
+ """Get the dimensionality of the policy's action space."""
559
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
560
+ return len(self.norm_stats[unnorm_key]["action"]["q01"])
561
+
562
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
563
+ """Get all the logged statistics for the given dataset."""
564
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
565
+ return self.norm_stats[unnorm_key]["action"]