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remyxai
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SpaceLlama3.1-hf

Feature Extraction
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Upload PrismaticForConditionalGeneration

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  2. generation_config.json +7 -0
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+ }
modeling_prismatic.py ADDED
@@ -0,0 +1,572 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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, AutoConfig
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
+ if not self.use_fused_vision_backbone:
134
+ # This branch is not used based on your current configuration.
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
+ # Adjusted to match the checkpoint dimensions
140
+ initial_projection_dim = 4096 # Match the checkpoint's expectation
141
+ self.fc1 = nn.Linear(2176, initial_projection_dim, bias=True)
142
+ self.fc2 = nn.Linear(initial_projection_dim, 4096, bias=True) # Match the checkpoint's expectation
143
+ #self.fc3 = nn.Linear(4096, self.llm_dim, bias=True) # Adding this layer manually
144
+
145
+ self.act_fn1 = nn.GELU()
146
+ self.act_fn2 = nn.GELU()
147
+
148
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
149
+ if not self.use_fused_vision_backbone:
150
+ projected_features = self.fc1(img_patches)
151
+ projected_features = self.act_fn1(projected_features)
152
+ projected_features = self.fc2(projected_features)
153
+ else:
154
+ projected_features = self.fc1(img_patches)
155
+ projected_features = self.act_fn1(projected_features)
156
+ projected_features = self.fc2(projected_features)
157
+ projected_features = self.act_fn2(projected_features)
158
+ projected_features = self.fc3(projected_features)
159
+
160
+ return projected_features
161
+
162
+
163
+ # === Main HF Class Definitions ===
164
+ @dataclass
165
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
166
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
167
+
168
+ loss: Optional[torch.FloatTensor] = None
169
+ logits: torch.FloatTensor = None
170
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
171
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
172
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
173
+
174
+ # Additions for VLMs
175
+ projector_features: Optional[torch.FloatTensor] = None
176
+
177
+
178
+ class PrismaticPreTrainedModel(PreTrainedModel):
179
+ config_class: PretrainedConfig = PrismaticConfig
180
+ base_model_prefix: str = "model"
181
+ supports_gradient_checkpointing: bool = True
182
+
183
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
184
+ _skip_keys_device_placement: str = "past_key_values"
185
+ _supports_flash_attn_2: bool = True
186
+
187
+ def _init_weights(self, module: nn.Module) -> None:
188
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
189
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
190
+ # https://github.com/TRI-ML/prismatic-vlms
191
+ std = (
192
+ self.config.initializer_range
193
+ if hasattr(self.config, "initializer_range")
194
+ else self.config.text_config.initializer_range
195
+ )
196
+
197
+ if hasattr(module, "class_embedding"):
198
+ module.class_embedding.data.normal_(mean=0.0, std=std)
199
+
200
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
201
+ module.weight.data.normal_(mean=0.0, std=std)
202
+ if module.bias is not None:
203
+ module.bias.data.zero_()
204
+ elif isinstance(module, nn.Embedding):
205
+ module.weight.data.normal_(mean=0.0, std=std)
206
+ if module.padding_idx is not None:
207
+ module.weight.data[module.padding_idx].zero_()
208
+
209
+ @property
210
+ def _supports_sdpa(self) -> bool:
211
+ """Check LLM supports SDPA Attention"""
212
+ return self.language_model._supports_sdpa
213
+
214
+
215
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
216
+ def __init__(self, config: PrismaticConfig) -> None:
217
+ super().__init__(config)
218
+
219
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
220
+ if config.use_fused_vision_backbone is None:
221
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
222
+
223
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
224
+ raise NotImplementedError(
225
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
226
+ "if you urgently need support for latest TIMM versions."
227
+ )
228
+
229
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
230
+ logger.warning(
231
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
232
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
233
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
234
+ f"use the above versions."
235
+ )
236
+
237
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
238
+ self.vision_backbone = PrismaticVisionBackbone(
239
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
240
+ )
241
+
242
+ # Create Multimodal Projector
243
+ self.projector = PrismaticProjector(
244
+ config.use_fused_vision_backbone,
245
+ vision_dim=self.vision_backbone.embed_dim,
246
+ llm_dim=config.text_config.hidden_size,
247
+ )
248
+
249
+ # Instantiate LLM Backbone
250
+ base_config = AutoConfig.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
251
+ base_config.attn_implementation = config._attn_implementation
252
+ base_config.vocab_size = 128320
253
+
254
+ self.language_model = AutoModelForCausalLM.from_config(base_config)
255
+ #self.language_model = AutoModelForCausalLM.from_config(
256
+ # config.text_config, attn_implementation=config._attn_implementation
257
+ #)
258
+ self.vocab_size = config.text_config.vocab_size
259
+ self.pad_token_id = config.pad_token_id
260
+
261
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
262
+ self.post_init()
263
+
264
+ # === `PreTrainedModel` Boilerplate ===
265
+ def get_input_embeddings(self) -> nn.Module:
266
+ return self.language_model.get_input_embeddings()
267
+
268
+ def set_input_embeddings(self, value: nn.Module) -> None:
269
+ self.language_model.set_input_embeddings(value)
270
+
271
+ def get_output_embeddings(self) -> nn.Module:
272
+ return self.language_model.get_output_embeddings()
273
+
274
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
275
+ self.language_model.set_output_embeddings(new_embeddings)
276
+
277
+ def get_decoder(self) -> nn.Module:
278
+ return self.language_model.get_decoder()
279
+
280
+ def set_decoder(self, decoder: nn.Module) -> None:
281
+ self.language_model.set_decoder(decoder)
282
+
283
+ def tie_weights(self) -> None:
284
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
285
+
286
+ def resize_token_embeddings(
287
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
288
+ ) -> nn.Embedding:
289
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
290
+
291
+ # Update config/instance variables
292
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
293
+ self.vocab_size = updated_embeddings.num_embeddings
294
+
295
+ return updated_embeddings
296
+
297
+ # === Core Prismatic VLM `forward()` Logic ===
298
+ def forward(
299
+ self,
300
+ input_ids: Optional[torch.LongTensor] = None,
301
+ attention_mask: Optional[torch.Tensor] = None,
302
+ pixel_values: Optional[torch.FloatTensor] = None,
303
+ labels: Optional[torch.LongTensor] = None,
304
+ inputs_embeds: Optional[torch.FloatTensor] = None,
305
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
306
+ use_cache: Optional[bool] = None,
307
+ output_attentions: Optional[bool] = None,
308
+ output_hidden_states: Optional[bool] = None,
309
+ output_projector_features: Optional[bool] = None,
310
+ return_dict: Optional[bool] = None,
311
+ ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
312
+ """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
313
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
314
+ output_hidden_states = (
315
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
316
+ )
317
+ output_projector_features = output_projector_features if output_projector_features is not None else False
318
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
319
+
320
+ # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
321
+ use_cache = use_cache and not self.training
322
+
323
+ # Instantiate Placeholder for Projector Features
324
+ projected_patch_embeddings = None
325
+
326
+ # Note :: We only support forward passes with the following cases:
327
+ # => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
328
+ # => Unimodal Forward :: (pixel_values is None)
329
+ # => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
330
+
331
+ # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
332
+ if input_ids.shape[1] == 1:
333
+ assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
334
+ assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
335
+ assert labels is None, "Unexpected key `labels` provided during cached generation!"
336
+
337
+ language_model_output = self.language_model(
338
+ input_ids=input_ids,
339
+ attention_mask=None,
340
+ position_ids=None,
341
+ past_key_values=past_key_values,
342
+ inputs_embeds=None,
343
+ labels=None,
344
+ use_cache=use_cache,
345
+ output_attentions=output_attentions,
346
+ output_hidden_states=output_hidden_states,
347
+ return_dict=return_dict,
348
+ )
349
+
350
+ # === Handle Unimodal Forward ===
351
+ elif pixel_values is None:
352
+ assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
353
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
354
+
355
+ language_model_output = self.language_model(
356
+ input_ids=input_ids,
357
+ attention_mask=attention_mask,
358
+ position_ids=None,
359
+ past_key_values=None,
360
+ inputs_embeds=None,
361
+ labels=labels,
362
+ use_cache=use_cache,
363
+ output_attentions=output_attentions,
364
+ output_hidden_states=output_hidden_states,
365
+ return_dict=return_dict,
366
+ )
367
+
368
+ # === Handle Multimodal Forward ===
369
+ elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
370
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
371
+
372
+ # Visual Feature Extraction
373
+ patch_features = self.vision_backbone(pixel_values)
374
+
375
+ # Projection Logic =>> Update Attention Mask
376
+ projected_patch_embeddings = self.projector(patch_features)
377
+ projected_patch_attention_mask = None
378
+ if attention_mask is not None:
379
+ projected_patch_attention_mask = torch.full(
380
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
381
+ fill_value=True,
382
+ dtype=attention_mask.dtype,
383
+ device=attention_mask.device,
384
+ )
385
+
386
+ # Get Input Embeddings (from Language Model Embeddings)
387
+ input_embeddings = self.get_input_embeddings()(input_ids)
388
+
389
+ # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
390
+ multimodal_embeddings = torch.cat(
391
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
392
+ )
393
+ multimodal_attention_mask = None
394
+ if attention_mask is not None:
395
+ multimodal_attention_mask = torch.cat(
396
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
397
+ )
398
+
399
+ # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
400
+ multimodal_labels = None
401
+ if labels is not None:
402
+ projected_patch_labels = torch.full(
403
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
404
+ fill_value=IGNORE_INDEX,
405
+ dtype=labels.dtype,
406
+ device=labels.device,
407
+ )
408
+ multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
409
+
410
+ # Dispatch to Language Model
411
+ language_model_output = self.language_model(
412
+ input_ids=None,
413
+ attention_mask=multimodal_attention_mask,
414
+ position_ids=None,
415
+ past_key_values=None,
416
+ inputs_embeds=multimodal_embeddings,
417
+ labels=multimodal_labels,
418
+ use_cache=use_cache,
419
+ output_attentions=output_attentions,
420
+ output_hidden_states=output_hidden_states,
421
+ return_dict=return_dict,
422
+ )
423
+
424
+ # === Otherwise =>> Assume Invalid! ===
425
+ elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
426
+ raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
427
+
428
+ else:
429
+ raise ValueError(
430
+ "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
431
+ f"=> `input_ids` = {input_ids is not None}\n"
432
+ f"=> `attention_mask` = {attention_mask is not None}\n"
433
+ f"=> `pixel_values` = {pixel_values is not None}\n"
434
+ f"=> `labels` = {labels is not None}\n"
435
+ f"=> `input_embeds` = {inputs_embeds is not None}\n"
436
+ f"=> `past_key_values` = {past_key_values is not None}\n"
437
+ f"=> `use_cache` = {use_cache}"
438
+ )
439
+
440
+ # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
441
+ if not return_dict:
442
+ if output_projector_features and (projected_patch_embeddings is not None):
443
+ return *language_model_output, projected_patch_embeddings
444
+
445
+ return language_model_output
446
+
447
+ return PrismaticCausalLMOutputWithPast(
448
+ loss=language_model_output.loss,
449
+ logits=language_model_output.logits,
450
+ past_key_values=language_model_output.past_key_values,
451
+ hidden_states=language_model_output.hidden_states,
452
+ attentions=language_model_output.attentions,
453
+ projector_features=projected_patch_embeddings,
454
+ )
455
+
456
+ # === GenerationMixin Methods ===
457
+ def prepare_inputs_for_generation(
458
+ self,
459
+ input_ids: Optional[torch.Tensor] = None,
460
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
461
+ inputs_embeds: Optional[torch.FloatTensor] = None,
462
+ pixel_values: Optional[torch.FloatTensor] = None,
463
+ attention_mask: Optional[torch.Tensor] = None,
464
+ **kwargs: str,
465
+ ) -> Dict[str, torch.Tensor]:
466
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
467
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
468
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
469
+ ):
470
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
471
+
472
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
473
+ if past_key_values is not None:
474
+ input_ids = input_ids[:, -1:]
475
+
476
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
477
+ if inputs_embeds is not None and past_key_values is None:
478
+ model_inputs = {"input_embeds": inputs_embeds}
479
+ else:
480
+ model_inputs = {"input_ids": input_ids}
481
+
482
+ # Make sure `pixel_values` are preserved in `model_inputs`
483
+ model_inputs.update(
484
+ {
485
+ "attention_mask": attention_mask,
486
+ "pixel_values": pixel_values,
487
+ "past_key_values": past_key_values,
488
+ "use_cache": kwargs.get("use_cache"),
489
+ }
490
+ )
491
+
492
+ return model_inputs
493
+
494
+ # Defer to Language Model (all handle this differently, with different return types)
495
+ def _reorder_cache(self, *args, **kwargs) -> Any:
496
+ return self.language_model._reorder_cache(*args, **kwargs)
497
+
498
+
499
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
500
+ config_class: PretrainedConfig = OpenVLAConfig
501
+
502
+ def __init__(self, config: OpenVLAConfig) -> None:
503
+ super().__init__(config)
504
+ self.norm_stats = config.norm_stats
505
+
506
+ # Compute action bins
507
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
508
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
509
+
510
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
511
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
512
+
513
+ def predict_action(
514
+ self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
515
+ ) -> np.ndarray:
516
+ """Thin wrapper around super().generate() that decodes predicted actions and de-normalizes them."""
517
+
518
+ # We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
519
+ # in order for the predictions to match the training configuration and be accurate.
520
+ input_ids = torch.cat(
521
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
522
+ )
523
+
524
+ # Run VLA inference
525
+ generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
526
+
527
+ # Extract predicted action tokens and translate into (normalized) continuous actions
528
+ predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
529
+ discretized_actions = self.vocab_size - predicted_action_token_ids
530
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
531
+ normalized_actions = self.bin_centers[discretized_actions]
532
+
533
+ # Unnormalize actions
534
+ action_norm_stats = self.get_action_stats(unnorm_key)
535
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
536
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
537
+ actions = np.where(
538
+ mask,
539
+ 0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
540
+ normalized_actions,
541
+ )
542
+
543
+ return actions
544
+
545
+ @staticmethod
546
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
547
+ if unnorm_key is None and len(norm_stats) != 1:
548
+ raise ValueError(
549
+ f"Your model was trained on more than one dataset. "
550
+ f"Please pass a `unnorm_key` from the following options to choose the statistics used for "
551
+ f"de-normalizing actions: {norm_stats.keys()}"
552
+ )
553
+
554
+ # If None, grab the (singular) dataset in `norm_stats` to use as `unnorm_key`
555
+ unnorm_key = unnorm_key if unnorm_key is not None else next(iter(norm_stats.keys()))
556
+ if unnorm_key not in norm_stats:
557
+ raise ValueError(
558
+ f"The `unnorm_key` you chose ({unnorm_key = }) is not in the available statistics. "
559
+ f"Please choose from: {norm_stats.keys()}"
560
+ )
561
+
562
+ return unnorm_key
563
+
564
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
565
+ """Get the dimensionality of the policy's action space."""
566
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
567
+ return len(self.norm_stats[unnorm_key]["action"]["q01"])
568
+
569
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
570
+ """Get all the logged statistics for the given dataset."""
571
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
572
+ return self.norm_stats[unnorm_key]["action"]