skaramcheti
commited on
Commit
•
cf0c8cb
1
Parent(s):
43a4a81
Upload OpenVLAForActionPrediction
Browse files- config.json +2 -1
- generation_config.json +7 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +989 -0
- modeling_prismatic.py +561 -0
config.json
CHANGED
@@ -4,7 +4,8 @@
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"OpenVLAForActionPrediction"
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],
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"auto_map": {
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-
"AutoConfig": "configuration_prismatic.OpenVLAConfig"
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},
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"hf_llm_id": "meta-llama/Llama-2-7b-hf",
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"image_resize_strategy": "resize-naive",
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"OpenVLAForActionPrediction"
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],
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"auto_map": {
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"AutoConfig": "configuration_prismatic.OpenVLAConfig",
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"AutoModelForVision2Seq": "modeling_prismatic.OpenVLAForActionPrediction"
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},
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"hf_llm_id": "meta-llama/Llama-2-7b-hf",
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"image_resize_strategy": "resize-naive",
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 32000,
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"transformers_version": "4.40.1"
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}
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model-00001-of-00003.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:10d8636256018712c5e5c823d12e22b5797f99bb721bd123bf6bf2379892be85
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size 6948961960
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model-00002-of-00003.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:2050b14f21d48904d269f48d5a980fecea87cd7b36641d9b0f015e72d1fe216a
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+
size 6971232040
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model-00003-of-00003.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:ea65305a1577f36f721965bf84c8caec0a948ce7ce84d754701637376c531fef
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size 1162406824
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model.safetensors.index.json
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modeling_prismatic.py
ADDED
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|
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
|
508 |
+
) -> np.ndarray:
|
509 |
+
"""Thin wrapper around .generate() that decodes predicted actions and unnormalizes them."""
|
510 |
+
# We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
|
511 |
+
# in order for the predictions to match the training configuration and be accurate.
|
512 |
+
input_ids = torch.cat(
|
513 |
+
(input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
|
514 |
+
)
|
515 |
+
|
516 |
+
# Run VLA inference
|
517 |
+
generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
|
518 |
+
|
519 |
+
# Extract predicted action tokens and translate into (normalized) continuous actions
|
520 |
+
predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
|
521 |
+
discretized_actions = self.vocab_size - predicted_action_token_ids
|
522 |
+
discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
|
523 |
+
normalized_actions = self.bin_centers[discretized_actions]
|
524 |
+
|
525 |
+
# Unnormalize actions
|
526 |
+
action_norm_stats = self.get_action_stats(unnorm_key)
|
527 |
+
mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
|
528 |
+
action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
|
529 |
+
actions = np.where(
|
530 |
+
mask,
|
531 |
+
0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
|
532 |
+
normalized_actions,
|
533 |
+
)
|
534 |
+
|
535 |
+
return actions
|
536 |
+
|
537 |
+
@staticmethod
|
538 |
+
def _check_unnorm_key(norm_stats, unnorm_key):
|
539 |
+
if unnorm_key is None:
|
540 |
+
assert len(norm_stats) == 1, (
|
541 |
+
f"Your model was trained on more than one dataset, "
|
542 |
+
f"please pass a `unnorm_key` from the following options to choose the statistics "
|
543 |
+
f"used for un-normalizing actions: {norm_stats.keys()}"
|
544 |
+
)
|
545 |
+
unnorm_key = next(iter(norm_stats.keys()))
|
546 |
+
|
547 |
+
assert unnorm_key in norm_stats, (
|
548 |
+
f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
|
549 |
+
f"please choose from: {norm_stats.keys()}"
|
550 |
+
)
|
551 |
+
return unnorm_key
|
552 |
+
|
553 |
+
def get_action_dim(self, unnorm_key=None):
|
554 |
+
"""Dimensionality of the policy's action space."""
|
555 |
+
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
|
556 |
+
return len(self.norm_stats[unnorm_key]["action"]["q01"])
|
557 |
+
|
558 |
+
def get_action_stats(self, unnorm_key=None):
|
559 |
+
"""Dimensionality of the policy's action space."""
|
560 |
+
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
|
561 |
+
return self.norm_stats[unnorm_key]["action"]
|