Spaces:
Running
on
L40S
Running
on
L40S
File size: 13,895 Bytes
f5e3203 2b6fecd f5e3203 2a04008 2b6fecd f5e3203 fed0a26 b22b950 f5e3203 2b6fecd 9220551 f5e3203 2a04008 2b6fecd 2a04008 80c639a 2a04008 2b6fecd 80c639a 2a04008 2b6fecd 2a04008 80c639a 2b6fecd 2a04008 2b6fecd 80c639a 2b6fecd 80c639a 2b6fecd 80c639a fed0a26 80c639a 2a04008 2b6fecd fed0a26 2b6fecd fed0a26 2a04008 fed0a26 80c639a 2b6fecd 2a04008 f5e3203 80c639a d19a19a dcd31e5 80c639a d0eb7f5 46f6023 d19a19a 46f6023 d19a19a 2b6fecd d19a19a 46f6023 d0eb7f5 46f6023 d0eb7f5 46f6023 d0eb7f5 46f6023 d0eb7f5 80c639a 46f6023 80c639a f5e3203 ad5bf1a f5e3203 80c639a d0eb7f5 80c639a f5e3203 80c639a d0eb7f5 80c639a f5e3203 d0eb7f5 f5e3203 ff97c38 d19a19a fed0a26 2a04008 fed0a26 f5e3203 d19a19a fed0a26 d19a19a f5e3203 fed0a26 f5e3203 fed0a26 898024b f5e3203 898024b f5e3203 80c639a d0eb7f5 f5e3203 d0eb7f5 d19a19a d0eb7f5 f5e3203 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
from collections.abc import Sequence
import random
from typing import Optional, List, Tuple
import gradio as gr
import spaces
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BayesianDetectorModel,
SynthIDTextWatermarkingConfig,
SynthIDTextWatermarkDetector,
SynthIDTextWatermarkLogitsProcessor,
)
# If the watewrmark is not detected, consider the use case. Could be because of
# the nature of the task (e.g., fatcual responses are lower entropy) or it could
# be another
_MODEL_IDENTIFIER = 'google/gemma-2b-it'
_DETECTOR_IDENTIFIER = 'google/synthid-spaces-demo-detector'
_PROMPTS: Tuple[str] = (
'Write an essay about my pets, a cat named Mika and a dog named Cleo.',
'Tell me everything you can about Portugal.',
'What is Hugging Face?',
)
_TORCH_DEVICE = (
torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
)
_ANSWERS: List[Tuple[str, str]] = []
_WATERMARK_CONFIG_DICT = dict(
ngram_len=5,
keys=[
654,
400,
836,
123,
340,
443,
597,
160,
57,
29,
590,
639,
13,
715,
468,
990,
966,
226,
324,
585,
118,
504,
421,
521,
129,
669,
732,
225,
90,
960,
],
sampling_table_size=2**16,
sampling_table_seed=0,
context_history_size=1024,
)
_WATERMARK_CONFIG = SynthIDTextWatermarkingConfig(
**_WATERMARK_CONFIG_DICT
)
tokenizer = AutoTokenizer.from_pretrained(
_MODEL_IDENTIFIER, padding_side="left"
)
tokenizer.pad_token_id = tokenizer.eos_token_id
model = AutoModelForCausalLM.from_pretrained(_MODEL_IDENTIFIER)
model.to(_TORCH_DEVICE)
logits_processor = SynthIDTextWatermarkLogitsProcessor(
**_WATERMARK_CONFIG_DICT,
device=_TORCH_DEVICE,
)
detector_module = BayesianDetectorModel.from_pretrained(_DETECTOR_IDENTIFIER)
detector_module.to(_TORCH_DEVICE)
detector = SynthIDTextWatermarkDetector(
detector_module=detector_module,
logits_processor=logits_processor,
tokenizer=tokenizer,
)
@spaces.GPU
def generate_outputs(
prompts: Sequence[str],
watermarking_config: Optional[SynthIDTextWatermarkingConfig] = None,
) -> Tuple[Sequence[str], torch.Tensor]:
tokenized_prompts = tokenizer(
prompts, return_tensors='pt', padding="longest"
).to(_TORCH_DEVICE)
input_length = tokenized_prompts.input_ids.shape[1]
output_sequences = model.generate(
**tokenized_prompts,
watermarking_config=watermarking_config,
do_sample=True,
max_length=500,
top_k=40,
)
output_sequences = output_sequences[:, input_length:]
detections = detector(output_sequences)
return (
tokenizer.batch_decode(output_sequences, skip_special_tokens=True),
detections
)
with gr.Blocks() as demo:
gr.Markdown(
'''
# Using SynthID Text in your Generative AI projects
[SynthID][synthid] is a Google DeepMind technology that watermarks and
identifies AI-generated content by embedding digital watermarks directly
into AI-generated images, audio, text or video.
SynthID Text is an open source implementation of this technology available
in Hugging Face Transformers that has two major components:
* A [logits processor][synthid-hf-logits-processor] that is
[configured][synthid-hf-config] on a per-model basis and activated when
calling `.generate()`; and
* A [detector][synthid-hf-detector] trained to recognized watermarked text
generated by a specific model with a specific configuraiton.
This Space demonstrates:
1. How to use SynthID Text to apply a watermark to text generated by your
model; and
1. How to indetify that text using a ready-made detector.
Note that this detector is trained specifically fore this demonstration. You
should maintain a specific watermarking configuration for every model you
use and protect that configuration as you would any other secret. See the
[end-to-end guide][synthid-hf-detector-e2e] for more on training your own
detectors, and the [SynthID Text documentaiton][raitk-synthid] for more on
how this technology works.
## Applying a watermark
Practically speaking, SynthID Text is a logits processor, applied to your
model's generation pipeline after [Top-K and Top-P][cloud-parameter-values],
that augments the model's logits using a pseudorandom _g_-function to encode
watermarking information in a way that balances generation quality with
watermark detectability. See the [paper][synthid-nature] for a complete
technical description of the algorithm and analyses of how different
configuration values affect performance.
Watermarks are [configured][synthid-hf-config] to parameterize the
_g_-function and how it is applied during generation. The following
configuration is used for all demos. It should not be used for any
production purposes.
```json
{
"ngram_len": 5,
"keys": [
654, 400, 836, 123, 340, 443, 597, 160, 57, 29,
590, 639, 13, 715, 468, 990, 966, 226, 324, 585,
118, 504, 421, 521, 129, 669, 732, 225, 90, 960
],
"sampling_table_size": 65536,
"sampling_table_seed": 0,
"context_history_size": 1024
}
```
Watermarks are applied by initializing a `SynthIDTextWatermarkingConfig`
and passing that as the `watermarking_config=` parameter in your call to
`.generate()`, as shown in the snippet below.
```python
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
SynthIDTextWatermarkingConfig,
)
# Standard model and tokenizer initialization
tokenizer = AutoTokenizer.from_pretrained('repo/id')
model = AutoModelForCausalLM.from_pretrained('repo/id')
# SynthID Text configuration
watermarking_config = SynthIDTextWatermarkingConfig(...)
# Generation with watermarking
tokenized_prompts = tokenizer(["your prompts here"])
output_sequences = model.generate(
**tokenized_prompts,
watermarking_config=watermarking_config,
do_sample=True,
)
watermarked_text = tokenizer.batch_decode(output_sequences)
```
## Try it yourself.
Lets use [Gemma 2B IT][gemma] to help you understand how watermarking works.
Using the text boxes below enter up to three prompts then click the generate
button. Some examples are provided to help get you started, but they are
fully editable.
Gemma will then generate watermarked and non-watermarked repsonses for each
non-empty prompt you provided.
[cloud-parameter-values]: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values
[gemma]: https://huggingface.co/google/gemma-2b
[raitk-synthid]: https://ai.google.dev/responsible/docs/safeguards/synthid-text
[synthid]: https://deepmind.google/technologies/synthid/
[synthid-hf-config]: https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/generation/configuration_utils.py
[synthid-hf-detector]: https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/generation/watermarking.py
[synthid-hf-detector-e2e]: https://github.com/huggingface/transformers/blob/v4.46.0/examples/research_projects/synthid_text/detector_bayesian.py
[synthid-hf-logits-processor]: https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/generation/logits_process.py
[synthid-nature]: https://www.nature.com/articles/s41586-024-08025-4
'''
)
prompt_inputs = [
gr.Textbox(value=prompt, lines=4, label='Prompt')
for prompt in _PROMPTS
]
generate_btn = gr.Button('Generate')
with gr.Column(visible=False) as generations_col:
gr.Markdown(
'''
## Human recognition of watermarked text
The primary goal of SynthID Text is to apply a watermark to generated text
wihtout affecting generation quality. Another way to think about this is
that generated text that carries a watermark should be imperceptible to
you, the reader, but easily perceived by a watermark detector.
The responses from Gemma are shown below. Use the checkboxes to mark which
responses you think are the watermarked, then click the "reveal" button to
see the true values.
The [research paper][synthid-nature] has an in-depth study examining human
perception of watermared versus non-watermarked text.
[synthid-nature]: https://www.nature.com/articles/s41586-024-08025-4
'''
)
generations_grp = gr.CheckboxGroup(
label='All generations, in random order',
info='Select the generations you think are watermarked!',
)
reveal_btn = gr.Button('Reveal', visible=False)
with gr.Column(visible=False) as detections_col:
gr.Markdown(
'''
## Detecting watermarked text
The only way to properly detect watermarked text is with a trained
classifier. This Space uses a pre-trained classifier hosted on Huggin Face
Hub. For production uses you will need to train your own classifiers to
recognize your watermarks. A [Bayesian detector][synthid-hf-detector] is
provided in Transformers, along with an
[end-to-end example][synthid-hf-detector-e2e] of how to train one of these
detectors.
You can see how your guesses compared to the actaul results below. As
above, the responses are displayed in checkboxes. If the box is checked,
then the text carries a watermark. Your correct guesses are annotated with
the "Correct" prefix.
[synthid-hf-detector]: https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/generation/watermarking.py
[synthid-hf-detector-e2e]: https://github.com/huggingface/transformers/blob/v4.46.0/examples/research_projects/synthid_text/detector_bayesian.py
'''
)
revealed_grp = gr.CheckboxGroup(
label='Ground truth for all generations',
info=(
'Watermarked generations are checked, and your selection are '
'marked as correct or incorrect in the text.'
),
)
gr.Markdown(
'''
## Limitations
SynthID Text watermarks are robust to some transformations, such as
cropping pieces of text, modifying a few words, or mild paraphrasing, but
this method does have limitations.
- Watermark application is less effective on factual responses, as there
is less opportunity to augment generation without decreasing accuracy.
- Detector confidence scores can be greatly reduced when an AI-generated
text is thoroughly rewritten, or translated to another language.
SynthID Text is not built to directly stop motivated adversaries from
causing harm. However, it can make it harder to use AI-generated content
for malicious purposes, and it can be combined with other approaches to
give better coverage across content types and platforms.
'''
)
reset_btn = gr.Button('Reset', visible=False)
def generate(*prompts):
prompts = [p for p in prompts if p]
standard, standard_detector = generate_outputs(prompts=prompts)
watermarked, watermarked_detector = generate_outputs(
prompts=prompts,
watermarking_config=_WATERMARK_CONFIG,
)
upper_threshold = 0.9501
lower_threshold = 0.1209
def decision(score: float) -> str:
if score > upper_threshold:
return 'Watermarked'
elif lower_threshold < score < upper_threshold:
return 'Indeterminate'
else:
return 'Not watermarked'
responses = [
(text, decision(score))
for text, score in zip(standard, standard_detector[0])
]
responses += [
(text, decision(score))
for text, score in zip(watermarked, watermarked_detector[0])
]
random.shuffle(responses)
_ANSWERS.extend(responses)
# Load model
return {
generate_btn: gr.Button(visible=False),
generations_col: gr.Column(visible=True),
generations_grp: gr.CheckboxGroup(
[response[0] for response in responses],
),
reveal_btn: gr.Button(visible=True),
}
generate_btn.click(
generate,
inputs=prompt_inputs,
outputs=[generate_btn, generations_col, generations_grp, reveal_btn]
)
def reveal(user_selections: list[str]):
choices: list[str] = []
value: list[str] = []
for (response, decision) in _ANSWERS:
if decision == "Watermarked":
if response in user_selections:
choice = f'Correct! {response}'
else:
choice = response
value.append(choice)
else:
choice = response
choices.append(choice)
return {
reveal_btn: gr.Button(visible=False),
detections_col: gr.Column(visible=True),
revealed_grp: gr.CheckboxGroup(choices=choices, value=value),
reset_btn: gr.Button(visible=True),
}
reveal_btn.click(
reveal,
inputs=generations_grp,
outputs=[
reveal_btn,
detections_col,
revealed_grp,
reset_btn
],
)
def reset():
_ANSWERS.clear()
return {
generations_col: gr.Column(visible=False),
detections_col: gr.Column(visible=False),
revealed_grp: gr.CheckboxGroup(visible=False),
reset_btn: gr.Button(visible=False),
generate_btn: gr.Button(visible=True),
}
reset_btn.click(
reset,
inputs=[],
outputs=[
generations_col,
detections_col,
revealed_grp,
reset_btn,
generate_btn,
],
)
if __name__ == '__main__':
demo.launch()
|