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lm-watermarking / demo_watermark.py

jwkirchenbauer

reduce model selection to just llama2,

ea229c1 about 2 months ago

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	# coding=utf-8
	# Copyright 2023 Authors of "A Watermark for Large Language Models"
	# available at https://arxiv.org/abs/2301.10226
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import os
	import argparse
	from pprint import pprint
	from functools import partial

	import numpy # for gradio hot reload
	import gradio as gr

	import torch

	from transformers import (AutoTokenizer,
	AutoModelForSeq2SeqLM,
	AutoModelForCausalLM,
	LogitsProcessorList)

	# from local_tokenizers.tokenization_llama import LLaMATokenizer

	from transformers import GPT2TokenizerFast
	OPT_TOKENIZER = GPT2TokenizerFast

	from watermark_processor import WatermarkLogitsProcessor, WatermarkDetector


	# ALPACA_MODEL_NAME = "alpaca"
	# ALPACA_MODEL_TOKENIZER = LLaMATokenizer
	# ALPACA_TOKENIZER_PATH = "/cmlscratch/jkirchen/llama"

	# FIXME correct lengths for all models
	API_MODEL_MAP = {
	"google/flan-ul2" : {"max_length": 1000, "gamma": 0.5, "delta": 2.0},
	"google/flan-t5-xxl" : {"max_length": 1000, "gamma": 0.5, "delta": 2.0},
	"EleutherAI/gpt-neox-20b" : {"max_length": 1000, "gamma": 0.5, "delta": 2.0},
	# "bigscience/bloom" : {"max_length": 1000, "gamma": 0.5, "delta": 2.0},
	# "bigscience/bloomz" : {"max_length": 1000, "gamma": 0.5, "delta": 2.0},
	}

	def str2bool(v):
	"""Util function for user friendly boolean flag args"""
	if isinstance(v, bool):
	return v
	if v.lower() in ('yes', 'true', 't', 'y', '1'):
	return True
	elif v.lower() in ('no', 'false', 'f', 'n', '0'):
	return False
	else:
	raise argparse.ArgumentTypeError('Boolean value expected.')

	def parse_args():
	"""Command line argument specification"""

	parser = argparse.ArgumentParser(description="A minimum working example of applying the watermark to any LLM that supports the huggingface 🤗 `generate` API")

	parser.add_argument(
	"--run_gradio",
	type=str2bool,
	default=True,
	help="Whether to launch as a gradio demo. Set to False if not installed and want to just run the stdout version.",
	)
	parser.add_argument(
	"--demo_public",
	type=str2bool,
	default=False,
	help="Whether to expose the gradio demo to the internet.",
	)
	parser.add_argument(
	"--model_name_or_path",
	type=str,
	default="facebook/opt-6.7b",
	help="Main model, path to pretrained model or model identifier from huggingface.co/models.",
	)
	parser.add_argument(
	"--prompt_max_length",
	type=int,
	default=None,
	help="Truncation length for prompt, overrides model config's max length field.",
	)
	parser.add_argument(
	"--max_new_tokens",
	type=int,
	default=200,
	help="Maximmum number of new tokens to generate.",
	)
	parser.add_argument(
	"--generation_seed",
	type=int,
	default=123,
	help="Seed for setting the torch global rng prior to generation.",
	)
	parser.add_argument(
	"--use_sampling",
	type=str2bool,
	default=True,
	help="Whether to generate using multinomial sampling.",
	)
	parser.add_argument(
	"--sampling_temp",
	type=float,
	default=0.7,
	help="Sampling temperature to use when generating using multinomial sampling.",
	)
	parser.add_argument(
	"--n_beams",
	type=int,
	default=1,
	help="Number of beams to use for beam search. 1 is normal greedy decoding",
	)
	parser.add_argument(
	"--use_gpu",
	type=str2bool,
	default=True,
	help="Whether to run inference and watermark hashing/seeding/permutation on gpu.",
	)
	parser.add_argument(
	"--seeding_scheme",
	type=str,
	default="simple_1",
	help="Seeding scheme to use to generate the greenlists at each generation and verification step.",
	)
	parser.add_argument(
	"--gamma",
	type=float,
	default=0.25,
	help="The fraction of the vocabulary to partition into the greenlist at each generation and verification step.",
	)
	parser.add_argument(
	"--delta",
	type=float,
	default=2.0,
	help="The amount/bias to add to each of the greenlist token logits before each token sampling step.",
	)
	parser.add_argument(
	"--normalizers",
	type=str,
	default="",
	help="Single or comma separated list of the preprocessors/normalizer names to use when performing watermark detection.",
	)
	parser.add_argument(
	"--ignore_repeated_bigrams",
	type=str2bool,
	default=False,
	help="Whether to use the detection method that only counts each unqiue bigram once as either a green or red hit.",
	)
	parser.add_argument(
	"--detection_z_threshold",
	type=float,
	default=4.0,
	help="The test statistic threshold for the detection hypothesis test.",
	)
	parser.add_argument(
	"--select_green_tokens",
	type=str2bool,
	default=True,
	help="How to treat the permuation when selecting the greenlist tokens at each step. Legacy is (False) to pick the complement/reds first.",
	)
	parser.add_argument(
	"--skip_model_load",
	type=str2bool,
	default=False,
	help="Skip the model loading to debug the interface.",
	)
	parser.add_argument(
	"--seed_separately",
	type=str2bool,
	default=True,
	help="Whether to call the torch seed function before both the unwatermarked and watermarked generate calls.",
	)
	parser.add_argument(
	"--load_fp16",
	type=str2bool,
	default=False,
	help="Whether to run model in float16 precsion.",
	)
	parser.add_argument(
	"--load_bf16",
	type=str2bool,
	default=False,
	help="Whether to run model in float16 precsion.",
	)
	args = parser.parse_args()
	return args

	def load_model(args):
	"""Load and return the model and tokenizer"""

	args.is_seq2seq_model = any([(model_type in args.model_name_or_path.lower()) for model_type in ["t5","T0"]])
	args.is_decoder_only_model = any([(model_type in args.model_name_or_path.lower()) for model_type in ["gpt","opt","bloom","llama"]])
	if args.is_seq2seq_model:
	model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name_or_path)
	elif args.is_decoder_only_model:
	if args.load_fp16:
	model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path,torch_dtype=torch.float16, device_map='auto')
	elif args.load_bf16:
	model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path,torch_dtype=torch.bfloat16, device_map='auto')
	else:
	model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path)
	else:
	raise ValueError(f"Unknown model type: {args.model_name_or_path}")

	if args.use_gpu:
	device = "cuda" if torch.cuda.is_available() else "cpu"
	if args.load_fp16 or args.load_bf16:
	pass
	else:
	model = model.to(device)
	else:
	device = "cpu"
	model.eval()

	tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

	return model, tokenizer, device


	from text_generation import InferenceAPIClient
	from requests.exceptions import ReadTimeout
	def generate_with_api(prompt, args):
	hf_api_key = os.environ.get("HF_API_KEY")
	if hf_api_key is None:
	raise ValueError("HF_API_KEY environment variable not set, cannot use HF API to generate text.")

	client = InferenceAPIClient(args.model_name_or_path, token=hf_api_key, timeout=60)

	assert args.n_beams == 1, "HF API models do not support beam search."
	generation_params = {
	"max_new_tokens": args.max_new_tokens,
	"do_sample": args.use_sampling,
	}
	if args.use_sampling:
	generation_params["temperature"] = args.sampling_temp
	generation_params["seed"] = args.generation_seed

	timeout_msg = "[Model API timeout error. Try reducing the max_new_tokens parameter or the prompt length.]"
	try:
	generation_params["watermark"] = False
	without_watermark_iterator = client.generate_stream(prompt, **generation_params)
	except ReadTimeout as e:
	print(e)
	without_watermark_iterator = (char for char in timeout_msg)
	try:
	generation_params["watermark"] = True
	with_watermark_iterator = client.generate_stream(prompt, **generation_params)
	except ReadTimeout as e:
	print(e)
	with_watermark_iterator = (char for char in timeout_msg)

	all_without_words, all_with_words = "", ""
	for without_word, with_word in zip(without_watermark_iterator, with_watermark_iterator):
	all_without_words += without_word.token.text
	all_with_words += with_word.token.text
	yield all_without_words, all_with_words


	def check_prompt(prompt, args, tokenizer, model=None, device=None):

	# This applies to both the local and API model scenarios
	if args.model_name_or_path in API_MODEL_MAP:
	args.prompt_max_length = API_MODEL_MAP[args.model_name_or_path]["max_length"]
	elif hasattr(model.config,"max_position_embedding"):
	args.prompt_max_length = model.config.max_position_embeddings-args.max_new_tokens
	else:
	args.prompt_max_length = 2048-args.max_new_tokens

	tokd_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True, truncation=True, max_length=args.prompt_max_length).to(device)
	truncation_warning = True if tokd_input["input_ids"].shape[-1] == args.prompt_max_length else False
	redecoded_input = tokenizer.batch_decode(tokd_input["input_ids"], skip_special_tokens=True)[0]

	return (redecoded_input,
	int(truncation_warning),
	args)



	def generate(prompt, args, tokenizer, model=None, device=None):
	"""Instatiate the WatermarkLogitsProcessor according to the watermark parameters
	and generate watermarked text by passing it to the generate method of the model
	as a logits processor. """

	print(f"Generating with {args}")
	print(f"Prompt: {prompt}")

	if args.model_name_or_path in API_MODEL_MAP:
	api_outputs = generate_with_api(prompt, args)
	yield from api_outputs
	else:
	tokd_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True, truncation=True, max_length=args.prompt_max_length).to(device)

	watermark_processor = WatermarkLogitsProcessor(vocab=list(tokenizer.get_vocab().values()),
	gamma=args.gamma,
	delta=args.delta,
	seeding_scheme=args.seeding_scheme,
	select_green_tokens=args.select_green_tokens)

	gen_kwargs = dict(max_new_tokens=args.max_new_tokens)

	if args.use_sampling:
	gen_kwargs.update(dict(
	do_sample=True,
	top_k=0,
	temperature=args.sampling_temp
	))
	else:
	gen_kwargs.update(dict(
	num_beams=args.n_beams
	))

	generate_without_watermark = partial(
	model.generate,
	**gen_kwargs
	)
	generate_with_watermark = partial(
	model.generate,
	logits_processor=LogitsProcessorList([watermark_processor]),
	**gen_kwargs
	)

	torch.manual_seed(args.generation_seed)
	output_without_watermark = generate_without_watermark(**tokd_input)

	# optional to seed before second generation, but will not be the same again generally, unless delta==0.0, no-op watermark
	if args.seed_separately:
	torch.manual_seed(args.generation_seed)
	output_with_watermark = generate_with_watermark(**tokd_input)

	if args.is_decoder_only_model:
	# need to isolate the newly generated tokens
	output_without_watermark = output_without_watermark[:,tokd_input["input_ids"].shape[-1]:]
	output_with_watermark = output_with_watermark[:,tokd_input["input_ids"].shape[-1]:]

	decoded_output_without_watermark = tokenizer.batch_decode(output_without_watermark, skip_special_tokens=True)[0]
	decoded_output_with_watermark = tokenizer.batch_decode(output_with_watermark, skip_special_tokens=True)[0]

	# mocking the API outputs in a whitespace split generator style
	all_without_words, all_with_words = "", ""
	for without_word, with_word in zip(decoded_output_without_watermark.split(), decoded_output_with_watermark.split()):
	all_without_words += without_word + " "
	all_with_words += with_word + " "
	yield all_without_words, all_with_words


	def format_names(s):
	"""Format names for the gradio demo interface"""
	s=s.replace("num_tokens_scored","Tokens Counted (T)")
	s=s.replace("num_green_tokens","# Tokens in Greenlist")
	s=s.replace("green_fraction","Fraction of T in Greenlist")
	s=s.replace("z_score","z-score")
	s=s.replace("p_value","p value")
	s=s.replace("prediction","Prediction")
	s=s.replace("confidence","Confidence")
	return s

	def list_format_scores(score_dict, detection_threshold):
	"""Format the detection metrics into a gradio dataframe input format"""
	lst_2d = []
	for k,v in score_dict.items():
	if k=='green_fraction':
	lst_2d.append([format_names(k), f"{v:.1%}"])
	elif k=='confidence':
	lst_2d.append([format_names(k), f"{v:.3%}"])
	elif isinstance(v, float):
	lst_2d.append([format_names(k), f"{v:.3g}"])
	elif isinstance(v, bool):
	lst_2d.append([format_names(k), ("Watermarked" if v else "Human/Unwatermarked")])
	else:
	lst_2d.append([format_names(k), f"{v}"])
	if "confidence" in score_dict:
	lst_2d.insert(-2,["z-score Threshold", f"{detection_threshold}"])
	else:
	lst_2d.insert(-1,["z-score Threshold", f"{detection_threshold}"])
	return lst_2d

	def detect(input_text, args, tokenizer, device=None, return_green_token_mask=True):
	"""Instantiate the WatermarkDetection object and call detect on
	the input text returning the scores and outcome of the test"""

	print(f"Detecting with {args}")
	print(f"Detection Tokenizer: {type(tokenizer)}")

	watermark_detector = WatermarkDetector(vocab=list(tokenizer.get_vocab().values()),
	gamma=args.gamma,
	seeding_scheme=args.seeding_scheme,
	device=device,
	tokenizer=tokenizer,
	z_threshold=args.detection_z_threshold,
	normalizers=args.normalizers,
	ignore_repeated_bigrams=args.ignore_repeated_bigrams,
	select_green_tokens=args.select_green_tokens)
	# for now, just don't display the green token mask
	# if we're using normalizers or ignore_repeated_bigrams
	if args.normalizers != [] or args.ignore_repeated_bigrams:
	return_green_token_mask = False

	error = False
	green_token_mask = None
	if input_text == "":
	error = True
	else:
	try:
	score_dict = watermark_detector.detect(input_text, return_green_token_mask=return_green_token_mask)
	green_token_mask = score_dict.pop("green_token_mask", None)
	output = list_format_scores(score_dict, watermark_detector.z_threshold)
	except ValueError as e:
	print(e)
	error = True
	if error:
	output = [["Error","string too short to compute metrics"]]
	output += [["",""] for _ in range(6)]


	html_output = "[No highlight markup generated]"

	if green_token_mask is None:
	html_output = "[Visualizing masks with ignore_repeated_bigrams enabled is not supported, toggle off to see the mask for this text. The mask is the same in both cases - only counting/stats are affected.]"

	if green_token_mask is not None:
	# hack bc we need a fast tokenizer with charspan support
	if "opt" in args.model_name_or_path:
	tokenizer = OPT_TOKENIZER.from_pretrained(args.model_name_or_path)

	tokens = tokenizer(input_text)
	if tokens["input_ids"][0] == tokenizer.bos_token_id:
	tokens["input_ids"] = tokens["input_ids"][1:] # ignore attention mask
	skip = watermark_detector.min_prefix_len
	charspans = [tokens.token_to_chars(i) for i in range(skip,len(tokens["input_ids"]))]
	charspans = [cs for cs in charspans if cs is not None] # remove the special token spans

	if len(charspans) != len(green_token_mask): breakpoint()
	assert len(charspans) == len(green_token_mask)

	tags = [(f'<span class="green">{input_text[cs.start:cs.end]}</span>' if m else f'<span class="red">{input_text[cs.start:cs.end]}</span>') for cs, m in zip(charspans, green_token_mask)]
	html_output = f'<p>{" ".join(tags)}</p>'

	return output, args, tokenizer, html_output

	def run_gradio(args, model=None, device=None, tokenizer=None):
	"""Define and launch the gradio demo interface"""
	check_prompt_partial = partial(check_prompt, model=model, device=device)
	generate_partial = partial(generate, model=model, device=device)
	detect_partial = partial(detect, device=device)


	css = """
	.green { color: black!important;line-height:1.9em; padding: 0.2em 0.2em; background: #ccffcc; border-radius:0.5rem;}
	.red { color: black!important;line-height:1.9em; padding: 0.2em 0.2em; background: #ffad99; border-radius:0.5rem;}
	"""

	with gr.Blocks(css=css) as demo:
	# Top section, greeting and instructions
	with gr.Row():
	with gr.Column(scale=9):
	gr.Markdown(
	"""
	## 💧 [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226) 🔍
	"""
	)
	with gr.Column(scale=1):
	gr.Markdown(
	"""
	[![](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/jwkirchenbauer/lm-watermarking)
	"""
	)
	# if model_name_or_path at startup not one of the API models then add to dropdown
	# all_models = sorted(list(set(list(API_MODEL_MAP.keys())+[args.model_name_or_path])))
	all_models = [args.model_name_or_path]
	model_selector = gr.Dropdown(
	all_models,
	value=args.model_name_or_path,
	label="Language Model",
	)

	# Construct state for parameters, define updates and toggles
	default_prompt = args.__dict__.pop("default_prompt")
	session_args = gr.State(value=args)
	# note that state obj automatically calls value if it's a callable, want to avoid calling tokenizer at startup
	session_tokenizer = gr.State(value=lambda : tokenizer)

	with gr.Tab("Welcome"):
	with gr.Row():
	with gr.Column(scale=2):
	gr.Markdown(
	"""
	Potential harms of large language models can be mitigated by watermarking a model's output.
	Watermarks are embedded signals in the generated text that are invisible to humans but algorithmically
	detectable, that allow anyone to later check whether a given span of text
	was likely to have been generated by a model that uses the watermark.

	This space showcases a watermarking approach that can be applied to _any_ generative language model.
	For demonstration purposes, the space demos a relatively small open-source language model.
	Such a model is less powerful than proprietary commercial tools like ChatGPT, Claude, or Gemini.
	Generally, prompts that entail a short, low entropy response such as the few word answer to a factual trivia question,
	will not exhibit a strong watermark presence, while longer watermarked outputs will produce higher detection statistics.
	"""
	)
	gr.Markdown(
	"""
	[Generate & Detect]: The first tab shows that the watermark can be embedded with
	negligible impact on text quality. You can try any prompt and compare the quality of
	normal text (Output Without Watermark) to the watermarked text (Output With Watermark) below it.
	You can also "see" the watermark by looking at the Highlighted tab where the tokens are
	colored green or red depending on which list they are in.
	Metrics on the right show that the watermark can be reliably detected given a reasonably small number of tokens (25-50).
	Detection is very efficient and does not use the language model or its parameters.

	[Detector Only]: You can also copy-paste the watermarked text (or any other text)
	into the second tab. This can be used to see how many sentences you could remove and still detect the watermark.
	You can also verify here that the detection has, by design, a low false-positive rate;
	This means that human-generated text that you copy into this detector will not be marked as machine-generated.

	You can find more details about how this watermark functions in our paper ["A Watermark for Large Language Models"](https://arxiv.org/abs/2301.10226), presented at ICML 2023.
	Additionally, read about our study on the reliabilty of this watermarking style in ["On the Reliability of Watermarks for Large Language Models"](https://arxiv.org/abs/2306.04634), presented at ICLR 2024.
	"""
	)

	with gr.Column(scale=1):
	gr.Markdown(
	"""
	![](https://drive.google.com/uc?export=view&id=1yVLPcjm-xvaCjQyc3FGLsWIU84v1QRoC)
	"""
	)

	with gr.Tab("Generate & Detect"):

	with gr.Row():
	prompt = gr.Textbox(label=f"Prompt", interactive=True,lines=10,max_lines=10, value=default_prompt)
	with gr.Row():
	generate_btn = gr.Button("Generate")
	with gr.Row():
	with gr.Column(scale=2):
	with gr.Tab("Output Without Watermark (Raw Text)"):
	output_without_watermark = gr.Textbox(interactive=False,lines=14,max_lines=14)
	with gr.Tab("Highlighted"):
	html_without_watermark = gr.HTML(elem_id="html-without-watermark")
	with gr.Column(scale=1):
	without_watermark_detection_result = gr.Dataframe(headers=["Metric", "Value"], interactive=False,row_count=7,col_count=2)
	with gr.Row():
	with gr.Column(scale=2):
	with gr.Tab("Output With Watermark (Raw Text)"):
	output_with_watermark = gr.Textbox(interactive=False,lines=14,max_lines=14)
	with gr.Tab("Highlighted"):
	html_with_watermark = gr.HTML(elem_id="html-with-watermark")
	with gr.Column(scale=1):
	with_watermark_detection_result = gr.Dataframe(headers=["Metric", "Value"],interactive=False,row_count=7,col_count=2)

	redecoded_input = gr.Textbox(visible=False)
	truncation_warning = gr.Number(visible=False)
	def truncate_prompt(redecoded_input, truncation_warning, orig_prompt, args):
	if truncation_warning:
	return redecoded_input + f"\n\n[Prompt was truncated before generation due to length...]", args
	else:
	return orig_prompt, args

	with gr.Tab("Detector Only"):
	with gr.Row():
	with gr.Column(scale=2):
	with gr.Tab("Text to Analyze"):
	detection_input = gr.Textbox(interactive=True,lines=14,max_lines=14)
	with gr.Tab("Highlighted"):
	html_detection_input = gr.HTML(elem_id="html-detection-input")
	with gr.Column(scale=1):
	detection_result = gr.Dataframe(headers=["Metric", "Value"], interactive=False,row_count=7,col_count=2)
	with gr.Row():
	detect_btn = gr.Button("Detect")

	# Parameter selection group
	with gr.Accordion("Advanced Settings",open=False):
	with gr.Row():
	with gr.Column(scale=1):
	gr.Markdown(f"#### Generation Parameters")
	with gr.Row():
	decoding = gr.Radio(label="Decoding Method",choices=["multinomial", "greedy"], value=("multinomial" if args.use_sampling else "greedy"))
	with gr.Row():
	sampling_temp = gr.Slider(label="Sampling Temperature", minimum=0.1, maximum=1.0, step=0.1, value=args.sampling_temp, visible=True)
	with gr.Row():
	generation_seed = gr.Number(label="Generation Seed",value=args.generation_seed, interactive=True)
	with gr.Row():
	n_beams = gr.Dropdown(label="Number of Beams",choices=list(range(1,11,1)), value=args.n_beams, visible=((not args.use_sampling) and (not args.model_name_or_path in API_MODEL_MAP)))
	with gr.Row():
	max_new_tokens = gr.Slider(label="Max Generated Tokens", minimum=10, maximum=1000, step=10, value=args.max_new_tokens)

	with gr.Column(scale=1):
	gr.Markdown(f"#### Watermark Parameters")
	with gr.Row():
	gamma = gr.Slider(label="gamma",minimum=0.1, maximum=0.9, step=0.05, value=args.gamma)
	with gr.Row():
	delta = gr.Slider(label="delta",minimum=0.0, maximum=10.0, step=0.1, value=args.delta)
	gr.Markdown(f"#### Detector Parameters")
	with gr.Row():
	detection_z_threshold = gr.Slider(label="z-score threshold",minimum=0.0, maximum=10.0, step=0.1, value=args.detection_z_threshold)
	with gr.Row():
	ignore_repeated_bigrams = gr.Checkbox(label="Ignore Bigram Repeats")
	with gr.Row():
	normalizers = gr.CheckboxGroup(label="Normalizations", choices=["unicode", "homoglyphs", "truecase"], value=args.normalizers)
	with gr.Row():
	gr.Markdown(f"_Note: sliders don't always update perfectly. Clicking on the bar or using the number window to the right can help. Window below shows the current settings._")
	with gr.Row():
	current_parameters = gr.Textbox(label="Current Parameters", value=args)
	with gr.Accordion("Legacy Settings",open=False):
	with gr.Row():
	with gr.Column(scale=1):
	seed_separately = gr.Checkbox(label="Seed both generations separately", value=args.seed_separately)
	with gr.Column(scale=1):
	select_green_tokens = gr.Checkbox(label="Select 'greenlist' from partition", value=args.select_green_tokens)


	with gr.Accordion("What do the settings do?",open=False):
	gr.Markdown(
	"""
	#### Generation Parameters:

	- Decoding Method : We can generate tokens from the model using either multinomial sampling or we can generate using greedy decoding.
	- Sampling Temperature : If using multinomial sampling we can set the temperature of the sampling distribution.
	0.0 is equivalent to greedy decoding, and 1.0 is the maximum amount of variability/entropy in the next token distribution.
	0.7 strikes a nice balance between faithfulness to the model's estimate of top candidates while adding variety. Does not apply for greedy decoding.
	- Generation Seed : The integer to pass to the torch random number generator before running generation. Makes the multinomial sampling strategy
	outputs reproducible. Does not apply for greedy decoding.
	- Number of Beams : When using greedy decoding, we can also set the number of beams to > 1 to enable beam search.
	This is not implemented/excluded from paper for multinomial sampling but may be added in future.
	- Max Generated Tokens : The `max_new_tokens` parameter passed to the generation method to stop the output at a certain number of new tokens.
	Note that the model is free to generate fewer tokens depending on the prompt.
	Implicitly this sets the maximum number of prompt tokens possible as the model's maximum input length minus `max_new_tokens`,
	and inputs will be truncated accordingly.

	#### Watermark Parameters:

	- gamma : The fraction of the vocabulary to be partitioned into the greenlist at each generation step.
	Smaller gamma values create a stronger watermark by enabling the watermarked model to achieve
	a greater differentiation from human/unwatermarked text because it is preferentially sampling
	from a smaller green set making those tokens less likely to occur by chance.
	- delta : The amount of positive bias to add to the logits of every token in the greenlist
	at each generation step before sampling/choosing the next token. Higher delta values
	mean that the greenlist tokens are more heavily preferred by the watermarked model
	and as the bias becomes very large the watermark transitions from "soft" to "hard".
	For a hard watermark, nearly all tokens are green, but this can have a detrimental effect on
	generation quality, especially when there is not a lot of flexibility in the distribution.

	#### Detector Parameters:

	- z-score threshold : the z-score cuttoff for the hypothesis test. Higher thresholds (such as 4.0) make
	_false positives_ (predicting that human/unwatermarked text is watermarked) very unlikely
	as a genuine human text with a significant number of tokens will almost never achieve
	that high of a z-score. Lower thresholds will capture more _true positives_ as some watermarked
	texts will contain less green tokens and achive a lower z-score, but still pass the lower bar and
	be flagged as "watermarked". However, a lowere threshold will increase the chance that human text
	that contains a slightly higher than average number of green tokens is erroneously flagged.
	4.0-5.0 offers extremely low false positive rates while still accurately catching most watermarked text.
	- Ignore Bigram Repeats : This alternate detection algorithm only considers the unique bigrams in the text during detection,
	computing the greenlists based on the first in each pair and checking whether the second falls within the list.
	This means that `T` is now the unique number of bigrams in the text, which becomes less than the total
	number of tokens generated if the text contains a lot of repetition. See the paper for a more detailed discussion.
	- Normalizations : we implement a few basic normaliations to defend against various adversarial perturbations of the
	text analyzed during detection. Currently we support converting all chracters to unicode,
	replacing homoglyphs with a canonical form, and standardizing the capitalization.
	See the paper for a detailed discussion of input normalization.
	"""
	)

	with gr.Accordion("What do the output metrics mean?",open=False):
	gr.Markdown(
	"""
	- `z-score threshold` : The cuttoff for the hypothesis test
	- `Tokens Counted (T)` : The number of tokens in the output that were counted by the detection algorithm.
	The first token is ommitted in the simple, single token seeding scheme since there is no way to generate
	a greenlist for it as it has no prefix token(s). Under the "Ignore Bigram Repeats" detection algorithm,
	described in the bottom panel, this can be much less than the total number of tokens generated if there is a lot of repetition.
	- `# Tokens in Greenlist` : The number of tokens that were observed to fall in their respective greenlist
	- `Fraction of T in Greenlist` : The `# Tokens in Greenlist` / `T`. This is expected to be approximately `gamma` for human/unwatermarked text.
	- `z-score` : The test statistic for the detection hypothesis test. If larger than the `z-score threshold`
	we "reject the null hypothesis" that the text is human/unwatermarked, and conclude it is watermarked
	- `p value` : The likelihood of observing the computed `z-score` under the null hypothesis. This is the likelihood of
	observing the `Fraction of T in Greenlist` given that the text was generated without knowledge of the watermark procedure/greenlists.
	If this is extremely _small_ we are confident that this many green tokens was not chosen by random chance.
	- `prediction` : The outcome of the hypothesis test - whether the observed `z-score` was higher than the `z-score threshold`
	- `confidence` : If we reject the null hypothesis, and the `prediction` is "Watermarked", then we report 1-`p value` to represent
	the confidence of the detection based on the unlikeliness of this `z-score` observation.
	"""
	)

	gr.HTML("""
	<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
	Follow the github link at the top and host the demo on your own GPU hardware to test out larger models.
	<br/>
	<a href="https://huggingface.co/spaces/tomg-group-umd/lm-watermarking?duplicate=true">
	<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
	<p/>
	""")

	# Register main generation tab click, outputing generations as well as a the encoded+redecoded+potentially truncated prompt and flag, then call detection
	generate_btn.click(fn=check_prompt_partial, inputs=[prompt,session_args,session_tokenizer], outputs=[redecoded_input, truncation_warning, session_args]).success(
	fn=generate_partial, inputs=[redecoded_input,session_args,session_tokenizer], outputs=[output_without_watermark, output_with_watermark]).success(
	fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark]).success(
	fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	# Show truncated version of prompt if truncation occurred
	redecoded_input.change(fn=truncate_prompt, inputs=[redecoded_input,truncation_warning,prompt,session_args], outputs=[prompt,session_args])
	# Register main detection tab click
	detect_btn.click(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result, session_args,session_tokenizer,html_detection_input], api_name="detection")

	# State management logic
	# define update callbacks that change the state dict
	def update_model(session_state, value): session_state.model_name_or_path = value; return session_state
	def update_sampling_temp(session_state, value): session_state.sampling_temp = float(value); return session_state
	def update_generation_seed(session_state, value): session_state.generation_seed = int(value); return session_state
	def update_gamma(session_state, value): session_state.gamma = float(value); return session_state
	def update_delta(session_state, value): session_state.delta = float(value); return session_state
	def update_detection_z_threshold(session_state, value): session_state.detection_z_threshold = float(value); return session_state
	def update_decoding(session_state, value):
	if value == "multinomial":
	session_state.use_sampling = True
	elif value == "greedy":
	session_state.use_sampling = False
	return session_state
	def toggle_sampling_vis(value):
	if value == "multinomial":
	return gr.update(visible=True)
	elif value == "greedy":
	return gr.update(visible=False)
	def toggle_sampling_vis_inv(value):
	if value == "multinomial":
	return gr.update(visible=False)
	elif value == "greedy":
	return gr.update(visible=True)
	# if model name is in the list of api models, set the num beams parameter to 1 and hide n_beams
	def toggle_vis_for_api_model(value):
	if value in API_MODEL_MAP:
	return gr.update(visible=False)
	else:
	return gr.update(visible=True)
	def toggle_beams_for_api_model(value, orig_n_beams):
	if value in API_MODEL_MAP:
	return gr.update(value=1)
	else:
	return gr.update(value=orig_n_beams)
	# if model name is in the list of api models, set the interactive parameter to false
	def toggle_interactive_for_api_model(value):
	if value in API_MODEL_MAP:
	return gr.update(interactive=False)
	else:
	return gr.update(interactive=True)
	# if model name is in the list of api models, set gamma and delta based on API map
	def toggle_gamma_for_api_model(value, orig_gamma):
	if value in API_MODEL_MAP:
	return gr.update(value=API_MODEL_MAP[value]["gamma"])
	else:
	return gr.update(value=orig_gamma)
	def toggle_delta_for_api_model(value, orig_delta):
	if value in API_MODEL_MAP:
	return gr.update(value=API_MODEL_MAP[value]["delta"])
	else:
	return gr.update(value=orig_delta)

	def update_n_beams(session_state, value): session_state.n_beams = value; return session_state
	def update_max_new_tokens(session_state, value): session_state.max_new_tokens = int(value); return session_state
	def update_ignore_repeated_bigrams(session_state, value): session_state.ignore_repeated_bigrams = value; return session_state
	def update_normalizers(session_state, value): session_state.normalizers = value; return session_state
	def update_seed_separately(session_state, value): session_state.seed_separately = value; return session_state
	def update_select_green_tokens(session_state, value): session_state.select_green_tokens = value; return session_state
	def update_tokenizer(model_name_or_path):
	# if model_name_or_path == ALPACA_MODEL_NAME:
	# return ALPACA_MODEL_TOKENIZER.from_pretrained(ALPACA_TOKENIZER_PATH)
	# else:
	return AutoTokenizer.from_pretrained(model_name_or_path)

	def check_model(value): return value if (value!="" and value is not None) else args.model_name_or_path
	# enforce constraint that model cannot be null or empty
	# then attach model callbacks in particular
	model_selector.change(check_model, inputs=[model_selector], outputs=[model_selector]).then(
	toggle_vis_for_api_model,inputs=[model_selector], outputs=[n_beams]
	).then(
	toggle_beams_for_api_model,inputs=[model_selector,n_beams], outputs=[n_beams]
	).then(
	toggle_interactive_for_api_model,inputs=[model_selector], outputs=[gamma]
	).then(
	toggle_interactive_for_api_model,inputs=[model_selector], outputs=[delta]
	).then(
	toggle_gamma_for_api_model,inputs=[model_selector,gamma], outputs=[gamma]
	).then(
	toggle_delta_for_api_model,inputs=[model_selector,delta], outputs=[delta]
	).then(
	update_tokenizer,inputs=[model_selector], outputs=[session_tokenizer]
	).then(
	update_model,inputs=[session_args, model_selector], outputs=[session_args]
	).then(
	lambda value: str(value), inputs=[session_args], outputs=[current_parameters]
	)
	# registering callbacks for toggling the visibilty of certain parameters based on the values of others
	decoding.change(toggle_sampling_vis,inputs=[decoding], outputs=[sampling_temp])
	decoding.change(toggle_sampling_vis,inputs=[decoding], outputs=[generation_seed])
	decoding.change(toggle_sampling_vis_inv,inputs=[decoding], outputs=[n_beams])
	decoding.change(toggle_vis_for_api_model,inputs=[model_selector], outputs=[n_beams])
	# registering all state update callbacks
	decoding.change(update_decoding,inputs=[session_args, decoding], outputs=[session_args])
	sampling_temp.change(update_sampling_temp,inputs=[session_args, sampling_temp], outputs=[session_args])
	generation_seed.change(update_generation_seed,inputs=[session_args, generation_seed], outputs=[session_args])
	n_beams.change(update_n_beams,inputs=[session_args, n_beams], outputs=[session_args])
	max_new_tokens.change(update_max_new_tokens,inputs=[session_args, max_new_tokens], outputs=[session_args])
	gamma.change(update_gamma,inputs=[session_args, gamma], outputs=[session_args])
	delta.change(update_delta,inputs=[session_args, delta], outputs=[session_args])
	detection_z_threshold.change(update_detection_z_threshold,inputs=[session_args, detection_z_threshold], outputs=[session_args])
	ignore_repeated_bigrams.change(update_ignore_repeated_bigrams,inputs=[session_args, ignore_repeated_bigrams], outputs=[session_args])
	normalizers.change(update_normalizers,inputs=[session_args, normalizers], outputs=[session_args])
	seed_separately.change(update_seed_separately,inputs=[session_args, seed_separately], outputs=[session_args])
	select_green_tokens.change(update_select_green_tokens,inputs=[session_args, select_green_tokens], outputs=[session_args])
	# register additional callback on button clicks that updates the shown parameters window
	generate_btn.click(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	detect_btn.click(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	# When the parameters change, display the update and also fire detection, since some detection params dont change the model output.
	delta.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	gamma.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	gamma.change(fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark])
	gamma.change(fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	gamma.change(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result,session_args,session_tokenizer,html_detection_input])
	detection_z_threshold.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	detection_z_threshold.change(fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark])
	detection_z_threshold.change(fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	detection_z_threshold.change(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result,session_args,session_tokenizer,html_detection_input])
	ignore_repeated_bigrams.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	ignore_repeated_bigrams.change(fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark])
	ignore_repeated_bigrams.change(fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	ignore_repeated_bigrams.change(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result,session_args,session_tokenizer,html_detection_input])
	normalizers.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	normalizers.change(fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark])
	normalizers.change(fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	normalizers.change(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result,session_args,session_tokenizer,html_detection_input])
	select_green_tokens.change(lambda value: str(value), inputs=[session_args], outputs=[current_parameters])
	select_green_tokens.change(fn=detect_partial, inputs=[output_without_watermark,session_args,session_tokenizer], outputs=[without_watermark_detection_result,session_args,session_tokenizer,html_without_watermark])
	select_green_tokens.change(fn=detect_partial, inputs=[output_with_watermark,session_args,session_tokenizer], outputs=[with_watermark_detection_result,session_args,session_tokenizer,html_with_watermark])
	select_green_tokens.change(fn=detect_partial, inputs=[detection_input,session_args,session_tokenizer], outputs=[detection_result,session_args,session_tokenizer,html_detection_input])

	# demo.queue(concurrency_count=3)
	demo.queue()

	if args.demo_public:
	demo.launch(share=True) # exposes app to the internet via randomly generated link
	else:
	demo.launch()

	def main(args):
	"""Run a command line version of the generation and detection operations
	and optionally launch and serve the gradio demo"""
	# Initial arg processing and log
	args.normalizers = (args.normalizers.split(",") if args.normalizers else [])
	print(args)

	if not args.skip_model_load:
	model, tokenizer, device = load_model(args)
	else:
	model, tokenizer, device = None, None, None
	tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
	if args.use_gpu:
	device = "cuda" if torch.cuda.is_available() else "cpu"
	else:
	device = "cpu"


	# terrapin example
	input_text = (
	"The diamondback terrapin or simply terrapin (Malaclemys terrapin) is a "
	"species of turtle native to the brackish coastal tidal marshes of the "
	"Northeastern and southern United States, and in Bermuda.[6] It belongs "
	"to the monotypic genus Malaclemys. It has one of the largest ranges of "
	"all turtles in North America, stretching as far south as the Florida Keys "
	"and as far north as Cape Cod.[7] The name 'terrapin' is derived from the "
	"Algonquian word torope.[8] It applies to Malaclemys terrapin in both "
	"British English and American English. The name originally was used by "
	"early European settlers in North America to describe these brackish-water "
	"turtles that inhabited neither freshwater habitats nor the sea. It retains "
	"this primary meaning in American English.[8] In British English, however, "
	"other semi-aquatic turtle species, such as the red-eared slider, might "
	"also be called terrapins. The common name refers to the diamond pattern "
	"on top of its shell (carapace), but the overall pattern and coloration "
	"vary greatly. The shell is usually wider at the back than in the front, "
	"and from above it appears wedge-shaped. The shell coloring can vary "
	"from brown to grey, and its body color can be grey, brown, yellow, "
	"or white. All have a unique pattern of wiggly, black markings or spots "
	"on their body and head. The diamondback terrapin has large webbed "
	"feet.[9] The species is"
	)

	args.default_prompt = input_text


	# Generate and detect, report to stdout
	if not args.skip_model_load:

	term_width = 80
	print("#"*term_width)
	print("Prompt:")
	print(input_text)

	# a generator that yields (without_watermark, with_watermark) pairs
	generator_outputs = generate(input_text,
	args,
	model=model,
	device=device,
	tokenizer=tokenizer)
	# we need to iterate over it,
	# but we only want the last output in this case
	for out in generator_outputs:
	decoded_output_without_watermark = out[0]
	decoded_output_with_watermark = out[1]

	without_watermark_detection_result = detect(decoded_output_without_watermark,
	args,
	device=device,
	tokenizer=tokenizer,
	return_green_token_mask=False)
	with_watermark_detection_result = detect(decoded_output_with_watermark,
	args,
	device=device,
	tokenizer=tokenizer,
	return_green_token_mask=False)

	print("#"*term_width)
	print("Output without watermark:")
	print(decoded_output_without_watermark)
	print("-"*term_width)
	print(f"Detection result @ {args.detection_z_threshold}:")
	pprint(without_watermark_detection_result)
	print("-"*term_width)

	print("#"*term_width)
	print("Output with watermark:")
	print(decoded_output_with_watermark)
	print("-"*term_width)
	print(f"Detection result @ {args.detection_z_threshold}:")
	pprint(with_watermark_detection_result)
	print("-"*term_width)


	# Launch the app to generate and detect interactively (implements the hf space demo)
	if args.run_gradio:
	run_gradio(args, model=model, tokenizer=tokenizer, device=device)

	return

	if __name__ == "__main__":

	args = parse_args()
	print(args)

	main(args)