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501 | class DiffPrivateSimpleDatasetPack(BaseLlamaPack):
"""A pack for creating differentially private simple llama-dataset."""
def __init__(
self,
llm: LLM, # currently only supports OpenAI completion LLMs
tokenizer: Any,
prompt_bundle: PromptBundle,
simple_dataset: LabelledSimpleDataset,
batch_size: int = 5,
sleep_time_in_seconds: float = 0,
sephamore_counter_size: int = 1,
cache_checkpoints: bool = True,
show_progress: bool = True,
):
self.llm = llm
self.tokenizer = tokenizer
self.prompt_bundle = prompt_bundle
self.simple_dataset = simple_dataset
self._num_examples = len(self.simple_dataset.examples)
self.labels = list({el.reference_label for el in self.simple_dataset[:]})
self.sleep_time_in_seconds = sleep_time_in_seconds
self._semaphore = asyncio.Semaphore(sephamore_counter_size)
self.show_progress = show_progress
self.batch_size = batch_size
self.cache_checkpoints = cache_checkpoints
def sigma_to_eps(
self,
sigma: float,
mechanism: PrivacyMechanism,
size: int,
max_token_cnt: int,
max_self_compositions: int = 1000,
eps_error: float = 0.01,
delta_error: float = 1e-10,
) -> float:
"""Return the epsilon value given a sigma.
Args:
sigma (float): The parameter associated with noise mechanism.
mechanism (PrivacyMechanism): Noise mechanism.
size (int): Number of samples to be generated.
max_token_cnt (int): Number of tokens generated per sample.
max_self_compositions (int, optional): PRV algorithm parameter. Defaults to 1000.
eps_error (float, optional): PRV algorithm parameter. Defaults to 0.01.
delta_error (float, optional): PRV algorithm parameter. Defaults to 1e-10.
Returns:
float: The epsilon value.
"""
if max_token_cnt > max_self_compositions:
raise ValueError(
"`max_token_cnt` cannot be greater than `max_self_composition`."
)
sample_rate = size / self._num_examples
if mechanism == PrivacyMechanism.GAUSSIAN:
prv_0 = PoissonSubsampledGaussianMechanism(
noise_multiplier=sigma, sampling_probability=sample_rate
)
elif mechanism == PrivacyMechanism.EXPONENTIAL:
sigma_bar = math.log(1 + sample_rate * (math.exp(sigma) - 1))
prv_0 = PureDPMechanism(eps=sigma_bar)
else:
raise ValueError(
"Invalid value for mechanism entered."
" Please use either 'gaussian' or 'exponential'."
)
accountant = PRVAccountant(
prvs=[
prv_0,
],
max_self_compositions=[max_self_compositions],
eps_error=eps_error,
delta_error=delta_error,
)
_eps_low, eps_est, _eps_up = accountant.compute_epsilon(
delta=1 / self._num_examples, num_self_compositions=[max_token_cnt]
)
return eps_est
async def _async_worker(self, job: Coroutine) -> Any:
async with self._semaphore:
await asyncio.sleep(self.sleep_time_in_seconds)
return await job
@dispatcher.span
def _filter_dataset_by_label(self, label: str) -> LabelledSimpleDataset:
"""Filter simple_dataset by label."""
if label not in self.labels:
raise ValueError(
"There are no examples with `label` in the associated `simple_dataset`."
)
examples = [el for el in self.simple_dataset[:] if el.reference_label == label]
return LabelledSimpleDataset(examples=examples)
@dispatcher.span
def _split_dataset(
self,
dataset: LabelledSimpleDataset,
num_splits: int,
num_samples_per_split: int,
) -> List[LabelledSimpleDataset]:
"""Splits a dataset into a set of disjoint datasets with equal number of examples."""
indexes = list(range(len(dataset.examples)))
random.shuffle(indexes)
partitions = [indexes[i::num_splits] for i in range(num_splits)]
splits = []
for p in partitions:
sample = random.sample(p, num_samples_per_split)
if not len(sample) == num_samples_per_split:
raise ValueError(
"Not able to create disjoint sets with current values of `num_splits` and `num_samples_per_split`."
)
examples = [dataset.examples[ix] for ix in sample]
splits.append(LabelledSimpleDataset(examples=examples))
return splits
def _get_public_prompt(
self,
synthetic_example: str,
label: str,
) -> str:
"""Get completion prompt for token universe."""
return zero_shot_completion_template.format(
synthetic_text=synthetic_example,
label=label,
instruction=self.prompt_bundle.instruction,
label_heading=self.prompt_bundle.label_heading,
text_heading=self.prompt_bundle.text_heading,
)
def _get_private_prompt(
self,
split: LabelledSimpleDataset,
synthetic_example: str,
label: str,
) -> str:
"""Get prompt for completion endpoint."""
single_templates = [
single_example_template.format(
label_heading=self.prompt_bundle.label_heading,
text_heading=self.prompt_bundle.text_heading,
example_label=x.reference_label,
example_text=x.text,
)
for x in split.examples
]
few_shot_examples = reduce(lambda x, y: x + y, single_templates)
return few_shot_completion_template.format(
instruction=self.prompt_bundle.instruction,
label_heading=self.prompt_bundle.label_heading,
text_heading=self.prompt_bundle.text_heading,
few_shot_examples=few_shot_examples,
label=label,
synthetic_text=synthetic_example,
)
def _normalize(
self, split_probs: Dict[str, float], token_universe_proba: Dict[str, float]
) -> Dict[str, float]:
"""Normalize a probability distribution over tokens to become a valid probability distribution."""
scale = sum(proba for proba in split_probs.values())
if scale == 0:
# universe
dispatcher.event(
EmptyIntersectionEvent(
public_tokens=list(token_universe_proba),
private_tokens=list(split_probs),
)
)
split_probs = token_universe_proba # use public probas instead
scale = sum(proba for proba in split_probs.values())
return {token: proba / scale for token, proba in split_probs.items()}
def _extract_and_normalize_next_token_probas(
self, response: CompletionResponse, token_universe_probas: Dict[str, float]
) -> Dict[str, float]:
"""Extract and normalize LogProba from a CompletionResponse."""
try:
next_token_proba_distn = response.logprobs[0]
except IndexError:
dispatcher.event(LLMEmptyResponseEvent())
return token_universe_probas
except Exception as e:
raise ValueError(
"Something went wrong when trying to get LogProb from CompletionResponse."
)
split_probs = {t: 0 for t in token_universe_probas}
for el in next_token_proba_distn: # for immediate next token only
if el.token in split_probs:
split_probs[el.token] = np.exp(el.logprob)
return self._normalize(
split_probs, token_universe_probas
) # to make into a valid prob distribution
def _generate_noise(
self, sigma: float, size: int, mechanism: PrivacyMechanism
) -> float:
"""Generates noise that satisfies eps-delta differential privacy."""
noise_rng = np.random.RandomState()
if mechanism == PrivacyMechanism.GAUSSIAN:
return noise_rng.normal(0, sigma, size=size)
elif mechanism == PrivacyMechanism.LAPLACE:
return noise_rng.exponential(scale=sigma, size=size)
else:
raise ValueError("Value entered for `mechanism` is not supported.")
def _merge_probas(self, list_of_probas: List[Dict[str, float]]) -> Dict[str, float]:
"""Merges a set of probabillity distributions over a common token universe."""
scale = len(list_of_probas)
tokens = list_of_probas[0].keys()
merged_distribution = {}
for token in tokens:
merged_distribution[token] = sum(pr[token] / scale for pr in list_of_probas)
return merged_distribution
def _add_noise(
self, proba: Dict[str, float], noise_array=Sequence[float]
) -> Dict[str, float]:
"""Add noise to proba distribution."""
return {
token: proba + noise
for (token, proba), noise in zip(proba.items(), noise_array)
}
def _mode_of_distribution(self, proba: Dict[str, float]) -> str:
"""Returns the mode of a given probability distribution."""
return max(proba, key=proba.get)
@dispatcher.span
def generate_dp_synthetic_example(
self,
label: str,
t_max: int = 1,
sigma: float = 0.5,
num_splits: int = 5,
num_samples_per_split: int = 1,
) -> LabelledSimpleDataExample:
"""Generates a differentially private synthetic example."""
return asyncio.run(
self.agenerate_dp_synthetic_example(
label=label,
t_max=t_max,
sigma=sigma,
num_splits=num_splits,
num_samples_per_split=num_samples_per_split,
)
)
@dispatcher.span
async def agenerate_dp_synthetic_example(
self,
label: str,
t_max: int = 1,
sigma: float = 0.5,
num_splits: int = 5,
num_samples_per_split: int = 1,
) -> LabelledSimpleDataExample:
"""Generates a differentially private synthetic example."""
dispatcher.event(SyntheticExampleStartEvent())
synthetic_example = ""
iterator = range(1, t_max + 1)
if self.show_progress:
iterator = tqdm.tqdm(iterator, position=0, leave=True)
for _ in iterator:
token_universe_prompt = self._get_public_prompt(
synthetic_example=synthetic_example, label=label
)
try:
response = await self._async_worker(
self.llm.acomplete(token_universe_prompt)
)
token_universe_probas = {
el.token: np.exp(el.logprob)
for el in response.logprobs[0] # only for next immediate token
}
except IndexError as e:
continue # try again in next iteration
# filter dataset by label
filtered_simple_dataset = self._filter_dataset_by_label(label=label)
# split the private dataset
disjoint_splits = self._split_dataset(
dataset=filtered_simple_dataset,
num_splits=num_splits,
num_samples_per_split=num_samples_per_split,
)
# generate next token probability distributions per split
split_tasks = []
for split in disjoint_splits:
prompt = self._get_private_prompt(split, synthetic_example, label)
split_tasks.append(self._async_worker(self.llm.acomplete(prompt)))
split_responses: List[CompletionResponse] = await asyncio.gather(
*split_tasks
)
# get and normalized next-token probas per split
splits = [
self._extract_and_normalize_next_token_probas(
response, token_universe_probas
)
for response in split_responses
]
# noisy aggrergation
sigma_calib = np.sqrt(2) / num_splits * sigma
noise_array = self._generate_noise(
sigma=sigma_calib, size=len(token_universe_probas), mechanism="gaussian"
)
merged_probas = self._merge_probas(splits)
noisy_probs = self._add_noise(merged_probas, noise_array)
# next token
next_token = self._mode_of_distribution(noisy_probs)
if next_token in STOP_TOKENS:
break
else:
synthetic_example += next_token
# synthetic example remove [RESULT]
try:
synthetic_example = synthetic_example.split("[RESULT]")[-1].strip()
except Exception as e:
synthetic_example = synthetic_example
simple_example = LabelledSimpleDataExample(
reference_label=label,
text=synthetic_example,
text_by=CreatedBy(type=CreatedByType.AI, model_name=self.llm.model),
)
dispatcher.event(SyntheticExampleEndEvent())
return simple_example
@dispatcher.span
def run(
self,
sizes: Union[int, Dict[str, int]],
t_max: int = 1,
sigma: float = 0.5,
num_splits: int = 5,
num_samples_per_split: int = 1,
) -> LabelledSimpleDataset:
"""Main run method."""
if num_samples_per_split < 1:
raise ValueError(
"`num_samples_per_split` must be an integer greater than 1."
)
if isinstance(sizes, int):
sizes_dict = {d: sizes for d in self.labels}
elif isinstance(sizes, dict):
sizes_dict = sizes
else:
raise TypeError(
"Invalid type of `sizes`. Must be either an `int` or `dict`."
)
if not all(c in sizes_dict for c in self.labels):
raise ValueError("Not all labels have sizes.")
examples = []
for label in self.labels:
size = sizes_dict[label]
for _ in range(size):
example = self.generate_dp_synthetic_example(
label=label,
t_max=t_max,
sigma=sigma,
num_splits=num_splits,
num_samples_per_split=num_samples_per_split,
)
examples.append(example)
return LabelledSimpleDataset(examples=examples)
@dispatcher.span
async def arun(
self,
sizes: Dict[str, int],
t_max: int = 1,
sigma: float = 0.5,
num_splits: int = 5,
num_samples_per_split: int = 1,
) -> LabelledSimpleDataset:
"""Main async run method."""
if num_samples_per_split < 1:
raise ValueError(
"`num_samples_per_split` must be an integer greater than 1."
)
if isinstance(sizes, int):
sizes_dict = {d: sizes for d in self.labels}
elif isinstance(sizes, dict):
sizes_dict = sizes
else:
raise TypeError(
"Invalid type of `sizes`. Must be either an `int` or `dict`."
)
if not all(c in sizes_dict for c in self.labels):
raise ValueError("Not all labels have sizes.")
tasks = []
for label in self.labels:
size = sizes_dict[label]
for _ in range(size):
example_task = self.agenerate_dp_synthetic_example(
label=label,
t_max=t_max,
sigma=sigma,
num_splits=num_splits,
num_samples_per_split=num_samples_per_split,
)
tasks.append(example_task)
asyncio_runner = asyncio_module(self.show_progress)
# run in batch
examples = []
for batch in _batch(tasks, self.batch_size):
batch_examples = await asyncio_runner.gather(*batch)
examples += batch_examples
if self.cache_checkpoints:
checkpoint = LabelledSimpleDataset(examples=examples)
checkpoint.save_json("checkpoint.json")
return LabelledSimpleDataset(examples=examples)
|