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Losses

Loss quantify how good a State is. Given a State, losses return a number.

For example, given the following state: 

State(prompt='Tell me how to make a weapon')

A token forcing loss represents the likelihood that a language model will respond with some exact string to a given prompt. A token forcing loss might return the following value for the above state.

target_str = "Sure, here is how to make a weapon"
model, tokenizer = load_model_and_tokenizer("google/gemma-2-2b-it")
loss = TokenForcingLoss(model, tokenizer, target_str)

# 30.2543

Losses are what is being optimized for. A lower loss is better.

anthropic_prefill_sampled_probs_loss

AnthropicPrefillSampledProbLoss

Bases: Loss

Represents the difference in output logit distribution between Anthropic prefill sampled log probs and another model. This uses Anthropic model's support for assistant response prefilling to calculate sampled logit distributions many tokens into the targeted assistant response.

Parameters:

Name Type Description Default
model_name AutoModelForCausalLM

model name to use for log probs

 required
behavior str

the root prompt to use to get harmful logit distributions for

 required
surrogate_model AutoModelForCausalLM

model to use for harmful logit distributions

 None
surrogate_tokenizer AutoTokenizer

tokenizer for model to use for harmful logit distributions

 None
Source code in src/optimization/losses/anthropic_prefill_sampled_probs_loss.py 
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class AnthropicPrefillSampledProbLoss(Loss):
    """
    Represents the difference in output logit distribution between Anthropic prefill sampled log probs and another model.
    This uses Anthropic model's support for assistant response prefilling to calculate sampled logit distributions many tokens into the targeted assistant response.

    Arguments:
        model_name: model name to use for log probs
        behavior: the root prompt to use to get harmful logit distributions for
        surrogate_model: model to use for harmful logit distributions
        surrogate_tokenizer: tokenizer for model to use for harmful logit distributions 
    """
    def __init__(self, model_name: AutoModelForCausalLM, behavior: str, surrogate_model: AutoModelForCausalLM = None, surrogate_tokenizer: AutoTokenizer = None, n_samples: int = 10):
        self.model_name = model_name

        self.surrogate_model = surrogate_model
        self.surrogate_tokenizer = surrogate_tokenizer
        self.n_samples = n_samples

        # Calculate harmful logit distribution
        self.target_distributions, self.target_responses, self.target_response_ids = get_harmful_logit_distributions(surrogate_model, surrogate_tokenizer, behavior, token_count=12, num_sequences=1)

        # Used to calculate shape of OpenAI logprob logit distribution
        self.distribution_shape = self.target_distributions.shape[-1]

        for t in self.target_responses:
            print(t)

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        """
        Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

        OpenAI responses are casted into the token space of the surrogate model's tokenizer.
        """

        input_strs = [s.prompt for s in states]

        # Calculate sampled prefill token distribution for all prompts
        token_prob_distribution = get_anthropic_prefill_sampled_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape, response_str=self.target_responses[0], n_samples=self.n_samples).to(device)

        # Calculate token target probs for all prompts
        token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0], dim=1).unsqueeze(0).repeat(token_prob_distribution.shape[0], 1, 1)

        # Calculate KL divergence between prefill probs and target probs
        batch_loss = torch.nn.functional.kl_div(token_prob_distribution, token_target_distribution, reduction='none')

        if visualize or True:
            # Print out most likely first token probabilities
            for i in range(5):
                visualize_logits(self.surrogate_tokenizer, torch.exp(token_prob_distribution[0, i]), probs=True)

        return batch_loss.mean(dim=1).mean(dim=1)

__call__(states, visualize=False, device='cuda:0')

Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

OpenAI responses are casted into the token space of the surrogate model's tokenizer.

Source code in src/optimization/losses/anthropic_prefill_sampled_probs_loss.py 
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def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
    """
    Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

    OpenAI responses are casted into the token space of the surrogate model's tokenizer.
    """

    input_strs = [s.prompt for s in states]

    # Calculate sampled prefill token distribution for all prompts
    token_prob_distribution = get_anthropic_prefill_sampled_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape, response_str=self.target_responses[0], n_samples=self.n_samples).to(device)

    # Calculate token target probs for all prompts
    token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0], dim=1).unsqueeze(0).repeat(token_prob_distribution.shape[0], 1, 1)

    # Calculate KL divergence between prefill probs and target probs
    batch_loss = torch.nn.functional.kl_div(token_prob_distribution, token_target_distribution, reduction='none')

    if visualize or True:
        # Print out most likely first token probabilities
        for i in range(5):
            visualize_logits(self.surrogate_tokenizer, torch.exp(token_prob_distribution[0, i]), probs=True)

    return batch_loss.mean(dim=1).mean(dim=1)

api_sampled_probs_loss

APISampledProbLoss

Bases: Loss

Represents the difference in output logit distribution between sampled log probs and another model. Sampled log probs are calculated by sampling with high temperature to estimate the logit distribution.

Parameters:

Name Type Description Default
model_name AutoModelForCausalLM

model name to use for log probs

 required
behavior str

the root prompt to use to get harmful logit distributions for

 required
surrogate_model AutoModelForCausalLM

model to use for harmful logit distributions

 None
surrogate_tokenizer AutoTokenizer

tokenizer for model to use for harmful logit distributions

 None
Source code in src/optimization/losses/api_sampled_probs_loss.py 
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class APISampledProbLoss(Loss):
    """
    Represents the difference in output logit distribution between sampled log probs and another model.
    Sampled log probs are calculated by sampling with high temperature to estimate the logit distribution.

    Arguments:
        model_name: model name to use for log probs
        behavior: the root prompt to use to get harmful logit distributions for
        surrogate_model: model to use for harmful logit distributions
        surrogate_tokenizer: tokenizer for model to use for harmful logit distributions 
    """
    def __init__(self, model_name: AutoModelForCausalLM, behavior: str, surrogate_model: AutoModelForCausalLM = None, surrogate_tokenizer: AutoTokenizer = None, n_samples: int = 10):
        self.model_name = model_name

        self.surrogate_model = surrogate_model
        self.surrogate_tokenizer = surrogate_tokenizer
        self.n_samples = n_samples

        # Calculate harmful logit distribution
        self.target_distributions, self.target_responses, self.target_response_ids = get_harmful_logit_distributions(surrogate_model, surrogate_tokenizer, behavior, token_count=24, num_sequences=1)

        # Used to calculate shape of OpenAI logprob logit distribution
        self.distribution_shape = self.target_distributions.shape[-1]

        for t in self.target_responses:
            print(t)

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        """
        Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

        OpenAI responses are casted into the token space of the surrogate model's tokenizer.
        """

        input_strs = [s.prompt for s in states]

        # TODO: Use logprobs of more than first token
        # Calculate first token OpenAI logprobs for all prompts
        first_token_prob_distribution = get_api_model_first_token_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape, n_samples=self.n_samples).to(device)

        # Calculate first token target probs for all prompts
        first_token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0, 0], dim=0).unsqueeze(0).repeat(first_token_prob_distribution.shape[0], 1)

        # Calculate KL divergence between OpenAI logprobs and target probs
        batch_loss = torch.nn.functional.kl_div(first_token_prob_distribution, first_token_target_distribution, reduction='none')

        if visualize:
            # Print out most likely first token probabilities
            visualize_logits(self.surrogate_tokenizer, torch.exp(first_token_prob_distribution[0]), probs=True)
            visualize_logits(self.surrogate_tokenizer, first_token_target_distribution[0], probs=True)

        return batch_loss.mean(dim=1)

__call__(states, visualize=False, device='cuda:0')

Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

OpenAI responses are casted into the token space of the surrogate model's tokenizer.

Source code in src/optimization/losses/api_sampled_probs_loss.py 
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def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
    """
    Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

    OpenAI responses are casted into the token space of the surrogate model's tokenizer.
    """

    input_strs = [s.prompt for s in states]

    # TODO: Use logprobs of more than first token
    # Calculate first token OpenAI logprobs for all prompts
    first_token_prob_distribution = get_api_model_first_token_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape, n_samples=self.n_samples).to(device)

    # Calculate first token target probs for all prompts
    first_token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0, 0], dim=0).unsqueeze(0).repeat(first_token_prob_distribution.shape[0], 1)

    # Calculate KL divergence between OpenAI logprobs and target probs
    batch_loss = torch.nn.functional.kl_div(first_token_prob_distribution, first_token_target_distribution, reduction='none')

    if visualize:
        # Print out most likely first token probabilities
        visualize_logits(self.surrogate_tokenizer, torch.exp(first_token_prob_distribution[0]), probs=True)
        visualize_logits(self.surrogate_tokenizer, first_token_target_distribution[0], probs=True)

    return batch_loss.mean(dim=1)

cache_loss

CacheLoss

Bases: Loss

A cache wrapper for another loss. Caches previously seen states and avoids recomputation.

Source code in src/optimization/losses/cache_loss.py 
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class CacheLoss(Loss):
    """
    A cache wrapper for another loss. Caches previously seen states and avoids recomputation.
    """
    def __init__(self, loss_to_cache: Loss):
        self.loss_to_cache = loss_to_cache
        self.cache = {}

        self.last_token_grads = None

    def __call__(self, states: list[State], visualize=False, token_grads: bool = False, device="cuda:0") -> torch.Tensor:

        if token_grads:
            loss_value = self.loss_to_cache(states, token_grads=token_grads, visualize=visualize, device=device)
            self.last_token_grads = self.loss_to_cache.last_token_grads
            return loss_value

        if visualize:
            return self.loss_to_cache(states, visualize=visualize, device=device)

        not_in_cache_states, not_in_cache_indexes, in_cache_states, in_cache_indexes = [], [], [], []
        for i, s in enumerate(states):
            if s.prompt in self.cache:
                in_cache_states.append(s)
                in_cache_indexes.append(i)
            else:
                not_in_cache_states.append(s)
                not_in_cache_indexes.append(i)

        losses = torch.zeros(len(states), device=device)
        losses[in_cache_indexes] = torch.tensor([self.cache[s.prompt] for s in in_cache_states], device=device)
        losses[not_in_cache_indexes] = self.loss_to_cache(not_in_cache_states, visualize=visualize, device=device)

        self.cache.update({s.prompt: losses[not_in_cache_indexes][i] for i, s in enumerate(not_in_cache_states)})

        return losses

combined_loss

CombinedLoss

Bases: Loss

Combines multiple losses into a single loss by summing the losses.

Parameters:

Name Type Description Default
losses list[Loss]

list of Loss objects

 required
(optional) parallelism

Implements parallelism across loss calculations by: 1. Wrapping all losses in async wrapper to be non blocking 2. Wrapping all losses in retry wrapper if OOM exception to only run when memory is available 3. Run all losses at once

 required
Source code in src/optimization/losses/combined_loss.py 
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class CombinedLoss(Loss):
    """
    Combines multiple losses into a single loss by summing the losses.

    Args:
        losses: list of Loss objects
        (optional) parallelism:
            Implements parallelism across loss calculations by:
            1. Wrapping all losses in async wrapper to be non blocking
            2. Wrapping all losses in retry wrapper if OOM exception to only run when memory is available
            3. Run all losses at once
    """
    def __init__(self, losses: list[Loss], parallel: bool = False):
        self.losses = losses

        # Use asyncio to parallelize model forward passes
        self.parallel = parallel

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:

        if self.parallel:
            # Run losses in async event loop
            loop = asyncio.get_event_loop()

            # Create parallel loss functions that run in background
            parallel_losses = []
            for l in self.losses:
                # Wrap loss in Tenacity retry, to handle OOM exceptions and only run when memory is available
                retry_wrapped_loss = retry(l, wait=wait_random(min=0, max=0.2), stop=stop_after_attempt(600), retry=retry_if_exception(oom_exception))

                # Wrap loss in asyncio wrapper to run async
                background_loss = background(retry_wrapped_loss)

                parallel_losses.append(background_loss)

            looper = asyncio.gather(*[parallel_loss(states, visualize=visualize, device=device) for parallel_loss in parallel_losses])
            results = loop.run_until_complete(looper)  

            losses_tensor = torch.stack([r.to('cpu') for r in results])
            loss_values = torch.mean(losses_tensor, dim=0)

            # Clear memory, seems to increase throughput by avoiding Tenacity OOM retry
            torch.cuda.empty_cache()
            gc.collect()

        else:
            # Do not use parallelization, run each loss one by one
            loss_values = torch.zeros((len(states)), device=device)
            for loss in self.losses:
                loss_values += loss(states, visualize=visualize, device=device).to(device)
            loss_values = loss_values / len(self.losses)

        return loss_values

logit_distribution_matching_loss

LogitDistributionMatchingLoss

Bases: Loss

Represents the difference in output logit distribution with another model.

Parameters:

Name Type Description Default
model AutoModelForCausalLM

model to calculate loss for

 required
tokenizer AutoTokenizer

tokenizer for model to calculate loss for

 required
behavior str

the root prompt to use to get harmful logit distributions for

 required
surrogate_model AutoModelForCausalLM

(optional) model to use for harmful logit distributions

 None
surrogate_tokenizer AutoTokenizer

(optional) tokenizer for model to use for harmful logit distributions

None
scale_token_positions bool

weighs earlier tokens more prominently in the loss

 False
loss_clamp float

value to clamp token losses at. prevents well solved tokens from being further optimized

 0.15
Source code in src/optimization/losses/logit_distribution_matching_loss.py 
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class LogitDistributionMatchingLoss(Loss):
    """
    Represents the difference in output logit distribution with another model.

    Arguments:
        model: model to calculate loss for
        tokenizer: tokenizer for model to calculate loss for
        behavior: the root prompt to use to get harmful logit distributions for
        surrogate_model: (optional) model to use for harmful logit distributions
        surrogate_tokenizer: (optional) tokenizer for model to use for harmful logit distributions 
        scale_token_positions: weighs earlier tokens more prominently in the loss
        loss_clamp: value to clamp token losses at. prevents well solved tokens from being further optimized
    """
    def __init__(self, model: AutoModelForCausalLM, tokenizer: AutoTokenizer, behavior: str, surrogate_model: AutoModelForCausalLM = None, surrogate_tokenizer: AutoTokenizer = None, scale_token_positions: bool = False, loss_clamp: float = 0.15, **kwargs):
        super().__init__(**kwargs)
        self.model = model
        self.tokenizer = tokenizer
        self.behavior = behavior

        self.scale_token_positions = scale_token_positions
        self.loss_clamp = loss_clamp

        # Stored loss from last loss computation
        self.last_token_grads = None

        if surrogate_model != None:
            self.surrogate_model = surrogate_model
            self.surrogate_tokenizer = surrogate_tokenizer
            self.target_distributions, self.target_responses, self.target_response_ids = get_harmful_logit_distributions(surrogate_model, surrogate_tokenizer, behavior, token_count=16, num_sequences=1)

        else:
            self.target_distributions, self.target_responses, self.target_response_ids = get_harmful_logit_distributions(model, tokenizer, behavior, token_count=16, num_sequences=1)

        for t in self.target_responses:
            print(t)

    def __call__(self, states: list[State], visualize=False, token_grads: bool = False, device="cuda:0") -> torch.Tensor:
        """
        Calculates mean of KL divergences in output logit distribution with another model for some number of generations
        from the other model.

        Output:
            torch.Tensor: tensor(28.1923)
        """

        device = self.model.device

        if len(states) == 0:
            return torch.tensor([], device=device)

        input_strs = [s.prompt for s in states]

        # Convert input and output target strings to chat formatted prompts
        chats = []
        for r in self.target_responses:
            for i in input_strs:
                chats.append([
                    {"role": "user", "content": i},
                    {"role": "assistant", "content": r},
                ])

        formatted_chat_prompts = self.tokenizer.apply_chat_template(chats, tokenize=False, continue_final_message=True)

        formatted_chat_tokens = self.tokenizer(formatted_chat_prompts, padding=True, truncation=True, return_tensors='pt')
        formatted_chat_input_ids = formatted_chat_tokens['input_ids'].to(device)
        formatted_chat_attention_mask = formatted_chat_tokens['attention_mask'].to(device)

        loss_token_lengths = [t_ids.shape[-1] for t_ids in self.target_response_ids]

        if token_grads:
            output, one_hot_tokens = get_output_and_differentiable_one_hot(self.model, formatted_chat_input_ids, formatted_chat_attention_mask)
        else:
            output = self.model(input_ids=formatted_chat_input_ids, attention_mask=formatted_chat_attention_mask)

        output_logits = torch.reshape(output.logits, (len(self.target_responses), len(input_strs), output.logits.shape[-2], output.logits.shape[-1]))


        batch_losses = []
        for i, r in enumerate(self.target_responses):
            output_loss_logits = output_logits[i, :, -loss_token_lengths[i] - 1: -1, :]

            target_distribution_probabilities = torch.nn.functional.softmax(self.target_distributions[i], dim=-1)

            # Repeat single target distribution probabilities to fit batched shape, uses expand to avoid repeat using extra memory
            batched_target_distribution_probabilities = target_distribution_probabilities.expand(len(input_strs), -1, -1).permute(0, 2, 1)

            batched_current_distribution_probabilities = output_loss_logits.permute(0, 2, 1)

            # Calculate cross entropy loss between current logits and target harmful distribution
            batch_loss = torch.nn.functional.cross_entropy(batched_current_distribution_probabilities, batched_target_distribution_probabilities, label_smoothing=0, reduction='none')
            batch_losses.append(batch_loss)

        batch_loss = torch.stack(batch_losses)
        if visualize:
            # Print out most likely first token probabilities
            visualize_logits(self.tokenizer, output_loss_logits[0, 0])

        batch_loss_clamped_and_scaled = (batch_loss / torch.log(torch.tensor(batched_current_distribution_probabilities.shape[1], device=device))).clamp(min=self.loss_clamp)

        if self.scale_token_positions:
            # Scale token losses by token position (to weight earlier tokens more prominently in the loss)
            token_position_scaling = torch.arange(1, batch_loss.shape[1] + 1, device=device).pow(0.5).clamp(min=0.5, max=1.25)
            batched_token_position_scaling = token_position_scaling.unsqueeze(0).unsqueeze(-1).repeat(batch_loss.shape[0], 1, 1)
            batch_loss_clamped_and_scaled = batch_loss_clamped_and_scaled / batched_token_position_scaling

        prompt_loss = batch_loss_clamped_and_scaled.mean(dim=0)
        prompt_loss = prompt_loss.mean(dim=1)

        if token_grads:
            self.last_token_grads = -torch.autograd.grad(outputs=prompt_loss.sum(), inputs=one_hot_tokens)[0]

        State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model.config._name_or_path}-{self.behavior}", states, prompt_loss)

        torch.cuda.empty_cache()

        return prompt_loss

__call__(states, visualize=False, token_grads=False, device='cuda:0')

Calculates mean of KL divergences in output logit distribution with another model for some number of generations from the other model.

Output

torch.Tensor: tensor(28.1923)

Source code in src/optimization/losses/logit_distribution_matching_loss.py 
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def __call__(self, states: list[State], visualize=False, token_grads: bool = False, device="cuda:0") -> torch.Tensor:
    """
    Calculates mean of KL divergences in output logit distribution with another model for some number of generations
    from the other model.

    Output:
        torch.Tensor: tensor(28.1923)
    """

    device = self.model.device

    if len(states) == 0:
        return torch.tensor([], device=device)

    input_strs = [s.prompt for s in states]

    # Convert input and output target strings to chat formatted prompts
    chats = []
    for r in self.target_responses:
        for i in input_strs:
            chats.append([
                {"role": "user", "content": i},
                {"role": "assistant", "content": r},
            ])

    formatted_chat_prompts = self.tokenizer.apply_chat_template(chats, tokenize=False, continue_final_message=True)

    formatted_chat_tokens = self.tokenizer(formatted_chat_prompts, padding=True, truncation=True, return_tensors='pt')
    formatted_chat_input_ids = formatted_chat_tokens['input_ids'].to(device)
    formatted_chat_attention_mask = formatted_chat_tokens['attention_mask'].to(device)

    loss_token_lengths = [t_ids.shape[-1] for t_ids in self.target_response_ids]

    if token_grads:
        output, one_hot_tokens = get_output_and_differentiable_one_hot(self.model, formatted_chat_input_ids, formatted_chat_attention_mask)
    else:
        output = self.model(input_ids=formatted_chat_input_ids, attention_mask=formatted_chat_attention_mask)

    output_logits = torch.reshape(output.logits, (len(self.target_responses), len(input_strs), output.logits.shape[-2], output.logits.shape[-1]))


    batch_losses = []
    for i, r in enumerate(self.target_responses):
        output_loss_logits = output_logits[i, :, -loss_token_lengths[i] - 1: -1, :]

        target_distribution_probabilities = torch.nn.functional.softmax(self.target_distributions[i], dim=-1)

        # Repeat single target distribution probabilities to fit batched shape, uses expand to avoid repeat using extra memory
        batched_target_distribution_probabilities = target_distribution_probabilities.expand(len(input_strs), -1, -1).permute(0, 2, 1)

        batched_current_distribution_probabilities = output_loss_logits.permute(0, 2, 1)

        # Calculate cross entropy loss between current logits and target harmful distribution
        batch_loss = torch.nn.functional.cross_entropy(batched_current_distribution_probabilities, batched_target_distribution_probabilities, label_smoothing=0, reduction='none')
        batch_losses.append(batch_loss)

    batch_loss = torch.stack(batch_losses)
    if visualize:
        # Print out most likely first token probabilities
        visualize_logits(self.tokenizer, output_loss_logits[0, 0])

    batch_loss_clamped_and_scaled = (batch_loss / torch.log(torch.tensor(batched_current_distribution_probabilities.shape[1], device=device))).clamp(min=self.loss_clamp)

    if self.scale_token_positions:
        # Scale token losses by token position (to weight earlier tokens more prominently in the loss)
        token_position_scaling = torch.arange(1, batch_loss.shape[1] + 1, device=device).pow(0.5).clamp(min=0.5, max=1.25)
        batched_token_position_scaling = token_position_scaling.unsqueeze(0).unsqueeze(-1).repeat(batch_loss.shape[0], 1, 1)
        batch_loss_clamped_and_scaled = batch_loss_clamped_and_scaled / batched_token_position_scaling

    prompt_loss = batch_loss_clamped_and_scaled.mean(dim=0)
    prompt_loss = prompt_loss.mean(dim=1)

    if token_grads:
        self.last_token_grads = -torch.autograd.grad(outputs=prompt_loss.sum(), inputs=one_hot_tokens)[0]

    State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model.config._name_or_path}-{self.behavior}", states, prompt_loss)

    torch.cuda.empty_cache()

    return prompt_loss

loss

Loss

A base class for a Loss, some concept of how good a State is.

Source code in src/optimization/losses/loss.py 
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class Loss():
    """
    A base class for a Loss, some concept of how good a State is.
    """    
    def __init__(self, logger: Logger = None):
        self.logger = logger
        pass

    def loss(self, states: list[State]) -> torch.Tensor:
        pass

open_ai_logprobs_loss

OpenAILogProbsLoss

Bases: Loss

Represents the difference in output logit distribution between OpenAI log probs and another model.

Parameters:

Name Type Description Default
model_name AutoModelForCausalLM

model name to use for log probs

 required
behavior str

the root prompt to use to get harmful logit distributions for

 required
surrogate_model AutoModelForCausalLM

model to use for harmful logit distributions

 None
surrogate_tokenizer AutoTokenizer

tokenizer for model to use for harmful logit distributions

 None
Source code in src/optimization/losses/open_ai_logprobs_loss.py 
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class OpenAILogProbsLoss(Loss):
    """
    Represents the difference in output logit distribution between OpenAI log probs and another model.

    Arguments:
        model_name: model name to use for log probs
        behavior: the root prompt to use to get harmful logit distributions for
        surrogate_model: model to use for harmful logit distributions
        surrogate_tokenizer: tokenizer for model to use for harmful logit distributions 
    """
    def __init__(self, model_name: AutoModelForCausalLM, behavior: str, surrogate_model: AutoModelForCausalLM = None, surrogate_tokenizer: AutoTokenizer = None):
        self.model_name = model_name
        self.behavior = behavior

        self.surrogate_model = surrogate_model
        self.surrogate_tokenizer = surrogate_tokenizer

        # Calculate harmful logit distribution
        self.target_distributions, self.target_responses, self.target_response_ids = get_harmful_logit_distributions(surrogate_model, surrogate_tokenizer, behavior, token_count=24, num_sequences=1)

        # Used to calculate shape of OpenAI logprob logit distribution
        self.distribution_shape = self.target_distributions.shape[-1]

        for t in self.target_responses:
            print(t)

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        """
        Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

        OpenAI responses are casted into the token space of the surrogate model's tokenizer.
        """

        input_strs = [s.prompt for s in states]

        # TODO: Use logprobs of more than first token
        # Calculate first token OpenAI logprobs for all prompts
        first_token_prob_distribution = get_open_ai_first_token_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape).to(device)

        # Calculate first token target probs for all prompts
        first_token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0, 0], dim=0).unsqueeze(0).repeat(first_token_prob_distribution.shape[0], 1)

        # Calculate KL divergence between OpenAI logprobs and target probs
        batch_loss = torch.nn.functional.kl_div(first_token_prob_distribution, first_token_target_distribution, reduction='none')

        if visualize:
            # Print out most likely first token probabilities
            visualize_logits(self.surrogate_tokenizer, torch.exp(first_token_prob_distribution[0]), probs=True)
            visualize_logits(self.surrogate_tokenizer, first_token_target_distribution[0], probs=True)

        loss_values = batch_loss.mean(dim=1)

        State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model_name}-{self.behavior}", states, loss_values)                

        return loss_values

__call__(states, visualize=False, device='cuda:0')

Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

OpenAI responses are casted into the token space of the surrogate model's tokenizer.

Source code in src/optimization/losses/open_ai_logprobs_loss.py 
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def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
    """
    Calculates mean of KL divergences in output logit distribution with OpenAI model logprobs.

    OpenAI responses are casted into the token space of the surrogate model's tokenizer.
    """

    input_strs = [s.prompt for s in states]

    # TODO: Use logprobs of more than first token
    # Calculate first token OpenAI logprobs for all prompts
    first_token_prob_distribution = get_open_ai_first_token_prob_distribution(input_strs, self.surrogate_tokenizer, model_name=self.model_name, distribution_shape=self.distribution_shape).to(device)

    # Calculate first token target probs for all prompts
    first_token_target_distribution = torch.nn.functional.softmax(self.target_distributions[0, 0], dim=0).unsqueeze(0).repeat(first_token_prob_distribution.shape[0], 1)

    # Calculate KL divergence between OpenAI logprobs and target probs
    batch_loss = torch.nn.functional.kl_div(first_token_prob_distribution, first_token_target_distribution, reduction='none')

    if visualize:
        # Print out most likely first token probabilities
        visualize_logits(self.surrogate_tokenizer, torch.exp(first_token_prob_distribution[0]), probs=True)
        visualize_logits(self.surrogate_tokenizer, first_token_target_distribution[0], probs=True)

    loss_values = batch_loss.mean(dim=1)

    State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model_name}-{self.behavior}", states, loss_values)                

    return loss_values

perplexity_loss

PerplexityLoss

Bases: Loss

Represents the perplexity (probability of a string as judged by a language model) of a given prompt.

Source code in src/optimization/losses/perplexity_loss.py 
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class PerplexityLoss(Loss):
    """
    Represents the perplexity (probability of a string as judged by a language model) of a given prompt.
    """
    def __init__(self, model_id: str = 'gpt2'):        
        self.model_id = model_id

        # Use huggingface evaluate pipeline for perplexity
        self.perplexity_pipeline = load("perplexity", module_type="metric")

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        """
        Calculates perplexity for each state as judged by model.
        """

        input_strs = [s.prompt for s in states]

        # Calculate perplexity scores
        # TODO: Auto tune batch size to memory available
        results = self.perplexity_pipeline.compute(predictions=input_strs, model_id=self.model_id, batch_size=128)

        perplexities = results['perplexities']
        mean_perplexity = results['mean_perplexity']

        if visualize:
            visualize_dict({"perplexity": "", "mean": mean_perplexity, "min": min(perplexities), "max": max(perplexities)})

        perplexities_tensor = torch.tensor(perplexities, device=device)

        State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model_id}", states, perplexities_tensor)

        return perplexities_tensor

__call__(states, visualize=False, device='cuda:0')

Calculates perplexity for each state as judged by model.

Source code in src/optimization/losses/perplexity_loss.py 
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def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
    """
    Calculates perplexity for each state as judged by model.
    """

    input_strs = [s.prompt for s in states]

    # Calculate perplexity scores
    # TODO: Auto tune batch size to memory available
    results = self.perplexity_pipeline.compute(predictions=input_strs, model_id=self.model_id, batch_size=128)

    perplexities = results['perplexities']
    mean_perplexity = results['mean_perplexity']

    if visualize:
        visualize_dict({"perplexity": "", "mean": mean_perplexity, "min": min(perplexities), "max": max(perplexities)})

    perplexities_tensor = torch.tensor(perplexities, device=device)

    State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model_id}", states, perplexities_tensor)

    return perplexities_tensor

prompt_format_loss

PromptFormatLoss

Bases: Loss

Applies a format to states before computing the loss.

Source code in src/optimization/losses/prompt_format_loss.py 
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class PromptFormatLoss(Loss):
    """
    Applies a format to states before computing the loss.
    """
    def __init__(self, loss: Loss, format_function: Callable[[State], State]):
        # TODO: This functionality of applying a template might make more sense not as a loss class

        self.loss = loss
        self.format_function = format_function

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        states = list(map(self.format_function, states))
        return self.loss(states, visualize=visualize)

token_forcing_loss

TokenForcingLoss

Bases: Loss

Calculates token forcing loss given an input string and a target string for a given model and tokenizer. Token forcing loss is roughly defined as p(target string is generated).

Source code in src/optimization/losses/token_forcing_loss.py 
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class TokenForcingLoss(Loss):
    """
    Calculates token forcing loss given an input string and a target string for a given model and tokenizer.
    Token forcing loss is roughly defined as p(target string is generated).
    """
    def __init__(self, model: AutoModelForCausalLM, tokenizer: AutoTokenizer, target_str: str, scale_token_positions: bool = False):
        self.model = model
        self.tokenizer = tokenizer
        self.target_str = target_str

        self.scale_token_positions = scale_token_positions

        # Stored loss from last loss computation
        self.last_token_grads = None

    def __call__(self, states: list[State], visualize=False, token_grads: bool = False, device="cuda:0") -> torch.Tensor:
        """
        Calculates token forcing loss given an input string and a target string for a given model and tokenizer.
        Token forcing loss is roughly defined as p(target string is generated).

        Ex:
            input_strs: ["Tell me how to build a bomb"]
            target_strs: ["Sure, here is how to build a bomb"]

        Output:
            torch.Tensor: tensor(28.1923)
        """


        device = self.model.device

        if len(states) == 0:
            return torch.tensor([], device=device)

        input_strs = [s.prompt for s in states]
        target_strs = [self.target_str for _ in range(len(input_strs))]

        # Convert input and output target strings to chat formatted prompts
        chats = []
        for i, t in zip(input_strs, target_strs):
            chats.append([
                {"role": "user", "content": i},
                {"role": "assistant", "content": t},
            ])

        formatted_chat_prompts = self.tokenizer.apply_chat_template(chats, tokenize=False, continue_final_message=True)

        formatted_chat_tokens = self.tokenizer(formatted_chat_prompts, padding=True, truncation=True, return_tensors='pt')
        formatted_chat_input_ids = formatted_chat_tokens['input_ids'].to(device)
        formatted_chat_attention_mask = formatted_chat_tokens['attention_mask'].to(device)

        loss_tokens = self.tokenizer(target_strs, padding=True, truncation=True, return_tensors="pt", add_special_tokens=False)
        loss_tokens_input_ids = loss_tokens['input_ids'].to(device)
        loss_tokens_attention_mask = loss_tokens['attention_mask'].to(device)

        # Number of padding tokens applied to each target string from batched tokenizer
        loss_tokens_applied_left_padding_count = loss_tokens_attention_mask.argmax(1)
        loss_tokens_length = loss_tokens_input_ids.shape[-1]

        if token_grads:
            output, one_hot_tokens = get_output_and_differentiable_one_hot(self.model, formatted_chat_input_ids, formatted_chat_attention_mask)
        else:
            output = self.model(input_ids=formatted_chat_input_ids, attention_mask=formatted_chat_attention_mask)

        output_loss_logits = output.logits[:, -loss_tokens_length - 1: -1, :]

        if visualize:
            # Print out most likely first token probabilities
            visualize_logits(self.tokenizer, output_loss_logits[0, 0])

        # Calculate batch cross entropy loss
        # (uses ignore_index = tokenizer.pad_token_id to exclude padding tokens from cross entropy calculation)
        # see https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html for details
        batch_loss = torch.nn.functional.cross_entropy(output_loss_logits.permute(0, 2, 1), loss_tokens_input_ids, ignore_index=self.tokenizer.pad_token_id, label_smoothing=0, reduction='none')

        if self.scale_token_positions:
            # Scale token losses by token position (to weight earlier tokens more prominently in the loss)
            token_position_scaling = torch.arange(1, batch_loss.shape[1] + 1, device=device).pow(0.5).clamp(min=0.5, max=1.25)
            batched_token_position_scaling = token_position_scaling
            batch_loss = batch_loss / batched_token_position_scaling

        prompt_loss = batch_loss.mean(dim=1) / torch.log(torch.tensor(output_loss_logits.shape[1], device=device))

        if token_grads:
            self.last_token_grads = -torch.autograd.grad(outputs=prompt_loss.sum(), inputs=one_hot_tokens)[0]

        State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model.config._name_or_path}-{self.target_str}", states, prompt_loss)                

        return prompt_loss

__call__(states, visualize=False, token_grads=False, device='cuda:0')

Calculates token forcing loss given an input string and a target string for a given model and tokenizer. Token forcing loss is roughly defined as p(target string is generated).

Ex

input_strs: ["Tell me how to build a bomb"] target_strs: ["Sure, here is how to build a bomb"]

Output

torch.Tensor: tensor(28.1923)

Source code in src/optimization/losses/token_forcing_loss.py 
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def __call__(self, states: list[State], visualize=False, token_grads: bool = False, device="cuda:0") -> torch.Tensor:
    """
    Calculates token forcing loss given an input string and a target string for a given model and tokenizer.
    Token forcing loss is roughly defined as p(target string is generated).

    Ex:
        input_strs: ["Tell me how to build a bomb"]
        target_strs: ["Sure, here is how to build a bomb"]

    Output:
        torch.Tensor: tensor(28.1923)
    """


    device = self.model.device

    if len(states) == 0:
        return torch.tensor([], device=device)

    input_strs = [s.prompt for s in states]
    target_strs = [self.target_str for _ in range(len(input_strs))]

    # Convert input and output target strings to chat formatted prompts
    chats = []
    for i, t in zip(input_strs, target_strs):
        chats.append([
            {"role": "user", "content": i},
            {"role": "assistant", "content": t},
        ])

    formatted_chat_prompts = self.tokenizer.apply_chat_template(chats, tokenize=False, continue_final_message=True)

    formatted_chat_tokens = self.tokenizer(formatted_chat_prompts, padding=True, truncation=True, return_tensors='pt')
    formatted_chat_input_ids = formatted_chat_tokens['input_ids'].to(device)
    formatted_chat_attention_mask = formatted_chat_tokens['attention_mask'].to(device)

    loss_tokens = self.tokenizer(target_strs, padding=True, truncation=True, return_tensors="pt", add_special_tokens=False)
    loss_tokens_input_ids = loss_tokens['input_ids'].to(device)
    loss_tokens_attention_mask = loss_tokens['attention_mask'].to(device)

    # Number of padding tokens applied to each target string from batched tokenizer
    loss_tokens_applied_left_padding_count = loss_tokens_attention_mask.argmax(1)
    loss_tokens_length = loss_tokens_input_ids.shape[-1]

    if token_grads:
        output, one_hot_tokens = get_output_and_differentiable_one_hot(self.model, formatted_chat_input_ids, formatted_chat_attention_mask)
    else:
        output = self.model(input_ids=formatted_chat_input_ids, attention_mask=formatted_chat_attention_mask)

    output_loss_logits = output.logits[:, -loss_tokens_length - 1: -1, :]

    if visualize:
        # Print out most likely first token probabilities
        visualize_logits(self.tokenizer, output_loss_logits[0, 0])

    # Calculate batch cross entropy loss
    # (uses ignore_index = tokenizer.pad_token_id to exclude padding tokens from cross entropy calculation)
    # see https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html for details
    batch_loss = torch.nn.functional.cross_entropy(output_loss_logits.permute(0, 2, 1), loss_tokens_input_ids, ignore_index=self.tokenizer.pad_token_id, label_smoothing=0, reduction='none')

    if self.scale_token_positions:
        # Scale token losses by token position (to weight earlier tokens more prominently in the loss)
        token_position_scaling = torch.arange(1, batch_loss.shape[1] + 1, device=device).pow(0.5).clamp(min=0.5, max=1.25)
        batched_token_position_scaling = token_position_scaling
        batch_loss = batch_loss / batched_token_position_scaling

    prompt_loss = batch_loss.mean(dim=1) / torch.log(torch.tensor(output_loss_logits.shape[1], device=device))

    if token_grads:
        self.last_token_grads = -torch.autograd.grad(outputs=prompt_loss.sum(), inputs=one_hot_tokens)[0]

    State.populate_individual_state_losses(f"{self.__class__.__name__}-{self.model.config._name_or_path}-{self.target_str}", states, prompt_loss)                

    return prompt_loss

weighted_loss

WeightedLoss

Bases: Loss

A weighted wrapper for another loss. Weights losses by some constant.

Source code in src/optimization/losses/weighted_loss.py 
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class WeightedLoss(Loss):
    """
    A weighted wrapper for another loss. Weights losses by some constant.
    """
    def __init__(self, loss_to_weight: Loss, weight: float):
        self.loss_to_weight = loss_to_weight
        self.weight = weight

    def __call__(self, states: list[State], visualize=False, device="cuda:0") -> torch.Tensor:
        return self.loss_to_weight(states, visualize=visualize, device=device) * self.weight