+
+
+ Back to top
+
+
+
+
+
+
+
+
+
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Literal, Optional, Sequence, TypeVar, Union, cast
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.layer_matching import (
+ LayerMatcher,
+ collect_matching_layers,
+ get_layer_name,
+ guess_hidden_layer_matcher,
+)
+from linear_relational.lib.token_utils import (
+ ensure_tokenizer_has_pad_token,
+ find_final_word_token_index,
+ make_inputs,
+ predict_all_token_probs_from_input,
+ predict_next_tokens_greedy,
+)
+from linear_relational.lib.torch_utils import get_device, untuple_tensor
+from linear_relational.lib.TraceLayerDict import TraceLayerDict
+from linear_relational.lib.util import batchify
+
+EditorSubject = Union[str, int, Callable[[str, list[int]], int]]
+
+
+
+[docs]
+@dataclass
+class ConceptSwapRequest:
+ text: str
+ subject: EditorSubject
+ remove_concept: str
+ add_concept: str
+
+
+
+
+[docs]
+@dataclass
+class ConceptSwapAndPredictGreedyRequest(ConceptSwapRequest):
+ predict_num_tokens: int = 1
+
+
+
+T = TypeVar("T")
+
+
+
+[docs]
+class CausalEditor:
+ """Modify model activations during inference to swap concepts"""
+
+ concepts: list[Concept]
+ model: nn.Module
+ tokenizer: Tokenizer
+ layer_matcher: LayerMatcher
+ layer_name_to_num: dict[str, int]
+
+ def __init__(
+ self,
+ model: nn.Module,
+ tokenizer: Tokenizer,
+ concepts: list[Concept],
+ layer_matcher: Optional[LayerMatcher] = None,
+ ) -> None:
+ self.concepts = concepts
+ self.model = model
+ self.tokenizer = tokenizer
+ self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+ ensure_tokenizer_has_pad_token(tokenizer)
+ num_layers = len(collect_matching_layers(self.model, self.layer_matcher))
+ self.layer_name_to_num = {}
+ for layer_num in range(num_layers):
+ self.layer_name_to_num[
+ get_layer_name(model, self.layer_matcher, layer_num)
+ ] = layer_num
+
+ @property
+ def device(self) -> torch.device:
+ return get_device(self.model)
+
+
+[docs]
+ def swap_subject_concepts_and_predict_greedy(
+ self,
+ text: str,
+ subject: EditorSubject,
+ remove_concept: str,
+ add_concept: str,
+ # if False, edit the subject token at every layer
+ edit_single_layer: int | Literal[False] = False,
+ predict_num_tokens: int = 1,
+ magnitude_multiplier: float = 1.0,
+ # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+ # if False, use the magnitude of the subject's original activation
+ use_remove_concept_projection_magnitude: bool = False,
+ ) -> str:
+ results = self.swap_subject_concepts_and_predict_greedy_bulk(
+ [
+ ConceptSwapAndPredictGreedyRequest(
+ text, subject, remove_concept, add_concept, predict_num_tokens
+ )
+ ],
+ magnitude_multiplier=magnitude_multiplier,
+ edit_single_layer=edit_single_layer,
+ use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+ )
+ return results[0]
+
+
+
+[docs]
+ def swap_subject_concepts_and_predict_greedy_bulk(
+ self,
+ requests: Sequence[ConceptSwapAndPredictGreedyRequest],
+ # if False, edit the subject token at every layer
+ edit_single_layer: int | Literal[False] = False,
+ magnitude_multiplier: float = 1.0,
+ # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+ # if False, use the magnitude of the subject's original activation
+ use_remove_concept_projection_magnitude: bool = False,
+ ) -> list[str]:
+ next_tokens = self.swap_subject_concepts_and_predict_tokens_greedy_bulk(
+ requests,
+ edit_single_layer=edit_single_layer,
+ magnitude_multiplier=magnitude_multiplier,
+ use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+ )
+ return [self.tokenizer.decode(tokens) for tokens in next_tokens]
+
+
+
+[docs]
+ def swap_subject_concepts_and_predict_tokens_greedy_bulk(
+ self,
+ requests: Sequence[ConceptSwapAndPredictGreedyRequest],
+ # if False, edit the subject token at every layer
+ edit_single_layer: int | Literal[False],
+ magnitude_multiplier: float = 1.0,
+ # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+ # if False, use the magnitude of the subject's original activation
+ use_remove_concept_projection_magnitude: bool = False,
+ batch_size: int = 12,
+ show_progress: bool = False,
+ ) -> list[list[int]]:
+ results: list[list[int]] = []
+ for batch in batchify(requests, batch_size, show_progress=show_progress):
+
+ def run_batch_fn() -> list[list[int]]:
+ max_num_tokens = max(req.predict_num_tokens for req in batch)
+ next_tokens = predict_next_tokens_greedy(
+ self.model,
+ self.tokenizer,
+ [req.text for req in batch],
+ num_tokens=max_num_tokens,
+ device=self.device,
+ )
+ return [
+ tokens[: req.predict_num_tokens]
+ for tokens, req in zip(next_tokens, batch)
+ ]
+
+ results.extend(
+ self._swap_subject_concepts_and_run_batch(
+ batch,
+ run_fn=run_batch_fn,
+ edit_single_layer=edit_single_layer,
+ magnitude_multiplier=magnitude_multiplier,
+ use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+ )
+ )
+ return results
+
+
+
+[docs]
+ def swap_subject_concepts_and_predict_all_token_probs_bulk(
+ self,
+ requests: Sequence[ConceptSwapRequest],
+ magnitude_multiplier: float = 1.0,
+ # if False, edit the subject token at every layer
+ edit_single_layer: int | Literal[False] = False,
+ # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+ # if False, use the magnitude of the subject's original activation
+ use_remove_concept_projection_magnitude: bool = False,
+ batch_size: int = 12,
+ show_progress: bool = False,
+ ) -> list[torch.Tensor]:
+ results: list[torch.Tensor] = []
+ for batch in batchify(requests, batch_size, show_progress=show_progress):
+
+ def run_batch_fn() -> list[torch.Tensor]:
+ inputs = make_inputs(
+ self.tokenizer, [req.text for req in batch], device=self.device
+ )
+ return predict_all_token_probs_from_input(self.model, inputs)
+
+ results.extend(
+ self._swap_subject_concepts_and_run_batch(
+ batch,
+ run_fn=run_batch_fn,
+ magnitude_multiplier=magnitude_multiplier,
+ edit_single_layer=edit_single_layer,
+ use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+ )
+ )
+ return results
+
+
+ def _swap_subject_concepts_and_run_batch(
+ self,
+ requests: Sequence[ConceptSwapRequest],
+ run_fn: Callable[[], T],
+ edit_single_layer: int | Literal[False],
+ magnitude_multiplier: float = 1.0,
+ # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+ # if False, use the magnitude of the subject's original activation
+ use_remove_concept_projection_magnitude: bool = False,
+ ) -> T:
+ """
+ Helper to run the given run_fn while swapping the subject concept for each request.
+ The run_fn should run the model with the same batch of inputs as specified in the requests
+ """
+ subj_tokens = [self._find_subject_token(req) for req in requests]
+ with torch.no_grad():
+ remove_concept_vectors = [
+ (
+ self._find_concept(req.remove_concept)
+ .vector.detach()
+ .clone()
+ .type(cast(torch.dtype, self.model.dtype))
+ .to(self.device)
+ )
+ for req in requests
+ ]
+ add_concept_vectors = [
+ (
+ self._find_concept(req.add_concept)
+ .vector.detach()
+ .clone()
+ .type(cast(torch.dtype, self.model.dtype))
+ .to(self.device)
+ )
+ for req in requests
+ ]
+
+ def edit_model_output(output: torch.Tensor, layer_name: str) -> torch.Tensor:
+ if (
+ edit_single_layer is not False
+ and self.layer_name_to_num[layer_name] != edit_single_layer
+ ):
+ return output
+ fixed_output = untuple_tensor(output)
+ for i, subj_token in enumerate(subj_tokens):
+ remove_concept_vector = remove_concept_vectors[i]
+ add_concept_vector = add_concept_vectors[i]
+ original_subj_act = fixed_output[i][subj_token]
+ if use_remove_concept_projection_magnitude:
+ base_magnitude = original_subj_act.dot(remove_concept_vector)
+ else:
+ base_magnitude = original_subj_act.norm()
+ magnitude = base_magnitude * magnitude_multiplier
+ fixed_output[i][subj_token] = original_subj_act + magnitude * (
+ add_concept_vector - remove_concept_vector
+ )
+ return output
+
+ with torch.no_grad(), TraceLayerDict(
+ self.model,
+ layers=self.layer_name_to_num.keys(),
+ edit_output=edit_model_output,
+ ):
+ return run_fn()
+
+ def _find_subject_token(self, query: ConceptSwapRequest) -> int:
+ text = query.text
+ subject = query.subject
+ if isinstance(subject, int):
+ return subject
+ if isinstance(subject, str):
+ return find_final_word_token_index(self.tokenizer, text, subject)
+ if callable(subject):
+ return subject(text, self.tokenizer.encode(text))
+ raise ValueError(f"Unknown subject type: {type(subject)}")
+
+ def _find_concept(self, concept_name: str) -> Concept:
+ for concept in self.concepts:
+ if concept.name == concept_name:
+ return concept
+ raise ValueError(f"Unknown concept: {concept_name}")
+
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+import torch
+from torch import nn
+
+
+
+[docs]
+class Concept(nn.Module):
+ """Linear Relation Concept (LRC)"""
+
+ layer: int
+ vector: torch.Tensor
+ object: str
+ relation: str
+ name: str
+ metadata: dict[str, Any]
+
+ def __init__(
+ self,
+ layer: int,
+ vector: torch.Tensor,
+ object: str,
+ relation: str,
+ metadata: Optional[dict[str, Any]] = None,
+ name: Optional[str] = None,
+ ) -> None:
+ super().__init__()
+ self.layer = layer
+ self.vector = vector
+ self.object = object
+ self.relation = relation
+ self.metadata = metadata or {}
+ self.name = name or f"{self.relation}: {self.object}"
+
+
+[docs]
+ def forward(self, activations: torch.Tensor) -> torch.Tensor:
+ vector = self.vector.to(activations.device, dtype=activations.dtype)
+ if len(activations.shape) == 1:
+ return vector @ activations
+ return vector @ activations.T
+
+
+
+from dataclasses import dataclass
+from typing import Callable, Optional, Sequence, Union
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.extract_token_activations import (
+ TokenLayerActivationsList,
+ extract_token_activations,
+)
+from linear_relational.lib.layer_matching import (
+ LayerMatcher,
+ collect_matching_layers,
+ get_layer_name,
+ guess_hidden_layer_matcher,
+)
+from linear_relational.lib.token_utils import (
+ ensure_tokenizer_has_pad_token,
+ find_final_word_token_index,
+)
+from linear_relational.lib.torch_utils import get_device
+from linear_relational.lib.util import batchify
+
+QuerySubject = Union[str, int, Callable[[str, list[int]], int]]
+
+
+
+
+
+
+
+
+
+
+
+[docs]
+@dataclass
+class QueryResult:
+ concept_results: dict[str, ConceptMatchResult]
+
+ @property
+ def best_match(self) -> ConceptMatchResult:
+ return max(self.concept_results.values(), key=lambda x: x.score)
+
+
+
+
+[docs]
+class ConceptMatcher:
+ """Match concepts against subject activations in a model"""
+
+ concepts: list[Concept]
+ model: nn.Module
+ tokenizer: Tokenizer
+ layer_matcher: LayerMatcher
+ layer_name_to_num: dict[str, int]
+ map_activations_fn: (
+ Callable[[TokenLayerActivationsList], TokenLayerActivationsList] | None
+ )
+
+ def __init__(
+ self,
+ model: nn.Module,
+ tokenizer: Tokenizer,
+ concepts: list[Concept],
+ layer_matcher: Optional[LayerMatcher] = None,
+ map_activations_fn: (
+ Callable[[TokenLayerActivationsList], TokenLayerActivationsList] | None
+ ) = None,
+ ) -> None:
+ self.concepts = concepts
+ self.model = model
+ self.tokenizer = tokenizer
+ self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+ self.map_activations_fn = map_activations_fn
+ ensure_tokenizer_has_pad_token(tokenizer)
+ num_layers = len(collect_matching_layers(self.model, self.layer_matcher))
+ self.layer_name_to_num = {}
+ for layer_num in range(num_layers):
+ self.layer_name_to_num[
+ get_layer_name(model, self.layer_matcher, layer_num)
+ ] = layer_num
+
+
+[docs]
+ def query(self, query: str, subject: QuerySubject) -> QueryResult:
+ return self.query_bulk([ConceptMatchQuery(query, subject)])[0]
+
+
+
+[docs]
+ def query_bulk(
+ self,
+ queries: Sequence[ConceptMatchQuery],
+ batch_size: int = 4,
+ verbose: bool = False,
+ ) -> list[QueryResult]:
+ results: list[QueryResult] = []
+ for batch in batchify(queries, batch_size, show_progress=verbose):
+ results.extend(self._query_batch(batch))
+ return results
+
+
+ def _query_batch(self, queries: Sequence[ConceptMatchQuery]) -> list[QueryResult]:
+ subj_tokens = [self._find_subject_token(query) for query in queries]
+ with torch.no_grad():
+ batch_subj_token_activations = extract_token_activations(
+ self.model,
+ self.tokenizer,
+ layers=self.layer_name_to_num.keys(),
+ texts=[q.text for q in queries],
+ token_indices=subj_tokens,
+ device=get_device(self.model),
+ # batching is handled already, so no need to batch here too
+ batch_size=len(queries),
+ show_progress=False,
+ )
+ if self.map_activations_fn is not None:
+ batch_subj_token_activations = self.map_activations_fn(
+ batch_subj_token_activations
+ )
+
+ results: list[QueryResult] = []
+ for raw_subj_token_activations in batch_subj_token_activations:
+ concept_results: dict[str, ConceptMatchResult] = {}
+ # need to replace the layer name with the layer number
+ subj_token_activations = {
+ self.layer_name_to_num[layer_name]: layer_activations[0]
+ for layer_name, layer_activations in raw_subj_token_activations.items()
+ }
+ for concept in self.concepts:
+ concept_results[concept.name] = _apply_concept_to_activations(
+ concept, subj_token_activations
+ )
+ results.append(QueryResult(concept_results))
+ return results
+
+ def _find_subject_token(self, query: ConceptMatchQuery) -> int:
+ text = query.text
+ subject = query.subject
+ if isinstance(subject, int):
+ return subject
+ if isinstance(subject, str):
+ return find_final_word_token_index(self.tokenizer, text, subject)
+ if callable(subject):
+ return subject(text, self.tokenizer.encode(text))
+ raise ValueError(f"Unknown subject type: {type(subject)}")
+
+
+
+@torch.no_grad()
+def _apply_concept_to_activations(
+ concept: Concept, activations: dict[int, torch.Tensor]
+) -> ConceptMatchResult:
+ score = concept.forward(activations[concept.layer]).item()
+ return ConceptMatchResult(
+ concept=concept.name,
+ score=score,
+ )
+
+from typing import Any, Literal
+
+import torch
+from torch import nn
+
+
+
+[docs]
+class InvertedLre(nn.Module):
+ """Low-rank inverted LRE, used for calculating subject activations from object activations"""
+
+ relation: str
+ subject_layer: int
+ object_layer: int
+ # store u, v, s, and bias separately to avoid storing the full weight matrix
+ u: nn.Parameter
+ s: nn.Parameter
+ v: nn.Parameter
+ bias: nn.Parameter
+ object_aggregation: Literal["mean", "first_token"]
+ metadata: dict[str, Any] | None = None
+
+ def __init__(
+ self,
+ relation: str,
+ subject_layer: int,
+ object_layer: int,
+ object_aggregation: Literal["mean", "first_token"],
+ u: torch.Tensor,
+ s: torch.Tensor,
+ v: torch.Tensor,
+ bias: torch.Tensor,
+ metadata: dict[str, Any] | None = None,
+ ) -> None:
+ super().__init__()
+ self.relation = relation
+ self.subject_layer = subject_layer
+ self.object_layer = object_layer
+ self.object_aggregation = object_aggregation
+ self.u = nn.Parameter(u, requires_grad=False)
+ self.s = nn.Parameter(s, requires_grad=False)
+ self.v = nn.Parameter(v, requires_grad=False)
+ self.bias = nn.Parameter(bias, requires_grad=False)
+ self.metadata = metadata
+
+ @property
+ def rank(self) -> int:
+ return self.s.shape[0]
+
+
+[docs]
+ def w_inv_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+ # group u.T @ vec to avoid calculating larger matrices than needed
+ return self.v @ torch.diag(1 / self.s) @ (self.u.T @ vec)
+
+
+
+[docs]
+ def forward(
+ self,
+ object_activations: torch.Tensor, # a tensor of shape (num_activations, hidden_activation_size)
+ normalize: bool = False,
+ ) -> torch.Tensor:
+ return self.calculate_subject_activation(
+ object_activations=object_activations,
+ normalize=normalize,
+ )
+
+
+
+[docs]
+ def calculate_subject_activation(
+ self,
+ object_activations: torch.Tensor, # a tensor of shape (num_activations, hidden_activation_size)
+ normalize: bool = False,
+ ) -> torch.Tensor:
+ # match precision of weight_inverse and bias
+ unbiased_acts = object_activations - self.bias.unsqueeze(0)
+ vec = self.w_inv_times_vec(unbiased_acts.T).mean(dim=1)
+
+ if normalize:
+ vec = vec / vec.norm()
+ return vec
+
+
+
+
+
+[docs]
+class LowRankLre(nn.Module):
+ """Low-rank approximation of a LRE"""
+
+ relation: str
+ subject_layer: int
+ object_layer: int
+ # store u, v, s, and bias separately to avoid storing the full weight matrix
+ u: nn.Parameter
+ s: nn.Parameter
+ v: nn.Parameter
+ bias: nn.Parameter
+ object_aggregation: Literal["mean", "first_token"]
+ metadata: dict[str, Any] | None = None
+
+ def __init__(
+ self,
+ relation: str,
+ subject_layer: int,
+ object_layer: int,
+ object_aggregation: Literal["mean", "first_token"],
+ u: torch.Tensor,
+ s: torch.Tensor,
+ v: torch.Tensor,
+ bias: torch.Tensor,
+ metadata: dict[str, Any] | None = None,
+ ) -> None:
+ super().__init__()
+ self.relation = relation
+ self.subject_layer = subject_layer
+ self.object_layer = object_layer
+ self.object_aggregation = object_aggregation
+ self.u = nn.Parameter(u, requires_grad=False)
+ self.s = nn.Parameter(s, requires_grad=False)
+ self.v = nn.Parameter(v, requires_grad=False)
+ self.bias = nn.Parameter(bias, requires_grad=False)
+ self.metadata = metadata
+
+ @property
+ def rank(self) -> int:
+ return self.s.shape[0]
+
+
+[docs]
+ def w_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+ # group v.T @ vec to avoid calculating larger matrices than needed
+ return self.u @ torch.diag(self.s) @ (self.v.T @ vec)
+
+
+
+[docs]
+ def forward(
+ self,
+ subject_activations: torch.Tensor, # a tensor of shape (num_activations, hidden_activation_size)
+ normalize: bool = False,
+ ) -> torch.Tensor:
+ return self.calculate_object_activation(
+ subject_activations=subject_activations,
+ normalize=normalize,
+ )
+
+
+
+[docs]
+ def calculate_object_activation(
+ self,
+ subject_activations: torch.Tensor, # a tensor of shape (num_activations, hidden_activation_size)
+ normalize: bool = False,
+ ) -> torch.Tensor:
+ # match precision of weight_inverse and bias
+ ws = self.w_times_vec(subject_activations.T)
+ vec = (ws + self.bias.unsqueeze(-1)).mean(dim=1)
+ if normalize:
+ vec = vec / vec.norm()
+ return vec
+
+
+
+
+
+[docs]
+class Lre(nn.Module):
+ """Linear Relational Embedding"""
+
+ relation: str
+ subject_layer: int
+ object_layer: int
+ weight: nn.Parameter
+ bias: nn.Parameter
+ object_aggregation: Literal["mean", "first_token"]
+ metadata: dict[str, Any] | None = None
+
+ def __init__(
+ self,
+ relation: str,
+ subject_layer: int,
+ object_layer: int,
+ object_aggregation: Literal["mean", "first_token"],
+ weight: torch.Tensor,
+ bias: torch.Tensor,
+ metadata: dict[str, Any] | None = None,
+ ) -> None:
+ super().__init__()
+ self.relation = relation
+ self.subject_layer = subject_layer
+ self.object_layer = object_layer
+ self.object_aggregation = object_aggregation
+ self.weight = nn.Parameter(weight, requires_grad=False)
+ self.bias = nn.Parameter(bias, requires_grad=False)
+ self.metadata = metadata
+
+
+[docs]
+ def invert(self, rank: int) -> InvertedLre:
+ """Invert this LRE using a low-rank approximation"""
+ u, s, v = self._low_rank_svd(rank)
+ return InvertedLre(
+ relation=self.relation,
+ subject_layer=self.subject_layer,
+ object_layer=self.object_layer,
+ object_aggregation=self.object_aggregation,
+ u=u.detach().clone(),
+ s=s.detach().clone(),
+ v=v.detach().clone(),
+ bias=self.bias.detach().clone(),
+ metadata=self.metadata,
+ )
+
+
+
+[docs]
+ def to_low_rank(self, rank: int) -> LowRankLre:
+ """Create a low-rank approximation of this LRE"""
+ u, s, v = self._low_rank_svd(rank)
+ return LowRankLre(
+ relation=self.relation,
+ subject_layer=self.subject_layer,
+ object_layer=self.object_layer,
+ object_aggregation=self.object_aggregation,
+ u=u.detach().clone(),
+ s=s.detach().clone(),
+ v=v.detach().clone(),
+ bias=self.bias.detach().clone(),
+ metadata=self.metadata,
+ )
+
+
+ @torch.no_grad()
+ def _low_rank_svd(
+ self, rank: int
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ # use a float for the svd, then convert back to the original dtype
+ u, s, v = torch.svd(self.weight.float())
+ low_rank_u: torch.Tensor = u[:, :rank].to(self.weight.dtype)
+ low_rank_v: torch.Tensor = v[:, :rank].to(self.weight.dtype)
+ low_rank_s: torch.Tensor = s[:rank].to(self.weight.dtype)
+ return low_rank_u, low_rank_s, low_rank_v
+
+
+from dataclasses import dataclass
+
+
+
+[docs]
+@dataclass(frozen=True, slots=True)
+class Prompt:
+ """A prompt for training LREs and LRCs"""
+
+ text: str
+ answer: str
+ subject: str
+ subject_name: str = "" # If not provided, will be set to subject
+ object_name: str = "" # If not provided, will be set to answer
+
+ def __post_init__(self) -> None:
+ if self.subject_name == "":
+ object.__setattr__(self, "subject_name", self.subject)
+ if self.object_name == "":
+ object.__setattr__(self, "object_name", self.answer)
+
+
+from collections import defaultdict
+from time import time
+from typing import Callable, Literal, Optional
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.balance_grouped_items import balance_grouped_items
+from linear_relational.lib.extract_token_activations import extract_token_activations
+from linear_relational.lib.layer_matching import (
+ LayerMatcher,
+ get_layer_name,
+ guess_hidden_layer_matcher,
+)
+from linear_relational.lib.logger import log_or_print, logger
+from linear_relational.lib.token_utils import PromptAnswerData, find_prompt_answer_data
+from linear_relational.lib.torch_utils import get_device
+from linear_relational.lib.util import group_items
+from linear_relational.Lre import InvertedLre, Lre
+from linear_relational.Prompt import Prompt
+from linear_relational.PromptValidator import PromptValidator
+from linear_relational.training.train_lre import ObjectAggregation, train_lre
+
+VectorAggregation = Literal["pre_mean", "post_mean"]
+
+
+
+[docs]
+class Trainer:
+ """Train LREs and concepts from prompts"""
+
+ model: nn.Module
+ tokenizer: Tokenizer
+ layer_matcher: LayerMatcher
+ prompt_validator: PromptValidator
+
+ def __init__(
+ self,
+ model: nn.Module,
+ tokenizer: Tokenizer,
+ layer_matcher: Optional[LayerMatcher] = None,
+ prompt_validator: Optional[PromptValidator] = None,
+ ) -> None:
+ self.model = model
+ self.tokenizer = tokenizer
+ self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+ self.prompt_validator = prompt_validator or PromptValidator(model, tokenizer)
+
+
+[docs]
+ def train_lre(
+ self,
+ relation: str,
+ subject_layer: int,
+ object_layer: int,
+ prompts: list[Prompt],
+ max_lre_training_samples: int | None = None,
+ object_aggregation: ObjectAggregation = "mean",
+ validate_prompts: bool = True,
+ validate_prompts_batch_size: int = 4,
+ move_to_cpu: bool = False,
+ verbose: bool = True,
+ seed: int | str | float = 42,
+ ) -> Lre:
+ processed_prompts = self._process_relation_prompts(
+ relation=relation,
+ prompts=prompts,
+ validate_prompts=validate_prompts,
+ validate_prompts_batch_size=validate_prompts_batch_size,
+ verbose=verbose,
+ )
+ prompts_by_object = group_items(processed_prompts, lambda p: p.object_name)
+ lre_train_prompts = balance_grouped_items(
+ items_by_group=prompts_by_object,
+ max_total=max_lre_training_samples,
+ seed=seed,
+ )
+ return train_lre(
+ model=self.model,
+ tokenizer=self.tokenizer,
+ layer_matcher=self.layer_matcher,
+ relation=relation,
+ subject_layer=subject_layer,
+ object_layer=object_layer,
+ prompts=lre_train_prompts,
+ object_aggregation=object_aggregation,
+ move_to_cpu=move_to_cpu,
+ )
+
+
+
+[docs]
+ def train_relation_concepts(
+ self,
+ relation: str,
+ subject_layer: int,
+ object_layer: int,
+ prompts: list[Prompt],
+ max_lre_training_samples: int | None = 20,
+ object_aggregation: ObjectAggregation = "mean",
+ vector_aggregation: VectorAggregation = "post_mean",
+ inv_lre_rank: int = 200,
+ validate_prompts_batch_size: int = 4,
+ validate_prompts: bool = True,
+ verbose: bool = True,
+ name_concept_fn: Optional[Callable[[str, str], str]] = None,
+ seed: int | str | float = 42,
+ ) -> list[Concept]:
+ processed_prompts = self._process_relation_prompts(
+ relation=relation,
+ prompts=prompts,
+ validate_prompts=validate_prompts,
+ validate_prompts_batch_size=validate_prompts_batch_size,
+ verbose=verbose,
+ )
+ prompts_by_object = group_items(processed_prompts, lambda p: p.object_name)
+ if len(prompts_by_object) == 1:
+ logger.warning(
+ f"Only one valid object found for {relation}. Results may be poor."
+ )
+ lre_train_prompts = balance_grouped_items(
+ items_by_group=prompts_by_object,
+ max_total=max_lre_training_samples,
+ seed=seed,
+ )
+ inv_lre = train_lre(
+ model=self.model,
+ tokenizer=self.tokenizer,
+ layer_matcher=self.layer_matcher,
+ relation=relation,
+ subject_layer=subject_layer,
+ object_layer=object_layer,
+ prompts=lre_train_prompts,
+ object_aggregation=object_aggregation,
+ ).invert(inv_lre_rank)
+
+ return self.train_relation_concepts_from_inv_lre(
+ relation=relation,
+ inv_lre=inv_lre,
+ prompts=processed_prompts,
+ vector_aggregation=vector_aggregation,
+ object_aggregation=object_aggregation,
+ object_layer=object_layer,
+ validate_prompts_batch_size=validate_prompts_batch_size,
+ validate_prompts=False, # we already validated the prompts above
+ name_concept_fn=name_concept_fn,
+ verbose=verbose,
+ )
+
+
+
+[docs]
+ def train_relation_concepts_from_inv_lre(
+ self,
+ inv_lre: InvertedLre | Callable[[str], InvertedLre],
+ prompts: list[Prompt],
+ vector_aggregation: VectorAggregation = "post_mean",
+ object_aggregation: ObjectAggregation | None = None,
+ relation: str | None = None,
+ object_layer: int | None = None,
+ validate_prompts_batch_size: int = 4,
+ extract_objects_batch_size: int = 4,
+ validate_prompts: bool = True,
+ name_concept_fn: Optional[Callable[[str, str], str]] = None,
+ verbose: bool = True,
+ ) -> list[Concept]:
+ if isinstance(inv_lre, InvertedLre):
+ if object_aggregation is None:
+ object_aggregation = inv_lre.object_aggregation
+ if object_layer is None:
+ object_layer = inv_lre.object_layer
+ if relation is None:
+ relation = inv_lre.relation
+ if object_aggregation is None:
+ raise ValueError(
+ "object_aggregation must be specified if inv_lre is a function"
+ )
+ if object_layer is None:
+ raise ValueError("object_layer must be specified if inv_lre is a function")
+ if relation is None:
+ raise ValueError("relation must be specified if inv_lre is a function")
+ processed_prompts = self._process_relation_prompts(
+ relation=relation,
+ prompts=prompts,
+ validate_prompts=validate_prompts,
+ validate_prompts_batch_size=validate_prompts_batch_size,
+ verbose=verbose,
+ )
+ start_time = time()
+ object_activations = self._extract_target_object_activations_for_inv_lre(
+ prompts=processed_prompts,
+ batch_size=extract_objects_batch_size,
+ object_aggregation=object_aggregation,
+ object_layer=object_layer,
+ show_progress=verbose,
+ move_to_cpu=True,
+ )
+ logger.info(
+ f"Extracted {len(object_activations)} object activations in {time() - start_time:.2f}s"
+ )
+ concepts: list[Concept] = []
+
+ with torch.no_grad():
+ for (
+ object_name,
+ activations,
+ ) in object_activations.items():
+ resolved_inv_lre = (
+ inv_lre
+ if isinstance(inv_lre, InvertedLre)
+ else inv_lre(object_name)
+ )
+ name = None
+ if name_concept_fn is not None:
+ name = name_concept_fn(relation, object_name)
+ concept = self._build_concept(
+ relation_name=relation,
+ layer=resolved_inv_lre.subject_layer,
+ inv_lre=resolved_inv_lre,
+ object_name=object_name,
+ activations=activations,
+ vector_aggregation=vector_aggregation,
+ name=name,
+ )
+ concepts.append(concept)
+ return concepts
+
+
+ def _process_relation_prompts(
+ self,
+ relation: str,
+ prompts: list[Prompt],
+ validate_prompts: bool,
+ validate_prompts_batch_size: int,
+ verbose: bool,
+ ) -> list[Prompt]:
+ valid_prompts = prompts
+ if validate_prompts:
+ log_or_print(f"validating {len(prompts)} prompts", verbose=verbose)
+ valid_prompts = self.prompt_validator.filter_prompts(
+ prompts, validate_prompts_batch_size, verbose
+ )
+ if len(valid_prompts) == 0:
+ raise ValueError(f"No valid prompts found for {relation}.")
+ return valid_prompts
+
+ def _build_concept(
+ self,
+ layer: int,
+ relation_name: str,
+ object_name: str,
+ activations: list[torch.Tensor],
+ inv_lre: InvertedLre,
+ vector_aggregation: VectorAggregation,
+ name: str | None,
+ ) -> Concept:
+ device = inv_lre.bias.device
+ dtype = inv_lre.bias.dtype
+ if vector_aggregation == "pre_mean":
+ acts = [torch.stack(activations).to(device=device, dtype=dtype).mean(dim=0)]
+ elif vector_aggregation == "post_mean":
+ acts = [act.to(device=device, dtype=dtype) for act in activations]
+ else:
+ raise ValueError(f"Unknown vector aggregation method {vector_aggregation}")
+ vecs = [
+ inv_lre.calculate_subject_activation(act, normalize=False) for act in acts
+ ]
+ vec = torch.stack(vecs).mean(dim=0)
+ vec = vec / vec.norm()
+ return Concept(
+ name=name,
+ object=object_name,
+ relation=relation_name,
+ layer=layer,
+ vector=vec.detach().clone().cpu(),
+ )
+
+ @torch.no_grad()
+ def _extract_target_object_activations_for_inv_lre(
+ self,
+ object_layer: int,
+ object_aggregation: Literal["mean", "first_token"],
+ prompts: list[Prompt],
+ batch_size: int,
+ show_progress: bool = False,
+ move_to_cpu: bool = True,
+ ) -> dict[str, list[torch.Tensor]]:
+ activations_by_object: dict[str, list[torch.Tensor]] = defaultdict(list)
+ prompt_answer_data: list[PromptAnswerData] = []
+ for prompt in prompts:
+ prompt_answer_data.append(
+ find_prompt_answer_data(self.tokenizer, prompt.text, prompt.answer)
+ )
+
+ layer_name = get_layer_name(self.model, self.layer_matcher, object_layer)
+ raw_activations = extract_token_activations(
+ self.model,
+ self.tokenizer,
+ layers=[layer_name],
+ texts=[prompt_answer.full_prompt for prompt_answer in prompt_answer_data],
+ token_indices=[
+ prompt_answer.output_answer_token_indices
+ for prompt_answer in prompt_answer_data
+ ],
+ device=get_device(self.model),
+ batch_size=batch_size,
+ show_progress=show_progress,
+ move_results_to_cpu=move_to_cpu,
+ )
+ for prompt, raw_activation in zip(prompts, raw_activations):
+ if object_aggregation == "mean":
+ activation = torch.stack(raw_activation[layer_name]).mean(dim=0)
+ elif object_aggregation == "first_token":
+ activation = raw_activation[layer_name][0]
+ else:
+ raise ValueError(
+ f"Unknown inv_lre.object_aggregation: {object_aggregation}"
+ )
+ activations_by_object[prompt.object_name].append(activation)
+ return activations_by_object
+
+' + + '' + + _("Hide Search Matches") + + "
" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/_static/styles/furo-extensions.css b/_static/styles/furo-extensions.css new file mode 100644 index 0000000..bc447f2 --- /dev/null +++ b/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n//