Coverage for pattern_lens / consts.py: 100%

1"""implements some constants and types""" 

2 

3import re 

4from typing import Literal 

5 

6import numpy as np 

7import torch 

8from jaxtyping import Float 

9 

10AttentionMatrix = Float[np.ndarray, "n_ctx n_ctx"] 

11"type alias for attention matrix" 

12 

13ActivationCacheNp = dict[str, np.ndarray] 

14"type alias for a cache of activations, like a transformer_lens.ActivationCache" 

15 

16ActivationCacheTorch = dict[str, torch.Tensor] 

17"type alias for a cache of activations, like a transformer_lens.ActivationCache but without the extras. useful for when loading from an npz file" 

18 

19DATA_DIR: str = "attn_data" 

20"default directory for attention data" 

21 

22ATTN_PATTERN_REGEX: re.Pattern = re.compile(r"blocks\.(\d+)\.attn\.hook_pattern") 

23"regex for finding attention patterns in model state dicts" 

24 

25SPINNER_KWARGS: dict = dict( 

26 config=dict(success="✔️ "), 

27) 

28"default kwargs for `muutils.spinner.Spinner`" 

29 

30DIVIDER_S1: str = "=" * 70 

31"divider string for separating sections" 

32 

33DIVIDER_S2: str = "-" * 50 

34"divider string for separating subsections" 

35 

36ReturnCache = Literal["numpy", "torch"] | None 

37"return type for a cache of activations"