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Added skip_bos Argument to LLM Attribution To Enable Wider Model Support for Attributing Against a Single Token #1322

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Added skip_bos Argument to LLM Attribution To Enable Wider Model Support for Attributing Against a Single Token #1322

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73dc20f
Update llm_attr.py
SulRash Aug 12, 2024
3b8b6aa
Update llm_attr.py
SulRash Aug 12, 2024
136b7da
Update llm_attr.py
SulRash Aug 12, 2024
fb93ce0
Merge branch 'master' into master
SulRash Aug 13, 2024
333852c
Changed error handling
SulRash Aug 13, 2024
5391085
Merge branch 'master' into master
SulRash Aug 15, 2024
f7373a3
Merge branch 'master' into master
SulRash Aug 18, 2024
382ae7a
Merge branch 'master' into master
SulRash Aug 29, 2024
90c1311
Fixed ufmt and linting check
SulRash Aug 29, 2024
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17 changes: 12 additions & 5 deletions captum/attr/_core/llm_attr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -290,9 +290,12 @@ def _forward_func(
# 1st element is the total prob, rest are the target tokens
# add a leading dim for batch even we only support single instance for now
if self.include_per_token_attr:
target_log_probs = torch.stack(
[total_log_prob, *log_prob_list], dim=0
).unsqueeze(0)
try:
target_log_probs = torch.stack(
[total_log_prob, *log_prob_list], dim=0
).unsqueeze(0)
except TypeError:
raise TypeError("It seems like you got an empty list of target tokens. If you are attributing only one target token (a single character or word) try using the skip_bos argument in the attribute function.")
else:
target_log_probs = total_log_prob
# pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[int,
Expand Down Expand Up @@ -327,6 +330,7 @@ def attribute(
inp: InterpretableInput,
target: Union[str, torch.Tensor, None] = None,
num_trials: int = 1,
skip_bos: bool = True,
# pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
# `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting
# errors.
Expand Down Expand Up @@ -382,8 +386,11 @@ def attribute(
assert gen_args is None, "gen_args must be None when target is given"

if type(target) is str:
# exclude sos
target_tokens = self.tokenizer.encode(target)[1:]
# exclude sos / bos
if skip_bos:
target_tokens = self.tokenizer.encode(target)[1:]
else:
target_tokens = self.tokenizer.encode(target)
target_tokens = torch.tensor(target_tokens)
elif type(target) is torch.Tensor:
target_tokens = target
Expand Down
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