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Add types to fix pyre errors in TextTemplateInput [1/n] #1356

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Add types to fix pyre errors in TextTemplateInput [1/n] #1356

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8 changes: 5 additions & 3 deletions captum/attr/_core/llm_attr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -412,8 +412,6 @@ def _format_model_input(self, model_input: Union[str, Tensor]) -> Tensor:
"""
# return tensor(1, n_tokens)
if isinstance(model_input, str):
# pyre-ignore[9] pyre/mypy thinks return type may be List, but it will be
# Tensor
return self.tokenizer.encode( # type: ignore
model_input, return_tensors="pt"
).to(self.device)
Expand Down Expand Up @@ -609,10 +607,14 @@ class created with the llm model that follows huggingface style
else next(self.model.parameters()).device
)

def _format_model_input(self, model_input: Tensor) -> Tensor:
def _format_model_input(self, model_input: Union[Tensor, str]) -> Tensor:
"""
Convert str to tokenized tensor
"""
if isinstance(model_input, str):
return self.tokenizer.encode( # type: ignore
model_input, return_tensors="pt"
).to(self.device)
return model_input.to(self.device)

def attribute(
Expand Down
76 changes: 41 additions & 35 deletions captum/attr/_utils/interpretable_input.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# pyre-strict
from abc import ABC, abstractmethod
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
from typing import Callable, cast, Dict, List, Optional, Tuple, Union

import torch

Expand Down Expand Up @@ -116,17 +116,19 @@ def to_tensor(self) -> Tensor:
pass

@abstractmethod
# pyre-fixme[3]: Return annotation cannot be `Any`.
def to_model_input(self, itp_tensor: Optional[Tensor] = None) -> Any:
def to_model_input(
self, perturbed_tensor: Optional[Tensor] = None
) -> Union[str, Tensor]:
"""
Get the (perturbed) input in the format required by the model
based on the given (perturbed) interpretable representation.
Args:
itp_tensor (Tensor, optional): tensor of the interpretable representation
of this input. If it is None, assume the interpretable
representation is pristine and return the original model input
perturbed_tensor (Tensor, optional): tensor of the interpretable
representation of this input. If it is None, assume the
interpretable representation is pristine and return the
original model input
Default: None.
Expand Down Expand Up @@ -198,13 +200,25 @@ class TextTemplateInput(InterpretableInput):
"""

values: List[str]
dict_keys: List[str]
baselines: Union[List[str], Callable[[], Union[List[str], Dict[str, str]]]]
n_features: int
n_itp_features: int
format_fn: Callable[..., str]
mask: Union[List[int], Dict[str, int], None]
formatted_mask: List[int]

def __init__(
self,
# pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
template: Union[str, Callable],
template: Union[str, Callable[..., str]],
values: Union[List[str], Dict[str, str]],
# pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
baselines: Union[List[str], Dict[str, str], Callable, None] = None,
baselines: Union[
List[str],
Dict[str, str],
Callable[[], Union[List[str], Dict[str, str]]],
None,
] = None,
mask: Union[List[int], Dict[str, int], None] = None,
) -> None:
# convert values dict to list
Expand All @@ -217,8 +231,8 @@ def __init__(
), f"the values must be either a list or a dict, received: {type(values)}"
dict_keys = []

self.values: List[str] = values
self.dict_keys: List[str] = dict_keys
self.values = values
self.dict_keys = dict_keys

n_features = len(values)

Expand Down Expand Up @@ -261,15 +275,12 @@ def __init__(

# internal compressed mask of continuous interpretable indices from 0
# cannot replace original mask of ids for grouping across values externally
# pyre-fixme[4]: Attribute must be annotated.
self.formatted_mask = [mask_id_to_idx[mid] for mid in mask]

n_itp_features = len(mask_ids)

# number of raw features and intepretable features
# pyre-fixme[4]: Attribute must be annotated.
self.n_features = n_features
# pyre-fixme[4]: Attribute must be annotated.
self.n_itp_features = n_itp_features

if isinstance(template, str):
Expand All @@ -280,7 +291,6 @@ def __init__(
f"received: {type(template)}"
)
template = template
# pyre-fixme[4]: Attribute annotation cannot contain `Any`.
self.format_fn = template

self.mask = mask
Expand All @@ -289,8 +299,6 @@ def to_tensor(self) -> torch.Tensor:
# Interpretable representation in shape(1, n_itp_features)
return torch.tensor([[1.0] * self.n_itp_features])

# pyre-fixme[14]: `to_model_input` overrides method defined in
# `InterpretableInput` inconsistently.
def to_model_input(self, perturbed_tensor: Optional[Tensor] = None) -> str:
values = list(self.values) # clone

Expand Down Expand Up @@ -321,18 +329,12 @@ def to_model_input(self, perturbed_tensor: Optional[Tensor] = None) -> str:
itp_val = perturbed_tensor[0][itp_idx]

if not itp_val:
# pyre-fixme[16]: Item `None` of `Union[None, Dict[str, str],
# List[typing.Any]]` has no attribute `__getitem__`.
values[i] = baselines[i]

if self.dict_keys:
dict_values = dict(zip(self.dict_keys, values))
# pyre-fixme[29]: `Union[typing.Callable[..., typing.Any], str]` is not
# a function.
input_str = self.format_fn(**dict_values)
else:
# pyre-fixme[29]: `Union[typing.Callable[..., typing.Any], str]` is not
# a function.
input_str = self.format_fn(*values)

return input_str
Expand Down Expand Up @@ -391,6 +393,14 @@ class TextTokenInput(InterpretableInput):
"""

inp_tensor: Tensor
itp_tensor: Tensor
itp_mask: Optional[Tensor]
values: List[str]
tokenizer: TokenizerLike
n_itp_features: int
baselines: int

def __init__(
self,
text: str,
Expand All @@ -401,11 +411,11 @@ def __init__(
inp_tensor = tokenizer.encode(text, return_tensors="pt")

# input tensor into the model of token ids
self.inp_tensor: Tensor = inp_tensor
self.inp_tensor = inp_tensor
# tensor of interpretable token ids
self.itp_tensor: Tensor = inp_tensor
self.itp_tensor = inp_tensor
# interpretable mask
self.itp_mask: Optional[Tensor] = None
self.itp_mask = None

if skip_tokens:
if isinstance(skip_tokens[0], str):
Expand All @@ -426,13 +436,11 @@ def __init__(
self.skip_tokens = skip_tokens

# features values, the tokens
self.values: List[str] = tokenizer.convert_ids_to_tokens(
self.itp_tensor[0].tolist()
)
self.tokenizer: TokenizerLike = tokenizer
self.n_itp_features: int = len(self.values)
self.values = tokenizer.convert_ids_to_tokens(self.itp_tensor[0].tolist())
self.tokenizer = tokenizer
self.n_itp_features = len(self.values)

self.baselines: int = (
self.baselines = (
baselines
if type(baselines) is int
else tokenizer.convert_tokens_to_ids([baselines])[0] # type: ignore
Expand All @@ -442,8 +450,6 @@ def to_tensor(self) -> torch.Tensor:
# return the perturbation indicator as interpretable tensor instead of token ids
return torch.ones_like(self.itp_tensor)

# pyre-fixme[14]: `to_model_input` overrides method defined in
# `InterpretableInput` inconsistently.
def to_model_input(self, perturbed_tensor: Optional[Tensor] = None) -> Tensor:
if perturbed_tensor is None:
return self.inp_tensor
Expand Down
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