Bump PyTorch and Python version

.conda/meta.yaml

@@ -13,10 +13,10 @@ build:
 
 requirements:
   host:
-    - python>=3.6
+    - python>=3.8
   run:
-    - numpy
-    - pytorch>=1.6
+    - numpy<2.0
+    - pytorch>=1.10
     - matplotlib-base
 
 test:

.github/workflows/lint.yml

@@ -13,7 +13,7 @@ jobs:
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     with:
       runner: linux.12xlarge
-      docker-image: cimg/python:3.9
+      docker-image: cimg/python:3.11
       repository: pytorch/captum
       script: |
         sudo chmod -R 777 .

.github/workflows/test-conda-cpu.yml

@@ -15,7 +15,7 @@ jobs:
   tests:
     strategy:
       matrix:
-        python_version: ["3.7", "3.8", "3.9", "3.10"]
+        python_version: ["3.8", "3.9", "3.10", "3.11"]
       fail-fast: false
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     with:

.github/workflows/test-pip-cpu-with-mypy.yml

@@ -17,7 +17,7 @@ jobs:
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     with:
       runner: linux.12xlarge
-      docker-image: cimg/python:3.6
+      docker-image: cimg/python:3.11
       repository: pytorch/captum
       script: |
         sudo chmod -R 777 .

.github/workflows/test-pip-cpu.yml

@@ -12,18 +12,17 @@ jobs:
   tests:
     strategy:
       matrix:
-        pytorch_args: ["-v 1.6", "-v 1.7", "-v 1.8", "-v 1.9", "-v 1.10", "-v 1.11", "-v 1.12", "-v 1.13"]
-        docker_img: ["cimg/python:3.6", "cimg/python:3.7"]
-        include:
-          - pytorch_args: "-v 2.0"
-            docker_img: "cimg/python:3.8"
+        pytorch_args: ["-v 1.10", "-v 1.11", "-v 1.12", "-v 1.13", "-v 2.0.0", "-v 2.1.0", "-v 2.2.0", "-v 2.3.0"]
+        docker_img: ["cimg/python:3.8", "cimg/python:3.9", "cimg/python:3.10", "cimg/python:3.11"]
         exclude:
+          - pytorch_args: "-v 1.10"
+            docker_img: "cimg/python:3.10"
+          - pytorch_args: "-v 1.10"
+            docker_img: "cimg/python:3.11"
           - pytorch_args: "-v 1.11"
-            docker_img: "cimg/python:3.6"
+            docker_img: "cimg/python:3.11"
           - pytorch_args: "-v 1.12"
-            docker_img: "cimg/python:3.6"
-          - pytorch_args: "-v 1.13"
-            docker_img: "cimg/python:3.6"
+            docker_img: "cimg/python:3.11"
       fail-fast: false
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     with:

README.md

@@ -47,8 +47,8 @@ Captum can also be used by application engineers who are using trained models in
 ## Installation
 
 **Installation Requirements**
-- Python >= 3.6
-- PyTorch >= 1.6
+- Python >= 3.8
+- PyTorch >= 1.10
 
 
 ##### Installing the latest release

captum/_utils/gradient.py

@@ -733,7 +733,7 @@ def _compute_jacobian_wrt_params(
     inputs: Tuple[Any, ...],
     labels: Optional[Tensor] = None,
     loss_fn: Optional[Union[Module, Callable]] = None,
-    layer_modules: List[Module] = None,
+    layer_modules: Optional[List[Module]] = None,
 ) -> Tuple[Tensor, ...]:
     r"""
     Computes the Jacobian of a batch of test examples given a model, and optional
@@ -805,7 +805,7 @@ def _compute_jacobian_wrt_params_with_sample_wise_trick(
     labels: Optional[Tensor] = None,
     loss_fn: Optional[Union[Module, Callable]] = None,
     reduction_type: Optional[str] = "sum",
-    layer_modules: List[Module] = None,
+    layer_modules: Optional[List[Module]] = None,
 ) -> Tuple[Any, ...]:
     r"""
     Computes the Jacobian of a batch of test examples given a model, and optional

captum/_utils/progress.py

@@ -3,7 +3,7 @@
 import sys
 import warnings
 from time import time
-from typing import cast, Iterable, Sized, TextIO
+from typing import cast, Iterable, Optional, Sized, TextIO
 
 from captum._utils.typing import Literal
 
@@ -51,7 +51,7 @@ class NullProgress:
     progress bars.
     """
 
-    def __init__(self, iterable: Iterable = None, *args, **kwargs):
+    def __init__(self, iterable: Optional[Iterable] = None, *args, **kwargs):
         del args, kwargs
         self.iterable = iterable
 
@@ -77,10 +77,10 @@ def close(self):
 class SimpleProgress:
     def __init__(
         self,
-        iterable: Iterable = None,
-        desc: str = None,
-        total: int = None,
-        file: TextIO = None,
+        iterable: Optional[Iterable] = None,
+        desc: Optional[str] = None,
+        total: Optional[int] = None,
+        file: Optional[TextIO] = None,
         mininterval: float = 0.5,
     ) -> None:
         """
@@ -155,11 +155,11 @@ def close(self):
 
 
 def progress(
-    iterable: Iterable = None,
-    desc: str = None,
-    total: int = None,
+    iterable: Optional[Iterable] = None,
+    desc: Optional[str] = None,
+    total: Optional[int] = None,
     use_tqdm=True,
-    file: TextIO = None,
+    file: Optional[TextIO] = None,
     mininterval: float = 0.5,
     **kwargs,
 ):

captum/attr/_core/noise_tunnel.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 from enum import Enum
-from typing import Any, cast, List, Tuple, Union
+from typing import Any, cast, List, Optional, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -80,7 +80,7 @@ def attribute(
         inputs: Union[Tensor, Tuple[Tensor, ...]],
         nt_type: str = "smoothgrad",
         nt_samples: int = 5,
-        nt_samples_batch_size: int = None,
+        nt_samples_batch_size: Optional[int] = None,
         stdevs: Union[float, Tuple[float, ...]] = 1.0,
         draw_baseline_from_distrib: bool = False,
         **kwargs: Any,

captum/attr/_utils/interpretable_input.py

@@ -1,3 +1,4 @@
+from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -45,7 +46,7 @@ def _scatter_itp_attr_by_mask(
     return attr
 
 
-class InterpretableInput:
+class InterpretableInput(ABC):
     """
     InterpretableInput is an adapter for different kinds of model inputs to
     work in Captum's attribution methods. Generally, attribution methods of Captum
@@ -94,6 +95,7 @@ class to create other types of customized input.
     is only allowed in certain attribution classes like LLMAttribution for now.)
     """
 
+    @abstractmethod
     def to_tensor(self) -> Tensor:
         """
         Return the interpretable representation of this input as a tensor
@@ -104,6 +106,7 @@ def to_tensor(self) -> Tensor:
         """
         pass
 
+    @abstractmethod
     def to_model_input(self, itp_tensor: Optional[Tensor] = None) -> Any:
         """
         Get the (perturbed) input in the format required by the model