build(deps): update captum requirement from <0.6.0,>=0.5.0 to >=0.5.0,<0.8.0

[dependabot]

Updates the requirements on captum to permit the latest version.

Release notes

Sourced from captum's releases.

Captum v0.7.0 Release

The Captum 0.7.0 release adds new functionalities for language model attribution, dataset level attribution, and a few improvements and bug fixes for existing methods.

Language Model Attribution

Captum 0.7.0 adds new APIs for language model attribution, making it substantially easier to define interpretable text features with corresponding baselines and masks. These new wrappers are compatible with most attribution methods in Captum and make it substantially easier to understand how aspects of a prompt impact an LLM’s predicted response. More details can also be found in our paper:

Using Captum to Explain Generative Language Models

Example:

from captum.attr import ShapleyValueSampling, LLMAttribution, TextTemplateFeature
shapley_values = ShapleyValueSampling(model)
llm_attr = LLMAttribution(shapley_values, tokenizer)
inp = TextTemplateInput(
# the text template
"{} lives in {}, {} and is a {}. {} personal interests include",
# the values of the features
["Dave", "Palm Coast", "FL", "lawyer", "His"],
# the reference baseline values of the features
baselines=["Sarah", "Seattle", "WA", "doctor", "Her"],
)
res = llm_attr.attribute(inp)

DataLoader Attribution

DataLoader Attribution is a new wrapper which provides an easy-to-use approach for obtaining attribution on a full dataset by providing a data loader rather than a single input (PR #1155, #1158).

Attribution Improvements

Captum 0.7.0 has added a few improvements to existing attribution methods including:

• Multi-task attribution for Shapley Values and Shapley Value Sampling is now supported, allowing users to get attributions for multiple target outputs simultaneously (PR #1173)
• LayerGradCam now supports returning attributions for each channel independently without summing across channels (PR #1086, thanks to @​dzenanz for this contribution)

Bug Fixes

• Visualization utilities were updated to use the new keyword argument visible to ensure compatibility with Matplotlib 3.7 (PR #1118)
• The default visualization mode in visualize_timeseries_attr has been fixed to appropriately utilize overlay_individual (PR #1152, thanks to @​teddykoker for this contribution)

Commits

• 833d6aa Updating version to 0.7.0 (#1219)
• 9f365be Add tests for PyTorch 1.10 - 2.1 (#1218)
• 37fe0b1 Correct default method argument for captum.attr.visualization.visualize_t… ...
• 645af96 Add docs for LLMAttribution (#1217)
• 47503f8 export LLMAttrResult & ProductBaselines (#1216)
• 2efc105 Implement viz utils in LLMAttributionResult (#1213)
• eaf1f4e Implement ProductBaselines (#1212)
• 5398892 add influence gpu tests not using DataParallel (#1185)
• 19d7e3f LLM Attribution GPU Tests (#1210)
• b321570 Add support for Lime and KernelShap in LLM Attribution (#1209)
• Additional commits viewable in compare view

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📚 Documentation preview 📚: https://pytorch-tabular--361.org.readthedocs.build/en/361/