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Add transformer_lens.tools.analysis.interchange: an intervention that sets a base input's activation inside an orthonormal subspace to its value on a source input, at one hook point and optionally one position per example. It comes with difference-of-means, probe and random bases to intervene on and a norm-matched random-subspace placebo, plus unit tests, GPT-2 integration tests and a docs page. Part of TransformerLensOrg#1879.
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run_with_intervention now raises unless the hook fires exactly once in the forward pass, as collect_activations already did. With positions, the activation must be [batch, pos, d_model] and positions must hold one index per example; other shapes raise instead of slicing the wrong axis. Add intervened_logits, which evaluates an intervention in batches without gradients and returns CPU logits, so memory is bounded by batch_size. The position tests now use a different position per row.
Add fit_das and LearnedSubspace to tools.analysis.interchange: DAS trains an orthonormal basis so that interchanging it makes the model produce the outputs a high-level causal model predicts for each base/source pair. The model is frozen during training and restored afterwards, and batches can be split to save memory. Includes unit tests on a planted direction, a GPT-2 test on held-out nouns, and a docs section. Part of TransformerLensOrg#1879.
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Hi @dipo101, please rebase this onto |
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Description
Part 2 of 3 for #1879. Stacked on #1892; only the last commit is new.
Adds
fit_dasandLearnedSubspace: distributed alignment search trains an orthonormal basis so that interchanging it makes the model produce the outputs a high-level causal model predicts for each base/source pair. Tested on a planted direction and on GPT-2 subject number with held-out nouns; docs section added.Type of change
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