From 004e8f64498a8cca52c9bb485baee8cb934dbfc7 Mon Sep 17 00:00:00 2001 From: David Chanin Date: Sat, 13 Apr 2024 13:32:43 +0100 Subject: [PATCH 1/8] feat: overhaul saving and loading From 44d2c6b597263e6b5af6ab94e4c95bd81ab825e8 Mon Sep 17 00:00:00 2001 From: github-actions Date: Sat, 13 Apr 2024 12:37:05 +0000 Subject: [PATCH 2/8] 0.2.0 Automatically generated by python-semantic-release --- CHANGELOG.md | 549 +++++++++++++++++++++++++------------------ pyproject.toml | 2 +- sae_lens/__init__.py | 2 +- 3 files changed, 317 insertions(+), 236 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 74da05b9..4e85bdf5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,559 +2,640 @@ +## v0.2.0 (2024-04-13) + +### Chore + +* chore: improving CI speed ([`9e3863c`](https://github.com/jbloomAus/SAELens/commit/9e3863c0e473797bd213ada901415834b934cb8c)) + +* chore: updating README.md with pip install instructions and PyPI badge ([`682db80`](https://github.com/jbloomAus/SAELens/commit/682db803ae6128d6b74cba968c23925d8f5effff)) + +### Feature + +* feat: overhaul saving and loading ([`004e8f6`](https://github.com/jbloomAus/SAELens/commit/004e8f64498a8cca52c9bb485baee8cb934dbfc7)) + +### Unknown + +* Merge pull request #80 from wllgrnt/will-update-tutorial + +bugfix - minimum viable updates to tutorial notebook ([`e51016b`](https://github.com/jbloomAus/SAELens/commit/e51016b01f3b0f30c83365c54430908779671d87)) + +* minimum viable fixes to evaluation notebook ([`b907567`](https://github.com/jbloomAus/SAELens/commit/b907567118ac891e712c5f31a5f0bc02d672008e)) + +* Merge pull request #76 from jbloomAus/faster-ci + +perf: improving CI speed ([`8b00000`](https://github.com/jbloomAus/SAELens/commit/8b000002764b3d2d8cebea2d8fe92e2092b1756e)) + +* try partial cache restore ([`392f982`](https://github.com/jbloomAus/SAELens/commit/392f982f413d02358d14a9a0702ee63857fd7232)) + +* Merge branch 'main' into faster-ci ([`89e1568`](https://github.com/jbloomAus/SAELens/commit/89e1568d114b49cd10778e902d4342c2c48a8359)) + +* Merge pull request #78 from jbloomAus/fix-artifact-saving-loading + +Fix artifact saving loading ([`8784c74`](https://github.com/jbloomAus/SAELens/commit/8784c74df08eece5d349cab9207ea1d544921c5b)) + +* remove duplicate code ([`6ed6af5`](https://github.com/jbloomAus/SAELens/commit/6ed6af55c5642629e019feeada1319df3cfd5c7a)) + +* set device in load from pretrained ([`b4e12cd`](https://github.com/jbloomAus/SAELens/commit/b4e12cd4b35e834402a9b915d4f02082e4eea711)) + +* fix typing issue which required ignore ([`a5df8b0`](https://github.com/jbloomAus/SAELens/commit/a5df8b00f33b1a6036ecf0517ad28f7fd92cb2fa)) + +* remove print statement ([`295e0e4`](https://github.com/jbloomAus/SAELens/commit/295e0e46421683a97e9871b7522a7c722e957e01)) + +* remove load with session option ([`74926e1`](https://github.com/jbloomAus/SAELens/commit/74926e162b47c04d71dbb151defbded6e27293b3)) + +* fix broken test ([`16935ef`](https://github.com/jbloomAus/SAELens/commit/16935efee3fb13195aa3931f2465b8f82e474cb8)) + +* avoid tqdm repeating during training ([`1d70af8`](https://github.com/jbloomAus/SAELens/commit/1d70af82278f5afb2406e6a8aa41ade3997f30b4)) + +* avoid division by 0 ([`2c7c6d8`](https://github.com/jbloomAus/SAELens/commit/2c7c6d857e6820c0a101765182393c591bf83369)) + +* remove old notebook ([`e1ad1aa`](https://github.com/jbloomAus/SAELens/commit/e1ad1aaeca1558cdc18174c70499f26f33bf26cf)) + +* use-sae-dict-not-group ([`27f8003`](https://github.com/jbloomAus/SAELens/commit/27f8003a6d655c0f08a2dd947ee662f68660b176)) + +* formatting ([`827abd0`](https://github.com/jbloomAus/SAELens/commit/827abd08e45aa4d436240442d9d2f843b260b346)) + +* improve artifact loading storage, tutorial forthcoming ([`604f102`](https://github.com/jbloomAus/SAELens/commit/604f102d54ab1f8981fb33c27addbd9f0f1128dc)) + +* add safetensors to project ([`0da48b0`](https://github.com/jbloomAus/SAELens/commit/0da48b044357eed17e5afffd3ce541e064185043)) + +* Merge pull request #71 from weissercn/main + +Addressing notebook issues ([`8417505`](https://github.com/jbloomAus/SAELens/commit/84175055ba5876b335cbc0de38bf709d0b11cec1)) + +* Merge pull request #70 from jbloomAus/update-readme-install + +chore: updating README.md with pip install instructions and PyPI badge ([`4d7d1e7`](https://github.com/jbloomAus/SAELens/commit/4d7d1e7db5e952c7e9accf19c0ccce466cdcf6cf)) + +* linting ([`1db0b5a`](https://github.com/jbloomAus/SAELens/commit/1db0b5ae7e091822c72bba0488d30fc16bc9a1c6)) + +* fixed graph name ([`ace4813`](https://github.com/jbloomAus/SAELens/commit/ace481322103737de2e80d688683d0c937ac5558)) + +* changed key for df_enrichment_scores, so it can be run ([`f0a9d0b`](https://github.com/jbloomAus/SAELens/commit/f0a9d0b30d52a163e0d715b1841a42009e444b64)) + +* fixed space in notebook 2 ([`2278419`](https://github.com/jbloomAus/SAELens/commit/2278419afb33a016b9a8bcf0759144b66cfe10da)) + +* fixed space in notebook 2 ([`24a6696`](https://github.com/jbloomAus/SAELens/commit/24a6696c07d7e06ac7f9487209bd4aa6d78412ee)) + +* fixed space in notebook ([`d2f8c8e`](https://github.com/jbloomAus/SAELens/commit/d2f8c8e28264368358a211a2a502e1638d7d9a25)) + +* fixed pickle backwards compatibility in tutorial ([`3a97a04`](https://github.com/jbloomAus/SAELens/commit/3a97a04097b9a0f480268661e38904926853cac3)) + + ## v0.1.0 (2024-04-06) ### Feature -* feat: release ([`c70b148`](https://github.com/jbloomAus/mats_sae_training/commit/c70b148fd49f880d424adb62a0817e03f2aacb44)) +* feat: release ([`c70b148`](https://github.com/jbloomAus/SAELens/commit/c70b148fd49f880d424adb62a0817e03f2aacb44)) ### Fix -* fix: removing paths-ignore from action to avoid blocking releases ([`28ff797`](https://github.com/jbloomAus/mats_sae_training/commit/28ff7971aeeba2d90d544c0b6a6fb7b743780ef4)) +* fix: removing paths-ignore from action to avoid blocking releases ([`28ff797`](https://github.com/jbloomAus/SAELens/commit/28ff7971aeeba2d90d544c0b6a6fb7b743780ef4)) -* fix: updating saevis version to use pypi ([`dbd96a2`](https://github.com/jbloomAus/mats_sae_training/commit/dbd96a2ef5d10012bfec43347b3ee920e59cdf9c)) +* fix: updating saevis version to use pypi ([`dbd96a2`](https://github.com/jbloomAus/SAELens/commit/dbd96a2ef5d10012bfec43347b3ee920e59cdf9c)) ### Unknown * Merge pull request #69 from chanind/remove-ci-ignore -fix: removing paths-ignore from action to avoid blocking releases ([`179cea1`](https://github.com/jbloomAus/mats_sae_training/commit/179cea190e8ae1a54659e325b8700d8d88026180)) +fix: removing paths-ignore from action to avoid blocking releases ([`179cea1`](https://github.com/jbloomAus/SAELens/commit/179cea190e8ae1a54659e325b8700d8d88026180)) -* Update README.md ([`1720ce8`](https://github.com/jbloomAus/mats_sae_training/commit/1720ce827f31b7ef769bd85345b9f1b996d9c3a8)) +* Update README.md ([`1720ce8`](https://github.com/jbloomAus/SAELens/commit/1720ce827f31b7ef769bd85345b9f1b996d9c3a8)) * Merge pull request #68 from chanind/updating-sae-vis -fix: hotfix updating saevis version to use pypi ([`a13cee3`](https://github.com/jbloomAus/mats_sae_training/commit/a13cee3b716624219523294a51b5e3655ad9630c)) +fix: hotfix updating saevis version to use pypi ([`a13cee3`](https://github.com/jbloomAus/SAELens/commit/a13cee3b716624219523294a51b5e3655ad9630c)) ## v0.0.0 (2024-04-06) ### Chore -* chore: adding more tests to ActivationsStore + light refactoring ([`cc9899c`](https://github.com/jbloomAus/mats_sae_training/commit/cc9899c7c7ecfa734bdb9260b80878092a0b3d4f)) +* chore: adding more tests to ActivationsStore + light refactoring ([`cc9899c`](https://github.com/jbloomAus/SAELens/commit/cc9899c7c7ecfa734bdb9260b80878092a0b3d4f)) -* chore: running isort to fix imports ([`53853b9`](https://github.com/jbloomAus/mats_sae_training/commit/53853b94c2809fe7145c92b6e6e68035ca5f2fe9)) +* chore: running isort to fix imports ([`53853b9`](https://github.com/jbloomAus/SAELens/commit/53853b94c2809fe7145c92b6e6e68035ca5f2fe9)) -* chore: setting up pyright type checking and fixing typing errors ([`351995c`](https://github.com/jbloomAus/mats_sae_training/commit/351995c1e0735073dd08a7fb0344cf0991d4a5a2)) +* chore: setting up pyright type checking and fixing typing errors ([`351995c`](https://github.com/jbloomAus/SAELens/commit/351995c1e0735073dd08a7fb0344cf0991d4a5a2)) -* chore: enable full flake8 default rules list ([`19886e2`](https://github.com/jbloomAus/mats_sae_training/commit/19886e28ed8358e787e1dad9b1dcfa5cf3dba7b5)) +* chore: enable full flake8 default rules list ([`19886e2`](https://github.com/jbloomAus/SAELens/commit/19886e28ed8358e787e1dad9b1dcfa5cf3dba7b5)) -* chore: using poetry for dependency management ([`465e003`](https://github.com/jbloomAus/mats_sae_training/commit/465e00333b9fae79ce73c277e7e1018e863002ca)) +* chore: using poetry for dependency management ([`465e003`](https://github.com/jbloomAus/SAELens/commit/465e00333b9fae79ce73c277e7e1018e863002ca)) -* chore: removing .DS_Store files ([`32f09b6`](https://github.com/jbloomAus/mats_sae_training/commit/32f09b67e68b116ee546da16059b34f8b0db23fa)) +* chore: removing .DS_Store files ([`32f09b6`](https://github.com/jbloomAus/SAELens/commit/32f09b67e68b116ee546da16059b34f8b0db23fa)) ### Unknown * Merge pull request #66 from chanind/pypi -feat: setting up sae_lens package and auto-deploy with semantic-release ([`34633e8`](https://github.com/jbloomAus/mats_sae_training/commit/34633e892624808fe1f98048beb63939e610e3af)) +feat: setting up sae_lens package and auto-deploy with semantic-release ([`34633e8`](https://github.com/jbloomAus/SAELens/commit/34633e892624808fe1f98048beb63939e610e3af)) -* Merge branch 'main' into pypi ([`3ce7f99`](https://github.com/jbloomAus/mats_sae_training/commit/3ce7f99fe7a6be2d87fb3fff484d6915419c0dcb)) +* Merge branch 'main' into pypi ([`3ce7f99`](https://github.com/jbloomAus/SAELens/commit/3ce7f99fe7a6be2d87fb3fff484d6915419c0dcb)) * Merge pull request #60 from chanind/improve-config-typing -fixing config typing ([`b8fba4f`](https://github.com/jbloomAus/mats_sae_training/commit/b8fba4fce043a41e3b997fae2d067eafc6264268)) +fixing config typing ([`b8fba4f`](https://github.com/jbloomAus/SAELens/commit/b8fba4fce043a41e3b997fae2d067eafc6264268)) -* setting up sae_lens package and auto-deploy with semantic-release ([`ba41f32`](https://github.com/jbloomAus/mats_sae_training/commit/ba41f327364901c40c6613a300e1ceaafe67e8fa)) +* setting up sae_lens package and auto-deploy with semantic-release ([`ba41f32`](https://github.com/jbloomAus/SAELens/commit/ba41f327364901c40c6613a300e1ceaafe67e8fa)) * fixing config typing -switch to using explicit params for ActivationsStore config instead of RunnerConfig base class ([`9be3445`](https://github.com/jbloomAus/mats_sae_training/commit/9be344512f15c24f5106b1e43200ea49b8c33981)) +switch to using explicit params for ActivationsStore config instead of RunnerConfig base class ([`9be3445`](https://github.com/jbloomAus/SAELens/commit/9be344512f15c24f5106b1e43200ea49b8c33981)) * Merge pull request #65 from chanind/fix-forgotten-scheduler-opts -passing accidentally overlooked scheduler opts ([`773bc02`](https://github.com/jbloomAus/mats_sae_training/commit/773bc02e6a3b5f2a2c45e156c7f236485cdd79fe)) +passing accidentally overlooked scheduler opts ([`773bc02`](https://github.com/jbloomAus/SAELens/commit/773bc02e6a3b5f2a2c45e156c7f236485cdd79fe)) -* passing accidentally overlooked scheduler opts ([`ad089b7`](https://github.com/jbloomAus/mats_sae_training/commit/ad089b7c0402d0cf4d1b8a37c597e9551b93b55e)) +* passing accidentally overlooked scheduler opts ([`ad089b7`](https://github.com/jbloomAus/SAELens/commit/ad089b7c0402d0cf4d1b8a37c597e9551b93b55e)) * Merge pull request #64 from chanind/lr-decay -adding lr_decay_steps and refactoring get_scheduler ([`c960d99`](https://github.com/jbloomAus/mats_sae_training/commit/c960d99ab9465fc86793c554d3ed810e9eb226ee)) +adding lr_decay_steps and refactoring get_scheduler ([`c960d99`](https://github.com/jbloomAus/SAELens/commit/c960d99ab9465fc86793c554d3ed810e9eb226ee)) -* adding lr_decay_steps and refactoring get_scheduler ([`fd5448c`](https://github.com/jbloomAus/mats_sae_training/commit/fd5448c5a016a8134a881732f6c61cd7a37eab67)) +* adding lr_decay_steps and refactoring get_scheduler ([`fd5448c`](https://github.com/jbloomAus/SAELens/commit/fd5448c5a016a8134a881732f6c61cd7a37eab67)) * Merge pull request #53 from hijohnnylin/neuronpedia_runner -Generate and upload Neuronpedia artifacts ([`0b94f84`](https://github.com/jbloomAus/mats_sae_training/commit/0b94f845739884146faead573f484cd1bc6d8737)) +Generate and upload Neuronpedia artifacts ([`0b94f84`](https://github.com/jbloomAus/SAELens/commit/0b94f845739884146faead573f484cd1bc6d8737)) -* format ([`792c7cb`](https://github.com/jbloomAus/mats_sae_training/commit/792c7cb235faace99acfff2416a29a88363ca245)) +* format ([`792c7cb`](https://github.com/jbloomAus/SAELens/commit/792c7cb235faace99acfff2416a29a88363ca245)) -* ignore type incorrectness in imported package ([`5fe83a9`](https://github.com/jbloomAus/mats_sae_training/commit/5fe83a9b09238201219b65df6b70ce074fed68eb)) +* ignore type incorrectness in imported package ([`5fe83a9`](https://github.com/jbloomAus/SAELens/commit/5fe83a9b09238201219b65df6b70ce074fed68eb)) * Merge pull request #63 from chanind/remove-eindex -removing unused eindex depencency ([`1ce44d7`](https://github.com/jbloomAus/mats_sae_training/commit/1ce44d7e3a3c51e013a68b0d7d82349da0ff8edf)) +removing unused eindex depencency ([`1ce44d7`](https://github.com/jbloomAus/SAELens/commit/1ce44d7e3a3c51e013a68b0d7d82349da0ff8edf)) -* removing unused eindex depencency ([`7cf991b`](https://github.com/jbloomAus/mats_sae_training/commit/7cf991ba534c6d9b5ea8a96cc1c4622183b211f9)) +* removing unused eindex depencency ([`7cf991b`](https://github.com/jbloomAus/SAELens/commit/7cf991ba534c6d9b5ea8a96cc1c4622183b211f9)) -* Safe to_str_tokens, fix memory issues ([`901b888`](https://github.com/jbloomAus/mats_sae_training/commit/901b888c98df7de77626adfbc31e96298585958e)) +* Safe to_str_tokens, fix memory issues ([`901b888`](https://github.com/jbloomAus/SAELens/commit/901b888c98df7de77626adfbc31e96298585958e)) -* Allow starting neuronpedia generation at a specific batch numbe ([`85d8f57`](https://github.com/jbloomAus/mats_sae_training/commit/85d8f578839687159b0c0ba6b1879746a23f3288)) +* Allow starting neuronpedia generation at a specific batch numbe ([`85d8f57`](https://github.com/jbloomAus/SAELens/commit/85d8f578839687159b0c0ba6b1879746a23f3288)) -* FIX: Linting 'do not use except' ([`ce3d40c`](https://github.com/jbloomAus/mats_sae_training/commit/ce3d40c61ceb1a1e7fd5c598371a3386f62a39d8)) +* FIX: Linting 'do not use except' ([`ce3d40c`](https://github.com/jbloomAus/SAELens/commit/ce3d40c61ceb1a1e7fd5c598371a3386f62a39d8)) -* Fix vocab: Ċ should be line break. Also set left and right buffers ([`205b1c1`](https://github.com/jbloomAus/mats_sae_training/commit/205b1c18fba46ef0b4629846bc4dc224c34bc2f6)) +* Fix vocab: Ċ should be line break. Also set left and right buffers ([`205b1c1`](https://github.com/jbloomAus/SAELens/commit/205b1c18fba46ef0b4629846bc4dc224c34bc2f6)) -* Merge ([`b159010`](https://github.com/jbloomAus/mats_sae_training/commit/b1590102bceb0d859d00a65fa68925e1a1ce2341)) +* Merge ([`b159010`](https://github.com/jbloomAus/SAELens/commit/b1590102bceb0d859d00a65fa68925e1a1ce2341)) -* Update Neuronpedia Runner ([`885de27`](https://github.com/jbloomAus/mats_sae_training/commit/885de2770279ed4188042985a500c3c393c57ced)) +* Update Neuronpedia Runner ([`885de27`](https://github.com/jbloomAus/SAELens/commit/885de2770279ed4188042985a500c3c393c57ced)) * Merge pull request #58 from canrager/main -Make prepend BOS optional: Default True ([`48a07f9`](https://github.com/jbloomAus/mats_sae_training/commit/48a07f928ec569bba5502af379c789747e15a9e0)) +Make prepend BOS optional: Default True ([`48a07f9`](https://github.com/jbloomAus/SAELens/commit/48a07f928ec569bba5502af379c789747e15a9e0)) -* make tests pass with use_bos flag ([`618d4bb`](https://github.com/jbloomAus/mats_sae_training/commit/618d4bbdbaf8fed344886eb292e2f5423f98fbbc)) +* make tests pass with use_bos flag ([`618d4bb`](https://github.com/jbloomAus/SAELens/commit/618d4bbdbaf8fed344886eb292e2f5423f98fbbc)) * Merge pull request #59 from chanind/fix-docs-deploy -attempting to fix docs deploy ([`cfafbe7`](https://github.com/jbloomAus/mats_sae_training/commit/cfafbe75026104d707c21d82efddd988bd1a3833)) +attempting to fix docs deploy ([`cfafbe7`](https://github.com/jbloomAus/SAELens/commit/cfafbe75026104d707c21d82efddd988bd1a3833)) -* force docs push ([`3aa179d`](https://github.com/jbloomAus/mats_sae_training/commit/3aa179d350d3602d873ebd5fa585d0a9c7475c79)) +* force docs push ([`3aa179d`](https://github.com/jbloomAus/SAELens/commit/3aa179d350d3602d873ebd5fa585d0a9c7475c79)) -* ignore type eror ([`e87198b`](https://github.com/jbloomAus/mats_sae_training/commit/e87198bba4c65a77be2e7c1b072e563c9497c9d0)) +* ignore type eror ([`e87198b`](https://github.com/jbloomAus/SAELens/commit/e87198bba4c65a77be2e7c1b072e563c9497c9d0)) -* format ([`67dfb46`](https://github.com/jbloomAus/mats_sae_training/commit/67dfb4685ba293db2266b1b4885964e2313104f9)) +* format ([`67dfb46`](https://github.com/jbloomAus/SAELens/commit/67dfb4685ba293db2266b1b4885964e2313104f9)) -* attempting to fix docs deploy ([`cda8ece`](https://github.com/jbloomAus/mats_sae_training/commit/cda8eceb0a8d356fd4f4b461a2fb473ab0071a1d)) +* attempting to fix docs deploy ([`cda8ece`](https://github.com/jbloomAus/SAELens/commit/cda8eceb0a8d356fd4f4b461a2fb473ab0071a1d)) -* Merge branch 'main' of https://github.com/jbloomAus/mats_sae_training into main ([`8aadcd3`](https://github.com/jbloomAus/mats_sae_training/commit/8aadcd3171748ecf0ff759c67522d04ac0f3c3a1)) +* Merge branch 'main' of https://github.com/jbloomAus/mats_sae_training into main ([`8aadcd3`](https://github.com/jbloomAus/SAELens/commit/8aadcd3171748ecf0ff759c67522d04ac0f3c3a1)) -* add prepend bos flag ([`c0b29cc`](https://github.com/jbloomAus/mats_sae_training/commit/c0b29cc5037bfeea88888f2bb90ebd2ecdf2ed05)) +* add prepend bos flag ([`c0b29cc`](https://github.com/jbloomAus/SAELens/commit/c0b29cc5037bfeea88888f2bb90ebd2ecdf2ed05)) -* fix attn out on run evals ([`02fa90b`](https://github.com/jbloomAus/mats_sae_training/commit/02fa90be6c70cfc93f13262462d3ec4cc86641b8)) +* fix attn out on run evals ([`02fa90b`](https://github.com/jbloomAus/SAELens/commit/02fa90be6c70cfc93f13262462d3ec4cc86641b8)) * Merge pull request #57 from chanind/optim-tests -Adding tests to get_scheduler ([`13c8085`](https://github.com/jbloomAus/mats_sae_training/commit/13c8085e1becb654e50fe69435a3f5814f0d2145)) +Adding tests to get_scheduler ([`13c8085`](https://github.com/jbloomAus/SAELens/commit/13c8085e1becb654e50fe69435a3f5814f0d2145)) * Merge pull request #56 from chanind/sae-tests -minor refactoring to SAE and adding tests ([`2c425ca`](https://github.com/jbloomAus/mats_sae_training/commit/2c425cabf7718c16eb62146972f389d2d5ff2a32)) +minor refactoring to SAE and adding tests ([`2c425ca`](https://github.com/jbloomAus/SAELens/commit/2c425cabf7718c16eb62146972f389d2d5ff2a32)) -* minor refactoring to SAE and adding tests ([`92a98dd`](https://github.com/jbloomAus/mats_sae_training/commit/92a98ddf8e6eba0b4cd4ac2e2350281f9ed2e2d0)) +* minor refactoring to SAE and adding tests ([`92a98dd`](https://github.com/jbloomAus/SAELens/commit/92a98ddf8e6eba0b4cd4ac2e2350281f9ed2e2d0)) -* adding tests to get_scheduler ([`3b7e173`](https://github.com/jbloomAus/mats_sae_training/commit/3b7e17368c7abfac2747f6fa8e3e03650914cfe5)) +* adding tests to get_scheduler ([`3b7e173`](https://github.com/jbloomAus/SAELens/commit/3b7e17368c7abfac2747f6fa8e3e03650914cfe5)) -* Generate and upload Neuronpedia artifacts ([`b52e0e2`](https://github.com/jbloomAus/mats_sae_training/commit/b52e0e2c427a4b74bebff5d94961725148c404f0)) +* Generate and upload Neuronpedia artifacts ([`b52e0e2`](https://github.com/jbloomAus/SAELens/commit/b52e0e2c427a4b74bebff5d94961725148c404f0)) * Merge pull request #54 from jbloomAus/hook_z_suppourt -notional support, needs more thorough testing ([`277f35b`](https://github.com/jbloomAus/mats_sae_training/commit/277f35b35b2b8f6475b5b8ae9f15356b168e9f50)) +notional support, needs more thorough testing ([`277f35b`](https://github.com/jbloomAus/SAELens/commit/277f35b35b2b8f6475b5b8ae9f15356b168e9f50)) * Merge pull request #55 from chanind/contributing-docs -adding a contribution guide to docs ([`8ac8f05`](https://github.com/jbloomAus/mats_sae_training/commit/8ac8f051c2686169f5143582ace162f7f337af50)) +adding a contribution guide to docs ([`8ac8f05`](https://github.com/jbloomAus/SAELens/commit/8ac8f051c2686169f5143582ace162f7f337af50)) -* adding a contribution guide to docs ([`693c5b3`](https://github.com/jbloomAus/mats_sae_training/commit/693c5b335527630dc246ebc55519008f87465913)) +* adding a contribution guide to docs ([`693c5b3`](https://github.com/jbloomAus/SAELens/commit/693c5b335527630dc246ebc55519008f87465913)) -* notional support, needs more thorough testing ([`9585022`](https://github.com/jbloomAus/mats_sae_training/commit/9585022366677c66d6cdbedd82d10711d1001fef)) +* notional support, needs more thorough testing ([`9585022`](https://github.com/jbloomAus/SAELens/commit/9585022366677c66d6cdbedd82d10711d1001fef)) -* Generate and upload Neuronpedia artifacts ([`4540268`](https://github.com/jbloomAus/mats_sae_training/commit/45402681bc4051bdbc160cd018c8c73ed4c7321f)) +* Generate and upload Neuronpedia artifacts ([`4540268`](https://github.com/jbloomAus/SAELens/commit/45402681bc4051bdbc160cd018c8c73ed4c7321f)) * Merge pull request #52 from hijohnnylin/fix_db_runner_assert -FIX: Don't check wandb assert if not using wandb ([`5c48811`](https://github.com/jbloomAus/mats_sae_training/commit/5c488116a0e5096e9e37d6d714dbd7a3835cb6fe)) +FIX: Don't check wandb assert if not using wandb ([`5c48811`](https://github.com/jbloomAus/SAELens/commit/5c488116a0e5096e9e37d6d714dbd7a3835cb6fe)) -* FIX: Don't check wandb assert if not using wandb ([`1adefda`](https://github.com/jbloomAus/mats_sae_training/commit/1adefda8f5c2f07c468ce63b10ba608faefc0877)) +* FIX: Don't check wandb assert if not using wandb ([`1adefda`](https://github.com/jbloomAus/SAELens/commit/1adefda8f5c2f07c468ce63b10ba608faefc0877)) -* add docs badge ([`f623ed1`](https://github.com/jbloomAus/mats_sae_training/commit/f623ed1b57f8753af3c995b68443b2459b258b42)) +* add docs badge ([`f623ed1`](https://github.com/jbloomAus/SAELens/commit/f623ed1b57f8753af3c995b68443b2459b258b42)) -* try to get correct deployment ([`777dd6c`](https://github.com/jbloomAus/mats_sae_training/commit/777dd6cfc57b3a8cd5e1d5056298d253ccb49d9f)) +* try to get correct deployment ([`777dd6c`](https://github.com/jbloomAus/SAELens/commit/777dd6cfc57b3a8cd5e1d5056298d253ccb49d9f)) * Merge pull request #51 from jbloomAus/mkdocs -Add Docs to the project. ([`d2ebbd7`](https://github.com/jbloomAus/mats_sae_training/commit/d2ebbd7b8ad1468da97f3e407934b8dd8e9e3f2f)) +Add Docs to the project. ([`d2ebbd7`](https://github.com/jbloomAus/SAELens/commit/d2ebbd7b8ad1468da97f3e407934b8dd8e9e3f2f)) -* mkdocs, test ([`9f14250`](https://github.com/jbloomAus/mats_sae_training/commit/9f142509da4e1a63ffc88a32a692dc5218aad96d)) +* mkdocs, test ([`9f14250`](https://github.com/jbloomAus/SAELens/commit/9f142509da4e1a63ffc88a32a692dc5218aad96d)) -* code cov ([`2ae6224`](https://github.com/jbloomAus/mats_sae_training/commit/2ae62243172088b309e40b3b4fca0ce43c08f19a)) +* code cov ([`2ae6224`](https://github.com/jbloomAus/SAELens/commit/2ae62243172088b309e40b3b4fca0ce43c08f19a)) * Merge pull request #48 from chanind/fix-sae-vis-version -Pin sae_vis to previous working version ([`3f8a30b`](https://github.com/jbloomAus/mats_sae_training/commit/3f8a30bf577efdaae717f1c6b930be35c9ad7883)) +Pin sae_vis to previous working version ([`3f8a30b`](https://github.com/jbloomAus/SAELens/commit/3f8a30bf577efdaae717f1c6b930be35c9ad7883)) -* fix suffix issue ([`209ba13`](https://github.com/jbloomAus/mats_sae_training/commit/209ba1324a775c30a0a1fc2941d0fdb46271ac53)) +* fix suffix issue ([`209ba13`](https://github.com/jbloomAus/SAELens/commit/209ba1324a775c30a0a1fc2941d0fdb46271ac53)) -* pin sae_vis to previous working version ([`ae0002a`](https://github.com/jbloomAus/mats_sae_training/commit/ae0002a67838ce33474d2697dce20d47356f8abb)) +* pin sae_vis to previous working version ([`ae0002a`](https://github.com/jbloomAus/SAELens/commit/ae0002a67838ce33474d2697dce20d47356f8abb)) -* don't ignore changes to .github ([`35fdeec`](https://github.com/jbloomAus/mats_sae_training/commit/35fdeec3d3c1e31c2eb884060e7550a8d5fa9b22)) +* don't ignore changes to .github ([`35fdeec`](https://github.com/jbloomAus/SAELens/commit/35fdeec3d3c1e31c2eb884060e7550a8d5fa9b22)) -* add cov report ([`971d497`](https://github.com/jbloomAus/mats_sae_training/commit/971d4979193efe3f30cc3d580df648a413fd16b2)) +* add cov report ([`971d497`](https://github.com/jbloomAus/SAELens/commit/971d4979193efe3f30cc3d580df648a413fd16b2)) * Merge pull request #40 from chanind/refactor-train-sae -Refactor train SAE and adding unit tests ([`5aa0b11`](https://github.com/jbloomAus/mats_sae_training/commit/5aa0b1121112024e44dc5a2d02e33e532b741ed9)) +Refactor train SAE and adding unit tests ([`5aa0b11`](https://github.com/jbloomAus/SAELens/commit/5aa0b1121112024e44dc5a2d02e33e532b741ed9)) -* Merge branch 'main' into refactor-train-sae ([`0acdcb3`](https://github.com/jbloomAus/mats_sae_training/commit/0acdcb395aaa42e0344191e680a4f521af6d430c)) +* Merge branch 'main' into refactor-train-sae ([`0acdcb3`](https://github.com/jbloomAus/SAELens/commit/0acdcb395aaa42e0344191e680a4f521af6d430c)) * Merge pull request #41 from jbloomAus/move_to_sae_vis -Move to sae vis ([`bcb9a52`](https://github.com/jbloomAus/mats_sae_training/commit/bcb9a5207339528d55d5c89640eec0b1a9c449cb)) +Move to sae vis ([`bcb9a52`](https://github.com/jbloomAus/SAELens/commit/bcb9a5207339528d55d5c89640eec0b1a9c449cb)) -* flake8 can ignore imports, we're using isort anyway ([`6b7ae72`](https://github.com/jbloomAus/mats_sae_training/commit/6b7ae72cd324929c7b53d4c1043ae422d8eec81d)) +* flake8 can ignore imports, we're using isort anyway ([`6b7ae72`](https://github.com/jbloomAus/SAELens/commit/6b7ae72cd324929c7b53d4c1043ae422d8eec81d)) -* format ([`af680e2`](https://github.com/jbloomAus/mats_sae_training/commit/af680e28e0978583758d7d584ff663f99482fdce)) +* format ([`af680e2`](https://github.com/jbloomAus/SAELens/commit/af680e28e0978583758d7d584ff663f99482fdce)) -* fix mps bug ([`e7b238f`](https://github.com/jbloomAus/mats_sae_training/commit/e7b238fd4813b76c647468949603b90fc37c1d8d)) +* fix mps bug ([`e7b238f`](https://github.com/jbloomAus/SAELens/commit/e7b238fd4813b76c647468949603b90fc37c1d8d)) -* more tests ([`01978e6`](https://github.com/jbloomAus/mats_sae_training/commit/01978e6dd141c269c403a4e84f5de2fb26af6057)) +* more tests ([`01978e6`](https://github.com/jbloomAus/SAELens/commit/01978e6dd141c269c403a4e84f5de2fb26af6057)) -* wip ([`4c03b3d`](https://github.com/jbloomAus/mats_sae_training/commit/4c03b3d3c4a89275d7b08d62d0c9ae18e0b865db)) +* wip ([`4c03b3d`](https://github.com/jbloomAus/SAELens/commit/4c03b3d3c4a89275d7b08d62d0c9ae18e0b865db)) -* more tests ([`7c1cb6b`](https://github.com/jbloomAus/mats_sae_training/commit/7c1cb6b827e1e455e715cf9aa1f6c82e687eaf2e)) +* more tests ([`7c1cb6b`](https://github.com/jbloomAus/SAELens/commit/7c1cb6b827e1e455e715cf9aa1f6c82e687eaf2e)) -* testing that sparsity counts get updated correctly ([`5b5d653`](https://github.com/jbloomAus/mats_sae_training/commit/5b5d6538c90d11fcc040ff05085642066c799518)) +* testing that sparsity counts get updated correctly ([`5b5d653`](https://github.com/jbloomAus/SAELens/commit/5b5d6538c90d11fcc040ff05085642066c799518)) -* adding some unit tests to _train_step() ([`dbf3f01`](https://github.com/jbloomAus/mats_sae_training/commit/dbf3f01ffb5ad3c2f403c6a81f7eab5c4646100c)) +* adding some unit tests to _train_step() ([`dbf3f01`](https://github.com/jbloomAus/SAELens/commit/dbf3f01ffb5ad3c2f403c6a81f7eab5c4646100c)) -* Merge branch 'main' into refactor-train-sae ([`2d5ec98`](https://github.com/jbloomAus/mats_sae_training/commit/2d5ec98164dd4fde0c34c0e966e78b9a3848b842)) +* Merge branch 'main' into refactor-train-sae ([`2d5ec98`](https://github.com/jbloomAus/SAELens/commit/2d5ec98164dd4fde0c34c0e966e78b9a3848b842)) -* Update README.md ([`d148b6a`](https://github.com/jbloomAus/mats_sae_training/commit/d148b6a2ad77e546b433cf2c3c5f9993f04228ba)) +* Update README.md ([`d148b6a`](https://github.com/jbloomAus/SAELens/commit/d148b6a2ad77e546b433cf2c3c5f9993f04228ba)) * Merge pull request #20 from chanind/activations_store_tests -chore: adding more tests to ActivationsStore + light refactoring ([`69dcf8e`](https://github.com/jbloomAus/mats_sae_training/commit/69dcf8e551415f6a3884a509ab4428818ad35936)) +chore: adding more tests to ActivationsStore + light refactoring ([`69dcf8e`](https://github.com/jbloomAus/SAELens/commit/69dcf8e551415f6a3884a509ab4428818ad35936)) -* Merge branch 'main' into activations_store_tests ([`4896d0a`](https://github.com/jbloomAus/mats_sae_training/commit/4896d0a4a850551d4e7bc91358adc7b98b7fc705)) +* Merge branch 'main' into activations_store_tests ([`4896d0a`](https://github.com/jbloomAus/SAELens/commit/4896d0a4a850551d4e7bc91358adc7b98b7fc705)) -* refactoring train_sae_on_language_model.py into smaller functions ([`e75a15d`](https://github.com/jbloomAus/mats_sae_training/commit/e75a15d3acb4b02a435d70b25a67e8103c9196b3)) +* refactoring train_sae_on_language_model.py into smaller functions ([`e75a15d`](https://github.com/jbloomAus/SAELens/commit/e75a15d3acb4b02a435d70b25a67e8103c9196b3)) -* suppourt apollo pretokenized datasets ([`e814054`](https://github.com/jbloomAus/mats_sae_training/commit/e814054a5693361ba51c9aed422b3a8b9d6d0fe6)) +* suppourt apollo pretokenized datasets ([`e814054`](https://github.com/jbloomAus/SAELens/commit/e814054a5693361ba51c9aed422b3a8b9d6d0fe6)) -* handle saes saved before groups ([`5acd89b`](https://github.com/jbloomAus/mats_sae_training/commit/5acd89b02c02fe43fae5cea9d1dadeff41424069)) +* handle saes saved before groups ([`5acd89b`](https://github.com/jbloomAus/SAELens/commit/5acd89b02c02fe43fae5cea9d1dadeff41424069)) -* typechecker ([`fa6cc49`](https://github.com/jbloomAus/mats_sae_training/commit/fa6cc4902f25843e1f7ae8f6b9841ecd4f074e9b)) +* typechecker ([`fa6cc49`](https://github.com/jbloomAus/SAELens/commit/fa6cc4902f25843e1f7ae8f6b9841ecd4f074e9b)) -* fix geom median bug ([`8d4a080`](https://github.com/jbloomAus/mats_sae_training/commit/8d4a080c404f3fa7c16e95e72d0be3b3a7812d0c)) +* fix geom median bug ([`8d4a080`](https://github.com/jbloomAus/SAELens/commit/8d4a080c404f3fa7c16e95e72d0be3b3a7812d0c)) -* remove references to old code ([`861151f`](https://github.com/jbloomAus/mats_sae_training/commit/861151fd04c5fc8963396f655c08818bcff08d1c)) +* remove references to old code ([`861151f`](https://github.com/jbloomAus/SAELens/commit/861151fd04c5fc8963396f655c08818bcff08d1c)) -* remove old geom median code ([`05e0aca`](https://github.com/jbloomAus/mats_sae_training/commit/05e0acafc690b7f6e08f8a8c62989afb21b7d1f1)) +* remove old geom median code ([`05e0aca`](https://github.com/jbloomAus/SAELens/commit/05e0acafc690b7f6e08f8a8c62989afb21b7d1f1)) * Merge pull request #22 from themachinefan/faster_geometric_median -Faster geometric median. ([`341c49a`](https://github.com/jbloomAus/mats_sae_training/commit/341c49ac91b8876b020c9a22c5ea3c0b7ea74b3e)) +Faster geometric median. ([`341c49a`](https://github.com/jbloomAus/SAELens/commit/341c49ac91b8876b020c9a22c5ea3c0b7ea74b3e)) -* makefile check type and types of geometric media ([`736bf83`](https://github.com/jbloomAus/mats_sae_training/commit/736bf83842f2e4be9baeee6ac12731b9b4834fc6)) +* makefile check type and types of geometric media ([`736bf83`](https://github.com/jbloomAus/SAELens/commit/736bf83842f2e4be9baeee6ac12731b9b4834fc6)) * Merge pull request #21 from schmatz/fix-dashboard-image -Fix broken dashboard image on README ([`eb90cc9`](https://github.com/jbloomAus/mats_sae_training/commit/eb90cc9d835da6febe4a4c5830a134237e3705eb)) +Fix broken dashboard image on README ([`eb90cc9`](https://github.com/jbloomAus/SAELens/commit/eb90cc9d835da6febe4a4c5830a134237e3705eb)) * Merge pull request #24 from neelnanda-io/add-post-link -Added link to AF post ([`39f8d3d`](https://github.com/jbloomAus/mats_sae_training/commit/39f8d3d8ff5659c9815f9a1ff3be7bf81370813c)) +Added link to AF post ([`39f8d3d`](https://github.com/jbloomAus/SAELens/commit/39f8d3d8ff5659c9815f9a1ff3be7bf81370813c)) -* Added link to AF post ([`f0da9ea`](https://github.com/jbloomAus/mats_sae_training/commit/f0da9ea8af61239c22de5b5778e9026f834fc929)) +* Added link to AF post ([`f0da9ea`](https://github.com/jbloomAus/SAELens/commit/f0da9ea8af61239c22de5b5778e9026f834fc929)) -* formatting ([`0168612`](https://github.com/jbloomAus/mats_sae_training/commit/016861243e11bc1b0f0fe1414bcde6c0a5eec334)) +* formatting ([`0168612`](https://github.com/jbloomAus/SAELens/commit/016861243e11bc1b0f0fe1414bcde6c0a5eec334)) -* use device, don't use cuda if not there ([`20334cb`](https://github.com/jbloomAus/mats_sae_training/commit/20334cb26bee5ddcc2ae6632b18a1f1933ab50f0)) +* use device, don't use cuda if not there ([`20334cb`](https://github.com/jbloomAus/SAELens/commit/20334cb26bee5ddcc2ae6632b18a1f1933ab50f0)) -* format ([`ce49658`](https://github.com/jbloomAus/mats_sae_training/commit/ce496583e397a71cec0ae376f9920e981417c0e8)) +* format ([`ce49658`](https://github.com/jbloomAus/SAELens/commit/ce496583e397a71cec0ae376f9920e981417c0e8)) -* fix tsea typing ([`449d90f`](https://github.com/jbloomAus/mats_sae_training/commit/449d90ffc54a548432da6aca3e47651bebfbf585)) +* fix tsea typing ([`449d90f`](https://github.com/jbloomAus/SAELens/commit/449d90ffc54a548432da6aca3e47651bebfbf585)) -* faster geometric median. Run geometric_median,py to test. ([`92cad26`](https://github.com/jbloomAus/mats_sae_training/commit/92cad26b4dc0fdd03d73a4a8dc9b1d5844308eb8)) +* faster geometric median. Run geometric_median,py to test. ([`92cad26`](https://github.com/jbloomAus/SAELens/commit/92cad26b4dc0fdd03d73a4a8dc9b1d5844308eb8)) -* Fix dashboard image ([`6358862`](https://github.com/jbloomAus/mats_sae_training/commit/6358862f1f52961bd444feb1b22a7dde27235021)) +* Fix dashboard image ([`6358862`](https://github.com/jbloomAus/SAELens/commit/6358862f1f52961bd444feb1b22a7dde27235021)) -* fix incorrect code used to avoid typing issue ([`ed0b0ea`](https://github.com/jbloomAus/mats_sae_training/commit/ed0b0eafdd7424bbcc83c39d400928536bba3378)) +* fix incorrect code used to avoid typing issue ([`ed0b0ea`](https://github.com/jbloomAus/SAELens/commit/ed0b0eafdd7424bbcc83c39d400928536bba3378)) -* add nltk ([`bc7e276`](https://github.com/jbloomAus/mats_sae_training/commit/bc7e2766109fd775292d8af1226b188bdec8d807)) +* add nltk ([`bc7e276`](https://github.com/jbloomAus/SAELens/commit/bc7e2766109fd775292d8af1226b188bdec8d807)) -* ignore various typing issues ([`6972c00`](https://github.com/jbloomAus/mats_sae_training/commit/6972c0016f1ce5a9b20960727fc7778a7655f833)) +* ignore various typing issues ([`6972c00`](https://github.com/jbloomAus/SAELens/commit/6972c0016f1ce5a9b20960727fc7778a7655f833)) -* add babe package ([`481069e`](https://github.com/jbloomAus/mats_sae_training/commit/481069ef129c37bfec9cd1cd94d43800c45ae969)) +* add babe package ([`481069e`](https://github.com/jbloomAus/SAELens/commit/481069ef129c37bfec9cd1cd94d43800c45ae969)) -* make formatter happy ([`612c7c7`](https://github.com/jbloomAus/mats_sae_training/commit/612c7c71ea5dfecc8f81bea3484976d82fb505ca)) +* make formatter happy ([`612c7c7`](https://github.com/jbloomAus/SAELens/commit/612c7c71ea5dfecc8f81bea3484976d82fb505ca)) -* share scatter so can link ([`9f88dc3`](https://github.com/jbloomAus/mats_sae_training/commit/9f88dc31dc01ce5b10a3772c013d99b9f9d4cf13)) +* share scatter so can link ([`9f88dc3`](https://github.com/jbloomAus/SAELens/commit/9f88dc31dc01ce5b10a3772c013d99b9f9d4cf13)) -* add_analysis_files_for_post ([`e75323c`](https://github.com/jbloomAus/mats_sae_training/commit/e75323c13ffda589b8c71ed9de5c7c1adb3d6ce7)) +* add_analysis_files_for_post ([`e75323c`](https://github.com/jbloomAus/SAELens/commit/e75323c13ffda589b8c71ed9de5c7c1adb3d6ce7)) -* don't block on isort linting ([`3949a46`](https://github.com/jbloomAus/mats_sae_training/commit/3949a4641c810c74f8ed093e92fabd31d8e2d02c)) +* don't block on isort linting ([`3949a46`](https://github.com/jbloomAus/SAELens/commit/3949a4641c810c74f8ed093e92fabd31d8e2d02c)) -* formatting ([`951a320`](https://github.com/jbloomAus/mats_sae_training/commit/951a320fdcb4e01c95bf2bac7a4929676562e323)) +* formatting ([`951a320`](https://github.com/jbloomAus/SAELens/commit/951a320fdcb4e01c95bf2bac7a4929676562e323)) -* Update README.md ([`b2478c1`](https://github.com/jbloomAus/mats_sae_training/commit/b2478c14d98cbfc2b0c2b0984d251f3c92d05a41)) +* Update README.md ([`b2478c1`](https://github.com/jbloomAus/SAELens/commit/b2478c14d98cbfc2b0c2b0984d251f3c92d05a41)) * Merge pull request #18 from chanind/type-checking -chore: setting up pyright type checking and fixing typing errors ([`bd5fc43`](https://github.com/jbloomAus/mats_sae_training/commit/bd5fc439e56fc1c9e0e6f9dd2613b77f57c763b2)) +chore: setting up pyright type checking and fixing typing errors ([`bd5fc43`](https://github.com/jbloomAus/SAELens/commit/bd5fc439e56fc1c9e0e6f9dd2613b77f57c763b2)) -* Merge branch 'main' into type-checking ([`57c4582`](https://github.com/jbloomAus/mats_sae_training/commit/57c4582c38906b9e6d887ceb25901de5734d76c9)) +* Merge branch 'main' into type-checking ([`57c4582`](https://github.com/jbloomAus/SAELens/commit/57c4582c38906b9e6d887ceb25901de5734d76c9)) * Merge pull request #17 from Benw8888/sae_group_pr -SAE Group for sweeps PR ([`3e78bce`](https://github.com/jbloomAus/mats_sae_training/commit/3e78bce5c4bccbb9555d7878dc48adf7cfd823cd)) +SAE Group for sweeps PR ([`3e78bce`](https://github.com/jbloomAus/SAELens/commit/3e78bce5c4bccbb9555d7878dc48adf7cfd823cd)) * Merge pull request #1 from chanind/sae_group_pr_isort_fix -chore: running isort to fix imports ([`dd24413`](https://github.com/jbloomAus/mats_sae_training/commit/dd24413749191fab0001f154a225eb991820a12b)) +chore: running isort to fix imports ([`dd24413`](https://github.com/jbloomAus/SAELens/commit/dd24413749191fab0001f154a225eb991820a12b)) -* black format ([`0ffcf21`](https://github.com/jbloomAus/mats_sae_training/commit/0ffcf21bbe73066cad152680255a25b73f98331c)) +* black format ([`0ffcf21`](https://github.com/jbloomAus/SAELens/commit/0ffcf21bbe73066cad152680255a25b73f98331c)) -* fixed expansion factor sweep ([`749b8cf`](https://github.com/jbloomAus/mats_sae_training/commit/749b8cff1a4ccb130a9ff6be698b766ae7642140)) +* fixed expansion factor sweep ([`749b8cf`](https://github.com/jbloomAus/SAELens/commit/749b8cff1a4ccb130a9ff6be698b766ae7642140)) -* remove tqdm from data loader, too noisy ([`de3b1a1`](https://github.com/jbloomAus/mats_sae_training/commit/de3b1a1fad3b176b2f547513374f0ff00b784975)) +* remove tqdm from data loader, too noisy ([`de3b1a1`](https://github.com/jbloomAus/SAELens/commit/de3b1a1fad3b176b2f547513374f0ff00b784975)) -* fix tests ([`b3054b1`](https://github.com/jbloomAus/mats_sae_training/commit/b3054b1e7cc938bee874db95a7ebfd911417a84a)) +* fix tests ([`b3054b1`](https://github.com/jbloomAus/SAELens/commit/b3054b1e7cc938bee874db95a7ebfd911417a84a)) -* don't calculate geom median unless you need to ([`d31bc31`](https://github.com/jbloomAus/mats_sae_training/commit/d31bc31689c84382044db1a8663df2d352674d7a)) +* don't calculate geom median unless you need to ([`d31bc31`](https://github.com/jbloomAus/SAELens/commit/d31bc31689c84382044db1a8663df2d352674d7a)) -* add to method ([`b3f6dc6`](https://github.com/jbloomAus/mats_sae_training/commit/b3f6dc6882ea052f6fecb2234d8ff4f6212184d7)) +* add to method ([`b3f6dc6`](https://github.com/jbloomAus/SAELens/commit/b3f6dc6882ea052f6fecb2234d8ff4f6212184d7)) -* flake8 and black ([`ed8345a`](https://github.com/jbloomAus/mats_sae_training/commit/ed8345ae4ade1445e7182f5c7deeb876203b6524)) +* flake8 and black ([`ed8345a`](https://github.com/jbloomAus/SAELens/commit/ed8345ae4ade1445e7182f5c7deeb876203b6524)) -* flake8 linter changes ([`8e41e59`](https://github.com/jbloomAus/mats_sae_training/commit/8e41e599d67a7d8eda644321eda8d0481f813f15)) +* flake8 linter changes ([`8e41e59`](https://github.com/jbloomAus/SAELens/commit/8e41e599d67a7d8eda644321eda8d0481f813f15)) -* Merge branch 'main' into sae_group_pr ([`082c813`](https://github.com/jbloomAus/mats_sae_training/commit/082c8138045557a592f57f53256d251f76902b4f)) +* Merge branch 'main' into sae_group_pr ([`082c813`](https://github.com/jbloomAus/SAELens/commit/082c8138045557a592f57f53256d251f76902b4f)) -* Delete evaluating.ipynb ([`d3cafa3`](https://github.com/jbloomAus/mats_sae_training/commit/d3cafa3f28029f4d12285a810df29e7de90311c2)) +* Delete evaluating.ipynb ([`d3cafa3`](https://github.com/jbloomAus/SAELens/commit/d3cafa3f28029f4d12285a810df29e7de90311c2)) -* Delete activation_storing.py ([`fa82992`](https://github.com/jbloomAus/mats_sae_training/commit/fa829924b0b5caab9c7653876a399732277c5d4e)) +* Delete activation_storing.py ([`fa82992`](https://github.com/jbloomAus/SAELens/commit/fa829924b0b5caab9c7653876a399732277c5d4e)) -* Delete lp_sae_training.py ([`0d1e1c9`](https://github.com/jbloomAus/mats_sae_training/commit/0d1e1c9a5f8b5c865f620bd97619cf108bf06796)) +* Delete lp_sae_training.py ([`0d1e1c9`](https://github.com/jbloomAus/SAELens/commit/0d1e1c9a5f8b5c865f620bd97619cf108bf06796)) -* implemented SAE groups ([`66facfe`](https://github.com/jbloomAus/mats_sae_training/commit/66facfef1094f311f00ee6a8abf53f62548802cf)) +* implemented SAE groups ([`66facfe`](https://github.com/jbloomAus/SAELens/commit/66facfef1094f311f00ee6a8abf53f62548802cf)) * Merge pull request #16 from chanind/flake-default-rules -chore: enable full flake8 default rules list ([`ad84706`](https://github.com/jbloomAus/mats_sae_training/commit/ad84706cc9d3d202669fa8175af9f181f323cd66)) +chore: enable full flake8 default rules list ([`ad84706`](https://github.com/jbloomAus/SAELens/commit/ad84706cc9d3d202669fa8175af9f181f323cd66)) -* implemented sweeping via config list ([`80f61fa`](https://github.com/jbloomAus/mats_sae_training/commit/80f61faac4dfdab27d03cec672641d898441a530)) +* implemented sweeping via config list ([`80f61fa`](https://github.com/jbloomAus/SAELens/commit/80f61faac4dfdab27d03cec672641d898441a530)) * Merge pull request #13 from chanind/poetry -chore: using poetry for dependency management ([`496f7b4`](https://github.com/jbloomAus/mats_sae_training/commit/496f7b4fa2d8835cec6673bec1ffa3d1f5fba268)) +chore: using poetry for dependency management ([`496f7b4`](https://github.com/jbloomAus/SAELens/commit/496f7b4fa2d8835cec6673bec1ffa3d1f5fba268)) -* progress on implementing multi-sae support ([`2ba2131`](https://github.com/jbloomAus/mats_sae_training/commit/2ba2131553b28af08d969304c2a2b68a8861fd98)) +* progress on implementing multi-sae support ([`2ba2131`](https://github.com/jbloomAus/SAELens/commit/2ba2131553b28af08d969304c2a2b68a8861fd98)) * Merge pull request #11 from lucyfarnik/fix-caching-shuffle-edge-case -Fixed edge case in activation cache shuffling ([`3727b5d`](https://github.com/jbloomAus/mats_sae_training/commit/3727b5d585efd3932fd49b540d11778c76bef561)) +Fixed edge case in activation cache shuffling ([`3727b5d`](https://github.com/jbloomAus/SAELens/commit/3727b5d585efd3932fd49b540d11778c76bef561)) * Merge pull request #12 from lucyfarnik/add-run-name-to-config -Added run name to config ([`c2e05c4`](https://github.com/jbloomAus/mats_sae_training/commit/c2e05c46b0c7d7159a29aaa243cd33a621437f38)) +Added run name to config ([`c2e05c4`](https://github.com/jbloomAus/SAELens/commit/c2e05c46b0c7d7159a29aaa243cd33a621437f38)) -* Added run name to config ([`ab2aabd`](https://github.com/jbloomAus/mats_sae_training/commit/ab2aabdcd9e293c32168549f9a99c25bf097a969)) +* Added run name to config ([`ab2aabd`](https://github.com/jbloomAus/SAELens/commit/ab2aabdcd9e293c32168549f9a99c25bf097a969)) -* Fixed edge case in activation cache shuffling ([`18fd4a1`](https://github.com/jbloomAus/mats_sae_training/commit/18fd4a1d43bbc8b7a490779f06e4f3c6983e3ea7)) +* Fixed edge case in activation cache shuffling ([`18fd4a1`](https://github.com/jbloomAus/SAELens/commit/18fd4a1d43bbc8b7a490779f06e4f3c6983e3ea7)) * Merge pull request #9 from chanind/rm-ds-store -chore: removing .DS_Store files ([`37771ce`](https://github.com/jbloomAus/mats_sae_training/commit/37771ce9a9bbd3b4bcf2d8749452f2f8e4516855)) +chore: removing .DS_Store files ([`37771ce`](https://github.com/jbloomAus/SAELens/commit/37771ce9a9bbd3b4bcf2d8749452f2f8e4516855)) -* improve readmen ([`f3fe937`](https://github.com/jbloomAus/mats_sae_training/commit/f3fe937303f0eb54e83771d1b307c3f09d1145fd)) +* improve readmen ([`f3fe937`](https://github.com/jbloomAus/SAELens/commit/f3fe937303f0eb54e83771d1b307c3f09d1145fd)) -* fix_evals_bad_rebase ([`22e415d`](https://github.com/jbloomAus/mats_sae_training/commit/22e415dfc0ab961470fd2c7ed476f4181e2afa44)) +* fix_evals_bad_rebase ([`22e415d`](https://github.com/jbloomAus/SAELens/commit/22e415dfc0ab961470fd2c7ed476f4181e2afa44)) -* evals changes, incomplete ([`736c40e`](https://github.com/jbloomAus/mats_sae_training/commit/736c40e16cb89cf7d8bd721518df268fb4a35ee7)) +* evals changes, incomplete ([`736c40e`](https://github.com/jbloomAus/SAELens/commit/736c40e16cb89cf7d8bd721518df268fb4a35ee7)) -* make tutorial independent of artefact and delete old artefact ([`6754e65`](https://github.com/jbloomAus/mats_sae_training/commit/6754e650e032efbc85b0dd11db480c96640bd0b6)) +* make tutorial independent of artefact and delete old artefact ([`6754e65`](https://github.com/jbloomAus/SAELens/commit/6754e650e032efbc85b0dd11db480c96640bd0b6)) -* fix MSE in ghost grad ([`44f7988`](https://github.com/jbloomAus/mats_sae_training/commit/44f7988e5a97bd93daedc3559cc4988caa128974)) +* fix MSE in ghost grad ([`44f7988`](https://github.com/jbloomAus/SAELens/commit/44f7988e5a97bd93daedc3559cc4988caa128974)) * Merge pull request #5 from jbloomAus/clean_up_repo -Add CI/CD, black formatting, pre-commit with flake8 linting. Fix some bugs. ([`01ccb92`](https://github.com/jbloomAus/mats_sae_training/commit/01ccb92c4dbc161a26b22d60b5c9e0fa2d560519)) +Add CI/CD, black formatting, pre-commit with flake8 linting. Fix some bugs. ([`01ccb92`](https://github.com/jbloomAus/SAELens/commit/01ccb92c4dbc161a26b22d60b5c9e0fa2d560519)) -* clean up run examples ([`9d46bdd`](https://github.com/jbloomAus/mats_sae_training/commit/9d46bdda9ab3aaf8246bb01cb270dadf3e5e06c9)) +* clean up run examples ([`9d46bdd`](https://github.com/jbloomAus/SAELens/commit/9d46bdda9ab3aaf8246bb01cb270dadf3e5e06c9)) -* move where we save the final artifact ([`f445fac`](https://github.com/jbloomAus/mats_sae_training/commit/f445facde3eb7c2011905501a7644f8ef6cbd6e6)) +* move where we save the final artifact ([`f445fac`](https://github.com/jbloomAus/SAELens/commit/f445facde3eb7c2011905501a7644f8ef6cbd6e6)) -* fix activations store innefficiency ([`07d38a0`](https://github.com/jbloomAus/mats_sae_training/commit/07d38a0eb5271d86fe2d5add8ae2ea5f7ae8aadc)) +* fix activations store innefficiency ([`07d38a0`](https://github.com/jbloomAus/SAELens/commit/07d38a0eb5271d86fe2d5add8ae2ea5f7ae8aadc)) -* black format and linting ([`479765b`](https://github.com/jbloomAus/mats_sae_training/commit/479765bc9ecc4f04fe76e1f4f447d0d0281e9045)) +* black format and linting ([`479765b`](https://github.com/jbloomAus/SAELens/commit/479765bc9ecc4f04fe76e1f4f447d0d0281e9045)) -* dummy file change ([`912a748`](https://github.com/jbloomAus/mats_sae_training/commit/912a748f7b5bff0193a997ee7369c312f073c35f)) +* dummy file change ([`912a748`](https://github.com/jbloomAus/SAELens/commit/912a748f7b5bff0193a997ee7369c312f073c35f)) -* try adding this branch listed specifically ([`7fd0e0c`](https://github.com/jbloomAus/mats_sae_training/commit/7fd0e0c3d3f951ae18b6b101043ba4a36c9933c4)) +* try adding this branch listed specifically ([`7fd0e0c`](https://github.com/jbloomAus/SAELens/commit/7fd0e0c3d3f951ae18b6b101043ba4a36c9933c4)) -* yml not yaml ([`9f3f1c8`](https://github.com/jbloomAus/mats_sae_training/commit/9f3f1c87ceed84afefef5bc6d4b519d538bcac91)) +* yml not yaml ([`9f3f1c8`](https://github.com/jbloomAus/SAELens/commit/9f3f1c87ceed84afefef5bc6d4b519d538bcac91)) -* add ci ([`91aca91`](https://github.com/jbloomAus/mats_sae_training/commit/91aca9142459e2a72478d4546ee0fa5a2910c161)) +* add ci ([`91aca91`](https://github.com/jbloomAus/SAELens/commit/91aca9142459e2a72478d4546ee0fa5a2910c161)) -* get units tests working ([`ade2976`](https://github.com/jbloomAus/mats_sae_training/commit/ade29762b4b94e02c3b44a66bedf150e721aec7c)) +* get units tests working ([`ade2976`](https://github.com/jbloomAus/SAELens/commit/ade29762b4b94e02c3b44a66bedf150e721aec7c)) -* make unit tests pass, add make file ([`08b2c92`](https://github.com/jbloomAus/mats_sae_training/commit/08b2c92f7fbfed38ea44312d53945cf1c9471d90)) +* make unit tests pass, add make file ([`08b2c92`](https://github.com/jbloomAus/SAELens/commit/08b2c92f7fbfed38ea44312d53945cf1c9471d90)) -* add pytest-cov to requirements.txt ([`ce526df`](https://github.com/jbloomAus/mats_sae_training/commit/ce526df64115113ecb68bd7f1a51b6fe11957553)) +* add pytest-cov to requirements.txt ([`ce526df`](https://github.com/jbloomAus/SAELens/commit/ce526df64115113ecb68bd7f1a51b6fe11957553)) -* seperate research from main repo ([`32b668c`](https://github.com/jbloomAus/mats_sae_training/commit/32b668ce96c8231eaaa93152d2b7d67dacc91dc5)) +* seperate research from main repo ([`32b668c`](https://github.com/jbloomAus/SAELens/commit/32b668ce96c8231eaaa93152d2b7d67dacc91dc5)) -* remove comma and set default store batch size lower ([`9761b9a`](https://github.com/jbloomAus/mats_sae_training/commit/9761b9ae5f0d4dd51d445ba1229f9b2ebc628e62)) +* remove comma and set default store batch size lower ([`9761b9a`](https://github.com/jbloomAus/SAELens/commit/9761b9ae5f0d4dd51d445ba1229f9b2ebc628e62)) -* notebook for Johny ([`39a18f2`](https://github.com/jbloomAus/mats_sae_training/commit/39a18f2f3fc138ba4743976b139071b7830c36a2)) +* notebook for Johny ([`39a18f2`](https://github.com/jbloomAus/SAELens/commit/39a18f2f3fc138ba4743976b139071b7830c36a2)) -* best practices ghost grads fix ([`f554b16`](https://github.com/jbloomAus/mats_sae_training/commit/f554b161e7b188d25ca9c21bf80629dc1b5b4e35)) +* best practices ghost grads fix ([`f554b16`](https://github.com/jbloomAus/SAELens/commit/f554b161e7b188d25ca9c21bf80629dc1b5b4e35)) * Update README.md -improved the hyperpars ([`2d4caf6`](https://github.com/jbloomAus/mats_sae_training/commit/2d4caf63e55d86967b906915c90a47094631276f)) +improved the hyperpars ([`2d4caf6`](https://github.com/jbloomAus/SAELens/commit/2d4caf63e55d86967b906915c90a47094631276f)) -* dashboard runner ([`a511223`](https://github.com/jbloomAus/mats_sae_training/commit/a511223ca3b5ee87930dc753b3164e00833dc17b)) +* dashboard runner ([`a511223`](https://github.com/jbloomAus/SAELens/commit/a511223ca3b5ee87930dc753b3164e00833dc17b)) -* readme update ([`c303c55`](https://github.com/jbloomAus/mats_sae_training/commit/c303c554b6599c38a9b78ef2f5e1651267d38fab)) +* readme update ([`c303c55`](https://github.com/jbloomAus/SAELens/commit/c303c554b6599c38a9b78ef2f5e1651267d38fab)) -* still hadn't fixed the issue, now fixed ([`a36ee21`](https://github.com/jbloomAus/mats_sae_training/commit/a36ee21bb351d279952c638442b3874de7756d0c)) +* still hadn't fixed the issue, now fixed ([`a36ee21`](https://github.com/jbloomAus/SAELens/commit/a36ee21bb351d279952c638442b3874de7756d0c)) -* fix mean of loss which broke in last commit ([`b4546db`](https://github.com/jbloomAus/mats_sae_training/commit/b4546dbdcea26d8812131572aa2c15fdfe4b4d28)) +* fix mean of loss which broke in last commit ([`b4546db`](https://github.com/jbloomAus/SAELens/commit/b4546dbdcea26d8812131572aa2c15fdfe4b4d28)) -* generate dashboards ([`35fa631`](https://github.com/jbloomAus/mats_sae_training/commit/35fa63110f2ad790036a7eef2e4b7d46fd54fa61)) +* generate dashboards ([`35fa631`](https://github.com/jbloomAus/SAELens/commit/35fa63110f2ad790036a7eef2e4b7d46fd54fa61)) * Merge pull request #3 from jbloomAus/ghost_grads_dev -Ghost grads dev ([`4d150c2`](https://github.com/jbloomAus/mats_sae_training/commit/4d150c255c2637f485875a5709002195b2e51ce0)) +Ghost grads dev ([`4d150c2`](https://github.com/jbloomAus/SAELens/commit/4d150c255c2637f485875a5709002195b2e51ce0)) -* save final log sparsity ([`98e4f1b`](https://github.com/jbloomAus/mats_sae_training/commit/98e4f1bb416b33d49bef1acc676cc3b1e9ed6441)) +* save final log sparsity ([`98e4f1b`](https://github.com/jbloomAus/SAELens/commit/98e4f1bb416b33d49bef1acc676cc3b1e9ed6441)) -* start saving log sparsity ([`4d6df6f`](https://github.com/jbloomAus/mats_sae_training/commit/4d6df6f41482e08530806495d22c5cedb1307448)) +* start saving log sparsity ([`4d6df6f`](https://github.com/jbloomAus/SAELens/commit/4d6df6f41482e08530806495d22c5cedb1307448)) -* get ghost grads working ([`e863ed7`](https://github.com/jbloomAus/mats_sae_training/commit/e863ed70afe1fb1d423262ee976196d2a69cf8b5)) +* get ghost grads working ([`e863ed7`](https://github.com/jbloomAus/SAELens/commit/e863ed70afe1fb1d423262ee976196d2a69cf8b5)) -* add notebook/changes for ghost-grad (not working yet) ([`73053c1`](https://github.com/jbloomAus/mats_sae_training/commit/73053c1fe9eed5a1e145f64a1fe11a300201544b)) +* add notebook/changes for ghost-grad (not working yet) ([`73053c1`](https://github.com/jbloomAus/SAELens/commit/73053c1fe9eed5a1e145f64a1fe11a300201544b)) -* idk, probs good ([`0407ad9`](https://github.com/jbloomAus/mats_sae_training/commit/0407ad9b3749ab6baa1bf6dd3040d2e394369836)) +* idk, probs good ([`0407ad9`](https://github.com/jbloomAus/SAELens/commit/0407ad9b3749ab6baa1bf6dd3040d2e394369836)) -* bunch of shit ([`1ec8f97`](https://github.com/jbloomAus/mats_sae_training/commit/1ec8f97330cc605d003757b9c8d6edb8040119bc)) +* bunch of shit ([`1ec8f97`](https://github.com/jbloomAus/SAELens/commit/1ec8f97330cc605d003757b9c8d6edb8040119bc)) -* Merge branch 'main' of github.com:jbloomAus/mats_sae_training ([`a22d856`](https://github.com/jbloomAus/mats_sae_training/commit/a22d85643d54e0924e5e4de8573671edf046bca5)) +* Merge branch 'main' of github.com:jbloomAus/mats_sae_training ([`a22d856`](https://github.com/jbloomAus/SAELens/commit/a22d85643d54e0924e5e4de8573671edf046bca5)) -* Reverse engineering the "not only... but" feature ([`74d4fb8`](https://github.com/jbloomAus/mats_sae_training/commit/74d4fb8d7299ee6ace0331a2d9b8cbab2c965bcb)) +* Reverse engineering the "not only... but" feature ([`74d4fb8`](https://github.com/jbloomAus/SAELens/commit/74d4fb8d7299ee6ace0331a2d9b8cbab2c965bcb)) * Merge pull request #2 from slavachalnev/no_reinit -Allow sampling method to be None ([`4c5fed8`](https://github.com/jbloomAus/mats_sae_training/commit/4c5fed8bbff8f13fdc534385caeb5455ff9d8a55)) +Allow sampling method to be None ([`4c5fed8`](https://github.com/jbloomAus/SAELens/commit/4c5fed8bbff8f13fdc534385caeb5455ff9d8a55)) -* Allow sampling method to be None ([`166799d`](https://github.com/jbloomAus/mats_sae_training/commit/166799d0a9d10c01d39fdbf3c7e338bc99cc4d96)) +* Allow sampling method to be None ([`166799d`](https://github.com/jbloomAus/SAELens/commit/166799d0a9d10c01d39fdbf3c7e338bc99cc4d96)) -* research/week_15th_jan/gpt2_small_resid_pre_3.ipynb ([`52a1da7`](https://github.com/jbloomAus/mats_sae_training/commit/52a1da72b25827b4ade2b13c679ac54c17def800)) +* research/week_15th_jan/gpt2_small_resid_pre_3.ipynb ([`52a1da7`](https://github.com/jbloomAus/SAELens/commit/52a1da72b25827b4ade2b13c679ac54c17def800)) -* add arg for dead neuron calc ([`ffb75fb`](https://github.com/jbloomAus/mats_sae_training/commit/ffb75fb118db527e4633768534b04b5ef5779122)) +* add arg for dead neuron calc ([`ffb75fb`](https://github.com/jbloomAus/SAELens/commit/ffb75fb118db527e4633768534b04b5ef5779122)) -* notebooks for lucy ([`0319d89`](https://github.com/jbloomAus/mats_sae_training/commit/0319d890d9da994f830b71361d4850b800f6862b)) +* notebooks for lucy ([`0319d89`](https://github.com/jbloomAus/SAELens/commit/0319d890d9da994f830b71361d4850b800f6862b)) -* add args for b_dec_init ([`82da877`](https://github.com/jbloomAus/mats_sae_training/commit/82da877da7bf273870eb1446d6c9584650876981)) +* add args for b_dec_init ([`82da877`](https://github.com/jbloomAus/SAELens/commit/82da877da7bf273870eb1446d6c9584650876981)) -* add geom median as submodule instead ([`4c0d001`](https://github.com/jbloomAus/mats_sae_training/commit/4c0d0014e4a5f0b5661963ce9fd9318c1cdfca78)) +* add geom median as submodule instead ([`4c0d001`](https://github.com/jbloomAus/SAELens/commit/4c0d0014e4a5f0b5661963ce9fd9318c1cdfca78)) -* add geom median to req ([`4c8ac9d`](https://github.com/jbloomAus/mats_sae_training/commit/4c8ac9d53585e60647e140d13903ef44e55aa8b6)) +* add geom median to req ([`4c8ac9d`](https://github.com/jbloomAus/SAELens/commit/4c8ac9d53585e60647e140d13903ef44e55aa8b6)) -* add-geometric-mean-b_dec-init ([`d5853f8`](https://github.com/jbloomAus/mats_sae_training/commit/d5853f8e075c731309cf208a5cd07b0ae0bdfa1e)) +* add-geometric-mean-b_dec-init ([`d5853f8`](https://github.com/jbloomAus/SAELens/commit/d5853f8e075c731309cf208a5cd07b0ae0bdfa1e)) -* reset feature sparsity calculation ([`4c7f6f2`](https://github.com/jbloomAus/mats_sae_training/commit/4c7f6f2d0960edc365369141671359c91281c600)) +* reset feature sparsity calculation ([`4c7f6f2`](https://github.com/jbloomAus/SAELens/commit/4c7f6f2d0960edc365369141671359c91281c600)) -* anthropic sampling ([`048d267`](https://github.com/jbloomAus/mats_sae_training/commit/048d267c0f964086e755c3ca245399c68be4d70d)) +* anthropic sampling ([`048d267`](https://github.com/jbloomAus/SAELens/commit/048d267c0f964086e755c3ca245399c68be4d70d)) -* get anthropic resampling working ([`ca74543`](https://github.com/jbloomAus/mats_sae_training/commit/ca74543a117f7bc25e9b40b1dd5e4109a2c0121d)) +* get anthropic resampling working ([`ca74543`](https://github.com/jbloomAus/SAELens/commit/ca74543a117f7bc25e9b40b1dd5e4109a2c0121d)) -* add ability to finetune existing autoencoder ([`c1208eb`](https://github.com/jbloomAus/mats_sae_training/commit/c1208eb61ebcc1dfbfc428b8b5fa090eb090bbd8)) +* add ability to finetune existing autoencoder ([`c1208eb`](https://github.com/jbloomAus/SAELens/commit/c1208eb61ebcc1dfbfc428b8b5fa090eb090bbd8)) -* run notebook ([`879ad27`](https://github.com/jbloomAus/mats_sae_training/commit/879ad272f541bb5acc05be7b2a042ac9d45479a6)) +* run notebook ([`879ad27`](https://github.com/jbloomAus/SAELens/commit/879ad272f541bb5acc05be7b2a042ac9d45479a6)) -* switch to batch size independent loss metrics ([`0623d39`](https://github.com/jbloomAus/mats_sae_training/commit/0623d39b323da05f4ae3b67df25841cabf83d200)) +* switch to batch size independent loss metrics ([`0623d39`](https://github.com/jbloomAus/SAELens/commit/0623d39b323da05f4ae3b67df25841cabf83d200)) -* track mean sparsity ([`75f1547`](https://github.com/jbloomAus/mats_sae_training/commit/75f15476400f26fddd2da9562be995e821745691)) +* track mean sparsity ([`75f1547`](https://github.com/jbloomAus/SAELens/commit/75f15476400f26fddd2da9562be995e821745691)) -* don't stop early ([`44078a6`](https://github.com/jbloomAus/mats_sae_training/commit/44078a6a7dd10423619ff3a348fd5276f9aad9b2)) +* don't stop early ([`44078a6`](https://github.com/jbloomAus/SAELens/commit/44078a6a7dd10423619ff3a348fd5276f9aad9b2)) -* name runs better ([`5041748`](https://github.com/jbloomAus/mats_sae_training/commit/5041748e9166d6e84d017053fe61e60e194cead2)) +* name runs better ([`5041748`](https://github.com/jbloomAus/SAELens/commit/5041748e9166d6e84d017053fe61e60e194cead2)) -* improve-eval-metrics-for-attn ([`00d9b65`](https://github.com/jbloomAus/mats_sae_training/commit/00d9b652d09f18c38dbd4c4e2376fcee3c457ac5)) +* improve-eval-metrics-for-attn ([`00d9b65`](https://github.com/jbloomAus/SAELens/commit/00d9b652d09f18c38dbd4c4e2376fcee3c457ac5)) -* add hook q ([`b061ee3`](https://github.com/jbloomAus/mats_sae_training/commit/b061ee30e9cf86226e625e5e2e05ea7b4cc4550e)) +* add hook q ([`b061ee3`](https://github.com/jbloomAus/SAELens/commit/b061ee30e9cf86226e625e5e2e05ea7b4cc4550e)) -* add copy suppression notebook ([`1dc893a`](https://github.com/jbloomAus/mats_sae_training/commit/1dc893a17db8a7866a598a4632034c91c139d862)) +* add copy suppression notebook ([`1dc893a`](https://github.com/jbloomAus/SAELens/commit/1dc893a17db8a7866a598a4632034c91c139d862)) -* fix check in neuron sampling ([`809becd`](https://github.com/jbloomAus/mats_sae_training/commit/809becdbd67b2e23c8be572188f8f098d770871a)) +* fix check in neuron sampling ([`809becd`](https://github.com/jbloomAus/SAELens/commit/809becdbd67b2e23c8be572188f8f098d770871a)) * Merge pull request #1 from jbloomAus/activations_on_disk -Activations on disk ([`e5f198e`](https://github.com/jbloomAus/mats_sae_training/commit/e5f198ea52741dc54e463c55f36409b2b559bf3a)) +Activations on disk ([`e5f198e`](https://github.com/jbloomAus/SAELens/commit/e5f198ea52741dc54e463c55f36409b2b559bf3a)) -* merge into main ([`94ed3e6`](https://github.com/jbloomAus/mats_sae_training/commit/94ed3e62636267d5cfe4163dcb37bbd30827e064)) +* merge into main ([`94ed3e6`](https://github.com/jbloomAus/SAELens/commit/94ed3e62636267d5cfe4163dcb37bbd30827e064)) -* notebook ([`b5344a3`](https://github.com/jbloomAus/mats_sae_training/commit/b5344a3048706e4b828c80eaccbc5b8b75eeb2ae)) +* notebook ([`b5344a3`](https://github.com/jbloomAus/SAELens/commit/b5344a3048706e4b828c80eaccbc5b8b75eeb2ae)) -* various research notebooks ([`be63fce`](https://github.com/jbloomAus/mats_sae_training/commit/be63fcea9db2555c1ac9803fc86008487ed97958)) +* various research notebooks ([`be63fce`](https://github.com/jbloomAus/SAELens/commit/be63fcea9db2555c1ac9803fc86008487ed97958)) -* Added activations caching to run.ipynb ([`054cf6d`](https://github.com/jbloomAus/mats_sae_training/commit/054cf6dc7082465b05bfbfd138566f41f1631471)) +* Added activations caching to run.ipynb ([`054cf6d`](https://github.com/jbloomAus/SAELens/commit/054cf6dc7082465b05bfbfd138566f41f1631471)) -* Added activations dir to gitignore ([`c4a31ae`](https://github.com/jbloomAus/mats_sae_training/commit/c4a31ae1e0876d475bf6881c1570e0fbb00fdff9)) +* Added activations dir to gitignore ([`c4a31ae`](https://github.com/jbloomAus/SAELens/commit/c4a31ae1e0876d475bf6881c1570e0fbb00fdff9)) -* Saving and loading activations from disk ([`309e2de`](https://github.com/jbloomAus/mats_sae_training/commit/309e2de8881e0b9e9f3b6a786501cb4336143890)) +* Saving and loading activations from disk ([`309e2de`](https://github.com/jbloomAus/SAELens/commit/309e2de8881e0b9e9f3b6a786501cb4336143890)) -* Fixed typo that threw out half of activations ([`5f73918`](https://github.com/jbloomAus/mats_sae_training/commit/5f73918a9447d63dcee9a73c6f9e2e23bec96abe)) +* Fixed typo that threw out half of activations ([`5f73918`](https://github.com/jbloomAus/SAELens/commit/5f73918a9447d63dcee9a73c6f9e2e23bec96abe)) -* minor speed improvement ([`f7ea316`](https://github.com/jbloomAus/mats_sae_training/commit/f7ea31679222ff03af0048a0730097eccb644de0)) +* minor speed improvement ([`f7ea316`](https://github.com/jbloomAus/SAELens/commit/f7ea31679222ff03af0048a0730097eccb644de0)) -* add notebook with different example runs ([`c0eac0a`](https://github.com/jbloomAus/mats_sae_training/commit/c0eac0a4f3889be932f50cc7485ef0cc236a46a3)) +* add notebook with different example runs ([`c0eac0a`](https://github.com/jbloomAus/SAELens/commit/c0eac0a4f3889be932f50cc7485ef0cc236a46a3)) -* add ability to train on attn heads ([`18cfaad`](https://github.com/jbloomAus/mats_sae_training/commit/18cfaad66432c827528e28d3969fcc6dbd16391a)) +* add ability to train on attn heads ([`18cfaad`](https://github.com/jbloomAus/SAELens/commit/18cfaad66432c827528e28d3969fcc6dbd16391a)) -* add gzip for pt artefacts ([`9614a23`](https://github.com/jbloomAus/mats_sae_training/commit/9614a237ef96a79383e1ead8d7769a427279b310)) +* add gzip for pt artefacts ([`9614a23`](https://github.com/jbloomAus/SAELens/commit/9614a237ef96a79383e1ead8d7769a427279b310)) -* add_example_feature_dashboard ([`e90e54d`](https://github.com/jbloomAus/mats_sae_training/commit/e90e54dd2e7cd6e2844710d9b8ba64a64ef25993)) +* add_example_feature_dashboard ([`e90e54d`](https://github.com/jbloomAus/SAELens/commit/e90e54dd2e7cd6e2844710d9b8ba64a64ef25993)) -* get_shit_done ([`ce73042`](https://github.com/jbloomAus/mats_sae_training/commit/ce7304265a01ce4e30537f58b148dd069149c474)) +* get_shit_done ([`ce73042`](https://github.com/jbloomAus/SAELens/commit/ce7304265a01ce4e30537f58b148dd069149c474)) -* commit_various_things_in_progress ([`3843c39`](https://github.com/jbloomAus/mats_sae_training/commit/3843c3976936e293b5c87b3501d6a7f397183f68)) +* commit_various_things_in_progress ([`3843c39`](https://github.com/jbloomAus/SAELens/commit/3843c3976936e293b5c87b3501d6a7f397183f68)) -* add sae visualizer and tutorial ([`6f4030c`](https://github.com/jbloomAus/mats_sae_training/commit/6f4030c5d411f7c2d23518ce247b553b216499e7)) +* add sae visualizer and tutorial ([`6f4030c`](https://github.com/jbloomAus/SAELens/commit/6f4030c5d411f7c2d23518ce247b553b216499e7)) -* make it possible to load sae trained on cuda onto mps ([`3298b75`](https://github.com/jbloomAus/mats_sae_training/commit/3298b75a5ff1c0a660840c6dfa8931a9faf226f7)) +* make it possible to load sae trained on cuda onto mps ([`3298b75`](https://github.com/jbloomAus/SAELens/commit/3298b75a5ff1c0a660840c6dfa8931a9faf226f7)) -* reduce hist freq, don't cap re-init ([`debcf0f`](https://github.com/jbloomAus/mats_sae_training/commit/debcf0fd62d4d252710d6b2f947efc33ba934ef7)) +* reduce hist freq, don't cap re-init ([`debcf0f`](https://github.com/jbloomAus/SAELens/commit/debcf0fd62d4d252710d6b2f947efc33ba934ef7)) -* add loader import to readme ([`b63f14e`](https://github.com/jbloomAus/mats_sae_training/commit/b63f14e01758a243487ac87bee710298bfef2603)) +* add loader import to readme ([`b63f14e`](https://github.com/jbloomAus/SAELens/commit/b63f14e01758a243487ac87bee710298bfef2603)) -* Update README.md ([`88f086b`](https://github.com/jbloomAus/mats_sae_training/commit/88f086b664f1c3750dd4b1b3fe0b79df1bfa180f)) +* Update README.md ([`88f086b`](https://github.com/jbloomAus/SAELens/commit/88f086b664f1c3750dd4b1b3fe0b79df1bfa180f)) -* improve-resampling ([`a3072c2`](https://github.com/jbloomAus/mats_sae_training/commit/a3072c26105b1053ece5390da26cdf8605c4a4b7)) +* improve-resampling ([`a3072c2`](https://github.com/jbloomAus/SAELens/commit/a3072c26105b1053ece5390da26cdf8605c4a4b7)) -* add readme ([`e9b8e56`](https://github.com/jbloomAus/mats_sae_training/commit/e9b8e56aac2ef694e6cab4a6fc24cf343e188ded)) +* add readme ([`e9b8e56`](https://github.com/jbloomAus/SAELens/commit/e9b8e56aac2ef694e6cab4a6fc24cf343e188ded)) -* fixl0_plus_other_stuff ([`2f162f0`](https://github.com/jbloomAus/mats_sae_training/commit/2f162f09be39a9bf56c40d1e461c064e4f2ed834)) +* fixl0_plus_other_stuff ([`2f162f0`](https://github.com/jbloomAus/SAELens/commit/2f162f09be39a9bf56c40d1e461c064e4f2ed834)) -* add checkpoints ([`4cacbfc`](https://github.com/jbloomAus/mats_sae_training/commit/4cacbfc2d32a509b8c34ccb940d0a21470c585ca)) +* add checkpoints ([`4cacbfc`](https://github.com/jbloomAus/SAELens/commit/4cacbfc2d32a509b8c34ccb940d0a21470c585ca)) -* improve_model_saving_loading ([`f6697c6`](https://github.com/jbloomAus/mats_sae_training/commit/f6697c6cfe654bee8b677401e423f2b09e682f73)) +* improve_model_saving_loading ([`f6697c6`](https://github.com/jbloomAus/SAELens/commit/f6697c6cfe654bee8b677401e423f2b09e682f73)) -* stuff ([`19d278a`](https://github.com/jbloomAus/mats_sae_training/commit/19d278a223bbdc2673616644f4da90492fe7b169)) +* stuff ([`19d278a`](https://github.com/jbloomAus/SAELens/commit/19d278a223bbdc2673616644f4da90492fe7b169)) -* Added support for non-tokenized datasets ([`afcc239`](https://github.com/jbloomAus/mats_sae_training/commit/afcc239de1aa91961a14e5734644d2ffaf4b764a)) +* Added support for non-tokenized datasets ([`afcc239`](https://github.com/jbloomAus/SAELens/commit/afcc239de1aa91961a14e5734644d2ffaf4b764a)) -* notebook_for_keith ([`d06e09b`](https://github.com/jbloomAus/mats_sae_training/commit/d06e09b70bd7fcf2419f9196970025097b7b55ef)) +* notebook_for_keith ([`d06e09b`](https://github.com/jbloomAus/SAELens/commit/d06e09b70bd7fcf2419f9196970025097b7b55ef)) -* fix resampling bug ([`2b43980`](https://github.com/jbloomAus/mats_sae_training/commit/2b43980cc2bae402e2016efaaa042f2cb545a02f)) +* fix resampling bug ([`2b43980`](https://github.com/jbloomAus/SAELens/commit/2b43980cc2bae402e2016efaaa042f2cb545a02f)) -* test pars ([`f601362`](https://github.com/jbloomAus/mats_sae_training/commit/f6013625963ebabdb86063f8db7a5e8339e37873)) +* test pars ([`f601362`](https://github.com/jbloomAus/SAELens/commit/f6013625963ebabdb86063f8db7a5e8339e37873)) -* further-lm-improvments ([`63048eb`](https://github.com/jbloomAus/mats_sae_training/commit/63048eb9e5f12a2f631b454f82a17563efcae328)) +* further-lm-improvments ([`63048eb`](https://github.com/jbloomAus/SAELens/commit/63048eb9e5f12a2f631b454f82a17563efcae328)) -* get_lm_working_well ([`eba5f79`](https://github.com/jbloomAus/mats_sae_training/commit/eba5f79ff267217a3f69b4dcab4903c98ed56055)) +* get_lm_working_well ([`eba5f79`](https://github.com/jbloomAus/SAELens/commit/eba5f79ff267217a3f69b4dcab4903c98ed56055)) -* basic-lm-training-currently-broken ([`7396b8b`](https://github.com/jbloomAus/mats_sae_training/commit/7396b8ba65a579d8aff0987c61ea9d7ac9177380)) +* basic-lm-training-currently-broken ([`7396b8b`](https://github.com/jbloomAus/SAELens/commit/7396b8ba65a579d8aff0987c61ea9d7ac9177380)) -* set_up_lm_runner ([`d1095af`](https://github.com/jbloomAus/mats_sae_training/commit/d1095afefb17f87a2a63841fbe6845b46650dde7)) +* set_up_lm_runner ([`d1095af`](https://github.com/jbloomAus/SAELens/commit/d1095afefb17f87a2a63841fbe6845b46650dde7)) -* fix old test, may remove ([`b407aab`](https://github.com/jbloomAus/mats_sae_training/commit/b407aabfcd9d8ff9a8752a92a26701dbc8da04a2)) +* fix old test, may remove ([`b407aab`](https://github.com/jbloomAus/SAELens/commit/b407aabfcd9d8ff9a8752a92a26701dbc8da04a2)) -* happy with hyperpars on benchmark ([`836298a`](https://github.com/jbloomAus/mats_sae_training/commit/836298a897e759e1f99a35c7d8195bcfb580afe3)) +* happy with hyperpars on benchmark ([`836298a`](https://github.com/jbloomAus/SAELens/commit/836298a897e759e1f99a35c7d8195bcfb580afe3)) -* improve metrics ([`f52c7bb`](https://github.com/jbloomAus/mats_sae_training/commit/f52c7bb142e019f9dd6d6f9d73ff3874bf33b529)) +* improve metrics ([`f52c7bb`](https://github.com/jbloomAus/SAELens/commit/f52c7bb142e019f9dd6d6f9d73ff3874bf33b529)) -* make toy model runner ([`4851dd1`](https://github.com/jbloomAus/mats_sae_training/commit/4851dd1a15ee57d658d41c8673fd6b3fe71a2b45)) +* make toy model runner ([`4851dd1`](https://github.com/jbloomAus/SAELens/commit/4851dd1a15ee57d658d41c8673fd6b3fe71a2b45)) -* various-changes-toy-model-test ([`a61b75f`](https://github.com/jbloomAus/mats_sae_training/commit/a61b75f058b79262e587a302319a1bb9136c353e)) +* various-changes-toy-model-test ([`a61b75f`](https://github.com/jbloomAus/SAELens/commit/a61b75f058b79262e587a302319a1bb9136c353e)) -* Added activation store and activation gathering ([`a85f24d`](https://github.com/jbloomAus/mats_sae_training/commit/a85f24d9389936dc7ad18aaf74d600669294b9cc)) +* Added activation store and activation gathering ([`a85f24d`](https://github.com/jbloomAus/SAELens/commit/a85f24d9389936dc7ad18aaf74d600669294b9cc)) -* First successful run on toy models ([`4927145`](https://github.com/jbloomAus/mats_sae_training/commit/4927145a6dfe9026d47c1bea1497667eb5a715fe)) +* First successful run on toy models ([`4927145`](https://github.com/jbloomAus/SAELens/commit/4927145a6dfe9026d47c1bea1497667eb5a715fe)) -* halfway-to-toy-models ([`feeb411`](https://github.com/jbloomAus/mats_sae_training/commit/feeb411b4526c0627a9a558cfcab91252f90046b)) +* halfway-to-toy-models ([`feeb411`](https://github.com/jbloomAus/SAELens/commit/feeb411b4526c0627a9a558cfcab91252f90046b)) -* Initial commit ([`7a94b0e`](https://github.com/jbloomAus/mats_sae_training/commit/7a94b0ebc34edd24e98df753d100e8a4c238d4f6)) +* Initial commit ([`7a94b0e`](https://github.com/jbloomAus/SAELens/commit/7a94b0ebc34edd24e98df753d100e8a4c238d4f6)) diff --git a/pyproject.toml b/pyproject.toml index 1a42aa38..9ca2ee14 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "sae-lens" -version = "0.1.0" +version = "0.2.0" description = "Training and Analyzing Sparse Autoencoders (SAEs)" authors = ["Joseph Bloom"] readme = "README.md" diff --git a/sae_lens/__init__.py b/sae_lens/__init__.py index 39e1de1c..2ba878ae 100644 --- a/sae_lens/__init__.py +++ b/sae_lens/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.1.0" +__version__ = "0.2.0" from .training.activations_store import ActivationsStore from .training.cache_activations_runner import cache_activations_runner From 676946654c89ea63f6244f7326cc970b9354f4e3 Mon Sep 17 00:00:00 2001 From: jbloom-md Date: Sat, 13 Apr 2024 15:58:45 +0100 Subject: [PATCH 3/8] fix: neuronpedia quicklist --- sae_lens/analysis/neuronpedia_integration.py | 24 ++++++++++++++------ 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/sae_lens/analysis/neuronpedia_integration.py b/sae_lens/analysis/neuronpedia_integration.py index f438049a..8a64e6d9 100644 --- a/sae_lens/analysis/neuronpedia_integration.py +++ b/sae_lens/analysis/neuronpedia_integration.py @@ -1,11 +1,21 @@ +import json +import urllib.parse import webbrowser -def open_neuronpedia( - feature_id: int, layer: int = 0, model: str = "gpt2-small", dataset: str = "res-jb" +def get_neuronpedia_quick_list( + features: list[int], + layer: int, + model: str = "gpt2-small", + dataset: str = "res-jb", + name: str = "temporary_list", ): - - path_to_html = f"https://www.neuronpedia.org/{model}/{layer}-{dataset}/{feature_id}" - - print(f"Feature {feature_id}") - webbrowser.open_new_tab(path_to_html) + url = "https://neuronpedia.org/quick-list/" + name = urllib.parse.quote(name) + url = url + "?name=" + name + list_feature = [ + {"modelId": model, "layer": f"{layer}-{dataset}", "index": str(feature)} + for feature in features + ] + url = url + "&features=" + urllib.parse.quote(json.dumps(list_feature)) + webbrowser.open(url) From afb417d0ccf219f427b5c53df6943dfd257ccc79 Mon Sep 17 00:00:00 2001 From: github-actions Date: Sat, 13 Apr 2024 15:04:40 +0000 Subject: [PATCH 4/8] 0.2.1 Automatically generated by python-semantic-release --- CHANGELOG.md | 7 +++++++ pyproject.toml | 2 +- sae_lens/__init__.py | 2 +- 3 files changed, 9 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4e85bdf5..92f33fc0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,13 @@ +## v0.2.1 (2024-04-13) + +### Fix + +* fix: neuronpedia quicklist ([`6769466`](https://github.com/jbloomAus/SAELens/commit/676946654c89ea63f6244f7326cc970b9354f4e3)) + + ## v0.2.0 (2024-04-13) ### Chore diff --git a/pyproject.toml b/pyproject.toml index 9ca2ee14..517e53ec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "sae-lens" -version = "0.2.0" +version = "0.2.1" description = "Training and Analyzing Sparse Autoencoders (SAEs)" authors = ["Joseph Bloom"] readme = "README.md" diff --git a/sae_lens/__init__.py b/sae_lens/__init__.py index 2ba878ae..905c2a00 100644 --- a/sae_lens/__init__.py +++ b/sae_lens/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.2.0" +__version__ = "0.2.1" from .training.activations_store import ActivationsStore from .training.cache_activations_runner import cache_activations_runner From 5cd5652a4b19ba985d20c229b6a92d17774bc6b9 Mon Sep 17 00:00:00 2001 From: jbloom-md Date: Sun, 14 Apr 2024 10:44:46 +0100 Subject: [PATCH 5/8] update readme --- README.md | 209 ++---------------------------------------------------- 1 file changed, 7 insertions(+), 202 deletions(-) diff --git a/README.md b/README.md index d417d5cf..e3dc2e4d 100644 --- a/README.md +++ b/README.md @@ -12,212 +12,17 @@ SAELens exists to help researchers: - Analyse sparse autoencoders / research mechanistic interpretability. - Generate insights which make it easier to create safe and aligned AI systems. -## Quick Start +Please refer to the [documentation](https://jbloomaus.github.io/SAELens/) for information on how to: +- Download and Analyse pre-trained sparse autoencoders. +- Train your own sparse autoencoders. +- Generate feature dashboards with the [SAE-Vis Library](https://github.com/callummcdougall/sae_vis/tree/main). -### Set Up +## Join the Slack! -This package is available on PyPI. You can install it via pip: +Feel free to join the [Open Source Mechanistic Interpretability Slack](https://join.slack.com/t/opensourcemechanistic/shared_invite/zt-1qosyh8g3-9bF3gamhLNJiqCL_QqLFrA) for support! -``` -pip install sae-lens -``` -### Loading Sparse Autoencoders from Huggingface - -[Previously trained sparse autoencoders](https://huggingface.co/jbloom/GPT2-Small-SAEs) can be loaded from huggingface with close to single line of code. For more details and performance metrics for these sparse autoencoder, read my [blog post](https://www.alignmentforum.org/posts/f9EgfLSurAiqRJySD/open-source-sparse-autoencoders-for-all-residual-stream). - -```python -import torch -from sae_lens import LMSparseAutoencoderSessionloader -from huggingface_hub import hf_hub_download - -layer = 8 # pick a layer you want. -REPO_ID = "jbloom/GPT2-Small-SAEs" -FILENAME = f"final_sparse_autoencoder_gpt2-small_blocks.{layer}.hook_resid_pre_24576.pt" -path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) -model, sparse_autoencoder, activation_store = LMSparseAutoencoderSessionloader.load_session_from_pretrained( - path = path -) -sparse_autoencoder.eval() -``` - -You can also load the feature sparsity from huggingface. - -```python -FILENAME = f"final_sparse_autoencoder_gpt2-small_blocks.{layer}.hook_resid_pre_24576_log_feature_sparsity.pt" -path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) -log_feature_sparsity = torch.load(path, map_location=sparse_autoencoder.cfg.device) - -``` -### Background - -We highly recommend this [tutorial](https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab). - - -## High Level - -### Motivation - -- **Accelerate SAE Research**: Support fast experimentation to understand SAEs and improve SAE training so we can train SAEs on larger and more diverse models. -- **Make Research like Play**: Support research into language model internals via SAEs. Good tooling can make research tremendously exciting and enjoyable. Balancing modifiability and reliability with ease of understanding / access is the name of the game here. -- **Build an awesome community**: Mechanistic Interpretability already has an awesome community but as that community grows, it makes sense that there will be niches. I'd love to build a great community around Sparse Autoencoders. - -### Goals - -#### **SAE Training**: SAE Training features will fit into a number of categories including: -- **Making it easy to train SAEs**: Training SAEs is hard for a number of reasons and so making it easy for people to train SAEs with relatively little expertise seems like the main way this codebase will create value. -- **Training SAEs on more models**: Supporting training of SAEs on more models, architectures, different activations within those models. -- **Being better at training SAEs**: Enabling methodological changes which may improve SAE performance as measured by reconstruction loss, Cross Entropy Loss when using reconstructed activation, L1 loss, L0 and interpretability of features as well as improving speed of training or reducing the compute resources required to train SAEs. -- **Being better at measuring SAE Performance**: How do we know when SAEs are doing what we want them to? Improving training metrics should allow better decisions about which methods to use and which hyperparameters choices we make. -- **Training SAE variants**: People are already training “Transcoders” which map from one activation to another (such as before / after an MLP layer). These can be easily supported with a few changes. Other variants will come in time and - -#### **Analysis with SAEs**: Using SAEs to understand neural network internals is an exciting, but complicated task. -- **Feature-wise Interpretability**: This looks something like "for each feature, have as much knowledge about it as possible". Part of this will feature dashboard improvements, or supporting better integrations with Neuronpedia. -- **Mechanistic Interpretability**: This comprises the more traditional kinds of Mechanistic Interpretability which TransformerLens supports and should be supported by this codebase. Making it easy to patch, ablate or otherwise intervene on features so as to find circuits will likely speed up lots of researchers. - -### Other Stuff - -I think there are lots of other types of analysis that could be done in the future with SAE features. I've already explored many different types of statistical tests which can reveal interesting properties of features. There are also things like saliency mapping and attribution techniques which it would be nice to support. -- Accessibility and Code Quality: The codebase won’t be used if it doesn’t work and it also won’t get used if it’s too hard to understand, modify or read. -Making the code accessible: This involves tasks like turning the code base into a python package. -- Knowing how the code is supposed to work: Is the code well-documented? This will require docstrings, tutorials and links to related work and publications. Getting aligned on what the code does is critical to sharing a resource like this. -- Knowing the code works as intended: All code should be tested. Unit tests and acceptance tests are both important. -- Knowing the code is actually performant: This will ensure code works as intended. However deep learning introduces lots of complexity which makes actually running benchmarks essential to having confidence in the code. - - -## Code Overview - -The codebase contains 2 folders worth caring about: - -- training: The main body of the code is here. Everything required for training SAEs. -- analysis: This code is mainly house the feature visualizer code we use to generate dashboards. It was written by Callum McDougal but I've ported it here with permission and edited it to work with a few different activation types. - -Some other folders: - -- tutorials: These aren't well maintained but I'll aim to clean them up soon. -- tests: When first developing the codebase, I was writing more tests. I have no idea whether they are currently working! - -I've been commiting my research code to the `Research` folder but am not expecting other people use or look at that. - - -### Training your own Sparse Autoencoder - -Sparse Autoencoders can be intimidating at first but it's fairly simple to train one once you know what each part of the config does. I've created a config class which you instantiate and pass to the runner which will complete your training run and log it's progress to wandb. - -Let's go through the major components of the config: -- Data: SAE's autoencode model activations. We need to specify the model, the part of the models activations we want to autoencode and the dataset the model is operating on when generating those activations. We now automatically detect if that dataset is tokenized and most huggingface datasets should be fine. One slightly annoying detail is that you need to know the dimensionality of those activations when contructing your SAE but you can get that in the transformerlens [docs](https://neelnanda-io.github.io/TransformerLens/generated/model_properties_table.html). Any language model in the table from those docs should work. -- SAE Parameters: Your expansion factor will determine the size of your SAE and the decoder bias initialization method should always be geometric_median or mean. Mean is faster but theoretically sub-optimal. I use another package to get the geometric median and it can be quite slow. -- Training Parameters: These are most critical. The right L1 coefficient (coefficient in the activation sparsity inducing term in the loss) changes with your learning rate but a good bet would be to use LR 4e-4 and L1 8e-5 for GPT2 small. These will vary for other models and playing around with them / short runs can be helpful. Training batch size of 4096 is standard and I'm not really sure whether there's benefit to playing with it. In theory a larger context size (one accurate to whatever the model was trained with) seems good but it's computationally cheaper to use 128. Learning rate warm up is important to avoid dead neurons. -- Activation Store Parameters: The activation store shuffles activations from forward passes over samples from your data. The larger it is, the better shuffling you'll get. In theory more shuffling is good. The total training tokens is a very important parameter. The more the better, but you'll often see good results having trained on a few hundred million tokens. Store batch batch size is a function of your gpu and how many forward passes of your model you want to do simultaneously when collecting activations. -- Dead Neurons / Sparsity Metrics: The config around resampling was more important when we were using resampling to avoid dead neurons (see Anthropic's post on this), but using ghost gradients, the resampling protcol is much simpler. I'd always set ghost grad to True and feature sampling method to None. The feature sampling window effects the dashboard statistics tracking feature occurence and the dead feature window tracks how many forward passes a neuron must not activate before we apply ghost grads to it. -- WANDB: Fairly straightfoward. Don't set log frequency too high or your dashboard will be slow! -- Device: I can run this code on my macbook with "mps" but mostly do runs with cuda. -- Dtype: Float16 maybe could work but I had some funky results and have left it at float32 for the time being. -- Checkpoints: I'd collected checkpoints on runs you care about but turn them off when tuning since it can be slow. - - -```python -import torch -import os -import sys - -os.environ["TOKENIZERS_PARALLELISM"] = "false" -os.environ["WANDB__SERVICE_WAIT"] = "300" - -from sae_lens.training.config import LanguageModelSAERunnerConfig -from sae_lens.training.lm_runner import language_model_sae_runner - -cfg = LanguageModelSAERunnerConfig( - - # Data Generating Function (Model + Training Distibuion) - model_name = "gpt2-small", - hook_point = "blocks.2.hook_resid_pre", - hook_point_layer = 2, - d_in = 768, - dataset_path = "Skylion007/openwebtext", - is_dataset_tokenized=False, - - # SAE Parameters - expansion_factor = 64, - b_dec_init_method = "geometric_median", - - # Training Parameters - lr = 0.0004, - l1_coefficient = 0.00008, - lr_scheduler_name="constantwithwarmup", - train_batch_size = 4096, - context_size = 128, - lr_warm_up_steps=5000, - - # Activation Store Parameters - n_batches_in_buffer = 128, - total_training_tokens = 1_000_000 * 300, - store_batch_size = 32, - - # Dead Neurons and Sparsity - use_ghost_grads=True, - feature_sampling_window = 1000, - dead_feature_window=5000, - dead_feature_threshold = 1e-6, - - # WANDB - log_to_wandb = True, - wandb_project= "mats_sae_training_gpt2", - wandb_entity = None, - wandb_log_frequency=100, - - # Misc - device = "cuda", - seed = 42, - n_checkpoints = 10, - checkpoint_path = "checkpoints", - dtype = torch.float32, - ) - -sparse_autoencoder = language_model_sae_runner(cfg) - -``` - - -## Loading a Pretrained Language Model - -Once your SAE is trained, the final SAE weights will be saved to wandb and are loadable via the session loader. The session loader will return: -- The model your SAE was trained on (presumably you're interested in studying this. It's always a HookedTransformer) -- Your SAE. -- An activations loader: from which you can get randomly sampled activations or batches of tokens from the dataset you used to train the SAE. (more on this in the tutorial) - -```python -from sae_lens import LMSparseAutoencoderSessionloader - -path ="path/to/sparse_autoencoder.pt" -model, sparse_autoencoder, activations_loader = LMSparseAutoencoderSessionloader.load_session_from_pretrained( - path -) - -``` -## Tutorials - -I wrote a tutorial to show users how to do some basic exploration of their SAE. -- `evaluating_your_sae.ipynb`: A quick/dirty notebook showing how to check L0 and Prediction loss with your SAE, as well as showing how to generate interactive dashboards using Callum's reporduction of [Anthropics interface](https://transformer-circuits.pub/2023/monosemantic-features#setup-interface). -- `logits_lens_with_features.ipynb`: A notebook showing how to reproduce the analysis from this [LessWrong post](https://www.lesswrong.com/posts/qykrYY6rXXM7EEs8Q/understanding-sae-features-with-the-logit-lens). - -## Example Dashboard - -WandB Dashboards provide lots of useful insights while training SAE's. Here's a screenshot from one training run. - -![screenshot](content/dashboard_screenshot.png) - - -## Example Output - -Here's one feature we found in the residual stream of Layer 10 of GPT-2 Small: - -![alt text](content/readme_screenshot_predict_pronoun_feature.png). Open `gpt2_resid_pre10_predict_pronoun_feature.html` in your browser to interact with the dashboard (WIP). - -Note, probably this feature could split into more mono-semantic features in a larger SAE that had been trained for longer. (this was was only about 49152 features trained on 10M tokens from OpenWebText). - - -## Citations and References: +## Citations and References Research: - [Towards Monosemanticy](https://transformer-circuits.pub/2023/monosemantic-features) From 96b1e120d78f5f05cd94aec7a763bc14849aa1d3 Mon Sep 17 00:00:00 2001 From: jbloom-md Date: Sun, 14 Apr 2024 12:36:25 +0100 Subject: [PATCH 6/8] add device override to session loader --- sae_lens/analysis/neuronpedia_integration.py | 2 + sae_lens/training/session_loader.py | 11 +- tutorials/basic_loading_and_analysing.ipynb | 452 + tutorials/evaluating_your_sae.ipynb | 366 - tutorials/logits_lens_with_features.ipynb | 151976 +++++++++++++++- 5 files changed, 151650 insertions(+), 1157 deletions(-) create mode 100644 tutorials/basic_loading_and_analysing.ipynb delete mode 100644 tutorials/evaluating_your_sae.ipynb diff --git a/sae_lens/analysis/neuronpedia_integration.py b/sae_lens/analysis/neuronpedia_integration.py index 8a64e6d9..84f628ad 100644 --- a/sae_lens/analysis/neuronpedia_integration.py +++ b/sae_lens/analysis/neuronpedia_integration.py @@ -19,3 +19,5 @@ def get_neuronpedia_quick_list( ] url = url + "&features=" + urllib.parse.quote(json.dumps(list_feature)) webbrowser.open(url) + + return url diff --git a/sae_lens/training/session_loader.py b/sae_lens/training/session_loader.py index 9e6df380..7835bb58 100644 --- a/sae_lens/training/session_loader.py +++ b/sae_lens/training/session_loader.py @@ -39,7 +39,7 @@ def load_sae_training_group_session( @classmethod def load_pretrained_sae( - cls, path: str + cls, path: str, device: str = "cpu" ) -> Tuple[HookedTransformer, SparseAutoencoderDictionary, ActivationsStore]: """ Loads a session for analysing a pretrained sparse autoencoder. @@ -47,6 +47,8 @@ def load_pretrained_sae( # load the SAE sparse_autoencoder = SparseAutoencoder.load_from_pretrained(path) + sparse_autoencoder.to(device) + sparse_autoencoder.cfg.device = device # load the model, SAE and activations loader with it. session_loader = cls(sparse_autoencoder.cfg) @@ -56,7 +58,7 @@ def load_pretrained_sae( return model, sae_group, activations_loader - def get_model(self, model_name: str): + def get_model(self, model_name: str) -> HookedTransformer: """ Loads a model from transformer lens. @@ -65,6 +67,9 @@ def get_model(self, model_name: str): # Todo: add check that model_name is valid - model = HookedTransformer.from_pretrained(model_name) + model = HookedTransformer.from_pretrained( + model_name, + device=self.cfg.device, + ) return model diff --git a/tutorials/basic_loading_and_analysing.ipynb b/tutorials/basic_loading_and_analysing.ipynb new file mode 100644 index 00000000..0e8d05b3 --- /dev/null +++ b/tutorials/basic_loading_and_analysing.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading and Analysing Pre-Trained Sparse Autoencoders" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For the most part I'll try to import functions and classes near where they are used\n", + "# to make it clear where they come from.\n", + "\n", + "import torch\n", + "\n", + "torch.set_grad_enabled(False)\n", + "\n", + "if torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading a pretrained Sparse Autoencoder\n", + "\n", + "SAE Lens provides utilities for downloading some previously trained Sparse Autoencoders from huggingface. We may improve the functionality here in the future and add more pre-trained SAEs. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sae_lens.toolkit.pretrained_saes import get_gpt2_res_jb_saes\n", + "\n", + "# let's start with a layer 8 SAE.\n", + "hook_point = \"blocks.8.hook_resid_pre\"\n", + "\n", + "# if the SAEs were stored with precomputed feature sparsities,\n", + "# those will be return in a dictionary as well.\n", + "saes, sparsities = get_gpt2_res_jb_saes(hook_point)\n", + "\n", + "print(saes.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to start analysing as quickly as possible, we have a \"session loader\" which will load the transformer_lens model and a dataloading class that can provide both activations and tokens from the the huggingface dataset the SAE was trained with." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sae_lens.training.session_loader import LMSparseAutoencoderSessionloader\n", + "\n", + "sparse_autoencoder = saes[hook_point]\n", + "sparse_autoencoder.to(device)\n", + "sparse_autoencoder.cfg.device = device\n", + "\n", + "loader = LMSparseAutoencoderSessionloader(sparse_autoencoder.cfg)\n", + "\n", + "# don't overwrite the sparse autoencoder with the loader's sae (newly initialized)\n", + "model, _, activation_store = loader.load_sae_training_group_session()\n", + "\n", + "# TODO: We should have the session loader go straight to huggingface." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Analysis\n", + "\n", + "Let's check some basic stats on this SAE in order to see how some basic functionality in the codebase works.\n", + "\n", + "We'll calculate:\n", + "- L0 (the number of features that fire per activation)\n", + "- The cross entropy loss when the output of the SAE is used in place of the activations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### L0 Test and Reconstruction Test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "\n", + "sparse_autoencoder.eval() # prevents error if we're expecting a dead neuron mask for who grads\n", + "\n", + "with torch.no_grad():\n", + " # activation store can give us tokens.\n", + " batch_tokens = activation_store.get_batch_tokens()\n", + " _, cache = model.run_with_cache(batch_tokens, prepend_bos=True)\n", + "\n", + " # Use the SAE\n", + " sae_out, feature_acts, loss, mse_loss, l1_loss, _ = sparse_autoencoder(\n", + " cache[sparse_autoencoder.cfg.hook_point]\n", + " )\n", + "\n", + " # save some room\n", + " del cache\n", + "\n", + " # ignore the bos token, get the number of features that activated in each token, averaged accross batch and position\n", + " l0 = (feature_acts[:, 1:] > 0).float().sum(-1).detach()\n", + " print(\"average l0\", l0.mean().item())\n", + " px.histogram(l0.flatten().cpu().numpy()).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that while the mean L0 is 64, it varies with the specific activation.\n", + "\n", + "To estimate reconstruction performance, we calculate the CE loss of the model with and without the SAE being used in place of the activations. This will vary depending on the tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformer_lens import utils\n", + "from functools import partial\n", + "\n", + "\n", + "# next we want to do a reconstruction test.\n", + "def reconstr_hook(activation, hook, sae_out):\n", + " return sae_out\n", + "\n", + "\n", + "def zero_abl_hook(activation, hook):\n", + " return torch.zeros_like(activation)\n", + "\n", + "\n", + "print(\"Orig\", model(batch_tokens, return_type=\"loss\").item())\n", + "print(\n", + " \"reconstr\",\n", + " model.run_with_hooks(\n", + " batch_tokens,\n", + " fwd_hooks=[\n", + " (\n", + " utils.get_act_name(\"resid_pre\", 10),\n", + " partial(reconstr_hook, sae_out=sae_out),\n", + " )\n", + " ],\n", + " return_type=\"loss\",\n", + " ).item(),\n", + ")\n", + "print(\n", + " \"Zero\",\n", + " model.run_with_hooks(\n", + " batch_tokens,\n", + " return_type=\"loss\",\n", + " fwd_hooks=[(utils.get_act_name(\"resid_pre\", 10), zero_abl_hook)],\n", + " ).item(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Specific Capability Test\n", + "\n", + "Validating model performance on specific tasks when using the reconstructed activation is quite important when studying specific tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example_prompt = \"When John and Mary went to the shops, John gave the bag to\"\n", + "example_answer = \" Mary\"\n", + "utils.test_prompt(example_prompt, example_answer, model, prepend_bos=True)\n", + "\n", + "logits, cache = model.run_with_cache(example_prompt, prepend_bos=True)\n", + "tokens = model.to_tokens(example_prompt)\n", + "sae_out, feature_acts, loss, mse_loss, l1_loss, _ = sparse_autoencoder(\n", + " cache[sparse_autoencoder.cfg.hook_point]\n", + ")\n", + "\n", + "\n", + "def reconstr_hook(activations, hook, sae_out):\n", + " return sae_out\n", + "\n", + "\n", + "def zero_abl_hook(mlp_out, hook):\n", + " return torch.zeros_like(mlp_out)\n", + "\n", + "\n", + "hook_point = sparse_autoencoder.cfg.hook_point\n", + "\n", + "print(\"Orig\", model(tokens, return_type=\"loss\").item())\n", + "print(\n", + " \"reconstr\",\n", + " model.run_with_hooks(\n", + " tokens,\n", + " fwd_hooks=[\n", + " (\n", + " hook_point,\n", + " partial(reconstr_hook, sae_out=sae_out),\n", + " )\n", + " ],\n", + " return_type=\"loss\",\n", + " ).item(),\n", + ")\n", + "print(\n", + " \"Zero\",\n", + " model.run_with_hooks(\n", + " tokens,\n", + " return_type=\"loss\",\n", + " fwd_hooks=[(hook_point, zero_abl_hook)],\n", + " ).item(),\n", + ")\n", + "\n", + "\n", + "with model.hooks(\n", + " fwd_hooks=[\n", + " (\n", + " hook_point,\n", + " partial(reconstr_hook, sae_out=sae_out),\n", + " )\n", + " ]\n", + "):\n", + " utils.test_prompt(example_prompt, example_answer, model, prepend_bos=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generating Feature Interfaces\n", + "\n", + "Feature dashboards are an important part of SAE Evaluation. They work by:\n", + "- 1. Collecting feature activations over a larger number of examples.\n", + "- 2. Aggregating feature specific statistics (such as max activating examples).\n", + "- 3. Representing that information in a standardized way" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "\n", + "def get_tokens(\n", + " activation_store,\n", + " n_batches_to_sample_from: int = 2**10,\n", + " n_prompts_to_select: int = 4096 * 6,\n", + "):\n", + " all_tokens_list = []\n", + " pbar = tqdm(range(n_batches_to_sample_from))\n", + " for _ in pbar:\n", + " batch_tokens = activation_store.get_batch_tokens()\n", + " batch_tokens = batch_tokens[torch.randperm(batch_tokens.shape[0])][\n", + " : batch_tokens.shape[0]\n", + " ]\n", + " all_tokens_list.append(batch_tokens)\n", + "\n", + " all_tokens = torch.cat(all_tokens_list, dim=0)\n", + " all_tokens = all_tokens[torch.randperm(all_tokens.shape[0])]\n", + " return all_tokens[:n_prompts_to_select]\n", + "\n", + "\n", + "all_tokens = get_tokens(activation_store) # should take a few minutes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sae_vis.data_config_classes import SaeVisConfig\n", + "from sae_vis.data_storing_fns import SaeVisData\n", + "\n", + "test_feature_idx_gpt = range(10)\n", + "\n", + "feature_vis_config_gpt = SaeVisConfig(\n", + " hook_point=hook_point,\n", + " features=test_feature_idx_gpt,\n", + " batch_size=2048,\n", + " minibatch_size_tokens=128,\n", + " verbose=True,\n", + ")\n", + "\n", + "sae_vis_data_gpt = SaeVisData.create(\n", + " encoder=sparse_autoencoder,\n", + " model=model,\n", + " tokens=all_tokens, # type: ignore\n", + " cfg=feature_vis_config_gpt,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "import os\n", + "import webbrowser\n", + "\n", + "for feature in test_feature_idx_gpt:\n", + " filename = f\"{feature}_feature_vis_demo_gpt.html\"\n", + " sae_vis_data_gpt.save_feature_centric_vis(filename, feature)\n", + " webbrowser.open(\"file://\" + os.path.abspath(filename))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, since generating feature dashboards can be done once per sparse autoencoder, for pre-trained SAEs in the public domain, everyone can use the same dashboards. Neuronpedia hosts dashboards which we can load via the intergration." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "from sae_lens.analysis.neuronpedia_integration import get_neuronpedia_quick_list\n", + "\n", + "# this function should open\n", + "neuronpedia_quick_list = get_neuronpedia_quick_list(\n", + " test_feature_idx_gpt,\n", + " layer=sparse_autoencoder.cfg.hook_point_layer,\n", + " model=\"gpt2-small\",\n", + " dataset=\"res-jb\",\n", + " name=\"A quick list we made\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mats_sae_training", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/evaluating_your_sae.ipynb b/tutorials/evaluating_your_sae.ipynb deleted file mode 100644 index d6ba6ed8..00000000 --- a/tutorials/evaluating_your_sae.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluating your SAE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import torch\n", - "import json\n", - "import plotly.express as px\n", - "from transformer_lens import utils\n", - "from datasets import load_dataset\n", - "from typing import Dict\n", - "from pathlib import Path\n", - "\n", - "from functools import partial\n", - "\n", - "sys.path.append(\"..\")\n", - "\n", - "from sae_lens.training.session_loader import LMSparseAutoencoderSessionloader\n", - "from sae_vis.data_fetching_fns import get_feature_data, FeatureData\n", - "from sae_vis.data_config_classes import SaeVisConfig\n", - "\n", - "if torch.backends.mps.is_available():\n", - " device = \"mps\"\n", - "else:\n", - " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "torch.set_grad_enabled(False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load your Autoencoder\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Start by downloading them from huggingface\n", - "from huggingface_hub import hf_hub_download\n", - "\n", - "REPO_ID = \"jbloom/GPT2-Small-SAEs\"\n", - "\n", - "\n", - "layer = 8 # any layer from 0 - 11 works here\n", - "FILENAME = f\"final_sparse_autoencoder_gpt2-small_blocks.{layer}.hook_resid_pre_24576.pt\"\n", - "\n", - "# this is great because if you've already downloaded the SAE it won't download it twice!\n", - "path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We can then load the SAE, dataset and model using the session loader\n", - "model, sparse_autoencoders, activation_store = (\n", - " LMSparseAutoencoderSessionloader.load_session_from_pretrained(path=path)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i, sae in enumerate(sparse_autoencoders):\n", - " hyp = sae.cfg\n", - " print(\n", - " f\"{i}: Layer {hyp.hook_point_layer}, p_norm {hyp.lp_norm}, alpha {hyp.l1_coefficient}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pick which sae you wnat to evaluate. Default is 0\n", - "sparse_autoencoder = list(sparse_autoencoders)[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the Autoencoder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### L0 Test and Reconstruction Test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sparse_autoencoder.eval() # prevents error if we're expecting a dead neuron mask for who grads\n", - "with torch.no_grad():\n", - " batch_tokens = activation_store.get_batch_tokens()\n", - " _, cache = model.run_with_cache(batch_tokens, prepend_bos=True)\n", - " sae_out, feature_acts, loss, mse_loss, l1_loss, _ = sparse_autoencoder(\n", - " cache[sparse_autoencoder.cfg.hook_point]\n", - " )\n", - " del cache\n", - "\n", - " # ignore the bos token, get the number of features that activated in each token, averaged accross batch and position\n", - " l0 = (feature_acts[:, 1:] > 0).float().sum(-1).detach()\n", - " print(\"average l0\", l0.mean().item())\n", - " px.histogram(l0.flatten().cpu().numpy()).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# next we want to do a reconstruction test.\n", - "def reconstr_hook(activation, hook, sae_out):\n", - " return sae_out\n", - "\n", - "\n", - "def zero_abl_hook(activation, hook):\n", - " return torch.zeros_like(activation)\n", - "\n", - "\n", - "print(\"Orig\", model(batch_tokens, return_type=\"loss\").item())\n", - "print(\n", - " \"reconstr\",\n", - " model.run_with_hooks(\n", - " batch_tokens,\n", - " fwd_hooks=[\n", - " (\n", - " utils.get_act_name(\"resid_pre\", 10),\n", - " partial(reconstr_hook, sae_out=sae_out),\n", - " )\n", - " ],\n", - " return_type=\"loss\",\n", - " ).item(),\n", - ")\n", - "print(\n", - " \"Zero\",\n", - " model.run_with_hooks(\n", - " batch_tokens,\n", - " return_type=\"loss\",\n", - " fwd_hooks=[(utils.get_act_name(\"resid_pre\", 10), zero_abl_hook)],\n", - " ).item(),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Specific Capability Test\n", - "\n", - "Validating model performance on specific tasks when using the reconstructed activation is quite important when studying specific tasks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_prompt = \"When John and Mary went to the shops, John gave the bag to\"\n", - "example_answer = \" Mary\"\n", - "utils.test_prompt(example_prompt, example_answer, model, prepend_bos=True)\n", - "\n", - "logits, cache = model.run_with_cache(example_prompt, prepend_bos=True)\n", - "tokens = model.to_tokens(example_prompt)\n", - "sae_out, feature_acts, loss, mse_loss, l1_loss, _ = sparse_autoencoder(\n", - " cache[sparse_autoencoder.cfg.hook_point]\n", - ")\n", - "\n", - "\n", - "def reconstr_hook(activations, hook, sae_out):\n", - " return sae_out\n", - "\n", - "\n", - "def zero_abl_hook(mlp_out, hook):\n", - " return torch.zeros_like(mlp_out)\n", - "\n", - "\n", - "hook_point = sparse_autoencoder.cfg.hook_point\n", - "\n", - "print(\"Orig\", model(tokens, return_type=\"loss\").item())\n", - "print(\n", - " \"reconstr\",\n", - " model.run_with_hooks(\n", - " tokens,\n", - " fwd_hooks=[\n", - " (\n", - " hook_point,\n", - " partial(reconstr_hook, sae_out=sae_out),\n", - " )\n", - " ],\n", - " return_type=\"loss\",\n", - " ).item(),\n", - ")\n", - "print(\n", - " \"Zero\",\n", - " model.run_with_hooks(\n", - " tokens,\n", - " return_type=\"loss\",\n", - " fwd_hooks=[(hook_point, zero_abl_hook)],\n", - " ).item(),\n", - ")\n", - "\n", - "\n", - "with model.hooks(\n", - " fwd_hooks=[\n", - " (\n", - " hook_point,\n", - " partial(reconstr_hook, sae_out=sae_out),\n", - " )\n", - " ]\n", - "):\n", - " utils.test_prompt(example_prompt, example_answer, model, prepend_bos=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generating Feature Interfaces" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vals, inds = torch.topk(feature_acts[0, -1].detach().cpu(), 10)\n", - "px.bar(x=[str(i) for i in inds], y=vals).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vocab_dict = model.tokenizer.vocab\n", - "vocab_dict = {\n", - " v: k.replace(\"Ġ\", \" \").replace(\"\\n\", \"\\\\n\") for k, v in vocab_dict.items()\n", - "}\n", - "\n", - "vocab_dict_filepath = Path(os.getcwd()) / \"vocab_dict.json\"\n", - "if not vocab_dict_filepath.exists():\n", - " with open(vocab_dict_filepath, \"w\") as f:\n", - " json.dump(vocab_dict, f)\n", - "\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "data = load_dataset(\n", - " \"NeelNanda/c4-code-20k\", split=\"train\"\n", - ") # currently use this dataset to avoid deal with tokenization while streaming\n", - "tokenized_data = utils.tokenize_and_concatenate(data, model.tokenizer, max_length=128)\n", - "tokenized_data = tokenized_data.shuffle(42)\n", - "all_tokens = tokenized_data[\"tokens\"]\n", - "\n", - "\n", - "# Currently, don't think much more time can be squeezed out of it. Maybe the best saving would be to\n", - "# make the entire sequence indexing parallelized, but that's possibly not worth it right now.\n", - "\n", - "total_batch_size = 4096 * 5\n", - "feature_idx = list(inds.flatten().cpu().numpy())\n", - "# max_batch_size = 512\n", - "# total_batch_size = 16384\n", - "# feature_idx = list(range(1000))\n", - "\n", - "\n", - "feature_vis_params = SaeVisConfig(\n", - " hook_point=sparse_autoencoder.cfg.hook_point,\n", - " minibatch_size_features=256,\n", - " minibatch_size_tokens=64,\n", - " features=feature_idx,\n", - " verbose=True\n", - ")\n", - "\n", - "\n", - "\n", - "tokens = all_tokens[:total_batch_size] \n", - "\n", - "feature_data: Dict[int, FeatureData] = get_feature_data(\n", - " encoder=sparse_autoencoder,\n", - " model=model,\n", - " tokens=tokens,\n", - " cfg=feature_vis_params\n", - ")\n", - "\n", - "feature_data.model = model\n", - "\n", - "for test_idx in list(inds.flatten().cpu().numpy()):\n", - " feature_data.save_feature_centric_vis(\n", - " f\"data_{test_idx:04}.html\",\n", - " feature_idx=test_idx\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will produce a number of html files which each contain a dashboard showing feature activation on the sample data. It currently doesn't process that much data so it isn't that useful. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mats_sae_training", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/logits_lens_with_features.ipynb b/tutorials/logits_lens_with_features.ipynb index c2bbe9ff..e38fa5c5 100644 --- a/tutorials/logits_lens_with_features.ipynb +++ b/tutorials/logits_lens_with_features.ipynb @@ -1,808 +1,151208 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Understanding SAE Features with the Logit Lens\n", - "\n", - "This notebook demonstrates how to use the mats_sae_training library to perform the analysis documented the post \"[Understanding SAE Features with the Logit Lens](https://www.alignmentforum.org/posts/qykrYY6rXXM7EEs8Q/understanding-sae-features-with-the-logit-lens)\". \n", - "\n", - "As such, the notebook will include sections for:\n", - "- Loading in GPT2-Small Residual Stream SAEs from Huggingface. \n", - "- Performing Virtual Weight Based Analysis of features (specifically looking at the logit weight distributions).\n", - "- Programmatically opening neuronpedia tabs to engage with public dashboards on [neuronpedia](https://www.neuronpedia.org/).\n", - "- Performing Token Set Enrichment Analysis (based on Gene Set Enrichment Analysis). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Up\n", - "\n", - "Here we'll load various functions for things like:\n", - "- downloading and loading our SAEs from huggingface. \n", - "- opening neuronpedia from a jupyter cell. \n", - "- calculating statistics of the logit weight distributions. \n", - "- performing Token Set Enrichment Analysis (TSEA) and plotting the results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "\n", - "import numpy as np\n", - "import torch\n", - "import plotly_express as px\n", - "\n", - "from transformer_lens import HookedTransformer\n", - "\n", - "# Model Loading\n", - "from sae_lens.analysis.toolkit import get_all_gpt2_small_saes, open_neuronpedia\n", - "\n", - "# Virtual Weight / Feature Statistics Functions\n", - "from sae_lens.analysis.feature_statistics import get_all_stats_dfs, get_W_U_W_dec_stats_df\n", - "\n", - "# Enrichment Analysis Functions\n", - "from sae_lens.analysis.tsea import (\n", - " get_enrichment_df,\n", - " manhattan_plot_enrichment_scores,\n", - " plot_top_k_feature_projections_by_token_and_category,\n", - ")\n", - "from sae_lens.analysis.tsea import (\n", - " get_baby_name_sets,\n", - " get_letter_gene_sets,\n", - " generate_pos_sets,\n", - " get_test_gene_sets,\n", - " get_gene_set_from_regex,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading GPT2 Small and SAE Weights\n", - "\n", - "This will take a while the first time you run it, but will be quick thereafter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = HookedTransformer.from_pretrained(\"gpt2-small\")\n", - "gpt2_small_sparse_autoencoders, gpt2_small_sae_sparsities = get_all_gpt2_small_saes()\n", - "W_dec_all_layers = torch.stack(\n", - " [\n", - " gpt2_small_sparse_autoencoders[i].W_dec.detach().cpu()\n", - " for i in gpt2_small_sparse_autoencoders.keys()\n", - " ],\n", - " dim=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Statistical Properties of Feature Logit Distributions\n", - "\n", - "In the post I study layer 8 (for no particular reason). At the end of this notebook is code for visualizing these statistics across all layers. Feel free to change the layer here and explore different layers. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# In the post, I focus on layer 8\n", - "layer = 8\n", - "\n", - "# get the corresponding SAE and feature sparsities.\n", - "sparse_autoencoder = gpt2_small_sparse_autoencoders[f\"blocks.{layer}.hook_resid_pre\"]\n", - "log_feature_sparsity = gpt2_small_sae_sparsities[f\"blocks.{layer}.hook_resid_pre\"].cpu()\n", - "W_dec = sparse_autoencoder.W_dec.detach().cpu()\n", - "\n", - "# calculate the statistics of the logit weight distributions\n", - "W_U_stats_df_dec, dec_projection_onto_W_U = get_W_U_W_dec_stats_df(\n", - " W_dec, model, cosine_sim=False\n", - ")\n", - "W_U_stats_df_dec[\"sparsity\"] = (\n", - " log_feature_sparsity # add feature sparsity since it is often interesting.\n", - ")\n", - "display(W_U_stats_df_dec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's look at the distribution of the 3rd / 4th moments. I found these aren't as useful on their own as joint distributions can be.\n", - "px.histogram(\n", - " W_U_stats_df_dec,\n", - " x=\"skewness\",\n", - " width=800,\n", - " height=300,\n", - " nbins=1000,\n", - " title=\"Skewness of the Logit Weight Distributions\",\n", - ").show()\n", - "\n", - "px.histogram(\n", - " W_U_stats_df_dec,\n", - " x=np.log10(W_U_stats_df_dec[\"kurtosis\"]),\n", - " width=800,\n", - " height=300,\n", - " nbins=1000,\n", - " title=\"Kurtosis of the Logit Weight Distributions\",\n", - ").show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = px.scatter(\n", - " W_U_stats_df_dec,\n", - " x=\"skewness\",\n", - " y=\"kurtosis\",\n", - " color=\"std\",\n", - " color_continuous_scale=\"Portland\",\n", - " hover_name=\"feature\",\n", - " width=800,\n", - " height=500,\n", - " log_y=True, # Kurtosis has larger outliers so logging creates a nicer scale.\n", - " labels={\"x\": \"Skewness\", \"y\": \"Kurtosis\", \"color\": \"Standard Deviation\"},\n", - " title=f\"Layer {8}: Skewness vs Kurtosis of the Logit Weight Distributions\",\n", - ")\n", - "\n", - "# decrease point size\n", - "fig.update_traces(marker=dict(size=3))\n", - "\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# then you can query accross combinations of the statistics to find features of interest and open them in neuronpedia.\n", - "tmp_df = W_U_stats_df_dec[[\"feature\", \"skewness\", \"kurtosis\", \"std\"]]\n", - "# tmp_df = tmp_df[(tmp_df[\"std\"] > 0.04)]\n", - "# tmp_df = tmp_df[(tmp_df[\"skewness\"] > 0.65)]\n", - "tmp_df = tmp_df[(tmp_df[\"skewness\"] > 3)]\n", - "tmp_df = tmp_df.sort_values(\"skewness\", ascending=False).head(10)\n", - "display(tmp_df)\n", - "\n", - "# if desired, open the features in neuronpedia\n", - "for feature in tmp_df.feature:\n", - " open_neuronpedia(feature, layer=layer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Token Set Enrichment Analysis\n", - "\n", - "We now proceed to token set enrichment analysis. I highly recommend reading my AlignmentForum post (espeically the case studies) before reading too much into any of these results. \n", - "Also read this [post](https://transformer-circuits.pub/2024/qualitative-essay/index.html) for good general perspectives on statistics here. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining Our Token Sets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import nltk\n", - "nltk.download('averaged_perceptron_tagger')\n", - "# get the vocab we need to filter to formulate token sets.\n", - "vocab = model.tokenizer.get_vocab() # type: ignore\n", - "\n", - "# make a regex dictionary to specify more sets.\n", - "regex_dict = {\n", - " \"starts_with_space\": r\"Ġ.*\",\n", - " \"starts_with_capital\": r\"^Ġ*[A-Z].*\",\n", - " \"starts_with_lower\": r\"^Ġ*[a-z].*\",\n", - " \"all_digits\": r\"^Ġ*\\d+$\",\n", - " \"is_punctuation\": r\"^[^\\w\\s]+$\",\n", - " \"contains_close_bracket\": r\".*\\).*\",\n", - " \"contains_open_bracket\": r\".*\\(.*\",\n", - " \"all_caps\": r\"Ġ*[A-Z]+$\",\n", - " \"1 digit\": r\"Ġ*\\d{1}$\",\n", - " \"2 digits\": r\"Ġ*\\d{2}$\",\n", - " \"3 digits\": r\"Ġ*\\d{3}$\",\n", - " \"4 digits\": r\"Ġ*\\d{4}$\",\n", - " \"length_1\": r\"^Ġ*\\w{1}$\",\n", - " \"length_2\": r\"^Ġ*\\w{2}$\",\n", - " \"length_3\": r\"^Ġ*\\w{3}$\",\n", - " \"length_4\": r\"^Ġ*\\w{4}$\",\n", - " \"length_5\": r\"^Ġ*\\w{5}$\",\n", - "}\n", - "\n", - "# print size of gene sets\n", - "all_token_sets = get_letter_gene_sets(vocab)\n", - "for key, value in regex_dict.items():\n", - " gene_set = get_gene_set_from_regex(vocab, value)\n", - " all_token_sets[key] = gene_set\n", - "\n", - "# some other sets that can be interesting\n", - "baby_name_sets = get_baby_name_sets(vocab)\n", - "pos_sets = generate_pos_sets(vocab)\n", - "arbitrary_sets = get_test_gene_sets(model)\n", - "\n", - "all_token_sets = {**all_token_sets, **pos_sets}\n", - "all_token_sets = {**all_token_sets, **arbitrary_sets}\n", - "all_token_sets = {**all_token_sets, **baby_name_sets}\n", - "\n", - "# for each gene set, convert to string and print the first 5 tokens\n", - "for token_set_name, gene_set in sorted(\n", - " all_token_sets.items(), key=lambda x: len(x[1]), reverse=True\n", - "):\n", - " tokens = [model.to_string(id) for id in list(gene_set)][:10] # type: ignore\n", - " print(f\"{token_set_name}, has {len(gene_set)} genes\")\n", - " print(tokens)\n", - " print(\"----\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performing Token Set Enrichment Analysis\n", - "\n", - "Below we perform token set enrichment analysis on various token sets. In practice, we'd likely perform tests accross all tokens and large libraries of sets simultaneously but to make it easier to run, we look at features with higher skew and select of a few token sets at a time to consider." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "features_ordered_by_skew = (\n", - " W_U_stats_df_dec[\"skewness\"].sort_values(ascending=False).head(5000).index.to_list()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# filter our list.\n", - "token_sets_index = [\n", - " \"starts_with_space\",\n", - " \"starts_with_capital\",\n", - " \"all_digits\",\n", - " \"is_punctuation\",\n", - " \"all_caps\",\n", - "]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "\n", - "# calculate the enrichment scores\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, # use the logit weight values as our rankings over tokens.\n", - " features_ordered_by_skew, # subset by these features\n", - " token_set_selected, # use token_sets\n", - ")\n", - "\n", - "manhattan_plot_enrichment_scores(\n", - " df_enrichment_scores, label_threshold=0, top_n=3 # use our enrichment scores\n", - ").show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = px.scatter(\n", - " df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x)).T,\n", - " x=\"starts_with_space\",\n", - " y=\"starts_with_capital\",\n", - " marginal_x=\"histogram\",\n", - " marginal_y=\"histogram\",\n", - " labels={\n", - " \"starts_with_space\": \"Starts with Space\",\n", - " \"starts_with_capital\": \"Starts with Capital\",\n", - " },\n", - " title=\"Enrichment Scores for Starts with Space vs Starts with Capital\",\n", - " height=800,\n", - " width=800,\n", - ")\n", - "# reduce point size on the scatter only\n", - "fig.update_traces(marker=dict(size=2), selector=dict(mode=\"markers\"))\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"1 digit\", \"2 digits\", \"3 digits\", \"4 digits\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"nltk_pos_PRP\", \"nltk_pos_VBZ\", \"nltk_pos_NNP\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"nltk_pos_VBN\", \"nltk_pos_VBG\", \"nltk_pos_VB\", \"nltk_pos_VBD\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"nltk_pos_WP\", \"nltk_pos_RBR\", \"nltk_pos_WDT\", \"nltk_pos_RB\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"a\", \"e\", \"i\", \"o\", \"u\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"negative_words\", \"positive_words\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = px.scatter(\n", - " df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x))\n", - " .T.reset_index()\n", - " .rename(columns={\"index\": \"feature\"}),\n", - " x=\"negative_words\",\n", - " y=\"positive_words\",\n", - " marginal_x=\"histogram\",\n", - " marginal_y=\"histogram\",\n", - " labels={\n", - " \"starts_with_space\": \"Starts with Space\",\n", - " \"starts_with_capital\": \"Starts with Capital\",\n", - " },\n", - " title=\"Enrichment Scores for Starts with Space vs Starts with Capital\",\n", - " height=800,\n", - " width=800,\n", - " hover_name=\"feature\",\n", - ")\n", - "# reduce point size on the scatter only\n", - "fig.update_traces(marker=dict(size=2), selector=dict(mode=\"markers\"))\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"contains_close_bracket\", \"contains_open_bracket\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\n", - " \"1910's\",\n", - " \"1920's\",\n", - " \"1930's\",\n", - " \"1940's\",\n", - " \"1950's\",\n", - " \"1960's\",\n", - " \"1970's\",\n", - " \"1980's\",\n", - " \"1990's\",\n", - " \"2000's\",\n", - " \"2010's\",\n", - "]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"positive_words\", \"negative_words\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores, label_threshold=0.98).show()" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding SAE Features with the Logit Lens\n", + "\n", + "This notebook demonstrates how to use the mats_sae_training library to perform the analysis documented the post \"[Understanding SAE Features with the Logit Lens](https://www.alignmentforum.org/posts/qykrYY6rXXM7EEs8Q/understanding-sae-features-with-the-logit-lens)\". \n", + "\n", + "As such, the notebook will include sections for:\n", + "- Loading in GPT2-Small Residual Stream SAEs from Huggingface. \n", + "- Performing Virtual Weight Based Analysis of features (specifically looking at the logit weight distributions).\n", + "- Programmatically opening neuronpedia tabs to engage with public dashboards on [neuronpedia](https://www.neuronpedia.org/).\n", + "- Performing Token Set Enrichment Analysis (based on Gene Set Enrichment Analysis). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Up\n", + "\n", + "Here we'll load various functions for things like:\n", + "- downloading and loading our SAEs from huggingface. \n", + "- opening neuronpedia from a jupyter cell. \n", + "- calculating statistics of the logit weight distributions. \n", + "- performing Token Set Enrichment Analysis (TSEA) and plotting the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import plotly_express as px\n", + "\n", + "from transformer_lens import HookedTransformer\n", + "\n", + "# Model Loading\n", + "from sae_lens.toolkit.pretrained_saes import get_gpt2_res_jb_saes\n", + "from sae_lens.analysis.neuronpedia_integration import get_neuronpedia_quick_list\n", + "\n", + "# Virtual Weight / Feature Statistics Functions\n", + "from sae_lens.analysis.feature_statistics import (\n", + " get_all_stats_dfs,\n", + " get_W_U_W_dec_stats_df,\n", + ")\n", + "\n", + "# Enrichment Analysis Functions\n", + "from sae_lens.analysis.tsea import (\n", + " get_enrichment_df,\n", + " manhattan_plot_enrichment_scores,\n", + " plot_top_k_feature_projections_by_token_and_category,\n", + ")\n", + "from sae_lens.analysis.tsea import (\n", + " get_baby_name_sets,\n", + " get_letter_gene_sets,\n", + " generate_pos_sets,\n", + " get_test_gene_sets,\n", + " get_gene_set_from_regex,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading GPT2 Small and SAE Weights\n", + "\n", + "This will take a while the first time you run it, but will be quick thereafter." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_sets_index = [\"boys_names\", \"girls_names\"]\n", - "token_set_selected = {\n", - " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", - "}\n", - "df_enrichment_scores = get_enrichment_df(\n", - " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", - ")\n", - "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded pretrained model gpt2-small into HookedTransformer\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tmp_df = df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x)).T\n", - "color = (\n", - " W_U_stats_df_dec.sort_values(\"skewness\", ascending=False)\n", - " .head(5000)[\"skewness\"]\n", - " .values\n", - ")\n", - "fig = px.scatter(\n", - " tmp_df.reset_index().rename(columns={\"index\": \"feature\"}),\n", - " x=\"boys_names\",\n", - " y=\"girls_names\",\n", - " marginal_x=\"histogram\",\n", - " marginal_y=\"histogram\",\n", - " # color = color,\n", - " labels={\n", - " \"boys_names\": \"Enrichment Score (Boys Names)\",\n", - " \"girls_names\": \"Enrichment Score (Girls Names)\",\n", - " },\n", - " height=600,\n", - " width=800,\n", - " hover_name=\"feature\",\n", - ")\n", - "# reduce point size on the scatter only\n", - "fig.update_traces(marker=dict(size=3), selector=dict(mode=\"markers\"))\n", - "# annotate any features where the absolute distance between boys names and girls names > 3\n", - "for feature in df_enrichment_scores.columns:\n", - " if abs(tmp_df[\"boys_names\"][feature] - tmp_df[\"girls_names\"][feature]) > 2.9:\n", - " fig.add_annotation(\n", - " x=tmp_df[\"boys_names\"][feature] - 0.4,\n", - " y=tmp_df[\"girls_names\"][feature] + 0.1,\n", - " text=f\"{feature}\",\n", - " showarrow=False,\n", - " )\n", - "\n", - "\n", - "fig.show()" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/13 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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441.121479e-080.1903660.2129153.326520-2.125533
.....................
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\n", + "" + ], + "text/plain": [ + " feature mean std skewness kurtosis sparsity\n", + "0 0 3.605430e-10 0.147306 0.181176 3.349324 -2.826817\n", + "1 1 -1.156584e-08 0.167641 0.123870 3.466491 -3.049473\n", + "2 2 -2.933682e-08 0.157237 0.097977 3.300671 -2.960210\n", + "3 3 2.656633e-10 0.174304 0.133900 3.522123 -2.822145\n", + "4 4 1.121479e-08 0.190366 0.212915 3.326520 -2.125533\n", + "... ... ... ... ... ... ...\n", + "24571 24571 1.506690e-08 0.205128 0.320509 3.330734 -1.933250\n", + "24572 24572 3.629150e-08 0.163037 0.112237 3.468479 -3.279720\n", + "24573 24573 2.686994e-08 0.166876 0.311090 4.092551 -2.592994\n", + "24574 24574 1.723016e-08 0.170027 0.359409 3.876928 -3.186663\n", + "24575 24575 -2.125306e-08 0.256167 0.357178 2.617407 -2.580951\n", + "\n", + "[24576 rows x 6 columns]" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# In the post, I focus on layer 8\n", + "layer = 8\n", + "\n", + "# get the corresponding SAE and feature sparsities.\n", + "sparse_autoencoder = gpt2_small_sparse_autoencoders[f\"blocks.{layer}.hook_resid_pre\"]\n", + "log_feature_sparsity = gpt2_small_sae_sparsities[f\"blocks.{layer}.hook_resid_pre\"].cpu()\n", + "W_dec = sparse_autoencoder.W_dec.detach().cpu()\n", + "\n", + "# calculate the statistics of the logit weight distributions\n", + "W_U_stats_df_dec, dec_projection_onto_W_U = get_W_U_W_dec_stats_df(\n", + " W_dec, model, cosine_sim=False\n", + ")\n", + "W_U_stats_df_dec[\"sparsity\"] = (\n", + " log_feature_sparsity # add feature sparsity since it is often interesting.\n", + ")\n", + "display(W_U_stats_df_dec)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for category in [\"boys_names\"]:\n", - " plot_top_k_feature_projections_by_token_and_category(\n", - " token_set_selected,\n", - " df_enrichment_scores,\n", - " category=category,\n", - " dec_projection_onto_W_U=dec_projection_onto_W_U,\n", - " model=model,\n", - " log_y=False,\n", - " histnorm=None,\n", - " )" - ] + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plotly.com" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "skewness=%{x}
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I found these aren't as useful on their own as joint distributions can be.\n", + "px.histogram(\n", + " W_U_stats_df_dec,\n", + " x=\"skewness\",\n", + " width=800,\n", + " height=300,\n", + " nbins=1000,\n", + " title=\"Skewness of the Logit Weight Distributions\",\n", + ").show()\n", + "\n", + "px.histogram(\n", + " W_U_stats_df_dec,\n", + " x=np.log10(W_U_stats_df_dec[\"kurtosis\"]),\n", + " width=800,\n", + " height=300,\n", + " nbins=1000,\n", + " title=\"Kurtosis of the Logit Weight Distributions\",\n", + ").show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "W_U_stats_df_dec_all_layers = get_all_stats_dfs(\n", - " gpt2_small_sparse_autoencoders, gpt2_small_sae_sparsities, model, cosine_sim=True\n", - ")\n", - "\n", - "display(W_U_stats_df_dec_all_layers.shape)\n", - "display(W_U_stats_df_dec_all_layers.head())" - ] - }, + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plotly.com" + }, + "data": [ + { + "hovertemplate": "%{hovertext}

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"linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Layer 8: Skewness vs Kurtosis of the Logit Weight Distributions" + }, + "width": 800, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "skewness" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "kurtosis" + }, + "type": "log" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter(\n", + " W_U_stats_df_dec,\n", + " x=\"skewness\",\n", + " y=\"kurtosis\",\n", + " color=\"std\",\n", + " color_continuous_scale=\"Portland\",\n", + " hover_name=\"feature\",\n", + " width=800,\n", + " height=500,\n", + " log_y=True, # Kurtosis has larger outliers so logging creates a nicer scale.\n", + " labels={\"x\": \"Skewness\", \"y\": \"Kurtosis\", \"color\": \"Standard Deviation\"},\n", + " title=f\"Layer {8}: Skewness vs Kurtosis of the Logit Weight Distributions\",\n", + ")\n", + "\n", + "# decrease point size\n", + "fig.update_traces(marker=dict(size=3))\n", + "\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's plot the percentiles of the skewness and kurtosis by layer\n", - "tmp_df = W_U_stats_df_dec_all_layers.groupby(\"layer\")[\"skewness\"].describe(\n", - " percentiles=[0.01, 0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]\n", - ")\n", - "tmp_df = tmp_df[[\"1%\", \"5%\", \"10%\", \"25%\", \"50%\", \"75%\", \"90%\", \"95%\", \"99%\"]]\n", - "\n", - "fig = px.area(\n", - " tmp_df,\n", - " title=\"Skewness by Layer\",\n", - " width=800,\n", - " height=600,\n", - " color_discrete_sequence=px.colors.sequential.Turbo,\n", - ").show()\n", - "\n", - "\n", - "tmp_df = W_U_stats_df_dec_all_layers.groupby(\"layer\")[\"kurtosis\"].describe(\n", - " percentiles=[0.01, 0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]\n", - ")\n", - "tmp_df = tmp_df[[\"1%\", \"5%\", \"10%\", \"25%\", \"50%\", \"75%\", \"90%\", \"95%\", \"99%\"]]\n", - "\n", - "fig = px.area(\n", - " tmp_df,\n", - " title=\"Kurtosis by Layer\",\n", - " width=800,\n", - " height=600,\n", - " color_discrete_sequence=px.colors.sequential.Turbo,\n", - ")\n", - "\n", - "fig.show()" + "data": { + "text/html": [ + "
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featureskewnesskurtosisstd
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" + ], + "text/plain": [ + " feature skewness kurtosis std\n", + "14215 14215 4.387646 55.515949 0.133079\n", + "19273 19273 3.638764 48.143658 0.115937\n", + "8992 8992 3.432152 44.810341 0.123028\n", + "895 895 3.228184 42.953152 0.112418" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# let's make a pretty color scheme\n", - "from plotly.colors import n_colors\n", - "\n", - "colors = n_colors(\"rgb(5, 200, 200)\", \"rgb(200, 10, 10)\", 13, colortype=\"rgb\")\n", - "\n", - "# Make a box plot of the skewness by layer\n", - "fig = px.box(\n", - " W_U_stats_df_dec_all_layers,\n", - " x=\"layer\",\n", - " y=\"skewness\",\n", - " color=\"layer\",\n", - " color_discrete_sequence=colors,\n", - " height=600,\n", - " width=1200,\n", - " title=\"Skewness cos(W_U,W_dec) by Layer in GPT2 Small Residual Stream SAEs\",\n", - " labels={\"layer\": \"Layer\", \"skewnss\": \"Skewness\"},\n", - ")\n", - "fig.update_xaxes(showticklabels=True, dtick=1)\n", - "\n", - "# increase font size\n", - "fig.update_layout(font=dict(size=16))\n", - "fig.show()\n", - "\n", - "# Make a box plot of the skewness by layer\n", - "fig = px.box(\n", - " W_U_stats_df_dec_all_layers,\n", - " x=\"layer\",\n", - " y=\"kurtosis\",\n", - " color=\"layer\",\n", - " color_discrete_sequence=colors,\n", - " height=600,\n", - " width=1200,\n", - " log_y=True,\n", - " title=\"log kurtosis cos(W_U,W_dec) by Layer in GPT2 Small Residual Stream SAEs\",\n", - " labels={\"layer\": \"Layer\", \"kurtosis\": \"Log Kurtosis\"},\n", - ")\n", - "fig.update_xaxes(showticklabels=True, dtick=1)\n", - "\n", - "# increase font size\n", - "fig.update_layout(font=dict(size=16))\n", - "fig.show()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# scatter\n", - "fig = px.scatter(\n", - " W_U_stats_df_dec_all_layers[W_U_stats_df_dec_all_layers.log_feature_sparsity >= -9],\n", - " # W_U_stats_df_dec_all_layers[W_U_stats_df_dec_all_layers.layer == 8],\n", - " x=\"skewness\",\n", - " y=\"kurtosis\",\n", - " color=\"std\",\n", - " color_continuous_scale=\"Portland\",\n", - " hover_name=\"feature\",\n", - " # color_continuous_midpoint = 0,\n", - " # range_color = [-4,-1],\n", - " log_y=True,\n", - " height=800,\n", - " # width = 2000,\n", - " # facet_col=\"layer\",\n", - " # facet_col_wrap=5,\n", - " animation_frame=\"layer\",\n", - ")\n", - "fig.update_yaxes(matches=None)\n", - "fig.for_each_yaxis(lambda yaxis: yaxis.update(showticklabels=True))\n", - "\n", - "# decrease point size\n", - "fig.update_traces(marker=dict(size=5))\n", - "fig.show()\n", - "fig.write_html(\"skewness_kurtosis_scatter_all_layers.html\")" + "data": { + "text/plain": [ + "'https://neuronpedia.org/quick-list/?name=temporary_list&features=%5B%7B%22modelId%22%3A%20%22gpt2-small%22%2C%20%22layer%22%3A%20%228-res-jb%22%2C%20%22index%22%3A%20%2214215%22%7D%2C%20%7B%22modelId%22%3A%20%22gpt2-small%22%2C%20%22layer%22%3A%20%228-res-jb%22%2C%20%22index%22%3A%20%2219273%22%7D%2C%20%7B%22modelId%22%3A%20%22gpt2-small%22%2C%20%22layer%22%3A%20%228-res-jb%22%2C%20%22index%22%3A%20%228992%22%7D%2C%20%7B%22modelId%22%3A%20%22gpt2-small%22%2C%20%22layer%22%3A%20%228-res-jb%22%2C%20%22index%22%3A%20%22895%22%7D%5D'" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mats_sae_training", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# then you can query accross combinations of the statistics to find features of interest and open them in neuronpedia.\n", + "tmp_df = W_U_stats_df_dec[[\"feature\", \"skewness\", \"kurtosis\", \"std\"]]\n", + "# tmp_df = tmp_df[(tmp_df[\"std\"] > 0.04)]\n", + "# tmp_df = tmp_df[(tmp_df[\"skewness\"] > 0.65)]\n", + "tmp_df = tmp_df[(tmp_df[\"skewness\"] > 3)]\n", + "tmp_df = tmp_df.sort_values(\"skewness\", ascending=False).head(10)\n", + "display(tmp_df)\n", + "\n", + "# if desired, open the features in neuronpedia\n", + "get_neuronpedia_quick_list(list(tmp_df.feature), layer=layer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Token Set Enrichment Analysis\n", + "\n", + "We now proceed to token set enrichment analysis. I highly recommend reading my AlignmentForum post (espeically the case studies) before reading too much into any of these results. \n", + "Also read this [post](https://transformer-circuits.pub/2024/qualitative-essay/index.html) for good general perspectives on statistics here. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining Our Token Sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nltk\n", + "\n", + "nltk.download(\"averaged_perceptron_tagger\")\n", + "# get the vocab we need to filter to formulate token sets.\n", + "vocab = model.tokenizer.get_vocab() # type: ignore\n", + "\n", + "# make a regex dictionary to specify more sets.\n", + "regex_dict = {\n", + " \"starts_with_space\": r\"Ġ.*\",\n", + " \"starts_with_capital\": r\"^Ġ*[A-Z].*\",\n", + " \"starts_with_lower\": r\"^Ġ*[a-z].*\",\n", + " \"all_digits\": r\"^Ġ*\\d+$\",\n", + " \"is_punctuation\": r\"^[^\\w\\s]+$\",\n", + " \"contains_close_bracket\": r\".*\\).*\",\n", + " \"contains_open_bracket\": r\".*\\(.*\",\n", + " \"all_caps\": r\"Ġ*[A-Z]+$\",\n", + " \"1 digit\": r\"Ġ*\\d{1}$\",\n", + " \"2 digits\": r\"Ġ*\\d{2}$\",\n", + " \"3 digits\": r\"Ġ*\\d{3}$\",\n", + " \"4 digits\": r\"Ġ*\\d{4}$\",\n", + " \"length_1\": r\"^Ġ*\\w{1}$\",\n", + " \"length_2\": r\"^Ġ*\\w{2}$\",\n", + " \"length_3\": r\"^Ġ*\\w{3}$\",\n", + " \"length_4\": r\"^Ġ*\\w{4}$\",\n", + " \"length_5\": r\"^Ġ*\\w{5}$\",\n", + "}\n", + "\n", + "# print size of gene sets\n", + "all_token_sets = get_letter_gene_sets(vocab)\n", + "for key, value in regex_dict.items():\n", + " gene_set = get_gene_set_from_regex(vocab, value)\n", + " all_token_sets[key] = gene_set\n", + "\n", + "# some other sets that can be interesting\n", + "baby_name_sets = get_baby_name_sets(vocab)\n", + "pos_sets = generate_pos_sets(vocab)\n", + "arbitrary_sets = get_test_gene_sets(model)\n", + "\n", + "all_token_sets = {**all_token_sets, **pos_sets}\n", + "all_token_sets = {**all_token_sets, **arbitrary_sets}\n", + "all_token_sets = {**all_token_sets, **baby_name_sets}\n", + "\n", + "# for each gene set, convert to string and print the first 5 tokens\n", + "for token_set_name, gene_set in sorted(\n", + " all_token_sets.items(), key=lambda x: len(x[1]), reverse=True\n", + "):\n", + " tokens = [model.to_string(id) for id in list(gene_set)][:10] # type: ignore\n", + " print(f\"{token_set_name}, has {len(gene_set)} genes\")\n", + " print(tokens)\n", + " print(\"----\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performing Token Set Enrichment Analysis\n", + "\n", + "Below we perform token set enrichment analysis on various token sets. In practice, we'd likely perform tests accross all tokens and large libraries of sets simultaneously but to make it easier to run, we look at features with higher skew and select of a few token sets at a time to consider." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "features_ordered_by_skew = (\n", + " W_U_stats_df_dec[\"skewness\"].sort_values(ascending=False).head(5000).index.to_list()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# filter our list.\n", + "token_sets_index = [\n", + " \"starts_with_space\",\n", + " \"starts_with_capital\",\n", + " \"all_digits\",\n", + " \"is_punctuation\",\n", + " \"all_caps\",\n", + "]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "\n", + "# calculate the enrichment scores\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, # use the logit weight values as our rankings over tokens.\n", + " features_ordered_by_skew, # subset by these features\n", + " token_set_selected, # use token_sets\n", + ")\n", + "\n", + "manhattan_plot_enrichment_scores(\n", + " df_enrichment_scores, label_threshold=0, top_n=3 # use our enrichment scores\n", + ").show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.scatter(\n", + " df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x)).T,\n", + " x=\"starts_with_space\",\n", + " y=\"starts_with_capital\",\n", + " marginal_x=\"histogram\",\n", + " marginal_y=\"histogram\",\n", + " labels={\n", + " \"starts_with_space\": \"Starts with Space\",\n", + " \"starts_with_capital\": \"Starts with Capital\",\n", + " },\n", + " title=\"Enrichment Scores for Starts with Space vs Starts with Capital\",\n", + " height=800,\n", + " width=800,\n", + ")\n", + "# reduce point size on the scatter only\n", + "fig.update_traces(marker=dict(size=2), selector=dict(mode=\"markers\"))\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"1 digit\", \"2 digits\", \"3 digits\", \"4 digits\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"nltk_pos_PRP\", \"nltk_pos_VBZ\", \"nltk_pos_NNP\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"nltk_pos_VBN\", \"nltk_pos_VBG\", \"nltk_pos_VB\", \"nltk_pos_VBD\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"nltk_pos_WP\", \"nltk_pos_RBR\", \"nltk_pos_WDT\", \"nltk_pos_RB\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"a\", \"e\", \"i\", \"o\", \"u\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"negative_words\", \"positive_words\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.scatter(\n", + " df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x))\n", + " .T.reset_index()\n", + " .rename(columns={\"index\": \"feature\"}),\n", + " x=\"negative_words\",\n", + " y=\"positive_words\",\n", + " marginal_x=\"histogram\",\n", + " marginal_y=\"histogram\",\n", + " labels={\n", + " \"starts_with_space\": \"Starts with Space\",\n", + " \"starts_with_capital\": \"Starts with Capital\",\n", + " },\n", + " title=\"Enrichment Scores for Starts with Space vs Starts with Capital\",\n", + " height=800,\n", + " width=800,\n", + " hover_name=\"feature\",\n", + ")\n", + "# reduce point size on the scatter only\n", + "fig.update_traces(marker=dict(size=2), selector=dict(mode=\"markers\"))\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"contains_close_bracket\", \"contains_open_bracket\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\n", + " \"1910's\",\n", + " \"1920's\",\n", + " \"1930's\",\n", + " \"1940's\",\n", + " \"1950's\",\n", + " \"1960's\",\n", + " \"1970's\",\n", + " \"1980's\",\n", + " \"1990's\",\n", + " \"2000's\",\n", + " \"2010's\",\n", + "]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"positive_words\", \"negative_words\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores, label_threshold=0.98).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_sets_index = [\"boys_names\", \"girls_names\"]\n", + "token_set_selected = {\n", + " k: set(v) for k, v in all_token_sets.items() if k in token_sets_index\n", + "}\n", + "df_enrichment_scores = get_enrichment_df(\n", + " dec_projection_onto_W_U, features_ordered_by_skew, token_set_selected\n", + ")\n", + "manhattan_plot_enrichment_scores(df_enrichment_scores).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_df = df_enrichment_scores.apply(lambda x: -1 * np.log(1 - x)).T\n", + "color = (\n", + " W_U_stats_df_dec.sort_values(\"skewness\", ascending=False)\n", + " .head(5000)[\"skewness\"]\n", + " .values\n", + ")\n", + "fig = px.scatter(\n", + " tmp_df.reset_index().rename(columns={\"index\": \"feature\"}),\n", + " x=\"boys_names\",\n", + " y=\"girls_names\",\n", + " marginal_x=\"histogram\",\n", + " marginal_y=\"histogram\",\n", + " # color = color,\n", + " labels={\n", + " \"boys_names\": \"Enrichment Score (Boys Names)\",\n", + " \"girls_names\": \"Enrichment Score (Girls Names)\",\n", + " },\n", + " height=600,\n", + " width=800,\n", + " hover_name=\"feature\",\n", + ")\n", + "# reduce point size on the scatter only\n", + "fig.update_traces(marker=dict(size=3), selector=dict(mode=\"markers\"))\n", + "# annotate any features where the absolute distance between boys names and girls names > 3\n", + "for feature in df_enrichment_scores.columns:\n", + " if abs(tmp_df[\"boys_names\"][feature] - tmp_df[\"girls_names\"][feature]) > 2.9:\n", + " fig.add_annotation(\n", + " x=tmp_df[\"boys_names\"][feature] - 0.4,\n", + " y=tmp_df[\"girls_names\"][feature] + 0.1,\n", + " text=f\"{feature}\",\n", + " showarrow=False,\n", + " )\n", + "\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Digging into Particular Features\n", + "\n", + "When we do these enrichments, I generate the logit weight histograms by category using the following function. It's important to make sure the categories you group by are in the columns of df_enrichment_scores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for category in [\"boys_names\"]:\n", + " plot_top_k_feature_projections_by_token_and_category(\n", + " token_set_selected,\n", + " df_enrichment_scores,\n", + " category=category,\n", + " dec_projection_onto_W_U=dec_projection_onto_W_U,\n", + " model=model,\n", + " log_y=False,\n", + " histnorm=None,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Appendix Results: Logit Weight distribution Statistics Accross All Layers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "W_U_stats_df_dec_all_layers = get_all_stats_dfs(\n", + " gpt2_small_sparse_autoencoders, gpt2_small_sae_sparsities, model, cosine_sim=True\n", + ")\n", + "\n", + "display(W_U_stats_df_dec_all_layers.shape)\n", + "display(W_U_stats_df_dec_all_layers.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's plot the percentiles of the skewness and kurtosis by layer\n", + "tmp_df = W_U_stats_df_dec_all_layers.groupby(\"layer\")[\"skewness\"].describe(\n", + " percentiles=[0.01, 0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]\n", + ")\n", + "tmp_df = tmp_df[[\"1%\", \"5%\", \"10%\", \"25%\", \"50%\", \"75%\", \"90%\", \"95%\", \"99%\"]]\n", + "\n", + "fig = px.area(\n", + " tmp_df,\n", + " title=\"Skewness by Layer\",\n", + " width=800,\n", + " height=600,\n", + " color_discrete_sequence=px.colors.sequential.Turbo,\n", + ").show()\n", + "\n", + "\n", + "tmp_df = W_U_stats_df_dec_all_layers.groupby(\"layer\")[\"kurtosis\"].describe(\n", + " percentiles=[0.01, 0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]\n", + ")\n", + "tmp_df = tmp_df[[\"1%\", \"5%\", \"10%\", \"25%\", \"50%\", \"75%\", \"90%\", \"95%\", \"99%\"]]\n", + "\n", + "fig = px.area(\n", + " tmp_df,\n", + " title=\"Kurtosis by Layer\",\n", + " width=800,\n", + " height=600,\n", + " color_discrete_sequence=px.colors.sequential.Turbo,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# let's make a pretty color scheme\n", + "from plotly.colors import n_colors\n", + "\n", + "colors = n_colors(\"rgb(5, 200, 200)\", \"rgb(200, 10, 10)\", 13, colortype=\"rgb\")\n", + "\n", + "# Make a box plot of the skewness by layer\n", + "fig = px.box(\n", + " W_U_stats_df_dec_all_layers,\n", + " x=\"layer\",\n", + " y=\"skewness\",\n", + " color=\"layer\",\n", + " color_discrete_sequence=colors,\n", + " height=600,\n", + " width=1200,\n", + " title=\"Skewness cos(W_U,W_dec) by Layer in GPT2 Small Residual Stream SAEs\",\n", + " labels={\"layer\": \"Layer\", \"skewnss\": \"Skewness\"},\n", + ")\n", + "fig.update_xaxes(showticklabels=True, dtick=1)\n", + "\n", + "# increase font size\n", + "fig.update_layout(font=dict(size=16))\n", + "fig.show()\n", + "\n", + "# Make a box plot of the skewness by layer\n", + "fig = px.box(\n", + " W_U_stats_df_dec_all_layers,\n", + " x=\"layer\",\n", + " y=\"kurtosis\",\n", + " color=\"layer\",\n", + " color_discrete_sequence=colors,\n", + " height=600,\n", + " width=1200,\n", + " log_y=True,\n", + " title=\"log kurtosis cos(W_U,W_dec) by Layer in GPT2 Small Residual Stream SAEs\",\n", + " labels={\"layer\": \"Layer\", \"kurtosis\": \"Log Kurtosis\"},\n", + ")\n", + "fig.update_xaxes(showticklabels=True, dtick=1)\n", + "\n", + "# increase font size\n", + "fig.update_layout(font=dict(size=16))\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# scatter\n", + "fig = px.scatter(\n", + " W_U_stats_df_dec_all_layers[W_U_stats_df_dec_all_layers.log_feature_sparsity >= -9],\n", + " # W_U_stats_df_dec_all_layers[W_U_stats_df_dec_all_layers.layer == 8],\n", + " x=\"skewness\",\n", + " y=\"kurtosis\",\n", + " color=\"std\",\n", + " color_continuous_scale=\"Portland\",\n", + " hover_name=\"feature\",\n", + " # color_continuous_midpoint = 0,\n", + " # range_color = [-4,-1],\n", + " log_y=True,\n", + " height=800,\n", + " # width = 2000,\n", + " # facet_col=\"layer\",\n", + " # facet_col_wrap=5,\n", + " animation_frame=\"layer\",\n", + ")\n", + "fig.update_yaxes(matches=None)\n", + "fig.for_each_yaxis(lambda yaxis: yaxis.update(showticklabels=True))\n", + "\n", + "# decrease point size\n", + "fig.update_traces(marker=dict(size=5))\n", + "fig.show()\n", + "fig.write_html(\"skewness_kurtosis_scatter_all_layers.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mats_sae_training", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From 356a8efba06e4f453d2f15afe9171b71d780819a Mon Sep 17 00:00:00 2001 From: jbloom-md Date: Sun, 14 Apr 2024 12:36:51 +0100 Subject: [PATCH 7/8] update sae loading code --- sae_lens/toolkit/pretrained_saes.py | 72 ++++++++++++++++++++++------- 1 file changed, 56 insertions(+), 16 deletions(-) diff --git a/sae_lens/toolkit/pretrained_saes.py b/sae_lens/toolkit/pretrained_saes.py index a143881b..49406fed 100644 --- a/sae_lens/toolkit/pretrained_saes.py +++ b/sae_lens/toolkit/pretrained_saes.py @@ -1,5 +1,6 @@ import json import os +from typing import Optional, Tuple import torch from huggingface_hub import hf_hub_download, list_files_info @@ -17,36 +18,75 @@ def load_sparsity(path: str) -> torch.Tensor: return sparsity -def get_gpt2_res_jb_saes() -> ( - tuple[dict[str, SparseAutoencoder], dict[str, torch.Tensor]] -): +def download_sae_from_hf( + repo_id: str = "jbloom/GPT2-Small-SAEs-Reformatted", + folder_name: str = "blocks.0.hook_resid_pre", + force_download: bool = False, +) -> Tuple[str, str, Optional[str]]: + + FILENAME = f"{folder_name}/cfg.json" + cfg_path = hf_hub_download( + repo_id=repo_id, filename=FILENAME, force_download=force_download + ) + + FILENAME = f"{folder_name}/sae_weights.safetensors" + sae_path = hf_hub_download( + repo_id=repo_id, filename=FILENAME, force_download=force_download + ) + + try: + FILENAME = f"{folder_name}/sparsity.safetensors" + sparsity_path = hf_hub_download( + repo_id=repo_id, filename=FILENAME, force_download=force_download + ) + except: # noqa + sparsity_path = None + + return cfg_path, sae_path, sparsity_path + + +def load_sae_from_local_path(path: str) -> Tuple[SparseAutoencoder, torch.Tensor]: + sae = SparseAutoencoder.load_from_pretrained(path) + sparsity = load_sparsity(path) + return sae, sparsity + + +def get_gpt2_res_jb_saes( + hook_point: Optional[str] = None, + device: str = "cpu", +) -> tuple[dict[str, SparseAutoencoder], dict[str, torch.Tensor]]: + """ + Download the sparse autoencoders for the GPT2-Small model with residual connections + from the repository of jbloom. You can specify a hook_point to download only one + of the sparse autoencoders if desired. + + """ GPT2_SMALL_RESIDUAL_SAES_REPO_ID = "jbloom/GPT2-Small-SAEs-Reformatted" GPT2_SMALL_RESIDUAL_SAES_HOOK_POINTS = [ f"blocks.{layer}.hook_resid_pre" for layer in range(12) ] + ["blocks.11.hook_resid_post"] + if hook_point is not None: + assert hook_point in GPT2_SMALL_RESIDUAL_SAES_HOOK_POINTS, ( + f"hook_point must be one of {GPT2_SMALL_RESIDUAL_SAES_HOOK_POINTS}" + f"but got {hook_point}" + ) + GPT2_SMALL_RESIDUAL_SAES_HOOK_POINTS = [hook_point] + saes = {} sparsities = {} for hook_point in tqdm(GPT2_SMALL_RESIDUAL_SAES_HOOK_POINTS): - # download the files required: - FILENAME = f"{hook_point}/cfg.json" - hf_hub_download(repo_id=GPT2_SMALL_RESIDUAL_SAES_REPO_ID, filename=FILENAME) - - FILENAME = f"{hook_point}/sae_weights.safetensors" - path = hf_hub_download( - repo_id=GPT2_SMALL_RESIDUAL_SAES_REPO_ID, filename=FILENAME - ) - FILENAME = f"{hook_point}/sparsity.safetensors" - path = hf_hub_download( - repo_id=GPT2_SMALL_RESIDUAL_SAES_REPO_ID, filename=FILENAME + _, sae_path, _ = download_sae_from_hf( + repo_id=GPT2_SMALL_RESIDUAL_SAES_REPO_ID, folder_name=hook_point ) # Then use our function to download the files - folder_path = os.path.dirname(path) - sae = SparseAutoencoder.load_from_pretrained(folder_path) + folder_path = os.path.dirname(sae_path) + sae = SparseAutoencoder.load_from_pretrained(folder_path, device=device) sparsity = load_sparsity(folder_path) + sparsity = sparsity.to(device) saes[hook_point] = sae sparsities[hook_point] = sparsity From 9a772ca6da155b5e97bc3109da74457f5addfbfd Mon Sep 17 00:00:00 2001 From: jbloom-md Date: Sun, 14 Apr 2024 12:37:51 +0100 Subject: [PATCH 8/8] add warning in run script --- scripts/run.ipynb | 1733 +++++++++++++++++++++++---------------------- 1 file changed, 872 insertions(+), 861 deletions(-) diff --git a/scripts/run.ipynb b/scripts/run.ipynb index 3ce81234..8e6cc3a4 100644 --- a/scripts/run.ipynb +++ b/scripts/run.ipynb @@ -1,912 +1,923 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Notebook with Example Config for Different Models / Hooks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook with Example Config for Different Models / Hooks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Warning: This notebook is a WIP and may not reflect current valid / optimal hyperparameters.\n", + "# We are hoping to provide more serious training examples / advice soon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using device: mps\n" - ] - } - ], - "source": [ - "import torch\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "if torch.cuda.is_available():\n", - " device = \"cuda\"\n", - "elif torch.backends.mps.is_available():\n", - " device = \"mps\"\n", - "else:\n", - " device = \"cpu\"\n", - "\n", - "print(\"Using device:\", device)\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: mps\n" + ] + } + ], + "source": [ + "import torch\n", + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "elif torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "else:\n", + " device = \"cpu\"\n", + "\n", + "print(\"Using device:\", device)\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gelu-2L\n", + "\n", + "An example of a toy language model we're able to train on." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MLP Out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"gelu-2l\",\n", + " hook_point=\"blocks.0.hook_mlp_out\",\n", + " hook_point_layer=0,\n", + " d_in=512,\n", + " dataset_path=\"NeelNanda/c4-tokenized-2b\",\n", + " is_dataset_tokenized=True,\n", + " # SAE Parameters\n", + " expansion_factor=[16, 32, 64],\n", + " b_dec_init_method=\"geometric_median\", # geometric median is better but slower to get started\n", + " # Training Parameters\n", + " lr=0.0012,\n", + " lr_scheduler_name=\"constantwithwarmup\",\n", + " l1_coefficient=0.00016,\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=128,\n", + " total_training_tokens=1_000_000 * 100,\n", + " store_batch_size=32,\n", + " # Resampling protocol\n", + " use_ghost_grads=True,\n", + " feature_sampling_window=5000,\n", + " dead_feature_window=5000,\n", + " dead_feature_threshold=1e-4,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_language_models_gelu_2l_test\",\n", + " wandb_log_frequency=10,\n", + " # Misc\n", + " device=device,\n", + " seed=42,\n", + " n_checkpoints=0,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GPT2 - Small" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residual Stream" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "layer = 3\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"gpt2-small\",\n", + " hook_point=f\"blocks.{layer}.hook_resid_pre\",\n", + " hook_point_layer=layer,\n", + " d_in=768,\n", + " dataset_path=\"Skylion007/openwebtext\",\n", + " is_dataset_tokenized=False,\n", + " # SAE Parameters\n", + " expansion_factor=32, # determines the dimension of the SAE.\n", + " b_dec_init_method=\"mean\", # geometric median is better but slower to get started\n", + " # Training Parameters\n", + " lr=0.0004,\n", + " l1_coefficient=0.00008,\n", + " lr_scheduler_name=\"constantwithwarmup\",\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " lr_warm_up_steps=5000,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=128,\n", + " total_training_tokens=1_000_000 * 300, # 200M tokens seems doable overnight.\n", + " store_batch_size=32,\n", + " # Resampling protocol\n", + " use_ghost_grads=True,\n", + " feature_sampling_window=2500,\n", + " dead_feature_window=5000,\n", + " dead_feature_threshold=1e-8,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_language_models_resid_pre_test\",\n", + " wandb_entity=None,\n", + " wandb_log_frequency=100,\n", + " # Misc\n", + " device=\"cuda\",\n", + " seed=42,\n", + " n_checkpoints=10,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pythia 70-M" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "import cProfile\n", + "\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"pythia-70m-deduped\",\n", + " hook_point=\"blocks.0.hook_mlp_out\",\n", + " hook_point_layer=0,\n", + " d_in=512,\n", + " dataset_path=\"EleutherAI/the_pile_deduplicated\",\n", + " is_dataset_tokenized=False,\n", + " # SAE Parameters\n", + " expansion_factor=64,\n", + " # Training Parameters\n", + " lr=3e-4,\n", + " l1_coefficient=4e-5,\n", + " train_batch_size=8192,\n", + " context_size=128,\n", + " lr_scheduler_name=\"constantwithwarmup\",\n", + " lr_warm_up_steps=10_000,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=64,\n", + " total_training_tokens=1_000_000 * 800,\n", + " store_batch_size=32,\n", + " # Resampling protocol\n", + " feature_sampling_window=2000, # Doesn't currently matter.\n", + " dead_feature_window=40000,\n", + " dead_feature_threshold=1e-8,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", + " wandb_entity=None,\n", + " wandb_log_frequency=20,\n", + " # Misc\n", + " device=\"cuda\",\n", + " seed=42,\n", + " n_checkpoints=0,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pythia 70M Hook Q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"../\")\n", + "\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"pythia-70m-deduped\",\n", + " hook_point=\"blocks.2.attn.hook_q\",\n", + " hook_point_layer=2,\n", + " hook_point_head_index=7,\n", + " d_in=64,\n", + " dataset_path=\"EleutherAI/the_pile_deduplicated\",\n", + " is_dataset_tokenized=False,\n", + " # SAE Parameters\n", + " expansion_factor=16,\n", + " # Training Parameters\n", + " lr=0.0012,\n", + " l1_coefficient=0.003,\n", + " lr_scheduler_name=\"constantwithwarmup\",\n", + " lr_warm_up_steps=1000, # about 4 million tokens.\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=128,\n", + " total_training_tokens=1_000_000 * 1500,\n", + " store_batch_size=32,\n", + " # Resampling protocol\n", + " feature_sampling_method=\"anthropic\",\n", + " feature_sampling_window=1000, # doesn't do anything currently.\n", + " feature_reinit_scale=0.2,\n", + " resample_batches=8,\n", + " dead_feature_window=60000,\n", + " dead_feature_threshold=1e-5,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_pythia_70M_hook_q_L2H7\",\n", + " wandb_entity=None,\n", + " wandb_log_frequency=100,\n", + " # Misc\n", + " device=\"mps\",\n", + " seed=42,\n", + " n_checkpoints=15,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tiny Stories" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MLP Out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "if device == \"cpu\" and torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"tiny-stories-1M\",\n", + " hook_point=\"blocks.1.mlp.hook_post\",\n", + " hook_point_layer=1,\n", + " d_in=256,\n", + " # dataset_path=\"roneneldan/TinyStories\",\n", + " # is_dataset_tokenized=False,\n", + " # Dan at Apollo pretokenized this dataset for us which will speed up training.\n", + " dataset_path=\"apollo-research/roneneldan-TinyStories-tokenizer-gpt2\",\n", + " is_dataset_tokenized=True,\n", + " # SAE Parameters\n", + " expansion_factor=16,\n", + " # Training Parameters\n", + " lr=1e-4,\n", + " lp_norm=1.0,\n", + " l1_coefficient=2e-4,\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=128,\n", + " total_training_tokens=1_000_000 * 20,\n", + " store_batch_size=32,\n", + " feature_sampling_window=500, # So we see the histograms.\n", + " dead_feature_window=250,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", + " wandb_log_frequency=10,\n", + " # Misc\n", + " device=device,\n", + " seed=42,\n", + " n_checkpoints=0,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hook Z\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gelu-2L\n", - "\n", - "An example of a toy language model we're able to train on." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", + "n_tokens_per_buffer (millions): 0.524288\n", + "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", + "Total training steps: 4882\n", + "Total wandb updates: 488\n", + "n_tokens_per_feature_sampling_window (millions): 262.144\n", + "n_tokens_per_dead_feature_window (millions): 131.072\n", + "We will reset the sparsity calculation 9 times.\n", + "Number tokens in sparsity calculation window: 2.05e+06\n", + "Loaded pretrained model tiny-stories-1M into HookedTransformer\n", + "Moving model to device: mps\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### MLP Out" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"gelu-2l\",\n", - " hook_point=\"blocks.0.hook_mlp_out\",\n", - " hook_point_layer=0,\n", - " d_in=512,\n", - " dataset_path=\"NeelNanda/c4-tokenized-2b\",\n", - " is_dataset_tokenized=True,\n", - " # SAE Parameters\n", - " expansion_factor=[16, 32, 64],\n", - " b_dec_init_method=\"geometric_median\", # geometric median is better but slower to get started\n", - " # Training Parameters\n", - " lr=0.0012,\n", - " lr_scheduler_name=\"constantwithwarmup\",\n", - " l1_coefficient=0.00016,\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=128,\n", - " total_training_tokens=1_000_000 * 100,\n", - " store_batch_size=32,\n", - " # Resampling protocol\n", - " use_ghost_grads=True,\n", - " feature_sampling_window=5000,\n", - " dead_feature_window=5000,\n", - " dead_feature_threshold=1e-4,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_language_models_gelu_2l_test\",\n", - " wandb_log_frequency=10,\n", - " # Misc\n", - " device=device,\n", - " seed=42,\n", - " n_checkpoints=0,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", + "n_tokens_per_buffer (millions): 0.524288\n", + "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", + "Total training steps: 4882\n", + "Total wandb updates: 488\n", + "n_tokens_per_feature_sampling_window (millions): 262.144\n", + "n_tokens_per_dead_feature_window (millions): 131.072\n", + "We will reset the sparsity calculation 9 times.\n", + "Number tokens in sparsity calculation window: 2.05e+06\n", + "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", + "n_tokens_per_buffer (millions): 0.524288\n", + "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", + "Total training steps: 4882\n", + "Total wandb updates: 488\n", + "n_tokens_per_feature_sampling_window (millions): 262.144\n", + "n_tokens_per_dead_feature_window (millions): 131.072\n", + "We will reset the sparsity calculation 9 times.\n", + "Number tokens in sparsity calculation window: 2.05e+06\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GPT2 - Small" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjbloom\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Residual Stream" + "data": { + "text/html": [ + "wandb version 0.16.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "layer = 3\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"gpt2-small\",\n", - " hook_point=f\"blocks.{layer}.hook_resid_pre\",\n", - " hook_point_layer=layer,\n", - " d_in=768,\n", - " dataset_path=\"Skylion007/openwebtext\",\n", - " is_dataset_tokenized=False,\n", - " # SAE Parameters\n", - " expansion_factor=32, # determines the dimension of the SAE.\n", - " b_dec_init_method=\"mean\", # geometric median is better but slower to get started\n", - " # Training Parameters\n", - " lr=0.0004,\n", - " l1_coefficient=0.00008,\n", - " lr_scheduler_name=\"constantwithwarmup\",\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " lr_warm_up_steps=5000,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=128,\n", - " total_training_tokens=1_000_000 * 300, # 200M tokens seems doable overnight.\n", - " store_batch_size=32,\n", - " # Resampling protocol\n", - " use_ghost_grads=True,\n", - " feature_sampling_window=2500,\n", - " dead_feature_window=5000,\n", - " dead_feature_threshold=1e-8,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_language_models_resid_pre_test\",\n", - " wandb_entity=None,\n", - " wandb_log_frequency=100,\n", - " # Misc\n", - " device=\"cuda\",\n", - " seed=42,\n", - " n_checkpoints=10,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.3" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pythia 70-M" + "data": { + "text/html": [ + "Run data is saved locally in /Users/josephbloom/GithubRepositories/mats_sae_training/scripts/wandb/run-20240326_191703-ec6k6v87" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "import cProfile\n", - "\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"pythia-70m-deduped\",\n", - " hook_point=\"blocks.0.hook_mlp_out\",\n", - " hook_point_layer=0,\n", - " d_in=512,\n", - " dataset_path=\"EleutherAI/the_pile_deduplicated\",\n", - " is_dataset_tokenized=False,\n", - " # SAE Parameters\n", - " expansion_factor=64,\n", - " # Training Parameters\n", - " lr=3e-4,\n", - " l1_coefficient=4e-5,\n", - " train_batch_size=8192,\n", - " context_size=128,\n", - " lr_scheduler_name=\"constantwithwarmup\",\n", - " lr_warm_up_steps=10_000,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=64,\n", - " total_training_tokens=1_000_000 * 800,\n", - " store_batch_size=32,\n", - " # Resampling protocol\n", - " feature_sampling_window=2000, # Doesn't currently matter.\n", - " dead_feature_window=40000,\n", - " dead_feature_threshold=1e-8,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", - " wandb_entity=None,\n", - " wandb_log_frequency=20,\n", - " # Misc\n", - " device=\"cuda\",\n", - " seed=42,\n", - " n_checkpoints=0,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" + "data": { + "text/html": [ + "Syncing run 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pythia 70M Hook Q" + "data": { + "text/html": [ + " View project at https://wandb.ai/jbloom/mats_sae_training_language_benchmark_tests" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"pythia-70m-deduped\",\n", - " hook_point=\"blocks.2.attn.hook_q\",\n", - " hook_point_layer=2,\n", - " hook_point_head_index=7,\n", - " d_in=64,\n", - " dataset_path=\"EleutherAI/the_pile_deduplicated\",\n", - " is_dataset_tokenized=False,\n", - " # SAE Parameters\n", - " expansion_factor=16,\n", - " # Training Parameters\n", - " lr=0.0012,\n", - " l1_coefficient=0.003,\n", - " lr_scheduler_name=\"constantwithwarmup\",\n", - " lr_warm_up_steps=1000, # about 4 million tokens.\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=128,\n", - " total_training_tokens=1_000_000 * 1500,\n", - " store_batch_size=32,\n", - " # Resampling protocol\n", - " feature_sampling_method=\"anthropic\",\n", - " feature_sampling_window=1000, # doesn't do anything currently.\n", - " feature_reinit_scale=0.2,\n", - " resample_batches=8,\n", - " dead_feature_window=60000,\n", - " dead_feature_threshold=1e-5,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_pythia_70M_hook_q_L2H7\",\n", - " wandb_entity=None,\n", - " wandb_log_frequency=100,\n", - " # Misc\n", - " device=\"mps\",\n", - " seed=42,\n", - " n_checkpoints=15,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" + "data": { + "text/html": [ + " View run at https://wandb.ai/jbloom/mats_sae_training_language_benchmark_tests/runs/ec6k6v87" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tiny Stories" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "Objective value: 116883.7422: 10%|█ | 10/100 [00:00<00:00, 128.72it/s]\n", + "/Users/josephbloom/GithubRepositories/mats_sae_training/sae_training/sparse_autoencoder.py:161: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " out = torch.tensor(origin, dtype=self.dtype, device=self.device)\n", + "100%|██████████| 10/10 [00:02<00:00, 4.93it/s] 405504/20000000 [00:14<08:53, 36739.57it/s]\n", + "100%|██████████| 10/10 [00:01<00:00, 5.01it/s]| 811008/20000000 [00:31<18:45, 17042.47it/s] \n", + "100%|██████████| 10/10 [00:01<00:00, 5.04it/s]| 1224704/20000000 [00:47<10:43, 29194.89it/s] \n", + "100%|██████████| 10/10 [00:02<00:00, 4.98it/s]| 1634304/20000000 [01:05<08:10, 37468.33it/s]\n", + "100%|██████████| 10/10 [00:02<00:00, 4.64it/s]| 2039808/20000000 [01:20<07:36, 39322.02it/s]\n", + "100%|██████████| 10/10 [00:01<00:00, 5.08it/s]| 2453504/20000000 [01:37<07:55, 36873.53it/s]\n", + "100%|██████████| 10/10 [00:01<00:00, 5.04it/s]| 2863104/20000000 [01:52<07:16, 39292.24it/s]\n", + "100%|██████████| 10/10 [00:01<00:00, 5.01it/s]| 3272704/20000000 [02:09<06:52, 40537.06it/s] \n", + "100%|██████████| 10/10 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[00:02<00:00, 4.95it/s]▊| 19656704/20000000 [12:59<00:07, 43804.93it/s]\n", + "4883| MSE Loss 0.000 | L1 0.000: 100%|█████████▉| 19996672/20000000 [13:14<00:00, 37714.53it/s]" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MLP Out" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model to checkpoints/sf7u2imk/final_sae_group_tiny-stories-1M_blocks.1.attn.hook_z_1024.pt\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import os\n", - "\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "if device == \"cpu\" and torch.backends.mps.is_available():\n", - " device = \"mps\"\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"tiny-stories-1M\",\n", - " hook_point=\"blocks.1.mlp.hook_post\",\n", - " hook_point_layer=1,\n", - " d_in=256,\n", - " # dataset_path=\"roneneldan/TinyStories\",\n", - " # is_dataset_tokenized=False,\n", - " # Dan at Apollo pretokenized this dataset for us which will speed up training.\n", - " dataset_path=\"apollo-research/roneneldan-TinyStories-tokenizer-gpt2\",\n", - " is_dataset_tokenized=True,\n", - " # SAE Parameters\n", - " expansion_factor=16,\n", - " # Training Parameters\n", - " lr=1e-4,\n", - " lp_norm=1.0,\n", - " l1_coefficient=2e-4,\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=128,\n", - " total_training_tokens=1_000_000 * 20,\n", - " store_batch_size=32,\n", - " feature_sampling_window=500, # So we see the histograms.\n", - " dead_feature_window=250,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", - " wandb_log_frequency=10,\n", - " # Misc\n", - " device=device,\n", - " seed=42,\n", - " n_checkpoints=0,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1dffd84a387d4cf48100fbe143287481", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.053 MB of 0.569 MB uploaded\\r'), FloatProgress(value=0.0935266880101429, max=1.0…" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hook Z\n", - "\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n" + ] }, { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", - "n_tokens_per_buffer (millions): 0.524288\n", - "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", - "Total training steps: 4882\n", - "Total wandb updates: 488\n", - "n_tokens_per_feature_sampling_window (millions): 262.144\n", - "n_tokens_per_dead_feature_window (millions): 131.072\n", - "We will reset the sparsity calculation 9 times.\n", - "Number tokens in sparsity calculation window: 2.05e+06\n", - "Loaded pretrained model tiny-stories-1M into HookedTransformer\n", - "Moving model to device: mps\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", - "n_tokens_per_buffer (millions): 0.524288\n", - "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", - "Total training steps: 4882\n", - "Total wandb updates: 488\n", - "n_tokens_per_feature_sampling_window (millions): 262.144\n", - "n_tokens_per_dead_feature_window (millions): 131.072\n", - "We will reset the sparsity calculation 9 times.\n", - "Number tokens in sparsity calculation window: 2.05e+06\n", - "Run name: 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07\n", - "n_tokens_per_buffer (millions): 0.524288\n", - "Lower bound: n_contexts_per_buffer (millions): 0.004096\n", - "Total training steps: 4882\n", - "Total wandb updates: 488\n", - "n_tokens_per_feature_sampling_window (millions): 262.144\n", - "n_tokens_per_dead_feature_window (millions): 131.072\n", - "We will reset the sparsity calculation 9 times.\n", - "Number tokens in sparsity calculation window: 2.05e+06\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjbloom\u001b[0m. 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details/current_learning_rate0.0001
details/n_training_tokens19988480
losses/ghost_grad_loss0.0
losses/l1_loss1.41017
losses/mse_loss8e-05
losses/overall_loss0.00036
metrics/CE_loss_score0.98362
metrics/ce_loss_with_ablation5.49512
metrics/ce_loss_with_sae2.71813
metrics/ce_loss_without_sae2.67199
metrics/explained_variance0.98647
metrics/explained_variance_std0.00905
metrics/l0166.02246
metrics/l2_norm1.39317
metrics/l2_ratio0.99823
metrics/mean_log10_feature_sparsity-1.53525
sparsity/below_1e-50
sparsity/below_1e-60
sparsity/dead_features0
sparsity/mean_passes_since_fired0.02051

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Run summary:


details/current_learning_rate0.0001
details/n_training_tokens19988480
losses/ghost_grad_loss0.0
losses/l1_loss1.41017
losses/mse_loss8e-05
losses/overall_loss0.00036
metrics/CE_loss_score0.98362
metrics/ce_loss_with_ablation5.49512
metrics/ce_loss_with_sae2.71813
metrics/ce_loss_without_sae2.67199
metrics/explained_variance0.98647
metrics/explained_variance_std0.00905
metrics/l0166.02246
metrics/l2_norm1.39317
metrics/l2_ratio0.99823
metrics/mean_log10_feature_sparsity-1.53525
sparsity/below_1e-50
sparsity/below_1e-60
sparsity/dead_features0
sparsity/mean_passes_since_fired0.02051

" ], - "source": [ - "import torch\n", - "import os\n", - "\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "if device == \"cpu\" and torch.backends.mps.is_available():\n", - " device = \"mps\"\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"tiny-stories-1M\",\n", - " hook_point=\"blocks.1.attn.hook_z\",\n", - " hook_point_layer=1,\n", - " d_in=64,\n", - " # dataset_path=\"roneneldan/TinyStories\",\n", - " # is_dataset_tokenized=False,\n", - " # Dan at Apollo pretokenized this dataset for us which will speed up training.\n", - " dataset_path=\"apollo-research/roneneldan-TinyStories-tokenizer-gpt2\",\n", - " is_dataset_tokenized=True,\n", - " # SAE Parameters\n", - " expansion_factor=16,\n", - " # Training Parameters\n", - " lr=1e-4,\n", - " lp_norm=1.0,\n", - " l1_coefficient=2e-4,\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=128,\n", - " total_training_tokens=1_000_000 * 20,\n", - " store_batch_size=32,\n", - " feature_sampling_window=500, # So we see the histograms.\n", - " dead_feature_window=250,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", - " wandb_log_frequency=10,\n", - " # Misc\n", - " device=device,\n", - " seed=42,\n", - " n_checkpoints=0,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toy Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sae_lens.training.toy_model_runner import SAEToyModelRunnerConfig, toy_model_sae_runner\n", - "\n", - "\n", - "cfg = SAEToyModelRunnerConfig(\n", - " # Model Details\n", - " n_features=200,\n", - " n_hidden=5,\n", - " n_correlated_pairs=0,\n", - " n_anticorrelated_pairs=0,\n", - " feature_probability=0.025,\n", - " model_training_steps=10_000,\n", - " # SAE Parameters\n", - " d_sae=240,\n", - " l1_coefficient=0.001,\n", - " # SAE Train Config\n", - " train_batch_size=1028,\n", - " feature_sampling_window=3_000,\n", - " dead_feature_window=1_000,\n", - " feature_reinit_scale=0.5,\n", - " total_training_tokens=4096 * 300,\n", - " # Other parameters\n", - " log_to_wandb=True,\n", - " wandb_project=\"sae-training-test\",\n", - " wandb_log_frequency=5,\n", - " device=\"mps\",\n", - ")\n", - "\n", - "trained_sae = toy_model_sae_runner(cfg)\n", - "\n", - "assert trained_sae is not None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run caching of activations to disk" + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "os.environ[\"WANDB__SERVICE_WAIT\"] = \"300\"\n", - "\n", - "from sae_lens.training.config import CacheActivationsRunnerConfig\n", - "from sae_lens.training.cache_activations_runner import cache_activations_runner\n", - "\n", - "cfg = CacheActivationsRunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"gpt2-small\",\n", - " hook_point=\"blocks.10.attn.hook_q\",\n", - " hook_point_layer=10,\n", - " hook_point_head_index=7,\n", - " d_in=64,\n", - " dataset_path=\"Skylion007/openwebtext\",\n", - " is_dataset_tokenized=False,\n", - " cached_activations_path=\"../activations/\",\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=16,\n", - " total_training_tokens=500_000_000,\n", - " store_batch_size=32,\n", - " # Activation caching shuffle parameters\n", - " n_shuffles_final=16,\n", - " # Misc\n", - " device=\"mps\",\n", - " seed=42,\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "cache_activations_runner(cfg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train an SAE using the cached activations stored on disk\n", - "Pass `use_cached_activations=True` into the config" + "data": { + "text/html": [ + " View run 1024-L1-0.0002-LR-0.0001-Tokens-2.000e+07 at: https://wandb.ai/jbloom/mats_sae_training_language_benchmark_tests/runs/ec6k6v87
Synced 7 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import os\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "os.environ[\"WANDB__SERVICE_WAIT\"] = \"300\"\n", - "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", - "from sae_lens.training.lm_runner import language_model_sae_runner\n", - "\n", - "cfg = LanguageModelSAERunnerConfig(\n", - " # Data Generating Function (Model + Training Distibuion)\n", - " model_name=\"gpt2-small\",\n", - " hook_point=\"blocks.10.hook_resid_pre\",\n", - " hook_point_layer=11,\n", - " d_in=768,\n", - " dataset_path=\"Skylion007/openwebtext\",\n", - " is_dataset_tokenized=False,\n", - " use_cached_activations=True,\n", - " # SAE Parameters\n", - " expansion_factor=64, # determines the dimension of the SAE.\n", - " # Training Parameters\n", - " lr=1e-5,\n", - " l1_coefficient=5e-4,\n", - " lr_scheduler_name=None,\n", - " train_batch_size=4096,\n", - " context_size=128,\n", - " # Activation Store Parameters\n", - " n_batches_in_buffer=64,\n", - " total_training_tokens=200_000,\n", - " store_batch_size=32,\n", - " # Resampling protocol\n", - " feature_sampling_method=\"l2\",\n", - " feature_sampling_window=1000,\n", - " feature_reinit_scale=0.2,\n", - " dead_feature_window=5000,\n", - " dead_feature_threshold=1e-7,\n", - " # WANDB\n", - " log_to_wandb=True,\n", - " wandb_project=\"mats_sae_training_gpt2_small\",\n", - " wandb_entity=None,\n", - " wandb_log_frequency=50,\n", - " # Misc\n", - " device=\"mps\",\n", - " seed=42,\n", - " n_checkpoints=5,\n", - " checkpoint_path=\"checkpoints\",\n", - " dtype=torch.float32,\n", - ")\n", - "\n", - "sparse_autoencoder = language_model_sae_runner(cfg)" + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20240326_191703-ec6k6v87/logs" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mats_sae_training", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" + "name": "stderr", + "output_type": "stream", + "text": [ + "4883| MSE Loss 0.000 | L1 0.000: : 20000768it [13:29, 37714.53it/s] /Users/josephbloom/miniforge3/envs/mats_sae_training/lib/python3.11/site-packages/wandb/sdk/wandb_run.py:2171: UserWarning: Run (ec6k6v87) is finished. The call to `_console_raw_callback` will be ignored. Please make sure that you are using an active run.\n", + " lambda data: self._console_raw_callback(\"stderr\", data),\n" + ] } + ], + "source": [ + "import torch\n", + "import os\n", + "\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "if device == \"cpu\" and torch.backends.mps.is_available():\n", + " device = \"mps\"\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"tiny-stories-1M\",\n", + " hook_point=\"blocks.1.attn.hook_z\",\n", + " hook_point_layer=1,\n", + " d_in=64,\n", + " # dataset_path=\"roneneldan/TinyStories\",\n", + " # is_dataset_tokenized=False,\n", + " # Dan at Apollo pretokenized this dataset for us which will speed up training.\n", + " dataset_path=\"apollo-research/roneneldan-TinyStories-tokenizer-gpt2\",\n", + " is_dataset_tokenized=True,\n", + " # SAE Parameters\n", + " expansion_factor=16,\n", + " # Training Parameters\n", + " lr=1e-4,\n", + " lp_norm=1.0,\n", + " l1_coefficient=2e-4,\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=128,\n", + " total_training_tokens=1_000_000 * 20,\n", + " store_batch_size=32,\n", + " feature_sampling_window=500, # So we see the histograms.\n", + " dead_feature_window=250,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_language_benchmark_tests\",\n", + " wandb_log_frequency=10,\n", + " # Misc\n", + " device=device,\n", + " seed=42,\n", + " n_checkpoints=0,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Toy Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sae_lens.training.toy_model_runner import (\n", + " SAEToyModelRunnerConfig,\n", + " toy_model_sae_runner,\n", + ")\n", + "\n", + "\n", + "cfg = SAEToyModelRunnerConfig(\n", + " # Model Details\n", + " n_features=200,\n", + " n_hidden=5,\n", + " n_correlated_pairs=0,\n", + " n_anticorrelated_pairs=0,\n", + " feature_probability=0.025,\n", + " model_training_steps=10_000,\n", + " # SAE Parameters\n", + " d_sae=240,\n", + " l1_coefficient=0.001,\n", + " # SAE Train Config\n", + " train_batch_size=1028,\n", + " feature_sampling_window=3_000,\n", + " dead_feature_window=1_000,\n", + " feature_reinit_scale=0.5,\n", + " total_training_tokens=4096 * 300,\n", + " # Other parameters\n", + " log_to_wandb=True,\n", + " wandb_project=\"sae-training-test\",\n", + " wandb_log_frequency=5,\n", + " device=\"mps\",\n", + ")\n", + "\n", + "trained_sae = toy_model_sae_runner(cfg)\n", + "\n", + "assert trained_sae is not None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run caching of activations to disk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "os.environ[\"WANDB__SERVICE_WAIT\"] = \"300\"\n", + "\n", + "from sae_lens.training.config import CacheActivationsRunnerConfig\n", + "from sae_lens.training.cache_activations_runner import cache_activations_runner\n", + "\n", + "cfg = CacheActivationsRunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"gpt2-small\",\n", + " hook_point=\"blocks.10.attn.hook_q\",\n", + " hook_point_layer=10,\n", + " hook_point_head_index=7,\n", + " d_in=64,\n", + " dataset_path=\"Skylion007/openwebtext\",\n", + " is_dataset_tokenized=False,\n", + " cached_activations_path=\"../activations/\",\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=16,\n", + " total_training_tokens=500_000_000,\n", + " store_batch_size=32,\n", + " # Activation caching shuffle parameters\n", + " n_shuffles_final=16,\n", + " # Misc\n", + " device=\"mps\",\n", + " seed=42,\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "cache_activations_runner(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train an SAE using the cached activations stored on disk\n", + "Pass `use_cached_activations=True` into the config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "os.environ[\"WANDB__SERVICE_WAIT\"] = \"300\"\n", + "from sae_lens.training.config import LanguageModelSAERunnerConfig\n", + "from sae_lens.training.lm_runner import language_model_sae_runner\n", + "\n", + "cfg = LanguageModelSAERunnerConfig(\n", + " # Data Generating Function (Model + Training Distibuion)\n", + " model_name=\"gpt2-small\",\n", + " hook_point=\"blocks.10.hook_resid_pre\",\n", + " hook_point_layer=11,\n", + " d_in=768,\n", + " dataset_path=\"Skylion007/openwebtext\",\n", + " is_dataset_tokenized=False,\n", + " use_cached_activations=True,\n", + " # SAE Parameters\n", + " expansion_factor=64, # determines the dimension of the SAE.\n", + " # Training Parameters\n", + " lr=1e-5,\n", + " l1_coefficient=5e-4,\n", + " lr_scheduler_name=None,\n", + " train_batch_size=4096,\n", + " context_size=128,\n", + " # Activation Store Parameters\n", + " n_batches_in_buffer=64,\n", + " total_training_tokens=200_000,\n", + " store_batch_size=32,\n", + " # Resampling protocol\n", + " feature_sampling_method=\"l2\",\n", + " feature_sampling_window=1000,\n", + " feature_reinit_scale=0.2,\n", + " dead_feature_window=5000,\n", + " dead_feature_threshold=1e-7,\n", + " # WANDB\n", + " log_to_wandb=True,\n", + " wandb_project=\"mats_sae_training_gpt2_small\",\n", + " wandb_entity=None,\n", + " wandb_log_frequency=50,\n", + " # Misc\n", + " device=\"mps\",\n", + " seed=42,\n", + " n_checkpoints=5,\n", + " checkpoint_path=\"checkpoints\",\n", + " dtype=torch.float32,\n", + ")\n", + "\n", + "sparse_autoencoder = language_model_sae_runner(cfg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mats_sae_training", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 }