Move from manual sharding to HF dataset builder.#391
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tom-pollak wants to merge 2 commits intodecoderesearch:mainfrom
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Move from manual sharding to HF dataset builder.#391tom-pollak wants to merge 2 commits intodecoderesearch:mainfrom
tom-pollak wants to merge 2 commits intodecoderesearch:mainfrom
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Before `CacheActivationConfig` had a inconsistent config file for some
interopability with `LanguageModelSAERunnerConfig`. It was kind of
unclear which parameters were necessary vs redundant, and just was
fairly unclear.
Simplified to the required arguments:
- `hf_dataset_path`: Tokenized or untokenized dataset
- `total_training_tokens`
- `model_name`
- `model_batch_size`
- `hook_name`
- `final_hook_layer`
- `d_in`
I think this scheme captures everything you need when attempting to
cache activations and makes it a lot easier to reason about.
Optional:
```
activation_save_path # defaults to "activations/{dataset}/{model}/{hook_name}
shuffle=True
prepend_bos=True
streaming=True
seqpos_slice
buffer_size_gb=2 # Size of each buffer. Affects memory usage and saving freq
device="cuda" or "cpu"
dtype="float32"
autocast_lm=False
compile_llm=True
hf_repo_id # Push to hf
model_kwargs # `run_with_cache`
model_from_pretrained_kwargs
```
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Oops previous commit wasn't merged yet, this is only for Move from manual sharding to HF dataset builder. |
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Old New Not a huge amount of difference in benchmarks, slightly faster. |
Depends on decoderesearch#389. Inspired by: https://opensourcemechanistic.slack.com/archives/C07EHMK3XC7/p1732413633220709 Instead of manually writing the single arrow shards, we can create a dataset builder that can do this more efficiently. This speeds up saving quite a lot, old method spent a some time calculating the fingerprint of the shard, which was unecessary and would require a hack to get around. > Along with this change, I also switched to a 1D activation scheme. - Previously the dataset was stored as a `(seq_len d_in)` array. - Now stored as a flat `d_in` Primary reason for this change is shuffling activations. I found that by using activations sequence, the activations are not properly shuffled. This is a problem with `ActivationCache` too but there's not a great solution for it there. You can observe this in the loss of the SAE by using small buffer sizes with either using cache or `ActivationStore`.
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Description
Depends on #389.
Inspired by:
https://opensourcemechanistic.slack.com/archives/C07EHMK3XC7/p1732413633220709
Instead of manually writing the single arrow shards, we can create a
dataset builder that can do this more efficiently. This speeds up saving
quite a lot, old method spent a some time calculating the fingerprint of
the shard, which was unecessary and would require a hack to get around.
(seq_len d_in)array.d_inPrimary reason for this change is shuffling activations. I found that by
using activations sequence, the activations are not properly shuffled.
This is a problem with
ActivationCachetoo but there's not a greatsolution for it there.
You can observe this in the loss of the SAE by using small buffer sizes
with either using cache or
ActivationStore.Fixes # (issue)
Type of change
Please delete options that are not relevant.
(d_in)activations vs(seq_len, d_in)Checklist:
You have tested formatting, typing and unit tests (acceptance tests not currently in use)
make check-cito check format and linting. (you can runmake formatto format code if needed.)