Collector refactor #81
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luciaquirke
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Nov 17, 2025
luciaquirke
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Nov 17, 2025
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…P, rest working + passing
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luciaquirke
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Nov 25, 2025
| batches: list[list[int]] | None = None, | ||
| target_modules: set[str] | None = None, | ||
| attention_cfgs: dict[str, AttentionConfig] | None = None, | ||
| attention_cfgs: dict[str, AttentionConfig] = {}, |
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I think it's best to not use mutable objects as default values because they are only instantiated once and so could be shared across uses of the function
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good point will change!
luciaquirke
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Nov 25, 2025
| cfg=cfg, | ||
| ) | ||
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| computer._compute(desc="New worker - Collecting gradients") |
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No underscore in compute since if it's a public fn. I feel like this class/function could also have a more concrete name, I'm not sure what though but in a sense every function is a computer
luciaquirke
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Nov 25, 2025
| Abstract base class for collectors that attach forward and backward hooks to model | ||
| layers. | ||
| Automatically discovers nn.Linear layers in the model, registers hooks during |
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nn.Linear layers -> supported modules
luciaquirke
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Nov 25, 2025
| implement custom logic. | ||
| Assumes model input shape is [N, S, I] where N=batch size, S=sequence length, | ||
| I=input dimension. |
luciaquirke
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Nov 25, 2025
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| target_modules: set[str] | None = None | ||
| """ | ||
| Set of module names to attach hooks to. Should consist only of nn.Linear modules. |
luciaquirke
reviewed
Nov 27, 2025
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| dtype: torch.dtype | ||
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| def setup(self) -> None: |
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Is this removable btw
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This PR introduces the new Collector class that will make it easy to implement custom hooks, e.g. for EK-FAC and generally allow for more flexible custom methods