modify tracin self influence helpers #994
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cc: @99warriors it looks like some of the tests related to progress bar are failing. Do you mind looking into it ? |
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 56efa7ca82253a71c3ea143f3e2f1cabbe483b58
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This pull request was exported from Phabricator. Differential Revision: D35603078 |
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 8a84e1ce98be36b8eb0ac82c504417bdf21a6539
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 8ce8db910a48b92b07efc4de1ec100d2939e4794
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| @@ -95,7 +96,7 @@ class TracInCPBase(DataInfluence): | |||
| def __init__( | |||
| self, | |||
| model: Module, | |||
| influence_src_dataset: Union[Dataset, DataLoader], | |||
| train_dataset: Union[Dataset, DataLoader], | |||
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@99warriors, do you mind fixing this naming also in the tutorials ? I remember that we used them explicitly as argument names.
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 8944c0b909a2c6daa589b94e3df22d5f9e651346
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 5f2164d1ebe75359c39b5a540a13eee6ab582900
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Differential Revision: D35603078 fbshipit-source-id: cd7ac1e59ffa5f57cfd9990626c44d7033636b7b
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Differential Revision: D35603078 fbshipit-source-id: 5c89f2f8ba5350c0c38e0cad6ccfd0929133c386
99warriors
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Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Differential Revision: D35603078 fbshipit-source-id: 87063052e68441b82514489f4d9f9ad29b396da4
Summary: Pull Request resolved: pytorch#994 change `TracInCP._self_influence_batch_tracincp` and `TracInCP._self_influence_batch_tracincp` `TracInCP._self_influence_batches_tracincp_fast` to be named `self_influence`, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence. The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by `influence` when running it in self influence mode. The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large. Minor change: for `influence_src_dataset` argument of all `__init__`'s, add description of what assumptions we make of the batches yielded by the dataloader. Reviewed By: NarineK Differential Revision: D35603078 fbshipit-source-id: 78d233a5da210424f3eed308ee563d3baeba4135
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Summary:
change
TracInCP._self_influence_batch_tracincpandTracInCP._self_influence_batch_tracincpTracInCP._self_influence_batches_tracincp_fastto be namedself_influence, which is now public, and now accept a DataLoader yielding batches (as well as a single batch, as before). The modified helper function can be called by external functions to compute self influence.The helper itself is also changed to improve efficiency, by reducing the number of times checkpoints are loaded. The modified helper, despite being able to compute self influence scores for a dataloader yielding batches, still only loads each checkpoint once, per call. This is because the modified helper now has an outer iteration over checkpoints, and an inner iteration over batches (the order of iteration is reversed compared to before). This helper is called by
influencewhen running it in self influence mode.The reason we cannot just increase the batch size to reduce the number of checkpoint loadings is that for large models (precisely those for which loading checkpoints is expensive), the model takes up too much memory, so that the batch size cannot be too large.
Minor change: for
influence_src_datasetargument of all__init__'s, add description of what assumptions we make of the batches yielded by the dataloader.Reviewed By: NarineK
Differential Revision: D35603078