add compute_intermediate_quantities to TracInCP
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 7f8c95ccc761ba5e1a59d78d0eccea999f6e0edb
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 505eddc34da93391975ba2579abd8dcc9c7560c5
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: cfac6edbd03e62ffbf68ee075661480e0be3df98
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 7fb76a6a8de16628993fb621accbb4337e02c987
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: 0f57a7a73d91a49f615a117a5ac3c2fdb2fb1bf8
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 3ce1d66f416b5c7e85dcb1392ac4412217cea811
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 675f9609e8a5f3612d6f7c9d046dc90d5e02ea72
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: d7a328a6227e5fae9e95b06188835524c2a5c86b
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 7d67eb9e22f0d01763838223e86097ccb97cc66a
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: 58e95b6da70406abb1536e83c3e63c07635deb08
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: cb8bc42468e3bd6799546522eb3e07ca52008c5b
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: 7cea23b0c3b55ca65a06b6d97e6145660b75c7b0
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: 06f1d22e57ce4001d4d4e8e29620a97dc734c8fc
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: 07ee5bb2ff7ac581d442a5621efb87cef6c7934d
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Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Differential Revision: https://internalfb.com/D40688327 fbshipit-source-id: 02d1ecbe66e8092f1be07ca831040e72d1b7ae37
Summary: Pull Request resolved: pytorch#1073 - For all `TracInCPBase` implementations, this adds an additional `test_loss_fn` initialization argument, which is the loss function to apply to test examples when computing the influence of a training example on a test example. With this change,the influence score is a sum over terms for each checkpoint, where each term is the gradient of `loss_fn` for a given training example, multiplied with the gradient of `test_loss_fn` for a given test example. Before, `test_loss_fn` was assumed to be the same as `loss_fn`. - checks regarding the reduction type of both `loss_fn` and `test_loss_fn` are now handled by helper functions `_check_tracincp_loss_fn` and `_check_tracincp_fast_loss_fn`. - documentation is updated. one detail: for `TracInCP`, we assume that `sample_wise_grads_per_batch` is applied to both `loss_fn` and `test_loss_fn` (if provided), and this is mentioned in the documentation. - `test_tracin_regression.test_tracin_regression` is slightly modified - `DataInfluenceConstructor` now can explicitly pass in the same loss function for both `loss_fn` and `test_loss_fn` (done when `duplicate_loss_fn=True`). Doing so would have the same effect as not passing in `test_loss_fn`, so the original tests are also applied to the case when `duplicate_loss_fn=True`, as the expected behavior should be the same as before. - a new test, `test_tracin_regression.test_tracin_constant_test_loss_fn` is added. For all implementations of `TracInCPBase`, it checks that if `test_loss_fn` is a constant loss function, the influence scores are all 0's. This should be the case, because if `test_loss_fn` is constant, its gradients would all be 0's, so that training examples have 0 influence on test examples. Differential Revision: https://internalfb.com/D41202866 fbshipit-source-id: e9eaf630be6b54f7d0c86c681dcf0c9e6dddb6f1
Summary: Pull Request resolved: pytorch#1068 This diff adds the `compute_intermediate_quantities` method to `TracInCP`, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case of `TracInCP`, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints. There is also an `aggregate` option that if True, returns not the influence embeddings of each example in the given dataset, but instead their *sum*. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the *sum* of the influence embeddings over the validation dataset (i.e. with `aggregate=True`) For tests, the tests currently used for `TracInCPFastRandProj.compute_intermediate_quantities` (`test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api`, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent`) are applied to `TracInCP.compute_intermediate_quantities`. In addition, `test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregate` is added to test the `aggregate=True` option, checking that with `aggregate=True`, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset. Reviewed By: cyrjano Differential Revision: D40688327 fbshipit-source-id: b673984927bb0a17cc21fb627dd76c8f765eb43a
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Summary:
This diff adds the
compute_intermediate_quantitiesmethod toTracInCP, which returns influence embeddings such that the influence of one example on another is the dot-product of their respective influence embeddings. In the case ofTracInCP, its influence embeddings are simply the parameter-gradients for an example, concatenated over different checkpoints.There is also an
aggregateoption that if True, returns not the influence embeddings of each example in the given dataset, but instead their sum. This is useful for the validation diff workflow (which is the next diff in the stack), where we want to calculate the influence of a given training example on an entire validation dataset. This can be accomplished by taking the dot-product of the training example's influence embedding with the sum of the influence embeddings over the validation dataset (i.e. withaggregate=True)For tests, the tests currently used for
TracInCPFastRandProj.compute_intermediate_quantities(test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_api,test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_consistent) are applied toTracInCP.compute_intermediate_quantities. In addition,test_tracin_intermediate_quantities.test_tracin_intermediate_quantities_aggregateis added to test theaggregate=Trueoption, checking that withaggregate=True, the returned influence embedding is indeed the sum of the influence embeddings for the given dataset.Reviewed By: cyrjano
Differential Revision: D40688327