[Profiler] Use parameter as key for optimizer state recording. #86753
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While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
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This was referenced Oct 12, 2022
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
This was referenced Oct 12, 2022
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
slgong-fb
approved these changes
Oct 17, 2022
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
aaronenyeshi
approved these changes
Oct 20, 2022
added 2 commits
October 21, 2022 11:42
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
…ing." While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) [ghstack-poisoned]
This was referenced Oct 23, 2022
sgrigory
pushed a commit
to sgrigory/pytorch
that referenced
this pull request
Oct 28, 2022
…ch#86753) While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) Pull Request resolved: pytorch#86753 Approved by: https://github.com/slgong-fb, https://github.com/aaronenyeshi
kulinseth
pushed a commit
to kulinseth/pytorch
that referenced
this pull request
Nov 5, 2022
…ch#86753) While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) Pull Request resolved: pytorch#86753 Approved by: https://github.com/slgong-fb, https://github.com/aaronenyeshi
kulinseth
pushed a commit
to kulinseth/pytorch
that referenced
this pull request
Dec 10, 2022
…ch#86753) While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all `torch.optim` optimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises. One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the `check_and_store` logic could be simplified and inlined by this joining of collected optimizer state. Differential Revision: [D40210703](https://our.internmc.facebook.com/intern/diff/D40210703/) Pull Request resolved: pytorch#86753 Approved by: https://github.com/slgong-fb, https://github.com/aaronenyeshi
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Stack from ghstack (oldest at bottom):
AccumulateGradname #86909While optimizer can store state however it likes, in practice most optimizer state corresponds to a particular parameter. (This is the case for all
torch.optimoptimizers.) Thus, it turns out to be ergonomic to collect using that structure. Note that this doesn't lock us into anything; we can always collect state with non Tensor keys if the use case arises.One simplification that arises is that Module and Optimizer collection has very similar structure. So similar, in fact, that it is possible to use a common template for config. I also found that a lot of the
check_and_storelogic could be simplified and inlined by this joining of collected optimizer state.Differential Revision: D40210703