Use new_empty in dropout #72078
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Use new_empty in dropout #72078
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zou3519
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Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: #72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c
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Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: #72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c (cherry picked from commit e51cf3e)
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Hey samdow. You merged this PR, but no release notes category and topic labels were added. The list of valid release and topic labels is available https://github.com/pytorch/pytorch/labels?q=release+notes+or+topic |
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Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: pytorch/pytorch#72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c (cherry picked from commit e51cf3e)
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Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: pytorch/pytorch#72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c (cherry picked from commit e51cf3e)
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Feb 9, 2022
Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: pytorch/pytorch#72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c (cherry picked from commit e51cf3e)
cyyever
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Feb 9, 2022
Summary: This will be needed by functorch to have the expected behavior of randomness: Dropout generates a tensor of the right size and then calls `bernoulli_` on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created as `empty_like` but this one just creates `empty` because it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls`_dropout_impl` here (not in native_functions.yaml) This shouldn't change the behavior outside of functorch Pull Request resolved: pytorch/pytorch#72078 Reviewed By: zou3519 Differential Revision: D33898338 Pulled By: samdow fbshipit-source-id: 9d9ed59d138d732d9647b2771ccf2ea97cffae1c (cherry picked from commit e51cf3e)
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This will be needed by functorch to have the expected behavior of randomness:
Dropout generates a tensor of the right size and then calls
bernoulli_on that. In order to get the expected behavior from ensembled creation, we'll need to make sure that the generated tensor is a batched tensor.This works mostly because most tensors are created asempty_likebut this one just createsemptybecause it needs a new shape, only for feature dropout. There is also no analogous version in CUDA because this directly calls_dropout_implhere (not in native_functions.yaml)This shouldn't change the behavior outside of functorch