From e41d6701c250ed3867b7f0de072d7d2453f755e1 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 3 Jan 2018 06:52:30 +0000 Subject: [PATCH] Deploy to GitHub Pages: e57a40b8a64a87370d7b9f46b86ec7887694229a --- develop/doc/_sources/design/backward.md.txt | 6 ++++-- develop/doc/design/backward.html | 5 +++-- develop/doc/searchindex.js | 2 +- develop/doc_cn/_sources/design/backward.md.txt | 6 ++++-- develop/doc_cn/design/backward.html | 5 +++-- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 16 insertions(+), 10 deletions(-) diff --git a/develop/doc/_sources/design/backward.md.txt b/develop/doc/_sources/design/backward.md.txt index 35f03692bb0..20fda7a98f5 100644 --- a/develop/doc/_sources/design/backward.md.txt +++ b/develop/doc/_sources/design/backward.md.txt @@ -106,9 +106,11 @@ See function `_addup_repetitive_outputs_` in `backward.py` for implementation de In our framework, variables can be marked as *no_gradient*, it means that the gradient of this variable is unnecessary and can be considered as zero in model training. Apparently, when all the outputs of some `grad_op` are marked as *no_gradient*, the `grad_op` itself can be skipped in backward pass. -But these unnecessary gradients still need to be creating and initialized by something, otherwise following `grad_op`s who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ `fill_zeros_like_op` to initialize them as all zeros. +Another situation is all the gradient inputs of some `grad_op` are marked as *no_gradient*, which means all of them can be considered as zeros. For `grad_op`s are in essence the propagation of gradients, all the outputs are definitely zeros when all gradient inputs are zeros. Therefore the `grad_op` can also be skipped. -This features are implemented in function `_remove_no_grad_branch_`. It checks new created `grad_op`s one-by-one, removes whose outputs are all in `no_grad_set` or inserts `fill_zeros_like_op` when its necessary. We can get the `no_grad_set` from the `_append_backward_ops_` argument `no_grad_dict` or generate it on the fly by scanning all variables' `no_gradient` attribute(True or False). +It should be noted that all these zero gradients still need to be creating and initialized by something, otherwise following `grad_op`s who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ `fill_zeros_like_op` to initialize them as all zeros. + +This features are implemented in function `_remove_no_grad_branch_`. It checks new created `grad_op`s one-by-one, removes who can be skipped and inserts `fill_zeros_like_op` when its necessary. We can get the `no_grad_set` from the `_append_backward_ops_` argument `no_grad_dict` or generate it on the fly by scanning all variables' `no_gradient` attribute(True or False). ### Creating Backward Variables diff --git a/develop/doc/design/backward.html b/develop/doc/design/backward.html index 297ad6f1fa8..29babfd4c39 100644 --- a/develop/doc/design/backward.html +++ b/develop/doc/design/backward.html @@ -301,8 +301,9 @@ for op in reversed(block.ops):

No Gradient VariablesΒΆ

In our framework, variables can be marked as no_gradient, it means that the gradient of this variable is unnecessary and can be considered as zero in model training. Apparently, when all the outputs of some grad_op are marked as no_gradient, the grad_op itself can be skipped in backward pass.

-

But these unnecessary gradients still need to be creating and initialized by something, otherwise following grad_ops who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ fill_zeros_like_op to initialize them as all zeros.

-

This features are implemented in function _remove_no_grad_branch_. It checks new created grad_ops one-by-one, removes whose outputs are all in no_grad_set or inserts fill_zeros_like_op when its necessary. We can get the no_grad_set from the _append_backward_ops_ argument no_grad_dict or generate it on the fly by scanning all variables’ no_gradient attribute(True or False).

+

Another situation is all the gradient inputs of some grad_op are marked as no_gradient, which means all of them can be considered as zeros. For grad_ops are in essence the propagation of gradients, all the outputs are definitely zeros when all gradient inputs are zeros. Therefore the grad_op can also be skipped.

+

It should be noted that all these zero gradients still need to be creating and initialized by something, otherwise following grad_ops who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ fill_zeros_like_op to initialize them as all zeros.

+

This features are implemented in function _remove_no_grad_branch_. It checks new created grad_ops one-by-one, removes who can be skipped and inserts fill_zeros_like_op when its necessary. We can get the no_grad_set from the _append_backward_ops_ argument no_grad_dict or generate it on the fly by scanning all variables’ no_gradient attribute(True or False).

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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/backward.md.txt b/develop/doc_cn/_sources/design/backward.md.txt index 35f03692bb0..20fda7a98f5 100644 --- a/develop/doc_cn/_sources/design/backward.md.txt +++ b/develop/doc_cn/_sources/design/backward.md.txt @@ -106,9 +106,11 @@ See function `_addup_repetitive_outputs_` in `backward.py` for implementation de In our framework, variables can be marked as *no_gradient*, it means that the gradient of this variable is unnecessary and can be considered as zero in model training. Apparently, when all the outputs of some `grad_op` are marked as *no_gradient*, the `grad_op` itself can be skipped in backward pass. -But these unnecessary gradients still need to be creating and initialized by something, otherwise following `grad_op`s who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ `fill_zeros_like_op` to initialize them as all zeros. +Another situation is all the gradient inputs of some `grad_op` are marked as *no_gradient*, which means all of them can be considered as zeros. For `grad_op`s are in essence the propagation of gradients, all the outputs are definitely zeros when all gradient inputs are zeros. Therefore the `grad_op` can also be skipped. -This features are implemented in function `_remove_no_grad_branch_`. It checks new created `grad_op`s one-by-one, removes whose outputs are all in `no_grad_set` or inserts `fill_zeros_like_op` when its necessary. We can get the `no_grad_set` from the `_append_backward_ops_` argument `no_grad_dict` or generate it on the fly by scanning all variables' `no_gradient` attribute(True or False). +It should be noted that all these zero gradients still need to be creating and initialized by something, otherwise following `grad_op`s who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ `fill_zeros_like_op` to initialize them as all zeros. + +This features are implemented in function `_remove_no_grad_branch_`. It checks new created `grad_op`s one-by-one, removes who can be skipped and inserts `fill_zeros_like_op` when its necessary. We can get the `no_grad_set` from the `_append_backward_ops_` argument `no_grad_dict` or generate it on the fly by scanning all variables' `no_gradient` attribute(True or False). ### Creating Backward Variables diff --git a/develop/doc_cn/design/backward.html b/develop/doc_cn/design/backward.html index bbb9d07b87a..ac37211a7b9 100644 --- a/develop/doc_cn/design/backward.html +++ b/develop/doc_cn/design/backward.html @@ -314,8 +314,9 @@ for op in reversed(block.ops):

No Gradient VariablesΒΆ

In our framework, variables can be marked as no_gradient, it means that the gradient of this variable is unnecessary and can be considered as zero in model training. Apparently, when all the outputs of some grad_op are marked as no_gradient, the grad_op itself can be skipped in backward pass.

-

But these unnecessary gradients still need to be creating and initialized by something, otherwise following grad_ops who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ fill_zeros_like_op to initialize them as all zeros.

-

This features are implemented in function _remove_no_grad_branch_. It checks new created grad_ops one-by-one, removes whose outputs are all in no_grad_set or inserts fill_zeros_like_op when its necessary. We can get the no_grad_set from the _append_backward_ops_ argument no_grad_dict or generate it on the fly by scanning all variables’ no_gradient attribute(True or False).

+

Another situation is all the gradient inputs of some grad_op are marked as no_gradient, which means all of them can be considered as zeros. For grad_ops are in essence the propagation of gradients, all the outputs are definitely zeros when all gradient inputs are zeros. Therefore the grad_op can also be skipped.

+

It should be noted that all these zero gradients still need to be creating and initialized by something, otherwise following grad_ops who take these gradients as inputs take the risk of using uninitialized memory. In our code, we employ fill_zeros_like_op to initialize them as all zeros.

+

This features are implemented in function _remove_no_grad_branch_. It checks new created grad_ops one-by-one, removes who can be skipped and inserts fill_zeros_like_op when its necessary. We can get the no_grad_set from the _append_backward_ops_ argument no_grad_dict or generate it on the fly by scanning all variables’ no_gradient attribute(True or False).

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