Cache graph functions and add If gradient test#637
Open
nfeybesse wants to merge 1 commit intotensorflow:masterfrom
Open
Cache graph functions and add If gradient test#637nfeybesse wants to merge 1 commit intotensorflow:masterfrom
nfeybesse wants to merge 1 commit intotensorflow:masterfrom
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This PR introduces a small cache of ConcreteFunction instances attached to a Graph.
When functions are attached via attachFunction, they are stored in a local cache
indexed by their defined name. This avoids repeatedly scanning the native
TensorFlow function library when resolving functions during gradient construction.
A helper method getFunctionCached(String prefix) is also added to allow quick lookup
of cached functions by name prefix.
In addition, this PR introduces IfGradientTest, a unit test validating correct
gradient propagation through a StatefulIf operation.