[TF] Reimplement unbroadcast using on-host axis calculation for performance.#24907
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rxwei merged 1 commit intoswiftlang:tensorflowfrom May 20, 2019
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[TF] Reimplement unbroadcast using on-host axis calculation for performance.#24907rxwei merged 1 commit intoswiftlang:tensorflowfrom
rxwei merged 1 commit intoswiftlang:tensorflowfrom
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…rmance. The inefficiency of `unbroadcast(toShape:)`, `unbroadcast(to:)`, and `unbroadcast(like:)` has caused significant performance problems during model training because it's performing a lot of TensorFlow operations to achieve axis calculation. We were forced to implement it this way in the early GPE era when neither send/receive nor per-op dispatch was available. This PR reimplements the unbroadcast operations in terms of host-side logic to compute axes to reduce along. This significantly reduces the TensorFlow opreation dispatch overhead. The base implementation changed from `broadcast(toShape:)` to `broadcast(to:)`. With the new implementation, differentiating broadcasting operators is 37% faster (see simple test script [here](https://gist.github.com/rxwei/e1488cac5379ba2bc3aff7490e18158f)). Note: - Since we now rely on the TensorFlow runtime less, more precondition checks and assertions are added to the newly implemented `unbroadcast(to:)` method. - The part of swiftlang#24408 that uses `Raw.broadcastGradientArgs(s0:s1:)` is still necessary for broadcasting binary operations to become faster. TODO: - Change `unbroadcast(toShape:)` tests added by swiftlang#24899 to use `unbroadcast(to:)`, since `unbroadcast(to:)` is now the base implementation.
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@swift-ci please test tensorflow |
dan-zheng
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Big 👍 to empirical benchmarking!
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The inefficiency of
unbroadcast(toShape:),unbroadcast(to:), andunbroadcast(like:)has caused significant performance problems during model training because it's performing a lot of TensorFlow operations to achieve axis calculation. We were forced to implement it this way in the early GPE era when neither send/receive nor per-op dispatch was available.This PR reimplements the unbroadcast operations in terms of host-side logic to compute axes to reduce along. This significantly reduces the TensorFlow opreation dispatch overhead. The base implementation changed from
broadcast(toShape:)tobroadcast(to:).With the new implementation, differentiating broadcasting operators is 37% faster (see simple test script here).
Note:
unbroadcast(to:)method.unbroadcast(to:)and improve derivative performance. #24408 that usesRaw.broadcastGradientArgs(s0:s1:)is still necessary for broadcasting binary operations to become faster.TODO:
unbroadcast(toShape:)tests added by [AutoDiff] Add more Tensorbroadcast/unbroadcastdifferentiation tests. #24899 to useunbroadcast(to:), sinceunbroadcast(to:)is now the base implementation.