Reuse HW device context in GPU encoder #3215
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Summary: In pytorch#3178, a mechanism to cache HW device context was introduced. This commit applies the reuse in StreamWriter, so that when using GPU video decoding and encoding, they are shared. This gives back about 250 - 300 MB of GPU memory. --- Q: What is HW device context? From https://ffmpeg.org/doxygen/4.1/structAVHWDeviceContext.html#details > This struct aggregates all the (hardware/vendor-specific) "high-level" state, i.e. > > state that is not tied to a concrete processing configuration. E.g., in an API that supports hardware-accelerated encoding and decoding, this struct will (if possible) wrap the state that is common to both encoding and decoding and from which specific instances of encoders or decoders can be derived. Pull Request resolved: pytorch#3215 Reviewed By: nateanl Differential Revision: D44504051 Pulled By: mthrok fbshipit-source-id: c52b4463af9ec6eeb01da85e7a4d6a47952aae1e
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This pull request was exported from Phabricator. Differential Revision: D44504051 |
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In #3178, a mechanism to cache HW device context was introduced.
This commit applies the reuse in StreamWriter, so that
when using GPU video decoding and encoding, they are shared.
This gives back about 250 - 300 MB of GPU memory.
Q: What is HW device context?
From https://ffmpeg.org/doxygen/4.1/structAVHWDeviceContext.html#details