Module: tfp.experimental.auto_batching.allocation_strategy
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Live variable analysis.
A variable is "dead" at some point if the compiler can find a proof that no
future instruction will read the value before that value is overwritten; "live"
otherwise.
This module implements a liveness analysis for the IR defined in
instructions.py.
Functions
optimize(...): Optimizes a Program's variable allocation strategy.
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# Module: tfp.experimental.auto_batching.allocation_strategy\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/auto_batching/allocation_strategy.py) |\n\nLive variable analysis.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tfp.experimental.auto_batching.frontend.allocation_strategy`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/allocation_strategy)\n\n\u003cbr /\u003e\n\nA variable is \"dead\" at some point if the compiler can find a proof that no\nfuture instruction will read the value before that value is overwritten; \"live\"\notherwise.\n\nThis module implements a liveness analysis for the IR defined in\ninstructions.py.\n\nFunctions\n---------\n\n[`optimize(...)`](../../../tfp/experimental/auto_batching/allocation_strategy/optimize): Optimizes a `Program`'s variable allocation strategy."]]
View source on GitHub