Module: tfp.experimental.auto_batching.frontend
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AutoGraph-based auto-batching frontend.
Modules
ab_type_inference
module: Type inference pass on functional control flow graph.
allocation_strategy
module: Live variable analysis.
dsl
module: Python-embedded DSL frontend for authoring autobatchable IR programs.
gast_util
module: Gast compatibility library. Supports 0.2.2 and 0.3.2.
instructions
module: Instruction language for auto-batching virtual machine.
lowering
module: Lowering the full IR to stack machine instructions.
st
module: A stackless auto-batching VM.
stack
module: Optimizing stack usage (pushes and pops).
tf_backend
module: TensorFlow (graph) backend for auto-batching VM.
vm
module: The auto-batching VM itself.
Classes
class Context
: Context object for auto-batching multiple Python functions together.
Functions
truthy(...)
: Normalizes Tensor ranks for use in if
conditions.
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Last updated 2023-11-21 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[],null,["# Module: tfp.experimental.auto_batching.frontend\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/frontend.py) |\n\nAutoGraph-based auto-batching frontend.\n\nModules\n-------\n\n[`ab_type_inference`](../../../tfp/experimental/auto_batching/type_inference) module: Type inference pass on functional control flow graph.\n\n[`allocation_strategy`](../../../tfp/experimental/auto_batching/allocation_strategy) module: Live variable analysis.\n\n[`dsl`](../../../tfp/experimental/auto_batching/dsl) module: Python-embedded DSL frontend for authoring autobatchable IR programs.\n\n[`gast_util`](../../../tfp/experimental/auto_batching/frontend/gast_util) module: Gast compatibility library. Supports 0.2.2 and 0.3.2.\n\n[`instructions`](../../../tfp/experimental/auto_batching/instructions) module: Instruction language for auto-batching virtual machine.\n\n[`lowering`](../../../tfp/experimental/auto_batching/lowering) module: Lowering the full IR to stack machine instructions.\n\n[`st`](../../../tfp/experimental/auto_batching/stackless) module: A stackless auto-batching VM.\n\n[`stack`](../../../tfp/experimental/auto_batching/stack_optimization) module: Optimizing stack usage (pushes and pops).\n\n[`tf_backend`](../../../tfp/experimental/auto_batching/tf_backend) module: TensorFlow (graph) backend for auto-batching VM.\n\n[`vm`](../../../tfp/experimental/auto_batching/virtual_machine) module: The auto-batching VM itself.\n\nClasses\n-------\n\n[`class Context`](../../../tfp/experimental/auto_batching/Context): Context object for auto-batching multiple Python functions together.\n\nFunctions\n---------\n\n[`truthy(...)`](../../../tfp/experimental/auto_batching/truthy): Normalizes Tensor ranks for use in `if` conditions.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Other Members ------------- ||\n|------------|-----------------------------------------------------------------------------------------------------------------------------|\n| TF_BACKEND | Instance of [`tfp.experimental.auto_batching.TensorFlowBackend`](../../../tfp/experimental/auto_batching/TensorFlowBackend) |\n\n\u003cbr /\u003e"]]