Module: tfp.experimental.auto_batching.instructions
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Instruction language for auto-batching virtual machine.
Classes
class Block
: A basic block.
class BranchOp
: A conditional.
class ControlFlowGraph
: A control flow graph (CFG).
class Function
: A function subject to auto-batching, callable with FunctionCallOp
.
class FunctionCallOp
: Call a Function
.
class GotoOp
: An unconditional jump.
class IndirectGotoOp
: Jump to the address in the reserved "program counter" variable.
class PopOp
: Restore the given variables from their stacks.
class PrimOp
: An arbitrary already-batched computation, a 'primitive operation'.
class Program
: An auto-batchable program.
class PushGotoOp
: Save an address for IndirectGotoOp
and unconditionally jump to another.
class TensorType
: TensorType(dtype, shape)
class Type
: Type(tensors,)
class VariableAllocation
: A token indicating how to allocate memory for an autobatched variable.
Functions
extract_referenced_variables(...)
: Extracts a set of the variable names referenced by the node in question.
halt_op(...)
: Returns a control transfer Op
that means "exit this graph".
interpret(...)
: Interprets a program in this instruction language and returns the result.
push_op(...)
: Returns an Op
that pushes values from vars_in
into vars_out
.
Other Members |
pc_var
|
'__program_counter__'
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21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.instructions\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/instructions.py) |\n\nInstruction language for auto-batching virtual machine.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tfp.experimental.auto_batching.frontend.instructions`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions), [`tfp.experimental.auto_batching.frontend.st.inst`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions), [`tfp.experimental.auto_batching.frontend.stack.inst`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions), [`tfp.experimental.auto_batching.stack_optimization.inst`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions), [`tfp.experimental.auto_batching.stackless.inst`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions)\n\n\u003cbr /\u003e\n\nClasses\n-------\n\n[`class Block`](../../../tfp/experimental/auto_batching/instructions/Block): A basic block.\n\n[`class BranchOp`](../../../tfp/experimental/auto_batching/instructions/BranchOp): A conditional.\n\n[`class ControlFlowGraph`](../../../tfp/experimental/auto_batching/instructions/ControlFlowGraph): A control flow graph (CFG).\n\n[`class Function`](../../../tfp/experimental/auto_batching/instructions/Function): A function subject to auto-batching, callable with `FunctionCallOp`.\n\n[`class FunctionCallOp`](../../../tfp/experimental/auto_batching/instructions/FunctionCallOp): Call a `Function`.\n\n[`class GotoOp`](../../../tfp/experimental/auto_batching/instructions/GotoOp): An unconditional jump.\n\n[`class IndirectGotoOp`](../../../tfp/experimental/auto_batching/instructions/IndirectGotoOp): Jump to the address in the reserved \"program counter\" variable.\n\n[`class PopOp`](../../../tfp/experimental/auto_batching/instructions/PopOp): Restore the given variables from their stacks.\n\n[`class PrimOp`](../../../tfp/experimental/auto_batching/instructions/PrimOp): An arbitrary already-batched computation, a 'primitive operation'.\n\n[`class Program`](../../../tfp/experimental/auto_batching/instructions/Program): An auto-batchable program.\n\n[`class PushGotoOp`](../../../tfp/experimental/auto_batching/instructions/PushGotoOp): Save an address for `IndirectGotoOp` and unconditionally jump to another.\n\n[`class TensorType`](../../../tfp/experimental/auto_batching/TensorType): TensorType(dtype, shape)\n\n[`class Type`](../../../tfp/experimental/auto_batching/Type): Type(tensors,)\n\n[`class VariableAllocation`](../../../tfp/experimental/auto_batching/instructions/VariableAllocation): A token indicating how to allocate memory for an autobatched variable.\n\nFunctions\n---------\n\n[`extract_referenced_variables(...)`](../../../tfp/experimental/auto_batching/instructions/extract_referenced_variables): Extracts a set of the variable names referenced by the node in question.\n\n[`halt_op(...)`](../../../tfp/experimental/auto_batching/instructions/halt_op): Returns a control transfer `Op` that means \"exit this graph\".\n\n[`interpret(...)`](../../../tfp/experimental/auto_batching/instructions/interpret): Interprets a program in this instruction language and returns the result.\n\n[`push_op(...)`](../../../tfp/experimental/auto_batching/instructions/push_op): Returns an `Op` that pushes values from `vars_in` into `vars_out`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Other Members ------------- ||\n|--------|-------------------------|\n| pc_var | `'__program_counter__'` |\n\n\u003cbr /\u003e"]]