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Latest development documentation · Updated 2026-09-08. A documentation snapshot for package 1.0.0.dev20260909 is not available.

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Automatic differentiation package - tensorplay.autograd

`tensorplay.autograd.backward`
`tensorplay.autograd.grad`	Compute and return the sum of gradients of outputs with respect to the inputs.

Forward-mode Automatic Differentiation

`tensorplay.autograd.forward_ad.make_dual`	Pairs `primal` with `tangent` at `level` (default: current).
`tensorplay.autograd.forward_ad.unpack_dual`	Returns `(tangent, primal)`; `tangent` is None for plain tensors.
`tensorplay.autograd.forward_ad.enter_dual_level`	Enters a new forward gradient level and returns its index
`tensorplay.autograd.forward_ad.exit_dual_level`	Exits the given (default: most recent) forward gradient level.

Functional higher level API

`tensorplay.autograd.functional.jacobian`	Compute the Jacobian of a given function.
`tensorplay.autograd.functional.hessian`	Compute the Hessian of a given scalar function.
`tensorplay.autograd.functional.vjp`	Compute the dot product between a vector `v` and the Jacobian of the given function at the point given by the inputs.
`tensorplay.autograd.functional.jvp`	Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector `v`.
`tensorplay.autograd.functional.vhp`	Compute the dot product between vector `v` and Hessian of a given scalar function at a specified point.
`tensorplay.autograd.functional.hvp`	Compute the dot product between the scalar function's Hessian and a vector `v` at a specified point.

Function

`tensorplay.autograd.function.Function`	Records operation history and defines formulas for differentiating ops.
`tensorplay.autograd.jvp`	Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector `v`.

Context method mixins

tensorplay.autograd.function.FunctionMeta

the name classproperty ("<Cls>Backward", used for node naming) and a friendlier repr for subclasses.

Custom Function utilities

`tensorplay.autograd.function.once_differentiable`	Decorator to make a custom autograd Function's backward run once, with gradients detached and grad-mode disabled inside.
`tensorplay.autograd.function.InplaceFunction`	In-place operations must call `ctx.mark_dirty` on the mutated inputs inside `forward`; this subclass exists only so historical code that subclasses it keeps working.
`tensorplay.autograd.function.NestedIOFunction`	Kept only for import compatibility; the modern contract is to define `forward` + `backward` on `Function` directly.

Numerical gradient checking

`tensorplay.autograd.gradcheck.gradcheck`	Check gradients computed via small finite differences against analytical gradients wrt tensors in `inputs` that are of floating point or complex type and with `requires_grad=True`.
`tensorplay.autograd.gradcheck.gradgradcheck`	Check gradients of gradients computed via small finite differences against analytical gradients wrt tensors in `inputs` and `grad_outputs` that are of floating point or complex type and with `requires_grad=True`.
`tensorplay.autograd.gradcheck.GradcheckError`	Error raised by `gradcheck()` and `gradgradcheck()`.
`tensorplay.autograd.gradcheck.get_numerical_jacobian_wrt_specific_input`

Profiler

`tensorplay.profiler.profiler.profile`	Context manager that records dispatched operations and annotations.
`tensorplay.profiler.profiler.record_function`	Mark a user-defined span in the active profiling session.
`tensorplay.autograd.profiler_util.EventList`	A list of profiling events with helper methods for analysis and visualization.
`tensorplay.autograd.profiler_util.FormattedTimesMixin`	Helpers for FunctionEvent and FunctionEventAvg.
`tensorplay.autograd.profiler_util.FunctionEvent`	Profiling information about a single function.
`tensorplay.autograd.profiler_util.FunctionEventAvg`	Averaged profiling statistics over multiple FunctionEvent objects.
`tensorplay.autograd.profiler_util.Interval`
`tensorplay.autograd.profiler_util.Kernel`
`tensorplay.autograd.profiler_util.MemRecordsAcc`	Acceleration structure for accessing mem_records in interval.
`tensorplay.autograd.profiler_util.StringTable`
`tensorplay.profiler.profiler.emit_nvtx`	Emit named ranges for active operations.
`tensorplay.profiler.profiler.emit_itt`	Emit instrumentation ranges for active operations.

Debugging and anomaly detection

`tensorplay.autograd.anomaly_mode.detect_anomaly`	Context-manager that enables anomaly detection for the autograd engine.
`tensorplay.autograd.anomaly_mode.set_detect_anomaly`	Context-manager that sets the anomaly detection for the autograd engine on or off.

Autograd graph

`tensorplay.autograd.graph.saved_tensors_hooks`	Context manager that installs a (pack, unpack) pair for every tensor saved via `Function()`'s `save_for_backward` while active.
`tensorplay.autograd.graph.save_on_cpu`	Store saved values in host memory and restore their original device.
`tensorplay.autograd.variable.Variable`
`tensorplay.autograd.variable.VariableMeta`