API migration guide

This page is the first stop when an older tenferro-rs example fails with cannot find module, cannot find function, or a changed-signature error. The current public API favors explicit extension traits, fallible constructors, and session-owned execution. Historical worklogs are not API documentation.

Removed modules and free functions

Older spelling	Current spelling
`tenferro_einsum::eager_tensor`	Import `tenferro_einsum::EagerEinsumExt` and call the trait method on the eager value.
`tenferro_linalg::eager_tensor`	Import `tenferro_linalg::EagerTensorLinalgExt` and call the trait method on the eager value.
`tenferro_runtime::traced_tensor`	Use the current traced tensor types and their extension traits, such as `tenferro_linalg::TracedTensorLinalgExt`.
`tenferro_einsum::einsum`	Use the current trait method, for example `TraceContextEinsumExt::einsum` or `TracedTensorEinsumExt::einsum`, for the receiver you have.
`tenferro_einsum::einsum_subscripts_with`	Import the owning einsum extension trait and call its session/context method.
`tenferro_linalg::svd`, `qr`, `eigh`, `solve`	Import `TensorLinalgExt`, `TypedTensorLinalgExt`, or `TensorReadLinalgExt` and call `.svd(...)`, `.qr(...)`, `.eigh(...)`, or `.solve(...)` on the input.

The owning crate’s prelude re-exports the public operation traits. For a first direct CPU program, the usual imports are:

use tenferro_cpu::CpuBackend;
use tenferro_linalg::prelude::*;
use tenferro_runtime::prelude::*;

Then keep execution inside one session:

let mut backend = CpuBackend::new();
let values = backend.with_backend_session(|session| input.svdvals(session))?;

Autodiff context changes

AdContextBuilder::with_core_rules() was removed. The core primitive rules are installed by the normal builder; start with:

let ad = tenferro_ad::AdContext::builder().build()?;

When an operation family supplies semantic AD rules, install that family explicitly instead:

let ad = tenferro_ad::AdContext::builder()
    .with_semantic_extension_rules(tenferro_linalg::semantic_ad_rules()?)?
    .build()?;

Fallible constructors and shape APIs

Constructors that validate storage, placement, or metadata return Result. Propagate the result instead of relying on an infallible constructor:

Older assumption	Current spelling
`EagerTensor::from_tensor_in(tensor, ctx)` returns a value	`EagerTensor::from_tensor_in(tensor, ctx)?`
`EagerTensor::requires_grad_in(tensor, ctx)` returns a value	`EagerTensor::requires_grad_in(tensor, ctx)?`
`TracedTensor::input_concrete_shape(dtype, shape)` is infallible	`TracedTensor::input_concrete_shape(dtype, shape)?`
`TypedTensor::from_vec_col_major(shape, data)` is infallible	`TypedTensor::from_vec_col_major(shape, data)?`
`TypedTensor::zeros(shape)` is infallible	`TypedTensor::zeros(shape)?`

reduce_sum uses an explicit optional axis list on eager values:

let total = value.reduce_sum(None)?;          // all axes
let columns = value.reduce_sum(Some(&[0]))?;  // selected axes

An empty slice remains distinct from None: use Some(&[]) when the API’s identity/no-axis behavior is what the program needs.

Finding the current method

Choose the value tier: direct concrete tensor, eager tensor, or traced tensor.
Import the *Ext trait owned by the operation crate.
Check the method’s receiver and session arity in the API cheatsheet.
Use the linear algebra guide, einsum guide, or custom operations guide for the relevant workflow.

Do not add a compatibility alias for a removed API. If a current example or error message contradicts this page, report the documentation gap through the issue-intake procedure after obtaining maintainer/user approval.