rlx-ir 0.2.16

Tensor IR for the RLX ML compiler — standalone, serializable, optimizable
Documentation 
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// RLX — versatile ML compiler + runtime.
// Copyright (C) 2026 Eugene Hauptmann, Nataliya Kosmyna.
// SPDX-License-Identifier: MIT OR Apache-2.0

//! Autodiff staging in the HIR → MIR → LIR pipeline.
//!
//! Differentiation is implemented in `rlx_autodiff` on MIR ([`crate::Graph`]).
//! This module documents how IR stages connect:
//!
//! ```text
//!  HIR (blocks)  ──lower──▶  MIR  ──prepare_graph_for_ad──▶  MIR (primitive)
//!                                    └── grad_with_loss ──▶  backward MIR Graph
//!  MIR (opt)     ──plan──▶   LIR  (inference compile only — not an AD input)
//! ```
//!
//! - **HIR**: use [`crate::hir::FusionPolicy::for_autodiff`] or `Direct` +
//!   `rlx_autodiff::prepare_graph_for_ad`.
//! - **MIR**: `rlx_autodiff::grad_with_loss` / `rlx_autodiff::grad_with_loss_module`.
//! - **LIR**: do not differentiate; lower from MIR first.

/// Named stages in a training-oriented compile flow.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AdPipelineStage {
    /// Block builders (`Graph::define`, `HirModule`).
    HirBuild,
    /// Tensor DAG after [`crate::hir::HirModule::lower_to_mir`].
    MirLowered,
    /// After `rlx_autodiff::prepare_graph_for_ad` (fused ops unfused, scans rewritten).
    MirPrepared,
    /// Gradient graph returned by `rlx_autodiff::grad_with_loss`.
    MirBackward,
}