rlx-tensor 0.2.14

// RLX — versatile ML compiler + runtime.
// Copyright (C) 2026 Eugene Hauptmann, Nataliya Kosmyna.
// SPDX-License-Identifier: MIT OR Apache-2.0

//! # rlx-tensor
//!
//! Symbolic tensor API for RLX — expression syntax with compiler-backed
//! execution. Compared to NumPy / ndarray / PyTorch / JAX:
//!
//! | | ndarray / NumPy | PyTorch | JAX | **rlx-tensor** |
//! |-|-----------------|---------|-----|----------------|
//! | Default execution | eager, host | eager, GPU | trace + XLA | **trace → fuse → backend** |
//! | Intermediate buffers | always materialized | often kept for autograd | XLA decides | **fusion + memory plan** |
//! | Device | host only | CUDA-centric | TPU/GPU | **CPU / Metal / CUDA / ROCm / wgpu / MLX / TPU** |
//! | Variable batch/seq | manual padding | manual | `jax.vmap` / shapes | **first-class `Dim::Dynamic`** |
//! | Slicing | views (cheap) | views | slice ops | **`s![]` → narrow (fusable)** |
//!
//! Symbolic [`Tensor`] handles carry **zero payload bytes** until you compile
//! and run — but with the `eval` feature you can also build from host data
//! NumPy-style and read results straight back, the graph staying lazy (so the
//! compiler fuses + memory-plans the whole expression) until you materialize:
//!
//! ```ignore
//! use rlx_tensor::Tensor; // requires feature = "eval"
//!
//! let a = Tensor::from_vec(vec![1.0, 2.0, 3.0], [3]);
//! let b = Tensor::ones([3]);
//! let c = (&a + &b).relu();        // graphs auto-merge, nothing runs yet
//! assert_eq!(c.to_vec(), vec![2.0, 3.0, 4.0]); // auto-selects fastest backend
//! // c.on(Device::Metal).to_vec()  // or pin an explicit backend
//! ```
//!
//! Reverse-mode autodiff (feature `grad`) returns gradient tensors:
//!
//! ```ignore
//! let loss = (&a * &b).sum([0], false);
//! let g = loss.grad(&[&a, &b]);     // [∂loss/∂a, ∂loss/∂b]
//! assert_eq!(g[0].to_vec(), b.to_vec());
//! ```
//!
//! JAX-shaped composable transforms (feature `transforms`) operate on [`Func`]
//! (a traced function of named inputs) and chain — `vmap(grad(f))`:
//!
//! ```ignore
//! let f = Func::new("f", |s| { let x = s.input("x", shape![3]); (&x * &x).sum([0], false) });
//! let batched_grad = f.grad(&["x"]).vmap(&["x"], 4);  // Func -> Func -> Func
//! let compiled = batched_grad.jit();                  // compile once
//! let out = compiled.run(&[("x", &xs)]);              // run many, no recompile
//! ```
//!
//! Materialization is compile-cached per thread (keyed by graph + constants +
//! device), so even plain `to_vec` / `run` reuse the compiled artifact.
//!
//! ```rust
//! use rlx_tensor::{ax, graph, rg, s, shape};
//!
//! // static shape
//! let g = graph("mlp", |g| {
//!     let x = g.input("x", shape![2, 4]);
//!     let w = g.param("w", shape![4, 3]);
//!     (&x.matmul(&w)).gelu()
//! });
//!
//! // dynamic batch + window slice (no copy in IR)
//! let g2 = graph("batched", |g| {
//!     let x = g.input("x", shape![?, 128]);
//!     x.slice(s![ax(), rg(0, 64)])
//! });
//! # let _ = (g, g2);
//! ```
//!
//! Or, with the `dsl` feature, declare the whole graph in a compact little
//! language with the `rlx!` macro — `@` is matmul, shapes and outputs are
//! inferred.

mod array;
#[cfg(feature = "eval")]
mod cache;
mod handle;
#[cfg(feature = "ndarray")]
mod interop;
/// Generic low-precision numerics — the `float_format!` macro + `fXmYeZ`
/// quantizer for emulated any-precision (nvf4 / f8 / bf8 / …) training.
pub mod lowp;
#[cfg(feature = "eval")]
mod model;
mod scalar;
#[cfg(feature = "optim")]
mod schedule;
mod scope;
mod slice;
mod tensor;
mod transform;

pub use array::{cat, stack};
pub use rlx_ir::op::{Activation, MaskKind};
pub use rlx_ir::{DType, Dim, Graph, NodeId, ScaleLayout, ScaledFormat, Shape};
/// Optimizers for [`Func::train_step`] (re-exported from `rlx_optim`).
/// Available with the `optim` feature.
#[cfg(feature = "optim")]
pub use rlx_optim::{Adam, AdamW, Lion, Muon, OptItem, Optimizer, Sgd};
#[cfg(feature = "optim")]
pub use schedule::LrSchedule;
pub use scope::{GraphScope, graph, graph_with};
pub use slice::{SliceAxis, SliceSpec, ax, ix, rg, tail};
pub use tensor::Tensor;
pub use transform::Func;
#[cfg(feature = "eval")]
pub use transform::Jitted;

#[cfg(feature = "eval")]
pub use cache::{cache_stats, clear_cache};
/// A compiled, runnable model with by-name weight binding. Available with the
/// `eval` feature.
#[cfg(feature = "eval")]
pub use model::Model;
/// Device selector for [`Tensor::on`] / [`Tensor::to_vec_on`] (re-exported
/// from `rlx_runtime`). Available with the `eval` feature.
#[cfg(feature = "eval")]
pub use rlx_runtime::Device;
/// Backend detection (re-exported from `rlx_runtime`): which devices this build
/// can use, and the fastest one. `to_vec` / `Func::run` pick automatically.
#[cfg(feature = "eval")]
pub use rlx_runtime::{available_devices, fastest_device, fastest_device_for, is_available};
#[cfg(feature = "eval")]
pub use tensor::Materialize;

#[doc(hidden)]
#[macro_export]
macro_rules! __dim {
    (?) => {
        $crate::Dim::Dynamic(0)
    };
    (? $sym:literal) => {
        $crate::Dim::Dynamic($sym)
    };
    ($n:expr) => {
        $crate::Dim::Static($n)
    };
}

/// Build a [`Shape`]. Default dtype is `F32`.
///
/// Each static dimension may be **any `usize` expression** — a literal, an
/// identifier, or arithmetic like `w / 2`, `w + heads`, `cfg.width * 2` — so graph
/// builders can size params/inputs from computed dimensions directly (no need to
/// hoist every derived size into a `let`). Dynamic dimensions use `?` → symbol 0
/// (repeat `?` for the same unknown size), `?1` → symbol 1, etc. A leading
/// `Dtype;` sets the element type (default `F32`).
///
/// ```rust
/// use rlx_tensor::{shape, DType};
///
/// let a = shape![2, 4];
/// let b = shape![F32; ?, 128];
/// let w = 64usize;
/// let c = shape![w + 1, w / 2, 3 * w];   // computed dimensions
/// assert_eq!(c.dtype(), DType::F32);
/// assert!(c.is_static());
/// assert!(!b.is_static());
/// ```
#[macro_export]
macro_rules! shape {
    ($dtype:ident; $($rest:tt)+) => {
        $crate::Shape::from_dims(&$crate::__shape_dims!([] ; $($rest)+), $crate::DType::$dtype)
    };
    ($($rest:tt)+) => {
        $crate::Shape::from_dims(&$crate::__shape_dims!([] ; $($rest)+), $crate::DType::F32)
    };
}

/// Token-tree muncher backing [`shape!`]: accumulates `Dim`s left to right,
/// dispatching `?`/`?N` to dynamic dims and everything else to a `usize` expression.
/// (A `tt`-list can't mix the `?` syntax with multi-token expressions, so we munch.)
#[doc(hidden)]
#[macro_export]
macro_rules! __shape_dims {
    // done (with or without a trailing comma)
    ([$($acc:tt)*] ; ) => { [ $($acc)* ] };
    ([$($acc:tt)*] ; ,) => { [ $($acc)* ] };
    // dynamic `?N`
    ([$($acc:tt)*] ; ? $sym:literal , $($rest:tt)*) => {
        $crate::__shape_dims!([$($acc)* $crate::Dim::Dynamic($sym),] ; $($rest)*)
    };
    ([$($acc:tt)*] ; ? $sym:literal) => {
        [ $($acc)* $crate::Dim::Dynamic($sym) ]
    };
    // dynamic `?` (symbol 0)
    ([$($acc:tt)*] ; ? , $($rest:tt)*) => {
        $crate::__shape_dims!([$($acc)* $crate::Dim::Dynamic(0),] ; $($rest)*)
    };
    ([$($acc:tt)*] ; ?) => {
        [ $($acc)* $crate::Dim::Dynamic(0) ]
    };
    // static dimension: any expression, more following
    ([$($acc:tt)*] ; $e:expr, $($rest:tt)*) => {
        $crate::__shape_dims!([$($acc)* $crate::Dim::Static($e),] ; $($rest)*)
    };
    // static dimension: any expression, last
    ([$($acc:tt)*] ; $e:expr) => {
        [ $($acc)* $crate::Dim::Static($e) ]
    };
}

#[cfg(feature = "dsl")]
#[doc(hidden)]
pub use rlx_macros::{__rlx_build, __rlx_expr};

/// Declare a computation [`Graph`] in a compact, readable little language.
///
/// `rlx! { … }` is a thin, versatile front-end over the shape-inferring graph
/// builder: you write the *math*, and shapes, node wiring, and outputs are
/// filled in for you. It expands to ordinary [`GraphScope`] / [`Tensor`]
/// calls (zero runtime cost) and evaluates to an [`rlx_ir::Graph`].
///
/// # Statements
/// | form | meaning |
/// |------|---------|
/// | `graph "name";` | *(optional, first)* names the graph |
/// | `input x: [dims];` | a graph input; the binding is auto-named `"x"` |
/// | `param w: [dims];` | a trainable parameter, auto-named `"w"` |
/// | `param w[N]: [dims];` | a parameter *family* `w_0 … w_{N-1}`, indexed `w[i]` |
/// | `param w @ "ir.name": [dims];` | override the IR/`set_param` name (`{i}` = family index) |
/// | `const eps = 1e-6 : F32;` | a broadcastable scalar constant |
/// | `const m = [[1,0],[0,1]] : F32;` | an N-D constant from a nested literal |
/// | `bind t;` / `bind a, b;` | adopt outer-scope Rust `Tensor`(s) of the same name (a baked constant / codebook param) as in-scope bindings |
/// | `let h = …;` | bind an intermediate to an expression |
/// | `fn blk(x, w) { … }` | a reusable subgraph, inlined at each call site (call positionally or named: `blk(w: b, x: a)`) |
/// | `repeat N { … }` | unroll the body `N` times at macro time (weight-tied stacks) |
/// | `repeat n { … }` | *runtime* count `n` → a `for _ in 0..n` loop; config-driven depth, rebound bindings are loop-carried |
/// | `repeat i in 0..N { … }` | unroll with index `i` (drives `w[i]`, distinct-weight stacks) |
/// | `scan h = h0 for N { … }` | compact `Op::Scan` loop (one body graph, not unrolled) |
/// | `tap a, b;` | expose intermediates as extra outputs (debugging) |
/// | `out y;` / `out a, b;` | mark graph outputs (defaults to the last `let`) |
///
/// `[dims]` uses the [`shape!`] grammar — literals, **runtime `usize`
/// expressions** (`[bt, d]`, `[d, ff]` size a graph from config values), `?`
/// for a dynamic axis, and an optional `DType;` prefix (`[F32; ?, 128]`).
///
/// # Expressions
/// * `a @ b` — matrix multiply (also `matmul(a, b)` / `mm(a, b)`)
/// * `+ - * / %` `**` — elementwise, with broadcasting and scalar promotion
///   (`x * 2.0`, `x ** 2`); `**` is power, `%` is `fmod`
/// * `== != < <= > >=` — elementwise comparison → a `Bool` mask (`x > 0.0`)
/// * `f(x)` — any no-extra-arg [`Tensor`] method: `gelu(x)`, `sqrt(x)`, …
/// * `maximum(a,b)` `minimum(a,b)` `pow(a,b)` `atan2(a,b)` `rem(a,b)` — binary
///   tensor ops (a scalar operand is promoted); `clamp(x, lo, hi)`;
///   `select(cond, a, b)` (mask select, aka `where_`)
/// * `linear(x, w, b)` — HF-style `x·Wᵀ + b` (W is `[out,in]`) as one fused
///   matmul+bias op; a wrapping activation folds in (`gelu(linear(x,w,b))`).
///   `matmul_t(a, b)` = `a·bᵀ`
/// * a user `fn`, called `blk(a, w)`, inlines its body here
/// * `x.method(args)` — the escape hatch: **any** `Tensor` method. A bare
///   argument naming a binding is validated and auto-borrowed, so
///   `q.attention(k, v, 8, 64, MaskKind::Causal)` reads naturally (and a typo'd
///   name is a clear DSL error). Other args are raw Rust; wrap an external
///   value as `(value)`, or prefix it with `~` (`~eps`, `~num_heads`), to pass
///   it BY VALUE — the escape a scalar identifier/const needs to reach an
///   `f32`/`usize`/enum parameter (`h.layer_norm(g, b, ~eps)`,
///   `q.attention(k, v, ~nh, ~dh, MaskKind::Causal)`) instead of `&f32`.
///
/// A definite static matmul shape mismatch (both operands statically 2-D with
/// incompatible inner dims) is reported at macro-expansion time.
///
/// Precedence follows NumPy: `.method(…)` > unary `-` > `**` > (`@` `*` `/` `%`)
/// > (`+` `-`) > (`== != < <= > >=`), left-associative except right-associative
/// `**` — so `x @ w * s` is `(x @ w) * s`.
///
/// # Example
/// ```rust
/// use rlx_tensor::rlx;
///
/// let g = rlx! {
///     graph "mlp";
///     input x: [?, 784];
///     param w1: [784, 256];   param b1: [256];
///     param w2: [256, 10];    param b2: [10];
///
///     let h = gelu(x @ w1 + b1);
///     let y = h @ w2 + b2;
///     out y;
/// };
///
/// assert_eq!(g.name, "mlp");
/// assert_eq!(g.outputs.len(), 1);
/// ```
///
/// The macro works identically whether imported from this crate
/// (`rlx_tensor::rlx!`) or the umbrella (`rlx::rlx!`) — the wrapper resolves
/// every emitted path through `$crate`.
///
/// Requires the `dsl` feature (on for umbrella `rlx` users via `tensor`).
#[cfg(feature = "dsl")]
#[macro_export]
macro_rules! rlx {
    ( $($body:tt)* ) => {{
        // Bring the handful of names the generated code references into a
        // block-local scope via `$crate` (robust across re-exports). The
        // proc macro below then emits only bare names + method/operator calls.
        #[allow(unused_imports)]
        use $crate::{GraphScope, DType, MaskKind, Activation, shape};
        $crate::__rlx_build! { $($body)* }
    }};
}

/// Evaluate a single `rlx!`-grammar expression over in-scope Rust [`Tensor`]
/// values — the "Rust bridge". Every identifier is a normal Rust variable, so
/// ordinary `for` loops and config drive the graph structure while the DSL
/// expresses the per-step math (e.g. `h = rlx_expr!(gelu(linear(h, w, b)))`).
/// This is what a macro-expanded `rlx! { … }` can't do: runtime-length loops.
/// Requires the `dsl` feature.
#[cfg(feature = "dsl")]
#[macro_export]
macro_rules! rlx_expr {
    ( $($body:tt)* ) => {{
        #[allow(unused_imports)]
        use $crate::{DType, MaskKind, Activation};
        $crate::__rlx_expr! { $($body)* }
    }};
}