---
title: "gparse-softmax — Mova code model"
description: "Mova code model of gparse/src/softmax.rs: components with their labels, obernants and the model text."
url: "https://groupants.elamur.ai/free/en/mova/gparse/src/softmax.rs.groupant/"
---
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# gparse-softmax

The groupant beside gparse/src/softmax.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.

## Facts

- **kind:** groupant
- **status:** partial source bound model
- **source:** [`gparse/src/softmax.rs`](https://github.com/ElamurAI/mova-free/blob/main/gparse/src/softmax.rs)
- **source SHA-256:** `61d258172479e2c3…`
- **repository folder:** [`gparse/src/softmax.rs.groupant`](https://github.com/ElamurAI/mova-free/tree/main/gparse/src/softmax.rs.groupant)
- **repository commit:** `48f2e6f74146`

> **Implementation material.** A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.

## Components

| component | label | where |
|---|---|---|
| **approximant** | partial | [approximant](#approximant) |
| **ast** | partial | `syntax.json` |
| **composition** | partial | [composition](#composition) |
| **cost** | symbolic source derived | [invariant](#invariant) |
| **descriptant** | present | [descriptant](#descriptant) |
| **evidence** | executed finite checks | [evidence](#evidence) |
| **invariant** | partial | [invariant](#invariant) |
| **tests** | executed bounded checks | [tests](#tests) |

## Obernants

The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.

| id | type | label | where |
|---|---|---|---|
| `behavior_complement` | behavior_complement | partial source derived model | [behavior_complement](#behavior-complement) |
| `reverse_reconstruction` | reverse_reconstruction | partial source derived model | [reverse_reconstruction](#reverse-reconstruction) |
| `gradient_transport` | gradient_transport | partial source derived model | [gradient_transport](#gradient-transport) |
| `layout_transport` | layout_transport | partial source derived model | [layout_transport](#layout-transport) |
| `failure_precedence` | failure_precedence | partial source derived model | [failure_precedence](#failure-precedence) |
| `numerical_observation` | numerical_observation | partial source derived model | [numerical_observation](#numerical-observation) |

## Model text


### descriptant

#### Source-bound description

last_dim dispatches nonempty CUDA F32 to contiguous custom Softmax; otherwise Candle reference last-dimension softmax. cuda_fwd validates contiguous offsets, row dimension/nonempty, i32 columns/u32 rows, allocates output and launches existing softmax_f32 with retained source offset and grid(rows,1,1), block(1,32,1). bwd returns p*(upstream-sum_last(p*upstream)).

##### Epoch 2026-10-31

Prospective model was recorded before edits. Added tests use bounded synthetic fixtures; production algorithms unchanged. Coverage is partial, not proof. No trained corpus, live teacher or throughput experiment was executed.

### approximant

#### Forward approximant

last_dim dispatches nonempty CUDA F32 to contiguous custom Softmax; otherwise Candle reference last-dimension softmax. cuda_fwd validates contiguous offsets, row dimension/nonempty, i32 columns/u32 rows, allocates output and launches existing softmax_f32 with retained source offset and grid(rows,1,1), block(1,32,1). bwd returns p*(upstream-sum_last(p*upstream)).

Domain: valid source-specific dimensions and resources stated in invariant; no automatic production substitution. Numerical observations are finite checks, not an end-to-end relative bound. Executable Rust surrogate: unsupported gap; existing exact tests are not surrogates. Future metric-specific caps may be 1/2/5/10%, never above 10%.

### invariant

#### Invariant and cost

Output shape matches input; finite real row relation sums probabilities to one, while numerical agreement is only finite-tested. Backward row-sum zero mathematically; singleton derivative zero. CPU custom kernel rejects; public wrapper routes CPU to reference.

##### Cost

O(elements) forward and backward plus reductions; contiguous conversion may allocate/copy O(elements). CUDA allocation/launch/device errors propagate; no speed measurement or all-domain error cap.

Allocation, finite arithmetic, platform dimensions and external failures are not universally certified.

### composition

#### Composition

last_dim dispatches nonempty CUDA F32 to contiguous custom Softmax; otherwise Candle reference last-dimension softmax. cuda_fwd validates contiguous offsets, row dimension/nonempty, i32 columns/u32 rows, allocates output and launches existing softmax_f32 with retained source offset and grid(rows,1,1), block(1,32,1). bwd returns p*(upstream-sum_last(p*upstream)).

##### Scoped children

The byte-scoped children project distinct internal relations through the parent syntax file. The parent combines them in actual source order. Remaining declarations/tests outside child ranges belong to this parent. Their correctness does not establish external caller preconditions.

##### Interfaces and limits

Output shape matches input; finite real row relation sums probabilities to one, while numerical agreement is only finite-tested. Backward row-sum zero mathematically; singleton derivative zero. CPU custom kernel rejects; public wrapper routes CPU to reference.

For positive normalized row p, compatible finite real logits are log(p)+constant; zero probabilities may be finite underflow/masking in f32 and do not uniquely identify -infinity. Gradient output admits upstream shifts by a row constant.

### models

## Typed model catalog

### behavior_complement

CPU/F64 inputs cannot use the custom CUDA F32 kernel through last_dim. Noncontiguous input is copied before custom invocation; raw custom rejects noncontiguous. CUDA error path does not silently use CPU fallback.

### reverse_reconstruction

For positive normalized row p, compatible finite real logits are log(p)+constant; zero probabilities may be finite underflow/masking in f32 and do not uniquely identify -infinity. Gradient output admits upstream shifts by a row constant.

### gradient_transport

VJP is diag(p)g-p(pᵀg); unlike no-backward upstream fused op, custom propagates into logits.

### layout_transport

contiguous_offsets start..end preserves narrow-view offset; transpose becomes contiguous before launch.

### failure_precedence

Raw CUDA path checks contiguous -> last dimension -> nonempty -> integer conversions -> dtype slice -> kernel -> alloc -> launch.

### numerical_observation

Selected widths 1,3,17,33,257 test abs forward/gradient/finite-difference error<2e-6; this is not relative percentage or universal guarantee.

### tests

#### Checks and limits

cpu_f64_uses_reference; fused_softmax_matches_reference_gradients_and_finite_differences

Selected commands run twice debug and twice release; exact receipts and source-to-test mapping retained in wave30 gparse external run directory. Tests do not certify allocation failure, all NaN/overflow inputs, trained-model accuracy or corpus timing. Compiled mutants cover selected boundaries; per-source coverage is recorded in the external map, not presumed.

See [source execution evidence](https://md.groupants.elamur.ai/free/en/mova/gparse/src/softmax.rs.groupant/#evidence) for exact names and unexecuted paths.

### evidence

#### Execution evidence — 2026-10-31

Source: gparse/src/softmax.rs

- cpu_f64_uses_reference: twice debug, twice release, all four exits 0.
- fused_softmax_matches_reference_gradients_and_finite_differences: twice debug, twice release, all four exits 0.
- synthetic_single_optimizer_step: twice debug, twice release, all four exits 0.

##### Controls

1 source-local compiled assertion mutants; 2 observed control tests including dependency mutations. Mutant compile exit0, assertion exit101, byte-exact restoration and positive rerun exit0. Per-source controls are listed in external source-test-map.json, not implied for every source.

##### Limits

Finite synthetic CPU/CUDA observations; no trained-corpus quality or all-input numeric guarantee.

CUDA softmax comparisons use absolute tolerance2e-6. GELU grid maxima observed: values0.0004734993, derivatives0.00086838007, finite difference0.0000005534244635896357; respective strict caps5e-4/1e-3/1e-5. These are finite observations, not percentage profiles or throughput claims.

Receipts: external wave30 gparse run native-commands.json, results.json, controls.json and source-test-map.json. Sorted exact test names/outcomes match per profile; numeric diagnostics and timings need not be byte-identical. Strict scoped library Clippy reports ten pre-existing production lints; appended test blocks were formatted independently without production reformatting.