Random Attention Matrix Test Case Generator

Description

Generate compatible bounded quantized query/key/value matrices, delegate dimensions, dot products, stable softmax weights, and the existing 12-significant-digit scaled-dot-product attention output; this is single-head unmasked attention, not training, a Transformer benchmark, or an accuracy claim.

A matrix construction produces numeric arrays with a target algebraic property. Floating-point residuals check how closely a computed result satisfies that property, while conditioning determines how sensitive it is to numerical error.

When to use this tool

Use this generator to create reproducible attention matrix test case cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply minimumValue, maximumValue, valueDivisor, queryCount, keyCount, keyWidth, valueWidth in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read queries, keys, values, weights, attendedValues, dimensions. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Draw shape-compatible query, key, and value matrices. Compute each score as a delegated dot product divided by the square root of keyWidth, apply softmax, and delegate scaled dot-product attention. Return weights, attended values, and matrix dimensions.^[1]

Delegated operations used for this result: scaled dot product attention; dimensions. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
minimumValue	Minimum Value	Required. Safe integer.
maximumValue	Maximum Value	Required. Safe integer.
valueDivisor	Value Divisor	Required. Finite number; strictly above 0.
queryCount	Query Count	Required. Safe integer; minimum 1; maximum 32.
keyCount	Key Count	Required. Safe integer; minimum 1; maximum 32.
keyWidth	Key Width	Required. Safe integer; minimum 1; maximum 16.
valueWidth	Value Width	Required. Safe integer; minimum 1; maximum 16.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
queries	Queries	Required. Array of Array of Finite number.; minimum items 1; maximum items 16.; minimum items 1; maximum items 32.
keys	Keys	Required. Array of Array of Finite number.; minimum items 1; maximum items 16.; minimum items 1; maximum items 32.
values	Values	Required. Array of Array of Finite number.; minimum items 1; maximum items 16.; minimum items 1; maximum items 32.
weights	Weights	Required. Array of Array of Finite number; minimum 0; maximum 1.; minimum items 1; maximum items 32.; minimum items 1; maximum items 32.
attendedValues	Attended Values	Required. Array of Array of Finite number.; minimum items 1; maximum items 16.; minimum items 1; maximum items 32.
dimensions	Dimensions	Required. Object; no unknown keys.
dimensions.queries	Queries	Required. Object; no unknown keys.
dimensions.queries.rows	Rows	Required. Safe integer; minimum 1; maximum 32.
dimensions.queries.columns	Columns	Required. Safe integer; minimum 1; maximum 16.
dimensions.keys	Keys	Required. Object; no unknown keys.
dimensions.keys.rows	Rows	Required. Safe integer; minimum 1; maximum 32.
dimensions.keys.columns	Columns	Required. Safe integer; minimum 1; maximum 16.
dimensions.values	Values	Required. Object; no unknown keys.
dimensions.values.rows	Rows	Required. Safe integer; minimum 1; maximum 32.
dimensions.values.columns	Columns	Required. Safe integer; minimum 1; maximum 16.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "minimumValue": -2,
    "maximumValue": 2,
    "valueDivisor": 1,
    "queryCount": 2,
    "keyCount": 2,
    "keyWidth": 1,
    "valueWidth": 1,
    "seed": 42
  },
  "output": {
    "queries": [
      [
        1
      ],
      [
        0
      ]
    ],
    "keys": [
      [
        2
      ],
      [
        1
      ]
    ],
    "values": [
      [
        -2
      ],
      [
        0
      ]
    ],
    "weights": [
      [
        0.7310585786300049,
        0.2689414213699951
      ],
      [
        0.5,
        0.5
      ]
    ],
    "attendedValues": [
      [
        -1.46211715726
      ],
      [
        -1
      ]
    ],
    "dimensions": {
      "queries": {
        "rows": 2,
        "columns": 1
      },
      "keys": {
        "rows": 2,
        "columns": 1
      },
      "values": {
        "rows": 2,
        "columns": 1
      }
    },
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

This is unmasked single-head attention without positional encoding, dropout, training, or a language model. The auxiliary weights inherit delegated rounding and extreme intermediate values can fail even when shapes agree.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Attention Matrix Test Case Generator — tool.io
Calculation or validation: machine-learning/scaled-dot-product-attention — tool.io
Calculation or validation: matrix/dimensions — tool.io

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