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Performance: optimize joiner output disposal loop #165

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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Original file line number Diff line number Diff line change
Expand Up @@ -13,3 +13,6 @@ Action: Apply loop unrolling for max reductions in high-frequency typed array op
## 2024-11-20 - Softmax math.exp 8x unrolling with local var cache
Learning: Unrolling the `Math.exp` accumulation loop to 8x and caching the multiplication `(tokenLogits[i] - maxLogit) * invTemp` into local variables before passing to `Math.exp` yields a measurable performance improvement (~4%) over the previous 4x unrolled implementation in the V8 engine, by reducing property access and allowing better instruction-level parallelism.
Action: Utilize 8x loop unrolling paired with local variable caching for tight floating-point accumulation loops over TypedArrays.
## 2025-04-25 - Object.values Array Allocation Hot Loop
Learning: Accessing properties of an ONNX tensor output object using `Object.values(out)` in a hot loop (like per-token decoding) causes severe garbage collection pressure and significant overhead compared to a `for...in` loop with a pre-allocated array (speedup: ~2.3x).
Action: Prefer `for...in` and a class-level recycled array over `Object.values()` when iterating small but frequently-generated objects (like session outputs) in hot paths.
16 changes: 9 additions & 7 deletions src/parakeet.js
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Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,7 @@ export class ParakeetModel {
this._targetLenTensor = new ort.Tensor('int32', this._targetLenArray, [1]);
this._encoderFrameBuffer = null; // Will be allocated when we know the dimension D
this._encoderFrameTensor = null; // Will be allocated when we know D
this._recycledOutputs = []; // Reusable array for joiner output disposal

// Incremental decode cache: stores decoder state at the end of the prefix
// keyed by a caller-provided cacheKey. This lets us skip decoding the
Expand Down Expand Up @@ -323,10 +324,11 @@ export class ParakeetModel {
const logits = out['outputs'];
const outputState1 = out['output_states_1'];
const outputState2 = out['output_states_2'];
const seenOutputs = new Set();
for (const value of Object.values(out)) {
if (!value || typeof value.dispose !== 'function' || seenOutputs.has(value)) continue;
seenOutputs.add(value);
this._recycledOutputs.length = 0; // Clear recycled array
for (const key in out) {
const value = out[key];
if (!value || typeof value.dispose !== 'function' || this._recycledOutputs.includes(value)) continue;
this._recycledOutputs.push(value);
if (value === logits || value === outputState1 || value === outputState2) continue;
value.dispose();
}
Comment on lines +327 to 334
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@coderabbitai coderabbitai Bot Apr 25, 2026

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⚠️ Potential issue | 🟡 Minor

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Disposal logic is correct and covered by existing tests; document the serial-use constraint.

The new flow (clear → for…in → dedup via includes → skip kept tensors → dispose) is behaviorally equivalent to the previous Set + Object.values version. Each unique disposable in out is disposed at most once, and logits / outputState1 / outputState2 are preserved. The clear-and-fill is fully synchronous after the await on joinerSession.run, so _recycledOutputs stays internally consistent within one call.

Existing decode tests (decode_loop.test.mjs) do exercise the disposal path via transcribe() calls, which invoke _runCombinedStep multiple times. The basic logic is sound.

One thing worth being explicit about: _recycledOutputs is now a class-level mutable buffer, so concurrent invocations of _runCombinedStep on the same ParakeetModel instance would clobber each other (same implicit constraint already applies to _targetIdArray / _targetTensor / _combState1 / _combState2). That's fine for the current call sites in transcribe, but worth documenting if streaming/long-audio paths ever start sharing a single model across overlapping in-flight calls. Add a brief comment in the constructor or class docstring noting this serial-use assumption.

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@src/parakeet.js` around lines 327 - 334, Add a short explanatory comment in
the ParakeetModel class (constructor or top-of-class docstring) stating that
_recycledOutputs is a class-level mutable buffer and that _runCombinedStep (and
related fields _targetIdArray, _targetTensor, _combState1, _combState2) must not
be invoked concurrently on the same ParakeetModel instance; note that callers
like transcribe and existing tests (decode_loop.test.mjs) rely on serial use and
overlapping in-flight calls will clobber these buffers.

Expand All @@ -339,12 +341,12 @@ export class ParakeetModel {
const failDecoderStep = (message) => {
logits?.dispose?.();

const disposed = new Set();
const disposed = [];
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@gemini-code-assist gemini-code-assist Bot Apr 25, 2026

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medium

The PR description states that a recycled class-level array (this._recycledOutputs) is used in failDecoderStep, but the implementation here still allocates a new local array [] on every call. While this is a failure path where performance is less critical, it is inconsistent with the stated optimization goal and the PR description.

      const disposed = this._recycledOutputs;
      disposed.length = 0;

const disposeUniqueState = (state) => {
if (!state) return;
for (const tensor of [state.state1, state.state2]) {
if (!tensor || tensor === this._combState1 || tensor === this._combState2 || disposed.has(tensor)) continue;
disposed.add(tensor);
if (!tensor || tensor === this._combState1 || tensor === this._combState2 || disposed.includes(tensor)) continue;
disposed.push(tensor);
tensor.dispose?.();
}
};
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