Pre-allocate Tensor/EValue in Java pipeline: 16s → 8.9s (RTF 1.9)
Reuse float arrays and Tensor/EValue objects across talker steps instead of creating new ones each iteration. Eliminates ~7s of GC overhead from thousands of JNI object allocations. Same validated audio quality as before, no C++ pipeline needed. Talker 35ms/step, CP 58ms/step, total 8.9s for 4.64s audio. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Kazeia Team
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@ -1074,44 +1074,51 @@ class Qwen3TtsEngine(
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nlog("PTE pipeline: prefill=${prefill.size}, trailing=${trailingEmbeds.size}")
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// Pre-allocate reusable buffers + Tensor/EValue (avoids GC overhead per step)
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val tEmbBuf = FloatArray(TALKER_DIM)
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val tMaskBuf = FloatArray(TALKER_PTE_KV_LEN)
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val tCosBuf = FloatArray(TALKER_HEAD_DIM)
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val tSinBuf = FloatArray(TALKER_HEAD_DIM)
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val tInputs = arrayOfNulls<org.pytorch.executorch.EValue>(4 + TALKER_LAYERS * 2)
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tInputs[0] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tEmbBuf, longArrayOf(1,1,TALKER_DIM.toLong())))
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tInputs[1] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tMaskBuf, longArrayOf(1,1,1,TALKER_PTE_KV_LEN.toLong())))
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tInputs[2] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tCosBuf, longArrayOf(1,1,TALKER_HEAD_DIM.toLong())))
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tInputs[3] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tSinBuf, longArrayOf(1,1,TALKER_HEAD_DIM.toLong())))
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for (i in 0 until TALKER_LAYERS) {
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tInputs[4+i*2] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tK[i], longArrayOf(1,TALKER_HEADS.toLong(),TALKER_PTE_KV_LEN.toLong(),TALKER_HEAD_DIM.toLong())))
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tInputs[5+i*2] = org.pytorch.executorch.EValue.from(org.pytorch.executorch.Tensor.fromBlob(tV[i], longArrayOf(1,TALKER_HEADS.toLong(),TALKER_PTE_KV_LEN.toLong(),TALKER_HEAD_DIM.toLong())))
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}
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// Talker step helper: update buffers → forward → extract outputs
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fun talkerStep(emb: FloatArray): Pair<FloatArray, FloatArray> {
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System.arraycopy(emb, 0, tEmbBuf, 0, TALKER_DIM)
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System.arraycopy(maskData, 0, tMaskBuf, 0, TALKER_PTE_KV_LEN)
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val pi = minOf(pos, tCos.size / TALKER_HEAD_DIM - 1)
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System.arraycopy(tCos, pi * TALKER_HEAD_DIM, tCosBuf, 0, TALKER_HEAD_DIM)
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System.arraycopy(tSin, pi * TALKER_HEAD_DIM, tSinBuf, 0, TALKER_HEAD_DIM)
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// KV buffers already point to tK[i]/tV[i] — data updated in-place below
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val out = talkerMod.forward(*tInputs.requireNoNulls())
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val hidden = out[0].toTensor().dataAsFloatArray
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val logits = out[1].toTensor().dataAsFloatArray
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for (i in 0 until TALKER_LAYERS) {
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val newK = out[2+i*2].toTensor().dataAsFloatArray
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val newV = out[3+i*2].toTensor().dataAsFloatArray
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System.arraycopy(newK, 0, tK[i], 0, tkvSize)
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System.arraycopy(newV, 0, tV[i], 0, tkvSize)
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}
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pos++
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return Pair(hidden, logits)
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}
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// ===== PREFILL =====
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val tPrefill = System.currentTimeMillis()
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for (step in prefill.indices) {
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// Unmask position
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val maskIdx = TALKER_PTE_KV_LEN - 1 - minOf(pos, TALKER_PTE_KV_LEN - 1)
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if (maskIdx >= 0) maskData[maskIdx] = 0f
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val cosSlice = FloatArray(TALKER_HEAD_DIM)
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System.arraycopy(tCos, pos * TALKER_HEAD_DIM, cosSlice, 0, TALKER_HEAD_DIM)
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val sinSlice = FloatArray(TALKER_HEAD_DIM)
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System.arraycopy(tSin, pos * TALKER_HEAD_DIM, sinSlice, 0, TALKER_HEAD_DIM)
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val inputs = mutableListOf(
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(prefill[step], longArrayOf(1, 1, TALKER_DIM.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(maskData.clone(), longArrayOf(1, 1, 1, TALKER_PTE_KV_LEN.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(cosSlice, longArrayOf(1, 1, TALKER_HEAD_DIM.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(sinSlice, longArrayOf(1, 1, TALKER_HEAD_DIM.toLong())))
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)
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for (i in 0 until TALKER_LAYERS) {
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inputs.add(org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(tK[i], longArrayOf(1, TALKER_HEADS.toLong(), TALKER_PTE_KV_LEN.toLong(), TALKER_HEAD_DIM.toLong()))))
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inputs.add(org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(tV[i], longArrayOf(1, TALKER_HEADS.toLong(), TALKER_PTE_KV_LEN.toLong(), TALKER_HEAD_DIM.toLong()))))
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}
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val out = talkerMod.forward(*inputs.toTypedArray())
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pastHidden = out[0].toTensor().dataAsFloatArray
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val logits = out[1].toTensor().dataAsFloatArray
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for (i in 0 until TALKER_LAYERS) {
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tK[i] = out[2 + i * 2].toTensor().dataAsFloatArray
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tV[i] = out[3 + i * 2].toTensor().dataAsFloatArray
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}
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pos++
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val (h, logits) = talkerStep(prefill[step])
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pastHidden = h
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if (step == prefill.size - 1) {
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for (j in CODEBOOK_SIZE until TALKER_VOCAB) { if (j != CODEC_EOS) logits[j] = Float.NEGATIVE_INFINITY }
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currentCb0 = sampleTopK(logits, 0.9f, 50)
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@ -1121,12 +1128,11 @@ class Qwen3TtsEngine(
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if (currentCb0 < 0 || currentCb0 == CODEC_EOS) return emptyArray()
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// ===== INTERLEAVED GENERATION =====
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// ===== INTERLEAVED GENERATION (reusing pre-allocated tensors) =====
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var totalTalkerMs = 0L; var totalCpMs = 0L
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for (genStep in 0 until maxGenTokens) {
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val codes = IntArray(NUM_CODEBOOKS); codes[0] = currentCb0
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// 1. CP: predict CB1-15
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val tCp = System.currentTimeMillis()
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val cpCodes = runCpPte(pastHidden!!, currentCb0)
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totalCpMs += System.currentTimeMillis() - tCp
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@ -1135,7 +1141,7 @@ class Qwen3TtsEngine(
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if (genStep < 3) nlog("Step ${genStep+1}: cb0=$currentCb0 cb1=${codes[1]}")
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// 2. Build next talker input
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// Build next talker input
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val codecSum = FloatArray(TALKER_DIM)
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addEmb(codecSum, codecEmb(codes[0]))
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for (cb in 1 until NUM_CODEBOOKS) addEmb(codecSum, cpEmb(cb - 1, codes[cb]))
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@ -1146,45 +1152,14 @@ class Qwen3TtsEngine(
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else -> sumEmb(codecSum, padE)
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}
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// 3. Talker step
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val maskIdx = TALKER_PTE_KV_LEN - 1 - minOf(pos, TALKER_PTE_KV_LEN - 1)
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if (maskIdx >= 0) maskData[maskIdx] = 0f
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val cosSlice = FloatArray(TALKER_HEAD_DIM)
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System.arraycopy(tCos, minOf(pos, tCos.size / TALKER_HEAD_DIM - 1) * TALKER_HEAD_DIM, cosSlice, 0, TALKER_HEAD_DIM)
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val sinSlice = FloatArray(TALKER_HEAD_DIM)
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System.arraycopy(tSin, minOf(pos, tSin.size / TALKER_HEAD_DIM - 1) * TALKER_HEAD_DIM, sinSlice, 0, TALKER_HEAD_DIM)
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val inputs = mutableListOf(
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(nextEmbed, longArrayOf(1, 1, TALKER_DIM.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(maskData.clone(), longArrayOf(1, 1, 1, TALKER_PTE_KV_LEN.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(cosSlice, longArrayOf(1, 1, TALKER_HEAD_DIM.toLong()))),
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org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(sinSlice, longArrayOf(1, 1, TALKER_HEAD_DIM.toLong())))
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)
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for (i in 0 until TALKER_LAYERS) {
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inputs.add(org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(tK[i], longArrayOf(1, TALKER_HEADS.toLong(), TALKER_PTE_KV_LEN.toLong(), TALKER_HEAD_DIM.toLong()))))
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inputs.add(org.pytorch.executorch.EValue.from(
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org.pytorch.executorch.Tensor.fromBlob(tV[i], longArrayOf(1, TALKER_HEADS.toLong(), TALKER_PTE_KV_LEN.toLong(), TALKER_HEAD_DIM.toLong()))))
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}
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val tTalker = System.currentTimeMillis()
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val out = talkerMod.forward(*inputs.toTypedArray())
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val (h, logits) = talkerStep(nextEmbed)
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totalTalkerMs += System.currentTimeMillis() - tTalker
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pastHidden = h
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pastHidden = out[0].toTensor().dataAsFloatArray
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val logits = out[1].toTensor().dataAsFloatArray
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for (i in 0 until TALKER_LAYERS) {
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tK[i] = out[2 + i * 2].toTensor().dataAsFloatArray
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tV[i] = out[3 + i * 2].toTensor().dataAsFloatArray
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}
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pos++
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// 4. Next CB0
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for (j in CODEBOOK_SIZE until TALKER_VOCAB) { if (j != CODEC_EOS) logits[j] = Float.NEGATIVE_INFINITY }
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val seen = HashSet<Int>(); for (prev in generatedCb0) seen.add(prev)
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for (tok in seen) { logits[tok] = if (logits[tok] > 0) logits[tok] / 1.05f else logits[tok] * 1.05f }
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