AI-Hypercomputer / AI-Hypercomputer/maxtext

Qwen3-Next: sequential GDN paths (lax.scan recurrence, solve_triangular) bound TPU step time

Abierto
#4,347 1 comentario 0 reacciones 1 asignado Reclamado por @parambole Ver en GitHub
Lenguaje dominante
Python
Estrellas
2.4k
Forks
608
Merge medio
2 d 19 h
PR fusionados (30 d)
158

Descripción

### Problem

In the Qwen3-Next Gated Delta Net (`jax_chunk_gated_delta_rule`), the two sequential computations dominate TPU step time:

- the inter-chunk recurrence runs as `lax.scan`, so the recurrent state round-trips HBM on every chunk step, and
- the UT-transform inverse `A = (I+S)^-1` uses `solve_triangular`, which substitutes row by row and barely uses the MXU.

Neither overlaps with other work, so they bound the step directly.

### Measurement

v5e-256, Qwen3-Next dense-8B config, seq 4096, bs 1, remat full (details in #4348):

| build | step time | tokens/s/device |
|---|---|---|
| main | 3.373s | 1,214 |
| #4348 (Pallas kernels) | **2.571s** | **1,593 (+31%)** |

Loss trajectories match to bf16 rounding level.

Guía de contribución

Abrir la guía de contribución

Línea de trabajo

Start at the Qwen3-Next Gated Delta Net entry point jax_chunk_gated_delta_rule and inspect the lax.scan recurrence and solve_triangular inverse described in the issue. Compare the Pallas-kernel results referenced in #4348, then benchmark the v5e-256, dense-8B, sequence-4096, batch-1 configuration; done means lower step time with loss matching to bf16 rounding.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
python
Área
machine-learning, performance
Tipo de issue
Refactorización
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Activo
Claridad
Bastante claro
Aptitud para principiantes
35/100

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