---
id: pr-flash-attention-2390
repo: Dao-AILab/flash-attention
pr: 2390
title: '[Cute,Sm100,Bwd] refine bwd swizzle for deterministic'
author: jayhshah
date: '2026-03-25'
url: https://github.com/Dao-AILab/flash-attention/pull/2390
source_category: upstream-code
architectures:
- sm100
tags:
- arch_pipeline
- attention_kv
- benchmark_test
- compiler_runtime
- cute
- scheduler_autotune
- sm100
- varlen
techniques:
- autotuning
- pipeline-stages
- swizzling
- tile-scheduling
hardware_features:
- blackwell
kernel_types:
- attention
- compiler
- runtime
- scheduler
languages:
- cute-dsl
captured_at: '2026-05-16'
status: merged
merge_sha: 5c7711e49bd64483ed6c2d339d78683c23860e11
inclusion_reason: Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.
changed_paths:
- benchmarks/benchmark_attn.py
- flash_attn/cute/tile_scheduler.py
artifact_dir: evidence/pull-bundles/flash-attention/gh-2390
head_ref_oid: 6757ec5a2890506155fb813cdab63a2382a9a3ae
---
# [Cute,Sm100,Bwd] refine bwd swizzle for deterministic

- Repository: `Dao-AILab/flash-attention`
- PR: [2390](https://github.com/Dao-AILab/flash-attention/pull/2390)
- Decision: `Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.`
- Reason: Mine shape sets, tolerance rules, warmup logic, and profile commands before mutating code.

## Changed Paths

- `benchmarks/benchmark_attn.py`
- `flash_attn/cute/tile_scheduler.py`

## Evidence Bundle

`evidence/pull-bundles/flash-attention/gh-2390`
