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Feat: Ring attention kernel and VAE optimization
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‎.github/workflows/UnitTests.yml‎

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- name: PyTest
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run: | #--deselect=src/maxdiffusion/tests/input_pipeline_interface_test.py
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export LIBTPU_INIT_ARGS='--xla_tpu_scoped_vmem_limit_kib=65536'
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HF_HUB_CACHE=/mnt/disks/github-runner-disk/ HF_HOME=/mnt/disks/github-runner-disk/ TOKENIZERS_PARALLELISM=false python3 -m pytest --deselect=src/maxdiffusion/tests/ltx_transformer_step_test.py -x
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# add_pull_ready:
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HF_HUB_CACHE=/mnt/disks/github-runner-disk/ HF_HOME=/mnt/disks/github-runner-disk/ TOKENIZERS_PARALLELISM=false python3 -m pytest --ignore=src/maxdiffusion/kernels/ --deselect=src/maxdiffusion/tests/ltx_transformer_step_test.py -x
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# add_pull_ready
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# if: github.ref != 'refs/heads/main'
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# permissions:
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# checks: read
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# pull-requests: write
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# needs: build
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# uses: ./.github/workflows/AddLabel.yml
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# uses: ./.github/workflows/AddLabel.yml

‎README.md‎

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[![Unit Tests](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml)
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# What's new?
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- **`2026/04/16`**: Support for Tokamax Ring Attention kernel is now added.
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- **`2026/03/31`**: Wan2.2 SenCache inference is now supported for T2V and I2V (up to 1.4x speedup)
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- **`2026/03/25`**: Wan2.1 and Wan2.2 Magcache inference is now supported
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- **`2026/03/25`**: LTX-2 Video Inference is now supported
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...
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```
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### Ring Attention
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We added ring attention support for Wan models. Below are the stats for one `720p` (81 frames) video generation (with CFG DP):
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| Accelerator | Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
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| -- | -- | -- | -- | -- | -- |
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| v7x-8 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context4-tp1 | 264.2 |
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| v7x-8 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context4-tp1 | **252.4** |
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| v7x-8 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context4-tp1 | 212.7 |
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| v7x-8 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context4-tp1 | **201.7** |
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| Accelerator | Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
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| -- | -- | -- | -- | -- | -- |
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| v7x-16 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context8-tp1 | 146.6 |
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| v7x-16 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context8-tp1 | **137.2** |
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| v7x-16 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context8-tp1 | **117.8** |
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| v7x-16 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context8-tp1 | 137.5 |
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(* There are some known stability issues for ring attention on 16 TPUs, please use `tokamax_flash` attention instead.)
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## Flux
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First make sure you have permissions to access the Flux repos in Huggingface.
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The full suite of -end-to end tests is in `tests` and `src/maxdiffusion/tests`. We run them with a nightly cadance.
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## Profiling
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To learn how to enable ML Diagnostics and XProf profiling for your runs, please see our [ML Diagnostics Guide](docs/profiling.md).
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To learn how to enable ML Diagnostics and XProf profiling for your runs, please see our [ML Diagnostics Guide](docs/profiling.md).

‎src/maxdiffusion/configs/base_wan_14b.yml‎

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# Replicates vae across devices instead of using the model's sharding annotations for sharding.
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replicate_vae: False
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vae_spatial: -1 # default to total_device * 2 // (dp)
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# matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision
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# Options are "DEFAULT", "HIGH", "HIGHEST"
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# Set true to load weights from pytorch
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from_pt: True
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split_head_dim: True
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attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring
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attention: 'flash' # Supported attention: dot_product, flash, tokamax_flash, cudnn_flash_te, ring, tokamax_ring
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flash_min_seq_length: 0
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# If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens.
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['out_channels', 'tensor'],
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['conv_out', 'context'],
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]
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vae_logical_axis_rules: [
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['activation_batch', 'redundant'],
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['activation_length', 'vae_spatial'],
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['activation_heads', null],
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['activation_kv_length', null],
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['embed', null],
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['heads', null],
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['norm', null],
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['conv_batch', 'redundant'],
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['out_channels', 'vae_spatial'],
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['conv_out', 'vae_spatial'],
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['conv_in', 'vae_spatial'],
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]
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data_sharding: [['data', 'fsdp', 'context', 'tensor']]
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# One axis for each parallelism type may hold a placeholder (-1)

‎src/maxdiffusion/configs/base_wan_1_3b.yml‎

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# Copyright 2023 Google LLC
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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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['out_channels', 'tensor'],
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['conv_out', 'context'],
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]
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vae_logical_axis_rules: [
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['activation_batch', 'redundant'],
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['activation_length', 'vae_spatial'],
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['activation_heads', null],
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['activation_kv_length', null],
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['embed', null],
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['heads', null],
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['norm', null],
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['conv_batch', 'redundant'],
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['out_channels', 'vae_spatial'],
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['conv_out', 'vae_spatial'],
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['conv_in', 'vae_spatial'],
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]
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data_sharding: [['data', 'fsdp', 'context', 'tensor']]
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# One axis for each parallelism type may hold a placeholder (-1)

‎src/maxdiffusion/configs/base_wan_27b.yml‎

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# Replicates vae across devices instead of using the model's sharding annotations for sharding.
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replicate_vae: False
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vae_spatial: 1
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# matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision
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vae_logical_axis_rules: [
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['activation_batch', 'redundant'],
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['activation_length', 'vae_spatial'],
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['activation_heads', null],
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['activation_kv_length', null],
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['embed', null],
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['heads', null],
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['norm', null],
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['conv_batch', 'redundant'],
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['out_channels', 'vae_spatial'],
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['conv_out', 'vae_spatial'],
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['conv_in', 'vae_spatial'],
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]
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# One axis for each parallelism type may hold a placeholder (-1)

‎src/maxdiffusion/configs/base_wan_i2v_14b.yml‎

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['out_channels', 'tensor'],
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['conv_out', 'context'],
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]
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vae_logical_axis_rules: [
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['activation_batch', 'redundant'],
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['activation_length', 'vae_spatial'],
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['activation_heads', null],
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['activation_kv_length', null],
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['embed', null],
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['heads', null],
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['norm', null],
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['conv_batch', 'redundant'],
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['out_channels', 'vae_spatial'],
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['conv_out', 'vae_spatial'],
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['conv_in', 'vae_spatial'],
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]
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# One axis for each parallelism type may hold a placeholder (-1)

‎src/maxdiffusion/configs/base_wan_i2v_27b.yml‎

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vae_logical_axis_rules: [
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['activation_batch', 'redundant'],
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['activation_length', 'vae_spatial'],
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['activation_heads', null],
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['activation_kv_length', null],
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['embed', null],
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['conv_batch', 'redundant'],
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['conv_out', 'vae_spatial'],
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['conv_in', 'vae_spatial'],
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]
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‎src/maxdiffusion/configuration_utils.py‎

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proxies=proxies,
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resume_download=resume_download,
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local_files_only=local_files_only,
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use_auth_token=use_auth_token,
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token=use_auth_token,
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user_agent=user_agent,
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subfolder=subfolder,
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revision=revision,

‎src/maxdiffusion/kernels/__init__.py‎

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‎src/maxdiffusion/kernels/splash_attention/__init__.py‎

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# Copyright 2025 DeepMind Technologies Limited. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Splash Attention kernels."""

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