AI-Hypercomputer · eltsai · Apr 17, 2026
@@ -57,11 +57,11 @@ jobs:
     - name: PyTest
       run: | #--deselect=src/maxdiffusion/tests/input_pipeline_interface_test.py
         export LIBTPU_INIT_ARGS='--xla_tpu_scoped_vmem_limit_kib=65536'
-        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
-#  add_pull_ready:
+        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
+#  add_pull_ready
 #    if: github.ref != 'refs/heads/main'
 #    permissions:
 #      checks: read
 #      pull-requests: write
 #    needs: build
-#    uses: ./.github/workflows/AddLabel.yml
+#    uses: ./.github/workflows/AddLabel.yml
@@ -17,6 +17,7 @@
 [![Unit Tests](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml)
 
 # What's new?
+- **`2026/04/16`**: Support for Tokamax Ring Attention kernel is now added.
 - **`2026/03/31`**: Wan2.2 SenCache inference is now supported for T2V and I2V (up to 1.4x speedup)
 - **`2026/03/25`**: Wan2.1 and Wan2.2 Magcache inference is now supported
 - **`2026/03/25`**: LTX-2 Video Inference is now supported
@@ -623,6 +624,24 @@ To generate images, run the following command:
     ...
   ```
 
+### Ring Attention
+We added ring attention support for Wan models. Below are the stats for one `720p` (81 frames) video generation (with CFG DP):
+| Accelerator |  Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
+| -- | -- | -- | -- | -- | -- | 
+| v7x-8 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context4-tp1 | 264.2 |
+| v7x-8 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context4-tp1 | **252.4** |
+| v7x-8 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context4-tp1 | 212.7 |
+| v7x-8 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context4-tp1 | **201.7** |
+
+| Accelerator |  Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
+| -- | -- | -- | -- | -- | -- | 
+| v7x-16 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context8-tp1 | 146.6 |
+| v7x-16 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context8-tp1 | **137.2** |
+| v7x-16 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context8-tp1 | **117.8** |
+| v7x-16 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context8-tp1 | 137.5 |
+
+(* There are some known stability issues for ring attention on 16 TPUs, please use `tokamax_flash` attention instead.)
+
   ## Flux
 
   First make sure you have permissions to access the Flux repos in Huggingface.

@@ -44,6 +44,7 @@ activations_dtype: 'bfloat16'
 
 # Replicates vae across devices instead of using the model's sharding annotations for sharding.
 replicate_vae: False
+vae_spatial: -1 # default to total_device * 2 // (dp)
 
 # matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision
 # Options are "DEFAULT", "HIGH", "HIGHEST"
@@ -60,7 +61,7 @@ jit_initializers: True
 # Set true to load weights from pytorch
 from_pt: True
 split_head_dim: True
-attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring, ulysses
+attention: 'flash' # Supported attention: dot_product, flash, tokamax_flash, cudnn_flash_te, ring, tokamax_ring, ulysses
 flash_min_seq_length: 0
 
 # If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens.
@@ -180,6 +181,19 @@ logical_axis_rules: [
                       ['out_channels', 'tensor'],
                       ['conv_out', 'context'],
                     ]
+vae_logical_axis_rules: [
+                          ['activation_batch', 'redundant'],
+                          ['activation_length', 'vae_spatial'],
+                          ['activation_heads', null],
+                          ['activation_kv_length', null],
+                          ['embed', null],
+                          ['heads', null],
+                          ['norm', null],
+                          ['conv_batch', 'redundant'],
+                          ['out_channels', 'vae_spatial'],
+                          ['conv_out', 'vae_spatial'],
+                          ['conv_in', 'vae_spatial'],
+                        ]
 data_sharding: [['data', 'fsdp', 'context', 'tensor']]
 
 # One axis for each parallelism type may hold a placeholder (-1)

@@ -1,4 +1,4 @@
-# Copyright 2023 Google LLC
+# Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -157,6 +157,19 @@ logical_axis_rules: [
                       ['out_channels', 'tensor'],
                       ['conv_out', 'context'],
                     ]
+vae_logical_axis_rules: [
+                          ['activation_batch', 'redundant'],
+                          ['activation_length', 'vae_spatial'],
+                          ['activation_heads', null],
+                          ['activation_kv_length', null],
+                          ['embed', null],
+                          ['heads', null],
+                          ['norm', null],
+                          ['conv_batch', 'redundant'],
+                          ['out_channels', 'vae_spatial'],
+                          ['conv_out', 'vae_spatial'],
+                          ['conv_in', 'vae_spatial'],
+                        ]
 data_sharding: [['data', 'fsdp', 'context', 'tensor']]
 
 # One axis for each parallelism type may hold a placeholder (-1)

@@ -44,6 +44,7 @@ activations_dtype: 'bfloat16'
 
 # Replicates vae across devices instead of using the model's sharding annotations for sharding.
 replicate_vae: False
+vae_spatial: 1
 
 # matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision
 # Options are "DEFAULT", "HIGH", "HIGHEST"
@@ -168,6 +169,19 @@ logical_axis_rules: [
                       ['out_channels', 'tensor'],
                       ['conv_out', 'context'],
                     ]
+vae_logical_axis_rules: [
+                          ['activation_batch', 'redundant'],
+                          ['activation_length', 'vae_spatial'],
+                          ['activation_heads', null],
+                          ['activation_kv_length', null],
+                          ['embed', null],
+                          ['heads', null],
+                          ['norm', null],
+                          ['conv_batch', 'redundant'],
+                          ['out_channels', 'vae_spatial'],
+                          ['conv_out', 'vae_spatial'],
+                          ['conv_in', 'vae_spatial'],
+                        ]
 data_sharding: [['data', 'fsdp', 'context', 'tensor']]
 
 # One axis for each parallelism type may hold a placeholder (-1)

@@ -163,6 +163,19 @@ logical_axis_rules: [
                       ['out_channels', 'tensor'],
                       ['conv_out', 'context'],
                     ]
+vae_logical_axis_rules: [
+                          ['activation_batch', 'redundant'],
+                          ['activation_length', 'vae_spatial'],
+                          ['activation_heads', null],
+                          ['activation_kv_length', null],
+                          ['embed', null],
+                          ['heads', null],
+                          ['norm', null],
+                          ['conv_batch', 'redundant'],
+                          ['out_channels', 'vae_spatial'],
+                          ['conv_out', 'vae_spatial'],
+                          ['conv_in', 'vae_spatial'],
+                        ]
 data_sharding: [['data', 'fsdp', 'context', 'tensor']]
 
 # One axis for each parallelism type may hold a placeholder (-1)

@@ -164,6 +164,19 @@ logical_axis_rules: [
                       ['out_channels', 'tensor'],
                       ['conv_out', 'context'],
                     ]
+vae_logical_axis_rules: [
+                          ['activation_batch', 'redundant'],
+                          ['activation_length', 'vae_spatial'],
+                          ['activation_heads', null],
+                          ['activation_kv_length', null],
+                          ['embed', null],
+                          ['heads', null],
+                          ['norm', null],
+                          ['conv_batch', 'redundant'],
+                          ['out_channels', 'vae_spatial'],
+                          ['conv_out', 'vae_spatial'],
+                          ['conv_in', 'vae_spatial'],
+                        ]
 data_sharding: [['data', 'fsdp', 'context', 'tensor']]
 
 # One axis for each parallelism type may hold a placeholder (-1)

@@ -394,7 +394,7 @@ def load_config(
             proxies=proxies,
             resume_download=resume_download,
             local_files_only=local_files_only,
-            use_auth_token=use_auth_token,
+            token=use_auth_token,
             user_agent=user_agent,
             subfolder=subfolder,
             revision=revision,

@@ -0,0 +1,15 @@
+# Copyright 2025 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Splash Attention kernels."""