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7d2e86a85a
* draft:add neuron as a legit backend
* feat: neuron-specific changes in the pipeline
* tests: eager tests
* draft: start with tp for flux2
* fix: style
* fix:apr_02 beta
* feat:add wan
* fix:pixart
* fix: rewrite flux swiglu activation to avoid gather op in neuron IR
* test: pixart compile mode on neuron
* cleanup & fix style
* merge: another change
* review: cleanup+suggestions
* fix: CIs style
* tests: add test units for tp
* fix: in case of text-encoder(s) on CPU
* review:cleanup+add test
* fix: style
* doc: remove it for now
* Add from_single_file support for SkyReelsV2 and ChronoEdit transformers (#13946)
SkyReels-V2 and ChronoEdit are both built on Wan, and their transformers have
the same keys as WanTransformer3DModel, so they reuse
convert_wan_transformer_to_diffusers (like WanVACE / WanAnimate). This lets the
community GGUF builds load directly.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* multi-GPU VAE Fix for Cosmos 3 (#13924)
fix(cosmos3): pin VAE latent norm buffers to encode output device
Under sharded placement (device_map="balanced"), vae.encode() runs on the
VAE's own device while the mean/inv_std buffers were pinned to x.device,
causing a cross-device RuntimeError. Compute raw_mu first, then pin the
normalization buffers to its device so all tensors share one device.
Co-authored-by: Atharva Joshi <atjoshi@smc521ge-0036.ipp2a2.colossus.nvidia.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* docs: fix repeated word typo in set_timesteps docstring (#13876)
* docs: fix repeated word typo in set_timesteps docstring
Removed the duplicate word "schedule" from the docstring for the sigmas argument in EulerDiscreteScheduler.set_timesteps.
* Update scheduling_euler_discrete.py
* Apply style fixes
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* clean some stuff to simplify code.
* clean more to remove permutation related shenanigans.
* revert: put torch.chunk back
* Update docs/source/en/training/distributed_inference.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Address Sayak's TP review nits (docstrings + stale comments)
- _modeling_parallel: fix stale comment claiming attention processors read
tp_degree at runtime (no longer true after the PackedColwise/Rowwise
refactor; processors are TP-agnostic via head_dim).
- tensor_parallel: single backticks instead of double in docstrings, add
input/output examples to _blocks_to_block_sizes and _resolve_tp_plan, and
drop the stale "applies the Flux2 fused-weight permutations" line from
apply_tensor_parallel (permuters were removed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Fix stale permuter reference in Flux2ParallelSelfAttnProcessor comment
The proportional QKV/MLP slice is now guaranteed by PackedColwiseParallel
block-by-block sharding, not the removed fused-weight permuter.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Derive TP backend inside apply_tensor_parallel from the mesh device type
Drops the `backend` parameter: apply_tensor_parallel already holds the TP
mesh, so it derives neuron-vs-default from `config._mesh.device_type` itself.
The caller no longer computes it. Documents why torch_device can't be used
(Neuron reports as "cpu" via torch's accelerator API). Removes the now-unused
is_torch_neuronx_available import from modeling_utils.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Simplify TP backend derivation to the mesh device type alone
Drop the redundant is_torch_neuronx_available() guard: a DeviceMesh("neuron", ...)
only exists when running on Neuron, so tp_mesh.device_type is the single source
of truth.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Remove incorrect "Neuron reports as cpu" rationale from TP backend docstring
Verified on the current native torch_neuronx drop that Neuron does surface as
the torch accelerator (torch._C._get_accelerator().type == "neuron",
get_device() == "neuron"); the old "reports as cpu" claim was stale. The
mesh-device-type derivation is unchanged — it's the device being sharded onto.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Drop stale "XLA" labels from Neuron device/runtime comments
Neuron is a native torch backend now (not XLA-routed): remove the "(XLA)"
parenthetical from the randn-on-CPU note in torch_utils and the "XLA runtime"
mention from the Neuron TP test docstring. Behavior unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* make style: reflow tensor_parallel docstrings
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Revert "revert: put torch.chunk back"
This reverts commit 7ea75f771a.
* fix: sharding for neuron + validate flux 1
* fix: change for check_repository_consistency
* feat: support qwen image as well
* Update docs/source/en/training/distributed_inference.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* review: keep bria and nucleusmoe out
* review: keep bria and nucleusmoe out
* review: keep bria and nucleusmoe out
* review: address agent review
* review: apply suggestions
* Update docs/source/en/training/distributed_inference.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* review: change example tp size to 4
* review: add flux tp test
* review: address comments
* review: improve the doc
* review:apply qwen rotary_emb per device
* review:remove redundant device def after PR #14383
* review: doc title suggestion
* review: restore copy comment
* review: remove cp divisibility explanation from doc on parallelism guide
* fix style
---------
Co-authored-by: Hz_Zhang <47402297+HaozheZhang6@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Atharva Joshi <atjoshi@nvidia.com>
Co-authored-by: Atharva Joshi <atjoshi@smc521ge-0036.ipp2a2.colossus.nvidia.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Ramkumar R <ramkumarashvanth09@gmail.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
31 lines
1.1 KiB
Markdown
31 lines
1.1 KiB
Markdown
<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License. -->
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# Parallelism
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Parallelism strategies help speed up diffusion transformers by distributing computations across multiple devices, allowing for faster inference/training times. Refer to the [Distributed inferece](../training/distributed_inference) guide to learn more.
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## ParallelConfig
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[[autodoc]] ParallelConfig
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## ContextParallelConfig
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[[autodoc]] ContextParallelConfig
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[[autodoc]] hooks.apply_context_parallel
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## TensorParallelConfig
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[[autodoc]] TensorParallelConfig
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[[autodoc]] hooks.apply_tensor_parallel
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