Bump triton-windows from 3.2.0.post19 to 3.4.0.post20 in /requirements/full

[dependabot]

Bumps triton-windows from 3.2.0.post19 to 3.4.0.post20.

Release notes

Sourced from triton-windows's releases.

v3.4.0-windows.post20

Note again that Triton 3.4 only works with PyTorch >= 2.8.

To install Triton 3.4 and prevent breaking with your installed PyTorch when a new version of Triton is released in future, you need to limit the version to be < 3.5:

pip install -U "triton-windows<3.5"

v3.3.1-windows.post19

This is identical with triton-windows 3.3.0.post19, but I bump the version number to match the official one.

The only difference between the official triton 3.3.0 and 3.3.1 is triton-lang/triton#6771 , which affects RTX 50xx GPUs. I've already added this patch since triton-windows 3.3.0.post14.

v3.3.0-windows.post19

• Fix JIT compilation using Clang

Note again that Triton 3.3 only works with PyTorch >= 2.7, and Triton 3.2 only works with PyTorch >= 2.6 .

To install Triton 3.3 and prevent breaking with your installed PyTorch when a new version of Triton is released in future, you need to limit the version to be < 3.4:

pip install -U "triton-windows<3.4"

v3.2.0-windows.post18

• Find MSVC and Windows SDK from environment variables set by Launch-VsDevShell.ps1 or VsDevCmd.bat, see woct0rdho/triton-windows#106
• Print cc_cmd when failed to compile for debugging

Although Triton 3.3 stable release is out, if I change triton-windows 3.3 to stable release, it may break things for many people using PyTorch 2.6 . I'll do this when PyTorch 2.7 stable release is out, and tell people to always specify the minor version when installing triton-windows.

empty

Here are some empty wheels named triton. You can add them to your build system if it tells you that some package requires triton rather than triton-windows, and also add triton-windows to the build system.

v3.2.0-windows.post17

Fix when multiple processes create __triton_launcher.pyd in parallel, see intel/intel-xpu-backend-for-triton#3270 . Now torch.compile autotune will work in general.

Note that ComfyUI enables cudaMalloc by default, but cudaMalloc does not work with CUDA graphs. Also, many models and nodes in ComfyUI are not compatible with CUDA graphs. You may use mode='max-autotune-no-cudagraphs' and see if it has speedup.

v3.2.0-windows.post16

Ensure temp files are closed when calling ptxas in parallel. I still need to investigate some bugs in PyTorch to make torch.compile autotune fully work, see unslothai/unsloth#1999

v3.2.0-windows.post15

Define Py_LIMITED_API and exclude new Python C API that cannot be compiled by TinyCC, see woct0rdho/triton-windows#92

v3.3.0-windows.post14

Fix getMMAVersionSafe for RTX 50xx (sm120), see woct0rdho/triton-windows#83

v3.2.0-windows.post13

TinyCC is bundled in the wheels, so you don't need to install MSVC to use Triton. Packages that directly call triton.jit, such as SageAttention, will just work.

You still need to install a C++ compiler if you use torch.compile targeting CPU. This may happen when you use nodes like 'CompileModel' in ComfyUI. Triton does not affect how PyTorch configures the C++ compiler in this case.

... (truncated)

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