Bump triton from 3.3.0 to 3.5.0

[dependabot]

Bumps triton from 3.3.0 to 3.5.0.

Release notes

Sourced from triton's releases.

Triton 3.4.0 Release

Highlights

Gluon Framework Comprehensive Enhancement

The Gluon framework has received major enhancements across all areas including new APIs, tensor memory management, layout operations, and synchronization primitives. Key additions include static_assert functionality, TensorDescriptor kernel arguments, async TMA operations, tensor memory implementation, thread synchronization barriers, and comprehensive tensor operations like split/join/reshape and reductions. (#7172, #7168, #7165, #7160, #7152, #7151, #7149, #7145, #7142, #7122, #7121, #7120, #7115, #7114, #7106, #7102, #7099, #7097, #7091, #7089, #7080, #7061, #7057, #7022, #7020, #7009, #7006, #7004, #7001, #6998, #6997, #6994, #6992, #6989, #6985, #6971, #6950)

Hardware Support Expansion

• AMD GFX950 Architecture Support - Comprehensive support for GFX950 including WMMA operations, performance optimizations, and architectural-specific features (#7175, #7171, #7127, #6744, #6594)
• Blackwell Enhanced TMEM Support - Improved tensor memory operations with better register usage and performance optimizations (#7160, #7079, #6817)
• Hopper WGMMA Improvements - Enhanced matrix multiplication with subtiling and prefetching optimizations (#7136, #6130)

Performance Optimizations

• Automatic Warp Specialization - Introduced automatic warp specialization optimization for enhanced kernel performance on NVIDIA GPUs (#6289, #6246, #6217)
• MMAv5 Pipelining - Re-enabled and improved MMAv5 pipelining with better performance and scheduling (#6732, #6613, #6256)
• TMA Operations Enhancement - Improved tensor memory access with better layout support and reduced register pressure (#6725, #6238, #6580)

New Features

Language and Frontend

• Aggregate Type Support - Added @tl.aggregate decorator for autogenerating Triton types from Python classes (#6970)
• JITFunction Constexpr Support - Enhanced constexpr support for function lists and improved JIT functionality (#6988, #6963, #7105)
• Enhanced Boolean Operations - Improved handling of boolean operators and scalars with chained operations (#6769)
• Bitonic Top-k and Sorting - Added support for bitonic top-k operations and improved sort implementations (#6461, #6486)
• Masked Histograms - Added support for masked histogram operations (#6695)
• Syntactic Sugar Additions - Added .item() as syntactic sugar for .reshape([]) (#6873)

Backend and Compilation

• Generic Swizzling Implementation - Implemented generic swizzling algorithm for convert_layout lowering (#6982)
• Enhanced Register Allocation - Improved dynamic register reallocation for warp specialization (#6877, #6694, #6407)
• TMA Reduce Operations - Added TMA reduce operations for descriptor-based reducing stores (#6580)
• Improved Subtiling - Enhanced subtiling code generation for tensor memory loading (#6415)
• BF16 Atomic Operations - Added support for BF16 atomic add operations (#6519)
• Stmatrix Support - Added comprehensive stmatrix support including transpose operations (#6910, #6899)

Hardware-Specific Features

• AMD AsyncCopy Optimizations - Enhanced AsyncCopy support in StreamPipeliner with improved memory operations (#6270, #6639, #6382)
• AMD Buffer Operations - Comprehensive improvements to buffer operations with better vectorization and alignment (#6126, #6145, #6329)
• AMD Ping-pong Scheduler - Enhanced ping-pong scheduler for better memory operation handling (#6254, #6301, #6198)
• NVIDIA PDL Support - Enabled Programmatic Dependent Launch for overlapping kernel execution (#6394)
• AMD HIP AOT Support - Added HIP Ahead-of-Time compilation support (#7007)

Improvements

Performance

• Routing Kernel Optimizations - Multiple performance improvements achieving up to 5% runtime reduction (#6866, #6546, #7040)
• Matrix Multiplication Enhancements - Enhanced persistent TMA matmul with epilogue subtiling and metadata alignment (#6724, #6882, #7123)
• SwiGLU Optimizations - Improved SwiGLU kernel performance and fused activation functions (#6797, #6553)
• Attention Kernel Fixes - Fixed and optimized attention tutorials with better performance metrics (#7037, #6839)

Developer Experience

... (truncated)

Changelog

Sourced from triton's changelog.

Releasing Triton

Triton releases provide a stable snapshot of the code base encapsulated into a binary that can easily be consumed through PyPI. Additionally, releases represent points in time when we, as the development team, can signal to the community that certain new features are available, what improvements have been made, and any changes that are coming that may impact them (i.e. breaking changes).

Release Compatibility Matrix

Following is the Release Compatibility Matrix for Triton releases:

Triton version	Python version	Manylinux version
3.2.0	>=3.9, <=3.13	glibc 2.17+ x86-64
3.1.0	>=3.8, <=3.12	glibc 2.17+ x86-64
3.0.0	>=3.8, <=3.12	glibc 2.17+ x86-64
2.3.1	>=3.7, <=3.12	glibc 2.17+ x86-64
2.3.0	>=3.7, <=3.12	glibc 2.17+ x86-64
2.2.0	>=3.7, <=3.12	glibc 2.17+ x86-64
2.1.0	>=3.7, <=3.11	glibc 2.17+ x86-64
2.0.0	>=3.6, <=3.11	glibc 2.17+ x86-64
1.1.1	>=3.6, <=3.9	glibc 2.17+ x86-64
1.1.0	>=3.6, <=3.9	glibc 2.17+ x86-64
1.0.0	>=3.6, <=3.9	glibc 2.17+ x86-64

Release Cadence

Following is the release cadence for year 2024/2025. All future release dates below are tentative. Please note: Patch Releases are optional.

Minor Version	Release branch cut	Release date	Patch Release date
3.5.0	Sep 2025	Oct 2025	---
3.4.0	Jun 2025	Jul 2025	---
3.3.0	Feb/Mar 2025	Apr 2025	---
3.2.0	Dec 2024	Jan 2025	---
3.1.0	Jun 2024	Oct 2024	---
3.0.0	Jun 2024	Jul 2024	---
2.3.0	Dec 2023	Apr 2024	May 2024
2.2.0	Dec 2023	Jan 2024	---

Release Cherry-Pick Criteria

After branch cut, we approach finalizing the release branch with clear criteria on what cherry picks are allowed in. Note: a cherry pick is a process to land a PR in the release branch after branch cut. These are typically limited to ensure that the team has sufficient time to complete a thorough round of testing on a stable code base.

• Regression fixes - that address functional/performance regression against the most recent release (e.g. 3.2 for 3.3 release)
• Critical fixes - critical fixes for severe issue such as silent incorrectness, backwards compatibility, crashes, deadlocks, (large) memory leaks
• Fixes to new features introduced in the most recent release (e.g. 3.2 for 3.3 release)
• Documentation improvements
• Release branch specific changes (e.g. change version identifiers or CI fixes)

Please note: No feature work allowed for cherry picks. All PRs that are considered for cherry-picks need to be merged on trunk, the only exception are Release branch specific changes. An issue is for tracking cherry-picks to the release branch is created after the branch cut. Only issues that have ‘cherry-picks’ in the issue tracker will be considered for the release.

Commits

• c3c476f [Release Only] Release 3.5.x triton to pypi
• 7416ffc [pick][release/3.5.x] Fold layout conversion for TMEM Store to fix perf drop ...
• 2766408 [cherry-pick] [Build] Cleanup Python 3.9 related code/docs (#8222) (#8288)
• 7ea55ff [cherry-pick] [release/3.5.x] Update MIN_PYTHON version to 3.10 #8167 (#8287)
• bbb06c0 [release/3.5.x] Revert "[BACKEND] hint to LLVM that we can bound threadIdx.x ...
• b0a1a75 [release/3.5.x][Backend] Fix ptxas workaround in convert_layout (#8155) (#8157)
• 5ae38bd [FRONTEND] Clone saved exception before raising (#8125)
• 6031203 [release/3.5.x] Update to triton-lang/llvm-project@7d5de3033187c8a3bb4d2e322f...
• 70cbcac [reland] Pick WarpSpec PRs and fixes to 3.5 release (#8092) (#8143)
• 615ab03 [release/3.5.x][AMD] Refactor llStore and llLoad to use proper ops (#8036) (#...
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