deps(deps): bump accelerate from 1.10.1 to 1.14.0

[dependabot]

Bumps accelerate from 1.10.1 to 1.14.0.

Release notes

Sourced from accelerate's releases.

v1.14.0: AMD ROCm support, FSDP2 hardening

FSDP2 Improvements

This release brings a large batch of FSDP2 fixes and quality-of-life improvements: correct dtype handling on load, sharding of embeddings/norms, QLoRA crash prevention, and a more robust auto-wrap policy.

• Fsdp2 fully_shard embedding and norm by @​SunMarc in #4015
• Fix fsdp2 load full state dict dtype mismatch by @​SunMarc in #4021
• Fix region compilation fsdpv2 by @​SunMarc in #4022
• [FSDP2] Cast model to uniform dtype before fully_shard to fix mixed-dtype AssertionError by @​roycho96 in #3985
• [FSDP2] Auto-exclude non-floating frozen Params4bit from fully_shard to prevent QLoRA crash by @​roycho96 in #3987
• fix(FSDP2): auto-wrap policy ignoring _no_split_modules fallback by @​JohnGiorgi in #3999
• fix: use key-based matching in fsdp2_load_full_state_dict by @​roycho96 in #3982
• fix: add missing model_has_params4bit guard to fsdp2_load_full_state_dict call by @​roycho96 in #3981
• Fix to-fsdp2: drop REMOVED / NOT_YET_IMPLEMENTED FSDP1 keys instead of leaking them by @​lollinng in #4065
• Prevent double-wrapping models in prepare_model() by @​joshuaswanson in #3977

AMD ROCm support

Accelerate now works end-to-end on AMD ROCm devices. Thanks @​Abdennacer-Badaoui!

• Make accelerate work end-to-end on AMD ROCm by @​Abdennacer-Badaoui in #4025

Neuron

Further Neuron improvements to reduce recompilation and cover missing device cases.

• Add padded allgather and broadcast for Neuron devices to reduce recompilation by @​czkkkkkk in #4000
• fix: add missing neuron device case by @​michaelbenayoun in #4042

Quantization & Offloading

We improved offloading support for quantized models, including Torchao, int8, and tied-weight handling.

• Torchao offload by @​SunMarc in #3973
• Fix int8 offload hook detachment statistics restoration by @​jiqing-feng in #4044
• Fix keep_in_fp32_modules not working for tied weights in load_and_quantize_model by @​jiqing-feng in #4043
• Fix dtype_byte_size for FP8 fnuz / e8m0fnu dtypes by @​lollinng in #4063

Data Loading

• Feat: Support dynamic batch size in BatchSamplerShard with even_batches by @​yuxinyuan in #3969
• Fix iterable dataset sharding condition when n_shards == num_processes by @​SunMarc in #3958
• Fix implicit padding in split_between_processes when apply_padding=False and num_samples < num_processes by @​3manifold in #4052

Minor fixes

• [DeepSpeed] allow kernels flash-attn in SP by @​kashif in #3959
• Fix: Conditionally import torch.distributed.algorithms.join in accelerator.py by @​0xDELUXA in #3962
• Fix is_hf_initialized attribute by @​SunMarc in #3976
• feat(utils): add max reduction type by @​imstevenpmwork in #4027

... (truncated)

Commits

• beb0672 Release: v1.14.0
• a5cb37c Fix all tests (#4072)
• 14d7cdc Support dynamic batch size in BatchSamplerShard with even_batches (#3969)
• 77eb5cf fix(tracker): guard init_trackers and log against None kwargs (#4026)
• 379c8e0 fix(security): remediate workflow vulnerability in .github/workflows/build-do...
• 61e6420 Fix to-fsdp2: actually drop REMOVED / NOT_YET_IMPLEMENTED FSDP1 keys (#4065)
• b120c1f tests: Gracefully handle missing set_device for mps (#4028)
• 418015a test: add regression test for no_split_module_classes accepting set type (#4048)
• 3f1acf8 Bump the actions group with 8 updates (#4068)
• a2e5292 docs: add aggregate profiler memory example (#4054)
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Reviewed commits

Commit	Summary
a4e296c	deps(deps): bump accelerate from 1.10.1 to 1.14.0

An analysis of the proposed change in requirements.txt shows that it is correct and follows best practices:

Analysis

• Correctness & Compatibility: Upgrading accelerate from 1.10.1 to 1.14.0 is a minor version bump. This is generally safe and ensures better compatibility with newer versions of dependency-heavy libraries like transformers (pinned to 4.57.3 in your file) and torch (pinned to 2.9.1).
• Performance & Security: Newer versions of accelerate typically include performance optimizations for distributed training (FSDP, DeepSpeed, etc.) and bug fixes.
• Pinning: The dependency remains strictly pinned (==), which is excellent practice for production environments to ensure reproducible builds.

No further changes are required for this modification.