chore(deps)(deps): bump accelerate from 1.11.0 to 1.15.0

[dependabot]

Bumps accelerate from 1.11.0 to 1.15.0.

Release notes

Sourced from accelerate's releases.

v1.15.0: FSDP2 activation memory, dtensor improvements

FSDP2

A large batch of FSDP2 work this release: two fixes that cut activation memory at long sequence lengths, tied-embedding support on torch >= 2.13, and a round of checkpointing correctness and scale fixes.

Activation checkpointing was wrapping each child of the matched layer (self_attn, mlp, the norms) instead of the layer itself, so every inter-child activation stayed saved for backward. It now wraps the layer.

There's also a new FSDP2-only activation_checkpointing_offload, which moves the remaining per-layer checkpoint inputs to pinned CPU memory. Gradients are exactly those of plain activation checkpointing:

# fsdp2.yaml
fsdp_config:
  fsdp_version: 2
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_activation_checkpointing: true
  fsdp_activation_checkpointing_offload: true

accelerate launch --config_file fsdp2.yaml train.py

• FSDP2 activation checkpointing: wrap the matched transformer layer itself, not each of its children by @​qgallouedec in #4172
• Add FSDP2 activation_checkpointing_offload: offload checkpointed layer inputs to pinned CPU memory by @​qgallouedec in #4175
• Fix FSDP2 tied-embedding models on torch >= 2.13: put the output embedding in the same fully_shard group by @​qgallouedec in #4171
• Fix FSDP2/PEFT/FULL_STATE_DICT dropping every rank's adapter shard except rank 0 by @​AmineDiro in #4206
• FSDP2: per-rank torch.save/load for SHARDED_STATE_DICT to fix 2800+ NPU checkpoint timeout by @​gygdh-001 in #4105
• Fix FSDP sharded checkpoint path resolution by @​HaomingSong in #4119
• Raise a clear error when FSDP is enabled on a mesh with no shard dimension by @​qgallouedec in #4180

DTensor

Two fixes for DTensor-sharded models, which you hit with FSDP2, tensor parallelism, or any N-D parallelism setup: gradient clipping no longer fails on the foreach op when plain tensors and DTensors are mixed, and prepare_model leaves an already-sharded model where it is:

• Clip grad norm support for dtensors by @​michaelbenayoun in #4219
• prepare_model: don't move DTensor-sharded models to the device by @​qgallouedec in #4181

Offloading & Quantization

An entire model can now be dispatched to disk, including tied weights — useful for tools like llm-compressor that compress large models on machines that can't hold them:

• [Offloading] Support full disk offloading by @​kylesayrs in #4083
• Fix disk offload crash on FP8 tensors by @​shoemoney in #4151

Trackers

Custom trackers can be registered by name and then selected from log_with= like any built-in one:

from accelerate import Accelerator
from accelerate.tracking import register_tracker_class
</tr></table> 

... (truncated)

Commits

• 6afc1e5 Release: v1.15.0
• 636a934 Fix nightly CI: tensor parallel size detection and DeepSpeed warmup steps (#4...
• 385d9fb docs: fix three dead links left by the docs restructure (#4230)
• c424d0b docs: fix garbled sentence in CONTRIBUTING.md (#4231)
• 0f7e35f Clip grad norm support for dtensors (#4219)
• b2ac509 Fix FSDP2/ PEFT/FULL_STATE_DICT dropping every rank's adapter shard except ...
• 33e8bc4 Fix load_accelerator_state only restoring one RNG backend (#4217)
• 50de3ad Treat MPS out-of-memory errors as OOM in find_executable_batch_size (#4227)
• ab5fe8e feat: add the neuron device branch in state (#4218)
• 9baf95b Fix MLFLOW_NESTED_RUN never being able to turn nesting off (#4223)
• Additional commits viewable in compare view