●Change8

Migrating to PyTorch v2.10.0

Version v2.10.0 introduces 6 breaking changes. This guide details how to update your code.

Released: 1/21/2026

6
Breaking Changes
6
Migration Steps
14
Affected Symbols

⚠️ Check Your Code

If you use any of these symbols, you need to read this guide:

torch.compiletorch.utils.data.datapipes.iter.groupingtorch.utils.data.datapipes.iter.shardingnn.attention.flex_attentiontorch.onnx.exporttorch.distributed.device_meshtorch.jittorch.profiler.export_memory_timelinetorch.cuda.memory._record_memory_historytorch.cuda.memory._export_memory_snapshottorch.condtorch.distributed._local_tensorLocalTensorMode@maybe_run_for_local_tensor

Breaking Changes

●Issue #1

Removed unused `data_source` argument from Sampler. If you have a custom sampler using this argument, please update it.

●Issue #2

Removed deprecated imports for `torch.utils.data.datapipes.iter.grouping`. Import `SHARDING_PRIORITIES`, `ShardingFilterIterDataPipe` from `torch.utils.data.datapipes.iter.sharding` instead.

●Issue #3

Removed Nested Jagged Tensor support from `nn.attention.flex_attention`.

●Issue #4

`fallback=False` is now the default in `torch.onnx.export`. To preserve 2.9 behavior, manually set `fallback=True` in the `torch.onnx.export` call.

●Issue #5

The ONNX exporter now uses the `dynamo=True` option without fallback by default. This is the recommended usage.

●Issue #6

Renamed `pytorch-triton` package to `triton`.

Migration Steps

  1. 1
    If using a custom sampler that utilizes the `data_source` argument, remove its usage.
  2. 2
    Update imports for grouping datapipes: change `from torch.utils.data.datapipes.iter.grouping import ...` to import from `torch.utils.data.datapipes.iter.sharding`.
  3. 3
    If using `torch.onnx.export`, replace usage of `dynamic_axes` with the `dynamic_shapes` argument.
  4. 4
    If using `torch.profiler.export_memory_timeline`, migrate to using `torch.cuda.memory._record_memory_history` and `torch.cuda.memory._export_memory_snapshot`.
  5. 5
    If relying on implicit device mesh slicing behavior that generated a warning, update code to explicitly manage flattened mesh bookkeeping.
  6. 6
    Replace usage of `torch.jit` APIs with `torch.compile` or `torch.export`.

Release Summary

PyTorch 2.10 introduces Python 3.14 support for torch.compile, new features like combo-kernels fusion and LocalTensor for distributed debugging, and removes several deprecated or legacy functionalities across ONNX, Dataloader, and nn modules.

Need More Details?

View the full release notes and all changes for PyTorch v2.10.0.

View Full Changelog