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PyTorch X.x.x Release Notes

Highlights

TODO

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

[Example] New interface for GraphTransformObserver to enable Node Level provenance tracking (#144277)

We now track a mapping between the nodes in the pre-grad and post-grad graph. See the issue for an example frontend to visualize the transformations. To update your GraphTransformObserver subclasses, instead of overriding on_node_creation and on_node_erase, there are new functions get_node_creation_hook, get_node_erase_hook, get_node_replace_hook and get_deepcopy_hook. These are registered on the GraphModule member of the GraphTransformObserver upon entry and exit of a with block

Version 2.6.0

class MyPrintObserver(GraphTransformObserver):
    def on_node_creation(self, node: torch.fx.Node):
        print(node)

Version 2.7.0

class MyPrintObserver(GraphTransformObserver):
    def get_node_creation_hook(self):
        def hook(node: torch.fx.Node):
            print(node)
        return hook

Deprecations

[Example] torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)

Users should use the dynamo=True option on torch.onnx.export.

Version 2.6.0

torch.onnx.dynamo_export(model, *args, **kwargs)

Version 2.7.0

torch.onnx.export(model, args, kwargs=kwargs, dynamo=True)

New features

Release Engineering

Python Frontend

Autograd

Dataloader

Linear Algebra

Nested Tensor (NJT)

torch.nn

torch.optim

Build Frontend

C++ Frontend

Distributed

Distributed Checkpoint (DCP)

DTensor

c10d

DistributedDataParallel (DDP)

FullyShardedDataParallel2 (FSDP2)

...

CPU

x86

aarch64

CUDA

MPS

ROCm

XPU

Profiler

torch.compile

Dynamo

AOTDispatcher

Inductor

torch.fx

torch.export

Quantization

ONNX

JIT

Lazy Tensor

torch.package

Improvements

Bug fixes

Performance

Documentation

Security

Developers