PyTorch 1.5 arrives with stable C++ frontend API

PyTorch 1.5 has been released. The open source machine learning library developed by Facebook AI Research has several new features on board, mainly a stable C++ frontend API that was still experimental until now.

Aside from several new features, this release also includes backwards incompatible changes and drops support for the language version Python 2 which reached its end of life in January 2020.

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Stable C++ frontend API

Now at parity with Python, the C++ frontend API has been declared stable in PyTorch 1.5. For example, this means that models can now be translated from the Python API to the C++ API, and that the behavior of the C++ optimizers has become identical to the Python API.

The stable version also comes with a new C++ tensor multi-dim indexing API: tensor.index({Slice(), 0, "...", mask}) behaves similar to the Python API’s tensor[:, 0, ..., mask] syntax and creates the same result. This removes the need for a workaround, which was previously achieved by combining narrow / select / index_select / masked_select.

Other updates

In PyTorch 1.5, the distributed RPC framework APIs have also been moved to stable mode, and additional features were added as experimental. The experimental features include a new API for binding C++ classes into PyTorch and TorchScript, which uses a syntax that is nearly identical to pybind11, and a new high-level autograd API.

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It should also be noted that several backwards incompatible changes are included in this release regarding Python, the C++ API, JIT, quantization, and RPC.

For more details, see the release notes and blog post.

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