# Changelog

All notable changes to metatensor-torch are documented here, following the [keep
a changelog](https://keepachangelog.com/en/1.1.0/) format. This project follows
[Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased](https://github.com/metatensor/metatensor/)

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#### Added

#### Fixed

#### Changed

#### Removed
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## [Version 0.6.1](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.6.1) - 2024-11-7

### Fixed

- Added missing data to `NeighborListOptions` serialization (#784)

### Changed

- `load_atomistic_model` now returns a `MetatensorAtomisticMode` instead of a
  raw TorchScript model. This allows to reload, modify and re-export a model
  (#783).

## [Version 0.6.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.6.0) - 2024-10-29

### Added

- a `is_atomistic_model` (Python only) function to check if an loaded module is an
  metatensor atomistic model (#697, #766)

### Changed

- the `System` class now supports boundary conditions along some axes but not
  others. This is implemented via a new `pbc` attribute. Any non-periodic
  dimension in a `System` must have the corresponding cell vector set to zero.
- `NeighborListOptions` can now request `strict` or non-strict neighbor lists (#770)

## [Version 0.5.5](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.5) - 2024-09-03

### Added

- a `"features"` standard output for atomistic models (#718)

### Fixed

- the Python wheels request the right versions of torch in their metadata (#724)

## [Version 0.5.4](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.4) - 2024-08-28

### Added

- `read_model_metadata` to load only the `ModelMetadata` from an exported
  atomistic model without having to load the whole model.
- Users can now store arbitrary additional metadata in `ModelMetadata.extra`
- Added `Labels.select` function to sub-select entries in labels
- Added support for serialization of TensorBlock with `TensorBlock::load`,
  `TensorBlock::load_buffer`, `TensorBlock::save`, `TensorBlock::save_buffer`
  and the corresponding functions in `metatensor.torch`.

## [Version 0.5.3](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.3) - 2024-07-15

### Changed

- `MetatensorAtomisticModel.save()` always saves models on the CPU.
- We now require Python >= 3.9

### Fixed

- Fixed a memory leak in `register_autograd_neighbors` (#684)

## [Version 0.5.2](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.2) - 2024-06-21

### Added

- `MetatensorAtomisticModel.save()` to save a wrapped model to a file.
- `TensorBlock.__len__` and `TensorBlock.shape`, which return the length and
  shape of the values in the block respectively (#640)
- `metatensor.torch.atomistic.ase_calculator.MetatensorCalculator` can now use
  [`vesin`](https://github.com/Luthaf/vesin) for faster neighbor list
  calculations (#659)
- When running atomistic models in the PyTorch profiler, different sections of
  the code now have meaningful names

### Deprecated

- `MetatensorAtomisticModel.export()` is deprecated in favor of `MetatensorAtomisticModel.save()`

### Fixed

- `metatensor.torch.atomistic.ase_calculator.MetatensorCalculator` uses the
  right device when computing stress/virial (#660)

## [Version 0.5.1](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.1) - 2024-05-14

### Added

- preprocessor macros containing the version number of metatensor-torch:
  `METATENSOR_TORCH_VERSION`, `METATENSOR_TORCH_VERSION_MAJOR`,
  `METATENSOR_TORCH_VERSION_MINOR`, and `METATENSOR_TORCH_VERSION_PATCH`.

## [Version 0.5.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.5.0) - 2024-05-02

### Changed

- We renamed `neighbors_list` to `neighbor_list` in all functions (#587)

### metatensor-torch Python

#### Changed

- The neighbor lists calculation in `MetatensorCalculator` (ASE calculator based
  on metatensor models) is now a lot faster (#586)
- Multiple small improvements related to custom TorchScript extensions (#584)
- There are reference output for neighbor list calculations, which should help
  writing interfaces to metatensor models in new simulation engines (#588)
- The wheels for `metatensor-torch` on PyPI now declare which versions of torch
  they are compatible with (#592)

## [Version 0.4.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.4.0) - 2024-04-11

### metatensor-torch C++

#### Added

- `ModelCapabilities::dtype`, used by the model to communicate the dtype it
  wants to use for inputs and outputs.
- The `load_model_extensions()` function to facilitate loading a model using
  TorchScript extensions.

#### Changed

- `System::add_data` now has an `override` parameter to replace custom data with
  a new value.

### metatensor-torch Python

#### Changed

- We now release wheels compatible with multiple torch versions on PyPI,
  removing the need to compile C++ code when installing this package.

#### Added

- `ModelCapabilities.dtype`, used by the model to communicate the dtype it
  wants to use for inputs and outputs.
- The `device` that should be used by a model inside the ASE's
  `MetatensorCalculator` can now be specified by the user.
- The `load_model_extensions()` and `load_atomistic_model` functions to
  facilitate loading a model using TorchScript extensions

## [Version 0.3.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.3.0) - 2024-03-01

### metatensor-torch C++

#### Added

- `ModelMetadata` to record metadata about a specific model such as it's name,
  authors, etc.
- Added `interaction_range` and `supported_devices` to `ModelCapabilities`

#### Changed

- `System::species` has been renamed to `System::types`.

### metatensor-torch Python

#### Added

- `ModelMetadata` to record metadata about a specific model such as it's name,
  authors, etc.
- Added `interaction_range` and `supported_devices` to `ModelCapabilities`

#### Changed

- `System.species` has been renamed to `System.types`.

## [Version 0.2.1](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.2.1) - 2024-01-26

### metatensor-torch C++

#### Added

- Offer serialization functionality as member functions (i.e. `TensorMap::load`)
  in addition to the existing free standing functions (i.e. `metatensor_torch::load`) (#453)
- In-memory serialization with `TensorMap::save_buffer`, `TensorMap::load_buffer`,
  and the respective free standing functions (#455)
- Serialization of Labels, with the same API as `TensorMap` (#455)


### metatensor-torch Python

#### Added

- Offer serialization functionality as member functions (i.e. `TensorMap.load`)
  in addition to the existing free standing functions (i.e. `metatensor.torch.load`) (#453)
- In-memory serialization with `TensorMap.save_buffer`, `TensorMap.load_buffer`,
  and the respective free standing functions (#455)
- Serialization of Labels, with the same API as `TensorMap` (#455)

## [Version 0.2.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.2.0) - 2024-01-08

### metatensor-torch C++

#### Added

- New classes specifically tailored for atomistic models (#405):
  - `System` defines the input of a model;
  - `NeighborsListOptions` allow a model to request a specific neighbors list;
  - `ModelRunOptions`, `ModelOutput` and `ModelCapabilities` allow to statically
    describe capabilities of a model, and request specific outputs from it.

- `TensorBlock::to`, `TensorMap::to`, and `System::to` to change the device or
  dtype of torch Tensor stored by metatensor
- `Labels::device`, `TensorBlock::device` and `TensorMap::device`; as well as
  `TensorMap::scalar_type`, and `TensorBlock::scalar_type` to query the current
  device and scalar type/dtype used by the data.
- `metatensor_torch::version` function, returning the version of the code as a
  string.

#### Fixed

- We now check that all tensors in a `TensorBlock`/`TensorMap` have the same
  dtype and device (#414)
- `keys_to_properties`, `keys_to_samples` and `components_to_properties` now
  keep the different Labels on the same device (#411)

### metatensor-torch Python

#### Added

- New classes specifically tailored for atomistic models (#405):
  - same classes as the C++ interfaces, in `metatensor.torch.atomistic`
  - `MetatensorAtomisticModel` as a way to wrap user-defined `torch.nn.Module`
    and export them in a unified way, handling unit conversions and metadata
    checks.
- [ASE](https://wiki.fysik.dtu.dk/ase/) calculator based on
  `MetatensorAtomisticModel` in `metatensor.torch.atomistic.ase_calculator`.
  This allow using arbitrary user-defined models to run simulations with ASE.

- `TensorBlock.to`, `TensorMap.to` and `System.to` to change the device or dtype
  of torch Tensor stored by metatensor
- `Labels.device`, `TensorBlock.device` and `TensorMap.device`; as well as
  `TensorMap.dtype`, and `TensorBlock.dtype` to query the current device and
  dtype used by the data.


## [Version 0.1.0](https://github.com/metatensor/metatensor/releases/tag/metatensor-torch-v0.1.0) - 2023-10-11

### metatensor-torch C++

#### Added

- TorchScript bindings to all metatensor-core class: `Labels`, `LabelsEntry`,
  `TensorBlock`, and `TensorMap`;
- Implementation of `mts_array_t`/`metatensor::DataArrayBase` for `torch::Tensor`;

### metatensor-torch Python

#### Added

- Expose TorchScript classes to Python;
- Expose all functions from `metatensor-operations` as TorchScript compatible code;