pytorch/torch/_namedtensor_internals.py

import torch
from torch._six import PY2
from collections import OrderedDict

"""
This file contains helper functions that implement experimental functionality
for named tensors in python. All of these are experimental, unstable, and
subject to change or deletion.
"""


def assert_namedtensor_build(api_name):
    if not torch._C._BUILD_NAMEDTENSOR:
        raise RuntimeError('NYI: {} is experimental and a part '
                           'of our named tensors project.'.format(api_name))


def check_serializing_named_tensor(tensor):
    if torch._C._BUILD_NAMEDTENSOR and tensor.has_names():
        raise RuntimeError(
            "NYI: Named tensors don't support serialization. Please drop "
            "names before serialization and/or serialize them seperately.")


def build_dim_map(tensor):
    """Returns a map of { dim: dim_name } where dim is a name if the dim is named
    and the dim index otherwise."""
    return OrderedDict([(idx if name is None else name, name)
                        for idx, name in enumerate(tensor.names)])


def unzip_namedshape(namedshape):
    if isinstance(namedshape, OrderedDict):
        namedshape = namedshape.items()
    if not hasattr(namedshape, '__iter__') and not isinstance(namedshape, tuple):
        raise RuntimeError(
            'Expected namedshape to be OrderedDict or iterable of tuples, got: {}'
            .format(type(namedshape)))
    if len(namedshape) == 0:
        raise RuntimeError('Expected namedshape to non-empty.')
    return zip(*namedshape)


def namer_api_name(inplace):
    if inplace:
        return 'names_'
    else:
        return 'renamed'


def is_ellipsis(item):
    if PY2:
        return item == '...'
    else:
        return item == Ellipsis or item == '...'


def expand_single_ellipsis(numel_pre_glob, numel_post_glob, names):
    return names[numel_pre_glob:len(names) - numel_post_glob]


def resolve_ellipsis(names, tensor_names, fn_name):
    ellipsis_indices = [i for i, name in enumerate(names) if is_ellipsis(name)]
    if len(ellipsis_indices) >= 2:
        raise RuntimeError('{}: More than one Ellipsis (\'...\') found in names ('
                           '{}). This function supports up to one Ellipsis.'
                           .format(fn_name, names))
    if len(ellipsis_indices) == 0:
        return names
    ellipsis_idx = ellipsis_indices[0]
    globbed_names = expand_single_ellipsis(ellipsis_idx, len(names) - ellipsis_idx - 1, tensor_names)
    return names[:ellipsis_idx] + globbed_names + names[ellipsis_idx + 1:]


def update_names_with_list(tensor, names, inplace):
    # Special case for tensor.renamed(None)
    if len(names) == 1 and names[0] is None:
        return tensor._update_names(None, inplace)

    return tensor._update_names(
        resolve_ellipsis(names, tensor.names, namer_api_name(inplace)), inplace)


def update_names_with_mapping(tensor, rename_map, inplace):
    dim_map = build_dim_map(tensor)
    for old_dim in rename_map.keys():
        new_dim = rename_map[old_dim]
        if old_dim in dim_map.keys():
            dim_map[old_dim] = new_dim
        else:
            raise RuntimeError(('{api_name}: Tried to rename dim \'{old_dim}\' to dim '
                                '{new_dim} in Tensor[{dims}] but dim \'{old_dim}\' does not exist')
                               .format(old_dim=old_dim, new_dim=new_dim, dims=tensor.names,
                                       api_name=namer_api_name(inplace)))
    return tensor._update_names(tuple(dim_map.values()), inplace)


def update_names(tensor, names, rename_map, inplace):
    """There are two usages:

    tensor.renamed(*names) returns a view on tensor with named dims `names`.
    `names` must be of length `tensor.dim()`; otherwise, if '*' is in `names`,
    then it is expanded greedily to be equal to the corresponding names from
    `tensor.names`.

    For example,
    ```
    >>> x = torch.empty(2, 3, 5, 7, names=('N', 'C', 'H', 'W'))
    >>> x.renamed('*', 'height', 'width').names
    ('N', 'C', 'height', 'width')

    >>> x.renamed('batch', '*', 'width').names
    ('batch', 'C', 'H', 'width')
    ```

    tensor.renamed(**rename_map) returns a view on tensor that has renamed dims
        as specified in the mapping `rename_map`.

    For example,
    ```
    >>> x = torch.empty(2, 3, 5, 7, names=('N', 'C', 'H', 'W'))
    >>> x.renamed(W='width', H='height').names
    ('N', 'C', 'height', 'width')
    ```

    Finally, tensor.renamed has an in-place version called tensor.names_.
    """
    assert_namedtensor_build(namer_api_name(inplace))

    has_names = len(names) > 0
    has_rename_pairs = bool(rename_map)
    if has_names and has_rename_pairs:
        raise RuntimeError('{api_name}: This function takes either positional '
                           'args or keyword args, but not both. Use tensor.{api_name}(*names) '
                           'to name dims and tensor.{api_name}(**rename_map) to rename '
                           'dims.'.format(api_name=namer_api_name(inplace)))

    # Special case for tensor.renamed(*[]), which is valid for a 0 dim tensor.
    if not has_names and not has_rename_pairs:
        return update_names_with_list(tensor, names, inplace)

    if has_names:
        return update_names_with_list(tensor, names, inplace)
    return update_names_with_mapping(tensor, rename_map, inplace)