def _concatenate_meta(tensors: Sequence[TensorLikeType],
dim: int) -> TensorLikeType:
if len(tensors) == 0:
msg = "concatenate expects at least one tensor, but received zero!"
raise ValueError(msg)
for tensor in tensors:
assert isinstance(tensor, TensorLike)
utils.check_same_dtype(*tensors)
utils.check_same_device(*tensors, allow_cpu_scalar_tensors=False)
shape = tensors[0].shape
utils.validate_idx(tensors[0].ndim, dim)
# Verifies same shape (except in the concat dimension)
concat_length = 0
for tensor in tensors:
for idx, (common_length, length) in enumerate(zip(shape,
tensor.shape)):
if idx == dim:
concat_length = concat_length + length
else:
assert length == common_length
new_shape = list(tensors[0].shape).copy()
new_shape[dim] = concat_length
return TensorMeta(
tensors[0],
shape=new_shape,
strides=utils.make_contiguous_strides_for(new_shape),
)
def _split_dim_meta(a: TensorLikeType, dim: int,
outer_length: int) -> TensorLikeType:
assert isinstance(a, TensorLike)
utils.validate_idx(a.ndim, dim)
utils.validate_dim_length(outer_length)
# Verifies the dim can be split with the specified lhs_length
_inner_length = a.shape[dim] / outer_length
inner_length: int = int(_inner_length)
if inner_length != _inner_length:
msg = "Attempting to split dimension of length {0}, but outer length of {1} divides it with a remainder!".format(
a.shape[dim], outer_length)
raise ValueError(msg)
new_shape: List[int] = []
new_strides: List[int] = []
for idx in range(a.ndim):
if idx == dim:
new_shape.extend((outer_length, inner_length))
new_strides.extend(
(a.stride()[idx] * inner_length, a.stride()[idx]))
else:
new_shape.append(a.shape[idx])
new_strides.append(a.stride()[idx])
return TensorMeta(a, shape=new_shape, strides=new_strides)
def _concatenate_meta(tensors: Sequence[TensorLikeType],
dim: int) -> TensorLikeType:
assert len(tensors) > 0
for tensor in tensors:
assert isinstance(tensor, TensorLike)
utils.check_same_dtype(tensors)
utils.check_same_device(tensors, allow_scalars=False)
shape = tensors[0].shape
utils.validate_idx(shape, dim)
# Verifies same shape (except in the concat dimension)
concat_length = 0
for tensor in tensors:
for idx, (common_length, length) in enumerate(zip(shape,
tensor.shape)):
if idx == dim:
concat_length = concat_length + length
else:
assert length == common_length
new_shape = list(tensors[0].shape).copy()
new_shape[dim] = concat_length
return TensorMeta(
tensors[0],
shape=new_shape,
strides=utils.make_contiguous_strides_for(new_shape),
)
def _collapse_view_meta(a: TensorLikeType, start: int,
end: int) -> TensorLikeType:
assert isinstance(a, TensorLike)
shape = a.shape
strides = a.stride()
utils.validate_idx(shape, start)
utils.validate_exclusive_idx(shape, end)
# Verifies end is strictly greater than start
# (Collapse requires a non-empty interval)
assert end > start
length = 1
stride = 1
for idx in range(start, end):
if idx != (end - 1):
assert strides[idx] == strides[idx + 1] * shape[idx + 1]
length = length * shape[idx]
stride = stride * strides[idx]
new_shape = shape[:start] + (length, ) + shape[end:]
new_strides = strides[:start] + (stride, ) + shape[end:]
return TensorMeta(a, shape=new_shape, strides=new_strides)
def _squeeze_meta(a: TensorLikeType, dimensions: Sequence) -> TensorLikeType:
assert isinstance(a, TensorLike)
for idx in dimensions:
utils.validate_idx(a.ndim, idx)
assert a.shape[idx] == 1
new_shape = []
new_strides = []
for idx in range(len(a.shape)):
if idx in dimensions:
continue
new_shape.append(a.shape[idx])
new_strides.append(a.stride()[idx])
return TensorMeta(a, shape=new_shape, strides=new_strides)
def _split_dim_meta(a: TensorLikeType, dim: int,
outer_length: int) -> TensorLikeType:
assert isinstance(a, TensorLike)
utils.validate_idx(a.shape, dim)
utils.validate_dim_length(outer_length)
# Verifies the dim can be split with the specified lhs_length
_inner_length = a.shape[dim] / outer_length
inner_length: int = int(_inner_length)
assert inner_length == _inner_length
new_shape: List[int] = []
new_strides: List[int] = []
for idx in a.shape:
if idx == dim:
new_shape.extend((outer_length, inner_length))
new_strides.extend(
(a.stride()[idx] * inner_length, a.stride()[idx]))
else:
new_shape.append(a.shape[idx])
new_strides.append(a.stride()[idx])
return TensorMeta(a, shape=new_shape, strides=new_strides)
def _collapse_view_helper(
a: TensorLikeType, start: int,
end: int) -> Tuple[Optional[ShapeType], Optional[StrideType]]:
assert isinstance(a, TensorLike)
# Special-case for zero dimensional tensors
if a.ndim == 0:
shape = (1, )
strides = (1, )
else:
shape = a.shape # type: ignore[assignment]
strides = a.stride()
utils.validate_idx(len(shape), start)
utils.validate_exclusive_idx(len(shape), end)
# Verifies end is strictly greater than start
# (Collapse requires a non-empty interval)
if end <= start:
msg = "Attempting to collapse but end, {0}, is less than or equal to start, {1}!".format(
end, start)
raise ValueError(msg)
length = 1
stride = 1
for idx in range(start, end):
if idx != (end - 1):
if not (strides[idx] == strides[idx + 1] * shape[idx + 1]):
return None, None
length = length * shape[idx]
stride = stride * strides[idx]
new_shape = shape[:start] + (length, ) + shape[end:]
new_strides = strides[:start] + (stride, ) + shape[end:]
return new_shape, new_strides
