Tensor

An n-dimensional array on a device. Operators run asynchronously and return at once; a read (numpy(), item(), tolist(), print) waits for the value. Build one with clika_runtime.tensor(...), the factories (zeros, ones, randn, ...), from_numpy or from_dlpack.

T (property)

The tensor with every dimension reversed (a view).

device (property)

(self) -> clika_runtime._core.Device

dtype (property)

The element dtype object (clika_runtime.float32, ...).

is_fake (property)

True for a declared, storage-free tensor (a parameter slot before load_state_dict binds it): its metadata reads, its data does not.

is_quantized (property)

True when a quantization scheme rides the tensor (quantized_view() names it).

mT (property)

The tensor with its last two dimensions swapped (a view).

nbytes (property)

Payload size in bytes at the tensor's own dtype.

ndim (property)

The number of dimensions.

shape (property)

The dimensions as a Size (a tuple with numel()).

__init__

__init__(self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

abs

absabs(self) -> clika_runtime._core.Tensor

abs(self) -> clika_runtime._core.Tensor

abs() -> Tensor

Method form of clika_runtime.ops.abs.

abs_

abs_abs_(self) -> object

abs_(self) -> object

abs_() -> Tensor

Method form of clika_runtime.ops.abs_.

Writes through this tensor's storage and returns this tensor, so calls chain.

acos

acosacos(self) -> clika_runtime._core.Tensor

acos(self) -> clika_runtime._core.Tensor

acos() -> Tensor

Method form of clika_runtime.ops.acos.

acos_

acos_acos_(self) -> object

acos_(self) -> object

acos_() -> Tensor

Method form of clika_runtime.ops.acos_.

Writes through this tensor's storage and returns this tensor, so calls chain.

acosh

acoshacosh(self) -> clika_runtime._core.Tensor

acosh(self) -> clika_runtime._core.Tensor

acosh() -> Tensor

Method form of clika_runtime.ops.acosh.

acosh_

acosh_acosh_(self) -> object

acosh_(self) -> object

acosh_() -> Tensor

Method form of clika_runtime.ops.acosh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

adaptive_avg_pool

adaptive_avg_pooladaptive_avg_pool(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_avg_pool.

adaptive_avg_pool1d

adaptive_avg_pool1dadaptive_avg_pool1d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool1d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool1d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_avg_pool1d.

adaptive_avg_pool2d

adaptive_avg_pool2dadaptive_avg_pool2d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool2d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool2d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_avg_pool2d.

adaptive_avg_pool3d

adaptive_avg_pool3dadaptive_avg_pool3d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool3d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_avg_pool3d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_avg_pool3d.

adaptive_max_pool

adaptive_max_pooladaptive_max_pool(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_max_pool.

adaptive_max_pool1d

adaptive_max_pool1dadaptive_max_pool1d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool1d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool1d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_max_pool1d.

adaptive_max_pool2d

adaptive_max_pool2dadaptive_max_pool2d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool2d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool2d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_max_pool2d.

adaptive_max_pool3d

adaptive_max_pool3dadaptive_max_pool3d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool3d(self, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

adaptive_max_pool3d(output_size) -> Tensor

Method form of clika_runtime.ops.adaptive_max_pool3d.

add

addadd(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

add(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

add(other, alpha=1.0, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.add.

add_

add_add_(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> object

add_(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> object

add_(other, alpha=1.0, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.add_.

Writes through this tensor's storage and returns this tensor, so calls chain.

add_layer_norm

add_layer_normadd_layer_norm(self, residual: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

add_layer_norm(self, residual: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

add_layer_norm(residual=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.add_layer_norm.

add_rms_norm

add_rms_normadd_rms_norm(self, residual: clika_runtime._core.Tensor | None = None, residual2: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

add_rms_norm(self, residual: clika_runtime._core.Tensor | None = None, residual2: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

add_rms_norm(residual=None, residual2=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.add_rms_norm.

all

allall(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

all(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

all(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.all.

allclose

allcloseallclose(self, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor

allclose(self, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor

allclose(other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor

Method form of clika_runtime.ops.allclose.

amax

amaxamax(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

amax(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

amax(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.amax.

amin

aminamin(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

amin(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

amin(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.amin.

aminmax

aminmaxaminmax(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

aminmax(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

aminmax(dim=None, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.aminmax.

any

anyany(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

any(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

any(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.any.

argmax

argmaxargmax(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor

argmax(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor

argmax(dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor

Method form of clika_runtime.ops.argmax.

argmin

argminargmin(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor

argmin(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor

argmin(dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor

Method form of clika_runtime.ops.argmin.

argsort

argsortargsort(self, dim: int = -1, descending: bool = False, stable: bool = False) -> clika_runtime._core.Tensor

argsort(self, dim: int = -1, descending: bool = False, stable: bool = False) -> clika_runtime._core.Tensor

argsort(dim=-1, descending=False, stable=False) -> Tensor

Method form of clika_runtime.ops.argsort.

asin

asinasin(self) -> clika_runtime._core.Tensor

asin(self) -> clika_runtime._core.Tensor

asin() -> Tensor

Method form of clika_runtime.ops.asin.

asin_

asin_asin_(self) -> object

asin_(self) -> object

asin_() -> Tensor

Method form of clika_runtime.ops.asin_.

Writes through this tensor's storage and returns this tensor, so calls chain.

asinh

asinhasinh(self) -> clika_runtime._core.Tensor

asinh(self) -> clika_runtime._core.Tensor

asinh() -> Tensor

Method form of clika_runtime.ops.asinh.

asinh_

asinh_asinh_(self) -> object

asinh_(self) -> object

asinh_() -> Tensor

Method form of clika_runtime.ops.asinh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

atan

atanatan(self) -> clika_runtime._core.Tensor

atan(self) -> clika_runtime._core.Tensor

atan() -> Tensor

Method form of clika_runtime.ops.atan.

atan2

atan2atan2(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

atan2(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

atan2(other) -> Tensor

Method form of clika_runtime.ops.atan2.

atan2_

atan2_atan2_(self, other: clika_runtime._core.Tensor) -> object

atan2_(self, other: clika_runtime._core.Tensor) -> object

atan2_(other) -> Tensor

Method form of clika_runtime.ops.atan2_.

Writes through this tensor's storage and returns this tensor, so calls chain.

atan_

atan_atan_(self) -> object

atan_(self) -> object

atan_() -> Tensor

Method form of clika_runtime.ops.atan_.

Writes through this tensor's storage and returns this tensor, so calls chain.

atanh

atanhatanh(self) -> clika_runtime._core.Tensor

atanh(self) -> clika_runtime._core.Tensor

atanh() -> Tensor

Method form of clika_runtime.ops.atanh.

atanh_

atanh_atanh_(self) -> object

atanh_(self) -> object

atanh_() -> Tensor

Method form of clika_runtime.ops.atanh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

atleast_1d

atleast_1datleast_1d(self) -> clika_runtime._core.Tensor

atleast_1d(self) -> clika_runtime._core.Tensor

atleast_1d() -> Tensor

Method form of clika_runtime.ops.atleast_1d.

atleast_2d

atleast_2datleast_2d(self) -> clika_runtime._core.Tensor

atleast_2d(self) -> clika_runtime._core.Tensor

atleast_2d() -> Tensor

Method form of clika_runtime.ops.atleast_2d.

atleast_3d

atleast_3datleast_3d(self) -> clika_runtime._core.Tensor

atleast_3d(self) -> clika_runtime._core.Tensor

atleast_3d() -> Tensor

Method form of clika_runtime.ops.atleast_3d.

avg_pool

avg_poolavg_pool(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], ceil_mode: bool, count_include_pad: bool, divisor_override: int | None) -> clika_runtime._core.Tensor

avg_pool(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], ceil_mode: bool, count_include_pad: bool, divisor_override: int | None) -> clika_runtime._core.Tensor

avg_pool(kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) -> Tensor

Method form of clika_runtime.ops.avg_pool.

avg_pool1d

avg_pool1davg_pool1d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], ceil_mode: bool = False, count_include_pad: bool = True) -> clika_runtime._core.Tensor

avg_pool1d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], ceil_mode: bool = False, count_include_pad: bool = True) -> clika_runtime._core.Tensor

avg_pool1d(kernel_size, stride=[], padding=[0], ceil_mode=False, count_include_pad=True) -> Tensor

Method form of clika_runtime.ops.avg_pool1d.

avg_pool2d

avg_pool2davg_pool2d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor

avg_pool2d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor

avg_pool2d(kernel_size, stride=[], padding=[0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor

Method form of clika_runtime.ops.avg_pool2d.

avg_pool3d

avg_pool3davg_pool3d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor

avg_pool3d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor

avg_pool3d(kernel_size, stride=[], padding=[0, 0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor

Method form of clika_runtime.ops.avg_pool3d.

batch_norm

batch_normbatch_norm(self, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

batch_norm(self, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

batch_norm(weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.batch_norm.

bernoulli_

bernoulli_bernoulli_(self, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

bernoulli_(self, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

bernoulli_(*, device=None) -> Tensor

Method form of clika_runtime.ops.bernoulli_.

Writes through this tensor's storage and returns this tensor, so calls chain.

binary_cross_entropy

binary_cross_entropybinary_cross_entropy(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

binary_cross_entropy(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

binary_cross_entropy(target, weight=None, reduction='mean') -> Tensor

Method form of clika_runtime.ops.binary_cross_entropy.

binary_cross_entropy_with_logits

binary_cross_entropy_with_logitsbinary_cross_entropy_with_logits(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean', pos_weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

binary_cross_entropy_with_logits(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean', pos_weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

binary_cross_entropy_with_logits(target, weight=None, reduction='mean', pos_weight=None) -> Tensor

Method form of clika_runtime.ops.binary_cross_entropy_with_logits.

bincount

bincountbincount(self, weights: clika_runtime._core.Tensor | None = None, minlength: int = 0) -> clika_runtime._core.Tensor

bincount(self, weights: clika_runtime._core.Tensor | None = None, minlength: int = 0) -> clika_runtime._core.Tensor

bincount(weights=None, minlength=0) -> Tensor

Method form of clika_runtime.ops.bincount.

bitwise_and

bitwise_andbitwise_and(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_and(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_and(other) -> Tensor

Method form of clika_runtime.ops.bitwise_and.

bitwise_and_

bitwise_and_bitwise_and_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_and_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_and_(other) -> Tensor

Method form of clika_runtime.ops.bitwise_and_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bitwise_left_shift

bitwise_left_shiftbitwise_left_shift(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_left_shift(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_left_shift(other) -> Tensor

Method form of clika_runtime.ops.bitwise_left_shift.

bitwise_left_shift_

bitwise_left_shift_bitwise_left_shift_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_left_shift_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_left_shift_(other) -> Tensor

Method form of clika_runtime.ops.bitwise_left_shift_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bitwise_not

bitwise_notbitwise_not(self) -> clika_runtime._core.Tensor

bitwise_not(self) -> clika_runtime._core.Tensor

bitwise_not() -> Tensor

Method form of clika_runtime.ops.bitwise_not.

bitwise_not_

bitwise_not_bitwise_not_(self) -> object

bitwise_not_(self) -> object

bitwise_not_() -> Tensor

Method form of clika_runtime.ops.bitwise_not_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bitwise_or

bitwise_orbitwise_or(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_or(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_or(other) -> Tensor

Method form of clika_runtime.ops.bitwise_or.

bitwise_or_

bitwise_or_bitwise_or_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_or_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_or_(other) -> Tensor

Method form of clika_runtime.ops.bitwise_or_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bitwise_right_shift

bitwise_right_shiftbitwise_right_shift(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_right_shift(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_right_shift(other) -> Tensor

Method form of clika_runtime.ops.bitwise_right_shift.

bitwise_right_shift_

bitwise_right_shift_bitwise_right_shift_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_right_shift_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_right_shift_(other) -> Tensor

Method form of clika_runtime.ops.bitwise_right_shift_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bitwise_xor

bitwise_xorbitwise_xor(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_xor(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

bitwise_xor(other) -> Tensor

Method form of clika_runtime.ops.bitwise_xor.

bitwise_xor_

bitwise_xor_bitwise_xor_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_xor_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

bitwise_xor_(other) -> Tensor

Method form of clika_runtime.ops.bitwise_xor_.

Writes through this tensor's storage and returns this tensor, so calls chain.

bmm

bmmbmm(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

bmm(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

bmm(other, bias=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.bmm.

broadcast_to

broadcast_tobroadcast_to(self, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

broadcast_to(self, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

broadcast_to(shape) -> Tensor

Method form of clika_runtime.ops.broadcast_to.

bucketize

bucketizebucketize(self, boundaries: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False) -> clika_runtime._core.Tensor

bucketize(self, boundaries: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False) -> clika_runtime._core.Tensor

bucketize(boundaries, out_int32=False, right=False) -> Tensor

Method form of clika_runtime.ops.bucketize.

bytes

bytesbytes(self) -> bytes

bytes(self) -> bytes

bytes() -> bytes

The host-contiguous payload at the tensor's OWN dtype; lossless for every dtype, bfloat16/float8/sub-byte included. Inside a tracing scope the value is not computed yet and this raises.

cast

castcast(self, target: clika_runtime._core.DataType, force_copy: bool = False) -> clika_runtime._core.Tensor

cast(self, target: clika_runtime._core.DataType, force_copy: bool = False) -> clika_runtime._core.Tensor

cast(target, force_copy=False) -> Tensor

Method form of clika_runtime.ops.cast.

cast_like

cast_likecast_like(self, reference: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

cast_like(self, reference: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

cast_like(reference) -> Tensor

Method form of clika_runtime.ops.cast_like.

causal_conv_update

causal_conv_updatecausal_conv_update(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

causal_conv_update(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

causal_conv_update(weight, bias, state, seq_lens=None, slot_ids=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.causal_conv_update.

ceil

ceilceil(self) -> clika_runtime._core.Tensor

ceil(self) -> clika_runtime._core.Tensor

ceil() -> Tensor

Method form of clika_runtime.ops.ceil.

ceil_

ceil_ceil_(self) -> object

ceil_(self) -> object

ceil_() -> Tensor

Method form of clika_runtime.ops.ceil_.

Writes through this tensor's storage and returns this tensor, so calls chain.

celu

celucelu(self, alpha: float = 1.0) -> clika_runtime._core.Tensor

celu(self, alpha: float = 1.0) -> clika_runtime._core.Tensor

celu(alpha=1.0) -> Tensor

Method form of clika_runtime.ops.celu.

celu_

celu_celu_(self, alpha: float = 1.0) -> object

celu_(self, alpha: float = 1.0) -> object

celu_(alpha=1.0) -> Tensor

Method form of clika_runtime.ops.celu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

chunk

chunkchunk(self, num_chunks: int, dim: int = 0) -> list[clika_runtime._core.Tensor]

chunk(self, num_chunks: int, dim: int = 0) -> list[clika_runtime._core.Tensor]

chunk(num_chunks, dim=0) -> list[Tensor]

Method form of clika_runtime.ops.chunk.

circular_pad

circular_padcircular_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

circular_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

circular_pad(pad) -> Tensor

Method form of clika_runtime.ops.circular_pad.

clamp

clampclamp(self, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

clamp(self, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

clamp(min=None, max=None) -> Tensor

Method form of clika_runtime.ops.clamp.

clamp_

clamp_clamp_(self, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> object

clamp_(self, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> object

clamp_(min=None, max=None) -> Tensor

Method form of clika_runtime.ops.clamp_.

Writes through this tensor's storage and returns this tensor, so calls chain.

clamp_max

clamp_maxclamp_max(self, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

clamp_max(self, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

clamp_max(max) -> Tensor

Method form of clika_runtime.ops.clamp_max.

clamp_max_

clamp_max_clamp_max_(self, max: clika_runtime._core.ops.ScalarOrTensor) -> object

clamp_max_(self, max: clika_runtime._core.ops.ScalarOrTensor) -> object

clamp_max_(max) -> Tensor

Method form of clika_runtime.ops.clamp_max_.

Writes through this tensor's storage and returns this tensor, so calls chain.

clamp_min

clamp_minclamp_min(self, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

clamp_min(self, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

clamp_min(min) -> Tensor

Method form of clika_runtime.ops.clamp_min.

clamp_min_

clamp_min_clamp_min_(self, min: clika_runtime._core.ops.ScalarOrTensor) -> object

clamp_min_(self, min: clika_runtime._core.ops.ScalarOrTensor) -> object

clamp_min_(min) -> Tensor

Method form of clika_runtime.ops.clamp_min_.

Writes through this tensor's storage and returns this tensor, so calls chain.

constant_pad

constant_padconstant_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

constant_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

constant_pad(pad, value=None) -> Tensor

Method form of clika_runtime.ops.constant_pad.

contiguous

contiguouscontiguous(self, force_copy: bool = False) -> clika_runtime._core.Tensor

contiguous(self, force_copy: bool = False) -> clika_runtime._core.Tensor

contiguous(force_copy=False) -> Tensor

Method form of clika_runtime.ops.contiguous.

conv

convconv(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv(weight, bias, stride, padding, dilation, groups, mode='constant', value=None, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv.

conv1d

conv1dconv1d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv1d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv1d(weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv1d.

conv2d

conv2dconv2d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv2d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv2d(weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv2d.

conv3d

conv3dconv3d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv3d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv3d(weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv3d.

conv_transpose

conv_transposeconv_transpose(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], groups: int, dilation: collections.abc.Sequence[int], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], groups: int, dilation: collections.abc.Sequence[int], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose(weight, bias, stride, padding, output_padding, groups, dilation, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv_transpose.

conv_transpose1d

conv_transpose1dconv_transpose1d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose1d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose1d(weight, bias=None, stride=[1], padding=[0, 0], output_padding=[0], groups=1, dilation=[1], *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv_transpose1d.

conv_transpose2d

conv_transpose2dconv_transpose2d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose2d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose2d(weight, bias=None, stride=[1, 1], padding=[0, 0, 0, 0], output_padding=[0, 0], groups=1, dilation=[1, 1], *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv_transpose2d.

conv_transpose3d

conv_transpose3dconv_transpose3d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose3d(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

conv_transpose3d(weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0, 0, 0, 0], output_padding=[0, 0, 0], groups=1, dilation=[1, 1, 1], *, activation='identity') -> Tensor

Method form of clika_runtime.ops.conv_transpose3d.

copy_

copy_copy_(self, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

copy_(self, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

copy_(src, *, target=None) -> Tensor

Method form of clika_runtime.ops.copy_.

Writes through this tensor's storage and returns this tensor, so calls chain.

copysign

copysigncopysign(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

copysign(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

copysign(other) -> Tensor

Method form of clika_runtime.ops.copysign.

copysign_

copysign_copysign_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

copysign_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

copysign_(other) -> Tensor

Method form of clika_runtime.ops.copysign_.

Writes through this tensor's storage and returns this tensor, so calls chain.

cos

coscos(self) -> clika_runtime._core.Tensor

cos(self) -> clika_runtime._core.Tensor

cos() -> Tensor

Method form of clika_runtime.ops.cos.

cos_

cos_cos_(self) -> object

cos_(self) -> object

cos_() -> Tensor

Method form of clika_runtime.ops.cos_.

Writes through this tensor's storage and returns this tensor, so calls chain.

cosh

coshcosh(self) -> clika_runtime._core.Tensor

cosh(self) -> clika_runtime._core.Tensor

cosh() -> Tensor

Method form of clika_runtime.ops.cosh.

cosh_

cosh_cosh_(self) -> object

cosh_(self) -> object

cosh_() -> Tensor

Method form of clika_runtime.ops.cosh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

count_nonzero

count_nonzerocount_nonzero(self, dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

count_nonzero(self, dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

count_nonzero(dims=[]) -> Tensor

Method form of clika_runtime.ops.count_nonzero.

cross

crosscross(self, other: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor

cross(self, other: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor

cross(other, dim=None) -> Tensor

Method form of clika_runtime.ops.cross.

cross_entropy

cross_entropycross_entropy(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

cross_entropy(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

cross_entropy(target, weight=None, ignore_index=None, reduction='mean') -> Tensor

Method form of clika_runtime.ops.cross_entropy.

cummax

cummaxcummax(self, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

cummax(self, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

cummax(dim) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.cummax.

cummin

cummincummin(self, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

cummin(self, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

cummin(dim) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.cummin.

cumprod

cumprodcumprod(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

cumprod(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

cumprod(dim, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.cumprod.

cumprod_

cumprod_cumprod_(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> object

cumprod_(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> object

cumprod_(dim, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.cumprod_.

Writes through this tensor's storage and returns this tensor, so calls chain.

cumsum

cumsumcumsum(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

cumsum(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

cumsum(dim, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.cumsum.

cumsum_

cumsum_cumsum_(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> object

cumsum_(self, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> object

cumsum_(dim, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.cumsum_.

Writes through this tensor's storage and returns this tensor, so calls chain.

deform_conv

deform_convdeform_conv(self, weight: clika_runtime._core.Tensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

deform_conv(self, weight: clika_runtime._core.Tensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

deform_conv(weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.deform_conv.

deg2rad

deg2raddeg2rad(self) -> clika_runtime._core.Tensor

deg2rad(self) -> clika_runtime._core.Tensor

deg2rad() -> Tensor

Method form of clika_runtime.ops.deg2rad.

deg2rad_

deg2rad_deg2rad_(self) -> object

deg2rad_(self) -> object

deg2rad_() -> Tensor

Method form of clika_runtime.ops.deg2rad_.

Writes through this tensor's storage and returns this tensor, so calls chain.

diag

diagdiag(self, diagonal: int = 0) -> clika_runtime._core.Tensor

diag(self, diagonal: int = 0) -> clika_runtime._core.Tensor

diag(diagonal=0) -> Tensor

Method form of clika_runtime.ops.diag.

diag_embed

diag_embeddiag_embed(self, offset: int = 0, dim1: int = -2, dim2: int = -1) -> clika_runtime._core.Tensor

diag_embed(self, offset: int = 0, dim1: int = -2, dim2: int = -1) -> clika_runtime._core.Tensor

diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor

Method form of clika_runtime.ops.diag_embed.

diagonal

diagonaldiagonal(self, offset: int = 0, dim1: int = 0, dim2: int = 1) -> clika_runtime._core.Tensor

diagonal(self, offset: int = 0, dim1: int = 0, dim2: int = 1) -> clika_runtime._core.Tensor

diagonal(offset=0, dim1=0, dim2=1) -> Tensor

Method form of clika_runtime.ops.diagonal.

diff

diffdiff(self, n: int = 1, dim: int = -1, prepend: clika_runtime._core.Tensor | None = None, append: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

diff(self, n: int = 1, dim: int = -1, prepend: clika_runtime._core.Tensor | None = None, append: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

diff(n=1, dim=-1, prepend=None, append=None) -> Tensor

Method form of clika_runtime.ops.diff.

dim

dimdim(self) -> int

dim(self) -> int

dim() -> int

The number of dimensions.

div

divdiv(self, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

div(self, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

div(other, rounding_mode='none', *, activation='identity') -> Tensor

Method form of clika_runtime.ops.div.

div_

div_div_(self, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> object

div_(self, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> object

div_(other, rounding_mode='none', *, activation='identity') -> Tensor

Method form of clika_runtime.ops.div_.

Writes through this tensor's storage and returns this tensor, so calls chain.

dot

dotdot(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

dot(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

dot(other) -> Tensor

Method form of clika_runtime.ops.dot.

dynamic_quantize

dynamic_quantizedynamic_quantize(self) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

dynamic_quantize(self) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

dynamic_quantize() -> tuple[Tensor, Tensor, Tensor]

Method form of clika_runtime.ops.dynamic_quantize.

element_size

element_sizeelement_size(self) -> int

element_size(self) -> int

element_size() -> int

Bytes per element (1 for the sub-byte dtypes).

elu

eluelu(self, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor

elu(self, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor

elu(alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor

Method form of clika_runtime.ops.elu.

elu_

elu_elu_(self, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> object

elu_(self, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> object

elu_(alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor

Method form of clika_runtime.ops.elu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

eq

eqeq(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

eq(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

eq(other) -> Tensor

Method form of clika_runtime.ops.eq.

eq_

eq_eq_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

eq_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

eq_(other) -> Tensor

Method form of clika_runtime.ops.eq_.

Writes through this tensor's storage and returns this tensor, so calls chain.

erf

erferf(self) -> clika_runtime._core.Tensor

erf(self) -> clika_runtime._core.Tensor

erf() -> Tensor

Method form of clika_runtime.ops.erf.

erf_

erf_erf_(self) -> object

erf_(self) -> object

erf_() -> Tensor

Method form of clika_runtime.ops.erf_.

Writes through this tensor's storage and returns this tensor, so calls chain.

erfc

erfcerfc(self) -> clika_runtime._core.Tensor

erfc(self) -> clika_runtime._core.Tensor

erfc() -> Tensor

Method form of clika_runtime.ops.erfc.

erfc_

erfc_erfc_(self) -> object

erfc_(self) -> object

erfc_() -> Tensor

Method form of clika_runtime.ops.erfc_.

Writes through this tensor's storage and returns this tensor, so calls chain.

erfinv

erfinverfinv(self) -> clika_runtime._core.Tensor

erfinv(self) -> clika_runtime._core.Tensor

erfinv() -> Tensor

Method form of clika_runtime.ops.erfinv.

erfinv_

erfinv_erfinv_(self) -> object

erfinv_(self) -> object

erfinv_() -> Tensor

Method form of clika_runtime.ops.erfinv_.

Writes through this tensor's storage and returns this tensor, so calls chain.

exp

expexp(self) -> clika_runtime._core.Tensor

exp(self) -> clika_runtime._core.Tensor

exp() -> Tensor

Method form of clika_runtime.ops.exp.

exp_

exp_exp_(self) -> object

exp_(self) -> object

exp_() -> Tensor

Method form of clika_runtime.ops.exp_.

Writes through this tensor's storage and returns this tensor, so calls chain.

expand

expandexpand(self, *args) -> clika_runtime._core.Tensor

expand(self, *args) -> clika_runtime._core.Tensor

expand(*sizes) -> Tensor

A broadcast view to the given sizes (-1 keeps a dimension); dims as separate arguments or one sequence.

expand_as

expand_asexpand_as(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

expand_as(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

expand_as(other) -> Tensor

Method form of clika_runtime.ops.expand_as.

exponential_

exponential_exponential_(self, lambd: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

exponential_(self, lambd: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

exponential_(lambd=1.0, *, device=None) -> Tensor

Method form of clika_runtime.ops.exponential_.

Writes through this tensor's storage and returns this tensor, so calls chain.

fake

fake(*args, **kwargs)

fake(spec: clika_runtime._core.FakeTensor) -> clika_runtime._core.Tensor

fake(spec) -> Tensor

A tensor with the shape, dtype and placement of a FakeTensor and no storage: a declared slot a checkpoint binds later. Its metadata reads; a data read raises until it is bound.

fast_gelu

fast_gelufast_gelu(self) -> clika_runtime._core.Tensor

fast_gelu(self) -> clika_runtime._core.Tensor

fast_gelu() -> Tensor

Method form of clika_runtime.ops.fast_gelu.

fill

fillfill(self, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor

fill(self, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor

fill(value, *, device=None) -> Tensor

Method form of clika_runtime.ops.fill.

fill_

fill_fill_(self, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

fill_(self, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

fill_(value, *, device=None) -> Tensor

Method form of clika_runtime.ops.fill_.

Writes through this tensor's storage and returns this tensor, so calls chain.

fill_diagonal

fill_diagonalfill_diagonal(self, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor

fill_diagonal(self, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor

fill_diagonal(fill_value, wrap=False, *, device=None) -> Tensor

Method form of clika_runtime.ops.fill_diagonal.

fill_diagonal_

fill_diagonal_fill_diagonal_(self, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

fill_diagonal_(self, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

fill_diagonal_(fill_value, wrap=False, *, device=None) -> Tensor

Method form of clika_runtime.ops.fill_diagonal_.

Writes through this tensor's storage and returns this tensor, so calls chain.

flatten

flattenflatten(self, start_dim: int = 0, end_dim: int = -1) -> clika_runtime._core.Tensor

flatten(self, start_dim: int = 0, end_dim: int = -1) -> clika_runtime._core.Tensor

flatten(start_dim=0, end_dim=-1) -> Tensor

Method form of clika_runtime.ops.flatten.

flip

flipflip(self, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

flip(self, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

flip(dims) -> Tensor

Method form of clika_runtime.ops.flip.

fliplr

fliplrfliplr(self) -> clika_runtime._core.Tensor

fliplr(self) -> clika_runtime._core.Tensor

fliplr() -> Tensor

Method form of clika_runtime.ops.fliplr.

flipud

flipudflipud(self) -> clika_runtime._core.Tensor

flipud(self) -> clika_runtime._core.Tensor

flipud() -> Tensor

Method form of clika_runtime.ops.flipud.

floor

floorfloor(self) -> clika_runtime._core.Tensor

floor(self) -> clika_runtime._core.Tensor

floor() -> Tensor

Method form of clika_runtime.ops.floor.

floor_

floor_floor_(self) -> object

floor_(self) -> object

floor_() -> Tensor

Method form of clika_runtime.ops.floor_.

Writes through this tensor's storage and returns this tensor, so calls chain.

floor_divide

floor_dividefloor_divide(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

floor_divide(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

floor_divide(other) -> Tensor

Method form of clika_runtime.ops.floor_divide.

floor_divide_

floor_divide_floor_divide_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

floor_divide_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

floor_divide_(other) -> Tensor

Method form of clika_runtime.ops.floor_divide_.

Writes through this tensor's storage and returns this tensor, so calls chain.

fmax

fmaxfmax(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

fmax(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

fmax(other) -> Tensor

Method form of clika_runtime.ops.fmax.

fmin

fminfmin(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

fmin(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

fmin(other) -> Tensor

Method form of clika_runtime.ops.fmin.

fmod

fmodfmod(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

fmod(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

fmod(other) -> Tensor

Method form of clika_runtime.ops.fmod.

fmod_

fmod_fmod_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

fmod_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

fmod_(other) -> Tensor

Method form of clika_runtime.ops.fmod_.

Writes through this tensor's storage and returns this tensor, so calls chain.

fold

foldfold(self, output_size: collections.abc.Sequence[int], kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor

fold(self, output_size: collections.abc.Sequence[int], kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor

fold(output_size, kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor

Method form of clika_runtime.ops.fold.

frac

fracfrac(self) -> clika_runtime._core.Tensor

frac(self) -> clika_runtime._core.Tensor

frac() -> Tensor

Method form of clika_runtime.ops.frac.

frac_

frac_frac_(self) -> object

frac_(self) -> object

frac_() -> Tensor

Method form of clika_runtime.ops.frac_.

Writes through this tensor's storage and returns this tensor, so calls chain.

frexp

frexpfrexp(self) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

frexp(self) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

frexp() -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.frexp.

from_bytes

from_bytes(*args, **kwargs)

from_bytes(data: bytes, shape: collections.abc.Sequence[int], dtype: clika_runtime._core.DataType, device: clika_runtime._core.ops.StreamOrDevice | None = None) -> clika_runtime._core.Tensor

Store a raw payload AS dtype: the entry for dtypes numpy cannot spell (bfloat16, float8, sub-byte codes). len(data) must equal the shape's packed byte size.

from_data

from_data(*args, **kwargs)

from_data(array: ndarray[order='C', device='cpu', writable=False], device: clika_runtime._core.ops.StreamOrDevice | None = None) -> clika_runtime._core.Tensor

Copy a C-contiguous numpy array (any numpy-native dtype) into a new tensor on device; unspecified consults the calling thread's ambient placement and falls back to the cpu.

gated_rms_norm

gated_rms_normgated_rms_norm(self, gate: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, eps: float | None = None) -> clika_runtime._core.Tensor

gated_rms_norm(self, gate: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, eps: float | None = None) -> clika_runtime._core.Tensor

gated_rms_norm(gate, normalized_shape, weight=None, eps=None) -> Tensor

Method form of clika_runtime.ops.gated_rms_norm.

gather

gathergather(self, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

gather(self, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

gather(dim, index) -> Tensor

Method form of clika_runtime.ops.gather.

ge

gege(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

ge(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

ge(other) -> Tensor

Method form of clika_runtime.ops.ge.

ge_

ge_ge_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

ge_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

ge_(other) -> Tensor

Method form of clika_runtime.ops.ge_.

Writes through this tensor's storage and returns this tensor, so calls chain.

geglu

geglugeglu(self, approximate: object | None = 'none') -> clika_runtime._core.Tensor

geglu(self, approximate: object | None = 'none') -> clika_runtime._core.Tensor

geglu(approximate='none') -> Tensor

Method form of clika_runtime.ops.geglu.

gelu

gelugelu(self, approximate: object | None = 'none') -> clika_runtime._core.Tensor

gelu(self, approximate: object | None = 'none') -> clika_runtime._core.Tensor

gelu(approximate='none') -> Tensor

Method form of clika_runtime.ops.gelu.

gelu_

gelu_gelu_(self, approximate: object | None = 'none') -> object

gelu_(self, approximate: object | None = 'none') -> object

gelu_(approximate='none') -> Tensor

Method form of clika_runtime.ops.gelu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

glu

gluglu(self, dim: int = -1) -> clika_runtime._core.Tensor

glu(self, dim: int = -1) -> clika_runtime._core.Tensor

glu(dim=-1) -> Tensor

Method form of clika_runtime.ops.glu.

grid_sample

grid_samplegrid_sample(self, grid: clika_runtime._core.Tensor, mode: object = 'bilinear', padding_mode: object = 'zeros', align_corners: bool = False) -> clika_runtime._core.Tensor

grid_sample(self, grid: clika_runtime._core.Tensor, mode: object = 'bilinear', padding_mode: object = 'zeros', align_corners: bool = False) -> clika_runtime._core.Tensor

grid_sample(grid, mode='bilinear', padding_mode='zeros', align_corners=False) -> Tensor

Method form of clika_runtime.ops.grid_sample.

group_norm

group_normgroup_norm(self, num_groups: int, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

group_norm(self, num_groups: int, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

group_norm(num_groups, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.group_norm.

gt

gtgt(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

gt(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

gt(other) -> Tensor

Method form of clika_runtime.ops.gt.

gt_

gt_gt_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

gt_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

gt_(other) -> Tensor

Method form of clika_runtime.ops.gt_.

Writes through this tensor's storage and returns this tensor, so calls chain.

hardshrink

hardshrinkhardshrink(self, lambd: float = 0.5) -> clika_runtime._core.Tensor

hardshrink(self, lambd: float = 0.5) -> clika_runtime._core.Tensor

hardshrink(lambd=0.5) -> Tensor

Method form of clika_runtime.ops.hardshrink.

hardshrink_

hardshrink_hardshrink_(self, lambd: float = 0.5) -> object

hardshrink_(self, lambd: float = 0.5) -> object

hardshrink_(lambd=0.5) -> Tensor

Method form of clika_runtime.ops.hardshrink_.

Writes through this tensor's storage and returns this tensor, so calls chain.

hardsigmoid

hardsigmoidhardsigmoid(self) -> clika_runtime._core.Tensor

hardsigmoid(self) -> clika_runtime._core.Tensor

hardsigmoid() -> Tensor

Method form of clika_runtime.ops.hardsigmoid.

hardsigmoid_

hardsigmoid_hardsigmoid_(self) -> object

hardsigmoid_(self) -> object

hardsigmoid_() -> Tensor

Method form of clika_runtime.ops.hardsigmoid_.

Writes through this tensor's storage and returns this tensor, so calls chain.

hardswish

hardswishhardswish(self) -> clika_runtime._core.Tensor

hardswish(self) -> clika_runtime._core.Tensor

hardswish() -> Tensor

Method form of clika_runtime.ops.hardswish.

hardswish_

hardswish_hardswish_(self) -> object

hardswish_(self) -> object

hardswish_() -> Tensor

Method form of clika_runtime.ops.hardswish_.

Writes through this tensor's storage and returns this tensor, so calls chain.

hardtanh

hardtanhhardtanh(self, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor

hardtanh(self, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor

hardtanh(min_val=-1.0, max_val=1.0) -> Tensor

Method form of clika_runtime.ops.hardtanh.

hardtanh_

hardtanh_hardtanh_(self, min_val: float = -1.0, max_val: float = 1.0) -> object

hardtanh_(self, min_val: float = -1.0, max_val: float = 1.0) -> object

hardtanh_(min_val=-1.0, max_val=1.0) -> Tensor

Method form of clika_runtime.ops.hardtanh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

hash_128

hash_128hash_128(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_128(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_128(seed=0) -> Tensor

Method form of clika_runtime.ops.hash_128.

hash_256

hash_256hash_256(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_256(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_256(seed=0) -> Tensor

Method form of clika_runtime.ops.hash_256.

hash_64

hash_64hash_64(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_64(self, seed: int = 0) -> clika_runtime._core.Tensor

hash_64(seed=0) -> Tensor

Method form of clika_runtime.ops.hash_64.

hash_tensor

hash_tensorhash_tensor(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, mode: object = 'xor_sum') -> clika_runtime._core.Tensor

hash_tensor(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, mode: object = 'xor_sum') -> clika_runtime._core.Tensor

hash_tensor(dims=[], keepdim=False, mode='xor_sum') -> Tensor

Method form of clika_runtime.ops.hash_tensor.

histogram

histogramhistogram(self, bins: int = 100, range: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, density: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

histogram(self, bins: int = 100, range: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, density: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

histogram(bins=100, range=None, weight=None, density=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.histogram.

huber_loss

huber_losshuber_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', delta: float = 1.0) -> clika_runtime._core.Tensor

huber_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', delta: float = 1.0) -> clika_runtime._core.Tensor

huber_loss(target, reduction='mean', delta=1.0) -> Tensor

Method form of clika_runtime.ops.huber_loss.

hypot

hypothypot(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

hypot(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

hypot(other) -> Tensor

Method form of clika_runtime.ops.hypot.

hypot_

hypot_hypot_(self, other: clika_runtime._core.Tensor) -> object

hypot_(self, other: clika_runtime._core.Tensor) -> object

hypot_(other) -> Tensor

Method form of clika_runtime.ops.hypot_.

Writes through this tensor's storage and returns this tensor, so calls chain.

index

indexindex(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry]) -> clika_runtime._core.Tensor

index(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry]) -> clika_runtime._core.Tensor

index(indices) -> Tensor

Method form of clika_runtime.ops.index.

index_add

index_addindex_add(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_add(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_add(dim, indices, src) -> Tensor

Method form of clika_runtime.ops.index_add.

index_add_

index_add_index_add_(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> object

index_add_(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> object

index_add_(dim, indices, src) -> Tensor

Method form of clika_runtime.ops.index_add_.

Writes through this tensor's storage and returns this tensor, so calls chain.

index_copy

index_copyindex_copy(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_copy(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_copy(dim, indices, src) -> Tensor

Method form of clika_runtime.ops.index_copy.

index_copy_

index_copy_index_copy_(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> object

index_copy_(self, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> object

index_copy_(dim, indices, src) -> Tensor

Method form of clika_runtime.ops.index_copy_.

Writes through this tensor's storage and returns this tensor, so calls chain.

index_fill

index_fillindex_fill(self, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

index_fill(self, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

index_fill(dim, indices, value) -> Tensor

Method form of clika_runtime.ops.index_fill.

index_fill_

index_fill_index_fill_(self, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> object

index_fill_(self, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> object

index_fill_(dim, indices, value) -> Tensor

Method form of clika_runtime.ops.index_fill_.

Writes through this tensor's storage and returns this tensor, so calls chain.

index_put

index_putindex_put(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor

index_put(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor

index_put(indices, value, accumulate=False) -> Tensor

Method form of clika_runtime.ops.index_put.

index_put_

index_put_index_put_(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> object

index_put_(self, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> object

index_put_(indices, value, accumulate=False) -> Tensor

Method form of clika_runtime.ops.index_put_.

Writes through this tensor's storage and returns this tensor, so calls chain.

index_select

index_selectindex_select(self, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_select(self, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

index_select(dim, index) -> Tensor

Method form of clika_runtime.ops.index_select.

inner

innerinner(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

inner(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

inner(other) -> Tensor

Method form of clika_runtime.ops.inner.

instance_norm

instance_norminstance_norm(self, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

instance_norm(self, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

instance_norm(weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.instance_norm.

interpolate

interpolateinterpolate(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], mode: object = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool = False, antialias: bool = False) -> clika_runtime._core.Tensor

interpolate(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], mode: object = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool = False, antialias: bool = False) -> clika_runtime._core.Tensor

interpolate(sizes=[], scale_factors=[], mode='nearest', align_corners=None, recompute_scale_factor=False, antialias=False) -> Tensor

Method form of clika_runtime.ops.interpolate.

is_contiguous

is_contiguousis_contiguous(self) -> bool

is_contiguous(self) -> bool

is_contiguous() -> bool

True when the elements are laid out densely in row-major order.

isclose

iscloseisclose(self, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor

isclose(self, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor

isclose(other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor

Method form of clika_runtime.ops.isclose.

isfinite

isfiniteisfinite(self) -> clika_runtime._core.Tensor

isfinite(self) -> clika_runtime._core.Tensor

isfinite() -> Tensor

Method form of clika_runtime.ops.isfinite.

isinf

isinfisinf(self) -> clika_runtime._core.Tensor

isinf(self) -> clika_runtime._core.Tensor

isinf() -> Tensor

Method form of clika_runtime.ops.isinf.

isnan

isnanisnan(self) -> clika_runtime._core.Tensor

isnan(self) -> clika_runtime._core.Tensor

isnan() -> Tensor

Method form of clika_runtime.ops.isnan.

isneginf

isneginfisneginf(self) -> clika_runtime._core.Tensor

isneginf(self) -> clika_runtime._core.Tensor

isneginf() -> Tensor

Method form of clika_runtime.ops.isneginf.

isposinf

isposinfisposinf(self) -> clika_runtime._core.Tensor

isposinf(self) -> clika_runtime._core.Tensor

isposinf() -> Tensor

Method form of clika_runtime.ops.isposinf.

item

itemitem(self) -> object

item(self) -> object

item() -> bool | int | float

The value of a one-element tensor as a Python number (waits for the value). Inside a tracing scope the value is not computed yet and this raises; clika_runtime.eval(tensor) computes it.

kl_div

kl_divkl_div(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', log_target: bool = False) -> clika_runtime._core.Tensor

kl_div(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', log_target: bool = False) -> clika_runtime._core.Tensor

kl_div(target, reduction='mean', log_target=False) -> Tensor

Method form of clika_runtime.ops.kl_div.

kron

kronkron(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

kron(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

kron(other) -> Tensor

Method form of clika_runtime.ops.kron.

kthvalue

kthvaluekthvalue(self, k: int, dim: int = -1, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

kthvalue(self, k: int, dim: int = -1, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

kthvalue(k, dim=-1, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.kthvalue.

l1_loss

l1_lossl1_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor

l1_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor

l1_loss(target, reduction='mean') -> Tensor

Method form of clika_runtime.ops.l1_loss.

layer_norm

layer_normlayer_norm(self, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

layer_norm(self, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

layer_norm(normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.layer_norm.

le

lele(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

le(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

le(other) -> Tensor

Method form of clika_runtime.ops.le.

le_

le_le_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

le_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

le_(other) -> Tensor

Method form of clika_runtime.ops.le_.

Writes through this tensor's storage and returns this tensor, so calls chain.

leaky_relu

leaky_reluleaky_relu(self, negative_slope: float = 0.01) -> clika_runtime._core.Tensor

leaky_relu(self, negative_slope: float = 0.01) -> clika_runtime._core.Tensor

leaky_relu(negative_slope=0.01) -> Tensor

Method form of clika_runtime.ops.leaky_relu.

leaky_relu_

leaky_relu_leaky_relu_(self, negative_slope: float = 0.01) -> object

leaky_relu_(self, negative_slope: float = 0.01) -> object

leaky_relu_(negative_slope=0.01) -> Tensor

Method form of clika_runtime.ops.leaky_relu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

linear

linearlinear(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor

linear(self, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor

linear(weight, bias=None, *, activation=None, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor

Method form of clika_runtime.ops.linear.

log

loglog(self) -> clika_runtime._core.Tensor

log(self) -> clika_runtime._core.Tensor

log() -> Tensor

Method form of clika_runtime.ops.log.

log10

log10log10(self) -> clika_runtime._core.Tensor

log10(self) -> clika_runtime._core.Tensor

log10() -> Tensor

Method form of clika_runtime.ops.log10.

log10_

log10_log10_(self) -> object

log10_(self) -> object

log10_() -> Tensor

Method form of clika_runtime.ops.log10_.

Writes through this tensor's storage and returns this tensor, so calls chain.

log1p

log1plog1p(self) -> clika_runtime._core.Tensor

log1p(self) -> clika_runtime._core.Tensor

log1p() -> Tensor

Method form of clika_runtime.ops.log1p.

log1p_

log1p_log1p_(self) -> object

log1p_(self) -> object

log1p_() -> Tensor

Method form of clika_runtime.ops.log1p_.

Writes through this tensor's storage and returns this tensor, so calls chain.

log2

log2log2(self) -> clika_runtime._core.Tensor

log2(self) -> clika_runtime._core.Tensor

log2() -> Tensor

Method form of clika_runtime.ops.log2.

log2_

log2_log2_(self) -> object

log2_(self) -> object

log2_() -> Tensor

Method form of clika_runtime.ops.log2_.

Writes through this tensor's storage and returns this tensor, so calls chain.

log_

log_log_(self) -> object

log_(self) -> object

log_() -> Tensor

Method form of clika_runtime.ops.log_.

Writes through this tensor's storage and returns this tensor, so calls chain.

log_sigmoid

log_sigmoidlog_sigmoid(self) -> clika_runtime._core.Tensor

log_sigmoid(self) -> clika_runtime._core.Tensor

log_sigmoid() -> Tensor

Method form of clika_runtime.ops.log_sigmoid.

log_sigmoid_

log_sigmoid_log_sigmoid_(self) -> object

log_sigmoid_(self) -> object

log_sigmoid_() -> Tensor

Method form of clika_runtime.ops.log_sigmoid_.

Writes through this tensor's storage and returns this tensor, so calls chain.

log_softmax

log_softmaxlog_softmax(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

log_softmax(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

log_softmax(dim=-1, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.log_softmax.

log_softmax_

log_softmax_log_softmax_(self, dim: int = -1) -> object

log_softmax_(self, dim: int = -1) -> object

log_softmax_(dim=-1) -> Tensor

Method form of clika_runtime.ops.log_softmax_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logaddexp

logaddexplogaddexp(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logaddexp(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logaddexp(other) -> Tensor

Method form of clika_runtime.ops.logaddexp.

logaddexp2

logaddexp2logaddexp2(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logaddexp2(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logaddexp2(other) -> Tensor

Method form of clika_runtime.ops.logaddexp2.

logical_and

logical_andlogical_and(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_and(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_and(other) -> Tensor

Method form of clika_runtime.ops.logical_and.

logical_and_

logical_and_logical_and_(self, other: clika_runtime._core.Tensor) -> object

logical_and_(self, other: clika_runtime._core.Tensor) -> object

logical_and_(other) -> Tensor

Method form of clika_runtime.ops.logical_and_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logical_not

logical_notlogical_not(self) -> clika_runtime._core.Tensor

logical_not(self) -> clika_runtime._core.Tensor

logical_not() -> Tensor

Method form of clika_runtime.ops.logical_not.

logical_not_

logical_not_logical_not_(self) -> object

logical_not_(self) -> object

logical_not_() -> Tensor

Method form of clika_runtime.ops.logical_not_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logical_or

logical_orlogical_or(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_or(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_or(other) -> Tensor

Method form of clika_runtime.ops.logical_or.

logical_or_

logical_or_logical_or_(self, other: clika_runtime._core.Tensor) -> object

logical_or_(self, other: clika_runtime._core.Tensor) -> object

logical_or_(other) -> Tensor

Method form of clika_runtime.ops.logical_or_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logical_xor

logical_xorlogical_xor(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_xor(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

logical_xor(other) -> Tensor

Method form of clika_runtime.ops.logical_xor.

logical_xor_

logical_xor_logical_xor_(self, other: clika_runtime._core.Tensor) -> object

logical_xor_(self, other: clika_runtime._core.Tensor) -> object

logical_xor_(other) -> Tensor

Method form of clika_runtime.ops.logical_xor_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logit

logitlogit(self, eps: float | None = None) -> clika_runtime._core.Tensor

logit(self, eps: float | None = None) -> clika_runtime._core.Tensor

logit(eps=None) -> Tensor

Method form of clika_runtime.ops.logit.

logit_

logit_logit_(self, eps: float | None = None) -> object

logit_(self, eps: float | None = None) -> object

logit_(eps=None) -> Tensor

Method form of clika_runtime.ops.logit_.

Writes through this tensor's storage and returns this tensor, so calls chain.

logsumexp

logsumexplogsumexp(self, dims: collections.abc.Sequence[int], keepdim: bool = False) -> clika_runtime._core.Tensor

logsumexp(self, dims: collections.abc.Sequence[int], keepdim: bool = False) -> clika_runtime._core.Tensor

logsumexp(dims, keepdim=False) -> Tensor

Method form of clika_runtime.ops.logsumexp.

lt

ltlt(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

lt(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

lt(other) -> Tensor

Method form of clika_runtime.ops.lt.

lt_

lt_lt_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

lt_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

lt_(other) -> Tensor

Method form of clika_runtime.ops.lt_.

Writes through this tensor's storage and returns this tensor, so calls chain.

masked_fill

masked_fillmasked_fill(self, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

masked_fill(self, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

masked_fill(mask, value) -> Tensor

Method form of clika_runtime.ops.masked_fill.

masked_fill_

masked_fill_masked_fill_(self, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> object

masked_fill_(self, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> object

masked_fill_(mask, value) -> Tensor

Method form of clika_runtime.ops.masked_fill_.

Writes through this tensor's storage and returns this tensor, so calls chain.

masked_scatter

masked_scattermasked_scatter(self, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

masked_scatter(self, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

masked_scatter(mask, source) -> Tensor

Method form of clika_runtime.ops.masked_scatter.

masked_scatter_

masked_scatter_masked_scatter_(self, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> object

masked_scatter_(self, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> object

masked_scatter_(mask, source) -> Tensor

Method form of clika_runtime.ops.masked_scatter_.

Writes through this tensor's storage and returns this tensor, so calls chain.

masked_select

masked_selectmasked_select(self, mask: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

masked_select(self, mask: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

masked_select(mask) -> Tensor

Method form of clika_runtime.ops.masked_select.

matmul

matmulmatmul(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor

matmul(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor

matmul(other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor

Method form of clika_runtime.ops.matmul.

max

maxmax(self, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max(self, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max(dim, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.max.

max_pool

max_poolmax_pool(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> clika_runtime._core.Tensor

max_pool(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> clika_runtime._core.Tensor

max_pool(kernel_size, stride, padding, dilation, ceil_mode) -> Tensor

Method form of clika_runtime.ops.max_pool.

max_pool1d

max_pool1dmax_pool1d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool1d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool1d(kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> Tensor

Method form of clika_runtime.ops.max_pool1d.

max_pool1d_with_indices

max_pool1d_with_indicesmax_pool1d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool1d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool1d_with_indices(kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.max_pool1d_with_indices.

max_pool2d

max_pool2dmax_pool2d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool2d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool2d(kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> Tensor

Method form of clika_runtime.ops.max_pool2d.

max_pool2d_with_indices

max_pool2d_with_indicesmax_pool2d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool2d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool2d_with_indices(kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.max_pool2d_with_indices.

max_pool3d

max_pool3dmax_pool3d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool3d(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor

max_pool3d(kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> Tensor

Method form of clika_runtime.ops.max_pool3d.

max_pool3d_with_indices

max_pool3d_with_indicesmax_pool3d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool3d_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool3d_with_indices(kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.max_pool3d_with_indices.

max_pool_with_indices

max_pool_with_indicesmax_pool_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool_with_indices(self, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

max_pool_with_indices(kernel_size, stride, padding, dilation, ceil_mode) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.max_pool_with_indices.

maximum

maximummaximum(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

maximum(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

maximum(other) -> Tensor

Method form of clika_runtime.ops.maximum.

maximum_

maximum_maximum_(self, other: clika_runtime._core.Tensor) -> object

maximum_(self, other: clika_runtime._core.Tensor) -> object

maximum_(other) -> Tensor

Method form of clika_runtime.ops.maximum_.

Writes through this tensor's storage and returns this tensor, so calls chain.

mean

meanmean(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

mean(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

mean(dims=[], keepdim=False, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.mean.

median

medianmedian(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

median(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

median(dim=None, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.median.

min

minmin(self, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

min(self, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

min(dim, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.min.

minimum

minimumminimum(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

minimum(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

minimum(other) -> Tensor

Method form of clika_runtime.ops.minimum.

minimum_

minimum_minimum_(self, other: clika_runtime._core.Tensor) -> object

minimum_(self, other: clika_runtime._core.Tensor) -> object

minimum_(other) -> Tensor

Method form of clika_runtime.ops.minimum_.

Writes through this tensor's storage and returns this tensor, so calls chain.

mish

mishmish(self) -> clika_runtime._core.Tensor

mish(self) -> clika_runtime._core.Tensor

mish() -> Tensor

Method form of clika_runtime.ops.mish.

mish_

mish_mish_(self) -> object

mish_(self) -> object

mish_() -> Tensor

Method form of clika_runtime.ops.mish_.

Writes through this tensor's storage and returns this tensor, so calls chain.

mm

mmmm(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

mm(self, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

mm(other, bias=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.mm.

mod

modmod(self, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor

mod(self, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor

mod(other, mode='python') -> Tensor

Method form of clika_runtime.ops.mod.

mod_

mod_mod_(self, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> object

mod_(self, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> object

mod_(other, mode='python') -> Tensor

Method form of clika_runtime.ops.mod_.

Writes through this tensor's storage and returns this tensor, so calls chain.

moe

moemoe(self, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.Tensor, fc2_experts: clika_runtime._core.Tensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.Tensor | None = None, fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

moe(self, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.Tensor, fc2_experts: clika_runtime._core.Tensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.Tensor | None = None, fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

moe(router_logits, fc1_experts, fc2_experts, top_k, fc1_bias=None, fc2_bias=None, fc3_experts=None, fc3_bias=None, e_score_correction_bias=None, router_weights=None, routing_mode=None, renormalize=None, n_group=None, topk_group=None, routed_scaling_factor=None, sparse_mixer_eps=None, apply_router_weight_on_input=None, *, activation=None, swiglu_fusion=None, swiglu_alpha=None, swiglu_beta=None, swiglu_limit=None, gelu_mode=None, shared_output=None) -> Tensor

Method form of clika_runtime.ops.moe.

mrope_rotary_embedding

mrope_rotary_embeddingmrope_rotary_embedding(self, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor

mrope_rotary_embedding(self, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor

mrope_rotary_embedding(position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor

Method form of clika_runtime.ops.mrope_rotary_embedding.

mrope_rotary_embedding_varlen

mrope_rotary_embedding_varlenmrope_rotary_embedding_varlen(self, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor

mrope_rotary_embedding_varlen(self, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor

mrope_rotary_embedding_varlen(position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor

Method form of clika_runtime.ops.mrope_rotary_embedding_varlen.

mse_loss

mse_lossmse_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor

mse_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor

mse_loss(target, reduction='mean') -> Tensor

Method form of clika_runtime.ops.mse_loss.

mul

mulmul(self, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

mul(self, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

mul(other, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.mul.

mul_

mul_mul_(self, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> object

mul_(self, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> object

mul_(other, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.mul_.

Writes through this tensor's storage and returns this tensor, so calls chain.

mv

mvmv(self, vec: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

mv(self, vec: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

mv(vec, bias=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.mv.

nan_to_num

nan_to_numnan_to_num(self, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor

nan_to_num(self, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor

nan_to_num(nan=None, posinf=None, neginf=None) -> Tensor

Method form of clika_runtime.ops.nan_to_num.

nan_to_num_

nan_to_num_nan_to_num_(self, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> object

nan_to_num_(self, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> object

nan_to_num_(nan=None, posinf=None, neginf=None) -> Tensor

Method form of clika_runtime.ops.nan_to_num_.

Writes through this tensor's storage and returns this tensor, so calls chain.

nanmean

nanmeannanmean(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

nanmean(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

nanmean(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.nanmean.

nanmedian

nanmediannanmedian(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

nanmedian(self, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

nanmedian(dim=None, keepdim=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.nanmedian.

nanquantile

nanquantilenanquantile(self, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor

nanquantile(self, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor

nanquantile(q, dim=None, keepdim=False, interpolation='linear') -> Tensor

Method form of clika_runtime.ops.nanquantile.

nansum

nansumnansum(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

nansum(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

nansum(dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.nansum.

narrow

narrownarrow(self, dim: int, start: clika_runtime._core.ops.IndexBound, length: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor

narrow(self, dim: int, start: clika_runtime._core.ops.IndexBound, length: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor

narrow(dim, start, length) -> Tensor

Method form of clika_runtime.ops.narrow.

ndim_host

ndim_hostndim_host(self) -> clika_runtime._core.Tensor

ndim_host(self) -> clika_runtime._core.Tensor

ndim_host() -> Tensor

Method form of clika_runtime.ops.ndim_host.

ne

nene(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

ne(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

ne(other) -> Tensor

Method form of clika_runtime.ops.ne.

ne_

ne_ne_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

ne_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

ne_(other) -> Tensor

Method form of clika_runtime.ops.ne_.

Writes through this tensor's storage and returns this tensor, so calls chain.

neg

negneg(self) -> clika_runtime._core.Tensor

neg(self) -> clika_runtime._core.Tensor

neg() -> Tensor

Method form of clika_runtime.ops.neg.

neg_

neg_neg_(self) -> object

neg_(self) -> object

neg_() -> Tensor

Method form of clika_runtime.ops.neg_.

Writes through this tensor's storage and returns this tensor, so calls chain.

nll_loss

nll_lossnll_loss(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

nll_loss(self, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor

nll_loss(target, weight=None, ignore_index=None, reduction='mean') -> Tensor

Method form of clika_runtime.ops.nll_loss.

nonzero

nonzerononzero(self) -> clika_runtime._core.Tensor

nonzero(self) -> clika_runtime._core.Tensor

nonzero() -> Tensor

Method form of clika_runtime.ops.nonzero.

norm

normnorm(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

norm(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor

norm(p=2.0, dims=[], keepdim=False) -> Tensor

Method form of clika_runtime.ops.norm.

normal_

normal_normal_(self, mean: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), stddev: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

normal_(self, mean: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), stddev: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

normal_(mean=0, stddev=1, *, device=None) -> Tensor

Method form of clika_runtime.ops.normal_.

Writes through this tensor's storage and returns this tensor, so calls chain.

normalize

normalizenormalize(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor

normalize(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor

normalize(p=2.0, dim=1, eps=None) -> Tensor

Method form of clika_runtime.ops.normalize.

normalize_

normalize_normalize_(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> object

normalize_(self, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> object

normalize_(p=2.0, dim=1, eps=None) -> Tensor

Method form of clika_runtime.ops.normalize_.

Writes through this tensor's storage and returns this tensor, so calls chain.

numel

numelnumel(self) -> int

numel(self) -> int

numel() -> int

The element count.

numel_host

numel_hostnumel_host(self) -> clika_runtime._core.Tensor

numel_host(self) -> clika_runtime._core.Tensor

numel_host() -> Tensor

Method form of clika_runtime.ops.numel_host.

numpy

numpynumpy(self) -> numpy.ndarray[]

numpy(self) -> numpy.ndarray[]

numpy() -> numpy.ndarray

A ZERO-COPY numpy view over the tensor's cpu storage, taken once the tensor's pending work has settled; mutations alias both ways, and the array keeps the storage alive. A later in-place op on the tensor writes asynchronously on the calling thread's placement, so synchronize it (crt.synchronize(), or crt.synchronize(stream) for an explicit stream) or call numpy() again before reading the view. A device tensor raises TypeError (move it first: t.to('cpu').numpy()); a dtype with no numpy twin raises TypeError; use bytes() or cast(). Inside a tracing scope the value is not computed yet and this raises; clika_runtime.eval(tensor) computes it.

outer

outerouter(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

outer(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

outer(other) -> Tensor

Method form of clika_runtime.ops.outer.

pad

padpad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], mode: object = 'constant', value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], mode: object = 'constant', value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor

pad(pad, mode='constant', value=None) -> Tensor

Method form of clika_runtime.ops.pad.

pdist

pdistpdist(self, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor

pdist(self, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor

pdist(p=2.0) -> Tensor

Method form of clika_runtime.ops.pdist.

permute

permutepermute(self, *args) -> clika_runtime._core.Tensor

permute(self, *args) -> clika_runtime._core.Tensor

permute(*dims) -> Tensor

A view with the dimensions reordered; dims as separate arguments or one sequence.

pixel_shuffle

pixel_shufflepixel_shuffle(self, upscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor

pixel_shuffle(self, upscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor

pixel_shuffle(upscale_factor, mode='crd') -> Tensor

Method form of clika_runtime.ops.pixel_shuffle.

pixel_unshuffle

pixel_unshufflepixel_unshuffle(self, downscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor

pixel_unshuffle(self, downscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor

pixel_unshuffle(downscale_factor, mode='crd') -> Tensor

Method form of clika_runtime.ops.pixel_unshuffle.

pow

powpow(self, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

pow(self, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

pow(exponent) -> Tensor

Method form of clika_runtime.ops.pow.

pow_

pow_pow_(self, exponent: clika_runtime._core.ops.ScalarOrTensor) -> object

pow_(self, exponent: clika_runtime._core.ops.ScalarOrTensor) -> object

pow_(exponent) -> Tensor

Method form of clika_runtime.ops.pow_.

Writes through this tensor's storage and returns this tensor, so calls chain.

prelu

preluprelu(self, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

prelu(self, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

prelu(weight=None) -> Tensor

Method form of clika_runtime.ops.prelu.

prelu_

prelu_prelu_(self, weight: clika_runtime._core.Tensor | None = None) -> object

prelu_(self, weight: clika_runtime._core.Tensor | None = None) -> object

prelu_(weight=None) -> Tensor

Method form of clika_runtime.ops.prelu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

prod

prodprod(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

prod(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

prod(dims=[], keepdim=False, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.prod.

put

putput(self, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor

put(self, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor

put(index, source, accumulate=False) -> Tensor

Method form of clika_runtime.ops.put.

put_

put_put_(self, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> object

put_(self, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> object

put_(index, source, accumulate=False) -> Tensor

Method form of clika_runtime.ops.put_.

Writes through this tensor's storage and returns this tensor, so calls chain.

qconv1d_woq

qconv1d_woqqconv1d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv1d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv1d_woq(weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv1d_woq.

qconv2d_woq

qconv2d_woqqconv2d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv2d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv2d_woq(weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv2d_woq.

qconv3d_woq

qconv3d_woqqconv3d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv3d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv3d_woq(weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv3d_woq.

qconv_transpose1d_woq

qconv_transpose1d_woqqconv_transpose1d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose1d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose1d_woq(weight, bias=None, stride=[1], padding=[0], output_padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv_transpose1d_woq.

qconv_transpose2d_woq

qconv_transpose2d_woqqconv_transpose2d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose2d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose2d_woq(weight, bias=None, stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv_transpose2d_woq.

qconv_transpose3d_woq

qconv_transpose3d_woqqconv_transpose3d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose3d_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

qconv_transpose3d_woq(weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], output_padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.qconv_transpose3d_woq.

qconv_transpose_woq

qconv_transpose_woqqconv_transpose_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qconv_transpose_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qconv_transpose_woq(weight, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qconv_transpose_woq.

qconv_woq

qconv_woqqconv_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qconv_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qconv_woq(weight, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity', compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qconv_woq.

qdeform_conv_woq

qdeform_conv_woqqdeform_conv_woq(self, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qdeform_conv_woq(self, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qdeform_conv_woq(weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qdeform_conv_woq.

qlinear_woq

qlinear_woqqlinear_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qlinear_woq(self, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qlinear_woq(weight, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qlinear_woq.

qlinear_woq_

qlinear_woq_qlinear_woq_(self, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qlinear_woq_(self, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qlinear_woq_(weight, out, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qlinear_woq_.

qmatmul_woq

qmatmul_woqqmatmul_woq(self, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qmatmul_woq(self, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qmatmul_woq(other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qmatmul_woq.

qmatmul_woq_

qmatmul_woq_qmatmul_woq_(self, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qmatmul_woq_(self, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor

qmatmul_woq_(other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor

Method form of clika_runtime.ops.qmatmul_woq_.

qmoe_woq

qmoe_woqqmoe_woq(self, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

qmoe_woq(self, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

qmoe_woq(router_logits, fc1_experts, fc2_experts, top_k, fc1_bias=None, fc2_bias=None, fc3_experts=None, fc3_bias=None, e_score_correction_bias=None, router_weights=None, routing_mode=None, renormalize=None, n_group=None, topk_group=None, routed_scaling_factor=None, sparse_mixer_eps=None, apply_router_weight_on_input=None, *, activation=None, swiglu_fusion=None, swiglu_alpha=None, swiglu_beta=None, swiglu_limit=None, gelu_mode=None, shared_output=None) -> Tensor

Method form of clika_runtime.ops.qmoe_woq.

quantile

quantilequantile(self, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor

quantile(self, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor

quantile(q, dim=None, keepdim=False, interpolation='linear') -> Tensor

Method form of clika_runtime.ops.quantile.

quantize

quantizequantize(self, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.QTensor

quantize(self, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.QTensor

quantize(scale, zero_point=None, quant_axis=-1, *, out_dtype=Undefined, block_size=0) -> QTensor

Method form of clika_runtime.ops.quantize.

quantize_

quantize_quantize_(self, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor

quantize_(self, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor quantize(self, out: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, block_size: int = 0) -> clika_runtime._core.Tensor

Overloaded function.

1. quantize_(self, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor

quantize_(out) -> QTensor

Method form of clika_runtime.ops.quantize_.

2. quantize_(self, out: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, block_size: int = 0) -> clika_runtime._core.Tensor

quantize_(out, scale, zero_point=None, quant_axis=-1, block_size=0) -> Tensor

Method form of clika_runtime.ops.quantize_.

quantize_dequantize

quantize_dequantizequantize_dequantize(self, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, code_dtype: clika_runtime._core.DataType = DataType.Undefined, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.Tensor

quantize_dequantize(self, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, code_dtype: clika_runtime._core.DataType = DataType.Undefined, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.Tensor

quantize_dequantize(scale, zero_point=None, quant_axis=-1, *, code_dtype=Undefined, out_dtype=Undefined, block_size=0) -> Tensor

Method form of clika_runtime.ops.quantize_dequantize.

quantize_to_scheme

quantize_to_schemequantize_to_scheme(self, scheme: str, scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor

quantize_to_scheme(self, scheme: str, scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor

quantize_to_scheme(scheme, scale=None) -> QTensor

Method form of clika_runtime.ops.quantize_to_scheme.

quick_gelu

quick_geluquick_gelu(self) -> clika_runtime._core.Tensor

quick_gelu(self) -> clika_runtime._core.Tensor

quick_gelu() -> Tensor

Method form of clika_runtime.ops.quick_gelu.

rad2deg

rad2degrad2deg(self) -> clika_runtime._core.Tensor

rad2deg(self) -> clika_runtime._core.Tensor

rad2deg() -> Tensor

Method form of clika_runtime.ops.rad2deg.

rad2deg_

rad2deg_rad2deg_(self) -> object

rad2deg_(self) -> object

rad2deg_() -> Tensor

Method form of clika_runtime.ops.rad2deg_.

Writes through this tensor's storage and returns this tensor, so calls chain.

random_

random_random_(self, low: int | None = None, high: int | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

random_(self, low: int | None = None, high: int | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

random_(low=None, high=None, *, device=None) -> Tensor

Method form of clika_runtime.ops.random_.

Writes through this tensor's storage and returns this tensor, so calls chain.

reciprocal

reciprocalreciprocal(self) -> clika_runtime._core.Tensor

reciprocal(self) -> clika_runtime._core.Tensor

reciprocal() -> Tensor

Method form of clika_runtime.ops.reciprocal.

reciprocal_

reciprocal_reciprocal_(self) -> object

reciprocal_(self) -> object

reciprocal_() -> Tensor

Method form of clika_runtime.ops.reciprocal_.

Writes through this tensor's storage and returns this tensor, so calls chain.

reflect_pad

reflect_padreflect_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

reflect_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

reflect_pad(pad) -> Tensor

Method form of clika_runtime.ops.reflect_pad.

reglu

reglureglu(self) -> clika_runtime._core.Tensor

reglu(self) -> clika_runtime._core.Tensor

reglu() -> Tensor

Method form of clika_runtime.ops.reglu.

relu

relurelu(self) -> clika_runtime._core.Tensor

relu(self) -> clika_runtime._core.Tensor

relu() -> Tensor

Method form of clika_runtime.ops.relu.

relu6

relu6relu6(self) -> clika_runtime._core.Tensor

relu6(self) -> clika_runtime._core.Tensor

relu6() -> Tensor

Method form of clika_runtime.ops.relu6.

relu6_

relu6_relu6_(self) -> object

relu6_(self) -> object

relu6_() -> Tensor

Method form of clika_runtime.ops.relu6_.

Writes through this tensor's storage and returns this tensor, so calls chain.

relu_

relu_relu_(self) -> object

relu_(self) -> object

relu_() -> Tensor

Method form of clika_runtime.ops.relu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

remainder

remainderremainder(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

remainder(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

remainder(other) -> Tensor

Method form of clika_runtime.ops.remainder.

remainder_

remainder_remainder_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

remainder_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

remainder_(other) -> Tensor

Method form of clika_runtime.ops.remainder_.

Writes through this tensor's storage and returns this tensor, so calls chain.

renorm

renormrenorm(self, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor

renorm(self, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor

renorm(p, dim, maxnorm, eps=None) -> Tensor

Method form of clika_runtime.ops.renorm.

renorm_

renorm_renorm_(self, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> object

renorm_(self, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> object

renorm_(p, dim, maxnorm, eps=None) -> Tensor

Method form of clika_runtime.ops.renorm_.

Writes through this tensor's storage and returns this tensor, so calls chain.

repeat

repeatrepeat(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

repeat(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

repeat(sizes) -> Tensor

Method form of clika_runtime.ops.repeat.

repeat_interleave

repeat_interleaverepeat_interleave(self, repeats: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, output_size: int | None = None) -> clika_runtime._core.Tensor

repeat_interleave(self, repeats: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, output_size: int | None = None) -> clika_runtime._core.Tensor

repeat_interleave(repeats, dim=None, output_size=None) -> Tensor

Method form of clika_runtime.ops.repeat_interleave.

replicate_pad

replicate_padreplicate_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

replicate_pad(self, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

replicate_pad(pad) -> Tensor

Method form of clika_runtime.ops.replicate_pad.

resample

resampleresample(self, orig_freq: int, new_freq: int, lowpass_filter_width: int = 16, rolloff: float = 0.945, beta: float | None = None) -> clika_runtime._core.Tensor

resample(self, orig_freq: int, new_freq: int, lowpass_filter_width: int = 16, rolloff: float = 0.945, beta: float | None = None) -> clika_runtime._core.Tensor

resample(orig_freq, new_freq, lowpass_filter_width=16, rolloff=0.945, beta=None) -> Tensor

Method form of clika_runtime.ops.resample.

reshape

reshapereshape(self, *args) -> clika_runtime._core.Tensor

reshape(self, *args) -> clika_runtime._core.Tensor

reshape(*shape) -> Tensor

A view with the given dimensions (one may be -1); dims as separate arguments or one sequence. Copies only when the layout cannot express the new shape.

reshape_as

reshape_asreshape_as(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

reshape_as(self, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

reshape_as(other) -> Tensor

Method form of clika_runtime.ops.reshape_as.

rfft

rfftrfft(self, n_fft: int | None = None, normalized: bool = False) -> clika_runtime._core.Tensor

rfft(self, n_fft: int | None = None, normalized: bool = False) -> clika_runtime._core.Tensor

rfft(n_fft=None, normalized=False) -> Tensor

Method form of clika_runtime.ops.rfft.

rms_norm

rms_normrms_norm(self, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

rms_norm(self, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor

rms_norm(normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor

Method form of clika_runtime.ops.rms_norm.

roll

rollroll(self, shifts: collections.abc.Sequence[int], dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

roll(self, shifts: collections.abc.Sequence[int], dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

roll(shifts, dims=[]) -> Tensor

Method form of clika_runtime.ops.roll.

rot90

rot90rot90(self, k: int = 1, dims: collections.abc.Sequence[int] = [0, 1]) -> clika_runtime._core.Tensor

rot90(self, k: int = 1, dims: collections.abc.Sequence[int] = [0, 1]) -> clika_runtime._core.Tensor

rot90(k=1, dims=[0, 1]) -> Tensor

Method form of clika_runtime.ops.rot90.

rotary_embedding

rotary_embeddingrotary_embedding(self, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

rotary_embedding(self, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

rotary_embedding(position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> Tensor

Method form of clika_runtime.ops.rotary_embedding.

rotary_embedding_varlen

rotary_embedding_varlenrotary_embedding_varlen(self, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

rotary_embedding_varlen(self, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor

rotary_embedding_varlen(cu_seqlens, seqlens=None, position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> Tensor

Method form of clika_runtime.ops.rotary_embedding_varlen.

round

roundround(self, decimals: int = 0) -> clika_runtime._core.Tensor

round(self, decimals: int = 0) -> clika_runtime._core.Tensor

round(decimals=0) -> Tensor

Method form of clika_runtime.ops.round.

round_

round_round_(self, decimals: int = 0) -> object

round_(self, decimals: int = 0) -> object

round_(decimals=0) -> Tensor

Method form of clika_runtime.ops.round_.

Writes through this tensor's storage and returns this tensor, so calls chain.

rsqrt

rsqrtrsqrt(self) -> clika_runtime._core.Tensor

rsqrt(self) -> clika_runtime._core.Tensor

rsqrt() -> Tensor

Method form of clika_runtime.ops.rsqrt.

rsqrt_

rsqrt_rsqrt_(self) -> object

rsqrt_(self) -> object

rsqrt_() -> Tensor

Method form of clika_runtime.ops.rsqrt_.

Writes through this tensor's storage and returns this tensor, so calls chain.

scatter

scatterscatter(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

scatter(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

scatter(dim, index, src) -> Tensor

Method form of clika_runtime.ops.scatter.

scatter_

scatter_scatter_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> object

scatter_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> object

scatter_(dim, index, src) -> Tensor

Method form of clika_runtime.ops.scatter_.

Writes through this tensor's storage and returns this tensor, so calls chain.

scatter_add

scatter_addscatter_add(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor

scatter_add(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor

scatter_add(dim, index, src, deterministic=False) -> Tensor

Method form of clika_runtime.ops.scatter_add.

scatter_add_

scatter_add_scatter_add_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> object

scatter_add_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> object

scatter_add_(dim, index, src, deterministic=False) -> Tensor

Method form of clika_runtime.ops.scatter_add_.

Writes through this tensor's storage and returns this tensor, so calls chain.

scatter_reduce

scatter_reducescatter_reduce(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor

scatter_reduce(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor

scatter_reduce(dim, index, src, reduce, include_self=True, deterministic=False) -> Tensor

Method form of clika_runtime.ops.scatter_reduce.

scatter_reduce_

scatter_reduce_scatter_reduce_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> object

scatter_reduce_(self, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> object

scatter_reduce_(dim, index, src, reduce, include_self=True, deterministic=False) -> Tensor

Method form of clika_runtime.ops.scatter_reduce_.

Writes through this tensor's storage and returns this tensor, so calls chain.

schedule

scheduleschedule(self) -> object

schedule(self) -> object

schedule() -> Tensor

Submit this tensor's deferred computation (work recorded under a tracing scope) on its stream without waiting for it; a tensor with no deferred work is left as it is. synchronize() waits either way; returns the same tensor.

select

selectselect(self, dim: int, index: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor

select(self, dim: int, index: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor

select(dim, index) -> Tensor

Method form of clika_runtime.ops.select.

selu

seluselu(self) -> clika_runtime._core.Tensor

selu(self) -> clika_runtime._core.Tensor

selu() -> Tensor

Method form of clika_runtime.ops.selu.

selu_

selu_selu_(self) -> object

selu_(self) -> object

selu_() -> Tensor

Method form of clika_runtime.ops.selu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sgn

sgnsgn(self) -> clika_runtime._core.Tensor

sgn(self) -> clika_runtime._core.Tensor

sgn() -> Tensor

Method form of clika_runtime.ops.sgn.

sgn_

sgn_sgn_(self) -> object

sgn_(self) -> object

sgn_() -> Tensor

Method form of clika_runtime.ops.sgn_.

Writes through this tensor's storage and returns this tensor, so calls chain.

shape_host

shape_hostshape_host(self) -> clika_runtime._core.Tensor

shape_host(self) -> clika_runtime._core.Tensor shape_host(self, dim: int) -> clika_runtime._core.Tensor shape_host(self, start: int | None, end: int | None) -> clika_runtime._core.Tensor

Overloaded function.

1. shape_host(self) -> clika_runtime._core.Tensor

shape_host() -> Tensor

Method form of clika_runtime.ops.shape_host.

2. shape_host(self, dim: int) -> clika_runtime._core.Tensor

shape_host(dim) -> Tensor

Method form of clika_runtime.ops.shape_host.

3. shape_host(self, start: int | None, end: int | None) -> clika_runtime._core.Tensor

shape_host(start, end) -> Tensor

Method form of clika_runtime.ops.shape_host.

sigmoid

sigmoidsigmoid(self) -> clika_runtime._core.Tensor

sigmoid(self) -> clika_runtime._core.Tensor

sigmoid() -> Tensor

Method form of clika_runtime.ops.sigmoid.

sigmoid_

sigmoid_sigmoid_(self) -> object

sigmoid_(self) -> object

sigmoid_() -> Tensor

Method form of clika_runtime.ops.sigmoid_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sign

signsign(self) -> clika_runtime._core.Tensor

sign(self) -> clika_runtime._core.Tensor

sign() -> Tensor

Method form of clika_runtime.ops.sign.

sign_

sign_sign_(self) -> object

sign_(self) -> object

sign_() -> Tensor

Method form of clika_runtime.ops.sign_.

Writes through this tensor's storage and returns this tensor, so calls chain.

silu

silusilu(self) -> clika_runtime._core.Tensor

silu(self) -> clika_runtime._core.Tensor

silu() -> Tensor

Method form of clika_runtime.ops.silu.

silu_

silu_silu_(self) -> object

silu_(self) -> object

silu_() -> Tensor

Method form of clika_runtime.ops.silu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sin

sinsin(self) -> clika_runtime._core.Tensor

sin(self) -> clika_runtime._core.Tensor

sin() -> Tensor

Method form of clika_runtime.ops.sin.

sin_

sin_sin_(self) -> object

sin_(self) -> object

sin_() -> Tensor

Method form of clika_runtime.ops.sin_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sinc

sincsinc(self) -> clika_runtime._core.Tensor

sinc(self) -> clika_runtime._core.Tensor

sinc() -> Tensor

Method form of clika_runtime.ops.sinc.

sinc_

sinc_sinc_(self) -> object

sinc_(self) -> object

sinc_() -> Tensor

Method form of clika_runtime.ops.sinc_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sinh

sinhsinh(self) -> clika_runtime._core.Tensor

sinh(self) -> clika_runtime._core.Tensor

sinh() -> Tensor

Method form of clika_runtime.ops.sinh.

sinh_

sinh_sinh_(self) -> object

sinh_(self) -> object

sinh_() -> Tensor

Method form of clika_runtime.ops.sinh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

size

sizesize(self, dim: int | None = None) -> object

size(self, dim: int | None = None) -> object

size(dim=None) -> Size | int

The shape as a Size, or one dimension's extent.

slice

sliceslice(self, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor

slice(self, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor slice(self, dim: collections.abc.Sequence[int], start: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], end: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], step: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = []) -> clika_runtime._core.Tensor

Overloaded function.

1. slice(self, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor

slice(dim, start=None, end=None, step=1) -> Tensor

Method form of clika_runtime.ops.slice.

2. slice(self, dim: collections.abc.Sequence[int], start: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], end: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], step: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = []) -> clika_runtime._core.Tensor

slice(dim, start=[], end=[], step=[]) -> Tensor

Method form of clika_runtime.ops.slice.

smooth_l1_loss

smooth_l1_losssmooth_l1_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', beta: float = 1.0) -> clika_runtime._core.Tensor

smooth_l1_loss(self, target: clika_runtime._core.Tensor, reduction: object = 'mean', beta: float = 1.0) -> clika_runtime._core.Tensor

smooth_l1_loss(target, reduction='mean', beta=1.0) -> Tensor

Method form of clika_runtime.ops.smooth_l1_loss.

snake

snakesnake(self, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor

snake(self, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor

snake(alpha=None, beta=None, eps=1e-9) -> Tensor

Method form of clika_runtime.ops.snake.

snake_

snake_snake_(self, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> object

snake_(self, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> object

snake_(alpha=None, beta=None, eps=1e-9) -> Tensor

Method form of clika_runtime.ops.snake_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softcap_logits

softcap_logitssoftcap_logits(self, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

softcap_logits(self, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

softcap_logits(cap) -> Tensor

Method form of clika_runtime.ops.softcap_logits.

softcap_logits_

softcap_logits_softcap_logits_(self, cap: clika_runtime._core.ops.ScalarOrTensor) -> object

softcap_logits_(self, cap: clika_runtime._core.ops.ScalarOrTensor) -> object

softcap_logits_(cap) -> Tensor

Method form of clika_runtime.ops.softcap_logits_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softmax

softmaxsoftmax(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

softmax(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

softmax(dim=-1, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.softmax.

softmax_

softmax_softmax_(self, dim: int = -1) -> object

softmax_(self, dim: int = -1) -> object

softmax_(dim=-1) -> Tensor

Method form of clika_runtime.ops.softmax_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softmin

softminsoftmin(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

softmin(self, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

softmin(dim=-1, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.softmin.

softmin_

softmin_softmin_(self, dim: int = -1) -> object

softmin_(self, dim: int = -1) -> object

softmin_(dim=-1) -> Tensor

Method form of clika_runtime.ops.softmin_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softplus

softplussoftplus(self, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor

softplus(self, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor

softplus(beta=1.0, threshold=20.0) -> Tensor

Method form of clika_runtime.ops.softplus.

softplus_

softplus_softplus_(self, beta: float = 1.0, threshold: float = 20.0) -> object

softplus_(self, beta: float = 1.0, threshold: float = 20.0) -> object

softplus_(beta=1.0, threshold=20.0) -> Tensor

Method form of clika_runtime.ops.softplus_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softshrink

softshrinksoftshrink(self, lambd: float = 0.5) -> clika_runtime._core.Tensor

softshrink(self, lambd: float = 0.5) -> clika_runtime._core.Tensor

softshrink(lambd=0.5) -> Tensor

Method form of clika_runtime.ops.softshrink.

softshrink_

softshrink_softshrink_(self, lambd: float = 0.5) -> object

softshrink_(self, lambd: float = 0.5) -> object

softshrink_(lambd=0.5) -> Tensor

Method form of clika_runtime.ops.softshrink_.

Writes through this tensor's storage and returns this tensor, so calls chain.

softsign

softsignsoftsign(self) -> clika_runtime._core.Tensor

softsign(self) -> clika_runtime._core.Tensor

softsign() -> Tensor

Method form of clika_runtime.ops.softsign.

softsign_

softsign_softsign_(self) -> object

softsign_(self) -> object

softsign_() -> Tensor

Method form of clika_runtime.ops.softsign_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sort

sortsort(self, dim: int = -1, descending: bool = False, stable: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

sort(self, dim: int = -1, descending: bool = False, stable: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

sort(dim=-1, descending=False, stable=False) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.sort.

split_by_size

split_by_sizesplit_by_size(self, chunk_size: int, dim: int = 0) -> list[clika_runtime._core.Tensor]

split_by_size(self, chunk_size: int, dim: int = 0) -> list[clika_runtime._core.Tensor]

split_by_size(chunk_size, dim=0) -> list[Tensor]

Method form of clika_runtime.ops.split_by_size.

split_with_sizes

split_with_sizessplit_with_sizes(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dim: int = 0) -> list[clika_runtime._core.Tensor]

split_with_sizes(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dim: int = 0) -> list[clika_runtime._core.Tensor]

split_with_sizes(sizes, dim=0) -> list[Tensor]

Method form of clika_runtime.ops.split_with_sizes.

sqrt

sqrtsqrt(self) -> clika_runtime._core.Tensor

sqrt(self) -> clika_runtime._core.Tensor

sqrt() -> Tensor

Method form of clika_runtime.ops.sqrt.

sqrt_

sqrt_sqrt_(self) -> object

sqrt_(self) -> object

sqrt_() -> Tensor

Method form of clika_runtime.ops.sqrt_.

Writes through this tensor's storage and returns this tensor, so calls chain.

square

squaresquare(self) -> clika_runtime._core.Tensor

square(self) -> clika_runtime._core.Tensor

square() -> Tensor

Method form of clika_runtime.ops.square.

square_

square_square_(self) -> object

square_(self) -> object

square_() -> Tensor

Method form of clika_runtime.ops.square_.

Writes through this tensor's storage and returns this tensor, so calls chain.

squeeze

squeezesqueeze(self, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

squeeze(self, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor

squeeze(dim=[]) -> Tensor

Method form of clika_runtime.ops.squeeze.

squeeze_

squeeze_squeeze_(self, dim: collections.abc.Sequence[int] = []) -> object

squeeze_(self, dim: collections.abc.Sequence[int] = []) -> object

squeeze_(dim=[]) -> Tensor

Method form of clika_runtime.ops.squeeze_.

Writes through this tensor's storage and returns this tensor, so calls chain.

ssd_update

ssd_updatessd_update(self, dt: clika_runtime._core.Tensor, a_rate: clika_runtime._core.Tensor, b_mat: clika_runtime._core.Tensor, c_mat: clika_runtime._core.Tensor, d_skip: clika_runtime._core.Tensor | None, dt_bias: clika_runtime._core.Tensor | None, gate: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, dt_softplus: bool = False) -> clika_runtime._core.Tensor

ssd_update(self, dt: clika_runtime._core.Tensor, a_rate: clika_runtime._core.Tensor, b_mat: clika_runtime._core.Tensor, c_mat: clika_runtime._core.Tensor, d_skip: clika_runtime._core.Tensor | None, dt_bias: clika_runtime._core.Tensor | None, gate: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, dt_softplus: bool = False) -> clika_runtime._core.Tensor

ssd_update(dt, a_rate, b_mat, c_mat, d_skip, dt_bias, gate, state, seq_lens=None, slot_ids=None, dt_softplus=False) -> Tensor

Method form of clika_runtime.ops.ssd_update.

std

stdstd(self, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor

std(self, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor

std(dims=[], correction=1, keepdim=False) -> Tensor

Method form of clika_runtime.ops.std.

stft

stftstft(self, n_fft: int, hop_length: int | None = None, win_length: int | None = None, window: clika_runtime._core.Tensor | None = None, center: bool = True, pad_mode: object = 'reflect', normalized: bool = False, onesided: bool = True) -> clika_runtime._core.Tensor

stft(self, n_fft: int, hop_length: int | None = None, win_length: int | None = None, window: clika_runtime._core.Tensor | None = None, center: bool = True, pad_mode: object = 'reflect', normalized: bool = False, onesided: bool = True) -> clika_runtime._core.Tensor

stft(n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True) -> Tensor

Method form of clika_runtime.ops.stft.

stride

stridestride(self, dim: int | None = None) -> object

stride(self, dim: int | None = None) -> object

stride(dim=None) -> tuple[int, ...] | int

The element strides, or one dimension's stride.

sub

subsub(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

sub(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor

sub(other, alpha=1.0, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.sub.

sub_

sub_sub_(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> object

sub_(self, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> object

sub_(other, alpha=1.0, *, activation='identity') -> Tensor

Method form of clika_runtime.ops.sub_.

Writes through this tensor's storage and returns this tensor, so calls chain.

sum

sumsum(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

sum(self, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor

sum(dims=[], keepdim=False, *, dtype=Undefined) -> Tensor

Method form of clika_runtime.ops.sum.

swiglu

swigluswiglu(self, alpha: float = 1.0, beta: float = 0.0, limit: float = inf) -> clika_runtime._core.Tensor

swiglu(self, alpha: float = 1.0, beta: float = 0.0, limit: float = inf) -> clika_runtime._core.Tensor

swiglu(alpha=1.0, beta=0.0, limit=None) -> Tensor

Method form of clika_runtime.ops.swiglu.

synchronize

synchronizesynchronize(self) -> object

synchronize(self) -> object

synchronize() -> Tensor

Wait until this tensor's pending work has completed (the point where an asynchronous failure surfaces); returns the same tensor.

t

tt(self) -> clika_runtime._core.Tensor

t(self) -> clika_runtime._core.Tensor

t() -> Tensor

The 2-D transpose (a view); a 0-d or 1-d tensor is returned as is.

take

taketake(self, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

take(self, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor

take(index) -> Tensor

Method form of clika_runtime.ops.take.

take_along_dim

take_along_dimtake_along_dim(self, index: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor

take_along_dim(self, index: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor

take_along_dim(index, dim=None) -> Tensor

Method form of clika_runtime.ops.take_along_dim.

tan

tantan(self) -> clika_runtime._core.Tensor

tan(self) -> clika_runtime._core.Tensor

tan() -> Tensor

Method form of clika_runtime.ops.tan.

tan_

tan_tan_(self) -> object

tan_(self) -> object

tan_() -> Tensor

Method form of clika_runtime.ops.tan_.

Writes through this tensor's storage and returns this tensor, so calls chain.

tanh

tanhtanh(self) -> clika_runtime._core.Tensor

tanh(self) -> clika_runtime._core.Tensor

tanh() -> Tensor

Method form of clika_runtime.ops.tanh.

tanh_

tanh_tanh_(self) -> object

tanh_(self) -> object

tanh_() -> Tensor

Method form of clika_runtime.ops.tanh_.

Writes through this tensor's storage and returns this tensor, so calls chain.

tensordot

tensordottensordot(self, other: clika_runtime._core.Tensor, dims_a: collections.abc.Sequence[int], dims_b: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

tensordot(self, other: clika_runtime._core.Tensor, dims_a: collections.abc.Sequence[int], dims_b: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

tensordot(other, dims_a, dims_b) -> Tensor

Method form of clika_runtime.ops.tensordot.

threshold

thresholdthreshold(self, threshold: float, value: float) -> clika_runtime._core.Tensor

threshold(self, threshold: float, value: float) -> clika_runtime._core.Tensor

threshold(threshold, value) -> Tensor

Method form of clika_runtime.ops.threshold.

threshold_

threshold_threshold_(self, threshold: float, value: float) -> object

threshold_(self, threshold: float, value: float) -> object

threshold_(threshold, value) -> Tensor

Method form of clika_runtime.ops.threshold_.

Writes through this tensor's storage and returns this tensor, so calls chain.

tile

tiletile(self, dims: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

tile(self, dims: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor

tile(dims) -> Tensor

Method form of clika_runtime.ops.tile.

to

toto(self, target: object, dtype: object | None = None) -> object

to(self, target: object, dtype: object | None = None) -> object

to(dtype) -> Tensor | to(device, dtype=None) -> Tensor

Convert to a dtype, or move to a device (a Device, a device string or a Stream), optionally converting too. A no-op move returns the same storage.

tolist

tolisttolist(self) -> object

tolist(self) -> object

tolist() -> list | bool | int | float

The values as nested Python lists (a Python number for a 0-d tensor); waits for the value. Inside a tracing scope the value is not computed yet and this raises; clika_runtime.eval(tensor) computes it.

topk

topktopk(self, k: int, dim: int = -1, largest: bool = True, sorted: bool = True) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

topk(self, k: int, dim: int = -1, largest: bool = True, sorted: bool = True) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]

topk(k, dim=-1, largest=True, sorted=True) -> tuple[Tensor, Tensor]

Method form of clika_runtime.ops.topk.

trace

tracetrace(self) -> clika_runtime._core.Tensor

trace(self) -> clika_runtime._core.Tensor

trace() -> Tensor

Method form of clika_runtime.ops.trace.

transpose

transposetranspose(self, dim0: int, dim1: int) -> clika_runtime._core.Tensor

transpose(self, dim0: int, dim1: int) -> clika_runtime._core.Tensor

transpose(dim0, dim1) -> Tensor

Method form of clika_runtime.ops.transpose.

tril

triltril(self, diagonal: int = 0) -> clika_runtime._core.Tensor

tril(self, diagonal: int = 0) -> clika_runtime._core.Tensor

tril(diagonal=0) -> Tensor

Method form of clika_runtime.ops.tril.

tril_

tril_tril_(self, diagonal: int = 0) -> object

tril_(self, diagonal: int = 0) -> object

tril_(diagonal=0) -> Tensor

Method form of clika_runtime.ops.tril_.

Writes through this tensor's storage and returns this tensor, so calls chain.

triu

triutriu(self, diagonal: int = 0) -> clika_runtime._core.Tensor

triu(self, diagonal: int = 0) -> clika_runtime._core.Tensor

triu(diagonal=0) -> Tensor

Method form of clika_runtime.ops.triu.

triu_

triu_triu_(self, diagonal: int = 0) -> object

triu_(self, diagonal: int = 0) -> object

triu_(diagonal=0) -> Tensor

Method form of clika_runtime.ops.triu_.

Writes through this tensor's storage and returns this tensor, so calls chain.

trunc

trunctrunc(self) -> clika_runtime._core.Tensor

trunc(self) -> clika_runtime._core.Tensor

trunc() -> Tensor

Method form of clika_runtime.ops.trunc.

trunc_

trunc_trunc_(self) -> object

trunc_(self) -> object

trunc_() -> Tensor

Method form of clika_runtime.ops.trunc_.

Writes through this tensor's storage and returns this tensor, so calls chain.

unflatten

unflattenunflatten(self, dim: int, sizes: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

unflatten(self, dim: int, sizes: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor

unflatten(dim, sizes) -> Tensor

Method form of clika_runtime.ops.unflatten.

unfold

unfoldunfold(self, kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor

unfold(self, kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor

unfold(kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor

Method form of clika_runtime.ops.unfold.

uniform_

uniform_uniform_(self, low: float = 0.0, high: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

uniform_(self, low: float = 0.0, high: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

uniform_(low=0.0, high=1.0, *, device=None) -> Tensor

Method form of clika_runtime.ops.uniform_.

Writes through this tensor's storage and returns this tensor, so calls chain.

unique

uniqueunique(self, sorted: bool = True, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

unique(self, sorted: bool = True, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

unique(sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]

Method form of clika_runtime.ops.unique.

unique_consecutive

unique_consecutiveunique_consecutive(self, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

unique_consecutive(self, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]

unique_consecutive(return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]

Method form of clika_runtime.ops.unique_consecutive.

unsqueeze

unsqueezeunsqueeze(self, dim: int) -> clika_runtime._core.Tensor

unsqueeze(self, dim: int) -> clika_runtime._core.Tensor

unsqueeze(dim) -> Tensor

Method form of clika_runtime.ops.unsqueeze.

unsqueeze_

unsqueeze_unsqueeze_(self, dim: int) -> object

unsqueeze_(self, dim: int) -> object

unsqueeze_(dim) -> Tensor

Method form of clika_runtime.ops.unsqueeze_.

Writes through this tensor's storage and returns this tensor, so calls chain.

upsample_bicubic2d

upsample_bicubic2dupsample_bicubic2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_bicubic2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_bicubic2d(sizes=[], scale_factors=[], align_corners=False) -> Tensor

Method form of clika_runtime.ops.upsample_bicubic2d.

upsample_bilinear2d

upsample_bilinear2dupsample_bilinear2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_bilinear2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_bilinear2d(sizes=[], scale_factors=[], align_corners=False) -> Tensor

Method form of clika_runtime.ops.upsample_bilinear2d.

upsample_linear1d

upsample_linear1dupsample_linear1d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_linear1d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_linear1d(sizes=[], scale_factors=[], align_corners=False) -> Tensor

Method form of clika_runtime.ops.upsample_linear1d.

upsample_nearest1d

upsample_nearest1dupsample_nearest1d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest1d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest1d(sizes=[], scale_factors=[]) -> Tensor

Method form of clika_runtime.ops.upsample_nearest1d.

upsample_nearest2d

upsample_nearest2dupsample_nearest2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest2d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest2d(sizes=[], scale_factors=[]) -> Tensor

Method form of clika_runtime.ops.upsample_nearest2d.

upsample_nearest3d

upsample_nearest3dupsample_nearest3d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest3d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor

upsample_nearest3d(sizes=[], scale_factors=[]) -> Tensor

Method form of clika_runtime.ops.upsample_nearest3d.

upsample_trilinear3d

upsample_trilinear3dupsample_trilinear3d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_trilinear3d(self, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor

upsample_trilinear3d(sizes=[], scale_factors=[], align_corners=False) -> Tensor

Method form of clika_runtime.ops.upsample_trilinear3d.

var

varvar(self, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor

var(self, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor

var(dims=[], correction=1, keepdim=False) -> Tensor

Method form of clika_runtime.ops.var.

view

viewview(self, *args) -> clika_runtime._core.Tensor

view(self, *args) -> clika_runtime._core.Tensor

view(*shape) -> Tensor

The same as reshape: a view with the given dimensions, dims as separate arguments or one sequence.

xlog1py

xlog1pyxlog1py(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

xlog1py(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

xlog1py(other) -> Tensor

Method form of clika_runtime.ops.xlog1py.

xlogy

xlogyxlogy(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

xlogy(self, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor

xlogy(other) -> Tensor

Method form of clika_runtime.ops.xlogy.

xlogy_

xlogy_xlogy_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

xlogy_(self, other: clika_runtime._core.ops.ScalarOrTensor) -> object

xlogy_(other) -> Tensor

Method form of clika_runtime.ops.xlogy_.

Writes through this tensor's storage and returns this tensor, so calls chain.

zero_

zero_zero_(self, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

zero_(self, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> object

zero_(*, device=None) -> Tensor

Method form of clika_runtime.ops.zero_.

Writes through this tensor's storage and returns this tensor, so calls chain.