clika_runtime functions
abs
abs(*args, **kwargs)
abs(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
abs(input) -> Tensor
The abs operator.
abs_
abs_(*args, **kwargs)
abs_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
abs_(self) -> Tensor
The abs_ operator.
acos
acos(*args, **kwargs)
acos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
acos(input) -> Tensor
The acos operator.
acos_
acos_(*args, **kwargs)
acos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
acos_(self) -> Tensor
The acos_ operator.
acosh
acosh(*args, **kwargs)
acosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
acosh(input) -> Tensor
The acosh operator.
acosh_
acosh_(*args, **kwargs)
acosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
acosh_(self) -> Tensor
The acosh_ operator.
adaptive_avg_pool
adaptive_avg_pool(*args, **kwargs)
adaptive_avg_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_avg_pool(input, output_size) -> Tensor
The adaptive_avg_pool operator.
adaptive_avg_pool1d
adaptive_avg_pool1d(*args, **kwargs)
adaptive_avg_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_avg_pool1d(input, output_size) -> Tensor
The adaptive_avg_pool1d operator.
adaptive_avg_pool2d
adaptive_avg_pool2d(*args, **kwargs)
adaptive_avg_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_avg_pool2d(input, output_size) -> Tensor
The adaptive_avg_pool2d operator.
adaptive_avg_pool3d
adaptive_avg_pool3d(*args, **kwargs)
adaptive_avg_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_avg_pool3d(input, output_size) -> Tensor
The adaptive_avg_pool3d operator.
adaptive_max_pool
adaptive_max_pool(*args, **kwargs)
adaptive_max_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_max_pool(input, output_size) -> Tensor
The adaptive_max_pool operator.
adaptive_max_pool1d
adaptive_max_pool1d(*args, **kwargs)
adaptive_max_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_max_pool1d(input, output_size) -> Tensor
The adaptive_max_pool1d operator.
adaptive_max_pool2d
adaptive_max_pool2d(*args, **kwargs)
adaptive_max_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_max_pool2d(input, output_size) -> Tensor
The adaptive_max_pool2d operator.
adaptive_max_pool3d
adaptive_max_pool3d(*args, **kwargs)
adaptive_max_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
adaptive_max_pool3d(input, output_size) -> Tensor
The adaptive_max_pool3d operator.
add
add(*args, **kwargs)
add(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor add(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
add(input, other, alpha=1.0, *, activation='identity') -> Tensor
The add operator.
add_
add_(*args, **kwargs)
add_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
add_(self, other, alpha=1.0, *, activation='identity') -> Tensor
The add_ operator.
add_layer_norm
add_layer_norm(*args, **kwargs)
add_layer_norm(input: clika_runtime._core.Tensor, 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(input, residual=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor]
The add_layer_norm operator.
add_rms_norm
add_rms_norm(*args, **kwargs)
add_rms_norm(input: clika_runtime._core.Tensor, 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(input, residual=None, residual2=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor]
The add_rms_norm operator.
all
all(*args, **kwargs)
all(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
all(input, dims=[], keepdim=False) -> Tensor
The all operator.
allclose
allclose(*args, **kwargs)
allclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor
allclose(input, other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor
The allclose operator.
amax
amax(*args, **kwargs)
amax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
amax(input, dims=[], keepdim=False) -> Tensor
The amax operator.
amin
amin(*args, **kwargs)
amin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
amin(input, dims=[], keepdim=False) -> Tensor
The amin operator.
aminmax
aminmax(*args, **kwargs)
aminmax(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
aminmax(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor]
The aminmax operator.
any
any(*args, **kwargs)
any(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
any(input, dims=[], keepdim=False) -> Tensor
The any operator.
arange
arange(start: 'Number', end: 'Number | None' = None, step: 'Number' = 1, *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
arange(start, end=None, step=1, *, dtype=None, device=None) -> Tensor
Evenly spaced values in [start, end); arange(n) counts from zero.
All-integer arguments produce an int64 tensor, any float argument
one of the default dtype; dtype overrides either.
argmax
argmax(*args, **kwargs)
argmax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor
argmax(input, dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor
The argmax operator.
argmin
argmin(*args, **kwargs)
argmin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor
argmin(input, dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor
The argmin operator.
argsort
argsort(*args, **kwargs)
argsort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> clika_runtime._core.Tensor
argsort(input, dim=-1, descending=False, stable=False) -> Tensor
The argsort operator.
as_tensor
as_tensor(data: 'object', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
as_tensor(data, dtype=None, device=None) -> Tensor
A tensor over data without a copy when none is needed: a Tensor
passes through, a c-contiguous writable numpy array on the CPU is
borrowed (the tensor shares the array's memory, as
:func:~clika_runtime.from_numpy does), and anything else enters
through :func:tensor. A requested dtype or device that
differs from the data's converts, which copies.
asin
asin(*args, **kwargs)
asin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
asin(input) -> Tensor
The asin operator.
asin_
asin_(*args, **kwargs)
asin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
asin_(self) -> Tensor
The asin_ operator.
asinh
asinh(*args, **kwargs)
asinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
asinh(input) -> Tensor
The asinh operator.
asinh_
asinh_(*args, **kwargs)
asinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
asinh_(self) -> Tensor
The asinh_ operator.
async_eval
async_eval(*trees: 'Any') -> 'None'
async_eval(*trees) -> None
Submit the deferred work behind every tensor in the trees (work recorded
under a tracing scope) on its stream and return without waiting; a later
read, or :func:eval, settles the values. A tensor with no deferred work
is left as it is.
atan
atan(*args, **kwargs)
atan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atan(input) -> Tensor
The atan operator.
atan2
atan2(*args, **kwargs)
atan2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atan2(input, other) -> Tensor
The atan2 operator.
atan2_
atan2_(*args, **kwargs)
atan2_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atan2_(self, other) -> Tensor
The atan2_ operator.
atan_
atan_(*args, **kwargs)
atan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atan_(self) -> Tensor
The atan_ operator.
atanh
atanh(*args, **kwargs)
atanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atanh(input) -> Tensor
The atanh operator.
atanh_
atanh_(*args, **kwargs)
atanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atanh_(self) -> Tensor
The atanh_ operator.
atleast_1d
atleast_1d(*args, **kwargs)
atleast_1d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atleast_1d(input) -> Tensor
The atleast_1d operator.
atleast_2d
atleast_2d(*args, **kwargs)
atleast_2d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atleast_2d(input) -> Tensor
The atleast_2d operator.
atleast_3d
atleast_3d(*args, **kwargs)
atleast_3d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
atleast_3d(input) -> Tensor
The atleast_3d operator.
attention
attention(*args, **kwargs)
attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor
attention(query, key, value, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, k_scale=None, v_scale=None) -> Tensor
The attention operator.
attention_over_cache
attention_over_cache(*args, **kwargs)
attention_over_cache(query: clika_runtime._core.Tensor, cache_key: clika_runtime._core.Tensor, cache_value: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
attention_over_cache(query, cache_key, cache_value, kvcache_start, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, k_scale=None, v_scale=None, head_sink=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None) -> Tensor
The attention_over_cache operator.
attention_varlen
attention_varlen(*args, **kwargs)
attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor
attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, k_scale=None, v_scale=None) -> Tensor
The attention_varlen operator.
avg_pool
avg_pool(*args, **kwargs)
avg_pool(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) -> Tensor
The avg_pool operator.
avg_pool1d
avg_pool1d(*args, **kwargs)
avg_pool1d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0], ceil_mode=False, count_include_pad=True) -> Tensor
The avg_pool1d operator.
avg_pool2d
avg_pool2d(*args, **kwargs)
avg_pool2d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
The avg_pool2d operator.
avg_pool3d
avg_pool3d(*args, **kwargs)
avg_pool3d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
The avg_pool3d operator.
batch_norm
batch_norm(*args, **kwargs)
batch_norm(input: clika_runtime._core.Tensor, 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(input, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor
The batch_norm operator.
bernoulli
bernoulli(*args, **kwargs)
bernoulli(probabilities: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
bernoulli(probabilities, *, device=None) -> Tensor
The bernoulli operator.
bernoulli_
bernoulli_(*args, **kwargs)
bernoulli_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> None
bernoulli_(self, *, device=None) -> None
The bernoulli_ operator.
binary_cross_entropy
binary_cross_entropy(*args, **kwargs)
binary_cross_entropy(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor
binary_cross_entropy(input, target, weight=None, reduction='mean') -> Tensor
The binary_cross_entropy operator.
binary_cross_entropy_with_logits
binary_cross_entropy_with_logits(*args, **kwargs)
binary_cross_entropy_with_logits(input: clika_runtime._core.Tensor, 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(input, target, weight=None, reduction='mean', pos_weight=None) -> Tensor
The binary_cross_entropy_with_logits operator.
bincount
bincount(*args, **kwargs)
bincount(input: clika_runtime._core.Tensor, weights: clika_runtime._core.Tensor | None = None, minlength: int = 0) -> clika_runtime._core.Tensor
bincount(input, weights=None, minlength=0) -> Tensor
The bincount operator.
bitwise_and
bitwise_and(*args, **kwargs)
bitwise_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_and(input, other) -> Tensor
The bitwise_and operator.
bitwise_and_
bitwise_and_(*args, **kwargs)
bitwise_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_and_(self, other) -> Tensor
The bitwise_and_ operator.
bitwise_left_shift
bitwise_left_shift(*args, **kwargs)
bitwise_left_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_left_shift(input, other) -> Tensor
The bitwise_left_shift operator.
bitwise_left_shift_
bitwise_left_shift_(*args, **kwargs)
bitwise_left_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_left_shift_(self, other) -> Tensor
The bitwise_left_shift_ operator.
bitwise_not
bitwise_not(*args, **kwargs)
bitwise_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
bitwise_not(input) -> Tensor
The bitwise_not operator.
bitwise_not_
bitwise_not_(*args, **kwargs)
bitwise_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
bitwise_not_(self) -> Tensor
The bitwise_not_ operator.
bitwise_or
bitwise_or(*args, **kwargs)
bitwise_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_or(input, other) -> Tensor
The bitwise_or operator.
bitwise_or_
bitwise_or_(*args, **kwargs)
bitwise_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_or_(self, other) -> Tensor
The bitwise_or_ operator.
bitwise_right_shift
bitwise_right_shift(*args, **kwargs)
bitwise_right_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_right_shift(input, other) -> Tensor
The bitwise_right_shift operator.
bitwise_right_shift_
bitwise_right_shift_(*args, **kwargs)
bitwise_right_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_right_shift_(self, other) -> Tensor
The bitwise_right_shift_ operator.
bitwise_xor
bitwise_xor(*args, **kwargs)
bitwise_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_xor(input, other) -> Tensor
The bitwise_xor operator.
bitwise_xor_
bitwise_xor_(*args, **kwargs)
bitwise_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
bitwise_xor_(self, other) -> Tensor
The bitwise_xor_ operator.
bmm
bmm(*args, **kwargs)
bmm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
bmm(input, other, bias=None, *, activation=None) -> Tensor
The bmm operator.
broadcast_tensors
broadcast_tensors(*args, **kwargs)
broadcast_tensors(tensors: collections.abc.Sequence[clika_runtime._core.Tensor]) -> list[clika_runtime._core.Tensor]
broadcast_tensors(tensors) -> list[Tensor]
The broadcast_tensors operator.
broadcast_to
broadcast_to(*args, **kwargs)
broadcast_to(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
broadcast_to(input, shape) -> Tensor
The broadcast_to operator.
bucketize
bucketize(*args, **kwargs)
bucketize(input: clika_runtime._core.Tensor, boundaries: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False) -> clika_runtime._core.Tensor
bucketize(input, boundaries, out_int32=False, right=False) -> Tensor
The bucketize operator.
cast
cast(*args, **kwargs)
cast(input: clika_runtime._core.Tensor, target: clika_runtime._core.DataType, force_copy: bool = False) -> clika_runtime._core.Tensor
cast(input, target, force_copy=False) -> Tensor
The cast operator.
cast_like
cast_like(*args, **kwargs)
cast_like(input: clika_runtime._core.Tensor, reference: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
cast_like(input, reference) -> Tensor
The cast_like operator.
causal_conv_update
causal_conv_update(*args, **kwargs)
causal_conv_update(input: clika_runtime._core.Tensor, 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(input, weight, bias, state, seq_lens=None, slot_ids=None, *, activation=None) -> Tensor
The causal_conv_update operator.
cdist
cdist(*args, **kwargs)
cdist(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor
cdist(x1, x2, p=2.0) -> Tensor
The cdist operator.
ceil
ceil(*args, **kwargs)
ceil(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
ceil(input) -> Tensor
The ceil operator.
ceil_
ceil_(*args, **kwargs)
ceil_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
ceil_(self) -> Tensor
The ceil_ operator.
celu
celu(*args, **kwargs)
celu(input: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor
celu(input, alpha=1.0) -> Tensor
The celu operator.
celu_
celu_(*args, **kwargs)
celu_(self: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor
celu_(self, alpha=1.0) -> Tensor
The celu_ operator.
chunk
chunk(*args, **kwargs)
chunk(input: clika_runtime._core.Tensor, num_chunks: int, dim: int = 0) -> list[clika_runtime._core.Tensor]
chunk(input, num_chunks, dim=0) -> list[Tensor]
The chunk operator.
circular_pad
circular_pad(*args, **kwargs)
circular_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
circular_pad(input, pad) -> Tensor
The circular_pad operator.
clamp
clamp(*args, **kwargs)
clamp(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor
clamp(input, min=None, max=None) -> Tensor
The clamp operator.
clamp_
clamp_(*args, **kwargs)
clamp_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor
clamp_(self, min=None, max=None) -> Tensor
The clamp_ operator.
clamp_max
clamp_max(*args, **kwargs)
clamp_max(input: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
clamp_max(input, max) -> Tensor
The clamp_max operator.
clamp_max_
clamp_max_(*args, **kwargs)
clamp_max_(self: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
clamp_max_(self, max) -> Tensor
The clamp_max_ operator.
clamp_min
clamp_min(*args, **kwargs)
clamp_min(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
clamp_min(input, min) -> Tensor
The clamp_min operator.
clamp_min_
clamp_min_(*args, **kwargs)
clamp_min_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
clamp_min_(self, min) -> Tensor
The clamp_min_ operator.
clone
clone(*args, **kwargs)
clone(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
clone(src) -> Tensor
The clone operator.
compile
compile(fn: 'Callable[..., Any]', signature: 'Sequence[TensorSpec]' = (), *, dynamic: 'bool | None' = None, fullgraph: 'bool' = False, optimize: 'bool | None' = None, specialize: 'bool | None' = None, place: 'PlaceLike' = None, dynamic_axes: 'DynamicAxes | None' = None, output_names: 'Sequence[str] | None' = None, graph_name: 'str | None' = None) -> 'CompiledFunction[Any, Any]'
compile(fn, signature=(), *, dynamic=None, fullgraph=False, optimize=None, specialize=None, place=None, dynamic_axes=None, output_names=None, graph_name=None) -> CompiledFunction
Wrap a callable or a module: the first call serves the eager result and
records the graph; later calls with the same input structure and matching
shapes run the graph and never call Python. A module returns a
:class:CompiledModule. The call takes and returns pytrees: tensor
leaves are the graph inputs and outputs, every other leaf is a static
value baked into the capture.
Guards. A call re-captures when its input structure or a static leaf's
value differs from the capture, and when a plain-data value the callable
closes over or reads from its module globals by name has changed since
the capture (numbers, strings, None and tuples, dicts, frozensets
of them up to a small size; a list or a set is read as an accumulator
the function may grow and is not tracked, and neither is a tensor, a
module, or any other object, so a mutated closure tensor keeps serving
the captured behavior until :meth:CompiledFunction.reset). Shapes are
the graph's
own guard: a drifted shape serves eagerly and re-captures with the
drifted axes dynamic; past CompiledFunction.kMaxRecaptures the
function serves eagerly until reset().
signature declares the inputs as :class:TensorSpec values (a -1
dim is dynamic) for the flattened tensor inputs, in order. Without one,
the first call derives a static signature. dynamic=True marks every
axis of every input dynamic at the first call, so one graph serves every
shape; dynamic_axes marks axes by input name, {"x": {0: "batch"}}
or {"x": [0]}. A call binds through the callable's signature, so a
tensor is named by its parameter (fn(x, m) and fn(x, mask=m) are
one structure named x and mask, absent optional parameters take
their defaults, **kwargs entries read by their keys); a call whose
one parameter holds a container names the tensors by the container's
own keys (fn(batch) reads input_ids), a container beside other
parameters joins the key to the parameter's name with ., a
positional-only or *args tensor is input_<i>, and a nested
position joins its path entries
with . (batch.tokens).
fullgraph=True raises :class:~clika_runtime.ClikaRTError when the
function cannot be recorded as one graph (a Python branch on a tensor
value, a data read during the recording); the default keeps serving such
a function eagerly. optimize and specialize select the capture's
graph optimization; place (a device, device string, or stream) homes
the captured graph and its weights; output_names names the tensor
outputs in flattening order and graph_name names the graph.
concat
concat(*args, **kwargs)
concat(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
concat(tensors, dim=0, *, activation='identity') -> Tensor
The concat operator.
constant_pad
constant_pad(*args, **kwargs)
constant_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor
constant_pad(input, pad, value=None) -> Tensor
The constant_pad operator.
contiguous
contiguous(*args, **kwargs)
contiguous(input: clika_runtime._core.Tensor, force_copy: bool = False) -> clika_runtime._core.Tensor
contiguous(input, force_copy=False) -> Tensor
The contiguous operator.
conv
conv(*args, **kwargs)
conv(input: clika_runtime._core.Tensor, 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(input, weight, bias, stride, padding, dilation, groups, mode='constant', value=None, *, activation='identity') -> Tensor
The conv operator.
conv1d
conv1d(*args, **kwargs)
conv1d(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor
The conv1d operator.
conv2d
conv2d(*args, **kwargs)
conv2d(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor
The conv2d operator.
conv3d
conv3d(*args, **kwargs)
conv3d(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor
The conv3d operator.
conv_transpose
conv_transpose(*args, **kwargs)
conv_transpose(input: clika_runtime._core.Tensor, 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(input, weight, bias, stride, padding, output_padding, groups, dilation, *, activation='identity') -> Tensor
The conv_transpose operator.
conv_transpose1d
conv_transpose1d(*args, **kwargs)
conv_transpose1d(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1], padding=[0, 0], output_padding=[0], groups=1, dilation=[1], *, activation='identity') -> Tensor
The conv_transpose1d operator.
conv_transpose2d
conv_transpose2d(*args, **kwargs)
conv_transpose2d(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0, 0, 0], output_padding=[0, 0], groups=1, dilation=[1, 1], *, activation='identity') -> Tensor
The conv_transpose2d operator.
conv_transpose3d
conv_transpose3d(*args, **kwargs)
conv_transpose3d(input: clika_runtime._core.Tensor, 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(input, 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
The conv_transpose3d operator.
copy
copy(*args, **kwargs)
copy(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), force_copy: bool = True) -> clika_runtime._core.Tensor
copy(src, *, target=None, force_copy=True) -> Tensor
The copy operator.
copy_
copy_(*args, **kwargs)
copy_(self: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
copy_(self, src, *, target=None) -> Tensor
The copy_ operator.
copy_into
copy_into(*args, **kwargs)
copy_into(out: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
copy_into(out, src, *, target=None) -> Tensor
The copy_into operator.
copy_to_cpu
copy_to_cpu(*args, **kwargs)
copy_to_cpu(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
copy_to_cpu(src) -> Tensor
The copy_to_cpu operator.
copysign
copysign(*args, **kwargs)
copysign(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
copysign(input, other) -> Tensor
The copysign operator.
copysign_
copysign_(*args, **kwargs)
copysign_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
copysign_(self, other) -> Tensor
The copysign_ operator.
cos
cos(*args, **kwargs)
cos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
cos(input) -> Tensor
The cos operator.
cos_
cos_(*args, **kwargs)
cos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
cos_(self) -> Tensor
The cos_ operator.
cosh
cosh(*args, **kwargs)
cosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
cosh(input) -> Tensor
The cosh operator.
cosh_
cosh_(*args, **kwargs)
cosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
cosh_(self) -> Tensor
The cosh_ operator.
cosine_similarity
cosine_similarity(*args, **kwargs)
cosine_similarity(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor
cosine_similarity(x1, x2, dim=1, eps=None) -> Tensor
The cosine_similarity operator.
count_nonzero
count_nonzero(*args, **kwargs)
count_nonzero(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor
count_nonzero(input, dims=[]) -> Tensor
The count_nonzero operator.
cross
cross(*args, **kwargs)
cross(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor
cross(input, other, dim=None) -> Tensor
The cross operator.
cross_entropy
cross_entropy(*args, **kwargs)
cross_entropy(input: clika_runtime._core.Tensor, 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(input, target, weight=None, ignore_index=None, reduction='mean') -> Tensor
The cross_entropy operator.
cummax
cummax(*args, **kwargs)
cummax(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
cummax(input, dim) -> tuple[Tensor, Tensor]
The cummax operator.
cummin
cummin(*args, **kwargs)
cummin(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
cummin(input, dim) -> tuple[Tensor, Tensor]
The cummin operator.
cumprod
cumprod(*args, **kwargs)
cumprod(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
cumprod(input, dim, *, dtype=Undefined) -> Tensor
The cumprod operator.
cumprod_
cumprod_(*args, **kwargs)
cumprod_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
cumprod_(self, dim, *, dtype=Undefined) -> Tensor
The cumprod_ operator.
cumsum
cumsum(*args, **kwargs)
cumsum(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
cumsum(input, dim, *, dtype=Undefined) -> Tensor
The cumsum operator.
cumsum_
cumsum_(*args, **kwargs)
cumsum_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
cumsum_(self, dim, *, dtype=Undefined) -> Tensor
The cumsum_ operator.
deform_conv
deform_conv(*args, **kwargs)
deform_conv(input: clika_runtime._core.Tensor, 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(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity') -> Tensor
The deform_conv operator.
deg2rad
deg2rad(*args, **kwargs)
deg2rad(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
deg2rad(input) -> Tensor
The deg2rad operator.
deg2rad_
deg2rad_(*args, **kwargs)
deg2rad_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
deg2rad_(self) -> Tensor
The deg2rad_ operator.
dequantize
dequantize(*args, **kwargs)
dequantize(input: clika_runtime._core.QTensor, *, target_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
dequantize(input, *, target_dtype=Undefined) -> Tensor
The dequantize operator.
dequantize_
dequantize_(*args, **kwargs)
dequantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
dequantize_(input, out) -> Tensor
The dequantize_ operator.
device
device(target: 'Device | str') -> '_ThreadLocalScope'
device(target) -> a placement region
Run the region on a device: operations inside run on the calling
thread's lane for it, and factories called inside place their tensors
there. Accepts a :class:Device or a device string ("cpu",
"cuda:0"); "meta" enters :func:meta_init instead, where every
tensor built is storage-free (Tensor.is_fake) and reports the
placement it will materialize on. For a specific stream use
:func:stream.
device_count
device_count(*args, **kwargs)
device_count(api: clika_runtime._core.Device) -> int device_count(kind: object) -> int
Overloaded function.
device_count(api: clika_runtime._core.Device) -> int
How many devices the backend exposes here (0 when it is unavailable). Brings the backend up on the first call.
device_count(kind: object) -> int
device_count(kind) -> int
How many devices the backend exposes (0 when it is unavailable).
diag
diag(*args, **kwargs)
diag(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor
diag(input, diagonal=0) -> Tensor
The diag operator.
diag_embed
diag_embed(*args, **kwargs)
diag_embed(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = -2, dim2: int = -1) -> clika_runtime._core.Tensor
diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor
The diag_embed operator.
diagonal
diagonal(*args, **kwargs)
diagonal(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = 0, dim2: int = 1) -> clika_runtime._core.Tensor
diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor
The diagonal operator.
diff
diff(*args, **kwargs)
diff(input: clika_runtime._core.Tensor, 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(input, n=1, dim=-1, prepend=None, append=None) -> Tensor
The diff operator.
div
div(*args, **kwargs)
div(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor div(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
div(input, other, rounding_mode='none', *, activation='identity') -> Tensor
The div operator.
div_
div_(*args, **kwargs)
div_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
div_(self, other, rounding_mode='none', *, activation='identity') -> Tensor
The div_ operator.
dot
dot(*args, **kwargs)
dot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
dot(input, other) -> Tensor
The dot operator.
dynamic_quantize
dynamic_quantize(*args, **kwargs)
dynamic_quantize(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]
dynamic_quantize(input) -> tuple[Tensor, Tensor, Tensor]
The dynamic_quantize operator.
eager
eager() -> '_EagerScope'
eager() -> an execution-mode region
Force immediate execution inside a :func:tracing region, for a value
the host must see (a metric, a control-flow decision). Outside tracing
execution is already eager, so the region changes nothing there.
einsum
einsum(*args, **kwargs)
einsum(equation: str, operands: collections.abc.Sequence[clika_runtime._core.Tensor]) -> clika_runtime._core.Tensor
einsum(equation, operands) -> Tensor
The einsum operator.
elu
elu(*args, **kwargs)
elu(input: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor
elu(input, alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor
The elu operator.
elu_
elu_(*args, **kwargs)
elu_(self: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor
elu_(self, alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor
The elu_ operator.
embedding
embedding(*args, **kwargs)
embedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
embedding(indices, weight, bias=None, *, activation=None) -> Tensor
The embedding operator.
empty
empty(*size: 'ShapeDim', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
empty(*size, dtype=None, device=None) -> Tensor
An uninitialized tensor of the given shape; the default dtype unless
dtype says otherwise.
empty_cache
empty_cache(*args, **kwargs)
empty_cache(device: object) -> None
empty_cache(device) -> None
Return the runtime pool's idle memory on the device to the driver (or the OS); memory in use is never touched.
empty_like
empty_like(input: 'Tensor', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
empty_like(input, *, dtype=None, device=None) -> Tensor
An uninitialized tensor of input's shape, dtype and device, each
overridable by keyword.
empty_strided
empty_strided(*args, **kwargs)
empty_strided(size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
empty_strided(size, stride, dtype, *, device=None) -> Tensor
The empty_strided operator.
eq
eq(*args, **kwargs)
eq(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
eq(input, other) -> Tensor
The eq operator.
eq_
eq_(*args, **kwargs)
eq_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
eq_(self, other) -> Tensor
The eq_ operator.
erf
erf(*args, **kwargs)
erf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erf(input) -> Tensor
The erf operator.
erf_
erf_(*args, **kwargs)
erf_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erf_(self) -> Tensor
The erf_ operator.
erfc
erfc(*args, **kwargs)
erfc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erfc(input) -> Tensor
The erfc operator.
erfc_
erfc_(*args, **kwargs)
erfc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erfc_(self) -> Tensor
The erfc_ operator.
erfinv
erfinv(*args, **kwargs)
erfinv(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erfinv(input) -> Tensor
The erfinv operator.
erfinv_
erfinv_(*args, **kwargs)
erfinv_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
erfinv_(self) -> Tensor
The erfinv_ operator.
eval
eval(*trees: 'Any') -> 'None'
eval(*trees) -> None
Wait until every tensor in the trees has its value. A tree is any nesting of dicts, lists, tuples, dataclasses and registered containers; leaves that are not tensors are skipped. Returns once every tensor's pending work (including work recorded under a tracing scope) has run, the point where an asynchronous failure surfaces.
exp
exp(*args, **kwargs)
exp(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
exp(input) -> Tensor
The exp operator.
exp_
exp_(*args, **kwargs)
exp_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
exp_(self) -> Tensor
The exp_ operator.
expand
expand(*args, **kwargs)
expand(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], bidirectional: bool = False) -> clika_runtime._core.Tensor
expand(input, shape, bidirectional=False) -> Tensor
The expand operator.
expand_as
expand_as(*args, **kwargs)
expand_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
expand_as(input, other) -> Tensor
The expand_as operator.
exponential_
exponential_(*args, **kwargs)
exponential_(self: clika_runtime._core.Tensor, lambd: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
exponential_(self, lambd=1.0, *, device=None) -> Tensor
The exponential_ operator.
eye
eye(n: 'int', m: 'int | None' = None, *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
eye(n, m=None, *, dtype=None, device=None) -> Tensor
An n by m (n by n when m is None) matrix with ones on
the diagonal; the default dtype unless dtype says otherwise.
fast_gelu
fast_gelu(*args, **kwargs)
fast_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
fast_gelu(input) -> Tensor
The fast_gelu operator.
fill
fill(*args, **kwargs)
fill(input: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
fill(input, value, *, device=None) -> Tensor
The fill operator.
fill_
fill_(*args, **kwargs)
fill_(self: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
fill_(self, value, *, device=None) -> Tensor
The fill_ operator.
fill_diagonal
fill_diagonal(*args, **kwargs)
fill_diagonal(input: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
fill_diagonal(input, fill_value, wrap=False, *, device=None) -> Tensor
The fill_diagonal operator.
fill_diagonal_
fill_diagonal_(*args, **kwargs)
fill_diagonal_(self: clika_runtime._core.Tensor, 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, wrap=False, *, device=None) -> Tensor
The fill_diagonal_ operator.
flatten
flatten(*args, **kwargs)
flatten(input: clika_runtime._core.Tensor, start_dim: int = 0, end_dim: int = -1) -> clika_runtime._core.Tensor
flatten(input, start_dim=0, end_dim=-1) -> Tensor
The flatten operator.
flip
flip(*args, **kwargs)
flip(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
flip(input, dims) -> Tensor
The flip operator.
fliplr
fliplr(*args, **kwargs)
fliplr(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
fliplr(input) -> Tensor
The fliplr operator.
flipud
flipud(*args, **kwargs)
flipud(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
flipud(input) -> Tensor
The flipud operator.
floor
floor(*args, **kwargs)
floor(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
floor(input) -> Tensor
The floor operator.
floor_
floor_(*args, **kwargs)
floor_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
floor_(self) -> Tensor
The floor_ operator.
floor_divide
floor_divide(*args, **kwargs)
floor_divide(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
floor_divide(input, other) -> Tensor
The floor_divide operator.
floor_divide_
floor_divide_(*args, **kwargs)
floor_divide_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
floor_divide_(self, other) -> Tensor
The floor_divide_ operator.
fmax
fmax(*args, **kwargs)
fmax(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
fmax(input, other) -> Tensor
The fmax operator.
fmin
fmin(*args, **kwargs)
fmin(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
fmin(input, other) -> Tensor
The fmin operator.
fmod
fmod(*args, **kwargs)
fmod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
fmod(input, other) -> Tensor
The fmod operator.
fmod_
fmod_(*args, **kwargs)
fmod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
fmod_(self, other) -> Tensor
The fmod_ operator.
fold
fold(*args, **kwargs)
fold(input: clika_runtime._core.Tensor, 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(input, output_size, kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor
The fold operator.
frac
frac(*args, **kwargs)
frac(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
frac(input) -> Tensor
The frac operator.
frac_
frac_(*args, **kwargs)
frac_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
frac_(self) -> Tensor
The frac_ operator.
frexp
frexp(*args, **kwargs)
frexp(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
frexp(input) -> tuple[Tensor, Tensor]
The frexp operator.
from_dlpack
from_dlpack(*args, **kwargs)
from_dlpack(array: ndarray[]) -> clika_runtime._core.Tensor
from_dlpack(array) -> Tensor
A tensor SHARING the memory of any DLPack producer (a numpy array, a torch tensor, another framework's array): no copy, mutations alias both ways, and the producer's buffer stays alive for the tensor's lifetime. The dtype and the device follow the producer (cpu, cuda, vulkan and metal devices).
from_numpy
from_numpy(*args, **kwargs)
from_numpy(array: object) -> clika_runtime._core.Tensor
A ZERO-COPY tensor BORROWING the array's memory (C-contiguous, writable, cpu): mutations alias both ways, and the tensor keeps the array alive for its whole lifetime. An op on the tensor runs asynchronously on the calling thread's placement, so a raw read of the array after an in-place op sees the write once that placement is synchronized: crt.synchronize() for the calling thread's lane, crt.synchronize(stream) when the op ran under an explicit stream; reads through numpy() settle on their own. Use tensor(...) for the copying entry; densify a strided array with np.ascontiguousarray(a) first.
full
full(size: 'Sequence[int] | int', fill_value: 'Number', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
full(size, fill_value, *, dtype=None, device=None) -> Tensor
A tensor filled with fill_value. Without dtype the fill value
picks the type: a bool fills a bool tensor, an int an int64 one,
a float one of the default dtype.
full_like
full_like(input: 'Tensor', fill_value: 'Number', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
full_like(input, fill_value, *, dtype=None, device=None) -> Tensor
A tensor of input's shape, dtype and device filled with
fill_value; dtype and device overridable by keyword.
gated_delta_update
gated_delta_update(*args, **kwargs)
gated_delta_update(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, beta: clika_runtime._core.Tensor, gate: clika_runtime._core.Tensor, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, scale: float | None = None, gate_bias: clika_runtime._core.Tensor | None = None, gate_scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
gated_delta_update(query, key, value, beta, gate, state, seq_lens=None, slot_ids=None, scale=None, gate_bias=None, gate_scale=None) -> Tensor
The gated_delta_update operator.
gated_rms_norm
gated_rms_norm(*args, **kwargs)
gated_rms_norm(input: clika_runtime._core.Tensor, 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(input, gate, normalized_shape, weight=None, eps=None) -> Tensor
The gated_rms_norm operator.
gather
gather(*args, **kwargs)
gather(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
gather(input, dim, index) -> Tensor
The gather operator.
ge
ge(*args, **kwargs)
ge(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
ge(input, other) -> Tensor
The ge operator.
ge_
ge_(*args, **kwargs)
ge_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
ge_(self, other) -> Tensor
The ge_ operator.
geglu
geglu(*args, **kwargs)
geglu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor
geglu(input, approximate='none') -> Tensor
The geglu operator.
gelu
gelu(*args, **kwargs)
gelu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor
gelu(input, approximate='none') -> Tensor
The gelu operator.
gelu_
gelu_(*args, **kwargs)
gelu_(self: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor
gelu_(self, approximate='none') -> Tensor
The gelu_ operator.
generate_rotary_cache
generate_rotary_cache(*args, **kwargs)
generate_rotary_cache(rotary_dim: int, max_positions: int, 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, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
generate_rotary_cache(rotary_dim, max_positions, 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, *, device=None) -> tuple[Tensor, Tensor]
The generate_rotary_cache operator.
get_default_dtype
get_default_dtype() -> 'Dtype'
get_default_dtype() -> dtype
The floating-point dtype a factory uses when dtype is not given, and
the dtype of a tensor built from Python floats; float32 until
:func:set_default_dtype changes it.
get_device_properties
get_device_properties(*args, **kwargs)
get_device_properties(device: clika_runtime._core.Device) -> clika_runtime._core.DeviceProperties get_device_properties(device: object) -> clika_runtime._core.DeviceProperties
Overloaded function.
get_device_properties(device: clika_runtime._core.Device) -> clika_runtime._core.DeviceProperties
The static capabilities of one device; raises RuntimeError when the device does not exist or its backend is not loaded.
get_device_properties(device: object) -> clika_runtime._core.DeviceProperties
get_device_properties(device) -> DeviceProperties
The static capabilities of a device (a Device or a device string).
get_printoptions
get_printoptions() -> 'dict[str, Any]'
get_printoptions() -> dict
The current print options (a copy): precision, threshold, edgeitems, linewidth, sci_mode.
glu
glu(*args, **kwargs)
glu(input: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor
glu(input, dim=-1) -> Tensor
The glu operator.
grid_sample
grid_sample(*args, **kwargs)
grid_sample(input: clika_runtime._core.Tensor, grid: clika_runtime._core.Tensor, mode: object = 'bilinear', padding_mode: object = 'zeros', align_corners: bool = False) -> clika_runtime._core.Tensor
grid_sample(input, grid, mode='bilinear', padding_mode='zeros', align_corners=False) -> Tensor
The grid_sample operator.
group_norm
group_norm(*args, **kwargs)
group_norm(input: clika_runtime._core.Tensor, 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(input, num_groups, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor
The group_norm operator.
group_query_attention
group_query_attention(*args, **kwargs)
group_query_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]
group_query_attention(query, key, value, past_key=None, past_value=None, kvcache_start=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, out_present_key=None, out_present_value=None, k_scale=None, v_scale=None, head_sink=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None) -> tuple[Tensor, Tensor, Tensor]
The group_query_attention operator.
group_query_attention_varlen
group_query_attention_varlen(*args, **kwargs)
group_query_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]
group_query_attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, past_key=None, past_value=None, kvcache_start=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, out_present_key=None, out_present_value=None, k_scale=None, v_scale=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None) -> tuple[Tensor, Tensor, Tensor]
The group_query_attention_varlen operator.
gt
gt(*args, **kwargs)
gt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
gt(input, other) -> Tensor
The gt operator.
gt_
gt_(*args, **kwargs)
gt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
gt_(self, other) -> Tensor
The gt_ operator.
hardshrink
hardshrink(*args, **kwargs)
hardshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor
hardshrink(input, lambd=0.5) -> Tensor
The hardshrink operator.
hardshrink_
hardshrink_(*args, **kwargs)
hardshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor
hardshrink_(self, lambd=0.5) -> Tensor
The hardshrink_ operator.
hardsigmoid
hardsigmoid(*args, **kwargs)
hardsigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hardsigmoid(input) -> Tensor
The hardsigmoid operator.
hardsigmoid_
hardsigmoid_(*args, **kwargs)
hardsigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hardsigmoid_(self) -> Tensor
The hardsigmoid_ operator.
hardswish
hardswish(*args, **kwargs)
hardswish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hardswish(input) -> Tensor
The hardswish operator.
hardswish_
hardswish_(*args, **kwargs)
hardswish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hardswish_(self) -> Tensor
The hardswish_ operator.
hardtanh
hardtanh(*args, **kwargs)
hardtanh(input: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor
hardtanh(input, min_val=-1.0, max_val=1.0) -> Tensor
The hardtanh operator.
hardtanh_
hardtanh_(*args, **kwargs)
hardtanh_(self: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor
hardtanh_(self, min_val=-1.0, max_val=1.0) -> Tensor
The hardtanh_ operator.
hash_128
hash_128(*args, **kwargs)
hash_128(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor
hash_128(input, seed=0) -> Tensor
The hash_128 operator.
hash_256
hash_256(*args, **kwargs)
hash_256(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor
hash_256(input, seed=0) -> Tensor
The hash_256 operator.
hash_64
hash_64(*args, **kwargs)
hash_64(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor
hash_64(input, seed=0) -> Tensor
The hash_64 operator.
hash_chain
hash_chain(*args, **kwargs)
hash_chain(rows: clika_runtime._core.Tensor, parent: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor
hash_chain(rows, parent, seed=0) -> Tensor
The hash_chain operator.
hash_tensor
hash_tensor(*args, **kwargs)
hash_tensor(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, mode: object = 'xor_sum') -> clika_runtime._core.Tensor
hash_tensor(input, dims=[], keepdim=False, mode='xor_sum') -> Tensor
The hash_tensor operator.
histogram
histogram(*args, **kwargs)
histogram(input: clika_runtime._core.Tensor, 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(input, bins=100, range=None, weight=None, density=False) -> tuple[Tensor, Tensor]
The histogram operator.
huber_loss
huber_loss(*args, **kwargs)
huber_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', delta: float = 1.0) -> clika_runtime._core.Tensor
huber_loss(input, target, reduction='mean', delta=1.0) -> Tensor
The huber_loss operator.
hypot
hypot(*args, **kwargs)
hypot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hypot(input, other) -> Tensor
The hypot operator.
hypot_
hypot_(*args, **kwargs)
hypot_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
hypot_(self, other) -> Tensor
The hypot_ operator.
index
index(*args, **kwargs)
index(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry]) -> clika_runtime._core.Tensor
index(input, indices) -> Tensor
The index operator.
index_add
index_add(*args, **kwargs)
index_add(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
index_add(input, dim, indices, src) -> Tensor
The index_add operator.
index_add_
index_add_(*args, **kwargs)
index_add_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
index_add_(self, dim, indices, src) -> Tensor
The index_add_ operator.
index_copy
index_copy(*args, **kwargs)
index_copy(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
index_copy(input, dim, indices, src) -> Tensor
The index_copy operator.
index_copy_
index_copy_(*args, **kwargs)
index_copy_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
index_copy_(self, dim, indices, src) -> Tensor
The index_copy_ operator.
index_fill
index_fill(*args, **kwargs)
index_fill(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
index_fill(input, dim, indices, value) -> Tensor
The index_fill operator.
index_fill_
index_fill_(*args, **kwargs)
index_fill_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
index_fill_(self, dim, indices, value) -> Tensor
The index_fill_ operator.
index_put
index_put(*args, **kwargs)
index_put(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor
index_put(input, indices, value, accumulate=False) -> Tensor
The index_put operator.
index_put_
index_put_(*args, **kwargs)
index_put_(self: clika_runtime._core.Tensor, 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, value, accumulate=False) -> Tensor
The index_put_ operator.
index_select
index_select(*args, **kwargs)
index_select(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
index_select(input, dim, index) -> Tensor
The index_select operator.
inner
inner(*args, **kwargs)
inner(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
inner(input, other) -> Tensor
The inner operator.
instance_norm
instance_norm(*args, **kwargs)
instance_norm(input: clika_runtime._core.Tensor, 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(input, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor
The instance_norm operator.
interpolate
interpolate(*args, **kwargs)
interpolate(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[], mode='nearest', align_corners=None, recompute_scale_factor=False, antialias=False) -> Tensor
The interpolate operator.
is_available
is_available(*args, **kwargs)
is_available(kind: object) -> bool
is_available(kind) -> bool
Whether the backend ("cuda", "vulkan", "metal", "cpu", ... or a Device) loads and initializes on this machine; the first call brings it up.
is_meta_init
is_meta_init() -> 'bool'
is_meta_init() -> bool
Whether the calling thread is inside a :func:meta_init (or
device("meta")) region.
isclose
isclose(*args, **kwargs)
isclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor
isclose(input, other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor
The isclose operator.
isfinite
isfinite(*args, **kwargs)
isfinite(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
isfinite(input) -> Tensor
The isfinite operator.
isinf
isinf(*args, **kwargs)
isinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
isinf(input) -> Tensor
The isinf operator.
isnan
isnan(*args, **kwargs)
isnan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
isnan(input) -> Tensor
The isnan operator.
isneginf
isneginf(*args, **kwargs)
isneginf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
isneginf(input) -> Tensor
The isneginf operator.
isposinf
isposinf(*args, **kwargs)
isposinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
isposinf(input) -> Tensor
The isposinf operator.
kl_div
kl_div(*args, **kwargs)
kl_div(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', log_target: bool = False) -> clika_runtime._core.Tensor
kl_div(input, target, reduction='mean', log_target=False) -> Tensor
The kl_div operator.
kron
kron(*args, **kwargs)
kron(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
kron(input, other) -> Tensor
The kron operator.
kthvalue
kthvalue(*args, **kwargs)
kthvalue(input: clika_runtime._core.Tensor, k: int, dim: int = -1, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
kthvalue(input, k, dim=-1, keepdim=False) -> tuple[Tensor, Tensor]
The kthvalue operator.
l1_loss
l1_loss(*args, **kwargs)
l1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor
l1_loss(input, target, reduction='mean') -> Tensor
The l1_loss operator.
layer_norm
layer_norm(*args, **kwargs)
layer_norm(input: clika_runtime._core.Tensor, 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(input, normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor
The layer_norm operator.
le
le(*args, **kwargs)
le(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
le(input, other) -> Tensor
The le operator.
le_
le_(*args, **kwargs)
le_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
le_(self, other) -> Tensor
The le_ operator.
leaky_relu
leaky_relu(*args, **kwargs)
leaky_relu(input: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor
leaky_relu(input, negative_slope=0.01) -> Tensor
The leaky_relu operator.
leaky_relu_
leaky_relu_(*args, **kwargs)
leaky_relu_(self: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor
leaky_relu_(self, negative_slope=0.01) -> Tensor
The leaky_relu_ operator.
linear
linear(*args, **kwargs)
linear(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, *, activation=None, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor
The linear operator.
linspace
linspace(start: 'Number', end: 'Number', steps: 'int', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
linspace(start, end, steps, *, dtype=None, device=None) -> Tensor
steps values evenly spaced from start to end inclusive; the
default dtype unless dtype says otherwise.
load
load(path: 'PathLike', *, device: 'Device | str | None' = None, mmap: 'bool | None' = None) -> 'Tensor | dict[str, Tensor] | Any'
load(path, *, device=None, mmap=None) -> Tensor | dict[str, Tensor] | pytree
Read a safetensors file written by :func:save (or by another tool)
onto device (None means the cpu). A file holding one tensor
under the reserved key loads as a :class:~clika_runtime.Tensor; a
file whose header metadata carries a pytree structure loads as that
pytree; anything else loads as a dict[str, Tensor] in header order.
mmap selects memory-mapped, on-demand loading; only the eager form
is served here, so mmap=True raises NotImplementedError.
load_with_metadata
load_with_metadata(path: 'PathLike', *, device: 'Device | str | None' = None, mmap: 'bool | None' = None) -> 'tuple[Tensor | dict[str, Tensor] | Any, dict[str, str]]'
load_with_metadata(path, *, device=None, mmap=None) -> (obj, dict[str, str])
:func:load plus the file's header metadata as a dict in header
order: the pairs save(..., metadata=) wrote, or whatever another
tool put there (a non-string value arrives as its JSON text). The
structure entries :func:load consumes (clika.format,
clika.treespec) are not part of the returned dict.
log
log(*args, **kwargs)
log(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log(input) -> Tensor
The log operator.
log10
log10(*args, **kwargs)
log10(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log10(input) -> Tensor
The log10 operator.
log10_
log10_(*args, **kwargs)
log10_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log10_(self) -> Tensor
The log10_ operator.
log1p
log1p(*args, **kwargs)
log1p(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log1p(input) -> Tensor
The log1p operator.
log1p_
log1p_(*args, **kwargs)
log1p_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log1p_(self) -> Tensor
The log1p_ operator.
log2
log2(*args, **kwargs)
log2(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log2(input) -> Tensor
The log2 operator.
log2_
log2_(*args, **kwargs)
log2_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log2_(self) -> Tensor
The log2_ operator.
log_
log_(*args, **kwargs)
log_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log_(self) -> Tensor
The log_ operator.
log_sigmoid
log_sigmoid(*args, **kwargs)
log_sigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log_sigmoid(input) -> Tensor
The log_sigmoid operator.
log_sigmoid_
log_sigmoid_(*args, **kwargs)
log_sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
log_sigmoid_(self) -> Tensor
The log_sigmoid_ operator.
log_softmax
log_softmax(*args, **kwargs)
log_softmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
log_softmax(input, dim=-1, *, dtype=Undefined) -> Tensor
The log_softmax operator.
log_softmax_
log_softmax_(*args, **kwargs)
log_softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor
log_softmax_(self, dim=-1) -> Tensor
The log_softmax_ operator.
logaddexp
logaddexp(*args, **kwargs)
logaddexp(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logaddexp(input, other) -> Tensor
The logaddexp operator.
logaddexp2
logaddexp2(*args, **kwargs)
logaddexp2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logaddexp2(input, other) -> Tensor
The logaddexp2 operator.
logical_and
logical_and(*args, **kwargs)
logical_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_and(input, other) -> Tensor
The logical_and operator.
logical_and_
logical_and_(*args, **kwargs)
logical_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_and_(self, other) -> Tensor
The logical_and_ operator.
logical_not
logical_not(*args, **kwargs)
logical_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_not(input) -> Tensor
The logical_not operator.
logical_not_
logical_not_(*args, **kwargs)
logical_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_not_(self) -> Tensor
The logical_not_ operator.
logical_or
logical_or(*args, **kwargs)
logical_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_or(input, other) -> Tensor
The logical_or operator.
logical_or_
logical_or_(*args, **kwargs)
logical_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_or_(self, other) -> Tensor
The logical_or_ operator.
logical_xor
logical_xor(*args, **kwargs)
logical_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_xor(input, other) -> Tensor
The logical_xor operator.
logical_xor_
logical_xor_(*args, **kwargs)
logical_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
logical_xor_(self, other) -> Tensor
The logical_xor_ operator.
logit
logit(*args, **kwargs)
logit(input: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor
logit(input, eps=None) -> Tensor
The logit operator.
logit_
logit_(*args, **kwargs)
logit_(self: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor
logit_(self, eps=None) -> Tensor
The logit_ operator.
logsumexp
logsumexp(*args, **kwargs)
logsumexp(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int], keepdim: bool = False) -> clika_runtime._core.Tensor
logsumexp(input, dims, keepdim=False) -> Tensor
The logsumexp operator.
lt
lt(*args, **kwargs)
lt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
lt(input, other) -> Tensor
The lt operator.
lt_
lt_(*args, **kwargs)
lt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
lt_(self, other) -> Tensor
The lt_ operator.
make_quantized
make_quantized(*args, **kwargs)
make_quantized(payload: clika_runtime._core.Tensor, scheme: str, logical_shape: collections.abc.Sequence[int], global_scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor
Construct a quantized weight from a raw packed payload (UInt8 block rows) under a named block scheme; logical_shape is the element shape it encodes, row-contiguous dimension first.
make_quantized_affine
make_quantized_affine(*args, **kwargs)
make_quantized_affine(codes: clika_runtime._core.Tensor, scales: clika_runtime._core.Tensor, biases: clika_runtime._core.Tensor, group_size: int, bits: int, logical_shape: collections.abc.Sequence[int]) -> clika_runtime._core.QTensor
Construct a float-bias affine weight from its planes: value = scale * code + bias per group. logical_shape is [out_features, in_features].
manual_seed
manual_seed(*args, **kwargs)
manual_seed(seed: int, device: object | None = None) -> None
manual_seed(seed, device=None) -> None
Seed a device's random-number generator so its random ops replay; None seeds the calling thread's ambient device.
masked_fill
masked_fill(*args, **kwargs)
masked_fill(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
masked_fill(input, mask, value) -> Tensor
The masked_fill operator.
masked_fill_
masked_fill_(*args, **kwargs)
masked_fill_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
masked_fill_(self, mask, value) -> Tensor
The masked_fill_ operator.
masked_scatter
masked_scatter(*args, **kwargs)
masked_scatter(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
masked_scatter(input, mask, source) -> Tensor
The masked_scatter operator.
masked_scatter_
masked_scatter_(*args, **kwargs)
masked_scatter_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
masked_scatter_(self, mask, source) -> Tensor
The masked_scatter_ operator.
masked_select
masked_select(*args, **kwargs)
masked_select(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
masked_select(input, mask) -> Tensor
The masked_select operator.
matmul
matmul(*args, **kwargs)
matmul(input: clika_runtime._core.Tensor, 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(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor
The matmul operator.
max
max(*args, **kwargs)
max(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
max(input, dim, keepdim=False) -> tuple[Tensor, Tensor]
The max operator.
max_pool
max_pool(*args, **kwargs)
max_pool(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride, padding, dilation, ceil_mode) -> Tensor
The max_pool operator.
max_pool1d
max_pool1d(*args, **kwargs)
max_pool1d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> Tensor
The max_pool1d operator.
max_pool1d_with_indices
max_pool1d_with_indices(*args, **kwargs)
max_pool1d_with_indices(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> tuple[Tensor, Tensor]
The max_pool1d_with_indices operator.
max_pool2d
max_pool2d(*args, **kwargs)
max_pool2d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> Tensor
The max_pool2d operator.
max_pool2d_with_indices
max_pool2d_with_indices(*args, **kwargs)
max_pool2d_with_indices(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> tuple[Tensor, Tensor]
The max_pool2d_with_indices operator.
max_pool3d
max_pool3d(*args, **kwargs)
max_pool3d(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> Tensor
The max_pool3d operator.
max_pool3d_with_indices
max_pool3d_with_indices(*args, **kwargs)
max_pool3d_with_indices(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> tuple[Tensor, Tensor]
The max_pool3d_with_indices operator.
max_pool_with_indices
max_pool_with_indices(*args, **kwargs)
max_pool_with_indices(input: clika_runtime._core.Tensor, 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(input, kernel_size, stride, padding, dilation, ceil_mode) -> tuple[Tensor, Tensor]
The max_pool_with_indices operator.
maximum
maximum(*args, **kwargs)
maximum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
maximum(input, other) -> Tensor
The maximum operator.
maximum_
maximum_(*args, **kwargs)
maximum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
maximum_(self, other) -> Tensor
The maximum_ operator.
mean
mean(*args, **kwargs)
mean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
mean(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor
The mean operator.
median
median(*args, **kwargs)
median(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
median(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor]
The median operator.
memory_stats
memory_stats(*args, **kwargs)
memory_stats(device: object) -> clika_runtime._core.MemoryStats
memory_stats(device) -> MemoryStats
A snapshot of the device's memory: physical free/total and the runtime pool's counters.
meshgrid
meshgrid(*args, **kwargs)
meshgrid(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], indexing: object = 'ij') -> list[clika_runtime._core.Tensor]
meshgrid(tensors, indexing='ij') -> list[Tensor]
The meshgrid operator.
meta_init
meta_init() -> '_MetaInitScope'
meta_init() -> a construction region
Mark the region as shape-only construction: a factory called inside
(zeros, empty, a layer's declared slots) is meant to mint a
storage-free tensor carrying shape, dtype, and placement only, so a
large model builds at zero bytes and materializes later through
Module.to_empty(device=...) or load_state_dict(..., assign=True).
device("meta") is the same region. The region sets the calling
thread's meta-init flag (:func:is_meta_init), which the tensor
factories read: a tensor built inside is storage-free
(Tensor.is_fake) and reports the placement it will materialize on
(the requested device, or the ambient one); there is no separate meta
device.
min
min(*args, **kwargs)
min(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
min(input, dim, keepdim=False) -> tuple[Tensor, Tensor]
The min operator.
minimum
minimum(*args, **kwargs)
minimum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
minimum(input, other) -> Tensor
The minimum operator.
minimum_
minimum_(*args, **kwargs)
minimum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
minimum_(self, other) -> Tensor
The minimum_ operator.
mish
mish(*args, **kwargs)
mish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
mish(input) -> Tensor
The mish operator.
mish_
mish_(*args, **kwargs)
mish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
mish_(self) -> Tensor
The mish_ operator.
mla_attention
mla_attention(*args, **kwargs)
mla_attention(q_nope: clika_runtime._core.Tensor, q_pe: clika_runtime._core.Tensor, new_ckv: clika_runtime._core.Tensor, new_kpe: clika_runtime._core.Tensor, ckv_cache: clika_runtime._core.Tensor, kpe_cache: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, scale: float, slot_ids: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
mla_attention(q_nope, q_pe, new_ckv, new_kpe, ckv_cache, kpe_cache, kvcache_start, cu_seqlens_q, scale, slot_ids=None) -> Tensor
The mla_attention operator.
mm
mm(*args, **kwargs)
mm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
mm(input, other, bias=None, *, activation=None) -> Tensor
The mm operator.
mod
mod(*args, **kwargs)
mod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor
mod(input, other, mode='python') -> Tensor
The mod operator.
mod_
mod_(*args, **kwargs)
mod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor
mod_(self, other, mode='python') -> Tensor
The mod_ operator.
moe
moe(*args, **kwargs)
moe(input: clika_runtime._core.Tensor, 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(input, 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
The moe operator.
mrope_rotary_embedding
mrope_rotary_embedding(*args, **kwargs)
mrope_rotary_embedding(input: clika_runtime._core.Tensor, 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(input, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor
The mrope_rotary_embedding operator.
mrope_rotary_embedding_qk_varlen
mrope_rotary_embedding_qk_varlen(*args, **kwargs)
mrope_rotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, 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) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
mrope_rotary_embedding_qk_varlen(query, key, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> tuple[Tensor, Tensor]
The mrope_rotary_embedding_qk_varlen operator.
mrope_rotary_embedding_varlen
mrope_rotary_embedding_varlen(*args, **kwargs)
mrope_rotary_embedding_varlen(input: clika_runtime._core.Tensor, 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(input, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor
The mrope_rotary_embedding_varlen operator.
ms_deform_attention
ms_deform_attention(*args, **kwargs)
ms_deform_attention(value: clika_runtime._core.Tensor, spatial_shapes: clika_runtime._core.Tensor, level_start_index: clika_runtime._core.Tensor, sampling_locations: clika_runtime._core.Tensor, attention_weights: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
ms_deform_attention(value, spatial_shapes, level_start_index, sampling_locations, attention_weights) -> Tensor
The ms_deform_attention operator.
mse_loss
mse_loss(*args, **kwargs)
mse_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor
mse_loss(input, target, reduction='mean') -> Tensor
The mse_loss operator.
mul
mul(*args, **kwargs)
mul(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor mul(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
mul(input, other, *, activation='identity') -> Tensor
The mul operator.
mul_
mul_(*args, **kwargs)
mul_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
mul_(self, other, *, activation='identity') -> Tensor
The mul_ operator.
multinomial
multinomial(*args, **kwargs)
multinomial(probabilities: clika_runtime._core.Tensor, num_samples: int, replacement: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
multinomial(probabilities, num_samples, replacement=False, *, device=None) -> Tensor
The multinomial operator.
mv
mv(*args, **kwargs)
mv(input: clika_runtime._core.Tensor, vec: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
mv(input, vec, bias=None, *, activation=None) -> Tensor
The mv operator.
nan_to_num
nan_to_num(*args, **kwargs)
nan_to_num(input: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor
nan_to_num(input, nan=None, posinf=None, neginf=None) -> Tensor
The nan_to_num operator.
nan_to_num_
nan_to_num_(*args, **kwargs)
nan_to_num_(self: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor
nan_to_num_(self, nan=None, posinf=None, neginf=None) -> Tensor
The nan_to_num_ operator.
nanmean
nanmean(*args, **kwargs)
nanmean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
nanmean(input, dims=[], keepdim=False) -> Tensor
The nanmean operator.
nanmedian
nanmedian(*args, **kwargs)
nanmedian(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
nanmedian(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor]
The nanmedian operator.
nanquantile
nanquantile(*args, **kwargs)
nanquantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor
nanquantile(input, q, dim=None, keepdim=False, interpolation='linear') -> Tensor
The nanquantile operator.
nansum
nansum(*args, **kwargs)
nansum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
nansum(input, dims=[], keepdim=False) -> Tensor
The nansum operator.
narrow
narrow(*args, **kwargs)
narrow(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound, length: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor
narrow(input, dim, start, length) -> Tensor
The narrow operator.
ndim
ndim(*args, **kwargs)
ndim(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
ndim(input) -> Tensor
The ndim operator.
ndim_host
ndim_host(*args, **kwargs)
ndim_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
ndim_host(input) -> Tensor
The ndim_host operator.
ne
ne(*args, **kwargs)
ne(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
ne(input, other) -> Tensor
The ne operator.
ne_
ne_(*args, **kwargs)
ne_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
ne_(self, other) -> Tensor
The ne_ operator.
neg
neg(*args, **kwargs)
neg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
neg(input) -> Tensor
The neg operator.
neg_
neg_(*args, **kwargs)
neg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
neg_(self) -> Tensor
The neg_ operator.
nll_loss
nll_loss(*args, **kwargs)
nll_loss(input: clika_runtime._core.Tensor, 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(input, target, weight=None, ignore_index=None, reduction='mean') -> Tensor
The nll_loss operator.
nms
nms(*args, **kwargs)
nms(boxes: clika_runtime._core.Tensor, scores: clika_runtime._core.Tensor, max_output_boxes_per_class: clika_runtime._core.ops.ScalarOrTensor | None = None, iou_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, score_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, center_point_box: bool = False) -> clika_runtime._core.Tensor
nms(boxes, scores, max_output_boxes_per_class=None, iou_threshold=None, score_threshold=None, center_point_box=False) -> Tensor
The nms operator.
nonzero
nonzero(*args, **kwargs)
nonzero(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
nonzero(input) -> Tensor
The nonzero operator.
norm
norm(*args, **kwargs)
norm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor
norm(input, p=2.0, dims=[], keepdim=False) -> Tensor
The norm operator.
normal
normal(*args, **kwargs)
normal(mean: clika_runtime._core.ops.ScalarOrTensor, stddev: clika_runtime._core.ops.ScalarOrTensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
normal(mean, stddev, shape, *, device=None) -> Tensor
The normal operator.
normal_
normal_(*args, **kwargs)
normal_(self: clika_runtime._core.Tensor, mean: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), stddev: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
normal_(self, mean=0, stddev=1, *, device=None) -> Tensor
The normal_ operator.
normalize
normalize(*args, **kwargs)
normalize(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor
normalize(input, p=2.0, dim=1, eps=None) -> Tensor
The normalize operator.
normalize_
normalize_(*args, **kwargs)
normalize_(self: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor
normalize_(self, p=2.0, dim=1, eps=None) -> Tensor
The normalize_ operator.
numel
numel(*args, **kwargs)
numel(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
numel(input) -> Tensor
The numel operator.
numel_host
numel_host(*args, **kwargs)
numel_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
numel_host(input) -> Tensor
The numel_host operator.
one_hot
one_hot(*args, **kwargs)
one_hot(indices: clika_runtime._core.Tensor, num_classes: int) -> clika_runtime._core.Tensor
one_hot(indices, num_classes) -> Tensor
The one_hot operator.
ones
ones(*size: 'ShapeDim', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
ones(*size, dtype=None, device=None) -> Tensor
A one-filled tensor; the default dtype unless dtype says otherwise.
ones_like
ones_like(input: 'Tensor', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
ones_like(input, *, dtype=None, device=None) -> Tensor
A one-filled tensor of input's shape, dtype and device, each
overridable by keyword.
outer
outer(*args, **kwargs)
outer(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
outer(input, other) -> Tensor
The outer operator.
pad
pad(*args, **kwargs)
pad(input: clika_runtime._core.Tensor, 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(input, pad, mode='constant', value=None) -> Tensor
The pad operator.
pairwise_distance
pairwise_distance(*args, **kwargs)
pairwise_distance(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), eps: float | None = None, keepdim: bool = False) -> clika_runtime._core.Tensor
pairwise_distance(x1, x2, p=2.0, eps=None, keepdim=False) -> Tensor
The pairwise_distance operator.
pdist
pdist(*args, **kwargs)
pdist(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor
pdist(input, p=2.0) -> Tensor
The pdist operator.
permute
permute(*args, **kwargs)
permute(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
permute(input, dims) -> Tensor
The permute operator.
pixel_shuffle
pixel_shuffle(*args, **kwargs)
pixel_shuffle(input: clika_runtime._core.Tensor, upscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor
pixel_shuffle(input, upscale_factor, mode='crd') -> Tensor
The pixel_shuffle operator.
pixel_unshuffle
pixel_unshuffle(*args, **kwargs)
pixel_unshuffle(input: clika_runtime._core.Tensor, downscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor
pixel_unshuffle(input, downscale_factor, mode='crd') -> Tensor
The pixel_unshuffle operator.
poisson
poisson(*args, **kwargs)
poisson(rates: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
poisson(rates, *, device=None) -> Tensor
The poisson operator.
pow
pow(*args, **kwargs)
pow(input: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
pow(input, exponent) -> Tensor
The pow operator.
pow_
pow_(*args, **kwargs)
pow_(self: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
pow_(self, exponent) -> Tensor
The pow_ operator.
prelu
prelu(*args, **kwargs)
prelu(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
prelu(input, weight=None) -> Tensor
The prelu operator.
prelu_
prelu_(*args, **kwargs)
prelu_(self: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
prelu_(self, weight=None) -> Tensor
The prelu_ operator.
prod
prod(*args, **kwargs)
prod(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
prod(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor
The prod operator.
put
put(*args, **kwargs)
put(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor
put(input, index, source, accumulate=False) -> Tensor
The put operator.
put_
put_(*args, **kwargs)
put_(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor
put_(self, index, source, accumulate=False) -> Tensor
The put_ operator.
q_embedding
q_embedding(*args, **kwargs)
q_embedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
q_embedding(indices, weight, bias=None, *, activation=None, out_dtype=Undefined) -> Tensor
The q_embedding operator.
qadd
qadd(*args, **kwargs)
qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
Overloaded function.
qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qadd(input, other, *, activation='identity') -> Tensor
The qadd operator.
qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qadd(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor
The qadd operator.
qadd_
qadd_(*args, **kwargs)
qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime.core.QTensor qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime.core.Tensor qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
Overloaded function.
qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qadd_(input, other, out, *, activation='identity') -> QTensor
The qadd_ operator.
qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qadd_(input, other, out, *, activation='identity') -> Tensor
The qadd_ operator.
qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qadd_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor
The qadd_ operator.
qconcat
qconcat(*args, **kwargs)
qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
Overloaded function.
qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qconcat(tensors, dim=0, *, activation='identity') -> Tensor
The qconcat operator.
qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qconcat(tensors, dim, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor
The qconcat operator.
qconcat_
qconcat_(*args, **kwargs)
qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime.core.QTensor qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
Overloaded function.
qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qconcat_(tensors, dim, out, *, activation='identity') -> QTensor
The qconcat_ operator.
qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qconcat_(tensors, dim, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor
The qconcat_ operator.
qconv
qconv(*args, **kwargs)
qconv(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, 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, value: float | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor
qconv(input, weight, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor
The qconv operator.
qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, 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, value: float | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv(input, weight, bias, stride, padding, dilation, groups, mode, value, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv operator.
qconv1d
qconv1d(*args, **kwargs)
qconv1d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv1d(input: clika_runtime._core.QTensor, 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(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor
The qconv1d operator.
qconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv1d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv1d operator.
qconv1d_woq
qconv1d_woq(*args, **kwargs)
qconv1d_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor
The qconv1d_woq operator.
qconv2d
qconv2d(*args, **kwargs)
qconv2d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv2d(input: clika_runtime._core.QTensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor
The qconv2d operator.
qconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv2d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv2d operator.
qconv2d_woq
qconv2d_woq(*args, **kwargs)
qconv2d_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor
The qconv2d_woq operator.
qconv3d
qconv3d(*args, **kwargs)
qconv3d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv3d(input: clika_runtime._core.QTensor, 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(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor
The qconv3d operator.
qconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv3d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv3d operator.
qconv3d_woq
qconv3d_woq(*args, **kwargs)
qconv3d_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor
The qconv3d_woq operator.
qconv_
qconv_(*args, **kwargs)
qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: 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') -> clika_runtime.core.QTensor qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, 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') -> clika_runtime.core.Tensor qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, 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') -> clika_runtime._core.Tensor
Overloaded function.
qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: 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') -> clika_runtime._core.QTensor
qconv_(input, weight, out, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> QTensor
The qconv_ operator.
qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, 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') -> clika_runtime._core.Tensor
qconv_(input, weight, out, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor
The qconv_ operator.
qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, 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') -> clika_runtime._core.Tensor
qconv_(input, weight, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor
The qconv_ operator.
qconv_transpose
qconv_transpose(*args, **kwargs)
qconv_transpose(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv_transpose(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor
qconv_transpose(input, weight, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor
The qconv_transpose operator.
qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv_transpose(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv_transpose operator.
qconv_transpose1d
qconv_transpose1d(*args, **kwargs)
qconv_transpose1d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv_transpose1d(input: clika_runtime._core.QTensor, 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(input, weight, bias=None, stride=[1], padding=[0], output_padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor
The qconv_transpose1d operator.
qconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv_transpose1d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv_transpose1d operator.
qconv_transpose1d_woq
qconv_transpose1d_woq(*args, **kwargs)
qconv_transpose1d_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1], padding=[0], output_padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor
The qconv_transpose1d_woq operator.
qconv_transpose2d
qconv_transpose2d(*args, **kwargs)
qconv_transpose2d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv_transpose2d(input: clika_runtime._core.QTensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor
The qconv_transpose2d operator.
qconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv_transpose2d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv_transpose2d operator.
qconv_transpose2d_woq
qconv_transpose2d_woq(*args, **kwargs)
qconv_transpose2d_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor
The qconv_transpose2d_woq operator.
qconv_transpose3d
qconv_transpose3d(*args, **kwargs)
qconv_transpose3d(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qconv_transpose3d(input: clika_runtime._core.QTensor, 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(input, 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
The qconv_transpose3d operator.
qconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qconv_transpose3d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qconv_transpose3d operator.
qconv_transpose3d_woq
qconv_transpose3d_woq(*args, **kwargs)
qconv_transpose3d_woq(input: clika_runtime._core.Tensor, 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(input, 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
The qconv_transpose3d_woq operator.
qconv_transpose_
qconv_transpose_(*args, **kwargs)
qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: 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') -> clika_runtime.core.QTensor qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, 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') -> clika_runtime.core.Tensor qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, 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') -> clika_runtime._core.Tensor
Overloaded function.
qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: 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') -> clika_runtime._core.QTensor
qconv_transpose_(input, weight, out, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> QTensor
The qconv_transpose_ operator.
qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, 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') -> clika_runtime._core.Tensor
qconv_transpose_(input, weight, out, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor
The qconv_transpose_ operator.
qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, 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') -> clika_runtime._core.Tensor
qconv_transpose_(input, weight, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor
The qconv_transpose_ operator.
qconv_transpose_woq
qconv_transpose_woq(*args, **kwargs)
qconv_transpose_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor
The qconv_transpose_woq operator.
qconv_woq
qconv_woq(*args, **kwargs)
qconv_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity', compute_mode='exact_fp') -> Tensor
The qconv_woq operator.
qdeform_conv
qdeform_conv(*args, **kwargs)
qdeform_conv(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, offset_groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qdeform_conv(input: clika_runtime._core.QTensor, 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') -> clika_runtime._core.Tensor
qdeform_conv(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity') -> Tensor
The qdeform_conv operator.
qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, offset_groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qdeform_conv(input, weight, offset, mask, bias, stride, padding, dilation, groups, offset_groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qdeform_conv operator.
qdeform_conv_woq
qdeform_conv_woq(*args, **kwargs)
qdeform_conv_woq(input: clika_runtime._core.Tensor, 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(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor
The qdeform_conv_woq operator.
qdiv
qdiv(*args, **kwargs)
qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
Overloaded function.
qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qdiv(input, other, *, activation='identity') -> Tensor
The qdiv operator.
qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qdiv(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor
The qdiv operator.
qdiv_
qdiv_(*args, **kwargs)
qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime.core.QTensor qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime.core.Tensor qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
Overloaded function.
qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qdiv_(input, other, out, *, activation='identity') -> QTensor
The qdiv_ operator.
qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qdiv_(input, other, out, *, activation='identity') -> Tensor
The qdiv_ operator.
qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qdiv_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor
The qdiv_ operator.
qfast_gelu
qfast_gelu(*args, **kwargs)
qfast_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qfast_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qfast_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qfast_gelu(input) -> Tensor
The qfast_gelu operator.
qfast_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qfast_gelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qfast_gelu operator.
qfast_gelu_
qfast_gelu_(*args, **kwargs)
qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qfast_gelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qfast_gelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qfast_gelu_(input, out) -> QTensor
The qfast_gelu_ operator.
qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qfast_gelu_(input, out) -> Tensor
The qfast_gelu_ operator.
qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qfast_gelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qfast_gelu_ operator.
qgelu
qgelu(*args, **kwargs)
qgelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qgelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qgelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qgelu(input) -> Tensor
The qgelu operator.
qgelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qgelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qgelu operator.
qgelu_
qgelu_(*args, **kwargs)
qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qgelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qgelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qgelu_(input, out) -> QTensor
The qgelu_ operator.
qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qgelu_(input, out) -> Tensor
The qgelu_ operator.
qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qgelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qgelu_ operator.
qhardswish
qhardswish(*args, **kwargs)
qhardswish(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qhardswish(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qhardswish(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qhardswish(input) -> Tensor
The qhardswish operator.
qhardswish(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qhardswish(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qhardswish operator.
qhardswish_
qhardswish_(*args, **kwargs)
qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qhardswish(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qhardswish(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qhardswish_(input, out) -> QTensor
The qhardswish_ operator.
qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qhardswish_(input, out) -> Tensor
The qhardswish_ operator.
qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qhardswish_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qhardswish_ operator.
qk_layer_norm
qk_layer_norm(*args, **kwargs)
qk_layer_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]
qk_layer_norm(query, key=None, value=None, query_weight=None, query_bias=None, key_weight=None, key_bias=None, head_dim=0, eps=None) -> tuple[Tensor, Tensor, Tensor]
The qk_layer_norm operator.
qk_layer_norm_
qk_layer_norm_(*args, **kwargs)
qk_layer_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor
qk_layer_norm_(query, key=None, value=None, query_weight=None, query_bias=None, key_weight=None, key_bias=None, head_dim=0, eps=None) -> Tensor
The qk_layer_norm_ operator.
qk_rms_norm
qk_rms_norm(*args, **kwargs)
qk_rms_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor]
qk_rms_norm(query, key=None, value=None, query_weight=None, key_weight=None, head_dim=0, eps=None) -> tuple[Tensor, Tensor, Tensor]
The qk_rms_norm operator.
qk_rms_norm_
qk_rms_norm_(*args, **kwargs)
qk_rms_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor
qk_rms_norm_(query, key=None, value=None, query_weight=None, key_weight=None, head_dim=0, eps=None) -> Tensor
The qk_rms_norm_ operator.
qleaky_relu
qleaky_relu(*args, **kwargs)
qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor
qleaky_relu(input, negative_slope=0.01) -> Tensor
The qleaky_relu operator.
qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qleaky_relu(input, negative_slope, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qleaky_relu operator.
qleaky_relu_
qleaky_relu_(*args, **kwargs)
qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qleaky_relu_(input, negative_slope, out) -> QTensor
The qleaky_relu_ operator.
qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qleaky_relu_(input, negative_slope, out) -> Tensor
The qleaky_relu_ operator.
qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qleaky_relu_(input, negative_slope, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qleaky_relu_ operator.
qlinear
qlinear(*args, **kwargs)
qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
qlinear(input, weight, bias=None, *, activation=None) -> Tensor
The qlinear operator.
qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qlinear(input, weight, bias, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qlinear operator.
qlinear_
qlinear_(*args, **kwargs)
qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime.core.QTensor qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
Overloaded function.
qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.QTensor
qlinear_(input, weight, out, bias=None, *, activation=None) -> QTensor
The qlinear_ operator.
qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor
qlinear_(input, weight, out, bias=None, *, activation=None) -> Tensor
The qlinear_ operator.
qlinear_woq
qlinear_woq(*args, **kwargs)
qlinear_woq(input: clika_runtime._core.Tensor, 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(input, weight, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor
The qlinear_woq operator.
qlinear_woq_
qlinear_woq_(*args, **kwargs)
qlinear_woq_(input: clika_runtime._core.Tensor, 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_(input, weight, out, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor
The qlinear_woq_ operator.
qmatmul
qmatmul(*args, **kwargs)
qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, transpose_a: bool, transpose_b: bool, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor
qmatmul(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor
The qmatmul operator.
qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, transpose_a: bool, transpose_b: bool, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qmatmul(input, other, bias, activation, transpose_a, transpose_b, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qmatmul operator.
qmatmul_
qmatmul_(*args, **kwargs)
qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime.core.QTensor qmatmul(input: clika_runtime._core.QTensor, 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) -> clika_runtime.core.Tensor qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor
Overloaded function.
qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.QTensor
qmatmul_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> QTensor
The qmatmul_ operator.
qmatmul_(input: clika_runtime._core.QTensor, 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) -> clika_runtime._core.Tensor
qmatmul_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor
The qmatmul_ operator.
qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor
qmatmul_(input, other, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor
The qmatmul_ operator.
qmatmul_woq
qmatmul_woq(*args, **kwargs)
qmatmul_woq(input: clika_runtime._core.Tensor, 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(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor
The qmatmul_woq operator.
qmatmul_woq_
qmatmul_woq_(*args, **kwargs)
qmatmul_woq_(input: clika_runtime._core.Tensor, 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_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor
The qmatmul_woq_ operator.
qmoe
qmoe(*args, **kwargs)
qmoe(input: clika_runtime._core.QTensor, 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(input: clika_runtime._core.QTensor, 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, fc2_bias: clika_runtime._core.Tensor | None, fc3_experts: clika_runtime._core.QTensor, fc3_bias: clika_runtime._core.Tensor | None, e_score_correction_bias: clika_runtime._core.Tensor | None, router_weights: clika_runtime._core.Tensor | None, routing_mode: object, renormalize: bool | None, n_group: int | None, topk_group: int | None, routed_scaling_factor: float | None, sparse_mixer_eps: float | None, apply_router_weight_on_input: bool | None, activation: object, swiglu_fusion: object, swiglu_alpha: float | None, swiglu_beta: float | None, swiglu_limit: float | None, gelu_mode: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor
Overloaded function.
qmoe(input: clika_runtime._core.QTensor, 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(input, 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
The qmoe operator.
qmoe(input: clika_runtime._core.QTensor, 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, fc2_bias: clika_runtime._core.Tensor | None, fc3_experts: clika_runtime._core.QTensor, fc3_bias: clika_runtime._core.Tensor | None, e_score_correction_bias: clika_runtime._core.Tensor | None, router_weights: clika_runtime._core.Tensor | None, routing_mode: object, renormalize: bool | None, n_group: int | None, topk_group: int | None, routed_scaling_factor: float | None, sparse_mixer_eps: float | None, apply_router_weight_on_input: bool | None, activation: object, swiglu_fusion: object, swiglu_alpha: float | None, swiglu_beta: float | None, swiglu_limit: float | None, gelu_mode: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor
qmoe(input, router_logits, fc1_experts, fc2_experts, top_k, fc1_bias, fc2_bias, fc3_experts, fc3_bias, e_score_correction_bias, router_weights, routing_mode, renormalize, n_group, topk_group, routed_scaling_factor, sparse_mixer_eps, apply_router_weight_on_input, activation, swiglu_fusion, swiglu_alpha, swiglu_beta, swiglu_limit, gelu_mode, out_scale, out_zero_point, out_quant_axis=-1, *, out_dtype=Undefined, shared_output=None) -> QTensor
The qmoe operator.
qmoe_woq
qmoe_woq(*args, **kwargs)
qmoe_woq(input: clika_runtime._core.Tensor, 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(input, 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
The qmoe_woq operator.
qmul
qmul(*args, **kwargs)
qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
Overloaded function.
qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qmul(input, other, *, activation='identity') -> Tensor
The qmul operator.
qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qmul(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor
The qmul operator.
qmul_
qmul_(*args, **kwargs)
qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime.core.QTensor qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime.core.Tensor qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
Overloaded function.
qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qmul_(input, other, out, *, activation='identity') -> QTensor
The qmul_ operator.
qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qmul_(input, other, out, *, activation='identity') -> Tensor
The qmul_ operator.
qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qmul_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor
The qmul_ operator.
qquick_gelu
qquick_gelu(*args, **kwargs)
qquick_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qquick_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qquick_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qquick_gelu(input) -> Tensor
The qquick_gelu operator.
qquick_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qquick_gelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qquick_gelu operator.
qquick_gelu_
qquick_gelu_(*args, **kwargs)
qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qquick_gelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qquick_gelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qquick_gelu_(input, out) -> QTensor
The qquick_gelu_ operator.
qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qquick_gelu_(input, out) -> Tensor
The qquick_gelu_ operator.
qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qquick_gelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qquick_gelu_ operator.
qrelu
qrelu(*args, **kwargs)
qrelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qrelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qrelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qrelu(input) -> Tensor
The qrelu operator.
qrelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qrelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qrelu operator.
qrelu_
qrelu_(*args, **kwargs)
qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qrelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qrelu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qrelu_(input, out) -> QTensor
The qrelu_ operator.
qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qrelu_(input, out) -> Tensor
The qrelu_ operator.
qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qrelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qrelu_ operator.
qsigmoid
qsigmoid(*args, **kwargs)
qsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qsigmoid(input) -> Tensor
The qsigmoid operator.
qsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qsigmoid(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qsigmoid operator.
qsigmoid_
qsigmoid_(*args, **kwargs)
qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qsigmoid(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qsigmoid(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qsigmoid_(input, out) -> QTensor
The qsigmoid_ operator.
qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qsigmoid_(input, out) -> Tensor
The qsigmoid_ operator.
qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qsigmoid_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qsigmoid_ operator.
qsilu
qsilu(*args, **kwargs)
qsilu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qsilu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qsilu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qsilu(input) -> Tensor
The qsilu operator.
qsilu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qsilu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qsilu operator.
qsilu_
qsilu_(*args, **kwargs)
qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qsilu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qsilu(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qsilu_(input, out) -> QTensor
The qsilu_ operator.
qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qsilu_(input, out) -> Tensor
The qsilu_ operator.
qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qsilu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qsilu_ operator.
qsub
qsub(*args, **kwargs)
qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
Overloaded function.
qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qsub(input, other, *, activation='identity') -> Tensor
The qsub operator.
qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qsub(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor
The qsub operator.
qsub_
qsub_(*args, **kwargs)
qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime.core.QTensor qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime.core.Tensor qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
Overloaded function.
qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor
qsub_(input, other, out, *, activation='identity') -> QTensor
The qsub_ operator.
qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qsub_(input, other, out, *, activation='identity') -> Tensor
The qsub_ operator.
qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
qsub_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor
The qsub_ operator.
qtanh
qtanh(*args, **kwargs)
qtanh(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qtanh(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
Overloaded function.
qtanh(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor
qtanh(input) -> Tensor
The qtanh operator.
qtanh(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
qtanh(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The qtanh operator.
qtanh_
qtanh_(*args, **kwargs)
qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor qtanh(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime.core.Tensor qtanh(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
qtanh_(input, out) -> QTensor
The qtanh_ operator.
qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
qtanh_(input, out) -> Tensor
The qtanh_ operator.
qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
qtanh_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The qtanh_ operator.
quantile
quantile(*args, **kwargs)
quantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor
quantile(input, q, dim=None, keepdim=False, interpolation='linear') -> Tensor
The quantile operator.
quantize
quantize(input: 'Tensor', scale: 'ScaleLike', zero_point: 'ZeroPointLike' = None, quant_axis: 'int' = -1, *, out_dtype: 'DtypeLike' = None, block_size: 'int' = 0) -> 'QTensor'
quantize(input, scale, zero_point=None, quant_axis=-1, *, out_dtype=None, block_size=0) -> QTensor
Quantize input at scale, a number or a tensor.
quantize(x, 0.02, out_dtype=clika_runtime.int8) quantizes per tensor
at a single scale; tensor scales carry per-channel and blocked schemes
exactly as :func:clika_runtime.ops.quantize takes them.
quantize_
quantize_(*args, **kwargs)
quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor quantize(input: clika_runtime._core.Tensor, 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.
quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
quantize_(input, out) -> QTensor
The quantize_ operator.
quantize_(input: clika_runtime._core.Tensor, 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_(input, out, scale, zero_point=None, quant_axis=-1, block_size=0) -> Tensor
The quantize_ operator.
quantize_dequantize
quantize_dequantize(input: 'Tensor', scale: 'ScaleLike', zero_point: 'ZeroPointLike' = None, quant_axis: 'int' = -1, *, code_dtype: 'DtypeLike' = None, out_dtype: 'DtypeLike' = None, block_size: 'int' = 0) -> 'Tensor'
quantize_dequantize(input, scale, zero_point=None, quant_axis=-1, *, code_dtype=None, out_dtype=None, block_size=0) -> Tensor
Quantize then dequantize in one call, at a number or tensor scale. The round trip reports what the quantized representation keeps; the standard fake-quantization step.
quantize_to_scheme
quantize_to_scheme(*args, **kwargs)
quantize_to_scheme(input: clika_runtime._core.Tensor, scheme: str, scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor
quantize_to_scheme(input, scheme, scale=None) -> QTensor
The quantize_to_scheme operator.
quantized_view
quantized_view(*args, **kwargs)
quantized_view(tensor: clika_runtime._core.Tensor) -> clika_runtime._core.QTensor
The typed quantized view of a tensor that carries a quantization scheme (Tensor.is_quantized); raises RuntimeError on a plain tensor.
quick_gelu
quick_gelu(*args, **kwargs)
quick_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
quick_gelu(input) -> Tensor
The quick_gelu operator.
rad2deg
rad2deg(*args, **kwargs)
rad2deg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
rad2deg(input) -> Tensor
The rad2deg operator.
rad2deg_
rad2deg_(*args, **kwargs)
rad2deg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
rad2deg_(self) -> Tensor
The rad2deg_ operator.
rand
rand(*size: 'ShapeDim', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
rand(*size, dtype=None, device=None) -> Tensor
Uniform samples in [0, 1); the default dtype unless dtype says
otherwise.
rand_like
rand_like(input: 'Tensor', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
rand_like(input, *, dtype=None, device=None) -> Tensor
Uniform samples in [0, 1) of input's shape, dtype and device,
each overridable by keyword.
randint
randint(*args: 'int | Sequence[int]', size: 'Sequence[int] | None' = None, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
randint(low=0, high, size, *, dtype=None, device=None) -> Tensor
Uniform integers in [low, high) of the given shape:
randint(high, size) or randint(low, high, size); size may
also be given by keyword. int64 unless dtype says otherwise.
randint_like
randint_like(input: 'Tensor', low: 'int', high: 'int | None' = None, *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
randint_like(input, low=0, high, *, dtype=None, device=None) -> Tensor
Uniform integers in [low, high) of input's shape, dtype and
device (randint_like(input, high) counts from zero); dtype and
device overridable by keyword.
randn
randn(*size: 'ShapeDim', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
randn(*size, dtype=None, device=None) -> Tensor
Standard-normal samples; the default dtype unless dtype says otherwise.
randn_like
randn_like(input: 'Tensor', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
randn_like(input, *, dtype=None, device=None) -> Tensor
Standard-normal samples of input's shape, dtype and device, each
overridable by keyword.
random_
random_(*args, **kwargs)
random_(self: clika_runtime._core.Tensor, low: int | None = None, high: int | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
random_(self, low=None, high=None, *, device=None) -> Tensor
The random_ operator.
randperm
randperm(*args, **kwargs)
randperm(n: clika_runtime._core.ops.ScalarOrTensor, *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
randperm(n, *, dtype=Int64, device=None) -> Tensor
The randperm operator.
reciprocal
reciprocal(*args, **kwargs)
reciprocal(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
reciprocal(input) -> Tensor
The reciprocal operator.
reciprocal_
reciprocal_(*args, **kwargs)
reciprocal_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
reciprocal_(self) -> Tensor
The reciprocal_ operator.
reflect_pad
reflect_pad(*args, **kwargs)
reflect_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
reflect_pad(input, pad) -> Tensor
The reflect_pad operator.
reglu
reglu(*args, **kwargs)
reglu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
reglu(input) -> Tensor
The reglu operator.
relu
relu(*args, **kwargs)
relu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
relu(input) -> Tensor
The relu operator.
relu6
relu6(*args, **kwargs)
relu6(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
relu6(input) -> Tensor
The relu6 operator.
relu6_
relu6_(*args, **kwargs)
relu6_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
relu6_(self) -> Tensor
The relu6_ operator.
relu_
relu_(*args, **kwargs)
relu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
relu_(self) -> Tensor
The relu_ operator.
remainder
remainder(*args, **kwargs)
remainder(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
remainder(input, other) -> Tensor
The remainder operator.
remainder_
remainder_(*args, **kwargs)
remainder_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
remainder_(self, other) -> Tensor
The remainder_ operator.
renorm
renorm(*args, **kwargs)
renorm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor
renorm(input, p, dim, maxnorm, eps=None) -> Tensor
The renorm operator.
renorm_
renorm_(*args, **kwargs)
renorm_(self: clika_runtime._core.Tensor, 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, dim, maxnorm, eps=None) -> Tensor
The renorm_ operator.
repeat
repeat(*args, **kwargs)
repeat(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
repeat(input, sizes) -> Tensor
The repeat operator.
repeat_interleave
repeat_interleave(*args, **kwargs)
repeat_interleave(input: clika_runtime._core.Tensor, repeats: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, output_size: int | None = None) -> clika_runtime._core.Tensor
repeat_interleave(input, repeats, dim=None, output_size=None) -> Tensor
The repeat_interleave operator.
replicate_pad
replicate_pad(*args, **kwargs)
replicate_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
replicate_pad(input, pad) -> Tensor
The replicate_pad operator.
requantize
requantize(*args, **kwargs)
requantize(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor
requantize(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor
The requantize operator.
requantize_
requantize_(*args, **kwargs)
requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime.core.QTensor requantize(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
Overloaded function.
requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor
requantize_(input, out) -> QTensor
The requantize_ operator.
requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor
requantize_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor
The requantize_ operator.
resample
resample(*args, **kwargs)
resample(input: clika_runtime._core.Tensor, orig_freq: int, new_freq: int, lowpass_filter_width: int = 16, rolloff: float = 0.945, beta: float | None = None) -> clika_runtime._core.Tensor
resample(input, orig_freq, new_freq, lowpass_filter_width=16, rolloff=0.945, beta=None) -> Tensor
The resample operator.
reset_peak_memory_stats
reset_peak_memory_stats(*args, **kwargs)
reset_peak_memory_stats(device: object) -> None
reset_peak_memory_stats(device) -> None
Restart the device's peak memory marks (peak_active_bytes, peak_reserved_bytes) from the present figures; nothing is allocated, freed or waited on. The measuring idiom: reset, run one step, read memory_stats(device).peak_active_bytes.
reshape
reshape(*args, **kwargs)
reshape(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.IndexBound]) -> clika_runtime._core.Tensor
reshape(input, shape) -> Tensor
The reshape operator.
reshape_as
reshape_as(*args, **kwargs)
reshape_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
reshape_as(input, other) -> Tensor
The reshape_as operator.
rfft
rfft(*args, **kwargs)
rfft(input: clika_runtime._core.Tensor, n_fft: int | None = None, normalized: bool = False) -> clika_runtime._core.Tensor
rfft(input, n_fft=None, normalized=False) -> Tensor
The rfft operator.
rms_norm
rms_norm(*args, **kwargs)
rms_norm(input: clika_runtime._core.Tensor, 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(input, normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor
The rms_norm operator.
roll
roll(*args, **kwargs)
roll(input: clika_runtime._core.Tensor, shifts: collections.abc.Sequence[int], dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor
roll(input, shifts, dims=[]) -> Tensor
The roll operator.
rot90
rot90(*args, **kwargs)
rot90(input: clika_runtime._core.Tensor, k: int = 1, dims: collections.abc.Sequence[int] = [0, 1]) -> clika_runtime._core.Tensor
rot90(input, k=1, dims=[0, 1]) -> Tensor
The rot90 operator.
rotary_embedding
rotary_embedding(*args, **kwargs)
rotary_embedding(input: clika_runtime._core.Tensor, 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(input, 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
The rotary_embedding operator.
rotary_embedding_qk
rotary_embedding_qk(*args, **kwargs)
rotary_embedding_qk(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, 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) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
rotary_embedding_qk(query, key, 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) -> tuple[Tensor, Tensor]
The rotary_embedding_qk operator.
rotary_embedding_qk_varlen
rotary_embedding_qk_varlen(*args, **kwargs)
rotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, 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) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
rotary_embedding_qk_varlen(query, key, 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) -> tuple[Tensor, Tensor]
The rotary_embedding_qk_varlen operator.
rotary_embedding_varlen
rotary_embedding_varlen(*args, **kwargs)
rotary_embedding_varlen(input: clika_runtime._core.Tensor, 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(input, 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
The rotary_embedding_varlen operator.
round
round(*args, **kwargs)
round(input: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor
round(input, decimals=0) -> Tensor
The round operator.
round_
round_(*args, **kwargs)
round_(self: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor
round_(self, decimals=0) -> Tensor
The round_ operator.
rsqrt
rsqrt(*args, **kwargs)
rsqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
rsqrt(input) -> Tensor
The rsqrt operator.
rsqrt_
rsqrt_(*args, **kwargs)
rsqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
rsqrt_(self) -> Tensor
The rsqrt_ operator.
save
save(obj: 'Tensor | Mapping[str, Tensor] | Any', path: 'PathLike', *, metadata: 'Metadata | None' = None) -> 'None'
save(obj, path, *, metadata=None) -> None
Write obj to a safetensors file at path: a
:class:~clika_runtime.Tensor, a dict of tensors (a
state_dict, kept in its own order), or any pytree of tensors,
flattened by key path with its structure in the file's header
metadata. metadata adds str -> str pairs to the header (a dict,
or (key, value) pairs), written in the given order after the
structure entries. Pending work on every tensor settles first.
Raises:
TypeError: when a leaf is not a tensor, a dict key is not a str, or
a metadata pair is not two strings.
ValueError: when a dict uses the reserved key .tensor, or a
metadata key starts with clika..
scaled_dot_product_attention
scaled_dot_product_attention(*args, **kwargs)
scaled_dot_product_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor
scaled_dot_product_attention(query, key, value, attn_mask=None, is_causal=False, q_scale=None, k_scale=None, v_scale=None) -> Tensor
The scaled_dot_product_attention operator.
scaled_dot_product_attention_varlen
scaled_dot_product_attention_varlen(*args, **kwargs)
scaled_dot_product_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor
scaled_dot_product_attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, attn_mask=None, is_causal=False, q_scale=None, k_scale=None, v_scale=None) -> Tensor
The scaled_dot_product_attention_varlen operator.
scatter
scatter(*args, **kwargs)
scatter(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
scatter(input, dim, index, src) -> Tensor
The scatter operator.
scatter_
scatter_(*args, **kwargs)
scatter_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
scatter_(self, dim, index, src) -> Tensor
The scatter_ operator.
scatter_add
scatter_add(*args, **kwargs)
scatter_add(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor
scatter_add(input, dim, index, src, deterministic=False) -> Tensor
The scatter_add operator.
scatter_add_
scatter_add_(*args, **kwargs)
scatter_add_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor
scatter_add_(self, dim, index, src, deterministic=False) -> Tensor
The scatter_add_ operator.
scatter_reduce
scatter_reduce(*args, **kwargs)
scatter_reduce(input: clika_runtime._core.Tensor, 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(input, dim, index, src, reduce, include_self=True, deterministic=False) -> Tensor
The scatter_reduce operator.
scatter_reduce_
scatter_reduce_(*args, **kwargs)
scatter_reduce_(self: clika_runtime._core.Tensor, 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, index, src, reduce, include_self=True, deterministic=False) -> Tensor
The scatter_reduce_ operator.
searchsorted
searchsorted(*args, **kwargs)
searchsorted(sorted_sequence: clika_runtime._core.Tensor, values: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False, side: bool | None = None, sorter: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor
searchsorted(sorted_sequence, values, out_int32=False, right=False, side=None, sorter=None) -> Tensor
The searchsorted operator.
select
select(*args, **kwargs)
select(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor
select(input, dim, index) -> Tensor
The select operator.
selu
selu(*args, **kwargs)
selu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
selu(input) -> Tensor
The selu operator.
selu_
selu_(*args, **kwargs)
selu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
selu_(self) -> Tensor
The selu_ operator.
set_default_dtype
set_default_dtype(dtype: 'DtypeLike') -> 'None'
set_default_dtype(dtype) -> None
Set the default floating-point dtype (clika_runtime.float32,
"bfloat16", ...). Only a floating-point dtype is accepted.
set_printoptions
set_printoptions(precision: 'int | None' = None, threshold: 'int | None' = None, edgeitems: 'int | None' = None, linewidth: 'int | None' = None, sci_mode: 'bool | None' = None) -> 'None'
set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, sci_mode=None) -> None
Set how tensors print. precision digits after the point (4 at
start), threshold the element count past which the printout
abbreviates with ... (1000), edgeitems the entries kept at each
end of an abbreviated dim (3), linewidth the characters per line
(80), sci_mode forces (True) or forbids (False) scientific notation;
None lets the values decide. An argument left None keeps its value.
sgn
sgn(*args, **kwargs)
sgn(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sgn(input) -> Tensor
The sgn operator.
sgn_
sgn_(*args, **kwargs)
sgn_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sgn_(self) -> Tensor
The sgn_ operator.
shape
shape(*args, **kwargs)
shape(input: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor shape(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor
Overloaded function.
shape(input: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor
shape(input, dim=None) -> Tensor
The shape operator.
shape(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor
shape(input, start, end) -> Tensor
The shape operator.
shape_host
shape_host(*args, **kwargs)
shape_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor shape_host(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor shape_host(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor
Overloaded function.
shape_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
shape_host(input) -> Tensor
The shape_host operator.
shape_host(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor
shape_host(input, dim) -> Tensor
The shape_host operator.
shape_host(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor
shape_host(input, start, end) -> Tensor
The shape_host operator.
sigmoid
sigmoid(*args, **kwargs)
sigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sigmoid(input) -> Tensor
The sigmoid operator.
sigmoid_
sigmoid_(*args, **kwargs)
sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sigmoid_(self) -> Tensor
The sigmoid_ operator.
sign
sign(*args, **kwargs)
sign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sign(input) -> Tensor
The sign operator.
sign_
sign_(*args, **kwargs)
sign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sign_(self) -> Tensor
The sign_ operator.
silu
silu(*args, **kwargs)
silu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
silu(input) -> Tensor
The silu operator.
silu_
silu_(*args, **kwargs)
silu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
silu_(self) -> Tensor
The silu_ operator.
sin
sin(*args, **kwargs)
sin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sin(input) -> Tensor
The sin operator.
sin_
sin_(*args, **kwargs)
sin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sin_(self) -> Tensor
The sin_ operator.
sinc
sinc(*args, **kwargs)
sinc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sinc(input) -> Tensor
The sinc operator.
sinc_
sinc_(*args, **kwargs)
sinc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sinc_(self) -> Tensor
The sinc_ operator.
sinh
sinh(*args, **kwargs)
sinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sinh(input) -> Tensor
The sinh operator.
sinh_
sinh_(*args, **kwargs)
sinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sinh_(self) -> Tensor
The sinh_ operator.
slice
slice(*args, **kwargs)
slice(input: clika_runtime._core.Tensor, 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(input: clika_runtime._core.Tensor, 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.
slice(input: clika_runtime._core.Tensor, 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(input, dim, start=None, end=None, step=1) -> Tensor
The slice operator.
slice(input: clika_runtime._core.Tensor, 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(input, dim, start=[], end=[], step=[]) -> Tensor
The slice operator.
smooth_l1_loss
smooth_l1_loss(*args, **kwargs)
smooth_l1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', beta: float = 1.0) -> clika_runtime._core.Tensor
smooth_l1_loss(input, target, reduction='mean', beta=1.0) -> Tensor
The smooth_l1_loss operator.
snake
snake(*args, **kwargs)
snake(input: clika_runtime._core.Tensor, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor
snake(input, alpha=None, beta=None, eps=1e-9) -> Tensor
The snake operator.
snake_
snake_(*args, **kwargs)
snake_(self: clika_runtime._core.Tensor, 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=None, beta=None, eps=1e-9) -> Tensor
The snake_ operator.
softcap_logits
softcap_logits(*args, **kwargs)
softcap_logits(input: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
softcap_logits(input, cap) -> Tensor
The softcap_logits operator.
softcap_logits_
softcap_logits_(*args, **kwargs)
softcap_logits_(self: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
softcap_logits_(self, cap) -> Tensor
The softcap_logits_ operator.
softmax
softmax(*args, **kwargs)
softmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
softmax(input, dim=-1, *, dtype=Undefined) -> Tensor
The softmax operator.
softmax_
softmax_(*args, **kwargs)
softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor
softmax_(self, dim=-1) -> Tensor
The softmax_ operator.
softmin
softmin(*args, **kwargs)
softmin(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
softmin(input, dim=-1, *, dtype=Undefined) -> Tensor
The softmin operator.
softmin_
softmin_(*args, **kwargs)
softmin_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor
softmin_(self, dim=-1) -> Tensor
The softmin_ operator.
softplus
softplus(*args, **kwargs)
softplus(input: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor
softplus(input, beta=1.0, threshold=20.0) -> Tensor
The softplus operator.
softplus_
softplus_(*args, **kwargs)
softplus_(self: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor
softplus_(self, beta=1.0, threshold=20.0) -> Tensor
The softplus_ operator.
softshrink
softshrink(*args, **kwargs)
softshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor
softshrink(input, lambd=0.5) -> Tensor
The softshrink operator.
softshrink_
softshrink_(*args, **kwargs)
softshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor
softshrink_(self, lambd=0.5) -> Tensor
The softshrink_ operator.
softsign
softsign(*args, **kwargs)
softsign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
softsign(input) -> Tensor
The softsign operator.
softsign_
softsign_(*args, **kwargs)
softsign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
softsign_(self) -> Tensor
The softsign_ operator.
sort
sort(*args, **kwargs)
sort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
sort(input, dim=-1, descending=False, stable=False) -> tuple[Tensor, Tensor]
The sort operator.
split_by_size
split_by_size(*args, **kwargs)
split_by_size(input: clika_runtime._core.Tensor, chunk_size: int, dim: int = 0) -> list[clika_runtime._core.Tensor]
split_by_size(input, chunk_size, dim=0) -> list[Tensor]
The split_by_size operator.
split_with_sizes
split_with_sizes(*args, **kwargs)
split_with_sizes(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dim: int = 0) -> list[clika_runtime._core.Tensor]
split_with_sizes(input, sizes, dim=0) -> list[Tensor]
The split_with_sizes operator.
sqrt
sqrt(*args, **kwargs)
sqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sqrt(input) -> Tensor
The sqrt operator.
sqrt_
sqrt_(*args, **kwargs)
sqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
sqrt_(self) -> Tensor
The sqrt_ operator.
square
square(*args, **kwargs)
square(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
square(input) -> Tensor
The square operator.
square_
square_(*args, **kwargs)
square_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
square_(self) -> Tensor
The square_ operator.
squeeze
squeeze(*args, **kwargs)
squeeze(input: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor
squeeze(input, dim=[]) -> Tensor
The squeeze operator.
squeeze_
squeeze_(*args, **kwargs)
squeeze_(self: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor
squeeze_(self, dim=[]) -> Tensor
The squeeze_ operator.
ssd_update
ssd_update(*args, **kwargs)
ssd_update(input: clika_runtime._core.Tensor, 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(input, dt, a_rate, b_mat, c_mat, d_skip, dt_bias, gate, state, seq_lens=None, slot_ids=None, dt_softplus=False) -> Tensor
The ssd_update operator.
stack
stack(*args, **kwargs)
stack(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0) -> clika_runtime._core.Tensor
stack(tensors, dim=0) -> Tensor
The stack operator.
std
std(*args, **kwargs)
std(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor
std(input, dims=[], correction=1, keepdim=False) -> Tensor
The std operator.
stft
stft(*args, **kwargs)
stft(input: clika_runtime._core.Tensor, 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(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True) -> Tensor
The stft operator.
stream
stream(stream_or_device: 'Stream | Device | str') -> '_PlacementContext'
stream(stream_or_device) -> a placement region
Run the region on a stream: operations inside issue onto it in order
and land their outputs there. A :class:Device (or a device string)
stands for that device's default stream at each operation.
sub
sub(*args, **kwargs)
sub(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor sub(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
sub(input, other, alpha=1.0, *, activation='identity') -> Tensor
The sub operator.
sub_
sub_(*args, **kwargs)
sub_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor
sub_(self, other, alpha=1.0, *, activation='identity') -> Tensor
The sub_ operator.
sum
sum(*args, **kwargs)
sum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor
sum(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor
The sum operator.
swiglu
swiglu(*args, **kwargs)
swiglu(input: clika_runtime._core.Tensor, alpha: float = 1.0, beta: float = 0.0, limit: float = inf) -> clika_runtime._core.Tensor
swiglu(input, alpha=1.0, beta=0.0, limit=None) -> Tensor
The swiglu operator.
synchronize
synchronize(target: 'Stream | Device | str | None' = None) -> 'None'
synchronize(target=None) -> None
Block until the targeted work has completed.
None (the default) synchronizes the stream the calling thread's
ambient placement resolves to: a set stream as is, a set device's
current lane for this thread, and, with nothing set, the calling
thread's current lane on the cpu. A :class:Device (or device string)
drains that device: every lane of that device is waited on, so the
device's queued work is complete on return, and no other device's lane
is waited on. A :class:Stream synchronizes itself. Prefer this
targeted form over ops.synchronize_all (which stalls every stream
in the process).
synchronize_all
synchronize_all(*args, **kwargs)
synchronize_all() -> None
synchronize_all() -> None
The synchronize_all operator.
synchronous
synchronous() -> '_SynchronousScope'
synchronous() -> an execution-mode region
Every operation inside completes before it returns: the calling
thread's lane is settled on enter, then the region runs one operation
at a time (deterministic stepping and debugging; pipelining is
given up). Nests inside :func:device / :func:stream regions.
take
take(*args, **kwargs)
take(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
take(self, index) -> Tensor
The take operator.
take_along_dim
take_along_dim(*args, **kwargs)
take_along_dim(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor
take_along_dim(input, index, dim=None) -> Tensor
The take_along_dim operator.
tan
tan(*args, **kwargs)
tan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
tan(input) -> Tensor
The tan operator.
tan_
tan_(*args, **kwargs)
tan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
tan_(self) -> Tensor
The tan_ operator.
tanh
tanh(*args, **kwargs)
tanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
tanh(input) -> Tensor
The tanh operator.
tanh_
tanh_(*args, **kwargs)
tanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
tanh_(self) -> Tensor
The tanh_ operator.
tensor
tensor(data: 'object', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
tensor(data, *, dtype=None, device=None) -> Tensor
A tensor from an array, a nested Python list, a number, or a tensor.
Arrays and lists copy in on the CPU and move to device when asked.
Lists of Python floats enter at the default dtype (:func:get_default_dtype);
ints at int64; bools at bool. Python data packs its own payload,
so no array library is needed; an array enters through the buffer
protocol or DLPack at its own dtype. When dtype names a type a
payload cannot spell directly (bfloat16, the float8 family,
sub-byte codes), the data enters at a carrier type and converts through
the runtime; pass raw payloads to :meth:Tensor.from_bytes to skip the
conversion entirely. A Tensor argument passes through, converted
when dtype or device ask for a change (the result may share
its storage). Inside a meta-init region the result is storage-free.
tensordot
tensordot(*args, **kwargs)
tensordot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dims_a: collections.abc.Sequence[int], dims_b: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
tensordot(input, other, dims_a, dims_b) -> Tensor
The tensordot operator.
threshold
threshold(*args, **kwargs)
threshold(input: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor
threshold(input, threshold, value) -> Tensor
The threshold operator.
threshold_
threshold_(*args, **kwargs)
threshold_(self: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor
threshold_(self, threshold, value) -> Tensor
The threshold_ operator.
tile
tile(*args, **kwargs)
tile(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor
tile(input, dims) -> Tensor
The tile operator.
to
to(*args, **kwargs)
to(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor to(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> list[clika_runtime._core.Tensor] to(src: clika_runtime._core.Tensor, device_str: str) -> clika_runtime._core.Tensor
Overloaded function.
to(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
to(src, *, target=None) -> Tensor
The to operator.
to(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> list[clika_runtime._core.Tensor]
to(tensors, *, target=None) -> list[Tensor]
The to operator.
to(src: clika_runtime._core.Tensor, device_str: str) -> clika_runtime._core.Tensor
to(src, device_str) -> Tensor
The to operator.
topk
topk(*args, **kwargs)
topk(input: clika_runtime._core.Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor]
topk(input, k, dim=-1, largest=True, sorted=True) -> tuple[Tensor, Tensor]
The topk operator.
trace
trace(fn: 'Callable[..., object]', inputs: 'object' = None, *, example_inputs: 'object' = None, signature: 'Sequence[TensorSpec] | None' = None, input_names: 'Sequence[str]' = (), output_names: 'Sequence[str]' = (), graph_name: 'str' = 'traced_model', run_transforms: 'bool | None' = None, bake: 'bool | None' = None, shape_fixation: 'bool | None' = None, place: 'PlaceLike' = None) -> 'TracedGraph'
trace(fn, example_inputs, *, input_names=(), output_names=(), graph_name="traced_model", run_transforms=None, bake=None, shape_fixation=None, place=None) -> TracedGraph
Record fn once into a runnable graph. Two roads name the inputs:
trace(fn, example_inputs=...) derives the graph inputs from example
tensors (their shapes and dtypes matter, their values are never read),
and trace(fn, signature) declares them as :class:TensorSpec values
(a -1 dim is dynamic). The second positional argument is read as a
signature when it is a sequence of TensorSpec values and as the
example inputs otherwise.
A module is called as module(*example_inputs); a plain callable is
called with example_inputs as its one argument (a list of tensors
for a list-in/list-out function, or any pytree). Tensor leaves feed the
graph in flattening order, static leaves are baked in, and the result's
tensor leaves are the graph outputs. Reading a tensor's value inside
fn during the recording raises. input_names and
output_names name the flattened tensors in order (defaults:
input_<i> and output_<i>; a dict key names its tensor).
The result carries the graph as .graph and answers the graph's own
methods directly (run, input_names, output_names,
nodes). run_transforms, bake and shape_fixation select
the graph optimization and finalization; place homes the finished
graph on a device, device string, or stream.
tracing
tracing() -> '_TracingScope'
tracing() -> an execution-mode region
Operations inside record into a lazy graph and return unevaluated
handles;
nothing runs until a value is read through clika_runtime.eval (a
plain read of a traced value raises). :func:eager re-enables
immediate execution for a nested region.
transpose
transpose(*args, **kwargs)
transpose(input: clika_runtime._core.Tensor, dim0: int, dim1: int) -> clika_runtime._core.Tensor
transpose(input, dim0, dim1) -> Tensor
The transpose operator.
tril
tril(*args, **kwargs)
tril(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor
tril(input, diagonal=0) -> Tensor
The tril operator.
tril_
tril_(*args, **kwargs)
tril_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor
tril_(self, diagonal=0) -> Tensor
The tril_ operator.
triu
triu(*args, **kwargs)
triu(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor
triu(input, diagonal=0) -> Tensor
The triu operator.
triu_
triu_(*args, **kwargs)
triu_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor
triu_(self, diagonal=0) -> Tensor
The triu_ operator.
trunc
trunc(*args, **kwargs)
trunc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
trunc(input) -> Tensor
The trunc operator.
trunc_
trunc_(*args, **kwargs)
trunc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
trunc_(self) -> Tensor
The trunc_ operator.
unflatten
unflatten(*args, **kwargs)
unflatten(input: clika_runtime._core.Tensor, dim: int, sizes: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor
unflatten(input, dim, sizes) -> Tensor
The unflatten operator.
unfold
unfold(*args, **kwargs)
unfold(input: clika_runtime._core.Tensor, 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(input, kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor
The unfold operator.
uniform_
uniform_(*args, **kwargs)
uniform_(self: clika_runtime._core.Tensor, low: float = 0.0, high: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
uniform_(self, low=0.0, high=1.0, *, device=None) -> Tensor
The uniform_ operator.
unique
unique(*args, **kwargs)
unique(input: clika_runtime._core.Tensor, 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(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
The unique operator.
unique_consecutive
unique_consecutive(*args, **kwargs)
unique_consecutive(input: clika_runtime._core.Tensor, 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(input, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
The unique_consecutive operator.
unsqueeze
unsqueeze(*args, **kwargs)
unsqueeze(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor
unsqueeze(input, dim) -> Tensor
The unsqueeze operator.
unsqueeze_
unsqueeze_(*args, **kwargs)
unsqueeze_(self: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor
unsqueeze_(self, dim) -> Tensor
The unsqueeze_ operator.
upsample_bicubic2d
upsample_bicubic2d(*args, **kwargs)
upsample_bicubic2d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor
The upsample_bicubic2d operator.
upsample_bilinear2d
upsample_bilinear2d(*args, **kwargs)
upsample_bilinear2d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor
The upsample_bilinear2d operator.
upsample_linear1d
upsample_linear1d(*args, **kwargs)
upsample_linear1d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor
The upsample_linear1d operator.
upsample_nearest1d
upsample_nearest1d(*args, **kwargs)
upsample_nearest1d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[]) -> Tensor
The upsample_nearest1d operator.
upsample_nearest2d
upsample_nearest2d(*args, **kwargs)
upsample_nearest2d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[]) -> Tensor
The upsample_nearest2d operator.
upsample_nearest3d
upsample_nearest3d(*args, **kwargs)
upsample_nearest3d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[]) -> Tensor
The upsample_nearest3d operator.
upsample_trilinear3d
upsample_trilinear3d(*args, **kwargs)
upsample_trilinear3d(input: clika_runtime._core.Tensor, 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(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor
The upsample_trilinear3d operator.
validate_rotary_dim
validate_rotary_dim(*args, **kwargs)
validate_rotary_dim(rotary_dim: int, head_dim: int = -1) -> None
validate_rotary_dim(rotary_dim, head_dim=-1) -> None
The validate_rotary_dim operator.
var
var(*args, **kwargs)
var(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor
var(input, dims=[], correction=1, keepdim=False) -> Tensor
The var operator.
version
version(*args, **kwargs)
version() -> str
The ClikaRT runtime version string (e.g. "0.1.0").
where
where(*args, **kwargs)
where(condition: clika_runtime._core.Tensor, input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor where(condition: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
Overloaded function.
where(condition: clika_runtime._core.Tensor, input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
where(condition, input, other) -> Tensor
The where operator.
where(condition: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor
where(condition) -> Tensor
The where operator.
xlog1py
xlog1py(*args, **kwargs)
xlog1py(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
xlog1py(input, other) -> Tensor
The xlog1py operator.
xlogy
xlogy(*args, **kwargs)
xlogy(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
xlogy(input, other) -> Tensor
The xlogy operator.
xlogy_
xlogy_(*args, **kwargs)
xlogy_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor
xlogy_(self, other) -> Tensor
The xlogy_ operator.
zero_
zero_(*args, **kwargs)
zero_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor
zero_(self, *, device=None) -> Tensor
The zero_ operator.
zeros
zeros(*size: 'ShapeDim', dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
zeros(*size, dtype=None, device=None) -> Tensor
A zero-filled tensor; the default dtype unless dtype says otherwise.
zeros_like
zeros_like(input: 'Tensor', *, dtype: 'DtypeLike' = None, device: 'DeviceLike' = None) -> 'Tensor'
zeros_like(input, *, dtype=None, device=None) -> Tensor
A zero-filled tensor of input's shape, dtype and device, each
overridable by keyword.