yobx.torch.interpreter._aten_methods#
- yobx.torch.interpreter._aten_methods.aten_meth_T(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Reverses all dimensions (tensor.T property).
- yobx.torch.interpreter._aten_methods.aten_meth___eq__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name='meth__eq___') str[source]#
equal
- yobx.torch.interpreter._aten_methods.aten_meth___radd__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed addition: y + x.
- yobx.torch.interpreter._aten_methods.aten_meth___rand__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed bitwise AND: y & x.
- yobx.torch.interpreter._aten_methods.aten_meth___rdiv__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed division: y / x.
- yobx.torch.interpreter._aten_methods.aten_meth___rmatmul__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed matrix multiplication: y @ x.
- yobx.torch.interpreter._aten_methods.aten_meth___rmod__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed remainder: y % x.
- yobx.torch.interpreter._aten_methods.aten_meth___rmul__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed multiplication: y * x.
- yobx.torch.interpreter._aten_methods.aten_meth___ror__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed bitwise OR: y | x.
- yobx.torch.interpreter._aten_methods.aten_meth___rpow__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed exponentiation: y ** x.
- yobx.torch.interpreter._aten_methods.aten_meth___rsub__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes reversed subtraction: y - x.
- yobx.torch.interpreter._aten_methods.aten_meth_abs(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
abs.
- yobx.torch.interpreter._aten_methods.aten_meth_aminmax(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, keepdim: bool = False) Tuple[str, str][source]#
Computes both minimum and maximum values.
- yobx.torch.interpreter._aten_methods.aten_meth_argsort(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int = -1, descending: bool = False, stable: bool = False) str[source]#
Returns the indices that would sort the tensor.
- yobx.torch.interpreter._aten_methods.aten_meth_bitwise_and(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes bitwise AND.
- yobx.torch.interpreter._aten_methods.aten_meth_bitwise_or(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes bitwise OR.
- yobx.torch.interpreter._aten_methods.aten_meth_bool(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
cast
- yobx.torch.interpreter._aten_methods.aten_meth_byte(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to uint8.
- yobx.torch.interpreter._aten_methods.aten_meth_char(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to int8.
- yobx.torch.interpreter._aten_methods.aten_meth_clamp_max(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, max_: str, name: str = 'meth_clamp_max') str[source]#
meth_clamp_max
- yobx.torch.interpreter._aten_methods.aten_meth_clamp_min(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, min_: str, name: str = 'meth_clamp_min') str[source]#
meth_clamp_min
- yobx.torch.interpreter._aten_methods.aten_meth_clone(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
identity
- yobx.torch.interpreter._aten_methods.aten_meth_conj(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Computes conjugate (identity for real tensors).
- yobx.torch.interpreter._aten_methods.aten_meth_contiguous(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
identity
- yobx.torch.interpreter._aten_methods.aten_meth_cos(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
cos
- yobx.torch.interpreter._aten_methods.aten_meth_cpu(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
identity
- yobx.torch.interpreter._aten_methods.aten_meth_detach(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
identity
- yobx.torch.interpreter._aten_methods.aten_meth_double(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to float64.
- yobx.torch.interpreter._aten_methods.aten_meth_eq(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name='meth_eq') str[source]#
equal
- yobx.torch.interpreter._aten_methods.aten_meth_erfinv(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Computes the inverse error function.
- yobx.torch.interpreter._aten_methods.aten_meth_exp(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
exp.
- yobx.torch.interpreter._aten_methods.aten_meth_expand(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *dims: List[int]) str[source]#
expand
- yobx.torch.interpreter._aten_methods.aten_meth_expand_as(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name: str = 'aten_meth_expand_as') str[source]#
expand_as
- yobx.torch.interpreter._aten_methods.aten_meth_flatten(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, start_dim: int = 0, end_dim: int = -1) str[source]#
flatten.
- yobx.torch.interpreter._aten_methods.aten_meth_float(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
cast
- yobx.torch.interpreter._aten_methods.aten_meth_half(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to float16.
- yobx.torch.interpreter._aten_methods.aten_meth_int(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to int32.
- yobx.torch.interpreter._aten_methods.aten_meth_item(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = 'aten_meth_item') str[source]#
float(x)
- yobx.torch.interpreter._aten_methods.aten_meth_log(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
log.
- yobx.torch.interpreter._aten_methods.aten_meth_logical_and(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes logical AND.
- yobx.torch.interpreter._aten_methods.aten_meth_logical_or(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes logical OR.
- yobx.torch.interpreter._aten_methods.aten_meth_logical_xor(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str) str[source]#
Computes logical XOR.
- yobx.torch.interpreter._aten_methods.aten_meth_long(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to int64.
- yobx.torch.interpreter._aten_methods.aten_meth_mH(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Computes conjugate transpose of the last two dimensions (tensor.mH property).
- yobx.torch.interpreter._aten_methods.aten_meth_mT(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Transposes the last two dimensions (tensor.mT property).
- yobx.torch.interpreter._aten_methods.aten_meth_masked_fill(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, mask: str, value: Any, name: str = 'aten_meth_masked_fill') str[source]#
masked_fill
- yobx.torch.interpreter._aten_methods.aten_meth_masked_fill_(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, mask: str, value: Any) str[source]#
masked
- yobx.torch.interpreter._aten_methods.aten_meth_max(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, keepdim: bool = False) str[source]#
max.
- yobx.torch.interpreter._aten_methods.aten_meth_mean(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: str | None = None, keepdim: bool = False) str[source]#
reducemean.
- yobx.torch.interpreter._aten_methods.aten_meth_min(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, keepdim: bool = False) str[source]#
min.
- yobx.torch.interpreter._aten_methods.aten_meth_neg(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
neg.
- yobx.torch.interpreter._aten_methods.aten_meth_new_zeros(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, size, dtype: torch.dtype | None = None, layout=None, device: torch.device | None = None, pin_memory=None, name: str = 'meth_new_zeros') str[source]#
Delegates to aten_new_zeros for the tensor.new_zeros(…) method call.
- yobx.torch.interpreter._aten_methods.aten_meth_numel(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = 'meth_numel') str[source]#
meth_numel
- yobx.torch.interpreter._aten_methods.aten_meth_permute(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *dims: Sequence[int]) str[source]#
permute.
- yobx.torch.interpreter._aten_methods.aten_meth_pow(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, exponent: str) str[source]#
pow
- yobx.torch.interpreter._aten_methods.aten_meth_relu(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
relu.
- yobx.torch.interpreter._aten_methods.aten_meth_repeat(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *repeats: List[int]) str[source]#
repeat
- yobx.torch.interpreter._aten_methods.aten_meth_reshape(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, *shape: List[int], name: str = 'reshape') str[source]#
reshape
- yobx.torch.interpreter._aten_methods.aten_meth_reshape_as(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, other: str) str[source]#
Reshapes tensor to match another tensor’s shape.
- yobx.torch.interpreter._aten_methods.aten_meth_shape(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = '.shape') str[source]#
shape
- yobx.torch.interpreter._aten_methods.aten_meth_short(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
Casts to int16.
- yobx.torch.interpreter._aten_methods.aten_meth_sigmoid(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
sigmoid.
- yobx.torch.interpreter._aten_methods.aten_meth_sin(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
sin
- yobx.torch.interpreter._aten_methods.aten_meth_size(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, name: str = '.size') str[source]#
size
- yobx.torch.interpreter._aten_methods.aten_meth_softmax(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int = -1) str[source]#
softmax.
- yobx.torch.interpreter._aten_methods.aten_meth_sqrt(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
sqrt.
- yobx.torch.interpreter._aten_methods.aten_meth_squeeze(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None) str[source]#
squeeze.
- yobx.torch.interpreter._aten_methods.aten_meth_std(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: Any | None = None, correction: float | None = 1, keepdim: bool = False) str[source]#
Computes the standard deviation.
- yobx.torch.interpreter._aten_methods.aten_meth_std_mean(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: Any | None = None, correction: float | None = 1, keepdim: bool = False) str[source]#
Computes the standard deviation and mean.
- yobx.torch.interpreter._aten_methods.aten_meth_sum(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, axis: str | None = None, keepdim: bool = False, dim: int | None = None) str[source]#
reducesum.
- yobx.torch.interpreter._aten_methods.aten_meth_t(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
transpose
- yobx.torch.interpreter._aten_methods.aten_meth_tanh(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#
tanh.
- yobx.torch.interpreter._aten_methods.aten_meth_to(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, *args: List[Any], name: str = '.to', **kwargs: Dict[str, Any]) str[source]#
cast
- yobx.torch.interpreter._aten_methods.aten_meth_transpose(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, dim0: int, dim1: int) str[source]#
transpose