IRs

PyTorch 2.0 offers two set of IRs for backends to interface with: Core Aten IR and Prims IR.

Core Aten IR

Core aten ops is the core subset of aten operators that can be used to compose other operators. Core aten IR is fully functional, and there is no inplace or _out variants in this opset. In contrast to Prims IR, core aten ops reuses the existing aten ops in “native_functions.yaml”, and it doesn’t further decompose ops into explicit type promotion and broadcasting ops. This opset is designed to serve as the functional IR to interface with backends.

Warning

This opset is still under active development, more ops will be added in the future.

Operator	Schema
aten._adaptive_avg_pool2d	_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor
aten._adaptive_avg_pool2d_backward	_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor
aten._adaptive_avg_pool3d	_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor
aten._cdist_forward	_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
aten._embedding_bag	_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)
aten._local_scalar_dense	_local_scalar_dense(Tensor self) -> Scalar
aten._log_softmax	_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
aten._native_batch_norm_legit	_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
aten._native_batch_norm_legit.no_stats	_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
aten._native_batch_norm_legit_no_training	_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)
aten._pdist_forward	_pdist_forward(Tensor self, float p=2) -> Tensor
aten._softmax	_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
aten._to_copy	_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor
aten.abs	abs(Tensor self) -> Tensor
aten.acos	acos(Tensor self) -> Tensor
aten.acosh	acosh(Tensor self) -> Tensor
aten.adaptive_avg_pool1d	adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor
aten.add.Scalar	add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
aten.add.Tensor	add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
aten.addmm	addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
aten.alias	alias(Tensor(a) self) -> Tensor(a)
aten.amax	amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
aten.amin	amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
aten.any	any(Tensor self) -> Tensor
aten.any.dim	any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
aten.any.dims	any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor
aten.arange.start_step	arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.argmax	argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
aten.argmin	argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
aten.as_strided	as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)
aten.asin	asin(Tensor self) -> Tensor
aten.asinh	asinh(Tensor self) -> Tensor
aten.atan	atan(Tensor self) -> Tensor
aten.atan2	atan2(Tensor self, Tensor other) -> Tensor
aten.atan2.out	atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
aten.atanh	atanh(Tensor self) -> Tensor
aten.avg_pool1d	avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor
aten.avg_pool2d	avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
aten.avg_pool2d_backward	avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
aten.avg_pool3d	avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
aten.bitwise_and.Scalar	bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor
aten.bitwise_and.Tensor	bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor
aten.bitwise_not	bitwise_not(Tensor self) -> Tensor
aten.bitwise_or.Scalar	bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor
aten.bitwise_or.Tensor	bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor
aten.bitwise_xor.Scalar	bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor
aten.bitwise_xor.Tensor	bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor
aten.bmm	bmm(Tensor self, Tensor mat2) -> Tensor
aten.cat	cat(Tensor[] tensors, int dim=0) -> Tensor
aten.ceil	ceil(Tensor self) -> Tensor
aten.clamp	clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
aten.clamp.Tensor	clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor
aten.clone	clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
aten.col2im	col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
aten.constant_pad_nd	constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor
aten.convolution	convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor
aten.convolution_backward	convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
aten.copy	copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor
aten.cos	cos(Tensor self) -> Tensor
aten.cosh	cosh(Tensor self) -> Tensor
aten.cumsum	cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
aten.diagonal	diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)
aten.div.Scalar	div.Scalar(Tensor self, Scalar other) -> Tensor
aten.div.Scalar_mode	div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor
aten.div.Tensor	div.Tensor(Tensor self, Tensor other) -> Tensor
aten.div.Tensor_mode	div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor
aten.embedding	embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
aten.embedding_dense_backward	embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor
aten.empty.memory_format	empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
aten.empty_strided	empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.eq.Scalar	eq.Scalar(Tensor self, Scalar other) -> Tensor
aten.eq.Tensor	eq.Tensor(Tensor self, Tensor other) -> Tensor
aten.erf	erf(Tensor self) -> Tensor
aten.exp	exp(Tensor self) -> Tensor
aten.expand	expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a)
aten.expm1	expm1(Tensor self) -> Tensor
aten.fill.Scalar	fill.Scalar(Tensor self, Scalar value) -> Tensor
aten.flip	flip(Tensor self, int[] dims) -> Tensor
aten.floor	floor(Tensor self) -> Tensor
aten.fmod.Scalar	fmod.Scalar(Tensor self, Scalar other) -> Tensor
aten.fmod.Tensor	fmod.Tensor(Tensor self, Tensor other) -> Tensor
aten.full	full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.full_like	full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
aten.gather	gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor
aten.ge.Scalar	ge.Scalar(Tensor self, Scalar other) -> Tensor
aten.ge.Tensor	ge.Tensor(Tensor self, Tensor other) -> Tensor
aten.gelu	gelu(Tensor self, *, str approximate=’none’) -> Tensor
aten.grid_sampler_2d	grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
aten.gt.Scalar	gt.Scalar(Tensor self, Scalar other) -> Tensor
aten.gt.Tensor	gt.Tensor(Tensor self, Tensor other) -> Tensor
aten.hardtanh	hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
aten.index.Tensor	index.Tensor(Tensor self, Tensor?[] indices) -> Tensor
aten.index_put	index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor
aten.index_select	index_select(Tensor self, int dim, Tensor index) -> Tensor
aten.isinf	isinf(Tensor self) -> Tensor
aten.isnan	isnan(Tensor self) -> Tensor
aten.le.Scalar	le.Scalar(Tensor self, Scalar other) -> Tensor
aten.le.Tensor	le.Tensor(Tensor self, Tensor other) -> Tensor
aten.leaky_relu	leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
aten.log	log(Tensor self) -> Tensor
aten.log10	log10(Tensor self) -> Tensor
aten.log1p	log1p(Tensor self) -> Tensor
aten.log2	log2(Tensor self) -> Tensor
aten.logical_and	logical_and(Tensor self, Tensor other) -> Tensor
aten.logical_not	logical_not(Tensor self) -> Tensor
aten.logical_or	logical_or(Tensor self, Tensor other) -> Tensor
aten.logical_xor	logical_xor(Tensor self, Tensor other) -> Tensor
aten.lt.Scalar	lt.Scalar(Tensor self, Scalar other) -> Tensor
aten.lt.Tensor	lt.Tensor(Tensor self, Tensor other) -> Tensor
aten.masked_scatter	masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor
aten.max.dim	max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
aten.max_pool2d_with_indices	max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
aten.max_pool2d_with_indices_backward	max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor
aten.max_pool3d_with_indices	max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
aten.maximum	maximum(Tensor self, Tensor other) -> Tensor
aten.mean	mean(Tensor self, *, ScalarType? dtype=None) -> Tensor
aten.mean.dim	mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
aten.min.dim	min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
aten.minimum	minimum(Tensor self, Tensor other) -> Tensor
aten.mm	mm(Tensor self, Tensor mat2) -> Tensor
aten.mul.Scalar	mul.Scalar(Tensor self, Scalar other) -> Tensor
aten.mul.Tensor	mul.Tensor(Tensor self, Tensor other) -> Tensor
aten.native_dropout	native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)
aten.native_group_norm	native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)
aten.native_group_norm_backward	native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
aten.native_layer_norm	native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)
aten.native_layer_norm_backward	native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
aten.ne.Scalar	ne.Scalar(Tensor self, Scalar other) -> Tensor
aten.ne.Tensor	ne.Tensor(Tensor self, Tensor other) -> Tensor
aten.neg	neg(Tensor self) -> Tensor
aten.nonzero	nonzero(Tensor self) -> Tensor
aten.permute	permute(Tensor(a) self, int[] dims) -> Tensor(a)
aten.pow.Scalar	pow.Scalar(Scalar self, Tensor exponent) -> Tensor
aten.pow.Tensor_Scalar	pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
aten.pow.Tensor_Tensor	pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
aten.prod	prod(Tensor self, *, ScalarType? dtype=None) -> Tensor
aten.prod.dim_int	prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
aten.rand	rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.randn	randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.randperm	randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.reciprocal	reciprocal(Tensor self) -> Tensor
aten.reflection_pad1d	reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor
aten.reflection_pad2d	reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor
aten.reflection_pad3d	reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor
aten.relu	relu(Tensor self) -> Tensor
aten.remainder.Scalar	remainder.Scalar(Tensor self, Scalar other) -> Tensor
aten.remainder.Tensor	remainder.Tensor(Tensor self, Tensor other) -> Tensor
aten.repeat	repeat(Tensor self, SymInt[] repeats) -> Tensor
aten.replication_pad2d	replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor
aten.replication_pad3d	replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor
aten.resize_	resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)
aten.round	round(Tensor self) -> Tensor
aten.rsqrt	rsqrt(Tensor self) -> Tensor
aten.scalar_tensor	scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
aten.scatter.src	scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
aten.scatter.value	scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
aten.scatter_add	scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
aten.scatter_reduce.two	scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor
aten.select.int	select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)
aten.select_scatter	select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor
aten.sigmoid	sigmoid(Tensor self) -> Tensor
aten.sign	sign(Tensor self) -> Tensor
aten.sin	sin(Tensor self) -> Tensor
aten.sinh	sinh(Tensor self) -> Tensor
aten.slice.Tensor	slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
aten.slice_scatter	slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
aten.sort	sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)
aten.split_with_sizes	split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]
aten.sqrt	sqrt(Tensor self) -> Tensor
aten.squeeze.dim	squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
aten.squeeze.dims	squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)
aten.sub.Scalar	sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
aten.sub.Tensor	sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
aten.sum.dim_IntList	sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
aten.sym_numel	sym_numel(Tensor self) -> SymInt
aten.sym_size.int	sym_size.int(Tensor self, int dim) -> SymInt
aten.sym_storage_offset	sym_storage_offset(Tensor self) -> SymInt
aten.sym_stride.int	sym_stride.int(Tensor self, int dim) -> SymInt
aten.tan	tan(Tensor self) -> Tensor
aten.tanh	tanh(Tensor self) -> Tensor
aten.topk	topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
aten.trunc	trunc(Tensor self) -> Tensor
aten.unsqueeze	unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
aten.upsample_bilinear2d.vec	upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
aten.upsample_nearest2d.vec	upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
aten.var.correction	var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor
aten.var.dim	var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
aten.view	view(Tensor(a) self, SymInt[] size) -> Tensor(a)
aten.where.self	where.self(Tensor condition, Tensor self, Tensor other) -> Tensor

Prims IR

Prims IR is a set of primitive operators that can be used to compose other operators. Prims IR is a lower level opset than core aten IR, and it further decomposes ops into explicit type promotion and broadcasting ops: prims.convert_element_type and prims.broadcast_in_dim. This opset is designed to interface with compiler backends.

Warning

This opset is still under active development, more ops will be added in the future.

Operator	Schema
prims.abs	(Tensor self) -> Tensor
prims.acos	(Tensor self) -> Tensor
prims.acosh	(Tensor self) -> Tensor
prims.asin	(Tensor self) -> Tensor
prims.asinh	(Tensor self) -> Tensor
prims.atan	(Tensor self) -> Tensor
prims.atanh	(Tensor self) -> Tensor
prims.cos	(Tensor self) -> Tensor
prims.cosh	(Tensor self) -> Tensor
prims.bessel_i0	(Tensor self) -> Tensor
prims.bessel_i0e	(Tensor self) -> Tensor
prims.bessel_i1	(Tensor self) -> Tensor
prims.bessel_i1e	(Tensor self) -> Tensor
prims.bessel_j0	(Tensor self) -> Tensor
prims.bessel_j1	(Tensor self) -> Tensor
prims.bitwise_not	(Tensor self) -> Tensor
prims.cbrt	(Tensor self) -> Tensor
prims.ceil	(Tensor self) -> Tensor
prims.conj_physical	(Tensor self) -> Tensor
prims.digamma	(Tensor self) -> Tensor
prims.erf	(Tensor self) -> Tensor
prims.erf_inv	(Tensor self) -> Tensor
prims.erfc	(Tensor self) -> Tensor
prims.erfcx	(Tensor self) -> Tensor
prims.exp	(Tensor self) -> Tensor
prims.expm1	(Tensor self) -> Tensor
prims.exp2	(Tensor self) -> Tensor
prims.fill	(Tensor self, Scalar value) -> Tensor
prims.floor	(Tensor self) -> Tensor
prims.imag	(Tensor(a) self) -> Tensor(a)
prims.isfinite	(Tensor self) -> Tensor
prims.lgamma	(Tensor self) -> Tensor
prims.log	(Tensor self) -> Tensor
prims.log1p	(Tensor self) -> Tensor
prims.log2	(Tensor self) -> Tensor
prims.log10	(Tensor self) -> Tensor
prims.ndtri	(Tensor self) -> Tensor
prims.neg	(Tensor self) -> Tensor
prims.real	(Tensor(a) self) -> Tensor(a)
prims.reciprocal	(Tensor self) -> Tensor
prims.round	(Tensor self) -> Tensor
prims.sign	(Tensor self) -> Tensor
prims.signbit	(Tensor self) -> Tensor
prims.sin	(Tensor self) -> Tensor
prims.sinh	(Tensor self) -> Tensor
prims.spherical_bessel_j0	(Tensor self) -> Tensor
prims.sqrt	(Tensor self) -> Tensor
prims.tan	(Tensor self) -> Tensor
prims.tanh	(Tensor self) -> Tensor
prims.trunc	(Tensor self) -> Tensor
prims.add	(Tensor self, Tensor other) -> Tensor
prims.atan2	(Tensor self, Tensor other) -> Tensor
prims.bitwise_and	(Tensor self, Tensor other) -> Tensor
prims.bitwise_or	(Tensor self, Tensor other) -> Tensor
prims.bitwise_xor	(Tensor self, Tensor other) -> Tensor
prims.div	(Tensor self, Tensor other) -> Tensor
prims.eq	(Tensor self, Tensor other) -> Tensor
prims.fmax	(Tensor self, Tensor other) -> Tensor
prims.fmin	(Tensor self, Tensor other) -> Tensor
prims.fmod	(Tensor self, Tensor other) -> Tensor
prims.frexp	(Tensor self) -> (Tensor mantissa, Tensor exponent)
prims.gcd	(Tensor self, Tensor other) -> Tensor
prims.ge	(Tensor self, Tensor other) -> Tensor
prims.gt	(Tensor self, Tensor other) -> Tensor
prims.hypot	(Tensor self, Tensor other) -> Tensor
prims.igamma	(Tensor self, Tensor other) -> Tensor
prims.igammac	(Tensor self, Tensor other) -> Tensor
prims.le	(Tensor self, Tensor other) -> Tensor
prims.lt	(Tensor self, Tensor other) -> Tensor
prims.maximum	(Tensor self, Tensor other) -> Tensor
prims.minimum	(Tensor self, Tensor other) -> Tensor
prims.mul	(Tensor self, Tensor other) -> Tensor
prims.ne	(Tensor self, Tensor other) -> Tensor
prims.nextafter	(Tensor self, Tensor other) -> Tensor
prims.pow	(Tensor self, Tensor other) -> Tensor
prims.remainder	(Tensor self, Tensor other) -> Tensor
prims.rsqrt	(Tensor self) -> Tensor
prims.shift_left	(Tensor self, Tensor other) -> Tensor
prims.shift_right_arithmetic	(Tensor self, Tensor other) -> Tensor
prims.sub	(Tensor self, Tensor other) -> Tensor
prims.zeta	(Tensor self, Tensor other) -> Tensor
prims.as_strided	(Tensor(a!) a, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor(a!)
prims.broadcast_in_dim	(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)
prims.collapse_view	(Tensor(a) a, int start, int end) -> Tensor(a)
prims.conj	(Tensor(a) a) -> Tensor(a)
prims.split_dim	(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)
prims.squeeze	(Tensor(a) a, int[] dimensions) -> Tensor(a)
prims.transpose	(Tensor(a) a, int[] permutation) -> Tensor(a)
prims.view_of	(Tensor(a) a) -> Tensor(a)
prims.view_of_dtype	(Tensor(a) a, ScalarType dtype) -> Tensor(a)
prims.as_strided_scatter	(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor
prims.collapse	(Tensor a, int start, int end) -> Tensor
prims.cat	(Tensor[] tensors, int dim) -> Tensor
prims.reshape	(Tensor a, SymInt[] shape) -> Tensor
prims.rev	(Tensor a, int[] dims) -> Tensor
prims.where	(Tensor pred, Tensor a, Tensor b) -> Tensor
prims.clone	(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
prims.convert_element_type	(Tensor a, ScalarType dtype) -> Tensor
prims.device_put	(Tensor a, Device device, bool non_blocking=False) -> Tensor
prims.item	(Tensor a) -> Scalar
prims.maximum_value	(ScalarType dtype) -> Scalar
prims.minimum_value	(ScalarType dtype) -> Scalar
prims.copy_strided	(Tensor a, SymInt[] stride) -> Tensor
prims.copy_to	(Tensor(a!) a, Tensor b) -> Tensor(a!)
prims.resize	(Tensor(a!) a, SymInt[] shape) -> Tensor(a!)
prims.amax	(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.amin	(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.prod	(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.sum	(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.xor_sum	(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.var	(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensor
prims.empty_strided	(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.empty_permuted	(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.scalar_tensor	(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> Tensor
prims.iota	(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.svd	(Tensor A, *, bool full_matrices) -> (Tensor U, Tensor S, Tensor Vh)
prims.normal	(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensor
prims.uniform	(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> Tensor
prims.fft_r2c	(Tensor self, *, int[] dim, bool onesided) -> Tensor
prims.fft_c2c	(Tensor self, *, int[] dim, bool forward) -> Tensor
prims.fft_c2r	(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensor
prims._make_token	() -> Tensor
prims._sink_tokens	(Tensor[] tokens) -> ()