IR

PyTorch 2.0 为后端提供两套 IR 以便进行交互：Core Aten IR 和 Prims IR。

Core Aten IR

Core aten 操作是 aten 操作的核心子集，可用于组合其他操作。Core aten IR 具有完整的功能，并且此操作集中没有 inplace 或 _out 变体。与 Prims IR 相比，core aten 操作重用了“native_functions.yaml”中现有的 aten 操作，并且没有进一步将操作分解为显式类型提升和广播操作。此操作集旨在用作与后端交互的功能性 IR。

警告

此操作集仍在积极开发中，将来会添加更多操作。

运算符	模式
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.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.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 是一组可以用来组合其他运算符的原始运算符。Prims IR 比核心 aten IR 更底层，它将运算进一步分解为显式的类型提升和广播运算：prims.convert_element_type 和 prims.broadcast_in_dim。这个运算符集旨在与编译器后端交互。

警告

此操作集仍在积极开发中，将来会添加更多操作。

运算符	模式
prims.abs	abs(Tensor self) -> Tensor
prims.acos	acos(Tensor self) -> Tensor
prims.acosh	acosh(Tensor self) -> Tensor
prims.asin	asin(Tensor self) -> Tensor
prims.asinh	asinh(Tensor self) -> Tensor
prims.atan	atan(Tensor self) -> Tensor
prims.atanh	atanh(Tensor self) -> Tensor
prims.cos	cos(Tensor self) -> Tensor
prims.cosh	cosh(Tensor self) -> Tensor
prims.bessel_i0	bessel_i0(Tensor self) -> Tensor
prims.bessel_i0e	bessel_i0e(Tensor self) -> Tensor
prims.bessel_i1	bessel_i1(Tensor self) -> Tensor
prims.bessel_i1e	bessel_i1e(Tensor self) -> Tensor
prims.bessel_j0	bessel_j0(Tensor self) -> Tensor
prims.bessel_j1	bessel_j1(Tensor self) -> Tensor
prims.bitwise_not	bitwise_not(Tensor self) -> Tensor
prims.cbrt	cbrt(Tensor self) -> Tensor
prims.ceil	ceil(Tensor self) -> Tensor
prims.conj_physical	conj_physical(Tensor self) -> Tensor
prims.digamma	digamma(Tensor self) -> Tensor
prims.erf	erf(Tensor self) -> Tensor
prims.erf_inv	erf_inv(Tensor self) -> Tensor
prims.erfc	erfc(Tensor self) -> Tensor
prims.erfcx	erfcx(Tensor self) -> Tensor
prims.exp	exp(Tensor self) -> Tensor
prims.expm1	expm1(Tensor self) -> Tensor
prims.exp2	exp2(Tensor self) -> Tensor
prims.fill	fill(Tensor self, Scalar value) -> Tensor
prims.floor	floor(Tensor self) -> Tensor
prims.imag	imag(Tensor self) -> Tensor
prims.isfinite	isfinite(Tensor self) -> Tensor
prims.lgamma	lgamma(Tensor self) -> Tensor
prims.log	log(Tensor self) -> Tensor
prims.log1p	log1p(Tensor self) -> Tensor
prims.log2	log2(Tensor self) -> Tensor
prims.log10	log10(Tensor self) -> Tensor
prims.ndtri	ndtri(Tensor self) -> Tensor
prims.neg	neg(Tensor self) -> Tensor
prims.real	real(Tensor self) -> Tensor
prims.reciprocal	reciprocal(Tensor self) -> Tensor
prims.round	round(Tensor self) -> Tensor
prims.sign	sign(Tensor self) -> Tensor
prims.signbit	signbit(Tensor self) -> Tensor
prims.sin	sin(Tensor self) -> Tensor
prims.sinh	sinh(Tensor self) -> Tensor
prims.spherical_bessel_j0	spherical_bessel_j0(Tensor self) -> Tensor
prims.sqrt	sqrt(Tensor self) -> Tensor
prims.tan	tan(Tensor self) -> Tensor
prims.tanh	tanh(Tensor self) -> Tensor
prims.trunc	trunc(Tensor self) -> Tensor
prims.add	add(Tensor self, Tensor other) -> Tensor
prims.atan2	atan2(Tensor self, Tensor other) -> Tensor
prims.bitwise_and	bitwise_and(Tensor self, Tensor other) -> Tensor
prims.bitwise_or	bitwise_or(Tensor self, Tensor other) -> Tensor
prims.bitwise_xor	bitwise_xor(Tensor self, Tensor other) -> Tensor
prims.div	div(Tensor self, Tensor other) -> Tensor
prims.eq	eq(Tensor self, Tensor other) -> Tensor
prims.fmax	fmax(Tensor self, Tensor other) -> Tensor
prims.fmin	fmin(Tensor self, Tensor other) -> Tensor
prims.fmod	fmod(Tensor self, Tensor other) -> Tensor
prims.frexp	frexp(Tensor self) -> (Tensor mantissa, Tensor exponent)
prims.gcd	gcd(Tensor self, Tensor other) -> Tensor
prims.ge	ge(Tensor self, Tensor other) -> Tensor
prims.gt	gt(Tensor self, Tensor other) -> Tensor
prims.hypot	hypot(Tensor self, Tensor other) -> Tensor
prims.igamma	igamma(Tensor self, Tensor other) -> Tensor
prims.igammac	igammac(Tensor self, Tensor other) -> Tensor
prims.le	le(Tensor self, Tensor other) -> Tensor
prims.lt	lt(Tensor self, Tensor other) -> Tensor
prims.maximum	maximum(Tensor self, Tensor other) -> Tensor
prims.minimum	minimum(Tensor self, Tensor other) -> Tensor
prims.mul	mul(Tensor self, Tensor other) -> Tensor
prims.ne	ne(Tensor self, Tensor other) -> Tensor
prims.nextafter	nextafter(Tensor self, Tensor other) -> Tensor
prims.pow	pow(Tensor self, Tensor other) -> Tensor
prims.remainder	remainder(Tensor self, Tensor other) -> Tensor
prims.rsqrt	rsqrt(Tensor self) -> Tensor
prims.shift_left	shift_left(Tensor self, Tensor other) -> Tensor
prims.shift_right_arithmetic	shift_right_arithmetic(Tensor self, Tensor other) -> Tensor
prims.sub	sub(Tensor self, Tensor other) -> Tensor
prims.zeta	zeta(Tensor self, Tensor other) -> Tensor
prims.as_strided	as_strided(Tensor(a!) a, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor(a!)
prims.broadcast_in_dim	broadcast_in_dim(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)
prims.collapse_view	collapse_view(Tensor(a) a, int start, int end) -> Tensor(a)
prims.conj	conj(Tensor(a) a) -> Tensor(a)
prims.slice	slice(Tensor(a) a, SymInt[] start_indices, SymInt[] limit_indices, SymInt[]? strides=None) -> Tensor(a)
prims.slice_in_dim	slice_in_dim(Tensor(a) a, SymInt start_index, SymInt limit_index, int stride=1, int axis=0) -> Tensor(a)
prims.split_dim	split_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)
prims.squeeze	squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a)
prims.transpose	transpose(Tensor(a) a, int[] permutation) -> Tensor(a)
prims.view_of	view_of(Tensor(a) a) -> Tensor
prims.view_of_dtype	view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor
prims.as_strided_scatter	as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor
prims.collapse	collapse(Tensor a, int start, int end) -> Tensor
prims.cat	cat(Tensor[] tensors, int dim) -> Tensor
prims.reshape	reshape(Tensor a, SymInt[] shape) -> Tensor
prims.rev	rev(Tensor a, int[] dims) -> Tensor
prims.where	where(Tensor pred, Tensor a, Tensor b) -> Tensor
prims.clone	clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
prims.convert_element_type	convert_element_type(Tensor a, ScalarType dtype) -> Tensor
prims.device_put	device_put(Tensor a, Device device) -> Tensor
prims.item	item(Tensor a) -> Scalar
prims.maximum_value	maximum_value(ScalarType dtype) -> Scalar
prims.minimum_value	minimum_value(ScalarType dtype) -> Scalar
prims.copy_strided	copy_strided(Tensor a, SymInt[] stride) -> Tensor
prims.copy_to	copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!)
prims.resize	resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!)
prims.amax	amax(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.amin	amin(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.prod	prod(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.sum	sum(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.xor_sum	xor_sum(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor
prims.var	var(Tensor inp, int[]? dims, *, float correction, ScalarType? output_dtype=None) -> Tensor
prims.empty_strided	empty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.empty_permuted	empty_permuted(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.scalar_tensor	scalar_tensor(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> Tensor
prims.iota	iota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> Tensor
prims.svd	svd(Tensor A, *, bool full_matrices) -> (Tensor U, Tensor S, Tensor Vh)
prims.normal	normal(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensor
prims.uniform	uniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> Tensor
prims.fft_r2c	fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensor
prims.fft_c2c	fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor
prims.fft_c2r	fft_c2r(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensor