in torch/overrides.py [0:0]
def get_testing_overrides() -> Dict[Callable, Callable]:
"""Return a dict containing dummy overrides for all overridable functions
Returns
-------
Dict[Callable, Callable]
A dictionary that maps overridable functions in the PyTorch API to
lambda functions that have the same signature as the real function
and unconditionally return -1. These lambda functions are useful
for testing API coverage for a type that defines ``__torch_function__``.
Examples
--------
>>> import inspect
>>> my_add = torch.overrides.get_testing_overrides()[torch.add]
>>> inspect.signature(my_add)
<Signature (input, other, out=None)>
"""
# Every function in the PyTorchAPI that can be overriden needs an entry
# in this dict.
#
# Optimally we would use inspect to get the function signature and define
# the lambda function procedurally but that is blocked by generating
# function signatures for native kernels that can be consumed by inspect.
# See Issue #28233.
Tensor = torch.Tensor
ret: Dict[Callable, Callable] = {
torch.abs: lambda input, out=None: -1,
torch.absolute: lambda input, out=None: -1,
torch.adaptive_avg_pool1d: lambda input, output_size: -1,
torch.adaptive_max_pool1d: lambda inputs, output_size: -1,
torch.acos: lambda input, out=None: -1,
torch.adjoint: lambda input: -1,
torch.arccos: lambda input, out=None: -1,
torch.acosh: lambda input, out=None: -1,
torch.arccosh: lambda input, out=None: -1,
torch.add: lambda input, other, out=None: -1,
torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1,
torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1,
torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1,
torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1,
torch.affine_grid_generator: lambda theta, size, align_corners: -1,
torch.all: lambda input, dim=None: -1,
torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1,
torch.alpha_dropout: lambda input, p, train, inplace=False: -1,
torch.amax: lambda input, dim=None: -1,
torch.amin: lambda input, dim=None: -1,
torch.aminmax: lambda input, dim=None, keepdim=False, out=None: -1,
torch.angle: lambda input, out=None: -1,
torch.any: lambda input, dim=None, keepdim=False, out=None: -1,
torch.argmax: lambda input: -1,
torch.argmin: lambda input: -1,
torch.argsort: lambda input, dim=None: -1,
torch.asin: lambda input, out=None: -1,
torch._assert_async: lambda input: -1,
torch.arcsin: lambda input, out=None: -1,
torch.asinh: lambda input, out=None: -1,
torch.arcsinh: lambda input, out=None: -1,
torch.atan: lambda input, out=None: -1,
torch.arctan: lambda input, out=None: -1,
torch.atan2: lambda input, other, out=None: -1,
torch.arctan2: lambda input, other, out=None: -1,
torch.atanh: lambda input, out=None: -1,
torch.arctanh: lambda input, out=None: -1,
torch.atleast_1d: lambda *tensors: -1,
torch.atleast_2d: lambda *tensors: -1,
torch.atleast_3d: lambda *tensors: -1,
torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1,
torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1,
torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count_tensor: -1,
torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1,
torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1,
torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
torch.batch_norm_stats: lambda input, eps: -1,
torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1,
torch.bernoulli: lambda input, generator=None, out=None: -1,
torch.bilinear: lambda input1, input2, weight, bias: -1,
torch.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, reduce=None,
reduction='mean', pos_weight=None: -1),
torch.bincount: lambda input, weights=None, minlength=0: -1,
torch.binomial: lambda count, prob, generator=None: -1,
torch.bitwise_and: lambda input, other, out=None: -1,
torch.bitwise_not: lambda input, out=None: -1,
torch.bitwise_or: lambda input, other, out=None: -1,
torch.bitwise_xor: lambda input, other, out=None: -1,
torch.bitwise_left_shift: lambda input, other, out=None: -1,
torch.bitwise_right_shift: lambda input, other, out=None: -1,
torch.block_diag: lambda *tensors: -1,
torch.bmm: lambda input, mat2, out=None: -1,
torch.broadcast_tensors: lambda *tensors: -1,
torch.broadcast_to: lambda self, size: -1,
torch.bucketize: lambda input, boundaries, out_int32=False, right=False, out=None: -1,
torch.cartesian_prod: lambda *tensors: -1,
torch.cat: lambda tensors, dim=0, out=None: -1,
torch.concat: lambda tensors, dim=0, out=None: -1, # alias for torch.cat
torch.cdist: lambda x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary': -1,
torch.ceil: lambda input, out=None: -1,
torch.celu: lambda input, alhpa=1., inplace=False: -1,
torch.chain_matmul: lambda *matrices, out=None: -1,
torch.channel_shuffle: lambda input, groups : -1,
torch.cholesky: lambda input, upper=False, out=None: -1,
torch.linalg.cholesky: lambda input, out=None: -1,
torch.linalg.cholesky_ex: lambda input, check_errors=False, out=None: -1,
torch.cholesky_inverse: lambda input, upper=False, out=None: -1,
torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1,
torch.choose_qparams_optimized: lambda input, numel, n_bins, ratio, bit_width: -1,
torch.chunk: lambda input, chunks, dim=0: -1,
torch.clamp: lambda input, min=None, max=None, out=None: -1,
torch.clip: lambda input, min=None, max=None, out=None: -1,
torch.clamp_min: lambda input, min, out=None: -1,
torch.clamp_max: lambda input, max, out=None: -1,
torch.column_stack: lambda tensors, out=None: -1,
torch.cov: lambda input, correction=1, fweights=None, aweights=None: -1,
torch.clone: lambda input: -1,
torch.combinations: lambda input, r=2, with_replacement=False: -1,
torch.complex: lambda real, imag: -1,
torch.copysign: lambda input, other, out=None: -1,
torch.polar: lambda abs, ang: -1,
torch.linalg.cond: lambda input, ord=None: -1,
torch.conj: lambda input, out=None: -1,
torch.conj_physical: lambda input, out=None: -1,
torch.resolve_conj: lambda input, out=None: -1,
torch.resolve_neg: lambda input, out=None: -1,
torch.constant_pad_nd: lambda input, pad, value=0: -1,
torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1,
torch.conv_tbc: lambda input, weight, bias, pad=0: -1,
torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.corrcoef: lambda input: -1,
torch.cos: lambda input, out=None: -1,
torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,
torch.cosh: lambda input, out=None: -1,
torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1,
torch.count_nonzero: lambda input: -1,
torch.cross: lambda input, other, dim=None, out=None: -1,
torch.linalg.cross: lambda input, other, dim=-1, out=None: -1,
torch.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean',
zero_infinity=False: -1),
torch.cummax: lambda input, dim, out=None: -1,
torch.cummin: lambda input, dim, out=None: -1,
torch.cumprod: lambda input, dim, out=None, dtype=None: -1,
torch.cumsum: lambda input, dim, out=None, dtype=None: -1,
torch.cumulative_trapezoid: lambda y, x=None, dim=-1: -1,
torch.logcumsumexp: lambda input, dim, out=None: -1,
torch.deg2rad: lambda input, out=None: -1,
torch.dequantize: lambda input: -1,
torch.det: lambda input: -1,
torch.linalg.det: lambda input: -1, # alias for torch.det # type: ignore[attr-defined]
torch.detach: lambda input: -1,
torch.diag: lambda input, diagonal=0, out=None: -1,
torch.diag_embed: lambda input, diagonal=0, out=None: -1,
torch.diagflat: lambda input, offset=0: -1,
torch.diff: lambda input, n=1, dim=-1, prepend=None, append=None, out=None: -1,
torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
torch.diagonal_scatter: lambda input, src, offset=0, dim1=0, dim2=1: -1,
torch.digamma: lambda input, out=None: -1,
torch.dist: lambda input, other, p=2: -1,
torch.div: lambda input, other, rounding_mode=None, out=None: -1,
torch.divide: lambda input, other, rounding_mode=None, out=None: -1,
torch.dot: lambda input, other, out=None: -1,
torch.dropout: lambda input, p, train, inplace=False: -1,
torch.dsmm: lambda input, mat2: -1,
torch.hsmm: lambda mat1, mat2: -1,
torch.dsplit: lambda input, indices_or_sections: -1,
torch.dstack: lambda tensors, out=None: -1,
torch.eig: lambda input, eigenvectors=False, out=None: -1,
torch.linalg.eig: lambda input, out=None: -1,
torch.linalg.eigvals: lambda input, out=None: -1,
torch.linalg.eigh: lambda input, UPLO="L", out=None: -1,
torch.linalg.eigvalsh: lambda input, UPLO="L", out=None: -1,
torch.einsum: lambda equation, *operands: -1,
torch.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
sparse=False: -1),
torch.embedding_bag: (lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False,
mode='mean', sparse=False, per_sample_weights=None, padding_idx=None: -1),
torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.eq: lambda input, other, out=None: -1,
torch.equal: lambda input, other: -1,
torch.erf: lambda input, out=None: -1,
torch.erfc: lambda input, out=None: -1,
torch.erfinv: lambda input, out=None: -1,
torch.exp: lambda input, out=None: -1,
torch.exp2: lambda input, out=None: -1,
torch.expm1: lambda input, out=None: -1,
torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1,
torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1,
torch.fused_moving_avg_obs_fake_quant: (lambda x, observer_on, fake_quant_on, averaging_const, running_min,
running_max, scale, zero_point, quant_min, quant_max, ch_axis,
per_row_fake_quant=False, symmetric_quant=False: -1),
torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1,
torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1,
torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1,
torch.fbgemm_linear_int8_weight_fp32_activation: (lambda input, weight, packed, col_offsets, weight_scale,
weight_zero_point, bias: -1),
torch.fbgemm_linear_quantize_weight: lambda input: -1,
torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1,
torch.fbgemm_pack_quantized_matrix: lambda input, a, b: -1,
torch.feature_alpha_dropout: lambda input, p, train: -1,
torch.feature_dropout: lambda input, p, train: -1,
torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.ifft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.rfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.irfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.hfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.ihfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.hfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.ihfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.hfftn: lambda input, s=None, dim=-1, norm=None: -1,
torch.fft.ihfftn: lambda input, s=None, dim=-1, norm=None: -1,
torch.fft.fftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.ifftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.rfftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.irfftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.fft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.ifft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.rfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.irfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.fftshift: lambda input, dim=None: -1,
torch.fft.ifftshift: lambda input, dim=None: -1,
torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fix: lambda input, out=None: -1,
torch.flatten: lambda input, start_dim=0, end_dim=-1: -1,
torch.flip: lambda input, dims: -1,
torch.fliplr: lambda input: -1,
torch.flipud: lambda input: -1,
torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1,
torch.floor: lambda input, out=None: -1,
torch.floor_divide: lambda input, other: -1,
torch.float_power: lambda input, exponent, out=None: -1,
torch.fmod: lambda input, other, out=None: -1,
torch.frac: lambda input, out=None: -1,
torch.frexp: lambda input, out=None: -1,
torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
torch.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1,
torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1,
torch.gcd: lambda input, other, out=None: -1,
torch.ge: lambda input, other, out=None: -1,
torch.greater_equal: lambda input, other, out=None: -1,
torch.geqrf: lambda input, out=None: -1,
torch.i0: lambda input, out=None: -1,
torch.inner: lambda input, other, out=None: -1,
torch.outer: lambda input, vec2, out=None: -1,
torch.ger: lambda input, vec2, out=None: -1, # alias for torch.outer
torch.gradient: lambda input, spacing=None, dim=None, edge_order=1: -1,
torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1,
torch.gru: lambda input, hx, params, has_biases, num_layers, gropout, train, bidirectional, batch_first: -1,
torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.gt: lambda input, other, out=None: -1,
torch.greater: lambda input, other, out=None: -1,
torch.hardshrink: lambda input, lambd=0.5: -1,
torch.heaviside: lambda input, values, out=None: -1,
torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction='mean': -1,
torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1,
torch.histogram: lambda input, bins=100, min=None, max=None, weight=None, density=False, out=None: -1,
torch.histogramdd: lambda input, bins, weight=None, density=False: -1,
torch.linalg.householder_product: lambda input, tau: -1,
torch.hspmm: lambda mat1, mat2, out=None: -1,
torch.hsplit: lambda input, indices_or_sections: -1,
torch.hstack: lambda tensors, out=None: -1,
torch.hypot: lambda input, other, out=None: -1,
torch.igamma: lambda input, other, out=None: -1,
torch.igammac: lambda input, other, out=None: -1,
torch.imag: lambda input, out=None: -1,
torch.index_add: lambda input, dim, index, source: -1,
torch.index_copy: lambda input, dim, index, source: -1,
torch.index_put: lambda input, indices, values, accumulate=False: -1,
torch.index_select: lambda input, dim, index, out=None: -1,
torch.index_fill: lambda input, dim, index, value: -1,
torch.isfinite: lambda tensor: -1,
torch.isin: lambda e, te, assume_unique=False, invert=False: -1,
torch.isinf: lambda tensor: -1,
torch.isreal: lambda tensor: -1,
torch.isposinf: lambda input, out=None: -1,
torch.isneginf: lambda input, out=None: -1,
torch.instance_norm: (lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps,
cudnn_enabled: -1),
torch.int_repr: lambda input: -1,
torch.inverse: lambda input, out=None: -1,
torch.linalg.inv: lambda input, out=None: -1,
torch.linalg.inv_ex: lambda input, check_errors=False, out=None: -1,
torch.is_complex: lambda input: -1,
torch.is_conj: lambda input: -1,
torch.is_neg: lambda input: -1,
torch.is_distributed: lambda input: -1,
torch.is_inference: lambda input: -1,
torch.is_floating_point: lambda input: -1,
torch.is_nonzero: lambda input: -1,
torch.is_same_size: lambda input, other: -1,
torch.is_signed: lambda input: -1,
torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1,
torch.isnan: lambda input: -1,
torch.istft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
normalized=False, onesided=None, length=None, return_complex=False: -1),
torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
torch.kron: lambda input, other: -1,
torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
torch.lcm: lambda input, other, out=None: -1,
torch.ldexp: lambda input, other, out=None: -1,
torch.le: lambda input, other, out=None: -1,
torch.less_equal: lambda input, other, out=None: -1,
torch.lerp: lambda input, end, weight, out=None: -1,
torch.lgamma: lambda input, out=None: -1,
torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None,
tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1,
torch.log: lambda input, out=None: -1,
torch.log_softmax: lambda input, dim, dtype=None: -1,
torch.log10: lambda input, out=None: -1,
torch.log1p: lambda input, out=None: -1,
torch.log2: lambda input, out=None: -1,
torch.logaddexp: lambda input, other, out=None: -1,
torch.logaddexp2: lambda input, other, out=None: -1,
torch.logdet: lambda input: -1,
torch.xlogy: lambda x, y, out=None: -1,
torch.logical_and: lambda input, other, out=None: -1,
torch.logical_not: lambda input, out=None: -1,
torch.logical_or: lambda input, other, out=None: -1,
torch.logical_xor: lambda input, other, out=None: -1,
torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
torch.logit: lambda input, eps=None: -1,
torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.lstsq: lambda input, A, out=None: -1,
torch.lt: lambda input, other, out=None: -1,
torch.less: lambda input, other, out=None: -1,
torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
torch.lu_solve: lambda b, LU_data, LU_pivots, out=None: -1,
torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1, # type: ignore[attr-defined] # noqa: B950
torch.masked_fill: lambda input, mask, value: -1,
torch.masked_scatter: lambda input, mask, source: -1,
torch.masked_select: lambda input, mask, out=None: -1,
torch.matmul: lambda input, other, out=None: -1,
torch.linalg.lu_factor: lambda input, pivot=True, out=None: -1,
torch.linalg.lu_factor_ex: lambda input, pivot=True, check_errors=False, out=None: -1,
torch.linalg.matmul: lambda input, other, out=None: -1, # alias for torch.matmul
torch.matrix_power: lambda input, n: -1,
torch.linalg.matrix_power: lambda input, n, out=None: -1,
torch.matrix_rank: lambda input, tol=None, symmetric=False: -1,
torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1,
torch.linalg.multi_dot: lambda tensors, out=None: -1,
torch.matrix_exp: lambda input: -1,
torch.linalg.matrix_exp: lambda input: -1,
torch.max: lambda input, out=None: -1,
torch.maximum: lambda input, other, out=None: -1,
torch.fmax: lambda input, other, out=None: -1,
torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False: -1),
torch.mean: lambda input, dim=None: -1,
torch.nanmean: lambda input, dim=None, keepdim=False, dtype=None, out=None: -1,
torch.median: lambda input, dim=None: -1,
torch.nanmedian: lambda input, dim=None: -1,
torch.meshgrid: lambda *tensors, **kwargs: -1,
torch.min: lambda input, out=None: -1,
torch.minimum: lambda input, other, out=None: -1,
torch.fmin: lambda input, other, out=None: -1,
torch.miopen_batch_norm: (lambda input, weight, bias, running_mean, running_var, training,
exponential_average_factor, epsilon: -1),
torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1,
torch.miopen_convolution_transpose: (lambda input, weight, bias, padding, output_padding, stride, dilation,
groups, benchmark, deterministic: -1),
torch.miopen_depthwise_convolution: (lambda input, weight, bias, padding, stride, dilation, groups, benchmark,
deterministic: -1),
torch.miopen_rnn: (lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first,
dropout, train, bidirectional, batch_sizes, dropout_state: -1),
torch.mm: lambda input, mat2, out=None: -1,
torch.mode: lambda input, dim=-1, keepdim=False, out=None: -1,
torch.movedim: lambda input, source, destination: -1,
torch.moveaxis: lambda input, source, destination: -1,
torch.msort: lambda input, descending=False, out=None: -1,
torch.mul: lambda input, other, out=None: -1,
torch.multiply: lambda input, other, out=None: -1,
torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1,
torch.mv: lambda input, vec, out=None: -1,
torch.mvlgamma: lambda input, p: -1,
torch.narrow: lambda input, dim, start, length: -1,
torch.narrow_copy: lambda input, dim, start, length: -1,
torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1,
torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
torch.native_dropout: lambda input, p, train: -1,
torch.native_layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
torch.native_multi_head_self_attention: lambda query, qkv_weight, qkv_bias, proj_weight, proj_bias, mask=None: -1,
torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1,
torch.native_norm: lambda input, p=2: -1,
torch.native_norm: lambda input, p=2: -1,
torch.native_norm: lambda input, p=2, dim=None, keepdim=False, dtype=None: -1,
torch.native_channel_shuffle: lambda input, groups : -1,
torch.ne: lambda input, other, out=None: -1,
torch.not_equal: lambda input, other, out=None: -1,
torch.neg: lambda input, out=None: -1,
torch.negative: lambda input, out=None: -1,
torch.nextafter: lambda input, other, out=None: -1,
torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1,
torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1,
torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1,
torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
torch.nn.functional.avg_pool2d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
count_include_pad=True, divisor_override=None: -1),
torch.nn.functional.avg_pool3d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
count_include_pad=True, divisor_override=None: -1),
torch.nn.functional.batch_norm: (lambda input, running_mean, running_var, weight=None, bias=None, training=False,
momentum=0.1, eps=1e-05: -1),
torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1,
torch.nn.functional.binary_cross_entropy: (lambda input, target, weight=None, size_average=None, reduce=None,
reduction="mean": -1),
torch.nn.functional.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None,
reduce=None, reduction="mean", pos_weight=None: -1),
torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1,
torch.nn.functional.cosine_embedding_loss: (lambda input1, input2, target, margin=0, size_average=None,
reduce=None, reduction='mean': -1),
torch.nn.functional.cross_entropy: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
reduce=None, reduction="mean", label_smoothing=0.0: -1),
torch.nn.functional.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0,
reduction='mean', zero_infinity=False: -1),
torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1,
torch.nn.functional.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0,
scale_grad_by_freq=False, sparse=False: -1),
torch.nn.functional.embedding_bag: (lambda input, weight, offsets=None, max_norm=None, norm_type=2,
scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None,
include_last_offset=False, padding_idx=None: -1),
torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1,
torch.nn.functional.fractional_max_pool2d: (lambda input, kernel_size, output_size=None, output_ratio=None,
return_indices=False, _random_samples=None: -1),
torch.nn.functional.fractional_max_pool2d_with_indices: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
_random_samples=None: -1),
torch.nn.functional.fractional_max_pool3d: (lambda input, kernel_size, output_size=None, output_ratio=None,
return_indices=False, _random_samples=None: -1),
torch.nn.functional.fractional_max_pool3d_with_indices: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
_random_samples=None: -1),
torch.nn.functional.gaussian_nll_loss: lambda input, target, var, full=False, eps=1e-06, reduction='mean': -1,
torch.nn.functional.gelu: lambda input: -1,
torch.nn.functional.glu: lambda input, dim=-1: -1,
torch.nn.functional.grid_sample: lambda input, grid, mode='bilinear', padding_mode='zeros', align_corners=None: -1,
torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1,
torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1,
torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1,
torch.nn.functional.hardtanh: lambda input, min_val=-1., max_val=1., inplace=False: -1,
torch.nn.functional.hinge_embedding_loss: (lambda input, target, margin=1.0, size_average=None, reduce=None,
reduction='mean': -1),
torch.nn.functional.instance_norm: (lambda input, running_mean=None, running_var=None, weight=None, bias=None,
use_input_stats=True, momentum=0.1, eps=1e-05: -1),
torch.nn.functional.interpolate: (lambda input, size=None, scale_factor=None, mode='nearest', align_corners=None,
recompute_scale_factor=None, antialias=False: -1),
torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1,
torch.nn.functional.linear: lambda input, weight, bias=None: -1,
torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1,
torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.logsigmoid: lambda input: -1,
torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
torch.nn.functional.margin_ranking_loss: (lambda input1, input2, target, margin=0, size_average=None,
reduce=None, reduction='mean': -1),
torch.nn.functional.max_pool1d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
ceil_mode=False, return_indices=False: -1),
torch.nn.functional.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False: -1),
torch.nn.functional.max_pool2d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
ceil_mode=False, return_indices=False: -1),
torch.nn.functional.max_pool2d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False: -1),
torch.nn.functional.max_pool3d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False: -1),
torch.nn.functional.max_pool3d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False: -1),
torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
torch.nn.functional.multi_head_attention_forward: (
lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v,
add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None,
need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None,
v_proj_weight=None, static_k=None, static_v=None: -1),
torch.nn.functional.multi_margin_loss: (lambda input, target, p=1, margin=1.0, weight=None, size_average=None,
reduce=None, reduction='mean': -1),
torch.nn.functional.multilabel_margin_loss: (lambda input, target, size_average=None, reduce=None,
reduction='mean': -1),
torch.nn.functional.multilabel_soft_margin_loss: (lambda input, target, weight=None, size_average=None,
reduce=None, reduction='mean': -1),
torch.nn.functional.nll_loss: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
reduce=None, reduction='mean': -1),
torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1,
torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1,
torch.nn.functional.pad: lambda input, pad, mode='constant', value=0: -1,
torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
torch.nn.functional.poisson_nll_loss: (lambda input, target, log_input=True, full=False, size_average=None,
eps=1e-08, reduce=None, reduction='mean': -1),
torch.nn.functional.prelu: lambda input, weight: -1,
torch.nn.functional.relu: lambda input, inplace=False: -1,
torch.nn.functional.relu6: lambda input, inplace=False: -1,
torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1,
torch.nn.functional.selu: lambda input, inplace=False: -1,
torch.nn.functional.silu: lambda input, inplace=False: -1,
torch.nn.functional.mish: lambda input, inplace=False: -1,
torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean', beta=1.: -1,
torch.nn.functional.huber_loss: lambda input, target, reduction='mean', delta=1.: -1,
torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1,
torch.nn.functional.softshrink: lambda input, lambd=0.5: -1,
torch.nn.functional.softsign: lambda input: -1,
torch.nn.functional.tanhshrink: lambda input: -1,
torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1,
torch.nn.functional.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06,
swap=False, size_average=None, reduce=None, reduction='mean': -1),
torch.nn.functional.triplet_margin_with_distance_loss: (lambda anchor, positive, negative, *,
distance_function=None, margin=1.0,
swap=False, reduction='mean': -1),
torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
torch.nonzero: lambda input, as_tuple=False: -1,
torch.argwhere: lambda input: -1,
torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.norm: lambda input, ord=None, dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.vector_norm: lambda input, ord=2, dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.matrix_norm: lambda input, ord='fro', dim=(-2, -1), keepdim=False, out=None, dtype=None: -1,
torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
torch.nuclear_norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
torch.numel: lambda input: -1,
torch.orgqr: lambda input, tau: -1,
torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1,
torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
torch.permute: lambda self, dim: -1,
torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1,
torch.pdist: lambda input, p=2: -1,
torch.pinverse: lambda input, rcond=1e-15: -1,
torch.linalg.pinv: lambda input, rcond=1e-15, hermitian=False: -1,
torch.pixel_shuffle: lambda input, upscale_factor: -1,
torch.pixel_unshuffle: lambda input, downscale_factor: -1,
torch.poisson: lambda input, generator=None: -1,
torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1,
torch.polygamma: lambda input, n, out=None: -1,
torch.positive: lambda input, out=None: -1,
torch.prelu: lambda input, weight: -1,
torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.pow: lambda input, exponent, out=None: -1,
torch.prod: lambda input, dtype=None: -1,
torch.put: lambda input, index, source, accumulate=False: -1,
torch.q_per_channel_axis: lambda input: -1,
torch.q_per_channel_scales: lambda input: -1,
torch.q_per_channel_zero_points: lambda input: -1,
torch.q_scale: lambda input: -1,
torch.q_zero_point: lambda input: -1,
torch.qr: lambda input, some=True, out=None: -1,
torch.linalg.qr: lambda input, mode='reduced', out=None: -1,
torch.quantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
torch.nanquantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1,
torch.quantize_per_tensor_dynamic: lambda input, dtype, reduce_range: -1,
torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1,
torch.quantized_gru_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
torch.quantized_lstm_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
torch.quantized_max_pool1d: (lambda input, kernel_size, stride=tuple(), padding=(0,),
dilation=(1,), ceil_mode=False: -1),
torch.quantized_max_pool2d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0),
dilation=(1, 1), ceil_mode=False: -1),
torch.quantized_rnn_relu_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
torch.quantized_rnn_tanh_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
torch.rad2deg: lambda input, out=None: -1,
torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.ravel: lambda input: -1,
torch.real: lambda input, out=None: -1,
torch.vdot: lambda input, other, out=None: -1,
torch.view_as_real: lambda input: -1,
torch.view_as_complex: lambda input: -1,
torch.reciprocal: lambda input, out=None: -1,
torch.relu: lambda input, inplace=False: -1,
torch.remainder: lambda input, other, out=None: -1,
torch.renorm: lambda input, p, dim, maxnorm, out=None: -1,
torch.repeat_interleave: lambda input, dim=None: -1,
torch.reshape: lambda input, shape: -1,
torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.roll: lambda input, shifts, dims=None: -1,
torch.rot90: lambda input, k=1, dims=(0, 1): -1,
torch.round: lambda input, out=None: -1,
torch.row_stack: lambda tensors, out=None: -1, # alias for torch.vstack
torch._rowwise_prune: (lambda weight, mask, compressed_indices_dtype: -1),
torch.rrelu: lambda input, lower=1. / 8, upper=1. / 3, training=False, inplace=False: -1,
torch.rsqrt: lambda input, out=None: -1,
torch.rsub: lambda input, other, alpha=1: -1,
torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.scatter: lambda input, dim, index, src: -1,
torch.scatter_add: lambda input, dim, index, src: -1,
torch._scatter_reduce: lambda input, dim, index, reduce, output_size=None, out=None: -1,
torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1,
torch.segment_reduce: lambda data, reduce="max", lengths=None, indices=None, axis=0, unsafe=False: -1,
torch.select: lambda input, dim, index: -1,
torch.select_scatter: lambda input, src, dim, index: -1,
torch.slice_scatter: lambda input, src, dim, start, end, step: -1,
torch.selu: lambda input, inplace=False: -1,
torch.sigmoid: lambda input, out=None: -1,
torch.sign: lambda input, out=None: -1,
torch.signbit: lambda input, out=None: -1,
torch.sgn: lambda input, out=None: -1,
torch.sin: lambda input, out=None: -1,
torch.sinc: lambda input, out=None: -1,
torch.sinh: lambda input, out=None: -1,
torch.slogdet: lambda input: -1,
torch.linalg.slogdet: lambda input: -1,
torch.smm: lambda input, mat2: -1,
torch.spmm: lambda input, mat2: -1,
torch.softmax: lambda input, dim, dtype=None: -1,
torch.solve: lambda input, A, out=None: -1,
torch.linalg.solve: lambda input, other, out=None: -1,
torch.sort: lambda input, dim=-1, descending=False, *, stable=False, out=None: -1,
torch.split: lambda tensor, split_size_or_sections, dim=0: -1,
torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
torch.sqrt: lambda input, out=None: -1,
torch.square: lambda input, out=None: -1,
torch.squeeze: lambda input, dim=None, out=None: -1,
torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.stack: lambda tensors, dim=0, out=None: -1,
torch.std: lambda input, dim=None: -1,
torch.std_mean: lambda input, dim=None: -1,
torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
pad_mode='reflect', normalized=False, onesided=True, return_complex=None: -1),
torch.sub: lambda input, other, out=None: -1,
torch.subtract: lambda input, other, out=None: -1,
torch.sum: lambda input, dim=None: -1,
torch.nansum: lambda input, dim=None: -1,
torch.svd: lambda input, some=True, compute_uv=True, out=None: -1,
torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1,
torch.linalg.svd: lambda input, full_matrices=True, out=None: -1,
torch.linalg.svdvals: lambda input, out=None: -1,
torch.symeig: lambda input, eigenvectors=False, upper=True, out=None: -1,
torch.swapaxes: lambda input, dim0, dim1: -1,
torch.swapdims: lambda input, axis0, axis1: -1,
torch.special.entr: lambda input: -1,
torch.special.erf: lambda input: -1,
torch.special.erfc: lambda input: -1,
torch.special.erfcx: lambda input: -1,
torch.special.erfinv: lambda input: -1,
torch.special.exp2: lambda input: -1,
torch.special.expm1: lambda input: -1,
torch.special.expit: lambda input: -1,
torch.special.polygamma: lambda input, n, out=None: -1,
torch.special.digamma: lambda input: -1,
torch.special.psi: lambda input: -1,
torch.special.gammainc: lambda input, other, out=None: -1,
torch.special.gammaincc: lambda input, other, out=None: -1,
torch.special.gammaln: lambda input: -1,
torch.special.i0: lambda input: -1,
torch.special.i0e: lambda input: -1,
torch.special.i1: lambda input: -1,
torch.special.i1e: lambda input: -1,
torch.special.logit: lambda input: -1,
torch.special.logsumexp: lambda input, dim, keepdim=False, out=None: -1,
torch.special.log1p: lambda input: -1,
torch.special.log_softmax: lambda input, dim, dtype=None: -1,
torch.special.round: lambda input: -1,
torch.special.sinc: lambda input: -1,
torch.special.softmax: lambda input, dim, dtype=None: -1,
torch.special.multigammaln: lambda input, p: -1,
torch.special.ndtri: lambda input: -1,
torch.special.ndtr: lambda input: -1,
torch.special.xlogy: lambda input, other, out=None: -1,
torch.special.xlog1py: lambda input, other, out=None: -1,
torch.special.zeta: lambda self, other, out=None: -1,
torch.t: lambda input: -1,
torch.take: lambda input, index: -1,
torch.take_along_dim: lambda input, indices, dim=None, out=None: -1,
torch.tan: lambda input, out=None: -1,
torch.tanh: lambda input, out=None: -1,
torch.linalg.tensorinv: lambda a, ind=2: -1,
torch.linalg.tensorsolve: lambda a, b, dims=None: -1,
torch.tensordot: lambda a, b, dims=2, out=None: -1,
torch.tensor_split: lambda input, indices_or_sections, dim=0: -1,
torch.threshold: lambda input, threshold, value, inplace=False: -1,
torch.tile: lambda input, dims: -1,
torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1,
torch.trace: lambda input: -1,
torch.transpose: lambda input, dim0, dim1: -1,
torch.trapz: lambda y, x=None, dim=-1: -1,
torch.trapezoid: lambda y, x=None, dim=-1: -1,
torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
torch.linalg.solve_triangular: lambda input, B, upper, left=True, unitriangular=False: -1,
torch.tril: lambda input, diagonal=0, out=None: -1,
torch.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False,
size_average=None, reduce=None, reduction='mean': -1),
torch.triu: lambda input, diagonal=0, out=None: -1,
torch.true_divide: lambda input, other: -1,
torch.trunc: lambda input, out=None: -1,
torch.unbind: lambda input, dim=0: -1,
torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1,
torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1,
torch.unsafe_chunk: lambda input, chunks, dim=0: -1,
torch.unsafe_split: lambda tensor, split_size_or_sections, dim=0: -1,
torch.unsafe_split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
torch.unsqueeze: lambda input, dim, out=None: -1,
torch.var: lambda input, dim=None: -1,
torch.var_mean: lambda input, dim=None: -1,
torch.vsplit: lambda input, indices_or_sections: -1,
torch.vstack: lambda tensors, out=None: -1,
torch.where: lambda condition, x=None, y=None: -1,
torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
Tensor.__floordiv__: lambda self, other: -1,
Tensor.__rfloordiv__: lambda self, other: -1,
Tensor.__ifloordiv__: lambda self, other: -1,
Tensor.__truediv__: lambda self, other: -1,
Tensor.__rtruediv__: lambda self, other: -1,
Tensor.__itruediv__: lambda self, other: -1,
Tensor.__lshift__: lambda self, other: -1,
Tensor.__rlshift__: lambda self, other: -1,
Tensor.__ilshift__: lambda self, other: -1,
Tensor.__rshift__: lambda self, other: -1,
Tensor.__rrshift__: lambda self, other: -1,
Tensor.__irshift__: lambda self, other: -1,
Tensor.__and__: lambda self, other: -1,
Tensor.__or__: lambda self, other: -1,
Tensor.__xor__: lambda self, other: -1,
Tensor.__float__: lambda self: -1,
Tensor.__complex__: lambda self: -1,
Tensor.__array__: lambda self, dtype: -1,
Tensor.__bool__: lambda self: -1,
Tensor.__contains__: lambda self, other: -1,
Tensor.__neg__: lambda self: -1,
Tensor.__invert__: lambda self: -1,
Tensor.__mod__: lambda self, other: -1,
Tensor.__rmod__: lambda self, other: -1,
Tensor.__imod__: lambda self, other: -1,
Tensor.__array_wrap__: lambda self, array: -1,
Tensor.__getitem__: lambda self, idx: -1,
Tensor.__deepcopy__: lambda self, memo: -1,
Tensor.__int__: lambda self: -1,
Tensor.__long__: lambda self: -1,
Tensor.__hash__: lambda self: -1,
Tensor.__index__: lambda self: -1,
Tensor.__len__: lambda self: -1,
Tensor.__format__: lambda self, format_spec: -1,
Tensor.__reduce_ex__: lambda self, proto: -1,
Tensor.__reversed__: lambda self: -1,
Tensor.__repr__: lambda self: -1,
Tensor.__setitem__: lambda self, k, v: -1,
Tensor.__setstate__: lambda self, d: -1,
Tensor.T.__get__: lambda self: -1,
Tensor.H.__get__: lambda self: -1,
Tensor.mT.__get__: lambda self: -1,
Tensor.mH.__get__: lambda self: -1,
Tensor._backward_hooks.__get__: lambda self: -1,
Tensor._base.__get__: lambda self: -1,
Tensor._cdata.__get__: lambda self: -1,
Tensor.grad.__get__: lambda self: -1,
Tensor._grad.__get__: lambda self: -1,
Tensor._grad_fn.__get__: lambda self: -1,
Tensor.grad_fn.__get__: lambda self: -1,
Tensor._version.__get__: lambda self: -1,
Tensor._autocast_to_reduced_precision: lambda self: -1,
Tensor._autocast_to_full_precision: lambda self: -1,
Tensor.data.__get__: lambda self: -1,
Tensor.device.__get__: lambda self: -1,
Tensor.dtype.__get__: lambda self: -1,
Tensor.is_cuda.__get__: lambda self: -1,
Tensor.is_xpu.__get__: lambda self: -1,
Tensor.is_leaf.__get__: lambda self: -1,
Tensor.retains_grad.__get__: lambda self: -1,
Tensor.is_meta.__get__: lambda self: -1,
Tensor.is_mlc.__get__: lambda self: -1,
Tensor.is_ort.__get__: lambda self: -1,
Tensor.is_mkldnn.__get__: lambda self: -1,
Tensor.is_quantized.__get__: lambda self: -1,
Tensor.is_sparse.__get__: lambda self: -1,
Tensor.is_sparse_csr.__get__: lambda self: -1,
Tensor.is_vulkan.__get__: lambda self: -1,
Tensor.layout.__get__: lambda self: -1,
Tensor.name.__get__: lambda self: -1,
Tensor.names.__get__: lambda self: -1,
Tensor.ndim.__get__: lambda self: -1,
Tensor.output_nr.__get__: lambda self: -1,
Tensor.requires_grad.__get__: lambda self: -1,
Tensor.shape.__get__: lambda self: -1,
Tensor.volatile.__get__: lambda self: -1,
Tensor.real.__get__: lambda self: -1,
Tensor.imag.__get__: lambda self: -1,
Tensor.__cuda_array_interface__.__get__: lambda self: -1,
Tensor.type: lambda self, dtype=None, non_blocking=False, **kwargs: -1,
Tensor._coalesced_: lambda self: -1,
Tensor._dimI: lambda self: -1,
Tensor._dimV: lambda self: -1,
Tensor._indices: lambda self: -1,
Tensor._is_view: lambda self: -1,
Tensor._nnz: lambda self: -1,
Tensor.crow_indices: lambda self: -1,
Tensor.col_indices: lambda self: -1,
Tensor._update_names: lambda self, names, inplace: -1,
Tensor._values: lambda self: -1,
Tensor.adjoint: lambda self: -1,
Tensor.align_as: lambda self, other: -1,
Tensor.align_to: lambda self, order, ellipsis_idx: -1,
Tensor.apply_: lambda self, callable: -1,
Tensor.as_strided: lambda self, size, stride: -1,
Tensor.as_strided_: lambda self, size, stride: -1,
Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1,
Tensor.bfloat16: lambda self, memory_format=torch.preserve_format: -1,
Tensor.bool: lambda self, memory_format=torch.preserve_format: -1,
Tensor.byte: lambda self, memory_format=torch.preserve_format: -1,
Tensor.char: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cauchy_: lambda self, median=0, sigma=1, *, generator=None: -1,
Tensor.coalesce: lambda self: -1,
Tensor._coalesced_: lambda self, coalesced: -1,
Tensor.contiguous: lambda self, memory_format=torch.contiguous_format: -1,
Tensor.copy_: lambda self, src, non_blocking=False: -1,
Tensor.cpu: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cuda: lambda self, memory_format=torch.preserve_format: -1,
Tensor.xpu: lambda self, memory_format=torch.preserve_format: -1,
Tensor.data_ptr: lambda self: -1,
Tensor.dense_dim: lambda self: -1,
Tensor.diagonal_scatter: lambda self, src, offset=0, dim1=0, dim2=1: -1,
Tensor.dim: lambda self: -1,
Tensor.double: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cdouble: lambda self, memory_format=torch.preserve_format: -1,
Tensor.element_size: lambda self: -1,
Tensor.expand: lambda self, size: -1,
Tensor.expand_as: lambda self, other: -1,
Tensor.exponential_: lambda self, lambd=1, *, generator=None: -1,
Tensor.fill_: lambda self, value: -1,
Tensor.fill_diagonal_: lambda self, value: -1,
Tensor.float: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cfloat: lambda self, memory_format=torch.preserve_format: -1,
Tensor.geometric_: lambda self, p, *, generator=None: -1,
Tensor.get_device: lambda self: -1,
Tensor.half: lambda self, memory_format=torch.preserve_format: -1,
Tensor.has_names: lambda self: -1,
Tensor.indices: lambda self: -1,
Tensor.int: lambda self, memory_format=torch.preserve_format: -1,
Tensor.is_coalesced: lambda self: -1,
Tensor.is_contiguous: lambda self: -1,
Tensor.is_inference: lambda self: -1,
Tensor.is_pinned: lambda self: -1,
Tensor.is_set_to: lambda self, tensor: -1,
Tensor.is_shared: lambda self: -1,
Tensor.item: lambda self: -1,
Tensor.log_normal_: lambda self, mean=1, std=2, *, generator=None: -1,
Tensor.log_softmax: lambda self, dim: -1,
Tensor.long: lambda self, memory_format=torch.preserve_format: -1,
Tensor.map_: lambda self, tensor, callable: -1,
Tensor.map2_: lambda self, x, y, callable: -1,
Tensor.mm: lambda self, mat2: -1,
Tensor.narrow_copy: lambda self, dimension, start, length: -1,
Tensor.ndimension: lambda self: -1,
Tensor.nelement: lambda self: -1,
Tensor.normal_: lambda self: -1,
Tensor.numpy: lambda self: -1,
Tensor.permute: lambda self, dim: -1,
Tensor.pin_memory: lambda self: -1,
Tensor.put_: lambda self, indices, tensor, accumulate=False: -1,
Tensor.qscheme: lambda self: -1,
Tensor.random_: lambda self, from_=0, to=None, *, generator=None: -1,
Tensor.record_stream: lambda self, stream: -1,
Tensor.refine_names: lambda self, names: -1,
Tensor.register_hook: lambda self, hook: -1,
Tensor.rename: lambda self, name: -1,
Tensor.repeat: lambda self, *size: -1,
Tensor.requires_grad_: lambda self, requires_grad=True: -1,
Tensor.reshape_as: lambda self, other: -1,
Tensor.resize: lambda self, *size: -1,
Tensor.resize_: lambda self, size: -1,
Tensor.resize_as: lambda self, other: -1,
Tensor.retain_grad: lambda self: -1,
Tensor.set_: lambda self, source=None, storage_offset=0, size=None, stride=None: -1,
Tensor.select_scatter: lambda self, src, dim, index: -1,
Tensor.share_memory_: lambda self: -1,
Tensor.short: lambda self, memory_format=torch.preserve_format: -1,
Tensor.size: lambda self: -1,
Tensor.slice_scatter: lambda self, src, dim, start, end, step: -1,
Tensor.sparse_dim: lambda self: -1,
Tensor.sparse_mask: lambda self, mask: -1,
Tensor.sparse_resize_: lambda self, size1, size2, dense_dim: -1,
Tensor.sparse_resize_and_clear_: lambda self, size1, size2, dense_dim: -1,
Tensor.sspaddmm: lambda self, mat1, mat2, beta=1, alpha=1, out=None: -1,
Tensor.storage: lambda self: -1,
Tensor._storage: lambda self: -1,
Tensor.storage_offset: lambda self: -1,
Tensor.storage_type: lambda self: -1,
Tensor.sum_to_size: lambda self, size: -1,
Tensor.tile: lambda self, *reps: -1,
Tensor.to: lambda self, dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format: -1,
Tensor.to_dense: lambda self: -1,
Tensor.to_sparse: lambda self: -1,
Tensor.tolist: lambda self: -1,
Tensor.to_mkldnn: lambda self: -1,
Tensor.type_as: lambda self, other: -1,
Tensor.unfold: lambda self, dimension, size, step: -1,
Tensor.uniform_: lambda self, from_=0, to=1: -1,
Tensor.values: lambda self: -1,
Tensor.view: lambda self, shape: -1,
Tensor.view_as: lambda self, other: -1,
Tensor.zero_: lambda self: -1,
Tensor.__dlpack__: lambda self, stream=None: -1,
Tensor.__dlpack_device__: lambda self: -1,
torch.linalg.lstsq: lambda self, b, cond=None, driver=None: -1,
}
ret2 = {}
ignored = get_ignored_functions()
for k, v in ret.items():
# Generate methods like __add__ and add_ by default from add
names = [
k.__name__, # Default method
k.__name__ + "_", # Inplace variant
"__" + k.__name__ + "__", # Dunder method
"__i" + k.__name__ + "__", # Inplace dunder method
"__r" + k.__name__ + "__", # Reverse dunder method
]
if k.__name__.startswith("bitwise_"):
# bitwise_<op> have dunder methods of the form __<op>__
# And so on.
subname = k.__name__[len("bitwise_"):]
names.extend([
"__" + subname + "__",
"__i" + subname + "__",
"__r" + subname + "__"
])
for name in names:
func = getattr(Tensor, name, None)
if callable(func) and func not in ret and func not in ignored:
ret2[func] = v
ret.update(ret2)
return ret