op_map = { "abs": { "phi_name": "abs", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "accuracy": { "phi_name": "accuracy", "inputs": { "x": "Out", "indices": "Indices", "label": "Label" }, "outputs": { "accuracy": "Accuracy", "correct": "Correct", "total": "Total" } }, "acos": { "phi_name": "acos", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "acosh": { "phi_name": "acosh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "adadelta": { "phi_name": "adadelta_", "inputs": { "param": "Param", "grad": "Grad", "avg_squared_grad": "AvgSquaredGrad", "avg_squared_update": "AvgSquaredUpdate", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "moment_out": "AvgSquaredGradOut", "inf_norm_out": "AvgSquaredUpdateOut", "master_param_out": "MasterParamOut" } }, "adagrad": { "phi_name": "adagrad_", "inputs": { "param": "Param", "grad": "Grad", "moment": "Moment", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "moment_out": "MomentOut", "master_param_out": "MasterParamOut" } }, "adam": { "phi_name": "adam_", "inputs": { "param": "Param", "grad": "Grad", "learning_rate": "LearningRate", "moment1": "Moment1", "moment2": "Moment2", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "master_param": "MasterParam", "skip_update": "SkipUpdate" }, "outputs": { "param_out": "ParamOut", "moment1_out": "Moment1Out", "moment2_out": "Moment2Out", "beta1_pow_out": "Beta1PowOut", "beta2_pow_out": "Beta2PowOut", "master_param_out": "MasterParamOut" }, "scalar": { "beta1": { "data_type": "float", "tensor_name": "Beta1Tensor" }, "beta2": { "data_type": "float", "tensor_name": "Beta2Tensor" }, "epsilon": { "data_type": "float", "tensor_name": "EpsilonTensor" } } }, "adamax": { "phi_name": "adamax_", "inputs": { "param": "Param", "grad": "Grad", "learning_rate": "LearningRate", "moment": "Moment", "inf_norm": "InfNorm", "beta1_pow": "Beta1Pow", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "moment_out": "MomentOut", "inf_norm_out": "InfNormOut", "master_param_out": "MasterParamOut" } }, "adamw": { "phi_name": "adamw_", "inputs": { "param": "Param", "grad": "Grad", "learning_rate": "LearningRate", "moment1": "Moment1", "moment2": "Moment2", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "master_param": "MasterParam", "skip_update": "SkipUpdate" }, "outputs": { "param_out": "ParamOut", "moment1_out": "Moment1Out", "moment2_out": "Moment2Out", "beta1_pow_out": "Beta1PowOut", "beta2_pow_out": "Beta2PowOut", "master_param_out": "MasterParamOut" }, "scalar": { "beta1": { "data_type": "float", "tensor_name": "Beta1Tensor" }, "beta2": { "data_type": "float", "tensor_name": "Beta2Tensor" }, "epsilon": { "data_type": "float", "tensor_name": "EpsilonTensor" } } }, "elementwise_add": { "phi_name": "add", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "sum": { "phi_name": "add_n", "inputs": { "inputs": "X" }, "outputs": { "out": "Out" } }, "addmm": { "phi_name": "addmm", "inputs": { "input": "Input", "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "attrs": { "alpha": "Alpha", "beta": "Beta" } }, "affine_grid": { "phi_name": "affine_grid", "inputs": { "input": "Theta" }, "outputs": { "output": "Output" }, "int_array": { "output_shape": { "data_type": "int", "tensor_name": "OutputShape" } } }, "reduce_all": { "phi_name": "all", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" } }, "allclose": { "phi_name": "allclose", "inputs": { "x": "Input", "y": "Other" }, "outputs": { "out": "Out" }, "scalar": { "rtol": { "data_type": "std::string", "tensor_name": "Rtol" }, "atol": { "data_type": "std::string", "tensor_name": "Atol" } } }, "reduce_amax": { "phi_name": "amax", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" } }, "reduce_amin": { "phi_name": "amin", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" } }, "angle": { "phi_name": "angle", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "reduce_any": { "phi_name": "any", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" } }, "range": { "phi_name": "arange", "inputs": { "start": "Start", "end": "End", "step": "Step" }, "outputs": { "out": "Out" } }, "arg_max": { "phi_name": "argmax", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "axis": { "data_type": "int64_t", "support_tensor": "True" } } }, "arg_min": { "phi_name": "argmin", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "axis": { "data_type": "int64_t", "support_tensor": "True" } } }, "argsort": { "phi_name": "argsort", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices" } }, "tensor_array_to_tensor": { "phi_name": "array_to_tensor", "inputs": { "x": "X" }, "outputs": { "out": "Out", "out_index": "OutIndex" } }, "as_complex": { "phi_name": "as_complex", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "as_real": { "phi_name": "as_real", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "asin": { "phi_name": "asin", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "asinh": { "phi_name": "asinh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "assert": { "phi_name": "assert", "inputs": { "cond": "Cond", "data": "Data" } }, "assign": { "phi_name": "assign", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "assign_value": { "phi_name": "assign_value", "outputs": { "out": "Out" } }, "atan": { "phi_name": "atan", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "atan2": { "phi_name": "atan2", "inputs": { "x": "X1", "y": "X2" }, "outputs": { "out": "Out" } }, "atanh": { "phi_name": "atanh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "auc": { "phi_name": "auc", "inputs": { "x": "Predict", "label": "Label", "stat_pos": "StatPos", "stat_neg": "StatNeg", "ins_tag_weight": "InsTagWeight" }, "outputs": { "auc": "AUC", "stat_pos_out": "StatPosOut", "stat_neg_out": "StatNegOut" } }, "batch_norm": { "phi_name": "batch_norm", "inputs": { "x": "X", "mean": "Mean", "variance": "Variance", "scale": "Scale", "bias": "Bias" }, "outputs": { "out": "Y", "mean_out": "MeanOut", "variance_out": "VarianceOut", "saved_mean": "SavedMean", "saved_variance": "SavedVariance", "reserve_space": "ReserveSpace" } }, "bce_loss": { "phi_name": "bce_loss", "inputs": { "input": "X", "label": "Label" }, "outputs": { "out": "Out" } }, "bernoulli": { "phi_name": "bernoulli", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "bicubic_interp_v2": { "phi_name": "bicubic_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" } }, "bilinear_tensor_product": { "phi_name": "bilinear", "inputs": { "x": "X", "y": "Y", "weight": "Weight", "bias": "Bias" }, "outputs": { "out": "Out" } }, "bilinear_interp_v2": { "phi_name": "bilinear_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" } }, "bincount": { "phi_name": "bincount", "inputs": { "x": "X", "weights": "Weights" }, "outputs": { "out": "Out" }, "scalar": { "minlength": { "data_type": "int", "support_tensor": "True" } } }, "bitwise_and": { "phi_name": "bitwise_and", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "bitwise_not": { "phi_name": "bitwise_not", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "bitwise_or": { "phi_name": "bitwise_or", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "bitwise_xor": { "phi_name": "bitwise_xor", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "bmm": { "phi_name": "bmm", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "box_coder": { "phi_name": "box_coder", "inputs": { "prior_box": "PriorBox", "prior_box_var": "PriorBoxVar", "target_box": "TargetBox" }, "outputs": { "output_box": "OutputBox" } }, "broadcast_tensors": { "phi_name": "broadcast_tensors", "inputs": { "input": "X" }, "outputs": { "out": "Out" } }, "c_concat": { "phi_name": "c_concat", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_embedding": { "phi_name": "c_embedding", "inputs": { "weight": "W", "x": "Ids" }, "outputs": { "out": "Out" } }, "cast": { "phi_name": "cast", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "ceil": { "phi_name": "ceil", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "celu": { "phi_name": "celu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "check_finite_and_unscale": { "phi_name": "check_finite_and_unscale_", "inputs": { "x": "X", "scale": "Scale" }, "outputs": { "out": "Out", "found_infinite": "FoundInfinite" } }, "cholesky": { "phi_name": "cholesky", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "cholesky_solve": { "phi_name": "cholesky_solve", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "class_center_sample": { "phi_name": "class_center_sample", "inputs": { "label": "Label" }, "outputs": { "remapped_label": "RemappedLabel", "sampled_local_class_center": "SampledLocalClassCenter" } }, "clip": { "phi_name": "clip", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "min": { "data_type": "float", "tensor_name": "Min" }, "max": { "data_type": "float", "tensor_name": "Max" } } }, "clip_by_norm": { "phi_name": "clip_by_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "coalesce_tensor": { "phi_name": "coalesce_tensor", "inputs": { "input": "Input" }, "outputs": { "output": "Output", "fused_output": "FusedOutput" }, "attrs": { "size_of_dtype": "user_defined_size_of_dtype" } }, "complex": { "phi_name": "complex", "inputs": { "real": "X", "imag": "Y" }, "outputs": { "out": "Out" } }, "concat": { "phi_name": "concat", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "axis" }, "scalar": { "axis": { "data_type": "int", "tensor_name": "AxisTensor" } } }, "conditional_block": { "phi_name": "conditional_block" }, "conj": { "phi_name": "conj", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "conv2d": { "phi_name": "conv2d", "inputs": { "input": "Input", "filter": "Filter" }, "outputs": { "out": "Output" } }, "conv2d_transpose": { "phi_name": "conv2d_transpose", "inputs": { "x": "Input", "filter": "Filter", "bias": "Bias" }, "outputs": { "out": "Output" }, "int_array": { "output_size": { "data_type": "int", "support_tensor": "True" } } }, "conv3d": { "phi_name": "conv3d", "inputs": { "input": "Input", "filter": "Filter" }, "outputs": { "out": "Output" } }, "conv3d_transpose": { "phi_name": "conv3d_transpose", "inputs": { "x": "Input", "filter": "Filter" }, "outputs": { "out": "Output" } }, "cos": { "phi_name": "cos", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "cosh": { "phi_name": "cosh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "crop_tensor": { "phi_name": "crop", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "int_array": { "shape": { "data_type": "int", "tensor_name": "Shape", "tensors_name": "ShapeTensor" }, "offsets": { "data_type": "int", "tensor_name": "Offsets", "tensors_name": "OffsetsTensor" } } }, "cross": { "phi_name": "cross", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim" } }, "softmax_with_cross_entropy": { "phi_name": "cross_entropy_with_softmax", "inputs": { "input": "Logits", "label": "Label" }, "outputs": { "softmax": "Softmax", "loss": "Loss" } }, "cumprod": { "phi_name": "cumprod", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "dim": "dim" } }, "cumsum": { "phi_name": "cumsum", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "data_norm": { "phi_name": "data_norm" }, "decode_jpeg": { "phi_name": "decode_jpeg", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "deformable_conv": { "phi_name": "deformable_conv", "inputs": { "x": "Input", "offset": "Offset", "filter": "Filter", "mask": "Mask" }, "outputs": { "out": "Output" } }, "depthwise_conv2d": { "phi_name": "depthwise_conv2d", "inputs": { "input": "Input", "filter": "Filter" }, "outputs": { "out": "Output" } }, "depthwise_conv2d_transpose": { "phi_name": "depthwise_conv2d_transpose", "inputs": { "x": "Input", "filter": "Filter", "bias": "Bias" }, "outputs": { "out": "Output" }, "int_array": { "output_size": { "data_type": "int", "support_tensor": "True" } } }, "dequantize_linear": { "phi_name": "dequantize_linear" }, "determinant": { "phi_name": "det", "inputs": { "x": "Input" }, "outputs": { "out": "Out" } }, "diag_v2": { "phi_name": "diag", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "diag_embed": { "phi_name": "diag_embed", "inputs": { "input": "Input" }, "outputs": { "out": "Out" } }, "diagonal": { "phi_name": "diagonal", "inputs": { "x": "Input" }, "outputs": { "out": "Out" } }, "digamma": { "phi_name": "digamma", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "dirichlet": { "phi_name": "dirichlet", "inputs": { "alpha": "Alpha" }, "outputs": { "out": "Out" } }, "dist": { "phi_name": "dist", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "distributed_push_sparse": { "phi_name": "distributed_push_sparse" }, "elementwise_div": { "phi_name": "divide", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "dot": { "phi_name": "dot", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "dropout": { "phi_name": "dropout", "inputs": { "x": "X", "seed_tensor": "Seed" }, "outputs": { "out": "Out", "mask": "Mask" }, "attrs": { "p": "dropout_prob", "is_test": "is_test", "mode": "dropout_implementation", "seed": "seed", "fix_seed": "fix_seed" } }, "dropout_nd": { "phi_name": "dropout_nd" }, "edit_distance": { "phi_name": "edit_distance", "inputs": { "hyps": "Hyps", "refs": "Refs", "hypslength": "HypsLength", "refslength": "RefsLength" }, "outputs": { "sequencenum": "SequenceNum", "out": "Out" } }, "eig": { "phi_name": "eig", "inputs": { "x": "X" }, "outputs": { "out_w": "Eigenvalues", "out_v": "Eigenvectors" } }, "eigh": { "phi_name": "eigh", "inputs": { "x": "X" }, "outputs": { "out_w": "Eigenvalues", "out_v": "Eigenvectors" } }, "eigvals": { "phi_name": "eigvals", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "eigvalsh": { "phi_name": "eigvalsh", "inputs": { "x": "X" }, "outputs": { "eigenvalues": "Eigenvalues", "eigenvectors": "Eigenvectors" }, "attrs": { "uplo": "UPLO" } }, "einsum": { "phi_name": "einsum", "inputs": { "x": "Operands" }, "outputs": { "out": "Out", "inner_cache": "InnerCache", "xshape": "XShape" } }, "elementwise_pow": { "phi_name": "elementwise_pow", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "elu": { "phi_name": "elu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "lookup_table_v2": { "phi_name": "embedding", "inputs": { "x": "Ids", "weight": "W" }, "outputs": { "out": "Out" }, "attrs": { "sparse": "is_sparse" } }, "empty": { "phi_name": "empty", "outputs": { "out": "Out" }, "int_array": { "shape": { "data_type": "int64_t", "tensor_name": "ShapeTensor", "tensors_name": "ShapeTensorList" } } }, "equal": { "phi_name": "equal", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "equal_all": { "phi_name": "equal_all", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "erf": { "phi_name": "erf", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "erfinv": { "phi_name": "erfinv", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "exp": { "phi_name": "exp", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "expand_v2": { "phi_name": "expand", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "shape": "shape" }, "int_array": { "shape": { "data_type": "int", "tensor_name": "Shape", "tensors_name": "expand_shapes_tensor" } } }, "expand_as_v2": { "phi_name": "expand_as", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "expm1": { "phi_name": "expm1", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "exponential": { "phi_name": "exponential_", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "lam": "lambda" } }, "eye": { "phi_name": "eye", "outputs": { "out": "Out" }, "scalar": { "num_rows": { "support_tensor": "True" }, "num_columns": { "support_tensor": "True" } } }, "fake_channel_wise_quantize_abs_max": { "phi_name": "fake_channel_wise_quantize_abs_max" }, "fake_channel_wise_quantize_dequantize_abs_max": { "phi_name": "fake_channel_wise_quantize_dequantize_abs_max" }, "fake_quantize_abs_max": { "phi_name": "fake_quantize_abs_max" }, "fake_quantize_dequantize_abs_max": { "phi_name": "fake_quantize_dequantize_abs_max" }, "fake_quantize_dequantize_moving_average_abs_max": { "phi_name": "fake_quantize_dequantize_moving_average_abs_max" }, "fake_quantize_moving_average_abs_max": { "phi_name": "fake_quantize_moving_average_abs_max" }, "fake_quantize_range_abs_max": { "phi_name": "fake_quantize_range_abs_max" }, "fc": { "phi_name": "fc", "inputs": { "input": "Input", "w": "W", "bias": "Bias" }, "outputs": { "out": "Out" }, "attrs": { "scale_in": "Scale_in", "scale_weights": "Scale_weights", "scale_out": "Scale_out" } }, "feed": { "phi_name": "feed", "outputs": { "out": "Out" } }, "fft_c2c": { "phi_name": "fft_c2c", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fft_c2r": { "phi_name": "fft_c2r", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fft_r2c": { "phi_name": "fft_r2c", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fill_any": { "phi_name": "fill", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "value": { "data_type": "float", "support_tensor": "True" } } }, "fill_diagonal": { "phi_name": "fill_diagonal", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, 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"data_type": "int", "tensor_name": "AxesTensor", "tensors_name": "AxesTensorList" } } }, "unstack": { "phi_name": "unstack", "inputs": { "x": "X" }, "outputs": { "out": "Y" } }, "update_loss_scaling": { "phi_name": "update_loss_scaling_", "inputs": { "x": "X", "found_infinite": "FoundInfinite", "prev_loss_scaling": "PrevLossScaling", "in_good_steps": "InGoodSteps", "in_bad_steps": "InBadSteps" }, "outputs": { "out": "Out", "loss_scaling": "LossScaling", "out_good_steps": "OutGoodSteps", "out_bad_steps": "OutBadSteps" }, "scalar": { "stop_update": { "data_type": "bool", "tensor_name": "StopUpdate" } } }, "viterbi_decode": { "phi_name": "viterbi_decode", "inputs": { "potentials": "Input", "transition_params": "Transition", "lengths": "Length" }, "outputs": { "scores": "Scores", "path": "Path" } }, "warpctc": { "phi_name": "warpctc", "inputs": { "logits": "Logits", "label": "Label", "logits_length": "LogitsLength", "labels_length": "LabelLength" }, "outputs": { "warpctcgrad": "WarpCTCGrad", "loss": "Loss" } }, "where": { "phi_name": "where", "inputs": { "condition": "Condition", "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "while": { "phi_name": "while" }, "yolo_box": { "phi_name": "yolo_box", "inputs": { "x": "X", "img_size": "ImgSize" }, "outputs": { "boxes": "Boxes", "scores": "Scores" } }, "yolov3_loss": { "phi_name": "yolo_loss", "inputs": { "x": "X", "gt_box": "GTBox", "gt_label": "GTLabel", "gt_score": "GTScore" }, "outputs": { "loss": "Loss", "objectness_mask": "ObjectnessMask", "gt_match_mask": "GTMatchMask" } }, "c_allgather": { "phi_name": "c_allgather", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_allreduce_max": { "phi_name": "c_allreduce_max", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_allreduce_sum": { "phi_name": "c_allreduce_sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_broadcast": { "phi_name": "c_broadcast", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_identity": { "phi_name": "c_identity", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_reduce_sum": { "phi_name": "c_reduce_sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_reducescatter": { "phi_name": "c_reducescatter", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_sync_calc_stream": { "phi_name": "c_sync_calc_stream", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_sync_comm_stream": { "phi_name": "c_sync_comm_stream", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "channel_shuffle": { "phi_name": "channel_shuffle", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "decayed_adagrad": { "phi_name": "decayed_adagrad", "inputs": { "param": "Param", "grad": "Grad", "moment": "Moment", "learning_rate": "LearningRate" }, "outputs": { "param_out": "ParamOut", "moment_out": "MomentOut" } }, "dpsgd": { "phi_name": "dpsgd", "inputs": { "param": "Param", "grad": "Grad", "learning_rate": "LearningRate" }, "outputs": { "param_out": "ParamOut" } }, "fetch_v2": { "phi_name": "fetch", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fill_constant_batch_size_like": { "phi_name": "full_batch_size_like", "inputs": { "input": "Input" }, "outputs": { "out": "Out" } }, "fused_elemwise_add_activation": { "phi_name": "fused_elemwise_add_activation", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out", "intermediate_out": "IntermediateOut" } }, "fusion_squared_mat_sub": { "phi_name": "fusion_squared_mat_sub", "inputs": { "x": "X", "y": "Y" }, "outputs": { "squared_x": "SquaredX", "squared_y": "SquaredY", "squared_xy": "SquaredXY", "out": "Out" } }, "get_tensor_from_selected_rows": { "phi_name": "get_tensor_from_selected_rows", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "lod_array_length": { "phi_name": "lod_array_length", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logspace": { "phi_name": "logspace", "inputs": { "start": "Start", "stop": "Stop", "num": "Num", "base": "Base" }, "outputs": { "out": "Out" } }, "lu": { "phi_name": "lu", "inputs": { "x": "X" }, "outputs": { "out": "Out", "pivots": "Pivots", "infos": "Infos" }, "attrs": { "pivot": "pivots" } }, "memcpy": { "phi_name": "memcpy", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "memcpy_d2h": { "phi_name": "memcpy_d2h", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "read_from_array": { "phi_name": "read_from_array", "inputs": { "array": "X", "i": "I" }, "outputs": { "out": "Out" } }, "recv_v2": { "phi_name": "recv_v2", "outputs": { "out": "Out" } }, "graph_reindex": { "phi_name": "reindex_graph", "inputs": { "x": "X", "neighbors": "Neighbors", "count": "Count", "hashtable_value": "HashTable_Value", "hashtable_index": "HashTable_Index" }, "outputs": { "reindex_src": "Reindex_Src", "reindex_dst": "Reindex_Dst", "out_nodes": "Out_Nodes" } }, "rrelu": { "phi_name": "rrelu", "inputs": { "x": "X" }, "outputs": { "out": "Out", "noise": "Noise" } }, "send_v2": { "phi_name": "send_v2", "inputs": { "x": "X" } }, "set_value": { "phi_name": "set_value", "inputs": { "x": "Input" }, "outputs": { "out": "Out" }, "int_array": { "starts": { "data_type": "int64_t", "tensors_name": "StartsTensorList" }, "ends": { "data_type": "int64_t", "tensors_name": "EndsTensorList" }, "steps": { "data_type": "int64_t", "tensors_name": "StepsTensorList" } } }, "set_value_with_tensor": { "phi_name": "set_value_with_tensor", "inputs": { "x": "Input" }, "outputs": { "out": "Out" }, "int_array": { "starts": { "data_type": "int64_t", "tensors_name": "StartsTensorList" }, "ends": { "data_type": "int64_t", "tensors_name": "EndsTensorList" }, "steps": { "data_type": "int64_t", "tensors_name": "StepsTensorList" } } }, "share_data": { "phi_name": "share_data", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sigmoid_cross_entropy_with_logits": { "phi_name": "sigmoid_cross_entropy_with_logits", "inputs": { "x": "X", "label": "Label" }, "outputs": { "out": "Out" } }, "skip_layernorm": { "phi_name": "skip_layernorm", "inputs": { "x": "X", "y": "Y", "scale": "Scale", "bias": "Bias" }, "outputs": { "out": "Out" } }, "sparse_momentum": { "phi_name": "sparse_momentum", "inputs": { "param": "Param", "grad": "Grad", "velocity": "Velocity", "index": "Index", "axis": "Axis", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "velocity_out": "VelocityOut", "master_param_out": "MasterParamOut" }, "scalar": { "axis": { "datatype": "int", "tensor_name": "Axis" } } }, "squared_l2_norm": { "phi_name": "squared_l2_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "temporal_shift": { "phi_name": "temporal_shift", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "uniform_random_batch_size_like": { "phi_name": "uniform_random_batch_size_like", "inputs": { "input": "Input" }, "outputs": { "out": "Out" } }, "write_to_array": { "phi_name": "write_to_array", "inputs": { "x": "X", "i": "I" }, "outputs": { "out": "Out" } } } op_info = { "abs": { "args": "Tensor x", "output": "Tensor(out)" }, "accuracy": { "args": "Tensor x, Tensor indices, Tensor label", "output": "Tensor(accuracy), Tensor(correct), Tensor(total)" }, "acos": { "args": "Tensor x", "output": "Tensor(out)" }, "acosh": { "args": "Tensor x", "output": "Tensor(out)" }, "adagrad_": { "args": "Tensor param, Tensor grad, Tensor moment, Tensor learning_rate, Tensor master_param, float epsilon = 1.0e-6f, bool multi_precision = false", "output": "Tensor(param_out), Tensor(moment_out), Tensor(master_param_out)" }, "adam_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false", "output": "Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out)" }, "adamax_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor moment, Tensor inf_norm, Tensor beta1_pow, Tensor master_param, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1.0e-8f, bool multi_precision = false", "output": "Tensor(param_out), Tensor(moment_out), Tensor(inf_norm_out), Tensor(master_param_out)" }, "adamw_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, float lr_ratio = 1.0f, float coeff = 0.01f, bool with_decay = false, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false", "output": "Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out)" }, "addmm": { "args": "Tensor input, Tensor x, Tensor y, float beta=1.0, float alpha=1.0", "output": "Tensor(out)" }, "affine_grid": { "args": "Tensor input, IntArray output_shape={}, bool align_corners=true", "output": "Tensor(output)" }, "allclose": { "args": "Tensor x, Tensor y, Scalar rtol=\"1e-5\", Scalar atol=\"1e-8\", bool equal_nan=false", "output": "Tensor(out)" }, "angle": { "args": "Tensor x", "output": "Tensor" }, "argmax": { "args": "Tensor x, Scalar(int64_t) axis, bool keepdims = false, bool flatten = false, DataType dtype = DataType::INT64", "output": "Tensor(out)" }, "argmin": { "args": "Tensor x, Scalar(int64_t) axis, bool keepdims = false, bool flatten = false, DataType dtype = DataType::INT64", "output": "Tensor(out)" }, "argsort": { "args": "Tensor x, int axis=-1, bool descending=false", "output": "Tensor(out), Tensor(indices)" }, "as_complex": { "args": "Tensor x", "output": "Tensor" }, "as_real": { "args": "Tensor x", "output": "Tensor" }, "as_strided": { "args": "Tensor input, int64_t[] dims = {}, int64_t[] stride = {}, int64_t offset = 0", "output": "Tensor" }, "asin": { "args": "Tensor x", "output": "Tensor(out)" }, "asinh": { "args": "Tensor x", "output": "Tensor(out)" }, "atan": { "args": "Tensor x", "output": "Tensor(out)" }, "atan2": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "atanh": { "args": "Tensor x", "output": "Tensor(out)" }, "auc": { "args": "Tensor x, Tensor label, Tensor stat_pos, Tensor stat_neg, Tensor ins_tag_weight, str curve = \"ROC\", int num_thresholds = (2 << 12) - 1, int slide_steps = 1", "output": "Tensor(auc), Tensor(stat_pos_out), Tensor(stat_neg_out)" }, "average_accumulates_": { "args": "Tensor param, Tensor in_sum_1, Tensor in_sum_2, Tensor in_sum_3, Tensor in_num_accumulates, Tensor in_old_num_accumulates, Tensor in_num_updates, float average_window = 0, int64_t max_average_window = INT64_MAX, int64_t min_average_window = 10000L", "output": "Tensor(out_sum_1), Tensor(out_sum_2), Tensor(out_sum_3), Tensor(out_num_accumulates), Tensor(out_old_num_accumulates), Tensor(out_num_updates)" }, "bce_loss": { "args": "Tensor input, Tensor label", "output": "Tensor" }, "bernoulli": { "args": "Tensor x", "output": "Tensor(out)" }, "bicubic_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, float[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "bilinear": { "args": "Tensor x, Tensor y, Tensor weight, Tensor bias", "output": "Tensor" }, "bilinear_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, float[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "bincount": { "args": "Tensor x, Tensor weights, Scalar(int) minlength = 0", "output": "Tensor(out)" }, "bitwise_and": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bitwise_not": { "args": "Tensor x", "output": "Tensor(out)" }, "bitwise_or": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bitwise_xor": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bmm": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "box_coder": { "args": "Tensor prior_box, Tensor prior_box_var, Tensor target_box, str code_type = \"encode_center_size\", bool box_normalized = true, int axis = 0, float[] variance = {}", "output": "Tensor(output_box)" }, "broadcast_tensors": { "args": "Tensor[] input", "output": "Tensor[]{input.size()}" }, "ceil": { "args": "Tensor x", "output": "Tensor(out)" }, "celu": { "args": "Tensor x, float alpha = 1.0", "output": "Tensor(out)" }, "check_finite_and_unscale_": { "args": "Tensor[] x, Tensor scale", "output": "Tensor[](out){x.size()}, Tensor(found_infinite)" }, "check_numerics": { "args": "Tensor tensor, str op_type = \"\", str var_name = \"\", int check_nan_inf_level = 0, int stack_height_limit = -1, str output_dir = \"\"", "output": "Tensor(stats), Tensor(values)" }, "cholesky": { "args": "Tensor x, bool upper=false", "output": "Tensor" }, "cholesky_solve": { "args": "Tensor x, Tensor y, bool upper=false", "output": "Tensor" }, "class_center_sample": { "args": "Tensor label, int num_classes, int num_samples, int ring_id = 0, int rank = 0, int nranks = 1, bool fix_seed = false, int seed = 0", "output": "Tensor(remapped_label), Tensor(sampled_local_class_center)" }, "clip": { "args": "Tensor x, Scalar(float) min, Scalar(float) max", "output": "Tensor(out)" }, "clip_by_norm": { "args": "Tensor x, float max_norm", "output": "Tensor(out)" }, "coalesce_tensor": { "args": "Tensor[] input, DataType dtype, bool copy_data = false, bool set_constant = false, bool persist_output = false, float constant = 0.0, bool use_align = true, int align_size = -1, int size_of_dtype = -1, int64_t[] concated_shapes = {}, int64_t[] concated_ranks = {}", "output": "Tensor[](output){input.size()}, Tensor(fused_output)" }, "complex": { "args": "Tensor real, Tensor imag", "output": "Tensor" }, "concat": { "args": "Tensor[] x, Scalar axis=0", "output": "Tensor" }, "conj": { "args": "Tensor x", "output": "Tensor (out)" }, "conv2d": { "args": "Tensor input, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, str padding_algorithm=\"EXPLICIT\", int[] dilations={1, 1}, int groups=1, str data_format=\"NCHW\"", "output": "Tensor" }, "conv3d": { "args": "Tensor input, Tensor filter, int[] strides={1, 1, 1}, int[] paddings={0, 0, 0}, str padding_algorithm=\"EXPLICIT\", int groups=1, int[] dilations={1, 1, 1}, str data_format=\"NCDHW\"", "output": "Tensor" }, "conv3d_transpose": { "args": "Tensor x, Tensor filter, int[] strides={1, 1, 1}, int[] paddings={0, 0, 0}, int[] output_padding={}, int[] output_size={}, str padding_algorithm=\"EXPLICIT\", int groups=1, int[] dilations={1, 1, 1}, str data_format=\"NCHW\"", "output": "Tensor(out)" }, "cos": { "args": "Tensor x", "output": "Tensor(out)" }, "cosh": { "args": "Tensor x", "output": "Tensor(out)" }, "crop": { "args": "Tensor x, IntArray shape = {}, IntArray offsets = {}", "output": "Tensor(out)" }, "cross": { "args": "Tensor x, Tensor y, int axis = 9", "output": "Tensor" }, "cross_entropy_with_softmax": { "args": "Tensor input, Tensor label, bool soft_label=false, bool use_softmax=true, bool numeric_stable_mode=true, int ignore_index=-100, int axis=-1", "output": "Tensor(softmax), Tensor(loss)" }, "cummax": { "args": "Tensor x, int axis=-1, DataType dtype = DataType::INT64", "output": "Tensor(out), Tensor(indices)" }, "cummin": { "args": "Tensor x, int axis=-1, DataType dtype = DataType::INT64", "output": "Tensor(out), Tensor(indices)" }, "cumprod": { "args": "Tensor x, int dim", "output": "Tensor(out)" }, "cumsum": { "args": "Tensor x, Scalar axis=-1, bool flatten=false, bool exclusive=false, bool reverse=false", "output": "Tensor(out)" }, "data": { "args": "str name, IntArray shape, DataType dtype, Place place", "output": "Tensor(out)" }, "depthwise_conv2d": { "args": "Tensor input, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, str padding_algorithm=\"EXPLICIT\", int groups=1, int[] dilations={1, 1}, str data_format=\"NCHW\"", "output": "Tensor(out)" }, "det": { "args": "Tensor x", "output": "Tensor" }, "diag": { "args": "Tensor x, int offset = 0, float padding_value = 0.0", "output": "Tensor" }, "diag_embed": { "args": "Tensor input, int offset = 0, int dim1 = -2, int dim2 = -1", "output": "Tensor(out)" }, "diagonal": { "args": "Tensor x, int offset = 0, int axis1 = 0, int axis2 = 1", "output": "Tensor" }, "digamma": { "args": "Tensor x", "output": "Tensor(out)" }, "dirichlet": { "args": "Tensor alpha", "output": "Tensor(out)" }, "dist": { "args": "Tensor x, Tensor y, float p = 2.0", "output": "Tensor" }, "dot": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "edit_distance": { "args": "Tensor hyps, Tensor refs, Tensor hypslength, Tensor refslength, bool normalized = false", "output": "Tensor(sequencenum), Tensor(out)" }, "eig": { "args": "Tensor x", "output": "Tensor(out_w), Tensor(out_v)" }, "eigh": { "args": "Tensor x, str UPLO = \"L\"", "output": "Tensor(out_w), Tensor(out_v)" }, "eigvals": { "args": "Tensor x", "output": "Tensor(out)" }, "eigvalsh": { "args": "Tensor x, str uplo = \"L\", bool is_test = false", "output": "Tensor(eigenvalues), Tensor(eigenvectors)" }, "elu": { "args": "Tensor x, float alpha = 1.0f", "output": "Tensor(out)" }, "equal_all": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "erf": { "args": "Tensor x", "output": "Tensor(out)" }, "erfinv": { "args": "Tensor x", "output": "Tensor(out)" }, "exp": { "args": "Tensor x", "output": "Tensor(out)" }, "expand": { "args": "Tensor x, IntArray shape = {}", "output": "Tensor(out)" }, "expand_as": { "args": "Tensor x, Tensor y, int[] target_shape = {}", "output": "Tensor(out)" }, "expm1": { "args": "Tensor x", "output": "Tensor(out)" }, "fft_c2c": { "args": "Tensor x, int64_t[] axes, str normalization, bool forward", "output": "Tensor" }, "fft_c2r": { "args": "Tensor x, int64_t[] axes, str normalization, bool forward, int64_t last_dim_size=0L", "output": "Tensor" }, "fft_r2c": { "args": "Tensor x, int64_t[] axes, str normalization, bool forward, bool onesided", "output": "Tensor" }, "fill": { "args": "Tensor x, Scalar value=0", "output": "Tensor(out)" }, "fill_diagonal": { "args": "Tensor x, float value=0, int offset=0, bool wrap=false", "output": "Tensor(out)" }, "fill_diagonal_tensor": { "args": "Tensor x, Tensor y, int64_t offset = 0, int dim1 = 0, int dim2 = 1", "output": "Tensor(out)" }, "flash_attn": { "args": "Tensor q, Tensor k, Tensor v, Tensor fixed_seed_offset, Tensor attn_mask, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = \"\"", "output": "Tensor(out), Tensor(softmax), Tensor(softmax_lse), Tensor(seed_offset)" }, "flash_attn_unpadded": { "args": "Tensor q, Tensor k, Tensor v, Tensor cu_seqlens_q, Tensor cu_seqlens_k, Tensor fixed_seed_offset, Tensor attn_mask, int64_t max_seqlen_q, int64_t max_seqlen_k, float scale, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = \"\"", "output": "Tensor(out), Tensor(softmax), Tensor(softmax_lse), Tensor(seed_offset)" }, "flatten": { "args": "Tensor x, int start_axis = 1, int stop_axis = 1", "output": "Tensor(out), Tensor(xshape)" }, "flip": { "args": "Tensor x, int[] axis", "output": "Tensor (out)" }, "floor": { "args": "Tensor x", "output": "Tensor(out)" }, "fmax": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "fmin": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "fold": { "args": "Tensor x, int[] output_sizes, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations", "output": "Tensor(out)" }, "frame": { "args": "Tensor x, int frame_length, int hop_length, int axis=-1", "output": "Tensor(out)" }, "full_int_array": { "args": "int64_t[] value, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "gather": { "args": "Tensor x, Tensor index, Scalar axis=0", "output": "Tensor(out)" }, "gather_nd": { "args": "Tensor x, Tensor index", "output": "Tensor(out)" }, "gather_tree": { "args": "Tensor ids, Tensor parents", "output": "Tensor(out)" }, "gaussian_inplace": { "args": "Tensor x, float mean=0, float std=1.0, int seed=0", "output": "Tensor(out)" }, "gelu": { "args": "Tensor x, bool approximate = false", "output": "Tensor(out)" }, "generate_proposals": { "args": "Tensor scores, Tensor bbox_deltas, Tensor im_shape, Tensor anchors, Tensor variances, int pre_nms_top_n, int post_nms_top_n, float nms_thresh, float min_size, float eta, bool pixel_offset=true", "output": "Tensor(rpn_rois), Tensor(rpn_roi_probs), Tensor(rpn_rois_num)" }, "grid_sample": { "args": "Tensor x, Tensor grid, str mode = \"bilinear\", str padding_mode = \"zeros\", bool align_corners = true", "output": "Tensor(out)" }, "group_norm": { "args": "Tensor x, Tensor scale, Tensor bias, float epsilon = 1e-5, int groups = -1, str data_layout = \"NCHW\"", "output": "Tensor(y), Tensor(mean), Tensor(variance)" }, "gumbel_softmax": { "args": "Tensor x, float temperature = 1.0, bool hard = false, int axis = -1", "output": "Tensor" }, "hardshrink": { "args": "Tensor x, float threshold = 0.5", "output": "Tensor (out)" }, "hardsigmoid": { "args": "Tensor x, float slope = 0.2, float offset = 0.5", "output": "Tensor (out)" }, "hardtanh": { "args": "Tensor x, float t_min=0, float t_max=24", "output": "Tensor(out)" }, "heaviside": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "histogram": { "args": "Tensor input, int64_t bins = 100, int min = 0, int max = 0", "output": "Tensor(out)" }, "huber_loss": { "args": "Tensor input, Tensor label, float delta", "output": "Tensor(out), Tensor(residual)" }, "i0": { "args": "Tensor x", "output": "Tensor(out)" }, "i0e": { "args": "Tensor x", "output": "Tensor(out)" }, "i1": { "args": "Tensor x", "output": "Tensor(out)" }, "i1e": { "args": "Tensor x", "output": "Tensor(out)" }, "imag": { "args": "Tensor x", "output": "Tensor (out)" }, "index_add": { "args": "Tensor x, Tensor index, Tensor add_value, int axis = 0", "output": "Tensor(out)" }, "index_put": { "args": "Tensor x, Tensor[] indices, Tensor value, bool accumulate=false", "output": "Tensor(out)" }, "index_sample": { "args": "Tensor x, Tensor index", "output": "Tensor" }, "index_select": { "args": "Tensor x, Tensor index, int axis = 0", "output": "Tensor(out)" }, "index_select_strided": { "args": "Tensor x, int64_t index, int axis = 0", "output": "Tensor(out)" }, "instance_norm": { "args": "Tensor x, Tensor scale, Tensor bias, float epsilon=1e-5", "output": "Tensor(y), Tensor(saved_mean), Tensor(saved_variance)" }, "inverse": { "args": "Tensor x", "output": "Tensor(out)" }, "is_empty": { "args": "Tensor x", "output": "Tensor(out)" }, "isclose": { "args": "Tensor x, Tensor y, Scalar(double) rtol=1e-5, Scalar(double) atol=1e-8, bool equal_nan=false", "output": "Tensor(out)" }, "isfinite": { "args": "Tensor x", "output": "Tensor(out)" }, "isinf": { "args": "Tensor x", "output": "Tensor(out)" }, "isnan": { "args": "Tensor x", "output": "Tensor(out)" }, "kldiv_loss": { "args": "Tensor x, Tensor label, str reduction = \"mean\"", "output": "Tensor(out)" }, "kron": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "kthvalue": { "args": "Tensor x, int k = 1, int axis = -1, bool keepdim = false", "output": "Tensor(out), Tensor(indices)" }, "label_smooth": { "args": "Tensor label, Tensor prior_dist, float epsilon = 0.0f", "output": "Tensor (out)" }, "lamb_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, float weight_decay, float beta1=0.9, float beta2=0.999, float epsilon=1.0e-6f, bool always_adapt=false, bool multi_precision=false", "output": "Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_outs)" }, "layer_norm": { "args": "Tensor x, Tensor scale, Tensor bias, float epsilon = 1e-5, int begin_norm_axis = 1", "output": "Tensor(out), Tensor(mean), Tensor(variance)" }, "leaky_relu": { "args": "Tensor x, float negative_slope = 0.02f", "output": "Tensor(out)" }, "lerp": { "args": "Tensor x, Tensor y, Tensor weight", "output": "Tensor(out)" }, "lgamma": { "args": "Tensor x", "output": "Tensor(out)" }, "linear_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, float[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "llm_int8_linear": { "args": "Tensor x, Tensor weight, Tensor bias, Tensor weight_scale, float threshold=6.0", "output": "Tensor(out)" }, "log": { "args": "Tensor x", "output": "Tensor(out)" }, "log10": { "args": "Tensor x", "output": "Tensor(out)" }, "log1p": { "args": "Tensor x", "output": "Tensor(out)" }, "log2": { "args": "Tensor x", "output": "Tensor(out)" }, "log_loss": { "args": "Tensor input, Tensor label, float epsilon", "output": "Tensor" }, "log_softmax": { "args": "Tensor x, int axis = -1", "output": "Tensor(out)" }, "logcumsumexp": { "args": "Tensor x, int axis=-1, bool flatten=false, bool exclusive=false, bool reverse=false", "output": "Tensor(out)" }, "logical_and": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "logical_not": { "args": "Tensor x", "output": "Tensor(out)" }, "logical_or": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "logical_xor": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "logit": { "args": "Tensor x, float eps = 1e-6f", "output": "Tensor(out)" }, "logsigmoid": { "args": "Tensor x", "output": "Tensor" }, "lstsq": { "args": "Tensor x, Tensor y, Scalar rcond=0.0f, str driver=\"gels\"", "output": "Tensor(solution), Tensor(residuals), Tensor(rank), Tensor(singular_values)" }, "lu": { "args": "Tensor x, bool pivot = true", "output": "Tensor(out), Tensor(pivots), Tensor(infos)" }, "lu_unpack": { "args": "Tensor x, Tensor y, bool unpack_ludata = true, bool unpack_pivots = true", "output": "Tensor(pmat), Tensor(l), Tensor(u)" }, "margin_cross_entropy": { "args": "Tensor logits, Tensor label, bool return_softmax = false, int ring_id = 0, int rank = 0, int nranks = 1, float margin1 = 1.0f, float margin2 = 0.5f, float margin3 = 0.0f, float scale = 64.0f", "output": "Tensor(softmax), Tensor(loss)" }, "masked_multihead_attention_": { "args": "Tensor x, Tensor cache_kv, Tensor bias, Tensor src_mask, Tensor cum_offsets, Tensor sequence_lengths, Tensor rotary_tensor, Tensor beam_cache_offset, Tensor qkv_out_scale, Tensor out_shift, Tensor out_smooth, int seq_len, int rotary_emb_dims, bool use_neox_rotary_style=false, str compute_dtype = \"default\", float out_scale=-1, int quant_round_type=1, float quant_max_bound=127.0, float quant_min_bound=-127.0", "output": "Tensor(out), Tensor(cache_kv_out), Tensor(beam_cache_offset_out)" }, "masked_select": { "args": "Tensor x, Tensor mask", "output": "Tensor (out)" }, "matrix_nms": { "args": "Tensor bboxes, Tensor scores, float score_threshold, int nms_top_k, int keep_top_k, float post_threshold=0., bool use_gaussian = false, float gaussian_sigma = 2., int background_label = 0, bool normalized = true", "output": "Tensor(out), Tensor(index), Tensor(roisnum)" }, "matrix_power": { "args": "Tensor x, int n", "output": "Tensor" }, "max_pool2d_with_index": { "args": "Tensor x, int[] kernel_size, int[] strides= {1, 1}, int[] paddings = {0, 0}, bool global_pooling = false, bool adaptive = false", "output": "Tensor(out), Tensor(mask)" }, "max_pool3d_with_index": { "args": "Tensor x, int[] kernel_size, int[] strides = {1, 1, 1}, int[] paddings = {0, 0, 0}, bool global_pooling = false, bool adaptive = false", "output": "Tensor(out), Tensor(mask)" }, "maxout": { "args": "Tensor x, int groups, int axis = 1", "output": "Tensor(out)" }, "mean_all": { "args": "Tensor x", "output": "Tensor" }, "memory_efficient_attention": { "args": "Tensor query, Tensor key, Tensor value, Tensor bias, Tensor cu_seqlens_q, Tensor cu_seqlens_k, Tensor causal_diagonal, Tensor seqlen_k, Scalar max_seqlen_q, Scalar max_seqlen_k, bool causal, double dropout_p, float scale, bool is_test", "output": "Tensor(output), Tensor(logsumexp), Tensor(seed_and_offset)" }, "merge_selected_rows": { "args": "Tensor x", "output": "Tensor(out)" }, "merged_adam_": { "args": "Tensor[] param, Tensor[] grad, Tensor[] learning_rate, Tensor[] moment1, Tensor[] moment2, Tensor[] beta1_pow, Tensor[] beta2_pow, Tensor[] master_param, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool multi_precision = false, bool use_global_beta_pow = false", "output": "Tensor[](param_out){param.size()}, Tensor[](moment1_out){param.size()}, Tensor[](moment2_out){param.size()}, Tensor[](beta1_pow_out){param.size()}, Tensor[](beta2_pow_out){param.size()}, Tensor[](master_param_out){param.size()}" }, "merged_momentum_": { "args": "Tensor[] param, Tensor[] grad, Tensor[] velocity, Tensor[] learning_rate, Tensor[] master_param, float mu, bool use_nesterov = false, str[] regularization_method = {}, float[] regularization_coeff = {}, bool multi_precision = false, float rescale_grad = 1.0f", "output": "Tensor[](param_out){param.size()}, Tensor[](velocity_out){param.size()}, Tensor[](master_param_out){param.size()}" }, "meshgrid": { "args": "Tensor[] inputs", "output": "Tensor[]{inputs.size()}" }, "mode": { "args": "Tensor x, int axis = -1, bool keepdim = false", "output": "Tensor(out), Tensor(indices)" }, "momentum_": { "args": "Tensor param, Tensor grad, Tensor velocity, Tensor learning_rate, Tensor master_param, float mu, bool use_nesterov = false, str regularization_method = \"\", float regularization_coeff = 0.0f, bool multi_precision = false, float rescale_grad = 1.0f", "output": "Tensor(param_out), Tensor(velocity_out), Tensor(master_param_out)" }, "multi_dot": { "args": "Tensor[] x", "output": "Tensor" }, "multiclass_nms3": { "args": "Tensor bboxes, Tensor scores, Tensor rois_num, float score_threshold, int nms_top_k, int keep_top_k, float nms_threshold=0.3, bool normalized=true, float nms_eta=1.0, int background_label=0", "output": "Tensor(out), Tensor(index), Tensor(nms_rois_num)" }, "multinomial": { "args": "Tensor x, Scalar(int) num_samples = 1, bool replacement = false", "output": "Tensor(out)" }, "multiplex": { "args": "Tensor[] inputs, Tensor index", "output": "Tensor" }, "mv": { "args": "Tensor x, Tensor vec", "output": "Tensor" }, "nanmedian": { "args": "Tensor x, IntArray axis = {}, bool keepdim = true", "output": "Tensor(out), Tensor(medians)" }, "nearest_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, float[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "nextafter": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "nll_loss": { "args": "Tensor input, Tensor label, Tensor weight, int64_t ignore_index = -100, str reduction = \"mean\"", "output": "Tensor(out), Tensor(total_weight)" }, "nms": { "args": "Tensor x, float threshold = 1.0f", "output": "Tensor(out)" }, "nonzero": { "args": "Tensor condition", "output": "Tensor(out)" }, "npu_identity": { "args": "Tensor x, int format = -1", "output": "Tensor" }, "numel": { "args": "Tensor x", "output": "Tensor(size)" }, "overlap_add": { "args": "Tensor x, int hop_length, int axis=-1", "output": "Tensor" }, "p_norm": { "args": "Tensor x, float porder=2, int axis=-1, float epsilon=1.0e-12f, bool keepdim=false, bool asvector=false", "output": "Tensor(out)" }, "pad3d": { "args": "Tensor x, IntArray paddings, str mode = \"constant\", float pad_value = 0.0, str data_format = \"NCDHW\"", "output": "Tensor(out)" }, "pixel_shuffle": { "args": "Tensor x, int upscale_factor=1, str data_format=\"NCHW\"", "output": "Tensor" }, "pixel_unshuffle": { "args": "Tensor x, int downscale_factor=1, str data_format=\"NCHW\"", "output": "Tensor" }, "poisson": { "args": "Tensor x", "output": "Tensor" }, "polygamma": { "args": "Tensor x, int n", "output": "Tensor(out)" }, "pow": { "args": "Tensor x, Scalar y=1.0f", "output": "Tensor(out)" }, "prelu": { "args": "Tensor x, Tensor alpha, str data_format=\"NCHW\", str mode=\"all\"", "output": "Tensor(out)" }, "prior_box": { "args": "Tensor input, Tensor image, float[] min_sizes, float[] max_sizes = {}, float[] aspect_ratios = {}, float[] variances = {}, bool flip=true, bool clip=true, float step_w=0.0, float step_h=0.0, float offset=0.5, bool min_max_aspect_ratios_order=false", "output": "Tensor(out), Tensor(var)" }, "psroi_pool": { "args": "Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height=1, int pooled_width=1, int output_channels=1, float spatial_scale=1.0", "output": "Tensor" }, "put_along_axis": { "args": "Tensor arr, Tensor indices, Tensor values, int axis, str reduce = \"assign\", bool include_self = true", "output": "Tensor(out)" }, "qr": { "args": "Tensor x, str mode = \"reduced\"", "output": "Tensor(q), Tensor(r)" }, "real": { "args": "Tensor x", "output": "Tensor (out)" }, "reciprocal": { "args": "Tensor x", "output": "Tensor(out)" }, "reindex_graph": { "args": "Tensor x, Tensor neighbors, Tensor count, Tensor hashtable_value, Tensor hashtable_index", "output": "Tensor(reindex_src), Tensor(reindex_dst), Tensor(out_nodes)" }, "relu": { "args": "Tensor x", "output": "Tensor(out)" }, "relu6": { "args": "Tensor x", "output": "Tensor" }, "renorm": { "args": "Tensor x, float p, int axis, float max_norm", "output": "Tensor(out)" }, "reverse": { "args": "Tensor x, IntArray axis", "output": "Tensor" }, "rms_norm": { "args": "Tensor x, Tensor bias, Tensor residual, Tensor norm_weight, Tensor norm_bias, float epsilon, int begin_norm_axis, float quant_scale, int quant_round_type, float quant_max_bound, float quant_min_bound", "output": "Tensor(out), Tensor(residual_out)" }, "rmsprop_": { "args": "Tensor param, Tensor mean_square, Tensor grad, Tensor moment, Tensor learning_rate, Tensor mean_grad, Tensor master_param, float epsilon = 1.0e-10f, float decay = 0.9f, float momentum = 0.0f, bool centered = false, bool multi_precision = false", "output": "Tensor(param_out), Tensor(moment_out), Tensor(mean_square_out), Tensor(mean_grad_out), Tensor(master_param_outs)" }, "roi_align": { "args": "Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height=1, int pooled_width=1, float spatial_scale=1.0, int sampling_ratio=-1, bool aligned=false", "output": "Tensor" }, "roi_pool": { "args": "Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height=1, int pooled_width=1, float spatial_scale=1.0", "output": "Tensor(out), Tensor(arg_max)" }, "roll": { "args": "Tensor x, IntArray shifts={}, int64_t[] axis={}", "output": "Tensor(out)" }, "round": { "args": "Tensor x", "output": "Tensor(out)" }, "rsqrt": { "args": "Tensor x", "output": "Tensor(out)" }, "scale": { "args": "Tensor x, Scalar scale=1.0, float bias=0.0, bool bias_after_scale=true", "output": "Tensor(out)" }, "scatter": { "args": "Tensor x, Tensor index, Tensor updates, bool overwrite=true", "output": "Tensor(out)" }, "scatter_nd_add": { "args": "Tensor x, Tensor index, Tensor updates", "output": "Tensor" }, "searchsorted": { "args": "Tensor sorted_sequence, Tensor values, bool out_int32 = false, bool right = false", "output": "Tensor(out)" }, "segment_pool": { "args": "Tensor x, Tensor segment_ids, str pooltype=\"SUM\"", "output": "Tensor(out), Tensor(summed_ids)" }, "selu": { "args": "Tensor x, float scale=1.0507009873554804934193349852946, float alpha=1.6732632423543772848170429916717", "output": "Tensor" }, "send_u_recv": { "args": "Tensor x, Tensor src_index, Tensor dst_index, str reduce_op = \"SUM\", IntArray out_size = {0}", "output": "Tensor(out), Tensor(dst_count)" }, "send_ue_recv": { "args": "Tensor x, Tensor y, Tensor src_index, Tensor dst_index, str message_op=\"ADD\", str reduce_op=\"SUM\", IntArray out_size={0}", "output": "Tensor(out), Tensor(dst_count)" }, "send_uv": { "args": "Tensor x, Tensor y, Tensor src_index, Tensor dst_index, str message_op = \"ADD\"", "output": "Tensor(out)" }, "sgd_": { "args": "Tensor param, Tensor learning_rate, Tensor grad, Tensor master_param, bool multi_precision=false", "output": "Tensor(param_out), Tensor(master_param_out)" }, "shape": { "args": "Tensor input", "output": "Tensor(out)" }, "shard_index": { "args": "Tensor input, int index_num, int nshards, int shard_id, int ignore_value=-1", "output": "Tensor(out)" }, "sigmoid": { "args": "Tensor x", "output": "Tensor" }, "sigmoid_cross_entropy_with_logits": { "args": "Tensor x, Tensor label, Tensor pos_weight, bool normalize=false, int ignore_index=-100", "output": "Tensor" }, "sign": { "args": "Tensor x", "output": "Tensor(out)" }, "silu": { "args": "Tensor x", "output": "Tensor" }, "sin": { "args": "Tensor x", "output": "Tensor(out)" }, "sinh": { "args": "Tensor x", "output": "Tensor(out)" }, "slogdet": { "args": "Tensor x", "output": "Tensor" }, "softplus": { "args": "Tensor x, float beta = 1.0, float threshold = 20.0f", "output": "Tensor" }, "softshrink": { "args": "Tensor x, float threshold = 0.5", "output": "Tensor" }, "softsign": { "args": "Tensor x", "output": "Tensor" }, "solve": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "spectral_norm": { "args": "Tensor weight, Tensor u, Tensor v, int dim = 0, int power_iters = 1, float eps = 1e-12f", "output": "Tensor" }, "sqrt": { "args": "Tensor x", "output": "Tensor(out)" }, "square": { "args": "Tensor x", "output": "Tensor" }, "squared_l2_norm": { "args": "Tensor x", "output": "Tensor(out)" }, "squeeze": { "args": "Tensor x, IntArray axis={}", "output": "Tensor(out), Tensor(xshape)" }, "stack": { "args": "Tensor[] x, int axis = 0", "output": "Tensor (out)" }, "stanh": { "args": "Tensor x, float scale_a=0.67f, float scale_b=1.7159f", "output": "Tensor(out)" }, "svd": { "args": "Tensor x, bool full_matrices = false", "output": "Tensor(u), Tensor(s), Tensor(vh)" }, "take_along_axis": { "args": "Tensor arr, Tensor indices, int axis", "output": "Tensor" }, "tan": { "args": "Tensor x", "output": "Tensor(out)" }, "tanh": { "args": "Tensor x", "output": "Tensor(out)" }, "tanh_shrink": { "args": "Tensor x", "output": "Tensor" }, "temporal_shift": { "args": "Tensor x, int seg_num, float shift_ratio = 0.25f, str data_format = \"NCHW\"", "output": "Tensor(out)" }, "tensor_unfold": { "args": "Tensor input, int64_t axis, int64_t size, int64_t step", "output": "Tensor" }, "thresholded_relu": { "args": "Tensor x, float threshold = 1.0", "output": "Tensor(out)" }, "top_p_sampling": { "args": "Tensor x, Tensor ps, Tensor threshold, int seed=-1", "output": "Tensor (out), Tensor(ids)" }, "topk": { "args": "Tensor x, Scalar(int) k = 1, int axis = -1, bool largest = true, bool sorted = true", "output": "Tensor(out), Tensor(indices)" }, "trace": { "args": "Tensor x, int offset = 0, int axis1 = 0, int axis2 = 1", "output": "Tensor" }, "triangular_solve": { "args": "Tensor x, Tensor y, bool upper=true, bool transpose=false, bool unitriangular=false", "output": "Tensor" }, "trilinear_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, float[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "trunc": { "args": "Tensor input", "output": "Tensor(out)" }, "unbind": { "args": "Tensor input, int axis = 0", "output": "Tensor[] {axis<0 ? input.dims()[input.dims().size()+axis]:input.dims()[axis]}" }, "unfold": { "args": "Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations", "output": "Tensor(out)" }, "uniform_inplace": { "args": "Tensor x, float min = -1.0, float max = 1.0, int seed = 0, int diag_num = 0, int diag_step = 0, float diag_val = 1.0", "output": "Tensor(out)" }, "unique_consecutive": { "args": "Tensor x, bool return_inverse = false, bool return_counts = false, int[] axis = {}, DataType dtype = DataType::FLOAT32", "output": "Tensor(out), Tensor(index), Tensor(counts)" }, "unpool3d": { "args": "Tensor x, Tensor indices, int[] ksize, int[] strides={1,1,1}, int[] paddings={0,0,0}, int[] output_size={0,0,0}, str data_format=\"NCDHW\"", "output": "Tensor(out)" }, "unsqueeze": { "args": "Tensor x, IntArray axis = {}", "output": "Tensor(out), Tensor(xshape)" }, "unstack": { "args": "Tensor x, int axis=0, int num=0", "output": "Tensor[](out){num}" }, "update_loss_scaling_": { "args": "Tensor[] x, Tensor found_infinite, Tensor prev_loss_scaling, Tensor in_good_steps, Tensor in_bad_steps, int incr_every_n_steps, int decr_every_n_nan_or_inf, float incr_ratio, float decr_ratio, Scalar stop_update=false", "output": "Tensor[](out){x.size()}, Tensor(loss_scaling), Tensor(out_good_steps), Tensor(out_bad_steps)" }, "view_dtype": { "args": "Tensor input, DataType dtype", "output": "Tensor(out)" }, "view_shape": { "args": "Tensor input, int64_t[] dims = {}", "output": "Tensor(out)" }, "viterbi_decode": { "args": "Tensor potentials, Tensor transition_params, Tensor lengths, bool include_bos_eos_tag = true", "output": "Tensor(scores), Tensor(path)" }, "warpctc": { "args": "Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank = 0, bool norm_by_times = false", "output": "Tensor(loss), Tensor(warpctcgrad)" }, "warprnnt": { "args": "Tensor input, Tensor label, Tensor input_lengths, Tensor label_lengths, int blank = 0, float fastemit_lambda = 0.0", "output": "Tensor(loss), Tensor(warprnntgrad)" }, "weight_dequantize": { "args": "Tensor x, Tensor scale, str algo=\"weight_only_int8\", DataType out_dtype=DataType::FLOAT16", "output": "Tensor(out)" }, "weight_only_linear": { "args": "Tensor x, Tensor weight, Tensor bias, Tensor weight_scale, str weight_dtype, int arch = 80", "output": "Tensor(out)" }, "weight_quantize": { "args": "Tensor x, str algo = \"weight_only_int8\", int arch = 80", "output": "Tensor(out), Tensor(scale)" }, "weighted_sample_neighbors": { "args": "Tensor row, Tensor colptr, Tensor edge_weight, Tensor input_nodes, Tensor eids, int sample_size, bool return_eids", "output": "Tensor(out_neighbors), Tensor(out_count), Tensor(out_eids)" }, "where": { "args": "Tensor condition, Tensor x, Tensor y", "output": "Tensor(out)" }, "yolo_box": { "args": "Tensor x, Tensor img_size, int[] anchors={}, int class_num = 1, float conf_thresh = 0.01, int downsample_ratio = 32, bool clip_bbox = true, float scale_x_y=1.0, bool iou_aware=false, float iou_aware_factor=0.5", "output": "Tensor(boxes), Tensor(scores)" }, "yolo_loss": { "args": "Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors={}, int[] anchor_mask={}, int class_num =1 , float ignore_thresh=0.7, int downsample_ratio=32, bool use_label_smooth=true, float scale_x_y=1.0", "output": "Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)" }, "adadelta_": { "args": "Tensor param, Tensor grad, Tensor avg_squared_grad, Tensor avg_squared_update, Tensor learning_rate, Tensor master_param, float rho, float epsilon, bool multi_precision", "output": "Tensor(param_out), Tensor(moment_out), Tensor(inf_norm_out), Tensor(master_param_out)" }, "add": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "add_n": { "args": "Tensor[] inputs", "output": "Tensor" }, "all": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "amax": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "amin": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "any": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "arange": { "args": "Tensor start, Tensor end, Tensor step, DataType dtype, Place place={}", "output": "Tensor(out)" }, "assign": { "args": "Tensor x", "output": "Tensor" }, "assign_out_": { "args": "Tensor x, Tensor output", "output": "Tensor(out)" }, "assign_value_": { "args": "Tensor output, int[] shape, DataType dtype, Scalar[] values, Place place = {}", "output": "Tensor(out)" }, "batch_norm": { "args": "Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics", "output": "Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)" }, "c_allgather": { "args": "Tensor x, int ring_id, int nranks, bool use_calc_stream", "output": "Tensor(out)" }, "c_allreduce_max": { "args": "Tensor x, int ring_id, bool use_calc_stream, bool use_model_parallel", "output": "Tensor(out)" }, "c_allreduce_sum": { "args": "Tensor x, int ring_id, bool use_calc_stream, bool use_model_parallel", "output": "Tensor(out)" }, "c_broadcast": { "args": "Tensor x, int ring_id=0, int root=0, bool use_calc_stream=false", "output": "Tensor(out)" }, "c_concat": { "args": "Tensor x, int rank, int nranks, int ring_id, bool use_calc_stream, bool use_model_parallel", "output": "Tensor(out)" }, "c_embedding": { "args": "Tensor weight, Tensor x, int64_t start_index=0, int64_t vocab_size=-1", "output": "Tensor(out)" }, "c_identity": { "args": "Tensor x, int ring_id, bool use_calc_stream, bool use_model_parallel", "output": "Tensor(out)" }, "c_reduce_sum": { "args": "Tensor x, int ring_id, int root_id, bool use_calc_stream", "output": "Tensor(out)" }, "c_sync_calc_stream": { "args": "Tensor x", "output": "Tensor(out)" }, "c_sync_comm_stream": { "args": "Tensor x, int ring_id", "output": "Tensor(out)" }, "cast": { "args": "Tensor x, DataType dtype", "output": "Tensor(out)" }, "channel_shuffle": { "args": "Tensor x, int groups, str data_format=\"NCHW\"", "output": "Tensor(out)" }, "conv2d_transpose": { "args": "Tensor x, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, int[] output_padding={}, IntArray output_size={}, str padding_algorithm=\"EXPLICIT\", int groups=1, int[] dilations={1, 1}, str data_format=\"NCHW\"", "output": "Tensor(out)" }, "copy_to": { "args": "Tensor x, Place place, bool blocking", "output": "Tensor(out)" }, "decode_jpeg": { "args": "Tensor x, str mode, Place place", "output": "Tensor(out)" }, "deformable_conv": { "args": "Tensor x, Tensor offset, Tensor filter, Tensor mask, int[] strides, int[] paddings, int[] dilations, int deformable_groups, int groups, int im2col_step", "output": "Tensor(out)" }, "depthwise_conv2d_transpose": { "args": "Tensor x, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, int[] output_padding={}, IntArray output_size={}, str padding_algorithm=\"EXPLICIT\", int groups=1, int[] dilations={1, 1}, str data_format=\"NCHW\"", "output": "Tensor(out)" }, "disable_check_model_nan_inf": { "args": "Tensor x, int flag = 0", "output": "Tensor(out)" }, "distribute_fpn_proposals": { "args": "Tensor fpn_rois, Tensor rois_num, int min_level, int max_level, int refer_level, int refer_scale, bool pixel_offset", "output": "Tensor[](multi_fpn_rois){max_level - min_level + 1}, Tensor[](multi_level_rois_num){max_level - min_level + 1}, Tensor(restore_index)" }, "divide": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "dropout": { "args": "Tensor x, Tensor seed_tensor, Scalar p, bool is_test, str mode, int seed, bool fix_seed", "output": "Tensor(out), Tensor(mask)" }, "einsum": { "args": "Tensor[] x, str equation", "output": "Tensor(out), Tensor[](inner_cache){x.size()}, Tensor[](xshape){x.size()}" }, "elementwise_pow": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "embedding": { "args": "Tensor x, Tensor weight, int64_t padding_idx=-1, bool sparse=false", "output": "Tensor" }, "embedding_grad_dense": { "args": "Tensor x, Tensor weight, Tensor out_grad, int64_t padding_idx=-1, bool sparse=false", "output": "Tensor(weight_grad)" }, "empty": { "args": "IntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "empty_like": { "args": "Tensor x, DataType dtype = DataType::UNDEFINED, Place place = {}", "output": "Tensor(out)" }, "enable_check_model_nan_inf": { "args": "Tensor x, int flag = 1", "output": "Tensor(out)" }, "equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "exponential_": { "args": "Tensor x, float lam", "output": "Tensor(out)" }, "eye": { "args": "Scalar num_rows, Scalar num_columns, DataType dtype=DataType::FLOAT32, Place place={}", "output": "Tensor(out)" }, "floor_divide": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "frobenius_norm": { "args": "Tensor x, IntArray axis, bool keep_dim, bool reduce_all", "output": "Tensor(out)" }, "full": { "args": "IntArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_": { "args": "Tensor output, IntArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_batch_size_like": { "args": "Tensor input, int[] shape, DataType dtype, Scalar value, int input_dim_idx, int output_dim_idx, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_like": { "args": "Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Place place = {}", "output": "Tensor(out)" }, "full_with_tensor": { "args": "Tensor shape, Tensor value, DataType dtype=DataType::FLOAT32", "output": "Tensor(out)" }, "fused_adam_": { "args": "Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] beta1_pows, Tensor[] beta2_pows, Tensor[] master_params, Tensor skip_update, Scalar beta1, Scalar beta2, Scalar epsilon, int chunk_size, float weight_decay, bool use_adamw, bool multi_precision, bool use_global_beta_pow", "output": "Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()}" }, "fused_batch_norm_act": { "args": "Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str act_type", "output": "Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)" }, "fused_bn_add_activation": { "args": "Tensor x, Tensor z, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str act_type", "output": "Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)" }, "fused_softmax_mask_upper_triangle": { "args": "Tensor X", "output": "Tensor(Out)" }, "gaussian": { "args": "IntArray shape, float mean, float std, int seed, DataType dtype, Place place={}", "output": "Tensor(out)" }, "greater_equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "greater_than": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "hardswish": { "args": "Tensor x", "output": "Tensor(out)" }, "hsigmoid_loss": { "args": "Tensor x, Tensor label, Tensor w, Tensor bias, Tensor path, Tensor code, int num_classes, bool is_sparse", "output": "Tensor(out), Tensor(pre_out), Tensor(w_out)" }, "increment": { "args": "Tensor x, float value = 1.0", "output": "Tensor(out)" }, "less_equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "less_than": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "linspace": { "args": "Tensor start, Tensor stop, Tensor number, DataType dtype, Place place", "output": "Tensor(out)" }, "logspace": { "args": "Tensor start, Tensor stop, Tensor num, Tensor base, DataType dtype, Place place={}", "output": "Tensor(out)" }, "logsumexp": { "args": "Tensor x, int64_t[] axis, bool keepdim, bool reduce_all", "output": "Tensor(out)" }, "matmul": { "args": "Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false", "output": "Tensor" }, "matrix_rank": { "args": "Tensor x, float tol, bool use_default_tol=true, bool hermitian=false", "output": "Tensor(out)" }, "matrix_rank_tol": { "args": "Tensor x, Tensor atol_tensor, bool use_default_tol=true, bool hermitian=false", "output": "Tensor(out)" }, "max": { "args": "Tensor x, IntArray axis={}, bool keepdim=false", "output": "Tensor(out)" }, "maximum": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "mean": { "args": "Tensor x, IntArray axis={}, bool keepdim=false", "output": "Tensor(out)" }, "memcpy_d2h": { "args": "Tensor x, int dst_place_type", "output": "Tensor" }, "memcpy_h2d": { "args": "Tensor x, int dst_place_type", "output": "Tensor" }, "min": { "args": "Tensor x, IntArray axis={}, bool keepdim=false", "output": "Tensor(out)" }, "minimum": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "mish": { "args": "Tensor x, float lambda", "output": "Tensor" }, "multiply": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "norm": { "args": "Tensor x, int axis, float epsilon, bool is_test", "output": "Tensor(out), Tensor(norm)" }, "not_equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "one_hot": { "args": "Tensor x, Scalar(int) num_classes", "output": "Tensor(out)" }, "ones": { "args": "IntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "ones_like": { "args": "Tensor x, DataType dtype=DataType::UNDEFINED, Place place={}", "output": "Tensor(out)" }, "pad": { "args": "Tensor x, int[] paddings, Scalar pad_value", "output": "Tensor" }, "pool2d": { "args": "Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm", "output": "Tensor(out)" }, "pool3d": { "args": "Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm", "output": "Tensor(out)" }, "prod": { "args": "Tensor x, IntArray dims, bool keep_dim, bool reduce_all", "output": "Tensor" }, "randint": { "args": "int low, int high, IntArray shape, DataType dtype=DataType::INT64, Place place={}", "output": "Tensor(out)" }, "randperm": { "args": "int n, DataType dtype, Place place={}", "output": "Tensor(out)" }, "read_file": { "args": "str filename = \"\", DataType dtype=DataType::UINT8, Place place=CPUPlace()", "output": "Tensor(out)" }, "remainder": { "args": "Tensor x, Tensor y", "output": "Tensor (out)" }, "repeat_interleave": { "args": "Tensor x, int repeats, int axis", "output": "Tensor(out)" }, "repeat_interleave_with_tensor_index": { "args": "Tensor x, Tensor repeats, int axis", "output": "Tensor(out)" }, "reshape": { "args": "Tensor x, IntArray shape", "output": "Tensor(out), Tensor(xshape)" }, "rnn": { "args": "Tensor x, Tensor[] pre_state, Tensor[] weight_list, Tensor sequence_length, Tensor dropout_state_in, float dropout_prob=0.0, bool is_bidirec=false, int input_size=10, int hidden_size=100, int num_layers=1, str mode=\"RNN_TANH\", int seed=0, bool is_test=false", "output": "Tensor(out), Tensor(dropout_state_out), Tensor[](state){pre_state.size()}, Tensor(reserve)" }, "rrelu": { "args": "Tensor x, float lower, float upper, bool is_test", "output": "Tensor(out), Tensor(noise)" }, "set_value": { "args": "Tensor x, IntArray starts, IntArray ends, IntArray steps, int64_t[] axes, int64_t[] decrease_axes, int64_t[] none_axes, int64_t[] shape, Scalar[] values", "output": "Tensor(out)" }, "set_value_with_tensor": { "args": "Tensor x, Tensor values, IntArray starts, IntArray ends, IntArray steps, int64_t[] axes, int64_t[] decrease_axes, int64_t[] none_axes", "output": "Tensor(out)" }, "slice": { "args": "Tensor input, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis", "output": "Tensor" }, "softmax": { "args": "Tensor x, int axis", "output": "Tensor(out)" }, "split": { "args": "Tensor x, IntArray sections, Scalar(int) axis", "output": "Tensor[]{sections.size()}" }, "split_with_num": { "args": "Tensor x, int num, Scalar(int) axis", "output": "Tensor[]{num}" }, "strided_slice": { "args": "Tensor x, int[] axes, IntArray starts, IntArray ends, IntArray strides", "output": "Tensor" }, "subtract": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "sum": { "args": "Tensor x, IntArray axis={}, DataType dtype=DataType::UNDEFINED, bool keepdim=false", "output": "Tensor(out)" }, "swish": { "args": "Tensor x", "output": "Tensor(out)" }, "sync_batch_norm_": { "args": "Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics", "output": "Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)" }, "tile": { "args": "Tensor x, IntArray repeat_times = {}", "output": "Tensor(out)" }, "trans_layout": { "args": "Tensor x, int[] perm", "output": "Tensor" }, "transpose": { "args": "Tensor x, int[] perm", "output": "Tensor(out)" }, "tril": { "args": "Tensor x, int diagonal", "output": "Tensor(out)" }, "tril_indices": { "args": "int rows, int cols, int offset, DataType dtype, Place place={}", "output": "Tensor(out)" }, "triu": { "args": "Tensor x, int diagonal", "output": "Tensor(out)" }, "triu_indices": { "args": "int row, int col, int offset, DataType dtype, Place place={}", "output": "Tensor(out)" }, "truncated_gaussian_random": { "args": "int[] shape, float mean, float std, int seed, DataType dtype=DataType::FLOAT32, Place place={}", "output": "Tensor(out)" }, "uniform": { "args": "IntArray shape, DataType dtype, Scalar min, Scalar max, int seed, Place place={}", "output": "Tensor(out)" }, "unique": { "args": "Tensor x, bool return_index, bool return_inverse, bool return_counts, int[] axis, DataType dtype=DataType::INT64", "output": "Tensor(out), Tensor(indices), Tensor(inverse), Tensor(counts)" }, "unpool": { "args": "Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format", "output": "Tensor(out)" }, "zeros": { "args": "IntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "zeros_like": { "args": "Tensor x, DataType dtype=DataType::UNDEFINED, Place place = {}", "output": "Tensor(out)" } }