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", "moment2_max": "Moment2Max", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "master_param": "MasterParam", "skip_update": "SkipUpdate" }, "outputs": { "param_out": "ParamOut", "moment1_out": "Moment1Out", "moment2_out": "Moment2Out", "moment2_max_out": "Moment2MaxOut", "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", "moment2_max": "Moment2Max", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "master_param": "MasterParam", "skip_update": "SkipUpdate" }, "outputs": { "param_out": "ParamOut", "moment1_out": "Moment1Out", "moment2_out": "Moment2Out", "moment2_max_out": "Moment2MaxOut", "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" }, "attrs": { "scale_x": "Scale_x", "scale_y": "Scale_y", "scale_out": "Scale_out" } }, "sum": { "phi_name": "add_n", "inputs": { "inputs": "X" }, "outputs": { "out": "Out" } }, "add_position_encoding": { "phi_name": "add_position_encoding", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "addmm": { "phi_name": "addmm", "inputs": { "input": "Input", "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "attrs": { "alpha": "Alpha", "beta": "Beta" } }, "affine_channel": { "phi_name": "affine_channel", "inputs": { "x": "X", "scale": "Scale", "bias": "Bias" }, "outputs": { "out": "Out" } }, "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" } }, "anchor_generator": { "phi_name": "anchor_generator", "inputs": { "input": "Input" }, "outputs": { "anchors": "Anchors", "variances_out": "Variances" } }, "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" }, "scalar": { "start": { "data_type": "double", "support_tensor": "True" }, "end": { "data_type": "double", "support_tensor": "True" }, "step": { "data_type": "double", "support_tensor": "True" } } }, "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_pos": { "phi_name": "assign_pos", "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" } }, "attention_lstm": { "phi_name": "attention_lstm", "inputs": { "x": "X", "c0": "C0", "h0": "H0", "attention_weight": "AttentionWeight", "attention_bias": "AttentionBias", "attention_scalar": "AttentionScalar", "attention_scalar_bias": "AttentionScalarBias", "lstm_weight": "LSTMWeight", "lstm_bias": "LSTMBias" }, "outputs": { "hidden": "Hidden", "cell": "Cell", "attentioned_x": "AttentionedX", "attention_fc_out": "AttentionFCOut", "lstm_x": "LSTMX", "lstm_out": "LSTMOUT" } }, "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" } }, "baddbmm": { "phi_name": "baddbmm", "inputs": { "input": "Input", "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "attrs": { "alpha": "Alpha", "beta": "Beta" } }, "barrier": { "phi_name": "barrier", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "batch_fc": { "phi_name": "batch_fc", "inputs": { "input": "Input", "w": "W", "bias": "Bias" }, "outputs": { "out": "Out" } }, "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" }, "attrs": { "data_format": "data_layout" } }, "bce_loss": { "phi_name": "bce_loss", "inputs": { "input": "X", "label": "Label" }, "outputs": { "out": "Out" } }, "beam_search_decode": { "phi_name": "beam_search_decode", "inputs": { "ids": "Ids", "scores": "Scores" }, "outputs": { "sentence_ids": "SentenceIds", "sentence_scores": "SentenceScores" } }, "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" }, "attrs": { "data_format": "data_layout" } }, "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" }, "attrs": { "data_format": "data_layout" } }, "bincount": { "phi_name": "bincount", "inputs": { "x": "X", "weights": "Weights" }, "outputs": { "out": "Out" }, "scalar": { "minlength": { "data_type": "int", "support_tensor": "True" } } }, "bipartite_match": { "phi_name": "bipartite_match", "inputs": { "dist_mat": "DistMat" }, "outputs": { "col_to_row_match_indices": "ColToRowMatchIndices", "col_to_row_match_dist": "ColToRowMatchDist" } }, "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" } }, "bn_act_xpu": { "phi_name": "bn_act_xpu", "attrs": { "data_format": "data_layout" } }, "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" } }, "c_softmax_with_cross_entropy": { "phi_name": "c_softmax_with_cross_entropy", "inputs": { "logits": "Logits", "label": "Label" }, "outputs": { "softmax": "Softmax", "loss": "Loss" } }, "c_split": { "phi_name": "c_split", "inputs": { "x": "X" }, "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" } }, "collect_fpn_proposals": { "phi_name": "collect_fpn_proposals", "inputs": { "multi_level_rois": "MultiLevelRois", "multi_level_scores": "MultiLevelScores", "multi_level_rois_num": "MultiLevelRoIsNum" }, "outputs": { "fpn_rois": "FpnRois", "rois_num": "RoisNum" }, "attrs": { "post_nms_topn": "post_nms_topN" } }, "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" } } }, "conv2d_transpose_bias": { "phi_name": "conv2d_transpose_bias", "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" } }, "correlation": { "phi_name": "correlation", "inputs": { "input1": "Input1", "input2": "Input2" }, "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" } } }, "cvm": { "phi_name": "cvm", "inputs": { "x": "X", "cvm": "CVM" }, "outputs": { "out": "Y" } }, "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" }, "attrs": { "scale_in": "Scale_in", "scale_out": "Scale_out", "scale_in_eltwise": "Scale_in_eltwise", "scale_weights": "Scale_weights" } }, "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": { "phi_name": "dequantize", "inputs": { "input": "Input" }, "outputs": { "output": "Output" }, "attrs": { "scale": "Scale", "shift": "Shift" } }, "dequantize_abs_max": { "phi_name": "dequantize_abs_max", "inputs": { "x": "X", "scale": "Scale" }, "outputs": { "out": "Out" } }, "dequantize_linear": { "phi_name": "dequantize_linear", "inputs": { "x": "X", "scale": "Scale", "zero_point": "ZeroPoint", "in_accum": "InAccum", "in_state": "InState" }, "outputs": { "y": "Y", "out_scale": "OutScale", "out_accum": "OutAccum", "out_state": "OutState" } }, "dequantize_log": { "phi_name": "dequantize_log", "inputs": { "x": "X", "dict": "Dict" }, "outputs": { "out": "Out" } }, "determinant": { "phi_name": "det", "inputs": { "x": "Input" }, "outputs": { "out": "Out" } }, "dgc_clip_by_norm": { "phi_name": "dgc_clip_by_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "dgc_momentum": { "phi_name": "dgc_momentum", "inputs": { "param": "Param", "grad": "Grad", "velocity": "Velocity", "learning_rate": "LearningRate", "master_param": "MasterParam", "current_step_tensor": "current_step", "nranks_tensor": "nranks" }, "outputs": { "param_out": "ParamOut", "velocity_out": "VelocityOut", "master_param_out": "MasterParamOut", "grad_out": "Grad_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" } }, "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" }, "scalar": { "p": { "support_tensor": "True" } } }, "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_dequantize_max_abs": { "phi_name": "fake_channel_wise_dequantize_max_abs", "inputs": { "x": "X", "scales": "Scales" }, "outputs": { "out": "Out" } }, "fake_channel_wise_quantize_abs_max": { "phi_name": "fake_channel_wise_quantize_abs_max", "inputs": { "x": "X" }, "outputs": { "out": "Out", "out_scale": "OutScale" } }, "fake_channel_wise_quantize_dequantize_abs_max": { "phi_name": "fake_channel_wise_quantize_dequantize_abs_max", "inputs": { "x": "X" }, "outputs": { "out": "Out", "out_scale": "OutScale" } }, "fake_dequantize_max_abs": { "phi_name": "fake_dequantize_max_abs", "inputs": { "x": "X", "scale": "Scale" }, "outputs": { "out": "Out" } }, "fake_quantize_abs_max": { "phi_name": "fake_quantize_abs_max", "inputs": { "x": "X" }, "outputs": { "out": "Out", "out_scale": "OutScale" } }, "fake_quantize_dequantize_abs_max": { "phi_name": "fake_quantize_dequantize_abs_max", "inputs": { "x": "X" }, "outputs": { "out": "Out", "out_scale": "OutScale" } }, "fake_quantize_dequantize_moving_average_abs_max": { "phi_name": "fake_quantize_dequantize_moving_average_abs_max", "inputs": { "x": "X", "in_scale": "InScale", "in_accum": "InAccum", "in_state": "InState" }, "outputs": { "out": "Out", "out_scale": "OutScale", "out_state": "OutState", "out_accum": "OutAccum" } }, "fake_quantize_moving_average_abs_max": { "phi_name": "fake_quantize_moving_average_abs_max", "inputs": { "x": "X", "in_scale": "InScale", "in_accum": "InAccum", "in_state": "InState" }, "outputs": { "out": "Out", "out_scale": "OutScale", "out_state": "OutState", "out_accum": "OutAccum" } }, "fake_quantize_range_abs_max": { "phi_name": "fake_quantize_range_abs_max", "inputs": { "x": "X", "in_scale": "InScale", "iter": "Iter" }, "outputs": { "out": "Out", "out_scale": "OutScale", "out_scales": "OutScales" } }, "faster_tokenizer": { "phi_name": "faster_tokenizer", "inputs": { "vocab": "Vocab", "text": "Text", "text_pair": "TextPair" }, "outputs": { "input_ids": "InputIds", "segment_ids": "SegmentIds" } }, "fc": { "phi_name": "fc", "inputs": { "input": "Input", "w": "W", "bias": "Bias" }, "outputs": { "out": "Out" }, "attrs": { "scale_in": "Scale_in", "scale_out": "Scale_out", "scale_weights": "Scale_weights" } }, "feed": { "phi_name": "feed", "outputs": { "out": "Out" } }, "fetch_barrier": { "phi_name": "fetch_barrier", "inputs": { "x": "X" }, "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" } }, "fill_diagonal_tensor": { "phi_name": "fill_diagonal_tensor", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "flash_attn_unpadded": { "phi_name": "flash_attn_unpadded", "scalar": { "max_seqlen_q": { "data_type": "int64_t", "support_tensor": "True" }, "max_seqlen_k": { "data_type": "int64_t", "support_tensor": "True" } } }, "flash_attn_v3_varlen": { "phi_name": "flash_attn_v3_varlen", "scalar": { "max_seqlen_q": { "data_type": "int64_t", "support_tensor": "True" }, "max_seqlen_k": { "data_type": "int64_t", "support_tensor": "True" } } }, "flash_attn_varlen_qkvpacked": { "phi_name": "flash_attn_varlen_qkvpacked", "scalar": { "max_seqlen_q": { "data_type": "int64_t", "support_tensor": "True" }, "max_seqlen_k": { "data_type": "int64_t", "support_tensor": "True" } } }, "flatten_contiguous_range": { "phi_name": "flatten", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xshape": "XShape" }, "attrs": { "start_axis": "start_axis", "stop_axis": "stop_axis" } }, "flip": { "phi_name": "flip", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "floor": { "phi_name": "floor", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "elementwise_floordiv": { "phi_name": "floor_divide", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "elementwise_fmax": { "phi_name": "fmax", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "elementwise_fmin": { "phi_name": "fmin", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "fold": { "phi_name": "fold", "inputs": { "x": "X" }, "outputs": { "out": "Y" } }, "frame": { "phi_name": "frame", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "frobenius_norm": { "phi_name": "frobenius_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "fill_constant": { "phi_name": "full", "outputs": { "out": "Out" } }, "fill_any_like": { "phi_name": "full_like", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "value": { "data_type": "float", "support_tensor": "True" } } }, "full_with_tensor": { "phi_name": "full_with_tensor", "int_array": { "shape": { "data_type": "int64_t", "support_tensor": "True" } } }, "fused_adam": { "phi_name": "fused_adam_", "inputs": { "params": "Params", "grads": "Grads", "learning_rate": "LearningRate", "moments1": "Moments1", "moments2": "Moments2", "moments2_max": "Moments2Max", "beta1_pows": "Beta1Pows", "beta2_pows": "Beta2Pows", "master_params": "MasterParams", "skip_update": "SkipUpdate" }, "outputs": { "params_out": "ParamsOut", "moments1_out": "Moments1Out", "moments2_out": "Moments2Out", "moments2_max_out": "Moments2MaxOut", "beta1_pows_out": "Beta1PowsOut", "beta2_pows_out": "Beta2PowsOut", "master_params_out": "MasterParamsOut" } }, "fused_attention": { "phi_name": "fused_attention", "inputs": { "x": "X", "ln_scale": "LnScale", "ln_bias": "LnBias", "qkv_weight": "QKVW", "qkv_bias": "QKVBias", "cache_kv": "CacheKV", "src_mask": "SrcMask", "out_linear_weight": "OutLinearW", "out_linear_bias": "OutLinearBias", "ln_scale_2": "Ln2Scale", "ln_bias_2": "Ln2Bias" }, "outputs": { "ln_mean": "LnMean", "ln_var": "LnVariance", "ln_out": "LnOut", "qkv_out": "QKVOut", "qkv_bias_out": "QKVBiasOut", "transpose_out_2": "TransposeOut2", "qk_out": "QKOut", "qktv_out": "QKTVOut", "softmax_out": "SoftmaxOut", "attn_dropout_mask_out": "AttnDropoutMaskOut", "attn_dropout_out": "AttnDropoutOut", "src_mask_out": "SrcMaskOut", "fmha_out": "FMHAOut", "out_linear_out": "OutLinearOut", "dropout_mask_out": "DropoutMaskOut", "ln_mean_2": "Ln2Mean", "ln_var_2": "Ln2Variance", "bias_dropout_residual_out": "BiasDropoutResidualOut", "cache_kv_out": "CacheKVOut", "out": "Y" } }, "fused_batch_norm_act": { "phi_name": "fused_batch_norm_act", "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" } }, "fused_bias_dropout_residual_layer_norm": { "phi_name": "fused_bias_dropout_residual_layer_norm", "inputs": { "x": "X", "residual": "Residual", "bias": "Bias", "ln_scale": "LnScale", "ln_bias": "LnBias" }, "outputs": { "bias_dropout_residual_out": "BiasDropoutResidualOut", "dropout_mask_out": "DropoutMaskOut", "ln_mean": "LnMean", "ln_variance": "LnVariance", "y": "Y" } }, "fused_bn_add_activation": { "phi_name": "fused_bn_add_activation_", "inputs": { "x": "X", "z": "Z", "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" } }, "fused_conv2d": { "phi_name": "fused_conv2d", "inputs": { "input": "Input", "filter": "Filter", "bias": "Bias", "residual_param": "ResidualData" }, "outputs": { "output": "Output" }, "attrs": { "scale_in": "Scale_in", "scale_out": "Scale_out", "scale_in_eltwise": "Scale_in_eltwise", "scale_weights": "Scale_weights" } }, "fused_conv2d_add_act": { "phi_name": "fused_conv2d_add_act", "inputs": { "input": "Input", "filter": "Filter", "bias": "Bias", "residual_data": "ResidualData" }, "outputs": { "output": "Output", "outputs": "Outputs" } }, "fused_conv3d": { "phi_name": "fused_conv3d", "inputs": { "input": "Input", "filter": "Filter", "bias": "Bias", "residual_param": "ResidualData" }, "outputs": { "output": "Output" }, "attrs": { "scale_in": "Scale_in", "scale_out": "Scale_out", "scale_in_eltwise": "Scale_in_eltwise", "scale_weights": "Scale_weights" } }, "fused_elementwise_add": { "phi_name": "fused_elementwise_add", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "fused_elementwise_div": { "phi_name": "fused_elementwise_div", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "fused_elementwise_mul": { "phi_name": "fused_elementwise_mul", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "fused_elementwise_sub": { "phi_name": "fused_elementwise_sub", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "fused_embedding_eltwise_layernorm": { "phi_name": "fused_embedding_eltwise_layernorm", "inputs": { "ids": "Ids", "embs": "Embs", "bias": "Bias", "scale": "Scale" }, "outputs": { "out": "Out" } }, "fused_embedding_fc_lstm": { "phi_name": "fused_embedding_fc_lstm", "inputs": { "ids": "Ids", "embeddings": "Embeddings", "weight_h": "WeightH", "bias": "Bias", "h0": "H0", "c0": "C0" }, "outputs": { "hidden": "Hidden", "cell": "Cell", "xx": "XX", "batched_input": "BatchedInput", "batched_hidden": "BatchedHidden", "batched_cell": "BatchedCell", "reordered_h0": "ReorderedH0", "reordered_c0": "ReorderedC0" } }, "fused_fc_elementwise_layernorm": { "phi_name": "fused_fc_elementwise_layernorm", "inputs": { "x": "X", "w": "W", "y": "Y", "bias0": "Bias0", "scale": "Scale", "bias1": "Bias1" }, "outputs": { "out": "Out", "mean": "Mean", "variance": "Variance" } }, "fused_feedforward": { "phi_name": "fused_feedforward", "inputs": { "x": "X", "dropout1_seed": "Dropout1Seed", "dropout2_seed": "Dropout2Seed", "linear1_weight": "Linear1Weight", "linear1_bias": "Linear1Bias", "linear2_weight": "Linear2Weight", "linear2_bias": "Linear2Bias", "ln1_scale": "Ln1Scale", "ln1_bias": "Ln1Bias", "ln2_scale": "Ln2Scale", "ln2_bias": "Ln2Bias" }, "outputs": { "out": "Out", "dropout1_mask": "Dropout1Mask", "dropout2_mask": "Dropout2Mask", "ln1_mean": "Ln1Mean", "ln1_variance": "Ln1Variance", "ln2_mean": "Ln2Mean", "ln2_variance": "Ln2Variance", "linear1_out": "Linear1Out", "ln1_out": "Ln1Out", "dropout1_out": "Dropout1Out", "dropout2_out": "Dropout2Out" }, "attrs": { "dropout1_seed_val": "dropout1_seed", "dropout2_seed_val": "dropout2_seed", "dropout1_prob": "dropout1_rate", "dropout2_prob": "dropout2_rate" } }, "fused_gate_attention": { "phi_name": "fused_gate_attention", "inputs": { "query": "Query", "key": "Key", "query_weight": "QueryWeight", "key_weight": "KeyWeight", "value_weight": "ValueWeight", "qkv_weight": "QKVWeight", "nonbatched_bias": "NonbatchedBias", "src_mask": "SrcMask", "gate_weight": "GateWeight", "gate_bias": "GateBias", "out_linear_weight": "OutLinearWeight", "out_linear_bias": "OutLinearBias" }, "outputs": { "query_transpose_out": "QueryTransposeOut", "key_transpose_out": "KeyTransposeOut", "value_transpose_out": "ValueTransposeOut", "qkv_transpose_out": "QKVTransposeOut", "softmax_out": "SoftmaxOut", "softmax_lse": "SoftmaxLse", "fmha_out": "FMHAOut", "gate_out": "GateOut", "out": "Out" } }, "fused_gemm_epilogue": { "phi_name": "fused_gemm_epilogue", "inputs": { "x": "X", "y": "Y", "bias": "Bias" }, "outputs": { "out": "Out", "reserve_space": "ReserveSpace" } }, "fused_gemm_epilogue_grad": { "phi_name": "fused_gemm_epilogue_grad", "inputs": { "x": "X", "y": "Y", "reserve_space": "ReserveSpace", "out_grad": "DOut" }, "outputs": { "x_grad": "DX", "y_grad": "DY", "bias_grad": "DBias" } }, "fused_multi_transformer_int8": { "phi_name": "fused_multi_transformer_int8", "inputs": { "x": "X", "ln_scale": "LnScale", "ln_bias": "LnBias", "qkv_w": "QKVW", "qkv_bias": "QKVBias", "cache_kv": "CacheKV", "time_step": "TimeStep", "src_mask": "SrcMask", "out_linear_w": "OutLinearW", "out_linear_bias": "OutLinearBias", "ffn_ln_scale": "FFNLnScale", "ffn_ln_bias": "FFNLnBias", "ffn1_weight": "FFN1Weight", "ffn1_bias": "FFN1Bias", "ffn2_weight": "FFN2Weight", "ffn2_bias": "FFN2Bias", "qkv_out_scale": "QKVOutScale", "out_linear_out_scale": "OutLinearOutScale", "ffn1_out_scale": "FFN1OutScale", "ffn2_out_scale": "FFN2OutScale" }, "outputs": { "cache_kv_out": "CacheKVOut", "out": "Out" } }, "fused_seqpool_cvm": { "phi_name": "fused_seqpool_cvm", "inputs": { "x": "X", "cvm": "CVM" }, "outputs": { "out": "Out" } }, "fused_transpose": { "phi_name": "fused_transpose", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fusion_gru": { "phi_name": "fusion_gru", "inputs": { "x": "X", "h0": "H0", "weight_x": "WeightX", "weight_h": "WeightH", "bias": "Bias" }, "outputs": { "reordered_h0": "ReorderedH0", "xx": "XX", "batched_input": "BatchedInput", "batched_out": "BatchedOut", "hidden": "Hidden" }, "attrs": { "scale_data": "Scale_data", "shift_data": "Shift_data", "scale_weights": "Scale_weights" } }, "fusion_lstm": { "phi_name": "fusion_lstm", "inputs": { "x": "X", "h0": "H0", "weight_x": "WeightX", "weight_h": "WeightH", "bias": "Bias", "c0": "C0" }, "outputs": { "out": "Out", "hidden": "Hidden", "cell": "Cell", "xx": "XX", "batched_input": "BatchedInput", "batched_hidden": "BatchedHidden", "batched_cell": "BatchedCell", "reordered_h0": "ReorderedH0", "reordered_c0": "ReorderedC0", "checked_cell": "CheckedCell" }, "attrs": { "scale_data": "Scale_data", "shift_data": "Shift_data", "scale_weights": "Scale_weights" } }, "fusion_repeated_fc_relu": { "phi_name": "fusion_repeated_fc_relu", "inputs": { "x": "X", "w": "W", "bias": "Bias" }, "outputs": { "relu_out": "ReluOut", "out": "Out" } }, "fusion_seqconv_eltadd_relu": { "phi_name": "fusion_seqconv_eltadd_relu", "inputs": { "x": "X", "filter": "Filter", "bias": "Bias" }, "outputs": { "out": "Out", "col_mat": "ColMat" }, "attrs": { "context_length": "contextLength", "context_start": "contextStart", "context_stride": "contextStride" } }, "fusion_seqpool_concat": { "phi_name": "fusion_seqpool_concat", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fusion_transpose_flatten_concat": { "phi_name": "fusion_transpose_flatten_concat", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "gather": { "phi_name": "gather", "inputs": { "x": "X", "index": "Index" }, "outputs": { "out": "Out" }, "scalar": { "axis": { "data_type": "int", "tensor_name": "Axis" } } }, "gather_nd": { "phi_name": "gather_nd", "inputs": { "x": "X", "index": "Index" }, "outputs": { "out": "Out" } }, "gather_tree": { "phi_name": "gather_tree", "inputs": { "ids": "Ids", "parents": "Parents" }, "outputs": { "out": "Out" } }, "gaussian_random": { "phi_name": "gaussian", "outputs": { "out": "Out" }, "int_array": { "shape": { "data_type": "int64_t", "tensor_name": "ShapeTensor", "tensors_name": "ShapeTensorList" } } }, "gelu": { "phi_name": "gelu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "generate_proposals_v2": { "phi_name": "generate_proposals", "inputs": { "scores": "Scores", "bbox_deltas": "BboxDeltas", "im_shape": "ImShape", "anchors": "Anchors", "variances": "Variances" }, "outputs": { "rpn_rois": "RpnRois", "rpn_roi_probs": "RpnRoiProbs", "rpn_rois_num": "RpnRoisNum" }, "attrs": { "pre_nms_top_n": "pre_nms_topN", "post_nms_top_n": "post_nms_topN" } }, "global_gather": { "phi_name": "global_gather", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "global_scatter": { "phi_name": "global_scatter", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "grad_add": { "phi_name": "grad_add", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "graph_khop_sampler": { "phi_name": "graph_khop_sampler", "inputs": { "row": "Row", "colptr": "Col_Ptr", "x": "X", "eids": "Eids" }, "outputs": { "out_src": "Out_Src", "out_dst": "Out_Dst", "sample_index": "Sample_Index", "reindex_x": "Reindex_X", "out_eids": "Out_Eids" } }, "graph_sample_neighbors": { "phi_name": "graph_sample_neighbors", "inputs": { "row": "Row", "colptr": "Col_Ptr", "x": "X", "eids": "Eids", "perm_buffer": "Perm_Buffer" }, "outputs": { "out": "Out", "out_count": "Out_Count", "out_eids": "Out_Eids" } }, "greater_equal": { "phi_name": "greater_equal", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "greater_than": { "phi_name": "greater_than", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "grid_sampler": { "phi_name": "grid_sample", "inputs": { "x": "X", "grid": "Grid" }, "outputs": { "out": "Output" } }, "group_norm": { "phi_name": "group_norm", "inputs": { "x": "X", "scale": "Scale", "bias": "Bias" }, "outputs": { "y": "Y", "mean": "Mean", "variance": "Variance" }, "attrs": { "data_format": "data_layout" } }, "gumbel_softmax": { "phi_name": "gumbel_softmax", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "hard_shrink": { "phi_name": "hardshrink", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "hard_sigmoid": { "phi_name": "hardsigmoid", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "hard_swish": { "phi_name": "hardswish", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "brelu": { "phi_name": "hardtanh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "hash": { "phi_name": "hash", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "runtime_shape": "ALL_KERNELS_MUST_COMPUTE_RUNTIME_SHAPE" } }, "elementwise_heaviside": { "phi_name": "heaviside", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "hinge_loss": { "phi_name": "hinge_loss", "inputs": { "logits": "Logits", "labels": "Labels" }, "outputs": { "loss": "Loss" } }, "histogram": { "phi_name": "histogram", "inputs": { "input": "X", "weight": "Weight" }, "outputs": { "out": "Out" } }, "hierarchical_sigmoid": { "phi_name": "hsigmoid_loss", "inputs": { "x": "X", "w": "W", "label": "Label", "bias": "Bias", "path": "PathTable", "code": "PathCode" }, "outputs": { "out": "Out", "pre_out": "PreOut", "w_out": "W_Out" } }, "huber_loss": { "phi_name": "huber_loss", "inputs": { "input": "X", "label": "Y" }, "outputs": { "out": "Out", "residual": "Residual" } }, "im2sequence": { "phi_name": "im2sequence", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "imag": { "phi_name": "imag", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "increment": { "phi_name": "increment", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "index_add": { "phi_name": "index_add", "inputs": { "x": "X", "index": "Index", "add_value": "AddValue" }, "outputs": { "out": "Out" } }, "index_elementwise_get": { "phi_name": "index_elementwise_get", "inputs": { "x": "x", "index": "index", "input_dims": "input_dims", "input_strides": "input_strides", "index_dims": "index_dims", "index_stride": "index_stride" }, "outputs": { "out": "Out" }, "attrs": { "slice_offset": "slice_offset", "accumulate": "accumulate", "is_combined": "is_combined" } }, "index_elementwise_put": { "phi_name": "index_elementwise_put" }, "index_elementwise_put_with_tensor": { "phi_name": "index_elementwise_put_with_tensor", "outputs": { "out": "Out" } }, "index_sample": { "phi_name": "index_sample", "inputs": { "x": "X", "index": "Index" }, "outputs": { "out": "Out" } }, "index_select": { "phi_name": "index_select", "inputs": { "x": "X", "index": "Index" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim" } }, "instance_norm": { "phi_name": "instance_norm", "inputs": { "x": "X", "scale": "Scale", "bias": "Bias" }, "outputs": { "y": "Y", "saved_mean": "SavedMean", "saved_variance": "SavedVariance" } }, "inverse": { "phi_name": "inverse", "inputs": { "x": "Input" }, "outputs": { "out": "Output" } }, "is_empty": { "phi_name": "is_empty", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "isclose": { "phi_name": "isclose", "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" } } }, "isfinite_v2": { "phi_name": "isfinite", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "isinf_v2": { "phi_name": "isinf", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "isnan_v2": { "phi_name": "isnan", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "kldiv_loss": { "phi_name": "kldiv_loss", "inputs": { "x": "X", "label": "Target" }, "outputs": { "out": "Loss" } }, "kron": { "phi_name": "kron", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "kthvalue": { "phi_name": "kthvalue", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices" } }, "l1_norm": { "phi_name": "l1_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "label_smooth": { "phi_name": "label_smooth", "inputs": { "label": "X", "prior_dist": "PriorDist" }, "outputs": { "out": "Out" } }, "lamb": { "phi_name": "lamb_", "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_outs": "MasterParamOut" } }, "layer_norm": { "phi_name": "layer_norm", "inputs": { "x": "X", "scale": "Scale", "bias": "Bias" }, "outputs": { "out": "Y", "mean": "Mean", "variance": "Variance" } }, "leaky_relu": { "phi_name": "leaky_relu", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "negative_slope": "alpha" } }, "bilinear_interp": { "phi_name": "legacy_bilinear_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" }, "attrs": { "data_format": "data_layout" } }, "expand": { "phi_name": "legacy_expand", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "shape": "expand_times" }, "int_array": { "shape": { "data_type": "int", "tensor_name": "ExpandTimes", "tensors_name": "expand_times_tensor" } } }, "generate_proposals": { "phi_name": "legacy_generate_proposals", "inputs": { "scores": "Scores", "bbox_deltas": "BboxDeltas", "im_info": "ImInfo", "anchors": "Anchors", "variances": "Variances" }, "outputs": { "rpn_rois": "RpnRois", "rpn_roi_probs": "RpnRoiProbs", "rpn_rois_num": "RpnRoisNum" }, "attrs": { "pre_nms_top_n": "pre_nms_topN", "post_nms_top_n": "post_nms_topN" } }, "matmul": { "phi_name": "legacy_matmul", "inputs": { "x": "X", "y": "Y", "out_grad": "DOut", "x_grad_grad": "DDX", "y_grad_grad": "DDY" }, "outputs": { "out": "Out", "x_grad": "DX", "y_grad": "DY" }, "attrs": { "transpose_x": "transpose_X", "transpose_y": "transpose_Y" } }, "nearest_interp": { "phi_name": "legacy_nearest_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" }, "attrs": { "data_format": "data_layout" } }, "reshape": { "phi_name": "legacy_reshape", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xshape": "XShape" }, "int_array": { "shape": { "data_type": "int", "tensor_name": "Shape", "tensors_name": "ShapeTensor" } } }, "lerp": { "phi_name": "lerp", "inputs": { "x": "X", "y": "Y", "weight": "Weight" }, "outputs": { "out": "Out" } }, "less_equal": { "phi_name": "less_equal", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "less_than": { "phi_name": "less_than", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "lgamma": { "phi_name": "lgamma", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "linear_interp_v2": { "phi_name": "linear_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" }, "attrs": { "data_format": "data_layout" } }, "linspace": { "phi_name": "linspace", "inputs": { "start": "Start", "stop": "Stop", "number": "Num" }, "outputs": { "out": "Out" } }, "lod_reset": { "phi_name": "lod_reset", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "log": { "phi_name": "log", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "log10": { "phi_name": "log10", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "log1p": { "phi_name": "log1p", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "log2": { "phi_name": "log2", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "log_loss": { "phi_name": "log_loss", "inputs": { "input": "Predicted", "label": "Labels" }, "outputs": { "out": "Loss" } }, "log_softmax": { "phi_name": "log_softmax", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logcumsumexp": { "phi_name": "logcumsumexp", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logical_and": { "phi_name": "logical_and", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "logical_not": { "phi_name": "logical_not", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logical_or": { "phi_name": "logical_or", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "logical_xor": { "phi_name": "logical_xor", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "logit": { "phi_name": "logit", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logsigmoid": { "phi_name": "logsigmoid", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "logsumexp": { "phi_name": "logsumexp", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "lookup_table": { "phi_name": "lookup_table", "inputs": { "w": "W", "ids": "Ids" }, "outputs": { "out": "Out" } }, "lrn": { "phi_name": "lrn", "inputs": { "x": "X" }, "outputs": { "out": "Out", "mid_out": "MidOut" } }, "lstsq": { "phi_name": "lstsq", "inputs": { "x": "X", "y": "Y" }, "outputs": { "solution": "Solution", "residuals": "Residuals", "rank": "Rank", "singular_values": "SingularValues" }, "scalar": { "rcond": { "data_type": "float", "support_tensor": "True" } } }, "lu_unpack": { "phi_name": "lu_unpack", "inputs": { "x": "X", "y": "Pivots" }, "outputs": { "pmat": "Pmat", "l": "L", "u": "U" } }, "margin_cross_entropy": { "phi_name": "margin_cross_entropy", "inputs": { "logits": "Logits", "label": "Label" }, "outputs": { "softmax": "Softmax", "loss": "Loss" } }, "masked_select": { "phi_name": "masked_select", "inputs": { "x": "X", "mask": "Mask" }, "outputs": { "out": "Y" } }, "match_matrix_tensor": { "phi_name": "match_matrix_tensor", "inputs": { "x": "X", "y": "Y", "w": "W" }, "outputs": { "out": "Out", "tmp": "Tmp" } }, "matmul_v2": { "phi_name": "matmul", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "attrs": { "transpose_x": "trans_x", "transpose_y": "trans_y" } }, "mul": { "phi_name": "matmul_with_flatten", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "matrix_nms": { "phi_name": "matrix_nms", "inputs": { "bboxes": "BBoxes", "scores": "Scores" }, "outputs": { "out": "Out", "index": "Index", "roisnum": "RoisNum" } }, "matrix_power": { "phi_name": "matrix_power", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "matrix_rank": { "phi_name": "matrix_rank", "inputs": { "x": "X", "tol_tensor": "TolTensor" }, "outputs": { "out": "Out" } }, "reduce_max": { "phi_name": "max", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "max_pool2d_with_index": { "phi_name": "max_pool2d_with_index", "inputs": { "x": "X" }, "outputs": { "out": "Out", "mask": "Mask" }, "attrs": { "kernel_size": "ksize" } }, "max_pool3d_with_index": { "phi_name": "max_pool3d_with_index", "inputs": { "x": "X" }, "outputs": { "out": "Out", "mask": "Mask" }, "attrs": { "kernel_size": "ksize" } }, "elementwise_max": { "phi_name": "maximum", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "maxout": { "phi_name": "maxout", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "reduce_mean": { "phi_name": "mean", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "mean": { "phi_name": "mean_all", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "memory_efficient_attention": { "phi_name": "memory_efficient_attention", "scalar": { "max_seqlen_q": { "data_type": "int64_t", "support_tensor": "True" }, "max_seqlen_k": { "data_type": "int64_t", "support_tensor": "True" } } }, "merge_selected_rows": { "phi_name": "merge_selected_rows", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "merged_adam_": { "phi_name": "merged_adam_", "inputs": { "param": "Param", "grad": "Grad", "learning_rate": "LearningRate", "moment1": "Moment1", "moment2": "Moment2", "moment2_max": "Moment2Max", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "moment1_out": "Moment1Out", "moment2_out": "Moment2Out", "moment2_max_out": "Moment2MaxOut", "beta1_pow_out": "Beta1PowOut", "beta2_pow_out": "Beta2PowOut", "master_param_out": "MasterParamOut" }, "scalar": { "beta1": { "data_type": "float", "support_tensor": "True" }, "beta2": { "data_type": "float", "support_tensor": "True" }, "epsilon": { "data_type": "float", "support_tensor": "True" } } }, "merged_momentum": { "phi_name": "merged_momentum_", "inputs": { "param": "Param", "grad": "Grad", "velocity": "Velocity", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "velocity_out": "VelocityOut", "master_param_out": "MasterParamOut" } }, "meshgrid": { "phi_name": "meshgrid", "inputs": { "inputs": "X" }, "outputs": { "out": "Out" } }, "reduce_min": { "phi_name": "min", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "elementwise_min": { "phi_name": "minimum", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "mish": { "phi_name": "mish", "inputs": { "x": "X", "lambda": "threshold" }, "outputs": { "out": "Out" } }, "mode": { "phi_name": "mode", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices" } }, "momentum": { "phi_name": "momentum_", "inputs": { "param": "Param", "grad": "Grad", "velocity": "Velocity", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "velocity_out": "VelocityOut", "master_param_out": "MasterParamOut" } }, "moving_average_abs_max_scale": { "phi_name": "moving_average_abs_max_scale", "inputs": { "x": "X", "in_accum": "InAccum", "in_state": "InState" }, "outputs": { "out": "Out", "out_scale": "OutScale", "out_state": "OutState", "out_accum": "OutAccum" } }, "multi_dot": { "phi_name": "multi_dot", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "multi_gru": { "phi_name": "multi_gru", "inputs": { "x": "X", "weight_x": "WeightX", "weight_h": "WeightH", "bias": "Bias", "scale_weights": "Scale_weights" }, "outputs": { "hidden": "Hidden" }, "attrs": { "scale_data": "Scale_data", "shift_data": "Shift_data" } }, "multiclass_nms": { "phi_name": "multiclass_nms", "inputs": { "bboxes": "BBoxes", "scores": "Scores" }, "outputs": { "out": "Out" } }, "multiclass_nms3": { "phi_name": "multiclass_nms3", "inputs": { "bboxes": "BBoxes", "scores": "Scores", "rois_num": "RoisNum" }, "outputs": { "out": "Out", "index": "Index", "nms_rois_num": "NmsRoisNum" } }, "multihead_matmul": { "phi_name": "multihead_matmul", "inputs": { "input": "Input", "w": "W", "bias": "Bias", "bias_qk": "BiasQK" }, "outputs": { "out": "Out" }, "attrs": { "transpose_q": "transpose_Q", "transpose_k": "transpose_K", "transpose_v": "transpose_V" } }, "multinomial": { "phi_name": "multinomial", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "num_samples": { "data_type": "int", "support_tensor": "True" } } }, "multiplex": { "phi_name": "multiplex", "inputs": { "inputs": "X", "index": "Ids" }, "outputs": { "out": "Out" } }, "elementwise_mul": { "phi_name": "multiply", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "mv": { "phi_name": "mv", "inputs": { "x": "X", "vec": "Vec" }, "outputs": { "out": "Out" } }, "nanmedian": { "phi_name": "nanmedian", "inputs": { "x": "X" }, "outputs": { "out": "Out", "medians": "MedianIndex" }, "int_array": { "axis": { "data_type": "int" } } }, "nearest_interp_v2": { "phi_name": "nearest_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" }, "attrs": { "data_format": "data_layout" } }, "nll_loss": { "phi_name": "nll_loss", "inputs": { "input": "X", "label": "Label", "weight": "Weight" }, "outputs": { "out": "Out", "total_weight": "Total_weight" } }, "nms": { "phi_name": "nms", "inputs": { "x": "Boxes" }, "outputs": { "out": "KeepBoxesIdxs" }, "attrs": { "threshold": "iou_threshold" } }, "where_index": { "phi_name": "nonzero", "inputs": { "condition": "Condition" }, "outputs": { "out": "Out" } }, "norm": { "phi_name": "norm", "inputs": { "x": "X" }, "outputs": { "out": "Out", "norm": "Norm" } }, "not_equal": { "phi_name": "not_equal", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "size": { "phi_name": "numel", "inputs": { "x": "Input" }, "outputs": { "size": "Out" } }, "one_hot_v2": { "phi_name": "one_hot", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "depth": { "data_type": "int", "tensor_name": "depth_tensor" } } }, "overlap_add": { "phi_name": "overlap_add", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "p_norm": { "phi_name": "p_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "pad": { "phi_name": "pad", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "pad_value": { "data_type": "float", "support_tensor": "True" } } }, "pad2d": { "phi_name": "pad2d" }, "pad3d": { "phi_name": "pad3d", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "pad_value": "value" }, "int_array": { "paddings": { "data_type": "int", "tensor_name": "Paddings" } } }, "partial_allgather": { "phi_name": "partial_allgather", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "partial_concat": { "phi_name": "partial_concat", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "partial_recv": { "phi_name": "partial_recv", "outputs": { "out": "Out" } }, "partial_sum": { "phi_name": "partial_sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "pixel_shuffle": { "phi_name": "pixel_shuffle", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "pixel_unshuffle": { "phi_name": "pixel_unshuffle", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "poisson": { "phi_name": "poisson", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "pool2d": { "phi_name": "pool2d", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "kernel_size": "ksize" }, "int_array": { "kernel_size": { "data_type": "int", "support_tensor": "True" } } }, "pool3d": { "phi_name": "pool3d", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "kernel_size": "ksize" } }, "pow": { "phi_name": "pow", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "y": "factor" }, "scalar": { "y": { "data_type": "float", "tensor_name": "FactorTensor" } } }, "prelu": { "phi_name": "prelu", "inputs": { "x": "X", "alpha": "Alpha" }, "outputs": { "out": "Out" } }, "print": { "phi_name": "print", "inputs": { "in": "In" }, "outputs": { "out": "Out" } }, "prior_box": { "phi_name": "prior_box", "inputs": { "input": "Input", "image": "Image" }, "outputs": { "out": "Boxes", "var": "Variances" } }, "reduce_prod": { "phi_name": "prod", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "psroi_pool": { "phi_name": "psroi_pool", "inputs": { "x": "X", "boxes": "ROIs", "boxes_num": "RoisNum" }, "outputs": { "out": "Out" } }, "put_along_axis": { "phi_name": "put_along_axis", "inputs": { "arr": "Input", "indices": "Index", "values": "Value" }, "outputs": { "out": "Result" }, "attrs": { "axis": "Axis", "reduce": "Reduce", "include_self": "Include_self" } }, "pylayer": { "phi_name": "pylayer" }, "qr": { "phi_name": "qr", "inputs": { "x": "X" }, "outputs": { "q": "Q", "r": "R" } }, "quantize": { "phi_name": "quantize", "inputs": { "input": "Input" }, "outputs": { "output": "Output" }, "attrs": { "scale": "Scale", "shift": "Shift", "include_self": "Include_self" } }, "quantize_linear": { "phi_name": "quantize_linear", "inputs": { "x": "X", "scale": "Scale", "zero_point": "ZeroPoint", "in_accum": "InAccum", "in_state": "InState" }, "outputs": { "y": "Y", "out_scale": "OutScale", "out_accum": "OutAccum", "out_state": "OutState" } }, "randint": { "phi_name": "randint", "outputs": { "out": "Out" }, "int_array": { "shape": { "data_type": "int64_t", "tensor_name": "ShapeTensor", "tensors_name": "ShapeTensorList" } } }, "randperm": { "phi_name": "randperm", "outputs": { "out": "Out" } }, "range_v2": { "phi_name": "range_v2", "inputs": { "start": "Start", "end": "End", "step": "Step" }, "outputs": { "out": "Out" }, "scalar": { "start": { "data_type": "double", "support_tensor": "True" }, "end": { "data_type": "double", "support_tensor": "True" }, "step": { "data_type": "double", "support_tensor": "True" } } }, "real": { "phi_name": "real", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "reciprocal": { "phi_name": "reciprocal", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "relu": { "phi_name": "relu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "relu6": { "phi_name": "relu6", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "elementwise_mod": { "phi_name": "remainder", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "renorm": { "phi_name": "renorm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "repeat_interleave": { "phi_name": "repeat_interleave", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "repeats": "Repeats", "axis": "dim" } }, "repeat_interleave_with_tensor_index": { "phi_name": "repeat_interleave_with_tensor_index", "inputs": { "x": "X", "repeats": "RepeatTensor" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim" } }, "requantize": { "phi_name": "requantize", "inputs": { "input": "Input" }, "outputs": { "output": "Output" }, "attrs": { "scale_in": "Scale_in", "scale_out": "Scale_out", "shift_in": "Shift_in", "shift_out": "Shift_out" } }, "reshape2": { "phi_name": "reshape", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xshape": "XShape" }, "int_array": { "shape": { "data_type": "int", "tensor_name": "Shape", "tensors_name": "ShapeTensor" } } }, "resnet_basic_block": { "phi_name": "resnet_basic_block", "inputs": { "x": "X", "filter1": "Filter1", "scale1": "Scale1", "bias1": "Bias1", "mean1": "Mean1", "var1": "Var1", "filter2": "Filter2", "scale2": "Scale2", "bias2": "Bias2", "mean2": "Mean2", "var2": "Var2", "filter3": "Filter3", "scale3": "Scale3", "bias3": "Bias3", "mean3": "Mean3", "var3": "Var3" }, "outputs": { "out": "Y", "conv1": "Conv1", "saved_mean1": "SavedMean1", "saved_invstd1": "SavedInvstd1", "mean1_out": "Mean1Out", "var1_out": "Var1Out", "conv2": "Conv2", "conv2_input": "Conv2Input", "saved_mean2": "SavedMean2", "saved_invstd2": "SavedInvstd2", "mean2_out": "Mean2Out", "var2_out": "Var2Out", "conv3": "Conv3", "saved_mean3": "SavedMean3", "saved_invstd3": "SavedInvstd3", "mean3_out": "Mean3Out", "var3_out": "Var3Out", "max_input1": "MaxInput1", "max_filter1": "MaxFilter1", "max_input2": "MaxInput2", "max_filter2": "MaxFilter2", "max_input3": "MaxInput3", "max_filter3": "MaxFilter3" } }, "resnet_unit": { "phi_name": "resnet_unit", "inputs": { "x": "X", "filter_x": "FilterX", "scale_x": "ScaleX", "bias_x": "BiasX", "mean_x": "MeanX", "var_x": "VarX", "z": "Z", "filter_z": "FilterZ", "scale_z": "ScaleZ", "bias_z": "BiasZ", "mean_z": "MeanZ", "var_z": "VarZ" }, "outputs": { "out": "Y", "bit_mask": "BitMask", "conv_x": "ConvX", "saved_mean_x": "SavedMeanX", "saved_invstd_x": "SavedInvstdX", "running_mean_x": "RunningMeanX", "running_var_x": "RunningVarX", "conv_z": "ConvZ", "saved_mean_z": "SavedMeanZ", "saved_invstd_z": "SavedInvstdZ", "running_mean_z": "RunningMeanZ", "running_var_z": "RunningVarZ" } }, "reverse": { "phi_name": "reverse", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "rmsprop": { "phi_name": "rmsprop_", "inputs": { "param": "Param", "mean_square": "MeanSquare", "mean_grad": "MeanGrad", "learning_rate": "LearningRate", "grad": "Grad", "moment": "Moment", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "moment_out": "MomentOut", "mean_square_out": "MeanSquareOut", "mean_grad_out": "MeanGradOut", "master_param_outs": "MasterParamOut" } }, "rnn": { "phi_name": "rnn", "inputs": { "x": "Input", "pre_state": "PreState", "weight_list": "WeightList", "sequence_length": "SequenceLength" }, "outputs": { "out": "Out", "dropout_state_out": "DropoutState", "state": "State", "reserve": "Reserve" } }, "roi_align": { "phi_name": "roi_align", "inputs": { "x": "X", "boxes": "ROIs", "boxes_num": "RoisNum" }, "outputs": { "out": "Out" } }, "roi_pool": { "phi_name": "roi_pool", "inputs": { "x": "X", "boxes": "ROIs", "boxes_num": "RoisNum" }, "outputs": { "out": "Out", "arg_max": "Argmax" } }, "roll": { "phi_name": "roll", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "int_array": { "shifts": { "data_type": "int64_t", "tensor_name": "ShiftsTensor" } } }, "round": { "phi_name": "round", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "row_conv": { "phi_name": "row_conv", "inputs": { "x": "X", "filter": "Filter" }, "outputs": { "out": "Out" } }, "rsqrt": { "phi_name": "rsqrt", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "save_combine": { "phi_name": "save_combine", "inputs": { "x": "X" } }, "scale": { "phi_name": "scale", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "scale": { "data_type": "float", "tensor_name": "ScaleTensor" }, "bias": { "data_type": "float", "support_tensor": "False" } } }, "scatter": { "phi_name": "scatter", "inputs": { "x": "X", "index": "Ids", "updates": "Updates" }, "outputs": { "out": "Out" } }, "scatter_nd_add": { "phi_name": "scatter_nd_add", "inputs": { "x": "X", "index": "Index", "updates": "Updates" }, "outputs": { "out": "Out" } }, "searchsorted": { "phi_name": "searchsorted", "inputs": { "sorted_sequence": "SortedSequence", "values": "Values" }, "outputs": { "out": "Out" } }, "seed": { "phi_name": "seed", "outputs": { "out": "Out" } }, "segment_pool": { "phi_name": "segment_pool", "inputs": { "x": "X", "segment_ids": "SegmentIds" }, "outputs": { "out": "Out", "summed_ids": "SummedIds" } }, "self_dp_attention": { "phi_name": "self_dp_attention", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "selu": { "phi_name": "selu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "graph_send_recv": { "phi_name": "send_u_recv", "inputs": { "x": "X", "src_index": "Src_index", "dst_index": "Dst_index" }, "outputs": { "out": "Out", "dst_count": "Dst_count" }, "int_array": { "out_size": { "data_type": "int64_t", "tensor_name": "Out_size" } } }, "graph_send_ue_recv": { "phi_name": "send_ue_recv", "inputs": { "x": "X", "y": "Y", "src_index": "Src_index", "dst_index": "Dst_index" }, "outputs": { "out": "Out", "dst_count": "Dst_count" }, "int_array": { "out_size": { "data_type": "int64_t", "tensor_name": "Out_size" } } }, "graph_send_uv": { "phi_name": "send_uv" }, "sequence_expand": { "phi_name": "sequence_expand", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "sequence_mask": { "phi_name": "sequence_mask", "inputs": { "x": "X" }, "outputs": { "y": "Y" }, "attrs": { "max_len": "maxlen" }, "scalar": { "max_len": { "data_type": "int", "tensor_name": "MaxLenTensor" } } }, "sequence_softmax": { "phi_name": "sequence_softmax", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sgd": { "phi_name": "sgd_", "inputs": { "param": "Param", "learning_rate": "LearningRate", "grad": "Grad", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "master_param_out": "MasterParamOut" } }, "shape": { "phi_name": "shape" }, "shard_index": { "phi_name": "shard_index", "inputs": { "input": "X" }, "outputs": { "out": "Out" } }, "share_buffer": { "phi_name": "share_buffer", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xout": "XOut" } }, "share_data": { "phi_name": "share_data_", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "shuffle_batch": { "phi_name": "shuffle_batch", "inputs": { "x": "X", "seed": "Seed" }, "outputs": { "out": "Out", "shuffle_idx": "ShuffleIdx", "seed_out": "SeedOut" } }, "shuffle_channel": { "phi_name": "shuffle_channel", "inputs": { "x": "X", "group": "group" }, "outputs": { "out": "Out" } }, "sigmoid": { "phi_name": "sigmoid", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sign": { "phi_name": "sign", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "silu": { "phi_name": "silu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sin": { "phi_name": "sin", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sinh": { "phi_name": "sinh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "slice": { "phi_name": "slice", "inputs": { "input": "Input" }, "outputs": { "out": "Out" }, "int_array": { "starts": { "data_type": "int", "tensor_name": "StartsTensor", "tensors_name": "StartsTensorList" }, "ends": { "data_type": "int", "tensor_name": "EndsTensor", "tensors_name": "EndsTensorList" } } }, "slogdeterminant": { "phi_name": "slogdet", "inputs": { "x": "Input" }, "outputs": { "out": "Out" } }, "soft_relu": { "phi_name": "soft_relu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "softmax": { "phi_name": "softmax", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "softplus": { "phi_name": "softplus", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "softshrink": { "phi_name": "softshrink", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "threshold": "lambda" } }, "softsign": { "phi_name": "softsign", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "solve": { "phi_name": "solve", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "sparse_batch_norm": { "phi_name": "sparse_batch_norm", "attrs": { "data_format": "data_layout" } }, "sparse_reshape": { "phi_name": "sparse_reshape", "int_array": { "shape": { "data_type": "int64_t", "tensor_name": "ShapeTensor", "tensors_name": "ShapeTensorList" } } }, "sparse_slice": { "phi_name": "sparse_slice", "int_array": { "starts": { "data_type": "int", "tensor_name": "StartsTensor", "tensors_name": "StartsTensorList" }, "ends": { "data_type": "int", "tensor_name": "EndsTensor", "tensors_name": "EndsTensorList" } } }, "sparse_sum": { "phi_name": "sparse_sum", "scalar": { "axis": { "data_type": "int", "tensor_name": "AxisTensor" } } }, "sparse_sync_batch_norm": { "phi_name": "sparse_sync_batch_norm", "attrs": { "data_format": "data_layout" } }, "spectral_norm": { "phi_name": "spectral_norm", "inputs": { "weight": "Weight", "u": "U", "v": "V" }, "outputs": { "out": "Out" } }, "split": { "phi_name": "split", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "scalar": { "axis": { "data_type": "int", "support_tensor": "True" } }, "int_array": { "sections": { "data_type": "int", "support_tensor": "True" } } }, "split_with_num": { "phi_name": "split_with_num", "scalar": { "axis": { "data_type": "int", "support_tensor": "True", "tensor_name": "AxisTensor" } } }, "sqrt": { "phi_name": "sqrt", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "square": { "phi_name": "square", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "squeeze2": { "phi_name": "squeeze", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xshape": "XShape" }, "attrs": { "axis": "axes" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "stack": { "phi_name": "stack", "inputs": { "x": "X" }, "outputs": { "out": "Y" } }, "stanh": { "phi_name": "stanh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "straight_through_estimator_grad": { "phi_name": "straight_through_estimator_grad", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "strided_slice": { "phi_name": "strided_slice", "inputs": { "x": "Input" }, "outputs": { "out": "Out" }, "int_array": { "starts": { "data_type": "int", "tensor_name": "StartsTensor", "tensors_name": "StartsTensorList" }, "ends": { "data_type": "int", "tensor_name": "EndsTensor", "tensors_name": "EndsTensorList" }, "strides": { "data_type": "int", "tensor_name": "StridesTensor", "tensors_name": "StridesTensorList" } } }, "elementwise_sub": { "phi_name": "subtract", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "reduce_sum": { "phi_name": "sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "axis": "dim", "keepdim": "keep_dim", "dtype": "out_dtype" }, "int_array": { "axis": { "data_type": "int", "support_tensor": "True" } } }, "svd": { "phi_name": "svd", "inputs": { "x": "X" }, "outputs": { "u": "U", "s": "S", "vh": "VH" } }, "swish": { "phi_name": "swish", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "sync_batch_norm": { "phi_name": "sync_batch_norm", "inputs": { "x": "X", "scale": "Scale", "bias": "Bias", "mean": "Mean", "variance": "Variance" }, "outputs": { "out": "Y", "mean_out": "MeanOut", "variance_out": "VarianceOut", "saved_mean": "SavedMean", "saved_variance": "SavedVariance", "reserve_space": "ReserveSpace" }, "attrs": { "data_format": "data_layout" } }, "take_along_axis": { "phi_name": "take_along_axis", "inputs": { "arr": "Input", "indices": "Index" }, "outputs": { "out": "Result" }, "attrs": { "axis": "Axis" } }, "tan": { "phi_name": "tan", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "tanh": { "phi_name": "tanh", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "tanh_shrink": { "phi_name": "tanh_shrink", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "tdm_child": { "phi_name": "tdm_child", "inputs": { "x": "X", "tree_info": "TreeInfo", "child_nums": "child_nums", "dtype": "dtype" }, "outputs": { "child": "Child", "leaf_mask": "LeafMask" } }, "tdm_sampler": { "phi_name": "tdm_sampler", "inputs": { "x": "X", "travel": "Travel", "layer": "Layer" }, "outputs": { "out": "Out", "labels": "Labels", "mask": "Mask" } }, "thresholded_relu": { "phi_name": "thresholded_relu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "tile": { "phi_name": "tile", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "int_array": { "repeat_times": { "data_type": "int", "tensor_name": "RepeatTimes", "tensors_name": "repeat_times_tensor" } } }, "top_k_v2": { "phi_name": "topk", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices" }, "scalar": { "k": { "data_type": "int", "tensor_name": "K" } } }, "top_k": { "phi_name": "topk_v1", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices" }, "scalar": { "k": { "data_type": "int", "tensor_name": "K" } } }, "trace": { "phi_name": "trace", "inputs": { "x": "Input" }, "outputs": { "out": "Out" } }, "transpose2": { "phi_name": "transpose", "inputs": { "x": "X" }, "outputs": { "out": "Out" }, "attrs": { "perm": "axis" } }, "triangular_solve": { "phi_name": "triangular_solve", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" } }, "tril_triu": { "phi_name": "tril_triu", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "trilinear_interp_v2": { "phi_name": "trilinear_interp", "inputs": { "x": "X", "out_size": "OutSize", "size_tensor": "SizeTensor", "scale_tensor": "Scale" }, "outputs": { "output": "Out" }, "attrs": { "data_format": "data_layout" } }, "trunc": { "phi_name": "trunc", "inputs": { "input": "X" }, "outputs": { "out": "Out" } }, "truncated_gaussian_random": { "phi_name": "truncated_gaussian_random", "outputs": { "out": "Out" } }, "unbind": { "phi_name": "unbind", "inputs": { "input": "X" }, "outputs": { "out": "Out" } }, "unfold": { "phi_name": "unfold", "inputs": { "x": "X" }, "outputs": { "out": "Y" } }, "uniform_random": { "phi_name": "uniform", "outputs": { "out": "Out" }, "scalar": { "min": { "data_type": "float", "support_tensor": "True" }, "max": { "data_type": "float", "support_tensor": "True" } }, "int_array": { "shape": { "data_type": "int64_t", "tensor_name": "ShapeTensor", "tensors_name": "ShapeTensorList" } } }, "uniform_random_inplace": { "phi_name": "uniform_inplace", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "unique": { "phi_name": "unique", "inputs": { "x": "X" }, "outputs": { "out": "Out", "indices": "Indices", "inverse": "Index", "counts": "Counts" } }, "unique_consecutive": { "phi_name": "unique_consecutive", "inputs": { "x": "X" }, "outputs": { "out": "Out", "index": "Index", "counts": "Counts" } }, "unpool": { "phi_name": "unpool", "inputs": { "x": "X", "indices": "Indices" }, "outputs": { "out": "Out" }, "attrs": { "padding": "paddings" }, "int_array": { "output_size": { "data_type": "int", "support_tensor": "True" } } }, "unpool3d": { "phi_name": "unpool3d", "inputs": { "x": "X", "indices": "Indices" }, "outputs": { "out": "Out" } }, "unsqueeze2": { "phi_name": "unsqueeze", "inputs": { "x": "X" }, "outputs": { "out": "Out", "xshape": "XShape" }, "attrs": { "axis": "axes" }, "int_array": { "axis": { "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" } } }, "view_shape": { "phi_name": "view_shape" }, "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" } }, "yolo_box_head": { "phi_name": "yolo_box_head", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "yolo_box_post": { "phi_name": "yolo_box_post", "inputs": { "boxes0": "Boxes0", "boxes1": "Boxes1", "boxes2": "Boxes2", "image_shape": "ImageShape", "image_scale": "ImageScale" }, "outputs": { "out": "Out", "nms_rois_num": "NmsRoisNum" } }, "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" } }, "box_clip": { "phi_name": "box_clip", "inputs": { "input": "Input", "im_info": "ImInfo" }, "outputs": { "output": "Output" } }, "c_allreduce_sum": { "phi_name": "c_allreduce_sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_identity": { "phi_name": "c_identity", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_scatter": { "phi_name": "c_scatter", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "channel_shuffle": { "phi_name": "channel_shuffle", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "chunk_eval": { "phi_name": "chunk_eval", "inputs": { "inference": "Inference", "label": "Label", "seq_length": "SeqLength" }, "outputs": { "precision": "Precision", "recall": "Recall", "f1_score": "F1-Score", "num_infer_chunks": "NumInferChunks", "num_label_chunks": "NumLabelChunks", "num_correct_chunks": "NumCorrectChunks" } }, "comm_init_all": { "phi_name": "comm_init_all" }, "crf_decoding": { "phi_name": "crf_decoding", "inputs": { "emission": "Emission", "transition": "Transition", "label": "Label", "length": "Length" }, "outputs": { "viterbi_path": "ViterbiPath" } }, "cross_entropy": { "phi_name": "cross_entropy", "inputs": { "x": "X", "label": "Label" }, "outputs": { "out": "Y" } }, "cross_entropy2": { "phi_name": "cross_entropy2", "inputs": { "x": "X", "label": "Label" }, "outputs": { "out": "Y", "x_shape": "XShape", "match_x": "MatchX" } }, "ctc_align": { "phi_name": "ctc_align", "inputs": { "input": "Input", "input_length": "InputLength" }, "outputs": { "output": "Output", "output_length": "OutputLength" } }, "cudnn_lstm": { "phi_name": "cudnn_lstm", "inputs": { "x": "Input", "init_h": "InitH", "init_c": "InitC", "w": "W", "weight_list": "WeightList", "sequence_length": "SequenceLength" }, "outputs": { "reserve": "Reserve", "state_out": "StateOut", "out": "Out", "last_h": "LastH", "last_c": "LastC" } }, "decayed_adagrad": { "phi_name": "decayed_adagrad", "inputs": { "param": "Param", "grad": "Grad", "moment": "Moment", "learning_rate": "LearningRate" }, "outputs": { "param_out": "ParamOut", "moment_out": "MomentOut" } }, "depend": { "phi_name": "depend", "inputs": { "x": "X", "dep": "Dep" }, "outputs": { "out": "Out" } }, "dgc": { "phi_name": "dgc", "inputs": { "u": "U", "v": "V", "grad": "Grad", "param": "Param" }, "outputs": { "u_out": "U_out", "v_out": "V_out", "encode_grad": "EncodeGrad", "grad_out": "Grad_out", "gather_buff": "GatherBuff" } }, "distribute_fpn_proposals": { "phi_name": "distribute_fpn_proposals", "inputs": { "fpn_rois": "FpnRois", "rois_num": "RoisNum" }, "outputs": { "multi_fpn_rois": "MultiFpnRois", "multi_level_rois_num": "MultiLevelRoIsNum", "restore_index": "RestoreIndex" } }, "distributed_fused_lamb_init": { "phi_name": "distributed_fused_lamb_init", "inputs": { "param": "Param", "grad": "Grad" }, "outputs": { "fp32_fused_param": "FP32FusedParam", "fp32_fused_grad": "FP32FusedGrad", "fp16_fused_param": "FP16FusedParam", "fp16_fused_grad": "FP16FusedGrad", "moment1": "Moment1", "moment2": "Moment2", "beta1_pow": "Beta1Pow", "beta2_pow": "Beta2Pow", "fused_param_offsets": "FusedParamOffsets", "fp32_shard_fused_param_offsets": "FP32ShardFusedParamOffsets", "fp16_shard_fused_param_offsets": "FP16ShardFusedParamOffsets", "param_info": "ParamInfo", "param_order": "ParamOrder", "param_out": "ParamOut", "master_param_out": "MasterParamOut", "grad_out": "GradOut", "global_scale": "GlobalScale", "step": "Step" } }, "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" } }, "flatten2": { "phi_name": "flatten2", "inputs": { "x": "X" }, "outputs": { "out": "Out", "x_shape": "XShape" } }, "ftrl": { "phi_name": "ftrl", "inputs": { "param": "Param", "squared_accumulator": "SquaredAccumulator", "linear_accumulator": "LinearAccumulator", "grad": "Grad", "learning_rate": "LearningRate" }, "outputs": { "param_out": "ParamOut", "squared_accum_out": "SquaredAccumOut", "linear_accum_out": "LinearAccumOut" } }, "fill_constant_batch_size_like": { "phi_name": "full_batch_size_like", "inputs": { "input": "Input" }, "outputs": { "out": "Out" } }, "fused_elemwise_activation": { "phi_name": "fused_elemwise_activation", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out", "intermediate_out": "IntermediateOut" } }, "fused_elemwise_add_activation": { "phi_name": "fused_elemwise_add_activation", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out", "intermediate_out": "IntermediateOut" } }, "fused_matmul": { "phi_name": "fused_matmul", "inputs": { "x": "X", "y": "Y", "residual_data": "ResidualData" }, "outputs": { "out": "Out" }, "attrs": { "scale_x": "Scale_x", "scale_y": "Scale_y", "scale_out": "Scale_out", "scale_in_eltwise": "Scale_in_eltwise", "fused_reshape_x": "fused_reshape_X", "fused_transpose_x": "fused_transpose_X", "fused_reshape_y": "fused_reshape_Y", "fused_transpose_y": "fused_transpose_Y", "fused_reshape_out": "fused_reshape_Out", "fused_transpose_out": "fused_transpose_Out" } }, "fused_softmax_mask": { "phi_name": "fused_softmax_mask", "inputs": { "x": "X", "mask": "Mask" }, "outputs": { "out": "Out" } }, "fused_softplus": { "phi_name": "fused_softplus", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "fused_token_prune": { "phi_name": "fused_token_prune", "inputs": { "attn": "Attn", "x": "X", "mask": "Mask", "new_mask": "NewMask" }, "outputs": { "slimmed_x": "SlimmedX", "cls_inds": "CLSInds" } }, "fusion_group": { "phi_name": "fusion_group", "inputs": { "inputs": "Inputs" }, "outputs": { "outs": "Outs" } }, "fusion_seqpool_cvm_concat": { "phi_name": "fusion_seqpool_cvm_concat", "inputs": { "x": "X", "cvm": "CVM" }, "outputs": { "out": "Out" } }, "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" } }, "gru": { "phi_name": "gru", "inputs": { "input": "Input", "h0": "H0", "weight": "Weight", "bias": "Bias" }, "outputs": { "batch_gate": "BatchGate", "batch_reset_hidden_prev": "BatchResetHiddenPrev", "batch_hidden": "BatchHidden", "hidden": "Hidden" } }, "gru_unit": { "phi_name": "gru_unit", "inputs": { "input": "Input", "hidden_prev": "HiddenPrev", "weight": "Weight", "bias": "Bias" }, "outputs": { "gate": "Gate", "reset_hidden_prev": "ResetHiddenPrev", "hidden": "Hidden" } }, "identity_loss": { "phi_name": "identity_loss", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "lars_momentum": { "phi_name": "lars_momentum_", "inputs": { "param": "Param", "grad": "Grad", "velocity": "Velocity", "learning_rate": "LearningRate", "master_param": "MasterParam" }, "outputs": { "param_out": "ParamOut", "velocity_out": "VelocityOut", "master_param_out": "MasterParamOut" } }, "crop": { "phi_name": "legacy_crop", "inputs": { "x": "X", "y": "Y" }, "outputs": { "out": "Out" }, "int_array": { "offsets": { "data_type": "int", "tensor_name": "Offsets" } } }, "limit_by_capacity": { "phi_name": "limit_by_capacity", "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" } }, "lookup_table_dequant": { "phi_name": "lookup_table_dequant", "inputs": { "w": "W", "ids": "Ids" }, "outputs": { "out": "Out" } }, "lstm": { "phi_name": "lstm", "inputs": { "input": "Input", "h0": "H0", "c0": "C0", "weight": "Weight", "bias": "Bias" }, "outputs": { "hidden": "Hidden", "cell": "Cell", "batch_gate": "BatchGate", "batch_cell_pre_act": "BatchCellPreAct" } }, "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" } }, "mp_allreduce_sum": { "phi_name": "mp_allreduce_sum", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "nce": { "phi_name": "nce", "inputs": { "input": "Input", "label": "Label", "weight": "Weight", "bias": "Bias", "sample_weight": "SampleWeight", "custom_dist_probs": "CustomDistProbs", "custom_dist_alias": "CustomDistAlias", "custom_dist_alias_probs": "CustomDistAliasProbs" }, "outputs": { "cost": "Cost", "sample_logits": "SampleLogits", "sample_labels": "SampleLabels" } }, "nop": { "phi_name": "nop", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "number_count": { "phi_name": "number_count", "inputs": { "numbers": "numbers" }, "outputs": { "out": "Out" } }, "partial_send": { "phi_name": "partial_send", "inputs": { "x": "X" } }, "prune_gate_by_capacity": { "phi_name": "prune_gate_by_capacity", "inputs": { "gate_idx": "GateIdx", "expert_count": "ExpertCount" }, "outputs": { "out_gate_idx": "NewGateIdx" } }, "pyramid_hash": { "phi_name": "pyramid_hash", "inputs": { "x": "X", "w": "W", "white_list": "WhiteList", "black_list": "BlackList" }, "outputs": { "out": "Out", "drop_pos": "DropPos", "x_temp_out": "X_Temp_Out" } }, "random_routing": { "phi_name": "random_routing", "inputs": { "prob": "Prob", "topk_value": "TopK_Value", "topk_idx": "TopK_Idx" }, "outputs": { "out": "Out" } }, "rank_attention": { "phi_name": "rank_attention", "inputs": { "x": "X", "rank_offset": "RankOffset", "rank_param": "RankParam" }, "outputs": { "input_help": "InputHelp", "out": "Out", "ins_rank": "InsRank" }, "attrs": { "max_rank": "MaxRank", "max_size": "MaxSize" } }, "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" } }, "sequence_conv": { "phi_name": "sequence_conv", "inputs": { "x": "X", "padding_data": "PaddingData", "filter": "Filter" }, "outputs": { "out": "Out" }, "attrs": { "padding_trainable": "paddingTrainable", "context_length": "contextLength", "context_start": "contextStart", "context_stride": "contextStride" } }, "sequence_pool": { "phi_name": "sequence_pool", "inputs": { "x": "X" }, "outputs": { "out": "Out", "max_index": "MaxIndex" } }, "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" } } }, "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_attention": { "phi_name": "sparse_attention", "inputs": { "q": "Q", "k": "K", "v": "V", "offset": "Offset", "columns": "Columns", "key_padding_mask": "KeyPaddingMask", "attn_mask": "AttnMask" }, "outputs": { "out": "Out", "sparse_dot_sdd": "SparseDotSdd", "softmax": "Softmax" } }, "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" }, "attrs": { "axis": "axis" }, "scalar": { "axis": { "data_type": "int", "tensor_name": "Axis" } } }, "squared_l2_norm": { "phi_name": "squared_l2_norm", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "stft": { "phi_name": "stft", "inputs": { "x": "X", "window": "Window" }, "outputs": { "out": "Out" } }, "c_sync_calc_stream": { "phi_name": "sync_calc_stream", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "c_sync_comm_stream": { "phi_name": "sync_comm_stream", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "temporal_shift": { "phi_name": "temporal_shift", "inputs": { "x": "X" }, "outputs": { "out": "Out" } }, "transfer_layout": { "phi_name": "transfer_layout", "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)" }, "accuracy_check": { "args": "Tensor x, Tensor y, str fn_name, double rtol=1e-5, double atol=1e-8, bool equal_nan=false", "output": "Tensor(out)" }, "acos": { "args": "Tensor x", "output": "Tensor(out)" }, "acosh": { "args": "Tensor x", "output": "Tensor(out)" }, "adadelta_": { "args": "Tensor param, Tensor grad, Tensor avg_squared_grad, Tensor avg_squared_update, Tensor learning_rate, Tensor master_param, float rho = 0.95f, float epsilon = 1.0e-6f, bool multi_precision = false", "output": "Tensor(param_out), Tensor(moment_out), Tensor(inf_norm_out), Tensor(master_param_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 moment2_max, 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, bool amsgrad = false", "output": "Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(moment2_max_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 moment2_max, 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, bool amsgrad = false", "output": "Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(moment2_max_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out)" }, "add_position_encoding": { "args": "Tensor x, float alpha = 1.0f, float beta = 1.0f", "output": "Tensor (out)" }, "addmm": { "args": "Tensor input, Tensor x, Tensor y, float beta=1.0, float alpha=1.0", "output": "Tensor(out)" }, "affine_channel": { "args": "Tensor x, Tensor scale, Tensor bias, str data_layout = \"AnyLayout\"", "output": "Tensor (out)" }, "affine_grid": { "args": "Tensor input, IntArray output_shape={}, bool align_corners=true", "output": "Tensor(output)" }, "all": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "all_gather": { "args": "Tensor x, int ring_id = 0, int nranks=0", "output": "Tensor(out)" }, "all_reduce": { "args": "Tensor x, int ring_id = 0, int reduce_type = 0", "output": "Tensor(out)" }, "all_to_all": { "args": "Tensor x, int ring_id = 0", "output": "Tensor(out)" }, "allclose": { "args": "Tensor x, Tensor y, Scalar(double) rtol=1e-5, Scalar(double) atol=1e-8, bool equal_nan=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)" }, "angle": { "args": "Tensor x", "output": "Tensor" }, "any": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "ap_facade": { "args": "Tensor[] xs, int64_t num_outputs, str custom_op_name, str infer_meta_func_name, str infer_symbolic_func_name, str serialized_attributes", "output": "Tensor[](out){num_outputs}" }, "ap_trivial_fusion_begin": { "args": "Tensor[] xs", "output": "Tensor(out)" }, "ap_trivial_fusion_end": { "args": "Tensor[] xs", "output": "Tensor(out)" }, "ap_variadic": { "args": "Tensor[] xs, int num_outputs, str code_module_lambda, str infer_symbolic_lambda, str infer_meta_lambda, str rnel_dispatch_lambda, str kernel_dispatch_const_data_lambda", "output": "Tensor[](out){num_outputs}" }, "apply_per_channel_scale": { "args": "Tensor x, Tensor scales", "output": "Tensor(out)" }, "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, bool stable=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" }, "asgd_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor d, Tensor y, Tensor n, Tensor master_param, bool multi_precision=false", "output": "Tensor(param_out), Tensor(d_out), Tensor(y_out), Tensor(master_param_out)" }, "asin": { "args": "Tensor x", "output": "Tensor(out)" }, "asinh": { "args": "Tensor x", "output": "Tensor(out)" }, "assign_out_": { "args": "Tensor x, Tensor output", "output": "Tensor(out)" }, "assign_pos": { "args": "Tensor x, Tensor cum_count, Tensor eff_num_len", "output": "Tensor(out)" }, "assign_value_": { "args": "Tensor output, int[] shape, DataType dtype, Scalar[] values, Place place = {}", "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)" }, "attention_lstm": { "args": "Tensor x, Tensor c0, Tensor h0, Tensor attention_weight, Tensor attention_bias, Tensor attention_scalar, Tensor attention_scalar_bias, Tensor lstm_weight, Tensor lstm_bias, str gate_activation = \"sigmoid\", str cell_activation = \"tanh\", str candidate_activation = \"tanh\"", "output": "Tensor (hidden), Tensor (cell), Tensor (attentioned_x), Tensor (attention_fc_out), Tensor (lstm_x), Tensor (lstm_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)" }, "baddbmm": { "args": "Tensor input, Tensor x, Tensor y, float beta=1.0, float alpha=1.0", "output": "Tensor(out)" }, "barrier": { "args": "Tensor x, int ring_id=0", "output": "Tensor(out)" }, "batch_fc": { "args": "Tensor input, Tensor w, Tensor bias", "output": "Tensor(out)" }, "bce_loss": { "args": "Tensor input, Tensor label", "output": "Tensor(out)" }, "beam_search": { "args": "Tensor pre_ids, Tensor pre_scores, Tensor ids, Tensor scores, int level, int beam_size, int end_id, bool is_accumulated = true", "output": "Tensor (selected_ids), Tensor (selected_scores), Tensor (parent_idx)" }, "bernoulli": { "args": "Tensor x", "output": "Tensor(out)" }, "bicubic_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] 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_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] 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)" }, "binomial": { "args": "Tensor count, Tensor prob", "output": "Tensor(out)" }, "bipartite_match": { "args": "Tensor dist_mat, str match_type = \"bipartite\", float dist_threshold = 0.5", "output": "Tensor (col_to_row_match_indices), Tensor (col_to_row_match_dist)" }, "bitwise_and": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bitwise_left_shift": { "args": "Tensor x, Tensor y, bool is_arithmetic = true", "output": "Tensor(out)" }, "bitwise_not": { "args": "Tensor x", "output": "Tensor(out)" }, "bitwise_or": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bitwise_right_shift": { "args": "Tensor x, Tensor y, bool is_arithmetic = true", "output": "Tensor(out)" }, "bitwise_xor": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "bmm": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "box_clip": { "args": "Tensor input, Tensor im_info", "output": "Tensor (output)" }, "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": { "args": "Tensor x, int ring_id = 0, int root = 0", "output": "Tensor(out)" }, "broadcast_tensors": { "args": "Tensor[] input", "output": "Tensor[]{input.size()}" }, "build_src_rank_and_local_expert_id": { "args": "Tensor expert_num_global_tensor, int64_t[] expert_num_global, int64_t num_local_experts", "output": "Tensor(vector), Tensor(local_expert_id)" }, "c_allreduce_sum": { "args": "Tensor x, int ring_id, bool use_calc_stream, bool use_model_parallel", "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_identity": { "args": "Tensor x, int ring_id, bool use_calc_stream, bool use_model_parallel", "output": "Tensor(out)" }, "c_scatter": { "args": "Tensor x, int ring_id = 0, int root = 0, int nranks = 0, bool use_calc_stream = false", "output": "Tensor(out)" }, "c_softmax_with_cross_entropy": { "args": "Tensor logits, Tensor label, int64_t ignore_index=-100, int ring_id=0, int rank=0, int nranks=0", "output": "Tensor(softmax), Tensor(loss)" }, "c_split": { "args": "Tensor x, int rank = 0, int nranks = 1, int ring_id = 0, bool use_model_parallel = true", "output": "Tensor(out)" }, "cal_aux_loss": { "args": "Tensor gate_prob, Tensor dispatch_mask, Tensor tokens_mask, Tensor dispatch_tokens_mask, int64_t num_experts, bool use_group, int64_t moe_k, float clip_min", "output": "Tensor(l_aux_loss), Tensor(seqlen_float), Tensor(ce)" }, "calc_reduced_attn_scores": { "args": "Tensor q, Tensor k, Tensor softmax_lse", "output": "Tensor(reduced_scores)" }, "cast": { "args": "Tensor x, DataType dtype", "output": "Tensor(out)" }, "ceil": { "args": "Tensor x", "output": "Tensor(out)" }, "celu": { "args": "Tensor x, float alpha = 1.0", "output": "Tensor(out)" }, "channel_shuffle": { "args": "Tensor x, int groups, str data_format=\"NCHW\"", "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)" }, "collect_fpn_proposals": { "args": "Tensor[] multi_level_rois, Tensor[] multi_level_scores, Tensor[] multi_level_rois_num, int post_nms_topn", "output": "Tensor (fpn_rois), Tensor (rois_num)" }, "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" }, "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)" }, "conv2d_transpose_bias": { "args": "Tensor x, Tensor filter, Tensor bias, 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)" }, "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)" }, "copy_to": { "args": "Tensor x, Place place, bool blocking", "output": "Tensor(out)" }, "copysign": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "correlation": { "args": "Tensor input1, Tensor input2, int pad_size, int kernel_size, int max_displacement, int stride1, int stride2, int corr_type_multiply=1", "output": "Tensor(out)" }, "cos": { "args": "Tensor x", "output": "Tensor(out)" }, "cosh": { "args": "Tensor x", "output": "Tensor(out)" }, "crf_decoding": { "args": "Tensor emission, Tensor transition, Tensor label, Tensor length", "output": "Tensor (viterbi_path)" }, "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)" }, "cross_entropy_with_softmax_bwd_w_downcast": { "args": "Tensor label, Tensor softmax, Tensor loss_grad", "output": "Tensor(input_grad)" }, "ctc_align": { "args": "Tensor input, Tensor input_length, int blank = 0, bool merge_repeated = true, int padding_value = 0", "output": "Tensor (output), Tensor (output_length)" }, "cudnn_lstm": { "args": "Tensor x, Tensor init_h, Tensor init_c, Tensor w, Tensor[] weight_list, Tensor sequence_length, float dropout_prob = 0.0, bool is_bidirec = false, int hidden_size = 100, int num_layers = 1, bool is_test = false, int seed = 0", "output": "Tensor (out), Tensor (last_h), Tensor (last_c), Tensor (reserve), Tensor (state_out)" }, "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, bool exclusive=false, bool reverse=false", "output": "Tensor(out)" }, "cumsum": { "args": "Tensor x, Scalar axis=-1, bool flatten=false, bool exclusive=false, bool reverse=false", "output": "Tensor(out)" }, "cvm": { "args": "Tensor x, Tensor cvm, bool use_cvm = true", "output": "Tensor (out)" }, "data": { "args": "str name, IntArray shape, DataType dtype, Place place", "output": "Tensor(out)" }, "decayed_adagrad": { "args": "Tensor param, Tensor grad, Tensor moment, Tensor learning_rate, float decay = 0.95f, float epsilon = 1.0e-6f", "output": "Tensor(param_out), Tensor(moment_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)" }, "depend": { "args": "Tensor x, Tensor[] dep", "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)" }, "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)" }, "dequantize_abs_max": { "args": "Tensor x, Tensor scale, float max_range", "output": "Tensor(out)" }, "dequantize_log": { "args": "Tensor x, Tensor dict", "output": "Tensor(out)" }, "det": { "args": "Tensor x", "output": "Tensor" }, "dgc": { "args": "Tensor u, Tensor v, Tensor grad, Tensor param, Tensor current_step, Tensor nranks, float m=0.9, bool use_nesterov=true, float[] sparsity={}, float rampup_begin_step=0.0, float rampup_step=0.0, float regular_coeff=0.0, int regular_type=0", "output": "Tensor(u_out), Tensor(v_out), Tensor(encode_grad), Tensor(grad_out), Tensor(k), Tensor(gather_buff)" }, "dgc_clip_by_norm": { "args": "Tensor x, Tensor current_step, float max_norm, float rampup_begin_step = -1.0", "output": "Tensor(out)" }, "dgc_momentum": { "args": "Tensor param, Tensor grad, Tensor velocity, Tensor learning_rate, Tensor master_param, Tensor current_step_tensor, Tensor nranks_tensor, float mu, bool use_nesterov = false, str regularization_method = \"\", float regularization_coeff = 0.0f, bool multi_precision = false, float rescale_grad = 1.0f, float rampup_begin_step = -1.0", "output": "Tensor (param_out), Tensor (velocity_out), Tensor (master_param_out), Tensor (grad_out)" }, "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)" }, "disable_check_model_nan_inf": { "args": "Tensor x, int flag = 0", "output": "Tensor(out)" }, "dist": { "args": "Tensor x, Tensor y, float p = 2.0", "output": "Tensor" }, "dot": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "dpsgd": { "args": "Tensor param, Tensor grad, Tensor learning_rate, float clip = 10.0f, float batch_size = 16.0f, float sigma = 1.0f, int seed = 0", "output": "Tensor(param_out)" }, "dropout": { "args": "Tensor x, Tensor seed_tensor, Scalar p = 0.5f, bool is_test = false, str mode = \"downgrade_in_infer\", int seed = 0, bool fix_seed = false", "output": "Tensor(out), Tensor(mask)" }, "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)" }, "embedding_grad_add_to": { "args": "Tensor token_indices, Tensor main_grad_, Tensor out_grad", "output": "Tensor(main_grad_out)" }, "embedding_with_scaled_gradient": { "args": "Tensor x, Tensor weight, int64_t padding_idx=-1", "output": "Tensor" }, "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_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, int64_t[] target_shape = {}", "output": "Tensor(out)" }, "expand_modality_expert_id": { "args": "Tensor expert_id, int64_t num_expert_per_modality, int64_t group_size, int64_t modality_offset, bool is_group_expert", "output": "Tensor(expert_id_out)" }, "expm1": { "args": "Tensor x", "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)" }, "fake_channel_wise_dequantize_max_abs": { "args": "Tensor x, Tensor[] scales, int[] quant_bits = {8}, int quant_axis = 0, int x_num_col_dims = 1", "output": "Tensor(out)" }, "fake_channel_wise_quantize_abs_max": { "args": "Tensor x, int bit_length = 8, int round_type = 1, int quant_axis = 0, bool is_test = false", "output": "Tensor(out), Tensor(out_scale)" }, "fake_channel_wise_quantize_dequantize_abs_max": { "args": "Tensor x, int bit_length = 8, int round_type = 1, int quant_axis = 0", "output": "Tensor(out), Tensor(out_scale)" }, "fake_dequantize_max_abs": { "args": "Tensor x, Tensor scale, float max_range", "output": "Tensor(out)" }, "fake_quantize_abs_max": { "args": "Tensor x, int bit_length = 8, int round_type = 1", "output": "Tensor(out), Tensor(out_scale)" }, "fake_quantize_dequantize_abs_max": { "args": "Tensor x, int bit_length = 8, int round_type = 1", "output": "Tensor(out), Tensor(out_scale)" }, "fake_quantize_dequantize_moving_average_abs_max": { "args": "Tensor x, Tensor in_scale, Tensor in_accum, Tensor in_state, float moving_rate = 0.9, int bit_length = 8, bool is_test = false, int round_type = 1", "output": "Tensor(out), Tensor(out_scale), Tensor(out_state), Tensor(out_accum)" }, "fake_quantize_moving_average_abs_max": { "args": "Tensor x, Tensor in_scale, Tensor in_accum, Tensor in_state, float moving_rate = 0.9, int bit_length = 8, bool is_test = false, int round_type = 1", "output": "Tensor(out), Tensor(out_scale), Tensor(out_state), Tensor(out_accum)" }, "fake_quantize_range_abs_max": { "args": "Tensor x, Tensor in_scale, Tensor iter, int window_size = 10000, int bit_length = 8, bool is_test = false, int round_type = 1", "output": "Tensor(out), Tensor(out_scale), Tensor(out_scales)" }, "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(double) 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_qkvpacked": { "args": "Tensor qkv, 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, Scalar max_seqlen_q, Scalar 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)" }, "flash_attn_v3": { "args": "Tensor q, Tensor k, Tensor v, Tensor q_v_, Tensor q_descale_, Tensor k_descale_, Tensor v_descale_, float softmax_scale, bool is_causal, int window_size_left, int window_size_right, float softcap, int num_splits, bool manual_set_pack_gqa, bool pack_gqa_, int sm_margin", "output": "Tensor(out), Tensor(softmax_lse)" }, "flash_attn_v3_varlen": { "args": "Tensor q, Tensor k, Tensor v, Tensor cu_seqlens_q, Tensor cu_seqlens_k, Tensor seqused_q, Tensor seqused_k, Tensor qv, Tensor q_descale, Tensor k_descale, Tensor v_descale, Scalar max_seqlen_q, Scalar max_seqlen_k, float softmax_scale, bool causal, int window_size_left, int window_size_right, float softcap, int num_splits, bool manual_set_pack_gqa, bool pack_gqa, int sm_margin", "output": "Tensor(out), Tensor(softmax_lse)" }, "flash_attn_varlen_qkvpacked": { "args": "Tensor qkv, Tensor cu_seqlens_q, Tensor cu_seqlens_k, Tensor fixed_seed_offset, Tensor attn_mask, Scalar max_seqlen_q, Scalar max_seqlen_k, float scale, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = \"\", bool varlen_padded = true", "output": "Tensor(out), Tensor(softmax), Tensor(softmax_lse), Tensor(seed_offset)" }, "flashmask_attention": { "args": "Tensor q, Tensor k, Tensor v, Tensor startend_row_indices, Tensor fixed_seed_offset, 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)" }, "flashmask_attention_v2": { "args": "Tensor q, Tensor k, Tensor v, Tensor startend_row_indices, Tensor block_mask, float softmax_scale, bool is_causal", "output": "Tensor(out), Tensor(softmax_lse)" }, "flatten": { "args": "Tensor x, int start_axis = 1, int stop_axis = 1", "output": "Tensor(out)" }, "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)" }, "fractional_max_pool2d": { "args": "Tensor x, int[] output_size, int[] kernel_size = {0, 0}, float random_u = 0.0, bool return_mask = true", "output": "Tensor(out), Tensor(mask)" }, "fractional_max_pool3d": { "args": "Tensor x, int[] output_size, int[] kernel_size = {0, 0, 0}, float random_u = 0.0, bool return_mask = true", "output": "Tensor(out), Tensor(mask)" }, "frame": { "args": "Tensor x, int frame_length, int hop_length, int axis=-1", "output": "Tensor(out)" }, "frobenius_norm": { "args": "Tensor x, IntArray axis, bool keep_dim, bool reduce_all", "output": "Tensor(out)" }, "ftrl": { "args": "Tensor param, Tensor squared_accumulator, Tensor linear_accumulator, Tensor grad, Tensor learning_rate, float l1=0.0f, float l2=0.0f, float lr_power=-0.5f", "output": "Tensor(param_out), Tensor(squared_accum_out), Tensor(linear_accum_out)" }, "full": { "args": "IntArray shape, Scalar(double) value, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_": { "args": "Tensor output, IntArray shape, Scalar(double) value, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_batch_size_like": { "args": "Tensor input, int[] shape, DataType dtype, Scalar(double) value, int input_dim_idx, int output_dim_idx, Place place=CPUPlace()", "output": "Tensor(out)" }, "full_int_array": { "args": "int64_t[] value, DataType dtype=DataType::FLOAT32, 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 value, IntArray shape, DataType dtype=DataType::FLOAT32", "output": "Tensor(out)" }, "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_rms_norm_quant": { "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), Tensor(inv_var)" }, "fused_softmax_mask": { "args": "Tensor x, Tensor mask", "output": "Tensor(out)" }, "fused_softmax_mask_upper_triangle": { "args": "Tensor X", "output": "Tensor(Out)" }, "gammaincc": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "gammaln": { "args": "Tensor x", "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": { "args": "IntArray shape, float mean, float std, int seed, DataType dtype, Place place={}", "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)" }, "global_gather": { "args": "Tensor x, Tensor local_count, Tensor global_count, int ring_id = 0", "output": "Tensor(out)" }, "global_scatter": { "args": "Tensor x, Tensor local_count, Tensor global_count, int ring_id = 0", "output": "Tensor(out)" }, "graph_khop_sampler": { "args": "Tensor row, Tensor colptr, Tensor x, Tensor eids, int[] sample_sizes, bool return_eids", "output": "Tensor(out_src), Tensor(out_dst), Tensor(sample_index), Tensor(reindex_x), Tensor(out_eids)" }, "graph_sample_neighbors": { "args": "Tensor row, Tensor colptr, Tensor x, Tensor eids, Tensor perm_buffer, int sample_size, bool return_eids, bool flag_perm_buffer", "output": "Tensor(out), Tensor(out_count), Tensor(out_eids)" }, "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_format = \"NCHW\"", "output": "Tensor(y), Tensor(mean), Tensor(variance)" }, "gru": { "args": "Tensor input, Tensor h0, Tensor weight, Tensor bias, str activation = \"tanh\", str gate_activation = \"sigmoid\", bool is_reverse = false, bool origin_mode = false, bool is_test=false", "output": "Tensor (batch_gate), Tensor (batch_reset_hidden_prev), Tensor (batch_hidden), Tensor (hidden)" }, "gru_unit": { "args": "Tensor input, Tensor hidden_prev, Tensor weight, Tensor bias, int activation = 2, int gate_activation = 1, bool origin_mode = false", "output": "Tensor (gate), Tensor (reset_hidden_prev), Tensor (hidden)" }, "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)" }, "hinge_loss": { "args": "Tensor logits, Tensor labels", "output": "Tensor (loss)" }, "histogram": { "args": "Tensor input, Tensor weight, int64_t bins = 100, float min = 0.0, float max = 0.0, bool density = false", "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)" }, "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)" }, "identity_loss": { "args": "Tensor x, int reduction = 1", "output": "Tensor(out)" }, "im2sequence": { "args": "Tensor x, Tensor y, int[] kernels, int[] strides = {1, 1}, int[] paddings = {0, 0, 0, 0}, int[] out_stride = {1, 1}", "output": "Tensor (out)" }, "imag": { "args": "Tensor x", "output": "Tensor (out)" }, "increment": { "args": "Tensor x, float value = 1.0", "output": "Tensor(out)" }, "index_add": { "args": "Tensor x, Tensor index, Tensor add_value, int axis = 0", "output": "Tensor(out)" }, "index_elementwise_get": { "args": "Tensor x, Tensor[] index, int64_t[] input_dims, int64_t[] input_strides, int64_t[] index_dims, int64_t[] index_stride, int64_t slice_offset = 0, bool accumulate = true, bool is_combined = false", "output": "Tensor (out)" }, "index_elementwise_put": { "args": "Tensor x, Tensor[] index, Scalar value, int64_t[] input_dims, int64_t[] input_strides, int64_t[] index_dims, int64_t[] index_strides, int64_t slice_offset", "output": "Tensor (out)" }, "index_elementwise_put_with_tensor": { "args": "Tensor x, Tensor[] index, Tensor value, int64_t[] input_dims, int64_t[] input_strides, int64_t[] index_dims, int64_t[] index_strides, int64_t slice_offset", "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)" }, "interp_antialias": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "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\", bool log_target = false", "output": "Tensor(out)" }, "kron": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "kthvalue": { "args": "Tensor x, int64_t k = 1, int axis = -1, bool keepdim = false", "output": "Tensor(out), Tensor(indices)" }, "l1_norm": { "args": "Tensor x", "output": "Tensor(out)" }, "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, double negative_slope = 0.02", "output": "Tensor(out)" }, "lerp": { "args": "Tensor x, Tensor y, Tensor weight", "output": "Tensor(out)" }, "lgamma": { "args": "Tensor x", "output": "Tensor(out)" }, "limit_by_capacity": { "args": "Tensor expert_count, Tensor capacity, int n_worker", "output": "Tensor(out)" }, "linear_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "linspace": { "args": "Tensor start, Tensor stop, Tensor number, DataType dtype, Place place", "output": "Tensor(out)" }, "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, double eps = 1e-6", "output": "Tensor(out)" }, "logsigmoid": { "args": "Tensor x", "output": "Tensor" }, "logspace": { "args": "Tensor start, Tensor stop, Tensor num, Tensor base, DataType dtype, Place place={}", "output": "Tensor(out)" }, "logsumexp": { "args": "Tensor x, int[] axis={}, bool keepdim=false, bool reduce_all=false", "output": "Tensor(out)" }, "lookup_table_dequant": { "args": "Tensor w, Tensor ids, int64_t padding_idx = -1", "output": "Tensor (out)" }, "lp_pool2d": { "args": "Tensor x, IntArray kernel_size, int64_t[] strides = {1,1}, int64_t[] paddings = {0,0}, bool ceil_mode = false, bool exclusive = true, str data_format = \"NCHW\", str pooling_type = \"\", bool global_pooling = false, bool adaptive = false, str padding_algorithm = \"EXPLICIT\", float norm_type = 0.0f", "output": "Tensor(out)" }, "lstm": { "args": "Tensor input, Tensor h0, Tensor c0, Tensor weight, Tensor bias, bool use_peepholes = true, bool is_reverse = false, bool is_test = false, str gate_activation = \"sigmoid\", str cell_activation = \"tanh\", str candidate_activation = \"tanh\"", "output": "Tensor (hidden), Tensor (cell), Tensor (batch_gate), Tensor (batch_cell_pre_act)" }, "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_solve": { "args": "Tensor b, Tensor lu, Tensor pivots, str trans", "output": "Tensor(out)" }, "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_fill": { "args": "Tensor x, Tensor mask, Tensor value", "output": "Tensor (out)" }, "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)" }, "match_matrix_tensor": { "args": "Tensor x, Tensor y, Tensor w, int dim_t = 1", "output": "Tensor (out), Tensor (tmp)" }, "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" }, "matrix_rank": { "args": "Tensor x, float tol, bool use_default_tol=true, bool hermitian=false", "output": "Tensor(out)" }, "matrix_rank_atol_rtol": { "args": "Tensor x, Tensor atol, Tensor rtol, 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)" }, "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, bool ceil_mode = 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, bool ceil_mode = false", "output": "Tensor(out), Tensor(mask)" }, "max_with_index": { "args": "Tensor x, Scalar(int64_t) dim, bool keepdim = false, bool flatten = false", "output": "Tensor(values), Tensor(indices)" }, "maxout": { "args": "Tensor x, int groups, int axis = 1", "output": "Tensor(out)" }, "mean": { "args": "Tensor x, IntArray axis={}, bool keepdim=false", "output": "Tensor(out)" }, "mean_all": { "args": "Tensor x", "output": "Tensor" }, "median": { "args": "Tensor x, IntArray axis = {}, bool keepdim = true, str mode=\"avg\"", "output": "Tensor(out), Tensor(medians)" }, "memcpy_d2h": { "args": "Tensor x, int dst_place_type", "output": "Tensor" }, "memcpy_h2d": { "args": "Tensor x, int dst_place_type", "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[] moment2_max, 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, bool amsgrad = false", "output": "Tensor[](param_out){param.size()}, Tensor[](moment1_out){param.size()}, Tensor[](moment2_out){param.size()}, Tensor[](moment2_max_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[](out){inputs.size()}" }, "min_with_index": { "args": "Tensor x, Scalar(int64_t) dim, bool keepdim = false, bool flatten = false", "output": "Tensor(values), Tensor(indices)" }, "mish": { "args": "Tensor x, float lambda", "output": "Tensor" }, "mode": { "args": "Tensor x, int axis = -1, bool keepdim = false", "output": "Tensor(out), Tensor(indices)" }, "moe_combine": { "args": "Tensor x, Tensor combine_weights, Tensor scatter_index", "output": "Tensor(y)" }, "moe_combine_auto": { "args": "Tensor x, Tensor combine_weights, Tensor scatter_index", "output": "Tensor(y)" }, "moe_combine_no_weight": { "args": "Tensor x, Tensor combine_weight, Tensor scatter_index, float epsilon = 1.0e-15", "output": "Tensor(y)" }, "moe_gate_dispatch": { "args": "Tensor x, Tensor gate_logits, Tensor corr_bias, int64_t k, int64_t capacity, bool use_pad", "output": "Tensor(y), Tensor(combine_weights), Tensor(scatter_index), Tensor(expert_offset), Tensor(expert_id)" }, "moe_gate_dispatch_and_quant": { "args": "Tensor x, Tensor gate_logits, Tensor corr_bias, int64_t k, int64_t capacity, bool use_pad, bool use_pow2_scale", "output": "Tensor(out_fp8), Tensor(scale), Tensor(combine_weights), Tensor(scatter_index), Tensor(expert_offset), Tensor(expert_id)" }, "moe_gate_dispatch_auto": { "args": "Tensor x, Tensor gate_logits, Tensor corr_bias, int64_t k, int64_t capacity, bool use_pad", "output": "Tensor(y), Tensor(combine_weights), Tensor(scatter_index), Tensor(expert_offset), Tensor(expert_id)" }, "moe_gate_dispatch_partial_nosoftmaxtopk": { "args": "Tensor x, Tensor combine_weights, Tensor expert_id, int64_t k, int64_t capacity, int64_t num_experts, bool use_pad, int64_t expert_start_index, int64_t expert_end_index, bool reverse_token_drop", "output": "Tensor(y), Tensor(combine_weights_out), Tensor(scatter_index), Tensor(scatter_index_rev), Tensor(expert_offset), Tensor(expert_nums_local)" }, "moe_gate_dispatch_permute": { "args": "Tensor x, Tensor gate_logits, Tensor corr_bias, int64_t k, int64_t capacity, int64_t world_size", "output": "Tensor(y), Tensor(combine_weights), Tensor(scatter_index), Tensor(expert_offset), Tensor(expert_id)" }, "moe_permute": { "args": "Tensor hidden_states, Tensor scale, Tensor expert_routemap_topk, Tensor expert_prob_topk, int num_experts, int[] tokens_per_expert, int padding_alignment, bool do_gather", "output": "Tensor(hidden_states_unzipped), Tensor(zipped_expertwise_rowmap), Tensor(token_prob_unzipped), Tensor(scale_unzipped)" }, "moe_unpermute": { "args": "Tensor hidden_states_unzipped, Tensor zipped_expertwise_rowmap, Tensor expert_routemap_topk, Tensor token_prob_unzipped, int total_zipped_tokens_num, int num_experts, bool use_mix_precision", "output": "Tensor(hidden_states), Tensor(expert_prob_topk)" }, "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)" }, "mp_allreduce_sum": { "args": "Tensor x, int ring_id = 0", "output": "Tensor(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" }, "nadam_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor momentum_decay_pow, Tensor beta2_pow, Tensor mu_product, Tensor moment1, Tensor moment2, Tensor master_param, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1.0e-8f, float momentum_decay = 0.004f, bool multi_precision = false", "output": "Tensor(param_out), Tensor(momentum_decay_pow_out), Tensor(beta2_pow_out), Tensor(mu_product_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(master_param_out)" }, "nanmedian": { "args": "Tensor x, IntArray axis = {}, bool keepdim = true, str mode=\"avg\"", "output": "Tensor(out), Tensor(medians)" }, "nearest_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] 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)" }, "norm": { "args": "Tensor x, int axis, float epsilon, bool is_test", "output": "Tensor(out), Tensor(norm)" }, "npu_identity": { "args": "Tensor x, int format = -1", "output": "Tensor" }, "numel": { "args": "Tensor x", "output": "Tensor(size)" }, "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)" }, "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)" }, "pad": { "args": "Tensor x, int[] paddings, Scalar pad_value", "output": "Tensor" }, "pad3d": { "args": "Tensor x, IntArray paddings, str mode = \"constant\", double pad_value = 0.0, str data_format = \"NCDHW\"", "output": "Tensor(out)" }, "partial_allgather": { "args": "Tensor x, int nranks, int rank, int ring_id = 0", "output": "Tensor(out)" }, "partial_concat": { "args": "Tensor[] x, int start_index = 0, int length = -1", "output": "Tensor(out)" }, "partial_sum": { "args": "Tensor[] x, int start_index = 0, int length = -1", "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)" }, "pool2d": { "args": "Tensor x, IntArray kernel_size, int64_t[] strides, int64_t[] 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, int64_t[] kernel_size, int64_t[] strides, int64_t[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm", "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)" }, "prod": { "args": "Tensor x, IntArray axis, bool keepdim, bool reduce_all", "output": "Tensor" }, "prune_gate_by_capacity": { "args": "Tensor gate_idx, Tensor expert_count, int64_t n_expert=0, int64_t n_worker=0", "output": "Tensor(out_gate_idx)" }, "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)" }, "pyramid_hash": { "args": "Tensor x, Tensor w, Tensor white_list, Tensor black_list, int num_emb = 0, int space_len = 0, int pyramid_layer = 2, int rand_len = 0, float drop_out_percent = 0, int is_training = 0, bool use_filter = true, int white_list_len = 0, int black_list_len = 0, int seed = 0, float lr = 0.0, str distribute_update_vars = \"\"", "output": "Tensor (out), Tensor (drop_pos), Tensor (x_temp_out)" }, "qr": { "args": "Tensor x, str mode = \"reduced\"", "output": "Tensor(q), Tensor(r)" }, "radam_": { "args": "Tensor param, Tensor grad, Tensor learning_rate, Tensor beta1_pow, Tensor beta2_pow, Tensor rho, Tensor moment1, Tensor moment2, 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(beta1_pow_out), Tensor(beta2_pow_out), Tensor(rho_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(master_param_out)" }, "randint": { "args": "int low, int high, IntArray shape, DataType dtype=DataType::INT64, Place place={}", "output": "Tensor(out)" }, "random": { "args": "Tensor x, int64_t from, int64_t to", "output": "Tensor(out)" }, "random_routing": { "args": "Tensor prob, Tensor topk_value, Tensor topk_idx", "output": "Tensor(out)" }, "randperm": { "args": "int n, DataType dtype, Place place={}", "output": "Tensor(out)" }, "rank_attention": { "args": "Tensor x, Tensor rank_offset, Tensor rank_param, int max_rank = 3, int max_size = 0", "output": "Tensor(input_help), Tensor(out), Tensor(ins_rank)" }, "read_file": { "args": "str filename = \"\", DataType dtype=DataType::UINT8, Place place=CPUPlace()", "output": "Tensor(out)" }, "real": { "args": "Tensor x", "output": "Tensor (out)" }, "reciprocal": { "args": "Tensor x", "output": "Tensor(out)" }, "reduce": { "args": "Tensor x, int ring_id = 0, int root_id = 0, int reduce_type = 0", "output": "Tensor(out)" }, "reduce_as": { "args": "Tensor x, Tensor target", "output": "Tensor(out)" }, "reduce_scatter": { "args": "Tensor x, int ring_id = 0, int nranks = 1", "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)" }, "repeat_interleave": { "args": "Tensor x, int repeats, int axis, int64_t output_size = -1", "output": "Tensor(out)" }, "repeat_interleave_with_tensor_index": { "args": "Tensor x, Tensor repeats, int axis, int64_t output_size = -1", "output": "Tensor(out)" }, "reshape": { "args": "Tensor x, IntArray shape", "output": "Tensor(out)" }, "restrict_nonzero": { "args": "Tensor condition, int64_t total_true_num", "output": "Tensor (out)" }, "reverse": { "args": "Tensor x, IntArray axis", "output": "Tensor" }, "rint": { "args": "Tensor x", "output": "Tensor(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)" }, "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)" }, "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, int decimals = 0 ", "output": "Tensor(out)" }, "rprop_": { "args": "Tensor param, Tensor grad, Tensor prev, Tensor learning_rate, Tensor master_param, Tensor learning_rate_range, Tensor etas, bool multi_precision=false", "output": "Tensor(param_out), Tensor(prev_out), Tensor(learning_rate_out), Tensor(master_param_out)" }, "rrelu": { "args": "Tensor x, float lower=1.0f/8, float upper=1.0f/3, bool is_test=false", "output": "Tensor(out), Tensor(noise)" }, "rsqrt": { "args": "Tensor x", "output": "Tensor(out)" }, "scale": { "args": "Tensor x, Scalar scale=1.0, Scalar 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)" }, "sequence_conv": { "args": "Tensor x, Tensor padding_data, Tensor filter, int context_length, bool padding_trainable = false, int context_start = 0, int context_stride = 1", "output": "Tensor (out)" }, "sequence_mask": { "args": "Tensor x, Scalar(int) max_len, DataType out_dtype", "output": "Tensor(y)" }, "sequence_pool": { "args": "Tensor x, bool is_test=false, str pooltype = \"AVERAGE\", float pad_value = 0.0", "output": "Tensor (out), Tensor (max_index)" }, "set": { "args": "Tensor x, Tensor source, int64_t[] dims = {}, int64_t[] stride = {}, int64_t offset = 0", "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)" }, "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)" }, "shape64": { "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)" }, "share_data": { "args": "Tensor x", "output": "Tensor (out)" }, "shuffle_batch": { "args": "Tensor x, Tensor seed, int startup_seed=0", "output": "Tensor(out), Tensor(shuffle_idx), Tensor(seed_out)" }, "shuffle_channel": { "args": "Tensor x, int group = 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(out)" }, "sin": { "args": "Tensor x", "output": "Tensor(out)" }, "sinh": { "args": "Tensor x", "output": "Tensor(out)" }, "slice": { "args": "Tensor input, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis", "output": "Tensor" }, "slogdet": { "args": "Tensor x", "output": "Tensor" }, "slogdet_v2": { "args": "Tensor x", "output": "Tensor(sign), Tensor(logdet)" }, "softplus": { "args": "Tensor x, double beta = 1.0, double threshold = 20.0", "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" }, "sparse_attention": { "args": "Tensor q, Tensor k, Tensor v, Tensor offset, Tensor columns, Tensor key_padding_mask, Tensor attn_mask", "output": "Tensor (out), Tensor (sparse_dot_sdd), Tensor (softmax)" }, "spectral_norm": { "args": "Tensor weight, Tensor u, Tensor v, int dim = 0, int power_iters = 1, float eps = 1e-12f", "output": "Tensor" }, "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}" }, "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)" }, "stack": { "args": "Tensor[] x, int axis = 0", "output": "Tensor (out)" }, "standard_gamma": { "args": "Tensor x", "output": "Tensor(out)" }, "stanh": { "args": "Tensor x, float scale_a=0.67f, float scale_b=1.7159f", "output": "Tensor(out)" }, "stft": { "args": "Tensor x, Tensor window, int n_fft, int hop_length, bool normalized, bool onesided", "output": "Tensor (out)" }, "strided_slice": { "args": "Tensor x, int[] axes, IntArray starts, IntArray ends, IntArray strides", "output": "Tensor" }, "sum": { "args": "Tensor x, IntArray axis={}, DataType dtype=DataType::UNDEFINED, bool keepdim=false", "output": "Tensor(out)" }, "svd": { "args": "Tensor x, bool full_matrices = false", "output": "Tensor(u), Tensor(s), Tensor(vh)" }, "svdvals": { "args": "Tensor x", "output": "Tensor(s)" }, "swiglu": { "args": "Tensor x, Tensor y", "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_format, 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)" }, "sync_calc_stream": { "args": "Tensor x", "output": "Tensor(out)" }, "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" }, "tdm_child": { "args": "Tensor x, Tensor tree_info, int child_nums, DataType dtype = DataType::INT32", "output": "Tensor (child), Tensor (leaf_mask)" }, "tdm_sampler": { "args": "Tensor x, Tensor travel, Tensor layer, bool output_positive=true, int[] neg_samples_num_list={}, int[] layer_offset={}, int seed = 0, int dtype=2", "output": "Tensor(out), Tensor(labels), Tensor(mask)" }, "temporal_shift": { "args": "Tensor x, int seg_num, float shift_ratio = 0.25f, str data_format = \"NCHW\"", "output": "Tensor(out)" }, "thresholded_relu": { "args": "Tensor x, float threshold = 1.0, float value = 0.0", "output": "Tensor(out)" }, "top_p_sampling": { "args": "Tensor x, Tensor ps, Tensor threshold, Tensor topp_seed, int64_t seed=-1, int k=0, str mode=\"truncate\"", "output": "Tensor (out), Tensor(ids), Tensor(topk_scores), Tensor(topk_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" }, "trans_layout": { "args": "Tensor x, int[] perm", "output": "Tensor" }, "transpose": { "args": "Tensor x, int[] perm", "output": "Tensor(out)" }, "triangular_solve": { "args": "Tensor x, Tensor y, bool upper=true, bool transpose=false, bool unitriangular=false", "output": "Tensor" }, "tril": { "args": "Tensor x, int diagonal=0", "output": "Tensor(out)" }, "tril_indices": { "args": "int rows, int cols, int offset, DataType dtype, Place place={}", "output": "Tensor(out)" }, "trilinear_interp": { "args": "Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format=\"NCHW\", int out_d=0, int out_h=0, int out_w=0, double[] scale={}, str interp_method=\"bilinear\", bool align_corners=true, int align_mode=1", "output": "Tensor(output)" }, "triu": { "args": "Tensor x, int diagonal=0", "output": "Tensor(out)" }, "triu_indices": { "args": "int row, int col, int offset, DataType dtype, Place place={}", "output": "Tensor(out)" }, "trunc": { "args": "Tensor input", "output": "Tensor(out)" }, "trunc_divide": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "truncated_gaussian_random": { "args": "int[] shape, float mean, float std, int seed, float a, float b, DataType dtype=DataType::FLOAT32, Place place={}", "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": { "args": "IntArray shape, DataType dtype, Scalar min, Scalar max, int seed, Place place={}", "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)" }, "uniform_random_batch_size_like": { "args": "Tensor input, int[] shape, int input_dim_idx = 0, int output_dim_idx = 0, float min=-1.0f, float max=1.0f, int seed=0, int diag_num=0, int diag_step=0, float diag_val=1.0f, DataType dtype=DataType::FLOAT32", "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)" }, "unpool": { "args": "Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format", "output": "Tensor(out)" }, "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)" }, "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)" }, "variance": { "args": "Tensor x, int64_t[] axis={}, bool keepdim=false", "output": "Tensor(out)" }, "view_dtype": { "args": "Tensor input, DataType dtype", "output": "Tensor(out)" }, "view_shape": { "args": "Tensor input, int64_t[] dims = {}", "output": "Tensor(out)" }, "view_slice": { "args": "Tensor input, int64_t begin_idx, int64_t end_idx", "output": "Tensor" }, "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\", int group_size = -1", "output": "Tensor(out)" }, "weight_only_linear": { "args": "Tensor x, Tensor weight, Tensor bias, Tensor weight_scale, str weight_dtype, int arch = 80, int group_size = -1", "output": "Tensor(out)" }, "weight_quantize": { "args": "Tensor x, str algo = \"weight_only_int8\", int arch = 80, int group_size = -1", "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_box_head": { "args": "Tensor x, int[] anchors, int class_num", "output": "Tensor(out)" }, "yolo_box_post": { "args": "Tensor boxes0, Tensor boxes1, Tensor boxes2, Tensor image_shape, Tensor image_scale, int[] anchors0, int[] anchors1, int[] anchors2, int class_num, float conf_thresh, int downsample_ratio0, int downsample_ratio1, int downsample_ratio2, bool clip_bbox, float scale_x_y, float nms_threshold", "output": "Tensor(out), Tensor(nms_rois_num)" }, "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)" }, "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)" }, "batched_gemm": { "args": "Tensor lhs, Tensor rhs, int64_t[] batch_sizes, bool trans_lhs, bool trans_rhs", "output": "Tensor(output)" }, "chunk_eval": { "args": "Tensor inference, Tensor label, Tensor seq_length, int num_chunk_types, str chunk_scheme = \"IOB\", int[] excluded_chunk_types = {}", "output": "Tensor (precision), Tensor (recall), Tensor (f1_score), Tensor (num_infer_chunks), Tensor (num_label_chunks), Tensor (num_correct_chunks)" }, "fast_ln": { "args": "Tensor x, Tensor scale, Tensor bias, float epsilon", "output": "Tensor(y), Tensor(mean), Tensor(invvar)" }, "fast_rms_norm": { "args": "Tensor x, Tensor scale, float epsilon", "output": "Tensor(y), Tensor(invvar)" }, "fp8_gemm_blockwise_": { "args": "Tensor A, Tensor A_scale, Tensor B, Tensor B_scale, Tensor input_result, Tensor bias, Tensor pre_gelu, Tensor workspace, bool transa, bool transb, bool grad, bool accumulate, bool use_split_accumulator, int math_sm_count, bool is_A_1d_scaled, bool is_B_1d_scaled", "output": "Tensor (output), Tensor (pre_gelu_out), Tensor (workspace_out)" }, "fp8_quant_blockwise": { "args": "Tensor x, float epsilon, bool using_1x128_vec_quant, bool input_transpose, bool output_scale_transpose, bool return_transpose_only, bool using_e5m2, bool using_pow2_scale", "output": "Tensor(out), Tensor(scale), Tensor(out_transposed), Tensor(scale_transposed)" }, "fused_rms_norm_ext": { "args": "Tensor x, Tensor scale, float epsilon", "output": "Tensor(y), Tensor(invvar)" }, "int_bincount": { "args": "Tensor x, int64_t low, int64_t high, int64_t dtype", "output": "Tensor(out)" }, "number_count": { "args": "Tensor numbers, int upper_range", "output": "Tensor(out)" }, "add": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "add_n": { "args": "Tensor[] inputs", "output": "Tensor" }, "arange": { "args": "Tensor start, Tensor end, Tensor step, DataType dtype, Place place={}", "output": "Tensor(out)" }, "assign": { "args": "Tensor x", "output": "Tensor" }, "batch_norm": { "args": "Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_format, 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_embedding": { "args": "Tensor weight, Tensor x, int64_t start_index=0, int64_t vocab_size=-1", "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)" }, "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)" }, "equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "floor_divide": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "fused_adam_": { "args": "Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] moments2_max, 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, bool amsgrad = false", "output": "Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](moments2_max_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()}" }, "fused_gemm_epilogue": { "args": "Tensor x, Tensor y, Tensor bias, bool trans_x, bool trans_y, str activation", "output": "Tensor(out), Tensor(reserve_space)" }, "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)" }, "less_equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "less_than": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "matmul": { "args": "Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false", "output": "Tensor" }, "maximum": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "min": { "args": "Tensor x, IntArray axis={}, bool keepdim=false", "output": "Tensor(out)" }, "minimum": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "multiply": { "args": "Tensor x, Tensor y", "output": "Tensor" }, "not_equal": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "range_v2": { "args": "Tensor start, Tensor end, Tensor step, DataType dtype, Place place={}", "output": "Tensor(out)" }, "remainder": { "args": "Tensor x, Tensor y", "output": "Tensor (out)" }, "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)" }, "softmax": { "args": "Tensor x, int axis", "output": "Tensor(out)" }, "subtract": { "args": "Tensor x, Tensor y", "output": "Tensor(out)" }, "sync_comm_stream": { "args": "Tensor[] x, int ring_id = 0", "output": "Tensor[](out){x.size()}" }, "tensor_unfold": { "args": "Tensor input, int64_t axis, int64_t size, int64_t step", "output": "Tensor" }, "tile": { "args": "Tensor x, IntArray repeat_times = {}", "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)" } }