// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include #include // NOTE(zhiqiu): Commonly, the inputs in auto-generated OP function are // determined by the OP`s proto automatically, i.e., all the inputs registered // in OpMaker. // However, some OPs have dispensable inputs, which means the input can // be none for some conditions. It is discovered that most dispensable inputs // is not used in imperative mode, so we drop those inputs when generating OP // functions. While, for very few OPs, the dispensable inputs are used, we // need to manually specify them in this map. std::map> op_ins_map = { {"layer_norm", {"X", "Scale", "Bias"}}, {"bincount", {"X", "Weights"}}, {"fused_attention", {"X", "LnScale", "LnBias", "QKVW", "QKVBias", "CacheKV", "SrcMask", "OutLinearW", "OutLinearBias", "Ln2Scale", "Ln2Bias"}}, {"fused_gate_attention", {"Query", "Key", "QueryWeight", "KeyWeight", "ValueWeight", "QKVWeight", "NonbatchedBias", "SrcMask", "GateWeight", "GateBias", "OutLinearWeight", "OutLinearBias"}}, {"fused_multi_transformer", {"X", "LnScale", "LnBias", "QKVW", "QKVBias", "CacheKV", "PreCaches", "RotaryPosEmb", "TimeStep", "SeqLengths", "SrcMask", "OutLinearW", "OutLinearBias", "FFNLnScale", "FFNLnBias", "FFN1Weight", "FFN1Bias", "FFN2Weight", "FFN2Bias"}}, {"fused_multi_transformer_int8", {"X", "LnScale", "LnBias", "QKVW", "QKVBias", "CacheKV", "TimeStep", "SrcMask", "OutLinearW", "OutLinearBias", "FFNLnScale", "FFNLnBias", "FFN1Weight", "FFN1Bias", "FFN2Weight", "FFN2Bias", "QKVOutScale", "OutLinearOutScale", "FFN1OutScale", "FFN2OutScale"}}, {"fused_bias_dropout_residual_layer_norm", {"X", "Residual", "Bias", "LnScale", "LnBias"}}, {"instance_norm", {"X", "Scale", "Bias"}}, {"gru_unit", {"Input", "HiddenPrev", "Weight", "Bias"}}, {"label_smooth", {"X", "PriorDist"}}, {"assign", {"X"}}, {"crop", {"X", "Y", "Offsets"}}, {"crop_tensor", {"X", "Shape", "Offsets"}}, {"reshape2", {"X", "Shape"}}, {"expand", {"X", "ExpandTimes"}}, {"slice", {"Input", "StartsTensor", "EndsTensor", "StartsTensorList", "EndsTensorList"}}, {"strided_slice", {"Input", "StartsTensor", "EndsTensor", "StridesTensor", "StartsTensorList", "EndsTensorList", "StridesTensorList"}}, {"set_value", {"Input", "ValueTensor", "StartsTensorList", "EndsTensorList", "StepsTensorList"}}, {"fake_quantize_dequantize_moving_average_abs_max", {"X", "InScale", "InAccum", "InState"}}, {"nll_loss", {"X", "Label", "Weight"}}, {"smooth_l1_loss", {"X", "Y", "InsideWeight", "OutsideWeight"}}, {"bilinear_tensor_product", {"X", "Y", "Weight", "Bias"}}, {"gather", {"X", "Index", "Axis"}}, {"repeat_interleave", {"X", "RepeatsTensor"}}, {"roi_pool", {"X", "ROIs", "RoisNum"}}, {"roi_align", {"X", "ROIs", "RoisNum"}}, {"prroi_pool", {"X", "ROIs", "BatchRoINums"}}, {"psroi_pool", {"X", "ROIs", "RoisNum"}}, {"collect_fpn_proposals", {"MultiLevelRois", "MultiLevelScores", "MultiLevelRoIsNum"}}, {"distribute_fpn_proposals", {"FpnRois", "RoisNum"}}, {"warpctc", {"Logits", "Label", "LogitsLength", "LabelLength"}}, {"hierarchical_sigmoid", {"X", "W", "Label", "PathTable", "PathCode", "Bias"}}, {"moving_average_abs_max_scale", {"X", "InAccum", "InState"}}, {"multiclass_nms3", {"BBoxes", "Scores", "RoisNum"}}, {"box_coder", {"PriorBox", "PriorBoxVar", "TargetBox"}}, {"momentum", {"Param", "Grad", "Velocity", "LearningRate", "MasterParam"}}, {"merged_momentum", {"Param", "Grad", "Velocity", "LearningRate", "MasterParam"}}, {"sparse_momentum", {"Param", "Grad", "Velocity", "Index", "LearningRate", "MasterParam"}}, {"rnn", {"Input", "PreState", "WeightList", "SequenceLength"}}, {"run_program", {"X", "Params"}}, {"fused_feedforward", {"Dropout1Seed", "Dropout2Seed", "Linear1Bias", "Linear2Bias", "Ln1Scale", "Ln1Bias", "Ln2Scale", "Ln2Bias"}}, {"faster_tokenizer", {"Text", "Vocab", "TextPair"}}, {"matrix_rank", {"X", "TolTensor"}}, {"adam", {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow", "MasterParam"}}, {"merged_adam", {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow", "MasterParam"}}, {"fused_adam", {"Params", "Grads", "LearningRate", "Moments1", "Moments2", "Beta1Pows", "Beta2Pows", "MasterParams", "SkipUpdate"}}, {"adamw", {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow", "MasterParam"}}, {"lamb", {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow", "MasterParam"}}, {"sparse_attention", {"Q", "K", "V", "Offset", "Columns", "KeyPaddingMask", "AttnMask"}}, {"sgd", {"Param", "LearningRate", "Grad", "MasterParam"}}, {"graph_khop_sampler", {"Row", "Eids", "Col_Ptr", "X"}}, {"nce", {"Input", "Label", "Weight", "Bias", "SampleWeight", "CustomDistProbs", "CustomDistAlias", "CustomDistAliasProbs"}}, {"yolov3_loss", {"X", "GTBox", "GTLabel", "GTScore"}}, {"check_finite_and_unscale", {"X", "Scale", "FloatStatus"}}, {"group_norm", {"X", "Scale", "Bias"}}, {"linear_chain_crf", {"Emission", "Transition", "Label", "Length"}}, {"crf_decoding", {"Emission", "Transition", "Label", "Length"}}, {"chunk_eval", {"Inference", "Label", "SeqLength"}}, {"sequence_mask", {"X", "MaxLenTensor"}}, {"graph_reindex", {"X", "Neighbors", "Count", "HashTable_Value", "HashTable_Index"}}, {"graph_sample_neighbors", {"Row", "Col_Ptr", "X", "Eids", "Perm_Buffer"}}, {"crop", {"X", "Y", "Offsets"}}, {"batch_norm", {"X", "Scale", "Bias", "Mean", "Variance", "MomentumTensor"}}, {"inplace_abn", {"X", "Scale", "Bias", "Mean", "Variance", "MomentumTensor"}}, {"linear_interp", {"X", "OutSize"}}, {"bilinear_interp", {"X", "OutSize"}}, {"trilinear_interp", {"X", "OutSize"}}, {"nearest_interp", {"X", "OutSize"}}, {"bicubic_interp", {"X", "OutSize"}}, {"resnet_basic_block", {"X", "Filter1", "Scale1", "Bias1", "Mean1", "Var1", "Filter2", "Scale2", "Bias2", "Mean2", "Var2", "Filter3", "Scale3", "Bias3", "Mean3", "Var3"}}, {"graph_send_recv", {"X", "Src_index", "Dst_index", "Out_size"}}, {"graph_send_ue_recv", {"X", "Y", "Src_index", "Dst_index", "Out_size"}}, }; // NOTE(zhiqiu): Like op_ins_map. // Commonly, the outputs in auto-generated OP function are determined by the // OP`s proto automatically, i.e., all the outputs registered in OpMaker. // However, some OPs have dispensable outputs, which means the output can // be none for some conditions. It is discovered that most dispensable outputs // is not used in imperative mode, so we drop those outputs when generating OP // functions. While, for very few OPs, the dispensable outputs are used, we // need to manually specify them in this map. std::map> op_outs_map = { {"fake_quantize_dequantize_moving_average_abs_max", {"Out", "OutScale", "OutAccum", "OutState"}}, {"batch_norm", {"Y", "MeanOut", "VarianceOut", "SavedMean", "SavedVariance", "ReserveSpace"}}, {"lstsq", {"Solution", "Residuals", "Rank", "SingularValues"}}, {"inplace_abn", {"Y", "MeanOut", "VarianceOut", "SavedMean", "SavedVariance", "ReserveSpace"}}, {"fused_attention", {"LnMean", "LnVariance", "LnOut", "QKVOut", "QKVBiasOut", "TransposeOut2", "QKOut", "QKTVOut", "SoftmaxOut", "AttnDropoutMaskOut", "AttnDropoutOut", "SrcMaskOut", "FMHAOut", "OutLinearOut", "DropoutMaskOut", "Ln2Mean", "Ln2Variance", "BiasDropoutResidualOut", "CacheKVOut", "Y"}}, {"fused_bias_dropout_residual_layer_norm", {"BiasDropoutResidualOut", "DropoutMaskOut", "LnMean", "LnVariance", "Y"}}, {"fused_gate_attention", {"QueryTransposeOut", "KeyTransposeOut", "ValueTransposeOut", "QKVTransposeOut", "SoftmaxOut", "FMHAOut", "GateOut", "Out"}}, {"sync_batch_norm", {"Y", "MeanOut", "VarianceOut", "SavedMean", "SavedVariance", "ReserveSpace"}}, {"unique", {"Out", "Index", "Indices", "Counts"}}, {"unique_consecutive", {"Out", "Index", "Counts"}}, {"generate_proposals", {"RpnRois", "RpnRoiProbs", "RpnRoisNum"}}, {"collect_fpn_proposals", {"FpnRois", "RoisNum"}}, {"matrix_nms", {"Out", "Index", "RoisNum"}}, {"distribute_fpn_proposals", {"MultiFpnRois", "RestoreIndex", "MultiLevelRoIsNum"}}, {"moving_average_abs_max_scale", {"Out", "OutScale", "OutAccum", "OutState"}}, {"multiclass_nms3", {"Out", "NmsRoisNum"}}, {"generate_proposals_v2", {"RpnRois", "RpnRoiProbs", "RpnRoisNum"}}, {"momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"merged_momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"sparse_momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"rnn", {"DropoutState", "Reserve", "Out", "State"}}, {"run_program", {"DOut", "CUDAGraph"}}, {"adam", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"merged_adam", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"fused_adam", {"ParamsOut", "Moments1Out", "Moments2Out", "Beta1PowsOut", "Beta2PowsOut", "MasterParamsOut"}}, {"adamw", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"sgd", {"ParamOut", "MasterParamOut"}}, {"lamb", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"fused_multi_transformer", {"CacheKVOut", "Out"}}, {"fused_multi_transformer_int8", {"CacheKVOut", "Out"}}, {"resnet_basic_block", {"Y", "Conv1", "SavedMean1", "SavedInvstd1", "Mean1Out", "Var1Out", "Conv2", "SavedMean2", "SavedInvstd2", "Mean2Out", "Var2Out", "Conv3", "SavedMean3", "SavedInvstd3", "Mean3Out", "Var3Out", "MaxInput1", "MaxFilter1", "MaxInput2", "MaxFilter2", "MaxInput3", "MaxFilter3"}}, }; // NOTE(zhiqiu): Commonly, the outputs in auto-generated OP function are // generated in C++ automatically. // However, some OPs need to pass the outputs from Python instead of generating // them in C++. There are mainly 2 reasons for that, // (1) Optimizer OPs need to update the input param in-place, like sgd. // So they need to pass the output which is same as input param. // (2) Very few python APIs has out in their arguments, like fill_constant. // So they need to pass the python output to C++. // Actually, this is not a good design, since it may break the SSA graph, // especially in declarative mode. // For those OPs, we need to manually specify the outs need to pass in this map. std::map> op_passing_outs_map = { {"sgd", {"ParamOut", "MasterParamOut"}}, {"rmsprop", {"ParamOut", "MomentOut", "MeanSquareOut", "MeanGradOut"}}, {"ftrl", {"ParamOut", "SquaredAccumOut", "LinearAccumOut"}}, {"adadelta", {"ParamOut", "AvgSquaredGradOut", "AvgSquaredUpdateOut"}}, {"adagrad", {"ParamOut", "MomentOut"}}, {"adamax", {"ParamOut", "MomentOut", "InfNormOut"}}, {"dpsgd", {"ParamOut"}}, {"decayed_adagrad", {"ParamOut", "MomentOut"}}, {"lars_momentum", {"ParamOut", "VelocityOut"}}, {"coalesce_tensor", {"Output", "FusedOutput"}}, {"adam", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"merged_adam", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"fused_adam", {"ParamsOut", "Moments1Out", "Moments2Out", "Beta1PowsOut", "Beta2PowsOut", "MasterParamsOut"}}, {"adamw", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"lamb", {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, {"average_accumulates", {"out_sum_1", "out_sum_2", "out_sum_3", "out_num_accumulates", "out_old_num_accumulates", "out_num_updates"}}, {"momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"merged_momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"sparse_momentum", {"ParamOut", "VelocityOut", "MasterParamOut"}}, {"batch_norm", {"MeanOut", "VarianceOut"}}, {"inplace_abn", {"MeanOut", "VarianceOut"}}, {"sync_batch_norm", {"MeanOut", "VarianceOut"}}, {"accuracy", {"Correct", "Total"}}, {"fill_constant", {"Out"}}, {"recv_v2", {"Out"}}, {"partial_recv", {"Out"}}, {"matmul", {"Out"}}, {"c_broadcast", {"Out"}}, {"c_sync_calc_stream", {"Out"}}, {"c_sync_comm_stream", {"Out"}}, {"c_reduce_sum", {"Out"}}, {"c_reduce_max", {"Out"}}, {"c_reduce_min", {"Out"}}, {"c_reduce_prod", {"Out"}}, {"c_reduce", {"Out"}}, {"c_scatter", {"Out"}}, {"barrier", {"Out"}}, {"fake_quantize_dequantize_moving_average_abs_max", {"Out", "OutScale", "OutAccum", "OutState"}}, {"fake_quantize_dequantize_abs_max", {"Out", "OutScale"}}, {"fake_channel_wise_quantize_dequantize_abs_max", {"Out", "OutScale"}}, {"check_finite_and_unscale", {"Out", "FoundInfinite"}}, {"update_loss_scaling", {"Out", "LossScaling", "OutGoodSteps", "OutBadSteps"}}, {"moving_average_abs_max_scale", {"Out", "OutScale", "OutAccum", "OutState"}}, {"rnn", {"DropoutState"}}, {"run_program", {"Out", "DOut", "OutScope", "CUDAGraph"}}, {"clear_float_status", {"FloatStatusOut"}}, {"get_float_status", {"FloatStatusOut"}}, {"assign", {"Out"}}, {"assign_value", {"Out"}}, {"split", {"Out"}}, {"concat", {"Out"}}, {"fused_multi_transformer", {"CacheKVOut"}}, {"fused_multi_transformer_int8", {"CacheKVOut"}}, {"group_norm", {"Mean", "Variance"}}, {"resnet_basic_block", {"Mean1Out", "Var1Out", "Mean2Out", "Var2Out", "Mean3Out", "Var3Out"}}, }; // NOTE(pangyoki): Tensor View Strategy. // In this case, a new output varbase will be created, and this varbase will // reuse the input varbase's allocation. // It's a map. The key of outer map is the view op name, the value is // a pair which implies the mapping relationship between the input and // output varbase. std::map> view_op_map = { {"squeeze2", {"X", "Out"}}, // "X" -> "Out" {"unsqueeze2", {"X", "Out"}}, {"reshape2", {"X", "Out"}}, {"flatten_contiguous_range", {"X", "Out"}}, }; // NOTE(pangyoki): Special inplace ops that are not supported in temporary. // The input and output of some inplace ops are special, such as // duplicate input. These inplace ops have no usage scenarios and // are not supported in temporary. std::set special_inplace_op_set = { "sum", // `sum` op has duplicate input "assign", // output of `assign` op is in `op_passing_outs_map` }; // NOTE(pangyoki): Special no_need_buffer ops that are not supported in // temporary. // sequence_conv op will raise error to get no_need_buffer info during // compiling. std::set special_no_need_buffer_op_set = { "sequence_conv", };