Handled dispensable tensors in AutoCodeGen for Eager Dygraph (#37723)

paddle/fluid/pybind/op_function_generator.cc

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/fluid/pybind/op_function_generator.h"
+
 #include <algorithm>
 #include <fstream>
 #include <iostream>
@@ -30,108 +32,6 @@
 #include "paddle/fluid/framework/fleet/ascend_wrapper.h"
 #endif
 
-// 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<std::string, std::set<std::string>> op_ins_map = {
-    {"layer_norm", {"X", "Scale", "Bias"}},
-    {"bincount", {"X", "Weights"}},
-    {"fused_attention",
-     {"X", "LnScale", "LnBias", "QKVW", "QKVBias", "SrcMask", "OutLinearW",
-      "OutLinearBias", "Ln2Scale", "Ln2Bias"}},
-    {"instance_norm", {"X", "Scale", "Bias"}},
-    {"gru_unit", {"Input", "HiddenPrev", "Weight", "Bias"}},
-    {"label_smooth", {"X", "PriorDist"}},
-    {"assign", {"X"}},
-    {"reshape2", {"X", "Shape"}},
-    {"expand", {"X", "ExpandTimes"}},
-    {"slice", {"Input", "StartsTensor", "EndsTensor"}},
-    {"fake_quantize_dequantize_moving_average_abs_max",
-     {"X", "InScale", "InAccum", "InState"}},
-    {"nll_loss", {"X", "Label", "Weight"}},
-    {"bilinear_tensor_product", {"X", "Y", "Weight", "Bias"}},
-    {"gather", {"X", "Index", "Axis"}},
-    {"roi_pool", {"X", "ROIs", "RoisNum"}},
-    {"roi_align", {"X", "ROIs", "RoisNum"}},
-    {"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"}},
-    {"sparse_momentum", {"Param", "Grad", "Velocity", "Index", "LearningRate"}},
-    {"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"}},
-    {"adamw",
-     {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow",
-      "Beta2Pow", "MasterParam"}},
-};
-
-// 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<std::string, std::set<std::string>> op_outs_map = {
-    {"fake_quantize_dequantize_moving_average_abs_max",
-     {"Out", "OutScale", "OutAccum", "OutState"}},
-    {"batch_norm",
-     {"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", "Y"}},
-    {"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"}},
-    {"sparse_momentum", {"ParamOut", "VelocityOut"}},
-    {"rnn", {"DropoutState", "Reserve", "Out", "State"}},
-    {"lamb",
-     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut"}},
-    {"run_program", {"DOut"}},
-    {"adam",
-     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut",
-      "MasterParamOut"}},
-    {"adamw",
-     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut",
-      "MasterParamOut"}},
-};
-
 // 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

paddle/fluid/pybind/op_function_generator.h

+// 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 <map>
+#include <set>
+#include <string>
+
+// 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<std::string, std::set<std::string>> op_ins_map = {
+    {"layer_norm", {"X", "Scale", "Bias"}},
+    {"bincount", {"X", "Weights"}},
+    {"fused_attention",
+     {"X", "LnScale", "LnBias", "QKVW", "QKVBias", "SrcMask", "OutLinearW",
+      "OutLinearBias", "Ln2Scale", "Ln2Bias"}},
+    {"instance_norm", {"X", "Scale", "Bias"}},
+    {"gru_unit", {"Input", "HiddenPrev", "Weight", "Bias"}},
+    {"label_smooth", {"X", "PriorDist"}},
+    {"assign", {"X"}},
+    {"reshape2", {"X", "Shape"}},
+    {"expand", {"X", "ExpandTimes"}},
+    {"slice", {"Input", "StartsTensor", "EndsTensor"}},
+    {"fake_quantize_dequantize_moving_average_abs_max",
+     {"X", "InScale", "InAccum", "InState"}},
+    {"nll_loss", {"X", "Label", "Weight"}},
+    {"bilinear_tensor_product", {"X", "Y", "Weight", "Bias"}},
+    {"gather", {"X", "Index", "Axis"}},
+    {"roi_pool", {"X", "ROIs", "RoisNum"}},
+    {"roi_align", {"X", "ROIs", "RoisNum"}},
+    {"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"}},
+    {"sparse_momentum", {"Param", "Grad", "Velocity", "Index", "LearningRate"}},
+    {"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"}},
+    {"adamw",
+     {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow",
+      "Beta2Pow", "MasterParam"}},
+};
+
+// 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<std::string, std::set<std::string>> op_outs_map = {
+    {"fake_quantize_dequantize_moving_average_abs_max",
+     {"Out", "OutScale", "OutAccum", "OutState"}},
+    {"batch_norm",
+     {"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", "Y"}},
+    {"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"}},
+    {"sparse_momentum", {"ParamOut", "VelocityOut"}},
+    {"rnn", {"DropoutState", "Reserve", "Out", "State"}},
+    {"lamb",
+     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut"}},
+    {"run_program", {"DOut"}},
+    {"adam",
+     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut",
+      "MasterParamOut"}},
+    {"adamw",
+     {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut",
+      "MasterParamOut"}},
+};