[Paddle-TRT] Fix AI-Rank BERT emb_eltwise_layernorm input order (#32482)

* fix airank bert emb order

* move input num check to converter

* add input num check

* add unused var check white list

paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass.cc

@@ -290,10 +290,20 @@ static int BuildFusion(Graph* graph, const std::string& name_scope
       ids.push_back(inner_pattern_ins[js[iter]].first->Name());
       embs.push_back(inner_pattern_ins[js[iter]].second->Name());
     }
+
     OpDesc new_op_desc;
     new_op_desc.SetType("fused_embedding_eltwise_layernorm");
     new_op_desc.SetInput("Ids", ids);
     new_op_desc.SetInput("Embs", embs);
+
+    new_op_desc.SetInput("WordId", {ids[0]});
+    new_op_desc.SetInput("PosId", {ids[1]});
+    new_op_desc.SetInput("SentId", {ids[2]});
+
+    new_op_desc.SetInput("WordEmbedding", {embs[0]});
+    new_op_desc.SetInput("PosEmbedding", {embs[1]});
+    new_op_desc.SetInput("SentEmbedding", {embs[2]});
+
     new_op_desc.SetInput("Bias", {end_pattern_biases[k]->Name()});
     new_op_desc.SetInput("Scale", {end_pattern_scales[k]->Name()});
     new_op_desc.SetOutput("Out", {end_pattern_out[k]->Name()});

paddle/fluid/framework/pipeline_trainer.cc

@@ -37,7 +37,7 @@ void PipelineTrainer::Initialize(const TrainerDesc& trainer_desc,
   int place_id = section_config.place_id();
 #if (defined PADDLE_WITH_NCCL)
   place_ = platform::CUDAPlace(place_id);
-#elif (defined WITH_ASCEND_CL)
+#elif (defined WITH_ASCEND_CL)  // NOLINT
   place_ = platform::NPUPlace(place_id);
 #endif
   worker_ = DeviceWorkerFactory::CreateDeviceWorker(

paddle/fluid/framework/unused_var_check.cc

@@ -53,27 +53,28 @@ static const std::unordered_set<std::string> &GetOpWithUnusedVarAllowSet() {
   // Use pointer here for safe static deinitialization
   static auto *allow_set = new std::unordered_set<std::string>({
       // called once
-      "batch_norm",                      // 0
-      "batch_norm_grad",                 // 0
-      "sync_batch_norm",                 // 0
-      "sync_batch_norm_grad",            // 0
-      "inplace_abn",                     // 0
-      "inplace_abn_grad",                // 0
-      "dgc_momentum",                    // 0
-      "fake_quantize_range_abs_max",     // 0
-      "rmsprop",                         // 0
-      "sequence_conv_grad",              // 0
-      "roi_perspective_transform_grad",  // 0
-      "fill_zeros_like",                 // 1
-      "fill_any_like",                   // 1
-      "nce_grad",                        // 1
-      "precision_recall",                // 1
-      "fusion_seqpool_cvm_concat",       // 2
-      "fused_batch_norm_act",            // 2
-      "fused_batch_norm_act_grad",       // 2
-      "data_norm",                       // 0
-      "data_norm_grad",                  // 0
-      "update_loss_scaling",             // 0
+      "batch_norm",                         // 0
+      "batch_norm_grad",                    // 0
+      "sync_batch_norm",                    // 0
+      "sync_batch_norm_grad",               // 0
+      "inplace_abn",                        // 0
+      "inplace_abn_grad",                   // 0
+      "dgc_momentum",                       // 0
+      "fake_quantize_range_abs_max",        // 0
+      "rmsprop",                            // 0
+      "sequence_conv_grad",                 // 0
+      "roi_perspective_transform_grad",     // 0
+      "fill_zeros_like",                    // 1
+      "fill_any_like",                      // 1
+      "nce_grad",                           // 1
+      "precision_recall",                   // 1
+      "fusion_seqpool_cvm_concat",          // 2
+      "fused_batch_norm_act",               // 2
+      "fused_batch_norm_act_grad",          // 2
+      "data_norm",                          // 0
+      "data_norm_grad",                     // 0
+      "update_loss_scaling",                // 0
+      "fused_embedding_eltwise_layernorm",  // 0
   });
   return *allow_set;
 }

paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc

@@ -34,8 +34,17 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
     VLOG(4) << "convert fluid EmbEltwiseLayerNorm op to tensorrt layer";
 
     framework::OpDesc op_desc(op, nullptr);
-    auto id_names = op_desc.Input("Ids");
-    auto emb_names = op_desc.Input("Embs");
+    auto word_id_name = op_desc.Input("WordId").front();
+    auto pos_id_name = op_desc.Input("PosId").front();
+    auto sent_id_name = op_desc.Input("SentId").front();
+    auto word_emb_name = op_desc.Input("WordEmbedding").front();
+    auto pos_emb_name = op_desc.Input("PosEmbedding").front();
+    auto sent_emb_name = op_desc.Input("SentEmbedding").front();
+    std::vector<std::string> id_names = {word_id_name, pos_id_name,
+                                         sent_id_name};
+    std::vector<std::string> emb_names = {word_emb_name, pos_emb_name,
+                                          sent_emb_name};
+
     int input_num = id_names.size();
 
     // Declare inputs
@@ -91,6 +100,12 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
       if (enable_int8) {
         output_fp16 = 1;
       }
+      PADDLE_ENFORCE_EQ(
+          input_num, 3,
+          platform::errors::InvalidArgument(
+              "When using oss and var-len, embedding_eltwise_layernorm op"
+              "should have 3 inputs only, but got %d.",
+              input_num));
       PADDLE_ENFORCE_EQ(
           output_fp16, 1,
           platform::errors::InvalidArgument(
@@ -125,15 +140,15 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
       plugin_ptr->fields = fields.data();
 
       std::vector<nvinfer1::ITensor*> plugin_inputs;
-      plugin_inputs.emplace_back(engine_->GetITensor(
-          engine_->network()->getInput(0)->getName()));  // word_embedding,
-                                                         // eval_placeholder_0
-      plugin_inputs.emplace_back(engine_->GetITensor(
-          engine_->network()->getInput(1)->getName()));  // sent_embedding,
-                                                         // eval_placeholder_1
-      plugin_inputs.emplace_back(engine_->GetITensor(
-          engine_->network()->getInput(2)->getName()));  // cu_seqlens,
-                                                         // eval_placeholder_2
+      plugin_inputs.emplace_back(
+          engine_->GetITensor(word_id_name));  // word_embedding,
+                                               // eval_placeholder_0
+      plugin_inputs.emplace_back(
+          engine_->GetITensor(sent_id_name));  // sent_embedding,
+                                               // eval_placeholder_1
+      plugin_inputs.emplace_back(
+          engine_->GetITensor(pos_id_name));  // cu_seqlens,
+                                              // eval_placeholder_2
       auto max_seqlen_tensor =
           engine_->GetITensor(engine_->network()->getInput(3)->getName());
       auto* shuffle_layer =