[CPU-PSLIB] Add dump_prob & Modify dump_param (#35414)

* Add dump_prob & Modify dump_param

* Add dump_prob & Modify dump_param

* Add dump_prob & Modify dump_param

* Add dump_prob & Modify dump_param

* Add dump_prob & Modify dump_param

cmake/external/pslib.cmake

@@ -19,7 +19,7 @@ IF((NOT DEFINED PSLIB_VER) OR (NOT DEFINED PSLIB_URL))
   MESSAGE(STATUS "use pre defined download url")
   SET(PSLIB_VER "0.1.1" CACHE STRING "" FORCE)
   SET(PSLIB_NAME "pslib" CACHE STRING "" FORCE)
-  SET(PSLIB_URL "https://pslib.bj.bcebos.com/pslib.tar.gz" CACHE STRING "" FORCE)
+  SET(PSLIB_URL "https://pslib.bj.bcebos.com/pslib_1.8.5.tar.gz" CACHE STRING "" FORCE)
 ENDIF()
 MESSAGE(STATUS "PSLIB_NAME: ${PSLIB_NAME}, PSLIB_URL: ${PSLIB_URL}")
 SET(PSLIB_SOURCE_DIR    "${THIRD_PARTY_PATH}/pslib")

paddle/fluid/framework/data_feed.cc

@@ -775,8 +775,12 @@ void MultiSlotDataFeed::PutToFeedVec(
                        total_instance * sizeof(int64_t));
     }
 
-    LoD data_lod{offset};
-    feed_vec_[i]->set_lod(data_lod);
+    // LoD data_lod{offset};
+    // feed_vec_[i]->set_lod(data_lod);
+    if (!use_slots_is_dense_[i]) {
+      LoD data_lod{offset};
+      feed_vec_[i]->set_lod(data_lod);
+    }
     if (use_slots_is_dense_[i]) {
       if (inductive_shape_index_[i] != -1) {
         use_slots_shape_[i][inductive_shape_index_[i]] =
@@ -1101,8 +1105,12 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec(
       CopyToFeedTensor(tensor_ptr, feasign, total_instance * sizeof(int64_t));
     }
     auto& slot_offset = offset[i];
-    LoD data_lod{slot_offset};
-    feed_vec_[i]->set_lod(data_lod);
+    // LoD data_lod{slot_offset};
+    // feed_vec_[i]->set_lod(data_lod);
+    if (!use_slots_is_dense_[i]) {
+      LoD data_lod{slot_offset};
+      feed_vec_[i]->set_lod(data_lod);
+    }
     if (use_slots_is_dense_[i]) {
       if (inductive_shape_index_[i] != -1) {
         use_slots_shape_[i][inductive_shape_index_[i]] =

paddle/fluid/framework/device_worker.h

@@ -149,6 +149,7 @@ class DeviceWorker {
   bool use_cvm_;
   bool no_cvm_;
   bool scale_sparse_gradient_with_batch_size_;
+  float dump_prob_;
   std::vector<std::string> all_param_;
 };
 
@@ -226,6 +227,7 @@ class DownpourWorker : public HogwildWorker {
   void CopySparseTable();
   void CopyDenseTable();
   void CopyDenseVars();
+  void DumpParam(std::ostringstream& os);
   virtual void DumpParam(const int batch_id);
 
   DownpourWorkerParameter param_;

paddle/fluid/framework/dist_multi_trainer.cc

@@ -78,6 +78,11 @@ void DistMultiTrainer::DumpWork(int tid) {
   std::string path = string::format_string(
       "%s/part-%03d-%05d", dump_fields_path_.c_str(), mpi_rank_, tid);
 
+  if (user_define_dump_filename_ != "") {
+    path = string::format_string("%s/part-%s", dump_fields_path_.c_str(),
+                                 user_define_dump_filename_.c_str());
+  }
+
   std::shared_ptr<FILE> fp = fs_open_write(path, &err_no, dump_converter_);
   while (1) {
     std::string out_str;

paddle/fluid/framework/downpour_worker.cc

@@ -78,6 +78,7 @@ void DownpourWorker::Initialize(const TrainerDesc& desc) {
   use_cvm_ = desc.use_cvm();
   // for sparse value accessor, embedding only
   no_cvm_ = desc.no_cvm();
+  dump_prob_ = desc.dump_prob();
   scale_sparse_gradient_with_batch_size_ =
       desc.scale_sparse_gradient_with_batch_size();
   scale_datanorm_ = desc.scale_datanorm();
@@ -131,6 +132,25 @@ void DownpourWorker::SetNeedDump(bool need_dump_field) {
   need_dump_field_ = need_dump_field;
 }
 
+void DownpourWorker::DumpParam(std::ostringstream& os) {
+  for (auto& param : dump_param_) {
+    Variable* var = thread_scope_->FindVar(param);
+    if (var == nullptr) {
+      continue;
+    }
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t len = tensor->numel();
+    std::string tensor_str;
+    try {
+      tensor_str = PrintLodTensor(tensor, 0, len);
+    } catch (std::exception& e) {
+      LOG(WARNING) << "catch exception, param:" << param;
+      continue;
+    }
+    os << "\t" << param << ":" << len << tensor_str;
+  }
+}
+
 void DownpourWorker::DumpParam(const int batch_id) {
   std::ostringstream os;
   for (auto& param : dump_param_) {
@@ -963,43 +983,47 @@ void DownpourWorker::TrainFiles() {
     }
     if (need_dump_field_) {
       size_t batch_size = device_reader_->GetCurBatchSize();
-      std::vector<std::string> ars(batch_size);
+      std::vector<std::ostringstream> ars(batch_size);
       for (auto& ar : ars) {
         ar.clear();
       }
       auto& ins_id_vec = device_reader_->GetInsIdVec();
       auto& ins_content_vec = device_reader_->GetInsContentVec();
       for (size_t i = 0; i < ins_id_vec.size(); i++) {
-        ars[i] += ins_id_vec[i];
-        ars[i] = ars[i] + "\t" + ins_content_vec[i];
-      }
-      for (auto& field : dump_fields_) {
-        Variable* var = thread_scope_->FindVar(field);
-        if (var == nullptr) {
-          continue;
-        }
-        LoDTensor* tensor = var->GetMutable<LoDTensor>();
-        if (!CheckValidOutput(tensor, batch_size)) {
+        srand((unsigned)time(NULL));
+        float random_prob = (float)rand() / RAND_MAX;  // NOLINT
+        if (random_prob >= dump_prob_) {
           continue;
         }
-        for (size_t i = 0; i < batch_size; ++i) {
+        ars[i] << ins_id_vec[i];
+        ars[i] << "\t" << ins_content_vec[i];
+
+        for (auto& field : dump_fields_) {
+          Variable* var = thread_scope_->FindVar(field);
+          if (var == nullptr) {
+            continue;
+          }
+          LoDTensor* tensor = var->GetMutable<LoDTensor>();
+          if (!CheckValidOutput(tensor, batch_size)) {
+            continue;
+          }
           auto output_dim = tensor->dims()[1];
           std::string output_dimstr =
               boost::lexical_cast<std::string>(output_dim);
-          ars[i] = ars[i] + "\t" + field + ":" + output_dimstr;
+          ars[i] << "\t" << field << ":" << output_dimstr;
           auto bound = GetTensorBound(tensor, i);
-          ars[i] += PrintLodTensor(tensor, bound.first, bound.second);
+          ars[i] << PrintLodTensor(tensor, bound.first, bound.second);
         }
-      }
-      // #pragma omp parallel for
-      for (size_t i = 0; i < ars.size(); i++) {
-        if (ars[i].length() == 0) {
+
+        if (need_dump_param_ && thread_id_ == 0) {
+          DumpParam(ars[i]);
+        }
+
+        if (ars[i].str().length() < 2) {
           continue;
         }
-        writer_ << ars[i];
-      }
-      if (need_dump_param_ && thread_id_ == 0) {
-        DumpParam(batch_cnt);
+
+        writer_ << ars[i].str();
       }
     }
 

paddle/fluid/framework/trainer_desc.proto

@@ -52,6 +52,7 @@ message TrainerDesc {
 
   optional string user_define_dump_filename = 24;
   optional bool scale_sparse_gradient_with_batch_size = 25 [ default = true ];
+  optional float dump_prob = 26 [ default = 1.0 ];
 
   // device worker parameters
   optional HogwildWorkerParameter hogwild_param = 101;

python/paddle/fluid/trainer_desc.py

@@ -131,6 +131,9 @@ class TrainerDesc(object):
         for loss in loss_names:
             self.proto_desc.loss_names.append(loss)
 
+    def _set_dump_prob(self, dump_prob):
+        self.proto_desc.dump_prob = dump_prob
+
     def _set_adjust_ins_weight(self, config_dict):
         self.proto_desc.adjust_ins_weight_config.need_adjust = \
                 config_dict.get("need_adjust", False)

python/paddle/fluid/trainer_factory.py

@@ -68,10 +68,15 @@ class TrainerFactory(object):
                     trainer._set_dump_fields_path(opt_info["dump_fields_path"])
                 if opt_info.get("dump_file_num") is not None:
                     trainer._set_dump_file_num(opt_info["dump_file_num"])
+                if opt_info.get("user_define_dump_filename") is not None:
+                    trainer._set_user_define_dump_filename(opt_info[
+                        "user_define_dump_filename"])
                 if opt_info.get("dump_converter") is not None:
                     trainer._set_dump_converter(opt_info["dump_converter"])
                 if opt_info.get("dump_param") is not None:
                     trainer._set_dump_param(opt_info["dump_param"])
+                if opt_info.get("dump_prob") is not None:
+                    trainer._set_dump_prob(opt_info["dump_prob"])
 
             if "fleet_desc" in opt_info:
                 device_worker._set_fleet_desc(opt_info["fleet_desc"])