[Cherry-Pick] logclean & embedding doc (#32009)

* fix en doc for emb (#31980)

* fix en doc for emb, test=document_fix;
Change-Id: I4757e67caacd7189f068493ed45a7445f87ffb40

* LOG CLEAN (#31819)

* upgrade vlog

* train from dataset fetch optimize

cmake/external/brpc.cmake

@@ -41,7 +41,7 @@ ExternalProject_Add(
         ${EXTERNAL_PROJECT_LOG_ARGS}
         # TODO(gongwb): change to de newst repo when they changed.
         GIT_REPOSITORY  "https://github.com/wangjiawei04/brpc"
-        GIT_TAG         "6d79e0b17f25107c35b705ea58d888083f59ff47"
+        GIT_TAG         "e203afb794caf027da0f1e0776443e7d20c0c28e"
         PREFIX          ${BRPC_SOURCES_DIR}
         UPDATE_COMMAND  ""
         CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}

paddle/fluid/distributed/service/brpc_ps_server.cc

@@ -57,7 +57,8 @@ uint64_t BrpcPsServer::start(const std::string &ip, uint32_t port) {
   std::unique_lock<std::mutex> lock(mutex_);
 
   std::string ip_port = ip + ":" + std::to_string(port);
-  VLOG(3) << "server of rank " << _rank << " starts at " << ip_port;
+  VLOG(0) << "running server with rank id: " << _rank
+          << ", endpoint: " << ip_port;
   brpc::ServerOptions options;
 
   int num_threads = std::thread::hardware_concurrency();
@@ -535,7 +536,7 @@ int32_t BrpcPsService::stop_server(Table *table,
   auto *p_server = _server;
   std::thread t_stop([p_server]() {
     p_server->stop();
-    LOG(INFO) << "Server Stoped";
+    VLOG(3) << "Server Stoped";
   });
   t_stop.detach();
   return 0;

paddle/fluid/distributed/service/brpc_utils.cc

@@ -331,7 +331,7 @@ std::string GetIntTypeEndpoint(const std::string& ip, const uint32_t& port) {
 
   while (hp->h_addr_list[i] != NULL) {
     int_ip = inet_ntoa(*(struct in_addr*)hp->h_addr_list[i]);
-    VLOG(0) << "Brpc Get host by name, host:" << ip << " -> ip: " << int_ip;
+    VLOG(3) << "Brpc Get host by name, host:" << ip << " -> ip: " << int_ip;
     break;
   }
 

paddle/fluid/distributed/service/env.h

@@ -39,7 +39,7 @@ struct PSHost {
 
   // |---ip---|---port---|--rank--|
   // |-32bit--|--20bit---|--12bit-|
-  // for pslib
+
   uint64_t serialize_to_uint64() {
     uint64_t host_label = 0;
     host_label = inet_addr(ip.c_str());
@@ -174,14 +174,12 @@ class PSEnvironment {
     host.ip = ip;
     host.port = port;
     host.rank = rank;
-    if (sign_set.count(rank) > 0) {
-      LOG(WARNING) << "ps-host :" << host.ip << ":" << host.port
-                   << ", rank:" << host.rank
-                   << " already register, ignore register";
-    } else {
+
+    if (sign_set.count(rank) == 0) {
       host_list.push_back(host);
       sign_set.insert(rank);
     }
+
     return 0;
   }
 

paddle/fluid/distributed/service/ps_client.cc

@@ -80,8 +80,7 @@ PSClient *PSClientFactory::create(const PSParameter &ps_config) {
   }
 
   TableManager::instance().initialize();
-  LOG(INFO) << "Create PSClient[" << service_param.client_class()
-            << "] success";
+  VLOG(3) << "Create PSClient[" << service_param.client_class() << "] success";
   return client;
 }
 }  // namespace distributed

paddle/fluid/distributed/service/service.cc

@@ -47,7 +47,7 @@ paddle::distributed::PSParameter load_from_prototxt(
 }
 
 void PSCore::init_gflag(const std::string& gflags) {
-  LOG(INFO) << "Init With Gflags:" << gflags;
+  VLOG(3) << "Init With Gflags:" << gflags;
   std::vector<std::string> flags = paddle::string::split_string(gflags);
   if (flags.size() < 1) {
     flags.push_back("-max_body_size=314217728");

paddle/fluid/distributed/table/depends/dense.h

@@ -89,7 +89,6 @@ class DSGD : public DenseOptimizer {
 
     auto blas = GetBlas<float>();
     float lr = *(global_learning_rate_) * (*learning_rate);
-    VLOG(4) << "DSGD LearningRate: " << lr;
     blas.VCOPY(update_numel, update_values + begin, grads.data());
     blas.SCAL(update_numel, lr, grads.data());
     blas.VSUB(update_numel, param + begin, grads.data(), param + begin);
@@ -157,7 +156,6 @@ class DAdam : public DenseOptimizer {
     beta2_pow[0] = beta2_pow[0] * beta2;
 
     float lr_ = *(global_learning_rate_)*learning_rate[0];
-    VLOG(4) << "DAdam LearningRate: " << lr_;
     lr_ *= sqrt(1 - beta2_pow[0]) / (1 - beta1_pow[0]);
 
     float* tmp_ = tmp.data();

paddle/fluid/distributed/table/depends/sparse.h

@@ -110,7 +110,6 @@ class SSGD : public SparseOptimizer {
       auto* value = block->Get(id);
 
       float learning_rate = *(global_learning_rate_) * (value + lr_offset)[0];
-      VLOG(4) << "SSGD LearningRate: " << learning_rate;
       float* param = value + param_offset;
 
       std::vector<float> grads;
@@ -166,7 +165,6 @@ class SAdam : public SparseOptimizer {
       if (!block->GetEntry(id)) continue;
       auto* values = block->Get(id);
       float lr_ = *(global_learning_rate_) * (values + lr_offset)[0];
-      VLOG(4) << "SAdam LearningRate: " << lr_;
       float* param = values + param_offset;
       float* moment1 = values + m1_offset;
       float* moment2 = values + m2_offset;

paddle/fluid/framework/details/build_strategy.cc

@@ -167,9 +167,6 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     AppendPassWithCheck(strategy_.fuse_bn_add_act_ops_, "fuse_bn_add_act_pass");
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) && !defined(__APPLE__)
     AppendPassWithCheck(strategy_.enable_auto_fusion_, "fusion_group_pass");
-#else
-    LOG(WARNING) << "fusion_group is not enabled for Windows/MacOS now, and "
-                    "only effective when running with CUDA GPU.";
 #endif
     AppendPassWithCheck(strategy_.fuse_elewise_add_act_ops_,
                         "fuse_elewise_add_act_pass");
@@ -271,12 +268,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     if (FLAGS_use_mkldnn) {
       AppendPass(pass_name);
     } else if (!strategy_.mkldnn_enabled_op_types_.empty()) {
-      LOG(WARNING)
-          << "mkldnn_enabled_op_types specify the operator type list to "
-             "use MKLDNN acceleration. It is null in default, means "
-             "that all the operators supported by MKLDNN will be "
-             "accelerated. And it should not be set when "
-             "FLAGS_use_mkldnn=false.";
+      VLOG(1) << "mkldnn_enabled_op_types specify the operator type list to "
+                 "use MKLDNN acceleration. It is null in default, means "
+                 "that all the operators supported by MKLDNN will be "
+                 "accelerated. And it should not be set when "
+                 "FLAGS_use_mkldnn=false.";
     }
 #else
     PADDLE_ENFORCE_NE(FLAGS_use_mkldnn, true,
@@ -409,26 +405,26 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
               << ", num_trainers:" << num_trainers_;
     } else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
       if (use_device != p::kCUDA) {
-        LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
-                        "GPU, skipped.";
+        VLOG(1) << "fuse_relu_depthwise_conv_pass is only supported on "
+                   "GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fusion_group_pass") {
       pass->Set<bool>("use_gpu", new bool((use_device == p::kCUDA)));
       if (use_device != p::kCUDA) {
-        LOG(WARNING) << "fusion_group_pass is only supported on GPU, skipped.";
+        VLOG(1) << "fusion_group_pass is only supported on GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fuse_bn_act_pass") {
       if (use_device != p::kCUDA) {
-        LOG(WARNING) << "fuse_bn_act_pass is only supported on "
-                        "GPU, skipped.";
+        VLOG(1) << "fuse_bn_act_pass is only supported on "
+                   "GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fuse_bn_add_act_pass") {
       if (use_device != p::kCUDA) {
-        LOG(WARNING) << "fuse_bn_add_act_pass is only supported on "
-                        "GPU, skipped.";
+        VLOG(1) << "fuse_bn_add_act_pass is only supported on "
+                   "GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "mkldnn_placement_pass") {

paddle/fluid/framework/device_worker.h

@@ -202,7 +202,7 @@ class DeviceWorker {
   Scope* root_scope_ = nullptr;
   Scope* thread_scope_;
   paddle::platform::Place place_;
-  int64_t batch_num_;
+  int64_t batch_num_ = 0;
   FetchConfig fetch_config_;
   bool use_cvm_;
   bool no_cvm_;

paddle/fluid/framework/hogwild_worker.cc

@@ -12,6 +12,7 @@ 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. */
 
+#include <ctime>
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/device_worker.h"
 #include "paddle/fluid/framework/device_worker_factory.h"
@@ -227,14 +228,32 @@ void HogwildWorker::PrintFetchVars() {
   // call count
   batch_num_++;
   int batch_per_print = fetch_config_.print_period();
-  if (thread_id_ == 0) {
-    if (batch_num_ % batch_per_print == 0) {
-      int fetch_var_num = fetch_config_.fetch_var_names_size();
-      for (int i = 0; i < fetch_var_num; ++i) {
-        platform::PrintVar(thread_scope_, fetch_config_.fetch_var_names(i),
-                           fetch_config_.fetch_var_str_format(i));
+  int fetch_var_num = fetch_config_.fetch_var_names_size();
+
+  if (fetch_var_num == 0) {
+    return;
+  }
+
+  if (thread_id_ == 0 && batch_num_ % batch_per_print == 0) {
+    time_t curtime;
+    time(&curtime);
+    char mbstr[80];
+    std::strftime(mbstr, sizeof(mbstr), "%Y-%m-%d %H:%M:%S",
+                  std::localtime(&curtime));
+
+    std::stringstream ss;
+    ss << "time: [" << mbstr << "], ";
+    ss << "batch: [" << batch_num_ << "], ";
+
+    for (int i = 0; i < fetch_var_num; ++i) {
+      platform::PrintVar(thread_scope_, fetch_config_.fetch_var_names(i),
+                         fetch_config_.fetch_var_str_format(i), &ss);
+      if (i < fetch_var_num - 1) {
+        ss << ", ";
       }
     }
+
+    std::cout << ss.str() << std::endl;
   }
 }
 

paddle/fluid/platform/lodtensor_printer.cc

@@ -27,24 +27,38 @@ namespace paddle {
 namespace platform {
 
 void PrintVar(framework::Scope* scope, const std::string& var_name,
-              const std::string& print_info) {
+              const std::string& print_info, std::stringstream* sstream) {
   framework::Variable* var = scope->FindVar(var_name);
   if (var == nullptr) {
-    VLOG(1) << "Variable Name " << var_name << " does not exist in your scope";
+    VLOG(0) << "Variable Name " << var_name << " does not exist in your scope";
     return;
   }
   framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
   if (tensor == nullptr) {
-    VLOG(1) << "tensor of variable " << var_name
+    VLOG(0) << "tensor of variable " << var_name
             << " does not exist in your scope";
     return;
   }
 
-  std::ostringstream sstream;
-  sstream << print_info << "\t";
-  sstream << var_name << "\t";
-  sstream << *tensor << "\t";
-  std::cout << sstream.str() << std::endl;
+  *sstream << print_info << ": ";
+
+#define PrintTensorCallback(cpp_type, proto_type) \
+  do {                                            \
+    if (tensor->type() == proto_type) {           \
+      *sstream << "[";                            \
+      auto* data = tensor->data<cpp_type>();      \
+      auto element_num = tensor->numel();         \
+      if (element_num > 0) {                      \
+        *sstream << data[0];                      \
+        for (int j = 1; j < element_num; ++j) {   \
+          *sstream << " " << data[j];             \
+        }                                         \
+      }                                           \
+      *sstream << "]";                            \
+    }                                             \
+  } while (0)
+
+  _ForEachDataType_(PrintTensorCallback);
 }
 
 }  // end namespace platform

paddle/fluid/platform/lodtensor_printer.h

@@ -26,6 +26,6 @@ class Scope;
 namespace paddle {
 namespace platform {
 void PrintVar(framework::Scope* scope, const std::string& var_name,
-              const std::string& print_info);
+              const std::string& print_info, std::stringstream* out);
 }  // end namespace platform
 }  // end namespace paddle

paddle/fluid/platform/lodtensor_printer_test.cc

@@ -18,5 +18,6 @@
 
 TEST(LodTensorPrinter, PrintVar) {
   paddle::framework::Scope scope;
-  paddle::platform::PrintVar(&scope, "NotAVar", "We don't have var");
+  std::stringstream ss;
+  paddle::platform::PrintVar(&scope, "NotAVar", "We don't have var", &ss);
 }

python/paddle/distributed/fleet/base/fleet_base.py

@@ -628,12 +628,13 @@ class Fleet(object):
         self.user_defined_optimizer = optimizer
 
         if strategy is not None:
-            warnings.warn(
-                "It is recommended to use DistributedStrategy "
-                "in fleet.init(). The strategy here is only for compatibility. "
-                "If the strategy in fleet.distributed_optimizer() is "
-                "not None, then it will overwrite the DistributedStrategy in fleet.init(), "
-                "which will take effect in distributed training.")
+            if self._is_collective:
+                warnings.warn(
+                    "It is recommended to use DistributedStrategy "
+                    "in fleet.init(). The strategy here is only for compatibility. "
+                    "If the strategy in fleet.distributed_optimizer() is "
+                    "not None, then it will overwrite the DistributedStrategy in fleet.init(), "
+                    "which will take effect in distributed training.")
             self._user_defined_strategy = copy.deepcopy(strategy)
 
         self._context = {}

python/paddle/distributed/fleet/runtime/the_one_ps.py

@@ -767,7 +767,7 @@ class TheOnePSRuntime(RuntimeBase):
         server = self._get_fleet_proto(is_server=True, is_sync=is_sync)
         proto_txt = str(server)
 
-        debug = bool(os.getenv("PSERVER_DEBUG", "0"))
+        debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))
         if debug:
             print("server: \n{}".format(proto_txt))
 

python/paddle/fluid/tests/unittests/test_monitor.py

@@ -17,6 +17,8 @@ TestCases for Monitor
 
 from __future__ import print_function
 import paddle
+paddle.enable_static()
+
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import numpy as np
@@ -52,6 +54,11 @@ class TestDatasetWithStat(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
+        embs = []
+        for x in slots_vars:
+            emb = fluid.layers.embedding(x, is_sparse=True, size=[100001, 4])
+            embs.append(emb)
+
         dataset = paddle.distributed.InMemoryDataset()
         dataset._set_batch_size(32)
         dataset._set_thread(3)
@@ -74,11 +81,17 @@ class TestDatasetWithStat(unittest.TestCase):
             for i in range(self.epoch_num):
                 for data in data_loader():
                     exe.run(fluid.default_main_program(), feed=data)
+
         else:
             for i in range(self.epoch_num):
                 try:
-                    exe.train_from_dataset(fluid.default_main_program(),
-                                           dataset)
+                    exe.train_from_dataset(
+                        fluid.default_main_program(),
+                        dataset,
+                        fetch_list=[embs[0], embs[1]],
+                        fetch_info=["emb0", "emb1"],
+                        print_period=1)
+
                 except Exception as e:
                     self.assertTrue(False)
 

python/paddle/nn/functional/input.py

@@ -148,9 +148,7 @@ def embedding(x, weight, padding_idx=None, sparse=False, name=None):
         sparse(bool): The flag indicating whether to use sparse update. This parameter only
             affects the performance of the backwards gradient update. It is recommended to set
             True because sparse update is faster. But some optimizers does not support sparse update,
-            such as :ref:`api_optimizer_AdadeltaOptimizer` , :ref:`api_optimizer_AdamaxOptimizer` ,
-            :ref:`api_optimizer_DecayedAdagradOptimizer` , :ref:`api_optimizer_FtrlOptimizer` ,
-            :ref:`api_optimizer_LambOptimizer` and :ref:`api_optimizer_LarsMomentumOptimizer` .
+            such as :ref:`api_paddle_optimizer_adadelta_Adadelta` , :ref:`api_paddle_optimizer_adamax_Adamax` , :ref:`api_paddle_optimizer_lamb_Lamb`.
             In these cases, sparse must be False. Default: False.
         padding_idx(int|long|None): padding_idx needs to be in the interval [-weight.shape[0], weight.shape[0]).
             If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted

python/paddle/nn/layer/common.py

@@ -1219,7 +1219,7 @@ class Embedding(layers.Layer):
     For specific usage, refer to code examples. It implements the function of the Embedding Layer.
     This layer is used to lookup embeddings vector of ids provided by :attr:`x` .
     It automatically constructs a 2D embedding matrix based on the
-    input :attr:`num_embeddings` and attr:`embedding_dim`.
+    input :attr:`num_embeddings` and :attr:`embedding_dim`.
 
     The shape of output Tensor is generated by appending an emb_size dimension to the
     last dimension of the input Tensor shape.
@@ -1231,9 +1231,9 @@ class Embedding(layers.Layer):
 
         Case 1:
 
-        input is a Tensor. padding_idx = -1
-            input.data = [[1, 3], [2, 4], [4, 127]
-            input.shape = [3, 2]
+        x is a Tensor. padding_idx = -1
+            x.data = [[1, 3], [2, 4], [4, 127]
+            x.shape = [3, 2]
         Given size = [128, 16]
         output is a Tensor:
             out.shape = [3, 2, 16]
@@ -1251,7 +1251,7 @@ class Embedding(layers.Layer):
     Parameters:
         num_embeddings (int): Just one element which indicate the size
             of the dictionary of embeddings.
-        embedding_dim:  Just one element which indicate the size of each embedding vector respectively.
+        embedding_dim (int):  Just one element which indicate the size of each embedding vector respectively.
         padding_idx(int|long|None): padding_idx needs to be in the interval [-num_embeddings, num_embeddings).
             If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
             to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup
@@ -1260,9 +1260,7 @@ class Embedding(layers.Layer):
         sparse(bool): The flag indicating whether to use sparse update. This parameter only
             affects the performance of the backwards gradient update. It is recommended to set
             True because sparse update is faster. But some optimizer does not support sparse update,
-            such as :ref:`api_optimizer_AdadeltaOptimizer` , :ref:`api_optimizer_AdamaxOptimizer` ,
-            :ref:`api_optimizer_DecayedAdagradOptimizer` , :ref:`api_optimizer_FtrlOptimizer` ,
-            :ref:`api_optimizer_LambOptimizer` and :ref:`api_optimizer_LarsMomentumOptimizer` .
+            such as :ref:`api_paddle_optimizer_adadelta_Adadelta` , :ref:`api_paddle_optimizer_adamax_Adamax` , :ref:`api_paddle_optimizer_lamb_Lamb`.
             In these case, sparse must be False. Default: False.
         weight_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
             default weight parameter property is used. See usage for details in :ref:`api_ParamAttr` . In addition,