Test dist word2vec (#7334)

* test dist word2vec

* multiple trainers work

paddle/framework/executor.cc

@@ -35,7 +35,7 @@ const std::string kFetchOpType = "fetch";
 
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
-void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
+static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
   if (var_type == proto::VarDesc::LOD_TENSOR) {
     var->GetMutable<LoDTensor>();
   } else if (var_type == proto::VarDesc::SELECTED_ROWS) {

paddle/framework/executor.h

@@ -45,7 +45,5 @@ class Executor {
   const platform::Place place_;
 };
 
-void CreateTensor(Variable* var, proto::VarDesc::VarType var_type);
-
 }  // namespace framework
 }  // namespace paddle

paddle/operators/recv_op.cc

@@ -32,6 +32,20 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+static void CreateTensorFromMessageType(framework::Variable *var,
+                                        sendrecv::VarType var_type) {
+  if (var_type == sendrecv::VarType::LOD_TENSOR) {
+    var->GetMutable<framework::LoDTensor>();
+  } else if (var_type == sendrecv::VarType::SELECTED_ROWS) {
+    var->GetMutable<framework::SelectedRows>();
+  } else {
+    PADDLE_THROW(
+        "VraibleMessage type %d is not in "
+        "[LoDTensor, SelectedRows]",
+        var_type);
+  }
+}
+
 void RunServer(Server **rpc_server,
                std::shared_ptr<detail::SendRecvServerImpl> service,
                const std::string &server_address) {
@@ -111,10 +125,10 @@ class RecvOp : public framework::OperatorBase {
         auto *merged_grad = recv_scope.FindVar(grad_var_name);
         if (merged_grad == nullptr) {
           auto *ptr = recv_scope.Var(grad_var_name);
-          framework::CreateTensor(ptr,
-                                  framework::ToVarType(merged_grad->Type()));
+          CreateTensorFromMessageType(ptr, v.second.type());
           VLOG(3) << "Create Variable " << grad_var_name
-                  << " on recv scope, which pointer is " << ptr;
+                  << " on recv scope, which pointer is " << ptr << " type is "
+                  << v.second.type();
         }
 
         if (trainer_count > 1) {

paddle/operators/sum_op.h

@@ -70,6 +70,7 @@ class SumKernel : public framework::OpKernel<T> {
     } else if (out_var->IsType<framework::SelectedRows>()) {
       PADDLE_ENFORCE(!in_place, "SelectedRows not support inplace sum now");
       auto *out = context.Output<SelectedRows>("Out");
+      out->mutable_rows()->clear();
       auto *out_value = out->mutable_value();
 
       // Runtime InferShape

python/paddle/v2/fluid/tests/book_distribute/test_dist_word2vec.py

+from __future__ import print_function
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import os
+
+PASS_NUM = 100
+EMBED_SIZE = 32
+HIDDEN_SIZE = 256
+N = 5
+BATCH_SIZE = 32
+IS_SPARSE = True
+TRAINERS = 2
+
+word_dict = paddle.dataset.imikolov.build_dict()
+dict_size = len(word_dict)
+
+first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
+second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
+third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
+forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
+next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+
+embed_first = fluid.layers.embedding(
+    input=first_word,
+    size=[dict_size, EMBED_SIZE],
+    dtype='float32',
+    is_sparse=IS_SPARSE,
+    param_attr='shared_w')
+embed_second = fluid.layers.embedding(
+    input=second_word,
+    size=[dict_size, EMBED_SIZE],
+    dtype='float32',
+    is_sparse=IS_SPARSE,
+    param_attr='shared_w')
+embed_third = fluid.layers.embedding(
+    input=third_word,
+    size=[dict_size, EMBED_SIZE],
+    dtype='float32',
+    is_sparse=IS_SPARSE,
+    param_attr='shared_w')
+embed_forth = fluid.layers.embedding(
+    input=forth_word,
+    size=[dict_size, EMBED_SIZE],
+    dtype='float32',
+    is_sparse=IS_SPARSE,
+    param_attr='shared_w')
+
+concat_embed = fluid.layers.concat(
+    input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
+predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
+cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
+avg_cost = fluid.layers.mean(x=cost)
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+train_reader = paddle.batch(
+    paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+t = fluid.DistributeTranspiler()
+# all parameter server endpoints list for spliting parameters
+pserver_endpoints = os.getenv("PSERVERS")
+# server endpoint for current node
+current_endpoint = os.getenv("SERVER_ENDPOINT")
+# run as trainer or parameter server
+training_role = os.getenv("TRAINING_ROLE",
+                          "TRAINER")  # get the training role: trainer/pserver
+t.transpile(
+    optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS)
+if training_role == "PSERVER":
+    if not current_endpoint:
+        print("need env SERVER_ENDPOINT")
+        exit(1)
+    pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+    exe.run(fluid.default_startup_program())
+    exe.run(pserver_prog)
+elif training_role == "TRAINER":
+    feeder = fluid.DataFeeder(
+        feed_list=[first_word, second_word, third_word, forth_word, next_word],
+        place=place)
+    exe.run(fluid.default_startup_program())
+    for pass_id in range(PASS_NUM):
+        for data in train_reader():
+            avg_cost_np = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+            print("avg_cost_np", avg_cost_np)
+            if avg_cost_np[0] < 5.0:
+                exit(
+                    0)  # if avg cost less than 10.0, we think our code is good.
+else:
+    print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+exit(1)