Fix and optimize async distribute lookup table

paddle/contrib/inference/paddle_inference_api_impl.h

@@ -54,7 +54,7 @@ class PaddlePredictorImpl : public PaddlePredictor {
   ~PaddlePredictorImpl() override{};
 
  private:
-  bool InitShared();
+  bool InitShared() override;
   bool SetFeed(const std::vector<PaddleTensor> &input_datas,
                std::vector<paddle::framework::LoDTensor> *feeds);
   bool GetFetch(const std::vector<paddle::framework::LoDTensor> &fetchs,

paddle/fluid/framework/selected_rows.cc

@@ -121,24 +121,29 @@ bool SelectedRows::HasKey(int64_t key) const {
 }
 
 std::vector<std::pair<int64_t, int64_t>> SelectedRows::Get(
-    std::vector<int64_t> keys, framework::Tensor* value) const {
+    const std::vector<int64_t>& keys, framework::Tensor* value) const {
   PADDLE_ENFORCE(value->IsInitialized(),
                  "The value tensor should be initialized.");
   std::vector<std::pair<int64_t, int64_t>> non_keys_pair;
-  int64_t value_width = value_->numel() / value_->dims()[0];
-  PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
-                    "output tensor should have the same shape with table "
-                    "execpt the dims[0].");
-
-  for (size_t i = 0; i < keys.size(); ++i) {
-    int64_t index = Index(keys[i]);
-    if (index == -1) {
-      non_keys_pair.push_back(std::make_pair(keys[i], static_cast<int64_t>(i)));
-    } else {
-      framework::VisitDataType(
-          framework::ToDataType(value_->type()),
-          TensorCopyVisitor(value, i * value_width, *value_.get(),
-                            index * value_width, value_width));
+  if (keys.empty()) {
+    VLOG(3) << "keys is empty, please check data!";
+  } else {
+    int64_t value_width = value_->numel() / value_->dims()[0];
+    PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
+                      "output tensor should have the same shape with table "
+                      "except the dims[0].");
+
+    for (size_t i = 0; i < keys.size(); ++i) {
+      int64_t index = Index(keys[i]);
+      if (index == -1) {
+        non_keys_pair.push_back(
+            std::make_pair(keys[i], static_cast<int64_t>(i)));
+      } else {
+        framework::VisitDataType(
+            framework::ToDataType(value_->type()),
+            TensorCopyVisitor(value, i * value_width, *value_.get(),
+                              index * value_width, value_width));
+      }
     }
   }
   return non_keys_pair;

paddle/fluid/framework/selected_rows.h

@@ -82,7 +82,7 @@ class SelectedRows {
    * @return a list of pair which contains the non-exists key and the index in
    * the value
    */
-  std::vector<std::pair<int64_t, int64_t>> Get(std::vector<int64_t> keys,
+  std::vector<std::pair<int64_t, int64_t>> Get(const std::vector<int64_t>& keys,
                                                framework::Tensor* value) const;
 
   /*

paddle/fluid/operators/detail/grpc_server.cc

@@ -177,11 +177,8 @@ class RequestPrefetch final : public RequestBase {
         program_(program),
         prefetch_ctx_(prefetch_ctx),
         req_id_(req_id) {
-    if (sync_mode_) {
-      request_.reset(new VariableResponse(scope, dev_ctx_, false));
-    } else {
-      request_.reset(new VariableResponse(scope, dev_ctx_, true));
-    }
+    // prefetch always create a new sub scope
+    request_.reset(new VariableResponse(scope, dev_ctx_, true));
     int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
@@ -198,10 +195,10 @@ class RequestPrefetch final : public RequestBase {
     std::string var_name = request_->OutVarname();
     VLOG(3) << "RequestPrefetch " << var_name;
     auto var_desc = program_->Block(0).FindVar(var_name);
-    framework::Scope* local_scope = &scope_->NewScope();
+    framework::Scope* local_scope = request_->GetMutableLocalScope();
     auto* var = local_scope->FindVar(var_name);
     InitializeVariable(var, var_desc->GetType());
-    executor_->RunPreparedContext(prefetch_ctx_, scope_);
+    executor_->RunPreparedContext(prefetch_ctx_, local_scope);
 
     SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply_);
 

paddle/fluid/operators/listen_and_serv_op.cc

@@ -207,6 +207,7 @@ static void AsyncUpdateThread(
   while (!exit_flag) {
     const detail::ReceivedMessage v = queue->Pop();
     auto recv_var_name = v.first;
+    VLOG(4) << "async update " << recv_var_name;
     auto var = v.second->GetVar();
     if (var == nullptr) {
       LOG(ERROR) << "Can not find server side var: " << recv_var_name;

paddle/fluid/operators/lookup_sparse_table_op.cc

@@ -127,7 +127,7 @@ class LookupSparseTableOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(-1.0f);
     AddAttr<float>("max",
                    "(float, default 1.0) "
-                   "Maximun value of uniform random")
+                   "Maximum value of uniform random")
         .SetDefault(1.0f);
     AddAttr<int>("seed",
                  "(int, default 0) "

paddle/fluid/operators/sgd_op.h

@@ -96,8 +96,12 @@ class SGDOpKernel : public framework::OpKernel<T> {
         return;
       }
 
-      size_t param_row_width = param.value().numel() / param.rows().size();
-      size_t grad_row_width = grad.value().numel() / grad.rows().size();
+      auto param_row_width = param.value().dims()[1];
+      auto grad_row_width = grad.value().dims()[1];
+      VLOG(4) << " param rows: " << param.rows().size()
+              << " param memory rows: " << param.value().dims()[0]
+              << " grad rows: " << grad.rows().size()
+              << " grad memory rows: " << grad.value().dims()[0];
       PADDLE_ENFORCE_EQ(param_row_width, grad_row_width,
                         "param_row should have the same size with grad_row");
 

python/paddle/fluid/framework.py

@@ -797,7 +797,7 @@ def rename_var(self, name, new_name):
         Rename variable in vars and ops' inputs and outputs
         """
         if not self.has_var(name):
-            raise ValueError("var %s is not in current" % name)
+            raise ValueError("var %s is not in current block" % name)
         v = self.var(name)
         if type(v) == Parameter:
             var_type = "Parameter"
@@ -843,6 +843,7 @@ def rename_var(self, name, new_name):
         self.vars[new_name] = var
         del self.vars[name]
         self.sync_with_cpp()
+        return var
 
     def remove_var(self, name):
         self.sync_with_cpp()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -273,15 +273,25 @@ def transpile(self,
                 if param_grad[0].name == self.table_name
             ][0]
             table_grad_var = self.table_param_grad[1]
-            self.table_grad_list = [
-                program.global_block().create_var(
-                    name="%s.trainer_%d.pserver_%d" %
-                    (table_grad_var.name, trainer_id, index),
-                    type=table_grad_var.type,
-                    shape=table_grad_var.shape,
-                    dtype=table_grad_var.dtype)
-                for index in range(len(self.pserver_endpoints))
-            ]
+            if self.sync_mode:
+                self.trainer_side_table_grad_list = [
+                    program.global_block().create_var(
+                        name="%s.trainer_%d.pserver_%d" %
+                        (table_grad_var.name, trainer_id, index),
+                        type=table_grad_var.type,
+                        shape=table_grad_var.shape,
+                        dtype=table_grad_var.dtype)
+                    for index in range(len(self.pserver_endpoints))
+                ]
+            else:
+                self.trainer_side_table_grad_list = [
+                    program.global_block().create_var(
+                        name="%s.pserver_%d" % (table_grad_var.name, index),
+                        type=table_grad_var.type,
+                        shape=table_grad_var.shape,
+                        dtype=table_grad_var.dtype)
+                    for index in range(len(self.pserver_endpoints))
+                ]
 
         grad_blocks = split_dense_variable(grad_list, len(pserver_endpoints))
         param_blocks = split_dense_variable(param_list, len(pserver_endpoints))
@@ -400,7 +410,8 @@ def transpile(self,
                 attrs={"axis": 0})
 
         if self.has_distributed_lookup_table:
-            self._replace_lookup_table_op_with_prefetch(program, eplist)
+            self._replace_lookup_table_op_with_prefetch(program,
+                                                        pserver_endpoints)
             self._split_table_grad_and_add_send_vars(program, pserver_endpoints)
 
     def get_trainer_program(self):
@@ -537,7 +548,7 @@ def __append_optimize_op__(op, block, grad_to_block_id):
         if self.has_distributed_lookup_table:
             pserver_index = self.pserver_endpoints.index(endpoint)
             table_opt_block = self._create_table_optimize_block(
-                pserver_index, pserver_program, pre_block_idx)
+                pserver_index, pserver_program, pre_block_idx, grad_to_block_id)
             prefetch_block = self._create_prefetch_block(
                 pserver_index, pserver_program, table_opt_block)
 
@@ -621,7 +632,8 @@ def _get_splited_name_and_shape(varname):
         return s_prog
 
     # transpiler function for dis lookup_table
-    def _replace_lookup_table_op_with_prefetch(self, program, eplist):
+    def _replace_lookup_table_op_with_prefetch(self, program,
+                                               pserver_endpoints):
         # 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op
         self.prefetch_input_vars = None
         self.prefetch_output_vars = None
@@ -670,7 +682,7 @@ def _replace_lookup_table_op_with_prefetch(self, program, eplist):
                         inputs={'X': self.prefetch_input_vars},
                         outputs={"Out": self.prefetch_output_vars},
                         attrs={
-                            "epmap": eplist,
+                            "epmap": pserver_endpoints,
                             RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                         })
 
@@ -707,11 +719,11 @@ def _split_table_grad_and_add_send_vars(self, program, pserver_endpoints):
                     inputs={
                         'Ids': [program.global_block().vars[table_grad_name]]
                     },
-                    outputs={"Out": self.table_grad_list})
+                    outputs={"Out": self.trainer_side_table_grad_list})
                 program.global_block().insert_op(
                     index=op_index + 2,
                     type="send_vars",
-                    inputs={'X': self.table_grad_list},
+                    inputs={'X': self.trainer_side_table_grad_list},
                     outputs={},
                     attrs={
                         "sync_send": True,
@@ -750,16 +762,7 @@ def _create_prefetch_block(self, pserver_index, pserver_program,
         return prefetch_block
 
     def _create_table_optimize_block(self, pserver_index, pserver_program,
-                                     pre_block_idx):
-        def _clone_var(block, var, persistable=True):
-            assert isinstance(var, Variable)
-            return block.create_var(
-                name=var.name,
-                shape=var.shape,
-                dtype=var.dtype,
-                type=var.type,
-                persistable=persistable)
-
+                                     pre_block_idx, grad_to_block_id):
         # STEP: create table optimize block
         # create table param and grad var in pserver program
         origin_param_var = self.origin_program.global_block().vars[
@@ -770,11 +773,11 @@ def _clone_var(block, var, persistable=True):
             dtype=origin_param_var.dtype,
             type=core.VarDesc.VarType.SELECTED_ROWS,
             persistable=True)
-        grad_var = _clone_var(
-            pserver_program.global_block(),
+        # parameter must be selected rows
+        param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+        grad_var = pserver_program.global_block().clone_variable(
             self.origin_program.global_block().vars[grad_var_name(
-                self.table_name)],
-            persistable=False)
+                self.table_name)])
 
         # create table optimize block in pserver program
         table_opt_op = [
@@ -788,7 +791,7 @@ def _clone_var(block, var, persistable=True):
         if self.sync_mode:
             # create grad vars in pserver program
             table_grad_var = self.table_param_grad[1]
-            table_grad_list = [
+            pserver_side_table_grad_list = [
                 pserver_program.global_block().create_var(
                     name="%s.trainer_%d.pserver_%d" %
                     (table_grad_var.name, index, pserver_index),
@@ -798,11 +801,21 @@ def _clone_var(block, var, persistable=True):
                 for index in range(self.trainer_num)
             ]
 
-            # append sum op for table_grad_list
+            # append sum op for pserver_side_table_grad_list
             table_opt_block.append_op(
                 type="sum",
-                inputs={"X": table_grad_list},
+                inputs={"X": pserver_side_table_grad_list},
                 outputs={"Out": [grad_var]})
+        else:
+            # in async_mode, for table gradient, it also need to be splited to each parameter server
+            origin_grad_name = grad_var.name
+            splited_grad_name = self.trainer_side_table_grad_list[
+                pserver_index].name
+            if not splited_grad_name.startswith(origin_grad_name):
+                raise ValueError("origin_grad_var: " + splited_grad_name +
+                                 " grad_var:" + grad_var.name)
+            grad_var = pserver_program.global_block().rename_var(
+                origin_grad_name, splited_grad_name)
 
         lr_var = pserver_program.global_block().vars[table_opt_op.input(
             "LearningRate")[0]]
@@ -818,6 +831,9 @@ def _clone_var(block, var, persistable=True):
             outputs=outputs,
             attrs=table_opt_op.attrs)
 
+        # add table parameter gradient and it's block id to grad_to_block_id
+        grad_to_block_id.append(grad_var.name + ":" + str(table_opt_block.idx))
+
         return table_opt_block
 
     # ====================== private transpiler functions =====================