[cherry-pick] fix fluid.embedding (#25328)

MrChengmo · web-flow · commit 38f9b71bdb2a · 2020-07-03T11:24:40.000+08:00
* test=release/1.8, cherry fix fluid.embedding
diff --git a/paddle/fluid/operators/distributed/parameter_prefetch.cc b/paddle/fluid/operators/distributed/parameter_prefetch.cc
@@ -209,16 +209,20 @@ void prefetchs(const std::vector<std::string>& id_var_names,
   TableAndEndpoints tables;
 
   for (auto& id_name : id_var_names) {
-    auto& id_tensor = scope.FindVar(id_name)->Get<framework::LoDTensor>();
-    auto* id_data = id_tensor.data<int64_t>();
+    auto* id_tensor =
+        scope.FindVar(id_name)->GetMutable<framework::LoDTensor>();
+    auto id_dims = id_tensor->dims();
+    id_tensor->Resize(framework::make_ddim(
+        {static_cast<int64_t>(id_dims[0] * id_dims[1]), 1}));
+    auto* id_data = id_tensor->data<int64_t>();
     std::vector<int64_t> ids;
 
-    for (int64_t i = 0; i < id_tensor.numel(); ++i) {
+    for (int64_t i = 0; i < id_tensor->numel(); ++i) {
       ids.push_back(id_data[i]);
       ids_union.push_back(id_data[i]);
     }
     ids_group.push_back(ids);
-    ids_lods.push_back(id_tensor.lod());
+    ids_lods.push_back(id_tensor->lod());
   }
 
   std::unordered_set<int64_t> s(ids_union.begin(), ids_union.end());
diff --git a/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc b/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc
@@ -26,7 +26,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInputs("Ids"),
                    "Input(Ids) of LookupTableOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("W"),
@@ -40,18 +40,18 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(table_dims.size(), 2,
                       "Only 2 dimensions of the 'Embedding' is supported.");
 
-    for (auto &ids_dim : ids_dims) {
+    for (auto& ids_dim : ids_dims) {
       PADDLE_ENFORCE_EQ(ids_dim.size(), 2,
                         "The dimension of the 'Ids' tensor must be 2.");
-      PADDLE_ENFORCE_EQ(ids_dim[1], 1,
-                        "The last dimension of the 'Ids' tensor must be 1.");
     }
 
     auto lookup_tables =
         ctx->Attrs().Get<std::vector<std::string>>("table_names");
     auto height_sections =
         ctx->Attrs().Get<std::vector<int64_t>>("height_sections");
     auto endpoints = ctx->Attrs().Get<std::vector<std::string>>("endpoints");
+    auto lookup_table_version =
+        ctx->Attrs().Get<std::string>("lookup_table_version");
 
     PADDLE_ENFORCE(lookup_tables.size() == height_sections.size() &&
                        lookup_tables.size() == endpoints.size() &&
@@ -61,8 +61,15 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel {
 
     auto outputs_dims = std::vector<framework::DDim>();
 
-    for (auto &ids_dim : ids_dims) {
-      outputs_dims.push_back(framework::make_ddim({ids_dim[0], table_dims[1]}));
+    for (auto& ids_dim : ids_dims) {
+      if (lookup_table_version == "lookup_table") {
+        outputs_dims.push_back(
+            framework::make_ddim({ids_dim[0], table_dims[1]}));
+      } else if (lookup_table_version == "lookup_table_v2") {
+        outputs_dims.push_back(framework::make_ddim(
+            {static_cast<int64_t>(ids_dim[0]), static_cast<int64_t>(ids_dim[1]),
+             static_cast<int64_t>(table_dims[1])}));
+      }
     }
 
     ctx->SetOutputsDim("Outputs", outputs_dims);
@@ -71,7 +78,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
+      const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::proto::VarType::Type(ctx.Attr<int>("dtype")),
         ctx.GetPlace());
@@ -81,7 +88,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel {
 template <typename T>
 class DistributedLookupTableKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext &context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto ids_vars = context.MultiInputVar("Ids");
     auto emb_vars = context.MultiOutput<framework::Tensor>("Embeddings");
 
@@ -93,10 +100,30 @@ class DistributedLookupTableKernel : public framework::OpKernel<T> {
     auto height_sections =
         context.Attr<std::vector<int64_t>>("height_sections");
     auto endpoints = context.Attr<std::vector<std::string>>("endpoints");
+    auto lookup_table_version =
+        context.Attr<std::string>("lookup_table_version");
 
     operators::distributed::prefetchs(
         id_names, out_names, embedding_name, false, lookup_tables, endpoints,
         height_sections, context, context.scope());
+
+    if (lookup_table_version == "lookup_table_v2") {
+      auto& scope = context.scope();
+      auto emb_dim =
+          scope.FindVar(embedding_name)->Get<framework::LoDTensor>().dims()[1];
+
+      for (size_t i = 0; i < id_names.size(); ++i) {
+        auto* id_var = scope.FindVar(id_names[i]);
+        auto* out_var = scope.FindVar(out_names[i]);
+        auto* id_tensor = id_var->GetMutable<framework::LoDTensor>();
+        auto* out_tensor = out_var->GetMutable<framework::LoDTensor>();
+
+        auto id_dims = id_tensor->dims();
+        out_tensor->Resize(framework::make_ddim(
+            {static_cast<int64_t>(id_dims[0]), static_cast<int64_t>(id_dims[1]),
+             static_cast<int64_t>(emb_dim)}));
+      }
+    }
   }
 };
 
@@ -134,6 +161,12 @@ class DistributedLookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
 
+    AddAttr<std::string>(
+        "lookup_table_version",
+        "(string, default lookup_table) "
+        "To distinguish between different versions of embedding OP")
+        .SetDefault(std::string("lookup_table"));
+
     AddAttr<int64_t>("padding_idx",
                      "(int64, default -1) "
                      "If the value is -1, it makes no effect to lookup. "
diff --git a/python/paddle/fluid/tests/unittests/dist_simnet_bow.py b/python/paddle/fluid/tests/unittests/dist_simnet_bow.py
@@ -92,8 +92,8 @@ def train_network(batch_size,
     # query
     q = fluid.layers.data(
         name="query_ids", shape=[1], dtype="int64", lod_level=1)
-    ## embedding
-    q_emb = fluid.layers.embedding(
+    # embedding
+    q_emb = fluid.embedding(
         input=q,
         is_distributed=is_distributed,
         size=[dict_dim, emb_dim],
@@ -104,10 +104,11 @@ def train_network(batch_size,
                 initializer=fluid.initializer.Constant(value=0.01),
                 name="__emb__"),
         is_sparse=is_sparse)
-    ## vsum
+    q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
+    # vsum
     q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
     q_ss = fluid.layers.softsign(q_sum)
-    ## fc layer after conv
+    # fc layer after conv
     q_fc = fluid.layers.fc(
         input=q_ss,
         size=hid_dim,
@@ -120,8 +121,8 @@ def train_network(batch_size,
     # pt
     pt = fluid.layers.data(
         name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
-    ## embedding
-    pt_emb = fluid.layers.embedding(
+    # embedding
+    pt_emb = fluid.embedding(
         input=pt,
         is_distributed=is_distributed,
         size=[dict_dim, emb_dim],
@@ -132,10 +133,11 @@ def train_network(batch_size,
                 initializer=fluid.initializer.Constant(value=0.01),
                 name="__emb__"),
         is_sparse=is_sparse)
-    ## vsum
+    pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
+    # vsum
     pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
     pt_ss = fluid.layers.softsign(pt_sum)
-    ## fc layer
+    # fc layer
     pt_fc = fluid.layers.fc(
         input=pt_ss,
         size=hid_dim,
@@ -147,8 +149,8 @@ def train_network(batch_size,
     # nt
     nt = fluid.layers.data(
         name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
-    ## embedding
-    nt_emb = fluid.layers.embedding(
+    # embedding
+    nt_emb = fluid.embedding(
         input=nt,
         is_distributed=is_distributed,
         size=[dict_dim, emb_dim],
@@ -159,10 +161,11 @@ def train_network(batch_size,
                 initializer=fluid.initializer.Constant(value=0.01),
                 name="__emb__"),
         is_sparse=is_sparse)
-    ## vsum
+    nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
+    # vsum
     nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
     nt_ss = fluid.layers.softsign(nt_sum)
-    ## fc layer
+    # fc layer
     nt_fc = fluid.layers.fc(
         input=nt_ss,
         size=hid_dim,
diff --git a/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py b/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
@@ -46,7 +46,7 @@ def _setup_config(self):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    #FIXME(typhoonzero): fix async tests later
+    # FIXME(typhoonzero): fix async tests later
     def notest_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
@@ -107,7 +107,7 @@ def _setup_config(self):
 
     def test_simnet_bow(self):
         need_envs = {
-            "IS_DISTRIBUTED": '1',
+            "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '1',
             'IS_SELF_CONTAINED_LR': '1'
         }
@@ -126,7 +126,7 @@ def _setup_config(self):
 
     def test_simnet_bow(self):
         need_envs = {
-            "IS_DISTRIBUTED": '1',
+            "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '1',
             'IS_SELF_CONTAINED_LR': '1'
         }
@@ -145,7 +145,7 @@ def _setup_config(self):
 
     def test_simnet_bow(self):
         need_envs = {
-            "IS_DISTRIBUTED": '1',
+            "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '1',
             'IS_SELF_CONTAINED_LR': '0'
         }
diff --git a/python/paddle/fluid/transpiler/distribute_transpiler.py b/python/paddle/fluid/transpiler/distribute_transpiler.py
@@ -50,8 +50,8 @@
 from ..distribute_lookup_table import find_distributed_lookup_table
 from . import collective
 
-LOOKUP_TABLE_TYPE = "lookup_table"
-LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
+LOOKUP_TABLE_TYPE = ["lookup_table", "lookup_table_v2"]
+LOOKUP_TABLE_GRAD_TYPE = ["lookup_table_grad", "lookup_table_v2_grad"]
 OP_NAME_SCOPE = "op_namescope"
 CLIP_OP_NAME_SCOPE = "@CLIP"
 OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
@@ -199,10 +199,10 @@ class DistributeTranspilerConfig(object):
     geo_sgd_need_push_nums = 100
 
     nccl_comm_num = 1
-    #The picture here illustrates the principle:
-    #https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
+    # The picture here illustrates the principle:
+    # https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
     use_hierarchical_allreduce = False
-    #Nccl ranks in a node when use hierarchical allreduce, it's set to gpu cards' number in most cases.
+    # Nccl ranks in a node when use hierarchical allreduce, it's set to gpu cards' number in most cases.
     hierarchical_allreduce_inter_nranks = 0
 
     # if mode is collective
@@ -445,7 +445,7 @@ def _transpile_collective(self,
 
     def _get_all_remote_sparse_update_op(self, main_program):
         sparse_update_ops = []
-        sparse_update_op_types = ["lookup_table", "nce"]
+        sparse_update_op_types = ["lookup_table", "nce", "lookup_table_v2"]
         for op in main_program.global_block().ops:
             if op.type in sparse_update_op_types and op.attr(
                     'remote_prefetch') is True:
@@ -475,7 +475,7 @@ def _update_remote_sparse_update_op(self, program,
                     ops.append(op)
                     used_ops.append(idx)
 
-            if op_type == "lookup_table":
+            if op_type in LOOKUP_TABLE_TYPE:
                 all_ops = program.global_block().ops
                 op_idxs = [all_ops.index(op) for op in ops]
                 inputs = [
@@ -521,7 +521,8 @@ def _update_remote_sparse_update_op(self, program,
                             "height_sections": height_sections,
                             "endpoints": endpoints,
                             "padding_idx": padding_idx,
-                            "trainer_id": self.trainer_id
+                            "trainer_id": self.trainer_id,
+                            "lookup_table_version": op_type
                         })
                 else:
                     raise ValueError(
@@ -609,10 +610,12 @@ def transpile(self,
                     )
 
                 assert trainers_num > self.config.hierarchical_allreduce_inter_nranks, \
-                    "trainers_num:{} < hierarchical_allreduce_inter_nranks:{}".format(trainers_num, self.config.hierarchical_allreduce_inter_nranks)
+                    "trainers_num:{} < hierarchical_allreduce_inter_nranks:{}".format(
+                        trainers_num, self.config.hierarchical_allreduce_inter_nranks)
 
                 assert trainers_num % self.config.hierarchical_allreduce_inter_nranks == 0, \
-                    "trainers_num:{} mod hierarchical_allreduce_inter_nranks:{} != 0".format(trainers_num, self.config.hierarchical_allreduce_inter_nranks)
+                    "trainers_num:{} mod hierarchical_allreduce_inter_nranks:{} != 0".format(
+                        trainers_num, self.config.hierarchical_allreduce_inter_nranks)
 
                 self.origin_program._hierarchical_allreduce_inter_nranks = \
                     int(self.config.hierarchical_allreduce_inter_nranks)
@@ -778,7 +781,7 @@ def transpile(self,
                 decay_dummy_output = program.global_block().create_var(
                     name=framework.generate_control_dev_var_name())
                 if self.config.runtime_split_send_recv:
-                    ## async mode, using communicator to merge and send
+                    # async mode, using communicator to merge and send
                     send_varnames = [self.counter_var.name]
                 else:
                     send_varnames = []
@@ -1015,7 +1018,7 @@ def get_trainer_program(self, wait_port=True):
 
             - Delete optimizer related op, because parameter updated on Pserver
             - After the op which computed gradient of each parameter, add ``Send_op`` and ``Recv_op`` 
-        
+
         Args:
             wait_port(bool): Whether to wait for the parameter server to be ready before returning to program, 
             default is True
@@ -1072,7 +1075,7 @@ def _get_trainer_startup_program(self, recv_vars, eplist):
         sparse_table_names = self._get_sparse_table_names()
 
         # self._fake_init_sparsetable(sparse_table_names)
-        #self._delete_trainer_optimizer(is_startup=True)
+        # self._delete_trainer_optimizer(is_startup=True)
 
         for varname, splited_var in six.iteritems(self.param_var_mapping):
             if varname in sparse_table_names:
@@ -1466,8 +1469,8 @@ def get_startup_program(self,
             Program: parameter server side startup program.
 
         Examples:
-	    .. code-block:: python
-            
+            .. code-block:: python
+
                 pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                 trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                 current_endpoint = "192.168.0.1:6174"
@@ -2661,7 +2664,7 @@ def _get_optimize_pass(self):
         for op in block.ops:
             if self._is_opt_role_op(op):
                 # Todo(chengmo): Whether clip related op belongs to Optimize guard should be discussed
-                # delete clip op from opt_ops when run in Parameter Server mode 
+                # delete clip op from opt_ops when run in Parameter Server mode
                 if OP_NAME_SCOPE in op.all_attrs(
                 ) and CLIP_OP_NAME_SCOPE in op.attr(
                         OP_NAME_SCOPE
@@ -2692,7 +2695,7 @@ def _get_optimize_pass(self):
         return opt_ops, params_grads
 
     def _get_distribute_update_vars(self):
-        #TODO(chengmo): find more powerful and simple way to deal with these special situation
+        # TODO(chengmo): find more powerful and simple way to deal with these special situation
         """
         This Function is used for a special model, like PyramidDnn which has pyramid hash op.
         Some Parameters don't use optimizing op to update its value, but updated in its BP process.
