Merge pull request #11342 from jacquesqiao/add-merge-splited-ids

Add merge_ids_op

paddle/fluid/framework/executor.cc

@@ -330,8 +330,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
   }
 
   for (auto& op : ctx->ops_) {
-    VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
+    VLOG(4) << place_ << " " << op->DebugStringEx(local_scope);
     op->Run(*local_scope, place_);
+    // NOTE! Please do not delete this line, it's usefull because the debug
+    // string before and after op.run are different, after run the output
+    // will have right shape which is usefull for debug.
+    VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
 
     if (FLAGS_benchmark) {
       VLOG(2) << "Memory used after operator " + op->Type() + " running: "

paddle/fluid/framework/operator.cc

@@ -69,6 +69,19 @@ static DDim GetDims(const Scope& scope, const std::string& name,
   }
 }
 
+static int GetRowSize(const Scope& scope, const std::string& name) {
+  Variable* var = scope.FindVar(name);
+  if (var == nullptr) {
+    return -1;
+  }
+
+  if (var->IsType<SelectedRows>()) {
+    return var->Get<SelectedRows>().rows().size();
+  }
+
+  return -1;
+}
+
 static LoD GetLoD(const Scope& scope, const std::string& name) {
   Variable* var = scope.FindVar(name);
   auto default_lod = LoD({{}});
@@ -153,6 +166,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < input.second.size(); ++i) {
       ss << input.second[i];
       if (scope) {
+        int row_size = GetRowSize(*scope, input.second[i]);
+        if (row_size >= 0) {
+          ss << "[row_size=" << row_size << "]";
+        }
         ss << "[" << GetDims(*scope, input.second[i], true) << "]";
         ss << "(" << GetLoD(*scope, input.second[i]) << ")";
       }
@@ -173,6 +190,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < output.second.size(); ++i) {
       ss << output.second[i];
       if (scope) {
+        int row_size = GetRowSize(*scope, output.second[i]);
+        if (row_size >= 0) {
+          ss << "[row_size=" << row_size << "]";
+        }
         ss << "[" << GetDims(*scope, output.second[i], true) << "]";
         ss << "(" << GetLoD(*scope, output.second[i]) << ")";
       }

paddle/fluid/operators/merge_ids_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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 "paddle/fluid/operators/merge_ids_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MergeIdsOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}");
+    AddInput(
+        "X",
+        "(LoDTensors) multi input tensor with shape{batch_num, N}, N is the "
+        "size of embedding table")
+        .AsDuplicable();
+    AddOutput("Out", "(LoDTensor) The merged outputs of the input tensors.");
+
+    AddComment(R"DOC(
+Merge multi LoDTensor's into one according to Ids's shard num.
+
+
+split_ids_op -> prefetch_op -> merge_ids_op
+
+
+merge_ids_op should be used after split_ids_op and prefetch_op, split_ids_op
+ will split input Ids into multiple tensors according to Id's shard number.
+prefetch_op will send them to parameter server to prefetch embedding value
+back. During split, the order of ids is disordered. In merge_ids_op we use
+the original Ids to restore the order of the fetched embedding value and
+ also pass the lod information to the merged output.
+
+
+Example:
+
+    Ids = [1,2,3,4,5,6] # 3 shared
+
+split_ids_op ->
+
+    Id0 = [3, 6] # id % 3 == 0
+    Id1 = [1, 4] # id % 3 == 1
+    Id2 = [2, 5] # id % 3 == 2
+
+prefetch_op ->
+
+    X0 = [[0.3 0.3]   # 3
+          [0.6 0.6]]  # 6
+    X1 = [[0.1 0.1]   # 1
+          [0.4 0.4]]  # 4
+    X2 = [[0.2 0.2]   # 2
+          [0.5 0.5]]  # 5
+
+merge_ids_op ->
+
+    Out = [[0.1 0.1]  # 1
+           [0.2 0.2]  # 2
+           [0.3 0.3]  # 3
+           [0.4 0.4]  # 4
+           [0.5 0.5]  # 5
+           [0.6 0.6]] # 6
+)DOC");
+  }
+};
+
+class MergeIdsOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Ids"), "MergeIdsOp must has input Ids.");
+    PADDLE_ENFORCE(ctx->HasInputs("X"), "MergeIdsOp must has input X.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"), "MergeIdsOp must has output Out.");
+
+    auto ids_var_type = ctx->GetInputsVarType("Ids").front();
+    auto ids_dims = ctx->GetInputDim("Ids");
+    if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
+      PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
+      PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+    }
+    auto x_var_type = ctx->GetInputsVarType("X");
+    for (auto &var_type : x_var_type) {
+      PADDLE_ENFORCE_EQ(var_type, framework::proto::VarType::LOD_TENSOR,
+                        "input X only support lod tensors");
+    }
+    ctx->ShareLoD("Ids", "Out");
+  }
+
+ private:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.MultiInput<framework::Tensor>("X").front()->type()),
+        ctx.GetPlace());
+  }
+};
+
+class MergeIdsOpInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    auto *input_var = block->Var(op_desc.Input("Ids")[0]);
+    for (auto &out_var : op_desc.Output("Out")) {
+      block->Var(out_var)->SetType(input_var->GetType());
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(merge_ids, ops::MergeIdsOp, ops::MergeIdsOpMaker,
+                  ops::MergeIdsOpInferVarType);
+REGISTER_OP_CPU_KERNEL(
+    merge_ids, ops::MergeIdsOpKernel<paddle::platform::CPUPlace, float>);

paddle/fluid/operators/merge_ids_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#pragma once
+
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/math/selected_rows_functor.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class MergeIdsOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto place = ctx.GetPlace();
+    if (!platform::is_cpu_place(place)) {
+      PADDLE_THROW("MergeIds do not support GPU kernel");
+    }
+    VLOG(3) << "run in MergeIdsOpKernel";
+
+    const auto *ids_var = ctx.InputVar("Ids");
+    PADDLE_ENFORCE(ids_var->IsType<framework::LoDTensor>(),
+                   "only support to merge Ids of LoDTensor");
+
+    const auto &ids_tensor = ids_var->Get<framework::LoDTensor>();
+    const auto &ids_dims = ids_tensor.dims();
+    const int64_t *ids = ids_tensor.data<int64_t>();
+
+    auto x_tensors = ctx.MultiInput<framework::LoDTensor>("X");
+
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
+
+    int batch_size = 0;
+    int embedding_size = 0;
+    for (auto &input : x_tensors) {
+      if (framework::product(input->dims()) != 0) {
+        if (embedding_size == 0) {
+          embedding_size = input->dims()[1];
+        }
+        PADDLE_ENFORCE_EQ(embedding_size, input->dims()[1],
+                          "embedding size of all input should be the same");
+        batch_size += input->dims()[0];
+      }
+    }
+    PADDLE_ENFORCE_EQ(
+        batch_size, ids_dims[0],
+        "the batch size of ids and merged embedding value should be the same");
+
+    const size_t shard_num = x_tensors.size();
+
+    if (shard_num == 1) {
+      VLOG(3) << "only one shard, we can copy the data directly";
+      TensorCopy(*x_tensors[0], place, out);
+    } else {
+      std::vector<int> in_indexs(shard_num, 0);
+      auto *out_data = out->mutable_data<T>(
+          framework::make_ddim({batch_size, embedding_size}), place);
+      // copy data from ins[shard_num] to out.
+      for (int i = 0; i < ids_dims[0]; ++i) {
+        int64_t id = ids[i];
+        size_t shard_id = static_cast<size_t>(id) % shard_num;
+        int index = in_indexs[shard_id];
+        memcpy(out_data + embedding_size * i,
+               x_tensors[shard_id]->data<T>() + index * embedding_size,
+               sizeof(T) * embedding_size);
+        in_indexs[shard_id] += 1;
+      }
+
+      for (size_t i = 0; i < shard_num; ++i) {
+        PADDLE_ENFORCE_EQ(in_indexs[i], x_tensors[i]->dims()[0],
+                          "after merge, all data in x_tensor should be used");
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestMergeIdsOp(OpTest):
+    def setUp(self):
+        self.op_type = "merge_ids"
+        ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
+        x0 = np.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]).astype('float32')
+        x1 = np.array([]).astype('float32')
+        x2 = np.array([[0.4, 0.5], [0.4, 0.5], [0.5, 0.6],
+                       [0.5, 0.6]]).astype('float32')
+        out = np.array([[0.1, 0.2], [0.4, 0.5], [0.4, 0.5], [0.2, 0.3],
+                        [0.5, 0.6], [0.5, 0.6], [0.3, 0.4]]).astype('float32')
+        self.inputs = {'Ids': ids, "X": [('x0', x0), ('x1', x1), ('x2', x2)]}
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -629,7 +629,7 @@ class DistributeTranspiler:
                 if op.type == LOOKUP_TABLE_TYPE:
                     continue_search_lookup_table_op = True
 
-                    op_index = list(all_ops).index(op)
+                    lookup_table_op_index = list(all_ops).index(op)
                     ids_name = op.input("Ids")
                     out_name = op.output("Out")
 
@@ -649,7 +649,7 @@ class DistributeTranspiler:
 
                     # insert split_ids_op
                     program.global_block().insert_op(
-                        index=op_index,
+                        index=lookup_table_op_index,
                         type="split_ids",
                         inputs={
                             'Ids': [
@@ -661,7 +661,7 @@ class DistributeTranspiler:
 
                     # insert prefetch_op
                     program.global_block().insert_op(
-                        index=op_index + 1,
+                        index=lookup_table_op_index + 1,
                         type="prefetch",
                         inputs={'X': prefetch_input_vars},
                         outputs={"Out": prefetch_output_vars},
@@ -672,16 +672,21 @@ class DistributeTranspiler:
 
                     # insert concat_op
                     program.global_block().insert_op(
-                        index=op_index + 2,
-                        type="concat",
-                        inputs={'X': prefetch_output_vars},
+                        index=lookup_table_op_index + 2,
+                        type="merge_ids",
+                        inputs={
+                            'Ids': [
+                                program.global_block().vars[varname]
+                                for varname in ids_name
+                            ],
+                            'X': prefetch_output_vars
+                        },
                         outputs={
                             "Out": [
                                 program.global_block().vars[varname]
                                 for varname in out_name
                             ]
-                        },
-                        attrs={"axis": 0})
+                        })
 
                     # delete lookup_table_op
                     delete_ops(program.global_block(), [op])