Add LoDRankTable (#5349)

* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add InferVarType

paddle/framework/CMakeLists.txt

@@ -45,8 +45,9 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
 
 cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context fill_constant_op)
+cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
 
-cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog)
+cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog lod_rank_table)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/framework/executor.cc

@@ -21,6 +21,7 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/framework/feed_fetch_type.h"
+#include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/scope.h"
@@ -70,10 +71,12 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
     var->GetMutable<FeedFetchList>();
   } else if (var_type == VarDesc::STEP_SCOPES) {
     var->GetMutable<std::vector<framework::Scope>>();
+  } else if (var_type == VarDesc::LOD_RANK_TABLE) {
+    var->GetMutable<LoDRankTable>();
   } else {
     PADDLE_THROW(
         "Variable type %d is not in "
-        "[LoDTensor, SelectedRows, FEED_MINIBATCH, FETCH_LIST]",
+        "[LoDTensor, SelectedRows, FEED_MINIBATCH, FETCH_LIST, LOD_RANK_TABLE]",
         var_type);
   }
 }

paddle/framework/framework.proto

@@ -116,6 +116,7 @@ message VarDesc {
     FEED_MINIBATCH = 3;
     FETCH_LIST = 4;
     STEP_SCOPES = 5;
+    LOD_RANK_TABLE = 6;
   }
   required string name = 1;
   required VarType type = 2;

paddle/framework/lod_rank_table.cc

+/* Copyright (c) 2016 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/framework/lod_rank_table.h"
+
+namespace paddle {
+namespace framework {
+void LoDRankTable::Reset(const LoD& lod, size_t level) {
+  this->coarse_lod_.clear();
+  this->items_.clear();
+  PADDLE_ENFORCE(level < lod.size(),
+                 "Cannot rank lod since the level %d is less than lod size %d",
+                 level, lod.size());
+  coarse_lod_.reserve(level);
+  for (size_t i = 0; i < level; ++i) {
+    coarse_lod_.push_back(lod[i]);
+  }
+  auto& vec = lod[level];
+  for (size_t i = 0; i < vec.size() - 1; ++i) {
+    TableItem item;
+    item.index = i;
+    item.length = vec[i + 1] - vec[i];
+    items_.emplace_back(item);
+  }
+  std::sort(items_.begin(), items_.end(),
+            [](const TableItem& a, const TableItem& b) {
+              return a.length > b.length;
+            });
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/lod_rank_table.h

+/* Copyright (c) 2016 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 "paddle/framework/lod_tensor.h"
+
+namespace paddle {
+namespace framework {
+
+// LoD Rank Table stores the `level` of `lod` which is ordered by sequence
+// length in descending order. It is useful when implement dynamic RNN and is
+// shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice
+// output operators.
+//
+// The table item contains two element. The length of sequence and the index of
+// sequence in that level.
+//
+// LoDRankTable also stores the coarse_lod, which is the lod information whose
+// level is less than input level, in order to restore the output LoD
+// information.
+class LoDRankTable {
+ public:
+  struct TableItem {
+    size_t index;
+    size_t length;
+  };
+
+  LoDRankTable() {}
+
+  void Reset(const LoD& lod, size_t level);
+
+  const std::vector<TableItem>& items() const { return this->items_; }
+
+  const LoD& coarse_lod() const { return this->coarse_lod_; }
+
+  size_t level() const { return coarse_lod_.size(); }
+
+ private:
+  LoD coarse_lod_;
+  std::vector<TableItem> items_;
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/var_desc.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <vector>
+#include "glog/logging.h"
 #include "paddle/framework/framework.pb.h"
 
 namespace paddle {

paddle/operators/CMakeLists.txt

@@ -141,6 +141,7 @@ set(DEPS_OPS
     pool_with_index_op
     nccl_op
     sequence_conv_op
+    lod_rank_table_op
     lstm_op)
 
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
@@ -149,6 +150,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
+op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
 if(WITH_GPU)
 op_library(nccl_op DEPS nccl_common)
 endif()

paddle/operators/lod_rank_table_op.cc

+/* Copyright (c) 2016 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/framework/lod_rank_table.h"
+#include "paddle/framework/op_registry.h"
+namespace paddle {
+namespace operators {
+
+class LoDRankTableOp : public framework::OperatorBase {
+ public:
+  LoDRankTableOp(const std::string &type,
+                 const framework::VariableNameMap &inputs,
+                 const framework::VariableNameMap &outputs,
+                 const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
+    auto *out =
+        scope.FindVar(Output("Out"))->GetMutable<framework::LoDRankTable>();
+    out->Reset(x.lod(), static_cast<size_t>(Attr<int>("level")));
+  }
+};
+
+class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LoDRankTableOpProtoMaker(framework::OpProto *proto,
+                           framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(LoDTensor) input lod tensor, must contain lod information.");
+    AddOutput("Out", "(LoDRankTable) The rank table of specific level.");
+    AddAttr<int>("level", "(int) the specific lod level to rank.")
+        .SetDefault(0)
+        .EqualGreaterThan(0);
+    AddComment(R"DOC(Create LoDRanTable by LoDTensor
+
+LoD Rank Table stores the `level` of `lod` which is ordered by sequence
+length in descending order. It is useful when implement dynamic RNN and is
+shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice
+output operators.
+)DOC");
+  }
+};
+
+class LoDRankTableInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("X"), "LoDRankTable must has input X");
+  }
+};
+
+class LoDRankTableInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDescBind &op_desc,
+                  framework::BlockDescBind *block) const override {
+    for (auto &o : op_desc.Output("Out")) {
+      block->Var(o)->SetType(framework::VarDesc::LOD_RANK_TABLE);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OPERATOR(lod_rank_table, paddle::operators::LoDRankTableOp,
+                  paddle::operators::LoDRankTableOpProtoMaker,
+                  paddle::operators::LoDRankTableInferShape,
+                  paddle::operators::LoDRankTableInferVarType,
+                  paddle::framework::EmptyGradOpMaker);

paddle/pybind/protobuf.cc

@@ -238,7 +238,8 @@ void BindVarDsec(py::module &m) {
       .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS)
       .value("FEED_MINIBATCH", VarDesc::FEED_MINIBATCH)
       .value("FETCH_LIST", VarDesc::FETCH_LIST)
-      .value("STEP_SCOPES", VarDesc::STEP_SCOPES);
+      .value("STEP_SCOPES", VarDesc::STEP_SCOPES)
+      .value("LOD_RANK_TABLE", VarDesc::LOD_RANK_TABLE);
 }
 
 void BindOpDesc(py::module &m) {

paddle/pybind/pybind.cc

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/framework/executor.h"
 #include "paddle/framework/feed_fetch_method.h"
 #include "paddle/framework/framework.pb.h"
+#include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/prune.h"
 #include "paddle/framework/selected_rows.h"
@@ -224,6 +225,9 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<LoDTensor>();
            },
            py::return_value_policy::reference)
+      .def("get_lod_rank_table",
+           [](Variable &self) { return self.GetMutable<LoDRankTable>(); },
+           py::return_value_policy::reference)
       .def("get_selected_rows",
            [](Variable &self) -> SelectedRows * {
              return self.GetMutable<SelectedRows>();
@@ -492,6 +496,15 @@ All parameter, weight, gradient are variables in Paddle.
   BindVarDsec(m);
   BindOpDesc(m);
 
+  py::class_<framework::LoDRankTable>(m, "LodRankTable")
+      .def("items", [](framework::LoDRankTable &table) {
+        std::vector<std::pair<size_t, size_t>> res;
+        for (auto &item : table.items()) {
+          res.push_back({item.index, item.length});
+        }
+        return res;
+      });
+
   m.def("op_support_gpu", OpSupportGPU);
 #ifdef PADDLE_WITH_CUDA
   m.def("get_cuda_device_count", platform::GetCUDADeviceCount);

python/paddle/v2/framework/framework.py

@@ -101,6 +101,10 @@ class Variable(object):
     def persistable(self):
         return self.desc.persistable()
 
+    @persistable.setter
+    def persistable(self, p):
+        self.desc.set_persistable(p)
+
     @property
     def name(self):
         return self.desc.name()

python/paddle/v2/framework/layers.py

@@ -729,3 +729,16 @@ class StaticRNN(object):
                 'states': memories,
                 'step_block': rnn_block
             })
+
+
+def lod_rank_table(x, level=0, program=None):
+    helper = LayerHelper("lod_rank_table", **locals())
+    table = helper.create_variable(
+        type=core.VarDesc.VarType.LOD_RANK_TABLE,
+        name=unique_name("lod_rank_table"))
+    helper.append_op(
+        type='lod_rank_table',
+        inputs={'X': x},
+        outputs={'Out': table},
+        attrs={'level': level})
+    return table

python/paddle/v2/framework/tests/test_lod_rank_table.py

+from paddle.v2.framework.layers import lod_rank_table, data
+from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.framework import g_program
+import paddle.v2.framework.core as core
+import numpy
+import unittest
+
+
+class TestLoDRankTable(unittest.TestCase):
+    def test_lod_rank_table(self):
+        x = data(name='x', shape=[100])
+        cpu = core.CPUPlace()
+        rank_table = lod_rank_table(x=x, level=1)
+        rank_table.persistable = True
+        exe = Executor(cpu)
+        scope = core.Scope()
+
+        tensor = core.LoDTensor()
+        tensor.set(numpy.random.random(size=(17, 100)), cpu)
+        tensor.set_lod([[0, 1, 3], [0, 5, 6, 7], [0, 3, 4, 9, 10, 13, 16, 17]])
+
+        exe.run(g_program, scope=scope, feed={'x': tensor})
+        var = scope.find_var(rank_table.name)
+        table = var.get_lod_rank_table()
+        self.assertEqual([(0, 5), (1, 1), (2, 1)], table.items())
+
+
+if __name__ == '__main__':
+    unittest.main()