Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_split

paddle/fluid/API.spec

@@ -118,6 +118,8 @@ paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name
 paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '66a622db727551761ce4eb73eaa7f6a4'))
 paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd50ac552b5d131468ed466d08bb2d38c'))
 paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fcd8301a0ce15f219c7a4bcd0c1e8eca'))
+paddle.fluid.layers.reduce_all (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '646ca4d4a2cc16084f59de44b6927eca'))
+paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'f36661060aeeaf6c6b1331e41b3726fa'))
 paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3'))
 paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce'))
 paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6'))
@@ -204,6 +206,7 @@ paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'sha
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8'))
+paddle.fluid.layers.rank (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'ee1386c42ecc8f424fe3fb21862fefc2'))
 paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42'))
 paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012'))
 paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b0daaa3fa4a0aa62f9b58c43d959eb25'))
@@ -272,6 +275,7 @@ paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, de
 paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8'))
 paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
 paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '2ec937ede953ded2fdff2675883900bb'))
+paddle.fluid.layers.linspace (ArgSpec(args=['start', 'stop', 'num', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '495e21e9a848c2d075a102802fc67756'))
 paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

paddle/fluid/framework/ir/graph_helper.cc

@@ -31,8 +31,8 @@ namespace paddle {
 namespace framework {
 namespace ir {
 namespace {
-void SortHelper(
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+void SortHelper(const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>,
+                               ir::NodeComp> &adj_list,
                 ir::Node *node, std::unordered_set<ir::Node *> *visited,
                 std::vector<ir::Node *> *ret) {
   visited->insert(node);
@@ -50,7 +50,8 @@ void SortHelper(
 
 bool HasCircleHelper(
     ir::Node *node,
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+    const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+        &adj_list,
     std::unordered_set<ir::Node *> *visited,
     std::unordered_set<ir::Node *> *in_trace,
     std::vector<std::vector<ir::Node *>> *circles) {
@@ -84,7 +85,8 @@ bool HasCircleHelper(
 }
 
 bool HasCircleInternal(
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+    const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+        &adj_list,
     std::vector<std::vector<ir::Node *>> *circles) {
   std::unordered_set<ir::Node *> visited;
   std::unordered_set<ir::Node *> in_trace;
@@ -107,8 +109,8 @@ bool FindCircleSubGraph(const Graph &graph,
 }
 
 std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
-  std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list =
-      BuildOperationAdjList(graph);
+  std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+      adj_list = BuildOperationAdjList(graph);
   PADDLE_ENFORCE(!HasCircleInternal(adj_list, nullptr));
   std::unordered_set<ir::Node *> visited;
   std::vector<ir::Node *> ret;
@@ -117,34 +119,30 @@ std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
       SortHelper(adj_list, adj.first, &visited, &ret);
     }
   }
+
   return ret;
 }
 
 // Build operator inlink edge table.
-std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
-    const Graph &graph) {
-  std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list;
+std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+BuildOperationAdjList(const Graph &graph) {
+  std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+      adj_list;
 
   for (auto &n : graph.Nodes()) {
     if (!n->IsOp()) continue;
     if (adj_list.find(n) == adj_list.end()) {
-      adj_list[n] = std::unordered_set<ir::Node *>();
+      adj_list[n] = std::set<ir::Node *, ir::NodeComp>();
     }
-    std::vector<ir::Node *> nodes;
     for (auto &var : n->inputs) {
       for (auto &adj_n : var->inputs) {
         PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
         VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
                 << " -> " << n->Name() << reinterpret_cast<void *>(n)
                 << "  via " << var->Name() << reinterpret_cast<void *>(var);
-        nodes.push_back(adj_n);
+        adj_list[n].insert(adj_n);
       }
     }
-    std::sort(nodes.begin(), nodes.end(), [](ir::Node *node1, ir::Node *node2) {
-      return node1->id() > node2->id();
-    });
-    adj_list[n].insert(std::make_move_iterator(nodes.begin()),
-                       std::make_move_iterator(nodes.end()));
   }
   return adj_list;
 }

paddle/fluid/framework/ir/graph_helper.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <map>
 #include <memory>
+#include <set>
 #include <vector>
 
 #include "paddle/fluid/framework/ir/graph.h"
@@ -25,6 +26,13 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
+// Compare nodes via node id.
+struct NodeComp {
+  bool operator()(ir::Node *const &node1, ir::Node *const &node2) const {
+    return node1->id() < node2->id();
+  }
+};
+
 // Test if the graph contains circle.
 bool HasCircle(const Graph &graph);
 
@@ -57,8 +65,8 @@ std::vector<Node *> TopologyVarientSort(const Graph &graph, SortKind sort_kind);
 void CleanIndividualNodes(Graph *graph);
 
 // Build an adjacency list of operations for the `graph`.
-std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
-    const Graph &graph);
+std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+BuildOperationAdjList(const Graph &graph);
 
 template <typename T>
 std::vector<T *> FilterByNodeWrapper(const Graph &graph) {

paddle/fluid/framework/op_desc.cc

@@ -241,6 +241,7 @@ OpDesc::OpDesc(const std::string &type, const VariableNameMap &inputs,
   outputs_ = outputs;
   attrs_ = attrs;
   need_update_ = true;
+  block_ = nullptr;
 }
 
 OpDesc::OpDesc(const OpDesc &other, BlockDesc *block) {

paddle/fluid/inference/api/analysis_predictor.cc

@@ -259,6 +259,9 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       return false;
     }
 
+    PADDLE_ENFORCE_NOT_NULL(input_ptr);
+    PADDLE_ENFORCE_NOT_NULL(inputs[i].data.data());
+
     if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),

paddle/fluid/inference/api/api.cc

@@ -54,6 +54,7 @@ PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
     memory_owned_ = other.memory_owned_;
   } else {
     Resize(other.length());
+    PADDLE_ENFORCE(!(other.length() > 0 && other.data() == nullptr));
     memcpy(data_, other.data(), other.length());
     length_ = other.length();
     memory_owned_ = true;

paddle/fluid/inference/api/api_impl.cc

@@ -169,6 +169,7 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
   std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
   // Hot fix the bug that result diff in multi-thread.
   // TODO(Superjomn) re-implement a real clone here.
+  PADDLE_ENFORCE_NOT_NULL(dynamic_cast<NativePaddlePredictor *>(cls.get()));
   if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(nullptr)) {
     LOG(ERROR) << "fail to call Init";
     return nullptr;
@@ -210,6 +211,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       return false;
     }
 
+    PADDLE_ENFORCE_NOT_NULL(input_ptr);
+    PADDLE_ENFORCE_NOT_NULL(inputs[i].data.data());
     if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
@@ -316,6 +319,8 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   }
 
   std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
+  PADDLE_ENFORCE_NOT_NULL(
+      dynamic_cast<NativePaddlePredictor *>(predictor.get()));
   if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }

paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc

@@ -47,6 +47,7 @@ struct DataRecord {
       num_lines++;
       std::vector<std::string> data;
       split(line, '\t', &data);
+      PADDLE_ENFORCE(data.size() >= 4);
       // load title1 data
       std::vector<int64_t> title1_data;
       split_to_int64(data[0], ' ', &title1_data);

paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc

@@ -214,28 +214,23 @@ TEST(Analyzer_Transformer, fuse_statis) {
 }
 
 // Compare result of NativeConfig and AnalysisConfig
-// void compare(bool use_mkldnn = false) {
-//   AnalysisConfig cfg;
-//   SetConfig(&cfg);
-//   if (use_mkldnn) {
-//     cfg.EnableMKLDNN();
-//   }
-//
-//   std::vector<std::vector<PaddleTensor>> input_slots_all;
-//   SetInput(&input_slots_all);
-//   CompareNativeAndAnalysis(
-//       reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
-//       input_slots_all);
-// }
-
-// TODO(yihuaxu):
-//    Disable compare and compare_mkldnn temporary, see
-//    https://github.com/paddlePaddle/Paddle/issues/16316 for details.
-// TEST(Analyzer_Transformer, compare) { compare(); }
-// #ifdef PADDLE_WITH_MKLDNN
-// TEST(Analyzer_Transformer, compare_mkldnn) { compare(true /* use_mkldnn */);
-// }
-// #endif
+void compare(bool use_mkldnn = false) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+TEST(Analyzer_Transformer, compare) { compare(); }
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_Transformer, compare_mkldnn) { compare(true /* use_mkldnn */); }
+#endif
 
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/op_use_default_grad_op_maker.spec

@@ -29,6 +29,8 @@ pool3d
 prelu
 quantize
 rank_loss
+reduce_all
+reduce_any
 reduce_max
 reduce_mean
 reduce_min

paddle/fluid/operators/detection/gpc.cc

@@ -24,6 +24,7 @@
  **/
 
 #include "paddle/fluid/operators/detection/gpc.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace gpc {
 
@@ -689,6 +690,7 @@ static bbox *create_contour_bboxes(gpc_polygon *p) {
 
   gpc_malloc<bbox>(box, p->num_contours * sizeof(bbox),
                    const_cast<char *>("Bounding box creation"));
+  PADDLE_ENFORCE_NOT_NULL(box);
 
   /* Construct contour bounding boxes */
   for (c = 0; c < p->num_contours; c++) {
@@ -852,6 +854,7 @@ void gpc_add_contour(gpc_polygon *p, gpc_vertex_list *new_contour, int hole) {
   /* Create an extended hole array */
   gpc_malloc<int>(extended_hole, (p->num_contours + 1) * sizeof(int),
                   const_cast<char *>("contour hole addition"));
+  PADDLE_ENFORCE_NOT_NULL(extended_hole);
 
   /* Create an extended contour array */
   gpc_malloc<gpc_vertex_list>(extended_contour,
@@ -969,6 +972,7 @@ void gpc_polygon_clip(gpc_op op, gpc_polygon *subj, gpc_polygon *clip,
   /* Build scanbeam table from scanbeam tree */
   gpc_malloc<double>(sbt, sbt_entries * sizeof(double),
                      const_cast<char *>("sbt creation"));
+  PADDLE_ENFORCE_NOT_NULL(sbt);
   build_sbt(&scanbeam, sbt, sbtree);
   scanbeam = 0;
   free_sbtree(&sbtree);
@@ -1604,6 +1608,7 @@ void gpc_tristrip_clip(gpc_op op, gpc_polygon *subj, gpc_polygon *clip,
   /* Build scanbeam table from scanbeam tree */
   gpc_malloc<double>(sbt, sbt_entries * sizeof(double),
                      const_cast<char *>("sbt creation"));
+  PADDLE_ENFORCE_NOT_NULL(sbt);
   build_sbt(&scanbeam, sbt, sbtree);
   scanbeam = 0;
   free_sbtree(&sbtree);

paddle/fluid/operators/linspace_op.cc

+/* Copyright (c) 2019 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/linspace_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LinspaceOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Start"),
+                   "Input(Start) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Stop"),
+                   "Input(Stop) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Num"),
+                   "Input(Num) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(OUt) of LinspaceOp should not be null.");
+
+    auto s_dims = ctx->GetInputDim("Start");
+    PADDLE_ENFORCE((s_dims.size() == 1) && (s_dims[0] == 1),
+                   "The shape of Input(Start) should be [1].");
+
+    auto e_dims = ctx->GetInputDim("Stop");
+    PADDLE_ENFORCE((e_dims.size() == 1) && (e_dims[0] == 1),
+                   "The shape of Input(Stop) should be [1].");
+
+    auto step_dims = ctx->GetInputDim("Num");
+    PADDLE_ENFORCE((step_dims.size() == 1) && (step_dims[0] == 1),
+                   "The shape of Input(Num) should be [1].");
+
+    ctx->SetOutputDim("Out", {-1});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
+    return framework::OpKernelType(
+        ctx.Input<framework::Tensor>("Start")->type(), ctx.device_context(),
+        layout_, library_);
+  }
+};
+
+class LinspaceOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Start",
+             "First entry in the sequence. It is a tensor of shape [1], should "
+             "be of type float32 or float64.");
+    AddInput("Stop",
+             "Last entry in the sequence. It is a tensor of shape [1], should "
+             "be of type float32 or float64.");
+    AddInput("Num",
+             "Number of entry in the sequence. It is a tensor of shape [1], "
+             "should be of type int32.");
+    AddOutput("Out", "A sequence of numbers.");
+    AddComment(R"DOC(
+    Return fixed number of evenly spaced values within a given interval. First entry is start, and last entry is stop. In the case when Num is 1, only Start is returned. Like linspace function of numpy.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(linspace, ops::LinspaceOp, ops::LinspaceOpMaker);
+REGISTER_OP_CPU_KERNEL(linspace, ops::CPULinspaceKernel<float>,
+                       ops::CPULinspaceKernel<double>);

paddle/fluid/operators/linspace_op.cu

+/* 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/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/linspace_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+
+namespace paddle {
+namespace operators {
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+template <typename T>
+__global__ void LinspaceKernel(T start, T step, int64_t size, T* out) {
+  CUDA_1D_KERNEL_LOOP(index, size) { out[index] = start + step * index; }
+}
+
+template <typename T>
+__global__ void LinspaceSpecialKernel(T start, T* out) {
+  out[0] = start;
+}
+
+template <typename T>
+class CUDALinspaceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* start_t = context.Input<framework::Tensor>("Start");
+    auto* stop_t = context.Input<framework::Tensor>("Stop");
+    auto* num_t = context.Input<framework::Tensor>("Num");
+    auto* out = context.Output<framework::Tensor>("Out");
+
+    framework::Tensor n;
+    framework::TensorCopy(*start_t, platform::CPUPlace(), &n);
+    T start = n.data<T>()[0];
+    framework::TensorCopy(*stop_t, platform::CPUPlace(), &n);
+    T stop = n.data<T>()[0];
+    framework::TensorCopy(*num_t, platform::CPUPlace(), &n);
+    int32_t num = n.data<int32_t>()[0];
+
+    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+
+    out->Resize(framework::make_ddim({num}));
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+
+    T step = 0;
+    if (num != 1) {
+      step = (stop - start) / (num - 1);
+    }
+
+    auto stream = context.cuda_device_context().stream();
+    int block = 512;
+    int grid = (num + block - 1) / block;
+    LinspaceKernel<T><<<grid, block, 0, stream>>>(start, step, num, out_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(linspace, ops::CUDALinspaceKernel<float>,
+                        ops::CUDALinspaceKernel<double>);

paddle/fluid/operators/linspace_op.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 <functional>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class CPULinspaceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    T start = context.Input<framework::Tensor>("Start")->data<T>()[0];
+    T stop = context.Input<framework::Tensor>("Stop")->data<T>()[0];
+    int32_t num = context.Input<framework::Tensor>("Num")->data<int32_t>()[0];
+    auto* out = context.Output<framework::Tensor>("Out");
+    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+
+    out->Resize(framework::make_ddim({num}));
+
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+
+    if (num > 1) {
+      T step = (stop - start) / (num - 1);
+      T value = start;
+      for (int i = 0; i < num; ++i) {
+        out_data[i] = value;
+        value += step;
+      }
+    } else {
+      out_data[0] = start;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reduce_ops/reduce_all_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/reduce_ops/reduce_all_op.h"
+
+REGISTER_REDUCE_OP(reduce_all);
+REGISTER_OP_CPU_KERNEL(reduce_all,
+                       ops::ReduceKernel<paddle::platform::CPUDeviceContext,
+                                         bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_all_op.cu

+// 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/reduce_ops/reduce_all_op.h"
+
+REGISTER_OP_CUDA_KERNEL(reduce_all,
+                        ops::ReduceKernel<paddle::platform::CUDADeviceContext,
+                                          bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_all_op.h

+// Copyright (c) 2019 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/fluid/operators/reduce_ops/reduce_op.h"
+
+namespace paddle {
+namespace operators {
+
+struct AllFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->all(dim);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reduce_ops/reduce_any_op.cc

+// Copyright (c) 2018 PaddlePaddle Authors. Any 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/reduce_ops/reduce_any_op.h"
+
+REGISTER_REDUCE_OP(reduce_any);
+REGISTER_OP_CPU_KERNEL(reduce_any,
+                       ops::ReduceKernel<paddle::platform::CPUDeviceContext,
+                                         bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.cu

+// Copyright (c) 2018 PaddlePaddle Authors. Any 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/reduce_ops/reduce_any_op.h"
+
+REGISTER_OP_CUDA_KERNEL(reduce_any,
+                        ops::ReduceKernel<paddle::platform::CUDADeviceContext,
+                                          bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.h

+// Copyright (c) 2019 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/fluid/operators/reduce_ops/reduce_op.h"
+
+namespace paddle {
+namespace operators {
+
+struct AnyFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->any(dim);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/squared_l2_distance_op.h

@@ -77,6 +77,9 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
     auto* x_g = context.Output<Tensor>(framework::GradVarName("X"));
     auto* y_g = context.Output<Tensor>(framework::GradVarName("Y"));
 
+    PADDLE_ENFORCE_NOT_NULL(x_g);
+    PADDLE_ENFORCE_NOT_NULL(y_g);
+
     auto sub_result = EigenMatrix<T>::From(*in0);
     auto out_grad = EigenMatrix<T>::From(*in1);
 
@@ -92,16 +95,14 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
     // propagate back to input
     auto& eigen_place =
         *context.template device_context<DeviceContext>().eigen_device();
-    if (x_g) {
+
     x_g->mutable_data<T>(context.GetPlace());
     // eigen matrix
     auto x_grad =
         EigenMatrix<T>::From(*x_g, framework::make_ddim({x_dims[0], cols}));
     // dimensions are same with subResult
     x_grad.device(eigen_place) = grad_mat;
-    }
 
-    if (y_g) {
     y_g->mutable_data<T>(context.GetPlace());
 
     PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
@@ -118,7 +119,6 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
       y_grad.device(eigen_place) = col_sum_res;
     }
   }
-  }
 };
 
 }  // namespace operators

python/paddle/fluid/layers/nn.py

@@ -73,6 +73,8 @@ __all__ = [
     'reduce_max',
     'reduce_min',
     'reduce_prod',
+    'reduce_all',
+    'reduce_any',
     'sequence_first_step',
     'sequence_last_step',
     'sequence_slice',
@@ -159,6 +161,7 @@ __all__ = [
     'sum',
     'slice',
     'shape',
+    'rank',
     'logical_and',
     'logical_or',
     'logical_xor',
@@ -4738,6 +4741,106 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
     return out
 
 
+def reduce_all(input, dim=None, keep_dim=False, name=None):
+    """
+    Computes the ``logical and`` of tensor elements over the given dimension.
+
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        dim (list|int|None): The dimension along which the logical and is computed.
+            If :attr:`None`, compute the logical and over all elements of
+            :attr:`input` and return a Tensor variable with a single element,
+            otherwise must be in the range :math:`[-rank(input), rank(input))`.
+            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
+        keep_dim (bool): Whether to reserve the reduced dimension in the
+            output Tensor. The result tensor will have one fewer dimension
+            than the :attr:`input` unless :attr:`keep_dim` is true.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: The reduced Tensor variable.
+
+    Examples:
+        .. code-block:: python
+        
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [True, True]]
+            # Each example is followed by the correspending output tensor.
+            fluid.layers.reduce_all(x)  # False 
+            fluid.layers.reduce_all(x, dim=0)  # [True, False]
+            fluid.layers.reduce_all(x, dim=-1)  # [False, True]
+            fluid.layers.reduce_all(x, dim=1,
+                                     keep_dim=True)  # [[False], [True]]
+
+    """
+    helper = LayerHelper('reduce_all', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    if dim is not None and not isinstance(dim, list):
+        dim = [dim]
+    helper.append_op(
+        type='reduce_all',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={
+            'dim': dim if dim != None else [0],
+            'keep_dim': keep_dim,
+            'reduce_all': True if dim == None else False
+        })
+    return out
+
+
+def reduce_any(input, dim=None, keep_dim=False, name=None):
+    """
+    Computes the ``logical or`` of tensor elements over the given dimension.
+
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        dim (list|int|None): The dimension along which the logical or is computed.
+            If :attr:`None`, compute the logical or over all elements of
+            :attr:`input` and return a Tensor variable with a single element,
+            otherwise must be in the range :math:`[-rank(input), rank(input))`.
+            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
+        keep_dim (bool): Whether to reserve the reduced dimension in the
+            output Tensor. The result tensor will have one fewer dimension
+            than the :attr:`input` unless :attr:`keep_dim` is true.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: The reduced Tensor variable.
+
+    Examples:
+        .. code-block:: python
+
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [False, False]]
+            # Each example is followed by the correspending output tensor.
+            fluid.layers.reduce_any(x)  # True
+            fluid.layers.reduce_any(x, dim=0)  # [True, False]
+            fluid.layers.reduce_any(x, dim=-1)  # [True, False]
+            fluid.layers.reduce_any(x, dim=1,
+                                     keep_dim=True)  # [[True], [False]]
+
+    """
+    helper = LayerHelper('reduce_any', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    if dim is not None and not isinstance(dim, list):
+        dim = [dim]
+    helper.append_op(
+        type='reduce_any',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={
+            'dim': dim if dim != None else [0],
+            'keep_dim': keep_dim,
+            'reduce_all': True if dim == None else False
+        })
+    return out
+
+
 def split(input, num_or_sections, dim=-1, name=None):
     """
     Split the input tensor into multiple sub-tensors.
@@ -9237,6 +9340,32 @@ def shape(input):
     return out
 
 
+def rank(input):
+    """
+    **Rank Layer**
+
+    Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
+
+    Args:
+        input (Variable): The input variable.
+
+    Returns:
+        Variable: The rank of the input variable.
+
+    Examples:
+        .. code-block:: python
+
+            input = layers.data(
+                name="input", shape=[3, 100, 100], dtype="float32")
+            rank = layers.rank(input) # 4
+    """
+
+    ndims = len(input.shape)
+    out = assign(np.array(ndims, 'int32'))
+
+    return out
+
+
 def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)

python/paddle/fluid/layers/tensor.py

@@ -24,26 +24,11 @@ from .layer_function_generator import templatedoc
 import numpy
 
 __all__ = [
-    'create_tensor',
-    'create_parameter',
-    'create_global_var',
-    'cast',
-    'tensor_array_to_tensor',
-    'concat',
-    'sums',
-    'assign',
-    'fill_constant_batch_size_like',
-    'fill_constant',
-    'argmin',
-    'argmax',
-    'argsort',
-    'ones',
-    'zeros',
-    'reverse',
-    'has_inf',
-    'has_nan',
-    'isfinite',
-    'range',
+    'create_tensor', 'create_parameter', 'create_global_var', 'cast',
+    'tensor_array_to_tensor', 'concat', 'sums', 'assign',
+    'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
+    'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite',
+    'range', 'linspace'
 ]
 
 
@@ -826,3 +811,45 @@ def range(start, end, step, dtype):
                 'Step': step},
         outputs={'Out': [out]})
     return out
+
+
+def linspace(start, stop, num, dtype):
+    """
+    Return fixed number of evenly spaced values within a given interval.
+
+    First entry is start, and last entry is stop. In the case when Num is 1, only Start is returned. Like linspace function of numpy.
+
+    Args:
+        start(float|Variable): First entry in the sequence. It is a float scalar, or a tensor of shape [1] with type 'float32'|'float64'.
+        stop(float|Variable): Last entry in the sequence. It is a float scalar, or a tensor of shape [1] with type 'float32'|'float64'.
+        num(int|Variable): Number of entry in the sequence. It is an int scalar, or a tensor of shape [1] with type int32.
+        dtype(string): 'float32'|'float64', the data type of the output tensor.
+
+    Returns:
+        Variable: The tensor variable storing a 1-D tensor. 
+
+    Examples:
+        .. code-block:: python
+
+             data = fluid.layers.linspace(0, 10, 5, 'float32') # [0.0,  2.5,  5.0,  7.5, 10.0]
+             data = fluid.layers.linspace(0, 10, 1, 'float32') # [0.0]
+
+    """
+    helper = LayerHelper("linspace", **locals())
+
+    if not isinstance(start, Variable):
+        start = fill_constant([1], dtype, start)
+    if not isinstance(stop, Variable):
+        stop = fill_constant([1], dtype, stop)
+    if not isinstance(num, Variable):
+        num = fill_constant([1], 'int32', num)
+
+    out = helper.create_variable_for_type_inference(dtype=start.dtype)
+
+    helper.append_op(
+        type='linspace',
+        inputs={'Start': start,
+                'Stop': stop,
+                'Num': num},
+        outputs={'Out': [out]})
+    return out

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

@@ -1925,6 +1925,13 @@ class TestBook(LayerTest):
             out = layers.flatten(x, axis=1, name="flatten")
             return (out)
 
+    def test_linspace(self):
+        program = Program()
+        with program_guard(program):
+            out = layers.linspace(20, 10, 5, 'float64')
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_linspace.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestLinspaceOpCommonCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([0]).astype(dtype),
+            'Stop': np.array([10]).astype(dtype),
+            'Num': np.array([11]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.arange(0, 11).astype(dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestLinspaceOpReverseCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([10]).astype(dtype),
+            'Stop': np.array([0]).astype(dtype),
+            'Num': np.array([11]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.arange(10, -1, -1).astype(dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestLinspaceOpNumOneCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([10]).astype(dtype),
+            'Stop': np.array([0]).astype(dtype),
+            'Num': np.array([1]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.array(10, dtype=dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -91,6 +91,78 @@ class TestProdOp(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestAllOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.outputs = {'Out': self.inputs['X'].all()}
+        self.attrs = {'reduce_all': True}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAllOpWithDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1]}
+        self.outputs = {'Out': self.inputs['X'].all(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAllOpWithKeepDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': np.expand_dims(
+                self.inputs['X'].all(axis=1), axis=1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.outputs = {'Out': self.inputs['X'].any()}
+        self.attrs = {'reduce_all': True}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOpWithDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1]}
+        self.outputs = {'Out': self.inputs['X'].any(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOpWithKeepDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': np.expand_dims(
+                self.inputs['X'].any(axis=1), axis=1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class Test1DReduce(OpTest):
     def setUp(self):
         self.op_type = "reduce_sum"