Merge develop

paddle/fluid/framework/ir/graph.h

@@ -142,8 +142,6 @@ class Graph {
     nodes_.erase(node);
   }
 
-  const ProgramDesc &program() const { return program_; }
-
  private:
   // This method takes ownership of `node`.
   ir::Node *AddNode(ir::Node *node) {
@@ -154,7 +152,7 @@ class Graph {
   }
 
   // NOTE: program_ shouldn't be exposed to user.
-  const ProgramDesc &program_;
+  const ProgramDesc program_;
   std::map<std::string, boost::any> attrs_;
   std::map<std::string, std::function<void(void)>> attr_dels_;
   std::map<ir::Node *, std::unique_ptr<ir::Node>> nodes_;

paddle/fluid/framework/ir/node.h

@@ -41,8 +41,7 @@ class Node {
   explicit Node(OpDesc* op_desc)
       : name_(op_desc->Type()),
         var_desc_(nullptr),
-        op_desc_(new OpDesc(*op_desc)),  // TODO(panyx0718) the pointer in the
-                                         // original OpDesc might go out.
+        op_desc_(new OpDesc(*op_desc, op_desc->Block())),
         type_(Type::kOperation) {}
 
   Type NodeType() const { return type_; }

paddle/fluid/framework/op_proto_maker.cc

@@ -129,6 +129,10 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
                                     "Optimized for variable")
       .SetDefault({});
 
+  AddAttr<std::vector<std::string>>(OpCreationCallstackAttrName(),
+                                    "Callstack for Op Creatation.")
+      .SetDefault({});
+
   Validate();
 }
 

paddle/fluid/framework/op_proto_maker.h

@@ -39,6 +39,7 @@ class OpProtoAndCheckerMaker {
  public:
   static const char *OpRoleAttrName() { return "op_role"; }
   static const char *OpRoleVarAttrName() { return "op_role_var"; }
+  static const char *OpCreationCallstackAttrName() { return "op_callstack"; }
 
   void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
 

paddle/fluid/framework/operator.cc

@@ -11,15 +11,17 @@ 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 <gflags/gflags.h>
-#include <glog/logging.h>
-
+#include "paddle/fluid/framework/operator.h"
 #include <algorithm>
-
+#include <sstream>
+#include <string>
+#include <vector>
+#include "gflags/gflags.h"
+#include "glog/logging.h"
 #include "paddle/fluid/framework/data_transform.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
 #include "paddle/fluid/framework/shape_inference.h"
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -127,19 +129,48 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
 }
 
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
-  VLOG(4) << place << " " << DebugStringEx(&scope);
-  if (platform::is_gpu_place(place)) {
+  try {
+    if (VLOG_IS_ON(4)) {
+      VLOG(4) << place << " " << DebugStringEx(&scope);
+    }
+    if (platform::is_gpu_place(place)) {
 #ifndef PADDLE_WITH_CUDA
-    PADDLE_THROW("Cannot run operator on place %s", place);
+      PADDLE_THROW("Cannot run operator on place %s", place);
 #else
-    auto dev_id = boost::get<platform::CUDAPlace>(place).device;
-    platform::SetDeviceId(dev_id);
+      auto dev_id = boost::get<platform::CUDAPlace>(place).device;
+      platform::SetDeviceId(dev_id);
 #endif
+    }
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    platform::RecordEvent record_event(Type(), pool.Get(place));
+    RunImpl(scope, place);
+    if (VLOG_IS_ON(3)) {
+      VLOG(3) << place << " " << DebugStringEx(&scope);
+    }
+  } catch (platform::EnforceNotMet exception) {
+    if (Attrs().count("sub_block") != 0) {
+      throw exception;
+    }
+
+    auto& callstack = Attr<std::vector<std::string>>(
+        OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
+
+    if (callstack.empty()) {
+      throw exception;
+    }
+    std::ostringstream sout;
+    sout << "Invoke operator " << Type() << " error.\n";
+    sout << "Python Callstacks: \n";
+    for (auto& line : callstack) {
+      sout << line;
+    }
+    sout << "C++ Callstacks: \n";
+    sout << exception.err_str_;
+    exception.err_str_ = sout.str();
+    throw exception;
+  } catch (...) {
+    std::rethrow_exception(std::current_exception());
   }
-  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
-  platform::RecordEvent record_event(Type(), pool.Get(place));
-  RunImpl(scope, place);
-  VLOG(3) << place << " " << DebugStringEx(&scope);
 }
 
 bool OperatorBase::HasInputs(const std::string& name) const {
@@ -167,7 +198,7 @@ const std::vector<std::string>& OperatorBase::Inputs(
 }
 
 bool OperatorBase::HasOutputs(const std::string& name) const {
-  if (outputs_.find(name) != outputs_.end()) {
+  if (outputs_.end() != outputs_.find(name)) {
     return true;
   } else {
     return false;

paddle/fluid/operators/concat_op.h

@@ -62,9 +62,21 @@ class ConcatGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* out_grad =
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto ins = ctx.MultiInput<framework::LoDTensor>("X");
     auto out_var_names = ctx.Outputs(framework::GradVarName("X"));
-    auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+    auto outs =
+        ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
+
+    {
+      auto dx = outs;
+      auto x = ins;
+      for (size_t i = 0; i < dx.size(); ++i) {
+        if (dx[i] != nullptr) {
+          dx[i]->set_lod(x[i]->lod());
+        }
+      }
+    }
+
     int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
 
     // get output tensor that the name is not kEmptyVarName

paddle/fluid/operators/elementwise_add_mkldnn_op.cc

@@ -137,9 +137,10 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel<T> {
 };
 
 template <typename T>
-class EltwiseAddMKLDNNGradKernel : public framework::OpKernel<T> {
+class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));

paddle/fluid/operators/elementwise_add_op.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
 #include "paddle/fluid/operators/math/blas.h"
 
@@ -136,9 +137,11 @@ elementwise_add_grad(const framework::ExecutionContext& ctx,
 }
 
 template <typename DeviceContext, typename T>
-class ElementwiseAddGradKernel : public framework::OpKernel<T> {
+class ElementwiseAddGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
+
     using Tensor = framework::Tensor;
 
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));

paddle/fluid/operators/elementwise_div_op.h

@@ -14,8 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
-
 namespace paddle {
 namespace operators {
 
@@ -53,9 +53,10 @@ struct DivGradDY {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseDivGradKernel : public framework::OpKernel<T> {
+class ElementwiseDivGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* x = ctx.Input<Tensor>("X");

paddle/fluid/operators/elementwise_max_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
 
 namespace paddle {
@@ -55,9 +56,10 @@ struct MaxGradDy {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseMaxGradKernel : public framework::OpKernel<T> {
+class ElementwiseMaxGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* x = ctx.Input<Tensor>("X");

paddle/fluid/operators/elementwise_min_op.h

@@ -14,8 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
-
 namespace paddle {
 namespace operators {
 
@@ -55,9 +55,10 @@ struct MinGradDy {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseMinGradKernel : public framework::OpKernel<T> {
+class ElementwiseMinGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* x = ctx.Input<Tensor>("X");

paddle/fluid/operators/elementwise_mul_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
 #include "paddle/fluid/operators/math/blas.h"
 
@@ -84,9 +85,10 @@ struct MulGradDY {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseMulGradKernel : public framework::OpKernel<T> {
+class ElementwiseMulGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* x = ctx.Input<Tensor>("X");

paddle/fluid/operators/elementwise_op.h

@@ -205,6 +205,20 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
   }
 };
 
+template <typename T>
+class ElemwiseGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* dx =
+        context.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    if (dx != nullptr) {
+      auto& dout =
+          *context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+      dx->set_lod(dout.lod());
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 

paddle/fluid/operators/elementwise_sub_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "paddle/fluid/operators/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise_op_function.h"
 
 namespace paddle {
@@ -50,9 +51,10 @@ struct SubGradDY {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseSubGradKernel : public framework::OpKernel<T> {
+class ElementwiseSubGradKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));

paddle/fluid/operators/math/concat.cc

@@ -71,7 +71,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::Tensor& input,
-                  const std::vector<const framework::Tensor*>& ref_inputs,
+                  const std::vector<const framework::LoDTensor*>& ref_inputs,
                   const int axis, std::vector<framework::Tensor*>* outputs) {
     // TODO(zcd): Add input data validity checking
     size_t num = outputs->size();

paddle/fluid/operators/math/concat.cu

@@ -189,7 +189,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::Tensor& input,
-                  const std::vector<const framework::Tensor*>& ref_inputs,
+                  const std::vector<const framework::LoDTensor*>& ref_inputs,
                   const int axis, std::vector<framework::Tensor*>* outputs) {
     // TODO(zcd): Add input data validity checking
     int o_num = outputs->size();

paddle/fluid/operators/math/concat.h

@@ -15,7 +15,7 @@ limitations under the License. */
 #pragma once
 #include <vector>
 #include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/framework/lod_tensor.h"
 
 namespace paddle {
 namespace operators {
@@ -57,7 +57,7 @@ template <typename DeviceContext, typename T>
 class ConcatGradFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor& input,
-                  const std::vector<const framework::Tensor*>& ref_inputs,
+                  const std::vector<const framework::LoDTensor*>& ref_inputs,
                   const int axis, std::vector<framework::Tensor*>* outputs);
 };
 

paddle/fluid/operators/mul_op.h

@@ -62,23 +62,31 @@ class MulGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims");
     int y_num_col_dims = ctx.template Attr<int>("y_num_col_dims");
-    const Tensor* x = ctx.Input<Tensor>("X");
-    const Tensor* y = ctx.Input<Tensor>("Y");
-    const Tensor x_matrix = x->dims().size() > 2
-                                ? framework::ReshapeToMatrix(*x, x_num_col_dims)
-                                : *x;
-    const Tensor y_matrix = y->dims().size() > 2
-                                ? framework::ReshapeToMatrix(*y, y_num_col_dims)
-                                : *y;
-    const Tensor* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto x_matrix = x->dims().size() > 2
+                        ? framework::ReshapeToMatrix(*x, x_num_col_dims)
+                        : static_cast<const Tensor&>(*x);
+    auto y_matrix = y->dims().size() > 2
+                        ? framework::ReshapeToMatrix(*y, y_num_col_dims)
+                        : static_cast<const Tensor&>(*y);
+    auto* dout = ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
 
     Tensor dout_mat;
     dout_mat.ShareDataWith(*dout);
     dout_mat.Resize({framework::flatten_to_2d(x->dims(), x_num_col_dims)[0],
                      framework::flatten_to_2d(y->dims(), y_num_col_dims)[1]});
 
-    Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    Tensor* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto* dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
+
+    if (dx != nullptr) {
+      dx->set_lod(x->lod());
+    }
+    if (dy != nullptr) {
+      dy->set_lod(y->lod());
+    }
+
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     if (dx) {

paddle/fluid/operators/sequence_softmax_cudnn_op.cu.cc

@@ -68,7 +68,9 @@ class SequenceSoftmaxGradCUDNNKernel : public framework::OpKernel<T> {
     auto* out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* x = ctx.Input<LoDTensor>("X");
     auto* x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
-
+    if (x_grad) {
+      x_grad->set_lod(x->lod());
+    }
     auto lod = x->lod();
     const size_t level = lod.size() - 1;
 

paddle/fluid/operators/sequence_softmax_op.h

@@ -66,6 +66,9 @@ class SequenceSoftmaxGradKernel : public framework::OpKernel<T> {
     auto* out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* x = ctx.Input<LoDTensor>("X");
     auto* x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    if (x_grad) {
+      x_grad->set_lod(x->lod());
+    }
 
     auto lod = x->lod();
     const size_t level = lod.size() - 1;

paddle/fluid/operators/top_k_op.cc

@@ -30,6 +30,8 @@ class TopkOp : public framework::OperatorWithKernel {
                    "Output(Indices) of TopkOp should not be null.");
 
     auto input_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(input_dims.size(), 2,
+                      "Rank of TopK op's input must be 2.");
     const int k = static_cast<int>(ctx->Attrs().Get<int>("k"));
 
     PADDLE_ENFORCE_GE(k, 1, "k must >= 1");

paddle/fluid/pybind/const_value.cc

@@ -40,6 +40,9 @@ void BindConstValue(pybind11::module* m) {
   op_proto_and_checker_maker.def(
       "kOpRoleVarAttrName",
       framework::OpProtoAndCheckerMaker::OpRoleVarAttrName);
+  op_proto_and_checker_maker.def(
+      "kOpCreationCallstackAttrName",
+      framework::OpProtoAndCheckerMaker::OpCreationCallstackAttrName);
 }
 
 }  // namespace pybind

python/paddle/fluid/framework.py

@@ -18,6 +18,7 @@ import collections
 import contextlib
 import re
 import six
+import traceback
 
 import numpy as np
 
@@ -499,6 +500,10 @@ class Operator(object):
         if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
             del op_attrs[role_var_name]
 
+        callstack_var_name = op_maker.kOpCreationCallstackAttrName()
+        op_attrs[callstack_var_name] = list(
+            reversed(traceback.format_stack()))[1:]
+
         if len(self.desc.type()) != 0:
             return
         if type is None:

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

@@ -67,7 +67,10 @@ class TestOperator(unittest.TestCase):
         self.assertEqual(mul_op.output("Out"), ["mul.out"])
         self.assertEqual(
             set(mul_op.attr_names),
-            set(["x_num_col_dims", "y_num_col_dims", "op_role", "op_role_var"]))
+            set([
+                "x_num_col_dims", "y_num_col_dims", "op_role", "op_role_var",
+                "op_callstack"
+            ]))
         self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
         self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
         self.assertEqual(mul_op.attr("x_num_col_dims"), 1)