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

paddle/framework/backward.cc

@@ -30,6 +30,7 @@ static void ForEachVarName(Map& names, T callback) {
   }
 }
 
+// return whether all the names + suffixes in the set
 static bool AllInSet(
     const std::map<std::string, std::vector<std::string>>& names,
     const std::string& suffix, const std::unordered_set<std::string>& set) {
@@ -48,7 +49,7 @@ static std::shared_ptr<OperatorBase> NOP() {
   return net_op;
 }
 
-//  Get backward operator from a forward operator, recursively implementation.
+//  Get backward operator from a forward operator, a recursive implementation.
 //
 //  no_grad_names the gradient variable names without gradient calculating.
 //
@@ -56,27 +57,30 @@ static std::shared_ptr<OperatorBase> NOP() {
 //  BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
 //  pass `uniq_id` through recursive calling.
 //
-//  returns The backward operator. For simple situation, it is a simple
-//  operator. For complex situation, it is a NetOp.
+//  returns The backward operator. In a simple situation, it may be a simple
+//  operator, in a complex situation, it maybe a NetOp.
 //
 //  See Backward.h for details
 static std::shared_ptr<OperatorBase> BackwardRecursive(
     const OperatorBase& forwardOp,
     std::unordered_set<std::string>& no_grad_names, size_t& uniq_id);
+
 std::shared_ptr<OperatorBase> BackwardRecursive(
     const OperatorBase& forwardOp,
     std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
   //  If all input gradients of forwarding operator do not need to calculate,
   //  just return an NOP. Not return null ptr because NOP does not take
-  //  too much time for calculation, but it is useful for simplifying logic.
-  if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
+  //  much time for calculation, but it is useful for simplifying logic.
+  if (AllInSet(forwardOp.inputs_ /*names*/, kGradVarSuffix /*suffix*/,
+               no_grad_names /*set*/)) {
     return NOP();
   }
 
   //  All output gradients of forwarding operator do not need to calculate.
   //  Then all input gradients cannot be computed at all, and we put them into
   //  `no_grad_names` set. Return an NOP.
-  if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
+  if (AllInSet(forwardOp.outputs_ /*names*/, kGradVarSuffix /*suffix*/,
+               no_grad_names /*set*/)) {
     ForEachVarName(forwardOp.inputs_,
                    [&no_grad_names](const std::string& name) -> bool {
                      no_grad_names.insert(GradVarName(name));
@@ -93,11 +97,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
 
     // Map from output gradient variable name to operator's indices in
-    // backward net. That operator generates that variable.
+    // backward net's ops_. That operator generates that variable.
     std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;
 
     size_t local_op_id = 0;
-    // reversely travel forwardNet
+    // reversely travel forwardNet and collect all duplicate outputs.
     for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
          ++it, ++local_op_id) {
       auto fwd = *it;
@@ -112,35 +116,41 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     // Get unique ID for this method.
     auto uid = uniq_id++;
     // TODO(dzh): more comment
+    // multiple operators which have the same output (y for example) may
+    // overwrite the same y variable when backward, special operations are token
+    // to handle this case. For each duplicate output, rename it to an alias
+    // (original name with a offset), append an `add` op for its operator,
+    // and finally sum all the alias variable to the final output variable y.
     using Pos = std::pair<size_t, std::shared_ptr<OperatorBase>>;
     std::list<Pos> insert_position;
     for (auto& dup_output_op : dup_output_ops) {
       const std::string& name = dup_output_op.first;
       auto& dup_op = dup_output_op.second;
+      // no duplicate output
       if (dup_op.size() == 1) continue;
-      std::vector<std::string> dup_outputs;
 
+      // process the duplicate outputs
+      std::vector<std::string> dup_outputs;
       for (size_t i = 0; i < dup_op.size(); ++i) {
+        // rename each duplicate output to an alias
         auto op_offset = dup_op[i];
         dup_outputs.push_back(name + "@RENAME@" + std::to_string(uid) + "@" +
                               std::to_string(i));
         net->ops_[op_offset]->Rename(name, dup_outputs.back());
       }
+      // collect all the offset to append `add` op for each alias
       insert_position.push_back(
-          {dup_op.back(),
-           OpRegistry::CreateOp(
-               "add", {{"X", {dup_outputs}}}, {{"Out", {name}}},
-               {{"input_format",
-                 std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
+          {dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}},
+                                               {{"Out", {name}}}, {})});
     }
 
+    // make sure the inserted `add` ops follow the BFS order.
     insert_position.sort(
         [](const Pos& l, const Pos& r) { return l.first > r.first; });
 
     for (auto& pos : insert_position) {
       net->InsertOp(pos.first + 1, pos.second);
     }
-
   } else {
     std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
 
@@ -176,7 +186,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
   net->type_ = "@GENERATED_BACKWARD@";
   net->CompleteAddOp();
   return net;
-}
+}  // namespace framework
 
 // See header for comments
 std::shared_ptr<OperatorBase> Backward(

paddle/platform/enforce.h

@@ -14,14 +14,21 @@ limitations under the License. */
 
 #pragma once
 
-#include <execinfo.h>
+#include <dlfcn.h>     // for dladdr
+#include <execinfo.h>  // for backtrace
 #include <iomanip>
+#include <memory>
 #include <sstream>
 #include <stdexcept>
 #include <string>
+
 #include "paddle/string/printf.h"
 #include "paddle/string/to_string.h"
 
+#ifdef __GNUC__
+#include <cxxabi.h>  // for __cxa_demangle
+#endif
+
 #ifndef PADDLE_ONLY_CPU
 
 #include "paddle/platform/dynload/cublas.h"
@@ -39,6 +46,19 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 
+namespace {
+#ifdef __GNUC__
+inline std::string demangle(std::string name) {
+  int status = -4;  // some arbitrary value to eliminate the compiler warning
+  std::unique_ptr<char, void (*)(void*)> res{
+      abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
+  return (status == 0) ? res.get() : name;
+}
+#else
+inline std::string demangle(std::string name) { return name; }
+#endif
+}
+
 struct EnforceNotMet : public std::exception {
   std::exception_ptr exp_;
   std::string err_str_;
@@ -48,15 +68,29 @@ struct EnforceNotMet : public std::exception {
       std::rethrow_exception(exp_);
     } catch (const std::exception& exp) {
       std::ostringstream sout;
+
       sout << string::Sprintf("%s at [%s:%d]", exp.what(), f, l) << std::endl;
-      sout << "Call Stacks: " << std::endl;
+      sout << "PaddlePaddle Call Stacks: " << std::endl;
+
       void* call_stack[TRACE_STACK_LIMIT];
-      int sz = backtrace(call_stack, TRACE_STACK_LIMIT);
-      auto line = backtrace_symbols(call_stack, sz);
-      for (int i = 0; i < sz; ++i) {
-        sout << line[i] << std::endl;
+      auto size = backtrace(call_stack, TRACE_STACK_LIMIT);
+      auto symbols = backtrace_symbols(call_stack, size);
+
+      Dl_info info;
+      for (int i = 0; i < size; ++i) {
+        if (dladdr(call_stack[i], &info)) {
+          auto demangled = demangle(info.dli_sname);
+          auto addr_offset = static_cast<char*>(call_stack[i]) -
+                             static_cast<char*>(info.dli_saddr);
+          sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
+                                  2 + sizeof(void*) * 2, call_stack[i],
+                                  demangled, addr_offset);
+        } else {
+          sout << string::Sprintf("%-3d %*0p %s\n", i, 2 + sizeof(void*) * 2,
+                                  call_stack[i]);
+        }
       }
-      free(line);
+      free(symbols);
       err_str_ = sout.str();
     }
   }
@@ -170,7 +204,7 @@ inline void throw_on_error(T e) {
  *    PADDLE_ENFORCE_EQ(a, b);
  *
  *    will raise an expression described as follows:
- *    "enforce a == b failed, 1 != 2" with detailed stack infomation.
+ *    "enforce a == b failed, 1 != 2" with detailed stack information.
  *
  *    extra messages is also supported, for example:
  *    PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)

python/paddle/trainer_config_helpers/evaluators.py

@@ -298,8 +298,8 @@ def pnpair_evaluator(
         input,
         label,
         info,
-        name=None,
-        weight=None, ):
+        weight=None,
+        name=None, ):
     """
     Positive-negative pair rate Evaluator which adapts to rank task like
     learning to rank. This evaluator must contain at least three layers.
@@ -308,27 +308,31 @@ def pnpair_evaluator(
 
     .. code-block:: python
 
-       eval = pnpair_evaluator(input, info, label)
+       eval = pnpair_evaluator(input, label, info)
 
-    :param name: Evaluator name.
-    :type name: None|basestring
     :param input: Input Layer name. The output prediction of network.
     :type input: LayerOutput
     :param label: Label layer name.
     :type label: LayerOutput
-    :param info: Label layer name. (TODO, explaination)
+    :param info: Info layer name. (TODO, explaination)
     :type info: LayerOutput
     :param weight: Weight Layer name. It should be a matrix with size
                   [sample_num, 1]. (TODO, explaination)
     :type weight: LayerOutput
+    :param name: Evaluator name.
+    :type name: None|basestring
     """
+    if not isinstance(input, list):
+        input = [input]
+    if label:
+        input.append(label)
+    if info:
+        input.append(info)
     evaluator_base(
-        name=name,
-        type="pnpair",
         input=input,
-        label=label,
-        info=info,
-        weight=weight)
+        type="pnpair",
+        weight=weight,
+        name=name, )
 
 
 @evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)