Merge branch 'release/1.3' into test/picked

test=release/1.3

paddle/fluid/API.spec

@@ -8,13 +8,13 @@ paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None
 paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
 paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.scope_guard ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
@@ -66,7 +66,7 @@ paddle.fluid.initializer.XavierInitializer.__init__ ArgSpec(args=['self', 'unifo
 paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
 paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
 paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
@@ -229,7 +229,7 @@ paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes',
 paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
 paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.Preprocessor.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Preprocessor.outputs ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
 paddle.fluid.layers.load ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,))
@@ -270,7 +270,7 @@ paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywo
 paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
 paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.DynamicRNN.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.DynamicRNN.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.memory ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32'))
 paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
@@ -346,12 +346,12 @@ paddle.fluid.contrib.StateCell.set_state ArgSpec(args=['self', 'state_name', 'st
 paddle.fluid.contrib.StateCell.state_updater ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.StateCell.update_states ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None))
-paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
@@ -456,7 +456,7 @@ paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', '
 paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
-paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
@@ -491,14 +491,14 @@ paddle.fluid.clip.ErrorClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'],
 paddle.fluid.clip.GradientClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.clip.GradientClipByNorm.__init__ ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.clip.GradientClipByGlobalNorm.__init__ ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',))
-paddle.fluid.profiler.cuda_profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.cuda_profiler ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.profiler.reset_profiler ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.profiler.profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.profiler ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
 paddle.fluid.profiler.start_profiler ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.profiler.stop_profiler ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
 paddle.fluid.unique_name.generate ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.unique_name.guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.unique_name.guard ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
 paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
 paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc

@@ -460,77 +460,6 @@ inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
   return node.inputs.size() == n;
 }
 
-NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
-  PADDLE_ENFORCE(!source.empty(),
-                 "Start points of topological sorting should not be empty!");
-  // CHECK all the inputs' in-degree is 0
-  for (auto *node : source) {
-    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
-  }
-
-  std::unordered_set<Node *> visited;
-  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
-
-  std::vector<Node *> inlink_visited;
-  while (!to_visit.empty()) {
-    std::vector<Node *> queue(to_visit.begin(), to_visit.end());
-    for (auto *p : queue) {
-      if (Agent(p).deleted()) {
-        visited.insert(p);
-        to_visit.erase(p);
-      }
-
-      inlink_visited.clear();
-
-      std::copy_if(p->inputs.begin(), p->inputs.end(),
-                   std::back_inserter(inlink_visited),
-                   [&](Node *x) -> bool { return visited.count(x) != 0; });
-
-      if (inlink_visited.size() == p->inputs.size()) {
-        sorted_.push_back(p);
-        for (auto *_ : p->outputs) {
-          if (!visited.count(_)) {
-            to_visit.insert(_);
-          }
-        }
-
-        to_visit.erase(p);
-        visited.insert(p);
-      }
-    }
-  }
-}
-
-NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
-    : sorted_(other.sorted_), cursor_(other.cursor_) {}
-
-Node &NodesTSIterator::operator*() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return *sorted_[cursor_];
-}
-
-NodesTSIterator &NodesTSIterator::operator++() {
-  if (++cursor_ >= sorted_.size()) {
-    sorted_.clear();
-    cursor_ = 0;
-  }
-  return *this;
-}
-NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
-  cursor_ = other.cursor_;
-  sorted_ = other.sorted_;
-  return *this;
-}
-
-bool NodesTSIterator::operator==(const NodesTSIterator &other) {
-  return sorted_ == other.sorted_ && cursor_ == other.cursor_;
-}
-
-Node *NodesTSIterator::operator->() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return sorted_[cursor_];
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h

@@ -30,6 +30,7 @@ namespace inference {
 namespace analysis {
 
 using framework::ir::Graph;
+using framework::ir::NodesTSIterator;
 
 const char kIsFunctionNode[] = "__is_function_node__";
 const char kFunctionNodeSubGraph[] = "__function_node_sub_graph__";
@@ -132,32 +133,6 @@ struct Agent {
   framework::ir::Node *x_;
 };
 
-// Topological sorting iterator on nodes.
-struct NodesTSIterator
-    : public std::iterator<std::forward_iterator_tag, framework::ir::Node *> {
-  NodesTSIterator() = default;
-  explicit NodesTSIterator(const std::vector<framework::ir::Node *> &source);
-  NodesTSIterator(NodesTSIterator &&other)
-      : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
-    other.cursor_ = 0;
-  }
-  NodesTSIterator(const NodesTSIterator &other);
-
-  framework::ir::Node &operator*();
-  NodesTSIterator &operator++();
-  // TODO(Superjomn) current implementation just compare the first
-  // element, need to compare the graph and all the elements in the queue and
-  // set.
-  NodesTSIterator &operator=(const NodesTSIterator &other);
-  bool operator==(const NodesTSIterator &other);
-  bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
-  framework::ir::Node *operator->();
-
- private:
-  std::vector<framework::ir::Node *> sorted_;
-  size_t cursor_{0};
-};
-
 // The nodes those have no input will be treated as start points.
 static std::vector<framework::ir::Node *> ExtractStartPoints(const Graph &g) {
   std::vector<framework::ir::Node *> result;

paddle/fluid/operators/fake_quantize_op.cc

@@ -21,26 +21,17 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVectorArrayMap =
-    Eigen::TensorMap<Eigen::Tensor<T, 1, MajorType, IndexType>>;
-
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using ConstEigenVectorArrayMap =
-    Eigen::TensorMap<const Eigen::Tensor<T, 1, MajorType, IndexType>>;
+template <typename T>
+struct Compare {
+ public:
+  bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
+};
 
 template <typename T>
 struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& ctx, const T* in,
                   const int num, T* out) {
-    Eigen::DSizes<Eigen::DenseIndex, 1> idim(num);
-    Eigen::DSizes<Eigen::DenseIndex, 1> odim(1);
-    Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>> in_e(in, idim);
-    Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> out_e(out, odim);
-
-    out_e = in_e.abs().maximum();
+    *out = *(std::max_element(in + 0, in + num, Compare<T>()));
   }
 };
 

paddle/fluid/operators/jit/gen/act.h

@@ -63,7 +63,6 @@ class VActFunc : public JitCode {
  public:
   explicit VActFunc(size_t code_size, void* code_ptr)
       : JitCode(code_size, code_ptr) {}
-  virtual const char* name() const = 0;
   virtual void genCode() = 0;
 
  protected:
@@ -269,7 +268,7 @@ class VActJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "VActJitCode";
     switch (type_) {
       case operand_type::RELU:
@@ -293,7 +292,7 @@ class VActJitCode : public VActFunc {
       default:
         break;
     }
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/blas.h

@@ -41,7 +41,7 @@ class VXXJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "VXXJitCode";
     if (scalar_index_ == 1) {
       base += "_Scalar";
@@ -62,7 +62,7 @@ class VXXJitCode : public JitCode {
     }
     base += (with_relu_ ? "_Relu" : "");
     base += "_D" + std::to_string(num_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/gru.h

@@ -49,7 +49,7 @@ class GRUJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "GRUJitCode";
     if (id_ == 0) {
       base += "_H1";
@@ -81,7 +81,7 @@ class GRUJitCode : public VActFunc {
     };
     AddTypeStr(act_gate_);
     AddTypeStr(act_cand_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/hopv.h

@@ -35,14 +35,14 @@ class HOPVJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "VXXJitCode";
     if (type_ == operand_type::MAX) {
       base += "_MAX";
     } else {
       base += "_SUM";
     }
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/jitcode.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <string>
 #include <type_traits>
 #include "paddle/fluid/operators/jit/gen_base.h"
 #include "paddle/fluid/platform/cpu_info.h"
@@ -59,7 +60,7 @@ typedef enum {
 } operand_type;
 
 #define DECLARE_JIT_CODE(codename) \
-  const char* name() const override { return #codename; }
+  std::string name() const override { return #codename; }
 
 class JitCode : public GenBase, public Xbyak::CodeGenerator {
  public:
@@ -68,7 +69,6 @@ class JitCode : public GenBase, public Xbyak::CodeGenerator {
             (code_size % 4096 != 0 ? (code_size / 4096 + 1) * 4096 : code_size),
             code_ptr) {}
 
-  virtual const char* name() const = 0;
   virtual void genCode() = 0;
 
   size_t getSize() const override { return CodeGenerator::getSize(); }

paddle/fluid/operators/jit/gen/lstm.h

@@ -53,7 +53,7 @@ class LSTMJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "LSTMJitCode";
     if (use_peephole_) {
       base += "_Peephole";
@@ -85,7 +85,7 @@ class LSTMJitCode : public VActFunc {
     AddTypeStr(act_gate_);
     AddTypeStr(act_cand_);
     AddTypeStr(act_cell_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/matmul.h

@@ -36,11 +36,11 @@ class MatMulJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "MatMulJitCode";
     base = base + "_M" + std::to_string(m_) + "_N" + std::to_string(n_) + "_K" +
            std::to_string(k_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/seqpool.h

@@ -38,7 +38,7 @@ class SeqPoolJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "SeqPoolJitCode";
     if (type_ == SeqPoolType::kSum) {
       base += "_Sum";
@@ -48,7 +48,7 @@ class SeqPoolJitCode : public JitCode {
       base += "_Sqrt";
     }
     base += ("_W" + std::to_string(w_));
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen_base.h

@@ -16,6 +16,7 @@
 
 #include <gflags/gflags.h>
 #include <memory>  // for unique_ptr
+#include <string>
 #include <vector>
 #include "paddle/fluid/operators/jit/kernel_base.h"
 
@@ -28,7 +29,7 @@ namespace jit {
 class GenBase : public Kernel {
  public:
   virtual ~GenBase() = default;
-  virtual const char* name() const = 0;
+  virtual std::string name() const = 0;
   virtual size_t getSize() const = 0;
   virtual const unsigned char* getCodeInternal() = 0;
   template <typename Func>

paddle/fluid/operators/row_conv_op.cc

@@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve
 unidirectional recurrent neural networks. The row convolution operator is 
 different from the 1D sequence convolution, and is computed as follows:
 
-Given an input sequence $in$ of length $t$ and input dimension $d$, 
-and a filter ($W$) of size $context \times d$, 
+Given an input sequence $X$ of length $t$ and input dimension $D$, 
+and a filter ($W$) of size $context \times D$,
 the output sequence is convolved as:
 
 $$
-out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
+out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i}
 $$
 
 In the above equation:
 
 * $Out_{i}$: The i-th row of output variable with shape [1, D].
 
-* $\\tau$: Future context size.
+* $context$: Future context size.
 
 * $X_{j}$: The j-th row of input variable with shape [1, D].
 
-* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
+* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D].
 
 More details about row_conv please refer to
 the design document

python/paddle/fluid/contrib/decoder/beam_search_decoder.py

@@ -22,7 +22,7 @@ This API is still under active development and may change drastically.
 
 from __future__ import print_function
 
-import contextlib
+from ...wrapped_decorator import signature_safe_contextmanager
 import numpy as np
 import six
 
@@ -419,7 +419,7 @@ class TrainingDecoder(object):
         self._state_cell = state_cell
         self._state_cell._enter_decoder(self)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.
@@ -613,7 +613,7 @@ class BeamSearchDecoder(object):
         self._word_dim = word_dim
         self._input_var_dict = input_var_dict
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.

python/paddle/fluid/contrib/inferencer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 
 from .. import core
 
@@ -105,7 +105,7 @@ class Inferencer(object):
 
         return results
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(main_program=self.inference_program):
             with executor.scope_guard(self.scope):

python/paddle/fluid/contrib/int8_inference/README.md

@@ -63,10 +63,10 @@ Notes:
 ## 4. How to reproduce the results
 * Small dataset
 ```bash
-python python/paddle/fluid/contrib/tests/test_calibration.py
+FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py
 ```
 
 * Full dataset
 ```bash
-DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
+FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
 ```

python/paddle/fluid/contrib/tests/CMakeLists.txt

@@ -6,5 +6,9 @@ if(APPLE OR WIN32 OR NOT WITH_MKL)
 endif()
 
 foreach(src ${TEST_OPS})
+    if(src MATCHES "test_calibration")
+        py_test(${src} SRCS ${src}.py ENVS FLAGS_use_mkldnn=true)
+    else()
         py_test(${src} SRCS ${src}.py)
+    endif()
 endforeach()

python/paddle/fluid/contrib/tests/test_calibration.py

@@ -199,7 +199,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
 
     def run_program(self, model_path, generate_int8=False, algo='direct'):
         image_shape = [3, 224, 224]
-        os.environ['FLAGS_use_mkldnn'] = 'True'
 
         fluid.memory_optimize(fluid.default_main_program())
 
@@ -241,9 +240,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
             label = label.reshape([-1, 1])
             running_program = calibrator.sampling_program.clone(
             ) if generate_int8 else infer_program.clone()
-            for op in running_program.current_block().ops:
-                if op.has_attr("use_mkldnn"):
-                    op._set_attr("use_mkldnn", True)
 
             t1 = time.time()
             _, acc1, _ = exe.run(

python/paddle/fluid/contrib/trainer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import os
 import errno
 import shutil
@@ -453,7 +453,7 @@ class Trainer(object):
             io.save_inference_model(param_path, feeded_var_names, target_vars,
                                     exe)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(
                 main_program=self.train_program,

python/paddle/fluid/executor.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import os
 import multiprocessing
 import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import six
 from .framework import Program, default_main_program, Variable
 from . import core
@@ -49,7 +49,7 @@ def _switch_scope(scope):
     return ex
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def scope_guard(scope):
     """
     Change the global/default scope instance by Python `with` statement. All

python/paddle/fluid/framework.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import collections
 from collections import defaultdict
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import os
 import re
 import traceback
@@ -111,7 +111,7 @@ class NameScope(object):
 _name_scope = NameScope()
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def name_scope(prefix=None):
     """
     Generate hierarchical name prefix for the operators.
@@ -1775,7 +1775,7 @@ class Program(object):
     def set_op_role_var(self, var_name):
         self._op_role_var = [var_name]
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _optimized_guard(self, param_and_grads):
         """
         A with guard to set :code:`Optimization` :code:`OpRole` and
@@ -1805,7 +1805,7 @@ class Program(object):
         self._op_role_var = tmp_var
         self._current_role = tmp_role
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _lr_schedule_guard(self, is_with_opt=False):
         """
         A with guard to set :code:`LRSched` :code:`OpRole` and
@@ -2459,7 +2459,7 @@ def switch_startup_program(program):
     return prev_program
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def program_guard(main_program, startup_program=None):
     """
     Change the global main program and startup program with `with` statement.
@@ -2524,7 +2524,7 @@ def _get_var(name, program=None):
     return program.global_block().var(name)
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def _imperative_guard(tracer):
     global _imperative_tracer_
     tmp_trace = _imperative_tracer_
@@ -2535,7 +2535,7 @@ def _imperative_guard(tracer):
     _imperative_tracer_ = tmp_trace
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def _imperative_place_guard(place):
     global _imperative_current_expected_place_
     tmp_place = _imperative_current_expected_place_

python/paddle/fluid/imperative/base.py

@@ -11,7 +11,7 @@
 # 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 contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import numpy as np
 
 from paddle.fluid import core
@@ -24,7 +24,7 @@ def enabled():
     return framework._in_imperative_mode()
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def guard(place=None):
     train = framework.Program()
     startup = framework.Program()

python/paddle/fluid/initializer.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 from . import framework
 import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 from .core import VarDesc
 from . import unique_name
 
@@ -49,7 +49,7 @@ def force_init_on_cpu():
     return _force_init_on_cpu_
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def init_on_cpu():
     """
     Force the variable to be inited on CPU.

python/paddle/fluid/layers/control_flow.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 
 from .layer_function_generator import autodoc, templatedoc
 from .tensor import assign, fill_constant
@@ -1532,7 +1532,7 @@ class DynamicRNN(object):
             outputs={'Out': [x_reordered]})
         return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
         The block for user to define operators in RNN. See the class docstring

python/paddle/fluid/layers/io.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import multiprocessing
 import os
 import six
@@ -1116,7 +1116,7 @@ class Preprocessor(object):
     def _is_completed(self):
         return self.sub_block and self.source_var_names and self.sink_var_names
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         self.status = Preprocessor.IN_SUB_BLOCK
         self.sub_block = self.main_prog._create_block()

python/paddle/fluid/optimizer.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from collections import defaultdict
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
 
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
 from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
@@ -1610,7 +1610,7 @@ class ModelAverage(Optimizer):
             },
             stop_gradient=True)
 
-    @contextmanager
+    @signature_safe_contextmanager
     def apply(self, executor, need_restore=True):
         """Apply average values to parameters of current model.
         """

python/paddle/fluid/profiler.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from . import core
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
 import os
 import six
 
@@ -35,7 +35,7 @@ NVPROF_CONFIG = [
 ]
 
 
-@contextmanager
+@signature_safe_contextmanager
 def cuda_profiler(output_file, output_mode=None, config=None):
     """The CUDA profiler.
     This fuctions is used to profile CUDA program by CUDA runtime application
@@ -217,7 +217,7 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
     core.disable_profiler(key_map[sorted_key], profile_path)
 
 
-@contextmanager
+@signature_safe_contextmanager
 def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
     """The profiler interface.
     Different from cuda_profiler, this profiler can be used to profile both CPU

python/paddle/fluid/recordio_writer.py

@@ -15,14 +15,14 @@
 from __future__ import print_function
 
 import os
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 from . import core
 __all__ = [
     'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
 ]
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def create_recordio_writer(filename,
                            compressor=core.RecordIOWriter.Compressor.Snappy,
                            max_num_records=1000):

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -355,6 +355,10 @@ class ControlFlowGraph(object):
                                                  is_forward).dtype()
                         cache_dtype = self._find_var(block_desc, cache_var,
                                                      is_forward).dtype()
+                        if x_dtype != cache_dtype:
+                            if PRINT_LOG:
+                                print("x_dtype and cache_dtype are different!")
+                            continue
 
                         if not compare_shape(x_shape, cache_shape, level):
                             continue

python/paddle/fluid/unique_name.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import collections
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import six
 import sys
 
@@ -68,7 +68,7 @@ def switch(new_generator=None):
     return old
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def guard(new_generator=None):
     if isinstance(new_generator, six.string_types):
         new_generator = UniqueNameGenerator(new_generator)

python/paddle/fluid/wrapped_decorator.py

+# 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.
+
+import decorator
+import contextlib
+
+__all__ = ['wrap_decorator', 'signature_safe_contextmanager']
+
+
+def wrap_decorator(decorator_func):
+    @decorator.decorator
+    def __impl__(func, *args, **kwargs):
+        wrapped_func = decorator_func(func)
+        return wrapped_func(*args, **kwargs)
+
+    return __impl__
+
+
+signature_safe_contextmanager = wrap_decorator(contextlib.contextmanager)

python/requirements.txt

@@ -11,3 +11,4 @@ graphviz
 six
 funcsigs
 pyyaml
+decorator