Merge branch 'unify_executor_interface' into add_parallel_executor_tests

Dockerfile

@@ -57,7 +57,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U
 # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
 # version(1.7.1 for now), which causes building documentation failed.
-RUN pip install --upgrade pip && \
+RUN pip install --upgrade pip==9.0.3 && \
     pip install -U wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark

doc/fluid/dev/index_cn.rst

@@ -4,6 +4,7 @@
 .. toctree::
   :maxdepth: 1
 
+  api_doc_std_cn.md
   new_op_cn.md
   new_op_kernel.md
   use_eigen_cn.md

doc/fluid/dev/index_en.rst

@@ -4,6 +4,7 @@ Development
 .. toctree::
   :maxdepth: 1
 
+  api_doc_std_en.md
   new_op_en.md
   new_op_kernel.md
   use_eigen_en.md

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -55,21 +55,21 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
   }
 }
 
-void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result, OpDesc *op,
+void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
+                                                const OpDesc &op,
                                                 const platform::Place &p,
                                                 const size_t &i) const {
   auto *op_handle = result->ops_.back().get();
-  op_handle->dev_ctxes_[p] = const_cast<platform::DeviceContext *>(
-      platform::DeviceContextPool::Instance().Get(p));
+  op_handle->dev_ctxes_[p] = platform::DeviceContextPool::Instance().Get(p);
 
-  auto var_names = op->InputArgumentNames();
+  auto var_names = op.InputArgumentNames();
 
   for (auto &each_var_name : var_names) {
     VarHandle *var = CreateOrGetLatestVarHandle(result, each_var_name, p, i);
     op_handle->AddInput(var);
   }
 
-  var_names = op->OutputArgumentNames();
+  var_names = op.OutputArgumentNames();
 
   for (auto &each_var_name : var_names) {
     CreateOpOutput(result, op_handle, each_var_name, p, i);
@@ -107,7 +107,7 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
       result.ops_.emplace_back(new SendOpHandle(*op, s, p));
       // Create inputs for output on original place and no ssa output
       // is created for send op.
-      CreateOpHandleIOs(&result, op, p, 0);
+      CreateOpHandleIOs(&result, *op, p, 0);
       continue;
     }
 
@@ -117,7 +117,7 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 
       result.ops_.emplace_back(new ComputationOpHandle(*op, s, p));
       auto *op_handle = result.ops_.back().get();
-      CreateOpHandleIOs(&result, op, p, i);
+      CreateOpHandleIOs(&result, *op, p, i);
 
       auto var_names = op->OutputArgumentNames();
 

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -45,8 +45,8 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
   std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
 
  private:
-  void CreateOpHandleIOs(SSAGraph *result, OpDesc *op, const platform::Place &p,
-                         const size_t &i) const;
+  void CreateOpHandleIOs(SSAGraph *result, const OpDesc &op,
+                         const platform::Place &p, const size_t &i) const;
 
  private:
   std::string loss_var_name_;

python/paddle/fluid/metrics.py

@@ -169,7 +169,7 @@ class Accuracy(MetricBase):
         return self.value / self.weight
 
 
-class ChunkEvalutor(MetricBase):
+class ChunkEvaluator(MetricBase):
     """
     Accumulate counter numbers output by chunk_eval from mini-batches and
     compute the precision recall and F1-score using the accumulated counter
@@ -177,7 +177,7 @@ class ChunkEvalutor(MetricBase):
     """
 
     def __init__(self, name=None):
-        super(ChunkEvalutor, self).__init__(name)
+        super(ChunkEvaluator, self).__init__(name)
         self.num_infer_chunks = 0
         self.num_label_chunks = 0
         self.num_correct_chunks = 0

python/paddle/fluid/parallel_executor.py

@@ -61,8 +61,8 @@ class ParallelExecutor(object):
                   main_program=test_program,
                   share_vars_from=train_exe)
 
-              train_loss, = train_exe.run([loss.name], feed_dict=feed_dict)
-              test_loss, = test_exe.run([loss.name], feed_dict=feed_dict)
+              train_loss, = train_exe.run([loss.name], feed=feed_dict)
+              test_loss, = test_exe.run([loss.name], feed=feed_dict)
         """
 
         self._places = []
@@ -123,22 +123,23 @@ class ParallelExecutor(object):
             allow_op_delay)
         self.scope = scope
 
-    def run(self, fetch_list, feed_dict={}):
+    def run(self, fetch_list, feed={}, feed_dict={}):
         """
         :param fetch_list: A list of variable names that will be fetched.
-        :param feed_dict: A dict mapping for feed variable name to LoDTensor
+        :param feed: A dict mapping for feed variable name to LoDTensor
           or numpy array.
         :return: fetched value list.
         """
-        if not isinstance(feed_dict, dict):
-            raise TypeError("feed_dict should be a dict")
+        feed = feed_dict
+        if not isinstance(feed, dict):
+            raise TypeError("feed should be a dict")
 
         feed_tensor_dict = {}
-        for i, feed_name in enumerate(feed_dict):
-            feed_tensor = feed_dict[feed_name]
+        for i, feed_name in enumerate(feed):
+            feed_tensor = feed[feed_name]
             if not isinstance(feed_tensor, core.LoDTensor):
                 feed_tensor = core.LoDTensor()
-                feed_tensor.set(feed_dict[feed_name], self._act_places[0])
+                feed_tensor.set(feed[feed_name], self._act_places[0])
             feed_tensor_dict[feed_name] = feed_tensor
 
         fetch_var_name = '@FETCHED_VAR_NAME@'