add executor.prepare

paddle/fluid/framework/executor.cc

@@ -14,12 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/executor.h"
 
-#include <set>
-
-#include "gflags/gflags.h"
 #include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
-#include "paddle/fluid/framework/feed_fetch_type.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -40,14 +36,13 @@ namespace {
 int kProgramId = -1;
 }  // namespace
 
-struct ExecutorPrepareContext {
-  ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id)
-      : prog_(prog), block_id_(block_id) {}
+ExecutorPrepareContext::ExecutorPrepareContext(
+    const framework::ProgramDesc& prog, size_t block_id)
+    : prog_(prog), block_id_(block_id) {}
 
-  const framework::ProgramDesc& prog_;
-  size_t block_id_;
-  std::vector<std::unique_ptr<OperatorBase>> ops_;
-};
+ExecutorPrepareContext::~ExecutorPrepareContext() {
+  VLOG(5) << "destroy ExecutorPrepareContext";
+}
 
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
@@ -101,9 +96,8 @@ static void CheckTensorNANOrInf(const std::string& name,
 void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
                    bool create_local_scope, bool create_vars) {
   platform::RecordBlock b(block_id);
-  auto* ctx = Prepare(pdesc, block_id);
-  RunPreparedContext(ctx, scope, create_local_scope, create_vars);
-  delete ctx;
+  auto ctx = Prepare(pdesc, block_id);
+  RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
 }
 
 // Check whether the block already has feed operators and feed_holder.
@@ -274,15 +268,15 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
   }
 }
 
-ExecutorPrepareContext* Executor::Prepare(const ProgramDesc& program,
-                                          int block_id) {
+std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
+    const ProgramDesc& program, int block_id) {
   auto* ctx = new ExecutorPrepareContext(program, block_id);
   PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), program.Size());
   auto& block = program.Block(block_id);
   for (auto& op_desc : block.AllOps()) {
     ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
   }
-  return ctx;
+  return std::unique_ptr<ExecutorPrepareContext>(ctx);
 }
 
 void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,

paddle/fluid/framework/executor.h

@@ -22,7 +22,16 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
-struct ExecutorPrepareContext;
+
+struct ExecutorPrepareContext {
+  ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id);
+  ~ExecutorPrepareContext();
+
+  const framework::ProgramDesc& prog_;
+  size_t block_id_;
+  std::vector<std::unique_ptr<OperatorBase>> ops_;
+};
+
 class Executor {
  public:
   // TODO(dzhwinter) : Do not rely on this function, it will be removed
@@ -47,8 +56,8 @@ class Executor {
            const std::string& feed_holder_name = "feed",
            const std::string& fetch_holder_name = "fetch");
 
-  static ExecutorPrepareContext* Prepare(const ProgramDesc& program,
-                                         int block_id);
+  static std::unique_ptr<ExecutorPrepareContext> Prepare(
+      const ProgramDesc& program, int block_id);
 
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           bool create_local_scope = true,

python/paddle/fluid/executor.py

@@ -235,6 +235,77 @@ def parselod(data):
             tensor.set_lod(lod)
             return tensor
 
+    def _get_program_cache(self, program_cache_key):
+        return self.program_caches.get(program_cache_key, None)
+
+    def _add_program_cache(self, program_cache_key, program):
+        self.program_caches[program_cache_key] = program
+
+    def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name,
+                            fetch_var_name):
+        tmp_program = program.clone()
+
+        global_block = tmp_program.global_block()
+
+        if feed_var_name in global_block.vars:
+            feed_var = global_block.var(feed_var_name)
+        else:
+            feed_var = global_block.create_var(
+                name=feed_var_name,
+                type=core.VarDesc.VarType.FEED_MINIBATCH,
+                persistable=True)
+
+        if fetch_var_name in global_block.vars:
+            fetch_var = global_block.var(fetch_var_name)
+        else:
+            fetch_var = global_block.create_var(
+                name=fetch_var_name,
+                type=core.VarDesc.VarType.FETCH_LIST,
+                persistable=True)
+
+        # prepend feed operators
+        if not has_feed_operators(global_block, feed, feed_var_name):
+            for i, name in enumerate(feed):
+                out = global_block.var(name)
+                global_block.prepend_op(
+                    type='feed',
+                    inputs={'X': [feed_var]},
+                    outputs={'Out': [out]},
+                    attrs={'col': i})
+
+        # append fetch_operators
+        if not has_fetch_operators(global_block, fetch_list, fetch_var_name):
+            for i, var in enumerate(fetch_list):
+                assert isinstance(var, Variable) or isinstance(var, str), (
+                    "Wrong type for fetch_list[%s]: %s" % (i, type(var)))
+                global_block.append_op(
+                    type='fetch',
+                    inputs={'X': [var]},
+                    outputs={'Out': [fetch_var]},
+                    attrs={'col': i})
+
+        return tmp_program
+
+    def _feed_data(self, program, feed, feed_var_name, scope):
+        # feed var to framework
+        for op in program.global_block().ops:
+            if op.desc.type() == 'feed':
+                feed_target_name = op.desc.output('Out')[0]
+                cur_feed = feed[feed_target_name]
+                if not isinstance(cur_feed, core.LoDTensor):
+                    cur_feed = self.aslodtensor(cur_feed)
+                idx = op.desc.attr('col')
+                core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
+            else:
+                break
+
+    def _fetch_data(self, fetch_list, fetch_var_name, scope):
+        outs = [
+            core.get_fetch_variable(scope, fetch_var_name, i)
+            for i in xrange(len(fetch_list))
+        ]
+        return outs
+
     def run(self,
             program=None,
             feed=None,
@@ -268,7 +339,6 @@ def run(self,
             raise TypeError("feed should be a map")
         if fetch_list is None:
             fetch_list = []
-
         if program is None:
             program = default_main_program()
 
@@ -278,79 +348,30 @@ def run(self,
         if scope is None:
             scope = global_scope()
 
-        program_cache = None
-        program_cache_key = get_program_cache_key(feed, fetch_list)
-
+        cache_key = get_program_cache_key(feed, fetch_list)
         if use_program_cache:
-            # find program cache by cache_key
-            program_cache = self.program_caches.get(program_cache_key, None)
-            # TODO(qiao): Should check program_cache and program are exactly the same.
+            cached_program = self._get_program_cache(cache_key)
+            if cached_program is None:
+                cached_program = self._add_feed_fetch_ops(
+                    program=program,
+                    feed=feed,
+                    fetch_list=fetch_list,
+                    feed_var_name=feed_var_name,
+                    fetch_var_name=fetch_var_name)
+                self._add_program_cache(cache_key, cached_program)
+            program = cached_program
         else:
-            self.program_caches.pop(program_cache_key, None)
-
-        if program_cache is None:
-            program_cache = program.clone()
-
-            if use_program_cache:
-                self.program_caches[program_cache_key] = program_cache
-
-            global_block = program_cache.global_block()
-
-            if feed_var_name in global_block.vars:
-                feed_var = global_block.var(feed_var_name)
-            else:
-                feed_var = global_block.create_var(
-                    name=feed_var_name,
-                    type=core.VarDesc.VarType.FEED_MINIBATCH,
-                    persistable=True)
-
-            if fetch_var_name in global_block.vars:
-                fetch_var = global_block.var(fetch_var_name)
-            else:
-                fetch_var = global_block.create_var(
-                    name=fetch_var_name,
-                    type=core.VarDesc.VarType.FETCH_LIST,
-                    persistable=True)
-
-            # prepend feed operators
-            if not has_feed_operators(global_block, feed, feed_var_name):
-                for i, name in enumerate(feed):
-                    out = global_block.var(name)
-                    global_block.prepend_op(
-                        type='feed',
-                        inputs={'X': [feed_var]},
-                        outputs={'Out': [out]},
-                        attrs={'col': i})
-
-            # append fetch_operators
-            if not has_fetch_operators(global_block, fetch_list,
-                                       fetch_var_name):
-                for i, var in enumerate(fetch_list):
-                    assert isinstance(var, Variable) or isinstance(var, str), (
-                        "Wrong type for fetch_list[%s]: %s" % (i, type(var)))
-                    global_block.append_op(
-                        type='fetch',
-                        inputs={'X': [var]},
-                        outputs={'Out': [fetch_var]},
-                        attrs={'col': i})
-
-        # feed var to framework
-        for op in program_cache.global_block().ops:
-            if op.desc.type() == 'feed':
-                feed_target_name = op.desc.output('Out')[0]
-                cur_feed = feed[feed_target_name]
-                if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = self.aslodtensor(cur_feed)
-                idx = op.desc.attr('col')
-                core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
-            else:
-                break
-
-        self.executor.run(program_cache.desc, scope, 0, True, True)
-        outs = [
-            core.get_fetch_variable(scope, fetch_var_name, i)
-            for i in xrange(len(fetch_list))
-        ]
+            self.program_caches.pop(cache_key, None)
+            program = self._add_feed_fetch_ops(
+                program=program,
+                feed=feed,
+                fetch_list=fetch_list,
+                feed_var_name=feed_var_name,
+                fetch_var_name=fetch_var_name)
+
+        self._feed_data(program, feed, feed_var_name, scope)
+        self.executor.run(program.desc, scope, 0, True, True)
+        outs = self._fetch_data(fetch_list, fetch_var_name, scope)
         if return_numpy:
             outs = as_numpy(outs)
         return outs

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

@@ -16,7 +16,6 @@
 
 import numpy
 import paddle.fluid.core as core
-
 from paddle.fluid.executor import Executor
 from paddle.fluid.layers import mul, data