Update code

paddle/framework/executor.cc

@@ -66,14 +66,6 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
   PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), pdesc.Size());
   auto& block = pdesc.Block(block_id);
 
-  if (VLOG_IS_ON(100)) {
-    std::ostringstream sout;
-    for (auto& name : scope->GetAllNames(false)) {
-      sout << name << ", ";
-    }
-    VLOG(100) << "Scope has variable " << sout.str();
-  }
-
   Scope* local_scope = scope;
   if (create_vars) {
     if (create_local_scope) {

paddle/framework/tensor_impl.h

@@ -134,6 +134,14 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
 #endif
     offset_ = 0;
   }
+
+  if (typeid(float).hash_code() == type.hash_code()) {
+    auto buf = reinterpret_cast<float*>(
+        reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
+    for (int64_t i = 0; i < this->numel(); ++i) {
+      buf[i] = NAN;
+    }
+  }
   return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                                  offset_);
 }

paddle/operators/sum_op.h

@@ -107,10 +107,12 @@ class SumKernel : public framework::OpKernel<T> {
               out_array.resize(i + 1);
             }
             if (out_array[i].numel() == 0) {
+              VLOG(10) << context.op().Output("Out") << " just copy";
               framework::CopyFrom(in_array[i], in_array[i].place(),
                                   context.device_context(), &out_array[i]);
               out_array[i].set_lod(in_array[i].lod());
             } else {
+              VLOG(10) << context.op().Output("Out") << " merged";
               PADDLE_ENFORCE(out_array[i].lod() == in_array[i].lod());
               auto in = EigenVector<T>::Flatten(in_array[i]);
               auto result = EigenVector<T>::Flatten(out_array[i]);

python/paddle/v2/fluid/executor.py

@@ -1,12 +1,31 @@
 import numpy as np
+import contextlib
+from framework import Program, default_main_program
 from . import core
-from framework import Program, default_main_program, Parameter, Variable
 
-__all__ = ['Executor', 'g_scope']
+__all__ = ['Executor', 'global_scope', 'scope_guard', 'switch_scope']
 
 g_scope = core.Scope()
 
 
+def global_scope():
+    return g_scope
+
+
+def switch_scope(scope):
+    global g_scope
+    ex = g_scope
+    g_scope = scope
+    return ex
+
+
+@contextlib.contextmanager
+def scope_guard(scope):
+    ex = switch_scope(scope)
+    yield
+    switch_scope(ex)
+
+
 def as_numpy(tensor):
     if isinstance(tensor, list):
         return [as_numpy(t) for t in tensor]
@@ -117,7 +136,7 @@ class Executor(object):
             raise TypeError()
 
         if scope is None:
-            scope = g_scope
+            scope = global_scope()
 
         program = program.clone()
         global_block = program.global_block()

python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py

@@ -170,7 +170,7 @@ def main():
 
     exe.run(fluid.default_startup_program())
 
-    embedding_param = fluid.g_scope.find_var(embedding_name).get_tensor()
+    embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
     embedding_param.set(
         load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
 

python/paddle/v2/fluid/tests/decorators.py

@@ -19,8 +19,10 @@ def prog_scope():
         def __fn__(*args, **kwargs):
             prog = fluid.Program()
             startup_prog = fluid.Program()
-            with fluid.program_guard(prog, startup_prog):
-                fn(*args, **kwargs)
+            scope = fluid.core.Scope()
+            with fluid.scope_guard(scope):
+                with fluid.program_guard(prog, startup_prog):
+                    fn(*args, **kwargs)
 
         return __fn__
 

python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py

@@ -298,7 +298,6 @@ class TestSimpleMulWithMemory(unittest.TestCase):
     @prog_scope()
     def test_forward_backward(self):
         py_rnn = TestSimpleMulWithMemory.SimpleMulWithMemory()
-
         data = fluid.layers.data(
             name=self.DATA_NAME, shape=[self.DATA_WIDTH], lod_level=1)
         data.stop_gradient = False
@@ -323,19 +322,18 @@ class TestSimpleMulWithMemory(unittest.TestCase):
         cpu = fluid.CPUPlace()
         exe = fluid.Executor(cpu)
         feed = py_rnn.to_feed(cpu)
-        for _ in xrange(2):
-            last_np, w_g, i_g = map(numpy.array,
-                                    exe.run(feed=feed,
-                                            fetch_list=[
-                                                last, self.PARAM_NAME + "@GRAD",
-                                                self.DATA_NAME + "@GRAD"
-                                            ],
-                                            return_numpy=False))
+        last_np, w_g, i_g = map(numpy.array,
+                                exe.run(feed=feed,
+                                        fetch_list=[
+                                            last, self.PARAM_NAME + "@GRAD",
+                                            self.DATA_NAME + "@GRAD"
+                                        ],
+                                        return_numpy=False))
         last_by_py, = py_rnn.exe().values()
-
         self.assertTrue(numpy.allclose(last_np, last_by_py))
         w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME)
-        print w_g[0], w_g_num[0]
+        # print w_g_num[0], w_g[0]
+
         self.assertTrue(numpy.allclose(w_g_num, w_g, rtol=0.1))
         i_g_num = py_rnn.get_numeric_gradient_of_input(self.DATA_NAME)
         i_g_num = i_g_num.reshape(i_g.shape)