Merge pull request #7027 from reyoung/feature/rnn_gradient_check

Feature/rnn gradient check

paddle/operators/shrink_rnn_memory_op.cc

@@ -116,9 +116,9 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
       auto height = dout_tensor.dims()[0];
       auto slice = dx_tensor.Slice(0, static_cast<int>(height));
       framework::CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx, &slice);
-      if (dx_tensor.dims()[0] < height) {
+      if (dx_tensor.dims()[0] > height) {
         auto rest_tensor = dx_tensor.Slice(
-            static_cast<int>(height), static_cast<int>(dout_tensor.dims()[0]));
+            static_cast<int>(height), static_cast<int>(dx_tensor.dims()[0]));
         math::set_constant(dev_ctx, &rest_tensor, 0.0f);
       }
     }

paddle/operators/sum_op.h

@@ -37,11 +37,11 @@ class SumKernel : public framework::OpKernel<T> {
     bool in_place = out_var == in_vars[0];
 
     if (out_var->IsType<framework::LoDTensor>()) {
-      auto *out = context.Output<Tensor>("Out");
+      auto *out = context.Output<LoDTensor>("Out");
+      if (!in_place) {
         out->mutable_data<T>(context.GetPlace());
-
+      }
       auto result = EigenVector<T>::Flatten(*out);
-
       if (!in_place) {
         math::SetConstant<DeviceContext, T> constant_functor;
         constant_functor(context.template device_context<DeviceContext>(), out,

paddle/operators/tensor_array_read_write_op.cc

@@ -130,9 +130,9 @@ class ReadFromArrayOp : public ArrayOp {
     auto &x_array = x->Get<framework::LoDTensorArray>();
     auto *out = scope.FindVar(Output("Out"));
     PADDLE_ENFORCE(out != nullptr, "Out must be set");
-    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
     size_t offset = GetOffset(scope, place);
     if (offset < x_array.size()) {
+      auto *out_tensor = out->GetMutable<framework::LoDTensor>();
       platform::DeviceContextPool &pool =
           platform::DeviceContextPool::Instance();
       auto &dev_ctx = *pool.Get(place);

paddle/pybind/tensor_py.h

@@ -77,10 +77,10 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
       } else if (paddle::platform::is_cpu_place(tensor.place())) {
         dst_tensor = tensor;
       }
-      return py::buffer_info(
-          dst_tensor.mutable_data<CUR_TYPE>(dst_tensor.place()),
-          sizeof(CUR_TYPE), py::format_descriptor<CUR_TYPE>::format(),
-          (size_t)framework::arity(dst_tensor.dims()), dims_outside, strides);
+      return py::buffer_info(dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
+                             py::format_descriptor<CUR_TYPE>::format(),
+                             (size_t)framework::arity(dst_tensor.dims()),
+                             dims_outside, strides);
     } else {
       constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
       return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor);

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

@@ -0,0 +1,29 @@
+import paddle.v2.fluid as fluid
+
+__all__ = ['many_times', 'prog_scope']
+
+
+def many_times(times):
+    def __impl__(fn):
+        def __fn__(*args, **kwargs):
+            for _ in range(times):
+                fn(*args, **kwargs)
+
+        return __fn__
+
+    return __impl__
+
+
+def prog_scope():
+    def __impl__(fn):
+        def __fn__(*args, **kwargs):
+            prog = fluid.Program()
+            startup_prog = fluid.Program()
+            scope = fluid.core.Scope()
+            with fluid.scope_guard(scope):
+                with fluid.program_guard(prog, startup_prog):
+                    fn(*args, **kwargs)
+
+        return __fn__
+
+    return __impl__