Merge branch 'develop' into has_attr

cmake/external/gzstream.cmake

@@ -27,13 +27,14 @@ SET(GZSTREAM_INCLUDE_DIR "${GZSTREAM_INSTALL_DIR}/include/" CACHE PATH "gzstream
 
 ExternalProject_Add(
         extern_gzstream
+        DEPENDS zlib
         GIT_REPOSITORY "https://github.com/jacquesqiao/gzstream.git"
         GIT_TAG ""
         PREFIX          ${GZSTREAM_SOURCES_DIR}
         UPDATE_COMMAND  ""
         CONFIGURE_COMMAND ""
         BUILD_IN_SOURCE 1
-        BUILD_COMMAND   make -j8
+        BUILD_COMMAND   make EXTERN_CPPFLAGS="-I${THIRD_PARTY_PATH}/install/zlib/include" EXTERM_LDFLAGS="-L${THIRD_PARTY_PATH}/install/zlib/lib" -j8
         INSTALL_COMMAND mkdir -p ${GZSTREAM_INSTALL_DIR}/lib/ && mkdir -p ${GZSTREAM_INSTALL_DIR}/include/
         && cp ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/libgzstream.a ${GZSTREAM_INSTALL_DIR}/lib
         && cp -r ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/gzstream.h ${GZSTREAM_INSTALL_DIR}/include

paddle/fluid/inference/api/demo_ci/run.sh

@@ -54,6 +54,9 @@ mkdir -p build
 cd build
 
 for WITH_STATIC_LIB in ON OFF; do
+# TODO(Superjomn) reopen this
+# something wrong with the TensorArray reset.
+:<<D
   # -----simple_on_word2vec-----
   rm -rf *
   cmake .. -DPADDLE_LIB=${inference_install_dir} \
@@ -74,6 +77,7 @@ for WITH_STATIC_LIB in ON OFF; do
       fi
     done
   fi
+D
   # ---------vis_demo---------
   rm -rf *
   cmake .. -DPADDLE_LIB=${inference_install_dir} \

paddle/fluid/operators/math/blas.h

@@ -168,6 +168,9 @@ class Blas {
   template <typename T>
   void SCAL(int n, const T a, T* x) const;
 
+  template <typename T>
+  T ASUM(int n, T* x, int inc) const;
+
   template <typename T>
   void BatchedGEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N,
                    int K, T alpha, const T* A, const T* B, T beta, T* C,
@@ -269,6 +272,11 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template SCAL<T>(args...);
   }
 
+  template <typename... ARGS>
+  T ASUM(ARGS... args) const {
+    return Base()->template ASUM<T>(args...);
+  }
+
   template <typename... ARGS>
   void BatchedGEMM(ARGS... args) const {
     Base()->template BatchedGEMM<T>(args...);

paddle/fluid/operators/math/blas_impl.h

@@ -84,6 +84,11 @@ struct CBlas<float> {
     platform::dynload::cblas_sscal(args...);
   }
 
+  template <typename... ARGS>
+  static float ASUM(ARGS... args) {
+    return platform::dynload::cblas_sasum(args...);
+  }
+
   template <typename... ARGS>
   static void GEMM_BATCH(ARGS... args) {
     platform::dynload::cblas_sgemm_batch(args...);
@@ -174,6 +179,11 @@ struct CBlas<double> {
     platform::dynload::cblas_dscal(args...);
   }
 
+  template <typename... ARGS>
+  static double ASUM(ARGS... args) {
+    return platform::dynload::cblas_dasum(args...);
+  }
+
   template <typename... ARGS>
   static void GEMM_BATCH(ARGS... args) {
     platform::dynload::cblas_dgemm_batch(args...);
@@ -268,6 +278,7 @@ struct CBlas<platform::float16> {
   static void VPOW(...) { PADDLE_THROW("float16 VPOW not supported on CPU"); }
   static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
   static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
+  static void ASUM(...) { PADDLE_THROW("float16 ASUM not supported on CPU"); };
 #ifdef PADDLE_WITH_MKLML
   static void GEMM_BATCH(...) {
     PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
@@ -476,6 +487,21 @@ void Blas<platform::CPUDeviceContext>::SCAL(int n, const T a, T *x) const {
 #endif
 }
 
+template <>
+template <typename T>
+T Blas<platform::CPUDeviceContext>::ASUM(int n, T *x, int inc) const {
+  auto sum = static_cast<T>(0.0);
+#ifdef PADDLE_WITH_MKLML
+  sum = CBlas<T>::ASUM(n, x, inc);
+#else
+  // TODO(jczaja): check if openblas does provide cblas_sasum/cblas_dasum
+  for (int c = 0; c < n; ++c) {
+    sum += x[c];
+  }
+#endif
+  return sum;
+}
+
 template <>
 template <typename T>
 void Blas<platform::CPUDeviceContext>::GEMV(bool trans_a, int M, int N, T alpha,

paddle/fluid/operators/math/softmax_impl.h

@@ -100,11 +100,8 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
 
     blas.VEXP(num_classes * batch_size, out_data, out_data);
     for (int n = 0; n < batch_size; ++n) {
-      entities[n] = out_data[n * num_classes];
-      for (int c = 1; c < num_classes; ++c) {
-        entities[n] += out_data[n * num_classes + c];
-      }
-      blas.SCAL(num_classes, 1.0f / entities[n], &out_data[n * num_classes]);
+      auto sum = blas.ASUM(num_classes, &out_data[n * num_classes], 1);
+      blas.SCAL(num_classes, 1.0f / sum, &out_data[n * num_classes]);
     }
   }
 };

paddle/fluid/operators/softmax_op.h

@@ -36,9 +36,7 @@ class SoftmaxKernel : public framework::OpKernel<T> {
     Tensor Out_2d = framework::ReshapeToMatrix(*Out, rank - 1);
 
 #ifdef PADDLE_ON_INFERENCE
-    math::SoftmaxFunctor<
-        DeviceContext, T,
-        std::is_same<DeviceContext, platform::CPUDeviceContext>::value>()(
+    math::SoftmaxFunctor<DeviceContext, T, true>()(
         context.template device_context<DeviceContext>(), &X_2d, &Out_2d);
 #else
     math::SoftmaxFunctor<DeviceContext, T, false>()(

paddle/fluid/platform/dynload/mklml.h

@@ -68,6 +68,8 @@ extern void* mklml_dso_handle;
   __macro(cblas_dgemm_batch);       \
   __macro(cblas_sdot);              \
   __macro(cblas_ddot);              \
+  __macro(cblas_sasum);             \
+  __macro(cblas_dasum);             \
   __macro(cblas_sscal);             \
   __macro(cblas_dscal);             \
   __macro(vsAdd);                   \

paddle/fluid/pybind/pybind.cc

@@ -398,7 +398,26 @@ All parameter, weight, gradient are variables in Paddle.
             },
         py::return_value_policy::copy);
 
-  py::class_<Scope>(m, "Scope", "")
+  py::class_<Scope>(m, "Scope", R"DOC(
+    Scope is an association of a name to Variable. All variables belong to Scope.
+
+    Variables in a parent scope can be retrieved from local scope.
+
+    You need to specify a scope to run a Net, i.e., `exe.Run(&scope)`.
+    One net can run in different scopes and update different variable in the
+    scope.
+
+    You can create var in a scope and get it from the scope.
+
+    Examples:
+        .. code-block:: python
+
+          # create tensor from a scope and set value to it.
+          param = scope.var('Param').get_tensor()
+          param_array = np.full((height, row_numel), 5.0).astype("float32")
+          param.set(param_array, place)
+
+        )DOC")
       .def("var",
            [](Scope &self, const std::string &name) -> Variable * {
              return self.Var(name);

python/paddle/dataset/wmt16.py

@@ -71,15 +71,16 @@ def __build_dict(tar_file, dict_size, save_path, lang):
             for w in sen.split():
                 word_dict[w] += 1
 
-    with open(save_path, "w") as fout:
-        fout.write("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))
+    with open(save_path, "wb") as fout:
+        fout.write(
+            cpt.to_bytes("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK)))
         for idx, word in enumerate(
                 sorted(
                     six.iteritems(word_dict), key=lambda x: x[1],
                     reverse=True)):
             if idx + 3 == dict_size: break
-            fout.write(word[0].encode('utf-8'))
-            fout.write('\n')
+            fout.write(cpt.to_bytes(word[0]))
+            fout.write(cpt.to_bytes('\n'))
 
 
 def __load_dict(tar_file, dict_size, lang, reverse=False):

python/paddle/fluid/initializer.py

@@ -33,13 +33,15 @@ def force_init_on_cpu():
     """
     The flag of whether force to init variables on CPU.
 
-    Returns::
+    Returns:
+        bool: the state if we should force init on CPU.
 
     Examples:
+
         .. code-block:: python
 
             if force_init_on_cpu():
-                pass
+                create_op('force_cpu': force_init_on_cpu())
 
     """
     return _force_init_on_cpu_