Fix layers.uniform_random (#13823)

* fix layers.uniform_random

* fix uniform_random
test=develop

* remove var type set
test=develop

* fix similar error
test=develop

paddle/fluid/operators/uniform_random_op.cc

@@ -115,14 +115,14 @@ class UniformRandomOpVarTypeInference : public framework::VarTypeInference {
   void operator()(const framework::OpDesc &op_desc,
                   framework::BlockDesc *block) const override {
     auto out_var_name = op_desc.Output("Out").front();
-    if (block->FindRecursiveOrCreateVar(out_var_name).GetType() ==
-        framework::proto::VarType::SELECTED_ROWS) {
-      block->FindRecursiveOrCreateVar(out_var_name)
-          .SetType(framework::proto::VarType::SELECTED_ROWS);
-    } else {
-      block->FindRecursiveOrCreateVar(out_var_name)
-          .SetType(framework::proto::VarType::LOD_TENSOR);
+    auto var_data_type = static_cast<framework::proto::VarType::Type>(
+        boost::get<int>(op_desc.GetAttr("dtype")));
+
+    auto out_var = block->FindRecursiveOrCreateVar(out_var_name);
+    if (out_var.GetType() != framework::proto::VarType::SELECTED_ROWS) {
+      out_var.SetType(framework::proto::VarType::LOD_TENSOR);
     }
+    out_var.SetDataType(var_data_type);
   }
 };
 

python/paddle/fluid/layers/ops.py

@@ -14,6 +14,8 @@
 
 from __future__ import print_function
 from .layer_function_generator import generate_layer_fn, generate_layer_fn_noattr
+from .. import core
+from ..framework import convert_np_dtype_to_dtype_
 
 __activations_noattr__ = [
     'sigmoid',
@@ -58,8 +60,11 @@ _uniform_random_ = generate_layer_fn('uniform_random')
 
 
 def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
+    locals_var = locals().keys()
+    if not isinstance(dtype, core.VarDesc.VarType):
+        dtype = convert_np_dtype_to_dtype_(dtype)
     kwargs = dict()
-    for name in locals():
+    for name in locals_var:
         val = locals()[name]
         if val is not None:
             kwargs[name] = val
@@ -78,8 +83,9 @@ _hard_shrink_ = generate_layer_fn('hard_shrink')
 
 
 def hard_shrink(x, threshold=None):
+    locals_var = locals().keys()
     kwargs = dict()
-    for name in locals():
+    for name in locals_var:
         val = locals()[name]
         if val is not None:
             kwargs[name] = val
@@ -99,12 +105,12 @@ _cum_sum_ = generate_layer_fn('cumsum')
 
 
 def cumsum(x, axis=None, exclusive=None, reverse=None):
+    locals_var = locals().keys()
     kwargs = dict()
-    for name in locals():
+    for name in locals_var:
         val = locals()[name]
         if val is not None:
             kwargs[name] = val
-
     return _cum_sum_(**kwargs)
 
 
@@ -121,8 +127,9 @@ _thresholded_relu_ = generate_layer_fn('thresholded_relu')
 
 
 def thresholded_relu(x, threshold=None):
+    locals_var = locals().keys()
     kwargs = dict()
-    for name in locals():
+    for name in locals_var:
         val = locals()[name]
         if val is not None:
             kwargs[name] = val