revert uniform_random_op

paddle/fluid/operators/uniform_random_op.cc

@@ -24,19 +24,8 @@ template <typename T>
 class CPUUniformRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    framework::Tensor* tensor(nullptr);
-    auto out_var = ctx.OutputVar("Out");
-    if (out_var->IsType<framework::LoDTensor>()) {
-      tensor = ctx.Output<framework::LoDTensor>("Out");
-    } else if (out_var->IsType<framework::SelectedRows>()) {
-      auto shape = ctx.Attr<std::vector<int>>("shape");
-      tensor = ctx.Output<framework::SelectedRows>("Out")->mutable_value();
-      tensor->Resize(framework::make_ddim(shape));
-    } else {
-      PADDLE_THROW("Only support LoDTensor and SelectedRows.");
-    }
+    auto* tensor = ctx.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(ctx.GetPlace());
-    data[0] = static_cast<T>(1000);
     unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
     std::minstd_rand engine;
     if (seed == 0) {

paddle/fluid/operators/uniform_random_op.cu

@@ -43,18 +43,7 @@ template <typename T>
 class GPUUniformRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    framework::Tensor* tensor(nullptr);
-    auto out_var = ctx.OutputVar("Out");
-    if (out_var->IsType<framework::LoDTensor>()) {
-      tensor = ctx.Output<framework::LoDTensor>("Out");
-    } else if (out_var->IsType<framework::SelectedRows>()) {
-      auto shape = ctx.Attr<std::vector<int>>("shape");
-      tensor = ctx.Output<framework::SelectedRows>("Out")->mutable_value();
-      tensor->Resize(framework::make_ddim(shape));
-    } else {
-      PADDLE_THROW("Only support LoDTensor and SelectedRows.");
-    }
-
+    auto* tensor = context.Output<framework::Tensor>("Out");
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     if (seed == 0) {

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

@@ -15,16 +15,6 @@
 import unittest
 import numpy as np
 from op_test import OpTest
-import paddle.fluid.core as core
-from paddle.fluid.op import Operator
-
-
-def output_hist(out):
-    hist, _ = np.histogram(out, range=(-5, 10))
-    hist = hist.astype("float32")
-    hist /= float(out.size)
-    prob = 0.1 * np.ones((10))
-    return hist, prob
 
 
 class TestUniformRandomOp(OpTest):
@@ -43,37 +33,11 @@ class TestUniformRandomOp(OpTest):
         self.check_output_customized(self.verify_output)
 
     def verify_output(self, outs):
-        hist, prob = output_hist(outs[0])
-        self.assertTrue(
-            np.allclose(
-                hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
-
-
-class TestUniformRandomOpSelectedRows(unittest.TestCase):
-    def get_places(self):
-        places = [core.CPUPlace()]
-        if core.is_compiled_with_cuda():
-            places.append(core.CUDAPlace(0))
-        return places
-
-    def test_check_output(self):
-        for place in self.get_places():
-            self.check_with_place(place)
-
-    def check_with_place(self, place):
-        scope = core.Scope()
-        out = scope.var("X").get_selected_rows()
-
-        op = Operator(
-            "uniform_random",
-            Out="X",
-            shape=[1000, 784],
-            min=-5.0,
-            max=10.0,
-            seed=10)
-        op.run(scope, place)
-        out_tensor = out.get_tensor()
-        hist, prob = output_hist(np.array(out_tensor))
+        tensor = outs[0]
+        hist, _ = np.histogram(outs[0], range=(-5, 10))
+        hist = hist.astype("float32")
+        hist /= float(outs[0].size)
+        prob = 0.1 * np.ones((10))
         self.assertTrue(
             np.allclose(
                 hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))