Add unit test

paddle/fluid/operators/CMakeLists.txt

@@ -267,7 +267,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
 else()
     set(DEPS_OPS ${DEPS_OPS} tensorrt_engine_op)
 endif()
-op_library(clip_by_norm_op DEPS selected_rows_functor)
+op_library(clip_by_norm_op DEPS selected_rows_functor selected_rows)
 op_library(sum_op DEPS selected_rows_functor)
 op_library(sgd_op DEPS selected_rows_functor)
 op_library(print_op DEPS lod_tensor)

paddle/fluid/operators/clip_by_norm_op.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
 #include "paddle/fluid/platform/transform.h"
 
@@ -23,6 +24,7 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using SelectedRows = framework::SelectedRows;
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
@@ -41,22 +43,24 @@ class ClipByNormKernel : public framework::OpKernel<T> {
 
       output = context.Output<Tensor>("Out");
       output->mutable_data<T>(context.GetPlace());
-    } else if (in_var->IsType<framework::SelectedRows>()) {
-      auto* x = context.Input<framework::SelectedRows>("X");
+    } else if (in_var->IsType<SelectedRows>()) {
+      auto* x = context.Input<SelectedRows>("X");
 
       // merge ids in selected rows first
       math::scatter::MergeAdd<DeviceContext, T> merge_func;
-      auto* merged_input = const_cast<framework::Scope&>(context.scope())
-                               .Var()
-                               ->GetMutable<framework::SelectedRows>();
+      SelectedRows* merged_input =
+          const_cast<framework::Scope&>(context.scope())
+              .Var()
+              ->GetMutable<SelectedRows>();
       merge_func(context.template device_context<DeviceContext>(), *x,
                  merged_input);
       input = &(merged_input->value());
 
-      auto* output_selected_rows = context.Output<SelectedRows>("Out");
-      output_selected_rows->set_rows(merged_input.rows());
-      output = output_selected_rows->mutable_data();
-      output->Resize(framework::make_ddim(merged_input.value().dims()));
+      SelectedRows* output_selected_rows = context.Output<SelectedRows>("Out");
+      output_selected_rows->set_rows(merged_input->rows());
+      output_selected_rows->set_height(merged_input->height());
+      output = output_selected_rows->mutable_value();
+      output->Resize(merged_input->value().dims());
     } else {
       PADDLE_THROW("Unexpected branch, input variable type is %s",
                    in_var->Type().name());

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

@@ -18,6 +18,8 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
+import paddle.fluid.core as core
+
 
 class TestClipByNormOp(OpTest):
     def setUp(self):
@@ -62,5 +64,41 @@ class TestCase3(TestClipByNormOp):
         self.max_norm = 1.0
 
 
+class TestClipByNormOpWithSelectedRows(OpTest):
+    def setUp(self):
+        self.initTestCase()
+
+        self.max_relative_error = 0.006
+
+        scope = core.Scope()
+        x_selected_rows = scope.var('X').get_selected_rows()
+        x_selected_rows.set_rows([1, 1, 2, 0])
+        x_tensor = x_selected_rows.get_tensor()
+        x_tensor = np.random.random((4, 1)).astype("float32")
+        x_tensor[np.abs(x_tensor) < self.max_relative_error] = 0.5
+
+        self.op_type = "clip_by_norm"
+        self.inputs = {'X': x_selected_rows, }
+        self.attrs = {}
+        self.attrs['max_norm'] = self.max_norm
+        y_tensor = np.zeros((3, 1))
+        y_tensor[0::1] = np.sum(x_tensor[0::1], x_tensor[1::1])
+        y_tensor[1::1] = x_tensor[2::1]
+        y_tensor[2::1] = x_tensor[3::1]
+        norm = np.sqrt(np.sum(np.square(y_tensor)))
+        if norm > self.max_norm:
+            output = self.max_norm * y_tensor / norm
+        else:
+            output = y_tensor
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def initTestCase(self):
+        self.shape = (100, )
+        self.max_norm = 1.0
+
+
 if __name__ == '__main__':
     unittest.main()