fix test

Author: smallv0221

paddle/fluid/operators/bincount_op.cu

@@ -63,16 +63,29 @@ void BincountCUDAInner(const framework::ExecutionContext& context) {
   }
   auto input_x = framework::EigenVector<InputT>::Flatten(*input);
 
-  framework::Tensor input_max_t;
+  framework::Tensor input_min_t, input_max_t;
   auto* input_max_data =
       input_max_t.mutable_data<InputT>({1}, context.GetPlace());
+  auto* input_min_data =
+      input_min_t.mutable_data<InputT>({1}, context.GetPlace());
+
   auto input_max_scala = framework::EigenScalar<InputT>::From(input_max_t);
+  auto input_min_scala = framework::EigenScalar<InputT>::From(input_min_t);
 
   auto* place = context.template device_context<DeviceContext>().eigen_device();
   input_max_scala.device(*place) = input_x.maximum();
+  input_min_scala.device(*place) = input_x.minimum();
 
-  Tensor input_max_cpu;
+  Tensor input_min_cpu, input_max_cpu;
   TensorCopySync(input_max_t, platform::CPUPlace(), &input_max_cpu);
+  TensorCopySync(input_min_t, platform::CPUPlace(), &input_min_cpu);
+
+  InputT input_min = input_min_cpu.data<InputT>()[0];
+
+  PADDLE_ENFORCE_GE(
+      input_min, static_cast<InputT>(0),
+      platform::errors::InvalidArgument(
+          "The elements in input tensor must be non-negative ints"));
 
   int64_t output_size =
       static_cast<int64_t>(input_max_cpu.data<InputT>()[0]) + 1L;

paddle/fluid/operators/bincount_op.h

@@ -43,6 +43,12 @@ void BincountInner(const framework::ExecutionContext& context) {
     return;
   }
 
+  PADDLE_ENFORCE_GE(
+      *std::min_element(input_data, input_data + input_numel),
+      static_cast<InputT>(0),
+      platform::errors::InvalidArgument(
+          "The elements in input tensor must be non-negative ints"));
+
   int64_t output_size = static_cast<int64_t>(*std::max_element(
                             input_data, input_data + input_numel)) +
                         1L;

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

@@ -47,7 +47,7 @@ def test_static_graph(self):
                                 'weights': w},
                           fetch_list=[output])
             actual = np.array(res[0])
-            expected = np.bincount(inputs, weights=weights)
+            expected = np.bincount(img, weights=w)
             self.assertTrue(
                 (actual == expected).all(),
                 msg='bincount output is wrong, out =' + str(actual))
@@ -70,6 +70,16 @@ def run_network(self, net_func):
         with fluid.dygraph.guard():
             net_func()
 
+    def test_input_value_error(self):
+        """Test input tensor should be non-negative."""
+
+        def net_func():
+            input_value = paddle.to_tensor([1, 2, 3, 4, -5])
+            paddle.bincount(input_value)
+
+        with self.assertRaises(ValueError):
+            self.run_network(net_func)
+
     def test_input_shape_error(self):
         """Test input tensor should be 1-D tansor."""
 
@@ -97,7 +107,7 @@ def net_func():
             input_value = paddle.to_tensor([1., 2., 3., 4., 5.])
             paddle.bincount(input_value)
 
-        with self.assertRaises(ValueError):
+        with self.assertRaises(TypeError):
             self.run_network(net_func)
 
     def test_weights_shape_error(self):

python/paddle/tensor/linalg.py

@@ -1318,15 +1318,16 @@ def bincount(x, weights=None, minlength=0, name=None):
             result2 = paddle.bincount(x, weights=w)
             print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]
     """
-    check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')
+    if x.dtype not in [paddle.int32, paddle.int64]:
+        raise TypeError("Elements in Input(x) should all be integers")
 
-    if paddle.min(x) < 0:
-        raise ValueError("Elements in Input(x) should all be non-negative")
     if in_dygraph_mode():
         return _C_ops.bincount(x, weights, "minlength", minlength)
 
     helper = LayerHelper('bincount', **locals())
 
+    check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')
+
     if weights is not None:
         check_variable_and_dtype(weights, 'Weights',
                                  ['int32', 'int64', 'float32', 'float64'],