reduce_mean grad error if keepdim=True and reduce_all=True

paddle/fluid/operators/reduce_ops/reduce_mean_op.part.cu

@@ -21,6 +21,4 @@ using CUDAReduceMeanGradKernel =
                           ops::MeanGradFunctor, true>;
 
 REGISTER_OP_CUDA_KERNEL(reduce_mean_grad, CUDAReduceMeanGradKernel<float>,
-                        CUDAReduceMeanGradKernel<double>,
-                        CUDAReduceMeanGradKernel<int>,
-                        CUDAReduceMeanGradKernel<int64_t>);
+                        CUDAReduceMeanGradKernel<double>);

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -236,8 +236,8 @@ class ReduceGradKernel : public framework::OpKernel<T> {
 
     if (reduce_all) {
       auto x = EigenVector<T>::Flatten(*input0);
-      auto x_reduce = EigenVector<T>::From(*input1);
-      auto x_reduce_grad = EigenVector<T>::From(*input2);
+      auto x_reduce = EigenVector<T>::Flatten(*input1);
+      auto x_reduce_grad = EigenVector<T>::Flatten(*input2);
       auto x_grad = EigenVector<T>::Flatten(*output);
       auto& place =
           *context.template device_context<DeviceContext>().eigen_device();

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

@@ -22,6 +22,8 @@
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 
+np.random.seed(10)
+
 
 class TestMeanOp(OpTest):
     def setUp(self):
@@ -74,10 +76,105 @@ def test_checkout_grad(self):
                 place, ['X'], 'Out', max_relative_error=0.8)
 
 
+def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
+    if isinstance(axis, list):
+        axis = tuple(axis)
+    if reduce_all:
+        axis = None
+    return np.mean(x, axis=axis, keepdims=keepdim)
+
+
+class TestReduceMeanOp(OpTest):
+    def setUp(self):
+        self.op_type = 'reduce_mean'
+        self.dtype = 'float64'
+        self.shape = [2, 3, 4, 5]
+        self.axis = [0]
+        self.keepdim = False
+        self.reduce_all = False
+        self.set_attrs()
+
+        np.random.seed(10)
+        x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
+        self.inputs = {'X': x_np}
+        self.outputs = {'Out': out_np}
+        self.attrs = {
+            'dim': self.axis,
+            'keep_dim': self.keepdim,
+            'reduce_all': self.reduce_all
+        }
+
+    def set_attrs(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], ['Out'])
+
+
+class TestReduceMeanOpDefaultAttrs(TestReduceMeanOp):
+    def setUp(self):
+        self.op_type = 'reduce_mean'
+        self.dtype = 'float64'
+        self.shape = [2, 3, 4, 5]
+
+        x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        out_np = np.mean(x_np, axis=0)
+        self.inputs = {'X': x_np}
+        self.outputs = {'Out': out_np}
+
+
+class TestReduceMeanOpFloat32(TestReduceMeanOp):
+    def set_attrs(self):
+        self.dtype = 'float32'
+
+
+class TestReduceMeanOpShape1D(TestReduceMeanOp):
+    def set_attrs(self):
+        self.shape = [100]
+
+
+class TestReduceMeanOpShape6D(TestReduceMeanOp):
+    def set_attrs(self):
+        self.shape = [2, 3, 4, 5, 6, 7]
+
+
+class TestReduceMeanOpAxisAll(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [0, 1, 2, 3]
+
+
+class TestReduceMeanOpAxisTuple(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = (0, 1, 2)
+
+
+class TestReduceMeanOpAxisNegative(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [-2, -1]
+
+
+class TestReduceMeanOpKeepdimTrue1(TestReduceMeanOp):
+    def set_attrs(self):
+        self.keepdim = True
+
+
+class TestReduceMeanOpKeepdimTrue2(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [0, 1, 2, 3]
+        self.keepdim = True
+
+
+class TestReduceMeanOpReduceAllTrue(TestReduceMeanOp):
+    def set_attrs(self):
+        self.reduce_all = True
+
+
 class TestMeanAPI(unittest.TestCase):
-    """
-    test paddle.tensor.stat.mean
-    """
+    # test paddle.tensor.stat.mean
 
     def setUp(self):
         self.x_shape = [2, 3, 4, 5]
@@ -128,6 +225,22 @@ def test_case(x, axis=None, keepdim=False):
         test_case(self.x, [0, 1, 2, 3])
         paddle.enable_static()
 
+    def test_fluid_api(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            x = fluid.data("x", shape=[10, 10], dtype="float32")
+            out = fluid.layers.reduce_mean(input=x, dim=1)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            x_np = np.random.rand(10, 10).astype(np.float32)
+            res = exe.run(feed={"x": x_np}, fetch_list=[out])
+        self.assertEqual(np.allclose(res[0], np.mean(x_np, axis=1)), True)
+
+        with fluid.dygraph.guard():
+            x_np = np.random.rand(10, 10).astype(np.float32)
+            x = fluid.dygraph.to_variable(x_np)
+            out = fluid.layers.reduce_mean(input=x, dim=1)
+        self.assertEqual(np.allclose(out.numpy(), np.mean(x_np, axis=1)), True)
+
     def test_errors(self):
         paddle.disable_static()
         x = np.random.uniform(-1, 1, [10, 12]).astype('float32')

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

@@ -67,22 +67,6 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
 
-class TestMeanOp(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_mean"
-        self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
-        self.attrs = {'dim': [1]}
-        self.outputs = {
-            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
-        }
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
 @skip_check_grad_ci(
     reason="reduce_max is discontinuous non-derivable function,"
     " its gradient check is not supported by unittest framework.")
@@ -318,21 +302,6 @@ def setUp(self):
         self.outputs = {'Out': self.inputs['X'].sum()}
 
 
-## reduction in multi dims
-class TestReduceMeanOpMultiAxises(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_mean"
-        self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
-        self.attrs = {'dim': [1, 2]}
-        self.outputs = {'Out': self.inputs['X'].mean(axis=(1, 2))}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
 @skip_check_grad_ci(
     reason="reduce_max is discontinuous non-derivable function,"
     " its gradient check is not supported by unittest framework.")
@@ -420,40 +389,6 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
 
-class TestReduceMeanWithDimOne(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_mean"
-        self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
-        self.attrs = {'dim': [1], 'keep_dim': False}
-        self.outputs = {
-            'Out': self.inputs['X'].mean(
-                axis=tuple(self.attrs['dim']), keepdims=False)
-        }
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
-class TestReduceMeanWithNumelOne(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_mean"
-        self.inputs = {'X': np.random.random((100, 1)).astype("float64")}
-        self.attrs = {'dim': [1], 'keep_dim': True}
-        self.outputs = {
-            'Out': self.inputs['X'].mean(
-                axis=tuple(self.attrs['dim']), keepdims=True)
-        }
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
 class TestReduceAll(OpTest):
     def setUp(self):
         self.op_type = "reduce_sum"
@@ -536,18 +471,6 @@ def test_errors(self):
             self.assertRaises(TypeError, fluid.layers.reduce_sum, x2)
 
 
-class TestReduceMeanOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # The input type of reduce_mean_op must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace())
-            self.assertRaises(TypeError, fluid.layers.reduce_mean, x1)
-            # The input dtype of reduce_mean_op  must be float32 or float64 or int32 or int64.
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.reduce_mean, x2)
-
-
 class API_TestSumOpError(unittest.TestCase):
     def test_errors(self):
         def test_dtype1():
@@ -649,24 +572,5 @@ def test_dygraph(self):
         self.assertTrue((out3 == np.sum(np_x, axis=(0, 1, 2))).all())
 
 
-class API_TestReduceMeanOp(unittest.TestCase):
-    def test_static(self):
-        with fluid.program_guard(fluid.Program(), fluid.Program()):
-            x = fluid.data("x", shape=[10, 10], dtype="float32")
-            out = fluid.layers.reduce_mean(input=x, dim=1)
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            x_np = np.random.rand(10, 10).astype(np.float32)
-            res = exe.run(feed={"x": x_np}, fetch_list=[out])
-        self.assertEqual(np.allclose(res[0], np.mean(x_np, axis=1)), True)
-
-    def test_dygraph(self):
-        with fluid.dygraph.guard():
-            x_np = np.random.rand(10, 10).astype(np.float32)
-            x = fluid.dygraph.to_variable(x_np)
-            out = fluid.layers.reduce_mean(input=x, dim=1)
-        self.assertEqual(np.allclose(out.numpy(), np.mean(x_np, axis=1)), True)
-
-
 if __name__ == '__main__':
     unittest.main()