modify datanorm op test=develop (#23030)

paddle/fluid/framework/unused_var_check.cc

@@ -53,7 +53,9 @@ const std::unordered_set<std::string> op_has_unsed_vars_white_list = {
     "precision_recall",                // 1
     "fusion_seqpool_cvm_concat",       // 2
     "fused_batch_norm_act",            // 2
-    "fused_batch_norm_act_grad"        // 2
+    "fused_batch_norm_act_grad",       // 2
+    "data_norm",                       // 0
+    "data_norm_grad",                  // 0
 };
 
 namespace paddle {

paddle/fluid/operators/data_norm_op.cc

@@ -51,6 +51,17 @@ class DataNormOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Means"), "");
     PADDLE_ENFORCE(ctx->HasOutput("Scales"), "");
     PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
+    bool enable_scale_and_shift =
+        ctx->Attrs().Get<bool>("enable_scale_and_shift");
+    if (enable_scale_and_shift) {
+      PADDLE_ENFORCE_EQ(
+          ctx->HasInput("scale_w"), true,
+          platform::errors::InvalidArgument(
+              "Input(scale_w) of DataNormOp should not be null."));
+      PADDLE_ENFORCE_EQ(ctx->HasInput("bias"), true,
+                        platform::errors::InvalidArgument(
+                            "Input(bias) of DataNormOp should not be null."));
+    }
 
     const auto x_dims = ctx->GetInputDim("X");
     const DataLayout data_layout = framework::StringToDataLayout(
@@ -72,6 +83,45 @@ class DataNormOp : public framework::OperatorWithKernel {
       PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSquareSum")[0], C);
     }
 
+    if (enable_scale_and_shift) {
+      auto scale_dim = ctx->GetInputDim("scale_w");
+      auto bias_dim = ctx->GetInputDim("bias");
+
+      PADDLE_ENFORCE_EQ(
+          scale_dim.size(), 1UL,
+          platform::errors::InvalidArgument("the dimensionof scale"
+                                            "must equal to 1. But received: "
+                                            "the shape of scale is [%s], "
+                                            "the dimensionof scale is [%d]",
+                                            scale_dim, scale_dim.size()));
+      PADDLE_ENFORCE_EQ(
+          bias_dim.size(), 1UL,
+          platform::errors::InvalidArgument("the dimension of bias"
+                                            "must equal to 1. But received: "
+                                            "the shape of bias is [%s],"
+                                            "the dimension of bias is [%d]",
+                                            bias_dim, bias_dim.size()));
+
+      bool check = true;
+      if ((!ctx->IsRuntime()) && (framework::product(scale_dim) <= 0 ||
+                                  framework::product(bias_dim) <= 0)) {
+        check = false;
+      }
+
+      if (check) {
+        PADDLE_ENFORCE_EQ(scale_dim[0], C,
+                          platform::errors::InvalidArgument(
+                              "the shape of scale must equal to [%d]"
+                              "But received: the shape of scale is [%d]",
+                              C, scale_dim[0]));
+        PADDLE_ENFORCE_EQ(bias_dim[0], C,
+                          platform::errors::InvalidArgument(
+                              "the shape of bias must equal to [%d]"
+                              "But received: the shape of bias is [%d]",
+                              C, bias_dim[0]));
+      }
+    }
+
     ctx->SetOutputDim("Y", x_dims);
     ctx->SetOutputDim("Means", {C});
     ctx->SetOutputDim("Scales", {C});
@@ -99,6 +149,17 @@ class DataNormOp : public framework::OperatorWithKernel {
                                          ctx, "BatchSquareSum"),
                       "BatchSquareSum input should be of float type");
 
+    bool enable_scale_and_shift = ctx.Attr<bool>("enable_scale_and_shift");
+    if (enable_scale_and_shift) {
+      PADDLE_ENFORCE_EQ(dn_param_type,
+                        OperatorWithKernel::IndicateVarDataType(ctx, "scale_w"),
+                        platform::errors::InvalidArgument(
+                            "scale_w input should be of float type"));
+      PADDLE_ENFORCE_EQ(dn_param_type,
+                        OperatorWithKernel::IndicateVarDataType(ctx, "bias"),
+                        platform::errors::InvalidArgument(
+                            "bias input should be of float type"));
+    }
     // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
     framework::LibraryType library = framework::LibraryType::kPlain;
     framework::DataLayout layout = framework::DataLayout::kAnyLayout;
@@ -133,6 +194,19 @@ class DataNormOpMaker : public framework::OpProtoAndCheckerMaker {
         "summary_decay_rate",
         "(float, default 0.9999999) The decay rate when update the summary")
         .SetDefault(0.9999999);
+    AddAttr<bool>(
+        "enable_scale_and_shift",
+        "(bool, default false) Set to true to enable scale and shift such as "
+        "batch_norm op")
+        .SetDefault(false);
+    AddInput("scale_w",
+             "scale_w is a 1-dimensional tensor of size C "
+             "that is applied to the output")
+        .AsDispensable();
+    AddInput("bias",
+             "bias is a 1-dimensional tensor of size C "
+             "that is applied to the output")
+        .AsDispensable();
     AddAttr<std::string>("data_layout", "").SetDefault("NCHW");
     AddAttr<bool>("sync_stats", "(bool, default false) only used in multi-GPU")
         .SetDefault(false);
@@ -194,7 +268,6 @@ class DataNormKernel<platform::CPUDeviceContext, T>
     // alloc memory
     T *y_data = y->mutable_data<T>(ctx.GetPlace());
 
-    Eigen::Array<T, Eigen::Dynamic, 1> inv_std(C);
     ConstEigenVectorArrayMap<T> b_size_arr(
         ctx.Input<Tensor>("BatchSize")->data<T>(), C);
     ConstEigenVectorArrayMap<T> b_sum_arr(
@@ -210,6 +283,7 @@ class DataNormKernel<platform::CPUDeviceContext, T>
 
     const T *means_data = mean_out->data<T>();
     const T *x_data = x->data<T>();
+
     const T *scales_data = scales->data<T>();
     const int slot_dim = ctx.Attr<int>("slot_dim");
     T min_precision = 1e-7f;
@@ -218,7 +292,8 @@ class DataNormKernel<platform::CPUDeviceContext, T>
       case DataLayout::kNHWC: {
         // if slot_dim is set and batch size is larger than zero, we choose
         // to check if show number is zero, if so, skip normalization.
-        if (slot_dim > 0 && N > 0) {
+        if (slot_dim > 0 && N > 0 &&
+            (!ctx.Attr<bool>("enable_scale_and_shift"))) {
           const int item_size = x->numel() / N;
           // location of show number in one embedding
           int offset = 0;
@@ -239,10 +314,56 @@ class DataNormKernel<platform::CPUDeviceContext, T>
             offset += item_size;
           }
         } else {
+          if (!ctx.Attr<bool>("enable_scale_and_shift") && slot_dim <= 0) {
             EigenArrayMap<T>(y_data, C, N) =
-              (ConstEigenArrayMap<T>(x->data<T>(), C, N).colwise() - means_arr)
+                (ConstEigenArrayMap<T>(x->data<T>(), C, N).colwise() -
+                 means_arr)
                     .colwise() *
                 scales_arr;
+          } else if (ctx.Attr<bool>("enable_scale_and_shift") &&
+                     slot_dim <= 0) {
+            const auto *scale_w = ctx.Input<Tensor>("scale_w");
+            const auto *bias = ctx.Input<Tensor>("bias");
+            ConstEigenVectorArrayMap<T> scale_w_arr(scale_w->data<T>(), C);
+            ConstEigenVectorArrayMap<T> bias_arr(bias->data<T>(), C);
+
+            Eigen::Array<T, Eigen::Dynamic, 1> new_scale =
+                scales_arr * scale_w_arr;
+            Eigen::Array<T, Eigen::Dynamic, 1> new_bias =
+                bias_arr - means_arr * scales_arr * scale_w_arr;
+            EigenArrayMap<T>(y_data, C, N) =
+                (ConstEigenArrayMap<T>(x->data<T>(), C, N).colwise() *
+                 new_scale)
+                    .colwise() +
+                new_bias;
+
+          } else {
+            const int item_size = x->numel() / N;
+            const auto *scale_w = ctx.Input<Tensor>("scale_w");
+            const auto *bias = ctx.Input<Tensor>("bias");
+            const T *scale_w_data = scale_w->data<T>();
+            const T *bias_data = bias->data<T>();
+            // location of show number in one embedding
+            int offset = 0;
+            for (int k = 0; k < N; ++k) {
+              for (int i = 0; i < item_size; i += slot_dim) {
+                if (x_data[offset + i] > -min_precision &&
+                    x_data[offset + i] < min_precision) {
+                  // show = 0
+                  memset(y_data + offset + i, 0, sizeof(T) * slot_dim);
+                } else {
+                  for (int j = i; j < i + slot_dim; ++j) {
+                    y_data[offset + j] = ((x_data[offset + j] - means_data[j]) *
+                                          scales_data[j]) *
+                                             scale_w_data[j] +
+                                         bias_data[j];
+                  }
+                }
+              }  // end for i
+
+              offset += item_size;
+            }  // end for k
+          }
         }
         break;
       }
@@ -274,7 +395,8 @@ class DataNormGradOp : public framework::OperatorWithKernel {
             "Output(BatchSquareSum) of DataNormGradOp should not be null."));
     PADDLE_ENFORCE(ctx->HasInput("Means"), "");
     PADDLE_ENFORCE(ctx->HasInput("Scales"), "");
-
+    bool enable_scale_and_shift =
+        ctx->Attrs().Get<bool>("enable_scale_and_shift");
     // check output
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("BatchSize")), "");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("BatchSum")), "");
@@ -294,6 +416,22 @@ class DataNormGradOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim(framework::GradVarName("BatchSize"), {C});
     ctx->SetOutputDim(framework::GradVarName("BatchSum"), {C});
     ctx->SetOutputDim(framework::GradVarName("BatchSquareSum"), {C});
+    if (enable_scale_and_shift) {
+      const bool has_scale_grad =
+          ctx->HasOutput(framework::GradVarName("scale_w"));
+      const bool has_bias_grad = ctx->HasOutput(framework::GradVarName("bias"));
+
+      PADDLE_ENFORCE_EQ((has_scale_grad == has_bias_grad), true,
+                        platform::errors::InvalidArgument(
+                            "Output(Scale@GRAD) and Output(Bias@GRAD)"
+                            "must be null or not be null at same time. "
+                            "But now, has Scale@Grad=[%d], has Bias@GRAD=[%d]",
+                            has_scale_grad, has_bias_grad));
+      if (has_scale_grad) {
+        ctx->SetOutputDim(framework::GradVarName("scale_w"), {C});
+        ctx->SetOutputDim(framework::GradVarName("bias"), {C});
+      }
+    }
   }
 
  protected:
@@ -353,18 +491,23 @@ class DataNormGradKernel<platform::CPUDeviceContext, T>
     const int C =
         (data_layout == DataLayout::kNCHW ? x_dims[1]
                                           : x_dims[x_dims.size() - 1]);
-
     // init output
     Tensor *d_x = nullptr;
     if (ctx.HasOutput(framework::GradVarName("X"))) {
       d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
     }
+
     auto *d_batch_size =
         ctx.Output<Tensor>(framework::GradVarName("BatchSize"));
     auto *d_batch_sum = ctx.Output<Tensor>(framework::GradVarName("BatchSum"));
     auto *d_batch_square_sum =
         ctx.Output<Tensor>(framework::GradVarName("BatchSquareSum"));
 
+    const T *mean_data = means->data<T>();
+    const T *inv_var_data = scales->data<T>();
+    ConstEigenVectorArrayMap<T> mean_arr(mean_data, C);
+    ConstEigenVectorArrayMap<T> inv_var_arr(inv_var_data, C);
+
     T *d_batch_size_data = d_batch_size->mutable_data<T>(ctx.GetPlace());
     T *d_batch_sum_data = d_batch_sum->mutable_data<T>(ctx.GetPlace());
     T *d_batch_square_sum_data =
@@ -372,7 +515,6 @@ class DataNormGradKernel<platform::CPUDeviceContext, T>
     EigenVectorArrayMap<T> d_batch_size_arr(d_batch_size_data, C);
     EigenVectorArrayMap<T> d_batch_sum_arr(d_batch_sum_data, C);
     EigenVectorArrayMap<T> d_batch_square_sum_arr(d_batch_square_sum_data, C);
-
     d_batch_size_arr.setZero();
     d_batch_sum_arr.setZero();
     d_batch_square_sum_arr.setZero();
@@ -392,9 +534,87 @@ class DataNormGradKernel<platform::CPUDeviceContext, T>
         if (d_x != nullptr) {
           EigenArrayMap<T> d_x_arr(d_x->mutable_data<T>(ctx.GetPlace()), C, N);
           d_x_arr.setZero();
+          if (!ctx.Attr<bool>("enable_scale_and_shift")) {
             for (int nc = 0; nc < N; ++nc) {
               d_x_arr.col(nc) = d_y_arr.col(nc) * scales_arr;
             }
+          } else {
+            const auto *scale_w = ctx.Input<Tensor>("scale_w");
+            auto *d_scale =
+                ctx.Output<Tensor>(framework::GradVarName("scale_w"));
+            auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("bias"));
+            ConstEigenVectorArrayMap<T> scale_arr(scale_w->data<T>(), C);
+            T *d_bias_data = nullptr;
+            T *d_scale_data = nullptr;
+
+            d_scale->mutable_data<T>(ctx.GetPlace());
+            d_bias->mutable_data<T>(ctx.GetPlace());
+            d_bias_data = d_bias->mutable_data<T>(ctx.GetPlace());
+            d_scale_data = d_scale->mutable_data<T>(ctx.GetPlace());
+
+            EigenVectorArrayMap<T> d_bias_arr(d_bias_data, C);
+            EigenVectorArrayMap<T> d_scale_arr(d_scale_data, C);
+            Tensor dy_sum;
+            dy_sum.Resize({C});
+            dy_sum.mutable_data<T>(ctx.GetPlace());
+            EigenVectorArrayMap<T> dy_sum_arr(
+                dy_sum.mutable_data<T>(ctx.GetPlace()), C);
+            Tensor dy_mul_x_sub_mean_mul_invstd_sum;
+            dy_mul_x_sub_mean_mul_invstd_sum.Resize({C});
+            dy_mul_x_sub_mean_mul_invstd_sum.mutable_data<T>(ctx.GetPlace());
+            EigenVectorArrayMap<T> dy_mul_x_sub_mean_mul_invstd_sum_arr(
+                dy_mul_x_sub_mean_mul_invstd_sum.mutable_data<T>(
+                    ctx.GetPlace()),
+                C);
+
+            dy_sum_arr.setZero();
+            dy_mul_x_sub_mean_mul_invstd_sum_arr.setZero();
+
+            if (slot_dim <= 0) {
+              for (int n = 0; n < N; ++n) {
+                dy_sum_arr += d_y_arr.col(n);
+                dy_mul_x_sub_mean_mul_invstd_sum_arr +=
+                    ((x_arr.col(n) - mean_arr) * inv_var_arr * d_y_arr.col(n));
+              }
+              if (d_scale && d_bias) {
+                d_bias_arr = dy_sum_arr;
+                d_scale_arr = dy_mul_x_sub_mean_mul_invstd_sum_arr;
+              }
+              for (int nc = 0; nc < N; ++nc) {
+                d_x_arr.col(nc) = d_y_arr.col(nc) * scales_arr * scale_arr;
+              }
+            } else {
+              int offset = 0;
+              const int item_size = x->numel() / N;
+              T *d_x_data = d_x->mutable_data<T>(ctx.GetPlace());
+              T *d_scale_data = d_scale->mutable_data<T>(ctx.GetPlace());
+              T *d_bias_data = d_bias->mutable_data<T>(ctx.GetPlace());
+              const T *dy_data = d_y->data<T>();
+              const T *scales_data = scales->data<T>();
+              const T *scale_w_data = scale_w->data<T>();
+              const T *x_data = x->data<T>();
+              for (int i = 0; i < item_size; i++) {
+                d_bias_data[i] = 0;
+                d_scale_data[i] = 0;
+              }
+              for (int k = 0; k < N; ++k) {
+                for (int i = 0; i < item_size; i += slot_dim) {
+                  if (!(x_data[offset + i] > -min_precision &&
+                        x_data[offset + i] < min_precision)) {
+                    // show != 0
+                    for (int j = i; j < i + slot_dim; ++j) {
+                      d_x_data[offset + j] = dy_data[offset + j] *
+                                             scales_data[j] * scale_w_data[j];
+                      d_bias_data[j] += dy_data[offset + j];
+                      d_scale_data[j] += (x_data[offset + j] - mean_data[j]) *
+                                         inv_var_data[j] * dy_data[offset + j];
+                    }
+                  }
+                }
+                offset += item_size;
+              }
+            }
+          }
         }
 
         if (slot_dim > 0 && N > 0) {
@@ -466,6 +686,8 @@ class DataNormGradMaker : public framework::SingleGradOpMaker<T> {
     op->SetInput("X", this->Input("X"));
     op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
 
+    op->SetInput("scale_w", this->Input("scale_w"));
+    op->SetInput("bias", this->Input("bias"));
     op->SetOutput("BatchSize", this->Input("BatchSize"));
     op->SetOutput("BatchSum", this->Input("BatchSum"));
     op->SetOutput("BatchSquareSum", this->Input("BatchSquareSum"));
@@ -481,6 +703,9 @@ class DataNormGradMaker : public framework::SingleGradOpMaker<T> {
                   this->InputGrad("BatchSum"));
     op->SetOutput(framework::GradVarName("BatchSquareSum"),
                   this->InputGrad("BatchSquareSum"));
+    op->SetOutput(framework::GradVarName("scale_w"),
+                  this->InputGrad("scale_w"));
+    op->SetOutput(framework::GradVarName("bias"), this->InputGrad("bias"));
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -3157,7 +3157,8 @@ def data_norm(input,
               do_model_average_for_mean_and_var=True,
               slot_dim=-1,
               sync_stats=False,
-              summary_decay_rate=0.9999999):
+              summary_decay_rate=0.9999999,
+              enable_scale_and_shift=False):
     """
     **Data Normalization Layer**
 
@@ -3206,6 +3207,7 @@ def data_norm(input,
         sync_stats(bool, Default False): When running with multiple GPU cards, using allreduce to sync the
             summary messages.
         summary_decay_rate(float, Default 0.9999999): The decay rate when updating summary.
+        enable_scale_and_shift(bool, Default False): do scale&shift after normalization.
 
     Returns:
         Variable: A tensor variable which is the result after applying data normalization on the input.
@@ -3236,12 +3238,35 @@ def data_norm(input,
     batch_size_default = 1e4
     batch_sum_default = 0.0
     batch_square_sum_default = 1e4
+    scale_w_default = 1.0
+    bias_default = 0.0
 
     if param_attr and isinstance(param_attr, dict):
         batch_size_default = param_attr.get("batch_size", 1e4)
         batch_sum_default = param_attr.get("batch_sum", 0.0)
         batch_square_sum_default = param_attr.get("batch_square", 1e4)
-
+    if enable_scale_and_shift:
+        scale_w_default = param_attr.get("scale_w", 1.0)
+        bias_default = param_attr.get("bias", 0.0)
+
+    # create scale and shift(bias) when enable_scale_and_shift is True
+    if name == None:
+        name = "dn"
+    if enable_scale_and_shift:
+        scale_w = helper.create_parameter(
+            attr=ParamAttr(
+                name=name + '.scale_w',
+                initializer=Constant(value=float(scale_w_default)),
+                trainable=True),
+            shape=param_shape,
+            dtype=input.dtype)
+        bias = helper.create_parameter(
+            attr=ParamAttr(
+                name=name + '.bias',
+                initializer=Constant(value=float(bias_default)),
+                trainable=True),
+            shape=param_shape,
+            dtype=input.dtype)
     # create parameter
     batch_size = helper.create_parameter(
         attr=ParamAttr(
@@ -3272,14 +3297,18 @@ def data_norm(input,
 
     data_norm_out = input if in_place else helper.create_variable(dtype=dtype)
 
-    helper.append_op(
-        type="data_norm",
-        inputs={
+    inputs = {
         "X": input,
         "BatchSize": batch_size,
         "BatchSum": batch_sum,
         "BatchSquareSum": batch_square_sum
-        },
+    }
+    if enable_scale_and_shift:
+        inputs["scale_w"] = scale_w
+        inputs["bias"] = bias
+    helper.append_op(
+        type="data_norm",
+        inputs=inputs,
         outputs={
             "Y": data_norm_out,
             "Means": means,
@@ -3292,7 +3321,8 @@ def data_norm(input,
             "epsilon": epsilon,
             "slot_dim": slot_dim,
             "sync_stats": sync_stats,
-            "summary_decay_rate": summary_decay_rate
+            "summary_decay_rate": summary_decay_rate,
+            "enable_scale_and_shift": enable_scale_and_shift
         })
 
     return helper.append_activation(data_norm_out)

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

@@ -24,6 +24,7 @@ import paddle.fluid.layers as layers
 import os
 from op_test import OpTest
 from paddle.fluid.framework import grad_var_name
+from paddle.fluid import Program, program_guard
 
 
 def _reference_testing(x, batch_size, batch_sum, batch_square_sum, slot_dim=-1):
@@ -72,7 +73,13 @@ class TestDataNormOpInference(unittest.TestCase):
     def __assert_close(self, tensor, np_array, msg, atol=1e-4):
         self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
 
-    def check_with_place(self, place, data_layout, dtype, shape, slot_dim=-1):
+    def check_with_place(self,
+                         place,
+                         data_layout,
+                         dtype,
+                         shape,
+                         slot_dim=-1,
+                         enable_scale_and_shift=False):
         """
         do forward and check
 
@@ -82,7 +89,7 @@ class TestDataNormOpInference(unittest.TestCase):
             dtype(dtype): np.float32
             shape(list): input shape
             slot_dim(int): dimension of one slot. Refer to data_norm api.
-
+            enable_scale_and_shift(bool): if enable scale and shift after normalization.
 
         """
         epsilon = 0.00001
@@ -127,6 +134,7 @@ class TestDataNormOpInference(unittest.TestCase):
         mean_tensor = create_or_get_tensor(scope, "mean", None, place)
         scales_tensor = create_or_get_tensor(scope, "scales", None, place)
 
+        if not enable_scale_and_shift:
             data_norm_op = Operator(
                 "data_norm",
                 # inputs
@@ -141,7 +149,34 @@ class TestDataNormOpInference(unittest.TestCase):
                 # attrs
                 epsilon=epsilon,
                 use_mkldnn=self.use_mkldnn,
-            slot_dim=slot_dim)
+                slot_dim=slot_dim,
+                enable_scale_and_shift=False)
+        else:
+            scale_w = np.ones(scale_shape).astype(np.float32)
+            bias = np.zeros(scale_shape).astype(np.float32)
+            scale_w_tensor = create_or_get_tensor(
+                scope, "scale_w",
+                OpTest.np_dtype_to_fluid_dtype(scale_w), place)
+            bias_tensor = create_or_get_tensor(
+                scope, "bias", OpTest.np_dtype_to_fluid_dtype(bias), place)
+            data_norm_op = Operator(
+                "data_norm",
+                # inputs
+                X="x_val",
+                BatchSize="batch_size",
+                BatchSum="batch_sum",
+                BatchSquareSum="batch_square_sum",
+                scale_w="scale_w",
+                bias="bias",
+                # outputs
+                Y="y_out",
+                Means="mean",
+                Scales="scales",
+                # attrs
+                epsilon=epsilon,
+                use_mkldnn=self.use_mkldnn,
+                slot_dim=slot_dim,
+                enable_scale_and_shift=True)
 
         data_norm_op.run(scope, place)
 
@@ -162,11 +197,13 @@ class TestDataNormOpInference(unittest.TestCase):
         for place in places:
             for data_format in ["NCHW", "NHWC"]:
                 for slot_dim in [-1, 1]:
+                    for enable_scale_and_shift in [False, True]:
                         self.check_with_place(
                             place,
                             data_format,
                             self.dtype, [2, 3],
-                        slot_dim=slot_dim)
+                            slot_dim=slot_dim,
+                            enable_scale_and_shift=enable_scale_and_shift)
 
 
 class TestDataNormOp(OpTest):
@@ -220,6 +257,130 @@ class TestDataNormOp(OpTest):
         self.check_grad(['X'], 'Y', no_grad_set=set([]))
 
 
+class TestDataNormOpWithEnableScaleAndShift(OpTest):
+    """
+    test class for data norm op
+    test forward and backward
+    """
+
+    def setUp(self):
+        """
+        init data norm op test env
+        """
+        self.op_type = 'data_norm'
+        self.use_mkldnn = False
+        epsilon = 0.00001
+        slot_dim = -1
+        enable_scale_and_shitf = True
+        x_shape = [2, 50]
+        scale_shape = [50]
+        tp = np.float32
+
+        x_val = np.random.uniform(-1, 1, x_shape).astype(tp)
+        batch_size = np.ones(scale_shape).astype(tp)
+        batch_size *= 1e4
+        batch_sum = np.zeros(scale_shape).astype(tp)
+        batch_square_sum = np.ones(scale_shape).astype(tp)
+        batch_square_sum *= 1e4
+        scale_w = np.ones(scale_shape).astype(tp)
+        bias = np.zeros(scale_shape).astype(tp)
+
+        y = np.array(x_val)
+
+        mean = np.zeros(x_shape).astype(tp)
+        scale = np.ones(x_shape).astype(tp)
+
+        self.inputs = {
+            "X": x_val,
+            "BatchSize": batch_size,
+            "BatchSum": batch_sum,
+            "BatchSquareSum": batch_square_sum,
+            "scale_w": scale_w,
+            "bias": bias
+        }
+        self.outputs = {"Y": y, "Means": mean, "Scales": scale}
+        self.attrs = {
+            "epsilon": epsilon,
+            "use_mkldnn": self.use_mkldnn,
+            "slot_dim": slot_dim,
+            "enable_scale_and_shift": True
+        }
+
+    def test_check_output(self):
+        """
+        test check forward, check output
+        """
+        self.check_output()
+
+    def test_check_grad(self):
+        """
+        test check backward, check grad
+        """
+        self.check_grad(['X'], 'Y', no_grad_set=set([]))
+
+
+class TestDataNormOpWithEnableScaleAndShift_1(OpTest):
+    """
+    test class for data norm op
+    test forward and backward
+    """
+
+    def setUp(self):
+        """
+        init data norm op test env
+        """
+        self.op_type = 'data_norm'
+        self.use_mkldnn = False
+        epsilon = 0.00001
+        slot_dim = 1
+        enable_scale_and_shitf = True
+        x_shape = [2, 50]
+        scale_shape = [50]
+        tp = np.float32
+
+        x_val = np.random.uniform(-1, 1, x_shape).astype(tp)
+        batch_size = np.ones(scale_shape).astype(tp)
+        batch_size *= 1e4
+        batch_sum = np.zeros(scale_shape).astype(tp)
+        batch_square_sum = np.ones(scale_shape).astype(tp)
+        batch_square_sum *= 1e4
+        scale_w = np.ones(scale_shape).astype(tp)
+        bias = np.zeros(scale_shape).astype(tp)
+
+        y = np.array(x_val)
+
+        mean = np.zeros(x_shape).astype(tp)
+        scale = np.ones(x_shape).astype(tp)
+
+        self.inputs = {
+            "X": x_val,
+            "BatchSize": batch_size,
+            "BatchSum": batch_sum,
+            "BatchSquareSum": batch_square_sum,
+            "scale_w": scale_w,
+            "bias": bias
+        }
+        self.outputs = {"Y": y, "Means": mean, "Scales": scale}
+        self.attrs = {
+            "epsilon": epsilon,
+            "use_mkldnn": self.use_mkldnn,
+            "slot_dim": slot_dim,
+            "enable_scale_and_shift": True
+        }
+
+    def test_check_output(self):
+        """
+        test check forward, check output
+        """
+        self.check_output()
+
+    def test_check_grad(self):
+        """
+        test check backward, check grad
+        """
+        self.check_grad(['X'], 'Y', no_grad_set=set([]))
+
+
 class TestDataNormOpWithSlotDim(OpTest):
     """
     test class for data norm op
@@ -399,5 +560,14 @@ class TestDataNormOpWithSyncStats(unittest.TestCase):
             os.remove(f)
 
 
+class TestDataNormOpErrorr(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            x2 = fluid.layers.data(name='x2', shape=[3, 4], dtype="int32")
+            #self.assertRaises(TypeError, fluid.data_norm, x2)
+            fluid.layers.data_norm(
+                input=x2, param_attr={}, enable_scale_and_shift=True)
+
+
 if __name__ == '__main__':
     unittest.main()