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paddle/operators/layer_norm_op.cc

@@ -33,29 +33,35 @@ class LayerNormOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Bias"), "");
-    PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"),
+                   "Output(Y) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Mean"),
+                   "Output(Mean) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Variance"),
+                   "Output(Variance) of LayerNormOp should not be null.");
 
     auto x_dim = ctx->GetInputDim("X");
     auto begin_norm_axis = ctx->Attrs().Get<int>("begin_norm_axis");
     PADDLE_ENFORCE_LT(begin_norm_axis, x_dim.size(),
-                      "'begin_norm_axis' must be less than the rank of X");
+                      "'begin_norm_axis' must be less than the rank of X.");
 
     auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
     int right = static_cast<int>(matrix_dim[1]);
-
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right);
+    if (ctx->HasInput("Scale")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right);
+    }
+    if (ctx->HasInput("Bias")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right);
+    }
 
     ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
     ctx->SetOutputDim("Mean", {left});
     ctx->SetOutputDim("Variance", {left});
-
     ctx->ShareLoD("X", "Y");
   }
 };
@@ -64,18 +70,26 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LayerNormOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The input tensor");
+    AddInput("X", "(LoDTensor) The input tensor.");
     AddInput("Scale",
-             "Scale is a 1-dimensional tensor of size H "
-             "that is applied to the output");
+             "(Tensor, optional) Scale is a 1-dimensional tensor of size "
+             "H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
+             "It is applied to the output.")
+        .AsDispensable();
     AddInput("Bias",
-             "Bias is a 1-dimensional tensor of size H "
-             "that is applied to the output");
-    AddOutput("Y", "result after normalization");
-    AddOutput("Mean", "Mean of the current mini batch.");
-    AddOutput("Variance", "Variance of the current mini batch.");
-
-    AddAttr<float>("epsilon", "")
+             "(Tensor, optional) Bias is a 1-dimensional tensor of size "
+             "H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
+             "It is applied to the output.")
+        .AsDispensable();
+    AddOutput("Y", "(LoDTensor) Result after normalization.");
+    AddOutput("Mean", "(Tensor) Mean of the current mini batch.")
+        .AsIntermediate();
+    AddOutput("Variance", "(Tensor) Variance of the current mini batch.")
+        .AsIntermediate();
+
+    AddAttr<float>("epsilon",
+                   "(float, default 1e-5) Constant for "
+                   "numerical stability")
         .SetDefault(1e-5)
         .AddCustomChecker([](const float &epsilon) {
           PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
@@ -83,7 +97,9 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
         });
     AddAttr<int>("begin_norm_axis",
                  "(int default:1), the "
-                 "axis of `begin_norm_axis ... Rank(X) - 1` will be normalized")
+                 "axis of `begin_norm_axis ... Rank(X) - 1` will be "
+                 "normalized. `begin_norm_axis` splits the tensor(`X`) to a "
+                 "matrix [N,H].")
         .SetDefault(1)
         .AddCustomChecker([](const int &begin_norm_axis) {
           PADDLE_ENFORCE_GT(begin_norm_axis, 0,
@@ -124,8 +140,7 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     int right = static_cast<int>(matrix_dim[1]);
 
     auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-    auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+
     auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
     auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
     auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
@@ -141,14 +156,32 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
                   .unaryExpr(add_epslion);
 
     auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-
     // TODO(zcd): Some thinking about output_map, is it appropriate that
     // `output_map` and `input_map` point to the same memory.
     auto inv_std = var_map.unaryExpr(inv_std_func);
-    output_map = (input_map - mean_map.replicate(1, right))
-                     .cwiseProduct(inv_std.replicate(1, right))
-                     .cwiseProduct(scale_map.replicate(left, 1)) +
-                 bias_map.replicate(left, 1);
+    if (scale && bias) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1)) +
+                   bias_map.replicate(left, 1);
+    } else if (scale) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1));
+    } else if (bias) {
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right)) +
+                   bias_map.replicate(left, 1);
+    } else {
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right));
+    }
   }
 };
 
@@ -158,11 +191,16 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     // check input
-    PADDLE_ENFORCE(ctx->HasInput("X"));
-    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Mean"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Variance"), "");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Scale"),
+                   "Input(Scale) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Mean"),
+                   "Input(Mean) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Variance"),
+                   "Input(Variance) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
+                   "Input(Y@GRAD) of LayerNormOp should not be null.");
 
     // check output
     if (ctx->HasOutput(framework::GradVarName("X"))) {
@@ -222,7 +260,6 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
     auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
     auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
     auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
     auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
@@ -254,35 +291,67 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
       auto triple_product_func = [](T ele) { return ele * ele * ele; };
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-      // dy_dx
-      auto dx_end = var_map.unaryExpr(inv_std_func)
-                        .replicate(1, right)
-                        .cwiseProduct(d_y_map)
-                        .cwiseProduct(scale_map.replicate(left, 1));
-      // dy_dmean_dx
-      auto dx_mean = (T(-1.0) / right) *
-                     var_map.unaryExpr(inv_std_func)
-                         .replicate(1, right)
-                         .cwiseProduct(d_y_map)
-                         .cwiseProduct(scale_map.replicate(left, 1))
-                         .rowwise()
-                         .sum()
-                         .replicate(1, right);
-      // dy_var_dx
-      auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                               .cwiseProduct(scale_map.replicate(left, 1))
-                               .cwiseProduct(d_y_map)
-                               .rowwise()
-                               .sum();
-      auto dvar_end = var_map.unaryExpr(inv_std_func)
-                          .unaryExpr(triple_product_func)
-                          .cwiseProduct(dvar_end_part)
-                          .replicate(1, right);
-      auto dx_var =
-          (T(-1.0) / right) *
-          (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-      d_x_map = dx_end + dx_mean + dx_var;
+      // TODO(zcd): these code can be refined
+      if (d_scale) {
+        auto scale_map =
+            ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+        // dy_dx
+        auto dx_end = var_map.unaryExpr(inv_std_func)
+                          .replicate(1, right)
+                          .cwiseProduct(d_y_map)
+                          .cwiseProduct(scale_map.replicate(left, 1));
+        // dy_dmean_dx
+        auto dx_mean = (T(-1.0) / right) *
+                       var_map.unaryExpr(inv_std_func)
+                           .replicate(1, right)
+                           .cwiseProduct(d_y_map)
+                           .cwiseProduct(scale_map.replicate(left, 1))
+                           .rowwise()
+                           .sum()
+                           .replicate(1, right);
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(scale_map.replicate(left, 1))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = var_map.unaryExpr(inv_std_func)
+                            .unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      } else {
+        // dy_dx
+        auto dx_end = var_map.unaryExpr(inv_std_func)
+                          .replicate(1, right)
+                          .cwiseProduct(d_y_map);
+        // dy_dmean_dx
+        auto dx_mean = (T(-1.0) / right) *
+                       var_map.unaryExpr(inv_std_func)
+                           .replicate(1, right)
+                           .cwiseProduct(d_y_map)
+                           .rowwise()
+                           .sum()
+                           .replicate(1, right);
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = var_map.unaryExpr(inv_std_func)
+                            .unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      }
     }
   }
 };