text: /*
  Copyright 2020 The OneFlow Authors. All rights reserved.
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  */
  #include "oneflow/core/framework/framework.h"
  
  namespace oneflow {
  
  REGISTER_USER_OP("prelu")
      .Input("x")
      .Input("alpha")
      .Output("y")
      .SetTensorDescInferFn([](user_op::InferContext* ctx) -> Maybe<void> {
        const Shape& x_shape = ctx->InputShape("x", 0);
        Shape* y_shape = ctx->OutputShape("y", 0);
        const Shape& alpha_shape = ctx->InputShape("alpha", 0);
        CHECK_EQ_OR_RETURN(alpha_shape.NumAxes(), 1);
        *y_shape = x_shape;
        return Maybe<void>::Ok();
      })
      .SetGetSbpFn([](user_op::SbpContext* ctx) -> Maybe<void> {
        const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0);
        ctx->NewBuilder()
            .Split(user_op::OpArg("x", 0), 1)
            .Split(user_op::OpArg("alpha", 0), 0)
            .Split(user_op::OpArg("y", 0), 1)
            .Build();
        FOR_RANGE(int64_t, i, 0, x_tensor.shape().NumAxes()) {
          if (i == 0) continue;
          ctx->NewBuilder()
              .Split(user_op::OpArg("x", 0), i)
              .Broadcast(user_op::OpArg("alpha", 0))
              .Split(user_op::OpArg("y", 0), i)
              .Build();
        }
        return Maybe<void>::Ok();
      })
      .SetDataTypeInferFn([](user_op::InferContext* ctx) -> Maybe<void> {
        *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0);
        return Maybe<void>::Ok();
      });
  
  REGISTER_USER_OP("prelu_grad")
      .Input("dy")
      .Input("x")
      .Input("alpha")
      .Output("dx")
      .Output("alpha_diff")
      .SetTensorDescInferFn([](user_op::InferContext* ctx) -> Maybe<void> {
        const Shape& x_shape = ctx->InputShape("x", 0);
        const Shape& dy_shape = ctx->InputShape("dy", 0);
        Shape* dx_shape = ctx->OutputShape("dx", 0);
        Shape* alpha_diff_shape = ctx->OutputShape("alpha_diff", 0);
        const Shape& alpha_shape = ctx->InputShape("alpha", 0);
        CHECK_EQ_OR_RETURN(alpha_shape.NumAxes(), 1);
        CHECK_OR_RETURN((alpha_shape.At(0) == x_shape.At(1)) || (alpha_shape.At(0) == 1));
        CHECK_EQ_OR_RETURN(dy_shape, x_shape);
        *dx_shape = x_shape;
        *alpha_diff_shape = alpha_shape;
        return Maybe<void>::Ok();
      })
      .SetGetSbpFn([](user_op::SbpContext* ctx) -> Maybe<void> {
        const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0);
        const user_op::TensorDesc& alpha_tensor =
            ctx->LogicalTensorDesc4InputArgNameAndIndex("alpha", 0);
        ctx->NewBuilder()
            .Split(user_op::OpArg("dy", 0), 0)
            .Split(user_op::OpArg("x", 0), 0)
            .Broadcast(user_op::OpArg("alpha", 0))
            .Split(user_op::OpArg("dx", 0), 0)
            .PartialSum(user_op::OpArg("alpha_diff", 0))
            .Build();
        ctx->NewBuilder()
            .PartialSum(user_op::OpArg("dy", 0))
            .Broadcast(user_op::OpArg("x", 0))
            .Broadcast(user_op::OpArg("alpha", 0))
            .PartialSum(user_op::OpArg("dx", 0))
            .PartialSum(user_op::OpArg("alpha_diff", 0))
            .Build();
        ctx->NewBuilder()
            .Split(user_op::OpArg("dy", 0), 1)
            .Split(user_op::OpArg("x", 0), 1)
            .Split(user_op::OpArg("alpha", 0), 0)
            .Split(user_op::OpArg("dx", 0), 1)
            .Split(user_op::OpArg("alpha_diff", 0), 0)
            .Build();
        FOR_RANGE(int64_t, i, 1, x_tensor.shape().NumAxes()) {
          ctx->NewBuilder()
              .Split(user_op::OpArg("dy", 0), i)
              .Split(user_op::OpArg("x", 0), i)
              .Split(user_op::OpArg("alpha", 0), 0)
              .Split(user_op::OpArg("dx", 0), i)
              .Split(user_op::OpArg("alpha_diff", 0), 0)
              .Build();
        }
        return Maybe<void>::Ok();
      })
      .SetDataTypeInferFn([](user_op::InferContext* ctx) -> Maybe<void> {
        *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0);
        *ctx->OutputDType("alpha_diff", 0) = ctx->InputDType("alpha", 0);
        return Maybe<void>::Ok();
      });
  
  REGISTER_USER_OP_GRAD("prelu").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op,
                                                           user_op::AddOpFn AddOp) -> Maybe<void> {
    if (op.NeedGenGradTensor4OpInput("x", 0) || op.NeedGenGradTensor4OpInput("alpha", 0)) {
      user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad");
      user_op::UserOpConfWrapper grad_op = builder.Op("prelu_grad")
                                               .Input("x", op.input("x", 0))
                                               .Input("dy", op.GetGradTensorWithOpOutput("y", 0))
                                               .Input("alpha", op.input("alpha", 0))
                                               .Output("dx")
                                               .Output("alpha_diff")
                                               .Build();
      AddOp(grad_op);
  
      if (op.NeedGenGradTensor4OpInput("x", 0)) {
        op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0);
      }
      if (op.NeedGenGradTensor4OpInput("alpha", 0)) {
        auto alpha_identity_op = user_op::UserOpConfWrapperBuilder(op.op_name() + "_alpha_identity")
                                     .Op("identity")
                                     .Input("in", grad_op.output("alpha_diff", 0))
                                     .Output("out")
                                     .Build();
        AddOp(alpha_identity_op);
        op.BindGradTensorWithOpInput(alpha_identity_op.output("out", 0), "alpha", 0);
      }
    }
    return Maybe<void>::Ok();
  });
  
  }  // namespace oneflow
  