Paddle/paddle/fluid/operators/group_norm_op.cc

/* Copyright (c) 2018 PaddlePaddle 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 "paddle/fluid/operators/group_norm_op.h"
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;

class GroupNormOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GroupNorm");
    OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "GroupNorm");
    OP_INOUT_CHECK(ctx->HasOutput("Mean"), "Output", "Mean", "GroupNorm");
    OP_INOUT_CHECK(ctx->HasOutput("Variance"), "Output", "Variance",
                   "GroupNorm");

    auto x_dim = ctx->GetInputDim("X");
    const std::string data_layout_str =
        ctx->Attrs().Get<std::string>("data_layout");
    const framework::DataLayout data_layout =
        framework::StringToDataLayout(data_layout_str);
    const int64_t channel_num =
        (data_layout == DataLayout::kNCHW ? x_dim[1] : x_dim[x_dim.size() - 1]);
    auto batch_size = x_dim[0];
    auto groups = ctx->Attrs().Get<int>("groups");
    PADDLE_ENFORCE_LE(
        groups, channel_num,
        platform::errors::InvalidArgument(
            "The Attr(groups) of Op(group_norm) must be less than or "
            "equal to the number of channels. But received: groups "
            "is [%s], channels is [%s], the Attr(data_layout) "
            "is [%s]. The error may come from wrong data_layout setting.",
            groups, channel_num, data_layout_str));
    PADDLE_ENFORCE_GE(
        groups, 1,
        platform::errors::InvalidArgument(
            "The Attr(groups) of Op(group_norm) must be "
            "greater than or equal to 1. But received: groups is [%s].",
            groups));

    if (ctx->HasInput("Scale")) {
      PADDLE_ENFORCE_EQ(
          ctx->GetInputDim("Scale").size(), 1UL,
          platform::errors::InvalidArgument(
              "The Input(Scale) of Op(group_norm) should be 1-D Tensor. "
              "But received: %u-D Tensor, the shape of Input(Scale) is [%s].",
              ctx->GetInputDim("Scale").size(), ctx->GetInputDim("Scale")));
      PADDLE_ENFORCE_EQ(
          ctx->GetInputDim("Scale")[0], channel_num,
          platform::errors::InvalidArgument(
              "The Input(Scale)'s first dimension size of Op(group_norm) must "
              "be equal to the number of channels. But received: the "
              "Input(Scale)'s first dimension size is [%s], the channels is "
              "[%s], the Attr(data_layout) is [%s]. The error may come "
              "from wrong data_layout setting.",
              ctx->GetInputDim("Scale")[0], channel_num, data_layout_str));
    }
    if (ctx->HasInput("Bias")) {
      PADDLE_ENFORCE_EQ(
          ctx->GetInputDim("Bias").size(), 1UL,
          platform::errors::InvalidArgument(
              "The Input(Bias) of Op(group_norm) should be 1-D Tensor. "
              "But received: %u-D Tensor, the shape of Input(Bias) is [%s].",
              ctx->GetInputDim("Bias").size(), ctx->GetInputDim("Bias")));
      PADDLE_ENFORCE_EQ(
          ctx->GetInputDim("Bias")[0], channel_num,
          platform::errors::InvalidArgument(
              "The Input(Bias)'s first dimension size of "
              "Op(group_norm) must be equal to the number of channels. "
              "But received: the Input(Bias)'s first dimension size is [%s], "
              "the channels is [%s], the Attr(data_layout) is [%s]. The "
              "error may come from wrong data_layout setting.",
              ctx->GetInputDim("Bias")[0], channel_num, data_layout_str));
    }

    ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
    ctx->SetOutputDim("Mean", {batch_size, groups});
    ctx->SetOutputDim("Variance", {batch_size, groups});
    ctx->ShareLoD("X", "Y");
  }
};

class GroupNormOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "The input tensor.");
    AddInput("Scale",
             "Scale 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();
    AddOutput("Y", "Result after normalization.");
    AddOutput("Mean", "Mean of each group.").AsIntermediate();
    AddOutput("Variance", "Variance of each group.").AsIntermediate();

    AddAttr<float>("epsilon",
                   "Constant for numerical stability [default 1e-5].")
        .SetDefault(1e-5)
        .AddCustomChecker([](const float &epsilon) {
          PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 1.0f,
                         "'epsilon' should be between 0.0 and 1.0.");
        });
    AddAttr<int>("groups", "The number of groups that divided from channels.")
        .AddCustomChecker([](const int &groups) {
          PADDLE_ENFORCE_GT(groups, 0, "'groups' should be greater than zero.");
        });
    AddAttr<std::string>("data_layout",
                         "An optional string from: \"NHWC\", \"NCHW\". ")
        .SetDefault("NCHW");
    AddComment(R"DOC(
Group Normalization

Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_
)DOC");
  }
};

class GroupNormGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {
    // check input
    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "GroupNormGrad");
    OP_INOUT_CHECK(ctx->HasInput("Variance"), "Input", "Variance",
                   "GroupNormGrad");
    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
                   framework::GradVarName("Y"), "GroupNormGrad");

    // check output
    if (ctx->HasOutput(framework::GradVarName("X"))) {
      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y"));
    }
    if (ctx->HasOutput(framework::GradVarName("Scale"))) {
      ctx->SetOutputDim(framework::GradVarName("Scale"),
                        ctx->GetInputDim("Scale"));
    }
    if (ctx->HasOutput(framework::GradVarName("Bias"))) {
      ctx->SetOutputDim(framework::GradVarName("Bias"),
                        ctx->GetInputDim("Bias"));
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    const auto *var = ctx.InputVar(framework::GradVarName("Y"));

    PADDLE_ENFORCE_NOT_NULL(
        var, platform::errors::InvalidArgument(
                 "Input(Y@GRAD) of GroupNormGradOp should not be null"));
    const Tensor *t = nullptr;
    if (var->IsType<Tensor>()) {
      t = &var->Get<Tensor>();
    } else if (var->IsType<LoDTensor>()) {
      t = &var->Get<LoDTensor>();
    }
    PADDLE_ENFORCE_NOT_NULL(
        t, platform::errors::InvalidArgument(
               "Input(Y@GRAD) Tensor of GroupNormGradOp should not be null"));
    return framework::OpKernelType(t->type(), ctx.GetPlace());
  }
};

template <typename T>
class GroupNormGradMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

  void Apply(GradOpPtr<T> op) const override {
    op->SetType("group_norm_grad");
    op->SetInput("Scale", this->Input("Scale"));
    op->SetInput("Bias", this->Input("Bias"));
    op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
    op->SetInput("Y", this->Output("Y"));
    op->SetInput("Variance", this->Output("Variance"));

    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetOutput(framework::GradVarName("Bias"), this->InputGrad("Bias"));
    op->SetOutput(framework::GradVarName("Scale"), this->InputGrad("Scale"));

    op->SetAttrMap(this->Attrs());
  }
};

DECLARE_INPLACE_OP_INFERER(GroupNormInplaceInToOut, {"X", "Y"});
DECLARE_INPLACE_OP_INFERER(GroupNormGradInplaceInToOut,
                           {framework::GradVarName("Y"),
                            framework::GradVarName("X")});

class GroupNormOpInferVarType
    : public framework::PassInDtypeAndVarTypeToOutput {
 protected:
  std::unordered_map<std::string, std::string> &GetInputOutputWithSameType()
      const override {
    static std::unordered_map<std::string, std::string> m{{"X", /*->*/ "Y"}};
    return m;
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
                  ops::GroupNormOpInferVarType,
                  ops::GroupNormGradMaker<paddle::framework::OpDesc>,
                  ops::GroupNormGradMaker<paddle::imperative::OpBase>,
                  ops::GroupNormInplaceInToOut);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp,
                  ops::GroupNormGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL(
    group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    group_norm_grad,
    ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, double>);