batch_norm_op.cc

/* Copyright (c) 2016 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/batch_norm_op.h"

#include <memory>
#include <string>
#include <unordered_map>

#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/platform/onednn_helper.h"
#include "paddle/fluid/prim/utils/static/composite_grad_desc_maker.h"
#include "paddle/fluid/prim/utils/static/desc_tensor.h"
#include "paddle/phi/infermeta/multiary.h"

namespace paddle::operators {

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

  bool is_test = ctx->Attrs().Get<bool>("is_test");
  bool trainable_stats = ctx->Attrs().Get<bool>("trainable_statistics");
  bool test_mode = is_test && (!trainable_stats);
  if (!test_mode) {
    OP_INOUT_CHECK(ctx->HasOutput("MeanOut"), "Output", "MeanOut", "BatchNorm");
    OP_INOUT_CHECK(
        ctx->HasOutput("VarianceOut"), "Output", "VarianceOut", "BatchNorm");
    OP_INOUT_CHECK(
        ctx->HasOutput("SavedMean"), "Output", "SavedMean", "BatchNorm");
    OP_INOUT_CHECK(ctx->HasOutput("SavedVariance"),
                   "Output",
                   "SavedVariance",
                   "BatchNorm");
  }

  // make sure Mean/MeanOut and Variance/VarianceOut share memory in Python
  PADDLE_ENFORCE_EQ(ctx->Inputs("Mean")[0],
                    ctx->Outputs("MeanOut")[0],
                    common::errors::InvalidArgument(
                        "Mean and MeanOut should share the same memory"));
  PADDLE_ENFORCE_EQ(
      ctx->Inputs("Variance")[0],
      ctx->Outputs("VarianceOut")[0],
      common::errors::InvalidArgument(
          "Variance and VarianceOut should share the same memory"));

  const auto x_dims = ctx->GetInputDim("X");

  for (int i = 0; i < x_dims.size(); i++) {
    PADDLE_ENFORCE_EQ(
        (x_dims[i] == -1) || (x_dims[i] > 0),
        true,
        common::errors::InvalidArgument(
            "Each dimension of input tensor is expected to be -1 or a "
            "positive number, but received %d. Input's shape is [%s].",
            x_dims[i],
            x_dims));
  }

  const DataLayout data_layout =
      common::StringToDataLayout(ctx->Attrs().Get<std::string>("data_layout"));

  if (ctx->IsRuntime() && ctx->HasInput("MomentumTensor")) {
    auto mom = ctx->Inputs("MomentumTensor");
    PADDLE_ENFORCE_EQ(mom.size(),
                      1,
                      common::errors::InvalidArgument(
                          "The input tensor MomentumTensor's size must be 1"
                          "But received: MomentumTensor's size is [%d]",
                          mom.size()));
  }

  PADDLE_ENFORCE_GE(
      x_dims.size(),
      2,
      common::errors::InvalidArgument(
          "ShapeError: the dimension of input "
          "X must greater than or equal to 2. But received: the shape of input "
          "X = [%s], the dimension of input X =[%d]",
          x_dims,
          x_dims.size()));
  PADDLE_ENFORCE_LE(
      x_dims.size(),
      5,
      common::errors::InvalidArgument(
          "ShapeError: the dimension of input X "
          "must smaller than or equal to 5. But received: the shape of input X "
          "= [%s], the dimension of input X = [%d]",
          x_dims,
          x_dims.size()));
  VLOG(4) << ctx->IsRunONEDNNKernel();
  VLOG(4) << data_layout;
  const int64_t C =
      ((ctx->IsRunONEDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
           ? x_dims[1]
           : x_dims[x_dims.size() - 1]);

  if (ctx->HasInput("Scale")) {
    auto scale_dim = ctx->GetInputDim("Scale");
    PADDLE_ENFORCE_EQ(
        scale_dim.size(),
        1UL,
        common::errors::InvalidArgument(
            "ShapeError: the dimension of scale must equal to 1."
            "But received: the shape of scale is [%s], the dimension "
            "of scale is [%d]",
            scale_dim,
            scale_dim.size()));
  }

  if (ctx->HasInput("Bias")) {
    auto bias_dim = ctx->GetInputDim("Bias");
    PADDLE_ENFORCE_EQ(
        bias_dim.size(),
        1UL,
        common::errors::InvalidArgument(
            "ShapeError: 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->HasInput("Scale") || !ctx->HasInput("Bias") ||
      ((!ctx->IsRuntime()) &&
       (common::product(ctx->GetInputDim("Scale")) <= 0 ||
        common::product(ctx->GetInputDim("Bias")) <= 0))) {
    check = false;
  }

  if (check) {
    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0],
                      C,
                      common::errors::InvalidArgument(
                          "ShapeError: the shape of scale must equal to [%d]"
                          "But received: the shape of scale is [%d]",
                          C,
                          ctx->GetInputDim("Scale")[0]));
    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0],
                      C,
                      common::errors::InvalidArgument(
                          "ShapeError: the shape of bias must equal to [%d]"
                          "But received: the shape of bias is [%d]",
                          C,
                          ctx->GetInputDim("Bias")[0]));
  }
  ctx->SetOutputDim("Y", x_dims);
  ctx->ShareLoD("X", "Y");
  VLOG(4) << x_dims;
  ctx->SetOutputDim("MeanOut", {C});
  ctx->SetOutputDim("VarianceOut", {C});
  if (!test_mode) {
    ctx->SetOutputDim("SavedMean", {C});
    ctx->SetOutputDim("SavedVariance", {C});
  }
  if (ctx->HasOutput("ReserveSpace")) {
    ctx->SetOutputDim("ReserveSpace", {-1});
  }
}

phi::KernelKey BatchNormOp::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
  // By default, the type of the scale, bias, mean,
  // and var tensors should both be float. (For float or float16 input tensor)
  // or double (For double input tensor).
  auto bn_param_type = framework::proto::VarType::FP32;
  if (input_data_type == framework::proto::VarType::FP64) {
    bn_param_type = framework::proto::VarType::FP64;
  }
  if (ctx.HasInput("Scale")) {
    PADDLE_ENFORCE_EQ(
        bn_param_type,
        framework::TransToProtoVarType(
            ctx.Input<DenseTensor>("Scale")->dtype()),
        common::errors::InvalidArgument("Scale input should be of float type"));
  }
  if (ctx.HasInput("Bias")) {
    PADDLE_ENFORCE_EQ(
        bn_param_type,
        framework::TransToProtoVarType(ctx.Input<DenseTensor>("Bias")->dtype()),
        common::errors::InvalidArgument("Bias input should be of float type"));
  }
  PADDLE_ENFORCE_EQ(
      bn_param_type,
      framework::TransToProtoVarType(ctx.Input<DenseTensor>("Mean")->dtype()),
      common::errors::InvalidArgument("Mean input should be of float type"));
  PADDLE_ENFORCE_EQ(bn_param_type,
                    framework::TransToProtoVarType(
                        ctx.Input<DenseTensor>("Variance")->dtype()),
                    common::errors::InvalidArgument(
                        "Variance input should be of float type"));
  return phi::KernelKey(input_data_type, ctx.GetPlace());
}

phi::KernelKey BatchNormOp::GetKernelTypeForVar(
    const std::string &var_name,
    const DenseTensor &tensor,
    const phi::KernelKey &expected_kernel_type) const {
#ifdef PADDLE_WITH_DNNL
  // Only input require reshaping, weights and
  // bias are having shape in NCHW order
  if ((var_name == "X") &&
      (expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
      (tensor.layout() != phi::DataLayout::ONEDNN)) {
    auto attrs = Attrs();
    auto ar = paddle::framework::AttrReader(attrs);
    const std::string data_layout = ar.Get<std::string>("data_layout");
    auto dl = common::StringToDataLayout(data_layout);
    // Some models may have intentionally set "AnyLayout" for pool
    // op. Treat this as NCHW (default data_format value)
    if (dl != phi::DataLayout::kAnyLayout) {
      return phi::KernelKey(tensor.place(), dl, expected_kernel_type.dtype());
    }
  }
#endif
  return phi::KernelKey(
      tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}

void BatchNormOpMaker::Make() {
  AddAttr<bool>("is_test",
                "(bool, default false) Set to true for inference only, false "
                "for training. Some layers may run faster when this is true.")
      .SetDefault(false);
  AddAttr<float>("momentum", "").SetDefault(0.9);
  AddAttr<float>("epsilon", "")
      .SetDefault(1e-5)
      .AddCustomChecker([](const float &epsilon) {
        PADDLE_ENFORCE_GE(
            epsilon,
            0.0f,
            common::errors::InvalidArgument(
                "'epsilon' should be greater or equal than 0.0."));
        PADDLE_ENFORCE_LE(epsilon,
                          0.001f,
                          common::errors::InvalidArgument(
                              "'epsilon' should be less or equal than 0.001."));
      });
  AddAttr<std::string>("data_layout", "").SetDefault("NCHW");
  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();
  AddInput("Mean",
           "The global mean (for training) or "
           "estimated mean (for testing)");
  AddInput("Variance",
           "The global variance (for training) "
           "or estimated Variance (for testing)");
  AddInput("MomentumTensor",
           "(phi::DenseTensor<float32>, optional) If provided, batch_norm will "
           "use this as momentum, this has a higher priority than "
           "attr(momentum), the shape of this tensor MUST BE [1].")
      .AsDispensable();
  AddOutput("Y", "result after normalization");
  AddOutput("MeanOut",
            "Share memory with Mean. "
            "Store the global mean when training");
  AddOutput("VarianceOut",
            "Share memory with Variance. "
            "Store the global Variance when training");
  AddOutput("SavedMean",
            "Mean of the current mini batch, "
            "will apply to output when training")
      .AsIntermediate();
  AddOutput("SavedVariance",
            "Variance of the current mini batch, "
            "will apply to output when training")
      .AsIntermediate();
  AddOutput("ReserveSpace",
            "Reserve GPU space for triggering the new semi-persistent "
            "NHWC kernel")
      .AsDispensable()
      .AsExtra();
  AddAttr<bool>("use_global_stats",
                "(bool, default false) Whether to use global mean and "
                "variance. In inference or test mode, set use_global_stats "
                "to true or is_test true. the behavior is equivalent. "
                "In train mode, when setting use_global_stats True, the "
                "global mean and variance are also used during train time, "
                "the BN acts as scaling and shifting.")
      .SetDefault(false);
  AddAttr<bool>("trainable_statistics",
                "(bool, default false) Whether to calculate mean and variance "
                "in test mode. If setting true in test mode, mean and variance "
                "will be calculated by current batch statistics.")
      .SetDefault(false);
  AddComment(R"DOC(
Batch Normalization.

Batch Norm has been implemented as discussed in the paper:
https://arxiv.org/pdf/1502.03167.pdf
Can be used as a normalizer function for conv2d and fully_connected operations.
The required data format for this layer is one of the following:
1. NHWC `[batch, in_height, in_width, in_channels]`
2. NCHW `[batch, in_channels, in_height, in_width]`

)DOC");
}

void BatchNormGradOp::InferShape(framework::InferShapeContext *ctx) const {
  // check input
  OP_INOUT_CHECK(ctx->HasInput("Scale"), "Input", "Scale", "BatchNormGrad");
  OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")),
                 "Input",
                 framework::GradVarName("Y"),
                 "BatchNormGrad");
  OP_INOUT_CHECK(
      ctx->HasInput("SavedMean"), "Input", "SavedMean", "BatchNormGrad");
  OP_INOUT_CHECK(ctx->HasInput("SavedVariance"),
                 "Input",
                 "SavedVariance",
                 "BatchNormGrad");

  // check output
  const bool has_scale_grad = ctx->HasOutput(framework::GradVarName("Scale"));
  const bool has_bias_grad = ctx->HasOutput(framework::GradVarName("Bias"));
  const bool has_x_grad = ctx->HasOutput(framework::GradVarName("X"));

  PADDLE_ENFORCE_EQ((has_scale_grad == has_bias_grad),
                    true,
                    common::errors::NotFound(
                        "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));

  const bool use_global_stats = ctx->Attrs().Get<bool>("use_global_stats");
  if (use_global_stats) {
    PADDLE_ENFORCE_EQ(
        !ctx->Attrs().Get<bool>("use_mkldnn"),
        true,
        common::errors::InvalidArgument(
            "Using global stats during training is not supported "
            "in oneDNN version of batch_norm_gradient kernel now."));
  }

  OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "BatchNormGrad");
  const auto x_dims = ctx->GetInputDim("X");
  const DataLayout data_layout =
      common::StringToDataLayout(ctx->Attrs().Get<std::string>("data_layout"));

  const int C = static_cast<int>(
      ((ctx->IsRunONEDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
           ? x_dims[1]
           : x_dims[x_dims.size() - 1]));

  // has_scale_grad == has_bias_grad, judge has_scale_grad is enough
  if (has_scale_grad) {
    ctx->SetOutputDim(framework::GradVarName("Scale"), {C});
    ctx->SetOutputDim(framework::GradVarName("Bias"), {C});
  }
  if (has_x_grad) {
    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
  }
}

phi::KernelKey BatchNormGradOp::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  const auto *var = ctx.InputVar(framework::GradVarName("Y"));
  if (var == nullptr) {
    PADDLE_THROW(
        common::errors::InvalidArgument("can't find gradient variable of Y"));
  }
  const DenseTensor *t = nullptr;
  if (var->IsType<DenseTensor>()) {
    t = &var->Get<DenseTensor>();
  }
  if (t == nullptr) {
    PADDLE_THROW(
        common::errors::InvalidArgument("gradient variable of Y is empty"));
  }

  auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
  return phi::KernelKey(data_type, ctx.GetPlace());
}

phi::KernelKey BatchNormGradOp::GetKernelTypeForVar(
    const std::string &var_name,
    const DenseTensor &tensor,
    const phi::KernelKey &expected_kernel_type) const {
#ifdef PADDLE_WITH_DNNL
  // Only input require reshaping, weights and
  // bias are having shape in NCHW order
  if (((var_name == "X") || (var_name == framework::GradVarName("Y"))) &&
      (expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
      (tensor.layout() != phi::DataLayout::ONEDNN)) {
    auto attrs = Attrs();
    auto ar = paddle::framework::AttrReader(attrs);
    const std::string data_layout = ar.Get<std::string>("data_layout");
    auto dl = common::StringToDataLayout(data_layout);
    // Some models may have intentionally set "AnyLayout" for pool
    // op. Treat this as NCHW (default data_format value)
    if (dl != phi::DataLayout::kAnyLayout) {
      return phi::KernelKey(tensor.place(), dl, expected_kernel_type.dtype());
    }
  }
#endif
  return phi::KernelKey(
      tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}

template <typename T>
void BatchNormGradMaker<T>::Apply(GradOpPtr<T> op) const {
  op->SetType(this->ForwardOpType() + "_grad");
  op->SetInput("X", this->Input("X"));
  op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));

  op->SetInput("Scale", this->Input("Scale"));
  op->SetInput("Bias", this->Input("Bias"));
  op->SetInput("SavedMean", this->Output("SavedMean"));
  op->SetInput("SavedVariance", this->Output("SavedVariance"));
  if (this->HasOutput("ReserveSpace")) {
    op->SetInput("ReserveSpace", this->Output("ReserveSpace"));
  }

  // used when setting use_global_stats True during training
  if (PADDLE_GET_CONST(bool, this->GetAttr("use_global_stats")) ||
      PADDLE_GET_CONST(bool, this->GetAttr("is_test"))) {
    op->SetInput("Mean", this->Output("MeanOut"));
    op->SetInput("Variance", this->Output("VarianceOut"));
  }

  op->SetInput("MeanOut", this->Output("MeanOut"));
  op->SetInput("VarianceOut", this->Output("VarianceOut"));

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

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

template <typename T>
void BatchNormDoubleGradMaker<T>::Apply(GradOpPtr<T> op) const {
  op->SetType("batch_norm_grad_grad");
  op->SetInput("X", this->Input("X"));
  op->SetInput("Scale", this->Input("Scale"));
  op->SetInput("SavedMean", this->Input("SavedMean"));
  op->SetInput("SavedVariance", this->Input("SavedVariance"));
  if (PADDLE_GET_CONST(bool, this->GetAttr("use_global_stats"))) {
    op->SetInput("Mean", this->Input("Mean"));
    op->SetInput("Variance", this->Input("Variance"));
  }
  op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
  op->SetInput("DDScale", this->OutputGrad(framework::GradVarName("Scale")));
  op->SetInput("DDBias", this->OutputGrad(framework::GradVarName("Bias")));
  op->SetInput("DY", this->Input(framework::GradVarName("Y")));

  op->SetAttrMap(this->Attrs());
  op->SetOutput("DX", this->InputGrad("X"));
  op->SetOutput("DScale", this->InputGrad("Scale"));
  op->SetOutput("DDY", this->InputGrad(framework::GradVarName("Y")));
}

void BatchNormDoubleGradOp::InferShape(
    framework::InferShapeContext *ctx) const {
  OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "BatchNormDoubleGrad");
  OP_INOUT_CHECK(
      ctx->HasInput("Scale"), "Input", "Scale", "BatchNormDoubleGrad");
  OP_INOUT_CHECK(
      ctx->HasInput("SavedMean"), "Input", "SavedMean", "BatchNormDoubleGrad");
  OP_INOUT_CHECK(ctx->HasInput("SavedVariance"),
                 "Input",
                 "SavedVariance",
                 "BatchNormDoubleGrad");

  const bool use_global_stats = ctx->Attrs().Get<bool>("use_global_stats");
  if (use_global_stats) {
    OP_INOUT_CHECK(ctx->HasInput("Variance"),
                   "Input",
                   "VarianceOut",
                   "BatchNormDoubleGrad");
  }

  OP_INOUT_CHECK(ctx->HasInput("DY"), "Input", "DY", "BatchNormDoubleGrad");

  // check output
  OP_INOUT_CHECK(ctx->HasOutput("DX"), "Output", "DX", "BatchNormDoubleGrad");

  const auto x_dims = ctx->GetInputDim("X");
  const DataLayout data_layout =
      common::StringToDataLayout(ctx->Attrs().Get<std::string>("data_layout"));
  const int C = static_cast<int>(
      ((ctx->IsRunONEDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
           ? x_dims[1]
           : x_dims[x_dims.size() - 1]));

  if (ctx->HasOutput("DX")) {
    ctx->SetOutputDim("DX", x_dims);
  }
  if (ctx->HasOutput("DScale")) {
    ctx->SetOutputDim("DScale", {C});
  }
  if (ctx->HasOutput("DDY")) {
    ctx->ShareDim("X", "DDY");
  }
}

phi::KernelKey BatchNormDoubleGradOp::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  const auto *var = ctx.InputVar("DY");
  if (var == nullptr) {
    PADDLE_THROW(
        common::errors::NotFound("cannot find gradient variable of Y"));
  }
  const DenseTensor *t = nullptr;
  if (var->IsType<DenseTensor>()) {
    t = &var->Get<DenseTensor>();
  }
  if (t == nullptr) {
    PADDLE_THROW(
        common::errors::InvalidArgument("gradient variable of Y is empty"));
  }
  return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"),
                        ctx.GetPlace());
}

DECLARE_INPLACE_OP_INFERER(BatchNormDoubleGradOpInplaceInferer, {"DY", "DDY"});

}  // namespace paddle::operators

namespace ops = paddle::operators;

DECLARE_INFER_SHAPE_FUNCTOR(batch_norm,
                            BatchNormInferShapeFunctor,
                            PD_INFER_META(phi::BatchNormInferMeta));

REGISTER_OPERATOR(batch_norm,
                  ops::BatchNormOp,
                  ops::BatchNormOpMaker,
                  ops::BatchNormOpInferVarType,
                  ops::BatchNormGradMaker<paddle::framework::OpDesc>,
                  ops::BatchNormGradMaker<paddle::imperative::OpBase>);

REGISTER_OPERATOR(batch_norm_grad,
                  ops::BatchNormGradOp,
                  ops::BatchNormDoubleGradMaker<paddle::framework::OpDesc>,
                  ops::BatchNormDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(batch_norm_grad_grad,
                  ops::BatchNormDoubleGradOp,
                  ops::BatchNormDoubleGradOpInplaceInferer);