matmul_v2_op.cc

//   Copyright (c) 2020 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/matmul_v2_op.h"
#include <string>
#include <vector>

namespace paddle {
namespace operators {

class MatMulV2Op : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "matmul_v2");
    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "matmul_v2");
    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "matmul_v2");
    bool trans_x = ctx->Attrs().Get<bool>("trans_x");
    bool trans_y = ctx->Attrs().Get<bool>("trans_y");

    std::vector<int64_t> dims_x =
        paddle::framework::vectorize(ctx->GetInputDim("X"));
    std::vector<int64_t> dims_y =
        paddle::framework::vectorize(ctx->GetInputDim("Y"));
    auto ndims_x = dims_x.size();
    auto ndims_y = dims_y.size();
    PADDLE_ENFORCE_GT(ndims_x, 0,
                      platform::errors::InvalidArgument(
                          "The Input(X) dims size must be greater than 0,"
                          " but reviced dims size is 0. "));
    PADDLE_ENFORCE_GT(ndims_y, 0,
                      platform::errors::InvalidArgument(
                          "The Input(Y) dims size must be greater than 0,"
                          " but reviced dims size is 0. "));

    bool x_broadcasted = false, y_broadcasted = false;
    if (ndims_x == 1) {
      dims_x.insert(dims_x.begin(), 1);
      ndims_x = 2;
      x_broadcasted = true;
    }

    if (ndims_y == 1) {
      dims_y.push_back(1);
      ndims_y = 2;
      y_broadcasted = true;
    }

    size_t M, N;
    if (trans_x) {
      M = dims_x[ndims_x - 1];
    } else {
      M = dims_x[ndims_x - 2];
    }
    if (trans_y) {
      N = dims_y[ndims_y - 2];
    } else {
      N = dims_y[ndims_y - 1];
    }

    std::vector<int64_t> new_dims;
    if (ndims_x > ndims_y) {
      new_dims.assign(dims_x.begin(), dims_x.end() - 2);
    } else if (ndims_x < ndims_y) {
      new_dims.assign(dims_y.begin(), dims_y.end() - 2);
    } else {
      new_dims.reserve(ndims_x);
      for (size_t i = 0; i < ndims_x - 2; ++i) {
        new_dims.push_back(std::max(dims_x[i], dims_y[i]));
      }
    }
    if (!x_broadcasted) {
      new_dims.push_back(M);
    }
    if (!y_broadcasted) {
      new_dims.push_back(N);
    }
    if (x_broadcasted && y_broadcasted) {
      new_dims.push_back(1);
    }

    auto out_dims = framework::make_ddim(new_dims);
    ctx->SetOutputDim("Out", out_dims);
    ctx->ShareLoD("X", /* --> */ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    auto input_data_type =
        OperatorWithKernel::IndicateOrPromoteVarDataTypes(ctx, "X", "Y");

#ifdef PADDLE_WITH_MKLDNN
    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
                                     framework::DataLayout::kMKLDNN,
                                     framework::LibraryType::kMKLDNN);
    }
#endif
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const framework::Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const {
    if (framework::IsComplexType(expected_kernel_type.data_type_)) {
      // only promote inputs’s types when contains complex input
      return framework::OpKernelType(tensor.type(), tensor.place(),
                                     tensor.layout());
    } else {
      return framework::OpKernelType(expected_kernel_type.data_type_,
                                     tensor.place(), tensor.layout());
    }
  }
};

class MatMulV2OpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "tensor of shape (d0, d1 ... M, K)");
    AddInput("Y", "tensor of shape (d0, d1 ... K, N)");
    AddOutput("Out", "tensor of shape (d0, d1 ... M, N)");
    AddAttr<bool>("trans_x",
                  "Set true to transpose the last two dimensions of X before "
                  "doing multiplication")
        .SetDefault(false);
    AddAttr<bool>("trans_y",
                  "Set true to transpose the last two dimensions of Y before "
                  "doing multiplication")
        .SetDefault(false);
    AddAttr<bool>("use_mkldnn",
                  "(bool, default false) Only used in mkldnn kernel")
        .SetDefault(false);
    AddAttr<std::string>(
        "mkldnn_data_type",
        "(string, default \"float32\"). Data type of mkldnn kernel")
        .SetDefault("float32")
        .InEnum({"float32", "bfloat16"});
    AddComment(
        R"DOC(Matrix multiplication Out = X * Y. A has shape (d0, d1 ... M, K), 
        B has shape (d0, d1 ... K, N), Out has shape ((d0, d1 ... M, N)). 
        In addition, it also follows the broadcast rule which is similar as
        numpy.matmul.
)DOC");
  }
};

class MatMulV2OpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(framework::InferShapeContext* context) const override {
    OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "matmul_v2");
    OP_INOUT_CHECK(context->HasInput("Y"), "Input", "Y", "matmul_v2");
    OP_INOUT_CHECK(context->HasInput(framework::GradVarName("Out")), "Input",
                   "Out@GRAD", "matmul_v2");
    auto x_dims = context->GetInputDim("X");
    auto y_dims = context->GetInputDim("Y");

    auto x_grad_name = framework::GradVarName("X");
    auto y_grad_name = framework::GradVarName("Y");

    if (context->HasOutput(x_grad_name)) {
      context->SetOutputDim(x_grad_name, x_dims);
    }
    if (context->HasOutput(y_grad_name)) {
      context->SetOutputDim(y_grad_name, y_dims);
    }
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    auto input_data_type = OperatorWithKernel::IndicateVarDataType(
        ctx, framework::GradVarName("Out"));

#ifdef PADDLE_WITH_MKLDNN
    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
                                     framework::DataLayout::kMKLDNN,
                                     framework::LibraryType::kMKLDNN);
    }
#endif
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const framework::Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const {
    if (framework::IsComplexType(expected_kernel_type.data_type_)) {
      // only promote inputs’s types when contains complex input
      return framework::OpKernelType(tensor.type(), tensor.place(),
                                     tensor.layout());
    } else {
      return framework::OpKernelType(expected_kernel_type.data_type_,
                                     tensor.place(), tensor.layout());
    }
  }
};

template <typename T>
class MatMulV2GradOpMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("matmul_v2_grad");
    op->SetInput("X", this->Input("X"));
    op->SetInput("Y", this->Input("Y"));
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
    op->SetAttrMap(this->Attrs());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(matmul_v2, ops::MatMulV2Op, ops::MatMulV2OpMaker,
                  ops::MatMulV2GradOpMaker<paddle::framework::OpDesc>,
                  ops::MatMulV2GradOpMaker<paddle::imperative::OpBase>);

REGISTER_OPERATOR(matmul_v2_grad, ops::MatMulV2OpGrad);

REGISTER_OP_CPU_KERNEL(
    matmul_v2, ops::MatMulV2Kernel<paddle::platform::CPUDeviceContext, float>,
    ops::MatMulV2Kernel<paddle::platform::CPUDeviceContext, double>,
    ops::MatMulV2Kernel<paddle::platform::CPUDeviceContext,
                        paddle::platform::complex<float>>,
    ops::MatMulV2Kernel<paddle::platform::CPUDeviceContext,
                        paddle::platform::complex<double>>);

REGISTER_OP_CPU_KERNEL(
    matmul_v2_grad,
    ops::MatMulV2GradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::MatMulV2GradKernel<paddle::platform::CPUDeviceContext, double>,
    ops::MatMulV2GradKernel<paddle::platform::CPUDeviceContext,
                            paddle::platform::complex<float>>,
    ops::MatMulV2GradKernel<paddle::platform::CPUDeviceContext,
                            paddle::platform::complex<double>>);