Merge pull request #10449 from reyoung/feature/clean_matmul

Rewrite Matmul, make code cleaner

paddle/fluid/operators/math/blas.cc

@@ -13,10 +13,40 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/math/blas.h"
+
+#include <utility>
 namespace paddle {
 namespace operators {
 namespace math {
-// Do nothing. Blas is a header only library.
+MatDescriptor CreateMatrixDescriptor(const framework::DDim &tensor_dim,
+                                     int num_flatten_cols, bool trans) {
+  PADDLE_ENFORCE_GT(tensor_dim.size(), 1);
+  MatDescriptor retv;
+  if (num_flatten_cols > 1) {
+    auto flatten_dim = framework::flatten_to_2d(tensor_dim, num_flatten_cols);
+    retv.height_ = flatten_dim[0];
+    retv.width_ = flatten_dim[1];
+  } else {
+    if (tensor_dim.size() == 2) {
+      retv.height_ = tensor_dim[0];
+      retv.width_ = tensor_dim[1];
+    } else {
+      auto dim_vec = framework::vectorize(tensor_dim);
+      retv.batch_size_ = 1;
+      for (size_t i = 0; i < dim_vec.size() - 2; ++i) {
+        retv.batch_size_ *= dim_vec[i];
+      }
+      retv.height_ = dim_vec[dim_vec.size() - 2];
+      retv.width_ = dim_vec[dim_vec.size() - 1];
+      retv.stride_ = retv.height_ * retv.width_;
+    }
+  }
+  if (trans) {
+    std::swap(retv.width_, retv.height_);
+  }
+  retv.trans_ = trans;
+  return retv;
+}
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/blas.h

@@ -46,6 +46,50 @@ namespace paddle {
 namespace operators {
 namespace math {
 
+/**
+ * Matrix Descriptor of a memory buffer.
+ *
+ * It is used for Blas::MatMul. MatMul operator can be batched.
+ * if Mat A is [BatchSize, H, W], Mat B is [BatchSize, H, W]. It will be a
+ * `batch_size` times of GEMM. The batched GEMM could be faster base on the
+ * implementation of the blas library. The batch size could be zero. If any
+ * matrix of `matmul` has a batch size, the will be a batched GEMM, too. e.g.,
+ * Mat A is [BatchSize, H1, W2], and Mat B [H2, W2], The result matrix wil be
+ * [BatchSize, H1, W2]
+ *
+ * The boolean flag, `trans`, describe the memory is the transpose of matrix or
+ * not. If the trans is true, the last two dims of matrix are transposed. The
+ * memory layout of the matrix is [Width, Height] or [BatchSize, Width, Height].
+ *
+ * The MatDescriptor is not only the dimension or shape of a matrix, it also
+ * contains the layout, stride of matrix. It is clearer to have a structure than
+ * reuse `DDim`.
+ */
+struct MatDescriptor {
+  int64_t height_;
+  int64_t width_;
+  int64_t stride_{0};
+  int64_t batch_size_{0};
+  bool trans_;
+};
+
+/**
+ * Create Matrix Descriptor from a tensor dim, num_flatten_cols, and transpose
+ * flag
+ *
+ * @param tensor_dim: The dimension of the tensor. The rank of this dimension
+ * must larger than 1.
+ *
+ * @param num_flatten_cols:  Reshape a tensor to a matrix. The matrix's first
+ * dimension(column length) will be the product of tensor's first `num_col_dims`
+ * dimensions. If num_flatten_cols is zero, the first N-2 dimension will be the
+ * batch_size of descriptor.
+ *
+ * @param trans: True if the matrix is transposed.
+ */
+extern MatDescriptor CreateMatrixDescriptor(const framework::DDim& tensor_dim,
+                                            int num_flatten_cols, bool trans);
+
 template <typename DeviceContext>
 class Blas {
  public:
@@ -90,6 +134,11 @@ class Blas {
                    int K, T alpha, const T* A, const T* B, T beta, T* C,
                    int batchCount, int64_t strideA, int64_t strideB) const;
 
+  template <typename T>
+  void MatMul(const framework::Tensor& mat_a, const MatDescriptor& dim_a,
+              const framework::Tensor& mat_b, const MatDescriptor& dim_b,
+              T alpha, framework::Tensor* mat_out, T beta) const;
+
  private:
   const DeviceContext& context_;
 };

paddle/fluid/operators/math/blas_impl.h

@@ -180,6 +180,31 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
 #endif
 }
 
+template <typename DeviceContext>
+template <typename T>
+void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
+                                 const MatDescriptor &dim_a,
+                                 const framework::Tensor &mat_b,
+                                 const MatDescriptor &dim_b, T alpha,
+                                 framework::Tensor *mat_out, T beta) const {
+  PADDLE_ENFORCE_EQ(dim_a.width_, dim_b.height_);
+  CBLAS_TRANSPOSE transA = !dim_a.trans_ ? CblasNoTrans : CblasTrans;
+  CBLAS_TRANSPOSE transB = !dim_b.trans_ ? CblasNoTrans : CblasTrans;
+  if (dim_a.batch_size_ == 0 && dim_b.batch_size_ == 0) {
+    this->template GEMM<T>(transA, transB, dim_a.height_, dim_b.width_,
+                           dim_a.width_, alpha, mat_a.data<T>(),
+                           mat_b.data<T>(), beta, mat_out->data<T>());
+  } else {
+    PADDLE_ENFORCE(dim_a.batch_size_ == dim_b.batch_size_ ||
+                   dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0);
+    this->template BatchedGEMM<T>(
+        transA, transB, dim_a.height_, dim_b.width_, dim_a.width_, alpha,
+        mat_a.data<T>(), mat_b.data<T>(), beta, mat_out->data<T>(),
+        dim_a.batch_size_ == 0 ? dim_b.batch_size_ : dim_a.batch_size_,
+        dim_a.stride_, dim_b.stride_);
+  }
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/matmul.h

-/* Copyright (c) 2017 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. */
-
-#pragma once
-#include <algorithm>
-#include <vector>
-#include "paddle/fluid/operators/math/blas.h"
-
-namespace paddle {
-namespace operators {
-namespace math {
-
-// Implements the logic of numpy matmul:
-// https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
-//
-// but allowing also for a, b to be transposed
-//
-// Both a & b can be 1- to 3-dimensional. Higher rank tensors are not supported
-// yet.
-template <typename DeviceContext, typename T>
-class MatMulFunctor {
- public:
-  void operator()(const DeviceContext& context, const framework::Tensor& a,
-                  bool trans_a, const framework::Tensor& b, bool trans_b,
-                  T alpha, framework::Tensor* out, T beta) {
-    auto dim_a = a.dims();
-    auto dim_b = b.dims();
-
-    PADDLE_ENFORCE(a.place() == b.place() && b.place() == out->place(),
-                   "Tensors must all be in the same place.");
-    PADDLE_ENFORCE_GE(dim_a.size(), 1,
-                      "Input tensor a must be at least 1-dimensional.");
-    PADDLE_ENFORCE_GE(dim_b.size(), 1,
-                      "Input tensor b must be at least 1-dimensional.");
-
-    std::vector<int64_t> out_dim;
-    int64_t batch_count = 1;
-    if (dim_a.size() > 3) {
-      PADDLE_ENFORCE(dim_b.size() == dim_a.size(),
-                     "The dimensions of X and Y must be the same, and both of "
-                     "them should be %d-dimensional.",
-                     dim_b.size());
-      // The first rank-2 dimensions are accumulated on the batch_count, and the
-      // last two dimensions are used for matrix multiplication.
-      for (int j = 0; j < dim_a.size() - 2; ++j) {
-        PADDLE_ENFORCE_EQ(dim_b[j], dim_a[j],
-                          "The %d-th dimension of X and Y must be the same.",
-                          j);
-        out_dim.push_back(dim_a[j]);
-        batch_count *= dim_a[j];
-      }
-    }
-
-    int M = 0, N = 0, kA = 0, kB = 0, batchCountA = 0, batchCountB = 0,
-        strideA = 0, strideB = 0;
-
-    switch (dim_a.size()) {
-      case 1:
-        // similar to np.matmul:
-        // prepend dimension 1 (no transpose) or append dimension 1 (transpose)
-        M = trans_a ? dim_a[0] : 1;
-        kA = trans_a ? 1 : dim_a[0];
-        break;
-      case 2:
-        M = trans_a ? dim_a[1] : dim_a[0];
-        kA = trans_a ? dim_a[0] : dim_a[1];
-        break;
-      case 3:
-        batchCountA = dim_a[0];
-        M = trans_a ? dim_a[2] : dim_a[1];
-        kA = trans_a ? dim_a[1] : dim_a[2];
-        strideA = M * kA;
-        break;
-      default:
-        batchCountA = batch_count;
-        size_t mat_s = dim_a.size() - 2;
-        M = trans_a ? dim_a[mat_s + 1] : dim_a[mat_s];
-        kA = trans_a ? dim_a[mat_s] : dim_a[mat_s + 1];
-        strideA = M * kA;
-    }
-
-    switch (dim_b.size()) {
-      case 1:
-        // similar to np.matmul:
-        // append dimension 1 (no transpose) or prepend dimension 1 (transpose)
-        kB = trans_b ? 1 : dim_b[0];
-        N = trans_b ? dim_b[0] : 1;
-        break;
-      case 2:
-        kB = trans_b ? dim_b[1] : dim_b[0];
-        N = trans_b ? dim_b[0] : dim_b[1];
-        break;
-      case 3:
-        batchCountB = dim_b[0];
-        kB = trans_b ? dim_b[2] : dim_b[1];
-        N = trans_b ? dim_b[1] : dim_b[2];
-        strideB = kB * N;
-        break;
-      default:
-        batchCountB = batch_count;
-        size_t mat_s = dim_b.size() - 2;
-        kB = trans_b ? dim_b[mat_s + 1] : dim_b[mat_s];
-        N = trans_b ? dim_b[mat_s] : dim_b[mat_s + 1];
-        strideB = kB * N;
-    }
-
-    PADDLE_ENFORCE_EQ(
-        kA, kB,
-        "First matrix's width must be equal with second matrix's height.");
-    if (batchCountA && batchCountB) {
-      PADDLE_ENFORCE_EQ(
-          batchCountA, batchCountB,
-          "When input tensors a and b are both batched, they must have the "
-          "same batch dimension.");
-    }
-    int batchCount = std::max(batchCountA, batchCountB);
-
-    CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-    CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
-
-    auto blas = GetBlas<DeviceContext, T>(context);
-
-    if (!batchCount) {
-      // regular matrix multiplication
-      blas.GEMM(transA, transB, M, N, kA, alpha, a.data<T>(), b.data<T>(), beta,
-                out->data<T>());
-    } else {
-      // batched matrix multiplication
-      blas.BatchedGEMM(transA, transB, M, N, kA, alpha, a.data<T>(),
-                       b.data<T>(), beta, out->data<T>(), batchCount, strideA,
-                       strideB);
-    }
-  }
-};
-
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/matmul_op.cc

@@ -12,14 +12,257 @@ 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_op.h"
 #include <algorithm>
+#include <utility>
 #include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detail/safe_ref.h"
+#include "paddle/fluid/operators/math/blas.h"
 
 namespace paddle {
 namespace operators {
+/**
+ * Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
+ * original x_dim is returned.
+ */
+static framework::DDim RowMatrixFromVector(const framework::DDim& x_dim) {
+  if (x_dim.size() > 1) {
+    return x_dim;
+  }
+  return framework::make_ddim({1, x_dim[0]});
+}
+
+/**
+ * Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
+ * original y_dim is returned.
+ */
+static framework::DDim ColumnMatrixFromVector(const framework::DDim& y_dim) {
+  if (y_dim.size() > 1) {
+    return y_dim;
+  }
+  return framework::make_ddim({y_dim[0], 1});
+}
+
+template <typename DeviceContext, typename T>
+class MatMulKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto& x =
+        detail::Ref(context.Input<framework::Tensor>("X"), "Cannot find X");
+    auto& y =
+        detail::Ref(context.Input<framework::Tensor>("Y"), "Cannot find Y");
+    auto* out = context.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+
+    auto blas = math::GetBlas<DeviceContext, T>(context);
+    auto mat_dim_a = math::CreateMatrixDescriptor(
+        RowMatrixFromVector(x.dims()), 0, context.Attr<bool>("transpose_X"));
+    auto mat_dim_b = math::CreateMatrixDescriptor(
+        ColumnMatrixFromVector(y.dims()), 0, context.Attr<bool>("transpose_Y"));
+    blas.MatMul(x, mat_dim_a, y, mat_dim_b, T(1), out, T(0));
+  }
+};
+
+// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
+// Identity op if the tensor is not of rank 3.
+static framework::Tensor FoldInitDims(const framework::Tensor& input) {
+  auto output = input;
+  auto in_dims = input.dims();
+  if (in_dims.size() == 3) {
+    output.Resize({in_dims[0] * in_dims[1], in_dims[2]});
+  }
+  return output;
+}
+
+// Reshape a rank-3 tensor from P x M x N to M x (P * N).
+// (Warning: This requires transposing data and writes into new memory.)
+// Identity op if the tensor is not of rank 3.
+template <typename DeviceContext, typename T>
+static framework::Tensor FoldHeadAndLastDims(const DeviceContext& context,
+                                             const framework::Tensor& input) {
+  auto in_dims = input.dims();
+  if (in_dims.size() != 3) {
+    return input;
+  }
+  framework::Tensor output;
+  output.Resize({in_dims[1], in_dims[0], in_dims[2]});
+  output.mutable_data<T>(context.GetPlace());
+  std::vector<int> axis = {1, 0, 2};
+  math::Transpose<DeviceContext, T, 3> trans;
+  trans(context, input, &output, axis);
+  output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
+
+  return output;
+}
+
+/**
+ * Reshape a tensor to 3-D or 2-D tensor by matrix descriptor.
+ *
+ * The shape would be [BatchSize, H, W] or [H, W].
+ * If transposed, `H,W` will be swapped.
+ */
+static void ReshapeTensorIntoMatrixSequence(
+    framework::Tensor* x, const math::MatDescriptor& descriptor) {
+  int64_t h, w;
+  h = descriptor.height_;
+  w = descriptor.width_;
+  if (descriptor.trans_) {
+    std::swap(w, h);
+  }
+  if (descriptor.batch_size_) {
+    x->Resize({descriptor.batch_size_, h, w});
+  } else {
+    x->Resize({h, w});
+  }
+}
+
+/**
+ * Reshape the x,y,out tensor to 3-D or 2-D tensor by matrix descriptor
+ * Out = matmul(x, y)
+ *
+ * This method will first calculate X,Y matrix sequence, and then calculate
+ * the out shape.
+ *
+ * Assume X = [BatchSize, H1, W1], Y = [BatchSize, H2, W2]
+ * The out = [BatchSize, H1, W2]
+ *
+ * If there is no batch size in `X` and `Y`, the out will be [H1, W2]
+ * If any of `X` and `Y` has batch size BatchSize, the out will have the
+ * BatchSize.
+ */
+static void ReshapeXYOutIntoMatrixSequence(framework::Tensor* x,
+                                           framework::Tensor* y,
+                                           framework::Tensor* out, bool trans_x,
+                                           bool trans_y) {
+  auto x_dim = RowMatrixFromVector(x->dims());
+  auto y_dim = ColumnMatrixFromVector(y->dims());
+  auto mat_dim_x = math::CreateMatrixDescriptor(x_dim, 0, trans_x);
+  auto mat_dim_y = math::CreateMatrixDescriptor(y_dim, 0, trans_y);
+  if (mat_dim_x.batch_size_ == 0 && mat_dim_y.batch_size_ == 0) {
+    out->Resize({mat_dim_x.height_, mat_dim_y.width_});
+  } else {
+    out->Resize({std::max(mat_dim_x.batch_size_, mat_dim_y.batch_size_),
+                 mat_dim_x.height_, mat_dim_y.width_});
+  }
+
+  ReshapeTensorIntoMatrixSequence(x, mat_dim_x);
+  ReshapeTensorIntoMatrixSequence(y, mat_dim_y);
+}
+
+// Using dimensional constraints on matrix multiplication, it is
+// straight-forward to check the following table for when X and Y
+// are both matrices.
+//
+// transpose_X | False    | True     | False    | True
+// transpose_Y | False    | False    | True     | True
+// -----------+----------+----------+----------+-----------
+//        dX = | dOut Y^T | Y dOut^T | dOut Y   | Y^T dOut^T
+//        dY = | X^T dOut | X dOut   | dOut^T X | dOut^T X^T
+//
+// When X is a vector of size K, we treat it instead as a matrix of shape
+// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
+// a matrix of shape (K, 1).
+//
+// When X and Y are both 3-dimensional tensors, then the first dimension
+// the batch dimension can be ignored and the exact same formulas apply
+// as for two matrices.
+//
+// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
+// up with formulas like
+//
+//   dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
+//
+// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
+// to X: (P * M) x K, dOut: (P * M) x N.
+template <typename DeviceContext, typename T>
+class MatMulGradKernel : public framework::OpKernel<T> {
+ public:
+  void MatMul(const framework::ExecutionContext& context,
+              const framework::Tensor& a, bool trans_a,
+              const framework::Tensor& b, bool trans_b,
+              framework::Tensor* out) const {
+    out->mutable_data<T>(context.GetPlace());
+    auto blas = math::GetBlas<DeviceContext, T>(context);
+    auto mat_dim_a = math::CreateMatrixDescriptor(a.dims(), 0, trans_a);
+    auto mat_dim_b = math::CreateMatrixDescriptor(b.dims(), 0, trans_b);
+    blas.MatMul(a, mat_dim_a, b, mat_dim_b, T(1), out, T(0));
+  }
+
+  void CalcInputGrad(const framework::ExecutionContext& context,
+                     const framework::Tensor& a, bool trans_a,
+                     bool is_fold_init_dims_a, const framework::Tensor& b,
+                     bool trans_b, bool is_fold_init_dims_b,
+                     framework::Tensor* out) const {
+    if (out == nullptr) return;
+    bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
+                        out->dims().size() == 2;
+    if (!need_combine) {
+      MatMul(context, a, trans_a, b, trans_b, out);
+    } else {
+      auto& ctx = context.template device_context<DeviceContext>();
+      MatMul(context, is_fold_init_dims_a
+                          ? FoldInitDims(a)
+                          : FoldHeadAndLastDims<DeviceContext, T>(ctx, a),
+             trans_a, is_fold_init_dims_b
+                          ? FoldInitDims(b)
+                          : FoldHeadAndLastDims<DeviceContext, T>(ctx, b),
+             trans_b, out);
+    }
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto x = *context.Input<framework::Tensor>("X");
+    auto y = *context.Input<framework::Tensor>("Y");
+    auto dout =
+        *context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
+    bool transpose_x = context.Attr<bool>("transpose_X");
+    bool transpose_y = context.Attr<bool>("transpose_Y");
+
+    ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
+    framework::DDim dx_dims;
+    if (dx) {
+      dx_dims = dx->dims();
+      if (dx_dims != x.dims()) {
+        dx->Resize(x.dims());
+      }
+    }
+
+    framework::DDim dy_dims;
+    if (dy) {
+      dy_dims = dy->dims();
+      if (dy_dims != y.dims()) {
+        dy->Resize(y.dims());
+      }
+    }
 
-using framework::Tensor;
+    if (transpose_x && transpose_y) {
+      CalcInputGrad(context, y, true, true, dout, true, false, dx);
+      CalcInputGrad(context, dout, true, true, x, true, false, dy);
+    } else if (transpose_x) {
+      CalcInputGrad(context, y, false, false, dout, true, false, dx);
+      CalcInputGrad(context, x, false, false, dout, false, true, dy);
+    } else if (transpose_y) {
+      CalcInputGrad(context, dout, false, false, y, false, true, dx);
+      CalcInputGrad(context, dout, true, true, x, false, true, dy);
+    } else {
+      CalcInputGrad(context, dout, false, false, y, true, false, dx);
+      CalcInputGrad(context, x, true, true, dout, false, true, dy);
+    }
+
+    if (dx) {
+      if (dx_dims != x.dims()) {
+        dx->Resize(dx_dims);
+      }
+    }
+    if (dy) {
+      if (dy_dims != y.dims()) {
+        dy->Resize(dy_dims);
+      }
+    }
+  }
+};
 
 class MatMulOp : public framework::OperatorWithKernel {
  public:
@@ -36,121 +279,41 @@ class MatMulOp : public framework::OperatorWithKernel {
 
     auto dim_x = context->GetInputDim("X");
     auto dim_y = context->GetInputDim("Y");
-    bool transpose_x = context->Attrs().Get<bool>("transpose_X");
-    bool transpose_y = context->Attrs().Get<bool>("transpose_Y");
-
-    PADDLE_ENFORCE_GE(dim_x.size(), 1,
-                      "Input tensor X must be at least 1-dimensional.");
-    PADDLE_ENFORCE_GE(dim_y.size(), 1,
-                      "Input tensor Y must be at least 1-dimensional.");
-
-    std::vector<int64_t> out_dim;
-    int64_t batch_count = 1;
-    if (dim_x.size() > 3) {
-      PADDLE_ENFORCE_EQ(
-          dim_y.size(), dim_x.size(),
-          "The dimensions of X and Y must be the same, and both of "
-          "them should be %d-dimensional.",
-          dim_x.size());
-
-      // The first rank-2 dimensions are accumulated on the batch_count, and the
-      // last two dimensions are used for matrix multiplication.
-      for (int j = 0; j < dim_x.size() - 2; ++j) {
-        PADDLE_ENFORCE_EQ(dim_y[j], dim_x[j],
-                          "The %d-th dimension of X and Y must be the same.",
-                          j);
-        out_dim.push_back(dim_x[j]);
-        batch_count *= dim_x[j];
-      }
-    }
 
-    int M = 0, N = 0, KX = 0, KY = 0, batchCountX = 0, batchCountY = 0;
-    bool remove_initial_dim = false, remove_final_dim = false;
-
-    switch (dim_x.size()) {
-      case 1:
-        if (transpose_x) {
-          M = dim_x[0];
-          KX = 1;
-        } else {
-          M = 1;
-          KX = dim_x[0];
-          remove_initial_dim = true;
-        }
-        break;
-      case 2:
-        M = transpose_x ? dim_x[1] : dim_x[0];
-        KX = transpose_x ? dim_x[0] : dim_x[1];
-        break;
-      case 3:
-        batchCountX = dim_x[0];
-        M = transpose_x ? dim_x[2] : dim_x[1];
-        KX = transpose_x ? dim_x[1] : dim_x[2];
-        break;
-      default:
-        batchCountX = batch_count;
-        size_t mat_s = dim_x.size() - 2;
-        M = transpose_x ? dim_x[mat_s + 1] : dim_x[mat_s];
-        KX = transpose_x ? dim_x[mat_s] : dim_x[mat_s + 1];
-        break;
-    }
+    auto mat_dim_x =
+        math::CreateMatrixDescriptor(RowMatrixFromVector(dim_x), 0,
+                                     context->Attrs().Get<bool>("transpose_X"));
+    auto mat_dim_y =
+        math::CreateMatrixDescriptor(ColumnMatrixFromVector(dim_y), 0,
+                                     context->Attrs().Get<bool>("transpose_Y"));
 
-    switch (dim_y.size()) {
-      case 1:
-        if (transpose_y) {
-          N = dim_y[0];
-          KY = 1;
-        } else {
-          N = 1;
-          KY = dim_y[0];
-          remove_final_dim = true;
-        }
-        break;
-      case 2:
-        KY = transpose_y ? dim_y[1] : dim_y[0];
-        N = transpose_y ? dim_y[0] : dim_y[1];
-        break;
-      case 3:
-        batchCountY = dim_y[0];
-        KY = transpose_y ? dim_y[2] : dim_y[1];
-        N = transpose_y ? dim_y[1] : dim_y[2];
-        break;
-      default:
-        batchCountY = batch_count;
-        size_t mat_s = dim_y.size() - 2;
-        KY = transpose_y ? dim_y[mat_s + 1] : dim_y[mat_s];
-        N = transpose_y ? dim_y[mat_s] : dim_y[mat_s + 1];
+    PADDLE_ENFORCE_EQ(mat_dim_x.width_, mat_dim_y.height_);
+    PADDLE_ENFORCE(mat_dim_x.batch_size_ == mat_dim_y.batch_size_ ||
+                   mat_dim_x.batch_size_ == 0 || mat_dim_y.batch_size_ == 0);
+    std::vector<int64_t> dim_out;
+    if (mat_dim_x.batch_size_ != 0) {
+      dim_out = framework::vectorize(dim_x);
+      dim_out[dim_out.size() - 2] = mat_dim_x.height_;
+      dim_out[dim_out.size() - 1] = mat_dim_y.width_;
+    } else if (mat_dim_y.batch_size_ != 0) {
+      dim_out = framework::vectorize(dim_y);
+      dim_out[dim_out.size() - 2] = mat_dim_x.height_;
+      dim_out[dim_out.size() - 1] = mat_dim_y.width_;
+    } else {
+      dim_out = {mat_dim_x.height_, mat_dim_y.width_};
     }
 
-    PADDLE_ENFORCE_EQ(
-        KX, KY,
-        "First matrix's width must be equal with second matrix's height.");
-    if (batchCountX && batchCountY) {
-      PADDLE_ENFORCE_EQ(
-          batchCountX, batchCountY,
-          "When Input(X) and Input(Y) are both three dimensional, they "
-          "must have the same batch dimension.");
+    if (dim_x.size() == 1 && dim_out[dim_out.size() - 2] == 1) {
+      std::swap(dim_out[dim_out.size() - 2], dim_out[dim_out.size() - 1]);
+      dim_out.resize(dim_out.size() - 1);
     }
-    int batchCount = std::max(batchCountX, batchCountY);
 
-    std::vector<int64_t> dim_out;
-    if (batchCount) {
-      if (dim_x.size() > 3) {
-        dim_out.insert(dim_out.begin(), out_dim.begin(), out_dim.end());
-      } else {
-        dim_out.push_back(batchCount);
-      }
+    if (dim_y.size() == 1 && dim_out[dim_out.size() - 1] == 1) {
+      dim_out.resize(dim_out.size() - 1);
     }
-    if (!remove_initial_dim) {
-      dim_out.push_back(M);
-    }
-    if (!remove_final_dim) {
-      dim_out.push_back(N);
-    }
-    if (dim_out.size() == 0) {
-      // We don't support 0-dimensional Tensors (scalars), so instead
-      // treat the output as a Tensor of shape (1, ) in this case.
-      dim_out.push_back(1);
+
+    if (dim_out.empty()) {
+      dim_out = {1};
     }
     context->SetOutputDim("Out", framework::make_ddim(dim_out));
     context->ShareLoD("X", /*->*/ "Out");
@@ -233,15 +396,40 @@ class MatMulOpGrad : public framework::OperatorWithKernel {
   }
 };
 
+class MatMulOpGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* retv = new framework::OpDesc();
+    retv->SetType("matmul_grad");
+    retv->SetInput("X", Input("X"));
+    retv->SetInput("Y", Input("Y"));
+    retv->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    retv->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    retv->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    retv->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(retv);
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(matmul, ops::MatMulOp, ops::MatMulOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::MatMulOpGradMaker);
 REGISTER_OPERATOR(matmul_grad, ops::MatMulOpGrad);
 REGISTER_OP_CPU_KERNEL(
     matmul, ops::MatMulKernel<paddle::platform::CPUDeviceContext, float>);
 REGISTER_OP_CPU_KERNEL(
     matmul_grad,
     ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, float>);
+
+#ifdef PADDLE_WITH_CUDA
+REGISTER_OP_CUDA_KERNEL(
+    matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>);
+REGISTER_OP_CUDA_KERNEL(
+    matmul_grad,
+    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>);
+#endif

paddle/fluid/operators/matmul_op.cu.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/matmul_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>);
-REGISTER_OP_CUDA_KERNEL(
-    matmul_grad,
-    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/matmul_op.h

-/* 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. */
-
-#pragma once
-#include <algorithm>
-#include <functional>
-#include <vector>
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/math_function.h"
-#include "paddle/fluid/operators/math/matmul.h"
-
-namespace paddle {
-namespace operators {
-namespace matmul_detail {
-
-using Tensor = framework::Tensor;
-using DDim = framework::DDim;
-using framework::make_ddim;
-using framework::vectorize;
-
-template <typename DeviceContext, typename T>
-class MatMulKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor& x = *context.Input<Tensor>("X");
-    const Tensor& y = *context.Input<Tensor>("Y");
-    Tensor* out = context.Output<Tensor>("Out");
-    out->mutable_data<T>(context.GetPlace());
-    bool transpose_x = context.Attr<bool>("transpose_X");
-    bool transpose_y = context.Attr<bool>("transpose_Y");
-
-    math::MatMulFunctor<DeviceContext, T>()(
-        context.template device_context<DeviceContext>(), x, transpose_x, y,
-        transpose_y, T(1), out, T(0));
-  }
-};
-
-template <typename T>
-inline Tensor Reshape(const Tensor& input, const DDim& dims) {
-  Tensor output;
-  output.ShareDataWith(input);
-  output.Resize(dims);
-  return output;
-}
-
-// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
-// Identity op if the tensor is not of rank 3.
-template <typename T>
-Tensor CombineBatchAndM(const Tensor& input) {
-  Tensor output;
-  output.ShareDataWith(input);
-  auto in_dims = input.dims();
-  if (in_dims.size() == 3) {
-    std::vector<int64_t> out_dims = {in_dims[0] * in_dims[1], in_dims[2]};
-    output.Resize(make_ddim(out_dims));
-  }
-  return output;
-}
-
-// Reshape a rank-3 tensor from P x M x N to M x (P * N).
-// (Warning: This requires transposing data and writes into new memory.)
-// Identity op if the tensor is not of rank 3.
-template <typename DeviceContext, typename T>
-Tensor CombineBatchAndN(const DeviceContext& context, const Tensor& input) {
-  Tensor output;
-  auto in_dims = input.dims();
-  if (in_dims.size() == 3) {
-    output.Resize({in_dims[1], in_dims[0], in_dims[2]});
-    output.mutable_data<T>(context.GetPlace());
-    std::vector<int> axis = {1, 0, 2};
-    math::Transpose<DeviceContext, T, 3> trans;
-    trans(context, input, &output, axis);
-    std::vector<int64_t> out_dims = {in_dims[1], in_dims[0] * in_dims[2]};
-    output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
-  } else {
-    output.ShareDataWith(input);
-  }
-  return output;
-}
-
-// Using dimensional constraints on matrix multiplication, it is
-// straight-forward to check the following table for when X and Y
-// are both matrices.
-//
-// transpose_X | False    | True     | False    | True
-// transpose_Y | False    | False    | True     | True
-// -----------+----------+----------+----------+-----------
-//        dX = | dOut Y^T | Y dOut^T | dOut Y   | Y^T dOut^T
-//        dY = | X^T dOut | X dOut   | dOut^T X | dOut^T X^T
-//
-// When X is a vector of size K, we treat it instead as a matrix of shape
-// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
-// a matrix of shape (K, 1).
-//
-// When X and Y are both 3-dimensional tensors, then the first dimension
-// the batch dimension can be ignored and the exact same formulas apply
-// as for two matrices.
-//
-// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
-// up with formulas like
-//
-//   dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
-//
-// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
-// to X: (P * M) x K, dOut: (P * M) x N.
-template <typename DeviceContext, typename T>
-class MatMulGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor& x = *context.Input<Tensor>("X");
-    const Tensor& y = *context.Input<Tensor>("Y");
-    const Tensor& dout = *context.Input<Tensor>(framework::GradVarName("Out"));
-    Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
-    Tensor* dy = context.Output<Tensor>(framework::GradVarName("Y"));
-    bool transpose_x = context.Attr<bool>("transpose_X");
-    bool transpose_y = context.Attr<bool>("transpose_Y");
-
-    std::vector<int64_t> x_dims = vectorize(x.dims());
-    std::vector<int64_t> y_dims = vectorize(y.dims());
-
-    // If X is a vector, reshape it to a matrix.
-    if (x_dims.size() == 1) {
-      x_dims.insert(x_dims.begin(), 1);
-    }
-
-    // If Y is a vector, reshape it to a matrix.
-    if (y_dims.size() == 1) {
-      y_dims.push_back(1);
-    }
-
-    int batch_count = 0;
-    // The first rank-2 dimensions are accumulated on the batch_count, and the
-    // last two dimensions are used for matrix multiplication.
-    if (x_dims.size() > 3) {
-      batch_count = accumulate(x_dims.begin(), x_dims.end() - 2, 1,
-                               std::multiplies<int>());
-    }
-    // Fix the dOut dimensions.
-    int M = 0, N = 0, batchCountX = 0, batchCountY = 0;
-
-    switch (x_dims.size()) {
-      case 2:
-        M = transpose_x ? x_dims[1] : x_dims[0];
-        break;
-      case 3:
-        batchCountX = x_dims[0];
-        M = transpose_x ? x_dims[2] : x_dims[1];
-        break;
-      default:
-        batchCountX = batch_count;
-        size_t mat_s = x_dims.size() - 2;
-        M = transpose_x ? x_dims[mat_s + 1] : x_dims[mat_s];
-    }
-
-    switch (y_dims.size()) {
-      case 2:
-        N = transpose_y ? y_dims[0] : y_dims[1];
-        break;
-      case 3:
-        batchCountY = y_dims[0];
-        N = transpose_y ? y_dims[1] : y_dims[2];
-        break;
-      default:
-        batchCountY = batch_count;
-        size_t mat_s = y_dims.size() - 2;
-        N = transpose_y ? y_dims[mat_s] : y_dims[mat_s + 1];
-    }
-    if (batchCountX && batchCountY) {
-      PADDLE_ENFORCE_EQ(
-          batchCountX, batchCountY,
-          "When Input(X) and Input(Y) are both three dimensional, they "
-          "must have the same batch dimension.");
-    }
-    int batchCount = std::max(batchCountX, batchCountY);
-    std::vector<int64_t> dout_dims = {M, N};
-    if (batchCount) {
-      if (x_dims.size() > 3) {
-        dout_dims.insert(dout_dims.begin(), x_dims.begin(), x_dims.end() - 2);
-      } else {
-        dout_dims.insert(dout_dims.begin(), batchCount);
-      }
-    }
-    Tensor X = Reshape<T>(x, make_ddim(x_dims));
-    Tensor Y = Reshape<T>(y, make_ddim(y_dims));
-    Tensor dOut = Reshape<T>(dout, make_ddim(dout_dims));
-
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    if (dx) {
-      dx->mutable_data<T>(context.GetPlace());
-      const Tensor& dOut_for_dX =
-          (x_dims.size() == 2 && y_dims.size() == 3)
-              ? CombineBatchAndN<DeviceContext, T>(dev_ctx, dOut)
-              : dOut;
-      if (x_dims.size() == 2 && y_dims.size() == 3) {
-        Y = transpose_y ? CombineBatchAndM<T>(Y)
-                        : CombineBatchAndN<DeviceContext, T>(dev_ctx, Y);
-      }
-      if (transpose_x) {
-        math::MatMulFunctor<DeviceContext, T>()(
-            dev_ctx, Y, transpose_y, dOut_for_dX, transpose_x, T(1), dx, T(0));
-      } else {
-        math::MatMulFunctor<DeviceContext, T>()(
-            dev_ctx, dOut_for_dX, transpose_x, Y, !transpose_y, T(1), dx, T(0));
-      }
-    }
-
-    if (dy) {
-      dy->mutable_data<T>(context.GetPlace());
-      const Tensor& dOut_for_dY = (y_dims.size() == 2 && x_dims.size() == 3)
-                                      ? CombineBatchAndM<T>(dOut)
-                                      : dOut;
-      if (y_dims.size() == 2 && x_dims.size() == 3) {
-        X = transpose_x ? CombineBatchAndN<DeviceContext, T>(dev_ctx, X)
-                        : CombineBatchAndM<T>(X);
-        dOut = CombineBatchAndM<T>(dOut);
-      }
-      if (transpose_y) {
-        math::MatMulFunctor<DeviceContext, T>()(
-            dev_ctx, dOut_for_dY, transpose_y, X, transpose_x, T(1), dy, T(0));
-      } else {
-        math::MatMulFunctor<DeviceContext, T>()(
-            dev_ctx, X, !transpose_x, dOut_for_dY, transpose_y, T(1), dy, T(0));
-      }
-    }
-  }
-};
-}  // namespace matmul_detail
-
-using matmul_detail::MatMulKernel;
-using matmul_detail::MatMulGradKernel;
-
-}  // namespace operators
-}  // namespace paddle

python/paddle/fluid/tests/unittests/test_matmul_op.py

@@ -111,21 +111,24 @@ class Generator(object):
 
 
 # Generate test cases for all possibilities
-for dim_X in [1, 2, 3]:
-    for dim_Y in [1, 2, 3]:
-        for transpose_X in [False, True]:
-            for transpose_Y in [False, True]:
-                test_name = (
-                    'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
-                        dim_X, dim_Y, transpose_X, transpose_Y))
-                shape_X, shape_Y = generate_compatible_shapes(
-                    dim_X, dim_Y, transpose_X, transpose_Y)
-                globals()[test_name] = type(test_name, (Generator, OpTest), {
-                    'shape_X': shape_X,
-                    'shape_Y': shape_Y,
-                    'transpose_X': transpose_X,
-                    'transpose_Y': transpose_Y,
-                })
+def inject_test(dim_x, dim_y, trans_x, trans_y):
+    test_name = ('TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
+        dim_x, dim_y, trans_x, trans_y))
+    shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
+                                                  trans_y)
+    globals()[test_name] = type(test_name, (Generator, OpTest), {
+        'shape_X': shape_x,
+        'shape_Y': shape_y,
+        'transpose_X': trans_x,
+        'transpose_Y': trans_y,
+    })
+
+
+for dim_X in (1, 2, 3):
+    for dim_Y in (1, 2, 3):
+        for transose_x in (False, True):
+            for transose_y in (False, True):
+                inject_test(dim_X, dim_Y, transose_x, transose_y)
 
 
 # Test case n-dim
@@ -149,7 +152,7 @@ def generate_compatible_shapes(dim, transpose_X, transpose_Y):
     return shape_X, shape_Y
 
 
-# Test case n-dim
+# # Test case n-dim
 for dim in [4]:
     for transpose_X in [False, True]:
         for transpose_Y in [False, True]: