[cherry-pick]add doublegrad op for matmul (#27800)

* add matmul doublegrad op

* fix compile errors

* modify code according to review

* delete float16

* delete GetDimForInput to be consitent with release/1.8

paddle/fluid/operators/matmul_op.cc

@@ -318,6 +318,181 @@ class MatMulGradKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class MatMulDoubleGradKernel : 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, bool flag,
+              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);
+
+    int head_number = 1;
+#if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
+    head_number = context.Attr<int>("head_number");
+#endif
+
+    if (head_number <= 1 && a.dims().size() == 3 && b.dims().size() <= 2) {
+      // the transpose_X must be false, if is true, the transpose cost much time
+      if (!trans_a) {
+        mat_dim_a.height_ *= mat_dim_a.batch_size_;
+        mat_dim_a.batch_size_ = 0;
+      }
+    }
+    blas.MatMul(a, mat_dim_a, b, mat_dim_b,
+                static_cast<T>(context.Attr<float>("alpha")), out,
+                static_cast<T>(flag));
+  }
+
+  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, bool flag,
+                     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, flag, 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, flag, 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::LoDTensor>("DOut");
+    auto *ddx = context.Input<framework::LoDTensor>("DDX");
+    auto *ddy = context.Input<framework::LoDTensor>("DDY");
+
+    auto *dx = context.Output<framework::LoDTensor>("DX");
+    auto *dy = context.Output<framework::LoDTensor>("DY");
+    auto *ddout = context.Output<framework::LoDTensor>("DDOut");
+
+    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());
+      }
+    }
+
+    framework::DDim ddout_dims;
+    if (ddout) {
+      ddout_dims = ddout->dims();
+      if (ddout_dims != dout.dims()) {
+        ddout->Resize(dout.dims());
+      }
+    }
+
+    bool ddout_flag = false;
+    if (ddx) {
+      auto ddx_mat = *ddx;
+      if (ddx_mat.dims() != x.dims()) {
+        ddx_mat.Resize(x.dims());
+      }
+      if (dy) {
+        if (transpose_x && transpose_y) {
+          // dy = dout' * ddx'
+          CalcInputGrad(context, dout, true, true, ddx_mat, true, false, false,
+                        dy);
+        } else if (transpose_x) {
+          // dy = ddx * dout
+          CalcInputGrad(context, ddx_mat, false, false, dout, false, true,
+                        false, dy);
+        } else if (transpose_y) {
+          // dy = dout' * ddx
+          CalcInputGrad(context, dout, true, true, ddx_mat, false, true, false,
+                        dy);
+        } else {
+          // dy = ddx' * dout
+          CalcInputGrad(context, ddx_mat, true, true, dout, false, true, false,
+                        dy);
+        }
+      }
+
+      if (ddout) {
+        CalcInputGrad(context, ddx_mat, transpose_x, true, y, transpose_y,
+                      false, ddout_flag, ddout);
+        ddout_flag = true;
+      }
+    }
+
+    if (ddy) {
+      auto ddy_mat = *ddy;
+      if (ddy_mat.dims() != y.dims()) {
+        ddy_mat.Resize(y.dims());
+      }
+      if (dx) {
+        if (transpose_x && transpose_y) {
+          // dx = ddy' * dout'
+          CalcInputGrad(context, ddy_mat, true, true, dout, true, false, false,
+                        dx);
+        } else if (transpose_x) {
+          // dx = ddy * dout'
+          CalcInputGrad(context, ddy_mat, false, false, dout, true, false,
+                        false, dx);
+        } else if (transpose_y) {
+          // dx = dout * ddy
+          CalcInputGrad(context, dout, false, false, ddy_mat, false, true,
+                        false, dx);
+        } else {
+          // dx = dout * ddy'
+          CalcInputGrad(context, dout, false, false, ddy_mat, true, false,
+                        false, dx);
+        }
+      }
+
+      if (ddout) {
+        CalcInputGrad(context, x, transpose_x, true, ddy_mat, transpose_y,
+                      false, ddout_flag, ddout);
+      }
+    }
+
+    if (dx) {
+      if (dx_dims != x.dims()) {
+        dx->Resize(dx_dims);
+      }
+    }
+
+    if (dy) {
+      if (dy_dims != y.dims()) {
+        dy->Resize(dy_dims);
+      }
+    }
+
+    if (ddout) {
+      if (ddout_dims != dout.dims()) {
+        ddout->Resize(ddout_dims);
+      }
+    }
+  }
+};
+
 class MatMulOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -602,6 +777,61 @@ class MatMulOpGradMaker : public framework::SingleGradOpMaker<T> {
     retv->SetAttrMap(this->Attrs());
   }
 };
+
+class MatMulOpDoubleGrad : 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");
+    OP_INOUT_CHECK(context->HasInput("Y"), "Input", "Y", "matmul");
+    OP_INOUT_CHECK(context->HasInput("DOut"), "Input", "DOut", "matmul");
+
+    if (context->HasOutput("DX") && context->HasInput("DDY")) {
+      context->ShareDim("X", "DX");
+    }
+
+    if (context->HasOutput("DY") && context->HasInput("DDX")) {
+      context->ShareDim("Y", "DY");
+    }
+
+    if (context->HasOutput("DDOut") &&
+        (context->HasInput("DDY") || context->HasInput("DDX"))) {
+      context->ShareDim("DOut", "DDOut");
+    }
+  }
+};
+
+template <typename T>
+class MatMulOpDoubleGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> retv) const override {
+    retv->SetType("matmul_grad_grad");
+    retv->SetInput("X", this->Input("X"));
+    retv->SetInput("Y", this->Input("Y"));
+    retv->SetInput("DOut", this->Input(framework::GradVarName("Out")));
+    retv->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
+    retv->SetInput("DDY", this->OutputGrad(framework::GradVarName("Y")));
+
+    auto ddx = this->OutputGrad(framework::GradVarName("X"));
+    auto ddy = this->OutputGrad(framework::GradVarName("Y"));
+
+    if (!ddx.empty() || !ddy.empty()) {
+      retv->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
+    }
+    retv->SetOutput(
+        "DX", ddy.empty() ? this->EmptyInputGrad() : this->InputGrad("X"));
+    retv->SetOutput(
+        "DY", ddx.empty() ? this->EmptyInputGrad() : this->InputGrad("Y"));
+
+    retv->SetAttrMap(this->Attrs());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -609,7 +839,10 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(matmul, ops::MatMulOp, ops::MatMulOpMaker,
                   ops::MatMulOpGradMaker<paddle::framework::OpDesc>,
                   ops::MatMulOpGradMaker<paddle::imperative::OpBase>);
-REGISTER_OPERATOR(matmul_grad, ops::MatMulOpGrad);
+REGISTER_OPERATOR(matmul_grad, ops::MatMulOpGrad,
+                  ops::MatMulOpDoubleGradMaker<paddle::framework::OpDesc>,
+                  ops::MatMulOpDoubleGradMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(matmul_grad_grad, ops::MatMulOpDoubleGrad);
 REGISTER_OP_CPU_KERNEL(
     matmul, ops::MatMulKernel<paddle::platform::CPUDeviceContext, float>,
     ops::MatMulKernel<paddle::platform::CPUDeviceContext, double>);
@@ -618,6 +851,11 @@ REGISTER_OP_CPU_KERNEL(
     ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, float>,
     ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, double>);
 
+REGISTER_OP_CPU_KERNEL(
+    matmul_grad_grad,
+    ops::MatMulDoubleGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MatMulDoubleGradKernel<paddle::platform::CPUDeviceContext, double>);
+
 #ifdef PADDLE_WITH_CUDA
 REGISTER_OP_CUDA_KERNEL(
     matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>,
@@ -630,4 +868,8 @@ REGISTER_OP_CUDA_KERNEL(
     ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::MatMulGradKernel<paddle::platform::CUDADeviceContext,
                           paddle::platform::float16>);
+REGISTER_OP_CUDA_KERNEL(
+    matmul_grad_grad,
+    ops::MatMulDoubleGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MatMulDoubleGradKernel<paddle::platform::CUDADeviceContext, double>);
 #endif

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

@@ -152,6 +152,38 @@ class TestMulDoubleGradCheck(unittest.TestCase):
             self.func(p)
 
 
+class TestMatmulDoubleGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        eps = 0.005
+        x_shapes = [[2], [2, 3], [2, 4, 3], [2, 3, 4, 5], [2, 3, 4]]
+        y_shapes = [[2], [3, 2], [2, 4, 5], [2, 3, 3, 5], [4, 3]]
+        transpose_xs = [False, True, True, False, False]
+        transpose_ys = [False, True, False, True, False]
+        dtypes = [np.float64, np.float64, np.float32, np.float32, np.float64]
+        typenames = ["float64", "float64", "float32", "float32", "float64"]
+        for i, (x_shape, y_shape, transpose_x, transpose_y, dtype, typename) \
+            in enumerate(zip(x_shapes, y_shapes, transpose_xs, transpose_ys, dtypes, typenames)):
+            x = layers.create_parameter(
+                dtype=typename, shape=x_shape, name='x{}'.format(i))
+            y = layers.create_parameter(
+                dtype=typename, shape=y_shape, name='y{}'.format(i))
+            out = layers.matmul(
+                x, y, transpose_x, transpose_y, name='out{}'.format(i))
+
+            x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
+            y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
+            gradient_checker.double_grad_check(
+                [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
 class TestReshapeDoubleGradCheck(unittest.TestCase):
     @prog_scope()
     def func(self, place):