Update elementwise double grad to save gpu memory (#19509)

* update elementwise double grad to save gpu memory, test=develop

* update elementwise_mul/div_grad_grad to save memory, test=develop

* remove eval function in eigen statement to save memory, test=develop

* add unittest for elementwise_div_grad_grad without dout, test=develop

* add unittest for elementwise_add_grad_grad without ddx, test=develop

* add float16 cuda kernel for elementwise double grad op, test=develop

paddle/fluid/operators/activation_op.cc

@@ -782,6 +782,8 @@ class SquareDoubleGradMaker
 DECLARE_INPLACE_OP_INFERER(ActivationGradOpInplaceInference,
                            {framework::GradVarName("Out"),
                             framework::GradVarName("X")});
+DECLARE_INPLACE_OP_INFERER(ActivationDoubleGradOpInplaceInference,
+                           {"DDX", "DDOut"});
 
 class PowGradOpDescMaker : public framework::SingleGradOpDescMaker {
  public:
@@ -896,7 +898,8 @@ REGISTER_OPERATOR(relu_grad, ops::ActivationOpGrad,
                   ops::ReluDoubleGradMaker);
 REGISTER_OPERATOR(
     relu_grad_grad,
-    ops::ActivationOpDoubleGrad2<ops::ReluGradFunctor<float>::FwdDeps()>);
+    ops::ActivationOpDoubleGrad2<ops::ReluGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInference);
 
 REGISTER_ACTIVATION_CPU_KERNEL(relu, Relu, ReluFunctor, ReluGradFunctor);
 
@@ -921,7 +924,8 @@ REGISTER_OPERATOR(leaky_relu_grad, ops::ActivationOpGrad,
                   ops::LeakyReluDoubleGradMaker);
 REGISTER_OPERATOR(
     leaky_relu_grad_grad,
-    ops::ActivationOpDoubleGrad2<ops::LeakyReluGradFunctor<float>::FwdDeps()>);
+    ops::ActivationOpDoubleGrad2<ops::LeakyReluGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInference);
 
 REGISTER_ACTIVATION_CPU_KERNEL(leaky_relu, LeakyRelu, LeakyReluFunctor,
                                LeakyReluGradFunctor);
@@ -945,7 +949,9 @@ REGISTER_OPERATOR(sqrt_grad, ops::ActivationOpGrad,
                   ops::SqrtDoubleGradMaker);
 REGISTER_OPERATOR(
     sqrt_grad_grad,
-    ops::ActivationOpDoubleGrad<ops::SqrtGradGradFunctor<float>::FwdDeps()>);
+    ops::ActivationOpDoubleGrad<ops::SqrtGradGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInference);
+
 REGISTER_ACTIVATION_CPU_KERNEL(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor);
 REGISTER_OP_CPU_KERNEL(
     sqrt_grad_grad, ops::SqrtDoubleGradKernel<plat::CPUDeviceContext,
@@ -967,7 +973,8 @@ REGISTER_OPERATOR(square_grad, ops::ActivationOpGrad,
                   ops::SquareDoubleGradMaker);
 REGISTER_OPERATOR(
     square_grad_grad,
-    ops::ActivationOpDoubleGrad<ops::SquareGradGradFunctor<float>::FwdDeps()>);
+    ops::ActivationOpDoubleGrad<ops::SquareGradGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInference);
 
 REGISTER_ACTIVATION_CPU_KERNEL(square, Square, SquareFunctor,
                                SquareGradFunctor);

paddle/fluid/operators/activation_op.h

@@ -1437,15 +1437,17 @@ struct SqrtGradGradFunctor : public BaseActivationFunctor<T> {
     auto* d = dev.eigen_device();
     auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
     auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
-    if (ddOut) {
-      auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
-      ddout.device(*d) = ddx * static_cast<T>(0.5) / out;
-    }
+    // sqrt GradGrad: ddy = 0.5 * ddx / y, dy = -1 * dx * ddx
+    // calculate dy first, so ddy can inplace ddx
     if (dOut) {
       auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
       auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
       dout.device(*d) = dx * ddx * static_cast<T>(-1) / out;
     }
+    if (ddOut) {
+      auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
+      ddout.device(*d) = ddx * static_cast<T>(0.5) / out;
+    }
   }
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
@@ -1459,15 +1461,17 @@ struct SquareGradGradFunctor : public BaseActivationFunctor<T> {
     auto* d = dev.eigen_device();
     auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
     auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
-    if (ddOut) {
-      auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
-      ddout.device(*d) = ddx * static_cast<T>(2) * x;
-    }
+    // square GradGrad: ddy=2x*ddx, dx=2dy*ddx
+    // calculate dx first, so ddy can inplace ddx
     if (dX) {
       auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
       auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
       dx.device(*d) = ddx * static_cast<T>(2) * dout;
     }
+    if (ddOut) {
+      auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
+      ddout.device(*d) = ddx * static_cast<T>(2) * x;
+    }
   }
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };

paddle/fluid/operators/elementwise/CMakeLists.txt

@@ -2,3 +2,5 @@ include(operators)
 register_operators()
 
 cc_test(test_elementwise_add_op_inplace SRCS test_elementwise_add_op_inplace.cc DEPS op_registry elementwise_add_op scope device_context enforce executor)
+cc_test(test_elementwise_div_grad_grad SRCS test_elementwise_div_grad_grad.cc DEPS op_registry elementwise_div_op scope device_context enforce executor)
+cc_test(test_elementwise_add_grad_grad SRCS test_elementwise_add_grad_grad.cc DEPS op_registry elementwise_add_op scope device_context enforce executor)

paddle/fluid/operators/elementwise/elementwise_add_op.cc

@@ -54,7 +54,9 @@ REGISTER_OPERATOR(elementwise_add_grad, ops::ElementwiseOpExplicitGrad,
                   ops::ElementwiseGradNoBufVarsInference,
                   ops::ElementwiseAddDoubleGradDescMaker);
 REGISTER_OPERATOR(elementwise_add_grad_grad,
-                  ops::ElementwiseOpDoubleGradWithoutDXDY);
+                  ops::ElementwiseOpDoubleGradWithoutDXDY,
+                  ops::ElementwiseDoubleGradOpInplace,
+                  ops::ElementwiseDoubleGradNoBufVarsInference);
 
 REGISTER_OP_CPU_KERNEL(
     elementwise_add,

paddle/fluid/operators/elementwise/elementwise_add_op.cu

@@ -36,4 +36,6 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, float>,
     ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, double>,
     ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, int>,
-    ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, int64_t>);
+    ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext,
+                                        plat::float16>);

paddle/fluid/operators/elementwise/elementwise_div_op.cc

@@ -80,7 +80,8 @@ REGISTER_OPERATOR(elementwise_div, ops::ElementwiseOp,
 
 REGISTER_OPERATOR(elementwise_div_grad, ops::ElementwiseOpGrad,
                   ops::ElementwiseDivDoubleGradDescMaker);
-REGISTER_OPERATOR(elementwise_div_grad_grad, ops::ElementwiseDivOpDoubleGrad);
+REGISTER_OPERATOR(elementwise_div_grad_grad, ops::ElementwiseDivOpDoubleGrad,
+                  ops::ElementwiseDivDoubleGradOpInplace);
 
 REGISTER_OP_CPU_KERNEL(
     elementwise_div,

paddle/fluid/operators/elementwise/elementwise_div_op.cu

@@ -37,6 +37,8 @@ REGISTER_OP_CUDA_KERNEL(
     elementwise_div_grad_grad,
     ops::ElementwiseDivDoubleGradKernel<paddle::platform::CUDADeviceContext,
                                         float>,
+    ops::ElementwiseDivDoubleGradKernel<paddle::platform::CUDADeviceContext,
+                                        paddle::platform::float16>,
     ops::ElementwiseDivDoubleGradKernel<paddle::platform::CUDADeviceContext,
                                         double>,
     ops::ElementwiseDivDoubleGradKernel<paddle::platform::CUDADeviceContext,

paddle/fluid/operators/elementwise/elementwise_div_op.h

@@ -148,20 +148,21 @@ class ElementwiseDivDoubleGradKernel : public framework::OpKernel<T> {
     GetDoubleGradSafeTensor<DeviceContext, T>(ctx, Out, ddX, &ddX_safe);
     GetDoubleGradSafeTensor<DeviceContext, T>(ctx, Y, ddY, &ddY_safe);
 
-    if (dOut) {
+    // ddOut = ddX / Y - Out * ddY / Y = (ddX - Out * ddY) / Y
+    // dY = Out * dX * ddY / Y - dX * ddX / Y
     // dOut = - dX * ddY
-      default_elementwise_mul<DeviceContext, T>(ctx, dX, &ddY_safe, dOut);
-      auto& place =
-          *ctx.template device_context<DeviceContext>().eigen_device();
-      auto dout = framework::EigenVector<T>::Flatten(*dOut);
-      dout.device(place) = static_cast<T>(-1) * dout;
+    // To save memory, (1) dout can be used as 'tmp' tensor, (2) ddout can
+    // inplace ddx
+    Tensor tmp;
+    if (dOut) {
+      tmp = *dOut;
+    } else {
+      auto& dev_ctx = ctx.template device_context<DeviceContext>();
+      tmp = ctx.AllocateTmpTensor<T, DeviceContext>(Out->dims(), dev_ctx);
     }
-
     if (dY) {
       // dX_div_Y = dX / Y;
-      auto& dev_ctx = ctx.template device_context<DeviceContext>();
-      Tensor dX_div_Y =
-          ctx.AllocateTmpTensor<T, DeviceContext>(Out->dims(), dev_ctx);
+      Tensor dX_div_Y = tmp;
       ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(
           ctx, dX, Y, axis, DivFunctor<T>(), &dX_div_Y);
 
@@ -179,14 +180,25 @@ class ElementwiseDivDoubleGradKernel : public framework::OpKernel<T> {
 
     if (ddOut) {
       // ddOut = ddX / Y - Out * ddY / Y = (ddX - Out * ddY) / Y
-      default_elementwise_mul<DeviceContext, T>(ctx, Out, &ddY_safe, ddOut);
+      default_elementwise_mul<DeviceContext, T>(ctx, Out, &ddY_safe, &tmp);
       ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          ctx, &ddX_safe, ddOut, 0, SubFunctor<T>(), ddOut);
+          ctx, &ddX_safe, &tmp, 0, SubFunctor<T>(), &tmp);
       ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(
-          ctx, ddOut, Y, axis, DivFunctor<T>(), ddOut);
+          ctx, &tmp, Y, axis, DivFunctor<T>(), ddOut);
+    }
+
+    if (dOut) {
+      // dOut = - dX * ddY
+      default_elementwise_mul<DeviceContext, T>(ctx, dX, &ddY_safe, dOut);
+      auto& place =
+          *ctx.template device_context<DeviceContext>().eigen_device();
+      auto dout = framework::EigenVector<T>::Flatten(*dOut);
+      dout.device(place) = static_cast<T>(-1) * dout;
     }
   }
 };
 
+DECLARE_INPLACE_OP_INFERER(ElementwiseDivDoubleGradOpInplace, {"DDX", "DDOut"});
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_mul_op.cc

@@ -77,7 +77,8 @@ REGISTER_OPERATOR(elementwise_mul, ops::ElementwiseOp,
                   ops::ElementwiseMulOpGradDescMaker);
 REGISTER_OPERATOR(elementwise_mul_grad, ops::ElementwiseOpGrad,
                   ops::ElementwiseMulDoubleGradDescMaker);
-REGISTER_OPERATOR(elementwise_mul_grad_grad, ops::ElementwiseOpDoubleGrad);
+REGISTER_OPERATOR(elementwise_mul_grad_grad, ops::ElementwiseOpDoubleGrad,
+                  ops::ElementwiseMulDoubleGradOpInplace);
 
 REGISTER_OP_CPU_KERNEL(
     elementwise_mul,

paddle/fluid/operators/elementwise/elementwise_mul_op.cu

@@ -94,4 +94,6 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, float>,
     ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, double>,
     ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, int>,
-    ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, int64_t>);
+    ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext,
+                                        plat::float16>);

paddle/fluid/operators/elementwise/elementwise_mul_op.h

@@ -146,37 +146,48 @@ class ElementwiseMulDoubleGradKernel : public framework::OpKernel<T> {
 
     if (ddout) ddout->mutable_data<T>(ctx.GetPlace());
 
-    // dx = dout * ddy
-    // dy = dout * ddx
     Tensor ddx_safe, ddy_safe;
     GetDoubleGradSafeTensor<DeviceContext, T>(ctx, x, ddx, &ddx_safe);
     GetDoubleGradSafeTensor<DeviceContext, T>(ctx, y, ddy, &ddy_safe);
-    int axis = ctx.Attr<int>("axis");
-    ElemwiseGradCompute<DeviceContext, T, MulGradDX<T>, MulGradDY<T>>(
-        ctx, ddx_safe, ddy_safe, *dout, *dout, axis, dx, dy, MulGradDX<T>(),
-        MulGradDY<T>());
 
+    // dx = dout * ddy
+    // dy = dout * ddx
     // ddout = ddx * y + x * ddy
+    // change computation sequence to save memory, so ddout can inplace ddx and
+    // dx can be used as 'tmp' tensor
+    // (1) dx = x * ddy
+    // (2) dy = dout * ddx
+    // (3) ddout = ddx * y
+    // (4) ddout = ddout + dx
+    // (5) dx = dout *ddy
     if (ddout) {
-      if (ddx && ddy) {
-        Tensor ddout_tmp;
-        ddout_tmp.mutable_data<T>(ddout->dims(), ctx.GetPlace());
+      // use dx to save memory, other than alloc tmp tensor
+      Tensor* ddout_tmp = dx;
 
-        default_elementwise_mul<DeviceContext, T>(ctx, ddx, y, ddout);
-        default_elementwise_mul<DeviceContext, T>(ctx, x, ddy, &ddout_tmp);
+      default_elementwise_mul<DeviceContext, T>(ctx, x, &ddy_safe, ddout_tmp);
+      int axis = ctx.Attr<int>("axis");
+      // NOTE: in the following ElemwiseGradCompute, for the
+      // first output tensor is nullptr, the branch to calculate first
+      // output tensor will not be activated, DivGradDx function will not
+      // be called and can be ignored, the first branch has little effect
+      // on running speed.
+      ElemwiseGradCompute<DeviceContext, T, MulGradDX<T>, MulGradDY<T>>(
+          ctx, ddx_safe, ddy_safe, *dout, *dout, axis, nullptr, dy,
+          MulGradDX<T>(), MulGradDY<T>());
+      default_elementwise_mul<DeviceContext, T>(ctx, &ddx_safe, y, ddout);
 
       auto& place =
           *ctx.template device_context<DeviceContext>().eigen_device();
       auto ddout_t = framework::EigenVector<T>::Flatten(*ddout);
-        auto ddout_tmp_t = framework::EigenVector<T>::Flatten(ddout_tmp);
+      auto ddout_tmp_t = framework::EigenVector<T>::Flatten(*ddout_tmp);
       ddout_t.device(place) = ddout_t + ddout_tmp_t;
-      } else {
-        if (ddx) default_elementwise_mul<DeviceContext, T>(ctx, ddx, y, ddout);
-        if (ddy) default_elementwise_mul<DeviceContext, T>(ctx, x, ddy, ddout);
-      }
+      default_elementwise_mul<DeviceContext, T>(ctx, dout, &ddy_safe, dx);
     }
   }
 };
 
+DECLARE_INPLACE_OP_INFERER(ElementwiseMulDoubleGradOpInplace, {"DDX", "DDOut"},
+                           {"X", framework::GradVarName("X")},
+                           {"Y", framework::GradVarName("Y")});
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -264,7 +264,18 @@ class ElementwiseOpDoubleGradWithoutDXDY
 
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    auto input_data_type = ctx.Input<Tensor>("DOut")->type();
+    framework::proto::VarType::Type input_data_type;
+    if (ctx.HasInput("DDX") == false) {
+      PADDLE_ENFORCE_EQ(ctx.HasInput("DDY"), true,
+                        "Input(DDY) should not be null");
+      input_data_type = ctx.Input<Tensor>("DDY")->type();
+    } else if (ctx.HasInput("DDY") == false) {
+      PADDLE_ENFORCE_EQ(ctx.HasInput("DDX"), true,
+                        "Input(DDX) should not be null");
+      input_data_type = ctx.Input<Tensor>("DDX")->type();
+    } else {
+      input_data_type = ctx.Input<Tensor>("DDX")->type();
+    }
 
 #ifdef PADDLE_WITH_MKLDNN
     if (platform::CanMKLDNNBeUsed(ctx)) {
@@ -321,8 +332,11 @@ DECLARE_INPLACE_OP_INFERER(ElementwiseOpInplace, {"X", "Out"});
 DECLARE_INPLACE_OP_INFERER(ElementwiseGradOpInplace,
                            {framework::GradVarName("Out"),
                             framework::GradVarName("X")});
+DECLARE_INPLACE_OP_INFERER(ElementwiseDoubleGradOpInplace, {"DDX", "DDOut"});
 
 DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ElementwiseGradNoBufVarsInference, "Y");
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ElementwiseDoubleGradNoBufVarsInference,
+                                      "Y", "DOut");
 
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_sub_op.cc

@@ -54,7 +54,9 @@ REGISTER_OPERATOR(elementwise_sub_grad, ops::ElementwiseOpExplicitGrad,
                   ops::ElementwiseGradNoBufVarsInference,
                   ops::ElementwiseSubDoubleGradDescMaker);
 REGISTER_OPERATOR(elementwise_sub_grad_grad,
-                  ops::ElementwiseOpDoubleGradWithoutDXDY);
+                  ops::ElementwiseOpDoubleGradWithoutDXDY,
+                  ops::ElementwiseDoubleGradOpInplace,
+                  ops::ElementwiseDoubleGradNoBufVarsInference);
 
 REGISTER_OP_CPU_KERNEL(
     elementwise_sub,

paddle/fluid/operators/elementwise/test_elementwise_add_grad_grad.cc

+// Copyright (c) 2019 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 <algorithm>
+#include <cstdlib>
+#include <memory>
+#include <random>
+#include <string>
+#include <vector>
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/operators/elementwise/test_elementwise_op_grad_grad.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/place.h"
+
+USE_OP(elementwise_add);
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class TestElementwiseAddGradGradWithoutDout
+    : public TestElementwiseOpGradGrad<T> {
+ public:
+  TestElementwiseAddGradGradWithoutDout(const platform::Place &place,
+                                        const framework::DDim &dims)
+      : TestElementwiseOpGradGrad<T>("elementwise_add_grad_grad", place, dims,
+                                     {"Y", "DOut", "DDY"}, {"DDOut"}) {}
+
+  using TestElementwiseOpGradGrad<T>::feed_datas_;
+  using TestElementwiseOpGradGrad<T>::expected_outs_;
+  using TestElementwiseOpGradGrad<T>::dims_;
+  void ComputeExpectedOuts() override {
+    size_t numel = static_cast<size_t>(framework::product(dims_));
+    std::vector<T> dy(numel);
+    std::vector<T> ddout(numel);
+    for (size_t i = 0; i < numel; ++i) {
+      // ddOut = ddX + ddY = ddY if ddX empty
+      ddout[i] = feed_datas_["DDY"][i];
+    }
+    expected_outs_["DDOut"] = ddout;
+  }
+
+  std::unique_ptr<framework::OperatorBase> CreateTestOp() override {
+    auto op = framework::OpRegistry::CreateOp(
+        this->op_type_, {{"Y", {"Y"}}, {"DOut", {"DOut"}}, {"DDY", {"DDY"}}},
+        {{"DDOut", {"DDOut"}}}, {{"use_mkldnn", false}, {"axis", 0}});
+    return op;
+  }
+};
+
+TEST(test_elementwise_add_grad_grad_without_ddx, cpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CPUPlace p;
+  TestElementwiseAddGradGradWithoutDout<float> test(p, dims);
+  ASSERT_TRUE(test.Check());
+}
+#ifdef PADDLE_WITH_CUDA
+TEST(test_elementwise_add_grad_grad_without_ddx, gpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CUDAPlace p(0);
+  TestElementwiseAddGradGradWithoutDout<float> test(p, dims);
+  ASSERT_TRUE(test.Check());
+}
+#endif
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/elementwise/test_elementwise_div_grad_grad.cc

+// Copyright (c) 2019 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 <algorithm>
+#include <cstdlib>
+#include <memory>
+#include <random>
+#include <string>
+#include <vector>
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/operators/elementwise/test_elementwise_op_grad_grad.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/place.h"
+
+USE_OP(elementwise_div);
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class TestElementwiseDivGradGradWithoutDout
+    : public TestElementwiseOpGradGrad<T> {
+ public:
+  TestElementwiseDivGradGradWithoutDout(const platform::Place &place,
+                                        const framework::DDim &dims)
+      : TestElementwiseOpGradGrad<T>("elementwise_div_grad_grad", place, dims,
+                                     {"Y", "Out", "DDX", "DDY", "DX"},
+                                     {"Y@GRAD", "DDOut"}) {}
+
+  using TestElementwiseOpGradGrad<T>::feed_datas_;
+  using TestElementwiseOpGradGrad<T>::expected_outs_;
+  using TestElementwiseOpGradGrad<T>::dims_;
+  void ComputeExpectedOuts() override {
+    size_t numel = static_cast<size_t>(framework::product(dims_));
+    std::vector<T> dy(numel);
+    std::vector<T> ddout(numel);
+    for (size_t i = 0; i < numel; ++i) {
+      // dY(Y@GRAD) = Out * dX * ddY / Y - dX * ddX / Y
+      dy[i] = (feed_datas_["DX"][i] / feed_datas_["Y"][i]) *
+              (feed_datas_["Out"][i] * feed_datas_["DDY"][i] -
+               feed_datas_["DDX"][i]);
+      // ddOut = ddX / Y - Out * ddY / Y = (ddX - Out * ddY) / Y
+      ddout[i] = (feed_datas_["DDX"][i] -
+                  feed_datas_["Out"][i] * feed_datas_["DDY"][i]) /
+                 (feed_datas_["Y"][i]);
+    }
+    expected_outs_["Y@GRAD"] = dy;
+    expected_outs_["DDOut"] = ddout;
+  }
+
+  std::unique_ptr<framework::OperatorBase> CreateTestOp() override {
+    auto op = framework::OpRegistry::CreateOp(
+        this->op_type_, {{"Y", {"Y"}},
+                         {"Out", {"Out"}},
+                         {"DDX", {"DDX"}},
+                         {"DDY", {"DDY"}},
+                         {"DX", {"DX"}}},
+        {{"Y@GRAD", {"Y@GRAD"}}, {"DDOut", {"DDOut"}}},
+        {{"use_mkldnn", false}, {"axis", 0}});
+    return op;
+  }
+};
+
+TEST(test_elementwise_div_grad_grad_without_dout, cpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CPUPlace p;
+  TestElementwiseDivGradGradWithoutDout<float> test(p, dims);
+  ASSERT_TRUE(test.Check());
+}
+
+#ifdef PADDLE_WITH_CUDA
+TEST(test_elementwise_div_grad_grad_without_dout, gpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CUDAPlace p(0);
+  TestElementwiseDivGradGradWithoutDout<float> test(p, dims);
+  ASSERT_TRUE(test.Check());
+}
+#endif
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/elementwise/test_elementwise_op_grad_grad.h

+// Copyright (c) 2019 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 <cstdlib>
+#include <map>
+#include <memory>
+#include <random>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/memory/memory.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace operators {
+
+// currently, this test class only support same dims
+template <typename T>
+class TestElementwiseOpGradGrad {
+ public:
+  TestElementwiseOpGradGrad(const std::string &op_type,
+                            const platform::Place &place,
+                            const framework::DDim &dims,
+                            const std::vector<std::string> &inputs,
+                            const std::vector<std::string> &outputs)
+      : op_type_(op_type),
+        place_(place),
+        dims_(dims),
+        inputs_(inputs),
+        outputs_(outputs) {}
+
+  void InitVarInScope(std::string var_name) {
+    in_out_tensors_[var_name] =
+        scope_.Var(var_name)->template GetMutable<framework::LoDTensor>();
+    in_out_tensors_[var_name]->Resize(dims_);
+    in_out_tensors_[var_name]->template mutable_data<T>(place_);
+  }
+
+  void InitFeedData(std::string var_name, size_t size) {
+    // generate random data
+    std::uniform_real_distribution<T> dist(static_cast<T>(10.0),
+                                           static_cast<T>(20.0));
+    std::mt19937 engine;
+    std::vector<T> data(size);
+    for (size_t i = 0; i < size; ++i) {
+      data[i] = dist(engine);
+    }
+    feed_datas_[var_name] = data;
+  }
+
+  void Setup() {
+    size_t numel = static_cast<size_t>(framework::product(dims_));
+    // init vars in scope and feed inputs
+    for (auto in_name : inputs_) {
+      InitVarInScope(in_name);
+      InitFeedData(in_name, numel);
+    }
+    for (auto out_name : outputs_) {
+      InitVarInScope(out_name);
+    }
+
+    // feeding: copy data to tensor, out tensor don't need init
+    auto bytes = sizeof(T) * numel;
+    for (auto &in_name : inputs_) {
+      auto dst = in_out_tensors_[in_name]->template data<T>();
+      auto src = feed_datas_[in_name].data();
+      auto src_place = platform::CPUPlace();
+      if (platform::is_cpu_place(place_)) {
+        auto dst_place = boost::get<platform::CPUPlace>(place_);
+        memory::Copy(dst_place, dst, src_place, src, bytes);
+      } else if (platform::is_gpu_place(place_)) {
+#ifdef PADDLE_WITH_CUDA
+        auto dst_place = boost::get<platform::CUDAPlace>(place_);
+        memory::Copy(dst_place, dst, src_place, src, bytes, nullptr);
+#else
+        PADDLE_THROW("Not compiled with cuda");
+#endif
+      }
+    }
+
+    // calculate expected outputs
+    ComputeExpectedOuts();
+  }
+
+  bool Check() {
+    Setup();
+    auto op = CreateTestOp();
+    op->Run(scope_, place_);
+    platform::DeviceContextPool::Instance().Get(place_)->Wait();
+    framework::LoDTensor cpu_out;
+    PADDLE_ENFORCE_EQ(scope_.kids().empty(), true, "scope has child scopes");
+
+    // get outputs from scope and compare them with expected_outs
+    bool all_equal = true;
+    for (auto &out_name : outputs_) {
+      auto &out_tensor =
+          scope_.FindVar(out_name)->template Get<framework::LoDTensor>();
+      if (platform::is_gpu_place(place_)) {
+        framework::TensorCopySync(out_tensor, platform::CPUPlace(), &cpu_out);
+      } else {
+        cpu_out = out_tensor;
+      }
+      auto *out_ptr = cpu_out.data<T>();
+      size_t numel = static_cast<size_t>(framework::product(dims_));
+      auto is_equal =
+          std::equal(out_ptr, out_ptr + numel, expected_outs_[out_name].data());
+      if (!is_equal) {
+        all_equal = false;
+        break;
+      }
+    }
+    return all_equal;
+  }
+
+  virtual std::unique_ptr<framework::OperatorBase> CreateTestOp() = 0;
+  virtual void ComputeExpectedOuts() = 0;
+  virtual ~TestElementwiseOpGradGrad() {}
+
+ protected:
+  std::string op_type_;
+  platform::Place place_;
+  framework::DDim dims_;
+  std::vector<std::string> inputs_;
+  std::vector<std::string> outputs_;
+  std::map<std::string, paddle::framework::LoDTensor *> in_out_tensors_;
+  std::map<std::string, std::vector<T>> feed_datas_;
+  std::map<std::string, std::vector<T>> expected_outs_;
+  framework::Scope scope_;
+};
+
+}  // namespace operators
+}  // namespace paddle