implementation of broadcast div backward by reduce

paddle/fluid/operators/elementwise/elementwise_div_op.cu

@@ -13,93 +13,46 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
-#include "paddle/fluid/operators/elementwise/elementwise_op_broadcast.cu.h"
-#include "paddle/fluid/platform/complex.h"
-#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 
 namespace paddle {
 namespace operators {
 
-template <typename T>
-static __global__ void SimpleElemwiseDivGradCUDAKernel(const T* x, const T* y,
-                                                       const T* out,
-                                                       const T* dout,
-                                                       int64_t size, T* dx,
-                                                       T* dy) {
-  int col = blockIdx.x * blockDim.x + threadIdx.x;
-
-  while (col < size) {
-    T o = dout[col];
-    dx[col] = o / y[col];
-    dy[col] = -o * out[col] / y[col];
-    col += blockDim.x * gridDim.x;
-  }
-}
-
-template <>
-__global__ void
-SimpleElemwiseDivGradCUDAKernel<paddle::platform::complex<float>>(
-    const paddle::platform::complex<float>* x,
-    const paddle::platform::complex<float>* y,
-    const paddle::platform::complex<float>* out,
-    const paddle::platform::complex<float>* dout, int64_t size,
-    paddle::platform::complex<float>* dx,
-    paddle::platform::complex<float>* dy) {
-  int col = blockIdx.x * blockDim.x + threadIdx.x;
-
-  while (col < size) {
-    paddle::platform::complex<float> o = dout[col];
-    paddle::platform::complex<float> y_conj(y[col].real, -y[col].imag);
-    paddle::platform::complex<float> out_div_y_conj((out[col] / y[col]).real,
-                                                    -(out[col] / y[col]).imag);
-    dx[col] = o / y_conj;
-    dy[col] = -o * out_div_y_conj;
-    col += blockDim.x * gridDim.x;
-  }
-}
-
-template <>
-__global__ void
-SimpleElemwiseDivGradCUDAKernel<paddle::platform::complex<double>>(
-    const paddle::platform::complex<double>* x,
-    const paddle::platform::complex<double>* y,
-    const paddle::platform::complex<double>* out,
-    const paddle::platform::complex<double>* dout, int64_t size,
-    paddle::platform::complex<double>* dx,
-    paddle::platform::complex<double>* dy) {
-  int col = blockIdx.x * blockDim.x + threadIdx.x;
-
-  while (col < size) {
-    paddle::platform::complex<double> o = dout[col];
-    paddle::platform::complex<double> y_conj(y[col].real, -y[col].imag);
-    paddle::platform::complex<double> out_div_y_conj((out[col] / y[col]).real,
-                                                     -(out[col] / y[col]).imag);
-    dx[col] = o / y_conj;
-    dy[col] = -o * out_div_y_conj;
-    col += blockDim.x * gridDim.x;
-  }
-}
-
 template <typename DeviceContext, typename T>
 typename std::enable_if<
-    std::is_same<DeviceContext, plat::CUDADeviceContext>::value>::type
-elementwise_div_grad(const framework::ExecutionContext& ctx,
-                     const framework::Tensor* x, const framework::Tensor* y,
-                     const framework::Tensor* out,
-                     const framework::Tensor* dout, framework::Tensor* dx,
-                     framework::Tensor* dy) {
-  dim3 block_size = dim3(ELEMENTWISE_BLOCK_SIZE, 1);
-  auto size = x->numel();
-  dim3 grid_size =
-      dim3((size + ELEMENTWISE_BLOCK_SIZE - 1) / ELEMENTWISE_BLOCK_SIZE, 1);
-  SimpleElemwiseDivGradCUDAKernel<
-      T><<<grid_size, block_size, 0,
-           ctx.template device_context<plat::CUDADeviceContext>().stream()>>>(
-      x->data<T>(), y->data<T>(), out->data<T>(), dout->data<T>(), size,
-      dx->mutable_data<T>(ctx.GetPlace()), dy->mutable_data<T>(ctx.GetPlace()));
+    std::is_same<DeviceContext, platform::CUDADeviceContext>::value>::type
+ElementwiseDivGrad(const framework::ExecutionContext& ctx,
+                   const framework::Tensor* x, const framework::Tensor* y,
+                   const framework::Tensor* out, const framework::Tensor* dout,
+                   framework::Tensor* dx, framework::Tensor* dy) {
+  int axis = ctx.Attr<int>("axis");
+  const auto& dev_ctx = ctx.template device_context<DeviceContext>();
+  const auto place = ctx.GetPlace();
+  if (dx != nullptr && dy != nullptr) {
+    dx->mutable_data<T>(place);
+    if (dx->IsSharedBufferWith(*dout)) {
+      dx->clear();
+      dx->mutable_data<T>(x->dims(), place);
+    }
+    std::vector<const framework::Tensor*> ins = {dout, out, y};
+    GetGradXAndYOut<ElementwiseType::kTernary, T>(
+        dev_ctx, place, axis, ins, dout, dx, dy, DivGradXYFunctor<T, T>());
+  } else if (dx != nullptr && dy == nullptr) {
+    dx->mutable_data<T>(place);
+    if (dx->IsSharedBufferWith(*dout)) {
+      dx->clear();
+      dx->mutable_data<T>(x->dims(), place);
+    }
+    std::vector<const framework::Tensor*> ins = {dout, y};
+    GetGradXOrYOut<ElementwiseType::kBinary, T>(dev_ctx, place, axis, ins, dout,
+                                                dx, DivGradXFunctor<T>());
+  } else if (dy != nullptr && dx == nullptr) {
+    std::vector<const framework::Tensor*> ins = {dout, out, y};
+    GetGradXOrYOut<ElementwiseType::kTernary, T>(
+        dev_ctx, place, axis, ins, dout, dy, DivGradYFunctor<T>());
+  }
 }
 
 }  // namespace operators

paddle/fluid/operators/elementwise/elementwise_div_op.h

@@ -111,26 +111,24 @@ struct DivDoubleDY {
 template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
-elementwise_div_grad(const framework::ExecutionContext& ctx,
-                     const framework::Tensor* x, const framework::Tensor* y,
-                     const framework::Tensor* out,
-                     const framework::Tensor* dout, framework::Tensor* dx,
-                     framework::Tensor* dy) {
+ElementwiseDivGrad(const framework::ExecutionContext& ctx,
+                   const framework::Tensor* x, const framework::Tensor* y,
+                   const framework::Tensor* out, const framework::Tensor* dout,
+                   framework::Tensor* dx, framework::Tensor* dy) {
   int axis = ctx.Attr<int>("axis");
+
   ElemwiseGradCompute<DeviceContext, T, DivGradDX<T>, DivGradDY<T>>(
       ctx, *x, *y, *out, *dout, axis, dx, dy, DivGradDX<T>(), DivGradDY<T>());
 }
 
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
-// cuda definition
 template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_same<DeviceContext, platform::CUDADeviceContext>::value>::type
-elementwise_div_grad(const framework::ExecutionContext& ctx,
-                     const framework::Tensor* x, const framework::Tensor* y,
-                     const framework::Tensor* out,
-                     const framework::Tensor* dout, framework::Tensor* dx,
-                     framework::Tensor* dy);
+ElementwiseDivGrad(const framework::ExecutionContext& ctx,
+                   const framework::Tensor* x, const framework::Tensor* y,
+                   const framework::Tensor* out, const framework::Tensor* dout,
+                   framework::Tensor* dx, framework::Tensor* dy);
 #endif
 
 template <typename DeviceContext, typename T>
@@ -146,15 +144,8 @@ class ElementwiseDivGradKernel : public ElemwiseGradKernel<T> {
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-    int axis = ctx.Attr<int>("axis");
 
-    if (dx != nullptr && dy != nullptr && (dx->dims() == dy->dims())) {
-      elementwise_div_grad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
-    } else {
-      ElemwiseGradCompute<DeviceContext, T, DivGradDX<T>, DivGradDY<T>>(
-          ctx, *x, *y, *out, *dout, axis, dx, dy, DivGradDX<T>(),
-          DivGradDY<T>());
-    }
+    ElementwiseDivGrad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
   }
 };
 

paddle/fluid/operators/elementwise/elementwise_functor.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/fluid/framework/array.h"
+#include "paddle/fluid/platform/complex.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/hostdevice.h"
@@ -113,6 +115,71 @@ struct MinFunctor {
   }
 };
 
+template <typename T>
+using Complex = paddle::platform::complex<T>;
+
+template <typename InT, typename OutT>
+struct DivGradXYFunctor {
+  inline HOSTDEVICE paddle::framework::Array<OutT, 2> operator()(const InT a,
+                                                                 const InT b,
+                                                                 const InT c) {
+    // dx = dout / y
+    // dy = - dout * out / y
+    paddle::framework::Array<OutT, 2> outs;
+    outs[0] = a / c;
+    outs[1] = -a * b / c;
+    return outs;
+  }
+};
+
+template <typename InT, typename OutT>
+struct DivGradXYFunctor<Complex<InT>, Complex<OutT>> {
+  inline HOSTDEVICE paddle::framework::Array<Complex<OutT>, 2> operator()(
+      const Complex<InT> a, const Complex<InT> b, const Complex<InT> c) {
+    paddle::framework::Array<Complex<OutT>, 2> outs;
+    Complex<InT> c_conj(c.real, -c.imag);
+    Complex<InT> out_div_c_conj((b / c).real, -(b / c).imag);
+    outs[0] = a / c_conj;
+    outs[1] = -a * out_div_c_conj;
+    return outs;
+  }
+};
+
+// Float div grad
+template <typename T>
+struct DivGradXFunctor {
+  inline HOSTDEVICE T operator()(const T& a, const T& b) const { return a / b; }
+};
+
+// Complex div grad
+template <typename T>
+struct DivGradXFunctor<Complex<T>> {
+  inline HOSTDEVICE Complex<T> operator()(const Complex<T>& a,
+                                          const Complex<T>& b) const {
+    Complex<T> b_conj(b.real, -b.imag);
+    return a / b_conj;
+  }
+};
+
+// Float mul and div
+template <typename T>
+struct DivGradYFunctor {
+  inline HOSTDEVICE T operator()(const T& a, const T& b, const T& c) const {
+    return -a * b / c;
+  }
+};
+
+// Complex mul and div
+template <typename T>
+struct DivGradYFunctor<Complex<T>> {
+  inline HOSTDEVICE Complex<T> operator()(const Complex<T>& a,
+                                          const Complex<T>& b,
+                                          const Complex<T>& c) const {
+    Complex<T> out_div_c_conj((b / c).real, -(b / c).imag);
+    return -a * out_div_c_conj;
+  }
+};
+
 // Fmax
 template <typename T>
 struct FMaxFunctor {

paddle/fluid/operators/elementwise/elementwise_op_function.h

@@ -43,6 +43,7 @@ limitations under the License. */
 #include <thrust/iterator/iterator_adaptor.h>
 
 #include "paddle/fluid/operators/elementwise/elementwise_op_broadcast.cu.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
 #include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
 
@@ -2619,5 +2620,77 @@ static inline std::vector<int> GetReduceDim(const framework::DDim &in,
   }
   return dims;
 }
+
+#if defined(__NVCC__) || defined(__HIPCC__)
+template <typename T>
+void ReduceWrapper(const platform::CUDADeviceContext &dev_ctx, int axis,
+                   framework::Tensor *src, framework::Tensor *dst) {
+  std::vector<int> reduce_dims = GetReduceDim(dst->dims(), src->dims(), axis);
+  TensorReduceFunctorImpl<T, T, kps::AddFunctor, kps::IdentityFunctor<T>>(
+      *src, dst, kps::IdentityFunctor<T>(), reduce_dims, dev_ctx.stream());
+}
+
+template <ElementwiseType ET, typename T, typename Functor>
+void GetGradXAndYOut(const platform::CUDADeviceContext &dev_ctx,
+                     const platform::Place &place, int axis,
+                     std::vector<const framework::Tensor *> ins,
+                     const framework::Tensor *dout, framework::Tensor *dx,
+                     framework::Tensor *dy, Functor func) {
+  framework::Tensor tmp_dx;
+  framework::Tensor tmp_dy;
+  dy->mutable_data<T>(place);
+  std::vector<framework::Tensor *> outs;
+  if (dx->dims() == dout->dims() && dy->dims() == dout->dims()) {
+    outs = {dx, dy};
+  } else if (dx->dims() != dout->dims() && dy->dims() == dout->dims()) {
+    tmp_dx.mutable_data<T>(dout->dims(), place);
+    outs = {&tmp_dx, dy};
+  } else if (dx->dims() == dout->dims() && dy->dims() != dout->dims()) {
+    tmp_dy.mutable_data<T>(dout->dims(), place);
+    outs = {dx, &tmp_dy};
+  } else if (dx->dims() != dout->dims() && dy->dims() != dout->dims()) {
+    tmp_dy.mutable_data<T>(dout->dims(), place);
+    tmp_dx.mutable_data<T>(dout->dims(), place);
+    outs = {&tmp_dx, &tmp_dy};
+  }
+
+  LaunchElementwiseCudaKernel<ET, T, T, decltype(func), 2>(dev_ctx, ins, &outs,
+                                                           axis, func);
+
+  if (dx->dims() != dout->dims() && dy->dims() == dout->dims()) {
+    ReduceWrapper<T>(dev_ctx, axis, &tmp_dx, dx);
+  } else if (dx->dims() == dout->dims() && dy->dims() != dout->dims()) {
+    ReduceWrapper<T>(dev_ctx, axis, &tmp_dy, dy);
+  } else if (dx->dims() != dout->dims() && dy->dims() != dout->dims()) {
+    ReduceWrapper<T>(dev_ctx, axis, &tmp_dx, dx);
+    ReduceWrapper<T>(dev_ctx, axis, &tmp_dy, dy);
+  }
+}
+
+template <ElementwiseType ET, typename T, typename Functor>
+void GetGradXOrYOut(const platform::CUDADeviceContext &dev_ctx,
+                    const platform::Place &place, int axis,
+                    std::vector<const framework::Tensor *> ins,
+                    const framework::Tensor *dout, framework::Tensor *dxy,
+                    Functor func) {
+  framework::Tensor tmp_dxy;
+  dxy->mutable_data<T>(place);
+
+  std::vector<framework::Tensor *> outs;
+  if (dxy->dims() != dout->dims()) {
+    tmp_dxy.mutable_data<T>(dout->dims(), place);
+    outs = {&tmp_dxy};
+  } else {
+    outs = {dxy};
+  }
+
+  LaunchElementwiseCudaKernel<ET, T, T>(dev_ctx, ins, &outs, axis, func);
+  if (dxy->dims() != dout->dims()) {
+    ReduceWrapper<T>(dev_ctx, axis, &tmp_dxy, dxy);
+  }
+}
+
+#endif
+
 }  // namespace operators
 }  // namespace paddle