未验证 提交 a85bf42a 编写于 作者: T tensor-tang 提交者: GitHub

Merge pull request #12681 from PaddlePaddle/revert-12554-refine_elementwise_add

Revert "Refine elementwise_add op"
...@@ -16,60 +16,6 @@ limitations under the License. */ ...@@ -16,60 +16,6 @@ limitations under the License. */
#include "paddle/fluid/operators/elementwise_add_op.h" #include "paddle/fluid/operators/elementwise_add_op.h"
#include "paddle/fluid/platform/float16.h" #include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void ElementwiseAddCUDAKernel(const T *x, const T *y, T *z, int n,
int post, int size) {
int idx_x = threadIdx.x + blockIdx.x * blockDim.x;
if (idx_x < size) {
int idx_y = idx_x / post - (idx_x / (n * post)) * n;
z[idx_x] = x[idx_x] + y[idx_y];
}
}
template <typename T>
class ElementwiseAddKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
using Tensor = framework::Tensor;
const auto x = ctx.Input<Tensor>("X");
const auto y = ctx.Input<Tensor>("Y");
auto z = ctx.Output<Tensor>("Out");
auto *z_data = z->mutable_data<T>(ctx.GetPlace());
auto &device = *(ctx.cuda_device_context().eigen_device());
const framework::DDim &x_dim = x->dims();
framework::DDim y_dim = y->dims();
int size = x->numel();
if (x_dim == y_dim) {
auto dim = framework::make_ddim({size});
auto z_eigen = framework::EigenTensor<T, 1>::From(*z, dim);
auto x_eigen = framework::EigenTensor<T, 1>::From(*x, dim);
auto y_eigen = framework::EigenTensor<T, 1>::From(*y, dim);
z_eigen.device(device) = x_eigen + y_eigen;
} else {
int axis = ctx.Attr<int>("axis");
axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
y_dim = trim_trailing_singular_dims(y_dim);
axis = (y_dim.size() == 0) ? x_dim.size() : axis;
int pre, n, post;
get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
int threads = 512;
int grids = (size + threads - 1) / threads;
auto stream = ctx.cuda_device_context().stream();
ElementwiseAddCUDAKernel<T><<<grids, threads, 0, stream>>>(
x->data<T>(), y->data<T>(), z_data, n, post, size);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators; namespace ops = paddle::operators;
namespace plat = paddle::platform; namespace plat = paddle::platform;
......
...@@ -144,41 +144,16 @@ class ElementwiseAddGradKernel : public framework::OpKernel<T> { ...@@ -144,41 +144,16 @@ class ElementwiseAddGradKernel : public framework::OpKernel<T> {
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out")); auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
// skip out, x, y
auto* out = dout;
auto *x = dout, *y = dout;
if (dx != nullptr) { if (platform::is_cpu_place(ctx.GetPlace()) && dx != nullptr &&
// In fact, we can just share memory, but it may cause a bug of memory dy != nullptr && (dx->dims() == dy->dims())) {
// optimizer elementwise_add_grad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
// dx->ShareDataWith(*dout);
framework::TensorCopy(*dout, ctx.GetPlace(),
ctx.template device_context<DeviceContext>(), dx);
}
if (dy == nullptr) return;
const framework::DDim& x_dim = dout->dims();
framework::DDim y_dim = dy->dims();
if (x_dim == y_dim) {
// dy->ShareDataWith(*dout);
framework::TensorCopy(*dout, ctx.GetPlace(),
ctx.template device_context<DeviceContext>(), dy);
} else { } else {
dy->mutable_data<T>(ctx.GetPlace()); default_elementwise_add_grad<DeviceContext, T>(ctx, x, y, out, dout, dx,
// Perform reduction to dout to calculate dy dy);
int axis = ctx.Attr<int>("axis");
axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
y_dim = trim_trailing_singular_dims(y_dim);
axis = (y_dim.size() == 0) ? x_dim.size() : axis;
auto& device =
*(ctx.template device_context<DeviceContext>().eigen_device());
int pre, n, post;
get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
auto eigen_dout = framework::EigenTensor<T, 3>::From(
*dout, framework::make_ddim({pre, n, post}));
auto eigen_dy =
framework::EigenTensor<T, 1>::From(*dy, framework::make_ddim({n}));
eigen_dy.device(device) = eigen_dout.sum(
framework::EigenDim<2>::From(framework::make_ddim({0, 2})));
} }
} }
}; };
......
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