Skip to content

[Accuracy diff No.148] Fix accuracy diff for paddle.Tensor.tile API #73454

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 23, 2025
Merged

[Accuracy diff No.148] Fix accuracy diff for paddle.Tensor.tile API #73454

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4962ae3
fix accuracy tile_grad
ooooo-create Jun 19, 2025
cb4d8db
apply review for clean useless code
ooooo-create Jun 21, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
56 changes: 43 additions & 13 deletions paddle/phi/kernels/impl/tile_grad_kernel_impl.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
#include <vector>

#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/cast_kernel.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/tile_grad_kernel.h"
Expand All @@ -33,20 +34,49 @@ void TileBackward(const Context& dev_ctx,
size_t reduce_size = reduce_dims_vec.size();
dev_ctx.template Alloc<T>(x_grad);

auto eigen_x_grad = EigenVector<T>::Flatten(*x_grad);
Eigen::DSizes<Eigen::DenseIndex, Dims * 2> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<Eigen::DenseIndex, Dims> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
if constexpr (std::is_same_v<T, dtype::float16> ||
std::is_same_v<T, dtype::bfloat16>) {
const DenseTensor out_grad_fp32 =
phi::Cast<T, Context>(dev_ctx, out_grad, DataType::FLOAT32);
DenseTensor x_grad_fp32;
x_grad_fp32.Resize(x_grad->dims());
dev_ctx.template Alloc<float>(&x_grad_fp32);
auto eigen_x_grad = EigenVector<float>::Flatten(x_grad_fp32);
Eigen::DSizes<Eigen::DenseIndex, Dims * 2> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<Eigen::DenseIndex, Dims> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
const auto eigen_out_grad_fp32 = EigenVector<float>::Flatten(out_grad_fp32);
auto& place = *dev_ctx.eigen_device();
funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, float, Dims>::Eval(
place, eigen_x_grad, eigen_out_grad_fp32, reduce_dims, reshape_dims);
if constexpr (std::is_same_v<T, dtype::float16>) {
phi::CastKernel<float, Context>(
dev_ctx, x_grad_fp32, DataType::FLOAT16, x_grad);
} else {
phi::CastKernel<float, Context>(
dev_ctx, x_grad_fp32, DataType::BFLOAT16, x_grad);
}
} else {
auto eigen_x_grad = EigenVector<T>::Flatten(*x_grad);
Eigen::DSizes<Eigen::DenseIndex, Dims * 2> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<Eigen::DenseIndex, Dims> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}

auto eigen_out_grad = EigenVector<T>::Flatten(out_grad);
auto& place = *dev_ctx.eigen_device();
funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Dims>::Eval(
place, eigen_x_grad, eigen_out_grad, reduce_dims, reshape_dims);
auto eigen_out_grad = EigenVector<T>::Flatten(out_grad);
auto& place = *dev_ctx.eigen_device();
funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Dims>::Eval(
place, eigen_x_grad, eigen_out_grad, reduce_dims, reshape_dims);
}
}

template <typename T, typename Context>
Expand Down
Loading