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Rename ctx to dev_ctx in elementwise_base.h #73827

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Jul 7, 2025
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Rename ctx to dev_ctx in elementwise_base.h #73827

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50 changes: 25 additions & 25 deletions paddle/phi/kernels/funcs/elementwise_base.h
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Original file line number Diff line number Diff line change
Expand Up @@ -202,14 +202,14 @@ class TransformFunctor {
TransformFunctor(const DenseTensor &x,
const DenseTensor &y,
DenseTensor *z,
const DeviceContext &ctx,
const DeviceContext &dev_ctx,
Functor func,
const bool is_xsize_larger = true)
: x_(x.data<T>()),
y_(y.data<T>()),
z_(ctx.template Alloc<OutType>(z)),
z_(dev_ctx.template Alloc<OutType>(z)),
nx_(x.numel()),
ctx_(ctx),
dev_ctx_(dev_ctx),
func_(func),
is_xsize_larger_(is_xsize_larger) {
if (is_xsize_larger_ == false) {
Expand All @@ -219,20 +219,20 @@ class TransformFunctor {

inline void Run() const {
phi::Transform<DeviceContext> trans;
trans(ctx_, x_, x_ + nx_, y_, z_, func_);
trans(dev_ctx_, x_, x_ + nx_, y_, z_, func_);
}

inline void RunRowWise(int n) const {
phi::Transform<DeviceContext> trans;
if (is_xsize_larger_) {
trans(ctx_,
trans(dev_ctx_,
x_,
x_ + nx_,
RowwiseTransformIterator<T, DeviceContext>(y_, n),
z_,
func_);
} else {
trans(ctx_,
trans(dev_ctx_,
y_,
y_ + nx_,
RowwiseTransformIterator<T, DeviceContext>(x_, n),
Expand All @@ -244,14 +244,14 @@ class TransformFunctor {
inline void RunMidWise(int n, int post) const {
phi::Transform<DeviceContext> trans;
if (is_xsize_larger_) {
trans(ctx_,
trans(dev_ctx_,
x_,
x_ + nx_,
MidWiseTransformIterator<T, DeviceContext>(y_, n, post),
z_,
func_);
} else {
trans(ctx_,
trans(dev_ctx_,
y_,
y_ + nx_,
MidWiseTransformIterator<T, DeviceContext>(x_, n, post),
Expand All @@ -265,7 +265,7 @@ class TransformFunctor {
const T *y_;
OutType *z_;
int64_t nx_;
const DeviceContext &ctx_;
const DeviceContext &dev_ctx_;
Functor func_;
bool is_xsize_larger_;
};
Expand All @@ -278,17 +278,17 @@ void CommonForwardBroadcastCPU(const DenseTensor &x,
int64_t *y_dims_array,
int64_t *out_dims_array,
int max_dim,
const CPUContext &ctx,
const CPUContext &dev_ctx,
Functor func,
const bool is_xsize_larger = true) {
std::vector<int64_t> index_array(max_dim, 0);
const T *x_data = x.data<T>();
const T *y_data = y.data<T>();
if (z && z->numel() == 0) {
ctx.Alloc<OutType>(z);
dev_ctx.Alloc<OutType>(z);
return;
}
OutType *out_data = ctx.Alloc<OutType>(z);
OutType *out_data = dev_ctx.Alloc<OutType>(z);

const int64_t out_size = std::accumulate(out_dims_array,
out_dims_array + max_dim,
Expand Down Expand Up @@ -731,7 +731,7 @@ __global__ void VectorizedElementwiseKernel(
}

template <typename OutT, typename Functor, int Arity, int NumOuts, int VecSize>
void LaunchElementwiseKernel(const KPDevice &ctx,
void LaunchElementwiseKernel(const KPDevice &dev_ctx,
const std::vector<const DenseTensor *> &ins,
std::vector<DenseTensor *> *outs,
Functor func) {
Expand All @@ -754,18 +754,18 @@ void LaunchElementwiseKernel(const KPDevice &ctx,
int block_size = 64;
int grid_size = 8;
int read_lens = kps::details::GetXpuReadLens(numel, block_size, grid_size);
auto stream = ctx.x_context()->xpu_stream;
auto stream = dev_ctx.x_context()->xpu_stream;
int64_t main_offset =
(numel / (read_lens * block_size)) * read_lens * block_size;
VectorizedElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSize>
<<<grid_size, block_size, 0, stream>>>(
ins_data, outs_data, numel, main_offset, read_lens, func);
#else
auto gpu_config =
phi::backends::gpu::GetGpuLaunchConfig1D(ctx, numel, VecSize);
phi::backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel, VecSize);
int64_t main_offset = (numel / (VecSize * gpu_config.GetBlockSize())) *
VecSize * gpu_config.GetBlockSize();
auto stream = ctx.stream();
auto stream = dev_ctx.stream();
VectorizedElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSize>
<<<gpu_config.block_per_grid, gpu_config.thread_per_block, 0, stream>>>(
ins_data, outs_data, numel, main_offset, VecSize, func);
Expand All @@ -775,18 +775,18 @@ void LaunchElementwiseKernel(const KPDevice &ctx,
template <typename OutT, typename Functor, int Arity, int NumOuts = 1>
typename std::enable_if<!NeedVectorized<OutT>::value, void>::type
ElementwiseKernelForDifferentVecSize(
const KPDevice &ctx,
const KPDevice &dev_ctx,
const std::vector<const DenseTensor *> &ins,
std::vector<DenseTensor *> *outs,
Functor func) {
LaunchElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSizeS>(
ctx, ins, outs, func);
dev_ctx, ins, outs, func);
}

template <typename OutT, typename Functor, int Arity, int NumOuts = 1>
typename std::enable_if<NeedVectorized<OutT>::value, void>::type
ElementwiseKernelForDifferentVecSize(
const KPDevice &ctx,
const KPDevice &dev_ctx,
const std::vector<const DenseTensor *> &ins,
std::vector<DenseTensor *> *outs,
Functor func) {
Expand All @@ -795,15 +795,15 @@ ElementwiseKernelForDifferentVecSize(
switch (vec_size) {
case VecSizeL:
LaunchElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSizeL>(
ctx, ins, outs, func);
dev_ctx, ins, outs, func);
break;
case VecSizeM:
LaunchElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSizeM>(
ctx, ins, outs, func);
dev_ctx, ins, outs, func);
break;
case VecSizeS:
LaunchElementwiseKernel<OutT, Functor, Arity, NumOuts, VecSizeS>(
ctx, ins, outs, func);
dev_ctx, ins, outs, func);
break;
default: {
PADDLE_THROW(common::errors::Unimplemented(
Expand All @@ -814,7 +814,7 @@ ElementwiseKernelForDifferentVecSize(
}

template <typename OutT, typename Functor, int NumOuts = 1>
void ElementwiseKernel(const KPDevice &ctx,
void ElementwiseKernel(const KPDevice &dev_ctx,
const std::vector<const DenseTensor *> &ins,
std::vector<DenseTensor *> *outs,
Functor func) {
Expand Down Expand Up @@ -850,14 +850,14 @@ void ElementwiseKernel(const KPDevice &ctx,
"but %dth output tensor`s shape is not.",
i));
}
ctx.template Alloc<OutT>((*outs)[i]);
dev_ctx.template Alloc<OutT>((*outs)[i]);
}
if (have_0_size) {
return;
}

ElementwiseKernelForDifferentVecSize<OutT, Functor, kArity, NumOuts>(
ctx, ins, outs, func);
dev_ctx, ins, outs, func);
}

#endif
Expand Down
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