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optimize phi::IntArray(common::vectorize()) in kernels/gpudnn #77195

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Merged
luotao1 merged 1 commit into from
Jan 6, 2026
Merged

optimize phi::IntArray(common::vectorize()) in kernels/gpudnn #77195

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5 changes: 1 addition & 4 deletions paddle/phi/kernels/gpudnn/affine_grid_grad_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -42,10 +42,7 @@ void AffineGridGradCudnnKernel(const Context& dev_ctx,
"Only support for CUDAPlace.Please switch your context from "
"CPUPlace to CUDAPlace or update your cudnn."));
if (output_grad.numel() == 0 || input_grad->numel() == 0) {
phi::Full<T, Context>(dev_ctx,
phi::IntArray(common::vectorize(input_grad->dims())),
0,
input_grad);
Full<T, Context>(dev_ctx, input_grad->dims(), 0, input_grad);
return;
}
auto handle = dev_ctx.cudnn_handle();
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3 changes: 1 addition & 2 deletions paddle/phi/kernels/gpudnn/affine_grid_kernel.cu
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Expand Up @@ -53,8 +53,7 @@ void AffineGridCudnnKernel(const Context& dev_ctx,
output->Resize(common::make_ddim({n, h_size_data[2], h_size_data[3], 2}));
T* output_data = dev_ctx.template Alloc<T>(output);
if (input.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(output->dims())), 0, output);
Full<T, Context>(dev_ctx, output->dims(), 0, output);
return;
}
ScopedSpatialTransformerDescriptor st_desc;
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6 changes: 1 addition & 5 deletions paddle/phi/kernels/gpudnn/conv_grad_kernel.cu
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Expand Up @@ -415,11 +415,7 @@ void ConvCudnnGradKernel(const Context& dev_ctx,
if (input.numel() == 0 || filter.numel() == 0) {
if (input_grad) dev_ctx.template Alloc<T>(input_grad);
if (filter_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(filter_grad->dims())),
0,
filter_grad);
Full<T, Context>(dev_ctx, filter_grad->dims(), 0, filter_grad);
}
return;
}
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3 changes: 1 addition & 2 deletions paddle/phi/kernels/gpudnn/conv_kernel.cu
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Expand Up @@ -306,8 +306,7 @@ void ConvCudnnKernel(const Context& dev_ctx,
const std::string& data_format,
DenseTensor* output) {
if (input.numel() == 0 || filter.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(output->dims())), 0, output);
Full<T, Context>(dev_ctx, output->dims(), 0, output);
return;
}
dev_ctx.template Alloc<T>(output);
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8 changes: 2 additions & 6 deletions paddle/phi/kernels/gpudnn/conv_transpose_grad_kernel.cu
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Expand Up @@ -61,18 +61,14 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& dev_ctx,
if (x.numel() == 0) {
if (dx) dev_ctx.template Alloc<T>(dx);
if (dfilter) {
phi::Full<T, Context>(dev_ctx,
phi::IntArray(common::vectorize(dfilter->dims())),
0,
dfilter);
Full<T, Context>(dev_ctx, dfilter->dims(), 0, dfilter);
}
return;
}
if (filter.numel() == 0) {
if (dfilter) dev_ctx.template Alloc<T>(dfilter);
if (dx) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(dx->dims())), 0, dx);
Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
}
return;
}
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3 changes: 1 addition & 2 deletions paddle/phi/kernels/gpudnn/conv_transpose_kernel.cu
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Expand Up @@ -251,8 +251,7 @@ void ConvTransposeRawGPUDNNKernel(const Context& dev_ctx,
const std::string& data_format,
DenseTensor* out) {
if (x.numel() == 0 || filter.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
Full<T, Context>(dev_ctx, out->dims(), 0, out);
return;
}

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15 changes: 5 additions & 10 deletions paddle/phi/kernels/gpudnn/pool_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,7 @@ void PoolRawGPUDNNKernel(const Context& dev_ctx,
const std::string& padding_algorithm,
DenseTensor* out) {
if (x.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), NAN, out);
Full<T, Context>(dev_ctx, out->dims(), NAN, out);
return;
}
PADDLE_ENFORCE_EQ(
Expand Down Expand Up @@ -268,11 +267,9 @@ void Pool2dGPUDNNKernel(const Context& dev_ctx,
DenseTensor* out) {
if (x.numel() == 0) {
if (pooling_type == "max") {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
Full<T, Context>(dev_ctx, out->dims(), 0, out);
} else { // for pooling_type == "avg"
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), NAN, out);
Full<T, Context>(dev_ctx, out->dims(), NAN, out);
}
return;
}
Expand Down Expand Up @@ -306,11 +303,9 @@ void Pool3dGPUDNNKernel(const Context& dev_ctx,
DenseTensor* out) {
if (x.numel() == 0) {
if (pooling_type == "max" || (!adaptive && pooling_type == "avg")) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
Full<T, Context>(dev_ctx, out->dims(), 0, out);
} else {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), NAN, out);
Full<T, Context>(dev_ctx, out->dims(), NAN, out);
}
return;
}
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6 changes: 2 additions & 4 deletions paddle/phi/kernels/onednn/elementwise_grad_kernel.cc
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Expand Up @@ -126,15 +126,13 @@ void ElementwiseGradKernel(const OneDNNContext& dev_ctx,
if (dx) {
dev_ctx.template Alloc<T>(dx);
if (dx->numel() != 0) {
phi::Full<T, OneDNNContext>(
dev_ctx, phi::IntArray(common::vectorize(dx->dims())), 0, dx);
Full<T, OneDNNContext>(dev_ctx, dx->dims(), 0, dx);
}
}
if (dy) {
dev_ctx.template Alloc<T>(dy);
if (dy->numel() != 0) {
phi::Full<T, OneDNNContext>(
dev_ctx, phi::IntArray(common::vectorize(dy->dims())), 0, dy);
Full<T, OneDNNContext>(dev_ctx, dy->dims(), 0, dy);
}
}
return;
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3 changes: 1 addition & 2 deletions paddle/phi/kernels/onednn/expand_grad_kernel.cc
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Expand Up @@ -29,8 +29,7 @@ void ExpandGradKernel(const Context& dev_ctx,

if ((in_grad && in_grad->numel() == 0) || out_grad.numel() == 0) {
dev_ctx.template Alloc<T>(in_grad);
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(in_grad->dims())), 0, in_grad);
Full<T, Context>(dev_ctx, in_grad->dims(), 0, in_grad);
return;
}

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6 changes: 1 addition & 5 deletions paddle/phi/kernels/onednn/prelu_grad_kernel.cc
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Expand Up @@ -31,11 +31,7 @@ void PReluGradKernel(const Context& dev_ctx,
if (x_grad->numel() == 0) {
dev_ctx.template Alloc<T>(x_grad);
if (alpha_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(alpha_grad->dims())),
0,
alpha_grad);
Full<T, Context>(dev_ctx, alpha_grad->dims(), 0, alpha_grad);
}
}
bool is_test = dev_ctx.HasDnnAttr("is_test")
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3 changes: 1 addition & 2 deletions paddle/phi/kernels/onednn/reduce_mean_kernel.cc
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Expand Up @@ -26,8 +26,7 @@ void MeanRawKernel(const Context& dev_ctx,
bool reduce_all,
DenseTensor* out) {
if (x.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(out->dims())), NAN, out);
Full<T, Context>(dev_ctx, out->dims(), NAN, out);
return;
}

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3 changes: 1 addition & 2 deletions paddle/phi/kernels/onednn/reshape_grad_kernel.cc
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Expand Up @@ -21,8 +21,7 @@ void ReshapeGradKernel(const Context& dev_ctx,
const DenseTensor& out_grad,
DenseTensor* x_grad) {
if ((x_grad && x_grad->numel() == 0) || out_grad.numel() == 0) {
phi::Full<T, Context>(
dev_ctx, phi::IntArray(common::vectorize(x_grad->dims())), 0, x_grad);
Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
return;
}

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