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Merged
luotao1 merged 3 commits into from
Jan 15, 2026
Merged

add phi::StringToDataLayout [fluid_ops] #77246

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f4ad0d7
add phi::StringToDataLayout
co63oc Jan 7, 2026
9532dcb
fix
co63oc Jan 8, 2026
bccb6ea
Merge branch 'develop' into n130
co63oc Jan 13, 2026
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4 changes: 3 additions & 1 deletion paddle/common/layout.h
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Expand Up @@ -144,7 +144,9 @@ using DataLayout = common::DataLayout;

namespace phi {
using DataLayout = common::DataLayout;
}
using common::DataLayoutToString;
using common::StringToDataLayout;
} // namespace phi

namespace paddle {
// In order to be compatible with the original custom operator Tensor interface
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/affine_channel_grad_kernel.cc
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Expand Up @@ -50,7 +50,7 @@ void AffineChannelGradKernel(const Context& dev_ctx,
auto* dscale = scale_grad;
auto* dbias = bias_grad;

const DataLayout layout = common::StringToDataLayout(data_layout);
const DataLayout layout = StringToDataLayout(data_layout);

auto dims = x->dims();
int N = static_cast<int>(dims[0]);
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/affine_channel_kernel.cc
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Expand Up @@ -46,7 +46,7 @@ void AffineChannelKernel(const Context& dev_ctx,
auto* y = out;
dev_ctx.template Alloc<T>(y);

const DataLayout layout = common::StringToDataLayout(data_layout);
const DataLayout layout = StringToDataLayout(data_layout);

auto dims = x->dims();
int N = static_cast<int>(dims[0]);
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4 changes: 2 additions & 2 deletions paddle/phi/kernels/cpu/batch_norm_grad_kernel.cc
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Expand Up @@ -59,7 +59,7 @@ void BatchNormGradFunctor(const Context& dev_ctx,
DenseTensor* bias_grad) {
const auto* d_y = &y_grad;

DataLayout data_layout = common::StringToDataLayout(data_layout_str);
DataLayout data_layout = StringToDataLayout(data_layout_str);

auto* d_x = x_grad;
auto* d_scale = scale_grad;
Expand Down Expand Up @@ -391,7 +391,7 @@ void BatchNormDoubleGradKernel(
"you want to use global status in pre_train model, "
"please set `use_global_stats = True`"));

const auto data_layout = common::StringToDataLayout(data_layout_str);
const auto data_layout = StringToDataLayout(data_layout_str);

const auto* ddX = x_grad_grad.get_ptr();
const auto* ddScale = scale_grad_grad.get_ptr();
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/batch_norm_kernel.cc
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Expand Up @@ -68,7 +68,7 @@ void BatchNormKernel(const Context& dev_ctx,

bool global_stats = test_mode || use_global_stats;

auto data_layout = common::StringToDataLayout(data_layout_str);
auto data_layout = StringToDataLayout(data_layout_str);

const auto& x_dims = x.dims();
PADDLE_ENFORCE_GE(
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/group_norm_grad_kernel.cc
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Expand Up @@ -60,7 +60,7 @@ void GroupNormGradKernel(const Context& dev_ctx,
}
return;
}
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
const auto scale_ptr = scale.get_ptr();
const auto bias_ptr = bias.get_ptr();
const auto& x_dims = y.dims();
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/group_norm_kernel.cc
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Expand Up @@ -52,7 +52,7 @@ void GroupNormKernel(const Context& dev_ctx,
}
return;
}
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
const auto scale_ptr = scale.get_ptr();
const auto bias_ptr = bias.get_ptr();

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6 changes: 3 additions & 3 deletions paddle/phi/kernels/cpu/interpolate_grad_kernel.cc
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Expand Up @@ -406,7 +406,7 @@ static void Interpolate1DCPUBwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -507,7 +507,7 @@ static void Interpolate2DCPUBwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -664,7 +664,7 @@ static void Interpolate3DCPUBwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

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6 changes: 3 additions & 3 deletions paddle/phi/kernels/cpu/interpolate_kernel.cc
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Expand Up @@ -557,7 +557,7 @@ static void Interpolate1DCPUFwd(
bool align_corners,
int align_mode,
DenseTensor* output) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(x.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -650,7 +650,7 @@ static void Interpolate2DCPUFwd(
bool align_corners,
int align_mode,
DenseTensor* output) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(x.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -813,7 +813,7 @@ static void Interpolate3DCPUFwd(
bool align_corners,
int align_mode,
DenseTensor* output) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n = 0, c = 0, in_d = 0, in_h = 0, in_w = 0;
funcs::ExtractNCDWH(x.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

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2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/temporal_shift_grad_kernel.cc
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Expand Up @@ -96,7 +96,7 @@ void TemporalShiftGradKernel(const Context& dev_ctx,
auto* input_grad = x_grad;
auto* output_grad = &out_grad;
int t = seg_num;
const DataLayout data_layout = common::StringToDataLayout(data_format_str);
const DataLayout data_layout = StringToDataLayout(data_format_str);

const int nt = static_cast<int>(output_grad->dims()[0]);
const int c = static_cast<int>(data_layout == DataLayout::NCHW
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/cpu/temporal_shift_kernel.cc
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Expand Up @@ -96,7 +96,7 @@ void TemporalShiftKernel(const Context& dev_ctx,
auto* input = &x;
auto* output = out;
int t = seg_num;
const DataLayout data_layout = common::StringToDataLayout(data_format_str);
const DataLayout data_layout = StringToDataLayout(data_format_str);

const int nt = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/funcs/data_layout_transform.h
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Expand Up @@ -44,7 +44,7 @@ inline OneDNNMemoryFormat ToOneDNNFormat(const DataLayout& layout) {
default:
PADDLE_THROW(
errors::InvalidArgument("Fail to convert layout %s to oneDNN format.",
::common::DataLayoutToString(layout)));
DataLayoutToString(layout)));
}
}

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2 changes: 1 addition & 1 deletion paddle/phi/kernels/funcs/sync_batch_norm_utils.h
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Expand Up @@ -425,7 +425,7 @@ void SyncBatchNormGradFunctor(
DenseTensor *bias_grad) {
double epsilon = static_cast<double>(epsilon_f);

const DataLayout layout = common::StringToDataLayout(data_layout_str);
const DataLayout layout = StringToDataLayout(data_layout_str);

const auto *d_y = &y_grad;

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2 changes: 1 addition & 1 deletion paddle/phi/kernels/fusion/onednn/fused_conv_kernel.cc
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Expand Up @@ -144,7 +144,7 @@ KernelKey ConvGetKernelTypeForVar(const GetKernelTypeForVarContext* ctx) {
(tensor.layout() != DataLayout::ONEDNN)) {
auto it = attrs.find("data_format");
const std::string data_format = PADDLE_GET_CONST(std::string, it->second);
auto dl = common::StringToDataLayout(data_format);
auto dl = StringToDataLayout(data_format);
// Some models may have intentionally set "AnyLayout" for conv
// op. Treat this as NCHW (default data_format value)
if (dl != DataLayout::ANY) {
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/fusion/xpu/bn_act_xpu_kernel.cc
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Expand Up @@ -33,7 +33,7 @@ void BNActXPUKernel(const Context& dev_ctx,
int act_type,
DenseTensor* y) {
using XPUType = typename XPUTypeTrait<T>::Type;
const auto data_layout = common::StringToDataLayout(data_layout_str);
const auto data_layout = StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
true,
common::errors::InvalidArgument(
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/affine_channel_grad_kernel.cu
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Expand Up @@ -98,7 +98,7 @@ void AffineChannelGradCUDAKernel(const Context& dev_ctx,
auto* dscale = scale_grad;
auto* dbias = bias_grad;

const DataLayout layout = common::StringToDataLayout(data_layout);
const DataLayout layout = StringToDataLayout(data_layout);

auto dims = dy->dims();
const int64_t num = dy->numel();
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/affine_channel_kernel.cu
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Expand Up @@ -56,7 +56,7 @@ void AffineChannelCUDAKernel(const Context& dev_ctx,
auto* y = out;
dev_ctx.template Alloc<T>(y);

const DataLayout layout = common::StringToDataLayout(data_layout);
const DataLayout layout = StringToDataLayout(data_layout);

auto dims = x->dims();
const int64_t num = x->numel();
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4 changes: 2 additions & 2 deletions paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
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Expand Up @@ -533,7 +533,7 @@ void BatchNormGradFunctor(const Context &dev_ctx,
DenseTensor *bias_grad) {
double epsilon = static_cast<double>(epsilon_f);

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);

const auto *d_y = &y_grad;

Expand Down Expand Up @@ -1407,7 +1407,7 @@ void BatchNormDoubleGradKernel(
"you want to use global status in pre_train model, "
"please set `use_global_stats = True`"));

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);

const DenseTensor *running_mean = nullptr;
const DenseTensor *running_variance = nullptr;
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/batch_norm_kernel.cu
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Expand Up @@ -570,7 +570,7 @@ void BatchNormKernel(const Context &dev_ctx,
}
double epsilon = epsilon_f;
const bool trainable_stats = trainable_statistics;
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
bool test_mode = is_test && (!trainable_stats);

// Get the size for each dimension.
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2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/conv_transpose_grad_kernel.cu
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Expand Up @@ -72,7 +72,7 @@ void DepthwiseConv2dTransposeGradKernel(const Context& dev_ctx,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
DenseTensor filter_ = filter;

if (!dx && !dfilter) {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/conv_transpose_kernel.cu
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Expand Up @@ -41,7 +41,7 @@ void DepthwiseConv2dTransposeKernel(const Context& dev_ctx,
Full<T, Context>(dev_ctx, out->dims(), 0, out);
return;
}
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
DenseTensor filter_ = filter;
dev_ctx.template Alloc<T>(out);

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2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv2d_bias_grad_kernel.cu
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Expand Up @@ -478,7 +478,7 @@ void DepthwiseConv2dBiasGradKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv2d_bias_kernel.cu
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Expand Up @@ -383,7 +383,7 @@ void DepthwiseConv2dBiasKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv3d_bias_grad_kernel.cu
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Expand Up @@ -657,7 +657,7 @@ void DepthwiseConv3dBiasGradKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NDHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv3d_bias_kernel.cu
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Expand Up @@ -328,7 +328,7 @@ void DepthwiseConv3dBiasKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NDHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv_grad_kernel.cu
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Expand Up @@ -95,7 +95,7 @@ void DepthwiseConvGradKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/depthwise_conv_kernel.cu
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Expand Up @@ -99,7 +99,7 @@ void DepthwiseConvKernel(const Context& dev_ctx,
auto filter_dims = filter.dims();

DDim in_data_dims;
const DataLayout data_layout = common::StringToDataLayout(data_format);
const DataLayout data_layout = StringToDataLayout(data_format);
if (data_layout != DataLayout::NHWC) {
in_data_dims = slice_ddim(in_dims, 2, in_dims.size());
} else {
Expand Down
2 changes: 1 addition & 1 deletion paddle/phi/kernels/gpu/group_norm_grad_kernel.cu
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Expand Up @@ -318,7 +318,7 @@ void GroupNormGradKernel(const Context& dev_ctx,
return;
}
using AccT = typename phi::dtype::MPTypeTrait<T>::Type;
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
const auto scale_ptr = scale.get_ptr();
const auto bias_ptr = bias.get_ptr();

Expand Down
4 changes: 2 additions & 2 deletions paddle/phi/kernels/gpu/group_norm_kernel.cu
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Expand Up @@ -727,7 +727,7 @@ void GroupNormNDHWCKernel(const Context& dev_ctx,
DenseTensor* residual_out,
DenseTensor* mean,
DenseTensor* var) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
if (data_layout != DataLayout::NHWC) {
PD_THROW("data_layout only supports NHWC and NDHWC");
}
Expand Down Expand Up @@ -1123,7 +1123,7 @@ void GroupNormGeneralCaseKernel(const Context& dev_ctx,
DenseTensor* mean,
DenseTensor* var) {
using AccT = typename phi::dtype::MPTypeTrait<T>::Type;
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
const auto scale_ptr = scale.get_ptr();
const auto bias_ptr = bias.get_ptr();
const auto x_dims = x.dims();
Expand Down
8 changes: 4 additions & 4 deletions paddle/phi/kernels/gpu/interpolate_grad_kernel.cu
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Expand Up @@ -1208,7 +1208,7 @@ static void Interpolate1DCUDABwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1317,7 +1317,7 @@ static void Interpolate2DCUDABwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1591,7 +1591,7 @@ static void InterpolateAA2DCUDABwd(
dev_ctx.template Alloc<T>(input_grad);
return;
}
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1812,7 +1812,7 @@ static void Interpolate3DCUDABwd(
bool align_corners,
int align_mode,
DenseTensor* input_grad) {
const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down
8 changes: 4 additions & 4 deletions paddle/phi/kernels/gpu/interpolate_kernel.cu
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Expand Up @@ -1010,7 +1010,7 @@ static void Interpolate1DCUDAFwd(
DenseTensor* output) {
auto* input_data = input.data<T>();

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1121,7 +1121,7 @@ static void Interpolate2DCUDAFwd(
DenseTensor* output) {
auto* input_data = input.data<T>();

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1367,7 +1367,7 @@ static void InterpolateAA2DCUDAFwd(
}
auto* input_data = input.data<T>();

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down Expand Up @@ -1601,7 +1601,7 @@ static void Interpolate3DCUDAFwd(
DenseTensor* output) {
auto* input_data = input.data<T>();

const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
const DataLayout data_layout = StringToDataLayout(data_layout_str);
int64_t n, c, in_d, in_h, in_w;
funcs::ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

Expand Down
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