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未验证 提交 2f2bf4e8 编写于 作者: 傅 傅剑寒 提交者： GitHub
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[AMP OP&Test] add bf16 fp16 type support for interpolate (#51153) 

* add bf16 fp16 type support for interpolate

* add bf16 fp16 support for interpolate in phi on cpu
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Showing with 232 addition and 161 deletion
paddle/phi/kernels/cpu/interpolate_grad_kernel.cc
浏览文件 @ 2f2bf4e8
......@@ -15,6 +15,7 @@
#include "paddle/phi/kernels/interpolate_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/interpolate_function.h"
......@@ -36,6 +37,7 @@ static void LinearInterpolationGrad(const DenseTensor& output_grad,
auto input_grad_t = EigenTensor<T, 3>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 3>::From(output_grad);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int l = 0; l < out_w; l++) {
int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
: static_cast<int>(ratio_w * l);
......@@ -51,11 +53,11 @@ static void LinearInterpolationGrad(const DenseTensor& output_grad,
for (int j = 0; j < c; j++) { // loop for channels
// linear interpolation grad
if (data_layout == DataLayout::kNCHW) {
const T grad = output_grad_t(i, j, l);
const MT grad = static_cast<MT>(output_grad_t(i, j, l));
input_grad_t(i, j, x_w) += static_cast<T>(grad * d_e);
input_grad_t(i, j, x_e) += static_cast<T>(grad * d_w);
} else {
const T grad = output_grad_t(i, l, j);
const MT grad = static_cast<MT>(output_grad_t(i, l, j));
input_grad_t(i, x_w, j) += static_cast<T>(grad * d_e);
input_grad_t(i, x_e, j) += static_cast<T>(grad * d_w);
}
......@@ -81,6 +83,9 @@ static void BilinearInterpolationGrad(const DenseTensor& output_grad,
auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int k = 0; k < out_h; k++) { // loop for images
int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
: static_cast<int>(ratio_h * k);
......@@ -105,13 +110,14 @@ static void BilinearInterpolationGrad(const DenseTensor& output_grad,
for (int j = 0; j < c; j++) { // loop for channels
// bilinear interpolation grad
if (data_layout == DataLayout::kNCHW) {
const T grad = output_grad_t(i, j, k, l);
// const T grad = output_grad_t(i, j, k, l);
const MT grad = static_cast<MT>(output_grad_t(i, j, k, l));
input_grad_t(i, j, y_n, x_w) += static_cast<T>(grad * d_s * d_e);
input_grad_t(i, j, y_s, x_w) += static_cast<T>(grad * d_n * d_e);
input_grad_t(i, j, y_n, x_e) += static_cast<T>(grad * d_s * d_w);
input_grad_t(i, j, y_s, x_e) += static_cast<T>(grad * d_n * d_w);
} else {
const T grad = output_grad_t(i, k, l, j);
const MT grad = static_cast<MT>(output_grad_t(i, k, l, j));
input_grad_t(i, y_n, x_w, j) += static_cast<T>(grad * d_s * d_e);
input_grad_t(i, y_s, x_w, j) += static_cast<T>(grad * d_n * d_e);
input_grad_t(i, y_n, x_e, j) += static_cast<T>(grad * d_s * d_w);
......@@ -173,24 +179,23 @@ static void BicubicInterpolationGrad(const DenseTensor& output_grad,
const DataLayout data_layout) {
auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int k = 0; k < out_h; k++) { // loop for images
T y_n = align_corners ? static_cast<T>(ratio_h * k)
: static_cast<T>(ratio_h * (k + 0.5) - 0.5);
MT y_n = align_corners ? ratio_h * k : ratio_h * (k + 0.5) - 0.5;
int input_y = floorf(y_n);
T y_t = y_n - input_y;
MT y_t = y_n - input_y;
for (int l = 0; l < out_w; l++) {
T x_n = align_corners ? static_cast<T>(ratio_w * l)
: static_cast<T>(ratio_w * (l + 0.5) - 0.5);
MT x_n = align_corners ? ratio_w * l : ratio_w * (l + 0.5) - 0.5;
int input_x = floorf(x_n);
T x_t = x_n - input_x;
MT x_t = x_n - input_x;
T x_coeffs[4];
T y_coeffs[4];
MT x_coeffs[4];
MT y_coeffs[4];
funcs::get_cubic_upsample_coefficients<T>(x_coeffs, x_t);
funcs::get_cubic_upsample_coefficients<T>(y_coeffs, y_t);
funcs::get_cubic_upsample_coefficients<MT>(x_coeffs, x_t);
funcs::get_cubic_upsample_coefficients<MT>(y_coeffs, y_t);
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
......@@ -202,13 +207,13 @@ static void BicubicInterpolationGrad(const DenseTensor& output_grad,
int access_y = std::max(std::min(input_y - 1 + jj, in_h - 1),
static_cast<int>(0));
if (data_layout == DataLayout::kNCHW) {
T grad = output_grad_t(i, j, k, l);
MT grad = static_cast<MT>(output_grad_t(i, j, k, l));
input_grad_t(i, j, access_y, access_x) +=
grad * y_coeffs[jj] * x_coeffs[ii];
static_cast<T>(grad * y_coeffs[jj] * x_coeffs[ii]);
} else {
T grad = output_grad_t(i, k, l, j);
MT grad = static_cast<MT>(output_grad_t(i, k, l, j));
input_grad_t(i, access_y, access_x, j) +=
grad * y_coeffs[jj] * x_coeffs[ii];
static_cast<T>(grad * y_coeffs[jj] * x_coeffs[ii]);
}
}
}
......@@ -238,6 +243,7 @@ static void TrilinearInterpolationGrad(const DenseTensor& output_grad,
auto input_grad_t = EigenTensor<T, 5>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 5>::From(output_grad);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int j = 0; j < out_d; j++) { // loop for D
int t_f = align_flag ? static_cast<int>(ratio_d * (j + 0.5) - 0.5)
: static_cast<int>(ratio_d * j);
......@@ -272,7 +278,7 @@ static void TrilinearInterpolationGrad(const DenseTensor& output_grad,
for (int i = 0; i < c; i++) { // loop for channels
// trilinear interpolation grad
if (data_layout == DataLayout::kNCHW) {
const T grad = output_grad_t(b, i, j, k, l);
const MT grad = static_cast<MT>(output_grad_t(b, i, j, k, l));
input_grad_t(b, i, t_f, y_n, x_w) +=
static_cast<T>(grad * d_b * d_s * d_e);
input_grad_t(b, i, t_f, y_n, x_e) +=
......@@ -290,7 +296,7 @@ static void TrilinearInterpolationGrad(const DenseTensor& output_grad,
input_grad_t(b, i, t_b, y_s, x_e) +=
static_cast<T>(grad * d_f * d_n * d_w);
} else {
const T grad = output_grad_t(b, j, k, l, i);
const MT grad = static_cast<MT>(output_grad_t(b, j, k, l, i));
input_grad_t(b, t_f, y_n, x_w, i) +=
static_cast<T>(grad * d_b * d_s * d_e);
input_grad_t(b, t_f, y_n, x_e, i) +=
......@@ -1038,7 +1044,9 @@ PD_REGISTER_KERNEL(bilinear_interp_grad,
ALL_LAYOUT,
phi::BilinearInterpGradKernel,
float,
double) {
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1047,7 +1055,9 @@ PD_REGISTER_KERNEL(nearest_interp_grad,
ALL_LAYOUT,
phi::NearestInterpGradKernel,
float,
double) {
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1056,7 +1066,9 @@ PD_REGISTER_KERNEL(trilinear_interp_grad,
ALL_LAYOUT,
phi::TrilinearInterpGradKernel,
float,
double) {
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1065,7 +1077,9 @@ PD_REGISTER_KERNEL(linear_interp_grad,
ALL_LAYOUT,
phi::LinearInterpGradKernel,
float,
double) {
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1074,7 +1088,9 @@ PD_REGISTER_KERNEL(bicubic_interp_grad,
ALL_LAYOUT,
phi::BicubicInterpGradKernel,
float,
double) {
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
paddle/phi/kernels/cpu/interpolate_kernel.cc
浏览文件 @ 2f2bf4e8
......@@ -15,6 +15,7 @@
#include "paddle/phi/kernels/interpolate_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/interpolate_function.h"
......@@ -43,6 +44,7 @@ static void LinearInterpolation(const DenseTensor& input,
auto input_t = EigenTensor<T, 3>::From(input);
auto output_t = EigenTensor<T, 3>::From(*output);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
std::vector<int> vx_w, vx_e;
std::vector<float> vd_w, vd_e;
......@@ -80,12 +82,14 @@ static void LinearInterpolation(const DenseTensor& input,
// linear interpolation
T out_t;
if (data_layout == DataLayout::kNCHW) {
out_t = input_t(i, j, vx_w[l]) * vd_e[l] +
input_t(i, j, vx_e[l]) * vd_w[l];
out_t =
static_cast<T>(static_cast<MT>(input_t(i, j, vx_w[l])) * vd_e[l] +
static_cast<MT>(input_t(i, j, vx_e[l])) * vd_w[l]);
output_t(i, j, l) = out_t;
} else {
out_t = input_t(i, vx_w[l], j) * vd_e[l] +
input_t(i, vx_e[l], j) * vd_w[l];
out_t =
static_cast<T>(static_cast<MT>(input_t(i, vx_w[l], j)) * vd_e[l] +
static_cast<MT>(input_t(i, vx_e[l], j)) * vd_w[l]);
output_t(i, l, j) = out_t;
}
}
......@@ -110,6 +114,7 @@ static void BilinearInterpolation(const DenseTensor& input,
auto input_t = EigenTensor<T, 4>::From(input);
auto output_t = EigenTensor<T, 4>::From(*output);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
std::vector<int> vy_n, vy_s;
std::vector<float> vd_n, vd_s;
......@@ -174,17 +179,27 @@ static void BilinearInterpolation(const DenseTensor& input,
// bilinear interpolation
T out_t;
if (data_layout == DataLayout::kNCHW) {
out_t = input_t(i, j, vy_n[k], vx_w[l]) * vd_s[k] * vd_e[l] +
input_t(i, j, vy_s[k], vx_w[l]) * vd_n[k] * vd_e[l] +
input_t(i, j, vy_n[k], vx_e[l]) * vd_s[k] * vd_w[l] +
input_t(i, j, vy_s[k], vx_e[l]) * vd_n[k] * vd_w[l];
out_t = static_cast<T>(
static_cast<MT>(input_t(i, j, vy_n[k], vx_w[l])) * vd_s[k] *
vd_e[l] +
static_cast<MT>(input_t(i, j, vy_s[k], vx_w[l])) * vd_n[k] *
vd_e[l] +
static_cast<MT>(input_t(i, j, vy_n[k], vx_e[l])) * vd_s[k] *
vd_w[l] +
static_cast<MT>(input_t(i, j, vy_s[k], vx_e[l])) * vd_n[k] *
vd_w[l]);
output_t(i, j, k, l) = out_t;
} else {
out_t = input_t(i, vy_n[k], vx_w[l], j) * vd_s[k] * vd_e[l] +
input_t(i, vy_s[k], vx_w[l], j) * vd_n[k] * vd_e[l] +
input_t(i, vy_n[k], vx_e[l], j) * vd_s[k] * vd_w[l] +
input_t(i, vy_s[k], vx_e[l], j) * vd_n[k] * vd_w[l];
out_t = static_cast<T>(
static_cast<MT>(input_t(i, vy_n[k], vx_w[l], j)) * vd_s[k] *
vd_e[l] +
static_cast<MT>(input_t(i, vy_s[k], vx_w[l], j)) * vd_n[k] *
vd_e[l] +
static_cast<MT>(input_t(i, vy_n[k], vx_e[l], j)) * vd_s[k] *
vd_w[l] +
static_cast<MT>(input_t(i, vy_s[k], vx_e[l], j)) * vd_n[k] *
vd_w[l]);
output_t(i, k, l, j) = out_t;
}
}
......@@ -206,6 +221,7 @@ static void NearestNeighborInterpolate(const DenseTensor& input,
const DataLayout& data_layout) {
auto input_t = EigenTensor<T, 4>::From(input);
auto output_t = EigenTensor<T, 4>::From(*output);
for (int k = 0; k < out_h; k++) { // loop for images
int in_k = (align_corners) ? static_cast<int>(ratio_h * k + 0.5)
: static_cast<int>(ratio_h * k);
......@@ -242,22 +258,23 @@ static void BicubicInterpolation(const DenseTensor& input,
const DataLayout data_layout) {
auto input_t = EigenTensor<T, 4>::From(input);
auto output_t = EigenTensor<T, 4>::From(*output);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int k = 0; k < out_h; k++) { // loop for images
T y_n = align_corners ? static_cast<T>(ratio_h * k)
: static_cast<T>(ratio_h * (k + 0.5) - 0.5);
MT y_n = align_corners ? static_cast<MT>(ratio_h * k)
: static_cast<MT>(ratio_h * (k + 0.5) - 0.5);
int input_y = floorf(y_n);
const T y_t = y_n - input_y;
const MT y_t = y_n - input_y;
for (int l = 0; l < out_w; l++) {
T x_n = align_corners ? static_cast<T>(ratio_w * l)
: static_cast<T>(ratio_w * (l + 0.5) - 0.5);
MT x_n = align_corners ? static_cast<MT>(ratio_w * l)
: static_cast<MT>(ratio_w * (l + 0.5) - 0.5);
int input_x = floorf(x_n);
const T x_t = x_n - input_x;
const MT x_t = x_n - input_x;
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
T coefficients[4];
MT coefficients[4];
// interp 4 times in x direction
for (int ii = 0; ii < 4; ii++) {
int access_y = std::max(std::min(input_y - 1 + ii, in_h - 1),
......@@ -271,35 +288,37 @@ static void BicubicInterpolation(const DenseTensor& input,
int access_x_3 =
std::max(std::min(input_x + 2, in_w - 1), static_cast<int>(0));
if (data_layout == DataLayout::kNCHW) {
coefficients[ii] =
cubic_interp<T>(input_t(i, j, access_y, access_x_0),
input_t(i, j, access_y, access_x_1),
input_t(i, j, access_y, access_x_2),
input_t(i, j, access_y, access_x_3),
coefficients[ii] = cubic_interp<MT>(
static_cast<MT>(input_t(i, j, access_y, access_x_0)),
static_cast<MT>(input_t(i, j, access_y, access_x_1)),
static_cast<MT>(input_t(i, j, access_y, access_x_2)),
static_cast<MT>(input_t(i, j, access_y, access_x_3)),
x_t);
} else {
coefficients[ii] =
cubic_interp<T>(input_t(i, access_y, access_x_0, j),
input_t(i, access_y, access_x_1, j),
input_t(i, access_y, access_x_2, j),
input_t(i, access_y, access_x_3, j),
coefficients[ii] = cubic_interp<MT>(
static_cast<MT>(input_t(i, access_y, access_x_0, j)),
static_cast<MT>(input_t(i, access_y, access_x_1, j)),
static_cast<MT>(input_t(i, access_y, access_x_2, j)),
static_cast<MT>(input_t(i, access_y, access_x_3, j)),
x_t);
}
}
// interp y direction
if (data_layout == DataLayout::kNCHW) {
output_t(i, j, k, l) = cubic_interp<T>(coefficients[0],
output_t(i, j, k, l) =
static_cast<T>(cubic_interp<MT>(coefficients[0],
coefficients[1],
coefficients[2],
coefficients[3],
y_t);
y_t));
} else {
output_t(i, k, l, j) = cubic_interp<T>(coefficients[0],
output_t(i, k, l, j) =
static_cast<T>(cubic_interp<MT>(coefficients[0],
coefficients[1],
coefficients[2],
coefficients[3],
y_t);
y_t));
}
}
}
......@@ -327,6 +346,7 @@ static void TrilinearInterpolation(const DenseTensor& input,
auto input_t = EigenTensor<T, 5>::From(input);
auto output_t = EigenTensor<T, 5>::From(*output);
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
std::vector<int> vt_f, vt_b;
std::vector<float> vd_f, vd_b;
......@@ -417,40 +437,42 @@ static void TrilinearInterpolation(const DenseTensor& input,
for (int l = 0; l < out_w; l++) {
// trilinear interpolation
if (data_layout == DataLayout::kNCHW) {
T out_t = input_t(b, i, vt_f[j], vy_n[k], vx_w[l]) * vd_b[j] *
vd_s[k] * vd_e[l] +
input_t(b, i, vt_f[j], vy_n[k], vx_e[l]) * vd_b[j] *
vd_s[k] * vd_w[l] +
input_t(b, i, vt_f[j], vy_s[k], vx_w[l]) * vd_b[j] *
vd_n[k] * vd_e[l] +
input_t(b, i, vt_f[j], vy_s[k], vx_e[l]) * vd_b[j] *
vd_n[k] * vd_w[l] +
input_t(b, i, vt_b[j], vy_n[k], vx_w[l]) * vd_f[j] *
vd_s[k] * vd_e[l] +
input_t(b, i, vt_b[j], vy_n[k], vx_e[l]) * vd_f[j] *
vd_s[k] * vd_w[l] +
input_t(b, i, vt_b[j], vy_s[k], vx_w[l]) * vd_f[j] *
vd_n[k] * vd_e[l] +
input_t(b, i, vt_b[j], vy_s[k], vx_e[l]) * vd_f[j] *
vd_n[k] * vd_w[l];
T out_t = static_cast<T>(
static_cast<MT>(input_t(b, i, vt_f[j], vy_n[k], vx_w[l])) *
vd_b[j] * vd_s[k] * vd_e[l] +
static_cast<MT>(input_t(b, i, vt_f[j], vy_n[k], vx_e[l])) *
vd_b[j] * vd_s[k] * vd_w[l] +
static_cast<MT>(input_t(b, i, vt_f[j], vy_s[k], vx_w[l])) *
vd_b[j] * vd_n[k] * vd_e[l] +
static_cast<MT>(input_t(b, i, vt_f[j], vy_s[k], vx_e[l])) *
vd_b[j] * vd_n[k] * vd_w[l] +
static_cast<MT>(input_t(b, i, vt_b[j], vy_n[k], vx_w[l])) *
vd_f[j] * vd_s[k] * vd_e[l] +
static_cast<MT>(input_t(b, i, vt_b[j], vy_n[k], vx_e[l])) *
vd_f[j] * vd_s[k] * vd_w[l] +
static_cast<MT>(input_t(b, i, vt_b[j], vy_s[k], vx_w[l])) *
vd_f[j] * vd_n[k] * vd_e[l] +
static_cast<MT>(input_t(b, i, vt_b[j], vy_s[k], vx_e[l])) *
vd_f[j] * vd_n[k] * vd_w[l]);
output_t(b, i, j, k, l) = out_t;
} else {
T out_t = input_t(b, vt_f[j], vy_n[k], vx_w[l], i) * vd_b[j] *
vd_s[k] * vd_e[l] +
input_t(b, vt_f[j], vy_n[k], vx_e[l], i) * vd_b[j] *
vd_s[k] * vd_w[l] +
input_t(b, vt_f[j], vy_s[k], vx_w[l], i) * vd_b[j] *
vd_n[k] * vd_e[l] +
input_t(b, vt_f[j], vy_s[k], vx_e[l], i) * vd_b[j] *
vd_n[k] * vd_w[l] +
input_t(b, vt_b[j], vy_n[k], vx_w[l], i) * vd_f[j] *
vd_s[k] * vd_e[l] +
input_t(b, vt_b[j], vy_n[k], vx_e[l], i) * vd_f[j] *
vd_s[k] * vd_w[l] +
input_t(b, vt_b[j], vy_s[k], vx_w[l], i) * vd_f[j] *
vd_n[k] * vd_e[l] +
input_t(b, vt_b[j], vy_s[k], vx_e[l], i) * vd_f[j] *
vd_n[k] * vd_w[l];
T out_t = static_cast<T>(
static_cast<MT>(input_t(b, vt_f[j], vy_n[k], vx_w[l], i)) *
vd_b[j] * vd_s[k] * vd_e[l] +
static_cast<MT>(input_t(b, vt_f[j], vy_n[k], vx_e[l], i)) *
vd_b[j] * vd_s[k] * vd_w[l] +
static_cast<MT>(input_t(b, vt_f[j], vy_s[k], vx_w[l], i)) *
vd_b[j] * vd_n[k] * vd_e[l] +
static_cast<MT>(input_t(b, vt_f[j], vy_s[k], vx_e[l], i)) *
vd_b[j] * vd_n[k] * vd_w[l] +
static_cast<MT>(input_t(b, vt_b[j], vy_n[k], vx_w[l], i)) *
vd_f[j] * vd_s[k] * vd_e[l] +
static_cast<MT>(input_t(b, vt_b[j], vy_n[k], vx_e[l], i)) *
vd_f[j] * vd_s[k] * vd_w[l] +
static_cast<MT>(input_t(b, vt_b[j], vy_s[k], vx_w[l], i)) *
vd_f[j] * vd_n[k] * vd_e[l] +
static_cast<MT>(input_t(b, vt_b[j], vy_s[k], vx_e[l], i)) *
vd_f[j] * vd_n[k] * vd_w[l]);
output_t(b, j, k, l, i) = out_t;
}
}
......@@ -1190,7 +1212,9 @@ PD_REGISTER_KERNEL(bilinear_interp,
phi::BilinearInterpKernel,
float,
double,
uint8_t) {
uint8_t,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1202,7 +1226,9 @@ PD_REGISTER_KERNEL(nearest_interp,
double,
int,
int64_t,
uint8_t) {
uint8_t,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1212,7 +1238,9 @@ PD_REGISTER_KERNEL(trilinear_interp,
phi::TrilinearInterpKernel,
float,
double,
uint8_t) {
uint8_t,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1222,12 +1250,20 @@ PD_REGISTER_KERNEL(linear_interp,
phi::LinearInterpKernel,
float,
double,
uint8_t) {
uint8_t,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL(
bicubic_interp, CPU, ALL_LAYOUT, phi::BicubicInterpKernel, float, double) {
PD_REGISTER_KERNEL(bicubic_interp,
CPU,
ALL_LAYOUT,
phi::BicubicInterpKernel,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
paddle/phi/kernels/gpu/interpolate_grad_kernel.cu
浏览文件 @ 2f2bf4e8
......@@ -60,6 +60,7 @@ __global__ void KeLinearInterpBw(T* in,
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
bool align_flag = (align_mode == 0 && !align_corners);
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (; tid < nthreads; tid += stride) {
int out_id_h = tid / output_w;
int out_id_w = tid % output_w;
......@@ -79,13 +80,12 @@ __global__ void KeLinearInterpBw(T* in,
: ratio_w * out_img_idx;
in_img_idx = (in_img_idx > 0) ? in_img_idx : 0; // w
int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0; // w_id
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
T src_w = static_cast<T>(ratio_w * (out_img_idx + 0.5) - 0.5);
src_w = (src_w > static_cast<T>(0)) ? src_w : static_cast<T>(0);
T w1lambda = align_flag
? static_cast<T>(static_cast<MT>(src_w) - in_img_idx)
: static_cast<T>(ratio_w * out_img_idx - in_img_idx);
T w2lambda = static_cast<T>(1.0) - w1lambda;
MT src_w = ratio_w * (out_img_idx + 0.5) - 0.5;
src_w = (src_w > 0) ? src_w : 0;
MT w1lambda =
align_flag ? src_w - in_img_idx : ratio_w * out_img_idx - in_img_idx;
MT w2lambda = 1.0 - w1lambda;
T* in_pos;
if (data_layout == DataLayout::kNCHW) {
......@@ -96,11 +96,17 @@ __global__ void KeLinearInterpBw(T* in,
const T* out_pos = &out[out_id_w];
if (data_layout == DataLayout::kNCHW) {
phi::CudaAtomicAdd(&in_pos[0], w2lambda * out_pos[0]);
phi::CudaAtomicAdd(&in_pos[w_id], w1lambda * out_pos[0]);
phi::CudaAtomicAdd(
&in_pos[0], static_cast<T>(w2lambda * static_cast<MT>(out_pos[0])));
phi::CudaAtomicAdd(
&in_pos[w_id],
static_cast<T>(w1lambda * static_cast<MT>(out_pos[0])));
} else {
phi::CudaAtomicAdd(&in_pos[0], w2lambda * out_pos[0]);
phi::CudaAtomicAdd(&in_pos[w_id * num_channels], w1lambda * out_pos[0]);
phi::CudaAtomicAdd(
&in_pos[0], static_cast<T>(w2lambda * static_cast<MT>(out_pos[0])));
phi::CudaAtomicAdd(
&in_pos[w_id * num_channels],
static_cast<T>(w1lambda * static_cast<MT>(out_pos[0])));
}
}
}
......@@ -469,6 +475,7 @@ __global__ void KeBicubicInterpBw(T* in,
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (; tid < nthreads; tid += stride) {
int out_id_h = tid / output_w;
......@@ -487,23 +494,21 @@ __global__ void KeBicubicInterpBw(T* in,
channel_id = tid % num_channels;
}
T in_img_idy = align_corners
? static_cast<T>(ratio_h * out_img_idy)
: static_cast<T>(ratio_h * (out_img_idy + 0.5) - 0.5);
MT in_img_idy = align_corners ? ratio_h * out_img_idy
: ratio_h * (out_img_idy + 0.5) - 0.5;
int input_y = floorf(static_cast<float>(in_img_idy));
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
const T y_t = static_cast<T>(static_cast<MT>(in_img_idy) - input_y);
T in_img_idx = align_corners
? static_cast<T>(ratio_w * out_img_idx)
: static_cast<T>(ratio_w * (out_img_idx + 0.5) - 0.5);
const MT y_t = in_img_idy - input_y;
MT in_img_idx = align_corners ? ratio_w * out_img_idx
: ratio_w * (out_img_idx + 0.5) - 0.5;
int input_x = floorf(static_cast<float>(in_img_idx));
const T x_t = static_cast<T>(static_cast<MT>(in_img_idx) - input_x);
const MT x_t = in_img_idx - input_x;
T x_coeffs[4];
T y_coeffs[4];
MT x_coeffs[4];
MT y_coeffs[4];
funcs::get_cubic_upsample_coefficients(x_coeffs, x_t);
funcs::get_cubic_upsample_coefficients(y_coeffs, y_t);
funcs::get_cubic_upsample_coefficients<MT>(x_coeffs, x_t);
funcs::get_cubic_upsample_coefficients<MT>(y_coeffs, y_t);
const T* out_pos = &out[out_id_h * output_w + out_id_w];
T* in_pos;
......@@ -524,7 +529,8 @@ __global__ void KeBicubicInterpBw(T* in,
access_x * num_channels + channel_id];
}
phi::CudaAtomicAdd(&in_pos[0],
(out_pos[0] * y_coeffs[j] * x_coeffs[i]));
static_cast<T>(static_cast<MT>(out_pos[0]) *
y_coeffs[j] * x_coeffs[i]));
}
}
}
......@@ -1568,7 +1574,8 @@ PD_REGISTER_KERNEL(bilinear_interp_grad,
phi::BilinearInterpGradKernel,
float,
double,
phi::dtype::float16) {
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1589,7 +1596,8 @@ PD_REGISTER_KERNEL(trilinear_interp_grad,
phi::TrilinearInterpGradKernel,
float,
double,
phi::dtype::float16) {
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1599,7 +1607,8 @@ PD_REGISTER_KERNEL(linear_interp_grad,
phi::LinearInterpGradKernel,
float,
double,
phi::dtype::float16) {
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
......@@ -1609,7 +1618,8 @@ PD_REGISTER_KERNEL(bicubic_interp_grad,
phi::BicubicInterpGradKernel,
float,
double,
phi::dtype::float16) {
phi::dtype::float16,
phi::dtype::bfloat16) {
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
}
paddle/phi/kernels/gpu/interpolate_kernel.cu
浏览文件 @ 2f2bf4e8
......@@ -82,26 +82,27 @@ __global__ void KeLinearInterpFw(const T* in,
in_img_idx = (in_img_idx > 0) ? in_img_idx : 0; // w
int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0; // w_id
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
T src_w = static_cast<T>(ratio_w * (out_img_idx + 0.5) - 0.5);
src_w = (src_w > static_cast<T>(0)) ? src_w : static_cast<T>(0);
T w1lambda = align_flag
? static_cast<T>(static_cast<MT>(src_w) - in_img_idx)
: static_cast<T>(ratio_w * out_img_idx - in_img_idx);
T w2lambda = static_cast<T>(1.0) - w1lambda;
MT src_w = ratio_w * (out_img_idx + 0.5) - 0.5;
src_w = (src_w > 0) ? src_w : 0;
MT w1lambda = align_flag ? (src_w - in_img_idx)
: (ratio_w * out_img_idx - in_img_idx);
MT w2lambda = 1.0 - w1lambda;
if (data_layout == DataLayout::kNCHW) {
const T* in_pos =
&in[out_id_h * out_id_w + channel_id * in_img_size + in_img_idx];
// linear interpolation
out[out_id_h * output_w + out_id_w] =
w2lambda * in_pos[0] + w1lambda * in_pos[w_id];
static_cast<T>(w2lambda * static_cast<MT>(in_pos[0]) +
w1lambda * static_cast<MT>(in_pos[w_id]));
} else {
const T* in_pos =
&in[out_id_h * input_w + in_img_idx * num_channels + channel_id];
// linear interpolation
out[out_id_h * output_w + out_id_w] =
w2lambda * in_pos[0] + w1lambda * in_pos[w_id * num_channels];
out[out_id_h * output_w + out_id_w] = static_cast<T>(
w2lambda * static_cast<MT>(in_pos[0]) +
w1lambda * static_cast<MT>(in_pos[w_id * num_channels]));
}
}
}
......@@ -308,15 +309,18 @@ __global__ void KeBilinearInterpNCHWFw(const T* in,
template <typename T>
__device__ __forceinline__ static T Kecubic_interp(
const T x0, const T x1, const T x2, const T x3, T t) {
T coeffs[4];
T a = static_cast<T>(-0.75);
T x_1 = t;
T x_2 = static_cast<T>(1.0) - t;
coeffs[0] = funcs::CubicConvolution2<T>(x_1 + static_cast<T>(1.0), a);
coeffs[1] = funcs::CubicConvolution1<T>(x_1, a);
coeffs[2] = funcs::CubicConvolution1<T>(x_2, a);
coeffs[3] = funcs::CubicConvolution2<T>(x_2 + static_cast<T>(1.0), a);
return x0 * coeffs[0] + x1 * coeffs[1] + x2 * coeffs[2] + x3 * coeffs[3];
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
MT coeffs[4];
MT a = static_cast<MT>(-0.75);
MT x_1 = static_cast<MT>(t);
MT x_2 = static_cast<MT>(1.0) - static_cast<MT>(t);
coeffs[0] = funcs::CubicConvolution2<MT>(x_1 + static_cast<MT>(1.0), a);
coeffs[1] = funcs::CubicConvolution1<MT>(x_1, a);
coeffs[2] = funcs::CubicConvolution1<MT>(x_2, a);
coeffs[3] = funcs::CubicConvolution2<MT>(x_2 + static_cast<MT>(1.0), a);
return static_cast<T>(
static_cast<MT>(x0) * coeffs[0] + static_cast<MT>(x1) * coeffs[1] +
static_cast<MT>(x2) * coeffs[2] + static_cast<MT>(x3) * coeffs[3]);
}
template <typename T>
......@@ -338,6 +342,7 @@ __global__ void KeBicubicInterpFw(const T* in,
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (; tid < nthreads; tid += stride) {
int out_id_h = tid / output_w;
......@@ -357,18 +362,16 @@ __global__ void KeBicubicInterpFw(const T* in,
channel_id = tid % num_channels;
}
T in_img_idy = align_corners
? static_cast<T>(ratio_h * out_img_idy)
: static_cast<T>(ratio_h * (out_img_idy + 0.5) - 0.5);
MT in_img_idy = align_corners ? ratio_h * out_img_idy
: ratio_h * (out_img_idy + 0.5) - 0.5;
int input_y = floorf(static_cast<float>(in_img_idy));
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
const T y_t = static_cast<T>(static_cast<MT>(in_img_idy) - input_y);
T in_img_idx = align_corners
? static_cast<T>(ratio_w * out_img_idx)
: static_cast<T>(ratio_w * (out_img_idx + 0.5) - 0.5);
const T y_t = static_cast<T>(in_img_idy - input_y);
MT in_img_idx = align_corners ? ratio_w * out_img_idx
: ratio_w * (out_img_idx + 0.5) - 0.5;
int input_x = floorf(static_cast<float>(in_img_idx));
const T x_t = static_cast<T>(static_cast<MT>(in_img_idx) - input_x);
const T x_t = static_cast<T>(in_img_idx - input_x);
T coefficients[4];
const T* in_pos_0;
......@@ -1460,6 +1463,7 @@ PD_REGISTER_KERNEL(bilinear_interp,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16,
int) {
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
......@@ -1486,6 +1490,7 @@ PD_REGISTER_KERNEL(trilinear_interp,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16,
int) {
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
......@@ -1498,6 +1503,7 @@ PD_REGISTER_KERNEL(linear_interp,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16,
int) {
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
......@@ -1510,6 +1516,7 @@ PD_REGISTER_KERNEL(bicubic_interp,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16,
int) {
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
......
paddle/phi/kernels/onednn/interpolate_kernel.cc
浏览文件 @ 2f2bf4e8
......@@ -232,7 +232,8 @@ PD_REGISTER_KERNEL(bilinear_interp,
ONEDNN,
phi::BilinearInterpKernel,
float,
phi::dtype::bfloat16) {}
phi::dtype::bfloat16,
phi::dtype::float16) {}
PD_REGISTER_KERNEL(nearest_interp,
OneDNN,
......@@ -240,5 +241,6 @@ PD_REGISTER_KERNEL(nearest_interp,
phi::NearestInterpKernel,
float,
phi::dtype::bfloat16,
phi::dtype::float16,
int8_t,
uint8_t) {}
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