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Fix perfornance issue of GroupNorm on CUDA when feature map is small. #46170

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Fix perfornance issue of GroupNorm on CUDA when feature map is small. #46170

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2 changes: 2 additions & 0 deletions aten/src/ATen/cuda/DeviceUtils.cuh
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
#pragma once

#include <cuda.h>
#include <c10/util/complex.h>
#include <c10/util/Half.h>
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36 changes: 18 additions & 18 deletions aten/src/ATen/native/cpu/group_norm_kernel.cpp
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Expand Up @@ -24,9 +24,9 @@ void GroupNormKernelImplInternal(
int64_t HxW,
int64_t group,
T eps,
Tensor* Y,
Tensor* mean,
Tensor* rstd) {
Tensor& Y,
Tensor& mean,
Tensor& rstd) {
TORCH_CHECK(X.numel() == N * C * HxW);
TORCH_CHECK(!gamma.defined() || gamma.numel() == C);
TORCH_CHECK(!beta.defined() || beta.numel() == C);
Expand All @@ -35,9 +35,9 @@ void GroupNormKernelImplInternal(
const T* X_data = X.data_ptr<T>();
const T* gamma_data = gamma.defined() ? gamma.data_ptr<T>() : nullptr;
const T* beta_data = beta.defined() ? beta.data_ptr<T>() : nullptr;
T* Y_data = Y->data_ptr<T>();
T* mean_data = mean->data_ptr<T>();
T* rstd_data = rstd->data_ptr<T>();
T* Y_data = Y.data_ptr<T>();
T* mean_data = mean.data_ptr<T>();
T* rstd_data = rstd.data_ptr<T>();
const T s = T(1) / static_cast<T>(D * HxW);
const bool gamma_null = (gamma_data == nullptr);
const bool beta_null = beta_data == nullptr;
Expand Down Expand Up @@ -94,9 +94,9 @@ void GroupNormKernelImpl(
int64_t HxW,
int64_t group,
double eps,
Tensor* Y,
Tensor* mean,
Tensor* rstd) {
Tensor& Y,
Tensor& mean,
Tensor& rstd) {
AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), "GroupNormKernelImpl", [&]() {
GroupNormKernelImplInternal<scalar_t>(
X,
Expand Down Expand Up @@ -268,9 +268,9 @@ void GroupNormBackwardKernelImplInternal(
int64_t C,
int64_t HxW,
int64_t group,
Tensor* dX,
Tensor* dgamma,
Tensor* dbeta) {
Tensor& dX,
Tensor& dgamma,
Tensor& dbeta) {
TORCH_CHECK(dY.numel() == N * C * HxW);
TORCH_CHECK(X.numel() == N * C * HxW);
TORCH_CHECK(mean.numel() == N * group);
Expand All @@ -282,9 +282,9 @@ void GroupNormBackwardKernelImplInternal(
const T* mean_data = mean.data_ptr<T>();
const T* rstd_data = rstd.data_ptr<T>();
const T* gamma_data = gamma.defined() ? gamma.data_ptr<T>() : nullptr;
T* dX_data = dX->defined() ? dX->data_ptr<T>() : nullptr;
T* dgamma_data = dgamma->defined() ? dgamma->data_ptr<T>() : nullptr;
T* dbeta_data = dbeta->defined() ? dbeta->data_ptr<T>() : nullptr;
T* dX_data = dX.defined() ? dX.data_ptr<T>() : nullptr;
T* dgamma_data = dgamma.defined() ? dgamma.data_ptr<T>() : nullptr;
T* dbeta_data = dbeta.defined() ? dbeta.data_ptr<T>() : nullptr;
Tensor ds = at::empty({N, C}, X.options());
Tensor db = at::empty({N, C}, X.options());
T* ds_data = ds.data_ptr<T>();
Expand Down Expand Up @@ -326,9 +326,9 @@ void GroupNormBackwardKernelImpl(
int64_t C,
int64_t HxW,
int64_t group,
Tensor* dX,
Tensor* dgamma,
Tensor* dbeta) {
Tensor& dX,
Tensor& dgamma,
Tensor& dbeta) {
AT_DISPATCH_FLOATING_TYPES(
X.scalar_type(), "GroupNormBackwardKernelImpl", [&]() {
GroupNormBackwardKernelImplInternal<scalar_t>(
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2 changes: 1 addition & 1 deletion aten/src/ATen/native/cuda/block_reduce.cuh
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
#pragma once

#include <THC/THCDeviceUtils.cuh>
#include <ATen/cuda/DeviceUtils.cuh>

namespace at {
namespace native {
Expand Down