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Normalization.cuh
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Normalization.cuh
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#pragma once
#include <THC/THCDeviceUtils.cuh>
#include <THC/THCGeneral.h>
#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#include <ATen/native/cuda/DeviceSqrt.cuh>
#include <ATen/native/cuda/LaunchUtils.h>
namespace at { namespace native {
#if defined(__HIP_PLATFORM_HCC__)
constexpr int WARP_SIZE = 64;
#else
constexpr int WARP_SIZE = 32;
#endif
// The maximum number of threads in a block
#if defined(__HIP_PLATFORM_HCC__)
constexpr int MAX_BLOCK_SIZE = 256;
#else
constexpr int MAX_BLOCK_SIZE = 512;
#endif
// Number of threads in a block given an input size up to MAX_BLOCK_SIZE
static int getNumThreads(int nElem) {
#if defined(__HIP_PLATFORM_HCC__)
int threadSizes[5] = { 16, 32, 64, 128, MAX_BLOCK_SIZE };
#else
int threadSizes[5] = { 32, 64, 128, 256, MAX_BLOCK_SIZE };
#endif
for (int i = 0; i != 5; ++i) {
if (nElem <= threadSizes[i]) {
return threadSizes[i];
}
}
return MAX_BLOCK_SIZE;
}
// Returns the index of the most significant 1 bit in `val`.
__device__ __forceinline__ int getMSB(int val) {
return 31 - __clz(val);
}
template <typename scalar_t, typename accscalar_t>
struct Float2 {
accscalar_t v1, v2;
__device__ Float2() {}
__device__ Float2(scalar_t v1, scalar_t v2) : v1(static_cast<accscalar_t>(v1)), v2(static_cast<accscalar_t>(v2)) {}
__device__ Float2(int v) : v1(static_cast<accscalar_t>(v)), v2(static_cast<accscalar_t>(v)) {}
__device__ Float2& operator+=(const Float2& a) {
v1 += a.v1;
v2 += a.v2;
return *this;
}
};
template <typename scalar_t, typename accscalar_t, typename PTA>
struct SumOp {
__device__ SumOp(const PTA& t) : tensor(t) {}
__device__ __forceinline__ accscalar_t operator()(int batch, int plane, int n) {
return static_cast<accscalar_t>(tensor[batch][plane][n]);
}
const PTA& tensor;
};
template <typename scalar_t, typename accscalar_t, typename PTA>
struct VarOp {
__device__ VarOp(accscalar_t m, const PTA& t) : mean(m), tensor(t) {}
__device__ __forceinline__ accscalar_t operator()(int batch, int plane, int n) {
accscalar_t val = tensor[batch][plane][n];
return (val - mean) * (val - mean);
}
const accscalar_t mean;
const PTA& tensor;
};
template <typename scalar_t, typename accscalar_t, typename PTA>
struct GradOp {
__device__ GradOp(accscalar_t m, const PTA& i, const PTA& g)
: mean(m), input(i), grad_output(g) {}
__device__ __forceinline__ Float2<scalar_t, accscalar_t> operator()(int batch, int plane, int n) {
accscalar_t g = grad_output[batch][plane][n];
accscalar_t c = static_cast<accscalar_t>(input[batch][plane][n]) - mean;
return Float2<scalar_t, accscalar_t>(g, g * c);
}
const accscalar_t mean;
const PTA& input;
const PTA& grad_output;
};
// Sum across all threads within a warp
template <typename T>
static __device__ __forceinline__ T warpSum(T val) {
for (int i = 0; i < getMSB(WARP_SIZE); ++i) {
val += WARP_SHFL_XOR(val, 1 << i, WARP_SIZE);
}
return val;
}
template <typename scalar_t, typename accscalar_t>
static __device__ __forceinline__ Float2<scalar_t, accscalar_t> warpSum(Float2<scalar_t, accscalar_t> value) {
value.v1 = warpSum(value.v1);
value.v2 = warpSum(value.v2);
return value;
}
// Sum across (batch, x/y/z) applying Op() pointwise
// this works by first having each thread sum it's part
// of the data. Then there is a double-shuffeling reduction.
// First each warp (of WARP_SIZE threads) uses warpSum to reduce its
// data to the "warp leader", who writes its value into shared memory.
// Then a single warp reads the remaining (at most WARP_SIZE) items
// and reduces them using another warpSum.
// The implicit assumption is that there are no more
// than WARP_SIZE**2 threads.
template<typename scalar_t, typename Op, typename PTA>
__device__ scalar_t reduce(Op op, PTA tensor, int plane) {
// first the reductions each thread does separately
scalar_t sum = static_cast<scalar_t>(0);
for (int batch = threadIdx.y; batch < tensor.size(0); batch += blockDim.y) {
for (int x = threadIdx.x; x < tensor.size(2); x += blockDim.x) {
sum += op(batch, plane, x);
}
}
// first warpSum to get one value per thread to
// one value per warp
sum = warpSum(sum);
// this writes each warps item into shared memory
// there are at most WARP_SIZE items left because
// there are at most WARP_SIZE**2 threads at the beginning
__shared__ scalar_t shared[WARP_SIZE];
__syncthreads();
int tid = threadIdx.x + threadIdx.y * blockDim.x;
if (tid % WARP_SIZE == 0) {
shared[tid / WARP_SIZE] = sum;
}
if (tid >= blockDim.x * blockDim.y / WARP_SIZE && tid < WARP_SIZE) {
// zero out the other entries in shared
shared[tid] = (scalar_t)0;
}
__syncthreads();
// now have a second warpSum to reduce the intermediate values
// from shared memory to a single number. The very first
// thread writes it to shared memory.
if (tid / WARP_SIZE == 0) {
sum = warpSum(shared[tid]);
if (tid == 0) {
shared[0] = sum;
}
}
__syncthreads();
// Everyone picks it up, should be broadcast into the whole grad_input
return shared[0];
}
template <typename scalar_t, typename accscalar_t, bool train, typename index_t>
__global__ void batch_norm_transform_input_kernel(
const PackedTensorAccessor<scalar_t, 3, RestrictPtrTraits, index_t> input,
PackedTensorAccessor<scalar_t, 3, RestrictPtrTraits, index_t> output,
const PackedTensorAccessor<typename std::conditional<train, accscalar_t, scalar_t>::type, 1, RestrictPtrTraits, index_t> mean_,
const PackedTensorAccessor<typename std::conditional<train, accscalar_t, scalar_t>::type, 1, RestrictPtrTraits, index_t> var_or_invstd,
const PackedTensorAccessor<scalar_t, 1, RestrictPtrTraits, index_t> weight,
const PackedTensorAccessor<scalar_t, 1, RestrictPtrTraits, index_t> bias,
accscalar_t epsilon) {
index_t plane = blockIdx.x;
if (plane >= input.size(1)) {
return;
}
accscalar_t gamma = weight.size(0) > 0 ? static_cast<accscalar_t>(weight[plane]) : static_cast<accscalar_t>(1);
accscalar_t beta = bias.size(0) > 0 ? static_cast<accscalar_t>(bias[plane]) : static_cast<accscalar_t>(0);
accscalar_t mean = static_cast<accscalar_t>(mean_[plane]);
accscalar_t invstd;
if (train) {
invstd = var_or_invstd[plane];
} else {
invstd = static_cast<accscalar_t>(1) / device_sqrt(static_cast<accscalar_t>(var_or_invstd[plane]) + epsilon);
}
index_t bs = input.size(0);
index_t fs = input.size(2);
index_t bstep = blockDim.y * gridDim.y;
for (index_t batch = threadIdx.y + blockIdx.y * blockDim.y; batch < bs; batch += bstep) {
auto o = output[batch][plane];
auto i = input[batch][plane];
for (index_t feature = threadIdx.x; feature < fs; feature += blockDim.x) {
o[feature] = static_cast<scalar_t>(gamma * (i[feature] - mean) * invstd + beta);
}
}
}
template<typename T>
struct InvStd {
__device__ __forceinline__ T operator()(T var, double epsilon) const {
T invstd = 0;
if (var != static_cast<T>(0) || epsilon != static_cast<T>(0)) {
invstd = static_cast<T>(1) / device_sqrt(var + epsilon);
}
return invstd;
}
};
template<typename T>
struct Var {
__device__ __forceinline__ T operator()(T var, double epsilon) const {
return var;
}
};
template <template<typename T> class VarTransform, typename input_scalar_t, typename stat_scalar_t, typename stat_accscalar_t, typename index_t>
__global__ void batch_norm_collect_statistics_kernel(
const PackedTensorAccessor<input_scalar_t, 3, RestrictPtrTraits, index_t> input,
const stat_accscalar_t epsilon,
const stat_accscalar_t momentum,
PackedTensorAccessor<stat_scalar_t, 1, RestrictPtrTraits, index_t> running_mean,
PackedTensorAccessor<stat_scalar_t, 1, RestrictPtrTraits, index_t> running_var,
PackedTensorAccessor<stat_accscalar_t, 1, RestrictPtrTraits, index_t> save_mean,
PackedTensorAccessor<stat_accscalar_t, 1, RestrictPtrTraits, index_t> save_transformed_var) {
__shared__ int shared_n[2 * 2 * WARP_SIZE + WARP_SIZE];
int plane = blockIdx.x;
int N = input.size(0) * input.size(2);
int tid = threadIdx.x + threadIdx.y * blockDim.x;
// Compute the mean and variance across (batch, x/y/z)
// this uses the Welford (in the for loop)/parallel algorithm (to sum across the block)
// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm
// and the parallel algorithm on the same page.
// We use two shuffles to reduce across the entire block.
// https://devblogs.nvidia.com/faster-parallel-reductions-kepler/ has a description.
stat_accscalar_t* shared_avg_var = (stat_accscalar_t*) &shared_n[WARP_SIZE];
// first the reductions each thread does separately
stat_accscalar_t avg = 0;
stat_accscalar_t var_n = 0;
int n = 0;
for (int batch = threadIdx.y; batch < input.size(0); batch += blockDim.y) {
for (int x = threadIdx.x; x < input.size(2); x += blockDim.x) {
stat_accscalar_t v = input[batch][plane][x];
stat_accscalar_t d1 = v - avg;
n++;
avg += d1 / n;
var_n += d1 * (v - avg);
}
}
// first warpSum to get one value per thread to
// one value per warp
for (int i = 0; i < getMSB(WARP_SIZE); ++i) {
stat_accscalar_t o_avg = WARP_SHFL_XOR(avg, 1 << i, WARP_SIZE);
int o_n = WARP_SHFL_XOR(n, 1 << i, WARP_SIZE);
stat_accscalar_t factor = 1.0 / fmaxf(1.0, n+o_n);
var_n += WARP_SHFL_XOR(var_n, 1 << i, WARP_SIZE) + (avg - o_avg) * (avg - o_avg) * n * o_n * factor;
avg = (n * avg + o_n * o_avg) * factor;
n += o_n;
}
// this writes each warps item into shared memory
// there are at most WARP_SIZE items left because
// there are at most WARP_SIZE**2 threads at the beginning
__syncthreads();
if (tid % WARP_SIZE == 0) {
shared_n[tid / WARP_SIZE] = n;
shared_avg_var[tid / WARP_SIZE * 2] = avg;
shared_avg_var[tid / WARP_SIZE * 2 + 1] = var_n;
}
__syncthreads();
// now have a second warpSum to reduce the intermediate values
// from shared memory to a single number. The very first
// thread writes it to shared memory.
if (tid < WARP_SIZE) {
n = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_n[tid] : 0);
avg = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_avg_var[2 * tid] : stat_accscalar_t(0));
var_n = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_avg_var[2 * tid + 1] : stat_accscalar_t(0));
}
for (int i = 0; i < getMSB(WARP_SIZE); ++i) {
stat_accscalar_t o_avg = WARP_SHFL_XOR(avg, 1 << i, WARP_SIZE);
int o_n = WARP_SHFL_XOR(n, 1 << i, WARP_SIZE);
stat_accscalar_t factor = 1.0 / fmaxf(1.0, n+o_n);
var_n += WARP_SHFL_XOR(var_n, 1 << i, WARP_SIZE) + (avg - o_avg) * (avg - o_avg) * n * o_n * factor;
avg = (n * avg + o_n * o_avg) * factor;
n += o_n;
}
// Save the mean, variance, and moving averages
if (tid == 0) {
if (save_mean.data() != NULL) {
save_mean[plane] = avg;
}
if (save_transformed_var.data() != NULL) {
save_transformed_var[plane] = VarTransform<stat_accscalar_t>{}(var_n / N, epsilon);
}
if (running_mean.data() != NULL) {
running_mean[plane] = static_cast<stat_scalar_t>((1 - momentum) * running_mean[plane] + momentum * avg);
}
if (running_var.data() != NULL) {
stat_accscalar_t unbiasedVar = var_n / (N - 1);
running_var[plane] = static_cast<stat_scalar_t>((1 - momentum) * running_var[plane] + momentum * unbiasedVar);
}
}
}
template <typename scalar_t, typename accscalar_t, typename index_t>
__global__ void batch_norm_backward_kernel(
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> input,
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> grad_output,
PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> grad_input,
PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> grad_weight,
PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> grad_bias,
const PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> weight,
const PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> running_mean,
const PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> running_var,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> save_mean,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> save_invstd,
bool train,
accscalar_t epsilon) {
index_t plane = blockIdx.x;
index_t N = grad_output.size(0) * grad_output.size(2);
accscalar_t mean, invstd;
if (train) {
mean = save_mean[plane];
invstd = save_invstd[plane];
} else {
mean = static_cast<accscalar_t>(running_mean[plane]);
invstd = static_cast<accscalar_t>(1) / device_sqrt(static_cast<accscalar_t>(running_var[plane]) + epsilon);
}
accscalar_t weight_val = weight.size(0) > 0 ? static_cast<accscalar_t>(weight[plane]) : accscalar_t(1);
accscalar_t norm = accscalar_t(1) / N;
// Compute two values across (batch, x/y/z) in one pass:
// 1. Sum(grad_output)
// 2. DotProduct(input - mean, grad_output)
GradOp<scalar_t, accscalar_t, PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t>> g(mean, input, grad_output);
Float2<scalar_t, accscalar_t> res = reduce<Float2<scalar_t, accscalar_t>, GradOp<scalar_t, accscalar_t,
PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t>>>(g, grad_output, plane);
accscalar_t grad_output_sum = res.v1;
accscalar_t dot_p = res.v2;
accscalar_t grad_mean = grad_output_sum * norm;
accscalar_t proj_scale = dot_p * norm * invstd * invstd;
accscalar_t grad_scale = invstd * weight_val;
if (grad_input.data() != NULL) {
for (int batch = threadIdx.y; batch < grad_output.size(0); batch += blockDim.y) {
for (int x = threadIdx.x; x < grad_output.size(2); x += blockDim.x) {
scalar_t go = grad_output[batch][plane][x];
if (train) {
scalar_t inp = input[batch][plane][x];
accscalar_t proj = (inp - mean) * proj_scale;
grad_input[batch][plane][x] = static_cast<scalar_t>((go - proj - grad_mean) * grad_scale);
} else {
grad_input[batch][plane][x] = static_cast<scalar_t>(go * grad_scale);
}
}
}
}
if (grad_weight.size(0) > 0) {
if (threadIdx.x == 0) {
grad_weight[plane] = static_cast<scalar_t>(dot_p * invstd);
}
}
if (grad_bias.size(0) > 0) {
if (threadIdx.x == 0) {
grad_bias[plane] = static_cast<scalar_t>(grad_output_sum);
}
}
}
template <typename scalar_t, typename accscalar_t, typename index_t>
__global__ void batch_norm_reduce_statistics_kernel(
const PackedTensorAccessor<accscalar_t, 2, RestrictPtrTraits, index_t> vec_mean,
const PackedTensorAccessor<accscalar_t, 2, RestrictPtrTraits, index_t> vec_invstd,
PackedTensorAccessor<accscalar_t, 1, RestrictPtrTraits, index_t> mean,
PackedTensorAccessor<accscalar_t, 1, RestrictPtrTraits, index_t> invstd,
PackedTensorAccessor<scalar_t, 1, RestrictPtrTraits, index_t> running_mean,
PackedTensorAccessor<scalar_t, 1, RestrictPtrTraits, index_t> running_var,
const accscalar_t epsilon,
const accscalar_t momentum,
const PackedTensorAccessor<scalar_t, 1, RestrictPtrTraits, index_t> counts) {
int feature_size = vec_mean.size(1);
int world_size = vec_mean.size(0);
int bid = blockIdx.x;
int tid = threadIdx.x;
// first the reductions each thread does separately
for (int i = bid*blockDim.x+tid; i < feature_size; i += gridDim.x*blockDim.x) {
accscalar_t avg = 0;
accscalar_t var_n = 0;
index_t n = 0;
for (int j = 0; j < world_size; j++) {
scalar_t count = counts[j];
accscalar_t m = vec_mean[j][i];
accscalar_t v = accscalar_t(1.0) / (vec_invstd[j][i]);
v = (v * v - epsilon) * count;
accscalar_t factor = 1.0 / (n + count);
var_n += v + (avg - m) * (avg - m) * n * count * factor;
avg = n * factor * avg + count * factor * m;
n += count;
}
mean[i] = avg;
invstd[i] = static_cast<accscalar_t>(1) / device_sqrt(var_n / n + epsilon);
if (running_mean.data() != NULL) {
running_mean[i] = static_cast<scalar_t>((1 - momentum) * running_mean[i] + momentum * avg);
}
accscalar_t unbiasedVar = var_n / (n - 1);
if (running_var.data() != NULL) {
running_var[i] = static_cast<scalar_t>((1 - momentum) * running_var[i] + momentum * unbiasedVar);
}
}
}
template <typename scalar_t, typename accscalar_t, typename index_t>
__global__ void batch_norm_backward_reduce_kernel(
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> input,
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> grad_output,
PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean,
PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> invstd,
PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean_dy,
PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean_dy_xmu,
PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> grad_weight,
PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> grad_bias) {
index_t plane = blockIdx.x;
index_t N = input.size(0) * input.size(2);
accscalar_t r_mean = mean[plane];
accscalar_t factor = invstd[plane];
GradOp<scalar_t, accscalar_t, PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t>> g(r_mean, input, grad_output);
Float2<scalar_t, accscalar_t> res = reduce<Float2<scalar_t, accscalar_t>, GradOp<scalar_t, accscalar_t,
PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t>>>(g, grad_output, plane);
accscalar_t norm = accscalar_t(1) / N;
if (threadIdx.x == 0) {
if (grad_weight.size(0) > 0) {
grad_weight[plane] = static_cast<scalar_t>(res.v2 * factor);
}
if (grad_bias.size(0) > 0) {
grad_bias[plane] = static_cast<scalar_t>(res.v1);
}
if (mean_dy.size(0) > 0) {
mean_dy[plane] = static_cast<accscalar_t>(res.v1 * norm);
}
if (mean_dy_xmu.size(0) > 0) {
mean_dy_xmu[plane] = static_cast<accscalar_t>(res.v2 * norm);
}
}
}
template <typename scalar_t, typename accscalar_t, typename index_t>
__global__ void batch_norm_backward_elemt_kernel(
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> input,
const PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> grad_output,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> invstd,
const PackedTensorAccessor<scalar_t, 1, DefaultPtrTraits, index_t> weight,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean_dy,
const PackedTensorAccessor<accscalar_t, 1, DefaultPtrTraits, index_t> mean_dy_xmu,
PackedTensorAccessor<scalar_t, 3, DefaultPtrTraits, index_t> grad_input) {
index_t plane = blockIdx.x;
if (plane >= input.size(1)) {
return;
}
accscalar_t m_c = mean[plane];
accscalar_t m_dy_c = mean_dy[plane];
accscalar_t factor_1_c = invstd[plane];
accscalar_t factor_2_c = weight.size(0) > 0 ? static_cast<accscalar_t>(weight[plane]) : static_cast<accscalar_t>(1);
factor_2_c *= factor_1_c;
factor_1_c = factor_1_c * factor_1_c * mean_dy_xmu[plane];
index_t bs = input.size(0);
index_t fs = input.size(2);
index_t bstep = blockDim.y * gridDim.y;
for (index_t batch = threadIdx.y + blockIdx.y * blockDim.y; batch < bs; batch += bstep) {
auto g_i = grad_input[batch][plane];
auto g_o = grad_output[batch][plane];
auto i = input[batch][plane];
for (index_t feature = threadIdx.x; feature < fs; feature += blockDim.x) {
g_i[feature] = static_cast<scalar_t>((g_o[feature] - m_dy_c - (i[feature] - m_c) * factor_1_c) * factor_2_c);
}
}
}
template <typename scalar_t, int64_t dim, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
static PackedTensorAccessor<scalar_t, dim, PtrTraits, index_t> packed_accessor_or_dummy(const Tensor& t) {
if (! t.defined()) {
const std::vector<index_t> zeros(dim);
return PackedTensorAccessor<scalar_t, dim, PtrTraits, index_t>(nullptr, zeros.data(), zeros.data());
}
return t.packed_accessor<scalar_t, dim, PtrTraits, index_t>();
}
template<typename scalar_t, typename index_t>
std::tuple<Tensor, Tensor, Tensor> batch_norm_cuda_template(const Tensor& input_, const Tensor& weight_, const Tensor& bias_,
const Tensor& running_mean_, const Tensor& running_var_,
bool train, double momentum, double epsilon) {
TensorArg input_arg{ input_, "input", 1 },
weight_arg{ weight_, "weight", 2 },
bias_arg{ bias_, "bias", 3 },
run_mean_arg{ running_mean_, "running_mean", 4 },
run_var_arg{ running_var_, "running_var", 5 };
CheckedFrom c = "batch_norm_cuda";
checkAllSameGPU(c, {input_arg, weight_arg, bias_arg, run_mean_arg, run_var_arg});
using accscalar_t = at::acc_type<scalar_t, true>;
int64_t n_input = input_.size(1);
Tensor save_mean_;
Tensor save_invstd_;
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto output_reshaped = at::empty_like(input_reshaped);
auto bs = input_reshaped.size(0);
auto features = input_reshaped.size(2);
auto input = input_reshaped.packed_accessor<scalar_t, 3, RestrictPtrTraits, index_t>();
auto input_options = input_.options();
if (input_.scalar_type() == at::ScalarType::Half) {
input_options = input_options.dtype(ScalarType::Float);
}
if (train) {
save_mean_ = at::empty({n_input}, input_options);
save_invstd_ = at::empty({n_input}, input_options);
} else {
save_mean_ = at::empty({0}, input_options);
save_invstd_ = at::empty({0}, input_options);
}
auto output = output_reshaped.packed_accessor<scalar_t, 3, RestrictPtrTraits, index_t>();
auto weight = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(weight_);
auto bias = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(bias_);
auto running_mean = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(running_mean_);
auto running_var = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(running_var_);
auto save_mean = save_mean_.packed_accessor<accscalar_t, 1, RestrictPtrTraits, index_t>();
auto save_invstd = save_invstd_.packed_accessor<accscalar_t, 1, RestrictPtrTraits, index_t>();
auto stream = at::cuda::getCurrentCUDAStream();
// The input_transform kernel is pointwise, but we need to balance reading parameters (save_var/mean,
// weight/bias) - which we only do once and have a for loop afterwards - with having many threads and blocks
// and good occupancy. Quite likely, we could go with even more blocks than 1024.
// The various planes are independent, so we use blocks for them.
int tf = std::max<int>(getNumThreads(input.size(2)/4),
std::min<int>(getNumThreads(input.size(2)), 64));
int tb = std::max<int>(64/tf, 1);
dim3 blocks_trans(input.size(1), std::max<int>(1, std::min<int>((256*1024)/input.size(1),
(input.size(0)+tb-1)/tb)));
blocks_trans.y = std::min<int>(blocks_trans.y, 65535);
dim3 threads_trans(tf, tb);
if (!train) {
batch_norm_transform_input_kernel<scalar_t, accscalar_t, false, index_t> <<<blocks_trans, threads_trans, 0, stream>>>
(input, output, running_mean, running_var, weight, bias, epsilon);
} else {
// for the reduction, we cannot use blocks for the batch dim, but if we have few threads in
// the feature dimension, we'll use some threads for blocks
dim3 blocks(input.size(1));
tf = getNumThreads(input.size(2));
dim3 threads(tf, std::max<int>(1, MAX_BLOCK_SIZE/tf));
batch_norm_collect_statistics_kernel<InvStd, scalar_t, scalar_t, accscalar_t, index_t> <<<blocks, threads, 0, stream>>>
(input, epsilon, momentum, running_mean, running_var, save_mean, save_invstd);
batch_norm_transform_input_kernel<scalar_t, accscalar_t, true, index_t> <<<blocks_trans, threads_trans, 0, stream>>>
(input, output, save_mean, save_invstd, weight, bias, epsilon);
}
THCudaCheck(cudaGetLastError());
return std::make_tuple(output_reshaped.view(input_.sizes()), save_mean_, save_invstd_);
}
template<typename scalar_t, typename index_t>
std::tuple<Tensor, Tensor, Tensor> batch_norm_backward_cuda_template(const Tensor& grad_out_, const Tensor& input_, const Tensor& weight_,
const Tensor& running_mean_, const Tensor& running_var_, const Tensor& save_mean_, const Tensor& save_invstd_,
bool train, double epsilon, std::array<bool,3> grad_input_mask) {
using accscalar_t = at::acc_type<scalar_t, true>;
Tensor grad_input_;
Tensor grad_input_reshaped;
Tensor grad_weight_;
Tensor grad_bias_;
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1});
auto grad_output_reshaped = grad_out_.reshape(input_reshaped.sizes());
if (grad_input_mask[0]) {
grad_input_ = at::empty_like(input_);
grad_input_reshaped = grad_input_.view(input_reshaped.sizes());
}
if (grad_input_mask[1]) {
grad_weight_ = at::empty_like(weight_);
}
if (grad_input_mask[2]) {
grad_bias_ = at::empty_like(weight_);
}
auto input = input_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_output = grad_output_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_input = packed_accessor_or_dummy<scalar_t, 3, DefaultPtrTraits, index_t>(grad_input_reshaped);
auto weight = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(weight_);
auto grad_weight = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(grad_weight_);
auto grad_bias = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(grad_bias_);
auto running_mean = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(running_mean_);
auto running_var = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(running_var_);
auto save_mean = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(save_mean_);
auto save_invstd = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(save_invstd_);
auto stream = at::cuda::getCurrentCUDAStream();
dim3 blocks(input.size(1));
int tf = getNumThreads(input.size(2));
dim3 threads(tf, std::max<int>(1, MAX_BLOCK_SIZE/tf));
batch_norm_backward_kernel<scalar_t, accscalar_t, index_t> <<<blocks, threads, 0, stream>>>
(input, grad_output, grad_input, grad_weight, grad_bias, weight, running_mean, running_var,
save_mean, save_invstd, train, epsilon);
THCudaCheck(cudaGetLastError());
return std::make_tuple(grad_input_, grad_weight_, grad_bias_);
}
template<typename scalar_t, typename index_t>
std::tuple<Tensor, Tensor> batch_norm_stats_cuda_template(const Tensor& input_, double epsilon) {
using accscalar_t = at::acc_type<scalar_t, true>;
int64_t n_input = input_.size(1);
Tensor dummy_mean_;
Tensor dummy_var_;
Tensor mean_;
Tensor invstd_;
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto bs = input_reshaped.size(0);
auto features = input_reshaped.size(2);
auto input = input_reshaped.packed_accessor<scalar_t, 3, RestrictPtrTraits, index_t>();
auto input_options = input_.options();
dummy_mean_ = at::empty({0}, input_options);
dummy_var_ = at::empty({0}, input_options);
// promote only mean_/invstd_ precision
if (input_.scalar_type() == at::ScalarType::Half) {
input_options = input_options.dtype(ScalarType::Float);
}
mean_ = at::empty({n_input}, input_options);
invstd_ = at::empty({n_input}, input_options);
auto mean = packed_accessor_or_dummy<accscalar_t, 1, RestrictPtrTraits, index_t>(mean_);
auto invstd = packed_accessor_or_dummy<accscalar_t, 1, RestrictPtrTraits, index_t>(invstd_);
auto dummy_mean = dummy_mean_.packed_accessor<scalar_t, 1, RestrictPtrTraits, index_t>();
auto dummy_invstd = dummy_var_.packed_accessor<scalar_t, 1, RestrictPtrTraits, index_t>();
auto stream = at::cuda::getCurrentCUDAStream();
dim3 blocks(input.size(1));
int tf = getNumThreads(input.size(2));
dim3 threads(tf, std::max<int>(1, MAX_BLOCK_SIZE/tf));
batch_norm_collect_statistics_kernel<InvStd, scalar_t, scalar_t, accscalar_t, index_t> <<<blocks, threads, 0, stream>>>
(input, epsilon, 0.0, dummy_mean, dummy_invstd, mean, invstd);
THCudaCheck(cudaGetLastError());
return std::make_tuple(mean_, invstd_);
}
template<typename scalar_t, typename index_t>
Tensor batch_norm_elemt_cuda_template(const Tensor& input_, const Tensor& weight_, const Tensor& bias_,
const Tensor& mean_, const Tensor& invstd_,
double epsilon) {
using accscalar_t = at::acc_type<scalar_t, true>;
int64_t n_input = input_.size(1);
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto output_reshaped = at::empty_like(input_reshaped);
auto bs = input_reshaped.size(0);
auto features = input_reshaped.size(2);
auto input = input_reshaped.packed_accessor<scalar_t, 3, RestrictPtrTraits, index_t>();
auto input_options = input_.options();
if (input_.scalar_type() == at::ScalarType::Half) {
input_options = input_options.dtype(ScalarType::Float);
}
auto output = output_reshaped.packed_accessor<scalar_t, 3, RestrictPtrTraits, index_t>();
auto weight = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(weight_);
auto bias = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(bias_);
auto mean = packed_accessor_or_dummy<accscalar_t, 1, RestrictPtrTraits, index_t>(mean_);
auto invstd = packed_accessor_or_dummy<accscalar_t, 1, RestrictPtrTraits, index_t>(invstd_);
auto stream = at::cuda::getCurrentCUDAStream();
// The input_transform kernel is pointwise, but we need to balance reading parameters (save_var/mean,
// weight/bias) - which we only do once and have a for loop afterwards - with having many threads and blocks
// and good occupancy. Quiet likely, we could go with even more blocks than 1024.
// The various planes are independent, so we use blocks for them.
int tf = std::max<int>(getNumThreads(input.size(2)/4),
std::min<int>(getNumThreads(input.size(2)), 64));
int tb = std::max<int>(64/tf, 1);
dim3 blocks_trans(input.size(1), std::max<int>(1, std::min<int>((256*1024)/input.size(1),
(input.size(0)+tb-1)/tb)));
dim3 threads_trans(tf, tb);
batch_norm_transform_input_kernel<scalar_t, accscalar_t, true, index_t> <<<blocks_trans, threads_trans, 0, stream>>>
(input, output, mean, invstd, weight, bias, epsilon);
THCudaCheck(cudaGetLastError());
return output_reshaped.view(input_.sizes());
}
template<typename scalar_t, typename accscalar_t, typename index_t>
std::tuple<Tensor, Tensor> batch_norm_gather_stats_cuda_template(const Tensor& mean_, const Tensor& invstd_,
const Tensor& running_mean_, const Tensor& running_var_,
double momentum, double epsilon, const Tensor& counts_) {
Tensor save_mean_;
Tensor save_invstd_;
auto features = mean_.size(1);
auto input_options = mean_.options();
if (mean_.scalar_type() == at::ScalarType::Half) {
input_options = input_options.dtype(ScalarType::Float);
}
save_mean_ = at::empty({features}, input_options);
save_invstd_ = at::empty({features}, input_options);
auto mean = packed_accessor_or_dummy<accscalar_t, 2, RestrictPtrTraits, index_t>(mean_);
auto invstd = packed_accessor_or_dummy<accscalar_t, 2, RestrictPtrTraits, index_t>(invstd_);
auto running_mean = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(running_mean_);
auto running_var = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(running_var_);
auto counts = packed_accessor_or_dummy<scalar_t, 1, RestrictPtrTraits, index_t>(counts_);
auto save_mean = save_mean_.packed_accessor<accscalar_t, 1, RestrictPtrTraits, index_t>();
auto save_invstd = save_invstd_.packed_accessor<accscalar_t, 1, RestrictPtrTraits, index_t>();
auto stream = at::cuda::getCurrentCUDAStream();
int block = getNumThreads(features);
int grid = std::max<int>(1, features/block);
batch_norm_reduce_statistics_kernel<scalar_t, accscalar_t, index_t> <<<grid, block, 0, stream>>>
(mean, invstd, save_mean, save_invstd, running_mean, running_var, epsilon, momentum, counts);
THCudaCheck(cudaGetLastError());
return std::make_tuple(save_mean_, save_invstd_);
}
template<typename scalar_t, typename index_t>
std::tuple<Tensor, Tensor, Tensor, Tensor> batch_norm_backward_reduce_cuda_template(const Tensor& grad_out_, const Tensor& input_,
const Tensor& mean_, const Tensor& invstd_,
const bool input_g, const bool weight_g, const bool bias_g) {
using accscalar_t = at::acc_type<scalar_t, true>;
int64_t n_input = input_.size(1);
Tensor mean_dy_;
Tensor mean_dy_xmu_;
Tensor grad_weight_;
Tensor grad_bias_;
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto grad_output_reshaped = grad_out_.reshape(input_reshaped.sizes());
if (input_g) {
mean_dy_ = at::empty_like(mean_);
mean_dy_xmu_ = at::empty_like(mean_);
}
auto grad_options = grad_out_.options();
if (weight_g) {
grad_weight_ = at::empty({n_input}, grad_options);
}
if (bias_g) {
grad_bias_ = at::empty({n_input}, grad_options);
}
auto input = input_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_output = grad_output_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_weight = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(grad_weight_);
auto grad_bias = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(grad_bias_);
auto mean = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_);
auto invstd = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(invstd_);
auto mean_dy = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_dy_);
auto mean_dy_xmu = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_dy_xmu_);
auto batch_size = input_reshaped.size(0);
auto feature_size = input_reshaped.size(2);
auto stream = at::cuda::getCurrentCUDAStream();
int block_y = std::min<int>(lastPow2(batch_size), MAX_BLOCK_SIZE/32);
int block_x = std::min<int>(getNumThreads(feature_size), MAX_BLOCK_SIZE/block_y);
const dim3 block(block_x, block_y);
const dim3 grid(n_input);
batch_norm_backward_reduce_kernel<scalar_t, accscalar_t, index_t> <<<grid, block, 0, stream>>>
(input, grad_output, mean, invstd, mean_dy, mean_dy_xmu, grad_weight, grad_bias);
THCudaCheck(cudaGetLastError());
return std::make_tuple(mean_dy_, mean_dy_xmu_, grad_weight_, grad_bias_);
}
template<typename scalar_t, typename index_t>
Tensor batch_norm_backward_elemt_cuda_template(const Tensor& grad_out_, const Tensor& input_,
const Tensor& mean_, const Tensor& invstd_,
const Tensor& weight_, const Tensor& mean_dy_, const Tensor& mean_dy_xmu_) {
using accscalar_t = at::acc_type<scalar_t, true>;
int64_t n_input = input_.size(1);
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto grad_output_reshaped = grad_out_.reshape(input_reshaped.sizes());
auto grad_input_reshaped = at::empty_like(input_reshaped);
auto bs = input_reshaped.size(0);
auto features = input_reshaped.size(2);
auto input = input_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_input = grad_input_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto grad_output = grad_output_reshaped.packed_accessor<scalar_t, 3, DefaultPtrTraits, index_t>();
auto mean = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_);
auto invstd = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(invstd_);
auto weight = packed_accessor_or_dummy<scalar_t, 1, DefaultPtrTraits, index_t>(weight_);
auto mean_dy = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_dy_);
auto mean_dy_xmu = packed_accessor_or_dummy<accscalar_t, 1, DefaultPtrTraits, index_t>(mean_dy_xmu_);
auto stream = at::cuda::getCurrentCUDAStream();
// The kernel is pointwise, but we need to balance reading parameters (save_var/mean,
// weight/bias) - which we only do once and have a for loop afterwards - with having many threads and blocks
// and good occupancy. Quiet likely, we could go with even more blocks than 1024.
// The various planes are independent, so we use blocks for them.
int tf = std::max<int>(getNumThreads(input.size(2)/4),
std::min<int>(getNumThreads(input.size(2)), 64));
int tb = std::max<int>(64/tf, 1);
dim3 blocks_trans(input.size(1), std::max<int>(1, std::min<int>((256*1024)/input.size(1),
(input.size(0)+tb-1)/tb)));
dim3 threads_trans(tf, tb);
batch_norm_backward_elemt_kernel<scalar_t, accscalar_t, index_t> <<<blocks_trans, threads_trans, 0, stream>>>
(input, grad_output, mean, invstd, weight, mean_dy, mean_dy_xmu, grad_input);
THCudaCheck(cudaGetLastError());
return grad_input_reshaped.view(input_.sizes());
}
template<typename input_scalar_t, typename stat_scalar_t, typename index_t>
std::tuple<Tensor, Tensor> batch_norm_update_stats_cuda_template(
const Tensor& input_, const Tensor& running_mean_, const Tensor& running_var_, double momentum) {
using stat_accscalar_t = at::acc_type<stat_scalar_t, true>;
int64_t n_channels = input_.size(1);
auto input_reshaped = input_.reshape({input_.size(0), input_.size(1), -1}); // internally we merge the feature dimensions
auto input_options = input_.options();
if (input_.scalar_type() == at::ScalarType::Half) {
input_options = input_options.dtype(ScalarType::Float);
}
Tensor save_mean_ = at::empty({n_channels}, input_options);
Tensor save_var_ = at::empty({n_channels}, input_options);
auto input = input_reshaped.packed_accessor<input_scalar_t, 3, RestrictPtrTraits, index_t>();
auto running_mean = packed_accessor_or_dummy<stat_scalar_t, 1, RestrictPtrTraits, index_t>(running_mean_);
auto running_var = packed_accessor_or_dummy<stat_scalar_t, 1, RestrictPtrTraits, index_t>(running_var_);
auto save_mean = save_mean_.packed_accessor<stat_accscalar_t, 1, RestrictPtrTraits, index_t>();
auto save_var = save_var_.packed_accessor<stat_accscalar_t, 1, RestrictPtrTraits, index_t>();
auto stream = at::cuda::getCurrentCUDAStream();
// for the reduction, we cannot use blocks for the batch dim, but if we have few threads in
// the feature dimension, we'll use some threads for blocks
dim3 blocks(input.size(1));
int tf = getNumThreads(input.size(2));
dim3 threads(tf, std::max<int>(1, MAX_BLOCK_SIZE/tf));
// NB: epsilon is unused by the Var transform, so we set it to 0
batch_norm_collect_statistics_kernel<Var, input_scalar_t, stat_scalar_t, stat_accscalar_t, index_t> <<<blocks, threads, 0, stream>>>
(input, 0., momentum, running_mean, running_var, save_mean, save_var);
THCudaCheck(cudaGetLastError());
return std::make_tuple(save_mean_, save_var_);
}
} } // namespace at::native