From 064d4462d3890fd5dda5c7845119e1a3854d477b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 26 Nov 2015 21:45:12 +0000
Subject: [PATCH] stuff
---
src/blas_kernels.cu | 271 +++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 267 insertions(+), 4 deletions(-)
diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu
index 636a9b5..4da31d1 100644
--- a/src/blas_kernels.cu
+++ b/src/blas_kernels.cu
@@ -1,6 +1,186 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
extern "C" {
#include "blas.h"
#include "cuda.h"
+#include "utils.h"
+}
+
+__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+ int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (index >= N) return;
+ int f = (index/spatial)%filters;
+
+ x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .00001f);
+}
+
+__global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
+{
+ int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (index >= N) return;
+ int f = (index/spatial)%filters;
+
+ delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
+}
+
+extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
+{
+ size_t N = batch*filters*spatial;
+ normalize_delta_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ variance_delta[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ variance_delta[i] += delta[index]*(x[index] - mean[i]);
+ }
+ }
+ variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
+}
+
+__global__ void spatial_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *spatial_variance_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= batch*filters) return;
+ int f = i%filters;
+ int b = i/filters;
+
+ int k;
+ spatial_variance_delta[i] = 0;
+ for (k = 0; k < spatial; ++k) {
+ int index = b*filters*spatial + f*spatial + k;
+ spatial_variance_delta[i] += delta[index]*(x[index] - mean[f]);
+ }
+ spatial_variance_delta[i] *= -.5 * pow(variance[f] + .00001f, (float)(-3./2.));
+}
+
+extern "C" void variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ variance_delta_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
+{
+ int k;
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= groups) return;
+ sum[i] = 0;
+ for(k = 0; k < n; ++k){
+ sum[i] += x[k*groups + i];
+ }
+}
+
+extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *spatial_variance_delta, float *variance_delta)
+{
+ spatial_variance_delta_kernel<<<cuda_gridsize(filters*batch), BLOCK>>>(x, delta, mean, variance, batch, filters, spatial, spatial_variance_delta);
+ check_error(cudaPeekAtLastError());
+ accumulate_kernel<<<cuda_gridsize(filters), BLOCK>>>(spatial_variance_delta, batch, filters, variance_delta);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void spatial_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *spatial_mean_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= batch*filters) return;
+ int f = i%filters;
+ int b = i/filters;
+
+ int k;
+ spatial_mean_delta[i] = 0;
+ for (k = 0; k < spatial; ++k) {
+ int index = b*filters*spatial + f*spatial + k;
+ spatial_mean_delta[i] += delta[index];
+ }
+ spatial_mean_delta[i] *= (-1./sqrt(variance[f] + .00001f));
+}
+
+extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *spatial_mean_delta, float *mean_delta)
+{
+ spatial_mean_delta_kernel<<<cuda_gridsize(filters*batch), BLOCK>>>(delta, variance, batch, filters, spatial, spatial_mean_delta);
+ check_error(cudaPeekAtLastError());
+ accumulate_kernel<<<cuda_gridsize(filters), BLOCK>>>(spatial_mean_delta, batch, filters, mean_delta);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ mean_delta[i] = 0;
+ for (j = 0; j < batch; ++j) {
+ for (k = 0; k < spatial; ++k) {
+ int index = j*filters*spatial + i*spatial + k;
+ mean_delta[i] += delta[index];
+ }
+ }
+ mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
+}
+
+extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ mean_delta_kernel<<<cuda_gridsize(filters), BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
+{
+ float scale = 1./(batch * spatial);
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ mean[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ mean[i] += x[index];
+ }
+ }
+ mean[i] *= scale;
+}
+
+__global__ void spatial_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ float scale = 1./(spatial*batch-1);
+ int k;
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= batch*filters) return;
+ int f = i%filters;
+ int b = i/filters;
+
+ variance[i] = 0;
+ for(k = 0; k < spatial; ++k){
+ int index = b*filters*spatial + f*spatial + k;
+ variance[i] += pow((x[index] - mean[f]), 2);
+ }
+ variance[i] *= scale;
+}
+
+__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ float scale = 1./(batch * spatial);
+ int j,k;
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ variance[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ variance[i] += pow((x[index] - mean[i]), 2);
+ }
+ }
+ variance[i] *= scale;
}
__global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY)
@@ -9,16 +189,34 @@
if(i < N) Y[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
}
+__global__ void pow_kernel(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) Y[i*INCY] = pow(X[i*INCX], ALPHA);
+}
+
+__global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) X[i*INCX] = ALPHA;
+}
+
__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < N) X[i*INCX] *= ALPHA;
}
-__global__ void mask_kernel(int n, float *x, float *mask)
+__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i < n && mask[i] == 0) x[i] = 0;
+ if(i < N) X[i*INCX] = ALPHA;
+}
+
+__global__ void mask_kernel(int n, float *x, float mask_num, float *mask)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < n && mask[i] == mask_num) x[i] = mask_num;
}
__global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY)
@@ -27,11 +225,58 @@
if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX];
}
+__global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) Y[i*INCY] *= X[i*INCX];
+}
+
+extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+ size_t N = batch*filters*spatial;
+ normalize_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, batch, filters, spatial);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
+{
+ mean_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, batch, filters, spatial, mean);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *spatial_mean, float *mean)
+{
+ mean_kernel<<<cuda_gridsize(filters*batch), BLOCK>>>(x, 1, filters*batch, spatial, spatial_mean);
+ check_error(cudaPeekAtLastError());
+ mean_kernel<<<cuda_gridsize(filters), BLOCK>>>(spatial_mean, batch, filters, 1, mean);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *spatial_variance, float *variance)
+{
+ spatial_variance_kernel<<<cuda_gridsize(batch*filters), BLOCK>>>(x, mean, batch, filters, spatial, spatial_variance);
+ check_error(cudaPeekAtLastError());
+ accumulate_kernel<<<cuda_gridsize(filters), BLOCK>>>(spatial_variance, batch, filters, variance);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ variance_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, mean, batch, filters, spatial, variance);
+ check_error(cudaPeekAtLastError());
+}
+
extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
{
axpy_ongpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY);
}
+extern "C" void pow_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
+{
+ pow_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX, Y, INCY);
+ check_error(cudaPeekAtLastError());
+}
+
extern "C" void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
{
axpy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY);
@@ -43,15 +288,27 @@
copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY);
}
+extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY)
+{
+ mul_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, INCX, Y, INCY);
+ check_error(cudaPeekAtLastError());
+}
+
extern "C" void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
{
copy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, OFFX, INCX, Y, OFFY, INCY);
check_error(cudaPeekAtLastError());
}
-extern "C" void mask_ongpu(int N, float * X, float * mask)
+extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask)
{
- mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask);
+ mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX)
+{
+ const_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
check_error(cudaPeekAtLastError());
}
@@ -60,3 +317,9 @@
scal_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
check_error(cudaPeekAtLastError());
}
+
+extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX)
+{
+ fill_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+ check_error(cudaPeekAtLastError());
+}
--
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