From a392bbd0c957a00e3782c96e7ced84a29ff9dd88 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 15 Mar 2016 05:33:02 +0000
Subject: [PATCH] Play along w/ alphago
---
src/blas_kernels.cu | 291 +++++++++++++++++++++++++++++++++++++++++----------------
1 files changed, 208 insertions(+), 83 deletions(-)
diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu
index 4da31d1..98366f8 100644
--- a/src/blas_kernels.cu
+++ b/src/blas_kernels.cu
@@ -1,6 +1,7 @@
#include "cuda_runtime.h"
#include "curand.h"
#include "cublas_v2.h"
+#include <assert.h>
extern "C" {
#include "blas.h"
@@ -14,7 +15,7 @@
if (index >= N) return;
int f = (index/spatial)%filters;
- x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .00001f);
+ x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f);
}
__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)
@@ -23,7 +24,7 @@
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);
+ delta[index] = delta[index] * 1./(sqrt(variance[f]) + .000001f) + 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)
@@ -45,29 +46,7 @@
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());
+ variance_delta[i] *= -.5 * pow(variance[i] + .000001f, (float)(-3./2.));
}
__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
@@ -81,38 +60,62 @@
}
}
-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)
+__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_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());
-}
+ const int threads = BLOCK;
+ __shared__ float local[threads];
-__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 id = threadIdx.x;
+ local[id] = 0;
- 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];
+ int filter = blockIdx.x;
+
+ int i, j;
+ for(j = 0; j < batch; ++j){
+ for(i = 0; i < spatial; i += threads){
+ int index = j*spatial*filters + filter*spatial + i + id;
+ local[id] += (i+id < spatial) ? delta[index] : 0;
+ }
}
- spatial_mean_delta[i] *= (-1./sqrt(variance[f] + .00001f));
+
+ if(id == 0){
+ mean_delta[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ mean_delta[filter] += local[i];
+ }
+ mean_delta[filter] *= (-1./sqrt(variance[filter] + .000001f));
+ }
}
-extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *spatial_mean_delta, float *mean_delta)
+__global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_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());
+ const int threads = BLOCK;
+ __shared__ float local[threads];
+
+ int id = threadIdx.x;
+ local[id] = 0;
+
+ int filter = blockIdx.x;
+
+ int i, j;
+ for(j = 0; j < batch; ++j){
+ for(i = 0; i < spatial; i += threads){
+ int index = j*spatial*filters + filter*spatial + i + id;
+
+ local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0;
+ }
+ }
+
+ if(id == 0){
+ variance_delta[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ variance_delta[filter] += local[i];
+ }
+ variance_delta[filter] *= -.5 * pow(variance[filter] + .000001f, (float)(-3./2.));
+ }
}
+
__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;
@@ -125,7 +128,7 @@
mean_delta[i] += delta[index];
}
}
- mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
+ mean_delta[i] *= (-1./sqrt(variance[i] + .000001f));
}
extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
@@ -134,6 +137,18 @@
check_error(cudaPeekAtLastError());
}
+extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ fast_mean_delta_kernel<<<filters, BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ fast_variance_delta_kernel<<<filters, BLOCK>>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
+ check_error(cudaPeekAtLastError());
+}
+
__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
{
float scale = 1./(batch * spatial);
@@ -150,26 +165,9 @@
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);
+ float scale = 1./(batch * spatial - 1);
int j,k;
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (i >= filters) return;
@@ -231,6 +229,7 @@
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;
@@ -238,28 +237,80 @@
check_error(cudaPeekAtLastError());
}
+__global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
+{
+ const int threads = BLOCK;
+ __shared__ float local[threads];
+
+ int id = threadIdx.x;
+ local[id] = 0;
+
+ int filter = blockIdx.x;
+
+ int i, j;
+ for(j = 0; j < batch; ++j){
+ for(i = 0; i < spatial; i += threads){
+ int index = j*spatial*filters + filter*spatial + i + id;
+ local[id] += (i+id < spatial) ? x[index] : 0;
+ }
+ }
+
+ if(id == 0){
+ mean[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ mean[filter] += local[i];
+ }
+ mean[filter] /= spatial * batch;
+ }
+}
+
+__global__ void fast_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ const int threads = BLOCK;
+ __shared__ float local[threads];
+
+ int id = threadIdx.x;
+ local[id] = 0;
+
+ int filter = blockIdx.x;
+
+ int i, j;
+ for(j = 0; j < batch; ++j){
+ for(i = 0; i < spatial; i += threads){
+ int index = j*spatial*filters + filter*spatial + i + id;
+
+ local[id] += (i+id < spatial) ? pow((x[index] - mean[filter]), 2) : 0;
+ }
+ }
+
+ if(id == 0){
+ variance[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ variance[filter] += local[i];
+ }
+ variance[filter] /= (spatial * batch - 1);
+ }
+}
+
+extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
+{
+ fast_mean_kernel<<<filters, BLOCK>>>(x, batch, filters, spatial, mean);
+ check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ fast_variance_kernel<<<filters, BLOCK>>>(x, mean, batch, filters, spatial, variance);
+ 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);
@@ -323,3 +374,77 @@
fill_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
check_error(cudaPeekAtLastError());
}
+
+__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
+{
+ int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (id >= size) return;
+ int i = id % minw;
+ id /= minw;
+ int j = id % minh;
+ id /= minh;
+ int k = id % minc;
+ id /= minc;
+ int b = id % batch;
+
+ int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
+ int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
+ out[out_index] += add[add_index];
+}
+
+extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
+{
+ int minw = (w1 < w2) ? w1 : w2;
+ int minh = (h1 < h2) ? h1 : h2;
+ int minc = (c1 < c2) ? c1 : c2;
+
+ int stride = w1/w2;
+ int sample = w2/w1;
+ assert(stride == h1/h2);
+ assert(sample == h2/h1);
+ if(stride < 1) stride = 1;
+ if(sample < 1) sample = 1;
+
+ int size = batch * minw * minh * minc;
+ shortcut_kernel<<<cuda_gridsize(size), BLOCK>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < n){
+ float diff = truth[i] - pred[i];
+ float abs_val = abs(diff);
+ if(abs_val < 1) {
+ error[i] = diff * diff;
+ delta[i] = diff;
+ }
+ else {
+ error[i] = 2*abs_val - 1;
+ delta[i] = (diff < 0) ? -1 : 1;
+ }
+ }
+}
+
+extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+ smooth_l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < n){
+ float diff = truth[i] - pred[i];
+ error[i] = diff * diff; //I know this is technically wrong, deal with it.
+ delta[i] = diff;
+ }
+}
+
+extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+ l2_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+ check_error(cudaPeekAtLastError());
+}
--
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