From 9ac78d8b84f6a059d2cefe22a10aa60de5b3feaf Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 04 Jan 2018 21:58:52 +0000
Subject: [PATCH] Fine tuning, use stopbackward=1 in the cfg-file in that layer where Backward should be stopped.
---
src/blas_kernels.cu | 61 ++++++++++++++++++++++--------
1 files changed, 45 insertions(+), 16 deletions(-)
diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu
index 684e66d..8e1cf19 100644
--- a/src/blas_kernels.cu
+++ b/src/blas_kernels.cu
@@ -23,7 +23,7 @@
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
- scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
+ scale_bias_kernel<<<dimGrid, dimBlock, 0, get_cuda_stream()>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
@@ -67,7 +67,7 @@
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
- add_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
+ add_bias_kernel<<<dimGrid, dimBlock, 0, get_cuda_stream()>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
@@ -223,6 +223,7 @@
local[id] += (i+id < spatial) ? delta[index] : 0;
}
}
+ __syncthreads();
if(id == 0){
mean_delta[filter] = 0;
@@ -251,6 +252,7 @@
local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0;
}
}
+ __syncthreads();
if(id == 0){
variance_delta[filter] = 0;
@@ -425,7 +427,7 @@
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);
+ normalize_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, x, mean, variance, batch, filters, spatial);
check_error(cudaPeekAtLastError());
}
@@ -446,6 +448,7 @@
local[id] += (i+id < spatial) ? x[index] : 0;
}
}
+ __syncthreads();
if(id == 0){
mean[filter] = 0;
@@ -474,6 +477,7 @@
local[id] += (i+id < spatial) ? pow((x[index] - mean[filter]), 2) : 0;
}
}
+ __syncthreads();
if(id == 0){
variance[filter] = 0;
@@ -486,13 +490,13 @@
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);
+ fast_mean_kernel<<<filters, BLOCK, 0, get_cuda_stream()>>>(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);
+ fast_variance_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance);
check_error(cudaPeekAtLastError());
}
@@ -516,13 +520,13 @@
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);
+ pow_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(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);
+ axpy_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY);
check_error(cudaPeekAtLastError());
}
@@ -539,20 +543,44 @@
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);
+ copy_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, X, OFFX, INCX, Y, OFFY, INCY);
+ check_error(cudaPeekAtLastError());
+}
+
+__global__ void flatten_kernel(int N, float *x, int spatial, int layers, int batch, int forward, float *out)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i >= N) return;
+ int in_s = i%spatial;
+ i = i/spatial;
+ int in_c = i%layers;
+ i = i/layers;
+ int b = i;
+
+ int i1 = b*layers*spatial + in_c*spatial + in_s;
+ int i2 = b*layers*spatial + in_s*layers + in_c;
+
+ if (forward) out[i2] = x[i1];
+ else out[i1] = x[i2];
+}
+
+extern "C" void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out)
+{
+ int size = spatial*batch*layers;
+ flatten_kernel<<<cuda_gridsize(size), BLOCK, 0, get_cuda_stream()>>>(size, x, spatial, layers, batch, forward, out);
check_error(cudaPeekAtLastError());
}
extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
{
int size = w*h*c*batch;
- reorg_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, w, h, c, batch, stride, forward, out);
+ reorg_kernel<<<cuda_gridsize(size), BLOCK, 0, get_cuda_stream()>>>(size, x, w, h, c, batch, stride, forward, out);
check_error(cudaPeekAtLastError());
}
extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask)
{
- mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask);
+ mask_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask);
check_error(cudaPeekAtLastError());
}
@@ -571,7 +599,7 @@
extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX)
{
- scal_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+ scal_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX);
check_error(cudaPeekAtLastError());
}
@@ -583,7 +611,7 @@
extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX)
{
- fill_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+ fill_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX);
check_error(cudaPeekAtLastError());
}
@@ -718,11 +746,12 @@
largest = (val>largest) ? val : largest;
}
for(i = 0; i < n; ++i){
- sum += exp(input[i]/temp-largest/temp);
+ float e = exp(input[i]/temp - largest/temp);
+ sum += e;
+ output[i] = e;
}
- sum = (sum != 0) ? largest/temp+log(sum) : largest-100;
for(i = 0; i < n; ++i){
- output[i] = exp(input[i]/temp-sum);
+ output[i] /= sum;
}
}
@@ -737,6 +766,6 @@
{
int inputs = n;
int batch = groups;
- softmax_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, offset, batch, input, temp, output);
+ softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, get_cuda_stream()>>>(inputs, offset, batch, input, temp, output);
check_error(cudaPeekAtLastError());
}
--
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