派生自 Algorithm/baseDetector
lib/detecter_tools/darknet/blas_kernels.cu
@@ -1,2312 +1,2473 @@ #include <cuda_runtime.h> #include <curand.h> #include <cublas_v2.h> #include <assert.h> #include <float.h> #include "blas.h" #include "dark_cuda.h" #include "utils.h" #include "tree.h" __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) #if CUDART_VERSION >= 9000 val += __shfl_xor_sync(0xffffffff, val, mask); #else val += __shfl_xor(val, mask); #endif return val; } __global__ void compare_2_arrays_kernel(float *one, float *two, int size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= size) return; const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]); if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff); } void compare_2_arrays_gpu(float *one, float *two, int size) { const int num_blocks = get_number_of_blocks(size, BLOCK); compare_2_arrays_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(one, two, size); CHECK_CUDA(cudaPeekAtLastError()); CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void mean_array_kernel(float *src, int size, float alpha, float *avg) { const int i = blockIdx.x*blockDim.x + threadIdx.x; if (i >= size) return; avg[i] = avg[i] * (1 - alpha) + src[i] * alpha; src[i] = avg[i]; } void mean_array_gpu(float *src, int size, float alpha, float *avg) { const int num_blocks = get_number_of_blocks(size, BLOCK); mean_array_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(src, size, alpha, avg); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= current_size) return; int f = (index / spatial) % filters; output[index] *= scale[f]; } void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, scale, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index]*x_norm[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i]; } } void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { backward_scale_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(x_norm, delta, batch, n, size, scale_updates); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= current_size) return; int f = (index / spatial) % filters; output[index] += biases[f]; } void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); add_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, biases, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i]; } } /* __global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int f1 = index / n; int f2 = index % n; if (f2 <= f1) return; float sum = 0; float norm1 = 0; float norm2 = 0; int b, i; for(b = 0; b < batch; ++b){ for(i = 0; i < size; ++i){ int i1 = b * size * n + f1 * size + i; int i2 = b * size * n + f2 * size + i; sum += output[i1] * output[i2]; norm1 += output[i1] * output[i1]; norm2 += output[i2] * output[i2]; } } norm1 = sqrt(norm1); norm2 = sqrt(norm2); float norm = norm1 * norm2; sum = sum / norm; for(b = 0; b < batch; ++b){ for(i = 0; i < size; ++i){ int i1 = b * size * n + f1 * size + i; int i2 = b * size * n + f2 * size + i; delta[i1] += - scale * sum * output[i2] / norm; delta[i2] += - scale * sum * output[i1] / norm; } } } void dot_error_gpu(layer l) { dot_kernel<<<cuda_gridsize(l.n*l.n), BLOCK, 0, get_cuda_stream()>>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } */ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) { backward_bias_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(bias_updates, delta, batch, n, size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; float mhat = m[index] / (1.f - powf(B1, t)); float vhat = v[index] / (1.f - powf(B2, t)); x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); } extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { adam_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, x, m, v, B1, B2, rate, eps, t); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) { scal_ongpu(n, B1, m, 1); scal_ongpu(n, B2, v, 1); axpy_ongpu(n, -decay*batch, w, 1, d, 1); axpy_ongpu(n, (1 - B1), d, 1, m, 1); mul_ongpu(n, d, 1, d, 1); axpy_ongpu(n, (1 - B2), d, 1, v, 1); adam_gpu(n, w, m, v, B1, B2, rate, eps, t); fill_ongpu(n, 0, d, 1); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; int f = (index / spatial) % filters; x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .00001f)); } extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); normalize_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, batch, filters, spatial); CHECK_CUDA(cudaPeekAtLastError()); } __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.F/(sqrtf(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) { size_t N = batch*filters*spatial; normalize_delta_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); CHECK_CUDA(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 * powf(variance[i] + .000001f, (float)(-3./2.)); } __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]; } } __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { 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] : 0; } } __syncthreads(); if(id == 0){ mean_delta[filter] = 0; for(i = 0; i < threads; ++i){ mean_delta[filter] += local[i]; } mean_delta[filter] *= (-1.F/sqrtf(variance[filter] + .000001f)); } } __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) { 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; } } __syncthreads(); if(id == 0){ variance_delta[filter] = 0; for(i = 0; i < threads; ++i){ variance_delta[filter] += local[i]; } variance_delta[filter] *= -.5 * powf(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; 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.F/sqrtf(variance[i] + .000001f)); } 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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta); CHECK_CUDA(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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta); CHECK_CUDA(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, 0, get_cuda_stream() >>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { float scale = 1.F/(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 variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) { float scale = 1.F/(batch * spatial - 1); 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] += powf((x[index] - mean[i]), 2); } } variance[i] *= scale; } __global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i >= N) return; int in_index = i; int in_w = i%w; i = i/w; int in_h = i%h; i = i/h; int in_c = i%c; i = i/c; int b = i%batch; int out_c = c/(stride*stride); int c2 = in_c % out_c; int offset = in_c / out_c; int w2 = in_w*stride + offset % stride; int h2 = in_h*stride + offset / stride; //printf("%d\n", offset); int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); // printf("%d %d %d\n", w2, h2, c2); //printf("%d %d\n", in_index, out_index); //if(out_index >= N || out_index < 0) printf("bad bad bad \n"); if(forward) out[out_index] = x[in_index]; else out[in_index] = x[out_index]; //if(forward) out[1] = x[1]; //else out[0] = x[0]; } __global__ void constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) { const float w = weights_gpu[i]; const float wu = weight_updates_gpu[i]; const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu); const float abs_limit = fabs(w * coef); if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign; } } extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { constrain_weight_updates_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, coef, weights_gpu, weight_updates_gpu); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; 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] = powf(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 constrain_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] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX])); } __global__ void constrain_min_max_kernel(int N, float MIN, float MAX, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) X[i*INCX] = fminf(MAX, fmaxf(MIN, X[i*INCX])); } __global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < N) { if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0; } } __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 scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) X[i*INCX] = X[i*INCX] * ALPHA + BETA; } __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; X[index*INCX] = ALPHA; } __global__ void mask_kernel_new_api(int n, float *x, float mask_num, float *mask, float val) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n && mask[i] == mask_num) x[i] = val; } __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) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX]; } __global__ void simple_copy_kernel(int size, float *src, float *dst) { int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) dst[index] = src[index]; } __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]; } __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; } } __syncthreads(); if(id == 0){ float mean_tmp = 0; for(i = 0; i < threads; ++i){ mean_tmp += local[i]; } mean_tmp /= spatial * batch; mean[filter] = mean_tmp; } } extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { fast_mean_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, batch, filters, spatial, mean); CHECK_CUDA(cudaPeekAtLastError()); } __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) ? powf((x[index] - mean[filter]), 2) : 0; } } __syncthreads(); if(id == 0){ float variance_tmp = 0; for(i = 0; i < threads; ++i){ variance_tmp += local[i]; } variance_tmp /= (spatial * batch);// -1); variance[filter] = variance_tmp; } } extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { fast_variance_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { 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) ? powf(x[index], 2) : 0; } } __syncthreads(); if (id == 0) { float v_tmp = 0; v_tmp = 0; for (i = 0; i < threads; ++i) { v_tmp += local[i]; } v_tmp /= (spatial * batch - 1); v_tmp = fmax(v_tmp, powf(mean[filter], 2)); const float alpha_cbn = 1.0f / minibatch_index; m_avg[filter] = alpha_cbn * mean[filter] + (1 - alpha_cbn) * m_avg[filter]; mean[filter] = m_avg[filter]; v_avg[filter] = alpha_cbn * v_tmp + (1 - alpha_cbn) * v_avg[filter]; float variance_tmp = fmax(0.0f, v_avg[filter] - powf(m_avg[filter], 2)); if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon); else variance[filter] = variance_tmp; //if (max_minibatch_index == minibatch_index) { if(rolling_mean_gpu) rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; if(rolling_variance_gpu) rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; } } } extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { fast_v_cbn_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void inverse_variance_kernel(int size, float *src, float *dst, float epsilon) { int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) dst[index] = 1.0f / sqrtf(src[index] + epsilon); } extern "C" void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon) { const int num_blocks = size / BLOCK + 1; inverse_variance_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; int f = (index / spatial) % filters; float val = 0; if(inverse_variance) val = (x[index] - mean[f]) * variance[f]; else val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon)); val *= scales[f]; val += biases[f]; if (!isnan(val) && !isinf(val)) x[index] = val; } extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); normalize_scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { mean_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, batch, filters, spatial, mean); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { variance_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance); CHECK_CUDA(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, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX, Y, INCY); CHECK_CUDA(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, 0, get_cuda_stream()>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) { copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY); } extern "C" void simple_copy_ongpu(int size, float *src, float *dst) { const int num_blocks = size / BLOCK + 1; simple_copy_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void memcpy_ongpu(void *dst, void *src, int size_bytes) { CHECK_CUDA(cudaMemcpyAsync(dst, src, size_bytes, cudaMemcpyDefault, get_cuda_stream())); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, INCX, Y, INCY); CHECK_CUDA(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, 0, get_cuda_stream()>>>(N, X, OFFX, INCX, Y, OFFY, INCY); CHECK_CUDA(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_CUDA(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, 0, get_cuda_stream()>>>(size, x, w, h, c, batch, stride, forward, out); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) { mask_kernel_new_api <<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask, val); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) { mask_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) { const_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) { constrain_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX) { constrain_min_max_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, MIN, MAX, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) { scal_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX) { scal_add_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, BETA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { //fill_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX); //CHECK_CUDA(cudaPeekAtLastError()); fill_kernel << <get_number_of_blocks(N, BLOCK), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void gradient_centralization_kernel(int filters, int f_size, float *in) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int tid = index % WARP_SIZE; const int f = index / WARP_SIZE; if (f >= filters) return; float mean = 0; for (int i = 0; i < f_size; i += WARP_SIZE) { mean += warpAllReduceSum(in[f*f_size + i + tid]); } mean = mean / f_size; for (int i = 0; i < f_size; i += WARP_SIZE) { in[f*f_size + i + tid] -= mean; } } extern "C" void gradient_centralization_gpu(int w, int h, int c, int f, float *in) { const int size = f * WARP_SIZE; const int f_size = c * h * w; if (f_size % WARP_SIZE == 0) { gradient_centralization_kernel << <get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >> > (f, f_size, in); CHECK_CUDA(cudaPeekAtLastError()); } } __device__ float relu(float src) { if (src > 0) return src; return 0; } __device__ float lrelu(float src) { const float eps = 0.001; if (src > eps) return src; return eps; } __device__ float grad_relu(float src) { return (src > 0); } __device__ float grad_lrelu(float src) { const float eps = 0.001; return (src > eps); } __global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float out_val = in[id]; int add_outputs = outputs_of_layers_gpu[0]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; float *add = layers_output_gpu[0]; out_val += add[add_index]; } out[id] = out_val; } __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float sum = 1, max_val = -FLT_MAX; if (weights_gpu && weights_normalization) { if (weights_normalization == SOFTMAX_NORMALIZATION) { for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (max_val < w) max_val = w; } } const float eps = 0.0001; sum = eps; for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } float out_val = 0; if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val = in[id] * w; // [0 or c or (c, h ,w)] } else out_val = in[id]; // layers for (int i = 0; i < n; ++i) { int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; float *add = layers_output_gpu[i]; if (weights_gpu) { const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val += add[add_index] * w; // [0 or c or (c, h ,w)] } else out_val += add[add_index]; } } out[id] = out_val; } extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; if (nweights == 0 && n == 1) { shortcut_singlelayer_simple_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } else { shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float grad = 1, sum = 1, max_val = -FLT_MAX; int i; if (weights_gpu && weights_normalization) { if (weights_normalization == SOFTMAX_NORMALIZATION) { for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (max_val < w) max_val = w; } } const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalization == RELU_NORMALIZATION) grad = w; else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1-w); delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] float weights_update_tmp = delta_in[id] * in[id] * grad;// / step; if (layer_step == 1 && (size/32) > (id/32 + 1)) { if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { weights_update_tmp = 0; } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) atomicAdd(&weight_updates_gpu[src_i / step], wu); } } else { if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp); //weight_updates_gpu[src_i / step] += weights_update_tmp; } } else delta_out[id] += delta_in[id]; // layers for (int i = 0; i < n; ++i) { int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; int out_index = id; float *layer_delta = layers_delta_gpu[i]; if (weights_gpu) { float *add = layers_output_gpu[i]; const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalization == RELU_NORMALIZATION) grad = w; else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1 - w); layer_delta[add_index] += delta_in[id] * w; float weights_update_tmp = delta_in[id] * add[add_index] * grad;// / step; if (layer_step == 1 && (size / 32) > (id / 32 + 1)) { if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { weights_update_tmp = 0; } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) atomicAdd(&weight_updates_gpu[weights_index], wu); //if(weights_gpu[weights_index] != 1) printf(" wu = %f, weights_update_tmp = %f, w = %f, weights_gpu[weights_index] = %f, grad = %f, weights_normalization = %d ", // wu, weights_update_tmp, w, weights_gpu[weights_index], grad, weights_normalization); } } else { if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp); //weight_updates_gpu[weights_index] += weights_update_tmp; } } else layer_delta[add_index] += delta_in[id]; } } } extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization) { const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 //printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step); //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; backward_shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu, weights_normalization); CHECK_CUDA(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, 0, get_cuda_stream()>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void simple_input_shortcut_kernel(float *in, int size, float *add, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; out[id] = in[id] + add[id]; } __global__ void input_shortcut_kernel(float *in, 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] = in[out_index] + add[add_index]; } extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { if (w1 == w2 && h1 == h2 && c1 == c2) { int size = batch * w1 * h1 * c1; simple_input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, add, out); CHECK_CUDA(cudaPeekAtLastError()); return; } 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; //input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); simple_copy_ongpu(w2 * h2 * c2 * batch, in, out); shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_x_ent_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 t = truth[i]; float p = pred[i]; error[i] = (t) ? -log(p) : 0; delta[i] = t - p; } } extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { softmax_x_ent_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, pred, truth, delta, error); CHECK_CUDA(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0); } } extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { weighted_sum_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, c); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ if(da) da[i] += dc[i] * s[i]; db[i] += dc[i] * (1-s[i]); ds[i] += dc[i] * a[i] + dc[i] * -b[i]; } } extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc) { weighted_delta_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, da, db, ds, dc); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ c[i] += a[i]*b[i]; } } extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) { mult_add_into_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, c); CHECK_CUDA(cudaPeekAtLastError()); } __device__ void softmax_device(int n, float *input, float temp, float *output) { int i; float sum = 0; float largest = -INFINITY; for(i = 0; i < n; ++i){ int val = input[i]; largest = (val>largest) ? val : largest; } for(i = 0; i < n; ++i){ float e = exp(input[i]/temp - largest/temp); sum += e; output[i] = e; } for(i = 0; i < n; ++i){ output[i] /= sum; } } __global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output) { int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(b >= batch) return; softmax_device(n, input + b*offset, temp, output + b*offset); } extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output) { int inputs = n; int batch = groups; softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, get_cuda_stream()>>>(inputs, offset, batch, input, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } __device__ void softmax_device_new_api(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -INFINITY; for (i = 0; i < n; ++i) { int val = input[i*stride]; largest = (val>largest) ? val : largest; } for (i = 0; i < n; ++i) { float e = expf(input[i*stride] / temp - largest / temp); sum += e; output[i*stride] = e; } for (i = 0; i < n; ++i) { output[i*stride] /= sum; } } __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= batch*groups) return; int b = id / groups; int g = id % groups; softmax_device_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); } extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { softmax_kernel_new_api << <cuda_gridsize(batch*groups), BLOCK, 0, get_cuda_stream() >> >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= N) return; int out_index = i; int out_w = i % (w*stride); i = i / (w*stride); int out_h = i % (h*stride); i = i / (h*stride); int out_c = i%c; i = i / c; int b = i%batch; int in_w = out_w / stride; int in_h = out_h / stride; int in_c = out_c; int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; if (forward) out[out_index] += scale * x[in_index]; else atomicAdd(x + in_index, scale * out[out_index]); } extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t size = w*h*c*batch*stride*stride; upsample_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(size, in, w, h, c, batch, stride, forward, scale, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= spatial*batch*groups) return; int s = id % spatial; id = id / spatial; int g = id % groups; int b = id / groups; int goff = group_offset[g] * spatial; int boff = b*stride; softmax_device_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); } extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) { int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups); int *tree_groups_offset = cuda_make_int_array_new_api(hier.group_offset, hier.groups); /* static int *tree_groups_size = 0; static int *tree_groups_offset = 0; if(!tree_groups_size){ tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); } */ int num = spatial*batch*hier.groups; softmax_tree_kernel <<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); CHECK_CUDA(cudaPeekAtLastError()); cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); } __global__ void fix_nan_and_inf_kernel(float *input, size_t size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) { input[index] = 1.0f / (fabs((float)index) + 1); // pseudo random value } } } extern "C" void fix_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); fix_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void reset_nan_and_inf_kernel(float *input, size_t size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) { input[index] = 0; } } } extern "C" void reset_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); reset_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void is_nan_or_inf_kernel(float *input, size_t size, int *pinned_return) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) *pinned_return = 1; } } extern "C" int is_nan_or_inf(float *input, size_t size) { int *pinned_return; CHECK_CUDA(cudaHostAlloc(&pinned_return, sizeof(int), cudaHostRegisterMapped)); *pinned_return = 0; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); is_nan_or_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size, pinned_return); CHECK_CUDA(cudaDeviceSynchronize()); int ret_val = *pinned_return; CHECK_CUDA(cudaFreeHost(pinned_return)); return ret_val; } __global__ void add_3_arrays_activate_kernel(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = 0; val += a1[index]; val += a2[index]; if (a3) val += a3[index]; if (a == LOGISTIC) val = 1.f / (1.f + expf(-val)); else if(a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); dst[index] = val; } } extern "C" void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (a != LOGISTIC && a != TANH) { printf(" add_3_arrays_activate() doesn't support activation %d, it supports only LOGISTIC and TANH \n", a); exit(EXIT_FAILURE); } add_3_arrays_activate_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, a3, size, a, dst); } __global__ void sum_of_mults_kernel(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { dst[index] = a1[index] * a2[index] + b1[index] * b2[index]; } } extern "C" void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); sum_of_mults_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, b1, b2, size, dst); } __global__ void activate_and_mult_kernel(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = a1[index]; if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); dst[index] = val * a2[index]; } } extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (a != TANH) { printf(" activat_and_mult() doesn't support activation %d, it supports only TANH \n", a); exit(EXIT_FAILURE); } activate_and_mult_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, size, a, dst); } __global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { if (scale_wh) { int osd_index = index % channel_size + (index / batch_size)*channel_size; out[index] = in_w_h_c[index] * scales_c[osd_index]; } else { out[index] = in_w_h_c[index] * scales_c[index / channel_size]; } } } extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { if (scale_wh) { int osd_index = index % channel_size + (index / batch_size)*channel_size; //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index] / channel_size); // l.delta * from out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here } else { int osd_index = index / channel_size; //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) int warp_id = index / 32; int index_warp_start = warp_id * 32; int osd_index_warp_start = index_warp_start / channel_size; int osd_index_warp_end = (index_warp_start + 31) / channel_size; if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel { float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from if (threadIdx.x % 32 == 0) { atomicAdd(&out_state_delta[osd_index], sum); //out_state_delta[osd_index] += sum; } } else { atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from } out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here } } } extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); backward_scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size, batch_size, scale_wh, in_scales_c, out_from_delta, in_from_output, out_state_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void sam_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { out[index] = in_w_h_c[index] * scales_c[index]; } } extern "C" void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_sam_kernel(float *in_w_h_c_delta, int size, int channel_size, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { out_state_delta[index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) out_from_delta[index] += in_scales_c[index] * in_w_h_c_delta[index]; // input * l.delta //out_state_delta[index] += in_w_h_c_delta[index]; //out_from_delta[index] = in_w_h_c_delta[index]; } } extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); backward_sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size, in_scales_c, out_from_delta, in_from_output, out_state_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { // rotate left or right if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floor(x_s); // round down int x_1 = ceil(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floor(y_s); int y_1 = ceil(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } } extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); smooth_rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float scale, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id > 0) { if (stage_id == 1) scale = 0.65; else if (stage_id == 2) scale = 0.8; else if (stage_id == 3) scale = 1.3; if (reverse) scale = 1 / scale; const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x_c + (x_d - x_c) / scale // Ysource = y_c + (y_d - y_c) / scale float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; int x_0 = floor(x_s); // round down int x_1 = ceil(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floor(y_s); int y_1 = ceil(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { //if (scale < 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } } } } extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); stretch_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id == 1 || stage_id == 2) { // rotate left or right if (stage_id == 2) angle = -angle; if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floor(x_s); // round down int x_1 = ceil(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floor(y_s); int y_1 = ceil(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } else if (stage_id == 3) { // flip for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } } } extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); sway_and_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { // if(reverse) if (stage_id == 0) { // simple copy for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = x + y*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 1) { // 90 degree clockwise rotation - 1 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - y) + x*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 2) { // 180 degree clockwise rotation - 2 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - x) + (kernel_size - 1 - y)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 3) { // 270 degree clockwise rotation - 3 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = y + (kernel_size - 1 - x)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } } } extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 8; // 8 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id == 1 || stage_id == 2 || stage_id == 3 || stage_id == 4) { float scale = 0.5; if (stage_id == 1) scale = 0.65; else if (stage_id == 2) scale = 0.8; else if (stage_id == 3) scale = 1.2; else if (stage_id == 4) scale = 1.4; if (reverse) scale = 1 / scale; const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x_c + (x_d - x_c) / scale // Ysource = y_c + (y_d - y_c) / scale float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; int x_0 = floor(x_s); // round down int x_1 = ceil(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floor(y_s); int y_1 = ceil(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { if(scale > 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } } else if (stage_id == 5 || stage_id == 6) { // rotate left or right if (stage_id == 6) angle = -angle; if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floor(x_s); // round down int x_1 = ceil(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floor(y_s); int y_1 = ceil(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } else if (stage_id == 7) { // flip for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } } } extern "C" void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); stretch_sway_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < current_size) { float val = 0; for (int i = 0; i < groups; ++i) { val += src_gpu[index + i*current_size]; } for (int i = 0; i < groups; ++i) { dst_gpu[index + i*current_size] = val / groups; } } } extern "C" void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) { const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); reduce_and_expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < current_size) { for (int i = 0; i < groups; ++i) { dst_gpu[index + i*current_size] = src_gpu[index]; } } } extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) { const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } #include <cuda_runtime.h> #include <curand.h> #include <cublas_v2.h> #include <assert.h> #include <float.h> #include "blas.h" #include "dark_cuda.h" #include "utils.h" #include "tree.h" __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) #if CUDART_VERSION >= 9000 val += __shfl_xor_sync(0xffffffff, val, mask); #else val += __shfl_xor(val, mask); #endif return val; } __global__ void compare_2_arrays_kernel(float *one, float *two, int size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= size) return; const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]); if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff); } void compare_2_arrays_gpu(float *one, float *two, int size) { const int num_blocks = get_number_of_blocks(size, BLOCK); compare_2_arrays_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(one, two, size); CHECK_CUDA(cudaPeekAtLastError()); CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void mean_array_kernel(float *src, int size, float alpha, float *avg) { const int i = blockIdx.x*blockDim.x + threadIdx.x; if (i >= size) return; avg[i] = avg[i] * (1 - alpha) + src[i] * alpha; src[i] = avg[i]; } void mean_array_gpu(float *src, int size, float alpha, float *avg) { const int num_blocks = get_number_of_blocks(size, BLOCK); mean_array_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(src, size, alpha, avg); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= current_size) return; int f = (index / spatial) % filters; output[index] *= scale[f]; } void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, scale, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index]*x_norm[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i]; } } void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { backward_scale_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(x_norm, delta, batch, n, size, scale_updates); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= current_size) return; int f = (index / spatial) % filters; output[index] += biases[f]; } void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); add_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, biases, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i]; } } /* __global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; int f1 = index / n; int f2 = index % n; if (f2 <= f1) return; float sum = 0; float norm1 = 0; float norm2 = 0; int b, i; for(b = 0; b < batch; ++b){ for(i = 0; i < size; ++i){ int i1 = b * size * n + f1 * size + i; int i2 = b * size * n + f2 * size + i; sum += output[i1] * output[i2]; norm1 += output[i1] * output[i1]; norm2 += output[i2] * output[i2]; } } norm1 = sqrt(norm1); norm2 = sqrt(norm2); float norm = norm1 * norm2; sum = sum / norm; for(b = 0; b < batch; ++b){ for(i = 0; i < size; ++i){ int i1 = b * size * n + f1 * size + i; int i2 = b * size * n + f2 * size + i; delta[i1] += - scale * sum * output[i2] / norm; delta[i2] += - scale * sum * output[i1] / norm; } } } void dot_error_gpu(layer l) { dot_kernel<<<cuda_gridsize(l.n*l.n), BLOCK, 0, get_cuda_stream()>>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } */ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) { backward_bias_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(bias_updates, delta, batch, n, size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; float mhat = m[index] / (1.f - powf(B1, t)); float vhat = v[index] / (1.f - powf(B2, t)); x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); } extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { adam_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, x, m, v, B1, B2, rate, eps, t); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) { scal_ongpu(n, B1, m, 1); scal_ongpu(n, B2, v, 1); axpy_ongpu(n, -decay*batch, w, 1, d, 1); axpy_ongpu(n, (1 - B1), d, 1, m, 1); mul_ongpu(n, d, 1, d, 1); axpy_ongpu(n, (1 - B2), d, 1, v, 1); adam_gpu(n, w, m, v, B1, B2, rate, eps, t); fill_ongpu(n, 0, d, 1); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; int f = (index / spatial) % filters; x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .00001f)); } extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); normalize_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, batch, filters, spatial); CHECK_CUDA(cudaPeekAtLastError()); } __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.F/(sqrtf(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) { size_t N = batch*filters*spatial; normalize_delta_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); CHECK_CUDA(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 * powf(variance[i] + .000001f, (float)(-3./2.)); } __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]; } } __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { 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] : 0; } } __syncthreads(); if(id == 0){ mean_delta[filter] = 0; for(i = 0; i < threads; ++i){ mean_delta[filter] += local[i]; } mean_delta[filter] *= (-1.F/sqrtf(variance[filter] + .000001f)); } } __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) { 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; } } __syncthreads(); if(id == 0){ variance_delta[filter] = 0; for(i = 0; i < threads; ++i){ variance_delta[filter] += local[i]; } variance_delta[filter] *= -.5 * powf(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; 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.F/sqrtf(variance[i] + .000001f)); } 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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta); CHECK_CUDA(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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta); CHECK_CUDA(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, 0, get_cuda_stream() >>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { float scale = 1.F/(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 variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) { float scale = 1.F/(batch * spatial - 1); 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] += powf((x[index] - mean[i]), 2); } } variance[i] *= scale; } __global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i >= N) return; int in_index = i; int in_w = i%w; i = i/w; int in_h = i%h; i = i/h; int in_c = i%c; i = i/c; int b = i%batch; int out_c = c/(stride*stride); int c2 = in_c % out_c; int offset = in_c / out_c; int w2 = in_w*stride + offset % stride; int h2 = in_h*stride + offset / stride; //printf("%d\n", offset); int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); // printf("%d %d %d\n", w2, h2, c2); //printf("%d %d\n", in_index, out_index); //if(out_index >= N || out_index < 0) printf("bad bad bad \n"); if(forward) out[out_index] = x[in_index]; else out[in_index] = x[out_index]; //if(forward) out[1] = x[1]; //else out[0] = x[0]; } __global__ void constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) { const float w = weights_gpu[i]; const float wu = weight_updates_gpu[i]; const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu); const float abs_limit = fabs(w * coef); if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign; } } extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { constrain_weight_updates_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, coef, weights_gpu, weight_updates_gpu); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; 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] = powf(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 constrain_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] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX])); } __global__ void constrain_min_max_kernel(int N, float MIN, float MAX, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) X[i*INCX] = fminf(MAX, fmaxf(MIN, X[i*INCX])); } __global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < N) { if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0; } } __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 scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) X[i*INCX] = X[i*INCX] * ALPHA + BETA; } __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; X[index*INCX] = ALPHA; } __global__ void mask_kernel_new_api(int n, float *x, float mask_num, float *mask, float val) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n && mask[i] == mask_num) x[i] = val; } __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) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX]; } __global__ void simple_copy_kernel(int size, float *src, float *dst) { int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) dst[index] = src[index]; } __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]; } __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; } } __syncthreads(); if(id == 0){ float mean_tmp = 0; for(i = 0; i < threads; ++i){ mean_tmp += local[i]; } mean_tmp /= spatial * batch; mean[filter] = mean_tmp; } } extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { fast_mean_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, batch, filters, spatial, mean); CHECK_CUDA(cudaPeekAtLastError()); } __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) ? powf((x[index] - mean[filter]), 2) : 0; } } __syncthreads(); if(id == 0){ float variance_tmp = 0; for(i = 0; i < threads; ++i){ variance_tmp += local[i]; } variance_tmp /= (spatial * batch);// -1); variance[filter] = variance_tmp; } } extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { fast_variance_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { 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) ? powf(x[index], 2) : 0; } } __syncthreads(); if (id == 0) { float v_tmp = 0; v_tmp = 0; for (i = 0; i < threads; ++i) { v_tmp += local[i]; } v_tmp /= (spatial * batch - 1); v_tmp = fmax(v_tmp, powf(mean[filter], 2)); const float alpha_cbn = 1.0f / minibatch_index; m_avg[filter] = alpha_cbn * mean[filter] + (1 - alpha_cbn) * m_avg[filter]; mean[filter] = m_avg[filter]; v_avg[filter] = alpha_cbn * v_tmp + (1 - alpha_cbn) * v_avg[filter]; float variance_tmp = fmax(0.0f, v_avg[filter] - powf(m_avg[filter], 2)); if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon); else variance[filter] = variance_tmp; //if (max_minibatch_index == minibatch_index) { if(rolling_mean_gpu) rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; if(rolling_variance_gpu) rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; } } } extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { fast_v_cbn_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void inverse_variance_kernel(int size, float *src, float *dst, float epsilon) { int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) dst[index] = 1.0f / sqrtf(src[index] + epsilon); } extern "C" void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon) { const int num_blocks = size / BLOCK + 1; inverse_variance_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; int f = (index / spatial) % filters; float val = 0; if(inverse_variance) val = (x[index] - mean[f]) * variance[f]; else val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon)); val *= scales[f]; val += biases[f]; if (!isnan(val) && !isinf(val)) x[index] = val; } extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); normalize_scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { mean_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, batch, filters, spatial, mean); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { variance_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance); CHECK_CUDA(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, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX, Y, INCY); CHECK_CUDA(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, 0, get_cuda_stream()>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) { copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY); } extern "C" void simple_copy_ongpu(int size, float *src, float *dst) { const int num_blocks = size / BLOCK + 1; simple_copy_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void memcpy_ongpu(void *dst, void *src, int size_bytes) { CHECK_CUDA(cudaMemcpyAsync(dst, src, size_bytes, cudaMemcpyDefault, get_cuda_stream())); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, INCX, Y, INCY); CHECK_CUDA(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, 0, get_cuda_stream()>>>(N, X, OFFX, INCX, Y, OFFY, INCY); CHECK_CUDA(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_CUDA(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, 0, get_cuda_stream()>>>(size, x, w, h, c, batch, stride, forward, out); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) { mask_kernel_new_api <<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask, val); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) { mask_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) { const_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) { constrain_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX) { constrain_min_max_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, MIN, MAX, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) { scal_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX) { scal_add_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, BETA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { //fill_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX); //CHECK_CUDA(cudaPeekAtLastError()); fill_kernel << <get_number_of_blocks(N, BLOCK), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void gradient_centralization_kernel(int filters, int f_size, float *in) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int tid = index % WARP_SIZE; const int f = index / WARP_SIZE; if (f >= filters) return; float mean = 0; for (int i = 0; i < f_size; i += WARP_SIZE) { mean += warpAllReduceSum(in[f*f_size + i + tid]); } mean = mean / f_size; for (int i = 0; i < f_size; i += WARP_SIZE) { in[f*f_size + i + tid] -= mean; } } extern "C" void gradient_centralization_gpu(int w, int h, int c, int f, float *in) { const int size = f * WARP_SIZE; const int f_size = c * h * w; if (f_size % WARP_SIZE == 0) { gradient_centralization_kernel << <get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >> > (f, f_size, in); CHECK_CUDA(cudaPeekAtLastError()); } } __device__ float relu(float src) { if (src > 0) return src; return 0; } __device__ float lrelu(float src) { const float eps = 0.001; if (src > eps) return src; return eps; } __device__ float grad_relu(float src) { return (src > 0); } __device__ float grad_lrelu(float src) { const float eps = 0.001; return (src > eps); } __global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float out_val = in[id]; int add_outputs = outputs_of_layers_gpu[0]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; float *add = layers_output_gpu[0]; out_val += add[add_index]; } out[id] = out_val; } __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float sum = 1, max_val = -FLT_MAX; if (weights_gpu && weights_normalization) { if (weights_normalization == SOFTMAX_NORMALIZATION) { for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (max_val < w) max_val = w; } } const float eps = 0.0001; sum = eps; for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } float out_val = 0; if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val = in[id] * w; // [0 or c or (c, h ,w)] } else out_val = in[id]; // layers for (int i = 0; i < n; ++i) { int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; float *add = layers_output_gpu[i]; if (weights_gpu) { const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val += add[add_index] * w; // [0 or c or (c, h ,w)] } else out_val += add[add_index]; } } out[id] = out_val; } extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization) { //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; if (nweights == 0 && n == 1) { shortcut_singlelayer_simple_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } else { shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; float grad = 1, sum = 1, max_val = -FLT_MAX; int i; if (weights_gpu && weights_normalization) { if (weights_normalization == SOFTMAX_NORMALIZATION) { for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (max_val < w) max_val = w; } } const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalization == RELU_NORMALIZATION) grad = w; else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1-w); delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] float weights_update_tmp = delta_in[id] * in[id] * grad;// / step; if (layer_step == 1 && (size/32) > (id/32 + 1)) { if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { weights_update_tmp = 0; } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) atomicAdd(&weight_updates_gpu[src_i / step], wu); } } else { if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp); //weight_updates_gpu[src_i / step] += weights_update_tmp; } } else delta_out[id] += delta_in[id]; // layers for (int i = 0; i < n; ++i) { int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; int out_index = id; float *layer_delta = layers_delta_gpu[i]; if (weights_gpu) { float *add = layers_output_gpu[i]; const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalization == RELU_NORMALIZATION) grad = w; else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1 - w); layer_delta[add_index] += delta_in[id] * w; float weights_update_tmp = delta_in[id] * add[add_index] * grad;// / step; if (layer_step == 1 && (size / 32) > (id / 32 + 1)) { if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { weights_update_tmp = 0; } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) atomicAdd(&weight_updates_gpu[weights_index], wu); //if(weights_gpu[weights_index] != 1) printf(" wu = %f, weights_update_tmp = %f, w = %f, weights_gpu[weights_index] = %f, grad = %f, weights_normalization = %d ", // wu, weights_update_tmp, w, weights_gpu[weights_index], grad, weights_normalization); } } else { if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp); //weight_updates_gpu[weights_index] += weights_update_tmp; } } else layer_delta[add_index] += delta_in[id]; } } } extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization) { const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 //printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step); //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; backward_shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu, weights_normalization); CHECK_CUDA(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, 0, get_cuda_stream()>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void simple_input_shortcut_kernel(float *in, int size, float *add, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; out[id] = in[id] + add[id]; } __global__ void input_shortcut_kernel(float *in, 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] = in[out_index] + add[add_index]; } extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { if (w1 == w2 && h1 == h2 && c1 == c2) { int size = batch * w1 * h1 * c1; simple_input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, add, out); CHECK_CUDA(cudaPeekAtLastError()); return; } 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; //input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); simple_copy_ongpu(w2 * h2 * c2 * batch, in, out); shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_x_ent_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 t = truth[i]; float p = pred[i]; error[i] = (t) ? -log(p) : 0; delta[i] = t - p; } } extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { softmax_x_ent_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, pred, truth, delta, error); CHECK_CUDA(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0); } } extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { weighted_sum_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, c); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ if(da) da[i] += dc[i] * s[i]; db[i] += dc[i] * (1-s[i]); ds[i] += dc[i] * a[i] + dc[i] * -b[i]; } } extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc) { weighted_delta_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, da, db, ds, dc); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ c[i] += a[i]*b[i]; } } extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) { mult_add_into_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, c); CHECK_CUDA(cudaPeekAtLastError()); } __device__ void softmax_device(int n, float *input, float temp, float *output) { int i; float sum = 0; float largest = -INFINITY; for(i = 0; i < n; ++i){ int val = input[i]; largest = (val>largest) ? val : largest; } for(i = 0; i < n; ++i){ float e = exp(input[i]/temp - largest/temp); sum += e; output[i] = e; } for(i = 0; i < n; ++i){ output[i] /= sum; } } __global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output) { int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(b >= batch) return; softmax_device(n, input + b*offset, temp, output + b*offset); } extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output) { int inputs = n; int batch = groups; softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, get_cuda_stream()>>>(inputs, offset, batch, input, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } __device__ void softmax_device_new_api(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -INFINITY; for (i = 0; i < n; ++i) { int val = input[i*stride]; largest = (val>largest) ? val : largest; } for (i = 0; i < n; ++i) { float e = expf(input[i*stride] / temp - largest / temp); sum += e; output[i*stride] = e; } for (i = 0; i < n; ++i) { output[i*stride] /= sum; } } __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= batch*groups) return; int b = id / groups; int g = id % groups; softmax_device_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); } extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { softmax_kernel_new_api << <cuda_gridsize(batch*groups), BLOCK, 0, get_cuda_stream() >> >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= N) return; int out_index = i; int out_w = i % (w*stride); i = i / (w*stride); int out_h = i % (h*stride); i = i / (h*stride); int out_c = i%c; i = i / c; int b = i%batch; int in_w = out_w / stride; int in_h = out_h / stride; int in_c = out_c; int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; if (forward) out[out_index] += scale * x[in_index]; else atomicAdd(x + in_index, scale * out[out_index]); } extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t size = w*h*c*batch*stride*stride; upsample_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(size, in, w, h, c, batch, stride, forward, scale, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= spatial*batch*groups) return; int s = id % spatial; id = id / spatial; int g = id % groups; int b = id / groups; int goff = group_offset[g] * spatial; int boff = b*stride; softmax_device_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); } extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) { int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups); int *tree_groups_offset = cuda_make_int_array_new_api(hier.group_offset, hier.groups); /* static int *tree_groups_size = 0; static int *tree_groups_offset = 0; if(!tree_groups_size){ tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); } */ int num = spatial*batch*hier.groups; softmax_tree_kernel <<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); CHECK_CUDA(cudaPeekAtLastError()); cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); } __global__ void fix_nan_and_inf_kernel(float *input, size_t size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) { input[index] = 1.0f / (fabs((float)index) + 1); // pseudo random value } } } extern "C" void fix_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); fix_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void reset_nan_and_inf_kernel(float *input, size_t size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) { input[index] = 0; } } } extern "C" void reset_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); reset_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } __global__ void is_nan_or_inf_kernel(float *input, size_t size, int *pinned_return) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) *pinned_return = 1; } } extern "C" int is_nan_or_inf(float *input, size_t size) { int *pinned_return; CHECK_CUDA(cudaHostAlloc(&pinned_return, sizeof(int), cudaHostRegisterMapped)); *pinned_return = 0; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); is_nan_or_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size, pinned_return); CHECK_CUDA(cudaDeviceSynchronize()); int ret_val = *pinned_return; CHECK_CUDA(cudaFreeHost(pinned_return)); return ret_val; } __global__ void add_3_arrays_activate_kernel(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = 0; if (a1) val += a1[index]; if (a2) val += a2[index]; if (a3) val += a3[index]; if (a == LOGISTIC) val = 1.f / (1.f + expf(-val)); else if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); else if (a == LEAKY) val = (val < 0) ? val*0.1 : val; dst[index] = val; } } extern "C" void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (!(a == LOGISTIC || a == TANH || a == LEAKY || a == LINEAR)) { printf(" add_3_arrays_activate() doesn't support activation %d, it supports only LOGISTIC and TANH \n", a); exit(EXIT_FAILURE); } add_3_arrays_activate_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, a3, size, a, dst); } __global__ void sum_of_mults_kernel(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { dst[index] = a1[index] * a2[index] + b1[index] * b2[index]; } } extern "C" void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); sum_of_mults_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, b1, b2, size, dst); } __global__ void activate_and_mult_kernel(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = a1[index]; if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); else if (a == LEAKY) val = (val < 0) ? val*0.1 : val; dst[index] = val * a2[index]; } } extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (!(a == TANH || a == LEAKY || a == LINEAR)) { printf(" activat_and_mult() doesn't support activation %d, it supports only TANH \n", a); exit(EXIT_FAILURE); } activate_and_mult_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, size, a, dst); } __global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { if (scale_wh) { int osd_index = index % channel_size + (index / batch_size)*channel_size; out[index] = in_w_h_c[index] * scales_c[osd_index]; } else { out[index] = in_w_h_c[index] * scales_c[index / channel_size]; } } } extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { if (scale_wh) { int osd_index = index % channel_size + (index / batch_size)*channel_size; //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index] / channel_size); // l.delta * from out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here } else { int osd_index = index / channel_size; //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) int warp_id = index / 32; int index_warp_start = warp_id * 32; int osd_index_warp_start = index_warp_start / channel_size; int osd_index_warp_end = (index_warp_start + 31) / channel_size; if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel { float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from if (threadIdx.x % 32 == 0) { atomicAdd(&out_state_delta[osd_index], sum); //out_state_delta[osd_index] += sum; } } else { atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from } out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here } } } extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); backward_scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size, batch_size, scale_wh, in_scales_c, out_from_delta, in_from_output, out_state_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void sam_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { out[index] = in_w_h_c[index] * scales_c[index]; } } extern "C" void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_sam_kernel(float *in_w_h_c_delta, int size, int channel_size, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { out_state_delta[index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) out_from_delta[index] += in_scales_c[index] * in_w_h_c_delta[index]; // input * l.delta //out_state_delta[index] += in_w_h_c_delta[index]; //out_from_delta[index] = in_w_h_c_delta[index]; } } extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); backward_sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size, in_scales_c, out_from_delta, in_from_output, out_state_delta); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { // rotate left or right if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floorf(x_s); // round down int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floorf(y_s); int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } } extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); smooth_rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float scale, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id > 0) { if (stage_id == 1) scale = 0.65; else if (stage_id == 2) scale = 0.8; else if (stage_id == 3) scale = 1.3; if (reverse) scale = 1 / scale; const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x_c + (x_d - x_c) / scale // Ysource = y_c + (y_d - y_c) / scale float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; int x_0 = floorf(x_s); // round down int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floorf(y_s); int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { //if (scale < 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } } } } extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); stretch_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id == 1 || stage_id == 2) { // rotate left or right if (stage_id == 2) angle = -angle; if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floorf(x_s); // round down int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floorf(y_s); int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } else if (stage_id == 3) { // flip for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } } } extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); sway_and_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 4; // 4 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { // if(reverse) if (stage_id == 0) { // simple copy for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = x + y*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 1) { // 90 degree clockwise rotation - 1 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - y) + x*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 2) { // 180 degree clockwise rotation - 2 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - x) + (kernel_size - 1 - y)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } else if (stage_id == 3) { // 270 degree clockwise rotation - 3 for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = y + (kernel_size - 1 - x)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; } } } } } extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; const int stage_step = (nweights / kernel_area) / 8; // 8 stages const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size if (i < nweights) { if (stage_id == 0) { // simple copy for (int x = 0; x < kernel_size; ++x) { for (int y = 0; y < kernel_size; ++y) { weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } else if (stage_id == 1 || stage_id == 2 || stage_id == 3 || stage_id == 4) { float scale = 0.5; if (stage_id == 1) scale = 0.65; else if (stage_id == 2) scale = 0.8; else if (stage_id == 3) scale = 1.2; else if (stage_id == 4) scale = 1.4; if (reverse) scale = 1 / scale; const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x_c + (x_d - x_c) / scale // Ysource = y_c + (y_d - y_c) / scale float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; int x_0 = floorf(x_s); // round down int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floorf(y_s); int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { if(scale > 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } } else if (stage_id == 5 || stage_id == 6) { // rotate left or right if (stage_id == 6) angle = -angle; if (reverse) angle = -angle; const float cos_a = cosf(angle * 3.14159265 / 180); const float sin_a = sinf(angle * 3.14159265 / 180); const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; float dropout_sum = 0; for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; int x_0 = floorf(x_s); // round down int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; int y_0 = floorf(y_s); int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; float c_x_1 = x_s - x_0; float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; else dropout_sum += c_x_0 * c_y_0; if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; else dropout_sum += c_x_1 * c_y_0; if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; else dropout_sum += c_x_0 * c_y_1; if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } // compensate for dropped items const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[x + y*kernel_size + i] *= coef; } } } else if (stage_id == 7) { // flip for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } } } } extern "C" void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); stretch_sway_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < current_size) { float val = 0; for (int i = 0; i < groups; ++i) { val += src_gpu[index + i*current_size]; } for (int i = 0; i < groups; ++i) { dst_gpu[index + i*current_size] = val / groups; } } } extern "C" void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) { const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); reduce_and_expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < current_size) { for (int i = 0; i < groups; ++i) { dst_gpu[index + i*current_size] = src_gpu[index]; } } } extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) { const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, int size, const float eps, float divider, const float clip, const float abs_add) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = src_gpu[index]; float sign = (val < 0) ? -1 : 1; // eps = 1 by default // eps = 2 - lower delta // eps = 0 - higher delta (linear) // eps = -1 - high delta (inverse number) // = (abs(x)*10+1)^(-1) float unsigned_val = powf(fabs(val)*10 + abs_add, eps); unsigned_val = unsigned_val / divider; if (unsigned_val > clip && clip != 0.0) unsigned_val = clip; if (isnan(unsigned_val) || isinf(unsigned_val)) unsigned_val = 0; dst_gpu[index] = unsigned_val * sign; } } extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps, float divider, float clip, float abs_add) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); mult_inverse_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, size, eps, divider, clip, abs_add); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void P_constrastive_f_det_kernel(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size) { const int il = blockIdx.x*blockDim.x + threadIdx.x; if (il < contrast_p_size) { const float sim = contrast_p[il].sim; const size_t i = contrast_p[il].i; const size_t j = contrast_p[il].j; const float numerator = expf(sim / temperature); float denominator = 0; int k; for (k = 0; k < contrast_p_size; ++k) { contrastive_params cp = contrast_p[k]; //if (k != i && labels[k] != labels[i]) { //if (k != i) { if (cp.i != i && cp.j == j) { //const float sim_den = cp.sim; ////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); //denominator += expf(sim_den / temperature); denominator += cp.exp_sim; } } float result = 0.9999; if (denominator != 0) result = numerator / denominator; if (result > 1) result = 0.9999; contrast_p[il].P = result; } } extern "C" void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(contrast_p_size, block_size); P_constrastive_f_det_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (labels, feature_size, temperature, contrast_p, contrast_p_size); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void coord_conv_kernel(float *dst, int w, int h, int chan, int batch, int type) { int i = blockIdx.x*blockDim.x + threadIdx.x; const int x = i % w; i = i / w; const int y = i % h; i = i / h; const int c = i % chan; //i = i / chan; //const int b = i % batch; if (type == 0) { if (c == 0) { const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1) dst[i] = x_val; // x - coord } else if (c == 1) { const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1) dst[i] = y_val; // y - coord } else if (c == 2) { const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1) const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1) const float rad_val = sqrtf(x_val*x_val + y_val*y_val); // [0; 1.414) dst[i] = rad_val; // rad - coord } } else if (type == 1) { if (c >= 0 && c <= 2) { dst[i] = 0; } } } extern "C" void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); coord_conv_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (dst, w, h, chan, b, type); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void forward_implicit_kernel(int size, int batch, int nweights, float *weight_gpu, float *output_gpu) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; output_gpu[id] = weight_gpu[id % nweights]; } extern "C" void forward_implicit_gpu(int batch, int nweights, float *weight_gpu, float *output_gpu) { int size = batch * nweights; forward_implicit_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, batch, nweights, weight_gpu, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_implicit_kernel(int size, int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; for (int i = 0; i < batch; ++i) { weight_updates_gpu[id] += delta_gpu[id + i * nweights]; } } extern "C" void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) { int size = nweights; backward_implicit_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, batch, nweights, weight_updates_gpu, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); }
