| const std::string im2col_str = "// This file is modified from the file located at\n// https://github.com/BVLC/caffe/blob/opencl/src/caffe/greentea/cl_kernels/im2col.cl\n// and is covered under the BSD 2-Clause License, as indicated in the LICENSE\n// file at the root of this repository.\n\n__kernel void im2col(const int n, __global const float* data_im,\n const int data_im_off,\n const int height, const int width,\n const int kernel_h, const int kernel_w,\n const int pad_h, const int pad_w,\n const int stride_h, const int stride_w,\n const int dilation_h, const int dilation_w,\n const int height_col, const int width_col,\n __global float* data_col, const int data_col_off) {\n\n for (int index = get_global_id(0); index < n;\n index += get_global_size(0)) {\n const int h_index = index / width_col;\n const int h_col = h_index % height_col;\n const int w_col = index % width_col;\n const int c_im = h_index / height_col;\n const int c_col = c_im * kernel_h * kernel_w;\n const int h_offset = h_col * stride_h - pad_h;\n const int w_offset = w_col * stride_w - pad_w;\n \n __global float* data_col_ptr = data_col + data_col_off;\n data_col_ptr += (c_col * height_col + h_col) * width_col + w_col;\n __global const float* data_im_ptr = data_im + data_im_off;\n data_im_ptr += (c_im * height + h_offset) * width + w_offset;\n \n for (int i = 0; i < kernel_h; ++i) {\n for (int j = 0; j < kernel_w; ++j) {\n int h_im = h_offset + i * dilation_h;\n int w_im = w_offset + j * dilation_w;\n *data_col_ptr =\n (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?\n data_im_ptr[i * dilation_h * width + j * dilation_w] : 0;\n data_col_ptr += height_col * width_col;\n }\n }\n }\n}\n\n__kernel void col2im(const int n, __global const float* data_col,\n const int data_col_off, const int channels,\n const int height, const int width,\n const int kernel_h, const int kernel_w,\n const int pad_h, const int pad_w,\n const int stride_h, const int stride_w,\n const int dilation_h, const int dilation_w,\n const int height_col, const int width_col,\n __global float* data_im, const int data_im_off) {\n\n for (int index = get_global_id(0); index < n; index += get_global_size(0)) {\n float val = 0;\n const int w_im = index % width + pad_w;\n const int h_im = (index / width) % height + pad_h;\n const int c_im = index / (width * height);\n int kernel_extent_w = (kernel_w - 1) * dilation_w + 1;\n int kernel_extent_h = (kernel_h - 1) * dilation_h + 1;\n // compute the start and end of the output\n const int w_col_start =\n (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1;\n const int w_col_end = min(w_im / stride_w + 1, width_col);\n const int h_col_start =\n (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1;\n const int h_col_end = min(h_im / stride_h + 1, height_col);\n \n // TODO: use LCM of stride and dilation to avoid unnecessary loops\n for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) {\n for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {\n int h_k = (h_im - h_col * stride_h);\n int w_k = (w_im - w_col * stride_w);\n if (h_k % dilation_h == 0 && w_k % dilation_w == 0) {\n h_k /= dilation_h;\n w_k /= dilation_w;\n int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) *\n height_col + h_col) * width_col + w_col;\n val += data_col[data_col_off + data_col_index];\n }\n }\n }\n data_im[data_im_off + index] = val;\n }\n}\n";const std::string pooling_str = "// This file is modified from the file located at\n// https://github.com/BVLC/caffe/blob/opencl/src/caffe/greentea/cl_kernels/pooling.cl\n// and is covered under the BSD 2-Clause License, as indicated in the LICENSE\n// file at the root of this repository.\n\n__kernel void max_pool_forward(\n const int nthreads, __global const float* bottom, const int channels, \n const int height, const int width,\n const int pooled_h, const int pooled_w,\n const int kernel_h, const int kernel_w,\n const int stride_h, const int stride_w,\n const int pad_h, const int pad_w,\n __global float* top, __global float* mask) {\n\n// printf(\"%d \", get_global_size(0));\n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n const int pw = i % pooled_w;\n const int ph = (i / pooled_w) % pooled_h;\n const int c = (i / pooled_w / pooled_h) % channels;\n const int n = i / pooled_w / pooled_h / channels;\n \n int hstart = ph * stride_h - pad_h;\n int wstart = pw * stride_w - pad_w;\n const int hend = min(hstart + kernel_h, height);\n const int wend = min(wstart + kernel_w, width);\n hstart = max(hstart, (int)0);\n wstart = max(wstart, (int)0);\n \n float maxval = -FLT_MAX;\n int maxidx = -1;\n __global const float* bottom_slice = bottom + (n * channels + c) * height * width;\n for (int h = hstart; h < hend; ++h) {\n for (int w = wstart; w < wend; ++w) {\n const int index = h * width + w;\n if (bottom_slice[index] > maxval) {\n maxidx = index;\n maxval = bottom_slice[maxidx];\n }\n }\n }\n top[i] = maxval;\n mask[i] = (float)maxidx;\n }\n}\n\n__kernel void ave_pool_forward(\n const int nthreads, __global const float* const bottom, const int channels, \n const int height, const int width,\n const int pooled_h, const int pooled_w,\n const int kernel_h, const int kernel_w,\n const int stride_h, const int stride_w, \n const int pad_h, const int pad_w, __global float* top) {\n \n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n const int pw = i % pooled_w;\n const int ph = (i / pooled_w) % pooled_h;\n const int c = (i / pooled_w / pooled_h) % channels;\n const int n = i / pooled_w / pooled_h / channels;\n int hstart = ph * stride_h - pad_h;\n int wstart = pw * stride_w - pad_w;\n int hend = min(hstart + kernel_h, height + pad_h);\n int wend = min(wstart + kernel_w, width + pad_w);\n const int pool_size = (hend - hstart) * (wend - wstart);\n hstart = max(hstart, (int)0);\n wstart = max(wstart, (int)0);\n hend = min(hend, height);\n wend = min(wend, width);\n float aveval = 0;\n __global const float* bottom_slice = bottom + (n * channels + c) * height * width;\n for (int h = hstart; h < hend; ++h) {\n for (int w = wstart; w < wend; ++w) {\n aveval += bottom_slice[h * width + w];\n }\n }\n top[i] = aveval / pool_size;\n }\n}\n\n__kernel void sto_pool_forward_train(\n const int nthreads, __global const float* bottom,\n const int channels, const int height, const int width,\n const int pooled_h, const int pooled_w, const int kernel_h,\n const int kernel_w, const int stride_h, const int stride_w,\n __global float* rand_idx, __global float* top) {\n \n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n const int pw = i % pooled_w;\n const int ph = (i / pooled_w) % pooled_h;\n const int c = (i / pooled_w / pooled_h) % channels;\n const int n = i / pooled_w / pooled_h / channels;\n \n const int hstart = ph * stride_h;\n const int hend = min(hstart + kernel_h, height);\n const int wstart = pw * stride_w;\n const int wend = min(wstart + kernel_w, width);\n float cumsum = 0.;\n __global const float* bottom_slice = bottom + (n * channels + c) * height * width;\n // First pass: get sum\n for (int h = hstart; h < hend; ++h) {\n for (int w = wstart; w < wend; ++w) {\n cumsum += bottom_slice[h * width + w];\n }\n }\n const float thres = rand_idx[i] * cumsum;\n // Second pass: get value, and set i.\n cumsum = 0;\n for (int h = hstart; h < hend; ++h) {\n for (int w = wstart; w < wend; ++w) {\n cumsum += bottom_slice[h * width + w];\n if (cumsum >= thres) {\n rand_idx[i] = ((n * channels + c) * height + h) * width + w;\n top[i] = bottom_slice[h * width + w];\n h = hend;\n w = wend;\n }\n }\n }\n }\n}\n\n__kernel void sto_pool_forward_test(\n const int nthreads, __global const float* const bottom, const int channels, \n const int height, const int width,\n const int pooled_h, const int pooled_w, \n const int kernel_h, const int kernel_w, \n const int stride_h, const int stride_w,\n __global float* top) {\n \n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n const int pw = i % pooled_w;\n const int ph = (i / pooled_w) % pooled_h;\n const int c = (i / pooled_w / pooled_h) % channels;\n const int n = i / pooled_w / pooled_h / channels;\n \n const int hstart = ph * stride_h;\n const int hend = min(hstart + kernel_h, height);\n const int wstart = pw * stride_w;\n const int wend = min(wstart + kernel_w, width);\n // We set cumsum to be 0 to avoid divide-by-zero problems\n float cumsum = FLT_MIN;\n float cumvalues = 0.;\n __global const float* bottom_slice = bottom + (n * channels + c) * height * width;\n // First pass: get sum\n for (int h = hstart; h < hend; ++h) {\n for (int w = wstart; w < wend; ++w) {\n cumsum += bottom_slice[h * width + w];\n cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];\n }\n }\n top[i] = cumvalues / cumsum;\n }\n}\n\n__kernel void max_pool_backward(const int nthreads,\n __global const float* top_diff,\n __global const float* mask,\n const int channels,\n const int height, const int width,\n const int pooled_h, const int pooled_w,\n const int kernel_h, const int kernel_w,\n const int stride_h, const int stride_w,\n const int pad_h, const int pad_w,\n __global float* bottom_diff) {\n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n // find out the local i\n // find out the local offset\n const int w = i % width;\n const int h = (i / width) % height;\n const int c = (i / width / height) % channels;\n const int n = i / width / height / channels;\n \n const int phstart =\n (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;\n const int phend = min((h + pad_h) / stride_h + 1, pooled_h);\n const int pwstart =\n (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;\n const int pwend = min((w + pad_w) / stride_w + 1, pooled_w);\n float gradient = 0.0f;\n const int offset = (n * channels + c) * pooled_h * pooled_w;\n __global const float* top_diff_slice = top_diff + offset;\n __global const float* mask_slice = mask + offset;\n for (int ph = phstart; ph < phend; ++ph) {\n for (int pw = pwstart; pw < pwend; ++pw) {\n if (mask_slice[ph * pooled_w + pw] == (float)(h * width + w)) {\n gradient += top_diff_slice[ph * pooled_w + pw];\n }\n }\n }\n bottom_diff[i] = gradient;\n }\n}\n\n__kernel void ave_pool_backward(const int nthreads,\n __global const float* top_diff,\n const int channels,\n const int height, const int width,\n const int pooled_h, const int pooled_w,\n const int kernel_h, const int kernel_w,\n const int stride_h, const int stride_w,\n const int pad_h, const int pad_w,\n __global float* bottom_diff) {\n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n // find out the local i\n // find out the local offset\n const int w = i % width + pad_w;\n const int h = (i / width) % height + pad_h;\n const int c = (i / width / height) % channels;\n const int n = i / width / height / channels;\n \n const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;\n const int phend = min(h / stride_h + 1, pooled_h);\n const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;\n const int pwend = min(w / stride_w + 1, pooled_w);\n float gradient = 0.0;\n __global const float* const top_diff_slice = top_diff + (n * channels + c) * pooled_h * pooled_w;\n for (int ph = phstart; ph < phend; ++ph) {\n for (int pw = pwstart; pw < pwend; ++pw) {\n // figure out the pooling size\n int hstart = ph * stride_h - pad_h;\n int wstart = pw * stride_w - pad_w;\n int hend = min(hstart + kernel_h, height + pad_h);\n int wend = min(wstart + kernel_w, width + pad_w);\n int pool_size = (hend - hstart) * (wend - wstart);\n gradient += top_diff_slice[ph * pooled_w + pw] / pool_size;\n }\n }\n bottom_diff[i] = gradient;\n }\n}\n\n__kernel void sto_pool_backward(\n const int nthreads, __global const float* rand_idx,\n __global const float* const top_diff, const int channels,\n const int height, const int width,\n const int pooled_h, const int pooled_w,\n const int kernel_h, const int kernel_w,\n const int stride_h, const int stride_w,\n __global float* bottom_diff) {\n\n for (int i = get_global_id(0); i < nthreads; i += get_global_size(0)) {\n // find out the local i\n // find out the local offset\n const int w = i % width;\n const int h = (i / width) % height;\n const int c = (i / width / height) % channels;\n const int n = i / width / height / channels;\n \n const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;\n const int phend = min(h / stride_h + 1, pooled_h);\n const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;\n const int pwend = min(w / stride_w + 1, pooled_w);\n float gradient = 0.0;\n __global const float* rand_idx_slice = rand_idx + (n * channels + c) * pooled_h * pooled_w;\n __global const float* top_diff_slice = top_diff + (n * channels + c) * pooled_h * pooled_w;\n for (int ph = phstart; ph < phend; ++ph) {\n for (int pw = pwstart; pw < pwend; ++pw) {\n gradient += top_diff_slice[ph * pooled_w + pw]\n * (i == (int) (rand_idx_slice[ph * pooled_w + pw])?1.0:0.0);\n }\n }\n bottom_diff[i] = gradient;\n }\n}\n\n";const std::string distribution_str = "// This code is adapted from https://github.com/amd/OpenCL-caffe/blob/stable/src/caffe/ocl/random.cl\n\n//Note: random generator has two parts\n//first part: the open sourced threefy random generator kernel from DE Shaw Research\n//second part. we wrap the kernel up to generate uniform, bernoulli and gaussion distribution generators.\n\n//begin: the open sourced random generator from DE Shaw Research\n//https://www.deshawresearch.com/resources_random123.html\ntypedef uint uint32_t;\n\nstruct r123array4x32 {\n uint32_t v[4];\n};\n\nenum r123_enum_threefry32x4 {\n R_32x4_0_0 = 10,\n R_32x4_0_1 = 26,\n R_32x4_1_0 = 11,\n R_32x4_1_1 = 21,\n R_32x4_2_0 = 13,\n R_32x4_2_1 = 27,\n R_32x4_3_0 = 23,\n R_32x4_3_1 = 5,\n R_32x4_4_0 = 6,\n R_32x4_4_1 = 20,\n R_32x4_5_0 = 17,\n R_32x4_5_1 = 11,\n R_32x4_6_0 = 25,\n R_32x4_6_1 = 10,\n R_32x4_7_0 = 18,\n R_32x4_7_1 = 20\n};\n\ninline uint32_t RotL_32(uint32_t x, unsigned int N) {\n return (x << (N & 31)) | (x >> ((32 - N) & 31));\n}\n\ntypedef struct r123array4x32 threefry4x32_ctr_t;\ntypedef struct r123array4x32 threefry4x32_key_t;\ntypedef struct r123array4x32 threefry4x32_ukey_t;\n\ninline threefry4x32_ctr_t threefry4x32_R(unsigned int Nrounds, threefry4x32_ctr_t in, threefry4x32_key_t k) {\n threefry4x32_ctr_t X;\n uint32_t ks[4 + 1];\n int i;\n ks[4] = 0x1BD11BDA;\n\n {\n ks[0] = k.v[0];\n X.v[0] = in.v[0];\n ks[4] ^= k.v[0];\n\n ks[1] = k.v[1];\n X.v[1] = in.v[1];\n ks[4] ^= k.v[1];\n\n ks[2] = k.v[2];\n X.v[2] = in.v[2];\n ks[4] ^= k.v[2];\n\n ks[3] = k.v[3];\n X.v[3] = in.v[3];\n ks[4] ^= k.v[3];\n }\n\n X.v[0] += ks[0];\n X.v[1] += ks[1];\n X.v[2] += ks[2];\n X.v[3] += ks[3];\n\n if (Nrounds > 0) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 1) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 2) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 3) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 3) {\n X.v[0] += ks[1];\n X.v[1] += ks[2];\n X.v[2] += ks[3];\n X.v[3] += ks[4];\n X.v[4 - 1] += 1;\n }\n\n if (Nrounds > 4) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 5) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 6) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 7) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 7) {\n X.v[0] += ks[2];\n X.v[1] += ks[3];\n X.v[2] += ks[4];\n X.v[3] += ks[0];\n X.v[4 - 1] += 2;\n }\n\n if (Nrounds > 8) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 9) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 10) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 11) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 11) {\n X.v[0] += ks[3];\n X.v[1] += ks[4];\n X.v[2] += ks[0];\n X.v[3] += ks[1];\n X.v[4 - 1] += 3;\n }\n\n if (Nrounds > 12) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 13) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 14) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 15) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 15) {\n X.v[0] += ks[4];\n X.v[1] += ks[0];\n X.v[2] += ks[1];\n X.v[3] += ks[2];\n X.v[4 - 1] += 4;\n }\n\n if (Nrounds > 16) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 17) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 18) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 19) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 19) {\n X.v[0] += ks[0];\n X.v[1] += ks[1];\n X.v[2] += ks[2];\n X.v[3] += ks[3];\n X.v[4 - 1] += 5;\n }\n\n if (Nrounds > 20) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 21) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 22) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 23) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 23) {\n X.v[0] += ks[1];\n X.v[1] += ks[2];\n X.v[2] += ks[3];\n X.v[3] += ks[4];\n X.v[4 - 1] += 6;\n }\n\n if (Nrounds > 24) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 25) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 26) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 27) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 27) {\n X.v[0] += ks[2];\n X.v[1] += ks[3];\n X.v[2] += ks[4];\n X.v[3] += ks[0];\n X.v[4 - 1] += 7;\n }\n\n if (Nrounds > 28) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 29) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 30) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 31) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 31) {\n X.v[0] += ks[3];\n X.v[1] += ks[4];\n X.v[2] += ks[0];\n X.v[3] += ks[1];\n X.v[4 - 1] += 8;\n }\n\n if (Nrounds > 32) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 33) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 34) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 35) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 35) {\n X.v[0] += ks[4];\n X.v[1] += ks[0];\n X.v[2] += ks[1];\n X.v[3] += ks[2];\n X.v[4 - 1] += 9;\n }\n\n if (Nrounds > 36) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 37) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 38) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 39) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 39) {\n X.v[0] += ks[0];\n X.v[1] += ks[1];\n X.v[2] += ks[2];\n X.v[3] += ks[3];\n X.v[4 - 1] += 10;\n }\n\n if (Nrounds > 40) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 41) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n if (Nrounds > 42) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 43) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 43) {\n X.v[0] += ks[1];\n X.v[1] += ks[2];\n X.v[2] += ks[3];\n X.v[3] += ks[4];\n X.v[4 - 1] += 11;\n }\n\n if (Nrounds > 44) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 45) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 46) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 47) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 47) {\n X.v[0] += ks[2];\n X.v[1] += ks[3];\n X.v[2] += ks[4];\n X.v[3] += ks[0];\n X.v[4 - 1] += 12;\n }\n\n if (Nrounds > 48) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 49) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 50) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 51) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 51) {\n X.v[0] += ks[3];\n X.v[1] += ks[4];\n X.v[2] += ks[0];\n X.v[3] += ks[1];\n X.v[4 - 1] += 13;\n }\n\n if (Nrounds > 52) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 53) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 54) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 55) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 55) {\n X.v[0] += ks[4];\n X.v[1] += ks[0];\n X.v[2] += ks[1];\n X.v[3] += ks[2];\n X.v[4 - 1] += 14;\n }\n\n if (Nrounds > 56) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 57) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 58) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 59) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 59) {\n X.v[0] += ks[0];\n X.v[1] += ks[1];\n X.v[2] += ks[2];\n X.v[3] += ks[3];\n X.v[4 - 1] += 15;\n }\n\n if (Nrounds > 60) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 61) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 62) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 63) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 63) {\n X.v[0] += ks[1];\n X.v[1] += ks[2];\n X.v[2] += ks[3];\n X.v[3] += ks[4];\n X.v[4 - 1] += 16;\n }\n\n if (Nrounds > 64) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_0_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_0_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 65) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_1_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_1_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 66) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_2_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_2_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 67) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_3_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_3_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 67) {\n X.v[0] += ks[2];\n X.v[1] += ks[3];\n X.v[2] += ks[4];\n X.v[3] += ks[0];\n X.v[4 - 1] += 17;\n }\n\n if (Nrounds > 68) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_4_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_4_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 69) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_5_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_5_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 70) {\n X.v[0] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_6_0);\n X.v[1] ^= X.v[0];\n X.v[2] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_6_1);\n X.v[3] ^= X.v[2];\n }\n\n if (Nrounds > 71) {\n X.v[0] += X.v[3];\n X.v[3] = RotL_32(X.v[3], R_32x4_7_0);\n X.v[3] ^= X.v[0];\n X.v[2] += X.v[1];\n X.v[1] = RotL_32(X.v[1], R_32x4_7_1);\n X.v[1] ^= X.v[2];\n }\n\n if (Nrounds > 71) {\n X.v[0] += ks[3];\n X.v[1] += ks[4];\n X.v[2] += ks[0];\n X.v[3] += ks[1];\n X.v[4 - 1] += 18;\n }\n return X;\n}\n//end: the open sourced random generator from DE Shaw Research\n\n// **************************\n// BERNOULLI DISTRIBUTION\n// **************************\n\n__kernel void PRNG_threefry4x32_bernoulli(\n__global float4 *randomnumber,\nthreefry4x32_ctr_t ctr_i,\nfloat inf, float sup,\nfloat threshold,\nuint nrounds, uint numrandom) {\n\n size_t gdx = get_global_id(0);\n\n uint maxUint = 0;\n maxUint--;\n float r = (float)maxUint;\n\n threefry4x32_ctr_t ctr = ctr_i;\n threefry4x32_ukey_t ukey;\n\n ukey.v[0] = ukey.v[1] = ukey.v[2] = ukey.v[3] = gdx;\n\n threefry4x32_ctr_t random4;\n\n if ( gdx < numrandom ) {\n random4 = threefry4x32_R(nrounds, ctr, ukey);\n float4 frnd;\n frnd.x = ( (((float)random4.v[0]) / r) * (sup - inf) + inf ) < threshold ? 1.0f : 0.0f;\n frnd.y = ( (((float)random4.v[1]) / r) * (sup - inf) + inf ) < threshold ? 1.0f : 0.0f;\n frnd.z = ( (((float)random4.v[2]) / r) * (sup - inf) + inf ) < threshold ? 1.0f : 0.0f;\n frnd.w = ( (((float)random4.v[3]) / r) * (sup - inf) + inf ) < threshold ? 1.0f : 0.0f;\n randomnumber[gdx] = frnd;\n }\n}\n\n// **************************\n// UNIFORM DISTRIBUTION (float)\n// **************************\n\n__kernel void PRNG_threefry4x32_uniform(\n__global float4 *randomnumber,\nthreefry4x32_ctr_t ctr_i,\nfloat inf, float sup,\nuint nrounds, uint numrandom) {\n\n size_t gdx = get_global_id(0);\n\n uint maxUint = 0;\n maxUint--;\n float r = (float)maxUint;\n\n threefry4x32_ctr_t ctr = ctr_i;\n threefry4x32_ukey_t ukey;\n\n ukey.v[0] = ukey.v[1] = ukey.v[2] = ukey.v[3] = gdx;\n\n threefry4x32_ctr_t random4;\n\n if ( gdx < numrandom ) {\n random4 = threefry4x32_R(nrounds, ctr, ukey);\n float4 frnd;\n frnd.x = ( (((float)random4.v[0]) / r) * (sup - inf) + inf );\n frnd.y = ( (((float)random4.v[1]) / r) * (sup - inf) + inf );\n frnd.z = ( (((float)random4.v[2]) / r) * (sup - inf) + inf );\n frnd.w = ( (((float)random4.v[3]) / r) * (sup - inf) + inf );\n randomnumber[gdx] = frnd;\n }\n}\n\n// **************************\n// UNIFORM DISTRIBUTION (uint)\n// **************************\n\n__kernel void PRNG_threefry4x32_uint_uniform(\n__global uint4 *randomnumber,\nthreefry4x32_ctr_t ctr_i,\nuint inf, uint sup,\nuint nrounds, uint numrandom) {\n\n size_t gdx = get_global_id(0);\n\n threefry4x32_ctr_t ctr = ctr_i;\n threefry4x32_ukey_t ukey;\n\n ukey.v[0] = ukey.v[1] = ukey.v[2] = ukey.v[3] = gdx;\n\n threefry4x32_ctr_t random4;\n\n if ( gdx < numrandom ) {\n random4 = threefry4x32_R(nrounds, ctr, ukey);\n uint4 frnd;\n frnd.x = random4.v[0] % (sup - inf) + inf;\n frnd.y = random4.v[1] % (sup - inf) + inf;\n frnd.z = random4.v[2] % (sup - inf) + inf;\n frnd.w = random4.v[3] % (sup - inf) + inf;\n randomnumber[gdx] = frnd;\n }\n}\n\n// **************************\n// GAUSSIAN DISTRIBUTION\n// **************************\n\n__kernel void PRNG_threefry4x32_gaussian(\n__global float4 *randomnumber,\nthreefry4x32_ctr_t ctr_i,\nfloat E, float V,\nuint nrounds, uint numrandom) {\n\n size_t gdx = get_global_id(0);\n\n uint maxUint = 0;\n maxUint--;\n float r = (float)maxUint;\n\n threefry4x32_ctr_t ctr = ctr_i;\n threefry4x32_ukey_t ukey1, ukey2;\n\n ukey1.v[0] = ukey2.v[1] = ukey1.v[2] = ukey2.v[3] = gdx;\n ukey2.v[0] = ukey1.v[1] = ukey2.v[2] = ukey1.v[3] = 0;\n\n threefry4x32_ctr_t random1, random2;\n\n if ( gdx < numrandom ) {\n random1 = threefry4x32_R(nrounds, ctr, ukey1);\n random2 = threefry4x32_R(nrounds, ctr, ukey2);\n float4 frnd1;\n\n float r1 = (((float)random1.v[0]) / r); // generate a random sequence of uniform distribution\n float r2 = (((float)random2.v[0]) / r);\n float r3 = (((float)random1.v[1]) / r);\n float r4 = (((float)random2.v[1]) / r);\n float r5 = (((float)random1.v[2]) / r);\n float r6 = (((float)random2.v[2]) / r);\n float r7 = (((float)random1.v[3]) / r);\n float r8 = (((float)random2.v[3]) / r);\n\n if(r2 == 0 || r4 == 0 || r6 == 0 || r8 == 0) {\n r2 += 0.0001;\n r4 += 0.0001;\n r6 += 0.0001;\n r8 += 0.0001;\n }\n\n frnd1.x = cos(2*M_PI*r1)*sqrt(-2.0*log(r2)) * V + E;// return a pseudo sequence of normal distribution using two above uniform noise data\n //frnd2.x = sin(2*M_PI*r1)*sqrt(-2.0*log(r2)); // return the quadrature counterpart of the foregoing pseudo normal distribution sequence\n frnd1.y = cos(2*M_PI*r3)*sqrt(-2.0*log(r4)) * V + E;// return a pseudo sequence of normal distribution using two above uniform noise data\n //frnd2.y = sin(2*M_PI*r3)*sqrt(-2.0*log(r4)); // return the quadrature counterpart of the foregoing pseudo normal distribution sequence\n frnd1.z = cos(2*M_PI*r5)*sqrt(-2.0*log(r6)) * V + E;// return a pseudo sequence of normal distribution using two above uniform noise data\n //frnd2.z = sin(2*M_PI*r5)*sqrt(-2.0*log(r6)); // return the quadrature counterpart of the foregoing pseudo normal distribution sequence\n frnd1.w = cos(2*M_PI*r7)*sqrt(-2.0*log(r8)) * V + E;// return a pseudo sequence of normal distribution using two above uniform noise data\n //frnd2.w = sin(2*M_PI*r7)*sqrt(-2.0*log(r8)); // return the quadrature counterpart of the foregoing pseudo normal distribution sequence\n\n randomnumber[gdx] = frnd1;\n }\n}\n";const std::string tensormath_str = "/**\n * Licensed to the Apache Software Foundation (ASF) under one\n * or more contributor license agreements. See the NOTICE file\n * distributed with this work for additional information\n * regarding copyright ownership. The ASF licenses this file\n * to you under the Apache License, Version 2.0 (the\n * \"License\"); you may not use this file except in compliance\n * with the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n// **************************************\n// Element-wise functions\n// **************************************\n\n// Sum is basically reduction.\n// This reduction code is serial reduction modified from AMD\'s example.\n// http://developer.amd.com/resources/documentation-articles/articles-whitepapers/opencl-optimization-case-study-simple-reductions/\n__kernel\nvoid clkernel_fabs(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = fabs(in[i]);\n}\n\n__kernel\nvoid clkernel_add_scalar(const int num, float x, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in[i] + x;\n}\n\n__kernel\nvoid clkernel_add(const int num, __global const float* in1, __global const float* in2,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in1[i] + in2[i];\n}\n\n__kernel\nvoid clkernel_clamp(const int num, float low, float high, __global const float* in,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = clamp(in[i], low, high);\n}\n\n__kernel\nvoid clkernel_divide_scalar_matx(const int num, __global const float* in1, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in1[i] / x;\n}\n\n__kernel\nvoid clkernel_divide_scalar_xmat(const int num, const float x, __global const float* in1,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = x / in1[i];\n}\n\n__kernel\nvoid clkernel_divide(const int num, __global const float* in1, __global const float* in2,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in1[i] / in2[i];\n}\n\n__kernel\nvoid clkernel_eltmult_scalar(const int num, const float x, __global const float* in,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in[i] * x;\n}\n\n__kernel\nvoid clkernel_eltmult(const int num, __global const float* in1, __global const float* in2,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in1[i] * in2[i];\n}\n\n__kernel\nvoid clkernel_exp(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = exp(in[i]);\n}\n\n__kernel\nvoid clkernel_le(const int num, __global const float* in, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] <= x) ? 1.0f : 0.0f;\n}\n\n__kernel\nvoid clkernel_log(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = log(in[i]);\n}\n\n__kernel\nvoid clkernel_lt(const int num, __global const float* in, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] < x) ? 1.0f : 0.0f;\n}\n\n__kernel\nvoid clkernel_ge(const int num, __global const float* in, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] >= x) ? 1.0f : 0.0f;\n}\n\n__kernel\nvoid clkernel_gt(const int num, __global const float* in, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] > x) ? 1.0f : 0.0f;\n}\n\n__kernel\nvoid clkernel_pow_scalar(const int num, const float x, __global const float* in,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = pow(in[i], x);\n}\n\n__kernel\nvoid clkernel_pow(const int num, __global const float* in1, __global const float* in2,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = pow(in1[i], in2[i]);\n}\n\n__kernel\nvoid clkernel_relu(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] >= 0.0f) ? in[i] : 0.0f;\n}\n\n__kernel\nvoid clkernel_set(const int num, const float x, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = x;\n}\n\n__kernel\nvoid clkernel_sigmoid(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = 1 / (1 + exp(-(in[i])));\n}\n\n__kernel\nvoid clkernel_sign(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = (in[i] > 0) - (in[i] < 0);\n}\n\n__kernel\nvoid clkernel_sqrt(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = sqrt(in[i]);\n}\n\n// kernel for square is called pow(2).\n\n__kernel\nvoid clkernel_subtract_scalar(const int num, __global const float* in, const float x,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in[i] - x;\n}\n\n__kernel\nvoid clkernel_subtract(const int num, __global const float* in1, __global const float* in2,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = in1[i] - in2[i];\n}\n\n// reduce3 kernel from\n// https://github.com/sschaetz/nvidia-opencl-examples/blob/master/OpenCL/src/oclReduction/oclReduction_kernel.cl\n__kernel\nvoid clkernel_sum(const int num, __global const float* in, __global float* out,\n __local float* sdata) {\n const int i = get_group_id(0)*(get_local_size(0)*2) + get_local_id(0);\n const int tid = get_local_id(0);\n sdata[tid] = (i < num) ? in[i] : 0.0f;\n\n // Perform the first level of reduction.\n if (i + get_local_size(0) < num) {\nsdata[tid] += in[i + get_local_size(0)];\n }\n barrier(CLK_LOCAL_MEM_FENCE);\n\n for (int s = get_local_size(0)/2; s > 0; s >>= 1) {\nif (tid > s) {\n sdata[tid] += sdata[tid + s];\n}\nbarrier(CLK_LOCAL_MEM_FENCE);\n }\n\n if (tid == 0) {\nout[get_group_id(0)] = sdata[0];\n }\n}\n\n__kernel\nvoid clkernel_tanh(const int num, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = tanh(in[i]);\n}\n\n// **************************************\n// Random functions\n// **************************************\n\n// See: distribution.cl\n\n// *********************************************************\n// BLAS functions, ref to http://docs.nvidia.com/cuda/cublas\n// *********************************************************\n\n__kernel\nvoid clkernel_amax(const int num, __global const float* in, __global int* ret,\n __local uint* sdata, __local size_t* temp) {\n const int gid = get_global_id(0);\n const int tid = get_local_id(0);\n\n for(int s = get_local_size(0)/2; s > 0; s >>= 1) {\nif (tid < s) {\n sdata[tid] = (in[sdata[tid]] > in[tid+s]) ? sdata[tid] : tid;\n}\nbarrier(CLK_LOCAL_MEM_FENCE);\n }\n if (tid == 0) {\nret[0] = sdata[0];\n }\n}\n\n\n/* TODO: Fix line 284:20.\n__kernel\nvoid clkernel_amin(const int num, __global const float* in, __global int* ret,\n __local float* sdata, __local size_t* temp) {\n const int gid = get_global_id(0);\n const int tid = get_local_id(0);\n\n // Initialize the values to pos infinity.\n sdata[tid] = (gid < num) ? in[gid] : INFINITY;\n barrier(CLK_LOCAL_MEM_FENCE);\n\n for(int s = get_local_size(0)/2; s > 0; s >>= 1) {\nif (tid < s) {\n sdata[tid] = (in[sdata[tid]] < in[tid+s]) ? sdata[tid] : tid;\n}\nbarrier(CLK_LOCAL_MEM_FENCE);\n }\n if (tid == 0) {\nret[0] = sdata[0];\n }\n}*/\n\n\n__kernel\nvoid clkernel_asum(const int num, __global const float* in, __global float* out,\n __local float* sdata) {\n const int tid = get_local_id(0);\n const int i = get_global_id(0);\n\n // Initialize\n sdata[tid] = (i < num) ? in[i] : INFINITY;\n // Perform the first level of reduction.\n if (i + get_local_size(0) < num) {\nsdata[tid] += in[i + get_local_size(0)];\n }\n barrier(CLK_LOCAL_MEM_FENCE);\n\n for(int s = get_local_size(0)/2; s > 0; s >>= 1) {\nif (tid < s) {\n sdata[tid] = fabs(sdata[tid + s]);\n}\nbarrier(CLK_LOCAL_MEM_FENCE);\n }\n if (tid == 0) {\nout[0] = sdata[0];\n }\n}\n\n__kernel\nvoid clkernel_axpy(const int num, float alpha, __global const float* in,\n __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = fma(alpha, in[i], out[i]);\n}\n\n// This kernel is essentially the same as Sum, except that during the process\n// of reading in data to the local memory, the value is also doubled.\n// Then, just before submitting the sum to out, we do a square-root on it.\n__kernel\nvoid clkernel_nrm2(const int num, __global const float* in, __global float* out,\n __local float* sdata) {\n const int i = get_group_id(0)*(get_local_size(0)*2) + get_local_id(0);\n const int tid = get_local_id(0);\n sdata[tid] = (i < num) ? (in[i] * in[i]) : 0.0f;\n\n // Perform the first level of reduction.\n if (i + get_local_size(0) < num) {\nsdata[tid] += in[i + get_local_size(0)];\n }\n barrier(CLK_LOCAL_MEM_FENCE);\n\n for (int s = get_local_size(0)/2; s > 0; s >>= 1) {\nif (tid > s) {\n sdata[tid] += sdata[tid + s];\n}\nbarrier(CLK_LOCAL_MEM_FENCE);\n }\n\n if (tid == 0) {\nout[get_group_id(0)] = sqrt(sdata[0]);\n }\n}\n\n__kernel\nvoid clkernel_scale(const int num, float x, __global float* out) {\n const int i = get_global_id(0);\n if (i >= num) return;\n out[i] = x * out[i];\n}\n\n__kernel\nvoid clkernel_dot(const int num, __global const float* in1, __global const float* in2,\n __global float* out, __local float* scratch) {\n const int i = get_global_id(0);\n if (i >= num) return;\n int offset = i << 2;\n scratch[i] = in1[offset] * in2[offset];\n\n}\n\n// First kernel from http://www.bealto.com/gpu-gemv_intro.html\n// y = \xce\xb1*A*v + \xce\xb2*y\n// fma(a, b, c) == (a * b) + c with infinite precision\n__kernel\nvoid clkernel_gemv(const int m, const int n, const float alpha,\n __global const float* A, __global const float* v,\n const float beta, __global float* out) {\n const int i = get_global_id(0);\n float sum = 0.0f;\n for (int k = 0; k < n; k++) {\n sum += fma(beta, out[i + m * k], alpha * A[i + m * k] * v[k]);\n }\n out[i] = sum;\n}\n\n// http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-dgmm\n// X[j] = x[j*inc(x)] if inc(x) \xe2\x89\xa5 0\n//= x[(\xcf\x87 \xe2\x88\x92 1)*|inc(x)| \xe2\x88\x92 j*|inc(x)|] if inc(x) < 0\n\n// C = diag( X )*A\n__kernel\nvoid clkernel_dgmm_left(const int nrow, const int ncol,\n__global const float* M, __global const float* v,\n__global float* out) {\n const uint gidx = get_global_id(0);\n\n uint offset = gidx * ncol;\n for (uint i = 0; i < ncol; i++) {\nout[offset + i] = M[offset + i] * v[i];\n }\n}\n\n// C = A*diag( X )\n__kernel\nvoid clkernel_dgmm_right(const int nrow, const int ncol,\n __global const float* M, __global const float* v,\n __global float* out) {\n const uint gidx = get_global_id(0);\n\n uint offset = gidx * ncol;\n for (uint i = 0; i < ncol; i++) {\nout[offset + i] = M[offset + i] * v[gidx];\n }\n}\n\n// TODO: Optimize with Reference from http://www.cedricnugteren.nl/tutorial.php?page=1\n// C = \xce\xb1*A*B + \xce\xb2*C\n__kernel\nvoid clkernel_gemm(const uint nrowA, const uint ncolB, const uint ncolA, const float alpha,\n __global const float* A, __global const float* B, const float beta,\n __global float* C, __local float* Asub, __local float* Bsub) {\n\n const uint lidx = get_local_id(0);\n const uint lidy = get_local_id(1);\n const uint TS = get_local_size(0); // Tile size\n const uint gidx = TS * get_group_id(0) + lidx; // Row ID of C (0..M)\n const uint gidy = TS * get_group_id(1) + lidy; // Row ID of C (0..N)\n\n // Initialise the accumulation register\n float acc = 0.0f;\n\n // Loop over all tiles\n const int numtiles = ncolA / TS;\n for (int t = 0; t < numtiles; t++) {\n const int tiledRow = TS * t + lidx;\n const int tiledCol = TS * t + lidy;\n Asub[lidy * TS + lidx] = A[tiledCol * nrowA + gidx];\n Bsub[lidy * TS + lidx] = B[gidy * ncolA + tiledRow];\n\n barrier(CLK_LOCAL_MEM_FENCE);\n\n for(int k = 0; k < TS; k++) {\n acc += Asub[k * TS + lidx] * Bsub[lidy * TS + k] * alpha;\n }\n\n barrier(CLK_LOCAL_MEM_FENCE);\n }\n\n C[gidy * nrowA + gidx] = fma(beta, C[gidy * nrowA + gidx], acc);\n}\n\n\n__kernel\nvoid clkernel_crossentropy(const uint batchsize, const uint dim,\n __global const float* p, __global const int* t,\n __global float* loss) {\n const uint gidx = get_global_id(0);\n if (gidx >= batchsize) return;\n\n int truth_idx = t[gidx];\n if (truth_idx <= 0) return;\n float prob_of_truth = p[gidx * dim + truth_idx];\n loss[gidx] = -log(fmax(prob_of_truth, -FLT_MIN));\n}\n\n\n__kernel\nvoid clkernel_softmaxentropy(const uint batchsize, const uint dim,\n __global const float* p, __global const int* t,\n __global float* grad) {\n const uint gidx = get_global_id(0);\n if (gidx >= batchsize) return;\n\n int truth_idx = t[gidx];\n if (truth_idx <= 0) return;\n grad[gidx * dim + truth_idx] -= 1.0;\n}\n\n\n__kernel\nvoid clkernel_rowmax(const uint nrow, const uint ncol,\n __global const float* in, __global float* out) {\n const uint row_id = get_global_id(0);\n if (row_id >= nrow) return;\n\n float row_max_val = -FLT_MAX;\n for (uint i = 0; i < ncol; i++) {\n row_max_val = fmax(row_max_val, in[row_id * ncol + i]);\n }\n\n out[row_id] = row_max_val;\n}\n\n\n// **************************************\n// Matrix functions\n// **************************************\n/*\n__kernel\nvoid clkernel_addcol(int nrow, int ncol, __global const float* A, __global const float* v, __global float* out) {\n const int i = get_global_id(0);\n const int j = get_global_id(1);\n if (i >= nrow) return;\n if (j >= ncol) return;\n ret[j] = A[j + nrow * i] + v[j];\n}\n\n__kernel\nvoid clkernel_addrow(int nrow, int ncol, __global const float* A, __global const float* v, __global float* out) {\n const int i = get_global_id(0);\n const int j = get_global_id(1);\n if (i >= nrow) return;\n if (j >= ncol) return;\n out[i] = A[i + ncol * j] + v[i];\n}\n\n__kernel\nvoid clkernel_outerproduct(int m, const int n, __global const float* in1, __global const float* in2, __global float* out) {\n const int col = get_global_id(0);\n const int row = get_global_id(1);\n\n // TODO: This\n}\n\n__kernel\nvoid clkernel_sumcol(int nrow, int ncol, __global const float* in, __global float* out) {\n const int i = get_global_id(0);\n if (i >= nrow) return;\n\n float sum = 0.0f;\n for (int j = 0; j < nrow; j++) {\nsum += input[nrow * i + j];\n }\n out[i] = sum;\n}\n*/\n__kernel\nvoid clkernel_sumrow(int nrow, int ncol, __global const float* in, __global float* out) {\n const int idx = get_global_id(0);\n if (idx >= nrow) return;\n\n float sum = 0.0f;\n for (int j = 0; j < ncol; j++) {\nsum += in[j + ncol * idx];\n }\n out[idx] = sum;\n}\n\n\n// Adapted from http://code.haskell.org/HsOpenCL/tests/bench/transpose.cl\n#define BLOCK_DIM 16\n__kernel\nvoid clkernel_transpose(uint nrow, uint ncol,\n__global const float* in, __global float* out,\n__local float* sdata) {\n uint gidx = get_global_id(0);\n uint gidy = get_global_id(1);\n\n if ((gidx < ncol) && (gidy < nrow)) {\nuint id_in = gidy * ncol + gidx;\nsdata[get_local_id(1) * (BLOCK_DIM+1) + get_local_id(0)] = in[id_in];\n }\n\n barrier(CLK_LOCAL_MEM_FENCE);\n\n gidx = get_group_id(1) * BLOCK_DIM + get_local_id(0);\n gidy = get_group_id(0) * BLOCK_DIM + get_local_id(1);\n if ((gidx < nrow) && (gidy < ncol)) {\nuint id_out = gidy * nrow + gidx;\nout[id_out] = sdata[get_local_id(0) * (BLOCK_DIM + 1) + get_local_id(1)];\n }\n}\n/*\n__kernel\nvoid clkernel_transpose2(uint nrow, uint ncol, __global const float* in, __global float* out, __local float* sdata) {\n const uint lidx = get_local_id(0);\n const uint lidy = get_local_id(1);\n const uint id0 = get_group_id(0) * ncol * lidx;\n const uint id1 = get_group_id(1) * nrow * lidy;\n\n if (id0 < nrow && id1 < ncol) {\nsdata[lidx][lidy] = in[id1 * nrow + id0];\n }\n\n barrier(CLK_LOCAL_MEM_FENCE);\n\n const uint new_id0 = get_group_id(1) * nrow + lidx;\n const uint new_id1 = get_group_id(0) * ncol + lidy;\n\n if (new_id0 < ncol && new_id1 < nrow) {\nout[new_id1 * ncol + new_id0] = sdata[lidx][lidy];\n }\n}*/\n\n__kernel\nvoid clkernel_diagvec_left(uint vsize, __global const float* vin, __global float* out) {\n const uint gid = get_global_id(0);\n\n for (uint i = 0; i < vsize; i++)\nout[gid * vsize + i] = (i == gid) ? vin[gid] : 0.0f;\n}\n\n\n__kernel\nvoid clkernel_diagvec_right(uint vsize, __global const float* vin, __global float* out) {\n const uint gid = get_global_id(0);\n\n for (uint i = 0; i < vsize; i++)\nout[gid * vsize + i] = (i == gid) ? vin[gid] : 0.0f;\n}\n"; |
