size_t pos_start = 0;
size_t pos_found = 0;
- if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {
+ if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
for (int j = 0; j < QK_K/16; ++j) {
if (quant_weights) {
- const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL;
+ const float * qw = quant_weights + QK_K * i + 16*j;
for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]);
} else {
for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l];
// Sample the next word X using top-k sampling
llama_sample_top_k(nullptr, candidates, int(k), 1);
- if (ctx) {
- ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
- }
+ ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();
// Update mu using the learning rate and error
*mu = *mu - eta * e;
- if (ctx) {
- ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
- }
+ ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
return X;
}