File size: 5,504 Bytes
9b19fd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | // thread safety test
// - Loads a copy of the same model on each GPU, plus a copy on the CPU
// - Creates n_parallel (--parallel) contexts per model
// - Runs inference in parallel on each context
#include <thread>
#include <vector>
#include <atomic>
#include "llama.h"
#include "arg.h"
#include "common.h"
#include "log.h"
#include "sampling.h"
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
common_init();
llama_backend_init();
llama_numa_init(params.numa);
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
//llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
// if (level == GGML_LOG_LEVEL_ERROR) {
// common_log_add(common_log_main(), level, "%s", text);
// }
//}, NULL);
auto cparams = common_context_params_to_llama(params);
// each context has a single sequence
cparams.n_seq_max = 1;
int dev_count = ggml_backend_dev_count();
int gpu_dev_count = 0;
for (int i = 0; i < dev_count; ++i) {
auto * dev = ggml_backend_dev_get(i);
if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
gpu_dev_count++;
}
}
const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split
//const int num_models = std::max(1, gpu_dev_count);
const int num_contexts = std::max(1, params.n_parallel);
std::vector<llama_model_ptr> models;
std::vector<std::thread> threads;
std::atomic<bool> failed = false;
for (int m = 0; m < num_models; ++m) {
auto mparams = common_model_params_to_llama(params);
if (m < gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = m;
} else if (m == gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = -1; // CPU model
} else {
mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;;
}
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
return 1;
}
models.emplace_back(model);
}
for (int m = 0; m < num_models; ++m) {
auto * model = models[m].get();
for (int c = 0; c < num_contexts; ++c) {
threads.emplace_back([&, m, c, model]() {
LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models);
llama_context_ptr ctx { llama_init_from_model(model, cparams) };
if (ctx == NULL) {
LOG_ERR("failed to create context\n");
failed.store(true);
return;
}
std::unique_ptr<common_sampler, decltype(&common_sampler_free)> sampler { common_sampler_init(model, params.sampling), common_sampler_free };
if (sampler == NULL) {
LOG_ERR("failed to create sampler\n");
failed.store(true);
return;
}
llama_batch batch = {};
{
auto prompt = common_tokenize(ctx.get(), params.prompt, true);
if (prompt.empty()) {
LOG_ERR("failed to tokenize prompt\n");
failed.store(true);
return;
}
batch = llama_batch_get_one(prompt.data(), prompt.size());
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("failed to decode prompt\n");
failed.store(true);
return;
}
}
const auto * vocab = llama_model_get_vocab(model);
std::string result = params.prompt;
for (int i = 0; i < params.n_predict; i++) {
llama_token token;
if (batch.n_tokens > 0) {
token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1);
} else {
token = llama_vocab_bos(vocab);
}
result += common_token_to_piece(ctx.get(), token);
if (llama_vocab_is_eog(vocab, token)) {
break;
}
batch = llama_batch_get_one(&token, 1);
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts);
failed.store(true);
return;
}
}
LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str());
});
}
}
for (auto & thread : threads) {
thread.join();
}
if (failed) {
LOG_ERR("One or more threads failed.\n");
return 1;
}
LOG_INF("All threads finished without errors.\n");
return 0;
}
|