mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-08-13 20:07:41 -04:00
8a4a856277
* Add support for Llada-8b: diffusion model * Add README * Fix README and convert_hf_to_gguf * convert_hf_to_gguf.py: address review comments * Make everything in a single example * Remove model-specific sampling * Remove unused argmax * Remove braced initializers, improve README.md a bit * Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps * Remove adding the mask token * Move add_add_bos_token to set_vocab * use add_bool in gguf_writer.py
684 lines
27 KiB
C++
684 lines
27 KiB
C++
#include "arg.h"
|
|
#include "chat.h"
|
|
#include "common.h"
|
|
#include "llama.h"
|
|
#include "log.h"
|
|
|
|
#include <limits.h>
|
|
|
|
#include <algorithm>
|
|
#include <cmath>
|
|
#include <cstring>
|
|
#include <limits>
|
|
#include <random>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
|
|
|
|
// Unified transfer scheduling methods
|
|
enum transfer_schedule {
|
|
TIMESTEP_BASED = 0, // Dream-style: (1.0 - s/t) * remaining
|
|
BLOCK_BASED = 1, // LLaDA-style: process in blocks with get_num_transfer_tokens
|
|
};
|
|
|
|
typedef bool (*diffusion_step_callback_t)(int32_t step,
|
|
int32_t total_steps,
|
|
const llama_token * tokens,
|
|
int32_t n_tokens,
|
|
void * user_data);
|
|
|
|
struct diffusion_params {
|
|
int32_t steps = 0;
|
|
float temperature = 0;
|
|
llama_token mask_token_id = LLAMA_TOKEN_NULL;
|
|
diffusion_step_callback_t step_callback = nullptr;
|
|
void * step_callback_user_data = nullptr;
|
|
int32_t seed = 0;
|
|
bool visual_mode = false;
|
|
bool shift_logits = false; // Shift logits by -1 after decode
|
|
|
|
float top_p = 0.;
|
|
int32_t top_k = 0.;
|
|
|
|
diffusion_algorithm algorithm = CONFIDENCE_BASED;
|
|
transfer_schedule schedule = TIMESTEP_BASED;
|
|
|
|
float cfg_scale = 0.; // Config scale for classifier-free guidance
|
|
float eps = 0.; // Timestep scheduling
|
|
int32_t block_length = 0; // Block size (for block scheduling)
|
|
float alg_temp = 0; // algorithm temperature (0.0 = deterministic)
|
|
bool add_gumbel_noise = false; // Add gumbel noise to the logits if temp > 0.0
|
|
|
|
int32_t max_length = 0; // Maximum sequence length
|
|
};
|
|
|
|
struct callback_data {
|
|
diffusion_params * diff_params;
|
|
const llama_vocab * vocab;
|
|
int32_t n_input;
|
|
};
|
|
|
|
static float calculate_confidence(const llama_token_data_array & cur_p,
|
|
diffusion_algorithm algorithm,
|
|
std::mt19937 & rng) {
|
|
switch (algorithm) {
|
|
case CONFIDENCE_BASED:
|
|
return cur_p.data[cur_p.selected].p; // Selected token probability
|
|
|
|
case ENTROPY_BASED:
|
|
{
|
|
float entropy = 0.0f;
|
|
const float epsilon = 1e-10f;
|
|
for (size_t i = 0; i < cur_p.size; i++) {
|
|
float prob = cur_p.data[i].p;
|
|
entropy += prob * logf(prob + epsilon);
|
|
}
|
|
return -entropy; // Higher entropy = lower confidence
|
|
}
|
|
|
|
case MARGIN_BASED:
|
|
return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
|
|
|
|
case RANDOM:
|
|
{
|
|
std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
|
|
return uniform(rng); // Random confidence
|
|
}
|
|
|
|
case ORIGIN:
|
|
return cur_p.data[cur_p.selected].p;
|
|
|
|
default:
|
|
return 0.0f;
|
|
}
|
|
}
|
|
|
|
// Unified transfer count calculation function
|
|
static int32_t calculate_transfer_count(int32_t step,
|
|
int32_t total_steps,
|
|
int32_t remaining_masked,
|
|
transfer_schedule schedule,
|
|
float eps,
|
|
const std::vector<int32_t> & num_transfer_tokens = {}) {
|
|
switch (schedule) {
|
|
case TIMESTEP_BASED:
|
|
{
|
|
float t = 1.0f - (float) step / total_steps * (1.0f - eps);
|
|
float s = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
|
|
float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
|
|
return (int32_t) (remaining_masked * p_transfer);
|
|
}
|
|
|
|
case BLOCK_BASED:
|
|
if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
|
|
return num_transfer_tokens[step];
|
|
}
|
|
return remaining_masked / (total_steps - step); // Fallback
|
|
|
|
default:
|
|
return remaining_masked / (total_steps - step);
|
|
}
|
|
}
|
|
|
|
static bool diffusion_step_callback(int32_t step,
|
|
int32_t total_steps,
|
|
const llama_token * tokens,
|
|
int32_t n_tokens,
|
|
void * user_data) {
|
|
(void) user_data;
|
|
|
|
callback_data * data = static_cast<callback_data *>(user_data);
|
|
|
|
auto print_progress_bar = [](int32_t step, int32_t total_steps) {
|
|
int progress_percent = (step * 100) / total_steps;
|
|
int progress_bars = (step * 50) / total_steps;
|
|
LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%",
|
|
step,
|
|
total_steps,
|
|
std::string(progress_bars, '=').c_str(),
|
|
std::string(50 - progress_bars, ' ').c_str(),
|
|
progress_percent);
|
|
};
|
|
|
|
if (data->diff_params->visual_mode) {
|
|
// Visual mode: clear
|
|
LOG_INF("\033[2J\033[H"); // Clear screen and move cursor to top-left
|
|
|
|
print_progress_bar(step, total_steps);
|
|
|
|
LOG_INF("\n");
|
|
|
|
std::string current_text = " ";
|
|
|
|
for (int32_t i = data->n_input; i < n_tokens; i++) {
|
|
std::string token_str;
|
|
if (tokens[i] != llama_vocab_mask(data->vocab)) {
|
|
char piece[256];
|
|
int n_chars = llama_token_to_piece(data->vocab, tokens[i], piece, sizeof(piece), 0, false);
|
|
if (n_chars > 0) {
|
|
piece[n_chars] = '\0';
|
|
token_str = piece;
|
|
}
|
|
} else {
|
|
token_str = " ";
|
|
}
|
|
|
|
current_text += token_str;
|
|
}
|
|
|
|
LOG_INF("%s\n", current_text.c_str());
|
|
} else {
|
|
print_progress_bar(step, total_steps);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
|
|
if (temperature == 0.0f) {
|
|
return;
|
|
}
|
|
|
|
std::uniform_real_distribution<double> uniform(0.0, 1.0);
|
|
for (int32_t i = 0; i < n_vocab; i++) {
|
|
double noise = uniform(rng);
|
|
// Prevent log(0)
|
|
noise = std::max(noise, 1e-20);
|
|
double gumbel_noise = std::pow(-std::log(noise), temperature);
|
|
logits[i] = std::exp(logits[i]) / gumbel_noise;
|
|
}
|
|
}
|
|
|
|
static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
|
|
std::vector<int32_t> num_transfer_tokens(steps);
|
|
|
|
int32_t base = mask_count / steps;
|
|
int32_t remainder = mask_count % steps;
|
|
|
|
for (int32_t i = 0; i < steps; i++) {
|
|
num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
|
|
}
|
|
|
|
return num_transfer_tokens;
|
|
}
|
|
|
|
static void diffusion_generate(llama_context * ctx,
|
|
const llama_token * input_tokens,
|
|
llama_token * output_tokens,
|
|
int32_t n_input,
|
|
const diffusion_params & params,
|
|
int32_t & n_generated) {
|
|
n_generated = 0;
|
|
if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
|
|
return;
|
|
}
|
|
|
|
const llama_model * model = llama_get_model(ctx);
|
|
|
|
// Initialize with input and pad with mask tokens
|
|
std::copy(input_tokens, input_tokens + n_input, output_tokens);
|
|
std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
|
|
|
|
std::mt19937 rng(params.seed);
|
|
|
|
llama_set_causal_attn(ctx, false);
|
|
|
|
int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
|
|
|
|
std::vector<llama_token_data> candidates(n_vocab);
|
|
std::vector<llama_token_data> conf_candidates;
|
|
conf_candidates.reserve(params.max_length);
|
|
std::vector<int32_t> mask_positions;
|
|
mask_positions.reserve(params.max_length);
|
|
|
|
// Setup sampler chain
|
|
struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
|
|
if (params.top_k > 0) {
|
|
llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
|
|
}
|
|
if (params.top_p < 1.0f) {
|
|
llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
|
|
}
|
|
if (params.temperature > 0.0f) {
|
|
llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
|
|
}
|
|
llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
|
|
|
|
struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
|
|
|
|
llama_batch batch = llama_batch_init(params.max_length, 0, 1);
|
|
batch.n_tokens = params.max_length;
|
|
|
|
// Pre-allocate buffers for CFG if needed
|
|
int32_t logits_size = n_vocab * params.max_length;
|
|
std::vector<float> cond_logits_buffer;
|
|
std::vector<llama_token> un_x_buffer;
|
|
if (params.cfg_scale > 0.0f) {
|
|
cond_logits_buffer.resize(logits_size);
|
|
un_x_buffer.resize(params.max_length);
|
|
}
|
|
|
|
// For block-based processing
|
|
std::vector<int32_t> num_transfer_tokens;
|
|
int32_t num_blocks = 1;
|
|
int32_t steps_per_block = params.steps;
|
|
|
|
if (params.schedule == BLOCK_BASED) {
|
|
GGML_ASSERT(params.max_length % params.block_length == 0);
|
|
num_blocks = params.max_length / params.block_length;
|
|
GGML_ASSERT(params.steps % num_blocks == 0);
|
|
steps_per_block = params.steps / num_blocks;
|
|
}
|
|
|
|
std::vector<float> confidence(params.max_length);
|
|
|
|
int64_t total_sampling_time = 0;
|
|
int64_t total_time = 0;
|
|
int64_t time_start = ggml_time_us();
|
|
|
|
for (int block_num = 0; block_num < num_blocks; block_num++) {
|
|
int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
|
|
int32_t block_end = (params.schedule == BLOCK_BASED) ?
|
|
std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
|
|
params.max_length;
|
|
|
|
// Count masked tokens in current block for block-based processing
|
|
if (params.schedule == BLOCK_BASED) {
|
|
int32_t block_mask_count = 0;
|
|
for (int i = block_start; i < block_end; i++) {
|
|
if (output_tokens[i] == params.mask_token_id) {
|
|
block_mask_count++;
|
|
}
|
|
}
|
|
num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
|
|
}
|
|
|
|
for (int32_t step = 0; step < steps_per_block; step++) {
|
|
int32_t global_step = block_num * steps_per_block + step;
|
|
|
|
if (params.step_callback) {
|
|
if (!params.step_callback(
|
|
global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Setup batch
|
|
for (int32_t i = 0; i < params.max_length; i++) {
|
|
batch.token[i] = output_tokens[i];
|
|
batch.pos[i] = i;
|
|
batch.n_seq_id[i] = 1;
|
|
batch.seq_id[i][0] = 0;
|
|
batch.logits[i] = 1;
|
|
}
|
|
|
|
float * logits = nullptr;
|
|
|
|
if (params.cfg_scale > 0.0f) {
|
|
int ret = llama_decode(ctx, batch);
|
|
if (ret != 0) {
|
|
LOG_ERR("Failed to generate conditional");
|
|
break;
|
|
}
|
|
float * cond_logits_ptr = llama_get_logits(ctx);
|
|
std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
|
|
|
|
// Unconditional generation (mask input)
|
|
std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
|
|
for (int32_t i = 0; i < n_input; i++) {
|
|
un_x_buffer[i] = params.mask_token_id;
|
|
}
|
|
|
|
for (int32_t i = 0; i < params.max_length; i++) {
|
|
batch.token[i] = un_x_buffer[i];
|
|
}
|
|
ret = llama_decode(ctx, batch);
|
|
if (ret != 0) {
|
|
LOG_ERR("Failed to generate unconditional");
|
|
break;
|
|
}
|
|
float * uncond_logits = llama_get_logits(ctx);
|
|
|
|
// Apply CFG
|
|
for (int32_t i = 0; i < logits_size; i++) {
|
|
cond_logits_buffer[i] =
|
|
uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
|
|
}
|
|
logits = cond_logits_buffer.data();
|
|
} else {
|
|
int ret = llama_decode(ctx, batch);
|
|
if (ret != 0) {
|
|
LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
|
|
break;
|
|
}
|
|
logits = llama_get_logits(ctx);
|
|
}
|
|
|
|
if (!logits) {
|
|
LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
|
|
break;
|
|
}
|
|
|
|
auto get_logits_for_pos = [&](int32_t pos) -> const float * {
|
|
if (params.shift_logits) {
|
|
return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
|
|
}
|
|
return logits + (pos) *n_vocab;
|
|
};
|
|
|
|
int64_t time_start_sampling = ggml_time_us();
|
|
|
|
mask_positions.clear();
|
|
for (int32_t i = 0; i < params.max_length; i++) {
|
|
if (output_tokens[i] == params.mask_token_id) {
|
|
// For block-based, only consider current block
|
|
if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) {
|
|
mask_positions.push_back(i);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (mask_positions.empty()) {
|
|
break;
|
|
}
|
|
|
|
if (params.add_gumbel_noise && params.temperature > 0.0f) {
|
|
add_gumbel_noise(logits, n_vocab, params.temperature, rng);
|
|
}
|
|
|
|
if (params.algorithm == ORIGIN) {
|
|
int32_t transfer_count = calculate_transfer_count(
|
|
step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
|
|
float p_transfer = (float) transfer_count / mask_positions.size();
|
|
|
|
for (int32_t pos : mask_positions) {
|
|
if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
|
|
const float * pos_logits = get_logits_for_pos(pos);
|
|
for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
|
|
candidates[token_id].id = token_id;
|
|
candidates[token_id].logit = pos_logits[token_id];
|
|
candidates[token_id].p = 0.0f;
|
|
}
|
|
|
|
llama_token_data_array cur_p = {
|
|
candidates.data(),
|
|
(size_t) n_vocab,
|
|
-1,
|
|
false,
|
|
};
|
|
|
|
llama_sampler_apply(sampler, &cur_p);
|
|
output_tokens[pos] = cur_p.data[cur_p.selected].id;
|
|
}
|
|
}
|
|
} else {
|
|
std::vector<std::pair<float, int32_t>> confidences;
|
|
std::vector<llama_token> sampled_tokens(mask_positions.size());
|
|
|
|
for (size_t i = 0; i < mask_positions.size(); i++) {
|
|
int32_t pos = mask_positions[i];
|
|
const float * pos_logits = get_logits_for_pos(pos);
|
|
|
|
for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
|
|
candidates[token_id].logit = pos_logits[token_id];
|
|
candidates[token_id].p = 0.0f;
|
|
candidates[token_id].id = token_id;
|
|
}
|
|
|
|
llama_token_data_array cur_p = {
|
|
candidates.data(),
|
|
candidates.size(),
|
|
-1,
|
|
false,
|
|
};
|
|
|
|
llama_sampler_apply(sampler, &cur_p);
|
|
llama_token sampled_token = cur_p.data[cur_p.selected].id;
|
|
|
|
float conf = calculate_confidence(cur_p, params.algorithm, rng);
|
|
|
|
sampled_tokens[i] = sampled_token;
|
|
confidences.emplace_back(conf, i);
|
|
}
|
|
|
|
int32_t transfer_count = calculate_transfer_count(
|
|
step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
|
|
|
|
if (transfer_count > 0) {
|
|
if (params.alg_temp == 0.0f) {
|
|
std::partial_sort(confidences.begin(),
|
|
confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
|
|
confidences.end(),
|
|
[](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
|
|
if (a.first != b.first) {
|
|
return a.first > b.first;
|
|
}
|
|
return a.second < b.second;
|
|
});
|
|
|
|
for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
|
|
int32_t mask_idx = confidences[i].second;
|
|
int32_t pos = mask_positions[mask_idx];
|
|
output_tokens[pos] = sampled_tokens[mask_idx];
|
|
}
|
|
} else {
|
|
conf_candidates.clear();
|
|
for (size_t i = 0; i < confidences.size(); i++) {
|
|
float conf_logit = confidences[i].first / params.alg_temp;
|
|
conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
|
|
}
|
|
|
|
llama_token_data_array conf_array = {
|
|
conf_candidates.data(),
|
|
conf_candidates.size(),
|
|
-1,
|
|
false,
|
|
};
|
|
|
|
for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
|
|
llama_sampler_apply(dist_sampler, &conf_array);
|
|
int32_t selected_idx = conf_array.selected;
|
|
int32_t mask_idx = selected_idx;
|
|
int32_t pos = mask_positions[mask_idx];
|
|
output_tokens[pos] = sampled_tokens[mask_idx];
|
|
|
|
conf_candidates[selected_idx].p = 0.0f;
|
|
conf_array.selected = -1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
int64_t time_end_sampling = ggml_time_us();
|
|
total_sampling_time += time_end_sampling - time_start_sampling;
|
|
}
|
|
}
|
|
|
|
int64_t time_end = ggml_time_us();
|
|
total_time += time_end - time_start;
|
|
|
|
LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
|
|
total_time / 1000.0,
|
|
total_time / 1000.0 / params.steps,
|
|
total_sampling_time / 1000.0 / params.steps);
|
|
|
|
llama_batch_free(batch);
|
|
llama_sampler_free(sampler);
|
|
llama_sampler_free(dist_sampler);
|
|
|
|
n_generated = params.max_length;
|
|
}
|
|
|
|
static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
|
|
if (!use_chat_template) {
|
|
return prompt;
|
|
}
|
|
|
|
auto chat_templates = common_chat_templates_init(model, "");
|
|
|
|
common_chat_templates_inputs inputs;
|
|
common_chat_msg user_msg;
|
|
user_msg.role = "user";
|
|
user_msg.content = prompt;
|
|
inputs.add_generation_prompt = true;
|
|
inputs.messages.push_back(user_msg);
|
|
|
|
auto result = common_chat_templates_apply(chat_templates.get(), inputs);
|
|
|
|
return result.prompt;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
ggml_time_init();
|
|
|
|
common_params params;
|
|
|
|
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DIFFUSION)) {
|
|
return 1;
|
|
}
|
|
|
|
common_init();
|
|
llama_backend_init();
|
|
|
|
llama_model_params model_params = llama_model_default_params();
|
|
model_params.n_gpu_layers = params.n_gpu_layers;
|
|
model_params.devices = params.devices.data();
|
|
model_params.use_mmap = params.use_mmap;
|
|
model_params.use_mlock = params.use_mlock;
|
|
model_params.check_tensors = params.check_tensors;
|
|
|
|
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
|
|
if (!model) {
|
|
LOG_ERR("error: failed to load model '%s'\n", params.model.path.c_str());
|
|
return 1;
|
|
}
|
|
|
|
if (!llama_model_is_diffusion(model)) {
|
|
LOG_ERR("error: unsupported model for diffusion");
|
|
llama_model_free(model);
|
|
return 1;
|
|
}
|
|
|
|
llama_context_params ctx_params = llama_context_default_params();
|
|
ctx_params.n_ctx = params.n_ctx;
|
|
ctx_params.n_batch = params.n_batch;
|
|
ctx_params.n_ubatch = params.n_ubatch;
|
|
ctx_params.flash_attn = params.flash_attn;
|
|
ctx_params.no_perf = params.no_perf;
|
|
ctx_params.type_k = params.cache_type_k;
|
|
ctx_params.type_v = params.cache_type_v;
|
|
|
|
llama_context * ctx = llama_init_from_model(model, ctx_params);
|
|
if (!ctx) {
|
|
LOG_ERR("error: failed to create context\n");
|
|
llama_model_free(model);
|
|
return 1;
|
|
}
|
|
|
|
llama_set_n_threads(ctx, params.cpuparams.n_threads, params.cpuparams_batch.n_threads);
|
|
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
std::string formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model);
|
|
|
|
std::vector<llama_token> input_tokens = common_tokenize(vocab,
|
|
formatted_prompt,
|
|
/*add special tokens*/ true,
|
|
/*parse special*/ true);
|
|
|
|
int n_input = input_tokens.size();
|
|
|
|
if (n_input >= params.n_ctx) {
|
|
LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, params.n_ctx);
|
|
llama_free(ctx);
|
|
llama_model_free(model);
|
|
return 1;
|
|
}
|
|
|
|
llama_token mask_token_id = llama_vocab_mask(vocab);
|
|
GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
|
|
|
|
bool visual_mode = params.diffusion.visual_mode;
|
|
|
|
int32_t n_generated = 0;
|
|
std::vector<llama_token> output_tokens(params.n_ubatch);
|
|
|
|
struct diffusion_params diff_params;
|
|
|
|
char shift_logits_str[8];
|
|
if (llama_model_meta_val_str(model, "diffusion.shift_logits", shift_logits_str, sizeof(shift_logits_str)) >= 0) {
|
|
diff_params.shift_logits = (strcmp(shift_logits_str, "true") == 0);
|
|
} else {
|
|
diff_params.shift_logits = true;
|
|
}
|
|
|
|
//Use either eps or block length, but not both
|
|
GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
|
|
|
|
if (params.diffusion.eps) {
|
|
diff_params.schedule = TIMESTEP_BASED;
|
|
diff_params.eps = params.diffusion.eps;
|
|
} else if (params.diffusion.block_length) {
|
|
diff_params.schedule = BLOCK_BASED;
|
|
diff_params.block_length = params.diffusion.block_length;
|
|
}
|
|
|
|
diff_params.mask_token_id = mask_token_id;
|
|
diff_params.seed = params.sampling.seed;
|
|
diff_params.temperature = params.sampling.temp;
|
|
diff_params.steps = params.diffusion.steps;
|
|
diff_params.algorithm = static_cast<diffusion_algorithm>(params.diffusion.algorithm);
|
|
diff_params.max_length = params.n_ubatch;
|
|
diff_params.top_p = params.sampling.top_p;
|
|
diff_params.top_k = params.sampling.top_k;
|
|
diff_params.visual_mode = params.diffusion.visual_mode;
|
|
diff_params.add_gumbel_noise = params.diffusion.add_gumbel_noise;
|
|
|
|
diff_params.step_callback = diffusion_step_callback;
|
|
callback_data cb_data = { &diff_params, vocab, n_input };
|
|
diff_params.step_callback_user_data = &cb_data;
|
|
|
|
const char * alg_names[] = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" };
|
|
const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" };
|
|
const char * alg_name =
|
|
(diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
|
|
const char * sched_name =
|
|
(diff_params.schedule >= 0 && diff_params.schedule <= 1) ? sched_names[diff_params.schedule] : "UNKNOWN";
|
|
|
|
LOG_INF("diffusion_params: - %-25s llama_token = %d\n", "mask_token_id", mask_token_id);
|
|
LOG_INF("diffusion_params: - %-25s u32 = %d\n", "steps", diff_params.steps);
|
|
LOG_INF("diffusion_params: - %-25s u32 = %d\n", "max_length", diff_params.max_length);
|
|
LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name);
|
|
LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "schedule", diff_params.schedule, sched_name);
|
|
LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "temperature", diff_params.temperature);
|
|
if (diff_params.schedule == TIMESTEP_BASED) {
|
|
LOG_INF("diffusion_params: - %-25s f32 = %.6f\n", "eps", diff_params.eps);
|
|
LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "alg_temp", diff_params.alg_temp);
|
|
}
|
|
if (diff_params.schedule == BLOCK_BASED) {
|
|
LOG_INF("diffusion_params: - %-25s u32 = %d\n", "block_length", diff_params.block_length);
|
|
LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "cfg_scale", diff_params.cfg_scale);
|
|
}
|
|
|
|
diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, diff_params, n_generated);
|
|
|
|
if (n_generated > 0) {
|
|
if (visual_mode) {
|
|
//clear screen and move cursor to top-left
|
|
LOG_INF("\033[2J\033[H");
|
|
}
|
|
|
|
output_tokens.erase(output_tokens.begin(), output_tokens.begin() + n_input);
|
|
std::string output_data = common_detokenize(vocab, output_tokens, false);
|
|
LOG_INF("\n%s\n", output_data.c_str());
|
|
} else {
|
|
LOG_INF("Error: diffusion generation failed\n");
|
|
}
|
|
|
|
llama_free(ctx);
|
|
llama_model_free(model);
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|