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Add LLaDA 8b Diffusion model (#14771)

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* 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
This commit is contained in:
Aman Gupta
2025-07-31 19:49:09 +08:00
committed by GitHub
parent 11490b3672
commit 8a4a856277
12 changed files with 931 additions and 385 deletions
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13
examples/diffusion/README.md Normal file
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@@ -0,0 +1,13 @@
# Diffusion Text Generation
This directory contains implementations for Diffusion LLMs (DLLMs)
More Info:
- https://github.com/ggml-org/llama.cpp/pull/14644
- https://github.com/ggml-org/llama.cpp/pull/14771
Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual`
Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual`

902
examples/diffusion/diffusion-cli.cpp
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@@ -5,344 +5,128 @@
#include "log.h"
#include <limits.h>
#include <string>
#include <vector>
#include <algorithm>
#include <cmath>
#include <cstring>
#include <limits>
#include <random>
#include <string>
#include <vector>
typedef bool (*diffusion_step_callback_t)(int32_t step,
int32_t total_steps,
const llama_token * tokens,
int32_t n_tokens,
void * user_data);
enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
enum diffusion_alg {
DIFFUSION_ALG_ORIGIN = 0,
DIFFUSION_ALG_MASKGIT_PLUS = 1,
DIFFUSION_ALG_TOPK_MARGIN = 2,
DIFFUSION_ALG_ENTROPY = 3,
// 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;
float eps;
float temperature;
float top_p;
int32_t top_k;
llama_token mask_token_id;
enum diffusion_alg algorithm;
float alg_temp;
diffusion_step_callback_t step_callback;
void * step_callback_user_data;
int32_t seed;
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
};
static diffusion_params diffusion_default_params() {
diffusion_params params = {};
params.steps = 64;
params.eps = 1e-3f;
params.temperature = 0.2f;
params.top_p = 0.95f;
params.top_k = 0;
params.mask_token_id = LLAMA_TOKEN_NULL;
params.algorithm = DIFFUSION_ALG_ORIGIN;
params.alg_temp = 0.0f;
params.step_callback = nullptr;
params.step_callback_user_data = nullptr;
params.seed = 0;
return params;
}
static void diffusion_generate(llama_context * ctx,
const llama_token * input_tokens,
llama_token * output_tokens,
int32_t n_input,
int32_t max_length,
struct diffusion_params params,
int32_t & n_generated) {
n_generated = 0;
if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || 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 + max_length, params.mask_token_id);
std::mt19937 rng(params.seed);
std::vector<float> timesteps(params.steps + 1);
for (int32_t i = 0; i <= params.steps; i++) {
timesteps[i] = 1.0f - (float) i / params.steps * (1.0f - params.eps);
}
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(max_length);
std::vector<int32_t> mask_positions;
mask_positions.reserve(max_length);
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(max_length, 0, 1);
batch.n_tokens = max_length;
int64_t total_sampling_time = 0;
int64_t total_time = 0;
int64_t time_start = ggml_time_us();
for (int32_t step = 0; step < params.steps; step++) {
if (params.step_callback) {
if (!params.step_callback(step, params.steps, output_tokens, max_length, params.step_callback_user_data)) {
break;
}
}
for (int32_t i = 0; i < 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;
}
int ret = llama_decode(ctx, batch);
if (ret != 0) {
LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, step, ret);
break;
}
float * raw_logits = llama_get_logits(ctx);
if (!raw_logits) {
LOG_ERR("%s: failed to get logits at step %d\n", __func__, step);
break;
}
auto get_logits_for_pos = [&](int32_t pos) -> const float * {
return pos == 0 ? raw_logits : raw_logits + (pos - 1) * n_vocab;
};
int64_t time_start_sampling = ggml_time_us();
mask_positions.clear();
for (int32_t i = 0; i < max_length; i++) {
if (output_tokens[i] == params.mask_token_id) {
mask_positions.push_back(i);
}
}
if (mask_positions.empty()) {
break;
}
float t = timesteps[step];
float s = timesteps[step + 1];
if (params.algorithm == DIFFUSION_ALG_ORIGIN) {
float p_transfer = (step < params.steps - 1) ? (1.0f - s / t) : 1.0f;
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 = {
/* .data = */ candidates.data(),
/* .size = */ (size_t) n_vocab, // Reset size to full vocab
/* .selected = */ -1,
/* .sorted = */ 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 = {
/* .data = */ candidates.data(),
/* .size = */ candidates.size(),
/* .selected = */ -1,
/* .sorted = */ false,
};
llama_sampler_apply(sampler, &cur_p);
llama_token sampled_token = cur_p.data[cur_p.selected].id;
float confidence = 0.0f;
if (params.algorithm == DIFFUSION_ALG_ENTROPY) {
const float epsilon = 1e-10f;
for (size_t j = 0; j < cur_p.size; j++) {
float prob = cur_p.data[j].p;
confidence += prob * logf(prob + epsilon);
}
} else if (params.algorithm == DIFFUSION_ALG_TOPK_MARGIN) {
confidence = cur_p.data[0].p - cur_p.data[1].p;
} else {
confidence = cur_p.data[cur_p.selected].p;
}
sampled_tokens[i] = sampled_token;
confidences.emplace_back(confidence, i);
}
int32_t num_transfer =
(step < params.steps - 1) ? (int32_t) (mask_positions.size() * (1.0f - s / t)) : mask_positions.size();
if (num_transfer > 0) {
if (params.alg_temp == 0.0f) {
std::partial_sort(confidences.begin(), confidences.begin() + num_transfer, 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;
});
} else {
conf_candidates.clear();
for (int32_t pos = 0; pos < max_length; pos++) {
float conf_logit = -std::numeric_limits<float>::infinity();
auto it = std::find(mask_positions.begin(), mask_positions.end(), pos);
if (it != mask_positions.end()) {
size_t mask_idx = std::distance(mask_positions.begin(), it);
conf_logit = confidences[mask_idx].first / params.alg_temp; // Apply temperature scaling
}
conf_candidates.emplace_back(llama_token_data{ pos, conf_logit, 0.0f });
}
llama_token_data_array conf_array = {
/* .data = */ conf_candidates.data(),
/* .size = */ conf_candidates.size(),
/* .selected = */ -1,
/* .sorted = */ false,
};
for (int32_t i = 0; i < num_transfer; i++) {
// Apply distribution sampler to get selected index
llama_sampler_apply(dist_sampler, &conf_array);
int selected_idx = conf_array.selected;
confidences[i].second = conf_candidates[selected_idx].id;
conf_candidates[selected_idx].p = 0.0f;
conf_array.selected = -1;
}
}
if (params.alg_temp == 0.0f) {
// Deterministic - use confidence order
for (int32_t i = 0; i < num_transfer; i++) {
int32_t mask_idx = confidences[i].second;
int32_t pos = mask_positions[mask_idx];
llama_token token = sampled_tokens[mask_idx];
output_tokens[pos] = token;
}
} else {
for (int32_t i = 0; i < num_transfer; i++) {
int32_t pos = confidences[i].second;
auto it = std::find(mask_positions.begin(), mask_positions.end(), pos);
if (it != mask_positions.end()) {
int32_t mask_idx = std::distance(mask_positions.begin(), it);
output_tokens[pos] = sampled_tokens[mask_idx];
}
}
}
}
}
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 = 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;
}
struct callback_data {
const common_params_diffusion * diff_params;
const llama_vocab * vocab;
int32_t n_input;
diffusion_params * diff_params;
const llama_vocab * vocab;
int32_t n_input;
};
static bool diffusion_step_callback(int32_t step,
int32_t total_steps,
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;
int32_t n_tokens,
void * user_data) {
(void) user_data;
callback_data * data = static_cast<callback_data *>(user_data);
@@ -350,11 +134,11 @@ static bool diffusion_step_callback(int32_t step,
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);
step,
total_steps,
std::string(progress_bars, '=').c_str(),
std::string(50 - progress_bars, ' ').c_str(),
progress_percent);
};
if (data->diff_params->visual_mode) {
@@ -391,6 +175,360 @@ static bool diffusion_step_callback(int32_t step,
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();
@@ -400,11 +538,6 @@ int main(int argc, char ** argv) {
return 1;
}
const char * alg_names[] = { "ORIGIN", "MASKGIT_PLUS", "TOPK_MARGIN", "ENTROPY" };
const char * alg_name = (params.diffusion.algorithm >= 0 && params.diffusion.algorithm <= 3) ?
alg_names[params.diffusion.algorithm] :
"UNKNOWN";
common_init();
llama_backend_init();
@@ -421,6 +554,12 @@ int main(int argc, char ** argv) {
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;
@@ -442,10 +581,12 @@ int main(int argc, char ** argv) {
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,
std::vector<llama_token> input_tokens = common_tokenize(vocab,
formatted_prompt,
/*add special tokens*/ true,
/*parse special*/ true);
int n_input = input_tokens.size();
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);
@@ -454,44 +595,79 @@ int main(int argc, char ** argv) {
return 1;
}
struct diffusion_params ldiff_params = diffusion_default_params();
ldiff_params.steps = params.diffusion.steps;
ldiff_params.eps = params.diffusion.eps;
ldiff_params.temperature = params.sampling.temp;
ldiff_params.top_p = params.sampling.top_p;
ldiff_params.top_k = params.sampling.top_k;
ldiff_params.algorithm = static_cast<enum diffusion_alg>(params.diffusion.algorithm);
ldiff_params.alg_temp = params.diffusion.alg_temp;
ldiff_params.seed = params.sampling.seed;
llama_token mask_token_id = llama_vocab_mask(vocab);
GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
LOG_INF("diffusion_params: - %-25s llama_token = %d\n", "mask_token_id", mask_token_id);
LOG_INF("diffusion_params: - %-25s u32 = %d\n", "steps", params.diffusion.steps);
LOG_INF("diffusion_params: - %-25s f32 = %.6f\n", "eps", params.diffusion.eps);
LOG_INF("diffusion_params: - %-25s u32 = %d (%s)\n", "algorithm", params.diffusion.algorithm,
alg_name);
LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "alg_temp", params.diffusion.alg_temp);
ldiff_params.mask_token_id = mask_token_id;
callback_data cb_data = { &params.diffusion, vocab, n_input };
ldiff_params.step_callback = diffusion_step_callback;
ldiff_params.step_callback_user_data = &cb_data;
int32_t n_generated = 0;
bool visual_mode = params.diffusion.visual_mode;
int32_t n_generated = 0;
std::vector<llama_token> output_tokens(params.n_ubatch);
diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, params.n_ubatch,
ldiff_params, n_generated);
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 (params.diffusion.visual_mode) {
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());