).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
add_opt(common_arg(
{"--diffusion-algorithm"}, "N",
- string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm),
+ string_format(
+ "diffusion algorithm: 0=DIFFUSION_ALGORITHM_ORIGIN, 1=DIFFUSION_ALGORITHM_ENTROPY_BASED, "
+ "2=DIFFUSION_ALGORITHM_MARGIN_BASED, 3=DIFFUSION_ALGORITHM_RANDOM, "
+ "4=DIFFUSION_ALGORITHM_CONFIDENCE_BASED (default: %d)", params.diffusion.algorithm),
[](common_params & params, int value) { params.diffusion.algorithm = value; }
).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
add_opt(common_arg(
+set(TARGET llama-diffusion)
+add_library(${TARGET} STATIC diffusion.cpp diffusion.h)
+target_link_libraries(${TARGET} PUBLIC llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PUBLIC cxx_std_17)
+
set(TARGET llama-diffusion-cli)
add_executable(${TARGET} diffusion-cli.cpp)
install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama-diffusion llama llama-common ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
### Core Diffusion Parameters
- `--diffusion-steps`: Number of diffusion steps (default: 256)
- `--diffusion-algorithm`: Algorithm for token selection
- - `0`: ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
- - `1`: ENTROPY_BASED - Entropy-based selection
- - `2`: MARGIN_BASED - Margin-based selection
- - `3`: RANDOM - Random selection
- - `4`: CONFIDENCE_BASED - Confidence-based selection (default)
+ - `0`: DIFFUSION_ALGORITHM_ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
+ - `1`: DIFFUSION_ALGORITHM_ENTROPY_BASED - Entropy-based selection
+ - `2`: DIFFUSION_ALGORITHM_MARGIN_BASED - Margin-based selection
+ - `3`: DIFFUSION_ALGORITHM_RANDOM - Random selection
+ - `4`: DIFFUSION_ALGORITHM_CONFIDENCE_BASED - Confidence-based selection (default)
- More documentation here https://github.com/DreamLM/Dream
- `--diffusion-visual`: Enable live visualization during generation
#include "arg.h"
#include "chat.h"
#include "common.h"
+#include "diffusion.h"
#include "llama.h"
#include "log.h"
#include <limits.h>
-#include <algorithm>
#include <clocale>
-#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,
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, const std::string & system_prompt, bool use_chat_template, llama_model * model) {
if (!use_chat_template) {
return prompt;
GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
if (params.diffusion.eps) {
- diff_params.schedule = TIMESTEP_BASED;
+ diff_params.schedule = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
diff_params.eps = params.diffusion.eps;
} else if (params.diffusion.block_length) {
- diff_params.schedule = BLOCK_BASED;
+ diff_params.schedule = DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED;
diff_params.block_length = params.diffusion.block_length;
}
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_names[] = {
+ "DIFFUSION_ALGORITHM_ORIGIN",
+ "DIFFUSION_ALGORITHM_ENTROPY_BASED",
+ "DIFFUSION_ALGORITHM_MARGIN_BASED",
+ "DIFFUSION_ALGORITHM_RANDOM",
+ "DIFFUSION_ALGORITHM_CONFIDENCE_BASED",
+ };
+ const char * sched_names[] = {
+ "DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED",
+ "DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED",
+ };
const char * alg_name =
(diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
const char * sched_name =
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) {
+ if (diff_params.schedule == DIFFUSION_TRANSFER_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) {
+ if (diff_params.schedule == DIFFUSION_TRANSFER_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);
}
--- /dev/null
+#include "diffusion.h"
+
+#include "log.h"
+
+#include <algorithm>
+#include <cstddef>
+#include <cmath>
+#include <cstring>
+#include <random>
+#include <utility>
+#include <vector>
+
+static float calculate_confidence(const llama_token_data_array & cur_p,
+ diffusion_algorithm algorithm,
+ std::mt19937 & rng) {
+ switch (algorithm) {
+ case DIFFUSION_ALGORITHM_CONFIDENCE_BASED:
+ return cur_p.data[cur_p.selected].p; // Selected token probability
+
+ case DIFFUSION_ALGORITHM_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 DIFFUSION_ALGORITHM_MARGIN_BASED:
+ return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
+
+ case DIFFUSION_ALGORITHM_RANDOM:
+ {
+ std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
+ return uniform(rng); // Random confidence
+ }
+
+ case DIFFUSION_ALGORITHM_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,
+ diffusion_transfer_schedule schedule,
+ float eps,
+ const std::vector<int32_t> & num_transfer_tokens = {}) {
+ switch (schedule) {
+ case DIFFUSION_TRANSFER_SCHEDULE_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 DIFFUSION_TRANSFER_SCHEDULE_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 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;
+}
+
+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 == DIFFUSION_TRANSFER_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 == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
+ int32_t block_end = (params.schedule == DIFFUSION_TRANSFER_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 == DIFFUSION_TRANSFER_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 != DIFFUSION_TRANSFER_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 == DIFFUSION_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;
+}
--- /dev/null
+#pragma once
+
+#include "llama.h"
+
+#include <cstdint>
+
+enum diffusion_algorithm {
+ DIFFUSION_ALGORITHM_ORIGIN = 0,
+ DIFFUSION_ALGORITHM_ENTROPY_BASED = 1,
+ DIFFUSION_ALGORITHM_MARGIN_BASED = 2,
+ DIFFUSION_ALGORITHM_RANDOM = 3,
+ DIFFUSION_ALGORITHM_CONFIDENCE_BASED = 4,
+};
+
+// Unified transfer scheduling methods
+enum diffusion_transfer_schedule {
+ DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED = 0, // Dream-style: (1.0 - s/t) * remaining
+ DIFFUSION_TRANSFER_SCHEDULE_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 = DIFFUSION_ALGORITHM_CONFIDENCE_BASED;
+ diffusion_transfer_schedule schedule = DIFFUSION_TRANSFER_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
+};
+
+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);
overview / pkg / ggml / sources / llama.cpp / commitdiff