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408 lines (339 loc) · 16.7 KB
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#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;
}
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