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#include <assert.h>
#include <algorithm>
#include <cstring>
#include <fstream>
#include <iostream>
#include <iterator>
#include <map>
#include <random>
#include <regex>
#include <set>
#include <sstream>
#include <string>
#include <unordered_map>
#include <vector>
#include "ggml/ggml.h"
#include "stable-diffusion.h"
static SDLogLevel log_level = SDLogLevel::INFO;
#define __FILENAME__ "stable-diffusion.cpp"
#define SD_LOG(level, format, ...) \
do { \
if (level < log_level) { \
break; \
} \
if (level == SDLogLevel::DEBUG) { \
printf("[DEBUG] %s:%-4d - " format "\n", __FILENAME__, __LINE__, ##__VA_ARGS__); \
} else if (level == SDLogLevel::INFO) { \
printf("[INFO] %s:%-4d - " format "\n", __FILENAME__, __LINE__, ##__VA_ARGS__); \
} else if (level == SDLogLevel::WARN) { \
fprintf(stderr, "[WARN] %s:%-4d - " format "\n", __FILENAME__, __LINE__, ##__VA_ARGS__); \
} else if (level == SDLogLevel::ERROR) { \
fprintf(stderr, "[ERROR] %s:%-4d - " format "\n", __FILENAME__, __LINE__, ##__VA_ARGS__); \
} \
} while (0)
#define LOG_DEBUG(format, ...) SD_LOG(SDLogLevel::DEBUG, format, ##__VA_ARGS__)
#define LOG_INFO(format, ...) SD_LOG(SDLogLevel::INFO, format, ##__VA_ARGS__)
#define LOG_WARN(format, ...) SD_LOG(SDLogLevel::WARN, format, ##__VA_ARGS__)
#define LOG_ERROR(format, ...) SD_LOG(SDLogLevel::ERROR, format, ##__VA_ARGS__)
#define GGML_FILE_MAGIC 0x67676d6c
#define TIMESTEPS 1000
/*================================================== Helper Functions ================================================*/
void set_sd_log_level(SDLogLevel level) {
log_level = level;
}
std::string sd_get_system_info() {
std::stringstream ss;
ss << "System Info: \n";
ss << " BLAS = " << ggml_cpu_has_blas() << std::endl;
ss << " SSE3 = " << ggml_cpu_has_sse3() << std::endl;
ss << " AVX = " << ggml_cpu_has_avx() << std::endl;
ss << " AVX2 = " << ggml_cpu_has_avx2() << std::endl;
ss << " AVX512 = " << ggml_cpu_has_avx512() << std::endl;
ss << " AVX512_VBMI = " << ggml_cpu_has_avx512_vbmi() << std::endl;
ss << " AVX512_VNNI = " << ggml_cpu_has_avx512_vnni() << std::endl;
ss << " FMA = " << ggml_cpu_has_fma() << std::endl;
ss << " NEON = " << ggml_cpu_has_neon() << std::endl;
ss << " ARM_FMA = " << ggml_cpu_has_arm_fma() << std::endl;
ss << " F16C = " << ggml_cpu_has_f16c() << std::endl;
ss << " FP16_VA = " << ggml_cpu_has_fp16_va() << std::endl;
ss << " WASM_SIMD = " << ggml_cpu_has_wasm_simd() << std::endl;
ss << " VSX = " << ggml_cpu_has_vsx() << std::endl;
return ss.str();
}
ggml_tensor* load_tensor_from_file(ggml_context* ctx, const std::string& file_path) {
std::ifstream file(file_path, std::ios::binary);
if (!file.is_open()) {
LOG_ERROR("failed to open '%s'", file_path.c_str());
return NULL;
}
int32_t n_dims;
int32_t length;
int32_t ttype;
file.read(reinterpret_cast<char*>(&n_dims), sizeof(n_dims));
file.read(reinterpret_cast<char*>(&length), sizeof(length));
file.read(reinterpret_cast<char*>(&ttype), sizeof(ttype));
if (file.eof()) {
LOG_ERROR("incomplete file '%s'", file_path.c_str());
return NULL;
}
int32_t nelements = 1;
int32_t ne[4] = {1, 1, 1, 1};
for (int i = 0; i < n_dims; ++i) {
file.read(reinterpret_cast<char*>(&ne[i]), sizeof(ne[i]));
nelements *= ne[i];
}
std::string name(length, 0);
file.read(&name[0], length);
ggml_tensor* tensor = ggml_new_tensor_4d(ctx, (ggml_type)ttype, ne[0], ne[1], ne[2], ne[3]);
const size_t bpe = ggml_type_size(ggml_type(ttype));
file.read(reinterpret_cast<char*>(tensor->data), ggml_nbytes(tensor));
return tensor;
}
static std::default_random_engine generator;
void set_random_seed(int seed) {
generator.seed(seed);
}
void ggml_tensor_set_f32_randn(struct ggml_tensor* tensor) {
float mean = 0.0;
float stddev = 1.0;
std::normal_distribution<float> distribution(mean, stddev);
for (int i = 0; i < ggml_nelements(tensor); i++) {
float random_number = distribution(generator);
ggml_set_f32_1d(tensor, i, random_number);
}
}
// set tensor[i, j, k, l]
// set tensor[l]
// set tensor[k, l]
// set tensor[j, k, l]
void ggml_tensor_set_f32(struct ggml_tensor* tensor, float value, int l, int k = 0, int j = 0, int i = 0) {
GGML_ASSERT(tensor->nb[0] == sizeof(float));
*(float*)((char*)(tensor->data) + i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0]) = value;
}
float ggml_tensor_get_f32(const ggml_tensor* tensor, int l, int k = 0, int j = 0, int i = 0) {
GGML_ASSERT(tensor->nb[0] == sizeof(float));
return *(float*)((char*)(tensor->data) + i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0]);
}
void print_ggml_tensor(struct ggml_tensor* tensor, bool shape_only = false) {
printf("shape(%zu, %zu, %zu, %zu)\n", tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
if (shape_only) {
return;
}
int range = 3;
for (int i = 0; i < tensor->ne[3]; i++) {
if (i >= range && i + range < tensor->ne[3]) {
continue;
}
for (int j = 0; j < tensor->ne[2]; j++) {
if (j >= range && j + range < tensor->ne[2]) {
continue;
}
for (int k = 0; k < tensor->ne[1]; k++) {
if (k >= range && k + range < tensor->ne[1]) {
continue;
}
for (int l = 0; l < tensor->ne[0]; l++) {
if (l >= range && l + range < tensor->ne[0]) {
continue;
}
printf(" [%d, %d, %d, %d] = %f\n", i, j, k, l, ggml_tensor_get_f32(tensor, l, k, j, i));
}
}
}
}
}
void copy_ggml_tensor(
struct ggml_tensor* dst,
const struct ggml_tensor* src) {
dst->nb[0] = src->nb[0];
dst->nb[1] = src->nb[1];
dst->nb[2] = src->nb[2];
dst->nb[3] = src->nb[3];
memcpy(((char*)dst->data), ((char*)src->data), ggml_nbytes(dst));
}
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
void set_timestep_embedding(struct ggml_tensor* timesteps, struct ggml_tensor* embedding, int dim, int max_period = 10000) {
// timesteps: [N,]
// embedding: [(dim + 1)/2, N]
int half = dim / 2;
std::vector<float> freqs(half);
for (int i = 0; i < half; ++i) {
freqs[i] = (float)std::exp(-std::log(max_period) * i / half);
}
for (int i = 0; i < timesteps->ne[0]; ++i) {
for (int j = 0; j < half; ++j) {
float arg = ggml_get_f32_1d(timesteps, i) * freqs[j];
ggml_tensor_set_f32(embedding, std::cos(arg), j, i);
ggml_tensor_set_f32(embedding, std::sin(arg), j + half, i);
}
if (dim % 2 != 0) {
*(float*)((char*)embedding->data + i * embedding->nb[1] + dim * embedding->nb[0]) = 0;
}
}
}
struct ggml_tensor* new_timestep_embedding(struct ggml_context* ctx, struct ggml_tensor* timesteps, int dim, int max_period = 10000) {
// timesteps: [N,]
// embedding: [(dim + 1)/2, N]
int acutual_dim = dim;
if (dim % 2 != 0) {
acutual_dim = dim + 1;
}
struct ggml_tensor* embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, acutual_dim, timesteps->ne[0]);
if (!ggml_get_no_alloc(ctx)) {
set_timestep_embedding(timesteps, embedding, dim, max_period);
}
return embedding;
}
std::vector<uint8_t> ggml_to_image_vec(struct ggml_tensor* t) {
int64_t w = t->ne[0];
int64_t h = t->ne[1];
int64_t c = t->ne[2];
std::vector<uint8_t> vec;
vec.resize(w * h * c);
uint8_t* data = (uint8_t*)vec.data();
for (int i = 0; i < h; i++) {
for (int j = 0; j < w; j++) {
for (int k = 0; k < c; k++) {
float value = ggml_tensor_get_f32(t, j, i, k);
value = (value + 1.0f) * 0.5f;
if (value < 0) {
value = 0;
} else if (value > 1) {
value = 1;
}
value *= 255.f;
*(data + i * w * c + j * c + k) = (uint8_t)value;
}
}
}
return vec;
}
void image_vec_to_ggml(const std::vector<uint8_t>& vec,
struct ggml_tensor* t) {
int64_t w = t->ne[0];
int64_t h = t->ne[1];
int64_t c = t->ne[2];
uint8_t* data = (uint8_t*)vec.data();
for (int i = 0; i < h; i++) {
for (int j = 0; j < w; j++) {
for (int k = 0; k < c; k++) {
float value = *(data + i * w * c + j * c + k);
value = value / 255.f;
value = 2 * value - 1;
ggml_tensor_set_f32(t, value, j, i, k);
}
}
}
}
/*================================================== CLIPTokenizer ===================================================*/
const std::string UNK_TOKEN = "<|endoftext|>";
const std::string BOS_TOKEN = "<|startoftext|>";
const std::string EOS_TOKEN = "<|endoftext|>";
const std::string PAD_TOEKN = "<|endoftext|>";
const int UNK_TOKEN_ID = 49407;
const int BOS_TOKEN_ID = 49406;
const int EOS_TOKEN_ID = 49407;
const int PAD_TOKEN_ID = 49407;
// Ref: https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
// TODO: implement bpe
class CLIPTokenizer {
private:
std::map<std::string, int32_t> encoder;
std::regex pat;
static std::string strip(const std::string& str) {
std::string::size_type start = str.find_first_not_of(" \t\n\r\v\f");
std::string::size_type end = str.find_last_not_of(" \t\n\r\v\f");
if (start == std::string::npos) {
// String contains only whitespace characters
return "";
}
return str.substr(start, end - start + 1);
}
static std::string whitespace_clean(std::string text) {
text = std::regex_replace(text, std::regex(R"(\s+)"), " ");
text = strip(text);
return text;
}
public:
CLIPTokenizer() = default;
std::string bpe(std::string token) {
std::string word = token + "</w>";
if (encoder.find(word) != encoder.end()) {
return word;
} else if (encoder.find(token) != encoder.end()) {
return token;
}
return UNK_TOKEN;
}
void add_token(std::string token, int32_t token_id) {
encoder[token] = token_id;
}
std::vector<int> tokenize(std::string text, size_t max_length = 0, bool padding = false) {
std::vector<int32_t> tokens = encode(text);
tokens.insert(tokens.begin(), BOS_TOKEN_ID);
if (max_length > 0) {
if (tokens.size() > max_length - 1) {
tokens.resize(max_length - 1);
} else {
if (padding) {
tokens.insert(tokens.end(), max_length - 1 - tokens.size(), PAD_TOKEN_ID);
}
}
}
tokens.push_back(EOS_TOKEN_ID);
return tokens;
}
std::vector<int> encode(std::string text) {
std::string original_text = text;
std::vector<int32_t> bpe_tokens;
text = whitespace_clean(text);
std::transform(text.begin(), text.end(), text.begin(), [](unsigned char c) { return std::tolower(c); });
std::regex pat(R"(<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[[:alpha:]]+|[[:digit:]]|[^[:space:][:alpha:][:digit:]]+)",
std::regex::icase);
std::smatch matches;
std::string str = text;
std::vector<std::string> token_strs;
while (std::regex_search(str, matches, pat)) {
for (auto& token : matches) {
std::istringstream iss(bpe(token));
std::vector<std::string> tokens{std::istream_iterator<std::string>{iss},
std::istream_iterator<std::string>{}};
for (const auto& bpe_token : tokens) {
bpe_tokens.push_back(encoder[bpe_token]);
token_strs.push_back(bpe_token);
}
}
str = matches.suffix();
}
std::stringstream ss;
ss << "[";
for (auto token : token_strs) {
ss << "\"" << token << "\", ";
}
ss << "]";
LOG_DEBUG("split prompt \"%s\" to tokens %s", original_text.c_str(), ss.str().c_str());
return bpe_tokens;
}
};
// Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/prompt_parser.py#L345
//
// Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
// Accepted tokens are:
// (abc) - increases attention to abc by a multiplier of 1.1
// (abc:3.12) - increases attention to abc by a multiplier of 3.12
// [abc] - decreases attention to abc by a multiplier of 1.1
// \( - literal character '('
// \[ - literal character '['
// \) - literal character ')'
// \] - literal character ']'
// \\ - literal character '\'
// anything else - just text
//
// >>> parse_prompt_attention('normal text')
// [['normal text', 1.0]]
// >>> parse_prompt_attention('an (important) word')
// [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
// >>> parse_prompt_attention('(unbalanced')
// [['unbalanced', 1.1]]
// >>> parse_prompt_attention('\(literal\]')
// [['(literal]', 1.0]]
// >>> parse_prompt_attention('(unnecessary)(parens)')
// [['unnecessaryparens', 1.1]]
// >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
// [['a ', 1.0],
// ['house', 1.5730000000000004],
// [' ', 1.1],
// ['on', 1.0],
// [' a ', 1.1],
// ['hill', 0.55],
// [', sun, ', 1.1],
// ['sky', 1.4641000000000006],
// ['.', 1.1]]
std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::string& text) {
std::vector<std::pair<std::string, float>> res;
std::vector<int> round_brackets;
std::vector<int> square_brackets;
float round_bracket_multiplier = 1.1f;
float square_bracket_multiplier = 1 / 1.1f;
std::regex re_attention(R"(\\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|\)|]|[^\\()\[\]:]+|:)");
std::regex re_break(R"(\s*\bBREAK\b\s*)");
auto multiply_range = [&](int start_position, float multiplier) {
for (int p = start_position; p < res.size(); ++p) {
res[p].second *= multiplier;
}
};
std::smatch m;
std::string remaining_text = text;
while (std::regex_search(remaining_text, m, re_attention)) {
std::string text = m[0];
std::string weight = m[1];
if (text == "(") {
round_brackets.push_back(res.size());
} else if (text == "[") {
square_brackets.push_back(res.size());
} else if (!weight.empty()) {
if (!round_brackets.empty()) {
multiply_range(round_brackets.back(), std::stod(weight));
round_brackets.pop_back();
}
} else if (text == ")" && !round_brackets.empty()) {
multiply_range(round_brackets.back(), round_bracket_multiplier);
round_brackets.pop_back();
} else if (text == "]" && !square_brackets.empty()) {
multiply_range(square_brackets.back(), square_bracket_multiplier);
square_brackets.pop_back();
} else if (text == "\\(") {
res.push_back({text.substr(1), 1.0f});
} else {
res.push_back({text, 1.0f});
}
remaining_text = m.suffix();
}
for (int pos : round_brackets) {
multiply_range(pos, round_bracket_multiplier);
}
for (int pos : square_brackets) {
multiply_range(pos, square_bracket_multiplier);
}
if (res.empty()) {
res.push_back({"", 1.0f});
}
int i = 0;
while (i + 1 < res.size()) {
if (res[i].second == res[i + 1].second) {
res[i].first += res[i + 1].first;
res.erase(res.begin() + i + 1);
} else {
++i;
}
}
return res;
}
/*================================================ FrozenCLIPEmbedder ================================================*/
struct ResidualAttentionBlock {
int32_t n_head;
int32_t d_model;
int32_t hidden_size; // n_head * d_model
int32_t intermediate_size;
// attention
struct ggml_tensor* q_w; // [hidden_size, hidden_size]
struct ggml_tensor* q_b; // [hidden_size, ]
struct ggml_tensor* k_w; // [hidden_size, hidden_size]
struct ggml_tensor* k_b; // [hidden_size, ]
struct ggml_tensor* v_w; // [hidden_size, hidden_size]
struct ggml_tensor* v_b; // [hidden_size, ]
struct ggml_tensor* out_w; // [hidden_size, hidden_size]
struct ggml_tensor* out_b; // [hidden_size, ]
// layer norm 1
struct ggml_tensor* ln1_w; // [hidden_size, ]
struct ggml_tensor* ln1_b; // [hidden_size, ]
// mlp
struct ggml_tensor* fc1_w; // [intermediate_size, hidden_size]
struct ggml_tensor* fc1_b; // [intermediate_size, ]
struct ggml_tensor* fc2_w; // [hidden_size, intermediate_size]
struct ggml_tensor* fc2_b; // [hidden_size, ]
// layer norm 2
struct ggml_tensor* ln2_w; // [hidden_size, ]
struct ggml_tensor* ln2_b; // [hidden_size, ]
size_t compute_params_mem_size(ggml_type wtype) {
double mem_size = 0;
mem_size += 4 * hidden_size * hidden_size * ggml_type_sizef(wtype); // q_w/k_w/v_w/out_w
mem_size += 8 * hidden_size * ggml_type_sizef(GGML_TYPE_F32); // q_b/k_b/v_b/out_b/ln1_w/ln1_b/ln2_w/ln2_b
mem_size += 2 * hidden_size * intermediate_size * ggml_type_sizef(wtype); // fc1_w/fc2_w
mem_size += intermediate_size * ggml_type_sizef(GGML_TYPE_F32); // fc1_b
mem_size += hidden_size * ggml_type_sizef(GGML_TYPE_F32); // fc2_b
mem_size += 16 * ggml_tensor_overhead(); // tensor overhead
return static_cast<size_t>(mem_size);
}
void init_params(struct ggml_context* ctx, ggml_type wtype) {
ln1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
ln1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
q_w = ggml_new_tensor_2d(ctx, wtype, hidden_size, hidden_size);
q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
k_w = ggml_new_tensor_2d(ctx, wtype, hidden_size, hidden_size);
k_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
v_w = ggml_new_tensor_2d(ctx, wtype, hidden_size, hidden_size);
v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
out_w = ggml_new_tensor_2d(ctx, wtype, hidden_size, hidden_size);
out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
fc1_w = ggml_new_tensor_2d(ctx, wtype, hidden_size, intermediate_size);
fc1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, intermediate_size);
fc2_w = ggml_new_tensor_2d(ctx, wtype, intermediate_size, hidden_size);
fc2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
ln2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
ln2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
}
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
tensors[prefix + "self_attn.q_proj.weight"] = q_w;
tensors[prefix + "self_attn.q_proj.bias"] = q_b;
tensors[prefix + "self_attn.k_proj.weight"] = k_w;
tensors[prefix + "self_attn.k_proj.bias"] = k_b;
tensors[prefix + "self_attn.v_proj.weight"] = v_w;
tensors[prefix + "self_attn.v_proj.bias"] = v_b;
tensors[prefix + "self_attn.out_proj.weight"] = out_w;
tensors[prefix + "self_attn.out_proj.bias"] = out_b;
tensors[prefix + "layer_norm1.weight"] = ln1_w;
tensors[prefix + "layer_norm1.bias"] = ln1_b;
tensors[prefix + "layer_norm2.weight"] = ln2_w;
tensors[prefix + "layer_norm2.bias"] = ln2_b;
tensors[prefix + "mlp.fc1.weight"] = fc1_w;
tensors[prefix + "mlp.fc1.bias"] = fc1_b;
tensors[prefix + "mlp.fc2.weight"] = fc2_w;
tensors[prefix + "mlp.fc2.bias"] = fc2_b;
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [N, n_token, hidden_size]
int64_t N = x->ne[2];
int64_t n_token = x->ne[1];
int64_t hidden_size = n_head * d_model;
struct ggml_tensor* r = x;
// layer norm 1
{
x = ggml_norm(ctx, x);
x = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ln1_w, x), x),
ggml_repeat(ctx, ln1_b, x));
}
// self-attention
{
struct ggml_tensor* q = ggml_add(ctx,
ggml_repeat(ctx, q_b, x),
ggml_mul_mat(ctx, q_w, x));
q = ggml_scale_inplace(ctx, q, ggml_new_f32(ctx, 1.0f / sqrt((float)d_model)));
q = ggml_reshape_4d(ctx, q, d_model, n_head, n_token, N); // [N, n_token, n_head, d_model]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, n_token, d_model]
q = ggml_reshape_3d(ctx, q, d_model, n_token, n_head * N); // [N * n_head, n_token, d_model]
struct ggml_tensor* k = ggml_add(ctx,
ggml_repeat(ctx, k_b, x),
ggml_mul_mat(ctx, k_w, x));
k = ggml_reshape_4d(ctx, k, d_model, n_head, n_token, N); // [N, n_token, n_head, d_model]
k = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3)); // [N, n_head, n_token, d_model]
k = ggml_reshape_3d(ctx, k, d_model, n_token, n_head); // [N * n_head, n_token, d_model]
struct ggml_tensor* v = ggml_add(ctx,
ggml_repeat(ctx, v_b, x),
ggml_mul_mat(ctx, v_w, x));
v = ggml_reshape_4d(ctx, v, d_model, n_head, n_token, N); // [N, n_token, n_head, d_model]
v = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3)); // [N, n_head, d_model, n_token]
v = ggml_reshape_3d(ctx, v, n_token, d_model, n_head * N); // [N * n_head, d_model, n_token]
struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q); // [N * n_head, n_token, n_token]
kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
kq = ggml_soft_max_inplace(ctx, kq);
struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq); // [N * n_head, n_token, d_model]
kqv = ggml_reshape_4d(ctx, kqv, d_model, n_token, n_head, N);
kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3)); // [N, n_token, n_head, d_model]
x = ggml_reshape_2d(ctx, kqv, d_model * n_head, n_token * N); // // [N * n_token, d_model * n_head]
}
// attention output
x = ggml_add(ctx, ggml_repeat(ctx, out_b, x), ggml_mul_mat(ctx, out_w, x));
// residual
x = ggml_add(ctx, x, r);
r = x;
// layer norm 2
{
x = ggml_norm(ctx, x);
x = ggml_add(ctx, ggml_mul(ctx, ggml_repeat(ctx, ln2_w, x), x),
ggml_repeat(ctx, ln2_b, x));
}
// mlp
x = ggml_mul_mat(ctx, fc1_w, x);
x = ggml_add(ctx, ggml_repeat(ctx, fc1_b, x), x);
x = ggml_gelu_quick_inplace(ctx, x);
x = ggml_mul_mat(ctx, fc2_w, x);
x = ggml_add(ctx, ggml_repeat(ctx, fc2_b, x), x);
// residual 2
x = ggml_add(ctx, x, r);
return x;
}
};
struct CLIPTextModel {
// network hparams
int32_t vocab_size = 49408;
int32_t max_position_embeddings = 77;
int32_t hidden_size = 768;
int32_t intermediate_size = 3072;
int32_t projection_dim = 768;
int32_t n_head = 12; // num_attention_heads
int32_t num_hidden_layers = 12;
// embeddings
struct ggml_tensor* position_ids;
struct ggml_tensor* token_embed_weight;
struct ggml_tensor* position_embed_weight;
// transformer
ResidualAttentionBlock resblocks[12];
struct ggml_tensor* final_ln_w;
struct ggml_tensor* final_ln_b;
CLIPTextModel() {
int d_model = hidden_size / n_head; // 64
for (int i = 0; i < num_hidden_layers; i++) {
resblocks[i].d_model = d_model;
resblocks[i].n_head = n_head;
resblocks[i].hidden_size = hidden_size;
resblocks[i].intermediate_size = intermediate_size;
}
}
size_t compute_params_mem_size(ggml_type wtype) {
double mem_size = 0;
mem_size += hidden_size * max_position_embeddings * ggml_type_sizef(GGML_TYPE_I32); // position_ids
mem_size += hidden_size * vocab_size * ggml_type_sizef(wtype); // token_embed_weight
mem_size += hidden_size * max_position_embeddings * ggml_type_sizef(wtype); // position_embed_weight
for (int i = 0; i < num_hidden_layers; i++) {
mem_size += resblocks[i].compute_params_mem_size(wtype);
}
mem_size += 2 * hidden_size * ggml_type_sizef(GGML_TYPE_F32); // final_ln_w/b
mem_size += ggml_tensor_overhead(); // object overhead
return static_cast<size_t>(mem_size);
}
void init_params(struct ggml_context* ctx, ggml_type wtype) {
position_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, max_position_embeddings);
for (int i = 0; i < max_position_embeddings; i++) {
ggml_set_i32_1d(position_ids, i, i);
}
token_embed_weight = ggml_new_tensor_2d(ctx, wtype, hidden_size, vocab_size);
position_embed_weight = ggml_new_tensor_2d(ctx, wtype, hidden_size, max_position_embeddings);
for (int i = 0; i < num_hidden_layers; i++) {
resblocks[i].init_params(ctx, wtype);
}
final_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
final_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
}
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
tensors[prefix + "embeddings.token_embedding.weight"] = token_embed_weight;
tensors[prefix + "embeddings.position_embedding.weight"] = position_embed_weight;
tensors[prefix + "final_layer_norm.weight"] = final_ln_w;
tensors[prefix + "final_layer_norm.bias"] = final_ln_b;
for (int i = 0; i < num_hidden_layers; i++) {
resblocks[i].map_by_name(tensors, prefix + "encoder.layers." + std::to_string(i) + ".");
}
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* input_ids) {
// input_ids: [N, n_token]
GGML_ASSERT(input_ids->ne[0] <= position_ids->ne[0]);
// token_embedding + position_embedding
struct ggml_tensor* x;
x = ggml_add(ctx,
ggml_get_rows(ctx, token_embed_weight, input_ids),
ggml_get_rows(ctx,
position_embed_weight,
ggml_view_1d(ctx, position_ids, input_ids->ne[0], 0))); // [N, n_token, hidden_size]
// transformer
for (int i = 0; i < num_hidden_layers; i++) {
x = resblocks[i].forward(ctx, x); // [N, n_token, hidden_size]
}
// final layer norm
{
x = ggml_norm(ctx, x);
x = ggml_add(ctx, ggml_mul(ctx, ggml_repeat(ctx, final_ln_w, x), x),
ggml_repeat(ctx, final_ln_b, x));
}
return x; // [N, n_token, hidden_size]
}
};
// ldm.modules.encoders.modules.FrozenCLIPEmbedder
struct FrozenCLIPEmbedder {
CLIPTokenizer tokenizer;
CLIPTextModel text_model;
struct ggml_tensor* forward(struct ggml_context* ctx, const std::string& prompt) {
std::vector<int32_t> tokens = tokenizer.tokenize(prompt, text_model.max_position_embeddings, true);
struct ggml_tensor* input_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, tokens.size());
memcpy(input_ids->data, tokens.data(), tokens.size() * ggml_element_size(input_ids));
struct ggml_tensor* hidden_states = text_model.forward(ctx, input_ids);
return hidden_states;
}
};
// Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/sd_hijack_clip.py#L283
struct FrozenCLIPEmbedderWithCustomWords {
CLIPTokenizer tokenizer;
CLIPTextModel text_model;
std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
size_t max_length = 0,
bool padding = false) {
auto parsed_attention = parse_prompt_attention(text);
{
std::stringstream ss;
ss << "[";
for (const auto& item : parsed_attention) {
ss << "['" << item.first << "', " << item.second << "], ";
}
ss << "]";
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
}
std::vector<int> tokens;
std::vector<float> weights;
for (const auto& item : parsed_attention) {
const std::string& curr_text = item.first;
float curr_weight = item.second;
std::vector<int> curr_tokens = tokenizer.encode(curr_text);
tokens.insert(tokens.end(), curr_tokens.begin(), curr_tokens.end());
weights.insert(weights.end(), curr_tokens.size(), curr_weight);
}
tokens.insert(tokens.begin(), BOS_TOKEN_ID);
weights.insert(weights.begin(), 1.0);
if (max_length > 0) {
if (tokens.size() > max_length - 1) {
tokens.resize(max_length - 1);
weights.resize(max_length - 1);
} else {
if (padding) {
tokens.insert(tokens.end(), max_length - 1 - tokens.size(), PAD_TOKEN_ID);
weights.insert(weights.end(), max_length - 1 - weights.size(), 1.0);
}
}
}
tokens.push_back(EOS_TOKEN_ID);
weights.push_back(1.0);
// for (int i = 0; i < tokens.size(); i++) {
// std::cout << tokens[i] << ":" << weights[i] << ", ";
// }
// std::cout << std::endl;
return {tokens, weights};
}
};
/*==================================================== UnetModel =====================================================*/
struct ResBlock {
// network hparams
int channels; // model_channels * (1, 1, 1, 2, 2, 4, 4, 4)
int emb_channels; // time_embed_dim
int out_channels; // mult * model_channels
// network params
// in_layers
struct ggml_tensor* in_layer_0_w; // [channels, ]
struct ggml_tensor* in_layer_0_b; // [channels, ]
// in_layer_1 is nn.SILU()
struct ggml_tensor* in_layer_2_w; // [out_channels, channels, 3, 3]
struct ggml_tensor* in_layer_2_b; // [out_channels, ]
// emb_layers
// emb_layer_0 is nn.SILU()
struct ggml_tensor* emb_layer_1_w; // [out_channels, emb_channels]
struct ggml_tensor* emb_layer_1_b; // [out_channels, ]
// out_layers
struct ggml_tensor* out_layer_0_w; // [out_channels, ]
struct ggml_tensor* out_layer_0_b; // [out_channels, ]
// out_layer_1 is nn.SILU()
// out_layer_2 is nn.Dropout(), p = 0 for inference
struct ggml_tensor* out_layer_3_w; // [out_channels, out_channels, 3, 3]
struct ggml_tensor* out_layer_3_b; // [out_channels, ]
// skip connection, only if out_channels != channels
struct ggml_tensor* skip_w; // [out_channels, channels, 1, 1]
struct ggml_tensor* skip_b; // [out_channels, ]
size_t compute_params_mem_size(ggml_type wtype) {
double mem_size = 0;
mem_size += 2 * channels * ggml_type_sizef(GGML_TYPE_F32); // in_layer_0_w/b
mem_size += out_channels * channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // in_layer_2_w
mem_size += 5 * out_channels * ggml_type_sizef(GGML_TYPE_F32); // in_layer_2_b/emb_layer_1_b/out_layer_0_w/out_layer_0_b/out_layer_3_b
mem_size += out_channels * emb_channels * ggml_type_sizef(wtype); // emb_layer_1_w
mem_size += out_channels * out_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // out_layer_3_w
mem_size += 10 * ggml_tensor_overhead(); // object overhead
if (out_channels != channels) {
mem_size += out_channels * channels * 1 * 1 * ggml_type_sizef(GGML_TYPE_F16); // skip_w
mem_size += out_channels * ggml_type_sizef(GGML_TYPE_F32); // skip_b
mem_size += 2 * ggml_tensor_overhead(); // object overhead
}
return static_cast<size_t>(mem_size);
}
void init_params(struct ggml_context* ctx, ggml_type wtype) {
in_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
in_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
in_layer_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, out_channels);
in_layer_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
emb_layer_1_w = ggml_new_tensor_2d(ctx, wtype, emb_channels, out_channels);
emb_layer_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, out_channels);
out_layer_3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
if (out_channels != channels) {
skip_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, channels, out_channels);
skip_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
}
}
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
tensors[prefix + "in_layers.0.weight"] = in_layer_0_w;
tensors[prefix + "in_layers.0.bias"] = in_layer_0_b;
tensors[prefix + "in_layers.2.weight"] = in_layer_2_w;
tensors[prefix + "in_layers.2.bias"] = in_layer_2_b;
tensors[prefix + "emb_layers.1.weight"] = emb_layer_1_w;
tensors[prefix + "emb_layers.1.bias"] = emb_layer_1_b;
tensors[prefix + "out_layers.0.weight"] = out_layer_0_w;
tensors[prefix + "out_layers.0.bias"] = out_layer_0_b;
tensors[prefix + "out_layers.3.weight"] = out_layer_3_w;
tensors[prefix + "out_layers.3.bias"] = out_layer_3_b;
if (out_channels != channels) {
tensors[prefix + "skip_connection.weight"] = skip_w;
tensors[prefix + "skip_connection.bias"] = skip_b;
}
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb) {
// x: [N, channels, h, w]
// emb: [N, emb_channels]
// in_layers
// group norm 32
auto h = ggml_group_norm(ctx, x);
h = ggml_add(ctx,
ggml_mul(ctx,
ggml_repeat(ctx,
ggml_reshape_4d(ctx, in_layer_0_w, 1, 1, in_layer_0_w->ne[0], 1),
h),
h),
ggml_repeat(ctx,
ggml_reshape_4d(ctx, in_layer_0_b, 1, 1, in_layer_0_b->ne[0], 1),
h));
// silu
h = ggml_silu_inplace(ctx, h);
// conv2d
h = ggml_conv_2d(ctx, in_layer_2_w, h, 1, 1, 1, 1, 1, 1);
h = ggml_add(ctx,
h,
ggml_repeat(ctx,
ggml_reshape_4d(ctx, in_layer_2_b, 1, 1, in_layer_2_b->ne[0], 1),
h)); // [N, out_channels, h, w]
// emb_layers
auto emb_out = ggml_silu(ctx, emb);
emb_out = ggml_mul_mat(ctx, emb_layer_1_w, emb_out);
emb_out = ggml_add(ctx, ggml_repeat(ctx, emb_layer_1_b, emb_out), emb_out); // [N, out_channels]
emb_out = ggml_reshape_4d(ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]); // [N, out_channels, 1, 1]
emb_out = ggml_repeat(ctx, emb_out, h); // [N, out_channels, h, w]
// out_layers
h = ggml_add(ctx, h, emb_out);
// group norm 32
h = ggml_group_norm_inplace(ctx, h);
h = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, out_layer_0_w, 1, 1, out_layer_0_w->ne[0], 1), h), h),
ggml_repeat(ctx, ggml_reshape_4d(ctx, out_layer_0_b, 1, 1, out_layer_0_b->ne[0], 1), h));
// silu
h = ggml_silu_inplace(ctx, h);
// dropout, skip for inference
// conv2d
h = ggml_conv_2d(ctx, out_layer_3_w, h, 1, 1, 1, 1, 1, 1);
h = ggml_add(ctx,
h,
ggml_repeat(ctx,
ggml_reshape_4d(ctx, out_layer_3_b, 1, 1, out_layer_3_b->ne[0], 1),
h)); // [N, out_channels, h, w
// skip connection
if (out_channels != channels) {
x = ggml_conv_2d(ctx, skip_w, x, 1, 1, 0, 0, 1, 1);
x = ggml_add(ctx,
x,
ggml_repeat(ctx,
ggml_reshape_4d(ctx, skip_b, 1, 1, skip_b->ne[0], 1),
x)); // [N, out_channels, h, w]
}
h = ggml_add(ctx, h, x);
return h; // [N, out_channels, h, w]
}
};
struct SpatialTransformer {
int in_channels; // mult * model_channels
int n_head; // num_heads
int d_head; // in_channels // n_heads
int depth = 1; // 1
int context_dim = 768; // hidden_size
// group norm
struct ggml_tensor* norm_w; // [in_channels,]
struct ggml_tensor* norm_b; // [in_channels,]
// proj_in
struct ggml_tensor* proj_in_w; // [in_channels, in_channels, 1, 1]
struct ggml_tensor* proj_in_b; // [in_channels,]
// transformer
struct
{
// layer norm 1
struct ggml_tensor* norm1_w; // [in_channels, ]
struct ggml_tensor* norm1_b; // [in_channels, ]
// attn1
struct ggml_tensor* attn1_q_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_k_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_v_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_out_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_out_b; // [in_channels, ]
// layer norm 2
struct ggml_tensor* norm2_w; // [in_channels, ]
struct ggml_tensor* norm2_b; // [in_channels, ]
// attn2
struct ggml_tensor* attn2_q_w; // [in_channels, in_channels]
struct ggml_tensor* attn2_k_w; // [in_channels, context_dim]
struct ggml_tensor* attn2_v_w; // [in_channels, context_dim]
struct ggml_tensor* attn2_out_w; // [in_channels, in_channels]