llama.cpp/examples/llava/clip.cpp

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// NOTE: This is modified from clip.cpp only for LLaVA,
// so there might be still unnecessary artifacts hanging around
// I'll gradually clean and extend it
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <iostream>
#include <map>
#include <regex>
#include <stdexcept>
#include <vector>
#include "clip.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
static std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), buf.size());
}
//
// key constants
//
#define KEY_FTYPE "general.file_type"
#define KEY_NAME "general.name"
#define KEY_DESCRIPTION "general.description"
#define KEY_HAS_TEXT_ENC "clip.has_text_encoder"
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
#define KEY_USE_GELU "clip.use_gelu"
#define KEY_N_EMBD "clip.%s.embedding_length"
#define KEY_N_FF "clip.%s.feed_forward_length"
#define KEY_N_BLOCK "clip.%s.block_count"
#define KEY_N_HEAD "clip.%s.attention.head_count"
#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
#define KEY_PROJ_DIM "clip.%s.projection_dim"
#define KEY_TOKENS "tokenizer.ggml.tokens"
#define KEY_N_POSITIONS "clip.text.context_length"
#define KEY_IMAGE_SIZE "clip.vision.image_size"
#define KEY_PATCH_SIZE "clip.vision.patch_size"
#define KEY_IMAGE_MEAN "clip.vision.image_mean"
#define KEY_IMAGE_STD "clip.vision.image_std"
//
// tensor name constants
//
#define TN_TOKEN_EMBD "%s.token_embd.weight"
#define TN_POS_EMBD "%s.position_embd.weight"
#define TN_CLASS_EMBD "v.class_embd"
#define TN_PATCH_EMBD "v.patch_embd.weight"
#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_PRE "%s.pre_ln.%s"
#define TN_LN_POST "%s.post_ln.%s"
#define TN_TEXT_PROJ "text_projection.weight"
#define TN_VIS_PROJ "visual_projection.weight"
#define TN_LLAVA_PROJ "mm.%d.%s"
//
// utilities to get data from a gguf file
//
static int get_key_idx(const gguf_context * ctx, const char * key) {
int i = gguf_find_key(ctx, key);
if (i == -1) {
fprintf(stderr, "key %s not found in file\n", key);
throw std::runtime_error(format("Missing required key: %s", key));
}
return i;
}
static uint32_t get_u32(const gguf_context * ctx, const std::string & key) {
const int i = get_key_idx(ctx, key.c_str());
return gguf_get_val_u32(ctx, i);
}
static float get_f32(const gguf_context * ctx, const std::string & key) {
const int i = get_key_idx(ctx, key.c_str());
return gguf_get_val_f32(ctx, i);
}
static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::string & name) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
if (!cur) {
throw std::runtime_error(format("%s: unable to find tensor %s\n", __func__, name.c_str()));
}
return cur;
}
static std::string get_ftype(int ftype) {
switch (ftype) {
case 0:
return "f32";
case 1:
return "f16";
case 2:
return "q4_0";
case 3:
return "q4_1";
case 6:
return "q5_0";
case 7:
return "q5_1";
case 8:
return "q8_0";
default:
throw std::runtime_error(format("%s: Unrecognized file type: %d\n", __func__, ftype));
}
}
//
// image data
//
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
//
// clip layers
//
struct clip_layer {
// attention
struct ggml_tensor * k_w;
struct ggml_tensor * k_b;
struct ggml_tensor * q_w;
struct ggml_tensor * q_b;
struct ggml_tensor * v_w;
struct ggml_tensor * v_b;
struct ggml_tensor * o_w;
struct ggml_tensor * o_b;
// layernorm 1
struct ggml_tensor * ln_1_w;
struct ggml_tensor * ln_1_b;
// ff
struct ggml_tensor * ff_i_w;
struct ggml_tensor * ff_i_b;
struct ggml_tensor * ff_o_w;
struct ggml_tensor * ff_o_b;
// layernorm 2
struct ggml_tensor * ln_2_w;
struct ggml_tensor * ln_2_b;
};
struct clip_vision_model {
struct clip_vision_hparams hparams;
// embeddings
struct ggml_tensor * class_embedding;
struct ggml_tensor * patch_embeddings;
struct ggml_tensor * position_embeddings;
struct ggml_tensor * pre_ln_w;
struct ggml_tensor * pre_ln_b;
std::vector<clip_layer> layers;
struct ggml_tensor * post_ln_w;
struct ggml_tensor * post_ln_b;
struct ggml_tensor * projection;
// LLaVA projection
struct ggml_tensor * mm_0_w;
struct ggml_tensor * mm_0_b;
struct ggml_tensor * mm_2_w;
struct ggml_tensor * mm_2_b;
};
struct clip_ctx {
bool has_text_encoder = false;
bool has_vision_encoder = false;
bool has_llava_projector = false;
struct clip_vision_model vision_model;
float image_mean[3];
float image_std[3];
bool use_gelu = false;
int32_t ftype = 1;
struct gguf_context * ctx_gguf;
struct ggml_context * ctx_data;
std::vector<uint8_t> buf_compute_meta;
// memory buffers to evaluate the model
ggml_backend_buffer_t params_buffer = NULL;
ggml_backend_buffer_t compute_buffer = NULL;
ggml_backend_t backend = NULL;
ggml_allocr * compute_alloc = NULL;
};
static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
return nullptr;
}
const auto & model = ctx->vision_model;
const auto & hparams = model.hparams;
const int image_size = hparams.image_size;
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
const int num_positions = num_patches + 1;
const int hidden_size = hparams.hidden_size;
const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head;
const int n_layer = hparams.n_layer;
//const int n_intermediate = hparams.n_intermediate;
//const int projection_dim = hparams.projection_dim;
const float eps = hparams.eps;
int batch_size = imgs->size;
if (ctx->has_llava_projector) {
GGML_ASSERT(batch_size == 1);
}
struct ggml_init_params params = {
/*.mem_size =*/ ctx->buf_compute_meta.size(),
/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
ggml_allocr_alloc(ctx->compute_alloc, inp_raw);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
float * data = (float *)malloc(ggml_nbytes(inp_raw));
for (size_t i = 0; i < imgs->size; i++) {
const int nx = imgs->data[i].nx;
const int ny = imgs->data[i].ny;
GGML_ASSERT(nx == image_size && ny == image_size);
const int n = nx * ny;
for (int b = 0; b < batch_size; b++) {
for (int k = 0; k < 3; k++) {
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
}
}
}
}
}
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
// concat class_embeddings and patch_embeddings
struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
ggml_allocr_alloc(ctx->compute_alloc, embeddings);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
embeddings = ggml_acc(ctx0, embeddings, inp,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
ggml_allocr_alloc(ctx->compute_alloc, positions);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
positions_data[i] = i;
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
embeddings =
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
// pre-layernorm
{
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
}
// loop over layers
for (int il = 0; il < n_layer - 1; il++) {
struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
//const size_t nb_q_w = model.layers[il].q_w->nb[0];
// layernorm1
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
model.layers[il].ln_1_b);
}
// self-attention
{
struct ggml_tensor * Q =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
struct ggml_tensor * K =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
struct ggml_tensor * V =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_inplace(ctx0, KQ);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));
cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size));
}
// attention output
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, embeddings);
embeddings = cur; // embeddings = residual, cur = hidden_states
// layernorm2
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
if (ctx->use_gelu) {
cur = ggml_gelu_inplace(ctx0, cur);
} else {
cur = ggml_gelu_quick_inplace(ctx0, cur);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
// residual 2
cur = ggml_add(ctx0, embeddings, cur);
embeddings = cur;
}
// llava projector
{
embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
ggml_allocr_alloc(ctx->compute_alloc, patches);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
patches_data[i] = i + 1;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);
}
embeddings = ggml_get_rows(ctx0, embeddings, patches);
// mm projection 0
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
embeddings = ggml_gelu(ctx0, embeddings);
embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
}
// build the graph
ggml_build_forward_expand(gf, embeddings);
ggml_free(ctx0);
return gf;
}
// read and create ggml_context containing the tensors and their data
struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
struct ggml_context * meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &meta,
};
struct gguf_context * ctx = gguf_init_from_file(fname, params);
if (!ctx) {
throw std::runtime_error(format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname));
}
if (verbosity >= 1) {
const int n_tensors = gguf_get_n_tensors(ctx);
const int n_kv = gguf_get_n_kv(ctx);
const int ftype = get_u32(ctx, KEY_FTYPE);
const std::string ftype_str = get_ftype(ftype);
const int idx_desc = get_key_idx(ctx, KEY_DESCRIPTION);
const std::string description = gguf_get_val_str(ctx, idx_desc);
const int idx_name = gguf_find_key(ctx, KEY_NAME);
if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
const std::string name = gguf_get_val_str(ctx, idx_name);
printf("%s: model name: %s\n", __func__, name.c_str());
}
printf("%s: description: %s\n", __func__, description.c_str());
printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
printf("%s: n_tensors: %d\n", __func__, n_tensors);
printf("%s: n_kv: %d\n", __func__, n_kv);
printf("%s: ftype: %s\n", __func__, ftype_str.c_str());
printf("\n");
}
const int n_tensors = gguf_get_n_tensors(ctx);
// kv
if (verbosity >= 3) {
const int n_kv = gguf_get_n_kv(ctx);
for (int i = 0; i < n_kv; ++i) {
const char * key = gguf_get_key(ctx, i);
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
}
printf("\n");
}
// data
size_t buffer_size = 0;
{
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(meta, name);
size_t tensor_size = ggml_nbytes(cur);
buffer_size += tensor_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i,
ggml_n_dims(cur), cur->name, tensor_size, offset);
}
}
}
buffer_size += n_tensors * 128 /* CLIP PADDING */;
clip_ctx * new_clip = new clip_ctx;
#ifdef GGML_USE_CUBLAS
new_clip->backend = ggml_backend_cuda_init(0);
printf("%s: CLIP using CUDA backend\n", __func__);
#endif
#ifdef GGML_USE_METAL
new_clip->backend = ggml_backend_metal_init();
printf("%s: CLIP using Metal backend\n", __func__);
#endif
if (!new_clip->backend) {
new_clip->backend = ggml_backend_cpu_init();
printf("%s: CLIP using CPU backend\n", __func__);
}
// model size and capabilities
{
int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);
new_clip->has_text_encoder = gguf_get_val_bool(ctx, idx);
idx = get_key_idx(ctx, KEY_HAS_VIS_ENC);
new_clip->has_vision_encoder = gguf_get_val_bool(ctx, idx);
idx = gguf_find_key(ctx, KEY_HAS_LLAVA_PROJ);
if (idx != -1) {
new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx);
}
GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
GGML_ASSERT(new_clip->has_vision_encoder);
GGML_ASSERT(!new_clip->has_text_encoder);
idx = get_key_idx(ctx, KEY_USE_GELU);
new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
if (verbosity >= 1) {
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0);
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
}
}
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors);
// load tensors
{
std::vector<uint8_t> read_buf;
struct ggml_init_params params = {
/*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
new_clip->ctx_data = ggml_init(params);
if (!new_clip->ctx_data) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
clip_free(new_clip);
return nullptr;
}
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
printf("cannot open model file for loading tensors\n");
clip_free(new_clip);
return nullptr;
}
// add tensors to context
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
struct ggml_tensor * t = ggml_get_tensor(meta, name);
struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t);
ggml_set_name(cur, name);
}
// alloc memory and offload data
new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size);
ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
ggml_allocr_alloc(alloc, cur);
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
fin.seekg(offset, std::ios::beg);
if (!fin) {
printf("%s: failed to seek for tensor %s\n", __func__, name);
clip_free(new_clip);
return nullptr;
}
int num_bytes = ggml_nbytes(cur);
if (ggml_backend_buffer_is_host(new_clip->params_buffer)) {
// for the CPU and Metal backend, we can read directly into the tensor
fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
} else {
// read into a temporary buffer first, then copy to device memory
read_buf.resize(num_bytes);
fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}
ggml_allocr_free(alloc);
fin.close();
}
// vision model
if (new_clip->has_vision_encoder) {
// load vision model
auto & vision_model = new_clip->vision_model;
auto & hparams = vision_model.hparams;
hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision"));
hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision"));
hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision"));
hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision"));
hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE);
hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE);
hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
for (int i = 0; i < 3; ++i) {
server : parallel decoding and multimodal (#3677) * implementing parallel decoding in server example * crash fixed * save dev progress * refactored sampling function * completion endpoint working * multiple client support * grammar + no stream completion * cached prompt support * chat.mjs support cached prompt + some fixes * server ui now support multiple clients * unused change reverted * fixed timings per slot * add context swap * add changes to README.md * llava multimodal integration * fixed tokens probs * add multimodal input - alfa * refactor code + remove unused comments + improved README.md * fix compilation errors with llvm * notify the user from server ui that multimodality is unavialable * some ci fixes * fix ci make build undefined ref errors * fix long prompt than ctx proposed in #3639 * fixed premature end due stop word * context shift fixed * fix llava implementation * sync README.md changes * readme change * update api like OpenAI * multimodal support enabled by default * fix make bui;d errors * fix multiple clients * fix zig build * new sampling API * latest changes of sampling API * server : coding-style normalization * server : coding-style normalization (part 2) * server : remove beam-search functionality * server : bug fix in ingest_images n_tokens is incremented internally by llama_batch_add * server : use refs + use llama_batch_clear() * server : snake case * server : minor sync * added thread safe pipeline * server : bach has to be allocated for n_parallel sequences * server : no need for atomic int - already using mutex * server : logs + minor code style * server : fix multibyte handle in partial response (#3706) * fix image load + view image in chat * make : silence stb warnings * clip : link to ggml, not to llama * server : fix switch fallthrough * server : fix crash in Debug on macOS (I have no idea why this fixes it!?) * server : refactor ctx_sampling init + n_ctx + names * server : bug fix for prompt caching * Do not save/load image_data to localStorage * editorconfig : new line in index.html * server : completion requests remember slot_id * Update readme to document multimodal in server * server : minor style * Update readme to document multimodal in server * server : hide ctx_sampling->prev behind API (#3696) * server : apply fix from #3722 * server : fix slot reuse * server : add comment about changing slot_state to bool --------- Co-authored-by: FSSRepo <go778sgt@gmail.com> Co-authored-by: Damian Stewart <d@damianstewart.com> Co-authored-by: Steward Garcia <57494570+FSSRepo@users.noreply.github.com> Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com> Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
2023-10-22 19:53:08 +00:00
new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));
new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));
}
if (verbosity >= 2) {
printf("\n%s: vision model hparams\n", __func__);
printf("image_size %d\n", hparams.image_size);
printf("patch_size %d\n", hparams.patch_size);
printf("v_hidden_size %d\n", hparams.hidden_size);
printf("v_n_intermediate %d\n", hparams.n_intermediate);
printf("v_projection_dim %d\n", hparams.projection_dim);
printf("v_n_head %d\n", hparams.n_head);
printf("v_n_layer %d\n", hparams.n_layer);
}
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
vision_model.layers.resize(hparams.n_layer);
for (int il = 0; il < hparams.n_layer; ++il) {
auto & layer = vision_model.layers[il];
layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight"));
layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight"));
layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight"));
layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight"));
layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight"));
layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight"));
layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight"));
layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight"));
layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias"));
layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias"));
layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias"));
layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias"));
layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias"));
layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias"));
layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias"));
layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias"));
}
}
ggml_free(meta);
new_clip->ctx_gguf = ctx;
// measure mem requirement and allocate
{
new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
clip_image_f32_batch batch;
batch.size = 1;
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf);
ggml_allocr_free(new_clip->compute_alloc);
new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size);
new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer);
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
}
return new_clip;
}
struct clip_image_u8 * clip_image_u8_init() {
return new clip_image_u8();
}
struct clip_image_f32 * clip_image_f32_init() {
return new clip_image_f32();
}
void clip_image_u8_free (struct clip_image_u8 * img) { delete img; }
void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
img->nx = nx;
img->ny = ny;
img->buf.resize(3 * nx * ny);
memcpy(img->buf.data(), data, img->buf.size());
}
bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
int nx, ny, nc;
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
if (!data) {
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
return false;
}
build_clip_img_from_data(data, nx, ny, img);
stbi_image_free(data);
return true;
}
bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
int nx, ny, nc;
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
if (!data) {
fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
return false;
}
build_clip_img_from_data(data, nx, ny, img);
stbi_image_free(data);
return true;
}
// normalize: x = (x - mean) / std
// TODO: implement bicubic interpolation instead of linear.
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
return false;
}
// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
if (pad2square && img->nx != img->ny) {
int longer_side = std::max(img->nx, img->ny);
temp->nx = longer_side;
temp->ny = longer_side;
temp->buf.resize(3 * longer_side * longer_side);
const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
// fill with background color
for (size_t i = 0; i < temp->buf.size(); i++) {
temp->buf[i] = bc[i % 3];
}
// copy from the input image
for (int y = 0; y < img->ny; y++) {
for (int x = 0; x < img->nx; x++) {
const int i = 3 * (y * img->nx + x);
const int j = 3 * (y * temp->nx + x);
temp->buf[j] = img->buf[i];
temp->buf[j+1] = img->buf[i+1];
temp->buf[j+2] = img->buf[i+2];
}
}
} else {
temp->nx = img->nx;
temp->ny = img->ny;
temp->buf.resize(img->buf.size());
memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
}
const int nx = temp->nx;
const int ny = temp->ny;
const int nx2 = ctx->vision_model.hparams.image_size;
const int ny2 = ctx->vision_model.hparams.image_size;
res->nx = nx2;
res->ny = ny2;
res->buf.resize(3 * nx2 * ny2);
const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
const int nx3 = int(nx / scale + 0.5f);
const int ny3 = int(ny / scale + 0.5f);
const auto & m3 = ctx->image_mean; // {0.48145466f, 0.4578275f, 0.40821073f};
const auto & s3 = ctx->image_std; // {0.26862954f, 0.26130258f, 0.27577711f};
for (int y = 0; y < ny3; y++) {
for (int x = 0; x < nx3; x++) {
for (int c = 0; c < 3; c++) {
// linear interpolation
const float sx = (x + 0.5f) * scale - 0.5f;
const float sy = (y + 0.5f) * scale - 0.5f;
const int x0 = std::max(0, (int)std::floor(sx));
const int y0 = std::max(0, (int)std::floor(sy));
const int x1 = std::min(x0 + 1, nx - 1);
const int y1 = std::min(y0 + 1, ny - 1);
const float dx = sx - x0;
const float dy = sy - y0;
const int j00 = 3 * (y0 * nx + x0) + c;
const int j01 = 3 * (y0 * nx + x1) + c;
const int j10 = 3 * (y1 * nx + x0) + c;
const int j11 = 3 * (y1 * nx + x1) + c;
const float v00 = temp->buf[j00];
const float v01 = temp->buf[j01];
const float v10 = temp->buf[j10];
const float v11 = temp->buf[j11];
const float v0 = v00 * (1.0f - dx) + v01 * dx;
const float v1 = v10 * (1.0f - dx) + v11 * dx;
const float v = v0 * (1.0f - dy) + v1 * dy;
const uint8_t v2 = std::min(std::max(std::round(v), 0.0f), 255.0f);
const int i = 3 * (y * nx3 + x) + c;
res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
}
}
}
clip_image_u8_free(temp);
return true;
}
void clip_free(clip_ctx * ctx) {
ggml_free(ctx->ctx_data);
gguf_free(ctx->ctx_gguf);
delete ctx;
}
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
return false;
}
clip_image_f32_batch imgs{};
imgs.size = 1;
imgs.data = img;
return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
}
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
return false;
}
int batch_size = imgs->size;
if(ctx->has_llava_projector) {
GGML_ASSERT(batch_size == 1); // TODO: support multiple images
}
// reset alloc buffer to clean the memory from previous invocations
ggml_allocr_reset(ctx->compute_alloc);
// build the inference graph
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
ggml_allocr_alloc_graph(ctx->compute_alloc, gf);
if (ggml_backend_is_cpu(ctx->backend)) {
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
}
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(ctx->backend)) {
ggml_backend_metal_set_n_cb(ctx->backend, n_threads);
}
#endif
ggml_backend_graph_compute(ctx->backend, gf);
// the last node is the embedding tensor
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
return true;
}
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
ggml_type type = GGML_TYPE_Q4_1;
switch (itype) {
case 2:
type = GGML_TYPE_Q4_0;
break;
case 3:
type = GGML_TYPE_Q4_1;
break;
case 6:
type = GGML_TYPE_Q5_0;
break;
case 7:
type = GGML_TYPE_Q5_1;
break;
case 8:
type = GGML_TYPE_Q8_0;
break;
default:
fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
return false;
};
auto * ctx_clip = clip_model_load(fname_inp, 2);
const auto & ctx_src = ctx_clip->ctx_gguf;
const auto & ctx_data = ctx_clip->ctx_data;
auto * ctx_out = gguf_init_empty();
gguf_set_kv(ctx_out, ctx_src);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", itype);
auto fout = std::ofstream(fname_out, std::ios::binary);
const int n_tensors = gguf_get_n_tensors(ctx_src);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx_src, i);
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
gguf_add_tensor(ctx_out, cur);
}
const size_t meta_size = gguf_get_meta_size(ctx_out);
for (size_t i = 0; i < meta_size; ++i) {
fout.put(0);
}
// regexes of tensor names to be quantized
const std::vector<std::string> k_names = {
".*weight",
};
std::vector<uint8_t> read_data(512);
std::vector<uint8_t> work(512);
std::vector<float> conv_buf(512);
std::vector<int64_t> hist_all(1 << 4, 0);
size_t total_size_org = 0;
size_t total_size_new = 0;
for (int i = 0; i < n_tensors; ++i) {
const std::string name = gguf_get_tensor_name(ctx_src, i);
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());
enum ggml_type new_type;
void * new_data;
size_t new_size;
bool quantize = false;
for (const auto & s : k_names) {
if (std::regex_match(name, std::regex(s))) {
quantize = true;
break;
}
}
// quantize only 2D tensors
quantize &= (ggml_n_dims(cur) == 2);
if (quantize) {
new_type = type;
const size_t n_elms = ggml_nelements(cur);
float * f32_data;
switch (cur->type) {
case GGML_TYPE_F32:
f32_data = (float *)cur->data;
break;
case GGML_TYPE_F16:
if (conv_buf.size() < n_elms) {
conv_buf.resize(n_elms);
}
for (size_t j = 0; j < n_elms; ++j) {
conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]);
}
f32_data = (float *)conv_buf.data();
break;
default:
printf("Please use an input file in f32 or f16\n");
return false;
}
if (work.size() < n_elms * 4) {
work.resize(n_elms * 4);
}
new_data = work.data();
std::vector<int64_t> hist_cur(1 << 4, 0);
switch (new_type) {
case GGML_TYPE_Q4_0: {
new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q4_1: {
new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_0: {
new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_1: {
new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q8_0: {
new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
default: {
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
return false;
}
}
for (size_t j = 0; j < hist_cur.size(); ++j) {
hist_all[j] += hist_cur[j];
}
} else {
new_type = cur->type;
new_data = cur->data;
new_size = ggml_nbytes(cur);
}
const size_t orig_size = ggml_nbytes(cur);
total_size_org += orig_size;
total_size_new += new_size;
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
fout.write((const char *)new_data, new_size);
size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
for (size_t j = 0; j < pad; ++j) {
fout.put(0);
}
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
}
// go back to beginning of file and write the updated metadata
fout.seekp(0, std::ios::beg);
std::vector<uint8_t> meta(meta_size);
gguf_get_meta_data(ctx_out, meta.data());
fout.write((const char *)meta.data(), meta_size);
fout.close();
clip_free(ctx_clip);
gguf_free(ctx_out);
{
printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); ++i) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); ++i) {
printf("%5.3f ", hist_all[i] / (float)sum_all);
}
printf("\n");
}
return true;
}
int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->vision_model.mm_2_b->ne[0];
}
int clip_n_patches(const struct clip_ctx * ctx) {
auto & params = ctx->vision_model.hparams;
return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
}
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
}