mirror of
https://github.com/ggerganov/llama.cpp.git
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66ba560256
* fix empty bug * Update MobileVLM-README.md added more results on devices * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update MobileVLM-README.md remove gguf links --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2028 lines
83 KiB
C++
2028 lines
83 KiB
C++
// NOTE: This is modified from clip.cpp only for LLaVA,
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// so there might be still unnecessary artifacts hanging around
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// I'll gradually clean and extend it
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// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
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#include "clip.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#ifdef GGML_USE_CUDA
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#include "ggml-cuda.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#define STB_IMAGE_IMPLEMENTATION
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#include "stb_image.h"
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <regex>
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#include <stdexcept>
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#include <vector>
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#include <sstream>
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#include <cinttypes>
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#include <limits>
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//#define CLIP_DEBUG_FUNCTIONS
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// RGB uint8 image
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struct clip_image_u8 {
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int nx;
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int ny;
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std::vector<uint8_t> buf;
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};
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// RGB float32 image (NHWC)
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// Memory layout: RGBRGBRGB...
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struct clip_image_f32 {
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int nx;
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int ny;
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std::vector<float> buf;
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};
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static std::string format(const char * fmt, ...) {
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va_list ap;
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va_list ap2;
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va_start(ap, fmt);
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va_copy(ap2, ap);
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int size = vsnprintf(NULL, 0, fmt, ap);
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GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
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std::vector<char> buf(size + 1);
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int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
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GGML_ASSERT(size2 == size);
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va_end(ap2);
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va_end(ap);
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return std::string(buf.data(), buf.size());
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}
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//
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// key constants
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//
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#define KEY_FTYPE "general.file_type"
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#define KEY_NAME "general.name"
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#define KEY_DESCRIPTION "general.description"
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#define KEY_HAS_TEXT_ENC "clip.has_text_encoder"
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#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
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#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
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#define KEY_USE_GELU "clip.use_gelu"
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#define KEY_N_EMBD "clip.%s.embedding_length"
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#define KEY_N_FF "clip.%s.feed_forward_length"
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#define KEY_N_BLOCK "clip.%s.block_count"
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#define KEY_N_HEAD "clip.%s.attention.head_count"
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#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
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#define KEY_PROJ_DIM "clip.%s.projection_dim"
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#define KEY_TOKENS "tokenizer.ggml.tokens"
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#define KEY_N_POSITIONS "clip.text.context_length"
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#define KEY_IMAGE_SIZE "clip.vision.image_size"
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#define KEY_PATCH_SIZE "clip.vision.patch_size"
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#define KEY_IMAGE_MEAN "clip.vision.image_mean"
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#define KEY_IMAGE_STD "clip.vision.image_std"
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#define KEY_PROJ_TYPE "clip.projector_type"
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#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
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#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
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#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
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//
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// tensor name constants
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//
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#define TN_TOKEN_EMBD "%s.token_embd.weight"
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#define TN_POS_EMBD "%s.position_embd.weight"
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#define TN_CLASS_EMBD "v.class_embd"
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#define TN_PATCH_EMBD "v.patch_embd.weight"
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#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
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#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
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#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
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#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
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#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
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#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
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#define TN_LN_1 "%s.blk.%d.ln1.%s"
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#define TN_LN_2 "%s.blk.%d.ln2.%s"
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#define TN_LN_PRE "%s.pre_ln.%s"
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#define TN_LN_POST "%s.post_ln.%s"
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#define TN_TEXT_PROJ "text_projection.weight"
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#define TN_VIS_PROJ "visual_projection.weight"
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#define TN_LLAVA_PROJ "mm.%d.%s"
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#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s"
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#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
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#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s"
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#define TN_IMAGE_NEWLINE "model.image_newline"
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enum projector_type {
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PROJECTOR_TYPE_MLP,
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PROJECTOR_TYPE_MLP_NORM,
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PROJECTOR_TYPE_LDP,
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PROJECTOR_TYPE_LDPV2,
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PROJECTOR_TYPE_UNKNOWN,
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};
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static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
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{ PROJECTOR_TYPE_MLP, "mlp" },
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{ PROJECTOR_TYPE_LDP, "ldp" },
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{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
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};
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//
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// utilities to get data from a gguf file
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//
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static int get_key_idx(const gguf_context * ctx, const char * key) {
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int i = gguf_find_key(ctx, key);
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if (i == -1) {
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fprintf(stderr, "key %s not found in file\n", key);
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throw std::runtime_error(format("Missing required key: %s", key));
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}
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return i;
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}
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static uint32_t get_u32(const gguf_context * ctx, const std::string & key) {
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const int i = get_key_idx(ctx, key.c_str());
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return gguf_get_val_u32(ctx, i);
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}
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static float get_f32(const gguf_context * ctx, const std::string & key) {
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const int i = get_key_idx(ctx, key.c_str());
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return gguf_get_val_f32(ctx, i);
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}
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static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::string & name) {
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struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
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if (!cur) {
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throw std::runtime_error(format("%s: unable to find tensor %s\n", __func__, name.c_str()));
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}
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return cur;
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}
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static std::string get_ftype(int ftype) {
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return ggml_type_name(static_cast<ggml_type>(ftype));
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}
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static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
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switch (type) {
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case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]);
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case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]);
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case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]);
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case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]);
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case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]);
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case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]);
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case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]);
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case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]);
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case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]);
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case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]);
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case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false";
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default: return format("unknown type %d", type);
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}
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}
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static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
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std::string result;
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for (size_t pos = 0; ; pos += search.length()) {
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auto new_pos = s.find(search, pos);
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if (new_pos == std::string::npos) {
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result += s.substr(pos, s.size() - pos);
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break;
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}
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result += s.substr(pos, new_pos - pos) + replace;
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pos = new_pos;
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}
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s = std::move(result);
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}
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static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
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const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
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switch (type) {
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case GGUF_TYPE_STRING:
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return gguf_get_val_str(ctx_gguf, i);
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case GGUF_TYPE_ARRAY:
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{
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const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
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int arr_n = gguf_get_arr_n(ctx_gguf, i);
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const void * data = gguf_get_arr_data(ctx_gguf, i);
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std::stringstream ss;
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ss << "[";
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for (int j = 0; j < arr_n; j++) {
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if (arr_type == GGUF_TYPE_STRING) {
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std::string val = gguf_get_arr_str(ctx_gguf, i, j);
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// escape quotes
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replace_all(val, "\\", "\\\\");
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replace_all(val, "\"", "\\\"");
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ss << '"' << val << '"';
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} else if (arr_type == GGUF_TYPE_ARRAY) {
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ss << "???";
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} else {
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ss << gguf_data_to_str(arr_type, data, j);
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}
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if (j < arr_n - 1) {
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ss << ", ";
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}
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}
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ss << "]";
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return ss.str();
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}
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default:
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return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
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}
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}
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static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
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size_t tensor_size = ggml_nbytes(tensor);
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printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
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prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
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tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
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}
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static projector_type clip_projector_type_from_string(const std::string & name) {
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for (const auto & kv : PROJECTOR_TYPE_NAMES) { // NOLINT
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if (kv.second == name) {
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return kv.first;
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}
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}
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return PROJECTOR_TYPE_UNKNOWN;
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}
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#ifdef CLIP_DEBUG_FUNCTIONS
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static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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std::cerr << "Failed to open file for writing: " << filename << std::endl;
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return;
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}
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// PPM header: P6 format, width, height, and max color value
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file << "P6\n" << img.nx << " " << img.ny << "\n255\n";
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// Write pixel data
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for (size_t i = 0; i < img.buf.size(); i += 3) {
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// PPM expects binary data in RGB format, which matches our image buffer
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file.write(reinterpret_cast<const char*>(&img.buf[i]), 3);
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}
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file.close();
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}
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static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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std::cerr << "Failed to open file for writing: " << filename << std::endl;
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return;
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}
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int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data
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int bytesPerPixel = 3;
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int widthInBytes = img.nx * bytesPerPixel;
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int paddingAmount = (4 - (widthInBytes % 4)) % 4;
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int stride = widthInBytes + paddingAmount;
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// Bitmap file header
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unsigned char fileHeader[14] = {
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'B','M', // Signature
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0,0,0,0, // Image file size in bytes
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0,0,0,0, // Reserved
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54,0,0,0 // Start of pixel array
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};
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// Total file size
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fileSize = 54 + (stride * img.ny);
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fileHeader[2] = (unsigned char)(fileSize);
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fileHeader[3] = (unsigned char)(fileSize >> 8);
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fileHeader[4] = (unsigned char)(fileSize >> 16);
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fileHeader[5] = (unsigned char)(fileSize >> 24);
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// Bitmap information header (BITMAPINFOHEADER)
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unsigned char infoHeader[40] = {
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40,0,0,0, // Size of this header (40 bytes)
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0,0,0,0, // Image width
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0,0,0,0, // Image height
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1,0, // Number of color planes
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24,0, // Bits per pixel
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0,0,0,0, // No compression
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0,0,0,0, // Image size (can be 0 for no compression)
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0,0,0,0, // X pixels per meter (not specified)
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0,0,0,0, // Y pixels per meter (not specified)
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0,0,0,0, // Total colors (color table not used)
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0,0,0,0 // Important colors (all are important)
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};
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// Width and height in the information header
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infoHeader[4] = (unsigned char)(img.nx);
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infoHeader[5] = (unsigned char)(img.nx >> 8);
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infoHeader[6] = (unsigned char)(img.nx >> 16);
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infoHeader[7] = (unsigned char)(img.nx >> 24);
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infoHeader[8] = (unsigned char)(img.ny);
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infoHeader[9] = (unsigned char)(img.ny >> 8);
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infoHeader[10] = (unsigned char)(img.ny >> 16);
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infoHeader[11] = (unsigned char)(img.ny >> 24);
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// Write file headers
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file.write(reinterpret_cast<char*>(fileHeader), sizeof(fileHeader));
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file.write(reinterpret_cast<char*>(infoHeader), sizeof(infoHeader));
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// Pixel data
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std::vector<unsigned char> padding(3, 0); // Max padding size to be added to each row
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for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top
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for (int x = 0; x < img.nx; ++x) {
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// Each pixel
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size_t pixelIndex = (y * img.nx + x) * 3;
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unsigned char pixel[3] = {
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img.buf[pixelIndex + 2], // BMP stores pixels in BGR format
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img.buf[pixelIndex + 1],
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img.buf[pixelIndex]
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};
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file.write(reinterpret_cast<char*>(pixel), 3);
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}
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// Write padding for the row
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file.write(reinterpret_cast<char*>(padding.data()), paddingAmount);
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}
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file.close();
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}
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// debug function to convert f32 to u8
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static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) {
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dst.nx = src.nx;
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dst.ny = src.ny;
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dst.buf.resize(3 * src.nx * src.ny);
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for (size_t i = 0; i < src.buf.size(); ++i) {
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dst.buf[i] = static_cast<uint8_t>(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255));
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}
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}
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#endif
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//
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// clip layers
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//
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struct clip_hparams {
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int32_t image_size;
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int32_t patch_size;
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int32_t hidden_size;
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int32_t n_intermediate;
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int32_t projection_dim;
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int32_t n_head;
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int32_t n_layer;
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float eps;
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char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default)
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int32_t image_grid_pinpoints[32];
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int32_t image_crop_resolution;
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};
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struct clip_layer {
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// attention
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struct ggml_tensor * k_w;
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struct ggml_tensor * k_b;
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struct ggml_tensor * q_w;
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struct ggml_tensor * q_b;
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struct ggml_tensor * v_w;
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struct ggml_tensor * v_b;
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struct ggml_tensor * o_w;
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struct ggml_tensor * o_b;
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// layernorm 1
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struct ggml_tensor * ln_1_w;
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struct ggml_tensor * ln_1_b;
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// ff
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struct ggml_tensor * ff_i_w;
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struct ggml_tensor * ff_i_b;
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struct ggml_tensor * ff_o_w;
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struct ggml_tensor * ff_o_b;
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// layernorm 2
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struct ggml_tensor * ln_2_w;
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struct ggml_tensor * ln_2_b;
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};
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struct clip_vision_model {
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struct clip_hparams hparams;
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// embeddings
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struct ggml_tensor * class_embedding;
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struct ggml_tensor * patch_embeddings;
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struct ggml_tensor * position_embeddings;
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struct ggml_tensor * pre_ln_w;
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struct ggml_tensor * pre_ln_b;
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std::vector<clip_layer> layers;
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struct ggml_tensor * post_ln_w;
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struct ggml_tensor * post_ln_b;
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struct ggml_tensor * projection;
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// LLaVA projection
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struct ggml_tensor * mm_0_w = NULL;
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struct ggml_tensor * mm_0_b = NULL;
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struct ggml_tensor * mm_2_w = NULL;
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struct ggml_tensor * mm_2_b = NULL;
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struct ggml_tensor * image_newline = NULL;
|
|
|
|
// Yi type models with mlp+normalization projection
|
|
struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4
|
|
struct ggml_tensor * mm_1_b = NULL;
|
|
struct ggml_tensor * mm_3_w = NULL;
|
|
struct ggml_tensor * mm_3_b = NULL;
|
|
struct ggml_tensor * mm_4_w = NULL;
|
|
struct ggml_tensor * mm_4_b = NULL;
|
|
|
|
// MobileVLM projection
|
|
struct ggml_tensor * mm_model_mlp_1_w;
|
|
struct ggml_tensor * mm_model_mlp_1_b;
|
|
struct ggml_tensor * mm_model_mlp_3_w;
|
|
struct ggml_tensor * mm_model_mlp_3_b;
|
|
struct ggml_tensor * mm_model_block_1_block_0_0_w;
|
|
struct ggml_tensor * mm_model_block_1_block_0_1_w;
|
|
struct ggml_tensor * mm_model_block_1_block_0_1_b;
|
|
struct ggml_tensor * mm_model_block_1_block_1_fc1_w;
|
|
struct ggml_tensor * mm_model_block_1_block_1_fc1_b;
|
|
struct ggml_tensor * mm_model_block_1_block_1_fc2_w;
|
|
struct ggml_tensor * mm_model_block_1_block_1_fc2_b;
|
|
struct ggml_tensor * mm_model_block_1_block_2_0_w;
|
|
struct ggml_tensor * mm_model_block_1_block_2_1_w;
|
|
struct ggml_tensor * mm_model_block_1_block_2_1_b;
|
|
struct ggml_tensor * mm_model_block_2_block_0_0_w;
|
|
struct ggml_tensor * mm_model_block_2_block_0_1_w;
|
|
struct ggml_tensor * mm_model_block_2_block_0_1_b;
|
|
struct ggml_tensor * mm_model_block_2_block_1_fc1_w;
|
|
struct ggml_tensor * mm_model_block_2_block_1_fc1_b;
|
|
struct ggml_tensor * mm_model_block_2_block_1_fc2_w;
|
|
struct ggml_tensor * mm_model_block_2_block_1_fc2_b;
|
|
struct ggml_tensor * mm_model_block_2_block_2_0_w;
|
|
struct ggml_tensor * mm_model_block_2_block_2_1_w;
|
|
struct ggml_tensor * mm_model_block_2_block_2_1_b;
|
|
|
|
// MobileVLM_V2 projection
|
|
struct ggml_tensor * mm_model_mlp_0_w;
|
|
struct ggml_tensor * mm_model_mlp_0_b;
|
|
struct ggml_tensor * mm_model_mlp_2_w;
|
|
struct ggml_tensor * mm_model_mlp_2_b;
|
|
struct ggml_tensor * mm_model_peg_0_w;
|
|
struct ggml_tensor * mm_model_peg_0_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;
|
|
projector_type proj_type = PROJECTOR_TYPE_MLP;
|
|
|
|
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_t backend = NULL;
|
|
ggml_gallocr_t 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_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
|
|
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 float eps = hparams.eps;
|
|
|
|
const 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_set_name(inp_raw, "inp_raw");
|
|
ggml_set_input(inp_raw);
|
|
|
|
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_set_name(embeddings, "embeddings");
|
|
ggml_set_input(embeddings);
|
|
|
|
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_set_name(positions, "positions");
|
|
ggml_set_input(positions);
|
|
|
|
embeddings =
|
|
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
|
|
|
|
// pre-layernorm
|
|
{
|
|
embeddings = ggml_norm(ctx0, embeddings, eps);
|
|
ggml_set_name(embeddings, "pre_ln");
|
|
|
|
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_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
cur = ggml_cont_3d(ctx0, KQV, 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_set_name(patches, "patches");
|
|
ggml_set_input(patches);
|
|
|
|
// shape [1, 576, 1024]
|
|
// ne is whcn, ne = [1024, 576, 1, 1]
|
|
embeddings = ggml_get_rows(ctx0, embeddings, patches);
|
|
|
|
// print_tensor_info(embeddings, "embeddings");
|
|
|
|
// llava projector
|
|
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
|
|
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);
|
|
|
|
} else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
|
|
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
|
|
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
|
|
// ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
|
|
// First LayerNorm
|
|
embeddings = ggml_norm(ctx0, embeddings, eps);
|
|
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
|
|
model.mm_1_b);
|
|
|
|
// GELU activation
|
|
embeddings = ggml_gelu(ctx0, embeddings);
|
|
|
|
// Second linear layer
|
|
embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
|
|
embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
|
|
|
|
// Second LayerNorm
|
|
embeddings = ggml_norm(ctx0, embeddings, eps);
|
|
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
|
|
model.mm_4_b);
|
|
}
|
|
else if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
|
|
// MobileVLM projector
|
|
int n_patch = 24;
|
|
struct ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
|
|
mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
|
|
mlp_1 = ggml_gelu(ctx0, mlp_1);
|
|
struct ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
|
|
mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
|
|
// mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
|
|
|
|
// block 1
|
|
struct ggml_tensor * block_1 = nullptr;
|
|
{
|
|
// transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
|
|
mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
|
|
mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
|
|
// stride = 1, padding = 1, bias is nullptr
|
|
block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
|
|
|
|
// layer norm
|
|
// // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
|
|
// block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
|
|
block_1 = ggml_norm(ctx0, block_1, eps);
|
|
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
|
|
|
|
// block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
|
|
// hardswish
|
|
struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
|
|
|
|
block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
|
|
// block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
|
|
// pointwise conv
|
|
block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
|
|
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
|
|
block_1 = ggml_relu(ctx0, block_1);
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
|
|
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
|
|
block_1 = ggml_hardsigmoid(ctx0, block_1);
|
|
// block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
|
|
block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
|
|
block_1 = ggml_mul(ctx0, block_1_hw, block_1);
|
|
|
|
int w = block_1->ne[0], h = block_1->ne[1];
|
|
block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
|
|
|
|
// block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
|
|
block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
|
|
|
|
// block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
|
|
block_1 = ggml_norm(ctx0, block_1, eps);
|
|
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
|
|
// block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
|
|
// residual
|
|
block_1 = ggml_add(ctx0, mlp_3, block_1);
|
|
}
|
|
|
|
// block_2
|
|
{
|
|
// stride = 2
|
|
block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
|
|
|
|
// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
|
|
// layer norm
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
|
|
// block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
|
|
block_1 = ggml_norm(ctx0, block_1, eps);
|
|
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
|
|
// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
|
|
// hardswish
|
|
struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
|
|
|
|
// not sure the parameters is right for globalAvgPooling
|
|
block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
|
|
// block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
|
|
// pointwise conv
|
|
block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
|
|
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
|
|
block_1 = ggml_relu(ctx0, block_1);
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
|
|
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
|
|
block_1 = ggml_hardsigmoid(ctx0, block_1);
|
|
|
|
// block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
|
|
block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
|
|
block_1 = ggml_mul(ctx0, block_1_hw, block_1);
|
|
|
|
int w = block_1->ne[0], h = block_1->ne[1];
|
|
block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
|
|
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
|
|
// block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
|
|
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
|
|
block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
|
|
|
|
|
|
// block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
|
|
block_1 = ggml_norm(ctx0, block_1, eps);
|
|
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
|
|
block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
|
|
// block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
|
|
}
|
|
embeddings = block_1;
|
|
}
|
|
else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2)
|
|
{
|
|
int n_patch = 24;
|
|
struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
|
|
mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
|
|
mlp_0 = ggml_gelu(ctx0, mlp_0);
|
|
struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
|
|
mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
|
|
// mlp_2 ne = [2048, 576, 1, 1]
|
|
// // AVG Pool Layer 2*2, strides = 2
|
|
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
|
|
// mlp_2 ne = [576, 2048, 1, 1]
|
|
mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
|
|
// mlp_2 ne [24, 24, 2048, 1]
|
|
mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
|
|
// weight ne = [3, 3, 2048, 1]
|
|
struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
|
|
peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
|
|
peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
|
|
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
|
|
peg_0 = ggml_add(ctx0, peg_0, mlp_2);
|
|
peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
|
|
embeddings = peg_0;
|
|
}
|
|
else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
// 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
|
|
const int n_kv = gguf_get_n_kv(ctx);
|
|
printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
|
|
__func__, n_kv, n_tensors, fname);
|
|
{
|
|
std::map<enum ggml_type, uint32_t> n_type;
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
enum ggml_type type = gguf_get_tensor_type(ctx, i);
|
|
|
|
n_type[type]++;
|
|
}
|
|
|
|
printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
|
for (int i = 0; i < n_kv; i++) {
|
|
const char * name = gguf_get_key(ctx, i);
|
|
const enum gguf_type type = gguf_get_kv_type(ctx, i);
|
|
const std::string type_name =
|
|
type == GGUF_TYPE_ARRAY
|
|
? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx, i)), gguf_get_arr_n(ctx, i))
|
|
: gguf_type_name(type);
|
|
|
|
std::string value = gguf_kv_to_str(ctx, i);
|
|
const size_t MAX_VALUE_LEN = 40;
|
|
if (value.size() > MAX_VALUE_LEN) {
|
|
value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
|
|
}
|
|
replace_all(value, "\n", "\\n");
|
|
|
|
printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
|
}
|
|
|
|
// print type counts
|
|
for (auto & kv : n_type) {
|
|
if (kv.second == 0) {
|
|
continue;
|
|
}
|
|
|
|
printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
|
}
|
|
}
|
|
|
|
// data
|
|
size_t model_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);
|
|
enum ggml_type type = gguf_get_tensor_type(ctx, i);
|
|
struct ggml_tensor * cur = ggml_get_tensor(meta, name);
|
|
size_t tensor_size = ggml_nbytes(cur);
|
|
model_size += tensor_size;
|
|
if (verbosity >= 3) {
|
|
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
|
|
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
|
|
}
|
|
}
|
|
}
|
|
|
|
clip_ctx * new_clip = new clip_ctx;
|
|
|
|
// update projector type
|
|
{
|
|
int idx = gguf_find_key(ctx, KEY_PROJ_TYPE);
|
|
if (idx != -1) {
|
|
const std::string proj_type = gguf_get_val_str(ctx, idx);
|
|
new_clip->proj_type = clip_projector_type_from_string(proj_type);
|
|
} else {
|
|
new_clip->proj_type = PROJECTOR_TYPE_MLP;
|
|
}
|
|
|
|
if (new_clip->proj_type == PROJECTOR_TYPE_MLP) {
|
|
if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) {
|
|
new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM;
|
|
}
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_USE_CUDA
|
|
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__, model_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__, model_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);
|
|
gguf_free(ctx);
|
|
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);
|
|
gguf_free(ctx);
|
|
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_ctx_tensors(new_clip->ctx_data, new_clip->backend);
|
|
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);
|
|
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);
|
|
gguf_free(ctx);
|
|
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);
|
|
}
|
|
}
|
|
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"));
|
|
|
|
try {
|
|
int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS);
|
|
int n = gguf_get_arr_n(ctx, idx);
|
|
const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx);
|
|
for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) {
|
|
hparams.image_grid_pinpoints[i] = pinpoints[i];
|
|
}
|
|
if (n < 32)
|
|
hparams.image_grid_pinpoints[n] = 0;
|
|
} catch (std::runtime_error & e) {
|
|
hparams.image_grid_pinpoints[0]=0;
|
|
}
|
|
|
|
try {
|
|
int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
|
|
strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
|
|
} catch (std::runtime_error & e) {
|
|
strcpy(hparams.mm_patch_merge_type, "flat");
|
|
}
|
|
|
|
try {
|
|
hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
|
|
} catch(const std::exception& e) {
|
|
hparams.image_crop_resolution = hparams.image_size;
|
|
}
|
|
|
|
int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
|
|
int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
|
|
|
|
const float * mean_data = (const float *)gguf_get_arr_data(ctx, idx_mean);
|
|
const float * std_data = (const float *)gguf_get_arr_data(ctx, idx_std);
|
|
|
|
for (int i = 0; i < 3; ++i) {
|
|
new_clip->image_mean[i] = mean_data[i];
|
|
new_clip->image_std[i] = std_data[i];
|
|
}
|
|
|
|
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);
|
|
printf("v_eps %f\n", hparams.eps);
|
|
printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
|
|
printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
|
|
printf("v_image_grid_pinpoints: ");
|
|
for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
|
|
printf("%d ", hparams.image_grid_pinpoints[i]);
|
|
}
|
|
printf("\n");
|
|
printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
|
|
|
|
}
|
|
|
|
try {
|
|
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"));
|
|
} catch(const std::exception& e) {
|
|
fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
|
|
}
|
|
|
|
// LLaVA projection
|
|
if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
|
|
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"));
|
|
try {
|
|
// Yi-type llava
|
|
vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
|
|
vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
|
|
} catch (std::runtime_error & e) { }
|
|
try {
|
|
// missing in Yi-type llava
|
|
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"));
|
|
} catch (std::runtime_error & e) { }
|
|
try {
|
|
// Yi-type llava
|
|
vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
|
|
vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
|
|
} catch (std::runtime_error & e) { }
|
|
try {
|
|
// Yi-type llava
|
|
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
|
|
vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
|
|
} catch (std::runtime_error & e) { }
|
|
try {
|
|
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
|
|
// fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
|
|
} catch (std::runtime_error & e) { }
|
|
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
|
|
// MobileVLM projection
|
|
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
|
vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias"));
|
|
vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight"));
|
|
vision_model.mm_model_mlp_3_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias"));
|
|
vision_model.mm_model_block_1_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight"));
|
|
vision_model.mm_model_block_1_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight"));
|
|
vision_model.mm_model_block_1_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias"));
|
|
vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight"));
|
|
vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias"));
|
|
vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight"));
|
|
vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias"));
|
|
vision_model.mm_model_block_1_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight"));
|
|
vision_model.mm_model_block_1_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight"));
|
|
vision_model.mm_model_block_1_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias"));
|
|
vision_model.mm_model_block_2_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight"));
|
|
vision_model.mm_model_block_2_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight"));
|
|
vision_model.mm_model_block_2_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias"));
|
|
vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight"));
|
|
vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias"));
|
|
vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight"));
|
|
vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias"));
|
|
vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
|
|
vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
|
|
vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
|
|
}
|
|
else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2)
|
|
{
|
|
// MobilVLM_V2 projection
|
|
vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
|
vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias"));
|
|
vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight"));
|
|
vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias"));
|
|
vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight"));
|
|
vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias"));
|
|
}
|
|
else {
|
|
std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
|
|
throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
|
|
}
|
|
|
|
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_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
|
|
clip_image_f32_batch batch;
|
|
batch.size = 1;
|
|
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
|
|
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
|
|
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
|
|
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; }
|
|
void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) {
|
|
if (batch->size > 0) {
|
|
delete[] batch->data;
|
|
batch->size = 0;
|
|
}
|
|
}
|
|
void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) {
|
|
if (batch->size > 0) {
|
|
delete[] batch->data;
|
|
batch->size = 0;
|
|
}
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
// Linear interpolation between two points
|
|
inline float lerp(float s, float e, float t) {
|
|
return s + (e - s) * t;
|
|
}
|
|
// Bilinear resize function
|
|
static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) {
|
|
dst.nx = target_width;
|
|
dst.ny = target_height;
|
|
dst.buf.resize(3 * target_width * target_height);
|
|
|
|
float x_ratio = static_cast<float>(src.nx - 1) / target_width;
|
|
float y_ratio = static_cast<float>(src.ny - 1) / target_height;
|
|
|
|
for (int y = 0; y < target_height; y++) {
|
|
for (int x = 0; x < target_width; x++) {
|
|
float px = x_ratio * x;
|
|
float py = y_ratio * y;
|
|
int x_floor = static_cast<int>(px);
|
|
int y_floor = static_cast<int>(py);
|
|
float x_lerp = px - x_floor;
|
|
float y_lerp = py - y_floor;
|
|
|
|
for (int c = 0; c < 3; c++) {
|
|
float top = lerp(
|
|
static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
|
|
static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
|
|
x_lerp
|
|
);
|
|
float bottom = lerp(
|
|
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
|
|
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
|
|
x_lerp
|
|
);
|
|
dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not
|
|
static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* dst, const float mean[3], const float std[3]) {
|
|
dst->nx = src->nx;
|
|
dst->ny = src->ny;
|
|
dst->buf.resize(src->buf.size());
|
|
|
|
for (size_t i = 0; i < src->buf.size(); ++i) {
|
|
int c = i % 3; // rgb
|
|
dst->buf[i] = (static_cast<float>(src->buf[i]) / 255.0f - mean[c]) / std[c];
|
|
}
|
|
}
|
|
|
|
inline float clip(float x, float lower, float upper) {
|
|
return std::max(lower, std::min(x, upper));
|
|
}
|
|
|
|
static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int target_width, int target_height) {
|
|
const int nx = img.nx;
|
|
const int ny = img.ny;
|
|
|
|
dst.nx = target_width;
|
|
dst.ny = target_height;
|
|
dst.buf.resize(3 * target_width * target_height);
|
|
|
|
float Cc;
|
|
float C[5];
|
|
float d0, d2, d3, a0, a1, a2, a3;
|
|
int i, j, k, jj;
|
|
int x, y;
|
|
float dx, dy;
|
|
float tx, ty;
|
|
|
|
tx = (float)nx / (float)target_width;
|
|
ty = (float)ny / (float)target_height;
|
|
|
|
// Bicubic interpolation; adapted from ViT.cpp, inspired from :
|
|
// -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36
|
|
// -> https://en.wikipedia.org/wiki/Bicubic_interpolation
|
|
|
|
for (i = 0; i < target_height; i++) {
|
|
for (j = 0; j < target_width; j++) {
|
|
x = (int)(tx * j);
|
|
y = (int)(ty * i);
|
|
|
|
dx = tx * j - x;
|
|
dy = ty * i - y;
|
|
|
|
for (k = 0; k < 3; k++) {
|
|
for (jj = 0; jj <= 3; jj++) {
|
|
d0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x - 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
|
|
d2 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
|
|
d3 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 2, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
|
|
a0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
|
|
|
|
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
|
|
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
|
|
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
|
|
|
|
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
|
|
|
|
d0 = C[0] - C[1];
|
|
d2 = C[2] - C[1];
|
|
d3 = C[3] - C[1];
|
|
a0 = C[1];
|
|
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
|
|
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
|
|
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
|
|
Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
|
|
|
|
const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f);
|
|
dst.buf[(i * target_width + j) * 3 + k] = float(Cc2);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// llava-1.6 type of resize_and_pad (black)
|
|
static void resize_and_pad_image(const clip_image_u8& image, clip_image_u8 &image_output, const std::pair<int, int>& target_resolution) {
|
|
int target_width = target_resolution.first;
|
|
int target_height = target_resolution.second;
|
|
|
|
float scale_w = static_cast<float>(target_width) / image.nx;
|
|
float scale_h = static_cast<float>(target_height) / image.ny;
|
|
|
|
int new_width, new_height;
|
|
|
|
if (scale_w < scale_h) {
|
|
new_width = target_width;
|
|
new_height = std::min(static_cast<int>(std::ceil(image.ny * scale_w)), target_height);
|
|
} else {
|
|
new_height = target_height;
|
|
new_width = std::min(static_cast<int>(std::ceil(image.nx * scale_h)), target_width);
|
|
}
|
|
|
|
clip_image_u8 resized_image;
|
|
// bilinear_resize(image, resized_image, new_width, new_height);
|
|
bicubic_resize(image, resized_image, new_width, new_height);
|
|
|
|
clip_image_u8 padded_image;
|
|
padded_image.nx = target_width;
|
|
padded_image.ny = target_height;
|
|
padded_image.buf.resize(3 * target_width * target_height, 0); // Initialize with black
|
|
|
|
// Calculate padding offsets
|
|
int pad_x = (target_width - new_width) / 2;
|
|
int pad_y = (target_height - new_height) / 2;
|
|
|
|
// Copy the resized image into the center of the padded buffer
|
|
for (int y = 0; y < new_height; ++y) {
|
|
for (int x = 0; x < new_width; ++x) {
|
|
for (int c = 0; c < 3; ++c) {
|
|
padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c];
|
|
}
|
|
}
|
|
}
|
|
image_output = std::move(padded_image);
|
|
}
|
|
|
|
/**
|
|
* Selects the best resolution from a list of possible resolutions based on the original size.
|
|
*
|
|
* @param original_size The original size of the image in the format (width, height).
|
|
* @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
|
|
* @return The best fit resolution in the format (width, height).
|
|
*/
|
|
static std::pair<int, int> select_best_resolution(const std::pair<int, int> & original_size, const std::vector<std::pair<int, int>> & possible_resolutions) {
|
|
int original_width = original_size.first;
|
|
int original_height = original_size.second;
|
|
std::pair<int, int> best_fit;
|
|
int max_effective_resolution = 0;
|
|
int min_wasted_resolution = std::numeric_limits<int>::max();
|
|
|
|
for (const auto& resolution : possible_resolutions) {
|
|
int width = resolution.first;
|
|
int height = resolution.second;
|
|
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
|
|
int downscaled_width = static_cast<int>(original_width * scale);
|
|
int downscaled_height = static_cast<int>(original_height * scale);
|
|
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
|
int wasted_resolution = (width * height) - effective_resolution;
|
|
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
|
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
|
max_effective_resolution = effective_resolution;
|
|
min_wasted_resolution = wasted_resolution;
|
|
best_fit = resolution;
|
|
}
|
|
}
|
|
|
|
return best_fit;
|
|
}
|
|
|
|
static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
|
|
std::vector<clip_image_u8*> patches;
|
|
int width = image.nx;
|
|
int height = image.ny;
|
|
for (int i = 0; i < height; i += patch_size) {
|
|
for (int j = 0; j < width; j += patch_size) {
|
|
clip_image_u8 *patch = clip_image_u8_init();
|
|
patch->nx = std::min(patch_size, width - j);
|
|
patch->ny = std::min(patch_size, height - i);
|
|
patch->buf.resize(3 * patch->nx * patch->ny);
|
|
for (int y = 0; y < patch->ny; ++y) {
|
|
for (int x = 0; x < patch->nx; ++x) {
|
|
for (int c = 0; c < 3; ++c) {
|
|
patch->buf[3 * (y * patch->nx + x) + c] = image.buf[3 * ((i + y) * width + (j + x)) + c];
|
|
}
|
|
}
|
|
}
|
|
patches.push_back(patch);
|
|
}
|
|
}
|
|
return patches;
|
|
}
|
|
|
|
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
|
|
// res_imgs memory is being allocated here, previous allocations will be freed if found
|
|
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
|
|
bool pad_to_square = true;
|
|
if (!ctx->has_vision_encoder) {
|
|
printf("This gguf file seems to have no vision encoder\n");
|
|
return false;
|
|
}
|
|
auto & params = ctx->vision_model.hparams;
|
|
// The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
|
|
if (strcmp(params.mm_patch_merge_type, "spatial_unpad") == 0) {
|
|
pad_to_square = false;
|
|
}
|
|
// free the previous res_imgs if any set
|
|
if (res_imgs->size > 0) {
|
|
clip_image_f32_batch_free(res_imgs);
|
|
}
|
|
res_imgs->data = nullptr;
|
|
res_imgs->size = 0;
|
|
|
|
// 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 (pad_to_square && 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 (this is the mean rgb color * 255)
|
|
|
|
// 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 {
|
|
if (params.image_grid_pinpoints[0] != 0) {
|
|
// "spatial_unpad" with "anyres" processing for llava-1.6
|
|
std::vector<std::pair<int, int>> possible_resolutions;
|
|
for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
|
|
possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
|
|
}
|
|
std::pair<int, int> best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions);
|
|
// clip_image_save_to_bmp(*img, "input.bmp");
|
|
resize_and_pad_image(*img, *temp, best_resolution); // we do not pad with mean-bg color anymore in llava-1.6
|
|
// clip_image_save_to_bmp(*temp, "resized.bmp");
|
|
// visually verify normalized image:
|
|
// normalize_image_u8_to_f32(*temp, *res, ctx->image_mean, ctx->image_std);
|
|
// {
|
|
// clip_image_u8 * temp2 = clip_image_u8_init();
|
|
// clip_image_convert_f32_to_u8(*res, *temp2);
|
|
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32.bmp");
|
|
// clip_image_u8_free(temp2);
|
|
// }
|
|
|
|
std::vector<clip_image_u8 *> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
|
|
|
|
clip_image_u8 *image_original_resize = clip_image_u8_init();
|
|
// bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
|
|
bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
|
|
patches.insert(patches.begin(), image_original_resize);
|
|
// clip_image_f32_batch_init(patches.size());
|
|
res_imgs->size = patches.size();
|
|
res_imgs->data = new clip_image_f32[res_imgs->size];
|
|
int num=0;
|
|
for (auto& patch : patches) {
|
|
normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
|
|
num++;
|
|
}
|
|
|
|
for (size_t i = 0; i < patches.size(); i++) {
|
|
// printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
|
|
clip_image_u8_free(patches[i]);
|
|
}
|
|
|
|
clip_image_u8_free(temp);
|
|
|
|
return true;
|
|
} 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;
|
|
// clip_image_save_to_bmp(*temp, "resized_vanilla.bmp");
|
|
|
|
const int nx2 = ctx->vision_model.hparams.image_size;
|
|
const int ny2 = ctx->vision_model.hparams.image_size;
|
|
clip_image_f32 * res = clip_image_f32_init();
|
|
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);
|
|
|
|
// {
|
|
// clip_image_u8 * temp2 = clip_image_u8_init();
|
|
// clip_image_convert_f32_to_u8(*res, *temp2);
|
|
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32_vanilla.bmp");
|
|
// clip_image_u8_free(temp2);
|
|
// }
|
|
// res_imgs.push_back(res);
|
|
|
|
res_imgs->size = 1;
|
|
res_imgs->data = new clip_image_f32[res_imgs->size];
|
|
res_imgs->data[0] = *res;
|
|
clip_image_f32_free(res);
|
|
|
|
return true;
|
|
}
|
|
|
|
ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.image_newline;
|
|
}
|
|
|
|
void clip_free(clip_ctx * ctx) {
|
|
ggml_free(ctx->ctx_data);
|
|
gguf_free(ctx->ctx_gguf);
|
|
|
|
ggml_backend_buffer_free(ctx->params_buffer);
|
|
ggml_backend_free(ctx->backend);
|
|
ggml_gallocr_free(ctx->compute_alloc);
|
|
delete ctx;
|
|
}
|
|
|
|
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
|
|
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
|
}
|
|
|
|
int32_t clip_image_size(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.hparams.image_size;
|
|
}
|
|
|
|
int32_t clip_patch_size(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.hparams.patch_size;
|
|
}
|
|
|
|
int32_t clip_hidden_size(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.hparams.hidden_size;
|
|
}
|
|
|
|
const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.hparams.mm_patch_merge_type;
|
|
}
|
|
|
|
const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.hparams.image_grid_pinpoints;
|
|
}
|
|
|
|
int clip_n_patches(const struct clip_ctx * ctx) {
|
|
const auto & params = ctx->vision_model.hparams;
|
|
|
|
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
|
|
|
|
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
|
|
n_patches /= 4;
|
|
}
|
|
|
|
return n_patches;
|
|
}
|
|
|
|
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
|
|
}
|
|
|
|
// build the inference graph
|
|
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
|
|
ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
|
|
|
|
// set inputs
|
|
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;
|
|
|
|
{
|
|
struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
|
|
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 * embeddings = ggml_graph_get_tensor(gf, "embeddings");
|
|
|
|
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);
|
|
}
|
|
|
|
{
|
|
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
|
|
|
|
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);
|
|
}
|
|
|
|
{
|
|
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
|
|
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);
|
|
}
|
|
|
|
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;
|
|
|
|
assert(itype < GGML_TYPE_COUNT);
|
|
type = static_cast<ggml_type>(itype);
|
|
|
|
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> work(512);
|
|
std::vector<float> conv_buf(512);
|
|
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;
|
|
if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
|
|
new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
|
|
// fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_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");
|
|
gguf_free(ctx_out);
|
|
return false;
|
|
}
|
|
|
|
if (work.size() < n_elms * 4) {
|
|
work.resize(n_elms * 4);
|
|
}
|
|
new_data = work.data();
|
|
|
|
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
|
|
} 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);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
|
|
return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
|
|
}
|
|
if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
|
|
return ctx->vision_model.mm_model_peg_0_b->ne[0];
|
|
}
|
|
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
|
|
return ctx->vision_model.mm_2_b->ne[0];
|
|
}
|
|
if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
|
|
return ctx->vision_model.mm_3_b->ne[0];
|
|
}
|
|
|
|
std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
|
|
throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
|
|
}
|