mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-10-31 23:28:51 +01:00
d34633d8db
Uses ggml functions instead of hardcoded names and adds support to quantize into the modern Q-K variants. This is just the bare minimum to get k-types working - a more refined choice of types would be needed to get best quality on low quantizations. I ran a few tests, it doesn't break anything I could notice and a Q6_K ViT works almost as well as Q8_0 but 3 times the inference speed.
1115 lines
40 KiB
C++
1115 lines
40 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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#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 "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_CUBLAS
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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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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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//
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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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//
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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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//
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// image data
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//
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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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//
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// clip layers
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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_vision_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;
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struct ggml_tensor * mm_0_b;
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struct ggml_tensor * mm_2_w;
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struct ggml_tensor * mm_2_b;
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};
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struct clip_ctx {
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bool has_text_encoder = false;
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bool has_vision_encoder = false;
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bool has_llava_projector = false;
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struct clip_vision_model vision_model;
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float image_mean[3];
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float image_std[3];
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bool use_gelu = false;
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int32_t ftype = 1;
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struct gguf_context * ctx_gguf;
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struct ggml_context * ctx_data;
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std::vector<uint8_t> buf_compute_meta;
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// memory buffers to evaluate the model
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ggml_backend_buffer_t params_buffer = NULL;
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ggml_backend_buffer_t compute_buffer = NULL;
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ggml_backend_t backend = NULL;
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ggml_allocr * compute_alloc = NULL;
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};
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static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
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if (!ctx->has_vision_encoder) {
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printf("This gguf file seems to have no vision encoder\n");
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return nullptr;
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}
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const auto & model = ctx->vision_model;
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const auto & hparams = model.hparams;
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const int image_size = hparams.image_size;
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const int patch_size = hparams.patch_size;
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const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
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const int num_positions = num_patches + 1;
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const int hidden_size = hparams.hidden_size;
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const int n_head = hparams.n_head;
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const int d_head = hidden_size / n_head;
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const int n_layer = hparams.n_layer;
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//const int n_intermediate = hparams.n_intermediate;
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//const int projection_dim = hparams.projection_dim;
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const float eps = hparams.eps;
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int batch_size = imgs->size;
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if (ctx->has_llava_projector) {
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GGML_ASSERT(batch_size == 1);
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}
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx->buf_compute_meta.size(),
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/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
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ggml_allocr_alloc(ctx->compute_alloc, inp_raw);
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if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
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float * data = (float *)malloc(ggml_nbytes(inp_raw));
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for (size_t i = 0; i < imgs->size; i++) {
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const int nx = imgs->data[i].nx;
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const int ny = imgs->data[i].ny;
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GGML_ASSERT(nx == image_size && ny == image_size);
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const int n = nx * ny;
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for (int b = 0; b < batch_size; b++) {
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for (int k = 0; k < 3; k++) {
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for (int y = 0; y < ny; y++) {
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for (int x = 0; x < nx; x++) {
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data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
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}
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}
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}
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}
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}
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ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
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free(data);
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}
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struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
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inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
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inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
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// concat class_embeddings and patch_embeddings
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struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
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ggml_allocr_alloc(ctx->compute_alloc, embeddings);
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if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
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void* zero_mem = malloc(ggml_nbytes(embeddings));
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memset(zero_mem, 0, ggml_nbytes(embeddings));
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ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
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free(zero_mem);
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}
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embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
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embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
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embeddings = ggml_acc(ctx0, embeddings, inp,
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embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
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struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
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ggml_allocr_alloc(ctx->compute_alloc, positions);
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if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
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int* positions_data = (int*)malloc(ggml_nbytes(positions));
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for (int i = 0; i < num_positions; i++) {
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positions_data[i] = i;
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}
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ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
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free(positions_data);
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}
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embeddings =
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ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
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// pre-layernorm
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{
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embeddings = ggml_norm(ctx0, embeddings, eps);
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
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}
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// loop over layers
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for (int il = 0; il < n_layer - 1; il++) {
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struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
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//const size_t nb_q_w = model.layers[il].q_w->nb[0];
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// layernorm1
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{
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cur = ggml_norm(ctx0, cur, eps);
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cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
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model.layers[il].ln_1_b);
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}
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// self-attention
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{
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struct ggml_tensor * Q =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
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Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
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Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
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Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
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Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
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struct ggml_tensor * K =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
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K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
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K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
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K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
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struct ggml_tensor * V =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
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V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
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V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
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V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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KQ = ggml_soft_max_inplace(ctx0, KQ);
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
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KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
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KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));
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cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size));
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}
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// attention output
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cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
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// re-add the layer input, e.g., residual
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cur = ggml_add(ctx0, cur, embeddings);
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embeddings = cur; // embeddings = residual, cur = hidden_states
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// layernorm2
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{
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cur = ggml_norm(ctx0, cur, eps);
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cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
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}
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
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if (ctx->use_gelu) {
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cur = ggml_gelu_inplace(ctx0, cur);
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} else {
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cur = ggml_gelu_quick_inplace(ctx0, cur);
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}
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
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// residual 2
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cur = ggml_add(ctx0, embeddings, cur);
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embeddings = cur;
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}
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// llava projector
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{
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embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
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struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
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ggml_allocr_alloc(ctx->compute_alloc, patches);
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if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
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int* patches_data = (int*)malloc(ggml_nbytes(patches));
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for (int i = 0; i < num_patches; i++) {
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patches_data[i] = i + 1;
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}
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ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
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free(patches_data);
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}
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embeddings = ggml_get_rows(ctx0, embeddings, patches);
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// mm projection 0
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embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
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embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
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|
|
|
embeddings = ggml_gelu(ctx0, embeddings);
|
|
|
|
embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
|
|
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
|
|
}
|
|
|
|
// build the graph
|
|
ggml_build_forward_expand(gf, embeddings);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
// read and create ggml_context containing the tensors and their data
|
|
struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|
struct ggml_context * meta = NULL;
|
|
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &meta,
|
|
};
|
|
|
|
struct gguf_context * ctx = gguf_init_from_file(fname, params);
|
|
if (!ctx) {
|
|
throw std::runtime_error(format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname));
|
|
}
|
|
|
|
if (verbosity >= 1) {
|
|
const int n_tensors = gguf_get_n_tensors(ctx);
|
|
const int n_kv = gguf_get_n_kv(ctx);
|
|
const int ftype = get_u32(ctx, KEY_FTYPE);
|
|
const std::string ftype_str = get_ftype(ftype);
|
|
const int idx_desc = get_key_idx(ctx, KEY_DESCRIPTION);
|
|
const std::string description = gguf_get_val_str(ctx, idx_desc);
|
|
const int idx_name = gguf_find_key(ctx, KEY_NAME);
|
|
if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
|
|
const std::string name = gguf_get_val_str(ctx, idx_name);
|
|
printf("%s: model name: %s\n", __func__, name.c_str());
|
|
}
|
|
printf("%s: description: %s\n", __func__, description.c_str());
|
|
printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
|
|
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
|
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
|
printf("%s: n_kv: %d\n", __func__, n_kv);
|
|
printf("%s: ftype: %s\n", __func__, ftype_str.c_str());
|
|
printf("\n");
|
|
}
|
|
const int n_tensors = gguf_get_n_tensors(ctx);
|
|
// kv
|
|
if (verbosity >= 3) {
|
|
const int n_kv = gguf_get_n_kv(ctx);
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
const char * key = gguf_get_key(ctx, i);
|
|
|
|
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
// data
|
|
size_t buffer_size = 0;
|
|
{
|
|
for (int i = 0; i < n_tensors; ++i) {
|
|
const char * name = gguf_get_tensor_name(ctx, i);
|
|
const size_t offset = gguf_get_tensor_offset(ctx, i);
|
|
struct ggml_tensor * cur = ggml_get_tensor(meta, name);
|
|
size_t tensor_size = ggml_nbytes(cur);
|
|
buffer_size += tensor_size;
|
|
if (verbosity >= 3) {
|
|
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i,
|
|
ggml_n_dims(cur), cur->name, tensor_size, offset);
|
|
}
|
|
}
|
|
}
|
|
|
|
buffer_size += n_tensors * 128 /* CLIP PADDING */;
|
|
|
|
clip_ctx * new_clip = new clip_ctx;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
new_clip->backend = ggml_backend_cuda_init(0);
|
|
printf("%s: CLIP using CUDA backend\n", __func__);
|
|
#endif
|
|
|
|
#ifdef GGML_USE_METAL
|
|
new_clip->backend = ggml_backend_metal_init();
|
|
printf("%s: CLIP using Metal backend\n", __func__);
|
|
#endif
|
|
|
|
|
|
if (!new_clip->backend) {
|
|
new_clip->backend = ggml_backend_cpu_init();
|
|
printf("%s: CLIP using CPU backend\n", __func__);
|
|
}
|
|
|
|
// model size and capabilities
|
|
{
|
|
int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);
|
|
new_clip->has_text_encoder = gguf_get_val_bool(ctx, idx);
|
|
|
|
idx = get_key_idx(ctx, KEY_HAS_VIS_ENC);
|
|
new_clip->has_vision_encoder = gguf_get_val_bool(ctx, idx);
|
|
|
|
idx = gguf_find_key(ctx, KEY_HAS_LLAVA_PROJ);
|
|
if (idx != -1) {
|
|
new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx);
|
|
}
|
|
|
|
GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
|
|
GGML_ASSERT(new_clip->has_vision_encoder);
|
|
GGML_ASSERT(!new_clip->has_text_encoder);
|
|
|
|
idx = get_key_idx(ctx, KEY_USE_GELU);
|
|
new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
|
|
|
|
if (verbosity >= 1) {
|
|
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
|
|
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
|
|
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
|
printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0);
|
|
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
|
}
|
|
}
|
|
|
|
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors);
|
|
|
|
// load tensors
|
|
{
|
|
std::vector<uint8_t> read_buf;
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(),
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
new_clip->ctx_data = ggml_init(params);
|
|
if (!new_clip->ctx_data) {
|
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
|
clip_free(new_clip);
|
|
return nullptr;
|
|
}
|
|
|
|
auto fin = std::ifstream(fname, std::ios::binary);
|
|
if (!fin) {
|
|
printf("cannot open model file for loading tensors\n");
|
|
clip_free(new_clip);
|
|
return nullptr;
|
|
}
|
|
|
|
// add tensors to context
|
|
for (int i = 0; i < n_tensors; ++i) {
|
|
const char * name = gguf_get_tensor_name(ctx, i);
|
|
struct ggml_tensor * t = ggml_get_tensor(meta, name);
|
|
struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t);
|
|
ggml_set_name(cur, name);
|
|
}
|
|
|
|
// alloc memory and offload data
|
|
new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size);
|
|
ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
|
|
for (int i = 0; i < n_tensors; ++i) {
|
|
const char * name = gguf_get_tensor_name(ctx, i);
|
|
struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
|
|
ggml_allocr_alloc(alloc, cur);
|
|
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
|
|
fin.seekg(offset, std::ios::beg);
|
|
if (!fin) {
|
|
printf("%s: failed to seek for tensor %s\n", __func__, name);
|
|
clip_free(new_clip);
|
|
return nullptr;
|
|
}
|
|
int num_bytes = ggml_nbytes(cur);
|
|
if (ggml_backend_buffer_is_host(new_clip->params_buffer)) {
|
|
// for the CPU and Metal backend, we can read directly into the tensor
|
|
fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
|
|
} else {
|
|
// read into a temporary buffer first, then copy to device memory
|
|
read_buf.resize(num_bytes);
|
|
fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
|
|
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
|
|
}
|
|
}
|
|
ggml_allocr_free(alloc);
|
|
fin.close();
|
|
}
|
|
|
|
// vision model
|
|
if (new_clip->has_vision_encoder) {
|
|
// load vision model
|
|
auto & vision_model = new_clip->vision_model;
|
|
auto & hparams = vision_model.hparams;
|
|
hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision"));
|
|
hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision"));
|
|
hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision"));
|
|
hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision"));
|
|
hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE);
|
|
hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE);
|
|
hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
|
|
hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
|
|
|
|
int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
|
|
int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
|
|
for (int i = 0; i < 3; ++i) {
|
|
new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));
|
|
new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));
|
|
}
|
|
|
|
if (verbosity >= 2) {
|
|
printf("\n%s: vision model hparams\n", __func__);
|
|
printf("image_size %d\n", hparams.image_size);
|
|
printf("patch_size %d\n", hparams.patch_size);
|
|
printf("v_hidden_size %d\n", hparams.hidden_size);
|
|
printf("v_n_intermediate %d\n", hparams.n_intermediate);
|
|
printf("v_projection_dim %d\n", hparams.projection_dim);
|
|
printf("v_n_head %d\n", hparams.n_head);
|
|
printf("v_n_layer %d\n", hparams.n_layer);
|
|
}
|
|
|
|
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
|
|
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
|
|
vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
|
|
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
|
|
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
|
|
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
|
|
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
|
|
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
|
|
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
|
|
|
|
vision_model.layers.resize(hparams.n_layer);
|
|
for (int il = 0; il < hparams.n_layer; ++il) {
|
|
auto & layer = vision_model.layers[il];
|
|
layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight"));
|
|
layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight"));
|
|
layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight"));
|
|
layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight"));
|
|
layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight"));
|
|
layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight"));
|
|
layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight"));
|
|
layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight"));
|
|
layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias"));
|
|
layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias"));
|
|
layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias"));
|
|
layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias"));
|
|
layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias"));
|
|
layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias"));
|
|
layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias"));
|
|
layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias"));
|
|
}
|
|
}
|
|
|
|
ggml_free(meta);
|
|
|
|
new_clip->ctx_gguf = ctx;
|
|
|
|
// measure mem requirement and allocate
|
|
{
|
|
new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
|
|
new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
|
|
clip_image_f32_batch batch;
|
|
batch.size = 1;
|
|
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
|
|
size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf);
|
|
ggml_allocr_free(new_clip->compute_alloc);
|
|
new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size);
|
|
new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer);
|
|
|
|
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
|
|
}
|
|
|
|
return new_clip;
|
|
}
|
|
|
|
struct clip_image_u8 * clip_image_u8_init() {
|
|
return new clip_image_u8();
|
|
}
|
|
|
|
struct clip_image_f32 * clip_image_f32_init() {
|
|
return new clip_image_f32();
|
|
}
|
|
|
|
void clip_image_u8_free (struct clip_image_u8 * img) { delete img; }
|
|
void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
|
|
|
|
static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
|
|
img->nx = nx;
|
|
img->ny = ny;
|
|
img->buf.resize(3 * nx * ny);
|
|
memcpy(img->buf.data(), data, img->buf.size());
|
|
}
|
|
|
|
bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
|
|
int nx, ny, nc;
|
|
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
|
|
if (!data) {
|
|
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
|
|
return false;
|
|
}
|
|
build_clip_img_from_data(data, nx, ny, img);
|
|
stbi_image_free(data);
|
|
return true;
|
|
}
|
|
|
|
bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
|
|
int nx, ny, nc;
|
|
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
|
|
if (!data) {
|
|
fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
|
|
return false;
|
|
}
|
|
build_clip_img_from_data(data, nx, ny, img);
|
|
stbi_image_free(data);
|
|
return true;
|
|
}
|
|
|
|
// normalize: x = (x - mean) / std
|
|
// TODO: implement bicubic interpolation instead of linear.
|
|
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
|
|
if (!ctx->has_vision_encoder) {
|
|
printf("This gguf file seems to have no vision encoder\n");
|
|
return false;
|
|
}
|
|
|
|
// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
|
|
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
|
|
|
|
clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
|
|
if (pad2square && img->nx != img->ny) {
|
|
int longer_side = std::max(img->nx, img->ny);
|
|
temp->nx = longer_side;
|
|
temp->ny = longer_side;
|
|
temp->buf.resize(3 * longer_side * longer_side);
|
|
const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
|
|
|
|
// fill with background color
|
|
for (size_t i = 0; i < temp->buf.size(); i++) {
|
|
temp->buf[i] = bc[i % 3];
|
|
}
|
|
|
|
// copy from the input image
|
|
for (int y = 0; y < img->ny; y++) {
|
|
for (int x = 0; x < img->nx; x++) {
|
|
const int i = 3 * (y * img->nx + x);
|
|
const int j = 3 * (y * temp->nx + x);
|
|
temp->buf[j] = img->buf[i];
|
|
temp->buf[j+1] = img->buf[i+1];
|
|
temp->buf[j+2] = img->buf[i+2];
|
|
}
|
|
}
|
|
} else {
|
|
temp->nx = img->nx;
|
|
temp->ny = img->ny;
|
|
temp->buf.resize(img->buf.size());
|
|
memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
|
|
}
|
|
|
|
const int nx = temp->nx;
|
|
const int ny = temp->ny;
|
|
|
|
const int nx2 = ctx->vision_model.hparams.image_size;
|
|
const int ny2 = ctx->vision_model.hparams.image_size;
|
|
|
|
res->nx = nx2;
|
|
res->ny = ny2;
|
|
res->buf.resize(3 * nx2 * ny2);
|
|
|
|
const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
|
|
|
|
const int nx3 = int(nx / scale + 0.5f);
|
|
const int ny3 = int(ny / scale + 0.5f);
|
|
|
|
const auto & m3 = ctx->image_mean; // {0.48145466f, 0.4578275f, 0.40821073f};
|
|
const auto & s3 = ctx->image_std; // {0.26862954f, 0.26130258f, 0.27577711f};
|
|
|
|
for (int y = 0; y < ny3; y++) {
|
|
for (int x = 0; x < nx3; x++) {
|
|
for (int c = 0; c < 3; c++) {
|
|
// linear interpolation
|
|
const float sx = (x + 0.5f) * scale - 0.5f;
|
|
const float sy = (y + 0.5f) * scale - 0.5f;
|
|
|
|
const int x0 = std::max(0, (int)std::floor(sx));
|
|
const int y0 = std::max(0, (int)std::floor(sy));
|
|
|
|
const int x1 = std::min(x0 + 1, nx - 1);
|
|
const int y1 = std::min(y0 + 1, ny - 1);
|
|
|
|
const float dx = sx - x0;
|
|
const float dy = sy - y0;
|
|
|
|
const int j00 = 3 * (y0 * nx + x0) + c;
|
|
const int j01 = 3 * (y0 * nx + x1) + c;
|
|
const int j10 = 3 * (y1 * nx + x0) + c;
|
|
const int j11 = 3 * (y1 * nx + x1) + c;
|
|
|
|
const float v00 = temp->buf[j00];
|
|
const float v01 = temp->buf[j01];
|
|
const float v10 = temp->buf[j10];
|
|
const float v11 = temp->buf[j11];
|
|
|
|
const float v0 = v00 * (1.0f - dx) + v01 * dx;
|
|
const float v1 = v10 * (1.0f - dx) + v11 * dx;
|
|
|
|
const float v = v0 * (1.0f - dy) + v1 * dy;
|
|
|
|
const uint8_t v2 = std::min(std::max(std::round(v), 0.0f), 255.0f);
|
|
|
|
const int i = 3 * (y * nx3 + x) + c;
|
|
|
|
res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
|
|
}
|
|
}
|
|
}
|
|
clip_image_u8_free(temp);
|
|
|
|
return true;
|
|
}
|
|
|
|
void clip_free(clip_ctx * ctx) {
|
|
ggml_free(ctx->ctx_data);
|
|
gguf_free(ctx->ctx_gguf);
|
|
|
|
delete ctx;
|
|
}
|
|
|
|
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
|
if (!ctx->has_vision_encoder) {
|
|
printf("This gguf file seems to have no vision encoder\n");
|
|
return false;
|
|
}
|
|
|
|
clip_image_f32_batch imgs{};
|
|
imgs.size = 1;
|
|
imgs.data = img;
|
|
return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
|
|
}
|
|
|
|
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
|
|
if (!ctx->has_vision_encoder) {
|
|
printf("This gguf file seems to have no vision encoder\n");
|
|
return false;
|
|
}
|
|
|
|
int batch_size = imgs->size;
|
|
if(ctx->has_llava_projector) {
|
|
GGML_ASSERT(batch_size == 1); // TODO: support multiple images
|
|
}
|
|
|
|
// reset alloc buffer to clean the memory from previous invocations
|
|
ggml_allocr_reset(ctx->compute_alloc);
|
|
|
|
// build the inference graph
|
|
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
|
|
ggml_allocr_alloc_graph(ctx->compute_alloc, gf);
|
|
|
|
if (ggml_backend_is_cpu(ctx->backend)) {
|
|
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
|
|
}
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (ggml_backend_is_metal(ctx->backend)) {
|
|
ggml_backend_metal_set_n_cb(ctx->backend, n_threads);
|
|
}
|
|
#endif
|
|
|
|
ggml_backend_graph_compute(ctx->backend, gf);
|
|
|
|
// the last node is the embedding tensor
|
|
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];
|
|
|
|
// copy the embeddings to the location passed by the user
|
|
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
|
|
return true;
|
|
}
|
|
|
|
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
|
|
|
|
ggml_type type = GGML_TYPE_Q4_1;
|
|
|
|
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> read_data(512);
|
|
std::vector<uint8_t> work(512);
|
|
std::vector<float> conv_buf(512);
|
|
std::vector<int64_t> hist_all(1 << 4, 0);
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
|
|
for (int i = 0; i < n_tensors; ++i) {
|
|
const std::string name = gguf_get_tensor_name(ctx_src, i);
|
|
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());
|
|
|
|
enum ggml_type new_type;
|
|
void * new_data;
|
|
size_t new_size;
|
|
|
|
bool quantize = false;
|
|
for (const auto & s : k_names) {
|
|
if (std::regex_match(name, std::regex(s))) {
|
|
quantize = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// quantize only 2D tensors
|
|
quantize &= (ggml_n_dims(cur) == 2);
|
|
|
|
if (quantize) {
|
|
new_type = type;
|
|
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");
|
|
return false;
|
|
}
|
|
|
|
if (work.size() < n_elms * 4) {
|
|
work.resize(n_elms * 4);
|
|
}
|
|
new_data = work.data();
|
|
|
|
std::vector<int64_t> hist_cur(1 << 4, 0);
|
|
|
|
switch (new_type) {
|
|
case GGML_TYPE_Q4_0: {
|
|
new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q4_1: {
|
|
new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q5_0: {
|
|
new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q5_1: {
|
|
new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q8_0: {
|
|
new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q2_K: {
|
|
new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q3_K: {
|
|
new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q4_K: {
|
|
new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q5_K: {
|
|
new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q6_K: {
|
|
new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
|
|
} break;
|
|
default: {
|
|
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
for (size_t j = 0; j < hist_cur.size(); ++j) {
|
|
hist_all[j] += hist_cur[j];
|
|
}
|
|
} else {
|
|
new_type = cur->type;
|
|
new_data = cur->data;
|
|
new_size = ggml_nbytes(cur);
|
|
}
|
|
const size_t orig_size = ggml_nbytes(cur);
|
|
total_size_org += orig_size;
|
|
total_size_new += new_size;
|
|
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
|
|
gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
|
|
fout.write((const char *)new_data, new_size);
|
|
size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
|
|
for (size_t j = 0; j < pad; ++j) {
|
|
fout.put(0);
|
|
}
|
|
|
|
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
|
|
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// go back to beginning of file and write the updated metadata
|
|
fout.seekp(0, std::ios::beg);
|
|
std::vector<uint8_t> meta(meta_size);
|
|
gguf_get_meta_data(ctx_out, meta.data());
|
|
fout.write((const char *)meta.data(), meta_size);
|
|
|
|
fout.close();
|
|
|
|
clip_free(ctx_clip);
|
|
gguf_free(ctx_out);
|
|
|
|
{
|
|
printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
|
|
printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
|
|
|
|
int64_t sum_all = 0;
|
|
for (size_t i = 0; i < hist_all.size(); ++i) {
|
|
sum_all += hist_all[i];
|
|
}
|
|
|
|
printf("%s: hist: ", __func__);
|
|
for (size_t i = 0; i < hist_all.size(); ++i) {
|
|
printf("%5.3f ", hist_all[i] / (float)sum_all);
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|
return ctx->vision_model.mm_2_b->ne[0];
|
|
}
|
|
|
|
int clip_n_patches(const struct clip_ctx * ctx) {
|
|
auto & params = ctx->vision_model.hparams;
|
|
|
|
return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
|
|
}
|
|
|
|
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
|
|
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
|
}
|