ver4a/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

mtmd : add vision support for llama 4 (#13282)

Browse source 
* wip llama 4 conversion

* rm redundant __init__

* fix conversion

* fix conversion

* test impl

* try this

* reshape patch_embeddings_0

* fix view

* rm ffn_post_norm

* cgraph ok

* f32 for pos embd

* add image marker tokens

* Llama4UnfoldConvolution

* correct pixel shuffle

* fix merge conflicts

* correct

* add debug_graph

* logits matched, but it still preceives the image incorrectly

* fix style

* add image_grid_pinpoints

* handle llama 4 preprocessing

* rm load_image_size

* rm unused line

* fix

* small fix 2

* add test & docs

* fix llava-1.6 test

* test: add notion of huge models

* add comment

* add warn about degraded quality
This commit is contained in:
Xuan-Son Nguyen 2025-05-19 13:04:14 +02:00 • committed by GitHub
parent f71f40a284
commit 92ecdcc06a
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
9 changed files with 424 additions and 82 deletions
Download patch file Download diff file Expand all files Collapse all files

74
tools/mtmd/clip-impl.h
View file

@ -4,6 +4,7 @@
#include <climits>
#include <cstdarg>
#include <cinttypes>
#include <string>
#include <map>
#include <sstream>
@ -44,7 +45,7 @@
// tensor name constants
//
#define TN_POS_EMBD "%s.position_embd.weight"
#define TN_POS_EMBD "v.position_embd.weight"
#define TN_CLASS_EMBD "v.class_embd"
#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat
#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1"
@ -110,6 +111,7 @@ enum projector_type {
PROJECTOR_TYPE_PIXTRAL,
PROJECTOR_TYPE_QWEN25VL,
PROJECTOR_TYPE_INTERNVL,
PROJECTOR_TYPE_LLAMA4,
PROJECTOR_TYPE_UNKNOWN,
};
@ -125,6 +127,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_IDEFICS3, "idefics3"},
{ PROJECTOR_TYPE_PIXTRAL, "pixtral"},
{ PROJECTOR_TYPE_INTERNVL, "internvl"},
{ PROJECTOR_TYPE_LLAMA4, "llama4"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {
@ -240,6 +243,11 @@ struct clip_image_u8_batch {
struct clip_image_f32_batch {
std::vector<clip_image_f32_ptr> entries;
// for llava-uhd style models, we need to know the grid size
// note: entries.size() == grid_x * grid_y + 1 (one overview image)
int grid_x = 0;
int grid_y = 0;
clip_image_f32_batch clone() const {
clip_image_f32_batch new_batch;
new_batch.entries.reserve(entries.size());
@ -358,6 +366,70 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
}
}
//
// debugging
//
static void print_tensor_shape(ggml_tensor * t) {
printf("%s.shape = [", t->name);
for (int i = 0; i < ggml_n_dims(t); ++i) {
printf("%" PRId64, t->ne[i]);
if (i < ggml_n_dims(t) - 1) {
printf(", ");
}
}
printf("]\n");
}
static void print_tensor_data(ggml_tensor * t, uint8_t * data, int64_t n) {
ggml_type type = t->type;
int64_t * ne = t->ne;
size_t * nb = t->nb;
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
printf("%s.data: [\n", t->name);
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
if (i2 == n && ne[2] > 2*n) {
printf(" ..., \n");
i2 = ne[2] - n;
}
printf(" [\n");
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
if (i1 == n && ne[1] > 2*n) {
printf(" ..., \n");
i1 = ne[1] - n;
}
printf(" [");
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
if (i0 == n && ne[0] > 2*n) {
printf("..., ");
i0 = ne[0] - n;
}
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v;
if (type == GGML_TYPE_F16) {
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
} else if (type == GGML_TYPE_F32) {
v = *(float *) &data[i];
} else if (type == GGML_TYPE_I32) {
v = (float) *(int32_t *) &data[i];
} else if (type == GGML_TYPE_I16) {
v = (float) *(int16_t *) &data[i];
} else if (type == GGML_TYPE_I8) {
v = (float) *(int8_t *) &data[i];
} else {
GGML_ABORT("fatal error");
}
printf("%8.4f", v);
if (i0 < ne[0] - 1) printf(", ");
}
printf("],\n");
}
printf(" ],\n");
}
printf(" ]\n");
}
}
//
// API used internally with mtmd
//