vbatts/llama.cpp
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Merge branch 'master' of https://github.com/ggerganov/llama.cpp into ceb/nomic-vulkan

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This commit is contained in:
Jared Van Bortel 2024-01-09 16:37:08 -05:00
commit 3773e1afe7
117 changed files with 12648 additions and 7516 deletions
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2
examples/CMakeLists.txt
View file

@ -31,8 +31,10 @@ else()
add_subdirectory(quantize-stats)
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(passkey)
add_subdirectory(speculative)
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(train-text-from-scratch)
if (LLAMA_METAL)
add_subdirectory(metal)

15
examples/baby-llama/baby-llama.cpp
View file

@ -575,10 +575,7 @@ static struct ggml_tensor * forward(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [n_past + N, N, n_head, 1]
@ -844,10 +841,7 @@ static struct ggml_tensor * forward_batch(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, n_batch]
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch);
// KQ_masked = mask_past(KQ_scaled)
@ -1131,10 +1125,7 @@ static struct ggml_tensor * forward_lora(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [n_past + N, N, n_head, 1]

61
examples/base-translate.sh Executable file
View file

@ -0,0 +1,61 @@
#!/bin/bash
#
# Few-shot translation example.
# Requires a base model (i.e. no fine-tuned or instruct models).
#
# Usage:
#
# cd llama.cpp
# make -j
#
# ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
#
if [ $# -lt 2 ]; then
echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
exit 1
fi
eargs=""
if [ $# -gt 2 ]; then
eargs="${@:3}"
fi
ftmp="__llama.cpp_example_tmp__.txt"
trap "rm -f $ftmp" EXIT
echo "Translate from English to French:
===
sea otter, peppermint, plush girafe:
sea otter => loutre de mer
peppermint => menthe poivrée
plush girafe => girafe peluche
===
violin
violin => violon
===
phone, computer, mouse, keyboard:
phone => téléphone
computer => ordinateur
mouse => souris
keyboard => clavier
===
" > $ftmp
echo "$2
" >> $ftmp
model=$1
# generate the most likely continuation until the string "===" is found
./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs

1
examples/batched/batched.cpp
View file

@ -69,6 +69,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(model, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
// initialize the context

2
examples/export-lora/export-lora.cpp
View file

@ -309,7 +309,7 @@ static struct ggml_cgraph * build_graph_lora(
) {
struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);
if (scaling != 1.0f) {
ab = ggml_scale(ctx, ab, ggml_new_f32(ctx, scaling));
ab = ggml_scale(ctx, ab, scaling);
}
struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);

2
examples/finetune/README.md
View file

@ -61,7 +61,7 @@ For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' L
--lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
```
The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values.
Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.

62
examples/finetune/finetune.cpp
View file

@ -3,15 +3,9 @@
#include "llama.h"
#include "common.h"
#include "train.h"
#include <unordered_map>
#include <vector>
#include <cassert>
#include <climits>
#include <cstring>
#include <cstdarg>
#include <ctime>
#include <random>
#include <stdexcept>
#include <algorithm>
#include <string>
@ -196,13 +190,13 @@ static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down";
static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up";
static void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
printf("%s: n_ctx: %u\n", __func__, params->n_ctx);
printf("%s: n_embd: %u\n", __func__, params->n_embd);
printf("%s: n_ff: %u\n", __func__, params->n_ff);
printf("%s: n_head: %u\n", __func__, params->n_head);
printf("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
printf("%s: n_layer: %u\n", __func__, params->n_layer);
printf("%s: n_vocab : %u\n", __func__, params->n_vocab);
printf("%s: n_ctx : %u\n", __func__, params->n_ctx);
printf("%s: n_embd : %u\n", __func__, params->n_embd);
printf("%s: n_ff : %u\n", __func__, params->n_ff);
printf("%s: n_head : %u\n", __func__, params->n_head);
printf("%s: n_head_kv : %u\n", __func__, params->n_head_kv);
printf("%s: n_layer : %u\n", __func__, params->n_layer);
printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps);
printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base);
printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale);
@ -269,7 +263,7 @@ static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_h
float rope_freq_scale = 1.0f;
GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
if (rope_freq_scale != 1.0f) {
hparams->rope_freq_scale = 1.0f / rope_freq_scale;
}
@ -612,6 +606,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
const int n_rot = hparams.n_embd_head();
const int n_embd_head = hparams.n_embd_head();
const int n_embd_gqa = hparams.n_embd_gqa();
const float rms_norm_eps = hparams.f_norm_rms_eps;
const float rope_freq_base = hparams.rope_freq_base;
const float rope_freq_scale = hparams.rope_freq_scale;
@ -680,10 +675,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
checkpoints.push_back(t01);
}
struct ggml_tensor * kv_scale = NULL;
if (!enable_flash_attn) {
kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));
}
const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
@ -781,32 +773,32 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
// make sure some tensors are not reallocated by inserting new temporary nodes depending on them
int n_leafs_before = gb->n_leafs;
int n_nodes_before = gb->n_nodes;
struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);
// output tensors
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
// input gradient
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
ggml_allocr_alloc(alloc, t36->grad);
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
// make sure base model tensors data cannot be used in viewable operations
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f));
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f));
}
// allocating checkpoints in one block to reduce memory fragmentation

2
examples/gguf/CMakeLists.txt
View file

@ -1,5 +1,5 @@
set(TARGET gguf)
add_executable(${TARGET} gguf.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

1
examples/gguf/gguf.cpp
View file

@ -1,5 +1,4 @@
#include "ggml.h"
#include "llama.h"
#include <cstdio>
#include <cinttypes>

34
examples/llama-bench/llama-bench.cpp
View file

@ -138,6 +138,7 @@ struct cmd_params {
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
std::vector<bool> mul_mat_q;
std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
int reps;
@ -155,6 +156,7 @@ static const cmd_params cmd_params_defaults = {
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* mul_mat_q */ {true},
/* tensor_split */ {{}},
/* reps */ 5,
@ -176,6 +178,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
printf(" -ts, --tensor_split <ts0/ts1/..> \n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
@ -309,6 +312,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
break;
}
params.main_gpu = split<int>(argv[i], split_delim);
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
} else if (arg == "-mmq" || arg == "--mul-mat-q") {
if (++i >= argc) {
invalid_param = true;
@ -383,6 +393,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
@ -400,6 +411,7 @@ struct cmd_params_instance {
int n_threads;
int n_gpu_layers;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
@ -428,6 +440,7 @@ struct cmd_params_instance {
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.mul_mat_q = mul_mat_q;
cparams.offload_kqv = !no_kv_offload;
return cparams;
}
@ -444,6 +457,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
/* .model = */ m,
@ -455,6 +469,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -476,6 +491,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
@ -491,6 +507,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -511,6 +528,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -559,6 +577,7 @@ struct test {
ggml_type type_v;
int n_gpu_layers;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
int n_prompt;
@ -579,6 +598,7 @@ struct test {
type_v = inst.type_v;
n_gpu_layers = inst.n_gpu_layers;
main_gpu = inst.main_gpu;
no_kv_offload = inst.no_kv_offload;
mul_mat_q = inst.mul_mat_q;
tensor_split = inst.tensor_split;
n_prompt = inst.n_prompt;
@ -640,7 +660,8 @@ struct test {
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "type_k", "type_v",
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
"n_gpu_layers", "main_gpu", "no_kv_offload",
"mul_mat_q", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
@ -659,7 +680,7 @@ struct test {
return INT;
}
if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
field == "f16_kv" || field == "mul_mat_q") {
field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -690,7 +711,8 @@ struct test {
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload),
std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts())
@ -851,6 +873,9 @@ struct markdown_printer : public printer {
if (field == "mul_mat_q") {
return "mmq";
}
if (field == "no_kv_offload") {
return "nkvo";
}
if (field == "tensor_split") {
return "ts";
}
@ -885,6 +910,9 @@ struct markdown_printer : public printer {
if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
fields.push_back("mul_mat_q");
}
if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
fields.push_back("no_kv_offload");
}
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
fields.push_back("tensor_split");
}

13
examples/llama.swiftui/README.md
View file

@ -1,7 +1,12 @@
# llama.swiftui
# llama.cpp/examples/llama.swiftui
Local inference of llama.cpp on an iPhone.
So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
Local inference of llama.cpp on an iPhone. This is a sample app that can be used as a starting
point for more advanced projects.
For usage instructions and performance stats, check the following discussion: https://github.com/ggerganov/llama.cpp/discussions/4508
![image](https://github.com/ggerganov/llama.cpp/assets/1991296/2b40284f-8421-47a2-b634-74eece09a299)
Video demonstration:
https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545

5
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -1,6 +1,5 @@
import Foundation
// import llama
import llama
enum LlamaError: Error {
case couldNotInitializeContext
@ -159,7 +158,7 @@ actor LlamaContext {
new_token_id = llama_sample_token_greedy(context, &candidates_p)
}
if new_token_id == llama_token_eos(context) || n_cur == n_len {
if new_token_id == llama_token_eos(model) || n_cur == n_len {
print("\n")
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()

5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
View file

@ -1,5 +0,0 @@
//
// Use this file to import your target's public headers that you would like to expose to Swift.
//
#import "llama.h"

92
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
View file

@ -7,51 +7,32 @@
objects = {
/* Begin PBXBuildFile section */
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 549479C52AC9E0F200E0F78B /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-fno-objc-arc -DGGML_SWIFT -DGGML_USE_METAL -O3"; }; };
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */; };
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */; };
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 8A39BE092AC7601000BFEB40 /* Accelerate.framework */; };
8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
/* End PBXBuildFile section */
/* Begin PBXFileReference section */
542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
542376092B0D9C40008E6A1C /* ggml-backend.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../ggml-backend.h"; sourceTree = "<group>"; };
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../ggml-backend.c"; sourceTree = "<group>"; };
542EA09B2AC8723900A8AEE9 /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = ../../ggml.c; sourceTree = "<group>"; };
542EA09C2AC8723900A8AEE9 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = ../../ggml.h; sourceTree = "<group>"; };
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../ggml-alloc.h"; sourceTree = "<group>"; };
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../ggml-alloc.c"; sourceTree = "<group>"; };
542EA0A12AC8729100A8AEE9 /* llama.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = llama.cpp; path = ../../llama.cpp; sourceTree = "<group>"; };
542EA0A22AC8729100A8AEE9 /* llama.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = llama.h; path = ../../llama.h; sourceTree = "<group>"; };
549479C52AC9E0F200E0F78B /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../ggml-metal.m"; sourceTree = "<group>"; };
549479C62AC9E0F200E0F78B /* ggml-metal.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-metal.h"; path = "../../ggml-metal.h"; sourceTree = "<group>"; };
549479C82AC9E10B00E0F78B /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../ggml-metal.metal"; sourceTree = "<group>"; };
549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "bridging-header.h"; sourceTree = "<group>"; };
8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
8A1C83782AC328BD0096AF73 /* ContentView.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ContentView.swift; sourceTree = "<group>"; };
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = "Preview Assets.xcassets"; sourceTree = "<group>"; };
8A39BE092AC7601000BFEB40 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */ = {isa = PBXFileReference; lastKnownFileType = wrapper; name = llama.cpp; path = ../..; sourceTree = "<group>"; };
F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = "<group>"; };
/* End PBXFileReference section */
/* Begin PBXFrameworksBuildPhase section */
@ -59,6 +40,7 @@
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
files = (
DF810E132B4A5BA200301144 /* llama in Frameworks */,
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
);
@ -67,30 +49,10 @@
/* End PBXFrameworksBuildPhase section */
/* Begin PBXGroup section */
8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = {
isa = PBXGroup;
children = (
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */,
542376092B0D9C40008E6A1C /* ggml-backend.h */,
542376062B0D9BEA008E6A1C /* ggml-quants.h */,
542376072B0D9BFB008E6A1C /* ggml-quants.c */,
549479C82AC9E10B00E0F78B /* ggml-metal.metal */,
549479C62AC9E0F200E0F78B /* ggml-metal.h */,
549479C52AC9E0F200E0F78B /* ggml-metal.m */,
542EA09B2AC8723900A8AEE9 /* ggml.c */,
542EA09C2AC8723900A8AEE9 /* ggml.h */,
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */,
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */,
542EA0A12AC8729100A8AEE9 /* llama.cpp */,
542EA0A22AC8729100A8AEE9 /* llama.h */,
);
name = llama.cpp;
sourceTree = "<group>";
};
8A1C836A2AC328BD0096AF73 = {
isa = PBXGroup;
children = (
8A08D1F62AC7383900FE6CD4 /* llama.cpp */,
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */,
8A907F312AC7134E006146EA /* llama.cpp.swift */,
8A3F84232AC4C891005E2EE8 /* models */,
8A1C83752AC328BD0096AF73 /* llama.swiftui */,
@ -115,19 +77,10 @@
8A9F7C4A2AC332BF008AE1EA /* UI */,
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */,
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */,
8A1C837C2AC328BE0096AF73 /* Preview Content */,
);
path = llama.swiftui;
sourceTree = "<group>";
};
8A1C837C2AC328BE0096AF73 /* Preview Content */ = {
isa = PBXGroup;
children = (
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */,
);
path = "Preview Content";
sourceTree = "<group>";
};
8A39BE082AC7601000BFEB40 /* Frameworks */ = {
isa = PBXGroup;
children = (
@ -155,7 +108,6 @@
8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
isa = PBXGroup;
children = (
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */,
8A907F322AC7134E006146EA /* LibLlama.swift */,
);
path = llama.cpp.swift;
@ -166,6 +118,7 @@
children = (
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */,
8A1C83782AC328BD0096AF73 /* ContentView.swift */,
F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */,
);
path = UI;
sourceTree = "<group>";
@ -195,6 +148,7 @@
);
name = llama.swiftui;
packageProductDependencies = (
DF810E122B4A5BA200301144 /* llama */,
);
productName = llama.swiftui;
productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
@ -241,9 +195,7 @@
isa = PBXResourcesBuildPhase;
buildActionMask = 2147483647;
files = (
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */,
8A3F84242AC4C891005E2EE8 /* models in Resources */,
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
);
runOnlyForDeploymentPostprocessing = 0;
@ -255,17 +207,12 @@
isa = PBXSourcesBuildPhase;
buildActionMask = 2147483647;
files = (
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */,
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */,
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */,
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
@ -395,11 +342,9 @@
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
@ -416,11 +361,12 @@
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
TARGETED_DEVICE_FAMILY = "1,2,7";
};
name = Debug;
};
@ -428,11 +374,9 @@
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
@ -449,10 +393,11 @@
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
TARGETED_DEVICE_FAMILY = "1,2,7";
};
name = Release;
};
@ -478,6 +423,13 @@
defaultConfigurationName = Release;
};
/* End XCConfigurationList section */
/* Begin XCSwiftPackageProductDependency section */
DF810E122B4A5BA200301144 /* llama */ = {
isa = XCSwiftPackageProductDependency;
productName = llama;
};
/* End XCSwiftPackageProductDependency section */
};
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
}

11
examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json
View file

@ -1,11 +0,0 @@
{
"colors" : [
{
"idiom" : "universal"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

27
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
View file

@ -4,6 +4,7 @@ import Foundation
class LlamaState: ObservableObject {
@Published var messageLog = ""
@Published var cacheCleared = false
let NS_PER_S = 1_000_000_000.0
private var llamaContext: LlamaContext?
private var defaultModelUrl: URL? {
@ -20,12 +21,12 @@ class LlamaState: ObservableObject {
}
func loadModel(modelUrl: URL?) throws {
messageLog += "Loading model...\n"
if let modelUrl {
messageLog += "Loading model...\n"
llamaContext = try LlamaContext.create_context(path: modelUrl.path())
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
} else {
messageLog += "Could not locate model\n"
messageLog += "Load a model from the list below\n"
}
}
@ -34,15 +35,29 @@ class LlamaState: ObservableObject {
return
}
let t_start = DispatchTime.now().uptimeNanoseconds
await llamaContext.completion_init(text: text)
let t_heat_end = DispatchTime.now().uptimeNanoseconds
let t_heat = Double(t_heat_end - t_start) / NS_PER_S
messageLog += "\(text)"
while await llamaContext.n_cur <= llamaContext.n_len {
while await llamaContext.n_cur < llamaContext.n_len {
let result = await llamaContext.completion_loop()
messageLog += "\(result)"
}
let t_end = DispatchTime.now().uptimeNanoseconds
let t_generation = Double(t_end - t_heat_end) / NS_PER_S
let tokens_per_second = Double(await llamaContext.n_len) / t_generation
await llamaContext.clear()
messageLog += "\n\ndone\n"
messageLog += """
\n
Done
Heat up took \(t_heat)s
Generated \(tokens_per_second) t/s\n
"""
}
func bench() async {
@ -56,10 +71,10 @@ class LlamaState: ObservableObject {
messageLog += await llamaContext.model_info() + "\n"
let t_start = DispatchTime.now().uptimeNanoseconds
await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
let _ = await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
let t_end = DispatchTime.now().uptimeNanoseconds
let t_heat = Double(t_end - t_start) / 1_000_000_000.0
let t_heat = Double(t_end - t_start) / NS_PER_S
messageLog += "Heat up time: \(t_heat) seconds, please wait...\n"
// if more than 5 seconds, then we're probably running on a slow device

6
examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json
View file

@ -1,6 +0,0 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

37
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
View file

@ -42,46 +42,27 @@ struct ContentView: View {
Button("Send") {
sendText()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Bench") {
bench()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Clear") {
clear()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Copy") {
UIPasteboard.general.string = llamaState.messageLog
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
}
}.buttonStyle(.bordered)
VStack {
VStack(alignment: .leading) {
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
)
.font(.system(size: 12))
.padding(.top, 4)
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
@ -89,7 +70,6 @@ struct ContentView: View {
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
@ -97,8 +77,6 @@ struct ContentView: View {
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
filename: "tinyllama-1.1b-f16.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
@ -106,7 +84,6 @@ struct ContentView: View {
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
filename: "phi-2-q4_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
@ -114,8 +91,6 @@ struct ContentView: View {
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
filename: "phi-2-q8_0.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
@ -123,15 +98,17 @@ struct ContentView: View {
modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
filename: "mistral-7b-v0.1.Q4_0.gguf"
)
.font(.system(size: 12))
Button("Clear downloaded models") {
ContentView.cleanupModelCaches()
llamaState.cacheCleared = true
}
.padding(8)
.font(.system(size: 12))
LoadCustomButton(llamaState: llamaState)
}
.padding(.top, 4)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
}
.padding()
}

2
examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift
View file

@ -93,7 +93,7 @@ struct DownloadButton: View {
print("Error: \(err.localizedDescription)")
}
}) {
Text("\(modelName) (Downloaded)")
Text("Load \(modelName)")
}
} else {
Text("Unknown status")

44
examples/llama.swiftui/llama.swiftui/UI/LoadCustomButton.swift Normal file
View file

@ -0,0 +1,44 @@
import SwiftUI
import UniformTypeIdentifiers
struct LoadCustomButton: View {
@ObservedObject private var llamaState: LlamaState
@State private var showFileImporter = false
init(llamaState: LlamaState) {
self.llamaState = llamaState
}
var body: some View {
VStack {
Button(action: {
showFileImporter = true
}) {
Text("Load Custom Model")
}
}
.fileImporter(
isPresented: $showFileImporter,
allowedContentTypes: [UTType(filenameExtension: "gguf", conformingTo: .data)!],
allowsMultipleSelection: false
) { result in
switch result {
case .success(let files):
files.forEach { file in
let gotAccess = file.startAccessingSecurityScopedResource()
if !gotAccess { return }
do {
try llamaState.loadModel(modelUrl: file.absoluteURL)
} catch let err {
print("Error: \(err.localizedDescription)")
}
file.stopAccessingSecurityScopedResource()
}
case .failure(let error):
print(error)
}
}
}
}

5
examples/llava/CMakeLists.txt
View file

@ -24,7 +24,8 @@ endif()
if (NOT MSVC)
target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
endif()
endif()
if(TARGET BUILD_INFO)
add_dependencies(llava BUILD_INFO)
endif()
@ -32,5 +33,5 @@ endif()
set(TARGET llava-cli)
add_executable(llava-cli llava-cli.cpp)
install(TARGETS llava-cli RUNTIME)
target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(llava PRIVATE cxx_std_11)

488
examples/llava/clip.cpp
View file

@ -16,12 +16,19 @@
#include "clip.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define CLIP_DEBUG
static std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
@ -119,26 +126,30 @@ static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::str
}
static std::string get_ftype(int ftype) {
switch (ftype) {
case 0:
return "f32";
case 1:
return "f16";
case 2:
return "q4_0";
case 3:
return "q4_1";
case 6:
return "q5_0";
case 7:
return "q5_1";
case 8:
return "q8_0";
default:
throw std::runtime_error(format("%s: Unrecognized file type: %d\n", __func__, ftype));
}
return ggml_type_name(static_cast<ggml_type>(ftype));
}
//
// image data
//
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
//
// clip layers
//
@ -196,39 +207,31 @@ struct clip_vision_model {
struct ggml_tensor * mm_2_b;
};
// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
struct clip_buffer {
uint8_t * data = NULL;
size_t size = 0;
void resize(size_t size) {
delete[] data;
data = new uint8_t[size];
this->size = size;
}
~clip_buffer() { delete[] data; }
};
struct clip_ctx {
bool has_text_encoder = false;
bool has_vision_encoder = false;
bool has_text_encoder = false;
bool has_vision_encoder = false;
bool has_llava_projector = false;
struct clip_vision_model vision_model;
float image_mean[3];
float image_std[3];
bool use_gelu = false;
int32_t ftype = 1;
struct ggml_context * ctx;
struct gguf_context * ctx_gguf;
struct ggml_context * ctx_data;
std::vector<uint8_t> buf_compute_meta;
// memory buffers to evaluate the model
clip_buffer buf_compute;
clip_buffer buf_alloc;
ggml_allocr * alloc = NULL;
ggml_backend_buffer_t params_buffer = NULL;
ggml_backend_buffer_t compute_buffer = NULL;
ggml_backend_t backend = NULL;
ggml_allocr * compute_alloc = NULL;
};
static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) {
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;
@ -249,28 +252,24 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
//const int projection_dim = hparams.projection_dim;
const float eps = hparams.eps;
int batch_size = imgs->size;
if(ctx->has_llava_projector) {
if (ctx->has_llava_projector) {
GGML_ASSERT(batch_size == 1);
}
const auto & buf_compute = ctx->buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.data,
/*.no_alloc =*/ false,
/*.mem_size =*/ ctx->buf_compute_meta.size(),
/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
params.no_alloc = true;
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
ggml_allocr_alloc(ctx->alloc, inp_raw);
ggml_allocr_alloc(ctx->compute_alloc, inp_raw);
if (!ggml_allocr_is_measure(ctx->alloc)) {
float * data = (float *)ggml_get_data(inp_raw);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
float * data = (float *)malloc(ggml_nbytes(inp_raw));
for (size_t i = 0; i < imgs->size; i++) {
const int nx = imgs->data[i].nx;
@ -283,12 +282,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
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].data[3 * (y * nx + x) + k];
data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
}
}
}
}
}
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
@ -298,42 +299,39 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
// concat class_embeddings and patch_embeddings
struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
ggml_allocr_alloc(ctx->alloc, embeddings);
if (!ggml_allocr_is_measure(ctx->alloc)) {
ggml_set_zero(embeddings);
ggml_allocr_alloc(ctx->compute_alloc, embeddings);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size);
ggml_allocr_alloc(ctx->alloc, temp);
embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, model.class_embedding, temp), 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]);
embeddings = ggml_acc(ctx0, embeddings, inp,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
ggml_allocr_alloc(ctx->alloc, positions);
if (!ggml_allocr_is_measure(ctx->alloc)) {
ggml_allocr_alloc(ctx->compute_alloc, positions);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
ggml_set_i32_1d(positions, i, i);
positions_data[i] = i;
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
embeddings =
ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings));
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
// pre-layernorm
{
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.pre_ln_w, embeddings), embeddings),
ggml_repeat(ctx0, model.pre_ln_b, embeddings));
}
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(ctx->alloc, KQ_scale);
if (!ggml_allocr_is_measure(ctx->alloc)) {
ggml_set_f32(KQ_scale, 1.0f / sqrt((float)d_head));
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
}
// loop over layers
@ -346,30 +344,30 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur),
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
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_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur));
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
Q = ggml_scale_inplace(ctx0, Q, KQ_scale);
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_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur));
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_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur));
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));
@ -385,7 +383,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
}
// attention output
cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur));
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);
@ -396,12 +394,11 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur),
ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
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, ggml_repeat(ctx0, model.layers[il].ff_i_b, cur), cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
if (ctx->use_gelu) {
cur = ggml_gelu_inplace(ctx0, cur);
@ -410,7 +407,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_o_b, cur), cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
// residual 2
cur = ggml_add(ctx0, embeddings, cur);
@ -423,23 +420,26 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
ggml_allocr_alloc(ctx->alloc, patches);
if (!ggml_allocr_is_measure(ctx->alloc)) {
for (int i = 0; i < num_patches; ++i) {
ggml_set_i32_1d(patches, i, i+1);
ggml_allocr_alloc(ctx->compute_alloc, patches);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
patches_data[i] = i + 1;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);
}
embeddings = ggml_get_rows(ctx0, embeddings, patches);
// mm projection 0
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_0_b, embeddings), 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, ggml_repeat(ctx0, model.mm_2_b, embeddings), embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
}
// build the graph
@ -452,7 +452,6 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
// 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 = {
@ -485,7 +484,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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);
@ -499,28 +498,41 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
// data
size_t ctx_size = 0;
size_t buffer_size = 0;
{
const int n_tensors = gguf_get_n_tensors(ctx);
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);
ctx_size += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
size_t tensor_size = ggml_nbytes(cur);
size_t padded_size = ggml_nbytes_pad(cur);
ctx_size += padded_size;
buffer_size += tensor_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i,
ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset);
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);
@ -545,21 +557,24 @@ struct clip_ctx * clip_model_load(const char * fname, const int 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__, (ctx_size / 1024.0 / 1024.0));
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 =*/ ctx_size,
/*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
/*.no_alloc =*/ true,
};
new_clip->ctx = ggml_init(params);
if (!new_clip->ctx) {
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;
@ -572,13 +587,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
return nullptr;
}
const int n_tensors = gguf_get_n_tensors(ctx);
// 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, t);
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) {
@ -586,10 +609,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
clip_free(new_clip);
return nullptr;
}
fin.read(reinterpret_cast<char *>(cur->data), ggml_nbytes(t));
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();
}
@ -598,20 +629,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// 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.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.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"));
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);
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));
new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));
}
if (verbosity >= 2) {
@ -625,35 +656,35 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
printf("v_n_layer %d\n", hparams.n_layer);
}
vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD);
vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD);
vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v"));
vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias"));
vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "weight"));
vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "bias"));
vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "bias"));
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, format(TN_ATTN_K, "v", il, "weight"));
layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight"));
layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight"));
layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight"));
layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight"));
layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight"));
layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight"));
layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight"));
layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias"));
layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias"));
layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias"));
layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias"));
layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias"));
layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias"));
layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias"));
layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias"));
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"));
}
}
@ -661,45 +692,45 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
new_clip->ctx_gguf = ctx;
// measure mem requirement and allocate
// measure mem requirement and allocate
{
static const size_t tensor_alignment = 32;
new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead());
new_clip->alloc = ggml_allocr_new_measure(tensor_alignment);
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 alloc_size = ggml_allocr_alloc_graph(new_clip->alloc, gf) + tensor_alignment;
ggml_allocr_free(new_clip->alloc);
new_clip->buf_alloc.resize(alloc_size);
new_clip->alloc = ggml_allocr_new(new_clip->buf_alloc.data, new_clip->buf_alloc.size, tensor_alignment);
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: total allocated memory: %.2f MB\n", __func__, (new_clip->buf_compute.size + alloc_size)/1024.0/1024.0);
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
}
return new_clip;
}
clip_image_u8 * make_clip_image_u8() {
auto img = new clip_image_u8();
return img;
struct clip_image_u8 * clip_image_u8_init() {
return new clip_image_u8();
}
clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }
void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; }
void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; }
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->size = nx * ny * 3;
img->data = new uint8_t[img->size]();
memcpy(img->data, data, img->size);
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);
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
if (!data) {
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
return false;
@ -711,7 +742,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
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);
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;
@ -723,7 +754,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
// normalize: x = (x - mean) / std
// TODO: implement bicubic interpolation instead of linear.
bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
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;
@ -732,18 +763,17 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
// 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 = make_clip_image_u8(); // we will keep the input image data here temporarily
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->size = 3 * longer_side * longer_side;
temp->data = new uint8_t[temp->size]();
uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
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->size; i++) {
temp->data[i] = bc[i % 3];
for (size_t i = 0; i < temp->buf.size(); i++) {
temp->buf[i] = bc[i % 3];
}
// copy from the input image
@ -751,17 +781,16 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
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->data[j] = img->data[i];
temp->data[j+1] = img->data[i+1];
temp->data[j+2] = img->data[i+2];
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->size = img->size;
temp->data = new uint8_t[temp->size]();
memcpy(&temp->data[0], &img->data[0], temp->size); // copy
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;
@ -772,8 +801,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
res->nx = nx2;
res->ny = ny2;
res->size = 3 * nx2 * ny2;
res->data = new float[res->size]();
res->buf.resize(3 * nx2 * ny2);
const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
@ -804,10 +832,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
const int j10 = 3 * (y1 * nx + x0) + c;
const int j11 = 3 * (y1 * nx + x1) + c;
const float v00 = temp->data[j00];
const float v01 = temp->data[j01];
const float v10 = temp->data[j10];
const float v11 = temp->data[j11];
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;
@ -818,7 +846,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
const int i = 3 * (y * nx3 + x) + c;
res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
}
}
}
@ -828,12 +856,13 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
}
void clip_free(clip_ctx * ctx) {
ggml_free(ctx->ctx);
ggml_free(ctx->ctx_data);
gguf_free(ctx->ctx_gguf);
delete ctx;
}
bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
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;
@ -845,8 +874,7 @@ bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32
return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
}
bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * 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;
@ -858,29 +886,29 @@ bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const cl
}
// reset alloc buffer to clean the memory from previous invocations
ggml_allocr_reset(ctx->alloc);
ggml_allocr_reset(ctx->compute_alloc);
// build the inference graph
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
ggml_allocr_alloc_graph(ctx->alloc, gf);
ggml_allocr_alloc_graph(ctx->compute_alloc, gf);
struct ggml_cplan plan = ggml_graph_plan(gf, n_threads);
if (plan.work_size > 0) {
plan.work_data = (uint8_t *)malloc(plan.work_size);
if (ggml_backend_is_cpu(ctx->backend)) {
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
}
ggml_graph_compute(gf, &plan);
#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];
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];
// copy the embeddings to the location passed by the user
memcpy(vec, ggml_get_data_f32(embeddings), ggml_nbytes(embeddings));
if (plan.work_size > 0) {
free(plan.work_data);
}
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
return true;
}
@ -888,32 +916,15 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
ggml_type type = GGML_TYPE_Q4_1;
switch (itype) {
case 2:
type = GGML_TYPE_Q4_0;
break;
case 3:
type = GGML_TYPE_Q4_1;
break;
case 6:
type = GGML_TYPE_Q5_0;
break;
case 7:
type = GGML_TYPE_Q5_1;
break;
case 8:
type = GGML_TYPE_Q8_0;
break;
default:
fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
return false;
};
assert(itype < GGML_TYPE_COUNT);
type = static_cast<ggml_type>(itype);
auto * ctx_clip = clip_model_load(fname_inp, 2);
auto ctx_clip = clip_model_load(fname_inp, 2);
const auto & ctx_src = ctx_clip->ctx_gguf;
const auto & ctx_data = ctx_clip->ctx;
const auto & ctx_data = ctx_clip->ctx_data;
auto ctx_out = gguf_init_empty();
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);
@ -966,6 +977,10 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
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;
@ -1010,6 +1025,21 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
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;
@ -1051,8 +1081,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
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);
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) {

48
examples/llava/clip.h
View file

@ -35,31 +35,14 @@ struct clip_vision_hparams {
float eps;
};
/** load mmproj model */
CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
/** free mmproj model */
CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
CLIP_API void clip_free(struct clip_ctx * ctx);
size_t clip_embd_nbytes(const struct clip_ctx * ctx);
int clip_n_patches(const struct clip_ctx * ctx);
int clip_n_mmproj_embd(const struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
uint8_t * data = NULL;
size_t size;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
float * data = NULL;
size_t size;
};
CLIP_API int clip_n_patches (const struct clip_ctx * ctx);
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
struct clip_image_u8_batch {
struct clip_image_u8 * data;
@ -71,21 +54,22 @@ struct clip_image_f32_batch {
size_t size;
};
struct clip_image_u8 * make_clip_image_u8();
struct clip_image_f32 * make_clip_image_f32();
CLIP_API void clip_image_u8_free(clip_image_u8 * img);
CLIP_API void clip_image_f32_free(clip_image_f32 * img);
CLIP_API struct clip_image_u8 * clip_image_u8_init ();
CLIP_API struct clip_image_f32 * clip_image_f32_init();
CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);
bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);
CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square);
CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs,
float * vec);
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype);
CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
#ifdef __cplusplus
}

88
examples/llava/llava-cli.cpp
View file

@ -39,73 +39,11 @@ static bool eval_string(struct llama_context * ctx_llama, const char* str, int n
return true;
}
// TODO: use common/sampling.h
static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
auto & sparams = params.sparams;
// out of user input, sample next token
const float temp = sparams.temp;
const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k;
const float top_p = sparams.top_p;
const float tfs_z = sparams.tfs_z;
const float typical_p = sparams.typical_p;
// const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n;
// const float repeat_penalty = sparams.repeat_penalty;
// const float alpha_presence = sparams.presence_penalty;
// const float alpha_frequency = sparams.frequency_penalty;
const int mirostat = sparams.mirostat;
const float mirostat_tau = sparams.mirostat_tau;
const float mirostat_eta = sparams.mirostat_eta;
// const bool penalize_nl = sparams.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx_llama);
auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
// Apply params.logit_bias map
for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx_llama, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token(ctx_llama, &candidates_p);
}
}
}
return id;
}
static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
int id = sample_id(ctx_llama, params);
static const char * sample(struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_llama,
int * n_past) {
const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
static std::string ret;
if (id == llama_token_eos(llama_get_model(ctx_llama))) {
ret = "</s>";
@ -174,8 +112,8 @@ struct llava_context {
};
static void show_additional_info(int /*argc*/, char ** argv) {
printf("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
@ -185,7 +123,7 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
auto prompt = params->prompt;
if (prompt_contains_image(prompt)) {
if (!params->image.empty()) {
printf("using base64 encoded image instead of command line image path\n");
fprintf(stderr, "using base64 encoded image instead of command line image path\n");
}
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
if (!embed) {
@ -217,16 +155,19 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
// generate the response
printf("\n");
fprintf(stderr, "\n");
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
for (int i = 0; i < max_tgt_len; i++) {
const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past);
const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
if (strcmp(tmp, "</s>") == 0) break;
printf("%s", tmp);
fflush(stdout);
}
llama_sampling_free(ctx_sampling);
printf("\n");
}
@ -302,6 +243,9 @@ int main(int argc, char ** argv) {
}
auto image_embed = load_image(ctx_llava, &params);
if (!image_embed) {
return 1;
}
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);

4
examples/llava/llava.cpp
View file

@ -10,7 +10,7 @@
#include "base64.hpp"
static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
clip_image_f32 * img_res = make_clip_image_f32();
clip_image_f32 * img_res = clip_image_f32_init();
if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
clip_image_f32_free(img_res);
@ -86,7 +86,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
}
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
clip_image_u8 * img = make_clip_image_u8();
clip_image_u8 * img = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
clip_image_u8_free(img);
fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);

5
examples/lookup/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET lookup)
add_executable(${TARGET} lookup.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

13
examples/lookup/README.md Normal file
View file

@ -0,0 +1,13 @@
# llama.cpp/examples/lookup
Demonstration of Prompt Lookup Decoding
https://github.com/apoorvumang/prompt-lookup-decoding
The key parameters for lookup decoding are `ngram_min`, `ngram_max` and `n_draft`. The first two determine the size of the ngrams to search for in the prompt for a match. The latter specifies how many subsequent tokens to draft if a match is found.
More info:
https://github.com/ggerganov/llama.cpp/pull/4484
https://github.com/ggerganov/llama.cpp/issues/4226

230
examples/lookup/lookup.cpp Normal file
View file

@ -0,0 +1,230 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
// max/min n-grams size to search for in prompt
const int ngram_max = 4;
const int ngram_min = 1;
// length of the candidate / draft sequence, if match is found
const int n_draft = params.n_draft;
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookup", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
int n_past = inp.size();
bool has_eos = false;
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
std::vector<llama_token> draft;
llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
const auto t_dec_start = ggml_time_us();
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
// print current draft sequence
LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft);
llama_sampling_accept(ctx_sampling, ctx, id, true);
const std::string token_str = llama_token_to_piece(ctx, id);
if (!params.use_color) {
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model)) {
has_eos = true;
}
++n_predict;
// check if the target token matches the draft
if (i_dft < (int) draft.size() && id == draft[i_dft]) {
LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
++n_accept;
++n_past;
++i_dft;
inp.push_back(id);
if (params.use_color) {
// color accepted draft token
printf("\033[34m%s\033[0m", token_str.c_str());
fflush(stdout);
}
continue;
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
draft.clear();
draft.push_back(id);
inp.push_back(id);
break;
}
if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// clean the cache of draft tokens that weren't accepted
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
llama_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// generate n_pred tokens through prompt lookup
auto prompt_lookup = [&]() -> void {
int inp_size = inp.size();
for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
const llama_token * ngram = &inp[inp_size - ngram_size];
for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
bool match = true;
for (int j = 0; j < ngram_size; ++j) {
if (inp[i + j] != ngram[j]) {
match = false;
break;
}
}
if (match) {
const int startIdx = i + ngram_size;
const int endIdx = startIdx + n_draft;
if (endIdx < inp_size) {
for (int j = startIdx; j < endIdx; ++j) {
LOG(" - draft candidate %d: %d\n", j, inp[j]);
draft.push_back(inp[j]);
llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
++n_drafted;
}
return;
}
}
}
}
return;
};
prompt_lookup();
llama_decode(ctx, batch_tgt);
++n_past;
draft.erase(draft.begin());
}
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ntarget:\n");
llama_print_timings(ctx);
llama_sampling_free(ctx_sampling);
llama_batch_free(batch_tgt);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

27
examples/main-cmake-pkg/CMakeLists.txt
View file

@ -7,28 +7,13 @@ find_package(Llama 0.0.1 REQUIRED)
# Bake common functionality in with target. Because applications
# using the relocatable Llama package should be outside of the
# source tree, main-cmake-pkg pretends the dependencies are built-in.
set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common")
add_library(common OBJECT
${_common_path}/common.h
${_common_path}/common.cpp
${_common_path}/console.h
${_common_path}/console.cpp
${_common_path}/grammar-parser.h
${_common_path}/grammar-parser.cpp
${_common_path}/sampling.h
${_common_path}/sampling.cpp
)
# WARNING: because build-info.h is auto-generated, it will only
# be available after the user has built the llama.cpp sources.
#
configure_file(${_common_path}/../build-info.h
${CMAKE_CURRENT_BINARY_DIR}/build-info.h
COPYONLY)
target_include_directories(common PUBLIC ${LLAMA_INCLUDE_DIR}
${CMAKE_CURRENT_BINARY_DIR})
add_library(common OBJECT)
file(GLOB _common_files
"${_common_path}/*.h"
"${_common_path}/*.cpp"
)
target_sources(common PRIVATE ${_common_files})
# If the common project was part of "main-cmake-pkg" the transient
# defines would automatically be attached. Because the common func-

87
examples/main/main.cpp
View file

@ -447,6 +447,21 @@ int main(int argc, char ** argv) {
LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
// group-attention state
// number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
int ga_i = 0;
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
if (ga_n != 1) {
GGML_ASSERT(ga_n > 0 && "grp_attn_n must be positive"); // NOLINT
GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
}
LOG_TEE("\n\n");
if (params.interactive) {
@ -508,37 +523,61 @@ int main(int argc, char ** argv) {
fflush(stdout);
}
// infinite text generation via context swapping
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
if (params.n_predict == -2) {
LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
if (ga_n == 1) {
// infinite text generation via context shifting
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
if (params.n_predict == -2) {
LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
}
const int n_left = n_past - params.n_keep - 1;
const int n_discard = n_left/2;
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
n_past -= n_discard;
if (ctx_guidance) {
n_past_guidance -= n_discard;
}
LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
LOG("clear session path\n");
path_session.clear();
}
} else {
// context extension via Self-Extend
while (n_past >= ga_i + ga_w) {
const int ib = (ga_n*ga_i)/ga_w;
const int bd = (ga_w/ga_n)*(ga_n - 1);
const int dd = (ga_w/ga_n) - ib*bd - ga_w;
const int n_left = n_past - params.n_keep - 1;
const int n_discard = n_left/2;
LOG("\n");
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n);
llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
n_past -= bd;
n_past -= n_discard;
ga_i += ga_w/ga_n;
if (ctx_guidance) {
n_past_guidance -= n_discard;
LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
}
LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
LOG("clear session path\n");
path_session.clear();
}
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)

5
examples/passkey/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET passkey)
add_executable(${TARGET} passkey.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

12
examples/passkey/README.md Normal file
View file

@ -0,0 +1,12 @@
# llama.cpp/example/passkey
See the following PRs for more info:
- https://github.com/ggerganov/llama.cpp/pull/3856
- https://github.com/ggerganov/llama.cpp/pull/4810
### Usage
```bash
make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250
```

296
examples/passkey/passkey.cpp Normal file
View file

@ -0,0 +1,296 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]);
return 1 ;
}
int seed = -1;
int n_junk = 250; // number of times to repeat the junk text
int n_keep = 32; // number of tokens in the prompt prefix
int n_grp = 1; // if more than 1 - perform LongLM SelfExtend
int i_pos = -1; // position of the passkey in the junk text
if (argc >= 2) {
params.model = argv[1];
}
if (argc >= 3) {
n_junk = std::stoi(argv[2]);
}
if (argc >= 4) {
n_grp = std::stoi(argv[3]);
}
if (argc >= 5) {
i_pos = std::stoi(argv[4]);
}
if (argc >= 6) {
seed = std::stoi(argv[5]);
}
if (seed == -1) {
seed = time(NULL);
}
srand(seed);
if (i_pos == -1) {
i_pos = rand() % n_junk;
}
const std::string prompt_prefix = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.";
const std::string prompt_suffix = " What is the pass key? The pass key is";
// generate junk text
params.prompt = prompt_prefix;
const int passkey = rand() % 50000 + 1;
for (int i = 0; i < n_junk; i++) {
if (i % n_junk == i_pos) {
params.prompt += " The pass key is " + std::to_string(passkey) + ". Remember it. " + std::to_string(passkey) + " is the pass key.";
}
params.prompt += " The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.";
}
params.prompt += prompt_suffix;
// init LLM
llama_backend_init(params.numa);
// initialize the model
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = 99; // offload all layers to the GPU
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
// initialize the context
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = seed;
ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
ctx_params.n_batch = 512;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
// tokenize the prefix and use it as a sink
const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size();
const int n_tokens_all = tokens_list.size();
// we leave a margin of 16 tokens for the generated text - it should contain just the passkey
const int n_predict = 16;
// total length of the sequences including the prompt
const int n_len = n_tokens_all + n_predict;
const int n_ctx = llama_n_ctx(ctx) - n_keep;
const int n_kv_req = llama_n_ctx(ctx);
const int n_batch = ctx_params.n_batch;
const int n_batch_grp = ctx_params.n_batch/n_grp;
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch);
// print the prompt token-by-token
LOG_TEE("\n");
LOG_TEE("prefix tokens: %d\n", n_tokens_prefix);
LOG_TEE("prompt tokens: %d\n", n_tokens_all);
//LOG_TEE("prompt: %s\n", params.prompt.c_str());
llama_batch batch = llama_batch_init(512, 0, 1);
int n_past = 0;
// fill the KV cache
for (int i = 0; i < n_ctx; i += n_batch) {
if (i > 0 && n_grp > 1) {
// if SelfExtend is enabled, we compress the position from the last batch by a factor of n_grp
const int ib = i/n_batch - 1;
const int bd = n_batch_grp*(n_grp - 1);
llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
n_past -= bd;
}
llama_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
}
if (i + n_batch >= n_tokens_all) {
batch.logits[batch.n_tokens - 1] = true;
}
if (llama_decode(ctx, batch) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
if (i + n_batch >= n_tokens_all) {
break;
}
}
for (int i = n_ctx; i < n_tokens_all; i += n_batch) {
const int n_discard = n_batch;
LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
n_past -= n_discard;
llama_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
}
if (i + n_batch >= n_tokens_all) {
batch.logits[batch.n_tokens - 1] = true;
}
if (llama_decode(ctx, batch) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
}
{
const int n_discard = n_past - n_ctx + n_predict;
if (n_discard > 0) {
LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
n_past -= n_discard;
}
}
LOG_TEE("\n");
LOG_TEE("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk);
LOG_TEE("\n");
// main loop
int n_cur = n_tokens_all;
int n_decode = 0;
LOG_TEE("%s", prompt_suffix.c_str());
fflush(stdout);
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
// sample the next token
{
auto n_vocab = llama_n_vocab(model);
auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// sample the most likely token
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream?
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
LOG_TEE("\n");
break;
}
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
n_decode += 1;
// prepare the next batch
llama_batch_clear(batch);
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_past++, { 0 }, true);
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
LOG_TEE("\n");
const auto t_main_end = ggml_time_us();
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
llama_print_timings(ctx);
fprintf(stderr, "\n");
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

2
examples/server/CMakeLists.txt
View file

@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()

76
examples/server/README.md
View file

@ -23,6 +23,7 @@ Command line options:
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default examples/server/public)
- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.
- `--embedding`: Enable embedding extraction, Default: disabled.
- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
@ -148,6 +149,8 @@ node index.js
`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
@ -164,37 +167,7 @@ node index.js
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
*Result JSON:*
Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
`content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
`stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
`generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
`model`: The path to the model loaded with `-m`
`prompt`: The provided `prompt`
`stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
`stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered
`stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided
`stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
`timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
`tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
`tokens_evaluated`: Number of tokens evaluated in total from the prompt
`truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
`slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
@ -202,6 +175,45 @@ node index.js
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
### Result JSON:
* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
```
{
"content": "<the token selected by the model>",
"probs": [
{
"prob": float,
"tok_str": "<most likely token>"
},
{
"prob": float,
"tok_str": "<second most likely tonen>"
},
...
]
},
```
Notice that each `probs` is an array of length `n_probs`.
- `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
- `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
- `model`: The path to the model loaded with `-m`
- `prompt`: The provided `prompt`
- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered
- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided
- `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
- `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
- `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
- `tokens_evaluated`: Number of tokens evaluated in total from the prompt
- `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
- **POST** `/tokenize`: Tokenize a given text.
*Options:*
@ -222,6 +234,8 @@ node index.js
`content`: Set the text to process.
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
*Options:*

693
examples/server/completion.js.hpp
View file

@ -74,355 +74,376 @@ unsigned char completion_js[] = {
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};
unsigned int completion_js_len = 5099;
unsigned int completion_js_len = 5346;

4591
examples/server/index.html.hpp
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File diff suppressed because it is too large Load diff

3693
examples/server/index.js.hpp
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File diff suppressed because it is too large Load diff

9
examples/server/public/completion.js
View file

@ -95,6 +95,15 @@ export async function* llama(prompt, params = {}, config = {}) {
break;
}
}
if (result.error) {
result.error = JSON.parse(result.error);
if (result.error.content.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error: ${result.error.content}`);
}
}
if (result.error) {
result.error = JSON.parse(result.error);
console.error(`llama.cpp error: ${result.error.content}`);

4
examples/server/public/index.html
View file

@ -427,7 +427,7 @@
}
if (data.timings) {
llamaStats.value = data.timings;
llamaStats.value = data;
}
}
@ -880,7 +880,7 @@
}
return html`
<span>
${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
</span>
`
}

238
examples/server/server.cpp
View file

@ -25,6 +25,7 @@
#include <thread>
#include <mutex>
#include <chrono>
#include <condition_variable>
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
@ -81,7 +82,7 @@ static inline bool is_base64(uint8_t c)
return (isalnum(c) || (c == '+') || (c == '/'));
}
static std::vector<uint8_t> base64_decode(std::string const &encoded_string)
static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
{
int i = 0;
int j = 0;
@ -208,10 +209,10 @@ struct slot_image
int32_t id;
bool request_encode_image = false;
float* image_embedding = nullptr;
float * image_embedding = nullptr;
int32_t image_tokens = 0;
clip_image_u8 img_data;
clip_image_u8 * img_data;
std::string prefix_prompt; // before of this image
};
@ -433,20 +434,27 @@ struct llama_client_slot
generated_token_probs.clear();
for (slot_image &img : images)
for (slot_image & img : images)
{
free(img.image_embedding);
delete[] img.img_data.data;
if (img.img_data) {
clip_image_u8_free(img.img_data);
}
img.prefix_prompt = "";
}
images.clear();
// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
}
bool has_budget(gpt_params &global_params) {
if (params.n_predict == -1 && global_params.n_predict == -1)
{
return true; // limitless
}
n_remaining = -1;
if(params.n_predict != -1)
if (params.n_predict != -1)
{
n_remaining = params.n_predict - n_decoded;
}
@ -454,7 +462,8 @@ struct llama_client_slot
{
n_remaining = global_params.n_predict - n_decoded;
}
return n_remaining > 0 || n_remaining == -1; // no budget || limitless
return n_remaining > 0; // no budget
}
bool available() const {
@ -542,7 +551,9 @@ struct llama_server_context
std::vector<task_result> queue_results;
std::vector<task_multi> queue_multitasks;
std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
std::condition_variable condition_tasks;
std::mutex mutex_results;
std::condition_variable condition_results;
~llama_server_context()
{
@ -761,6 +772,42 @@ struct llama_server_context
slot->prompt = "";
}
slot->sparams.penalty_prompt_tokens.clear();
slot->sparams.use_penalty_prompt_tokens = false;
const auto &penalty_prompt = data.find("penalty_prompt");
if (penalty_prompt != data.end())
{
if (penalty_prompt->is_string())
{
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
if (slot->params.n_predict > 0)
{
slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
}
slot->sparams.use_penalty_prompt_tokens = true;
}
else if (penalty_prompt->is_array())
{
const auto n_tokens = penalty_prompt->size();
slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
const int n_vocab = llama_n_vocab(model);
for (const auto &penalty_token : *penalty_prompt)
{
if (penalty_token.is_number_integer())
{
const auto tok = penalty_token.get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
slot->sparams.penalty_prompt_tokens.push_back(tok);
}
}
}
slot->sparams.use_penalty_prompt_tokens = true;
}
}
slot->sparams.logit_bias.clear();
if (json_value(data, "ignore_eos", false))
@ -813,24 +860,17 @@ struct llama_server_context
{
for (const auto &img : *images_data)
{
std::string data_b64 = img["data"].get<std::string>();
const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
slot_image img_sl;
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
int width, height, channels;
std::vector<uint8_t> image_buffer = base64_decode(data_b64);
data_b64.clear();
auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3);
if (!data) {
img_sl.img_data = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
{
LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
return false;
}
LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height);
img_sl.img_data.nx = width;
img_sl.img_data.ny = height;
img_sl.img_data.size = width * height * 3;
img_sl.img_data.data = new uint8_t[width * height * 3]();
memcpy(img_sl.img_data.data, data, width * height * 3);
stbi_image_free(data);
LOG_TEE("slot %i - loaded image\n", slot->id);
img_sl.request_encode_image = true;
slot->images.push_back(img_sl);
}
@ -885,6 +925,7 @@ struct llama_server_context
llama_sampling_free(slot->ctx_sampling);
}
slot->ctx_sampling = llama_sampling_init(slot->sparams);
llama_set_rng_seed(ctx, slot->params.seed);
slot->command = LOAD_PROMPT;
all_slots_are_idle = false;
@ -992,6 +1033,12 @@ struct llama_server_context
slot.generated_text += token_str;
slot.has_next_token = true;
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
{
// we can change penalty_prompt_tokens because it is always created from scratch each request
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
}
// check if there is incomplete UTF-8 character at the end
bool incomplete = false;
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
@ -1062,7 +1109,7 @@ struct llama_server_context
}
// check the limits
if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
{
slot.stopped_limit = true;
slot.has_next_token = false;
@ -1098,8 +1145,8 @@ struct llama_server_context
{
continue;
}
clip_image_f32 img_res;
if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true))
clip_image_f32 * img_res = clip_image_f32_init();
if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
{
LOG_TEE("Error processing the given image");
clip_free(clp_ctx);
@ -1114,11 +1161,12 @@ struct llama_server_context
return false;
}
LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding))
if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
{
LOG_TEE("Unable to encode image\n");
return false;
}
clip_image_f32_free(img_res);
img.request_encode_image = false;
}
@ -1127,7 +1175,7 @@ struct llama_server_context
void send_error(task_server& task, std::string error)
{
std::lock_guard<std::mutex> lock(mutex_results);
std::unique_lock<std::mutex> lock(mutex_results);
task_result res;
res.id = task.id;
res.multitask_id = task.multitask_id;
@ -1135,6 +1183,7 @@ struct llama_server_context
res.error = true;
res.result_json = { { "content", error } };
queue_results.push_back(res);
condition_results.notify_all();
}
void add_multi_task(int id, std::vector<int>& sub_ids)
@ -1144,6 +1193,7 @@ struct llama_server_context
multi.id = id;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
condition_tasks.notify_one();
}
void update_multi_task(int multitask_id, int subtask_id, task_result& result)
@ -1155,6 +1205,7 @@ struct llama_server_context
{
multitask.subtasks_remaining.erase(subtask_id);
multitask.results.push_back(result);
condition_tasks.notify_one();
}
}
}
@ -1173,7 +1224,7 @@ struct llama_server_context
{"n_ctx", slot.n_ctx},
{"model", params.model_alias},
{"seed", slot.params.seed},
{"temp", slot.sparams.temp},
{"temperature", slot.sparams.temp},
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
@ -1183,6 +1234,8 @@ struct llama_server_context
{"repeat_penalty", slot.sparams.penalty_repeat},
{"presence_penalty", slot.sparams.penalty_present},
{"frequency_penalty", slot.sparams.penalty_freq},
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
{"mirostat", slot.sparams.mirostat},
{"mirostat_tau", slot.sparams.mirostat_tau},
{"mirostat_eta", slot.sparams.mirostat_eta},
@ -1200,7 +1253,7 @@ struct llama_server_context
void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
{
std::lock_guard<std::mutex> lock(mutex_results);
std::unique_lock<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
@ -1219,7 +1272,7 @@ struct llama_server_context
{
std::vector<completion_token_output> probs_output = {};
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
if (probs_pos < probs_stop_pos)
{
@ -1236,11 +1289,12 @@ struct llama_server_context
}
queue_results.push_back(res);
condition_results.notify_all();
}
void send_final_response(llama_client_slot &slot)
{
std::lock_guard<std::mutex> lock(mutex_results);
std::unique_lock<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
@ -1278,7 +1332,7 @@ struct llama_server_context
{
probs = std::vector<completion_token_output>(
slot.generated_token_probs.begin(),
slot.generated_token_probs.begin() + slot.sent_token_probs_index);
slot.generated_token_probs.end());
}
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
}
@ -1296,11 +1350,12 @@ struct llama_server_context
}
queue_results.push_back(res);
condition_results.notify_all();
}
void send_embedding(llama_client_slot &slot)
{
std::lock_guard<std::mutex> lock(mutex_results);
std::unique_lock<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
@ -1328,6 +1383,7 @@ struct llama_server_context
};
}
queue_results.push_back(res);
condition_results.notify_all();
}
int request_completion(json data, bool infill, bool embedding, int multitask_id)
@ -1351,6 +1407,7 @@ struct llama_server_context
// otherwise, it's a single-prompt task, we actually queue it
queue_tasks.push_back(task);
condition_tasks.notify_one();
return task.id;
}
@ -1358,13 +1415,10 @@ struct llama_server_context
{
while (true)
{
std::this_thread::sleep_for(std::chrono::microseconds(5));
std::lock_guard<std::mutex> lock(mutex_results);
if (queue_results.empty())
{
continue;
}
std::unique_lock<std::mutex> lock(mutex_results);
condition_results.wait(lock, [&]{
return !queue_results.empty();
});
for (int i = 0; i < (int) queue_results.size(); i++)
{
@ -1460,12 +1514,13 @@ struct llama_server_context
void request_cancel(int task_id)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
std::unique_lock<std::mutex> lock(mutex_tasks);
task_server task;
task.id = id_gen++;
task.type = CANCEL_TASK;
task.target_id = task_id;
queue_tasks.push_back(task);
condition_tasks.notify_one();
}
int split_multiprompt_task(task_server& multiprompt_task)
@ -1491,7 +1546,7 @@ struct llama_server_context
void process_tasks()
{
std::lock_guard<std::mutex> lock(mutex_tasks);
std::unique_lock<std::mutex> lock(mutex_tasks);
while (!queue_tasks.empty())
{
task_server task = queue_tasks.front();
@ -1563,6 +1618,7 @@ struct llama_server_context
std::lock_guard<std::mutex> lock(mutex_results);
queue_results.push_back(aggregate_result);
condition_results.notify_all();
queue_iterator = queue_multitasks.erase(queue_iterator);
}
@ -1593,8 +1649,10 @@ struct llama_server_context
LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
kv_cache_clear();
}
// avoid 100% usage of cpu all time
std::this_thread::sleep_for(std::chrono::milliseconds(5));
std::unique_lock<std::mutex> lock(mutex_tasks);
condition_tasks.wait(lock, [&]{
return !queue_tasks.empty();
});
}
for (llama_client_slot &slot : slots)
@ -1652,7 +1710,6 @@ struct llama_server_context
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_decoded += 1;
slot.n_past += 1;
}
@ -1870,6 +1927,7 @@ struct llama_server_context
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1)
{
slot.t_start_genereration = ggml_time_us();
@ -1965,6 +2023,10 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
printf(" --log-disable disables logging to a file.\n");
printf("\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf("\n");
}
static void server_params_parse(int argc, char **argv, server_params &sparams,
@ -2328,6 +2390,49 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
log_set_target(stdout);
LOG_INFO("logging to file is disabled.", {});
}
else if (arg == "--override-kv")
{
if (++i >= argc) {
invalid_param = true;
break;
}
char * sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
} else {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
params.kv_overrides.push_back(kvo);
}
else
{
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@ -2335,6 +2440,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
exit(1);
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back(llama_model_kv_override());
params.kv_overrides.back().key[0] = 0;
}
if (invalid_param)
{
@ -2393,26 +2502,33 @@ json oaicompat_completion_params_parse(
llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters
//
// For parameters that are defined by the OpenAI documentation (e.g.
// temperature), we explicitly specify OpenAI's intended default; we
// need to do that because sometimes OpenAI disagrees with llama.cpp
//
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("uknown"));
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.8);
llama_params["top_k"] = json_value(body, "top_k", 40);
llama_params["top_p"] = json_value(body, "top_p", 0.95);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", 0);
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", false);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
@ -3026,7 +3142,17 @@ int main(int argc, char **argv)
{
prompt = "";
}
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
json image_data;
if (body.count("image_data") != 0) {
image_data = body["image_data"];
}
else
{
image_data = "";
}
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1);
task_result result = llama.next_result(task_id);
return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
});

14
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -369,10 +369,7 @@ static struct ggml_tensor * llama_build_train_graphs(
checkpoints.push_back(t00);
checkpoints.push_back(t01);
struct ggml_tensor * kv_scale = NULL;
if (!enable_flash_attn) {
kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));
}
const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
@ -444,14 +441,13 @@ static struct ggml_tensor * llama_build_train_graphs(
// make sure some tensors are not reallocated by inserting new temporary nodes depending on them
int n_leafs_before = gb->n_leafs;
int n_nodes_before = gb->n_nodes;
struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);
// output tensors
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
// input gradient
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
ggml_allocr_alloc(alloc, t36->grad);