vbatts/llama.cpp
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Merge branch 'master' into gg/flash-attn

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-04-24 14:00:32 +03:00
commit 8937ec5307
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GPG key ID: 449E073F9DC10735
57 changed files with 1576 additions and 8245 deletions
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7
examples/batched.swift/Sources/main.swift
View file

@ -153,7 +153,7 @@ while n_cur <= n_len {
// const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream? -> mark the stream as finished
if new_token_id == llama_token_eos(model) || n_cur == n_len {
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
i_batch[i] = -1
// print("")
if n_parallel > 1 {
@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
var result = [CChar](repeating: 0, count: 8)
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
if nTokens < 0 {
let actualTokensCount = -Int(nTokens)
result = .init(repeating: 0, count: actualTokensCount)
@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
model,
token,
&result,
Int32(result.count)
Int32(result.count),
false
)
assert(check == actualTokensCount)
} else {

4
examples/batched/batched.cpp
View file

@ -191,8 +191,8 @@ int main(int argc, char ** argv) {
//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream? -> mark the stream as finished
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
i_batch[i] = -1;
LOG_TEE("\n");
if (n_parallel > 1) {

2
examples/beam-search/beam-search.cpp
View file

@ -47,7 +47,7 @@ struct beam_search_callback_data {
// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
// For example, eob can be flagged due to maximum token length, stop words, etc.
static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
}
// Function matching type llama_beam_search_callback_fn_t.

10
examples/infill/infill.cpp
View file

@ -586,7 +586,7 @@ int main(int argc, char ** argv) {
// deal with eot token in infill mode
if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
if(is_interacting && !params.interactive_first) {
if (is_interacting && !params.interactive_first) {
// print an eot token
printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
}
@ -651,8 +651,8 @@ int main(int argc, char ** argv) {
// LOG_TEE("took new input\n");
is_interacting = false;
}
// deal with end of text token in interactive mode
else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
// deal with end of generation tokens in interactive mode
else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
LOG("found EOS token\n");
if (params.interactive) {
@ -731,8 +731,8 @@ int main(int argc, char ** argv) {
}
}
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
// end of generation
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
break;
}

2
examples/llama.android/app/src/main/cpp/llama-android.cpp
View file

@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop(
const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
return env->NewStringUTF("");
}

6
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -158,7 +158,7 @@ actor LlamaContext {
new_token_id = llama_sample_token_greedy(context, &candidates_p)
}
if new_token_id == llama_token_eos(model) || n_cur == n_len {
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
print("\n")
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
@ -322,7 +322,7 @@ actor LlamaContext {
defer {
result.deallocate()
}
let nTokens = llama_token_to_piece(model, token, result, 8)
let nTokens = llama_token_to_piece(model, token, result, 8, false)
if nTokens < 0 {
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@ -330,7 +330,7 @@ actor LlamaContext {
defer {
newResult.deallocate()
}
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
return Array(bufferPointer)
} else {

122
examples/llava/clip.cpp
View file

@ -3,6 +3,7 @@
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
#include "log.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
@ -23,7 +24,6 @@
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <iostream>
#include <map>
#include <regex>
#include <stdexcept>
@ -145,7 +145,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
static int get_key_idx(const gguf_context * ctx, const char * key) {
int i = gguf_find_key(ctx, key);
if (i == -1) {
fprintf(stderr, "key %s not found in file\n", key);
LOG_TEE("key %s not found in file\n", key);
throw std::runtime_error(format("Missing required key: %s", key));
}
@ -247,7 +247,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
size_t tensor_size = ggml_nbytes(tensor);
printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
}
@ -265,7 +265,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
std::cerr << "Failed to open file for writing: " << filename << std::endl;
LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
return;
}
@ -284,7 +284,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
std::cerr << "Failed to open file for writing: " << filename << std::endl;
LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
return;
}
@ -515,7 +515,7 @@ struct clip_ctx {
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");
LOG_TEE("This gguf file seems to have no vision encoder\n");
return nullptr;
}
@ -879,21 +879,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
const int idx_name = gguf_find_key(ctx, KEY_NAME);
if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
const std::string name = gguf_get_val_str(ctx, idx_name);
printf("%s: model name: %s\n", __func__, name.c_str());
LOG_TEE("%s: model name: %s\n", __func__, name.c_str());
}
printf("%s: description: %s\n", __func__, description.c_str());
printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
printf("%s: n_tensors: %d\n", __func__, n_tensors);
printf("%s: n_kv: %d\n", __func__, n_kv);
printf("%s: ftype: %s\n", __func__, ftype_str.c_str());
printf("\n");
LOG_TEE("%s: description: %s\n", __func__, description.c_str());
LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
LOG_TEE("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
LOG_TEE("%s: n_tensors: %d\n", __func__, n_tensors);
LOG_TEE("%s: n_kv: %d\n", __func__, n_kv);
LOG_TEE("%s: ftype: %s\n", __func__, ftype_str.c_str());
LOG_TEE("\n");
}
const int n_tensors = gguf_get_n_tensors(ctx);
// kv
const int n_kv = gguf_get_n_kv(ctx);
printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
__func__, n_kv, n_tensors, fname);
{
std::map<enum ggml_type, uint32_t> n_type;
@ -904,7 +904,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
n_type[type]++;
}
printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(ctx, i);
const enum gguf_type type = gguf_get_kv_type(ctx, i);
@ -920,7 +920,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
replace_all(value, "\n", "\\n");
printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
}
// print type counts
@ -929,7 +929,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
continue;
}
printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
}
}
@ -944,7 +944,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
size_t tensor_size = ggml_nbytes(cur);
model_size += tensor_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
}
}
@ -971,18 +971,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
#ifdef GGML_USE_CUDA
new_clip->backend = ggml_backend_cuda_init(0);
printf("%s: CLIP using CUDA backend\n", __func__);
LOG_TEE("%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__);
LOG_TEE("%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__);
LOG_TEE("%s: CLIP using CPU backend\n", __func__);
}
// model size and capabilities
@ -1006,15 +1006,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
if (verbosity >= 1) {
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
LOG_TEE("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
LOG_TEE("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
LOG_TEE("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
LOG_TEE("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
}
}
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
// load tensors
{
@ -1027,7 +1027,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
new_clip->ctx_data = ggml_init(params);
if (!new_clip->ctx_data) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
LOG_TEE("%s: ggml_init() failed\n", __func__);
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
@ -1035,7 +1035,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
printf("cannot open model file for loading tensors\n");
LOG_TEE("cannot open model file for loading tensors\n");
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
@ -1057,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
fin.seekg(offset, std::ios::beg);
if (!fin) {
printf("%s: failed to seek for tensor %s\n", __func__, name);
LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
@ -1128,23 +1128,23 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
if (verbosity >= 2) {
printf("\n%s: vision model hparams\n", __func__);
printf("image_size %d\n", hparams.image_size);
printf("patch_size %d\n", hparams.patch_size);
printf("v_hidden_size %d\n", hparams.hidden_size);
printf("v_n_intermediate %d\n", hparams.n_intermediate);
printf("v_projection_dim %d\n", hparams.projection_dim);
printf("v_n_head %d\n", hparams.n_head);
printf("v_n_layer %d\n", hparams.n_layer);
printf("v_eps %f\n", hparams.eps);
printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
printf("v_image_grid_pinpoints: ");
LOG_TEE("\n%s: vision model hparams\n", __func__);
LOG_TEE("image_size %d\n", hparams.image_size);
LOG_TEE("patch_size %d\n", hparams.patch_size);
LOG_TEE("v_hidden_size %d\n", hparams.hidden_size);
LOG_TEE("v_n_intermediate %d\n", hparams.n_intermediate);
LOG_TEE("v_projection_dim %d\n", hparams.projection_dim);
LOG_TEE("v_n_head %d\n", hparams.n_head);
LOG_TEE("v_n_layer %d\n", hparams.n_layer);
LOG_TEE("v_eps %f\n", hparams.eps);
LOG_TEE("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
LOG_TEE("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
LOG_TEE("v_image_grid_pinpoints: ");
for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
printf("%d ", hparams.image_grid_pinpoints[i]);
LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
}
printf("\n");
printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
LOG_TEE("\n");
LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
}
@ -1155,7 +1155,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
} catch(const std::exception& e) {
fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
LOG_TEE("%s: failed to load vision model tensors\n", __func__);
}
// LLaVA projection
@ -1184,7 +1184,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
} catch (std::runtime_error & e) { }
try {
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
// fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
// LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
} catch (std::runtime_error & e) { }
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection
@ -1264,7 +1264,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
}
return new_clip;
@ -1304,7 +1304,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
int nx, ny, nc;
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
if (!data) {
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
return false;
}
build_clip_img_from_data(data, nx, ny, img);
@ -1316,7 +1316,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
int nx, ny, nc;
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
if (!data) {
fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
LOG_TEE("%s: failed to decode image bytes\n", __func__);
return false;
}
build_clip_img_from_data(data, nx, ny, img);
@ -1506,7 +1506,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
@ -1545,7 +1545,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
bool pad_to_square = true;
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
LOG_TEE("This gguf file seems to have no vision encoder\n");
return false;
}
auto & params = ctx->vision_model.hparams;
@ -1622,7 +1622,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
}
for (size_t i = 0; i < patches.size(); i++) {
// printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
// LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
clip_image_u8_free(patches[i]);
}
@ -1765,7 +1765,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
LOG_TEE("This gguf file seems to have no vision encoder\n");
return false;
}
@ -1777,7 +1777,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
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");
LOG_TEE("This gguf file seems to have no vision encoder\n");
return false;
}
@ -1939,7 +1939,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
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));
// LOG_TEE("%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;
@ -1958,7 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
f32_data = (float *)conv_buf.data();
break;
default:
printf("Please use an input file in f32 or f16\n");
LOG_TEE("Please use an input file in f32 or f16\n");
gguf_free(ctx_out);
return false;
}
@ -1985,7 +1985,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
fout.put(0);
}
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
}
@ -2001,8 +2001,8 @@ 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);
LOG_TEE("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
}
return true;

51
examples/llava/llava-cli.cpp
View file

@ -1,4 +1,5 @@
#include "ggml.h"
#include "log.h"
#include "common.h"
#include "clip.h"
#include "llava.h"
@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
*n_past += n_eval;
@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
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))) {
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
size_t img_base64_str_start, img_base64_str_end;
find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
return NULL;
}
@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
if (!embed) {
fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
LOG_TEE("%s: could not load image from base64 string.\n", __func__);
return NULL;
}
@ -112,8 +113,8 @@ struct llava_context {
};
static void show_additional_info(int /*argc*/, char ** argv) {
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");
LOG_TEE("\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]);
LOG_TEE(" 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) {
@ -123,18 +124,18 @@ 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()) {
fprintf(stderr, "using base64 encoded image instead of command line image path\n");
LOG_TEE("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) {
fprintf(stderr, "%s: can't load image from prompt\n", __func__);
LOG_TEE("%s: can't load image from prompt\n", __func__);
return NULL;
}
params->prompt = remove_image_from_prompt(prompt);
} else {
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
if (!embed) {
fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
return NULL;
}
}
@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
system_prompt = prompt.substr(0, image_pos);
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
printf("system_prompt: %s\n", system_prompt.c_str());
LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
printf("user_prompt: %s\n", user_prompt.c_str());
LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
} else {
@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
}
@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
// generate the response
fprintf(stderr, "\n");
LOG_TEE("\n");
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
std::string response = "";
@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) {
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__);
LOG_TEE("%s: error: unable to load model\n" , __func__);
return NULL;
}
@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) {
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
if (ctx_llama == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
return NULL;
}
@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) {
llama_backend_free();
}
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
LOG_TEE("%s", text);
}
int main(int argc, char ** argv) {
ggml_time_init();
@ -266,6 +273,14 @@ int main(int argc, char ** argv) {
show_additional_info(argc, argv);
return 1;
}
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("llava", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
llama_log_set(llama_log_callback_logTee, nullptr);
#endif // LOG_DISABLE_LOGS
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
gpt_print_usage(argc, argv, params);
show_additional_info(argc, argv);
@ -274,7 +289,7 @@ int main(int argc, char ** argv) {
auto ctx_llava = llava_init(&params);
if (ctx_llava == NULL) {
fprintf(stderr, "%s: error: failed to init llava\n", __func__);
LOG_TEE("%s: error: failed to init llava\n", __func__);
return 1;
}

36
examples/llava/llava.cpp
View file

@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
if (newline_tmp->buffer == NULL) {
printf("newline_tmp tensor buffer is NULL\n");
LOG_TEE("newline_tmp tensor buffer is NULL\n");
}
ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
} else {
model.newline->data = newline_tmp->data;
if (model.newline->data == NULL) {
printf("newline_tmp tensor data is NULL\n");
LOG_TEE("newline_tmp tensor data is NULL\n");
}
}
@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
img_res_v.size = 0;
img_res_v.data = nullptr;
if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
LOG_TEE("%s: unable to preprocess image\n", __func__);
delete[] img_res_v.data;
return false;
}
@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
delete[] img_res_v.data;
if (!encoded) {
fprintf(stderr, "Unable to encode image\n");
LOG_TEE("Unable to encode image\n");
return false;
}
@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
if (!encoded) {
fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
return false;
}
}
const int64_t t_img_enc_batch_us = ggml_time_us();
printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
const int32_t * image_grid = clip_image_grid(ctx_clip);
@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
// clip_image_save_to_bmp(*tmp, "image_feature.bmp");
}
printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
const int64_t t_img_enc_end_us = ggml_time_us();
float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
return true;
}
@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
if (n_image_embd != n_llama_embd) {
printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
return false;
}
return true;
@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
if (!image_embd) {
fprintf(stderr, "Unable to allocate memory for image embeddings\n");
LOG_TEE("Unable to allocate memory for image embeddings\n");
return false;
}
int n_img_pos;
if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
LOG_TEE("%s: cannot encode image, aborting\n", __func__);
free(image_embd);
return false;
}
@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
}
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
LOG_TEE("%s : failed to eval\n", __func__);
return false;
}
*n_past += n_eval;
@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
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__);
LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
return NULL;
}
@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
if (!image_embed_result) {
clip_image_u8_free(img);
fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
LOG_TEE("%s: coulnd't embed the image\n", __func__);
return NULL;
}
@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
auto file = fopen(path, "rb");
if (file == NULL) {
fprintf(stderr, "%s: can't read file %s\n", __func__, path);
LOG_TEE("%s: can't read file %s\n", __func__, path);
return false;
}
@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
if (buffer == NULL) {
fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
perror("Memory allocation error");
fclose(file);
return false;
@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
long image_bytes_length;
auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
if (!loaded) {
fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
LOG_TEE("%s: failed to load %s\n", __func__, image_path);
return NULL;
}

2
examples/lookahead/lookahead.cpp
View file

@ -299,7 +299,7 @@ int main(int argc, char ** argv) {
}
fflush(stdout);
if (id == llama_token_eos(model)) {
if (llama_token_is_eog(model, id)) {
has_eos = true;
}

1
examples/lookup/lookup-stats.cpp
View file

@ -30,7 +30,6 @@ int main(int argc, char ** argv){
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_set_rng_seed(ctx, params.seed);
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt

3
examples/lookup/lookup.cpp
View file

@ -38,7 +38,6 @@ int main(int argc, char ** argv){
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_set_rng_seed(ctx, params.seed);
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt
@ -141,7 +140,7 @@ int main(int argc, char ** argv){
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model)) {
if (llama_token_is_eog(model, id)) {
has_eos = true;
}

9
examples/main/main.cpp
View file

@ -240,7 +240,6 @@ int main(int argc, char ** argv) {
return 1;
}
session_tokens.resize(n_token_count_out);
llama_set_rng_seed(ctx, params.seed);
LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
}
}
@ -795,8 +794,8 @@ int main(int argc, char ** argv) {
}
}
// deal with end of text token in interactive mode
if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
// deal with end of generation tokens in interactive mode
if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
LOG("found EOS token\n");
if (params.interactive) {
@ -920,8 +919,8 @@ int main(int argc, char ** argv) {
}
}
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
// end of generation
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
LOG_TEE(" [end of text]\n");
break;
}

2
examples/parallel/parallel.cpp
View file

@ -359,7 +359,7 @@ int main(int argc, char ** argv) {
// client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
if (client.n_decoded > 2 &&
(id == llama_token_eos(model) ||
(llama_token_is_eog(model, id) ||
(params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
client.response.find("User:") != std::string::npos ||
client.response.find('\n') != std::string::npos)) {

4
examples/passkey/passkey.cpp
View file

@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
// 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) {
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
LOG_TEE("\n");
break;

21
examples/server/CMakeLists.txt
View file

@ -1,12 +1,29 @@
set(TARGET server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET}
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
set(TARGET_SRCS
server.cpp
utils.hpp
httplib.h
)
set(PUBLIC_ASSETS
index.html
index.js
completion.js
json-schema-to-grammar.mjs
)
foreach(asset ${PUBLIC_ASSETS})
set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
list(APPEND TARGET_SRCS ${output})
add_custom_command(
DEPENDS "${input}"
OUTPUT "${output}"
COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
)
endforeach()
add_executable(${TARGET} ${TARGET_SRCS})
install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>

496
examples/server/completion.js.hpp
View file

@ -1,496 +0,0 @@
unsigned char completion_js[] = {
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};
unsigned int completion_js_len = 5909;

10
examples/server/deps.sh
View file

@ -8,13 +8,3 @@ PUBLIC=$DIR/public
echo "download js bundle files"
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
echo >> $PUBLIC/index.js # add newline
FILES=$(ls $PUBLIC)
cd $PUBLIC
for FILE in $FILES; do
echo "generate $FILE.hpp"
# use simple flag for old version of xxd
xxd -i $FILE > $DIR/$FILE.hpp
done

2883
examples/server/index.html.hpp
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1911
examples/server/index.js.hpp
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1826
examples/server/json-schema-to-grammar.mjs.hpp
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10
examples/server/public/index.html
View file

@ -881,11 +881,11 @@
.replace(/&/g, '&amp;')
.replace(/</g, '&lt;')
.replace(/>/g, '&gt;')
.replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
.replace(/__(.*?)__/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*/g, '<em>$1</em>')
.replace(/_(.*?)_/g, '<em>$1</em>')
.replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
.replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');

7
examples/server/server.cpp
View file

@ -854,7 +854,7 @@ struct server_context {
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
slot.params.seed = json_value(data, "seed", default_params.seed);
slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
@ -1028,7 +1028,6 @@ struct server_context {
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
return false;
}
llama_set_rng_seed(ctx, slot.params.seed);
}
slot.command = SLOT_COMMAND_LOAD_PROMPT;
@ -1118,7 +1117,7 @@ struct server_context {
bool process_token(completion_token_output & result, server_slot & slot) {
// remember which tokens were sampled - used for repetition penalties during sampling
const std::string token_str = llama_token_to_piece(ctx, result.tok);
const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
slot.sampled = result.tok;
// search stop word and delete it
@ -1201,7 +1200,7 @@ struct server_context {
});
}
if (result.tok == llama_token_eos(model)) {
if (llama_token_is_eog(model, result.tok)) {
slot.stopped_eos = true;
slot.has_next_token = false;

2
examples/server/tests/README.md
View file

@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
cd ../../..
mkdir build
cd build
cmake ../
cmake -DLLAMA_CURL=ON ../
cmake --build . --target server
```

57
examples/server/tests/features/results.feature Normal file
View file

@ -0,0 +1,57 @@
@llama.cpp
@results
Feature: Results
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
And a model file test-model-00001-of-00003.gguf
And 128 as batch size
And 256 KV cache size
And 128 max tokens to predict
Scenario Outline: Multi users completion
Given <n_slots> slots
And continuous batching
Then the server is starting
Then the server is healthy
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given concurrent completion requests
Then the server is busy
Then the server is idle
And all slots are idle
Then all predictions are equal
Examples:
| n_slots |
| 1 |
| 2 |

34
examples/server/tests/features/steps/steps.py
View file

@ -61,6 +61,7 @@ def step_server_config(context, server_fqdn, server_port):
context.server_metrics = False
context.server_process = None
context.seed = None
context.draft = None
context.server_seed = None
context.user_api_key = None
context.response_format = None
@ -107,6 +108,11 @@ def step_n_gpu_layer(context, ngl):
context.n_gpu_layer = ngl
@step('{draft:d} as draft')
def step_draft(context, draft):
context.draft = draft
@step('{n_ctx:d} KV cache size')
def step_n_ctx(context, n_ctx):
context.n_ctx = n_ctx
@ -254,6 +260,15 @@ def step_n_tokens_predicted(context, predicted_n):
assert_n_tokens_predicted(context.completion, predicted_n)
@step('all predictions are equal')
@async_run_until_complete
async def step_predictions_equal(context):
n_completions = await gather_tasks_results(context)
assert n_completions >= 2, "need at least 2 completions"
assert_all_predictions_equal(context.tasks_result)
context.tasks_result = []
@step('the completion is truncated')
def step_assert_completion_truncated(context):
step_assert_completion_truncated(context, '')
@ -1020,6 +1035,23 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
f' {n_predicted} <> {expected_predicted_n}')
def assert_all_predictions_equal(completion_responses):
content_0 = completion_responses[0]['content']
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content 0: {content_0}")
i = 1
for response in completion_responses[1:]:
content = response['content']
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content {i}: {content}")
assert content == content_0, "contents not equal"
i += 1
async def gather_tasks_results(context):
n_tasks = len(context.concurrent_tasks)
@ -1148,6 +1180,8 @@ def start_server_background(context):
server_args.extend(['--ubatch-size', context.n_ubatch])
if context.n_gpu_layer:
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
if context.draft is not None:
server_args.extend(['--draft', context.draft])
if context.server_continuous_batching:
server_args.append('--cont-batching')
if context.server_embeddings:

4
examples/server/utils.hpp
View file

@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse(
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Some chat templates don't use EOS token to stop generation
// We must add their end sequences to list of stop words
llama_params["stop"].push_back("<|im_end|>"); // chatml
llama_params["stop"].push_back("<end_of_turn>"); // gemma
// Handle "response_format" field
if (body.contains("response_format")) {

4
examples/simple/simple.cpp
View file

@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
// 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) {
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
LOG_TEE("\n");
break;

2
examples/speculative/speculative.cpp
View file

@ -360,7 +360,7 @@ int main(int argc, char ** argv) {
}
}
if (token_id == llama_token_eos(model_tgt)) {
if (llama_token_is_eog(model_tgt, token_id)) {
has_eos = true;
}
++n_predict;