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llava : use logger in llava-cli (#6797)

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This change removes printf() logging so llava-cli is shell scriptable.
This commit is contained in:
Justine Tunney
2024-04-21 08:19:04 -04:00
committed by GitHub
parent b97bc3966e
commit 89b0bf0d5d
3 changed files with 111 additions and 96 deletions
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122
examples/llava/clip.cpp
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@@ -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;