vbatts/llama.cpp
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Add comment for llama_log_callback and replace remaining printf calls

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grahameth 2023-08-02 01:16:12 +02:00
parent c857a33b19
commit 98369f62c5
2 changed files with 27 additions and 21 deletions
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42
llama.cpp
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@ -1766,7 +1766,7 @@ static struct ggml_cgraph * llama_build_graph(
} }
#if 0 #if 0
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__, LLAMA_LOG_INFO("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0, ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0, lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0, lctx.get_buf_max_mem(1)/1024.0/1024.0,
@ -1827,7 +1827,7 @@ static bool llama_eval_internal(
ggml_allocr_alloc_graph(lctx.alloc, gf); ggml_allocr_alloc_graph(lctx.alloc, gf);
#endif #endif
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
// for big prompts, if BLAS is enabled, it is better to use only one thread // for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
@ -2014,7 +2014,7 @@ struct llama_tokenizer {
left_sym.n += right_sym.n; left_sym.n += right_sym.n;
right_sym.n = 0; right_sym.n = 0;
//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size); //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain // remove the right sym from the chain
left_sym.next = right_sym.next; left_sym.next = right_sym.next;
@ -3022,7 +3022,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
tensor.data = read_data.addr; tensor.data = read_data.addr;
model_loader->load_data_for(tensor); model_loader->load_data_for(tensor);
printf("[%4zu/%4zu] %36s - %16s, type = %6s, ", LLAMA_LOG_INFO("[%4zu/%4zu] %36s - %16s, type = %6s, ",
++idx, model_loader->tensors_map.tensors.size(), ++idx, model_loader->tensors_map.tensors.size(),
tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(), tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
ggml_type_name(tensor.type)); ggml_type_name(tensor.type));
@ -3044,7 +3044,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type = tensor.type; new_type = tensor.type;
new_data = tensor.data; new_data = tensor.data;
new_size = tensor.size; new_size = tensor.size;
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0); LLAMA_LOG_INFO("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else { } else {
new_type = quantized_type; new_type = quantized_type;
#ifdef GGML_USE_K_QUANTS #ifdef GGML_USE_K_QUANTS
@ -3109,7 +3109,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data = (float *) f32_conv_buf.addr; f32_data = (float *) f32_conv_buf.addr;
} }
printf("quantizing to %s .. ", ggml_type_name(new_type)); LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
fflush(stdout); fflush(stdout);
work.resize(nelements * 4); // upper bound on size work.resize(nelements * 4); // upper bound on size
@ -3159,7 +3159,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
} }
} }
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0); LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0; int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) { for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i]; hist_all[i] += hist_cur[i];
@ -3168,18 +3168,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (tot_count > 0) { if (tot_count > 0) {
for (size_t i = 0; i < hist_cur.size(); i++) { for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements)); LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
} }
} }
printf("\n"); LLAMA_LOG_INFO("\n");
} }
total_size_org += tensor.size; total_size_org += tensor.size;
total_size_new += new_size; total_size_new += new_size;
file_saver.write_tensor(tensor, new_type, new_data, new_size); file_saver.write_tensor(tensor, new_type, new_data, new_size);
} }
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
{ {
int64_t sum_all = 0; int64_t sum_all = 0;
@ -3188,11 +3188,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
} }
if (sum_all > 0) { if (sum_all > 0) {
printf("%s: hist: ", __func__); LLAMA_LOG_INFO("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) { for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all)); LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
} }
printf("\n"); LLAMA_LOG_INFO("\n");
} }
} }
} }
@ -3250,10 +3250,9 @@ struct llama_context * llama_new_context_with_model(
unsigned percentage = (unsigned) (100 * progress); unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) { while (percentage > *cur_percentage_p) {
*cur_percentage_p = percentage; *cur_percentage_p = percentage;
fprintf(stderr, "."); LLAMA_LOG_INFO(".");
fflush(stderr);
if (percentage >= 100) { if (percentage >= 100) {
fprintf(stderr, "\n"); LLAMA_LOG_INFO("\n");
} }
} }
}; };
@ -3308,14 +3307,14 @@ struct llama_context * llama_new_context_with_model(
// measure memory requirements for the graph // measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment; size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0); LLAMA_LOG_INFO("%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
// debug - for comparison with scratch buffer // debug - for comparison with scratch buffer
//size_t prev_req = //size_t prev_req =
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) + // MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
// MEM_REQ_SCRATCH1().at(ctx->model.type) + // MEM_REQ_SCRATCH1().at(ctx->model.type) +
// MEM_REQ_EVAL().at(ctx->model.type); // MEM_REQ_EVAL().at(ctx->model.type);
//fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0); //LLAMA_LOG_INFO("%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
// recreate allocator with exact memory requirements // recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc); ggml_allocr_free(ctx->alloc);
@ -3679,7 +3678,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
n_tensors++; n_tensors++;
if (n_tensors % 4 == 0) { if (n_tensors % 4 == 0) {
fprintf(stderr, "."); LLAMA_LOG_INFO(".");
} }
} }
} }
@ -4316,5 +4315,6 @@ static void llama_log_internal(llama_log_level level, const char * format, ...)
static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data) { static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data) {
(void) level; (void) level;
(void) user_data; (void) user_data;
fprintf(stderr, "%s", text); fputs(text, stderr);
fflush(stderr);
} }

6
llama.h
View file

@ -91,6 +91,12 @@ extern "C" {
LLAMA_LOG_LEVEL_WARN = 3, LLAMA_LOG_LEVEL_WARN = 3,
LLAMA_LOG_LEVEL_INFO = 4 LLAMA_LOG_LEVEL_INFO = 4
}; };
// Signature for logging events
// Note that text includes the new line character at the end for most events.
// If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
// if it exists.
// It might not exist for progress report where '.' is output repeatedly.
typedef void (*llama_log_callback)(llama_log_level level, const char * text, void * user_data); typedef void (*llama_log_callback)(llama_log_level level, const char * text, void * user_data);
struct llama_context_params { struct llama_context_params {