vbatts/llama.cpp
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llama : code style fixes + progress print fix

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This commit is contained in:
Georgi Gerganov 2023-05-20 12:29:08 +03:00
parent ffe9652bc1
commit f67bc3c363
2 changed files with 41 additions and 34 deletions
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31
.gitignore vendored
View file

@ -1,5 +1,15 @@
*.o
*.a
/*.o
/*.a
/*.sh
/*.log
/*.org
/ppl-*.txt
/qnt-*.txt
/perf-*.txt
*.bin
.DS_Store
.build/
.cache/
@ -21,8 +31,9 @@ build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
prompts/
models/*
*.bin
wikitext-2-raw/
/main
/quantize
@ -33,6 +44,7 @@ models/*
/benchmark-matmult
/vdot
/Pipfile
/libllama.so
build-info.h
arm_neon.h
@ -43,17 +55,4 @@ __pycache__
zig-out/
zig-cache/
ppl-*.txt
qnt-*.txt
perf-*.txt
examples/jeopardy/results.txt
/prompts
*.sh
*.log
*.py
*.txt
/wikitext-2-raw/
*.org
/libllama.so

44
llama.cpp
View file

@ -1010,21 +1010,28 @@ static void llama_model_load_internal(
ml->ggml_ctx = ctx;
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.norm = ml->get_tensor("norm.weight", {n_embd}, GGML_BACKEND_CPU);
ggml_backend backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_output = GGML_BACKEND_CUDA;
} else {
backend_output = GGML_BACKEND_CPU;
model.norm = ml->get_tensor("norm.weight", {n_embd}, GGML_BACKEND_CPU);
// "output" tensor
{
ggml_backend backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_output = GGML_BACKEND_CUDA;
} else {
backend_output = GGML_BACKEND_CPU;
}
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
}
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
const int i_gpu_start = n_layer - n_gpu_layers;
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : GGML_BACKEND_CUDA;
auto & layer = model.layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
@ -1036,13 +1043,15 @@ static void llama_model_load_internal(
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend);
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend);
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
if (backend == GGML_BACKEND_CUDA) {
vram_total += ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)
+ ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm)
+ ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
vram_total +=
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
}
}
}
@ -1077,7 +1086,7 @@ static void llama_model_load_internal(
}
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
#else
(void) n_gpu_layers;
(void) n_gpu_layers;
#endif
}
@ -2192,7 +2201,7 @@ struct llama_context * llama_init_from_file(
unsigned * cur_percentage_p = (unsigned *) ctx;
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
++*cur_percentage_p;
*cur_percentage_p = percentage;
fprintf(stderr, ".");
fflush(stderr);
if (percentage >= 100) {
@ -2442,8 +2451,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
base_t = model_loader->get_tensor(
base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
lt.data = (uint8_t *) lt.ggml_tensor->data;
model_loader->load_data_for(lt);
lt.ggml_tensor->data = lt.data;