vbatts/llama.cpp
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llama : check if model architecture is known

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Georgi Gerganov 2023-08-22 19:03:08 +03:00
parent 4ed3469c68
commit 8bd7f06b58
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GPG key ID: 449E073F9DC10735
1 changed files with 27 additions and 29 deletions
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llama.cpp
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@ -1359,7 +1359,7 @@ static const char * llama_model_type_name(e_model type) {
}
static void llama_model_load_internal(
const std::string & fname,
llama_model_loader & ml,
llama_model & model,
llama_vocab & vocab,
int n_ctx,
@ -1379,8 +1379,6 @@ static void llama_model_load_internal(
void * progress_callback_user_data) {
model.t_start_us = ggml_time_us();
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
model.n_gpu_layers = n_gpu_layers;
auto & hparams = model.hparams;
@ -1390,7 +1388,7 @@ static void llama_model_load_internal(
// read hparams
{
struct gguf_context * ctx = ml->ctx_gguf;
struct gguf_context * ctx = ml.ctx_gguf;
std::string tokenizer_name;
GGUF_GET_KEY(ctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, "tokenizer.ggml.model");
@ -1452,7 +1450,7 @@ static void llama_model_load_internal(
} break;
}
model.ftype = ml->ftype;
model.ftype = ml.ftype;
hparams.n_ctx = n_ctx;
@ -1473,7 +1471,7 @@ static void llama_model_load_internal(
// read vocab
{
struct gguf_context * ctx = ml->ctx_gguf;
struct gguf_context * ctx = ml.ctx_gguf;
vocab.id_to_token.resize(hparams.n_vocab);
@ -1515,7 +1513,7 @@ static void llama_model_load_internal(
{
// hparams
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->fver));
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, general_arch.c_str());
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
@ -1533,7 +1531,7 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype));
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9);
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml.n_elements*1e-9);
// general kv
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, general_name.c_str());
@ -1557,7 +1555,7 @@ static void llama_model_load_internal(
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(ctx_size, mmapped_size);
ml.calc_sizes(ctx_size, mmapped_size);
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
@ -1572,7 +1570,7 @@ static void llama_model_load_internal(
struct ggml_init_params params = {
/*.mem_size =*/ model.buf.size,
/*.mem_buffer =*/ model.buf.data,
/*.no_alloc =*/ ml->use_mmap,
/*.no_alloc =*/ ml.use_mmap,
};
model.ctx = ggml_init(params);
@ -1608,7 +1606,7 @@ static void llama_model_load_internal(
const auto llm = LLM<LLM_ARCH_LLAMA>();
model.tok_embeddings = ml->create_tensor(ctx, llm(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.tok_embeddings = ml.create_tensor(ctx, llm(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// "output" tensor
{
@ -1629,8 +1627,8 @@ static void llama_model_load_internal(
backend_output = GGML_BACKEND_CPU;
}
model.norm = ml->create_tensor(ctx, llm(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml->create_tensor(ctx, llm(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
model.norm = ml.create_tensor(ctx, llm(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, llm(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
if (backend_norm == GGML_BACKEND_GPU) {
vram_weights += ggml_nbytes(model.norm);
}
@ -1649,18 +1647,18 @@ static void llama_model_load_internal(
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
layer.attention_norm = ml->create_tensor(ctx, llm(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attention_norm = ml.create_tensor(ctx, llm(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wq = ml->create_tensor(ctx, llm(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml->create_tensor(ctx, llm(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml->create_tensor(ctx, llm(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml->create_tensor(ctx, llm(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.wq = ml.create_tensor(ctx, llm(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, llm(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, llm(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, llm(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml->create_tensor(ctx, llm(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm = ml.create_tensor(ctx, llm(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.w1 = ml->create_tensor(ctx, llm(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.w2 = ml->create_tensor(ctx, llm(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.w3 = ml->create_tensor(ctx, llm(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.w1 = ml.create_tensor(ctx, llm(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.w2 = ml.create_tensor(ctx, llm(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.w3 = ml.create_tensor(ctx, llm(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
@ -1671,7 +1669,7 @@ static void llama_model_load_internal(
}
}
ml->done_getting_tensors();
ml.done_getting_tensors();
// print memory requirements
{
@ -1734,8 +1732,8 @@ static void llama_model_load_internal(
}
// populate `tensors_by_name`
for (int i = 0; i < ml->n_tensors; ++i) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, ml->get_tensor_name(i));
for (int i = 0; i < ml.n_tensors; ++i) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
}
@ -1746,13 +1744,13 @@ static void llama_model_load_internal(
}
#endif
ml->load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
ml.load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
model.mapping = std::move(ml->mapping);
model.mapping = std::move(ml.mapping);
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
@ -1786,10 +1784,10 @@ static bool llama_model_load(
const llm_arch arch = llama_arch_from_string(arch_name);
if (arch == LLM_ARCH_UNKNOWN) {
throw std::runtime_error("unknown architecture: " + arch_name);
throw std::runtime_error("unknown model architecture: '" + arch_name + "'");
}
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers,
llama_model_load_internal(*ml, model, vocab, n_ctx, n_batch, n_gpu_layers,
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true;