vbatts/llama.cpp
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llama2.c converter: cleanups + take n_ff from config

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ochafik 2023-08-26 23:09:22 +01:00
parent 0722e58ac2
commit c58792c768
1 changed files with 1 additions and 28 deletions
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29
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
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@ -23,7 +23,6 @@
#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens" #define KV_TOKENIZER_LIST "tokenizer.ggml.tokens"
#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type" #define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type"
#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores" #define KV_TOKENIZER_SCORES "tokenizer.ggml.scores"
#define KV_TOKENIZER_MERGES "tokenizer.ggml.merges"
#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id" #define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id"
#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id" #define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id"
#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id" #define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id"
@ -35,15 +34,10 @@
#define KV_EMBEDDING_LENGTH "llama.embedding_length" #define KV_EMBEDDING_LENGTH "llama.embedding_length"
#define KV_BLOCK_COUNT "llama.block_count" #define KV_BLOCK_COUNT "llama.block_count"
#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length" #define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length"
#define KV_USE_PARALLEL_RESIDUAL "llama.use_parallel_residual"
#define KV_TENSOR_DATA_LAYOUT "llama.tensor_data_layout"
#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count" #define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count"
#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv" #define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv"
#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon" #define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon"
#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count" #define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count"
#define KV_ROPE_SCALE_LINEAR "llama.rope.scale_linear"
#define TN_TOKEN_EMBD "token_embd.weight" #define TN_TOKEN_EMBD "token_embd.weight"
#define TN_OUTPUT_NORM "output_norm.weight" #define TN_OUTPUT_NORM "output_norm.weight"
@ -329,11 +323,6 @@ struct train_params {
int mem_compute1_gb; int mem_compute1_gb;
}; };
uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
void print_params(struct my_llama_hparams * params) { void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
@ -534,21 +523,6 @@ struct llama_file {
return std::string(chars.data(), len); return std::string(chars.data(), len);
} }
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() { ~llama_file() {
if (fp) { if (fp) {
std::fclose(fp); std::fclose(fp);
@ -708,8 +682,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
// for rms-att-weight // for rms-att-weight
int row_length = model->hparams.n_embd; int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams; const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd; int n_ff = model->hparams.n_ff;
int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i]; auto & layer = model->layers[i];