vbatts/llama.cpp
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convert : fix layer names

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Georgi Gerganov 2023-08-14 21:06:07 +03:00
parent 01080a5a51
commit c35fc0bbb0
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3 changed files with 57 additions and 57 deletions
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gguf-llama.cpp
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@ -626,7 +626,7 @@ struct gguf_file_loader {
hparams.n_embd = read_u32("llama.embedding_length");
hparams.n_ff = read_u32("llama.feed_forward_length");
hparams.n_head = read_u32("llama.attention.head_count");
hparams.n_layer = read_u32("llama.layer_count");
hparams.n_layer = read_u32("llama.block_count");
hparams.n_rot = read_u32("llama.rope.dimension_count");
hparams.f_rms_norm_eps = read_f32("llama.attention.layer_norm_rms_epsilon");
@ -1373,7 +1373,7 @@ 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.tok_embeddings = ml->get_tensor("token_embd.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
// "output" tensor
{
@ -1394,7 +1394,7 @@ static void llama_model_load_internal(
backend_output = GGML_BACKEND_CPU;
}
model.norm = ml->get_tensor("norm.weight", {n_embd}, backend_norm);
model.norm = ml->get_tensor("output_norm.weight", {n_embd}, backend_norm);
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
if (backend_norm == GGML_BACKEND_GPU) {
vram_weights += ggml_nbytes(model.norm);
@ -1413,20 +1413,20 @@ static void llama_model_load_internal(
auto & layer = model.layers[i];
std::string layers_i = "layers." + std::to_string(i);
std::string layers_i = "blk." + std::to_string(i);
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
layer.attention_norm = ml->get_tensor(layers_i + ".attn_norm.weight", {n_embd}, backend);
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
layer.wq = ml->get_tensor(layers_i + ".attn_q.weight", {n_embd, n_embd}, backend_split);
layer.wk = ml->get_tensor(layers_i + ".attn_k.weight", {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml->get_tensor(layers_i + ".attn_v.weight", {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml->get_tensor(layers_i + ".attn_output.weight", {n_embd, n_embd}, backend_split);
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_split);
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
layer.w1 = ml->get_tensor(layers_i + ".ffn_gate.weight", {n_embd, n_ff}, backend_split);
layer.w2 = ml->get_tensor(layers_i + ".ffn_down.weight", { n_ff, n_embd}, backend_split);
layer.w3 = ml->get_tensor(layers_i + ".ffn_up.weight", {n_embd, n_ff}, backend_split);
if (backend == GGML_BACKEND_GPU) {
vram_weights +=