diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 9afea8a7e..72ad4d4ea 100644 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -18,6 +18,7 @@ from sentencepiece import SentencePieceProcessor # compatible with python < 3.9 NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' + def count_model_parts(dir_model: str) -> int: num_parts = 0 for filename in os.listdir(dir_model): @@ -28,10 +29,12 @@ def count_model_parts(dir_model: str) -> int: print("gguf: found " + str(num_parts) + " model parts") return num_parts + if len(sys.argv) < 3: print("Usage: convert-h5-to-ggml.py dir-model ftype\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") + sys.exit(1) @@ -43,7 +46,7 @@ last_dir = os.path.basename(os.path.normpath(dir_model)) # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 -# + # map from ftype to string ftype_str = ["f32", "f16"] @@ -52,6 +55,7 @@ if len(sys.argv) > 2: ftype = int(sys.argv[2]) if ftype < 0 or ftype > 1: print("Invalid ftype: " + str(ftype)) + sys.exit(1) fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" @@ -70,14 +74,14 @@ num_parts = count_model_parts(dir_model) if num_parts > 1: print("gguf: Only models with a single datafile are supported.") - sys.exit() -gguf_writer = gguf.GGUFWriter.open(fname_out) + sys.exit() +llm_arch = "llama" +gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch) print("gguf: get model metadata") -llm_arch = "llama" block_count = hparams["num_hidden_layers"] head_count = hparams["num_attention_heads"] @@ -89,21 +93,20 @@ else: if "_name_or_path" in hparams: hf_repo = hparams["_name_or_path"] else: - hf_repo="" + hf_repo = "" -gguf_writer.add_architecture(llm_arch) +gguf_writer.add_architecture() gguf_writer.add_name(last_dir) -gguf_writer.add_file_type( "All tensors F32" if ftype == 0 else "Most tensors F16, some F32") gguf_writer.add_source_hf_repo(hf_repo) -gguf_writer.add_tensor_data_layout(llm_arch, "Meta AI original pth") -gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"]) -gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"]) -gguf_writer.add_block_count(llm_arch, block_count) -gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(llm_arch, head_count) -gguf_writer.add_head_count_kv(llm_arch, head_count_kv) -gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"]) +gguf_writer.add_tensor_data_layout("Meta AI original pth") +gguf_writer.add_context_length(hparams["max_position_embeddings"]) +gguf_writer.add_embedding_length(hparams["hidden_size"]) +gguf_writer.add_block_count(block_count) +gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) +gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) +gguf_writer.add_head_count(head_count) +gguf_writer.add_head_count_kv(head_count_kv) +gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) # TOKENIZATION @@ -125,19 +128,23 @@ if Path(dir_model + "/tokenizer.model").is_file(): score: float piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") + text = piece.encode("utf-8") score = tokenizer.get_score(i) - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): toktype = 2 - if tokenizer.is_control(i): toktype = 3 + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 # TODO: How to determinate if a token is user defined? # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto # if tokenizer.is_user_defined(i): toktype = 4 - if tokenizer.is_unused(i): toktype = 5 - if tokenizer.is_byte(i): toktype = 6 + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 tokens.append(text) scores.append(score) @@ -193,10 +200,10 @@ tensor_map = gguf.get_tensor_name_map(block_count) # tensor info print("gguf: get tensor metadata") -part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) ) +part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts)) for part_name in part_names: - print("gguf: loading model part '"+ part_name + "'") + print("gguf: loading model part '" + part_name + "'") model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") for name in model_part.keys(): @@ -218,11 +225,12 @@ for part_name in part_names: elif name.endswith(".bias") and name[:-5] in tensor_map: name = tensor_map[name[:-5]] + ".bias" else: - print( "Can not map tensor '" + name + "'" ) + print("Can not map tensor '" + name + "'") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype + old_dtype = data_dtype # if f32 desired, convert any float16 to float32 if ftype == 0 and data.dtype == np.float16: @@ -236,69 +244,19 @@ for part_name in part_names: if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data_dtype = np.float16 - data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4 + print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype)) - gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes) + data = data.astype(data_dtype) + + gguf_writer.add_tensor(name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensor metadata") -gguf_writer.write_ti_data_to_file() - -# tensor data -print("gguf: convert and write tensor data") - -part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) ) - -for part_name in part_names: - print("gguf: loading model part '"+ part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # we don't need these - if name == "rope.freqs": - continue - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: - print( "Can not map tensor '" + name + "'" ) - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data.dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.write_tensor_data(data) +print("gguf: write tensors") +gguf_writer.write_tensors_to_file() gguf_writer.close()