diff --git a/convert-gptneox-h5-to-gguf.py b/convert-gptneox-h5-to-gguf.py index 79876eee3..11cf19282 100644 --- a/convert-gptneox-h5-to-gguf.py +++ b/convert-gptneox-h5-to-gguf.py @@ -13,6 +13,8 @@ from pathlib import Path from transformers import AutoTokenizer # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py + + def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. @@ -34,6 +36,7 @@ def bytes_to_unicode(): cs = [chr(n) for n in cs] return dict(zip(bs, cs)) + def count_model_parts(dir_model: str) -> int: num_parts = 0 for filename in os.listdir(dir_model): @@ -44,6 +47,7 @@ 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") @@ -58,7 +62,7 @@ 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"] @@ -67,6 +71,7 @@ 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" @@ -77,30 +82,30 @@ with open(dir_model + "/config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "GPTNeoXForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0] ) + print("Model architecture not supported: " + hparams["architectures"][0]) + sys.exit() # get number of model parts num_parts = count_model_parts(dir_model) -gguf_writer = gguf.GGUFWriter.open(fname_out) +llm_arch = "gptneox" +gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch) print("gguf: get model metadata") -llm_arch = "gptneox" block_count = hparams["num_hidden_layers"] -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_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, int( hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])) ) -gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"]) -gguf_writer.add_parallel_residual(llm_arch, hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) -gguf_writer.add_layer_norm_eps(llm_arch, hparams["layer_norm_eps"]) +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(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"]))) +gguf_writer.add_head_count(hparams["num_attention_heads"]) +gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) +gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) # TOKENIZATION @@ -124,14 +129,14 @@ if Path(dir_model + "/tokenizer.json").is_file(): print("gguf: get gpt2 tokenizer vocab") - vocab_size = len( tokenizer_json["model"]["vocab"] ) + vocab_size = len(tokenizer_json["model"]["vocab"]) # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py tokenizer = AutoTokenizer.from_pretrained(dir_model) reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} byte_encoder = bytes_to_unicode() - byte_decoder = {v:k for k, v in byte_encoder.items()} + byte_decoder = {v: k for k, v in byte_encoder.items()} for i in range(vocab_size): if i in reverse_vocab: @@ -146,8 +151,9 @@ if Path(dir_model + "/tokenizer.json").is_file(): text.extend(c.encode('utf-8')) else: print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") - padding_token = f"[PAD{i}]".encode("utf8") - text = bytearray(padding_token) + pad_token = f"[PAD{i}]".encode("utf8") + text = bytearray(pad_token) + tokens.append(text) gguf_writer.add_token_list(tokens) @@ -201,7 +207,7 @@ else: ) 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(): @@ -223,11 +229,12 @@ 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: @@ -241,77 +248,21 @@ 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") - -if num_parts == 0: - part_names = ("pytorch_model.bin",) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -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.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"): - 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_to_file(data) +print("gguf: write tensors") +gguf_writer.write_tensors_to_file() gguf_writer.close() - -print("gguf: model successfully exported to '" + fname_out + "'" ) +print("gguf: model successfully exported to '" + fname_out + "'") print("")