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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,236 @@ | ||
| import argparse | ||
| import json | ||
| import sys | ||
| import os | ||
| import torch | ||
| import numpy as np | ||
| from pathlib import Path | ||
| import gguf | ||
| from sentencepiece import SentencePieceProcessor # type: ignore[import] | ||
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| try: | ||
| from safetensors import safe_open | ||
| except ImportError: | ||
| print("Please install `safetensors` python package") | ||
| sys.exit(1) | ||
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| def count_model_parts(dir_model: Path) -> int: | ||
| # get number of model parts | ||
| num_parts = 0 | ||
| for filename in os.listdir(dir_model): | ||
| if filename.startswith("model-00"): | ||
| num_parts += 1 | ||
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| if num_parts > 0: | ||
| print("gguf: found " + str(num_parts) + " model parts") | ||
| return num_parts | ||
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| def parse_args() -> argparse.Namespace: | ||
| parser = argparse.ArgumentParser(description="Convert a PLaMo model to a GGML compatible file") | ||
| parser.add_argument( | ||
| "--vocab-only", action="store_true", | ||
| help="extract only the vocab", | ||
| ) | ||
| parser.add_argument( | ||
| "--outfile", type=Path, | ||
| help="path to write to; default: based on input", | ||
| ) | ||
| parser.add_argument( | ||
| "model", type=Path, | ||
| help="directory containing model file, or model file itself (*.bin)", | ||
| ) | ||
| parser.add_argument( | ||
| "ftype", type=int, choices=[0, 1], default=1, nargs='?', | ||
| help="output format - use 0 for float32, 1 for float16", | ||
| ) | ||
| return parser.parse_args() | ||
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| args = parse_args() | ||
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| dir_model = args.model | ||
| ftype = args.ftype | ||
| if not dir_model.is_dir(): | ||
| print(f'Error: {args.model} is not a directory', file = sys.stderr) | ||
| sys.exit(1) | ||
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| # possible tensor data types | ||
| # ftype == 0 -> float32 | ||
| # ftype == 1 -> float16 | ||
|
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| # map from ftype to string | ||
| ftype_str = ["f32", "f16"] | ||
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| if args.outfile is not None: | ||
| fname_out = args.outfile | ||
| else: | ||
| # output in the same directory as the model by default | ||
| fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' | ||
|
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| print("gguf: loading model "+dir_model.name) | ||
|
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| with open(dir_model / "config.json", "r", encoding="utf-8") as f: | ||
| hparams = json.load(f) | ||
|
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| if hparams["architectures"][0] != "PlamoForCausalLM": | ||
| print("Model architecture not supported: " + hparams["architectures"][0]) | ||
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| sys.exit(1) | ||
|
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| # get number of model parts | ||
| num_parts = count_model_parts(dir_model) | ||
|
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| # from add PLaMo model #3557 | ||
| # https://github.com/ggerganov/llama.cpp/pull/3557/files | ||
|
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| ARCH=gguf.MODEL_ARCH.PLAMO | ||
| gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) | ||
|
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| print("gguf: get model metadata") | ||
|
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| block_count = hparams["num_hidden_layers"] | ||
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| gguf_writer.add_name("PLaMo") | ||
| gguf_writer.add_context_length(4096) # not in config.json | ||
| gguf_writer.add_embedding_length(hparams["hidden_size"]) | ||
| gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) | ||
| gguf_writer.add_block_count(block_count) | ||
| gguf_writer.add_head_count(hparams["num_attention_heads"]) | ||
| gguf_writer.add_head_count_kv(hparams["num_attention_heads"] // hparams["n_shared_head"]) | ||
| gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) | ||
| gguf_writer.add_file_type(ftype) | ||
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| # TOKENIZATION | ||
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| print("gguf: get tokenizer metadata") | ||
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| tokens: list[bytes] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
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| tokenizer_model_file = dir_model / 'tokenizer.model' | ||
| if not tokenizer_model_file.is_file(): | ||
| print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) | ||
| sys.exit(1) | ||
|
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| # vocab type sentencepiece | ||
| print("gguf: get sentencepiece tokenizer vocab, scores and token types") | ||
|
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| tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) | ||
|
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| for i in range(tokenizer.vocab_size()): | ||
| text: bytes | ||
| score: float | ||
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| piece = tokenizer.id_to_piece(i) | ||
| text = piece.encode("utf-8") | ||
| score = tokenizer.get_score(i) | ||
|
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| toktype = 1 # defualt to normal token type | ||
| if tokenizer.is_unknown(i): | ||
| toktype = 2 | ||
| if tokenizer.is_control(i): | ||
| toktype = 3 | ||
|
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| # toktype = 4 is user-defined = tokens from added_tokens.json | ||
|
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| if tokenizer.is_unused(i): | ||
| toktype = 5 | ||
| if tokenizer.is_byte(i): | ||
| toktype = 6 | ||
|
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| tokens.append(text) | ||
| scores.append(score) | ||
| toktypes.append(toktype) | ||
|
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| gguf_writer.add_tokenizer_model("llama") | ||
| gguf_writer.add_token_list(tokens) | ||
| gguf_writer.add_token_scores(scores) | ||
| gguf_writer.add_token_types(toktypes) | ||
| gguf_writer.add_sep_token_id(5) | ||
| gguf_writer.add_pad_token_id(3) | ||
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| special_vocab = gguf.SpecialVocab(dir_model) | ||
| special_vocab.add_to_gguf(gguf_writer) | ||
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| # TENSORS | ||
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| tensor_map = gguf.get_tensor_name_map(ARCH,block_count) | ||
|
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| # params for qkv transform | ||
| n_head = hparams["num_attention_heads"] | ||
| n_head_kv = hparams["num_key_value_heads"] | ||
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| head_dim = hparams["hidden_size"] // n_head | ||
|
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| # tensor info | ||
| print("gguf: get tensor metadata") | ||
|
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| if num_parts == 0: | ||
| part_names = iter(("model.safetensors",)) | ||
| else: | ||
| part_names = ( | ||
| f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1) | ||
| ) | ||
|
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| for part_name in part_names: | ||
| if args.vocab_only: | ||
| break | ||
| print("gguf: loading model part '" + part_name + "'") | ||
| model_part = safe_open(dir_model / part_name, framework="pt") | ||
|
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| for name in model_part.keys(): | ||
| if "self_attn.rotary_emb.inv_freq" in name: | ||
| continue | ||
| data = model_part.get_tensor(name) | ||
|
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| old_dtype = data.dtype | ||
|
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| # convert any unsupported data types to float32 | ||
| if data.dtype != torch.float16 and data.dtype != torch.float32: | ||
| data = data.to(torch.float32) | ||
|
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| data = data.squeeze().numpy() | ||
|
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| # map tensor names | ||
| new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) | ||
| if new_name is None: | ||
| print("Can not map tensor '" + name + "'") | ||
| sys.exit() | ||
|
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| n_dims = len(data.shape) | ||
| data_dtype = data.dtype | ||
|
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| # if f32 desired, convert any float16 to float32 | ||
| if ftype == 0 and data_dtype == np.float16: | ||
| data = data.astype(np.float32) | ||
|
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| # 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) | ||
|
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| # 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) | ||
|
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| print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
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| gguf_writer.add_tensor(new_name, data) | ||
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| print("gguf: write header") | ||
| gguf_writer.write_header_to_file() | ||
| print("gguf: write metadata") | ||
| gguf_writer.write_kv_data_to_file() | ||
| if not args.vocab_only: | ||
| print("gguf: write tensors") | ||
| gguf_writer.write_tensors_to_file() | ||
|
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| gguf_writer.close() | ||
|
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| print(f"gguf: model successfully exported to '{fname_out}'") | ||
| print("") |
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