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build(python): Package scripts with pip-0517 compliance
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| #!/usr/bin/env python3 | ||
| from __future__ import annotations | ||
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| import json | ||
| import os | ||
| import struct | ||
| import sys | ||
| from pathlib import Path | ||
| from typing import Any, BinaryIO, Sequence | ||
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| import numpy as np | ||
| import torch | ||
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| if 'NO_LOCAL_GGUF' not in os.environ: | ||
| sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) | ||
| import gguf | ||
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| NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} | ||
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| def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: | ||
| fout.write(b"ggla"[::-1]) # magic (ggml lora) | ||
| fout.write(struct.pack("i", 1)) # file version | ||
| fout.write(struct.pack("i", params["r"])) | ||
| # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int | ||
| # but some models ship a float value instead | ||
| # let's convert to int, but fail if lossless conversion is not possible | ||
| assert ( | ||
| int(params["lora_alpha"]) == params["lora_alpha"] | ||
| ), "cannot convert float to int losslessly" | ||
| fout.write(struct.pack("i", int(params["lora_alpha"]))) | ||
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| def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None: | ||
| sname = name.encode("utf-8") | ||
| fout.write( | ||
| struct.pack( | ||
| "iii", | ||
| len(shape), | ||
| len(sname), | ||
| NUMPY_TYPE_TO_FTYPE[data_type.name], | ||
| ) | ||
| ) | ||
| fout.write(struct.pack("i" * len(shape), *shape[::-1])) | ||
| fout.write(sname) | ||
| fout.seek((fout.tell() + 31) & -32) | ||
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| if __name__ == '__main__': | ||
| if len(sys.argv) < 2: | ||
| print(f"Usage: python {sys.argv[0]} <path> [arch]") | ||
| print( | ||
| "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" | ||
| ) | ||
| print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") | ||
| sys.exit(1) | ||
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| input_json = os.path.join(sys.argv[1], "adapter_config.json") | ||
| input_model = os.path.join(sys.argv[1], "adapter_model.bin") | ||
| output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") | ||
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| if os.path.exists(input_model): | ||
| model = torch.load(input_model, map_location="cpu") | ||
| else: | ||
| input_model = os.path.join(sys.argv[1], "adapter_model.safetensors") | ||
| # lazy import load_file only if lora is in safetensors format. | ||
| from safetensors.torch import load_file | ||
| model = load_file(input_model, device="cpu") | ||
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| arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" | ||
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| if arch_name not in gguf.MODEL_ARCH_NAMES.values(): | ||
| print(f"Error: unsupported architecture {arch_name}") | ||
| sys.exit(1) | ||
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| arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] | ||
| name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone | ||
|
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| with open(input_json, "r") as f: | ||
| params = json.load(f) | ||
|
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| if params["peft_type"] != "LORA": | ||
| print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") | ||
| sys.exit(1) | ||
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| if params["fan_in_fan_out"] is True: | ||
| print("Error: param fan_in_fan_out is not supported") | ||
| sys.exit(1) | ||
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| if params["bias"] is not None and params["bias"] != "none": | ||
| print("Error: param bias is not supported") | ||
| sys.exit(1) | ||
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| # TODO: these seem to be layers that have been trained but without lora. | ||
| # doesn't seem widely used but eventually should be supported | ||
| if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: | ||
| print("Error: param modules_to_save is not supported") | ||
| sys.exit(1) | ||
|
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| with open(output_path, "wb") as fout: | ||
| fout.truncate() | ||
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| write_file_header(fout, params) | ||
| for k, v in model.items(): | ||
| orig_k = k | ||
| if k.endswith(".default.weight"): | ||
| k = k.replace(".default.weight", ".weight") | ||
| if k in ["llama_proj.weight", "llama_proj.bias"]: | ||
| continue | ||
| if k.endswith("lora_A.weight"): | ||
| if v.dtype != torch.float16 and v.dtype != torch.float32: | ||
| v = v.float() | ||
| v = v.T | ||
| else: | ||
| v = v.float() | ||
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| t = v.detach().numpy() | ||
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| prefix = "base_model.model." | ||
| if k.startswith(prefix): | ||
| k = k[len(prefix) :] | ||
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| lora_suffixes = (".lora_A.weight", ".lora_B.weight") | ||
| if k.endswith(lora_suffixes): | ||
| suffix = k[-len(lora_suffixes[0]):] | ||
| k = k[: -len(lora_suffixes[0])] | ||
| else: | ||
| print(f"Error: unrecognized tensor name {orig_k}") | ||
| sys.exit(1) | ||
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| tname = name_map.get_name(k) | ||
| if tname is None: | ||
| print(f"Error: could not map tensor name {orig_k}") | ||
| print(" Note: the arch parameter must be specified if the model is not llama") | ||
| sys.exit(1) | ||
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| if suffix == ".lora_A.weight": | ||
| tname += ".weight.loraA" | ||
| elif suffix == ".lora_B.weight": | ||
| tname += ".weight.loraB" | ||
| else: | ||
| assert False | ||
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| print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") | ||
| write_tensor_header(fout, tname, t.shape, t.dtype) | ||
| t.tofile(fout) | ||
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| print(f"Converted {input_json} and {input_model} to {output_path}") | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| #!/usr/bin/env python3 | ||
| import argparse | ||
| import os | ||
| import sys | ||
| from pathlib import Path | ||
| from pprint import pprint | ||
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| import torch | ||
| from sentencepiece import SentencePieceProcessor | ||
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| if 'NO_LOCAL_GGUF' not in os.environ: | ||
| sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) | ||
| import gguf | ||
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| def _flatten_dict(dct, tensors, prefix=None): | ||
| assert isinstance(dct, dict) | ||
| for key in dct.keys(): | ||
| new_prefix = prefix + '.' + key if prefix is not None else key | ||
| if isinstance(dct[key], torch.Tensor): | ||
| tensors[new_prefix] = dct[key] | ||
| elif isinstance(dct[key], dict): | ||
| _flatten_dict(dct[key], tensors, new_prefix) | ||
| else: | ||
| raise ValueError(type(dct[key])) | ||
| return None | ||
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| def _get_sentencepiece_tokenizer_info(dir_model: Path): | ||
| tokenizer_path = dir_model / 'adept_vocab.model' | ||
| print('gguf: getting sentencepiece tokenizer from', tokenizer_path) | ||
| tokenizer = SentencePieceProcessor(str(tokenizer_path)) | ||
| print('gguf: adding tokens') | ||
| tokens: list[bytes] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
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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 | ||
| if tokenizer.is_unknown(i): | ||
| toktype = 2 | ||
| if tokenizer.is_control(i): | ||
| toktype = 3 | ||
| 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) | ||
| pass | ||
| return tokens, scores, toktypes | ||
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| def main(): | ||
| parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file") | ||
| parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") | ||
| parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") | ||
| parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") | ||
| parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") | ||
| args = parser.parse_args() | ||
| sys.path.append(str(args.adept_inference_dir)) | ||
| persimmon_model = torch.load(args.ckpt_path) | ||
| hparams = persimmon_model['args'] | ||
| pprint(hparams) | ||
| tensors: dict[str, torch.Tensor] = {} | ||
| _flatten_dict(persimmon_model['model'], tensors, None) | ||
|
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| arch = gguf.MODEL_ARCH.PERSIMMON | ||
| gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch]) | ||
|
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| block_count = hparams.num_layers | ||
| head_count = hparams.num_attention_heads | ||
| head_count_kv = head_count | ||
| ctx_length = hparams.seq_length | ||
| hidden_size = hparams.hidden_size | ||
|
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| gguf_writer.add_name('persimmon-8b-chat') | ||
| gguf_writer.add_context_length(ctx_length) | ||
| gguf_writer.add_embedding_length(hidden_size) | ||
| gguf_writer.add_block_count(block_count) | ||
| gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) | ||
| # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 | ||
| gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) | ||
| gguf_writer.add_head_count(head_count) | ||
| gguf_writer.add_head_count_kv(head_count_kv) | ||
| gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) | ||
| gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon) | ||
|
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| tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) | ||
| 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_bos_token_id(71013) | ||
| gguf_writer.add_eos_token_id(71013) | ||
|
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| tensor_map = gguf.get_tensor_name_map(arch, block_count) | ||
| print(tensor_map) | ||
| for name in tensors.keys(): | ||
| data = tensors[name] | ||
| if name.endswith(".self_attention.rotary_emb.inv_freq"): | ||
| continue | ||
| old_dtype = data.dtype | ||
| # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) | ||
| data = data.to(torch.float32).squeeze().numpy() | ||
| new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) | ||
| if new_name is None: | ||
| print("Can not map tensor '" + name + "'") | ||
| sys.exit() | ||
| n_dims = len(data.shape) | ||
| print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
| gguf_writer.add_tensor(new_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 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 '{args.outfile}'") | ||
| print("") | ||
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| if __name__ == '__main__': | ||
| main() | ||
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