mirror of
https://github.com/ggerganov/llama.cpp.git
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scripts: Generalize convert scripts (#3838)
* Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
This commit is contained in:
Galunid
GitHub
parent
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commit
a75fa576ab
@ -1,247 +0,0 @@
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#!/usr/bin/env python3
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# HF bloom --> gguf conversion
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from __future__ import annotations
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import argparse
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import json
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import os
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import re
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import struct
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import sys
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from pathlib import Path
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from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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num_parts += 1
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if num_parts > 0:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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# Supported Models:
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# https://huggingface.co/bigscience/bloom-1b7
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# https://huggingface.co/bigscience/bloom-3b
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# https://huggingface.co/bigscience/bloom-7b1
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# https://huggingface.co/Langboat/bloom-1b4-zh
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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sys.exit(1)
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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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:
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hparams = json.load(f)
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if hparams["architectures"][0] != "BloomForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit(1)
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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ARCH=gguf.MODEL_ARCH.BLOOM
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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["n_layer"]
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gguf_writer.add_name("Bloom")
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n_embed = hparams.get("hidden_size", hparams.get("n_embed"))
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n_head = hparams.get("n_head", hparams.get("num_attention_heads"))
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gguf_writer.add_context_length(hparams.get("seq_length", n_embed))
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gguf_writer.add_embedding_length(n_embed)
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gguf_writer.add_feed_forward_length(4 * n_embed)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(n_head)
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gguf_writer.add_head_count_kv(n_head)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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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[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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print("gguf: get gpt2 tokenizer vocab")
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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# The number of tokens in tokenizer.json can differ from the expected vocab size.
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# This causes downstream issues with mismatched tensor sizes when running the inference
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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added_vocab = tokenizer.get_added_vocab()
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
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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
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n_head_kv = hparams.get("n_head_kv", n_head)
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head_dim = n_embed // n_head
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# tensor info
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print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(dir_model / part_name, map_location="cpu")
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has_lm_head = True
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if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys():
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has_lm_head = False
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for original_name in model_part.keys():
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data = model_part[original_name]
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name = re.sub(r'transformer\.', '', original_name)
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old_dtype = data.dtype
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
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# Map bloom-style qkv_linear to gpt-style qkv_linear
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# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
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# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
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qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
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data = np.concatenate(
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(qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
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qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
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qkv_weights[:, 2, :, :].reshape((-1, n_embed))),
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axis=0
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)
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print("re-format attention.linear_qkv.weight")
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elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
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qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
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data = np.concatenate(
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(qkv_bias[:, 0, :].reshape((n_embed,)),
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qkv_bias[:, 1, :].reshape((n_embed,)),
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qkv_bias[:, 2, :].reshape((n_embed,))),
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axis=0
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)
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print("re-format attention.linear_qkv.bias")
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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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
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(new_name, data)
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if not has_lm_head and name == "word_embeddings.weight":
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gguf_writer.add_tensor("output.weight", data)
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print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) # noqa
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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if not args.vocab_only:
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print("gguf: write tensors")
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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}'")
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print("")
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@ -1,253 +0,0 @@
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#!/usr/bin/env python3
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# HF falcon--> gguf conversion
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from __future__ import annotations
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import argparse
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import contextlib
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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def count_model_parts(dir_model: Path, prefix: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith(prefix):
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num_parts += 1
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if num_parts > 0:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file")
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parser.add_argument(
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"--vocab-only", action="store_true",
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help="extract only the vocab",
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)
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parser.add_argument(
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"--outfile", type=Path,
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help="path to write to; default: based on input",
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)
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parser.add_argument(
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"model", type=Path,
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help="directory containing model file, or model file itself (*.bin)",
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)
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parser.add_argument(
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"ftype", type=int, choices=[0, 1], default=1, nargs='?',
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help="output format - use 0 for float32, 1 for float16",
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)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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sys.exit(1)
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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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:
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hparams = json.load(f)
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if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"):
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit(1)
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# get number of model parts
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num_parts = count_model_parts(dir_model, "model-00")
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if num_parts:
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is_safetensors = True
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from safetensors import safe_open
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else:
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is_safetensors = False
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num_parts = count_model_parts(dir_model, "pytorch_model-")
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ARCH=gguf.MODEL_ARCH.FALCON
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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.get("num_hidden_layers")
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if block_count is None:
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block_count = hparams["n_layer"] # old name
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n_head = hparams.get("num_attention_heads")
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if n_head is None:
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n_head = hparams["n_head"] # old name
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n_head_kv = hparams.get("num_kv_heads")
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if n_head_kv is None:
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n_head_kv = hparams.get("n_head_kv", 1) # old name
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gguf_writer.add_name("Falcon")
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gguf_writer.add_context_length(2048) # not in config.json
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gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(n_head)
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gguf_writer.add_head_count_kv(n_head_kv)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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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[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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print("gguf: get gpt2 tokenizer vocab")
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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# The number of tokens in tokenizer.json can differ from the expected vocab size.
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# This causes downstream issues with mismatched tensor sizes when running the inference
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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for i in range(vocab_size):
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tokens.append(reverse_vocab[i])
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scores.append(0.0) # dummy
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toktypes.append(gguf.TokenType.NORMAL)
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
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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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head_dim = hparams["hidden_size"] // n_head
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# tensor info
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print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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elif is_safetensors:
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part_names = (
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f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1)
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)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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if is_safetensors:
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ctx = safe_open(dir_model / part_name, framework="pt", device="cpu")
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else:
|
||||
ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu"))
|
||||
|
||||
with ctx as model_part:
|
||||
for name in model_part.keys():
|
||||
data = model_part.get_tensor(name) if is_safetensors else model_part[name]
|
||||
|
||||
old_dtype = data.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
||||
data = data.to(torch.float32)
|
||||
|
||||
# QKV tensor transform
|
||||
# The original query_key_value tensor contains n_head_kv "kv groups",
|
||||
# each consisting of n_head/n_head_kv query weights followed by one key
|
||||
# and one value weight (shared by all query heads in the kv group).
|
||||
# This layout makes it a big pain to work with in GGML.
|
||||
# So we rearrange them here,, so that we have n_head query weights
|
||||
# followed by n_head_kv key weights followed by n_head_kv value weights,
|
||||
# in contiguous fashion.
|
||||
# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
|
||||
|
||||
if "query_key_value" in name:
|
||||
qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
|
||||
q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
|
||||
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
||||
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
||||
data = torch.cat((q,k,v)).reshape_as(data)
|
||||
|
||||
data = data.squeeze().numpy()
|
||||
|
||||
# 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()
|
||||
|
||||
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(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()
|
||||
if not args.vocab_only:
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
@ -1,221 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# HF gptneox--> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import AutoTokenizer # type: ignore[import]
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
|
||||
import gguf
|
||||
|
||||
|
||||
def count_model_parts(dir_model: Path) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.startswith("pytorch_model-"):
|
||||
num_parts += 1
|
||||
|
||||
if num_parts > 0:
|
||||
print("gguf: found " + str(num_parts) + " model parts")
|
||||
return num_parts
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert a GPT-NeoX 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()
|
||||
|
||||
args = parse_args()
|
||||
|
||||
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)
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
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'
|
||||
|
||||
print("gguf: loading model "+dir_model.name)
|
||||
|
||||
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])
|
||||
|
||||
sys.exit()
|
||||
|
||||
# get number of model parts
|
||||
num_parts = count_model_parts(dir_model)
|
||||
|
||||
ARCH=gguf.MODEL_ARCH.GPTNEOX
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
block_count = hparams["num_hidden_layers"]
|
||||
|
||||
gguf_writer.add_name(dir_model.name)
|
||||
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
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
# gpt2 tokenizer
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
|
||||
# The number of tokens in tokenizer.json can differ from the expected vocab size.
|
||||
# This causes downstream issues with mismatched tensor sizes when running the inference
|
||||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
assert max(tokenizer.vocab.values()) < vocab_size
|
||||
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
||||
|
||||
# tensor info
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = iter(("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:
|
||||
if args.vocab_only:
|
||||
break
|
||||
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]
|
||||
|
||||
# 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
|
||||
|
||||
old_dtype = data.dtype
|
||||
|
||||
# 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
|
||||
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)
|
||||
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(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()
|
||||
if not args.vocab_only:
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
890
convert-hf-to-gguf.py
Executable file
890
convert-hf-to-gguf.py
Executable file
@ -0,0 +1,890 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
from enum import IntEnum
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from torch import Tensor
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
|
||||
import gguf
|
||||
|
||||
|
||||
###### MODEL DEFINITIONS ######
|
||||
|
||||
class SentencePieceTokenTypes(IntEnum):
|
||||
NORMAL = 1
|
||||
UNKNOWN = 2
|
||||
CONTROL = 3
|
||||
USER_DEFINED = 4
|
||||
UNUSED = 5
|
||||
BYTE = 6
|
||||
|
||||
|
||||
class Model:
|
||||
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
|
||||
self.dir_model = dir_model
|
||||
self.ftype = ftype
|
||||
self.fname_out = fname_out
|
||||
self.is_big_endian = is_big_endian
|
||||
self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
|
||||
self.is_safetensors = self._is_model_safetensors()
|
||||
self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
|
||||
self.part_names = self._get_part_names()
|
||||
self.hparams = Model.load_hparams(self.dir_model)
|
||||
self.model_arch = self._get_model_architecture()
|
||||
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess)
|
||||
|
||||
def set_vocab(self):
|
||||
self._set_vocab_gpt2()
|
||||
|
||||
def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
|
||||
for part_name in self.part_names:
|
||||
print(f"gguf: loading model part '{part_name}'")
|
||||
ctx: ContextManager[Any]
|
||||
if self.is_safetensors:
|
||||
from safetensors import safe_open
|
||||
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
|
||||
else:
|
||||
ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
|
||||
|
||||
with ctx as model_part:
|
||||
for name in model_part.keys():
|
||||
data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
|
||||
yield name, data
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_name(self.dir_model.name)
|
||||
self.gguf_writer.add_block_count(self.hparams.get(
|
||||
"n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
|
||||
))
|
||||
if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
|
||||
self.gguf_writer.add_context_length(n_ctx)
|
||||
if (n_embd := self.hparams.get("hidden_size")) is not None:
|
||||
self.gguf_writer.add_embedding_length(n_embd)
|
||||
if (n_ff := self.hparams.get("intermediate_size")) is not None:
|
||||
self.gguf_writer.add_feed_forward_length(n_ff)
|
||||
if (n_head := self.hparams.get("num_attention_head")) is not None:
|
||||
self.gguf_writer.add_head_count(n_head)
|
||||
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
for name, data_torch in self.get_tensors():
|
||||
# we don't need these
|
||||
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
||||
continue
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
def write(self):
|
||||
self.write_tensors()
|
||||
self.gguf_writer.write_header_to_file()
|
||||
self.gguf_writer.write_kv_data_to_file()
|
||||
self.gguf_writer.write_tensors_to_file()
|
||||
self.gguf_writer.close()
|
||||
|
||||
def write_vocab(self):
|
||||
self.gguf_writer.write_header_to_file()
|
||||
self.gguf_writer.write_kv_data_to_file()
|
||||
self.gguf_writer.close()
|
||||
|
||||
@staticmethod
|
||||
def count_model_parts(dir_model: Path, prefix: str) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.endswith(prefix):
|
||||
num_parts += 1
|
||||
|
||||
return num_parts
|
||||
|
||||
@staticmethod
|
||||
def load_hparams(dir_model):
|
||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
@staticmethod
|
||||
def from_model_architecture(model_architecture):
|
||||
if model_architecture == "StableLMEpochForCausalLM":
|
||||
return StableLMModel
|
||||
if model_architecture == "GPTNeoXForCausalLM":
|
||||
return GPTNeoXModel
|
||||
if model_architecture == "BloomForCausalLM":
|
||||
return BloomModel
|
||||
if model_architecture == "MPTForCausalLM":
|
||||
return MPTModel
|
||||
if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
|
||||
return BaichuanModel
|
||||
if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
|
||||
return FalconModel
|
||||
if model_architecture == "GPTBigCodeForCausalLM":
|
||||
return StarCoderModel
|
||||
if model_architecture == "GPTRefactForCausalLM":
|
||||
return RefactModel
|
||||
if model_architecture == "PersimmonForCausalLM":
|
||||
return PersimmonModel
|
||||
return Model
|
||||
|
||||
def _is_model_safetensors(self) -> bool:
|
||||
return Model.count_model_parts(self.dir_model, ".safetensors") > 0
|
||||
|
||||
def _get_part_names(self):
|
||||
if self.is_safetensors:
|
||||
if self.num_parts == 1: # there's only one .safetensors file
|
||||
return ("model.safetensors",)
|
||||
return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1))
|
||||
|
||||
if self.num_parts == 1: # there's only one .bin file
|
||||
return ("pytorch_model.bin",)
|
||||
return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
|
||||
|
||||
def _get_model_architecture(self) -> gguf.MODEL_ARCH:
|
||||
arch = self.hparams["architectures"][0]
|
||||
if arch == "GPTNeoXForCausalLM":
|
||||
return gguf.MODEL_ARCH.GPTNEOX
|
||||
if arch == "BloomForCausalLM":
|
||||
return gguf.MODEL_ARCH.BLOOM
|
||||
if arch == "MPTForCausalLM":
|
||||
return gguf.MODEL_ARCH.MPT
|
||||
if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
|
||||
return gguf.MODEL_ARCH.BAICHUAN
|
||||
if arch == "FalconForCausalLM":
|
||||
return gguf.MODEL_ARCH.FALCON
|
||||
if arch == "GPTBigCodeForCausalLM":
|
||||
return gguf.MODEL_ARCH.STARCODER
|
||||
if arch == "GPTRefactForCausalLM":
|
||||
return gguf.MODEL_ARCH.REFACT
|
||||
if arch == "PersimmonForCausalLM":
|
||||
return gguf.MODEL_ARCH.PERSIMMON
|
||||
|
||||
raise NotImplementedError(f'Architecture "{arch}" not supported!')
|
||||
|
||||
def _set_vocab_gpt2(self):
|
||||
dir_model = self.dir_model
|
||||
hparams = self.hparams
|
||||
tokens: list[bytearray] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
from transformers import AutoTokenizer # type: ignore[attr-defined]
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
assert max(tokenizer.vocab.values()) < vocab_size
|
||||
|
||||
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
pad_token = f"[PAD{i}]".encode('utf-8')
|
||||
tokens.append(bytearray(pad_token))
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def _set_vocab_sentencepiece(self):
|
||||
from sentencepiece import SentencePieceProcessor
|
||||
|
||||
tokenizer_path = self.dir_model / 'tokenizer.model'
|
||||
|
||||
tokens: list[bytes] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
if not tokenizer_path.is_file():
|
||||
print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
tokenizer = SentencePieceProcessor(str(tokenizer_path))
|
||||
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
|
||||
|
||||
for token_id in range(vocab_size):
|
||||
piece = tokenizer.id_to_piece(token_id)
|
||||
text = piece.encode("utf-8")
|
||||
score = tokenizer.get_score(token_id)
|
||||
|
||||
toktype = SentencePieceTokenTypes.NORMAL
|
||||
if tokenizer.is_unknown(token_id):
|
||||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||||
elif tokenizer.is_control(token_id):
|
||||
toktype = SentencePieceTokenTypes.CONTROL
|
||||
elif tokenizer.is_unused(token_id):
|
||||
toktype = SentencePieceTokenTypes.UNUSED
|
||||
elif tokenizer.is_byte(token_id):
|
||||
toktype = SentencePieceTokenTypes.BYTE
|
||||
|
||||
tokens.append(text)
|
||||
scores.append(score)
|
||||
toktypes.append(toktype)
|
||||
|
||||
added_tokens_file = self.dir_model / 'added_tokens.json'
|
||||
if added_tokens_file.is_file():
|
||||
with open(added_tokens_file, "r", encoding="utf-8") as f:
|
||||
added_tokens_json = json.load(f)
|
||||
|
||||
for key in added_tokens_json:
|
||||
tokens.append(key.encode("utf-8"))
|
||||
scores.append(-1000.0)
|
||||
toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
|
||||
|
||||
self.gguf_writer.add_tokenizer_model("llama")
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_scores(scores)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
|
||||
class StableLMModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
self.gguf_writer.add_rope_dimension_count(
|
||||
int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
|
||||
)
|
||||
self.gguf_writer.add_layer_norm_eps(1e-5)
|
||||
|
||||
|
||||
class GPTNeoXModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams["num_hidden_layers"]
|
||||
|
||||
self.gguf_writer.add_name(self.dir_model.name)
|
||||
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
||||
self.gguf_writer.add_rope_dimension_count(
|
||||
int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
|
||||
)
|
||||
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
||||
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
|
||||
|
||||
|
||||
class BloomModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_name("Bloom")
|
||||
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
||||
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
||||
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
|
||||
self.gguf_writer.add_embedding_length(n_embed)
|
||||
self.gguf_writer.add_feed_forward_length(4 * n_embed)
|
||||
self.gguf_writer.add_block_count(self.hparams["n_layer"])
|
||||
self.gguf_writer.add_head_count(n_head)
|
||||
self.gguf_writer.add_head_count_kv(n_head)
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams["n_layer"]
|
||||
tensors = dict(self.get_tensors())
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
has_lm_head = True
|
||||
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
||||
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
||||
|
||||
for name, data_torch in tensors.items():
|
||||
if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys():
|
||||
has_lm_head = False
|
||||
|
||||
name = re.sub(r'transformer\.', '', name)
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
|
||||
# Map bloom-style qkv_linear to gpt-style qkv_linear
|
||||
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
|
||||
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
|
||||
qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
|
||||
data = np.concatenate(
|
||||
(
|
||||
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
|
||||
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
|
||||
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
|
||||
),
|
||||
axis=0,
|
||||
)
|
||||
print("re-format attention.linear_qkv.weight")
|
||||
elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
|
||||
qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
|
||||
data = np.concatenate(
|
||||
(
|
||||
qkv_bias[:, 0, :].reshape((n_embed,)),
|
||||
qkv_bias[:, 1, :].reshape((n_embed,)),
|
||||
qkv_bias[:, 2, :].reshape((n_embed,)),
|
||||
),
|
||||
axis=0,
|
||||
)
|
||||
print("re-format attention.linear_qkv.bias")
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
if not has_lm_head and name == "word_embeddings.weight":
|
||||
self.gguf_writer.add_tensor("output.weight", data)
|
||||
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
|
||||
class MPTModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams["n_layers"]
|
||||
self.gguf_writer.add_name(self.dir_model.name)
|
||||
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
|
||||
self.gguf_writer.add_head_count(self.hparams["n_heads"])
|
||||
if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
|
||||
self.gguf_writer.add_head_count_kv(kv_n_heads)
|
||||
self.gguf_writer.add_layer_norm_eps(1e-5)
|
||||
if self.hparams["attn_config"]["clip_qkv"] is not None:
|
||||
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
|
||||
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
for name, data_torch in self.get_tensors():
|
||||
# we don't need these
|
||||
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
||||
continue
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
# note: MPT output is tied to (same as) wte in original model;
|
||||
# for easier implementation in llama.cpp it's duplicated in GGUF, though :/
|
||||
if new_name == "token_embd.weight":
|
||||
self.gguf_writer.add_tensor("output.weight", data)
|
||||
|
||||
|
||||
class BaichuanModel(Model):
|
||||
def set_vocab(self):
|
||||
self._set_vocab_sentencepiece()
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams["num_hidden_layers"]
|
||||
head_count = self.hparams["num_attention_heads"]
|
||||
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
||||
hf_repo = self.hparams.get("_name_or_path", "")
|
||||
|
||||
ctx_length = 0
|
||||
if "max_sequence_length" in self.hparams:
|
||||
ctx_length = self.hparams["max_sequence_length"]
|
||||
elif "max_position_embeddings" in self.hparams:
|
||||
ctx_length = self.hparams["max_position_embeddings"]
|
||||
elif "model_max_length" in self.hparams:
|
||||
ctx_length = self.hparams["model_max_length"]
|
||||
else:
|
||||
print("gguf: can not find ctx length parameter.")
|
||||
sys.exit()
|
||||
|
||||
self.gguf_writer.add_name(self.dir_model.name)
|
||||
self.gguf_writer.add_source_hf_repo(hf_repo)
|
||||
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
||||
self.gguf_writer.add_context_length(ctx_length)
|
||||
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
||||
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
||||
self.gguf_writer.add_head_count(head_count)
|
||||
self.gguf_writer.add_head_count_kv(head_count_kv)
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
||||
|
||||
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
||||
if self.hparams["rope_scaling"].get("type") == "linear":
|
||||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
||||
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
||||
|
||||
def write_tensors(self):
|
||||
# Collect tensors from generator object
|
||||
model_kv = dict(self.get_tensors())
|
||||
block_count = self.hparams["num_hidden_layers"]
|
||||
head_count = self.hparams["num_attention_heads"]
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
||||
|
||||
for i in range(block_count):
|
||||
if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None:
|
||||
print(f"Unpacking and permuting layer {i}")
|
||||
model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \
|
||||
self._reverse_hf_permute_part(w, 0, head_count, head_count)
|
||||
model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \
|
||||
self._reverse_hf_permute_part(w, 1, head_count, head_count_kv)
|
||||
model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \
|
||||
self._reverse_hf_part(w, 2)
|
||||
del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"]
|
||||
|
||||
for name, data_torch in model_kv.items():
|
||||
# we don't need these
|
||||
if name.endswith(".rotary_emb.inv_freq"):
|
||||
continue
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
|
||||
if n_kv_head is not None and n_head != n_kv_head:
|
||||
n_head //= n_kv_head
|
||||
|
||||
return (
|
||||
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
||||
.swapaxes(1, 2)
|
||||
.reshape(weights.shape)
|
||||
)
|
||||
|
||||
def _reverse_hf_permute_part(
|
||||
self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
|
||||
) -> Tensor:
|
||||
r = weights.shape[0] // 3
|
||||
return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
|
||||
|
||||
def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
|
||||
r = weights.shape[0] // 3
|
||||
return weights[r * n_part:r * n_part + r, ...]
|
||||
|
||||
|
||||
class FalconModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams.get("num_hidden_layers")
|
||||
if block_count is None:
|
||||
block_count = self.hparams["n_layer"] # old name
|
||||
|
||||
n_head = self.hparams.get("num_attention_heads")
|
||||
if n_head is None:
|
||||
n_head = self.hparams["n_head"] # old name
|
||||
|
||||
n_head_kv = self.hparams.get("num_kv_heads")
|
||||
if n_head_kv is None:
|
||||
n_head_kv = self.hparams.get("n_head_kv", 1) # old name
|
||||
|
||||
self.gguf_writer.add_name("Falcon")
|
||||
self.gguf_writer.add_context_length(2048) # not in config.json
|
||||
self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
|
||||
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
||||
self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_head_count(n_head)
|
||||
self.gguf_writer.add_head_count_kv(n_head_kv)
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("num_hidden_layers")
|
||||
if block_count is None:
|
||||
block_count = self.hparams["n_layer"] # old name
|
||||
|
||||
n_head = self.hparams.get("num_attention_heads")
|
||||
if n_head is None:
|
||||
n_head = self.hparams["n_head"] # old name
|
||||
|
||||
n_head_kv = self.hparams.get("num_kv_heads")
|
||||
if n_head_kv is None:
|
||||
n_head_kv = self.hparams.get("n_head_kv", 1) # old name
|
||||
|
||||
head_dim = self.hparams["hidden_size"] // n_head
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
|
||||
for name, data_torch in self.get_tensors():
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
# QKV tensor transform
|
||||
# The original query_key_value tensor contains n_head_kv "kv groups",
|
||||
# each consisting of n_head/n_head_kv query weights followed by one key
|
||||
# and one value weight (shared by all query heads in the kv group).
|
||||
# This layout makes it a big pain to work with in GGML.
|
||||
# So we rearrange them here,, so that we have n_head query weights
|
||||
# followed by n_head_kv key weights followed by n_head_kv value weights,
|
||||
# in contiguous fashion.
|
||||
# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
|
||||
|
||||
if "query_key_value" in name:
|
||||
qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
|
||||
q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
|
||||
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
||||
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
||||
data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
class StarCoderModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams["n_layer"]
|
||||
|
||||
self.gguf_writer.add_name("StarCoder")
|
||||
self.gguf_writer.add_context_length(self.hparams["n_positions"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
||||
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
||||
self.gguf_writer.add_head_count_kv(1)
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
|
||||
class RefactModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
hidden_dim = self.hparams["n_embd"]
|
||||
inner_dim = 4 * hidden_dim
|
||||
hidden_dim = int(2 * inner_dim / 3)
|
||||
multiple_of = 256
|
||||
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
|
||||
block_count = self.hparams["n_layer"]
|
||||
|
||||
self.gguf_writer.add_name("Refact")
|
||||
# refact uses Alibi. So this is from config.json which might be used by training.
|
||||
self.gguf_writer.add_context_length(self.hparams["n_positions"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
||||
|
||||
self.gguf_writer.add_feed_forward_length(ff_dim)
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
||||
self.gguf_writer.add_head_count_kv(1)
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
def write_tensors(self):
|
||||
hidden_dim = self.hparams["n_embd"]
|
||||
inner_dim = 4 * hidden_dim
|
||||
hidden_dim = int(2 * inner_dim / 3)
|
||||
multiple_of = 256
|
||||
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
n_head = self.hparams["n_head"]
|
||||
n_head_kv = 1
|
||||
head_dim = self.hparams["n_embd"] // n_head
|
||||
block_count = self.hparams["n_layer"]
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
|
||||
tensors = dict(self.get_tensors())
|
||||
for i in range(block_count):
|
||||
if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None:
|
||||
tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim]
|
||||
tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:]
|
||||
del tensors[f"transformer.h.{i}.attn.kv.weight"]
|
||||
if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None:
|
||||
tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w
|
||||
del tensors[f"transformer.h.{i}.attn.q.weight"]
|
||||
if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None:
|
||||
tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim]
|
||||
tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:]
|
||||
del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
||||
|
||||
for name, data_torch in tensors.items():
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
class PersimmonModel(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
||||
head_count = self.hparams["num_attention_heads"]
|
||||
head_count_kv = head_count
|
||||
hidden_size = self.hparams["hidden_size"]
|
||||
|
||||
self.gguf_writer.add_name('persimmon-8b-chat')
|
||||
self.gguf_writer.add_embedding_length(hidden_size)
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
||||
self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
|
||||
self.gguf_writer.add_head_count(head_count)
|
||||
self.gguf_writer.add_head_count_kv(head_count_kv)
|
||||
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
||||
|
||||
def set_vocab(self):
|
||||
self._set_vocab_sentencepiece()
|
||||
# self.gguf_writer.add_bos_token_id(71013)
|
||||
# self.gguf_writer.add_eos_token_id(71013)
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
|
||||
for name, data_torch in self.get_tensors():
|
||||
if name.endswith(".self_attention.rotary_emb.inv_freq"):
|
||||
continue
|
||||
old_dtype = data_torch.dtype
|
||||
# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
|
||||
data = data_torch.to(torch.float32).squeeze().numpy()
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
n_dims = len(data.shape)
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
###### CONVERSION LOGIC ######
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert a huggingface 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(
|
||||
"--outtype", type=str, choices=["f32", "f16"], default="f16",
|
||||
help="output format - use f32 for float32, f16 for float16",
|
||||
)
|
||||
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
|
||||
parser.add_argument(
|
||||
"model", type=Path,
|
||||
help="directory containing model file",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
args = parse_args()
|
||||
|
||||
dir_model = args.model
|
||||
if not dir_model.is_dir():
|
||||
print(f'Error: {args.model} is not a directory', file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
ftype_map = {
|
||||
"f32": gguf.GGMLQuantizationType.F32,
|
||||
"f16": gguf.GGMLQuantizationType.F16,
|
||||
}
|
||||
|
||||
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-{args.outtype}.gguf'
|
||||
|
||||
print(f"Loading model: {dir_model.name}")
|
||||
|
||||
hparams = Model.load_hparams(dir_model)
|
||||
|
||||
model_class = Model.from_model_architecture(hparams["architectures"][0])
|
||||
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
|
||||
|
||||
print("Set model parameters")
|
||||
model_instance.set_gguf_parameters()
|
||||
|
||||
print("Set model tokenizer")
|
||||
model_instance.set_vocab()
|
||||
|
||||
if args.vocab_only:
|
||||
print(f"Exporting model vocab to '{fname_out}'")
|
||||
model_instance.write_vocab()
|
||||
else:
|
||||
print(f"Exporting model to '{fname_out}'")
|
||||
model_instance.write()
|
||||
|
||||
print(f"Model successfully exported to '{fname_out}'")
|
||||
@ -1,227 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# HF mpt--> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import AutoTokenizer # type: ignore[import]
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
|
||||
import gguf
|
||||
|
||||
|
||||
def count_model_parts(dir_model: Path) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.startswith("pytorch_model-"):
|
||||
num_parts += 1
|
||||
|
||||
if num_parts > 0:
|
||||
print("gguf: found " + str(num_parts) + " model parts")
|
||||
return num_parts
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert an MPT 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()
|
||||
|
||||
args = parse_args()
|
||||
|
||||
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)
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
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'
|
||||
|
||||
print("gguf: loading model "+dir_model.name)
|
||||
|
||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
||||
hparams = json.load(f)
|
||||
|
||||
if hparams["architectures"][0] != "MPTForCausalLM":
|
||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
||||
|
||||
sys.exit()
|
||||
|
||||
# get number of model parts
|
||||
num_parts = count_model_parts(dir_model)
|
||||
|
||||
ARCH=gguf.MODEL_ARCH.MPT
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
block_count = hparams["n_layers"]
|
||||
|
||||
gguf_writer.add_name(dir_model.name)
|
||||
gguf_writer.add_context_length(hparams["max_seq_len"])
|
||||
gguf_writer.add_embedding_length(hparams["d_model"])
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_feed_forward_length(4 * hparams["d_model"])
|
||||
gguf_writer.add_head_count(hparams["n_heads"])
|
||||
if kv_n_heads := hparams["attn_config"].get("kv_n_heads"):
|
||||
gguf_writer.add_head_count_kv(kv_n_heads)
|
||||
gguf_writer.add_layer_norm_eps(1e-05)
|
||||
if hparams["attn_config"]["clip_qkv"] is not None:
|
||||
gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"])
|
||||
gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"])
|
||||
|
||||
# TOKENIZATION
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
# gpt2 tokenizer
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but
|
||||
# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to
|
||||
# accomodate some "reserved" tokens; this is causing problems down the line in
|
||||
# llama.cpp, so we pad the vocab with dummy tokens:
|
||||
|
||||
vocab_size = hparams["vocab_size"]
|
||||
|
||||
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
||||
|
||||
# tensor info
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = iter(("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:
|
||||
if args.vocab_only:
|
||||
break
|
||||
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
|
||||
|
||||
# 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
|
||||
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print("Cannot map tensor '" + name + "'")
|
||||
continue # for the sake of compatibility with some old published models, don't quit
|
||||
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(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
||||
|
||||
gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
# note: MPT output is tied to (same as) wte in original model;
|
||||
# for easier implementation in llama.cpp it's duplicated in GGUF, though :/
|
||||
if new_name == "token_embd.weight":
|
||||
gguf_writer.add_tensor("output.weight", data)
|
||||
|
||||
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()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
@ -1,272 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# HF refact--> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import AutoTokenizer # type: ignore[import]
|
||||
|
||||
if "NO_LOCAL_GGUF" not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf"))
|
||||
import gguf
|
||||
|
||||
def count_model_parts(dir_model: Path) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.startswith("pytorch_model-"):
|
||||
num_parts += 1
|
||||
|
||||
if num_parts > 0:
|
||||
print("gguf: found " + str(num_parts) + " model parts")
|
||||
return num_parts
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Convert a Refact 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()
|
||||
|
||||
|
||||
args = parse_args()
|
||||
|
||||
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)
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
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"
|
||||
|
||||
print("gguf: loading model " + dir_model.name)
|
||||
|
||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
||||
hparams = json.load(f)
|
||||
|
||||
if hparams["architectures"][0] != "GPTRefactForCausalLM":
|
||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
# get number of model parts
|
||||
num_parts = count_model_parts(dir_model)
|
||||
|
||||
ARCH = gguf.MODEL_ARCH.REFACT
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
# Get refact feed forward dimension
|
||||
hidden_dim = hparams["n_embd"]
|
||||
inner_dim = 4 * hidden_dim
|
||||
hidden_dim = int(2 * inner_dim / 3)
|
||||
multiple_of = 256
|
||||
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
|
||||
block_count = hparams["n_layer"]
|
||||
|
||||
gguf_writer.add_name("Refact")
|
||||
# refact uses Alibi. So this is from config.json which might be used by training.
|
||||
gguf_writer.add_context_length(hparams["n_positions"])
|
||||
gguf_writer.add_embedding_length(hparams["n_embd"])
|
||||
|
||||
gguf_writer.add_feed_forward_length(ff_dim)
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_head_count(hparams["n_head"])
|
||||
gguf_writer.add_head_count_kv(1)
|
||||
gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"])
|
||||
gguf_writer.add_file_type(ftype)
|
||||
|
||||
# TOKENIZATION
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
# gpt2 tokenizer
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
|
||||
# The number of tokens in tokenizer.json can differ from the expected vocab size.
|
||||
# This causes downstream issues with mismatched tensor sizes when running the inference
|
||||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
assert max(tokenizer.vocab.values()) < vocab_size
|
||||
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
|
||||
|
||||
# params for qkv transform
|
||||
n_head = hparams["n_head"]
|
||||
n_head_kv = 1
|
||||
|
||||
head_dim = hparams["n_embd"] // n_head
|
||||
|
||||
# tensor info
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = iter(("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:
|
||||
if args.vocab_only:
|
||||
break
|
||||
print("gguf: loading model part '" + part_name + "'")
|
||||
model_part = torch.load(dir_model / part_name, map_location="cpu")
|
||||
|
||||
for i in range(block_count):
|
||||
if f"transformer.h.{i}.attn.kv.weight" in model_part:
|
||||
data = model_part[f"transformer.h.{i}.attn.kv.weight"]
|
||||
model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[
|
||||
: n_head_kv * head_dim
|
||||
]
|
||||
model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[
|
||||
n_head_kv * head_dim :
|
||||
]
|
||||
del model_part[f"transformer.h.{i}.attn.kv.weight"]
|
||||
if f"transformer.h.{i}.attn.q.weight" in model_part:
|
||||
model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[
|
||||
f"transformer.h.{i}.attn.q.weight"
|
||||
]
|
||||
del model_part[f"transformer.h.{i}.attn.q.weight"]
|
||||
if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part:
|
||||
data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
||||
model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim]
|
||||
model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:]
|
||||
del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
||||
|
||||
for name in model_part.keys():
|
||||
data = model_part[name]
|
||||
|
||||
old_dtype = data.dtype
|
||||
|
||||
# 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
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
|
||||
if new_name is None:
|
||||
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(
|
||||
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()
|
||||
if not args.vocab_only:
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
@ -1,210 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# HF starcoder --> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import AutoTokenizer # type: ignore[import]
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
|
||||
import gguf
|
||||
|
||||
|
||||
def count_model_parts(dir_model: Path) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.startswith("pytorch_model-"):
|
||||
num_parts += 1
|
||||
|
||||
if num_parts > 0:
|
||||
print("gguf: found " + str(num_parts) + " model parts")
|
||||
return num_parts
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert a StarCoder 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, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
|
||||
return parser.parse_args()
|
||||
|
||||
args = parse_args()
|
||||
|
||||
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)
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
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'
|
||||
|
||||
print("gguf: loading model "+dir_model.name)
|
||||
|
||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
||||
hparams = json.load(f)
|
||||
|
||||
if hparams["architectures"][0] != "GPTBigCodeForCausalLM":
|
||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
# get number of model parts
|
||||
num_parts = count_model_parts(dir_model)
|
||||
|
||||
ARCH=gguf.MODEL_ARCH.STARCODER
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
block_count = hparams["n_layer"]
|
||||
|
||||
gguf_writer.add_name("StarCoder")
|
||||
gguf_writer.add_context_length(hparams["n_positions"])
|
||||
gguf_writer.add_embedding_length(hparams["n_embd"])
|
||||
gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_head_count(hparams["n_head"])
|
||||
gguf_writer.add_head_count_kv(1)
|
||||
gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
|
||||
gguf_writer.add_file_type(ftype)
|
||||
|
||||
# TOKENIZATION
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
# gpt2 tokenizer
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
|
||||
# The number of tokens in tokenizer.json can differ from the expected vocab size.
|
||||
# This causes downstream issues with mismatched tensor sizes when running the inference
|
||||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
assert max(tokenizer.vocab.values()) < vocab_size
|
||||
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
||||
|
||||
# params for qkv transform
|
||||
n_head = hparams["n_head"]
|
||||
n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
|
||||
|
||||
head_dim = hparams["n_embd"] // n_head
|
||||
|
||||
# tensor info
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = iter(("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:
|
||||
if args.vocab_only:
|
||||
break
|
||||
print("gguf: loading model part '" + part_name + "'")
|
||||
model_part = torch.load(dir_model / part_name, map_location="cpu")
|
||||
|
||||
for name in model_part.keys():
|
||||
data = model_part[name]
|
||||
|
||||
old_dtype = data.dtype
|
||||
|
||||
# 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
|
||||
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)
|
||||
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, "=>", new_name + ", shape = " + str(data.shape) + ", " + 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()
|
||||
if not args.vocab_only:
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
@ -26,7 +26,7 @@ from pathlib import Path
|
||||
from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar
|
||||
|
||||
import numpy as np
|
||||
from sentencepiece import SentencePieceProcessor # type: ignore[import]
|
||||
from sentencepiece import SentencePieceProcessor
|
||||
|
||||
import os
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
@ -328,7 +328,7 @@ class BpeVocab:
|
||||
|
||||
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
tokenizer = self.bpe_tokenizer
|
||||
from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
|
||||
from transformers.models.gpt2 import tokenization_gpt2
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
|
||||
|
||||
for i, _ in enumerate(tokenizer):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user