mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-10 01:10:35 +00:00
146 lines
4.0 KiB
Python
Executable File
146 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import json
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import struct
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import sys
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from pathlib import Path
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import gguf
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import numpy as np
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from sentencepiece import SentencePieceProcessor
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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if not 2 <= len(sys.argv) < 4:
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print("Usage: {} dir-model [ftype]\n".format(Path(__file__).name))
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print(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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sys.exit(1)
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# output in the same directory as the model
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dir_model = Path(sys.argv[1])
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# possible data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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#
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if len(sys.argv) > 2:
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ftype = int(sys.argv[2])
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if ftype < 0 or ftype > 1:
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print("Invalid ftype: " + str(ftype))
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sys.exit(1)
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fname_out = dir_model / ("ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".gguf")
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ARCH = gguf.MODEL_ARCH.MPT
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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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config = AutoConfig.from_pretrained(dir_model)
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block_count = config.n_layers
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gguf_writer.add_name("Replit")
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gguf_writer.add_context_length(config.max_seq_len)
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gguf_writer.add_embedding_length(config.d_model)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_feed_forward_length(4 * config.d_model)
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gguf_writer.add_head_count(config.n_heads)
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gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
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gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
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gguf_writer.add_file_type(ftype)
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clip_qkv = config.attn_config.clip_qkv
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if clip_qkv is not None:
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gguf_writer.add_clamp_kqv(clip_qkv)
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print("gguf: get sentencepiece tokenizer vocab")
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tokenizer = SentencePieceProcessor(str(dir_model / "spiece.model"))
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#print(tokenizer.encode('I believe the meaning of life is'))
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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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for i in range(tokenizer.vocab_size()):
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tokens.append(tokenizer.id_to_piece(i).encode('utf-8'))
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scores.append(tokenizer.get_score(i))
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toktype = gguf.TokenType.NORMAL
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if tokenizer.is_unknown(i):
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toktype = gguf.TokenType.UNKNOWN
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elif tokenizer.is_control(i):
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toktype = gguf.TokenType.CONTROL
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elif tokenizer.is_unused(i):
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toktype = gguf.TokenType.UNUSED
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elif tokenizer.is_byte(i):
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toktype = gguf.TokenType.BYTE
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toktypes.append(toktype)
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gguf_writer.add_tokenizer_model("llama") # sentencepiece
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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)
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special_vocab.add_to_gguf(gguf_writer)
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print("gguf: get tensor metadata")
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model = AutoModelForCausalLM.from_pretrained(dir_model, config=config, low_cpu_mem_usage=True)
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#print(model)
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tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
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list_vars = model.state_dict()
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for name in list_vars.keys():
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print(name, list_vars[name].shape, list_vars[name].dtype)
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print(config)
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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print("Processing variable:", name, "with shape:", data.shape)
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n_dims = len(data.shape)
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0
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if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
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print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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elif ftype == 1 or data.dtype != np.float32:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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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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gguf_writer.add_tensor(new_name, data)
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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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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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